diff --git a/README.md b/README.md index e52d2ffee..fa5faf4b0 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ There are a number of examples for how to create and run networks on github whic https://github.com/Hjorthmedh/Snudda/tree/master/examples/notebooks -## Command line examle +## Command line example Once installed Snudda can also be run from the command line, using the snudda command. Below is a small list of the relevant commands that can be used. diff --git a/examples/notebooks/Parkinson/GenerateParkinsonNetwork-example-10k.ipynb b/examples/notebooks/Parkinson/GenerateParkinsonNetwork-example-10k.ipynb index 17afda112..5fe0759b4 100644 --- a/examples/notebooks/Parkinson/GenerateParkinsonNetwork-example-10k.ipynb +++ b/examples/notebooks/Parkinson/GenerateParkinsonNetwork-example-10k.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "212baaee-00c7-4ab6-bc80-b68cd613e5c2", "metadata": {}, "outputs": [], @@ -68,12 +68,205 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "a87ebe54-8a06-4065-99ae-cd0b01eb6dce", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jupyter environment detected. Enabling Open3D WebVisualizer.\n", + "[Open3D INFO] WebRTC GUI backend enabled.\n", + "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n", + "Adding Striatum with 10000 neurons (stay_inside=False)\n", + "Using cube for striatum\n", + "Neurons for striatum read from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum\n", + "Adding neurons: FS from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/fs/3/mechanisms.json\n", + "Adding neurons: dSPN from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/10/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/10/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/11/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/11/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/12/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/12/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/13/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/13/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/14/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/14/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/15/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/15/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/16/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/16/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/17/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/17/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/18/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/18/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/19/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/19/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/20/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/20/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/21/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/21/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/22/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/22/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/23/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/23/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/24/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/24/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/25/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/25/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/26/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/26/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/27/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/27/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/28/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/28/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/29/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/29/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/3/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/30/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/30/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/31/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/31/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/32/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/32/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/33/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/33/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/34/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/34/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/35/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/35/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/4/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/4/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/5/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/5/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/6/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/6/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/7/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/7/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/8/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/8/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/9/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/dspn/9/mechanisms.json\n", + "Adding neurons: iSPN from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/10/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/10/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/11/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/11/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/12/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/12/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/13/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/13/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/14/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/14/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/15/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/15/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/16/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/16/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/17/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/17/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/18/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/18/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/19/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/19/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/20/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/20/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/21/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/21/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/22/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/22/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/23/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/23/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/24/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/24/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/25/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/25/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/26/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/26/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/27/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/27/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/28/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/28/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/29/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/29/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/3/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/30/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/30/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/31/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/31/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/32/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/32/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/33/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/33/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/34/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/34/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/35/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/35/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/4/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/4/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/5/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/5/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/6/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/6/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/7/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/7/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/8/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/8/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/9/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/ispn/9/mechanisms.json\n", + "Adding neurons: ChIN from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/mechanisms.json\n", + "Adding neurons: LTS from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/mechanisms.json\n", + "No directory $SNUDDA_DATA/neurons/striatum/ngf, skipping NGF cells.\n", + "Reading connectivity from ../../../../BasalGangliaData/Parkinson/20221213/connectivity/network-config.json\n", + "Writing networks/PD-example-10k/PD0/network-config.json\n" + ] + } + ], "source": [ "from snudda import SnuddaInit\n", "\n", @@ -83,14 +276,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "9cc72b7a-2185-4232-ac57-0c60cc09b599", "metadata": {}, "outputs": [], "source": [ "os.environ[\"IPYTHONDIR\"] = os.path.join(os.path.abspath(os.getcwd()), \".ipython\")\n", "os.environ[\"IPYTHON_PROFILE\"] = \"default\"\n", - "os.system(\"ipcluster start -n 4 --profile=$IPYTHON_PROFILE --ip=127.0.0.1 --log-level ERROR 2> parallel-log.txt &\")\n", + "os.system(\"ipcluster start -n 6 --profile=$IPYTHON_PROFILE --ip=127.0.0.1 --log-level ERROR 2> parallel-log.txt &\")\n", "\n", "import time\n", "time.sleep(10) # Wait for ipcluster to start" @@ -98,10 +291,79 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4af8e087-ae0b-4259-914c-348b191b9f6a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Placing neurons\n", + "Network path: networks/PD-example-10k/PD0\n", + "Creating missing directory networks/PD-example-10k/PD0/log\n", + "Created directory networks/PD-example-10k/PD0/log\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "Generating 43854 points for networks/PD-example-10k/PD0/mesh/Striatum-cube-mesh-0.0004989626526218353.obj\n", + "n_points = 41705, previous close_pairs = 88592\n", + "n_points = 39666, previous close_pairs = 72961\n", + "n_points = 37734, previous close_pairs = 60742\n", + "n_points = 35907, previous close_pairs = 50789\n", + "n_points = 34184, previous close_pairs = 42385\n", + "n_points = 32563, previous close_pairs = 35762\n", + "n_points = 31046, previous close_pairs = 29793\n", + "n_points = 29634, previous close_pairs = 25172\n", + "n_points = 28324, previous close_pairs = 21205\n", + "n_points = 27119, previous close_pairs = 17586\n", + "n_points = 26020, previous close_pairs = 14847\n", + "n_points = 25020, previous close_pairs = 12725\n", + "n_points = 24119, previous close_pairs = 10830\n", + "n_points = 23315, previous close_pairs = 9131\n", + "n_points = 22604, previous close_pairs = 7628\n", + "n_points = 22345, previous close_pairs = 6311\n", + "n_points = 16540, previous close_pairs = 5805\n", + "Filtering 16540 points..\n", + "Filtering, keeping inside points: 13576 / 16540\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 2.9s\n", + "Touch detection\n", + "Network path: networks/PD-example-10k/PD0\n", + "Creating missing directory networks/PD-example-10k/PD0/voxels\n", + "Created directory networks/PD-example-10k/PD0/voxels\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "importing SnuddaDetect from snudda.detect.detect on engine(s)\n", + "importing ProjectionDetection from snudda.detect.projection_detection on engine(s)\n", + "HyperID 122 completed - 8378973 synapses found (139.5 s)\n", + "HyperID 115 completed - 10503202 synapses found (171.5 s)\n", + "HyperID 170 completed - 13355309 synapses found (210.2 s)\n", + "HyperID 164 completed - 7218765 synapses found (110.1 s)\n", + "Suppressing printouts for hyper voxels that complete in < 100 seconds.\n", + "HyperID 163 completed - 17581736 synapses found (263.5 s)\n", + "HyperID 121 completed - 20384980 synapses found (298.3 s)\n", + "HyperID 114 completed - 25205670 synapses found (339.2 s)\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 400.9s\n", + "Prune synapses\n", + "Network path: networks/PD-example-10k/PD0\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "No file networks/PD-example-10k/PD0/pruning_merge_info.json\n", + "importing SnuddaPrune from snudda.detect.prune on engine(s)\n", + "prune_synapses_parallel (5545921/117880579 synapses, 4.7% kept): 247.6s\n", + "prune_synapses_parallel (1499/3332 gap_junctions, 45.0% kept): 0.1s\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 747.2s\n" + ] + } + ], "source": [ "from snudda import Snudda\n", "snd_pd0 = Snudda(network_path=network_path_pd0, parallel=True, ipython_profile=\"default\")\n", @@ -110,17 +372,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "248d9449-313a-4294-b7ed-7f69e7b6713d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "Writing input spikes to networks/PD-example-10k/PD0/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "importing SnuddaInput from snudda.input.input on engine(s)\n", + "Writing spikes to networks/PD-example-10k/PD0/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 819.1s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "snd_pd0.setup_input(input_config=input_config)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "240070ac-728d-404a-ae6a-0b7f904a7109", "metadata": {}, "outputs": [], @@ -140,12 +430,203 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4afe0e37-0604-4f31-845a-40673556d922", "metadata": { + "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding Striatum with 10000 neurons (stay_inside=False)\n", + "Using cube for striatum\n", + "Neurons for striatum read from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum\n", + "Adding neurons: FS from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/3/mechanisms.json\n", + "Adding neurons: dSPN from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/10/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/10/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/11/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/11/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/12/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/12/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/13/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/13/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/14/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/14/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/15/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/15/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/16/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/16/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/17/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/17/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/18/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/18/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/19/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/19/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/20/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/20/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/21/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/21/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/22/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/22/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/23/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/23/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/24/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/24/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/25/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/25/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/26/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/26/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/27/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/27/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/28/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/28/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/29/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/29/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/3/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/30/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/30/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/31/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/31/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/32/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/32/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/33/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/33/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/34/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/34/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/35/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/35/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/4/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/4/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/5/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/5/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/6/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/6/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/7/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/7/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/8/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/8/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/9/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/dspn/9/mechanisms.json\n", + "Adding neurons: iSPN from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/10/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/10/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/11/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/11/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/12/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/12/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/13/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/13/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/14/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/14/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/15/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/15/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/16/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/16/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/17/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/17/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/18/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/18/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/19/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/19/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/20/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/20/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/21/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/21/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/22/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/22/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/23/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/23/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/24/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/24/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/25/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/25/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/26/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/26/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/27/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/27/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/28/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/28/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/29/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/29/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/3/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/30/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/30/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/31/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/31/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/32/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/32/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/33/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/33/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/34/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/34/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/35/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/35/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/4/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/4/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/5/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/5/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/6/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/6/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/7/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/7/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/8/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/8/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/9/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/ispn/9/mechanisms.json\n", + "Adding neurons: ChIN from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/mechanisms.json\n", + "Adding neurons: LTS from dir /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/mechanisms.json\n", + "Parameter file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/parameters.json\n", + "Mechanism file not found: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/mechanisms.json\n", + "No directory $SNUDDA_DATA/neurons/striatum/ngf, skipping NGF cells.\n", + "Reading connectivity from ../../../../BasalGangliaData/Parkinson/20221213/connectivity/network-config-PD-basic.json\n", + "Writing networks/PD-example-10k/PD2-ref/network-config.json\n" + ] + } + ], "source": [ "from snudda import SnuddaInit\n", "\n", @@ -155,10 +636,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "ce37d134-8091-4e3b-8285-96e64e20dbee", - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Placing neurons\n", + "Network path: networks/PD-example-10k/PD2-ref\n", + "Creating missing directory networks/PD-example-10k/PD2-ref/log\n", + "Created directory networks/PD-example-10k/PD2-ref/log\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2-ref/network-config.json\n", + "Generating 43854 points for networks/PD-example-10k/PD2-ref/mesh/Striatum-cube-mesh-0.0004989626526218353.obj\n", + "n_points = 41705, previous close_pairs = 88592\n", + "n_points = 39666, previous close_pairs = 72961\n", + "n_points = 37734, previous close_pairs = 60742\n", + "n_points = 35907, previous close_pairs = 50789\n", + "n_points = 34184, previous close_pairs = 42385\n", + "n_points = 32563, previous close_pairs = 35762\n", + "n_points = 31046, previous close_pairs = 29793\n", + "n_points = 29634, previous close_pairs = 25172\n", + "n_points = 28324, previous close_pairs = 21205\n", + "n_points = 27119, previous close_pairs = 17586\n", + "n_points = 26020, previous close_pairs = 14847\n", + "n_points = 25020, previous close_pairs = 12725\n", + "n_points = 24119, previous close_pairs = 10830\n", + "n_points = 23315, previous close_pairs = 9131\n", + "n_points = 22604, previous close_pairs = 7628\n", + "n_points = 22345, previous close_pairs = 6311\n", + "n_points = 16540, previous close_pairs = 5805\n", + "Filtering 16540 points..\n", + "Filtering, keeping inside points: 13576 / 16540\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 3.4s\n", + "Touch detection\n", + "Network path: networks/PD-example-10k/PD2-ref\n", + "Creating missing directory networks/PD-example-10k/PD2-ref/voxels\n", + "Created directory networks/PD-example-10k/PD2-ref/voxels\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2-ref/network-config.json\n", + "importing SnuddaDetect from snudda.detect.detect on engine(s)\n", + "importing ProjectionDetection from snudda.detect.projection_detection on engine(s)\n", + "HyperID 136 completed - 7659202 synapses found (125.8 s)\n", + "HyperID 192 completed - 7720534 synapses found (131.0 s)\n", + "HyperID 128 completed - 8013509 synapses found (135.8 s)\n", + "HyperID 184 completed - 8573563 synapses found (143.0 s)\n", + "Suppressing printouts for hyper voxels that complete in < 100 seconds.\n", + "HyperID 191 completed - 11196105 synapses found (174.8 s)\n", + "HyperID 135 completed - 10844160 synapses found (185.7 s)\n", + "HyperID 129 completed - 5705545 synapses found (111.0 s)\n", + "HyperID 185 completed - 6272969 synapses found (123.4 s)\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2-ref/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 291.5s\n", + "Prune synapses\n", + "Network path: networks/PD-example-10k/PD2-ref\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "No file networks/PD-example-10k/PD2-ref/pruning_merge_info.json\n", + "importing SnuddaPrune from snudda.detect.prune on engine(s)\n", + "prune_synapses_parallel (2057874/67275811 synapses, 3.1% kept): 165.0s\n", + "prune_synapses_parallel (1499/3332 gap_junctions, 45.0% kept): 0.2s\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 511.9s\n" + ] + } + ], "source": [ "from snudda import Snudda\n", "snd_pd2_ref = Snudda(network_path=network_path_pd2_ref, parallel=True, ipython_profile=\"default\")\n", @@ -167,19 +720,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "0e35a69c-c9d1-4434-a272-e6bfae4b4e93", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/Parkinson/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2-ref/network-config.json\n", + "Writing input spikes to networks/PD-example-10k/PD2-ref/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2-ref/network-config.json\n", + "importing SnuddaInput from snudda.input.input on engine(s)\n", + "Writing spikes to networks/PD-example-10k/PD2-ref/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 582.5s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "snd_pd2_ref.setup_input(input_config=input_config)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "638dcf39-a9f2-485a-8a50-a44103c96674", "metadata": {}, "outputs": [], @@ -189,10 +770,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "499564e6-bdd5-40dd-b31b-003111b19dbd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/.local/lib/python3.9/site-packages/ipyparallel/apps/ipclusterapp.py:3: UserWarning: ipyparallel.apps.ipclusterapp is deprecated in ipyparallel 7. Use ipyparallel.cluster\n", + " warnings.warn(f\"{__name__} is deprecated in ipyparallel 7. Use ipyparallel.cluster\")\n", + "2024-03-08 15:13:21.467 [IPClusterStop] Stopping cluster \n", + "2024-03-08 15:13:21.467 [IPClusterStop] Stopping controller\n", + "2024-03-08 15:13:21.670 [IPClusterStop] Stopping engine(s): 1709905790\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "os.system(f\"IPYTHONDIR={os.path.join(os.path.abspath(os.getcwd()), '.ipython')} && IPYTHON_PROFILE=default && ipcluster stop\")" ] @@ -207,10 +810,10547 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "0b4a0e8f-b58f-465f-ad06-70ed0df8da38", - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/1/DR-rat-Mar-13-08-1-536-R-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/1/DR-rat-Mar-13-08-1-536-R-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/1/DR-rat-Mar-13-08-1-536-R-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/2/MTC180800A-IDB-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/2/MTC180800A-IDB-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/2/MTC180800A-IDB-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/optim_chin_morph_renamed2019-11-08.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/optim_chin_morph_renamed2019-11-08.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/optim_chin_morph_renamed2019-11-08.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/4/lts_morp_9862_centered_no_axon_resampled-var4.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/lts_morp_9862_centered_no_axon_resampled-var4.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/4/lts_morp_9862_centered_no_axon_resampled-var4.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/5/lts_morp_9862_centered_no_axon_resampled-var5.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/lts_morp_9862_centered_no_axon_resampled-var5.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/5/lts_morp_9862_centered_no_axon_resampled-var5.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/3/MTC251001A-IDB-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/3/MTC251001A-IDB-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/3/MTC251001A-IDB-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/0/BE104E-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/0/BE104E-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/0/BE104E-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/1/lts_morp_9862_centered_no_axon_resampled-var1.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/lts_morp_9862_centered_no_axon_resampled-var1.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/1/lts_morp_9862_centered_no_axon_resampled-var1.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/0/lts_morp_9862_centered_no_axon_resampled-var0.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/lts_morp_9862_centered_no_axon_resampled-var0.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/0/lts_morp_9862_centered_no_axon_resampled-var0.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/6/lts_morp_9862_centered_no_axon_resampled-var6.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/lts_morp_9862_centered_no_axon_resampled-var6.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/6/lts_morp_9862_centered_no_axon_resampled-var6.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/7/lts_morp_9862_centered_no_axon_resampled-var7.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/lts_morp_9862_centered_no_axon_resampled-var7.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/7/lts_morp_9862_centered_no_axon_resampled-var7.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/8/lts_morp_9862_centered_no_axon_resampled-var8.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/lts_morp_9862_centered_no_axon_resampled-var8.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/8/lts_morp_9862_centered_no_axon_resampled-var8.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/3/lts_morp_9862_centered_no_axon_resampled-var3.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/lts_morp_9862_centered_no_axon_resampled-var3.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/3/lts_morp_9862_centered_no_axon_resampled-var3.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/2/lts_morp_9862_centered_no_axon_resampled-var2.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/lts_morp_9862_centered_no_axon_resampled-var2.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/2/lts_morp_9862_centered_no_axon_resampled-var2.swc-cache.pickle\n", + "Creating directory networks/PD-example-10k/PD2\n", + "Writing new network to networks/PD-example-10k/PD2/network-synapses.hdf5\n", + "Loading synapses into memory.\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/1/DR-rat-Mar-13-08-1-536-R-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/1/DR-rat-Mar-13-08-1-536-R-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/1/DR-rat-Mar-13-08-1-536-R-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/0/BE104E-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/0/BE104E-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/0/BE104E-cor-rep-res3-61.swc-cache.pickle\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/utils/swap_to_degenerated_morphologies.py:295: RuntimeWarning: invalid value encountered in cast\n", + " edited_synapses[:, 10] = new_sec_x * 1000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/2/MTC180800A-IDB-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/2/MTC180800A-IDB-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/2/MTC180800A-IDB-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/3/MTC251001A-IDB-cor-rep-res3-61.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/fs/3/MTC251001A-IDB-cor-rep-res3-61.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD3/neurons/striatum/fs/3/MTC251001A-IDB-cor-rep-res3-61.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/lts_morp_9862_centered_no_axon_resampled-var6.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/6/lts_morp_9862_centered_no_axon_resampled-var6.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/6/lts_morp_9862_centered_no_axon_resampled-var6.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/optim_chin_morph_renamed2019-11-08.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/optim_chin_morph_renamed2019-11-08.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710/optim_chin_morph_renamed2019-11-08.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/lts_morp_9862_centered_no_axon_resampled-var0.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/0/lts_morp_9862_centered_no_axon_resampled-var0.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/0/lts_morp_9862_centered_no_axon_resampled-var0.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/lts_morp_9862_centered_no_axon_resampled-var7.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/7/lts_morp_9862_centered_no_axon_resampled-var7.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/7/lts_morp_9862_centered_no_axon_resampled-var7.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/lts_morp_9862_centered_no_axon_resampled-var4.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/4/lts_morp_9862_centered_no_axon_resampled-var4.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/4/lts_morp_9862_centered_no_axon_resampled-var4.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/lts_morp_9862_centered_no_axon_resampled-var5.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/5/lts_morp_9862_centered_no_axon_resampled-var5.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/5/lts_morp_9862_centered_no_axon_resampled-var5.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/lts_morp_9862_centered_no_axon_resampled-var1.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/1/lts_morp_9862_centered_no_axon_resampled-var1.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/1/lts_morp_9862_centered_no_axon_resampled-var1.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/lts_morp_9862_centered_no_axon_resampled-var2.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/2/lts_morp_9862_centered_no_axon_resampled-var2.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/2/lts_morp_9862_centered_no_axon_resampled-var2.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/lts_morp_9862_centered_no_axon_resampled-var8.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/8/lts_morp_9862_centered_no_axon_resampled-var8.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/8/lts_morp_9862_centered_no_axon_resampled-var8.swc-cache.pickle\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/lts_morp_9862_centered_no_axon_resampled-var3.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2/neurons/striatum/lts/3/lts_morp_9862_centered_no_axon_resampled-var3.swc\n", + "\n", + "Failed to load cache from /home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0/neurons/striatum/lts/3/lts_morp_9862_centered_no_axon_resampled-var3.swc-cache.pickle\n", + "1000000 / 5545921\n", + "2000000 / 5545921\n", + "3000000 / 5545921\n", + "4000000 / 5545921\n", + "5000000 / 5545921\n", + "5545921 / 5545921\n", + "Processing neuron 0\n", + "No axon for neuron ChIN_0 (92)\n", + "Processing neuron 100\n", + "No axon for neuron ChIN_0 (154)\n", + "No axon for neuron ChIN_0 (155)\n", + "Processing neuron 200\n", + "No axon for neuron ChIN_0 (220)\n", + "No axon for neuron LTS_4 (221)\n", + "Processing neuron 300\n", + "No axon for neuron ChIN_0 (305)\n", + "No axon for neuron LTS_4 (306)\n", + "No axon for neuron LTS_5 (307)\n", + "Processing neuron 400\n", + "No axon for neuron ChIN_0 (499)\n", + "Processing neuron 500\n", + "No axon for neuron ChIN_0 (500)\n", + "No axon for neuron ChIN_0 (501)\n", + "Processing neuron 600\n", + "No axon for neuron ChIN_0 (650)\n", + "Processing neuron 700\n", + "Processing neuron 800\n", + "Processing neuron 900\n", + "Processing neuron 1000\n", + "No axon for neuron ChIN_0 (1016)\n", + "No axon for neuron ChIN_0 (1017)\n", + "No axon for neuron ChIN_0 (1087)\n", + "No axon for neuron LTS_1 (1088)\n", + "Processing neuron 1100\n", + "No axon for neuron ChIN_0 (1167)\n", + "No axon for neuron ChIN_0 (1168)\n", + "No axon for neuron LTS_0 (1169)\n", + "No axon for neuron LTS_6 (1170)\n", + "No axon for neuron LTS_6 (1171)\n", + "Processing neuron 1200\n", + "No axon for neuron ChIN_0 (1258)\n", + "No axon for neuron LTS_7 (1259)\n", + "Processing neuron 1300\n", + "Processing neuron 1400\n", + "Processing neuron 1500\n", + "No axon for neuron ChIN_0 (1555)\n", + "Processing neuron 1600\n", + "No axon for neuron ChIN_0 (1642)\n", + "No axon for neuron ChIN_0 (1643)\n", + "No axon for neuron ChIN_0 (1644)\n", + "Processing neuron 1700\n", + "No axon for neuron ChIN_0 (1755)\n", + "No axon for neuron ChIN_0 (1756)\n", + "No axon for neuron LTS_8 (1757)\n", + "Processing neuron 1800\n", + "No axon for neuron ChIN_0 (1845)\n", + "Processing neuron 1900\n", + "No axon for neuron ChIN_0 (1923)\n", + "Processing neuron 2000\n", + "No axon for neuron ChIN_0 (2008)\n", + "No axon for neuron ChIN_0 (2009)\n", + "Processing neuron 2100\n", + "Processing neuron 2200\n", + "No axon for neuron ChIN_0 (2231)\n", + "Processing neuron 2300\n", + "No axon for neuron ChIN_0 (2328)\n", + "Processing neuron 2400\n", + "No axon for neuron ChIN_0 (2430)\n", + "No axon for neuron ChIN_0 (2431)\n", + "Processing neuron 2500\n", + "No axon for neuron ChIN_0 (2540)\n", + "Processing neuron 2600\n", + "Processing neuron 2700\n", + "No axon for neuron LTS_0 (2736)\n", + "No axon for neuron LTS_5 (2737)\n", + "Processing neuron 2800\n", + "Processing neuron 2900\n", + "No axon for neuron ChIN_0 (2930)\n", + "No axon for neuron ChIN_0 (2931)\n", + "Processing neuron 3000\n", + "No axon for neuron ChIN_0 (3040)\n", + "Processing neuron 3100\n", + "No axon for neuron ChIN_0 (3144)\n", + "No axon for neuron ChIN_0 (3145)\n", + "No axon for neuron LTS_3 (3146)\n", + "No axon for neuron LTS_4 (3147)\n", + "Processing neuron 3200\n", + "Processing neuron 3300\n", + "Processing neuron 3400\n", + "No axon for neuron ChIN_0 (3421)\n", + "Processing neuron 3500\n", + "Processing neuron 3600\n", + "No axon for neuron ChIN_0 (3662)\n", + "No axon for neuron ChIN_0 (3663)\n", + "No axon for neuron LTS_3 (3664)\n", + "No axon for neuron LTS_5 (3665)\n", + "Processing neuron 3700\n", + "No axon for neuron ChIN_0 (3774)\n", + "No axon for neuron ChIN_0 (3775)\n", + "No axon for neuron ChIN_0 (3776)\n", + "No axon for neuron LTS_0 (3777)\n", + "Processing neuron 3800\n", + "No axon for neuron ChIN_0 (3859)\n", + "Processing neuron 3900\n", + "No axon for neuron ChIN_0 (3949)\n", + "Processing neuron 4000\n", + "No axon for neuron ChIN_0 (4074)\n", + "No axon for neuron LTS_2 (4075)\n", + "Processing neuron 4100\n", + "No axon for neuron ChIN_0 (4165)\n", + "No axon for neuron ChIN_0 (4166)\n", + "No axon for neuron LTS_2 (4167)\n", + "Processing neuron 4200\n", + "Processing neuron 4300\n", + "No axon for neuron ChIN_0 (4309)\n", + "Processing neuron 4400\n", + "No axon for neuron ChIN_0 (4447)\n", + "No axon for neuron ChIN_0 (4448)\n", + "No axon for neuron LTS_2 (4449)\n", + "No axon for neuron LTS_6 (4450)\n", + "No axon for neuron LTS_6 (4451)\n", + "No axon for neuron LTS_7 (4452)\n", + "Processing neuron 4500\n", + "No axon for neuron ChIN_0 (4580)\n", + "No axon for neuron LTS_3 (4581)\n", + "Processing neuron 4600\n", + "No axon for neuron LTS_4 (4685)\n", + "Processing neuron 4700\n", + "No axon for neuron ChIN_0 (4795)\n", + "No axon for neuron ChIN_0 (4796)\n", + "No axon for neuron ChIN_0 (4797)\n", + "No axon for neuron LTS_2 (4798)\n", + "No axon for neuron LTS_3 (4799)\n", + "Processing neuron 4800\n", + "No axon for neuron ChIN_0 (4898)\n", + "No axon for neuron LTS_2 (4899)\n", + "Processing neuron 4900\n", + "Processing neuron 5000\n", + "No axon for neuron ChIN_0 (5059)\n", + "No axon for neuron ChIN_0 (5060)\n", + "Processing neuron 5100\n", + "No axon for neuron LTS_1 (5153)\n", + "No axon for neuron LTS_4 (5154)\n", + "Processing neuron 5200\n", + "No axon for neuron ChIN_0 (5258)\n", + "No axon for neuron LTS_2 (5259)\n", + "No axon for neuron LTS_4 (5260)\n", + "Processing neuron 5300\n", + "No axon for neuron ChIN_0 (5374)\n", + "No axon for neuron ChIN_0 (5375)\n", + "No axon for neuron ChIN_0 (5376)\n", + "No axon for neuron ChIN_0 (5377)\n", + "Processing neuron 5400\n", + "Processing neuron 5500\n", + "No axon for neuron ChIN_0 (5595)\n", + "Processing neuron 5600\n", + "Processing neuron 5700\n", + "No axon for neuron ChIN_0 (5712)\n", + "Processing neuron 5800\n", + "No axon for neuron ChIN_0 (5800)\n", + "No axon for neuron ChIN_0 (5801)\n", + "Processing neuron 5900\n", + "No axon for neuron ChIN_0 (5919)\n", + "Processing neuron 6000\n", + "No axon for neuron ChIN_0 (6017)\n", + "No axon for neuron LTS_2 (6018)\n", + "No axon for neuron LTS_5 (6019)\n", + "Processing neuron 6100\n", + "No axon for neuron LTS_7 (6122)\n", + "No axon for neuron LTS_7 (6123)\n", + "Processing neuron 6200\n", + "No axon for neuron ChIN_0 (6239)\n", + "No axon for neuron ChIN_0 (6240)\n", + "No axon for neuron LTS_6 (6241)\n", + "No axon for neuron LTS_8 (6242)\n", + "Processing neuron 6300\n", + "No axon for neuron ChIN_0 (6346)\n", + "No axon for neuron LTS_7 (6347)\n", + "Processing neuron 6400\n", + "No axon for neuron ChIN_0 (6451)\n", + "No axon for neuron LTS_1 (6452)\n", + "Processing neuron 6500\n", + "Processing neuron 6600\n", + "Processing neuron 6700\n", + "No axon for neuron ChIN_0 (6761)\n", + "No axon for neuron ChIN_0 (6762)\n", + "Processing neuron 6800\n", + "No axon for neuron ChIN_0 (6864)\n", + "No axon for neuron ChIN_0 (6865)\n", + "Processing neuron 6900\n", + "No axon for neuron LTS_6 (6990)\n", + "Processing neuron 7000\n", + "No axon for neuron ChIN_0 (7047)\n", + "No axon for neuron LTS_5 (7048)\n", + "Processing neuron 7100\n", + "No axon for neuron ChIN_0 (7145)\n", + "No axon for neuron ChIN_0 (7146)\n", + "No axon for neuron ChIN_0 (7147)\n", + "No axon for neuron ChIN_0 (7148)\n", + "No axon for neuron ChIN_0 (7149)\n", + "Processing neuron 7200\n", + "No axon for neuron LTS_4 (7258)\n", + "No axon for neuron LTS_6 (7259)\n", + "Processing neuron 7300\n", + "No axon for neuron ChIN_0 (7371)\n", + "No axon for neuron LTS_5 (7372)\n", + "No axon for neuron LTS_8 (7373)\n", + "Processing neuron 7400\n", + "No axon for neuron ChIN_0 (7489)\n", + "No axon for neuron ChIN_0 (7490)\n", + "No axon for neuron ChIN_0 (7491)\n", + "No axon for neuron ChIN_0 (7492)\n", + "No axon for neuron ChIN_0 (7493)\n", + "No axon for neuron LTS_8 (7494)\n", + "Processing neuron 7500\n", + "No axon for neuron ChIN_0 (7590)\n", + "No axon for neuron ChIN_0 (7591)\n", + "No axon for neuron ChIN_0 (7592)\n", + "Processing neuron 7600\n", + "Processing neuron 7700\n", + "No axon for neuron ChIN_0 (7702)\n", + "No axon for neuron ChIN_0 (7703)\n", + "Processing neuron 7800\n", + "No axon for neuron ChIN_0 (7810)\n", + "No axon for neuron ChIN_0 (7811)\n", + "No axon for neuron ChIN_0 (7812)\n", + "No axon for neuron ChIN_0 (7813)\n", + "No axon for neuron LTS_0 (7814)\n", + "Processing neuron 7900\n", + "No axon for neuron LTS_0 (7900)\n", + "No axon for neuron LTS_7 (7901)\n", + "No axon for neuron ChIN_0 (7990)\n", + "No axon for neuron ChIN_0 (7991)\n", + "No axon for neuron LTS_0 (7992)\n", + "Processing neuron 8000\n", + "No axon for neuron ChIN_0 (8085)\n", + "No axon for neuron LTS_1 (8086)\n", + "No axon for neuron LTS_6 (8087)\n", + "Processing neuron 8100\n", + "No axon for neuron ChIN_0 (8184)\n", + "No axon for neuron ChIN_0 (8185)\n", + "Processing neuron 8200\n", + "No axon for neuron ChIN_0 (8266)\n", + "No axon for neuron ChIN_0 (8267)\n", + "Processing neuron 8300\n", + "No axon for neuron ChIN_0 (8398)\n", + "Processing neuron 8400\n", + "Processing neuron 8500\n", + "Processing neuron 8600\n", + "No axon for neuron LTS_2 (8604)\n", + "No axon for neuron LTS_8 (8605)\n", + "Processing neuron 8700\n", + "No axon for neuron LTS_3 (8700)\n", + "No axon for neuron LTS_8 (8701)\n", + "No axon for neuron LTS_8 (8725)\n", + "No axon for neuron ChIN_0 (8747)\n", + "No axon for neuron ChIN_0 (8748)\n", + "No axon for neuron LTS_1 (8749)\n", + "No axon for neuron LTS_3 (8750)\n", + "No axon for neuron LTS_5 (8751)\n", + "No axon for neuron LTS_7 (8752)\n", + "Processing neuron 8800\n", + "No axon for neuron ChIN_0 (8875)\n", + "No axon for neuron ChIN_0 (8876)\n", + "Processing neuron 8900\n", + "Processing neuron 9000\n", + "No axon for neuron ChIN_0 (9090)\n", + "No axon for neuron ChIN_0 (9091)\n", + "No axon for neuron LTS_0 (9092)\n", + "Processing neuron 9100\n", + "No axon for neuron LTS_1 (9172)\n", + "Processing neuron 9200\n", + "Processing neuron 9300\n", + "No axon for neuron LTS_3 (9355)\n", + "No axon for neuron LTS_8 (9356)\n", + "Processing neuron 9400\n", + "No axon for neuron LTS_0 (9435)\n", + "No axon for neuron LTS_5 (9436)\n", + "Processing neuron 9500\n", + "No axon for neuron LTS_1 (9522)\n", + "Processing neuron 9600\n", + "Processing neuron 9700\n", + "No axon for neuron ChIN_0 (9715)\n", + "Processing neuron 9800\n", + "No axon for neuron LTS_4 (9812)\n", + "No axon for neuron LTS_6 (9813)\n", + "No axon for neuron ChIN_0 (9893)\n", + "Processing neuron 9900\n", + "No axon for neuron LTS_8 (9980)\n", + "Synapse degeneration recovery...\n", + "Running post degeneration pruning of synapses\n", + "1000000 / 5545921\n", + "1000000 / 2825190\n", + "2000000 / 5545921\n", + "3000000 / 5545921\n", + "2000000 / 2825190\n", + "4000000 / 5545921\n", + "5000000 / 5545921\n", + "2825190 / 2825190\n", + "Unable to compensate for 27503.2 degenerated synapses.\n", + "Post pruning. Keeping 1800534/2825190 (63.731%)\n", + "Keeping 1800534 out of 5545921 synapses (32.466 %)\n", + "Loading synapses into memory.\n", + "1499 / 1499\n", + "Keeping 1499 out of 1499 gap junctions (100.000 %)\n", + "Writing new input data to networks/PD-example-10k/PD2/input-spikes.hdf5\n", + "Processed input to FS_1 (0), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (1), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (10), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (100), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1000), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1001), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1002), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1003), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1004), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1005), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1006), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1007), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1008), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1009), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (101), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1010), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1011), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1012), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1013), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1014), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1015), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1016), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1017), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (1018), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1019), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (102), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1020), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1021), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1022), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1023), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1024), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1025), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1026), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1027), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1028), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1029), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (103), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1030), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1031), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1032), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1033), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1034), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1035), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1036), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1037), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1038), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1039), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (104), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1040), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1041), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1042), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1043), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1044), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1045), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1046), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1047), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1048), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1049), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (105), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1050), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1051), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1052), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1053), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1054), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1055), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1056), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1057), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1058), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1059), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (106), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1060), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1061), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1062), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1063), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1064), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1065), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1066), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1067), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1068), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1069), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (107), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1070), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1071), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1072), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1073), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1074), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1075), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1076), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1077), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1078), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1079), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (108), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1080), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1081), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1082), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1083), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1084), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1085), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1086), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1087), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (1088), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1089), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (109), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1090), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1091), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1092), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1093), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1094), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1095), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1096), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (1097), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (1098), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1099), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (11), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (110), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1100), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1101), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1102), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1103), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1104), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1105), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1106), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1107), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1108), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1109), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (111), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1110), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1111), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1112), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1113), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1114), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1115), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1116), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1117), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1118), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1119), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (112), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1120), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1121), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1122), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1123), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1124), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1125), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1126), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1127), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1128), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1129), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (113), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1130), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1131), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1132), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1133), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1134), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1135), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1136), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1137), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1138), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1139), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (114), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1140), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1141), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1142), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1143), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1144), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1145), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1146), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1147), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1148), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1149), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (115), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1150), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1151), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1152), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1153), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1154), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1155), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1156), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1157), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1158), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1159), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (116), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1160), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1161), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1162), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1163), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1164), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1165), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1166), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1167), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1168), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (1169), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (117), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (1170), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (1171), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (1172), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (1173), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (1174), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1175), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1176), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1177), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1178), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1179), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (118), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1180), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1181), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1182), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1183), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1184), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1185), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1186), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1187), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1188), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1189), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (119), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1190), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1191), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1192), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1193), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1194), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1195), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1196), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1197), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1198), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1199), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (12), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (120), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1200), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1201), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1202), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1203), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1204), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1205), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1206), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1207), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1208), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1209), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (121), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1210), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1211), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1212), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1213), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1214), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1215), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1216), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1217), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1218), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1219), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (122), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1220), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1221), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1222), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1223), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1224), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1225), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1226), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1227), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1228), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1229), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (123), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1230), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1231), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1232), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1233), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1234), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1235), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1236), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1237), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1238), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1239), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (124), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1240), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1241), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1242), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1243), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1244), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1245), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1246), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1247), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1248), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1249), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (125), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1250), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1251), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1252), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1253), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1254), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1255), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1256), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1257), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1258), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (1259), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (126), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1260), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1261), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1262), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1263), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1264), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1265), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1266), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1267), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1268), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1269), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (127), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1270), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (1271), keeping 20 out of 200 inputs (plus remapping 0 inputs)(10.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1272), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1273), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1274), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1275), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1276), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1277), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1278), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1279), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (128), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1280), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1281), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1282), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1283), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1284), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1285), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1286), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1287), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1288), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1289), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (129), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1290), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1291), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1292), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1293), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1294), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1295), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1296), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1297), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1298), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1299), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (13), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (130), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1300), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1301), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1302), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1303), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1304), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1305), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1306), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1307), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1308), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1309), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (131), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1310), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1311), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1312), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1313), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1314), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1315), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1316), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1317), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1318), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1319), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (132), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1320), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1321), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1322), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1323), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1324), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1325), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1326), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1327), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1328), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1329), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (133), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1330), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1331), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1332), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1333), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1334), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1335), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1336), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1337), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1338), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1339), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (134), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1340), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1341), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1342), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1343), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1344), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1345), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1346), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1347), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1348), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1349), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (135), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1350), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1351), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1352), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1353), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (1354), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (1355), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1356), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1357), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1358), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1359), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (136), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1360), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1361), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1362), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1363), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1364), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1365), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1366), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1367), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1368), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1369), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (137), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1370), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1371), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1372), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1373), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1374), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1375), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1376), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1377), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1378), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1379), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (138), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1380), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1381), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1382), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1383), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1384), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1385), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1386), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1387), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1388), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1389), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (139), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1390), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1391), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1392), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1393), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1394), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1395), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1396), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1397), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1398), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1399), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (14), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (140), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1400), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1401), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1402), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1403), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1404), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1405), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1406), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1407), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1408), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1409), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (141), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1410), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1411), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1412), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1413), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1414), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1415), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1416), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1417), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1418), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1419), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (142), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1420), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1421), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1422), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1423), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1424), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1425), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1426), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1427), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1428), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1429), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (143), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1430), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1431), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1432), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1433), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1434), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1435), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1436), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1437), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1438), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1439), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (144), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1440), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1441), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1442), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1443), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1444), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1445), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1446), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1447), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1448), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1449), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (145), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1450), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1451), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1452), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1453), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1454), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1455), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1456), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1457), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1458), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1459), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (146), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to FS_1 (1460), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1461), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1462), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1463), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1464), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1465), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1466), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1467), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1468), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1469), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (147), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1470), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1471), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1472), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1473), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1474), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1475), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1476), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1477), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1478), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1479), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (148), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1480), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1481), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1482), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1483), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1484), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1485), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1486), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1487), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1488), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1489), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (149), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1490), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1491), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1492), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1493), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1494), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1495), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1496), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1497), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1498), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1499), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (15), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (150), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1500), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1501), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1502), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1503), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1504), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1505), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1506), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1507), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1508), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1509), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (151), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1510), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1511), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1512), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1513), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1514), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1515), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1516), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1517), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1518), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1519), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (152), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1520), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1521), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1522), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1523), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1524), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1525), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1526), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1527), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1528), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1529), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (153), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1530), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1531), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1532), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1533), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1534), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1535), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1536), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1537), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1538), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1539), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (154), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1540), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1541), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1542), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1543), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1544), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1545), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1546), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1547), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1548), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1549), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (155), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1550), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1551), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1552), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1553), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1554), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1555), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (1556), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (1557), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (1558), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1559), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (156), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1560), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1561), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1562), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1563), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1564), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1565), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1566), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1567), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1568), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (1569), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (157), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1570), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1571), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1572), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1573), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1574), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1575), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1576), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1577), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1578), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1579), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (158), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1580), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1581), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1582), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1583), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1584), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1585), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1586), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1587), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1588), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1589), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (159), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1590), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1591), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1592), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1593), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1594), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1595), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1596), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1597), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1598), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1599), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (16), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (160), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1600), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1601), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1602), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1603), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1604), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1605), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (1606), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1607), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1608), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1609), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (161), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1610), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1611), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1612), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1613), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1614), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1615), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1616), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1617), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1618), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1619), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (162), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1620), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1621), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1622), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1623), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1624), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1625), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1626), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1627), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1628), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1629), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (163), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1630), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1631), keeping 156 out of 200 inputs (plus remapping 0 inputs)(78.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1632), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1633), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1634), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1635), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1636), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1637), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1638), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1639), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (164), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1640), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1641), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1642), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1643), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1644), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1645), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1646), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1647), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1648), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1649), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (165), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1650), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1651), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1652), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1653), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1654), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1655), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (1656), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1657), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1658), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1659), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (166), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1660), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1661), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1662), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to FS_0 (1663), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (1664), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1665), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1666), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (1667), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1668), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1669), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (167), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1670), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1671), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1672), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1673), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1674), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1675), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1676), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1677), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1678), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1679), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (168), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1680), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1681), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1682), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1683), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1684), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1685), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1686), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1687), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1688), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1689), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (169), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1690), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1691), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1692), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1693), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1694), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1695), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1696), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1697), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1698), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1699), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (17), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (170), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1700), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1701), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1702), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1703), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1704), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1705), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1706), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1707), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1708), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1709), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (171), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1710), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1711), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1712), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1713), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1714), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1715), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1716), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1717), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1718), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1719), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (172), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1720), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1721), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1722), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1723), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1724), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1725), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1726), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1727), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1728), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1729), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (173), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1730), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1731), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1732), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1733), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1734), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1735), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1736), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1737), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1738), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1739), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (174), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1740), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1741), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1742), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1743), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1744), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1745), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1746), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1747), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1748), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1749), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (175), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1750), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1751), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1752), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1753), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (1754), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1755), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1756), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (1757), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (1758), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1759), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (176), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (1760), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1761), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1762), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1763), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1764), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1765), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1766), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1767), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1768), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (1769), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (177), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1770), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (1771), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1772), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (1773), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (1774), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1775), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1776), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1777), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1778), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1779), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (178), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1780), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1781), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (1782), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1783), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1784), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1785), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1786), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1787), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1788), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1789), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (179), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1790), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1791), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1792), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1793), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1794), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1795), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1796), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1797), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1798), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1799), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (18), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (180), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1800), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1801), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1802), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1803), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1804), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1805), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1806), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1807), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1808), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1809), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (181), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1810), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1811), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1812), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1813), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1814), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1815), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1816), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1817), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1818), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1819), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (182), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1820), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1821), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1822), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1823), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1824), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1825), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1826), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1827), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1828), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1829), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (183), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1830), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1831), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1832), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1833), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1834), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1835), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1836), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1837), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1838), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1839), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (184), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1840), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1841), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1842), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (1843), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (1844), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1845), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (1846), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1847), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1848), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1849), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (185), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (1850), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (1851), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1852), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (1853), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (1854), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (1855), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1856), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (1857), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1858), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1859), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (186), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (1860), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1861), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (1862), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1863), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1864), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1865), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (1866), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (1867), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1868), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1869), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (187), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1870), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1871), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1872), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1873), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1874), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1875), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1876), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1877), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1878), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1879), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (188), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (1880), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1881), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1882), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1883), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1884), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1885), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1886), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1887), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1888), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1889), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (189), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1890), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1891), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1892), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1893), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1894), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (1895), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1896), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1897), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1898), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1899), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (19), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (190), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (1900), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1901), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (1902), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1903), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1904), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1905), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1906), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (1907), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1908), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1909), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (191), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1910), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1911), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (1912), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1913), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1914), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1915), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1916), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1917), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1918), keeping 159 out of 200 inputs (plus remapping 0 inputs)(79.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1919), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (192), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (1920), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (1921), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (1922), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (1923), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (1924), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (1925), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (1926), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1927), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (1928), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1929), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (193), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1930), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (1931), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1932), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1933), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (1934), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (1935), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (1936), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (1937), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (1938), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (1939), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (194), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (1940), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (1941), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (1942), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1943), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1944), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (1945), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1946), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (1947), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1948), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1949), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (195), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (1950), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1951), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (1952), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (1953), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1954), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1955), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1956), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (1957), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1958), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1959), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (196), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1960), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (1961), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1962), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1963), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (1964), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (1965), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1966), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (1967), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (1968), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (1969), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (197), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (1970), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (1971), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (1972), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (1973), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1974), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (1975), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1976), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1977), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (1978), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (1979), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (198), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1980), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (1981), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1982), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (1983), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1984), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1985), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1986), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1987), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (1988), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1989), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (199), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1990), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (1991), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (1992), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1993), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (1994), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1995), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1996), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (1997), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1998), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (1999), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (20), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (200), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2000), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2001), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2002), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2003), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2004), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2005), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2006), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2007), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2008), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2009), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (201), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to FS_3 (2010), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2011), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2012), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2013), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2014), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2015), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2016), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2017), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2018), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2019), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (202), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2020), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2021), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2022), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2023), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2024), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2025), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2026), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2027), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2028), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2029), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (203), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2030), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2031), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2032), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2033), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2034), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2035), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2036), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2037), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2038), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2039), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (204), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2040), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2041), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2042), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2043), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2044), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2045), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2046), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2047), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2048), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2049), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (205), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2050), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2051), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2052), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2053), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2054), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2055), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2056), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2057), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2058), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2059), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (206), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2060), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2061), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2062), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2063), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2064), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2065), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2066), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2067), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2068), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2069), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (207), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2070), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2071), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2072), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2073), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2074), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2075), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2076), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2077), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2078), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2079), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (208), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2080), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2081), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2082), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2083), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2084), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2085), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2086), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2087), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2088), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2089), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (209), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2090), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2091), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2092), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2093), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2094), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2095), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2096), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2097), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2098), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2099), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (21), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (210), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2100), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2101), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2102), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2103), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2104), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2105), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2106), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (2107), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2108), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2109), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (211), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2110), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2111), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2112), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2113), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2114), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2115), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2116), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2117), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2118), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2119), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (212), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2120), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2121), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2122), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2123), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2124), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2125), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2126), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2127), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2128), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2129), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (213), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2130), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2131), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2132), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2133), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2134), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2135), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2136), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2137), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2138), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2139), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (214), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2140), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2141), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2142), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2143), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2144), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2145), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2146), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2147), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2148), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2149), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (215), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2150), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2151), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2152), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2153), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2154), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2155), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2156), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2157), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2158), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2159), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (216), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2160), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2161), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2162), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2163), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2164), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2165), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2166), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2167), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2168), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2169), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (217), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2170), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2171), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2172), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2173), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2174), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2175), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2176), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2177), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2178), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2179), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (218), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2180), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2181), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2182), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2183), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2184), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2185), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2186), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2187), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2188), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2189), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (219), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2190), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2191), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2192), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2193), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2194), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2195), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2196), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2197), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2198), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2199), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (22), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (220), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2200), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2201), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2202), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2203), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2204), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2205), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2206), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2207), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2208), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2209), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (221), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2210), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2211), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2212), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2213), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2214), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2215), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2216), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2217), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2218), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2219), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (222), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2220), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2221), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2222), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2223), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2224), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2225), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2226), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2227), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2228), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2229), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (223), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2230), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2231), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2232), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2233), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2234), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2235), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2236), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2237), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2238), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2239), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (224), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2240), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2241), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2242), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2243), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2244), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2245), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2246), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2247), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2248), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2249), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (225), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2250), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2251), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2252), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2253), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2254), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2255), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2256), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2257), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2258), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2259), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (226), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2260), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2261), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2262), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2263), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2264), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2265), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2266), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2267), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2268), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2269), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (227), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2270), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2271), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2272), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2273), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2274), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2275), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2276), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2277), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2278), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2279), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (228), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2280), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2281), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2282), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2283), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2284), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2285), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2286), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2287), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2288), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2289), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (229), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2290), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2291), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2292), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2293), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2294), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2295), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2296), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2297), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2298), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2299), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (23), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (230), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2300), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2301), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2302), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2303), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2304), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2305), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2306), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2307), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2308), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2309), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (231), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2310), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2311), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2312), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2313), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2314), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2315), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2316), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2317), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2318), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2319), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (232), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2320), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2321), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2322), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2323), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2324), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2325), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2326), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2327), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2328), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (2329), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (233), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to FS_3 (2330), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2331), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2332), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2333), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2334), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2335), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2336), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2337), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2338), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2339), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (234), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2340), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2341), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2342), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2343), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2344), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2345), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2346), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2347), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2348), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2349), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (235), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2350), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2351), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2352), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2353), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2354), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2355), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2356), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2357), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2358), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2359), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (236), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2360), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2361), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2362), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2363), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2364), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2365), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2366), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2367), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2368), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2369), keeping 157 out of 200 inputs (plus remapping 0 inputs)(78.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (237), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2370), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2371), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2372), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2373), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2374), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2375), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2376), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2377), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2378), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2379), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (238), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2380), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2381), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2382), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2383), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2384), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2385), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2386), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2387), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2388), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2389), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (239), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2390), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2391), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2392), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2393), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2394), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2395), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2396), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2397), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2398), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2399), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (24), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (240), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2400), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2401), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2402), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2403), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2404), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2405), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2406), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2407), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2408), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2409), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (241), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2410), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2411), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2412), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2413), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2414), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2415), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2416), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2417), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2418), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2419), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (242), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2420), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2421), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2422), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2423), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2424), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2425), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2426), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2427), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2428), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2429), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (243), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2430), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2431), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2432), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2433), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2434), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2435), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2436), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2437), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2438), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2439), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (244), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2440), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2441), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2442), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2443), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2444), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2445), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2446), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2447), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2448), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2449), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (245), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2450), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2451), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2452), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2453), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2454), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2455), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2456), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2457), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2458), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2459), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (246), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2460), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2461), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2462), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2463), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2464), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2465), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2466), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2467), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2468), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2469), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (247), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2470), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2471), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2472), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2473), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2474), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2475), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2476), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2477), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2478), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2479), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (248), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2480), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2481), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2482), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2483), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2484), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2485), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2486), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2487), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2488), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2489), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (249), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2490), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2491), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2492), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2493), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2494), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2495), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2496), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2497), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2498), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2499), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (25), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (250), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2500), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2501), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2502), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2503), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2504), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2505), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2506), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2507), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2508), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2509), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (251), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2510), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2511), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2512), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2513), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2514), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2515), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2516), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2517), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2518), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2519), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (252), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2520), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2521), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2522), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2523), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2524), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2525), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2526), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2527), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2528), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2529), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (253), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2530), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2531), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2532), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2533), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2534), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2535), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2536), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2537), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2538), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2539), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (254), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2540), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (2541), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2542), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2543), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2544), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2545), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2546), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2547), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2548), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2549), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (255), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2550), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2551), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2552), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2553), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2554), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2555), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2556), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2557), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2558), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2559), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (256), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2560), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2561), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2562), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2563), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2564), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2565), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2566), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2567), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2568), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2569), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (257), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2570), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2571), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2572), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2573), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2574), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2575), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2576), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2577), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2578), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2579), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (258), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2580), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2581), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2582), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2583), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2584), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2585), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2586), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2587), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2588), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2589), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (259), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2590), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2591), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2592), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2593), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2594), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2595), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2596), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2597), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2598), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2599), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (26), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (260), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2600), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2601), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2602), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2603), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2604), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2605), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2606), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2607), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2608), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2609), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (261), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2610), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2611), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2612), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2613), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2614), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2615), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2616), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (2617), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2618), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2619), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (262), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2620), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2621), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2622), keeping 166 out of 200 inputs (plus remapping 0 inputs)(83.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2623), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2624), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2625), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2626), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2627), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2628), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2629), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (263), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2630), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2631), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2632), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (2633), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2634), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2635), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2636), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2637), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2638), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2639), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (264), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2640), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2641), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2642), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2643), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2644), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2645), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2646), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2647), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2648), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2649), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (265), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2650), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2651), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2652), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2653), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2654), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2655), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2656), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2657), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2658), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2659), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (266), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2660), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2661), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2662), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2663), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2664), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2665), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2666), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2667), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2668), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2669), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (267), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2670), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2671), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2672), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2673), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2674), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2675), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2676), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2677), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2678), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2679), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (268), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2680), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2681), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2682), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2683), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2684), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2685), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2686), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2687), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2688), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2689), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (269), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2690), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2691), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2692), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2693), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2694), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2695), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2696), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2697), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2698), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2699), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (27), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (270), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2700), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2701), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2702), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2703), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2704), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2705), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2706), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (2707), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2708), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2709), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (271), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (2710), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2711), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2712), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2713), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2714), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2715), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2716), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2717), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2718), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2719), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (272), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2720), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2721), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2722), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2723), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2724), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2725), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2726), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2727), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2728), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2729), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (273), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2730), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2731), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2732), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2733), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2734), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2735), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (2736), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (2737), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (2738), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (2739), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (274), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2740), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2741), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2742), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2743), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2744), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2745), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2746), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (2747), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2748), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2749), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (275), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2750), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2751), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2752), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2753), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2754), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2755), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2756), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2757), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2758), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2759), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (276), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2760), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2761), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2762), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2763), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2764), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2765), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2766), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (2767), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2768), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2769), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (277), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2770), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2771), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2772), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2773), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2774), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2775), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2776), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2777), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (2778), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2779), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (278), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2780), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (2781), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2782), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2783), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2784), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2785), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2786), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2787), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2788), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2789), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (279), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2790), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2791), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2792), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2793), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2794), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2795), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2796), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2797), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2798), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2799), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (28), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (280), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2800), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2801), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2802), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2803), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2804), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2805), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2806), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2807), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2808), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2809), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (281), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2810), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2811), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2812), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2813), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2814), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (2815), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (2816), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2817), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2818), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2819), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (282), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2820), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2821), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2822), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2823), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (2824), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2825), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2826), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2827), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (2828), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2829), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (283), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (2830), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (2831), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2832), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (2833), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2834), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2835), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2836), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2837), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2838), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2839), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (284), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2840), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2841), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2842), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (2843), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2844), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2845), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2846), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2847), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2848), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2849), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (285), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (2850), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2851), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2852), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (2853), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2854), keeping 23 out of 200 inputs (plus remapping 0 inputs)(11.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2855), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2856), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2857), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2858), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2859), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (286), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2860), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2861), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2862), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2863), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2864), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (2865), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2866), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2867), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2868), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2869), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (287), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2870), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2871), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2872), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2873), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2874), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2875), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2876), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2877), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2878), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2879), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (288), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2880), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2881), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (2882), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2883), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2884), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2885), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2886), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2887), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2888), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (2889), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (289), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2890), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (2891), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2892), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2893), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2894), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2895), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2896), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2897), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2898), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2899), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (29), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (290), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2900), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2901), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (2902), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2903), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (2904), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (2905), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2906), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (2907), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2908), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (2909), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (291), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2910), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2911), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (2912), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2913), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2914), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2915), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (2916), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2917), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (2918), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (2919), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (292), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (2920), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2921), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (2922), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2923), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (2924), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2925), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2926), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2927), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (2928), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (2929), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (293), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2930), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (2931), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (2932), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (2933), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2934), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (2935), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2936), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (2937), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2938), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2939), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (294), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (2940), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2941), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (2942), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (2943), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (2944), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (2945), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (2946), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (2947), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (2948), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2949), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (295), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (2950), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (2951), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2952), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (2953), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2954), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (2955), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2956), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (2957), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2958), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (2959), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (296), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (2960), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2961), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2962), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (2963), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2964), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (2965), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (2966), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2967), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2968), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (2969), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (297), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2970), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2971), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (2972), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (2973), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (2974), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2975), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (2976), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (2977), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2978), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (2979), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (298), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (2980), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2981), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2982), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2983), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2984), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (2985), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2986), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2987), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (2988), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (2989), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (299), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2990), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2991), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (2992), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2993), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2994), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (2995), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (2996), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2997), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (2998), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (2999), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (30), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (300), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (3000), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3001), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3002), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3003), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3004), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3005), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3006), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3007), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3008), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3009), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (301), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3010), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3011), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3012), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3013), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3014), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3015), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3016), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3017), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3018), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3019), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (302), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3020), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3021), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3022), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3023), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3024), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3025), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3026), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3027), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3028), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3029), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (303), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3030), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3031), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3032), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3033), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3034), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3035), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3036), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3037), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3038), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3039), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (304), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3040), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3041), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3042), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3043), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3044), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3045), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3046), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3047), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3048), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3049), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (305), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3050), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3051), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3052), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3053), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3054), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3055), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3056), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3057), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3058), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3059), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (306), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3060), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3061), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3062), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3063), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3064), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3065), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3066), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3067), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3068), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3069), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (307), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3070), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3071), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3072), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3073), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3074), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3075), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3076), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3077), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3078), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3079), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to FS_2 (308), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3080), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3081), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3082), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3083), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3084), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3085), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3086), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3087), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3088), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3089), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (309), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3090), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3091), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3092), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3093), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3094), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3095), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3096), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3097), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3098), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3099), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (31), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (310), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3100), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3101), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3102), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3103), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3104), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3105), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3106), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3107), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3108), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3109), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (311), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3110), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3111), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3112), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3113), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3114), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3115), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3116), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3117), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3118), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3119), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (312), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3120), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3121), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3122), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3123), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3124), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3125), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3126), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3127), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3128), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3129), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (313), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3130), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3131), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3132), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3133), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3134), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3135), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3136), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3137), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3138), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3139), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (314), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3140), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3141), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3142), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3143), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3144), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3145), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (3146), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (3147), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3148), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (3149), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (315), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (3150), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (3151), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (3152), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (3153), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3154), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3155), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3156), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3157), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3158), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3159), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (316), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3160), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3161), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3162), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3163), keeping 22 out of 200 inputs (plus remapping 0 inputs)(11.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3164), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3165), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3166), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3167), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3168), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3169), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (317), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3170), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3171), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3172), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3173), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3174), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3175), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3176), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3177), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3178), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3179), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (318), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3180), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3181), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3182), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3183), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3184), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3185), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3186), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3187), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3188), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3189), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (319), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3190), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3191), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3192), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3193), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3194), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3195), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3196), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3197), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3198), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3199), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (32), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (320), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3200), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3201), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3202), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3203), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3204), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3205), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3206), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3207), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3208), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3209), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (321), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3210), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3211), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3212), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3213), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3214), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3215), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3216), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3217), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3218), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3219), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (322), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3220), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3221), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3222), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3223), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3224), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3225), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3226), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3227), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3228), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3229), keeping 156 out of 200 inputs (plus remapping 0 inputs)(78.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (323), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3230), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3231), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3232), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3233), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3234), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3235), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3236), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3237), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3238), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3239), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (324), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3240), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3241), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3242), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3243), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3244), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3245), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3246), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3247), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3248), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3249), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (325), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3250), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3251), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3252), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3253), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3254), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3255), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3256), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3257), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3258), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3259), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (326), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3260), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3261), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3262), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3263), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3264), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3265), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3266), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3267), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3268), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3269), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (327), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3270), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3271), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3272), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3273), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3274), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3275), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3276), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3277), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3278), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3279), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (328), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3280), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3281), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3282), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3283), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3284), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3285), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3286), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3287), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3288), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3289), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (329), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3290), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3291), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3292), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3293), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3294), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3295), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3296), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3297), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3298), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3299), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (33), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (330), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3300), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3301), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3302), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3303), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3304), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3305), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3306), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3307), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3308), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3309), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (331), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3310), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3311), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3312), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3313), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3314), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3315), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3316), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3317), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3318), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3319), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (332), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3320), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3321), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3322), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3323), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (3324), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (3325), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3326), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3327), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3328), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3329), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (333), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3330), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3331), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3332), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3333), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3334), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3335), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3336), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3337), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3338), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3339), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (334), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3340), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3341), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3342), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3343), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3344), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3345), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3346), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3347), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3348), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3349), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (335), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3350), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3351), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3352), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3353), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3354), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3355), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3356), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3357), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3358), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3359), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (336), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3360), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3361), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3362), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3363), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3364), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3365), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3366), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3367), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3368), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3369), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (337), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3370), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3371), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3372), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3373), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3374), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3375), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3376), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3377), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3378), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (3379), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (338), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3380), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3381), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3382), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3383), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3384), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3385), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3386), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3387), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (3388), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3389), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (339), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3390), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3391), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3392), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3393), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3394), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3395), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3396), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3397), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3398), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3399), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (34), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (340), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3400), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3401), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3402), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3403), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3404), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3405), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3406), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3407), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3408), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3409), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (341), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3410), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3411), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3412), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3413), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3414), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3415), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3416), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3417), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3418), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3419), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (342), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3420), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3421), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3422), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3423), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (3424), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3425), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3426), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3427), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3428), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3429), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (343), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3430), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3431), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3432), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3433), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3434), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3435), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3436), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3437), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3438), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3439), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (344), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3440), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3441), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3442), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3443), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3444), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3445), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3446), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3447), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3448), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3449), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (345), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3450), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3451), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3452), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3453), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3454), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3455), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3456), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3457), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3458), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3459), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (346), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3460), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3461), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3462), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3463), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3464), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3465), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3466), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3467), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3468), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3469), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (347), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3470), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3471), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3472), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3473), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3474), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3475), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3476), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3477), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3478), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3479), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (348), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3480), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3481), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3482), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3483), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3484), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3485), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3486), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3487), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3488), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (3489), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (349), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3490), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3491), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3492), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3493), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3494), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3495), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3496), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3497), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3498), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3499), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (35), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (350), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3500), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (3501), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3502), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3503), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3504), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3505), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3506), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3507), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3508), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3509), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (351), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3510), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3511), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3512), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3513), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3514), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3515), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3516), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3517), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3518), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3519), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (352), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3520), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3521), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3522), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3523), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3524), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3525), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3526), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3527), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3528), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3529), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (353), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3530), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3531), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to FS_1 (3532), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (3533), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3534), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3535), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3536), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3537), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3538), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3539), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (354), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3540), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3541), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3542), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3543), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3544), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3545), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3546), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3547), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3548), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3549), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (355), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3550), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3551), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3552), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3553), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3554), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3555), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3556), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3557), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3558), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3559), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (356), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3560), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3561), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3562), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3563), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3564), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3565), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3566), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3567), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3568), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3569), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (357), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3570), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3571), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3572), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3573), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3574), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3575), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3576), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3577), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3578), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3579), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (358), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3580), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3581), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3582), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3583), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3584), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3585), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3586), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3587), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3588), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3589), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (359), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3590), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3591), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3592), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3593), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3594), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3595), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3596), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3597), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3598), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3599), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (36), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (360), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3600), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3601), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3602), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3603), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3604), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3605), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3606), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3607), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3608), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3609), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (361), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3610), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3611), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3612), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3613), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3614), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3615), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3616), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3617), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3618), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3619), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (362), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3620), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3621), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (3622), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3623), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3624), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3625), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3626), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3627), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3628), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3629), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (363), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3630), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3631), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3632), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3633), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3634), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3635), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3636), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3637), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3638), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3639), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (364), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3640), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3641), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3642), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3643), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3644), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3645), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3646), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3647), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3648), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3649), keeping 158 out of 200 inputs (plus remapping 0 inputs)(79.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (365), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3650), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3651), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3652), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3653), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3654), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3655), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3656), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3657), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3658), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3659), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (366), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3660), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3661), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3662), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3663), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (3664), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (3665), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (3666), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3667), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3668), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3669), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (367), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3670), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3671), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3672), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3673), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3674), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3675), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3676), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3677), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3678), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3679), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (368), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3680), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3681), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3682), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3683), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3684), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3685), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3686), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3687), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3688), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3689), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (369), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3690), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3691), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3692), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3693), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3694), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3695), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3696), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3697), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3698), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3699), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (37), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (370), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (3700), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (3701), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3702), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3703), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3704), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (3705), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3706), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3707), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3708), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3709), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (371), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3710), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3711), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3712), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3713), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3714), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3715), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3716), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (3717), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3718), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3719), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (372), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3720), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3721), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3722), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3723), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3724), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3725), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3726), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3727), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3728), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3729), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (373), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3730), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3731), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3732), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3733), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3734), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3735), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3736), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3737), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (3738), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3739), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (374), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3740), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3741), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3742), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3743), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3744), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3745), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3746), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3747), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3748), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3749), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (375), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3750), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3751), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3752), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3753), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3754), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3755), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3756), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3757), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3758), keeping 152 out of 200 inputs (plus remapping 0 inputs)(76.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3759), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (376), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (3760), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3761), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3762), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3763), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3764), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3765), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3766), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (3767), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (3768), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3769), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (377), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3770), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3771), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3772), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3773), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3774), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3775), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3776), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (3777), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3778), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3779), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (378), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3780), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3781), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3782), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3783), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3784), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3785), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3786), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3787), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3788), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3789), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (379), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3790), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3791), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3792), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3793), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3794), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3795), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3796), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3797), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3798), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (3799), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (38), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (380), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3800), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3801), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3802), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3803), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3804), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3805), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3806), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3807), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3808), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3809), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (381), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3810), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3811), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3812), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (3813), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (3814), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3815), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3816), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3817), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3818), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3819), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (382), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3820), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3821), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3822), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3823), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3824), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3825), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (3826), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (3827), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3828), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3829), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (383), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3830), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3831), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3832), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (3833), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3834), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3835), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (3836), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3837), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3838), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3839), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (384), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (3840), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3841), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3842), keeping 152 out of 200 inputs (plus remapping 0 inputs)(76.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3843), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3844), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (3845), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3846), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3847), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3848), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3849), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (385), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3850), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3851), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (3852), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3853), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3854), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3855), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3856), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3857), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3858), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3859), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (386), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to FS_3 (3860), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3861), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3862), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3863), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3864), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3865), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3866), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3867), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (3868), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3869), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (387), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3870), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3871), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3872), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3873), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3874), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3875), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (3876), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (3877), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3878), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3879), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (388), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3880), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3881), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3882), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3883), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3884), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3885), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3886), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3887), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3888), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3889), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (389), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3890), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3891), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3892), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (3893), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3894), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3895), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3896), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3897), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (3898), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (3899), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (39), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (390), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (3900), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (3901), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3902), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (3903), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (3904), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3905), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3906), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (3907), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3908), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3909), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (391), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (3910), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (3911), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3912), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (3913), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (3914), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (3915), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3916), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3917), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (3918), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3919), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (392), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3920), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (3921), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (3922), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (3923), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (3924), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3925), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3926), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3927), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (3928), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (3929), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (393), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (3930), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (3931), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (3932), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3933), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (3934), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (3935), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (3936), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3937), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (3938), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3939), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (394), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3940), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (3941), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3942), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (3943), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3944), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (3945), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3946), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3947), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (3948), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (3949), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (395), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (3950), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3951), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3952), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3953), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3954), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3955), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (3956), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3957), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (3958), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (3959), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (396), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3960), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3961), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3962), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3963), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (3964), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3965), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (3966), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3967), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3968), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (3969), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (397), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3970), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3971), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (3972), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3973), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (3974), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (3975), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (3976), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (3977), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (3978), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3979), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (398), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (3980), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3981), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3982), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (3983), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3984), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (3985), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (3986), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3987), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (3988), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3989), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (399), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (3990), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3991), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (3992), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3993), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3994), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3995), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (3996), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (3997), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3998), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (3999), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (40), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (400), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4000), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4001), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4002), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4003), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4004), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4005), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4006), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4007), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4008), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4009), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (401), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4010), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4011), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4012), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4013), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4014), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4015), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4016), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4017), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4018), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4019), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (402), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4020), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4021), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4022), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4023), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4024), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4025), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4026), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4027), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4028), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4029), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (403), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4030), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4031), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4032), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4033), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4034), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4035), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4036), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4037), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4038), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4039), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (404), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4040), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4041), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4042), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4043), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4044), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4045), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4046), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4047), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4048), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4049), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (405), keeping 155 out of 200 inputs (plus remapping 0 inputs)(77.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4050), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4051), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4052), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4053), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4054), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4055), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (4056), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4057), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4058), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4059), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (406), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4060), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4061), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4062), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4063), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4064), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4065), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4066), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4067), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4068), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4069), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (407), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4070), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4071), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4072), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4073), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4074), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (4075), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (4076), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (4077), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4078), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4079), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (408), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4080), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4081), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4082), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4083), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4084), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4085), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4086), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4087), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4088), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4089), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (409), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4090), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4091), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4092), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4093), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4094), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4095), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4096), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4097), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4098), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4099), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (41), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (410), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4100), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4101), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4102), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4103), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4104), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4105), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4106), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4107), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4108), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (4109), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (411), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4110), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4111), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4112), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4113), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4114), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4115), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4116), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4117), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4118), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4119), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (412), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4120), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4121), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4122), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4123), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4124), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4125), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4126), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4127), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4128), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4129), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (413), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4130), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4131), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4132), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4133), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4134), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4135), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4136), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4137), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4138), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4139), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (414), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4140), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4141), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4142), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4143), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4144), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4145), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4146), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4147), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4148), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4149), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (415), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4150), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4151), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4152), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4153), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4154), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4155), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4156), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4157), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4158), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4159), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (416), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4160), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4161), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4162), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4163), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4164), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4165), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4166), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (4167), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (4168), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (4169), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (417), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (4170), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (4171), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (4172), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4173), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4174), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4175), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4176), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4177), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4178), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4179), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (418), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4180), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4181), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4182), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4183), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4184), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4185), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4186), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4187), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4188), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4189), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (419), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4190), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4191), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4192), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4193), keeping 23 out of 200 inputs (plus remapping 0 inputs)(11.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4194), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4195), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4196), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4197), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4198), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4199), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (42), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (420), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4200), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4201), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4202), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4203), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4204), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4205), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4206), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4207), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4208), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4209), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (421), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4210), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4211), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4212), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4213), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4214), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4215), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4216), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4217), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4218), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4219), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (422), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4220), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4221), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4222), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4223), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4224), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4225), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4226), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4227), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4228), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4229), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (423), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4230), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4231), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4232), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4233), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4234), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4235), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4236), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4237), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4238), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4239), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (424), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4240), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4241), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4242), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4243), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4244), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4245), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4246), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4247), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4248), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4249), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (425), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4250), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4251), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4252), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4253), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4254), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4255), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4256), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4257), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4258), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4259), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (426), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4260), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4261), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4262), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4263), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4264), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4265), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4266), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4267), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4268), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4269), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (427), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4270), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4271), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4272), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4273), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4274), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4275), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4276), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4277), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4278), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4279), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (428), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4280), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4281), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4282), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4283), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4284), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4285), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4286), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4287), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4288), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4289), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (429), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4290), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4291), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4292), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4293), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4294), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4295), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4296), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4297), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4298), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4299), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (43), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (430), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4300), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4301), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4302), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4303), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4304), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4305), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4306), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4307), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (4308), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4309), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (431), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to FS_0 (4310), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (4311), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4312), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4313), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4314), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4315), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4316), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4317), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4318), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4319), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (432), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4320), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4321), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4322), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4323), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4324), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4325), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4326), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4327), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4328), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4329), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (433), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4330), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4331), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4332), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4333), keeping 18 out of 200 inputs (plus remapping 0 inputs)(9.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4334), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4335), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4336), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4337), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4338), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4339), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (434), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4340), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4341), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4342), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4343), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4344), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4345), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4346), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4347), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4348), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4349), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (435), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4350), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4351), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4352), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4353), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4354), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4355), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4356), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4357), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4358), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4359), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (436), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4360), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4361), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4362), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4363), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (4364), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (4365), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4366), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4367), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4368), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4369), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (437), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4370), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4371), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4372), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4373), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4374), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4375), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4376), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4377), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4378), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4379), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (438), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4380), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4381), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4382), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4383), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4384), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4385), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4386), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4387), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4388), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4389), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (439), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4390), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4391), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4392), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4393), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4394), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4395), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4396), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4397), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4398), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4399), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (44), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (440), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4400), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4401), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4402), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4403), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4404), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4405), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4406), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4407), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4408), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4409), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (441), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4410), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4411), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4412), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4413), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4414), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4415), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4416), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4417), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4418), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (4419), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (442), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4420), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4421), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4422), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4423), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4424), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4425), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4426), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4427), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4428), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4429), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (443), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4430), keeping 155 out of 200 inputs (plus remapping 0 inputs)(77.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4431), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4432), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4433), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4434), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4435), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4436), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4437), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4438), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4439), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (444), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4440), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4441), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4442), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4443), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4444), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4445), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (4446), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4447), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4448), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (4449), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (445), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (4450), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (4451), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (4452), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (4453), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (4454), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4455), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4456), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4457), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4458), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4459), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (446), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4460), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4461), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4462), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4463), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4464), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4465), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4466), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4467), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4468), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4469), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (447), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4470), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4471), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4472), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4473), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4474), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4475), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4476), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4477), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4478), keeping 25 out of 200 inputs (plus remapping 0 inputs)(12.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4479), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (448), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4480), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4481), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4482), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4483), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4484), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4485), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4486), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4487), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4488), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4489), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (449), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4490), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4491), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4492), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4493), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4494), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4495), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4496), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4497), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4498), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4499), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (45), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (450), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4500), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4501), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4502), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4503), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4504), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4505), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4506), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4507), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4508), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4509), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (451), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4510), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4511), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4512), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4513), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4514), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4515), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4516), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4517), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4518), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4519), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (452), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4520), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4521), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4522), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4523), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4524), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4525), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4526), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4527), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4528), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4529), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (453), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4530), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4531), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4532), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4533), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4534), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4535), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4536), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4537), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4538), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4539), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (454), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4540), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4541), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4542), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4543), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4544), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4545), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4546), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4547), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4548), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4549), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (455), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4550), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4551), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4552), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (4553), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4554), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4555), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4556), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4557), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4558), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4559), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (456), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4560), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4561), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4562), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4563), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4564), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4565), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4566), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4567), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4568), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4569), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (457), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4570), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4571), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4572), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4573), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4574), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4575), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4576), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4577), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4578), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (4579), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (458), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4580), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (4581), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4582), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4583), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4584), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4585), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4586), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4587), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4588), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4589), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (459), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4590), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4591), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4592), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4593), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4594), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4595), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4596), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4597), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4598), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4599), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (46), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (460), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4600), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4601), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4602), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4603), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4604), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4605), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4606), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4607), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4608), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4609), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (461), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4610), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4611), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4612), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4613), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4614), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4615), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4616), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4617), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4618), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (4619), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (462), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4620), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4621), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4622), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4623), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4624), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4625), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4626), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4627), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4628), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4629), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (463), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4630), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4631), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4632), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4633), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4634), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4635), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4636), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4637), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4638), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4639), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (464), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4640), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4641), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4642), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4643), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4644), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4645), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4646), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4647), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4648), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4649), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (465), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4650), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4651), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4652), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4653), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4654), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4655), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4656), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4657), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4658), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4659), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (466), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4660), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4661), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4662), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4663), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4664), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (4665), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (4666), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4667), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4668), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4669), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (467), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4670), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4671), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4672), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4673), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4674), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4675), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4676), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4677), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4678), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4679), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (468), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4680), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4681), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4682), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4683), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (4684), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (4685), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (4686), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (4687), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4688), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4689), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (469), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4690), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4691), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4692), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4693), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4694), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4695), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4696), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4697), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4698), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4699), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (47), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (470), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4700), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4701), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4702), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4703), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4704), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4705), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4706), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4707), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4708), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4709), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (471), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4710), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4711), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4712), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4713), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4714), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4715), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4716), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4717), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4718), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4719), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (472), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4720), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4721), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4722), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4723), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4724), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4725), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4726), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4727), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4728), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4729), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (473), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4730), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4731), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4732), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4733), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4734), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4735), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4736), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4737), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4738), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4739), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (474), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4740), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4741), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4742), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4743), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4744), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4745), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (4746), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4747), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4748), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4749), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (475), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4750), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4751), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4752), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4753), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4754), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4755), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4756), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4757), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4758), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4759), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (476), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4760), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4761), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4762), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4763), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4764), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4765), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4766), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4767), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (4768), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4769), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (477), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4770), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4771), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4772), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4773), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4774), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4775), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4776), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4777), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4778), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4779), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (478), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4780), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (4781), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4782), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (4783), keeping 158 out of 200 inputs (plus remapping 0 inputs)(79.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4784), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4785), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4786), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4787), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4788), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4789), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (479), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4790), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4791), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (4792), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (4793), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (4794), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4795), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4796), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4797), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (4798), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (4799), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (48), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (480), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to FS_0 (4800), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (4801), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4802), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (4803), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4804), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4805), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4806), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4807), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4808), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4809), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (481), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4810), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4811), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4812), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4813), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4814), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4815), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4816), keeping 23 out of 200 inputs (plus remapping 0 inputs)(11.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4817), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4818), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4819), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (482), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4820), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4821), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (4822), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4823), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (4824), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4825), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4826), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4827), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (4828), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4829), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (483), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4830), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4831), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4832), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4833), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4834), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4835), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4836), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4837), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4838), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (4839), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (484), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4840), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4841), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4842), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4843), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (4844), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (4845), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4846), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4847), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (4848), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4849), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (485), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4850), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4851), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4852), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4853), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4854), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4855), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4856), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4857), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4858), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4859), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (486), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4860), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4861), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4862), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4863), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4864), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4865), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4866), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4867), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4868), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4869), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (487), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (4870), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4871), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4872), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4873), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4874), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4875), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4876), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4877), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4878), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4879), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (488), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (4880), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (4881), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (4882), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (4883), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4884), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4885), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (4886), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4887), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4888), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (4889), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (489), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (4890), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (4891), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (4892), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4893), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (4894), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (4895), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (4896), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (4897), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (4898), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (4899), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (49), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (490), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (4900), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (4901), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (4902), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4903), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4904), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4905), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (4906), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4907), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4908), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4909), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (491), keeping 159 out of 200 inputs (plus remapping 0 inputs)(79.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4910), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4911), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4912), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (4913), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4914), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4915), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4916), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (4917), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4918), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4919), keeping 25 out of 200 inputs (plus remapping 0 inputs)(12.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (492), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4920), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (4921), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (4922), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (4923), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4924), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (4925), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (4926), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4927), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (4928), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4929), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (493), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (4930), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4931), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (4932), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (4933), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4934), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (4935), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (4936), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4937), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4938), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4939), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (494), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (4940), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4941), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (4942), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (4943), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (4944), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4945), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4946), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4947), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (4948), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4949), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (495), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (4950), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4951), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4952), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (4953), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4954), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (4955), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4956), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4957), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4958), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (4959), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (496), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4960), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (4961), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4962), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4963), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4964), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (4965), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4966), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4967), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (4968), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4969), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (497), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (4970), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (4971), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4972), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (4973), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (4974), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (4975), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4976), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (4977), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (4978), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (4979), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (498), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4980), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4981), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4982), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4983), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (4984), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (4985), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (4986), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4987), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4988), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (4989), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (499), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (4990), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (4991), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4992), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4993), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (4994), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (4995), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (4996), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (4997), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4998), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (4999), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (50), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (500), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5000), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5001), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5002), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5003), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5004), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5005), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5006), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5007), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5008), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5009), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (501), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5010), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5011), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5012), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5013), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5014), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5015), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5016), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5017), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5018), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5019), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (502), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5020), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5021), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5022), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5023), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5024), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5025), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5026), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5027), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5028), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5029), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (503), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5030), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5031), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5032), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5033), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5034), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5035), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5036), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5037), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5038), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5039), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (504), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5040), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5041), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5042), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5043), keeping 157 out of 200 inputs (plus remapping 0 inputs)(78.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5044), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5045), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5046), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5047), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5048), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5049), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (505), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5050), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5051), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5052), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5053), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5054), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5055), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5056), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5057), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5058), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5059), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (506), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5060), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (5061), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (5062), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5063), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5064), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5065), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5066), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5067), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5068), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5069), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (507), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5070), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5071), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5072), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5073), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5074), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5075), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5076), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5077), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5078), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5079), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (508), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5080), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5081), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5082), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5083), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5084), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5085), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5086), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5087), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5088), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5089), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (509), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5090), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5091), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5092), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5093), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5094), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5095), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5096), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5097), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5098), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5099), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (51), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (510), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5100), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5101), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5102), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5103), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5104), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5105), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5106), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5107), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5108), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5109), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (511), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5110), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5111), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5112), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5113), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5114), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5115), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5116), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5117), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5118), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5119), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (512), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5120), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5121), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5122), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5123), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5124), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5125), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5126), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5127), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5128), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5129), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (513), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5130), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5131), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5132), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5133), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5134), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5135), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5136), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5137), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5138), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5139), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (514), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5140), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5141), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5142), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5143), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5144), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5145), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5146), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5147), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5148), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5149), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (515), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5150), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5151), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5152), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (5153), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (5154), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (5155), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5156), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5157), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5158), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5159), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (516), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5160), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5161), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5162), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (5163), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5164), keeping 25 out of 200 inputs (plus remapping 0 inputs)(12.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5165), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5166), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5167), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5168), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5169), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (517), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5170), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5171), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5172), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5173), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5174), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5175), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5176), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5177), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5178), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5179), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (518), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5180), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5181), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5182), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5183), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5184), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5185), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5186), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5187), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5188), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5189), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (519), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5190), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5191), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5192), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5193), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5194), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5195), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5196), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5197), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5198), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5199), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (52), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (520), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5200), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5201), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5202), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5203), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5204), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5205), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5206), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5207), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5208), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5209), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (521), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5210), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5211), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5212), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5213), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5214), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5215), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5216), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5217), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5218), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5219), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (522), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5220), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5221), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5222), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5223), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5224), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5225), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5226), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5227), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5228), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5229), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (523), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5230), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5231), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5232), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5233), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5234), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5235), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5236), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5237), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5238), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5239), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (524), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5240), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5241), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5242), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5243), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5244), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5245), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5246), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5247), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5248), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5249), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (525), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5250), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5251), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5252), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5253), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5254), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5255), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5256), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5257), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5258), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (5259), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (526), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (5260), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (5261), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (5262), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5263), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5264), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5265), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5266), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5267), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5268), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5269), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (527), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5270), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5271), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5272), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5273), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5274), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5275), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5276), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5277), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5278), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5279), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (528), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5280), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5281), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5282), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5283), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5284), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (5285), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5286), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5287), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5288), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5289), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (529), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5290), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5291), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5292), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5293), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5294), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5295), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5296), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5297), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5298), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5299), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (53), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (530), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5300), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5301), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5302), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5303), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5304), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5305), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5306), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5307), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5308), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5309), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (531), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5310), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5311), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5312), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5313), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5314), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5315), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5316), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5317), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5318), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5319), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (532), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5320), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5321), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5322), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5323), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5324), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5325), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5326), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5327), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5328), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5329), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (533), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5330), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5331), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5332), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5333), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5334), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5335), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5336), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5337), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5338), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5339), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (534), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5340), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5341), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5342), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5343), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5344), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5345), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5346), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5347), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5348), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5349), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (535), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5350), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5351), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5352), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5353), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5354), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5355), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5356), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5357), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5358), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5359), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (536), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5360), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5361), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5362), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5363), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5364), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5365), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5366), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5367), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5368), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5369), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (537), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5370), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5371), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5372), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5373), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5374), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5375), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5376), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5377), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (5378), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (5379), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (538), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to FS_2 (5380), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5381), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5382), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5383), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5384), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5385), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5386), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5387), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5388), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5389), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (539), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5390), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5391), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5392), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5393), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5394), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5395), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5396), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5397), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5398), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5399), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (54), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (540), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5400), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5401), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5402), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5403), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5404), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5405), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5406), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5407), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5408), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5409), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (541), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5410), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5411), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5412), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5413), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5414), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5415), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5416), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5417), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5418), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5419), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (542), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5420), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5421), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5422), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5423), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5424), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5425), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5426), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5427), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5428), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5429), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (543), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5430), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5431), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5432), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5433), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5434), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5435), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5436), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5437), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5438), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5439), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (544), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5440), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5441), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5442), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5443), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5444), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5445), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5446), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5447), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5448), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5449), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (545), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5450), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5451), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5452), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5453), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5454), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5455), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5456), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5457), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5458), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5459), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (546), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5460), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5461), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5462), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5463), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5464), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5465), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5466), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5467), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5468), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5469), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (547), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5470), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5471), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5472), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5473), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5474), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5475), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to FS_2 (5476), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5477), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5478), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5479), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (548), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5480), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5481), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5482), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5483), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5484), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5485), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5486), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5487), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5488), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5489), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (549), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5490), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5491), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5492), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5493), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5494), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5495), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5496), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5497), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5498), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5499), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (55), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (550), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5500), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5501), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5502), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5503), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5504), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5505), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5506), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5507), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5508), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5509), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (551), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5510), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5511), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5512), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5513), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5514), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5515), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5516), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5517), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5518), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5519), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (552), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5520), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5521), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5522), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5523), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5524), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5525), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5526), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5527), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5528), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5529), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (553), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5530), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5531), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5532), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5533), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5534), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5535), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5536), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5537), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5538), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5539), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (554), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5540), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5541), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5542), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5543), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5544), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5545), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5546), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5547), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5548), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5549), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (555), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5550), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5551), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5552), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5553), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5554), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5555), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5556), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5557), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5558), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5559), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (556), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5560), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5561), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5562), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5563), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5564), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5565), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5566), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5567), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5568), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5569), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (557), keeping 152 out of 200 inputs (plus remapping 0 inputs)(76.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5570), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5571), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5572), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5573), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5574), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5575), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5576), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5577), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5578), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5579), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (558), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5580), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5581), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5582), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5583), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5584), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5585), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5586), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5587), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5588), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5589), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (559), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5590), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5591), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5592), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5593), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5594), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5595), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (5596), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (5597), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5598), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5599), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (56), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (560), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5600), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5601), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5602), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5603), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5604), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5605), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5606), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5607), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5608), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5609), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (561), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5610), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (5611), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (5612), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5613), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5614), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5615), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5616), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5617), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5618), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5619), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (562), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5620), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5621), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5622), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5623), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5624), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5625), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5626), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5627), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5628), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5629), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (563), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5630), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5631), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5632), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5633), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5634), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5635), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5636), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5637), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5638), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5639), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (564), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5640), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5641), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5642), keeping 155 out of 200 inputs (plus remapping 0 inputs)(77.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5643), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5644), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5645), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5646), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5647), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5648), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5649), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (565), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5650), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5651), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5652), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5653), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5654), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5655), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5656), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5657), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5658), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5659), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (566), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5660), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5661), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5662), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5663), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5664), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5665), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5666), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5667), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5668), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5669), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (567), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5670), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5671), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5672), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5673), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5674), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5675), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5676), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5677), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5678), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5679), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (568), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5680), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5681), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5682), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5683), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5684), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5685), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5686), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5687), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5688), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5689), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (569), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5690), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5691), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5692), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5693), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5694), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5695), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5696), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5697), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5698), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5699), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (57), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (570), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5700), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5701), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5702), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5703), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (5704), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5705), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5706), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5707), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5708), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (5709), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (571), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5710), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5711), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5712), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (5713), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5714), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5715), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5716), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5717), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5718), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5719), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (572), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5720), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (5721), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5722), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5723), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5724), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5725), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5726), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5727), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5728), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5729), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (573), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5730), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5731), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5732), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5733), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5734), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5735), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5736), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5737), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5738), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5739), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (574), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (5740), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5741), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5742), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5743), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5744), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5745), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5746), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5747), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5748), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5749), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (575), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5750), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5751), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5752), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5753), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5754), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5755), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5756), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5757), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5758), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5759), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (576), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5760), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5761), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5762), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5763), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5764), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5765), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5766), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5767), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5768), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5769), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (577), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5770), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5771), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5772), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5773), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5774), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5775), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5776), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5777), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5778), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5779), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (578), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5780), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5781), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5782), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5783), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5784), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5785), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5786), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5787), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5788), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5789), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (579), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5790), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (5791), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (5792), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5793), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5794), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5795), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5796), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5797), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5798), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5799), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (58), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (580), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5800), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5801), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (5802), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5803), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5804), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5805), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5806), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (5807), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5808), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5809), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (581), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5810), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5811), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (5812), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5813), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5814), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (5815), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5816), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5817), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5818), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5819), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (582), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5820), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5821), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (5822), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5823), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5824), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5825), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5826), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5827), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5828), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5829), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (583), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5830), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5831), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5832), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5833), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5834), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5835), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (5836), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5837), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5838), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5839), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (584), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5840), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (5841), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5842), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5843), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5844), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5845), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5846), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5847), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5848), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5849), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (585), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5850), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5851), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5852), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5853), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5854), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5855), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (5856), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5857), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5858), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (5859), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (586), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5860), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (5861), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (5862), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5863), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5864), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5865), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5866), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5867), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5868), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (5869), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (587), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5870), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5871), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5872), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5873), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5874), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5875), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5876), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5877), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5878), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5879), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (588), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5880), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5881), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5882), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5883), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5884), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5885), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5886), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5887), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5888), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (5889), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (589), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5890), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5891), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5892), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5893), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5894), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5895), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5896), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5897), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5898), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5899), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (59), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (590), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5900), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5901), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5902), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5903), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5904), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5905), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5906), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (5907), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (5908), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (5909), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (591), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5910), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5911), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (5912), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (5913), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5914), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (5915), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5916), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5917), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (5918), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (5919), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (592), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (5920), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (5921), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (5922), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (5923), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (5924), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5925), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5926), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5927), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (5928), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (5929), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (593), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (5930), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5931), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (5932), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5933), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5934), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5935), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (5936), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5937), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5938), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5939), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (594), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (5940), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (5941), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5942), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (5943), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (5944), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (5945), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5946), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (5947), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (5948), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (5949), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (595), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5950), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (5951), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5952), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (5953), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5954), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (5955), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5956), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (5957), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5958), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (5959), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (596), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (5960), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5961), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (5962), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (5963), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (5964), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5965), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (5966), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5967), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (5968), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5969), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (597), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (5970), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (5971), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5972), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (5973), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5974), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (5975), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (5976), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (5977), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (5978), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5979), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (598), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (5980), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (5981), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5982), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5983), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (5984), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5985), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (5986), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5987), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (5988), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (5989), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (599), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (5990), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5991), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (5992), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5993), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (5994), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (5995), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5996), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (5997), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5998), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (5999), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (60), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (600), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6000), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6001), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6002), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6003), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6004), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6005), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6006), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6007), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6008), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6009), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (601), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6010), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6011), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6012), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6013), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6014), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6015), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6016), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6017), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (6018), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (6019), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (602), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6020), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6021), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6022), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6023), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6024), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6025), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6026), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6027), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6028), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6029), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (603), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6030), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6031), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6032), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6033), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6034), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6035), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6036), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6037), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6038), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6039), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (604), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6040), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6041), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6042), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6043), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6044), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6045), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6046), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6047), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6048), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6049), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (605), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6050), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6051), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6052), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6053), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6054), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6055), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6056), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6057), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6058), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6059), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (606), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6060), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6061), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6062), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6063), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6064), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6065), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6066), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6067), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6068), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6069), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (607), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6070), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6071), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6072), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6073), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6074), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6075), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6076), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6077), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6078), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6079), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (608), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6080), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6081), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6082), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6083), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6084), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6085), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6086), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6087), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6088), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6089), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (609), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6090), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6091), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6092), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6093), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6094), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6095), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6096), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6097), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6098), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6099), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (61), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (610), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6100), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6101), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6102), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6103), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6104), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6105), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6106), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6107), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6108), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6109), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (611), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6110), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6111), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6112), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6113), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6114), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6115), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6116), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6117), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6118), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6119), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (612), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6120), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6121), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (6122), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (6123), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6124), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (6125), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (6126), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6127), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6128), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6129), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (613), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6130), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6131), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6132), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6133), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6134), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6135), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6136), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6137), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6138), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6139), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (614), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6140), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6141), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6142), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6143), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6144), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6145), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6146), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6147), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6148), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6149), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (615), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6150), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6151), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6152), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6153), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6154), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6155), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6156), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6157), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6158), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6159), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (616), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6160), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6161), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6162), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6163), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6164), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6165), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6166), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6167), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6168), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6169), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (617), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6170), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6171), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (6172), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6173), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6174), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6175), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6176), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6177), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6178), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6179), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (618), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6180), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6181), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6182), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6183), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6184), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6185), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6186), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6187), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6188), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6189), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (619), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6190), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6191), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6192), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6193), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6194), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6195), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6196), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6197), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6198), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6199), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (62), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (620), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6200), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6201), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6202), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6203), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6204), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6205), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6206), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6207), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6208), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6209), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (621), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6210), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6211), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6212), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6213), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6214), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6215), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6216), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6217), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6218), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6219), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (622), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6220), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6221), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (6222), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6223), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6224), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6225), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6226), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6227), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6228), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6229), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (623), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6230), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6231), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6232), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6233), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6234), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6235), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6236), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6237), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6238), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6239), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (624), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6240), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (6241), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (6242), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6243), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6244), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6245), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6246), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6247), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6248), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6249), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (625), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6250), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6251), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6252), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6253), keeping 24 out of 200 inputs (plus remapping 0 inputs)(12.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6254), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6255), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6256), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (6257), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6258), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6259), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (626), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6260), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6261), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6262), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6263), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6264), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6265), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6266), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6267), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6268), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6269), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (627), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6270), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6271), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6272), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6273), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6274), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6275), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6276), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6277), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6278), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6279), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (628), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6280), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6281), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (6282), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6283), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6284), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6285), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6286), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6287), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6288), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6289), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (629), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6290), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6291), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6292), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6293), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6294), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6295), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6296), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6297), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6298), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6299), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (63), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (630), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6300), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6301), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6302), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6303), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6304), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6305), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6306), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6307), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6308), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6309), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (631), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6310), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6311), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6312), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6313), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6314), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6315), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6316), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6317), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6318), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6319), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (632), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6320), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6321), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6322), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6323), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6324), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6325), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6326), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6327), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6328), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6329), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (633), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6330), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6331), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6332), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6333), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6334), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6335), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6336), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6337), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6338), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6339), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (634), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6340), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6341), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6342), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6343), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6344), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6345), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6346), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (6347), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (6348), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6349), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (635), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6350), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6351), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6352), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6353), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6354), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6355), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6356), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6357), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6358), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6359), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (636), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6360), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6361), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6362), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6363), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6364), keeping 21 out of 200 inputs (plus remapping 0 inputs)(10.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (6365), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (6366), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6367), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6368), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6369), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (637), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6370), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6371), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6372), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6373), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6374), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6375), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6376), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6377), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6378), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6379), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (638), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6380), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6381), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6382), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6383), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6384), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6385), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6386), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6387), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6388), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6389), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (639), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6390), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6391), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6392), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6393), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6394), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6395), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6396), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6397), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6398), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6399), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (64), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (640), keeping 152 out of 200 inputs (plus remapping 0 inputs)(76.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6400), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6401), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6402), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6403), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6404), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6405), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6406), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6407), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6408), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6409), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (641), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6410), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6411), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6412), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6413), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6414), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6415), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6416), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6417), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6418), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6419), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (642), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6420), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6421), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6422), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6423), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6424), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6425), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6426), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6427), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6428), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6429), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (643), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6430), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6431), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6432), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6433), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6434), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6435), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6436), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6437), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6438), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6439), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (644), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6440), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6441), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6442), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6443), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6444), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6445), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6446), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6447), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6448), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6449), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (645), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6450), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6451), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (6452), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (6453), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6454), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (6455), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6456), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6457), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6458), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6459), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (646), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6460), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6461), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6462), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6463), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6464), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6465), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6466), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6467), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6468), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6469), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (647), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6470), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6471), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6472), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6473), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6474), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6475), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6476), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6477), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6478), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6479), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (648), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6480), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6481), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6482), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6483), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6484), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6485), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6486), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6487), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6488), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6489), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (649), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6490), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6491), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6492), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6493), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6494), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6495), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6496), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6497), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6498), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6499), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (65), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (650), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6500), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6501), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6502), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6503), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6504), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6505), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6506), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6507), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6508), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6509), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (651), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6510), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6511), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6512), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6513), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6514), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6515), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6516), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6517), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6518), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6519), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (652), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6520), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6521), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6522), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6523), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6524), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6525), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6526), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6527), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6528), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6529), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (653), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6530), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6531), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6532), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6533), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6534), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6535), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6536), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6537), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6538), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6539), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (654), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6540), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6541), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6542), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6543), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6544), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6545), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6546), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6547), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6548), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6549), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (655), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6550), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6551), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6552), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6553), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6554), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6555), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6556), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6557), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6558), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6559), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (656), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6560), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6561), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6562), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6563), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6564), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6565), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6566), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6567), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6568), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6569), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (657), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6570), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6571), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6572), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6573), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6574), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6575), keeping 25 out of 200 inputs (plus remapping 0 inputs)(12.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6576), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6577), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6578), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6579), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (658), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6580), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6581), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6582), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6583), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6584), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6585), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6586), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6587), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6588), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6589), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (659), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6590), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6591), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6592), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6593), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6594), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6595), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6596), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6597), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6598), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6599), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (66), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (660), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6600), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6601), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6602), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6603), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6604), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6605), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6606), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6607), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6608), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6609), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (661), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6610), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6611), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6612), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6613), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6614), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6615), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6616), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6617), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6618), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6619), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (662), keeping 20 out of 200 inputs (plus remapping 0 inputs)(10.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6620), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6621), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6622), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6623), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6624), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6625), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6626), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6627), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6628), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6629), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (663), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6630), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6631), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6632), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6633), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6634), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6635), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6636), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6637), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6638), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6639), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (664), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6640), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6641), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6642), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6643), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6644), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6645), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6646), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6647), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (6648), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (6649), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (665), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6650), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6651), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6652), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6653), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6654), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6655), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6656), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (6657), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6658), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6659), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (666), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6660), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6661), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6662), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6663), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6664), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6665), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6666), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6667), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6668), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6669), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (667), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6670), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6671), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6672), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6673), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6674), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6675), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6676), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6677), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6678), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6679), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (668), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (6680), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6681), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6682), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6683), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6684), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6685), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6686), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6687), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6688), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6689), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (669), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6690), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6691), keeping 155 out of 200 inputs (plus remapping 0 inputs)(77.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6692), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6693), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6694), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6695), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (6696), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6697), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6698), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6699), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (67), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (670), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6700), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6701), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6702), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6703), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6704), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6705), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6706), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6707), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6708), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6709), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (671), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6710), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6711), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6712), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6713), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6714), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6715), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6716), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6717), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6718), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6719), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (672), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6720), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6721), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6722), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6723), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6724), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6725), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6726), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6727), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6728), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6729), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (673), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6730), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6731), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6732), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6733), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6734), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6735), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6736), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6737), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6738), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6739), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (674), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6740), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6741), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6742), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6743), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6744), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6745), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6746), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6747), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6748), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6749), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (675), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6750), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6751), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6752), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6753), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6754), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6755), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6756), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6757), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6758), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6759), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (676), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6760), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6761), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6762), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (6763), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (6764), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (6765), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6766), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6767), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6768), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6769), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (677), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6770), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6771), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6772), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (6773), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6774), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6775), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6776), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (6777), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6778), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6779), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (678), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6780), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6781), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6782), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6783), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6784), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6785), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6786), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (6787), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6788), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6789), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (679), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6790), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6791), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6792), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6793), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (6794), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6795), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6796), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6797), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (6798), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6799), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (68), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (680), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6800), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6801), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (6802), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6803), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6804), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6805), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6806), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6807), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6808), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6809), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (681), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (6810), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6811), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6812), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6813), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6814), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6815), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6816), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6817), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6818), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6819), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (682), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6820), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6821), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6822), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (6823), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6824), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (6825), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6826), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (6827), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6828), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6829), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (683), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6830), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6831), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6832), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6833), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6834), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6835), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6836), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6837), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6838), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6839), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (684), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6840), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6841), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (6842), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6843), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (6844), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (6845), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (6846), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6847), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6848), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6849), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (685), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6850), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6851), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (6852), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6853), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6854), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6855), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6856), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6857), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6858), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (6859), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (686), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6860), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (6861), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6862), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6863), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6864), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (6865), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (6866), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (6867), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (6868), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6869), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (687), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (6870), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (6871), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (6872), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6873), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6874), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6875), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6876), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (6877), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6878), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (6879), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (688), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6880), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (6881), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6882), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (6883), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (6884), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6885), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6886), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (6887), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (6888), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6889), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (689), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6890), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6891), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6892), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (6893), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (6894), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6895), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (6896), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6897), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (6898), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6899), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (69), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (690), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6900), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (6901), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6902), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (6903), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6904), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6905), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (6906), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (6907), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6908), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6909), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (691), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6910), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6911), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6912), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (6913), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6914), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (6915), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6916), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (6917), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6918), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6919), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (692), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (6920), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6921), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (6922), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (6923), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (6924), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6925), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6926), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6927), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6928), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (6929), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (693), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (6930), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6931), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6932), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6933), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (6934), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (6935), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6936), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6937), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (6938), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6939), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (694), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (6940), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6941), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6942), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6943), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (6944), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (6945), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6946), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (6947), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6948), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6949), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (695), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6950), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6951), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6952), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6953), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6954), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (6955), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6956), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (6957), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6958), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6959), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (696), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (6960), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6961), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6962), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (6963), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6964), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (6965), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6966), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6967), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6968), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (6969), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (697), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (6970), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6971), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (6972), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6973), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6974), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (6975), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (6976), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6977), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (6978), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (6979), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (698), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (6980), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6981), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (6982), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6983), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (6984), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (6985), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (6986), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6987), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6988), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (6989), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (699), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (6990), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6991), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (6992), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6993), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6994), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (6995), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (6996), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (6997), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6998), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (6999), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (70), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (700), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7000), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7001), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7002), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7003), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7004), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7005), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7006), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7007), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7008), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7009), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (701), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7010), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7011), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7012), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7013), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7014), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7015), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7016), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7017), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7018), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7019), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (702), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7020), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7021), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7022), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7023), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7024), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7025), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7026), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7027), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7028), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7029), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (703), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7030), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7031), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7032), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7033), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7034), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7035), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7036), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7037), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7038), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7039), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (704), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7040), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7041), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7042), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7043), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7044), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7045), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7046), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7047), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (7048), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (7049), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (705), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to FS_1 (7050), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (7051), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7052), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7053), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7054), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7055), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7056), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7057), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7058), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7059), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (706), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7060), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7061), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7062), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7063), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7064), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7065), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7066), keeping 22 out of 200 inputs (plus remapping 0 inputs)(11.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7067), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7068), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7069), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (707), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7070), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7071), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7072), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7073), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7074), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7075), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7076), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7077), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7078), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7079), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (708), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7080), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7081), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7082), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7083), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7084), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7085), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7086), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7087), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7088), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7089), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (709), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7090), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7091), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7092), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7093), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7094), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7095), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (7096), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (7097), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7098), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7099), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (71), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (710), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7100), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7101), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7102), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7103), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7104), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7105), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7106), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7107), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7108), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7109), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (711), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7110), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7111), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7112), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7113), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7114), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7115), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7116), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7117), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7118), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7119), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (712), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7120), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7121), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7122), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7123), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7124), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7125), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7126), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7127), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7128), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7129), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (713), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7130), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7131), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7132), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7133), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7134), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7135), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7136), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7137), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7138), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7139), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (714), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7140), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7141), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7142), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7143), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7144), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7145), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7146), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7147), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7148), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7149), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (715), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7150), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7151), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7152), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7153), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7154), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7155), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7156), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7157), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7158), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7159), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (716), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7160), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7161), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7162), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7163), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7164), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7165), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7166), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7167), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7168), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7169), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (717), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7170), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7171), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7172), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7173), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7174), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7175), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7176), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7177), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7178), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7179), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (718), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7180), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7181), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7182), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7183), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7184), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7185), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7186), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7187), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7188), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7189), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (719), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7190), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7191), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7192), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7193), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7194), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7195), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7196), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7197), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7198), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7199), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (72), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (720), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7200), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7201), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7202), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7203), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7204), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7205), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7206), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7207), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7208), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7209), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (721), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7210), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7211), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (7212), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (7213), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7214), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7215), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7216), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7217), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7218), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7219), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (722), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7220), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7221), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7222), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7223), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7224), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7225), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7226), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7227), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7228), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7229), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (723), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7230), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7231), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7232), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7233), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7234), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7235), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7236), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7237), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7238), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7239), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (724), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7240), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7241), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7242), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7243), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7244), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7245), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7246), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7247), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7248), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7249), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (725), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7250), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7251), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7252), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7253), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7254), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7255), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7256), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7257), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (7258), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (7259), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (726), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7260), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7261), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7262), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7263), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7264), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7265), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7266), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7267), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7268), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7269), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (727), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7270), keeping 152 out of 200 inputs (plus remapping 0 inputs)(76.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7271), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7272), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7273), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7274), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7275), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7276), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7277), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7278), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7279), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (728), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7280), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7281), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7282), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to FS_2 (7283), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (7284), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7285), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7286), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7287), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7288), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7289), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (729), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7290), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7291), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7292), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7293), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7294), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7295), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7296), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7297), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7298), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7299), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (73), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (730), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7300), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7301), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7302), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7303), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7304), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7305), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7306), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7307), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7308), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7309), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (731), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7310), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7311), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7312), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7313), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7314), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7315), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7316), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7317), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7318), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7319), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (732), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7320), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7321), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7322), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7323), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7324), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7325), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7326), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7327), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7328), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7329), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (733), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7330), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7331), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7332), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7333), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7334), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7335), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7336), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7337), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7338), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7339), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (734), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7340), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7341), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7342), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7343), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7344), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7345), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7346), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7347), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7348), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7349), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (735), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7350), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7351), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7352), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7353), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7354), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7355), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7356), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7357), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7358), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7359), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (736), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7360), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7361), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7362), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7363), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7364), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7365), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7366), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7367), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7368), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7369), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (737), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7370), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7371), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (7372), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (7373), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (7374), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (7375), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7376), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7377), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7378), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7379), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (738), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7380), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7381), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7382), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7383), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7384), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7385), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7386), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7387), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7388), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7389), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (739), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7390), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7391), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7392), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7393), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7394), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7395), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7396), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7397), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7398), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7399), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (74), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (740), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7400), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7401), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7402), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7403), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7404), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7405), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7406), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7407), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7408), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7409), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (741), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7410), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7411), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7412), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7413), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7414), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7415), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7416), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7417), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7418), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7419), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (742), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7420), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7421), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7422), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7423), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7424), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7425), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7426), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7427), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7428), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7429), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (743), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7430), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7431), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7432), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7433), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7434), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7435), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7436), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7437), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7438), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7439), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (744), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7440), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7441), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7442), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7443), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7444), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7445), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7446), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7447), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7448), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7449), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (745), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7450), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7451), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7452), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7453), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7454), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7455), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7456), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7457), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7458), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7459), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (746), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7460), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7461), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7462), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7463), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7464), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7465), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7466), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7467), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7468), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7469), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (747), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7470), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7471), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7472), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7473), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7474), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7475), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7476), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7477), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7478), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7479), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (748), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7480), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7481), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7482), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7483), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7484), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7485), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7486), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7487), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7488), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7489), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (749), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7490), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7491), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7492), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7493), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (7494), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (7495), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7496), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7497), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7498), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7499), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (75), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (750), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7500), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7501), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7502), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7503), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7504), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7505), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7506), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7507), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7508), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7509), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (751), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7510), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7511), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7512), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7513), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7514), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7515), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7516), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7517), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7518), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7519), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (752), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7520), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7521), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7522), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7523), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7524), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7525), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7526), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7527), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7528), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7529), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (753), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7530), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7531), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7532), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7533), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7534), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7535), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7536), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7537), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7538), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7539), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (754), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7540), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7541), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7542), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7543), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7544), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7545), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7546), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7547), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7548), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7549), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (755), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7550), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7551), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7552), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7553), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7554), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7555), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7556), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7557), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7558), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7559), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (756), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7560), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7561), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7562), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7563), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7564), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7565), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7566), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7567), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7568), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7569), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (757), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7570), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7571), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7572), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7573), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7574), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7575), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7576), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7577), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7578), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7579), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (758), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7580), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7581), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7582), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7583), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7584), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7585), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7586), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7587), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7588), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7589), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (759), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7590), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7591), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7592), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7593), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7594), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7595), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7596), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7597), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7598), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7599), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (76), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (760), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7600), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7601), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7602), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7603), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7604), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7605), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7606), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7607), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7608), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7609), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (761), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7610), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7611), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7612), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7613), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7614), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7615), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7616), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7617), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7618), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7619), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (762), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7620), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7621), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7622), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7623), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7624), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7625), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7626), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7627), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7628), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7629), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (763), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7630), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7631), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7632), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7633), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7634), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7635), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7636), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7637), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7638), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7639), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (764), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7640), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7641), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7642), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7643), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7644), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7645), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7646), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7647), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7648), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (7649), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (765), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7650), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7651), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7652), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7653), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7654), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7655), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7656), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7657), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7658), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7659), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (766), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7660), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7661), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7662), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7663), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7664), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7665), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7666), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7667), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7668), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7669), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (767), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7670), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7671), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7672), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7673), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7674), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7675), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7676), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7677), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7678), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7679), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (768), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7680), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7681), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7682), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7683), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7684), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7685), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7686), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7687), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7688), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7689), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (769), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7690), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7691), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7692), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (7693), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7694), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7695), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7696), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7697), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7698), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7699), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (77), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (770), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7700), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7701), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7702), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7703), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (7704), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (7705), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (7706), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7707), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7708), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7709), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (771), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7710), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7711), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7712), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7713), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7714), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7715), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7716), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7717), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7718), keeping 23 out of 200 inputs (plus remapping 0 inputs)(11.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7719), keeping 23 out of 200 inputs (plus remapping 0 inputs)(11.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (772), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7720), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7721), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7722), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7723), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7724), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7725), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7726), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7727), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7728), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7729), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (773), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7730), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7731), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7732), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7733), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7734), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7735), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7736), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7737), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7738), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7739), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (774), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (7740), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7741), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7742), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7743), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7744), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7745), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7746), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7747), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7748), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7749), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (775), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7750), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7751), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7752), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7753), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7754), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7755), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7756), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7757), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7758), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7759), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (776), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7760), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7761), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7762), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7763), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7764), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7765), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7766), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7767), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7768), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7769), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (777), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7770), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7771), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7772), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7773), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7774), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7775), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7776), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7777), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7778), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7779), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (778), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7780), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (7781), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7782), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (7783), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (7784), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7785), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7786), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7787), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7788), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7789), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (779), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7790), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7791), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7792), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7793), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7794), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7795), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7796), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7797), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7798), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7799), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (78), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (780), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7800), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7801), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7802), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (7803), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7804), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7805), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7806), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7807), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7808), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7809), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (781), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7810), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7811), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7812), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7813), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (7814), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7815), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7816), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7817), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7818), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7819), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (782), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (7820), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7821), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (7822), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7823), keeping 25 out of 200 inputs (plus remapping 0 inputs)(12.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7824), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (7825), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7826), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7827), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7828), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (7829), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (783), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7830), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7831), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7832), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7833), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7834), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7835), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7836), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (7837), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (7838), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7839), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (784), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7840), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7841), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7842), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (7843), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7844), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7845), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7846), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (7847), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7848), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7849), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (785), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7850), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7851), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7852), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7853), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7854), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7855), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7856), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7857), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (7858), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7859), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (786), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7860), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7861), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7862), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7863), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (7864), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7865), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7866), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7867), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7868), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7869), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (787), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (7870), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7871), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7872), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7873), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (7874), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7875), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (7876), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7877), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7878), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7879), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (788), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7880), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7881), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7882), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (7883), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7884), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7885), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (7886), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7887), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7888), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7889), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (789), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7890), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7891), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (7892), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (7893), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (7894), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7895), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7896), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7897), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7898), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7899), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (79), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (790), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (7900), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (7901), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (7902), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (7903), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (7904), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (7905), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (7906), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7907), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7908), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7909), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (791), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7910), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7911), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7912), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7913), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (7914), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (7915), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7916), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7917), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7918), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (7919), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (792), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (7920), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7921), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7922), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (7923), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (7924), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7925), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (7926), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (7927), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7928), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (7929), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (793), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7930), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (7931), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7932), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (7933), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7934), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (7935), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (7936), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (7937), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (7938), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7939), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (794), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (7940), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7941), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (7942), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (7943), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7944), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7945), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (7946), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (7947), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (7948), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7949), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (795), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (7950), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7951), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7952), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7953), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (7954), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7955), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7956), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (7957), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7958), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (7959), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (796), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7960), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7961), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (7962), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (7963), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7964), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7965), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (7966), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (7967), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7968), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (7969), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (797), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7970), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (7971), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7972), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (7973), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (7974), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7975), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (7976), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7977), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (7978), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7979), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (798), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (7980), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7981), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (7982), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (7983), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (7984), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (7985), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7986), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (7987), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7988), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (7989), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (799), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7990), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (7991), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (7992), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (7993), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (7994), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (7995), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7996), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (7997), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7998), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (7999), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (80), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (800), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8000), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8001), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8002), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8003), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8004), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8005), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8006), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8007), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8008), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8009), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (801), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8010), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8011), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8012), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8013), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8014), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8015), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8016), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8017), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8018), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8019), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (802), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8020), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8021), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8022), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8023), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8024), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8025), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8026), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8027), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8028), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8029), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (803), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8030), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8031), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8032), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8033), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8034), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8035), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8036), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8037), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8038), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8039), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (804), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8040), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8041), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8042), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8043), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8044), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8045), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8046), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8047), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8048), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8049), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (805), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8050), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8051), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8052), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8053), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8054), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8055), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8056), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8057), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8058), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8059), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (806), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8060), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8061), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8062), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8063), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8064), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8065), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8066), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8067), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8068), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8069), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (807), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8070), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8071), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8072), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8073), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8074), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8075), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8076), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8077), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8078), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8079), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (808), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8080), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8081), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8082), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8083), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8084), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8085), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (8086), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (8087), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (8088), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8089), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (809), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8090), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8091), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8092), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8093), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8094), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8095), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8096), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8097), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8098), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8099), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (81), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (810), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8100), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8101), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8102), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8103), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8104), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8105), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8106), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8107), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8108), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8109), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (811), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8110), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8111), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8112), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8113), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8114), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8115), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8116), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8117), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8118), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8119), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (812), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8120), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8121), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8122), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8123), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8124), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8125), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8126), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8127), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8128), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8129), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (813), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8130), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8131), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8132), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8133), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8134), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8135), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8136), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8137), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8138), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8139), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (814), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8140), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8141), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8142), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8143), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8144), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8145), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8146), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8147), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8148), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8149), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (815), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8150), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8151), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8152), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8153), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8154), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8155), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8156), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8157), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8158), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8159), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (816), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8160), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8161), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8162), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8163), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8164), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8165), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8166), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8167), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8168), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8169), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (817), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8170), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8171), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8172), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8173), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8174), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8175), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8176), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8177), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8178), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8179), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (818), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8180), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8181), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8182), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8183), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8184), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8185), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (8186), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (8187), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8188), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8189), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (819), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8190), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8191), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8192), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8193), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8194), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8195), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8196), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8197), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8198), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8199), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (82), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (820), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8200), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8201), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8202), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8203), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8204), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8205), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8206), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8207), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8208), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8209), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (821), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8210), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8211), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8212), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8213), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8214), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8215), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8216), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8217), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8218), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8219), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (822), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8220), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8221), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8222), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8223), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8224), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8225), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8226), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8227), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8228), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8229), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (823), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8230), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8231), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8232), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8233), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8234), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8235), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8236), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8237), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8238), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8239), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (824), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8240), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8241), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8242), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8243), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8244), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8245), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8246), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8247), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8248), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8249), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (825), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8250), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8251), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8252), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8253), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8254), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8255), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8256), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8257), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8258), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8259), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (826), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8260), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8261), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8262), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8263), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8264), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8265), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8266), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8267), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8268), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8269), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (827), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8270), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8271), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8272), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8273), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8274), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8275), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8276), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8277), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8278), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8279), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to FS_0 (828), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8280), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8281), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8282), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8283), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8284), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8285), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8286), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8287), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8288), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8289), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (829), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8290), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8291), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8292), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8293), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8294), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8295), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8296), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8297), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8298), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8299), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (83), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (830), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8300), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8301), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8302), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8303), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8304), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8305), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8306), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8307), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8308), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8309), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (831), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8310), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8311), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8312), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8313), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8314), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8315), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8316), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (8317), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8318), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8319), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (832), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8320), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8321), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8322), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8323), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8324), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8325), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8326), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8327), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8328), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8329), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (833), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8330), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8331), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8332), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8333), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8334), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8335), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8336), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8337), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8338), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8339), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (834), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8340), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8341), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8342), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8343), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8344), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8345), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8346), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8347), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8348), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8349), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (835), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8350), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8351), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8352), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8353), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8354), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8355), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8356), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8357), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8358), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8359), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (836), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8360), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8361), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8362), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8363), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8364), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8365), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8366), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8367), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8368), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8369), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (837), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8370), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8371), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8372), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8373), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8374), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8375), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8376), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8377), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8378), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8379), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (838), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8380), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8381), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8382), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8383), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8384), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8385), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8386), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8387), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8388), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8389), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (839), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8390), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8391), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8392), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8393), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8394), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8395), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8396), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8397), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8398), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8399), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (84), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (840), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8400), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8401), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8402), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8403), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8404), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8405), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8406), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8407), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8408), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8409), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (841), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8410), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8411), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8412), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8413), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8414), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8415), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8416), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8417), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8418), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8419), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (842), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8420), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8421), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8422), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8423), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8424), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8425), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8426), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8427), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8428), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8429), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (843), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8430), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8431), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8432), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8433), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8434), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8435), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8436), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8437), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8438), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8439), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (844), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8440), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8441), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8442), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8443), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8444), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8445), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8446), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8447), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8448), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8449), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (845), keeping 25 out of 200 inputs (plus remapping 0 inputs)(12.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8450), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8451), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8452), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8453), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8454), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8455), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8456), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8457), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8458), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8459), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (846), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8460), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8461), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8462), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8463), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8464), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8465), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8466), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8467), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8468), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8469), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (847), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8470), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8471), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8472), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8473), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8474), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8475), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8476), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8477), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8478), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8479), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (848), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8480), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (8481), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (8482), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (8483), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8484), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8485), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8486), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8487), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8488), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8489), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (849), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8490), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8491), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8492), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8493), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8494), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8495), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8496), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8497), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8498), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8499), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (85), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (850), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8500), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8501), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8502), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8503), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8504), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8505), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8506), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8507), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8508), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8509), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (851), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8510), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8511), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8512), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8513), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8514), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8515), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8516), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8517), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8518), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8519), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (852), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8520), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8521), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8522), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8523), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8524), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8525), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8526), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8527), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8528), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8529), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (853), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8530), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8531), keeping 38 out of 200 inputs (plus remapping 0 inputs)(19.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8532), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8533), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8534), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8535), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8536), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8537), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8538), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8539), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (854), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8540), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8541), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8542), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8543), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8544), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8545), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8546), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8547), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8548), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8549), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (855), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8550), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8551), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8552), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8553), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8554), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8555), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8556), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8557), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8558), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8559), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (856), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8560), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8561), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8562), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8563), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8564), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8565), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8566), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8567), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8568), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8569), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (857), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8570), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8571), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8572), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8573), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8574), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8575), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8576), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8577), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8578), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8579), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (858), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8580), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8581), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8582), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8583), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8584), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8585), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8586), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8587), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8588), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8589), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (859), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8590), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8591), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8592), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8593), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8594), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8595), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8596), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8597), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8598), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8599), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (86), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (860), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8600), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8601), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8602), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8603), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to LTS_2 (8604), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (8605), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (8606), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8607), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8608), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8609), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (861), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8610), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8611), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8612), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8613), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8614), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8615), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8616), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8617), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8618), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8619), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (862), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8620), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8621), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8622), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8623), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8624), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8625), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8626), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8627), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8628), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8629), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (863), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8630), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8631), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8632), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8633), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8634), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8635), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8636), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8637), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8638), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8639), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (864), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8640), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8641), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8642), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8643), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8644), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8645), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8646), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8647), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8648), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8649), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (865), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8650), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8651), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8652), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8653), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8654), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8655), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8656), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8657), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8658), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8659), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (866), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8660), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8661), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8662), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8663), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8664), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8665), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8666), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8667), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8668), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8669), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (867), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8670), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8671), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8672), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8673), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8674), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8675), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8676), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8677), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8678), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8679), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (868), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8680), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8681), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8682), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8683), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8684), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8685), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8686), keeping 158 out of 200 inputs (plus remapping 0 inputs)(79.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8687), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8688), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8689), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (869), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8690), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8691), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8692), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8693), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8694), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8695), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8696), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8697), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8698), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8699), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (87), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (870), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (8700), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (8701), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8702), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8703), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8704), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8705), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8706), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8707), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8708), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8709), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (871), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8710), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8711), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8712), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8713), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8714), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8715), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8716), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8717), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8718), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8719), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (872), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8720), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8721), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8722), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8723), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8724), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (8725), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8726), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8727), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8728), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8729), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (873), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8730), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8731), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8732), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8733), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8734), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8735), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8736), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8737), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8738), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8739), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (874), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8740), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8741), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8742), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8743), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8744), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8745), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8746), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8747), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8748), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (8749), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (875), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (8750), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (8751), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_7 (8752), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (8753), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8754), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8755), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8756), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8757), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8758), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8759), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (876), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8760), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8761), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8762), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8763), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8764), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8765), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8766), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8767), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8768), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (8769), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (877), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8770), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8771), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8772), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8773), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8774), keeping 23 out of 200 inputs (plus remapping 0 inputs)(11.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8775), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8776), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8777), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8778), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8779), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (878), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8780), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8781), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8782), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8783), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (8784), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8785), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8786), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8787), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8788), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8789), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (879), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (8790), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8791), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8792), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8793), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8794), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (8795), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8796), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8797), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8798), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8799), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (88), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (880), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8800), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (8801), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (8802), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8803), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8804), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8805), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8806), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8807), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8808), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8809), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (881), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8810), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8811), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (8812), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8813), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8814), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8815), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8816), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8817), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8818), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8819), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (882), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8820), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8821), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8822), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8823), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8824), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8825), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8826), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (8827), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (8828), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8829), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (883), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8830), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8831), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8832), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8833), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8834), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (8835), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8836), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8837), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8838), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8839), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (884), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8840), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8841), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8842), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8843), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8844), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8845), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8846), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8847), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8848), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8849), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (885), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (8850), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8851), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8852), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8853), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8854), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8855), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8856), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8857), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8858), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8859), keeping 154 out of 200 inputs (plus remapping 0 inputs)(77.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (886), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8860), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8861), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8862), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8863), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8864), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8865), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (8866), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8867), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8868), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8869), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (887), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (8870), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8871), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8872), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8873), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8874), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8875), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (8876), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (8877), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8878), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8879), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (888), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (8880), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8881), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8882), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8883), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (8884), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (8885), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (8886), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8887), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8888), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8889), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (889), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (8890), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (8891), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8892), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8893), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (8894), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8895), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8896), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (8897), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (8898), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (8899), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (89), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (890), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8900), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8901), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8902), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (8903), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (8904), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8905), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8906), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8907), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (8908), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8909), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (891), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (8910), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8911), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (8912), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (8913), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (8914), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8915), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8916), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8917), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8918), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8919), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (892), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (8920), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (8921), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (8922), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (8923), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8924), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (8925), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (8926), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (8927), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8928), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (8929), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (893), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (8930), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (8931), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8932), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (8933), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (8934), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (8935), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (8936), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8937), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (8938), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (8939), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (894), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8940), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (8941), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (8942), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8943), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (8944), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8945), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (8946), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8947), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (8948), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (8949), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (895), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8950), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8951), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (8952), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8953), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (8954), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8955), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8956), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (8957), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8958), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (8959), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (896), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (8960), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8961), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8962), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (8963), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8964), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (8965), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (8966), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8967), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8968), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8969), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (897), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8970), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (8971), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8972), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (8973), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8974), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (8975), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (8976), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8977), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8978), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (8979), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (898), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8980), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (8981), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (8982), keeping 155 out of 200 inputs (plus remapping 0 inputs)(77.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8983), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8984), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (8985), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8986), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8987), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (8988), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (8989), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (899), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8990), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8991), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8992), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (8993), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8994), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8995), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8996), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8997), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (8998), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (8999), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (90), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (900), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to FS_3 (9000), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9001), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9002), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9003), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9004), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9005), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9006), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9007), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9008), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9009), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (901), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9010), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9011), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9012), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9013), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9014), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9015), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9016), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9017), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9018), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9019), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (902), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9020), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9021), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9022), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9023), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9024), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9025), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9026), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9027), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9028), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9029), keeping 29 out of 200 inputs (plus remapping 0 inputs)(14.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (903), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9030), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9031), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9032), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9033), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9034), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9035), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9036), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9037), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9038), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9039), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (904), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9040), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9041), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9042), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9043), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9044), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9045), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9046), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9047), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9048), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9049), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (905), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9050), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9051), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9052), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9053), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9054), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9055), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9056), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9057), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9058), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9059), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (906), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9060), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9061), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9062), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9063), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9064), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9065), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9066), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9067), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9068), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9069), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (907), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (9070), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9071), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9072), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9073), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9074), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9075), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9076), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9077), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9078), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9079), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (908), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9080), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9081), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9082), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9083), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9084), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9085), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9086), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9087), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9088), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9089), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (909), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (9090), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (9091), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (9092), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (9093), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9094), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9095), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9096), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9097), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9098), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9099), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (91), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (910), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9100), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9101), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9102), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9103), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9104), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9105), keeping 41 out of 200 inputs (plus remapping 0 inputs)(20.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9106), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9107), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9108), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9109), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (911), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9110), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9111), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9112), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9113), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9114), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9115), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9116), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9117), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9118), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9119), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (912), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9120), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9121), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9122), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9123), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9124), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9125), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9126), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9127), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9128), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9129), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (913), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9130), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9131), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9132), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9133), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9134), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9135), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9136), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9137), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9138), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9139), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (914), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9140), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9141), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9142), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9143), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9144), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9145), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9146), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9147), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9148), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9149), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (915), keeping 151 out of 200 inputs (plus remapping 0 inputs)(75.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9150), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9151), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9152), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9153), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9154), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (9155), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9156), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9157), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9158), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9159), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (916), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9160), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9161), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9162), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9163), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9164), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9165), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9166), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9167), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9168), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9169), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (917), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9170), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9171), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (9172), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9173), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9174), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9175), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9176), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9177), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9178), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9179), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (918), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9180), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9181), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9182), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9183), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9184), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9185), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9186), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9187), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9188), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9189), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (919), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9190), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9191), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9192), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9193), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9194), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9195), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9196), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9197), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9198), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9199), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (92), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (920), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9200), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9201), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9202), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9203), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9204), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9205), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9206), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9207), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9208), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9209), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (921), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9210), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9211), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9212), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9213), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9214), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9215), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9216), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9217), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9218), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9219), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (922), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9220), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9221), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9222), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9223), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9224), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9225), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9226), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9227), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9228), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9229), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (923), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9230), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9231), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9232), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9233), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9234), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9235), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9236), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9237), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9238), keeping 146 out of 200 inputs (plus remapping 0 inputs)(73.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9239), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (924), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9240), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9241), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9242), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9243), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9244), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9245), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9246), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9247), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9248), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9249), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (925), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9250), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9251), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9252), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9253), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9254), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9255), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (9256), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (9257), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9258), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9259), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (926), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9260), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9261), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9262), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9263), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9264), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9265), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9266), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9267), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9268), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9269), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (927), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9270), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9271), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9272), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9273), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9274), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9275), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9276), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9277), keeping 28 out of 200 inputs (plus remapping 0 inputs)(14.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9278), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9279), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (928), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9280), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9281), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9282), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9283), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9284), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9285), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9286), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9287), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9288), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9289), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (929), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9290), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9291), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9292), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9293), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9294), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9295), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9296), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9297), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9298), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9299), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (93), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (930), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9300), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9301), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9302), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9303), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9304), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9305), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9306), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9307), keeping 47 out of 200 inputs (plus remapping 0 inputs)(23.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9308), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9309), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (931), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9310), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9311), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9312), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9313), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9314), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9315), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9316), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9317), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9318), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9319), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (932), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9320), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9321), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9322), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9323), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9324), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9325), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9326), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9327), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9328), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9329), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (933), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9330), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9331), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9332), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9333), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9334), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9335), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9336), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9337), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9338), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9339), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (934), keeping 53 out of 200 inputs (plus remapping 0 inputs)(26.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9340), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9341), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9342), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9343), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9344), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9345), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9346), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9347), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9348), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9349), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (935), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9350), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9351), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9352), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9353), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9354), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to LTS_3 (9355), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (9356), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (9357), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9358), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9359), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (936), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9360), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9361), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9362), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9363), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9364), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9365), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9366), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9367), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9368), keeping 26 out of 200 inputs (plus remapping 0 inputs)(13.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9369), keeping 33 out of 200 inputs (plus remapping 0 inputs)(16.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (937), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9370), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9371), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9372), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9373), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9374), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9375), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9376), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9377), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9378), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9379), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (938), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9380), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9381), keeping 54 out of 200 inputs (plus remapping 0 inputs)(27.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9382), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9383), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9384), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9385), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9386), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9387), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9388), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9389), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (939), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9390), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9391), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9392), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9393), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9394), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9395), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9396), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9397), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9398), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9399), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (94), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (940), keeping 30 out of 200 inputs (plus remapping 0 inputs)(15.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9400), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9401), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9402), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9403), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9404), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9405), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9406), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9407), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9408), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9409), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (941), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9410), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (9411), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (9412), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9413), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9414), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9415), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9416), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9417), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9418), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9419), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (942), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9420), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9421), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9422), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9423), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9424), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9425), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9426), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9427), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9428), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9429), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (943), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9430), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9431), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9432), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9433), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9434), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to LTS_0 (9435), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_5 (9436), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (9437), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (9438), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9439), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (944), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9440), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9441), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9442), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9443), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9444), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9445), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9446), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9447), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9448), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9449), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (945), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9450), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9451), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9452), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9453), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9454), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9455), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9456), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9457), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9458), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9459), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (946), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9460), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9461), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9462), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9463), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9464), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9465), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9466), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9467), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9468), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9469), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (947), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9470), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9471), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9472), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9473), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9474), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9475), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9476), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9477), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9478), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9479), keeping 36 out of 200 inputs (plus remapping 0 inputs)(18.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (948), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9480), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9481), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9482), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9483), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9484), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9485), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9486), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9487), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9488), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9489), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (949), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9490), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9491), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9492), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9493), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9494), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9495), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9496), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9497), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9498), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9499), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (95), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (950), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9500), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9501), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9502), keeping 140 out of 200 inputs (plus remapping 0 inputs)(70.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9503), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9504), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9505), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9506), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9507), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9508), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9509), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (951), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9510), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9511), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9512), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9513), keeping 150 out of 200 inputs (plus remapping 0 inputs)(75.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9514), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9515), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9516), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9517), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9518), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9519), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (952), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9520), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9521), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to LTS_1 (9522), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_1 (9523), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9524), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9525), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9526), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9527), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9528), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9529), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (953), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9530), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9531), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9532), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9533), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9534), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9535), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9536), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9537), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9538), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9539), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (954), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9540), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9541), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9542), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9543), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9544), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9545), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9546), keeping 48 out of 200 inputs (plus remapping 0 inputs)(24.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9547), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9548), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9549), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (955), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9550), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9551), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9552), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9553), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9554), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9555), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9556), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9557), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9558), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9559), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (956), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9560), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9561), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9562), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9563), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9564), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9565), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9566), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9567), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9568), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9569), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (957), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9570), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9571), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9572), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9573), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9574), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9575), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9576), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9577), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9578), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9579), keeping 50 out of 200 inputs (plus remapping 0 inputs)(25.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (958), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9580), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9581), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9582), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9583), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9584), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9585), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9586), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9587), keeping 98 out of 200 inputs (plus remapping 0 inputs)(49.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9588), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9589), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (959), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9590), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9591), keeping 64 out of 200 inputs (plus remapping 0 inputs)(32.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9592), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9593), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9594), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9595), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9596), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9597), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9598), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9599), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (96), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (960), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9600), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9601), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9602), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9603), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (9604), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (9605), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9606), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9607), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9608), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9609), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (961), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9610), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9611), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9612), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9613), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9614), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9615), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to FS_0 (9616), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9617), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9618), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9619), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (962), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9620), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9621), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9622), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9623), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9624), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9625), keeping 35 out of 200 inputs (plus remapping 0 inputs)(17.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9626), keeping 39 out of 200 inputs (plus remapping 0 inputs)(19.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9627), keeping 24 out of 200 inputs (plus remapping 0 inputs)(12.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9628), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9629), keeping 31 out of 200 inputs (plus remapping 0 inputs)(15.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (963), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9630), keeping 27 out of 200 inputs (plus remapping 0 inputs)(13.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9631), keeping 37 out of 200 inputs (plus remapping 0 inputs)(18.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9632), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9633), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9634), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9635), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9636), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9637), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9638), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9639), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (964), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9640), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9641), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9642), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9643), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9644), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9645), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9646), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9647), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9648), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9649), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (965), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9650), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9651), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9652), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9653), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9654), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9655), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9656), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9657), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9658), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9659), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (966), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9660), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9661), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9662), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9663), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9664), keeping 51 out of 200 inputs (plus remapping 0 inputs)(25.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9665), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9666), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9667), keeping 46 out of 200 inputs (plus remapping 0 inputs)(23.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9668), keeping 45 out of 200 inputs (plus remapping 0 inputs)(22.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9669), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (967), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9670), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9671), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9672), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9673), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9674), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9675), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9676), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9677), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9678), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9679), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (968), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9680), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9681), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (9682), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9683), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9684), keeping 109 out of 200 inputs (plus remapping 0 inputs)(54.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9685), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9686), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9687), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9688), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9689), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (969), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9690), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9691), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9692), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9693), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9694), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9695), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9696), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9697), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9698), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9699), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (97), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (970), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9700), keeping 148 out of 200 inputs (plus remapping 0 inputs)(74.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9701), keeping 153 out of 200 inputs (plus remapping 0 inputs)(76.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9702), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9703), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9704), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9705), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9706), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (9707), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9708), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9709), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (971), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9710), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9711), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9712), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9713), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9714), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (9715), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9716), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9717), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9718), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9719), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (972), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9720), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9721), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9722), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9723), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9724), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9725), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9726), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9727), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9728), keeping 32 out of 200 inputs (plus remapping 0 inputs)(16.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9729), keeping 93 out of 200 inputs (plus remapping 0 inputs)(46.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (973), keeping 42 out of 200 inputs (plus remapping 0 inputs)(21.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9730), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9731), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9732), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9733), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9734), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9735), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9736), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9737), keeping 40 out of 200 inputs (plus remapping 0 inputs)(20.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9738), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9739), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (974), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9740), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9741), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9742), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9743), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9744), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9745), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9746), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9747), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9748), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9749), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (975), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9750), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9751), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_33 (9752), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9753), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9754), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9755), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_0 (9756), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (9757), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9758), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9759), keeping 106 out of 200 inputs (plus remapping 0 inputs)(53.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (976), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9760), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9761), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9762), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9763), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9764), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9765), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9766), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9767), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9768), keeping 60 out of 200 inputs (plus remapping 0 inputs)(30.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9769), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_2 (977), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9770), keeping 56 out of 200 inputs (plus remapping 0 inputs)(28.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9771), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9772), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9773), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9774), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (9775), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9776), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9777), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9778), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9779), keeping 147 out of 200 inputs (plus remapping 0 inputs)(73.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (978), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9780), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9781), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9782), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9783), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9784), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9785), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9786), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9787), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9788), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9789), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (979), keeping 88 out of 200 inputs (plus remapping 0 inputs)(44.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9790), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9791), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9792), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9793), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9794), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9795), keeping 96 out of 200 inputs (plus remapping 0 inputs)(48.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9796), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9797), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9798), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9799), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (98), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (980), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9800), keeping 103 out of 200 inputs (plus remapping 0 inputs)(51.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9801), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9802), keeping 74 out of 200 inputs (plus remapping 0 inputs)(37.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9803), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9804), keeping 85 out of 200 inputs (plus remapping 0 inputs)(42.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9805), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9806), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9807), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9808), keeping 136 out of 200 inputs (plus remapping 0 inputs)(68.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9809), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (981), keeping 90 out of 200 inputs (plus remapping 0 inputs)(45.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9810), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9811), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to LTS_4 (9812), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to LTS_6 (9813), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to FS_2 (9814), keeping 200 out of 200 inputs (plus remapping 0 inputs)(100.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9815), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9816), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9817), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9818), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_1 (9819), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (982), keeping 57 out of 200 inputs (plus remapping 0 inputs)(28.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9820), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9821), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9822), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9823), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9824), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9825), keeping 130 out of 200 inputs (plus remapping 0 inputs)(65.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9826), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9827), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9828), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_10 (9829), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (983), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9830), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9831), keeping 81 out of 200 inputs (plus remapping 0 inputs)(40.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_12 (9832), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_13 (9833), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9834), keeping 68 out of 200 inputs (plus remapping 0 inputs)(34.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_15 (9835), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9836), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_17 (9837), keeping 69 out of 200 inputs (plus remapping 0 inputs)(34.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9838), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9839), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_11 (984), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_21 (9840), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9841), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9842), keeping 101 out of 200 inputs (plus remapping 0 inputs)(50.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9843), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9844), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9845), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9846), keeping 86 out of 200 inputs (plus remapping 0 inputs)(43.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9847), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9848), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9849), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (985), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9850), keeping 76 out of 200 inputs (plus remapping 0 inputs)(38.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9851), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9852), keeping 118 out of 200 inputs (plus remapping 0 inputs)(59.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9853), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9854), keeping 44 out of 200 inputs (plus remapping 0 inputs)(22.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9855), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9856), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9857), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9858), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9859), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (986), keeping 99 out of 200 inputs (plus remapping 0 inputs)(49.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_3 (9860), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_4 (9861), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9862), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9863), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9864), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_9 (9865), keeping 61 out of 200 inputs (plus remapping 0 inputs)(30.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9866), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (9867), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (9868), keeping 89 out of 200 inputs (plus remapping 0 inputs)(44.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9869), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (987), keeping 141 out of 200 inputs (plus remapping 0 inputs)(70.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9870), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (9871), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (9872), keeping 142 out of 200 inputs (plus remapping 0 inputs)(71.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9873), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9874), keeping 137 out of 200 inputs (plus remapping 0 inputs)(68.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9875), keeping 129 out of 200 inputs (plus remapping 0 inputs)(64.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9876), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9877), keeping 107 out of 200 inputs (plus remapping 0 inputs)(53.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_25 (9878), keeping 123 out of 200 inputs (plus remapping 0 inputs)(61.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9879), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_14 (988), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_27 (9880), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9881), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9882), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9883), keeping 100 out of 200 inputs (plus remapping 0 inputs)(50.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_31 (9884), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9885), keeping 79 out of 200 inputs (plus remapping 0 inputs)(39.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9886), keeping 138 out of 200 inputs (plus remapping 0 inputs)(69.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9887), keeping 124 out of 200 inputs (plus remapping 0 inputs)(62.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9888), keeping 63 out of 200 inputs (plus remapping 0 inputs)(31.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9889), keeping 49 out of 200 inputs (plus remapping 0 inputs)(24.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (989), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_34 (9890), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9891), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9892), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to ChIN_0 (9893), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9894), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_0 (9895), keeping 95 out of 200 inputs (plus remapping 0 inputs)(47.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_2 (9896), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9897), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (9898), keeping 71 out of 200 inputs (plus remapping 0 inputs)(35.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9899), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_3 (99), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (990), keeping 102 out of 200 inputs (plus remapping 0 inputs)(51.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9900), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_4 (9901), keeping 113 out of 200 inputs (plus remapping 0 inputs)(56.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9902), keeping 59 out of 200 inputs (plus remapping 0 inputs)(29.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9903), keeping 65 out of 200 inputs (plus remapping 0 inputs)(32.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_5 (9904), keeping 62 out of 200 inputs (plus remapping 0 inputs)(31.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9905), keeping 121 out of 200 inputs (plus remapping 0 inputs)(60.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_6 (9906), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_7 (9907), keeping 43 out of 200 inputs (plus remapping 0 inputs)(21.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9908), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_8 (9909), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_15 (991), keeping 80 out of 200 inputs (plus remapping 0 inputs)(40.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9910), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_9 (9911), keeping 22 out of 200 inputs (plus remapping 0 inputs)(11.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9912), keeping 97 out of 200 inputs (plus remapping 0 inputs)(48.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_11 (9913), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_14 (9914), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_16 (9915), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_18 (9916), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_19 (9917), keeping 52 out of 200 inputs (plus remapping 0 inputs)(26.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9918), keeping 111 out of 200 inputs (plus remapping 0 inputs)(55.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_20 (9919), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_17 (992), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_22 (9920), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_23 (9921), keeping 105 out of 200 inputs (plus remapping 0 inputs)(52.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9922), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_24 (9923), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9924), keeping 94 out of 200 inputs (plus remapping 0 inputs)(47.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_25 (9925), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9926), keeping 135 out of 200 inputs (plus remapping 0 inputs)(67.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_26 (9927), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_27 (9928), keeping 84 out of 200 inputs (plus remapping 0 inputs)(42.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_28 (9929), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_19 (993), keeping 77 out of 200 inputs (plus remapping 0 inputs)(38.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_29 (9930), keeping 58 out of 200 inputs (plus remapping 0 inputs)(29.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9931), keeping 78 out of 200 inputs (plus remapping 0 inputs)(39.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9932), keeping 67 out of 200 inputs (plus remapping 0 inputs)(33.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_30 (9933), keeping 66 out of 200 inputs (plus remapping 0 inputs)(33.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9934), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_31 (9935), keeping 114 out of 200 inputs (plus remapping 0 inputs)(57.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_32 (9936), keeping 55 out of 200 inputs (plus remapping 0 inputs)(27.50 %) (0.00 % remapped)\n", + "Processed input to dSPN_34 (9937), keeping 34 out of 200 inputs (plus remapping 0 inputs)(17.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9938), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to dSPN_35 (9939), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (994), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9940), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9941), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9942), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9943), keeping 115 out of 200 inputs (plus remapping 0 inputs)(57.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_1 (9944), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9945), keeping 72 out of 200 inputs (plus remapping 0 inputs)(36.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_5 (9946), keeping 87 out of 200 inputs (plus remapping 0 inputs)(43.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9947), keeping 149 out of 200 inputs (plus remapping 0 inputs)(74.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_6 (9948), keeping 126 out of 200 inputs (plus remapping 0 inputs)(63.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_7 (9949), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (995), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_8 (9950), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9951), keeping 116 out of 200 inputs (plus remapping 0 inputs)(58.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9952), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9953), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9954), keeping 119 out of 200 inputs (plus remapping 0 inputs)(59.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_10 (9955), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9956), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9957), keeping 82 out of 200 inputs (plus remapping 0 inputs)(41.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_12 (9958), keeping 91 out of 200 inputs (plus remapping 0 inputs)(45.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_13 (9959), keeping 108 out of 200 inputs (plus remapping 0 inputs)(54.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (996), keeping 120 out of 200 inputs (plus remapping 0 inputs)(60.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_16 (9960), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9961), keeping 139 out of 200 inputs (plus remapping 0 inputs)(69.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_18 (9962), keeping 143 out of 200 inputs (plus remapping 0 inputs)(71.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9963), keeping 125 out of 200 inputs (plus remapping 0 inputs)(62.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_20 (9964), keeping 122 out of 200 inputs (plus remapping 0 inputs)(61.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9965), keeping 117 out of 200 inputs (plus remapping 0 inputs)(58.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_22 (9966), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (9967), keeping 112 out of 200 inputs (plus remapping 0 inputs)(56.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_24 (9968), keeping 131 out of 200 inputs (plus remapping 0 inputs)(65.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9969), keeping 132 out of 200 inputs (plus remapping 0 inputs)(66.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (997), keeping 133 out of 200 inputs (plus remapping 0 inputs)(66.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_26 (9970), keeping 145 out of 200 inputs (plus remapping 0 inputs)(72.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_28 (9971), keeping 144 out of 200 inputs (plus remapping 0 inputs)(72.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9972), keeping 83 out of 200 inputs (plus remapping 0 inputs)(41.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_29 (9973), keeping 73 out of 200 inputs (plus remapping 0 inputs)(36.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_30 (9974), keeping 92 out of 200 inputs (plus remapping 0 inputs)(46.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9975), keeping 75 out of 200 inputs (plus remapping 0 inputs)(37.50 %) (0.00 % remapped)\n", + "Processed input to iSPN_32 (9976), keeping 70 out of 200 inputs (plus remapping 0 inputs)(35.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_33 (9977), keeping 134 out of 200 inputs (plus remapping 0 inputs)(67.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9978), keeping 104 out of 200 inputs (plus remapping 0 inputs)(52.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_35 (9979), keeping 110 out of 200 inputs (plus remapping 0 inputs)(55.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_21 (998), keeping 127 out of 200 inputs (plus remapping 0 inputs)(63.50 %) (0.00 % remapped)\n", + "Processed input to LTS_8 (9980), keeping 0 out of 0 inputs (plus remapping 0 inputs)(0.00 %) (0.00 % remapped)\n", + "Processed input to iSPN_23 (999), keeping 128 out of 200 inputs (plus remapping 0 inputs)(64.00 %) (0.00 % remapped)\n" + ] + } + ], "source": [ "network_file_pd0 = os.path.join(network_path_pd0, \"network-synapses.hdf5\")\n", "network_file_pd2_ref = os.path.join(network_path_pd2_ref, \"network-synapses.hdf5\")\n", @@ -231,7 +11371,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "ac942a43-86bc-40c3-8d5e-526aa17a13d7", "metadata": {}, "outputs": [], @@ -249,7 +11389,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4dcb44ac-d3d8-4f32-b086-02a6efaf355e", "metadata": {}, "outputs": [], @@ -262,10 +11402,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "1cafa122", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "7623" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# neuron_id = 55\n", "neuron_id" @@ -273,10 +11424,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "1ea26248", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "Plotting 200 external synapses\n", + "Plotting 200 external synapses\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:118: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(xs=coords[:, 0], ys=coords[:, 1], zs=coords[:, 2],\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:125: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(xs=syn_coords[:, 0], ys=syn_coords[:, 1], zs=syn_coords[:, 2],\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:158: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:159: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels(y_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:160: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_zticklabels(z_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:158: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:159: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels(y_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:160: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_zticklabels(z_labels)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure written: networks/PD-example-10k/PD0/figures/input-to-7623-dSPN_18-and-internal-synapses.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "from snudda.plotting.plot_degeneration import PlotDegeneration\n", @@ -293,10 +11494,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "8cc7faff", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "Reading SNUDDA_DATA=../../../../BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2/network-synapses.hdf5\n", + "Plotting 200 external synapses\n", + "Plotting 127 external synapses\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:118: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(xs=coords[:, 0], ys=coords[:, 1], zs=coords[:, 2],\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:125: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(xs=syn_coords[:, 0], ys=syn_coords[:, 1], zs=syn_coords[:, 2],\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:158: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:159: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels(y_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:160: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_zticklabels(z_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:158: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:159: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels(y_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:160: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_zticklabels(z_labels)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure written: networks/PD-example-10k/PD2/figures/input-to-7623-dSPN_18-and-internal-synapses.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "from snudda.plotting.plot_degeneration import PlotDegeneration\n", @@ -310,10 +11561,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "84c3217c-8447-4eab-8712-494d24c97b38", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD0 from networks/PD-example-10k/PD0/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/Parkinson/20221213/PD2 from networks/PD-example-10k/PD2-ref/network-config.json\n", + "Plotting 200 external synapses\n", + "Plotting 200 external synapses\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:118: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(xs=coords[:, 0], ys=coords[:, 1], zs=coords[:, 2],\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:125: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(xs=syn_coords[:, 0], ys=syn_coords[:, 1], zs=syn_coords[:, 2],\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:158: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:159: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels(y_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:160: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_zticklabels(z_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:158: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_xticklabels(x_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:159: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_yticklabels(y_labels)\n", + "/home/hjorth/HBP/Snudda/snudda/plotting/plot_input_locations.py:160: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax.set_zticklabels(z_labels)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure written: networks/PD-example-10k/PD2-ref/figures/input-to-7623-dSPN_18-and-internal-synapses.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "from snudda.plotting.plot_degeneration import PlotDegeneration\n", @@ -327,7 +11628,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "5cb8b804-88b0-4fb7-af11-163bbca7d133", "metadata": {}, "outputs": [], @@ -340,10 +11641,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "4753a05f-c299-4f34-8b19-1545a324a03b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "pd0_input_info.plot_input_count(\"PD0-input_example.png\")\n", "pd2_input_info.plot_input_count(\"PD2-input_example.png\")" @@ -351,10 +11673,92 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "e3dca220-b719-48d9-8cd1-a0c3eaa1b3e3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WT\n", + "Loading networks/PD-example-10k/PD0/network-synapses.hdf5\n", + "Loading config data from HDF5\n", + "Loading 9981 neurons with 5545921 synapses and 1499 gap junctions\n", + "Load done. 1.3\n", + "ChIN -> LTS: 2285 synapses\n", + "ChIN -> dSPN: 38828 synapses\n", + "ChIN -> iSPN: 46605 synapses\n", + "FS -> FS: 2427 synapses\n", + "FS -> LTS: 299 synapses\n", + "FS -> dSPN: 203034 synapses\n", + "FS -> iSPN: 123519 synapses\n", + "LTS -> ChIN: 3108 synapses\n", + "LTS -> dSPN: 10990 synapses\n", + "LTS -> iSPN: 7966 synapses\n", + "dSPN -> ChIN: 1492 synapses\n", + "dSPN -> dSPN: 1071065 synapses\n", + "dSPN -> iSPN: 281530 synapses\n", + "iSPN -> ChIN: 1502 synapses\n", + "iSPN -> dSPN: 1164165 synapses\n", + "iSPN -> iSPN: 2587106 synapses\n", + "\n", + "PD2\n", + "Loading networks/PD-example-10k/PD2/network-synapses.hdf5\n", + "Loading config data from HDF5\n", + "Loading 9981 neurons with 1800534 synapses and 1499 gap junctions\n", + "Load done. 0.5\n", + "ChIN -> LTS: 2285 synapses\n", + "ChIN -> dSPN: 5384 synapses\n", + "ChIN -> iSPN: 15227 synapses\n", + "FS -> FS: 3145 synapses\n", + "FS -> LTS: 345 synapses\n", + "FS -> dSPN: 165639 synapses\n", + "FS -> iSPN: 113834 synapses\n", + "LTS -> ChIN: 3108 synapses\n", + "LTS -> dSPN: 3098 synapses\n", + "LTS -> iSPN: 2864 synapses\n", + "dSPN -> ChIN: 1492 synapses\n", + "dSPN -> dSPN: 241274 synapses\n", + "dSPN -> iSPN: 89791 synapses\n", + "iSPN -> ChIN: 1502 synapses\n", + "iSPN -> dSPN: 281883 synapses\n", + "iSPN -> iSPN: 869663 synapses\n", + "\n", + "PD2 degenerated\n", + "Loading networks/PD-example-10k/PD2-ref/network-synapses.hdf5\n", + "Loading config data from HDF5\n", + "Loading 9981 neurons with 2057874 synapses and 1499 gap junctions\n", + "Load done. 0.7\n", + "ChIN -> LTS: 2189 synapses\n", + "ChIN -> dSPN: 10387 synapses\n", + "ChIN -> iSPN: 17184 synapses\n", + "FS -> FS: 4296 synapses\n", + "FS -> LTS: 528 synapses\n", + "FS -> dSPN: 200334 synapses\n", + "FS -> iSPN: 141325 synapses\n", + "LTS -> ChIN: 3135 synapses\n", + "LTS -> dSPN: 2988 synapses\n", + "LTS -> iSPN: 2660 synapses\n", + "dSPN -> ChIN: 1470 synapses\n", + "dSPN -> dSPN: 301854 synapses\n", + "dSPN -> iSPN: 96214 synapses\n", + "iSPN -> ChIN: 1399 synapses\n", + "iSPN -> dSPN: 306631 synapses\n", + "iSPN -> iSPN: 965280 synapses\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cmd_str1 = f\"snudda_load {network_path_pd0}/network-synapses.hdf5 --countSyn\"\n", "cmd_str2 = f\"snudda_load {network_path_pd2}/network-synapses.hdf5 --countSyn\"\n", @@ -370,10 +11774,1311 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "b59beba2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WT\n", + "Loading networks/PD-example-10k/PD0/network-synapses.hdf5\n", + "Loading config data from HDF5\n", + "Loading 9981 neurons with 5545921 synapses and 1499 gap junctions\n", + "Load done. 1.4\n", + "List neurons pre-synaptic to neuron_id = 7623 (dSPN_18)\n", + "The neuron receives 2250 synapses\n", + "103 : dSPN_13 (3 synapses)\n", + "189 : dSPN_31 (2 synapses)\n", + "202 : iSPN_15 (2 synapses)\n", + "212 : iSPN_26 (3 synapses)\n", + "360 : dSPN_33 (3 synapses)\n", + "413 : FS_2 (3 synapses)\n", + "434 : dSPN_18 (2 synapses)\n", + "505 : dSPN_1 (3 synapses)\n", + "537 : iSPN_0 (2 synapses)\n", + "556 : iSPN_16 (2 synapses)\n", + "585 : dSPN_19 (3 synapses)\n", + "599 : dSPN_28 (3 synapses)\n", + "601 : dSPN_31 (4 synapses)\n", + "604 : dSPN_33 (4 synapses)\n", + "631 : iSPN_23 (4 synapses)\n", + "634 : iSPN_24 (3 synapses)\n", + "637 : iSPN_26 (3 synapses)\n", + "641 : iSPN_29 (4 synapses)\n", + "644 : iSPN_30 (2 synapses)\n", + "718 : iSPN_20 (4 synapses)\n", + "744 : FS_1 (3 synapses)\n", + "745 : dSPN_0 (4 synapses)\n", + "750 : dSPN_2 (2 synapses)\n", + "760 : dSPN_12 (3 synapses)\n", + "763 : dSPN_14 (4 synapses)\n", + "771 : dSPN_22 (3 synapses)\n", + "773 : dSPN_23 (3 synapses)\n", + "774 : dSPN_24 (3 synapses)\n", + "776 : dSPN_25 (3 synapses)\n", + "781 : dSPN_28 (2 synapses)\n", + "783 : dSPN_32 (4 synapses)\n", + "789 : iSPN_2 (3 synapses)\n", + "799 : iSPN_9 (2 synapses)\n", + "818 : iSPN_26 (2 synapses)\n", + "841 : dSPN_6 (5 synapses)\n", + "853 : dSPN_17 (3 synapses)\n", + "884 : dSPN_34 (2 synapses)\n", + "1019 : dSPN_0 (3 synapses)\n", + "1036 : dSPN_18 (4 synapses)\n", + "1038 : dSPN_20 (4 synapses)\n", + "1039 : dSPN_22 (3 synapses)\n", + "1042 : dSPN_24 (2 synapses)\n", + "1054 : iSPN_1 (3 synapses)\n", + "1055 : iSPN_1 (3 synapses)\n", + "1063 : iSPN_11 (3 synapses)\n", + "1064 : iSPN_11 (3 synapses)\n", + "1070 : iSPN_21 (2 synapses)\n", + "1071 : iSPN_24 (3 synapses)\n", + "1072 : iSPN_26 (6 synapses)\n", + "1074 : iSPN_27 (2 synapses)\n", + "1076 : iSPN_28 (3 synapses)\n", + "1078 : iSPN_31 (3 synapses)\n", + "1147 : iSPN_16 (3 synapses)\n", + "1201 : dSPN_18 (1 synapses)\n", + "1207 : dSPN_25 (4 synapses)\n", + "1225 : iSPN_1 (3 synapses)\n", + "1245 : iSPN_22 (3 synapses)\n", + "1265 : dSPN_3 (3 synapses)\n", + "1269 : dSPN_8 (4 synapses)\n", + "1272 : dSPN_11 (2 synapses)\n", + "1291 : dSPN_25 (3 synapses)\n", + "1294 : dSPN_26 (3 synapses)\n", + "1307 : iSPN_0 (2 synapses)\n", + "1311 : iSPN_4 (3 synapses)\n", + "1356 : dSPN_0 (5 synapses)\n", + "1359 : dSPN_2 (4 synapses)\n", + "1370 : dSPN_8 (2 synapses)\n", + "1372 : dSPN_8 (3 synapses)\n", + "1381 : dSPN_15 (3 synapses)\n", + "1383 : dSPN_16 (4 synapses)\n", + "1384 : dSPN_17 (3 synapses)\n", + "1387 : dSPN_18 (2 synapses)\n", + "1397 : dSPN_27 (3 synapses)\n", + "1404 : dSPN_31 (4 synapses)\n", + "1411 : iSPN_0 (2 synapses)\n", + "1413 : iSPN_0 (3 synapses)\n", + "1414 : iSPN_1 (4 synapses)\n", + "1423 : iSPN_6 (5 synapses)\n", + "1425 : iSPN_6 (3 synapses)\n", + "1429 : iSPN_11 (4 synapses)\n", + "1433 : iSPN_13 (2 synapses)\n", + "1453 : iSPN_30 (1 synapses)\n", + "1462 : dSPN_0 (4 synapses)\n", + "1464 : dSPN_2 (3 synapses)\n", + "1466 : dSPN_3 (3 synapses)\n", + "1469 : dSPN_5 (3 synapses)\n", + "1470 : dSPN_7 (3 synapses)\n", + "1471 : dSPN_8 (2 synapses)\n", + "1477 : dSPN_14 (2 synapses)\n", + "1478 : dSPN_14 (2 synapses)\n", + "1488 : dSPN_20 (3 synapses)\n", + "1494 : dSPN_24 (3 synapses)\n", + "1497 : dSPN_27 (4 synapses)\n", + "1500 : dSPN_31 (2 synapses)\n", + "1501 : dSPN_32 (3 synapses)\n", + "1503 : dSPN_32 (4 synapses)\n", + "1506 : iSPN_1 (3 synapses)\n", + "1521 : iSPN_12 (3 synapses)\n", + "1522 : iSPN_13 (2 synapses)\n", + "1524 : iSPN_14 (4 synapses)\n", + "1526 : iSPN_14 (3 synapses)\n", + "1528 : iSPN_16 (2 synapses)\n", + "1545 : iSPN_30 (3 synapses)\n", + "1557 : FS_0 (4 synapses)\n", + "1560 : dSPN_1 (2 synapses)\n", + "1565 : dSPN_7 (4 synapses)\n", + "1568 : dSPN_8 (2 synapses)\n", + "1570 : dSPN_11 (3 synapses)\n", + "1573 : dSPN_12 (2 synapses)\n", + "1576 : dSPN_14 (2 synapses)\n", + "1579 : dSPN_16 (3 synapses)\n", + "1588 : dSPN_22 (2 synapses)\n", + "1599 : dSPN_28 (1 synapses)\n", + "1607 : iSPN_0 (4 synapses)\n", + "1618 : iSPN_16 (4 synapses)\n", + "1620 : iSPN_17 (6 synapses)\n", + "1621 : iSPN_19 (4 synapses)\n", + "1623 : iSPN_21 (4 synapses)\n", + "1627 : iSPN_26 (4 synapses)\n", + "1635 : iSPN_31 (3 synapses)\n", + "1639 : iSPN_35 (4 synapses)\n", + "1641 : iSPN_35 (3 synapses)\n", + "1811 : iSPN_8 (1 synapses)\n", + "1821 : iSPN_16 (2 synapses)\n", + "1935 : dSPN_24 (3 synapses)\n", + "1969 : iSPN_10 (3 synapses)\n", + "2010 : FS_3 (4 synapses)\n", + "2014 : dSPN_5 (3 synapses)\n", + "2028 : dSPN_14 (3 synapses)\n", + "2029 : dSPN_14 (5 synapses)\n", + "2052 : dSPN_29 (2 synapses)\n", + "2059 : dSPN_35 (3 synapses)\n", + "2062 : iSPN_1 (2 synapses)\n", + "2085 : iSPN_18 (3 synapses)\n", + "2093 : iSPN_26 (3 synapses)\n", + "2100 : iSPN_33 (4 synapses)\n", + "2132 : dSPN_14 (3 synapses)\n", + "2249 : dSPN_16 (4 synapses)\n", + "2330 : FS_3 (3 synapses)\n", + "2336 : dSPN_5 (2 synapses)\n", + "2347 : dSPN_12 (3 synapses)\n", + "2352 : dSPN_16 (3 synapses)\n", + "2354 : dSPN_18 (5 synapses)\n", + "2368 : dSPN_28 (3 synapses)\n", + "2374 : dSPN_33 (3 synapses)\n", + "2381 : dSPN_35 (2 synapses)\n", + "2383 : iSPN_2 (3 synapses)\n", + "2384 : iSPN_2 (3 synapses)\n", + "2391 : iSPN_8 (4 synapses)\n", + "2408 : iSPN_19 (3 synapses)\n", + "2428 : iSPN_35 (2 synapses)\n", + "2451 : dSPN_14 (3 synapses)\n", + "2460 : dSPN_20 (3 synapses)\n", + "2507 : iSPN_15 (3 synapses)\n", + "2612 : iSPN_16 (3 synapses)\n", + "2614 : iSPN_17 (1 synapses)\n", + "2621 : iSPN_28 (3 synapses)\n", + "2624 : iSPN_31 (3 synapses)\n", + "2645 : dSPN_8 (3 synapses)\n", + "2649 : dSPN_11 (2 synapses)\n", + "2657 : dSPN_16 (3 synapses)\n", + "2674 : dSPN_30 (4 synapses)\n", + "2720 : iSPN_26 (3 synapses)\n", + "2879 : dSPN_22 (3 synapses)\n", + "2932 : FS_3 (3 synapses)\n", + "2933 : dSPN_0 (3 synapses)\n", + "2935 : dSPN_1 (3 synapses)\n", + "2938 : dSPN_3 (2 synapses)\n", + "2944 : dSPN_10 (3 synapses)\n", + "2958 : dSPN_23 (3 synapses)\n", + "2960 : dSPN_24 (4 synapses)\n", + "2962 : dSPN_25 (3 synapses)\n", + "2964 : dSPN_26 (3 synapses)\n", + "2970 : dSPN_30 (3 synapses)\n", + "2972 : dSPN_30 (3 synapses)\n", + "2975 : dSPN_34 (3 synapses)\n", + "2978 : iSPN_2 (3 synapses)\n", + "2988 : iSPN_5 (4 synapses)\n", + "2989 : iSPN_6 (5 synapses)\n", + "2995 : iSPN_8 (5 synapses)\n", + "2997 : iSPN_12 (3 synapses)\n", + "2999 : iSPN_13 (3 synapses)\n", + "3004 : iSPN_15 (3 synapses)\n", + "3015 : iSPN_21 (8 synapses)\n", + "3017 : iSPN_21 (4 synapses)\n", + "3019 : iSPN_23 (4 synapses)\n", + "3020 : iSPN_24 (3 synapses)\n", + "3034 : iSPN_32 (3 synapses)\n", + "3038 : iSPN_33 (3 synapses)\n", + "3044 : dSPN_3 (4 synapses)\n", + "3047 : dSPN_5 (3 synapses)\n", + "3051 : dSPN_7 (5 synapses)\n", + "3057 : dSPN_11 (6 synapses)\n", + "3061 : dSPN_13 (8 synapses)\n", + "3063 : dSPN_13 (3 synapses)\n", + "3066 : dSPN_14 (4 synapses)\n", + "3069 : dSPN_18 (3 synapses)\n", + "3072 : dSPN_19 (3 synapses)\n", + "3074 : dSPN_20 (3 synapses)\n", + "3075 : dSPN_22 (3 synapses)\n", + "3083 : dSPN_27 (3 synapses)\n", + "3085 : dSPN_27 (3 synapses)\n", + "3086 : dSPN_27 (3 synapses)\n", + "3097 : dSPN_34 (3 synapses)\n", + "3102 : iSPN_5 (3 synapses)\n", + "3108 : iSPN_8 (3 synapses)\n", + "3109 : iSPN_8 (5 synapses)\n", + "3111 : iSPN_9 (4 synapses)\n", + "3112 : iSPN_11 (4 synapses)\n", + "3113 : iSPN_12 (3 synapses)\n", + "3118 : iSPN_18 (5 synapses)\n", + "3122 : iSPN_20 (3 synapses)\n", + "3123 : iSPN_20 (3 synapses)\n", + "3126 : iSPN_21 (5 synapses)\n", + "3128 : iSPN_23 (2 synapses)\n", + "3129 : iSPN_23 (4 synapses)\n", + "3131 : iSPN_23 (4 synapses)\n", + "3132 : iSPN_26 (3 synapses)\n", + "3135 : iSPN_28 (3 synapses)\n", + "3137 : iSPN_29 (4 synapses)\n", + "3139 : iSPN_31 (4 synapses)\n", + "3141 : iSPN_32 (5 synapses)\n", + "3143 : iSPN_35 (3 synapses)\n", + "3150 : FS_2 (8 synapses)\n", + "3152 : FS_3 (9 synapses)\n", + "3153 : FS_3 (5 synapses)\n", + "3154 : dSPN_1 (3 synapses)\n", + "3164 : dSPN_10 (3 synapses)\n", + "3169 : dSPN_13 (4 synapses)\n", + "3170 : dSPN_15 (4 synapses)\n", + "3173 : dSPN_16 (2 synapses)\n", + "3174 : dSPN_17 (3 synapses)\n", + "3176 : dSPN_19 (3 synapses)\n", + "3179 : dSPN_21 (6 synapses)\n", + "3180 : dSPN_22 (3 synapses)\n", + "3183 : dSPN_27 (3 synapses)\n", + "3184 : dSPN_28 (4 synapses)\n", + "3186 : dSPN_32 (2 synapses)\n", + "3187 : dSPN_33 (5 synapses)\n", + "3190 : dSPN_34 (4 synapses)\n", + "3193 : iSPN_2 (3 synapses)\n", + "3194 : iSPN_2 (3 synapses)\n", + "3195 : iSPN_3 (3 synapses)\n", + "3197 : iSPN_6 (4 synapses)\n", + "3199 : iSPN_7 (5 synapses)\n", + "3200 : iSPN_7 (4 synapses)\n", + "3202 : iSPN_9 (5 synapses)\n", + "3203 : iSPN_10 (4 synapses)\n", + "3204 : iSPN_10 (3 synapses)\n", + "3205 : iSPN_10 (5 synapses)\n", + "3207 : iSPN_12 (5 synapses)\n", + "3211 : iSPN_15 (2 synapses)\n", + "3212 : iSPN_15 (6 synapses)\n", + "3214 : iSPN_17 (3 synapses)\n", + "3215 : iSPN_20 (3 synapses)\n", + "3216 : iSPN_20 (5 synapses)\n", + "3218 : iSPN_22 (4 synapses)\n", + "3221 : iSPN_23 (8 synapses)\n", + "3222 : iSPN_23 (4 synapses)\n", + "3223 : iSPN_23 (4 synapses)\n", + "3225 : iSPN_25 (4 synapses)\n", + "3226 : iSPN_26 (3 synapses)\n", + "3227 : iSPN_26 (3 synapses)\n", + "3228 : iSPN_26 (5 synapses)\n", + "3229 : iSPN_26 (4 synapses)\n", + "3230 : iSPN_27 (3 synapses)\n", + "3232 : iSPN_28 (3 synapses)\n", + "3236 : iSPN_32 (2 synapses)\n", + "3240 : iSPN_33 (3 synapses)\n", + "3241 : iSPN_33 (3 synapses)\n", + "3243 : iSPN_34 (4 synapses)\n", + "3244 : iSPN_34 (4 synapses)\n", + "3245 : iSPN_34 (3 synapses)\n", + "3295 : dSPN_23 (3 synapses)\n", + "3328 : dSPN_0 (4 synapses)\n", + "3339 : dSPN_8 (5 synapses)\n", + "3367 : dSPN_31 (4 synapses)\n", + "3381 : iSPN_6 (4 synapses)\n", + "3404 : iSPN_23 (2 synapses)\n", + "3416 : iSPN_33 (3 synapses)\n", + "3426 : dSPN_0 (2 synapses)\n", + "3455 : dSPN_16 (3 synapses)\n", + "3480 : dSPN_31 (4 synapses)\n", + "3487 : iSPN_0 (3 synapses)\n", + "3488 : iSPN_3 (3 synapses)\n", + "3560 : dSPN_16 (3 synapses)\n", + "3584 : dSPN_31 (6 synapses)\n", + "3610 : iSPN_6 (4 synapses)\n", + "3613 : iSPN_8 (4 synapses)\n", + "3614 : iSPN_8 (1 synapses)\n", + "3636 : iSPN_20 (3 synapses)\n", + "3652 : iSPN_29 (2 synapses)\n", + "3656 : iSPN_31 (5 synapses)\n", + "3675 : dSPN_7 (3 synapses)\n", + "3677 : dSPN_7 (4 synapses)\n", + "3681 : dSPN_16 (3 synapses)\n", + "3683 : dSPN_17 (3 synapses)\n", + "3685 : dSPN_19 (3 synapses)\n", + "3690 : dSPN_23 (3 synapses)\n", + "3692 : dSPN_24 (3 synapses)\n", + "3694 : dSPN_26 (3 synapses)\n", + "3698 : dSPN_27 (3 synapses)\n", + "3700 : dSPN_27 (2 synapses)\n", + "3702 : dSPN_31 (4 synapses)\n", + "3703 : dSPN_31 (3 synapses)\n", + "3710 : dSPN_33 (3 synapses)\n", + "3716 : iSPN_3 (2 synapses)\n", + "3733 : iSPN_11 (3 synapses)\n", + "3734 : iSPN_12 (4 synapses)\n", + "3735 : iSPN_12 (2 synapses)\n", + "3739 : iSPN_14 (2 synapses)\n", + "3743 : iSPN_17 (4 synapses)\n", + "3749 : iSPN_21 (4 synapses)\n", + "3753 : iSPN_22 (4 synapses)\n", + "3755 : iSPN_23 (4 synapses)\n", + "3759 : iSPN_26 (2 synapses)\n", + "3760 : iSPN_26 (3 synapses)\n", + "3763 : iSPN_28 (4 synapses)\n", + "3765 : iSPN_29 (4 synapses)\n", + "3767 : iSPN_30 (5 synapses)\n", + "3769 : iSPN_32 (4 synapses)\n", + "3772 : iSPN_35 (4 synapses)\n", + "3773 : iSPN_35 (6 synapses)\n", + "3783 : dSPN_4 (2 synapses)\n", + "3784 : dSPN_4 (4 synapses)\n", + "3802 : dSPN_21 (3 synapses)\n", + "3819 : dSPN_35 (3 synapses)\n", + "3827 : iSPN_4 (1 synapses)\n", + "3828 : iSPN_5 (3 synapses)\n", + "3839 : iSPN_16 (3 synapses)\n", + "3891 : dSPN_24 (4 synapses)\n", + "3950 : FS_1 (3 synapses)\n", + "3981 : dSPN_16 (3 synapses)\n", + "4012 : dSPN_30 (3 synapses)\n", + "4021 : iSPN_0 (2 synapses)\n", + "4025 : iSPN_2 (4 synapses)\n", + "4044 : iSPN_11 (2 synapses)\n", + "4060 : iSPN_25 (3 synapses)\n", + "4076 : FS_1 (4 synapses)\n", + "4077 : FS_3 (5 synapses)\n", + "4078 : dSPN_2 (3 synapses)\n", + "4079 : dSPN_2 (4 synapses)\n", + "4083 : dSPN_5 (3 synapses)\n", + "4085 : dSPN_7 (4 synapses)\n", + "4086 : dSPN_7 (3 synapses)\n", + "4087 : dSPN_8 (3 synapses)\n", + "4089 : dSPN_9 (3 synapses)\n", + "4093 : dSPN_12 (3 synapses)\n", + "4094 : dSPN_12 (3 synapses)\n", + "4096 : dSPN_14 (4 synapses)\n", + "4097 : dSPN_14 (4 synapses)\n", + "4100 : dSPN_20 (3 synapses)\n", + "4104 : dSPN_23 (3 synapses)\n", + "4107 : dSPN_26 (3 synapses)\n", + "4108 : dSPN_27 (6 synapses)\n", + "4109 : dSPN_28 (2 synapses)\n", + "4111 : dSPN_31 (5 synapses)\n", + "4113 : dSPN_32 (3 synapses)\n", + "4114 : dSPN_32 (2 synapses)\n", + "4115 : dSPN_32 (4 synapses)\n", + "4116 : dSPN_32 (3 synapses)\n", + "4117 : dSPN_33 (4 synapses)\n", + "4118 : dSPN_34 (5 synapses)\n", + "4121 : dSPN_35 (3 synapses)\n", + "4122 : dSPN_35 (3 synapses)\n", + "4124 : iSPN_2 (3 synapses)\n", + "4125 : iSPN_4 (4 synapses)\n", + "4130 : iSPN_7 (5 synapses)\n", + "4133 : iSPN_11 (5 synapses)\n", + "4138 : iSPN_15 (4 synapses)\n", + "4139 : iSPN_17 (2 synapses)\n", + "4141 : iSPN_19 (5 synapses)\n", + "4143 : iSPN_19 (3 synapses)\n", + "4144 : iSPN_19 (4 synapses)\n", + "4147 : iSPN_22 (5 synapses)\n", + "4151 : iSPN_27 (3 synapses)\n", + "4152 : iSPN_27 (2 synapses)\n", + "4153 : iSPN_28 (5 synapses)\n", + "4156 : iSPN_30 (2 synapses)\n", + "4157 : iSPN_31 (5 synapses)\n", + "4160 : iSPN_32 (3 synapses)\n", + "4163 : iSPN_34 (3 synapses)\n", + "4168 : FS_0 (6 synapses)\n", + "4181 : dSPN_4 (3 synapses)\n", + "4196 : dSPN_11 (3 synapses)\n", + "4227 : dSPN_31 (3 synapses)\n", + "4246 : iSPN_4 (4 synapses)\n", + "4265 : iSPN_10 (2 synapses)\n", + "4304 : iSPN_33 (2 synapses)\n", + "4422 : iSPN_22 (3 synapses)\n", + "4456 : dSPN_0 (3 synapses)\n", + "4481 : dSPN_13 (3 synapses)\n", + "4486 : dSPN_16 (2 synapses)\n", + "4509 : dSPN_30 (4 synapses)\n", + "4511 : dSPN_31 (3 synapses)\n", + "4518 : dSPN_33 (2 synapses)\n", + "4553 : iSPN_20 (3 synapses)\n", + "4561 : iSPN_25 (3 synapses)\n", + "4586 : dSPN_6 (3 synapses)\n", + "4597 : dSPN_14 (5 synapses)\n", + "4598 : dSPN_14 (3 synapses)\n", + "4599 : dSPN_14 (3 synapses)\n", + "4602 : dSPN_16 (4 synapses)\n", + "4604 : dSPN_16 (3 synapses)\n", + "4608 : dSPN_18 (3 synapses)\n", + "4611 : dSPN_20 (3 synapses)\n", + "4618 : dSPN_27 (3 synapses)\n", + "4621 : dSPN_30 (3 synapses)\n", + "4630 : dSPN_33 (4 synapses)\n", + "4633 : dSPN_34 (4 synapses)\n", + "4634 : dSPN_35 (3 synapses)\n", + "4647 : iSPN_9 (3 synapses)\n", + "4660 : iSPN_17 (3 synapses)\n", + "4665 : iSPN_20 (5 synapses)\n", + "4670 : iSPN_23 (5 synapses)\n", + "4671 : iSPN_23 (3 synapses)\n", + "4677 : iSPN_26 (2 synapses)\n", + "4686 : FS_2 (5 synapses)\n", + "4693 : dSPN_1 (3 synapses)\n", + "4694 : dSPN_1 (3 synapses)\n", + "4695 : dSPN_2 (1 synapses)\n", + "4704 : dSPN_8 (2 synapses)\n", + "4709 : dSPN_11 (4 synapses)\n", + "4710 : dSPN_12 (2 synapses)\n", + "4714 : dSPN_15 (3 synapses)\n", + "4722 : dSPN_18 (5 synapses)\n", + "4735 : dSPN_27 (2 synapses)\n", + "4736 : dSPN_27 (3 synapses)\n", + "4738 : dSPN_31 (5 synapses)\n", + "4739 : dSPN_32 (3 synapses)\n", + "4745 : iSPN_2 (5 synapses)\n", + "4747 : iSPN_5 (4 synapses)\n", + "4755 : iSPN_9 (3 synapses)\n", + "4757 : iSPN_10 (3 synapses)\n", + "4764 : iSPN_15 (2 synapses)\n", + "4767 : iSPN_18 (3 synapses)\n", + "4774 : iSPN_24 (4 synapses)\n", + "4778 : iSPN_27 (4 synapses)\n", + "4781 : iSPN_27 (3 synapses)\n", + "4782 : iSPN_28 (2 synapses)\n", + "4786 : iSPN_30 (3 synapses)\n", + "4788 : iSPN_30 (3 synapses)\n", + "4801 : FS_3 (5 synapses)\n", + "4803 : dSPN_1 (3 synapses)\n", + "4805 : dSPN_3 (4 synapses)\n", + "4823 : dSPN_14 (4 synapses)\n", + "4825 : dSPN_15 (1 synapses)\n", + "4826 : dSPN_15 (3 synapses)\n", + "4831 : dSPN_20 (2 synapses)\n", + "4833 : dSPN_20 (3 synapses)\n", + "4835 : dSPN_24 (3 synapses)\n", + "4836 : dSPN_26 (4 synapses)\n", + "4845 : dSPN_33 (2 synapses)\n", + "4851 : iSPN_3 (4 synapses)\n", + "4852 : iSPN_3 (3 synapses)\n", + "4854 : iSPN_4 (3 synapses)\n", + "4860 : iSPN_7 (3 synapses)\n", + "4864 : iSPN_8 (3 synapses)\n", + "4873 : iSPN_14 (3 synapses)\n", + "4881 : iSPN_20 (2 synapses)\n", + "4883 : iSPN_22 (2 synapses)\n", + "4885 : iSPN_23 (3 synapses)\n", + "4886 : iSPN_23 (3 synapses)\n", + "4887 : iSPN_24 (4 synapses)\n", + "4901 : FS_1 (4 synapses)\n", + "4905 : dSPN_2 (3 synapses)\n", + "4917 : dSPN_8 (4 synapses)\n", + "4941 : dSPN_26 (3 synapses)\n", + "4944 : dSPN_28 (3 synapses)\n", + "4953 : dSPN_31 (5 synapses)\n", + "4969 : iSPN_5 (1 synapses)\n", + "4972 : iSPN_7 (2 synapses)\n", + "5065 : dSPN_4 (3 synapses)\n", + "5139 : iSPN_22 (3 synapses)\n", + "5409 : dSPN_28 (2 synapses)\n", + "5602 : dSPN_3 (3 synapses)\n", + "5611 : dSPN_8 (2 synapses)\n", + "5618 : dSPN_11 (3 synapses)\n", + "5643 : dSPN_28 (4 synapses)\n", + "5665 : iSPN_4 (3 synapses)\n", + "5695 : iSPN_24 (2 synapses)\n", + "5765 : iSPN_1 (3 synapses)\n", + "5790 : iSPN_26 (4 synapses)\n", + "5793 : iSPN_30 (2 synapses)\n", + "5799 : iSPN_35 (2 synapses)\n", + "5903 : iSPN_24 (2 synapses)\n", + "6027 : dSPN_4 (3 synapses)\n", + "6056 : dSPN_23 (4 synapses)\n", + "6057 : dSPN_25 (3 synapses)\n", + "6059 : dSPN_26 (4 synapses)\n", + "6070 : iSPN_1 (3 synapses)\n", + "6072 : iSPN_2 (2 synapses)\n", + "6077 : iSPN_4 (2 synapses)\n", + "6078 : iSPN_5 (5 synapses)\n", + "6086 : iSPN_9 (2 synapses)\n", + "6087 : iSPN_10 (4 synapses)\n", + "6091 : iSPN_12 (4 synapses)\n", + "6095 : iSPN_12 (4 synapses)\n", + "6107 : iSPN_23 (4 synapses)\n", + "6121 : iSPN_35 (4 synapses)\n", + "6133 : dSPN_4 (2 synapses)\n", + "6203 : iSPN_14 (4 synapses)\n", + "6243 : FS_1 (4 synapses)\n", + "6244 : FS_1 (4 synapses)\n", + "6245 : dSPN_0 (5 synapses)\n", + "6248 : dSPN_7 (5 synapses)\n", + "6249 : dSPN_7 (3 synapses)\n", + "6250 : dSPN_8 (2 synapses)\n", + "6256 : dSPN_10 (3 synapses)\n", + "6257 : dSPN_11 (3 synapses)\n", + "6258 : dSPN_13 (6 synapses)\n", + "6262 : dSPN_16 (5 synapses)\n", + "6272 : dSPN_23 (4 synapses)\n", + "6281 : dSPN_29 (3 synapses)\n", + "6285 : dSPN_35 (3 synapses)\n", + "6290 : iSPN_3 (3 synapses)\n", + "6296 : iSPN_7 (5 synapses)\n", + "6298 : iSPN_7 (2 synapses)\n", + "6307 : iSPN_10 (4 synapses)\n", + "6311 : iSPN_14 (3 synapses)\n", + "6313 : iSPN_15 (3 synapses)\n", + "6322 : iSPN_21 (3 synapses)\n", + "6323 : iSPN_22 (4 synapses)\n", + "6327 : iSPN_28 (4 synapses)\n", + "6338 : iSPN_32 (1 synapses)\n", + "6345 : iSPN_35 (4 synapses)\n", + "6453 : FS_0 (2 synapses)\n", + "6494 : dSPN_33 (3 synapses)\n", + "6671 : dSPN_12 (4 synapses)\n", + "6702 : dSPN_33 (3 synapses)\n", + "6713 : iSPN_4 (3 synapses)\n", + "6721 : iSPN_8 (2 synapses)\n", + "6729 : iSPN_12 (3 synapses)\n", + "6731 : iSPN_14 (1 synapses)\n", + "6764 : FS_2 (2 synapses)\n", + "6770 : dSPN_8 (3 synapses)\n", + "6992 : iSPN_5 (2 synapses)\n", + "7003 : iSPN_16 (3 synapses)\n", + "7019 : iSPN_23 (2 synapses)\n", + "7088 : dSPN_26 (3 synapses)\n", + "7153 : dSPN_1 (4 synapses)\n", + "7193 : dSPN_20 (3 synapses)\n", + "7199 : dSPN_23 (3 synapses)\n", + "7200 : dSPN_24 (3 synapses)\n", + "7214 : dSPN_33 (3 synapses)\n", + "7217 : iSPN_0 (3 synapses)\n", + "7234 : iSPN_16 (2 synapses)\n", + "7238 : iSPN_18 (3 synapses)\n", + "7239 : iSPN_20 (3 synapses)\n", + "7252 : iSPN_31 (2 synapses)\n", + "7255 : iSPN_34 (3 synapses)\n", + "7258 : LTS_4 (7 synapses)\n", + "7264 : iSPN_24 (2 synapses)\n", + "7269 : iSPN_28 (3 synapses)\n", + "7274 : iSPN_30 (2 synapses)\n", + "7276 : iSPN_31 (4 synapses)\n", + "7281 : iSPN_34 (6 synapses)\n", + "7302 : dSPN_19 (3 synapses)\n", + "7308 : dSPN_23 (3 synapses)\n", + "7320 : dSPN_35 (3 synapses)\n", + "7322 : dSPN_35 (3 synapses)\n", + "7330 : iSPN_7 (4 synapses)\n", + "7335 : iSPN_10 (4 synapses)\n", + "7337 : iSPN_14 (4 synapses)\n", + "7345 : iSPN_19 (4 synapses)\n", + "7350 : iSPN_21 (5 synapses)\n", + "7355 : iSPN_28 (4 synapses)\n", + "7360 : iSPN_30 (2 synapses)\n", + "7365 : iSPN_33 (3 synapses)\n", + "7369 : iSPN_34 (3 synapses)\n", + "7381 : dSPN_4 (4 synapses)\n", + "7420 : dSPN_28 (4 synapses)\n", + "7431 : iSPN_2 (3 synapses)\n", + "7470 : iSPN_23 (3 synapses)\n", + "7476 : iSPN_28 (4 synapses)\n", + "7487 : iSPN_34 (2 synapses)\n", + "7495 : FS_3 (4 synapses)\n", + "7503 : dSPN_12 (3 synapses)\n", + "7508 : dSPN_17 (1 synapses)\n", + "7516 : dSPN_23 (3 synapses)\n", + "7517 : dSPN_23 (4 synapses)\n", + "7518 : dSPN_23 (3 synapses)\n", + "7522 : dSPN_26 (2 synapses)\n", + "7524 : dSPN_28 (2 synapses)\n", + "7533 : iSPN_0 (7 synapses)\n", + "7534 : iSPN_0 (7 synapses)\n", + "7536 : iSPN_3 (4 synapses)\n", + "7538 : iSPN_4 (3 synapses)\n", + "7540 : iSPN_4 (4 synapses)\n", + "7542 : iSPN_4 (4 synapses)\n", + "7544 : iSPN_8 (1 synapses)\n", + "7548 : iSPN_11 (3 synapses)\n", + "7549 : iSPN_11 (4 synapses)\n", + "7551 : iSPN_15 (4 synapses)\n", + "7555 : iSPN_17 (3 synapses)\n", + "7562 : iSPN_19 (2 synapses)\n", + "7564 : iSPN_20 (2 synapses)\n", + "7568 : iSPN_21 (2 synapses)\n", + "7569 : iSPN_21 (6 synapses)\n", + "7578 : iSPN_29 (3 synapses)\n", + "7580 : iSPN_31 (4 synapses)\n", + "7585 : iSPN_33 (7 synapses)\n", + "7587 : iSPN_34 (4 synapses)\n", + "7593 : dSPN_0 (3 synapses)\n", + "7598 : dSPN_4 (3 synapses)\n", + "7602 : dSPN_7 (2 synapses)\n", + "7605 : dSPN_8 (3 synapses)\n", + "7606 : dSPN_10 (2 synapses)\n", + "7607 : dSPN_10 (5 synapses)\n", + "7612 : dSPN_11 (4 synapses)\n", + "7613 : dSPN_11 (3 synapses)\n", + "7616 : dSPN_14 (3 synapses)\n", + "7617 : dSPN_14 (4 synapses)\n", + "7621 : dSPN_17 (2 synapses)\n", + "7628 : dSPN_22 (2 synapses)\n", + "7631 : dSPN_22 (3 synapses)\n", + "7632 : dSPN_24 (3 synapses)\n", + "7635 : dSPN_25 (2 synapses)\n", + "7639 : dSPN_28 (4 synapses)\n", + "7644 : dSPN_30 (3 synapses)\n", + "7647 : dSPN_30 (3 synapses)\n", + "7648 : dSPN_30 (3 synapses)\n", + "7650 : dSPN_33 (5 synapses)\n", + "7651 : dSPN_33 (4 synapses)\n", + "7652 : dSPN_33 (3 synapses)\n", + "7654 : dSPN_35 (5 synapses)\n", + "7656 : iSPN_2 (4 synapses)\n", + "7662 : iSPN_7 (3 synapses)\n", + "7664 : iSPN_9 (3 synapses)\n", + "7666 : iSPN_9 (4 synapses)\n", + "7669 : iSPN_11 (3 synapses)\n", + "7671 : iSPN_12 (3 synapses)\n", + "7673 : iSPN_14 (3 synapses)\n", + "7675 : iSPN_16 (3 synapses)\n", + "7677 : iSPN_17 (2 synapses)\n", + "7679 : iSPN_18 (2 synapses)\n", + "7681 : iSPN_20 (5 synapses)\n", + "7692 : iSPN_28 (3 synapses)\n", + "7694 : iSPN_30 (3 synapses)\n", + "7695 : iSPN_30 (4 synapses)\n", + "7697 : iSPN_32 (5 synapses)\n", + "7700 : iSPN_34 (3 synapses)\n", + "7701 : iSPN_34 (3 synapses)\n", + "7702 : ChIN_0 (21 synapses)\n", + "7712 : dSPN_3 (2 synapses)\n", + "7715 : dSPN_6 (3 synapses)\n", + "7717 : dSPN_8 (3 synapses)\n", + "7734 : dSPN_19 (4 synapses)\n", + "7799 : iSPN_28 (4 synapses)\n", + "7872 : iSPN_8 (4 synapses)\n", + "7903 : FS_1 (2 synapses)\n", + "7930 : dSPN_20 (4 synapses)\n", + "7954 : iSPN_2 (2 synapses)\n", + "8048 : iSPN_6 (4 synapses)\n", + "8119 : dSPN_22 (3 synapses)\n", + "8151 : iSPN_9 (2 synapses)\n", + "8163 : iSPN_23 (2 synapses)\n", + "8171 : iSPN_31 (2 synapses)\n", + "8189 : dSPN_2 (3 synapses)\n", + "8192 : dSPN_4 (2 synapses)\n", + "8199 : dSPN_12 (4 synapses)\n", + "8220 : iSPN_0 (4 synapses)\n", + "8239 : iSPN_16 (3 synapses)\n", + "8282 : dSPN_14 (4 synapses)\n", + "8283 : dSPN_14 (3 synapses)\n", + "8309 : iSPN_1 (3 synapses)\n", + "8400 : dSPN_2 (3 synapses)\n", + "8424 : dSPN_24 (2 synapses)\n", + "8441 : iSPN_1 (3 synapses)\n", + "8443 : iSPN_6 (4 synapses)\n", + "8457 : iSPN_14 (3 synapses)\n", + "8650 : iSPN_3 (2 synapses)\n", + "8871 : iSPN_33 (2 synapses)\n", + "8917 : dSPN_21 (4 synapses)\n", + "8919 : dSPN_21 (2 synapses)\n", + "8952 : iSPN_13 (3 synapses)\n", + "8953 : iSPN_14 (3 synapses)\n", + "9156 : iSPN_16 (3 synapses)\n", + "9238 : iSPN_18 (3 synapses)\n", + "9747 : dSPN_28 (2 synapses)\n", + "\n", + "PD2\n", + "Loading networks/PD-example-10k/PD2/network-synapses.hdf5\n", + "Loading config data from HDF5\n", + "Loading 9981 neurons with 1800534 synapses and 1499 gap junctions\n", + "Load done. 0.6\n", + "List neurons pre-synaptic to neuron_id = 7623 (dSPN_18)\n", + "The neuron receives 816 synapses\n", + "189 : dSPN_31 (2 synapses)\n", + "413 : FS_2 (3 synapses)\n", + "631 : iSPN_23 (4 synapses)\n", + "641 : iSPN_29 (2 synapses)\n", + "744 : FS_1 (3 synapses)\n", + "763 : dSPN_14 (2 synapses)\n", + "783 : dSPN_32 (3 synapses)\n", + "789 : iSPN_2 (2 synapses)\n", + "1019 : dSPN_0 (2 synapses)\n", + "1038 : dSPN_20 (3 synapses)\n", + "1070 : iSPN_21 (2 synapses)\n", + "1072 : iSPN_26 (4 synapses)\n", + "1074 : iSPN_27 (2 synapses)\n", + "1172 : FS_1 (3 synapses)\n", + "1201 : dSPN_18 (1 synapses)\n", + "1207 : dSPN_25 (3 synapses)\n", + "1245 : iSPN_22 (3 synapses)\n", + "1269 : dSPN_8 (4 synapses)\n", + "1356 : dSPN_0 (3 synapses)\n", + "1370 : dSPN_8 (2 synapses)\n", + "1383 : dSPN_16 (2 synapses)\n", + "1387 : dSPN_18 (2 synapses)\n", + "1404 : dSPN_31 (4 synapses)\n", + "1413 : iSPN_0 (3 synapses)\n", + "1414 : iSPN_1 (4 synapses)\n", + "1423 : iSPN_6 (3 synapses)\n", + "1425 : iSPN_6 (3 synapses)\n", + "1429 : iSPN_11 (4 synapses)\n", + "1470 : dSPN_7 (3 synapses)\n", + "1477 : dSPN_14 (2 synapses)\n", + "1488 : dSPN_20 (3 synapses)\n", + "1501 : dSPN_32 (3 synapses)\n", + "1522 : iSPN_13 (2 synapses)\n", + "1557 : FS_0 (4 synapses)\n", + "1568 : dSPN_8 (2 synapses)\n", + "1620 : iSPN_17 (3 synapses)\n", + "1621 : iSPN_19 (4 synapses)\n", + "1623 : iSPN_21 (3 synapses)\n", + "1627 : iSPN_26 (4 synapses)\n", + "1639 : iSPN_35 (4 synapses)\n", + "1811 : iSPN_8 (1 synapses)\n", + "1969 : iSPN_10 (2 synapses)\n", + "2010 : FS_3 (3 synapses)\n", + "2059 : dSPN_35 (3 synapses)\n", + "2100 : iSPN_33 (3 synapses)\n", + "2132 : dSPN_14 (2 synapses)\n", + "2249 : dSPN_16 (3 synapses)\n", + "2330 : FS_3 (4 synapses)\n", + "2352 : dSPN_16 (3 synapses)\n", + "2354 : dSPN_18 (5 synapses)\n", + "2391 : iSPN_8 (3 synapses)\n", + "2428 : iSPN_35 (2 synapses)\n", + "2460 : dSPN_20 (2 synapses)\n", + "2621 : iSPN_28 (2 synapses)\n", + "2649 : dSPN_11 (2 synapses)\n", + "2657 : dSPN_16 (3 synapses)\n", + "2720 : iSPN_26 (3 synapses)\n", + "2879 : dSPN_22 (2 synapses)\n", + "2932 : FS_3 (3 synapses)\n", + "2933 : dSPN_0 (3 synapses)\n", + "2972 : dSPN_30 (3 synapses)\n", + "2989 : iSPN_6 (3 synapses)\n", + "2995 : iSPN_8 (5 synapses)\n", + "3004 : iSPN_15 (3 synapses)\n", + "3015 : iSPN_21 (6 synapses)\n", + "3019 : iSPN_23 (4 synapses)\n", + "3044 : dSPN_3 (3 synapses)\n", + "3047 : dSPN_5 (3 synapses)\n", + "3051 : dSPN_7 (2 synapses)\n", + "3057 : dSPN_11 (3 synapses)\n", + "3061 : dSPN_13 (6 synapses)\n", + "3063 : dSPN_13 (2 synapses)\n", + "3066 : dSPN_14 (3 synapses)\n", + "3069 : dSPN_18 (3 synapses)\n", + "3072 : dSPN_19 (3 synapses)\n", + "3074 : dSPN_20 (3 synapses)\n", + "3075 : dSPN_22 (3 synapses)\n", + "3086 : dSPN_27 (3 synapses)\n", + "3109 : iSPN_8 (4 synapses)\n", + "3111 : iSPN_9 (4 synapses)\n", + "3112 : iSPN_11 (3 synapses)\n", + "3118 : iSPN_18 (3 synapses)\n", + "3122 : iSPN_20 (3 synapses)\n", + "3126 : iSPN_21 (3 synapses)\n", + "3128 : iSPN_23 (2 synapses)\n", + "3131 : iSPN_23 (4 synapses)\n", + "3132 : iSPN_26 (3 synapses)\n", + "3141 : iSPN_32 (3 synapses)\n", + "3150 : FS_2 (8 synapses)\n", + "3152 : FS_3 (11 synapses)\n", + "3153 : FS_3 (5 synapses)\n", + "3164 : dSPN_10 (2 synapses)\n", + "3169 : dSPN_13 (4 synapses)\n", + "3180 : dSPN_22 (3 synapses)\n", + "3184 : dSPN_28 (3 synapses)\n", + "3186 : dSPN_32 (2 synapses)\n", + "3190 : dSPN_34 (4 synapses)\n", + "3194 : iSPN_2 (3 synapses)\n", + "3199 : iSPN_7 (4 synapses)\n", + "3202 : iSPN_9 (5 synapses)\n", + "3203 : iSPN_10 (4 synapses)\n", + "3205 : iSPN_10 (3 synapses)\n", + "3207 : iSPN_12 (4 synapses)\n", + "3212 : iSPN_15 (3 synapses)\n", + "3216 : iSPN_20 (3 synapses)\n", + "3218 : iSPN_22 (2 synapses)\n", + "3221 : iSPN_23 (4 synapses)\n", + "3222 : iSPN_23 (2 synapses)\n", + "3223 : iSPN_23 (3 synapses)\n", + "3225 : iSPN_25 (2 synapses)\n", + "3226 : iSPN_26 (3 synapses)\n", + "3228 : iSPN_26 (3 synapses)\n", + "3229 : iSPN_26 (3 synapses)\n", + "3232 : iSPN_28 (3 synapses)\n", + "3241 : iSPN_33 (3 synapses)\n", + "3243 : iSPN_34 (4 synapses)\n", + "3328 : dSPN_0 (3 synapses)\n", + "3339 : dSPN_8 (4 synapses)\n", + "3367 : dSPN_31 (3 synapses)\n", + "3416 : iSPN_33 (2 synapses)\n", + "3426 : dSPN_0 (2 synapses)\n", + "3487 : iSPN_0 (2 synapses)\n", + "3584 : dSPN_31 (6 synapses)\n", + "3636 : iSPN_20 (3 synapses)\n", + "3652 : iSPN_29 (2 synapses)\n", + "3698 : dSPN_27 (3 synapses)\n", + "3735 : iSPN_12 (2 synapses)\n", + "3739 : iSPN_14 (2 synapses)\n", + "3749 : iSPN_21 (3 synapses)\n", + "3763 : iSPN_28 (2 synapses)\n", + "3765 : iSPN_29 (3 synapses)\n", + "3767 : iSPN_30 (4 synapses)\n", + "3773 : iSPN_35 (4 synapses)\n", + "3783 : dSPN_4 (2 synapses)\n", + "3819 : dSPN_35 (2 synapses)\n", + "3891 : dSPN_24 (3 synapses)\n", + "3950 : FS_1 (2 synapses)\n", + "3981 : dSPN_16 (2 synapses)\n", + "4025 : iSPN_2 (4 synapses)\n", + "4044 : iSPN_11 (2 synapses)\n", + "4060 : iSPN_25 (2 synapses)\n", + "4076 : FS_1 (4 synapses)\n", + "4077 : FS_3 (4 synapses)\n", + "4079 : dSPN_2 (3 synapses)\n", + "4086 : dSPN_7 (3 synapses)\n", + "4087 : dSPN_8 (2 synapses)\n", + "4089 : dSPN_9 (3 synapses)\n", + "4093 : dSPN_12 (3 synapses)\n", + "4097 : dSPN_14 (3 synapses)\n", + "4107 : dSPN_26 (3 synapses)\n", + "4108 : dSPN_27 (4 synapses)\n", + "4109 : dSPN_28 (2 synapses)\n", + "4111 : dSPN_31 (4 synapses)\n", + "4122 : dSPN_35 (3 synapses)\n", + "4138 : iSPN_15 (3 synapses)\n", + "4147 : iSPN_22 (4 synapses)\n", + "4152 : iSPN_27 (1 synapses)\n", + "4153 : iSPN_28 (3 synapses)\n", + "4157 : iSPN_31 (4 synapses)\n", + "4163 : iSPN_34 (2 synapses)\n", + "4168 : FS_0 (3 synapses)\n", + "4227 : dSPN_31 (3 synapses)\n", + "4486 : dSPN_16 (2 synapses)\n", + "4597 : dSPN_14 (4 synapses)\n", + "4599 : dSPN_14 (3 synapses)\n", + "4602 : dSPN_16 (2 synapses)\n", + "4604 : dSPN_16 (3 synapses)\n", + "4608 : dSPN_18 (3 synapses)\n", + "4633 : dSPN_34 (3 synapses)\n", + "4634 : dSPN_35 (2 synapses)\n", + "4677 : iSPN_26 (2 synapses)\n", + "4686 : FS_2 (5 synapses)\n", + "4693 : dSPN_1 (2 synapses)\n", + "4704 : dSPN_8 (2 synapses)\n", + "4709 : dSPN_11 (4 synapses)\n", + "4710 : dSPN_12 (2 synapses)\n", + "4736 : dSPN_27 (2 synapses)\n", + "4738 : dSPN_31 (2 synapses)\n", + "4745 : iSPN_2 (3 synapses)\n", + "4747 : iSPN_5 (3 synapses)\n", + "4757 : iSPN_10 (3 synapses)\n", + "4764 : iSPN_15 (2 synapses)\n", + "4781 : iSPN_27 (3 synapses)\n", + "4801 : FS_3 (6 synapses)\n", + "4803 : dSPN_1 (3 synapses)\n", + "4805 : dSPN_3 (4 synapses)\n", + "4823 : dSPN_14 (2 synapses)\n", + "4825 : dSPN_15 (1 synapses)\n", + "4826 : dSPN_15 (2 synapses)\n", + "4835 : dSPN_24 (3 synapses)\n", + "4851 : iSPN_3 (2 synapses)\n", + "4886 : iSPN_23 (2 synapses)\n", + "4901 : FS_1 (3 synapses)\n", + "5065 : dSPN_4 (2 synapses)\n", + "5643 : dSPN_28 (2 synapses)\n", + "5765 : iSPN_1 (3 synapses)\n", + "5790 : iSPN_26 (3 synapses)\n", + "5903 : iSPN_24 (2 synapses)\n", + "6091 : iSPN_12 (3 synapses)\n", + "6107 : iSPN_23 (2 synapses)\n", + "6244 : FS_1 (6 synapses)\n", + "6248 : dSPN_7 (3 synapses)\n", + "6250 : dSPN_8 (2 synapses)\n", + "6257 : dSPN_11 (3 synapses)\n", + "6258 : dSPN_13 (6 synapses)\n", + "6262 : dSPN_16 (4 synapses)\n", + "6285 : dSPN_35 (3 synapses)\n", + "6290 : iSPN_3 (2 synapses)\n", + "6296 : iSPN_7 (4 synapses)\n", + "6298 : iSPN_7 (2 synapses)\n", + "6307 : iSPN_10 (2 synapses)\n", + "6338 : iSPN_32 (1 synapses)\n", + "6453 : FS_0 (2 synapses)\n", + "6455 : FS_2 (5 synapses)\n", + "6721 : iSPN_8 (2 synapses)\n", + "6731 : iSPN_14 (1 synapses)\n", + "6764 : FS_2 (3 synapses)\n", + "7088 : dSPN_26 (3 synapses)\n", + "7274 : iSPN_30 (2 synapses)\n", + "7281 : iSPN_34 (3 synapses)\n", + "7330 : iSPN_7 (3 synapses)\n", + "7335 : iSPN_10 (4 synapses)\n", + "7350 : iSPN_21 (3 synapses)\n", + "7355 : iSPN_28 (3 synapses)\n", + "7381 : dSPN_4 (3 synapses)\n", + "7495 : FS_3 (5 synapses)\n", + "7533 : iSPN_0 (7 synapses)\n", + "7542 : iSPN_4 (3 synapses)\n", + "7544 : iSPN_8 (1 synapses)\n", + "7562 : iSPN_19 (2 synapses)\n", + "7564 : iSPN_20 (2 synapses)\n", + "7580 : iSPN_31 (2 synapses)\n", + "7585 : iSPN_33 (5 synapses)\n", + "7605 : dSPN_8 (3 synapses)\n", + "7607 : dSPN_10 (4 synapses)\n", + "7613 : dSPN_11 (3 synapses)\n", + "7621 : dSPN_17 (2 synapses)\n", + "7628 : dSPN_22 (2 synapses)\n", + "7632 : dSPN_24 (3 synapses)\n", + "7639 : dSPN_28 (4 synapses)\n", + "7644 : dSPN_30 (3 synapses)\n", + "7648 : dSPN_30 (3 synapses)\n", + "7650 : dSPN_33 (4 synapses)\n", + "7651 : dSPN_33 (3 synapses)\n", + "7654 : dSPN_35 (4 synapses)\n", + "7656 : iSPN_2 (3 synapses)\n", + "7666 : iSPN_9 (3 synapses)\n", + "7671 : iSPN_12 (3 synapses)\n", + "7694 : iSPN_30 (3 synapses)\n", + "7695 : iSPN_30 (4 synapses)\n", + "7700 : iSPN_34 (3 synapses)\n", + "7701 : iSPN_34 (3 synapses)\n", + "7702 : ChIN_0 (15 synapses)\n", + "7715 : dSPN_6 (3 synapses)\n", + "7717 : dSPN_8 (3 synapses)\n", + "7734 : dSPN_19 (2 synapses)\n", + "7799 : iSPN_28 (3 synapses)\n", + "7872 : iSPN_8 (4 synapses)\n", + "7903 : FS_1 (2 synapses)\n", + "7930 : dSPN_20 (3 synapses)\n", + "8151 : iSPN_9 (2 synapses)\n", + "8171 : iSPN_31 (2 synapses)\n", + "8220 : iSPN_0 (3 synapses)\n", + "8282 : dSPN_14 (2 synapses)\n", + "8283 : dSPN_14 (3 synapses)\n", + "8953 : iSPN_14 (2 synapses)\n", + "9156 : iSPN_16 (3 synapses)\n", + "9238 : iSPN_18 (2 synapses)\n", + "\n", + "PD2 degenerated\n", + "Loading networks/PD-example-10k/PD2-ref/network-synapses.hdf5\n", + "Loading config data from HDF5\n", + "Loading 9981 neurons with 2057874 synapses and 1499 gap junctions\n", + "Load done. 0.8\n", + "List neurons pre-synaptic to neuron_id = 7623 (dSPN_18)\n", + "The neuron receives 989 synapses\n", + "197 : iSPN_6 (2 synapses)\n", + "448 : dSPN_28 (3 synapses)\n", + "599 : dSPN_28 (3 synapses)\n", + "601 : dSPN_31 (3 synapses)\n", + "637 : iSPN_26 (3 synapses)\n", + "639 : iSPN_27 (3 synapses)\n", + "719 : iSPN_22 (3 synapses)\n", + "745 : dSPN_0 (5 synapses)\n", + "747 : dSPN_1 (2 synapses)\n", + "774 : dSPN_24 (3 synapses)\n", + "783 : dSPN_32 (3 synapses)\n", + "841 : dSPN_6 (2 synapses)\n", + "881 : dSPN_33 (2 synapses)\n", + "909 : iSPN_23 (2 synapses)\n", + "1042 : dSPN_24 (4 synapses)\n", + "1043 : dSPN_24 (3 synapses)\n", + "1054 : iSPN_1 (4 synapses)\n", + "1070 : iSPN_21 (2 synapses)\n", + "1147 : iSPN_16 (4 synapses)\n", + "1172 : FS_1 (3 synapses)\n", + "1207 : dSPN_25 (4 synapses)\n", + "1225 : iSPN_1 (4 synapses)\n", + "1255 : iSPN_34 (2 synapses)\n", + "1269 : dSPN_8 (3 synapses)\n", + "1328 : iSPN_14 (2 synapses)\n", + "1356 : dSPN_0 (4 synapses)\n", + "1367 : dSPN_6 (3 synapses)\n", + "1370 : dSPN_8 (3 synapses)\n", + "1375 : dSPN_11 (2 synapses)\n", + "1387 : dSPN_18 (4 synapses)\n", + "1425 : iSPN_6 (4 synapses)\n", + "1429 : iSPN_11 (3 synapses)\n", + "1462 : dSPN_0 (3 synapses)\n", + "1474 : dSPN_11 (2 synapses)\n", + "1478 : dSPN_14 (3 synapses)\n", + "1479 : dSPN_15 (3 synapses)\n", + "1505 : iSPN_0 (1 synapses)\n", + "1506 : iSPN_1 (3 synapses)\n", + "1510 : iSPN_4 (2 synapses)\n", + "1512 : iSPN_5 (3 synapses)\n", + "1520 : iSPN_12 (3 synapses)\n", + "1529 : iSPN_16 (3 synapses)\n", + "1530 : iSPN_17 (2 synapses)\n", + "1533 : iSPN_18 (3 synapses)\n", + "1540 : iSPN_27 (3 synapses)\n", + "1549 : iSPN_33 (2 synapses)\n", + "1578 : dSPN_15 (3 synapses)\n", + "1612 : iSPN_9 (2 synapses)\n", + "1623 : iSPN_21 (3 synapses)\n", + "1636 : iSPN_32 (2 synapses)\n", + "1932 : dSPN_23 (2 synapses)\n", + "2010 : FS_3 (3 synapses)\n", + "2015 : dSPN_5 (2 synapses)\n", + "2018 : dSPN_7 (3 synapses)\n", + "2028 : dSPN_14 (3 synapses)\n", + "2037 : dSPN_17 (4 synapses)\n", + "2045 : dSPN_23 (3 synapses)\n", + "2085 : iSPN_18 (2 synapses)\n", + "2092 : iSPN_26 (3 synapses)\n", + "2330 : FS_3 (7 synapses)\n", + "2346 : dSPN_12 (3 synapses)\n", + "2374 : dSPN_33 (3 synapses)\n", + "2375 : dSPN_33 (6 synapses)\n", + "2412 : iSPN_21 (3 synapses)\n", + "2657 : dSPN_16 (3 synapses)\n", + "2685 : dSPN_34 (3 synapses)\n", + "2932 : FS_3 (4 synapses)\n", + "2935 : dSPN_1 (2 synapses)\n", + "2943 : dSPN_9 (2 synapses)\n", + "2961 : dSPN_25 (3 synapses)\n", + "2975 : dSPN_34 (4 synapses)\n", + "2988 : iSPN_5 (2 synapses)\n", + "2989 : iSPN_6 (3 synapses)\n", + "2997 : iSPN_12 (5 synapses)\n", + "3000 : iSPN_13 (2 synapses)\n", + "3038 : iSPN_33 (3 synapses)\n", + "3041 : FS_0 (5 synapses)\n", + "3055 : dSPN_9 (2 synapses)\n", + "3057 : dSPN_11 (3 synapses)\n", + "3061 : dSPN_13 (4 synapses)\n", + "3062 : dSPN_13 (3 synapses)\n", + "3063 : dSPN_13 (2 synapses)\n", + "3067 : dSPN_15 (2 synapses)\n", + "3074 : dSPN_20 (2 synapses)\n", + "3079 : dSPN_24 (3 synapses)\n", + "3088 : dSPN_28 (3 synapses)\n", + "3092 : dSPN_29 (2 synapses)\n", + "3106 : iSPN_8 (3 synapses)\n", + "3109 : iSPN_8 (4 synapses)\n", + "3110 : iSPN_9 (3 synapses)\n", + "3111 : iSPN_9 (3 synapses)\n", + "3118 : iSPN_18 (3 synapses)\n", + "3123 : iSPN_20 (2 synapses)\n", + "3128 : iSPN_23 (3 synapses)\n", + "3131 : iSPN_23 (3 synapses)\n", + "3132 : iSPN_26 (3 synapses)\n", + "3148 : FS_0 (5 synapses)\n", + "3149 : FS_1 (6 synapses)\n", + "3150 : FS_2 (13 synapses)\n", + "3151 : FS_3 (4 synapses)\n", + "3152 : FS_3 (9 synapses)\n", + "3153 : FS_3 (6 synapses)\n", + "3156 : dSPN_2 (4 synapses)\n", + "3158 : dSPN_5 (6 synapses)\n", + "3161 : dSPN_6 (4 synapses)\n", + "3163 : dSPN_9 (3 synapses)\n", + "3165 : dSPN_10 (3 synapses)\n", + "3169 : dSPN_13 (5 synapses)\n", + "3176 : dSPN_19 (2 synapses)\n", + "3177 : dSPN_20 (3 synapses)\n", + "3178 : dSPN_21 (1 synapses)\n", + "3179 : dSPN_21 (3 synapses)\n", + "3181 : dSPN_23 (2 synapses)\n", + "3184 : dSPN_28 (3 synapses)\n", + "3190 : dSPN_34 (2 synapses)\n", + "3191 : dSPN_35 (3 synapses)\n", + "3192 : iSPN_0 (3 synapses)\n", + "3200 : iSPN_7 (4 synapses)\n", + "3202 : iSPN_9 (3 synapses)\n", + "3203 : iSPN_10 (5 synapses)\n", + "3206 : iSPN_11 (2 synapses)\n", + "3211 : iSPN_15 (3 synapses)\n", + "3216 : iSPN_20 (4 synapses)\n", + "3219 : iSPN_22 (3 synapses)\n", + "3221 : iSPN_23 (4 synapses)\n", + "3222 : iSPN_23 (3 synapses)\n", + "3223 : iSPN_23 (2 synapses)\n", + "3226 : iSPN_26 (3 synapses)\n", + "3228 : iSPN_26 (4 synapses)\n", + "3236 : iSPN_32 (2 synapses)\n", + "3240 : iSPN_33 (4 synapses)\n", + "3241 : iSPN_33 (4 synapses)\n", + "3244 : iSPN_34 (3 synapses)\n", + "3356 : dSPN_21 (3 synapses)\n", + "3375 : dSPN_35 (2 synapses)\n", + "3379 : iSPN_4 (3 synapses)\n", + "3426 : dSPN_0 (4 synapses)\n", + "3471 : dSPN_27 (3 synapses)\n", + "3493 : iSPN_6 (1 synapses)\n", + "3553 : dSPN_14 (2 synapses)\n", + "3610 : iSPN_6 (4 synapses)\n", + "3703 : dSPN_31 (3 synapses)\n", + "3721 : iSPN_7 (4 synapses)\n", + "3731 : iSPN_10 (3 synapses)\n", + "3735 : iSPN_12 (3 synapses)\n", + "3742 : iSPN_15 (2 synapses)\n", + "3750 : iSPN_21 (3 synapses)\n", + "3760 : iSPN_26 (3 synapses)\n", + "3784 : dSPN_4 (3 synapses)\n", + "3842 : iSPN_18 (2 synapses)\n", + "3928 : iSPN_18 (2 synapses)\n", + "4012 : dSPN_30 (3 synapses)\n", + "4025 : iSPN_2 (3 synapses)\n", + "4070 : iSPN_33 (2 synapses)\n", + "4076 : FS_1 (4 synapses)\n", + "4077 : FS_3 (5 synapses)\n", + "4079 : dSPN_2 (2 synapses)\n", + "4097 : dSPN_14 (3 synapses)\n", + "4100 : dSPN_20 (3 synapses)\n", + "4107 : dSPN_26 (2 synapses)\n", + "4111 : dSPN_31 (3 synapses)\n", + "4113 : dSPN_32 (3 synapses)\n", + "4115 : dSPN_32 (4 synapses)\n", + "4116 : dSPN_32 (2 synapses)\n", + "4120 : dSPN_35 (3 synapses)\n", + "4125 : iSPN_4 (6 synapses)\n", + "4130 : iSPN_7 (3 synapses)\n", + "4133 : iSPN_11 (3 synapses)\n", + "4143 : iSPN_19 (2 synapses)\n", + "4147 : iSPN_22 (3 synapses)\n", + "4149 : iSPN_23 (3 synapses)\n", + "4157 : iSPN_31 (4 synapses)\n", + "4158 : iSPN_31 (3 synapses)\n", + "4160 : iSPN_32 (4 synapses)\n", + "4161 : iSPN_33 (5 synapses)\n", + "4163 : iSPN_34 (3 synapses)\n", + "4168 : FS_0 (2 synapses)\n", + "4172 : FS_3 (2 synapses)\n", + "4230 : dSPN_35 (1 synapses)\n", + "4231 : dSPN_35 (2 synapses)\n", + "4236 : iSPN_1 (4 synapses)\n", + "4271 : iSPN_14 (3 synapses)\n", + "4303 : iSPN_32 (3 synapses)\n", + "4391 : iSPN_8 (3 synapses)\n", + "4394 : iSPN_10 (2 synapses)\n", + "4597 : dSPN_14 (3 synapses)\n", + "4598 : dSPN_14 (3 synapses)\n", + "4599 : dSPN_14 (4 synapses)\n", + "4600 : dSPN_15 (2 synapses)\n", + "4602 : dSPN_16 (3 synapses)\n", + "4606 : dSPN_17 (3 synapses)\n", + "4608 : dSPN_18 (3 synapses)\n", + "4630 : dSPN_33 (4 synapses)\n", + "4662 : iSPN_18 (3 synapses)\n", + "4686 : FS_2 (8 synapses)\n", + "4699 : dSPN_6 (3 synapses)\n", + "4725 : dSPN_21 (3 synapses)\n", + "4736 : dSPN_27 (2 synapses)\n", + "4737 : dSPN_31 (3 synapses)\n", + "4738 : dSPN_31 (3 synapses)\n", + "4739 : dSPN_32 (3 synapses)\n", + "4747 : iSPN_5 (4 synapses)\n", + "4765 : iSPN_15 (3 synapses)\n", + "4774 : iSPN_24 (3 synapses)\n", + "4776 : iSPN_25 (3 synapses)\n", + "4781 : iSPN_27 (3 synapses)\n", + "4800 : FS_0 (3 synapses)\n", + "4801 : FS_3 (7 synapses)\n", + "4803 : dSPN_1 (4 synapses)\n", + "4805 : dSPN_3 (4 synapses)\n", + "4812 : dSPN_7 (3 synapses)\n", + "4825 : dSPN_15 (3 synapses)\n", + "4830 : dSPN_19 (3 synapses)\n", + "4836 : dSPN_26 (2 synapses)\n", + "4843 : dSPN_31 (2 synapses)\n", + "4851 : iSPN_3 (2 synapses)\n", + "4852 : iSPN_3 (2 synapses)\n", + "4864 : iSPN_8 (2 synapses)\n", + "4871 : iSPN_13 (2 synapses)\n", + "4879 : iSPN_17 (3 synapses)\n", + "4885 : iSPN_23 (3 synapses)\n", + "4977 : iSPN_10 (2 synapses)\n", + "4978 : iSPN_11 (3 synapses)\n", + "4984 : iSPN_14 (2 synapses)\n", + "5396 : dSPN_11 (3 synapses)\n", + "5643 : dSPN_28 (3 synapses)\n", + "5652 : dSPN_33 (2 synapses)\n", + "5710 : iSPN_35 (3 synapses)\n", + "5727 : dSPN_14 (5 synapses)\n", + "6047 : dSPN_17 (3 synapses)\n", + "6050 : dSPN_18 (2 synapses)\n", + "6056 : dSPN_23 (5 synapses)\n", + "6059 : dSPN_26 (3 synapses)\n", + "6065 : dSPN_32 (3 synapses)\n", + "6095 : iSPN_12 (4 synapses)\n", + "6109 : iSPN_27 (2 synapses)\n", + "6116 : iSPN_32 (4 synapses)\n", + "6221 : iSPN_23 (3 synapses)\n", + "6243 : FS_1 (2 synapses)\n", + "6244 : FS_1 (6 synapses)\n", + "6257 : dSPN_11 (3 synapses)\n", + "6258 : dSPN_13 (2 synapses)\n", + "6262 : dSPN_16 (3 synapses)\n", + "6267 : dSPN_17 (3 synapses)\n", + "6274 : dSPN_24 (3 synapses)\n", + "6281 : dSPN_29 (3 synapses)\n", + "6313 : iSPN_15 (2 synapses)\n", + "6326 : iSPN_27 (3 synapses)\n", + "6345 : iSPN_35 (3 synapses)\n", + "6455 : FS_2 (5 synapses)\n", + "6494 : dSPN_33 (4 synapses)\n", + "6649 : iSPN_24 (1 synapses)\n", + "6683 : dSPN_23 (2 synapses)\n", + "6764 : FS_2 (2 synapses)\n", + "6770 : dSPN_8 (2 synapses)\n", + "7038 : iSPN_32 (2 synapses)\n", + "7088 : dSPN_26 (2 synapses)\n", + "7123 : iSPN_16 (4 synapses)\n", + "7151 : dSPN_0 (2 synapses)\n", + "7188 : dSPN_17 (2 synapses)\n", + "7235 : iSPN_16 (4 synapses)\n", + "7238 : iSPN_18 (2 synapses)\n", + "7266 : iSPN_26 (4 synapses)\n", + "7294 : dSPN_11 (3 synapses)\n", + "7314 : dSPN_27 (3 synapses)\n", + "7335 : iSPN_10 (4 synapses)\n", + "7337 : iSPN_14 (2 synapses)\n", + "7345 : iSPN_19 (3 synapses)\n", + "7350 : iSPN_21 (3 synapses)\n", + "7355 : iSPN_28 (2 synapses)\n", + "7364 : iSPN_32 (3 synapses)\n", + "7369 : iSPN_34 (4 synapses)\n", + "7495 : FS_3 (6 synapses)\n", + "7511 : dSPN_19 (3 synapses)\n", + "7513 : dSPN_20 (3 synapses)\n", + "7518 : dSPN_23 (4 synapses)\n", + "7533 : iSPN_0 (5 synapses)\n", + "7542 : iSPN_4 (3 synapses)\n", + "7545 : iSPN_9 (3 synapses)\n", + "7581 : iSPN_31 (6 synapses)\n", + "7585 : iSPN_33 (2 synapses)\n", + "7598 : dSPN_4 (3 synapses)\n", + "7604 : dSPN_8 (3 synapses)\n", + "7613 : dSPN_11 (3 synapses)\n", + "7616 : dSPN_14 (2 synapses)\n", + "7619 : dSPN_15 (2 synapses)\n", + "7621 : dSPN_17 (3 synapses)\n", + "7631 : dSPN_22 (3 synapses)\n", + "7632 : dSPN_24 (2 synapses)\n", + "7634 : dSPN_24 (3 synapses)\n", + "7639 : dSPN_28 (3 synapses)\n", + "7650 : dSPN_33 (3 synapses)\n", + "7656 : iSPN_2 (3 synapses)\n", + "7662 : iSPN_7 (4 synapses)\n", + "7664 : iSPN_9 (4 synapses)\n", + "7669 : iSPN_11 (3 synapses)\n", + "7671 : iSPN_12 (2 synapses)\n", + "7694 : iSPN_30 (3 synapses)\n", + "7697 : iSPN_32 (2 synapses)\n", + "7699 : iSPN_34 (5 synapses)\n", + "7700 : iSPN_34 (2 synapses)\n", + "7810 : ChIN_0 (24 synapses)\n", + "7870 : iSPN_6 (1 synapses)\n", + "7950 : iSPN_1 (2 synapses)\n", + "8114 : dSPN_18 (4 synapses)\n", + "8138 : iSPN_2 (3 synapses)\n", + "8214 : dSPN_26 (2 synapses)\n", + "8230 : iSPN_8 (3 synapses)\n", + "8736 : iSPN_17 (3 synapses)\n", + "8907 : dSPN_13 (2 synapses)\n", + "8917 : dSPN_21 (2 synapses)\n", + "9156 : iSPN_16 (3 synapses)\n", + "9504 : iSPN_18 (3 synapses)\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cmd_str1 = f\"snudda_load {network_path_pd0}/network-synapses.hdf5 --listPre {neuron_id}\"\n", "cmd_str2 = f\"snudda_load {network_path_pd2}/network-synapses.hdf5 --listPre {neuron_id}\"\n", @@ -389,10 +13094,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ebeab2bb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added: 12, removed: 805, kept: 605\n" + ] + }, + { + "data": { + "image/png": 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pU0SkGO2h49Vsr4WSyev1eoyOjvrJ5Mlw9lpBNMkqEIFxKMHGC4ijPNkvXMhRnoxqaLVa6PV6lJSUcCnKH3NwZBXDCDU7FWiZJJfLl3wMPp9Pq7VQ5zAwMIChoaGw235sBHvschaRWFt8FpLJMwyDS5cuUZm8Wq32sziKNawkWQUi2HUjWV4jIyPo6OigpE/Iiz0OQcjL6/XCZrPR4XcuRfnjC46sYhREbUWGKckXbWJiAp2dncjJyUFpaemyv4ALVVZOpxMtLS1wOBwR74Wxnz+WBRbRBlsmPzU1herqaqo2DLQ4irZMfrkgdkurgUBHeUL6RqMRQ0NDaG9vD+oo7/P5KBEtlqLMkdfaBkdWMQa2ZdKHH36I+vp6KBQKeDwedHZ2YmZmBtXV1fRLvVyEEljodDq0tLQgKSkJmzdvXrIreyxK168kxGIxVCoVMjIygsrkiUsEmfEig7argStZWS2GwNk4l8tFr1t/fz8d7BYKhXTWkO0oH6ptSNz+uRTltQeOrGIIwUQUDMPAZDKhpaUFUqk0qGXSchBIJkQCPzw8jPLycmRnZy9rAfukVVZsBJ7nYjJ59qBtpDL5aJ1vrJBVIMRisZ+jPBnsHhsbg9VqxcmTJ/1cSQId5YORF5eivLbAkVWMIDBunnyBJiYmMDY2hsLCQhQWFkZ9MWEPBTscDrS2tsLpdGL79u2Ij49f9vMHIyufz4fh4WEAgFqtjqiaWAnyW0ks9LqCyeTJoO1SZPLLRSyTVSDIYLfdbodcLkdBQQHN8iLtVnZ4Z0JCwoLkxaUoxz44slplLGSZ5PV6MTk5ia1bt1L1WbRBFv/Z2Vm0trYiOTl5WW2/UM9PYLPZ0NLSAp/PB7FYjOHhYaqwI62wWIn3uNJgD9rm5+cHnVWSSqV+5BVNmfxCDhaxCvKdIY7y7HYrIa/JyUl4PB4olUoq1mDvFZL9LIJgKcps8mKrDTlcOXBktYoINTtFZOI8Hg/r1q1bMaIiMJvNaGpqQkVFBbKysqL6JSTPxTAMZmZm0NbWhoyMDBQVFVESM5vN0Ov1fnNLhLgCF+S1VFkt9zwDZ5U8Hg9dgAMVc+Tfcm4y1lJlRUDIig12u5XEodhsNirYYFtqqVQqWt1HQl58Pn+eYGOtXbu1Bo6sVgmh4ubJflFZWRlGRkZW9E7X4XBgdHQ0qm2/QJAvcE9PD8bGxuhMGLvlSaqJgoICv7klsiArFApKXp9kCIVCJCcnIzk5GcBl0QEhL+Imz259RSqTX6tktdhr5PF4kMvlkMvlyM7O9tsrNBqNGB0dDekoT/4+HPLisrxWFhxZXWEEzk4RorLb7WhtbYXL5aLEQdRiK4GZmRm0trZCLpdDKpWuCFEBoNlGMzMzqKurg0KhWPDxgfM3RAWm1+vR09MDh8OBvr4+pKamQq1W04DAWMVKLlhisdhPMUdi7Jcqk1+rZBVpNlngXmEoR3k2eRFHefL3XIrylQdHVlcQ5G6MEBDphWs0GrS3tyMtLc3PMmmpJrOLnUNfXx9GR0dRWVkJHo+HsbGxqB6DQKfTobm5GQBQU1OzpLRetgqMYRicPXsWiYmJsFqtlMzZVkdXUj23EFajVcmOsQ8lkw9VPZBzjoVrFwmCtQEjBY8331GekJdOp8Pg4CD1PgxUabI9DQGOvFYSHFldAYSKm/d6vejp6cHk5CTWrVuHjIwMv79bTnxHMNjtdrS0tMDj8dAqZ2pqakXskIjZbWlpKbq6uqJS/ZAvvVqtRnJyMr0j1uv1NKZCKBT67XdFU+a/1HNereMGk8nr9fqQMvnVHApeKqJBVoFg+0Hm5eUtqNIkBMZ2lAf8yStYijJ7SJkTbIQHjqxWGIEiCvKBtlgsaGlpAZ/PR319PeLi4ub9bTTJihjepqWloaKign6ZopUUTEAyrmw2G2prayGXy9HV1RW1Y7C/0Ow7YrKokP0uYpYqk8lo1XWl4uxjEezWV2AeFVmAyYxdSkpKTBB9OLgSCsZAlSb5nBmNRmg0GvT29lJHeTZ5AaEd5Z1OJxoaGlBfX09daohYg/w/R17+4MhqBRFsdophGIyPj6Orqwu5ubkoKSkJ+WWLBlmx237BDG+jqa5jR9vX1dVBJBLR8w82ILtUhDrfYCaz7Dh7u93u59PHnr2JNmJdsRi4AHs8Hnz44YeQSqWU6FdSJh8tkO/WlQT7c0biUBZy4g/mKE9ArimXorw4OLJaAYSanfJ4POjo6IBOpwvLMmm5ZEXafl6vN6S4IRr7YgzDYGRkBH19fSgpKUFeXt68L9ZKVFaLQSgU+vnNOZ1O2gbr6OigszekbfhJSQQOBrLg5+fnQywWh5TJk2tFvPlWG7EwGxaOozyp8Mk/r9c7zyVjoRTl73//+zhw4ACuu+66VXmNsYDV/7R9zBBqdopYJslkMtTX14fVYlkOWZG2X3p6OsrLy0NWEMttA7rdbrS3t8NkMmHbtm1QqVR+v2fPWa02JBKJnwCBzN4QmTyAecPJyyWvtUJ+5P0h57uQTL6/v9+vSl2KTD5aWIk9q+UiUNEajPilUikYhoFWq4VKpZoXhwL4k9epU6dQV1e3aq8pFsCRVRQRbHaKYRgMDQ2hv78/YsukpZCVz+dDb28vxsbGsH79+nmijUAsp7Kam5tDc3Mz4uLiUF9fH7RNFG2yilbbMnD2hr2HQ/YhPi6hiuEgkKwCEYlM/kqOFMQiWQUikPjdbjfGxsYwNjaGoaEhWK1WKBQKut/F3lsl5GW1WpcVBfRxQGy/y2sEbFPMQMukS5cuYWRkBFu3bkVRUVFEd9qRkpXNZsO5c+eg0+lQX1+/KFGRY0S6+DMMg7GxMZw7dw5ZWVnYsmXLggt5sNccaxUH2cPJz8/H5s2bsWvXLpSXl0MkEmFsbAynTp3CuXPn0NfXh9nZ2UVDFWOhkowEi5FVIIhMvrKyEvX19aitrUVaWhqsVitaW1tx8uRJtLS0YHR0FGazecWux1ogq0CIRCIkJCRAKpWitrYWO3fuRH5+Ps2OO3nyJC5cuID+/n40NzdDr9fDarXivffeQ35+Pv278+fPL3icQ4cOoby8HFKpFFVVVfjzn//s93uGYfD4448jIyMDMpkM11xzDfr6+ujv33//fUqWgf8uXLhAH/fGG2+guroacXFxyMvLw09+8hO/44R6nunp6YiuG1dZLRMkDTaUZZJKpcKOHTuWpEKLhKw0Gg3a2tqQmZmJsrKysFsykVYqJKpkdnYWmzdvpq2OxY4RTXn8lSCCwH0Ikq+k1+vR19cHh8OBhIQE2jIMNXAba6QcCpGSFRvBZPKBQ7bsOSW1Wh2VFiuwNskKAN2zAuZXrcRR3mAw4IknnsC7774LhmHwi1/8Ag888ABuu+02PP/889i/fz96enro37Fx5swZ3HbbbXjyySdx44034rXXXsMtt9yCxsZGrF+/HgDw1FNP4Wc/+xleeeUVFBQU4LHHHsP+/fvR2dkJqVSK+vp6TE1N+T3vY489huPHj2Pr1q0AgL/85S/40pe+hJ///Oe47rrr0NXVhbvvvhsymQzf+MY3/P62p6fHLxMv2HkvBI6slohQs1NEfTcyMrKkZF02+Hz+onfwPp8PPT09mJiYwPr165Genh7xMcIlEovFgubmZohEorD33YDoLtir5Q0YmK+00MCtWq1eE7JvNpZDVoEINmRLWqxarRb9/f0QiUR+goOlXq+PA1kFgjjKp6en44033kB/fz82b96M/Px8/L//9//w85//HA8++CDi4uLw0ksv4Tvf+c6853j22Wdx/fXX49vf/jYA4Ic//CGOHTuGX/ziF3juuefAMAyeeeYZPProo7j55psBAL/+9a+RlpaGw4cP49Zbb4VYLPZbT9xuN9566y3cd9999HPy6quv4pZbbsG9994LACgsLMR3v/td/PjHP8Y//dM/+X2eUlNT5+1pRwKOrJaAUHHz7KHbaHjtLdais9ls1CEi1KzWYgh38Z+cnERHRwfy8vJQXFwc0QIR7Bhk43itgrh8E6PUwIFbcn2mp6eRnJwc807ybFeVaCOYmzxRyy1XJv9xJKtAkHb+I488grvuugtDQ0Ow2+2YmppCQ0ND0L9paGjAgw8+6Pez/fv34/DhwwCAoaEhTE9P45prrqG/VyqVqK2tRUNDA2699dZ5z/n2229Dp9Phq1/9Kv2Z0+mct+7IZDKMj49jZGQE+fn59OfV1dVwOp1Yv349vv/972PHjh1hvX4CjqwiBLlLDAxom56eRnt7O9LT0/2GbpeDhaoecrzMzEyUl5cv+Qu7WIvO6/Wiu7sb09PT2LhxY8SlOzlGrAksoolgA7dk5kyj0aC/vx9SqdTPWSPWhpOvpNVSMKl3oFouWIR9MHwSyIrYoeXm5gIACgoKAABpaWno7u4O+jfT09M0qJIgLS2N7hOR/y70mEC8+OKL2L9/P7Kzs+nP9u/fj29+85u48847sXfvXvT39+Ppp58GAExNTSE/Px8ZGRl47rnnsHXrVjidTrzwwgvYs2cPzp07h82bN4d1DQCOrMIGe3bqgw8+wM6dOyGXy+liPjU1taQ23EIIRlY+nw/d3d2YnJyMyvEWqt5I5cbj8VBfX7/k6iAWCWYlQSoJ4PLdJAC6GA8NDaG9vd1vOHm1ZN9srKYvYDCZPGmxkv3BYDJ50opfK/uCbERCsjabDQBWtTofHx/HO++8gzfeeMPv53fffTcGBgZw4403wu12IyEhAffffz++//3v09dXVlaGsrIy+jf19fUYGBjAT3/6U7z66qthnwNHVmEg2OwUMbtsbm6GQCBYchtuIQSSldVqRUtLCyWPaByPLS1nf+nZgo3lVG7kGB/nymoxBC7G7A30rq4u+iUn1cZqDCfHwnAtQWCEPVsm39nZCY/HQ6NQAMTMeUeCSCor0hKdmZnx+7lGowl5s5qeng6NRhPy8eS/Go3GTzWs0WjoDRYbBw8eRFJSEm666Sa/n/N4PPz4xz/GE088genpaaSkpOD48eMALu9fhUJNTQ1OnToV8vfBwJHVIiDuyWxJOp/Px9TUFEZGRha1TFoO2GQ1NTWFjo4OZGVloaysLGrHI89DcoHInNb4+HhQc92lIBjBWCwWWK1WqNXqVa8qVgILESp7A53tjq7X6zE6OgoANBSQ7fC90ucbqxVKMDd5vV4PvV4PADh79uyCbvKxCK/XG3Yr2Ol0QigU4sSJE/jMZz4D4PL39fjx4/MUdwR1dXU4fvw4HnjgAfqzY8eO0cHigoICpKen4/jx45Sc5ubmcO7cOXz961/3ey6GYXDw4EHccccdIc9ZIBAgKysLAPC73/0OdXV1Czr0NDc3R7y2cGQVAqTtR9R+hKhIhTU2NoZNmzbRu+WVAJ/Ph9frRUdHB6amplBVVTWvx7xcsCsrh8OB5uZm6soerSFENlmxvRFFIhFcLpefim6xqmItVlYLIZjsO9Bglq2cU6vVS4paWQyxTFZssK9XamoqTp06herqairYIOIWtlgjWjL5aMLr9YatgLRarVAqlXjhhRewbds21NTU4JlnnoHVaqVihzvuuANZWVl48sknAQD3338/du/ejaeffhoHDhzA66+/josXL+L5558HcPk6PvDAA/jRj36EkpISKl3PzMzELbfc4nf8EydOYGhoCHfddde8c5udncWbb76JPXv2wOFw4ODBgzh06BA++OAD+phnnnkGBQUFWLduHRwOB1544QWcOHEC7777bkTXjCOrIAhlmWQ0GmkbrrKyckWJCrgsFTWZTPD5fCvSZgQ+qqxmZ2fR0dGB1NRUVFZWRrXaIQRDiHd2dhbV1dVQKBRwuVz0LpkkI7MX5sA+fawtOosh0vPl8Xh+8RSByrmurq6oRtkTrBWyYoN0HRISEqBUKv1k8nq9fp4jeqzExgCRtQGtVivS0tLw/e9/H48//jimp6dRXV2No0eP0pvX0dFRv25LfX09XnvtNTz66KN45JFHUFJSgsOHD9MZKwB4+OGHYbVacc8998BoNGLnzp04evTovOvz4osvor6+HuXl5UHP75VXXsFDDz0EhmFQV1eH999/HzU1NfT3LpcL3/rWtzAxMYG4uDhs2LAB7733Hvbu3Rv29QIAHvNxuk2NAtjVFJGkMwyD4eFh9Pf3o6ioCJOTkygtLV2SMi5cTE5Oor29HUKhEHv27FmxvrzX68WxY8cgEAhQUVHhp/SJFk6ePImCggKMjIxAIBCgurqaVlXsLyx7odHr9Zibm6MqOhLx0dnZiaSkpBU5z2jC5XLh1KlT2L17d1SJ3+12w2g0Upk88egjLUOlUrmkz4rRaERnZyfq6+ujdq4rDeLYstCixyZ7g8EAs9nsZyrL9uW7kmhubkZKSgptnS2EN998E//zP/+zqGPFxx1cZfU3hJqdcjqdaGtrg9VqpUatWq2WVl3RhtfrRVdXFzQaDQoKCqDRaFaMqJxOJ1pbWwEgLBf4pYK8puzsbLrfFuz6sedxCgoKqKRZr9fTiA+iApPL5UtemNcyRCKRn5M8ER/o9XpMTk7C4/H47XeFu3+zViurxd7/hWTybF++cGTy0UQklZXFYgmamPBJA0dWmB83z26Ntba2Qq1Wo76+nm4uhlpslwuiLhQKhaivr4fVap1ndxItGAwGNDc3U0XVSphk+nw+9Pf3w+FwID8/P2QbIRQCVXQOhwOtra3U6Z24RpDFKFYi7dlY6fMJFB/YbLZ5w8kLtVUJ1qIEfCkzVpHI5Fcy8ywSsrLZbCuyBbDW8IkmK7ZlElvtxw4sLC8vR3Z2tt8XmajmogniEMFWF9rt9hWJnCctzdLSUuTm5kKr1Ub9OE6nEy0tLXA6nXSPhY2lLIxSqRQymQwqlQrZ2dnUf45E2hMhAiGvj7NLejCwneRzcnKC7t8QJ3lSebHD/9YaWUVDbr+QTJ6deUYIP1pu8pHuWXGV1SeYrEKJKGw226KBhdGsrLxeLzo7O6HVaue14qIZaw9c3u9oa2vD3NycX/ZUtKPtjUYjmpqakJiYiM2bN+P8+fNRfX6ysLL959h7EyTwjrR3yH7XlZTIx8JWcGBb1ev1hnSKWIvjAyuREhysUiXkNTo6CoZh/Nqscrl8SSTPkVXk+ESSVbC4eeAjC6OMjIwFAwujVVmx2347duyYp8KJJlmZTCY0NzdDoVDMy56KlhycYRiMjo6it7fXLzE41Jd5qQ7fwcDemygqKqLtHb1ej56eHjidTpoKvFqDt6sNgUDgFwrIboFNT0/D7XajsbHximdSLRUrPcgcmHnGdpPX6/UYHBxcskyezDWGA46sLuMTRVah4uYjtUyKBolMTEygs7NzQWPYaByHZE/19PSgqKgIBQUF875M0TiOx+NBR0cH9Ho9tm7d6tf2i3ZESDhgt3cCB0lHRkbA4/Ho3fFCeznLRSwTIvsaJSYmYmRkBGlpafOc5JdbRawUrrQvYCg3+VAy+YVm4iKpCsnw/CcdnxiyCtX2M5vNaGlpodVNOIuWQCBYchuQ5EHNzMwsqsBbLomwCWTLli0hP/DLrawCo0MCv6DRXuAiPVf2ICk7FViv12N6ehq9vb1RN5qNhTZgJGAYBkKhEFlZWfMyqcieoFAo9NvvWu15pdU2sQ3WZg2ciYuLi/OrvEQiEXw+HxiG4SqrCPGJIKtQcfNjY2Po7u6OOPZiqXtWZrMZzc3NEIvFQdt+wY4DLO1LSY4lkUiCEggby6l8SOs0JycnpO1UrHkDLiSRHxwcjNrsEjnftYBAgUWwKiIw1oM9r7QaTvKrTVaBCCaTZwdQtre3Q6FQUJPjcD/HVqt12XFDHwd8rMkqcHaKbZnU0dEBg8EQdtotGwKBAC6XK6LzIHda+fn5KCoqCutLtlSyIi3G/Px8FBcXL7pgLkVgwfYQXKx1GusWScEk8uzZJa/XG9PtsGhgMTUge2+msLDQj+DZTvJsgl9p0UaskVUghEKh30wc2SOcnZ0FcDlzihjykrTpYNeMk65fRuy+08sEMaANHPI1Go04ffo0PB4P6uvrIyYqILLKyuPxoK2tDb29vdi0aVNEprdssgoHXq8X7e3t6O7uRnV1NUpKSsJaVCMlE6fTiQsXLmB2dhZ1dXWL7vHFWmW1GIgibN26ddixYwe2bNmCxMRE6HQ6XLx4EadPn0ZnZyemp6fhdDqDPkcsk3MwRCpdJwRfWlqK2tpa7NixAzk5OXA6nejq6sLJkyfR1NSE4eFhzM3Nrcj1iHWyCgTZIywoKACfz8f27duRkZEBu92Ojo4Ov2tGbNaAy5XVmTNnkJ+fD6lUitra2kXdLA4dOoTy8nJIpVJUVVXhz3/+s9/vGYbB448/joyMDMhkMlxzzTXo6+ujv3///ffpmhn478KFC/Rxb7zxBqqrqxEXF4e8vDz85Cc/mXcu77//PjZv3gyJRILi4mK8/PLLS7p+H7vKKlTcPMMwGBwcxMDAAIqLi5Gfn7+suPlwCCSwFRdpjz8SsrLZbGhqagKfz484eyqSvTG9Xo+Wlhao1Wps2bIlrGn/WK+sFkJgsGIwibxcLvezhFqLMvDlzlkFOskHSr4BzBtOXm51utbIioCIKwLTpgOv2euvv46hoSH09fWhra0Nv/rVr7B9+3Y888wz2L9/P3p6eoJavp05cwa33XYbnnzySdx444147bXXcMstt6CxsZF6Az711FP42c9+hldeeYWa2O7fvx+dnZ2QSqWor6+fZ0jw2GOP4fjx49i6dSsA4C9/+Qu+9KUv4ec//zmuu+46dHV14e6774ZMJqNu8ENDQzhw4ADuvfde/Pa3v8Xx48dx1113ISMjA/v374/oun2svAEDRRRsy6TW1lbYbDZUV1fTnvFSMTY2Bo1GQ9+0YOcxPj6O7u7usFtxoZ7nnXfewe7duxckH7JvtNT4kIaGBhoZsNC5jIyMoK+vD2VlZcjJyQn7NTU2NiIpKQl5eXn0Z6TyjfRcu7q6IJFIFszKuZJwu93UMUKv11OJfHx8PMbGxrB379410TIcHR2FyWRCVVVV1J+bOMmT62QymSASifwELUtxkh8aGoLD4UBFRUXUz3klYTQa0dHRsWCsO8MwaGxsxJ/+9Cf813/9F/h8PtRqNa6++mp87nOfw/3334/77rsP3/nOd+b97Re/+EVYrVb86U9/oj/bvn07qqur8dxzz4FhGGRmZuJb3/oWHnroIQCXR1vS0tLw8ssvB420d7vdyMrKwn333YfHHnsMAHD77bfD7Xbj0KFD9HE///nP8dRTT2F0dBQ8Hg//8i//giNHjqC9vZ0+5tZbb4XRaMTRo0cjum4fm8oq1OzUzMwM2trakJSUhE2bNkXF92shNSBR4Ol0uiXth7FBsrNCVT0+nw89PT2YmJhYVmrwYpUVaWWaTCa/YeJwsZYrq8UgEomCSuTJvsTJkyf9XDVWM+11IaykgwXbST4/P59Wp3q93q86ZQ9wh/M9XcuV1WLVN4/Hw5YtW7B+/Xo8/fTT+NGPfoSrrroKx48fx/T0NK655ho0NDQE/duGhgY8+OCDfj/bv38/Dh8+DOAyyZPnIFAqlaitrUVDQ0NQsnr77beh0+loJAlweTsgcC9NJpNhfHwcIyMjyM/PR0NDg99xyLmwc7bCxZonKzI7NTQ0hNTUVEgkknmWSRUVFcjKyoralzHU4j43N4fm5mbIZDLs2LEjKrlDoY5lt9v9nDaW4+23EJlYLBY0NTXR1sBSLIzW2p7VUsGWyKvVapw9exYbN25cUYl8tHAl7ZYCVXNut3uePx8RHhB/vmCk9HEmKwKdTgcAyM/Px44dO2g19vDDD6O7uzvo30xPT8/LvUtLS8P09DT9PflZqMcE4sUXX8T+/fv90g7279+Pb37zm7jzzjuxd+9e9Pf34+mnnwZwOSw2Pz8/5LnMzc3BbrdHdPO2psmK3fbr6emhsx/EMsnn84W0TFoOAgUW7MHbgoICFBUVrSgxzszMoLW1FWlpaaioqFj2Hkko6frU1BTa29uRl5cXtlgj1PNHk6zWChaSyBMFHXtRXk0X+dX0BhSJREhNTaX7LyQ52WAwoK2tjQ4nByYBR+ICEUuIhKzcbjcArKoacHx8HO+88w7eeOMNv5/ffffdGBgYwI033gi3242EhATcf//9+P73v78in+M1S1aBs1PEAonEv2dmZqKsrGxFPsxsuyWPx4P29vYly+AXA5usGIZBf38/hoeHUVlZGVYWTrjHYJMJu724cePGZed2BSMrl8sFnU63pPDAWK2sFkOgRN7pdFJXjY6OjlWVyMeSkW2g8MBqtdI9QbaTvNPpXJPDspFUhKQ7Yzab/X6u0WhCtv3T09Oh0WhCPp78V6PR+EXLazQaGnHPxsGDB5GUlISbbrrJ7+c8Hg8//vGP8cQTT2B6ehopKSk4fvw4ANA95VDnkpCQEHFLfM2RFXt2ih03z+fz0dfXRzeJox3/zgaprEwmE1paWiCTyRYdvF3OsXw+n5+L+fbt26M6JMiurEi0vdfrjWo6MZtgiNEtqYwTEhJoWyghIWHRWPu1gHAIVSKR+JmmWq1W6PV6P8cIcl2WKkKI5HxjsaXGVmMSJ/m5uTlaeZG9L/Z+V6y77UdSWZF5zjNnzuBLX/oSgMtkd/z4caq4C0RdXR2OHz/uty907Ngx1NXVAQAVUx0/fpyS09zcHM6dO4evf/3rfs/FMAwOHjyIO+64I2TLWiAQ0Bvn3/3ud6irq6OzZXV1dfNk8+xziQRriqx8Ph88Hk9QyySn00lzoFZ6E5vP58PtduP8+fMoLCxEYWHhii2ifD4fJpOJ5mpt3rw56uFwpLLS6XRoaWlBSkpKVKPt2ZUbcQ0pLi5Geno6VdORzXYAiwoS1mpltRACJfLEMSJQhLBSEvlYqqwWAp/Ph0qlgkqlgsViQXx8PBQKBQwGA4aHh2lQISH4WBwliNRxXSKR4MUXX0RNTQ1qamrwzDPPwGq1UrHDHXfcgaysLDz55JMAgPvvvx+7d+/G008/jQMHDuD111/HxYsX8fzzzwO4/Fl74IEH8KMf/QglJSVUup6ZmYlbbrnF7/gnTpzA0NAQ7rrrrnnnNjs7izfffBN79uyBw+HAwYMHcejQIXzwwQf0Mffeey9+8Ytf4OGHH8bf//3f48SJE3jjjTdw5MiRiK/bmiCrhWanRkdH0dPTA7FYjKKiohUnKrfbjd7eXvh8PtTU1KyowSSpPPr7+1FeXh6RXDxS6HQ6epzA/K5ogAwsa7VabNmyBSqVCi6XC1KpFJmZmbTlYzabodPpqCBBJpP5LdBrYUElWM65sh0jioqKqAhhpVzk1wpZseHz+UKGKQZep1hyko80JVipVOLRRx/F448/junpaVRXV+Po0aO0ezQ6Our3murr6/Haa6/h0UcfxSOPPIKSkhIcPnyYzlgBlwUaVqsV99xzD4xGI3bu3ImjR4/OmwV98cUXUV9fHzI49ZVXXsFDDz0EhmFQV1eH999/HzU1NfT3BQUFOHLkCL75zW/i2WefRXZ2Nl544YWIZ6yANTBnFSpunqTFGo1GbNiwAQMDA8jMzPRTq0QbJGZDKpXCYDBg//79K/YFd7lcaGtrw+zsLIqLi1FUVLQix3G73dTRY9u2bcueQQuGlpYW6HQ6yGQybNq0CVKpNKw5K+KtRiovh8MBsVgMiUSC0tLSmI75sFqtuHjxInbv3h3152ZL5Ek7DFi8Il0Ivb294PP5KC4ujvr5rhSampqQlpaGzMzMoL8n14l8hgwGAxiG8RtOXo106Uiu9QcffID77rsPg4ODMftZv1KI6cqKPTtF9qWAy5HsLS0tiI+Px44dOyAWizE8PLwiUfOA/0BsUVERMjMz8f7776/Y3ajJZEJTUxOdTVkpd2uz2Uz3jrKzs1eEqHQ6HTQaDeRyOWprayO6qw30VrPb7eju7qb7ajwez2+BXm0XcDZW8h5wIRd5diIwe79rMYn8Wq2sFvo8sa8T20k+2L7gYpEe0YTX6w27lW+xWD6WXpRLQUySVajcKWKZNDg46BfuBywvtmMhsNN1SU4TqfKinVTKDi8kllAXL15ckSwoYnZbUFAAh8OxIjEew8PD6O/vh0qlikr7RSaTIT4+HnK5HMXFxXSBnpycRE9PD51vihXboyu1wERDIu/z+dbcghjpnBXbST4vLw9erxdzc3PQ6/V+kR7s/a6VmIPjUoKXhpgjq1C5Uw6HA62trXA4HKipqZlXBawEWRmNRrS0tMxL1yVfEK/XG7UPM1sCzw4vjHa0vc/nQ1dXF+19p6SkoKurK6qVAHktRqMRNTU1mJqaivpQcOACHWxPh8jA1Wo1nc35JGAxibzH4/Frhcnl8jVZWS1XwSgQCOh1AC7fmBqNRhgMBgwMDNCoGPZwcjRugCIlq+UM/H+cEFNkRfYx2NUU8NEQbEpKSkg1XDTJil0VBDO9JecWLRIh7bhgEvhokpXdbkdzczPdDCWy9GgO7VqtVjQ1NUEsFqOurg4SiQTT09MrnhTMHiwNTAYeHh6m3mrk30q3e2JpKziURJ7kdwmFQjqO4XQ6r0grLBqItoOFSCTyazsTkjcYDJTk2aKWpd4AcWS1NMQEWZG2H1H7scmgt7cXY2Njiw7BCgQC2p5bDoiwwWw2L+iDt9QAxkCMj4/TnKtghrfRIqvZ2Vm0tLQEdb3g8XhReS1arRatra3Izs5GaWkpXUiibZG02HMF29MJbPcQGXhSUlJY2UsejwddXV1wOBwoLi6md+NrDaEk8j09PZibm8Pp06fptSFVx2q3U0Nhpe2WAkneZrNR8hoeHvbbM01MTAzbSZ5rAy4Nq05Wodp+VqsVLS0tYBgG9fX1i95dCIXCkNlC4YIINxISEhb1wWO7WCwFXq8XnZ2d0Gq12LRpE23ZBGK5ZMXe56uoqAiqllxK+GLgMYizxvr16/2m4oHQZEV+Hsnd6VKIjz2bU1hYSFuGOp0OXV1dcLvdfi3DYBvax48fR29vLxiGQUtLC77whS8gISFh0XONdRCJvFwuh0qlQnp6Om2n9vb2Rl0iH01cSW9AHo8HuVwOuVxOh5PNZjMMBoOfqIXdXg21fkRy3hxZfYRVJSt2NUUk6QAwOTmJzs7OiCIvltMGZLf9AoUbobAcErFarWhuboZAIFg03n45x3G73WhtbYXFYkFtbW3IxTUax7BarSGdNUIRzGrtkwS2DMkdM7stxm4ZCgQC9Pb2Up82s9mM4eFhbNiw4Yqf+0qBvBeBPn0kY0mv1/vlUsWCi/xqGtmy90yJkzzZ7xodHfUb4iZiDbJ9wbUBl4ZVIatQs1Ok1aLVarFhw4aIPOmW2pYjbT+LxRJR/MVSyZFkTwW2ykJhqURCZsKIOGQhIchS23RmsxmNjY1QKBSoq6sLeYxYdl0Pdscc6Bwhk8koUQEf5TMthFjaswoHoW4cAqXfpJ26VIl8NBFLrusCgQBJSUnUG9TlclFFJttJXq1Ww+12h32TZrVal+3N+XHBqpAVW5xAPmxzc3NoaWmBWCxetNoIBqFQGDF5kLafUqlcdEEPRKQk4vP50N3djcnJyYi8C5dCwmQfLFwrqKWIRSYnJ9HR0RGWy3wsx3oEItA5wm634//+7//mPW5ubg5Wq3XBodJYaZeFg3Ck6zweL6hE3mAwUIl8fHw8Ja+VdJEnrjaxQlaBEIvFQZ3k9Xo9vF4vmpub5ykyg11/rg34EVatDUjKYPZs0XLiNSKpdBiGwdDQEAYGBsJu+y3neGwVXqTmsMSHMBx4vV50dXVBo9EsuA8W7BjhkgnbkZ1I3xdDLFdWi2F8fByTk5NBf3fhwgWaeEsqi1g3UQ2FpbRklyKRjxaBk89ArJJVIIiTfEZGBrRaLaqqqqibPGk9s68VuVm32WwcWf0Nq/pOu1wuNDU1YXBwEFu2bFly/DsQPnm4XC5cunQJY2NjqKmpmSdLDxfhVjxarRZnzpxBQkICamtrI3YxD7eCs9lsOHfuHMxmM+rr68MmKiB8AnA6nbhw4QJ0Oh3q6+vDIqpInj8WMTs7O+/6JycnY8+ePbjqqqtQUVEBkUiEkZERnDp1ChcuXMDAwADm5ubW1GuOxv4hUc+tW7cOO3bsoPOCBoMBFy9exOnTp9HR0YGpqalli6ECOzNrBeS8ExISkJubi40bN2LXrl1Yv349ZDIZpqam0NDQgCeffBJf/epXMTg4iA8++AD5+fmQSqWora3F+fPnFzzGoUOHUF5eDqlUiqqqqnmu5wzD4PHHH0dGRgZkMhmuueYa9PX10d+///77dGsm8N+FCxfo49555x26T52SkoLPfvazGB4eXvR5QgU8LoZVq6z0ej21FCKWSctBOGSl1+vR0tIClUoVcdsv2PEWIhGfz4f+/n6MjIxg3bp1If3LFkM4ZEXm0DIyMlBeXh7xFzicY5BYD7VajS1btiw7gyoWghyXis985jNUWMBOvHU6nbTVMzExAY/HQ93yiRghVluD0Ra7LOQiT9rUy5HIr1WyYqueCdhqVQA0/mh4eBjDw8MYGBhAXl4evvSlL0Gr1WL//v3o6ekJupd15swZ3HbbbXjyySdx44034rXXXsMtt9yCxsZGamT71FNP4Wc/+xleeeUV6ri+f/9+dHZ20kTwqakpv+d97LHHcPz4cWzduhUAMDQ0hJtvvhkPPvggfvvb38JkMuGb3/wmPvOZz6CxsdHvb3t6evzEXUvdg1s1srLb7SgoKEBubm5UviQLkRVbvl1aWhqVYy60wDscDrS0tMDtdi87qXih/SS2ZHw5hLgQAbBTkJfaMl1Iur6Uc71SIK4pgccPpYCTSCRIT09Heno6jEYj2traoFKpMDMzg/7+fojF4lUTIyyGlc6zWshFPlAin5iYuGiuGflOxCr5h0IwsgqEUCjE1Vdfjb179+IPf/gDrr76atx555147733kJ+fj8bGRrz00kv4zne+M+9vn332WVx//fX49re/DQD44Q9/iGPHjuEXv/gFnnvuOTAMg2eeeQaPPvoobr75ZgDAr3/9a6SlpeHw4cO49dZbIRaL/YId3W433nrrLdx33330el+6dAlerxc/+tGP6Gt56KGHcPPNN8Ptdvt9tlNTU8MWri2EVSOr7OzsqAzxEoQiK6fTidbWVtjt9qA2TdE+HsmESk5OXlIFEohQpOhyudDS0gK73b7sMMZQhEj2wEisx1LjUK70UHC0cOHCBVit1nk/D2eBJMbLubm5yM3NpdLmQL8+dujkalYJV3qMIFiUPdnvCpTIJyYmzmufB7rcrBUQ2Xo45+1yueB0OrFnzx7cfvvtuP322wFcFvc0NDQE/ZuGhgY8+OCDfj/bv38/Dh8+DOByRTQ9PY1rrrmG/l6pVKK2thYNDQ249dZb5z3n22+/DZ1OR/OzAGDLli3g8/k4ePAg7rzzTlgsFrz66qu45ppr5t2EVVdXw+l0Yv369fj+97+PHTt2LPrag2HVh4KjBdKWY3/pdDodWltbkZiYiE2bNkU1tDCQRNjVWzQzoYKRldFoRHNzM1UxRoMQAwnAbrejqakJPB4P9fX1y3I0D0ZWTqcTo6OjiI+Pj8h09kotTsSNIxDhigQCX2+gtJktRmhra4PP5/ObX4pWQnO4WG1vQJlMhqysLCqRX8xFfi0a7wKRzVjpdDoAmNcxSUtLQ3d3d9C/mZ6enqc0TktLo/tE5L8LPSYQL774Ivbv3+9nKFBQUIB3330XX/jCF/AP//AP8Hq981KBMzIy8Nxzz2Hr1q1wOp144YUXsGfPHpw7dw6bN28O5xL4YdXIKtofNPIBIB+GgYEBDA0NoaysbEVCC9kCC5fLhdbWVthstgWHb5d6HEJW7JZcMM/CpSKQTHQ6HZqbm5Geno6Kiopl3/EHnqPJZEJjYyOkUikmJiaog0RSUlJYGUMrWVn5fD6cOnUKjY2N824SeDwebrjhhrCv+UKPC7TysVgs0Ol00Gq16Ovrg0QiodcjMTEx6unQgVhtsmKDx+PReJz8/PygEvm4uDgwDAODwbCiEvloIxKyIp/zK33jwsb4+DjeeecdvPHGG34/n56ext13342vfOUruO2222A2m/H444/jc5/7HI4dOwYej4eysjKUlZXRv6mvr8fAwAB++tOf4tVXX434XD5WlRVwWRXX09MDu90edeIIPB5p7ZAqZ6HB2KWCkJXX60VHRwdmZ2eX1ZJb6BhsJ49Q1kxLAbvNSDbXi4uLqS0TaQGRjCGRSEQXarVa7bdQh1vVTE9Pw2KxICsrCzKZDL29vdDr9RgYGEBzczMmJiZQUFCAz3/+86iurqbPOzg4iObm5qBEtXv3buTk5IT1miMhVHZ0BXFDIPs5xP2b3TJcibTbWCKrQASTyI+Pj2NsbIxK5Bezy4oVRDIbRva6A1vRGo3Gb0+JjfT0dGg0mpCPJ//VaDR+tmgajQbV1dXznu/gwYNISkrCTTfd5Pfz//7v/4ZSqcRTTz1Ff/ab3/wGOTk5OHfuHLZv3x70/GpqanDq1Kmgv1sMHxuyIh+ACxcuIDk5Oeptv0DweDyYTCZMTEwsWXgQDvh8PjweD86ePQuhULjsllwwEDJpaWmhsR7RDGIklVtnZyempqawadMmJCUlweVyAYCfg0Tg3k5HRwddqJOSkhYlAbfbjX//93/H22+/Da/XS8MrR0dHsWvXLqSmpoLH46GrqwtHjhzBL3/5S6SkpODb3/427rrrLqriC0R5eXnQL/NKQCAQ+C3ODoeDtgzHxsYARN/yKJbJKhDEg0+r1WL79u3URT5wZulKOexHgkgqKzJfefbsWbpf5PP5cPz4cXzjG98I+jd1dXU4fvw4HnjgAfqzY8eOoa6uDsDl9l16ejqOHz9OP89zc3M4d+4cvv71r/s9F8MwOHjwIO644455N+E2m20e6ZLXtZCyuLm5eZ53aLj4WLQBiSoOAHJzc5c1rxUO3G43tFotbDYbtm3btqIO3CaTCTabDXl5eWH7JEYKp9MJu91OZavRHmz1er0wm820r01aOMEQuLdDFmqdToexsTEwDAOBQICpqSmo1Wr4fD784Q9/wODgIDweD+x2Oy5cuACJREJbwQCwYcMGZGRkwGazIS4ujkaxCIVC9PX14bvf/S5OnDiBjIwMZGVl+XlVAkBjYyMOHTqEnTt34itf+coVdSKXSqXIzMxEZmZm0P0cmUzmFzq5lJu0tbYHxBZYhJLIsx32CXkt9fpEC5GQlcViAQC88sorqK+vR01NDZ555hlYrVZKXnfccQeysrLw5JNPAgDuv/9+7N69G08//TQOHDiA119/HRcvXsTzzz8P4PK6+8ADD+BHP/oRSkpKqHQ9MzMTt9xyi9/xT5w4gaGhIdx1113zzu3AgQP46U9/ih/84Ae0DfjII48gLy8PmzZtAgA888wzKCgowLp16+BwOPDCCy/gxIkTePfdd5d07dZ8ZUXkxU6nk+bRrOSXbm5ujkaqk/2ElYDP50NfXx9GRkYgEolQUVGxIsfRarXo7OwEn8/H1q1bo06GJpMJvb294PP5qK2tjXihYC/UPp8Pg4ODmJmZwcTEBFpbW/HCCy+gqakJJpMJPB4Pn/70p6naKDs7G++88w4A/7Ycj8dDSkoKrr/+egBAVVUV5ubmUFpaCpFIRD8/5G/Ysu5/+7d/w+TkJB599FG89957+O///m/odDps3rwZ//iP/4iCggKIRCI6KxNtBNvPIS1D4kG3FJf0tVRZAaHbaWyJPICQEnl2oOKVfN2RmtjGx8fj3//93/H444/TwNSjR49SgcTo6Kjfdaivr8drr72GRx99FI888ghKSkpw+PBhOmMFAA8//DCsVivuueceGI1G7Ny5E0ePHp3XsXnxxRdRX1+P8vLyeed29dVX47XXXsNTTz2Fp556CnFxcairq8PRo0dppe9yufCtb30LExMTiIuLw4YNG/Dee+9h7969EV83AOAxqzRhyTAMbQMtFbOzs2htbUVycjIqKytx5swZrFu3jt6VRxMMw9A7tcLCQohEImi1Wjoktxw4HA64XC44HA662JC02+LiYnR3dy/5DQ4F9oxWYWEhRkZGcPXVV0f1GMQ/MC0tDWaz2U+yyn7/I1ksJicnodVqUV1djbfeegvf/e53YTQa4XQ6kZOTg0996lOw2WyUdF599VVYrVZIJBJ8+tOfRnp6OjweD2QyGRwOB7xeL/1yeb1eMAzj9/khLVJyvmRf7bOf/Sx+8IMfwG63IysrC/v27YNKpUJ8fDyUSiWsVitcLhfy8/OxceNG5ObmRumqLgy2BNxgMIDH4/m1xEK1kD/88ENs2rRpWSMQVxLT09OYmJjAli1bIvq7wOsDwM/maKUHt4eHh2G1WrFu3bpFH3v+/Hl8+ctfxuTk5Jq6kVgprMnKiu0OUVFRQds2KxFtD1yeKO/s7MTs7Cw2b96MpKQkTExMROVYIyMjuHjxIkwmE1wuF0QiERiGwYYNG7Bt2zY4nc6op+wGxnoQYUW0QEIziX9gOC7lkYDcX2k0GszNzYHP5+Paa69FWloabWEKBAKYzWZKiE6nE3/4wx+gVqvh8XjwhS98AQqFAj6fD263GzabDWq1miYCkGqKiE/4fD7EYjGysrKQnp6O5uZmuN1uJCUloaamhkaeO51OaLVaeq79/f3QaDSor6+HwWCAxWKB0WiE1+tFTk4O1q1bF1WxDFsCTjKX9Ho9pqam0NPT49cyZLtGfFwqq8UQSiLPVmGu5OB2pFlWq6kEjDWsuT0r4g7hcrnmDcNGKy2YDYvFgubmZohEIj9xw3LDF4HLC0RzczNsNhtsNhucTiekUilEIhFmZmZw+PBhOJ1O8Hg8mM3mqNz1Bov1MJvNUSNEMqzsdDqxfft2yOVyzMzMrEhL7OTJkzCbzbj22mupKwJx4zYajTh9+jSNYyAVksFggFQqxQcffICamhoIhUKcO3cOGo0GV111FeLi4tDb2wvg8vtDqtuqqiq/9o1KpUJycjL9vC00oOp0OnHy5EkaG0+ee3p6Gs3NzdizZw8YhoFGo0FaWhrS09Nhs9mQlpa2LPEEO3OpoKAAbrebildIS4yo6KJ9Q7TSiIbjRmBLNdjgdrRd5CPds4plZeOVxqpWVpE6GxAPvNTU1KDuENGurKamptDe3o7c3FyUlJTM8/Na7rHIwsrjXY6V93q9kEqlYBgGk5OTcLlcsNvtAC63aWpqatDe3g6dTge1Wo3y8nJkZGSE/WEOFeux3KRggrm5Oer3yFZjLmS3FOlxyd9MTEzg2LFjYBgGCQkJ8Hg8mJubg0gkQlNTE7q7uyGXy5Gbm4uioiKkpKSgpKQEycnJ8Pl8KCkpwdtvv43XXnsNdrsdPB4Pf/nLX+Dz+ZCQkICUlBTcc889KCkpwauvvjrvvfd4PLBYLODxeOjs7KTebmxiJI8lj4+Li4PT6fR7zW63Gx988AGtqLu6usAwDK10kpOTUV5eDp/Ph5SUlGWpTsmeLjEgZodOMgyDxsZGv5GBWFLRBWIl4kFCDW4bDIaoSeS9Xm/Y1RoXD+KPNdEGJGKD0dFRVFZWIisrK+jjokVWJHtqamoKGzduDGq8GI3Kis/no6SkBKdPn4ZIJIJcLkdCQgIUCgUmJyfh8/loTpfJZEJDQwM0Gg2sVitGR0cxNDREh4MX8jtcLNYjGnZIhNiDZWithPns66+/Tom8v78fycnJSEpKgsViwZ49e/DMM8/AbrcjPz9/XpuNKMZycnKwb98+vPvuu7BYLMjLy6MLFRnUbW5uniduIYPDOTk56OvrQ29vL2ZmZvDP//zPqKyshEwmg8fjQU9PD5KTk5GWloaWlhY4HI6g18Lj8YDP50MqlcJms/n9TqPRQKPRgM/nQy6XY+/evSguLo7KNSTBitnZ2Thx4gTKy8thsVj8VHRsleGVVEAuhiuRZRU4uE0iPUjlJRAIIpbIkxvScMC1Af0R82Rlt9vR0tICj8ezqClsNMjKZrOhubkZAKjMOhiiUVlNT09jamoKtbW1yMrKoouqw+HAiRMnMDg4SCsvuVwOl8tF77j5fD6MRiPa29sxMTEBu91O78AnJyfh8XjoPAPZXwmVpUUqq6XsWzAMg97eXoyNjYUk9miSFXkurVaLjRs3gs/nY2hoCGazGXK5HHq9Hn/9618XDLfk8/kQiUQQi8W4+uqrce2116KtrQ0ffvhh0OiKwPPXarW4/vrr8eCDD+Ls2bMwmUwoKytDSUkJfQyxyqmtrQUAJCUlobu7GwKBAMPDw357eGRTP5gPIQHZfzp27Bi8Xq+fM8ByQV6bUqlEamoqCgsL/VR03d3dcLvdUCqVtPJa7fbUlQ5eZEvkAxOlI5HIR6oG5CqrjxDTbUDS9ktLS0NFRcWib/JyyYr4wWVmZi4atbGcyooIEMbHx7Fhw4Z5C6tcLse1116LoaEhTE5OYmJiAnV1dRgfH4fBYIDH46ELhVQqxezsLE6ePAm5XI7p6Wn09vZSrzmpVIqUlJQFTXXZUu1IFiCyP+VwOFBXVwe5XB7y+aNZWTU1NcFisaC+vh58Ph9lZWX44x//CKfTicrKykXVoOfPn8e5c+foZyU9PR0CgWDBc2Rfl7179+LGG28EAFx11VVhnXN+fj7y8/MBXHbxOH78OGw2GxISEnDjjTdifHwcfX19GB8fX1Ala7fb8c4770AqlSIvLy+sYy8G8rrZr5FtNMswjF/LkFQVpKJQq9VXPHRytVOCF5LIB8bYs11HvF5vRAILjqw+QkxWVuy2XyTRF0slK/bx1q9fH9aEdaSx9gSB8SGhFnipVIqKigqUlZXh3XffhVKpRHJyMlQqFXp7e2EwGGAwGKg4wuPx0AVYJBLBaDRCq9WiuLgYpaWlaGhogEgkwrp16+YJNcgiFckCwBZqFBUVwWw2L+jpFy2y6u7uxu9+9zvU1tbS1ll8fDxqamrQ3d2Nn/70p36k7Ha7YTKZAFz+fDQ3N6OpqcnvOTUaDUQiUcj3VCAQUKfs1NRUZGVlYXR0dMEKY6HXm52djdtuuw0Oh4POQSmVSlRWVqKvr48qTUdGRuaJX8iC19XVtaJkxQap7InLCLuqGBsbQ2dnJxQKhZ8QYaVbhqtNVoFYyEWe7Tpit9vpiMRiN4YcWfkj5sgqkrZfIAQCQcSzW0vNniLEGEk1QsIfk5KSwo4PCSQSsifEMAwuXLiACxcuQCaTQaFQwOv1QigUYmJiAg6HAyKRCGNjY3j33XfR19cHHo+HiooKfPvb3/bb9yNf+nAJZXp6Gm1tbcjOzsaZM2dw7NgxAEBFRQVuuummedcjWgIO4HKOzvT0NJxOJ9LS0uixtm3bhqeeegqVlZXw+Xzo6urC2NgYBgcHqaghVIVHRBCpqakwGo1UVerz+SCTyfCpT32K7luwHTWItQ9blBDu5rlYLJ5XjfB4PJSWlqK0tBQAqHpxfHwcZ8+epW1CgUAQ1b2MSLOhArOpXC4XrSq6urqoMTGxyFrMmHip57yaThSLIZREnvg9jo+PLyqRt1qtUbU9W+tY1VuTwA8wiYAnsupI7yoiraxmZ2dx5swZOn0dyfEiWeAZhsHQ0BAuXbqEoqIiVFVVhf1FI/tTwYxVt2zZgvXr10MsFsPlciE9PR0KhYLOBKWkpGBmZgYOhwPl5eUoKirC2NgYXn75ZYyMjMwbyl3stZD9qfb2dmzcuBGNjY2YmZmhv+/q6sKvfvUrOmwZ+LfRgNlspplh5Hm9Xi/kcjmtiBsaGnDixAl0dnbC4XD4OVEEA5/PR0FBAW699VZ87Wtfw913340bb7wRV199NT7/+c8jMzOTvg9EkEDiyCsrK/1i7S9evIjBwcEF95/CBZ/PR1JSEjZu3EjPg1Q3xNImGlissloMYrGYturr6+uxbds2JCUlwWAw4MKFCzhz5gy6urqg0WiWbQRAEGuV1UIgEvn8/HyIxWKsX78eZWVlEAgEGBoawsmTJ3HhwgUMDAxAr9fT77rNZkNXV9eajLQnz7V582ZIJBIUFxfj5ZdfXtZ1jIlbE7KHMzY2FnYbLhjCJSuGYahv3FLdxdmRJAt9aTweD9ra2mAymbBt27YlJWaypdCB50AiqM1mMyYnJ5GdnY2SkhKcO3cOFosFdrsdcXFxEIlESExMpC3VN998E2q1Gvv27aNO4gu1Nd1uN1paWmCz2ej81Pj4+LzHGY1GvPTSSygpKcHOnTuhVqujelctFovh8/kwOzsLu91O70jZd9r9/f1htWjj4+NRWVkJpVJJ9yiJoiucFhufz58Xa0+qLp1OB6/Xi7a2Nlp5LceAWK1W44tf/CJcLhfEYnHUvTWB6Ph1BrYMiZJVr9djZGQEHR0dUZldWmtehgRerxdisZiKVYDLnxtSmXZ2duLUqVN47733MDo6ipmZGTz//PPYvn07nnnmmTUTaT80NIQDBw7g3nvvxW9/+1scP34cd911FzIyMrB///4lXbtVJyubzYaWlhb4fD7U19eH3MMJB0TmvRDYCbvLiRAhX7CF5ibMZjOam5uXbRC70P4Yec2Tk5OUeBmGgVQqxcjICE6ePAmPx4OioiJ6fD6fD7fbjdnZWXz44Yf40pe+BCB05WGxWNDY2Ai5XO4Xg6JWq6nqjQ23243Ozk4MDg7i7//+70OSbaRgGAZutxsSiYQ6bBOiTUxMpJWxWq0OWt0BlxdToVCIDRs2YOfOnVHdW2FLnbVaLQYGBqBQKKh7RFxc3LKk4Dweb0Vmn6JJVoFgCzGAy98/0g7r6OiA1+v1m10Kt2Xo8/liSkofLoKdt0QiQXp6OtLT08EwDJ0F/MlPfgKGYfDII4/gmmuuwac+9SkcOXJkTUTaP/fccygoKMDTTz8N4PIWwalTp/DTn/50yWS1qnW0RqPBmTNnkJCQQO/Wl4PFKiuDwYDTp0/TqI3lZF0Rx4JQi/Dk5CTOnj2L9PR0bN26dVlqqVBk5fF40NLSgpGREdTU1NAKkcfjITMzE9u3b8eePXug1+thtVrpnA/50Hm9XszOztL9nGDH0Gg0OHv2LDIyMrB582Y/Yv7c5z6HvLy8kC1Np9OJ4eHhBRefcNuDDMPgxRdfxJEjR6hL/Llz59DX14e+vj6sX7+eHmfv3r1BFYEymQzV1dW47rrrsGvXrhVd7AgpFhQUYMuWLdi5cycKCgrg9XrR3d2NkydPorm5GWNjY7BarSsaKLkYyPt/JSoVshhWVlZix44d2LJlCxITEzE7O+vXMtRqtTQiIxjWcmW1UCXJ4/FQXl6Ohx56CF6vF1//+tfxxhtvoKCgAJcuXcI111yzYKQ9O64euBxpTx6/WKR9MCwWaU8q58BI+8XOZSlYden6unXrltz2C0QosmKHCkYzeyoYiZCB4snJyZBzR9E4jtVqRVNTE8Ricciqjcfj4Stf+QrS0tLQ2toa9DU7nU789re/hdFohNFoxNatW1FUVORndFtVVRU07C0xMRG33XYbOjs78fbbbwc9b6VSCZ/PN+/8yYwYScQlG/Gh0NXVhYMHD0Kr1VIhzczMDHp6evDQQw/huuuuo49VKBQ4cOAADh06BJvNBoZhoFKp8P/9f/9f1L3ewkUoKXiwwMmV8KRbCKvlCxgY7xHM7ogt/05ISKAL/VrasyIghsjh3CSxI+137dqFXbt2AbjsmL4WIu1Dncvc3BzsdvuSbMRWlazS0tKi6uUXjKzcbjfa29uXtWcU7vEcDgeam5vh9XpDDuAuBYFkRebBcnJy5tlABTvHAwcO4FOf+hROnz6N8+fP+21ye71emhNlNBoxMTGBz33uc5iZmaFGt4t5ElZUVMDlcqGnpwcmkwkWiwUCgQC1tbXQaDT44x//CI/HQ2XfLpeLSq95PB5mZ2fR398PqVTqt2Czv9RtbW0YGBigd6ZyuRxOpxMZGRn48pe/PO+c1Go1Pv/5z2NoaAjx8fEoLi6+oovbYjNbkQROEnm71WrF1NQUfD4fUlNTo/ZZjhUT21B2R3q9Hm1tbXR2UK1Ww+12rzmyImtFOGS11iPtVwKrvmcVTQSSB/GqUygUKxIqyCYRolBLSUlBZWVlVFtM7Nj5xaqdUODxeNi2bRs0Gg2GhoaoNJv0mElb0+l04tVXX8WGDRtw/fXXh3WHz+PxUF1dPS9Jd3Z2Fr/+9a/h9Xrhdrvx9ttvY3R0FHK5HFu2bMH58+fR0NAAiUSCu+++GyUlJdDpdOjt7YXL5YJSqURcXBxaWlrwwgsvwG6305YjqZgW2gtj75WsNggh8Xg8qFQqv4U2VOAkIXGSv2W1Wum+ldPphEwmi8oeVqyQVSAC7Y4sFgv0ej1mZmZgsVjQ19cHk8lEb25iWcoOREZW5LNALMUI1kqkfahzSUhIWLI586q3AaMJ9uzT2NgYenp6gnrVRQvEoHRwcBADAwMoLy+nG/7RPo7L5cKlS5eoGm8pDuwSiQQ333wzent78corr0AikfjNQLGvUVdXFwoKChAXF0flqZHCbrfD5/PB6/WCx+NBLBZTA93JyUl88MEH+PDDDwEAR44cwT/8wz/QTWW73Y6pqSn87Gc/Q2NjI1wuF0pLS6HX6+mizzAMnUlaCczMzNBjZWRkwOl0QiAQICEhgZKkQCCATqfD3NwcDT0kIGGRg4ODOHr0KGZmZhAXF4cdO3bglltuCTkqQVxHpqenaRU8MDAAg8EAhmHoMLJarUZGRsayK4xYJSs2eDwe4uPjER8fj7y8PJw9exapqanwer0YHBykbiCBjhGxBNIVCOdai8Vi8Pl8tLa20p+tpUj7wLZg4LksBbF9KxIhyDBna2srTW9diSBGAj6fj/7+frhcLtTU1KzYAB9x2FCpVH5qvKVAKBQiOzsb7e3tGB8fp61EpVIJsVhM85ycTid+97vfITExEWKxGBUVFdi3bx94PB6txhb70mVkZCA+Ph56vZ7+jP037IiSpqYm/OpXv0JhYSEVgohEIpw7dw7FxcUQCoU0tr6xsRE2mw1SqRR7/havsdzF1ufzwW63QywW02BNMoDscrlom5O4S5C9FrFYTN0K5ubmoFarsX79emRnZ2N4eBg2mw0mkwkOhwMzMzOw2+0YHR2Fz+fDpz/9aczOzoLP5yM9PZ0KjBiGQUdHB5qamujNF3tj3mw2Q6lUYmBgAP39/VCpVLR9upS20Vogq2AgKkLgo2o00DGCkNdyolaihUj22UjX4A9/+ANeeeWVNRdpf++99+IXv/gFHn74Yfz93/89Tpw4gTfeeANHjhxZ6uX7eJGVw+Gg/92xY8eKRhyYzWbYbDYq514pb7TJyUmYzWakpqZi06ZNUVlUyABtf38/GhoacOHCBWzatAlbt26FRCKhlQ1xAfd6vejo6EBWVha0Wi0mJiaQkJCAq666asF9E6FQiLKyMpw+fRoej2feNZJIJNi2bRuAyxvJhw8fxr/8y79QckxLS0N+fj6USiUkEgm8Xi8SEhKQkZGBzs5OyOVyKBQKnDlzhu7vqNXqiNpBU1NT6OrqwuzsLMRiMVQqFTZs2EAFJ0QMYbFY4HA4oNVqMTIyAoZhEB8fT1OqU1JS0N3djZaWFjqwLBQKsXPnTvpYkUiE8+fPY3h4GNPT07hw4QL4fD7sdjvS09Nxxx13oLi4GFqtFt3d3bBYLHTR4vP5kMlk8Hq98Hg8SElJQUVFBd3XmZmZoeGB7H2/cK7FWlTWBarqpFIpMjMzkZmZ6ecYodFo0NvbC6lU6ucYsRotw0hMbEn773vf+96ajLQvKCjAkSNH8M1vfhPPPvsssrOz8cILLyxZtg6sYqw9EJ1oewKS1eT1erF3794VJaqJiQl0dnbSttZShooXA4n1mJycpNYt0fKCAy4HF/7Hf/wHmpubaXKtQqHAunXrIBKJ4PV6cdVVV0EoFILH4yEuLg4FBQXQarW0msjMzMSBAwfo7Ai74iOuHT09Pbh06RId2mUvil6vF2azmQ76/td//RcYhsHGjRupDJbP52NqagrJycl0GJZ4IV577bXYunUrTCYTdDod9Ho9bQeRPSCFQjFvITabzbhw4QL+9Kc/wel0Iisri1ZUWVlZyM3NRWZmJs6cOQOdTgez2Uw93UwmE6ampnDu3DlkZGRg3759kMvlNCX47Nmz6Ovrg16vh0qlwnXXXQeZTAahUAiLxYIjR47AarVCrVajrq4OSqUSCoUCFosFiYmJ2LlzJwwGA2ZnZ+l3g00mDMNAKBQiISEBVVVVqKysxPT0NLxeLw2DJPtder0eMpkM2dnZyMzMDHotgMsjHV1dXaivr4/a52ulcfLkSWzcuDGs8ROPx0MFLHq9Hna7nX5GSMvwSpC1TqdDX18ftm/fvuhjia8nCWDk8DGorIipp0ajoRZAK8W/xHOO3OWQdk604XQ60dzcTP0Re3p6ov6aysvLccstt2DXrl0wmUz4v//7P5hMJly8eBHAZal1UVERcnJyqCfe9PQ0LBYLXTyHh4fx+9//nu5H1dbWIi8vD16vF+3t7dDr9aivr8euXbtw7733or29Hdu2bYNCoYBGo0FhYSGNx5ienqavMTs7G0KhkBKPQqGASCSizuhCoZC2vgKHTklLTqfTYWRkhP6eLEx2ux3PP/88/vSnP2FgYAC33norJT+GYaDX6+niXl1djcbGRkrmXq8XFosFKSkpyMzMhFKphEwm86t81q9fj/z8fAwODlJxBKmIGIah4oikpCR6N0vSfIHLqke2lZdIJILP54NUKqXWUTweDzabDX19fbDZbDAajQAut8W2bdsGpVKJkZERzM7OgsfjwWQyYWJiwk8ez3ZKj0bq7pVGJOcsFAqRnJyM5ORkAP4ms6OjowA+EuMs12lkIUSaEiwQCFbsXNYi1jRZkewpHo+H+vp6yGSyqORMBYPdbkdzczMYhqHHGh8fjzpZGY1GNDU1Qa1WY926dRAKhUt2eA8GUvEMDAzgwIED1H6JJOKaTCYaBnj48GEUFBTQ2TT2F420p7RaLZKTk2GxWHDhwgUkJyejpaUFfD4fdXV1kEql8Hq9+NKXvoSNGzfCYrEgJycHR48exY9//GNUV1fDYrHgvffeo889OzsLn88HhUJBA+7Yd74Mw8BgMGBubg69vb3Iysqid59sA1HiDk6Iq7OzE01NTXjzzTfh8/mQn59PCZAQsM/nQ1FREfh8PkpLS1FYWIjXX38dp06dontUpBU3NzcHp9NJKxaPx0OJNDs7GwqFAiaTCTabjW5KCwQCpKSkwO1208wx8rekMmXP4pD0YblcjtnZWTpAzOfzYbVaodfrkZCQAIlEAqPRCIvFApPJhPHxcT9LsHXr1kEsFvs5pRPbo1hX0QXDcuasAk1m5+bmoNfrqdOITCbzaxlGS9kbaZaVXC5fczcRK4k1qwbUaDRoa2tDVlYWysrK6Jsa7Wh74KNcrfT0dFRUVNBjRZMY2QrGwMHlaJGVx+NBe3s7jEbjPKupiooK/PCHP8QPf/hDXLx4kbb2yJc4NTWVfnnIeZFZF7FYDLfbDYfDQVVa69ato9eJPJ6o1wDg5ptvxq9//WscPHhw3nlevHiR9sGdTicVc5AFiqS2fu9730NLSwuUSiWeffZZXH311X7PE5g5ZDQa8Ytf/AICgQAbNmyAQCCgikWRSASLxYJ169b5KR+FQiH+7u/+DhaLBV1dXdTV3mg0QqlU4uLFizR2xeVy0VBMg8EApVJJr51EIsHs7CxV+vX09KC9vd1P2EJA3CQSExOxZ88eGqiZkpKC4eFh6olIwv5MJhONk4iLi8PMzAyEQiEdTZDL5VTVyXZKZ/sYEmESqbxiQZAQCux9vOWCxLMolUoUFBTA4/HMy6UiKs9QbeVwEYlFFCErDh9hzd1SsYML169fP2/mIJpkxTa8rays9IvVIMeKBol4vV50dnZiZmYGW7ZsmTcbFA2ystlsaGxsDOl4wePxUFBQgMcffxxPPvkk2traAIBmaFksFtpGYdszOZ1O6HQ6SKVSyOVyFBUVUaIlJr4zMzN0QSR7iTweD7///e/xD//wD3jttdf8zsXn8+HixYvYsGEDrUYCyUqlUmHXrl3YsGED3nzzTXz5y1/GyZMnUVRUFPT1WywWPPjggzhz5gw2b95Mh5FTUlLQ398PvV6P+Ph43HrrrTCZTEhISKCLklwux7333gutVouxsTHw+XxotVrMzs5Cp9Oho6ODTuYTwsjOzsbExARMJhPkcjlEIhHS09PpnlJaWhoEAgG6u7tRVFREF0ylUgmXywWFQkFFJiKRCLm5uQCAnJwc2hbOyclBXl4eRkdH4fF4kJmZibi4OKSmpiItLQ0zMzOQSCRYv379vFEHYnuUnp4OrVaLwcFBJCQkUEHCSlUX0QAh9pWoOoRCIVJSUugNi81mg8FgoNU527w43Ch7gkiDFzmy8seqk1WojKFgYDtEhAouJPL15cLlcqG1tRU2my2k4W00Kiu73Y6mpibaygzWo14uWc3OzqKlpQWZmZl+VWiwY9TX1+ONN97A+fPnodFoEB8fj7m5Obz22muQy+VITk6mJO1yuWC322EwGCCVSvG1r32NpuECl2e1SJvWZDKht7eXpvKSxeCXv/wl7r//fjz77LM4duwY7HY7iouLsWXLFrS3t6OqqgpOpxM+n4/OnpCFk8fjQa1W4+/+7u/wy1/+Etu2bUNJSQkqKyuxZcsWfOYzn6HDif/xH/+B48ePw+v1Ym5ujsY2sMnjnnvuQUJCAlXzEdl+SkoK3n77bRw8eBAzMzPIycmhDvbXXXcdtm3bhoaGBrjdbqSlpWHDhg3o6OgAn89HRUUF5HI5UlJScNVVV8FoNGJ2dhZqtRpWq5W6s5eXl2PDhg2wWq1wOp0Qi8W0DcpGbm4uUlJS4PP5qOlroGIrKSkJ27dvh8VigUKhCEuEIBQKaZoxu7ro7e2F0+n0k8fHQqQ9sDJkFYi4uDjExcXRtjJpGU5MTKC7uzsic+KltAHXmkpzJbHqZBUuyIK7WMR9NCork8mE5uZmxMfHLzjXtNxj6XQ6NDc3z2svBmKppMj2RAxWGbLBvmmQyWTYvXs3/V1raysYhsGf//xnrFu3DsXFxVSpR87L6XTi17/+NR5++GF6F0+k30lJSdBqtfjlL3+JCxcuQC6XY//+/fjGN76BuLg4VFRU4LnnnvM7n1/+8pc4fvw4FQdMT0+jrq4Ou3fvnlcVqtVqJCcno7y8HAUFBXC5XHjzzTfx6quvYu/evRgcHMTp06fhdDrhcrnQ2dkJ4PJ+EAlyvPvuu1FfX4/z58/DZDLB6XTiRz/6EXp6evwc3D/96U+joqICUqkUPB4P7e3t4PP5EAqFWL9+PbKysnDs2DH4fD46IM7j8WCxWDA6OjrP3DMQ4RBLOC26hISEsI2aA6Xr7OqCjDFEI3AyWriSZMUGn8+HSqWCSqVCYWGhX5R9T08PdV0h1yWwZRgpWXEpwf6IebJiWwwttuACy2/NjY+Po6urC0VFRSgoKFjwzoY4S0QKNomEk6dFIj0iAVHkGQyGsAaWF3KQr6qqwqc//Wk0Nzfjr3/9K06fPo3MzExaCQqFQkilUlitVvz2t79FWVkZ3G43nE4neDweRkdHadrt0NAQeDwebak89NBDQY+5fft2HDp0CFNTU7BYLODxeDh//jzS09P99sOAy4vX7t27kZaWRgl048aN+Otf/4pXXnkF1113HcRiMcxmMxVOqNVqzMzMoLe3FwzD4LbbbqPXgewhEfspAoVCgeLiYkpU5PFkkLq9vR3t7e3zXgvDMGAYBpOTkwu/aauEhYaCydgCaW36fD4qAyf5VIE+hitNIpEmG68UAs2J2SrD4eFhP6VqYmLikiorDh9h1clqoTag0+lEa2srHA5H2BZDS20DsveNwnW+WAoxskUO4bpeRNoGtNlsaGpqglAoRF1dXVh99YWi53k8Hr72ta9hcHAQf/zjHzE5OYne3l7Ex8dj48aNUCqVcLvdmJmZgVQqxczMDEQiEW3bnTlzhkaZkC+rwWDA73//ezzwwANBB6o3b96Mhx56CL/85S/R09MDt9sNl8uFP/7xj9Bqtdi1axeEQiGcTifNuCIqusTERDAMg0996lPo6OhAeno6br/9dgwPD2N0dBTXXnstpFIpGIZBamoqTpw4QY+bkZEBqVSKqampeb5shHyXukiuxDxeNBCJg8VCgZMTExNgGAaJiYlRCZwMBbJ3udpkxUYwUiehk0R9KRQKERcXB71eD6VSuSBxcfNV87HqZBUKer0eLS0tSExMxKZNm8KW1y6lNRcogQ/3CxYpiYQT67Hc45DWYkZGBk2/DQeLBSTy+Xw88sgjiIuLw+uvv47p6Wn09PRAKBRSR3MS+87j8eD1euFwOODxeKibQF1dHfLy8mA2m9HY2AiNRoPTp08jKysLhYWF897jAwcOYN++fRgbG8ORI0dobPzZs2cxMDCA6667DvHx8TAYDGhvb8emTZuQnJxMr5darUZ9fT08Hg9cLhdKSkpQWFjotwhs27YNH374IRiGwWc/+1mUlJQAuKwAfeONN+gME3BZ/djY2Ihdu3aFvVDyeDzw+XxUVVXhqquuCutvrjSWY7cUaDZrNpuh0+miFjgZDGshHoStRCXqS9JO7+rqgtvt9gudDNyf4tqA8xFz7zjDMBgcHMSlS5dQWFiIjRs3RjQHEilZabVaNDQ0QKVSoba2NqI7wUiORY6TkpIScRjjYkQCfNRabGxsRFlZGSorKyP6Qi9UWQGXK8+BgQFcddVVeO+99/DTn/4U5eXlGB8fR1tbG1XWBbbHfD4f8vLycO2112Lnzp0oLCzE+vXrcdNNNyE7Oxt33XUXbr/9dvzXf/0XrFbrvONKpVKUlJTggQcewK9//Wu8/vrr2LNnD7Zt2waj0Yh33nkHv//979He3o7+/n643W76npAqiLRrifMDGyKRiBI7MdkFgJSUFJSVlVFHdGIrReyp5ubm4HK5FrQqIj6MV111FTZu3BizC2y0hoKJaGWlAyfXAlkFgjikZGRkoL6+Htu2bUNSUhIMBgMuXryI06dPo7OzE8PDw5icnITNZsPQ0BDy8/MhlUpRW1uL8+fPL3iMQ4cOoby8HFKpFFVVVfOMZBmGweOPP46MjAzIZDJcc8016Ovro79///336Xcm8N+FCxcAAN///veD/p59A/jyyy/P+300KuxVr6zYX3S32422tjaYzeYlG8OGSyDsvbB169bR4dhIEI7wYTmxHuzjLERWxLtPp9MtObNrIUIkikUy6CuRSHDHHXfg9ttvR09PDzo7O/Hyyy/D4XBQUiCGt1arFUlJSUhNTaW2SmKxGKmpqdi3bx+GhoZw6tQpvP/++0hKSsINN9xAB2UDERcXh6qqKhw4cAC9vb0wmUzYs2cPfvjDH+J73/seOjo6UFZWRuXe7LC7uLg4eDweCAQCvxsFp9NJ97RIa4kcW6FQ4IYbbkBqaioEAgFNW7bb7Thx4gTm5ubwzDPPQCQSUXkzMe0VCAQoLCxEbW0tdDoduru74Xa7/Vpkq5lVxMZKGdmuVODkWvQyBD4SWJDFneSZsfcB//KXv+C73/0uJBIJHA4Hvv3tb+P222/Hf//3f2P//v3o6ekJGuh65swZ3HbbbXjyySdx44034rXXXsMtt9yCxsZG6g341FNP4Wc/+xleeeUVamK7f/9+dHZ2QiqVor6+HlNTU37P+9hjj+H48ePYunUrgMvx9ffee6/fY/bt20c9PgkSEhLQ09ND/z8a79eqegMCl/dwiOdac3MzFAoFNmzYsGSFUXd3N3w+HyorK0M+xuVyoaWlBXa7HZs2bVpS3AZwOYRsaGgopO09W/6+nOOMj49jcnISNTU1837HJpJNmzYt2ROxo6MDIpFoXuSGwWBAU1MTUlNTQ1ZrHo8H//mf/4kPP/wQSqUSXq8XQqEQSqUSKpWK3kGT10+qLrfbDbfbTZ3VMzIysH79emzdujVkerTVasXFixdRX1+Pubk5xMfHQywWw+Vy4a677sLZs2dRWlqKbdu20VkvsVgMo9EIj8cDqVQKpVJJ97vGx8cRHx8PgUAAuVxOxRM2mw1ZWVmw2+2w2+3UcYI8X3FxMfbt2+c3QExuTKampiAUCiGTyagLNRlkJou10WhcMGzySmJ0dBRzc3N+hqcrDXbgJNvTMTBwMhiIpD4cj71YwoULF5CXl7doevjExAS2bNkCmUxGUwkOHDiAU6dO4b777sN3vvOdeX/zxS9+EVarFX/605/oz7Zv347q6mo899xzYBgGmZmZ+Na3vkVFTSaTCWlpaXj55Zdx6623zntOt9uNrKws3HfffXjssceCnmtLSwuqq6vx4Ycf0jb3yy+/jAceeMCvhR4NrHplxTAMRkdH0dPTE5YCbzEIBIIFlXNGoxHNzc1QKpWor69fltXMQpUVCX5cTP4e7nGCVT16vR7Nzc1Uzr+c1kgwocvY2Bi6u7tRWlqK3NzcBdtd//RP/4T09HS0t7dDoVBAKpXiz3/+MyorK8Hj8eBwOGj1QqoegUBA4zG8Xi9cLheN1AhFVuQ8yV05gVgsxi9+8Qvs2LEDp0+fRk5ODhQKBYRCIb2LBUBtmmZnZ+FwOJCfnw+RSETVXKT9IpPJkJubi/j4eAiFQhqLIhKJqFCCWHC53W7k5OQgPT0dJSUlKCkpwcTEBGZnZ/3Omx3hTsxV2WGTgVEfV6p6WI2IkFCBkzqdDmNjY9TBg1wP9k3YWq2swnWwSElJgd1ux+c//3kcPHgQ7e3t1Dm+oaEh6N80NDTgwQcf9PvZ/v37cfjwYQDA0NAQpqen/UYnlEolamtr0dDQEJSs3n77beh0OhpJEgwvvPACSktL5+3HWiwW5OXlwefzYfPmzXjiiSewbt26RV/7Qlh1surv78fIyEhQ54alQCgUBiUQtp1RcXEx8vPzl/2BD6UGJA7w0Qp+DCQrQvC9vb1RC3xktwF9Ph+6u7sxNTUVtjIyPj4ed955J/1/h8MBq9WKlpYWZGdnQyaTQafTobi4mIYQApfvsFUqFRQKBZRKJfR6PQYHB+l8VljedT4f+L29EExMYOfGjfjtyAguXbqE2tpapKamgsfjQSgUUufzDz/8EDqdDgkJCRCJRCgvL4dcLqdVflJSEiWupKQkOBwOut9FWoXp6emYnJyEXq+HQCCA2WyGXC4Pu3pmm6sGa5GJxeKIoz6WiljIs2JHfPh8PirUmJiYQFdXFxQKBRUjEO/KtYZwHSx0Oh0YhqF7wFVVVaiqqsK5c+fQ3d0d9G+mp6dpdAhBWloapqen6e/Jz0I9JhAvvvgi9u/fH1LF6nA48Nvf/nZepVdWVoaXXnoJGzZsgMlkwn/+53+ivr4eHR0dy1LErjpZ5eTkICsrK2qRHsH2rNh7OtEiRWA+ibBjPaqrq5eUrrvYcYjEfnZ2Flu3bqW+d9E4Bqlumpub4XK5UFdXt+R9FalUikceeQQnT56ERqPB9PQ0br75Zmg0GnR2dmJ6ehomk4lGW8TFxUEmk0GhUKCoqAgCgQBDQ0Po6OiAUqmkd+HBhCD8ri4I3nsPl3p7Md7ejorCQgwODWFychL79+/3E044nU44HA54vV4YDAa89957kMvlSE1NhV6vR0ZGBsxmMzo7O5GamoobbriB+hMSjz0e73K0fENDA+Li4iCVSmEwGOB0OilZRdJdD9zDIC0ynU6H/v5+OBwOqhxLSkqKurNBLJAVG3w+n1pPkeFb0i7s7OyE2+2GQCDA+Ph4TO39LYZI5qyA8Ia/Vwrj4+N455138MYbb4R8zB//+EeYzWZ85Stf8ft5XV2d39ZIfX09Kioq8Ktf/Qo//OEPl3xOq05WMpksKvZIBIGtOSIXF4lE1AU8WmATY2CsRzS/QISsHA4HmpqaACDqr4UMwDY0NCA+Ph6bN29e9t28TCbDddddBwD461//ipGREeTk5OCOO+7A0aNHodVqaQovyYNyuVzo6urC9ddfj+LiYj/3hKGhIdqSO336NCQSCfLy8pCu0QAALMnJgM+HnLQ0zMzNwWazoaurC0lJSZBIJHC5XBgYGIDNZgPwURS3RqNBSkoKlEolrFYrurq6aKVUUFAQ8vWlpqZiZGQENpsNarU6bMeIxRDYImNXXUNDQxCJREsOmwyGWG+riUQipKWlIS0tjaYGaDSaZQVOrgbCJSvyvgcaDmg0mpACLWIrFurx5L8ajcavxa7RaGjEPRsHDx5EUlISbrrpppDn+cILL+DGG2+cV60FQiQSYdOmTejv71/wcYshNt/VZYDdBiTO7NnZ2SgtLY1664CQiMFgQHNzM400j/ZGOZ/Ph8fjwZkzZxYUOiwHNpsNWq0WhYWFKC4ujuriNTo6CqfTifz8fOpjt3//fronNj09Db1eD4/HA5vNhjNnzqC3txf3338/bbtlZWWhsrISw8PD+MlPfoLh4WG4XC6kpqbirj17sNvhQPLkJLRGI6ZmZuBiGDp3NT4+DpPJBK1WS1tsJCpeKBSis7WVzr2MjY1hYGAA69atg9PpxO9+9zvs2LGDGsmyUVpaCpVKRVuHwcyBowH2sCkRIxHrI+IgsVDY5GKItcpqIRCHkbi4OGzcuJFWyHq9HgMDA7Db7VFzSY8myBhHOGuDSCSCRCLxa/n5fD4cP34c3/jGN4L+TV1dHY4fP44HHniA/uzYsWO0wikoKEB6ejqOHz9OyWlubg7nzp3D17/+9XnnevDgQdxxxx0h99qHhobw17/+FW+//fair8fr9aKtrQ2f+tSnFn3sQlh1sor2B4k4WPT09GB0dHTJcvFwQIZfL168OC/WI1pgGAYzMzNwuVyorKxETk7Oko/hcrkwPj5O92OcTieSk5NpBMXQ0BDGxsbQ2tqKAwcOzKvc7HY7JicnqchgMcIkYZUajQYymcxv7ysuLg7FxcXw+Xx07obP56O5uRkdHR04c+YMmpubcdVVV0EsFtMbgQ8//BBdXV208mtpacG/PP88Cng8mMfGMO50wgJgW00NKioqaMuor68PPT09VOxB2qoCnw+2vykMCWpra7Fx40a43W40Nzfj6NGj+Na3voUNGzb4vT6BQBBSCLJSYFv4lJSULBo2GY6wZy2RFeA/FyYQCIIGK7Jd0oMFTl5p0M9bmDeyEokEf/nLX/DKK6+gpqYGzzzzDKxWKxU73HHHHcjKysKTTz4JALj//vuxe/duPP300zhw4ABef/11XLx4Ec8//zyAy2vVAw88gB/96EcoKSmh0vXMzEzccsstfsc+ceIEhoaGcNddd4U8v5deegkZGRm44YYb5v3uBz/4AbZv347i4mIYjUb85Cc/wcjIyILPFw5WnayiDZ/PB7vdjpmZGdTV1a3YFLjX66VlLbmDjzZ8Ph86Ozuh0WggEAiC3t0TMAwDrVYLj8dDpbEWi4W6jMfFxaGjo4NWGSS5lmEYKBQKOohIKpmOjg4UFRVhZGQESqUS5eXlOHz4ME1WLSkpwZe//GVUVlYGXQDIeIDT6cT27dvx1ltv4fDhw9BqtUhISMC+fftw9dVXIz8/H3K5HAMDA+jo6MDAwAC9C2VvyKanp6Ovrw+HDx+mKaokOE83O4sMkQidTifMfzt+QkIC7fmT/SZynch+UkJCAirKyyFXKDA7O4vBwUFYLBZkZ2fD7XZjdnYWWVlZiI+Px5tvvompqSkqzlls0blSEyGhwiaHh4dpwCKpukLJwdcaWS0kVAh2PYIFTqrVaiiVyism1CDdnnCP5/V68a1vfQuPP/44TSY/evQobbmNjo76PVd9fT1ee+01PProo3jkkUdQUlKCw4cP+40jPPzww7BarbjnnntgNBqxc+dOHD16dN5N6Ysvvoj6+vp5bv4EPp8PL7/8Mu68886g3wODwYC7774b09PTSExMxJYtW3DmzJkFx4nCwarPWZGAuGjAYDCgsbERXq8XV1999Yr1rtmxHiaTCfv27Yu68zSJQyE5UmNjY/j0pz/t9xiXywWdTgeJRILR0VF0dnbC5/MhKSkJHo8H09PTmJubg0gkQkJCAk3fnZqaov1wErnR399Pv7yErIaGhpCamkrJX6VSITc3Fx0dHZiZmUF6ejoOHDiA+++/3292xGKxoLGxET6fj7qQP/HEE7BYLHR/RKlU4tprr8W//uu/ore3F//8z/+MiYkJAB99sTMzM3HNNddALpdT8cepU6cwPT2NsrIyqNVqNDQ0wOf1Qs0wmHI44P3ba7r11lupEtPj8eDtt99GR0cHgMsy982bN2Pjxo2QSCRUSj88PIz3338f69evR2VlJR0olslksNvtkEgk6OvrQ29vL2pra/G5z30OO3bsCCoOGhsbg8FgmFeNXUmQrDGdTgeDwUAjVUilQW4yenp6IBAIUFxcvGrnGgkGBwfhdDpRUVER0d+xAyf1ej18Pp+fPH4lBQ12ux0NDQ3Yu3fvojcGXq8XiYmJGBwcXHDP9JOGVa+sonFHxzAMRkZG0NfXh7y8PAwPD68YUbFjPcrKyvDee+9FLXJ+bGwMQ0ND8Pl8sFqtdOZoeHgYbrcb09PTtKXpdDrxwQcfQKvVUnGCTCZDXFwcRkZGAFzeh3K73WAYBiaTCWKxGEKh0C/GnQgPrFYrEhISIBQKabtQLpejtLQURqMRPT09UCqVGBgYoDlJLpcL586dw//+7//ic5/7HN2bMpvNmJ6expkzZ9DZ2YmRkREqouHxeBCJRLDZbDh16hS+9a1v4aqrroLT6YTX64VMJkNNTQ3UajUmJibQ3t6O0tJSOBwOyOVyrF+/HmazGUNDQxgcHITVaoVYJIIRAJHVZGZmIisri3622PlZAFBZWYn169dDLpdDIpFQiya1Wo3MzEycP38eDoeDxtgXFhbSDCpC7IODg3j33XdRV1eHqqoqxMXFUed3YqRLBolXa8NfIpH4ycHn5uboHBO76nI6nWvKNHWp9lDswEmGYWCxWKDT6a5I4CTbvWIxEAEQ5w3oj1Unq+WCuJgbDAZs3boVUqmUzulEW94bGOtBitJoJBMbDAacO3cOc3NztAIhRENmgCYmJihZTU1NYXp6GiqVCnN/U74RwQfZt2MXzT6fD/n5+TCbzZDJZMjMzITdbqfkNDg4CK/Xi4yMDExNTWFwcJBaqAgEAjpL5PF4oFAo4HA4IJFIkJaWhuHhYTQ0NGB0dBRWqxWTk5O4ePEiVCoV9Hq93/Uhi7jP54NWq6VzVQaDAUKhEDfeeCPy8vJgt9uhVqtx6tQpHDp0CHw+H9dccw1ycnKQn5+Prq4uWCwWMAwDp8sFJ+takkFfMkDM9i7j8/morKykhMLj8SAWiahVlMvlgtPppF5oaWlpSE1NpdXq0NAQBAIBlEolPv3pT9PZsNbWVvT19SE7O5u2O2ZmZjA1NYWampp5rgVutxtTU1Pg8/mYm5vD5OQklEolNmzYELUxDjbYWUxFRUV+bumk0rDb7UGHcGMN0fAG5PF4iI+PR3x8/BUJnIw0HgTgyCoQa5qsLBYLmpubqYs5qRKA8KfFwwGJZzeZTH6ehTweL2rR9iSBlM/nQywWY25ujpKO3W6HQCDwW0CEQiH4fD4VCxDvPbfbjerqakxOTmJkZITehSYnJ9PHXHPNNVCr1TTt1+Px4Pz587h48SImJiZgs9mQmJhIF3virUec1O12O41adzqdSExMxMjICCYmJpCSkkIHgH0+H63g2MRJ8p/IF5hI0m+66SYUFRVRB3eJREJbgDweD62trXQvKzk5GZmZmZiamsLc3BxycnIgl8tpu8tisUCpVFKPQtJizMvLoy1RkUgEl8sFk0aDObsdQyMjGB4a8ntfNBoN3nnnHaSnp9OKjs/nY+/evUhLS4PH44FcLkdZWRnMZjM1001MTERcXBxMJhNaWlpw7bXX+n2eTp06hcnJSbjdbno9ybUJZqsVbbDd0js6OqhnInsIl+x1kWHoWIHP54t6tbrSgZORkhVRBHL4CKtOVku9W5menkZbWxtyc3NRUlLipw4CIh/ACwUypyWRSILGekQj2t7pdGJkZARCoZC29AgZkdeRkJCAwsJC+jckon54eBiJiYmoqamhcedkFml6eho6nQ5CoRBzc3NwOBx+M2B8Pp9urj7wwAM4dOgQbQ0JBAK8+eab6OrqgkAgwM0334zU1FS89dZbmJiY8BNxJCUlwWQyITU1lbb4RCIRVfiFeo/lcjkVUEgkEmq7RNqTAoEApaWlsNls0Gg0mJ2dxV//+ldUVFSgqqoKfD4fJpMJAwMDKCkpgUAggMPhoLNP8fHxYBiGznFlZmYiPz8fcXFxsFqtEAqFMBgMOHr4MOKcTrh9PsTxeLAJhfD87T1lGAazs7PQarV+565QKGjlK5PJKKk7nU5YLBY4nU4olUpkZGTMmyM0GAzQaDRQ/E3YQaJUfD4fWlpaoNVqERcXh5ycHGi1WphMJqSnp2PDhg0r1lKUyWTIy8tDYWGh395OW1vbFcmoigQr7bq+EoGTkdw8c5H2wbHqZBUpiEvExMQENmzYMG8gjXxootGaI3NaOTk5foTIxnIrK6PRiKamJqjVatx8882Ynp6G2WzGyMgIDAYD4uPjsWPHDpo8SsDn87F161a6gJHfkXMUiUTIycmBSqXCpUuXoFQqUVVVFXKxIwo99uT51q1bMTo6CrlcjurqakilUvzzP/8z3nzzTZw7dw5erxeZmZnYvXs3XnjhBdjtdsTHx0MkEqG6uhoulwuTk5NgGIaKNzQaDaRSKdRqNU3dBT6qtogzOpGY5+bmoqCggLoY9PX1IScnB263mxJkbm4uGIaBRqNBWloaJVuHw4GZmRmkpqYiMzMTX/3qVyEUCjE2Nga5XA6RSISsrCysq6zEpoQEmCYncai9HeNaLSUom802T9lH9kjj4+OhUCjAMAwMBgOtRtra2iCRSJCVlQWVSjXP6JjsHdrtdkpk5MbEbrdjZGQEbrcbLS0ttDImw8w7d+5c8mctFAJb5oF7O8EyqkjVdSUVdQRXOiIkGoGT4VotAZdvANeKK8eVREyQ1UJpwWw4HA60tLRQl4hgm8KkNbccsook1mM5ldX4+Di6urr8vArJLFJFRQXMZjNtJw0PD88jRVKBhAKpPgsKCvwsh0K9jsD3gJiysiGVSvHlL38Z1113HVpbW5GXl4eioiK0tLTgrbfeQkpKCrKzs5GZmUmfk0TDE/9BkUhEfzY6Ogqz2QyFQoGRkRFkZ2dTEYZMJqPkKhQKkZWVRduRZH7K6XRidnYWOTk5SElJodWYQCCgERWkVTg3N4e//OUvMJvN1K39jjvuwN/93d/R13fD3yqxI0eO4Bvf+AYVo7Cv+ebNm+F2u9Ha2or4+Hjk5eXBYDDQoMnjx4+Dx+MhOzsbSqWSikfIDYVSqUR1dTU6OzvpeZKWKbn5YUvsye86OjqwYcOGqDllECzkYEEyqkhOFblp0Ol06OjooMq1K6GoY5/varYllxI4GWkbMFaGmWMJMUFW4YA4jCcnJ2PdunULvvHLISt2rMf27dsXNSZdSmXFNooNNaNFvNEIwglgJGCTbbDqMxjCvWFgC03Wr19Pv7C7du2Cw+GATqeDXC73q+DIRjF7touEIhYWFlKlWn9/P5WSk0WcgBCfXC6n+zwmkwk9PT0YGhqCXq9HfHw8ZDIZVCoVtFotGIaBRCLB3NwctFotnnjiCTAMA7FYTD8/zz33HK6++mq/fUiJRIIbbrgBYrEYFouFnkNRURGddbvvvvuQn5+PN954Az09PUhOTobD4cClS5foax0ZGcFvfvMbHD16FLW1tXj00UfpcYqLi1FUVETDRqempmC1WimxBgNRiUabrCIRIwVaHwVT1LGrrpWIPVltsmIjGJkToQbJMFOpVPTzG861Jm1ADv6IebJiL45lZWVhOTgQYUKkWEqsR6SV1VI9BMONtidikLm5ubDIliAcMiR397Ozs35CE41Gg66uLrqPFezak+qA/J5cW5FIBLFYDJlMhrS0NEgkEhrSyP5bUuEQ4QV5zPj4uN+AtkqlQmFhITIyMhAXFweXy4WUlBTI5XJoNBqqeiOfocbGRjzxxBP49re/7XfTIBQKkZubC6PRSOfFdu7cCbFYDB6Ph4MHD+KFF17AI488grfffhuPPvoojR8hpC8QCJCdnQ2xWIx3330XLpcL3/zmN6nTCflXXFyM4uJi6qbf1dWFyclJWCwWei3ZzhXRxlKVs8EUdcRwlkS3r0TYZCyRVSBCBU4S55gzZ84sGjjJkVVwxMQ7HuqL4vF40NzcjJGREWzbtm3BTCU2llLtTE5O4ty5c8jOzsamTZvCVvtEciyTyYSGhgZIJBLU1tZG9OUNh6xsNhvOnj0Lt9uNurq6iMIeF4u1J3Jus9mMuro6SlQulwsTExNUBh9MUEFUhy6Xiw6AE8UfkcVLJBLEx8cjLS0NmZmZUCqVSEtLQ35+Pvh8PoxGI8xmM/XGm56ehlKpnFdluFwuTE9P0z0CMmNGHN0BUF9AMmh8+PBhPProo37Xl0SHkM+BSqWCRCLB7OwsTUO++uqr8fzzz+Oaa67B7bffDplMBplMRvekCgsLwTAMJiYmMDk5id/85jfYtWsXdu3ahUOHDgVt6+bl5eH666/HV7/6VXzxi19EbW0tSktLsXHjRhw4cGBFFGLRGvMQCoVITU1FeXk56uvraSrAzMwMzp07h4aGBvT29mJ2dnZZbfpYN94lICMTOTk5SEtLQ3p6OsrLy6kC9tSpU7h06RKGhob8bnKsVit0Ot2ajLQP51yWipitrMxmM5qamiCTyYKq8BZCJG3A5cZ6hFtZkYyrpQZMLkZWZFg5IyMD5eXlEd95LlRZmc1mXLp0CSqVClVVVTAYDDhz5gwcDgfNrbJarbRiCWwp2mw29Pf3w2azQSqVorCwEBKJhO5vEOIiJsQMwyA7OxtbtmyBSCTCtddei9nZWfplJ3fzJpMJVquVvt/kvVCr1XS+KzExESkpKVQZCfhbIRFC++CDD/Dee+9Rl3gA+PrXv44TJ05Aq9ViZmYGNpuNSpsnJibQ39+Pp556ChMTE3jsscewc+dO9Pf3w2g04je/+Q2VfGs0GkrSTqcTDMPgN7/5DcbGxvDNb34z6GeBx+PRdttKYyXsloKFTbLnmJYTNhnNsZQrBWKavFDg5NzcHF566SVotVp0d3fjxRdfRG1tLZ555pk1E2kfzrksFatutwSADokSkIU9Pz9/SQ7g58+fR2Zm5qJBX+yW3KZNm5bUpmhqakJiYiLy8/OD/p5Nhhs2bFhyxtUHH3yA9evXzwtCZIdKLieIkagS9+7d6/dzrVaLlpYWKtLw+Xw4cuQIpqamaCx9fn4+LBYLnb26dOkSbDYbhEIhfD4fWltbcezYMSQlJaG6uhoFBQXg8/mIi4uDQCCAVCpFeno61q1bh/j4eMzOzsJsNsNms+HChQv42c9+BqvVCsbnQ5xcTmXqw8PD0Ol0iIuLo+fC5/Nx00030TkysViM1tZWdHV1wWQyzXvdcrkcGRkZ8Hq9+MIXvoADBw4gKyuLvk+HDh3CvffeC7fbTZ0xbDYbenp6aFWXn5+Pn//856iqqgJwuSPwj//4j2hoaADDMJienobH44FUKkVNTQ11zbfb7cjPz8e1114bkfFstHHp0iVkZ2dfEWIEMC9s0mg0RhQ2ee7cORQVFa2IH+dKobu7GyKRCEVFRUF/7/P5MDQ0hP/5n//BSy+9BI/Hgw0bNmD//v249tprceedd66JSPvFzmU5iInKipARW3iwnPDCcCqraMV6LFRZRSvIkBwnsPIhRrdarXbZQYzBBneHhoYwMDDgp4h0uVyYmpqC3W6nbhRWqxU8Hg8pKSmoqqrC1NQU3Uti55WZTCbY7XaqCOTz+VAoFFi/fj1qa2vpe5CZmYmZmRk0NzejsbHR72bGarWiubnZ79yJPQ2pzlpaWlBVVQWJRIKmpia0t7eHfI94PB5cLhfq6+og9PnQfekSOjs6kPu3maPdu3fjX//1X3HhwgXqgD8wMAAejwej0QihUEidOAiEQiH+6Z/+CXK5HOfOncPs7CytdLOysug1U6vVOH/+PLZs2UIDH5cb97EUXGkj23DDJkO5R8TynlUoeL3eBefT+Hw+ioqK8MQTT+B///d/cfXVV+Oee+7BO++8g29/+9vYt2/fmoi0X+xcloOYICsANFiQYZhlL+wLkRW7EolGrEeoPau5uTk0NjZCqVRGJcgwcE+JVIVerxd1dXXLlgwHphGTZGW2kIJAIBDQxYKck1qtRl5eHo4ePQqNRgOXy0UVf/X19ejp6YHFYoHBYMDs7Cxyc3OhUqlQXFyMDRs2UKIiIgOSO0UcI8K5AeHxeMjMzIRQKMSxY8fgdrsRFxeHjIwMmEwmas/EhsfjQUpKCpLEYihnZwG3GwaVCgwuS/9JO2vbtm1wuVywWCyQy+X44IMPoFAoUFpaivj4eLz11lu0MlGpVIiPj8fdd9+Nhx9+GPfeey/Onz9PDYFLS0tpppZOp4PH48HOnTupmpLEWwiFwqiGLIbCaruuLxQ2OTg4OC9scq2SVTg3xCTSPj09HV/84hfxxS9+EcBlx/Senp6gfxNLkfaLnctyEBNkNTs7i6amJqSmpqKiomLZ/ehQCxuJhJ+ZmYlavH2wympqagrt7e0oLCykzt/ROA4hE0KEZA8pGv17Ulk5nU40NjYCCJ5GLJVKUVRUhIGBAQBAcnIydu3aBblcjoaGBhpnwjAMXWQ8Hg++8Y1v4NixY/B6vSgsLMRVV12FjIwMZGdn0/MnlTV5f9555x0ayhjMtikQ27ZtQ2lpKXg8HqampnDy5EkAl1saCQkJsNlsdG+L3GTw+Xz4rFbkzM0hsagIBokEYocDSXI5po1Gel7E7YPP52PDhg0wm81ISkpCbm4ulEolhEIhjh49isTERLpPQ1qMTz/9NH74wx/iz3/+M86cOQOfz4eUlBTEx8ejoKAAMzMzcDgckEqlfvEWpNogIYtKpRLJyclR8apjY7XJKhCLhU0CoJ+ztTKPFKmjzmoOBS8n0n4lERNk5XA4UFpauugeU7gIRlbsWI/6+vqoWcawj8UwDHp7ezE2NrasNmYwELJaCSIEPgqSbGhoQGJiol9r1Ov1wuPxUNl2XV0dUlJSMDk5CZPJhJMnTyI9PR3T09NUSECez263Q6FQ4JZbbsEdd9wR0jGbDNm6XC7U1NRAJpOho6MDc3Nzfi4PhLADSUsqlSIvL48+JisrC6mpqTD+jXCI6pC8Vz6fDxKJhLYic8fGMG61QpSdjbjkZMz9zXWdONKzj2mz2SgpkuMRgYrJZILRaITRaERlZSU8Hg+Ki4vxyiuv4M9//jO+973voa+vD2lpaXSWrqOjA4cOHcJnP/tZfOELX6DPGxiySKouUm0kJycjKSlp2Q7hsUZWbAQLmzx79iysVisaGxuXFDa5Ggi3GkxKSqKxNmyslUj7xc5lOYgJssrJyVnSXFQoBM5Zzc7OoqWlBenp6aioqIhqC4HP58PtdtOwQeK/F+05CVItGAwGbNy4MagqaDkgSrmcnBw/Euzr68O5c+fgcDigVCqxfv16ZGZmoqurC2NjYwAuixQmJyeRlZUFhUJBJ/Dj4+OpkzhpUwZbFG02G5qbmyGTybBt2zYIhUJYrVZqXMtuTxIPwbm5ORQVFSE9PR0zMzNobW1FXFwcvUFwuVyQSqV0psvj8SAxMRFms5m2KIkJ8JmmJrSIRBB6vRALhbj+hhvwtZtvps4UbW1t0Ol0sNlsVP1I3CoSExP9lKpkJowspgaDAevWrYNMJsOePXvw4x//GG+88QYyMzMhkUgoaUqlUjz77LNQq9V+prcEMpkM2dnZtNogVVdfXx/d4yHkJZPJIiKftSIFB0BfG5FGB4ZNBnr2xcrrCreyEovFUCgUGB4epj9bS5H2i53LchATZBVtCIVCKhEmIgES6xFtEA+6hoYGOkwc7b0Fj8dD91u2b98e1egA4qAwODgI4LIr+cTEBBU1vPPOO9Qfz2Aw0CgP4qoOfBRpYDQakZ2dDaFQiKqqqrAqS6PRSPPBSktL6Y2ETqfD+Pg4HewFLpPitddei9TUVL9KpqysDDabjf7zejzw+nwoKirCjh07kJaWBp/Ph/T0dPzqV79CX1+fX+Xt9XphYRgkxMfDzjD4f8eOIbuyEt/97neRlJSEoqIi2Gw2DA4Ooq2tDaOjo2hra4PRaIRarb4cM/I3VwwSTUJal2NjY5iamqJzWsXFxfjxj3+MF198kVahxOV7bm4OZ86cCUpWbATb4yFVF9nrI8RF7H4W+wzEyqK+GNjD4Xw+H4mJiUhMTERxcbGfFJwk6QYLm1wNRNIGTElJwenTp9dkpP1i57IcxARZRfuLQqqd5ubmebEe0QaxmykqKlrUf28psNlsdA8pPz8/qkTl9XppFtjmzZtpTEhHRwcdwA3MxSKtPbZhsMvlglgsvhyE+Le4lnCIampqCp2dnSgtLfWT3Pt8Pmg0GjidTuq+7vV6/a4xifcYHR2FRCJBXFwcpqamIJVKoVQqoY6PR3p6OuLi4sAwDFQqFTZt2oS33noL/f39tNVGKnCfzwfb36I6nE4n/vKXv+C73/0ugI/Ua1VVVSgrK0N2djb6+/uxZcsWukdF7kLZ7R6SFUaIcWhoCMPDwxCLxTCbzfRxTqeTzmctZZKE7PEQZZ3BYIBOp0NPTw9cLhd1kSBVVyCWGma4GiDXJ9j5SqXSoGGTJEWbXXUlJCRcUYKOhKykUinuvPPONRlpH865LBUxQVbRBrF9UalUEQ8UhwuGYdDX14fp6WkoFIoViQQng76ZmZlwOp1R/XIR9SXZgyJ7L52dnTAYDHQ4NxBisZjGfzgcDgAfDfXy+Xwa7LgQSDU3OjqKjRs3+s3L2O12vP7662hubkZVVRVtEWq1WqSlpVEpPBFJpKamwm63Y3x8HAaDARkZGdTphM/n03ag1+vF0NAQtmzZQkUOgdeTZGyJxWK4XC688847uPbaa/0WBrFYjF27duGVV17BK6+8QuXrgc9FKj+SE6XRaCgxkhsA8nuGYZCQkIDJyUm/AculQCAQIDk5GcnJyXSeSafTYWZmBn19fX7efWzPurVSWZFqfjFyXShscnx8HAAocSUlJa141RXuIDNpIX/xi18MWY28//778372+c9/Hp///OdDPi+Px8MPfvAD/OAHP1jw+K+99tqCv+fz+bT9HwqLnctS8bEjK41Gg6GhIYhEImzZsmVF7hhJfIPNZkNRURHd74kWSARFX18fbV+S/ZtowGQy4eLFi3SIl9zVj42NwWw2zzsOqRoyMjKwfv16KJVKnD9/ng67knMm5LEQiCLTaDRi27Zt8yrFI0eO4MiRIygoKKAOCPv27UNTUxP1hSRt1pmZGZw9e5bK4Xk8HtSJiX7vOTsAUiKR4Atf+AIOHTpELZnY8Pl8dG8pNTUVx44dw//93/9RifoNN9xAN6izsrIglUqpSIO92JPFPyMjA0VFRdBoNBgYGPAbcyAmuwaDATKZDLt27YJUKsX1118f9vu4GNjzTGwXCZ1OR737iFqTtFpjHeGSVSACndJJ1UWSD+Lj4/2qrmivG5FEhJA9Xw7++NiQFdtpPDc3l6buRhsWiwWNjY2Qy+Woq6uDTqebF8y3HIQa9A3XyHYxTE9Po7W1lbqWt7S0wOfzweFw0EWWtIWIXJu0T3bs2EHbJ0ajESqVChqNBmazGQKBADk5OQs6jpAhaQCoqamZ53M3NDSEZ599FnNzc0hMTITFYkFOTg4kEglSUlL8spPcbjcGBgYwOjpKAyeTkpKgVKn8npPP5yM1NRWJiYmorq6GQCDAQw89hJ///OcYCkgFBj6KsTcYDHC73bDZbHj//feRmJiIM2fO4OGHH0ZxcTH4fD727duHZ599Fg6Hg+6jAR+NAWzduhXFxcW0pUqGqYl7h9PphEKhoK2YG264AQaDAcBlIYFcLqd7X9G48w9MwyUedLOzs2htbYVcLl/VnKpwQL4Dy6kEeTweVWIuFjaZlJS0bD9GIrqJNCKEgz9igqyW24Jgx3rU1dXR0L1oQ6PRoLW11c8GKlokAlzeu2hqaoLP55snr1/ucRiGwcDAAIaGhpCXl0crK5INRUC+UCqVCllZWdi6dSuNtWe3uxISEmA0GpGQkECdCEpLS0OqIC0WC5qbm5GQkICKigpotVq43W6kpqYiLi4OfX19+Ld/+zd0dXXB4/EgKysL5eXlUCgU0Ol0SExMpHJ40roSCoVYv349hoeHaRKy2+32W1wEAgF2796NhIQEHDt2DBMTExAKhbjjjjvwk5/8BG63mxI1eQ/IMDGJFq+pqYFIJILBYMAvf/lL3HzzzUhKSkJaWhruueceHDp0iBI9+7N8+vRpFBYWIj4+Hrt378bExAS8Xi/kcjm8Xi/++Mc/0vdUJBIhPj4e7733Hux2O1wuF+Lj4+Hz+eicWE5ODoqKiqioYzlge/cNDw+juroaTqfTL6eK3SaLlYh1sicYzbZlqLDJycnJqIRNkgo+HLIie8Kc6/p8xARZLQfBYj3cbndUkoIJFgpjjEasPXC5NUd8BoPZPy2HrLxeL9ra2mAymbB9+3a62ctu4bGRlpaGG264ASqVKuTiQJwiyEK6kNWTTqfDhQsXIBKJMDk5iQ8//BCtra0YGhqCUCjEvn37oNFocO7cOSpIGBkZQVFREbxe7zxxDGnjrFu3DrOzs5DL5RgcHKSu72z4fD7YbDZotVqMjY2Bz+fT/b+0tDRMTU35vYdTU1NoaWlBSUkJdDod4uPjIRQKMTs7i+TkZFy8eBFbt25Fbm4uxsbGYLVa8ZnPfAaNjY3zWmlarRZdXV1Yt24d3T9h4+GHH8aRI0cogXo8HszNzVGfQ5vNRq+7wWDA2NgY2trasHXrVmzcuHGRdz18kAFupVJJoy2IcIgECrKrrpVok4WLKxFpH+2wyUjIiihrI0lM+KRgTZMVMbwNHJBdblIwG2RY1Wq1Bs2HWm6sPRCeIztbuRYJHA4HHZ6sq6ujNj9VVVXo7OykCiOTyQSbzYbU1FTs379/QaICLreUFhNSAB9Nw5M9JUKMOTk5GBkZwdjYGF566SVYLBZYrVb6vpGE3YGBAWRlZWFmZgZSqRTJycmw2+04ceIE3nzzTfzgBz9Abm4usrOzIZPJIBKJ/NqZHo8HQ0NDkMvl1KGDVGcZGRkYGRmZ9/51dHRQp4R9+/YhMzMTCoWCCnf+53/+B/Hx8bj55ptp1lpJSQn9GzYaGhpQWloadGaFx+Nh7969aGpqgsVigUQiQV9fHyVTdlowgc1mw7lz5+Dz+VBcXByVRS1QYBGYU8VesEmb7EqKE9i40lZLkYRNEsFKIMjnLZxqkJAV1wacj5ggq0hL+sViPaJFVhaLhcaUhApjXE5lFYnjxVIqK+KknpKSQp2+gcvXe926ddSFQSgUwm634/jx4zSrarntFoZh0N3djaamJuh0Or/9HODyhvfVV1+NsbExHDp0CC6XCyUlJVCr1dBqtZS4JBIJdDodmpqa0NvbC4VCAbfbTdtypCrQ6/VQ///tnXd4VGX6/u/JJJn03gvpjUB6gYCAGimCgIqC6xp7+a66KmJdxLKuKBYQdJdldRfQRakC0iF0CCW9hySkl8lMyiQzmUw9vz/ye9+dSZ1JJmQC53NdXO7OnJk55+Sc9znv8z7PfTs5gcvl9tvn7OxsBAUFwdTUlFYwqlQqzJw5E/n5+QOqsRNycnJgYmICa2trVFZWIjQ0FF5eXrh27RpVv4iPj4eDgwPs7e1x+fJlrc+3t7fjwoULSEpKGlAiydraGjNnzgQA6vlVXl4OhUIBc3NzWjlI/pFZ1/nz51FUVIS7774b3t7eI/obkb/TcNWAfQdskiZraGigxQmas66xrCwcT13AvkGcPLxoFqwMZDapj6WJRCKhbtks2hhFsAJ0t1XXxWmXzHZGU5Lb0tKC/Px8TJo0CSEhIYN+z0hnVn1nbMM9SekbrMhsbSixXs0bwsLCApaWlvS8jTRQEcv5U6dO4dSpU3BxcaHH1vdvzOFwMGnSJPzf//0fLl68iICAAKhUKnh7e+P69evIycmBi4sLLToIDw9HbW0tZDIZoqOjcfXqVXR3d9PZBeldcnBw6KfSLRAI+g1ydnZ2WLp0KX755RfIZDI4OzsjLCwMDMOgpKQEHR0daGtrw8mTJ2FiYgI/Pz+qxahWq5GVlYXAwEC4u7vDwcEB06dPR15eHn06JhQXF6Onpwd2dna0rHwgiSRzc3Pcd999iI2NRWNjIw2GDQ0NqK2tBZ/P17qmxWIxioqK4O7uTtfL9B3Iyd9D17913zSZZnFCXl5eb0XmGDbiGpOILTGbJKlTUrBC2gQsLCzoep+u51cikejl7XUnYTTBShd0tfXQ1LTTV01CsxCh7/rUQJCZlT6BkUjxDDVjG+h3dAlWpP+rtrZWZ31C4pxrb29PleJdXV3h4uKis2CqUqnE6dOncfbsWVRWVqKpqYlWzWn+zkBYWVkhNTUVjY2NqKmpgbu7O6ytrZGfn4+ysjLMnTsXkydPhpWVFZ1Vu7m5oaSkBOXl5fDw8ICDgwMaGhpw9epVPPLII/3SWmKxuN8gp1Qq8eijjyIvLw+lpaW499574eHhAS6Xi4CAAOzdu5eucZFzb25urmUUmZmZidOnT2PRokV4+OGHce+99+L333/vp5Df2dmJmJgYiEQilJSUQC6Xw8nJiQYvTSNKMgASiOPwiRMnaJqQaB1yuVwUFBRQYeGAgACEhYXByspKp0F9qCZbXdAsTlCr1ejq6oJQKERdXV0/yxNDyB8ZU7DSRLNgxc/PT8tssra2looUDGc2SVT9WfozIYKVpq1HaGjosPb2Iw1WSqUS+fn56OrqGnB9aqjf0jVYCYVC5ObmwsfHB2FhYTrfvLoEK6VSiYKCArr/uuS9SaBSq9WIjo6GXC6HUCikvk2kbJzMBjStQUQiEVVMuH79Oq5evYrW1lbagNnV1UWlr4arJjMxMYG3tzdcXFwgFAppas7Kyoqm8LhcLq0AvHHjBgDAx8eHnvvi4mJMnjyZ7l/f2VXf86dUKlFTUwOVSkWr7cggQtakLl68SIONQCCASCSCq6sr1R20s7ODTCbD9u3bMXnyZEyePBmFhYVUvor8dnNzM8zMzBAREUGfwoVCIZqbm2kBAwlcA1WccTgcWtVYVlZGF/pJSlIikUAmk6GxsRF1dXVITU2lgZV810CDvL4zq6EwMTGhJeGkEbe1tRVtbW2oq6sDh8OhgWukorPGGqz6otkm4ODggKqqKjg7O2tJYmmudZFxipStszOr/hh9sBqJrQdJYemzliSRSJCTkwMej0cLEXRBU3ZoqJtIs9F38uTJeq8z9PWz6otUKkV2djbMzMwwbdo0nfZfM1CRc2ZhYUEFU5VKJUpLS1FTU4OKigqYmZmhrq4O1dXVqKurg7m5OSZNmkR7hiwtLREaGgqBQIDm5mbU1dXRPh6ZTIaZM2ciICCA6uf1hfy+l5cX5s6di8LCQmpe2XcA9/PzQ0VFBfz8/CCRSGBra4uYmBiqWk2+v2/asS+tra2Qy+UwMTGhhpCEkJAQOpt3d3enbrrkuuLxeDA3N6eNtkKhEAAQERGB5uZmagoJQKtCVfMpnBQwkM/n5eWBYRg4OzvT4EX+lhYWFpg+fTqSk5PR3d0NCwsL3Lhxg65jkYKSxsZGdHR00FJsUphD0oSa6UJDBqu+8Hg8LfkjIjpbU1MzYqPJiSS6SyB9cr6+vlqSWG1tbVSI2N7eHqdOnQKXy4VEIoG/vz+am5sRHR2NTZs2ISkpadDv3717Nz744ANUV1cjJCQEX3zxBe6//376PsMw+PDDD/Gvf/0LHR0dmDFjBv7xj38gJCQEQK8iRl+HcMK1a9eoqgrDMPj666+xZcsW1NTUwMXFBX/605/wl7/8ZcjvaWpqun1U14GB16xGY+uhT5GFQCBAXl4efHx8tMRUdf0dAEPOejTNDBMTE/uVMOvCUDOr9vZ25OTkwN3dXWdV+YEClSZKpRJHjx7FpUuXIJFI4ObmRlNzNTU1VLOMlFgTWSNra2t0dXWhs7OTrrEwDIPw8HB4enpSpXNS9k4UIPoeq5OTE/V6OnXqFBYsWEDXBohmIXFf7erqorMcNzc3OqCRBxZyLQw0wyB29TU1Nejs7ISLiwu9FjkcDgIDA5GZmYmpU6dizZo1cHZ2xrZt2+j5IutKpIkX6A1ylZWVKC0t7fd3GuimNTMz0+rzIbM4TU07ErhsbW3pbwK9qiLW1tbo7OwE0HsfkeBFVN01/2neE5rFQWMdADRFZwFQo8m2tjbU1NRoifMOZTQ5UWZWmvQtsNCUxAJ6Kzxra2tx6tQpXLt2DWq1GrNmzcLKlSuRnZ2NefPm0dR3Xy5fvozHHnsMa9euxaJFi7Bjxw4sXboU2dnZVI9v3bp12LhxI7Zt20ZFbOfNm4fi4mJYWFggJSUFTU1NWt/7wQcfID09HQkJCfS11157DSdOnMBXX32FqVOnoq2tTcshm1BWVgY7Ozv6/w3lEGE0waovo7X10CVYaSqOR0ZG6lSK3Rdykw/2W0SDDxjYzFBXBgtWDQ0NVAx2uPQo8L/qL7K/AwWq8vJybN++HSdOnKBlyqWlpZBKpUhMTER7ezu1/5BIJLCzs6MCsGVlZbh8+TJaWlpo3xEZqFQqlVZJeWlpKby9vWn5fN9ZEKlUrKiowObNm/HQQw8hMDAQarUaDQ0NKCgogJmZGS0ZVigUuPfee5GZmQmlUgkOhwMulwuFQkFnE5q/QfbDxsYGUqkUJ0+exKJFi2gzLrEEUSgUKC8vh4ODAzw9PWlKEugdiCwsLDBz5kxERkYC6A0+wcHBKCsr0/q9Y8eOwd/ff8jeHE11heDgYMhkMgiFQgiFQlRXV9OBztXVFU5OTnB0dERqair27dtHZ3KaWQXNNCApniGBi6i9A70PJ6SS8lYEg8GMJquqqqjRJAlemuumEzFYDZd1sbKyQnh4OE6cOIHJkydDJpMhPj4emzdvRnl5OVxcXPDvf/+7nysvAHz77beYP38+3nrrLQDAX//6V5w8eRLfffcdNm/eDIZhsGHDBqxevRpLliwBAGzfvh3u7u7Yv38/VqxYQdcdCQqFAgcOHMCrr75Kz3tJSQn+8Y9/oLCwEGFhYQB610cHws3NbUQP5MNhdH91EkBycnIQFhaGyMjIEV2cwwUrpVKJ3Nxc1NXVITk5eUSBCgAdEAcKJCKRCBkZGbC2tkZSUtKoDB/7BiuGYVBWVoaSkhLExsYOWvGnCRmoNJ+m+37m5s2b+OWXX5CVlYWWlhZqJEjkgsRiMd22paWFLvarVCq0tLTg6NGjaGxspErkJL3W1tYGkUhEy6/FYjFiYmLwhz/8gd4omiXacrmclkcDvTcAKWE/fvw49u/fj/r6epw8eRLp6ek4duwY1Wq0tLTUOlYywPVdx5JIJFAoFHB2dsY999yDnp4ebNq0CdeuXYNAIKBPxN7e3mhoaMCTTz6J3377jRZckO8zMzNDWFgYysrKIJPJAGBAcVulUonjx4/r9Xfn8Xjw9vZGdHQ05syZgylTpsDU1BTl5eU4e/YssrKyIJPJEBYWRtf0TExMkJmZSQMRgYj/EmUOhUKB4uJieHt70wBHdAKJzNOtgMykQ0JCkJycjGnTpsHNzY1qWF6+fJk6SBMR44mErorrcrkcDQ0NCAkJwTfffIPi4mLU1NRg7ty5yMjIGPAzGRkZSE1N1Xpt3rx5dPuqqio0NzdrbWNvb4/k5ORBv/PgwYNobW2lliQA8PvvvyMwMBCHDh1CQEAA/P398dxzzw04s4qJiYGnpyfuu+8+XLp0adjj1hWjmVlxOBxaIGAIW4+hghWx3SB2FqMtrx2o14qUjgcHB8Pf33/UaRbNYKVUKmmJtL6FFENVf0mlUpw5c4am7YiPllgspumxpqYmuLi4ID8/nyqUt7e3o7q6Gh0dHXTdydzcHDwejwaEyspKKJVKuLu7Y/bs2Xjuuefg6OiIzs5OetNonqPOzk6IxWKEhYVRAV0zMzOo1WqUlJSguLiYHpNEIoFarUZkZCQcHR1hbW1N/b+IJpvmDJLMqCQSCZycnODj4wMPDw8EBwdj7969OH78OJYtW0ZLwqdPn46Ojg4UFBTgu+++w8yZM2FhYUFnImq1GnK5HNevX0dlZSWSk5Ph6uoKKysrreAOAJWVlaivrx+Rt5qJiQmdbRAPLzLramtro/sjl8vR0tKCkydPYv78+QNe30QxxcfHB0FBQVozbvK/h1rrGksGM5qsqKiAVCqFubk5amtr4ezsPCHKvHUNVq2trdTOhuDl5QV3d/d+KWVCc3NzP7ded3d3NDc30/fJa4Nt05cff/wR8+bN07pGb968iZqaGuzevRvbt2+HSqXCG2+8gWXLluH06dMAelPSmzdvRkJCAmQyGX744QfMmTMHV69eRVxc3LDHPxxGE6zEYjEyMzNpDnW0AaSvWzCBpBe9vLwQFhZmkJuvr5r2WFjbk2BFAi0pBNGlomq49SkA1C5cJBLB3Nyc6v0VFRVRUzvSc9bY2Eg1/aytrVFUVET7mHp6emBhYQGpVAoHBwe68G9qaor29nb4+fkhMTGR7s+NGzf6zQCA3mbZ5ORkmJqa0qo28lQdFBQEW1tbdHZ20vNOyriJwjiZGZG1D81jJtYZ5PtIetLBwQGurq5Qq9VUVZ3YixDR3oKCAgBAXFwc1Va0trZGR0cHdQ8WiUSYMWMGwsLCkJWVpXVcarUaR44cwUMPPaRljTISrKysMGnSJEyaNAmhoaE4ePAgXXvgcrmor69HcXExddUlkJ6ooKAg+Pn5aZ0fzfQmCVwDrXVpphfHkr5Gk2VlZejq6kJbWxtu3rypVVU3UO+aMaCPlxUAnSScxgqiOLNr1y6t19VqNa16DQ0NBdAb1OLj41FWVoawsDD6j5CSkoLKykqsX78eP/3006j3zWiCVX19Pdzc3BASEmKwAKJ5gzEMQ6VxRlKNNxRkwCPWIVKp1ODW9mSwzsjIgKenJ8LDww1SSEGorq5GU1MTeDweDTjEpZfH41HV8ri4OPj4+MDb2xtOTk746quvcO3aNao2weFwIJVK6SDv7u6OgIAATJkyBfHx8bSSrbS0FHK5nDo694XIPXV3d9OAbGpqSp/4SbqNfLZveoj4RBE01zqIOCwpJyYzL6lUCoFAgOeeew5mZmYwNTUFj8eDSqVCfHw8rK2tUVtbi7y8PNTW1lJJqGXLlmmtx7W3t+PSpUtITEyk5cqadHV1oa6ubtTBShMbGxssXryYrl2RWVZnZycuXrwIa2truLq6wsTEBFVVVYiIiBgy9T3QWhe5jjTbAMg1datmXVwuF7a2tggLC9Mymrxx4wbkcjkcHBxo8BpIMGA8IBmI4SCVrH3Tr3w+f9BqOg8PD/D5/EG3J//l8/nU3ob8f2Jxr8l//vMfODs7Y/HixVqvk7VaEqiA3qpXoNcMUjNIaZKUlISLFy8O+J6+GE2wCg8PN6j4rGawIkKuHR0dY+IazOVy0d3djeLiYlhbW2PatGkGl0sRCARQKBSYPHkyJk2aNOz2JJVDBpahAhWfz0dFRQXEYjE4HA4sLS3R09MDDocDHx8fBAYG0sGfpOeIO211dTUA0DUqV1dX8Pl82Nvb4+6778bjjz+OhISEfr8fFhYGiUQCPp+P2tpaWs2miWbVmyaXLl2iMwUStADQviUS6AZTReFwODRFKZfLYWNjA4Zh0NLSgs7OTkRFRaGqqopKHXE4HPj7+8PJyQlRUVH4/fff0dHRQb+rubkZXl5etEKRw+FAIpGgoKAAs2bNwqFDh7SU7dVqNVU6N2QKy8bGBvfddx+uX78OhUKBqKgohISE0PW/uro6iEQicLlcKoHl4uIy7LVKglDfWZdmwQY5F2OdLiTqKmR/BjKaFAqFqKiooAoSpJdpvGZdusotmZubw8HBAY2NjVqfTU9PxyuvvDLgZ6ZPn4709HS8/vrr9LWTJ09i+vTpAHqLIDw8PJCenk6DU2dnJ65evYr/+7//0/ouhmHwn//8B2lpaf2uiRkzZkCpVKKyshJBQUEAQHsdyex8IHJzc7WC5GgwmmBl6LwzCVbd3d3IycmBqakppk+fPiZWByqVCqWlpfDz86N6e4aCFFLU19fDxMRE50A10NNvX4goZ3l5Odrb22FnZweJRAIfHx+Eh4fTAf27775DRkYG3Nzc0NnZidLSUtTW1uLBBx+Eo6Mj1TITiUS0qfG5557Dq6++OuigpdlrFBgYiK+++konuS0AdLbW09ODjIwM8Pl8mJubo62tDbm5uUhNTUVubi5EIhFNQwKgaS2pVApLS0skJSVBrVajvb0dSqUS9vb2cHR0hEAgoIMiCVYKhQJdXV1wdnbG5MmTkZGRQbUGr169Sr2RyO8Q9+LOzk5ERESguLhYKy1dV1eHzs5Ogz84eXp69nsqNjc3h0wmg0QiQVxcHLhcLoRCIWpqamjlHRn0del3GmrWNdbpQs3UriYczuBGk6WlpVq6fc7Ozrc01aaP8aKfnx+ysrKwbds2JCUlYcOGDZBIJLTYIS0tDd7e3li7di2A3nLy2bNn4+uvv8bChQvx66+/IjMzk7oMczgcvP766/j0008REhJCS9e9vLywdOlSrd8+ffo0qqqq8Nxzz/Xbr9TUVMTFxeGZZ57Bhg0boFar8fLLL+O+++6js60NGzYgICAAkZGR6OnpwQ8//IDTp0/jxIkTIz11WhhNsDI0XC4XYrFY77SZPpDUYnd3N3x9fQedCo8Uoh/Y3d2N2NhYZGZm6rRPwxVSqFQqFBYW0puY9CmZm5vD0tISUVFR8Pf3x4ULF/Dpp5+isLAQPT098PHxQWdnJxoaGqBQKBATE4PU1FTcuHGDqlTIZDLExMQgLS1N5/PN4XDg4eGBjRs3YsmSJcOmTBwdHSESiWBvb48pU6aAz+fDxsaG2ouYmprioYceQlZWFnJyciAQCMDhcGBlZQWGYWgpNFFiJwGWYRh4eHigsLAQERERkEgkdHZmYmICHx8fcDgcREdHo6mpCc3NzTA1NaX9Z0qlEm5ublql7eR82djY0NkY+dsaMpMwGAzTa2/T0NCA+Ph42v/i4OCA4OBg9PT00CINYtlCAhdJkQ7FcLMuQxdp6Fq6PpjRZEtLC8rLy3VSSzcU+qxZ2draYtmyZVizZg3tZTx27BgtkKitrdXa15SUFOzYsQOrV6/G+++/j5CQEOzfv5/2WAG9VjQSiQQvvPACOjo6MHPmTBw7dqxfdfKPP/6IlJQUhIeH99svExMT/P7773j11Vcxa9YsWFtbY8GCBfj666/pNnK5HG+++SYaGhpgZWWFqKgonDp1atCGY33hMLo+zo4xZB3BEDAMg2vXrqGjowORkZEjqrwaDs1GX9IzosusR1e6u7uRlZUFS0tLREdHQ6VS4ezZs5g3b96gT766rE+pVCpcuHABpaWlNMCQXiEiZvvAAw+gtLQUa9asQXl5OYRCIaRSKWJiYhAaGkoH2dTUVDz44IPYt28frl27BplMBm9vbyxbtmzAC34wlEol/va3vyE9PR1qtbrfQq0mHR0d6OrqAo/Hg5WVFZqamrBv3z7awLtt2zaaS2cYBr/99hvOnTsHhUJBB86goCCtNS5yrtRqNYRCIRVJrq2tpeeMDL5EaFQkEuHkyZMwMzNDeHg4uru7aWP5zJkztaS6uFwu7OzstNTdzczM8Kc//WlM7TWIIG9rayvi4uKGXUMls0wit9XT06OlX6jvGlDfhmTNoWaks66ioiKqvzdSNNXSW1tbtTyqnJ2dR9ViMhDXr1+Hn5+fTs2xc+bMwVtvvYUVK1YYdB9uB267mRUJIkSNYCwCFWn0JWXNJSUlBu1JaW1tRW5urlbFIvn+wfLfmk+xQ61PCYVCquhNRFpJMQFR0q6vr8e//vUv8Hg8WuJKBHKB3rURkUiEOXPmwMLCAsuXL8ddd90FmUxG7ef1obm5GRKJBPfeey/Vk8vIyMD06dO11p3EYjG2b9+OpUuXwtbWFgqFApaWlvDy8kJTUxNWrFhBAxXwP7+ogwcPQiqVoqysjMreaCpQaJ4re3t73Lx5E4GBgWhoaKDvEeUNGxsbqoE3d+5ctLW1gWEYVFVVQSQSobOzE6GhoVqiraRvjBgsEg4fPozg4GBMmTLF4GlwtVqNgoICSCQSJCYm6jQA9y2NJ/qFAoGAejdpqsYPF2iGa0geyazLEE3BfdXSSTq8ubkZN27cGLUz8ED7rOvMqru722gKQ4yN2ypYEXkmsrZDvIsMCfGIcnFxoQ3LQ0kh6UtdXR1KS0sRHh4OX19f+rrmTa954WsWUpB01VADH8MwMDc3h7m5ORQKBa3wIbI9mZmZ2LdvH1Xt9vf3R3t7O27cuAGxWIzi4mLY2trCzc2NziS5XK7WvuqLUqmEtbU1eDweLC0tYW9vDycnJ3R3d9P0XG1tLX766ScolUpalt7e3k7TVWQtKTs7mwrvEgmkkpISFBUVgWEY2NjYoL6+nmrumZmZaSk3kF4/Ho8HFxcX2idlbW0NhUIBLpdLS/uJOaRcLkdPTw+USiVttPXy8oKFhQWtlCNVlSqVCiqVCmKxGL/99huampqQlpaGP/7xjwYLWKQPT6lUIiEhYcSzN7IGRFTE29raIBQKUVRUBKVSqaVfqItQMTD60nhDK1gQ0eKBjCYLCwuhVqu1jCZHsuat65oVmbmzLsEDYzTBarQ3KllcJ/p4tbW1/XyFRguRNurrEWUIs0dNQ8mBBHs1gxVhsEIKhmGot1PfajpnZ2cEBASgrq4O9vb2CAsLw+TJkyESifDll1/it99+g7u7O/z9/dHV1QVra2tYWVnRQcbKygpubm5YsmQJpk2bNqpjJpAGWk1lDVNTU2RnZ9PBPSsriz6J37hxg56jtrY2mJqa4i9/+QuSkpIgFArB5/OpkrmTkxMNMiRIHDx4EH5+fuDxeIiPj4erqys4HA5kMhlOnToFgUAAMzMzzJw5E2fOnIFUKoWZmRltdibnIjg4GI6Ojujp6cGOHTvouh/pLyMzVgDUUoT8zczMzODj4wOBQICffvoJoaGhmDJlCl0/IsK5+iKXy2lBUXx8vN4WOYPRdzZCrEDIPWFra6ulGj/SIo2+13TfWddYyy0NZjTZ2NiI0tJS2NjY0CCtq9GkPmtWEomEtQgZBKMJViOFPHXfuHFDazZCBgdDoBlIYmNj+/XHjHZmRbxuZDLZoIaSfYNV30IKouTA4/Fot3lXVxccHBwQFRVFy0fNzMwQExODkJAQmJmZwdLSEgqFAt9++y327dtHc/hkxiIWi6FQKBAUFITo6GikpKQgICAAs2bNMlh5vqWlJdzd3cHn8+lARfppOjo6UFFRoTVLvnHjBjo6OuDg4ACRSIRnn32WLuKSmQBRMhcIBAgKCqIpTKC36IE095aUlNC+O6FQiOLiYpibm8Pf35/2gJFzL5fLYW5uDhsbG5ibm2P27NlwdnbGL7/8QvUSQ0JCqFo7WQs0Mel1GiZN05oDnIeHB8zMzHD9+nVcunQJV65cQV5eHuRyOUJDQ/H6669j/vz5Og2KPT09yM7OhrW1NaZOnTpmgzqH8z8DxsDAQFoaLxAIaHpcs0hjpKXxA8mD3YqiFILmcfY1mszPz6e6mWTWNdgMVt9gxc6sBmZCBytiHyIUCpGQkEAVnQHDWdtrBpJp06YN+NQzkNySrhAjRisrK0ybNm3QJ2GSTuqb+ycSSIWFhZDJZLCwsIBMJkN3dzc6OzvR3d1NB0tSCUYW/IHeAW7Pnj1UD4z4WWVmZlKBV2tra6SmpuLPf/6zQYtINLGxsUFLSwsduJRKJXg8Huzt7anpoiYtLS1obW3FpEmTtOwQCJpK5l999RUKCwtRX1/frzxerVYjJiYG7u7uMDExQVJSEnbu3ElnRpaWlujs7KSzuq6uLho4jx49iscee4xWG/J4PPq03dnZCQsLC1RXV0Mul8PHxwdRUVH91rG4XC7c3d0hlUqhUCgQGhqK69evo7u7G5mZmVi1ahU6Ojrw2GOPDXn+yHXk7OyMiIiIWypBZG5uDk9PT3h6elIrEFJdWFhYSEvjXV1ddTLzHGzW1dXVRRueia3LrWxI1jSaJD2Hra2tqK+vR0lJCezs7GhBClmzJPeqLsGKNLuzM6uBmbDBajg1c0MEK7FYTJ9Uhwokg0k7DQcppPD29tbJiJHM4DQr/hoaGnDlyhU0NTXB1NSUpqtICTcJXlKpVEu2H+jVh7tw4QIKCgrg7OwMU1NT1NXVQS6Xo6OjA+3t7bCxscH8+fPHNFC1t7fTQEUW3onOIFGEGIwVK1YgNjZ20Pc7OztRXV2NTZs24V//+heOHj1KJZJCQkLg6uoKDw8POtjZ2tpizpw5kMvlcHFxQVJSEk6ePAmZTEb7reRyOTgcDlpaWpCfn08N9jo7O1FbWwtvb2+4urqip6cH9fX1tES+oaEB5ubm9KFDKpWisrKS9mcxDAMej4ewsDDk5ORALpejtrYWr7zyCo4ePYp33nmHKrv3Pcbs7Gx4e3sjODh4XLXyTEz+ZwUSEhICqVRKU5tEHonMupycnHQuje/u7kZBQQF8fX3h7OystVYL3Hr9Qk11fM3ZJbmnORwOLYsHoFOwIuuj7MxqYIwmWOlzgxH/Jjc3N0yePHnAi3O0wYoMREQPb6j9G8nMqra2FmVlZYiIiNB5bcLExETLA4rD4YDP51MVB6KUbWtrS32ezMzM4OTk1K9Cj8/no6ioCEDvzZGUlASlUom6ujpcv34dbW1tsLGxwbx58/Dhhx8aVBqoL2VlZeju7tayLrG1tYWVlRW6u7u1ApcmPj4+ePnllwf9XqFQiPz8fAQGBsLPzw+pqan405/+hF9//RWLFi2Cu7t7P8NFoLcxUywWw97eHiEhIRCLxbh27Rq6urroNmRfz549Cx8fH8yfPx8nTpxAS0sLjh8/DicnJyiVSmr/QGYL5G9EZsGmpqaQy+VUZ1Amk9GmYnK8crkcJ06cgFAoxKZNm6iCAABqDkmUsI0NS0vLfqaDQqGQKtQ7OjpqFcQMhEgkQnZ2dr9jvNUNyUPRd3ZJZl11dXUAgLy8PLrWNVjjNVlj10WY+k7EaIIVMLABoyYM8z97+7CwMK0S5L6MNFiRMuTKykpMmTJFJ6mQwSxCBoKohvP5/H6py6H2iWEYWFpaIjs7Gy4uLnBzc6P6Z5p2FDweD/7+/pg6dSqEQiF9wiMzT9LIXFVVhSlTpuD69eu0iVWhUMDNzQ1BQUFQKBSYOXMmXnnllTENVEDvEyWPx6PiskDvrJPH48HV1RX3338/LC0tcePGDeTl5QHovVYWLlw46PlraGhAaWkpIiMjtXTVVq1ahQsXLsDZ2Zmm6vqmpszNzVFYWIiKigo4OjrCyckJgYGBKCgo6HdNKZVKVFdXIyIiAsnJyZBIJLh27RqOHz8OtVpNm4RbWlpga2tLZ+ekUCMsLAxFRUVISUmBUqlEc3MziouL6UDM5XJhb29PNeCeeOIJLFy4EC+88AIAoKCgAGFhYXppXcrlcjqLA0CNHs3MzBASEjJm6g4DySMJBAJaEGNlZUXfJ426pPo2KCio38xec62L3H9j2ZCsKyYmJnBwcICDgwO8vLyoMEFbWxtqa2sHNZrs7u6GpaWlUYrxGgNGFayGQq1Wo7i4GC0tLTrZ248kWBFlh/b2dr00BHWdWcnlcrp4Pn36dJ0GBc1CioSEBHR1dUEgEKCyshIFBQXUHRfovSmJGDBR5NaEBEqhUAgPDw+Ul5dDIBDQc2VqagpLS0t4enpi2rRpSEtLuyVP6x4eHmhubqb6h0qlEg4ODjTNExwcDLVaDUdHR7S3t6OhoQFubm5IS0sb8HzdvHkTtbW1iI2N7XedBAYGYsaMGZDL5XRdQalU9isCCA8Ph7e3NwQCAYRC4bAPI52dnZg5cybCwsKQlpaGTZs2Yfv27WhsbERwcDBcXFwgEolQUFAAb29v+Pj4QCwWw9zcHCKRCN3d3QgODkZlZSUtwiCSVHPmzKEOyE1NTThw4ADKy8uxfPlyxMfHD9lsKhaLUVtbC4VCAVdXVzQ1NaGsrAwKhQIuLi4IDAykbQlA70xt1qxZY55K1JRH0iwZFwqFKCgooD1tIpEIwcHBw6agNRu8gf4NyeM16yLFFZpGkyKRqJ/R5NWrV+mMKzAwcMJZ2pPvWrlyJYqKiuDr64vVq1fjqaeeMsBZ7GVCBCuyPsUwjM729voGK80eLX01BHWZWZH1LxsbG2p9MRx9hWg1n9hCQkLokykZ5EnZLVEn1xxwiCI8aQK+evUqJBIJVXIg60VWVlaYP38+ZsyYcctSKIGBgWhsbMTly5epT5RUKkVhYSH9WyiVSlo12NPTg+eff15LUgboHaBKS0shFAqRmJg4aDpl1qxZ2LBhAyIiIqBSqTBp0iQtDyEyoJBBkmjNCQQC2iDdF6VSiZqaGtrE/eqrr8La2hr//Oc/UVdXB5lMhqamJtTU1GDBggVUT5GwdetW+sBA3Jhra2vpTIPMAoOCgmjhBpfLHTBQkZl4e3s7rly5AoFAgK6uLrrmRt7v6OiAQCAA0PvAIJVK0dbWNmDwHmv6lozX1dXhxo0bsLCwQEVFBfh8Pj0XupSMj0VD8kjoWwmouaanKXd17NgxnD9/nl6PE83SvqqqCgsXLsRLL72E//73v0hPT8dzzz0HT09PzJs3zyDn0mjkloDeAbXvoE9y8s7OzoiMjNR5iiyTyXDmzBnMnTt32ItQlzWwoWhqakJ1dTVVOu6LQCBAXl6eTutfBF2tPQia5cNCoZAqoLu6usLc3Jw2iarValRWVkIsFtNmRXNzc1hZWSE0NBRxcXG3tIOeYRhcuXIFN2/epIMTcao9evQoZsyYAScnJzrIXL9+HU888QQee+wxrXNCmnl7enoQGxs75AONUCjEU089haysLHR2duLZZ5+lbrkEc3NzPProo1o2GmVlZTh16hS1j9eE+GEtW7aMBj6lUon169fj1KlT4PP5qK6uBsMweOCBBxASEkKrOTMyMlBSUgKlUolJkyYhOjoaFhYW1PBx5syZ4HK5sLGxocUzZNBbsGABAgMDwefzUVNTA5FIBKlUCrFYDLlcjvb2dq1KUvJZtVoNHo8Ha2trmJmZ0W18fX1x1113jWuRRktLCwoKCmgKl1SoCoVCtLa2wsTERKtIQ9/A2rc03hAyUIPR1taGsrKyQccGTWJiYtDS0oLnn38eR44cQUVFBZydnfHGG28MaGm/fPlySCQSHDp0iL42bdo0xMTEUEt7Ly8vvPnmm1i1ahWA3vU/d3d3bN26dUBJJ4VCAW9vb7z66qv44IMPAPS2d0RFRWlZ2vflnXfeweHDh1FYWEhfW7FiBTo6OnDs2LFhj10XjHpmRdQcQkNDMWnSJL1uIBLUhuseJ2WnI/kNzd8aaGbFMAxqampQXl6OyMjIIf2DNNFMX+gSqID+C7xtbW0QCAQoKCigmosikQhdXV20UIA8YZuamsLX1xdRUVG3XOqlp6cH5eXlVGaGlICbm5vD29sb+fn5iI2NBZfLRVtbGx544AH84Q9/0PoOmUyG3NxccLlcJCQkDDt4ubi4YMuWLfj+++/x/fffU9ULzdm0TCbD1atX8eCDD9LXQkNDqbOxpjUJQa1WIzMzk8oSubq64rXXXkN8fDz4fD62b9+Ouro69PT0QCqV0u/o6uqiDycRERHgcrmQSCTw8vJCXFwcpFIprUDkcDj0+MRiMXbt2gU/Pz+o1WqIxWLqpEtmUGRQJo3WmvcCaRqfPHky7SEb72pCPp+PwsJCTJ06lc4mzM3N4eXlBS8vL6jVanR0dEAoFNJUuIODAw1eoymN17zvyHajLY3XtWxdLpejuroaAQEB+Oabb/DNN9/g5s2b+Oijj4a0tF+5cqXWa/PmzcP+/fsBDG9pP1CwGs7Sfv78+WAYBqmpqVi3bh1Ns2dkZGj9DtkXTeuS0WKUwYqsrTQ3NyMuLo66hOqDZrAaaPAiKaOmpqYR/wZhoDUrzTW2xMRErTTTYJDgQb5Ll0DV0tKCtrY2mJubw8fHB2ZmZjTFU11dDYFAgICAAPD5fNpnRFITZmZm4PF48Pb2xpQpU/qVtt8Kbt68ia6uLkilUpqK1FThyMrKQnt7OywtLeHk5IR77rlH6/MSiQQ5OTmwt7en8le64OXlhb/+9a84cuTIgE/SHA4HR48exZw5c2gRB4fDQVxcHN0vTYia+7Rp06jdPFl7cXFxQUREBIKDg7Fx40aUlZXBwsKC9paRdUOgN0gSPTpifklmPmQmRq4J8v8rKyupozJ5jVSOmpmZ0cHXxsYGlpaWVG7Lw8MD7u7uWmos40lTUxN9ih/MYdvExAROTk5wcnJCaGioVml8ZWUllcki+oWjVY0fbWm8rlJLra2tUKvVWvdgYGAgPDw8UFZWNuBnjMnSfrB96ezspJY8o8XogpVMJkNOTg7UajVSUlJGfJDkohpo3YoUOgylGKEPfWdWRPKGqHfrW0hB9n+oAUShUKCgoAAlJSVQqVSwsrLCjRs34ODgAFNTU1RVVaG1tRVcLhdVVVV0PYR8N5fLhYWFBVxcXBAcHEztM271oFVfX0/XD8ixE3HRiooKqFQqlJeXw8HBAe+9956WmntHRwftU9N3RqBQKHDw4EE8+uijgwoDV1dXo6ioCDNnzqSvc7lczJ49W2v/gF7FlM7OThQXFyMpKYnKEnV2dlLvKLFYjEceeQRNTU3o6OiAp6cnqqqqUFNTg4aGBnrtJyUl0YcnTYsSMugR+SgAWte3TCaDubk5vR5Ius/GxgaOjo5ITEyEl5eXUVabNTQ0oKysDNHR0Xo9OPYtjSdFGiUlJZDL5Vqq8brch0PNugYq0iD/ezDuFEv7W4FRBSuRSITMzEy916cGY6Aii66uLmRnZ8PW1nbIRl990AyK5Pvt7Ox01mbTd30K6H2SIQ67ZBbS0dFBg5ZMJtOqdmMYBg4ODhCLxXBwcEBkZCQCAwOplXt2djZMTEzoOpcuDZujhQQluVyu9bparcaxY8e07ODDw8Mxd+5c+v9bWlpQWFioU6XYQFRWVuLmzZu0GKUvxBV19+7dWsEK6L2uUlNT4ebmhry8PEgkEpiZmUEmk0EoFNLtNBtHg4KC6GK6QCBAW1sbeDwe4uLiEBgYiN9++w25ubno6urClStXEBYWhpSUFHoNk7VFhUIBPp8PExMTWqSh2b+lVCqptuOUKVPoIrinp+ewFbTjRV1dHcrLyxEbG6tTK8dgcLncfh5WfbUiNfULh5vxDDfr0qVIQ9dgRSzt+977E8XSfrB9sbOzM1gANqpg1dzcjICAAIOlJfoGKz6fj/z8fPj7+xs0N09mVqSR2M/PT+fvH0mgAkBvFHKMJP1DeqtIXwcAWmxA5IBCQ0MRHBwMU1NT2sND1gIEAgFt2HR2dtYq0jA0LS0tWoaEQO/5UCgUqK6u1no9ISGBHgcZ3KZMmaKTR9Bgvz0UJ0+eRHNzM7Zv344333yzX0Ak8lje3t7Yt28fpFIpOBwOGhoaqDFkXywsLODj4wMfHx86CxAIBPDz88PLL79Me/yIRUVPTw86Ojqo7JSjoyP4fD7OnTuH/Px8JCYmwt/fH2q1Gl1dXRCLxejo6KDOyQkJCfj4449HFQDGmpqaGty8eRNxcXE6pcp1hZT929jY0NJ4Ynmfl5cHhmG0VON1ub77zrp0KY3XNViZm5vD3d1dq8JOrZ44lvbTp0/HkSNHtD6nuS+GwKiCVXh4uEGFKokMEum9uXnzJqZOnTrok8pIIQKbeXl5OjcSAyMrpCBoljQrlUqYmppCJpNBoVAgLCwMwcHBaG1thUgkgo2NDR0IyA3cl75rARKJBAKBAA0NDVT3jAQuXRaxdYGYOmrC4XAgkUj63fzh4eFarrejHdw0g0nfZvTCwkJcv34dQG9KNz4+HmFhYXj88cfx4osvaj09a6brOBwOurq6wOfzh+3R6zsLICrmtra26OzspG0Kd999N2xtbelvMgyD4uJinDt3DocPHwYALesREqS4XC6OHj2KzMxMrF69GgsWLDCKdSlNSD+cpoPxWKGpFamZnq2traW9TuSe0vQiGwxdS+MVCoXOavGRkZE4e/bshLS0f+mll/Ddd9/h7bffxjPPPIPTp09j165d9Bo1BEYVrAx9MxHBy7y8PHR0dCA5OdngN4VaraaK3voqUuhTSNEXW1tbJCQkwNvbG83NzeDz+Zg0aRJ8fHxgb28PHo834rSP5lNpQEAAZDIZ7ee6efMmLCws6EA7GnM6Ho+nJexKIGsypIrR2toa8fHxKCoqQnt7OxITE0ct9hkUFAQbGxvaDEtmpdevX8e1a9e0tu3u7kZ+fj6KiorQ1NSETz75ROt9EqjIgJWbm4tJkybp7Dirqe7t6uqKrKws2NragsvlIjMzE+bm5lSSyNHREe+++y5KSkpw5MgRKs9EUlABAQGYMmUKzMzMEBgYiLNnz+Ivf/kLRCLRsGK4twqGYVBZWYn6+nrEx8ffci28vulZkr4VCoW0f01TNX64VP5A6UKi/UjsXjSNUQcrjff09MQ999wzIS3tAwICcPjwYbzxxhv49ttv4ePjgx9++MFgPVaAkfVZqVSqEQnCDsbly5chl8thaWmJ2NhYg6eySMm0UqlEV1cXUlNTh72wB/LrGU2Qrq2tRXl5OSZPnqzzjG40qFQq2s9FGkpJmbYuN7YmlZWVOHz4MLXiICgUCmRmZuLEiRMAgLvvvhvvv/8+TExMEBsbOyIDvIEQi8UoKiqCQqFAVFQUvv32W3z++edDfoYEEPJEqVarsWfPHjQ0NECpVILL5cLKygpxcXEwNzdHU1MTXFxcEBMTM2xJPZEW8vf3h7+/P52xt7e30/45hUIBJycnmJub44cffsBvv/1GlTBkMhnmz58PHx8ftLe3w8HBAbm5ubh+/TocHBywc+dOqkgwXjBMr+N0U1MT4uPjjU4HTzMd3traiu7ubjg6OtLgRdorhkMmk9GHjoiICDrjIv+A/mtdTz31FOLi4rRUIVj+x20brNra2uhNmpiYaPDudFK+TIoV0tPTcffddw85kGquT422Y16tVuPGjRv0CcyQ+X5dYRgGIpGIBq7u7m44OTnRWddQM4umpibs3r27X6ACetfjWltb8dNPP8HGxgaff/45goODERUVZTAzwYFQq9WIjIxEXV3dkBqVERERuH79Oh202tvbcfDgQbS3t8PR0ZH2OrW3t1N9v8mTJw/5lEl64kJDQwcVNiYFKaRIQyQSQS6Xo6amBubm5jhz5gyUSiX8/f0hlUrB4/GQm5uL3NxcyOVyuLq64oUXXsDLL788LjYUDMOgrKwMAoEA8fHxE8K+nbQhCIVCtLe309J4V1dXODo6DngPy+VyZGZmwtbWFlOmTNEKbkM1JD/88MNYsmQJ3njjjVtybBMNowpWarWapn5GA2kmtrKygpeXFwICAgywd/+jpaUFeXl5CAgIoAuOx48fx6xZswa9AUdaSDEQRK1BKpUiNjZ2XMtdNSHrXGQgtbW1pYGrr9L0/v37afpU8xIkBRbNzc04ceIE1q5di/DwcERERNwS+aeSkhI8/vjjqKmp0TJ87Mv333+vpXtWV1dHXYVNTU0hkUi0God5PB5WrFgxYEFIU1MTiouL+4nuDgdRdiCzAIVCgfT0dDQ3N8PW1hYtLS24du0a2traYGJiAi6XCwcHB8TExOCRRx6Bo6PjkNesIWEYBiUlJWhra0N8fLzRXLP6oFkUIxQKoVQqtUrjifpKVlYWrK2tMWXKlCGvWc3S+JqaGiQnJyMtLY2uObFoc1sFK81G39jYWNTX18PKygrBwcEG2T9NRfa+hRqk8mWgtIYhA5VUKkVubi7Mzc0RFRV1yzXcdKXvQErkn5ydndHR0YETJ05QWxAC+dtLpVJqyfHzzz8jMDDwlhYHCAQCbN68GevWrRtU89HOzg43b97UGnSJn5FCocC5c+eo5QPQG6xSU1MxefJkre8hlY1RUVGjUrdXq9U0XXju3Dl8+umnVBnD0tIScXFxMDMzQ0VFBaqrq2Fubg5nZ2fcfffd+Oqrr3QWbR7pvhUXF0MkEiE+Pl7ntTxjRnOWKxQKIRKJaBO3jY0NVV3Rhbq6OsyfPx+pqan4/vvvx6Ty9nbgtglWcrkcubm5UCgUiI2NhZWVFYqKimBqamqQpjWVSoWioiK0tbUhNja2382dnp6OxMRErQIOkp8eacVfX0QiEXJzc+Hm5kYFUycCZN2lubkZGRkZ6OzspEFAsxFYqVSioaEBly9fRlVVFebOnYvdu3ePyz53dXVh6tSpdF0OAB2ETE1NUVRUhE8++QRPPPFEv88qlUqcOHECZWVltFLTwcEB999/P10s76sOb8g0LsMw+PHHH/HNN9+Az+fj3nvvper5UqkUBw8ehFAohImJCczMzLBo0SL8+OOPEIlEqKioAI/Hw+TJkw2yNqhWq1FYWAixWIz4+HiDrTcaG93d3cjKytJSvSCl8UNZ3jc1NWH+/Pm466678K9//csoG7aNhduiGlCzETcuLo6uaxjK2l4mkyE7OxtAr1DkQE+GfX+rbyHFaAMVMUskvj7GVoY8FKS6qqmpiT7tA/0rADs7O3Hp0iVUVFTAwsIC995773jtMmxtbbF69Wq89tprAHr/fg8++CAVvI2IiEBjY+OAnzU1NcXcuXMRERGByspKcDgcBAUFaQWqsrIy8Pn8IdXhRwqHw8Gzzz4Ld3d3/PWvf4Wbmxt6enogFoup+CuxPZHL5Th16hStMOTz+eByuYiLi8Pf/va3UcmQqdVq5Ofno6enBwkJCbftjEGhUCA/Px+2traIiooCh8OBSCSiyiVFRUW00lPTfJHP52PhwoVITk5mA5UOGFWwGgnNzc0oKCig60eag7hm+fNIIZbhjo6OmDJlyqAXFOmlAPpLJ41mBkRkf6qqqjB16tRBNdMmAnV1dVpVUcD/9O1IFV1YWBhqa2vh7++PJUuWjOv+Pvfcc9i+fTut6vLx8aFisKRRejDdM1NTUwQEBPRbLyUpsY6ODiQlJY3Z2g2Hw8EDDzyAGTNmYPXq1bCysoKFhQVdVyTbkP/+/PPPcHd3h7OzM/h8Pvbv34+Kigps3LgRERERej8ckb5DhUKB+Ph4o01XjxaFQoHs7GzweDxERUXRe51Y+WjagAiFQpSXl+P1119HZGQkbezeunUrG6h0YGLkkQaANIgWFBQgKipqQMUIU1PTUc2smpubcfXqVapIPtQFRbrV+ypdj7bir6ioCHV1dUhMTJzQgQpAvzUqzcoo0svl4+ODyMhIzJkzB2VlZSguLoZAIDBos7g+7Nmzh7YkcLlcrb+nWq2mzsW6QAbwrq4uJCYm3pIiAycnJ7zxxhuor69HQUEBjh8/jvb2dgC995CZmRkcHR0hl8tpIG1oaIBYLMaVK1ewaNEifPnll3qdf5VKhdzcXKhUqts6UCmVSuTk5MDc3BzR0dGD3utEuSQmJgZz5szB+++/j4yMDLS3t+Pw4cNYuHAhNm7cSHv+WAbGqIKVPlJDubm5aGhowLRp0/qp/RJ0dfDtC2laJIGw74xtIEgaUDNQjSZVR6qKxGIxkpKSbnnjpCERi8W4fv06XSfRDFLA/6qiFAoFOBwOkpOT8fnnn2Pq1KkwMTFBaWkpzp49i7y8PDQ2NvbTEhxL3Nzc8Ntvv+HgwYN0MCJ/W5VKRWdJw0GewJVKJRISEm7p2k1ISAg++OAD2NjYQCKR0JS0paUlJk+eDLVaDUtLS4jFYuopp2ng+O9//xsHDhzQ6beUSiWys7PBMAxd37sdIcdpamqqNaMaju7ubvzzn/9ESkoKWltbUVhYiAULFuD48ePs7GoYjKrAAsCAPkGadHd3IycnB2ZmZoiJiRkyD15fX4/GxsYhbaH7omltHxcXp5PiBcMwuHbtGlxcXODj4wMulzuqQEVsL2xsbDB16tQJfRGLxWKcOHECLS0t6O7upqaCmjc3EWFtbW1FR0cHUlJS8OKLL9L3SeUVKYvv6uqCvb29lvzTreDChQu4evWq1uyQWLM8/PDDg36OrHlaWFgMO0Mfa/h8PtLT0/Gf//yHqvIHBATAzs4OBw4cQE9PT78KSHNzc8ydOxfvvPMOXXcZaFZIArKZmRmio6Mn9HU7FGRGZWJigpiYGJ2Ps7OzE0uXLoW9vT0OHDhwW1RF3kqM7rGnr06bJq2trcjNzYWnpyfCw8OHfZrRNw3Y09ODnJwccDgcna3tSSGFk5MTbt68ifr6eri6usLNzQ0ODg56B622tjbk5eXBx8dn3I3wDAExgOPxeFT+iijBm5qa0lkK6atSq9X9PKs4HA5sbW1ha2uLwMBALfXyyspKLfmnkZxzXUlJSUFPTw+1nyBBkqTVBkIqlSIrK0tvv62xwt3dHX/4wx/wwAMPoKysDAzDUImd8vLyfj5dkydPhq+vL7q7u7FlyxaqzhEUFESbY+3t7WmgIgF5vI9zrCApTn0DlVgsxrJly2BlZYXffvuNDVQjwOhmVnK5fMBgVVtbi7KyMoSHh8PX11en7yIK4n0tHgZCJBIhOzsbzs7OwzbzEfoWUjAMg7a2NmqoB4AOos7OzsNe2A0NDSgtLUV4eDi8vb11OELjRqlUYt++faivrwcAWpjQ0tICmUxGzRblcjkuXLiAy5cvw8XFBadPn0ZgYKDOv0HOObHn0LQ5GYs0VFNTE/bs2QOZTAYOh4PIyEjMnz+/33ZisRjZ2dm01cDYHzxOnjyJxx9/nPaHBQUF4b777qOZgps3b6K9vR1ubm7405/+BAcHB3rOifnkcNmOiYxKpUJOTg4A6NVH1d3djWXLlkGtVuPIkSNGJzE1UTC6mVVfiGswn8/XWSiWoGvpOqkoDA4OpppswzFYo6+mkjbRGCP+UuRJ1NXVVWvRmRSL1NfXIzY21mh9h/SFNASbmprS8xQcHIy2tjaabjIxMYGpqSl6enrA4XCQmJioc6ACemfPbm5u1OyQnPPy8nL09PRQ+SeiMGAIRCKR1npbbW0tGhoatB4wRCIRcnJy4Ovre8ubmkdKeHg4AgMDUVlZiZ6eHri4uMDU1BStra1wcnJCeHg4uFwuTE1NcfnyZWqF09nZCR6PB6VSifPnz8PR0ZGe84kgqaQLZEZF1uJ0DVQ9PT147LHHIJPJcPz4cTZQjQKjC1aaaUAiFKtSqXR23NVkuGBFgkR1dTWio6N19kbSRZGCw+HA0dERjo6OCAkJoWsutbW1KC4upje0s7MzKioq0NXVhaSkpHHRbDM0DMOgpaUFV69ehVQqpeXpNjY2aG5upsGLGB8qlUpMmTIFlZWVtK9pJGiec02bk6amJpSWlg4p/6QPnZ2dUCgU9DoViUTYuXMnZs6ciaSkJLS2tiIvL2/ExpDjhY+PDx599FFs2rQJCoWCCue6uLjQBmLSZgD0tiI0NTXB19cXDz30EDgcDtXSIw9pVlZWNHCNRqF/PCFVnCqVSquPczhkMhn++Mc/UsWWsbZBud0xujQgqUYi/U0ODg4jLjIQi8XIyMjAfffd1+89lUqFgoICiEQixMXF6VxtNxoPKgJx5+Xz+ejo6ACXy4Wvry88PT0N5hU1nuTn5yMvLw8tLS00UAG9ZdSWlpaora2l508mk6Gjo4NWP16+fHlMjl9T/kkoFILH42mtc+kziPL5fPz0008Dpqu9vb3h4OCA2NjYW6KCb2hUKhV2796N//znP6isrMSkSZNgY2MDS0tLhISEwNLSElwul96PFhYWcHJywooVK/rdo0SQmJxzANTQ09nZeUKUtJP2BIVCoVegUigUSEtLQ01NDdLT00fVXM3Si9HNrID/peUCAwNHlUIhMytSSk7o6elBdnY2uFwupk+frlOO3ZCKFJaWlnB0dERNTQ1txBQKhbh69Sp4PB7c3NzGvFhgrGAYBqWlpWAYhpapk9mytbU1regjA55MJqP9PcSefSwGMXNzc3h5ecHLy0tLkLSgoABqtVrL5mS433d3d6fpsr40NDSgvb0d0dHR/a67iQCXy8WKFSuwYsUKtLW1QSKRYO/evdixYwdEIhGio6NpKp7MtmxtbVFbW4vu7m64ubnB2dmZpnfd3d3h7u5OFfqFQiGqqqpQWFgIBwcHOusyxowCCVRyuVyvQKVUKvHss8+isrISZ86cYQOVgTC6mVVJSQlu3ryJqKioEVuWE+RyOU6fPk0XiYH/eQa5urpi8uTJIyqkGG0qgwyS/v7+CAgIoANaX68oDoejVSwwEUqBGYah2nM9PT3o7u6GqakpLC0t0dPTQ4OTQqHA9evXUVxcDKVSCZFIhKVLl2Lr1q23fH87OzvpOZdIJDRF6+rqOmjqWSwWY9u2beju7h7wfS6Xi+TkZMyYMWMsd/+WwDAMrly5gurqapiZmVEVD7VaDTs7O/B4PJSWlkIsFoNhGHh6eiI1NRXOzs5QKpW4ceMGpFIp/Pz8qFgvMSYUCARoa2uDpaUlDVz6znTHAk2pKH0am1UqFV588UXk5ubi9OnTBnclv5MxumBVV1cHKysrgyxEqlQqnDx5Evfccw/Mzc3R2NiIoqIihISEwM/PT+dCCtLEOlp9P4ZhUFdXh4qKCkyePHnIC1nTBK6lpQUKhQLOzs5wc3ODi4uLUadQ+Hw+Lly4QNNt3t7eaGhogFAohFwuh4mJCXp6erB9+3a0tLQA6E0Rnjt3Tq/iirFAc82lvb0d1tbWtBWhr925QCDApUuXUFVVNaAPG4fDQVpa2qgfuowFkpqfNGmS1t9p165daG1tpUUzFhYWCAkJwdy5c3HhwgWUlpZCrVbD2toaCxcu7FckRSo6SbpQrVbTdOF4XOtqtZpa8OgbqF599VVcvnwZZ86cuS0qeo0Jo0sDenp6GkxahzydKZVKVFdXo7a2FjExMTrLFhnS2kOtVqOsrAwtLS2Ii4sbVmXbxMQETk5OcHJyQmhoKMRiMVpaWqgwpqOjI00XGlvPhoWFBVxcXDB58mTqkrpr1y4A/0ufCoVCtLa20s+kpaWNe6ACACsrK0yaNAmTJk2iRQYCgQBZWVngcrlaM11nZ2eEhITAwcEBnZ2dKC8v12qoJbPM2NhYxMbGjvtsYTSQjERAQABVcCcQ7yxyn5C1SKC35YSk4/l8Po4ePYrFixdrPYz2regkM11yrWs2gOvq1DtSiEp8d3e3XoFKrVbjjTfewPnz53H27Fk2UI0BRhesDAnR5isqKoJUKsW0adN0nrEZopCCoFAoUFBQgJ6enhGJl2o2xQYFBUEqlaKlpQV8Ph9lZWWwtbWlgWu8CzRIijMoKAh+fn4AtNf3uFwu1Go1HBwcMHPmTGRkZIBhGERHR4/bPg+GmZkZPD094enpqeUXVVJSAoVCQfuPiOttbm4uLl26pKXCIhKJcPHiRahUKr2UVIyJ9vZ25ObmIjg4eMAex+nTp0OhUKCmpgYmJiawtrZGZGQkAMDR0RE3b96kPWlCoRCnT5/GwoULYWJiAqFQCJlMBnd3d1ptaG9vD3t7eyoCS2ZclZWVtDDGxcVlUKfekUIClUQiQXx8vM79Ymq1Gm+//TZOnjyJM2fOTKgK0ImE0aUBDWltL5VKcf78edja2upsUUA00cjsbrQaf1KpFDk5ObCwsMDUqVMNntIgVW4tLS1obW2FhYUFDVz29va3NHDV19ejrKysn+Mtyd+TWSrQG8C7urqQm5uL5uZmbN26FbGxsbdsX0eDQqFAVlYW5HI5zMzMIJFI6NO/iYkJjh8/Tj27rK2tIZfL4e/vj6VLl473rusNKcMPDQ2Fj4/PkNuSKldbW1ua6uvq6sKhQ4fQ3NxMXRBMTU0xdepUODs7U71EDw8PzJs3b8h7VLMwRiAQ0HQhceodTTMywzAoLCxEV1eXXnYmarUaq1evxp49e3DmzBmEhISMeB9Yhua2DVbt7e3IycmBSqXSOfXXt5BitIGqo6MDeXl5cHd3R2ho6JingUiBBlHQIIoRbm5ucHJyGrPf1zQSjI6O7tfUvHfvXtTW1tK/K+mtkkqlaGhoQFhYGN5+++0JUTknl8uRnZ1Nlba5XC59+ifFAubm5mhra0N1dTWtipw2bRqSk5PHe/f1gsySIyIiRlWGLxAIcPjwYbS2ttKZF5HaUqvV4PF46O7uxn333dfPUmUwGIZBV1cXPe9isRj29va0qlOfDAPDMCgqKkJnZ6deBpEMw+CTTz7B9u3bcebMGSpbxTI23JZpwIaGBhQXFyM0NBS1tbU6fcaQjr5Ab/l9cXHxLW0M5XK5NPdPCjRaWlpo2srFxYWWFhtqhkcURtra2gY1ErSzs9MKlBwOB2ZmZjAzM0NkZCQeffTRCRGopFIpsrOzYWtrqyXJZWFhAV9fX/j6+tLeIj6fj56eHkgkEri6usLPzw8qlWpCVHQCQEtLCwoKCjBlypRBXQ10xdXVFffeey+OHDlCy/lJRWF3dzeUSiVMTEz0OjccDgd2dnaws7NDUFCQlmfUzZs3wePxaOAaKl3IMAyKi4shEon0UsNnGAaff/45/vOf/+D06dNsoLoFGN3MajTW9gzD4MaNG6irq0NMTAxcXFyQkZGBgICAISvvDFlIwTAMqqqqUF1dbTRmieQplMy4JBIJlSFyc3MbsV2FUqlEfn4+ZDIZYmNjBy30qKmpwa5du7RmrECvQd3TTz89IWwkJBIJsrKy4OrqivDwcJ2uEbVaDZFIRCs6ZTIZPe+urq5Ga/He3NyMoqIiTJ061aCVjCUlJcjOzgaHw0FSUhLMzMxw4cIFKJVKBAUFYcaMGQaZ/ZN0ISmOUSqVWtWFJMXHMAx90EpISNC5UIlhGKxfvx7r169Heno6YmJiRr3PLMNz2wQrpVKJvLw8ujhKmgyvXbsGLy+vQfPthiykIM2t7e3tiImJMVoPqu7ubjqAikQiarnt5uamc3OmTCajVi1RUVGDztTEYjE++OADuLm5aT05E2V7XUSGxxui8+fj46OTt9lAMAxD5Z8EAgE6OzvpeTeGwhhCY2MjSktLERUVRXuiDIlmUz3Qu/6nVCphYWExJsevaS8jFArpeXdxcUFXVxc6OzuRmJioV6D67rvv8MUXX+D48eNITEw0+D6zDMxtEay6u7uptXRMTIzWwJmVlQUXFxdamUYwdCGFXC5HXl4e1Go1YmJijPapuS9yuZwGLs3mTDc3N9jZ2Q14TiQSCbKzs+Ho6DhsY/XPP/+MrKwseHh4aH0XwzATYp2qra0Nubm5WtWNhkAmk9En/9bW1lHJPxmK+vp63LhxAzExMbeNmHJfyHmvqqqCVCrVOu+Ojo5DpiIZhsGWLVvw8ccf4+jRo5g+ffot3HMWo2v80Hfwam9vR0ZGBpycnAbsixhIzJYEKUMFKolEgmvXrsHc3PyWu8COFnNzc3h7eyM2NhazZ89GUFAQNQu8cOECSkpK0NraSp+IOzo6cP36dXh4eAzrz6RSqbBp0yZqOUFgGAZCodDoA1VLSwtycnIQHh5u0EAFgDZLE6vzsLAwqld57tw5FBQUgM/nG6wydjhqa2tRXl5+W6n+D4S5uTlV2khJScHkyZPB4XBQUlKCs2fPUgfyviawDMNg69at+PDDD3Hw4MERBarvv/8e/v7+sLCwQHJyMq5duzbk9rt370Z4eDitJD5y5Ei/fVqzZg08PT1haWmJ1NRUlJeXD/hdMpkMMTEx4HA4yM3Npa+fPXsWS5YsobqkMTEx+O9//6v3sd0KjH+xYAjq6+tRUlKCsLCwQYsY+gYrzfUp0oc1Gm4ns0RNLTfNvqKioiKoVCrY2NhAJBJRK5XhqKysRFNTE2JjY7XOi1KpBJ/PH8MjGT0NDQ0oKysz+LrNQGg2GxMNPWIsWVBQoLXONRYN4NXV1aiqqkJcXBzs7e0N/v3GAlnTbmlpob1x1tbWcHFxQVhYGMRiMYRCIRoaGlBSUgJbW1tcunQJiYmJqKqqwrvvvouDBw9i1qxZev/2zp07sXLlSmzevBnJycnYsGED5s2bh7KysgGvr8uXL+Oxxx7D2rVrsWjRIuzYsQNLly5FdnY2pkyZAgBYt24dNm7ciG3btiEgIAAffPAB5s2bh+Li4n7Xydtvvw0vLy/k5eX1+52oqCi88847cHd3x6FDh5CWlgZ7e3ssWrRI7+McS4wuDUjM+IbbpqysDA0NDYiJiRlSKJIIpIaHhxu0kAK4/cwSB4NhGJSXl6O2thY8Hg8ymYwuWA9VKJCXl4dnnnkGS5Ys0dqmpaUFXl5eePPNN2/VIegFGbwHKsO/1ZD1RYFAgI6ODtjY2NDz3lf+SV9Iy0FdXR3i4uJuawsLYgfU1NSEhISEYX22SHr85Zdfxrlz56BQKDB//nz86U9/wj333KO3T1dycjISExPx3XffAehd7vD19cWrr76Kd999t9/2y5cvh0QiwaFDh+hr06ZNQ0xMDDZv3gyGYeg9tGrVKgC9a6vu7u7YunUrVqxYQT939OhRrFy5Env37kVkZCRycnKGLApZuHAh3N3d8e9//1uvYxxrjG5mNdzNp1AokJeXRxUphisI4HK5UCqVBq/4Ky8vR0NDw22fNiE3eWNjIxISEuDg4EALBchivJ2dnZaCBsHf3x9JSUng8Xha51utViMtLW08DmdIyLE2NDQgPj7eKAZvKysr+Pn5wc/PT0v+qaamBqampnR9UV81h75/19vZFLDvseoSaEh6PC0tDRcvXsTq1ashFArx5z//GU1NTbh48SLi4+N1+n1if/Pee+/R10xMTJCamoqMjIwBP5ORkYGVK1dqvTZv3jzs378fAFBVVYXm5makpqbS9+3t7ZGcnIyMjAwarPh8Pp5//nns379f5wArEokQERGh07a3EqMLVkPR3d2NrKwsWFpaYtq0aTr1CpGmTZL7H22gUqlUKCwshFgsvm3MEgdDrVajqKgIIpEIiYmJ9Fitra1hbW0Nf39/yGQy+uRfUVEBKysrGriEQuGA5yckJMQoSvo1IWXMra2tWsdqTPSVfyJqDkVFRVAqlbSvaDjxV5IOI+7bxnishqSyspIGKn2O9dChQ3jxxRdpCg4ANmzYgJKSEgQFBen8PUKhECqVql+/mru7O0pLSwf8THNz84DbNzc30/fJa4NtwzAMnnrqKbz00ktISEhAdXX1sPu6a9cuXL9+Hf/85z91OrZbiVEGK023YEJraytyc3Ph5eWFsLAwna09uFwu2tvbUV9fDzc3t1Hl/Ht6epCbmwsul4vExMRRybsYO2QGq1QqkZiYOGiqj8fjwcfHBz4+PlAqlVT66ffff8e3334LHx8fODs79ytbNyaIyrZEItGrjHk8MTExoTJD4eHhVM2BiL8Srygi/kogfmNCoVDnWcZEprKyks6U9QlUx44dw9NPP42tW7dqyWRxOBxMnjx5DPbU8GzatAldXV1aM7qhOHPmDJ5++mn861//otqOxoRRBqu+1NXVobS0FBEREcPqkxFI2o88hfL5fNy4cQP29vZU5UEfQdmuri7k5OTA2dkZERERE1pBezh6enqQk5MDHo+HhIQEnZt2TU1N4ezsjF9++QXNzc2YM2cOGhsbUVtbSwsyGIYxqiBP+vOUSqVemnDGRF81B02vqPLycmot7+rqivr6enR0dCAhIUFvQeWJBlmP0zfNmZ6ejrS0NGzZsgXLli0b9X64uLiAy+X2Kyri8/mDihV4eHgMuT35L5/P15LC4vP5dD3q9OnTyMjI6PegmZCQgMcffxzbtm2jr507dw4PPPAA1q9fb5QpesAIS9c1IU22N27cQHx8vN6BSq1Ww9TUFP7+/khMTMRdd90FDw8PCIVCXLp0CVeuXEFVVVW/0uq+CAQCXL9+Hb6+vjobNk5UxGIxrl27Bjs7O8TExOitLvGvf/0LAoEAZmZmUCqV8Pb2RnZ2Nrq6umhvW1dXF86dO4eGhoZhi2nGErKWAEAvlW1jx9LSEr6+voiLi8OcOXOoHFFmZiaam5thb28PsVhsMCseY6Sqqgq1tbWIj4/XK1CdP38ef/jDH/Ddd9/hD3/4g0GyAObm5oiPj0d6ejp9Ta1WIz09fdAS+OnTp2ttDwAnT56k2xNVHs1tOjs7cfXqVbrNxo0bkZeXh9zcXOTm5tLS9507d+Jvf/sb/dzZs2excOFCfPHFF3jhhRdGfbxjhVHOrDgcDuRyOXJzcyGTyTB9+nSd0xVDFVLweDyq4aZQKGgz7M2bN2FpaUlnXKTKimEY1NbWorKyEpGRkaPWSDN2SBk+MdfT90YlOms+Pj7gcDiwtLREV1cX2tra0NHRAZVKBR6PR6s3SYkwme32TVmNJT09PcjOzoa1tTWmTp162z6AkCKM5uZmWFlZITg4GO3t7SgrK+tX1Xm7BOvq6mrU1NQgPj5eLxWZy5cv49FHH8XXX3+NJ5980qDp6pUrV+LJJ59EQkICkpKSsGHDBkgkEjz99NMAev3cvL29sXbtWgDAa6+9htmzZ+Prr7/GwoUL8euvvyIzMxNbtmwB0DtGvv766/j0008REhJCS9e9vLxo2rJvOw8J2kFBQfTB/8yZM1i0aBFee+01PPzww3S9y9zc3OgKx4wyWEkkEmRmZsLKygrTpk3T6emePLXrKp1kZmYGLy8veHl5aYmPZmZmwszMDG5ubpBKpejo6EB8fPxt3X8C/E8PbqRl+Gq1GtevX4e3tzdtsuZyuWhtbaWpwOjoaHh4eMDKygoRERFwc3PTUiwvLy8f0pnXUBAFDicnJ9oUeruiUqmofiNJc7q5uSE0NJRWdZKHBmOUf9KXmpoaVFVV6R2orl27hmXLluGzzz7D888/b/BjX758OQQCAdasWYPm5mbExMTg2LFj9AG4trZW64EpJSUFO3bswOrVq/H+++8jJCQE+/fvpz1WQG/vlEQiwQsvvICOjg7MnDkTx44d02vNddu2beju7sbatWtpoASA2bNn4+zZs6M/cANidH1WAHDx4kXY2dkhNDRUL+t5orIwGkUKlUoFgUCAsrIy6lfk7u4+ovLgiQDDMKipqcHNmzdHpQd38eJF5OTkQCqVaj04nDx5ErW1tXjppZcwZ84cKBQKTJo0acCUrkKhoBYnQqEQZmZmNHAZSoKIWLN7e3tP+Cbu4VCpVMjNzYVKpUJsbOyQFYKaVZ1tbW3g8XhavmgT4bonWRB9m5uzs7PxwAMP4IMPPsAbb7xxW18TExmjDFZ9pU6Goq8H1WhvKmKWaGlpicjISGon39LSApVKRQfPvhVuExFSwkye9EY6e5RKpfjxxx/R09OjFagEAgGqqqrw2Wef6W2hQEqziVI8wzBaFicjOffE8XYga/bbDaVSidzcXDAMg9jYWL3WHokvGgleAGhZvLOzs1Gq5NfV1aGiokLvQJWfn4+FCxfirbfewjvvvMMGKiPGKIOVUqnUafHX0IoUHR0dyM3NhYeHRz+zRCKDQwKXXC6ng6eLi4tR3sBDodkvFhsbO6q1oqysLJw+fVrrtYCAAEydOhV+fn6jLgXXlCBqaWlBT08PnJ2d6bnXZa2FGAmGhYXd1mojQO8MNScnB1wuFzExMaN6qNI89wKBAN3d3WMu/6Qv9fX1VNfQwcFB588VFxdjwYIFeOWVV7BmzRo2UBk5EzZYEbdZYPRCtADQ1NSE4uJihISEDGuWSGwHSOCSSCR08JwIC9UKhYI+dcfExIx6f/fv34+Kigo6uzUzM8PDDz8MX19fQ+yuFppWGy0tLejq6qI9RYO1IxCljTuhSEahUFAn46ioKIPP/jVtTkQiEWxsbOh1b2Njc8sHfKLhGBcXp1egKisrw4IFC/Dss8/i008/ZQPVBMAog9VQ1vb6FlIMh6Yl+9SpU0e0ZiORSGjgIoOnu7u70Tx5akLSnFZWVpg6deqoBjO1Wo1Lly7Rqk2g98HB398fDz744C1Z5yAFGi0tLWhvb6faeW5ubrCxsaHpoejo6CE1JG8HSCk++duO9fmXy+VaNidkjXE4d15DQR5CYmNj4ejoqPPnKioqsGDBAjz22GNYt27dhFiPY5lgwcqQhRTkd4qLi9HR0YHY2FiD6KP19PTQwNXR0UF189zc3MZdLaCzsxM5OTlwc3PT2e12KNLT0+kMDeidUcXExGDmzJnjsp5HtPNaWlrQ2toKDocDtVqN0NBQeHt739aDkkwmQ1ZWFmxsbDBlypRbfqwqlYqq9AsEAqhUKi13Xl2k0fSBBCp9vbeqq6sxf/58LF26FBs2bLitr4nbjQkTrAxdSHErzBI1jQ1bW1thbW1NA9etTpm0trYiLy+PFheM9rd7enqwZcsWrWIYe3t7PPHEE+OujEB0/vh8PpycnNDe3g4A9Kn/diiO0YQ0/Do4OCAyMnLcU1oMw6Czs5MGLolEAkdHR3r+R3t9NDU1oaSkRO/Zcl1dHebNm4f58+fj73//OxuoJhhGGaz6ugWTGZVKpTJIIYVYLEZubi7s7OwQGRl5SwYuzad+oVAICwsLGrgGc+Q1FI2NjSgpKcHkyZO1pFlGQ35+Po4fP671WkBAgEHkaUaDWq1GYWEhurq6EBcXB0tLSzAMg46ODvrgIJPJaHWbq6urwZ/6byVSqRSZmZlUBmy8A9VASKVSGrja29tpL52rq6ve135zczOKi4v1DlRNTU2YN28eZs+ejS1bttxWDyt3CkYfrAxd8dfa2or8/Hz4+voiKChoXG5uUhpMyrK5XC4NXIa0NGcYBlVVVaipqUFUVJRB12wOHDiA8vJyrZnuww8/PK4l4SqVCnl5eZDL5YiLixuwcIQUaJBUrVgshoODAy0SGO9ZoT5IJBJkZWXBzc0NYWFhRhmo+kJ66QQCAYRCIUxMTGjgcnJyGjKI8Pl8FBYWIjo6Wq+1ZT6fjwULFiAxMRFbt25lA9UExaiDFVmfMlSgqq+vR1lZGSIiIuDl5WWgvR0dxJGXz+fTfiJXV1e4u7vDyclpxIFLrVZTde3Y2Fi9uvl14fTp08jPz6deYT4+PnjkkUfGrYSflGubmJjopWnY96l/vKvbdEUsFiMrKwteXl4TtrlZrVajo6ODPrSRdhCyzqX5sNHS0oKCggJERUXpZS8jFApx//33Y8qUKfj5558nXIsJy/8wymClUqkgl8tp+fpoCymIWWJjYyOio6P1qhy6lZB0FXnqJx5FpJ9I1ydCIrEjlUoRGxtr8NmCUqnErl270NjYCIZhYGFhgT/84Q/jVm0nk8mQnZ0NS0vLUVU4Er1I8tSvqeLg4OBgNAGhq6sLWVlZ8PX1HZGGozFC2kHI+e/q6oK9vT1cXV1hYmKC8vJyTJ06dUAL+MFoa2vDwoULERQUhJ07d07odC+LkQarv/3tb+Dz+ViyZAmSkpJGNW1XqVTUq2i0za+3EqJO3tLSAj6fr9UIO9Q6i1wupw2h0dHRY3KDZmZm4ty5c2AYBiYmJjA1NcV99903Lu6i3d3dyM7OhoODg0EV8QdScSAl8cOlq8YSkUiE7Oxs+Pv7IyAgYFz24VbQ09MDoVCIhoYGdHZ2gsfjwcPDQ+cHh46ODjzwwAPw9PTEvn37jL73kWV4jLIcJikpCW1tbXj44YcRERGBVatW4cKFC4P2Xg1GT08Prl+/DqVSiaSkpAkTqID/eRQFBwdjxowZSE5Ohp2dHWpra3Hu3DlkZ2ejvr5eqxqvu7sb165dg6WlJeLi4gweqLq6unD69Gnk5eXRfSRtBONhi97V1YXr16/D1dUVkZGRBq3uIuuIkZGRmDVrFqKjo2FqaorS0lKcO3cOeXl5aGpq0ioEGms6OjqQnZ2NwMDA2zpQAYCFhQUsLCwgkUgQGRmJ8PBwagh67tw5FBUV0exDXzo7O/HQQw/BxcUFe/bsGVGg+v777+Hv7w8LCwskJyfj2rVrQ26/e/duhIeHw8LCAlOnTqV2HASGYbBmzRp4enrC0tISqampKC8vH/C7ZDIZYmJiwOFwkJubS1/v6enBU089halTp8LU1FTLFPJOwChnVoSenh6cOnUKe/fuxcGDB2FmZoZFixbhwQcfxMyZM4ccjDs7O5Gbm3tbmiVKpVKaKhSJRLC3t4ednR0aGxvh5eWlswCwPrS3t2PHjh2QSqXUPoWc08jISMydO/eWpqM6OjqQk5MDf39/g5Ti64qmeolAIIBYLIajoyOd8Y5VE3hbWxtyc3MREhIyJsogxgZptZg8ebKWQaFmZadAIEBPTw+cnJzQ3d0NLy8vuLq64qGHHoK5uTkOHz48ohT4zp07kZaWhs2bNyM5ORkbNmzA7t27UVZWNmAa8vLly5g1axbWrl2LRYsWYceOHfjiiy+QnZ1NVdK/+OILrF27Ftu2baN2HgUFBSguLu53zbz22msoLy/H0aNHkZOTQ80UJRIJVq1ahbi4OOzduxcWFhbYv3+/3sc3UTHqYKWJQqHAmTNnsGfPHhw4cAAqlQoLFy7Egw8+iDlz5mg9PdXV1aG8vByBgYHw8/O7LXL6gyGTyXDz5k3U19cDAGxtbalKvD423sNx4sQJ5Ofn03PJ4XAQGhqKyZMnIyAg4JaeY6FQiPz8fISGhupsyDlWkAIN0gRua2tLA5ehbDbIwH0n6BoC/zveiIiIYVstiPzTunXr8NNPP8HS0hKurq749ddfkZSUNKLzn5ycjMTERHz33XcAegtBfH198eqrr+Ldd9/tt/3y5cshkUhw6NAh+tq0adMQExODzZs3g2EYeHl54c0338SqVasA9KZz3d3dsXXrVqxYsYJ+7ujRo1i5ciX27t2LyMhIrWClyVNPPYWOjo47KlhNmNIYMzMzzJ07F3PnzsXf//53XLhwAXv27MHLL7+M7u5u3H///Vi8eDGuXr2Kw4cP4+jRo4NaRt9OCAQCNDU1ISoqCk5OTsMaSo4UhUIBExMTqNVqMAwDKysrzJgx45YXqxANx8jISKP4+1paWmLSpEmYNGkSlR8i59/CwoKuc9nb24/o/AsEAuTn5xu0R86YIQagugQqALC2toa1tTW++eYbVFZWoqGhAZGRkbjnnnvg7u6OVatW4U9/+pPOv08kq9577z36momJCVJTU5GRkTHgZzIyMrBy5Uqt1+bNm0cDSVVVFZqbm5Gamkrft7e3R3JyMjIyMmiw4vP5eP7557F///4JtWRxq5gwwUoTU1NT3H333bj77ruxceNGXL58Gbt27cKzzz6L7u5uzJo1C1euXMF9991n0NmFMcEwDCorK1FXV6eljTacoaS7u/uIBs7JkyejqakJEokEpqamuOeee255oCIzZn37bG4V5ubm9Pxr9tLl5uaCw+FoFWjokpYmfUVTpky57QV4gf9ZuISHh+sVmGUyGf74xz/SqlAHBwdIpVKcPn1a7/UqoVAIlUrV73y7u7ujtLR0wM80NzcPuD1x3SX/HWobhmHw1FNP4aWXXkJCQgKqq6v12u87gQkZrDThcrmYMmUKPv74YwQHB+Pjjz/GxYsXsWbNGjz//POYO3culixZggULFhi812i8UKvVKC4uRnt7OxITEwcsbjA1NYW7uzvc3d2hUqmoNxQZOMmMS1fB0YCAADz00ENob2+nM7VbhWZzs77q2uOFZqO3Zj9RSUkJFAqFVj/RQGuvRFJI376iiUp7eztycnIQFhamVw+kXC7Hk08+iaamJqSnp9Nrw9LSEgsXLhyjvTU8mzZtQldXl9aMjkWbCR+sAOCXX36BpaUlLly4AFtbWyxZsgRffPEFcnNzsWfPHnz++ef4v//7P6SmpmLx4sVYuHDhiNMy441SqaQ25YmJiTot6HO5XKoSoDlwFhUV6WUo2dnZidLSUlRXVyM+Pv6WBA1Ng8jBArOxY2JiAicnJzg5OSEsLAxdXV0QCASorq5GUVFRP38oYntxJyjFA/8rliGCw7qiUCjw3HPPoaqqCqdPn9ZL0HYwSD8jn8/Xep3P5w+advbw8Bhye/JfPp+vNWPk8/l0Per06dPIyMjop1GakJCAxx9/HNu2bRvVcd0OTJgCi6Eg2oGDDbQMw6CwsBB79uzBb7/9hrKyMsyZMwdLly7FokWL4OTkNCECl0wmQ05ODszMzGgp9WjQx1AyNzcXp0+fpufZ09MTjz766JhWWZIZZEdHB+Lj4yeUFJKudHd303VGkUgEHo8HmUyGyZMnG43KylhC+saCg4P1qnJUKpV48cUXkZeXhzNnzhg0TZqcnIykpCRs2rQJQO91OGnSJLzyyiuDFlh0d3fj999/p6+lpKQgKipKq8Bi1apVePPNNwH0Pvi5ubnRAova2lp0dnbSzzc2NmLevHnYs2cPkpOT+xUS3YkFFrdFsNIHhmFQVlaGvXv3Yt++fcjPz8ddd92FpUuX4oEHHoCbm5tRBi6xWIycnBw4OjoatPmV0NdQkjjCEr3Cn376CV1dXTAxMQHDMLC2tsaTTz45ZgvBRIWjp6cHcXFxY6KKb2xUVlaiuroatra26OrquqVix+MBCVRBQUHDGp5qolKp8MorryAjIwNnz541eFDfuXMnnnzySfzzn/9EUlISNmzYgF27dqG0tBTu7u5IS0uDt7c31q5dC6C3dH327Nn4/PPPsXDhQvz666/47LPP+pWuf/7551ql6/n5+QOW7LJOjwAALCZJREFUrgO9ViYBAQH9qgGLi4shl8uxZs0adHV1Yf369QAwYMXg7cYdF6w0IcaLJHBlZmYiJSUFS5YsweLFi+Hl5WUUAwRZeL6V4ruaYq/t7e0oLy9HT08Pfd/DwwN//OMfx2RfiJMx0HsT3gkyOVVVVaiurkZcXBzs7e1pgQzpJzIxMaEl8aPRjDQWOjs7kZWVRdtLdEWtVuP111/HmTNncObMGb2CnD589913+PLLL9Hc3IyYmBhs3LgRycnJAIA5c+bA398fW7dupdvv3r0bq1evRnV1NUJCQrBu3Trcf//99H2GYfDhhx9iy5Yt6OjowMyZM/H3v/8doaGhA/7+YMHK398fNTU1/ba/E4bxOzpYacIwDGpra7Fv3z7s27cPGRkZSExMxJIlS7BkyRJMmjRpXAIXqQgLDQ0dt2bQnp4eHDx4UOsm8fHxwZIlSww+syKpTh6PNya27MYGqeqsr69HfHz8gIUrROyYpAuJseFA6dqJANE2JA3duqJWq/H222/j8OHDOHv27G2v4sGiDRusBoBhGDQ2NuK3337D3r17cfHiRURHR9PAdatmN7W1taioqMCUKVP0EvAcC3JycnDhwgXweDwoFApwuVyEhIRQQ0l3d/dRN8FKpVJkZWXB3t7e4PJJxggRWG5qakJ8fLxOxSOaxoZ907Wurq5Gny4dTaD6y1/+gr179+LMmTMICQkZu51kMUrYYDUMDMOgpaUF+/fvx969e3H27FlERERgyZIlWLp06Zj4CGmqxMfGxsLe3t6g3z8Smpqa8Pvvv0Mul4PD4SAiIgJ33XWXwQwlxWIxsrOzJ5Q302gga6ctLS2Ij48fcT8gUXAQCAQQiUSws7PTUtAwJsRiMTIzM+Hn56fXrIhhGHz88cf46aefcObMGYSHh4/hXrIYK2yw0gOGYdDW1oYDBw5g3759OHXqFAIDA7FkyRI8+OCDBil8IE63nZ2diI2NNaoBp7a2FlVVVbC2tkZUVJRWw+VQhpKOjo5DBh+RSIScnJzbyvJiKBiGQUlJCdra2gxa5SiTyWjgam1thZWVFQ1c412gQfy3yN9YVxiGwdq1a7FlyxacPn2aFiyw3HmwwWoUdHR04Pfff8e+fftw/PhxeHt7Y+nSpVi6dCmio6P1DlxEVVqlUiE2NnbC2hqo1WrahEwMJUng6lscQHTggoODx2yx3Jgg5fgikQjx8fFjJnxLCjTIrJf02unTCG4oJBIJMjMz4ePjg6CgIJ0/xzAMvvnmG2zYsAGnT59GdHT0GO4li7HDBisD0dXVhcOHD2Pfvn04evQoXFxcsHjxYixduhSJiYnDDg49PT3IycmBhYXFbVVYMJShJBm47xTdOzJrFovFiI+Pv2XrS6RAg/wN1Go1/Rs4OzuPaYEGCVTe3t56rfUyDINNmzZh3bp1OHHiBBISEsZsH1kmBmywGgO6u7tx7Ngx7N27F4cPH4atrS0WL16MJUuWYPr06f0CUVdXF3JycuDi4oLw8PDbtrCAFAe0tLSgsbERcrkc9vb28PX1HVR26HZBrVZT9+b4+PhxmzVr/g0EAgGkUilV0HBzczPofnV3dyMzMxOenp4IDg7WK1D985//xCeffIJjx45h2rRpBtsnlokLG6zGmJ6eHpw8eZJ6cvF4POrJNWPGDBw7dgzHjh3Dm2++ecutNsYDhmFQXV2NqqoqhIeHo6enBy0tLRCLxbSqzdCD5nijUqmQl5cHhUIxJqaYo4H00wkEAnR2dlIreTc3t1G1JZBA5eHhgZCQEL0C1X/+8x+8//77OHz4MO66664R7wPL7QUbrG4hcrlcy5NLKpVCKpUiLS0N33zzzW01QA+EZql2XFycVk8RMZTk8/l00HR3d4erq+uElllSqVTIzc2l65DGFKj6QqzkW1pa0NbWBmtraxq49LGYkUqlyMzMhJubm15GoAzD4KeffsJbb72F33//HXPmzBnF0bDcbrDBahxgGAZffPEF/vrXv2LOnDnIycmBVCrFwoULsXTpUtxzzz1jtvA+XqjVaq0KuKGe2mUymZZ6BjE0NLSh5FijVCqRk5MDDoeDmJiYCdW8q1AotAo0zMzMaOBycHAYNFVNApWrq6teLQgMw2DXrl149dVXsW/fPsydO9eQh8NyG8AGq3Fg3bp1WL9+PQ4fPoy4uDioVCpcunQJe/fuxW+//QaRSIT58+dj6dKluO+++ya8EZtKpUJBQQG6u7sRFxenVyBWKBS0Aba1tZUaSrq7u8PGxsZo06YKhQI5OTkwNTVFdHT0hC6Y6VvdqVarqUo8USkHemdmmZmZcHZ2Rnh4uF5/m3379uGll17Czp07J5S1B8utgw1W40BdXR2USuWAjZFqtRpXr16lgYvP51NPrvnz5084Ty6lUqlVjj+aNJhSqdRqQjY3N6czLmOyfJHL5cjOzr4tJaOIUj95gOjp6YGzszMcHBxQV1cHZ2dnRERE6PW3+P333/HMM8/gv//9L5YuXTp2O88yobk9y86MHF9f30E7+E1MTDB9+nR89dVXKC8vx7lz5xAWFobPPvsM/v7+WL58OXbs2IGOjg6jF68kFuEcDgfx8fGjXq8xNTWFh4cHoqKiMHv2bISFhVHR2/Pnz6OkpAStra1Qq9UGOgL9kclkyMrKgqWl5YSfUQ0Eh8OBg4MDQkJCkJKSguTkZFhbW6OyshI9PT2QSCSora2FVCrV6fuOHj2KZ599Flu3bh1xoPr+++/h7+8PCwsLJCcn49q1a0Nuv3v3boSHh8PCwgJTp07FkSNHtN5nGAZr1qyBp6cnLC0tkZqaivLy8gG/SyaTISYmBhwOh4ovE4ijg4WFBXx9fbFu3boRHR9LL2ywMmJMTEwQHx+PtWvXorS0FNeuXUNsbCy+/fZbBAQE4OGHH8b27dvR2tpqdIGrp6cH169fh6WlJWJiYgw+aJMm18jISMyaNQtTp04Fh8NBYWEhzp8/j6KiIggEAqhUKoP+7lCQNJiNjQ2mTp1627YgEDgcDszMzCAQCODh4YGZM2fCw8MDQqEQly5dQkZGBiorK9HV1TXg9Zmeno4nn3wSW7ZswbJly0a0Dzt37sTKlSvx4YcfIjs7G9HR0Zg3bx5aWloG3P7y5ct47LHH8OyzzyInJ4c28RcWFtJt1q1bh40bN2Lz5s24evUqrK2tMW/ePC3XAcLbb789oEVJZ2cn5s6dCz8/P2RlZeHLL7/ERx99hC1btozoOFnYNOCEhGEYlJaWUjPJgoICzJo1i3pyubq6jmtKTCKRIDs7e0QpodGij6GkISEivMRvzFhSkmOJXC5HZmYm7OzsEBkZqXXMCoUCQqEQAoGAFmi4ubmhvr4ed911F65cuYJHHnkEmzZtwpNPPjni85WcnIzExER89913AHrT6L6+vnj11VcHNUqUSCQ4dOgQfW3atGmIiYnRMkp88803sWrVKgC9cmDu7u7UKJFw9OhRrFy5Env37kVkZKSWncc//vEP/OUvf0FzczOt8n333Xexf/9+lJaWjuhY73Ru70e/2xQiJPvBBx8gKysLJSUlmDt3Ln7++WcEBwdjwYIF2Lx5MxobG2/5jEskEuH69evw9PS85YEK+F+aKjQ0FDNmzEBiYiKsra1RVVWFc+fOIScnhzYkGwrSU+Ts7HxHBaqsrCzY2tr2C1QAYGZmBk9PT5qyDQ8Ph0gkosaFDzzwAJ544gk88sgjIz5fZB9SU1PpayYmJkhNTUVGRsaAn8nIyNDaHgDmzZtHt6+qqkJzc7PWNvb29khOTtb6Tj6fj+effx4//fTTgAVQGRkZmDVrllY7yrx581BWVob29vYRHe+dDhusJjgcDgfBwcF45513cOXKFVRUVGDp0qXYt28fwsPDcd9992HTpk2ora0d88DV1taG7OxsBAQE6KVYMFZwOBzY2toiKCgI06dPx7Rp02ghwPnz55GVlYW6uroB0zu6QuSE3Nzc9K6Am6iQIGFtbT1goOoLSdkmJSVh9+7dYBgGs2bNwokTJ+Dq6ooHH3wQFRUVeu+HUCiESqXqZ2nv7u6O5ubmAT/T3Nw85Pbkv0NtwzAMnnrqKbz00kuDykAN9juav8GiH2ywuo3gcDjw9/fHypUrceHCBdTU1OCxxx7D0aNHMXXqVMyZMwfffPMNKisrDR64WlpakJubi7CwML2cX28l1tbWCAgIQHJyMmbMmAFXV1fw+XxcvHgR165dQ3V1Nbq7u3X+PmJ54eXlpVfz60RGoVAgOzsbVlZWmDJlil7rctnZ2XjkkUfwt7/9DadOnUJ5eTmuXbuG+Ph4o7DB0ZVNmzahq6sL77333njvyh3FmAQrY63OYRgGX331FUJDQ8Hj8eDt7Y2//e1vozpWY4XD4cDb2xuvvvoqzpw5g7q6Ojz77LM4d+4c4uPjMWPGDHzxxRcoKysbdeBqaGhAYWEhpkyZMuBiszFiaWmJSZMmISEhAbNmzYKXlxfa2tpw+fJlXLlyBTdv3oRYLB703HR2diIzMxO+vr5GMYu8FSgUClrpqG8BSX5+PhYvXox3330Xr7/+OjgcDjgcDqZMmYLVq1fD1dVV7/0hPV58Pl/rdT6fDw8PjwE/4+HhMeT25L9DbXP69GlkZGSAx+PB1NQUwcHBAICEhAQ8+eSTQ/6O5m+w6IfBg5WxVucAwGuvvYYffvgBX331FUpLS3Hw4EEkJSUZ5sCNGA6HAw8PD7z00ks4ceIEmpqa8OqrryIzMxPTpk1DUlISPv30UxQVFeld9l1TU4MbN24gJiZm3N2MR4q5uTl8fHwQFxeH2bNnw8/PD11dXbh69SouX76M8vJyiEQiGrhEIhF1u9XHm2kiQ2ZU5IFSn0BVXFyMRYsW4fXXX8fbb79tsMBubm6O+Ph4pKen09fUajXS09Mxffr0AT8zffp0re0B4OTJk3T7gIAAeHh4aG3T2dmJq1ev0m02btyIvLw85ObmIjc3lz5c79y5kz78Tp8+HefPn4dCodD6nbCwMDg6Ohrg6O88DF4NaKzVOSUlJYiKikJhYSHCwsIMecgTFlI5d/DgQezbtw8nTpyAj48PdUEeypOLYRhUVFSgoaEBcXFxsLOzu8V7P/b0NZQ0NTWFnZ0dWltbERQUZLTpTkOjVCqRnZ0NMzMzvX3aysrKsGDBAjz33HP461//avAZ6M6dO/Hkk0/in//8J5KSkrBhwwbs2rULpaWlcHd3pwUda9euBdD7cDx79mx8/vnnWLhwIX799Vd89tlnyM7OpsaOX3zxBT7//HNs27YNAQEB+OCDD5Cfn4/i4uIB1Veqq6sREBCgNd6IRCKEhYVh7ty5eOedd1BYWIhnnnkG69evxwsvvGDQc3CnYNCZlTFX5/z+++8IDAzEoUOHEBAQAH9/fzz33HNoa2sb1TFPZEjlXFpaGvbv3w8+n49PPvkE1dXVmDdvHqZOnYr33nsP165d05pxKZVK5Obmorm5GYmJibdloAJA3Y6nTJmC2bNnw8fHBwKBAEDvdVlcXAyhUDiuTchjDQlUpqamiIqK0itQVVRUYNGiRXjiiSfwySefjEmqdPny5fjqq6+wZs0axMTEIDc3F8eOHaPFDLW1tWhqaqLbp6SkYMeOHdiyZQuio6OxZ88e7N+/X8uB+O2338arr76KF154AYmJiRCLxTh27JheMmH29vY4ceIEqqqqEB8fjzfffBNr1qxhA9UoMGjTyVDVOYP1FoxFdU51dXW/37l58yZqamqwe/dubN++HSqVCm+88QaWLVuG06dPj+h4bzdsbW2xYsUKrFixAhKJhHpyLVmyBPb29njggQewYMECfP3113BwcMCPP/542wnuDkZbWxuqqqqoUSQxlCwpKdEylNTUypvoECFeLpertxpHdXU1Fi1ahIcffhhffPHFmDZIv/LKK3jllVcGfO/s2bP9XnvkkUfwyCOPDPp9HA4Hn3zyCT755BOdft/f33/Atc2oqChcuHBBp+9gGZ6JIwM9BLpU56jVashkMmzfvh2hoaEAgB9//BHx8fEoKytjU4N9sLa2xsMPP4yHH34YUqkUJ0+exK+//ooHH3wQDMNg+fLluHr1KmbMmDGh1MRHQktLCwoKChAZGUkXxx0dHeHo6IjQ0FBqZlhRUYHCwkKtwGXMliBDQaxNTExM9FYgqaurw/3334/7778fGzZsuO2VPFhuDQa9ioy5OsfT0xOmpqY0UAFAREQEgN5UAcvgWFpaYsaMGaisrMRdd92FXbt2wdLSEmlpaQgODsbLL7+MU6dOGbTR1lhobm5GQUEBpk6dOuA1zOFwYG9vr6WVZ2tri5qaGpw7dw7Z2dmor6+fUOdGpVIhJycHAPQOVE1NTVi4cCHuvfdefP/992ygYjEYBr2SjLk6Z8aMGVAqlaisrKTfc+PGDQC4YxbKR8PatWvh4+ODo0eP4qGHHsIPP/yApqYm/PLLL7CwsMCLL76IwMBAvPjiizh69OioGm2NhcbGRhQXFyMqKkqnSkcOhwMbGxsEBgZi2rRpSElJgZOTExobG3H+/Hlcv34dtbW1Rn1uyIyKYRjExsbqFaiam5tx//33IyUlBVu2bLlt0qEsxoHBqwGNtTpHrVYjMTERNjY22LBhA9RqNV5++WXY2dnhxIkThjwFtyUymQxcLnfQlJ9KpcLFixeptUlnZycWLFiAJUuWTEhPrvr6ety4cQPR0dFwdnYe9ff19PRQWw1jNZRUqVRadi76pHcFAgHuv/9+TJ06FT///PNtnxpmufWMiZDtd999hy+//BLNzc2IiYnBxo0bkZycDACYM2cO/P39sXXrVrr97t27sXr1alRXVyMkJATr1q3D/fffT99nGAYffvghtmzZgo6ODsycORN///vftVJ6mgwUrIDeJ+VXX30VJ06cgLW1NS0WcHJyMvQpuKNRq9W4cuUKDVwtLS2YO3culi5divnz58PGxma8d3FIamtrUVlZiZiYmDHpiZHL5RAKheDz+WhrazMKQ0m1Wo28vDwoFArExcXpFWxaW1uxcOFCBAcHY+fOnRN2nY7FuGFV11nGFLVajezsbOzZswf79u1DfX097r33XixduhT3338/7OzsjEr9obq6GlVVVYiNjYWDg8OY/54xGEqSQCWXyxEXF6dXsOno6MCiRYvg7e2NvXv3agm3srAYEjZYsdwy1Go1CgoKqLVJeXk57rnnHixZsgSLFi2Co6PjuAaumzdvora2dtyanFUqlZZ9PIfDoYHL0dFxTIoV1Go18vPzIZPJ9A5UnZ2dWLJkCRwdHbF///47po2BZXxggxXLuMAwDEpKSmjgKioqop5cixYtuqWeXAzDoLKykqpx2Nra3pLfHQq1Wo329nbqy8UwDFxdXeHm5gYnJyeDFC+QhwepVKq3k7NYLMaDDz4ICwsLHDp0CJaWlqPeHxaWoWCDFcu4Q6Sb9u7di3379iEnJwcpKSlYsmQJFi9eDE9PzzELXAzD4MaNG+Dz+YiPjzeaYgdNxsJQUq1Wo7CwEBKJBPHx8Xql77q7u/Hwww8DAA4fPmz0a5AstwdssGIxKhiGQU1NDQ1cV69eRXJyMhYvXowlS5bA19fXYIGLOC4LhULEx8dPiIpFhmEgFovB5/PR0tICqVQKJycnuLu7w8XFRaegM5pAJZVKsXz5cnR3d+PYsWO3rdQWi/HBduzBOC1NPvroI2qjoPnPGJ/8DQnx5HrzzTdx8eJFVFdXY/ny5Thy5AimTJmCOXPmYP369bh58+aorE0YhkFxcTFaW1uRkJAwIQIV8D9DyeDgYKSkpAxqKCmTyQb8PMMwKCoqglgs1jtQyWQy/PGPf0RnZyeOHDnCBiqWW8odP7PauXMn0tLSsHnzZiQnJ2PDhg3YvXs3ysrKBmwEvXz5MmbNmoW1a9di0aJF2LFjB7744ot+fWFr167V6gsrKCgYsC/stddeQ3l5OY4ePapVai8WiyEWi7W2vffee5GYmKhV9n+nwDAM+Hw+fvvtN+zduxfnzp1DZGQkli5diiVLluhlfqhWq1FUVITOzk7Ex8ffNoUBUqkUAoEAfD4fIpEIdnZ2tEDDysqKBipy3DweT+fvlsvleOKJJ1BfX4/09HS23YPllnPHBytjtTTpS15eHmJiYnD+/HncddddBjwDEw+GYdDa2ooDBw5g7969SE9PR0hICJYsWYIHH3wQERERgwYuUlRAUmD6DNgTCZlMRpuQ29raYG1tDQ6HA4VCgcTERL0CtEKhwDPPPIMbN27gzJkzcHFxGcM9Z2EZmDs6DWjMliZ9+eGHHxAaGnrHByqgNxXm4uKCZ599FocPH0ZzczPefvttFBYW4q677kJcXBw++ugj5Obmatl3dHd3IysrC1KpFAkJCbdtoAIAHo9HDSVnzZoFMzMzdHd302u+r6HkYCiVSrz00ksoKSnBqVOnRhyoxiPVvnjxYkyaNAkWFhbw9PTEE088gcbGRq1tdu3ahZiYGFhZWcHPzw9ffvnliI6PZey5o4PVUJYmxH6kL2NhaTIcPT09+O9//4tnn31WtwO7g+BwOHB0dERaWhoOHDgAPp+Pjz76CDdv3sTcuXMRFRWF999/H+fOncPChQvx73//W++1mokMKcvv6elBSkoK5syZg+DgYPT09CA7OxsXL15EWVkZ2tvb+wUulUqFV155BVlZWTh16lS/a1pXxss9/O6778auXbtQVlaGvXv3orKyEsuWLaPvHz16FI8//jheeuklFBYW4u9//zvWr19PsywsRgZzB9PQ0MAAYC5fvqz1+ltvvcUkJSUN+BkzMzNmx44dWq99//33jJubG8MwDHPp0iUGANPY2Ki1zSOPPMI8+uijDMMwzLfffsvMmDGDUSqVDMMwTFVVFQOAycnJGfA3d+zYwZiamjLNzc16H+OdjFgsZnbv3s08/PDDDJfLZczMzJhnn32WOXnyJNPZ2clIJJLb+p9YLGaysrKY48ePM62trf3e7+rqYmpqapjr168zhw8fpv+2bdvGCAQC5plnnmECAwOZmpqaUf0dkpKSmJdffpn+f5VKxXh5eTFr164dcPtHH32UWbhwodZrycnJzIsvvsgwDMOo1WrGw8OD+fLLL+n7HR0dDI/HY3755ZdB9+PAgQMMh8Nh5HI5wzAM89hjjzHLli3T2mbjxo2Mj48Po1ar9TtIljHnjp5ZGbOliSY//PADFi1aNOIn2zsVa2tr3HfffWhqasJdd92Fn376CXK5HI8++ijCwsLwxhtv4Pz581AqleO9qwaH+f/9YwKBYNAiEhMTE7i4uGDy5MmYPXs2oqOjUVtbizfffBOenp7YuXMnVq1aNSoh3/FMtWvS1taG//73v0hJSaHNzzKZrN95sbS0RH19PWpqavQ/WJYx5Y4OVsZsaUKoqqrCmTNn2BTgCPn4449hZ2eHI0eOYPny5di+fTuam5vxww8/QKlU4oknnkBwcDBeeeUVpKenTyjfqcEggaqlpQUJCQk6qUuQdOof//hHrFixAg4ODvjjH/+I9evXw9XVFQ899NCg5fBDMV6pdsI777wDa2trODs7o7a2FgcOHKDvzZs3D/v27UN6ejrUajVu3LiBr7/+GkCvLxeLcXHH6/ivXLkSTz75JBISEqiliUQiwdNPPw0A/SxNXnvtNcyePRtff/01tTTJzMzEli1bAPTe9K+//jo+/fRThISE0NJ1Ly8vLF26FAAwadIkrX0gCgBBQUHw8fHReu/f//43PD09sWDBgrE8Dbctn376KbhcrlYxhbm5OXWyVSgUOHfuHPbs2YPnn38ecrkcixYtwpIlS3DPPfdMuCIMhmFQXl4OPp+vc6DS/OzHH3+MPXv24OLFiwgLC6P9aOfPn59w5wIA3nrrLTz77LOoqanBxx9/jLS0NBw6dAgcDgfPP/88KisrsWjRIigUCtjZ2eG1117DRx99xJpGGiF3fLBavnw5BAIB1qxZQy1Njh07Rp/aamtrtS7clJQU7NixA6tXr8b777+PkJAQ7N+/n/ZYAcDbb78NiUSCF154gVqaHDt2TO9+HrVaja1bt+Kpp55ijexGyHCVlmZmZkhNTUVqaiq+//57XLx4EXv27MHrr7+Orq4u6smVmppq9I3DzP+XrWpubtZbkYNhGNobeObMGYSFhQHoffiKjIxEZGTkiPZprFPtnp6eWtv0bf1wcXGBi4sLQkNDERERAV9fX1y5cgXTp08Hh8PBF198gc8++wzNzc1wdXWlGZHAwMARHS/L2HHH91mxsAwE8eQiQrtCoZB6cs2bN88o9fAqKirQ0NCAhIQEvZROGIbB119/jY0bNyI9PR3R0dEG3a/k5GQkJSVh06ZNAHrP7aRJk/DKK68M2svY3d2N33//nb6WkpKCqKgorV7GVatW4c033wTQm2p3c3Pr18uoSW1tLfz8/HDmzBnMmTNnwG3S0tJQUVGBy5cvj/KoWQzOOBV2sLBMGFQqFXPt2jXm7bffZkJCQhhLS0vmgQceYH788UemsbFx3Kv+JBIJk5+fzxw5coTh8/l6Vwx+9tlnjKOjI3P9+vUxOX+//vorw+PxmK1btzLFxcXMCy+8wDg4ONDq1ieeeIJ599136faXLl1iTE1Nma+++oopKSlhPvzwQ8bMzIwpKCig23z++eeMg4MDc+DAASY/P59ZsmQJExAQwEilUoZhGObKlSvMpk2bmJycHKa6uppJT09nUlJSmKCgIKanp4dhGIYRCATMP/7xD6akpITJyclh/vznPzMWFhbM1atXx+Q8sIwONlixsOiBSqVicnJymL/85S/M5MmTGXNzc2bBggXM5s2bmbq6OkYsFt/yQFVQUMAcPnx4RIHqq6++Yuzt7ZmMjIwxPW+bNm1iJk2axJibmzNJSUnMlStX6HuzZ89mnnzySa3td+3axYSGhjLm5uZMZGQkc/jwYa331Wo188EHHzDu7u4Mj8dj7r33XqasrIy+n5+fz9x9992Mk5MTw+PxGH9/f+all15i6uvr6TYCgYCZNm0aY21tzVhZWTH33nuv1n6xGBdsGpCFZYQwGp5c+/btQ3FxMWbPnk09uVxcXMbck6uqqgo1NTWIj4/Xy4eLYRj8+9//xl/+8hccOXIEM2fOHMO9ZGEZPWywYmExAMz/L24ggSs3NxczZsygnlweHh4GD1zV1dWorq4eUaD66aef8NZbb+H3338fdP2GhcWYYOszJyDGaGkCAMePH8e0adNga2sLV1dXPPzww6iurh7NoU4YOBwOQkJC8N577+HatWu4ceMGFi1ahN27dyMsLAzz5s3Dd999h7q6ulFZmxBqampQVVWlt7MxwzC02Xfv3r1soGKZMLDBaoIxXjprhLfffhteXl79Xq+qqqK9Sbm5uTh+/DiEQiEeeughwx38BIHD4SAgIACrVq3CpUuXUFVVhUceeQSHDx9GZGQk7r77bmzYsAFVVVUjCly1tbW4efMm4uPj9faU2rdvH/785z9j165dmDt3rt6/zcIybozPUhnLSBlPnbUjR44w4eHhTFFRUT8tw927dzOmpqaMSqWirx08eFBLi+1OR61WM42Njcz333/P3HPPPYypqSkTExPDfPTRR0xOTo5OxRmlpaXMoUOHRlSF+MsvvzBWVlbM/v37x/tUsLDoDTuzmkAYs6VJfHw8TExM8J///AcqlQoikQg//fQTUlNTqRbbnQ6Hw4Gnpyf+9Kc/4dSpU2hsbMTLL7+MK1euIDk5GdOmTcNnn32G4uLiAWdc9fX1qKioQGxsLOzt7fX67SNHjuDZZ5/Ftm3bsGTJEkMdEgvLLYMNVhMIY7Y0CQgIwIkTJ/D++++Dx+PBwcEB9fX12LVrl/4HegfA4XDg6uqK5557DkeOHEFzczNWrVqF/Px83HXXXYiPj8fHH3+MvLw8qNVqbNiwAZs3b0ZsbCwcHBz0+q1Tp07hqaeewg8//KBlkcHCMpFggxXLsGzatAldXV147733Bt2mubkZzz//PJ588klcv34d586dg7m5OZYtW2aQgoLbGSIi++STT+LgwYPg8/lYs2YNKioqkJqaioCAAHz44Ydwc3PTe43q3LlzePzxx/H9998PquzAwjIRYIPVBMKYLU2+//572NvbY926dYiNjcWsWbPw888/Iz09HVevXh3lkd9Z2NnZ4Q9/+AP27t2L9evXQywWIyUlBZ999hkiIyPxzjvvICMjAyqVasjvuXTpEpYvX45vvvkGaWlpY97zxcIylrDBagJhzJYm3d3d/ZSqifiuprU8i+6Qys9Dhw7h3LlzaG5uxrfffouOjg4sW7YM4eHhWLly5YCeXFevXsWyZcvw2Wef4bnnnmMDFcvEZ5wLPFj0ZDx01voykLNxeno6w+FwmI8//pi5ceMGk5WVxcybN4/x8/Njuru7x+Zk3OYUFBQwJ0+eHPA9qVTKHDp0iHnmmWcYZ2dnxtXVlXnmmWeYgwcPMmfPnmUcHByYb775hnW8ZbltYIPVBORW66z1ZaBgxTAM88svvzCxsbGMtbU14+rqyixevJgpKSkZ9fGyDI1cLmdOnDjBvPjii4yrqyvD4XCY1atXs4GK5baClVtiYbmNUCqV2L59O55++mk29cdyW8EGKxYWFhYWo4ctsGBhYQEwPpqTixcvxqRJk2BhYQFPT0888cQTaGxs1NrmTtacZPkfbLBiYWEZN83Ju+++G7t27UJZWRn27t2LyspKrcZlVnOShTKuK2YsLCxGwXhqTmpy4MABLT1JVnOShcDOrFhuGcZqbbJr1y7ExMTAysoKfn5++PLLL0d1nBON8dSc1KStrQ3//e9/kZKSQvUkWc1JFgIbrFhuCcZqbXL06FE8/vjjeOmll1BYWIi///3vWL9+Pb777jvDHbyRM16ak4R33nkH1tbWcHZ2Rm1tLQ4cOEDfYzUnWSjjPbVjuTMwVmuTxx57jFm2bJnW9hs3bmR8fHzumD6lhoYGBgBz+fJlrdffeustJikpacDPmJmZMTt27NB67fvvv2fc3NwYhultRgfANDY2am3zyCOPMI8++qjWawKBgCkrK2NOnDjBzJgxg7n//vvpuW9qamJCQkKYt956i8nOzmbOnTvHzJ49m7n33nvvmL8PSy/szIplzDFmaxOZTAYLCwut1ywtLVFfX4+amhr9D3YCMl6ak5q/Hxoaivvuuw+//vorjhw5gitXrgBgNSdZ/gcbrFjGHGO2Npk3bx727duH9PR0qNVq3LhxA19//TUAoKmpSc8jnZiMl+bkQBAdSZlMBoDVnGT5H2ywYrlt0cXa5Pnnn8crr7yCRYsWwdzcHNOmTaNWGn0HyduZlStX4l//+he2bduGkpIS/N///R8kEgmefvppAEBaWprWeXzttddw7NgxfP311ygtLcVHH32EzMxMvPLKKwB6bU9ef/11fPrppzh48CAKCgqQlpYGLy8vLF26FECv2O53332H3Nxc1NTU4PTp03jssccQFBREA9rChQtx/fp1fPLJJygvL0d2djaefvpp+Pn5ITY29taeJJbxZbzzkCy3PzKZjOFyucxvv/2m9XpaWhqzePHiAT/j6+vLrF+/Xuu1NWvWMFFRUQzDMExlZeWA+oSzZs1i/vznPzMMwzBLlixhTExMGC6XS/8BYLhcLpOWlqb1OaVSydTX1zMymYw5cuQIA4BpaWkZ+UFPQG615mR+fj5z9913M05OTgyPx2P8/f2Zl156iamvr9f6HlZzkoVhWG1AlltEcnIykpKSsGnTJgC9KZxJkybhlVdewbvvvttv++XLl6O7uxu///47fS0lJQVRUVHYvHkzGIaBl5cXVq1ahTfffBNAb5rJzc0NW7duxYoVK1BbW4vOzk76+cbGRsybNw979uxBcnIyfHx8BtzXtLQ0VFRU4PLly4Y8BSwsLKNhnIMlyx2CsVqbCAQC5h//+AdTUlLC5OTkMH/+858ZCwsL5urVq2NzIlhYWEaE6XgHS5Y7g+XLl0MgEGDNmjVobm5GTEwMjh07RgskamtrtdaIUlJSsGPHDqxevRrvv/8+QkJCsH//fkyZMoVu8/bbb0MikeCFF15AR0cHZs6ciWPHjvWr7huObdu2YdWqVWAYBtOnT8fZs2eRlJRkmANnYWExCGwakIWFhYXF6Llzyp1YWFhYWCYsbLBiYWFhYTF62GDFwsLCwmL0sMGKhYWFhcXoYYMVCwsLC4vRwwYrFhYdGQ8/Ln9/f3A4HK1/n3/+udY2+fn5uOuuu2BhYQFfX1+sW7fOMAfMwmJMjG+bFwvLxODXX39lzM3NmX//+99MUVER8/zzzzMODg4Mn88fcPtLly4xXC6XWbduHVNcXMysXr16wKZme3t7Zv/+/UxeXh6zePHifk3Nfn5+zCeffMI0NTXRf2KxmL4vEokYd3d35vHHH2cKCwuZX375hbG0tGT++c9/jt3JYGEZB9hgxcKiA+Plx+Xn59dPI1GTv//974yjoyMjk8noa++88w4TFham1/GxsBg7bBqQhWUYxtv2/fPPP4ezszNiY2Px5ZdfQqlUav3OrFmzYG5urvU7ZWVlaG9vH/lBs7AYGazcEgvLMAzlx1VaWjrgZwxl+/7nP/8ZcXFxcHJywuXLl/Hee++hqakJ33zzDf2egICAft9B3nN0dNT3cFlYjBI2WLGwGDErV66k/zsqKgrm5uZ48cUXsXbtWvB4vHHcMxaWWwubBmRhGYbxtn3XJDk5GUqlEtXV1UP+juZvsLDcDrDBioVlGIzJ9j03NxcmJiZwc3Ojv3P+/HkoFAqt3wkLC2NTgCy3F+Nd4cHCMhEYDz+uy5cvM+vXr2dyc3OZyspK5ueff2ZcXV21XI47OjoYd3d35oknnmAKCwuZX3/9lbGysmJL11luO9hgxcKiI7fa9j0rK4tJTk5m7O3tGQsLCyYiIoL57LPPmJ6eHq3vycvLY2bOnMnweDzG29ub+fzzzw1/8Cws4wzrZ8XCwsLCYvSwa1YsLCwsLEYPG6xYWFhYWIweNlixsLCwsBg9bLBiYWFhYTF62GDFwsLCwmL0sMGKhYWFhcXoYYMVCwsLC4vRwwYrFhYWFhajhw1WLCwsLCxGDxusWFhYWFiMHjZYsbCwsLAYPf8PEK5ONzXfAB8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from snudda.plotting.plot_degeneration_and_growth import PlotDegenerationAndGrowth\n", "pdg = PlotDegenerationAndGrowth(original_network_path=network_path_pd0, \n", @@ -403,10 +13136,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "026277af", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added: 267, removed: 982, kept: 428\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from snudda.plotting.plot_degeneration_and_growth import PlotDegenerationAndGrowth\n", "pdg = PlotDegenerationAndGrowth(original_network_path=network_path_pd0, \n", @@ -417,10 +13178,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "322eb303", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Assuming volume type: cube [cube or full]\n", + "Only using 20000 neurons of the connection data\n", + "Number of neurons: 9981\n", + "Synapse row 0 - 0.0 % time: 0.09590765554457903 seconds\n", + "Synapse row 1000000 - 18.03127018938784 % time: 1.0616571977734566 seconds\n", + "Synapse row 1909184 - 34.42501254525624 % time: 1.92861302010715 seconds\n", + "Synapse row 2818368 - 50.81875490112463 % time: 2.797268974594772 seconds\n", + "Synapse row 3727552 - 67.21249725699302 % time: 3.64993095677346 seconds\n", + "Synapse row 4636736 - 83.60623961286142 % time: 4.508365733548999 seconds\n", + "Created connection matrix 5.277761960402131 seconds\n", + "GJ row : 0 - 0.0 % time : 0.00397101417183876 seconds\n", + "Created gap junction connection matrix 0.007229821756482124 seconds\n", + "Creating population dictionary\n", + "Done.\n", + "Taking corner neurons: Keeping 9981/9981\n", + "Calculating synapse distance histogram\n", + "Creating dist histogram\n", + "n_synapses = 5545921, at 0\n", + "n_synapses = 5545921, at 1000000\n", + "n_synapses = 5545921, at 2000000\n", + "n_synapses = 5545921, at 3000000\n", + "n_synapses = 5545921, at 4000000\n", + "n_synapses = 5545921, at 5000000\n", + "Created distance histogram (optimised) in 5.037051769904792 seconds\n", + "Saving cache to networks/PD-example-10k/PD0/network-synapses.hdf5-cache\n", + "Assuming volume type: cube [cube or full]\n", + "Only using 20000 neurons of the connection data\n", + "Number of neurons: 9981\n", + "Synapse row 0 - 0.0 % time: 0.15086539834737778 seconds\n", + "Synapse row 1000000 - 55.53907896213013 % time: 1.1732734804973006 seconds\n", + "Created connection matrix 1.8826652746647596 seconds\n", + "GJ row : 0 - 0.0 % time : 7.021873747929931 seconds\n", + "Created gap junction connection matrix 7.025150118395686 seconds\n", + "Creating population dictionary\n", + "Done.\n", + "Taking corner neurons: Keeping 9981/9981\n", + "Calculating synapse distance histogram\n", + "Creating dist histogram\n", + "n_synapses = 1800534, at 0\n", + "n_synapses = 1800534, at 1000000\n", + "Created distance histogram (optimised) in 1.1954982774332166 seconds\n", + "Saving cache to networks/PD-example-10k/PD2/network-synapses.hdf5-cache\n", + "Assuming volume type: cube [cube or full]\n", + "Only using 20000 neurons of the connection data\n", + "Number of neurons: 9981\n", + "Synapse row 0 - 0.0 % time: 0.15342048462480307 seconds\n", + "Synapse row 1000000 - 48.593840050459846 % time: 1.1488829534500837 seconds\n", + "Synapse row 1528937 - 74.29692002522992 % time: 1.69627130869776 seconds\n", + "Created connection matrix 2.150094443000853 seconds\n", + "GJ row : 0 - 0.0 % time : 0.00559756625443697 seconds\n", + "Created gap junction connection matrix 0.008765995502471924 seconds\n", + "Creating population dictionary\n", + "Done.\n", + "Taking corner neurons: Keeping 9981/9981\n", + "Calculating synapse distance histogram\n", + "Creating dist histogram\n", + "n_synapses = 2057874, at 0\n", + "n_synapses = 2057874, at 1000000\n", + "n_synapses = 2057874, at 2000000\n", + "Created distance histogram (optimised) in 2.205943351611495 seconds\n", + "Saving cache to networks/PD-example-10k/PD2-ref/network-synapses.hdf5-cache\n" + ] + } + ], "source": [ "from snudda.analyse.analyse import SnuddaAnalyse\n", "\n", @@ -439,10 +13268,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "7c4aae21-f5c3-448a-ab70-2093af5ae325", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability dSPN to iSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [9999993.]\n", + "P(d<5e-05) = 0.05690174608830866\n", + "P(d<0.0001) = 0.04190766202779609\n", + "Plotting connection probability dSPN to iSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [9999973.]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-dSPN-to-iSPN-synapses-3D-dist.png\n" + ] + }, + { + "data": { + "text/plain": [ + "({},\n", + " 'networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-dSPN-to-iSPN-synapses-3D-dist.png')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"dSPN\", \"iSPN\", dist_3d=True, exp_max_dist=[50e-6, 100e-6], exp_data_detailed=[(3, 47), (3, 66)], return_ax=True, show_plot=False, save_figure=False)\n", "sa_pd2.plot_connection_probability(\"dSPN\", \"iSPN\", dist_3d=True, ax=ax, colour=\"blue\")" @@ -450,10 +13346,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "67b0d479-7013-467d-8ce8-455580c04e61", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 111037 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-dSPN-to-iSPN-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 38338 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-dSPN-to-iSPN-per-cell.png\n" + ] + } + ], "source": [ "sa_pd0.plot_num_synapses_per_pair(\"dSPN\", \"iSPN\")\n", "sa_pd2.plot_num_synapses_per_pair(\"dSPN\", \"iSPN\")" @@ -461,10 +13410,113 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "3b532731-e3dd-4ceb-a2be-9c3a45dc666e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability dSPN to dSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [9997913.]\n", + "P(d<5e-05) = 0.2403874233427431\n", + "P(d<0.0001) = 0.16350460079408388\n", + "Plotting connection probability dSPN to dSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [9997995.]\n", + "P(d<5e-05) = 0.07303581626825488\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-dSPN-to-dSPN-synapses-3D-dist.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 364064 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-dSPN-to-dSPN-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 91309 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-dSPN-to-dSPN-per-cell.png\n" + ] + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"dSPN\", \"dSPN\", dist_3d=True, exp_max_dist=[50e-6, 100e-6], exp_data_detailed=[(5, 19), (3, 43)], return_ax=True, show_plot=False, save_figure=False)\n", "sa_pd2.plot_connection_probability(\"dSPN\", \"dSPN\", dist_3d=True, ax=ax, colour=\"blue\", exp_colour=\"blue\", exp_data_detailed=[(0, 7)], exp_max_dist=[50e-6])\n", @@ -475,10 +13527,113 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "8ddd9987-00ca-4dda-ba06-ed2f0ce87e16", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability iSPN to iSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [9997947.]\n", + "P(d<5e-05) = 0.3467361431045165\n", + "P(d<0.0001) = 0.26447794456800217\n", + "Plotting connection probability iSPN to iSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [9997954.]\n", + "P(d<5e-05) = 0.1572475310608474\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-iSPN-to-iSPN-synapses-3D-dist.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 764560 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-iSPN-to-iSPN-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 285880 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-iSPN-to-iSPN-per-cell.png\n" + ] + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"iSPN\", \"iSPN\", dist_3d=True, exp_max_dist=[50e-6, 100e-6], exp_data_detailed=[(14, 39), (7, 31)], return_ax=True, show_plot=False, save_figure=False)\n", "# PD connectivity from Taverna et al 2008\n", @@ -490,10 +13645,113 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "ee31e26b-4673-4770-b43e-748e1cc5f204", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability iSPN to dSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [9999990.]\n", + "P(d<5e-05) = 0.2550776873995422\n", + "P(d<0.0001) = 0.17106050832749395\n", + "Plotting connection probability iSPN to dSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [9999988.]\n", + "P(d<5e-05) = 0.08756865350031592\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-iSPN-to-dSPN-synapses-3D-dist.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 375033 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-iSPN-to-dSPN-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 101795 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-iSPN-to-dSPN-per-cell.png\n" + ] + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"iSPN\", \"dSPN\", dist_3d=True, exp_max_dist=[50e-6, 100e-6], exp_data_detailed=[(13, 47), (10, 80)], return_ax=True, show_plot=False, save_figure=False)\n", "sa_pd2.plot_connection_probability(\"iSPN\", \"dSPN\", dist_3d=True, ax=ax, colour=\"blue\", exp_colour=\"blue\", exp_max_dist=[50e-6], exp_data_detailed=[(3, 12)])\n", @@ -504,10 +13762,111 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "dee2ef42-6a0d-4749-91c0-42d342026f1d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability FS to FS (synapses)\n", + "Centering in None : Keeping 132/132\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [17292.]\n", + "P(d<0.00025) = 0.12740637031851593\n", + "Plotting connection probability FS to FS (synapses)\n", + "Centering in None : Keeping 132/132\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [17292.]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-FS-to-FS-synapses-3D-dist.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 132/132\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 811 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-FS-to-FS-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 132/132\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 963 connections\n" + ] + }, + { + "data": { + "image/png": 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4ceNQrVq1og7FZDCRMk5MpIiISFmxLMhZq1Yt9OzZE97e3gD+l0SJooj4+HjMnz8fderUwcSJE5GWllYcIRERERHJVqSJ1LNnz/DTTz/Bw8MDnTp1wpYtWwDkJFA1a9ZEQEAAKleuDFEUkZmZiWXLlqF169Z49uxZUYZFREREpBdFkkiFhYVh2LBhcHV1xeTJk3Hjxg2IoghBEPD2229j3759uHXrFlasWIH79+9j/fr1aNiwIURRxI0bN/D9998XRVhEREREeqW3OVKJiYlYs2YNVq5ciZs3bwKANMHc2dkZH3zwAUaPHo3q1aurrZ+ZmYnOnTvj5MmTqFu3Lm7cuKGPsEwW50gZJ86RIiIiZbJvlzt27BhWrlyJ7du3IyMjA8D/Eihvb2+MGzcO/fv3L/DOvDJlymDMmDE4efIkYmJi5IZFREREVORkJVINGjTArVu3APwvebK3t8f777+PcePGFXrfPScnJwDAq1ev5IRFREREVCxkJVK5l/AAwNPTEx999BGGDh0KGxsbndqrWLEivL29pbv6iIiIiIyZrESqTJkyGDRoEMaNG4e2bdvKDqZVq1YIDQ2V3Q4RERFRcZCVSD18+BAVK1bUVyxEREREJkVWIpWWloa0tDQ4OTnB2tpa63oZGRnSCuf53cVHREREZOxkrSPl7u6OWrVqITg4uFD1QkNDpbpEREREpkr2gpxylqEqhm3+iIiIiIpMsey1R0RERFQSGSSRSk1NBQCdl0kgIiIiMgYGSaQOHz4MAKhSpYohuiciIiLSC63v2jt27BiOHTum9rkNGzYgMjJSY31RFPH8+XOcO3cOISEhEAQB7du3L1SwRERERMZE60QqNDQU3377bZ7HRVHExo0bC9WpKIqwtLTEhAkTClWPiIiIyJgU6tKeKIoqP/k9XtCPl5cXdu/eDS8vL72/ICIiIqLiovWI1IgRI+Dr6yudi6KIzp07QxAEzJkzBx06dNBY38zMDLa2tqhZsybKly+va7xERERERkPrRKpGjRqoUaOG2ucaN24MHx8fvQVFREREZApkbRETEhICICeRIiIiIiptZCVSHIUiIiKi0owrmxMRERHpiIkUERERkY60urRnbm4OABAEAVlZWXke19Xr7RERERGZEq0SKeU1o7R5nIiIiKg00CqR8vb2hiAIWj9OREREVBpolUiFhoYW6nEiIiKi0oCTzYmIiIh0xESKiIiISEdMpIiIiIh0VOSJ1OPHjzFx4kR4eXmhadOmGD58OC5dulTU3RIREREVOVmJ1PHjx2Fvbw8HBwecOHEiz/OPHz9Gq1atsGzZMly4cAFXrlzBunXr0KpVKwQHB8vpmoiIiMjgZCVSO3bsQFpaGipUqIA33ngjz/Off/45Hjx4AFEUVX4yMzMxbNgwJCcny+meiIiIyKBkJVJnzpyBIAjo1q1bnueePn2KTZs2QRAENG3aFOfPn0dSUhLmzp0rPb969Wo53RMREREZlKxE6tGjRwAAT0/PPM/t3btX2v7lzz//hKenJ+zt7TFt2jR06NABALBv3z453RMREREZlKxE6unTpwAAJyenPM+FhYUBAGrVqoWWLVuqPNenTx+IoogrV67I6Z6IiIjIoGQlUqmpqQCA7OzsPM+dPHkSgiCgU6dOeZ5zdXUFACQmJsrpnoiIiMigZCVStra2AID4+HiVx+Pj43H9+nUAQPv27fPUMzc3B8BNj4mIiMi0yUqk6tSpAwA4dOiQyuM7duyQjnPnQylLSEgAAFSoUEFO90REREQGJSuR6tSpE0RRxMGDB6WJ4/fu3cO8efMAALVr10bdunXz1Lt48SKAnPlT+rRr1y74+fnB3d0d1tbWcHJyQvv27bFw4UKkpKTorZ/Tp0/j119/xYgRI9CqVSu4u7vD1tYWVlZWcHZ2hq+vL2bPno2YmBi99UlERETGRxBlXF+7c+cOGjVqhMzMTABA5cqV8fTpU2nO1JIlSzBhwoQ89WrWrIl79+5h3LhxWLZsma7dS9LS0jB06FDs2rUr3zJubm7YtGkT2rZtK7s/W1tbPH/+vMByVlZWmDlzJqZNm1boPlJSUuDg4IDk5GTY29vrEiYVASv3gYYOQaOMu5sMHQIRUaliIadyrVq18Ouvv2LMmDFQKBQqc6W6du2Kjz/+OE+dU6dOISYmBoIgoGPHjnK6BwAoFAr4+fnhwIEDAABnZ2cEBATAw8MDiYmJCAoKQnh4OGJjY9GrVy+Eh4ejYcOGsvt1cnJC69at4enpiZo1a8LBwQGvXr3C3bt3sXfvXoSHhyMjIwNfffUVXr16hRkzZsjuk4iIiIyLrBGpXJGRkfjrr78QFRWFcuXKoXv37hg1ahQsLPLmaZMnT8a2bdsgCALOnTuH8uXLy+p7xYoVGDt2LADAw8MDR48ehbOzs0qZzz//HIsWLQIAdOzYUVqaQVeXL19Go0aNIAhCvmXWrl2LESNGQBRFWFhYICYmBlWrVtW6D45IGSeOSBERkTK9JFKGolAo4ObmJi0MevbsWXh5eakt17JlS0RGRgIADh48iO7duxd5fH369MHu3bsBAH/99RdGjRqldV0mUsaJiRQRESmTNdnc0MLCwqQkysfHR20SBeQst6A8VysoKKhY4mvUqJF0/Pjx42Lpk4iIiIqPSSdS+/fvl4579eqlseybb76ptl5RioqKko5dXFyKpU8iIiIqPiadSF26dEk6btWqlcayLi4ucHNzAwDExcVJa1kVld27d2P79u0AAGtra/Tu3btI+yMiIqLiJ+uuvVwKhQK7d+/G/v37cfnyZTx79gzp6ekF1hMEAbdv39a53xs3bkjHNWvWLLB8zZo1ERsbK9WtXLmyzn3nCgsLk7a6yczMRGxsLIKDgxEcHAwAsLCwwPLly/NMgCciIiLTJzuRunr1KgYNGoSrV6+qPK7NHHZNd71pIykpSTp2dHQssHylSpXU1pXjiy++wL///pvncUEQ4OPjg9mzZ8Pb27vAdjIyMpCRkSGd63MBUSIiIioashKphIQEdOnSBfHx8VLiZGFhAUdHR1hZWeklQE3S0tKkY2tr6wLLly1bVjrO3XC5qLi6uqJbt25qV3ZXZ968eZg9e3aRxkRERET6JWuO1MKFCxEXFwcAaNasGfbv34+0tDQ8fPgQ0dHRWv2YuoiICIiiCFEUkZaWhsjISHz77bdITU3F119/jSZNmuDw4cMFtjNt2jQkJydLP7mXIImIiMh4yRqR2rt3L4CczYtPnDgBGxsbvQSlLVtbWzx79gwAkJ6eDltbW43lX758KR3b2dnpPZ5y5crB09MTnp6eGDZsGN544w08fPgQvXv3xpkzZ9CkSZN861pZWRXLKB4RERHpj6wRqdytXkaPHl3sSRQAlVXRnzx5UmD5p0+fqq1bFGrWrIn58+cDyJmEPnfu3CLtj4iIiIqfrETK0tISAODu7q6PWAqtfv360rE2lwmVyyjXLSrKa1eFhoYWeX9ERERUvGQlUrVq1QIA6fb/4qZ8qez06dMay8bFxUnzjpycnPSy9EFBlC8f5l6CJCIiopJDViLVv39/iKKo1WTqotCzZ0/puKDVyvft2ycdF7QKur7cunVLOi6OxI2IiIiKl6xE6uOPP4abmxu2bduG8PBwfcWkNR8fH2nrldDQUJw7d05tOYVCgaVLl0rngwcPLpb4li9fLh136NChWPokIiKi4iMrkXJwcMCOHTvg6OiI3r17Y+3atcjOztZXbAUyNzfHjBkzpHN/f3/Ex8fnKTd16lRERkYCyEloevTooba9wMBACIIAQRDg6+urtszy5csREhKiccFRhUKB+fPn47fffpMe++ijj7R4RURERGRKZC1/MGrUKABAo0aNcPToUYwcORJTpkxBq1at4OjoCDMzzXmaIAj466+/5ISAgIAAbN++HYcOHcKVK1fg6emJgIAAeHh4IDExEUFBQThx4gSAnDv1VqxYIau/iIgIjBs3Dm5ubujWrRuaNGkCJycnlClTBklJSbh8+TJ27tyJu3fvSnWmTZsGHx8fWf0SERGR8ZGVSOWO4AD/2+7lyZMnBc5XUiY3kbKwsMDWrVsxZMgQ7NmzB48fP8acOXPylKtWrRo2btyIRo0ayeovV2xsLFatWqWxjIODA+bNm4dx48bppU8iIiIyLrL32tNmT738yN1rL5ednR12796NnTt3Yu3atTh9+jTi4+NhZ2eH2rVro1+/fhgzZgwcHBxk97V06VK88847CAsLw/nz53H79m08efIEr169gq2tLZydndG0aVP06NEDfn5+eumTiIiIjJMgysiEYmJiZAdQo0YN2W2URCkpKXBwcEBycjLs7e0NHQ79Pyv3gYYOQaOMu5sMHQIRUakia0SKSRARERGVZrLu2iMiIiIqzZhIEREREelI9mRzZa9evcK///6Lq1evIjExEZmZmSrrPBERERGVJHpJpDIzM/Hdd99h2bJlSE5OVnnu9URqypQp2LlzJ9zc3HDkyBF9dE9ERERkELIv7T19+hRt27bF3LlzkZSUBFEUpR91+vbti6ioKISGhuLMmTNyuyciIiIyGNmJVP/+/REZGQlRFNGhQwesWLFC4+W8Dh06oFq1agAK3miYiIiIyJjJSqS2bduGsLAwCIKAzz//HMePH0dAQACaN2+usV7Xrl0hiiJOnjwpp3siIiIig5KVSP3zzz8AgKZNm+KHH37Qul7Tpk0BADdu3JDTPREREZFByUqk/vvvPwiCgPfee69Q9ZydnQEACQkJcronIiIiMihZiVRuIlSrVq1C1bO0tASQc7cfERERkamSlUhZW1sDKHxClJuAVahQQU73RERERAYlK5GqUqUKAODatWuFqhcREQEAqFmzppzuiYiIiAxKViLVsWNHiKKIzZs357tu1OuePHmCrVu3QhAE+Pj4yOmeiIiIyKBkJVLDhg0DANy6dQtz584tsHxmZiaGDRuGFy9eQBAEjBgxQk73RERERAYle0Sqd+/eEEURM2fOxJgxYxAVFZWn3IsXL7B9+3a0adMGhw4dgiAIGDZsGBo0aCCneyIiIiKDEkRtr8nlIzk5Ge3bt8e1a9cgCAKAnEnoL1++hCAIqFixIpKSkpCdnQ0AEEURzZo1w4kTJ2BjYyP/FZRQKSkpcHBwQHJyMuzt7Q0dDv0/K/eBhg5Bo4y7mwwdAhFRqSJ7ixgHBwdERERg0KBB0h57uUkUkLMXn0KhkJ7z8/NDWFgYkygiIiIyebJHpJRdunQJa9asQVhYGO7evYukpCTY2tqiWrVq8PHxgb+/P1q1aqWv7ko0jkgZJ45IERGRMr0mUqQ/TKSMExMpIiJSJvvSHhEREVFpxUSKiIiISEdMpIiIiIh0ZKFNoVGjRhVJ54Ig4K+//iqStomIiIiKmlaTzc3MzKTlDPRNoVAUSbumjpPNjRMnmxMRkTKtRqQAaLWXniAIGsu9/nxRJWdERERExUGrRCo6Ojrf5169eoWpU6di27ZtsLW1xbBhw9ClSxfUqVMH5cqVw/PnzxEVFYUjR45g/fr1SE1NRb9+/TB//nxYWGidxxEREREZHdnrSA0aNAhbtmxBx44dsWHDBri4uORbNi4uDoMGDcLx48cxcOBABAUFyem6ROOlPePES3tERKRM1l17mzdvxubNm1GtWjXs2bNHYxIFAM7OztizZw9cXV2xadMmbNmyRU73RERERAYlK5FatWoVBEHAqFGjYGtrq1UdW1tbfPDBBxBFEatWrZLTPREREZFByUqkLl26BABo2LBhoerllr948aKc7omIiIgMSlYi9fTpUwBAampqoerlls+tT0RERGSKZCVSlStXBgAcOHCgUPX279+vUp+IiIjIFMlKpLy9vSGKIrZt24bt27drVWfHjh3Ytm0bBEGAt7e3nO6JiIiIDEpWIjVhwgSYmeU0MWjQIEydOhWPHz9WW/bx48eYNm0aBg0aBCBnMc4JEybI6Z6IiIjIoGSvIzVr1ix8++230irlZmZmaNCgAerUqQMbGxu8ePECUVFRuH79OrKzs6WVzWfOnImZM2fKfwUlFNeRMk5cR4qIiJTJXlp81qxZcHBwwNdff4309HQoFApcvXoVV69eVSmXm0BZW1vj+++/x6effiq3ayIiIiKDknVpL9dnn32GK1eu4NNPP0WNGjUgimKeH3d3d5VyRERERKZO9qU9dRISEvDw4UOkpaXB1tYWVatW5R16hcRLe8aJl/aIiEhZkewaXLlyZSZOREREVOLp5dIeERERUWlUJCNSRGQYvPRIRFS8OCJFREREpCMmUkREREQ6YiJFREREpCMmUkREREQ6YiJFREREpCMmUkREREQ6YiJFREREpCMmUkREREQ6kpVIBQcH6ysOIiIiIpMjK5Hq2bMn6tSpgwULFiA+Pl5fMRERERGZBNmX9qKjo/HVV1/Bzc0NgwYNwpEjR/QRFxEREZHRk5VIDR8+HNbW1hBFEa9evcKWLVvQvXt31KtXDz/++COePHmirziJiIiIjI6sRGr16tV4+PAhfv75ZzRu3BiiKEIURdy+fRtffvklqlWrhiFDhiA0NFRP4RIREREZD9mX9hwcHDB+/HhcvHgR4eHh8Pf3l0apMjMzsXHjRnTp0gUNGjTAkiVLkJiYqI+41dq1axf8/Pzg7u4Oa2trODk5oX379li4cCFSUlL01k9qaiq2bt2KTz75BO3bt0flypVhaWkJe3t7NGjQAP7+/jhw4ABEUdRbn0RERGR8BLEI/tonJydj7dq1+OOPP3D58uWcjgQBAFCmTBkMGDAAo0ePRseOHfXSX1paGoYOHYpdu3blW8bNzQ2bNm1C27ZtZfW1ePFifP3110hPTy+wbMeOHbFu3TpUr1690P2kpKTAwcEBycnJsLe31yVUKgJW7gMNHYJJy7i7ydAhEBHpVZEkUspOnTqF5cuXY8uWLXj58mVOp/+fVDVo0ABjxoyBv78/ypcvr1P7CoUCb731Fg4cOAAAcHZ2RkBAADw8PJCYmIigoCCEh4cDACpUqIDw8HA0bNhQ59czduxYrFixAgDg6uqKrl27okWLFnByckJ6ejoiIiKwbt06pKWlAQBq1qyJiIgIODk5FaofJlLGiYmUPEykiKikKfJEKldycjK++eYbLFu2DIIgQBRFKaEqW7Yshg8fjq+++gqurq6FanfFihUYO3YsAMDDwwNHjx6Fs7OzSpnPP/8cixYtApAzShQWFqbz6xg3bhzu3LmDzz//HF26dIGZWd6rozExMejRowdu3LgBABg5ciRWrVpVqH6YSBknJlLyMJEiopKmyBOprKwsbN26FStXrpQmnavrUhAElC1bFkuWLEFAQIBWbSsUCri5ueHRo0cAgLNnz8LLy0ttuZYtWyIyMhIAcPDgQXTv3l2n15OYmIiKFSsWWO7ChQto1qwZAMDGxgYJCQmwsbHRuh8mUsaJiZQ8TKSIqKQpsi1ioqKi8MUXX8DV1VW6cy/3rr7WrVtj9erVePDgARYvXoz69etDFEW8ePECY8eOxcGDB7XqIywsTEqifHx81CZRAGBubo4JEyZI50FBQTq/Lm2SKADw9PRE/fr1AQAvXrxAVFSUzn0SERGRcdJrIvXq1Sts2LABnTt3Rv369bFo0SIkJCRAFEWULVsWH3zwAc6ePYuIiAgMHz4cVapUwaeffopr165hzZo1sLGxgSiKmD9/vlb97d+/Xzru1auXxrJvvvmm2npFSXkkKXd+GBEREZUcFvpo5NatW1i5ciXWrFmDp0+fAvjf5bsGDRpg3Lhx8Pf3h4ODQ75tvP/++7h58ybmzp2LK1euaNXvpUuXpONWrVppLOvi4gI3NzfExsYiLi4OCQkJqFy5slb96CIzMxM3b96UzmvUqFFkfREREZFhyEqkgoKCsHLlSmnydm7yZGlpib59+2LcuHHw9fXVur3WrVsDgJSMFSR3MjeQc3dcQWrWrInY2FipblEmUv/88w+Sk5MBAF5eXnBxcdFYPiMjAxkZGdK5Pte9IiIioqIhK5EaOnSodAceAFSrVg2jR4/Ghx9+WGDioE6ZMmUKVT4pKUk6dnR0LLB8pUqV1NbVt4SEBHz55ZfS+fTp0wusM2/ePMyePbvIYiIiIiL908ulve7du2PcuHF4++231S4HoK3WrVsjJCRE6/K5azUBgLW1dYHly5YtKx2npqYWLjgtZWZmon///oiPjwcA9O3bF++++26B9aZNm4ZJkyZJ5ykpKXBzcyuSGImIiEg/ZCVSn3/+OcaMGYPatWvrJZgKFSrAx8dHL20ZQnZ2NkaNGoXjx48DAGrXrq31+lFWVlawsrIqyvCIiIhIz2QlUj/88IO+4tCJra0tnj17BgBIT0+Hra2txvLKd87Z2dnpNRZRFDF27FisX78eAFC9enUcPnwYFSpU0Gs/REREZDxkLX8watQojBo1SlroUluXL1/GqFGj8MEHH8jpXmVbmSdPnhRYXnkSu65b0qgjiiI++ugj/PHHHwBy5oodPXoU7u7ueuuDiIiIjI+sRCowMBBr1qzBvXv3ClXvwYMHCAwMRGBgoJzupQUvASA6OrrA8spllOvKIYoiPv74YyxfvhxAzv57ISEhervcSURERMaryFY2Lw5NmjSRjk+fPq2xbFxcnLT0gZOTk16WPshNon7//XcAQNWqVRESEoI6derIbpuIiIiMn0ESKYVCAQCwsJB302DPnj2l44JWK9+3b590XNAq6Np4PYmqUqUKQkJCULduXdltExERkWkwSCKVe4lN7ma8Pj4+0npVoaGhOHfunNpyCoUCS5culc4HDx4sq18A+OSTT6QkysXFBSEhIahXr57sdomIiMh06CWREgRBq3IvXrzAiRMn8PPPP0MQBDRs2FBWv+bm5pgxY4Z07u/vL63fpGzq1KnShPgOHTqgR48eatsLDAyEIAgQBEHjiuzjx4/Hb7/9BiAniQoNDdXbnCsiIiIyHVpfW5s9eza+/fbbPI+Looi+ffvq1Lk2C1UWJCAgANu3b8ehQ4dw5coVeHp6IiAgAB4eHkhMTERQUBBOnDgBIOdOvRUrVsjqb/r06Vi2bBmAnARy4sSJuHbtGq5du6axnpeXF6pXry6rbyIiIjIuhZqklLsVjLaPa+Lr64tPPvmk0PVeZ2Fhga1bt2LIkCHYs2cPHj9+jDlz5uQpV61aNWzcuBGNGjWS1V9uUgbkvO5p06ZpVW/16tUYMWKErL6JiIjIuGidSLm7u+dZdfzYsWMQBAEeHh4F7nVnZmYGW1tb1KxZE127dkWvXr1kbSejzM7ODrt378bOnTuxdu1anD59GvHx8bCzs0Pt2rXRr18/jBkzBg4ODnrpj4iIiAgABFGX4aT/Z2ZmBkEQsH37dvTp00efcZV6KSkpcHBwQHJysuxJ+aQ/Vu4DDR2CScu4u8nQIRAR6ZWs9Qe8vb0hCEKBo1FEREREJZGsRCo0NFRPYRARERGZHpNe2ZyIiIjIkJhIEREREelIq0t7yutHKS+AqW5dqcJSbo+IiIjIlGh1117u3XnA//bJe/1xXSm3R//Du/aME+/ak4d37RFRSaP1ZHNRFNUmTTJWT5CdhBEREREZklaJVEhISKEeJyIiIioNtEqkXl/RvKDHiYiIiEoD3rVHREREpCMmUkREREQ6YiJFREREpCMmUkREREQ60mqyubm5eZF0LggCsrKyiqRtIiIioqKmVSIlZ60oIiIiopJKq0TK29ubi2cSERERvUarRCo0NLSIwyAiIiIyPZxsTkRERKQjJlJEREREOmIiRURERKQjJlJEREREOtJqsvm3334rHc+YMUPt47pSbo+IiIjIlAiiFotEmZmZScsfKBQKtY/rSrk9+p+UlBQ4ODggOTkZ9vb2hg6H/p+V+0BDh2DSMu5uMnQIRER6pdWIFJCzKKe6pEnOYp1cm4qIiIhMmVaJVEhISKEeJyIiIioNtLq0R8WPl/aMEy/tlWy89EhEhcW79oiIiIh0xESKiIiISEdaTzYvjPj4eDx8+BCpqamws7ND1apV4eTkVBRdERERERmM3hKpmJgY/PLLL9iyZQtiY2PzPF+9enX4+fnh448/Ro0aNfTVLREREZHB6OXS3q+//opGjRphyZIliI2NhSiKeX7u3buHRYsWoVGjRvjtt9/00S0RERGRQckekZo3bx6mT58OIGdNKTMzM3h4eKBu3booV64cnj9/jqioKFy9ehXZ2dl48eIFxo8fj5SUFEydOlX2CyAiIiIyFFnLH5w7dw5t2rSBQqGAubk5JkyYgMmTJ6Nq1ap5yj569AiLFy/GTz/9JJX/77//0Lx5c1kvoKTi8gfGicsflGxc/oCICkvWpb1ffvkFCoUCgiBg3bp1WLRokdokCgCqVKmChQsXYv369QCA7OxsLF26VE73RERERAYlK5EKCQmBIAh46623MGjQIK3qDBw4EH369IEoilwZnYiIiEyarEQqLi4OAPDWW28Vql7v3r1V6hMRERGZIlmJVPny5VX+W9T1iIiIiIyJrETKw8MDAHDr1q1C1YuKilKpT0RERGSKZCVSw4YNgyiKWLt2LTIzM7Wqk5mZicDAQAiCgPfff19O90REREQGJSuRGjFiBHx9fXHz5k0MHToUL1++1Fg+PT0dw4YNw61bt9CpUyeMGDFCTvdEREREBiUrkRIEATt37kS/fv2wdetWNGzYED/++CPOnz+PtLQ0iKKItLQ0REZGYuHChWjYsCG2bt2KAQMGYMeOHXp6CURERESGodWCnObm5gU2lNuMIAhalxEEAVlZWVoFWtpwQU7jxAU5SzYuyElEhaXVFjGFWfxcm7IyFlMnIiIiMhpaJVLe3t4aR5qIiIiISiOtEqnQ0NAiDoOIiIjI9MiabE5ERERUmjGRIiIiItIREykiIiIiHTGRIiIiItKRVpPNtfHixQvs3LkTERERuH//PlJSUqBQKDTWEQQBR44c0VcIRERERMVKL4nU8uXL8dVXXyE5OVnrOqIockkFIiIiMmmyE6nvvvsOM2fO1GqRzdzEiQtyEhERUUkga47U9evXMXPmTABAvXr1cOTIEWnjYkEQsGPHDqSlpeHSpUtYsGABqlSpAgAYOXIk0tPTC7z0R0RERGTMZI1ILV++HKIowsbGBsHBwahevXqeMjY2NmjUqBEaNWqEgIAAvPPOOwgMDMTz58+xYcMGOd0TERERGZSsEaljx45BEAT4+fmpTaJeV758eezYsQMVK1bE5s2bsWvXLjnd57Fr1y74+fnB3d0d1tbWcHJyQvv27bFw4UKkpKTorR+FQoHLly8jMDAQ48ePR7t27WBjYwNBECAIAkaMGKG3voiIiMh4yRqRunfvHgCgbdu2ap/PzMzM81iFChUwfPhwLF68GH///Tf69OkjJwQAQFpaGoYOHZonMUtISEBCQgJOnTqFX375BZs2bco31sIYOHAgtm3bJrsdIiIiMm2yRqRSU1MBAJUrV1Z5vGzZsirPv6558+YAgDNnzsjpHkDO6JCfn5+URDk7O2P69On4559/sGzZMnTo0AEAEBsbi169euHatWt66VNZxYoVUbduXdntEhERkWmRNSJVrlw5pKSk5Bl5cnBwQHp6ujRi9bqsrCwAQFxcnJzuAQB//vknDhw4AADw8PDA0aNH4ezsLD3/8ccf4/PPP8eiRYvw7NkzjBkzBmFhYbL6bN26NRo2bIgWLVqgRYsWqFmzJgIDAzFy5EhZ7RIREZFpkZVIubu74+LFi3kSovr16+Px48cIDw9XW+/ChQsAgDJlysjpHgqFArNnz5bO//77b5UkKteCBQtw5MgRREZG4vjx4wgODkb37t117verr77SuS4RERGVHLIu7Xl6ekIURVy6dEnlcW9vbwBASEgIzp49q/LcnTt38Oeff0IQBDRs2FBO9wgLC8OjR48AAD4+PvDy8lJbztzcHBMmTJDOg4KCZPVLREREBMhMpHx9fQEAR48eVXnc398fFhYWyM7ORufOnfHFF19g5cqV+OKLL9CyZUukpaUBAAYPHiyne+zfv1867tWrl8ayb775ptp6RERERLqSdWnv7bffhrm5OWJiYnDy5Em0b98eAFC7dm189dVX+Pbbb5GWloZFixblqevl5YVx48bJ6V5lJKxVq1Yay7q4uMDNzQ2xsbGIi4tDQkJCnknyRERERIUha0SqUqVKuHnzJq5evZrnMt2sWbOwYMEClCtXDqIoSj9AzvIBhw4dkj1H6saNG9JxzZo1CyyvXEa5LhEREZEuZO+1pymBmTJlCiZMmIBTp07h8ePHKFeuHFq2bCltFSNXUlKSdOzo6Fhg+UqVKqmtawwyMjKQkZEhnetzAVEiIiIqGrITqYJYWVlJc6n0LXeuFQBYW1sXWD53fSsg/zWuDGXevHkqdyASERGR8SvyRIq0M23aNEyaNEk6T0lJgZubmwEjIip9rNwHGjqEfGXc3WToEIhIjSJLpJKSkpCamgo7OzuUL1++SPqwtbXFs2fPAADp6emwtbXVWP7ly5fSsZ2dXZHEpCsrKytYWVkZOgwiIiIqBFmTzZWlpaVh2bJl8PX1hZ2dHSpVqgR3d3dUqlQJdnZ26NSpE3777TeVy3FyKSdoT548KbD806dP1dYlIiIi0oVeEqndu3ejbt26mDhxIo4fP47nz5+r3Kn3/PlzhIWFYfz48ahbty727Nmjj25Rv3596Tg6OrrA8spllOsSERER6UJ2IrV27Vr069cP8fHxUuJkZ2eHZs2aoUOHDmjWrBns7e2l5+Li4tC3b1/8/fffsoNv0qSJdHz69GmNZePi4hAbGwsAcHJy4hpSREREJJusRCoqKgpjx46FQqGAKIp49913cerUKSQnJ+PcuXM4fvw4zp07h6SkJERERKB///4AgOzsbIwZMwa3b9+WFXzPnj2l44JWK9+3b590XNAq6ERERETakJVILVmyBOnp6RAEAT/88AO2bt2KNm3aqC3bunVrbN68GT/++COAnHWTlixZIqd7+Pj4wMXFBQAQGhqKc+fOqS2nUCiwdOlS6Vzu1jREREREgMxEKjg4GIIgwNvbG59//rlWdSZNmgQfHx+IooiDBw/K6R7m5uaYMWOGdO7v74/4+Pg85aZOnYrIyEgAQIcOHdCjRw+17QUGBkIQBAiCUGRrXxEREVHJIWv5gwcPHgAABgwYUKh6AwYMwLFjx6T6cgQEBGD79u04dOgQrly5Ak9PTwQEBMDDwwOJiYkICgrCiRMnAOTcqbdixQrZfUZHR+Ovv/5SeezixYvS8fnz5zF9+nSV5zt37ozOnTvL7puIiIiMh6xEytbWFhkZGXB2di5UPScnJ6m+XBYWFti6dSuGDBmCPXv24PHjx5gzZ06ectWqVcPGjRvRqFEj2X3GxMRg7ty5+T5/8eJFlcQqN04mUkRERCWLrEt7derUAQDcu3evUPVy756rW7eunO4ldnZ22L17N3bs2IF+/frBzc0NVlZWcHR0RJs2bbBgwQJcvnwZ7du310t/RERERAAgiKIo6lr5559/xmeffQYvLy+cPn0agiAUWEcURbRs2RKRkZH46aefMH78eF27L9FSUlLg4OCA5ORk2NvbGzoc+n/GvIUIlWzcIobIOMkakRo7diyaNm2K8+fP47PPPtOqzqRJk3D+/Hl4enpizJgxcronIiIiMihZiZSVlRX27t2LNm3a4JdffkHbtm2xZcsWaf+7XElJSdi8eTPatWuHpUuXol27dti7dy/KlCkjK3giIiIiQ9Lq0l6tWrU0Pv/q1Ss8ePBA5dJehQoVUK5cOTx//lxKrERRhCAIqFq1KiwtLSEIguxFOUsqXtozTry0R4bCS3tExkmru/bu3r0LQRCQX86Vu/YSAKlMYmIiEhMT85QDgIcPH0pJFREREZGp0iqRql69OpMeIiIiotdoPSJFRERERKpkTTYnIiIiKs2YSBERERHpiIkUERERkY5k7bX3uidPnmDv3r2IiIjAo0ePkJqaCjs7O1StWhVt2rRB79694ejoqM8uiYiIiAxGL4nUixcv8MUXX2DVqlXIyMhQW2bFihWwsrLChx9+iAULFqBs2bL66JqIiIjIYGRf2nvy5AlatWqF33//Henp6RBFMd+f9PR0/Prrr2jVqhWePn2qj/iJiIiIDEb2iFT//v1x7do1AEDZsmXx3nvvoUePHqhXrx5sbW2RlpaGmzdv4uDBg9iwYQNevHiBq1evon///ggNDZXbPREREZHBaLVFTH62b9+O/v37QxAENGvWDNu2bUONGjXyLR8TE4MBAwbg7NmzEAQB27ZtwzvvvKNr9yUat4gxTtwihgyFW8QQGSdZl/Y2bNgAAKhcuTIOHTqkMYkCgBo1auDAgQNwcnICAPzzzz9yuiciIiIyKFmJ1L///gtBEDBq1ChUrFhRqzqVKlXCBx98AFEU8e+//8rpnoiIiMigZM2Rio+PBwA0bdq0UPWaNGmiUp+IiDQz9svKvPRIpZWsEakyZcoAADIzMwtVL7e8paWlnO6JiIiIDEpWIlW1alUAwPHjxwtVLywsDADg6uoqp3siIiIig5KVSPn6+kIURfz999+4cOGCVnUiIyOxbt06CIIAX19fOd0TERERGZSsROrDDz+EIAh49eoVunbtim3btmksv23bNnTr1g2ZmZkQBAEBAQFyuiciIiIyKFmTzb28vDB27Fj8/vvvSExMhJ+fH2rVqoVu3bqhXr16KFeuHJ4/f45bt27h0KFDuH37NkRRhCAIGDt2LJo3b66v10FERERU7GQtyAkA2dnZGD58ONavX5/ToCDkWza3q2HDhiEwMBBmZrJ3qCmxuCCncTL2O6eIDIV37VFpJTuTMTMzw99//42NGzfCy8tL4157LVq0wObNm7F27VomUURERGTyZI9Ive7evXv4999/8ejRI6SmpsLOzg5VqlRBmzZtUL16dX12VaJxRMo4cUSKSD2OSFFpJWuO1Nq1awEALi4u6N69OwCgevXqTJiIiIioVJB1fW3EiBEYOXIkTpw4oa94iIiIiEyGrETK1tYWAODh4aGXYIiIiIhMiaxEqkqVKgCAV69e6SUYIiIiIlMiK5Hq1KkTAOD06dN6CYaIiIjIlMi6a+/8+fNo3bo1bGxscPXqVe6dp0el9a493hVHZJp41x6VVrJGpJo3b465c+ciNTUV3bp1w8WLF/UVFxEREZHRk738gYuLC958803s378fXl5eeOONN9CxY0dUq1YNZcuWLbANf39/OSEQERERGYysS3tmZmYqW8Lk7qOndeeCgKysLF27L9F4aY+ITAkv7VFpJWtECvjf/nn5nRMRERGVVLISqdWrV+srDiIiIiKTIyuRGj58uL7iICIiIjI5su7aIyIiIirNdB6RevDgAS5evIjk5GQ4ODigSZMmqFatmj5jIyIiIjJqhU6k/vvvP3z22WeIiIjI81zbtm2xZMkStG7dWi/BERERERmzQl3aCw4Ohq+vLyIiIiCKYp6fU6dOwcfHBwcPHiyqeImIiIiMhtaJVGpqKoYPH4709HRpiYM6deqgffv2qFOnjlQuIyMDw4cPR0pKiv6jJSIiIjIiWidSf//9N+Li4iAIAlq2bIkrV67g5s2bOHHiBG7evImrV69Kl/QSEhLw999/F1nQRERERMZA60Rq//79AABHR0ccPHgQDRs2VHm+QYMG2L9/P5ycnFTKExEREZVUWidSFy9ehCAI8Pf3R4UKFdSWqVChAvz9/SGKIi5duqS3IImIiIiMkdZ37SUmJgIAmjVrprGcp6cnAODp06e6R0VERCbF2PfJ5F6AVFS0HpF6/vw5AMDOzk5jOVtbWwDAy5cvZYRFREREZPy4sjkRERGRjphIEREREemo0ImUIAhFEQcRERGRySn0FjF9+/bVqpwoijA3N9dYRhAEZGVlFTYEIiIiIqOg06bFuSubqyMIgjRqpakcERERkakrVCKlTWLE5ImIiIhKC60Tqezs7KKMg4iIiMjk8K49IiIiIh2VqERq165d8PPzg7u7O6ytreHk5IT27dtj4cKFSElJKTF9EhERkXEQxBIwqSktLQ1Dhw7Frl278i3j5uaGTZs2oW3btibRZ0pKChwcHJCcnAx7e3s5oZoUY99mgohME7eIoaJi8omUQqHAW2+9hQMHDgAAnJ2dERAQAA8PDyQmJiIoKAjh4eEAcjZVDg8PR8OGDY2+TyZSRESlBxM902XyidSKFSswduxYAICHhweOHj0KZ2dnlTKff/45Fi1aBADo2LEjwsLCjL5PJlJERKUHEynTZdKJlEKhgJubGx49egQAOHv2LLy8vNSWa9myJSIjIwEABw8eRPfu3Y26TyZSRESlBxMp02XSk83DwsKkhMbHx0dtQgMA5ubmmDBhgnQeFBRkUn0SERGRcdJpZXNjsX//fum4V69eGsu++eabauuZQp9ERFSyGfNoPEfLNDPpEalLly5Jx61atdJY1sXFBW5ubgCAuLg4JCQkmEyfREREZJxMekTqxo0b0nHNmjULLF+zZk3ExsZKdStXrmwSfRIRERmKMY+WAYYfMTPpRCopKUk6dnR0LLB8pUqV1NY1hj4zMjKQkZEhnScnJwNAqVvUU8x+ZegQiIjIhBTl30k7OzsIgqCxjEknUmlpadKxtbV1geXLli0rHaemphpVn/PmzcPs2bPzPJ57aZCIiIjycnBwKLK2tblz3qQTqZJk2rRpmDRpknSenZ2NxMREVKpUqcBs2JBSUlLg5uaG2NjYUrVMgzHiZ2E8+FkYF34exsPUPgs7O7sCy5h0ImVra4tnz54BANLT02Fra6ux/MuXL6Vjbd6c4uzTysoKVlZWKo+VL19epxgNwd7e3iS+FKUBPwvjwc/CuPDzMB4l6bMw6bv2lBONJ0+eFFj+6dOnausae59ERERknEw6kapfv750HB0dXWB55TLKdY29TyIiIjJOJp1INWnSRDo+ffq0xrJxcXHSMgROTk46L0NgiD6NmZWVFWbOnJnnsiQVP34WxoOfhXHh52E8SuJnYdKJVM+ePaXjglYO37dvn3Rc0IrkxtanMbOyssKsWbNK1JfCVPGzMB78LIwLPw/jURI/C5NOpHx8fODi4gIACA0Nxblz59SWUygUWLp0qXQ+ePBgk+qTiIiIjJNJJ1Lm5uaYMWOGdO7v74/4+Pg85aZOnYrIyEgAQIcOHdCjRw+17QUGBkIQBAiCAF9f32Lpk4iIiEyXIIqiaOgg5MjKykKvXr1w6NAhADn72wUEBMDDwwOJiYkICgrCiRMnAOTcNXfixAk0atRIbVuBgYEYOXIkgJyRp9DQ0CLvk4iIiEyXySdSQM6K4UOGDMGePXvyLVOtWjVs3LgR7du3z7eMtomUPvskIiIi02XSl/Zy2dnZYffu3dixYwf69esHNzc3WFlZwdHREW3atMGCBQtw+fJlvSY0huhTn3bt2gU/Pz+4u7vD2toaTk5OaN++PRYuXFhk+xYZok9jlpqaiq1bt+KTTz5B+/btUblyZVhaWsLe3h4NGjSAv78/Dhw4AH39W8fX11e6dK3Nz927d/XSr6kw5PvD78b/zJo1q1CfQ0HTMbRRWr8bCoUCly9fRmBgIMaPH4927drBxsZGep0jRowodJtRUVGYMmUKGjduDAcHB9ja2qJ+/fr4+OOPpeku+maIPlWIVKqkpqaKffr0EQHk++Pm5iaeOnXKpPs0dosWLRKtra01vie5Px07dhRjYmJk9+nj46NVf7k/0dHR8l+oCTHE+8PvRl4zZ84s1OeQ+zNy5Eid+yyt341+/fppfJ3Dhw8vVHsrVqwQy5Ytm2975ubm4uzZs/X6GgzR5+tMeosYKhyFQgE/Pz8cOHAAAODs7Jxnbld4eDhiY2PRq1cvhIeHo2HDhibXpym4efMm0tPTAQCurq7o2rUrWrRoAScnJ6SnpyMiIgLr1q1DWloajh8/Dl9fX0RERMDJyUkv/W/fvr3AMvrqyxQVx/vD74Z6gwcPRrNmzQos9+rVKwwbNgyZmZkAgFGjRuml/9L03VAoFCrnFStWRKVKlXDr1q1Ct7Vu3TqMGTMGAGBmZobBgwejS5cusLCwQHh4ONasWYOMjAxpDakvv/xSdvyG6FOtIk3TyKgsX75cytI9PDzEx48f5ykzefJklZEQU+zTFIwdO1bs3r27GBwcLCoUCrVl7t69K9avX18v/+IWRdV/dVNexf3+8Lshz7Zt26T3pn79+rLaKq3fjblz54pTp04VN2/eLN65c0cURVFcvXp1oUek4uPjRXt7exGAaGZmJu7cuTNPmVOnTok2NjYiANHCwkK8fv26rNgN0Wd+StdvTSmWlZUlVqlSRfqCnD17Nt9yzZo1k8odPHjQpPo0FU+fPtWqXGRkpPS+2NjYiM+fP9e5z9L6x0Jbxfn+8Lsh31tvvSW9LwsWLJDVFr8b/6NLIvXFF19IdcaPH59vuUWLFknl3nvvPVlxGqLP/JSIyeZUsLCwMDx69AhAzh2JXl5easuZm5tjwoQJ0nlQUJBJ9WkqKlasqFU5T09PaY/GFy9eICoqqijDomLC74Y8jx49knaWsLCwgL+/v4EjKt02btwoHX/22Wf5lgsICEC5cuUA5Nxg8fLlS5PqMz9MpEoJ5e1sCtqu5s0331RbzxT6LIns7e2l46L4nwAVP3435FmzZo00v6d3797SbhNU/K5evYqYmBgAQMOGDVGzZs18y9rZ2aFjx44AgOfPn+PYsWMm06cmTKRKiUuXLknHrVq10ljWxcUFbm5uAHI2Xk5ISDCZPkuazMxM3Lx5UzqvUaOGXtp966234OrqijJlyqBChQpo1KgRAgICEBISopf2TV1Rvz/8bsizevVq6fiDDz7Qa9v8bhROYX6XXy+jXNfY+9SEiVQpcePGDelYU/auroxyXWPvs6T5559/kJycDADw8vLS27+89+7di4cPH+LVq1dISkrC1atX8eeff6Jz587o0qWLdNmptCrq94ffDd0dP35c+sdFlSpV9L4hPL8bhcO/LQCXPyglkpKSpGNHR8cCy1eqVEltXWPvsyRJSEhQuV13+vTpstusUKECunXrhpYtW8LV1RXm5uZ48OABjhw5gv3790MURRw9ehTt2rVDREREqbtkUlzvD78bulu1apV0PHz4cJibm+ulXX43dMO/LUykSo20tDTp2NrausDyZcuWlY5TU1NNps+SIjMzE/3795c2xO7bty/effddWW3OmzcPLVq0QJkyZfI8N2nSJJw5cwb9+/fHvXv3EBMTg1GjRmHfvn2y+jQlxfn+8Luhm9TUVGzevFk619faUfxu6I5/W3hpj8joZGdnY9SoUTh+/DgAoHbt2ir/CtdVu3bt1P6hyNWyZUscOHAAVlZWAHImNp8+fVp2v6aC74/x27hxI54/fw4A6NixI+rWrauXdvnZkxxMpEoJW1tb6Th3RW1NlO8Os7OzM5k+TZ0oihg7dizWr18PAKhevToOHz6MChUqFEv/DRs2xPvvvy+da9qUuzTS1/vD74ZulP9Boe9J5gXhd0M9/m1hIlVqlC9fXjp+8uRJgeWfPn2qtq6x92nKRFHERx99hD/++AMAUK1aNRw9ehTu7u7FGkenTp2k42vXrhVr36ZAH+8PvxuFd/36dZw6dQpAzpIgfn5+xR4Dvxt58W8LE6lSI3dRRwCIjo4usLxyGeW6xt6nqRJFER9//DGWL18OIGf/vZCQENSuXbvYY6lcubJ0XNonNqujj/eH343C++uvv6TjwYMHw8bGpthj4HcjL/5tYSJVajRp0kQ6LujaflxcHGJjYwHkbM6p/D8PY+/TFOUmUb///jsAoGrVqggJCUGdOnUMEo/yv/BK6+iHJvp4f/jdKJysrCz8/fff0nlxX9bLxe9GXoX5XX69TOPGjU2mT02YSJUSPXv2lI4LWh1Z+W4UOWu0GKJPU/N6ElWlShWEhITobRKtLpQXHiytox+a6OP94XejcPbu3Yu4uDgAOX8IW7dubZA4+N3Iy8PDA9WrVweQc7nz7t27+ZZNS0uTbqKxsbGBj4+PyfSpUZHs4EdGJysrS3RxcSn0JqkHDhwwqT5NzUcffSS9bhcXlyLbnVxbN27cEK2traWYIiIiDBqPsdHX+8PvRuG8/fbb0nuwZMkSg8RQWr4bumxaPGXKlEJvIDx48GBZcRqiz/wwkSpFfvvtN+kXqlGjRmJcXFyeMp9//rlUpkOHDvm2pfxl8/HxKZY+S5pPPvlEL0mUNp/Fzz//LIaHh2ts59y5c6K7u7vUVvfu3XWKxxTp8/3hd0O/Hj16JFpYWIgAxDJlyogJCQla1+V3o/B0SaTi4uJEOzs7EYBoZmYm7ty5M0+ZiIgI0cbGRgQgWlhYiNeuXcu3vdz+AYjR0dHF0qccXJCzFAkICMD27dtx6NAhXLlyBZ6enggICICHhwcSExMRFBSEEydOAMi5/r9ixQqT7NMUTJ8+HcuWLQMACIKAiRMn4tq1awXeCeTl5SUNaRfG0aNHMXHiRNSuXRtdu3ZF48aNUalSJZibm+Phw4c4cuQI9u3bh+zsbAA5e/op72dW0hni/eF3Qztr165FVlYWAOCdd97RaiXrwijN343o6GiVSfwAcPHiRen4/PnzeXZU6Ny5Mzp37qzymJOTE3755ReMGDEC2dnZePfddzF48GB069YN5ubmCA8Px5o1a6SlCmbPno0GDRrIit0QfearSNIzMlopKSniW2+9pZLxv/5TrVq1Av+Fpu2/uvXZZ0ni4+Oj8f3I72f16tV52tLms3jnnXe07qNHjx7igwcPivYNMDL6fH/43dCv+vXr63xpk98NzUJCQgr9/6CZM2fm295vv/2mcvnz9R9zc3NxxowZBcalXCe/ESl99ykHR6RKGTs7O+zevRs7d+7E2rVrcfr0acTHx8POzg61a9dGv379MGbMGDg4OJh0n6Rq0aJFePvtt/Hvv//iwoULiI+Px5MnT5CRkQEHBwe4u7ujXbt2GDp0KNq0aWPocIudod4ffjc0Cw8PlzaZdXNzQ7du3fTeB78b+jNu3Dh07doVy5cvx4EDBxAbG4vs7GxUrVoVXbp0wejRo9G8eXOT7/N1wv9nf0RERERUSFz+gIiIiEhHTKSIiIiIdMREioiIiEhHTKSIiIiIdMREioiIiEhHTKSIiIiIdMREioiIiEhHTKSIiIiIdMREioiIiEhHTKSIiIiIdMREioiIiEhHTKSISMWsWbMgCAIEQUBoaKihwzFJ6enp+OGHH9CuXTtUqFAB5ubm0nt69+5dQ4dHRHrERIpKpdw/ark/Bw4cKLDO3bt3pfJvvPFGMURJpujly5fw9vbGl19+iYiICCQlJSE7O9vQYRFREbEwdABExmDatGno0aMHBEEwdChk4pYvX47Tp08DADw8PDBmzBi4urrC3NwcAODk5GTI8IhIz5hIEQGIjIxEUFAQhgwZYuhQyMTt3bsXQM6o58GDB1GtWjUDR0RERYmX9qhUs7a2hplZztfgm2++watXrwwcEZm62NhYADkjT0yiiEo+JlJUqlWqVAnvv/8+AODOnTtYsWKFgSMiU5eRkQEgJ0knopKPiRSVet9++y2srKwAAHPmzEFaWprObY0YMULru7MCAwOlsoGBgXmeV57cPmLECADA48eP8fXXX6Nx48awt7eHo6MjOnbsiE2bNkEURZX6ly9fRkBAAOrXrw8bGxtUqlQJvXv31ulOvKNHj2LgwIGoUaMGrK2t4ezsjN69e2Pr1q1at6FQKLB+/Xr4+fnB3d0d5cqVg62tLerXr4+AgACcOXNGY31179e5c+cwduxY1KtXD3Z2dvm+l9pKSkrC/Pnz0bFjRzg7O6NMmTJwcnLCG2+8gXnz5iEpKUltPeU7HWNiYgAAMTExeW5q0DW248ePY9SoUWjYsCHs7OxgaWkJJycneHh4oGfPnpgzZw5u3rypUqdVq1YQBAHm5ubSKJkmoiiidu3aEAQBZcuWxbNnz6Tn1P0uJiUl4fvvv4eXlxfKly+PcuXKwcPDA1OmTEF8fHyB/d28eROLFy/Gu+++i7p168LW1lZ6v729vfHdd9/hyZMnBbaTG5evry8A4NmzZ5g7dy68vLxQsWJFlbgeP35cYHuPHz/G7Nmz0aFDBzg6OsLS0hIODg6oXbs22rVrh48++gj79u0r8AaCyMhITJw4EZ6enqhYsSKsrKxQtWpV9O7dG6tWrUJWVlaBsejyuZMBiESlEAARgOjq6iqKoih+9tln0mOzZ89WWyc6Oloq06FDB7Vlhg8fLpWJjo7WGMPq1aulsqtXr9bY3/Dhw8UTJ06ITk5O0mOv/4wePVrMzs4WRVEUV6xYIVpYWORb9vfff883rpkzZ0rlQkJCxEmTJuXbDgCxb9++Ynp6usbXeunSJbFBgwYa2wEgfvLJJ2JWVpZW79eCBQtEc3PzPG2oey+1sXfvXrFixYoa46tYsaK4d+9eje+Zpp/CxqZQKMQxY8Zo1Xbv3r1V6v7111/SczNmzCiwr4MHD0rl/f39VZ57/Xfx7NmzYvXq1fONxdnZWbx06VK+fa1Zs0ar12Rvby/u2bNHY9y5ZX18fMRLly6JNWrUyLe98uXLiwcOHMi3rX379ol2dnZaxZaQkKC2jfT0dHHUqFGiIAga6zdq1Ei8ffu22jbkfO5U/DjZnAjA119/jb/++gspKSn48ccfMW7cOFSuXNnQYUnu3buHvn37Ijk5GSNGjICPjw+sra1x+vRp/P7773j58iVWrlyJdu3awd7eHmPGjIGjoyNGjRoFT09PZGVlYe/evdi0aRMAYMKECfD19UWDBg009vvLL79g27ZtcHBwwKhRo9CiRQsoFAqEh4djzZo1yMjIwI4dOzBkyJB8R6fOnz8PHx8fpKamAgA6duyI3r17o0aNGsjOzsbFixcRGBiIuLg4LFu2DJmZmQVeYt20aRP2798PW1tb+Pv7o3Xr1rC0tMTVq1fh4uJS6Pf34MGDeOedd6RRgjZt2mDw4MGoWrUqHj16hA0bNiAiIgKJiYl45513sGfPHvTo0UOqP3jwYDRr1gwAMHr0aCQkJKBy5cpYuXKlSj9eXl6FimvZsmXSe2FnZ4cBAwagRYsWqFy5MjIzM3H//n2cOXMGhw8fzlN38ODBmDx5MpKSkrBq1SrMmDFDunNQHeX3fMyYMfmWi42NRa9evZCQkID+/fujW7duqFixIu7evYuVK1ciKioKcXFxGDRoECIjI2FpaZmnjRcvXkAQBHh6esLb2xsNGjRAxYoVAQD379/H4cOHceDAAaSkpKB///44efJkge9dcnIy3nnnHcTExMDb2xsDBgyAs7Mz7t27h/Xr1yMyMhJJSUno27cvwsLC0KpVK5X6Dx8+xMCBA6URaR8fH/Tu3RsuLi6wsrLCkydPcPnyZRw5ciTfUaCsrCz07NlTGvWtWrUqBg8ejKZNm8LGxgb379/Htm3bcOLECVy5cgXe3t44f/58nv/XyPncyQAMnckRGQJeG5ESRVH87rvvpMcnTJiQp44hR6Tw/6MhZ86cyVMuJCRE+tevu7u7WKlSJbFVq1bi06dP85SdMWOG1N5HH32kNq7XR1fq1q0rxsbG5il36dIlsXLlylK5oKCgPGWeP38u1qpVSwQg2tjYiLt27VLbZ1JSktipUyeprUOHDuUpo/x+ARDr1asnxsTEqG2vMFJTU0VnZ2ep3VmzZkkje7mys7NV3jtnZ2cxJSVFbXu5IyI1atSQHVujRo1EAGKFChXEu3fv5lvu5cuXYkRERJ7HJ06cKMWc33sviqL46NEjaQSzcePGeZ5//XfRzs5OPHbsWJ5yqampYrNmzaRyW7duVdvf5cuXxVu3buUbjyiK4qFDh0QbGxsRgNilS5d8yynHBUBcsGBBnjJZWVniJ598IpXx8PAQFQqFSpmFCxdKzy9dulRjbBEREeLLly/zPD516lSpjYCAALVlRFEUf/75Z6nc0KFD8zwv93On4sVEikoldYlUWlqa6OLiIgIQy5QpkycRMnQitX79+nzb6tq1q1TOysoq3//5vnjxQrS1tRUBiLVq1VJbRjmRMjMzE8+fP59vvzt37pTKNm/ePM/zyn8w/v7773zbEUVRfPLkiWhvby8CEHv27JnneeX3SxAE8dy5cxrb09bSpUuldnv16qWxbM+ePaWyP/30k9oy+kykrKysRACin5+fTvWvX78uxfvWW2/lW27u3LlSuV9++SXP86//Lq5atSrftvbv3y+V+/DDD3WKO9f06dOltu7fv6+2jHJc/fr1y7cthUIhtmzZUiq7c+dOleeVL6U9f/680LHGxcWJ1tbWIgCxa9euBZYfMmSICEA0NzfP89rkfu5UvDjZnOj/lStXDjNmzAAAZGZm4ptvvjFwRP/j5OSEQYMG5fu88krrb7/9NmrUqKG2XNmyZdGyZUsAQHR0NNLT0zX22717d+mSlTp9+vRB/fr1AeRcwrtz547K82vWrAEAuLq6FrhGV+5keAAIDQ2V7n5T54033kDz5s01tqetbdu2ScdffvmlxrJfffWV2npFpVy5cgCAS5cuITMzs9D169evj86dOwMA9u/fr3bSuSiK+PPPPwEANjY20l2s+XF0dNRYplOnTrCwyJk1cvny5ULHrEz59zoiIqLA8l988UW+z5mZmWHy5MnS+ZYtW1Sez32vAeDs2bOFCRMAsHHjRun7NGXKlALLDx8+HEDOTRhHjhxRG4uunzsVL86RIlLy4YcfYvHixYiKisI///yDKVOmoGnTpoYOCy1bttQ4v0V5XlDr1q01tpVbVhRFJCUlaZxT1LVr1wJj69q1K27cuAEA+O+//1CrVi0AQEpKCiIjIwEAVapUwa5duwpsKzd5Sk9PR3R0dL5zuDp27FhgW9oQRRH//fcfgJwkoqCtfzp06IBy5crh+fPnOH36NLKzs6V1yIpC9+7dsWHDBly/fh1dunTBpEmT0KNHD9jY2Gjdxrhx43D06FEoFAr89ddfmDVrlsrzwcHBiI6OBgAMGjQIDg4OGttr1aqVlCipY2VlBUdHRzx+/Fjlzj91Tpw4gaCgIPz333+4c+cOUlNT813L7f79+xrbsre3L/B3X/n3Ofdzz9W9e3csXrwYANCvXz98+eWX8PPzy/cfJa8LCwuTjuPi4rBjxw6N5R88eCAdX716NU8scj93Kj5MpIiUWFpa4rvvvsPgwYORnZ2NadOmSStVG1KlSpU0Pp+7fENhyxY0IlW3bt0CY1Mu8/DhQ+k4NjZWukX8zJkzePfddwtsS1liYmK+z+lrocuUlBS8ePECAFC7du0CkyIzMzPUqVMHFy5cwMuXL5GUlCRNki4KCxYswIkTJ3D//n2cOHECJ06cgKWlJby8vNC+fXv4+vqie/fuGtes6tu3L6pWrYqHDx9i1apV+Oabb1SScuUJ8ZommedydHQssEzu71h+v19paWkYNmwYdu7cWWBbuVJSUjQ+n7t0gyaOjo4oX748kpKSVH5XAaBHjx7w9/fH2rVr8eTJE0yZMgVTpkxBzZo10a5dO3h7e6NXr15wc3NT27bycif+/v7avaj/9/rvuj4+dyo+vLRH9JqBAwdKdwjt27dP5V+ahlKYUQ99jpAoX+7QpkzunXkA8l1zSVuaLmmULVtWVtu5lOPV5rUCgK2trdr6RaF69eo4f/48Pv30Uylhe/XqFf79918sWbIE77zzDpydnTFjxox8L4VaWFjgww8/BJCT3O7fv1967vHjx9JIoaenJ9q0aVNgTPr4/Ro0aJCURJUrVw4DBw7EvHnzsGbNGmzevBnbt2/H9u3bMWfOHKmOQqHQ2Ka2n19uOXXrxQUGBiIwMFBlFDo6Ohr//PMPxo4dixo1aqB3797SCKwyOb/vr/+u6+Nzp+LDRIroNYIgYP78+dL51KlTi6Sfgv4wGIPnz58XqoydnZ10rJxw9OvXD2LOzS1a/+QusFiUlOPV5rUCqn+AlesXFUdHRyxZsgRxcXHSH1I/Pz/pD2xKSgrmzJmDXr165btI5OjRo6VRKOURKOWFIbUZjdKH8PBw7Nu3DwDQpEkTREVFYePGjZg6dSr8/f0xYMAA9O3bF3379i3UPDhtP7/ccsq/n7kEQcDw4cNx4cIF3L17F+vWrcPHH3+MRo0aAci5FLxv3z60atUKly5dUqmr3F5KSkqhftfVLdSqj8+digcTKSI1unXrJs2nOHXqFLZv365VPeXLZgVNEtVm1WZDi4qKKlSZqlWrSseurq7SsTYraxuCvb29NEJx586dAv8gZWdn4/bt2wByRsXKly9f1CFKLCws0Lp1a3z66afYtGkT4uPjsXnzZmlO09GjR/P9PXV1dUWfPn0A5Iyy3r9/H6Io4o8//gCQM0ozdOjQYnkdwcHB0vH333+vcY5e7twtbdy+fTvP6v6ve/r0qTRypPy7qk6NGjUwdOhQLFu2DJcvX8bVq1fh4+MDIGckUvnGA0D1crM+f9/lfO5UPJhIEeVj/vz50pyLr7/+WqsRpAoVKkjHypNJ1Tl58qS8AIvBoUOHCiyjvCig8qUhR0dH6V/y586dQ1xcnP4DlEkQBGlhxufPnyM8PFxj+fDwcGlEqlWrVkU60bwg5ubmGDBggMrk8ePHj+dbfty4cQAgTToPDg6W5vW89957sLe3L8pwJcrbtNSpU0djWeXLkAVJSUnJM4H8dfn9rmqjYcOG2Lp1q/SZv/5e5yZZQOHiLqzCfu5U9JhIEeWjRYsW8PPzAwBcu3ZNq33SchMHABpXHb5x44Z0ecOYHTp0CBcvXsz3+b179+L69esAclbtrlmzpsrzyrd45y4tYWz69+8vHS9YsEBjWeVLvsr1DEn5Pde0f1vXrl2lGwP++usv/P7779JzxXVZD1Cdy6RpxPPUqVOFTkh+/PHHfJ/Lzs6W7soDgAEDBhSqbSDnRo7chPP193rw4MHSiPTixYuLfMRZ28+dih4TKSINvvvuO+lW7yVLlhRYvlu3blL5X3/9Ve0figcPHqB///4m8T8/hUKBgQMH5rnDCci5ZfuDDz6QztWt4fPxxx/D3d0dQM7cnC+//DLf29uBnMuhmzZtwq+//io/eC2NGDECzs7OAHISQ+UJzsrmzJkjJb/Ozs4YOXJkkcb16NEjTJ48WbqUqE5WVpZ0eQ6AxjW/BEHA2LFjAeRcesqd7O3l5SWtLVYclLdmmT17tto7+y5evIgBAwYUeKnudVu2bFFJlnJlZ2dj0qRJ0ohVo0aNpDXLlGM5ePCgxsu7QUFB0qXB19/ratWqYcKECQBy7l7t0aNHnnXVXnfhwoU8Say+P3cqelz+gEiDunXr4sMPP8Ty5cu1mszq4uICf39/rFq1CsnJyWjdujXGjRuHpk2bIiMjA6dPn8aaNWvw4sULDBo0CBs3biyGV6G7/v37Y+vWrWjUqBE++OADeHl5QaFQ4OTJkwgMDJT+CPbr10/tgqE2NjbYtWsXvL29kZSUhB9++AHr1q3DgAED4OnpCXt7e7x48QKxsbE4d+4cDh8+jJSUFJUErajZ2tpizZo16N27tzRytn//fgwaNAhVqlTB48ePsWHDBpw6dQpAzpyVNWvWFPlE84yMDCxevBiLFy9GixYt0LFjRzRs2BAVKlRAWloa7ty5g6CgIOkPbq1atTB48GCNbY4YMQJff/21SvJSnKNRQM7vSvXq1XHv3j2cOXMG9evXx4cffog6dergxYsXOHbsGDZs2IBXr15h+PDh0qKuBWnWrBlSUlIwefJk7Nq1CwMGDICTkxNiY2Oxfv16nD9/HkDOPMbVq1fnuSwbEhKCWbNmwcnJCT169ECzZs3g4uICMzMzPHr0CAcPHlS51P36HCkgZ87XhQsXEBwcjHPnzqFBgwbo06cPOnbsiCpVqiA7O1vasy8kJAQ3b96Eubm5yj6HRfG5UxErjuXTiYwN1GwRk5+HDx9Ke37l/uS3RYwo5uwb16ZNmzx7gOX+lC1bVly7dm2htogZPny4xhgLaktZQdvYKG8RExISIk6ePDnf1wJAfOedd/LdUyxXVFSUxvdE+UcQBHHGjBmyXqMu9uzZI1aoUEFjbBUqVBD37NmjsR19bRFz9+5drd4vIGd/vKioKK3aVf787ezsxNTU1ALrFOZ3URQLfg/OnDkjOjo65vt6zM3Nxfnz54shISHSYzNnzlTbVu7zPj4+4qVLl0R3d/d823VwcBAPHDigth1fX1+t3uty5cpp3CInMzNTnDx5srR3YUE/r79HRfW5U9HhiBRRAapUqYJPP/0U33//vVblHRwccOzYMSxfvhxBQUG4du0aMjMz4erqih49emDChAmoX7++VnOujMGPP/6IN998EytWrEBERATi4uLg4OCAFi1a4IMPPtBqrknt2rURERGB4OBgbN68GSdPnsTDhw+RmpoKGxsbuLq6wsPDAz4+Pnj77bfzzLUqDr1798adO3fw+++/Y+/evbhx4waSkpJQvnx51KtXD71798ZHH31UbHfq1ahRA7dv38bBgwdx8uRJXLx4Effu3UNqairKlCkDFxcXNG/eHP3798fAgQM1rjaurHv37tIoz5AhQ9QuA1DUWrRogYsXL2LRokXYs2cPYmJiYGFhgapVq6JTp04YPXo0vLy8EBoaWqh2GzdujPPnz2PZsmXYtm0boqOjkZGRgerVq+Ott97C5MmTUaVKFbV1d+/ejcOHD+PYsWM4d+4coqKi8OTJE4iiiPLly6NBgwbo2rUrPvzwQ413/FlaWuLHH3/E+PHjsWrVKoSEhODWrVtITEyEmZkZKlWqhHr16qFNmzbo0aMHvL29VeoX1edORUcQxUJehCYiIpPVt29faX7UuXPn9LZnoaHk3lnr4+NT6MSLSB842ZyIqJSIjY3Fnj17AOTc/m/qSRSRMWAiRURUSsyaNUtaD+3TTz81bDBEJQQvrhIRlVBRUVGIiopCamoq9uzZg7Vr1wLI2Zpl4MCBBo6OqGRgIkVEVEKtW7cOs2fPVnnMxsZG7e3/RKQbfpOIiEo4QRDg6uqKAQMG4PTp02jRooWhQyIqMXjXHhEREZGOOCJFREREpCMmUkREREQ6YiJFREREpCMmUkREREQ6YiJFREREpCMmUkREREQ6YiJFREREpCMmUkREREQ6+j/VMg+Gcyn9cQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-FS-to-FS-per-cell.png\n" + ] + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"FS\", \"FS\", dist_3d=True, exp_max_dist=[250e-6], exp_data_detailed=[(7, 12)], return_ax=True, show_plot=False, save_figure=False)\n", "sa_pd2.plot_connection_probability(\"FS\", \"FS\", dist_3d=True, ax=ax, colour=\"blue\")\n", @@ -518,10 +13877,113 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "ce2efe34-447e-4184-81bf-438c03aeaf9b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability FS to iSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [637956.]\n", + "P(d<0.0001) = 0.46944847862279054\n", + "P(d<0.00015) = 0.34335868353766474\n", + "P(d<0.00025) = 0.1516185416337494\n", + "Plotting connection probability FS to iSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [637956.]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-FS-to-iSPN-synapses-3D-dist.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 33041 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-FS-to-iSPN-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 30099 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-FS-to-iSPN-per-cell.png\n" + ] + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"FS\", \"iSPN\", dist_3d=True, exp_max_dist=[100e-6, 150e-6, 250e-6], exp_data_detailed=[(6, 9), (21, 54), (27,77)], return_ax=True, show_plot=False, save_figure=False)\n", "sa_pd2.plot_connection_probability(\"FS\", \"iSPN\", dist_3d=True, ax=ax, colour=\"blue\")\n", @@ -532,10 +13994,113 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "dd5e3b8e-3a8c-4bbe-a89d-c014ae39bd59", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting connection probability FS to dSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requested: 10000000.0 calculated [637956.]\n", + "P(d<0.0001) = 0.6467987202070676\n", + "P(d<0.00015) = 0.4924724043489086\n", + "P(d<0.00025) = 0.22740965372719305\n", + "Plotting connection probability FS to dSPN (synapses)\n", + "Centering in None : Keeping 4833/4833\n", + "Counting connections\n", + "Requested: 10000000.0 calculated [637956.]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/snudda/analyse/analyse.py:1438: RuntimeWarning: invalid value encountered in divide\n", + " p_con = np.divide(count_con, count_all)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-distance-dependent-connection-probability-FS-to-dSPN-synapses-3D-dist.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 49174 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD0/figures/Network-number-of-synapses-from-FS-to-dSPN-per-cell.png\n", + "Plotting number of connections\n", + "Only analysing centre post synaptic neurons, sideLen = 0.00025\n", + "Centering in None : Keeping 4833/4833\n", + "Calculating max synapses\n", + "Calculating mean synapses\n", + "Plotting 40437 connections\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wrote networks/PD-example-10k/PD2/figures/Network-number-of-synapses-from-FS-to-dSPN-per-cell.png\n" + ] + } + ], "source": [ "ax = sa_pd0.plot_connection_probability(\"FS\", \"dSPN\", dist_3d=True, exp_max_dist=[100e-6, 150e-6, 250e-6], exp_data_detailed=[(8, 9), (29, 48), (48,90)], return_ax=True, show_plot=False, save_figure=False)\n", "sa_pd2.plot_connection_probability(\"FS\", \"dSPN\", dist_3d=True, ax=ax, colour=\"blue\")\n", diff --git a/examples/notebooks/ProjectionExample/composite_axon_projections.ipynb b/examples/notebooks/ProjectionExample/composite_axon_projections.ipynb index 911cfe161..497fe0b5a 100644 --- a/examples/notebooks/ProjectionExample/composite_axon_projections.ipynb +++ b/examples/notebooks/ProjectionExample/composite_axon_projections.ipynb @@ -11,12 +11,12 @@ "\n", "To define a new projection, the following should be defined in a .json-file:\n", " - a mapping between points in the source and target regions (intermediate values are interpolated),\n", - " - a list of morphologies to use, and optionally,\n", + " - a list of axon morphologies to use, and optionally,\n", " - the rotation of the termination zone.\n", "\n", "### Minimal Example of a Configuration File\n", - "For example, the file may look like this:\n", - "\n", + "For example, the file may look like this\n", + "(File: ```Snudda/snudda/data/InputAxons/GPe2Striatum/example-projection-config.json```)\n", "\n", "```\n", "{\n", @@ -62,7 +62,7 @@ "\n", "\n", "### Configuration File Using Paths\n", - "If the number of points used in the mapping is large, alternatively, a path can be specified:\n", + "The source and destination points can be explicitly given in the configuration file, or if the number of points used in the mapping is large, alternatively, a path can be specified:\n", "\n", "```\n", "{\n", @@ -129,7 +129,7 @@ "create_cube_mesh(mesh_file_a, [5e-3,0,0], 300e-6, \"Volume A - connect structures example\")\n", "create_cube_mesh(mesh_file_b, [-5e-3,0,0], 300e-6, \"Volume B - connect structures example\")\n", "\n", - "duration = 1.0" + "duration = 0.5" ] }, { @@ -169,10 +169,10 @@ "\n", "proj_file = os.path.join(\"$SNUDDA_DATA\", \"InputAxons\", \"GPe2Striatum\", \"example-projection-config.json\")\n", "\n", - "si.add_neurons(name=\"dSPN\", num_neurons=20, volume_id=\"VolumeA\",\n", + "si.add_neurons(name=\"dSPN\", num_neurons=10, volume_id=\"VolumeA\",\n", " neuron_dir=os.path.join(\"$DATA\",\"neurons\",\"striatum\",\"dspn\"),\n", " axon_config=proj_file)\n", - "si.add_neurons(name=\"iSPN\", num_neurons=20, volume_id=\"VolumeB\",\n", + "si.add_neurons(name=\"iSPN\", num_neurons=10, volume_id=\"VolumeB\",\n", " neuron_dir=os.path.join(\"$DATA\",\"neurons\",\"striatum\",\"ispn\"))\n", "\n", "# Normally we would use add_neuron_target to connect the neurons in the same volume together, \n", @@ -185,7 +185,7 @@ " target_name=\"iSPN\",\n", " region_name=\"VolumeA\",\n", " connection_type=\"GABA\",\n", - " # projection_file=proj_file, # -- moved to add_neuron\n", + " # projection_file=proj_file, # -- moved to add_neuron, since the axon belongs to a neuron\n", " projection_name=\"ExampleProjection\",\n", " dist_pruning=SPN2SPNdistDepPruning,\n", " f1=None, soft_max=None, mu2=None, a3=None,\n", @@ -199,6 +199,36 @@ "si.write_json()" ] }, + { + "cell_type": "markdown", + "id": "04b78438-f811-4d22-819c-11894f749beb", + "metadata": {}, + "source": [ + "## Example projection\n", + "\n", + "When adding the axon to the neuron, then ```axon_config``` is added to the neuron definition (excerpt from ```network-config.json``` file). \n", + "\n", + "```\n", + "\"neurons\": {\n", + " \"dSPN\": {\n", + " \"num_neurons\": 20,\n", + " \"neuron_type\": \"neuron\",\n", + " \"rotation_mode\": \"random\",\n", + " \"volume_id\": \"VolumeA\",\n", + " \"axon_config\": \"$SNUDDA_DATA/InputAxons/GPe2Striatum/example-projection-config.json\",\n", + " \"neuron_path\": {\n", + " \"dSPN_0\": \"/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508\",\n", + " \"dSPN_1\": \"/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521\",\n", + " \"dSPN_2\": \"/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503\",\n", + " \"dSPN_3\": \"/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521\"\n", + " }\n", + " }\n", + " }\n", + "```\n", + "\n", + "Also, in the ```connectivity``` block we have specified ```projection_name``` corresponding to the name specified in the axon configuration file." + ] + }, { "cell_type": "code", "execution_count": 3, @@ -234,49 +264,26 @@ "\n", "Reading SNUDDA_DATA=None from networks/composite_axon_example/network-config.json\n", "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/composite_axon_example/network-synapses.hdf5\n", - "Generating 10649 points for networks/composite_axon_example/mesh/volume_A.obj\n", - "n_points = 10128, previous close_pairs = 20878\n", - "n_points = 9633, previous close_pairs = 17156\n", - "n_points = 9165, previous close_pairs = 14277\n", - "n_points = 8722, previous close_pairs = 11912\n", - "n_points = 8305, previous close_pairs = 9919\n", - "n_points = 7913, previous close_pairs = 8366\n", - "n_points = 7547, previous close_pairs = 6946\n", - "n_points = 7208, previous close_pairs = 5900\n", - "n_points = 6894, previous close_pairs = 4948\n", - "n_points = 6605, previous close_pairs = 4095\n", - "n_points = 6341, previous close_pairs = 3492\n", - "n_points = 6102, previous close_pairs = 2995\n", - "n_points = 5886, previous close_pairs = 2542\n", - "n_points = 5694, previous close_pairs = 2132\n", - "n_points = 5525, previous close_pairs = 1781\n", - "n_points = 5507, previous close_pairs = 1470\n", - "n_points = 4073, previous close_pairs = 1434\n", - "Filtering 4073 points..\n", - "Filtering, keeping inside points: 2962 / 4073\n", - "Generating 10649 points for networks/composite_axon_example/mesh/volume_B.obj\n", - "n_points = 10129, previous close_pairs = 21239\n", - "n_points = 9636, previous close_pairs = 17463\n", - "n_points = 9169, previous close_pairs = 14493\n", - "n_points = 8728, previous close_pairs = 12091\n", - "n_points = 8312, previous close_pairs = 10079\n", - "n_points = 7920, previous close_pairs = 8513\n", - "n_points = 7553, previous close_pairs = 7066\n", - "n_points = 7211, previous close_pairs = 5986\n", - "n_points = 6894, previous close_pairs = 5018\n", - "n_points = 6604, previous close_pairs = 4141\n", - "n_points = 6338, previous close_pairs = 3541\n", - "n_points = 6098, previous close_pairs = 3028\n", - "n_points = 5882, previous close_pairs = 2565\n", - "n_points = 5689, previous close_pairs = 2167\n", - "n_points = 5517, previous close_pairs = 1816\n", - "n_points = 5492, previous close_pairs = 1491\n", - "n_points = 4049, previous close_pairs = 1443\n", - "Filtering 4049 points..\n", - "Filtering, keeping inside points: 2918 / 4049\n", + "No n_putative_points and putative_density, setting n_putative_points = 10782\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 10782 points for networks/composite_axon_example/mesh/volume_A.obj\n", + "Filtering, keeping inside points: 2987 / 4103\n", + "neuron_name = 'dSPN_0', num = 2, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508'\n", + "neuron_name = 'dSPN_1', num = 3, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521'\n", + "neuron_name = 'dSPN_2', num = 3, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503'\n", + "neuron_name = 'dSPN_3', num = 2, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521'\n", + "No n_putative_points and putative_density, setting n_putative_points = 10782\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 10782 points for networks/composite_axon_example/mesh/volume_B.obj\n", + "Filtering, keeping inside points: 2936 / 4072\n", + "neuron_name = 'iSPN_0', num = 3, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611'\n", + "neuron_name = 'iSPN_1', num = 3, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603'\n", + "neuron_name = 'iSPN_2', num = 2, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527'\n", + "neuron_name = 'iSPN_3', num = 2, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529'\n", + "importing SnuddaPlace from snudda.place on engine(s)\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 0.6s\n", + "Execution time: 2.1s\n", "Touch detection\n", "Network path: networks/composite_axon_example\n", "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/ProjectionExample/.ipython/profile_default/security/ipcontroller-client.json\n", @@ -289,18 +296,18 @@ "Reading SNUDDA_DATA=None from networks/composite_axon_example/network-config.json\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 16.6s\n", + "Execution time: 13.6s\n", "Prune synapses\n", "Network path: networks/composite_axon_example\n", "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/ProjectionExample/.ipython/profile_default/security/ipcontroller-client.json\n", "\n", "No file networks/composite_axon_example/pruning_merge_info.json\n", "importing SnuddaPrune from snudda.detect.prune on engine(s)\n", - "prune_synapses_parallel (16/44 synapses, 36.4% kept): 0.0s\n", + "prune_synapses_parallel (9/19 synapses, 47.4% kept): 0.0s\n", "prune_synapses_parallel (0/0 gap_junctions, 0.0% kept): 0.0s\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 17.3s\n" + "Execution time: 14.3s\n" ] } ], @@ -334,13 +341,13 @@ "Writing spikes to networks/composite_axon_example/input-spikes.hdf5\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 18.7s\n" + "Execution time: 15.2s\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -364,9 +371,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-02-28 13:02:32.160 [IPClusterStop] Stopping cluster \n", - "2024-02-28 13:02:32.160 [IPClusterStop] Stopping controller\n", - "2024-02-28 13:02:32.300 [IPClusterStop] Stopping engine(s): 1709121724\n" + "2024-06-18 15:52:26.863 [IPClusterStop] Stopping cluster \n", + "2024-06-18 15:52:26.863 [IPClusterStop] Stopping controller\n", + "2024-06-18 15:52:26.979 [IPClusterStop] Stopping engine(s): 1718718722\n" ] }, { @@ -384,6 +391,15 @@ "os.system(\"ipcluster stop\")" ] }, + { + "cell_type": "markdown", + "id": "c64bf4b3-46c8-45b3-b88e-a56e41941eed", + "metadata": {}, + "source": [ + "## Plotting network\n", + "Here we can see that the dSPN and iSPN in this example are located far from each other, so the only connections between dSPN and iSPN will be using the projection feature." + ] + }, { "cell_type": "code", "execution_count": 7, @@ -402,7 +418,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -454,19 +470,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "mpiexec -n 4 snudda simulate networks/composite_axon_example --time 1.0\n", + "mpiexec -n 4 snudda simulate networks/composite_axon_example --time 0.5\n", "args.ipython_profile = None\n", "args.ipython_profile = None\n", "args.ipython_profile = None\n", "args.ipython_profile = None\n", - "numprocs=4\n", - "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=1.0, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, neuromodulation=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", + "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=0.5, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", "Using input file networks/composite_axon_example/input-spikes.hdf5\n", - "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=1.0, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, neuromodulation=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", - "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=1.0, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, neuromodulation=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", + "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=0.5, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", "Using input file networks/composite_axon_example/input-spikes.hdf5\n", - "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=1.0, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, neuromodulation=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", + "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=0.5, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", "Using input file networks/composite_axon_example/input-spikes.hdf5\n", + "args: Namespace(action='simulate', path='networks/composite_axon_example', network_file=None, input_file=None, output_file=None, time=0.5, snudda_data=None, simulation_config=None, record_volt=True, randomseed=None, disable_synapses=None, disable_gj=None, mech_dir=None, profile=False, verbose=False, exportCoreNeuron=False, record_all=None, ipython_profile=None)\n", "Using input file networks/composite_axon_example/input-spikes.hdf5\n", "Reading SNUDDA_DATA=None from networks/composite_axon_example/network-config.json\n", "Reading SNUDDA_DATA=None from networks/composite_axon_example/network-config.json\n", @@ -492,141 +507,162 @@ "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/composite_axon_example/network-synapses.hdf5\n", "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/composite_axon_example/network-synapses.hdf5\n", "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/composite_axon_example/network-synapses.hdf5\n", - "2 : Memory status: 60% free\n", - "1 : Memory status: 60% free\n", - "0 : Memory status: 60% free\n", - "3 : Memory status: 60% free\n", + "0 : Memory status: 61% free\n", + "0 : Memory status: 61% free\n", + "0 : Memory status: 61% free\n", + "0 : Memory status: 61% free\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603/modulation.json\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611/modulation.json\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603/modulation.json\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", - "1 : Memory status: 60% free\n", - "3 : Memory status: 60% free\n", - "0 : Memory status: 60% free\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", - "2 : Memory status: 60% free\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "0 : Memory status: 60% free\n", "Added 0.0 gap junctions to simulation (0 total)\n", - "Added 16 synapses to simulation (16 total)\n", - "2 : Memory status: 60% free\n", - "1 : Memory status: 60% free\n", - "3 : Memory status: 60% free\n", + "Added 9 synapses to simulation (9 total)\n", "0 : Memory status: 60% free\n", "0 : Memory status: 60% free\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Added 9 synapses to simulation (9 total)\n", "0 : Memory status: 60% free\n", - "Running simulation for 1000.0 ms.\n", - "1 : Memory status: 60% free\n", - "1 : Memory status: 60% free\n", - "Running simulation for 1000.0 ms.\n", - "2 : Memory status: 60% free\n", - "2 : Memory status: 60% free\n", - "Running simulation for 1000.0 ms.\n", - "3 : Memory status: 60% free\n", - "3 : Memory status: 60% free\n", - "Running simulation for 1000.0 ms.\n", - "Running simulation for 1.0 s\n", - "Running simulation for 1.0 s\n", - "Running simulation for 1.0 s\n", - "Running simulation for 1.0 s\n", - "Running Neuron simulator 1000 ms, with dt=0.025\n", - "Running Neuron simulator 1000 ms, with dt=0.025\n", - "Running Neuron simulator 1000 ms, with dt=0.025\n", - "Running Neuron simulator 1000 ms, with dt=0.025\n", - "1% done. Elapsed: 0.8 s, estimated time left: 82.2 s\n", - "99% done. Elapsed: 73.4 s, estimated time left: 0.7 s\n", + "0 : Memory status: 60% free\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Added 9 synapses to simulation (9 total)\n", + "0 : Memory status: 60% free\n", + "0 : Memory status: 60% free\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Added 9 synapses to simulation (9 total)\n", + "0 : Memory status: 60% free\n", + "0 : Memory status: 60% free\n", + "0 : Memory status: 60% free\n", + "Running simulation for 500.0 ms.\n", + "0 : Memory status: 60% free\n", + "0 : Memory status: 60% free\n", + "0 : Memory status: 60% free\n", + "Running simulation for 500.0 ms.\n", + "0 : Memory status: 60% free\n", + "Running simulation for 500.0 ms.\n", + "Running simulation for 0.5 s\n", + "Running Neuron simulator 500 ms, with dt=0.025\n", + "Running simulation for 0.5 s\n", + "Running Neuron simulator 500 ms, with dt=0.025\n", + "Running simulation for 0.5 s\n", + "Running Neuron simulator 500 ms, with dt=0.025\n", + "0 : Memory status: 60% free\n", + "0 : Memory status: 60% free\n", + "Running simulation for 500.0 ms.\n", + "Running simulation for 0.5 s\n", + "Running Neuron simulator 500 ms, with dt=0.025\n", + " 1% done. Elapsed: 1.1 s, estimated time left: 110.4 s\n", + " 1% done. Elapsed: 1.1 s, estimated time left: 111.4 s\n", + " 1% done. Elapsed: 1.1 s, estimated time left: 111.5 s\n", + " 1% done. Elapsed: 1.1 s, estimated time left: 111.3 s\n", + "100% done. Elapsed: 103.1 s, estimated time left: 0.0 s\n", "Neuron simulation finished\n", - "Simulation run time: 74.2 s\n", + "Simulation run time: 103.1 s\n", "Simulation done, saving output\n", + "Writing network output to networks/composite_axon_example/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to networks/composite_axon_example/simulation/output.hdf5\n", + "Program run time: 108.6s\n", + "100% done. Elapsed: 103.8 s, estimated time left: 0.0 s\n", "Neuron simulation finished\n", - "Simulation run time: 74.2 s\n", + "Simulation run time: 103.8 s\n", "Simulation done, saving output\n", + "Writing network output to networks/composite_axon_example/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to networks/composite_axon_example/simulation/output.hdf5\n", + "Program run time: 109.3s\n", + "100% done. Elapsed: 104.0 s, estimated time left: 0.0 s\n", "Neuron simulation finished\n", - "Simulation run time: 74.1 s\n", + "Simulation run time: 104.0 s\n", "Simulation done, saving output\n", + "Writing network output to networks/composite_axon_example/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to networks/composite_axon_example/simulation/output.hdf5\n", + "Program run time: 109.5s\n", + "100% done. Elapsed: 104.3 s, estimated time left: 0.0 s\n", "Neuron simulation finished\n", - "Simulation run time: 74.2 s\n", + "Simulation run time: 104.3 s\n", "Simulation done, saving output\n", "Writing network output to networks/composite_axon_example/simulation/output.hdf5\n", "Using sample dt = None (sample step size None)\n", - "Worker 1/4 writing data to networks/composite_axon_example/simulation/output.hdf5\n", - "Worker 2/4 writing data to networks/composite_axon_example/simulation/output.hdf5\n", - "Worker 3/4 writing data to networks/composite_axon_example/simulation/output.hdf5\n", - "Worker 4/4 writing data to networks/composite_axon_example/simulation/output.hdf5\n", - "Program run time: 76.9s\n" + "Worker 1/1 writing data to networks/composite_axon_example/simulation/output.hdf5\n", + "Program run time: 109.8s\n" ] }, { @@ -660,22 +696,14 @@ "text": [ "Loading networks/composite_axon_example/simulation/output.hdf5\n", "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", - "8: (iSPN_3, default, )\n", - "10: (iSPN_3, default, )\n", - "11: (iSPN_3, default, )\n", - "15: (iSPN_3, default, )\n", - "17: (iSPN_3, default, )\n", - "22: (dSPN_2, default, )\n", - "25: (dSPN_2, default, )\n", - "28: (dSPN_2, default, )\n", - "33: (dSPN_2, default, )\n", - "35: (dSPN_2, default, )\n", + "1: (iSPN_3, default, )\n", + "9: (iSPN_3, default, )\n", "Saving figure to networks/composite_axon_example/figures/spike-raster.png\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/examples/notebooks/README.md b/examples/notebooks/README.md index 404c938e3..a39641f8b 100644 --- a/examples/notebooks/README.md +++ b/examples/notebooks/README.md @@ -8,6 +8,7 @@ Here is a collection of Jupyter Notebooks, some of the workflows are split over * [simple_network_parallel](simple_network_parallel.ipynb) how to use ipyparallel when running Snudda * [custom_slice_example](custom_slice_example.ipynb) shows how to create custom slice and define your own connectivity rules for neuron types. * [population_unit_network](population_unit_network.ipynb) how to define population units. +* [population_unit_mesh](population_unit_mesh.ipynb) how to define population units specified by volume meshes. * [example_of_density_function](example_of_density_function.ipynb) how to specify density variations using a function of (x,y,z) in a volume. * [example_of_neuron_rotations](example_of_neuron_rotations.ipynb) shows how to rotate neurons based on position. * [bend_morphologies](bend_morphologies.ipynb) shows how to make the neurons bend the axons and dendrites at the edge of the mesh, to keep them constrained to the volume. @@ -42,3 +43,5 @@ Here is a collection of Jupyter Notebooks, some of the workflows are split over ## Additional notebooks * [Paired current injection](validation/synapses/network_pair_pulse_simulation.ipynb) in a simulated network * [Neuromodulation examples](https://github.com/jofrony/Neuromodulation-software/tree/main/examples) in a network +* [Minimal current injection example](neuron_with_current_injection.ipynb) + diff --git a/examples/notebooks/VirtualNeurons/VirtualNeurons.ipynb b/examples/notebooks/VirtualNeurons/VirtualNeurons.ipynb index 84eed8934..504452137 100644 --- a/examples/notebooks/VirtualNeurons/VirtualNeurons.ipynb +++ b/examples/notebooks/VirtualNeurons/VirtualNeurons.ipynb @@ -137,27 +137,38 @@ "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/VirtualNeurons/.ipython/profile_default/security/ipcontroller-client.json\n", "\n", "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/data from networks/virtual_network_example/network-config.json\n", - "Generating 771 points for networks/virtual_network_example/mesh/Striatum-cube-mesh-0.00010749824478388102.obj\n", - "n_points = 734, previous close_pairs = 1365\n", - "n_points = 699, previous close_pairs = 1122\n", - "n_points = 665, previous close_pairs = 923\n", - "n_points = 633, previous close_pairs = 772\n", - "n_points = 603, previous close_pairs = 645\n", - "n_points = 575, previous close_pairs = 534\n", - "n_points = 549, previous close_pairs = 449\n", - "n_points = 525, previous close_pairs = 372\n", - "n_points = 503, previous close_pairs = 305\n", - "n_points = 484, previous close_pairs = 250\n", - "n_points = 466, previous close_pairs = 212\n", - "n_points = 450, previous close_pairs = 178\n", - "n_points = 436, previous close_pairs = 147\n", - "n_points = 427, previous close_pairs = 120\n", - "n_points = 324, previous close_pairs = 103\n", - "Filtering 324 points..\n", - "Filtering, keeping inside points: 141 / 324\n", + "No n_putative_points and putative_density, setting n_putative_points = 780\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 780 points for networks/virtual_network_example/mesh/Striatum-cube-mesh-0.00010749824478388102.obj\n", + "n_points = 742, previous close_pairs = 1402\n", + "n_points = 706, previous close_pairs = 1147\n", + "n_points = 672, previous close_pairs = 941\n", + "n_points = 640, previous close_pairs = 787\n", + "n_points = 610, previous close_pairs = 661\n", + "n_points = 582, previous close_pairs = 551\n", + "n_points = 556, previous close_pairs = 465\n", + "n_points = 532, previous close_pairs = 387\n", + "n_points = 510, previous close_pairs = 318\n", + "n_points = 490, previous close_pairs = 256\n", + "n_points = 472, previous close_pairs = 217\n", + "n_points = 456, previous close_pairs = 181\n", + "n_points = 442, previous close_pairs = 150\n", + "n_points = 430, previous close_pairs = 125\n", + "n_points = 427, previous close_pairs = 102\n", + "n_points = 331, previous close_pairs = 96\n", + "Filtering 331 points..\n", + "Filtering, keeping inside points: 142 / 331\n", + "neuron_name = 'dSPN_0', num = 12, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508'\n", + "neuron_name = 'dSPN_1', num = 12, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521'\n", + "neuron_name = 'dSPN_2', num = 14, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503'\n", + "neuron_name = 'dSPN_3', num = 12, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521'\n", + "neuron_name = 'iSPN_0', num = 12, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611'\n", + "neuron_name = 'iSPN_1', num = 12, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603'\n", + "neuron_name = 'iSPN_2', num = 14, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527'\n", + "neuron_name = 'iSPN_3', num = 12, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529'\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 0.1s\n", + "Execution time: 0.2s\n", "Touch detection\n", "Network path: networks/virtual_network_example\n", "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/VirtualNeurons/.ipython/profile_default/security/ipcontroller-client.json\n", @@ -169,18 +180,18 @@ "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/data from networks/virtual_network_example/network-config.json\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 5.3s\n", + "Execution time: 7.6s\n", "Prune synapses\n", "Network path: networks/virtual_network_example\n", "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/VirtualNeurons/.ipython/profile_default/security/ipcontroller-client.json\n", "\n", "No file networks/virtual_network_example/pruning_merge_info.json\n", "importing SnuddaPrune from snudda.detect.prune on engine(s)\n", - "prune_synapses_parallel (7138/149366 synapses, 4.8% kept): 0.2s\n", + "prune_synapses_parallel (7386/150743 synapses, 4.9% kept): 0.2s\n", "prune_synapses_parallel (0/0 gap_junctions, 0.0% kept): 0.0s\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 5.8s\n" + "Execution time: 8.2s\n" ] } ], @@ -227,97 +238,105 @@ "text": [ "Writing to networks/virtual_network_example/modified-network.hdf5\n", "Keeping 100 neurons.\n", - "Making neuron id 0 (iSPN_3) virtual (old ID 0)\n", - "Making neuron id 1 (dSPN_2) virtual (old ID 1)\n", - "Making neuron id 2 (dSPN_3) virtual (old ID 2)\n", + "Making neuron id 0 (dSPN_2) virtual (old ID 0)\n", + "Making neuron id 1 (dSPN_1) virtual (old ID 1)\n", + "Making neuron id 2 (dSPN_1) virtual (old ID 2)\n", "Making neuron id 3 (dSPN_2) virtual (old ID 3)\n", - "Making neuron id 4 (dSPN_1) virtual (old ID 4)\n", - "Making neuron id 5 (iSPN_0) virtual (old ID 5)\n", - "Making neuron id 6 (iSPN_2) virtual (old ID 6)\n", - "Making neuron id 7 (dSPN_2) virtual (old ID 7)\n", - "Making neuron id 8 (iSPN_1) virtual (old ID 8)\n", - "Making neuron id 9 (dSPN_0) virtual (old ID 9)\n", - "Making neuron id 10 (dSPN_2) virtual (old ID 10)\n", - "Making neuron id 12 (dSPN_1) virtual (old ID 12)\n", - "Making neuron id 14 (dSPN_3) virtual (old ID 14)\n", - "Making neuron id 17 (dSPN_1) virtual (old ID 17)\n", - "Making neuron id 21 (dSPN_2) virtual (old ID 21)\n", + "Making neuron id 4 (iSPN_1) virtual (old ID 4)\n", + "Making neuron id 5 (iSPN_2) virtual (old ID 5)\n", + "Making neuron id 7 (iSPN_2) virtual (old ID 7)\n", + "Making neuron id 8 (iSPN_3) virtual (old ID 8)\n", + "Making neuron id 10 (iSPN_3) virtual (old ID 10)\n", + "Making neuron id 11 (dSPN_1) virtual (old ID 11)\n", + "Making neuron id 12 (iSPN_0) virtual (old ID 12)\n", + "Making neuron id 14 (iSPN_3) virtual (old ID 14)\n", + "Making neuron id 15 (iSPN_1) virtual (old ID 15)\n", + "Making neuron id 16 (iSPN_3) virtual (old ID 16)\n", + "Making neuron id 20 (iSPN_2) virtual (old ID 20)\n", "Making neuron id 22 (iSPN_2) virtual (old ID 22)\n", - "Making neuron id 23 (iSPN_0) virtual (old ID 23)\n", - "Making neuron id 24 (dSPN_0) virtual (old ID 24)\n", - "Making neuron id 25 (dSPN_3) virtual (old ID 25)\n", - "Making neuron id 26 (dSPN_1) virtual (old ID 26)\n", - "Making neuron id 27 (dSPN_0) virtual (old ID 27)\n", - "Making neuron id 28 (iSPN_2) virtual (old ID 28)\n", - "Making neuron id 29 (iSPN_1) virtual (old ID 29)\n", - "Making neuron id 30 (dSPN_3) virtual (old ID 30)\n", - "Making neuron id 31 (dSPN_3) virtual (old ID 31)\n", - "Making neuron id 32 (iSPN_3) virtual (old ID 32)\n", - "Making neuron id 33 (iSPN_0) virtual (old ID 33)\n", - "Making neuron id 35 (dSPN_0) virtual (old ID 35)\n", - "Making neuron id 36 (dSPN_0) virtual (old ID 36)\n", - "Making neuron id 37 (iSPN_3) virtual (old ID 37)\n", - "Making neuron id 38 (iSPN_0) virtual (old ID 38)\n", - "Making neuron id 39 (iSPN_2) virtual (old ID 39)\n", - "Making neuron id 41 (iSPN_1) virtual (old ID 41)\n", - "Making neuron id 42 (iSPN_1) virtual (old ID 42)\n", - "Making neuron id 43 (iSPN_0) virtual (old ID 43)\n", - "Making neuron id 45 (dSPN_2) virtual (old ID 45)\n", - "Making neuron id 46 (iSPN_1) virtual (old ID 46)\n", - "Making neuron id 49 (dSPN_2) virtual (old ID 49)\n", - "Making neuron id 50 (dSPN_1) virtual (old ID 50)\n", + "Making neuron id 24 (dSPN_3) virtual (old ID 24)\n", + "Making neuron id 25 (dSPN_1) virtual (old ID 25)\n", + "Making neuron id 26 (iSPN_1) virtual (old ID 26)\n", + "Making neuron id 27 (dSPN_2) virtual (old ID 27)\n", + "Making neuron id 28 (dSPN_3) virtual (old ID 28)\n", + "Making neuron id 29 (iSPN_3) virtual (old ID 29)\n", + "Making neuron id 30 (dSPN_0) virtual (old ID 30)\n", + "Making neuron id 31 (dSPN_1) virtual (old ID 31)\n", + "Making neuron id 32 (dSPN_3) virtual (old ID 32)\n", + "Making neuron id 34 (dSPN_0) virtual (old ID 34)\n", + "Making neuron id 35 (iSPN_1) virtual (old ID 35)\n", + "Making neuron id 36 (iSPN_1) virtual (old ID 36)\n", + "Making neuron id 38 (iSPN_2) virtual (old ID 38)\n", + "Making neuron id 40 (dSPN_2) virtual (old ID 40)\n", + "Making neuron id 41 (dSPN_2) virtual (old ID 41)\n", + "Making neuron id 43 (dSPN_0) virtual (old ID 43)\n", + "Making neuron id 45 (iSPN_2) virtual (old ID 45)\n", + "Making neuron id 47 (iSPN_2) virtual (old ID 47)\n", + "Making neuron id 48 (dSPN_3) virtual (old ID 48)\n", + "Making neuron id 49 (dSPN_3) virtual (old ID 49)\n", + "Making neuron id 50 (dSPN_3) virtual (old ID 50)\n", "Making neuron id 51 (dSPN_2) virtual (old ID 51)\n", - "Making neuron id 52 (iSPN_2) virtual (old ID 52)\n", - "Making neuron id 53 (dSPN_3) virtual (old ID 53)\n", - "Making neuron id 54 (iSPN_0) virtual (old ID 54)\n", - "Making neuron id 55 (dSPN_1) virtual (old ID 55)\n", - "Making neuron id 56 (iSPN_3) virtual (old ID 56)\n", - "Making neuron id 59 (iSPN_0) virtual (old ID 59)\n", + "Making neuron id 52 (dSPN_2) virtual (old ID 52)\n", + "Making neuron id 53 (iSPN_1) virtual (old ID 53)\n", + "Making neuron id 54 (dSPN_2) virtual (old ID 54)\n", + "Making neuron id 55 (dSPN_3) virtual (old ID 55)\n", + "Making neuron id 57 (dSPN_1) virtual (old ID 57)\n", + "Making neuron id 58 (iSPN_1) virtual (old ID 58)\n", "Making neuron id 60 (iSPN_1) virtual (old ID 60)\n", - "Making neuron id 61 (iSPN_3) virtual (old ID 61)\n", - "Making neuron id 63 (iSPN_1) virtual (old ID 63)\n", - "Making neuron id 65 (iSPN_1) virtual (old ID 65)\n", - "Making neuron id 67 (dSPN_3) virtual (old ID 67)\n", - "Making neuron id 68 (iSPN_1) virtual (old ID 68)\n", - "Making neuron id 69 (iSPN_1) virtual (old ID 69)\n", - "Making neuron id 71 (iSPN_1) virtual (old ID 71)\n", - "Making neuron id 72 (iSPN_2) virtual (old ID 72)\n", - "Making neuron id 75 (iSPN_0) virtual (old ID 75)\n", - "Making neuron id 76 (iSPN_3) virtual (old ID 76)\n", - "Making neuron id 77 (iSPN_2) virtual (old ID 77)\n", - "Making neuron id 78 (iSPN_3) virtual (old ID 78)\n", - "Making neuron id 79 (dSPN_0) virtual (old ID 79)\n", - "Making neuron id 80 (dSPN_0) virtual (old ID 80)\n", - "Making neuron id 81 (dSPN_0) virtual (old ID 81)\n", - "Making neuron id 82 (iSPN_2) virtual (old ID 82)\n", - "Making neuron id 83 (dSPN_3) virtual (old ID 83)\n", - "Making neuron id 84 (dSPN_2) virtual (old ID 84)\n", - "Making neuron id 85 (iSPN_2) virtual (old ID 85)\n", - "Making neuron id 86 (iSPN_2) virtual (old ID 86)\n", - "Making neuron id 87 (iSPN_2) virtual (old ID 87)\n", - "Making neuron id 88 (dSPN_2) virtual (old ID 88)\n", - "Making neuron id 89 (iSPN_0) virtual (old ID 89)\n", - "Making neuron id 90 (iSPN_3) virtual (old ID 90)\n", - "Making neuron id 91 (dSPN_1) virtual (old ID 91)\n", - "Making neuron id 92 (iSPN_2) virtual (old ID 92)\n", - "Making neuron id 93 (dSPN_1) virtual (old ID 93)\n", + "Making neuron id 61 (iSPN_2) virtual (old ID 61)\n", + "Making neuron id 63 (iSPN_2) virtual (old ID 63)\n", + "Making neuron id 64 (dSPN_0) virtual (old ID 64)\n", + "Making neuron id 65 (iSPN_3) virtual (old ID 65)\n", + "Making neuron id 66 (dSPN_3) virtual (old ID 66)\n", + "Making neuron id 68 (dSPN_2) virtual (old ID 68)\n", + "Making neuron id 69 (iSPN_0) virtual (old ID 69)\n", + "Making neuron id 70 (dSPN_2) virtual (old ID 70)\n", + "Making neuron id 72 (dSPN_0) virtual (old ID 72)\n", + "Making neuron id 73 (dSPN_1) virtual (old ID 73)\n", + "Making neuron id 74 (iSPN_0) virtual (old ID 74)\n", + "Making neuron id 75 (dSPN_3) virtual (old ID 75)\n", + "Making neuron id 76 (iSPN_0) virtual (old ID 76)\n", + "Making neuron id 77 (iSPN_0) virtual (old ID 77)\n", + "Making neuron id 78 (dSPN_0) virtual (old ID 78)\n", + "Making neuron id 79 (dSPN_2) virtual (old ID 79)\n", + "Making neuron id 80 (dSPN_1) virtual (old ID 80)\n", + "Making neuron id 81 (iSPN_3) virtual (old ID 81)\n", + "Making neuron id 82 (dSPN_2) virtual (old ID 82)\n", + "Making neuron id 83 (dSPN_2) virtual (old ID 83)\n", + "Making neuron id 84 (iSPN_1) virtual (old ID 84)\n", + "Making neuron id 86 (iSPN_3) virtual (old ID 86)\n", + "Making neuron id 87 (iSPN_0) virtual (old ID 87)\n", + "Making neuron id 88 (iSPN_2) virtual (old ID 88)\n", + "Making neuron id 89 (iSPN_3) virtual (old ID 89)\n", + "Making neuron id 90 (dSPN_3) virtual (old ID 90)\n", + "Making neuron id 91 (dSPN_2) virtual (old ID 91)\n", + "Making neuron id 92 (iSPN_0) virtual (old ID 92)\n", + "Making neuron id 93 (iSPN_0) virtual (old ID 93)\n", "Making neuron id 94 (iSPN_3) virtual (old ID 94)\n", - "Making neuron id 95 (iSPN_0) virtual (old ID 95)\n", + "Making neuron id 95 (iSPN_2) virtual (old ID 95)\n", "Making neuron id 96 (dSPN_3) virtual (old ID 96)\n", - "Making neuron id 97 (dSPN_0) virtual (old ID 97)\n", - "Making neuron id 98 (dSPN_2) virtual (old ID 98)\n", - "Making neuron id 99 (iSPN_0) virtual (old ID 99)\n", - "0/7138 synapses processed\n", - "7138/7138 synapses processed\n", + "Making neuron id 97 (dSPN_1) virtual (old ID 97)\n", + "Making neuron id 98 (iSPN_0) virtual (old ID 98)\n", + "Making neuron id 99 (iSPN_1) virtual (old ID 99)\n", + "0/7386 synapses processed\n", + "7386/7386 synapses processed\n", "Filtering done.\n", "Copying synapses and gap junctions\n", - "7138 / 7138 synapse rows parsed\n", + "7386 / 7386 synapse rows parsed\n", "Synapse matrix written.\n", - "Keeping 7138 synapses (out of 7138)\n", + "Keeping 7386 synapses (out of 7386)\n", "0 / 0 gap junction rows parsed\n", "Gap junction matrix written.\n", "Keeping 0 gap junctions (out of 0)\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hjorth/HBP/Snudda/venv/lib/python3.9/site-packages/numpy/core/numeric.py:330: RuntimeWarning: invalid value encountered in cast\n", + " multiarray.copyto(a, fill_value, casting='unsafe')\n" + ] } ], "source": [ @@ -385,13 +404,13 @@ "Writing spikes to networks/virtual_network_example/input-spikes.hdf5\n", "stop_parallel disabled, to keep pool running.\n", "\n", - "Execution time: 6.5s\n" + "Execution time: 8.9s\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -413,9 +432,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-02-28 12:35:49.233 [IPClusterStop] Stopping cluster \n", - "2024-02-28 12:35:49.234 [IPClusterStop] Stopping controller\n", - "2024-02-28 12:35:49.347 [IPClusterStop] Stopping engine(s): 1709120132\n" + "2024-05-30 13:02:10.901 [IPClusterStop] Stopping cluster \n", + "2024-05-30 13:02:10.901 [IPClusterStop] Stopping controller\n", + "2024-05-30 13:02:11.059 [IPClusterStop] Stopping engine(s): 1717066911\n" ] } ], @@ -488,58 +507,38 @@ "output_type": "stream", "text": [ "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/data from networks/virtual_network_example/network-config.json\n", - "0 : Memory status: 85% free\n", + "0 : Memory status: 86% free\n", "Empty mod_file field for ChIN -> dSPN synapses. This channel is IGNORED.\n", "Empty mod_file field for ChIN -> iSPN synapses. This channel is IGNORED.\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527/modulation.json\n", "Warning: Old format of parameter config, using parameter_id = 0.\n", - "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529/modulation.json\n", - "0 : Memory status: 86% free\n", + "0 : Memory status: 87% free\n", "Added 0.0 gap junctions to simulation (0 total)\n", - "ERROR: Added only 1613 out of 7138 synapses! (80 virtual neurons)\n", - "0 : Memory status: 86% free\n", + "ERROR: Added only 1523 out of 7386 synapses! (80 virtual neurons)\n", + "0 : Memory status: 87% free\n", "Running simulation for 0.5 s\n", "Running Neuron simulator 500 ms, with dt=0.025\n", - "1% done. Elapsed: 0.7 s, estimated time left: 65.7 s\n", + "1% done. Elapsed: 0.8 s, estimated time left: 75.8 s\n", "Neuron simulation finished\n", - "Simulation run time: 61.7 s\n", + "Simulation run time: 69.4 s\n", "Writing network output to networks/virtual_network_example/simulation/output.hdf5\n", "Using sample dt = None (sample step size None)\n", "Worker 1/1 writing data to networks/virtual_network_example/simulation/output.hdf5\n" @@ -567,7 +566,7 @@ "output_type": "stream", "text": [ "Loading networks/virtual_network_example/simulation/output.hdf5\n", - "Failed sanity check on neuron ID, not all neurons simulated? [11 13 15 16 18 19 20 34 40 44 47 48 57 58 62 64 66 70 73 74]\n" + "Failed sanity check on neuron ID, not all neurons simulated? [ 6 9 13 17 18 19 21 23 33 37 39 42 44 46 56 59 62 67 71 85]\n" ] } ], @@ -595,15 +594,15 @@ "Loading network info from networks/virtual_network_example/modified-network.hdf5\n", "Loading input info from networks/virtual_network_example/input-spikes.hdf5\n", "Loading networks/virtual_network_example/simulation/output.hdf5\n", - "Failed sanity check on neuron ID, not all neurons simulated? [11 13 15 16 18 19 20 34 40 44 47 48 57 58 62 64 66 70 73 74]\n", + "Failed sanity check on neuron ID, not all neurons simulated? [ 6 9 13 17 18 19 21 23 33 37 39 42 44 46 56 59 62 67 71 85]\n", "Plotting traces: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n", "Plotted 100 traces (total 20)\n", - "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/VirtualNeurons/networks/virtual_network_example/figures/Network-voltage-trace--dSPN-iSPN.pdf\n" + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/VirtualNeurons/networks/virtual_network_example/figures/Network-voltage-trace--iSPN-dSPN.pdf\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/examples/notebooks/neuromodulation/REAME.md b/examples/notebooks/neuromodulation/REAME.md new file mode 100644 index 000000000..5743b0257 --- /dev/null +++ b/examples/notebooks/neuromodulation/REAME.md @@ -0,0 +1,13 @@ +# TODO -- Neuromdulation + +- Run short simulation of each neuron, without dopamine, then turn on dopamine. +- dSPN should increase frequency, iSPN should decrease frequency + +- Add dopamine cascade for iSPN, how do we model D1 and D2 receptors? +- How do we treat diffusion of dopamine? (it should be external, axial diffusion) +- Does cAMP diffuse internally? + +- Direct stimulation of dopamine, can we use a dopamine axon? +- Volymetric transmission, where two cells might read the same spatial concentration. + +(- Tripartite synapse) \ No newline at end of file diff --git a/examples/notebooks/neuromodulation/bath_current_injection.json b/examples/notebooks/neuromodulation/bath_current_injection.json new file mode 100644 index 000000000..47f99f0f3 --- /dev/null +++ b/examples/notebooks/neuromodulation/bath_current_injection.json @@ -0,0 +1,10 @@ +{ + "0": { + "time": [0, 0.5, 1, 10], + "current": [0, 600e-12, 0, 0] + }, + "1": { + "time": [0, 0.5, 1, 10], + "current": [0, 500e-12, 0, 0] + } +} diff --git a/examples/notebooks/neuromodulation/config/mesh/cube-mesh-5e-05.obj b/examples/notebooks/neuromodulation/config/mesh/cube-mesh-5e-05.obj new file mode 100644 index 000000000..f86cd9190 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/mesh/cube-mesh-5e-05.obj @@ -0,0 +1,33 @@ +# Generated by create_cube_mesh.py +# Striatum cube mesh, centre: [0.00475 0.004 0.00775], side: 5e-05 + +g cube + +v 4725.000000 3975.000000 7725.000000 +v 4725.000000 3975.000000 7775.000000 +v 4725.000000 4025.000000 7725.000000 +v 4725.000000 4025.000000 7775.000000 +v 4775.000000 3975.000000 7725.000000 +v 4775.000000 3975.000000 7775.000000 +v 4775.000000 4025.000000 7725.000000 +v 4775.000000 4025.000000 7775.000000 + +vn 0.0 0.0 1.0 +vn 0.0 0.0 -1.0 +vn 0.0 1.0 0.0 +vn 0.0 -1.0 0.0 +vn 1.0 0.0 0.0 +vn -1.0 0.0 0.0 + +f 1//2 7//2 5//2 +f 1//2 3//2 7//2 +f 1//6 4//6 3//6 +f 1//6 2//6 4//6 +f 3//3 8//3 7//3 +f 3//3 4//3 8//3 +f 5//5 7//5 8//5 +f 5//5 8//5 6//5 +f 1//4 5//4 6//4 +f 1//4 6//4 2//4 +f 2//1 6//1 8//1 +f 2//1 8//1 4//1 diff --git a/examples/notebooks/neuromodulation/config/network.json b/examples/notebooks/neuromodulation/config/network.json new file mode 100644 index 000000000..8d6dfb43f --- /dev/null +++ b/examples/notebooks/neuromodulation/config/network.json @@ -0,0 +1,12 @@ +{ + "network_path": "my_network", + "snudda_data": "/home/hjorth/HBP/BasalGangliaData/data", + + "random_seed": { + "master_seed": 1234 + }, + + "regions": { + "Striatum" : "striatum.json" + } +} diff --git a/examples/notebooks/neuromodulation/config/neurons/chin.json b/examples/notebooks/neuromodulation/config/neurons/chin.json new file mode 100644 index 000000000..74bed8749 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/neurons/chin.json @@ -0,0 +1,18 @@ +{ + "ChIN": { + "neuron_path": { + "ChIN_0": "$SNUDDA_DATA/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710" + }, + + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "stay_inside_mesh": false, + "fraction": 0.011, + "axon_density": [ + "r", + "5000*1e12/3*exp(-r/120e-6)", + 0.00035 + ] + } +} diff --git a/examples/notebooks/neuromodulation/config/neurons/dspn.json b/examples/notebooks/neuromodulation/config/neurons/dspn.json new file mode 100644 index 000000000..a02d212b6 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/neurons/dspn.json @@ -0,0 +1,17 @@ +{ + "dSPN": { + "neuron_path": { + "dSPN_0": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026", + "dSPN_1": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20211026", + "dSPN_2": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20211028", + "dSPN_3": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c9_D1-mWT-1215MSN03-v20211026" + }, + + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "stay_inside_mesh": false, + "fraction": 0.475 + } + +} diff --git a/examples/notebooks/neuromodulation/config/neurons/fs.json b/examples/notebooks/neuromodulation/config/neurons/fs.json new file mode 100644 index 000000000..48812e0c5 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/neurons/fs.json @@ -0,0 +1,16 @@ +{ + "FS": { + "neuron_path": { + "FS_0": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e160628_FS2-mMTC180800A-IDB-v20210210", + "FS_1": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e161024_FS16-mDR-rat-Mar-13-08-1-536-R-v20210210", + "FS_2": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e161205_FS1-mBE104E-v20210209", + "FS_3": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e161205_FS1-mMTC180800A-IDB-v20210210" + }, + + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "stay_inside_mesh": false, + "fraction": 0.013 + } +} diff --git a/examples/notebooks/neuromodulation/config/neurons/ispn.json b/examples/notebooks/neuromodulation/config/neurons/ispn.json new file mode 100644 index 000000000..d217aa2b6 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/neurons/ispn.json @@ -0,0 +1,16 @@ +{ + "iSPN": { + "neuron_path": { + "iSPN_0": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20211026", + "iSPN_1": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20211026", + "iSPN_2": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20211026", + "iSPN_3": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20211026" + }, + + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "stay_inside_mesh": false, + "fraction": 0.475 + } +} diff --git a/examples/notebooks/neuromodulation/config/neurons/lts.json b/examples/notebooks/neuromodulation/config/neurons/lts.json new file mode 100644 index 000000000..7d2231503 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/neurons/lts.json @@ -0,0 +1,27 @@ +{ + "LTS": { + "neuron_path" : { + "LTS_0" : "$SNUDDA_DATA/neurons/striatum/lts/LTS_180118_morp_9862_updated_20210301", + "LTS_1" : "$SNUDDA_DATA/neurons/striatum/lts/LTS_180118_morp_9862_updated_April2022" + }, + + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "stay_inside_mesh": false, + "fraction": 0.007, + "axon_density": [ + "xyz", + "12*3000*1e12*( 0.25*exp(-(((x-200e-6)/100e-6)**2 + ((y-0)/50e-6)**2 + ((z-0)/30e-6)**2)) + 1*exp(-(((x-300e-6)/300e-6)**2 + ((y-0)/15e-6)**2 + ((z-0)/10e-6)**2)) + 1*exp(-(((x-700e-6)/100e-6)**2 + ((y-0)/15e-6)**2 + ((z-0)/15e-6)**2)) )", + [ + -0.0002, + 0.0009, + -0.0001, + 0.0001, + -3e-05, + 3e-05 + ] + ] + } +} + diff --git a/examples/notebooks/neuromodulation/config/pop1.json b/examples/notebooks/neuromodulation/config/pop1.json new file mode 100644 index 000000000..3948cfc28 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/pop1.json @@ -0,0 +1,16 @@ +{ + "populations": { + "method": "radial_density", + "centres": [[ 0.00475, 0.004, 0.00775 ], + [ 0.00475, 0.004, 0.00775 ]], + "probability_functions": [ + "(d < 300e-6) * 1", + "(d < 300e-6) * 1" + ], + "unit_id": [ 1, 2 ], + "neuron_types": [["dSPN", "iSPN"], + ["dSPN", "iSPN"]], + "num_neurons": [ 4000, 4000 ] + + } +} diff --git a/examples/notebooks/neuromodulation/config/striatum.json b/examples/notebooks/neuromodulation/config/striatum.json new file mode 100644 index 000000000..33a6931a7 --- /dev/null +++ b/examples/notebooks/neuromodulation/config/striatum.json @@ -0,0 +1,29 @@ +{ + "Striatum": { + "num_neurons": 10, + "volume": { + "mesh_file": "config/mesh/cube-mesh-5e-05.obj", + "d_min": 1.5e-5, + "num_putative_points": 1000, + "random_seed": 123456, + "!density": { + "my_neuron": { + "density_function": "abs(x)" + } + }, + "!neuron_orientation": { + "my_neuron": { + "rotation_mode": "vector_field", + "rotation_field_file": "my_rotation_file.json" + } + } + }, + "neurons": ["neurons/dspn.json", + "neurons/ispn.json", + "!neurons/fs.json", + "!neurons/chin.json", + "!neurons/lts.json"], + "connectivity": ["$SNUDDA_DATA/connectivity/striatum/striatum-connectivity.json"], + "!populations": "pop1.json" + } +} diff --git a/examples/notebooks/neuromodulation/data/DA-bath-experiment.json b/examples/notebooks/neuromodulation/data/DA-bath-experiment.json new file mode 100644 index 000000000..4ce9541ce --- /dev/null +++ b/examples/notebooks/neuromodulation/data/DA-bath-experiment.json @@ -0,0 +1,38 @@ +{ + "network_file": "networks/neuromodulation_bath/network-synapses.hdf5", + "input_file": "networks/neuromodulation_bath/input-spikes.hdf5", + "output_file": "networks/neuromodulation_bath/simulation/output.hdf5", + "log_file": "networks/neuromodulation_bath/log/network-simulation-bath.txt", + "time": 4.0, + "record_all_soma": true, + "record_rxd_species_all": [0, 1], + "record_density_mechanism": { + "kir_ms.modulation_factor": { + "neuron_id": [0, 1], + "section_id": [-1, -1], + "section_x": [0.5, 0.5] + }, + "naf_ms.modulation_factor": { + "neuron_id": [0, 1], + "section_id": [-1, -1], + "section_x": [0.5, 0.5] + }, + "kaf_ms.modulation_factor_g": { + "neuron_id": [0, 1], + "section_id": [-1, -1], + "section_x": [0.5, 0.5] + }, + "kaf_ms.modulation_factor_shift": { + "neuron_id": [0, 1], + "section_id": [-1, -1], + "section_x": [0.5, 0.5] + } + }, + "rxd_enable_extracellular": false, + "bath_application": { + "DA": { + "time": [0, 1.99, 2, 4], + "concentration": [0, 0, 60e-6, 60e-6] + } + } +} diff --git a/examples/notebooks/neuromodulation/data/DASyn.mod b/examples/notebooks/neuromodulation/data/DASyn.mod new file mode 100644 index 000000000..ee1ace957 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/DASyn.mod @@ -0,0 +1,38 @@ +NEURON { + POINT_PROCESS DASyn + RANGE quanta, tau, open +} +UNITS { + (mM) = (milli / liter) +} + +PARAMETER { + quanta = 1e-4 (mM/ms) + tau = 10 (ms) +} + +INITIAL { + open = 0 +} + +STATE { + open (1) +} + +BREAKPOINT {SOLVE state METHOD cnexp} + +DERIVATIVE state { + open' = -open/tau +} + + +: 33000 dopamine molecules per vesicle : Omiatek, D., Bressler, A., +: Cans, AS. et al. The real catecholamine content of secretory vesicles +: in the CNS revealed by electrochemical cytometry. Sci Rep 3, 1447 +: (2013). https://doi.org/10.1038/srep01447 + + + +NET_RECEIVE(weight) { + open = open + weight +} diff --git a/examples/notebooks/neuromodulation/data/Dopamine decay time constant estimation.ipynb b/examples/notebooks/neuromodulation/data/Dopamine decay time constant estimation.ipynb new file mode 100644 index 000000000..077c81285 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/Dopamine decay time constant estimation.ipynb @@ -0,0 +1,160 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6775ac3-3e0b-4da5-b8d8-812268bc75a9", + "metadata": {}, + "source": [ + "### Dopamine decay time constant estimation\n", + "\n", + "Cragg SJ, Hille CJ, Greenfield SA. Dopamine release and uptake dynamics within nonhuman primate striatum in vitro. J Neurosci. 2000;20(21):8209-8217. doi:10.1523/JNEUROSCI.20-21-08209.2000\n", + "\n", + "Data from marmoset.\n", + "\n", + "Link: https://www.jneurosci.org/content/20/21/8209\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1088162f-0a1f-488a-b3a2-acf7936fdf66", + "metadata": {}, + "source": [ + "![alt text](cragg2000-fig3A.jpg \"Cragg2000-fig3A\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fd090813-c857-4375-b805-9e0993853fb0", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "\n", + "pio.renderers.default = 'iframe'\n", + "pio.templates.default = 'simple_white'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6011c253-d60c-450d-b4e6-e4f220e59efa", + "metadata": {}, + "outputs": [], + "source": [ + "def cubic_interpolate(time, values, dt=1e-3):\n", + " from scipy.interpolate import CubicSpline \n", + "\n", + " t, v = time, values # shorthand\n", + " cs = CubicSpline(x=t, y=v)\n", + " \n", + " t = np.arange(start=np.min(t), stop=np.max(t), step=dt)\n", + " \n", + " return t, cs\n", + "\n", + "def mask_crossing(arr, threshold):\n", + " mask = np.array(arr > threshold, dtype=np.int8)\n", + " mask[1:] = np.diff(mask)\n", + " mask[0] = 0\n", + "\n", + " return mask" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6bf1ad61-3ba2-4d1f-8f90-46d84a89ae57", + "metadata": {}, + "outputs": [], + "source": [ + "# digitized data, using fig 3A from Cragg et al. 2000\n", + "original_time=np.array([1.2660550458715598, 1.4999999999999998, 1.7339449541284402, 2.0091743119266052, 2.229357798165138, 2.5045871559633026, 2.738532110091744, 3, 3.2477064220183482, 3.4954128440366983, 0.23394495412844019, 0.5091743119266054, 0.7293577981651376, 0.9908256880733946])\n", + "original_concentration=np.array([1.8634538152610438, 1.6626506024096384, 1.0200803212851401, 0.618473895582329, 0.4497991967871484, 0.3373493975903612, 0.25702811244979884, 0.20080321285140545, 0.17670682730923692, 0.1686746987951806, -0.008032128514056325, -0.008032128514056325, 0.008032128514056325, 0.2730923694779117])\n", + "\n", + "# unsorted when digitzed\n", + "idxs = np.argsort(original_time)\n", + "original_time=original_time[idxs]\n", + "original_concentration=original_concentration[idxs]\n", + "\n", + "# use spline interpolation\n", + "time, cs = cubic_interpolate(time=original_time, values=original_concentration, dt=1e-3)\n", + "concentration = cs(time,0)\n", + "\n", + "# get y-axis crossing of 1/exp(t/tau) where t=tau\n", + "mask=mask_crossing(concentration, np.exp(-1))\n", + "cross_idx = np.min(np.where(mask==-1)[0])\n", + "peak_idx = np.argmax(concentration)\n", + "tau=time[cross_idx] - time[peak_idx]\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2b977194-b506-4a01-bd9e-dc756259ad6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# plot\n", + "fig=go.Figure()\n", + "fig.add_trace(go.Scatter(x=original_time, y=original_concentration, mode='markers', name=\"raw data (Cragg 2000 fig3A)\", marker=dict(size=15, color='red')))\n", + "fig.add_trace(go.Scatter(x=time, y=concentration, mode='lines', name=\"interpolated\", line=dict(color='black')))\n", + "fig.add_trace(go.Scatter(x=time[[peak_idx, cross_idx]], y=concentration[[peak_idx, cross_idx]], marker=dict(size=15, color='black'), mode='markers', name='idxs'))\n", + "fig.update_layout(title=f\"tau = {tau:.3f} (s)\", xaxis_title='time (s)', yaxis_title='concentration (uM)')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eccb454e-700f-453d-baf5-71bb62531312", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "nrn", + "language": "python", + "name": "nrn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/data/JSON/reaction_diffusion_D1.json b/examples/notebooks/neuromodulation/data/JSON/reaction_diffusion_D1.json new file mode 100644 index 000000000..3a4908801 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/JSON/reaction_diffusion_D1.json @@ -0,0 +1,573 @@ +{ + "species": { + "GaolfGDP": { + "initial_concentration": 1.0083120895466201e-11, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 1.0083120895466201e-11, + "boundary_condition": false + }, + "Gbgolf": { + "initial_concentration": 2.98851246006536e-08, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 2.98851246006536e-08, + "boundary_condition": false + }, + "GaolfGTP": { + "initial_concentration": 8.91348109605658e-12, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 8.91348109605658e-12, + "boundary_condition": false + }, + "D1RDAGolf": { + "initial_concentration": 2.00890216216344e-09, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 2.00890216216344e-09, + "boundary_condition": false + }, + "Golf": { + "initial_concentration": 1.4530725722822101e-06, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 1.4530725722822101e-06, + "boundary_condition": false + }, + "D1RGolf": { + "initial_concentration": 5.150334009549751e-07, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 5.150334009549751e-07, + "boundary_condition": false + }, + "D1RDA": { + "initial_concentration": 5.95922532787641e-09, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 5.95922532787641e-09, + "boundary_condition": false + }, + "D1R": { + "initial_concentration": 1.47699847155499e-06, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 1.47699847155499e-06, + "boundary_condition": false + }, + "cAMP": { + "initial_concentration": 3.81860143351998e-08, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 3.81860143351998e-08, + "boundary_condition": false + }, + "AC5": { + "initial_concentration": 2.66944644058834e-09, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 2.66944644058834e-09, + "boundary_condition": false + }, + "AC5GaolfGTP": { + "initial_concentration": 1.1809040707886602e-10, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 1.1809040707886602e-10, + "boundary_condition": false + }, + "PDE4": { + "initial_concentration": 1.50680848289944e-06, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 1.50680848289944e-06, + "boundary_condition": false + }, + "PKA": { + "initial_concentration": 1.15714133868944e-06, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 1.15714133868944e-06, + "boundary_condition": false + }, + "PKAcAMP2": { + "initial_concentration": 3.2824343126378e-09, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 3.2824343126378e-09, + "boundary_condition": false + }, + "PKAcAMP4": { + "initial_concentration": 8.673741393198342e-11, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 8.673741393198342e-11, + "boundary_condition": false + }, + "PKAreg": { + "initial_concentration": 3.9489489583994e-08, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 3.9489489583994e-08, + "boundary_condition": false + }, + "PKAc": { + "initial_concentration": 3.6607805792436603e-09, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 3.6607805792436603e-09, + "boundary_condition": false + }, + "DA": { + "initial_concentration": 2e-08, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 2e-08, + "boundary_condition": false + }, + "AMP": { + "initial_concentration": 0.0, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 0.0, + "boundary_condition": true + }, + "PDE4_cAMP": { + "initial_concentration": 4.931915171005611e-07, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 4.931915171005611e-07, + "boundary_condition": false + }, + "PDE10c": { + "initial_concentration": 5.78101285574062e-10, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 5.78101285574062e-10, + "boundary_condition": false + }, + "PDE10": { + "initial_concentration": 3.96456253552482e-07, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 3.96456253552482e-07, + "boundary_condition": false + }, + "PDE10_cAMP": { + "initial_concentration": 3.0278168362869404e-07, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 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[ + "soma_internal", + "dend_internal" + ] + }, + "irrevreaction_14": { + "reactants": "PKAc_Target1", + "products": "PKAc + Target1p", + "forward_rate": 10.0, + "backward_rate": null, + "regions": [ + "soma_internal", + "dend_internal" + ] + }, + "irrevreaction_15": { + "reactants": "PP1_Target1p", + "products": "PP1 + Target1", + "forward_rate": 5.0, + "backward_rate": null, + "regions": [ + "soma_internal", + "dend_internal" + ] + } + } +} \ No newline at end of file diff --git a/examples/notebooks/neuromodulation/data/SBML/Nair2015-D1-BIOMD0000000635_url.xml b/examples/notebooks/neuromodulation/data/SBML/Nair2015-D1-BIOMD0000000635_url.xml new file mode 100644 index 000000000..c6dac8c4a --- /dev/null +++ b/examples/notebooks/neuromodulation/data/SBML/Nair2015-D1-BIOMD0000000635_url.xml @@ -0,0 +1,7586 @@ + + + + + +
Nair2015 - Interaction between +neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1 +Neuron)
+
+

This model is described in the article:

+
+ Sensing Positive versus + Negative Reward Signals through Adenylyl Cyclase-Coupled GPCRs + in Direct and Indirect Pathway Striatal Medium Spiny + Neurons. +
+
Nair AG, Gutierrez-Arenas O, + Eriksson O, Vincent P, Hellgren Kotaleski J.
+
J. Neurosci. 2015 Oct; 35(41): + 14017-14030
+

Abstract:

+
+

Transient changes in striatal dopamine (DA) concentration + are considered to encode a reward prediction error (RPE) in + reinforcement learning tasks. Often, a phasic DA change occurs + concomitantly with a dip in striatal acetylcholine (ACh), + whereas other neuromodulators, such as adenosine (Adn), change + slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs + for these neuromodulators in striatal medium spiny neurons + (MSNs), which play important roles in plasticity. However, + little is known about the interaction between these + neuromodulators via GPCRs. The interaction between these + transient neuromodulator changes and the effect on cAMP/PKA + signaling via Golf- and Gi/o-coupled GPCR are studied here + using quantitative kinetic modeling. The simulations suggest + that, under basal conditions, cAMP/PKA signaling could be + significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an + ACh dip is required to gate a subset of D1R/Golf-dependent PKA + activation. Furthermore, the interaction between ACh dip and DA + peak, via D1R and M4R, is synergistic. In a similar fashion, + PKA signaling in D2+ MSNs is under basal inhibition via + D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting + A2aR/Golf, but D2+ MSNs could also respond to the DA peak via + other intracellular pathways. This study highlights the + similarity between the two types of MSNs in terms of high basal + AC inhibition by Gi/o and the importance of interactions + between Gi/o and Golf signaling, but at the same time predicts + differences between them with regard to the sign of RPE + responsible for PKA activation.Dopamine transients are + considered to carry reward-related signal in reinforcement + learning. An increase in dopamine concentration is associated + with an unexpected reward or salient stimuli, whereas a + decrease is produced by omission of an expected reward. Often + dopamine transients are accompanied by other neuromodulatory + signals, such as acetylcholine and adenosine. We highlight the + importance of interaction between acetylcholine, dopamine, and + adenosine signals via adenylyl-cyclase coupled GPCRs in shaping + the dopamine-dependent cAMP/PKA signaling in striatal neurons. + Specifically, a dopamine peak and an acetylcholine dip must + interact, via D1 and M4 receptor, and a dopamine dip must + interact with adenosine tone, via D2 and A2a receptor, in + direct and indirect pathway neurons, respectively, to have any + significant downstream PKA activation.

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This model is hosted on + BioModels Database + and identified by: + BIOMD0000000635.

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To cite BioModels Database, please use: + BioModels Database: + An enhanced, curated and annotated resource for published + quantitative kinetic models.

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To the extent possible under law, all copyright and related or + neighbouring rights to this encoded model have been dedicated to + the public domain worldwide. Please refer to + CC0 + Public Domain Dedication for more information.

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+ + + + + + + + + + Gutenkunst + Ryan + + rgutenk@email.arizona.edu + + University of Arizona + + + + + + + + + Fortier + Alyssa + + alyssalynfortier@email.arizona.edu + + University of Arizona + + + + + Mollasalehi + Niloufar + + Nmollasalehi@email.arizona.edu + + University of Arizona + + + + + Hunjan + Anoop + + ahunjan@email.arizona.edu + + University of Arizona + + + + + Courtright + janet + + jcourtright@email.arizona.edu + + University of Arizona + + + + + Gee + Kevin + + kevingee@email.arizona.edu + + University of Arizona + + + + + + 2017-05-02T13:29:37Z + + + 2017-05-02T15:48:35Z + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

The Gs protein is thought to be homologous to the Golf protein complex. Beta1 is listed because no information was provided on the specific beta subunit used.

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The Golf is made up of the alpha, beta, and gamma subunits. The beta and gamma subunits of the Gs protein are used because Gs is homologous to the Golf protein, and Gbolf anf Ggolf could not be found.

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The Golf is made up of the alpha, beta, and gamma subunits. The beta and gamma subunits of the Gs protein are used because Gs is homologous to the Golf protein, and Gbolf and Ggolf could not be found.

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The Golf is made up of the alpha, beta, and gamma subunits. The beta and gamma subunits of the Gs protein are used because Gs is homologous to the Golf protein, and Gbolf and Ggolf could not be found.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Protein phosphatase 2B is also known as calcineurin.

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Assuming PP2Bc refers to entire catalytic part and not just the gamma catalytic subunit.

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D32 appears to be a shortened name for DARPP32 and is used instead of writing it out.

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This appears to be a phosphorylated verison of D32, which is the shortened name for DARPP32 used in this model.

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This was assumed to be a modified version of PP2A. The three subunits of PP2A (alpha isoforms) are included.

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The B56 and p are assumed to be modifications of PP2A. hasPart is used instead of isVersionOf, since PP2A consists of multiple subunits.

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PP1 consists of one catalytic and one regulatory subunit. The alpha catalytic subunit and regulatory subunit A were used here.

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The Gi protein consists of three subunits: alpha, beta, and gamma. Alpha-1, beta-1, and gamma-2 were used because they are lowest numbers found on uniprot, although many different versions of the subunits exist.

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+ The units of Acetyl-Choline (ACh) are in surface density instead (molecule/um^2) of volume density (mole/L) because there is a conflict for the second order binding to the receptor even when the original value of the rate constant is in volume units. The error is displayed as entities of different unitis cant be added. The solution was to express both the rate constant and the neurotransmitter in the same surface density units than the receptor. As Avogadro number is 6x10^23 and the conversion factor from L to um^3 is 10^15, 1mole/L equals 6x10^8 molecules/um^3. +The original values of [ACh] and the second order rate are (Falkenburger, 2010), + +[ACh] = 100uM + +k = 2.8 (uM^-1)*(s^-1) + +k*[ACh] = 280 s^-1 + +which converted with the factor 1mole/L = 6x10^8 molecules/um^3 + + [ACh] = 100uM = 6x10^4 molecules/um^3 + +k = 2.8 (uM^-1)*(s^-1) = 2.8 x10^6 (M^-1)*(s^-1) = 4.7x10^-3 (um^3/molecules)*(s^-1) + +k*[ACh] = 280 s^-1 + +which, for the sake of unit congruency (stupid program!) can be arbitrarily reduced to a surface density like, + +[ACh] = 6x10^4 molecules/um^2 + +k = 4.7x10^-3 (um^2/molecules)*(s^-1) + +k*[ACh] = 280 s^-1 + +As the ACh is a boundary element, i.e. its concentration wont be perturbed by the interaction with the system, the product k*[ACh] is a determined by the boundary function of ACh. Thus, while the conversions made above make the concentration and the rate constant meaninful when read, even after the second one (an imaginary 1um think extracellular layer of ACh at 100uM) for numeric purposes they are just a pain in the ass and make no differences. It is easier to trick the program by stating, + +[ACh] = 100 molecule/micrometer^2 + +k = 2.8 (micrometer^2/molecule)*(s^-1) + +Even when these numbers have cant be read straightforward. + + +

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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AKAR3 is a FRET-based A-kinase activity reporter, or a biosensor, developed by Allen and Zhang (2006). It was applied in Castro et al (2013) to measure PKA dynamics. In this model it is used to measure PKA dynamics in the D1+MSN model. AKAR3p is assumed to be the phosphorylated version of AKAR3.

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AKAR3 is a FRET-based A-kinase activity reporter, or a biosensor, developed by Allen and Zhang (2006). It was applied in Castro et al (2013) to measure PKA dynamics. In this model it is used to measure PKA dynamics in the D1+MSN model. AKAR3p is assumed to be the phosphorylated version of AKAR3.

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The second term is now obsolete, but more accurately describes this reaction.

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The second term is now obsolete, but more accurately describes this reaction.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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a/examples/notebooks/neuromodulation/data/SBML/Nair2015-D2-BIOMD0000000636_url.xml b/examples/notebooks/neuromodulation/data/SBML/Nair2015-D2-BIOMD0000000636_url.xml new file mode 100644 index 000000000..a671b79c1 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/SBML/Nair2015-D2-BIOMD0000000636_url.xml @@ -0,0 +1,7933 @@ + + + + + +
Nair2015 - Interaction between +neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 +Neuron)
+
+

This model is described in the article:

+
+ Sensing Positive versus + Negative Reward Signals through Adenylyl Cyclase-Coupled GPCRs + in Direct and Indirect Pathway Striatal Medium Spiny + Neurons. +
+
Nair AG, Gutierrez-Arenas O, + Eriksson O, Vincent P, Hellgren Kotaleski J.
+
J. Neurosci. 2015 Oct; 35(41): + 14017-14030
+

Abstract:

+
+

Transient changes in striatal dopamine (DA) concentration + are considered to encode a reward prediction error (RPE) in + reinforcement learning tasks. Often, a phasic DA change occurs + concomitantly with a dip in striatal acetylcholine (ACh), + whereas other neuromodulators, such as adenosine (Adn), change + slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs + for these neuromodulators in striatal medium spiny neurons + (MSNs), which play important roles in plasticity. However, + little is known about the interaction between these + neuromodulators via GPCRs. The interaction between these + transient neuromodulator changes and the effect on cAMP/PKA + signaling via Golf- and Gi/o-coupled GPCR are studied here + using quantitative kinetic modeling. The simulations suggest + that, under basal conditions, cAMP/PKA signaling could be + significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an + ACh dip is required to gate a subset of D1R/Golf-dependent PKA + activation. Furthermore, the interaction between ACh dip and DA + peak, via D1R and M4R, is synergistic. In a similar fashion, + PKA signaling in D2+ MSNs is under basal inhibition via + D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting + A2aR/Golf, but D2+ MSNs could also respond to the DA peak via + other intracellular pathways. This study highlights the + similarity between the two types of MSNs in terms of high basal + AC inhibition by Gi/o and the importance of interactions + between Gi/o and Golf signaling, but at the same time predicts + differences between them with regard to the sign of RPE + responsible for PKA activation.Dopamine transients are + considered to carry reward-related signal in reinforcement + learning. An increase in dopamine concentration is associated + with an unexpected reward or salient stimuli, whereas a + decrease is produced by omission of an expected reward. Often + dopamine transients are accompanied by other neuromodulatory + signals, such as acetylcholine and adenosine. We highlight the + importance of interaction between acetylcholine, dopamine, and + adenosine signals via adenylyl-cyclase coupled GPCRs in shaping + the dopamine-dependent cAMP/PKA signaling in striatal neurons. + Specifically, a dopamine peak and an acetylcholine dip must + interact, via D1 and M4 receptor, and a dopamine dip must + interact with adenosine tone, via D2 and A2a receptor, in + direct and indirect pathway neurons, respectively, to have any + significant downstream PKA activation.

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+
+

This model is hosted on + BioModels Database + and identified by: + BIOMD0000000636.

+

To cite BioModels Database, please use: + BioModels Database: + An enhanced, curated and annotated resource for published + quantitative kinetic models.

+
+
+

To the extent possible under law, all copyright and related or + neighbouring rights to this encoded model have been dedicated to + the public domain worldwide. Please refer to + CC0 + Public Domain Dedication for more information.

+
+ + + + + + + + + + Gutenkunst + Ryan + + rgutenk@email.arizona.edu + + University of Arizona + + + + + + + + + Fortier + Alyssa + + alyssalynfortier@email.arizona.edu + + University of Arizona + + + + + Mollasalehi + Niloufar + + Nmollasalehi@email.arizona.edu + + University of Arizona + + + + + Hunjan + Anoop + + ahunjan@email.arizona.edu + + University of Arizona + + + + + Courtright + janet + + jcourtright@email.arizona.edu + + University of Arizona + + + + + Gee + Kevin + + kevingee@email.arizona.edu + + University of Arizona + + + + + + 2017-05-02T16:01:53Z + + + 2017-05-16T13:20:10Z + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

The Gs protein is thought to be homologous to the Golf protein complex. Beta1 is listed because no information was provided on the specific beta subunit used.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

The Golf is made up of the alpha, beta, and gamma subunits. The beta and gamma subunits of the Gs protein are used because Gs is homologous to the Golf protein, and Gbolf and Ggolf could not be found.

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The Gi protein consists of three subunits: alpha, beta, and gamma. Alpha-1, beta-1, and gamma-2 were used because they are lowest numbers found on uniprot, although many different versions of the subunits exist.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + The units of Acetyl-Choline (ACh) are in surface density instead (molecule/um^2) of volume density (mole/L) because there is a conflict for the second order binding to the receptor even when the original value of the rate constant is in volume units. The error is displayed as "entities of different unitis can't be added". The solution was to express both the rate constant and the neurotransmitter in the same surface density units than the receptor. As Avogadro number is 6x10^23 and the conversion factor from L to um^3 is 10^15, 1mole/L equals 6x10^8 molecules/um^3. +The original values of [ACh] and the second order rate are (Falkenburger, 2010), + +[ACh] = 100uM + +k = 2.8 (uM^-1)*(s^-1) + +k*[ACh] = 280 s^-1 + +which converted with the factor 1mole/L = 6x10^8 molecules/um^3 + + [ACh] = 100uM = 6x10^4 molecules/um^3 + +k = 2.8 (uM^-1)*(s^-1) = 2.8 x10^6 (M^-1)*(s^-1) = 4.7x10^-3 (um^3/molecules)*(s^-1) + +k*[ACh] = 280 s^-1 + +which, for the sake of unit congruency (stupid program!) can be arbitrarily reduced to a surface density like, + +[ACh] = 6x10^4 molecules/um^2 + +k = 4.7x10^-3 (um^2/molecules)*(s^-1) + +k*[ACh] = 280 s^-1 + +As the ACh is a boundary element, i.e. its concentration won't be perturbed by the interaction with the system, the product k*[ACh] is a determined by the boundary function of ACh. Thus, while the conversions made above make the concentration and the rate constant meaninful when read, even after the second one (an imaginary 1um think extracellular layer of ACh at 100uM) for numeric purposes they are just a pain in the ass and make no differences. It is easier to trick the program by stating, + +[ACh] = 100 molecule/micrometer^2 + +k = 2.8 (micrometer^2/molecule)*(s^-1) + +Even when these numbers have can't be read straightforward. + + + + + + + + + + + + + + + + + + + + + + + +

PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

+ + + + + + + + + + + + + + + + + + + + + +

PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

+ + + + + + + + + + + + + + + + + + + + + +

PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

+ + + + + + + + + + + + + + + + + + + + + + +

PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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The second term is now obsolete, but more accurately describes this reaction.

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The second term is now obsolete, but more accurately describes this reaction.

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a/examples/notebooks/neuromodulation/data/SBML/Nair2015-D2-BIOMD0000000636_url_UPDATED.xml b/examples/notebooks/neuromodulation/data/SBML/Nair2015-D2-BIOMD0000000636_url_UPDATED.xml new file mode 100644 index 000000000..292173491 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/SBML/Nair2015-D2-BIOMD0000000636_url_UPDATED.xml @@ -0,0 +1,7933 @@ + + + + + +
Nair2015 - Interaction between +neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 +Neuron)
+
+

This model is described in the article:

+
+ Sensing Positive versus + Negative Reward Signals through Adenylyl Cyclase-Coupled GPCRs + in Direct and Indirect Pathway Striatal Medium Spiny + Neurons. +
+
Nair AG, Gutierrez-Arenas O, + Eriksson O, Vincent P, Hellgren Kotaleski J.
+
J. Neurosci. 2015 Oct; 35(41): + 14017-14030
+

Abstract:

+
+

Transient changes in striatal dopamine (DA) concentration + are considered to encode a reward prediction error (RPE) in + reinforcement learning tasks. Often, a phasic DA change occurs + concomitantly with a dip in striatal acetylcholine (ACh), + whereas other neuromodulators, such as adenosine (Adn), change + slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs + for these neuromodulators in striatal medium spiny neurons + (MSNs), which play important roles in plasticity. However, + little is known about the interaction between these + neuromodulators via GPCRs. The interaction between these + transient neuromodulator changes and the effect on cAMP/PKA + signaling via Golf- and Gi/o-coupled GPCR are studied here + using quantitative kinetic modeling. The simulations suggest + that, under basal conditions, cAMP/PKA signaling could be + significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an + ACh dip is required to gate a subset of D1R/Golf-dependent PKA + activation. Furthermore, the interaction between ACh dip and DA + peak, via D1R and M4R, is synergistic. In a similar fashion, + PKA signaling in D2+ MSNs is under basal inhibition via + D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting + A2aR/Golf, but D2+ MSNs could also respond to the DA peak via + other intracellular pathways. This study highlights the + similarity between the two types of MSNs in terms of high basal + AC inhibition by Gi/o and the importance of interactions + between Gi/o and Golf signaling, but at the same time predicts + differences between them with regard to the sign of RPE + responsible for PKA activation.Dopamine transients are + considered to carry reward-related signal in reinforcement + learning. An increase in dopamine concentration is associated + with an unexpected reward or salient stimuli, whereas a + decrease is produced by omission of an expected reward. Often + dopamine transients are accompanied by other neuromodulatory + signals, such as acetylcholine and adenosine. We highlight the + importance of interaction between acetylcholine, dopamine, and + adenosine signals via adenylyl-cyclase coupled GPCRs in shaping + the dopamine-dependent cAMP/PKA signaling in striatal neurons. + Specifically, a dopamine peak and an acetylcholine dip must + interact, via D1 and M4 receptor, and a dopamine dip must + interact with adenosine tone, via D2 and A2a receptor, in + direct and indirect pathway neurons, respectively, to have any + significant downstream PKA activation.

+
+
+
+

This model is hosted on + BioModels Database + and identified by: + BIOMD0000000636.

+

To cite BioModels Database, please use: + BioModels Database: + An enhanced, curated and annotated resource for published + quantitative kinetic models.

+
+
+

To the extent possible under law, all copyright and related or + neighbouring rights to this encoded model have been dedicated to + the public domain worldwide. Please refer to + CC0 + Public Domain Dedication for more information.

+
+ + + + + + + + + + Gutenkunst + Ryan + + rgutenk@email.arizona.edu + + University of Arizona + + + + + + + + + Fortier + Alyssa + + alyssalynfortier@email.arizona.edu + + University of Arizona + + + + + Mollasalehi + Niloufar + + Nmollasalehi@email.arizona.edu + + University of Arizona + + + + + Hunjan + Anoop + + ahunjan@email.arizona.edu + + University of Arizona + + + + + Courtright + janet + + jcourtright@email.arizona.edu + + University of Arizona + + + + + Gee + Kevin + + kevingee@email.arizona.edu + + University of Arizona + + + + + + 2017-05-02T16:01:53Z + + + 2017-05-16T13:20:10Z + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

The Gs protein is thought to be homologous to the Golf protein complex. Beta1 is listed because no information was provided on the specific beta subunit used.

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The Golf is made up of the alpha, beta, and gamma subunits. The beta and gamma subunits of the Gs protein are used because Gs is homologous to the Golf protein, and Gbolf and Ggolf could not be found.

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The Gi protein consists of three subunits: alpha, beta, and gamma. Alpha-1, beta-1, and gamma-2 were used because they are lowest numbers found on uniprot, although many different versions of the subunits exist.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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PDE10c appears to be a modification of PDE10 and does not appear to be a different subunit or molecule. Thus, both are written as a version of PDE10a.

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The second term is now obsolete, but more accurately describes this reaction.

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The second term is now obsolete, but more accurately describes this reaction.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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This identifier may not work in the pathway browser, but works properly when entered into reactome.org search bar.

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Yapo2017- cAMP/PKA signalling in D1 dopamine receptor expressing medium-spiny neurons
+
+

This model is described in the article:

+
+ Detection of phasic dopamine + by D1 and D2 striatal medium spiny neurons. +
+
Yapo C, Nair AG, Clement L, Castro + LR, Hellgren Kotaleski J, Vincent P.
+
J. Physiol. (Lond.) 2017 Aug; :
+

Abstract:

+
+

The phasic release of dopamine in the striatum determines + various aspects of reward and action selection, but the + dynamics of dopamine effect on intracellular signalling remains + poorly understood. We used genetically-encoded FRET biosensors + in striatal brain slices to quantify the effect of transient + dopamine on cAMP or PKA-dependent phosphorylation level, and + computational modelling to further explore the dynamics of this + signalling pathway. Medium-sized spiny neurons (MSNs), which + express either D1 or D2 dopamine receptors, responded to + dopamine by an increase or a decrease in cAMP, respectively. + Transient dopamine showed similar sub-micromolar efficacies on + cAMP in both D1 and D2 MSNs, thus challenging the commonly + accepted notion that dopamine efficacy is much higher on D2 + than on D1 receptors. However, in D2 MSNs, the large decrease + in cAMP level triggered by transient dopamine did not translate + in a decrease in PKA-dependent phosphorylation level, owing to + the efficient inhibition of Protein Phosphatase 1 by DARPP-32. + Simulations further suggested that D2 MSNs can also operate in + a "tone-sensing" mode, allowing them to detect transient dips + in basal dopamine. Overall, our results show that D2 MSNs may + sense much more complex patterns of dopamine than previously + thought. This article is protected by copyright. All rights + reserved.

+
+
+
+

This model is hosted on + BioModels Database + and identified by: + MODEL1701170000.

+

To cite BioModels Database, please use: + BioModels Database: + An enhanced, curated and annotated resource for published + quantitative kinetic models.

+
+
+

To the extent possible under law, all copyright and related or + neighbouring rights to this encoded model have been dedicated to + the public domain worldwide. Please refer to + CC0 + Public Domain Dedication for more information.

+
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Yapo2017 - A2AR/cAMP/PKA signalling in D2 dopamine receptor expressing medium-spiny neurons
+
+

This model is described in the article:

+
+ Detection of phasic dopamine + by D1 and D2 striatal medium spiny neurons. +
+
Yapo C, Nair AG, Clement L, Castro + LR, Hellgren Kotaleski J, Vincent P.
+
J. Physiol. (Lond.) 2017 Aug; :
+

Abstract:

+
+

The phasic release of dopamine in the striatum determines + various aspects of reward and action selection, but the + dynamics of dopamine effect on intracellular signalling remains + poorly understood. We used genetically-encoded FRET biosensors + in striatal brain slices to quantify the effect of transient + dopamine on cAMP or PKA-dependent phosphorylation level, and + computational modelling to further explore the dynamics of this + signalling pathway. Medium-sized spiny neurons (MSNs), which + express either D1 or D2 dopamine receptors, responded to + dopamine by an increase or a decrease in cAMP, respectively. + Transient dopamine showed similar sub-micromolar efficacies on + cAMP in both D1 and D2 MSNs, thus challenging the commonly + accepted notion that dopamine efficacy is much higher on D2 + than on D1 receptors. However, in D2 MSNs, the large decrease + in cAMP level triggered by transient dopamine did not translate + in a decrease in PKA-dependent phosphorylation level, owing to + the efficient inhibition of Protein Phosphatase 1 by DARPP-32. + Simulations further suggested that D2 MSNs can also operate in + a "tone-sensing" mode, allowing them to detect transient dips + in basal dopamine. Overall, our results show that D2 MSNs may + sense much more complex patterns of dopamine than previously + thought. This article is protected by copyright. All rights + reserved.

+
+
+
+

This model is hosted on + BioModels Database + and identified by: + MODEL1701170001.

+

To cite BioModels Database, please use: + BioModels Database: + An enhanced, curated and annotated resource for published + quantitative kinetic models.

+
+
+

To the extent possible under law, all copyright and related or + neighbouring rights to this encoded model have been dedicated to + the public domain worldwide. Please refer to + CC0 + Public Domain Dedication for more information.

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"dspn,dspn": { + "fake_glutamate": { + "conductance": [ + 1e-9, + 1e-3 + ], + "channel_parameters": { + "mod_file": "tmGlut", + "parameter_file": "data/tmglut_DA_parameters.json" + }, + "cluster_size": 1, + "cluster_spread": null, + "pruning": { + "f1": null, + "soft_max": null, + "mu2": null, + "a3": null, + "cluster_pruning": false + } + }, + + "!DA": { + "conductance": [ + 1e-9, + 1e-3 + ], + "channel_parameters": { + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e16 + }, + "mod_file": "DASyn" + }, + "cluster_size": 1, + "cluster_spread": null, + "pruning": { + "f1": null, + "soft_max": null, + "mu2": null, + "a3": null, + "cluster_pruning": false + } + } + } + } + } + } +} diff --git a/examples/notebooks/neuromodulation/data/connectivity_v2.json b/examples/notebooks/neuromodulation/data/connectivity_v2.json new file mode 100644 index 000000000..9708f3f1e --- /dev/null +++ b/examples/notebooks/neuromodulation/data/connectivity_v2.json @@ -0,0 +1,28 @@ +{ + "regions": { + "Cube": { + "connectivity": { + "dspn,dspn": { + "GABA": { + "conductance": [ + 1e-9, + 1e-3 + ], + "channel_parameters": { + "mod_file": "tmGabaA" + }, + "cluster_size": 1, + "cluster_spread": null, + "pruning": { + "f1": null, + "soft_max": null, + "mu2": null, + "a3": null, + "cluster_pruning": false + } + } + } + } + } + } +} diff --git a/examples/notebooks/neuromodulation/data/convert_sbml_to_json.sh b/examples/notebooks/neuromodulation/data/convert_sbml_to_json.sh new file mode 100755 index 000000000..8be36dcd2 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/convert_sbml_to_json.sh @@ -0,0 +1,20 @@ +# python ../../../../snudda/utils/sbml_to_snudda.py SBML/MODEL_speedy_reduced2.xml JSON/robert_reaction_diffusion.json + +# python ../../../../snudda/utils/sbml_to_snudda.py SBML/Robert-MODEL_speedy_reduced2_UPDATED.xml JSON/reaction_diffusion_D1.json --conc_scale_factor 1e-9 + +python ../../../../snudda/utils/sbml_to_snudda.py SBML/Robert-MODEL_speedy_reduced2_UPDATED_notarget.xml JSON/reaction_diffusion_D1.json --conc_scale_factor 1e-9 + +# python ../../../../snudda/utils/sbml_to_snudda.py SBML/Nair_2016_optimized_UPDATED.xml JSON/reaction_diffusion_D1.json + +# python ../../../../snudda/utils/sbml_to_snudda.py SBML/Nair2015-D1-BIOMD0000000635_url.xml JSON/reaction_diffusion_D1.json + +python ../../../../snudda/utils/sbml_to_snudda.py SBML/Nair2015-D2-BIOMD0000000636_url_UPDATED.xml JSON/reaction_diffusion_D2.json --conc_scale_factor 1e-9 + +#python ../../../../snudda/utils/sbml_to_snudda.py SBML/Yapo2017-D1-MODEL1701170000_url.xml JSON/reaction_diffusion_D1.json + +# python ../../../../snudda/utils/sbml_to_snudda.py SBML/Yapo2017-D2-MODEL1701170001_url.xml JSON/reaction_diffusion_D2.json + +# python ../../../../snudda/utils/sbml_to_snudda.py SBML/Robert-MODEL_speedy_reduced2.xml JSON/robert_reaction_diffusion.json + + + diff --git a/examples/notebooks/neuromodulation/data/converted-neuromodulation-model.json b/examples/notebooks/neuromodulation/data/converted-neuromodulation-model.json new file mode 100644 index 000000000..843f5f128 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/converted-neuromodulation-model.json @@ -0,0 +1,636 @@ +{ + "species": { + "GaolfGDP": { + "initial_concentration": 0.0100831208954662, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 0.0100831208954662 + }, + "Gbgolf": { + "initial_concentration": 29.8851246006536, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 29.8851246006536 + }, + "GaolfGTP": { + "initial_concentration": 0.00891348109605658, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 0.00891348109605658 + }, + "D1RDAGolf": { + "initial_concentration": 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124.575 -50.8201 27.5941 0.075 1743 +1745 2 123.614 -48.5953 24.9715 0.075 1744 +1746 2 122.809 -46.2468 22.4051 0.075 1745 +1747 2 122.154 -43.7657 19.9196 0.075 1746 +1748 2 126.827 -58.9614 33.3757 0.075 1741 +1749 2 126.896 -59.0475 36.5579 0.075 1748 +1750 2 126.393 -59.8047 39.1049 0.075 1749 +1751 2 124.477 -61.7759 40.5652 0.075 1750 +1752 2 122.825 -62.8793 43.0001 0.075 1751 +1753 2 121.165 -63.7474 45.5696 0.075 1752 +1754 2 118.65 -64.9262 47.0292 0.075 1753 +1755 2 115.977 -66.2692 47.8604 0.075 1754 +1756 2 114.409 -67.3152 50.362 0.075 1755 +1757 2 113.743 -68.372 53.2753 0.075 1756 +1758 2 113.83 -70.1528 55.8384 0.075 1757 +1759 2 114.98 -72.765 56.9487 0.075 1758 +1760 2 116.237 -75.1852 58.5438 0.075 1759 +1761 2 116.418 -78.189 59.5344 0.075 1760 +1762 2 116.277 -81.2546 60.2368 0.075 1761 +1763 2 116.308 -83.8863 61.9723 0.075 1762 +1764 2 116.363 -86.2733 64.0786 0.075 1763 +1765 2 116.736 -89.0175 65.5841 0.075 1764 +1766 2 117.324 -92.0306 66.4049 0.075 1765 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200.818 -64.7581 9.67551 0.075 1834 +1836 2 201.667 -61.0104 9.25371 0.075 1835 +1837 2 202.008 -57.2341 9.78442 0.075 1836 +1838 2 202.677 -53.7908 11.4227 0.075 1837 +1839 2 201.432 -67.6699 13.1974 0.075 1834 +1840 2 202.33 -66.4338 14.1485 0.075 1839 +1841 2 203.072 -64.8231 10.3986 0.075 1840 +1842 2 198.434 -80.2681 1.31984 0.075 1830 +1843 2 199.533 -80.6926 -2.49236 0.075 1842 +1844 2 200.162 -80.6661 -6.32425 0.075 1843 +1845 2 202.087 -80.7804 -9.68167 0.075 1844 +1846 2 204.939 -81.9036 -9.73889 0.075 1845 +1847 2 207.225 -83.4391 -8.37952 0.075 1846 +1848 2 209.035 -85.3804 -6.92759 0.075 1847 +1849 2 210.83 -86.112 -4.58195 0.075 1848 +1850 2 212.824 -86.2801 -2.26527 0.075 1849 +1851 2 215.353 -87.2719 -1.27627 0.075 1850 +1852 2 217.905 -88.9431 -0.820958 0.075 1851 +1853 2 220.355 -90.4238 0.296838 0.075 1852 +1854 2 222.657 -92.0699 1.53549 0.075 1853 +1855 2 224.354 -94.5189 2.31247 0.075 1854 +1856 2 225.668 -96.8614 3.74766 0.075 1855 +1857 2 227.223 -98.4133 5.82633 0.075 1856 +1858 2 229.605 -99.9457 7.05545 0.075 1857 +1859 2 231.605 -101.233 8.84562 0.075 1858 +1860 2 233.214 -102.56 11.1152 0.075 1859 +1861 2 234.829 -104.753 12.3909 0.075 1860 +1862 2 236.222 -107.416 13.0938 0.075 1861 +1863 2 237.683 -109.828 14.3404 0.075 1862 +1864 2 201.662 -80.1483 -13.2022 0.075 1845 +1865 2 202.154 -79.9794 -16.7371 0.075 1864 +1866 2 202.343 -79.4918 -20.2916 0.075 1865 +1867 2 202.069 -82.7275 -19.9537 0.075 1866 +1868 2 202.869 -85.8701 -19.6009 0.075 1867 +1869 2 204.455 -88.6717 -19.2434 0.075 1868 +1870 2 206.7 -90.9455 -18.623 0.075 1869 +1871 2 208.235 -93.1834 -17.0634 0.075 1870 +1872 2 208.664 -95.7471 -15.1103 0.075 1871 +1873 2 208.935 -98.2641 -13.0537 0.075 1872 +1874 2 208.808 -100.378 -10.5824 0.075 1873 +1875 2 206.687 -102.474 -9.55521 0.075 1874 +1876 2 202.245 -76.6369 -21.6925 0.075 1866 +1877 2 203.094 -75.646 -24.5105 0.075 1876 +1878 2 205.62 -74.3004 -25.8321 0.075 1877 +1879 2 207.672 -73.6537 -27.7485 0.075 1878 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125.662 -169.229 41.6801 0.075 1947 +1949 2 124.971 -169.736 38.7487 0.075 1948 +1950 2 123.269 -169.072 36.4491 0.075 1949 +1951 2 120.793 -167.595 35.5594 0.075 1950 +1952 2 63.6597 -107.457 42.436 0.075 1917 +1953 2 65.3642 -109.8 43.7453 0.075 1952 +1954 2 66.4431 -110.846 46.9117 0.075 1953 +1955 2 67.5346 -111.657 50.1552 0.075 1954 +1956 2 68.8656 -112.289 53.3503 0.075 1955 +1957 2 54.3087 -107.061 37.1881 0.075 1915 +1958 2 52.132 -108.154 35.1074 0.075 1957 +1959 2 49.6376 -107.889 33.1046 0.075 1958 +1960 2 46.9485 -107.181 31.4941 0.075 1959 +1961 2 43.8485 -107.243 31.2518 0.075 1960 +1962 2 40.6822 -107.588 31.8959 0.075 1961 +1963 2 37.5077 -107.926 32.5085 0.075 1962 +1964 2 34.2769 -108.134 32.7157 0.075 1963 +1965 2 31.4518 -108.434 34.1185 0.075 1964 +1966 2 29.3932 -108.834 36.5991 0.075 1965 +1967 2 26.9526 -109.041 38.6887 0.075 1966 +1968 2 23.9183 -109.092 39.2571 0.075 1967 +1969 2 20.7386 -109.1 38.6214 0.075 1968 +1970 2 17.9001 -108.744 37.1349 0.075 1969 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0.075 1992 +1994 2 79.3347 -79.4452 52.1105 0.075 1993 +1995 2 82.1393 -80.625 51.7149 0.075 1994 +1996 2 84.0437 -81.6611 53.4363 0.075 1995 +1997 2 85.8699 -83.0001 55.4966 0.075 1996 +1998 2 87.7583 -85.4011 55.729 0.075 1997 +1999 2 89.4716 -87.7893 56.4524 0.075 1998 +2000 2 91.315 -89.4824 58.2043 0.075 1999 +2001 2 93.7776 -90.6751 59.3843 0.075 2000 +2002 2 96.605 -91.8599 59.6131 0.075 2001 +2003 2 99.0852 -93.4662 60.2552 0.075 2002 +2004 2 101.018 -95.517 61.4529 0.075 2003 +2005 2 102.156 -97.7159 60.3625 0.075 2004 +2006 2 102.597 -99.3889 57.8292 0.075 2005 +2007 2 103.558 -101.639 56.2045 0.075 2006 +2008 2 105.076 -104.203 55.526 0.075 2007 +2009 2 106.661 -106.705 54.7953 0.075 2008 +2010 2 108.56 -109.106 54.715 0.075 2009 +2011 2 110.569 -111.435 54.8084 0.075 2010 +2012 2 112.495 -113.832 54.8322 0.075 2011 +2013 2 114.339 -116.295 54.8329 0.075 2012 +2014 2 115.794 -119 54.9578 0.075 2013 +2015 2 117.027 -121.809 55.1013 0.075 2014 +2016 2 118.565 -124.465 54.9545 0.075 2015 +2017 2 120.167 -127.068 55.2238 0.075 2016 +2018 2 121.673 -129.684 55.8118 0.075 2017 +2019 2 123.43 -132.081 55.7291 0.075 2018 +2020 2 125.471 -134.27 55.0179 0.075 2019 +2021 2 127.19 -136.751 54.4904 0.075 2020 +2022 2 128.385 -139.538 54.7538 0.075 2021 +2023 2 129.415 -141.884 56.3941 0.075 2022 +2024 2 47.0579 -69.3705 52.779 0.075 1982 +2025 2 47.1662 -67.56 55.2167 0.075 2024 +2026 2 46.2851 -64.2836 55.9517 0.075 2025 +2027 2 46.4419 -60.9657 56.7024 0.075 2026 +2028 2 46.7084 -57.5466 56.958 0.075 2027 +2029 2 46.7577 -54.0716 57.0873 0.075 2028 +2030 2 46.1783 -51.1649 58.7681 0.075 2029 +2031 2 45.67 -51.2347 55.648 0.075 2030 +2032 2 45.0917 -51.2562 52.5605 0.075 2031 +2033 2 43.0687 -50.5186 50.4911 0.075 2032 +2034 2 40.0196 -49.9244 49.9373 0.075 2033 +2035 2 37.0656 -49.3912 49.0913 0.075 2034 +2036 2 34.6416 -48.7065 47.2563 0.075 2035 +2037 2 32.7718 -47.9635 44.8322 0.075 2036 +2038 2 31.8351 -46.9664 42.028 0.075 2037 +2039 2 31.9383 -46.3598 38.9539 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101.517 43.0842 1.88249 0.075 4306 +4308 2 97.869 30.6667 4.77329 0.075 4304 +4309 2 100.809 29.5783 4.98801 0.075 4308 +4310 2 103.318 28.6794 6.48887 0.075 4309 +4311 2 105.821 27.7916 8.15383 0.075 4310 +4312 2 108.504 27.0078 9.53927 0.075 4311 +4313 2 111.471 26.8619 10.5659 0.075 4312 +4314 2 114.459 26.4154 11.4223 0.075 4313 +4315 2 117.402 25.529 11.8384 0.075 4314 +4316 2 120.36 24.561 12.0011 0.075 4315 +4317 2 123.465 24.2216 12.3482 0.075 4316 +4318 2 126.551 24.0986 12.9266 0.075 4317 +4319 2 129.536 23.4842 13.5928 0.075 4318 +4320 2 132.358 22.3373 14.2563 0.075 4319 +4321 2 134.967 20.6809 14.8229 0.075 4320 +4322 2 137.557 18.9225 15.1019 0.075 4321 +4323 2 140.224 17.2885 15.3934 0.075 4322 +4324 2 142.78 15.4961 15.607 0.075 4323 +4325 2 145.19 13.4927 15.65 0.075 4324 +4326 2 147.827 11.8284 15.2598 0.075 4325 +4327 2 150.248 10.5338 16.6829 0.075 4326 +4328 2 152.649 9.27274 18.2705 0.075 4327 +4329 2 155.152 8.10512 19.7438 0.075 4328 +4330 2 157.9 7.16283 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32.8076 0.075 4352 +4354 2 177.126 3.84475 33.1252 0.075 4353 +4355 2 179.846 2.50397 33.1795 0.075 4354 +4356 2 182.417 0.897227 33.4264 0.075 4355 +4357 2 185.112 -0.411061 33.9548 0.075 4356 +4358 2 187.998 -0.726217 34.5996 0.075 4357 +4359 2 190.874 -0.801079 35.5264 0.075 4358 +4360 2 193.49 -1.20665 36.9392 0.075 4359 +4361 2 195.551 -2.08987 38.9953 0.075 4360 +4362 2 197.612 -2.97296 41.051 0.075 4361 +4363 2 200.098 -3.80122 42.4717 0.075 4362 +4364 2 202.935 -4.34566 43.3256 0.075 4363 +4365 2 205.926 -4.41694 43.8755 0.075 4364 +4366 2 208.883 -4.45348 44.5862 0.075 4365 +4367 2 211.874 -4.24546 45.0655 0.075 4366 +4368 2 214.883 -3.90705 45.3072 0.075 4367 +4369 2 217.898 -3.51461 45.3835 0.075 4368 +4370 2 220.914 -3.12281 45.48 0.075 4369 +4371 2 223.943 -3.03094 45.72 0.075 4370 +4372 2 226.917 -3.40627 46.0866 0.075 4371 +4373 2 229.645 -4.12807 46.9731 0.075 4372 +4374 2 231.891 -5.00308 48.8173 0.075 4373 +4375 2 233.702 -6.00709 51.0456 0.075 4374 +4376 2 235.628 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295.355 -27.2761 56.2331 0.075 4398 +4400 2 298.072 -27.6893 54.9583 0.075 4399 +4401 2 300.86 -27.8528 53.9095 0.075 4400 +4402 2 303.884 -27.9336 53.8416 0.075 4401 +4403 2 306.084 -28.462 55.8749 0.075 4402 +4404 2 307.295 -29.2495 58.4965 0.075 4403 +4405 2 308.297 -30.092 61.2427 0.075 4404 +4406 2 309.357 -31.1043 63.8803 0.075 4405 +4407 2 310.628 -32.7288 66.0944 0.075 4406 +4408 2 312.124 -35.1314 67.2097 0.075 4407 +4409 2 313.56 -37.4998 68.4655 0.075 4408 +4410 2 315.001 -39.8235 69.7972 0.075 4409 +4411 2 316.486 -41.9931 71.282 0.075 4410 +4412 2 318.039 -43.5778 73.3631 0.075 4411 +4413 2 319.53 -45.1601 75.4908 0.075 4412 +4414 2 321.024 -46.8337 77.5428 0.075 4413 +4415 2 322.482 -48.7906 79.2841 0.075 4414 +4416 2 323.831 -51.2688 80.4213 0.075 4415 +4417 2 84.2412 44.3478 3.33583 0.075 4247 +4418 2 85.1104 48.4491 1.53492 0.075 4417 +4419 2 84.3897 51.3014 2.23872 0.075 4418 +4420 2 84.5812 54.2604 2.84868 0.075 4419 +4421 2 84.4942 57.226 2.62034 0.075 4420 +4422 2 84.6172 59.9938 1.58769 0.075 4421 +4423 2 85.2426 62.529 0.0897999 0.075 4422 +4424 2 86.4361 65.058 -1.06937 0.075 4423 +4425 2 86.8509 67.9737 -1.67472 0.075 4424 +4426 2 87.5865 70.8241 -2.2863 0.075 4425 +4427 2 88.6689 73.5755 -2.92458 0.075 4426 +4428 2 89.9147 76.2703 -3.51441 0.075 4427 +4429 2 90.7702 79.1485 -3.89954 0.075 4428 +4430 2 91.6322 82.0232 -4.29572 0.075 4429 +4431 2 92.453 84.8078 -5.04248 0.075 4430 +4432 2 93.1576 87.4771 -6.27529 0.075 4431 +4433 2 93.4788 90.1967 -7.56553 0.075 4432 +4434 2 93.6963 93.0259 -8.61207 0.075 4433 +4435 2 94.0893 95.8531 -9.60275 0.075 4434 +4436 2 94.6055 98.6838 -10.5316 0.075 4435 +4437 2 94.9506 101.598 -11.2729 0.075 4436 +4438 2 95.2262 104.511 -12.0517 0.075 4437 +4439 2 95.1189 107.425 -12.8119 0.075 4438 +4440 2 94.9526 110.301 -13.6954 0.075 4439 +4441 2 95.0791 113.088 -14.8647 0.075 4440 +4442 2 95.0818 115.702 -16.3915 0.075 4441 +4443 2 95.0846 118.316 -17.9186 0.075 4442 +4444 2 94.9519 120.85 -19.5569 0.075 4443 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2 91.3457 -142.758 19.1899 0.075 1921 +1923 2 94.057 -141.905 20.5652 0.075 1922 +1924 2 96.5927 -141.784 22.4331 0.075 1923 +1925 2 98.6357 -141.777 21.7531 0.075 1924 +1926 2 99.1769 -140.818 18.8137 0.075 1925 +1927 2 100.774 -139.912 16.4344 0.075 1926 +1928 2 103.766 -139.286 15.8472 0.075 1927 +1929 2 106.621 -137.95 16.0329 0.075 1928 +1930 2 108.735 -136.726 17.9031 0.075 1929 +1931 2 111.16 -135.9 19.56 0.075 1930 +1932 2 113.451 -137.38 20.5556 0.075 1931 +1933 2 115.031 -139.741 21.6485 0.075 1932 +1934 2 116.159 -142.57 22.0109 0.075 1933 +1935 2 117.274 -145.399 22.4085 0.075 1934 +1936 2 118.482 -147.967 23.1342 0.075 1935 +1937 2 119.383 -149.467 20.6107 0.075 1936 +1938 2 120.283 -150.967 18.0872 0.075 1937 +1939 2 121.093 -152.941 16.1839 0.075 1938 +1940 2 121.936 -155.818 15.5501 0.075 1939 +1941 2 123.333 -158.274 14.5518 0.075 1940 +1942 2 124.749 -160.313 12.7603 0.075 1941 +1943 2 126.082 -162.371 10.9158 0.075 1942 +1944 2 127.534 -164.634 9.46021 0.075 1943 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65.2897 0.075 2012 +2014 2 127.529 -87.0695 65.688 0.075 2013 +2015 2 127.55 -89.7111 66.967 0.075 2014 +2016 2 127.589 -91.9962 69.026 0.075 2015 +2017 2 127.652 -94.3092 71.0383 0.075 2016 +2018 2 128.121 -97.0815 72.2858 0.075 2017 +2019 2 128.7 -99.99 73 0.075 2018 +2020 2 131.11 -123.81 15.9333 0.075 1961 +2021 2 132.189 -125.316 12.8626 0.075 2020 +2022 2 133.298 -128.062 10.8598 0.075 2021 +2023 2 134.159 -130.819 8.74254 0.075 2022 +2024 2 136.011 -132.441 6.30638 0.075 2023 +2025 2 136.178 -135.139 4.30202 0.075 2024 +2026 2 135.53 -137.86 2.06667 0.075 2025 +2027 2 133.963 -122.675 15.6687 0.075 2020 +2028 2 136.929 -121.849 15.5195 0.075 2027 +2029 2 139.181 -119.772 15.1146 0.075 2028 +2030 2 140.898 -117.262 14.4545 0.075 2029 +2031 2 143.263 -115.369 14.6026 0.075 2030 +2032 2 145.873 -114.112 13.8982 0.075 2031 +2033 2 147.681 -112.843 11.9165 0.075 2032 +2034 2 148.147 -110.303 10.3088 0.075 2033 +2035 2 150.293 -108.226 9.92323 0.075 2034 +2036 2 152.937 -106.642 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56.6025 55.395 -13.2467 0.165 6030 +6032 2 56.475 56.6775 -13.4533 0.165 6031 +6033 2 56.035 58.065 -13.66 0.165 6032 +6034 2 55.7067 58.7667 -13.8667 0.165 6033 +6035 2 55.0533 60.7833 -13.7644 0.165 6034 +6036 2 54.8 61.74 -13.7133 0.165 6035 +6037 2 54.0733 65.04 -13.56 0.165 6036 +6038 2 53.6633 66.9367 -13.56 0.165 6037 +6039 2 53.3167 68.6033 -13.6222 0.165 6038 +6040 2 53.16 70.3467 -13.6844 0.165 6039 +6041 2 53.055 70.97 -13.7467 0.165 6040 +6042 2 53.03 71.88 -13.7467 0.165 6041 +6043 2 42.46 8.89 -8.53333 0.165 5984 +6044 2 39.89 6.03 -8.53333 0.165 6043 +6045 2 39.0925 4.9375 -8.53333 0.165 6044 +6046 2 38.0425 3.955 -8.53333 0.165 6045 +6047 2 37.5967 3.49667 -8.53333 0.165 6046 +6048 2 37.015 3.29 -8.53333 0.165 6047 +6049 2 36.12 3.02 -8.53333 0.165 6048 +6050 2 47.84 10.87 -6.76 0.275 5982 +6051 2 50.2333 12.72 -6.76 0.275 6050 +6052 2 51.5033 13.3667 -7.48889 0.275 6051 +6053 2 52.255 13.64 -7.85333 0.275 6052 +6054 2 52.78 13.77 -8.94667 0.275 6053 +6055 2 53.81 12.85 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95.0525 0.193333 0.22 6706 +6708 2 76.8967 95.6533 0.195556 0.22 6707 +6709 2 77.6825 98.045 -0.473333 0.22 6708 +6710 2 77.99 98.73 -0.697778 0.22 6709 +6711 2 78.1375 101.085 -1.12 0.22 6710 +6712 2 77.9325 102.403 -1.10333 0.22 6711 +6713 2 77.52 103.782 -1.09333 0.22 6712 +6714 2 77.14 104.965 -1.09333 0.22 6713 +6715 2 76.5825 106.22 -1.29333 0.22 6714 +6716 2 76.3333 106.717 -1.36 0.22 6715 +6717 2 76.05 107.255 -1.49333 0.22 6716 +6718 2 75.7 107.82 -1.89333 0.22 6717 +6719 2 67.26 53.83 7.14667 0.22 6672 +6720 2 70.4367 54.29 6.22222 0.22 6719 +6721 2 72.2575 54.4475 5.12667 0.22 6720 +6722 2 73.842 54.524 4.79467 0.22 6721 +6723 2 74.778 54.766 4.57333 0.22 6722 +6724 2 75.2625 54.7975 4.57333 0.22 6723 +6725 2 76.355 55.195 4.74667 0.22 6724 +6726 2 77.4475 55.7975 4.92 0.22 6725 +6727 2 78.5475 56.3375 4.69 0.22 6726 +6728 2 79.5425 56.9825 4.46 0.22 6727 +6729 2 80.01 57.1667 4.19111 0.22 6728 +6730 2 81.27 58.06 3.65333 0.22 6729 +6731 2 82.4833 59.41 3.65333 0.22 6730 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0.22 6756 +6758 2 110.953 75.73 5.94333 0.22 6757 +6759 2 112.335 76.0325 6.43333 0.22 6758 +6760 2 113.4 76.5025 6.85667 0.22 6759 +6761 2 115.02 77.7117 7.28 0.22 6760 +6762 2 115.434 78.01 7.28 0.22 6761 +6763 2 116.216 78.556 7.51467 0.22 6762 +6764 2 117.044 78.986 7.74933 0.22 6763 +6765 2 117.47 79.2525 7.86667 0.22 6764 +6766 2 118.483 79.5325 8.45 0.22 6765 +6767 2 119.812 79.828 9.14933 0.22 6766 +6768 2 120.52 79.76 9.38133 0.22 6767 +6769 2 120.877 79.845 9.61333 0.22 6768 +6770 2 121.19 79.79 9.61333 0.22 6769 +6771 2 121.46 79.41 9.61333 0.22 6770 +6772 2 121.84 79.18 9.61333 0.22 6771 +6773 2 97.64 72.53 5.32 0.055 6748 +6774 2 98.035 76.31 2.34 0.055 6773 +6775 2 99.705 78.525 1.66 0.055 6774 +6776 2 101.88 79.435 0.293333 0.055 6775 +6777 2 103.22 79.09 -0.733333 0.055 6776 +6778 2 54.48 47.68 7.42667 0.165 6654 +6779 2 55.33 51.758 7.42667 0.165 6778 +6780 2 55.42 52.2925 7.42667 0.165 6779 +6781 2 55.4725 53.5875 7.42667 0.165 6780 +6782 2 55.49 54.96 7.37667 0.165 6781 +6783 2 55.4125 56.2525 7.32667 0.165 6782 +6784 2 55.37 56.8333 7.29333 0.165 6783 +6785 2 55.315 57.38 7.22667 0.165 6784 +6786 2 55.1 57.71 7.22667 0.165 6785 +6787 2 55.97 58.9 7.22667 0.165 6786 +6788 2 58.636 59.06 7.22667 0.165 6787 +6789 2 60.0967 59.1283 7.23556 0.165 6788 +6790 2 60.532 59.152 7.23733 0.165 6789 +6791 2 61.534 59.27 7.24267 0.165 6790 +6792 2 62.538 59.508 7.248 0.165 6791 +6793 2 63.614 59.856 7.09333 0.165 6792 +6794 2 64.0875 59.9375 7.05333 0.165 6793 +6795 2 65.225 60.6425 6.85333 0.165 6794 +6796 2 65.7267 61.0233 6.72 0.165 6795 +6797 2 66.89 62.15 6.12 0.165 6796 +6798 2 68.25 63.6367 5.49333 0.165 6797 +6799 2 68.9 64.31 5.01333 0.165 6798 +6800 2 71.1775 67.3675 3.89333 0.165 6799 +6801 2 72.12 68.834 3.784 0.165 6800 +6802 2 72.4375 69.185 3.81333 0.165 6801 +6803 2 73.1225 70.0475 3.92 0.165 6802 +6804 2 74.574 70.91 4.04 0.165 6803 +6805 2 75.1375 71.1325 4.06 0.165 6804 +6806 2 76.36 71.5225 4.09333 0.165 6805 +6807 2 76.9033 71.5867 4.09333 0.165 6806 +6808 2 78.5767 72.34 4.16889 0.165 6807 +6809 2 79.525 72.79 4.20667 0.165 6808 +6810 2 81.475 74.405 4.32 0.165 6809 +6811 2 83.7733 76.84 5.31556 0.165 6810 +6812 2 84.6225 79.0825 5.81333 0.165 6811 +6813 2 84.7825 80.42 5.81333 0.165 6812 +6814 2 84.9625 81.8675 5.81333 0.165 6813 +6815 2 85.36 83.345 5.81333 0.165 6814 +6816 2 85.6067 84.0267 5.81333 0.165 6815 +6817 2 85.865 84.715 5.81333 0.165 6816 +6818 2 88.155 86.295 7.06 0.165 6817 +6819 2 91.6433 87.88 8.30667 0.165 6818 +6820 2 92.8867 88.8233 8.30667 0.165 6819 +6821 2 93.8225 90.8525 8.55 0.165 6820 +6822 2 93.885 92.17 8.79333 0.165 6821 +6823 2 93.9775 93.355 9.03667 0.165 6822 +6824 2 94.04 93.9867 9.28 0.165 6823 +6825 2 94.06 94.455 9.28 0.165 6824 +6826 2 94.14 94.78 9.28 0.165 6825 +6827 2 54.02 59.32 7.2 0.165 6786 +6828 2 54.2933 62.5967 7.2 0.165 6827 +6829 2 55.1367 64.1867 7.05333 0.165 6828 +6830 2 55.6 64.745 6.98 0.165 6829 +6831 2 56.38 65.6 6.76 0.165 6830 +6832 2 56.06 67.05 6.06667 0.165 6831 +6833 2 55.552 70.7 6.06667 0.165 6832 +6834 2 55.4175 71.255 6.06667 0.165 6833 +6835 2 54.9775 72.645 6.06667 0.165 6834 +6836 2 54.76 73.29 6.06667 0.165 6835 +6837 2 54.28 75.0133 6.08444 0.165 6836 +6838 2 53.505 77.8183 6.11111 0.165 6837 +6839 2 53.6343 78.7586 6.12 0.165 6838 +6840 2 53.575 79.0833 6.12 0.165 6839 +6841 2 53.7783 79.735 6.34667 0.165 6840 +6842 2 53.974 79.93 6.392 0.165 6841 +6843 2 54.286 80.706 6.664 0.165 6842 +6844 2 54.556 81.55 6.936 0.165 6843 +6845 2 54.675 82.0575 7.14 0.165 6844 +6846 2 54.97 83.432 8.09333 0.165 6845 +6847 2 55.02 84.32 8.4 0.165 6846 +6848 2 55.09 84.66 8.63 0.165 6847 +6849 2 55.432 85.984 9.01333 0.165 6848 +6850 2 55.588 86.948 9.01333 0.165 6849 +6851 2 55.952 87.844 9.01333 0.165 6850 +6852 2 56.605 89.1333 9.01333 0.165 6851 +6853 2 56.776 89.57 9.01333 0.165 6852 +6854 2 56.955 90.05 9.01333 0.165 6853 +6855 2 57.22 90.5033 9.01333 0.165 6854 +6856 2 57.36 90.92 9.01333 0.165 6855 +6857 2 57.43 91.37 9.01333 0.165 6856 +6858 2 58.51 67.2 6.97333 0.165 6831 +6859 2 60.04 68.57 6.97333 0.165 6858 +6860 2 59.58 70.8 5.70667 0.165 6859 +6861 2 60.7033 74.2667 5.58667 0.165 6860 +6862 2 61.46 76.545 5.43667 0.165 6861 +6863 2 61.93 77.8375 5.34667 0.165 6862 +6864 2 62.1833 78.3367 5.34667 0.165 6863 +6865 2 62.6233 79.54 6.16889 0.165 6864 +6866 2 62.3975 80.9875 7.19667 0.165 6865 +6867 2 61.43 81.642 7.81333 0.165 6866 +6868 2 60.45 81.682 7.81333 0.165 6867 +6869 2 59.9625 81.6825 7.81333 0.165 6868 +6870 2 59.4467 81.6367 7.81333 0.165 6869 +6871 2 58.795 81.32 7.81333 0.165 6870 +6872 2 58.12 81.13 7.81333 0.165 6871 +6873 2 61.02 69.7 5.68 0.165 6859 +6874 2 62.635 71.97 3.73667 0.165 6873 +6875 2 63.3225 73.08 3.3 0.165 6874 +6876 2 64.06 74.1675 2.98333 0.165 6875 +6877 2 64.9125 75.11 3.35 0.165 6876 +6878 2 65.3467 75.57 3.49333 0.165 6877 +6879 2 65.88 75.975 3.78 0.165 6878 +6880 2 66.45 76.2 4.64 0.165 6879 +6881 2 66.34 77.58 4.64 0.165 6880 +6882 2 65.194 79.29 4.64 0.165 6881 +6883 2 65.098 80.052 4.51467 0.165 6882 +6884 2 65.054 80.886 4.39467 0.165 6883 +6885 2 65.048 81.802 4.27733 0.165 6884 +6886 2 65.125 82.2675 4.18667 0.165 6885 +6887 2 65.3375 83.3125 4.28333 0.165 6886 +6888 2 65.5875 84.3125 4.53667 0.165 6887 +6889 2 65.6933 84.8067 4.70222 0.165 6888 +6890 2 66.7867 86.0467 5.02667 0.165 6889 +6891 2 67.305 86.56 5.02667 0.165 6890 +6892 2 67.93 87.3 5.02667 0.165 6891 +6893 2 67.56 88.66 5.70667 0.165 6892 +6894 2 67.968 91.53 5.50133 0.165 6893 +6895 2 68.076 92.444 5.18933 0.165 6894 +6896 2 68.052 93.484 4.87733 0.165 6895 +6897 2 68.094 94.4 4.63733 0.165 6896 +6898 2 68.036 95.31 4.392 0.165 6897 +6899 2 67.852 96.37 4.18133 0.165 6898 +6900 2 67.692 97.37 4.216 0.165 6899 +6901 2 67.502 98.196 3.90667 0.165 6900 +6902 2 67.3925 98.6425 3.84667 0.165 6901 +6903 2 66.705 99.3875 3.18333 0.165 6902 +6904 2 66.4433 99.72 2.80444 0.165 6903 +6905 2 65.2133 100.453 1.62222 0.165 6904 +6906 2 63.6 101.307 1.14222 0.165 6905 +6907 2 62.0233 102.07 1.03111 0.165 6906 +6908 2 59.6625 103.123 0.913333 0.165 6907 +6909 2 58.9167 103.417 0.831111 0.165 6908 +6910 2 58.215 103.9 0.666667 0.165 6909 +6911 2 55.775 104.715 0.126667 0.165 6910 +6912 2 52.335 106.112 -1.12333 0.165 6911 +6913 2 51.1775 106.735 -1.36 0.165 6912 +6914 2 50.045 107.345 -1.20667 0.165 6913 +6915 2 49.5267 107.637 -1.15556 0.165 6914 +6916 2 47.245 107.27 -0.9 0.165 6915 +6917 2 46.5367 107.057 -0.746667 0.165 6916 +6918 2 45.855 106.675 -0.746667 0.165 6917 +6919 2 45.5 106.12 -0.746667 0.165 6918 +6920 2 68.85 88.34 5.50667 0.165 6892 +6921 2 70.956 91.006 5.65867 0.165 6920 +6922 2 71.28 91.92 5.87467 0.165 6921 +6923 2 71.726 92.734 6.09067 0.165 6922 +6924 2 72.242 93.492 6.256 0.165 6923 +6925 2 72.776 94.286 6.42133 0.165 6924 +6926 2 73.352 95.064 6.58667 0.165 6925 +6927 2 74.076 95.804 6.58667 0.165 6926 +6928 2 74.77 96.582 6.58667 0.165 6927 +6929 2 75.472 97.378 6.58667 0.165 6928 +6930 2 75.78 97.8275 6.58667 0.165 6929 +6931 2 76.6025 98.9625 6.80333 0.165 6930 +6932 2 77.03 99.4433 6.87556 0.165 6931 +6933 2 78.4233 100.543 7.16444 0.165 6932 +6934 2 80.4067 101.713 7.45333 0.165 6933 +6935 2 82.4867 102.213 7.09333 0.165 6934 +6936 2 84.98 102.89 6.64333 0.165 6935 +6937 2 86.806 103.078 6.51733 0.165 6936 +6938 2 87.34 103.27 6.55333 0.165 6937 +6939 2 88.705 103.125 6.73333 0.165 6938 +6940 2 89.3567 102.983 6.85333 0.165 6939 +6941 2 91.0633 102.527 6.58222 0.165 6940 +6942 2 92.155 102.295 6.32667 0.165 6941 +6943 2 94.415 102.16 5.56 0.165 6942 +6944 2 98.1233 102.713 5.57778 0.165 6943 +6945 2 100.087 103.22 5.58667 0.165 6944 +6946 2 101.75 104.137 5.58667 0.165 6945 +6947 2 102.84 105.46 5.58667 0.165 6946 +6948 2 104.095 107.7 5.58667 0.165 6947 +6949 2 104.362 109.14 5.58667 0.165 6948 +6950 2 104.567 109.82 5.58667 0.165 6949 +6951 2 104.495 110.61 5.58667 0.165 6950 +6952 2 104.12 111.25 5.58667 0.165 6951 +6953 2 68.75 77.05 2.36 0.165 6880 +6954 2 69.67 77.45 2.36 0.165 6953 +6955 2 69.73 77.69 2.36 0.165 6954 +6956 2 72.0475 80.98 2.68 0.165 6955 +6957 2 73.015 81.8475 2.50667 0.165 6956 +6958 2 74.21 83.326 2.33333 0.165 6957 +6959 2 74.916 84.242 2.33333 0.165 6958 +6960 2 75.422 85.198 2.33333 0.165 6959 +6961 2 75.926 86.25 2.33333 0.165 6960 +6962 2 76.362 87.308 2.33333 0.165 6961 +6963 2 76.782 88.442 2.33333 0.165 6962 +6964 2 77.262 89.384 2.33333 0.165 6963 +6965 2 78.0533 90.4183 2.33333 0.165 6964 +6966 2 78.5767 90.9883 2.33333 0.165 6965 +6967 2 79.215 91.41 2.33333 0.165 6966 +6968 2 80.0283 91.6367 2.33333 0.165 6967 +6969 2 80.358 91.748 2.33333 0.165 6968 +6970 2 80.72 91.885 2.33333 0.165 6969 +6971 2 81.99 92.2075 2.33333 0.165 6970 +6972 2 83.2525 92.65 2.33333 0.165 6971 +6973 2 84.445 93.2725 2.33333 0.165 6972 +6974 2 85.455 94.04 2.65 0.165 6973 +6975 2 86.2475 94.7825 2.96667 0.165 6974 +6976 2 87.2475 95.315 3.28333 0.165 6975 +6977 2 88.2775 95.775 3.6 0.165 6976 +6978 2 89.3425 96.305 3.6 0.165 6977 +6979 2 90.33 97.0725 3.6 0.165 6978 +6980 2 90.7567 97.5267 3.6 0.165 6979 +6981 2 91.18 98.14 3.6 0.165 6980 +6982 2 91.58 98.8 3.6 0.165 6981 +6983 2 91.64 100.57 3.28 0.165 6982 +6984 2 91.7425 103.697 3.28 0.165 6983 +6985 2 91.595 105.14 3.28 0.165 6984 +6986 2 91.275 106.538 3.21 0.165 6985 +6987 2 90.8825 107.698 3.14 0.165 6986 +6988 2 90.66 108.968 3.07 0.165 6987 +6989 2 90.634 110.732 2.832 0.165 6988 +6990 2 90.695 111.31 2.79 0.165 6989 +6991 2 90.78 111.96 2.72 0.165 6990 +6992 2 90.84 112.465 2.58 0.165 6991 +6993 2 90.94 112.96 2.16 0.165 6992 +6994 2 91.33 113.87 2.16 0.165 6993 +6995 2 92.0825 117.14 2.19667 0.165 6994 +6996 2 92.1467 117.79 2.20889 0.165 6995 +6997 2 92.37 118.21 2.23333 0.165 6996 +6998 2 92.63 118.8 2.30667 0.165 6997 +6999 2 91.11 114.2 2.16 0.165 6993 +7000 2 88.922 116.792 2.16 0.165 6999 +7001 2 88.6325 117.207 2.16 0.165 7000 +7002 2 88.39 118.57 2.17667 0.165 7001 +7003 2 88.2633 119.173 2.18222 0.165 7002 +7004 2 88.235 119.925 2.19333 0.165 7003 +7005 2 88.44 121.22 2.22667 0.165 7004 +7006 2 92.17 100.45 2.62667 0.165 6982 +7007 2 94.9133 100.9 2.41333 0.165 7006 +7008 2 95.785 101.22 2.30667 0.165 7007 +7009 2 99.1367 101.223 1.98667 0.165 7008 +7010 2 101.313 101.037 1.98667 0.165 7009 +7011 2 102.35 101.005 1.98667 0.165 7010 +7012 2 105.72 102.065 1.52 0.165 7011 +7013 2 109.137 103.723 1.05333 0.165 7012 +7014 2 110.825 105.188 1.05333 0.165 7013 +7015 2 111.353 105.717 1.05333 0.165 7014 +7016 2 111.675 106.315 1.05333 0.165 7015 +7017 2 113.935 107.425 0.94 0.165 7016 +7018 2 117.832 109.155 0.826667 0.165 7017 +7019 2 118.473 109.537 0.826667 0.165 7018 +7020 2 119.81 110.52 0.826667 0.165 7019 +7021 2 120.475 111.01 0.826667 0.165 7020 +7022 2 121.97 112.82 1.69333 0.165 7021 +7023 2 124.44 115.26 2.56 0.165 7022 +7024 2 125.095 115.895 2.56 0.165 7023 +7025 2 126.23 116.72 2.56 0.165 7024 +7026 2 71.49 77.5 0.826667 0.055 6954 +7027 2 71.8775 75.09 0.57 0.055 7026 +7028 2 72.2725 74.35 0.443333 0.055 7027 +7029 2 72.9925 73.68 0.0666667 0.055 7028 +7030 2 73.2867 73.4933 0.00888889 0.055 7029 +7031 2 74.3367 72.7967 -0.746667 0.055 7030 +7032 2 75.3333 72.0833 -1.51111 0.055 7031 +7033 2 76.69 72.0133 -2.16889 0.055 7032 +7034 2 78.3967 72.7567 -2.76444 0.055 7033 +7035 2 79.2967 73.6467 -2.70667 0.055 7034 +7036 2 79.9233 74.8133 -2.18222 0.055 7035 +7037 2 79.68 75.395 -1.4 0.055 7036 +7038 2 81.76 75.935 -0.506667 0.055 7037 +7039 2 84.435 75.795 0.68 0.055 7038 +7040 2 86.905 76.28 1.87333 0.055 7039 +7041 2 89.775 77.43 2.91333 0.055 7040 +7042 2 93.485 78.46 3.81333 0.055 7041 +7043 2 97.595 79.02 4.60667 0.22 7042 +7044 2 101.13 80.305 6.21333 0.22 7043 +7045 2 104.295 81.985 7.77333 0.22 7044 +7046 2 107.657 83.2033 8.14667 0.22 7045 +7047 2 109.717 83.4733 7.88889 0.22 7046 +7048 2 110.665 83.785 7.76 0.22 7047 +7049 2 113.065 84.655 7.63333 0.22 7048 +7050 2 116.617 86.0333 8.20889 0.11 7049 +7051 2 118.857 86.54 9.00444 0.11 7050 +7052 2 120.453 87.2067 9.78222 0.11 7051 +7053 2 122.37 87.9733 9.91111 0.11 7052 +7054 2 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185.748 110.26 5.29333 0.165 12576 +12578 2 185.477 110.893 5.63111 0.165 12577 +12579 2 184.158 112.552 6.72267 0.165 12578 +12580 2 183.815 112.893 6.82667 0.165 12579 +12581 2 183.423 113.223 7 0.165 12580 +12582 2 183.29 113.6 7 0.165 12581 +12583 2 183.28 113.72 7 0.165 12582 +12584 2 184.68 85.34 -2.12 0.165 12561 +12585 2 186.708 87.605 -2.75667 0.165 12584 +12586 2 187.125 88.71 -3.10333 0.165 12585 +12587 2 187.026 90.258 -3.50667 0.165 12586 +12588 2 186.978 90.7125 -3.50667 0.165 12587 +12589 2 186.86 91.2933 -3.50667 0.165 12588 +12590 2 186.493 92.66 -4.56444 0.165 12589 +12591 2 186.415 93.22 -5.09333 0.165 12590 +12592 2 186.106 96.632 -6.928 0.22 12591 +12593 2 185.966 97.746 -7.21067 0.055 12592 +12594 2 185.634 98.462 -7.65067 0.055 12593 +12595 2 185.44 98.8675 -7.82 0.055 12594 +12596 2 185.015 99.74 -8.37 0.055 12595 +12597 2 184.555 100.745 -8.98 0.055 12596 +12598 2 184.127 101.678 -9.68333 0.055 12597 +12599 2 183.86 102.843 -10.1167 0.055 12598 +12600 2 183.297 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} + } +} \ No newline at end of file diff --git a/examples/notebooks/neuromodulation/data/extraction.ipynb b/examples/notebooks/neuromodulation/data/extraction.ipynb new file mode 100644 index 000000000..32d22d07d --- /dev/null +++ b/examples/notebooks/neuromodulation/data/extraction.ipynb @@ -0,0 +1,1233 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b9d1ca96-2e8e-43fb-9834-755b3e766a02", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload \n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "f83305e7-8789-43b1-b6bb-205b7b58e7a2", + "metadata": {}, + "source": [ + "Digitized data from Planert 2013, Fig. 4 B & D.\n", + "\n", + "![image](planert2013/Planert2013-Fig4-DA-pone.0057054.g004.png)\n", + "\n", + "Reference: \n", + "\n", + "Planert H, Berger TK, Silberberg G. Membrane properties of striatal direct and indirect pathway neurons in mouse and rat slices and their modulation by dopamine. PLoS One. 2013;8(3):e57054. doi:10.1371/journal.pone.0057054" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6044676f-9fa5-4674-9581-9ed27c939cd5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "pio.renderers.default = 'iframe'\n", + "pio.templates.default = 'seaborn'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "47641278-fa81-4867-8343-318f9b48d1e5", + "metadata": {}, + "outputs": [], + "source": [ + "def postprocess(filename):\n", + " raw_data = np.genfromtxt(filename, delimiter=',')\n", + " pre = raw_data[::2]\n", + " post = raw_data[1::2]\n", + " \n", + " assert post.shape == pre.shape, f\"length mismatch ({post.shape=}) != ({pre.shape=})\"\n", + "\n", + " slopes = (post[:,1] - pre[:,1]) / (post[:,0] - pre[:,0])\n", + " pre_y = post[:,1]-slopes\n", + " pre_x = np.zeros(pre_y.shape)\n", + "\n", + " post_x = post[:,0]\n", + " post_y = post[:,1]\n", + "\n", + " return pre_x, pre_y, post_x, post_y" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db483a6b-0ca8-4e78-932e-7336986c6b61", + "metadata": {}, + "outputs": [ + { + "ename": "type", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m (df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpre\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m-\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpre\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mround(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m))\n", + "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" + ] + } + ], + "source": [ + "(df['pre'] - df['pre'].round(0).astype(int))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "efe6417a-7b86-4084-9bf6-a16f852f230d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing planert2013/d1-60mV.csv...\n", + "Processing planert2013/d1-80mV.csv...\n", + "Processing planert2013/d2-60mV.csv...\n", + "Processing planert2013/d2-80mV.csv...\n" + ] + } + ], + "source": [ + "table_data = {'post' : [], 'pre' : [], 'holding': [], 'receptor': [], 'filename': []}\n", + "\n", + "for filename, holding, receptor in [('planert2013/d1-60mV.csv', -60, 'D1'),\n", + " ('planert2013/d1-80mV.csv', -80, 'D1'),\n", + " ('planert2013/d2-60mV.csv', -60, 'D2'),\n", + " ('planert2013/d2-80mV.csv', -80, 'D2')]:\n", + " print(f\"Processing {filename}...\")\n", + " pre_x, pre_y, post_x, post_y = postprocess(filename)\n", + " table_data['post'] += list(post_y)\n", + " table_data['pre'] += list(pre_y)\n", + " table_data['holding'] += [holding] * len(pre_y)\n", + " table_data['receptor'] += [receptor] * len(pre_y)\n", + " table_data['filename'] += [filename] * len(pre_y)\n", + "\n", + "df = pd.DataFrame(table_data)\n", + "df['change'] = (df['post'] - df['pre']) / df['pre']\n", + "df['experiment'] = df['receptor'] + '-' + df['holding'].astype(str)\n", + "\n", + "# The figure shows spike counts so we round the digitized values\n", + "# to the nearest integer\n", + "df['i_pre'] = df['pre'].round(0).astype(int)\n", + "df['i_post'] = df['post'].round(0).astype(int)\n", + "df['i_change%'] = ((df['i_post'] - df['i_pre']) / df['i_pre'])*100\n", + "\n", + "# check rounding error\n", + "df['pre_diff'] = (df['pre'] - df['i_pre']).abs()\n", + "df['post_diff'] = (df['post'] - df['i_post']).abs()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8a3d700a-cdbb-428d-994d-80b33a46e062", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.scatter(df, y='i_change%', x='experiment', color='experiment')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "751a95e0-90d1-4626-afaa-9b43251ff4b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.box(df, y='i_change%', x='experiment', color='experiment')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e1123f05-6425-44b4-a9af-4ffdb3386ff9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.box(df, y='post', x='experiment', color='experiment')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6d16d5a3-4ec8-4da1-89d5-a6ddb148e9a3", + "metadata": {}, + "outputs": [], + "source": [ + "def plot(df, receptor, holding):\n", + " _df = df.loc[(df['receptor'] == receptor) & (df['holding'] == holding)]\n", + " \n", + " fig = go.Figure()\n", + "\n", + " for _, row in _df.iterrows():\n", + " fig.add_trace(go.Scatter(x=['pre','post'], y=[row['i_pre'], row['i_post']]))\n", + "\n", + " fn = _df['filename'].unique()\n", + " fig.update_layout(title=f'Files: {fn}', height=500, width=500)\n", + " \n", + " return fig\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cfabc090-19f5-4473-b8fa-c41f71839526", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(df, 'D1', -60)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2411b58f-c843-490b-b21a-7204dfbfdd3a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(df, 'D2', -60)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9afa8f71-009f-46fd-83ca-850c6c8fd653", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(df, 'D1', -80)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "61ce7b86-8704-4f9c-b330-14af76f3a6f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(df, 'D2', -80)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b5b0228-1960-45d4-867f-20ee8f67789c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "df1f7a1c-9c20-4add-a906-51346ccc14c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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3911.1680334.000000-80D2planert2013/d2-80mV.csv1.792008D2--80411175.0000000.0000000.168033
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54.8743684.141989-60D1planert2013/d1-60mV.csv0.176818D1--604525.0000000.1419890.125632
77.8826073.120437-60D1planert2013/d1-60mV.csv1.526123D1--6038166.6666670.1204370.117393
86.8883594.139035-60D1planert2013/d1-60mV.csv0.664243D1--604775.0000000.1390350.111641
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30.0815793.768613-60D1planert2013/d1-60mV.csv-0.978353D1--6040-100.0000000.2313870.081579
40.9228674.994933-60D1planert2013/d1-60mV.csv-0.815239D1--6051-80.0000000.0050670.077133
423.0737703.995902-80D2planert2013/d2-80mV.csv-0.230769D2--8043-25.0000000.0040980.073770
04.9339210.925110-60D1planert2013/d1-60mV.csv4.333333D1--6015400.0000000.0748900.066079
451.0655742.018735-80D2planert2013/d2-80mV.csv-0.472158D2--8021-50.0000000.0187350.065574
3714.0573774.000000-80D2planert2013/d2-80mV.csv2.514344D2--80414250.0000000.0000000.057377
414.0573773.994194-80D2planert2013/d2-80mV.csv0.015819D2--80440.0000000.0058060.057377
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\n", + "7 D1--60 3 8 166.666667 0.120437 0.117393 \n", + "8 D1--60 4 7 75.000000 0.139035 0.111641 \n", + "22 D1--80 5 10 100.000000 0.119758 0.106204 \n", + "1 D1--60 2 5 150.000000 0.008344 0.100139 \n", + "11 D1--60 5 9 80.000000 0.226579 0.097651 \n", + "3 D1--60 4 0 -100.000000 0.231387 0.081579 \n", + "4 D1--60 5 1 -80.000000 0.005067 0.077133 \n", + "42 D2--80 4 3 -25.000000 0.004098 0.073770 \n", + "0 D1--60 1 5 400.000000 0.074890 0.066079 \n", + "45 D2--80 2 1 -50.000000 0.018735 0.065574 \n", + "37 D2--80 4 14 250.000000 0.000000 0.057377 \n", + "41 D2--80 4 4 0.000000 0.005806 0.057377 \n", + "6 D1--60 4 6 50.000000 0.017258 0.054903 \n", + "35 D2--60 2 0 -100.000000 0.067425 0.049834 \n", + "21 D1--80 5 8 60.000000 0.050430 0.043233 \n", + "28 D2--60 3 7 133.333333 0.083150 0.043189 \n", + "43 D2--80 5 0 -100.000000 0.167213 0.040984 \n", + "40 D2--80 5 4 -20.000000 0.037705 0.036885 \n", + "34 D2--60 2 1 -50.000000 0.018761 0.036545 \n", + "31 D2--60 4 0 -100.000000 0.029666 0.033223 \n", + "38 D2--80 4 8 100.000000 0.259035 0.032787 \n", + "10 D1--60 5 6 20.000000 0.050891 0.029409 \n", + "15 D1--80 4 1 -75.000000 0.246245 0.029224 \n", + "32 D2--60 3 2 -33.333333 0.039326 0.026578 \n", + "30 D2--60 4 2 -50.000000 0.068106 0.026578 \n", + "36 D2--80 10 6 -40.000000 0.224331 0.024590 \n", + "44 D2--80 2 4 100.000000 0.181983 0.024590 \n", + "2 D1--60 3 0 -100.000000 0.088352 0.020395 \n", + "9 D1--60 4 8 100.000000 0.244715 0.010075 \n", + "12 D1--60 6 7 16.666667 0.020459 0.009667 \n", + "13 D1--60 4 4 0.000000 0.047635 0.007587 \n", + "33 D2--60 3 1 -66.666667 0.063277 0.003322 \n", + "29 D2--60 4 4 0.000000 0.079290 0.003322 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# points with rounding differences larger than 0.4 were manually checked. \n", + "# Two of these were adjusted by hand in the .csv files after manual inspection.\n", + "df.sort_values('post_diff')[::-1]\n" + ] + }, + { + "cell_type": "raw", + "id": "a2def532-75a0-4ee2-82d7-e4bae835a4f2", + "metadata": {}, + "source": [ + "df.sort_values('i_change%')[['i_pre', 'i_post', 'i_change%', 'experiment']]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "nrn", + "language": "python", + "name": "nrn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/data/input.json b/examples/notebooks/neuromodulation/data/input.json new file mode 100644 index 000000000..1da880ff1 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/input.json @@ -0,0 +1,28 @@ +{ + "0": { + "cortical" : { + "generator" : "poisson", + "start" : [0], + "end" : [1], + "frequency" : [40], + "conductance" : 0.5e-9, + "num_inputs" : 100, + "mod_file": "tmGlut" + }, + "DA" : { + "generator" : "poisson", + "start" : [0.1], + "end" : [1], + "frequency" : [40], + "conductance" : 0.5e-9, + "num_inputs" : 100, + "mod_file": "DASyn", + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e9 + } + } + } +} diff --git a/examples/notebooks/neuromodulation/data/input_v2.json b/examples/notebooks/neuromodulation/data/input_v2.json new file mode 100644 index 000000000..66cc7553c --- /dev/null +++ b/examples/notebooks/neuromodulation/data/input_v2.json @@ -0,0 +1,30 @@ +{ + "0": { + "cortical" : { + "generator" : "poisson", + "start" : [0], + "end" : [1], + "frequency" : [10], + "conductance" : 0.5e-9, + "input_correlation": 0.5, + "num_inputs" : 50, + "mod_file": "tmGlut" + }, + "DA" : { + "generator" : "poisson", + "start" : [0.1], + "end" : [0.2], + "frequency" : [40], + "conductance" : 0.5e-9, + "num_inputs" : 100, + "num_soma_synapses" : 10, + "mod_file": "DASyn", + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e16 + } + } + } +} diff --git a/examples/notebooks/neuromodulation/data/input_v4.json b/examples/notebooks/neuromodulation/data/input_v4.json new file mode 100644 index 000000000..95d49c4ea --- /dev/null +++ b/examples/notebooks/neuromodulation/data/input_v4.json @@ -0,0 +1,77 @@ +{ + "0": { + "cortical_background": { + "generator": "poisson", + "frequency": 1.2 + }, + "thalamic_background": { + "generator": "poisson", + "frequency": 1.2 + }, + "cortical" : { + "generator" : "poisson", + "start" : [0], + "end" : [2], + "frequency" : [10], + "conductance" : 0.5e-9, + "input_correlation": 0.75, + "num_inputs" : 20, + "parameter_file": "data/tmglut_DA_parameters.json", + "mod_file": "tmGlut" + }, + "DA" : { + "generator" : "poisson", + "start" : [0.3], + "end" : [1.3], + "frequency" : [10], + "conductance" : 0.5e-9, + "num_inputs" : 100, + "num_soma_synapses" : 10, + "mod_file": "DASyn", + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e18 + } + } + }, + "1": { + "cortical_background": { + "generator": "poisson", + "frequency": 1.2 + }, + "thalamic_background": { + "generator": "poisson", + "frequency": 1.2 + }, + "cortical" : { + "generator" : "poisson", + "start" : [0], + "end" : [2], + "frequency" : [10], + "conductance" : 0.5e-9, + "input_correlation": 0.75, + "num_inputs" : 20, + "parameter_file": "data/tmglut_DA_parameters.json", + "mod_file": "tmGlut" + }, + "DA" : { + "generator" : "poisson", + "start" : [0.3], + "end" : [1.3], + "frequency" : [10], + "conductance" : 0.5e-9, + "num_inputs" : 100, + "num_soma_synapses" : 10, + "mod_file": "DASyn", + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e18 + } + } + } + +} diff --git a/examples/notebooks/neuromodulation/data/input_v6_bath_on_off.json b/examples/notebooks/neuromodulation/data/input_v6_bath_on_off.json new file mode 100644 index 000000000..6257fc438 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/input_v6_bath_on_off.json @@ -0,0 +1,23 @@ +{ + "dspn": { + "cortical_background": { + "generator": "poisson", + "frequency": 1.2 + }, + "thalamic_background": { + "generator": "poisson", + "frequency": 1.2 + }, + "cortical" : { + "generator" : "poisson", + "start" : [0], + "end" : [10], + "frequency" : [10], + "conductance" : 0.5e-9, + "input_correlation": 0.75, + "num_inputs" : 20, + "parameter_file": "data/tmglut_DA_parameters.json", + "mod_file": "tmGlut" + } + } +} diff --git a/examples/notebooks/neuromodulation/data/mechanisms/DASyn.mod b/examples/notebooks/neuromodulation/data/mechanisms/DASyn.mod new file mode 100644 index 000000000..ee1ace957 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/DASyn.mod @@ -0,0 +1,38 @@ +NEURON { + POINT_PROCESS DASyn + RANGE quanta, tau, open +} +UNITS { + (mM) = (milli / liter) +} + +PARAMETER { + quanta = 1e-4 (mM/ms) + tau = 10 (ms) +} + +INITIAL { + open = 0 +} + +STATE { + open (1) +} + +BREAKPOINT {SOLVE state METHOD cnexp} + +DERIVATIVE state { + open' = -open/tau +} + + +: 33000 dopamine molecules per vesicle : Omiatek, D., Bressler, A., +: Cans, AS. et al. The real catecholamine content of secretory vesicles +: in the CNS revealed by electrochemical cytometry. Sci Rep 3, 1447 +: (2013). https://doi.org/10.1038/srep01447 + + + +NET_RECEIVE(weight) { + open = open + weight +} diff --git a/examples/notebooks/neuromodulation/data/mechanisms/kaf_ms.mod b/examples/notebooks/neuromodulation/data/mechanisms/kaf_ms.mod new file mode 100644 index 000000000..71b01ea79 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/kaf_ms.mod @@ -0,0 +1,168 @@ +TITLE Fast A-type potassium current (Kv4.2) + +COMMENT + +Neuromodulation is added as functions: + + modulationDA = 1 + modDA*(maxModDA-1)*levelDA + +where: + + modDA [0]: is a switch for turning modulation on or off {1/0} + maxModDA [1]: is the maximum modulation for this specific channel (read from the param file) + e.g. 10% increase would correspond to a factor of 1.1 (100% +10%) {0-inf} + levelDA [0]: is an additional parameter for scaling modulation. + Can be used simulate non static modulation by gradually changing the value from 0 to 1 {0-1} + + Further neuromodulators can be added by for example: + modulationDA = 1 + modDA*(maxModDA-1) + modulationACh = 1 + modACh*(maxModACh-1) + .... + + etc. for other neuromodulators + + + +[] == default values +{} == ranges + +ENDCOMMENT + + +NEURON { + SUFFIX kaf_ms + USEION k READ ek WRITE ik + RANGE gbar, gk, ik, q + RANGE modDA, maxModDA, levelDA + RANGE modACh, maxModACh, levelACh + RANGE modShift +} + +UNITS { + (S) = (siemens) + (mV) = (millivolt) + (mA) = (milliamp) +} + +PARAMETER { + gbar = 0.0 (S/cm2) + q = 1 : room temperature (unspecified) + :q = 2 : body temperature 35 C (Du 2017) + :q = 3 : body temperature 35 C + modDA = 0 + maxModDA = 1 + levelDA = 0 + modShift = 0 + modACh = 0 + maxModACh = 1 + levelACh = 0 + + + +} + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + gk (S/cm2) + minf + mtau (ms) + hinf + htau (ms) +} + +STATE { m h } + +BREAKPOINT { + SOLVE states METHOD cnexp + gk = gbar*m*m*h*modulationDA() + modShift = modulationACh() + ik = gk*(v-ek) +} + +DERIVATIVE states { + rates() + m' = (minf-m)/mtau*q + h' = (hinf-h)/htau*q +} + +INITIAL { + rates() + m = minf + h = hinf +} + +PROCEDURE rates() { + UNITSOFF + minf = 1/(1+exp((v-(-10+modShift))/(-17.7))) + mtau = 0.9+1.1/(1+exp((v-(-30))/10)) + hinf = 1/(1+exp((v-(-75.6))/11.8)) + htau = 14 + UNITSON + + :Du 2017 + :LOCAL alpha, beta, sum + :UNITSOFF + :alpha = 1.5/(1+exp((v-4)/(-17))) + :beta = 0.6/(1+exp((v-10)/9)) + :sum = alpha+beta + :minf = alpha/sum + :mtau = 1/sum + : + :alpha = 0.105/(1+exp((v-(-121))/22)) + :beta = 0.065/(1+exp((v-(-55))/(-11))) + :sum = alpha+beta + :hinf = alpha/sum + :htau = 1/sum + :UNITSON +} + + +FUNCTION modulationDA() { + : returns modulation factor + + modulationDA = 1 + modDA*(maxModDA-1)*levelDA +} + +FUNCTION modulationACh() { + : returns modulation factor + + modulationACh = 1 + modACh*(maxModACh-1)*levelACh +} + +COMMENT + +Original data by Tkatch et al (2000) [1]. Neostriatal neurons were acutely +dissociated from young adult rats, age P28-P42. Electrophysiological +recordings were done at unspecified temperature (room temperature 20-22 C +assumed). Potentials were not corrected for the liquid junction potential +(estimated 1-2 mV). + +Activation m^1 matches experimental data [1, Fig.2C]. Activation time +constants fit tabulated data [1, Fig.2B]. Slope of inactivation function +fitted to the data [1, Fig.3B] with half inactivation potential -75.6 +mV. Temperature factor q between 1.5 [3] and 3 [2] was used for body +temperature. Conductance kinetics of m2h type is used [2], no corrections +for m^2 applied. Later modification by Du [4] is close to this model. + +[1] Tkatch T, Baranauskas G, Surmeier DJ (2000) Kv4.2 mRNA abundance and +A-type K(+) current amplitude are linearly related in basal ganglia and +basal forebrain neurons. J Neurosci 20(2):579-88. + +[2] Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, +O'Donnell P, Finkel LH (2005) NMDA/AMPA ratio impacts state transitions +and entrainment to oscillations in a computational model of the nucleus +accumbens medium spiny projection neuron. J Neurosci 25(40):9080-95. + +[3] Evans RC, Morera-Herreras T, Cui Y, Du K, Sheehan T, Kotaleski JH, +Venance L, Blackwell KT (2012) The effects of NMDA subunit composition on +calcium influx and spike timing-dependent plasticity in striatal medium +spiny neurons. PLoS Comput Biol 8(4):e1002493. + +[4] Du K, Wu YW, Lindroos R, Liu Y, Rózsa B, Katona G, Ding JB, +Kotaleski JH (2017) Cell-type-specific inhibition of the dendritic +plateau potential in striatal spiny projection neurons. Proc Natl Acad +Sci USA 114:E7612-E7621. + +ENDCOMMENT diff --git a/examples/notebooks/neuromodulation/data/mechanisms/kdr_ms.mod b/examples/notebooks/neuromodulation/data/mechanisms/kdr_ms.mod new file mode 100644 index 000000000..1a1476890 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/kdr_ms.mod @@ -0,0 +1,79 @@ +TITLE Fast delayed rectifier potassium current (Kv3.1/3.2) + +NEURON { + SUFFIX kdr_ms + USEION k READ ek WRITE ik + RANGE gbar, gk, ik +} + +UNITS { + (S) = (siemens) + (mV) = (millivolt) + (mA) = (milliamp) +} + +PARAMETER { + gbar = 0.0 (S/cm2) + q = 1 +} + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + gk (S/cm2) + minf + mtau (ms) +} + +STATE { m } + +BREAKPOINT { + SOLVE states METHOD cnexp + gk = gbar*m + ik = gk*(v-ek) +} + +DERIVATIVE states { + rates() + m' = (minf-m)/mtau*q +} + +INITIAL { + rates() + m = minf +} + +PROCEDURE rates() { + UNITSOFF + minf = 1/(1+exp((v-(-13))/(-6))) + mtau = 11.1 + UNITSON +} + +COMMENT + +Original data and model by Baranauskas et al (1999) [1] for globus +pallidus neurons from young adult rat. The temperature was not +specified. Potentials were not corrected for the liquid junction +potential, which was estimated to be 1-2 mV. + +Kinetics of m^1 type is used as in [2,3]. Room temperature 20-23 C +is assumed. + +NEURON implementation by Alexander Kozlov . + +[1] Baranauskas G, Tkatch T, Surmeier DJ (1999) Delayed rectifier currents +in rat globus pallidus neurons are attributable to Kv2.1 and Kv3.1/3.2 +K(+) channels. J Neurosci 19(15):6394-404. + +[2] Migliore M, Hoffman DA, Magee JC, Johnston D (1999) Role of an +A-type K+ conductance in the back-propagation of action potentials in the +dendrites of hippocampal pyramidal neurons. J Comput Neurosci 7(1):5-15. + +[3] Evans RC, Morera-Herreras T, Cui Y, Du K, Sheehan T, Kotaleski JH, +Venance L, Blackwell KT (2012) The effects of NMDA subunit composition on +calcium influx and spike timing-dependent plasticity in striatal medium +spiny neurons. PLoS Comput Biol 8(4):e1002493. + +ENDCOMMENT diff --git a/examples/notebooks/neuromodulation/data/mechanisms/kir.mod b/examples/notebooks/neuromodulation/data/mechanisms/kir.mod new file mode 100644 index 000000000..9fee08193 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/kir.mod @@ -0,0 +1,122 @@ +TITLE Non-inactivating inwardly rectifying potassium current (Kir2.3) + + +NEURON { + SUFFIX kir + USEION k READ ek WRITE ik + USEION PKA READ PKAi VALENCE 0 + + RANGE gbar, gk, ik, shift + RANGE mod_pka_g_min, mod_pka_g_max, mod_pka_g_half, mod_pka_g_slope + RANGE modulation_factor +} + +UNITS { + (S) = (siemens) + (mV) = (millivolt) + (mA) = (milliamp) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + gbar = 0.0 (S/cm2) + shift = 0.0 (mV) + q = 1 : body temperature 35 C + mod_pka_g_min = 1 (1) + mod_pka_g_max = 1 (1) + mod_pka_g_half = 0.000100 (mM) + mod_pka_g_slope = 0.01 (mM) +} + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + gk (S/cm2) + minf + mtau (ms) + PKAi (mM) + modulation_factor (1) +} + +STATE { m } + +BREAKPOINT { + SOLVE states METHOD cnexp + modulation_factor=modulation(PKAi, mod_pka_g_min, mod_pka_g_max, mod_pka_g_half, mod_pka_g_slope) + gk = gbar*m*modulation_factor + ik = gk*(v-ek) +} + +DERIVATIVE states { + rates() + m' = (minf-m)/mtau*q +} + +INITIAL { + rates() + m = minf +} + +: TODO: These parameters should NOT be hardcoded, and use function instead of procedure? +PROCEDURE rates() { + UNITSOFF + minf = 1/(1+exp((v-(-82)-shift)/13)) + mtau = 1/(exp((v-(-103))/(-14.5))+0.125/(1+exp((v-(-35))/(-19)))) + UNITSON +} + +FUNCTION modulation(conc (mM), mod_min (1), mod_max (1), mod_half (mM), mod_slope (mM)) (1) { + : returns modulation factor + modulation = mod_min + (mod_max-mod_min) / (1 + exp(-(conc - mod_half)/mod_slope)) +} + + +COMMENT + +2024-05-28 : Wilhelm Thunberg, Johannes Hjorth (KTH, Stockholm) +Adding neuromodulation using RxD + +Original model by Wolf et al (2005) [1] for the rat MSN cells from the +nucleus accumbens. The activation curve was fitted to a mouse Kir2.1 +channel expressed in HEK cells [2] and shifted to match extracellular +concentration of K in rat. Measured half-activation values are -109.3 +mV (striatonigral MSN) and -113.2 mV (striatopallidal MSN) [6, Supp +Tab.1]. Time constants were derived from Aplysia data [3] and adjusted +to match the rat experiments [1]. Time constant was further tuned [4] +to fit the rat data below -80 mV [5]. Kinetics is corrected to the body +temperature 35 C [4]. + +Non-inactivating Kir current was observed in cells expressing Kir2.2 +and/or Kir2.3 [5]. Activation variable with m^1 kinetics is used [1,4]. +Smooth fit of the time constants by Alexander Kozlov . + +[1] Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, +O'Donnell P, Finkel LH (2005) NMDA/AMPA ratio impacts state transitions +and entrainment to oscillations in a computational model of the nucleus +accumbens medium spiny projection neuron. J Neurosci 25(40):9080-95. + +[2] Kubo Y, Murata Y (2001) Control of rectification and permeation by +two distinct sites after the second transmembrane region in Kir2.1 K+ +channel. J Physiol 531, 645-660. + +[3] Hayashi H, Fishman HM (1988) Inward rectifier K+ channel kinetics +from analysis of the complex conductance of aplysia neuronal membrane. +Biophys J 53, 747-757. + +[4] Steephen JE, Manchanda R (2009) Differences in biophysical properties +of nucleus accumbens medium spiny neurons emerging from inactivation of +inward rectifying potassium currents. J Comput Neurosci 27(3):453-70 + +[5] Mermelstein PG, Song WJ, Tkatch T, Yan Z, Surmeier DJ (1998) +Inwardly rectifying potassium (IRK) currents are correlated with IRK +subunit expression in rat nucleus accumbens medium spiny neurons. J +Neurosci 18(17):6650-61. + +[6] Shen W, Tian X, Day M, Ulrich S, Tkatch T, Nathanson NM, Surmeier DJ +(2007) Cholinergic modulation of Kir2 channels selectively elevates +dendritic excitability in striatopallidal neurons. Nat Neurosci +10(11):1458-66. + +ENDCOMMENT diff --git a/examples/notebooks/neuromodulation/data/mechanisms/naf_ms.mod b/examples/notebooks/neuromodulation/data/mechanisms/naf_ms.mod new file mode 100644 index 000000000..f676971f6 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/naf_ms.mod @@ -0,0 +1,133 @@ +TITLE Fast transient sodium current + +COMMENT + +Neuromodulation is added as functions: + + modulationDA = 1 + modDA*(maxModDA-1)*levelDA + +where: + + modDA [0]: is a switch for turning modulation on or off {1/0} + maxModDA [1]: is the maximum modulation for this specific channel (read from the param file) + e.g. 10% increase would correspond to a factor of 1.1 (100% +10%) {0-inf} + levelDA [0]: is an additional parameter for scaling modulation. + Can be used simulate non static modulation by gradually changing the value from 0 to 1 {0-1} + + Further neuromodulators can be added by for example: + modulationDA = 1 + modDA*(maxModDA-1) + modulationACh = 1 + modACh*(maxModACh-1) + .... + + etc. for other neuromodulators + + + +[] == default values +{} == ranges + +ENDCOMMENT + +NEURON { + SUFFIX naf_ms + USEION na READ ena WRITE ina + RANGE gbar, gna, ina + RANGE modDA, maxModDA, levelDA + RANGE modACh, maxModACh, levelACh +} + +UNITS { + (S) = (siemens) + (mV) = (millivolt) + (mA) = (milliamp) +} + +PARAMETER { + gbar = 0.0 (S/cm2) + :q = 1 : room temperature 22 C + q = 1.8 : body temperature 35 C + modDA = 0 + maxModDA = 1 + levelDA = 0 + modACh = 0 + maxModACh = 1 + levelACh = 0 +} + +ASSIGNED { + v (mV) + ena (mV) + ina (mA/cm2) + gna (S/cm2) + minf + mtau (ms) + hinf + htau (ms) +} + +STATE { m h } + +BREAKPOINT { + SOLVE states METHOD cnexp + gna = gbar*m*m*m*h*modulationDA()*modulationACh() + ina = gna*(v-ena) +} + +DERIVATIVE states { + rates() + m' = (minf-m)/mtau*q + h' = (hinf-h)/htau*q +} + +INITIAL { + rates() + m = minf + h = hinf +} + +PROCEDURE rates() { + UNITSOFF + :minf = 1/(1+exp((v-(-25.5))/(-9.2))) + :mtau = 0.33+1/(exp((v-(-62))/14)+exp((v-(-60))/(-17))) + :hinf = 1/(1+exp((v-(-63.2))/6)) + :htau = 0.6+1/(exp((v-(-44))/8)+exp((v-(-99))/(-44))) + minf = 1/(1+exp((v-(-25))/(-10))) + mtau = 0.33+1/(exp((v-(-62))/14)+exp((v-(-60))/(-17))) + hinf = 1/(1+exp((v-(-62))/6)) + htau = 0.6+1/(exp((v-(-44))/8)+exp((v-(-99))/(-44))) + UNITSON +} + +FUNCTION modulationDA() { + : returns modulation factor + + modulationDA = 1 + modDA*(maxModDA-1)*levelDA +} + +FUNCTION modulationACh() { + : returns modulation factor + + modulationACh = 1 + modACh*(maxModACh-1)*levelACh +} + +COMMENT + +Original data by Ogata and Tatebayashi (1990) [1]. Neostriatal neurons +of medium size (putative medium spiny neurons) freshly isolated from +the adult guinea pig brain (either sex, 200 g). Data compensated for +the liquid junction potential (-13 mV). Experiments carried out at room +temperature (22 C). Conductance fitted by m3h kinetics. + +Smooth fit of mtau and htau data [1] by Alexander Kozlov +assuming natural logarithm of tau values [1, Figs. 5 and 9] and +temperature correction factor of 1.8 [2] as suggested by Robert Lindroos +. + +[1] Ogata N, Tatebayashi H (1990) Sodium current kinetics in freshly +isolated neostriatal neurones of the adult guinea pig. Pflugers Arch +416(5):594-603. + +[2] Schwarz JR (1986) The effect of temperature on Na currents in rat +myelinated nerve fibres. Pflugers Arch. 406(4):397-404. + +ENDCOMMENT diff --git a/examples/notebooks/neuromodulation/data/mechanisms/tmglut.mod b/examples/notebooks/neuromodulation/data/mechanisms/tmglut.mod new file mode 100644 index 000000000..8b860becd --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/tmglut.mod @@ -0,0 +1,274 @@ +TITLE Glutamatergic synapse with short-term plasticity (stp) + +COMMENT +stp can be turned of by setting use_stp == 0 + +-------------------------------------------- + +Neuromodulation is added as functions: + + modulationDA = 1 + modDA*(maxModDA-1)*levelDA + +where: + + modDA [0]: is a switch for turning modulation on or off {1/0} + maxModDA [1]: is the maximum modulation for this specific channel (read from the param file) + e.g. 10% increase would correspond to a factor of 1.1 (100% +10%) {0-inf} + levelDA [0]: is an additional parameter for scaling modulation. + Can be used simulate non static modulation by gradually changing the value from 0 to 1 {0-1} + + Further neuromodulators can be added by for example: + modulationDA = 1 + modDA*(maxModDA-1) + modulationACh = 1 + modACh*(maxModACh-1) + .... + + etc. for other neuromodulators + + + +[] == default values +{} == ranges + +ENDCOMMENT + +NEURON { + THREADSAFE + POINT_PROCESS tmGlut + RANGE tau1_ampa, tau2_ampa, tau1_nmda, tau2_nmda + RANGE g_ampa, g_nmda, i_ampa, i_nmda, nmda_ratio + RANGE e, g, i, q, mg + RANGE tau, tauR, tauF, U, u0 + RANGE ca_ratio_ampa, ca_ratio_nmda, mggate, use_stp + RANGE failRateDA, failRateACh, failRate + RANGE modDA, maxMod_AMPADA, levelDA, maxMod_AMPAACh, levelACh + RANGE maxMod_NMDADA, modACh, maxMod_NMDAACh + NONSPECIFIC_CURRENT i + USEION cal WRITE ical VALENCE 2 +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) + (mM) = (milli/liter) +} + +PARAMETER { + : q = 2 --- We have manually corrected these + tau1_ampa= 1.1 (ms) : ORIG 2.2, ampa same as in Wolf? not same as in Du et al 2017 (and glutamate.mod) + tau2_ampa = 5.75 (ms) : ORIG 11.5 ms, tau2 > tau1 + tau1_nmda= 2.76 (ms) : ORIG 5.52 ms Chapman et al 2003; table 1, adult rat (rise time, rt = 12.13. rt ~= 2.197*tau (wiki;rise time) -> tau = 12.13 / 2.197 ~= 5.52 + tau2_nmda = 115.5 (ms) : ORIG 231 ms, Chapman et al 2003; table 1, adult rat + nmda_ratio = 0.5 (1) + e = 0 (mV) + tau = 3 (ms) + tauR = 100 (ms) : tauR > tau + tauF = 800 (ms) : tauF >= 0 + U = 0.3 (1) <0, 1> + u0 = 0 (1) <0, 1> + ca_ratio_ampa = 0.005 + ca_ratio_nmda = 0.1 + mg = 1 (mM) + + modDA = 0 + maxMod_AMPADA = 1 + modACh = 0 + maxMod_AMPAACh = 1 + levelACh = 0 + + + maxMod_NMDADA = 1 + levelDA = 0 + + maxMod_NMDAACh = 1 + + failRateDA = 0 + failRateACh = 0 + failRate = 0 + use_stp = 1 : to turn of use_stp -> use 0 +} + +ASSIGNED { + v (mV) + i (nA) + i_ampa (nA) + i_nmda (nA) + ical (nA) + ical_ampa (nA) + ical_nmda (nA) + g (uS) + g_ampa (uS) + g_nmda (uS) + factor_ampa + factor_nmda + x + +} + +STATE { + A_ampa (uS) + B_ampa (uS) + A_nmda (uS) + B_nmda (uS) +} + +INITIAL { + LOCAL tp_ampa, tp_nmda + A_ampa = 0 + B_ampa = 0 + + tp_ampa = (tau1_ampa*tau2_ampa)/(tau2_ampa-tau1_ampa) * log(tau2_ampa/tau1_ampa) + factor_ampa = -exp(-tp_ampa/tau1_ampa) + exp(-tp_ampa/tau2_ampa) + factor_ampa = 1/factor_ampa + + A_nmda = 0 + B_nmda = 0 + tp_nmda = (tau1_nmda*tau2_nmda)/(tau2_nmda-tau1_nmda) * log(tau2_nmda/tau1_nmda) + factor_nmda = -exp(-tp_nmda/tau1_nmda) + exp(-tp_nmda/tau2_nmda) + factor_nmda = 1/factor_nmda +} + +BREAKPOINT { + LOCAL itot_nmda, itot_ampa, mggate + SOLVE state METHOD cnexp + + : NMDA + mggate = 1 / (1 + exp(-0.062 (/mV) * v) * (mg / 3.57 (mM))) + g_nmda = (B_nmda - A_nmda) * modulationDA_NMDA()*modulationACh_NMDA() + itot_nmda = g_nmda * (v - e) * mggate + ical_nmda = ca_ratio_nmda*itot_nmda + i_nmda = itot_nmda - ical_nmda + + : AMPA + g_ampa = (B_ampa - A_ampa) * modulationDA_AMPA() * modulationACh_AMPA() + itot_ampa = g_ampa*(v - e) + ical_ampa = ca_ratio_ampa*itot_ampa + i_ampa = itot_ampa - ical_ampa + + : total values + ical = ical_nmda + ical_ampa + g = g_ampa + g_nmda + i = i_ampa + i_nmda + + : printf("%g\t%g\t%g\t%g\t%g\n",tau1_ampa,B_ampa,A_ampa,B_nmda,A_nmda) + : printf("%g\t%g\t%g\t%g\t%g\n",v,g_nmda,g,i,ical) +} + +DERIVATIVE state { + A_ampa' = -A_ampa/tau1_ampa + B_ampa' = -B_ampa/tau2_ampa + A_nmda' = -A_nmda/tau1_nmda + B_nmda' = -B_nmda/tau2_nmda +} + +NET_RECEIVE(weight (uS), y, z, u, tsyn (ms)) { + LOCAL weight_ampa, weight_nmda + INITIAL { + y = 0 + z = 0 + u = u0 + tsyn = t + : printf("t\t t-tsyn\t y\t z\t u\n") + + } + + if ( weight <= 0 ) { +VERBATIM + return; +ENDVERBATIM + } + if( urand() > failRate*(1 + modDA*(failRateDA-1)*levelDA + modACh*(failRateACh-1)*levelACh)) { + + z = z*exp(-(t-tsyn)/tauR) + z = z + (y*(exp(-(t-tsyn)/tau) - exp(-(t-tsyn)/tauR)) / (tau/tauR - 1) ) + y = y*exp(-(t-tsyn)/tau) + x = 1-y-z + if (tauF > 0) { + u = u*exp(-(t-tsyn)/tauF) + u = u + U*(1-u) + } else { + u = U + } + + if (use_stp > 0) { + : We divide by U to normalise, so that g gives amplitude + : of first activation + weight_ampa = weight *x*u / U + } else { + weight_ampa = weight + } + + weight_nmda = weight_ampa*nmda_ratio + + A_ampa = A_ampa + weight_ampa*factor_ampa + B_ampa = B_ampa + weight_ampa*factor_ampa + A_nmda = A_nmda + weight_nmda*factor_nmda + B_nmda = B_nmda + weight_nmda*factor_nmda + + y = y + x*u + : printf("** %g\t%g\t%g\t%g\t%g\n", t, t-tsyn, y, z, u) + tsyn = t + } +} + +FUNCTION urand() { + urand = scop_random(1) +} + + +FUNCTION modulationDA_NMDA() { + : returns modulation factor + + modulationDA_NMDA = 1 + modDA*(maxMod_NMDADA-1)*levelDA +} + +FUNCTION modulationACh_NMDA() { + : returns modulation factor + + modulationACh_NMDA = 1 + modACh*(maxMod_NMDAACh-1)*levelACh +} + +FUNCTION modulationDA_AMPA() { + : returns modulation factor + + modulationDA_AMPA = 1 + modDA*(maxMod_AMPADA-1)*levelDA +} + +FUNCTION modulationACh_AMPA() { + : returns modulation factor + + modulationACh_AMPA = 1 + modACh*(maxMod_AMPAACh-1)*levelACh +} + +COMMENT +(2019-11-29) Synaptic failure rate (fail) added. Random factor, no +reproducibility guaranteed in parallel sim. + +(2019-08-21) We normalise the activation by U, to make sure that g specifies + the conductance of the first actvation + +(2019-06-05) Q-factor was calculated in INITAL block, which meant if +the synapse was reinitalised then the time constants changed with each +initalise. Updated: Johannes Hjorth, hjorth@kth.se + +- updates by Robert Lindroos (robert.lindroos at ki.se): +Missing line calculating Ca ratio of NMDA current fixed. The whole block were updated since +plotting ratios for both nmda and ampa gave 0. +- switch for turning of short term dynamics added. If used this synapse will summate. + +Implementation of glutamatergic synapse model with short-term facilitation +and depression based on modified tmgsyn.mod [1] by Tsodyks et al [2]. +Choice of time constants and calcium current model follows [3]. +NEURON implementation by Alexander Kozlov . + +[1] tmgsyn.mod, ModelDB (https://senselab.med.yale.edu/ModelDB/), +accession number 3815. + +[2] Tsodyks M, Uziel A, Markram H (2000) Synchrony generation in recurrent +networks with frequency-dependent synapses. J Neurosci. 20(1):RC50. + +[3] Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, +O'Donnell P, Finkel LH (2005) NMDA/AMPA ratio impacts state transitions +and entrainment to oscillations in a computational model of the nucleus +accumbens medium spiny projection neuron. J Neurosci 25(40):9080-95. +ENDCOMMENT diff --git a/examples/notebooks/neuromodulation/data/mechanisms/vecevent.mod b/examples/notebooks/neuromodulation/data/mechanisms/vecevent.mod new file mode 100644 index 000000000..ce917ccf9 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/mechanisms/vecevent.mod @@ -0,0 +1,80 @@ +: Vector stream of events + +NEURON { + THREADSAFE + ARTIFICIAL_CELL VecStim + POINTER ptr +} + +ASSIGNED { + index + etime (ms) + ptr +} + + +INITIAL { + index = 0 + element() + if (index > 0) { + net_send(etime - t, 1) + } +} + +NET_RECEIVE (w) { + if (flag == 1) { + net_event(t) + element() + if (index > 0) { + net_send(etime - t, 1) + } + } +} + +DESTRUCTOR { +VERBATIM + void* vv = (void*)(_p_ptr); + if (vv) { + hoc_obj_unref(*vector_pobj(vv)); + } +ENDVERBATIM +} + +PROCEDURE element() { +VERBATIM + { void* vv; int i, size; double* px; + i = (int)index; + if (i >= 0) { + vv = (void*)(_p_ptr); + if (vv) { + size = vector_capacity(vv); + px = vector_vec(vv); + if (i < size) { + etime = px[i]; + index += 1.; + }else{ + index = -1.; + } + }else{ + index = -1.; + } + } + } +ENDVERBATIM +} + +PROCEDURE play() { +VERBATIM + void** pv; + void* ptmp = NULL; + if (ifarg(1)) { + ptmp = vector_arg(1); + hoc_obj_ref(*vector_pobj(ptmp)); + } + pv = (void**)(&_p_ptr); + if (*pv) { + hoc_obj_unref(*vector_pobj(*pv)); + } + *pv = ptmp; +ENDVERBATIM +} diff --git a/examples/notebooks/neuromodulation/data/planert2013/Planert2013-Fig4-DA-pone.0057054.g004.png b/examples/notebooks/neuromodulation/data/planert2013/Planert2013-Fig4-DA-pone.0057054.g004.png new file mode 100644 index 000000000..c4b433e6b Binary files /dev/null and b/examples/notebooks/neuromodulation/data/planert2013/Planert2013-Fig4-DA-pone.0057054.g004.png differ diff --git a/examples/notebooks/neuromodulation/data/planert2013/d1-60mV.csv b/examples/notebooks/neuromodulation/data/planert2013/d1-60mV.csv new file mode 100644 index 000000000..4ae1768db --- /dev/null +++ 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4.057377049180332 +0.3333333333333339, 3.6885245901639436 +1, 3.0737704918032875 +0.32432432432432456, 3.278688524590166 +1, 0.04098360655738276 +0.05405405405405439, 2.2950819672131217 +0.9909909909909924, 3.9754098360655803 +0.05405405405405439, 1.9672131147541023 +1, 1.0655737704918096 diff --git a/examples/notebooks/neuromodulation/data/tmglut_DA_parameters.json b/examples/notebooks/neuromodulation/data/tmglut_DA_parameters.json new file mode 100644 index 000000000..9f4fd28d5 --- /dev/null +++ b/examples/notebooks/neuromodulation/data/tmglut_DA_parameters.json @@ -0,0 +1,39 @@ +{ + "low": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.2, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + }, + "mid": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1.15, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.3, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + }, + "high": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1.3, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.6, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + } + +} diff --git a/examples/notebooks/neuromodulation/dspn/dspn_experiment_config.json b/examples/notebooks/neuromodulation/dspn/dspn_experiment_config.json new file mode 100644 index 000000000..2e4366611 --- /dev/null +++ b/examples/notebooks/neuromodulation/dspn/dspn_experiment_config.json @@ -0,0 +1,10 @@ +{ + "network_file": "../networks/dspn_modulation/network-synapses.hdf5", + "input_file": "../networks/dspn_modulation/input-spikes.hdf5", + "output_file": "../networks/dspn_modulation/simulation/dspn-output.hdf5", + "log_file": "../networks/dspn_modulation/log/network-simulation-log.txt", + "sample_dt": 0.01, + "time": 11, + "record_all_soma": true, + "record_rxd_species_concentration_all_compartments": [["PKA", [0]], ["DA", [0]]] +} diff --git a/examples/notebooks/neuromodulation/dspn/dspn_neuromodulation_tuning.ipynb b/examples/notebooks/neuromodulation/dspn/dspn_neuromodulation_tuning.ipynb new file mode 100644 index 000000000..ab38fb242 --- /dev/null +++ b/examples/notebooks/neuromodulation/dspn/dspn_neuromodulation_tuning.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d4c170ea-f2de-419b-b3f7-f30b34e1505a", + "metadata": {}, + "source": [ + "# Tuning of dSPN neuromodulation\n", + "\n", + "## Setup network with dSPN population\n", + "Create a network of disconnected dSPN neurons where each morphology key / parameter key combination is represented.\n", + "\n", + "Here we use the ```setup_network``` function in InputTuning to create the network." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ac751b7f-1198-4f73-b605-f45031d16828", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 neuron models in ../data/dspn\n", + "Skipping neuron type SBML\n", + "Skipping neuron type dspn_rxd\n", + "Skipping neuron type dspn_no_rxd\n", + "Skipping neuron type mechanisms\n", + "Skipping neuron type JSON\n", + "Writing network config file to ../networks/dspn_modulation/network-config.json\n", + "Reading SNUDDA_DATA=../../../../../BasalGangliaData/data/ from ../networks/dspn_modulation/network-config.json\n", + "Generating 10928 points for data/mesh/InputTestMesh.obj\n", + "Filtering, keeping inside points: 9341 / 10217\n", + "neuron_name = 'str_dspn_e150602_c1_D1_mWT_0728MSN01_v20211026', num = 1, neuron_path = '../data/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026'\n", + "Reading SNUDDA_DATA=../../../../../BasalGangliaData/data/ from ../networks/dspn_modulation/network-config.json\n", + "No d_view specified, running distribute neurons in serial\n", + "No connections specified in connectivity_distribution.\n", + "No file ../networks/dspn_modulation/pruning_merge_info.json\n" + ] + } + ], + "source": [ + "import os\n", + "from snudda.input.input_tuning import InputTuning\n", + "\n", + "snudda_data = \"../../../../../BasalGangliaData/data/\"\n", + "network_path = os.path.join(\"..\", \"networks\", \"dspn_modulation\")\n", + "input_tuning = InputTuning(network_path, snudda_data=snudda_data)\n", + "\n", + "#neurons_path = os.path.join(\"$DATA\", \"neurons\", \"striatum\")\n", + "neurons_path = os.path.join(\"..\", \"data\")\n", + "\n", + "input_tuning.setup_network(neurons_path=neurons_path, \n", + " num_replicas=1,\n", + " neuron_types=\"dspn\",\n", + " reaction_diffusion_file=\"../data/JSON/robert_reaction_diffusion.json\",\n", + " morphology_key=\"m22be6817\",\n", + " network_random_seed=1234)\n", + "input_tuning = None" + ] + }, + { + "cell_type": "markdown", + "id": "907b3cdc-008a-470a-9c18-b673947a5ee2", + "metadata": {}, + "source": [ + "## Generate synaptic input for the neuron population\n", + "\n", + "Setup glutamate input, and GABA input. Also generate dopamine input.\n", + "\n", + "| time (s) | glutamate (Hz) | GABA (Hz) | DA (Hz) |\n", + "| --- | --- | --- | --- |\n", + "| 1 - 3 | 10 | 5 | 0 |\n", + "| 5 - 7 | 10 | 5 | 5 |\n", + "| 9 - 11 | 10 | 5 | 10 |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ede302ac-ba27-4fee-9a86-db7081d00bc4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading SNUDDA_DATA=../../../../../BasalGangliaData/data/ from ../networks/dspn_modulation/network-config.json\n", + "Writing input spikes to ../networks/dspn_modulation/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=../../../../../BasalGangliaData/data/ from ../networks/dspn_modulation/network-config.json\n", + "Writing spikes to ../networks/dspn_modulation/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from snudda import Snudda\n", + "snd = Snudda(network_path=network_path)\n", + "snd.setup_input(input_config=\"input.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "d0ad4a94-2043-4ea2-9a9c-e980870254d5", + "metadata": {}, + "source": [ + "## Simulate" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "df61067c-9fc0-4d7f-99ab-79f1ed31384b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpirun -n 6 snudda simulate ../networks/dspn_modulation --time 11 --simulation_config dspn_experiment_config.json\n" + ] + } + ], + "source": [ + "duration = 11 # 4.5\n", + "simulation_config = \"dspn_experiment_config.json\"\n", + "exec_cmd = f\"mpirun -n 6 snudda simulate {network_path} --time {duration} --simulation_config {simulation_config}\" \n", + "print(exec_cmd)\n", + "# os.system(exec_cmd)" + ] + }, + { + "cell_type": "markdown", + "id": "c1d060c0-8250-4dd3-a243-e03a59d2b781", + "metadata": {}, + "source": [ + "## Plot PKA level" + ] + }, + { + "cell_type": "markdown", + "id": "9bd68bbe-4152-42c6-94d8-e9efc1cab0a5", + "metadata": {}, + "source": [ + "## Determine parameters:\n", + "\n", + "KIR: ```mod_pka_g_min```, ```mod_pka_g_max```, ```mod_pka_g_half```, ```mod_pka_g_slope```\n", + "\n", + "Kaf: ```mod_pka_g_min```, ```mod_pka_g_max```, ```mod_pka_g_half```, ```mod_pka_g_slope```, ```mod_pka_shift_min```, ```mod_pka_shift_max```, ```mod_pka_shift_half```, ```mod_pka_shift_slope```\n" + ] + }, + { + "cell_type": "markdown", + "id": "f6b32b68-fa7f-43c0-bf33-9c4420995c4e", + "metadata": {}, + "source": [ + "## Plot the PKA activation curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f0ff623-0d4e-4cd1-95c0-4092b3ba4f7b", + "metadata": {}, + "outputs": [], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "\n", + "output_file = os.path.join(network_path, \"simulation\", \"dspn-output.hdf5\")\n", + "nd = SnuddaLoadSimulation(output_file)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKA\", 0)[0][0]\n", + "data_da = nd.get_data(\"DA\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "787c874a-1eb3-4ae1-90b2-027bad46ba85", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da, label=\"DA\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "# plt.legend()\n", + "plt.title(\"DA\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff187fa9-6ef5-4d47-a64a-7390865ccf59", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_pka, label=\"PKA\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "# plt.legend()\n", + "plt.title(\"PKA\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c22ae2e9-f355-4bc2-911a-04052676e2e3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/dspn/input.json b/examples/notebooks/neuromodulation/dspn/input.json new file mode 100644 index 000000000..cfe7223e2 --- /dev/null +++ b/examples/notebooks/neuromodulation/dspn/input.json @@ -0,0 +1,38 @@ +{ + "dspn": { + "cortical": { + "generator": "poisson", + "start": [0.5, 2, 3.5], + "end": [1.5, 3, 4.5], + "frequency": [10, 10, 10] + }, + + "GABA": { + "generator": "poisson", + "type": "GABA", + "start": [0.5, 2, 3.5], + "end": [1.5, 3, 4.5], + "frequency": [5, 5, 5], + "num_inputs": 100, + "conductance": 5e-10, + "mod_file": "tmGabaA", + "parameter_file": "$DATA/synapses/striatum/PlanertFitting-DD-tmgaba-fit.json" + }, + "Dopamine": { + "generator": "poisson", + "type": "GABA", + "start": [2, 3.5], + "end": [3, 4.5], + "frequency": [5, 10], + "num_inputs": 100, + "conductance": 5e-9, + "mod_file": "DASyn", + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e9 + } + } + } +} diff --git a/examples/notebooks/neuromodulation/dspn/test-modulation.json b/examples/notebooks/neuromodulation/dspn/test-modulation.json new file mode 100644 index 000000000..542df5cf9 --- /dev/null +++ b/examples/notebooks/neuromodulation/dspn/test-modulation.json @@ -0,0 +1,208 @@ +{ + "abc": [ + { + "param_name": "mod_pka_g_min_kir_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_max_kir_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1.25 + }, + { + "param_name": "mod_pka_g_half_kir_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_g_slope_kir_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + }, + { + "param_name": "mod_pka_g_min_cal12_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_max_cal12_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1.20 + }, + { + "param_name": "mod_pka_g_half_cal12_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_g_slope_cal12_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + }, + { + "param_name": "mod_pka_g_min_cal13_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_max_cal13_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1.20 + }, + { + "param_name": "mod_pka_g_half_cal13_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_g_slope_cal13_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + }, + { + "param_name": "mod_pka_g_min_naf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_max_naf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 0.9 + }, + { + "param_name": "mod_pka_g_half_naf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_g_slope_naf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + }, + + { + "param_name": "mod_pka_g_min_kas_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_max_kas_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 0.8 + }, + { + "param_name": "mod_pka_g_half_kas_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_g_slope_kas_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + }, + + + { + "param_name": "mod_pka_g_min_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_max_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 0.9999 + }, + { + "param_name": "mod_pka_g_half_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_g_slope_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + }, + + + { + "param_name": "mod_pka_shift_min_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 0 + }, + { + "param_name": "mod_pka_shift_max_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 3 + }, + { + "param_name": "mod_pka_shift_half_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 12.5e-12 + }, + { + "param_name": "mod_pka_shift_slope_kaf_ms", + "type": "range", + "sectionlist": ["soma", "basal"], + "dist_type": "uniform", + "value": 1e-12 + } + + + + ] +} diff --git a/examples/notebooks/neuromodulation/neuromodulation_bath.ipynb b/examples/notebooks/neuromodulation/neuromodulation_bath.ipynb new file mode 100644 index 000000000..ef2218924 --- /dev/null +++ b/examples/notebooks/neuromodulation/neuromodulation_bath.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "138f1fa5-37b6-4563-a588-309e5b21b9d5", + "metadata": {}, + "source": [ + "# Neuromodulation example\n", + "\n", + "This neuromodulation creates a small network of neurons. We also use the reaction diffusion model by Anu G Nair 2015.\n", + "\n", + "To generate the ```reaction_diffusion.json``` file in ```data/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/``` from the xml file we run ```data/convert_sbml_to_json.sh```\n", + "\n", + "To get the RxD functionality of the ```DA_syn``` we must specify the \"RxD\" block in the connectivity block of the network configuration. See ```data/connectivity.json```\n", + "\n", + "```\n", + " \"channel_parameters\":\n", + "\t\t\"RxD\": {\n", + "\t\t \"species_name\": \"DA\",\n", + "\t\t \"flux_variable\": \"open\",\n", + "\t\t \"region\": \"internal\",\n", + " \"weight_scaling\": 1e9,\n", + "\t\t},\n", + "\n", + " ...\n", + " }\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1746feed-04de-4020-a2cb-f884e8e9698a", + "metadata": {}, + "source": [ + "## Network setup\n", + "\n", + "This simulation models bath appliation of dopamine to the network. The concentrations starts low, then at 2 seconds it increases." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b55f23d-62ac-4433-8639-07870af8c40a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading connectivity from data/connectivity.json\n", + "Adding neurons: dspn from dir data/dspn\n", + "Writing networks/neuromodulation_bath/network-config.json\n", + "Writing networks/neuromodulation_bath/network-config.json\n", + "Placing neurons\n", + "Network path: networks/neuromodulation_bath\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_bath/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 90\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 90 points for networks/neuromodulation_bath/mesh/Cube-cube-mesh-3.6763882578080044e-05.obj\n", + "Filtering, keeping inside points: 5 / 35\n", + "neuron_name = 'dspn_0', num = 4, neuron_path = 'data/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.0s\n", + "Touch detection\n", + "Network path: networks/neuromodulation_bath\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_bath/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "Processing hyper voxel : 0/48 (4 neurons)\n", + "Processing hyper voxel : 1/48 (4 neurons)\n", + "Processing hyper voxel : 4/48 (4 neurons)\n", + "Processing hyper voxel : 5/48 (4 neurons)\n", + "Processing hyper voxel : 12/48 (4 neurons)\n", + "Processing hyper voxel : 13/48 (4 neurons)\n", + "Processing hyper voxel : 16/48 (4 neurons)\n", + "Processing hyper voxel : 17/48 (4 neurons)\n", + "Processing hyper voxel : 14/48 (3 neurons)\n", + "Processing hyper voxel : 6/48 (2 neurons)\n", + "Processing hyper voxel : 18/48 (2 neurons)\n", + "Processing hyper voxel : 25/48 (2 neurons)\n", + "Processing hyper voxel : 29/48 (1 neurons)\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_bath/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.9s\n", + "Prune synapses\n", + "Network path: networks/neuromodulation_bath\n", + "No file networks/neuromodulation_bath/pruning_merge_info.json\n", + "Read 518 out of total 518 synapses\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.9s\n" + ] + } + ], + "source": [ + "import os\n", + "from snudda import Snudda\n", + "\n", + "neuron_path = os.path.join(\"data\", \"dspn\")\n", + "network_path = os.path.join(\"networks\", \"neuromodulation_bath\")\n", + "connectivity_path = os.path.join(\"data\", \"connectivity.json\")\n", + "\n", + "snudda = Snudda(network_path=network_path)\n", + "si = snudda.init_tiny(neuron_paths=neuron_path, neuron_names=\"dspn\", number_of_neurons=[4], \n", + " connection_config=connectivity_path, random_seed=12345)\n", + "\n", + "# si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"reaction_diffusion.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"data/JSON/reaction_diffusion_D1.json\"\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation\"] = \"test-modulation.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation_key\"] = \"abc\"\n", + "\n", + "si.write_json()\n", + "\n", + "snudda.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "41e96e4a-0f52-4d77-8a1f-ba8d270b9b78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_bath/network-synapses.hdf5\n", + "Writing input spikes to networks/neuromodulation_bath/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_bath/network-synapses.hdf5\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 2 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 2 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 2 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 3 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 3 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 3 (meta modified by input_config)\n", + "Writing spikes to networks/neuromodulation_bath/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 1.1s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_config = os.path.join(\"data\", \"input_v5_bath.json\")\n", + "snudda.setup_input(input_config=input_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aa0341ff-115d-43f9-a46a-dd6cf074ca69", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpirun -n 4 snudda simulate networks/neuromodulation_bath --time 4.0 --simulation_config data/DA-bath-experiment.json --mechdir /home/hjorth/BasalGangliaData/data/neurons/mechanisms\n" + ] + } + ], + "source": [ + "sim_time = 4.0\n", + "n_workers = 4\n", + "sim_config = \"data/DA-bath-experiment.json\"\n", + "mech_dir = \"/home/hjorth/BasalGangliaData/data/neurons/mechanisms\"\n", + "\n", + "run_str = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config} --mechdir {mech_dir}\"\n", + "print(run_str)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51848ea4-d25b-429b-bd62-0502ccf03e3c", + "metadata": {}, + "outputs": [], + "source": [ + "os.system(run_str)" + ] + }, + { + "cell_type": "markdown", + "id": "8159ba12-a064-4eb9-ab51-72fba54b3a1c", + "metadata": {}, + "source": [ + "### Add PKA and DA recordings\n", + "Here we add recordings from a compartment that receives synaptic input from its neighbour. This has been checked by looking at the synapse_connection matrix (snudda_load command)." + ] + }, + { + "cell_type": "markdown", + "id": "bfdb2c9a-4d32-453d-84b8-2c872bfd4050", + "metadata": {}, + "source": [ + "## Load the data and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d84ee6f3-4193-4509-ad4a-0d93dff5e3d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_bath/simulation/output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "2: (dspn_0, p7aa400d6, mf702205f)\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "\n", + "output_file = os.path.join(network_path, \"simulation\", \"output.hdf5\")\n", + "nd = SnuddaLoadSimulation(output_file)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKAc\", 1)[0][1]\n", + "data_da = nd.get_data(\"DA\", 1)[0][1]\n", + "data_da_external = nd.get_data(\"DA\", 0)[0][0]\n", + "\n", + "# This is saved with add_rxd_internal_concentration_recording_all -- check that it worked \n", + "data_pka_all0 = nd.get_data(\"PKAc\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bd0f4edf-a622-490b-b39f-18f516975737", + "metadata": {}, + "outputs": [], + "source": [ + "data_types = nd.list_data_types(0)\n", + "all_species_data = nd.get_all_data(neuron_id=0, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a63a7152-730e-4868-a81a-cc87fccaf9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data[data_type][0][0].T[0], name=data_type))\n", + "\n", + "fig.update_layout(xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d31a7d49-55b1-479c-942a-17cbd87fa412", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_bath/simulation/output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "2: (dspn_0, p7aa400d6, mf702205f)\n", + "Saving figure to networks/neuromodulation_bath/figures/spike-raster.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "fig_file_raster = f\"spike-raster.png\"\n", + "\n", + "time_range_zoom = (0,sim_time)\n", + "spr = SnuddaPlotSpikeRaster2(network_path=network_path)\n", + "\n", + "spr.plot_spike_raster(fig_file=fig_file_raster, time_range=time_range_zoom)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "35751562-8631-4430-9911-ffd1ffeda516", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure networks/neuromodulation_bath/figures/spike-frequency-pop-units0.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spr.plot_spike_histogram(label_text=\"dSPN\", bin_size=0.25)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fc082055-9e38-4ed1-b842-40e166548071", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_bath/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_bath/input-spikes.hdf5\n", + "Loading networks/neuromodulation_bath/simulation/output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "2: (dspn_0, p7aa400d6, mf702205f)\n", + "Plotting traces: [0, 1, 2, 3]\n", + "Plotted 4 traces (total 4)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_bath/figures/Network-voltage-trace--dspn-0-1-2-3.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "sim_file = os.path.join(network_path, \"simulation\", \"output.hdf5\")\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt = PlotTraces(output_file=sim_file)\n", + "# Use trace_id to specify which traces\n", + "ax = pt.plot_traces(offset=0, time_range=(0,sim_time),fig_size=(10,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0484dc59-0ae4-41fa-bfd5-441f8ddf6290", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting traces: [0]\n", + "Plotted 1 traces (total 4)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_bath/figures/Network-voltage-trace--dspn-0.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting traces: [1]\n", + "Plotted 1 traces (total 4)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_bath/figures/Network-voltage-trace--dspn-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax0 = pt.plot_traces(offset=0, time_range=(0,sim_time),fig_size=(10,4), trace_id=0)\n", + "ax1 = pt.plot_traces(offset=0, time_range=(0,sim_time),fig_size=(10,4), trace_id=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "191cf2cb-a61f-4b9e-89a9-4fe963f0343b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/neuromodulation_bath_current.ipynb b/examples/notebooks/neuromodulation/neuromodulation_bath_current.ipynb new file mode 100644 index 000000000..a6c00e6d7 --- /dev/null +++ b/examples/notebooks/neuromodulation/neuromodulation_bath_current.ipynb @@ -0,0 +1,450 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "138f1fa5-37b6-4563-a588-309e5b21b9d5", + "metadata": {}, + "source": [ + "# Neuromodulation example (bath)\n", + "\n", + "This neuromodulation creates a small network of neurons. We also use the reaction diffusion model by Anu G Nair 2015.\n", + "\n", + "To generate the ```reaction_diffusion.json``` file in ```data/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/``` from the xml file we run ```data/convert_sbml_to_json.sh```\n", + "\n", + "Reference for DA modulation cascade:\n", + "\n", + "Lindroos R, Dorst MC, Du K, et al. Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2. Front Neural Circuits. 2018;12:3. Published 2018 Feb 6. doi:10.3389/fncir.2018.00003\n", + "\n", + "Here we use the data from Planert:\n", + "Planert H, Berger TK, Silberberg G. Membrane properties of striatal direct and indirect pathway neurons in mouse and rat slices and their modulation by dopamine. PLoS One. 2013;8(3):e57054. doi:10.1371/journal.pone.0057054\n", + "\n", + "The neurons are held at -60mV, or -80mV, then a current injection is applied for 500ms to trigger spiking.\n", + "\n", + "Figure 4:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1746feed-04de-4020-a2cb-f884e8e9698a", + "metadata": {}, + "source": [ + "## Network setup\n", + "\n", + "We have two neurons. The first neuron (Neuron 0) receives external input (cortical from t=0s and DA from t=0.1s). The cortical input will activate the first neuron, and through activation of synapses on the second neuron, we will see the dopamine level increase in the second neuron (Neuron 1).\n", + "\n", + "The first neuron also receives direct DA activation from external input (starting at 100ms)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b55f23d-62ac-4433-8639-07870af8c40a", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from snudda import Snudda\n", + "\n", + "neuron_path = os.path.join(\"data\", \"dspn\")\n", + "network_path = os.path.join(\"networks\", \"neuromodulation_bath_current\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad92699c-455c-4fcd-b742-4391c78a0fd5", + "metadata": {}, + "outputs": [], + "source": [ + "snudda = Snudda(network_path=network_path)\n", + "si = snudda.init_tiny(neuron_paths=neuron_path, neuron_names=\"dspn\", number_of_neurons=[2], \n", + " random_seed=123)\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"data/JSON/reaction_diffusion_D1.json\"\n", + "\n", + "# How the ion channels are modified by DA\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation\"] = \"test-modulation.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation_key\"] = \"abc\"\n", + "\n", + "si.write_json()\n", + "\n", + "snudda.create_network()" + ] + }, + { + "cell_type": "markdown", + "id": "38468f7e-6ef2-464c-bfe9-1cb7cc9ea46b", + "metadata": {}, + "source": [ + "### No synaptic input, only current injections!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae7ebf60-1556-4e07-81c6-85156c90bfad", + "metadata": {}, + "outputs": [], + "source": [ + "sim_output_neuromodulation_ON = os.path.join(network_path, \"simulation\", \"output_neuromodulation_ON.hdf5\")\n", + "sim_output_neuromodulation_OFF = os.path.join(network_path, \"simulation\", \"output_neuromodulation_OFF.hdf5\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d019b580-4193-4321-9ee8-9bdd3988209f", + "metadata": {}, + "outputs": [], + "source": [ + "# mech_dir = os.path.join(\"..\", \"..\", \"..\", \"..\", \"BasalGangliaData\", \"data\", \"neurons\", \"mechanisms\")\n", + "mech_dir = \"/home/hjorth/BasalGangliaData/data/neurons/mechanisms\"\n", + "sample_dt = None # 0.00005\n", + "\n", + "# sim = snudda.simulate(time=0, mech_dir=mech_dir, verbose=True, sample_dt=sample_dt, output_file=sim_output_neuromodulation_ON)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "207658ef-0747-4c87-babb-d3076e3f7a33", + "metadata": {}, + "outputs": [], + "source": [ + "# sim_config_on = os.path.join(\"data\", \"da_experiment_on.json\")\n", + "# sim_config_off = os.path.join(\"data\", \"da_experiment_off.json\")\n", + "\n", + "snudda = None\n", + "\n", + "sim_config_on = os.path.join(\"data\", \"da_experiment_cur_inj_on_bath.json\")\n", + "sim_config_off = os.path.join(\"data\", \"da_experiment_cur_inj_off_bath.json\")\n", + "\n", + "\n", + "sim_time = 1\n", + "n_workers = 1" + ] + }, + { + "cell_type": "markdown", + "id": "56760ccb-c832-4852-b696-323198f8d771", + "metadata": {}, + "source": [ + "## Running simulations\n", + "\n", + "To see progress of the two simulations in log files ```networks/neuromodulation_ON_OFF/log/network-simulation-ON.txt``` and ```networks/neuromodulation_ON_OFF/log/network-simulation-OFF.txt```." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b6b0fa2-93d2-4d0b-92ef-3c4949ac3355", + "metadata": {}, + "outputs": [], + "source": [ + "run_str_on = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_on} --mechdir {mech_dir}\"\n", + "print(run_str_on)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bc7d396-09b0-46cc-89da-ff3c356b2b6e", + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "os.system(run_str_on)" + ] + }, + { + "cell_type": "markdown", + "id": "0c9d6057-e439-40e1-b7e7-4d55bdec3ce7", + "metadata": {}, + "source": [ + "### Rerun without neuromodulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f3648c5-2f5f-4b5b-b438-5eb15bffdd61", + "metadata": {}, + "outputs": [], + "source": [ + "run_str_off = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_off} --mechdir {mech_dir} --disable_rxd_neuromodulation\"\n", + "print(run_str_off)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3cdbcb1-7520-4a7c-93d8-61071697b8ca", + "metadata": {}, + "outputs": [], + "source": [ + "os.system(run_str_off)" + ] + }, + { + "cell_type": "markdown", + "id": "bfdb2c9a-4d32-453d-84b8-2c872bfd4050", + "metadata": {}, + "source": [ + "## Load the data and plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d84ee6f3-4193-4509-ad4a-0d93dff5e3d9", + "metadata": {}, + "outputs": [], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "\n", + "nd = SnuddaLoadSimulation(sim_output_neuromodulation_ON)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKAc\", 1)[0][1]\n", + "data_da = nd.get_data(\"DA\", 1)[0][1]\n", + "data_da_external = nd.get_data(\"DA\", 0)[0][0]\n", + "\n", + "# This is saved with add_rxd_internal_concentration_recording_all -- check that it worked \n", + "data_pka_all0 = nd.get_data(\"PKAc\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b72e5c19-3c2b-4c35-8488-e59b5a9d6db7", + "metadata": {}, + "outputs": [], + "source": [ + "nd.list_data_types(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd0f4edf-a622-490b-b39f-18f516975737", + "metadata": {}, + "outputs": [], + "source": [ + "data_types = nd.list_data_types(0)\n", + "all_species_data = nd.get_all_data(neuron_id=0, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()\n", + "voltage = nd.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a63a7152-730e-4868-a81a-cc87fccaf9f4", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " idx = time >= 0.0\n", + " fig.add_trace(go.Scatter(x=time[idx], y=all_species_data[data_type][0][0].T[0][idx], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad49456c-9e8c-4f16-b0de-524c84d33d35", + "metadata": {}, + "outputs": [], + "source": [ + "fig.write_html(\"ask-jeanette2.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "315118b2-d7da-4c3c-a089-4769f28511e2", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " idx = time > 0.01\n", + " y=all_species_data[data_type][0][0].T[0][idx]\n", + " fig.add_trace(go.Scatter(x=time[idx], y=y/y[0], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "511489ad-b3d6-4301-a8bd-fa870cf456e4", + "metadata": {}, + "outputs": [], + "source": [ + "data_types2 = nd.list_data_types(0)\n", + "all_species_data2 = nd.get_all_data(neuron_id=1, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()\n", + "voltage = nd.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d231b4e4-4a56-47b5-84f6-e13b6e721125", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data2:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data2[data_type][0][1].T[0], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81722b1c-c30c-445e-a5f5-abaa823f0ce4", + "metadata": {}, + "outputs": [], + "source": [ + "nd_off = SnuddaLoadSimulation(sim_output_neuromodulation_OFF)\n", + "time_off = nd_off.get_time()\n", + "voltage_off = nd_off.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e9ce899-6243-448b-a4ac-2ba3e83f522e", + "metadata": {}, + "outputs": [], + "source": [ + "fig = go.Figure()\n", + "\n", + "sct_on = go.Scatter(x=time, y=voltage[0][0][:,0], name=\"DA (0.3-1.3s)\", opacity=0.5)\n", + "sct_off = go.Scatter(x=time_off, y=voltage_off[0][0][:,0], name=\"No DA\", opacity=0.5)\n", + "fig.add_traces([sct_on, sct_off])\n", + "fig.write_image(\"example-trace.png\", scale=2, height=800, width=1200)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1c558be-b54e-41e7-a64c-6c98e955ed2a", + "metadata": {}, + "outputs": [], + "source": [ + "fig = go.Figure()\n", + "\n", + "sct_on = go.Scatter(x=time, y=voltage[0][1][:,0], name=\"DA (0.3-1.3s)\", opacity=0.5)\n", + "sct_off = go.Scatter(x=time_off, y=voltage_off[0][1][:,0], name=\"No DA\", opacity=0.5)\n", + "fig.add_traces([sct_on, sct_off])" + ] + }, + { + "cell_type": "markdown", + "id": "cd855cab-eb67-4c0c-a20c-56f23eb04d9e", + "metadata": {}, + "source": [ + "## Plotting simulation with DA modulation \n", + "\n", + "DA is active from 0.3 to 1.3 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc082055-9e38-4ed1-b842-40e166548071", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt = PlotTraces(output_file=sim_output_neuromodulation_ON)\n", + "# Use trace_id to specify which traces\n", + "ax = pt.plot_traces(offset=0, time_range=None,fig_size=(10,4))" + ] + }, + { + "cell_type": "markdown", + "id": "d5ab977a-2171-4f7a-8db8-0d4fcaff82a6", + "metadata": {}, + "source": [ + "## Plot simulation, with neuromodulation disabled" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "191cf2cb-a61f-4b9e-89a9-4fe963f0343b", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt_off = PlotTraces(output_file=sim_output_neuromodulation_OFF)\n", + "# Use trace_id to specify which traces\n", + "ax_off = pt_off.plot_traces(offset=0, time_range=None,fig_size=(10,4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "398790f7-d211-4fff-9249-77b28e14c859", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3380b41c-d95e-4f8c-95bc-614fa76d2733", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/neuromodulation_example_anu.ipynb b/examples/notebooks/neuromodulation/neuromodulation_example_anu.ipynb new file mode 100644 index 000000000..2634d3c87 --- /dev/null +++ b/examples/notebooks/neuromodulation/neuromodulation_example_anu.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "138f1fa5-37b6-4563-a588-309e5b21b9d5", + "metadata": {}, + "source": [ + "# Neuromodulation example\n", + "\n", + "This neuromodulation creates a small network of neurons. We also use the reaction diffusion model by Anu G Nair 2015.\n", + "\n", + "To generate the ```reaction_diffusion.json``` file in ```data/dspn_rxd``` from the xml file we run ```data/convert_sbml_to_json.sh```\n", + "\n", + "To get the RxD functionality of the ```DA_syn``` we must specify the \"RxD\" block in the connectivity block of the network configuration. See ```data/connectivity.json```\n", + "\n", + "```\n", + " \"channel_parameters\":\n", + "\t\t\"RxD\": {\n", + "\t\t \"species_name\": \"DA\",\n", + "\t\t \"flux_variable\": \"open\",\n", + "\t\t \"region\": \"internal\",\n", + " \"weight_scaling\": 1e9,\n", + "\t\t},\n", + "\n", + " ...\n", + " }\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1746feed-04de-4020-a2cb-f884e8e9698a", + "metadata": {}, + "source": [ + "## Network setup\n", + "\n", + "We have two neurons. The first neuron (Neuron 0) receives external input (cortical from t=0s and DA from t=0.1s). The cortical input will activate the first neuron, and through activation of synapses on the second neuron, we will see the dopamine level increase in the second neuron (Neuron 1).\n", + "\n", + "The first neuron also receives direct DA activation from external input (starting at 100ms)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b55f23d-62ac-4433-8639-07870af8c40a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading connectivity from data/connectivity.json\n", + "Adding neurons: neuron from dir data/dspn_rxd\n", + "Writing networks/neuromodulation_example_anu/network-config.json\n", + "Placing neurons\n", + "Network path: networks/neuromodulation_example_anu\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 63\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 63 points for networks/neuromodulation_example_anu/mesh/Cube-cube-mesh-2.917951293943981e-05.obj\n", + "Filtering, keeping inside points: 4 / 26\n", + "neuron_name = 'neuron', num = 2, neuron_path = 'data/dspn_rxd'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.0s\n", + "Touch detection\n", + "Network path: networks/neuromodulation_example_anu\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "Processing hyper voxel : 0/27 (2 neurons)\n", + "Processing hyper voxel : 1/27 (2 neurons)\n", + "Processing hyper voxel : 3/27 (2 neurons)\n", + "Processing hyper voxel : 4/27 (2 neurons)\n", + "Processing hyper voxel : 9/27 (2 neurons)\n", + "Processing hyper voxel : 12/27 (2 neurons)\n", + "Processing hyper voxel : 13/27 (2 neurons)\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.5s\n", + "Prune synapses\n", + "Network path: networks/neuromodulation_example_anu\n", + "No file networks/neuromodulation_example_anu/pruning_merge_info.json\n", + "Read 67 out of total 67 synapses\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.5s\n" + ] + } + ], + "source": [ + "import os\n", + "from snudda import Snudda\n", + "\n", + "neuron_path = os.path.join(\"data\", \"dspn_rxd\")\n", + "network_path = os.path.join(\"networks\", \"neuromodulation_example_anu\")\n", + "connectivity_path = os.path.join(\"data\", \"connectivity.json\")\n", + "\n", + "snudda = Snudda(network_path=network_path)\n", + "snudda.init_tiny(neuron_paths=neuron_path, neuron_names=\"neuron\", number_of_neurons=[2], \n", + " connection_config=connectivity_path, random_seed=12345)\n", + "snudda.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "41e96e4a-0f52-4d77-8a1f-ba8d270b9b78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "Writing input spikes to networks/neuromodulation_example_anu/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "Writing spikes to networks/neuromodulation_example_anu/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.5s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_config = os.path.join(\"data\", \"input.json\")\n", + "snudda.setup_input(input_config=input_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d019b580-4193-4321-9ee8-9bdd3988209f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using input file networks/neuromodulation_example_anu/input-spikes.hdf5\n", + "NEURON mechanisms already compiled, make sure you have the correct version of NEURON modules.\n", + "If you delete x86_64, aarch64, arm64 directories (or nrnmech.dll) then you will force a recompilation of the modules.\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "Using network_file: networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "Using input_file: networks/neuromodulation_example_anu/input-spikes.hdf5\n", + "Using output_file: networks/neuromodulation_example_anu/simulation/output.hdf5\n", + "Using logFile: networks/neuromodulation_example_anu/log/network-simulation-log.txt-0\n", + "Worker 0 : Loading network from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "Loading config file networks/neuromodulation_example_anu/network-config.json\n", + "0 : Memory status: 62% free\n", + "Distributing neurons.\n", + "Setup neurons\n", + "Node 0 - cell 0 neuron\n", + "Parsing neuromodulation json: None\n", + "Parsing species\n", + "Parsing rates.\n", + "Parsing reactions\n", + "Parsing done.\n", + "Neuron neuron (0) resting voltage = -80.0\n", + "!!! Popping extra segment from neuron -- temp fix!\n", + "Node 0 - cell 1 neuron\n", + "Parsing neuromodulation json: None\n", + "Parsing species\n", + "Parsing rates.\n", + "Parsing reactions\n", + "Parsing done.\n", + "Neuron neuron (1) resting voltage = -80.0\n", + "!!! Popping extra segment from neuron -- temp fix!\n", + "0 : Memory status: 62% free\n", + "Adding gap junctions.\n", + "connect_network_gap_junctions_local\n", + "Finding node local gap junctions...\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Adding synapses.\n", + "connect_network_synapses\n", + "Build node cache\n", + "Node cache built.\n", + "Build node cache\n", + "Node cache built.\n", + "Added 67 on worker 0\n", + "Added 67 synapses to simulation (67 total)\n", + "0 : Memory status: 67% free\n", + "Adding external (cortical, thalamic) input from networks/neuromodulation_example_anu/input-spikes.hdf5\n", + "0 : Memory status: 67% free\n", + "0 : Memory status: 67% free\n", + "Time set to 0 ms. No simulation run.\n", + "Program run time: 10.6s\n" + ] + } + ], + "source": [ + "mech_dir = os.path.join(\"data\", \"mechanisms\")\n", + "sim = snudda.simulate(time=0, mech_dir=mech_dir, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "8159ba12-a064-4eb9-ab51-72fba54b3a1c", + "metadata": {}, + "source": [ + "### Add PKA and DA recordings\n", + "Here we add recordings from a compartment that receives synaptic input from its neighbour. This has been checked by looking at the synapse_connection matrix (snudda_load command)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "88bf71a5-19da-4e2e-8e90-29546a56cea1", + "metadata": {}, + "outputs": [], + "source": [ + "sim.add_rxd_concentration_recording(species=\"PKA\", neuron_id=1,\n", + " region=\"dend_internal\",\n", + " sec_type=\"dend\",\n", + " sec_id=4,\n", + " sec_x=0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "27ec8a70-1fbc-4093-9d8c-cb73494b626e", + "metadata": {}, + "outputs": [], + "source": [ + "sim.add_rxd_concentration_recording(species=\"DA\", neuron_id=1,\n", + " region=\"dend_internal\",\n", + " sec_type=\"dend\",\n", + " sec_id=4,\n", + " sec_x=0.25)" + ] + }, + { + "cell_type": "markdown", + "id": "93f8a148-edbf-40e5-ab7f-1dca76bc2d32", + "metadata": {}, + "source": [ + "### Add additional PKA and DA recorings, for input" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6b9f2c09-d17f-4848-990d-9cc9b7859c39", + "metadata": {}, + "outputs": [], + "source": [ + "sim.add_rxd_concentration_recording(species=\"DA\", neuron_id=0,\n", + " region=\"dend_internal\",\n", + " sec_type=\"dend\",\n", + " sec_id=7,\n", + " sec_x=0.233)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f5f9b57-9e65-4a09-87ab-cc6da2ce332e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation for 0.2 s\n", + "Running Neuron simulator 200 ms, with dt=0.025\n", + " 1% done. Elapsed: 0.5 s, estimated time left: 46.0 s\n", + " 2% done. Elapsed: 0.9 s, estimated time left: 44.4 s\n", + " 3% done. Elapsed: 1.3 s, estimated time left: 43.2 s\n", + " 4% done. Elapsed: 1.8 s, estimated time left: 42.4 s\n", + " 5% done. Elapsed: 2.2 s, estimated time left: 41.8 s\n", + " 10% done. Elapsed: 4.4 s, estimated time left: 39.4 s\n", + " 20% done. Elapsed: 8.7 s, estimated time left: 34.8 s\n", + " 30% done. Elapsed: 13.0 s, estimated time left: 30.4 s\n", + " 40% done. Elapsed: 17.4 s, estimated time left: 26.0 s\n", + " 50% done. Elapsed: 21.7 s, estimated time left: 21.7 s\n", + " 60% done. Elapsed: 26.0 s, estimated time left: 17.3 s\n", + " 70% done. Elapsed: 30.3 s, estimated time left: 13.0 s\n", + " 80% done. Elapsed: 34.7 s, estimated time left: 8.7 s\n", + " 90% done. Elapsed: 39.0 s, estimated time left: 4.3 s\n", + "100% done. Elapsed: 43.3 s, estimated time left: 0.0 s\n", + "Neuron simulation finished\n", + "Simulation done.\n", + "Simulation run time: 45.0 s\n" + ] + } + ], + "source": [ + "sim.run(t=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "053125fc-9117-459b-a3e8-725ef45db53a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing network output to networks/neuromodulation_example_anu/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to networks/neuromodulation_example_anu/simulation/output.hdf5\n" + ] + } + ], + "source": [ + "sim.record.write()" + ] + }, + { + "cell_type": "markdown", + "id": "bfdb2c9a-4d32-453d-84b8-2c872bfd4050", + "metadata": {}, + "source": [ + "## Load the data and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d84ee6f3-4193-4509-ad4a-0d93dff5e3d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_example_anu/simulation/output.hdf5\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadNetworkSimulation\n", + "\n", + "output_file = os.path.join(network_path, \"simulation\", \"output.hdf5\")\n", + "nd = SnuddaLoadNetworkSimulation(output_file)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKA\", 1)[0][1]\n", + "data_da = nd.get_data(\"DA\", 1)[0][1]\n", + "data_da_external = nd.get_data(\"DA\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d98d057c-752c-458b-bd52-a726f42dc90a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jJkyYYO5/6tSpmD17Nry9veHp6YnU1FTExcVxgjYREbULQghsPFKCD7ecwMGzWvPyIRHemBgbioTeAXCTt+2LObYkSQPS4sWLAQAjRoywWJ6eno4pU6YAAJYsWWIx9+fK6f9/blNQUICLFy+a25w9exYTJkzApUuX4Ofnh9tuuw07d+6En5+fuc2iRYvg4OCApKQk6PV6JCYm4uOPP26BURIREbUeIQR+PVyC9zb9gWPqcgD1Z52NH9gFk+LCEBXgKXGFbYNMCCGkLqIt0ul0UCqV0Gq18PTkDxsREUnv0FktXv/pCHafKgMAeLg44fG4MEy9LQI+HpwmAjT989suJmkTERGR9coqa/HGz0fxXc5ZAICrswOeuq0rnro9Al7ucomra5sYkIiIiNooIQR+PFiMBT8cxqX/nqo/ProLXkyMRJCXm8TVtW0MSERERG3QpQo95vznEDYdrb/ReqS/AguT+iE6tLPElbUPDEhERERtTHbBJcz8Zj9KdHo4O8qQMrIHpo/oBrkTT9W3FQYkIiKiNsJkEvhg8wn8X8YfMAmgm18nfPjoQPQK5MlCtsaARERE1AZU1dbhhW8P4Je8+msG/iUmGAse6NMhbwPSGvhdJSIisnNqbQ2e+mIP8s7p4Owowxvj+uEvg3i7q5bEgERERGTHTl6owGOf7cJ5bQ28O8nxyaQYDA73lrqsdo8BiYiIyE4dU+vw2Ge7cbFCj65+nbB8yhCE+rhLXVaHwIBERERkhw6e1WDS0t3QVhvQK9ATX04dAl9eDbvVMCARERHZmT9KyvH4svpwFB3qheVThkDp7ix1WR0KAxIREZEdKbpUhcc+2wVNlQEDQrzw5dRYeLjw47q18YpSREREduJCuR6PLd2F0nI9Iv0V+PyJwQxHEmFAIiIisgM1BiOe/SoHRWVVCPV2x5dTh/BGsxJiQCIiIpKYEAIvrzmEnMLL8HR1wvInBkPl6Sp1WR0aAxIREZHEPtl6Emv2n4OjgwwfT4xBVz8PqUvq8BiQiIiIJLT7VBne2nAMADD/vt64rYevxBURwIBEREQkmbLKWsxYuR8mAYwf2AWPx4VLXRL9FwMSERGRBEwmgdnf5kKtq0E3v054/YG+UpdEf8KAREREJIEvsk8jM/8CXJwc8OGjA9GJp/PbFQYkIiKiVlZ4qRJvbsgHAPy/e3qhV6CnxBXR1RiQiIiIWpHJJPDSdwdRbTAirqsPHosNk7okagQDEhERUSv6cmchdp8qg7vcEW892B8ODjKpS6JGMCARERG1khJdjfmU/rl3RSHE213iiuhaGJCIiIhayRs/H0VlrRG3hHhhIg+t2TUGJCIiolaw8+QlrMs9D5kMeP2Bvjy0ZucYkIiIiFqYwWjC/HWHAQCPDglFv2ClxBXRjTAgERERtbBv9pxBfkk5Ors746XESKnLoSZgQCIiImpBVbV1+L+M4wCAmfE94eUul7giagoGJCIiohaUvv00LpTrEertjglDQqUuh5qIAYmIiKiFXK6sxZLMAgDACwk9IXfix25bwS1FRETUQhZnFaBcX4fegZ64r3+Q1OVQMzAgERERtYBLFXp8mV0IAHgpMZKn9bcxDEhEREQtYNn2U6g2GNGvixIjIv2kLoeaiQGJiIjIxrTVBnyxo37vUcqo7pDJuPeorWFAIiIisrEvs0+jXF+Hnv4euLOXv9TlkBUYkIiIiGyoutaIpdtOAQCSR3bn3KM2igGJiIjIhtbsP4vLVQaEeLvhnn6BUpdDVmJAIiIishEhBJZvPw0AmBwXDidHfsy2VdxyRERENrLtxEUcL61AJ7kjHhocInU5dBMkDUhpaWkYPHgwFAoFVCoVxo4di/z8fIs2n376KUaMGAFPT0/IZDJoNBqb9DtixAjIZDKLx7PPPmvL4RERUQeT/t+9R38ZFAJPV2dpi6GbImlAysrKQnJyMnbu3ImNGzfCYDAgISEBlZWV5jZVVVUYM2YMXn75ZZv2CwDTpk1DcXGx+fHWW2/ZbGxERNSxnLpYic3HSiGTAZNvDZe6HLpJTlK++YYNGyyeL1++HCqVCjk5ORg+fDgAYObMmQCAzMxMm/YLAO7u7ggICLCueCIiItTPOyq8VIUPMo4DAEZGqhDh20niquhmSRqQrqbVagEA3t7erdLv119/ja+++goBAQG477778Morr8Dd3b3RPvR6PfR6vfm5TqezaY1ERNQ2GE0CR4t12HO6DHtOl2H3qcu4WPG/z4cJQ0IlrI5sxW4CkslkwsyZMzFs2DD07du3xft99NFHERYWhqCgIBw8eBBz5sxBfn4+1qxZ02g/aWlpWLBggc3qIiKituNShR5Zf1zAlvwL2PrHBWirDRbr5Y4O6B3kieE9/TA6SiVRlWRLdhOQkpOTkZeXh23btrVKv08//bT56379+iEwMBCjR49GQUEBunXr1qCfuXPnYvbs2ebnOp0OISE8Q4GIqL0q0dVg/cFirD94HrlnNBDif+sULk4YGNYZQyK8MTjcG/2DlXB1dpSuWLI5uwhIKSkpWL9+PbZu3Yrg4GBJ+o2NjQUAnDhxotGA5OLiAhcXF5vVRkRE9qdSX4cfD5zH97nnsOtUmUUo6hPkiZGRKoyM8sOAYC9e46idkzQgCSGQmpqKtWvXIjMzExEREZL1m5ubCwAIDORVT4mIOpp8dTm+3lWItfvOoVxfZ14eE9YZ9w8IQmKfAAQoXSWskFqbpAEpOTkZK1aswLp166BQKKBWqwEASqUSbm5uAAC1Wg21Wo0TJ04AAA4dOgSFQoHQ0FDzpOvRo0dj3LhxSElJaVK/BQUFWLFiBe6++274+Pjg4MGDmDVrFoYPH47+/fu39reBiIgkIIRAdsElfJR5AttPXDIvD/dxx0ODQ3D/gCAEd278xB1q/2RC/HkHYiu/uazxG/ilp6djypQpAIBXX3210cnRf24THh6OKVOm4NVXX21Sv2fOnMFjjz2GvLw8VFZWIiQkBOPGjcPf//53eHp6Nql2nU4HpVIJrVbb5NcQEZH0hBDYfKwUH2w+gdwzGgCAo4MMd/byx2NDw3BrNx/eYLYda+rnt6QBqS1jQCIiantyz2jwxk9Hsft0GQDAxckBjwwOwbThXbm3qINo6ue3XUzSJiIiakkluhr886ej+OHAeQD1wWjKreF46vau8FPwBBxqiAGJiIjaLZNJYMXuIrz5yzGU6+sgkwHjorvgxYRIBHm5SV0e2TEGJCIiapcKL1XixdUHsOf0ZQDAgBAv/HNsX/TtopS4MmoLGJCIiKjdWbPvLF75Pg+VtUa4yx3xUmIkHo8LhyMnX1MTMSAREVG7UVVbh/+3Ng9r958DAAyJ8Ma/HhrACdjUbAxIRETULpy9XIVpX+TgaLEOjg4yPD+6B5JHdudeI7IKAxIREbV5e06X4dkvc3Cpsha+HnIsfiwGg8O9pS6L2jAGJCIiatM25BVjxspc1BpN6B3oiX9PHoQuPEONbhIDEhERtVnf7j2Dv/3nIEwCSOzjj0UP3wJ3OT/a6Obxp4iIiNqkZdtO4bX1RwAADw8KwRvj+3G+EdkMAxIREbU5X+4sNIejp4d3xdy7oq55H04iazAgERFRm/KfnPprHAHAcyO64aXESIYjsjkHqQsgIiJqqt8Oq/HSdwcAAFNuDWc4ohbDgERERG3CobNaPL8qFyYBPDQoGPPu7c1wRC2GAYmIiOxesbYaUz/fg2qDEXf09MMb4/rBgROyqQUxIBERkV2rMRjx1Od7UVquR09/D3zwaDScHPnxRS2LP2FERGTX5q3Lw+HzOvh0kmPp5MHwdHWWuiTqABiQiIjIbq3eewbf7j0LmQz4YEI0Qrx501lqHQxIRERkl/LV5XhlXf3p/LPie+LW7r4SV0QdCQMSERHZndo6E55ftR81BhNu7+GLlJHdpS6JOhgGJCIisjvvZxzHMXU5vDvJ8a+HbuEZa9TqGJCIiMiu5J7RYHFWAQDgH2P7wk/hInFF1BExIBERkd3Q1xnxwre5MJoE7h8QhLv7BUpdEnVQDEhERGQ3Ps06iYILlfBTuOC1B/pIXQ51YAxIRERkF4ouVeHDLScAAK/c2xte7nKJK6KOjAGJiIgkJ4TAqz8ehr7OhFu7+eC+/jy0RtJiQCIiIsltOlqKzcdK4ewow2sP9OVNaElyDEhERCQpg9GEtJ+PAgCeur0ruqs8JK6IiAGJiIgktmp3EU5erIRPJzmSeUFIshMMSEREJJkKfR3e23QcADAzvgc8XJwkroiontU/iSaTCSdOnEBpaSlMJpPFuuHDh990YURE1P59mlWAS5W1iPDthEeGhEpdDpGZVQFp586dePTRR1FYWAghhMU6mUwGo9Fok+KIiKj9KqusxWfbTgEA5oyJhLMjD2qQ/bAqID377LMYNGgQfvrpJwQGBvJsAyIiarbPfj+Jqloj+nbxRGKfAKnLIbJgVUA6fvw4vvvuO3Tvzsl0RETUfJqqWnyRXQgAmDGqB/+jTXbHqv2ZsbGxOHHihK1rISKiDmLZ9tOo0NehV6An7uztL3U5RA1YtQcpNTUVL7zwAtRqNfr16wdnZ2eL9f3797dJcURE1P7oagxI314/9yh1VHfuPSK7ZFVASkpKAgA8+eST5mUymQxCCE7SJiKi61qxqwjlNXXoofLAGM49IjtlVUA6deqUresgIqIOwGA04fMdpwEATw/vCgcH7j0i+2RVQAoLC7N1HURE1AH8kqdGsbYGvh4uuP+WIKnLIbomqy86UVBQgNTUVMTHxyM+Ph4zZsxAQUFBs/pIS0vD4MGDoVAooFKpMHbsWOTn51u0+fTTTzFixAh4enpCJpNBo9E0qe+PPvoI4eHhcHV1RWxsLHbv3m2xvqamBsnJyfDx8YGHhweSkpJQUlLSrPqJiKjphBBY+vtJAMCkoWFwcXKUuCKia7MqIP3666/o3bs3du/ejf79+6N///7YtWsX+vTpg40bNza5n6ysLCQnJ2Pnzp3YuHEjDAYDEhISUFlZaW5TVVWFMWPG4OWXX25yv9988w1mz56N+fPnY9++fRgwYAASExNRWlpqbjNr1iz8+OOPWL16NbKysnD+/HmMHz++ye9BRETNk1N4GQfOaiF3csDEobxqNtk5YYVbbrlFzJkzp8HyOXPmiOjoaGu6FEIIUVpaKgCIrKysBuu2bNkiAIjLly/fsJ8hQ4aI5ORk83Oj0SiCgoJEWlqaEEIIjUYjnJ2dxerVq81tjh49KgCI7OzsJtWq1WoFAKHVapvUnoioIzOZTOK5r3JE2Jz14q+rD0hdDnVgTf38tmoO0tGjR/Htt982WP7kk0/ivffeszqsabVaAIC3t7fVfdTW1iInJwdz5841L3NwcEB8fDyys7MBADk5OTAYDIiPjze3iYqKQmhoKLKzszF06NAG/er1euj1evNznU5ndY1ERO1ZbZ0Jf5SUI++cFnnntfhDXYHjpeW4XGUAAEy+NVzaAomawKqA5Ofnh9zcXPTo0cNieW5uLlQqlVWFmEwmzJw5E8OGDUPfvn2t6gMALl68CKPRCH9/ywuP+fv749ixYwAAtVoNuVwOLy+vBm3UanWj/aalpWHBggVW10VE1F4Va6ux62QZdp8uw6GzWuSry1FrNDVoJ5MBDwwIQu8gTwmqJGoeqwLStGnT8PTTT+PkyZO49dZbAQDbt2/Hm2++idmzZ1tVSHJyMvLy8rBt2zarXt/S5s6dazE2nU6HkJAQCSsiIpKGpqoWWX9cwO/HL2L3qTIUlVU1aOPp6oR+wUr0DVIiKlCBHioFuvl5wE3OidnUNlgVkF555RUoFAq8++675kNZQUFBePXVVzFjxoxm95eSkoL169dj69atCA4OtqYkM19fXzg6OjY4I62kpAQBAfUXJAsICEBtbS00Go3FXqQ/t7mai4sLXFxcbqo2IqK2quBCBTYeKcHmo6XYW1gGk/jfOgcZ0LeLEkPCvTEwrDP6dVEiuLMbr5BNbZpVAUkmk2HWrFmYNWsWysvLAQAKhaLZ/QghkJqairVr1yIzMxMRERHWlGNBLpcjJiYGGRkZGDt2LID6w3cZGRlISUkBAMTExMDZ2RkZGRnmq4Ln5+ejqKgIcXFxN10DEVF7oNbWYP3B8/g+9xzyzlnOu4z0V2BElB+GdvXBoLDOULg6X6MXorbJqoD0Z9YEoyuSk5OxYsUKrFu3DgqFwjz/R6lUws3NDUD9fCG1Wm2+Oe6hQ4egUCgQGhpqnsw9evRojBs3zhyAZs+ejcmTJ2PQoEEYMmQI3nvvPVRWVuKJJ54w9z916lTMnj0b3t7e8PT0RGpqKuLi4hqdoE1E1FEYTQJbjpXiy52F2Hr8AsR/9xQ5Ochwa3df3NlLhZFRKgR3dpe2UKIW1uSANHDgQGRkZKBz586Ijo6+7q7Tffv2NanPxYsXAwBGjBhhsTw9PR1TpkwBACxZssRicvTw4cMbtCkoKMDFixfNbR5++GFcuHAB8+bNg1qtxi233IINGzZYTNxetGgRHBwckJSUBL1ej8TERHz88cdNqpuIqL3R1RiwYlcRvtpZiLOXq83LB4V1xgPRXXBPv0B4d5JLWCFR65IJIcSNmwELFizASy+9BHd3d7z66qvXDUjz58+3WYH2SqfTQalUQqvVwtOTZ2QQUdt0ubIWy7afwvIdp1FeUwcAULo54+HBIXh0SCjCfTtJXCGRbTX187vJAYksMSARUVtWoa/DkswCLNt+ClW1RgBAD5UHpt3eFfffEgRXZ55tRu1TUz+/rZqD1LVrV+zZswc+Pj4WyzUaDQYOHIiTJ09a0y0REbUwo0lg9d4zeOe3P3Cxov7it70DPZE6qjsS+wTAwYFnnhEBVgak06dPw2g0Nliu1+tx9uzZmy6KiIhs79BZLeb85yCOFNefkRbu446/3dULiX38eUo+0VWaFZB++OEH89e//vorlEql+bnRaERGRoZNTtUnIiLbqTEYsWjTH/js91MwmgQ8XZ0wY3QPPB4XDrmTVfcsJ2r3mhWQrlxXSCaTYfLkyRbrnJ2dER4ejnfffddmxRER0c05fF6L1JX7cfJCJQDg3v6BePX+PvD14IVvia6nWQHJZKq/t05ERAT27NkDX1/fFimKiIhujhACX+4sxD9+OoraOhNUChf8Y2xfJPRp/G4BRGTJqjlIp06dsnUdRERkI1W1dXhp9UH8dKgYADA6SoV3/jIAnXkdI6Ims/pK2pWVlcjKykJRURFqa2st1llzPzYiIrp5xdpqPPX5Xhw+r4Ozowx/u6sXnhwWzknYRM1kVUDav38/7r77blRVVaGyshLe3t64ePEi3N3doVKpGJCIiCRw6KwWUz/fg9JyPXw6yfHp4zGICfOWuiyiNsmq0xdmzZqF++67D5cvX4abmxt27tyJwsJCxMTE4J133rF1jUREdAPZBZfwyKfZKC3Xo6e/B75PHsZwRHQTrApIubm5eOGFF+Dg4ABHR0fo9XqEhITgrbfewssvv2zrGomI6Dq25JdiSvpuVNYaMay7D/4z/VaEePNmskQ3w6qA5OzsDAeH+peqVCoUFRUBAJRKJc6cOWO76oiI6Lo2HinB01/shb7OhPheKiydPBgKV2epyyJq86yagxQdHY09e/agR48euOOOOzBv3jxcvHgRX375Jfr27WvrGomIqBE7TlxE8tf7YDAK3Ns/EIsevgXOjrzwI5EtWPWb9MYbbyAwMBAA8M9//hOdO3fG9OnTceHCBXz66ac2LZCIiBraX3QZT32xF7VGE8b0CcB7DEdENtXsPUhCCKhUKvOeIpVKhQ0bNti8MCIiatzJCxV4YvkeVNUacVt3X/zfhFvgxHBEZFPN/o0SQqB79+6ca0REJAFtlQFPfb4XmioDBoR44ZNJMXBxcpS6LKJ2p9kBycHBAT169MClS5daoh4iIrqGOqMJySv24eTFSgQpXfHZ44PQycXq6/0S0XVYtU924cKFeOmll5CXl2freoiI6Bre+PkYtp24CHe5Iz6bPBh+Ct5wlqilWPVfj8cffxxVVVUYMGAA5HI53NzcLNaXlZXZpDgiIqq3IU+NZdvr74O56OFb0DvIU+KKiNo3qwLSokWLeF8fIqJWcqasCn/97gAA4JnhXZHYJ0DiiojaP6sC0pQpU2xcBhERNcZgNCF15X7oauoQHeqFFxMjpS6JqEOwag6So6MjSktLGyy/dOkSHB15NgURka18tOUEcs9o4OnqhA8mRPNaR0StxKrfNCFEo8v1ej3kcvlNFURERPUOn9fiw80nAAD/GNcPwZ15fzWi1tKsQ2zvv/8+AEAmk+Gzzz6Dh4eHeZ3RaMTWrVsRFRVl2wqJiDogg9GEF1cfRJ1JYEyfANzXP1Dqkog6lGYFpEWLFgGo34O0ZMkSi8Npcrkc4eHhWLJkiW0rJCLqgBZnFuBosQ5e7s54fWxfnhhD1MqaFZBOnao/xXTkyJFYs2YNOnfu3CJFERF1ZIWXKvHhlvpDawvu78PrHRFJwKqz2LZs2WLrOoiI6L9e+/EIautMuK27L+4fECR1OUQdklUByWg0Yvny5cjIyEBpaSlMJpPF+s2bN9ukOCKijibjaAkyjpXCyUGGV+/vw0NrRBKxKiA9//zzWL58Oe655x707ctj40REtlBjMGLBj0cAAFNvi0B3lccNXkFELcWqgLRq1Sp8++23uPvuu21dDxFRh/VldiGKyqrg7+mC1NE9pC6HqEOz6jpIcrkc3bt3t3UtREQdlq7GgI8y6ydmz76zJzxcrPr/KxHZiFUB6YUXXsD//d//XfOCkURE1DyfZBVAU2VAN79OSBoYLHU5RB2eVf9F2bZtG7Zs2YJffvkFffr0gbOzs8X6NWvW2KQ4IqKOoFRXg6Xb6i+j8lJiFJx4OxEiyVkVkLy8vDBu3Dhb10JE1CF9uOUEagwmRId6IbGPv9TlEBGsDEjp6em2roOIqEMq1dVg1Z4zAICXEiJ5VjCRnbB6P25dXR02bdqETz75BOXl5QCA8+fPo6KiwmbFERG1d59tO4XaOhMGhnohrpuP1OUQ0X9ZtQepsLAQY8aMQVFREfR6Pe68804oFAq8+eab0Ov1vB8bEVETXK6sxVc7CwEAqaN6cO8RkR2xag/S888/j0GDBuHy5ctwc3MzLx83bhwyMjJsVhwRUXuWvv0UqmqN6BPkiRGRflKXQ0R/YtUepN9//x07duyAXC63WB4eHo5z587ZpDAiovasUl+H5TtOAwCSR3bn3iMiO2PVHiSTyQSj0dhg+dmzZ6FQKJrcT1paGgYPHgyFQgGVSoWxY8ciPz/fok1NTQ2Sk5Ph4+MDDw8PJCUloaSk5Lr9ymSyRh9vv/22uU14eHiD9QsXLmxy7UREN2PNvrPQ1dQh3McdY/oESF0OEV3FqoCUkJCA9957z/xcJpOhoqIC8+fPb9btR7KyspCcnIydO3di48aNMBgMSEhIQGVlpbnNrFmz8OOPP2L16tXIysrC+fPnMX78+Ov2W1xcbPFYtmwZZDIZkpKSLNq99tprFu1SU1ObXDsRkbVMJoH0/+49mnxrOBwcuPeIyN7IhBWXwz579iwSExMhhMDx48cxaNAgHD9+HL6+vti6dStUKpVVxVy4cAEqlQpZWVkYPnw4tFot/Pz8sGLFCjz44IMAgGPHjqFXr17Izs7G0KFDm9Tv2LFjUV5ebjE/Kjw8HDNnzsTMmTOtqlWn00GpVEKr1cLT09OqPoioY8rML8WU9D3wcHFC9txRULg63/hFRGQTTf38tmoPUnBwMA4cOID/9//+H2bNmoXo6GgsXLgQ+/fvtzocAYBWqwUAeHt7AwBycnJgMBgQHx9vbhMVFYXQ0FBkZ2c3qc+SkhL89NNPmDp1aoN1CxcuhI+PD6Kjo/H222+jrq7O6tqJiJoqfftpAMBDg0IYjojslNV3Q3RycsLEiRMxceJEmxRiMpkwc+ZMDBs2DH379gUAqNVqyOVyeHl5WbT19/eHWq1uUr+ff/45FApFg8NyM2bMwMCBA+Ht7Y0dO3Zg7ty5KC4uxr/+9a9G+9Hr9dDr9ebnOp2uGaMjIqpXcKECWX9cgEwGTLk1XOpyiOgarApIaWlp8Pf3x5NPPmmxfNmyZbhw4QLmzJnT7D6Tk5ORl5eHbdu2WVPSNS1btgwTJ06Eq6urxfLZs2ebv+7fvz/kcjmeeeYZpKWlwcXFpUE/aWlpWLBggU1rI6KO41KFHnsLL2Pl7iIAwOgoFUJ93CWuioiuxaqA9Mknn2DFihUNlvfp0wePPPJIswNSSkoK1q9fj61btyI4+H93sQ4ICEBtbS00Go3FXqSSkhIEBNz4rI/ff/8d+fn5+Oabb27YNjY2FnV1dTh9+jQiIyMbrJ87d65FqNLpdAgJCblhv0TUMZXqapB98hJ2nSrD7lNlOFFqeZeBR2NDJaqMiJrCqoCkVqsRGBjYYLmfnx+Ki4ub3I8QAqmpqVi7di0yMzMRERFhsT4mJgbOzs7IyMgwn4GWn5+PoqIixMXF3bD/pUuXIiYmBgMGDLhh29zcXDg4OFxzDpWLi0uje5aIiADAaBI4cFaDLcdKsflYKQ6fb3gYvqe/BwaGdkZcNx+MjLR+viYRtTyrAlJISAi2b9/eINBs374dQUFBTe4nOTkZK1aswLp166BQKMzzipRKJdzc3KBUKjF16lTMnj0b3t7e8PT0RGpqKuLi4izOYIuKikJaWhrGjRtnXqbT6bB69Wq8++67Dd43Ozsbu3btwsiRI6FQKJCdnY1Zs2bhscceQ+fOnZv77SCiDkoIgf1nNPgh9zzWHyzGxYr/zVOUyYC+QUrERnhjSIQ3Bod7o3Mn+XV6IyJ7YlVAmjZtGmbOnAmDwYBRo0YBADIyMvDXv/4VL7zwQpP7Wbx4MQBgxIgRFsvT09MxZcoUAMCiRYvg4OCApKQk6PV6JCYm4uOPP7Zon5+fbz4D7opVq1ZBCIEJEyY0eF8XFxesWrUKr776KvR6PSIiIjBr1iyLQ2hERNdSrK3Gyt1nsHb/WZwpqzYvV7g4YXhPP4yMUmFEpB98PbjXmaitsuo6SEII/O1vf8P777+P2tpaAICrqyvmzJmDefPm2bxIe8TrIBF1LEIIbD9xCV/uPI1NR0thNNX/6XSXOyKhtz8euKULbuvhC2dHq66eQkStpKmf31YFpCsqKipw9OhRuLm5oUePHh1qjg4DElHHYDQJ/HyoGB9tOYFj6nLz8qFdvTFhSCgSegfATe4oYYVE1BxN/fy2+jpIAODh4YHBgwffTBdERHbJZBL4PvccPtx8Aicv1t/+qJPcEUkxwXhsaBh6+jf9vpNE1PZYFZAqKyuxcOFCZGRkoLS0FCaTyWL9yZMnbVIcEZEUdhRcxD9/Omo+E03p5ownhoVjyq3h8HLnRGuijsCqgPTUU08hKysLkyZNQmBgIGQy3miRiNq+c5pqvPrDYWw8UgKgftL1syO6YfKt4fBwuakd7kTUxlj1G//LL7/gp59+wrBhw2xdDxFRqzOaBL7MPo23fs1HVa0Rjg4yTIwNxfOje8CHZ6IRdUhWBaTOnTubbyhLRNSWFV6qxKxvcrGvSAMAGBzeGW+M64cenGNE1KFZdT7q66+/jnnz5qGqqsrW9RARtZp1uedwz/vbsK9IAw8XJ/xjbF9883QcwxERWbcH6d1330VBQQH8/f0RHh4OZ2dni/X79u2zSXFERC2hxmDEvHV5+HbvWQDAkHBvLHrkFnTxcpO4MiKyF1YFpLFjx9q4DCKi1lFaXoOnv8hB7hkNHGRAyqgemDGqO5x4gUci+pObulBkR8YLRRK1PXnntJj2xV4Ua2ugdHPGxxMHYlh3X6nLIqJW1CoXiszJycHRo0cBAH369EF0dPTNdEdE1GJ2nLiIp77Yi6paI7r5dcLSyYMR7ttJ6rKIyE5ZFZBKS0vxyCOPIDMzE15eXgAAjUaDkSNHYtWqVfDz87NljUREN2XjkRIkr9iH2joThnX3wccTY6B0c77xC4mow7LqoHtqairKy8tx+PBhlJWVoaysDHl5edDpdJgxY4atayQistqPB87j2a9yUFtnQmIffyybMpjhiIhuyKo5SEqlEps2bWpwH7bdu3cjISEBGo3GVvXZLc5BIrJ/vx1WY/rX+2A0CYyP7oK3HuzPydhEHVyLzkEymUwNTu0HAGdn5wb3ZSMiksK24xeRsmK/ORy985cBcHDgbZGIqGms+q/UqFGj8Pzzz+P8+fPmZefOncOsWbMwevRomxVHRGSNA2c0ePrLvag11h9We+vB/gxHRNQsVgWkDz/8EDqdDuHh4ejWrRu6deuGiIgI6HQ6fPDBB7aukYioyc5rqs1nq93ewxfvT4jmYTUiajarDrGFhIRg37592LRpE44dOwYA6NWrF+Lj421aHBFRc1Tq6/DU53txoVyPSH8FPp44EC5OjlKXRURtULP+W7V582b07t0bOp0OMpkMd955J1JTU5GamorBgwejT58++P3331uqViKiaxJCYPa3uThSrINPJzk+mzwICleerUZE1mlWQHrvvfcwbdq0Rmd9K5VKPPPMM/jXv/5ls+KIiJpq6bZT+PVwCeSODvj08RiEeLtLXRIRtWHNCkgHDhzAmDFjrrk+ISEBOTk5N10UEVFz7Cu6jIW/1B/uf+W+3ogJ85a4IiJq65oVkEpKSho9vf8KJycnXLhw4aaLIiJqKk1VLVJX7EedSeCe/oF4LDZU6pKIqB1oVkDq0qUL8vLyrrn+4MGDCAwMvOmiiIia6pV1h3FOU40wH3csHN8PMhlP5yeim9esgHT33XfjlVdeQU1NTYN11dXVmD9/Pu69916bFUdEdD0/HyrGjwfOw9FBhvcfieakbCKymWbdaqSkpAQDBw6Eo6MjUlJSEBkZCQA4duwYPvroIxiNRuzbtw/+/v4tVrC94K1GiKR1sUKPhEVbUVZZi5SR3fFiYqTUJRFRG9Aitxrx9/fHjh07MH36dMydOxdXspVMJkNiYiI++uijDhGOiEh689cdRlllLaICFEgd3V3qcoionWn2hSLDwsLw888/4/Llyzhx4gSEEOjRowc6d+7cEvURETWw5VgpfjpUDEcHGd75ywBeDJKIbM6qK2kDQOfOnTF48GBb1kJEdEM1BiNe/fEwAGDqbRHo20UpcUVE1B7xBkVE1KZ8uvUkCi9Vwd/TBTNG95C6HCJqpxiQiKjNOFNWhY+2nAAA/P2e3vBwsXonOBHRdTEgEVGb8dav+dDXmRDX1Qf39uc114io5TAgEVGbcOisFj8eOA+ZDPj7vb14QUgialEMSETUJry5of5ea2Nv6YI+QZyYTUQtiwGJiOze1j8uYNuJi5A7OmD2nT2lLoeIOgAGJCKya0IIvPVr/d6jx4aGIcTbXeKKiKgjYEAiIruWmX8Beed0cJc7InlkN6nLIaIOggGJiOyWEALvbz4OoH7vkY+Hi8QVEVFHwYBERHZrR8El7C/SwMXJAU/dHiF1OUTUgTAgEZHd+uC/e48mDAmFSuEqcTVE1JEwIBGRXdpXdBk7T5bB2VGGZ+7oKnU5RNTBSBqQ0tLSMHjwYCgUCqhUKowdOxb5+fkWbWpqapCcnAwfHx94eHggKSkJJSUl1+13ypQpkMlkFo8xY8ZYtCkrK8PEiRPh6ekJLy8vTJ06FRUVFTYfIxFZZ9m2UwCAB27pgkClm8TVEFFHI2lAysrKQnJyMnbu3ImNGzfCYDAgISEBlZWV5jazZs3Cjz/+iNWrVyMrKwvnz5/H+PHjb9j3mDFjUFxcbH6sXLnSYv3EiRNx+PBhbNy4EevXr8fWrVvx9NNP23yMRNR85zXV+CVPDQB4chjnHhFR65P0To8bNmyweL58+XKoVCrk5ORg+PDh0Gq1WLp0KVasWIFRo0YBANLT09GrVy/s3LkTQ4cOvWbfLi4uCAgIaHTd0aNHsWHDBuzZsweDBg0CAHzwwQe4++678c477yAoKMhGIyQia3yRXQijSSCuqw96B3lKXQ4RdUB2NQdJq9UCALy9vQEAOTk5MBgMiI+PN7eJiopCaGgosrOzr9tXZmYmVCoVIiMjMX36dFy6dMm8Ljs7G15eXuZwBADx8fFwcHDArl27Gu1Pr9dDp9NZPIjI9qpq67BydxEA4MnbuPeIiKRhNwHJZDJh5syZGDZsGPr27QsAUKvVkMvl8PLysmjr7+8PtVp9zb7GjBmDL774AhkZGXjzzTeRlZWFu+66C0aj0dyvSqWyeI2TkxO8vb2v2W9aWhqUSqX5ERISchOjJaJrWbPvHLTVBoT5uGNUlOrGLyAiagGSHmL7s+TkZOTl5WHbtm033dcjjzxi/rpfv37o378/unXrhszMTIwePdqqPufOnYvZs2ebn+t0OoYkIhsQQuBMWTVyz2pw8IwGPx48DwB4PC4cjg4yiasjoo7KLgJSSkqKeaJ0cHCweXlAQABqa2uh0Wgs9iKVlJRcc35RY7p27QpfX1+cOHECo0ePRkBAAEpLSy3a1NXVoays7Jr9uri4wMWFV/ElulkGowkHzmiwo+AS9hVdxoEzGlyuMli08XBxwvjoLhJVSEQkcUASQiA1NRVr165FZmYmIiIs5xvExMTA2dkZGRkZSEpKAgDk5+ejqKgIcXFxTX6fs2fP4tKlSwgMDAQAxMXFQaPRICcnBzExMQCAzZs3w2QyITY21kajI6IrCi5UYPPRUmwvuIjdp8pQVWu0WC93dECvIE8MCFaibxcl4rr6oHMnuUTVEhEBMiGEkOrNn3vuOaxYsQLr1q1DZGSkeblSqYSbW/11T6ZPn46ff/4Zy5cvh6enJ1JTUwEAO3bsMLePiopCWloaxo0bh4qKCixYsABJSUkICAhAQUEB/vrXv6K8vByHDh0y7wW66667UFJSgiVLlsBgMOCJJ57AoEGDsGLFiibVrtPpoFQqodVq4enJs2yI/kwIgdwzGvx2pAS/HVaj4EKlxXrvTnLEdfPBkHBv3BLihahABVycHCWqlog6kqZ+fku6B2nx4sUAgBEjRlgsT09Px5QpUwAAixYtgoODA5KSkqDX65GYmIiPP/7Yon1+fr75DDhHR0ccPHgQn3/+OTQaDYKCgpCQkIDXX3/d4hDZ119/jZSUFIwePdrc//vvv99ygyXqAM6UVWHNvnP4z76zKCqrMi93dpRhaFcf3NHTD7d280VUgAIOnF9ERHZM0j1IbRn3IBHVqzOasPFICT7PPo2dJ8vMyzvJHTEySoWEPgEYEekHT1dnCaskIqrXJvYgEVHbpasx4JvdZ7B8x2mc01QDAGQyYFg3XyTFdEFinwC4y/knhojaJv71IqJmKa8xIH37afz795Mor6kDUD+naGJsKCYMCUWQF++bRkRtHwMSETVJjcGI9O2n8cnWAmj+e1p+D5UHnro9Ag/c0gWuzpxkTUTtBwMSEV2XEAIbj5Tg9Z+O4ExZ/aG0bn6dMDO+J+7pF8jJ1kTULjEgEdE1nSmrwt+/z0PWHxcAAAGerngpMRJjo7vwKtdE1K4xIBFRA0IIrNhdhDd+OorKWiPkjg546vYIJI/sjk4u/LNBRO0f/9IRkYVSXQ1eWH0Avx+/CAAYEu6NNx/sjwjfThJXRkTUehiQiMgsu+ASUlfux8UKPVycHPDXMVF44tZwzjMiog6HAYmIIITAkqyTePvXYzAJICpAgQ8fHYjuKg+pSyMikgQDElEHV1tnwt/+cxBr9p8DAIwf2AX/HNsPbnKetk9EHRcDElEHpq024Nkvc5B98hIcHWR49f4+eCw2FDIZD6kRUcfGgETUQV0o1+Oxz3Yhv6QcneSO+PixGNzR00/qsoiI7AIDElEHVKKrwaP/3omCC5Xw93RB+pQh6B3Emy4TEV3BgETUwRRrq/Hov3fh1MVKBCldsfLpoQjz4Sn8RER/xoBE1IGUVdZi4mf14Si4sxtWThuKEG93qcsiIrI7DEhEHURVbR2eXL4HJy9UIlDpilVPD0VwZ4YjIqLGOEhdABG1PIPRhOe+3ofcMxoo3Zzx5dQhDEdERNfBgETUAfzzp6PIzL8AV2cHLJsyGN1VCqlLIiKyawxIRO3ct3vPYPmO0wCA9x6ORkxYZ2kLIiJqAxiQiNqx/UWX8fe1eQCA50f3wJi+ARJXRETUNjAgEbVTmqpaJH+9D7VGExJ6++P50T2kLomIqM1gQCJqh4QQ+Nt/DuG8tgbhPu5496EBcHDg7UOIiJqKAYmoHVq5+ww2HFbD2VGG9ydEQ+HqLHVJRERtCgMSUTtTcKECr60/DAB4MSES/YO9pC2IiKgNYkAiakdMJoE53x1EjcGE27r7YtrtXaUuiYioTWJAImpHvtpViL2Fl9FJ7og3H+zPeUdERFZiQCJqJ85pqvHmL8cAAHPuikIXLzeJKyIiarsYkIjaiVe+z0NlrRGDwjrjsdgwqcshImrTGJCI2oEtx0qx+VgpnB1lWJjEQ2tERDeLAYmojautM+H19UcAAE8Oi0B3lYfEFRERtX0MSERt3Oc7TuPkxUr4erggZVR3qcshImoXGJCI2rCLFXq8n3EcAPDXMZG8ICQRkY0wIBG1YR9tOYFyfR36dVHiwYHBUpdDRNRuMCARtVHnNNX4emcRgPq9R5yYTURkOwxIRG3UBxnHUWs0ITbCG7d195W6HCKidoUBiagNOnWxEqtzzgIAXkqMhEzGvUdERLbEgETUBr2fcRxGk8DISD8MCveWuhwionaHAYmojTlTVoUfDpwHAMy6s6fE1RARtU8MSERtzGe/n4TRJHB7D1/0D/aSuhwionZJ0oCUlpaGwYMHQ6FQQKVSYezYscjPz7doU1NTg+TkZPj4+MDDwwNJSUkoKSm5Zp8GgwFz5sxBv3790KlTJwQFBeHxxx/H+fPnLdqFh4dDJpNZPBYuXNgi4ySylYsVeqzacwYAMP2ObhJXQ0TUfkkakLKyspCcnIydO3di48aNMBgMSEhIQGVlpbnNrFmz8OOPP2L16tXIysrC+fPnMX78+Gv2WVVVhX379uGVV17Bvn37sGbNGuTn5+P+++9v0Pa1115DcXGx+ZGamtoi4ySylc93nIa+zoQBwUrEdfORuhwionbLSco337Bhg8Xz5cuXQ6VSIScnB8OHD4dWq8XSpUuxYsUKjBo1CgCQnp6OXr16YefOnRg6dGiDPpVKJTZu3Gix7MMPP8SQIUNQVFSE0NBQ83KFQoGAgIAWGBmR7ZhMArVGE3Q1Bvz795MAgOkjuvHMNSKiFiRpQLqaVqsFAHh715+Vk5OTA4PBgPj4eHObqKgohIaGIjs7u9GAdK1+ZTIZvLy8LJYvXLgQr7/+OkJDQ/Hoo49i1qxZcHKyq28J2bnaOhO01Qbzo6q2DpV6I6oN//231ojK2jrzv1W1RlTpjdDXGVFrNKG2rv6hrzNZPP/z13UmYfGeYT7uuLM3gz0RUUuymzRgMpkwc+ZMDBs2DH379gUAqNVqyOXyBsHG398farW6Sf3W1NRgzpw5mDBhAjw9Pc3LZ8yYgYEDB8Lb2xs7duzA3LlzUVxcjH/961+N9qPX66HX683PdTpdM0dIbYG+zoiLFbW4UK5Hqa4GFyr0KNXpcaFCj0sVemirDdBUGaCrNkBTbUBVrbFV63N2lOH50T3gyKtmExG1KLsJSMnJycjLy8O2bdts1qfBYMBDDz0EIQQWL15ssW727Nnmr/v37w+5XI5nnnkGaWlpcHFxadBXWloaFixYYLPaSBomk8A5TTVOX6rEmbJqnLlchbOXq3GmrApnL1fhYkWtVf16ujrB080ZHi5OcJc7wl1e/28nFye4yR3RSe4IN7kTOskd4S53hIuTI+RODvUPR4f/ff3f5y5XPXe+8q+jA8MREVErsIuAlJKSgvXr12Pr1q0IDv7fDTcDAgJQW1sLjUZjsReppKTkhnOHroSjwsJCbN682WLvUWNiY2NRV1eH06dPIzIyssH6uXPnWoQqnU6HkJCQJo6QpFCiq8Ghs1ocL63A8ZJyHC+twInSClQbrr/XR+7oAD+FC3wVLvDzcIHKs/5fX4ULvNycofzvw8u9/l+FqzNDCxFROyNpQBJCIDU1FWvXrkVmZiYiIiIs1sfExMDZ2RkZGRlISkoCAOTn56OoqAhxcXHX7PdKODp+/Di2bNkCH58bn+2Tm5sLBwcHqFSqRte7uLg0umeJ7ENVbR32FWpw4KwGB87U/1ui0zfaVu7ogFAfd4R0dkOItztCOrsjxNsNwZ3d0cXLDV7uzpwATUTUwUkakJKTk7FixQqsW7cOCoXCPK9IqVTCzc0NSqUSU6dOxezZs+Ht7Q1PT0+kpqYiLi7OYoJ2VFQU0tLSMG7cOBgMBjz44IPYt28f1q9fD6PRaO7X29sbcrkc2dnZ2LVrF0aOHAmFQoHs7GzMmjULjz32GDp37izJ94KaR19nxL5CDbJPXkJ2wUXkntHAYLSczOwgA3qoFIgKVKCHygPdVQr08PdAmLc7nBx5jVQiIro2mRBC3LhZC735Nf6Xnp6ejilTpgCon2T9wgsvYOXKldDr9UhMTMTHH39scYhNJpOZX3P69OkGe6Ku2LJlC0aMGIF9+/bhueeew7Fjx6DX6xEREYFJkyZh9uzZTd5LpNPpoFQqodVqb3j4jmxDW2XAlvxSbDxSgsz8UlReNUG6i5cbBoZ1xoBgJQaEeKFPkCfc5XZxFJmIiOxEUz+/JQ1IbRkDUuuorjXityNq/GffOew4cdHilHdfDxcM6+6DuK4+uLWbL0K83XhojIiIrqupn9/87zXZHSEE9p/RYOWuIvx8qNhiT1FPfw8k9A7Anb390a+LEg6cHE1ERC2AAYnsRm2dCT8fKkb69lM4cFZrXh7i7Ybx0cEYG90FEb6dJKyQiIg6CgYkklyNwYgVu4qwJKsApeX1Z57JnRxw/4AgPDw4BIPCOvPQGRERtSoGJJKMvs6IlbuK8HHm/4KRSuGCSUPD8GhsKHw8eFkFIiKSBgMStTohBDYdLcU/fjqCwktVAOrPQEse2R0PxgRD7sRT8ImISFoMSNSqCi5U4NUfDuP34xcB1O8xmjG6B/4yKBguTo4SV0dERFSPAYlahdEksHTbSbzz2x+orTNB7uiAqbdHIHlkd3i48MeQiIjsCz+ZqMWdvFCBF1YfwP4iDQDg9h6+eP2BvgjnGWlERGSnGJCoRf1w4Dzm/ucgKmuNULg44e/39sJDg0J4VhoREdk1BiRqEfo6I15ffwRf7SwCAMRGeGPRw7cgyMtN4sqIiIhujAGJbO5ShR5Pf5mDnMLLAICUkd0xM74HbxBLRERtBgMS2VTBhQo8uXwPCi9VwdPVCf83IRojI1VSl0VERNQsDEhkM/uKLuOJ9D3QVhsQ4u2G9CmD0V2lkLosIiKiZmNAIpvYfaoMT6TvRmWtEbeEeOGzyYPgyythExFRG8WARDdt+4mLeOrzvag2GHFrNx98NnkQ3OX80SIioraLn2J0U/acLsPUz/egxmDCHT398MmkGLg684rYRETUtjEgkdWOqXWYurw+HI2M9MOSSTG8XQgREbULPO+arHKmrAqPL90NXU0dBoV1xscTGY6IiKj9YECiZiuvMeDJ5XtQWq5HpL8CSycPhpuc4YiIiNoPBiRqFpNJYNY3uTheWoEAT1d8/uQQKN2dpS6LiIjIphiQqFne3ZiPTUdLIXdywCeTYhCgdJW6JCIiIptjQKIm23ikBB9tKQAAvJXUHwNCvKQtiIiIqIUwIFGTFGur8dJ3BwAAU2+LwNjoLhJXRERE1HIYkOiGjCaBmatyoakyoF8XJeaMiZK6JCIiohbFgEQ39PGWE9h1qgyd5I74YEI05E78sSEiovaNn3R0XUeLdfi/jOMAgNfH9kW4byeJKyIiImp5DEh0TXVGE/763UHUmQQSevtjHOcdERFRB8GARNf02bZTOHROC09XJ/xjbF/IZDKpSyIiImoVDEjUqNMXK7Fo4x8AgL/f2xsqT17viIiIOg4GJGrUP346An2dCcO6++AvMcFSl0NERNSqGJCogS35pdh0tBRODjIsuJ+H1oiIqONhQCILtXUmvP7jEQDAlFvD0V3lIXFFRERErY8BiSx8vuM0Tl6shK+HHDPie0hdDhERkSQYkMhMW23Ah1tOAABeSoyEp6uzxBURERFJgwGJzP699SS01Qb0UHngwZgQqcshIiKSDAMSAQAulOuxbPspAMALCZFwdODEbCIi6rgYkAgA8NGWE6iqNWJAsBKJffylLoeIiEhSDEiEYm01VuwqAgC8lBjF0/qJiKjDY0AifPb7KdQaTRgS7o1h3X2kLoeIiEhyDEgd3OXKWqzcXb/36LmR3bj3iIiICBIHpLS0NAwePBgKhQIqlQpjx45Ffn6+RZuamhokJyfDx8cHHh4eSEpKQklJyXX7FUJg3rx5CAwMhJubG+Lj43H8+HGLNmVlZZg4cSI8PT3h5eWFqVOnoqKiwuZjtHfLd5xGVa0RfYI8cUdPP6nLISIisguSBqSsrCwkJydj586d2LhxIwwGAxISElBZWWluM2vWLPz4449YvXo1srKycP78eYwfP/66/b711lt4//33sWTJEuzatQudOnVCYmIiampqzG0mTpyIw4cPY+PGjVi/fj22bt2Kp59+usXGao8q9XVYvuM0AGD6CO49IiIiukImhBBSF3HFhQsXoFKpkJWVheHDh0Or1cLPzw8rVqzAgw8+CAA4duwYevXqhezsbAwdOrRBH0IIBAUF4YUXXsCLL74IANBqtfD398fy5cvxyCOP4OjRo+jduzf27NmDQYMGAQA2bNiAu+++G2fPnkVQUNANa9XpdFAqldBqtfD09LTd96BcD32d0Wb9XU/69tNYuu0Uwn3ckfHCCJ7aT0RE7V5TP7+dWrGmG9JqtQAAb29vAEBOTg4MBgPi4+PNbaKiohAaGnrNgHTq1Cmo1WqL1yiVSsTGxiI7OxuPPPIIsrOz4eXlZQ5HABAfHw8HBwfs2rUL48aNa9CvXq+HXq83P9fpdDc/4Ea8sPoAtv5xoUX6vpanbu/KcERERPQndhOQTCYTZs6ciWHDhqFv374AALVaDblcDi8vL4u2/v7+UKvVjfZzZbm/v+W1fP78GrVaDZVKZbHeyckJ3t7e1+w3LS0NCxYsaPa4mkvuKIOLU+sd+YwMUGD8wC6t9n5ERERtgd0EpOTkZOTl5WHbtm1Sl9KouXPnYvbs2ebnOp0OISG2vx3HZ5MH27xPIiIiah67OM0/JSUF69evx5YtWxAcHGxeHhAQgNraWmg0Gov2JSUlCAgIaLSvK8uvPtPtz68JCAhAaWmpxfq6ujqUlZVds18XFxd4enpaPIiIiKh9kjQgCSGQkpKCtWvXYvPmzYiIiLBYHxMTA2dnZ2RkZJiX5efno6ioCHFxcY32GRERgYCAAIvX6HQ67Nq1y/yauLg4aDQa5OTkmNts3rwZJpMJsbGxthwiERERtUGSBqTk5GR89dVXWLFiBRQKBdRqNdRqNaqrqwHUT66eOnUqZs+ejS1btiAnJwdPPPEE4uLiLCZoR0VFYe3atQAAmUyGmTNn4h//+Ad++OEHHDp0CI8//jiCgoIwduxYAECvXr0wZswYTJs2Dbt378b27duRkpKCRx55pElnsBEREVH7JukcpMWLFwMARowYYbE8PT0dU6ZMAQAsWrQIDg4OSEpKgl6vR2JiIj7++GOL9vn5+eYz4ADgr3/9KyorK/H0009Do9Hgtttuw4YNG+Dq6mpu8/XXXyMlJQWjR4829//++++3zECJiIioTbGr6yC1JS11HSQiIiJqOU39/LaLSdpERERE9oQBiYiIiOgqDEhEREREV2FAIiIiIroKAxIRERHRVRiQiIiIiK7CgERERER0FQYkIiIioqswIBERERFdRdJbjbRlVy5ArtPpJK6EiIiImurK5/aNbiTCgGSl8vJyAEBISIjElRAREVFzlZeXQ6lUXnM978VmJZPJhPPnz0OhUEAmk9msX51Oh5CQEJw5c6bd3uOtvY+R42v72vsY2/v4gPY/Ro7PekIIlJeXIygoCA4O155pxD1IVnJwcEBwcHCL9e/p6dkuf+j/rL2PkeNr+9r7GNv7+ID2P0aOzzrX23N0BSdpExEREV2FAYmIiIjoKgxIdsbFxQXz58+Hi4uL1KW0mPY+Ro6v7WvvY2zv4wPa/xg5vpbHSdpEREREV+EeJCIiIqKrMCARERERXYUBiYiIiOgqDEhEREREV2FAagEfffQRwsPD4erqitjYWOzevfu67VevXo2oqCi4urqiX79++Pnnny3WCyEwb948BAYGws3NDfHx8Th+/LhFm7KyMkycOBGenp7w8vLC1KlTUVFRYfOxAbYdn8FgwJw5c9CvXz906tQJQUFBePzxx3H+/HmLPsLDwyGTySweCxcutPvxAcCUKVMa1D5mzBiLNq25/QDbj/Hq8V15vP322+Y29roNDx8+jKSkJHN97733nlV91tTUIDk5GT4+PvDw8EBSUhJKSkpsOawm1/JnTRlfWloaBg8eDIVCAZVKhbFjxyI/P9+izYgRIxpsv2effdbWQzOz9RhfffXVBvVHRUVZtGnL27Cx3y+ZTIbk5GRzm9bchs0Z37///W/cfvvt6Ny5Mzp37oz4+PgG7SX5HBRkU6tWrRJyuVwsW7ZMHD58WEybNk14eXmJkpKSRttv375dODo6irfeekscOXJE/P3vfxfOzs7i0KFD5jYLFy4USqVSfP/99+LAgQPi/vvvFxEREaK6utrcZsyYMWLAgAFi586d4vfffxfdu3cXEyZMsPvxaTQaER8fL7755htx7NgxkZ2dLYYMGSJiYmIs+gkLCxOvvfaaKC4uNj8qKirsfnxCCDF58mQxZswYi9rLysos+mmt7ddSY/zz2IqLi8WyZcuETCYTBQUF5jb2ug13794tXnzxRbFy5UoREBAgFi1aZFWfzz77rAgJCREZGRli7969YujQoeLWW29tE+NLTEwU6enpIi8vT+Tm5oq7775bhIaGWmyfO+64Q0ybNs1i+2m1WpuPr6XGOH/+fNGnTx+L+i9cuGDRpi1vw9LSUouxbdy4UQAQW7ZsMbdprW3Y3PE9+uij4qOPPhL79+8XR48eFVOmTBFKpVKcPXvW3EaKz0EGJBsbMmSISE5ONj83Go0iKChIpKWlNdr+oYceEvfcc4/FstjYWPHMM88IIYQwmUwiICBAvP322+b1Go1GuLi4iJUrVwohhDhy5IgAIPbs2WNu88svvwiZTCbOnTtns7EJYfvxNWb37t0CgCgsLDQvCwsLa/SPgq21xPgmT54sHnjggWu+Z2tuPyFaZxs+8MADYtSoURbL7HUb/tm1arxRnxqNRjg7O4vVq1eb2xw9elQAENnZ2TcxmoZaYnxXKy0tFQBEVlaWedkdd9whnn/+eWtKbraWGOP8+fPFgAEDrvm69rYNn3/+edGtWzdhMpnMy1prG97M+IQQoq6uTigUCvH5558LIaT7HOQhNhuqra1FTk4O4uPjzcscHBwQHx+P7OzsRl+TnZ1t0R4AEhMTze1PnToFtVpt0UapVCI2NtbcJjs7G15eXhg0aJC5TXx8PBwcHLBr1y67Hl9jtFotZDIZvLy8LJYvXLgQPj4+iI6Oxttvv426ujrrB9OIlhxfZmYmVCoVIiMjMX36dFy6dMmij9bYfkDrbMOSkhL89NNPmDp1aoN19rgNbdFnTk4ODAaDRZuoqCiEhoZa/b7W1mILWq0WAODt7W2x/Ouvv4avry/69u2LuXPnoqqqymbveUVLjvH48eMICgpC165dMXHiRBQVFZnXtadtWFtbi6+++gpPPvlkg5upt/Q2tMX4qqqqYDAYzD9/Un0O8ma1NnTx4kUYjUb4+/tbLPf398exY8cafY1arW60vVqtNq+/sux6bVQqlcV6JycneHt7m9vYQkuM72o1NTWYM2cOJkyYYHGDwhkzZmDgwIHw9vbGjh07MHfuXBQXF+Nf//rXTY7qf1pqfGPGjMH48eMRERGBgoICvPzyy7jrrruQnZ0NR0fHVtt+QOtsw88//xwKhQLjx4+3WG6v29AWfarVasjl8gah/nrfp5aq5WaZTCbMnDkTw4YNQ9++fc3LH330UYSFhSEoKAgHDx7EnDlzkJ+fjzVr1tjkfa9oqTHGxsZi+fLliIyMRHFxMRYsWIDbb78deXl5UCgU7Wobfv/999BoNJgyZYrF8tbYhrYY35w5cxAUFGQORFJ9DjIgkd0wGAx46KGHIITA4sWLLdbNnj3b/HX//v0hl8vxzDPPIC0tze4vtf/II4+Yv+7Xrx/69++Pbt26ITMzE6NHj5awspaxbNkyTJw4Ea6urhbL2/I27EiSk5ORl5eHbdu2WSx/+umnzV/369cPgYGBGD16NAoKCtCtW7fWLrPZ7rrrLvPX/fv3R2xsLMLCwvDtt982urezLVu6dCnuuusuBAUFWSxvC9tw4cKFWLVqFTIzMxv8DWltPMRmQ76+vnB0dGxw1kNJSQkCAgIafU1AQMB121/590ZtSktLLdbX1dWhrKzsmu9rjZYY3xVXwlFhYSE2btxosfeoMbGxsairq8Pp06ebP5BraMnx/VnXrl3h6+uLEydOmPtoje0HtPwYf//9d+Tn5+Opp566YS32sg1t0WdAQABqa2uh0Whs9r7W1nIzUlJSsH79emzZsgXBwcHXbRsbGwsA5p9jW2npMV7h5eWFnj17WvwetodtWFhYiE2bNjX5dxCw7Ta8mfG98847WLhwIX777Tf079/fvFyqz0EGJBuSy+WIiYlBRkaGeZnJZEJGRgbi4uIafU1cXJxFewDYuHGjuX1ERAQCAgIs2uh0OuzatcvcJi4uDhqNBjk5OeY2mzdvhslkMv8C2Ov4gP+Fo+PHj2PTpk3w8fG5YS25ublwcHBosEv1ZrTU+K529uxZXLp0CYGBgeY+WmP7AS0/xqVLlyImJgYDBgy4YS32sg1t0WdMTAycnZ0t2uTn56OoqMjq97W2FmsIIZCSkoK1a9di8+bNiIiIuOFrcnNzAcD8c2wrLTXGq1VUVKCgoMBcf1vfhlekp6dDpVLhnnvuuWHbltiG1o7vrbfewuuvv44NGzZYzCMCJPwctGpqN13TqlWrhIuLi1i+fLk4cuSIePrpp4WXl5dQq9VCCCEmTZok/va3v5nbb9++XTg5OYl33nlHHD16VMyfP7/R0/y9vLzEunXrxMGDB8UDDzzQ6OmN0dHRYteuXWLbtm2iR48eLXaavy3HV1tbK+6//34RHBwscnNzLU4/1ev1QgghduzYIRYtWiRyc3NFQUGB+Oqrr4Sfn594/PHH7X585eXl4sUXXxTZ2dni1KlTYtOmTWLgwIGiR48eoqamxtxPa22/lhjjFVqtVri7u4vFixc3eE973oZ6vV7s379f7N+/XwQGBooXX3xR7N+/Xxw/frzJfQpRf4p4aGio2Lx5s9i7d6+Ii4sTcXFxbWJ806dPF0qlUmRmZlr8DlZVVQkhhDhx4oR47bXXxN69e8WpU6fEunXrRNeuXcXw4cNtPr6WGuMLL7wgMjMzxalTp8T27dtFfHy88PX1FaWlpeY2bXkbClF/tlhoaKiYM2dOg/dszW3Y3PEtXLhQyOVy8d1331n8/JWXl1u0ae3PQQakFvDBBx+I0NBQIZfLxZAhQ8TOnTvN6+644w4xefJki/bffvut6Nmzp5DL5aJPnz7ip59+slhvMpnEK6+8Ivz9/YWLi4sYPXq0yM/Pt2hz6dIlMWHCBOHh4SE8PT3FE088YfHDZa/jO3XqlADQ6OPK9TtycnJEbGysUCqVwtXVVfTq1Uu88cYbFgHDXsdXVVUlEhIShJ+fn3B2dhZhYWFi2rRpFh+sQrTu9rP1GK/45JNPhJubm9BoNA3W2fM2vNbP4B133NHkPoUQorq6Wjz33HOic+fOwt3dXYwbN04UFxe3ifFd63cwPT1dCCFEUVGRGD58uPD29hYuLi6ie/fu4qWXXmqx6yC1xBgffvhhERgYKORyuejSpYt4+OGHxYkTJyzesy1vQyGE+PXXXwWABp8PQrT+NmzO+MLCwhod3/z5881tpPgclAkhhHX7noiIiIjaJ85BIiIiIroKAxIRERHRVRiQiIiIiK7CgERERER0FQYkIiIioqswIBERERFdhQGJiIiI6CoMSERERERXYUAionZjypQpGDt2rGTvP2nSJLzxxhtNavvII4/g3XffbeGKiMhavJI2EbUJMpnsuuvnz5+PWbNmQQgBLy+v1inqTw4cOIBRo0ahsLAQHh4eN2yfl5eH4cOH49SpU1Aqla1QIRE1BwMSEbUJarXa/PU333yDefPmIT8/37zMw8OjScGkpTz11FNwcnLCkiVLmvyawYMHY8qUKUhOTm7ByojIGjzERkRtQkBAgPmhVCohk8kslnl4eDQ4xDZixAikpqZi5syZ6Ny5M/z9/fHvf/8blZWVeOKJJ6BQKNC9e3f88ssvFu+Vl5eHu+66Cx4eHvD398ekSZNw8eLFa9ZmNBrx3Xff4b777rNY/vHHH6NHjx5wdXWFv78/HnzwQYv19913H1atWnXz3xwisjkGJCJq1z7//HP4+vpi9+7dSE1NxfTp0/GXv/wFt956K/bt24eEhARMmjQJVVVVAACNRoNRo0YhOjoae/fuxYYNG1BSUoKHHnromu9x8OBBaLVaDBo0yLxs7969mDFjBl577TXk5+djw4YNGD58uMXrhgwZgt27d0Ov17fM4InIagxIRNSuDRgwAH//+9/Ro0cPzJ07F66urvD19cW0adPQo0cPzJs3D5cuXcLBgwcBAB9++CGio6PxxhtvICoqCtHR0Vi2bBm2bNmCP/74o9H3KCwshKOjI1QqlXlZUVEROnXqhHvvvRdhYWGIjo7GjBkzLF4XFBSE2tpai8OHRGQfGJCIqF3r37+/+WtHR0f4+PigX79+5mX+/v4AgNLSUgD1k623bNlintPk4eGBqKgoAEBBQUGj71FdXQ0XFxeLieR33nknwsLC0LVrV0yaNAlff/21eS/VFW5ubgDQYDkRSY8BiYjaNWdnZ4vnMpnMYtmVUGMymQAAFRUVuO+++5Cbm2vxOH78eINDZFf4+vqiqqoKtbW15mUKhQL79u3DypUrERgYiHnz5mHAgAHQaDTmNmVlZQAAPz8/m4yViGyHAYmI6E8GDhyIw4cPIzw8HN27d7d4dOrUqdHX3HLLLQCAI0eOWCx3cnJCfHw83nrrLRw8eBCnT5/G5s2bzevz8vIQHBwMX1/fFhsPEVmHAYmI6E+Sk5NRVlaGCRMmYM+ePSgoKMCvv/6KJ554AkajsdHX+Pn5YeDAgdi2bZt52fr16/H+++8jNzcXhYWF+OKLL2AymRAZGWlu8/vvvyMhIaHFx0REzceARET0J0FBQdi+fTuMRiMSEhLQr18/zJw5E15eXnBwuPafzKeeegpff/21+bmXlxfWrFmDUaNGoVevXliyZAlWrlyJPn36AABqamrw/fffY9q0aS0+JiJqPl4okojIBqqrqxEZGYlvvvkGcXFxN2y/ePFirF27Fr/99lsrVEdEzcU9SERENuDm5oYvvvjiuheU/DNnZ2d88MEHLVwVEVmLe5CIiIiIrsI9SERERERXYUAiIiIiugoDEhEREdFVGJCIiIiIrsKARERERHQVBiQiIiKiqzAgEREREV2FAYmIiIjoKgxIRERERFf5/5yWZ619oiABAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da, label=\"DA\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d3bda015-1674-45ae-96f0-69167fd2cc89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_pka, label=\"PKA\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0e281a93-c7de-45cf-9723-98f3a19bbca0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da_external, label=\"DA from external?\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20144fff-adf6-4c4b-956d-97deb916d7e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d31a7d49-55b1-479c-942a-17cbd87fa412", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_example_anu/simulation/output.hdf5\n", + "Saving figure to networks/neuromodulation_example_anu/figures/spike-raster.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "fig_file_raster = f\"spike-raster.png\"\n", + "\n", + "time_range_zoom = (0,0.1)\n", + "spr = SnuddaPlotSpikeRaster2(network_path=network_path)\n", + "\n", + "spr.plot_spike_raster(fig_file=fig_file_raster, time_range=time_range_zoom)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fc082055-9e38-4ed1-b842-40e166548071", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_example_anu/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_example_anu/input-spikes.hdf5\n", + "Loading networks/neuromodulation_example_anu/simulation/output.hdf5\n", + "Plotting traces: [0, 1]\n", + "Plotted 2 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_example_anu/figures/Network-voltage-trace--neuron-0-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "sim_file = os.path.join(network_path, \"simulation\", \"output.hdf5\")\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt = PlotTraces(output_file=sim_file)\n", + "# Use trace_id to specify which traces\n", + "ax = pt.plot_traces(offset=0, time_range=(0,0.5),fig_size=(10,4))" + ] + }, + { + "cell_type": "markdown", + "id": "e3532a44-6084-4701-a8a3-28df6e106524", + "metadata": {}, + "source": [ + "## TODO!! Also add ability to have external synapses affect RxD concentrations" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "97789ccd-8c45-47e8-aeec-3b752b4c9942", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20.0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.neurons[0].icell.soma[0](0.5).naf_ms.gbar" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7b2a5b48-6450-475e-b842-478e75d2cf32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1e-08" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.neurons[0].icell.soma[0](0.5).pas.g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "191cf2cb-a61f-4b9e-89a9-4fe963f0343b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/neuromodulation_example_anu_on_real_dspn.ipynb b/examples/notebooks/neuromodulation/neuromodulation_example_anu_on_real_dspn.ipynb new file mode 100644 index 000000000..1a2ea9967 --- /dev/null +++ b/examples/notebooks/neuromodulation/neuromodulation_example_anu_on_real_dspn.ipynb @@ -0,0 +1,1022 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "138f1fa5-37b6-4563-a588-309e5b21b9d5", + "metadata": {}, + "source": [ + "# Neuromodulation example\n", + "\n", + "This neuromodulation creates a small network of neurons. We also use the reaction diffusion model by Anu G Nair 2015.\n", + "\n", + "To generate the ```reaction_diffusion.json``` file in ```data/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/``` from the xml file we run ```data/convert_sbml_to_json.sh```\n", + "\n", + "To get the RxD functionality of the ```DA_syn``` we must specify the \"RxD\" block in the connectivity block of the network configuration. See ```data/connectivity.json```\n", + "\n", + "```\n", + " \"channel_parameters\":\n", + "\t\t\"RxD\": {\n", + "\t\t \"species_name\": \"DA\",\n", + "\t\t \"flux_variable\": \"open\",\n", + "\t\t \"region\": \"internal\",\n", + " \"weight_scaling\": 1e9,\n", + "\t\t},\n", + "\n", + " ...\n", + " }\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8f73162b-5ed5-40bc-a557-b73994a94bbd", + "metadata": {}, + "source": [ + "## TODO: Ska vi ha jämn placering av alla DA synapser för att få en jämnare fördelning av DA?\n", + "\n", + "## TODO: Hur ska vi göra modulering av dopamine" + ] + }, + { + "cell_type": "markdown", + "id": "1746feed-04de-4020-a2cb-f884e8e9698a", + "metadata": {}, + "source": [ + "## Network setup\n", + "\n", + "We have two neurons. The first neuron (Neuron 0) receives external input (cortical from t=0s and DA from t=0.1s). The cortical input will activate the first neuron, and through activation of synapses on the second neuron, we will see the dopamine level increase in the second neuron (Neuron 1).\n", + "\n", + "The first neuron also receives direct DA activation from external input (starting at 100ms)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b55f23d-62ac-4433-8639-07870af8c40a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading connectivity from data/connectivity.json\n", + "Adding neurons: dspn from dir data/dspn\n", + "Writing networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Writing networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Placing neurons\n", + "Network path: networks/neuromodulation_example_anu_with_real_dspn\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 63\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 63 points for networks/neuromodulation_example_anu_with_real_dspn/mesh/Cube-cube-mesh-2.917951293943981e-05.obj\n", + "Filtering, keeping inside points: 4 / 26\n", + "neuron_name = 'dspn_0', num = 2, neuron_path = 'data/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n", + "Touch detection\n", + "Network path: networks/neuromodulation_example_anu_with_real_dspn\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "Processing hyper voxel : 0/27 (2 neurons)\n", + "Processing hyper voxel : 1/27 (2 neurons)\n", + "Processing hyper voxel : 3/27 (2 neurons)\n", + "Processing hyper voxel : 4/27 (2 neurons)\n", + "Processing hyper voxel : 9/27 (2 neurons)\n", + "Processing hyper voxel : 10/27 (2 neurons)\n", + "Processing hyper voxel : 12/27 (2 neurons)\n", + "Processing hyper voxel : 13/27 (2 neurons)\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.9s\n", + "Prune synapses\n", + "Network path: networks/neuromodulation_example_anu_with_real_dspn\n", + "No file networks/neuromodulation_example_anu_with_real_dspn/pruning_merge_info.json\n", + "Read 90 out of total 90 synapses\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.9s\n" + ] + } + ], + "source": [ + "import os\n", + "from snudda import Snudda\n", + "\n", + "neuron_path = os.path.join(\"data\", \"dspn\")\n", + "network_path = os.path.join(\"networks\", \"neuromodulation_example_anu_with_real_dspn\")\n", + "connectivity_path = os.path.join(\"data\", \"connectivity.json\")\n", + "\n", + "snudda = Snudda(network_path=network_path)\n", + "si = snudda.init_tiny(neuron_paths=neuron_path, neuron_names=\"dspn\", number_of_neurons=[2], \n", + " connection_config=connectivity_path, random_seed=12345)\n", + "\n", + "# si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"reaction_diffusion.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"data/JSON/reaction_diffusion_D1.json\"\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation\"] = \"test-modulation.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation_key\"] = \"abc\"\n", + "\n", + "\n", + "si.write_json()\n", + "\n", + "snudda.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "41e96e4a-0f52-4d77-8a1f-ba8d270b9b78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "Writing input spikes to networks/neuromodulation_example_anu_with_real_dspn/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 0 (meta modified by input_config)\n", + "Writing spikes to networks/neuromodulation_example_anu_with_real_dspn/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 1.1s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#input_config = os.path.join(\"data\", \"input_v2.json\")\n", + "input_config = os.path.join(\"data\", \"input_v3.json\")\n", + "snudda.setup_input(input_config=input_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d019b580-4193-4321-9ee8-9bdd3988209f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPI Rank: 0, Size: 1\n", + "Using input file networks/neuromodulation_example_anu_with_real_dspn/input-spikes.hdf5\n", + "NEURON mechanisms already compiled, make sure you have the correct version of NEURON modules.\n", + "If you delete x86_64, aarch64, arm64 directories (or nrnmech.dll) then you will force a recompilation of the modules.\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "MPI Rank: 0, Size: 1 -- NEUON: This is node 0 out of 1\n", + "Using network_file: networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "Using input_file: networks/neuromodulation_example_anu_with_real_dspn/input-spikes.hdf5\n", + "Using output_file: networks/neuromodulation_example_anu_with_real_dspn/simulation/output.hdf5\n", + "Using logFile: networks/neuromodulation_example_anu_with_real_dspn/log/network-simulation-log.txt-0\n", + "Worker 0 : Loading network from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "Loading config file networks/neuromodulation_example_anu_with_real_dspn/network-config.json\n", + "0 : Memory status: 66% free\n", + "Distributing neurons.\n", + "Setup neurons\n", + "numprocs=1\n", + "Node 0 - cell 0 dspn_0\n", + "Neuron dspn_0 (0) resting voltage = -86.0\n", + "!!! Popping extra segment from neuron -- temp fix!\n", + "Node 0 - cell 1 dspn_0\n", + "Neuron dspn_0 (1) resting voltage = -86.0\n", + "!!! Popping extra segment from neuron -- temp fix!\n", + "Build node cache dspn_0 (dspn_0[0])\n", + "Forcing rxd update...\n", + "Updating node data... (takes ≈ 1 microcentury)\n", + "RxD update completed.\n", + "Node cache built.\n", + "Build node cache dspn_0 (dspn_0[1])\n", + "Node cache built.\n", + "0 : Memory status: 71% free\n", + "Adding gap junctions.\n", + "connect_network_gap_junctions_local\n", + "Finding node local gap junctions...\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Adding synapses.\n", + "connect_network_synapses\n", + "Added 90 on worker 0\n", + "Added 90 synapses to simulation (90 total)\n", + "0 : Memory status: 71% free\n", + "Adding external (cortical, thalamic) input from networks/neuromodulation_example_anu_with_real_dspn/input-spikes.hdf5\n", + "0 : Memory status: 71% free\n", + "0 : Memory status: 71% free\n", + "Time set to 0 ms. No simulation run.\n", + "Program run time: 3.3s\n" + ] + } + ], + "source": [ + "mech_dir = os.path.join(\"..\", \"..\", \"..\", \"..\", \"BasalGangliaData\", \"data\", \"neurons\", \"mechanisms\")\n", + "sample_dt = None # 0.00005\n", + "sim = snudda.simulate(time=0, mech_dir=mech_dir, verbose=True, sample_dt=sample_dt)" + ] + }, + { + "cell_type": "markdown", + "id": "8159ba12-a064-4eb9-ab51-72fba54b3a1c", + "metadata": {}, + "source": [ + "### Add PKA and DA recordings\n", + "Here we add recordings from a compartment that receives synaptic input from its neighbour. This has been checked by looking at the synapse_connection matrix (snudda_load command)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1dc9fba9-131c-4bff-a7dc-3fa3047452f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.add_synapse_variable_recording(source_id=0, dest_id=1, variable=\"modulation_factor_ampa\", synapse_type=\"fake_glutamate\")\n", + "sim.add_synapse_variable_recording(source_id=0, dest_id=1, variable=\"modulation_factor_nmda\", synapse_type=\"fake_glutamate\")\n", + "sim.add_synapse_variable_recording(source_id=1, dest_id=0, variable=\"modulation_factor_ampa\", synapse_type=\"fake_glutamate\")\n", + "sim.add_synapse_variable_recording(source_id=1, dest_id=0, variable=\"modulation_factor_nmda\", synapse_type=\"fake_glutamate\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea777c3d-232f-45d4-a26c-d85d5d4bb3c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.add_synapse_variable_recording(source_id=0, dest_id=1, variable=\"g\", synapse_type=\"fake_glutamate\")\n", + "sim.add_synapse_variable_recording(source_id=0, dest_id=1, variable=\"g\", synapse_type=\"fake_glutamate\")\n", + "sim.add_synapse_variable_recording(source_id=1, dest_id=0, variable=\"g\", synapse_type=\"fake_glutamate\")\n", + "sim.add_synapse_variable_recording(source_id=1, dest_id=0, variable=\"g\", synapse_type=\"fake_glutamate\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "22a1e1bd-a3f7-4032-b1d5-c7d31a1a82ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.add_external_input_variable_recording(neuron_id=0, input_type=\"cortical\", variable=\"modulation_factor_ampa\")\n", + "sim.add_external_input_variable_recording(neuron_id=0, input_type=\"cortical\", variable=\"modulation_factor_nmda\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "88bf71a5-19da-4e2e-8e90-29546a56cea1", + "metadata": {}, + "outputs": [], + "source": [ + "sim.add_rxd_concentration_recording(species=\"PKAc\", neuron_id=1,\n", + " region=\"dend_internal\",\n", + " sec_id=4,\n", + " sec_x=0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "27ec8a70-1fbc-4093-9d8c-cb73494b626e", + "metadata": {}, + "outputs": [], + "source": [ + "sim.add_rxd_concentration_recording(species=\"DA\", neuron_id=1,\n", + " region=\"dend_internal\",\n", + " sec_id=4,\n", + " sec_x=0.25)" + ] + }, + { + "cell_type": "markdown", + "id": "93f8a148-edbf-40e5-ab7f-1dca76bc2d32", + "metadata": {}, + "source": [ + "### Add additional PKA and DA recorings, for input" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6b9f2c09-d17f-4848-990d-9cc9b7859c39", + "metadata": {}, + "outputs": [], + "source": [ + "sim.add_rxd_concentration_recording(species=\"DA\", neuron_id=0,\n", + " region=\"dend_internal\",\n", + " sec_id=7,\n", + " sec_x=0.233)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4d9216d8-8a84-4370-bfc1-88f6cc496a13", + "metadata": {}, + "outputs": [], + "source": [ + "# sim.add_rxd_internal_concentration_recording_all(species=\"PKA\", neuron_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a9f436d4-4b12-4aca-87bc-1aebb9475456", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recording all RxD species from neurons: 0\n" + ] + } + ], + "source": [ + "sim.add_rxd_internal_concentration_recording_all_species(neuron_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "65a75d7a-fdcb-46b0-8148-d4752a3d0748", + "metadata": {}, + "outputs": [], + "source": [ + "# Add density mechanism record\n", + "sim.add_density_mechanism_recording(neuron_id=0, sec_id=0, sec_x=0.5, density_mechanism=\"kir_ms\", variable=\"modulation_factor\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9f5f9b57-9e65-4a09-87ab-cc6da2ce332e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation for 0.5 s\n", + "Running Neuron simulator 500 ms, with dt=0.025\n", + " 1% done. Elapsed: 1.0 s, estimated time left: 102.9 s\n", + " 2% done. Elapsed: 2.1 s, estimated time left: 100.9 s\n", + " 3% done. Elapsed: 3.1 s, estimated time left: 99.8 s\n", + " 4% done. Elapsed: 4.1 s, estimated time left: 98.7 s\n", + " 5% done. Elapsed: 5.2 s, estimated time left: 98.1 s\n", + " 10% done. Elapsed: 10.4 s, estimated time left: 93.7 s\n", + " 20% done. Elapsed: 21.1 s, estimated time left: 84.3 s\n", + " 30% done. Elapsed: 31.8 s, estimated time left: 74.2 s\n", + " 40% done. Elapsed: 42.2 s, estimated time left: 63.4 s\n", + " 50% done. Elapsed: 52.8 s, estimated time left: 52.8 s\n", + " 60% done. Elapsed: 63.1 s, estimated time left: 42.1 s\n", + " 70% done. Elapsed: 73.6 s, estimated time left: 31.6 s\n", + " 80% done. Elapsed: 84.3 s, estimated time left: 21.1 s\n", + " 90% done. Elapsed: 94.9 s, estimated time left: 10.5 s\n", + "100% done. Elapsed: 105.5 s, estimated time left: 0.0 s\n", + "Neuron simulation finished\n", + "Simulation done.\n", + "Simulation run time: 107.4 s\n" + ] + } + ], + "source": [ + "sim.run(t=500)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "053125fc-9117-459b-a3e8-725ef45db53a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing network output to networks/neuromodulation_example_anu_with_real_dspn/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to networks/neuromodulation_example_anu_with_real_dspn/simulation/output.hdf5\n" + ] + } + ], + "source": [ + "sim.record.write()" + ] + }, + { + "cell_type": "markdown", + "id": "bfdb2c9a-4d32-453d-84b8-2c872bfd4050", + "metadata": {}, + "source": [ + "## Load the data and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d84ee6f3-4193-4509-ad4a-0d93dff5e3d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_example_anu_with_real_dspn/simulation/output.hdf5\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "\n", + "output_file = os.path.join(network_path, \"simulation\", \"output.hdf5\")\n", + "nd = SnuddaLoadSimulation(output_file)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKAc\", 1)[0][1]\n", + "data_da = nd.get_data(\"DA\", 1)[0][1]\n", + "data_da_external = nd.get_data(\"DA\", 0)[0][0]\n", + "\n", + "# This is saved with add_rxd_internal_concentration_recording_all -- check that it worked \n", + "data_pka_all0 = nd.get_data(\"PKAc\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bd0f4edf-a622-490b-b39f-18f516975737", + "metadata": {}, + "outputs": [], + "source": [ + "data_types = nd.list_data_types(0)\n", + "all_species_data = nd.get_all_data(neuron_id=0, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a63a7152-730e-4868-a81a-cc87fccaf9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data[data_type][0][0].T[0], name=data_type))\n", + "\n", + "fig.update_layout(xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1e49669a-1e97-4e19-8586-4acff6e21d69", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_69811/2856066121.py:7: RuntimeWarning:\n", + "\n", + "invalid value encountered in divide\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import plotly.graph_objects as go\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " yy = all_species_data[data_type][0][0].T[0]\n", + " fig.add_trace(go.Scatter(x=time, y=yy/np.max(yy), name=data_type))\n", + "\n", + "fig.update_layout(xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0b572560-b42f-4920-bdf3-6718800d01f1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_69811/4119170436.py:7: RuntimeWarning:\n", + "\n", + "invalid value encountered in divide\n", + "\n", + "/tmp/ipykernel_69811/4119170436.py:7: RuntimeWarning:\n", + "\n", + "divide by zero encountered in divide\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import plotly.graph_objects as go\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " yy = all_species_data[data_type][0][0].T[0]\n", + " fig.add_trace(go.Scatter(x=time, y=yy/yy[0], name=data_type))\n", + "\n", + "fig.update_layout(xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adb36614-f0e8-44ac-a36d-9119b48d4bee", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e6554026-bb50-4209-a1f7-37565af50529", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure()\n", + "for data_type in all_species_data:\n", + " plt.plot(time, all_species_data[data_type][0][0].T[0], label=data_type)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3febf45e-b1a0-45ce-b791-266ed03ed741", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.00003623, 1.00003623, 1.00003623, ..., 1.25 , 1.25 ,\n", + " 1.25 ])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_species_data[data_type][0][0].T[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "d98d057c-752c-458b-bd52-a726f42dc90a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da, label=\"DA\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d3bda015-1674-45ae-96f0-69167fd2cc89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_pka, label=\"PKAc\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "0e281a93-c7de-45cf-9723-98f3a19bbca0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da_external, label=\"DA from external?\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "#plt.legend()\n", + "plt.title(\"DA from external?\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20144fff-adf6-4c4b-956d-97deb916d7e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d31a7d49-55b1-479c-942a-17cbd87fa412", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_example_anu_with_real_dspn/simulation/output.hdf5\n", + "Saving figure to networks/neuromodulation_example_anu_with_real_dspn/figures/spike-raster.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "fig_file_raster = f\"spike-raster.png\"\n", + "\n", + "time_range_zoom = (0,0.1)\n", + "spr = SnuddaPlotSpikeRaster2(network_path=network_path)\n", + "\n", + "spr.plot_spike_raster(fig_file=fig_file_raster, time_range=time_range_zoom)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fc082055-9e38-4ed1-b842-40e166548071", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_example_anu_with_real_dspn/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_example_anu_with_real_dspn/input-spikes.hdf5\n", + "Loading networks/neuromodulation_example_anu_with_real_dspn/simulation/output.hdf5\n", + "Plotting traces: [0, 1]\n", + "Plotted 2 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_example_anu_with_real_dspn/figures/Network-voltage-trace--dspn-0-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "sim_file = os.path.join(network_path, \"simulation\", \"output.hdf5\")\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt = PlotTraces(output_file=sim_file)\n", + "# Use trace_id to specify which traces\n", + "ax = pt.plot_traces(offset=0, time_range=(0,1),fig_size=(10,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0484dc59-0ae4-41fa-bfd5-441f8ddf6290", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting traces: [0]\n", + "Plotted 1 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_example_anu_with_real_dspn/figures/Network-voltage-trace--dspn-0.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting traces: [1]\n", + "Plotted 1 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_example_anu_with_real_dspn/figures/Network-voltage-trace--dspn-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax0 = pt.plot_traces(offset=0, time_range=(0,1),fig_size=(10,4), trace_id=0)\n", + "ax1 = pt.plot_traces(offset=0, time_range=(0,1),fig_size=(10,4), trace_id=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "dafbc17d-34f0-413e-b26a-08e51e8fff35", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method plot_traces in module snudda.plotting.plot_traces:\n", + "\n", + "plot_traces(trace_id=None, offset=0.15, colours=None, skip_time=None, time_range=None, line_width=1, fig_size=None, mark_current=None, mark_current_y=None, title=None, fig_name=None, mark_depolarisation_block=True, mark_spikes=True) method of snudda.plotting.plot_traces.PlotTraces instance\n", + " Plot the traces of neuron trace_id\n", + " \n", + " Args:\n", + " trace_id (int or list) : ID of trace to show, can be integer or list\n", + " offset (float) : Offset between multiple traces, float or None\n", + " colours : What colour to plot\n", + " skip_time (float) : Skip portion of the start, modifies time shown\n", + " time_range (float, float) : Range to plot\n", + " mark_current (list) : List of tuples of start, end time\n", + " mark_current_y (float) : Y-coordinate of where to mark the current\n", + " title (str) : Plot title\n", + " fig_name (str) : Figure file to save to\n", + "\n" + ] + } + ], + "source": [ + "help(pt.plot_traces)" + ] + }, + { + "cell_type": "markdown", + "id": "e3532a44-6084-4701-a8a3-28df6e106524", + "metadata": {}, + "source": [ + "## TODO!! Also add ability to have external synapses affect RxD concentrations" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "97789ccd-8c45-47e8-aeec-3b752b4c9942", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19.67872706705603" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.neurons[0].icell.soma[0](0.5).naf_ms.gbar" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7b2a5b48-6450-475e-b842-478e75d2cf32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0005424674497187078" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim.neurons[0].icell.soma[0](0.5).pas.g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "191cf2cb-a61f-4b9e-89a9-4fe963f0343b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/neuromodulation_on_off.ipynb b/examples/notebooks/neuromodulation/neuromodulation_on_off.ipynb new file mode 100644 index 000000000..f6c58f5d1 --- /dev/null +++ b/examples/notebooks/neuromodulation/neuromodulation_on_off.ipynb @@ -0,0 +1,615 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "138f1fa5-37b6-4563-a588-309e5b21b9d5", + "metadata": {}, + "source": [ + "# Neuromodulation example \n", + "\n", + "This neuromodulation creates a small network of neurons. We also use the reaction diffusion model by Anu G Nair 2015.\n", + "\n", + "To generate the ```reaction_diffusion.json``` file in ```data/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/``` from the xml file we run ```data/convert_sbml_to_json.sh```\n", + "\n", + "Reference for DA modulation cascade:\n", + "\n", + "Lindroos R, Dorst MC, Du K, et al. Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2. Front Neural Circuits. 2018;12:3. Published 2018 Feb 6. doi:10.3389/fncir.2018.00003" + ] + }, + { + "cell_type": "markdown", + "id": "1746feed-04de-4020-a2cb-f884e8e9698a", + "metadata": {}, + "source": [ + "## Network setup\n", + "\n", + "We have two neurons. The first neuron (Neuron 0) receives external input (cortical from t=0s and DA from t=0.1s). The cortical input will activate the first neuron, and through activation of synapses on the second neuron, we will see the dopamine level increase in the second neuron (Neuron 1).\n", + "\n", + "The first neuron also receives direct DA activation from external input (starting at 100ms)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b55f23d-62ac-4433-8639-07870af8c40a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading connectivity from data/connectivity_v2.json\n", + "Adding neurons: dspn from dir data/dspn\n", + "Writing networks/neuromodulation_ON_OFF/network-config.json\n", + "Writing networks/neuromodulation_ON_OFF/network-config.json\n", + "Placing neurons\n", + "Network path: networks/neuromodulation_ON_OFF\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 63\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 63 points for networks/neuromodulation_ON_OFF/mesh/Cube-cube-mesh-2.917951293943981e-05.obj\n", + "Filtering, keeping inside points: 2 / 26\n", + "neuron_name = 'dspn_0', num = 2, neuron_path = 'data/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.0s\n", + "Touch detection\n", + "Network path: networks/neuromodulation_ON_OFF\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "Processing hyper voxel : 0/36 (2 neurons)\n", + "Processing hyper voxel : 1/36 (2 neurons)\n", + "Processing hyper voxel : 4/36 (2 neurons)\n", + "Processing hyper voxel : 5/36 (2 neurons)\n", + "Processing hyper voxel : 12/36 (2 neurons)\n", + "Processing hyper voxel : 13/36 (2 neurons)\n", + "Processing hyper voxel : 16/36 (2 neurons)\n", + "Processing hyper voxel : 17/36 (2 neurons)\n", + "Processing hyper voxel : 14/36 (1 neurons)\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.5s\n", + "Prune synapses\n", + "Network path: networks/neuromodulation_ON_OFF\n", + "No file networks/neuromodulation_ON_OFF/pruning_merge_info.json\n", + "Read 31 out of total 31 synapses\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.6s\n" + ] + } + ], + "source": [ + "import os\n", + "from snudda import Snudda\n", + "\n", + "neuron_path = os.path.join(\"data\", \"dspn\")\n", + "network_path = os.path.join(\"networks\", \"neuromodulation_ON_OFF\")\n", + "connectivity_path = os.path.join(\"data\", \"connectivity_v2.json\")\n", + "\n", + "snudda = Snudda(network_path=network_path)\n", + "si = snudda.init_tiny(neuron_paths=neuron_path, neuron_names=\"dspn\", number_of_neurons=[2], \n", + " connection_config=connectivity_path, random_seed=123)\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"data/JSON/reaction_diffusion_D1.json\"\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation\"] = \"test-modulation.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation_key\"] = \"abc\"\n", + "\n", + "si.write_json()\n", + "\n", + "snudda.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "41e96e4a-0f52-4d77-8a1f-ba8d270b9b78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF/network-synapses.hdf5\n", + "Writing input spikes to networks/neuromodulation_ON_OFF/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF/network-synapses.hdf5\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 1 (meta modified by input_config)\n", + "Writing spikes to networks/neuromodulation_ON_OFF/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 3.5s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_config = os.path.join(\"data\", \"input_v4.json\")\n", + "snudda.setup_input(input_config=input_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ae7ebf60-1556-4e07-81c6-85156c90bfad", + "metadata": {}, + "outputs": [], + "source": [ + "sim_output_neuromodulation_ON = os.path.join(network_path, \"simulation\", \"output_neuromodulation_ON.hdf5\")\n", + "sim_output_neuromodulation_OFF = os.path.join(network_path, \"simulation\", \"output_neuromodulation_OFF.hdf5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d019b580-4193-4321-9ee8-9bdd3988209f", + "metadata": {}, + "outputs": [], + "source": [ + "# mech_dir = os.path.join(\"..\", \"..\", \"..\", \"..\", \"BasalGangliaData\", \"data\", \"neurons\", \"mechanisms\")\n", + "mech_dir = \"/home/hjorth/BasalGangliaData/data/neurons/mechanisms\"\n", + "sample_dt = None # 0.00005\n", + "\n", + "# sim = snudda.simulate(time=0, mech_dir=mech_dir, verbose=True, sample_dt=sample_dt, output_file=sim_output_neuromodulation_ON)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "207658ef-0747-4c87-babb-d3076e3f7a33", + "metadata": {}, + "outputs": [], + "source": [ + "# sim_config_on = os.path.join(\"data\", \"da_experiment_on.json\")\n", + "# sim_config_off = os.path.join(\"data\", \"da_experiment_off.json\")\n", + "\n", + "snudda = None\n", + "\n", + "sim_config_on = os.path.join(\"data\", \"da_experiment_on.json\")\n", + "sim_config_off = os.path.join(\"data\", \"da_experiment_off.json\")\n", + "\n", + "\n", + "sim_time = 1\n", + "n_workers = 1" + ] + }, + { + "cell_type": "markdown", + "id": "56760ccb-c832-4852-b696-323198f8d771", + "metadata": {}, + "source": [ + "## Running simulations\n", + "\n", + "To see progress of the two simulations in log files ```networks/neuromodulation_ON_OFF/log/network-simulation-ON.txt``` and ```networks/neuromodulation_ON_OFF/log/network-simulation-OFF.txt```." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6b6b0fa2-93d2-4d0b-92ef-3c4949ac3355", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpirun -n 1 snudda simulate networks/neuromodulation_ON_OFF --time 1 --simulation_config data/da_experiment_on.json --mechdir /home/hjorth/BasalGangliaData/data/neurons/mechanisms\n" + ] + } + ], + "source": [ + "run_str_on = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_on} --mechdir {mech_dir}\"\n", + "print(run_str_on)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bc7d396-09b0-46cc-89da-ff3c356b2b6e", + "metadata": {}, + "outputs": [], + "source": [ + "run_str_on = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_on} --mechdir {mech_dir}\"\n", + "print(run_str_on)\n", + "os.system(run_str_on)" + ] + }, + { + "cell_type": "markdown", + "id": "0c9d6057-e439-40e1-b7e7-4d55bdec3ce7", + "metadata": {}, + "source": [ + "### Rerun without neuromodulation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0f3648c5-2f5f-4b5b-b438-5eb15bffdd61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpirun -n 1 snudda simulate networks/neuromodulation_ON_OFF --time 1 --simulation_config data/da_experiment_off.json --mechdir /home/hjorth/BasalGangliaData/data/neurons/mechanisms --disable_rxd_neuromodulation\n" + ] + } + ], + "source": [ + "run_str_off = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_off} --mechdir {mech_dir} --disable_rxd_neuromodulation\"\n", + "print(run_str_off)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3cdbcb1-7520-4a7c-93d8-61071697b8ca", + "metadata": {}, + "outputs": [], + "source": [ + "os.system(run_str_off)" + ] + }, + { + "cell_type": "markdown", + "id": "bfdb2c9a-4d32-453d-84b8-2c872bfd4050", + "metadata": {}, + "source": [ + "## Load the data and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d84ee6f3-4193-4509-ad4a-0d93dff5e3d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_ON_OFF/simulation/output_neuromodulation_ON.hdf5\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "\n", + "nd = SnuddaLoadSimulation(sim_output_neuromodulation_ON)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKAc\", 1)[0][1]\n", + "data_da = nd.get_data(\"DA\", 1)[0][1]\n", + "data_da_external = nd.get_data(\"DA\", 0)[0][0]\n", + "\n", + "# This is saved with add_rxd_internal_concentration_recording_all -- check that it worked \n", + "data_pka_all0 = nd.get_data(\"PKAc\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bd0f4edf-a622-490b-b39f-18f516975737", + "metadata": {}, + "outputs": [], + "source": [ + "data_types = nd.list_data_types(0)\n", + "all_species_data = nd.get_all_data(neuron_id=0, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()\n", + "voltage = nd.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a63a7152-730e-4868-a81a-cc87fccaf9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data[data_type][0][0].T[0], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "511489ad-b3d6-4301-a8bd-fa870cf456e4", + "metadata": {}, + "outputs": [], + "source": [ + "data_types2 = nd.list_data_types(0)\n", + "all_species_data2 = nd.get_all_data(neuron_id=1, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()\n", + "voltage = nd.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d231b4e4-4a56-47b5-84f6-e13b6e721125", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data2:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data2[data_type][0][1].T[0], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "81722b1c-c30c-445e-a5f5-abaa823f0ce4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_ON_OFF/simulation/output_neuromodulation_OFF.hdf5\n" + ] + } + ], + "source": [ + "nd_off = SnuddaLoadSimulation(sim_output_neuromodulation_OFF)\n", + "time_off = nd_off.get_time()\n", + "voltage_off = nd_off.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1e9ce899-6243-448b-a4ac-2ba3e83f522e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "\n", + "sct_on = go.Scatter(x=time, y=voltage[0][0][:,0], name=\"DA (0.3-1.3s)\", opacity=0.5)\n", + "sct_off = go.Scatter(x=time_off, y=voltage_off[0][0][:,0], name=\"No DA\", opacity=0.5)\n", + "fig.add_traces([sct_on, sct_off])\n", + "fig.write_image(\"example-trace.png\", scale=2, height=800, width=1200)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c1c558be-b54e-41e7-a64c-6c98e955ed2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "\n", + "sct_on = go.Scatter(x=time, y=voltage[0][1][:,0], name=\"DA (0.3-1.3s)\", opacity=0.5)\n", + "sct_off = go.Scatter(x=time_off, y=voltage_off[0][1][:,0], name=\"No DA\", opacity=0.5)\n", + "fig.add_traces([sct_on, sct_off])" + ] + }, + { + "cell_type": "markdown", + "id": "cd855cab-eb67-4c0c-a20c-56f23eb04d9e", + "metadata": {}, + "source": [ + "## Plotting simulation with DA modulation \n", + "\n", + "DA is active from 0.3 to 1.3 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fc082055-9e38-4ed1-b842-40e166548071", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_ON_OFF/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_ON_OFF/input-spikes.hdf5\n", + "Loading networks/neuromodulation_ON_OFF/simulation/output_neuromodulation_ON.hdf5\n", + "Plotting traces: [0, 1]\n", + "Plotted 2 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_ON_OFF/figures/Network-voltage-trace--dspn-0-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt = PlotTraces(output_file=sim_output_neuromodulation_ON)\n", + "# Use trace_id to specify which traces\n", + "ax = pt.plot_traces(offset=0, time_range=None,fig_size=(10,4))" + ] + }, + { + "cell_type": "markdown", + "id": "d5ab977a-2171-4f7a-8db8-0d4fcaff82a6", + "metadata": {}, + "source": [ + "## Plot simulation, with neuromodulation disabled" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "191cf2cb-a61f-4b9e-89a9-4fe963f0343b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_ON_OFF/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_ON_OFF/input-spikes.hdf5\n", + "Loading networks/neuromodulation_ON_OFF/simulation/output_neuromodulation_OFF.hdf5\n", + "Plotting traces: [0, 1]\n", + "Plotted 2 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_ON_OFF/figures/Network-voltage-trace--dspn-0-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt_off = PlotTraces(output_file=sim_output_neuromodulation_OFF)\n", + "# Use trace_id to specify which traces\n", + "ax_off = pt_off.plot_traces(offset=0, time_range=None,fig_size=(10,4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "398790f7-d211-4fff-9249-77b28e14c859", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/neuromodulation_on_off_bath.ipynb b/examples/notebooks/neuromodulation/neuromodulation_on_off_bath.ipynb new file mode 100644 index 000000000..ce2bebab7 --- /dev/null +++ b/examples/notebooks/neuromodulation/neuromodulation_on_off_bath.ipynb @@ -0,0 +1,617 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "138f1fa5-37b6-4563-a588-309e5b21b9d5", + "metadata": {}, + "source": [ + "# Neuromodulation example (bath)\n", + "\n", + "This neuromodulation creates a small network of neurons. We also use the reaction diffusion model by Anu G Nair 2015.\n", + "\n", + "To generate the ```reaction_diffusion.json``` file in ```data/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/``` from the xml file we run ```data/convert_sbml_to_json.sh```\n", + "\n", + "Reference for DA modulation cascade:\n", + "\n", + "Lindroos R, Dorst MC, Du K, et al. Basal Ganglia Neuromodulation Over Multiple Temporal and Structural Scales-Simulations of Direct Pathway MSNs Investigate the Fast Onset of Dopaminergic Effects and Predict the Role of Kv4.2. Front Neural Circuits. 2018;12:3. Published 2018 Feb 6. doi:10.3389/fncir.2018.00003\n", + "\n", + "Here we use the data from Planert:\n", + "Planert H, Berger TK, Silberberg G. Membrane properties of striatal direct and indirect pathway neurons in mouse and rat slices and their modulation by dopamine. PLoS One. 2013;8(3):e57054. doi:10.1371/journal.pone.0057054\n", + "\n", + "Figure 4:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1746feed-04de-4020-a2cb-f884e8e9698a", + "metadata": {}, + "source": [ + "## Network setup\n", + "\n", + "We have two neurons. The first neuron (Neuron 0) receives external input (cortical from t=0s and DA from t=0.1s). The cortical input will activate the first neuron, and through activation of synapses on the second neuron, we will see the dopamine level increase in the second neuron (Neuron 1).\n", + "\n", + "The first neuron also receives direct DA activation from external input (starting at 100ms)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b55f23d-62ac-4433-8639-07870af8c40a", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from snudda import Snudda\n", + "\n", + "neuron_path = os.path.join(\"data\", \"dspn\")\n", + "network_path = os.path.join(\"networks\", \"neuromodulation_ON_OFF_bath\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ad92699c-455c-4fcd-b742-4391c78a0fd5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding neurons: dspn from dir data/dspn\n", + "Writing networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "Writing networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "Placing neurons\n", + "Network path: networks/neuromodulation_ON_OFF_bath\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF_bath/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 63\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 63 points for networks/neuromodulation_ON_OFF_bath/mesh/Cube-cube-mesh-2.917951293943981e-05.obj\n", + "Filtering, keeping inside points: 2 / 26\n", + "neuron_name = 'dspn_0', num = 2, neuron_path = 'data/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.0s\n", + "Touch detection\n", + "Network path: networks/neuromodulation_ON_OFF_bath\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF_bath/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "No connections specified in connectivity_distribution.\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n", + "Prune synapses\n", + "Network path: networks/neuromodulation_ON_OFF_bath\n", + "No file networks/neuromodulation_ON_OFF_bath/pruning_merge_info.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n" + ] + } + ], + "source": [ + "snudda = Snudda(network_path=network_path)\n", + "si = snudda.init_tiny(neuron_paths=neuron_path, neuron_names=\"dspn\", number_of_neurons=[2], \n", + " random_seed=123)\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"reaction_diffusion\"] = \"data/JSON/reaction_diffusion_D1.json\"\n", + "\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation\"] = \"test-modulation.json\"\n", + "si.network_data[\"regions\"][\"Cube\"][\"neurons\"][\"dspn\"][\"modulation_key\"] = \"abc\"\n", + "\n", + "si.write_json()\n", + "\n", + "snudda.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "41e96e4a-0f52-4d77-8a1f-ba8d270b9b78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up inputs, assuming input.json exists\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF_bath/network-synapses.hdf5\n", + "Writing input spikes to networks/neuromodulation_ON_OFF_bath/input-spikes.hdf5\n", + "Reading SNUDDA_DATA=None from networks/neuromodulation_ON_OFF_bath/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/neuromodulation_ON_OFF_bath/network-synapses.hdf5\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 0 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical_background with cortical_background input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of thalamic_background with thalamic_background input for neuron dspn_0 1 (meta modified by input_config)\n", + "!!! Warning, combining definition of cortical with cortical input for neuron dspn_0 1 (meta modified by input_config)\n", + "Writing spikes to networks/neuromodulation_ON_OFF_bath/input-spikes.hdf5\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.8s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_config = os.path.join(\"data\", \"input_v6_bath_on_off.json\")\n", + "snudda.setup_input(input_config=input_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae7ebf60-1556-4e07-81c6-85156c90bfad", + "metadata": {}, + "outputs": [], + "source": [ + "sim_output_neuromodulation_ON = os.path.join(network_path, \"simulation\", \"output_neuromodulation_ON.hdf5\")\n", + "sim_output_neuromodulation_OFF = os.path.join(network_path, \"simulation\", \"output_neuromodulation_OFF.hdf5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d019b580-4193-4321-9ee8-9bdd3988209f", + "metadata": {}, + "outputs": [], + "source": [ + "# mech_dir = os.path.join(\"..\", \"..\", \"..\", \"..\", \"BasalGangliaData\", \"data\", \"neurons\", \"mechanisms\")\n", + "mech_dir = \"/home/hjorth/BasalGangliaData/data/neurons/mechanisms\"\n", + "sample_dt = None # 0.00005\n", + "\n", + "# sim = snudda.simulate(time=0, mech_dir=mech_dir, verbose=True, sample_dt=sample_dt, output_file=sim_output_neuromodulation_ON)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "207658ef-0747-4c87-babb-d3076e3f7a33", + "metadata": {}, + "outputs": [], + "source": [ + "# sim_config_on = os.path.join(\"data\", \"da_experiment_on.json\")\n", + "# sim_config_off = os.path.join(\"data\", \"da_experiment_off.json\")\n", + "\n", + "snudda = None\n", + "\n", + "sim_config_on = os.path.join(\"data\", \"da_experiment_on_bath.json\")\n", + "sim_config_off = os.path.join(\"data\", \"da_experiment_off_bath.json\")\n", + "\n", + "\n", + "sim_time = 1\n", + "n_workers = 1" + ] + }, + { + "cell_type": "markdown", + "id": "56760ccb-c832-4852-b696-323198f8d771", + "metadata": {}, + "source": [ + "## Running simulations\n", + "\n", + "To see progress of the two simulations in log files ```networks/neuromodulation_ON_OFF/log/network-simulation-ON.txt``` and ```networks/neuromodulation_ON_OFF/log/network-simulation-OFF.txt```." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b6b0fa2-93d2-4d0b-92ef-3c4949ac3355", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpirun -n 1 snudda simulate networks/neuromodulation_ON_OFF_bath --time 1 --simulation_config data/da_experiment_on_bath.json --mechdir /home/hjorth/BasalGangliaData/data/neurons/mechanisms\n" + ] + } + ], + "source": [ + "run_str_on = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_on} --mechdir {mech_dir}\"\n", + "print(run_str_on)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bc7d396-09b0-46cc-89da-ff3c356b2b6e", + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "os.system(run_str_on)" + ] + }, + { + "cell_type": "markdown", + "id": "0c9d6057-e439-40e1-b7e7-4d55bdec3ce7", + "metadata": {}, + "source": [ + "### Rerun without neuromodulation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0f3648c5-2f5f-4b5b-b438-5eb15bffdd61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mpirun -n 1 snudda simulate networks/neuromodulation_ON_OFF_bath --time 1 --simulation_config data/da_experiment_off_bath.json --mechdir /home/hjorth/BasalGangliaData/data/neurons/mechanisms --disable_rxd_neuromodulation\n" + ] + } + ], + "source": [ + "run_str_off = f\"mpirun -n {n_workers} snudda simulate {network_path} --time {sim_time} --simulation_config {sim_config_off} --mechdir {mech_dir} --disable_rxd_neuromodulation\"\n", + "print(run_str_off)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3cdbcb1-7520-4a7c-93d8-61071697b8ca", + "metadata": {}, + "outputs": [], + "source": [ + "os.system(run_str_off)" + ] + }, + { + "cell_type": "markdown", + "id": "bfdb2c9a-4d32-453d-84b8-2c872bfd4050", + "metadata": {}, + "source": [ + "## Load the data and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d84ee6f3-4193-4509-ad4a-0d93dff5e3d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_ON_OFF_bath/simulation/output_neuromodulation_ON.hdf5\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "\n", + "nd = SnuddaLoadSimulation(sim_output_neuromodulation_ON)\n", + "time = nd.get_time()\n", + "data_pka = nd.get_data(\"PKAc\", 1)[0][1]\n", + "data_da = nd.get_data(\"DA\", 1)[0][1]\n", + "data_da_external = nd.get_data(\"DA\", 0)[0][0]\n", + "\n", + "# This is saved with add_rxd_internal_concentration_recording_all -- check that it worked \n", + "data_pka_all0 = nd.get_data(\"PKAc\", 0)[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bd0f4edf-a622-490b-b39f-18f516975737", + "metadata": {}, + "outputs": [], + "source": [ + "data_types = nd.list_data_types(0)\n", + "all_species_data = nd.get_all_data(neuron_id=0, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()\n", + "voltage = nd.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a63a7152-730e-4868-a81a-cc87fccaf9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data[data_type][0][0].T[0], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "511489ad-b3d6-4301-a8bd-fa870cf456e4", + "metadata": {}, + "outputs": [], + "source": [ + "data_types2 = nd.list_data_types(0)\n", + "all_species_data2 = nd.get_all_data(neuron_id=1, exclude=[\"spikes\", \"voltage\"])\n", + "time = nd.get_time()\n", + "voltage = nd.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d231b4e4-4a56-47b5-84f6-e13b6e721125", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"iframe\" # Do not save plots in the notebook, they can get BIG\n", + "\n", + "fig = go.Figure()\n", + "for data_type in all_species_data2:\n", + " fig.add_trace(go.Scatter(x=time, y=all_species_data2[data_type][0][1].T[0], name=data_type))\n", + "\n", + "fig.update_layout(title=\"With DA modulation\", xaxis_title=\"Time (s)\", yaxis_title=\"Concentration\", width=1000, height=800)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "81722b1c-c30c-445e-a5f5-abaa823f0ce4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading networks/neuromodulation_ON_OFF_bath/simulation/output_neuromodulation_OFF.hdf5\n" + ] + } + ], + "source": [ + "nd_off = SnuddaLoadSimulation(sim_output_neuromodulation_OFF)\n", + "time_off = nd_off.get_time()\n", + "voltage_off = nd_off.get_data(\"voltage\", [0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1e9ce899-6243-448b-a4ac-2ba3e83f522e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "\n", + "sct_on = go.Scatter(x=time, y=voltage[0][0][:,0], name=\"DA (0.3-1.3s)\", opacity=0.5)\n", + "sct_off = go.Scatter(x=time_off, y=voltage_off[0][0][:,0], name=\"No DA\", opacity=0.5)\n", + "fig.add_traces([sct_on, sct_off])\n", + "fig.write_image(\"example-trace.png\", scale=2, height=800, width=1200)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c1c558be-b54e-41e7-a64c-6c98e955ed2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure()\n", + "\n", + "sct_on = go.Scatter(x=time, y=voltage[0][1][:,0], name=\"DA (0.3-1.3s)\", opacity=0.5)\n", + "sct_off = go.Scatter(x=time_off, y=voltage_off[0][1][:,0], name=\"No DA\", opacity=0.5)\n", + "fig.add_traces([sct_on, sct_off])" + ] + }, + { + "cell_type": "markdown", + "id": "cd855cab-eb67-4c0c-a20c-56f23eb04d9e", + "metadata": {}, + "source": [ + "## Plotting simulation with DA modulation \n", + "\n", + "DA is active from 0.3 to 1.3 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fc082055-9e38-4ed1-b842-40e166548071", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_ON_OFF_bath/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_ON_OFF_bath/input-spikes.hdf5\n", + "Loading networks/neuromodulation_ON_OFF_bath/simulation/output_neuromodulation_ON.hdf5\n", + "Plotting traces: [0, 1]\n", + "Plotted 2 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_ON_OFF_bath/figures/Network-voltage-trace--dspn-0-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt = PlotTraces(output_file=sim_output_neuromodulation_ON)\n", + "# Use trace_id to specify which traces\n", + "ax = pt.plot_traces(offset=0, time_range=None,fig_size=(10,4))" + ] + }, + { + "cell_type": "markdown", + "id": "d5ab977a-2171-4f7a-8db8-0d4fcaff82a6", + "metadata": {}, + "source": [ + "## Plot simulation, with neuromodulation disabled" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "191cf2cb-a61f-4b9e-89a9-4fe963f0343b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from networks/neuromodulation_ON_OFF_bath/network-synapses.hdf5\n", + "Loading input info from networks/neuromodulation_ON_OFF_bath/input-spikes.hdf5\n", + "Loading networks/neuromodulation_ON_OFF_bath/simulation/output_neuromodulation_OFF.hdf5\n", + "Plotting traces: [0, 1]\n", + "Plotted 2 traces (total 2)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/neuromodulation_ON_OFF_bath/figures/Network-voltage-trace--dspn-0-1.pdf\n" + ] + }, + { + "data": { + "image/png": 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ccc166j7g/edeqn/eqp6fqueVB68Jui0WC2JiYhAXF6cvy8nJQVxcHGJjY/PcJjY21mF9ANi6dau+fvXq1REVFeWwzvXr1/H999/nu08AsFqtCA4OdngRERGVVGK4nTuwe7gcRAWlTR6kXbtO2ed/9+XuiYk8ddzSSv+eM3pNmFMoOXf9VxWi+H3iVVfj+PHjsWTJEnz00Uc4cuQIRo4cifT0dAwZMgQAMHDgQIeB1v70pz9h8+bNmD17No4ePYqpU6fihx9+wOjRowEABoMBY8eOxfTp0/H555/j4MGDGDhwICpUqIBevXp54hSJiIicp1w5ICoKydWq4XkAv5jNQFTU7eVEJdF/r9nDfn54HsDlKlWKf83+d59JFSvieQCHrFb33Af/PW5C+fLuPW5pVa4ccsqVQzyA54Hb/YJV+rz/ez0dDQjA8wB+j4xU4/z+e16XKldW+z4RL7NgwQKpUqWKWCwWadmypezdu1d/75FHHpFBgwY5rP+vf/1L6tSpIxaLRRo0aCAbN250eD8nJ0dee+01iYyMFKvVKh06dJBjx44VqkypqakCQFJTU4t8XkRERC5x86Ys/8c/BIBERUaK3Lzp6RIR3dvNm9KkcWMBIIsWLnTONXvzpsx85x0BILVr1XLffXDzpkydMkUASK2aNXn/uVjqpUsCQACI5OSo93nfvCktH3hAAMjYP/1JnfO7eVPmv/uu++9PJyhoHOg1o5drRo8erWeq7/btt9/mWtanTx/06dMn3/0ZDAa88cYbeOONN5xVRCIiopLDakWONqCQwQBYrfden8jTrNbb1ypuR09OuWatVuhjILvzPvDUcUupHB+f//2Pip+31QrDf5vN54ioc353/E457Z4vYbyqeTkRERERlR45Oer08BRFpj6ikoHXk3dh0E1ERKQ4lQIXKl1UCCx4/7lPafisDVorEAXujTupdj53Y9BNRERERCWSM4MoTwdkqgcV5F6evp6pcBh0ExERKY6VfSLP4f1HzqRqplv1hwgMuomIiBSnemWG1OOKwEK1IIVyK03fdapdz6qdz90YdBMRERFRieSKIMrdlfvSFAiS66ma6VYdg24iIiLFsXJG3sqZ166ng1/eh67n6b+xO2hBt2rnqtr53I1BNxERkeJUr8yQelQKLBhskyvwuvIuDLqJiIhKCVbSyFto16pKfbo9ffzSQIWHNPejavNyV9zzJQmDbiIiIsWpWokh9ZWGIIqoKHhveBcG3URERESkPE8FKapn8EqS0hCIqprpVv1vx6CbiIhIcapXZkg9rgwsVAtWqHRS7TpW7XzuxqCbiIhIcapXZkhdznxg5Kn7gA+93Kc0fNaqZrpVx6CbiIiIiEoklQILlc6FPE+160n1ByYMuomIiBSnemWG1OOKKcM8FaSoFhyVZKXhu067nkrDuaqEQTcREZHiWOknb6XStavSuZDnqXY9qT7gIINuIiKiUkLVygypy5nXrKdHLyfXK02ftWrnqnrmnkE3ERGR4lSvzJC6eO0SOVIt2C4tGHQTERERkfI4ern6StNnrVrwrdr53I1BNxERkeJUr8yQulSap5v3ITmD6n2fVcWgm4iISHGlKftDanHmtcs+3eorTd91qp2r6vcJg24iIiIiKlG0KcNUqoirdC7kOcx0eycG3URERIpj5Yy8lTOvXTYrJ5Wodl2plrm/G4NuIiIixalemSF18dqlwigN14uqmW7VzuduDLqJiIiIqERSIdNdGgJBcj/Vg1TVMOgmIiJSHCtn5K1UuHZVOAdvUZoecKh2Xan+t2PQTUREREQlkgqjl2tUC5LIM1RtXq46Bt1ERESK83SwQVRUKgQWKpyDtyhN33WqXVeqnc/dGHQTEREpTvXKDKnHFVOGsU83qUC7jnldeRcG3URERERUIqkUWPDhl+updL3cj2rXk+rN5hl0ExERKa40VURJLSpkulUNIsgzVA1OVf+dYtBNRERUSqhWSSN1uaJ5uYb3gbpK09+2NJ2rChh0ExERKY6VM/JWKmS/eP+RM6ma6VbtfO7GoJuIiEhxqldmSF3OvHY9FcDz/nMfFR7SFJSq15Wq58Wgm4iIiIhKJDYvJ3LETLd3YtBNRESkuNKU/SE1qDhlmOpBRUlQmr7rVL2etHtfNQy6iYiIFKdq5YzUp0IQxfuPXEG160r1h1MMuomIiIioRFIh003uo8JDmvth83LvxKCbiIhIcaWhIkpqceWUYe6mwjlQycPryrsw6CYiIiKiEsmZD4z48El9pelvrFrQrdr53I1BNxERkeJUr8yQulQYvVz1vqrkGapeT6qeF4NuIiIixalaiSF1uaLfqqfuA95/5Eyq9ulWvZWC1wTdV69exYABAxAcHIzQ0FAMHToUN27cuOc2N2/exAsvvIAyZcogMDAQvXv3RlJSkv7+zz//jP79+6Ny5crw8/NDvXr18O6777r6VIiIiIjoHlQNLMi1StP1ouq5csowDxswYAB+/fVXbN26FV988QW+++47PPfcc/fcZty4cdiwYQPWrFmD7du34+LFi3jyySf19+Pj41GuXDmsXLkSv/76K/7yl79g0qRJeO+991x9OkRERG6jegaB1KNiplvVIIncS9XrSdXz0pg9XYCCOHLkCDZv3oz9+/ejRYsWAIAFCxagW7dumDVrFipUqJBrm9TUVHz44YdYtWoV2rdvDwD4xz/+gXr16mHv3r1o3bo1/vjHPzpsU6NGDezZswfr1q3D6NGjXX9iREREbqB6ZYbUo12rKjww4n3nPipcLwWl2nWl2vnczSsy3Xv27EFoaKgecANAx44dYTQa8f333+e5TXx8PGw2Gzp27Kgvi46ORpUqVbBnz558j5Wamorw8HDnFZ6IiKiEUL1SQ+px5jVbmgIyUvfvze9x7+QVme7ExESUK1fOYZnZbEZ4eDgSExPz3cZisSA0NNRheWRkZL7b7N69G6tXr8bGjRvvWZ6srCxkZWXp/3/9+vUCnAUREZFnsJJG3kal1hkqnUtJd2egnZOTA6PRK/KLRaLa9aTqQxKNR6/EiRMnwmAw3PN19OhRt5Tl0KFD6NmzJ6ZMmYJOnTrdc90ZM2YgJCREf1WuXNktZSQiIiIqDVTq002eoXoQx+vZu3g00/3SSy9h8ODB91ynRo0aiIqKwqVLlxyWZ2dn4+rVq4iKispzu6ioKNy6dQvXrl1zyHYnJSXl2ubw4cPo0KEDnnvuObz66qv3LfekSZMwfvx4/f+vX7/OwJuIiEos1SufpB6VssOcp9t97vyuy87OhsVi8WBpXEOle+NOqp3P3TwadEdERCAiIuK+68XGxuLatWuIj49HTEwMAOCbb75BTk4OWrVqlec2MTEx8PHxQVxcHHr37g0AOHbsGM6dO4fY2Fh9vV9//RXt27fHoEGD8Oabbxao3FarFVartUDrEhEReZqqlTRSlysGUuPo5aWL6g8bVbueVDufu3lFR4d69eqhS5cuGD58OPbt24ddu3Zh9OjR6Nevnz5y+YULFxAdHY19+/YBAEJCQjB06FCMHz8e27ZtQ3x8PIYMGYLY2Fi0bt0awO0m5e3atUOnTp0wfvx4JCYmIjExEZcvX/bYuRIRETmbVplRdf5TUg8DVSqKO68XVYNu3hveySsGUgOATz75BKNHj0aHDh1gNBrRu3dvzJ8/X3/fZrPh2LFjyMjI0JfNnTtXXzcrKwudO3fGokWL9PfXrl2Ly5cvY+XKlVi5cqW+vGrVqjhz5oxbzouIiMjVVK18krpU6tPNIMkzVP/eU+16Uu187uY1QXd4eDhWrVqV7/vVqlXL9cfy9fXFwoULsXDhwjy3mTp1KqZOnerMYhIREZU4qldmSF2uuHbdfT/w/nOfu0cvVxEf4ngnr2heTkREREVnt9s9XQSiQnFFYOHpIIxBknt5+u/taqpdT6r/vRh0ExERKU61yhmpT6Vsngrn4C1KQ6Zbw+vKuzDoJiIiUhwrZ+RtXDHNlqcCeU4Z5hmqB92qUf3+YNBNRESkONUrM6QeVw6kxlH81VUaMt0qtQK5k+rdoIoUdF+7dg1Lly7FpEmTcPXqVQDAjz/+iAsXLji1cERERFR8WuWTwQZ5G2cGTp66D1QNkko6VYNujWrXk2rnc7dCj17+yy+/oGPHjggJCcGZM2cwfPhwhIeHY926dTh37hw+/vhjV5STiIiIikj1yiepR6Upw8gzVP/eU+16Vu187lboTPf48eMxePBgnDhxAr6+vvrybt264bvvvnNq4YiIiKj4VK98knpUCrqZ6XafOz9jVT9vVa8n1X+nCh1079+/H88//3yu5RUrVkRiYqJTCkVERETOw76s5G1UmjJMteDIW6gexKl2Xan+9yp00G21WnH9+vVcy48fP46IiAinFIqIiIicR9XMCKlPhaBbw/vP9TiQmvfS/l6q/t0KHXT36NEDb7zxBmw2G4DbT83PnTuHP//5z+jdu7fTC0hERETFwymLyNuo2Lyc3EvV4E1Vqt8nhQ66Z8+ejRs3bqBcuXLIzMzEI488glq1aiEoKAhvvvmmK8pIRERExcDKJ3kbVwbd7q7cq5qZLImY6fZeqp6XptCjl4eEhGDr1q3YuXMnfvnlF9y4cQPNmzdHx44dXVE+IiIiKibVKzOkHpUy3eQZqgbdGtWuZ22ebtXOS1PooFvTtm1btG3b1pllISIiIhdQtRJD6mOmmwqDmW7vpdr53K3QQff8+fPzXG4wGODr64tatWrh4YcfhslkKnbhiIiIqPjYp5u8jSsCC9UzafS/vzGg/t9ZtfNT/Xeq0EH33LlzcfnyZWRkZCAsLAwAkJKSAn9/fwQGBuLSpUuoUaMGtm3bhsqVKzu9wERERFQ4qmZ8SF0q9ukm12Om23upel6aQg+k9tZbb+GBBx7AiRMnkJycjOTkZBw/fhytWrXCu+++i3PnziEqKgrjxo1zRXmJiIiokFSvzJB6VOrTrXoGryS5M9OtatCt6nmpfn8UOtP96quv4t///jdq1qypL6tVqxZmzZqF3r1749SpU5g5cyanDyMiIiohVK/MkHpUynST+5SGoFvV61j17h+FznQnJCQgOzs71/Ls7GwkJiYCACpUqIC0tLTil46IiIiKTdVKGqlLpaCb95/7sHm591L1vDSFDrrbtWuH559/HgcOHNCXHThwACNHjkT79u0BAAcPHkT16tWdV0oiIiIqMlUrn6Q+FZqXqxpElESlYSA1VYNT1bthFDro/vDDDxEeHo6YmBhYrVZYrVa0aNEC4eHh+PDDDwEAgYGBmD17ttMLS0RERIWnaiWN1KXS6OW879ynNDQvV/W8VL9PCt2nOyoqClu3bsXRo0dx/PhxAEDdunVRt25dfZ127do5r4RERERULKpXZkg9KjUvVz2DV5KUpublqlH94XChg25NdHQ0oqOjnVkWIiIicgFW+snbuCIr7emgm1yvNGS6VQ1OVf17aYoUdP/+++/4/PPPce7cOdy6dcvhvTlz5jilYEREROQcqldmSD2ueFDk6SnDyPXYp9t7qXpemkIH3XFxcejRowdq1KiBo0ePomHDhjhz5gxEBM2bN3dFGYmIiKgYVK/MkHrYvJyKojQ1L1ftelL1vDSFHkht0qRJmDBhAg4ePAhfX1/8+9//xvnz5/HII4+gT58+rigjERERFYOqlRhSlysCVU8FYXdmX8m1SlPzctWo/nCq0EH3kSNHMHDgQACA2WxGZmYmAgMD8cYbb+Cdd95xegGJiIioeFTPIJB6XNm83N3BmKrBX0lUGoJuVYNT1c7nboUOugMCAvR+3OXLl8dvv/2mv3flyhXnlYyIiIicQtXKJ6nLlc3L3U3VIKkkuvMzVv3zVu38VL9PCt2nu3Xr1ti5cyfq1auHbt264aWXXsLBgwexbt06tG7d2hVlJCIiomLIzs4GoG5lhtTjykw3Ry9XV2nIdKvackm187lboYPuOXPm4MaNGwCA119/HTdu3MDq1atRu3ZtjlxORERUArFPKXkbFTPd5HqlIehW9bxUfZigKXTQXaNGDf3fAQEBWLx4sVMLRERERM7FTDd5G1cOpMbRy9VVGkYv16h2Pan+cLjQfbpr1KiB5OTkXMuvXbvmEJATERFRyaB6ZYbU44qAwlP3gerBX0nCebq9l+oPhwsddJ85cybPL62srCxcuHDBKYUiIiIi59EqM0TewhWBhacq9Qy63YfNy72X6g+HC9y8/PPPP9f//dVXXyEkJET/f7vdjri4OFSrVs2phSMiIqLiY/NW8jauCLq1Sj2bl6vrzs9Y1eBUo9r15Kn7010KHHT36tULAGAwGDBo0CCH93x8fFCtWjXMnj3bqYUjIiKi4lM9g0DqcWXQ7W6qBhElEZuXey8G3f+lPS2qXr069u/fj7Jly7qsUEREROQ8DLrJ27gisPD0QGrkeqWhebmqQanq3aAKPXr56dOnXVEOIiIichHVMwikHpUy3aoGf570+++/Iy0tDfXq1XNYbrPZ9H+r+rmrmulWvRtGgYLu+fPnF3iHY8aMKXJhiIiIyPmY6SZvo1KmW9UgyZNiYmJw9epVhyAbAG7duqX/W9XPW9XgVPXfqQIF3XPnzi3QzgwGA4NuIiKiEkbVjA+pyxUBhadGL1c9mPCES5cu5bm8NGS6VT0v1VtkFSjoZpNyIiIi76VqZoTU5cpMt7vxvnOd69evIzg4WP//OzPdqj7sUPX7XNWHCZpCz9N9JxFR7g9ORESkGlUrn6QulZqXqz5AlCedO3fO4f/vDLpVDeJUPS/Vf6eKFHR//PHHaNSoEfz8/ODn54fGjRtjxYoVzi4bEREROYGqlTRSl0qZbgbdzpWRkaH/++6g+87PWtXEoKpjBKiawdcUevTyOXPm4LXXXsPo0aPx4IMPAgB27tyJESNG4MqVKxg3bpzTC0lERERFp3pfOVKXCqOX8/5zruPHj+v/TkpKcnivNPXpVu16UvW8NIXOdC9YsADvv/8+3nnnHfTo0QM9evTAzJkzsWjRokKNcl5YV69exYABAxAcHIzQ0FAMHToUN27cuOc2N2/exAsvvIAyZcogMDAQvXv3znVzapKTk1GpUiUYDAZcu3bNBWdARETkGcy0kTe5efOm/m82L6e73Rl0X7lyxeG90jB6uarnpepDEk2hg+6EhAS0adMm1/I2bdogISHBKYXKy4ABA/Drr79i69at+OKLL/Ddd9/hueeeu+c248aNw4YNG7BmzRps374dFy9exJNPPpnnukOHDkXjxo1dUXQiIiKPYqWfvMn169ddsl9PBd2q91V1t1OnTun/vjvovvO7TtUgTtXm5ar/ThU66K5Vqxb+9a9/5Vq+evVq1K5d2ymFutuRI0ewefNmLF26FK1atULbtm2xYMECfPrpp7h48WKe26SmpuLDDz/EnDlz0L59e8TExOAf//gHdu/ejb179zqs+/777+PatWuYMGGCS8pPRETkSZ6aKomoKNLT0/V/q9C8XNXgz1Pu7MedkpLi8F5paF6u6ve46t0wCtyn+9ChQ2jYsCHeeOMNPP300/juu+/0Pt27du1CXFxcnsG4M+zZswehoaFo0aKFvqxjx44wGo34/vvv8cQTT+TaJj4+HjabDR07dtSXRUdHo0qVKtizZw9at24NADh8+DDeeOMNfP/99w5PzoiIiFTBTBt5k7S0NP3fzqyA3xmQuRPvP+e6cOGC/u97Bd2qBm/MdHunAme6GzdujFatWuHKlSv45ptvULZsWaxfvx7r169H2bJlsW/fvjyDX2dITExEuXLlHJaZzWaEh4cjMTEx320sFgtCQ0MdlkdGRurbZGVloX///vjb3/6GKlWqFLg8WVlZuH79usOLiIiopGKln7yJq4JuT2XSVB8gyt2SkpJgNpthMBhy1cFLQ/NyVan+O1XgoHv79u1o0KABJkyYgG7dusFkMmHu3LmIj4/HypUr0axZs0IffOLEiTAYDPd8HT16tND7LahJkyahXr16+L//+79CbTdjxgyEhITor8qVK7uohERERMXHSj95k7uzl87iqUwagz/nunr1KqxWK0wm0z2DblW/71TNdKv+O1XgoPuhhx7CsmXLkJCQgAULFuDMmTNo164d6tSpg3feeSffjPO9vPTSSzhy5Mg9XzVq1EBUVBQuXbrksG12djauXr2KqKioPPcdFRWFW7du5RqJPCkpSd/mm2++wZo1a2A2m2E2m9GhQwcAQNmyZTFlypR8yz1p0iSkpqbqr/Pnzxf63ImIiNyFlX7yJpcvX9b/zT7ddLdr164hKCgIZrPZof8/4DjyPT9376L636vQ83QHBARgyJAhGDJkCE6ePIl//OMfWLhwIV577TV06dIFn3/+eYH3FRERgYiIiPuuFxsbi2vXriE+Ph4xMTEAbgfMOTk5aNWqVZ7bxMTEwMfHB3FxcejduzcA4NixYzh37hxiY2MBAP/+97+RmZmpb7N//3788Y9/xI4dO1CzZs18y2O1WmG1Wgt8nkRERJ6kegaB1KKNSG00Gp1aEffUfaB6MOFu6enpqFKlCjIyMnIF3VlZWfq/Vfy+u/NaUu38VP+dKnTQfadatWph8uTJqFq1KiZNmoSNGzc6q1wO6tWrhy5dumD48OFYvHgxbDYbRo8ejX79+qFChQoAbg+q0KFDB3z88cdo2bIlQkJCMHToUIwfPx7h4eEIDg7Giy++iNjYWH0QtbsDa+1Lvl69ern6ghMREXkrEYHBYPB0MYgK5OrVqwAAHx8fp2anPRX8Muh2rqysLJQpUwbJyckOyTPgdqbbYDBARJT83O9uwasSVYNtTZGD7u+++w7Lli3Dv//9bxiNRjz99NMYOnSoM8vm4JNPPsHo0aPRoUMHGI1G9O7dG/Pnz9fft9lsOHbsGDIyMvRlc+fO1dfNyspC586dsWjRIpeVkYiIqKTR+jiaTCblB6ohNWh9uq1WKzPd5CAnJwd2ux1RUVE4d+4cbty44fD+rVu39KBbxSDuzvEOVDw/QN3zKlTQffHiRSxfvhzLly/HyZMn0aZNG8yfPx9PP/00AgICXFVGAEB4eDhWrVqV7/vVqlXL9Ufy9fXFwoULsXDhwgId49FHH1X2D01ERKWTNiaKxWLJlRUiKom0bJ7FYlEi0y0iMBqNrGM6we+//w4AqFixIgICApCcnOzwflZWFkwmE3JycpR82KG1AlGNljRV+T4pcNDdtWtXfP311yhbtiwGDhyIP/7xj6hbt64ry0ZERETFdPHiRQC3H0Tf2RqMqKRKTU2FwWBwep9uT1TmtZYmZrMZt27dcvvxVXPs2DEAQPXq1REQEJBrRHqbzQaTyQSbzaZk8KY9kFItONXmXvfx8VF2vu4CB90+Pj5Yu3YtHn/8cZhMJleWiYiIiJxEm13E399f2SwJqeXq1aswmUwu6xLhzmBFu+dYd3aOEydOAABq1KiBoKCgPINus/l2eKNipjs1NRXA7aBbJVrQbbVaYbPZPFwa1yhw0F2YUcmJiIioZEhKSgIABAUFebgkRAWTkpICq9UKo9HotOyw1vfX3cGKNv0Zu3c4hxZ0N2rUCMHBwbkC6+zsbH2GIZUywRot6DabzUqdn/Y75e/vn6ufvirUekxCREREDrQ+jyEhIR4uCVHBpKamIjAw0KnNyxMSEgC4P1jR7j+LxeK2Y6pMC7qrVq2qzzR0Z7bbZrPBz88PgJqZ7rS0NADqtZzQWmQFBgYq9TDhTgy6iYiIFKYNPBQVFQUAyvaXI3Wkp6cjODhYHxDLGbSg28fHxyn7K6g7R2Kn4jt//rzeCkILurWADbj9/RYYGAhAzUy31l3BYrEodX7atM0qPxxm0E1ERKQwLeguW7YsAHAwJyrxsrKyEB4eDpPJ5LTAQhvF39fX163BitZs1tWz/JQWly5dQnBwMACgTJkyAP73QCUnJwciogduKma6teDU39/fwyVxLu28wsLClHqYcCcG3URERApLTEyEyWTSM3w3b970cImI8pednY3s7GxUrFjRqZlurW+1v7+/Wyv12uwBWlaWiic1NVV/gKj9V3uwoWWBw8LCAKiZ6daCU6vVqtT5aQ9OtAcqKmLQTUREpLArV67A19eXQTd5hZ9++gkAUL9+facG3Vrf6qCgILcGK1rTZy0rS8Vz8+ZNVKpUCQBQrlw5AP8Lus+cOQPgf8G4ipnulJQUGAwGGAwGTxfFqRITE2E2m93e/cOdGHQTEREpLCUlBUFBQfpATllZWR4uEVH+9u3bBwBo0aKFU5uXa0G3uzNpWrN2bXAvKrpLly5BRFCjRg0A/wu6tb/t+fPnAQCRkZEA4JLp5jwtNTUVZrMZBoNBqUy39nBYm+5NRQy6iYiIFHbt2jVUrFiRmW7yCr/88gsAIDY21qmZbi3jHB4e7tZg5cqVKzCZTMplJj3h22+/BQA0a9YMAFC+fHkA/2tyffToUQC3W0kAag4amZaWBh8fH+Wup9TUVAQFBelBt4p/OwbdREREisrIyEB2djbq1q2rV2ZsNpuHS0WUvwMHDsBkMqFcuXJOnd4rKSkJRqPR7VN3Xbt2TckgyRP27t0LAGjfvj2A/wXdWl9ubTqxpk2bAlDzuy49PV3Jechv3LiBsLAw/eGwigN+MugmIiJS1NatWwEADzzwAJuXk1f47bffEBERAQBObV5+5coVWCwWt89vfO3aNfj6+sJoZJW7uH755RcYDAbUrl0bAPSpwbRBuM6dOweDwaD3n1cxW3rjxg0EBAQo9RAnJycHN2/eRNWqVZVukcVvACIiIkWtW7cOANCvXz8G3VTi5eTk4OrVq6hTpw4AODXTfe3aNfj5+bm9z2hqairCwsKUCpI85fTp07mmXrNarXpf7osXL8JqteqZYBWzpRkZGQgPD1fqejp27BgAoGHDhvr9qeLfjkE3ERGRonbt2gVfX19ERUUx6KYS7z//+Q9EBN27dwfg3Ex3WlqaQ59Rd8nIyEC5cuWUCpI8JSkpCVFRUQ7LgoKC9OngEhISULZsWfj6+gJQs3n5rVu3EBkZqdRAajt27AAAtGzZUs90q/g7xaCbiIhIQRkZGTh16hSaN28OAEpnEEgNH3/8MQDgueeeA+DcTHdGRgbCwsLcOlBTTk4OsrOzUalSJQbdxZSTk4OMjAx95HJN2bJlkZqaCuB2q4KaNWsqG3RnZGRARFCxYkVPF8WpfvzxRwBA27Zt9YfDbF5OREREXmHmzJkQEYwaNQoA9MoMg24qqXbs2IEyZcogNDQUgPOC7pycHNy6dcuhz6g77gOt2WzNmjX1vuQqzh3tDocPH4aIoEmTJg7Lq1atiszMTPz0008QETRv3lz/rlMt6D58+DAAoFq1akplun/88UeYTCZERUUpPeAng24iIiIFzZ8/H/7+/ujfvz8A6P0cVWy2R97vxx9/REpKCv7whz/oy5zVFFwb1frOUfzdEXRr059FR0crPRWSO3zxxRcAgM6dOzssf+SRRwAAEyZMAAAMHDhQ2cBNmxKtVq1aSrWcuHPwRA6kRkRERF7j7bffRkpKCp5//nl91GRmuqkk++tf/woAeP311/VlPj4+Tsnmff/99wCAxo0bu7VSHx8fD+D27AFaIKhiMOEOWr9fLcjW9OvXDwAQFxcHq9WqTxcGqBd0Hz9+HABQr149ZYLunJwcpKSkIDo6GgDYp5uIiIi8w7Vr1/DXv/4VoaGhmDVrlr5c1SaXpIa4uDhUqlQJVapU0Zc5a3qvn376CQDQqlUrj2S669evr/T8w+7w66+/5jkQXvXq1VG9enUAwP/93/85vKdaqwIt0924cWNlmpfv378fIoI2bdoAUPt3yr1DOBIREZFL9ezZEzabDZ988onD3MCs9FNJ9a9//Qs3b97EkCFDHJY7q3l5fHy8Pr+zOzNpp06dQkBAAIxGo9IDRLlDYmKiPj/33X755Rfs2LEDXbt2dVhut9vdUTS3OX36NHx8fGA2m5XJdH/yyScAgL59+wKA0rNsMNNNRESkiC+//BLfffcd2rZti27dujm8p/LcteTdZs6cCYPBgIkTJzos1wLk4jpx4gTCwsIAuHcU/6SkJJQtWxaA2s1mXe3q1avIyspCs2bN8nw/MDAwV8BtMBiUy5YmJCQgKCgIAJTJdG/duhUWiwWNGzcGAGX74wMMuomIiJQxaNAgmM1mfPbZZ7neY9BNJdHNmzdx4MABNG7cGP7+/g7vaYFqcUb8zs7ORlJSkp4l1fbpjkp9WloaqlWrBgDs010MGzZsAAB06NChUNup1rw8JSUF5cqVA+Dc6fQ86bfffnNowcBMNxEREZVob775Ji5fvoyxY8ciPDw81/sMuqkkmj17NnJycjB+/Phc7zmjS8S6deuQk5ODPn36OOzT1ZV6bQqrli1bAlC7r6qrxcXFAYDDyPb3YzAYlAu6MzMzUblyZQCA0Wj0+qD7hx9+gM1mQ6dOnfRlKv9OMegmIiLycjdv3sS0adMQEhKCd955J891tMoMK/1UkixZsgQWiyXXIFiAc7LDy5cvBwAMHz4cgPsy3evXrwcA9OjRw+G4KmbwXO3AgQOwWq15PkzMj2pB98WLFyEielZYhUz34sWLAfzv3gTc2xLF3TiQGhERkZcbPHgwsrKysHTpUofB0+7ETBuVNBcvXsTZs2fRqVOnPK9bZ2S6v/vuO0RFRSE4OBiA+5qv7tixAwaDQR+VmUF30Z0+fRrly5cv1DaqBd179uwBADRp0gSAGkH3pk2bEBgYiHr16unLVJ7akpluIiIiL3b27Fn861//Qu3atfPMFmpUbrZH3kmbm3vq1Kl5vl/cObW/+eYbpKen4+mnn9aXuWsgtcOHDyM0NFR/mMCHXkVz9epVpKen44EHHijUdqoF3fv27QMAtG7dGsDt6fS8Oei+du0aEhIS8OCDDzosV/l3ikE3ERGRF+vduzdEBP/617/uuR6bl1NJ8+9//xthYWGIjY3N8/3iZqVff/11AMBrr72Wa5+urNRrg7c1aNAg13GZ6S6c1atXA7g9FWJhGAyGYg3AV9L8/PPPAICGDRsCcN50ep4ye/ZsAMCoUaMclqvcvJxBNxERkZfaunUr4uPj0bFjRzRt2vSe6/r6+gJQb0Rf8k7fffcdrl275pCFvpsWWBQlUD19+jS+++47NGnSRJ+2C3BPpX7NmjUQEfTu3VtfxqC7aDZu3AgADp9lQaiW6f7tt9/0Od8B7890L1++HL6+vnj88ccdljPTTURERCXOwIEDYTKZ9GzQvWhBt4oZBPI+r776KgDgjTfeyHed4jQvHzhwIADg448/dljujuD3k08+AQD88Y9/1Jc5o396afTTTz8hJCRE//4qKKPRCLvd7qJSuV9iYiIiIyP1//fmPt2//fYbfv/99zzHcmDQTURERCXKm2++icTERIwaNapAo/qyTymVFImJidi5cycaNWqkzzucl6JesydOnMDOnTvRokULNG7c2OE9Lfh1ZRZ03759CA8P1wdvA3j/FUVOTg4SEhIQHR1d6G1VynTn5OQgPT0dNWrU0Jdp17E30kYrf/PNN3O9547701MYdBMREXmZI0eOYMqUKQgLC8O8efMKtA0r/VQS5OTkoEOHDhARvP/++/dct6hZ6SFDhgDIneUGXD+K+NWrV3H58mW0atXKYTmblxfe999/j5ycHLRr167Q26qU6T527BhExKELkclk8lyBiuHcuXP49ttv0bRpU71/+p2Y6SYiIiKPy87OxmeffYYHHngAIoINGzbkO0XY3bT1VMwgkPf4wx/+gMOHD2PQoEG5Ri6+W1EC1cTEROzevRvNmzd3mIpIo1XqXXUfzJ07FwDw4osvOixn8/LC0waHHDBgQKG3VSno3rZtGwDo088B3pvpHjJkCEQEH374YZ7vqzz2iHcPfUdERKSw06dP46OPPsI333yDX3/9FVevXgVwu0L58ccf3zdoyQsz3eQpkydPxqZNm/Dggw9i+fLl912/KFnpkSNHQkTw3nvv3XOfRQ1+v/vuOyxbtgy///47AgMD0bhxYwwbNgxVqlQBAKxatQoWiwVdu3Z12E7lDJ6rbN++HWazOc+M6P0YDAZlgu7du3cDgEPG3xsz3UeOHMG2bdvQuHFjNG/ePM91VB69nEE3ERFRCfLrr7/i9ddfx5YtW5CamgrgdgWyXLly6NixIzp16oRnn30WUVFRRdq/ihkEKvlOnDiBt99+G+XLl8d3331XoG0KO71XRkYGNmzYgOrVq+c7DVlRp87buXMnBg0ahFOnTgG4fU+KCD777DNMmzYNgYGBqF27Nk6dOoXOnTvne1zefwV3/PhxVKxYsUjbGo1GZaYM++mnn2C1WhEaGqovu7Pvs6emD7ty5QqGDBmC1NRUPPLIIzh27BiuXbuG9u3bY8KECQ7levvtt/Up/D799NN896nygJ8MuomIiEqAq1ev4qGHHsLhw4cBABERERgwYACGDRuGhx9+uMDNyO9HxcoMlXzDhw+HiGDr1q0FvpYLOw7BhAkTYLfbMXPmzHzXKWymOzs7G127dsXXX38No9GIfv364W9/+xsqVaqEnJwcfPvtt1i+fDm++eYb/PTTTyhXrlyeWXxmugvn+vXrSE9PR5cuXYq0vUpB99mzZ1G+fHmHZXdex54Ium/evIk6deogJSUFBoMBO3bsAHD7YdTWrVsxdepUDB48GM2bN8df/vIXXLlyBUFBQVi6dGme3T40bF5ORERELnPr1i3Uq1cPly5dQvfu3TF79mzUrVvX6cdRqckleY/ExER89913aNq0KRo0aFDg7QoTqGZnZ+Mf//gHypQpg6eeeuq++yxIpf7WrVuoU6cOzp49i1atWmH9+vUOLUyMRiPat2+P9u3b33df7NNdOFo29A9/+EORtjeZTEp81926dQs3btzAo48+6rBcC7Rv3rwJf39/t5dr1KhRSElJwdy5czFmzBjs2bMHjRo1QmBgID744ANMmTIFH3zwAYDb1/7LL7+Mt99++74P3FQe8JMDqREREXnYM888g0uXLmHatGn44osvXBJwa1TMIFDJNnXqVIgIFixYUKjtCjOQ2vjx43Hz5k1MnTq1QPssyH3Qvn17nD17FuPHj8fevXuL3KUDUDuD5wqbNm0CAPTu3btI26uS6V6/fj0AoEOHDg7LPfkQJycnBytWrEDFihUxduxYGI1GPPjggwgODobRaMTIkSNx6dIlbNu2DdOnT8e1a9cwc+bMArVwUTnoZqabiIjIgy5evIh169ahdu3aePXVV116LJXmriXvsXbtWoSEhKBt27aF2k7LSt8v6H7vvfewYMECVKxYEaNHjy7QPu8XrKxcuRK7du3CY489htmzZxei1MU7Lt32ww8/IDg4GIGBgUXa3mQyeVXgdu7cOYSHh+c6Xy3jP3jwYIflrp767l6WLl2K7OxsjB8//p7rPfroo7ky9Pej8iwbzHQTERF50DPPPAMRwYoVK1x+LAbd5G6//PILkpOT0aNHj0JvGxAQAADIzMzMd524uDiMGTMGoaGhOHTo0H33WdBM95/+9CdYrVZ8/vnnhShx/oo6gFtplJOTg4sXL6JRo0ZF3oe3ZLoTExNRoUIFVK1aFUFBQWjbti1u3Lihv79jxw6EhoY6DKIGODYvd7eFCxfCaDTe9wFXcah4nzDoJiIi8pBDhw5h+/bteOCBB9CqVSuXH49BN7mb1txbG7m4MIKDgwHAIQi5U05ODvr27QuTyYSffvopV2CSl4JMSfTFF1/g6tWrGD58uN4svLhUbjbrbJs3b4aI5Jp2rTC8Jehu0aIFEhIS0L9/f7Rs2RK7du1CxYoVERcXhz59+uDKlSt5fg6eup5u3bqFQ4cOoUmTJnoZXEHF3yk2LyciIvKQZ555BgaDAf/85z/dcjyj0cjmreRWX331FcqXL4/q1asXetugoCAA+We6X331VSQnJ2Py5MmoWrVqgfZZkL7Vr732GgwGA958881Clrh4x6XbtCbVQ4YMKfI+TCZTiQ+6J02ahAsXLmDs2LGYO3cuAOD999/H6NGj0bFjRwBA5cqVsXjx4lzbeirT/e677yInJwcvvviiS4+jwiB4d2Omm4iIyAO++eYbHDx4EJ07d0bNmjXdckwG3eROX375JTIyMjBgwIAiba8F3RkZGbneu3btGv72t7+hTJkymDZtWoH3eb8M4c2bN/Hzzz+jRYsWeqbdGVSef9jZvv32WwQEBKBChQpF3ofJZIKIOLFUznXz5k3MmTMHZcqUcRgzYOTIkfjll18wcuRI7N27F+fOncvzOvRUpnvJkiUwm80YNGiQy45hMBiUvE+8Jui+evUqBgwYgODgYISGhmLo0KH5NjfS3Lx5Ey+88ALKlCmDwMBA9O7dG0lJSbnWW758ORo3bgxfX1+UK1cOL7zwgqtOg4iICMDtLI7RaHRLX26NyWRi0E1uM2PGDADAX/7ylyJtrwUbeQXdTz31FLKzs/Hxxx8Xag77+2Wc582bBxHBn/70pyKUOH9sXl4w169fx/nz54vd3aakZ7pHjx6NW7du4d133811/TZo0ACLFi2652dQmJH9neXdd9/FiRMn0KFDh0Ldc0WhYqbba5qXDxgwAAkJCdi6dStsNhuGDBmC5557DqtWrcp3m3HjxmHjxo1Ys2YNQkJCMHr0aDz55JPYtWuXvs6cOXMwe/Zs/O1vf0OrVq2Qnp6OM2fOuOGMiIiotHr//fdx7tw5DBw4EGXLlnXbcc1mM4Nucovs7Gzs2bMHderUKVBf67xogcXdTWhXrlyJuLg4tGrVCt26dSvUPrVmufkFv8uXL4fZbEb//v2LUOL8qTwqszNpzaxHjRpVrP2YzeYSG3RrD4sqVqxY5FYg2nXsjqA7IyMDbdu2xYEDBxAUFIR//etfLj2eqmOPeEXQfeTIEWzevBn79+9HixYtAAALFixAt27dMGvWrDybn6SmpuLDDz/EqlWr0L59ewDAP/7xD9SrVw979+5F69atkZKSgldffRUbNmxwmP+ucePG7jkxIiIqdTIyMjBu3DgEBARgyZIlbj02g25ylzlz5iA7O9spGeM7g+7ff/8dQ4YMgb+/P7Zs2VLkfeaVScvIyMDx48fRunVrl2XymOnOX05ODubMmQM/Pz888cQTxdqXxWIpsUH3u+++C5vNhsmTJxd5H65oOZGdna0H85o9e/aga9euSE1NRc+ePfGvf/3LpQOoAeoG3V7RvHzPnj0IDQ3VA24A6NixI4xGI77//vs8t4mPj4fNZtMHIgCA6OhoVKlSBXv27AEAbN26FTk5Obhw4QLq1auHSpUq4emnn8b58+dde0JERFRqPfPMM8jKysKSJUtcXnm5m4+PDyv9VGg5OTmFDmDmz58Pq9WKESNGFOvYBoNBD7pzcnLw4IMPIjs7Gxs2bChWn+u87oO5c+e6pGn5nVQMJpxl2rRpuH79OiZOnFjshx4lOeieP38+LBZLse4NZ87TvXXrVgQFBcHHxwdWqxXNmjXD+PHj0aVLF7Rp0wY3btzAwoULsX79erf8ZhkMBiUfDntF0J2YmIhy5co5LDObzQgPD0diYmK+21gsllxNmiIjI/VtTp06hZycHLz11luYN28e1q5di6tXr+Kxxx675x87KysL169fd3gRERHdz7Fjx/DZZ5+hfv36Tm++WhAMuqkwbt26hZ49e8JkMsFkMiEwMBAvvPDCfYOZPXv24MKFC+jRo0exgyct6P7ll18QExODc+fOYcyYMXorxqLKK9P98ccfw8fHB3369CnWvu+F91/esrOz8fbbbyMkJASvvvpqsffn6+tbIoPuY8eO4dy5c+jSpUux7o2Czjd/P//+97/RuXNnZGVl4amnnkK1atVw8OBBzJ07F1999RWio6Nx6tSpYjf3LwxVxx7xaPPyiRMn4p133rnnOkeOHHHZ8XNycmCz2TB//nx06tQJAPDPf/4TUVFR2LZtGzp37pzndjNmzCjSfJNERFS6DRw4EABc3icuPxaLhZk2KpC4uDg88cQTSEtLQ/369VG7dm3s2bMHixYtwo4dO/DTTz/lGzSMGjUKBoMB7733XrHLYTAYsGPHDjRp0gQA0LNnT7z77rvF3u/dwW9GRgZOnDiB2NhYlzUtNxgMSg4Q5QwjR47EzZs38xxYrCisVmuBRi/PycnBiRMnkJmZieTkZJw+fRrHjx/HxYsX0alTJ/0721n+/Oc/AwBmzpxZrP1YrVYAxct037p1C4MGDYLFYsGRI0ccpvU7dOgQqlSp4tQR/AvKaDS6dYA4d/Fo0P3SSy9h8ODB91ynRo0aiIqKwqVLlxyWZ2dn4+rVq4iKispzu6ioKNy6dQvXrl1zyHYnJSXp25QvXx4AUL9+ff39iIgIlC1bFufOncu3TJMmTcL48eP1/79+/ToqV658z/MgIqLS7bfffsO+ffvQunVrNGjQwCNlsFgsuSr9+/fvx5IlS2AymdC1a1f06NHDI2WjkuPzzz9Hr169YDKZMH/+fIc5eQcMGIBVq1ahQ4cO2LZtW65tp0+fjp9++gmPPvporlaKhfXDDz/o1+uwYcMwduxYp9w7eQW/c+bMcXnTcoDNy/Oyf/9+fPjhh6hatSqee+45p+zTz8/vvkF3XFwcunfvnm+A98knn2D8+PH45ptvnDLeU05ODr788ktUrlwZdevWLda+tL7XxckIDxw4EOnp6Vi0aJFDwA0ADRs2LFb5ikPZsUfECxw+fFgAyA8//KAv++qrr8RgMMiFCxfy3ObatWvi4+Mja9eu1ZcdPXpUAMiePXtEROTYsWMCQL7++mt9neTkZDEajfLVV18VuHypqakCQFJTUwt7akREVEo88sgjAkAOHjzosTI0a9ZMrFar/v+vvPKKAHB41a5dO9/fVlKf3W6XkJAQsVgscubMmTzXeeyxxwSAjBw5Ul+WlZUlbdq0EQASEREhKSkpxSpHWlqaBAYGCgCpVatWsfZ1N4PBIB07dnRYVrt2bfHx8RG73e7UY93JaDRKu3btXLb/ksJms8nLL78sffr0kX379t133bCwMDEajXLq1CmnlaFfv35yrzAnJSVFfHx8xMfHR8aNGyevvfaazJ49W9auXSuHDh2SzMxMefnll8VoNIrZbJadO3cWu0yzZ88WADJ79uxi72vDhg0CQBYtWlSk7X/++WcBIHXr1i12WZwtLCxMqlat6uliFFhB40CvCLpFRLp06SLNmjWT77//Xnbu3Cm1a9eW/v376+///vvvUrduXfn+++/1ZSNGjJAqVarIN998Iz/88IPExsZKbGysw3579uwpDRo0kF27dsnBgwfl8ccfl/r168utW7cKXDYG3UREdC8vvviiAJAWLVp4tBytW7cWs9ksIiLTpk0TAFKxYkU5deqUJCQkyIABAwSAWCwWOXTokEfLSp4xbtw4ASAzZszIdx273S61a9cWAPL666/L5s2bpUyZMgJAOnfuLDabrdjlaNSokQAQq9UqderUKfb+7nR38JuWliYGg0Hatm3r1OPczWQyyUMPPeTSY3haVlaWREVFOTzIGzFiRL7rd+nSRQDIO++849RyDBkyRABIVlZWnu936tRJAMiGDRvuuZ+dO3eK2WwWHx+fYj8UqFixolitVqc82Nm8ebMAkHnz5hVp+ypVqojBYJCTJ08WuyzOFhERIeXLl/d0MQpMuaA7OTlZ+vfvL4GBgRIcHCxDhgyRtLQ0/f3Tp08LANm2bZu+LDMzU0aNGiVhYWHi7+8vTzzxhCQkJDjsNzU1Vf74xz9KaGiohIeHyxNPPCHnzp0rVNkYdBMRlU7nz5+XCRMmSM+ePWXGjBm5KnirV6+WihUrCgCpVq1asbN/xfXoo4+K0WiUy5cvi8lkkjJlykhmZqbDOps2bRKj0SjBwcH5VlhJXUFBQRIeHn7f9dLS0iQsLEwPrIxGo1MCp/T0dGnQoIEAkH79+klwcLBUq1at2Pu9k8lkkocfflj//+nTpwsAWb16tVOPczez2Zwr+aOap556SgDI5MmT5cKFC1KrVq1crSJERLZv3y4RERECQNq0aeP0cowaNUoAyOXLl3O9p7V8bdasWYH29fXXX4vBYJCwsDBJT08vUnni4+MFgDz11FNF2v5u27ZtEwAya9asQm87c+ZMASDPP/+8U8ribBUqVJCIiAhPF6PAlAu6SzIG3UREpYvNZpMHH3wwV9NsPz8/+eijj0REZPz48QJAzGazDB482KXNVguqd+/eAkAGDx6cq3vVnebPny8ApGfPnu4tIHnUzp078wyQ8pOZmSmzZs2SV155Jc/gprCOHz8uoaGhAkAGDhwoIrezXlFRUcXe953uDn4bNWokJpPJ5feoj4+PxMTEuPQYnpSVlSVms9mhafDdrSJEbl9nJpNJTCaTjBkzxiWf+8svvywA8szkat/dR48eLfD+5s2bpzfH1sqblJQk06dPl48++ui+59C+fXsBIGfPni3cieRj9+7dAkDeeuutQm2Xnp4uFotFgoODS8RvUl6qVq0qoaGhInL7d7RRo0Yl+gEwg243YtBNRFS6aE0i27ZtK7t37xa73S4LFy4UPz8/ASDly5cXAFK1alWHVlmepjVzt1qtUqZMmXuu27Rp01x9Brdv3y4DBgyQcePGybJly+Tnn38usRU3Kjztur67VaA7rF+/XsxmsxgMBlmwYIG+/M4KuLNYLBY9+LXb7WIymaRRo0ZOPUZefH193XIcT3n11VcFgCxdutRheWZmpt7k/MUXXxSz2Sxms/m+/b2LY8qUKQJADhw44LD87NmzAqBIDz+GDh0qAKRTp04yZswYMRqN+gPX0NDQfMfryMzMFJPJ5NT+0/v37xcAMnXq1EJt17NnTwEgn376qdPK4my1a9eWwMBAERHx9/e/72+VpzHodiMG3UREpcfJkyfzrbRlZmZKt27dxGKxSMOGDUvc03mtWSEAGTdu3D3XTUlJkeDgYAEgTZo0kaCgoFyZfe3Vrl07Bt8K8PX1lcqVK7v9uFOnThUA4uvrKzt27HB4r0GDBuLn5+fU4/n6+krDhg1FRGTt2rUCQKZPn+7UY+QlICBAoqOjXX4cTwkLC5OAgIA837t8+bI+MJ7ZbM71d3Y27btu+/btDsu1QQDj4+OLtN+2bdvq33tRUVGydu1aee211/QB1zZv3pxrm8mTJwsAWbFiRZGOmZeDBw8KAHn11VfvuZ7NZpMJEyZI1apVxWKxCAD92i+ptHt+xYoVBTpHT2PQ7UYMuomISo8OHToIAK8caGzTpk16hbEgv1mpqakSHR0tBoNBypUrJ6NGjZKkpCQ5efKkrFq1SiZNmiQxMTF69oe8lzYa8sSJE9163AkTJugBTF4Z9tjYWH3wP2cJCAjQB2fTBtRyRx0uODhYatSocc91UlJSZPLkyVKjRg0xGAxisVgK3YTYE9avXy8AZNSoUfmuc/78eZk4caJbZkdYuHChAJCNGzfqy86ePSsGg0EaNGhQ5P3a7XZ57bXXZPHixQ7Ld+/eLRaLRQwGg97FSFO2bFnx9/cv8jHzovVLf+WVV/Jd5+DBgxISEqJ3fWrcuLG0bt1azp8/79SyOFvz5s3FYrHIgw8+KAaDIde4IyUNg243YtBNRFQ6JCcnF7vS5kl2u10iIyPlmWeecep+tamiVq1a5dT9kvs8/PDDAsAtg/1lZWXJunXr9Kxj1apV861Yd+nSRQwGg1OPHxwcLNWrVxcRkfDwcClXrpxT95+f8PDwe7Yk2LZtm/j4+OhdQNq0aSPh4eEeeRhSWNHR0WI0GktMd5qPPvpI/07q3r27+Pr66g8c9+7d65JjHj9+XM/maw9KtC49L774olOPdebMmXu2WDp//rxYLBYxGo0yd+5cpx7b1bQHbf7+/lKlShVPF+e+ChoH3p5ZnYiIiO7r2WefhYjg3Xff9XRRisRoNCIxMdHp+/3qq68QERGBYcOGoXfv3rBYLE4/BjnH2rVr8csvv2Ds2LEIDw8HAOTk5GDPnj2oVasWQkNDnXKcixcv4p133oHNZkO/fv1Qv3597N69G6tWrcK6detgs9kAAI0aNcIPP/yQ7zVTpkwZiAhu3brltOvKbDbDZrPhxo0buHr1Kh5//HGn7Legx71bTk4ORo8ejQ8++AAmkwlr1qzBU089BQC4efMmatWqhbfffhvR0dEYNGiQW8qq+fvf/44NGzagXr166N27Nx544AEYjUaHdf7xj3/g6NGj6NSpEwIDA91avvwEBQUBAF5//XUcO3YM1apVQ5MmTfDHP/4RrVq1cskxa9eujd9++w0NGzbE5MmTMWXKFNhsNlStWhXz5s1z6rGsVisAIDs7O8/3H330Udy6dQubN29G586dnXpsVwsLC0N2djays7Px2GOPebo4zuOWRwCKY6abiEh9a9asKdQ0M6XN4sWLBYD079/f00WhfIwYMULP9pnNZn26oblz5woAmT17tlOOM3XqVDEYDPmOARAVFSUzZ84sUDPj119/XQDI7t27nVI2EZFy5cpJVFSULFq0yOl9be+lYsWKuQaF2rFjh5QtW1b/XPIajCs5OVkCAwPFaDTKzp073VJWu92ut0S482WxWGTEiBF6ywTtMwwKCnLKCPbOoo3uDUAefPBBtx47PT1dRo4cKdHR0TJo0CCXNI9OTk4WIO850LW/yYABA5x+XHfQBqyDC1slOBObl7sRg24iIrWlpKSI1WoVX19fj8+1XZLVrl1bDAaDHD582NNF8Vpjx46VgIAA8ff3l8jISD1AfPDBB4s12vO+ffv0ptxr167Vp+Zq3769WCwW8fX1dcpgeJs3bxYAUq5cOdm9e7ecPHlSJk2aJM8//7zMnj270P15N27cKABk3rx5xS6byO2BpcLDwyUsLEzvz+2uAQ+rVq0qISEh+v/37dtXgNtznGvTaeXn0KFDYjabxWq1Om3aqbxcuHBBFi9eLPXq1RMA8uijj4rNZpP4+HiZOHGilCtXTgCIj4+PPktDSEiIW/ppF8aFCxf0wM0To/G7WmZmpgCQIUOGOCzPysoSX19f8ff3L3EDeRaUNrCit+SGGXS7EYNuIiK1NWnSRADI2rVrPV2UEu3w4cNiMBikdu3ani5KocydO1ciIiKkWrVq+c5dXhw2m61A6w0bNkwASEREhNSsWVMiIyMlKipKIiIi9EpoixYt5Pjx44U6vt1ul4iICDEajXLq1CkRuV1pr1Gjhh5AbdiwodDndTebzSbBwcFiNpudlvVMSUlxWgsKm82mT12F/07z5OzpyO7lzqmQZs2apY8knZSUVKDttcHuIiMjXTJbwDvvvOPQQiG/sR9WrFghlStXltDQUOnWrVuJ6cd9p1WrVgkAr+gTXFR53RfdunUTALJs2TIPlar44uLiBIBUqFDB00UpEAbdbsSgm4hITXa7Xfr06SMApHfv3p4ujld45plncs3vXZK99dZbAkD8/f31eaKdUWHdsGGDNG/eXPz9/fXpsGbOnJnv+tu2bRMAUqtWrTwDqgsXLsgjjzyiB0QxMTEFbnqpTZ9092BcdrtdlixZ4rQsZb9+/ZzaTF0TEBAgVatWLfZ+evfurTeF1j5Hdz4gunP6Mz8/PwkKCip08Pzaa68JAOnVq5dTyzZv3jwBIGXLlpWVK1fKmTNnnLp/dytTpowAkA4dOni6KC5z93Wwbt06/cGct5s3b56cOnVKTp48WeCHUp7CoNuNGHQTEalj+/bt0rBhQz1YAiBNmzblPNQFlJWVJQEBAeLr61vip3r5+uuvxWAwSEREhKSnp8uZM2ckKChIDAZDkfv5nj9/Xpo2bao3G65evbr06NFDb84dFhYmRqNRrFarDBw4ULKyssRms0lISIiYzeb7BsCHDh2SmJgYPSPZv3//e2bS7Xa7BAUFSUBAQIGv4cWLF0tAQICYzWZp3rx5gaYY2r9/vwBwyTzUrVu3FoPBcN+MakpKiowePVoeeuihXPMzz58/X88sN2jQQIxGo/43OnnypNPLnBdtKiStCX5RRyTXri9n9e/Wuh6Eh4crUZfVxigwGo1KBKD5MRgM0qVLFxG53YojMDBQLBaLx7tA2e12Wb16tYwePVo6dOgg1atXFz8/PzEYDFKjRo1CdT9q3bq1mEymEv37y6DbjRh0E5GrpaamyrRp02TGjBmSnp7u6eIoa/Xq1XplrXbt2tK9e3evm26lJNCm63niiSc8XZR82e12CQ4OFh8fH4es3vnz5yUgIEAMBoN8+umnhdrnzp07xWQyCQDp2rWrQ70gKytL2rRpI2FhYRIbGysVK1bUM+BVq1YtdL/ls2fPSp06dQSABAYGytSpUyUrK0u2bNkiL774ogwbNkx2796tZ/KnTp2a535sNpssWbJEpk2bJkuWLJEHH3xQAEhAQIA0btxYHzzrfv3JK1Wq5NB83Zk+++wzASBDhw7Nd51hw4blGrzt1VdfFbvdrn8GYWFhkpqaKs2aNdPXMRgMUrZsWbdU6lu3bi1ms1m6du0qAIrcBD8hIUGMRqNUrFjRKeWKjIwUg8EgFStWlPDwcGnXrl2huzC42vHjx6Vx48bi5+cnlStXvuf0hBEREeLr6yuhoaH3nKLN2xmNRmnXrp2I/K8FhKd/r1JTUx26wxgMBgkMDJQ6dero826bzeYCPzDy9fW979z2nsag240YdBORK61Zs0YsFovD6LHr1q3zdLGUs337djEajeLn5+fSgYpKiwYNGggAOXDggKeLkqeXX35ZAMiMGTNyvXfmzBnx9/cXg8FQ4HstLS1NAgICxGQyFbhCuWzZMr2pc1GbC8+fP18CAgLyHSlcC8rzCirHjBkjZrM51/qPPvqo3kph8+bNYjKZxMfHR/bv359nGaZNmyYAZOTIkUU6h4KoUqWK3s3jzsz+oUOH9L7pNWrUkM2bN8v58+f1Qb608wsODtYz9rGxsfq5Tpw4UQDIK6+84rKya9q1aydGo1ECAwOlfPnyxdrXyJEj87x+x40bJ76+vhIWFiZjx469b2sTrW+59tuifW5Go7HE/M4cPnxY7/pRu3ZtfS7zGjVq5GrRoD2gGT58uNSrV098fX09VGrXM5vNEhsbKyK3m9MHBAR4uEQiTzzxhAC35w/Pq4vC7t27xWw2i8lkuu/4GVoLDGfPce5sDLrdiEE3EbnKq6++qmfDVqxYIR999JH4+vqKwWCQNWvWeLp4yti/f7+YzWbx8fGRn3/+2dPFUcKpU6fEYDBIlSpVSlzTwNTUVDGbzRIREZHvOidPnhRfX18xmUwFenAQExMjAGTp0qVOLGnB2O12Wbp0qfTo0UOmTp0qp06dkvPnz8vzzz8vVatWzTN46tKliwCQ8uXLy/z582X37t3y6aef5tmUPC4uTs/gWywWiYiIkMGDB8uZM2dkxYoVYjQaJSwszKV/55SUFGnUqJE+ANo777wj3bp1E4PBIAaDQYYOHepwfJvNJl26dJEKFSrI1KlTHd7r2bOnns3XBpkzm82SnJzssvKL/G+0ci0oLKzLly/LsmXLZP78+ZKamiqhoaF61tBut+sBT5kyZSQkJEQAiJ+fX74DQNpsNr2Z/YQJE/TPKD4+Xvz8/MRoNMq2bduKc8pOUalSJTEYDHqAnZ6eLn369NHLXr9+fYmLixMRkejoaDEajZKWlqb34S9JU5k5k8VikZiYGDl8+LAAkIEDB3q0PAkJCQUaSHPfvn16ImHkyJH5fm88++yzAsAlrWeciUG3GzHoJiJX0PqlVahQwaEyeOrUKb1C5IqRlkubs2fPitVqLVSGkgpm1KhRAkBiY2NLVODdo0cPASDr16+/53r79+/X+1/PnDnT4Ry0Lh8NGjQQq9XqksGtXEV7mNe2bdsC/10OHjwo3bp1k2bNmukBnfby9/fPc35pV5g1a5ZDdr527dpy9OjRQu1Duy6rV68uIiJbtmwRAPLwww+7osi6cePG6eUubPPtXr16OXzmJpNJnnnmGf1hiBaAtmjRQm8JsGTJEv3abNy4sQwePFg++ugjyczMlJSUFH36r0aNGuU63uHDh8XHx0d8fHw82tRc6//+7LPP5novKSlJOnfurHcr0K7Ljh07iojIpk2bitV3vqTz9fWVRo0ayZAhQwSA2+7B/HTo0EEAyO7du++77qlTp6RatWoCQGrWrJlnP/SwsDCHKfZKKgbdbsSgm4ic7cKFC2IymSQ0NDTPwYMOHz4sFotFzGazxMfHe6CEasjMzJTw8HAxGAxOmTKJctOyimFhYdK+fXsZNmyYxMTESNu2bWXBggVuD8bXr18vBoNB6tevX+D1tebbwcHB0q9fP70PttZ8uV69ejJ69OgS9WAhP3a7XaxWq4SHhxervPv375cnnnhCnnrqKbfXf1JSUmTNmjVFzmAuXrxYAMcpsbS+7B999JGzipmLNpib0Wgs1HZTpkzRg+MVK1bI8uXL9X6zUVFR8uSTT0rz5s1l8uTJYrfbZezYsfqYFAcOHHBoTn/3y2w253sdaAMNBgUFubwVQH7q1asnRqPxnoODXb58WQYOHCihoaESEhKidw+y2+0SEBBQpFHivUFAQIDUqVNHypcvr09F5yknT54U4Pago4UxYsQIvQvMvn37JDMzU6ZPny7h4eECQMaOHeuiEjsPg243YtBNRM6mNaPcsWNHvuvs3r1bTCaTWK1Wt42+q5oHHnhAAMisWbM8XRSljR07Vn+4oVX0tX8HBgbK/PnzXV6GTZs26feVxWIp1Ai6drtdpkyZIn5+fnp3j0cffVTWrl3rdZV5rf+1Oz7zksput8uCBQsc+jtrffLNZrPLxnRISUmR8PBwef311wu1jclkkjJlyjhca3a7XcaMGaPfR927d5ezZ89KdHS0fo3eHWCbTCYpX768wxgAK1euvOfxly5dqj80c/YD3pUrV0qnTp3k1VdfzXME/vj4eIfMdVHMmDFDAEifPn2KU9QSKSQkRKpVqyYGg0EeeeQRj5alc+fOAkAOHTpU6G2XLVumt9TQXlarVcaPH++Ckjofg243YtBNVHrZ7XZZv369zJ07t8Bz5t7PggULBIA8+eST9113w4YN+uigJX0uy5Lk8uXLUr9+fQEgPXr08HRxSg273e6QhZo+fbo+NVtERITLxin49NNP9Qzjww8/XOQpdWw2W4Gmzyqp0tPTxc/Pr1DTh5Um27dvFwBSuXLlEvP5aHOfb9q0Kc/3T548KfXq1XMIWIYMGSIit1tEPfbYY9K1a1eZNGmS1KpVSywWi/j5+Un16tXv+VD3TrNmzRKj0SgGg0EmTpzolM9m0KBBuYKsFi1aSIsWLaRy5cpSt25dfRaB4s4Z3rBhQwEgH3zwQbHLXZKEh4fr3QdmzpzpsXLY7Xbx8fHRu2sUxalTp6RFixYSExMjK1euLDH3X0Ew6HYjBt1EpVN8fLzeBEp7BQUFyerVq4u8z5SUFPHx8ZHAwEDJysoq0Dba9ExlypS57xy/dHtKKC3Q69evn1f9uKvIZrPJ2LFj9RGJLRaL1KhRI98go7BSU1PFx8dH/Pz8JCEhwSn79FaPPPKIy5tQezut33VefYjdLT09XUwmk1StWvW+6+7bt0/at28v06ZNc0lZDh8+rPcBN5lMMmDAgCJ/d2rZ5+joaMnMzJQFCxZI5cqVxWQyidlsltDQUPH19RWLxSKLFi0qdtlTU1MlODhYDAaDUuN2aH8PAC77bktJSZHJkyfLsmXL8v17a4mC2bNnu6QMJR2Dbjdi0E3kHZKSkmTMmDEyZcoU2bZtmyxZsqTQ8/BqDh06JGazWYxGo0yYMEE2bdokY8aM0Zv0FWX6Gbvdrjd//eyzzwq1rTbti9lszjVSLzmqXbu2ALjnPK9UcJmZmbJ06VJZtWpVsa679PR0GTFihDRq1EgfHGrYsGHFLp/W7PF+g6Z5q6ysrAI9oNMGZnT1YGEqqFu3rgDQR8T2lMGDBwuAEjN1l91ul3nz5ukDYD322GO51klNTc01DondbpfDhw9Lenq6PqVXRETEfaczcyZt2jFfX98idcc6dOiQjBgxokSN/aGNLeHv7++S/WszOGiBfeXKlfMcSbxGjRpiNpvz7CJQGjDodiMG3UQlnzY3ZF4DyURFRcmuXbvk8uXLcuHCBenRo4fExMTIO++8k2cQkZCQIAEBAWI0GnM1z0tNTZXKlSsLAHnttdcKXL7Lly/r2/Xr169I5xgXFyfBwcECQHx8fKRdu3a5Ko0pKSny5JNPStu2bUvEVDDuNmnSJAEggwYN8nRRlLBjxw69eaNW+du4cWOx93v58mX94UjdunWL/Pu6f/9+ASDNmjUrdplKGrvdLr1799b79DZp0iTfwa7WrVsnBoNBwsLC3BroeKuEhAQxm80SFBRU4BZHzpaWliZms7nY83m7itZqQnswZrfbpX379g4DtJnNZilXrpw+PdSdTck90U1DG0TR39+/UNNQ7dixw6HP8eDBg11YyoLTpil01fdb7dq1xWAwyPLly/V54Y1Go7z44ot63ejMmTP5PoApLRh0uxGDbqKSTZuD2Wg0ypQpU6Rv377yxBNPyIABA6RChQp5BuJapi0kJER69Ogho0aNkunTp0tMTIxeyc2viWZWVpaUL19eABSoqbndbtefWI8ZM6ZY52q322X+/PlSs2ZN/VyqVasmPXv2lNatW0tgYKAA0M9h7ty5xTqeNzl48KAYDAaJjIws0S0Bjh8/LosWLZKJEyd6fAqYe0lOThar1Spms1nmz58vb731llgsFjEYDMXqYnEnLdMXEhJSpGmLqlevLgaDwWUDY3nK+fPn9e+M6OhoefTRR/VgZsWKFfp6R48elQYNGujvFXZqrdJs0aJFAsBjA1RpAWxJyXLfzW6364O2RUZG6g98W7RoIQMGDJCYmBhp3ry5hIWFSY0aNeTFF1+U9u3bS61atZw2/klRrF69Wh8HJT09/b7rHzx4UHx8fMRsNsu2bdv0sUBKwiBfHTt2FADy8ssvO33fK1euzJUE2Ldvn1SsWFHPep85c0Z/+HLgwAGnl8FbMOh2IwbdRCXXhQsX9Kfsd4+OqQWfUVFRDvPONm3aVB+t+M5RXrX1mzdvft8BaJKSksTPz0/MZvN9B4HR5l919tQYly9flv79++sPELRA28fHR3bs2KH3R/d0E0p3sNvtEhUVJQaDocQGsqNHj86zNUaPHj1K5EOCli1b5uoKcecc8j///LNTjrNo0SIxGAzi4+MjmzdvLvB2H3zwgQCQgQMHOqUcznLo0CGJiYmRGjVqyPTp0wu9/YEDB/Qmny+++KK+fP369fro6uXLl5fo6Gj9nn/88cc50GIRaPMOd+jQQebNmyfTp0+XSZMmScOGDcVkMonFYpGaNWsWuDtQamqq9OzZU6xWq5hMJnnooYfyDPy0gKdt27bOPiWnstls8tRTT0l4eLiEhITIhAkTPF2kAlmyZIkAkEcffTTfdex2u8yePVusVqsYjUb9d9Jms+kPvDz90Hr//v3SvHnzIg8MeS+VKlUSHx+fPFt6jBkzxuE3qrR3WWHQ7UYMuolKrjp16ug/DNWrV5fp06fLzz//LKtXr5YNGzbk6oP02GOPCQCZOHGiw/KEhARZv359nnNm50cbCbdSpUr5Bk3z5s3TswOuYrfb5ejRo2IwGPSHC1arVbZs2SJms1n8/f0LdV6FMWXKFAkPD5eoqCgZN26cXsFMTk6W7du3F+u4mZmZcvToUdm9e7ds3rxZ1qxZI/v3789z3bFjxxa5r31hJSUlyVNPPSVt27YtcL9xbS7rChUqyJgxY/Rzad68eYmsfK9YsUIASKdOnXK9d+DAATEajRIcHOy0psxxcXF6Fr1Xr173vW5sNpsEBASIn5+fx5oH5yUpKUk/Dy1wbtGiRYEfqthsNgkODhaj0ZhnH/X09HTp1auXhIaGisVikYYNGxZpCh+6zW63S6tWrfJsCdW0aVNp1KiR/qBswYIF99xXQkKChIaGCgCpWrWqPn5HUFCQwwOqrKwsCQgIEKvVWqBMLBWNNi/79u3bc72Xlpamtxbz9fXN1WUmLS1Nf2hd1HFhSrKDBw8KAOnbt2++6+zcuVOqVKkijzzySKnty61h0O1GDLqJis5ut8tbb70lgwcPzjdgKojk5GSZNGmS9OvXT95//32x2WwyefJkvZLUtWvXApenfPnyYjAYZPPmzXL06FFZuHBhkUcG1UbC7d+/f673VqxY4bZ+llWrVhWDwSA///yzbNy4UYxGo1gsFvnrX/8qAOSBBx6QTz/9VHr37i3Tp093yo+oNh9wUFCQ3qzdaDTqo1Rrr0qVKsm+ffsKvF+73S5PPvlknt0CAEitWrXk8uXL+vo2m00sFouUK1eu2OckcrvJ7kcffSRLliyRefPmOfRNjI+P17ONWpbx0UcfvWfgt3r16nsGX926dRMA0rNnT6eUv7C2bNkiK1eu1K+JrKws8ff3F19f33yDAq1pbl5BeVGdOnVKb87q4+MjPXv2zPc748UXXxQAMm/ePKcd3xm6du0qAGTDhg1it9v1Vi5t2rQp0PbagIkzZsxwcUnpTvHx8bJx40bZu3evxMfHO9ynSUlJEhoaKgaDId/vsaysLClTpowAkIULF+rLP/jgAzEajWI0GvXprPr06SMAZPHixa49qVIuKSlJjEajVK5c2WH5vn379Kbyw4cPz/eB2IULF8Tf31+vK6hE69Zz+PBhTxfFKzDodiMG3URFk56eLpUqVXIImHr16lXogG/cuHF5Nh3XXgMGDCjU/k6dOpVr4BeDwSCTJk0q1H5ERJYtW6bvIzIyUi5cuCB2u12effZZAW4PPJXXD1tSUpLeB66gc4uuW7dOypcvL+Hh4fLyyy/rn6M2eNiIESP0dePi4sRkMonBYMizX3twcHCBmwfb7XbZt2+fHDx4UNatWyfjx4+Xvn37CgApV66cHnCuXr1azw4NHjxYpk+fLj179tTnf71zntELFy7IypUrHZrEnjlzRh/YBYA0aNBAJk6cKG+99ZbMnz9fli1bph83NDRUH1RKC/7vrOwWlbb/u18dOnSQadOmidFoFJPJJKtXr5asrCxp166dAJDw8PA8M46ZmZni7+8vVqv1ntlbLdsWGBgoLVq0kFdffdVlU8TEx8dLu3btJDo6Wp9aDYD4+fnJ5s2bZcCAAQLgvlP5aGVeu3atU8u3bt06h2s2NDTUod9ramqqmM1miYyMdOpxi+v8+fNiMBikfv36Dsu1hyqjRo267z6qVq0qFoulRHY3KM0OHz4sJpNJAgMD82zGr90L77zzTq73Dhw4IEFBQQJAz35HR0e7o9il3pAhQxy+y5YuXSoGg0FMJlOBpio7evSoWCwWsVgsSnXfKF++vAQFBXm6GF6DQbcbMegmKryEhAS9X9T48ePl7Nmz0qRJEwFuzzddkIAvMzNT71eqNZtu0qSJ1KpVS6xWq1itVvnLX/5SpPLt379fWrduLYMHD5aPPvpIHxhNm5t04cKFMnbsWFm1apVcuHDBIbOqOX/+vJhMJr2Pqxa4aOddv379XN8bNptNevXqpQeW2n+bNWt2z2yp1n/VYrHoT+m1ZuRawH93Rf348eO5Am6LxSKtW7fW/32/J92HDx92CMzufAUGBhYoMDx+/LieBerSpYuMGDHC4fw7duwogwYNErPZLAaDQR566CGZP39+vvtbuHCh3p3AbrdLRESE+Pn5FTtQ0VpO1K9fX1atWiVr166VdevWSdOmTfVzDggIkPj4eIftZs6cKQaDQYxGY67B9zp16iQAZOnSpfc8tt1ul+HDh+tz2WrHi4iIkI4dO8oDDzwgTZs2LVBF8fDhw7J+/XqHfoBJSUmyfv16/SGBdq1WrFhRJkyYIDNmzHB4EFWzZs37Hic1NVWsVqv4+/u7pIn32bNnZdiwYfo1PmvWLBH5X1P9kjS1j4jon+3d2VC73a43ZV22bFm+2586dUoASPfu3V1dVCoC7QGryWRyeECrdW15/PHH8902LS1NH/CuTJky+Y5CT85ls9n0Vjvz58/XW2YVZsDBTZs26b8L3mDq1KnSunXrPB8Aidye4QSAdO7c2c0l814Mut2IQTdR4TzxxBN65f3u0bqnT5+uZz7vNUjJ8ePH9T5VPXv2lEqVKonVanVZBshms+lZCC0gvPsVEBAgK1eu1Ldp2LChAJCdO3dKXFycw7rDhw/PdYy9e/fqff7q168vcXFxYrPZpHfv3nrgnNfT9A0bNujZ6aSkJLHb7bJ48WLp3r27NGjQQGJjY/Mdvdlut8sHH3wghw4dklmzZukPF7QRSv38/PLd9ujRo+Lr6ytGo1HGjRsnr776qsyfP1+OHj0qFy5cKFSLhaysLD3Y146/cOFC/TPUAsz7DWCn0ZoXa336hw4dWuCy5EWrWJUvXz7P8zp48KDMnz8/324C8fHx+qB8ISEhYrFY9GboBW1arLHb7bJlyxbp3bu3BAYGisFgELPZrAfjzZo1y/ezHz9+vMN16OPjk+t6bt68eZ6D/50/f14qVqwogYGBBZ5uR3sYlFf3Cme5cOGCninUHgCZTKZc4zJ40u7duwWAxMTE5Pl+amqqBAYGitFozHdk56eeekoAsI92CRYXFycREREC3J41onv37gLc7sNdkN8mrSUUuc+qVav0776goCC5cOFCofehtYB66623XFBC59G6Lmjf+S1btsx1vWktw0rqqPklEYNuN2LQTaVFWlpasQfceueddwSANGzYMN95og8fPixhYWH5ZgAPHjwoVqtVDAaDzJo1SxISEtzyZFabFzc6Olrmzp0rx48fl6VLl8qoUaNk8ODBehA1d+5cmT17tgCQJ598Ut/+0KFDUrduXb3yvXnzZv0Hb/ny5XqzttmzZ+c69uuvv64HFUOGDJG6detKdHS09OvXT8xms1PnPR02bJgA0Ad6CgoKypXJ3759u/43KOjIvQWxceNGWbhwoUNF4NChQ0Wa6qhNmzZ6BaM43892u13Cw8PFx8enSBUyTXJysjRq1EgiIiKkadOmUqtWLXn22WedVsm22Wz6A62GDRvm2u/ixYv1Bxrz58+Xvn37SkxMjLRr105GjBgh8+fPv+9I+0WhdQlwVf/ApUuX5noApt2L1apVc9p9YbfbZezYsVKmTBkJCwuTZ599VrKysiQ1NVU++uijfPuX22w2CQsLE5PJdM+pyw4ePCgmk0nMZnOuh0t2u118fX2lQoUKTjkXch273S5Dhw7Vr8UaNWq4ZHRpcp4PPvhA+vbtm2eLtYIo6D3uSZ999pnDb4PWIuipp55yWK9BgwZiNpv58KcQGHS7EYNuUl1CQoI+ijJwe6CqkydPFno/+/fvF6PRKGFhYffNgl6+fFnPXj3xxBP6dC3PPvusWCwWMRqNsmnTJhERGTlypAAocBbUVZKSkvTsO3C7r2lezWq1EdK1gFDLLgcGBt4zMFm5cqXeTF3rR6YFx86e9uuDDz7Qm+1qmUOtyf/XX38tRqNRzGZziWvCeyetlcCdrQ+KQnvyP3nyZCeVzLW08QLuHHgtLi5ODAaDBAcHu/236vDhw2IwGKR27dpO3/dHH32kt/K484GI3W6XESNG6PdKcb8b9u/fL+XKldPvay2bqXV5uLPlQP369WXmzJl6qwctC5Zfc8477dy5U2+10Lt3b5k0aZKsWLFCfxD2+uuvF+s8yH2Sk5MZbJciO3fu1B+ylLSANS0tTfz8/MRisTg8WGjWrJkA0MeNSU9PF6PRKE2bNvVUUb0Sg243YtBNKkpOTpbx48c7DFz1yCOPSJcuXfTmrCtWrCjwvrSm2UajMVef1/ycP39eqlSpkiuL5evrqwfcIiLlypWTwMDAIp2ns6WlpcnAgQPvOy/umTNn5K233pKWLVuKj4+PxMTEFKiClpyc7BCYJyQkuOwH3m63y4YNG6RatWr6Z//www+LwWAQi8VSKpq52mw28fX1leDg4BJXkboXbayD1157TdauXSs+Pj7i4+NTpIdlztC/f3+Hyp0zbNmyRQwGgwQGBubbAmHz5s1iNpvFbDYX+HvnbgcPHhSz2SxGo9HhwcsHH3wg5cuXl5YtW8oHH3wgEyZMcJhCymAw6H21CzMw1oEDB/SWPne+QkNDS/3UPEQlmfag71799wvDbrfLZ599Jjt37szz/W3btuU5Yr7NZpP27duLj4+PhIeH62Om3F1nS09Pl6CgIDEajfL888/rrducPfil6hh0uxGDbvJmFy5ckM6dO0v16tWld+/ecvDgQfnoo4/0LKrVapUWLVo4NAXfsWOH3neyT58++QYjycnJ8uSTT+rZ2ccee0wOHjxY6DImJyfLxo0bZefOnbmatx8+fDhXM25yvgkTJjgEAIUdEd5baYOn3W8O3pLGZrNJVFSU/veyWq1Obw1RGNo0Y0WdN3vfvn0SFRUlPj4+0qhRI2nXrp3+8Of48eP33Hbnzp1iMpmKlPFOSEgQf39/MRqNsnv37gJtY7fb5aOPPpKWLVuK2WyW0NDQIjVxz8zMlEOHDsmyZctk3bp1XvXQh6i00sYmmTZtWrH2Y7fbpVatWvp3eJs2bfTvgKysLIcBPOvUqSMHDhyQw4cPy759+/SpFWvXri3h4eFiNBpl4MCBeR5Ha4GodSW736CelBuDbjdi0E3e4K233pKQkBAJDQ2VIUOGyNmzZ+Xll1/WszLaIE/ay9/f/55PO1NTU/XsdcWKFR0qldu3b5dGjRrpGfIaNWrIF1984ZJKY+fOnQXAfSveVHybNm2SBx54QG9Cr/ooyikpKXqW2xtp3UK6dOlSIpq5aqM79+nT577r2u12WbJkiYwfP14mTpyoN7muV6+eXkGsWbNmge/7LVu26E3Be/bsec+B4Gw2mwwaNEifexmArFq1qsDnSUSlV1ZWlt4VZcuWLUXejzadWb9+/aRjx44CQJo2bSqpqan6QKfdu3fXB1i8+zVs2LACH+v8+fOydu1aPtgrIgbdbsSgm0q6RYsW6U8xtWZG2iskJETPYp88eVIGDhwozzzzjKSnpxdo3y+//LLel7FZs2Z6htxgMEjLli1ly5Yt+g+GwWAo9ijSd8rMzBSTyVSg6YvIeex2uz5IWUHmFi7oPp988kmxWq0SFRXl1MHZiiIlJUWv2KxevdqjZVGJloG5V9eEs2fP5vqe8vHxybeJZUEdPXrUoatEdHS0LF682KGiuXTpUr2JZUREhDz44IOyZs2aYh2XiEqXs2fPio+Pj1it1iINvpmUlCQGg0GqVaumL+vXr5/eRQ+Aw7R08fHxMnbsWJk0aZJMmzZN1q9f75TzoIJh0O1GDLqpJEtNTRWLxSKBgYH6wD6bN2+WZ555xmmBzZYtW8Tf31/vwzhmzBi9P/OUKVP0J7Rahbd8+fIyffr0YvdPfP755wWAfPrpp844DSoEu90uNWrUEAAFmhv6Xi5cuCCVKlUSAFKpUiXx8fFxyn6L4uzZs9K1a1f94dHLL7/s9jKo7OjRo/ccVC0tLU0CAgLEYDDI5MmT5fjx47Jhw4Yijyqcl59//lnat2+vT7FmNpslNjZWn1kgICBAlixZ4rTjEVHpo03lWa9evUJvq2Wvt2/f7rB80KBBEhQUVKBBGcl9GHS7EYNuKsm05teeePJpt9vF39/fYRCqESNG6AFNREREkSvTWVlZYrFYpFy5cs4sMhVCamqqhISEiMFgyHf6t/vRph4DIOPHjxeR282itf0WtB9tcdntdhk9erSeRYiMjGSG00WeeeYZASCLFy/O9d5DDz0kAGT58uUuL4fNZpN58+bpg0WazWbp0qVLvnOtExEVRu/evfP9rstPZmammM1mqVq1qusKRk7FoNuNGHRTSaU9aW3evLm+7PLly8Xu3/nBBx9IxYoVpXbt2jJ58mRJTk7Ocz1tEKq755222+0ydepUPbNZlH5EL774ogCQZcuWFekcyDmOHz8uPj4+YrFY7jnHs81mk2HDhonZbBY/Pz8ZNGiQLFy4UJ96bOPGjbn2azKZJDAwsEgDb93P0aNHZd26dbJx40Z59tln9cC/YsWKeY4GS86jDapmsVgcHgYuX75cAEi7du08WDoiIufIysqSwMBAMZlMBe4eo83xzhZ83oNBtxsx6CZPW7RokVSuXFlq1aqlZ+fS0tLE19dXrFar3tS7Xbt2en/GRx55pNAZnYMHD+pT4FitVj1jrWWt+/btq48ubrfbxc/PT0JCQvLd3yuvvCIApG/fvoUqR3JysphMJma5S4jNmzeLwWCQ8PDwXGMBrF+/Xvz8/PTrpHz58vogM8DtOZTzG9F+wYIFAkB69erllHImJCTIsGHDJDQ0NNegM+XLl/e6Ecq92ebNm/Xm3f7+/tK5c2fx8fERf3//Ao8nQURU0sXHx+vTFt6v5VZ6erqYTCapWLGim0pHzsCg240YdJM77NixQ4YPHy7du3eXHj16yIEDB+T8+fPy+OOP60GwNhL56NGj9YGutFF3tUE4WrZsqU9pYbVapWzZsuLj4yOVKlWScePG5Tu39IoVK8RgMIjBYJABAwbo2cdNmzZJ165d9YGPAgMD5eDBg/oUU/PmzbvnedWrVy/Pvkv3EhsbW+yRQcm5Zs+erQ9OlZWVJSdPnpQuXbro19kTTzzhMEfzzp07Zf78+ff93mzcuLEAKFTz9fPnz8vSpUv1zPuOHTscpl4JCwuToUOHyuLFi2XBggXMbHtIamqqjB8/XsqXLy8AxGQy8Z4mIuXs3btXD7zj4+PzXU+rN7Frk3dh0O1GDLrJVbKysmTMmDESFhaW55QQ2qtZs2aSnp4uKSkp+uBWAKRJkyby8ssv6yMGN23aVN/34sWLxc/PT8LDw6Vp06YO2ciRI0c6lGPt2rViMBgkICDgnqMOL1u2TIxGo/4qW7bsfc/x/PnzYjKZJCwsrEDNzN966y0BIJ06dbrvuuRegwcP1gem0q6lRo0aFWsQrISEBDGZTBIVFXXfde12uzzzzDP6NE9ac3FtxNf27dsXewRsco0zZ87orWSIiFSze/duMRqNEhYWlu8gsmXKlJHAwEA3l4yKi0G3GzHoJlc4c+aMBAYGCgAJDg6WYcOG6VNPnDlzRoYOHSpDhw6VuLg4h+12796dKyg3Go3SvXv3+44WHhcXJ9WrVxcA0r9/fxER2bZtmxiNRvHz85OzZ8/et9x79+6VsmXLSkREhBw4cKBA56oF0gMGDMh3nS1btkhMTIzelL24I5+TayxYsECqVKkijz32WL7NxgtrzJgxAkCmT59+z/WaN28uAKR27dqybNkyadWqlT5S9p3zyBMREbnbrFmzBIA8/vjjud7bvn27AHDqtKrkHgWNAw0iIqBiuX79OkJCQpCamorg4GBPF4e81C+//IJvvvkGFSpUQI8ePVClShVcuXIF77//Pp5//vl8t8vOzsZPP/2EsLAwmM1mREdHw2az4Z133gEAPPbYY2jYsCGMRmOBypGTk4PmzZvj559/RpkyZZCSkgKTyYQDBw6gQYMGTjnXvNStWxfHjx/HZ599hh49eujLN23ahOeeew4XLlyAwWBA8+bNsWXLFoSHh7usLFSy5OTkIDw8HJmZmUhOTkZgYGCudV555RX87W9/w5NPPol///vfHiglERHRvT3wwAP44Ycf8Omnn6Jv37768jZt2mDPnj24fPkyypYt68ESUmEVNA5k0O0EDLrVdPXqVVgsljwr+EVx8eJFfPLJJ4iNjUXbtm315Z999hkmTJiAkydP6ssMBgNEBJMnT8abb76pL79y5Qo+++wz/Oc//8Hly5eRkZGBw4cPIycnx2Hb9evXOwSuhZWTk4PevXtj+/btiIyMxIoVK9CiRYsi768gzp07hzp16iArKwvR0dHIzMxEUlISbt68CbPZjP79+2P+/PkIDQ11aTmoZFq7di369OmD7t2744svvnB4LzExERUrVkRkZCR+//33Aj9gIiIicqfr168jKioKdrsdp0+fRoUKFXD9+nWEhYWhQYMG+OWXXzxdRCqkAseBrk+6O0dycrI888wzEhQUJCEhIfLHP/7xvv2/MjMzZdSoURIeHi4BAQHy5JNPSmJiosM6+/btk/bt20tISIiEhoZKp06d5KeffipU2di8XC2pqalSu3ZtvWl2dHR0roEvkpKSZPv27QVu4qz15dH26ePjI35+fnrfU6359+bNm/VptgBIbGysHDp0SPr16ydBQUEOTca1QTkaNWokkyZNknHjxknfvn0LNSBZSZOQkCAtW7YUHx8fCQoKkvr168ugQYOKPcUZqaFRo0YCIFe3hbZt2woA9tcmIqISb9OmTfqgnpMnT5ZWrVoJgFxTZ5J3UK55edeuXZGQkIAPPvgANpsNQ4YMwQMPPIBVq1blu83IkSOxceNGLF++HCEhIRg9ejSMRiN27doFALhx4waqVq2KHj16YOLEicjOzsaUKVOwc+dOnD9/Hj4+PgUqGzPdaqlTpw5OnDiB7t2749atW/j6668hImjXrh06d+6Mt99+G9euXQMA+Pj4YMGCBfds/p2Tk4OoqCgkJydj5cqV+OGHH/D111/DZrOhQoUKaN68Of7617/qGfXWrVvj+++/R2xsLPbs2aPvJyIiAl26dEGnTp3Qo0cPXmtU6pw+fRo1a9ZEtWrVcOrUKQDAnj170KZNG7Rp00b/biciIirJ/va3v2HixIl6S8UHHngA+/bt83CpqCiUynQfPnxYAMj+/fv1ZV9++aUYDAZ9YKm7Xbt2TXx8fByG3T9y5IgAkD179oiIyP79+wWAnDt3Tl/nl19+EQBy4sSJApePme7iOX/+vERHR0tAQICUKVNGunbtWqzRjotDmxd44MCB+rIzZ87oAzRpr9q1a8vkyZP17PPs2bPz3efo0aMFgEyePPm+xz979qwAkBYtWojI7RHGBwwYcM8Rw4lKk0GDBgkAfU7tqlWritFozPe3gIiIqCSy2+0SHx8v27ZtK9DsLVQyKTV6+YcffiihoaEOy2w2m5hMJlm3bl2e28TFxQmAXM1Sq1SpInPmzBERkevXr0uZMmVkypQpkpWVJRkZGfKnP/1J6tWrV6iRkRl0F93s2bPFYrEIAKlVq5ZERkbq8/ru3bvXZcfNzMyUJUuWyNdff+2wvFy5cuLr65vr779hwwYBIBaLRQICAgSAhISESHx8vISHhwuAXKOIi4gcPXpUDAaDVKpUqUDl6tq1a64HTET0PzabTQICAsTX11fmzZuX6yEZERERkbsUNA70itFmEhMTUa5cOYdlZrMZ4eHhSExMzHcbi8WSa9ClyMhIfZugoCB8++23WLlyJfz8/BAYGIjNmzfjyy+/hNlszrc8WVlZuH79usOL/icnJwedOnWCyWSCv78/XnrpJWRnZ+da5+GHH8ZLL70EHx8fbNy4ESdOnEBiYiI2bNiA7OxsPPTQQzh9+rTTyxcXF4ewsDAMHz4cHTt2RN26dZGRkYGvvvoKly5dwv/93/85/P1v3bqFfv36wWKxICEhATdu3MDs2bNx/fp1tG7dGqtWrYLZbEbPnj2RkZHhcKxu3boBADZu3Hjfcl29ehVfffUVoqOjXT5oGZG3MpvNWLhwIW7evImxY8ciMDAQS5Ys8XSxiIiIiPLl0aB74sSJMBgM93wdPXrUZcfPzMzE0KFD8eCDD2Lv3r3YtWsXGjZsiO7duyMzMzPf7WbMmIGQkBD9VblyZZeV0RuNHj0aW7duRZ06deDv7485c+bAarWifPnyaNu2LZ577jlUqlQJO3bsQOfOnXH9+nU9OAWAxx9/HF999RWys7MRExOTK5Atju+//x6dOnVCTk4O5s6di2eeeQbHjx9HzZo1MW7cOBgMBsyePdthmyeffBLp6emYP3++Pk3V+PHj8fXXX8Nut6NXr154/fXXcePGDXTp0kXf7s0338SpU6cwePBgNG7c+L5l69q1K3JycrBw4UKnnS+RigYNGoQnn3wSVatWxa5du2CxWDxdJCIiIqL8uSnznqdLly7JkSNH7vnKyspyWfPypUuXSrly5Rz6UWRlZYm/v7/885//zLfcN2/elNTUVP11/vz5UtG8/OzZszJlyhTZuHGj2O122bt3r6xevVrS09P1dX7++WcxGAxSpUoVfdkHH3wgrVq1kjJlyugjeBuNRpkwYcI9jzd//nwBIHXr1nX4G23YsEEefvhh6dKli3z22Wdy4MAB2blzp8TFxd2zT0xSUpL4+fmJyWSSgwcP6sunTp2q99WuXr267N69W29evnHjRgEgMTExee7zs88+E4PBIP7+/vrok4sWLZKEhAQxmUwSFhaWb5lsNptMmTJFevfuLY8//rgAkG7dut3zMyEiIiIiopJBqT7d2kBqP/zwg77sq6++KtBAamvXrtWXHT161GEgtfnz50tUVJTk5OTo62j9BT/55JMCl6809Olet26dw5RXd75MJpNMmzZNUlJSJDQ0VIxGoxw9ejTffV24cKHAfeaHDRsmAKRy5coyevRo6dKliwDQp9q6++Xn5+fwN9ecOXNGypUrJwAcBtfT1KpVK9e+qlevLmazWXx8fO45sNuqVav08phMJjGZTFK/fv18+3mL3H64U6NGDYfj1axZkwNpEBERERF5CaWCbhGRLl26SLNmzeT777+XnTt3Su3ataV///76+7///rvUrVtXvv/+e33ZiBEjpEqVKvLNN9/IDz/8ILGxsRIbG6u/f+TIEbFarTJy5Eg5fPiwHDp0SP7v//5PQkJC5OLFiwUum+pB95o1a8RgMIivr69s2LBBXnnlFenQoYNMnDhR5s2bJ2XKlHEIhOfNm+fU4w8aNEh8fHz04LRZs2aSkpIiSUlJMmvWLJk6dapMmzZNJk+eLL6+vrlGE587d65etqlTp+baf2pqqhiNRqlZs6Zs2bJFpkyZIh07dtQfMixZsuS+Zfz555/1OYS118MPP5xrvaysLJk9e7b+AGDUqFGSlpYmmzdvZsBNRERERORFlAu6k5OTpX///hIYGCjBwcEyZMgQSUtL098/ffq0AJBt27bpyzIzM2XUqFESFhYm/v7+8sQTT0hCQoLDfrds2SIPPvighISESFhYmLRv317PhBeUykH3+fPnxcfHR/z8/OTMmTN5rmOz2eSZZ56RBg0a5Nvc3xkOHTqUa+qsrKwsGTlypFSrVk26d+8uR48e1UcTX7x4sT5dV1hYmMTHx+fap91ul9q1awsA2bBhg8N7aWlphZ6GKC4uTipWrKiPwH748GH9vVWrVonZbNYz4gWZQoyIiIiIiEqmgsaBBhERp3UQL6UKPCm6F2rRogXi4+Px9ddfo0OHDp4uju7EiRMYNmwY9uzZA5vNBj8/P2RmZsJiseChhx7Ctm3bkJOTAwCoXr06fvnlFwQGBjrsIyMjAw888AAOHz6MESNG4P3333da+TZt2oQ//OEP8PX1xYkTJ7Bp0yYMHz4c/v7+ePfdd/HHP/4RRqNXTB5ARERERER5KGgcyFo/5WvXrl2Ij49H+/btS1TA/d1336FevXr47rvvUKFCBSxbtgwZGRnYsGEDLBYL4uLiYDAY9PWXLl2aK+C+evUqqlSpgsOHD2PQoEFODbiB21OFrVq1ChkZGahevTqGDx+O4OBgHDt2DMOGDWPATURERERUSjDT7QSqZrofeugh7Ny5E0lJSbnmSfeUH374AbGxsTAYDNi1axceeOCBXOtkZ2fDbDbj119/RbNmzSAi2LlzJ1q1agUAuHbtGurUqYPLly9j5syZePnll11W3vfeew+vvPIKKleujF27dqFs2bIuOxYREREREblPQeNABt1OoGLQnZ2dDV9fX9SuXRtHjhzxdHEAAL/++iuaN28Ou92OHTt2IDY29r7b7Nq1C4888gjsdjtCQ0MRGRmJ8+fPIyMjAzNmzMDEiRPdUHIiIiIiIlINm5dTsSxcuBB2ux0vvviip4sCAIiLi0OLFi2QnZ2NL7/8skABNwA8+OCD+O233/DYY48hKCgI586dg4hg0aJFDLiJiIiIiMjlmOl2AhUz3XXq1MHp06eRlZXl8f7Hr7/+OqZOnQofHx+sWbMGPXv29Gh5iIiIiIiIChoHmt1YJvISV65cwYkTJ/DII494POB++umnsWbNGkRGRuKHH35ApUqVPFoeIiIiIiKiwmDQTblMmTIFADB16lSPlSE7OxsPPvgg9u3bh+bNm2PPnj2wWCweKw8REREREVFRsE835bJ69WoEBwfj0Ucf9cjxb9y4gTp16mDfvn144oknEB8fz4CbiIiIiIi8EoNucrBnzx4kJyd7rN/0zZs3Ubt2bZw+fRpjx47FunXrPFIOIiIiIiIiZ2DQXQq89NJLMBgMWLBgwX3XHTNmDAwGA2bOnOmGkjnKyclBkyZNkJiYiClTpmDu3LluLwMREREREZEzMeguBcqUKQPg9rRb95KYmIgffvgBLVq0QFRUlMvLdejQIYwYMQKNGjVC1apVUb58eRw/fhzDhw/3aH9yIiIiIiIiZ2HQXQr07t0bAPDLL7/ccz1tTu68MuJHjhxB9erVYbFYEBkZiXnz5hW5PDdu3ECrVq3QqFEjfPDBBzh+/DiuX7+OtLQ0DBkyBH//+9+LvG8iIiIiIqKShPN0O4E3zNNtMBhgMBiQnZ2d5zRgV69eRUREBCpXrowzZ844vHft2jVUrlwZ6enpaNKkCY4dO4bMzEw0btwYu3btQmBgYIHL8eGHH+JPf/oT0tPT8dhjj+Hdd99FvXr1int6REREREREblXQOJCZ7lKiQoUKEBH06NEDAJCRkYEZM2agRYsWaNWqFerWrYucnBx8+OGHDtvl5OSgadOmuHHjBv7xj3/gwIEDuHHjBvr06YNffvkF1apVw8WLF+97/KVLlyIkJATDhg1DdnY2li9fji1btjDgJiIiIiIipTHT7QTekOnetGkTunfvDgAwmUyw2+0AbmfATSYTsrOz0aVLF3z55Zf6Njk5OWjZsiXi4+Mxbtw4zJkzx2Gfc+bMwUsvvQR/f3/s27cPDRo0yHXcGzduoEmTJjh16hR8fX0xbtw4TJ06lVOAERERERGRV2Ommxx069YNtWrVAgA94AYAEUF2djZ8fX0BAL/99hsAIDs7GzExMYiPj0ffvn1zBdwAMH78eHz66ae4efMmmjVrhm+//dbh/d9++w1VqlTBqVOnMHjwYKSmpuKtt95iwE1ERERERKUGM91O4A2ZbgC4desW3nvvPVy7dg0WiwWXL1/GpUuXcOvWLRw4cACnT58GANSoUQNJSUlIT09Hv3798M9//vOe+/3222/RqVMn2O12rFq1Cn379sVnn32GPn36IDs7G++++64+SBsREREREZEKChoHMuh2Am8Juu/n119/xahRo7B//34EBQXhlVdewUsvvVTgbVu2bImMjAy9+brVasXmzZvx6KOPurbgREREREREbsag241UCbqL6+LFixg5ciSSk5NRs2ZNzJ07F+Hh4Z4uFhERERERkdMVNA40u7FMpLgKFSrgs88+83QxiIiIiIiISgwOpEZERERERETkIgy6iYiIiIiIiFyEQTcRERERERGRizDoJiIiIiIiInIRBt1ERERERERELsKgm4iIiIiIiMhFGHQTERERERERuQiDbiIiIiIiIiIXYdBNRERERERE5CIMuomIiIiIiIhchEE3ERERERERkYsw6CYiIiIiIiJyEbOnC6ACEQEAXL9+3cMlISIiIiIiInfQ4j8tHswPg24nSEtLAwBUrlzZwyUhIiIiIiIid0pLS0NISEi+7xvkfmE53VdOTg4uXryIoKAgGAwGTxcnl+vXr6Ny5co4f/48goODPV0cKuV4PVJJwWuRSgpei1SS8HqkksIbrkURQVpaGipUqACjMf+e28x0O4HRaESlSpU8XYz7Cg4OLrEXLJU+vB6ppOC1SCUFr0UqSXg9UklR0q/Fe2W4NRxIjYiIiIiIiMhFGHQTERERERERuQiD7lLAarViypQpsFqtni4KEa9HKjF4LVJJwWuRShJej1RSqHQtciA1IiIiIiIiIhdhppuIiIiIiIjIRRh0ExEREREREbkIg24iIiIiIiIiF2HQrYiFCxeiWrVq8PX1RatWrbBv3757rr9mzRpER0fD19cXjRo1wqZNm9xUUioNCnM9LlmyBA899BDCwsIQFhaGjh073vf6JSqown43aj799FMYDAb06tXLtQWkUqOw1+K1a9fwwgsvoHz58rBarahTpw5/q8lpCns9zps3D3Xr1oWfnx8qV66McePG4ebNm24qLanqu+++wx/+8AdUqFABBoMB69evv+823377LZo3bw6r1YpatWph+fLlLi+nMzDoVsDq1asxfvx4TJkyBT/++COaNGmCzp0749KlS3muv3v3bvTv3x9Dhw7FgQMH0KtXL/Tq1QuHDh1yc8lJRYW9Hr/99lv0798f27Ztw549e1C5cmV06tQJFy5ccHPJSTWFvRY1Z86cwYQJE/DQQw+5qaSkusJei7du3cJjjz2GM2fOYO3atTh27BiWLFmCihUrurnkpKLCXo+rVq3CxIkTMWXKFBw5cgQffvghVq9ejcmTJ7u55KSa9PR0NGnSBAsXLizQ+qdPn0b37t3Rrl07/PTTTxg7diyGDRuGr776ysUldQIhr9eyZUt54YUX9P+32+1SoUIFmTFjRp7rP/3009K9e3eHZa1atZLnn3/epeWk0qGw1+PdsrOzJSgoSD766CNXFZFKiaJci9nZ2dKmTRtZunSpDBo0SHr27OmGkpLqCnstvv/++1KjRg25deuWu4pIpUhhr8cXXnhB2rdv77Bs/Pjx8uCDD7q0nFS6AJD//Oc/91znlVdekQYNGjgs69u3r3Tu3NmFJXMOZrq93K1btxAfH4+OHTvqy4xGIzp27Ig9e/bkuc2ePXsc1geAzp0757s+UUEV5Xq8W0ZGBmw2G8LDw11VTCoFinotvvHGGyhXrhyGDh3qjmJSKVCUa/Hzzz9HbGwsXnjhBURGRqJhw4Z46623YLfb3VVsUlRRrsc2bdogPj5eb4J+6tQpbNq0Cd26dXNLmYk03hzDmD1dACqeK1euwG63IzIy0mF5ZGQkjh49muc2iYmJea6fmJjosnJS6VCU6/Fuf/7zn1GhQoVcX6pEhVGUa3Hnzp348MMP8dNPP7mhhFRaFOVaPHXqFL755hsMGDAAmzZtwsmTJzFq1CjYbDZMmTLFHcUmRRXlenzmmWdw5coVtG3bFiKC7OxsjBgxgs3Lye3yi2GuX7+OzMxM+Pn5eahk98dMNxGVGG+//TY+/fRT/Oc//4Gvr6+ni0OlSFpaGp599lksWbIEZcuW9XRxqJTLyclBuXLl8Pe//x0xMTHo27cv/vKXv2Dx4sWeLhqVQt9++y3eeustLFq0CD/++CPWrVuHjRs3Ytq0aZ4uGpHXYKbby5UtWxYmkwlJSUkOy5OSkhAVFZXnNlFRUYVan6iginI9ambNmoW3334bX3/9NRo3buzKYlIpUNhr8bfffsOZM2fwhz/8QV+Wk5MDADCbzTh27Bhq1qzp2kKTkoryvVi+fHn4+PjAZDLpy+rVq4fExETcunULFovFpWUmdRXlenzttdfw7LPPYtiwYQCARo0aIT09Hc899xz+8pe/wGhkDo/cI78YJjg4uERnuQFmur2exWJBTEwM4uLi9GU5OTmIi4tDbGxsntvExsY6rA8AW7duzXd9ooIqyvUIADNnzsS0adOwefNmtGjRwh1FJcUV9lqMjo7G/7dzPyFR/GEcxz+bukrskqG0GljhPwLR2g4JeTA6CB2KLgkdFvNgoCV4EQzC7ZBSEEFop6A/p/5QtyLrsicjKtpSSZQU9KJGVmB6KXs6ufzMiN9W3x2a3i8YmJ2Z3X2+8DDMZ5jvDA8P6+XLl6nl4MGDqTeklpSUZLJ8+MivnBfr6ur05s2b1I0fSRofH1dxcTGBG7/lV/pxaWlpTbBeuSFkZu6KBb7zV2cYr9/kht938+ZNy83NtWvXrtnr16/t2LFjlp+fb7Ozs2ZmFovFrKurK3X84OCgZWdn2/nz5210dNTi8bjl5OTY8PCwV0OAj6Tbj2fPnrVgMGh37tyxmZmZ1LKwsODVEOAT6fbi93h7Of6UdHtxenrawuGwnThxwsbGxuzevXu2adMmO3PmjFdDgI+k24/xeNzC4bDduHHDJicn7dGjR1ZWVmaNjY1eDQE+sbCwYMlk0pLJpEmyCxcuWDKZtKmpKTMz6+rqslgsljp+cnLS1q9fb52dnTY6OmqXLl2yrKwsGxgY8GoI/xuh2yf6+vpsy5YtFgwGbffu3fbkyZPUvvr6emtqalp1/O3bt62ystKCwaBVVVXZ/fv3M1wx/Cydfty6datJWrPE4/HMFw7fSffc+F+EbvxJ6fbi48ePrba21nJzc620tNR6enrsy5cvGa4afpVOP37+/NlOnz5tZWVllpeXZyUlJdbW1mYfPnzIfOHwlUQi8cNrwJX+a2pqsvr6+jXf2blzpwWDQSstLbWrV69mvO5fETDjuRAAAAAAAFxgTjcAAAAAAI4QugEAAAAAcITQDQAAAACAI4RuAAAAAAAcIXQDAAAAAOAIoRsAAAAAAEcI3QAAAAAAOELoBgAAAADAEUI3AAD4qaNHj+rQoUNelwEAwF8p2+sCAACAdwKBwE/3x+NxXbx4UWaWoYoAAPAXQjcAAP+wmZmZ1PqtW7fU3d2tsbGx1LZQKKRQKORFaQAA+AKPlwMA8A8rKipKLRs2bFAgEFi1LRQKrXm8fO/evWpvb1dHR4c2btyoSCSiy5cva3FxUc3NzQqHwyovL9eDBw9W/dfIyIj279+vUCikSCSiWCymd+/eZXjEAABkFqEbAACk7fr16yosLNTTp0/V3t6u1tZWHT58WHv27NGLFy/U0NCgWCympaUlSdLHjx+1b98+RaNRPX/+XAMDA5qbm1NjY6PHIwEAwC1CNwAASNuOHTt06tQpVVRU6OTJk8rLy1NhYaFaWlpUUVGh7u5uzc/Pa2hoSJLU39+vaDSq3t5ebd++XdFoVFeuXFEikdD4+LjHowEAwB3mdAMAgLTV1NSk1rOyslRQUKDq6urUtkgkIkl6+/atJOnVq1dKJBI/nB8+MTGhyspKxxUDAOANQjcAAEhbTk7Oqs+BQGDVtpW3on/9+lWS9OnTJx04cEDnzp1b81vFxcUOKwUAwFuEbgAA4NyuXbt09+5dbdu2TdnZXH4AAP4dzOkGAADOHT9+XO/fv9eRI0f07NkzTUxM6OHDh2pubtby8rLX5QEA4AyhGwAAOLd582YNDg5qeXlZDQ0Nqq6uVkdHh/Lz87VuHZcjAAD/CpiZeV0EAAAAAAB+xK1lAAAAAAAcIXQDAAAAAOAIoRsAAAAAAEcI3QAAAAAAOELoBgAAAADAEUI3AAAAAACOELoBAAAAAHCE0A0AAAAAgCOEbgAAAAAAHCF0AwAAAADgCKEbAAAAAABHCN0AAAAAADjyDRrxb8CiikDnAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from snudda.plotting.plot_traces import PlotTraces\n", + "pt_off = PlotTraces(output_file=sim_output_neuromodulation_OFF)\n", + "# Use trace_id to specify which traces\n", + "ax_off = pt_off.plot_traces(offset=0, time_range=None,fig_size=(10,4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "398790f7-d211-4fff-9249-77b28e14c859", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/neuron_modulation_of_channels.ipynb b/examples/notebooks/neuromodulation/neuron_modulation_of_channels.ipynb new file mode 100644 index 000000000..a9639106d --- /dev/null +++ b/examples/notebooks/neuromodulation/neuron_modulation_of_channels.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "85a1b59a-8154-428c-80b8-372822469a39", + "metadata": {}, + "source": [ + "# Neuronmodulation of Channels\n", + "\n", + "This example shows how to model neuromodulation, with release from synapses, and effects on ion channels.\n", + "\n", + "First version has dSPN, iSPN" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a7561a68-c3e9-4768-a865-90717ab4dbd4", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "network_path = os.path.join(\"networks\", \"striatum10\")\n", + "config_file = os.path.join(\"config\", \"network.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5e6871b5-1064-4d14-b33a-ea784cb0c959", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading config/network.json\n", + "Loading config/striatum.json\n", + "Loading config/neurons/dspn.json\n", + "Loading config/neurons/ispn.json\n", + "Loading /home/hjorth/HBP/BasalGangliaData/data/connectivity/striatum/striatum-connectivity.json\n", + "Setting network_path = '/home/hjorth/HBP/Snudda/examples/notebooks/neuromodulation/networks/striatum10'\n", + "Writing to file networks/striatum10/network-config.json\n" + ] + } + ], + "source": [ + "from snudda import Snudda\n", + "\n", + "snd = Snudda(network_path)\n", + "snd.import_config(network_config_file=config_file, overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3a98f99d-44f3-419e-a4b1-7ef5d1144fd5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Placing neurons\n", + "Network path: networks/striatum10\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/data from networks/striatum10/network-config.json\n", + "Generating 4097 points for config/mesh/cube-mesh-5e-05.obj\n", + "n_points = 3892, previous close_pairs = 184169\n", + "n_points = 3697, previous close_pairs = 161727\n", + "n_points = 3512, previous close_pairs = 141453\n", + "n_points = 3336, previous close_pairs = 123994\n", + "n_points = 3169, previous close_pairs = 109010\n", + "n_points = 3010, previous close_pairs = 96085\n", + "n_points = 2859, previous close_pairs = 84760\n", + "n_points = 2716, previous close_pairs = 74773\n", + "n_points = 2580, previous close_pairs = 66058\n", + "n_points = 2451, previous close_pairs = 58336\n", + "n_points = 2328, previous close_pairs = 51560\n", + "n_points = 2211, previous close_pairs = 45604\n", + "n_points = 2100, previous close_pairs = 40392\n", + "n_points = 1995, previous close_pairs = 35745\n", + "n_points = 1895, previous close_pairs = 31636\n", + "n_points = 1800, previous close_pairs = 28002\n", + "n_points = 1710, previous close_pairs = 24811\n", + "n_points = 1624, previous close_pairs = 21985\n", + "n_points = 1542, previous close_pairs = 19417\n", + "n_points = 1464, previous close_pairs = 17152\n", + "n_points = 1390, previous close_pairs = 15145\n", + "n_points = 1320, previous close_pairs = 13364\n", + "n_points = 1254, previous close_pairs = 11808\n", + "n_points = 1191, previous close_pairs = 10429\n", + "n_points = 1131, previous close_pairs = 9221\n", + "n_points = 1074, previous close_pairs = 8115\n", + "n_points = 1020, previous close_pairs = 7154\n", + "n_points = 969, previous close_pairs = 6300\n", + "n_points = 920, previous close_pairs = 5562\n", + "n_points = 874, previous close_pairs = 4894\n", + "n_points = 830, previous close_pairs = 4304\n", + "n_points = 788, previous close_pairs = 3788\n", + "n_points = 748, previous close_pairs = 3313\n", + "n_points = 710, previous close_pairs = 2900\n", + "n_points = 674, previous close_pairs = 2533\n", + "n_points = 640, previous close_pairs = 2211\n", + "n_points = 608, previous close_pairs = 1924\n", + "n_points = 577, previous close_pairs = 1683\n", + "n_points = 548, previous close_pairs = 1465\n", + "n_points = 520, previous close_pairs = 1272\n", + "n_points = 494, previous close_pairs = 1113\n", + "n_points = 469, previous close_pairs = 973\n", + "n_points = 445, previous close_pairs = 845\n", + "n_points = 422, previous close_pairs = 735\n", + "n_points = 401, previous close_pairs = 644\n", + "n_points = 381, previous close_pairs = 565\n", + "n_points = 362, previous close_pairs = 491\n", + "n_points = 344, previous close_pairs = 425\n", + "n_points = 327, previous close_pairs = 377\n", + "n_points = 311, previous close_pairs = 328\n", + "n_points = 295, previous close_pairs = 290\n", + "n_points = 280, previous close_pairs = 248\n", + "n_points = 266, previous close_pairs = 219\n", + "n_points = 253, previous close_pairs = 193\n", + "n_points = 241, previous close_pairs = 170\n", + "n_points = 230, previous close_pairs = 148\n", + "n_points = 219, previous close_pairs = 127\n", + "n_points = 209, previous close_pairs = 108\n", + "n_points = 200, previous close_pairs = 92\n", + "n_points = 121, previous close_pairs = 79\n", + "Filtering 121 points..\n", + "Filtering, keeping inside points: 27 / 121\n", + "neuron_name = 'dSPN_0', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026'\n", + "neuron_name = 'dSPN_1', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20211026'\n", + "neuron_name = 'dSPN_2', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20211028'\n", + "neuron_name = 'dSPN_3', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c9_D1-mWT-1215MSN03-v20211026'\n", + "neuron_name = 'iSPN_0', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20211026'\n", + "neuron_name = 'iSPN_1', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20211026'\n", + "neuron_name = 'iSPN_2', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20211026'\n", + "neuron_name = 'iSPN_3', num = 1, neuron_path = '$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20211026'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 1.4s\n", + "Touch detection\n", + "Network path: networks/striatum10\n", + "Creating missing directory networks/striatum10/voxels\n", + "Created directory networks/striatum10/voxels\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/data from networks/striatum10/network-config.json\n", + "No d_view specified, running distribute neurons in serial\n", + "Processing hyper voxel : 20/64 (8 neurons)\n", + "Processing hyper voxel : 5/64 (8 neurons)\n", + "Processing hyper voxel : 25/64 (8 neurons)\n", + "Processing hyper voxel : 21/64 (8 neurons)\n", + "Processing hyper voxel : 4/64 (6 neurons)\n", + "Processing hyper voxel : 9/64 (6 neurons)\n", + "Processing hyper voxel : 37/64 (4 neurons)\n", + "Processing hyper voxel : 24/64 (4 neurons)\n", + "Processing hyper voxel : 8/64 (3 neurons)\n", + "Processing hyper voxel : 17/64 (3 neurons)\n", + "Processing hyper voxel : 36/64 (2 neurons)\n", + "Processing hyper voxel : 26/64 (2 neurons)\n", + "Processing hyper voxel : 41/64 (2 neurons)\n", + "Processing hyper voxel : 22/64 (2 neurons)\n", + "Processing hyper voxel : 40/64 (1 neurons)\n", + "Processing hyper voxel : 16/64 (1 neurons)\n", + "Processing hyper voxel : 10/64 (1 neurons)\n", + "Processing hyper voxel : 33/64 (1 neurons)\n", + "Processing hyper voxel : 6/64 (1 neurons)\n", + "Processing hyper voxel : 1/64 (1 neurons)\n", + "Processing hyper voxel : 0/64 (1 neurons)\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/BasalGangliaData/data from networks/striatum10/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 3.0s\n", + "Prune synapses\n", + "Network path: networks/striatum10\n", + "No file networks/striatum10/pruning_merge_info.json\n", + "Read 1071 out of total 1071 synapses\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 3.1s\n" + ] + } + ], + "source": [ + "snd.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee5af2af-3d62-412f-9c06-2b1b3b1fb3e0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/plot_hh_modulation_parameters.ipynb b/examples/notebooks/neuromodulation/plot_hh_modulation_parameters.ipynb new file mode 100644 index 000000000..260ba4c08 --- /dev/null +++ b/examples/notebooks/neuromodulation/plot_hh_modulation_parameters.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6416efc6-6a3c-4763-9c5d-4ba602910efd", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "44b6c7e1-fc41-4335-9292-da06344bc7a2", + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "pio.templates.default = 'plotly_dark'\n", + "pio.renderers.default = 'iframe'\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "298c8b70-4724-4d5b-8e2e-5b729256c9d8", + "metadata": {}, + "outputs": [], + "source": [ + "def make_sigmoid(val, half, slope, maximum, minimum):\n", + " sigmoid = (maximum-minimum)*1/(1 + np.exp(-(val - half) / slope)) + minimum\n", + " return sigmoid\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dddb5dcb-6563-456a-9f63-3081ac170fc4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'param_name': 'mod_pka_g_min_kir_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1},\n", + " {'param_name': 'mod_pka_g_max_kir_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1.25},\n", + " {'param_name': 'mod_pka_g_half_kir_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 12.5},\n", + " {'param_name': 'mod_pka_g_slope_kir_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1},\n", + " {'param_name': 'mod_pka_g_min_cal12_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1},\n", + " {'param_name': 'mod_pka_g_max_cal12_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1.25},\n", + " {'param_name': 'mod_pka_g_half_cal12_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 12.5},\n", + " {'param_name': 'mod_pka_g_slope_cal12_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1},\n", + " {'param_name': 'mod_pka_g_min_cal13_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1},\n", + " {'param_name': 'mod_pka_g_max_cal13_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1.25},\n", + " {'param_name': 'mod_pka_g_half_cal13_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 12.5},\n", + " {'param_name': 'mod_pka_g_slope_cal13_ms',\n", + " 'type': 'range',\n", + " 'sectionlist': 'all',\n", + " 'dist_type': 'uniform',\n", + " 'value': 1}]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mod_pka_g_min, mod_pka_g_max, mod_pka_g_half, mod_pka_g_slope \n", + "{\n", + " \"MYMODULATIONKEY-kir\" : {\n", + " \"kir\": {\n", + " \"mod_pka_g_min\": 1,\n", + " \"mod_pka_g_max\": 1.25,\n", + " \"mod_pka_g_half\": 12.5,\n", + " \"mod_pka_g_slope\": 1\n", + " }\n", + " },\n", + " \n", + "}\n", + "\n", + "[\n", + " {\n", + " \"param_name\": \"mod_pka_g_min_kir_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_max_kir_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1.25\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_half_kir_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 12.5\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_slope_kir_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1\n", + " },\n", + "\n", + " {\n", + " \"param_name\": \"mod_pka_g_min_cal12_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_max_cal12_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1.25\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_half_cal12_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 12.5\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_slope_cal12_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1\n", + " },\n", + " \n", + " {\n", + " \"param_name\": \"mod_pka_g_min_cal13_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_max_cal13_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1.25\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_half_cal13_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 12.5\n", + " },\n", + " {\n", + " \"param_name\": \"mod_pka_g_slope_cal13_ms\",\n", + " \"type\": \"range\",\n", + " \"sectionlist\": \"all\",\n", + " \"dist_type\": \"uniform\",\n", + " \"value\": 1\n", + " }\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "13880088-3b08-414d-842a-a26c3631e684", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "values = np.arange(0, 150, 0.1)\n", + "ss = make_sigmoid(values, half=3.6, slope=1, maximum=1.25, minimum=0.75)\n", + "\n", + "\n", + "fig=go.Figure(go.Scatter(x=values, y=ss))\n", + "fig.update_layout(xaxis_range=[0, 20])\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "nrn", + "language": "python", + "name": "nrn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuromodulation/reaction_diffusion.json b/examples/notebooks/neuromodulation/reaction_diffusion.json new file mode 100644 index 000000000..843f5f128 --- /dev/null +++ b/examples/notebooks/neuromodulation/reaction_diffusion.json @@ -0,0 +1,636 @@ +{ + "species": { + "GaolfGDP": { + "initial_concentration": 0.0100831208954662, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 0.0100831208954662 + }, + "Gbgolf": { + "initial_concentration": 29.8851246006536, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 29.8851246006536 + }, + "GaolfGTP": { + "initial_concentration": 0.00891348109605658, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + 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b/examples/notebooks/neuromodulation/test-modulation.json new file mode 120000 index 000000000..3d77250d3 --- /dev/null +++ b/examples/notebooks/neuromodulation/test-modulation.json @@ -0,0 +1 @@ +dspn/test-modulation.json \ No newline at end of file diff --git a/examples/notebooks/neuron_with_current_injection.ipynb b/examples/notebooks/neuron_with_current_injection.ipynb new file mode 100644 index 000000000..b24f0e873 --- /dev/null +++ b/examples/notebooks/neuron_with_current_injection.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6ee7f4a3-33bb-482d-b794-336608122095", + "metadata": {}, + "source": [ + "## Current injection simulation\n", + "This notebook runs a predefined neuron with current injection. Nothing fancy, very basic." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "81e0db5d-53f0-4bfc-993c-5e1aa864ffe7", + "metadata": {}, + "outputs": [], + "source": [ + "from snudda import Snudda" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7621c263-a32b-4c05-be7d-5486d07f6f2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding neurons: dSPN from dir ../../snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/\n", + "Writing network/single_neuron/network-config.json\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "network_path = \"network/single_neuron\"\n", + "ss = Snudda(network_path=network_path)\n", + "ss.init_tiny(neuron_paths=[\"../../snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/\"],\n", + " neuron_names=[\"dSPN\"], number_of_neurons=[1], random_seed=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "978c1d9c-6b3a-4622-a2bd-9afd03854b74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Placing neurons\n", + "Network path: network/single_neuron\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 46\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 46 points for network/single_neuron/mesh/Cube-cube-mesh-2.3159794767993218e-05.obj\n", + "Filtering, keeping inside points: 1 / 20\n", + "neuron_name = 'dSPN', num = 1, neuron_path = '../../snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.0s\n", + "Touch detection\n", + "Network path: network/single_neuron\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "No connections specified in connectivity_distribution.\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n", + "Prune synapses\n", + "Network path: network/single_neuron\n", + "No file network/single_neuron/pruning_merge_info.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n" + ] + } + ], + "source": [ + "ss.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4c86c755-abd2-4b86-9819-31c30257f81e", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_config = {\"current_injection_info\" : {\"0\": {\"time\": [0, 0.4999, 0.5, 0.9999, 1, 10],\n", + " \"current\": [0, 0, 300e-12, 300e-12, 0, 0]}}}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b21c9ea6-a0c0-40aa-b0ab-a78c7e0f185d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPI Rank: 0, Size: 1\n", + "Using input file None\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "NEURON mechanisms already compiled, make sure you have the correct version of NEURON modules.\n", + "If you delete x86_64, aarch64, arm64 directories (or nrnmech.dll) then you will force a recompilation of the modules.\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "Warning: No external synaptic input file given!\n", + "MPI Rank: 0, Size: 1 -- NEURON: This is node 0 out of 1\n", + "Using network_file: network/single_neuron/network-synapses.hdf5\n", + "Using input_file: None\n", + "Using output_file: network/single_neuron/simulation/output.hdf5\n", + "Using logFile: network/single_neuron/log/network-simulation-log.txt-0\n", + "Worker 0 : Loading network from network/single_neuron/network-synapses.hdf5\n", + "Loading config file network/single_neuron/network-config.json\n", + "0 : Memory status: 73% free\n", + "Distributing neurons.\n", + "Setup neurons\n", + "numprocs=1\n", + "Warning: Old format of parameter config, using parameter_id = 0.\n", + "Warning! No modulation key specified, ignoring /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508/modulation.json\n", + "Node 0 - cell 0 dSPN\n", + "Neuron dSPN (0) resting voltage = -86.0\n", + "!!! Popping extra segment from neuron -- temp fix!\n", + "0 : Memory status: 73% free\n", + "Adding gap junctions.\n", + "connect_network_gap_junctions_local\n", + "Finding node local gap junctions...\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Adding synapses.\n", + "connect_network_synapses\n", + "Added 0 on worker 0\n", + "Added 0 synapses to simulation (0 total)\n", + "0 : Memory status: 73% free\n", + "Parsing current_injection_info.\n", + "Adding current injection to neuron 0: time = [ 0. 0.4999 0.5 0.9999 1. 10. ], current = [0.e+00 0.e+00 3.e-10 3.e-10 0.e+00 0.e+00], interpolate\n", + "No input file given, not adding external input!\n", + "0 : Memory status: 73% free\n", + "0 : Memory status: 73% free\n", + "Running simulation for 2000.0 ms.\n", + "Running simulation for 2.0 s\n", + "Running Neuron simulator 2000 ms, with dt=0.025\n", + " 1% done. Elapsed: 0.1 s, estimated time left: 10.2 s\n", + " 2% done. Elapsed: 0.2 s, estimated time left: 10.1 s\n", + " 3% done. Elapsed: 0.3 s, estimated time left: 10.1 s\n", + " 4% done. Elapsed: 0.4 s, estimated time left: 10.0 s\n", + " 5% done. Elapsed: 0.5 s, estimated time left: 9.9 s\n", + " 10% done. Elapsed: 1.0 s, estimated time left: 9.3 s\n", + " 20% done. Elapsed: 2.1 s, estimated time left: 8.4 s\n", + " 30% done. Elapsed: 3.1 s, estimated time left: 7.3 s\n", + " 40% done. Elapsed: 4.2 s, estimated time left: 6.3 s\n", + " 50% done. Elapsed: 5.2 s, estimated time left: 5.2 s\n", + " 60% done. Elapsed: 6.2 s, estimated time left: 4.2 s\n", + " 70% done. Elapsed: 7.3 s, estimated time left: 3.1 s\n", + " 80% done. Elapsed: 8.3 s, estimated time left: 2.1 s\n", + " 90% done. Elapsed: 9.4 s, estimated time left: 1.0 s\n", + "100% done. Elapsed: 10.4 s, estimated time left: 0.0 s\n", + "Neuron simulation finished\n", + "Simulation done.\n", + "Simulation run time: 10.4 s\n", + "Simulation done, saving output\n", + "Writing network output to network/single_neuron/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to network/single_neuron/simulation/output.hdf5\n", + "Program run time: 10.6s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ss.simulate(simulation_config=simulation_config, verbose=True, time=2.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eecaaa29-0047-4fd8-b7f5-d15a038e3cce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network/single_neuron/simulation/output.hdf5\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "sls = SnuddaLoadSimulation(network_path=network_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "69dbe223-b41a-4b6f-b54a-b55430981676", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['spikes', 'voltage']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sls.list_data_types(neuron_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7fe9e765-20ee-4216-a854-83c0066d30c0", + "metadata": {}, + "outputs": [], + "source": [ + "time = sls.get_time()\n", + "neuron_id = 0\n", + "voltage = sls.get_data(neuron_id=0, data_type=\"voltage\")[0][neuron_id]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a41a9158-94d3-4dee-9026-a5a4b4f04431", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'voltage')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, voltage)\n", + "plt.xlabel(\"time\")\n", + "plt.ylabel(\"voltage\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "903550c5-1082-4eb4-bcf8-e0d14a402311", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/neuron_with_current_injection_BG_data.ipynb b/examples/notebooks/neuron_with_current_injection_BG_data.ipynb new file mode 100644 index 000000000..f5eea17b9 --- /dev/null +++ b/examples/notebooks/neuron_with_current_injection_BG_data.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6ee7f4a3-33bb-482d-b794-336608122095", + "metadata": {}, + "source": [ + "## Current injection simulation\n", + "This notebook runs a predefined neuron with current injection. Nothing fancy, very basic. This version uses BasalGangliaData" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "81e0db5d-53f0-4bfc-993c-5e1aa864ffe7", + "metadata": {}, + "outputs": [], + "source": [ + "from snudda import Snudda" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7621c263-a32b-4c05-be7d-5486d07f6f2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding neurons: dSPN from dir ../../../BasalGangliaData/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/\n", + "Writing network/single_neuron/network-config.json\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "network_path = \"network/single_neuron\"\n", + "ss = Snudda(network_path=network_path)\n", + "ss.init_tiny(neuron_paths=[\"../../../BasalGangliaData/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/\"],\n", + " neuron_names=[\"dSPN\"], number_of_neurons=[1], random_seed=123,\n", + " morphology_key=[\"m22be6817\"],\n", + " parameter_key=[\"p1863c9a5\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "978c1d9c-6b3a-4622-a2bd-9afd03854b74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Placing neurons\n", + "Network path: network/single_neuron\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "No n_putative_points and putative_density, setting n_putative_points = 46\n", + "(this must be larger than the number of neurons you want to place)\n", + "Generating 46 points for network/single_neuron/mesh/Cube-cube-mesh-2.3159794767993218e-05.obj\n", + "Filtering, keeping inside points: 1 / 20\n", + "neuron_name = 'dSPN', num = 1, neuron_path = '../../../BasalGangliaData/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.0s\n", + "Touch detection\n", + "Network path: network/single_neuron\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "No d_view specified, running distribute neurons in serial\n", + "Traceback (most recent call last):\n", + " File \"/home/hjorth/HBP/Snudda/snudda/neurons/morphology_data.py\", line 483, in load_cache\n", + " raise ValueError(f\"Cache mismatch. Different paths:\\nRequested: {self.swc_file}\\nCached: {data['swc_file']}\")\n", + "ValueError: Cache mismatch. Different paths:\n", + "Requested: ../../../BasalGangliaData/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/morphology/WT-0728MSN01-cor-rep-ax-res3-var7.swc\n", + "Cached: /home/hjorth/HBP/BasalGangliaData/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/morphology/WT-0728MSN01-cor-rep-ax-res3-var7.swc\n", + "\n", + "Failed to load cache from ../../../BasalGangliaData/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026/morphology/WT-0728MSN01-cor-rep-ax-res3-var7.swc-cache.pickle\n", + "No connections specified in connectivity_distribution.\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n", + "Prune synapses\n", + "Network path: network/single_neuron\n", + "No file network/single_neuron/pruning_merge_info.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.1s\n" + ] + } + ], + "source": [ + "ss.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4c86c755-abd2-4b86-9819-31c30257f81e", + "metadata": {}, + "outputs": [], + "source": [ + "simulation_config = {\"current_injection_info\" : {\"0\": {\"time\": [0, 0.4999, 0.5, 0.9999, 1, 10],\n", + " \"current\": [0, 0, 300e-12, 300e-12, 0, 0]}}}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b21c9ea6-a0c0-40aa-b0ab-a78c7e0f185d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MPI Rank: 0, Size: 1\n", + "Using input file None\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "NEURON mechanisms already compiled, make sure you have the correct version of NEURON modules.\n", + "If you delete x86_64, aarch64, arm64 directories (or nrnmech.dll) then you will force a recompilation of the modules.\n", + "Reading SNUDDA_DATA=None from network/single_neuron/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from network/single_neuron/network-synapses.hdf5\n", + "Warning: No external synaptic input file given!\n", + "MPI Rank: 0, Size: 1 -- NEURON: This is node 0 out of 1\n", + "Using network_file: network/single_neuron/network-synapses.hdf5\n", + "Using input_file: None\n", + "Using output_file: network/single_neuron/simulation/output.hdf5\n", + "Using logFile: network/single_neuron/log/network-simulation-log.txt-0\n", + "Worker 0 : Loading network from network/single_neuron/network-synapses.hdf5\n", + "Loading config file network/single_neuron/network-config.json\n", + "0 : Memory status: 73% free\n", + "Distributing neurons.\n", + "Setup neurons\n", + "numprocs=1\n", + "Node 0 - cell 0 dSPN\n", + "Neuron dSPN (0) resting voltage = -86.0\n", + "!!! Popping extra segment from neuron -- temp fix!\n", + "Build node cache dSPN (dSPN[0])\n", + "Forcing rxd update...\n", + "Updating node data... (takes ≈ 1 microcentury)\n", + "RxD update completed.\n", + "Node cache built.\n", + "0 : Memory status: 73% free\n", + "Adding gap junctions.\n", + "connect_network_gap_junctions_local\n", + "Finding node local gap junctions...\n", + "Added 0.0 gap junctions to simulation (0 total)\n", + "Adding synapses.\n", + "connect_network_synapses\n", + "Added 0 on worker 0\n", + "Added 0 synapses to simulation (0 total)\n", + "0 : Memory status: 73% free\n", + "Parsing current_injection_info.\n", + "Adding current injection to neuron 0: time = [ 0. 0.4999 0.5 0.9999 1. 10. ], current = [0.e+00 0.e+00 3.e-10 3.e-10 0.e+00 0.e+00], interpolate\n", + "No input file given, not adding external input!\n", + "0 : Memory status: 73% free\n", + "0 : Memory status: 73% free\n", + "Running simulation for 2000.0 ms.\n", + "Running simulation for 2.0 s\n", + "Running Neuron simulator 2000 ms, with dt=0.025\n", + " 1% done. Elapsed: 0.2 s, estimated time left: 15.4 s\n", + " 2% done. Elapsed: 0.3 s, estimated time left: 15.4 s\n", + " 3% done. Elapsed: 0.5 s, estimated time left: 15.2 s\n", + " 4% done. Elapsed: 0.6 s, estimated time left: 14.9 s\n", + " 5% done. Elapsed: 0.8 s, estimated time left: 14.7 s\n", + " 10% done. Elapsed: 1.5 s, estimated time left: 13.6 s\n", + " 20% done. Elapsed: 3.0 s, estimated time left: 12.1 s\n", + " 30% done. Elapsed: 4.5 s, estimated time left: 10.5 s\n", + " 40% done. Elapsed: 6.0 s, estimated time left: 9.0 s\n", + " 50% done. Elapsed: 7.5 s, estimated time left: 7.5 s\n", + " 60% done. Elapsed: 9.0 s, estimated time left: 6.0 s\n", + " 70% done. Elapsed: 10.5 s, estimated time left: 4.5 s\n", + " 80% done. Elapsed: 12.0 s, estimated time left: 3.0 s\n", + " 90% done. Elapsed: 13.5 s, estimated time left: 1.5 s\n", + "100% done. Elapsed: 15.0 s, estimated time left: 0.0 s\n", + "Neuron simulation finished\n", + "Simulation done.\n", + "Simulation run time: 15.1 s\n", + "Simulation done, saving output\n", + "Writing network output to network/single_neuron/simulation/output.hdf5\n", + "Using sample dt = None (sample step size None)\n", + "Worker 1/1 writing data to network/single_neuron/simulation/output.hdf5\n", + "Program run time: 15.4s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ss.simulate(simulation_config=simulation_config, verbose=True, time=2.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eecaaa29-0047-4fd8-b7f5-d15a038e3cce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network/single_neuron/simulation/output.hdf5\n" + ] + } + ], + "source": [ + "from snudda.utils import SnuddaLoadSimulation\n", + "sls = SnuddaLoadSimulation(network_path=network_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "69dbe223-b41a-4b6f-b54a-b55430981676", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['spikes', 'voltage']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sls.list_data_types(neuron_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7fe9e765-20ee-4216-a854-83c0066d30c0", + "metadata": {}, + "outputs": [], + "source": [ + "time = sls.get_time()\n", + "neuron_id = 0\n", + "voltage = sls.get_data(neuron_id=0, data_type=\"voltage\")[0][neuron_id]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a41a9158-94d3-4dee-9026-a5a4b4f04431", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'voltage')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, voltage)\n", + "plt.xlabel(\"time\")\n", + "plt.ylabel(\"voltage\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "903550c5-1082-4eb4-bcf8-e0d14a402311", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/population_unit_mesh.ipynb b/examples/notebooks/population_unit_mesh.ipynb new file mode 100644 index 000000000..b7e295658 --- /dev/null +++ b/examples/notebooks/population_unit_mesh.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "354adfb1-1505-4a16-9116-f546882f06c7", + "metadata": {}, + "source": [ + "# Specify population units with meshes\n", + "\n", + "This example shows how to use obj mesh files to specify the population units." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ade4a161-acb2-4895-adda-42eeff2e07a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cube for striatum\n", + "Neurons for striatum read from /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum\n", + "FS: Skipping neuron because, num_neurons =0\n", + "Adding neurons: dSPN from dir /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn\n", + "Adding neurons: iSPN from dir /home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn\n", + "ChIN: Skipping neuron because, num_neurons =0\n", + "LTS: Skipping neuron because, num_neurons =0\n", + "No directory $DATA/neurons/striatum/ngf, skipping NGF cells.\n", + "Writing networks/population_unit_mesh_network/network-config.json\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "from snudda import SnuddaInit\n", + "from snudda.place.create_cube_mesh import create_cube_mesh\n", + "\n", + "network_path = os.path.join(\"networks\",\"population_unit_mesh_network\")\n", + "config_file = os.path.join(network_path, \"network-config.json\")\n", + "si = SnuddaInit(config_file=config_file, random_seed=12345)\n", + "si.define_striatum(num_dSPN=1000, num_iSPN=1000, num_FS=0, num_LTS=0, num_ChIN=0,\n", + " volume_type=\"cube\", neurons_dir=\"$DATA/neurons\")\n", + "\n", + "mesh_file_A = os.path.join(network_path, \"mesh\", \"example-mesh-A.obj\")\n", + "mesh_file_B = os.path.join(network_path, \"mesh\", \"example-mesh-B.obj\")\n", + "\n", + "create_cube_mesh(file_name=mesh_file_A, centre_point=[0.00475, 0.004, 0.00775], side_len=0.0001) \n", + "create_cube_mesh(file_name=mesh_file_B, centre_point=[0.00475, 0.004-0.00015, 0.00775-0.00015], side_len=0.0001) \n", + "\n", + "# The centre of the cube is [0.00475, 0.004, 0.00775]. num_neurons is optional\n", + "si.add_population_unit_mesh(structure_name=\"Striatum\", neuron_types=[\"dSPN\"], mesh_file=mesh_file_A, fraction_of_neurons=0.7)\n", + "si.add_population_unit_mesh(structure_name=\"Striatum\", neuron_types=[\"dSPN\", \"iSPN\"], mesh_file=mesh_file_B, fraction_of_neurons=0.8)\n", + " \n", + "si.write_json(config_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9e7be703-7b87-4eda-afe5-3c6dce854545", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"IPYTHONDIR\"] = os.path.join(os.path.abspath(os.getcwd()), \".ipython\")\n", + "os.environ[\"IPYTHON_PROFILE\"] = \"default\"\n", + "os.system(\"ipcluster start -n 4 --profile=$IPYTHON_PROFILE --ip=127.0.0.1 --log-level ERROR 2> parallel-log.txt &\")\n", + "\n", + "import time\n", + "time.sleep(10) # Wait for ipcluster to start" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "642f6922-9a17-4a6e-9d52-f8373882487b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Placing neurons\n", + "Network path: networks/population_unit_mesh_network\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=None from networks/population_unit_mesh_network/network-config.json\n", + "Generating 9874 points for networks/population_unit_mesh_network/mesh/Striatum-cube-mesh-0.00029179512939439816.obj\n", + "n_points = 9392, previous close_pairs = 19707\n", + "n_points = 8935, previous close_pairs = 16128\n", + "n_points = 8502, previous close_pairs = 13425\n", + "n_points = 8092, previous close_pairs = 11224\n", + "n_points = 7706, previous close_pairs = 9382\n", + "n_points = 7342, previous close_pairs = 7911\n", + "n_points = 7002, previous close_pairs = 6580\n", + "n_points = 6685, previous close_pairs = 5565\n", + "n_points = 6391, previous close_pairs = 4666\n", + "n_points = 6121, previous close_pairs = 3866\n", + "n_points = 5874, previous close_pairs = 3310\n", + "n_points = 5650, previous close_pairs = 2832\n", + "n_points = 5448, previous close_pairs = 2409\n", + "n_points = 5268, previous close_pairs = 2030\n", + "n_points = 5108, previous close_pairs = 1698\n", + "n_points = 5072, previous close_pairs = 1417\n", + "n_points = 3726, previous close_pairs = 1346\n", + "Filtering 3726 points..\n", + "Filtering, keeping inside points: 2660 / 3726\n", + "neuron_name = 'dSPN_0', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508'\n", + "neuron_name = 'dSPN_1', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521'\n", + "neuron_name = 'dSPN_2', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503'\n", + "neuron_name = 'dSPN_3', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521'\n", + "neuron_name = 'iSPN_0', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611'\n", + "neuron_name = 'iSPN_1', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603'\n", + "neuron_name = 'iSPN_2', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527'\n", + "neuron_name = 'iSPN_3', num = 250, neuron_path = '/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529'\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 0.5s\n", + "Touch detection\n", + "Network path: networks/population_unit_mesh_network\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "Reading SNUDDA_DATA=None from networks/population_unit_mesh_network/network-config.json\n", + "importing SnuddaDetect from snudda.detect.detect on engine(s)\n", + "importing ProjectionDetection from snudda.detect.projection_detection on engine(s)\n", + "Suppressing printouts for hyper voxels that complete in < 100 seconds.\n", + "HyperID 31 completed - 9147519 synapses found (125.5 s)\n", + "Reading SNUDDA_DATA=None from networks/population_unit_mesh_network/network-config.json\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 145.0s\n", + "Prune synapses\n", + "Network path: networks/population_unit_mesh_network\n", + "Reading IPYPARALLEL connection info from /home/hjorth/HBP/Snudda/examples/notebooks/.ipython/profile_default/security/ipcontroller-client.json\n", + "\n", + "No file networks/population_unit_mesh_network/pruning_merge_info.json\n", + "importing SnuddaPrune from snudda.detect.prune on engine(s)\n", + "prune_synapses_parallel (908575/17042558 synapses, 5.3% kept): 41.1s\n", + "prune_synapses_parallel (0/0 gap_junctions, 0.0% kept): 0.0s\n", + "stop_parallel disabled, to keep pool running.\n", + "\n", + "Execution time: 201.2s\n" + ] + } + ], + "source": [ + "from snudda import Snudda\n", + "snd = Snudda(network_path=network_path, parallel=True, ipython_profile=\"default\")\n", + "snd.create_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "358378fd-963a-4ca3-8cc7-df961d78d9d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-29 15:10:12.158 [IPClusterStop] Stopping cluster \n", + "2024-04-29 15:10:12.158 [IPClusterStop] Stopping controller\n", + "2024-04-29 15:10:12.290 [IPClusterStop] Stopping engine(s): 1714396001\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.system(\"ipcluster stop\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6630fb8f-8e9d-419f-9ea1-48a158da79cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading SNUDDA_DATA=None from networks/population_unit_mesh_network/network-config.json\n", + "Reading SNUDDA_DATA=/home/hjorth/HBP/Snudda/snudda/utils/../data from networks/population_unit_mesh_network/network-synapses.hdf5\n", + "Population unit 0 has 1959 neurons\n", + "Population unit 1 has 26 neurons\n", + "Population unit 2 has 15 neurons\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import PlotNetwork\n", + "\n", + "pn = PlotNetwork(network_path)\n", + "pn.plot_populations()\n", + "\n", + "# The commented code below also plots the network, but with the option to plot more detail of the neurons:\n", + "# pn = PlotNetwork(network_path)\n", + "# pn.plot(plot_axon=False, plot_dendrite=False, plot_synapses=False, colour_population_unit=True)\n", + "\n", + "pn.close() # Close the hdf5 file so others can use it (or better shut down kernel after)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/parallel/KTH_PDC/README.md b/examples/parallel/KTH_PDC/README.md index 9fe69b930..a5dbb6c0e 100644 --- a/examples/parallel/KTH_PDC/README.md +++ b/examples/parallel/KTH_PDC/README.md @@ -23,4 +23,10 @@ After the network is created, you can start a simulation: sbatch Dardel_simulate.job ``` +Note that you might need to change python version from 3.9 to the installed python version in ```Dardel_simulate.job```: + +``` +export PYTHONPATH=$SNUDDA_DIR/snudda_env/lib/python3.9/ +``` + You can find the generated network files and simulation in ```Snudda/examples/parallel/KTH_PDC/networks/test_10k```. diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Analyse_sten_5_FS_LTS.ipynb b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Analyse_sten_5_FS_LTS.ipynb new file mode 100644 index 000000000..16d1b1b97 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Analyse_sten_5_FS_LTS.ipynb @@ -0,0 +1,1126 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bc09aea9", + "metadata": {}, + "source": [ + "# Verifying that lateral GABA inhibtion affects firing rate in a population" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "98f5da90", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import os\n", + "import numpy as np\n", + "network_path = os.path.join(\"..\", \"networks\", \"sten_5_SPN_FS_LTS\")\n", + "network_file = os.path.join(network_path, \"network-synapses.hdf5\")\n", + "simulation_file_with_synapses = os.path.join(network_path, \"simulation\", \"output-with-synapses-sten_5A.hdf5\")\n", + "# simulation_file_no_synapses = os.path.join(network_path, \"simulation\", \"output-no-synapses-sten_5A.hdf5\")\n", + "duration = 18\n", + "\n", + "# Local path for Snudda data\n", + "snudda_data = \"/home/hjorth/HBP/BasalGangliaData/data\"" + ] + }, + { + "cell_type": "markdown", + "id": "1b4c8039", + "metadata": {}, + "source": [ + "# Plot network\n", + "\n", + "In this particular example with SPN, FS and LTS we do not use the population units." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "51d7a120", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Population unit 0 has 31998 neurons\n", + "Population unit 1 has 4000 neurons\n", + "Population unit 2 has 4000 neurons\n", + "Population unit 0 has 31998 neurons\n", + "Population unit 1 has 4000 neurons\n", + "Population unit 2 has 4000 neurons\n" + ] + }, + { + "data": { + "image/png": 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M0tISc3NzHD58mEaj4RoXTU1NOabgXu/bg7IPg3mwz97N0gdpmlagoLKH1ioQUNld2c3Ef7ey+8EEyI4ePcrS0hKvvPIKW7ZsueH3NqrWImzz7nL21qEqorf5f+XvxQiorryc15d1u13XPVDKf82Pzi3dgECGAAcwMCabPrBpB5vC2KwksKxBKDMz1ilZ4KBSQ13n8vIyi4uLnD9/nvfee4/h4WFXijg+Pn5DdcTN7KOuCbid3a7s9VagoNfr0e123b2uQEFlH5ZVIKCyOzbb+vduFiqbA79XW1lZcYKsAwcO0Gw2Nx2f7fuvaNs6V41Z+XrrLG0krZQA5O1o1fNA9D4wQPEHQTCg+C/n/MsVEjYitKxEGIYOhFiWQikJnRcG9y7Qv2Vl5kPzU06J6FoseFL/fc3TwsICKysrN/QoEFtwq26GD9qZPczMw81AmYDnzUoSK1BQ2QdpFQio7LamhUqd8u5lUbK58Ls99/nz5zl69Ci+77N///5NAYA+q3PJypv7QJ7z7/f7RFF0Q06/3DZYn5PjtAu3Fmw532azOZASUAWCcuy206B19mVdgNUqWF2AwElZc6Bxa0w21SDmoSxMLIsVN0uhiNXYsmULMzMzPPHEE6718tLSEseOHaPX6zE+Ps7U1BSTk5OMjY3dUHf/oOzDSAe8n/NZ8Knj2XetDBbtvgcVKKjsflkFAiq7pd0L/b+Z3csGQFEU8e6777K4uMirr77KwYMHb+tY7NjKgj1gQFSnsi5bCbCwsMDS0hIzMzPU63U6nQ5RFFGv192CbRdqOx5bXgiDkbfAQb/fd3S61R7Y+bV0f5qmbjMhO49We+B53gAYEKBQykLXqe/Zc9n7Y4FA+f/13WazSbPZZMuWLfi+73oUSFNgexRYFuNBOK0PIx1wr9Uum9nNQEEcx45lWlpaYnh4mNHR0QoUVHZfrAIBld3UkiThypUrXLt2jWeeeeZ9Rz13ExkuLy/z1ltvMTw8zIEDB5w47XbHUOSrc1oHKcet3L0Ajj6vzYbGx8c5c+YMQRDQarUYGRlhcnKS4eHhG+YgDMMBxb6ocTmkRqPhHLPv+9TrdaIoolar3dADoNykxlYVaM+CchdC2+yo2+3SbDYHShd7vZ5LI9g5sOfR+MqVC/qjf5cBoEDB8PAwO3fuJMsy1tfXWVhYYGFhgSiK+NGPfsTk5KTbG2FkZOQDc1gfN+ZhM1Bw9uxZtm/f7hghPSN2M6T7CUwq+/hbBQIqu8EUfcRx7Gqh3+9id6dMgBa648eP8+STT7J3796BcrebLfQ2/x1F0QDdXc596zP62draGocPH2Z1dZVnn32WyclJABYXF7l+/ToXL17k+PHjjI+Puxz48PCwc+iWXVCOX7qJcj2/RIIai50fLfi25bKN4PWdJEkcm6AoXY7dphVkZdBh51CAyI6j7PhhUNhmKzXK6ZNWq+W6Gf7Jn/wJTz/9NL1ej4WFBU6fPu2EiOpmuBmw0rnv9pn7qKUD7tasbkTiU8sUwObdDCtQUNmtrAIBlQ2YHIqcls1Pvx+7kyi+3+9z6NAhVlZWeP31150zvt0xbFmcVdXbn8Ngy1w5xqWlJd555x0mJiZ46aWXgBwUNJtN13J3y5YtThy4urrK3Nwc/X7f1dar5M7m14MgcDR/uZOgonsBCOtYrUBRnxUoUFvi8nXBxuJvHbR15mJDyk7L8zyXotCYyg7EdkDcjB3QPbDqd92L4eFhtm3bxp49e0jTlHa7zfz8PFevXuX48ePUajXHFKhxkb3Hd5N++jBAwIPeuMne95ulD1TJAhUoqOz2VoGAyoDB2v8y3Xw/QMDtjrO4uMjBgwcZGxvjwIEDmyrOb1ZhYJ2GFcWpNlsRdb/fHxBcnTp1ijNnzvDYY4+xa9cufN93pX62MZDn5f36G40G4+PjjvrudDqsr69z7tw5er0ew8PDTExMuOY8cr5yytZBdTqdAYCg67NAxV6brToAXLmi7Udgvyfhoa7VaggU7ZerBGxkqc8IPGzWEMr2M7BpBTEj9tpkymcrTbO8vMzKygqXLl3iyJEj1Ot1V3UwOTlJvV6/q978D1oT8KBz8bc652agQO+0mALP8ypQUNmAVSCgshvEf3aht/Tw+7FbOfDTp09z8uRJ9u3bx2OPPXbTRe5WY9HxrRjPlurp/+M4Zn19nSNHjrC2tsZzzz3nFO21Ws0tlr1ez4230Wi49IEWzTiOGRsbc4upxHErKyucO3eONE0ZHx93LXybzSZJklCv19081+v1G6K4MmCwoKV8Pf1+H9/3aTQaTjNRZg3q9brrgSB2wlYSaEw6ny2dhMGyS/1b82DTGhYAlEGG/WMdehAETExMMDY2xp49e4jjmJWVFZaXl7l48SJHjhyh1Wo5xmVyctLpGzazj3s6AO5OjKj7JLOgoN/vu+fKggJbLVPZo2EVCHjE7Xa1//eTCSg78F6vxzvvvEO73eaTn/yk62J3M7NRp/6tv8u19UoHiF5WdBpFEd/97nfZsmULr7/+uqNN7Th1PNHkcrAWZChCrtVq1Ot1xsbGaDabzM7OkmUZy8vLrK6usrS0xNmzZ/F9n6mpKRcJT0xMuP7y1snbMVh6XddshYD2unS99nv1en2grBM2tsnVNdq5tf+2Jj2ABSzlMVuRocZlnY4YCFn5ujSGqakp1wQqiiK3ZfLJkydZX19nZGTEpQ4mJiacNsKO60HZhwEC7H24W7sTUCDWzAoNK1Dw8bYKBDyiVnaWN8u93i8QUM7nz8/P8/bbbzM5OcmBAwduGeFZK1Po1mH2er0Bp1jOX8/PzxNFEXv37mXnzp1uwRP9rcWvTI1L7GepcZXtycFZDYDnedTrdaamppiengbynf2Wl5e5evUqZ86ccVoDpShsnr8MCHRtGlOz2RxIAaiRkaI61fdv1tvAMg5iBPr9vnP0tkNhuY/BZo1r9P8WBGwGDoAbrk9jtqJI+5larcbMzAyTk5M8+eST9Pt9lpeXWV5e5vjx43S7XcbGxhwgsON5EHa/SwTv9Jz36xorUFAZVCDgkbQy/X8r8dXNaPy7NZuzPnnyJKdPn+aZZ55h9+7dd7SoWKdinYgt/1tfX3eLlJxyr9cDNtoNB0HA9u3bncOzTr/b7TpaVBsGJUlCt9sdKP2zDljnkPO07IR1EiMjIzQaDbZu3UqtVnN6gqWlJa5cucLFixedw5ucnHS0vs5pO8qp7NDOi+d5bhMly1QIlFiQJaAgEKBz2Q6GqlKwTscCAjEY9jgWcNiqArsfg9VIyCwNbQGHAI6sXq+zZcsWZmdn8X2fXq/H0tISCwsLXLx4kSzLeOedd5ienmZqaorR0dEP1Ek/7OmAuzULCixw+/73v8/OnTvdvJc1BRUo+GhbBQIeMbOR450or+8nE9Dv9/nBD35Ar9fjzTffZGxs7Lbf00JrqWYbJcuBlZ2Q6OqFhQWOHTvG1NQU+/fv56233gI2KG4tYpZar9fr1Go1V3oVxzH9fn/AocoB6nPADYyBgJbSCQImWZYxOjrK0NAQq6urLqXQ6/U4deoUvV6PVqvF6Ogow8PDjIyM3EDn6/4JvAik6Fo0Jo2z2+0OsCg2CrdzoXnVuVRFYRsb2Yhf/69rs6JCyziUr1/3x4oSBSYsg6FjW3ZG/99qtVwKJooi/uRP/oTp6WlWV1c5f/48WZYNlCPe7x4FH7V0wN2Y1W0o7WWB2c1aHFeg4KNnFQh4RExOztag38nLer+EgXEcc+zYMbZu3cqrr746kMu92XjLVLL+1vhFo1vnZevqVd+/b98+du3a5YR0Orctt9O/9bf9XK1Wc6kG2MifWwGlKHs5Vzlcu6mQHJxK5fTdRqPB448/7hx6FEXMz8+zuLjIlStXiOOYiYkJhoaGGB4eHijblEO082JTJZoz2+FQ82BTJppDu5GSxJACFRYAWBCh89m5ty2S7f3Uc7eZANICU12TfU7tWCyzZLUFO3bscNfS7XZZXFxkcXGR06dP43meAwTaMlnzdC+O60GnAyx4e5Bm78NmTEGv17tlSWIFCh5uq0DAI2CiYe+l9a9dbO91oTxx4gSrq6vs2LGDF1988bbHsQu7VaXbvy1AsNR7GIb0ej2OHj3K8vIyr776KlNTU+47NhcehuFAS9ZGo+Fy9Pr9Zg4UcIp6MQNyrLDRtdAumPq9jXJtN0CxGjrvzMwMExMT+L7P2tqaa8977tw5p6ofHx9nbGyM0dFRdx02FaB5tPc9iiLHHJT7CkgcqmsQqFKvAtiIDOWMNPdlR22bC9n7GcfxwNwpspQI0/7OPg/llITOVQYElikQi7J7927SNGVtbY35+XkuX77M0aNHCYLAAYKZmRmGh4fv6tl+0ExAGdw9KNsM7FimoHyvy6BALILdLKuyh8cqEPAxNiv0sRTs3ZhdfO/2u51Oh4MHDxLHMVNTU4yPj9/RMcp5ZdioSS83rrGRqOd5XL16laNHjzI0NMTLL7/M0NCQ29hHcyHKHwYddpIkLuqXXqAc9droWiV4dgdB/bGCQY1VaQQJ7ETr22iq0+m4RVPHUmpg9+7dtNttlpeXWVtb48qVK5w/f57h4WFHd0uIKMGjLQXUuS040TXZz5Z/ZxkMG8lLUFjWQgg8CfwIAOk+lRsZCXDISZRZBlv1YMGJ1Q7YZ6LMXOnfIyMjDA8Ps2PHDrIsY2VlhcXFRS5fvsyxY8dotVquFFGNi273nD4qIOB2TZHs2lIGBerXYVNo1Q6JD49VIOBjaoq6bG74Xl62stO7U7t69SrvvPMOW7du5bnnnuPtt9++47TCZp+TA7MLvhXnZVnGhQsXOHnyJHv27GHnzp3ueuXorXPS93RdAgZyMGIE5OT1vWaz6Rr1iDLvdDoDC6XNX1tnJyDQaDRc5C4g0O12B9IJ5QXfOt3h4WGGhobYtWsXQRDQ7XZZX1/n0qVLnDx5kmaz6ervJyYmnJMW1a/za1yWAbBbJStStyDAtiCWVkKVEhJUWqpYmgqrP7BRu67ZtkHW9ev89nlI09SJHG1KSEyFFRbq3tvv266N2s8A8nLVdrvNysoK58+f57333mNoaMgBgs16FDyo/LysnLZ6kOe923NWoOCjYxUI+BiaFsXvfOc7PPvss26huxfTy3+nDjxNU44ePcqFCxfYv38/O3bscMfZTGBoKdwypVw+rn5n88ZSzb/77rssLS3x8ssvMz097QCQPlOmIy3Fr02EbOSpjntydoArF1Tkrp/bkj1R+kov2HPYSNfm2FWOlaZ5XwM5WZtusNG6nLoYjqGhIaanp9m7dy+9Xo/l5WUWFhY4efKkK6NTOaKqKCDXOsix9ft9V3VgtRb6WXmfAF2/ZUdgo4TTjtUyBeX0gE0dyJFbkNHr9dx5LbOy2XNUfp42Y5Q0vvJxwjBkcnKS2dlZIO9RsLS05PQEhw4dchtJKYVgAamezQ/S7DvwIO1+aB/uFBToPa1AwYOzCgR8jEwOT4t4eavbe7FyKdqtbH193W33e+DAgYEca9mxlwVd+ox96W00qGjSRpme57G2tsaPfvQj6vU6b7zxBo1GY1MBm1Wly1Fbul+5csgdohy7dVpW0KeoWnMkAKDjWwCgz1nGYDPdwNDQ0A06B53LfkciQzlQ2+xJeoLJyUmeeOIJOp2OSx8sLi66fgXT09OMjo4yPj4+oPS2YEOLsZyvyiA1LgucLGtgWQY9N5bFsb0X9Dk9u5Zh0OcEIvQMCLRZEKL7qLTRzRyzvT57bvusQw4KpqenXXpFjYsWFxddjwKAy5cv43ke4+PjAxqLD8I+DBCge3i/2YebgQIB7EuXLhFFETt27KhAwQdsFQj4mJgiKSsCu1n0fTdWFubdzC5fvsyhQ4fYsWMHzz777A2LRnksVs2u8dvITc7FCtZsqVgcx5w9e9bR/4899thAnl3O0VLFtjzOCprkuNbW1qjX69TrdefQRFPLMWlBlMMVOCiLDHXNco7dbtddkyJy389b/oZhSL/fH2jwozlKksRVBKhhkDoBlsV8tsUw4GhW9SeYm5tjbW2N8fFxlpeXuXTpEmmaui6GAgZysJbut+VhOr4cuQVc1lEJWOjnuh4LyCxgg1zRr8Veu+VpLpWmsZsMCRBYpuJWTsIyGZahKDNelqkQ6JmZmWFmZgbfz3sU/OAHPyCKIo4ePUqv12N8fNylGMbGxu6749Q8PUgn+KBSEOX7tra2RrfbZevWrQM9MsrVBxUoeP9WgYCPgWlxLpdU3S8QIEe2mSVJwpEjR7h06RIvvPAC27Ztu+lxygIu+/KW6WIbYdq/1WTn6NGjzM/Ps3//fqampgYWK31XQEAiPTkiRddyLBJOrq+vu7x9EATu33LcNh8tsZtlC2Cj1E2LlCoOms2mi/oFPjS3coy9Xs+JBwVUPM+j0+m4KFN7H+hcug6BDwtSLIjRgtlsNnnssccckFpeXub69euu4Y5EiCpH1F4HVjsgRsCyERYA2YoE/duyBPaZsloDXaNtRKRjiCUo9zTQs2J73mv+5cj17Ng0jE0X2JRFucpA57eshhgZ3/d5/PHHmZiYoNPpsLCwwNLSEhcuXCDLMsbHx136QJsmvR/Ts/sg7cPUISg1AINskX2G9BnbcKoCBXdnFQj4CJsVp20WJdwPEHCr47Tbbd566y183+fAgQMMDQ3d8hjWWcKtaU0bhVmHEkUR3/ve96jVarzwwgtucx/rpGx0bKNES8/b1IAWV20gZMWUcqSNRoNOp+MiVTkuOYiyUr7f7zsAYinxJEnodDruGBIfypHpvJ7nuXI50d4qf5QwUboFLYCWHZAzLc+/dXSe5zE6OkqtVmNqaopGozFQebC2tuZ2RhwfH2d6eto1PiqDHhuJ2y6Mej41p7oOew80b1aBbgGbngfd56GhIXcvbbpqM+pfc2z/tiDQXks58rciQ8sg2dSF7tXQ0BBDQ0Ps2LED3/dZX19ncXGRhYWFG3oUTE5OMjw8fNfO6oOg5e/knPDhVyRYZgk27pUFn5tpCipQcHurQMBH1Daj/zdbBG171ns1ORBrc3NzvPvuu+zevZunn356YJGwlLZ9CeUQ7aJr/7YLuXXYiji1w9zOnTvZvXv3wIJgnYL+bfPOtnOfnLStrfe8jba7a2tr7li2dt9GmtbJ6fs6t5yzbYCjfL9+p66Amg8xBhpjee7FEjSbzYEtksuUu65VYxsaGhpQ7NtFUyBFIENtlycmJti2bZvTXKytrXH+/HlOnjzJ+Pi4YwpGRkZcKiNNU6ejkFnHXdYAaCz6d9kpl1mj9fV1d48s0NJ3Ldiy82dZCPtzW8KpY2l8lknRPNqf67jlygPL7gwNDVGv15mdnSVNU9fnYX5+npMnTxKG4UA3w1ardVtnZUHPgzI96w/6vJbF2szuBhTYfQ8e9HV8FKwCAR9BU3RVpv/L9kEwAXEcc/jwYa5evcrLL7/s1NR2bOWF0VKy5c/ZBd++0Nbhqtvg0tISs7OzDgAody9HI4cq51oW0mlxkEPo9XrOuTYaDTef9XqdTqfjxqqFRU7DAgrrXLRwNZtN1tbWBlToNvrWNWpsnucN1KRL21AGRKLMm82m62OgtIXGLe1Ap9MZAEMadxRFTnGvZ0gpDoEN3Sff910P/p07dxIEAe12m6WlJU6cOEG/33egYHR01OXwdZzyuS0DYKN0S/trXLo2CyI0Vi3wAjL2edL+D5pvC44sC6HyTDVNsmOx4EpzaMGkjmfTGZo/fceycyrrHBkZYc+ePQCuR8GVK1c4evQo9XqdiYkJ17ioDAZ1vg8jHfBhOM4yE3A7u1NQUO5mWIGCCgR8pEwPtaVWb7UoWEX2+zFRo6urqxw8eJBarcZnPvMZms3mDeOz0RAwQAdbB2BBgBXQWaAQBAHLy8u88847NJtNV/uv79tjiA7Xiy1nYh2OxpNlmattt+V5aZoyPz/PlStXGBsbY2hoyEUjug7b6EcLinVEwMCmN3KEijQl1tO8at5s+aFYCgngbBrFNt5RJA+4FIPm0kZA9vpE1+s+yXGXGRvNvwUuUsyLKYiiyIGCa9eukaYpY2NjjI2NOYBm2RIdS/fO5vmtENSKDQXO7POs+6DjC8xovDbvL/BZbv6k+bRgWscSuNC82PSSrcKwrJLVh+hZv1lppFiAiYkJHn/88YEtky9evMjhw4cZHh52jYsmJibcfH4YTMCH4SgFoO/VbgYKBP5g8xbHjyIoqEDAR8S0+JXV17cyLf7v1zwv78R3/vx5HnvsMZ566qlNXxabAtC/9ceq9a1T04tqHZUW0wsXLnD48GF27NjBvn37OHnypFPW2yhWAEBKe+XRrUMQdQy4PLy6BmpfgEuXLnHixAlmZma4cOEC3W6XWq3G6OgozWaTsbEx53x0P2wULfZBc64yQetE7W6Edg5sCaSU0NaJWScj4GJZBmCgR4HGaRv9WEZCzrBer7vKBAEaAQObSpI+QWWOkPdHaLVabNmyxVVXrK2tce3aNRYXF/F9n3PnzjEyMuIcsJy6nK1yuGJCbNQGOEdqnWA5Ei+DGin/rTYBcLoNyzyUz6Xz6RmTg7BAZDPGyv5cc1yms8spNfuzqakpJicn3fO5vLzM6uoqZ86cYW1tjZGREZrNpmMsrGBO9kGwBBYAPUi73+fdDBToOdT7pOdHuyY+88wz9+38D7NVIOAhN/uw3o7+L9v9SAeITrtw4QKf+MQnmJmZuePvlpkB/b+N1Kxg0DqzY8eOcfXqVZ555hmn/rcOTU5TVK0WDYn7AOfIFFmXAYeci9INq6urvPDCCwPjWl1dZXV1lbm5Oebm5gjDkLGxMRqNBkNDQzfkTOVo1GTHOpAy/a55kIMX6Gg2m84RSS9g+wTYvgA2r62xWKAih2T1CbDBWijtUXaMug7Nkc3p61r0eUX1yoNPTEzQ6/V47733aLVaXL16laWlJZrNJtu2bWNsbMwBCNgQTMqhWWGefZYsoBLIs7vb6Rmz6QyZnhn9v507Oy92zvQZgQxL+9v5tr+zTNWtomgbmcr02Vqt5hoXqSxyaWmJubk5+v0+f/zHf8zo6OjA/hG6V3ezPtyJfVTSAXdrek9kdp39p//0n/KFL3yhAgGVffimReJW4r9b2ftNB6ysrPDWW2+RZRnPPffcbQGAZQBsZL/Zoqlo3C7IWZaxvLzM4cOHCYKAV199lXq9PlBjb6PJ8gIcxzHtdntAyCjnIrYgjmOXLxewOHLkCCMjI7zxxhskSd5RT052fHyc4eFhtm7d6srz1JEP8mh4amrKbWtrKwsUWaZp6sR8orzV1KgciYqe1HwpGoWN+y+gY3sYCFhoTnS9WZa5Bke6F9IEWGAgMCV2Qf8vBsA6S4EEPZc2PaUtkZXa2LJlC7OzsyRJwurqKr1ej7m5OVZXV92eB8PDw4yNjQ2wKponS61LVKm5sp+zjYY0B7Ziwb4LGptNS2nulOqBwc2qrCO0wFVjsc+cvmvvkX0mNDaNN0kSN88yfUZjmpycdGm1/fv3u8oDNdVRi2ilD+6XA/0w0wEP8rwWFKyvr9/1ZlIfZatAwENqWkTsQna3dq9MQJZlnD9/nqNHj/LEE09w9erV2y4qdiG10boWcNgQVdlSPgECgGvXrnHo0CF27tzJ008/7Y5lc3g6hxyGjlGr1Yjj2AEGRfjSBtgmPorI5+fnOXv2LI899hi7d+92x7YRt9USKELbunUrvV6P9fV1er0ei4uLzM3NAdBqtZicnHTiLltGqHnyfZ+VlRXHVIjRsBGwAIRKAlVdIIdhVf1BELheAhIHWrahLGyzjXLkXO3cSoEvx2YFeLDxbNp9AXQ+CSjts6BzaVMeVTjMz8+zvLzMyZMn6XQ6jI2NuUY7s7OzTqApbUer1RqYsyRJ6Ha7A/Ona1ZqQ++AGA24kdJXWsf38yZA6vgox1zWrug+2GeyrHcQuLOCUpuqskyO2Crdc5u20bEta6Cqg9nZWZeisZqCNE0HyhFHR0cHxqh7cyf2cWUCbmVKvzwqVoGAh8xsBHS39H/ZtMDcjUVRxKFDh1haWuK1115jamqK69ev3xJM2Dy/XWSkxJYARwDA0ueQU+gnTpxwDYfUlc0KsORMbeta5fJ1ndbhKRpVDb7K33TcCxcucP36dZ577jlarZZzJtaR2EhXrAVsaAikK9Ai2+/3WVxc5NKlS8zNzdFsNhkdHXUNeDQWOXbNlRyH5l/zVs7l6zoljLNRsHVS1nFlWeYa/uhe2QXWNhGyolObA9fc6h5ajYPuhcYjfcFmwEcNk0Sxz87OMjMzw2OPPcba2hrtdpt2u83p06c5efKkc2DDw8MMDw87wGEjdNs5zj53+lm323VzKsduI3aNRffPPmeA+7yAQTl9ZQWNFuDaToeAAxk2wtd1iH2IomigBNKewwIy+3PP2+hRsHPnTqIocq2il5aWOHPmDADj4+NOjDgyMjIgWryVPSpMgLV2u10xAZV9OKbF5r/+1//K1q1bXa32vZqi4Du1paUlDh48yPDwMJ/5zGdc5GQZBbuw2wXJpgJsCkCORY7Pgpt6vU673eadd94hyzI+9alPDWzxqwgfuMG5adtdidg0RttQR8cSkyAHevz4cRqNBvv376der9NqtVxJm84jRw+D+WdF0TqPmA5t/DM0NMT27dvxvLzG/vr161y7dg3AldPps7b00NLDtv+9peTtLoOixq2zVf5Y12wXUiu61M9arZZjYnRsHUPnUD5cEbi+q8/JEVtGxwrqLB2vsepc+l2SJIyOjjI2NuYATrvddt0hz549S5qmzMzMMDo6yuTk5EBHRjldzZM9brk6QqDJjkdAR2yBBVA2rWDnz2oKbFVCHMcMDQ05kGU1GeUdFvVuWfBs58WCC12P/W5ZaKhzqRxx586dZNlGfw1thqT0gloc36pHwaPGBCh9JmD/KFgFAh4Ss4p3S7O+H7vTdECWZZw5c4YTJ07w1FNP8fjjjw+cuxz92HyoBQOWErV17GWhlz5/6dIljhw5wrZt23jssccGok4dW01iynSrnJz66itSsyJA20SmVqs5RmN0dJQ9e/a4a2w2mw6QdLvdAVGYxqLqAxvlaT407jRNB/rsT05OMjY2xvr6usvjLy8vc+HCBSB3wkoztNttN9f6oxJMOSzll9M0dZ+XyREBrnlPFOU7DEpDYZ2atAdyUJ1OZ6CsT85R1w844CKWpN1uuzmW87W5d8sA2VbAFjTa8lHR+wI+Ehru3LmT9fV11tfXB5rtqFxxy5YtA+kLqxMRWLAAVKBRDJPGb3s/6L5obm06xd5vy27pHmkcmksBmzKQ1Risky3rCjqdjgPMZTCymc5G90AmvYAqD9I0ZWVlhYWFBa5cucLx48ep1+sD6QNb+vthOeMPkwlYX1+/ZffTj5tVIOBDNhtBWEHW+xH0ye4EBPT7fd555x1WV1d54403mJiY2PQ4WvTsIiWHbfOd9udW+GevKUkSTp48yeXLl3nhhReYmpoaoEkbjQbdbtc1foENJyedhMoB1fxGtfgS9ckJ6nznz59nbm6OIAjYtm2bAxIq4wMGegJovHKUtkWvxqI0gsYuJ6ljaU4UATabTWq1GmNjY0CeVrhw4QL9fp/Dhw8zMjLC1NSUU87rXEplaG40H0phWMei31kHZVMPyn+HYej0E5Y1sPd6fX2dZrPpAJh1OJYl0XXZ1I9tx6wIXeBEeg2bXhAYkJ5BrIieH6ngt2/fTpZlrK2tsbq6ytWrVzl9+rQTQl6+fNk1LtpM0GiFgzAYVetzVmAoAKDrFTgS42JZG70rFojo+jR/FkzYd0vPiZ5B2x7avlM23WbFiHbtKJsF6L7vMzExwdjYmBu3GhddvHiRI0eO0Gw2HSCwnRQflFlw/WFYxQRU9sDMqoPhxjaw79dud5zFxUUOHjzI2NgYn/nMZ1zkc7Pj6FhlWtlGOjBYHWBz1nEcs7S0xOHDh4njmBdeeIGRkZGBSMou2rZZixYGUeiip60+QNGe7VqXJAnHjx8nSRJeeukljh07BjAAFMQ2CGDY69TiLACk8j0BFUtBy5FZBySnUY6OgyBgbGyMfr9Pv99ndnaWlZUVrly54tT7O3bsYHx8fIAR0KKvrohytup8qHPovN1u1y3kGqeAgaJLG40KjPZ6PXdOCfnUIEngRJUANvWjORPlr59ZZ6heA5Z1EJAT26LPWwGjBRkqRZRDPnfuHHNzc66uXrsiDg0NMT4+7qoxdI0W4Nnny6YDLECwkb79jn1HyroLPWfWueuz5XSBwF05BWFFtbbyxJ7bAhDL4OnnFjxYXYI+r42OdA+XlpZYXFzk7NmzrK2tEYYhx44dc5UH5XXCjuX9speaE+BDAQHSZlQgoLIP1LTA2L719uUR3fh+7WYgIMsyTp06xalTp3j66acdNW4jfY1J4xJYKS981qnYfKxNC3S7XVde92d/9mfs2LHDtaHVoi6QYFX5dgG0witpAsQ2KMpU1KlOe/Pz8xw+fJipqSl27tzpmtbIsZcXY113WexlS/SU71YkKMBhBXUCRTaHb7UNYgw6nY5boBuNBrOzs0xOTjoauN/vu4V4ZGSErVu3MjY2NrAjnU23lEGY/ohOto7AggIp4hWF2zy01QuUHWMQBK7WXykGK2qVc7OUttgZu+VzmqbunglA6d9WRGd3WLROVr9rNBq8+uqrrK+vs7S0xMrKCpcvXybLMldXPzo66pTfFuzpnrTbbXce++wJ+Oi91JyUmYTy+OzvbBrAArpyVK9z6J7ZVJB9v+w9sSxQuRrHgirLOGpsFkSEYei2TAY4duwY7XabLMs4efIk6+vrTpchRsGKMsvpjXuxcuXFgzSl2arqgMo+MLOUNmze+e+DZAJ6vR5vv/02nU6HT37yk4yPjwM37tpWdiw2N2zzj7ZUzEaiojK1cM7NzdFut3n55ZfZsWPHQKRjF7WVlZUB6lRO1fadlyOxC6JSEpA7mKtXr3L27Fn27dtHq9UaSFcov68F0y602k9A16tmOjYvLNMWwbYsTAChvNOfqgmso4jj2IEJuxjLKQ4NDTE9Pe26mnW7Xa5cuUIcx4yNjTEyMkKr1RooZwPcdej/rbbBRqQqS5MjUVQvcKocvZoYKXcuOl+AphwBK/LVua1uwjqkfr/vxt/pdEiSvJeAhHWWPrc5cDllme69rrvZbLJ161YmJibYs2cPaZqysLDA6uqq02Oo8qDZbLqNkMoA17IVsBGBlx2ofS5t+gNwANlS8rb5kn3nLJum/y8LBy0LYMeje2iDB71/dh0QCLCdKzXOzdYiz/MYHh7m6aefBnAlsQsLCxw+fNjtH6HKA/V7eD+MgGXNHrStra0BVJqAyj4Yk0OzVNxmZqnF92Pl48zPz/P2228zOTnJJz7xiYHWo2WnDwx8V7+3bIAVW2lRsVSe53msr6/zzjvv0Ov1GBoaYtu2bW4B1yJmnYKl1G2+U45fC7PNe0slLmr4+PHjrK6u8uyzz7pSnyTJmwBJda7z21pwwEWrWoBUn69r0veUkrDUsBZ2mzcvz6dd0MVGSMugvgI2Py9wJceoXelWVlZYWVnh9OnTZFnGyMgI4+PjjIyMDNwLjdEq9TVviuLLzk9RscZpBZeWIbEqe6VJyiJGfUf6C7uwK/K0jITGpr0QBOAsC2Q1CTaNI1Al0CjtiCoHNLa1tTXnyJaXl6nX60xPTzMyMsLw8PBAMyrLTNlzWxAsJ1zO0dvxyvRslDUE9t0rs3Bpmg4wX3qubOpps7VEx7XvrE3bWDGlvT57rHJuvtFosG3bNtcASmWxSvNFUeS2nZ6ammJkZOSunfnt1scP0lQe+GGJEj8Mq0DAAzC7cFiR0s1MC8/7NStSOnHiBGfOnOHZZ59l165dm+YOyy9/eaEqL2g2r2oFcyqjm5+f57333mNmZoapqSmOHTvmojZVQ1jBluZGx1Qet9vtut/ZaFnnkrhPjWeGh4d5/vnnAVy06fu+O44iWy2oAhnq2S8HYJkKzZOAlcYtUZ3Nd+t7cpLNZnNApGgjSUsNWwClz9iFWoxAo9FgfHyc8fFxduzY4Zza1atXuXjxIp7nDXSOU895ASc5D51D823vg8Zaq9UGIlnb818ORs5QoMaWhK6srAxcq3VKtgeEniUrKG00Gu7eliNyK2aU87dzqH4F9lyAK+EbGRlxwlMBw+vXr3Ps2DHXBXJkZMQJ6MqsidiSMh1vnanm1moQBHLKbcAtEBUQE/ARmNO7UU4t2OfJPqubmWVXrAlUldMOOrc1XbPA/7Zt29i+fTuQO9HFxUVWVlY4d+4cWZY5keHExATDw8M3BS12jB+WKHBtbc2N8VGxCgR8wFam/+8E4d7PdEAURXz/+9+n3+/zqU99aqB7GGwu5LGLmKXqtfgr4rbRmBY5LWZS/+/fv5+pqSkWFhYGHK4VkWl+lJOWkE1RsF3MrdO0wsQrV65w8uRJ9u3bx9jY2A1RkK1g0HVo8xwbSepams2muw/l+m4J9+RcNd4kSVzTHR1PEbzmSwu4zZ9LC6FxlnUEemZEmdv7oIV4dnaWbdu2EYYhq6urtNttzp49S5LkZZRTU1MDDZRsrl/9AADXXEeUsc2J22oPfV7n1/dgY78Jm8qBjaoLC7DKLIqoct07S5cDrvJAWhPdVws+7LOrz1sRqU1n+b7vOhVu3bqV9fV1VlZWXOWGIkO15RXdbXUVZcannHe3YkI5T+tsrT7AAoEyKNazaysV7K6SZZBu32fLCup8mhON1wIIm46zoMECAMt2aa4FsLQOCBTMz89z4sQJfP/2PQosUH7Q9qiVB0IFAj5Qk4O4k+jf2v0SBq6urtLpdJicnOS11167IYooU5D2hdfvtUhZStQ6VjkkKwJ85513iOOY119/fUD9bxc6u9Bbp6yIXAtQt9t1yFwLrwDS0NAQnU6H48ePMz8/z/79+53iXk7U0p22VE40vHVcOof9t/5f39Fc2QVQugCb79f82Fx8ed6sc7Q/z7LMlR6qy6CiQoEBCSDtIqzxT01NOZZAjnFlZYWrV68CeZSsTnxDQ0MOaK2vr7txaMxyrvbeWUZB16hGSjZtpDnT/ZZ4T05c2gyJEvX8CexoHAJbAhX2WbBMjBydziNQKYZDPxPIKetRdC/Fnuzdu9ftFdFutzl//rzLgQ8NDQ2wQpoLa2KhylG+/l3W2Vghn33OrD5Bn7UshJgtq5+wn9X/65mzz6gFpeX50O/tdeln5bSGfTbs+IaGhmi1WuzYsYMsy0s75+fnuXz58k17FFRMwIO1CgR8AKYXWovN3ea33i8TkKYpx48f5+zZs4RhyEsvveR+Xs79a9EpC6EEAuzfdkzWQWgBvnr1Ku+++y7btm3jmWeecTSqdSAqhRIDoFynaH/bnEffEU2vn2usi4uLrq759ddfd33my9SxHJau00ZeNurWQqhKBp1fi6RNfdjj2zJFu3BrUbULu8YCedQtEWKZeZHgUnl72yJZoEs/k7Ozi7nOVa/XqdVqbk/6JMm7812/fp3Tp08TBAFbtmxx4i6lH2y0qD+WvoeNVIX6Fei5EOCxx7HXpc8lSeIcvt4VwDEOOqdM39UzZJsnKboW06FGVbBRuWDBjcC5+k3YLZJtuV4QBAMOKooiVlZWuHbtGv1+nx/84AcMDw+7qoPJyckBcAAbjJx1uvqMfb/semGdoMDBzXoXCNiU9S32WZSVq2H092ZrlGUE7Dusa9B7XwYa9n5b3U4QBC6NpWdrdXXV7Xmgd1nlr7br54Oydrv9SFUGQAUC7rvpZb0b+r9s7wcEdDodDh48SBzHvPTSS7z77rvud+Xo347Z5qEtlW7zw/al12fkAE+dOsXZs2d55pln2LVrl4v0ZHZDGxu16dhy4JbqF+1s1epSya+srHDy5En27NnDnj17HOWrxVAgxXYADMPQRXq6zjJg0Hj0e5WqCRhYGl5zp8/YSF5CNNhweLo+5YnhRv2Hzj00NOTAgXY9tDoBUcFRFLlmPZZ5svOguYPcwc3MzDAxMeGqGNbW1lz3uFqtxpYtWxgZGXFCPvu8KKouz58WezsvFrAoarfsUnkONGYLMuy7pGdQwNGWZdrUBmywNorQ7fNsU1FK4eid07xaAGS3cg7D0HUo7HQ6PP/881y/fp2FhQXOnz9PEARuN79y6kDvmd4fgQMLvnXNVkOguS4LS23kbp2yZew0F/b9tX9bB26Bsb6r50n/1ryXAZ7m26YI7Xg2Syt4Xq5dmZ6edsdeWlpyDbT+5E/+xO00ebMeBffbxAQ8SlaBgPtkepluVvt/N3av6YArV65w6NAhtm3bxrPPPjsQ3WyWK4SNSNk6GUv16VrKNK/9+bvvvku73eaNN95gZGTELdzlnLAWay0iWqAVpemY9t9atBXZra+vc+7cOa5cucLzzz/P5OSkEyIqgpQjkRBQx7Sqdht56fhWXS7nLnBkdR2WUfF9353fRn72+DaKKyvGbUWBzq2cvahk62jtcyExnsCFTdvYhVrfE3OgcSt/Ozw8zM6dO0mSxG3iMzc3x8rKCiMjI0xMTLj2w7q3yrWLOleUr3tsxwiDOgGbDtEY1YjIgks9hxJDAi4Hrjm2nxVotHOm+6uUTbk6pwxk9GxbmtymrjqdDkNDQwPlgFNTU0xOTlKv11ldXXV7Rpw6dcqBrtHRUUZHRwdArq5F20zr3FbsV34n7fftOmO/o/nWc21/Z5lA+47b9JddD2xFiD2G5s+m9DR/9n0vr4MWMOq6dJ1BEDA9Pe2eH22ZvLi4eEOPgvu9ZbLsUdtGGCoQcF9ML9G7777L3r17XQ/ye7W7ZQLSNOXo0aNcvHiR/fv3O6WuPc5m4ylHb+XUhY3abASixWB+fp53332XqakpPvGJTwwIiRRF6TiWhpRyWw5DneJ0bJtqkNPxfd+1BF5aWuLFF190DrAcQSqtIKevjntaqOTEbERmo7CyhsGmLDS+Wq3m2u4KCFjFvebCzqXOYZXlsNHgRQp0jc868fX19YF74fu5MFFzqftmqVd7DdbRqbugHLG9Zim4h4eHnchudXWV69evO6GjOszZY+iaxdYomrURoNWkSEwo6lfjsOkTjcnS3jatY8s3pWsobzEtRy1gpO9rzpSGslE14OZImyzBRorKMlO2TC+OY7d5z/bt212aYX5+njNnzrgeCJOTk05sqPPq+nReW75rWzKL8reOWNdvWS09p3rO7Rjl2HVMvZP2WbVaCh3PMmplQFoejwUUuo7N0o06jn1eBbC00+Ts7Ky7JwIFR48epdfruX0RJicnGR8fHwBC92JVOqCyuzZFu0mS96ffvXv3wAYc92KWKr+dra+v89ZbbwHw6U9/egDFlql7jddSd3JMNre3GeJXFKWfX7x4kZMnT/Lkk08yOzs7ELGU6U1FiRYY2NynKNeRkRFHP1qFfZrmzV5OnjwJwL59+wYctk1nWM2AbWBULtPT76xiHzZKumy0VqaPxSro2FaBbsFLmqYDpV7WWdu5FhuicQkQ2fNakGAbvQhclRc/gY1yPtcyEZYytoDQOhzlueUQVldXWVlZ4fDhw47OVX8CVZ6IKpcTKjcNst0CO52Oo+PlXAR8RMsrWg6CwM2N5toCDLt5k5xos9l0zthS/zZFY2l6O296vpIkuSE3rXvcbrfdltKWpYDcMY+Pj7sUC8Dy8jIrKyucP3+eTqfjcuRDQ0Musk3TDbGf1Z9YFX+Z7dFzYEGS7oV9vsWs6Dt2fdA7YZkCnVPvjpyz3gs9O57n3bDVsmUYbHlkmeXQHNsNx2xAIVOPgm3btgF56lOgYG5ujjiOHUBVM6i7BQXqzvkoWQUC7tFsJFDOf75fsznnW9mlS5d499132blzJ88888wND3yZArTjts5FDsnS/loEbD5WdPWxY8eIoojXX3/d5a6t85f62zb+ieO8Ta51DvYaFQ3U63UX9er3165dY25ujtnZWa5eveoWQBu9K9rTPbARsY6l8eh7Gq/Om2UZy8vLzM3NAbiufHIc1uGKJbFiLetY7KJmwZPVVcjZlB1Mr9ej1WoNLLI6rxZ03SPLsNhmPPqurR4ogyMrwlQEqmdaz2GZZZGAcPv27W7fhatXr3LixAm38czo6OhAxGpZD41JZrUWFojq/Pbz+ox6MShlYktTbYmpzX3DYHRq0zRxHLuyNqUe9OzL2YkVUarGAodOpzPQPlrMVpqmrK2t0ev1GB4eJssy10jHvg/aCCmK8h3/hoeHnTPLso19HOTg7bOgZ90+73bOdP/tu69jaLwWOOi4egd7vZ7bN8KmxizjUA4gLIMAG02x7DpkwXCZNRATsFk6wVqr1RqoPFhfX3egQD0KpM+YnJx09/hWtr6+XoGAym5vWsDL4r87dd63s9ulA5Ik4ciRI1y+fJkXX3yRrVu33vQ4MCiOsk7fLs4WvduFU9fZ7/ddM5WpqSlHx1uHJHGW8s+2hlmLgpr7yDQmuzCJ5k3TlLNnz9Lv93nxxRfp9XpcvXrVjVMlcromm/e3EZJAiDQC+r7GqoXt4sWLLC8vs2fPHhdlqNkN4OrELbuRZZlLC1jqVQuf3dtAYEUOQHOtXL3mMooiVldXXQRtAZP0Ev1+nziKCJIMb6lP2qyRjKUkDZ/EaBDs5kPKi+tnmgOlNiwQtKJGGw3aDn9qVywadnl5mbW1Nc6ePeuc5rVr1xgbG3NVDtaRyXnp2u11aqxyKGmab5+sn2kLaUvR29SBnkeBGIEbVXCMjIy450RO34JY+07YZ0ZzIAZBYxfToLmGjc1o7Ptoe1WozHP79u34vs/a2hpLS0ssLCxw8eJFN7fKg2tuymyOde5iqiz9r+jdsj6Wet+MHRRY1/tlG2xpDqUFsWyhWBjr4O06Y89lwYkNRPReWO3G7UzPxPDwMLt27SLLMtdEa3FxkdOnT+P7/gAoGBoauuHY7XbbpR8eFatAwF2aaMpyDgzu78Y/NzvO2toaBw8exPd9Dhw44BbXmx1HC4ReTNjI9UvYZa/BKr/1EidJXspz6NAhnnzySbZt2zYAMLRw2LFrsZQ+QtGudfb6nhZi1ZoDrKys8N577zE0NMSzzz7rFhgdQ9G7hIa2sY6iRNti1Zam2XELuJw9e5YgCHj66acJgsBt+atFtNvtMj8/z9LSkpsrLdC2jE3UpwUlGrtAi31u+v0+nU7HOU0buVmNQZk1ydKU4No6fqeDh4/XDojmV4lDn2w6b1CUNfNovxX7eJ0+w6sJWejjX1uj1qyR+h5ev0ffg7Dh40UJ4UKXZKWDNxzCljGSICGJYrLVHqx28FtNvOlhYjbSB2JhxsbGXKOmKIo4ceIEaZqXq6rca2pqyn3OikKtA7PPryJQy2wIbNuKiHLKQ1oRm36yuWz9TiyP1XvYHL3+1n3UvZTuRABUv9MW0OW9M2y0rnttI+tWq8X4+DjDw8Ns3brVPXMrKyssLy+7yPbMmTNMTk4yOzs7kD6xYEW9E6wT1nNsUwuae41Nz6/VAZQbLQk02P0R9Fmd36bGtL5YbYANnqxZBtKuE/dinuc5Mab2j1hdXWVxcZFr165x4sQJwjB0gABgx44dN9UE/It/8S/43/63/43Lly/z8ssv83/8H/8Hn/zkJ296/v/wH/4D/+Af/APOnDnDvn37+Kf/9J/yF//iX3S/z7KMf/gP/yG/9Vu/xdLSEp/5zGf4P//P/5N9+/a5zxw7doxf/MVf5E//9E/p9/u89NJL/KN/9I/4b//b/9Z95n/+n/9n/vRP/5RDhw7x3HPPudTw3VgFAu7Q7OJSjpZld5PLv5XdjFG4ePEi7733Hnv27GHfvn23zXdpfNaxlM0ic+v09e+1tTUuXLhAmqa89NJLTE9PDwCE8vm0cIpeV1Sin9k8ZHnB1xxfuHCBM2fOsHfvXmZmZvD9XARmQYDN53reRrvecgmRIiBF5YrE19bW3EJ7+fJlWq0We/bsceOXgxF4kZALcHvZLy8vs7y8DOTU5OjoKENDQ86RqNGPBUiaN0uxh0FI1onwoxSvHkCtAFhF+sLOs9dPCdMUuhFZJwZqePjUCOjRxYtTalfaBNQIiPBqPvXEI05TRnyPZr1OLYqoBSkpGd1+xEgQECQRvX5MP4nISKGdEFxfIN7VIpjvE7fbBPg06h7x3DrsGsKfzAGoFnrdX7EeYRiyfft2Zmdn6ff7Azv7AWzZsoWJiYmBPQwEohS5613QHKhiQk7XRqBirGSiuqU7ULWHjZxVRmmdtn2OpEfQvVNPBJsuE7iTs7X3WaBUx1f/CW0QZXtl6LnRuQUMdu/eTbfb5fvf/z5hGHLp0iWOHz/O8PAwW7ZscekDAW4BDwvoLVC2rKXVC1iHLXZEjJv2j9C8q6JBKSNb8aF5syk0OzdWb6Hv6f9138r7TLxf833f6S8ef/xxkiRhZWXF6Qm+9KUvuaBhYmKCy5cvO+3Bv//3/56vfOUrfP3rX+fNN9/ka1/7Gp/73Oc4evTopqzBd77zHf76X//r/Nqv/Rpf+MIX+N3f/V2++MUv8qMf/YgXXngBgF//9V/nN37jN/jt3/5t9u7dyz/4B/+Az33uc7z33nuOqfvCF77Avn37+Pa3v02r1eJrX/saX/jCFzh58qQbG8D/8D/8D3zve9/j7bffvqe5qUDAHdjN6P+yfVDpgDiOOXz4MFevXuWVV15xIqPbmRYhK8jTzy2QsRSqXVjn5+c5ePCgi26GhoZctGbFe5ZStOVaVlFvFetyFuWcfbfb5cSJEywuLrJ//36GhoZcrto2fwHcv210IxZBjluLtSIelfJpsb9y5QrtdpudO3cORLC2LtwKDXV/lbdVLnJ1dZX5+XnXkQ9gdHSUmZkZ133MdmSDDYfXqtWZWsvwrywR4JMCPuDhExPh+036W5uEYY3wQpsw8/HIqOERAxkZNUJ8AjxCIKVOAx+fDEijhELShe+H1P2AZq1GLQjo9PvU/Dx9QAqB55F5AX6WkpGQERCe7+D7Hj51/CCgFoZEaYp/YR2G6yTBRv7ZlgNKEyIHqs5x27dvJ0nyCo8kSVheXubMmTP4vu+6GEpTIABn76NYFjXtGR4eHqC+19bWBjoMik2RkwnD0HWk1FitvkJO0TpRPcu6PmlLrIhP91NgRN/TdwQmAMeQ6XOWzbPvqY5tNR3T09Ps3LnTpbfU3vjIkSMD2yVPTU05MKWUjtJOep80p0rrWVBgx6E51zVoPFa4qXnWu2XBhV1XLNNi04/2XRbLcz9BQNmCYKMJ1BNPPMGPfvQjvv3tb/NLv/RL/NEf/RE7d+7k2Wef5Sd+4if45je/yf/4P/6P/I2/8TcA+PrXv87/8//8P/zbf/tv+aVf+qUbjv2//+//O3/hL/wFfvEXfxGAf/SP/hHf/OY3+ef//J/z9a9/nSzL+NrXvsav/Mqv8Jf/8l8G4Hd+53fYunUrv/d7v8dP/dRPcf36dY4fP86/+Tf/xjV7+yf/5J/wm7/5m64MHOA3fuM3gFw3VYGAD8jKTu5W+an7mQ7Qy9But3nrrbeo1+t85jOfuevKAysG2yx6h0HGQJ87d+4cp06dYteuXTSbTU6cOOGaywwPDw9EPGr1KyRtc3tadKyYCBiItDzPY2FhgUOHDjE2Nsbzzz8/UDOuOVWkoyhddfI2tSEqX0DA1jnbSPDUqVMAPPHEE4yOjrooT45CZgGS2AtL/2ZZ5pTx7XabqB9BmrLWWef06dMAboe/iYkJmolH1O7hBxAO19my1iABuqyTklInJMMnwCcjIEsjGpf6ZNSoF+4+zd07EDLqNQjCgDhJiFOPkDoBAeAVn0lJ8ah5AaHng19UeiQpSZzQj2NqvkeWpqRZBllKCmR41NOURrNJmmX0M0jSlPVenzhJyMjITi2QPDHunKT0DU6wlqR4cUY/693QyVB6ArV+Vnve8+fPO6A1OTlJo9FwDZ4ELqwDkrhSoNk6Mpv31h/LVmmcut8CCnqPlaqBDdBWjqZ1bj2H9vmxTlCskvoh6Lm0ini9hyo3taBKz7PGJ1ar0WiwY8cOut2uExmeOnWK48ePux4PrVbL7YwnpkZjF+thdRDa/0LgRnOjMkKBeK0Xdt50X2xfB90HvXs2ZVYWKOu+iDmwwP+DtMnJSf7qX/2rfO1rX+OXf/mX+fN//s/zn//zf+YP//APOXbsGP/kn/wT91nf9/nsZz/Ld7/73U2P9d3vfpevfOUrAz/73Oc+x+/93u8BcPr0aS5fvsxnP/tZ9/vx8XHefPNNvvvd7/JTP/VTTE9P88wzz/A7v/M7vPrqqzQaDf7lv/yXzM7O8tprr93Xa69AwE1MD6GNBm4nULmf6QCA8+fPc+zYMR5//HGefPLJu0bGdiEsswtWyW7zzlEUcfToUebn53nuueeYnJxkcXHRLXRSc4tGhY0F0aYRtHDZ2nArOrMLwIULFzh69Cg7d+5kenp6oAzM5uTtdrD6o8VVC4ackI20dC5FpqJkH3/8cUd7iunRWG2dtECUjehtuaHv+/gJNOd7DHdjQgK2NicJntzDmpdHp+3FZcYu9ki9BmQJdTzAI8AjISSjQUpKQEhCSkhIRs4K5LG/h0+Kj49HSEbCkFewK3gEvs+wN0ScpGQUc53lnx8K6zTrdcIgIIPciWcQk5F5EKUZeF7++ywjTTMSUrzC8TYbDcig0+/TTbqkJICH3wsgyYiIBpyY53lkC22eSScJjlwnIaY31ibYNUGWpERxTOJnzpmLZQrD0EU43W6XtbU15ubmiKKIkZERpqenGRoacqkm+67o/y1otBGp53luu2YL8uS80nSjkZWl1W3/AjmyZrPp9ChyooqErVpe3RwVhYdh6JghOUdbFSG2QcdqtVoDjZcseLKiSTnukZERduzYQRRFLC0tOYZqcXGRLMuYnp52LJZ0OAJXVsOic1lWw2pwLO0vx6/1xa4lVlNgxYJlcaCtaLBpID1P9vMfpGVZ5jQBU1NT/JW/8ld48803+c3f/M0bxNdbt27lyJEjmx7n8uXLm35eaTD9favPeJ7HH/7hH/LFL37RlTrOzs7yjW98w2kY7pdVIGATswIY2KCVb2f3Kx2gY5w8eZJXX33VtdW8ldk8vX15rCOzCmAheKvWXltb49ChQ4yOjvLJT37yBrWzRfKw0eNdAECRg41W7A6AjUaDVqvlHHSn0+H06dNcvXqVZ5991jlzRfibLRCiRcu6BCtc0qIt4KbxXblyhfX1dcIwZOfOne7zdrc8LfpapG3+1zIAiizjdpe0nVCPIjw8QhoEePjdjPqZNmP7JmhtadHoD5Osd8Hz8DNISHKHTMQGGADIikge6tSAjBo14txlk8fp5JG97xP4PqnnESQZiecVn0kgSwnwXfTaqNVIsox+FOUcgx/gpxlpkuBlEHketcAn8jx8fGr49JMED/DDkFoYQBZClNEDMlJCcsZgrXBwcsDNbkZj0ScpIEtAQHdlhdp7HQJCPBJa9Qa1J5ukrY08sNVRhGHI2NgYe/fudf3lVV8vpmB0dBTP8xgaGhrYWtjzPJdGshG4nFo5ChfYtztKtlotNxYBSysIho1KDftcwgb9LWAhEG7z4bCx3bEVOdr0l3QCditnPc96fy0wlWOWCFNVGbt27aLX67mNpM6dO0cYhq59tBgXXYMYE/0t8aTGo/ul9cUKWC2raLd3tn9bhqS8turdFrDS52/GYt5va7fbrt/Fh2lZlvGzP/uzzM7O8sd//Me0Wi3+9b/+1/ylv/SX+MEPfuAawt0Pq0CAMRsx3gn9X7b7wQQsLy9z8OBBAF577TXGx8dvO2aNW6Yx27I8KzySeMpqAs6fP8+ZM2d46qmn2LNnj3PSlm7VgmAjbC0cZZVxmf7VfOo7qnKo1Wp84hOfGGhAooVQi4EWOxs9KTUB3HTjFDEFnU6HkydPsra2RhAEjI2N0Wg0BhZ320Gu0+m461K+Vg5kfX19Q7G+2CZZXwIa+DTx8fAJCPAJg4AUj/6RK6Rbh0kXOiRkhFlIQkyGR0pcxPf5fWoSElIn9GqEgU8/jkgKpx94IWkWF+fwaAYBmecRBD5NPyD2fbpRQuhnZGkeRcVZSuDli3OnAFdJltEIgpz69338IKDT6+UpgHTjPvueR5RG1Cl2DMwyQt+jUavhJT5pmpCQEnsbvQ/CMMQDgksr+EFAGqf4ng9ZilekJ2IifHzC2Cc4sUowXiObbpI2Q4Ki4Y8cp+65tmOemZlxzmVhYYFr165x8eJF96zU63W2bNky0HFSzkz6ADnYfr/P6uqqEwdaWl4gAQb3kdDfSn9tlvu2pYM28rXvh2Un9I6GYeieLTt+gRKdN8sy99zb7ZyVMkmSxDElGoOA0NDQkGvs1W636fV6XL9+nTNnzjilvLZMVrdE27BI64BSbxq75rTb7Q5UEsAGgyCHbisqbLSv+bNpN/3sfgRWd2rtdntgK+GZmRmnH7J25cqVAXGetW3btt3y8/r7ypUrA878ypUrvPLKKwB8+9vf5vd///dZXFx0guTf/M3f5Jvf/Ca//du/vakW4V6tAgGF3an471b2fjQBysMfO3aMJ554wpWwlD9TjvgtrW6dv40+9P+KHjqdjsudR1HEqVOnWFhY4MUXXxzoxW8dq15aOy+W2rdCQ5tqUISmz/V6Pc6fP897773Hzp07eeyxxwb6vWseRTvadADkUc7y8vLAZ3S/ABdBSQSVJIkTzOzatYvr168PKMj1/1KP2w6Bysta1bIAQ9yPCNZXGaJJLqOL8anTo0tGcf3EpEDtygodstxBZgE+NbJcdgdkRPhAQkBII8gXzF4/ZxYCfILAhzSjh0efjDoZie+TJDFJ4hF6PoHn0Qh9+v2ELAzpJQm1sIaXZaz3+yRJHwhoBiG1Zo0ECMhICzYhSxP6aZ+gSEBkaUo9qNMIAuq1GiQJ3TghzjKytNAajITUmk38IrqOogjv6jppLyHNUmLSArJk1KmTAUHBDAQeeB7EC13CxR6tsTq97S0619bw4pSk4ZGOtvCDQbGZnsGZmRm2bNlCrVaj3W5z5swZOp0Ob7/99kAuXM2s9C7IiVl2R0BA71Ic550ALa2vMeg50DOnCNoKCT3Pc82C9LxYdiDLsoEmWyo31DO7urrqxqeIWEBX6QLAKejLaS/LCohJ0Lg0B2NjY4RhyO7du6nValy9epXFxUWXgpRI01bb2NbEAtD6mU2X2fdW82TXRq0T5fVV98OmXx5E9G/PX2YC6vU6r732Gt/61rf44he/COTr3re+9S1+7ud+btPjfPrTn+Zb3/oWv/ALv+B+9s1vfpNPf/rTAOzdu5dt27bxrW99yzn9lZUVvve97/EzP/MzAC5lWU6DlNO698MqEMBg6997cf6ye00HRFHEoUOHWFpa4rXXXmNqaorTp08PHMvSafq3fVFuNmYrOrKb4wCsrq5y7NgxxsbGeP3110mSZKBznBC8FjhF82Unb8dhBXb9ft85ah3jwoULXL9+naeffprx8fGBnHu73XbXaCMNLfy2VrxWqzka2FL1VoC0urrKiRMn3GYjMHivtTBZhkKmz1mRV5qm1PyAcLVHurhO/vrk5H2HPhE9UhJShqgRkpISUitIcYr/QuD5NIKApLiWkCKXnaWFY0+J6OEBNa9OmhZRU5ySEhU8QkYYBLkoMNsoQQzCgCRNGZbWwarcAXyPXhwT+h79LB/PUKNBu9slymIC8rkMfB/f90iBXj+iFuSjj9OcQYjSiLCbQTfCrwWQZiT9Ps1rXfJkQe7w0yzCJyQjo+4FJFlCSkaURHhpiu8HxFlKvLhGsrhCza+B5xGkGelQl/TJyQEHagGo7s3o6Cizs7Mu7x3HeXvjubk5tz/B2NiYq8PXscrVKTYPLubMlgva539xcdGBAMs6WdrfihX1e5vysKDCil9t6kKpCfts6vm331cqwUbSZeCj1IZlJRQwaG527NhBmuYdAxcWFkiShD/90z9lbGyMqakpRkdHXTmiDUDiON+kyYImqxHRO2Sdmq380O+1rmje9Fw/CDAgJqOcDvjKV77Cl7/8ZV5//XU++clP8rWvfY12u+2qBX76p3+anTt38mu/9msA/PzP/zw//uM/zj/7Z/+Mz3/+8/y7f/fv+OEPf8i/+lf/Csjn4xd+4Rf4x//4H7Nv3z5XIrhjxw4HND796U8zOTnJl7/8Zb761a/SarX4rd/6LU6fPs3nP/95N7YTJ06wtrbG5cuX6XQ6rk/A888/f8fbMD/SIMA+dDaSvVdTdHo3trS0xFtvvcXo6Cif+cxnBlTA5XKh8ktkHbNF19Yp24hekUW/3+fy5cucPn2aJ554gn379g3k9O15giBwAiLRqZq7cq2vFjjly0WrS5C3trZGHMc8++yzjI2NDeRYbbpA0Y3VBthcohYH25pWY9A8qf539+7dTExMOMACG7SjaH2xDPq5TX/Y++CtR/iXFgkIqZES5Rp5PKBJg5gued1+Dg08AuQQ/TzuJiUh8Gr4QYAHZJ5H6Ht5vr7fJy60/yEeGdDLIjx8ojgFPOrUSIlZ7/do1Bt4SULm+YR49KJ+nsf3wEuL/gRpWhQL5uV9/TSllgowQbNeJyvATTeBuBAf1v2AehCSpGnh/FNSP+cu0jTXG2RxQu3Ycq5zSBPCRo00qBFkKa1ajShOiNOEOItpBHUiaQyAFA8Cn1atRpKkdPopPh4NP7+P3SzBX0/xFrskU80BSl/3x6rS9dzUajUmJyfZsmULTz75JO12m8XFRRYWFrh+/TppmjI9Pe0cmSJZtS62rJSeKTm4NE0dwNVzIcerZ1lOXIJAwH1XZlMVcsxZljE8PDwAHMTY2dScWvgqRaIUld4PvaPSBZTXgTAMb2hoZAMJvffj4+N4nsfy8jLPP/88KysrrKysMDc3R5blFTFqq63dEfUOKz0jgK150ztrWRWrExAw0u+d6PYBiAJhI/ou7yL4kz/5k1y7do2vfvWrXL58mVdeeYVvfOMbTth37ty5gTEeOHCA3/3d3+VXfuVX+Pt//++zb98+fu/3fs/1CAD4O3/n79But/lbf+tvsbS0xI/92I/xjW98w1V/zczM8I1vfINf/uVf5id+4ieIooj9+/fzH//jf+Tll192x/mbf/Nv8p//8392//7EJz4B5BUIjz/++B1d9yMLAoRY3333XTzP49lnn33faPNuNAFZlnf/OnHiBE899RSPP/74DQIZLQjlXLd1iIp+9Xs9jPZnlkFIkoSTJ08yPz/PU089xcjICGtra64GW85e37P0ohZJK/rRC2OVvfbF7vV6rK+vc/z4cTzPY/fu3W7uFb0ootd5LBuwWXpGkaHqrQXitDPd6dOn6fV67N27171UtsWw5lGLs/KsWsS63S5RFDnKtt/vE/g+3qV5KLLbkAEZXToE1BkmpM4YKVlRxOcT4gMBKTEJSZEVTwjSAE9RepLghSGeB4FXI85iauS5/ixJiQuhX0xU1BPkvEJCRqffoebVckfu5/y6F/jUPQ/fg26SkqQxnufTajRy/iCKiJOEJI4IgL6X9wdI05SGJ9V9SpwmRN0iX+0HjDYbuTrBC0mBftejm0R5VYHv59x+P6HZaBCSgwafjMDzCf0G+B5hUWYYBLWcG8gg832yNCOs1Qg9j0a9Rpyk1LKUJIP08jr9kcHtdC3YVTSr/RZs6+ggCJzD0+Y8nU6HdrvN0tISy8vLzikODw+7HRR1LlvTbt8pwLXVFbBUikHpL1t1oyoCm0aQANWmJgSadU06p32v5GAVtercyr1L2CcQYcsWdR7f32heBBvBhE1tdDodx/KpR7/dSGp+fp6VlRVOnTqF53lMT08zNTXF7OysAyh2LbCBQllLYZkXrS/lviAPwtRAzGoCZD/3cz93U/r/j/7oj2742Ze+9CW+9KUv3fRcnufxq7/6q/zqr/7qTT/z+uuv8wd/8Ae3HPNm575beyRBgF5SLRTlrmz3aneaDuj3+7zzzjusra3xxhtvOKq6fKwyoLAAQAudzmdpUnsewJXFLS0tcfToUYaGhlwuSlGIOpjZ+uEgCBw6luMWOLElgJa+FOKXY52bm+PKlSvs3buXy5cvu0Xcip3U491S+Ypq5Nz1HTsXtn5agOPkyZNMTEywbds2xySIDrU9Bey5bJczjcsKuLIsI5pfwycuFPu5wK1TtOFJ6ZFRz/P3LvPtkZDS9OtEaUauEMiBQEZGL0mIkgjwibMML8tIszSn0MkIR5sk9Qz/ak6vg+e+nzMNuaCwl/UJIw/PDwjDAC8t8ohZVmgVUvzMI03SvNEPHv24W1QRAFFO+SdZruWv12ukSQ5ZMiJqfogfBCRZRi0I8P1iBPU6cQ/8LMULQ8IkIfN9ggzqtZB+r0ecpjTqDZK0AF9ZroPI35MEPwhJkwQ/8AmAwPfw8GiEAWmW6xC8LKOxGtEbyXUUFpRagavy+npm9W+rMFeqa3JykpmZGZcDXllZ4eLFi5w9e5ahoSG2bNnC2NiYA542VQAbwFtMgGXZrOPTcyVHr3fRahz0TNsqHQuG7TMvVk7PsNXblDUOVoyrklv9zG5Lba9HYxQYaLfbwMYumTbvrzncs2cP6+vrbiOpkydPuvTL2NgYMzMzDoDofdM6IubPrm1KT9jSyQdlKg98kDqEh8EeKRCgh8wK5+6Fwr+Z3QkTsLCwwMGDB5mYmODAgQM3tLm1xyo7eBv128Ypll4sR0n63rVr13jvvffYvXs3u3fvBnAvY3kxkBO0KmktovZ32p5XkbMWZXVlO3nyJOvr67zwwgvU63UuXrzo5sc6We3CpmuxIEfAwm6AoghFACIMQ1ZXV7l48SJPPPEEW7dudbXLui7Nmehb2zFNOUibmwzw4OoqftejiU9C3nkvJUX99PJIPyOkjl9Q/0VsQ4ZPn4go7RelgzkPkOHj+TlIaIZ14jQlSvpFoiAlpA54pEtdsvEQCnld3iiojk9Gj37BNhTlhEFIvUgp+AWdHnhQIyEiLaAD7hrz9ERGK2iQeR5pnJCR4Hk5IK75PoEf5FF6ktDwPfoF05P5Pv0oJsvytAVBAEnCUKMJpJCB5wc06nknhDAMaPmh64TYjaIixeDlPEkYEkcxfpbRi2KSOCHwvWKmIM0yanNdUm+dzmMt/FrocsnawMamqqyDlsNTikrPunWYo6OjLt8tIV+/3+fcuXO0222azaarrxerpOfU5lyVWrClpSpNtHoVz/NclA0bnS8tOBVYFauhd9D+vNytT0yXrjOO8zbBdltqK+KzYB82NmyyvQIEZPRvAZNy2nRiYsJpMcRurK6ucv36dc6dO0ej0WBiYsL90XGsTqLcYElrxK30Tvfb1tbWXB+HR8keGRCgh79ML78fRX/ZbnWsLMu71J06dYpnnnmG3bt33/CwWZrfOnH777KDtqr4cv9z/f/169fpdDq88MILLmepl0uo3wrmRIcpJ1em8hTNqOROlHmn06HRaLC4uMiRI0cYGRnh2WefHYiwFb0otytnniSJU1Tbl19gqNw0RU1dwjB0YsMXX3yR6elpN6/6rm0dq99psdQiaWnX0A+oXW/jkdAnF/RFxHik9Iu2PU2KjoRFf72MlIg8b69UQV4zEFOjhkeDkJBQaoEwj4z9LCUmJiYhICAhyoGA7+Mv94mg6B4IgQdJ0QAIPEJCwMf3PLwgxM/yrn9ecT2e7xMk+Ti7aZpH+El+nuFGi7B4XtMMstSjpug0zUv7Qt/H9/wcqKQpvSQmg6KhsMdIPZ//VJFwEJKkCUma0Ax8vFqN0Peo1+p4WUqaZoRphpem4G/chyiK8IMAPI9uHFELQhq1kNTLBYr9OCJLErxzq/hDIWkG/mSL2pZJwnrNAQA9T2IEpLYXVa7n15YAK42lKF59+Lds2UIcx24Dn7m5OTzPY2pqiuHhYdcIyKbg7PsHOHpfv1NELFZLQAUGHax6V9gyOVthY9MKlq2wrITSd1rzlO6z756OrXNbIa+cud5ZvXcqp9Q7aFk0rUONRsNpLpIkYWFhgZWVFU6fPk2n03HVGxYU2PJMzY0Ajublg7b19fVNUwEfd/vYgwA5TatYtc73foOAzdIBvV6Pt99+m06nw5tvvunqPstjtCjY5hDL59DvyqIZXad6/K+srLCwsECtVuP11193Qh99T8i9TM0BLjWg+bKCwDAMHVWohUmR98LCAmfOnGH37t2Mj4+7F9rWPsNGi1a7gCqiExjQomYXL8A1HErTlDNnzpAkCS+99JIrebQsiiJ+bfVq1dJaYJSfdWKl+XUgIo9XFbOmRUmfT4cOIS3CnMQmwKdHh5wVqIHrAeAVbXeCAgzkn++nMX7qFw4zj3lrBMQkeR+Aoo1vTELdqzPaahFmsNTtkpFS92o06nXSJCFOU8jyJkCB7+dSxSwjgQIQ5BF7lmSsJ/08+vZCPDzSjFzUFwQ5+Z9l1DyPKMt7AmR4xGlCP29gjOfnlQghEPo+nu8z1GzQ70dERTqD4hj5oT18zyfNUrzi0aqFAb4XkqYZ/TgiihMgo+X7EPiQpqRePq5GENDu9+gleWOlWpyQreT/z2pEspKSPTkFbFDucnyWlhf4U2rAbnFdVqjb2vWRkRHq9TqTk5P4vs/q6irdbtcJDefn510Xw5GRkQHnKuBhU0q2ukGOFnAtgPWuDA8PO2ev9UnPrN472CgRXF9fd+ub3hPrrG3wIJGvSmEVBOgYAlGaU3UI1RphtQSaOx1bc6o1pdvt0mg0nBBz9+7d+H6+5fT169c5evQocRy7nSW134Fdp3VNWhM/SKuYgI+hKf/k1N2bUEsfNBNw/fp13n77baanp/nEJz5xQ+2/xmmjehgEAZZO1Atolcv6uRZBgEuXLnH48GG3F7l1eKK/lb8fGhoaWDD08ul67Euvz1hhE+QLhjbkef7552m1WgPARjSnxmcrMiRosspsnUuRkSIefa7f73P06FF27NjBrl27Brq+eZ7nNq7R/Kyurg7kXSWwWl9fdx3RPM+jEWf0um1iMhpFK18PCAnx8YgdI9AjJiDEJ8ajT7/o6x+QEVBz7X0LJ0xKXKQVGnh55z6/KOfKe/sVlQY+GTmd72UhI7U6zVqNAI/hNGGll0fjyvlnnkeaZdQD3+XU0ywj9TzCLCP2Q3yvuHfqUpglZL3ESRxTPBIignzfQfB8on5MGAZESUySged71MIQP8sIAp80zTUF/aIBUZqmZH7eJCnOMpI050SSLCWNM5I01z14WUatFpIlCVGStzb2gAjI+lFRGZELCwOADLICHHkEeToFP5/PpXUaiw2y2TyKV1qp/KwqYrXPvQCnQKbYAaXVxCTYMtWRkRGazabba0KR/aVLl9w5pqamnHJ+fHzcdeqzvfIFVPTMlfU91vELIMgRp2nq2hZbAaTeSZXuaX2Qo9Zzb4MUCQx1HUoJ6L2zlQpiV/Te6lp0fIEP/azdbg+Aa42pXq8P7CzZ7/dd5cE777zj5v/KlStMTU0xNDTkwIXVLXwQdrNthD/u9rEFAZbevlXp3/0EAVYTkKYpJ0+e5MyZMzz33HPs3Llz0zHc7OHWS1hmCezPyihfx3rvvfe4cuUKzzzzDEtLS3Q6HZdXVyRgNwyxqmHRorDRAa3X69Hv911TE9jQD2iBOn78OK1Wi+eff94tTHZByrK8kmB4eNj9W4uN/khFLYZAFK5VOUMOcK5du8bevXtd/l/OX5+r1Wp0Oh0X/ViR4dramgMIul9BEBAvrFG/1CWkVTjthD49KPgAqLnNeTrEBBQb6ZAnACIShoiLTYBSMqKi3U8OIXx8YlKiIrb2Us8BDDwPP8vLBkkTUj+kVW/gBz79OCbIyOl9P6fYM88jinOn7vuQBT61glKP05QkSzf2AyjKGNMkT1t4eHSJXOfCIO9TSC2o0U9ivCylXmvgBwF+lpHEMWmaFbsMFvsYFBsR9fy8e6Dv5VUL/TgudhaAbhLjZ0VJoJcXTPq+T9TvE6cZvX6XhIyalzM9SZrmfQg8CFOPxPNIkkJ7QEbo1fLug0FAmnqQZXiLMeGO0IEAOSs9o1Z9bx2rfq9KF0XtAo3ahErfBVzawZb0jY+Ps2PHDiDvu6ENfCDPlY+NjTlHpvfLUuh678RcAAPpOr3TjUaD4eHhAQGvSgMFCKzwVWBalLxtkGWdqrQ8Nj1gr1nvoT2HWBUbpOg7Cgxszl+CTLF3+rwadI2NjbmA5tKlS5w+fZrl5WXOnj2L7/tMTEwwNTXFzMzMB0rXVyDgY2I2t7uZiKVsd6rovxPTsbrdLgcPHiSKIj71qU/dthf1ZiDAioKsqEh/W4EP5Ava2toaR44coV6v88Ybb+B5+e58ZdGezb3L4erlV77fUpdWwKeyOuuQz5w5w44dO9i+fbtz8JoPm/e3OgTtzmbp006nMxAhaVHTvVQzoWvXrvHkk0/SbDZZW1tzgEbzYDu0CSDYVEAjDcgurRC3V/LivfFpwpkxgrlVQurOifkkhWAvz6MHpjgwhEIfAHp6fHKNQJOAHhF9egT4NBkGQhqYRinE+AUH4Pt5mVxKhkdSJAcK8aQPZDmlHqcZraKDXpKmhEGer4+zFD/NaPq+K9dL44x+loHn46UxcZafMyjASI+YhJgGTeoUbXM9jyCJ8fyQoXoNfJ9mELCSdUiSmCjJ9Q+9NI/e60Any9sQN8IAzyPfyyDOux00/Lw9cZLmuf3Q84gL0NUzYCEFmr4HXkCU9okTyLxcapn4Hn5adMLMcvYiSfp5syQCatmGM4eNVrpyUHpPYJAJFPiXM7Nlanq+bYlar9dzm/dYMZ/+LeAwOTnJrl27nPNbWlri5MmTTkOg1rxDQ0PuPFYnYEv57LtnlflaE/Qe6Xc2tScNgsap8WhdscJKWy0lU8rAAhEBAR1T113WWln9gT5rAy2r07H5/izL3AZRL730Emmasry8zMLCApcvX+bEiRM0m02mpqbcNsB32hDnTqzcMvhRsY8VCCjT/7cDAICj5u6HKcL+0z/9U2ZnZ3nuuecG6H8r+pNZ5F3+uaXFbXMNjVdRRZIkXLp0iaNHj7Jr1y6eeOIJ5+T1UkrJb8GEnKUWTu1PLjCj1qB2N0B73IsXL3L+/HmefPJJpqam3LG1CGmRlGhwM8FPkiQuCtN3BRSs+LHdbnPy5Ekgb/8rIAMMNFhSRGi3DRYI8PBormeMZQ2y9hr1IsvvLa8TLa8X+fucko8LgV6LjD5pwQzEBckf0iHKnVBB44cE9B2lHhMRFeR7So8uFBUEWfGZwPQbiNIeFCV/PvniGKU5zR4EdXwfur0+cZYRZQmqUQCfJjVGhloEnu+2A07TFNK8UVCWZcSJ+hQo2vOpZTk30aRBGAQ0ggA8iDyfWlCUmGW5826ENdpJRD/u43k+XpbvOtjNEmp+nTDIywh9D9IU+mmK7+csROgV7IAiwKAAQh40wjqB79Pp9+hGBfgjJcu8nIFIU2p4xEWlhQ+F5iIrmJaAaDQgKfLbMNgy2z7jcpA2XaB30moD7I54+owAgq1vl6jUUvp6R5UqUOWBNkFaWFhgcXGRS5cuAXlDHqXrrJhX+hirytf7A3nuWueweXixX0p1aB2x64UVyNpyXAFmUfYC3vqcTQ/Y91w/s9U8mnOBFqVWLAtj1zwrOtZ7rDkeHx93LY6zLGNpaYnFxUXOnj3Lu+++y8jIiAMEEhneq1VMwEfcpG69k+jf2v1iAiRSS9OUF154wVGEcKPwz+bo9G/YWJgslaafl/Prct5xHHPixAkuX77Ms88+63YctIpgKetttK0xZNlG6Z+ajqjbmc4ralTOe319naNHj5JlGS+88IIDGVq4JCgSNbm+vu6u0dKQNuevRa3f7zsHr8V3fn6eubk5ZmdnuX79OrVazXVXk5Ja4E8Liq04qNfrtJdXSa9cY5g6PfLcc50aLVrOJSck1IpY3C+y9CENIKZLPod5O9x8w9+QBkPUiMgb7xSxa9EvICyEg1CnQZMmNQIS/Hw/gaK8MCWll2fEqdGg5dcIfZ9OmuV5/zQlSnKX72UpHllRHZBDgcz38DyfehgSZfk2vaHv550IPZ80S+gUbEaTOikUefj8qiMSoiTNa/prIfUg31QoJqPm+fhZVmwAlLvewPdphiGB5xOl+Xw3aiGkGe04oh9F9NOURhhClpH5PmmWEUVxXtaYBgS+VwgCYbjRoBnmEWXquiBAluXtiv3AL+4J5GmZDO2pkBKzGEY0C7ZHjFcQBATdlPpih2i9iz9Sy/sMeBvltXIyFnxaVkrPqs2Ry7mKxZLj0rn1bxttKx2mVNiePXtYWVlxufD5+XkuXLhAq9VyIkNtqyyHpm6BNuJWR077XlqArQCh2+26wKG8GZauwZYX2/mRE9c7atMlzWbTtQkGBhgKjUPzqXHbNUbzpc/YKgetS1YjoXsyMzPDzMwMkKdNFhcXWVxc5NixY/R6PcbGxhwoGB8fvysx4draWgUCPoqmB+9eW//eD03A+vo6Bw8edIjb7i5lAYCl1/WA27Fa4Z9evM3y3XLcKysrHD16FN/3Xe//MvVvx2CjHxt5yGkq16gyQAs+JLxbWFjg+PHjbN26ldnZWQAXddfrdReFK21QHrtddPv9vuuLrgVHlKO6/509e5b19XX27t3L2NiY2xtdjIXmU/Nl85FWwFS/sg7UihK9jIik6Oufu+t8W988gs+gKMLLqwI2PpPn9Pt0yKsF/MI55pvlxCRFzj2gQUiNGikZdRrkFQP5FsIhAb6Xb9zjZxlpGtOlT52MMAxIk9xpg083SfDSlCjLGYR6UCPwfaIoJiIptiXO+xZ4WVa4UY96GJClCXHmUSegX4zNJyzG4NEnF91Bvm9Bt58x1mpRCwP6UUy33ydNMzIvZ0eGaiFhrQbFhkWhl7cV7vQj0iwlSxL6Suf4BUhIU/q9Hvg+nhfQTxIyLyT0PFLDSKX9Pp00oebn+og4y4WHcRzR8APGaqP0oygHRlk+NzERqe8NOGXf98nOLJH1YzrE+bUtdsgWM9Jt4/TZAMgCnoriy4JUOS45NL0zAsl6viw4FzDVuiDHa4V6AtWTk5MDn22325w+fZooihgeHnYiQyn4dQylMGyHT12L/i0tjWVF9f/9ft8JYtXG1zY0EhMn1kRtkG0nUTlzW+qr1F2323U9FYCBkkfLqkiQa/VBuj7LCt7MkdfrdbZu3era93Y6HQcK1JNEWyVrLm/lG9bX12+7a+vH0R5MU+YPyPQw6kW6WwAAGyBAL/Ld2pUrV/jOd77D+Pg4b7zxhhuXTIuEzbtrMdCYRb3r5dDLnCR5+05L2fV6PbrdLpcuXeL73/8+Y2NjPP300wPjl9NWhG+pUjldKyTSwiYhlISAlrnIsoxr165x5MgRnnzySfbu3eteTlGJqjFWdKHcn5y7XngtZvZ+CcDIVldXOXr0KJ1Oh+eee47JyckBQaQiDi0qVgxlF8harVYo9ROGGKZBjSZ1hhgiKKh8NQAC6BcgICtq83PSXXvhKVb1SYArrLFOm4R+cYyccvfxi+h/iBat4jgxMXkuu09EnPWJkjxqznf0K0BDCp0kpk+fNuvESY9u1iMhpk8v77Dn5WV9db9GvRZCVNTRZxlRmhauD1KvWNDDkLrfJAci+Q6AiSuAzJUPPWJSUgLfo+4HhRPPik6BIcPNJvh5P8R6kOf6M/IyxH7UpxfHRElCPQzJ0hQvSUhT6PX7ZEEAXt7PoBnmTZWajQZ1PyBOMzq9HnGaFeWE+czXfY96kDMcfhDk1ECWVz0EXpi3JAbqJ5cZPtHm6WiC2es+9dOreP0eeYeFvPywTp0Qj+Bqd0BACwzsT1HWEVgGzTpMPW9WdGyjXquJ0Xuhd1nnswLZRqPB5OQku3fv5vnnn+fpp59menqaXq/H8ePHeeutt7h8+TL9fp+1tbUB6l3vu85XZhjFhAk4KKhQ2kFO147dCiIVFAi4qMWyBU36nt5BVe/YNcFWO9k1Q+uEnVO7BtqSzdtZq9Vix44d7N+/nx/7sR/j9ddfZ3p6mqWlJX70ox/xx3/8x7zzzjtcuHDBiSqtra+vb8oE/It/8S94/PHHaTabvPnmm3z/+9+/5Tj+w3/4Dzz77LM0m01efPFF/t//9/8d+H2WZXz1q19l+/bttFotPvvZz3L8+PGBzxw7doy//Jf/MjMzM4yNjfFjP/Zj/Kf/9J8GPnPu3Dk+//nPuy2if/EXf/GeUtsfSSbAIm6bV7oXs9GkFQLdztI05ciRI8zNzfHCCy+wbdu2gRziZh2w9G8r+tELZNkCfdceRzn0brfLiRMnmJub4/nnn2d8fNzl8uVoy1FDkmx03ZN62lJ39o8WQ+XX1Vyo3W6zvLzM/v37nXim1Wq5e5CmG5uw2KhfEb/NIerflva3oijt/jczM8Ps7Kwbkz7X7XYZGRlxC4iNBEVtqlFLrVbDi1KGaBWRvV/I+XJdvxT7ytAHUvJ7NaIs77fXp2BWCo1/TklneMT0ifGBFi3yhIBPQB2PoFDf5wyDNgWSC87hQlAIEFWql0e+GRERvUJVkHcmjFwrIo8Gdcgy4jQi7cfU/aIta/GMRHFML4ld6V7s+zT8gMgPiON+0RI4JiTfT6BoOcRYo0kY1vLqgjgh9PLuf34Q5D0J4jhX5ae54LAf9fOKASBJE+p+DpzCWr6XQf5Me9T9fDtk38/bGcf9Pu2oiw/0icnImy5nmU9TZaJybFl+7H6SkOEVLIAaNOX7E4aeT5J5+JkHMcTk2yRnRWEhBRuTpRD7AbHv02q1Bna41FpgdSp69wTI1Vve5rMFDPTHOk/Y6FJpy1UFvvU5gWWxb/V6nUajwdTUFNu3b6fT6XDp0iXa7TaHDh0C8rJe5cvF4GkN0PuoSFuBg9YGfcbqA6xWwpb9lQGSmDabuhQroHdczITWG72bNhhS4GODAjGSd+P4b2ae5zEyMsLIyAi7d+8mTVOny7h69SrHjx93bMzx48d55ZVXXJ8Aa//+3/97vvKVr/D1r3+dN998k6997Wt87nOf4+jRo44Jtfad73yHv/7X/zq/9mu/xhe+8AV+93d/ly9+8Yv86Ec/chsI/fqv/zq/8Ru/wW//9m+7HQQ/97nP8d577zkG5Qtf+AL79u3j29/+Nq1Wi6997Wt84Qtf4OTJk87XfP7zn2fbtm185zvf4dKlS/z0T/80tVqN//V//V/vbq6yew2BPyTLssw1thBN9n4emCRJ+OY3v8lP/MRP3LHStN1uc/DgQQBeeeUV5xSzLOMP/uAP+PEf//GB/JtVwOqlsRGJReJaPM6dO8fS0hLPP/+8u752u80Pf/hDPM9ztfhC4hIGWcFPEARcuXKFubk5nnvuuQEhoc4nMCAkrgVK5UUrKysu37Z9+3amp6dv0A3I0euccubqPdDtdl3b4CiK2LVr18C9FPrPsnz3v8uXL/P4448zMTHhzqFF5PDhw2zbts01cNFiYkVUGo8U2bUIwvMrhMXefKEX0ssiR+Dntf8BKQl1Apq08IOQfrJOl36Ric6KrgC+4w5SoF+wAA1aNIqomiLjnhYpg4SUmlMBZPTpE5FQK8oHU7TTX0hChEe+T0BIQJ0mCQlt1oiJGGKIYYaLtESPOjVGw2a+vW+SFOAgF+Zl6hmQ5eMIPA/Pg34ak7vugGajTkGC0CocQa8fFU48Iysa92RZCmnqSvbqQY2UlLhoCJCmCfUiYgz8HNzESUKWKur36Gd5a+Bur0svi3KGxQshS1wXwpqXp5XqYUin36cXJ8V+DIXwlwiK++EXIkzI6NPDNzUcee1BUiSA6iSu9qJP1KrTJMSLwR8J6Q0HZK2N9t1Jkm8nK2epZ9umrY4cOcKuXbsYGRkZ0BfIbNWOvqtAw6bE9N5K6a9UngUUYuHW19fZtWuXqzpYWVlxz/22bdsYHR1lamrKbaolBm5tbc0JHu37Kb2P1iTbI6Fer3P+/HnXF6EMXLRWiP5X+sBWBwkQ6HoEDqxpjFojFaW/+OKLAwHT+13ny5Ykias8+Jt/82/y1ltvUa/XefPNN/mFX/gF/tyf+3NMTEzw5ptv8sYbb/DP//k/B/L1e/fu3fxP/9P/xC/90i/dcNyf/MmfpN1u8/u///vuZ5/61Kd45ZVX+PrXv06WZezYsYP/5X/5X/jbf/tvA7C8vMzWrVv5v/6v/4uf+qmf4vr162zZsoX/8l/+C//Nf/PfADkzOjY2xje/+U0++9nP8v/9f/8fX/jCF5ibm3PpkK9//ev83b/7d7l27dpdVU18pNIBiiJPnDjBqVOn7on+L5ultO/ELl26xHe/+10mJyf51Kc+NVBSohfIHksPr5y+BQVWnFcGCpZSj+OYubk5vve97zE0NMRzzz03sC+6FiEhb1Fpdm70c73stvWoHLbdAEXq/0OHDrFnzx5GRkYG1LuKBmyZlC0Z6na7rkHP8PCwUxxLJSzQYUuITp8+zeXLl3nuuedc10Ndk1IhGrMWHJuT1DgkVGw0GoQxcH7RZe8hJM7UqJciUk9JiYu2vTkoSJJ+sQNAiE9AkyYhAV36RaOgvM4+RO17FZmqNbCPdg1sUKMe1AlrTQK/jl8QcBExPSKXSEiKkWTk6oWQvD47TxXUadLEJygcGtSoERDm2/WmKb0kop/mkXWU5oCgFobUg3ouFCzmerjepFVv4QUBvh8w1GjQCEP6SUI3iojTJC/rC3zqQZh3C8wgS4te9b6fnzOOyLK02PwnoJ+m9KKIuGCHSHPOpd/v0Y0ier0+nX6PfhbhkRZiyowwqFHz8w2CoiyP+nu9vFNgnPXoZzH9LCoYm6AAWkFRH5DrGpKi/0J+T0JaNAuA5ReaDh+Keo2g08HrRIRRRrbYxb+wgtfuu9SYnJI2trKiVkXI9t3U7/R5C/T1vCq6t/S6XXtsBC6wYBX+luofGRlh165dvPjii7z44os88cQTBEHA3Nwcb731FsePH+fq1assLy8PVBrofbLnV/8PG5joPbPgW2yC/tb7aNcerWNaeywborXCpg10bJuOtVUY9o/WuvtlQRAwNTXFU089xR/90R9x/Phxdu3aRRiG/L2/9/eYnp7m9ddf54c//CGf/exn3fd83+ezn/0s3/3udzc97ne/+92BzwN87nOfc5/XGmc/Mz4+zptvvuk+Mz09zTPPPMPv/M7v0G63ieOYf/kv/yWzs7O89tpr7jwvvviiAwA6z8rKCu++++5dzcVHIh1go1U9ZDZ//H5sM8e9mSVJwuHDh7ly5QovvfTSplQQbHT6swjWlhzpZQAGFgkbTdgcfpIknDp1ykXHo6OjDiCoJMgieOX+tJhY6tLSlKId9Tu9fBr/qVOnWFxc5Omnn2ZycpJr1665l11OPggCl0PT/bHXn2WZ60pohX+abzVpieOYkydPut0NrXpbC5XGbalE389bk9oFxWoCwjDEu7xCSFBsABThE+PjF8K4PFvfpEZETFaU6PWJiv6AFHqCPMbs0ccUcQJZQWZDQp+8r1/xjaCGn3gF9Z/gJXmknB8zLGR6hXCtEOplRZohLfoS+EDcj8hIaVAnJqHlNSHLG/f4hMRZQieNqGV1Aj8kTWM8Pxc/ZplAaHH//VzbEAZ5U6EozYjiGD9NST2Pfq/rri1JEtKshu8lxa6CivZD6mFItx+B55NlKYmXtzn2MugmeYRf83xqIWSeR7fXc70F8oRM/n99IvwMwiT/nHoGdOMOMSGBH+SzVYCOfsGS5KLNfJ60h0ILXNvlfBbzfRvDIi2TFLAtb85Ud5sxdQpJZzq3iP/UrItWJTxVwyv77OmZFJi2gNu274WNDYKU7lLjHJu6sk5PP7N5fL3HAsRS5ct5joyMsGXLFrd//NLSEgsLC5w/f540TV0ff22VrPdKDlkMgTQMSZKwvr5Ot9t19LiAtt5rbdZkS/J0DCuULFcJaE4k5LXroZ1fuzbqWOWf3U/bsmULrVaLn/mZn+Gv/tW/ysWLF/m//+//mz/7sz8bcLQAW7du5ciRI5se5/Lly5t+/vLly+73+tnNPuN5Hn/4h3/IF7/4RUZHR/F9n9nZWb7xjW8wOTl5y/PYc9ypPfQgQA+qFdLZblX3w24HAtbW1njrrbcIw5ADBw44qt9SW9bZWgrbCuDsZ/QC6yWQA5dzk7P9r//1vxJFEc8//zwjIyNuoVAeXAuKupJZACJKUAtUebc+IXItZlrYTpw4QaPRYP/+/e6FU9c9vfw6phYtO482gpHD1mJnd0KLoojLly9z5cqVgVaitnRLbIEWB9/33XVknQiv3cPLPBpbxgkaoRMjBVFCI/bxOyKOgyJ+7xcb+uQkf1SwACk+9aJ9T0xCh17h/kOaRW06UCjsPbfbX15R0CPDywV/RNRp4CfiF3JHn5JBf6O5UB2fTuHEakUnwrSg6H0yatRMxULeZzAgL7sLazXiJMnr8YuK+V6WkGZR/r0sL+VL0wQvzfcE0P6GYZE2CIKAwMvv3zrF9sSenzv0JOck4rhbdEgo2AwvoF6wTPhxUY2gz0dF6WNAnEQkaY0wyHcjlJ5Fjh5ysBIV7jomJcn6+NSggGb5boshw/UmeB5Jp1OAt8A5c69IarT8FlGaENAvOAKfbtHTId9+uU9AQK3YBtorAFZScEFKyyRxRhZspOjUTKfsmGwqT+kCu5mQnl2rN5DwVd9Vvl7vVppu1NBvFvkq+LHlenofxJBKBzA5OUmz2WTXrl2uW+ji4iInTpzA931mZmYYHx9nZGRkoBrCOmO92/q97bppxX4aq95vAQm75pW1Fna9sK3ClcLTpl5lkaNND9xvy7J8W2k1dtu5cyd/7a/9NX7+53/+Aznf7cbysz/7s8zOzvLHf/zHtFot/vW//tf8pb/0l/jBD37A9u3b7+v5HmoQYB9uS/3bvPb9sFuBgIsXL/Lee++xZ88e9u3bNxDF24dSlLYcscZaLge0jtIuEFYsqFK7drvNrl272Llzp1sgtEDpPMrrCUgo4tYYbKmfFfjoxVR6QDW3Z8+eZffu3Wzfvn2glaotMbI5ReUwrVJfiuS1tTU3FluKpMXj8uXLzM/P89hjjzE0NES73XbXJ6GU3R3OASsgvNYh7q4UUbpPcr5HUB/Gq0G93SaPN4doBA16Sa/YpS9XwXuu7hwSooLG94re/3nsrpx8Tiv79IpEQJMGKbFjBZJCIaDa9bwcr1fkpbXdsFcI+9KC5g/JCMmK76iJT73oOSCHHREXCYKUwACFftQt2IeEuGAQItpF6iImyPIoLyyAQD3Iz1ULAsjyfv9hluUOHWhH/bzywasRhjVSzyON81x73vcgJAhqG04gimgEAXHm54K9TK17lI+P6fR6tBoNgnRD9JpmWZ6S8GA17WongwKCBeSbNmU0ioRKUFQUdKPIMTl5QkeAyXcMiiL9gLDQCVDoPHJmImdS8vRDVsxcgEeT3OGEXkgPP4dsJk2nZ1ZAxvO8gR0t1QxLzJil88sMguhzMXj6fwn6rHOUWNAKl4GBnQItA2HbfVvmr9lsMj4+zvbt290GROvr61y6dInFxUWCIGD79u2uIY8NIqw+wep2yjod/dxWaalE2AIigRjNh4IFgRqJkRVolCP++6kJ2Mza7faAMHBmZsbpqqxduXJloAzc2rZt2275ef2twMd+5pVXXgHg29/+Nr//+7/P4uKi22zuN3/zN/nmN7/Jb//2b/NLv/RLbNu27YYqBZ33ZmO7mT2UmgA9aMofl3P/96O239pmx4vjmLfffpujR4/yyiuv8MwzzwwAAEtNKWKV4y234CzTWHJoevBtftD3fU6cOMGZM2doNBo88cQTAC7CFShSDj4IAqcOFtpWz3yL3DWvVkeg646iiLNnz3L27FmefPJJ14xDgMI6fIEKuxCIQdB5VClgdQMCHL1ej9XVVd577z3m5+d59tlnHfqu1WquCYkAiBYty5iMJSGNbt78plaQ8AE+QT+m0RZlnMfISZJvnCOHKmFely7rtMnIqeWcoveKdropdWo0qBfRvKhlr2i7GxAWwrw+Ahj5b5XVz6NcjxrNYox+EcmGeF4uEVSiQdvzul0Eiz8AIT4N6gz7QwzXhwvX5xcVC15R/Jar3+uFfiEiJk77dNN+vstg4BMEYe6g04IFKHL7YZA3/vHJUwSeB34YFjAkp9rrfo1akG99nKZ5Z4G4SCUkaVawI0WaiRSv6LuQJDFRnBClKd04yoWCSZqXC+LngKZozVwoIGjQoB40i2NluZYgjujRKxgRQZ/UaQF6qebfL8BCj6wQBTZpArWiPiMtmIIaWSHgzCFavmlScH7NPfc2ZZVlmRPtlZmussO0XfzkALW26PNiNvVuiomzfS70jul7en+1Nsrh2rXDvut6Zy2rIKHs9PQ0zz//PC+++KJLIZw9e5aDBw9y7NgxLl++7NKMOudmjJ7EgJ630atBAETMghx/OUix7ZY9zxsoUbYaBmv67Adl5Y6B9Xqd1157jW9961vuZ2ma8q1vfYtPf/rTmx7j05/+9MDnAb75zW+6z+/du5dt27YNfGZlZYXvfe977jPr6+vAjWkPy0Z9+tOf5p133uHq1asD5xkbG+P555+/q+t+6JiAzej/8o3/oEHA6uoqb731Fo1GgwMHDgw0vtBLUVaryrGWG/ToM/pbC4TNYesF6ff7vP3228RxzFNPPcXp06fdC2VRud0ARWIjvex2kdCuYLaDn8akBUJCS8/zePnllx1Vbx9AsQFWiGc1DmI1pEq2+w8IRIi1WF5eZnFxkcnJSfbu3euOpz8CFVpobN4wDEN8z2doLaVBSES+e1/uFHyCoinPEJ5zCoBzaFFBzdeoEdOlVlQEeEUpWZ0aYUG/axPevK1QQr41sKoBMgIaRdTfxSsi4KRICKjMMK/8rxETAUFerx6EuWPGp0ajUBbI8efpiRZ1arU6XlrD8/2NlrsZNMOQbhzRpOncYIBPjRpR4VBzqlzlh9D0fQjCDSfDRk15P44JwgA/I68e0LNMnsQIwgbNMMh1BOTiQLIUL8np/STrk5F378sK0BDgU68XbZ2zNE8/AHGWksZdvAIwDdEkKPQRjTCkH+edE+Mk1zV4mUcv7tMr0i3DNKG4rqwYIaQ0w5yKzyKPuGALPCieATEqCfl2zvm9qVEvWhGrkgO8xIOlLjQCok6CNxSSFO+TBf76W05Mz66lu602wDpGqfEtuyVwoShfjtLmwaUJECOhdIXd0MiKCfVu6niq29e4dVx1MpyamnJ9CJQ6yLKMS5cuuY17VBEEG43ItPZoe2/93AYadp3U+qnr0JpoAyxVfdk1dLM1936a0gHlPgFf+cpX+PKXv8zrr7/OJz/5Sb72ta/Rbrf5G3/jbwDw0z/90+zcuZNf+7VfA+Dnf/7n+fEf/3H+2T/7Z3z+85/n3/27f8cPf/hD/tW/+ldAfh9/4Rd+gX/8j/8x+/btcyWCO3bs4Itf/CKQO/jJyUm+/OUv89WvfpVWq8Vv/dZvcfr0aT7/+c8D8N/9d/8dzz//PP/9f//f8+u//utcvnyZX/mVX+Fnf/ZnXTrlTu2hAgE2b3Yr5f8HpQnIsozz589z9OhR9u7dy5NPPrnpGMqIVOOVs7LRr8CFFgsYzG/pOFevXuW9995j69atPP300ywsLAwABPXSh43SPtt/XwuQ2IVer8fa2pqLMjSmZrPpoo+lpSVOnTrF1q1b2b59u0P7tkWo6odF42uLYCtsEtWvORSS1SInulDXNDs7y7Zt2wbAhaVRdWypqhX1dDodRruiqfNO/xEx60TUCqFZnWZRl5/QJCwiwFwB0Ccx1Jca5+YOWU1+e674Lyf480/m0aNgAaRF9jmjTpNchqeWQ17BOeTH1znzhsIxWZLR8EMSoJ75RV9ANSAqGvgEIfUwgCQ/txfkXQQ9IM6g5gVou+EojfPqhVqNNPEJsyLGzXItROZ7rPV6+F5EkiZEWd69IIh9twNgnKTEaY+aX8dPU3pJXhY5XGuQBgH4OS2fZXnv/zDOCMKAONV2xEkxBxQdEKDX6xSzgat48B1T0Ss0D3kL4ZofkBWtiSHD83JQ4iV5mV+eDgkJ/QZJmhDguVSLj0+tGF+v0Bg0vBZJlhSSzNSBrHx++9S8RtGC2SeiR0JGmOZ3vXGlW4CrvGw02D1GNhQ6hyaHCwykAETbSwhr3337vquXRznFVQb3cvq2CZZAhMzu0imgIsCvd1if6Xa7riW3xqd1qayBUGrg0KFDzMzMsLa2xtzcHGmaMjY25rrwSbyotIM0Q7bPiNUGCBwpENL5BbCs3sjOETBwnA/C1DyovNnbT/7kT3Lt2jW++tWvcvnyZV555RW+8Y1vOBHeuXPnBgKmAwcO8Lu/+7v8yq/8Cn//7/999u3bx+/93u+5HgEAf+fv/B3a7TZ/62/9LZaWlvixH/sxvvGNb7hgc2Zmhm984xv88i//Mj/xEz9BFEXs37+f//gf/yMvv/wykPus3//93+dnfuZn+PSnP83w8DBf/vKX+dVf/dW7vvaHCgTogbwVAIAPhgno9/scPHiQxcVFXn31VdeDv2w3exDtg6vo1ba2lRO26QM5vZMnT3L+/Hn27dvn8jmqgNALqhfL1izL6WoBsWyEpTL1OVt9cPHiRS5evMj+/fuZmJhw+39r8VL/cgkVBRDW19cHUgRS+0uwp8XLUpfdbpcLFy44qnLLli0uatHCVV7oFLW4qoBOhHd5jTEaqOd+Urjk0EXeDaTkzwqHnRZCPgqXHxPTIS3a/ECTFg1CYlLWiy5zfiEO7BclaPouBakfFZUAFKVnXboFJMj3HohI8akVTqhLjx71AmY0CemmuZsJ8HJAkOUtfyNSAjySJGatkxTVAkWe2PfpRQmJB3XPI/A8OklU0NoBvTim5vuEQUA/yfsEeFkefa+nPTYq6z2GCMDPc+1RnDiFRJrKZeZpjygD+n1izyMtOve1goCg5jtwV8tqG42BgDjNSyd9CpEbeXfCJjXq9Rr9fo8a+TOTZnlEHyUJcZY4METxDEdJDnBGvGHwckBU94s6+/ypKDY+yrdPVt+HKOuhRkwZKfnOkJHTZ3hZSlB0J8w3bkqceLTorpAnNbw6wfkO2ePD0AgHGEq903KA5X4AtgmY/Y4YLasXKDs9VbdYnVAURbRaLfc+6L3WMWCwR7+YOY3NivesPkesm2Xj7Jo1OTnJzp07ybKMlZUV1tfX3Ta/jUaD6elptxWwAiCtTXY/hfKc6F3XeQRuyhqCD1oHIFPgUm4WBPBzP/dz/NzP/dym3/ujP/qjG372pS99iS996Us3PZfnefzqr/7qLR3266+/zh/8wR/ccsyPPfbYDd0I78UeKhCgB+l2dr9BQJqmnDp1ivHxcQ4cOHBbOkUvuhydze/LeauBjc2FyRHr716vx7vvvkuv1+ONN95geHjYIWXY6F1g621hQxxoBTS2xljfD8PQ7XQmZ726usrhw4dJkoRPfOITrkTPAgX7fR230WgMiJJ03aIyy6kNjWVlZYWzZ89Sr9eZmppidXV1QP8AGxUOGofuSZqmBP2U2tk1vDRXuK9DkbXvke/iF9JinLwmPHOuLi7ccuiFRFnPRagU2nNt7hPRJaHGCCFN6qjCf431Ikuf0/QhjcKlx/TpEFBjiGHiouQwbwrkkxFTK1rURKT0WKdPXJxjmIyiDwFRnkdPPZqNJlGvVygZADzCeo2o3y/y4CmkuRbAz4pUTRDgJ0lxpD5J5hEmPnESUgvzPQuCMCDr9/EKR6emO/g+NT+gF+UNk+p+jUZQp1Wr0+/36MR5pYCXpkRpih8EdHsRrWZR151BEsekcYLn+9R9D2o11jptenQLLURALayTJilk+aynSd71z/fA8z38uGBWsryPYi7YzPv9EecuPPByNiP0vLwhUj8n9wMCar6fyyjjBJKYwKsRZNphUFsOhWiDqIw+agqVZhnNoE6W5CCxFTaKfgsZXdap0YCs2KtxqUM8O+SiVVuebJ9l69j1zsoRllvz2jy/BQKi6i0rZvPym607+r5tvGPFjNpWWAygdhrUeDUOXV8ZgOh6xsbGmJqaYseOHURRxPLyMqurq5w5c4Y4jt0Wv6Ojow6waK22+gRb4i1tgObRzteDAgCQV4AFQTCQ+n1U7KEDAXdioprer1AkyzLOnj3LwsICU1NTvPbaa3d0PL3w5TIeOWSNS84NuCEHtrS0xKFDh5idnWX//v3OGWuzDdUC6zp7vd4AFSn6f2hoaEAoJPWymkxojEmSd8h6++232bJlC88995yLBPRdW49vj2erHaw4yjYcyrLshuhdO6TNzMywdetWtw2oFiab07RqYdcBcKFDdn2NGKhRx8enQVhE1vXCaXp5JjoIiZI+HTpEZAQFPZ/T4rnjjenhERDRL7oHeoWLzUv7GtRI8UjxadBw7j3PsSeFo887BHbp0qdLjTpeASDqheOXcE/6Achr072ivE1COvUi6Pa6BdzwCrkgxFFcROU5qGn4dYLCtWUFEzDSbNCNAqKkVxwv35QnJe+z3+l1CsCUFPAoo+UPE2cZYZaSpHnaJE0DvFpekkiQ6y4ano/vQ9RPSbKcWehHMd0o10bk2xYnJGlKGtTxUrViyjdXDjxtu5vgFVUZpM08XZJlxFE+5rS44hykNJwDz5++lKZXlIt52jkxIySkWasRFg5mNeriAa2wQRDUIE3y7YmLMs96EJB5AUmc0ieiT0yTJt0kr+LwvSBXEvg+pDl3lGs7cpDRX+qy2oxdrto6dDlbGz2L5dK7DBs5cjl7sXp2LdJ7Z6N9OWSrO9DaArkT1bqgVIPeTzF4lvK3mqEsy2i1WhslnCZIKQcgWgvEfOjnw8PD7NixgzAMabfbrK6ucunSJeI4drv5NZtNF2ErGNFa4nkerVbLsSC6jgcJAGCjMuBOgtCPmz1UIOBOzYpP7EN6N9bv9zl06BArKyvMzs4yNDR01w+epc5UZqcXyNJgZcR98uRJzpw5w759+9i9e7dTAlvxnaXvbWcum1YQbVgGJSsrK8Rx7PL3gFP/K+WgRcrm2kQTyixDoLnR8Ww+VOe26tXz589z/fp1du/ezfT0tDuf5kSLmFIIA/k/zyM4vUgvahMVi37eEDYkpFbE+B61guCNSekka3SJCjcZUCvq+Pv0gI0StqhwyyrPazFEROQa0ST0aBX7DAhkhPj06LuSsgCvKBrMo/Bh8oY0aSFGTB2Rn0edtUIKlysF4qLzn1cAk7SQ0vk0vDCn3rMML0sLqt9nJGzQajRIkoRuHBdtgfOa/jDwSdMw34wny52/l6YkacpGJ0SfPmleAlnQ5ut99eDPdQlJktJPU9I8ZCfyM2pe3lEwzfJnOJIGwM+rCCio+yzLiJOoSMPkLEuWZaRZXMgqc8aintWoBTWSNCUpIvZ6UCdNkxxoFKkYSSX7RKRpwlCzhZdlxEle7tg07a9Tz6MR5M9t3lwon3vfD6ilecoG3yOO8/oLjxTt2tClT0hAmJG3RE6l86ixsaeEzzptLl9eBGByctIxhbbG3T77+v1m2iClAyDfe8OW+ZYrAlQqq2Mp+NH7omhbwFyiXFv+q/dKlL90DVo7NR67gZnYw3JLX6tjyLLMsRY6jrbxffLJJ+l0OqysrLC0tMT8/DyQd8KTnkAlhDpWub3whwECHsVthOEjCgLKqPtubXFxkYMHDzI6OsqBAwc4ffr0XaUXyhUCFkVbKs1WAWRZXmb07rvv0ul0ePPNNxkdHXXnlUZAL5198WyO3Z5PeSwtRvrdyMjIwOZKQRBw6dIlnn/+ebZs2eJEhvqjBUUpCmkZrP5A863r04tqu53pmg8dOoTneTz33HMDUZNKCWGDYdDxJVjs9/t4Kz28SNX7eQ+4pIhmfchLA/06SSolfESnKCFTd/+QsMgQZzSLDnF58ViPHhSldX7xmaAQDvZo0KRHH1Wg+2BK0/LkgLYGhj4heQ16LorLRYu5QqFOVnxLbifvMBgVxX15T4I6acEOeESZl0fhgU8YNOjHeb671WiQpCkREAYhmZfneIPUI0nzNEfmeZDm1HacCsglxTjz/gV5a92oENrl6YVGUMcPAkLfI4lj4jTXBnieR5Sl+J5HmualheAR+AHaJ4DMgyTLx+AFxFlCg1z02C8KMrVzoVf83U9iVALpWADPw8uCgg3Jre4F+FlISr4HARlEBeht1etKbFD381LHXhwTF3nuehjm2y2neRqkH0VuE6gc1OV9E1o0icl3aMxSz1VX+AVAzHsOdBimwVOERKPDLMR54x2ACxcuMDIy4nZ6K6vebZtuYAA064+cvxX2Wkpajlnvn/6t8mABAP1eNL9AgEqIJdjT+xaGoaPtxUgoCLDHCILA7Tyq3gaWHdTnhoaG3BomUDM9Pc2uXbuIoojFxUWWl5e5cuUKZ8+epdlsMjY2xsjIiAM7YikENG6nDbuf1m63B1rAP0r2UIGAO73homzUhOZOLcsyTp8+zcmTJ9m3bx+PPfaYc2RyePqj8VghoHXu9phy2AICtg5YNjc3x3vvvcfU1BSvvvqqYx70gtfrdbrd7kB0nySJE+LZfgLq9Q0btL0FDKL1l5eXOXLkCJ7n8fTTTzvkbulFK8TU36Lm1bNfEYRecHuftMBFUUSn0+HUqVNs2bKFnTt3OtpRUYZAi45j9Qu2IiG7ukodijK4hB49UhJcnX0hCOtkGUmWO9B6wQsE1AtC2SucdlaU4nl0iOjSJy2ka3mv+aToEugXfQGSgn3wiqY1ftGGJi0y/2HhVvOUgTLOSiqk+KREqDMgeDQIiAqmISqOFRLQoEbePjfvd58ASdonSQtg6+WgICHfuM9PU/pJQj+JC2CU99ere/l3CQJCzyeNY5IsQl0I3OY8Bc2dA6ei536S/ztKMvLNgTJ8L985sObn2xol8UZ7Yz/FCfFa9TpxmuYJAN8nSIvGPVlE5IR4flFZoR4CCXlnQFCvxqhIS/gFo+Lju02N+kTkuYG84DPwvBwUFGMLPY9OWuSckzzX34siQj/A8wIaXt7tMUq9QiQYOmFiliRkhAXo29h3MKSG+hEGRYVJgybDqyHDj83SmZnh9OnTjI+Pu5w45JGudq6z74ycr9YYOf40TR0FbcG13klVGig1CBssKOBAsxi7oaGhgUBBOW6lCfQ55f31/tpARGNTPxKxmmWztfy2b4LWL6VCBFrGxsZoNpvs2bOHMAxZXl5mZWWFubk5jh8/zsTEBDMzMwMp1dtVid1PUzrgQTMQD4M9VCDgTk1I9m6id9Xgt9ttPvnJTzI+Pu5+Z0tsytS3nKMFCELA1vSSWScqIHD69GnOnTvH008/ze7duwcU+3K2VtVvv6+coxXuNZtNt0DoBbTK/ziOuXLlCsePH+fJJ58c6Degz5SBjW1aouNakREwAD7sZkG1Wo3FxUXm5uZ47LHH2L59u1sgLI2oc6psyVYQaMFJ0xQvzgvnciV+l4ykSANktFnPM/UROZWc5DnnOmFRSFajR4Q6yAc0yNvZUkCEOj2iIl+fmxxD7uaggU+LevGtjMDtAahtgHNnWSOkRlhE1x516sTkW/QqD+/TQJvlSNvgAQ2a1AomIiQgzOsCiuLGGOI8hdAnI0vyzY6CICBKNrY3jskYZYiwVoM0yzf+8XPhXZYF1DwPspQQCudfo+mFhH5AP4ldVUOaxOQbCqc0/BqeX4AJoNPtFSxM7ASXXtGRcL3bI5QgLi36RWQZYRwW/E1WzHpagKPYMSZZIR5shCFpktIjIyUXMTaoF0kVbRiUl1j6QUicpLR7PZq1OmmxU2Lo+aReRpzlPQjJvJyzycDzIMskB02BkLwiIS6eEck6a4SA74dkaVKwOnlZY6O4mpgYrnfxtuZ0v0pr5bB6vR5Xr17l7NmzAK49r8D76uqqc2xixmx+34JxW/orx6wIXYBCFTkS0/V6vYFUhdaGTqczIM6za54CFqtlsMJr2wTNRuh2DdM6orWj3GlQTJ9NZ8rpqzPoysqKEw3/6Ec/Ynx8nImJiYHqgw/S1tbWqnTAR83upkJgYWGBgwcPMjExwYEDBwZqbe2x5GStiYKzZTz2wdcCABsNPRTxRlHE4cOHHfCYnJx0AEJIW9/XS6Z/K2qwdfS26Y/ABgyKjqIo4tixY1y5coV9+/YxNDTkXn6b69PLJwds1cK6Zol4rCDR5gC1kJ09e5aVlRWeeeYZhoeHB8r+dD5Lc1oA0O/33f3Q9YYTDZL5LpDQpU+toG/zRj8NYiIWWaKZNMk3gsn77aszoP7OiqgyF3r5Bemft/Dp0iXfOibOI1z+f/b+PNiyszrvxz97PMOduu/tSd0tdbe6NSGERiR1Bxs7KJbzg68hgw04FfCE4z9MYSvYsSmgXJh8qZDg4IGUymUs48SKDXZFdtmOiCKHOGVkBiMkNLV6HtR9e7rddzjDnn9/rLXe857bLSEB8lcENiW6+95z9tl7n73ftdaznudZFS1SR8QrkcG+gQoQA1Cef+6q/g4tr3oM6NNXoFsq+yEZoqFPETlhpUlAREoCQUTY1CSB9N1DGlphQl03JFFMHEdUVU1WDoGAqq6JwhjqRtkKFQUVcSU1dhTIIJ+8LkiDCIKQsgGpwQEakigRZ8BKWAuiR4i1k662rlFI2dQ0ZUUTQNAIy94UFmINLMTLdp0QBGaWFFBXhfo0mP8B+jclxWLTFRvCUCYXCsoTE6hUL9dUQX4+mhFQNzVF0xDXggIEdUMQNKInSGLiRvwTqrqmrCqqSJKdRpGXkoBMzaVMBaKeiJIwhSFNLWOJjKsBNXEYEyrHod/rk+fjHiFWDXe7XXbt2kUQBCwuLpJlGUePHnV++K1Wy7XdAIe0+dwmm0vik47zPHfcGVtX7O8m4fWTB1vH/OTaArBfANgx+d4shkT4yb6dpwV3X05oyKe/fpgawQiEPmHRRylszet0OkxNTVGWJadPn+bmm29maWmJhYUFp8M35cHs7Ky7Rt/Krd/vX1Ie+J2wvaKSgJeS7b2YJMBIeIcOHeKaa67h8ssvf0Foy998SYvfJ/PhMb914JMBq6ri/PnzPPnkk0xNTXHDDTc4ty2/KvaRBR/K9x8we6gtQzdLYBjPuG3M5969e6nrmuuvv971Df3j9TX5vjPfaq2/tSJgZIBi8KI5z/V6PQ4dOkSSJNx4441uUbN9GNPXIEKfKe0nVX4VAtBMQHMuA+2mm62sVOkpITDABupKvxkF5YUDL62DlI7rP5daPSekru7LGShhryQlVgc5Ybg3BG5wj02u65CyQkGpYsCMApsDIP32moyMGCEtpiRaxYaqboAWCXGUEIQBRaE1dlPoUUNcQ6CBOokTorCirGRaYCuJCcKIooC4FkOjGJVgYaY86hcYS9UsjPdKK9mcoLSgKgQ+2Uuj/64pKmm12ACiJI5pBQl10zAohnq1AxIgo2JIQbeBIIioy0JIjQ4FMEqmoDpRGNOOE4q6pl3XRIkEnCqIaDcBkCjrYhSEG0VgpAUhCFHZlGS5kCKbUP0AajnXuoEwjAjrirKSb72kVqqoJHfStEHPopEUUqAC6gZrUkgiE7SIVTUwbDLKOBxL+u15tWTd4P41a9YQxzHr1q1zle65c+dYWlpyHu82xMfaBqaWieN4zLbX1gALtoYQwmhMcV3XDAYDl7jbM2bPoCUC/gwS8zKxcwHG1hd7Rv311FcQ2NphybwNQorjeEwiaL8HHFnZEEmfXG3nOTk5yfT0NJs3bwYEql9YWODUqVM8++yztFotZmdnXWKwuqj7RraVlZXvJgHfbtvXcw0cDoc8/vjjDIdD7rjjDjeI4VLb6iTAr859yZ+PCPhaXHuNZdRmO7xz5042bdo05hngJw62+dCf/zMj6vnzt+1nljREUcRwOOTMmTPs3buXbdu2sWHDBhfozdXL9mk/Nxh/dcJhpEE7Fv/Bt+w+DENOnTrFc889x9TUFFu3bnX7t4TFKoTV5wGMeZDXdU3Uy6nPr1D1hwRrOlR5TkBGqqC59dBl+Isw2tuktGkTE9Ig5LCmlpVcvOjMyifSmlLGx8i7pT/cpaOhWyD/Ut0FcnL1BRCkINBgGxDQpkOqLYCQSGH5ASkJbWJaxPQZ6jjiSAmIgSYmKUnS1roWbRiolBFzs4eqKkjiLnmZi8a+0aSvLInDhqoWLb0ES4HqzRVxImopj0CoeGkYUDZSyYYKiku9LUlARUMcqGwPsfmtqwoiq7ZrojSlVL8BFSAShwmtWjgGOSVhMx72I0Y2zaG2SibTFnXTUNY1QazciiAgDsSiqWlqUgs6dYy5IEBE0Mj3X2jilVU5BCmJ8iDiKGKg/XGzFa6cLXBIzlBpozZaWBwHoyCiagoqIKxFqmhIT0xMHIm7wqDOBOFYO+2eJ2vT2XhgGB8lbvd5q9Vy43xBAk6v12NxcZHFxUVAUIG1a9eOBXVfUgyM8WrsmTSrXvPut+1Sw3n8/roVN03TODTA9uuvPz6iBzi00f5uQd7+NJWRb15ma6X9fLXhmR2rJQq2RtnnzszMMDMzw44dOyjLksXFRRYWFjh8+DBPPPEEU1NTDiWYmZn5hsji31UHvIK2S/XbL7W9EBJw9uxZHn/8cdatW8ctt9wyRqb5evu6VJD2g/dqva69NgxDB433ej3uvPNOJwGynr8Pp/kzte2mtwfW2gr2uQbrtVQmlue5M9mo69q5/1133XXMzs6OPVR+D9IWJJ/g5+uWgyBwvv8mFer3+24BsR6mJQDbtm1zFqM+zGeMY0tujLxk529T08qyJDx2QSpIJY+VFxYRL/dA2dkSKkvXi69dbSl89ZqGkqBOsKE7Ldq6zBs2UGstavbBme6tokVbe896L2COgzZ7PlYUwoSJ0l9OiN2+BUBOCJHAlRIxdCmFmA9XQExNXuQKvYtlbYSEyb52xSsqmThQxrSDFnkhfIOanLqJaKqGAUNqKmJScgbaXmhJ4hOENAGUlbZ5GmHfp02kob+hUjPjFgFxpCTUonBnV9cVYSDJUlaVVNmQuq4ZkoOyHdJaeBTS9hjotycwf6rEP8V1SEhJY5Eb5nlG3TRUZamEx0ZaD0bEa2rKpkE8HQv9tgXxMSslaWukEEBiMHQQksQJ/aKApiQJIv2OhGkhlk/SjrH5CiGKyjVC2ExcminH3BDKgKRa2i51lBC0E8Klgi1MUM8vwVQyNsXTR/bseYIRsmgJwuTkpBuY1ev1WFlZ4dSpU2MogU0A9CF5Q8xMEeQz6g2FsCTckhBfOeQXLwbTW7Be3e8H3PNrmxUePj/B1lBbI4zUaMdiwd54CIZq+p/rc4j8BGA1chvHMXNzc87RNc9zFhYWOH/+PE8//TRFUTAzM+OSgqmpqReFMPf7/e8mAd9u2/NB+Pv37+fIkSNcd911bNmy5UXdAHZD+qxUe+BWm+fY7y0JsAx5YWGBr371qw4yNx2s368z+AtGWbT/mfag+hC59fGsAl/du3vyyScpy5JXvepVLvu2npyPTmRZ5gK8L2P0Jwz6JCGrMAwZsArg8ccfJ8syrrzySiYnJx1z2U+UfEmUfU6SJExPT3PixAl3LdrnhtSlDf8R4liiBjKx9qltvG6PIZl2iyPENEgkd0LvaumyngY6kKjSikYpaAI2t7WyDEnokGo3v6JkwNAR/aSGRqv/iDiIKBoxmTH3AKmJS+0fN471H2hnW/rmhdLOJKAKepBop7mkrZ4HUnVG2vwQpUFGTlInYgzUSBpTMqAANSSSLryY8wS0SGiFEUEUMixL4iAgjEKGVU3Z5MrQD5SgKO/NKYmqlKYyV4OIOBRZYkNA1Qh/v6mFTFjVBUOGRCR6ZVEmvQTUOIipGiH9pUlCU5a0kpQgDAkakRKGkbybRsx5oromUGlkXRVkzeiZjpRSCKMWQ0xEGiUEATLcp6pI49ihJU0jo4LqptZ7SBJH8XZIgYaOtntqoKkLVYjYfIFAvxdhNRS16UkqoqqhdWyFlJSCNtm589TnGjpbNxGt7YyRhv1g5yfjfkAfDoekacr09DTdbpdut8twOGQ4HLKwsOAkdYYgGLRvgdh31/R77z6R2HcxHQ6HDmmw19ia5M8JsfOwZMKef1s3fK6SrUn294mJCcqydOjnaiKi/d2QVR9N9bkRL7Y1nKYpmzZtYtOmTTSNyLAtKTh69CiASwjWrl3r5I6rt5WVlee1iv+/ffu2tUfybyqQXtaXvvQlTp8+ze7du9m6deuLvpHsgbIq3x4cv2+9el8+SfDw4cP83d/9HVu2bGHHjh1jPTU/C7aHz6+cLSmwjNggP+urG7PWPt/2c+7cOb785S8zMzPDdddd58YJG5N4ZWXFMYdXZ/22EFhgt0rdl+XYw26/NxgyjmN27tzpSIb+eQHOrdAWPiP+2IAREMlTWoY0KwNd5FWapjItGcKDhlhISZTIFWkSEGv9LyQ9C6ohjfRvG+uOG/8b2lGbtroOyhQ589gzgpz53Eufu1IOOwjrXMJmSZ+BQuCFOhPIVL4+JRkZNrDYzkqGB8no2lzPSob/xIhRj0ggRUgn0/Vk/0OWqiWKJsPMi4XaGKspsQ0ilhG8MQFxLMlsGkb6+VBoCGu0z50jk/pU9e+OMwCiMCIIA+qmZljk5FXhzH7yunTtFUs8SioyhMBZUVI1NUkYk0QREZCkCUkio5PLumaY54o4QBhFtLSqjRPR/UvzAJIg1lZKoi0fMX4SjUZCEoakcUwYCo6SqYFS3RijAK38LVUJ9B4KFVVqNFkrsBXEyIeBskJQBEHONiQlpU2MTTEcqU8CouNLDoXyYXYbt2tBrtVquWA/OTk5pku3yt2Sgi1btrBr1y62bdvmiHCnT5/m4MGDHDx4kIWFBTcUyNqDPhfAgrvfzrS1xtY2g+XtmbSJpJ1OZ8y11JAOOw//mO2zrMgwfxL7zy/UrHVi65tPcLTgb4nTpRDZr7cFQUC322Xr1q3ccMMNvO51r+PGG29kamqKU6dO8YUvfIFHHnmEp59+mlOnTo0hHM/XDvjEJz7B9u3babfb3HHHHXzxi198wWP4zGc+w7XXXku73eaGG264yNu/aRo++MEPctlll9HpdLjrrrvYt2+f+/3nPve5MfTG/+9LX/qSe92nP/1pbrrpJrrdLtu2bePf//t//5Kulb+94pCAF9sO8CWCp0+f5mtf+xobN27kuuuue8k9IT+rtgrdh89g3OceRmS8p59+mqWlJW666SampqZYXFwcy75X+3T7v7NM3c+Q4zh2Yz99vwHzLM/znOPHj3P06FGuueYa1q1b51AKH31YDU36BCEL0LbZw2zwZL/fdw9InuecPXvWwZRXXHHFWBafpimtVovBYOAWJX/xsc/1h69UVUV1dkkpf1L/xipES2gpFN9QkDPQhkBCxCRtDVomaWsYGetqYAxk0l4rlJn21A2NgAQUqoPPKUkI3dgfM7ORxT2g1r6z9IYlU5aucaPthEIxCKnrTVBWAR1tApjZjLgW5gplSyhOqambWvULhboa6mKo0L4MvBmNzHWT9IgcTyEhdMGuBKIGqlpQgjAIIAiIKygIlKBohL2IGFMYRMRRShJHNFVJUdcEQYikPLUmR6WmM2KwY16DamJMLOI6bYuEBE1NXtZEYUgrFpuiJgwxHkMBBGFI0TTOCXBkmGzPrrj+Cemvoh2KcZD4KIS0kphuBMM8J6tqOrGMJE7ihLCKoDE+h01wlAZDhNk1G09D0rJGEyE5vpwAs1uWYxH/AvnbCgNskqUTHy4NiNZ0x8i+vvGNH5RtDbEAbs+9Ef4MRjfkzsh8mzZtcuTd+fl59/zaKODZ2Vk3G8BHCSyI+MZEdoz2/NsxmXzX1pNut+vaj7Ym2jrjJwDGBbBzhBEvwdYvO2drAxh50DhIfjvC3vfNbD6fYPv27VRVxYULF1hYWODIkSM8+eST/Kf/9J/YuHEj+/fvdxP6bPujP/oj7rnnHu69917uuOMOPv7xj3P33Xezd+9eNmzYcNHnff7zn+ftb387H/nIR3jTm97E/fffz1ve8ha+8pWvuCmCH/3oR/mN3/gNPvWpT7kxwnfffTdPPfUU7XabPXv2cPLkybH9fuADH+Dhhx/mtttuA+C///f/zr/4F/+C3/zN3+QHfuAHePrpp3nXu95Fp9N53kFHL7QFzTd7pb/Fm8HXX2976qmn3M127Ngxrr/+escmfalbv9/nr//6r/mBH/iBS7JhfZjPLtf58+f56le/yuTkJNdcc427yVdWVnj00Uf53u/9Xvr9viMN+cmDryTwe3e+YdBgMOCpp55yrlvWT9y/fz/9fp9rrrnGjb20KgOkErdFoGnE5/uZZ55hzZo1zM7OuoXGPt+HAX0FglUXhw8fptfrsW3bNo4cOcLNN988xli2c7PvzaxKLeGwa2fH/9RTT7Ft2zaqowvEqmGXsbBCv7N+cqUBJiPHPOfMzU3Ifaatl/DZoaMhOiCiUVhbkI8kFinaoB5of9hQBlO/S7AVMmGigcAY86Zvbxywn1NoW0ECaYEMpJXQKf91CejQpdQ0otHwO8kECYnbZ0bOUJOEFikpCUOVIiYktGghw5E0gdL9JIpflJTamw8FYaAhDCPSRIh3w6JgWGfqhR95lXLjoHz0PUFd04QhTV26hkOuqZK1Z0ps0qDQ6yI1aUpJtNmgpslBTCdJaCUJlfV96xqCgDSOJdmoGwZFQdAIo7wohZcQhiLVa6gJg4i6qSSZiCKGuRgxTXc7JAq196tKyI11TRJFDIuCvM4dylQqKTRSdMEGHKNtHFMvGN/EXCprKrF4DiLyJtPX1y5pk6suyU13xzrK6ZYLgr75mLlw+n18KzCapqHb7Tp+kCUFRqAzHs5zzz3H5s2bmZiYcO5/llgvLy9z4cIF15YzG3SbdeBD+fbcw8jW2HcFtIJiaWmJ8+fPs3PnzrFevRUMPofKEAzb96WcB30LcztvQxMtobhw4QLHjx/nlltuAUYF08u15XnOH/zBH/Dwww/z4IMPUtc13/M938Ndd93FXXfdxc/+7M9y++2381u/9VvuuC+//HLe/e5380u/9EsX7e+tb30rvV6PP//zP3c/u/POO7npppu49957aZqGzZs386//9b/mve99LwCLi4ts3LiR3/u93+Ntb3vbRfssioItW7bw7ne/mw984AMA/OiP/ihFUfCZz3zGve43f/M3+ehHP8rRo0dfNAJu2ysOCXixW13XzM/P02q12LNnzzcl7/Cdvfw2gB/07c8gCDh69Cj79u3jyiuvHPP+t/fZv6uqco5d5qK1srJCWZYOCjSDHr9vGIahg+dsQVlZWWHv3r3Mzc3xmte8xkmBrMdvvXvL1Pv9vjsW31bYZ/D6vXy/XQCSTBw8eJAoinjVq1411nqxhxhGZiI2stSupV1X/7rYzdlut2nWr6E8fUGrZoPkA+2jxxroQzq0tEdeKrTdKBQuaUFDTUIb46ZLYIeqLkWUF2ql0YhMzCx5Eq2FKyUJhiQqJWvIlEcumv7a1YCJ6v0jQgaKGVjfv6NVuhgVCcLRo0+k7QdJVkI3QEiOVDgPtftfRaZ8ACMrSsCHlFDFbrWC2GJjAyCDkdqucg1roAxI4sj5/QtaUGjYEo5EHAT0m0LElbV8flyn7mgENRH4XqYDhsockG8sxmYFmGtBqUlQSbsRtUZV1xRVReUFPrM5zvKMRoNtU4JZQzfqA1BRkzRCAmwlwp9oh6Lnj4KQMIqYaLeJypKiLMUtsCzIahkk1ahjgVA2E1qkdFttiqqCRuYdFASKT4SYZkM4FrH8LpBAVJcpQ/pUmK2waT9CgkCq+KqXUXeSi/hAVtn7ltl+W85+Zn4aBqv7lTJIxW//tve2221mZ2e5/PLLHZ/ASIZWBExNTbFu3TpHxrXPM36BFSa2Tli7cnXgtyLBEhBfUZQkCSsrK2OJh5GhbY1aPQzJRyOsVeArA3wU9qUGthezpWnKj//4j/PjP/7jvOENb+BHfuRHmJyc5KGHHuKjH/0oS0tLvO9973OvD8OQu+66i0ceeeSS+3vkkUe45557xn52991388ADDwBw6NAh5ufnueuuu9zvZ2ZmuOOOO3jkkUcumQT82Z/9GefOnePHf/zH3c+yLLvI4rjT6XD8+HGOHDnC9u3bX9J1eMUlAS/my56fn+fEiRN0Oh3uvPPObypb9IEQP4j5aIQdU57nPPPMM1y4cIHXvva1rF27dgxeMyKMz+a3YGuwme/97b/XEgFfyWBqgBMnTnD06FF27drFZZdd5hYLIyiCcCLMR9zP1u2BteMyuNGgPPMKsIc8yzIuXLjAkSNHmJ2d5bLLLnMyRPAm/CGtEUtGjEBkm/mMgzxsnU7HoRWtVgs2tmjOFZSV1dwj9neowKw46kmglZ669Oaldy7+9AHQRqFgEgfrSgVeE9QChEt/3ix05XWFLvgxsXb2ayX3QUJCGqTaZ04Q4xpBF6ImBgbYNMKUtqIDMjsgwHQI0nlvFFY374BCbYVjDZ8tQjIyBgwxBUKbFrE6EGbk2KAfs0/OFGGwwUrm9lfX0vnPq4KwChyvIqQm16o40uPJm5IWKa2oRVkpVwFDSWSfKS3nu19R0lIMxnrtEYFyJ2p1JQwwG568LGjqhiaSuQZy31TkZUAShoRhRNRouheK13/eZJrKSAoUBgmRcWvynLKWT+42DUkAVS3tBHv+enXfJTAS2EtNKKU1o10Siqp21y/0lkFzhgyDiLBpqJqaqvR9Iq0tk2hKGRA0FRxdIaCk7ibku9a5FqPP+/GheZ84aC06W2cskPp9chgRbf1CBRhTGc3MzDj0cGVlhfPnz3P27FnOnDlDEARMT0879vylLNdtbfC5UfaZfoFjJD47VqvsDQ1cjQTAuDupFS5+wLdWon2WvcdPFl6urd/vs3PnTv7JP/kn/MzP/AzHjh3jiiuuYOPGjWOv27hxI88888wl9zE/P3/J11vrxv58odes3j75yU9y9913Oxk2SGLx8z//8/zYj/0Y3//938/+/fv52Mc+BsDJkye//ZOAF9qqqmLv3r2cOHGCTZs2uaD9zWx+pms3JHCRkcXS0hKPPvoonU6H22+/fUxqZz01e5DtQTBijT3kzhY3EOmQBWp7v8/mtwf/zJkz9Pt9du3axZo1a9zx2INoC4Dtzypz3/sfuEiba/+u65p+v+8Yx/Pz8ywuLrJp0ybncOizfPv9viMQ2cNulY9l8lZJ+K0d38bUrsGpmYLWwoBpJsDp6UXTX2iTINI+dK4/Ewc++Z9V6oGiAwTQDhNhoTcmWgO0JqyUemYeAtJeaGmCMdBKvdDfiUNdrTUvqjlPophBKS50hYa/SANzhdnQBhrcu8REmmygyoca6eDL1IERrGxd6lpfJ5u4Gw5YoqJLh5iUmMgMfzHvg5E2Xjj7DeK2mCpXolAwXwJ1oRoF1S1UhqvUZMo+MI+/hoYWKZWSG2UGQUCpXIiAkIqaPgMlKSa0QjE1KsuCvBY/hRppZciAo0JTPCCOoa5ppSlhVVHnIw5IK5Hnp24asqqSiYmIrLBf6t1QCZLWBAF5nSFDoiQZbWmyF+t9VFJQV4mMFm4asjJHkq5GEanILYhxFNHUWtFWghX5FkgxEWEQ0jSVfqNCoaSfw7GI5rIpl9xbgLQ1w/69Gh0YDAaucPB5QpaA++1Jvye/GrG0Kr3VarF+/Xo2b95M0zQO4j969ChHjx51Ejqbd2CcIFsvLAnwEVJzD/XXXd8x0LhStp74nCA/gbF10fZjSKSPWrqn92XuWDdNQ6/Xc+1Vu+7/X2/Hjx/ns5/9LJ/+9KfHfv6ud72LAwcO8KY3vYmiKJienuY973kPv/IrvzLWzn6x27dNEtDr9XjssccIgoA9e/Zw7tw5R1b7ZjcL9qslh3azHjt2jL1793LFFVewdetWV/VagPMHaFhlbNW6/zoYZdpG+rGM3xaIfr9PXdesrKxw9uxZ4jjmxhtvHAvCtjAY895sSS2Z8asEP0O3c63r2vXx7bVFUXDw4EHX97LpXv6iAyNLYRjJJn3vcl8CaS0KW+zyPCcFOHieuj+gS057cg3VSkmgLPRYgfNICXAWqCrE1jdS2FYq6UDZ7tKZbpqG5apHRcOEzqEXyZrs0dj54i2YK+8A5QkIYWyoVXehJkICnkuwyympytohAG261FT0GdImRmR4tVaIQoOr9ej8uQAGNxstsmSgKgMJ55ULvwJTD3UccqWSt1L3WaqKICCijYzkDR1RUHgLjaIg1npo3P9ChcnN3FeQlEz9CsQ6ucBUGTXmxJgSBzEhMUUj13BAn5qALglxKBr2YZ4zZEhKi7o2D4JaGxkhddOIwVBdEycJyzre1pCEqXabVprSzzL6RcmEPidJElJXFVlV0c+GQvpTgqGaJxPocUekhCRkDGn0OlFAXBrCI1oLczFsogjqipbKJB2kzUhPgdcioTHeSKykS0naOD+gXt+lCUftN58Fv9r62/cS8MmEhkr6/hp+RW77tGLE1hZ7Hi0A29pmZjp1LSZi/X6fCxcu8NxzzwGwYcMG1q1b56abVlVFr9dz64YhF/Z8+wWFPwXQEgB/3fMTGH/eia2f/lRS/z3+319ONGC1bfC6dYLmrI4xp06dYtOmTZfcx6ZNm17w9fbnqVOnuOyyy8Zec9NNN120v/vuu4+5uTl+6Id+aOznQRDw7/7dv+P//X//X+bn51m/fj0PP/wwAFdeeeWLPOPR9oqTCF7qiz5x4gSf//znmZ2d5Y477nByuBdyDHypmz1Y/laWJV/72tc4cOAAr3nNa9i2bZvLZAFHsvNhP1/+Z+iAvc5IQObQ5bPnTToUx7HzHOh2u8zOzo5NArOq3580aC0F4xUYTG/EPV92Yy0BX0YUBAFHjhxhcnKSq6++eoxvYK+3h9Ne7+uAfTjTSI2WCPkJU3FuhV3MQV+mAnZJCVZWgMzB/NLzj9S4BTIKN0XQ6vJcA3ihwTvS4N1nhR49coZUjVRc1ls2a2CZ5FcyoE+mzn45FRLWYloqJUs0+CUaJAtqSmUmNIpGpMQkmoAU2q23YBFo4JQUI3PHERPTpY2Q+gqG9OkzYECmZyc8h5hUq+wSmWjY0UQmV5KfQNKmbBAegani5eoU2CyFmtjtWVCTNi1ti4QqQZQgHapMsVZ2PNSEgSQfLX1XK0mIo4gkkCvQoU1bSYZZPWSYZTSNcB+GZNp6GY0QjggIGqHhlSjc26DYREAcRWJ3TEMYxaSJKD2aBuIwJIkldSmrirJuqMNQkQwUrUjx3RwabAy0oB1lI4hBSEi71SFutei2ZaZjUNVUldw7ZSXk0TAMXbsqVYFmpRwOu4MyhwpJolXmowrdJHbW97bgZoHcqnlL0Ffr5y1omnTPyLh+QmCJuq2JRka0JML+bq6fcRwzMTHB1q1bufnmm7npppuYnp7m5MmTfPGLX3QyxOXlZcqydDwFS06MAGj7bLVatNtt1qxZ45BBe53fJoUR/2E1guHLk/21xUdoX65ttUQwTVNuvfVWF1xBkq2HH36Y3bt3X3Ifu3fvHns9wEMPPeRev2PHDjZt2jT2mqWlJb7whS9ctM+mabjvvvt4xzve8by2yFEUsWXLFtI05b/+1//K7t27Wb9+/Us7cV7hSEBVVU7TeeONN47JMl7qFMHn23ySjq/x7ff7PPbYY7Tbbe68886Lsm8/E/b7e5axmpe2H4x9wsxq5m4YhvT7fY4fP86JEye44YYbnH+4OYRZ4uAftwVYP+s2UqIhE/aZfsvCFqMLFy5w4sQJNm/ezPT0tHvgDMq3KsQfKOKjHJZQGPHRdzr0UYMgCOC5Za0qKyVsidCuplICoE0PFAFcpDB6pPV/RciQIebu3iKlhRBkBk5MaNMDpAqWnros0W1SBXwrbMBPh1RHAiuao7CvcASMpGhDaCT4xExSSeedkJg2UGrtbfPzDGIWVCBWhCDABtQGFAzJnA1uiw4JCT2WyRjS0t5zgygfQsw9UTgTOUPEjjehUgFjiRHqpC0RETBgqGcb6d8rIjoaMEWkaFfXeBiSeOVOBxA2uX43ETkNWd5HlBHisxAq4bHUmrn0JJZyrFL/Z3okgTZqAm2hSIWfMsyGVHVD2kBVV1R1SNjUhEFIFDTQNBRlRVWrUVcQEEUhQRDSRA1JNXIDlJSsIKR2ZEgxmbLXKGkvCkmCgLyS+3VIRbsJKQtxfCjqkbtgrYhQStu7ZrlDfWJkPHWpbZ1ak2z/mbH1w54VS7DNDMyeF1s3/PdY4LRCxN5vCYMfKH0DNHvmwzB0ZENb6yzhT5KEyclJJiYm2LJlCydOnCDPc06fPs3y8jLdbpfp6Wnn6+8Hcdu3BX9rNfqB3IiHTSPSQP+91hL1JcT+ufhr9Mux1XV9SZ+Ae+65h3e+853cdttt3H777Xz84x+n1+s5kt473vEOtmzZwkc+8hEA3vOe9/D617+ej33sY7zxjW/kD//wD/nyl7/Mb//2b7vz+Lmf+zk+/OEPc9VVVzmJ4ObNm3nLW94y9tl/9Vd/xaFDh/ipn/qpi4737Nmz/PEf/zHf933fx3A45L777uMzn/kM//t//+9v6PxfsUnAysoKX/3qV0mShH/wD/6B61XZdin4/hvZfAMNC3wnTpxg7969bN++nSuvvHIMprPX+xKZ4XDo+v9+Ru/PFfAz3rIsXRJg29LSEo899hhRFLFnzx6iKOLIkSOusrdEwNoIfqXgW/Ja/85kOT7x0GBASxKOHj3KcDjkuuuuc4uUHastLnYevje6n3RYZeBfG8DJnaqqIk1TTh46xhwWqIy8V2ET3nJyGgcWmztcSIFU0CltxHNPRgYltJTeZqayhZLvOtSU9Bhqa6FmqGqAQBdqAb6FFiayPwPfhUVeOWW97L9QtcBIdz5yApDzENVCmy5iyluQM6Sl7QGZSi/6B7PtrfXfEpRjOkoCNClhoWK8jo5CFoJbo736ikKDW0rHXcdC6WuhohoJModxoC0IUw+Ygl6CpDUKYrqk2rqp0aHB3ndhw4bQ/nqtBMdavzszNBJ0pFBWQwCKSPSQIUoNORENA1ImdEyv3EdxGFLWBWXdkNSxjokOmUxjsqqkqWsGRU5RCA0zDVKiMKBqGvJCxkenmjiFinAUlKS0haMRjPrtg2pI1MQURUkdQr8oyWtBY6omIA5jgrpSBEhwBjNsiohIgoi80fkDyu0olI9QpQmRToe059NXDtl646tozG/DHDztOTajIEPi/LXIX4cMSvfJh5aE274MKZycnLzIL8CfWgqScExOTrJhwwanTlpcXGR+fp66rlm3bp0b3tPpdFyR46OS9vmWePhcAjte3yfAJyDaOdh5vpwEQTNC8zkBIJK/M2fO8MEPfpD5+XluuukmHnzwQUfsswmHtu3Zs4f777+f97///bzvfe/jqquu4oEHHnAeAQC/+Iu/SK/X46d/+qe5cOECr3vd63jwwQcvim+f/OQn2bNnD9dee+0lj/lTn/oU733ve2maht27d/O5z32O22+//Rs6/1ecT0BZlhw5coSnn36abdu2sWvXrktmgBcuXODRRx/l+7//+78ln/s3f/M3bNu2jXPnznHu3Dle85rXsG7dOnfzWdULI4mcEQBtM9juC1/4Atdff72DZvy2AeB0vpbxLiws8MQTT7B582Z27tzptPpHjx5lZWWFHTt2jGXvPuvWCHm+46ERAu1Be+6558iyzBGEsixj3759BEHAjh07XGZugdv6dv1+31ULQRBw6NAhrrzySocU+COCbZGxB9scxEAILjPErKPFkIwWCQltzMTFbHrAht6GOvGt1voyo0ubFm369KgJtKZvadVeUpBpABLoekCmbYOaiIgJJkErNAlqgSP2GUUPV1WXqs8X8LrS7nugda+Q6mJXRWfK6g9dIB8NBApd912CqYTfEFMhWBLTpuVQC5mZUNCiRUpCrtWmafVHWvZAiYc2KaFSJn9IQktrV3PHq921MltmIcJFAovTphul1EBeZcqIqEjoYAOPSgpaJKRRyqDKlGZoksVA9ynDnQu9DhEim5Q+ekGHCT1jST1CEuIgIghCwiikqSryekgStmnHCQQQB2IC1QQBK9mQrJL7KglaJJEkYoNStA9yTUKiIKZoCmoqIgKiqEMrSSAIGQ4HDJuB8AECmU5ZuWVQvjVpIEhSakbQkggGeq/I9Mmakli/o5xK8Ki5KSffszXBKntLmq1Ct2dkZmZmjMdjM0fSNGVxcZH9+/c7qa5V1VZgGKfJn0Pi8w18orG5GPoTS60nb7B/EMh8kMnJSdavXz+G5tmzPRgMHNEwDEPm5uZYs2aNQxPtnI0nZMogIyxbcWPrWhiG7N27l06nw86dOy8K9tZeeDmSgPn5ea6++mr6/f7LMqb4lb694pCA+fl5nn32WW6++WbWrVv3vK/7elMEX+oWBAH79u2j2+2yZ88el5lZNmo3vwV9X8rnW2raA2mVty+3sc2H7g8fPsxzzz3Hrl272Lhx4xhPwJIFYKxCtxaBwfo+Mcf2bYG9qmQOgCEFy8vLHDhwgPXr17N+/foxlMCO1wgyPvHRjsOYv8ZRMBTCbznY+Q+HQ86ePctM3GFL2dG+dYWo6iMF6aWeNxNfQMNSoAQ+qYOl9ht5+5VuOW603itJCBwgbZ3xGuggXvfWH5fkQxdcZI6gWP9kWsuqmQoNJUMFfiWgSY87oWaoLHY5chnJIxz8REmCuVr2Si0ZavsgoqJQ0mBFrHRHmygorgk1LVp01P/AJICB4gpSe0rl2ddpizEBfeUFyHU0hKRSXEDaIBURDYWyFmT0UJeUNBpBzXEVqy9ArEE0cu2CnIqmsukDEiLbpJjaPtJEqkXqnY/5CNhESDPnEeuiptFRUEWumpBEnATrmrKSKnYiTYiDgE4U01QyljlUFEDuH8EppDVRQ9No4iUTFatqqEQ+KJsCSzoB6qYiCkLiJGWYyx2E3pO2T/FViKmagSJLchfJvVgoQlMIyrPYJ9jQcuiZkX39yXr2vPjPsd9jN/6PJQqrq3bfR8AMx1br623d8vlD9lxbwm5wvY9WWKEwMTHhKnoj+oZh6NQEc3NzXHnllaysrLC8vMxzzz3Hs88+S7fbZc2aNaxZs8ZB7L5qyP7ur732uZca9vZykwJ7vZ5Ljr4Tt1dcErBp0yamp6e/7hfiV8bf7A3y3HPPsby8zNzcHLfccsslkQe7Ue3h9aE8Hxq33pXBcH5Pz5IEQxCefvpp8jznta99rdPsrrb1Xe3oB+NmPT78548Ptb/7vbhTp05x8uRJrrnmGiYmJhw6YQmEQfeAqxJWX1uTpPjciH6/7wyD7NgvXLjAhQsXxGsgb1OvDMcqxoCQDqnWiKV2h23wa+Tg3EphZzNzCTV89egDfdp0NFEQEmFFhrj8STA2n3ur9QtNGBqV/BmNL9QgFhDSZYJA+7uibDdrYUhJNZgHjhEeKslRql1JXBqHY1SI56CpBWqFkOWMM3rUpArUm1OfDgQilSE/NEQK2ecU+r9KP8968uLhL0mMvFbg8D5owpRociCkxFollg1hENFKUpfk5rofc2UcKReQ9wQBYZgSVqWiE4Z7CEeg8pCZQFM34YLkiBd/qsiGzYqAshEYPAlkMY6QmQCxSdPKkjhOCONYhg4BZvMr3gbStReeRAxBQBIoRF03FE1JXhf4plIxsm8hkQY0ReFQHOGUiBEVaOsoighL+TRBm4Z6v8aabGnvvsyI+xVVklM047bjVkz4qJ09hyaxXe266bc9jVzsr1G+O6Ghjj4HwRIKP0kwMp/ZE9u65jv42Xpma40VO6tHCU9PTzvDoiRJHEJw5MgRVlZWWLNmDevWrWNmZoZut+umn/pwvx2XISb2M7/F+nIlAisrK0xMTLzs5MNX6vaKSwJ86OiFNr9H9o1qOn3ioZlsXCoB8PtwvpmQ3+u3YGoPrN8+WM0lOH36NHv37mXTpk3cdNNNDhq0atuydt8MxPpsfhvAZ84a9GcP0WAwGIPbLly4QFVV7Nq1ywX61ZPE/D6iIQeGSqy+7v7mmwZlWcaxY8fIsox169Yx152hObqoILbA0FKPV64vn2Icf6mWMx2xOxoj3CCyL2kCWLUeEOkoYFmAB+r5nmiVKQHZEAP5DBtGbKE/1aREdOyjHnZLg2CjSoSKipQUcxcMXbiWerBQl7/KhZlKK24U65AEwrwQEiJKj7gotDto0dIWQ0nFAKg1fMtxC4e9oc+AkoouXcSHINFqGA1gdgySlKSaSJT6PSSIC2DEJFlTUA9rPXcjCqrBjenfqVxArIG6EpQj13OpMBa+Sikp9KpavWwYRoPJEpsgIA0jsqrAyHp1g6AAKG+kLClVIVGWJYQRRVkQBppANJJgWUIU2PcapFRBQFlVxIEkd50goGgap64gCKgDQYNyasImcLwMkadCyQoxCQ0RQS2EwA5tCk1oahpjAhAQ0FY+RH3hPOWFJcq0Rbxp2j2rPp/G5oTYM2XWulapWzIAuGTBlxNbguDLDY1TYMm4tet8pNBX/hh52H5u7UBfZWTHZ0iRfbYlMt1u151fHMdOZrh161aWl5fp9Xr0ej1OnDgBwNzcHLOzs8zNzTmUwo7fkA0/+fCThZdj6/V6FznwfSdtr7gk4MV+2VbpGunlpW5GPIzjmD179rBv374xmN7ffFKKQfT2EPlMeGDs3xaALfPNsoznnnuOAwcOcPXVV7tevyUZ9mAZmmAIgF8h2INiQdrIQ1mWsbi46CB54wkURcGhQ4domoYbbrjBJSa+SZFttqgYDJllmfMhsAdytdWoD/NVVcWxY8eo65rLLruMqakp0meXsJlrLVKMGhgqDD/qbcfYaNohA4YMMUZ3h5ZW8iMPfpHMjexbM/oMnWFNqlV+paQ+CYMglWOHNkNylf9FWrmLq5wEspyeMtkzckIaunQUyq8QE2OZFJDqDL8I3FRBC8Cl/i3R/5ldMECulegEk4yMjCRRaKjIyBnQo6XyOyELVth0BJk/IMOQOkgvv0XCgAyI6JBQAC1CGnftAswVwLCPGAt8teIUIQ0FLdpe/W/6+5AwCMgbo8vZ9IURjlJRM0mHjNSDyc3bUVKyIQNSWgSNQvWKAQlOUbOSmYoh1MRQg3RdENaK7gQNdS3iQyGL1tjo4ZKKqK6JkpimKmga4RTUQUDZSCsmDhPaqcw1CKrMJU4ZBTERbbpIG2pIqe2TsC4x0mZNRIuO3i05NomxIaalCU9BQZhXZGca8pnORTNE+v2+UxVZe81mBtjaslprn+e524fPqvfdBa3f76+NPurgtxbtvQbb+4x+4/f43gZW3BRF4aTaBuNb8tDv9x0/YWJigjVr1jheUa/X48KFC5w7d47Dhw/T6XRYu3Yt69atG2uFrFYHvJybKQO+iwR8m20+A/albidOnODJJ5/kiiuu4Kqrrhrr419q800u/Grdemd+X88eCsvI7WHL85ynnnqK5eVlXvWqV7F+/foxiZ/B75YMwKgnbw+lwWKrEQbfS9ySlYmJCU6fPs2hQ4dYv369MxTy9+ufn1UN/jna3/1M3NAIX51hPclDhw6NPdDhUHTmpWOl17RpYZaukYKp3aBFBQybHLMKAtHhC8ic0KLNgAGVVsMFKDFLVRqMuremDq8x1bxUtIH2xjOt7lO1yxnQ0/68ufzbxL0CGwokyELj2glu/C5tImJsylxEwAqF7kPaF7H2/0XVIMOD5NzErEZ6z6mGlpHZsJATDWSXSnuoqIeZ1ppsMFGORaIkxFFABEkAatUgWLUfuAAvSZMkIAQBRSOoVtw0lHXEQOcLtMOYJhL6ZV7kNGQObRHegvgqtJM2UVXrwKZCr0Cj514xIEfIkB3q2vAB80uUa9tnSJeUdjjBdBhTNQHL1RIRlcL9ETDUfQX6vRt6JC4AcSnfAGFIK44oS0ksgiAkDsUJMAhQdKnCNB9iviTtFKNZ2ibIj1zzVKWWYmwlI6FjxTtCRqqValAQTrVcwPYd+Hw1j+/P4XsM2M+M+2OwvI8E+twlv5DwpbqWtPuvs/XAvD18G2ArtIyTYGREwKGDPpnRPs+k0fZzW6eTJGF6epo1a9a4QL+4uMjCwgIHDhyg3+9z4MABlpeXmZ2dZXp6+mWTBfpbr9f7pmbPfLtv39ZJwEuVCVZVxTPPPMP8/PxFvgMvZl8WdP1M2c+2fajPhnmYFv/ZZ59lYmKCW265xVXSxgy2h9PgPz8hsercEgy/HeG3KXyVQlmWnDlzhrNnz3LllVe6doBl/nbtDDL0Ww92LWyk6Oo2h28Ran8uLCw4z+qZmRmX2CSDEVGtxDz/Q+3e1qpxb8jVelWCidTWBTb13ch7Q9eZL3TBHg3QES67VeX6bWHyP+MgxARkDOkzICTSznrmmP8mJ2zRIaYm1Yq7IWfAEHMrNKg6IGTAgAFDMkraxMpvkKBszP6Shi6pSyJCTB1QE6sfocHpcjQ1E0xg3oiB4hPGcyi1Xhe7WmGzSyph/WpJfQpVR1itDeh+xGMhVMREGStUQUArjqlKyMqcMIigkdo2IiCrK6IA0jgijmKaakS/lBZKRBiI+14YGAdCCJ9iJiRtAbEYDmhFkt5klbn9WSUmvohDoNV0oQmo68olKyBBSjQMlQbxSFEbSX4yhkSNmTGHo3HDpagMBuVQz9oQqEATky45FT11TkxJSIMOIQ15Y4TTWsmSNghKkotUER8ZT4ymogEhNadPn3b9cCPrGQpgVbORfm0t8WXFloRbK8AIwZaUW9C1/RtC588q8ZMOYCwJsM+3NcFUBfY6n2xoXAK/mPAly/bZtj9ftWCEZVtn1q1b5wjgf/M3f8O6devo9/s899xz1HXN2rVr3Zhkm3T4rd4u5RHwnbS94pKAl/IlvxTXwNW2w6ulIC+0L7ux7Ya2qt2H0/2WgD8NbGFhgf3797Nt2zYuu+wy9xD4Qzh8kyELsr46wBIDg9SMdOjLFy24J0nC008/TafTYdeuXVRV5UYa23sAlzzY59r5+QmNnbefYPgEwjiO2bdvH+fPn2f79u1MT08DODgya2ogd/1+q7RLhZdt8MyQDNNgx9osCDUoV0DBkFxB8MYhC+IsKJWZfG8dWhpUaw0FwhCQqlkC9DJ9hOAXUzqSHVrxm0AxJyTRrnvtAmek/fpYwW+bVC89cBhqAhDra2wwUk7uHPUiRuJBkyg2+nPp5gvoPyQjIaZFjHHfLTGKiBjo0TU0KraslbHeULBC3gyIAhgOIOhUmjwUZAyogDZth8SIsU5O0yQ0dahWyzVFU0kyE8TEoQTSuhJ6Z2IwMDHDWo6kpNT7Q3r4A3JNiNqaNok3hPE6mrohaaVkTU5Ty3c+kaQMixzoylVqGoqqUs5FqKhMBXWtxy1AvjgBJHov1O5e68YpdSVtjUKVBFklqaAgOTJYqNbWR8uxK8yVQar/spHkaqhTJ20MtFhXlx6XonGpW0qIjKpOmZoSZY6/tdtt1q5dy+TkpCsaTFfvb5akm6GQcX6MYGjIo/mPmLTQ5/lY0m/rq613ljT4LT9bb3z0wdYpQxwN/bMkxI7T1j8fXfSJiH6f30cZ7Tg3bNjA9PQ0TdOwsrLCwsICZ86cYf/+/SRJ4hKC2dnZ53XSe6nbdzkB38bbi3UNnJ+f54knnmDLli1cc801l4SYvl4SEIahs86EEfxlGa7PDzBY/siRI5w+fZrrr7+ebrfrXmuQfhAETopogdxaA/6EMbPktCl/foVgkp08zzl//jyHDx9my5YtzM3NuYfPbnC/fWAPtSUCq3uIllzYa+y8jbGc5znPPvssYRhy1VVXOahwzA608MYPUzkWtznJJUFC2dQkwJAcUfibXK5RS1YJwDGxs5bpM9RgWdFlgggj6AkUnFPTURKfANShBvyKSboMNThJT9twCdQGWKB2CeuiFxcLGQiRqXuxpgECAcu8g4SAjJo+qGWQWQDb+ZvZjCVCkp5kSh6sNB2olIY3pAJa2ARFSYnE0DfXEFVRqb+AJS8i+RucymFNSt0KqRtJDgQYD5XcKFfDZgSYgW9NQ1zVDLRSj0mJgoRuK6WqVMpZQ12VVI34/vdUXAkwVC+AWo2YxVa3oKvXFGymooxLDpuYJoemljZAN+4ShyHtNKXORWVg8wDlCKVfX+s30LiE0oSHYOLPhJRW0qaTJAyriirPqcuSsqm8bx1yTUrEWklYEJW2GKxBkDb2beau3STJU6miV2ktociAcVMCPabosrWsiRvWrl3rkMFer8dwOOTkyZMAbqqoGfD4lbkhhrYOGUfItw42xZGR+YAxro7vT2AcJZ/r5BcEtq7Zs2/PvCEYvhIBBAWwAsWQRZ+z5EsiDUH0f+8nAbY2B0HA1NQUU1NTbNu2zaGZpjp48sknmZqacgmB77PwUreVlZXvIgGvtM1umK+3fT0Iv65rnnnmGWfDu3qE4+p9PR8xEEbDLPwenWW7Bpn7blhnz56l2+1yxx13uPfb6/0emX32pRIQWwisN2iEHDtn/8E7c+YM586dY/v27a7nZltVVWNTBu09NqrY+At+QmKLhCULtrAY8efIkSNs2rTJjRq2xWEwGEjykTfEZ3Ol341saY2AJVWeje+tFAFoHLRvDHXzfE+VCCc1dqSwuvkIRNRKr7PmQEGOVMcpFTJoSLzsZEKdQOuCN/SoscmCFTVDre1kYl5MWwmBEOhQnLb+WwLjkIaWntMI0FYnN8x3UAiCEswN63B0O6yvPqDQvnKiAamgr/ufZELPucJkj9JiMTsf6f6nG9dSUTD/t39DeWGBLT94N4ESI8Xwxq4fmnDJuQlZ05IO0Ww0QF4U5JU1bMSBoKwbSp1kIKK/iEn9jjJFJCZp02eo/hCokZPUyTklCSVBHSEzGUqyMqKmpS0g9KdmdyTXTBgjJn8s1ZVR1AFtl9rpSJ+mYVAUDKuaqpEzTfS7TGhjY5nNEEo8DmywlKVINn+hUQZHQ4s2JRmV3nfyTYh5lVkMSVtITKeCpSHBXNtNCZyYmKDb7To1wHA4ZGVlhaIonFnZzMwMaZoyMTHhEDyrnn1mvyUDFoj9tcaH6G2tWd1G9AO//3uf9Gv7sDaEzxnwnQeN82DogG9Q5PMUrLXpW63753SpLYoi5ubmmJubA4SbtLCwwMLCAk8++SRlWbJmzRqXFLwUyd932wHfxtsLJQHm/d80DXv27Pm6cM/z7ctuTv/GtqDsQ1uWQJw+fZqTJ08yNTXFjTfeCDDWizfYzs+A7eG27NgYvvZQ+z08y9INsqvrmkOHDtHr9Zz+37fy9bXC9lDbACJ/cIkZgVirwSYA+kgHyGjjpmm44oormJqaGoMQrboJgoB0YaChahSqRUbVkCkxrSFGQoq56TVU9BENe6wVmrgDou0CY39nLjw0+nMJFAU27EXqvZwMHMYgTHmpGwNqIg1R0AJKMmrQcT+o6l8IesIFMJqd6BlqbTt0kMozJGeI6MwDQg04JWJyLPMJClX6y1RE8U7MWaJcgXCy42YBjJIJSBklFzmFHnGt/w5o0UYCa6ZnJj4Aa+58LcO/e4qIrmtrxNrhD4kVwi9U22DQd0Cblp6lECKLWjATs3GuKUnClKKWwClDmRpiapK4S1VW2DCdREmQUkmjihDptYuTYeVV5TlBlZBGo+kRFvSFg2HWUSNFQps2wtg3AyRJSdIooRVJ0hFXteIsDVUQ0DRi9gwBaSi8krCW+RMxMakKRBsCpR7aeGnhiYg5VI7YFIsWQPgggkV1aWmSI+qWqrfMMC6pIllPLLD7ah/rfYehzBBZXFxkcXGRM2fOuLXIkgh7pn0XQn8zRBLGScAWFG19sNesHnrmT0U10y9/NoEP8/s8JVurLBkwJNIvcmzNWZ1sWOHxYqv5NE3ZtGkTmzZtomlkFPD58+dZWFjg4MGDxHHM7Oysu64vJDvv9/vf0e2AV9wUwZeyPZ9r4KlTp/j85z/PzMwMd95554v6glcnAX42bYQdC4Z+T99HAQ4dOsS+ffu47LLL6Ha7LqDaA+snED4ZZ3l52bUajIlrFfVqyaElDWEYMhgMeOKJJ0iShFe96lVj8wvss2Gc2OPbdlpPz5IT36TEqpBWqzXGJG6ahp07d9Ltdh1akGWZIx42TUO+2KM8v6Lh2HrmpbLqwcxySkrlAxgeUGiFl2v4FdmbDfeRyrhW2BkDW6mBIX0KDTgxKbGa7gbIZMA2HYSQJzDzkJyMgbK/JaT2NBERnrs9HBWhIwhWxMoEF/peiY2staSlhTkQiI+dBTNTyssEwwHmwQ/Q/8sHaSZGsxQiUmyKnwQ7XKCTeXUm3QNLD4bkDMh0HmEf019M3nojORk9TXckqZIOujgyDFSVgIa7QkOpwPllU4iZDpDECd00JQ1bxKE4BQo8LiG2pCEre9isgQKzgpLKOkJY94H7n7yzFXdp03Fnk5cjpCPU/xlzQtozkgS1aGFkwkiDdqz4zrDKWc4ych04ZMlG0FTuXhySk9U5aSSaAEs6bByzKUEMHYn0PiGU46kRjkCm6ZcIBXNM8hprU6WmpFhccc98p9NxvCHfDMhagWmasn79erZu3eoqX4CDBw/y9NNPs7CwMDYlcDgcur+vlgyuVvP4qIJJFlebm9maYSiAvd64AL5lsR23rSEwSjYMfbB9GwppSIKfENjfvxG5dxAETE5Ocvnll3PjjTfyvd/7vVx//fW0Wi2OHz/O3/zN3/CFL3yBffv2ce7cuYuSpudDAj7xiU+wfft22u02d9xxB1/84hdf8Dg+85nPcO2119Jut7nhhhv4y7/8y7HfN03DBz/4QccNu+uuu9i3b5/7/ec+97mxwtD/70tf+pJ73Wc/+1nuvPNOpqamWL9+Pf/sn/0zDh8+/JKvm22vSCTgG20H1HXNs88+y/Hjx7n++uvHZja/2H3ZTQm46tygK8tujbxnn11VFU899RRZlnHbbbexsLDA4uKiy9pt/76iwbcGNRtgy85tYbDjseBqpJ9Wq8WZM2c4dOgQW7duZe3ata7C93kKdh3tGGx/do19XwK72fyen51nnuccO3YMwGXVljBY8mCDWcqyJJy/QEngFnDfnichcsYu4qdXqTO+dHoFim4hk/6kAu/Tp6Cio4EiBLqkrsOeaZfcCGep63PXGuBLZdSHXoohC7pRi6yzXeuf5kQXOUKfVKIZhZr+iHa/Uk6AoBwhiaoAWtoyyKkJNewYYUw+wz6lYc3/759TIL14EMMZMS8asRYMTxH2ecESjSrZA4WrpTI1ueKowjb6paEbJZFyDaTVIi2EUivjioKMSJMZYUuYL2ITIHa8QUBlSSWRq4BrZXyEjJwAQ63hK4cFCRUzUW5EFCpMrQlWCtSxGAIB2hCo9RpUmjyKU0NKqA2TQlstNrnRTH1r+lWhP7XZFJW7GwsGRMByUWmrSBCLkpJQ07qaBPGXtDFJFWmd6hWu1NxK6JgRKaEiBpmeZ6o4ThzGNIrwNU1Du912z8vU1JRba8ym1wL5xMQEg8EAEJMdC/bHjx93a1en03ETAP2q3n+O/X59FEWu9WfPsP3dihbjIhkj328dGHoIIxVDv98fMxeyNRMkyBr/yVoXfhHly56/FZLAMAxZu3Yta9euZefOnRRF4VCCvXv3kmUZa9as4dFHH+Xqq69meXmZK6+8cmwff/RHf8Q999zDvffeyx133MHHP/5x7r77bvbu3TumKrPt85//PG9/+9v5yEc+wpve9Cbuv/9+3vKWt/CVr3zFDRD66Ec/ym/8xm/wqU99yk0QvPvuu3nqqadot9vs2bPHcURs+8AHPsDDDz/MbbfdBsChQ4d485vfzD333MMf/MEfsLi4yM///M/zT//pP+UrX/nKN3S9XpFJwIvd/EA8GAz46le/Sl3X7N69+yXrPm1fdlP7D40FaL9Hb4FveXmZp556ig0bNrBz507qumZlZQXA2ehaRW6oAoyrIAw2s0Du8w98EqG97vTp08zPz3P99de7qsIY++122zH9/cwfGJs5YO/xyTqGUFhFEUURS0tLHD9+nHa77XqMlqwMBgOHStg1qZeHWjlJYIlJtR4ym5pGQX6pXiV0jODxFhP0WVZ6V+4WW2FjDxVJkPpbflZo1QyZwvmWeAwYaO9fwF/DCBLt+oKNi7HpeGgCYQFdHo9c7YMh1jl44hrYIkYoW6KVl8+SXr7AwRE1oVbXoWIaLQ0yETJ6N8Jkeh26VM5yqHLHVmsQNPqjiCPF9AaXYpnywI4dhMoomMqk2hpbklBqa6BkpPwQ6mFISe4hMEIsjBPpvRd1TVaZi6IkOIELuZJIBHrM0KjDoY0yMgtmaUN06AraEpigM4IgoKltjoLw81sqGTUC3iQxMujJNAfSgDHOQTuKCOOYvKqoSkEoLJmrMGfBhjZtbUSJwXOIzFdIiAjDmLLOASF91kSKw2TuOAI924qGPjVtCjqk5PopklwN5fqtmaYMRlP2DF0zxrzPlvf1/4PBwKEDTdMwPT1NmqasW7eOXq/HysoK58+fZzAYcObMGVqtFhs2bHDmN37Pf3VBYIm78YOMnGhtCisobJiROZsaYrFapu0jHX5F789VsYTCXwN9NAS+9bMCkiRhw4YNbNiwwa1bCwsL/M//+T953/veR57n7N+/n82bN/OP/tE/4oorruDXfu3XeNe73uXGBt977738xV/8Bb/7u7/LL/3SL130Gb/+67/OD/7gD/ILv/ALAPzqr/4qDz30EL/1W7/FvffeS9M0fPzjH+f9738/b37zmwH4/d//fTZu3MgDDzzA2972NtfisK0oCv70T/+Ud7/73e56/N3f/R1VVfHhD3/YJUzvfe97efOb3+yIoSAzaXbs2HHRcb7+9a/nc5/73NjPvu2TgLIsOX36NF/72tfYuHEj11133TcEKdm+/CzXgqX1vnw//jAMOXbsGM899xxXXXUV69evHyPYGPxlD81qOZ8vq7HPtH6cJRE2Ctiy8l6vx5EjR4iiyMFdxhuwAN/pdNxozNXMfkM1fF6BHZslPLZINE3D6dOnOXv2LBs3bmRqaooDBw64xMKSFUscHFO4n5ECxqq3iXuGBAhvPsQm2JXkrgotsPG9EjhrDUIdlZgVagtcqFmtvFIkXibxqxg4EqFV+xnQIVKL3ZKEFgEyr0CAankQWkQktKnIFIpOHPSca1vAb01kFEoxFPB3QEZFnxwUcE8xsVnm6IOByvVKEtqkJF41b1S0AWarLDMMFJnCJIg2tCegpNHjtp51Wxsrwi+RCYEpknwFDhmQc6gcuTB0PfsCMyeS8bkRkd4neZVTgDIfzGZYpJmixhDGhoH9IrYUJMFEkxEdl7wMGBLXEdQjt/6gDtVZoSRUeN+IkHINKtcGCvVOkqskCWYSpoTGhyltzqHZ+4icNMIf7hxpUmAcDknysrqnLgsy6trSRrOXNp+JmIBJzJPCRiqNHA9kRgQEaUyoKF+WZU7+65PhfNMgg/gtKbAiwYoMIxi2223Wr1/PxMQEZ8+eZTAYcPr0aYfcmR26Vem2b98B0NZLH9n01U8+Gdqv2i0x8HlBljTY+Vjwt8/y11tfnWQFzmoO07d6C4KAbrdLt9vl/vvvJ89z3vCGN3DFFVdw33338TM/8zPs2LGDAwcO8Mu//MvufWEYctddd/HII49ccr+PPPII99xzz9jP7r77bh544AFAKvj5+Xnuuusu9/uZmRnuuOMOHnnkEd72trddtM8/+7M/49y5cy4RAbj11lsJw5D77ruPH/uxH2NlZYX//J//M3fdddeYZPLyyy8fQxXss7/3e7/3os95RSYBL/bLj6KIs2fPcuzYMa6//no2b978DX/mpYiBdiPaTWoPQlmW7Nu3jyzLuPnmm+l2uy57NajLZ/CbntevvE0GNBgMnPzQqnCflWvynAsXLrBv3z42b97MFVdcMYZYAA6WN7mf/d5fZGz/xk/wUQc/ccrznAMHDlDXNdu3b3c2xGEYsry8zPT0tKtO/BZCGIYkM12aQZ9AwVE04Dda82eUukAm2Che6U8Xaqwjyu9IQ7TNCJB3JKoJEAohWrmZW6A//CVSsV+sVaeZu4RaW7dp06NHhbQETIYm9sMNCSUosmCmQeKgb1JCkR2aP1ytKUjGiGNQYqOE5CjF7692gW3AELOTkURDzl/Sh0Sr1MpV1iJ9i11NbRP8ICCjVDhdQpzwKxo6tDEDG0m5KnxiXahpDHrMbVLMWgk9gryu1GVR+AIjZYOo7BNi2klCv7CWh5noiMDP0IxEExcx9hloC0VaLqVW4IQBSR0pJgE2T6LShGTAEBsSFCiiJN+ptEaquqSuArI8w+YA2HdYaSrq71vspWXeIAGkQcRyPaTPUO+8CFNf5Hq/2hTLQnGURF9hhtXo/9tExIaasoFIg5wVB+bS588EscTcnntbJ6wIGQwGjqDsB+Z+v+8Y9Bs3bqQsSxYWFlheXubgwYMATE1NMTMzM8YDsHXBqv3VyAFwkTLJ9y3xVQSGaphRkK0pfnC3z7xU39s/J1tvX+7N/Fje9ra38aM/+qMsLS3xJ3/yJ/zET/zERWqyjRs38swzz1xyP/Pz85d8/fz8vPu9/ez5XrN6++QnP8ndd9/N1q1b3c927NjB//gf/4Mf+ZEf4V/9q39FVVXs3r37Iv5BFEUOVRgOh7zlLW9h9+7d/Mqv/MpFn/OKTAJezGYa27Is2b179zct8bgUMdA2u4HruubcuXM89dRTzM7OcsMNN4x5FViQ9UcAg7QF7GEBXHbtW4bag2iB3LJ2kxueOnWKa665hpmZGWB089oiYg+ab1fsa4BhfAqhTf7zkwBj2R46dIgoirj66qvdg22ERUML2u22e0jLsiTLMun5TbRptWcohlItRl7X2UIDSG9anO4kZFj1BxJoDeKOFb4WuFdkhCbiqshJgbby3UdjXUMSOgQahFKt6IWLXpIryiAL/Og7txpZ2gP2brTtkGHa9ECV97VC0hZMZpigxwAR2CWKEQgWMATMMy/QFEl8EIZMKJnNOASpC5vy6bEeVajVaoOMwu2oq6CoLUwnH2q1rBMCGZnYSjPD6lhxOwy1340GLCEFthE+gBH+hAoZKHNe2jwmLFSz31poj6a2l9Qm1F79yA8wIKQVxiRNQtFk7tg6tIGGoF4mDCZpGsUhwoCwlj1ZV74hIqdkpC0Rh8ZCMZO6Mn9F9OgEtYhBiYo1kUoER3yDmDSKKCtBr1JspoQgFuI5IYhJzoAlxa3Q7zXWVEvUHImyI3LEnjkkPLXIYLbjnvs4jmm322PPoRUHxhvwq3/jCvmVtV+Z+xwgq/Ivu+wy1q5d64jAS0tLnD171vkOzMzMMDMzIzM+vIBuCYG1C1a3KCwp8EmHxr73HVXNjtdHVC3RseO3c/AlhTByUf37tg2enp7m7rvvftk/8+ttx48f57Of/Syf/vSnx34+Pz/Pu971Lt75znfy9re/neXlZT74wQ/yz//5P+ehhx66ZAH9Ez/xEywvL/PQQw9d8np+WyYBZ8+e5bHHHqPb7TI9Pf0t0Xj6Jjk+QdAnwxw5coT9+/ezc+dOrrjiCgBX+fuZrJFt7GGHkVTHz7Z9GN0eNN+BcGlpiaIoWFhY4MYbb2RmZmZsspghDgbzt1qti8yEbFIZjJQBPkLgtyUWFxc5ceKETP+bm3MJhPEF/M0Qh9UEyU7aJqpjBYVrBpS0Med70eYbP9s6uWYhbOS8iEKDqDKVCSmo6JMREdNR/bnB/Q1m5hOo+Yt47pmjvFVy1v1tdJ9C/BLovNTPbrvKT14rWEONOR3KdLwUm4qXaDJhvfCCkVJB9AIpQ4YasASCz9xRl3RpK7SckyDjg2tFBWpCd6yBVqvilCifaj8V9YHZBmN1rbY0LOGyGXsNZq6EwvSBBrgWbQJEapjSomKk5qgx+2fR/JcuwZH2RG2EU8Vuau3+V4wmG0ry05AGLaq6VovnkCgIxHwIqJignSRUufTei1qSSFNkyOTHtnICRkODckJNGMWBEr1WYsYk7ZhY0Y1Iz8f8BRIiWkHKoBQsxtIvETiWmPOjIBkhK6DojvBI5Le1cg8qAmWfiHGVJGwNDXHWJmqP2opG9rWg6rsA+oHSXmt/N+MwKxpMljsxMeH6+pZAWLvOTMXMtMwUSGfPnuXo0aOEYcj69euZnJx065jfCjXE0lcGGN8pSRInIzTCIYycB/2A79sa+6oAW4NtLfGljy9nImCkxqmpKfezdevWEUURp06dGnvtqVOnxnr2/rZp06YXfL39eerUqTHC+qlTp7jpppsu2t99993H3NwcP/RDPzT280984hPMzMzw0Y9+1P3sv/yX/8Lll1/OF77wBe68886x13/4wx/ms5/9LF/84hfHztHfXpESwedrBzRNw759+3j00Ue59tprufzyy1+UY+CL2fwkwIfmTCL3zDPPcOTIEW6++WYHx1uSADhWrGXSBp35ckIj1lngttf4ML312ZaWlnjssceoqoqrrrqK6enpMUjNevM2ptOuQ5IkTvPvqwTsHA26j6KIbrfrFo7Tp09z4sQJrrrqKi6//HIn/cuyjKWlJcdQtsTAFgDfzazVapGeyyjzgc4BLLTKt6AmVWio/eOMgh49Sq2pY62w27RoM0FCqot/pdwB6+yKyY4MEYJlCobY6BjIgSEZfTIG9FmhxwolJbkGzxBz0au1128d3RKZH5BTaegUpMKSBnGSyxjSoyRTBEOq6R4DxKlAVA4ZJQOGyNwE6CgcLtWxfWrkWBGWnphroJH+MiXFCaTeUX+BWHvVohhIVBIp7AQZjGSDhMRnUCDsnk46yBhgUrg2XTp0NIjKUWR6BQTDEdJfjxX6DBjQZ8iQPn0G9OR7qSvdn43zsUZPoz19MNXCStXDuvUEIU0QaOgVQ6KgGkkKpZYvQdPKlp6XzJPIGKh7ZKhXVJQBYvEUK+sipY04UA40iYoVKZDrk1MwaIas0Kck09aJjQJq0aaFWBLXDBko1XH0n3BKBOfJsaRV0gnjkaS06ESxI+76SJ31x20dscRgdYvOl/cBYwHTdw604sIgeV/mZ0jl1NQUGzduZOfOnezYsYMNGzaQ5zkHDx5k7969FEXBsWPHHDJpRYbfvrC1wEciLCmx3/suqD6aYAmCnxCslkD7nKWXc1s9QChNU2699VYefvhh97O6rnn44YfZvXv3Jfexe/fusdcDPPTQQ+71O3bsYNOmTWOvWVpa4gtf+MJF+2yahvvuu493vOMdFxVf/X7/oqTI7hPfEwLgT/7kT/jQhz7Epz/9aXbu3Pm85/9tgwQMh0Mef/xxsixzGsmTJ09+y5MAY81aELWRw2macuedd7oM217ry+t89YDduD6M7icYRgrySTT2MC0sLHDs2DG2bdvG0aNHXcXgGPj1yPHL4Dl7kAC3iAwGg7EeHIwmEprudzgccuDAAYbDIVdddRWTk5Nj8OPqG6tpxNPb2gG2MLVaLYIGsrPn6dEjI9eecewqJYNeZZCwhAQQ2mCsdX1C7MJ+QEBH5WwgFa4Qz1Jd8isKoIt5xMUEyCwAqdKsHWE3uvS6J0m1M24T6BrtrecY/95QhoKGFCEgVqoXLxCFQkJESa26/0AJi7WO4UWrVTEiaiubvFIo3j5jRCGTBkDfKRoChnqFWlrZZ1Qkbs5CghkeNdgiadMTK0xSWGpVK74H8p5MmfDiwxBTYoz9DOuhx0omFCC90fBsnoE2mglNMkKMjREHqfrs15oGSAgejXVulGTZqNVuQ1FXDimAgF4lmICxDuyYjHIXauui9n4T05DGMVmZYXMjJeCniBagT66VuiU/JpGUJE54JjaJ0Wycrc3U8hCsWFPGkpGqJFMVwFqmEVOogj42REmaIUU9ItDZ82t8oDAMHbfIJyL7UrzhcEiapi4Q+L79MBruZchC04gxkS9ThpE5kEH6QRC4cb/bt29nZWWFY8eOcf78eU6cOEEURaxZs4Y1a9awdu1aF5xtrLFV+r7hz2ovAUsIfPmiT8C+lIPh6mTp5dou5RNwzz338M53vpPbbruN22+/nY9//OP0ej1H0nvHO97Bli1b+MhHPgLAe97zHl7/+tfzsY99jDe+8Y384R/+IV/+8pf57d/+bXeOP/dzP8eHP/xhrrrqKicR3Lx5M295y1vGPvuv/uqvOHToED/1Uz910bG+8Y1v5D/+x//Ihz70IdcOeN/73se2bdu4+eab3eueeOIJ3vGOd/Bv/s2/4frrr3e8gzRNmZ2dHdvnt0UScO7cOR577DHWrVvHLbfc4oLw17MNfimb3Xg+PH/ixAmeeuoprrjiCq688soxwyC7Kf2H0H5mPTgz3LBEwB4+e53tx1QBRVFw5MgRzp8/z0033cTk5CQnTpxwD5hv0+n/t7riX15edsiEHZdN/rIFIggClpaWeOaZZ5iammLLli0O4rP3+NfZZD+WGJhe2WcIV/2cnKFqpKFDgnXVpdKPHbTaokOjKIAYClV06CKT+cQ5v02s0+4CWmrJaiY8CbG+CgbAlAbAWPv8IgkUJ79Ek5CQgCG5/iyhJtOWhAVE6dPLjANRFaD7kdkGEsCl3paaNKbRSn001jjTaQMluOOIFCgOFCI210I52oaUiAGZdpAjzFgpAbpMkJEzoGKZIW0vMJqqYIK250VYafe/UbAcdT1sNLSaKVHijgVSBHi3br8IEUemOaavr7XGNgVGQ65qioqKvBGIvtTAVzn8JlAkRa6FpA5QNgKXm8FSHCTUTe0SBlEmiCMhKPnP3VXiHtnSpG5YDqhoyOgTIF7+IV2G9JRyCRNEju0vLSkzoo5cu0cIo7WiGpIGtEnddxpq8tBSfMjSvTap8AaCkLoxbsDIHyLUAsKeY3/wlyEClgQY2maB0pQ/xiEyIrG1/OwZNETA2gUW5FcTAf1g7HsL+DbhNmel3+9z/vx5jh8/ztNPP83k5CQzMzO0222HXvpmY7Z++NJkn8RoP4MR+dr8C1bLCF/uzdoiq5OAt771rZw5c4YPfvCDzM/Pc9NNN/Hggw86Yp+1UGzbs2cP999/P+9///t53/vex1VXXcUDDzzgPAIAfvEXf5Fer8dP//RPc+HCBV73utfx4IMPjrVQQAiBe/bs4dprr73oeP/hP/yH3H///Xz0ox/lox/9KN1ul927d/Pggw+ODZ768pe/TL/f58Mf/jAf/vCH3c8vJREMmpcba/kGNoPOm6bhwIEDHDp0iGuvvZatW7eO3Rznzp3jiSee4PWvf/235HMffvhhXvva19Jut3n66ac5deoU119/PXNzc2NuV/bA+A+RPZx5nrO0tMTf/u3f8oY3vIEgCJzcx4JoFEUOajci0HA4ZO/evURRxDXXXMPk5CRFUfDkk0+yYcMG1q1b5zLwoijo9XpjcJotKFYJ+D3FKIr4yle+wlVXXcXU1BR5nnPy5EmOHTvG1q1bmZ2ddVwCS2BMYWBZfVVVHDt2jMFgwPr16111Ye2HIAhIwoj64HMMKLQi7tA4oFeqz4KSNhMY7a7W4FJhc+1Dhqq+j9WJTxjmgS7Rpf4PXZAF/heCoASTjFor34BEkwioWdQAP0WbNl2dJGdu/gJJ59rF7el+YwRp6BAoBG/98cT1tHO1jLEQIRMLK1JHYIvp0kHm0wtZzJIOIQPaGQW0lMkv2IcM2mkrw1/MhKQ3LzLHiJwcNMES+p0I69rEigKUrlUQEOjQ41x76RKkC0ommJC+Nca0b9TuF01Ahoitk0gtE3U1DDTZCBXnMCWHBelCmR5tOgiXQP4dOSJdoNr7mkkmmOi0GWY5vTrDJHe4O0gaNKm2U4YUxECqyUqgrYtlljVdi2gpkjRkAIhc0vghUuGrxBZzpJAWVqONkURJm5EG9GVNDVPQu9PGRVdMM0GHLmbhbE0sM7Uq5qZprZtyct9er+fgeRjxbCxZN37A/Pw8c3NzrF271j2bMLIHtrWy0+k43xBbd+z5XY1U2rrhtxQMol9ZWeHs2bPcfvvtY4nJcDhkaWmJLMs4f/68kzIbL6vdbjvDInMetc/3iYc+18rWqqNHj7K8vMxrXvOasQLLP9aXY1tcXOTyyy/n7NmzY+6M30nbKxYJyLKMxx9/nMFgwB133OHG1Prbi50i+GK3KIro9Xo8/vjjNE3D7bff7uxxL/VaX1NvQdjaCYBLHPy2gU8UtIfhwoUL7N+/n/Xr17Np0yb3oJgCwNj4vseAZcx2DWx8r98esM/2s+0syzh8+DBnzpzhqquucl4EfiVi77FeolUtPkpgigeQlkeSyCKZaL1vXvUBI3JYTkmbhEKZ9onysAutZ0OFxqeZ0vdGtEg0OIuJzhBTGZgMT0e9IoN82pjeXUSIISUmjgM0RAlRq8sENQV9+o5tIEB7zRSw4n1OhnX+jSgXqOJA5uYFGjBKTWYCCnyJW6LVc+yIgJUGCoPbjQQpVriRA/LRatKklRI4ZchSo2Q+CZE5Nggp0Aq2YaDyOmuliLAywubl2XczoI/IKIXCKEN/Opi1r1y1WJMHgb9L0DOtVVqYagWtRFHECEqaCsKtt6SjS6RnYMiBIkZlSV1XmDTS/jOXRaveoXTITKV4g6lP7PxkONSKEkClldHDxlDL+SR6xTK9Ig0yedJoiiY/tZkKZnQ0xEiAgsakek+USmpttBUj1NCIhpD6XI96dsIVBGmaujXCZ8rbM+Vr5Y1o7K8d5sBnbTn7z585Yjwke5+v6beE33g9llD4Q4Js3bDWg6ERU1NTruW4tLTEuXPnWFxcpNPpsH79emZmZli7dq07F59zZZ/vEERdX0zp4KsEXi6/ANt6vR7AdwcIvdK2xcVFvvCFL7B27VpuvvnmDF+twwABAABJREFUsZvC376V7QCQG/SJJ55g8+bN7Nq1ywVF+xy/VeAbAPlkFvPv918DuFaDVdX254kTJzhx4gTXXnsta9ascS58WZY5Jq9PwLMkwN6/+oEFxnpzlv2bZGffvn2EYcirX/3qMYjQVAMgJkX+CFNgTAlgUGOapo7w444Jm/bWqKSr0b52iFm8FLpE2xBfYWJb7W7QsLDOU1qgY1xAgrgJAlcYyTCF922z30YWwIW2GqR6NgNZ6wTXGiLRGhBspnzlAoUxwKUuNfJgqBz/ChRmFlBYeOkr+joxKRoR+AJ3npnzGoSUtgLucs1C9QgotPZNXCdcet0lJnCM9BpKGhARu6q6Vla6MST6DMgeP0x/+TTTe26nHUyT0iFjgBkpC66RaLVuARxQaN/8DCoKbfrIQKgOHdc6CTRVyDE5pgyLGpLR0Yq91vOMlbFhVz4npymMiR+yzFBr/tBp9FMlL5pBlAH2ZlKEUvliRZkKhsp5MAvphpHvYkWfIdAwpMTA3UjvO5kaUGCciJgJJohYUZxoSE1X0QQ55wzTcATInISIhCRMGNY9WtSURUGsMkFL4n0SnJHqfFa9bYbyBUHA8vIyVVU5oqGtX74fiSUSRVE4TpEFY/tMv6iwfdhakmXZWIJgdsNWmRsZ2JBKK6L6/T7Hjx9n//79rFmzhunpadauXesmI/rMf5ND+hLB1VLIl3Pr9XqOQ/Wdur0ik4BOp8NVV13Fli1bXjAL9P3+v5lssa5r9u/fT5ZlbNu2jV27dl3ESvUZ9n5PzR5KGGW5ljzkeT52g/mTCIui4OjRoywtLXHDDTcwNTU11mO37Hi1nbFl8CbZMYmiVRa+9a9l+pbNHzp0iDVr1rB9+/axBMLaCrYI2X6NaOT3GX27ULsuBl82TaMYAG6RF8meQLbSNy5pk1JQaU3bokb04jGhBuyInJoBOabVF8lVSUqCmdzIO00eKO2ASv8UM2GUxCeSNksMakUURPYnki5xOdRKi0aBdxTcHVWjgjiMJvuV+nN7vcw6kFaCjReGWsmFNg4ZUlLFD4wj0aVHDxlBK0qJAJthEGnNKoiIOBAONDzKvVcw1PmLMKBmBlg5k9FeL02RhA7Ja7ZT/s1pFh/+HJ27/h9Nj0TG2dKUq9FKWzAU43GEpODYA2bhawOMbT7AQL/HrtIgSwI3XS/F3A5sXkGg94BhF422C3IF6kPMrKdPRqX3k7H/xZA3p0NKjwFmwCRqhIZEUxpxYxTkqePsh9FEwDr/tcchCRUjkTQxJGJIiXRtK733dN1AkokJ5ZJIs8i+MfODCGjqSu+9YIxdXxSFg+9B5tr7Mjp7DeB0+H7v3Yh9wFglbUm/vy4YEdiIxU3TjA0ps4LBWhVlWbKysuJ+7rv7+RJHaz3Y2mQ9c+MYLS8vs7i4yNNPP00YhszNzTEzM8P09PQYI381wdonMr+ciYDvZfCdur0ik4BWqzXmkvR8m585Ph9a8PW2LMt47LHHyLKM6elpNx7XArrPlPf7Zj4732fHwmgCl/+QWOCvqoqVlRX27dvHxMQEN9xwg5MFWb/PHjyfIWvB2Y7Jevb2gFoP0WaU+2qEU6dOURQF09PTbN261cF7RgQyhr8tFn5LwB7+VqvlzE38c/aTgeFwSKnVm1nxprQx45sOLSK62j/P1Ha3cqQy0/AL/C+Aq7n228wBk/jJHHur+RsSWoQIKS/U6izUwCQuf5EGCCH5WRtB9lySYDB6445d5sKjZzDShvsKghRDHHQCHqOhRAKsm9RwiPgKpFqfDrV7n2KOgwkRQyoSGp1REBEzEioagJ+5z+rpMcQMtOHR6PGee/IQ3eu3giYQUqHOMP0PvpfBk/t1D6FiIWLH2ycj1aq2rfhHQkuZGIXC6gLuV0o5NNtjU3sI8jIkUbwmZYKAHLMlFt1+rGG3IWGCUL/ZSjEisfaNnU7fUqkBOS1aiArERkM16rw41NRDWkjWAon1mo6+s4hSr72M/W1rspER06LWVCfQpHR0XuIRWGtDKcaIoxUmixSiaALubjZfCTGrCgmpPYIvMDbF06TF1kf3ny9bX/w2oAVyS+CNE2TFSV3XDrWzwsD4S4AjLftKBJ+YaGRk+1wf9fSLAzs+e60lH8YRuOyyy9yclXPnznHmzBkOHDhAq9Vi3bp1zMzMkOc5nU5nbO2z9evlVAesrKx8R48RhldoEvBiN1/W940kAQsLCzz22GPMzs5yyy238Oijj7obHkYMVuuH+0N3fLjdd+/yf2cB3Sp7I/kcPnyYK6+8kvXr1wM4xrBf1a9GHPwWg5mKrM7ifXgvz3OyLOPo0aNuoqHJhXy+gAV86yP6kiVLhHzOADA2Ac0MRZaXlzl//jwpsIkW4uJmhj+RBpkBk3QIGFn41trdFgA7o02XnIGr4GMNzRaujNNdqJK9QlQIQqbLXRIhC3JEQkWokLT4wMf0FSxvMeIUmBdAqUG2Qip+I+lJIiGtgUT/tOoxRIiEVrlnwCSjxCB2+87xTYqEjDhUnX9EqcdkASXSlMdc8k1w1yYgUwviClSdIMG/6kOx/zDdq7drjW1jiE25UNG8aisZOS26tBX2NvWEYQvG5LBvsNDaWMid0uZJlAEyalVUar4T0nLNj8ghBaXTGRQK7st7hdhnxMiUQoOp+ULasaTEDMlINTkxB0hrQdT674iGDl2EfCmIioyslne0dN8ZDYV+WkpLuQQilZRNPse8GjJNbuQ7lcFCsZJdBZuKVGpoCVuOuS2KH0RBEoxMwnypsD9Fz55j30zMfmfPZKvVGuMhWbJua4cRdq1YMUMhQxl8FQDgiHu+5blfDMBo0JlfIPlmRL5L6urfV1XlPPu3b99OlmXO1vjQoUP0+33a7TZhGLrEwJc9v1ybyQO/iwR8m24+4e2lbE3TcPjwYfbv388111zD5Zdf7h4gu+lX9/p9RYCffPgPrS/F8fvzYRgyGAw4fPgw58+f58Ybb2TNmjVjD7Cdjz1ktijYA2csWjuGIBhN61o9p9tGku7bt49Op8NrXvMa9u3b5x5GH24sy9I9fPb53W7XwY7mCOb7I9hrbZE5ffo0WZaxdu1a1qxZAxd6BOd7iAFtSp+e9twzzOkuweb5mcksmFGOVIGlQuKxVmdg/O+QxEH7xiQIHYQsMkKbcpc7iL/UBEH+LSa1o7kBemdo1x2HP0hrYzRtEEaJgY0djsEF3BBJAFqMHOVsjkAC6jVofvcSbnMy104wL37pt4eYhbLNu0uVfBgQKN99lIy0iSm7FeVrtjsOgzANhtonjymJyIOC3JtjUGhFL2Erw9QYYplrAjeTTQ401EKiR2eEwYiIFiEdN8RIbIlklHCO2fCmJIRE5AxZpiYlIybRIGzX2KYCxMTUhK4xJC5/xjuptWVhDoeNTvlboccEk4DNXIiURBi4xDTAyKiB/jbSBVFQJzN1kkSnh8wwGJDR0CJQVCJC5l2UOjcicHeiaCBsWNKAZaA5fZqJiQkX/O3Zssrf1g4Lfr5E0NY6Y/jbM+ijAPbfar6SrW0+1G4GQD4qYYHfCh6/ILFgb+uVn3DYcdpMEXuPX0itNkVat24d69evJ45jHn/8cZIkodfr8dxzzxGGIbOzs6xZs4Z169aNyd++ldtqo6DvxO0VmQS8lKzMIKgXuxVFwde+9jWWlpa4/fbbnRc/jHMM7N++/t7PuA0u89sFMCLWGBEnTVOWlpZ48sknCcOQ22+/3QVPP8Ne3YKwB8sQAv9h9Y/DMnnbjAD47LPPsnnzZjZv3uweXkMpBoOB25/Jdy6l1bXkwham1YlJWZacPHmSPM/ZsmULa9askd91OgS0iM4PKSnIFIAXTbyNvRX+e8mIyBdq1Waq9EChYKsFxepGAk7KaJq8BCyD1guls0mvVwJzqJ8TOq4A+plG/FsEDYOjnv8U0usXz/kRzG93m08MtCTA0ADxkB+ZyibYGCCzCwrJNDEZVetoxdsoV2Fkb1upxY51ncUNr3JIQ6PXzZQHlnwA9DV8i5dBQXlkiWjbtKobAnXECzTpkL53or4BMqtBgq4pFBIiWkpCFMym0ECc6jmHiJui+PALelPqsYf6fVWapIiKYkih32+oFtKCrTRUKusLnBQPGvpktBwdNNDrbUG5cqlVSMd996M0LtCWUaxtDjFTbvS6BcSO7CcJUovCmT0X2NxKsReSb63Q9KVDl5TUJSzmbdEiYGUyorey4qZ8As61055La8+BrFUWoEyl5A/68SF5P+Bb29E8QWxd8Nn4/loSx7FDFgG3rvjyQEtUVpsY+a/1yX6WyFxKGeUXTL4h2dq1a7n88sspy5J+v8+5c+c4efKka53Ozs66xMB3U/xmtu8mAa/QJADGzSZeaHspCoGlpSUeffRRJiYm2LNnz0UB1LJfX/pnf/cHedjnWuC2m/pSycipU6d4+umnWb9+veM5+Nm+Vff28NrD5g/cMDKPPaz24K+WKVZVxenTpzl58iRXX301a9euvYjLYPu1jN035/ClQHEcMzEx4eyNfT2zoQQ2qnLr1q1jmXowLGnOrzBE2Ncm35KA1qOlNKsAY843Ct8H5AwR69YUU4bLOF3pyk6p3QsaIiut5UoFmCsCUsyOxjjjoQbwkDYwYKjB14YbSyA2SaAFc7urxBjGEhU0EOKqVluOrJLPkEQhpXJhyqD0RtMbGWkcKHgsMwsSTKnQKNweuQpXxgfH2p2XK9rViluEc42ruQ21sKfHgm2lxMpsfi9T216LDUlqkZLqp4urXqjXTLgXqZLjGg2DoXIzJNiH2gwwoqAYCAlxMyIl0ZFKIZViAW1VIBhaYeZSJRVd9RcUjwTBHCYI9dsSlCRT4ya53mLWE2HjhGr97sXcV5QpLf1csVu2plJFyZBGk1Fpy0wyqVe0cVhGREvv0YCQLo1rLUhKKS0VSd7ULoihHr20IoZEs7Os6SZMT09TVZXT3Pf7/bHR39PT067dZs+abfYc27Nra4nfEjT0bjVp2AjFPpxvpEJLKKwYsGMxlZAlGPY59tn2fjseX1LoG6bZPv33WSJhyYHf+gwCmYNgLYGqqjh//jwLCwvs3buXPM+ZmZlxScE3A+d/Nwl4BScBL3Z7sUmAuV1deeWVXHnllZe8afzevj8YyCpyC/a+0YX9zOcK2M9OnTrFuXPn2LFjB7Ozs2PQnH2OBXfbny+LWa2vtf/s4Vr9mfv27SPPc66//npX6dvx+tpiqygMqQBc4mGKAXsY/eOxP63/D7Bt2zaAUSUThDQHT2v3VYJGW6VhNhK2UO14okQzo+y1aBNSk2mNXur7DSYeSfWkGhRtfE7j6mX5TmVKYK4Nh5iSigEFHU01Ug1CNpDYqvhJJBGw4GmT/zoIIlAxCvSGICSM2gE5o8RgJCeENiFt2pit8JAefVACIBpGLVUw1wCZrdCmS6IMgZG2wVz4R058gYLhNqegjUkwLbGQvfcPnWbqjtc6kl/j/d6gbDSsF5izQKVVcqREvJwWFQktJRAGygUwa2RBE+z7MJJeToNNExC4XLZIWQXWsrDBUjp/T1s/cj+lREy4MU+xuiwGFGrbK2dUajU+QppC5SQEY5hAQFcTnhIxs2oFKU0Aw3qINSWklSEoS6Z4REvPMHZogiSiMupY2i1oktcQU64MqJaWSedm3CAvMwCzscLD4dA9V4Bz47O1wNYHP9D6Vb+tJxb0w1CsiH1Y3zfsMYmhmX35BYOvFrACwkcb7Jn3UUxrhRoC4bcRALe2+Kokv9Vq66CtNT7XacOGDWzYsMGRIRcWFlhYWODw4cNEUeQSgtnZ2YuKuxfaLmUZ/J22/V+RBLxQO6CqKp566inOnDnDLbfc8oKuUH4QtP36GSowdmP6DHyT9pjZxsrKCr1ej5tuuonp6WmyLGMwGIw9hHVdu54eMPYg+tp7e71fFfjDiPr9voPMzOrT5y50Oh263a5zJ/RHl8JonoC1Mezh7Pf7DhWw4wY4f/48nU6HzZs3u4TJmZtcyDFmfa0VoI1gDQlZpq9wdUVHu6ZCSpOlPlTCWKM/s7Ew1hM3/rglA/YZkhjkGhCsZi2ICOjQwcbaxBqEzBNfHPHG4XwjAhrb38JZqK8x+L2NEf8iDV2VkhlFClgol8HY/7Ue+UCvTwto0UZIkWaCU2vAtyFHjcM1JFCPHPNMJx8osiDSQ3NPbFMrgG+WuQEwtWM9pjWQil2uSQuTBNo3VrL41ccIbtpJqJV049AA8wMIte6X/rhwMQrXkEl12JER88SXP2LAgApJVuQ+kVHCEWZGVDFgST0iRoOUGwKGFGrPK6wLGSGcK7Jg94ukSpHiFUJQHc1qMOTI0h5o1N2yYdBkTupqd2ShEylHkxTl3ouVmxI5hUeumIw0byRRjKgZEuZi6NycKqjWTFGFI56PoWg25c9QguFw6Krt+fl51qxZ44oJv+fvjx33k3cfLfAJf+btYVbihhBaQuHzhvzAbOufv3bY+gEjpML+biimFUu+4ZjfDrXP9BOA53MJDILAEQy3bt1KXdcsLi66eStPPfUUk5OTLiGYmZl5wdbBysrKd3wS8IqcIvhSthdyDez1evzt3/4tvV6PPXv2fF1bSD/wG+nGr859QqBN9Vp9o/Z6PR599FGapuHyyy93znoGt1m2bGM3/UrfkgAz3PDZvxaM2+02nU7HzfpeWFhg3759rFu3jquvvnqMXGTB2Yx+7BwNdTBfAL+dYfIkGCUldq42KnN2dtYlALYPmzLYWi6U6y5M6ZhQWeG5WtZKPWsjehsqEg3MmTrrTdBBhuq2sKo0VKA71zSg0SrYdN4j+9fGBW4zphHntlAr21pnFAo4PEVEBwnoXWKmVWFuA3OMgGjVYxthhndJnVwtUq5CoH3xmJgOHf2MwAUaMDteWIMgBHJlcq2UzfOu1qRCDINycnqccoGno/1qQVBMhDhqW7QQcyLj4SeM5IyRo1tKsoUeuSQtlfNd6J1doq5WVK0QKWZi9sBijmwNma67Dom+wgb0yLEHmA7A5gZIvW9BukTQGxscZbjI4LkLhCtDvZcKTZTkfAf0yfTfkbYz7B7oKwfFvv9ae/2hfg+VolD2LRvyMmTIMsv06evnieAx0kRjoEZMliTavSdESrufbfRzrWdprSXBBHIKmgvnXZAsy5JWq+VswmFEmtuxYwfr1q1za8uFCxc4ePAgx48fZ2FhgSzLSNPUEYQBl8Tbn6uRQL/irirxzfeVR1bE2LH5vgO+EsqO0ydP+/MNfHTA93LpdDquGLFjsM/1Sd4vFt4Pw5C1a9eyc+dOXvva1/K6172Obdu2URQFTz/9NP/n//wfHnvsMY4dO0av17uoxdzv9y8pEfzEJz7B9u3babfb3HHHHXzxi198weP4zGc+w7XXXku73eaGG27gL//yL8d+3zQNH/zgB7nsssvodDrcdddd7Nu3z/3+c5/7nLuWq//70pe+BMCv/MqvXPL332w74xWLBHyznID5+XmeeOIJtm7dytVXX/2iDCfshrXPX531WtCzgOw/WFUl3vrPPvssW7duZWVlxX1J9tAZk9/ebwmB7dfOx++j2Xvscy2ZiKKIkydPcvLkSa655hpnNmTwok9YtMXFtx32TUl8Qg8wds5N0zgvcf86me95EAQMBgM6nY7AivQ13Em/PWOIP0zHYP2QkA4drZykmswp1U2/0KBkg3lqrYnNKa7RICTObLKUS3DvM3RSPiMEwpCYlDZi4StK8o4SAUsF3guFrSPX4+8wCvzGwgdYbVVrTHY5zsRV/VIlmjVtoBA+WrHLQKShku1KKq/zXdPXwCU97pgWU4gvf6UtiZBleirWE5fDCYwRIBJKIz6OWgQJI6sbC1k1Q3L1wQc0mCbr1hK1b2RYAEmuV1xIfh1SJc5JdTxkQK3XJdF0yRoNA4YkmuzEGh472gSR4c4lCaIosNbFUI893DJBxAQlK7p32b+4+ZX6CWY51UHmRohMNKfQdwRk2oZIVdTYIkVmOkhqVirIX+leE2I6TKshU6A/l+vYMLKtbtFg1te18hJEn1Ip8pJjjBFLKM21sE1MHY5c8ayaHw6HYza/9kxu376dNE0dwri4uOi8++fm5picnGR6etqhfPacW1vTZ+37sj/ATRtdXdFnWea4Qf5aYdW8rWFmZGTVvr9W+SiqrTm2HvquiU3TjM09+Ea9AdI0ZePGjWzcuJGmaRzB8Ny5cxw4cIAkSZidneXkyZNcf/319Ho9LrvssrF9/NEf/RH33HMP9957L3fccQcf//jHufvuu9m7dy8bNmy46DM///nP8/a3v52PfOQjvOlNb+L+++/nLW95C1/5ylfcAKGPfvSj/MZv/Aaf+tSn3ATBu+++m6eeeop2u82ePXscx8q2D3zgAzz88MPcdtttALz3ve/lZ37mZ8Ze84Y3vIHXvva1L/k6+dsrNgl4sdvqJKCua/bu3ctzzz3Hq1/9ajZt2vSi9+UrDfwgaDe1BT2z6rQHot/vc+DAAU6dOsU111zD7Ows+/btG2Pwmk7XD8y+Ax+M+mA2UMhn/logt8Ti4MGDLC0tce2119JqtcZIPpYImKzPEhpzGLNFwDJ5/2d2rvYw93o9lwBs2rSJ+fl59zvbv9/7KydjwpVCg2RDplWdVdQQ0nFVkqirbXSLOeHFavwjQ2fNYd6EXTJAOKRGCGqhwumSXlgrQlIQgIohMgCoTRebhWfa8pKKiEB7/jUtJGBOIFV1l5Z2gW1qH0zQRnj2AwKE2JZqGyIgc5WgMfyNOmeSRjBTo9i1GQr9U/rqEsSF2lg5zkStKYdMCRBZW6qpyQRdSg9VQc/SAk+sIU8aEhY+I69atv57w/D8aRb/6r9R0bCSpsz8P2+ipTB9oKE1xISZlSZFtUvnZJOmg52PqA0ETaj0nqg1ATKSoYzyHSriIcnVCivuiDt0Vf/fYKoI+SbNc0J8/ycIWGHIkIyWpowNNjchJolSwirUwUh9RapsjJV5QAhGs6IsjwDoYFMwK+WChJgdtBldy72YO7zE2lPWyOsi7YMsL0g7Xbc2wIg4Z21CvxAyNC6KIjZu3MjmzZsBnHf/8ePHHYw/NzfnRgPbs2lrli8FhtH6ae08C+y+PbBPkLa1wQoFX6KcZZk7br+A8lFIK54M4fSJyT6v4VuxWZU8MTHBFVdcQVVVrnXwH/7Df+Dzn/88ExMT3HLLLfz1X/81u3fvJkkSfu3Xfo13vetdbmzwvffey1/8xV/wu7/7u/zSL/3SRZ/z67/+6/zgD/4gv/ALvwDAr/7qr/LQQw/xW7/1W9x77700TcPHP/5x3v/+9/PmN78ZgN///d9n48aNPPDAA7ztbW8jTdOxWFUUBX/6p3/Ku9/9bhcfJicnx1oXjz32GE899RT33nvvN3Wdvu2TAD9wD4dDvvrVr1JVFbt3737JMIkhAZZU+Kx4XxLj9+OGwyGPP/44VVVxyy23jMkK/aBrE8EMEbDfW9D2kw5fMeCjEyA9rGeffZaZmRmuvvpqiqKg3+87dq7vWmi8gKYRx0Ef6jNdsgVxXzpk2fu5c+c4e/YsaZqyZcsWV3n4PUjL5KMokuOYiIhXlrQSg45WiwXixT+BaLDNTrfBxrZI6Ba4XoD1CjN5ReuvUWhtKaBdKEReAT0ycqSCjwlVFGckvQKZL1jpUBkZeGPmQkY67FM7M6C28hVMSWDWwwP6xAQuVck07E6Q6rnUCiRDSu1wA6nII21vhC6QGTpgw40qamzEsHgM5I5DYNC4BXMJwLBCT/v7sQL1Wk1iqUCjQ3LM6lcGOkmbIFRHu4SGHkuf+2+k23eQ3noTa/W+y/R7CBUOlySsUGhfzH5KRrP4bLoeSDpQuuQgwIidJTVTTGC2SCv0GakvpIkx1CQuBGV2WEIpAV/aTYFLampHFBVip9lRyz1SklPQqUriKGKlEslhroRBQ05qKncu1gpKCenSoaBwTSizFi7IlSZoBEY5T0HCSnxEJtRmUx3igr+1Grvd7hgK6KuR7O++Fbi5eM7MzLB9+3byPOfChQsMBgOeffZZmqZhZmaGmZkZJiYmHCnYnlu/h29EPpM2+2uI72TYbrddi9Ne58sN/WP0Cyf7z+c/2brhr3cv5+YTCP/yL/+So0eP8ta3vpWVlRV++Id/mH6/z/d93/fx5S9/mV/+5V927wvDkLvuuotHHnnkkvt95JFHuOeee8Z+dvfdd/PAAw8AcOjQIebn57nrrrvc72dmZrjjjjt45JFHeNvb3nbRPv/sz/6Mc+fOuUTkUtvv/M7vcPXVV/M93/M9L+UyXLS9YpOAF3tDWCZ77tw5HnvsMdavX8+rXvWqb0hH6lvoWvCGEfHFn7sdx/HYZ27bto2maRx5z5KTpmlYWlpy0r/VUjybBGY/s5G+MBoEZByC06dPc/jwYS677DI2bNjgTH4sw+73++R5TrfbHXvArYKwP+36+kZFlnDYuR09epRz586xZcsW52vg9x3NQtgSDVtUVoohyZou4YUlJphEHPQKNeWpMBvWUmH+ROHzyIHmIeZNLzIzY4yb5hqFpkNVAZQEpOqiJ0FHFm6p0xL8nr68b4k+QwZMaJtB2AHiWii2tsIPsKTEWgSFpiIlDX0aTTYkXEvgMm298MUTNfJtMC95881PMEVDpPC4WQL3yVwf2XwDRH1gM+kECem5doHUm8Z5QFnugUspZBvqgGaTN4YYCTEl0muXAMXffZXOrlto3XCNchViSdWWCsJpo1I25AxIEMsnYc6bZ15DSqrAug2LNidHC9wJETLDz4x+xM1RbHsNnTHTKDE1MsMnSXwiNWwOXPhtFEmBFbVCTvXayP4rvTpa91eNVuk5MalyC2JMPGiOjCMTKGlfiJRSwr+QHDMX4Bm74vLNiN4FtYMe+QYESQhaRJhawNA0I+9mWebWIFsnfAUTjOR9FsBnZ2edMshG/p46dYrhcMjExATdbpe1a9e6NoPve2JrqR/UDba3NajVarn9G0Jpx2NB3QI84M7F9yiw9dFPQGyN9dGBl3u74oormJ2d5V/+y3/JT/7kT/L444/zx3/8x/z5n/+5m4Fg28aNG3nmmWcuuZ/5+flLvn5+ft793n72fK9ZvX3yk5/k7rvvfl77/OFwyB/8wR9cEpl4qdsrNgl4sVsYhpw7d45jx45x3XXXvaiZA5fafPmdBUS7Ef1s3Pr4hw8f5siRI+zcuZNNmza5atvPen0zj9WkF0MGfFas/wD6vb08zzl9+jRHjx7l6quvdvMNzK3Lby/Yw2Xvs/aCVfuWjNjnDYfDsR5eGIY8++yz9Ho9duzYQafTGXP9smue57nbr8GBRjKq4pCYUoOBeLSloF3cUkOxdFjRxXc0lKWioaClhq8iFpP6cES0irT6CghIMRJgS7nbUvlXDu43GZ946EtAyYAlMrqEtOnSps2QIV3amJzMxslaK6ClKgebY2+BbeTOV6qF8RSRqw4D9/uKEpkdYL75EpIl8cnpYayHkTGR+RUU2OiejFLPQRj4UvuLo3+AGQVFCrvX2o2WKtSOGYX3zYnPeOwxzZU3Ea2VqjkmRayDQ9JpCfY5JTWV2gKNKI8N5sxo5EYJywPt79tAH9HOZ9SqOIgItD8faatnqO0P9LzEsEeQi4JFFmnR0WZERBIkNE2tCZpZLwXedzYaACXHJUTNgaZFoaZSLTUkGrjGTYVpMgKHDwgl0KSUpoqwyY4jD4XR926DkEYmyNKYCIcVwWTLcWl8GNyee3+ynfXLV68XwEVrjCXmU1NTxHHMunXrCIKAc+fOcf78ec6cOQOIAdHc3JxTDlkL0eYYrG4D+Mdm66F9vgV9Owbr89ta6ssbfVWAv67Y/v8+EgDber0eU1NThGHITTfdxIYNG/i3//bf/r19/qW248eP89nPfpZPf/rTz/ua//bf/hvLy8u8853v/KY/79s6CcjznFOnTpFlGXfccQfT09MveR9+z381RO9L9nyZy9e+9jV6vR6vfvWrx1oOtg9fNuhL72DkQmi9OdunP7wniiJnGGKJwfnz57nzzjvdMfkLQKvVchWFEXqsovdRB/9hs4fcR0zMayCOY6666qqx8/IlOz5h09AE+zOOY/JhJv1kVghdAE8xn/iYgIFS2mwxFytck+KFGhhisuUlyimImdTKU6q6ltbZ1nvNqGjRwsbvlpR0GUn9xKFPbHzE485c9mpi5eCHbnk3cqD52kmi0mVS4d9FV6XbYCFpbAz1bCtNWVIl+oX0WEJG1EqtvMKyVqEiLTMfAhk/jFIAR0nACEAfBXP5LNMtSF88oXbkvFwDsI3frdw5S0IicLlcrUQh82jtBDXitxdQkJK6BGagKElnrO1RarUvA59FIyCOeWKakzv0Q66oYDc5AzpqwWRVvrkKot+S6AQidZmQGQmh4guh3ksgao2CUcCdoENJnwJ0SFVDRq2aFbn3An1fpslqofRAO54WKcJlSDAegLVPYhJaJGpCJIjVkCG5+kB0aWvDxpAv4Q3YsKmQiCaIKFdxgXwSMODWI/u7wfK+4Y/NCIiiyMmbbZ++8VkURczNzTE9Pe3QxaWlJc6ePctgMHBcgomJibFj8IO7VfOrzc6MX2RrmX2+rYGrTcn8Y/R/5k9k/fvaVpsF2UhkU0LZdurUqefll23atOkFX29/njp1aoyEeOrUKW666aaL9nffffcxNzfHD/3QDz3vcf/O7/wOb3rTmy5CF76R7RUrEfx6N8OFCxf4/Oc/7yQi32wCAOOOgX5Atxv+woULfPGLXySKIm6++WYHu/v7s+rfHkofcofx0Zg+BDcYDJwUx6Q2eZ5z7NgxAK655hra7bYj1gBuUTD/Adu37yhoSYMfwP1s2x6+5eVlnnjiCdavX8+VV17pXmvHY/1Ae0+73SbPcxYXF5080IhIcRK7MFppgDXSmrjGJ2rxKoQqs6eptUmwzIAefQnaUwlDbOa8yNhE2z9i1ZcUnuTLiHBSs40Y2eIUOND3tTG4WFADCQJD3V+u4LakHZEDzRtEpDgaOtS4fQnRMKdgqIwA8983Wx/c30taqjG35McMhzr6X+JA7pFJkgXwHDEvGiITEQfKYo+1fVJQ0WNAhpkO5fT0PaX7LPV61/0mjJzyJdGpGWBTATNWKNysgpBa1QTSKsgpyR2lr3S4QkJLCYkjR0cwnoKw8hsC+vR1ELIM2ikoGdCwpIHVUIEOKR1S9QmQ77Zocg2ysYZ4sYPqOIEpLFGyTM2y3ovC2Ki01eLXQSPzpUiTorb6T0Kojgdo60AmFsr0ykivlFgsyRRFCfzSygg0SQzJaRhSUjSle2b8gGkB3Z4lv+q39WQ1cufP+fAle1Yo+OuS8XmMRX/NNddw4403smPHDqIo4siRIwCcOHGChYUFyrJ0sj9bByyJsPXGuAZWnFj7wEceV69Hq71R/FbC39dm6gGfbJemKbfeeisPP/yw+1ld1zz88MPs3r37kvvZvXv32OsBHnroIff6HTt2sGnTprHXLC0t8YUvfOGifTZNw3333cc73vGOMSTI3w4dOsT/+l//i5/8yZ98aSf8PNu3HRLQNA3Hjh1j79697Nq1iziOn7ev8mL25ffX/ADrB8u6rnnuuefYu3cvO3bsYNu2bQ5qL8vSMfkt2Psogq9/tYfRHiLr0/vjQ42wd+LECQ4dOsSOHTvYu3ev0/Xb8RrJz4YF2UPvzxwwQxCfvWtIh0l1QDLS5557jiuuuIKpqamx62PBHkYzzScnJ8myzB2TZfD2uWmaUp/KdLkzeFWG1UBMqYOETN6Vq0DLvOVTDYGJSq9s+luhgVVMfDKt0BptLsgUQgkjRrRrXG/dyH9tDR+B9tBrapcYyLcnPgcjl360Mq7IWKGmcUoAQyLaBKR03PGJHjxXCFxg4RpxzAvISUgVjzCTIqFBpjRKuhNoPMPGH8lmEw/ND6AEFc9J3T/JJDa/rhhA0JHAHzByPIyRmQgQkipHwQKf0B8LjAApxyeeA4IA4Bo5lVb9ENAikemE2BjoUitp+W5qAmxoUITMCYi0kdA/eJT4yk2UlLSdnXSjREW7+qaFkJaCr2QoFfuRRLCloVbOqqWvl9aRoSoBRpYMiegQa7sl1wRJ7lezfYZAnSdtpJWkqzLFcqjtBbnTLbkpqDn7uYeZ/b5/rAnlyLAo0mZQlgRMRKNhP9YLL8vSzQmwfwNujbAguVreCzjvEJ8UvLq/b4XPasKeSQw3btzIV77yFTZu3Ei/3+fEiRNEUcTatWsdqc7aF2YEZEZmhkoaydAn/fkB3ncZtDXX5xX9fbcDVhPI77nnHt75zndy2223cfvtt/Pxj3+cXq/nSHrveMc72LJlCx/5yEcAeM973sPrX/96Pvaxj/HGN76RP/zDP+TLX/4yv/3bvw1IovNzP/dzfPjDH+aqq65yEsHNmzfzlre8Zeyz/+qv/opDhw7xUz/1U897zL/7u7/LZZddxj/+x//4W3INvq2SgLIsefLJJ1lYWODWW291es+XMkDohTZ/HLHBb3Vds3//fk6dOsWNN97IunXrxvrvRo6xh9i4AT7JzgJkWZb0er0xja4Po1v/fd++fZw9e5ZXvepVTE9Pc/DgQSclskqhKArXQrDWgREaffJQGIZMTEywvLzsXuNLFQ8ePEiv1+Pqq68eg/7sAbbFB3DQpCUVlr37VUEURUQV9J13ugUAmQVnnIAIGRubuV6uOeZLItClgw1pEYaBTX4TWNxc/2WUa02mWnUj0snWqGNd5qDw0fggISVmjORIpuEuFAlI1bXAQF2QoCjw8RCbMChhYYBJ53IKSqRKF8c4QTmkIozIKFikYeRAKLK7Gt+et3IBX/gUoyRgCvH5H+pRWUNAus0yIjfpyPla9W9nbWZNESGTTGAjjdskmoI07jjFBVAIlkKMFH1GjCgietSYEVGo3XXwJzta6EXbA+KkKO+RIHzh8T8jOX816WuuJUrEtTAlYtpRAs0jQuynBRUJVBLaaPiXu0gSvRTj+cv3k7vjSgn1fggoNCkM9PjEVMmGI0vSZqoVGd4caeIq99cSPXpkjpth1suG6kx83/cwYEVbI3LlUkS/kU3IutHr9UiSxPECrELOsoyJiYkxjw9LsM0gyJ5tC+a+A19RFHS7XbcW+GRmGLUt/XaDkYwN2Zybm2PLli0MBgP6/T4rKyscPXqUZ555htnZWTZu3Mj09PSYWsHWDt+90FcaGFKwGgWwY7dE5f8LToC/vfWtb+XMmTN88IMfZH5+nptuuokHH3zQQe9Hjx4dW8P37NnD/fffz/vf/37e9773cdVVV/HAAw84jwCAX/zFX6TX6/HTP/3TXLhwgde97nU8+OCDDl217ZOf/CR79uzh2muvveTx1nXN7/3e7/FjP/Zj3xD5/VLbKzYJWH0jrKys8Oijj9JqtdizZ4+rSl/IMfClbj77P0kS+v0+X/va16jr2g0c8jPq1TIZ/2GwfZl3t/3cr8QtgNrf+/0+TzzxBEEQ8JrXvIbp6WmnD67rmomJiTFr46qqnMGI35Pze/j++N8oitzikmUZBw8epN1uc80117jzsQfYEIkkSRzHwBaMpaUlpzu262XXJM9zosPnsfnuRlQLGM2gbxTWlwXXJGtCblumVPZAqFauRgW0mfWh1qrCGZAQ1qiBzMiR0P5Dx+okWsHboB2BwEfVuGm+Y60wDUyPtA1gd5iEmFDpiLXjBjSECgjL8U4gxjyFahgMds6U4geGUOD87SzArJC7B9MkhOasJ02P2gumkiQk6pZnAjXjPPiz8+xczXLZzH1G3vyCYkgv3Ib7JmT0XEIi/0kqZiTDSlM082zINOgFeqzyeWK6I8iCURb7zL7lh6joIKRDOepEQf8MmTBYUJGqokDuHRsXPCBSHoAEWTEwKhj5HsTadJKkUe6OUO9OmxkAERNMKe3UJlyKSkAcBjISOpingSgZUOZ/jUk8C1CjY7n3rcVS0qKJW6x0Q5o0grKk0TXLoHmfLNc0zViLz7bBYOCKiXa77YjIVo1bBQ5cJLnzg76tDT5Eb/vw1UJWSJid8WWXXUYYhs6b4MSJE1RVxdzcHDMzM0xNTTljITsXP1j6a5PvF+B7nPjve7mTAVNgXMo2+Gd/9mf52Z/92Uu+73Of+9xFP/vhH/5hfviHf/h5PysIAj70oQ/xoQ996AWP6f7773/B34dh6FrE36rtFZsE+NuJEyd48skn2bZtG7t27Rq7sZ7PMfDFbKuDmGXWVSXWvV/72tfYuHEjV199tXs4hsPhWJvAbliD4v1+vE/4M/tNf7CPwXLmJ2COVNu3b3fnZv24MJRhIPY+I+8YCuJrh32bYD8xsEXlwoULHDp0iM2bN7Np0yZnUezDcT4Byc7PX6h8tYElPHmeky/2KejTpuVsXAH6KskDCx+VWywDBdFHRDsJRQLC1wr9C6Wqo2FsQIFMbUupaJRwJ1ZBYg6EDnmJyDWoirBMmP/WU5fF3Bz8hHjW130U5OLspnsWQmFBm5gWCQWZW/wjjFNeIk7wUvWVzsBWvOzNcjYE12NPaNzIYmHjg0D0KOQ92qS6H5naGC9B0ikBpA31MJVBqvu2JKIAVijp0rjq1mibZvErzIWaUs2Paiq3WEgbZXQ8K2OIgPA0YIAMGBIjY0kGGtdPFxA/oCSmhVApI0YKEmmLyPdrQVxQDjGOtkZJrLRBQYyEuR8pylIrjpMqApFpO8YSOxSpEhmhcBGsPZKq+LF/9izpuikqck1FJQEScmAXM8Qe4VWCIaQqig017c3KJeqllGZ2ws0L8YsBq+bt2fMh8zAMnYrHCgB79uz1VsXbmmImZIZUBipHNMTO0D6r3G3zrX/t37Ym2NjxTqfDmjVr5F4oClZWVlhYWODAgQN0u13Wr1/v/Ats375E2W+b+koI3ynRT0hers18T77TZwe8opOAuq555plnOHnyJDfeeOMlLRu/2STAAr9vs3v06FFOnDgxJjn0mbB+kAVcb8yHxup65MsfRZFj6ANjTnvD4ZATJ05w/PhxrrnmGjZu3DjWQrDP9b0DjAtgKIJvB2zB2SR8tgBYdn7ixAnm5+fZvn27GzVs5+UHfB/ZsGTHztmOwRYTQxfCMCQ7vUhAxZC+dlJtyIxY8NgIVsAtnqnSqHIyWlr99RmQYaYxqGFMTUSOgdujkbC1o+3BaMhPpB1eq6TFWrhFl4BSEwNj20vgMX/8Ebwr0+WEbNYoSW2oRxOCUyDk+p6O+/tQe81G6BOsINNjMwTBoHob/mNbokHJ+vg+nI++f8Q2hxX6BAR6RdHrOyIuCuN99bAks9IxUxxJChr3HVX691IdEEJsYJPZFw+96115xyNVszkDhC4VQttAQqqL9NqU2q+XtskSOV1tRcgoIhs1HWkYzxz0bh6IgZ53gflF2gyGiMEY4pMof0Cg/xobYlW5OyjCCIIxnQlpjdQ0mmAIKTVWbEKQiQRTbIwmV4TKh5BW0goNQzLaFyCam3bPW7/fd9wdvzoH3HNsz59fAKz26LekwGf2W8C1RMA2e63t05z+LAgbWuknFfZ7XxlgFrzr1693x3r27FkWFxc5ceIEdV0zOzvL9PQ0s7OzrojxK3z7DH899RVWLyca0Ov1gO8mAa/YJGA4HPLFL36RpmnYvXv3JYc8wNefIvj1Nl/6YlDZ/Pw8t956K2vXrr3o9b6Vr++K5f8ZhqFz3jKoy25yy9AtgB48eJBz585x4403Mjs76xIav99nZELfTMSYwnbuxhOwBMN+Z9fH9js/P8/OnTvdDG47Dmuv2GYogy1KRvix3xl8uJp/kEy0qXu2XIqhq8jNQq0Srbsf6VQ5Aa3NXEbIg9KNlQAhJLJJbECQhP1Ykwczy7FhLl2tYq0TPZq2B7HuIyFijSrsl+irw3vjXmftAjF5AajpkpMhga6tVWRKTAuBq1e0Yjd0wUiDFqwD3a8lBmLYI5ufwnYZTUUwRYDNCggwKZ4FQNlPhqEREsSEM2Cu+PL6lndOhhJMMIFY9UoFLeS1EUxekNPVUUgSqAUrqYEuCQ1DbIm2Xrj9Pdczq8nIFIoX4p2MW2qREJNSUDBkoIZSCTkicZRjqEm0Wi+oGBkcyZ8j1YQ1F8TAqEObUlsPZnOcUWD2xBUVCSMxaEKkqYP5QlTaVIiJO3K00n6JtKUh91uJTXgMnbIk0PtBvqlUCYWVQ2GoM/L+kKiVuOLAh+WtzWbrkSlv/PaevW817G4BH8b19sbg99cBe7ZtnbHnf2VlxZGVLfmw592Ow0cA7bPsv263y/T0NLt27XKuoxcuXODo0aOkaeqShrm5uTEi9qWSgJd76/V6Y6jHd+r2ik0CgiBgdnaWXbt2veCXZDfSN5M11nXNysoKjz32GEEQOEKeZair9+3f/DCaKugz5a2nbrpaS2LsZ4uLi06Tf8stt7iH0NffmtTGArJZ/9pDbVm8vcbX2doDbtyBQ4cOAXDddde5OeR+L9+gfjvXS73G9u1PP4SR/WnTNBw5fYINJEwi89xlMltFSwONdZrN8rfROrTQ+lkGshiIHWI2N21S1WGjdeSoNjQDFrFkKVygG/EIUAmhhD+b/WZ8hQbUBAfl8490+naWK4ysfXNEESCDfszRfxTYcz16SwjsZ5aqGsqQglrNyCZQf63JQUXgXhfSpkPBkJyKDuNWwzmSCMgkRCPljSpz+0yTRFrAzhhiokdpnMg8g1otcyxI2ndlw4kyBe19LoVdq2D/WZaPPUnr+1+vga92fIE2KQWZ9vrN4AhEqdCioVGLKEN5ckLVcxTUqtMXJr5xJMQO2kiLqPeiqPdjYkyaOKXplQ18khFIIabbN6TIhjiVFNTVedJojpiATtyCuiYNYqIqci0qSZ4iTY4q9z1mCHvFtCETjMZHF1lNMt1yPX0Yb7HZv62FCKN2n1/BDwYD11qwxADGK2kfVbQCxi8e0jR101LNrdQUPoZkWuIwHA5pt9sjKbAmAj4x2NYJUztt2rSJzZs3Oxm0tSKffvppJicnWbNmjTvn1ft4ubder+fQie/k7RWbBBhh7ettfu/bZ/e/2M3keE899RTbt293khgYOWL5FTPgArwFP3tQ7AG1gGo3tcF0lt2fO3eOQ4cOsXXrVi6//HImJyfdw2f9+aZpnAxv9WAjy+KtKjASkb+IGPdgcXGRgwcPsnHjRk6dOuUIhLYvy/Z9+K3T6bge5erX2b8tSTH0YWVlhfn5eebm5pibXUc9v0J/+SyZBrMghyrMSeJYg27myGxmwao8aAXfYwdSS2Up/AAhZQlVL3HoQKUBIKZBjFsERRgSaf8+dKptY9GPxt1YD9ykhUa2M3a9bSOXPvT1EjqsyodRcI8YBfgYQxTk9zabwP5tPxsRFkc+BAa1S1COMRh+5IAnx9zW9xWHzpOdO0jntteSM0pC7L/Y+8yMjNwF4poeDRGZejjE+v+hBvOQmDYlhUMVbAhUSsOQgsHf7SOf30fT1OR/9ygTt97MEsKnmGI0rEhQBRHVNZoSCdweOiaEyPqsyz5y8cs1ybPrYwmNJW9t2kSkdIkoNWlskajcVL5xcRcUqybjAJhKIFEDoIQQojm98jVNrYOnwpA0alPnokbonzpFsnG9tr5sgFDlOBo26bJUnkMDtDwOkQVgwLUQ/XXH/u5L+nzHTn/UuSX8fkHhG/ysVvL4MmZfiWDriC85tGMA3Lpj64YVPZZ8+KintUjjOGbDhg2sX7/etRMvXLjAwsICCwsL1HXN448/ztq1a1m/fr1bg17ObWVl5Zsew/t/w/aKTQJe7Ob3qV9qElDXNU8//TQnTpxwnIMzZ86MEf/8rNSX1/mwm8lrYJQQ+DIZ26qqYmFhgcOHD3PdddexYcMG9z4fZrMH3jJu+9Nsgu1B6/V6LrmwzRYKS25OnDjBrl27SJKEU6dOucDuM4r9fqLNTEjT1JmD+NAgjCSCExMTZFnG6dOnWVpacglNEAQ0l03AZEP17CHyR/4PzK2lc+s/0MVc6vEWLSIiZFK7mPU21LSIvc624AUJCYXa+pYuNNj8dqlZjeRVETPQuruhJKVFh4Q+pVa/YhNrBkZGCsw0OZBgklBoh9kqOzPzkaOSfZvO3+YKWGCyo08YuQAm4AiA1i6wBKJgvD1glbXMXqgJGWpNPgro1hIwkVEAnDn4KPFw6FXZsp8uHU0ehowMkUfmSqKgiFUdIWNxYyIyLhCxBhmwI60CYVXI+0MCFv7Pw5RnTxM0DUESUzVQr7nCnZfZK5tA04YXGcnOgnNOQaiYkCA5taowzPGwdr8TQqepGSwZEFlirFiTtJfMidK0KrVLKCLM3DgnVmMjkZ2Oxv+iry9rQbLCOCYv5J5saGhtXEPOea/VIoTAyH1DAbk6SZqk9Hy+THupYHZ2dqy9aCQ/vyXobz63yG85WjC2wsAc/GxdsOfVJyHbemIkQksc/P1ZC8FXH61GXgeDgVuj7Ge+9wlcWjaYJAkbNmxg48aNHD16lJWVFTqdDidPnmTfvn1MTk5KQaETEb+RAu/rbb1ez61X38nbKzYJeLFfjGXBL5UcaBMH67rmzjvvpNOR5d0eDH/fwCX378Ne1rMzyN5uWqvqi6Lg8OHD5HnObbfdxvT09BhJxucP+EmILRI2E9zIgNZLXH2slqE/88wzrKys8OpXv5p2u+0GG9lx+m0DX67j9+PiOGYwGLjj8h0D7bqcPHmSXq/H9u3babfbY5MK6+Ullh7537SGQ9bcehcRqKlORkiiPf1AtdTithcQalUoi7o0CWSJnWEGcZqz0GzTBUsH6/cZKFVMNgngOUYdM5hfYFyxk5HjkLmDfU0ChIsQ03UiMvS1bSKtI43cF1AyoSmMBXeD362HbxW4Qfyj4T8jVKBAkgWr2Fn1O0lvEmXTS/VtrzFInKKgShLaoEiFeTJKmtRlioy+WvBam0Q26Z2XyKxGq75TQk1RRIdv4Dnqs7BIff4c7SAgDwPiqqJOU7J9Xyaa/Ie0Nyaa9MhA31G7xUKwjUWyJkBNQa2hFDdlICJioAHdgn9FQxsbrdTQpkOODHSuNGkxyaOxREasCJFHVo5ngOOlmLwwVKRJxiVVdIMJBrmNig71tTEhUzRknPtff8X0938/MTFDNcSSKYW1ciRGiJERgg16n5ycdKPF7WdN0zA5OcnKyoqTERrSaMN8LFm3dcGSeiuK6rp2Fb25Ctp6UlWVa1MaEmDPrj3ztn6tXmfNLdVakjBqEw6HwzHVkrUuDYWA0fpuLY+JiQl27tzpWp8XLlxgcXGRp59+mrIsnVGRTzD8ZrdLGQV9J26v2CQAXjxB5KUqBFZPHPQVAlb52uf7gXb1Z6wOwv4x+zBenud87WtfY2ZmhltvvdUlDrZPn4Hv98cA16ezBMBPKnzijiUvpv9vtVrccMMNDjnwZUD2ufYQ2zHYYmGLkK86MAjQdM1mahSGITt37nSwX57nI0nS0gprdt5Ka+ccbSaJlI4lQ3MaNWwJXVXaoYXoyeV/suCnyCQ/gVtHXnaNUrgyF+xEQFa5altgeeknDxiSAB1klpskHKV2mSElpUG8CaRmLBBv+REs3ybSwNEwQUKubH+pQmM9uhHsm+kx2JW/WNY3Sgxg9DAaQdFY/MJ+kHDeVpBeWh6FSyoyIMyBRirUoIapcJKAQPkOQtET093EoSaBhltJE8zxX46oZKgtgEp1HkYutNHPKW3W0tm8mfrQIZIAsiQlrCracxuZ2CgueRmj4LeWkSNiRcUAUXykSmZslM/g2yUXiFeftWJaeg4ZBTkDpf8lLqnIFVeKSejS0R7/SJIaYsoEczKwK1Rr2mPVfEAYxJSNIFR1U+ln2NUSEaL5Vna/53WktJXNUJErARE9lwyI0pQ109MOmjcznn6/755NC3SWhPvyXcAhgsYHsHXL/vRZ/zZ9EEZtPKvGfTKxPbP2WdbDNyjfoH1b43xioqGxg8HAJSfGXzC0widvG+ppx26tTFtfzdJ48+bN1HVNr9fjwoULnDt3jgMHDjiC4dzcHGvXrv2GUYLvJgGyvaKTgBe7vdgkoGkaDh8+zP79+7n22mvZunXrWEVswc6C42ro34fq7WHxOQl2Q/uB/fz58+zfv9/1/y2Y+omFnzQAY4EZcCOG/WBvGbQPJy4vL7Nv3z62bt3Kzp07HbRniQNIBWIPr5/kmAOhHZOPOvjM5CAI6PV6zM/PMzExwWWXXeZIhPYwd7tdwgamw5R851ZwgV3G8hqbX+rtxi2nQgoUONo4/QW1ksZqrcltJG2knWSpKzMySmXDWyUtNrSN0/OL5K+hS5fSJRpi/iNMdJkSIFPmBhRq+SpSRRBoV4bP2PQ4k9eJG17BpBIYh5hm3gJ5oCwE4Qr45j/GATBkwILmJJZABEqBtH66tDMKNWCqaegCw1MrMDlFkA2pH91Lcuv3Itx/S3VgwABxPZS0QgyDIm18SIqSONJeSqVVeIXMOxiqx6KMA44oyKjTtdTJMZJenyYIyOOYaGaL7tdG6ApS0Qe62m4olUg38hSsXIJk6InJIHNFPQK9g3DfXKCJoSBIoj2Q+00QBptF2WjKFmKGUCWV8kZQ2+MK02SIWVFG2AR6n9VkDIlJKdUTstLef04uKEJsTobjnA3jmETTM2T1yAjHAvTk5KQrIs6ePev65LYG2ZrkE+esMPDlvf7vbV3p9XrOv99aAAbv++0GG5BmSb6tqbZeWXJh/zZSoKEQ/vAiOzd/PbE1xuafWNIBOP8Un6tgWxiK4+nU1BTbtm2jqirHJThw4ACDwYDp6Wnm5uaYnZ1lamrqRaME300CZPu/IgnwiXPPt9n0v8XFRW6//XZmZmbGfu8/cBbo4eL5An6CACM7TFMH1HVNp9NxsNy+ffu4+uqrWb9+PTDenzNjDt/Ew9fn2nH7U7h8y17APTzmNbBr1y7m5uYcrGgPs8F7dg7tdpuqqhy50RYan3hoskD7mTkNZlnGxo0bmZmZGUsQ7DqlaUr5xGEippQUJySwgMCNDbJ6T6DSnJDo/8/enwdbdlVX3uhvrd2d5naZN/tUKjMlpUgklEhctYkA2wgLGwyiCpehXjyo5y6iwnZhK8IOhwNTEXZVUEFBhXDYUQpHYT7z/PgMhG0BBgNCGAujFEKpXimlsu/b2592N2u9P9aa+657JYEAwQe2N5Gk8txz9tln37PWnHPMMcb041zFfa/01aALgDI4yA2UcfTAAhmwW6A901ug+MQHOBlAI3B55a9EZsdLEE+8qav8cdWy9oE997C+uyaP4TBEZthrjFcxKH/uzCcsYkqk63TFf9/8eUQCKEmBSPrcO0hbQfnEJvGBy7klZkQUWFKcpG90yzgjW15LZ+9eihPPkE/txDBKRUWTBinOAEiSp9jD8O4+ilXQEozuJIMO78ho0KXrEZsGbj7CgO7RswxPHaLSEcN2mwhLrjQZlp5PANzvfClJT7wIMEeUFC6cCzFSPBOs/7kQLD1G5tsVuU8rbd3waJL5rn/pf+eGSGmsdTV+QuoCu/9+5Qxr1keDBrFPGg2Vl7VGvv0Se5Mi14TBJ6oO5ehT+OTBSRndJEGxFXLfhwodjVBEmjSO6vUfBk1JsEWWLL14accdP36cRqNBu91mYmKirvQFVRS/kHDvkIpb9hfp8YdcpZCHJK8Vz5OwCGo0Gsv2gbA4AJZxlcICKvycodRZnhPyC0Ke1Uo+RHgO4Qrs2LGDwWDA9PQ0MzMzHD9+vFaVhXMOXuwQTsC/9uPHdoogvHRewHdDAhYXF3nggQcoy5Ldu3c/LwFYea5QFieHLACB3IX4EhpyJElCu91Ga82+ffuw1nLllVeyfv36+nkhMUcWWzgPQOD0sC8nmbgsdsms5VpOnTpVzxpYtWpVfe2SoQtPAaiJQ/Iz+WxyTkkuXoicJDLDZrNZ38MQopQkJs9zpDvuwkpMw3uwuf+l9d8RES2aPpAKvcsBwRp8YmA8EA/iY2dqwZ3UgTENMpooMlwP2m3kpu5DC7u/oKBP31eiGrHITWmQU9Bn4BkEjjQ45l0KlkyH8AiGO1zYccZFLuEpa1h53PMOxPRHpgWCC/pL1kdLEsKQIOgSGx00BJbeVfswLq6L7pNkTEy9lvSOOyg84bHlUwDn4Zh6lEWEhPikyvHXBww8L8CNEio8UF56pz2ZaxChGQ769I89QetVt9Dc+irsmjXkzSaJVgy7pxHnwoyEFMUI+OG/LmSmvjnU8YkgLFXOompYGqO8dJ9yD/kDngLo9ACVr+alyaOIQGl0FBIFNQOG9Bl47oitvxPuHrlvquOlWGRWgUglXcoR+wTJ9fuj+t0iT0LNPOPFNYgyUgZVh16vV0t6+/1+TeyVQ9A3ad9t3LiRLMvqwT7NZrOugI8dO8bMzEy9RwihN4Tww2BfFEX9foISrPQXkGJBAv9KFFFUAALR93o9+v0+g8Gg3tPCQkG8TCTJEQQy5D5JASfXG0oMwyTixWJBo9Fg8+bNXHPNNdx6661cc801NJtNTp48yTe/+U0eeughDh48yOzs7DK0FXjeBEGAP/uzP6v5TTfddBMPPfTQC76vHJ/5zGfYuXMnjUaDa665hi9+8YvLfm6t5QMf+AAbN26k2Wxy2223ceDAgfrnX//61+vPt/LPt7/97WXn+fCHP8yVV15JlmVs3ryZ//7f//t3vLaXevyLQAK+UxJw5swZnnrqKbZt28YVV1zxXRMLOVeYqYa9+jA5CIOmQPgzMzPs27ev9vkXUoz0xiSYS3CVhSJBW84t1bgsoHDxysLo9XocP36c0dFRrrnmmporIJl7SNKR65TevZCEoiiqCX8h8UfkStL6OHjwYP05Q+vPkHQk7Yler4cmp+krdI31vXZ8TSjUPOurrIKcCkWPxPerrQ8QS7Cqk3WViMhPGglupptY5roKPvdVWlUHmj5LzH7r0whVE8AEKXA0xCE5fRwzPaHlq0gHaltfu0d+m3f1oTOmGfjQJDC2g+8LxLI3hxoaT1hizluWDIRC+WAMNIlo0vLX7OpcaZuUVHQZkiP+ABEZLUrcBMOcIS1Gcdp/J7sbeuld4tMwx41IPAqgfBg19HxTQ9AYgwxFNigqFp54BlUsMPaG12NQtLZM0uKV9E9cZPjtrxBbaKH8wB6XzPXJkQmBmW8ntCmZx7UJshX3RSyUBU4XYqb4EjZokBDTx814dFW98S0Mny5J7xmnDVhKeyTAS9oZ06HrE8LIJwfKkwWp/SsG9BErJPe5ZKYBiAOhUzkoP0PBfd5i71NEU6+i4aH/UHYnPfaQnNtsNpdB/a1Wi4mJCdasWYO1loWFhVqVA9RDiERyKPuRnD+E2SXISwECDlGUBEX2EVnvZVnS6/Xq9kWWZfT7/dppUAqAfr9f75uSYIRTTUOUQloLtcnYCqnjSnn2Szm01kxMTDAxMcFll11GnufMzs4yPT3N008/TVVVrFq1ihMnTrB9+/bnSQQ/9alPceedd3L33Xdz0003cdddd3H77bfXdu4rjwceeIB3v/vdfPCDH+Stb30rn/zkJ7njjjt45JFH6uFBH/rQh/iTP/kT/vIv/7KeHnj77bezb98+Go0Gu3fv5syZM8vO+4d/+Ifcd999XH/99fVj73vf+/jKV77Chz/8Ya655pq6ZfRyHP9ikoCV7QBjDPv37+fUqVMvajn8YucSyD38QobkldBEKAyax48f5+jRo2zdupU1a9YwOzu7jMUffsmBZT4Dof+3PC4/kwRBIMI8z+n1ehw5coS1a9dyySWX1ItKkg1YWuywNAGw1+vV0KEEb2EYy+INRxp3Oh2OHj1Kq9Xi0ksv5eTJkzWKAdToSKgxzrKMwTzYcWfdkwXA99AzsJ0dryNoGR/yW2S+ynSb/cDD1Aph4y8J6zTWW+cY309359W+IjMeil5ywnOJgQsTMlpW0aFPmyaOk249lKz8hL4KGXwkNYQjsLnatGCACtoY1FfnrrBvSoyGLsvd/URCKItPql9BGZykz1XrMiY3RlzyXO+79AOOJEkqcEI6NzXPWfM622SHEVSUdBn4arlgxPfFBwxIiGjQ9gHR8TH6dHzLwrVZ+hTIAB8L5KMTtLZvI6Hp++/OtklvGWF007/DzlmEQW89dwCWhiQ5/ob19zSUEi4hIA7RcYdruyhif/ecX6SzjWp4GL70ssPENyB0XccvmSsp315yrxnWZ3fGRJqSIQUpumYUZDUx0nkUuARSCKrOcTD2v5vYX4VTUzRo0KdPF+guXGTs6FFGtm6tE2VJwiXwSwtPjpDQJ1W+JPZxHLNhwwZGR0cpy5KZmRnm5uYANxp83bp1dSIh+0Io25MgLRV8iGqGrb0Qzg8TimaziTGmbkcCtaIhdCcMyYRhOyAkVcOSlbocUmT8ICoAIRiuX7++5kdMT0/zoQ99iC9+8YukacrOnTv5/Oc/z0//9E/zv/7X/+LXfu3X6pHBd999N1/4whf4i7/4C37/93//eef/6Ec/ypvf/GZ+93d/F4A//uM/5t577+VP//RPufvuu7HWctddd/H+97+ft7/97QB84hOfYP369dxzzz28613vIk1TNmzYUJ+zKAo++9nP8lu/9Vv1Z3/mmWf43//7f/PUU0/V3jnbt2//vu/LyuPHuh3wUo+VkwTFcnhmZobdu3e/5AQAlqMKoQQwzFpXkgmVUhw8eJDDhw9z1VVXsWXLlmVfXunNr+x9hVIdyZIFIpP3l6RDkgOlFIuLixw5coTt27ezadOmGqaTPp3M+5bkKITTQlcx2SAkGZBqRFCEc+fOcfToUdasWcOWLVvq+yDJhSQKQP2z0dFRJiYmaI6DpuM3fDF5kU6tzGwvEbNet5m6ZEG03A7adXP3XK3v2gspGdQ1vAQPqQ6bNGgyzhhtRmjSIiVlhKbnCbg5801aZDRr62E3LsZ1xlMvw7PAIn2vOXAOei7xcBjE/LE5hhS+Wo5oeQW9q+wNiVYMWBoBHELdTTRtNA0cQuESH9eXl8FBooGXqXlO6yBL1r1LgqqD6MAHIOMJa3KdObmfxVCSYymw9OgwYOATIkemE+Mci/Vtg9hLIl0iUPkWSUJMum4C/LXJoChRdKhIk006HwZnCOwsdcUp0YVxRYFDaBQOyZB/iyJDfAaW5itE/i7EPrzLN6tERlPhwfrYe1Ao6zz7E988AWfckzNkgFhTVwywDKn83TMewXAtmIyYkgGVTxqU/5YIqTLyV+T4GgVOc+G8Eg3Q37sXmw/Bo36yBgV5kzUOS7798nj4M0n+JSEAagng+Pg4l19+OZdeeikjIyNcvHiRY8eOceTIEU6fPs38/HwdgKXvL/uKXJMUOqE3iRQHghoILykcWS7nkc8kf4fIYrinynvIHhUWSGHV/3LIAMNzjYyMsHXrVv7qr/6Kw4cPc/XVV5MkCXfeeSerVq16HvSvtea2225jz549L3jOPXv2cNttty177Pbbb6+ff+TIEc6ePbvsOePj49x0000ves7Pfe5zTE9P14kIwOc//3kuu+wy/v7v/57t27ezbds2fvVXf/VfBxLw/XACZmZmeOyxx1izZg1XX3319+wL/WKthfBLGjrsDQYDnn76aYbDIVNTU4yOjtbnqI1zAuZuGJAFARDITfpxQq4JSTSSmR89epSFhQWuu+46YGlksUD6AumJZn/l5xFpj9ZuKmGYYIQs4hMnTnDhwgXWr19fm5oIhChQYFgBSFUj9qPZVVex8NgTlE9+m3TqKpz4rlkHAGfh6vzpI08UA0WDjKHfjofnZ4nWTeL87YfeUtZxwyNiUo8aVD69aNCg5YHxyrcP5Gel3+hj8CHC0c0MsQ/zOTLm2IG/CZWXrgH+eitfjTq64uqtk1Q+CRCCWUqG8v9tgIa/RqnwBfZXdergYHjXA3eoh/LXOkKEIq1Dnuv9x76yN/SPzBJvX01oV5xhvNzR1b8Df30D8tr90MkdlW8kuDZB7K9k6FMw7a9w4F+1tBKd38BEW9H1d1Cm+jkGvrvSHkKVlLvs/ruJKEOW5JNtGhTkHvFx90eMdcQ3QX7PCuNth2MqBvXvp/AQv/bJWuxxiy4DnOdD5Nsdjr1RSMLCUvvFqThi35KSKtUNEHLckdTjMCIlzLD1+8p4KGjQxGAZ0Kf31DHUzAxEEeYVr6jJfLKnwFKvXda4JP/dbpc8z2m323VLURA8IQtLm1A4RK1Wq+YggSO/DQYDTpw4Ue9B69ato91u02w2axRiGZ8naH2GBkRhoiJoqLQ7QzQzhPMFRZWWpqCOIalQ9pUQdZX78534AD/IMTExwerVq/n5n/95/st/+S/s2bOH3bt3P48jsH79ep599tkXPMfZs2dZv379855/9uzZ+ufy2Is9Z+XxsY99jNtvv70eXAdw+PBhjh07xmc+8xk+8YlPUFUVv/M7v8M73/lOvva1r31vH/wFjh/rJOClHgLhHzlyhIMHD/KKV7ziedX4SzlkEa4kw0gQDvtkxriRvDJuWHryspiF9CfV/8pkJAz6sgAlaMsCg+V+Afv376fZbHLNNdfUi0g2EoHlJaFYWFioE5WVCymE+oSTIH/KsuS5554D4BWveMWyBblyiIjAl7LIrbU1zFkUBc2rX8n0E4+T+A219DC6CwoaoUQlxJ5V7ayCjW8X2GOPgbkGtWFDDYOL+7uT37mUIiWiSdND37LdW4ZeR689uJshE+uh9H1bGZPrgo/IEgt/HWAoaND0hDhYYqMrWozQ9TPqxEFf+bBd1qS65wca97ndVbgAJ+TEhIIKGZNrMYwwgnAYnK7C0dlycoZbVzE8eJ5VV2xmQF7D7BZNiwYduh66rmoegsW1Gsog4PbJ0bi5CwPfsEnrO6lZ9K0VcFWzkxe2afq62UHh0sVfgvYdsXMJ5pfkJ/JtAmnX5AwZ+gRiyf1vyTFRZJNuLoAgMU4O2qb0UwAij7Yoj5zkdFEUDHAeA975z/92BZmRfn7LOwamiO+gSyQT5RJDa0EaHBXKcwE0fQb+uysugSXiRdDf+xD9c+ewSpOsW0vqp3aGazDsk7darboaD5NsCYaiOup0OjQajZrvE65zqdCFFyDBdfPmzTU5cH5+vg5Co6OjjI6O1nr78HzyR5IFuWY5v6x32TPFJ0DQRwn8shfKHiGtCLkP0soNUQ/Z+1byGV7OI5QIbt26FaCG7f+fOE6ePMmXv/xlPv3pTy97XPbpT3ziE1x55ZWASxampqbYv3//S7LX/07Hv4gkQGvN2bNnsdZyww031LOuv5dDqm5gGdwlCYAESXBf3BMnTnD06FF27NhRL7DBYFAHc0kUZEGszKQlmxZ5TtgnCyWBSZIwGAyYm5vj0ksvZfv27fXrQ6mhvFaIOjL8Q+D9MLEISY+SecviFwXAK17xilpGKNcaWhTL+GBZ/ALvyWeWBCi74nL0+RKzTqSA2m/YrovcOX2OxqatOBMe978eAxJSJm54i+8oWyxNyjoMu+pVIPOMJg3aiOGsUxM46p5Y46a4Ke+WnB4DGmTIwNcGCRWGgfcTEC54Ewex5+RkNHzdXiGOcdIPFsOiCPGOdwmADBUShrtzO3TVb1LXyI7tnvjkp2BAmwzjq92CpXl8ct8Kaa5o6M4fp81ahFzoutNu/I7yCY5U3iA2xqa+phFi31u3yPjbGJEKOp2DxsH0MqDISQSFP+9+J67/nhDVcxeF2BexZPAsSZdLjeQ3WPn7IlLJhKURzSVL6EnkUy15rugl3Plz3ypwSZxrFIjVsxOFGpw6oPJtgNi3QErEuArfgok8HpT6pLwCK9TUgoyMRKUYK78TEJvmlDZDTxZNd06hz/0DNopoTk0ReS6OBEbp0/d6PZRSdcW/stcuCX7YApC5IvJvqcYlEIds/NADZWxsrPb3EIXC+fPnOX36dI0MSnUve0w4NlzOKUijIIBS+IT8pxAxkD1H9jshPoeVvyAJsp/JZ5ME5+U+Op0Oo6OjAKxZs6ZugYbHuXPnlvXsw2PDhg3f8fny97lz59i4ceOy51x77bXPO9/HP/5xJicnedvb3rbs8Y0bNxLHcZ0AgBsEB3D8+PEfOAn4seYEvJRffKfT4dSpU1RVxe7du3+gBEAq9tDsRxZtWOU/88wznDhxgmuvvZYtW7Y8r8cP1ItDFmPYE4MlQ57QYU+g/ZAncPHiRebm5hgbG6vNMuRaV3IIZOEppWprTcnGJciLpCeUPzabTfI85+TJk0xMTHDppZfWQV+qCUmEwlaF8A+azWZ9DSuTCj0xweKTD+BMa4Y4Ap4j+eXlIv1jT3tJl0iwSmTWu8MMnC7e1V8GMWkR05cmGaDqXqzxQcKRs4Ri5oBphwdoMr/9C7CrfL/YQc0lYmUrtjSVJ9wtQcDKc80dXiAzEJYq4bI2/JG6RippeXyAEBr9yOKu6yjHvqq1aAYM6NKj47n6XXoMamohlH33+tAhUbTqQ8/uj1my+RWFgryvq7YdNz4lJkGTkdHycsLSB+gCKKdlFHLsz+3cBVxwFSNgN9dR7voSH8QlOEOMd4wwPpFa8kcQTwC5V9ojL6KoaAIL33iAi/+8h/7eR9AnuqgZKHvGP9chGzmmDvKShLlzO1Soh9giSSc/9t+UnNITTQvPnigpqMqSSGliJUljTElB13bp0cMNkoprGqH73ZXkDIjaoEcnSIsCe/83sD7Bbzaby6aMhjB4qEYKkQDZowSNDIl8aZrW1basRVmvoWJoJacnTVPWrFnDVVddxVVXXcX69etrM54nn3ySM2fOsLCwUO9Hcg2yR4bzTEQJFf7JsqwO6CtbrOHeJS1Q2R/DfX+lVPvlPHq9Xo0EpGnK1NQU991337JrvO+++7jlllte8PW33HLLsucD3HvvvfXzt2/fzoYNG5Y9Z2FhgW9961vPO6e1lo9//OO85z3vqRMtOV772tdSliWHDh2qHxO0VhCMH+T4iUYCzp49y5NPPsnExERtUvP9HOGXTL6wK7+4kok/+eSTaK2Zmpqq2bdyjna7XcP8ko2HOlcJqmEAD/t74SIvy5IzZ85w/vx5NmzYUPf8QhcuIepItS4Of41Gg36/v6zFEGpxV0pwLly4wOzsLFu2bGF0dJThcFi/RpAJSU6kDRBOMgyTBDn/YDCodfn9YYeFb+4h3XYto5ubDjR98jjF8adobljvt23Xwbd+UxXKlpsXaHxF6YD+lEYN5ZZ1bVjURK7cS8ZUDdIXiFVt5gfxDilp0/BkRBfU+2VJHLvtXBALUdRHNRXNDSESEpjjOsSeUuYG3zoHRMfot75eznEyOAnA4FKUlnf+u/BPX6S9fhuTUzdifLD07uvIOBsXWCuanv++sO8h2lNT9QChxAsiDZXv97vBQa6X7siFMjsBlloU+NaJhM8Ey9kvfQ6zdgv5qnUUhx+h3RpjYveNdQqnsJ4c5yhxCme+5CSWTh7YJvPvnS+r9GMSX1GLX4Dz1m+wVJWIfXADh+Lk53IGww5UFXaYECUXiNe2qQoRBVpkSE+o1JCZC+D8AeR30EST0WBA7pkqNmgkUX8PAVQUkWqNqjSJKejSA5xhkySqwkPJGXgkyD0at1LKRSg7Hbh4kea6dctcQ8MCQNaqrOmwhSjVuaxH4fasJA8KIz/U+su6lP0mZPtL0RJFUd0WSJKEiYkJOp0OCwsLnDlzhizLWL16NRMTEzXvSa47dEAND7kG2ZdkzwuTG9l7pV0grwv5Aj8MFEDUAiEH4M477+S9730v119/PTfeeCN33XUX3W63Jum95z3vYfPmzXzwgx8EnGzvDW94Ax/5yEd4y1vewl//9V/z8MMP8+d//ueA21t/+7d/m//23/4bO3bsqCWCmzZt4o477lh2PV/72tc4cuQIv/qrv/q8a73tttt4zWtewy//8i9z1113YYzhN37jN3jTm960DB34fo+fyCTAGMNzzz3HyZMn2bVrF0VRcPr06Zfl3CulK+B+mefOnWP//v1s2rSJyy67bJkyQF63kuEfBl1x7ZKAKnr9lfwDGSd85MgRsizj6quvptPpMD09DfC8KWNyDpkxIOeB5YxcgfPEFlQ2n8OHDwOwZcuW2gAojuMabhQ4MGxdhEmN8AEkcSrLks7cHJ2n95Hv3Ys2FUWcUHYWKJ/dQ+/JgrQsycqCxFqSq6dwlqyu4ndWK6mXxrkA6CyFnIGsn+XmO7uu3nOWvw4REAKdI4MlvirPfQDSnvVtPYyrvNd7RefkCRYe/Ucab3wrjZYiJaXHvGcxWMpqQDtyjgeqxi0i3xmO6mBvcORATYIQE0vPOShxMHcLkao5znpGTlRV5Hm/fkXJUusg80ETpGduUJ2K0aunvApfwiseMXGPiba99PV/01+hTOIDML6n7qySRdeuyXa9DrPJVb9jm95IVkBCA03pA6SbtVBR+HAtBEyZo2cp/O9hgKvIMw+0u6twr3AYiiFFnBXlM5Y0fQNgfu+TRMkI8apVVN0+46+/FRn8VDUcliPBP/XtBuGbVEAbN5cgnAPhPAMKnzYK9iSpYlSTBw2GYV7SjDKUVhgjY6i8VNKLAiO0bwNUiGeApnLpQhRjioIUlsHgso5kTQoKIJW9FBiS+Idom7xWArAk/DUfJyhQwkQ99PwP5c0hbC+V/apVq2oYe2FhgYWFBZ555hmstaxZs4bVq1czPj5eV/Ph0CAJ+pLMSBEj+4TsFbJXhQijHCv3s5f7WGkb/Eu/9EtcuHCBD3zgA5w9e5Zrr72WL33pSzWx7/jx48vUC7t37+aTn/wk73//+/mDP/gDduzYwT333FN7BAD83u/9Ht1ul1//9V9nbm6OW2+9lS996Us1kVuOj33sY+zevZudO3c+7zq11nz+85/nt37rt3j9619Pu93m537u5/jIRz7ystwHZX9YWMvLcISVsRzD4ZDHHnuMoii47rrraLfbnD17liNHjrwobPNS3ke+fPPz8zz88MP8zM/8TP2YyP+uvvpqNmzYUAduCdoh+1+ycq01Bw4cQCnFFVdcwcLCAsaYelqhMYZer1fLhaSXf/HiRZ588kkmJyfZsmULjUaD2dlZ5ubm2LRpE0ANn4WMWkkyQjhNKnm5xv379zMyMsLExAS9Xq82qZiYmKDZbNJoNBgdHa2lSK1WqyYpSVUyMzPD/Pw8l1566TKCoSzioig4f++9DJ59FqU0JomJrWVYlqA1aI02hmae09y8mdbUrYg1kPgDSC/fjY4d1mx+RwsbeAjWBficHgrFCC0SmljcsCBw5MMhFX16dVBwEsHItwYSz6S3LOy9j4WTJ7Gjq1jzM28ECs5+/Z8ohkOSskRZSxPINu5ATV2JGPsoxnDGPa4THSP2SG4sTc+z8wWGb7LEtG94y57+c0fJF0+STE3hrG1zZLRwBnT37mV44SLR2u1kU1cyQkZKTJc+4p3YJK7HI4uWwcnbXAAZeBpl5cNW6lOE0hMZhV/h+v/OeMfV9e6etUkRPb2MABLf/ISI3Ad8WHL6k/Rk4D9HRsT83kdpTe1CJjA2WG6Y1AOYAXVwL61NOxm5pE2vA8X+J0mmrqF3ooPeMkLkX+vIg24ug9NsZP5KqlpiOOKfs6TuxzM8MpylspOFlhhPDFRegaAQu+s2TeI4oV92Paei9CJDIZkuSUHd98x9+rP/8A9YIFu3jvab3lSv3XDNWGvrqXzGmLo3X5YlR48eZcOGDYyNjS1rO0p1L5W+JPndbhdwgb/dbtdqH1HthAODpF3Z7/frwHzmzBlWr15d2xe3Wq0aRRQJsbgBdjod5ufna2//VqvFyMhInXCEMwYk4IeeJIIU7Nmzhze84Q31vhjuYStlgy/XIYnMk08++QP31H/Sj58oJGB2dpbHHnuM1atXMzU1VWfGL3WA0IsdKyF6Id4BPPnkk8zNzXHdddcxMjKybJBPCFkJVC/BMoT1ZQHIgpQFLtcs/cFTp07VZMPx8fEa5g/7ZqHaQN5feAfCQ5DPIkE5NFISY5HZ2VmiKGLTpk3LJEaiFsiybJkxklQwkvGH90oQgjiOUd0u5smniIFBIyOuDKYqiXVE2Wq5n2tNN2swNjWFrglpiQ+dFQMGHoh3E/QaNMh0zNC4Tq0bZqMR1zw3ImbJ890NDBI7IBDzIJlO59jhvgryLYh4aoqMNlRdFvY+QjJ1DVWRkxQFFkuuI/I4po2hDZ6057wIY5xTnJjSuErUXakEOKlwHUFR1bB6d66gmjvJqhtfy5Dctx40YoI0/Y0HaCiF1Zp06koi8J+xQIx7xHyowvqA5N7NJQUGjSH11EPlTXbcUCIPC2Pq97VoUl/PZkDf36dhTbyTKYeChiiGyCRHmfynvJgzpiQnw0kD+08fozd9jt43v8Xoa29y68R/LzWwuPcJTHcO2m1W3ThFhk8gRmD11G66dCm2jNTPl5rRYmn4ZMd9J5wHgQyTcn9bP7gpokJ4/qW/U9arOQrfDvABCDdH0s1JKChL1/RxvAcnNnS+CQ1KhrWY1CElhvkHvo2NIkhS2n6/ErKcrFWgNu2RVl7ITQJqjo0EUylChFwo6zGssmFJWSQS4FCCKK08KVgk4ZfrEWKiBGohJY6OjtYDj6RFuLi4yNzcHEePHsVay+rVqxkbG2P16tU1gVj2qPB9gdq6XI5wX/thyQOBmov1b7MDfkKSAGstx44dq4fxXHrppcu+HC/kGPi9HOGXTWCsXq/HE088QZZl7N69myiKlk3tCgOxvDasmCULzvO8RgxCAl/YNtBac/DgQc6fP8/VV1/NxMQEeZ7XDFyR/ggDX84hbQapDEKTD0EYhBSklNMZd7tdyrJkfHycDRs2LGsphImJnEvgRUEbVk4zlA1KZpnn586RKj9StiwpoxgaTRgOiPsD0qLEZBnVmknATQJs+FkCA0/bckgAuPkBCbGKqZSTfikPSMc4q17nBigz4yURVAEwiw9ujgDoBuaUHop3z3Ud5YT21E7MwYt0jz+NPdmlqRNM0cW02ySTG0le8SoaI3gjHvEpFPa99UwCVQfwcErgECifPcNi97SnKjaxeRc1fZ7JN70F55eg64QhRTHAMvm63WQ0GTIkx3hSo1TaVY1sODg8r6/GEf6c7M+JL8u6YbDE/9e0vSxROPe51x5Qs92pq3b5PA3vQ+BAfZlmYGmQMKCgiYw80r47bsmPzdM/+iRJa4TxtdtrjoBIKAGSjVcyfPqf2fj614MnGVpKIt97T7yPJPU9in0rpqqTIEdXLGvURTwGXLKkfKopbAtT38uSIRFDSmJPRDXAgNhjHg4VcHci94ljShPXgHHJRN61lMeOQf8C9sQxqi1baF51E+0N6xnZsIHBYLCMeBzC+yvJvrKmYLnzpxQSIScnlPu2Wq06qQgNfUJ5ryQBwhmQPUP2lkajscyvQPYTua6V5mwywe+yyy6jKArm5uZYWFjg1KlTxHFcJwVr1qx5HnIo1/TD9AR4oUMQk39LAn7MkwDJNJ966ilmZ2e5/vrra5gqPFZ+KX+Q9xN/7L1793LJJZewffv2moATZr/hIpF/yznEejdNU7rd7jIGv1T3EuTzPOfpp58G4NWvfjVjfta4nAuWTEVCjkHYAxRy4UpmsaARAgWKz/jY2BhjY2P1+UUdIImK9PpDI5KiKGqUAJb6dYIYSBK2CBRao6whUgqjFBQ5URSjjNOTD6sKHa+ioPK8akeqWqyNaSxNWgwZEqMobYGpoEsPkXnFnr6Xknpo1qEDxgen0lPChDDmtATGQ74ZsbekKf1zHbe7RXnFRtLta8gi6D3eI40jWj/7JiISRHYX+XrYIQ7K9+GFSihqfk0D8dhTjJBgdm5BcynzFPS/eh9Jv0+rKGhmTboMiDBkNDyDoSKtWQ+ua51i/f9rhgyR9CbxsHzug2Hil7VTALjPLaC7tAucFt+Z5wzo+Zp+6fqth8ItS1p/kemJhM5p4yPPD3BBtommReZHDpc16J5uHUOlP4PdmNXJArgALHqH9qYG0ZlVNGkjDoaC3CS+Py9zDGJv6+Out+ETRDdXQfvrcK8TDknuwXtDgjgkKs88cSndwoHzJDtU/VurPEIyRpPCJ5qVZ6W4yZbGt1AcJlKcOELn1LPEgwGRdXhCefgwVZ6TK4WamKj3ElnDwqqXoC18ACGugeMEjIyM1GsvJBKvZO3LHwnYK4uQUHMvvXsh94U9fdlrpJhI03TZe4m6QJIXKZ5kNLKcZ3Z2lvn5eU6cOMH+/fvr6X4TExP180JJ4I/q6HQ6AP82Spgf8ySg1+vx4IMPkqYpu3fvftGxkD9oOwCWgpoQ5V75yleyadOmOhCG/X9Y6l3J+0tlHC4wWYzyb1mYwtyfnZ3lueeeY/369WzcuLE2/wjdCSVLDlsHoXRRFrnAeuHmElYTJ06cAFyPb3JycpmmWLLykKErcNlKGZPAeKLplcUbRREXLlxgEcg2bsLOzmDznEg5tnVlDarI0da59sXlrOeWJ1gUfTqIw5xMHxxjlC49+vQRBzgBoo3vwIpjmwj0HOHO+dPl3iXPkdIcfiCGvMI/T4gYMvAcb9fHT6OY7qwfmXrlDf4bomjQ9HSxJclhRobMoXc/d2HLsd/bvjeMxxqa9OkxSszorp8i6swzdtlWz2BwgW1IjvJQvqs9S//eDR8GHSif1NoLxdIw3eXqg5y+T05yMpo1hc/dg8pPH3DeDMqnC86KOCVngJNEuqCfeqpb4Z/vkJUIiyH2DZnCMwlyInJfTbvfr0tHko3OW7/vGymJFxGmLMH7jakpn/wlNYoAhkU/zAiWjIeGuFHQTY8kiclUTo6bIJD4zyQBXbQilth/h/o41kJCg8HCCfL+OtImXqiJb/gUJFhPA41qbkGEm1+RkhARMzz5HLrfRyuIlGZ45ixg6Z49Q/7QQ4y99S1U69bVAVTsvaXaFrgelg/zkuparMFDgh0stQvkMUkSwuo/DLRSqITE5pC0LD+XawxNzGQPW4kahPtCyHUYHx9nYmKCzZs3MxgM6HQ6zM7OcvjwYbIsY9WqVfV+Fs4O+GEfQgr8UScfP47Hj3USkKYpmzdvZuvWrd/xl7VyMMX3c+R5zhNPPFH3qFavXl1/kQU+E2heKvWVEj8JlCFyIMlAr9dbNqDjxIkTnDx5sh41LHC/bAQhd0AWlZAK5dzhlELhDIScAHEVe+KJJxgdHWV8fHyZtCiUJcp7ynnk80iSIYOJ5P6E92Q4HHLx4kWKomDDhg1EP/1TLH7hi9DtYYY5VRyRJymZ0lhTklQl6sRJsqkpHz6cSlsDIyTeelfVcP7QS9AiEmRW/BAxdzFe4x750G58cJaOtTPOTck8T9+NwTVUdc/cegjfeH56TEKWD+hXFY2dG7z4MBziW/nAH/mrFwnaAE2M9X3h3EP84EBjqeozmqh1bcy6cYYolpj1lQ+oIkp0n6hBw1+d47eLPM9501sfrLycDSf3i30F3GfoCXQO9WiQIsx8V812SVhVe+6VRJ5VYfyVu4RDvPJLepS1AsH4fnoVtFm0V3U4HUDhuQRLScoSQW/oYfkIPLvepXYZhkFNAKXu74djhbVvb4jqQ/vufISlTaseVGU9WqHQNHCTDB2kb2p8pMSS06cxNcX0175ONj6KHdtClOc0rt5K4XkobWIKf9czWv7euzN1nztEPlgkqkqSylBWJZlNGSgNzQRdFSx+5SsMfvZnaYyM1JW3yHwlCAP1PhbqxUWWGyp0QoWSGIQJiiBBXwoXeQ+xFG+328smk8peIvyAUMEgiILsLbL/iYmQFGeCgMp1hXun7EehrXG/3+fMmTMURcH999/P+Pg4k5OTTE5O1pbrP6yj2+3WXir/2o8f6yQgSZKXNC0phNq/nyRgYWGBRx55hJGREW666Sbuv//+JbObFWS48AgZuhJABYqXRTscDmvmvkDnx44d49y5c1x99dWMjo7WiyWc3Cd9Oqka5P2lNx9ad8rzZEORwN3tdnnyySdZu3Ytk5OTzM/PA9QJRUgsXKkqkD6gXBc4UlCv16thSmlFiMrg8ssvd0lFHDPyS/+B6swZin+6n2JhHluVmKqCJMFYS2IMi1/4EqM/9Wai9qivDCGl6ZUAMnTI+HrO+qDrusna17xuGl9Eg6ZnwJf+mbEPX7EPpaLkd5WlU6eLp4CtWeaxbzRYUxEr0ao3sB4xGCKjiqhHEnmsw3eUndlRSemrzQoZXONseaRTbXyfvyTx3foCJ5WT9ohrlcQ4R7scoVCKIY+q0wCXQKQelnfcChkK7KpZNy0w83fLknh0Yb47R9IWLoO7UykxAyLf947qe50jNjtLsxSkXh74mY/O/dAlba43r0h9SqM8wtMn94C89UE/CgB2E9T/rmUR++cJP0FUCwYZ2Sxjgsua+BcT0fF1fqMO+Y61LxbFgigIhlgCEz/zUwz27mVwYC+NLVcx8K2CNi1in3hJQlf254masLD3Mbrnz5JWFQ1jMNYSp5lDfyINeUGc55TGUJYVnU6nhqNXrVrF+Pj4MhRA9hZZdxJ4JQEI/QNClz1Z0+EfSShkDxOTMmudHXCv16v3FWkvSItAWgGy90ihELYNw/cSKeJK10DZY0LDMTE2kuvZtWsX09PTzMzMcPTo0ZpLIHyDlQY6P+ixUh74r/n4sU4CXuohiyJcOC/1OH36NE8//TSXXXYZ27ZtA5ZLcELTHIG/Q5JOyJQXiF6G+AhcL2z7oijYt28fZVkyNTUFLJGCJBEIPbphibQYuonBUvsidBMbDod1T/HkyZOcOHGCV77ylXULQV4nGfBKgp9ch1yXZPEymCTcgLTWDAaD2p9h06ZNy9AQYwzVmjWM/NJ/YPb/+r/AWCdgGw6JKzdhzwy6dL7wGaKRUbqjE7RefTPVmBtFM/Rd7tgHj1A/0PeDWpznfkLk2e5i9lr53q8NOAGRh66V38ILypqIJ8NtnFGOoxvOnn4KqxS9Ex0aWxzSILK3JZNdF4pcb92p20Xil3jOQlID6Q6MloE3C/Q9qVCaC+7clcdF3HQCCZ0VKQ1iD8ML4mGxHu527yvv0aRB6f3sHeAuCZOhZOArWHdNSXscKOkH3AgXRFNUbSAkyYTyPXmofE8/IqFHD8uSEZFLShwrI60TC7H9LWn4REsRU/iKX/lkyY0udowBJyt0/IGYJXnfCHh5Y0XL40DG35vc+yc69b4YBhU+TVsyEBp0oOwWlFlCNCHJhPcVmJqi4fGkHn3fbHCETFEGzD2yh4VjT6O1oowiWkVJYg2l0hCDsRarFHFlyJOYKo5BKUajiOa6dSwsLDAYDJidnWV2dhZw/JqRkZG6sg518qH7qOwPIaTfbDaXtUVDZ0BJ6qVwkNcJiin7Zsj9CX0G5Hzh2hfFk7xGkoCwJRmqC+S/JZEQ/oE8JqORN2zYgLWWxcVFZmdnOXbsGPv27WN0dLRGCUZHR3/gCr7T6dQ2zf/ajx/7JEC+UN/tOS9kTfmdDmMMzz77LGfOnOHaa69l7dq1ADU0JhU2LGlVw56/fHllUQlrXsh6smikrz49Pc2BAwfYsGEDmzdvXiavk8w7hPOAZbCbVOVCxpGsWhaf9OmGwyFHjx6l2+2ya9eueuhIv9+vF1673abX69WLM3RIlMUt6IIEfkE25B53Oh3Onz9fewzAEjIgKEqWZRBF6KIgtmAtNI3Fao1JEnIhSBU59uJZkjG3CbuhPI6j3vZBrkSmuBsf+JyJkEKm3y0ZAjmjIesrxBIxgIlJaNCm7wmIFTIMxzn0OXi5ojM9S37yFKVWDA/spbHlDTiyYhtNn5Ky7gPja9gKSxPtiXoZBQVNWiyNlYUlI2IYoV1zEZzGPfXz7nLcqNq4Ri8MmpZXTEhSk3tIPvMhyvokB5+AgExrVHQZ4DwMS69c0JReZtggo+jO031mL83rX0vlWzMyF8F5M+T0KWgQk9JGRICuZ780IAiWxgS7BKuqA6+zhk5IaeL8+TV9eh6HEavg1JP23OccMqRHjkggO7gNq8IZD8k0STfcqfQUzASF82DQLA2QcuoCR1wFGBmB/oiMtlrS+C8lA87QOPbfHYcGWfLOBRa+/g908wEmSaiMIS5K0qqkTFNypVGmAqVR1oJ1LI4iirCRJn5kL6O/8AusWrWKsixrUu7s7Gytv5dDSNChsig04gmPUP4n+47wB8KCIQzCst8AtVfAYDCg3++TJAmtVovhcFgXIWGbUvajlShFWKTIHgXLFVjh33LecL+11jI2NsbExASXX345w+GQmZkZpqenOXHiBEqpmmA4OTn5fTnF9nq9f1MG+OPHPgl4qcf3IhMUw6GyLLnllltotVr1zySohosszG7DKj0k/4WvC6v4oig4c+YMhw4dYseOHVxyySV1UBYYXhaVZNShoUjo2y2MfbkOed9Qy/vss8+SZRm7du2qp5KFpJuwPQHUgT4kEcpnDu9tyEAGOH/+PJs2bao3ipXSxaqqUL0e+cICoEgUVFrTiWOiqoSqQilARegsoygLihNdqhEoD++lCzQa60iv3kJF6gNil8W9z9CeeiUJI8gIm8pr5oVHEBHRo4/MAXQtBRcych8kS185ik1sjGJYWXqP7cH6CWsmiqi6C/S/dC9m541k28bJkYA3xFW4DRQRo954RiYdxoiLoPatAallXQB015SyNM1Q1RZBGS1k7G3pU4RhzTdQvioXwZ9rgMiooNwnEcrzC2Jv3SuzFdw1J+QewE+IyNqrya6ZQtkBWo3QIKkTKBmM49KLkogBMSk5uX/O0F//0rAf6yt7J+PDJ0zOR9BNkEyQYVEC7cf+9+fSh9j/1paIjnP7TpJddUmdBMxTkgAxAwY+1I/SwnoZae6bFy0iUjK6niSZUtH3581w5kTCN0iAUaRxY3xLw40vGvr7WozAcNBFK42qSnIdUWmNJaa0ljLSlKYijjSUJbHSRJUhbzaJi5zhzCzFcEjaaNTOnY1Gg40bNxJFUT36d25urkYITp06xfj4eO3OF44DF7JgyImSwBvae8sfQQFk/5L1He5Fgv4ZY+oWg6CaUryAUxXJ3iGPyzWFvADZz1aiBaHSKmxxhHsaOIRk48aNbNy4EWMMi4uLTE9Pc/LkSZ555hlGR0frhGBsbOwltYQ7nc6yff9f8/EvJgl4qTLB0HDoVa961bIvnxzhQgrhopVfrpD0ErL4Q4JfVVUcPXqUq666itWrV9ePt9vtGpIL3bTC3lzILQDXx5JeHCwt3iiKWFxc5MCBA7XSQNABgfpEKxyOLw7bEJJEhPLDUJoon/fUqVOAg/8bjUa9Aci1yPkG+5+jc/8/0fTXWkQxReTlSUa5+eoKkjzH9HroOGbmuW9BPqRZVDQwYE8yd+RJxt768/QHcP5rX6SwlvzLJxjZfi2NK9cRM0rpSX5L9LuKlIgh4jZgyTFEPjApNBkpPfpA5WVlMXGUc35mFm0s2XBAol3Psiw6FI9/nYXHFZW1JDtvJNu5GTGVSX23v6hDGjV5TkKZ9eQ5UeNXnqQolrOOva59wBS/AKHRuUpWTHkqL1gbZ9SnHcafKcfQx/iqtSIn860PF8CVH3aEZxtoz55IaWWTlD7g575l4NoMmQ+x1pMnDUP6/tqdFW/i0QkZCJSz1K93swIEu8FjHa7XHPmgDPirqvznLHw1H7NISZVDctUlNRcjB6ReHvr73UD5+zKgJPYYBDXLw9kiL3Ek+uE6RnQWLrlKSes2VOSveYiqeRkmSYirikpHaAUozaIkwZddRnnmNGVREitFWlUYpYkGA4giqEpUULCEZGJwAU9aev1+n06nQxRFzM/P15we8fhvtVr1vhcilHJOcSENVQDhLAGB9WWfCFVCYRCW6xOCYEgEFIJfiETIa2WQkFiahz8LWwaSgKzcW1/o0FrXCdFll11Gnuc1SvDkk09irWXVqlU1l2ClPa8cK+cG/Gs+fuz1ES+1Z/PdZILWWo4fP87DDz/M9u3b2bVr1wsmAPD8hGKl/l4WjSy2MKuVACpB2RjDrl27mJiYqF+fZRmtVot2u02WZbXzl0iGQpg+lOOEDmKhTPDMmTM8/fTTXH755Vx55ZX1BiJ/FhYWWFxcrJ0QO51OndmH/IXwPSVZENlQVVUcPny4/n1IG6Tf79Pr9VhcXKTb7Tri4NGjdPc8QFq46lNrTYXrkZqqIjaGUkFqLakxpJFGW0ujs0grL4irAiwk1mKLIWc/93nO3f8VV8sqRb+qOHfsCXpH5jx87Jzy3By4ge+rO/Z+i7bX1ovDW0xGk5Sk7gGHw2cm3nQbyabLsK02oLDtcdKpN9J++9sZf/vbGLvup9GnD5B4KD6pq3OnSig80F750DtkQJ8eA/rIrAHHcpd6PvZ1s60DDx5BWBpdLH59hgFdX+0vDTPGowExDcaYpOUNiYUjIN/y2GMD7rGEBqO+daBZmtooI5kqxAPB+jTADQou/Gd0AaIp6w/xEXDBv4GrtFM/3mjojYWFyGh9ayfx1MLY+/l16RFhPc/Cw9jp8gQjAiaACWLGGaGN+10ZIN9/Hof4WK8qyH2ryM1MqILrc/dkaaZDA5e8FRT06NOj6++J8ZZLlt6+k+RKURlLkg/Jhjml1gwiTd5skr1yJ+n4OFHl758x6KoktoYYSEfH6v0oLCKstTV/KITYwY2k3bZtG5dffjmTk5OUZcnx48d59tlnOX36NIuLi3WxIC3JULIsyXk42jc8vyCTIcdA9qDBYPC84WShNFiuOYqiOuivTDzCfVaQhtDkKORZyRG+/jsdaZqyYcMGrr76am699VauvfZaRkZGOHPmDHv27OFb3/oWBw8eZGZmZhm6+0JJwJ/92Z+xbds2Go0GN910Ew899NB3fO/PfOYz7Ny5k0ajwTXXXMMXv/jF532GD3zgA2zcuJFms8ltt93GgQMH6p9//etffx6RU/58+9vfBuDo0aMv+PMHH3zwu96bl3r8i0ECvlMSUFUV+/bt48KFC0xNTbF69erveK6V/IJwUUgADok3IQEnz3MuXLjA/v37mZiYYHFxkXa7Xff1pE8WJg8i1ZHRm2G1LpwBWJIkhpvHyZMnOXv2LK94xSsYGxurUQJBEcJrk3PIZhAegkCEUwpl8VdVxZEjR1i7di1r1qzhmWeeeV4bRFi+SZLQefBbJMMcY91GXKiIvHJVOFFMrhQmiigtqNgwMIZUa3RlUFq71yQJpiyJjYE4cqZDzQZRXhCXBWp0gmj7anKGiPjOKQrcyGEhA0YemK88ChD50GqADDfBL/WBUfz9R6euJuIqf9bU97RdxR1tHWd06+tpURHT8oTD0oerpb574t+3T+FxgtjzGkyQeVv/euuv0A3etad6ZI0Wg6PPsHj+GKPXvI7WJSM+kLnJiQ0sA3p1pV6Qo8lIVANlnYFNjx4C/0td7OR8kVdC4O6IUuTWTR5M3Vm8GZEgGfLpbA37ix1y5NOnEvzntJ4pIe2BITEpQ9x45RhqyWPJkjW0+B9kOFWHS1SMT5n8uvTnjHHKjNizMQxDOh3Dhf0PMkJEwoZ6ZLOjBA6IcAmLCa6tDXQRH4klboNjO4i7gaHAMkaTkg6DE8+4tkUcEeU5KopJyoIqa2DLgs4//AOp0kRFQRVFDBoNosGQKkkptaL92t31mpG1LmtNVD9hW1D2IwnwIyMjrF27tobsO50OFy5cqG19RZcvrcBQLij/vbLVKXubPC48gLB9EA4JEzRS9rIXaiXKfhzOLpC9Z2WyIQVU6C8Qcghe6qGUqo3Qtm/fTlEUzM7OMj09zb59+6iqigcffJAoijhx4sQydcCnPvUp7rzzTu6++25uuukm7rrrLm6//Xb279/PunXrnvdeDzzwAO9+97v54Ac/yFvf+lY++clPcscdd/DII4/UA4Q+9KEP8Sd/8if85V/+ZT1B8Pbbb2ffvn00Gg12795dK6vk+MM//EPuu+8+rr/++mWPf/WrX+Xqq6+u/z05Ofk93ZvvdPyLSQIE/l559Pt9Hn30UZRS7N69+0XhofCQL3DIAQgdA6UylsdkMIdSiunpaZ577jkuu+wyJiYmuHjx4rJAHsJxIgMUhq48ppSi3+/XLn6yKCTDF83+c889R1EUvOpVr1o2JUyy/ZA4KIsXqN9DyD2h6iE0DamqioWFBc6fP8+WLVuYmJhgMHC2rYI2hORIOV80O0ucD4mspdTCqreklcFYsJETnZmqpIw0kVVYnwBQFFitKRSQxBRVRZ41IB+CUuiyoDUYErXXYTD0vLa/6UOPk5Y5yVizHqMrI4oA3yEPB8/G3tE/9V19UcA72DzyFfXQV5EpDRpE3stAIOKqrv1zZMRshaZJE7HbiXxNnzPEDd/pYIAmo4jxTkqLhcMPkbciVGMd4z/3ZtQiRPQoyEh9x94FLDc0GM8ciCnJrSP1uYE4EUNyImKaNHxgzxmifF/eOewZK8mJC/MyHlhge+P/PyLFmfMsiTQdHc/UjQuQ5ED5Kt9pHpyUM6acKzFjA6x2dbfDPmJyilptoT0p0nEFWKaeEGmgQ0Ic32Jh77cpWhtJdk6h23gpqFMRyPU7VYYjN5aUdWqR1tfrsA7rWy2RT4CG1lItwuKBb9GYupr27W9iYe9euucvoI0hqpxPhclzKmvJIk2U96mimDyOnN11s0FsDMntt6NHR+tgKxLi4XBIo9GoCw8JomEFba2t1T+yL2itGRkZqd0E5+fnmZubq9sG7XabRqPB2NhYrTqQwkD2hbCgkLUdthTEfCjkQoVKopADFP4bqIsCeZ/QmyTkKMjnlH1LrukHZe4nScK6detY5w2aut0ujz32GH/zN3/D448/zsTEBO12mze/+c18+MMf5td+7dfqscF33303X/jCF/iLv/gLfv/3f/955/7oRz/Km9/8Zn73d38XgD/+4z/m3nvv5U//9E+5++67sdZy11138f73v5+3v/3tAHziE59g/fr13HPPPbzrXe+qUQw5iqLgs5/9LL/1W7/1vM8+OTm57Lkv5/FjnwT8IO2A6elpHnvsMdavX89VV131kj0EwgAKLMtMw8elKpdK+9ixY1y8eJEbbriBsbExOp1OHfRD1r/A7CEMJhl62I8PPbtD85BOp8PRo0dZtWoVmzdvptvtLpMpSg9O3lfaFCHRJvRUkE1FZIByH8+dO8fi4iLbt2+vOQyygUkCEPICxHEwnZykGA5qmZRVECcJSV6gTOXBWUUex+iqgqpy7HBj6p6r9psQSYKyButth63WlFmKjHkZ+A54n4Gv8R0V0I2O7WNRZMS0yLD1/0CqW+1la+K3L6rx2AcngeZHaXnNuIPGnSOeq57BMsoozogo9SB+5Tv7Lri4frPyFXBBWfTp3PcVxt78sywNoompgNZ1N1K1INIZ/e6QwXN76VoY2zlFOjJehzDtQ7XxfICSJl26CMKQe9Be+StJyRicOofa3MJ5BIqpUVkjIxB7gx+XIHS82a6j9eU0aNSaABFOdj020AQv0HNQfouYBi2v4R+SoJn+xmfJrcGuXo/aspNk6xgNbN2OcERLZzw0oFjGL5BxPj0c4C/DjBtTN6E8ruA4E1WNRnQRn4TCJxAlKSl44qAjPApKUPhvhuNnDA7PsHD0cYwCnRe0Hhxgk4ji/AVQCpWmjAwG5FWJ0pokTsiGA6o4oVCKyFhMVaKThNFdr6YaG1sGx/d6vXrtS1AOHUAlaZfiRgKyrFvZH4wxtRPo+Pi44+T4OR/SL1dKsW7dOlqt1rKevuwr0h4EamJf6EKolKqfF/IHwsQgZPpLGzOcuCqH7KWhKiAM/N+P18t3O5RSjIyM8L73vY/3ve99/Mqv/ArWusFKv/7rv86JEyeWVfxaa2677Tb27Nnzgufbs2cPd95557LHbr/9du655x4Ajhw5wtmzZ7ntttvqn4+Pj3PTTTexZ88e3vWudz3vnJ/73OeYnp6uE5HweNvb3sZgMODKK6/k937v93jb2972/dyGFzx+7JOAl3qESYC1lqNHj3Lw4EF27tzJli1bvu9zhX1/OeSLLQt1OBzy1FNPYa3l+uuvp91uL+vR9fv9+oseLiJ5D9kUBIaTwUFC5pMJYMI1OHToEJdffnntACjQvCQMYVWeZVmdFMjiEthxJfFPUImFhQUOHz6MtZbLL7+cZrO5jNAj91h4CQJptlotd9033oD6/Lkly968wDSceYpVmgrQ1mKtIh7maL8BNno98ijCliVxWWCThHiYM4xjuo2Gq36jmIHWFJ0LtNnqIWdLm8TXpA7YdyCwxYWlrB4248buSAdZAqSzvB34Ct355TsLG1t332PvLwiuKta+S+yUAG4YjZtQ4M5V1KQ464OmG1Tjaufhs8fRgz52GqrJkpQMN22goEgh7sHCkefoHH4csgwzHLBw/gKTP/8f3O/M16qRF8JFtZGNMxaKSb1MMWVJt1+RbJ5gSO5HDzcwnjvh5H5RDc3HdVBeqsHddELrkybrP5Gu2xuzX/oyNEaY+Kmf8sOIxdLZheT+/v2YqiDesoX21C0++XD3zSk7lE+ITO0zID4OkX8nXVM/3e+h8tcjG1lGysCbBIk8sQKqDugR998xDhnQvsnjMIkltKH3yEGGnfPY/iLGWtARNo7J56dBQarAaI01lkGagjFUSkGk6Uexsw22BmMNjbKEqqSz92GiTRsZWbOmVgUIH0fgcumry3oSiD+c5yFVtCTrspeE7UVrLSMjI4yPj7N27dpaYjwYDDhy5AhAPf539erVaK3rtSt7nuwdoYtoWFC02+26IJDiaDAY1DNRpAUqe1ySJPXzQmVSmFDIuX4UR1EU3HTTTfz+7/8+p06d4pJLLuGtb33rsuesX7+eZ5999gVff/bsWdavX/+855/1yiL5+zs9Z+XxsY99jNtvv51LLrmkfmxkZISPfOQjvPa1r0Vrzd/8zd9wxx13cM8997xsicC/qCRAmK5PPfUUc3Nz3HDDDUxMTHzf54LlHgShMY/8e25ujieeeILx8XGuuOKKOriHph1CvAv7cYIMyKIKofvQVUvOlWUZ09PTnDt3jquuuoqxsbHnEYlgiXgTchVkE5FFHqIZ0iaQBdrv9zl06BAA27dvr68/tCsFlo1ElXOKuiDbsIH8F95K9eUvU1WGrCpRlaFKU6o4RhcFSZFDHJMap8hWpkJZi40cVU4DRVmhqopUKbpKQZxgy5KiqIimz2IPz9C8bDUy276kZEifiw8+DN0FJt/4M4jVkMGNHDaeaV/Wgc41CCofQnsUpDi/eEcYy2jQ8o2Cyve9I0oGOPW4aOLFO9/19sWJznkKiKVvhGVIQkX3+D6HfJw+Rjx5lW8+zNP55wcoL5wBYzBx7Bzoej1UHKHXr/XXYYDIw9qlb0/4qpHKd/zdsCEnc3PTAV1KZHw65AiBQnwTqaShIiLxRLiKxBsGF1Rewuj8D43nXriBPBFNMlSSUcycp7j3y7Te9DOeBhljyek++SSLBx+heelVRFM7fTKEN3uKyL2EUSx6w9HMEa6/3/OhvwW00GRkdQLiJIFVbVMsDoMuEYP5b36Fkdt/1v9enK1yRkZaI0pu/HS+7xj9Mwfc3bAWFTsLKp0PsVUFSUoycK2pFIispTCGTCmqvIA4xhSFU78Yl0ImngNTPLCH/ptuW0YuXok4ypRPge5lP5Kf9/v9ul23kjMQsu8luZBCYmRkhCRJ2Lx5c90rv3DhAmfPnqXRaDA5ObnMRVT2sdCkSJCIlXuMtAsEjZQWgCCXISFR9riwJRIiHz+qJCAkBsp7XnvttT+S936h4+TJk3z5y1/m05/+9LLH16xZswxxuOGGGzh9+jT/83/+z39LAlYeYrYjA4duueWWFx049N0OqcqB5wVY+cIKK//YsWNcdtllbNy4sV60Yb9MsukwEIeOYJJgCAkv7OmHvTtjDJ1Oh6mpqRoBqKqqRiIkW5fFJbyB0O545f0IZwRorVlYWODIkSOMjY2xdu3aZXMIQlkiLA0yClslYn6SpinR5s3Yd76T7te/TnX2LJHWFEpTGgcbG62xFlQUY6xFeyKVVYphFBEZQxUnDOOIyELW6zFME2i1sFVJXin0sw9SzK4nunKK4cgC1eFp5k8/i16YJzUV3T0PseqWG4k9S36IQfkqUztRoO+mV5TkPigJ2U35xwsfKqXi1eTzs8Tjk74idn10B14P60fAQc2Le/eSTU2RMupr2yEL//QAUXeB9oZLGbnmenJvaxszwZpb38qFvV8iP3kSXeSkRUmRpQxwWvwCgxPDDevA6UJ97MOZ8YmNs+YBi5vWqH0P3XjlgTPKKXwrwjn9O6Nid2bnHhgReQZF5FEB44mUYvsrfAAYe+Nb6Ox9CHXwaXp/8zeQpOibf46Zx+5nmPdort9Ma+o6rxJYmr/ncBLxfHDERceycElAhyUJ3xK733gcxv1d+D8Jhj4O4pd+fzEDlAX53r20pm6o2Q/KX4EkJPmzp+gefdIlRVqDgciCLnJsZUjiGFPkoDWtqkIXBVZBK4oYomhWFflwiGk2KMsKpRUma1AWBZVSVMMhAy/zDYcANRqNZY/Jmg9HeYfkuqqqls0REfQwVA3J/tFsNuskXgqCNE1ptVps3LixVgvNz89z+vRprLWcPn2a8fFxsiyr17QkGoIgCjIh7cCwXSn7huxlUugIGiAFB/A8EuSP6ghtg9esWUMURZw7d27Zc86dO/eiffgNGzZ8x+fL3+fOnWPjxo3LnvNCycbHP/5xJicnX1Jgv+mmm7j33nu/6/Ne6vFjnwS81MxwMBhw7tw5tm7dypVXXvkD9ZVCYqBcQ0jSiaKIY8eOcfr0aa655hpWr15df5lD3by8VvppgiCEkp0w2xfSjyww+VzPPfcc1louvfRSRkZGavheEozp6ennDQ+RlkQ4+GN0dBRwk79C+V9RFMzMzHDixAkuvfTSGuoLCYK9Xm8ZuVAWvUCb0ooQWHMwGNAYGaF7cZrSWgpfaWIMfa1Jywqr/VyC4YAoTXwSoIlNRWosg7KkbDRQpqI5GBCbip6OsL5lUBrL4MxZqpmvgvW8b62Rr0x5/gznP/d5Wps30Zq6ETfkp/Bys9h73IvTnpPmNUjIaOC8BMSux02qM77ir3IJpJnnBpTeptZ4O1uoZg35w1+jHPaZv+cedDZKUQ5YHPZRcUIax2Sv/+na6iYm9UHQMj7108we+bgXPzrrncRaosUBDTQVSyN/3Wca1vK/AueJ4DT3ommwqDqFcfV/SUFB34fdypMJXc88p48bAZx5dKDESSsTz52I/Oft18G5pHLT9qam6HcWKWbOoIuCzj9/DrVmI9mqcUYu3YUb/ewSsMJTOEFMj0ryszn56WNkr9lRV/ll/dtxRwVeyTGs75kbzBT535VLwHonunD+WTh/gbR0xs7dA+fIdqzFElEyxAxh8NReqvMXsPmQ2HobaYVztrQWXeRUsVtbwzgmNZZCaxLlrrsyhsgaVFURRZrKunQsiSLSQZ9+mlKmKShF9c//TPS2t9Wwv7QCZQ0J8VfWcq/XW9YGCCftydoLg6hSSxbCstYFphdPEanSu90uWmsmJiaYmJhg9erVHDp0iDRNOXv2LIuLi4yOjrJmzRra7Tbtdrvep8ICQfbK0B9FCoeVJMeQuySvD4uRH9URIgFpmjI1NcV9993HHXfcUV/nfffdx2/+5m++4OtvueUW7rvvPn77t3+7fuzee+/llltuARyKumHDBu6777466C8sLPCtb32L//yf//Oyc1lr+fjHP8573vOel2R7/9hjjy1LLH7Q48c+Cfhuh7WWQ4cOce7cOcbGxti5c+cPfM5wXkBI5pMs/PHHH2cwGHDzzTfX8H3Y34Kl5CVk/gu0LklF2NsLs/k0TRkMBszPz3PkyBG2bdvGzMzMMnlhqFSQbF/UBeFCCxdo6BAm/53nOcePH2dhYYGtW7fW6gnhFYTJT2hpKu0NkQclSVIPRZHKxVqLjWMn76ucgz3KfelU5KSAi0rRjmNyFMMsQxtLq7CoYkCSxBRlidEKdERaFFR5jtEaZaHUiipJwFSosqS1YRM5TSLbZXjmDK1LLiFqrKM7WKTYd4yxq7YhnyYCeuS0yagouPjtvUzeMEV+6CzJ5RtIGcd6amBKRqZTSuNG1SZrNZYFYib9UBsnJtQeYm6imXnma9Dr0Iwi4rKi6J2n0hqdOYKiWreZkhhxOlT+fQpfzze3X0Xv4JPkaUa68VLat0yRsBqFJfM0wsrrAxzvvaCk8JMMpdJfalIkRDRo47z6Kzp0SUgcT4Co7q4PfY3vevTSNpGZgSWZbz0U5PWkPpf4GD+YZ0C6cwr17Xsx/S5lnGDOnaL9iuvJ1k/Wn1VcHoXtLxMB+w/f65CBdZuJLmkhYWFlSu++iYYM5eV+4kjokqDet/fC9EWyqsSUFUWaks134fg3yfcp4sk19AY5cT6gMhVR5Ux9iDQN470sLJTGj1UyFaVSaB1RmYLSk1QLQOmYtHJcBmMdyqWMce0twIieMs9haDGdDtHoaB3sQ/RPkoCVpmASTCVIyP4kyUIo8wvHFMueET4nLEykaJD3Eca6OJuKe+HZs2fJsqzmEaxdu7bmCITk6HDvkEJDjrD9GcoWw2v9URySXIUSwTvvvJP3vve9XH/99dx4443cdddddLvdmqT3nve8h82bN/PBD34QgPe973284Q1v4CMf+Qhvectb+Ou//msefvhh/vzP/xxw+/9v//Zv89/+239jx44dtURw06ZNdaIhx9e+9jWOHDnCr/7qrz7vWv/yL/+SNE257rrrAPjbv/1b/uIv/oL/83/+z8t2P36ik4CiKHjiiSfodDps376dxcXF7/6il3BIRS//LVrYxcXFuv9/ww1uznzIoA0hMfHml+eEDN+Q0BNm84ISGGM4f/48x44d4+qrr2bjxo0sLCzUicVgMFhGFgr5A/IeYv4RzhSQQxKJoihq4su2bdvq95aFHXoLhJuHfD5Yap1Iq6HT6Sy1IoZDiolxOHeOyFqqJEHFMUmvRxXFUBREcUyhHN2rVJpmOfRGKxApha4qlFEkleMMFFqTrN+AjsaZPf60n01QkjQabJi6EWjQYZEcfO96yRymONtHTSboxIWihq+1Yxqkl08x9+QxVq1eS3Wmg964ClA0aNGI/IjVLKLfH9JBM3jiWRrX3oo2mkh85VGeWpcwtvu1zlp3Hqrn9lKcPI4uCrRSRO0xVt16iyfNRTjSXUQcp6iyYIAhXrOZ6ugz6LWbGLnlVpwj4QBFTEXhQzxehpdgMSzSxc1KiMlIGU7Pw6TnfvjuPDQo6XoqHjS8dFF7cqPT+TsRoCLCGQ6DUPEiUgrOMV/fW6d70CTgJYXp+hFWv/X/zUIxzfy3HyQ5d4bixD7YdS0W62cnloh9svX0zHzvUxQKlNLkT/4TjUt+zlXUuCQhYwkVWJr8J26FjquxSEH/4DTR7IxLMiwUzYZj8y/MEhlHptRnz5LFrv1kUFSNBmVVORmqtVitQLk2h9aayFiaqsIuLqKTmKoylFlK5dfFEEuZusFc1hgSpRiWFb00pYojUlOhy5JhnKC8MZis4bB1JyN94ziuiYGCIoq3QGjzKwmCrNFQritBXooEKTZkbcveEGrzBf0TDkCz2Vzm4T83N8fJkyc5ePAgIyMj9RREaU9IAbVy9HDIUZJkp6qqupUp+8uLGbi93Een06mRUYBf+qVf4sKFC3zgAx/g7NmzXHvttXzpS1+qiX3Hjx9fhi7v3r2bT37yk7z//e/nD/7gD9ixYwf33HNP7REA8Hu/93t0u11+/dd/nbm5OW699Va+9KUvPU+m/rGPfYzdu3e/aAH7x3/8xxw7dow4jtm5cyef+tSneOc73/my3Ysf+yTgxSCixcVFHn30UVqtFrt37+b8+fPMzc39wO8X9ulhyV/77Nmz7Nu3j23btrFp06a6jy6QVyjDC3v5oRWnfJ6QPCiBVxKBoig4ePAgs7Oz7Nq1i9WrV9eLVxaKKAHAVfJjY2M1vBdKcmSjWckNELj+4MGDNJtNLr300mXmR4PBYBnsJwE//PJKi0ApRavVqquZPM9rLsTCV78K8/OoKMYaA8Ziy5w8zUApMlMRV6Wr5lFERY42BkxFkfgeJhZblBSJk+upqqJ1yTWk6zOG5w5TljlWQZRkPhiLtt/9/oa44KEAuyFhce9eVk/dxICClnPOpyRnfHXCoNxMEcdEo87kRsbhWFNRKUVeOeHh4qf+b0zaoIeivWt37R9Q+T629QQ6DaTjDaIb3gg35Cw8+Szl8SdZ9aafJmW1Kw4pfXfaQdAl7k918QDj2y4nm3oDEW5+oEWcEZ2bgRvwg+cyxDRJMJQ0acPsDOX9f0/85l9ANUUNoT33QXm541JP3oHrTurXIka8/Z3iImdx7zNESYPhYJru6ZOo295OMuKmCLZoUPj2g0V5mV9MkrRYt/v1dD5/D1Wee1xCrj3BWSM5R8PB3mcZnj5KkaYQaRgMaQ2gauClf9QcAXCSPpH4pThin5j9DGaOkpUltqqI04TKWEzp2w5xjDaGxEP2hb8ztqqIrEN1ullKMswdahTHlKZCxxpT5I4MOBxSaI0d5qRKYSNNpSNUUaAijRLCX9bAyTUhKdxUQ20NqQ+Y4XCfLMuWeQXIXiRJgcD5YbtR1qogceEoYVjqt4fBa6XySdp3Ieqw8j2kdTkyMsLo6Chbtmyh3+8zNzfH4uJizSVYs2YNExMTrFq16gVJhoJ2yF4TGhTJniUo5w/7eKFRwr/5m7/5ovD/17/+9ec99ou/+Iv84i/+4ou+h1KKP/qjP+KP/uiPvuO1fPKTn3zRn733ve/lve9973d8/Q96/NgnAS90nDlzhqeeeopt27ZxxRVXLAusL8chcLlAaQcOHKj7/2LbKV9iCeaSecvrJWAKZBZCX7JQZcEJYXAwGNTM/KuvvnrZdL4wexetcKfTwVrL/Px8Da/JucTi0xhDr9erYUdwC+DgwYNccsklTE5O1gtRFqBUJpIMSXUgi1Q+Y8gels8GbkEPTp2iuOgkVVZrrILEOLkVxmCUwmhNGUVgLUlZopKYYaKwUeR013mOKSviKEJVFSpJ0EVBNBKT0GLV7bcx/8C3yecuoDrz5F4NbjG0vTFshtt8nQd8SXtqynezIaKg8BK/MVpoNWT+W1/GrttINrWbJg1iFXkRmwGrqYYXaL/jl4jSSSz46tu9qyPfObqZ8UE3q5ODiOSaHTQm16EYwaA8o93p/GM0/UL7EDlAdYaMveGtyDyCCBl3FFHR9719Z1hUem4Dnu5YUpGt2sD4O95DToGhoOnRCnFLbPn7ExP7zryD0hOfAIim3kH9lqq/QHHqAKlWWGuont1L8/opnNd/HxCrIQBFURsyZbQvfzX97ll/H4bezCf2fAtY2LuX4tw5VCMDY6AyoBW9px9FT13HytowQ7gNS4nBkokQgJveFysFVUVsLFopqsjJ+mwcsZilYG3dioiqilg7CWmhNVUj89+/kjKOSYGyMqgsw3a7oB16oYxTEbSMYykY4+7dMEkYWEOkNHFVYcoCFUWodZsoypKiLOtRtiGaF/p5RFFU24vLfiSV8nA4rIuMkLAnswckKQ+lw+Hzwz1TEL9wzxNjIlhCL4uiqPcCcIS6devW1cqgTqfD6dOnOXToUD0FcNWqVbTb7fo9BfoPnU1Dj4CVUuQfxiEqjBAJ+Nd8/EQlAcYYDhw4wIkTJ3j1q1+9zNwhlPX9oEcIaT322GMURcH111+/bFHI9UjCIMxX6ZUL1CeLM5T/rfTLVkrR6/XYv39/3Y+ToB+ahgjE9kILN1w04v4VGnKE2fXBgwfZsWMHo6Oj9TWIL0GIXMhGIoiFfD65ZvnMw+Gw3pykWhksdhwXQAFKgQFrDTaO/T8VWGcmVCpQyqCtdbK5OCYqSgaRJqsqWkOn+C/TjLIqqY6epLz6CmKaNHffRIwhqaDnu/OuOnROgM6KVnkTHXeIPfAAiHy1WzEPhx5lYtCnOPwc2dQuUkZppSl5WVKWOSUalU2ga4c7jZgMuY64JiOmx9DLBBNPqov8O1U0No36it452VvPizc18D5k8b4vMfnKG6k8a730xDcFZMRAi5iIjMifwVB4vb/x6oGSPsaT5lISEjLcIB2XCIpCQsyS3CAlGa/kkJTKJxiWiHW33kr38Fmiy1YzDvSfO+8Y9T6hcumf82Ao/HU7zgKYLZtpjm72960kpYBBhVmsMPmQ4uw5VJqi8pzIa/BFc9DGTSYMq/4Ml8TkXldRsGQmBBaTrsZUZ5zltNagICoNVisGWUalPMlQuwQ0rkqyvADl+vyqqlBxjB4MyaKIqCqxcUzRalIagxodwRpLWlXuZ8oTUq2l0pqkMlQWUuu+04lyTghFmjF2801YD4eHRFqpimUth9p58QkRXkAo9RX5nfBwZI2Gkr1wv+r1ejVvIJQ6yx4gvAMJ9hKkRfYn779y6uDo6CgTExNceuml5HleOxeeOHECrTWTk5OMjIywYcOGuoUBLOMEyDX+sJGA4XBIVVX/NkDIHz/2SYAsBAnIeZ5z8803P+8X+FKnCL6U95Pq/YEHHmBsbIxrrrmmzoalIpZevixiCZihdl7IfhKMw4pboDzp/x88eJArrriCzZs314G+23WBK2wziKGQZPaSVIRmG9JDFGheMnvxqRYDoFDCE2qWZQPJsgxjTG1qEm4oIblHPpskT1VVkbRbjlSlNMaWjrwXxVBWWOOGBkVaEZcVJo6psoxoOCCzljTPGSpFrDUNa8mMpdAK4yewdY4+hb76EiCmTRODoRe56jglJqXhJYFDUs9kN56EVp4Z0t7YJvXdZXH9hwkmbv457JHn6D/wRYq/+xyNW34OfemVxHFMJz9Bdfg84zuniKohlsgL66yvoYVH736Serh7wMCL6zQNMnp0KalIycDXxcr31Rf2/iPVkaMUSjGYaJDgvA9zCh/AXaXqrIMjL110DolihORmFOQe5jekuFG6jaRFrBUM3bij3FvxlL4F4M7vgPrIV/8yldH9LCa5bB0KRUYTdWWDfuAmWIE3NHaph2NKpI4rMTqgosWALo7MN0rcSLCNeTp7H0PFMVYpUmtplCWdrAFa0QCaRLRw6dIihgxo0vRmSVAyJMXSJPM+ASW0RjCRJreG2Bq0jlER2FXrGGmuYmH6GLYqXYIQRagid70YH5gjLCrPiSNNYtzI66KqGGiPCimFiTVFHMEQRqxlaCps7GSaWTEk9tyGCOOmCL56FyM33kgRBNnBYEC/369VPitheliyFpZ9BpZL8MSILGz5hbLf0KlU2nehM+FKVz8ZACSHTDSUfS8kGoazUATRFCRi48aNbN68mTzP6Xa7zM/Pc+HCBY4ePcr4+Pgyd0O5/hCJCM3MXu5D9tV/SwLc8WOfBADMz8/z6KOPMj4+zmte85plMhk5Xs52wMzMDFVVsXXr1mXuTRJQJRiHX9JwyE+o85fHJdiGNptlWXL48GHOnz/Pq1/9alatWlUnB6H2VsyCwgRDFqcsfHm+IAdyHqkKDhw4UF9Ls9ms0QoZAxraBVtr61nbIkuSa5JERuC0UEopSU0cx+iRETpaY4yFSKON01WbsiSKY1TuHPWyPEdpjU1iqAw60lS5IwzaYoiNIwprnQWxBVMZyAdcvP+bTFz3WtLRJg1SqrzP8KEHiXfeRGPNuG8MlD48OyCdytJ7+puMbPyZYKiOYUCBZkBGQjk/Q6k01eIcc1/5v91nwLUuWrt/HueRhwf4RWHvRtq6d3I2wsZjBO698Y8ZElIP+TsmfoVzO8wPnsQ+ux/VyKiiiN7pi0xcsZrYy/KEP+9cAp1AsOeFlxaxD1LEPgWx3tdApIOpVkRa0wcGDLxNb+XRDPH9d0iG8coAp1hw3gHCH7A+cRCLZucFKFJG698xIyOh8oyH3kN7GWarabz6cn/Oih45Cs3o1BSDvXtJ1TiJ7WHmZzD5ACz0pmcZBdqM+HvQofAthZiWRz3ctbsmg6Hfg86hRyDLIEmJjCEuS9ARxeIsUWTIJtdhKCnmF6AsKZOEwkLi7akrLE1bkuSOyGmVwsQxNnHW1dEwJ9YKqzU0m/Sqkmbfz5ywlqLRoPStiLQsyKMYOz6BzTKyQBW00pBM2n3SSpTWnzxHdPtS+cvPw2JAyMJiDxxW+fI+L1Q8SEIfyoJlv5CAXJZlLTcMx56vTCSEQyBFw/j4OKtXr2bHjh10Oh3m5uaYm5vj2LFjGGM4dOgQq1atYvXq1XW7VPaZH0Yi0Ol0ai7Tvx0/AUlAr9fjoYce4vLLL2f79u0v2it6OdoBxhiee+45Tp48iVKKyy+//AXJKxI0BQYXOC8k0sj5ZIGAW8iDwaCG8WSy1dTUFCMjI3X/ThZnaBYiiyGE4QW+k4UfZutRFNFsNpmZmeHYsWOsX7+ebdu21eMxpZffbDbr1kHYzgj/PRwO6w1G7oU8L5wVLtcdxzFFr+cCfRyhhr7qAJS1xAoiU0HhNPtpaSiiyMkHi4Koqii0r7hQ9OOY2BpsWaGTGFuVtGYu0v/q39FPUlfRlQWpsdBdxK5ZQ9MPBRJzmcGpGYaP/CPEmvl/+ibr3vBmUpzHf87QO8sp2LyV/MkHMDrCxDEDLwWrrCXLBxRV6YOdg74jLF269BmgsAz37mVs6rUsjaABR8hzyINGZhy6iQIRFd29TxAfe4ZEK+eSqDX5ocfQV1xB6nv5LigvTT9wPIScJVc+5Tv5unYQsD68V90z5EwSxSMMGVLM9tAt0Fmbyicvbmqf8ubBmpQIZ6jrNP0u6VH0fXLlXBJS3xaxRAtgZi6SbNsYSAotvb0PYU4cY/zfT2G8O4MD8ZcmDq6fmkI8/BOg3LuX/uwsVVEwu/cR4qmbADzK4n5PnTPTNDaOe22FG6aUD6D3zXuJywJMRYlCKSirEtIMtKJYWKQoZtCJQ6W0giqKqbRCl5CK9M6431LfT//DWtLB0LWuIqcWyKqKvKoovcmVNcYRCfOC2Bh0FNWkz/L++2lu24qdmKiLCWkjSpsv9N2XdSR9fln7sp9IS0A8ASSBD2W9ghhIoh9KhUPkVNoMUomH/J5QLh2ucdlzQoQzNCOTvSn8nGKMtH79ejZt2sRgMOChhx4ijmOOHz/OgQMH6oRh9erVjIyM/FCSgF6vx8jIyA+Vd/CTdPzYJwHtdpvdu3c/j8m58ggZp9/PFyfPcx5//HGGwyHXX389Dz744PNIK3LIAgwXp8BYAm+FWbZk+BJYB4MBTzzxBCMjI1x55ZXP8+YPuQeSladpWkN/IXwvG4W0CWBJ4RD6DExOTtbXVxRF7fQV8hlCcg64xdLv95fJA8PKIczaZdMCMJ0Ogy98AQTetAZURFLkaGtdlYQiU26IkLWWSkFUOYJgkSQuCBuDVqKzdpbCVkckZUVhDSqOKcsCpTRxWZJYS3OrmGi4+e3q/Dydf7oHW5VEkSZqNCBJEZMbwFf0npS3dg3F6jUwP0NclRilKLKUwliyiUkiCt8nX5pEB45VXu7dT3XqNL3OVxm9ejfVGhlx48iHw8AOV/m6OqJJMnUVPQZUB/YTWUtclNjFOWa+8WVWv+4mNOt9e6NDSgs3oVD5FoD1qQUMyH0CEHnCn6Kky/RX/56k16UxMkZeDMhHJpj82Xd6Yl7pkwU8sdF40WHmA7lo9Esiz3JwyYb7jjR8IpCNWRb+8X46R1fRHF1DRsnw1DHywvWzUzQxIxgKxMpY5g9mHjEp/VCfVVNT5Pf/M5XSdC+eo3romzS3vIpoMiM/Pcfi0aewsxfor5okXbXFjexdOMegM0tSFDSNxRY5eRSRpykmTSF2hFSSGNIEO8yxjczhHf2eM/xRmiQf0soLSJyVVIkls2CNRWnliH5xTDkcYnREVBbOBAhcUldWxNY4HwytGDQbbtpgWVEuLNBas6b28ZchXLKGJcEXhC/0IAmhfgnmggZIkSCvlXUuM0KksJAEQloEIdlQeERhUSPFQChvFlKf7Bnys7BdERqNhbboQkwW9EIKkcsuu6zmUE1PT9eFSxRFrFmzhjVr1rBq1aqXZKTzUo5Op0Or1fq3JMAfP/ZJAPBdEwBYsrMNiS4v9VhYWODRRx9lbGyM6667rv5yhIsuNN5Y+Z6ymEISX5IktTZ/OBzWAfzixYvs37+fzZs31zOhJVsWSF6CqyxQkRGKA6BsDrKwJJMPiUSnT59menqarVu3Mj4+vszIQ3T9QuoLkwcZETwzM7Ns5oFMHpSsH1hGfAxlidPnz1NEMTaKMElKlee0/b1MjCEZDFDGcbkjYxyxajgkM5ZSawrtdNVYiMsKW1YQRRRakXikAK0ZaouKYrJ8SGzcf3f23k9+8iyUAzfmNc9pm4ph5DbavKpQ3UUf4Nw1aGISr5c3RIy95S3M3/8A5cnDLmBbiC7dSrZuDXgIHTQ9BjT84KKoA4vnDtFQkC/OMXv/Z8nTjDVv/XkajAKWhDZdulQUaN97r7D0HjtAfvQQzbWb4PQxF6RKKM+dZOZTx2i/9T9g22N0HnmG0de82gf5yFfxmsLz45Vn6CfEnn1/gemv70FZJ1y0nXniqmL8up/ylXzszY5k4qHx6ZAD+3NPDSy8jFCjSP1gJYdDDEj8nTPA2Ft/gXN//zl6cxcZArpyyWGeZnQHhtFG4SmIlhjlPQ2kt+8m/8WeT9Ge3Ern8GMOjZm9iLlwH7HWzjIasGlKujhPuTjv2P/GkDQbtPoDmtaSY7GRxlhDoTW6rKji2CWmceLMgeIIYw00mpjhwOc1ysn+yoIoTlDGkpY5hdIMGw2MsRRF6fgtSmGTFMoSqxVVFIGxaONwm1JpSqVIdITWTgoZkvdC+V5o7StVtOwfkmTL2pX1GDqbytoUXwBBJYGa5BuSEEOCsuyZUtxIYhImAuFrQrWQ7AMhchhyh0LyYLhXhJJBeU6j0WDz5s1s3ryZqloajXzkyBGefvppxsbGmJycrEmG328QfyF54L/m4yciCQir0xc7wiTge8kYRW542WWXcdllly17rzDjDgNgWDWH/71Sxy8LQoLp4cOHOXPmDDt37mRycrJmqYZ+AfIZ5L8FRRAJoRASVzJswen4h8MhJ0+epNfrsWPHjlomGHoXhLwEaU+E0kWpCsChHcJHCBddo9GoTYnk88umNADKOCbKcwfvFwVohbWgioLY8wSMgqioMFqjPbPaJAkqcsFRlyVG5F2A0tqdz2u9C+M2+EopsqpEVyWDg4dcUlSVZL7doJVGK9BVSTMHlU8Tzy4Sr1rlJXipcwWMM4ZlhWaC9s4pFs4eI9YRxlSkay6nxNDd+y36J/YzcuUN6Ms2Yxd7FIcfpXvmJFmkyYZDSr9x6jL3tbrro8fEvh0QeV/+GMUM5cFHibRm9I2vo9i7mt7hZyiLgmZ7nOzm26C9GsMMtpyns/d+JqfeSIX1YV968ZYBOQkDpv/hq+iqRDXbjL/xdZi9z2IuniFpt8iuuI5kwxbHWIhSiqog9RWvyBolyGu8CQ59hvQ8IdFNZFxy6qcmNkbRGOOXbGZ4+AhVFNGJIrR18sbO03tJp26lwg1UMj6ZcuOIZLizU3QkRIyOTlAoRV9rVNbEMiCyhqGFqtlAlRUmslCUYEpMFJHlheOQGMtAK8okwVpolBVWga0UJk2Jc8fnSXp90Iq+l+ol+ZA0sAtWpiIxzkkwMoYkz+mOtCmNpWEMhQ/2KtLODCuKiIsBpjJESYStKhpVRaQ1qdI0Nm2q16H0pKXCFyvvMCEXmV5oBtRut+ugLYiBEHeFIyBBWBBDcSEM1QVC4gtbiLA00lw4S2JYtLLKl9agoBchQhEmGKI2CNFUSQKkiAgRXElelFKsXr2aNWvWcMUVVzAYDJienmZ6erpGCSQhWL169QvyxF7s6HQ6tTzz346fkCTgpRwShF4qL8Bay3PPPfeCcsMwoMmXP+zryxFm4tLbk0UdauiNMRw8eJCFhQV27dpVO3hJ9i3nCOE2oH58pepAqnJJHqQ6GAwGHD58GKUUu3btWsYMDrN1IQXKIg4JSHI9wheQhCr8bLDUV5MEQ55XVd7G1lQ0qwpd5Jg4oVBu8lxiHKwvsLgBTBShDMTWMsRiqxJtLEo7ixhVVlRWYauSMoqoFGhjSbwEK64c+SsyFYmFIorQWlHipreZKHJJR+nnzBtD92t/x8gd74Booq59i6piSJ/O/AzdPV8k85tjrDWpguLb/0hx5DBlHLOw70HifRCXBcZa4mYLUxlKrUEpSmsoV61lQExJ1we7iIiECvHIUwwe3uuMZJKY8ukTrLr5LbSu/1lK66DTIbN09n6DwbH9DhWJImZP/n9pv/2dGDQZLRok3thHsfjYPqIN2xi/7tXEjJBTkU39jNv8KzeoqMQ5DZqqovQmP9onKBpNTkGJISWt2wIKWNy7h/FXXUeRrXL3y6cODiFwvIHm1OthUNG9eKZmy1dVhTp/gZm9/8zI1BT4sceGgozUGx4bxNMQYtT0OdS6jbSunKI9AebRg/TOHqRstVwFTuVMgCKNjRtEgwG6LFFJymKzSeGTTOIE2++RWNy/y4Ioz1FRRGwNUQWDoqxZ/6pwNtWFjomNcXbB2qVamTHk/QGmkUHl2kGR1kRFgdLauROmKXEUUw0HRHECyvFgGu+4A9toUASsfFhy9xQkQIK4OJSuW7duGYonyUIYVCXghy25lfB7yGUKpYnhuSRgS9EhCYZ4GAhSIRyhMHkRhZTA/KGkUYJ+mGCERmth21M+z0p1QIgSGGOYm5tjenqaw4cP8/TTT9eKg8nJye8a4FdaBv9rP/7FJAHw0hUC0v8X//8XkopIL03OJ8lAWH3LH4HqV47VbTabVFXF7Ows7Xaba6+9tpbvyaKR3pqwbbMso9vt1jA/ULuGyUIOK3np983MzHDy5EkmJye55JJL6gw7TdNayRAmAlLdywKXhdjr9WpTkNAbXKqNUOYoc7FFbVDfo5ERmm+8jfwf/oFmUZAag00SCq3JE8d2t0qBMagspVSa1BiGUUSZJOjBkDLSxBa0ctKtxFoqC7ooiRSUcUI8GKDikrSsSCT50wptDZV2LoRGO5a2MoaoKkmUcr7uRU73M3+N2biFbGQcl7p06MwsMhy4OfK5drbCSmvyh75CbK1DFbSiSlLnHKeUCyimovCbuy5K4rLE7LwJS0lGTOLNeQYUvhHgpteVcx2SSKOKkt5j36C1fQvN1ZcxHFr6dp7Zv/97zKCL9ja2ylooc6b/8T5Gf/pWmox4EiIUhw8646XrXk1JwwfZJnESYyrngS+GPZbE+w8YCownUqbIaGVDgSb2gseYmFWYXs70P3yW1Xe8G+Mpecq3U8RgqKRCv/ZGhvf9I1Gvg8XxJYy1RBcu0Pvil2iNjTIsDCNjLUambiNBMaRD5fkImgaN617DBD1KShJiyiSlUs7oiDxHxRHkBqUjFJYqTWlWBmUq0nVr4OJFTF5Sak2VpNgid34VOsKkKaooKOMYYyyRqWhWJXkUU6WOnOgST+vaVjGgnAdly1rSnh9qHDlEiiimUpD1+xitHVKxahWq1SQdGaXxyp2kO3Y8b/1IDz9UC8g6P3jwII1Go65yZQ3LniTrFlzrUQoDCaZhC0GCbjiwCFiWGIROgbLnhUVEiE6GSIScXxKCsIWwMrBLhR8GfbmeUIK9koP1QnuzkAd37NhRty+np6c5cuQISZKwevXqF0UJBAn4t8MdP3x/xpfheKmwzUtJAhYXF9mzZw9RFHHLLbe8YAIQ9spgKeDLFzTswYe9fHlcXivuWVEUsWvXrmV+ArKo5byyAAVmk76c/JFALRrfMOmYn5/n5MmTrFu3rh4P2u/3XT/Wu4XJppDnOb1ej263S7fbra2B5XPLZ5X2gzFuhLGYjIjN8JYtW2pHw36/z8WLF2segjGGbHI1Y299C2zaRKSckUoKKOOY/5VWFFlGaSErS6o4ItcajKVME6fZxlIp7ZTs1qKUdm6ClUFXFSQJxlrKKKKfphRpSqkjIuvMdVDOrjXy7QSbOJe4GMc1iIsCjh+he+BpBgceo3voEOnGy2lfMUVzy+WkO66jSBJywEQxVZIwVAoqQ2MwcAxyHbnZ8ZXBJClUFbqqKK1Bf+tL2L176e/9JpGHvRNiYi/ki5ln9W3vZPwd/4EobWKiiM6TeynLisqepvdPX8X0O5g4okgz8iQmx5JHEf3pc8z+7d8y/Y2/Z/rhL7DwuY+TP/xVmrsuJyV1lTIlFUNHiLNOQuhAf0uPeUpf8Uc4eyEZZuT+bZdxDUpy+lHEII7pz/YoyX0TwpL5hCBC0SIjocnqN/400egElVLoOHbyO+PkjIP5eZLeAuWp08zu/SpDFjwqJCZFpbc39qocoDt/htxpRCGJ0UVJoywYHQxoDga1cU9cVqQnTtPICzJTkQ2HJGWBimNUZWj1emR5QVY5/b5JEhrDnKg/cL+3xM/giCKMdu0D8KqFer1bUmuJrEGXJdZUxMopEVKgsXYtq/5f/5Gxd7yD9ptvJ9q6tSYCSoUN1MFS1qAYbR06dIjVq1dzxRVX1BP/JFEYDofLxo73+/1athcOPZN9KWyThj+Tx0LekbQUpHhQSjEyMlIXA6E3iKiGpP26MrGQ65afha3G8DOFbc2wxfq9HM1mk82bN7Nr1y5e//rX88pXvpI4jjl8+DDf+MY3eOSRRzh27Bjnz5/HGLNsgqAcf/Znf8a2bdtoNBrcdNNNtYrqxY7PfOYz7Ny5k0ajwTXXXMMXv/jFZT+31vKBD3yAjRs30mw2ue222zhw4ED9869//evLyNbhn29/+9vPe7+DBw/Whkwv9/ETkQS81OO7tQPOnj3Lgw8+yObNm7nuuutesI+0UkoTPi6Vd9jvD+cDhMjA+fPneeaZZ9i4cWNtwxsSDUPyocjsQn2tQIJhAiKLGKgTlFOnTtUzqycnJ5f1AsMsPMzKh8MhCwsLNR8gJOxI0iHvJ0mJEI5GR0frCqTdbrNu3To2bNjA+Ph4rVLo9XrMzc3RaTZJ77iDxq//Gs1f+AVia2gaS6OqaOY5Wb9PaoyzD1ZOx54WBc3ctUMoS7QfK9yoDGnpdPEmTR3pq3JQtI20Uw/gIN9SR1TGYHVEkSRYrSjjiG6aUmQNijSjSFNMFKO0U/oba8EYes89TL7vQZpj62iNTtDIh861T2t6OmLYbLj3Q7l+dOWc9XQcE1clkbGYOKbMMmw+RO1/DnPoEPP3fprhmTNY5mB6nu4/fY58715SlZAmm5j89+9AA8mmV9Av+8w9foThmeOkxjhOxaBPXylMFKPLioZWkGb0Lp6md+oURb/nZHaPPU5E5IcjKTJSlAWs0xOkJMTDRcpnTzA4fpqKDnimvkKReMqiAoa+MeAseAYMevMYLOkqfOcenyBUaFJfxWe+r99g/KdeR7xqHUWSQhRTNpuYNMNEGm0M2hjyEyc5//d/z4WvfpXFf/girgFkPedB0yRz72Sdnw86gthp+Uul6FtHJrXGMswaFJGGckia56SVISm9K4OOaJUF7eGAkcUFkrKgMRyghwOHFDUafmBVCUqjqpLIWqgMJooocN/PSilMo0EVJ66dkiau+q8MZZqRW4MeGa2DqOwVQvILCXNh5S38nEOHDjE5Ocmll15aB/4weZA9JAzy4XNCO16B9yW4hP32EMkMJYdSmYfkwzCQh1MBpSiR4iuUN4eE6vD14YwSaRGEe+wPeghKsGPHDm6++WZuvvlm1q1bx9zcHL/5m7/Jjh07+Ku/+itmZmbqgXOf+tSnuPPOO/mv//W/8sgjj/DqV7+a22+/nfPnz7/gezzwwAO8+93v5ld+5Vd49NFHueOOO7jjjjt46qmn6ud86EMf4k/+5E+4++67+da3vkW73eb222+vuVa7d+/mzJkzy/786q/+Ktu3b+f6669f9n5FUfDud7+b173udT/w/Xmh419UO+DFXAPFLOf48ePs2rWrngz1YkfY4woh/tCaE6iz2pXmGzLa+Nprr2U4HDI7O7uMNCgLUxIDgcHk8ShyQ3ukVy8LUxah9PoOHTpEt9vlyiuvrDNxufaVRByB6kJTo16vV7cFRO4o1b5MKgx7lePj4zQajVo2GEKLSZKwdu3aeqNZWFig2+3WxKIsy9jw7/895Ze/gr5wwWWfScwAV90Z5UhkCovRCl2WkCTYPHfEq37f8S4UKOVIWFordFmBtZRJ4toLVvmWg28fKDCFm0tgtWYQRzAoSY0b9UojQxcFaWUoIo0aDp0ev7OA1hFRZchURa4jtLWYXs9tvB6CLpKYyEJWFO7nCnKrMEqRW0srTdCVwcxfYGHPF6i0Iw/qytC8ZBvWGpSK0GoTIzfdTrblCleFr9tIecDLXrWTmxkdUcaarN9jpDIsDAeAolSGKHKahWp2jsQnAImX4ClTMmBATMXC/ffSP3ccrSP6ChYeiZh8w9th1QR4doAL/kXtPaBRLD55mMZwQGPjRiKcPh/wREdYUvEXwSMw/tobGZmH3rN7GS7MgN/8+yhGPBlUGUvW75EYA+cKsvUOydCeI5ASM7Hhcopnv8VQlWAtkR/ZO2ylpEVJVuXOEChOHAnUuCpdR5FLHo0hBRJjyIxB5TllmpJVzk8gKgqIJK1xnyIFrLIMdESiDNpYIiHwKoVSjv/hiAzOEcDqiJnt2+mdPcvk5OSyMd+h6U4oG5bHLly4wNq1a9mwYcMyjg1QB2PZK8LEXFoJYdEi+1dYXct+IAmK9ORX9vblCIuXWv4bMPrD9R/KlWV/CVECOUeIUsg+JkkBvPzmQM1mk0suuYRLLrmEj3/843zlK1/hf/yP/8ETTzzB5OQkr3vd6zhw4ADvfOc767HBd999N1/4whf4i7/4C37/93//eef86Ec/ypvf/GZ+93d/F3BT/u69917+9E//lLvvvhtrLXfddRfvf//7efvb3w7AJz7xCdavX88999zDu971rnpksxxFUfDZz36W3/qt33oeEvL+97+fnTt38sY3vpEHHnjgZb0/8BOSBPwg7YCiKHj88cfp9Xov2v9/sfcUAoxk7ZLJroTPJegOBgP27duHMYapqSmyLOPcuXN1ph8a+oSeAsIHCH0CpKqWQ+AzWWjPPPMMjUajhgwlqAP1wBEJyNISCJm8oZOhVPzhZ5LkQ5KE8H7KIYhB+PnA2XHKYJEkSWqXsGNlCW/8GUaAUUA99hjVsWPe2c75r+cKrHLbv7EWohirwGQZWVWSGovxhXiF8qNfDZGFqCy8zx3kiZviV+iIKIsofeC2WlPEMTrSxEXp0AQLJo5RkSauHHGue+hx3CUpVFkRJW7D13ECWlEpRRXF5FXFiDWkZYnVrp6OjCHXUT1vXrvOBHmcYMuSRuWu0o5vYEhFal1nPd3+SuewV52jt+9B8iQh9cQy7VsqUVGQVAZr3e+3Uq6C70YxqaloKCjoE5GQ+tkFMrlvbu+36Zw/iYpdkuS4DJa5f/w7Rv/dO7BoIocVMMIIuXckjIiJBxddEpxMUjBA0yAmZujNgmIyLBWK1JsaFd6tMIPxiqq9Fs6dRhtnOlQmidPwVyVJpGE4ZBDHRA9/leLK64l3rAUa+F818RVraV5YR3T2FFWaoJRmEMUukVOKWGmiskQBff87jgBVVSRaoYdDP8DKcQSwfnRtUdCyYI0hjyJUFIE1FEpRKCdvzJMYWxkibVBVRVwZlHKkVgYDNyUziqCqaOQ58ZNPMDs1tcyZTv7IGhYovigK8jzn4sWLNdwrazkMymESERYPktjLWhPejwRoqc7lPBLMQ0KgqASAei8qy7LmB5VlSZZl9b4inyH0CxD+k5w7NBgKEdKVSGj4+b6fVsD3crTbbd7xjndw//33Mz4+zm/8xm/w+c9/nt/5nd/hueeeq5+ntea2225jz549L3iePXv2cOeddy577Pbbb+eee+4B4MiRI5w9e5bbbrut/vn4+Dg33XQTe/bs4V3vetfzzvm5z32O6enpOhGR42tf+xqf+cxneOyxx/jbv/3b7/ejf8fjJyIJeKmHVK5ydDodHnnkEdrtNrfccstLkg7Kl1CCm/T3V0oPwwxeKcX8/DyPP/44a9asYdu2bXW/K5ToSQCW7DtsOYRsXRkBLO8tvbeyLFlYWOCRRx5h/fr1bNiwod4MYIlVK/+WxRzOGxD53+joaP0eg8GgnjQYZuohYiEbSggpCls5vE4hNoVM5zVr1jA5OYlSioWFBebm5jizuMjE6dPExsH4UVm4DT9JKKqCypOutLWooqTy12TjmBJn5YrSlFqRlCVxUaItJKYiqSqKOGaYJk6OaC0UBbG3JrbWYpSDdhXUUwojrcgjN1QmwpKUhshUDD1z3JiKSmswrjouqtIFFqxzhysLCq0pk5ShhVXbthEzwvDAY5TGJTDKGIdCZBnNV78CgyZnSGYboDTWDqlyKHqLEEfoYU6loIojdBwR574lop3vvbaedGcteZoSTYzQo0NG2yUaRGQk9OjTPbHfueZpRaEiWh6SL7UiP3gRdcV6EjRNWpRYEmRan8KeOYdVml4xTUrpuQSRVz3IqF9NeeE82dr1GAxDFBkJBV3s/m+78/n7R+S0CMZzPlTskJphVTLc9wD2KUPzitfQuOZK+vMdjB8XGPv1OYhd1W6tpYoUpqrIej2iOKZQgHVtJm2clDIzhtgYKh253zHaza3QEf04RlclhdKQxBiPEljlfu9Yx2UZxgmNPKeRF85AKorJjKG0Fj3ok2qnRFFHj7Ftaor4iivodDpMT0/XfvVKKVatWlVb1vZ6PWZmZhgdHWXTpk3LquWwLQdLRY4k9fLvMPhKAgBL8t7Q8CtM+MM+fcgPEOliaHAm6z00CJOEQda/BHvZVwQNWElmFlQjVET9KOV6vV6PzZs3c9lll/GLv/iL/PZv/zYf/ehHlz1n/fr1PPvssy/4+rNnzz4PTV6/fn1NlJa/v9NzVh4f+9jHuP3225fZ1E9PT/Of/tN/4q/+6q8YGxv73j7k93D8i0oCQiTg3LlzPPHEE8vGDX+v5wp78LBUNYeyQen/79u3j+3bt9e/xFD6JwlE6CcQjheWhRGqBsKKQXS5gmps3bq1Hs8Z9tdCwpEkGuEGIVVHeJ9C9CHcCMLFLRl/lmV1pSFIBiwlRGLzKcmDvEeIHrTbbUZGRoiUYvbTn6b0MDs46V5RVRDHRFphrQKtUHGEripynObfRpEj+ik3dhhriXyiYLXjB6TGUJZOFWC0MykqrHUWsFFEZX3wVprCVOjYORGmVU6VpuiydP1fY7FJ7CcdamdNrJ1kLEWRmopIKUeTixMKr9pftXkra268g/LIPtj3kCPlxQ6mTjdeQuuNb0DptQzNwFPyBmQ2o8QSN8eJwTHXtWYIVJEmG+ZQVZRRjI0jImMp/bjaxBh0a4yxqZ/2wd+Z/TRJibQiNRFJPiQuS/pZikkzKuVaJ8oYBkeeJK0qGq+4iqj2CHTfp97hg9hiQJzEdM5fIKdCk5PQQuyBSyrUcwdIkgy9dj0NUm83XFI9eYxUacqqclMC44jIQhU5Bz6d59jYE8SUpSwtKkkYHnqMwdEnMHnB+L//dxTz57GRRhlDPMwp04TIWqK8cFK8JKGII1JrIXfug9pCapwFsDEWrFNJNIwhGg7pZm7epLKQ5ENKa6nihDyKycoSGzmb4IGNnahTR6BKdFWR+QSQOCHziaaOI7QxqP3Poa+8ktHRUdrtNouLizWJb2ZmhpmZmXpNrFq1qt43QmJdSKKTvSEMmuE6lPUuSUDI8ZE1HbYAZE+SPS18vbQwwiRjJWIYtgckmIcVPiyftip7SFhASAvhR5kAgDMLWjk34Ed9DeFx8uRJvvzlL/PpT3962eO/9mu/xn/8j/+R17/+9T/U9/+JSAK+l3ZAWZYcOHCAo0ePcs011yzru3wv7ydVrLx36AAo1XBRFBw9epRz584xNTXFqlWr6uwZlvz1JZGQBKDT6dQBV7T4ISlQFrgs3l6vx6lTpyjLkquuuqqeKSAEnpB4BEvqhpVBWpILIQJJeyBN01piJJ4DoToh/B3ItcqQk36/v8xTQJzOJEGRQzarRqNRnzNbvx577hyqKN0Y1iRBAZU1aJ3S7vacB7vviw+TpoN/rWNnF0lMUbpxtYVSxNY6WZe1ziwoihhmqXutESscnLLAGsokJaoqVJxQxs4jL7EWVTozXZNmJFqTe/5AGTn5X1LkVFkDXRTEwyFVmpAr7QK1tTSGQ9bffANaa7JLL2VQeQWJ1m44za7XEUeXkFeF1+BbKqBDjwE9MjKa6zfRP3bEJTBRhC5LRjdcQnvLK6niBPpdmD5G98xpqmEfaw1mfob+o4/Svu7VWNyMwYIhkWqiSckmJikvnnP3bjig9OeN4xjVmaPc9wCd2ZNw8w0oVqFZpPvIMwyPPY0qClJraCjN8BvfJH3da714r8vw0YP0p0/SOHeKbOfVRNt3oIipGKBQqCKntI4xEJUFUQkmzYisG7SUrdvMwvmTjh/gWyUqily73RjU1q0MHj3kfAGsBVF5eHlfbN1I6oF2BsraVA5BihNUWVDFEYMkIev3UToiyXOsqahiJz0tyhJlnEW1KnKGPgnOk5gESCcn6c3MOP/EosDEMVQlmSkplUJFMSb2nBStiCs3wjgM1GmakqYpY2NjKKVYXFxkfn4egNnZWWZnZ2uEoNVqLZveJy094emE0mFZY/K3tBIlyEtrL7Tplb1Dfh5FUa0wCHv14WwRKULEZChEM0NvAnl9qGySgidsZ8h9kc/wowzCoTpgzZo1RFHEuXPnlj3n3LlzLxo7NmzY8B2fL38LYTt8zrXXXvu883384x9ncnKSt73tbcse/9rXvsbnPvc5PvzhDwNLw+niOObP//zP+eVf/uXv4VO/+PEvSh2glOLs2bOcOXOGm2+++ftKAOQI2wDy5ZWsVaQ5TzzxBNPT09x8882sWbPmeZV9mqa0Wq16gUpGLAsglMlIpb2yKh8MBjz33HPMz8+jlKLdbteLRhIAQRYEoZBkaKWrl1QlsGQRLH+k4mi1WvW/ZeMaGRmpR4wKUhHyG+SQ5EgSGq31MgniSvnjyBvegLKu0tae2BaVpZv8Nhw4yRcuaEelzKh3PWBlKhrDnFbhnAGTqqqDB5F2REGlsFXlfd9dv76sKkqlXKUcaaxWxPmQaJi7KYfGMExiqjRzGvdI10qEqHBEMxtFJMOhc4nLMmwUeV6DR1aspdXYThLH0FhFPPVT4FnVNoqZve8zdM+f9E6CBuW9BBP/P1BO7ZAkgIWqovmqWxm5+TaizZcQrV9Lsu0KRqZuZ+1b/z+s+oV/h45ip7I48jgLX/gbqtqLP6eoSrTSNKZ+hipJMMaSWEsGZLgKOM0LEhT9k0eZ+fwXGJhp5v76/0dx6HGX7FhLMsxp93o0z56m93d/R/fsDGc/+1nmTz4D89POw//gfrp7v4mhJCMlI6H1ih3u94lFWRgKQ74siIsCc/oYibVEOsIoVXs5GBS6KNFnzqDOHKJRuOCUa+0UBpF2lsHWUsQJVZqSa2fCFKFQ1llSmShyCgkdYbVCxTFEMXkUOZlqpJ1KxDhUxUTaSUOTFKKI1T/103XiaCzosiDNC+ckWFWk/T5RWTieQBSjrSXacUWdfK/0GJEEYM2aNezcuZMdO3awYcMGyrLk1KlTdREzNzf3POmxVPqyPmVdZllWJw+h6Y8EWpH6hVW/7CODwYD9+/ezcePGuu8vff1er1cnASFfKfw8svfKPifXJ0XPSuOfsDUQogIvhzrgpRxhEpCmKVNTU9x33331z40x3Hfffdxyyy0v+Ppbbrll2fMB7r333vr527dvZ8OGDcues7CwwLe+9a3nndNay8c//nHe8573PK9dvWfPHh577LH6zx/90R8xOjrKY489xjve8Y7v/wasOH4ikICXcnQ6Hc6cOYPWmltvvfUHGjYREl/CaloyYOn/j46O8prXvKZGDaR3F8L+8uWWPl5YgQN11i2vCa0+Z2dn60FDV199NY888sgywo28h3gOhP27kEm88jMI419gf3lfQQlgqX0QEgTFN0AUCqE6QhIPkRDJf8uCl8QirGTYtYsG0P/KVzCzs44XYC1p7DzeqyzDVIakqhz0bx2xD+VsWhWOIe4QeoUygLUMlWYYucluWVVRGFeFEmkGVgbyQmod6a5SylWhUYxtJBRAYi2xNRgDpDEVisxbIGut6wmCxkJSWSJjHFfA37/BYIYkmyBSiuzVryXdvJXy8W/S7XWINqyBSefZ71CAkojUj+2N6PfOkZ86ToLzM4iSmOKRr7F46CnWvekXsSgKT/eLdUqariG58WfpPfxVMmMxvUUMHQwpFZqKHsoq7GSMvmw7g8NHSMoSXZSub64VSmsocmwUY3uLpDphmLWIeguYKMYo5UhxSYyxlrHXv4PZZx+i0JpMNOi+FTM88CjZK64kGVlHEjXR7RaNxiimt4CNY8fHsJYkjr0TAY4PAUSeCFlFEboqydIUk+doO0QlTvdA5fgfqixdxa8gHgzc8CWsmwvgzZUi61oB2aBPZSxVI6NSlZsPoDS2LLFJwsBYsih23zP/OYzW9MqS6Ov/SENr+tZ6wyHnRljFEWWa0igLtLFUVUFWVbRe+UqSq69ehtZlWcZwOGRmZoZ+v8/69esZGxurA+TY2BgjIyNs2rSp9vI4e/YsR48erWVvExMTNJvNutiAJZt0SQqk5Rd6mYQ8Idl3BD0cDofs37+fyy+/nPXr19fQvvCQoiha5uwXIp0hYVi4QCKhDsmJYdIRopQvpJQK1Qk/rGOlT8Cdd97Je9/7Xq6//npuvPFG7rrrLrrdbk3Se8973sPmzZv54Ac/CMD73vc+3vCGN/CRj3yEt7zlLfz1X/81Dz/8MH/+538OuH32/8/enwdbkl13/ehn752Z59x5vjVP3VVdVd3V6m61rB5seZTdgCRbMgZZJn62MZZlHvAQAmTAAxGEAzMYgmdDwFMEWAbjQJZlZGNjPRu1DB6EZHVVT1Vd89A117115+GczD28P3aurH1ul6Qe1JJK1o6oqKp7z8mTmSf3Gr7ru77r/e9/Pz/7sz/Lvn372LNnDz/90z/N1q1beec739lzLk8++STnzp3jR3/0R19yngcPHuz5/+c+9zm01hw6dOhLeTvujCDgi0FFN27c4Nlnn2V4eLjJbF/r52VZ1tMKJ5vgxo0bPPfcc+zcuZO77rqrIdrJ+1JCYdq2I7X4tMMgFd1IZXxXV1dZXFzk2LFjbNmyhenpaaRbISU+yvmlbX+yZLMJPF+WZYM05HneGJP0HNMARO55WZYN+iCBghielIcgwYJseLjFq0ij/DSw8t7Tf//9DO3ezcr/90P4G9fRKFxVRWa9dag64/OqNuqoOPRFRWi9JBDabUprozMKcXCMCnFOYEDV4kAW5eoyi1YYo8H52LKWFzhjyKuKUFUYrSMrPkQdeeM9qkY5jIqCv94YnI18AF+VZFpDUHijcSgWnnmKwQe+CU+EVYutd9HeuZ/KOZbWV+myQnnzJt5AMdqHos76WWHp//dxVB38xIb9kuGqQg/aWqNPYSgwqDihDsPA9s2sHKlbVY1h7amnGHn4W1D1LIBVu8jy7/42utvF1FwKbS0ZAesDWquIdmiPcQ5zeYmJ73gHy7/1X/Eh9sqHVouuMQw/+hdgApi5QuE8LaMxnQ6Z0VQqzoFY++1fY/iR72Klf5C1P/kdKlfRIuBthc7zOJ4ZcEpRaU1l6uFSSqO9pfAOG2A9RIRHZxldHYmcLevwWeRF6DKiNwZoV5bMOxQqjgCuVQZb1qKtQwcPZSSVdmsuCSFm71WW4ayJiJIPkelIfJaWZ2fJA+RlSdvF78BkeYMsrOeRgKomJ2h98zdjDhxoHGGq0imCQWNjY80MALjF4ZE91t/fz9DQEJOTkyiluHnzJsvLy5w+fRqIHIKJiYkGtZO9KLZE2PzpoB9JFIqiYHV1tdEWOHv2LNu3b2dycrIh/qWti2KvUjlgKRvK3u/r62s0BTZyi1KEIP1bfp4mLyk68Xot6bxIg4B3v/vdzMzM8DM/8zNcu3aNBx98kE984hMNse/FF1/sQTIef/xxfvVXf5Wf+qmf4h/9o3/Evn37+PjHP97jnD/4wQ+yurrKj/3Yj7GwsMA3fdM38YlPfKIpo8r6j//xP/L4449z4MCB1+2av9hS4cuFwbyGlRJS0hVC4MyZM5w7d47777+fsiyZmZnh4Ycffs2fd/bsWRYXF7n33nubzzp//jznzp3j0KFDbNu2ramRCWkGelEAQQD++I//mIceeqh58CQTl7JC2n1grWV2dpZLly6xd+/exlkrFZWk3vjGNzabZGOQkiIDQvARyVGITvnKlSusrq6ye/fuHrESrXUTzacGSQyAXItkD4IqyPWnioZi8OCW05efp7XJEAL2+HFW//N/hjrDy4ESRVkUhOBja1zNYld1d4ALoRGO8cFjlYayS1G0ojY8ik5eT3kjyhMPdLtR2EVFbQGfZ9gASit8COQ+YKoSV8+PNz62Dsa2v0BXKVoucgOCtTFz1gofqLvZ49z4EEJsYQuBsbd8DwNb7yLPMtqtFt21dWY/+ztUZcn6hdM4E0WSMBlD97wJs307K5/9FH7xJtq6SHDzPqIeIcDAAMPf+/+KTZBKowJxsl4IlMFy8eMfQjuLIeCspb13L/0PP0ybKW5+/MNY24mBlOjhB8iqMpYdfM15CJAHz9g3vp186y4W/+A3Wb92AZSmVAr6+hl+x9sAWP71X4/178yQV5bcO6yOg3PyWu3Rj4xj11ea+xaMYa3II+lSxwClW0szt2onLlB+R1AfH/vyLfVcDx+DklJrjCIOqiJOmIyzIRzrWU5pdFTwc5EjYHygGwLkEYHIrENrxVqeY00GIdDX7eKUotNuxRJDHQCq+npGVpYjQTPLIn9FwWpeYPrajP3Ij0QdiLqtNjWti4uLzM3NsXnz5qajRvaLvC6V8pZ9mTrw/v5+VlZWWF5eZmFhgU6nw+DgIFu2bKHdbjeIpMiMi02QDiCZA1CWJSsrK5w6dYrt27c30rrtdrtnPLokEpIciIOHXrVB4QJIyUGCCfldykOSjgJjDCdOnGDTpk1s3bq1sbsbSwdf6hVC4J577uFjH/sY3/iN3/i6fc6dtO4IJOB2y1rLs88+y/LyMo8++ihDQ0NcuXLlZQ8Q+kJLIlR54Muy5MSJE8zPz/PQQw8xPj7ebAJ5qAXulw2dIgkpSSbV/t7Yiue95/Lly9y4cYNDhw414zLTwRxSl5OfS2YvjltQhY3RNtAgA3KeKXIhmggDAwPNppXjikgS9PbzpupfaZDWbrdpt9uN1PDGzxbDoIDV3/wtrI6CMb4ocNaR2Yqs7EZiV1Xia2hROYcqu2QhDh3qZFEjIFcQtKEEsrwgq1v3Kq0IIcL0tr4/oS4jKB9q4lkcXJQ5iw/RqDtjotOtJx8GpeOwouTZypwjVB6TmagYaGJ2qVAxi/eOzu99hGpsitbWrayUFasXTtOptWU0geA9Wa01sHjqKbLjn0VbiwmevK51k2WU3S66yDFll0LpeI4+YgLeezp0ccyR2RLjAypEPfv1ixdZv3QJXUWURBtNN8RpjIHYeeGLVlTnMwrjQyRiKgVbJ5h/6n/TmblE7gMZEWHwrmLpv38cPTEdh+cEcHVZJXMxCLBFjqkqnFa41WVKU2d6eYYzMfgyWYY2BuU9rarCEgWishpJckZHR+sDmXdoHYWXvXOQGZR3EVlQMfiySpFphQqg8kgGdRIUB+hTmpJa4Q9h08eBVU7rKPdcIyS5tfiqikFKjQqFqiKoKNhEPTXQKoVX8TzbTzxBGWILaJp9W2uZm5ujqiq2bdvWg5Rt7LIRInGqdZK23UmiMD4+ztTUVFOrX1pa4tSpUxRFwdTUVA8iKjZCbKYkCRcuXODuu+9uRIJSUR9pbRbbJ/tW2gY3chMk6E+nAorNEjsjXUO362yQ65R78nqv28kG/1led2QQsLq6yuHDh2m32zz22GNNHfp2YkGvdAnJDuKmWV1d5bnnniPPcx555JHms9J6+8a2l3STyPE6nc5L5DWldtfX18fa2hqnTp3COcehQ4caYRGgp1NBNqxE2yncCLdGgcrmTqNrifbn5+ebn6XXYa1lZWWl6S+WJQGKBAKDg4MNWxnoMSAC/wv0mBIf5XcSMITLlymXl+Nxg4vQdpbhdJSVVc5hQqBSEFRs1dI+1oI7KkNlGdo7VM0Z8EWOqwVjsuBRNo4aDibWfqk7DZTWFDXRcD3PI+HQOlQWFQKd8lTaRGfo4yhcE2pmulZRutY5MuditlrkrOQZrtVGdTu0AG8rIODnrrM6PxMdVR67CFSeYephN8p7ssxgnUMFH2cN+kDhHc57KmOo2m2yegriWriMC2MoAgV9gMLimH3q6di+FizWZDhjKJWm1VnH5wXrdfChjMG52KcfvKf0HqMUmVIYW1IYA9Yy/5FfwmuDqWxdlohlla6Po4vVzFVA0a1b9ioTZz1Eqed4bKU0VgV8lrOexal8pVJkWmOsAxuleb2KKn2G2A1QyUyIukyRaY3xLsoQOYtVEHwg07UYjQ+g4sjeYB2ld6zXz68LUeO/VArna6lnE0tJvtXCRlgFrT3BZFQhloiMdfRRUunYv5GFQB+Rv1ARxaVcCKDiJEkFPQG9PPeXL18GYHR0tKeUJvtBgnpxzuI85TUSBKSsenHK7XabwcFBJicn2bx5M51Oh6WlJU6fPk1VVQ3vYGJioinXzc3Nce7cOfbu3cvw8HAztEjsA9wi7qUywIIaiq2Qc5ZR4mIvUmcu/AAJKFKOUIp0bExKXs91u3LAn/V1RwQB6YMh9f/t27dzzz339EBHX4ogQB5iiaIPHz7M9PQ0d999d5OJC/wlG3NjK1wa4comSgMECSTEUMzNzXH8+HHGx8fZtWtX4yQFhpNsXIaGpP+Wz0jvgZQB5DVilCS773a7HD16lMHBwaYlSaBIMUJpZ4Fk/SKjnJKDNhoOuR8SrKQkoBSeVErRXV6OhDNryQIY5wi+QmcRmtUEbB7br6KoTcD5gM0LqrqdMCNquqM02BgMOGsjQU4p2lmGrhx5VdE1BpcZgtLYPI4adlrjjKFVt5rF6wrkStF1NqroC+nNGAyBKvgoVmQMmdasS7BQluQqkg5VlhOUpii7VMFTtYqYLQOhW0a52iwjOEumFJmLU/i0iqRE7xyuZssXzkVCojHc/PXfoMoN2jpaA8MM3PsI5cxJ/IsvoqsK5Syq1cZr3Uw9xDlsuxWdqlLYELsDFGCMxjgfs22lcAGcVgRt0ApclsVAJ8TvIlAb6wBeKboqii0VweKVRquAsRWhFs4xStEhKvdFXYICqyB3jkIyYufQZZzyZ+uAxDpHoXUcIKVU5DFoTde04tjoPMNa15QKgjF0Kourp1Iqpci8I3dxyJOyFm2iqqCpLK7I6XpPRogcgMxgqwpdl5yECJm7qFqZOYfyjrU8Z70oyLSJiEueo6zFAHnd/ir7UrQApqamGhuQOnjJnCW7lnY8CdDb7XaTiUuA3W63e8TB5G8hBm7atInp6WlCCCwsLHD58mXOnj3L4OAgrVaLGzdusG3bNtrtdlPuk9ZkITKm55XW6fv7+3s+W8oUKboonIaUSJzqFcgxpbQgdiRtaX49l6CzXw8Cbq07IgiA6FzOnj3L2bNnue+++5o6UrpeaxAgzlxrzfz8PCsrK9xzzz1s3bq1gfDTGpg83Buh/XRjyaYHGnhfHKwxhqtXr3LixAl27drVEACBBp6XlZYmJACBW/MLZEPJ5wkbWd6XRvJ79+6l0+mwsrLSDMnQWjMxMdEgCBIMiOGSzxSNAgku5HylXUk+U+DQ9L4IFCrv0zt30u1rU6ytY4KDuv2POvO0mWlgfGeiszYmTsHzqMjd8g5f14sJISoLGkOpfKyZO0dWlRjJVisLOVhlQGsy58B7qqyu7DtPu6pQSmOCZ73VximFqUpy5yNJUO59HcCYLEMrg6oqvI5BSQmEsktOlBhWLpLXXM3Ed7U0LUWsx2NMFDRS8TqUUthWQe49oarIAVNVVG2oQiTv+bkZOn/4P6IyYpbVrZF9WGfRpacMkNV966FbRkVBApnSZLbCWIcVgqRWYB0qi0qM2saRzS7PWc+jPK9BYayNUvmqLitoQ8t78rKMw3aKosmQPQGnDG3rWKsVAY33ZM5ifZ3hKiiIJYwIrccR0SaL8r02ROFg731UFTQZ1jtE418RAxvv6kFUzqPbBboma0a1hDiPAmIwkdsSbQw2V3SDQusYMGa10FMVPFmIRFTtA0U9RKrKc0IWh06FEKi8Bm0wucKMjjYOtNPpNH3kMtY7DYDT/Zp27sjvxbasra3R39/f7KV0j8trUiEvUQeVZKW/v78RFpufn28mml6+fJn5+XkmJycZGBhouEDCcdrI/hf+jyQv3W63QQEF5hcbKImC2EVpOQR6WhQlmUmFyeSzXs8lyo1fDwJurTsiCLDW8vTTT7O4uMgjjzzyeSUUN8oGv5rlvefYsWNcu3aNVqvF1q1bm2MKQVEQgLQ9D24fzYrghjhQyQhCCFy/fp2zZ89y7733Mjo62iOpmUJlqSqXEGxEnEecsSAGQKM7ntYC5Tiy4fr7+xkYGGBycpL19XXW19eZmZlpznt6eprh4WH6+vp6ygppR0DKV5A/AvlLPTQVL0nrm8YYLl68yNp9hxh/+mm817RqIlYVYg97yDI8YLwjuGj0UWCVbmr7UToWMAqnNFlpobIR9g5xwp9Azl4pbB7V4bTzUWs+ywnEGnvIC3AlzmQYrYld5rV2f55jyrU684RKaVyrRel9I9+rtEKF6HRCCPi8oHTrBFU7AediZq7iGOWOyWiVJVm3G7N/Y1B5RqevHVGDymKsxRsT0Yh406NWf13HDjrODtAhZrQWT8hyUEQiI2BU/N5ji2OJMRm6roFrbSL5riyjkzOa4CIZznoHZQWtFpXJ6AZPK8/I6+epcp6CSKywWscav3OEvEZyfMAYWMszfF6gnKOwNflPG2yeRe2FAJmPCItXUT8RHztA8m6Xjja4Vgu6JbmqwAeMryIfQBu8qpXriha67npwxuB9oJBSlzZk1oLzVFke6/zdEtOKKo1AJINWFmM0tg4MtYmDqkKIKJSxccqgzeLnBmcZeMMbyCcmmr0mAcCWLVsarY1UNU/KagKliyOV2noqIZ7ul9RGAU1gsLa29hLujrzGe8/Nmzc5d+4c99xzDwMDA6yurjYJgJALJyYmGB4e7pH7lix/I4Inex9ulQJTtDJFDyQJkddIgnT06FEmJiaa0bjyuvRzXg+C4MrKSg/n4evrDgkCID5sjz/+eM8oy43rtSIBVVXx1FNPYa3lgQce4Lnnnms26cYsNkUFhCUrkFcqySnIgLxf/j5x4gTLy8s8/PDDPW01aT9tOsVPjIE42dXV1ZcIiGy8lrTtUO6PGBu4JYOcZRnDw8NMTU01zOHFxcUGJRgdHW1GBQsykNYAJTBImb1p7TMtR8hnHz9+HGstu7/tW1k+fw5zcw6sBa0xWuGrWLOnrsM7RYR2s1vDgCqiY8+rCu8DmDjlLTgXDXQep/z5Mmtq+laG+NT3JQNKa6HVis7EaHTdfqgCkVBXlhH+r5EKEYTxnQ6hVeC1wjlPO0Qeg8sLlHfklY3a9bW2vdKKrNNBaYUrCvIAeBdbGAn4qqKSwMd5iqoi1KURF+KUukKmFRYFmMjYd8YQqpjdFlLDNTV5riZgQnRyma573FXMdEPwZFUVM3ylqDITJZt9nIiHjuI9eE9FHNMcTAzMnDG0rKUAUDryMELNwPchnl9mUEpT2CoSIr3H+4B2Jbk2GGcjmU9FvkIkgWZ47zCZwdiAqbUJMgW6lurtaoPX8btoOYdSgXUVyzsmz/A6tvsFZyMKE2LJQ9XaBNQDlFzwtKyjm8dnKVPx3ldG47KMyrk4YIpbZZLcVrS0IkxOkn3jN9LetavZhxIA7Nmzp7ERabab2pB0iJdk8IKopXtUmPlKxfZhQRLTKYCCTkrwIPZoZmaGK1eusGvXroa939fXx9TUFDt27GimlS4vL/PCCy/gvWfTpk3NvA+xG0IAlH2ejiiW0oaUG+W1KZdBzqssS55++mmGh4c5dOhQc2/Ejsi1yEqJhF+KoEDQldezA+FOW3dEEJDn+csSSJAHLYWXXu6SwTwjIyPce++9rK+v92jxA80DDTSON22huV1fvNQHBWrrdDo8++yzaK154IEHXqLsJY5243HlPqS9t7LZ0nYeybyVUk37ofxfEIE0s9Bas76+3kiLeu8ZHh5uIvTl5WVu3rzJwsJCc6+2bNnC2NhYD4FI2n8EwZDgJYU/RYHs8uXLFEXB7t27Y5mg1r43Pkr7qjxHZyHqvetYEy6zHExUDsy8w4aArYe/eBWDBF3ZCHMbE515VYH3dLMI/WtfZ5h5RldrcudRtiIPgVUCwQM+EAqDqspYh+52o36/0azk/RTrHVyRx156YrbdjQ8JvqrIlCZ4FxUK1S2ymy0KdPDoLIstiCh0WUZmugLtYkmg8J7g6xp8EXkEIYRIevQZWd0G56sK1yrqerjGmzaqsgTvMN6hfYayDpdFBrsnELKM0O2ga2TCBQ8+BgcK0N6R2Vjvt97jixxCVGwsCOTaEMpuhPVR5NqADvhuF/Kc0oMJAW0ylO3gjb5FxqzRG7wnr+KwqJAF2j5Esl/N6Ld5hrOWTMXxyWUeryMvS8hMHPesFbmJAbInxIDLOgrjWIdYTjGBblHgnGagsqiqwviA9g6f51htKHXkILgix9WO3udF1IswJn7HCpzJcN7Tabdpt1oMvvU7GKxbh4XcW1UVly5dwlrLPffc08N6T9HJjSU0uMX9EbKf8Glk/ojseXGyKZqYOuHUebZaLZaWlrhy5Qrbt29vlEvFlomdbLVajI6OMj4+zvbt25uJn+fPn+fYsWOMjIwwNTXF9PQ0o6OjjZ1Ja/mp4p/sdUE00i4C5xzPPPMMIyMjHDp0qAdBBXpQjLTkmpYlXitKsLKy8vVSwIZ1RwQB0CtL+fmWPOCpct3LWdeuXeO5557jrrvu4q677gLoaeVLNx3cEruQjSAOTjZhSrIRR93tdllYWODYsWOMjY2xc+fOZl63RMqprG6e56yvrwM0MJ+ciyAQwjuQzxT4TjIGia7TuQJyL1P+QIomSGYhxx4dHW0IQc0EwKtXuXr1KgMDA43wycDAAK1WqxEiSWFPuUedTofLly/TarXYs2dPc5/10CDu2rU4T74o8LVQj/IOk+VYorHGBwYChLLE5DkdVWeQzpIrFTsIqPvJfYhZX6in4IUAWtOqKpz3lHlOR2v6tMGpej6BMYQ8qsbFPnZDVhTRORDbEu3oCFlZUVjbyBIba+s2RxdJjllGCKBsLXQTAplSeDTK2drhRzlgbWo4HodycWaBUarpRNABXFFEbkRZ0pKyjAJVReeirYuOL6vbOH3Aqzju1hpNqCyZjv341M4t95F4aHUcrBMVFwNF3bVAjZhURVFn1TbOVKhLBrrTQXfqzDrLKNstdLeksA7tIqrgszy2OYZAqeMwHyNZoY7EO+1clHNWgLUUJhIKPbE7witFm4AqctaI5MBWVd4qi9RIiAqQec+Ain3/gYDqdOMY6rKL8R6XZehOh0rFVtDMe2yWoa0ltNo1h6HuGzVZJCsag89iBwTA4lu/AzswgF9aaobQVFXFuXPn0Fpz3333Nftxo2iOBMSpM06D+LTlFm6Rijd236TiQmnrsHQX5HnOzZs3uXTpEvv27WvKkfI7+Yy05Ch2SgSItm3bRgiBxcVFFhYWuHTpEkVRMD09zdjYGOPj4z1CZ865pkSZZvZiA733HD58mMHBwZ4A4HYrRRS/GEqQvvblrNXV1UY6/esrrjsmCHg5K4WiX84KIXDq1ClefPFFHnjgAaanp5vfiURwCrWnjl4+R6L8jRG6QO+yCVZWVrhw4QL79+9n+/btPW2CojGQRtkSCEjdTxx82n0gm0w+M50QlhIT0w2ZbiSB50MIDbwv5L6UISyfNzAwwNDQUHPt3W6XS5cuNde/adOmRstcjE3Kf5idnWVgYKAhdQoykT/8MOHcudi25Vyd2Zq6n9+RmYzc1TwEHWvWLhANtPMEHfv6c+9xxoB3UXoWhc8yvI3Xrp2Lvegq8gus0Vgf+99DCOTra/G+ak0oCjra0Oc9oXbs3ntcDccr78kBHwKu0yHP8sjG9wHrIm8gU/WoYu/xZTeqDpq6D93HAKEMkYxWKoUyhlxpnLOEooWqqqhX7xR5FR1sHjzKuho2J44WCBF6ty5OyLNGR9JjgKLTrT9H0ec8uq7Hu7rrIQ91d0Xt5EJVgo4ll65SjUYCSuN1hPOdii1zLevoEBELXVVxaE5RgK0iwc97FIEsBHyex+6BLMMCpiojQbIoosqj1hCiSqL1NdfBObzJUM5RGUOZZ5gidhdIh0NellhtoA6A8qqipYjOvUZiMh+HDVkTWO1rE4whCzQTBVVe61GYDGXjgKCgVSSL5hm6DpJMnjM8NMTa+nrz3Lfb7ab9V7qVZP/JXpOAW5BKsVXSLiz7L+06kiREkhkh50kwLdmx7C9x8DJu/OrVq9x9990N9C2JhCiAiu0SXo9IDkuC02q1yPOckZGRRt9gZWWFpaUlzpw5wwsvvMDY2BjDw8OMj483r09tsQQq1lqeeeaZlxUAbFwpSpBeswQHKT/p5aAEa2trDAwMfFk6Ee6U9TUVBKRZ8hdb8mCurq7y6KOPvgQiSiH0jfV/uFVPl00rkf3GrgGlFGtra6ysrPDAAw807UJyDkICTCEvERpS6tbADunxFwcrAYIYE8kQpMVIWpUkkxCoPjVMKWtZNpO8J0Uk5L3GxGljYiSGhoaYmprC+yidLBkDRB7B0NAQ7Xab2dlZ5ufnGyJQitKEEMh276b7Td+E+6M/jtl1llFWFXndvqa8j9PhgNI5tMkorK3JfuBIUJkip/JZhLjreq7SGu9j+2AWQhwDq+MI28w6OkURR8AaE0fbVhXWOFQNo6s8Hq9dVVQm6gfIxDs5vi7LeE4mlh5CgLLOrqJ7CijrIIu8A1WXDnxV1SQzRW5tJM/lBWtEhj0mi8Q/FFZrrNLkIcoJOxTG2yZwybSm1Coy3wEqjybEc0SjvaNVWchCREvqQECHEAclhUBev86UXfRA3BPBOpxWGK2ovCcvXZw94D3tYLBlhdU68hVc5AoQPHlVj5JWihYVpTFxXJIitjPqOKgn2Ng+qLIY7AWlKHXslsi7XXz93WityTudKNijwNt69kKIPAOlIy+hZS15reBIiMJNWSB2lCiNc7Hs1Onri+fT7cQpgBBVDqMBwGRRKMqoWKpq7dpF/8QEE3X2vrq62nAAnHOcPXuWkZERhoaGmu6cVD9fnJYE9bLnJVCXwDkV5ZGV6nSkDlxeLy2Fi4uLTQCQEpLTUqK0GkqbYKPbUSciEkxsVPkcHx9ncnKS3bt3s76+zsLCAjdv3uT8+fP09/czOTnJyMgI4+PjPV0OR44cYWhoiPvuu+8l3KVXsjaWD1Ib/HJRgpWVla8jARvWHRMEvJxyAPASxa3brdXVVY4cOUKr1eKxxx677ayBjbrX4jilZge9ffwpA1/+ttbywgsv0Ol02L59O6Ojo43krmxiQQrSdjrJDuQzxenLQy1tehIgyEaWoCBFFNK+XikZCGTX19fXfF6qe7Cx7VHug1zrRrJOf38/7Xa74REsLCywsrLCpUuXmns1NTXFxMREY8xSGHR1dZWBb/xGzKFDrD11mM4zT0fHpDVBRW5AJAnGrC22oCkKBdYHVKajY/Ae7Vx0gFqD0bQlw9UGnK/r+R5fG04RqvE2tiU6FcmHWWXJXJwNYOoZAdb7OOnOu+jotUZ3u0Akz5m6K0Ag8CrPUE6GG8XgQ8hxJgSyYKMkcc0ByHx0zqWOYkkQ+/YJEeHwWscNWwY6NWwu/f6ZrXBAUbRwVYcijxm21pHfkNd1fKWiPoKcv6nKuiyhKVEx6FAOnWW0Ouus5UWsoWdRhpmqoqxKTJaRdbr4qsIQeQCtqsJ4R1drvIvfm9M6kiq1JlcaVZa0vYuDnyqLzXI6mYkO3HtsAN9qRenhelaAV4quizMBSq1pE9CVQ9fSz04piqqKtXul0M6Di6WK4OIAIe8sWkc9AaOgpA4K6mFEEiCVRUEnyyLiEqCbGUoHg8Yw8qaHmxHB3ntu3LjBxMRETyA8Pz/PjRs3yLKMgYEBxsfHG1KfZOxpxi97EWg0AGQ8t6B7QsSV9juxV2tra00GXpYlN27c4Nq1axw6dKhJLoAeRFIcvpQC0+FAKVqY8oZSuytBwcjICKOjo+zcuRNrLTdv3mRubo6TJ082+314eJgrV64wODjIvffe2wQVX6r1+coGqWaBnLPYrK+rBb503TFBwMtdxpgv2CY4OzvLM888w7Zt214iNpQucdACj22MLFPhC4GKgcYpr6+vc/LkSVqtFlu2bGkcqmyyFC4UEg3cIhLK8SU7kLq+GJB0aFBKQkqdrBgB2dwSQIhjl/a+NECQa+jr62v6geX6hLcgWYsYNzFoYrAmJiYYGxvDOcfi4iL9/f3MzMwwMzPD4OBgAx8CDTkpz3P6duyg2LyZ/m9+C4v/8ucpa8KaCoGWC1gds+FKKwrvI28A0N0SpVWsfSvoy3Iq5WgpTV7Zpm3PmbpzIARclsUM00emf67Aa4OuKoL3GK3JsxzqeQGegG+3Y4ZaVVEetixjr3oZ34MCFSKrv1TSQaLRZYUysTXQhyivq1RdB9cZSkVHZ31AmVijVjXzPSrSOSpi7dtZC1lEI6x12Fpdz9SSuU4ptNL4qoxz7mvEQXmPszaKI2VxwmLua5JrCOjKkteM+U6W1ZyMEMf/ekdrrcQrYjZvDN3KUvb3YeSeAblWmBAdd91LQVXD/KYscUo35QRfSwcbZzF5AT6WUlye0QHaCqrKURiNqwM9HSv9KBsDPV0HVv3e18hMFFwSrkEA8KGWYcgAha4HEymtKGyFVZrVVkFRVrSFmAq0QsAGHz9Ha7j3PnQ9UKbT6XDp0iUmJyfZtm1bUxobGhpifHy8mQkirXkAIyMj9Pf39/BuZKCX2AGxCeK0JcmQpEBKh4IuiDMLITAzM8Ps7Cz3338/09PTjdppu91upMDT+r3YGmNMU2YYGBh4SceB2FRZUlZIiYFFUfQMOpN24zNnzjRJxaVLl5iammJoaOh1geI/H7lQbKJcy+XLl78k0vJfS+trMgi4HRIQQuDChQucOnWKe++9l23btn3RY0l0nJIAN36WRKBp9Lm0tMTJkyfZunUre/bs4cUXX2weyJREqHUc7CHtdFrrRvRDIDuB4uU1sklFkStV7ZLAQFoLJUBICUWpzoGUDVJSoPQJp+RBgTWBHonRlNAkBkwM1unTp5sRyBCZ0N1ul/n5ea5fv94ENVIiyLKMtbW1eN9DICgaXQCjFKqyGJXhbUXLmNju5Rwuj4+wCbHFTJs4krYF5GWXtnRnSOuUNvia1OdrEpy2FqU05PX3WOQ4aV8jEvNQUPlaW8A78JEXYLwneAcmqwfc1BByXSLQdaeCq92i1vWEQh/r5EpFQRoDMVO3lizPsdaRU6sPBqKAj3Oxm8DEQCTLc8p4i6iURqmAIrb5Ka8hM/hQz1BQkbjY8h7d6eLzPGbBRMKjUnEkcrCWAR8otaLbblMpRdvH5yjTmqyyVK0CVxT0ORfbBjODCdDRbar1dXTdOeEg6i3o6MCVi2UFn2VR+tdGRT6rKpzSdQeHpxUqqqKI3EnvsXmOA3JnySobBzgVkXTYqnkenTzH5zGA0jq2fgZbUdU6AvEhiVB/0JrKOwKKtSzDGE2rHhxUtlpk6+u0nKOtYmBptmym/S3f3ATgly5dor+/n/Hx8UaBToh54mDa7TZbtmxh69atdLtdVldXuXHjRmObxsfHm9KB7DUR6pG9KDYBaBIFsQ9pciJ8m3379jX1fgnU02RGkIWUmyDJgiADEnykA39kr2+cHCo2If1b7NTs7CyTk5PcfffdDbnwwoULGGOYnJxkamqqGVz0eqzboQSXL1/mP//n/8zu3btfl8+8U9cdEwS83OjxdkGAc46jR49y8+ZNvuEbvqGBrV/usdJMOz2P1MmKo75y5QoXLlzgvvvuY8uWLU20nZLxUtQgPWZKKvTeN5lDSgQSByzqYJLlpyJDYpT6+vqa/t2NnACgR59ANkwqaCRBhZyz1B3lnojR6nQ6jZFZX1/nwoULTE9Ps23btpdwJ0SlrNPpNKqFN2/eBGhmqo+OjKBGRlBLy8RWekfIIqkO7/CoKOxTVRilYlbvY7+7UxmFj1B05mQCHxAc6EDQMbutINbOMwMo2tZSWofPTCTp1f3iunYYJgRaqDjcqCFxKpyPUHhQUoMO+BDLBVFZztcOPtSteirC0N5FyLoOaAjUs+xjeSKrn6/MR2g7h9gBUTtKQ6DjY7ZrqhLfinwR6x2lNtCuGfLaRCJkVaGKAusjElFUFdQtcqG+R8bU+v9KgTFUec56q02wNor8WEseAlUIsSVS9pdkipVF9fej19bIQuxEcLUCoqvvtReI2ccAzNSfi44lDUOUGQ5ZiMGTjz37ldIUIVDgsVkUBFI+DofKTV1/rixBxYAP0QZwMVj0RSsGUNahDFC0CFqTO0tf6eN1ZQa6HVp5RlUTGk1VwdWr6IUFumNjDbw9PT3doGopWTft9El1PUZGRpiYmGjGCQt8DpE/MzY2xuDgYE/5TRz++vp6T/cP0Ow54QAcOHCgCbSlbCnk5pQDlAYEYi9SuF9+ng4sEoeaioKlnIbU4QrXanh4mHvvvRetNYODg+zatQvvPfPz88zOznLq1KlmrPLk5GRjF16PJYHSO9/5Tp544gk+/OEPvy6fc6euOyYIeLlro2pgp9PhyJEjADz22GOvSCkqdd7y0MsmTUsBkgWcOHGCGzdu8OCDDzI5OQnQbKgU8pONltbWU37AxkldwkCWYEEY/AK1ScRfFAUrKys96EUaxaf9t1IbTGto3ntWVlbo6+vrqWOKQRCCUVVVTcYvBsEYw+LiIhcuXGDr1q2MjY31lEBSYyPnMzAwwJYtW+h2uywtLbG4uMiVK1e4cuUKw9/4jRT/83cJiqhG50Nsx1Ngym5EB4LH2ygMlJlIHvMhEsSCUgTviAKzAW+iLLBxDhPiBL5V77CtNirPGrne3EW4GB114Sui84Uo85sphSlLMBkhy6ikJKEiyU5pRUDjasa7UQJPx24CVdVKd1IeUHF6n/zMh7rds+5asPVzqOvOCaULtHcEFycQeqPRLmr0KxvV7kKexU6BzNBScUJi3ulQFjkdo8kUDBCvl1odEBe18VEalxtclqOtI89ioJG5KCNcZVG6N4ToxDMgmBzlQ2y7c1Ex0JUVWVXGQUZ1Bg6qHqGoUd6iVVRYdFpHHkeWx3IPAVcPbSrzrO5iiCUa5x2ZjvclFEWcF1EHBNoYqhCVGytUJC52uygd96s1BcFERKSw0FGQKd20AppAbCFVOgacISIrTilWLl9mcX2dycnJRv5b7II4y7RdV+D7tA4u/Jm+vj5GRkaaMsJ63W0gSqBTU1OMjo42dkaShfSPc47r16+zsLDAvffe2yAAfX19zQCwtNQpyUhq/9KMPm3tS9saU9JimvGn5yb/Fw2UkZGRhgQoSZIEMRMTE0xMTLB//37W1taYnZ1tgoK+vr4mIEh1SF7rmp2d5R3veAcPPPAAv/RLv/SayIlfi+trLghIHffCwgJHjhxhYmLiVTFT02NJJC2OTX4vNbBnnnmGTqfTjABOmf/pxD1xtgK1yZAQgfglqxBhHRkGJH+ng33k/KTWJ4IhKXlINp+cv2gTiBMXgqLU9NN2IwlaUmaxkJPSwMIYw+zsLJcvX2bv3r1NRpKKNkl7kqiiifGQ76QoCkZHR9m0aRNra2uRffzt307fZz9La3UVnUU2e3A2Zmg2OiBFnM6nAvhQ8wQyKdNEQqD3kX3uVew8iK1nBpPnqODjND0TVQVdTQZEG4KKcLaqbISxTYYvS3Q9RVBFvBtnstjOZzQ2RKU9R6xvOxSFKCX6OKFOmP0qeHxQVK1I4vP1uWMtoXZ0XkfSYIVCZzmaEPv7W1Exz9g4fTHrdurBO4rMZBjv8K4ePuQ9vlUQVJxbEHygUirOJciknBIVATOl4qRDX7fr1fMbShUFjqr6/xkaXwcIhtil4bMcTRU5A0rTKssYmBQF1OetiTMQTM3md6oODLWGevyzV7E9tFQ13B9qjg6RA2C1xhYFvhtnM5R5HmWKnSPLTJwq6FxdmomlIm0tudFU2lBlGTnQDkRdCGNwZYnLDJlTUdHROrQPdLUi6JzFixeZ2r+fiYmJJqCVfZbW+VOkUDqLRP8/RRTF8Q4MDBBCaNqGV1ZWmJ2d5cUXXyTLMsbHxxuUwBjTlBxmZmZYWFhodADgVkeTdPhIR1Fqu2TfpfteugHkGGKfhLMg1ydJwUaOlLQ0HjlyhIGBAe69994e3oBc80Y0t7+/n507dzbkwrm5OWZnZzl69CjW2qYbYXJy8lXL/M7NzfGOd7yDffv28Su/8iuvW/nhTl53zB15peWAy5cvc+zYMfbt28euXbteFRnl8/EL5FhKqUZpUGQwJUAAmkE7El2nG06CAYEU05YgIduk8pvy2qtXrzYyv6IDLtG6lA0EuksJPmnnQrqhZUlQIqiCGBvnXBM4bGzFkUDjxRdfZGFhgf3799Nut3sYxykcKdctxkVqlGIgBDUZHx+Pn7d5M903PsT6wgLzi4v0f/SjmLo+nuGxWkXNfyIhrh1AB09WlVjrUHkenXaex1qwj3Pqvci1Bl+T6jS5i3MGMqWoiPXszFpcnqONwRFZ/UopHKHpnS+9R4V6tkSAkOUEHx2I6nQjQ11KBRAH9ajYl+/qfn0bgLweF21tnE6oVKytZyaK/4QonKMqi84zuqGe8EfskiiqKpYblKLtHZ1Qb25FlDUOKg4vUrEmXxmDre8d9dQ/m2esFUVsV3SezFraZWyLDErRpR5GZLt4rekagw1EQqFSKO/xVWzxC8Zg+tpUzhMIUao3RMlftIp6PKEmJToHJvIoYlkkRAEla2O/fwhUWVRpLFwt8FNZ+oCsLFlpFbgsp78qMV4TnIUQpYXLVpucALVCoHIOjcF5T07AdjpgYlsnPkTUqbKoEMdQmyyD4Bl44AHGx8cb55qigOI0pbaedv+EEHqUAaUmnw4WE1vS39/P6OgoExMTVFXVtBafOnUK732DEEgp4eDBg00AIERbcdCCQkggIs5ceDtiwzbaGTlv+bnYFrErYjvETkGcYXDkyBH6+/s5cOBATwCQon9faGVZxvT0dDMBcWVlpZE8Pn78eDMyWdoQX449X1hY4Hu+53vYsWMHH/nIR27bBfb1dQcFAS93GWO4fv066+vrPPTQQw0s/2qPlQYBqRMFuH79OkeOHGnGGkOvjKgsmeXdarVYW1trjpMy+FOSXV9fX5M9SPTvvWd6eprBwUHm5+c5deoUa2trDcGmr6+vQRVSTkDakiiw4traWoM4hHBLqlSyFgkoZNOnwj9pi5H3nvPnz9PpdLj77rt7yh7p50kLk9Qj03KHfK7AlykRCmggwqE/+iPWlMbVnseKT9QAAN4gSURBVM2G0PTDa23QPo7rNZXFBY9qt2Iff13KyFSt+OcDyleYOuvzCrAOXbeE6eCxRYtAiJCwcw1UTIiKco64cbS16CKiCaJI542OinrdEqNq+d28wNeQtdHRcRoTGf5oFeVyiVC7ygx9ZRkdo1KoMvIuchVFgbTWGBvb54K1BIFm4wOLNbFVMtORHxCUokTVk/Q0BihsSajnLnitoCholSXex/kKpckibF8L5bSdxYVAX4hyxUVfH1lZ0lHgW21ciDMW8qqi31pKE9v9usGjiyK2X9bkQ+0DlTEYauEhH79zI1MpTVar+HXwLo4JFsXCKoBtFWQh8hoy73GtViytEMsv3lqC1gRNJPl1uygfv0vlfdQJINSti3ULY/0s6aqqu0miYqBXsQNDb9/G6F139ex/IfamxFvhycjv4ZZwWavV6ilVSrlNAmOxN9KfLwTdycnJRuN/YWGBU6dONe9fW1sjz3Pa9QhjQeWEk5CihqlDlz9wy5GnrYpid+AWarERyZD3yrj1gYEBDh482OzptDSSBg4vZymlGBoaYmhoiLvuuouyLLl58yazs7McOXIEpVQTEExMTNzWuS8tLfG93/u9TE5O8uu//uuvSEH2z9r6mgoCqqpiZmYG5xyPPvroayaapEFAmklbazl//jxnzpxh3759TfQKt7LjVAdAHLk4xJQol9bi0s0ixxOmrmzm/v5+iqJgeHiYTqfD6uoqly5dYmlpifHxccbHxxkcHGxgLxEtSWF8aReS7FumhokRkwBBDERqAKR9sKoqTp8+zdDQELt3726CA7mulEUs/08DDkEe5J7I9W5ERRpU4upVHIHgY1apVKzVamL9nNqYuzwHazE+0CpLCh1Z+d5Fw+5MrNNLBuxVFKlRZXS6aIO2FaYocLVaXtHpor2nrPvMYy+goixyrMkiYmBMLBdQt+m1Wxjroma/idJ+zrs4u95oyhDIa5JebOurMy4FpdKYLLbWtX1UJdRKo2stAAUYndFyceod1C2POva9AzhfTxrUcVKg11G0KLNxkqEJsYYfgsw3CLQJYF0cwhTiSOag4rjfyND0tAn4bgefZbRR6E4HV5MrHaJxECIhUms6JkPp2O3hfdRx8HmG94EqODIdhxgZFa9Xe0+ftXS9jzoAeY4uK0Kekwdfj/qNdX+UiURJIqmyozRt5dFG4wKRnIjCmiiz3O8sWS0hrYzGBxWFhJzFmgwIsYshBIyKQZ7ZtYuh7/7uBl2TQF3sg6BaZVmytrbWOM90X8t+F/g/heXTklt/f3+DjGmtGRgYaLL2kZERZmZmMMZw8OBBVldXmzkcSik2b97M2NgYo6OjDXLX7XabBET2fErySx22EIzTOSkS0Mg+TMupYiuefvrplygBpmhoGti/2iUtiNJqvbi4yOzsLOfOneP5559vSJfz8/M89NBDrK2t8X3f93309/fz8Y9//OsTA7/IumOCgC8WSa6srHD48GGMMY2e/WtdqVRl2t538uRJZmdneeihh5qNm/bbi8NOz1kIQKmTk8268X1A44SFUyDwnEz9stY2AcHo6GjDIbh582bjnKUvV/rwxdGmksjCQBamf0rg2xi0yP87nQ5nz55ly5YtbNq0qXlPf39/g1oMDw83qocp5C9lDSk7yM+LomgMnmQfKeM63HUX4fBhvNKxI8A5cmfr8kAc8uJdHI+b1edqnCMQa9iq1p1XSuHyHFdZshrKDjbq9ussR7uohtfxAd+O348JnhA8gYxSaaiH+URnYii1jrB2CGA9usjx9ewCHwI6eFqVjcN1akZ7yDNQQYYVRzVDUxMKjaHSmty7iCSEAFrjfOxvd85DjXJk1hIfmQjHU19rXgcMQcdShBJIOMQMPLYzGoyryOpRxM7oSJzslnitmtkEyvtmEqFTNWzuPD6LMwCCdZFrEQK+5lwo72seYIgTEn38XaniTAVvIpkxzd7xdWufc4SioMwMmY9kzVDrPOA9znlcXuCcxWUG63zUGDCaji7oI3ZjGBuVFW0WJaMBKuKMCF1zP4qqorCO9dzRyXPQpp7dENGuwbe9DVXvn1TkRwR8+vv7G8cpgXDaaifkWbEHIvSVkoslMJfSgexxcZ4hBM6cOdOQAIuiYGhoqNmbMzMzrKyscPr0aZRSbNq0qRkVLvwb+RzZf+mwsnQGgJxrag9SxFL2bVmWPPXUUwwMDLxECjjVV/lSL601Y2NjjI2NsW/fPtbX15mdneXYsWO85z3vYXh4GKUUU1NTPPnkk0255Ovr8687Jgj4QuvGjRs8++yz7Nq1C2MMS0tLX5LjpsJDApE/99xzKKV485vf3MDcWutm08pmTntx+/r6emqHaQ9wKsiREvikBShV8JIMXYyNZBByDqOjo4yOjhJCYHl5mYWFBV588UXyPG82zvDwcPNZojMgZECIwYfAi3ALtpRrWVtb48KFC+zevZupqanGwIhwSQojpllAq9VqMg3hPEjgI62OgkKkXAG5X8XOnXQGBlGddVxlY7ubdZQECq1rgZ2aBBhCQ3qrlMLV/eOO6NB0iNCxIjqkjDjyV5ddvIpT6tq2otOhnl8QHbEqooOwAQqlYqboXVQ2zLLoDOsAICOS08hjG2YkBQIqDtIJPk5B1N7Hscd1Z4GC2KoWosPty3O0iwI7MfuNQYzXmiL4RoAoEDkJPhCzWQKqnsyX2SoOUbIWXasFZkQoXIdYew/1/12Ijlvr7NYMBqUoQsAS2fpaxc4G5T1OgdGKvm4XFaJ2QgCsiiqJ7bJEEecilCq2PypCnLRYVug8dm2EEK9RWxvvB3F0tFKKrN6HmihbTJZhlUIXBaUxkAVaVYVSmm67wA4MoG7caBQSlQ9URREnR9rYqREjR49WivUipyoKVFEQ1tYjarJ9OwPf/m3kw8PN8yxOWvaiZNuyh8TBC+lvI5IItxCClFckjnrjvpFE4OLFiywtLXH//ff37C+g2fcjIyPs3LmzqaffuHGD+fl5RkZGmJ6ebroa0gA/RRlTFFLsScplSsse1tqmBPD5ZgG8HgHA7VZfXx87duxgx44dnDp1ire97W1cv36dlZUVdu3axbd927fxtre9jXe/+92vqTT8tbzu6CAghMDZs2c5e/Ys999/P5s3b+bFF1/8kilCpapdc3NzHD58mPHxcQ4ePNi8Roh+abufODigp31ISgNSm5MlTl6ybRkBDPTAhqkuwEYFP8nqBXZst9tMTEzQbrdZWFhgZmaGkydPopRqBHrSmr3UzIS9L0uifuccMzMz3Lhxg7179zI8PNxcd19fXzNOOM0A5HxStUIJcuSa0uBDso/UKMlxjTEMfv+76fz+/8JevEgZYDUz9NV1/sz76OR1Pde+DpR0nrGuNLoo4lx578lCdDqxU8BibHR2SoHOonyuVpFZH0KIindK46xFtVoYpSlsiQmx97+q682KQFbPOogMtyi+U2lN2WqRSxsekdCoQ8BmGZXJYh07xGFDWil88BHGFyeiDbiYFZe5joFCx9KuA5qILETdf0LA1up4WVWiAqgaMlH1sZX3TbbufU1y1Joqy9Heo0PsnfcQmf+1umBWBxG6LClbBXkAYytUVcbyA2CVhlaBXu8QnMPmRfy8ykatAOsogJZ3WBu1EUwAW5ctMqVpd7tQywJ7EzUXbB6nM+begfP4EFEhHUIt7eyx3qOHhul/17tY++3fJrc2Ekjr0kBhbRwVnGfxGIDODGbzZty3fisLnQ57t2yhXXcBpGqbQuZLS4MycjwV3EnJsCnZN89zVldXAZo+fgkmUlSsr6+vQR3OnDnD2toaBw8ebIIMa20TVMseFAfd19fH8PBwQy5cWVlhZWWFZ555pqdFb/PmzY00sNiklECc7sGUBNnpdF71MKDXc3W7Xd73vvfRbrc5ceIEIyMjHD9+nN/5nd/hYx/7GN/6rd/69SDg8ywVUm/0VbzSbBVirfz5559nYWGBN77xjQzXEfuVK1e4ePEijzzyyGv+zJMnT9LpdBgbG+P5559nx44d7Nmzp2fDysZJHXuqzCcw/f/9v/+Xt7zlLQ07XupzKZkIYqYt0NzGernI+KaQeQo5Shuh3B/nXOOsV1ZWmrrl8vIys7OzdLtd+vv72bRpE5OTk02WIoJAEB3E8vIyV65caVqSxHCkiIZ8R6let5RSxHilpYf0Psjr0s4DiAHJ6upq07Io9c1qdpa1GzeY/+3foa/bpa/bjTr1RqMDtMqyngMQiW82r7PzqqS/NppdpWOPvY3wf/A+9pBneVTtC54KFR2dr2WHa1a98YGWrchc1MAvUVE2WCsKYi25yrIo++ssVRHrxq2a7IhSGGvRzlFlGZ2+PkoVp92peOHossQA1juMyWILnNFk1uJDwBoTEYXa2fg8iy2GxCxaRuS2bFRHhJjhe4jdFSFQBh9bIbWO6AhQFnkc+WtjG19oUJPIsPeZiddNrOU7rXHGoF0c3GNshXMe1deHtxVZiOI9XiusNnTrskTbWtpVxXpfHyUB4+r7X1nyEAOhEMBlBq8NTse5AcHayIVQ8Rq7Nckw8zV3wTq6RpNv3469cgXtPD7L4nyE4GmVFX7TNMtDQwzOzuLynPyNb6QzNcVSWbJ3796GlJsKe8nzLs96qvgJt4J1CVylhi57XBytBNTCARDETwIIKRVkWcaJEyfodDrs378fraPGgEgSC9QvRMJ0iFBauhTeD0S2vNTTy7JkamqqQQpkjkiqJSLnnHY8SADwhje84asmACjLkh/8wR/k4sWLfPKTn2R8fPwrfUp31LojkYD19XUOHz5MlmU8/vjjPZnr52vrezVLa83CwgLXrl3j/vvvZ3Jysgcik00jEbXA2eKk5d9pxC2bU4IaUQSUYwoUJ1lEqjSYBhYbmcXCJk7rfxIQyEYWpy8Q2urqKsvLy83o0aGhIcbGxhgZGWlKGN1ulwsXLqCU4g1veANwq/Yp17iRDQy97GepQ4rREI5DqrQm15uSClPyZGqA/dAQl2/cYPCtb6X4zd+MdWUCvs5+K63RCrR1ZEGhnI/Sv8bg6zq31zUs7ivy4KnyPA7ksRWZswQUWW4INurQG2Nie15Z4dttrNYEH6cbZi5OswtB45TGtVrRSToblfhqmeGofQ8mBAobW+mwNmr6t1qgAsZ7ylogqe0cGQpTH58QYiYcQhzeoxWVDE7qxnZEV5MTUZBVFUarOG9AEXX+tYZ6aE5RZ/Qhz1F14IRz5DVhsMpM1FnQcWJi5j066IhK+CihbPv6Iq+hLh3YvIACQtlFZXnsWggeVEQY8prU542hFPRL18OYbByolCtNpRSKgLYWVFRnFKGhDOJ3qiL5MPjItdBlSa4UPhjWZ2ZjYEAcN6yrOCK4/aY3Mfhd38mQ96ytrbE0NxeTi7JsHKFA+qlqnmT6aXYs+70oiqZFTxy67IG0NCiogixJatLSoQQWJ0+exHvPgQMHenQ6pO9fzkHOTYaMie0Rx512MQgKsHPnTtbX15tk4OzZs82wo9HR0aZsIOiH2Khnn32WoaGhryoEwFrLj/7oj3Lu3DmefPLJrwcAr2LdcUHA3NwcR44cYcuWLRw4cOAlD+NGxcBXu5xzXL16lbW1NR577DEGBwcbp5U6r1QoJCURirNO62gCa6cZxtraWg+MngYRYkQkq5cefCkHCHQnTh5oiHtAA9+J0UhlP0MIDA0NMTAw0LD75+fnuXbtGidPnmxGg167do3R0VH279/f9BinnQDyOSnjOW2XSq9L4P5U9EiWtDSlBjMNhKQc0u12uXz5cjPBzN91F9XRo3TPnsGdOk3QilJn5GVJEWKRXKsoj9stWrUfiWI+/WVFpiKzvFJR/lbXtWdlNN7Ftj+VGYzzUS7YGKpuF59H5TzjLNoYsBXO+6g+WE+ny2uRmhA8ufMQPKWKLYSu7r33IWCcJStVlBAIMUiwWXS0eR0g+OBrTf6ohpcpRXAen+dxVgFQOYcxIU4sDIaQ54SyC3mOsVVEAkLAiNNWsfxAVUWGvY/Nj8oHKhUHCKm6NVGFWL5w3RKbZfWcBoOyDmN0ZN0rhTcaq+vuA+lKEMi+rDBKYzFUeKpQj8etxZNQEXGwtor3VMoRWURU4qwCg8/y2NoZQsz0WwXVeoe2jq2CSsUyV+UceV3OsUVOtnMng+94e3TGzjE0NMTCwgIQp96trq5y9uzZZh9t27aNiYmJHkU9sQ0p8Vf2t5Th5Of9/f1NkJzum5RcJ/tRAl1rLWfPnkUpxd69e3uCBtnvYm9kb8l+lnkcUqKTrqSUtS/nPTAwwPT0NHv27GFtbY35+XmWl5c5ceIEzrmGXzQ1NUVRFDz99NMMDQ01QkBfDctay/ve9z6OHTvGk08+ydTU1Ff6lO7IdUcFAS+++CInTpzgwIED7Nix47av+VIgAZ1Oh8OHDyOa30NDQ82x4dZmEmOQsufT6FsceEqSkWAgrZfLSkV2UjhdMmfJilNCoKiCpcdOWxPTYw4MDDSfJ3oFcnypJUpQMD8/z4ULF/Des7S01Gimi2NOAx6BEMWQpdLIAlNKB4XcC+FApPdPXif3Ne1icC5OJJyZmWlqns45GBvFfOPj9D3+GNVHfx1/4gTBWjr9fahuF6OiDK13cahNlWUYABsV8lQ9eCdkOU5B1SoaJryqnbUOkY1fGRMzeQWm043aAr6Gy7WiUApVlRhtIkqgiNC/92A0IdRSwVmG9V2Q4FFpirq0UGUR5jdVFCpyBCjL6Oxr4h51X7ytBxtpiIOG+vsolaaoKqyPw3qqvIjKfLaKBD0VVQMhlges0WAyjPeooCiVisFOrcJnFDXCEjsUgoljm5WzuMqSZ6C6nTjK2Rgy67BEpEHh6SqDC4FCxRHGhkhc1AqCqqV563G/spRSzVyIoBSuLOkjYKwFFB0TuyBa3ZKss07H9dHJI9KQVbENsK+zHoOtEPA+QhvhwoUeiPz06dOUZcnOnTubcprsq7W1Na5fv8758+fp7+9namqKgYGBJjMW7o/sW0H15DkXSH2jEt/GDFuefTnWc889B9Aob6Zom+zjNOlIa/iCyKV1frELqXy57MH0mOPj42zevJmDBw8yPz/fjEQ+e/Zs5OMMDrJ79+6XKAF+pZZzjr/1t/4WTz31FH/wB3/A5s2bv9KndMeuOyYI6Ha7nD9/nocffvgLQj6vNQgQqWFR53rxxRd7yG7i3DZm+uIcpf99I/NfoPHUOKQwuHxGytCVTSyGQOBK2fhCDpL7A7cUwFJhEMlkhG2fohniZBcWFhq4Uyae3X333YyMjDTKZSdOnKDb7TI4OMjExETT8yzXmJYn0s4IKXHked4zElnOTzQMBP0QFUWgCRzm5+eZmZlhcnKyuQ/SB93pdCjLkvZ3v4Py19apLrxIWRS4EBj0NVqiiOQ15yi1xpt4rYIZ2RDwOsNWJSYzhFocyDgb9fSVgjzOpyfEDD330Rk5Z/FZC22j1kBQKkoJhBBZ8s5H/fw8jjIunIvBh3d4pXFFganKGHzUaojGO6hCFNEBTNdj8wxftKDTwWc5VtQHiaqHvqoic7+G5lXd++6CR2c5ylkIkRiIAq9M3f7v8TrOLUARUREULWvR1MObQpxvoKxFBRvbH/M8kikDZFpHiWDvIgJBILdxxLDLsqgYmGVNCYUAxlt8ltMKsXPCq6j3H3QMJoxzUcNBUAnvhd4Qpw16T15VtK1joVWgrQxkiqjFmtJUWWx71CHOAZC9ePHiRbz37Nu3r0fJUjgx/f39TXC9vr7O0tISFy5cAGDTpk2Mjo7S19f3krJgioqlaFfqyDc6aEkWjh8/TgiBvXv3NuWFtGUw5d+kROKUb2OMadriNhIO05+lCCbc0gEwxjA1NdWIFD399NNNgHTkyBGKomh+PzY29hVBBbz3/J2/83f4oz/6Iz71qU+xdevWL/s5fC2tOyYIaLfbfPM3f/MXfZ0EAa+mT3Wj1PDMzExPhC0bJm2nkf+nNb80o02DhW6324iLSN07VePaaDjgVo1RjiMkH8lm5HPFaAAvMUbiLNMgQDQKBH4XgzAzM8PFixc5cOBAI0tsTBz/OT4+HuuotUGUkoJMQRNDIucNNMQ/732jQijIgVx/mt0ItyFVNLxx4wY3btxgx44dzfWnA01CCM3Y5ezbvp31//zL4KPYja26qDqDV8rjtYntfHmO66w3WW/Q0Vlkui7XFAV5t4sORJGiPIt6+t6hXBwu5KqqHsOr4mjhGqIORIcmZDeEIe5900Ova5KeNxpVlmTB41VAWRXb7og97JmO8LpzngxF1e2iiJyGqm5nVM6jyopM1wqAAQoUrqoiX0ApAoEqL8gAUzpU8NhMU7bahKqKr9FRJMiEyE1oWYsOUWnQicCSjsRAbIUOUXrZZRkoFZ15PXzHKYXplrF+76MTjjyKqH1g+/ooQsB0O2ht6vsUuyyCi4GH7joou4R2ux55nOGdR6sopwxRXtgHT1+oCZHE8o62LuoluJosaQzFw2/COdcEAPfcc0/D2E/Je2I7pDw1OjrK5OQk27ZtY2FhgZWVFc6cOYO1lna7zdDQUFNH73Q6Pe2vaXut7DcJ0sU+ZFnG0aNH0Vqze/fuBsqXJEL2Q7vdbvbL7UqSsu9T3pD8XuyBcBhSwrEE3em5VlXVDAN6wxve0JyPTAF84YUXKMuSiYmJ16zv/0qW954PfvCD/P7v/z5/8Ad/wM6dO1/3z/xaX3dMEAD0PKSfb6Va1y83Sg0hcPLkSS5evNgjNZy24kkgkMLhG8l7KbQtGy4l+aXHkmsRuF7qgumxxNHLv6WOKBmLnLsYFflMCQwkCJBsW85B2vXS+7W6usq5c+dYXl7mvvvua7gFKdtZGMpjY2Ps3LmThYUFlpeXuXHjBhcvXmRgYICJiQmGh4d7Mo4syxqeArx0nLJkN9KemJYUzp8/z8rKSkOQkkAiLc2k78/27GbsXe9i9n/+LsZFgZ6i1ULddRfrx45ijUYZTXAOXxR0a8JbVpUY51FaU7Xb4KKugKEeblMLz6A0ykiffz2Qp86SgzI1oQ1wjmAMufdoH4f4BAVBaTIfh+JUKo4szuvZ9qGupQcdywYheLyv2/OCR3lNThQlsmVJ0e6LAYA2KOcwzkexIV/fe60wPirnVVpRaR1FkXy85jKLXQ8mi5K5uqpwWT3rQCk6RRFLC7XMrnAWFDTyu9pHLkXIMtZDoF1Vte6AwplInjQikeADOsvi+OAQtQxExTGrqtimaAzaaEIV2zG9tXF6IHHYktGasijohoBRtS5HFoWFlI4BiCbQ0YbQasWJj1qRbd5M3yNv5sSJExhj2Lt3b8++kCw4za5lBLegbqLUOTg4yNTUVIMQSAtulmVs3ryZkZGRJsiWMlmqICr7VvbrCy+8QF9fH7t27WrKZ6n4WFo+kGde9ldKDhS70el0ejROhDQotiHt4JFzkqBAuAXPPfccQ0NDPdoEkgxMTk6yf/9+VldXmZmZ4erVq42+v6AEItrzpVzee37yJ3+S3/qt3+JTn/oUe/bs+ZIe/8/quqOCgJez5IHdyMT9fEvaXlZXV3n00Ucb0Rs51u1IQGlkL/9OXyMEIHFmAvmlGblsxjSYEKcvQYScvzjK1EDI+8S4pO9JgwuB4eXc0gxbUIm1tTVOnTpFWZbcf//9jdMWR91ut3tanaS/eWhoiHa73ZRn1tfXG4EiYwzT09OMjY0xNDTUkJSkpUnY1oIIyERFua/WWk6fPk1RFLzxjW9s2Nhp14PcR+FIyPnm997L+K5dZNeu0T8wQLZ7N2s3b7J08iTGRsZ+uyrxAapWi1JriioQsqiup9fXqISRbW1NbssIvi5hUI/9rcV7AlG6tsyzWJvulhRFC+Vs7DaoW91kgJGt29qcjqJAwXl0ZupgIRCCqR15/IzMWYJzlNqg8zzODKhJdEFBKEvyEFX9jKsdhLOEvIhkQi8Ex1Bn6rUyodKRiOh8bKEzGdYYXIhBklWKMs8i4z/LIm/BWjKIAkneoU0t1FR3O8SyQog9/SrOEAjekwGdLDr8vCxpVxUo1UwotEpHNMLE9/s8Bxt5AVWIg5lQitxZqCp8q6CjNUFHkmPszKiDl1Y7zmGwlmEF+fbt9L/73Zw6cwalFHv27Gn2dkrKS0t7gkrJfpcOHrEB4jinp6fZsWNHM/BneXm5yeonJyfp7+9vbIrsQ9nLVVVx6tQpxsfHOXDgQBOQiD1JHXfaaZMik8YY1tfXG7sgmf5GhT9JSFIeQoogyM+73W7TBSC24HZLKcXg4CCDg4Ps2bOn0fefmZnhxRdfbK5f9P1f6/S+EAL/5J/8E37t136NT33qU+zbt+81He/r69a6o4KAl4MEpH28X2ytra1x+PBhWq0Wjz322EsGUWzkF0hkLg5JHFoaoUvQIA5Lfi4Eu5Sc19fX1wPFy6ZcX1+n2+32MI1TIpIYAYEXRbEshdbTrCZtvZPPkuBhfX2do0ePMjQ0xMGDB3vq82mZQgKA1ICkOuNaawYHB9m6dSvWWmZnZ1lcXOTYsWONQRgaGmq+H4E80+9TAjdrLadOnaLdbvcMZpLhSHCr5LG2ttacp3SGlGWJLgraBw5QtNusrq5StVq09uzGnj1X99ZHXX1rdMy0W0XMYOsWP0KgKKvoMJXG43GF/LyLDjEjD0pHmNxH6VpT2Vgf73YISmPb7TiExkc4H1tFDQEdpYbliatCnA+g8FGy1kX2vlca43zUtjexXS9kUWWvG29E/N6dI1tbBxVV+YKpSZhG43Qt0FTLLDsigqF0HCxktUbXjPuKOAI570bxH2tq5Tjv6RqN8TVZkkDQedQzUPXURUUsfShFZh1e12Q+o+nUcH2tJRyHLkXiBZ2BwTh10IfYDREC1B0IwUaCo8/jFMEQIA+xbdMXOV2jI9dAaXRVooPHV2VUHCRQ9vUz+Bf+AqfPnqUoCrZu3drsEwmCU2QP6BmjK0FzSshNW3HlfaLKOTAw0OyBxcVFXnzxRbz3DY9mfHwcY+JY7dOnTzM6OsqhQ4d6oPy0Q0b2bDoaPEUVZH+LnZGMPt276b6XfZLaiTRxeuaZZxgaGmpKAC933U7ff2ZmhjNnzvDcc88xNjbWoAT9/f0v+7hyzT/3cz/Hhz/8YZ588kkOHDjwit7/9fWF1x0VBLyclTqDL7Ru3rzJ008/zdatWxsxjo1rYxCQQngCF6Zwf1o+SJ2ntZbp6WkuXrzIpUuX2LRpE8PDw43RSBnAKblHoHphHEtrnRAMU+csWYucp8w0SPkF8p52u433nrm5OV544QV27NjBzp07e1j8glykxEW5doEbpRSRBidiiESVcMeOHXQ6HRYXFzlz5gxAMxZUVAsFWbDWsr6+zsmTJ9m8eXPT8rMxwBHDJkZNRITSlikJGNfX15vvcvT7v5/VX/5lqvPn4whfFWVqtRbiW11WqGv3WVnG2n0I+CzHaUUICp1HmFyjwbl6eqChsDbqzhNnCugQcC7K3Bobs/mgVGxB9LHu7ghUNSehow3KKTLvIoFOqwiTE4cSRSTCRMgeoniP1nHAUVWiVEQjdGVp1c+trwf74B268rH1sK69+8pCHjNsWwstURRRRU9HzQBjLUWrhbUVZHFIEXlBaStaRLU/K4G3D7H+r2J2XlSxtGF9QLcKjNLkVRXnLAB5VaGzjKIq64mPoumv6aLoKk2hY0eEsVXUBtAKpWrWv9IobQhVRZ5lOK3JA/QHWC0KikOH4K49nLl+Ha0127Ztaxxq2oFzO9g65dWk3Svp+1LnC7GrSFAt6TQYGxvDOcfKygqzs7NcuHCBoaGhRoRs//79zX5L22vl2CmRUJALGU8uqGKqNyDBQlpmEy5Revx0SVKTSgG/FsKf1rf0/e+55x7W1taYnZ1ldna2p/04tQGfb4UQ+Nf/+l/z7//9v+fJJ5/k0KFDr/q8vr5uv+4YxUDgJU7t861PfepTPPTQQ4yOjt7299JqePDgQbZv3/55j9PtdvnUpz7Fd33XdzWOJ50lII4orb0L+z0lBkmvbgiBhYUFFhYWuHnzJt7HGeEi0JPW6CQLgFulBoEoJVuWjEUMm2T+4hAFwkyJiFIfvHHjBidOnGDXrl1s2rSpBw4UNEKIUWIQNiIx6dCftB1RYEx5vda66ZNeXFyk2+0yPz+PtZaBgYFGwMhay8WLF9mxY0dTakiDOrmeNChIg5/UqIuh6+vrawY3QTRQ9uRJytVVuk89RfnC8XgsBF0BEzzKZOTra2ilqYoCC7g8JziLUZrM2Qi9E0l6poq16xBiy59WxAE29YhbYx1BK5SNGX7QJs6szzI6RSv24mex716HOLQor2JbH0qxlue4LCrzWaMJJsPWXQFFtyQXlUJbYbShqEq0ioQ+q6IYT24tXsduArSJ8LyOLYWYOBjI5VnURKhJhWgdR+rmeV3+0FRZhnOW3NdDk4jIhYk3OPILQogBUqugCpAphdNxXoMuS7zR5M5jie2ZeVVh8xwXApVSVO02rfvvJxw+HLkVzqIrS1kUTStlqLsHcI5WiDoHuTGYJ54gHDiA955Lly6RZRmTk5PNwJ+07CbZvTz/8gzLcyf7G+gRrUrFrGQ/prX2tMQgTq4sS7rdLqdPn272iAz6Gh4ebspqsmdSG5KWJuWc046fFBlIUQX5vexj2Tdy/q1Wi7Ise6YBvlbo/gstay1zc3PMzMwwOzuL956JiQmmpqaYmJjoEX4LIfCLv/iL/It/8S/4vd/7Pd70pje9buf1Z3l9zSEB8PnbBL33HD9+nKtXr37RVkM5DtwSB0kdScoFkGPDLbEigdRTxrHWupHo3b9/P0tLS9y8eZOzZ8+ytrbG+Ph4048smzyF9WVyWapVntYHU6ZwCq2nWbPWmkuXLnH58mUOHjzYQPRyPUJiStv05LPTSWpi3ARVkFqkMPWldxpoAoVWq8Xw8DDOObZt24Zzjps3b3LlypXm+xKURIyBBDlyLamjFwRBMiBplZJzy/O86ZqQ83HOEfbsIQuB4r77KJ/8FN0nP0nm46Acn2mMV9jgKdttcmtRLjoXVVWRSOejBC0+Kg2iDYWNjPvArcl9QevYDRCIs+tRtJSquwJM7Ms3ps68NaYsyXyg2yqodAGoiB7o6MQNcZiRKCS2fOyLx7s4JKkel6y1wvsIpUdEwuPQkRcQArp+nrPg0S7UJMQ6i/eRB4GOsL9S0hYY4nXgY8tdZSmCx2kTuwlshUERYnckLgR0kROcJ9caYyvQhirPKYvIMajW1+lDRfJeXV7xShHyjDA4wMjb38byc8+hul28UnSLPLYcek+lcoyJQ4DQhvYP/j/krRb5yAhlHYxfunSpUcFLJ3hKwJoGzbI3JOOGW+PABYWTZyutt8u+STP2VMZXkEDZY+fOnWPLli1s376dPM9ZWlpiZWWFc+fOcerUKUZHRxkeHmZ6erpHiVRsSrrnU0EtCThEdCtFyNIEYmNwUVUVzzzzTIMAvJ4BgNjH6enpZvz60tJSg5AcPXqUa9eucezYMd75znfyuc99jn/2z/4Zn/jEJ74eALyO644KAl4u2zTdyLLKsuSZZ56h2+3y2GOPvay6VBoEyOZIs0xZKWQvDjgl7kgNUCJ00fxut9tMTk4yMjKCc47V1VUuX77M0tISw8PDbNmyhbGxscb5ShuSGJW0ZpgGAWI4xAmnwz9Onz7N8vIyhw4d6snUxemvra31IBBiyMSYCYNfHLv8PK0zppm6kCTT+ybnm+c5k5OTrK6usrS0xF133cXq6ipHjx4FYHp6momJCUZGRoBbRjeFZtO6aMpiTrUQ0oAs5VPkrRatt/0FBt76Hfi5ORZ+4RdRVRmdJ4FgMpyJfec4S+EcVasNOuCDInOe3Dqc8rGurlXMmpWKcr+2G/V360xaoShV7CzwSuF0RjfLCXlOVlVRkKd+vVPQMaaW2Y1DkbyNw3xyiINyyqpWQ4wEP6WIDH7rYktdnsUShY/qfaqqInkvz6Issg84G3kP1KWQ4H2cUugcVVGArWjlMXgwKk5phNhxYKyDPJZUMhQqxM4Elxlsq0A7G2cU1F0VWkUdAOEZmFaBW+/EaYR5TkfKCiiWdu1i7dw5xu+9l+zIERzQzYvIvdCR91CUXTIfKDODW1xE33UXzjnW19e5evUqmzdvZmxsrClfidNL96eU42QvyLMiAfjq6ipFUTRolXOu4abIc7xxr8ieS8m53ntOnjzJ1NQUO3bsaALmoaEh+vv7G3Kh6GGcOXOmkfEWZc90yJeca1r6kusAmjZAsV/p3pTgAeDIkSONEqAc50vN6v98SynFyMgIIyMj3H333XQ6Hf7X//pfPPXUU/y7f/fvcM7xPd/zPczPz7O+vv71scCv07qjgoCXu9L2GICVlRUOHz7M4OAgjz766MuOdlNijfxfsuCUKSztbPJacXiShaaknvSzxZD09/ejtWZoaIiJiQk6nQ7Ly8vMz89z9uxZ+vr6GB8fZ2JiohEoabVaTdQPtzZ6mjnIZhbnLWIk99xzT3MNAovKhEIxcsJLSGFGyZ5SjfI0E5LhJXBL/TAdRSz3CWjuw9mzZ1laWuKhhx5iamqKqqrYvHkzMzMzzM3NcePGDSDKug4NDTVz1FNkJoVF5T7L95QiIdLDLQFP02ZlDG58nPZf/WHWf+W/opaWolMMcSodqnaYeRGdKCGK9oTYaocPKKNjVuocKs/xxOE+Wquo+hcUhDhDoDJRXU85F1X4iG2IVSuy+XVlyYyJRLcsXluE8g225gYYk0OegbX1BMJILCSEuhNBY4sc1tbJucUxcErjauGgQEBlWeQuBI9TGmUMpixjq13NYaiIHQrKuYiEWIf2Ls5nqM89WBuHCaEigTHUSoo+llg6eR7lkOu9kdkqdhK0CoJ1JMoY9PW1GX/oIZaHhpjZfw/jZ06jVlYwLkoUYy3aBEKWsRZCzTGIx11ZWeH69ets3bqVnTt3srKy8hIHLXtW7ITsqbR8JkFD2iGQlv3kOUufNXkmJRiVfV+WJcePH2fTpk3s3LmzRzY4DU5brRZTU1NMT09jrW3GgV+4cAGtNVu2bGFwcLCpo6f8I+99z0ChlCCY8mVkiQ6AEILT6/pKrXa7zdve9rZGGv4nf/InuXz5Mn/9r/91bty4wVvf+lb+23/7b6+YWPj19YXX12wQII57ZmaGZ555hp07d7Jv375XHOVubBOUzDIl76RdBem/xTGLil8amKR1SQkOUlh98+bNjTLe7Owsc3NzPPfcc2it2bx5czMmWIyBiIkISiB/a60bTfChoSF2797dYxzkc2U6Wdp+p5RqauqpHKoEAykEmcL1As/La9LWxpSNLURBGUiyuLjY3LehoSHGx8dxLs4MkEEnAOPj40xPTzcMbgkuUgMoZQIhTsnvpXyRciWa4G3bNha+/92oa9fZMjyEWVpi9Td/C2syyiwy7QkBbIBuzPJ9lpNnGWp1Fa+iJLABjIs6BJ5AUJEtn9cO22YZ1sc5A9o7nPdYY8DkBKXqcceeTGm8dwRt4pAeW6FMRjAmdhDUAUjIFDYvaHW7sSWPyE0IXkYGBwx1Bq5jSx/eRWKh0VB243AhBbrTiedeE/ysMUDdcaBAuyj5mzmPU5HgSBX1GCqjYisfgTzcUky0xqAIeJEcJqB0bJPE+5qnEAOKDGB5me5/+RVG/vb/m6F9+yh/9EdZPn6C7FNPxvPLMnSnw3q7Taco6BseZvDegywuLXH9+vUGQVpdXe0h9qVKnxKcynMrjlmenW63S19fX7MHJahNSwkbp/6lCIMcf21tjaNHj7Jp0ya2bdsG0DM5MIXp09JhCIHR0dGmBXFlZaWZbdDtdpmenmZkZIQtW7Y0+z4NyNNSx+04As8++yyDg4Pcd999zZ77ciEAn2+FEPi1X/s1/t7f+3v8xm/8Bt/1Xd8FwC/+4i9y7Ngx/s//+T9fDwBeh3VHEQNTYt4XWkeOHGlIgadPn+a+++571dKSf/AHf8ADDzzA6OhoYxxSBygZhsCMEgQIs17aicQ4bJQaTqFFIRE6FyWCRZZXyIYAN27caMRJIGbIg4ODjI+PN3wBgeitjSOEjx8/ztatW5v2qJTQJNMFRTUt7TiQIEBKC/LzFA2R602HAkktUmYeyHuFrLi+vs6ZM2cYGxtjz549zTWmcKxk7LeTXl1ZWWkGngwNDTEyMtLDI5BMX2SK5fPF4AI95Rv53NOnTwOwb9++BsUp//RPWfyfv0u1soInkIWAqmJpQOko/ZsF0M5SivCNj61xTsXAQakImRfOU2rFel5ERUGjyZynk2dURSu22ZVlJAU6HyVzbRWhfAK6iu12KtTy1WVJUNDJC5wxGB/lcmM5Ier0Z86ReYf3gZDn0SlnWaOoaKyN+gQuOuO8fr8i4IizFrQPZC6OPXYq9vJnPnILtI9te0HriHAI09u5KGWsVBTygRgMuPq7zDPyqqJV6ws4oiiSqhUPbZ6hv+u7GHj44Vstc3NzrPzKf6WzthpLHHlOVeTkf+7PMbh9O5cuXeKuu+5iaGioB3kSLsjG4DQVyBGHLkGBMYaRkRGstayurvaUsMRRi+OUY6byvYLUHTt2jK1btzI5OdkgUhKEyPMpJTxpH061ReTYKU+hqipWV1eZm5tjdXW1CZiFT7BxmqD8bUycBvi5z32Ovr6+HiEgueaNbdJfzvUbv/Eb/PiP/zgf+chHeNvb3vYVO48/a+urYx7kl3gZY7hy5Qrnz5/nzW9+82vSlk6Zv5LRSr1bDIeI4IijEeOQtq6lGUkKL6a/T6eMSWYhgYUYjU2bNrFnzx4efvhh9u/fT1EUXLx4kT/90z/lhRde4OrVq6yurmKtZX5+nuPHj3P33Xc396CBwOvzXFlZaVT4pLVJ6o9ptwLQaIgPDg420L8YVDEyacAjRi4VOVlaWuLo0aNMTU2xe/fuhq0tDjnPc/r6+ujv728yGSlL5HneDDo5dOgQDz/8MNPT0ywuLnL06FFOnjzJzMwMnU6nOR7QtGuleu3pOZVlydmzZ2m32+zfv79Bf6y19D36KOM/9ZMM/+W/RKuy5JXFUNf0jakn2dkI0+cZPgS6xkTym9ZYpXA+trM5rXEmTiVUKrL+UVCUFVlVklURscDaml1vI5HfOYLzqBDIrCV3jrwsUSGK/becoxAyX5bFKX9A3u3GNjzi0B58rY5Yix4RYjbv6+dCZ4aKSOqzRB5DmRf4PHYREGIwkFc2tk3GQkbs9/cOU5Ml48jgKFfsvI/nXFbkZUVWOzcrXR2hHvnsQz0KOaITDkV16lRPJhuGhxl4348x+K530fqu72Lku9/BxI/8CGFkhEuXLgExSJ6dnW32jThSySCl1CUKgELky7KseRZlj8vUSnHe4ojTIEA6X1KejgTWzz//PJs3b2bbtm3N+2WvbOTayP5P0TR5zcZyV6vVYuvWrbzxjW/k0UcfZdu2bXQ6HZ5//nn+9E//lFOnTnHjxo2mFCbXVFUVR44cYWBggPvvv7+nvHk7rtOXc/2P//E/eN/73sev/MqvvOIA4N/9u3/H7t27abfbPPLII3z2s5/9gq//6Ec/yoEDB2i329x///38z//5P3t+H0LgZ37mZ9iyZQt9fX289a1v5dSpU7c9Vrfb5cEHH0QpxdNPP33b15w+fbqRV/9qXF9z5QCBjgEef/zx16xnLZs8ddhpT38Kjaf1uVar1cMeFqOSZg5AI6crWXOq4w23CHWp4ZD3Dg0NMTk52Uj4Li0tce3aNU6cOEF/fz/WWvbv39+gGGLsgB7oX1Y6T10MiBg+4SGIQZGxqSGE5rPEYMs9EaIhxLGqc3NznDx5kl27djE1NdUYtpThLFmbZEJp+5MIAcnvWq0W09PTbNq0iaqqmJubY25ujosXLzZaBSLzKtC/BANiZJeXlzl16lQD14rBlwCkMZJax6E7NTSuvcM6h9OGyntsXuCUwuU55DmqLKPmv1bktRMPKhIHlQ9YBZnSVMpjNPWY3Sjta0yGdy4KBvlACB5tYttfFmqZXhP5ARAnCOoQIoxPwNcjjjMgsxa8jrLF4ZaYFkClFWhNbqPQz7oPVH19EVHQmtLXJQXnYxnEaPBEON856iJFfD5U5ES4vij1q4iDirzWtG1FXlm67RbdLIutgJWlWxQRVSnLOGY5BEyW4WycHdCemGz2SAiR8Z9lGdnu3c33uLCwwOLiInv27GF4eLiRsb58+TJFUTA6OsrAwEDD7Jf9CzSOW/a2BMJra2tNz39aU5fnf319vUEQ5BhwC6lUSnH69Gm2bdvG5ORkExAL2VBekyIQcm4ppJ/aFAkeoHcwkCQGEmgsLi42/fhlWTYcg7GxMY4dO0Z/fz/33ntvT9khJQh/Jdbv/u7v8tf+2l/jwx/+MO985ztf0Xs/8pGP8IEPfID/8B/+A4888gj/5t/8G5544glOnDjB9PT0S17/J3/yJ7znPe/h537u53j729/Or/7qr/LOd76Tw4cPNxoE/+Jf/At+4Rd+gV/+5V9mz549/PRP/zRPPPEEx44de4k/+eAHP8jWrVt55plnbnt+VVXxnve8h7e85S38yZ/8ySu6ti/XuqPKAWm2fLu1tLTE4cOHMcYwNjb2moQlZFN+5jOfYevWrWzatKmnR7iqqgbClkwFbnUwpO198h6RBU03nmQaafSfthtK5iJGTxy0lB+AxmFDNBBnzpxhYWGBkZGRRqVwenqa0dHRRlJUAo0QoqCO9FBLZiwiO8I7MMbQ39/fZPYS1LRaLdq1Kp+UatKaq1zzzMwM58+fZ8+ePUxMTPTUQtOhRp1OpwkixPimYilyX4WbkEKfcm5aa1ZWVpibm2vmxY+NjTExMdF0WGitWVtb48SJE2zatImJiYnm+5NnrCiKpiSzurjI8r/8ebytyMqSwnuqEGi5mP3azNA1GUprijqTdxDr3TZqCGTOkjkXM2wdpwfqKt6zKou1d1xUHMyEfOejEqHXUV8/856QReKd1YZSazBRu195j3ZxMh/W0qoqTIhT+0xVYY2hyrM4gbAeRWysjdMPiUOPyqIgl2AiL3De0Wctxnt8lsWygo7qgyHesNgVULQInXV8qx0DnZqwp3wkOmoFZZZRmYz1Vit2U3jfCAtlIQYzhfeRB1G0mPihHyRs2dKoQsp3IgHd4uIiN2/eZPfu3U1brSBwUnZaXFzk+vXrzX4aHx9nbGysOVb6jKWBaFq3l1JC2vmSZuXp+1dWVjh79iy7d+9uAgAJmtPygzhhQRLlZyK+Ja+TsoIE0ykKKfs4RTXSzgGR8Z6dnW2mhO7cuZOJiYlGyltshlzDlxsN+OQnP8l73vMePvShD/Ge97znFX/+I488wjd8wzfwb//tvwWi3d6xYwd/62/9Lf7BP/gHL3n9u9/9blZXV/nt3/7t5mePPvooDz74IP/hP/wHQghs3bqVv/t3/y5/7+/9PQAWFxfZtGkTH/7wh/n+7//+5n2/+7u/ywc+8AE+9rGPcd9993HkyBEefPDBns/7iZ/4Ca5cucJ3fMd38P73v7+xR19N62sGCbh27RrPPfccd911FyGEZtO8miVwtjjmVCdAHE4K1QvhSDafvF4Qg5Q1nDpyoKmhp4GEODiBLdPMWJZkB5I5C5/gzJkzZFnGm9/8ZoaHh+l2u9y4cYP5+XnOnTvXjAIdHBxsYHIR5ZHzh1vjjwcGBprzT8cQizESp50KkMh1y6jjixcvMjMzw/79+5uMTAKAjb3bUtfvdDoNeiHEJ8mE0jZIMbISbMh9GR8fZ3x8vMncbt68ycWLF+l2u82Alxs3brB9+/amhiz3VVjWYmirqkIXBYPf890s/frH8Ch8COTdqNKnAhgLuTYoH0VzVKvAO4dyNYs+1CQ+78krS9AKpw1BRQnfCkUeAtoHdIhDhxTUwkMu1vh1FNshENsNi9hz72rp4la3A9SDh+pyhfKxS6Fqt6OOgYraBUbHvyGKHXmTEaqKfu9QtopEP+8bvkGoOw+81lR5Rk6IZQFTlzmshSyPxzYar3ScaUCcqmizjJAXKKNpOQsYgjZYY+iGGDi1yipOIGy1GXjn9xC2bGm+k/R711ozPz/PzZs32bNnTxMYpIRdpRTtdrsZarW8vNygBDdu3GiY+MPDw012J/tbyKViC1KinQS4sgfk/ISrcuLECfbt28fExERD8pU9LXtf+CayT/r7+5vAP0WhJLDtdrs9yU/KQZDAIe18gBjwCApy/fp1JicnmZqaYn5+nosXL6KUagR6RNv/yx0A/J//83/4gR/4Af7tv/23ryoAKMuSp556in/4D/9h8zOtNW9961v59Kc/fdv3fPrTn+YDH/hAz8+eeOIJPv7xjwNw7tw5rl27xlvf+tbm9yMjIzzyyCN8+tOfboKA69ev8973vpePf/zjn5es+OSTT/LRj36Up59+mt/4jd94Rdf25Vx3fBAgme+5c+d4wxvewKZNmzh//vxtxYJe7kp7f4XBm3IDUrEQySbkfeKs0zpcSvJJAwPoHUssRkzY7BIICEyXtgQBTZlCKcXq6iqnT59mZGSEvXv3NkZsZGSEgYEBtmzZgnOOGzduMDc3x5UrV5oBKBIQiIMXQqCslJ8g5y/kPYFOgZ5MXe7F2bNnWV1d5f777++BReW7k8AhNWaSFUGUGJb7kkL5IYSeOQySxSmlGoEhaQscHBxkbGysQW/OnTvXtB4uLy+jdZx7IMZZa90gAKnQi3ngAca3baP6zGdx16/hjh6LCoI+jr/NnIvzCFAUjz2G/+M/wQUXBXC0ITcGKo0NnirLo5OvKnKtKbIMHTxFrVyIUgQUXgU61kPRous9ShFnGRiD1SZOK9Tg8xxbFGhrcQF8brDek/uoM+CUwmUFKFAuBgkEMCEGMZUCXxS4PCMDci8tgxpKK8L/KBVllDEGX+v6B23Q1hLyHOMdqrKgY2mk4z0m1w2HYPhH30vnf/0+5uw51ts56wo0CoqCge94K2Z4CLNtGyqRzZZgXMpPly5dYnl5md27dzelnjSrFUQozbxl5O/OnTtx7tZI3EuXLjUBgRDsqqpiYGCg2WPyzKeBZlpSkkDz5MmT7N27l61bt/a0o0qpLy1xpV1B8ictVYjdEQKr7Iu0nCD6BXALIZCgWGzP4cOH6e/vb1DRHTt2EEJo9AhOnz7N0aNHG9W+L9dI4D/+4z/mL//lv8y/+lf/ih/6oR96VQGIcD82bdrU8/NNmzZx/Pjx277n2rVrt339tWvXmt/Lzz7fa0II/PAP/zA//uM/zpve9CbOnz//ks+5efMmP/zDP8yv/MqvMDw8/Iqv7cu57qggYOOD4pzjueeeY2FhgUcffZShoSHg8ysGvprPks0ocL4YnNQASGAgBkNqh7IhU5W9dDNLECFZqLTqee+b2eGCKKS1QamTSy3+2rVrnD59mj179jQyyBtRCXFwW7ZsYdOmTZRlyeLiIisrK5w+fRqlVBMQDA0N9bQsSlCU8hvk3+12u6fDQZCLsiw5ceIESine8IY3kGUZKysrPaRIQR02Qpmi9Cf3SO5x2v8s9znVZkjr+fIdiuMQo3n9+nWWlpY4ePBgU364ceMGL774YjPkZXR0lHa73XA4JEjx3mOmpuj73ncBsPSRj2D/5NNURJEdQuzV12OjtN/2NoI2dP74j1G1aJPVBteqdftVJOCFvj5sFX+va51/D2iTNZm3rrsByDK6zmG1gsygbYXOC3w9JlhnGetakzlH8CFO5SOiBrmNQkHB6Kg6qKKMr/Me8qzO1g0Ghc1yVAh0Q8BrQ6E0qq7vhyzDmjhauZVlRGafppNFpcKsjARFjAEf0Hmc5pebjPZf/suY7dvgPe9BnTxJ9dRTtCpLsWkTAw8+gBkfv9V2R0SD5FkQJ3zz5s0mAJASgJTN0rq6PGOyr1LCrhBMJyYmmnLY3Nwcly5dwhjDli1bGBkZYXBwsOG4SCAAt9QBxQYsLCxw7tw59u/fz9TUVPN7+fz+/v4GzZPnNnX4qZiPXH9KMgSaBAJuzQJot9s9/CI5N7lXTz/9dBMApLZTa90gADISeHZ29ss2Evizn/0s3/d938c//af/lPe+971fUULiq1m/+Iu/yPLycg8CsXG9973v5Qd+4Af45m/+5i/jmb26dUcFAenqdDpN/f/xxx/v0Zz+UgQBaTYvNfgUppPPkPa92xkgIfVJq5tE9SnSkLKRU2U7caoba3XikMSgSDS/d+9epqamGoOXwu1ShgB6PmNkZITx8XF2797N3NxcExCUZdn0IQ8PD/cYWSFPpSQm6OVAdDodjh8/zvDwMLt37wZ6WybTrEoyoLSMALcye6mRiuiLlA7E6QuSINcqSEqqgSDXfuHCBW7evMkb3vCGJpsTLsS2bdtYW1tjaWmJK1eukGVZc/3ibFK2eFEU9H/3d7M6N4c9dRrnHbrVwo6PM/KD/0+sb/+5J2g99hjhxQtRM//AARb/5c+j5+dQxjSDg3TRwjsblf+UzGcIMZvWCm1MnACoNF5H+D1oHYfmhEClo/BQCJ7Mx3F9OjOEsqw7EBSgY7cCClpFHGFMNABe6UheVDFjD8HT1QZrNJYI5Q84h6qq2AWRGaoix5RxamLwHlfkuAAULXSI2gShrvsbH2h957eRP/BArIFXFWH3bvp37aKfWzyMkHTYpGJX7XYb5xynT5/GWsu2bduabpk0S08D5dRppoFqyvKX52ZwcJDh4WG2b99Op9NhbW2NM2fO4L1neHiYkZGRZqCX2ALRmxBBLwkAQgisrq42U0A3ltdScp+ci5QXUvJfeq4S+ErbbWqb0sRC7plzrukCEB0AKWtsdLhK3RoJvHv37tuOBJaAYGJiokFcXu06fPgw73rXu/jH//gf8zf+xt94TQHA5OQkxpiG8yHr+vXrbN68+bbv2bx58xd8vfx9/fp1ttTlKPm/1PuffPJJPv3pTzeJlaw3velN/JW/8lf45V/+ZZ588kl+67d+i5//+Z8Hbn3fWZbxoQ99iB/5kR951df9pV53ZBCwsLDAkSNHmJqa4t57730Jq3WjMM8rXRKFi4GZn59ndHS0IdNIRiEbVhCAdGiIQIAbWwZTZ5wygNPWwZT0J5+TbnoJKC5fvszVq1e59957G01+cf7ynvTYaa1Tzkmc7tTUFKOjo2zatInV1VUWFha4fv06p0+fpr+/vxlyJIZNMqG0VOK9Z3V1tSHbbd68uYfwKFldt9ttjLh8d1L3FLKf3Cdx9O12u0eQRWqkqZyzXLMYfGGTd7tdzp8/T7fbfYk8alpSEZnWHTt2sL6+ztLSEmfPnsU516Akad+2yjKGf/zHWb98GX/lCqavn+LA/phx1/e1f8d2/Latt1Ql2+04V6CeJJj5KNyTKU1V5KDjGGIZTkTdDaBqUaGq3aLUhv6/9H1kTx3GnzjRSPH6KHYcuwTqWj1ApaKIUVlrDIDC5wZcPRa6LFHG0K1KfFFQ6pyCEAODAMoYnLVkOvIXtI/CQGtFi8IYcucoyooyM/jM4FWG8uHWcCLtaT36aBwRzC1ESRyffB/pqN70Z9Zarly50gQAgoYJUiBM/na73VODl+8faFAz2deS4afGWdpRhUcgqn2XL19uPmt8fLxBCES8Z8+ePUxNTfU8Vxun9qUdLunPQgiNrHFaWpT9LklI6vgl4EmTjpQY/NxzzzUBQJpovByHu3EksGiSnDx5km6328w3eTVlg2effZbv/u7v5id+4if4O3/n77xmBKAoCh5++GE++clPNl0F3ns++clP8jf/5t+87Xsee+wxPvnJT/L+97+/+dnv//7v89hjjwGwZ88eNm/ezCc/+cnG6S8tLfGZz3yGv/7X/zoAv/ALv8DP/uzPNu+/cuUKTzzxBB/5yEd45JFHgMg9SBPR3/zN3+Sf//N/zp/8yZ80olFfLeuOCgKUUly5coWjR4+yb98+du3addsH6bUiASnsPjU1xerqKs888wx9fX1MTk42whxpfRBoanvi0FJIMq1vynvEAab1TMl8JaJPnaAYEecc586dY2VlhQceeKAnQxanLOz+tJSwsQc5PadUQa3dbjMxMdG00F2/fp35+XmuXLnC6Ohokxm12+0euH1ubq4pS0xOTjbXXRQFy8vLAKytrTVoghi4dDpbX19fEyBkWda0Y8n1a62bIELuWfqdSflA7qsIEymlegLGoih6RJjEcMtx5R7s2rWrgUsvXrxIWZaMjIw09yGEgJqYoJiebgIjoPnOxJHJs9D3jY+z+rGPoQnk9chgTBTTaX3Dm1i/dJlsaRE9NUX3zJlIJlQKow1BxSE+qigYOHgQfegQ5f/nF9AzM/g8KvXpqoqCP2VFpRTrRYHKon6BVwpvFNqHOF9ARclfn2Wx/U/FuQe5ihk8IU4brLTG5DkqRMTBKCJKYDR4jTIqtiSGqEVgXZROxjvyTZsY+t7vRY+O4uvAXAJfCehkv8jzCLfgb2stZ86cwVrLXXfdRZ7nzf2UIE6IqeIMU8GdNAOXLhbZZ/LMpVm0IBHiqKWbQCZgSreBnP/u3bsZHh7uQS/keZbPFn6AkF038l6ERJvagzRwSFuUjTHN0K+0HCmB6fPPP8/g4CD3339/z7FezUoJtjISeGZm5lWVDY4dO8bb3/523v/+9/MTP/ETX7ISwAc+8AF+6Id+iDe96U28+c1v5t/8m3/D6uoqf/Wv/lUAfvAHf5Bt27bxcz/3cwD87b/9t/mWb/kW/tW/+le87W1v47/9t//G5z73OT70oQ8B0Q68//3v52d/9mfZt29f0yK4devWJtDYuXNnzzkMDg4CcPfddzfl2IMHD/a85nOf+xxa66/KUch3VBDQ7XY5efIkDz74YDNr/nbrtQYBYky89z01NdGyf/bZZ8myjKmpKcbHxxkeHu6JtGUzCxogG3ZjvRroQQBSlrNs3tTAWRvH6T777LNorXnjG9/YZMeCHmzMNOQcUthd6olA874UVhSYVHgHW7ZsYevWrYQQWF5ebiR8+/r6mJqaYmpqiqWlJc6fP9+gEuKwJSNLywApwUkyGkEpxPAJRCr3IzWEqbGWeyPZXMrbWFtb49SpUw3UCTTHlSUtVvKelHmdnsv09DSTk5OUZcn8/DzXrl3jwoULTZ+28AgkeBMnkwZyxhjMN3wD/v9+huzFF6OCX1HgAgy+6WEG3vnOnr7zhQ//Mp1z56B+f2Y0JdD/Pd+NqZ8Vtm7FLS5ACLSqirZzkQdQf5+2yBtUwRU5FXGsb2Et7W/9NuzFFymPvYDXmvWduzCPP8YAoGZnWfnUp7B5BiiQ3naTYZRoJUSiojUZds8eRh55M8ZaKsAAra1bYWwskhiTwE1q5ALzy36Q+yXQtdaay5cvA3DPPfc0QV4aQAiHJA0CUmQwbS9NZwMIH0T+n5LtUg6IHCOEwPDwMKOjo3Q6HS5dusT09DTXrl3j/PnzzT6QjhsJYiUpkP2UdrWktkq6DoQMLOeS7gFBCjaSCGXfHj16lIGBgR60KyUhv5alVOwUGhgYeMVlgxMnTvD2t7+d973vffz0T//0l5QD8O53v5uZmRl+5md+hmvXrvHggw/yiU98oiH2ybnJevzxx/nVX/1Vfuqnfop/9I/+Efv27ePjH/94j3P+4Ac/yOrqKj/2Yz/GwsIC3/RN38QnPvGJLwth8iux7iidAIgiN19M1GJlZYVPf/rTfOd3fucrPr44W4Hg0vq+bOSyLFlYWGicgfe+QQikhi7vTeuIqeBPKiSUQpNprV2gQiHJra+vN4OQ7rrrrqbnPa1zinOU90nwkbYlSSeABCBCuhPCofxMXpPWHlutVpOh37x5k4WFBebm5siyjImJCTZt2tQQNOXz5F5u5CWIYxQyoVK3uhLE8ImxS2vEcg9TDoE4T8m4Op0OJ0+eZHp6mi1btjS/TwmPcs/TEorcO3H+UlaQ80szxbW1NVZWVlhaWmqmnG3atKmZOCdODW5xRi5dusTKwgL3OIc7foKqyCn27ye/777GgQkpMTjH4qf+gM4f/zHZ6iphahL9nd/JUC32kuc53TNnmfnQh0BBf7cbe/yNkA9rTolS+HsP0v4rfwXtPb7bpT0+TquWhF6cmeXM+XNs3bGj0Yrw3rP2ySfpfOYzcaiQdxiTobpdBt/z/fh2m+7KCmQZ6wMDzNdZrta62QspBC9ZrDz3EtjJdyCqlXKvjDFcvnyZqqrYu3dvEyBrrVlfX+9BtAQilz0Dt5AEKRGsra01wYUxUaI3DZoF7heHL89f2g4rAeCNGzc4dOgQY2NjrK+vs7q62ohVLS0tMTIy0ozLlcBHnhlx8gLpyx4QVE0CyFQvQLpv5BlNkww5z6effrqZBSDPqdyjL6XTvd1KywYzMzN0u13+03/6Tzz44IM8/PDDvO997+MHfuAH+Of//J9/Udv99fXlX3dcECCZ5Bda6+vr/O///b954oknXtEGSJ0i0JOZiBFP23CEyXv9+vUmKrbWNgNM0mlfcItsKAZcDBJEoqOQ5Tb2JRtjWFhY4Pjx42zbto0tW7Y02bQQ1qC3jCGOTFQApeZZlmVDaoJbQkNpO6LA9BIEpApnGw3RqVOn6Ha7bNu2reERKKUaLYL+/v4GZk/rmxCRHTG8KQqROlvRFZD3ybWmbVVyn+QeCy9h165dTE9PN8YeaBCCNECT3u0UiRHEBOjpOkgdVUpmNMY0eu4LCwsopZicnGRsbIy+vr4mAFBKcfDgwR70Rq5ZSiNpQCZMecqSqs525dobJviRI6x+9NfJ19fIQuzLV3lBuPtuzL330n9gP9XUVPMsyXNXFEUjLX3fffeR53lPe6q1Fn/uHN0//COYnaHYuZPi4YcxtVz1RsStqioWFhYagmWr1WLz5s0MDg42AZWUddbX13skpcXZitMTBGD//v3NvZZzl4AufW4kMBDUS747eY2UlVI57L6+viYYKcuyZzqlIDiyx5xz3Lx5k8uXL7N3717GxsZ6MnRp1VtdXW2m/83PzzdzPYRTk84xkO9fsmwJlATdkGuRICDl0EhA4ZxrSpUHDhxo9oQgBl9upxtC1Ev4l//yX/I7v/M7HD9+nKmpKX78x3+cd7zjHTz88MNfDwS+ytbXZBBQliVPPvkk3/md3/my2KwbyTXiINMgYOPP02w0zViXl5eZm5tjZmaG9fV1JicnG6EOgSWFlS7GKM2Kbudsrl69ytmzZ9m7d28TAKTtThtZz8bckihOGdPiZOWz5V4J1C61eHGYqUNM75EEAidOnCCE0EC16+vr5HnO0tISKysr3Lhxg9XVVSYmJprsMHVycmzJlqSPP4U72+12gxRA5BSk2Vn6fUDUjj937hz33HNPU7MXRyvvWV9f7yFmbgyA0u4CoMnM04FIKQKRcj3k351OpwkKJHCR0pKowKUojrxPvvuUFS4GP22pTEtEWmvodlFnzpAZQ/u++/D1ZwjKI3C4PA/GmIb4KCUceQbluxD0SngaMrhGymTyjIjjSlvVJLuW58C5OBly06ZNDA4ONsGY3EMJUoXDobVm//79jaNOAya59yn0L4N05FkS9EzupVy7QLqpIqbcX3lG5L5WVdXIf8/NzXHhwgX27t1Lf39/E2hIgC3Hke9Svpfl5WUWFxdZWFhoUJLBwUEGBgaa504CqhQtSXUIZO+lJUdBqJ555hmGhoaaEkBa1nitTP7Xsi5evMgTTzzBt37rt/Lt3/7t/M7v/A6f+MQn6O/v55/9s3/GD/3QD33Fzu3rq3fdcUFAWk/7fMt7z+/93u/x7d/+7T2tg7dbYrzkmJIJyKZLIfoU1pRsfWOLjhhzYcrPzMwwOzvL6uoqY2NjjI+Ps3Xr1kaJT+rk8h5xhGJcz507x6VLlzhw4EAPD0IMlkCGYiDTLEOQBcmK5Jhwq59YjKs4wI2T++T98hndbpf19XVOnTrF6Ogou3btahyHBETSBQBxlLNkRZ1Oh4mJiR6Gder4NrKg5d6mbVMrKyvN/U+DnCzLuH79OmfPnuXgwYMMDQ2hlGqmI/b39/eIvcj0NjGqch/S7zdFYiTTlB5vCQBF8lUcgmyntHvi+eefb35WVRVjY2NNfbm/v7+59pWVFSA6JuGCyDHTQEEyQkEx5P5I5piWdeS7TNs8syxjeXmZS5cuNffKOdfTRpfee4Gmq6piZWWlJ6NOeRoyQU+yaLm3SqmmbLK0tNSoNgoELoRQiASywcFBdu3a1cNlkUBKygbpswIRVRoYGGicp6B16R6XeyfnXZZlQ+qS71eOJ99Hf38/KysrXLx4kZ07dzak2I2lA3HA8pzK95gGdd1ul4WFBW7cuMHy8jKjo6NNkiDiRPLdbpQdT78TQfQOHz7ccADSboF0z38l1tWrV3niiSf4lm/5Fj70oQ81NqeqKv7wD/+QiYkJHnjgga/Y+X199a6vySAghMDv/d7v8Za3vOULzp9Onac4nvTnqVFIiTzys41EpDSz2/j+qqoayVIxAKJZL5mdvE6u8fjx46yurnLPPffQarWaerwQjcRIyuelzOlU4EQy/JSEBL11d3E6qeZ/WrqQz1hcXOSFF15gy5Yt7Ny5s0fRTIb7iKNJjaNkfqL3Pj8/3+gUiFqfOJI0s5TzE6haHDncmrZYliVXrlxhbm6O+++//yXtXymLOv2u5L6LVrzA1QMDA4QQpafT0oEcT5Tg5D1A85ylsxU6nQ5nzpxp5FnTmvbVq1dZXl5mbGyMqampniBCArvUmQgSkorXCCQu1yROTsZdSylFMnwJuGZnZ5mZmeGhhx5iZGSE9fX1ngBJ3iPHF8e+kbOSBskSKEhZRIKTVK/CWsvg4CCdTof5+XkuX77cPIsjIyMsLi4yMDDQqNqlyNRGkmU6DVDOL90TKbolz4IEpqnjHhoawlrbcI0kqBH0YHFxkWvXrrF79+7mGRX1QeGQbNQKkWmEAsvLeaTJxtraWjP0a2lpiaGhoQYxGxkZaZ5NsSPyWfJcPPXUUz2k5dSMp2XGL/e6du0af/7P/3keeeQRfumXfukrikZ8fb289TVZnJFo+Qt1CKQBQAony/tT+HwjMXDjZ8kGlNekmaps4P7+fnbt2sXDDz/MW97yFrZt28bc3Byf/vSn+dznPseZM2dYWlpqDPDTTz/djKkUDXw5bzHM8ifN1sVBSdCQttZtPK+0rCEGOyXCiXMUKPz69escPXqUu+66i7vvvrtBANJsPj2eGHHJ0AQOvvfee3nTm97E5s2bWVlZ4dlnn+XZZ5/lypUrPbPbU4eTkqNSZ9XpdDh9+jSzs7McOnSoydLkGuU7lqBErisdEQs09y9t70zrznJN6bnJ9yHnI/ddKcXy8jInT55sFBrlddJRcc899zRdLnNzczz33HMcPXqUmZmZpl9eAiL5HuWeynclwZJ8B0APrJ12Ysi1zM7OcuPGDR544IGmXCJL3ieqkRLYpc5MnjERz9nYaicBgdyb9D7JcyutZYcOHeK+++5j06ZNLC4uAhHWv3jxYoM4iNMXeF72tmTM8n+B6OUZSb83uTY5n5RjkO7jPL81xrrdbjftgPfeey9DQ0NNdi3vl2dIbIfwAyRwliBVvrsU5p+amuLuu+/moYce4vHHH2fr1q2sr6/zzDPP8NnPfpbjx49z7dq15lmUYKksSz73uc8xMDDQBAC329tfiTUzM8M73vEOHnroIf7Tf/pPrzgA+GocCXzixAm+7du+jU2bNtFut7nrrrv4qZ/6qS84yO5OW3dUiyC8VDr4860vFARsDABud0z5eWpkbxcAiDPa+PNUtzwlBgqEv3379kahTBCC8+fPN+InQ0NDHDhwoHFc4qBls6dljlS8Jz1/cX4pyXBjW5H8W4y5GFrJ6oSwODMzw4ULF9i/fz/T09MvqYnLZ6eGf6Mjl6BgbW2N/v5+RkdH2bx5M3fffTc3btxgYWGBo0eP0tfXx8TEBH19fYyMjDTHTb8XQVxOnjyJc4777ruvB00RRELug5QR0iWGNa39i+MTB5vWvtOyT/o+yXIFfp+fn+fFF19kz549bNq0qYfsKI5TvmdxuhMTE6yurrK+vs4LL7xAlmXN5Ee5fglkBJUQFEgctBimtLadlhHOnTtHp9PhgQceYHBwsHmvfN9yTLke4X4IJyMNkiQg0Vr3yCunpNrUcaVchLSEYq1ldnaWrVu3MjEx0SBFly9f5vLly03JZGBg4CVzOiRTlu9B7pP8TjodhMch+yF9hgRVSANZpaIeybVr1zh06FBTv08RobSbR3gDKaK3cR/Iz8Q2wC19kCzL2L59O1u3bgXiPIv5+XlOnTpFVVWNxPHY2BjPP/98gwDI957an40JzZdr3bx5k3e84x3s37+f//Jf/kvPd/Jy1lfrSOA8z/nBH/xB3vjGNzI6OsozzzzDe9/7Xrz3/NN/+k9f4V366lx3XDkgdWRfaP3hH/4hBw8ebERrgB6n/IUCgFe6UmeXZtkb66tpDTs9J8l0Zmdnef7552m1Wqyvrzdtd+Pj44yMjPQEAul8AoEM0yElaU1fPiOt86bZLNyq56dkO2HPnzx5ssm0RRREyhZCbpLXSq1dIHUxsIIGiPRuygdwzvWMi11bW2N1dZXr16/jnGs6LcbHx5vrXV1d5cyZMwwMDLBz587GgUsgIzVeCcZS8l96D4CmjJJmj2npQdrMpPUQbgVOkinLd7+wsNCoyG3evLmB5AUyTssdwv+Q7wZu6RisrKzQ7Xa5evVqcw9EwlbunXAV0gBPAgPvfVNnr6qK8+fPs7a2xv333998NxI4pg5OOB3pdyccijSYlEx8o1OV+yNBiZRhJAtOSzidTocTJ06wdetWpqenm2uQAFdUG69cudLcm7R+LgiYPI+yp8XJyn1KCZ8SOKTomQQCgr5cv36dCxcucPDgwaa7JnXeQr6TvS38FnmmUp5QWnZK+RNpeU6+r1Q0S363srLCzZs3uXnzJisrK+R5zvbt29myZUvTivuFEpUvx1pYWODtb38727Zt42Mf+9gX5WHdbn21jwRO1wc+8AH+9E//lD/8wz98xdf51bjuOCTg5a6NSEDq/IEvWQAgx0odqhw3hUnTn6dLjPnFixc5ffp0E7hI/X12dpZTp04RQmjqhVu2bGmctRi61KmnJQ4xMOnsg9RIyb/hFilJHG232+XEiROsrKzw8MMPN9ljWu+UGnXKQRBoVhyjnFuaPUkgIPColC2MMQwPDxNCYM+ePczMzHDz5k3Onz/PyZMnm/7zy5cvs3nzZnbv3t0Y1ZSYJUZW7r84OHHo4ghSspWc88rKSpMli2NKlenEkaVZeVVVzMzMcO7cueY7lOdNgjx5JsXxyHvle5TnNMuyprVseHiYqqpYXl7mwoULWGsZHR1lYmKi6cNPSXAS0Mh3aK3l1KlTeO85cOBAT2eBvCfNSFPSazoMSpZcT3rPgB4Gv/xOrj8NWtJg4YUXXmD37t2MjY313AfRsWi1WgwPDzM8PNwgUjIBEyKPYHJysiFrpr31KYfhdp0yqTR1quHx4osvMjMzw/33399D0JRnWY4px5A/cEuKW65ZApRWq9UEVKldkO9bOANyjvIapaKuf7vd5ubNm4yNjTE5OcnCwgKf+cxn6OvrY3p6mqmpKUZGRr4iAcDS0hLvete7mJ6e5qMf/eirCgC+2kcCp+v06dN84hOf4Hu/93tf6WV+1a4/E0FAahTglqLfl3J9oQ34hX4XQuD06dNcunSJhx56iLGxscbICoP8wIEDDav4/PnznD59mk2bNjXqXGmgkcKlt4P7gZ4AQu5LihhI8HDs2DG01tx33309GyQlSYpTEQcvNXkxmpL1CXExhYTTFikxsNJqJU5FnN3OnTubssTly5cb4y4EQ6lRr6+vNxmxXIfcg9SIyzOREh/lXMWZSFYqULI4hPS6IfIVrly5wuXLl3nwwQcbCD9tH00Dh9RZpPwFuWdph4fWurm+ycnJJkiZnZ1lcXGxyY6FbS9OR3QITp48SV9fH7t37+4pRaTPn/xfUA25frnmtAtiY9Ai3/Htym9piUqcZQhRx+HkyZPcc889jI6O9rShAj2flba8yQwLyZDn5uY4c+YMQCNvmzpTCY5S7oScV0pmlWf6ypUrzMzMcOjQoUaIK0VXpKwhnAQ5ljjsdB+kwaVcjwSeEjTK9y88Hgk6ZX8JiiI6AKLlsHv3bqy1LCwsMDs720DY0oo8MTHxiuH4V7NWVlb4i3/xLzI4OMh//+///VUr6n01jwSW9fjjj3P48GG63S4/9mM/xj/5J//kFV3jV/O644KAlxvtSub3cur/X6nlXNT6Xl5e5s1vfnMjKiPnmBKdpL1w7969zM/Ps7i4yLlz5zh27Bijo6NMT0+zdevWl/SWi9FKa9Jph0WanUhAsL6+zrFjxxgaGuKuu+4Ceqcqptm9OHdxMPKZQGMIxZGKg5LPTI+TGlpxgqnTDCE0teL9+/czPDzM8vIyN2/e5MyZMw27enh4uGHaC3N8Y+YqQUY6DEjg4qqqmrJK+j2k1ylGXOreZ8+ebZyHMLsF5k3rwOLY05+ngWpaSpLzSbNN+Vmr1WrG4C4uLjI3N8fVq1cpiqKZaZBlGSdPnmRkZKRnxoaw3oGerNPaKEkt90l+lhIoxSHK79P7Jz+TZzhFneT/Mr76zJkz7Nq1i8HBwZ5ulo3PoQQyKeoCkbshvBGIZQM5LsSAYHJystkLIsQjTlWCHXFaZVly6dIl5ufnue+++xrVT+dcU3qQ7zMlgcrzLsFgWmZK2+JSjkK6f+RepyJAaTAeQhwGlAYAEixmWcamTZsa0uni4iIzMzOcOXOG5557rrkHImX8pV5ra2v8pb/0l8iyjN/8zd98XT7j9V4vZySwrI985CMsLy/zzDPP8Pf//t/n53/+5/ngBz/4ZTjL13/9/9v77rgor+z9Z+i9914EQaVZwBZLYmKMCLibsjERk7gm7sZoYtomMbppGjWJRk3dfN3U3SiCYl+i0RQ1RqWDgICABQaGNtQBZu7vD37nemcAo0hT3+fzmY8y884799555z3nnvOc59x0TsC1Qsy3iYSfoQKVSoX09HTo6ekhMjKyS3te3RC/GEKkH/iwYcO4GMulS5dQVFTEQ4a6vRXE3ZBuDpGMkUaj4SWARFSimz5wxRjqOhG6IWBS4BN3t8AVVjXlXummTLt2KrkTUxxA5y6ytLSUiwBZWVnBxMQEVlZW8PPzQ0tLC08bnDt3ju8YqQ2wmMMWd/1UPkZjoxJHMYJAOzMqz6S1pOfPnTuHxsZGjBw5kjsdosEloy6WTOo6esQXEMPzRMij0DyNSXQg9PT0eBSAGh1VVlaioKAAQCf/wNnZWatfghjOpl2wGC2h75iIlGLom4wfgb4zYsWLDoyoVUD/1tXVoaSkBJ7/X55YN0QuckRoTKLjRdcYjYmuD2tra5ibm8PV1RUtLS2oq6vjKRBzc3Nei09dQMlxoahRRUUFamtrMWLECJiammp9R+RE07haWlq0DLfowIopGfF3Q06XOE9af917khhRECMAYvqAjhHfY2NjAxsbGwQEBPBGP3QtmJub894Gf9To51rQ0tKCv/zlL2hvb8fBgwe51kJvMZRbAhM8PT0BACNGjIBarcaTTz6J559//pYogbwlnQC6SSiVSq7yNpQcgMbGRqSlpcHW1lars524ExR32qLRAMBvyEDnDdDCwgLDhg2DSqXiJWD043dycoKLiwtnSos6AQTaxVVVVeHs2bPw9/fnLVsJ4pjIwSIuAJ1DtyZcNGqkkkfhWZEkJe4mKYoBXDGiJSUluHz5MsLCwrRU4SgkS/K0Li4uGD58OBQKBW9ypKenx3kElHuWyWS8bEu8oZLRpddFTQIykERC6+joQF5eHhhjvJMjCdlQSoGMvxhWF0lgpH0gksxEcicAXi5I60y7dTEPTdeIoWFnq9u6ujrY2trC2NgYxcXFUKlUnFxpY2PDd8i026ddMTllNAc6NzlsIocBuFLtImpM0E2RIg5kSJVKJS5evIhhw4bBwsJCK8Ij5sLJedAl9YnRLXIUKeokOvoGBgawtbWFra0tJx9WV1ejrKyMOwOkBQEApaWlqK6uxogRI2BmZqblyIqOm25UggiwND4xUkOgudD/yekUnUGR2U9r2tHRgczMTJibmyM0NJTPX4wWXQ1UjkxCXqQLkZqaCj09Pe4Q2NnZXbcRU6lUePTRR6FUKpGSkgIrK6vren93GMotgbuDeB3eCk7ATVcdIJZCdQf6YVEPbFKpoxz6YCppAZ2lNJmZmfDy8oKfn98f/qDF3TvNTdw1kVESS6Uo9Eo//traWr4boNI7Oo4+/8KFC1xtj0Lq4m5cvNkB4LX2IhdAN+8udlCkmygZDuCKgaN50S6dwrUqlQolJSWor6/H8OHDuUALGVgyfGJUQpRf1tfX51r+FRUVaGtrg7OzM+zs7HhzFwqvi84L3aipykBPT487DXp6eqirq8PZs2dhamqK4cOHaxkJyqVTrpexTi18cpgoUkBKeSSdKzomtK6igRAJbhRyJ4NN521ubsaFCxe4Q0QGsrW1FUqlEnV1dVytz9bWlq8nrQFwpQUy1e2LOX8xgiTmwkUnhHb+ohNIhMkRI0bwygRR8IeuAZonXR8UwSBHhSI13ZXi0TgpfUHfA6WgjIyM0NjYiNraWsjlct5AqL29HcHBwTzyIhpnkf9BFSnk0NJvgIisdO2TU0cOAZ1HlKmm64PWVUzXdXR0NgMyMzNDaGhonxoZisaIjX7s7e159z/dHbEu2traMH/+fFy8eBGHDx+GnZ1dn41t27ZtWLBgAT777DPeEnj79u3Iy8uDs7Nzl5bAx48fx9SpU/Huu+/ylsCrV6/WKhFcu3Yt3n33Xa0SwczMzG5LBAGgpKQEvr6+WtUB3333HQwNDRESEgJjY2OcPn0azz33HKZPn45vv/22z+Y/mLilnIDuCICNjY2Qy+Vcx97Ozg7Ozs5wdHTsFZP1RnDx4kXk5+djxIgRWiGq64G4e6IbsEjIEisgaNdC5Yf0oHwqhVFLS0tRVVWF0NBQzkYXmfPAlfSE2I1Qo+mURgbAb6CigyIKuoj5WDFMTOcSG9LQebOysqDRaBAcHMwbEVEUgxwGuonSjZbmL/IhKGxN8sW1tbXo6OjgTV10u72JVQyiUQI6ozjZ2dlwcHCAt7c3d74o5CveSEn3XmTOi6x7WgMKJYv1/sRpodfIGFKHQnqNwvFNTU0oLi6Gh4cHv7bpdyI6Wfr6+qitreWKhUZGRlo5dFoDUqUkx1J01kQugS7ZlL5fInjK5XKUlJTA39+fRxtE0h9jjK+HLslOl0MhqlGSiiJFLGisavWVzoHkNOjrdzbSEksiz507p1VeaWdnx6suyAGm65IiMa3/v1Mifcd0TYtkWHIkRaKkvv6VroFimoMqYohv0tHRgbS0NJibmyMkJKRfd5lE0CSHQKlUwsrKikcJSNac0N7ejieeeAIFBQU4cuSIVul1X2HLli1Yv349bwm8adMmviOfNm0afHx88OWXX/LjExISsGLFCpSUlCAgIADr1q3DfffdpzXHVatW4fPPP+ctgT/++GMEBgZ2+/ndOQHbtm3DunXrUFBQAMYYvL298eijj+K55567ZVoL3zJOAP0ARfKOLpqbm7lDQJKt1PLzj7zgGwFjDOfOneMhbVtb2xs6l8gVoPmSEQSuEPLEcLwYXiZnoLKyku9QAgMD4ebmprWzA7SJmHRzE/OxtDuiXa/4uhgeph0xOTAiMY4MKeVXm5ubkZ6eDkNDQ4waNYrPV3QCxPI/0RiJYWOxTIt21vTZDQ0NvK+DSqWCjY0N3xGJGgJi2FyhUCA/Px8eHh5wcXHRklYW88Pid0CfJz4HXGHim5qaanWBZOxKRzyxBI/eK7L7KcJQV1eHoqIiBAYG8ioOMZxOTggZR4oadHR0oLq6mnf9AwBLS0tYWlrCzs6Ol+qRUyhGkGhMorEWc+eGhoa4ePEiysvLtbom0q5XJA2K+XVaB7GCguYt/r5F9rtYmkhRHRof/Usqh0BnmVdraytv5lRXV4e6ujpUV1fzpl9EtiUJYIoKUepE5H6QowB0OkOkqSCmucTfAb1HvE+JJMCQkBCtVNxAgFKJxK0xNjZGamoqfH19ceedd2Lp0qXIyMjAkSNHujDuJdzcuOmcAAoL6j53vRUARCaTy+Wor6/X6gHel0xXqgBobGxERETENdWiXgtoNyWWHYk7S+CK8RcZySITPi0tjZOnampqeP6c1kLMAdNnieQsMkJ0ExTTC2JJGO22KM8KXClhFHftFLnJzMyEg4MDAgICuIFpbGzkxgAAJ/aJIWCqF6cdppjOEAlejGn3Vmhra+NGQKlUcjIZlSeq1WooFAqcPXsWvr6+cPz/bXlpDcjAiDl0coRonhRSps+na4Oep10roN2ZkNIstPaUC6cwvkKhwMWLFxEQEABLS0stKVtywsT50rjE9AV9z7W1taiqquLRHXd3d1hbW/PvhloC029NrKEnR4YiENXV1bhw4QIvtyPtBXJ0xAoK2t1TlIHmTmtCuXoiZFLVhKhRQLoTxP8Rr3Mal7GxMUpKSqBSqRASEqJ1fYrVKVR10tDQAEtLSx4xIqeOritKZdCDuBr0+VQaKDpHYpQDAE/nEAcgLCxswB0AXajVatTU1OCNN95AcnIylEolTExMsHbtWsybN4+XwEq4NXDTOwF0o72REkCVSsWle2tra2FpackdAiIQ9QZiBUBYWFifpx9EYhEZGwpR6jKpRfJZU1MTMjMzYWtri4CAAH4jbGxsRFVVFcrLy9HR0cF3xg4ODlo3Jvpcyr8Tc17cgZmYmGjdZOk7E6MIgHZtek1NDbKzs+Hq6go/Pz++i6WbJ+28yQDQzZQiDLRDpLwxfQf0PJ1Pt2yQbv5kYGtqaqBUKqFQKHj4XS6XIyAgAK6urtBoNJwESEZX1DcggyWqyJHBp+Pps+k7orA2rR8ALSNN605yw8Rqv3jxIgIDA3n9fHt7Z7tfCn9ThEaseCDHij5LTBvRMcT5kMvlaG1thZWVFTw8PLT6WIhkR9rxGhgY4MKFC1AoFPD394e+fqeuv5ieIR4EcQjISdHld9A6iQ5mR0cHb1ktpnBojcTNAP2tp6fHHQCZTIagoCC+BuSY0vdE3ACKSNXW1nKNDj09Pe4QUNqAnC0xMiE65eL/6ToTqws0Gg2vAggNDR10zpIIjUaDpUuX4uDBg7j//vvx008/ITc3F3fccQfi4+Px2GOPDfYQJfQBblonQNwJA30nmUmCNJWVlaiuruYMe2dn5y55squhoaEB6enpXSoA+hriblPcYYpOgLi7qq2tRXp6Otzd3eHv78+PF2vo29raoFQqeWiQwuXU5UzscEYMenF3A1yRx6UxUp4d0CY7qlQqGBkZ8coEX19f2Nra8nwpRRvEkLG4oxdDxmKoWUwVUDiX/iXHgIyLGI4XtRk6OjpQUlICuVzOjZOTkxMsLCxgbW3N110sK6TdKLHHLSwsuNNBxo6uVTGsTaQ/MV1C56BcPuXE29vbeajdx8cHpqamnJMhauSLu05ydnTz+TQekRUvqjmK10NLSwuam5u5fDE12xFlh8vKylBXVwdfX1/+26SIhHidihUZYopETHXRd0JrQuPVLcejklNyglpbW7UiRoaGhsjLy4NMJkNoaCj/fJon/QbomhB37iLhtr6+nncVlMlkcHJygq2tLVxdXXmahdZZvA5FyW6R56HRaJCbm6vVC6C3G5m+hkajwYsvvogDBw7gyJEj8PX1BdBZTbFnzx7uIEi4+XHTOQEA+E6Iht5fBpby55WVlVAoFDAxMeEOAXVZ6w4KhQJZWVnw9vaGr69vv/+oxfyj7tcpGkfqAhgYGAgPDw9+U9LN5Yuhel0CEZErHRwcYGtrqxVOFm9gotEWHTcylkR0k8lkXOQkKCgI9vb23PjRjZXCqmQwyDGgeYm7QjJGIqERuLJDFFnpFJJuaWnRKiMlx6K0tBTl5eUIDw+HqakpysvLUVNTA4VCAQBcx56048lYiZUVZJzoexJzwrQmZJzFmngxhULXNzkPNK7g4GC+oxXPbWBgwI2zoaGhVntjAqWldAmNbW1tWpwEcoyoKyVxI6jqwsTEBDY2Njyl1NraCn9/f+7wiNcRfTdiqSXQSaBsb2/n0Riau+gAiPwKUTSH1puuKyLhAVfEkM6fP4/29nYEBgZqladS6oYiQHRecsjo+qNKDvre2tvbUV9fj7q6Oj5nKr90d3fXIsnqOjX0d1tbG7KysjgJkH47Q6HkTKPR4NVXX0VSUhKOHj2KYcOGDfaQJPQjbrpWwpcuXUJJSYlWfq2/YGhoCFdXV4SFhWHatGkYNmwYWlpacPr0afz666/Iz89HXV2d1o/8woULyMjIQFBQ0DWVAPYFaLcn5ifpZks3trKyMuTl5SE8PBweHh5aqQSRWCcy+ek16vEeGRmJqKgo2NnZobKyEidPnkR6ejouXLiAlpYWAFfKDsU0De2CyFEBroS8S0pKUFJSgrCwMLi5uXHjTEaCnBQxr01EN3HdycCIMrwiP0IsVRTfQ2MRyxcZ6yRyXrp0CcHBwTykbmNjw6+HoKAgvvNNS0tDcXExamtr+fdNN3MikwHa4W/628jIiEc96L2i8aDKBnIIiouLUVNTg5CQEFhaWvK2t3QO6rwoRhRaWlq01DOJy0CGTpTaFUluosMitqZ2cHBAcHAwoqKieIXE2bNnUVVVBQsLC7S0tGiRBsWoh7jzpvNTSaTY84CiGvR9idoJRPQTSzCJoEppKPqcvLw8qNVqDBs2DDKZjHdqFFMadK2T86EbsaBri65jQ0ND2NvbY9iwYYiIiMC4ceNgbW2N6upqHD9+HKdOnUJJSQmUSqWW80MOlUwm490ARRLgYHMBgM41eOONN5CQkIBDhw5dtwMwFNsBHz16FLGxsXB1dYW5uTnCw8Px3XffXde8bmXcdGJBe/fuxZIlSxAaGoq4uDjExsbC39+/342tvr4+l+kk4kxlZSXS0tKgr9/ZH5wanIwZM2bAyTM9pUM0Gg3y8/NRVVWFcePGddt5TDf8ClwxmrRDoR0atXX19vaGSqVCdXU138mTFoGrqysn1OkSocQufMXFxaiurkZ4eDhvGiTWnIu7QJGNTeFfMvhiWZnodND8RUNC76e/KdxLuV1yAGpqajBixAit71F0UExNTWFlZQUvLy/e6a2mpgYXL17kzX/Mzc15qB4Az2XTWooOgUgGFMmTBgYGMDc3h56eHnJzc9HY2Ijg4GAetqadL5XfiXXsra2tWs1ziLdAURE6P62PyBkQ02xi5YW469ZoNLCxsUFVVRVvbKVUKpGfn89fc3Bw0LoWRGeQPpuiPeLn0L89OaokAkY7c/EaJsN99uxZyGQyno4j/gpFouh6aWlpgbGxMWf1i70syImh8YvzoO/L2NgYHh4ecHd3R0dHB5f1Li0thYGBARwcHHjqgDgA5ubmGDFihFZ6ZrDTAIwxrFmzBl999RWOHDmCoKCg63r/UG0HfPz4cYSGhuLll1+Gs7Mz9u7di/j4eFhbWyM6Ovo6V+nWw02XDmCMoaqqCjt37kRSUhK/WMkhCAoKGtAfk0ajQXV1NfLy8ngu0tnZGU5OTrCzsxtU776jowNZWVloaWlBREREt1UPusYI0G4QRH93l6elmyTljWtra6FQKFBdXQ0jIyPOIbCxseE3fzK8586dQ1tbG/+x0+fRucjYiDtY4i2QAaFzimRDSmvQc0ReFPO04o5MDH1rNBpkZWWhra0NQUFBvP0xAJ5vJn0CWl/gij48GeTa2loeLqcyVFGpj5wayoGTM0PiMbqsdQDIyclBW1sbvL29uZGmnTVdd7RexIqneQNXdvP0OWI9f3t7O5qbm7UMM33v5ByJTW7Igejo6OxQqK+vDx8fH25Y1epO2eGGhgYoFAo0NjbCzMyMSxzTGEnFEgBvhkT1/0ZGRmhpaenSO0D8nsWOkGIVhp6eHrKzs6HRaODn5wdra2v+PZHTQfOiSABpAdBOn6SvRfEnMvhksMXPFVOTosNSU1PDHWU6xszMDGFhYVpphsEGYwzvv/8+Nm3ahMOHDyMsLOy6z3EztQOePXs2nJ2dsXXr1uue562Gmy4SQIScp556Ck8++SRqa2uRnJyMxMRErFu3Dn5+foiNjUVcXJyW5nZ/oa2tDUVFRTA1NUVUVBTX8s/NzYVarYajoyOcnJxgb28/oPk+lUqFtLQ0GBoaYty4cT2yjmmnJxLtxF0OHSOGR8nY0LF0Q3Z3d4e7uzvUajWqqqpQUVGBjIwMGBoaajlFBQUFMDExwejRo6Gnd0U1j4wgSfYS6VCUbyVjQEZepVJpySjTDVtkwItVEuIuVtzltre3IysrC/r6+hg+fDjMzc35eUTWPxlGeh8ZYNGYODs7w83NDWq1GnV1dVAqlTh//jzMzMx4a1yx/a2xsTHfgYpkSzK2Z8+ehZGREUaMGMEdDrF5D82d3k9td8lJozUQ14K+V12jT2MSywxFwikZRZVKxfs0BAQE8DWi9bWxsYGtrS2cnZ3R1NTEu/6VlZVBJpPBzs6Oy1kbGBhwAqVareZOkG5ZJ41Rl9woOoutra0oLi6GkZERPD09eXpMTCdQFInORecQHTxaA7qGRAIi/V5011OMZNFzDg4OXFPi1KlT/Jhjx45xtT5HR8dBFZ5hjGHTpk3YuHEjUlJSeuUA3EztgIFOZyI4OPh6pnjL4qZzAkTQzeTxxx/H448/jvr6euzZsweJiYmYPn063N3deYQgPDy8zx2ChoYGpKWlwc7OjoccqaXp8OHDoVQqIZfLUVBQgLa2Nh4WJEGa/kJPvQl6gm4qQYwOiE4C7cLIUNCOSLz5kVNAu19ap8rKSmRnZ6O1tRXm5uacPCbuqkVGNZGyRMla0XhT/pYMhbj7Fw0jnZPGrxtup9BwTk4ObGxs4OPjo2U8gSvhZXGuNF4yHGJel8asp6cHd3d3eHl58QhWZWUlzp49C6Cz0oA6/pGhEqMSZGgtLS3h6+vL50EpBdHgkJETlevECgBxnek5Er0RUxE0BiIBUgidHA9ai/z8fFhYWMDPz09rbej8ohSwiYkJzMzM4OLigpaWFq7amJOTAysrKzg7O3PDLMotE0lPJDVSJIfEe8RcfVNTE/Ly8mBnZwd/f38+VkrD0LVLxpyiIeQg6uvr87QE7fSJGEhcCdFJFn8r4vVH46FrkEqFLS0tuRIgNfmpqKjga0kbBuqtMBBgjOHTTz/F2rVrcfDgQYwdO7ZX57kZ2gETtm/fjlOnTuGzzz67prnd6ripnQBdWFtb49FHH8Wjjz6KhoYG7N+/H4mJiZg1axYcHBwQExODuLg4jBs37oYdgqqqKmRlZcHHx6fbCgCZTAZra2tYW1sjICCAG8Li4mLk5OTA3t4eTk5OcHR07NPa4JqaGmRkZMDT07PXXAnxRifWbeuS7ro7nv4WXyOi2qVLl/iuJz8/H21tbbCzs4OlpSXXIhAbtxDjn85HBpiMFe2eKU1AxkEcN4XOxXwzvUa77uzsbLi4uCAgIEDLwBMZUWSui33faecvGk/xuhL5EBqNBra2trCxsYGfnx/nEBQWFoIxBldXV1hZWcHCwoJ3PiR5Yg8PDx7xoMgBnZcMFlVMAOBpH1pL2mGLpYNkaMnAUvid/ibioOhE0Wfk5OTA3NyclwFSGF3kbehGM8jwWlhYwMrKircBbmpq4nr2+vr6cHV1haOjIw/h03cmOnpipQfQuUtvaWlBXl4eLCwsEBgYyJ09SruIpEQ9PT1OXgTAXxMjYbraAeRsiiRXAjmTYqpJdArS0tI48Y2cGbHJj9jno7S0FIaGhjxCYGtr22/RTMYYtm7dijfeeAP79+/H+PHj++Vz+hPX0w4YAI4cOYLHH38c//rXvzBy5Mh+Ht3NgVvKCRBhaWmJhx56CA899BCam5tx8OBBJCUlYe7cubC0tMScOXMQFxeHCRMmXHeY/sKFCygoKMDIkSN7bHUpQiaT8TCw2P63rKwMubm5vKGNqGHfG5SXlyM3NxdBQUFwd3fv9XmAK7tf0bBfzaEQGe30oJBreXk5zp49Cz8/Pzg7O0Mmk/F1qKqqwsWLF1FYWAgHBwceSdHtFEi7LVGRjW7MYvqCbtB0kyZngYwI7SwNDQ3R1NSE3NxceHp6wtPTUytFIIbmRfIg8QLEFIXoGJDRJANE6ycK/lBKixyChoYGNDU1oaSkBK2trbCzs0NDQwNcXFzg4+PDlfnIGNH3IY6TyIC0JmIJJYWaKWduamrK8+3kRNC60drS90n8AQMDAz5GW1tbuLu7c96BWN5maGjIuQ4iB4OuB4p4UBTD2NgYTk5OcHV15UJHRUVF0Gg0cHR0hIWFBU8RESFQnBPQmZLJzc3lRE1yWEVDTd85yTSTA0nrKqZhKEpA60VrI64pQYwGiYRUxjrLADMyMmBiYoJRo0b1+PsxMjKCm5sbTyHV1NSgqqqK8xqoNXhfRhAZY/jmm2/w6quvYs+ePZg8efINne9maAf8008/Yc6cOdiwYQPi4+N7N9FbEDcdMfBG0draih9++AFJSUlITk6GkZER5syZg7lz52LSpElX3ZUzxlBQUMBrx/uiAqC5uZmrFSqVStjY2PAGR9eaJ2SM8VK7kJCQfmnuca3QJRiWlpYiPz8fwcHB/EetG8pnjPHdYHV1NZqamrg4EVUa6F6m4o6dHroEQTpGzGnTjZ5Cx35+frxnAnDFmRFv/OLuTlQ4pHOKu1Oanyi5TO8VyYWk30/HE0dAoVCgqKiIk9ZsbW35zpkqKMQIhNgoiOYLXKnCIMeMcqVi2LuhoYGvFUU7xJQHvUach/z8fLi5ufGUSUtLCxfcoWiCmLsXrwlScRQjE6S6SOF4Msa0FkqlEpcvX0ZNTQ2PFllYWHCJZvrc7Oxs7piQ+BSNhb4f+r7oWqLqAloPsUKBSILkBInaBqJzKzq8wJWGQhRBSE9P5w6AGLm5nt8SiXZVVlaiubkZdnZ2N8wjYIxh27ZtWLp0KZKSknDPPff06jy6iIqKQmRkJDZv3gyg8xrw8vLCkiVLeiQGNjc3Y8+ePfy5iRMnIjQ0VIsY+MILL+D5558H0NkO2MnJiRMDy8rKeN8L4Eo74B07diAqKgoeHh4AOssEo6OjsXbtWjz99NN9Mt9bBbedEyCira0NR44cQWJiInbt2gWNRoPo6GjExcVh2rRpWrvyuro6XnPclz0ARLS2tqKyspL3M6B86dX6GWg0GuTl5UGhUCAiIoKXAA42GGMoLi5GcXExRo4cCXt7e61oglhqJYZ36QZNrOqGhgZYWVnBwcEBzs7OMDMz4zdgsV87vZfEX+gzAG1pacYYysvLeWtbJycnvisVd8ViyF8kolF6ALhSiUBzIMMk1sjTzl+siycDSdoKtINuaGhAdnY2vL294eHhgdbWVlRXV/OeBra2ttwImJiYcGdDdHxIU4DkicnRoh4E5OCoVCo0NTVxp5eUFMmwirXzra2tKCgogJeXFxwdHXkrYyJDEpNerNwQc+Pk/NCa0HG0zrSm5GxRtILSQ1RtoVAoeOMvGxsbLgXs6OgIDw8PXuFAZZn0fenp6fEIgJi+IR6CyDmhNaO0kqi7IWo30HoTRKdArVYjMzOTOwB0DYgE1t6AeARVVVWoq6vrNY8gMTERixcvxvbt2zF79uxej0cXQ7Ud8JEjRxAdHY1ly5ZpqRxSB83bHbe1EyCio6MDv/zyCxISErBr1y60tLRg9uzZiImJwbBhw/DQQw/hzjvvxLp16wZE31ulUvEGR7W1tbCwsOAOAfUz6OjoQGZmJlQqFSIiIoZMa0tyTKg9MTWAIVBennKslH8WiYZiLr2iooLf+MzMzGBnZwd7e3seKiaDROFb2l2K9eBkeMrKylBaWorhw4fziImoHy8SIck4iaWS5Kzo6sXrGjLaLYpOCs1dJFJS7ri2thZ5eXnw9/fn0sy0s6fSw/r6et7kh9pBU4QAuMJoJwPPGOPKlmLuH+i8vkT5bdLk19fXh6WlpVaL4nPnzsHPzw92dnZa0RuaE0VYiFBIzpGYXqG5ihwTcZ3JESQpYiKGilLJ5JBQ6aFCoYC+vj7c3Nxgbm7O3ydGCShVI6Y5RP6CmK6gKIDoNJLx1uUMiNcrfa8UPaAUAFUniefuK4g8gurq6mvmEezevRsLFy7Ed999h7i4uD4bD2EotgN+7LHHtNIChKlTp+Lo0aN9NvebFZIT0A3UajWOHz+OHTt2YPv27ZDL5XBzc8Pbb7+N6OjofokCXA3d9TOws7ODQqGAsbExwsLChkzjEbVajaysLDQ3N2P06NE8/EvGTpelTzd2kbSme1Om95BkbXl5ORQKBUxNTeHs7MwdAuBKuJvyx83NzXynXVRUhMrKSoSEhMDMzEyLLCiSuWgetNMVnyOSGu0QyfCToQKuhNCBK7Xp1AxITGOQEamsrERRURF3TMghIVIbGVtRxIYa21RVVUEmk8HBwQFWVlY8skGhbdGBISNNBpWIlJQKoDGScE59fT3y8vIQEBAAR0dHrQgH9Vsgh4B4AAQ6jl4n54wMpZmZGVcKpKgAYwwWFhbc6Io9HQBopXJycnJga2sLY2NjNDc3c4fA2dmZkyuNjY1hamrKxyUKHYm8EiIKUhUArQvpVehWJug6AASVSoWMjAzeC0CMfJHD0R8QeQRVVVVaPAIbGxu+Odi/fz8WLFiAL7/8Eg888EC/jEXCzQfJCbgK9u/fj7/85S+YN28ezM3NkZycjIqKCtx9992Ii4vDvffeO+Dh946ODly4cAHFxcV850QRAisrqxsKN94o2trakJ6eDplMhvDwcG7A6cYPXCEYimxrCuXrllaJuycx3EqGg8LEoiF0dnbmIT4iAVJqoq6ujre2FcP/IglSdDroHOJumUrYxFpxcgLEygXKSYutfcVyPcrnX7p0CZcuXUJgYCCsra15PlzcaYtcA3IwxJB7VVUV3xmTAbC3t4eNjY1WLwIxvw+Ah/01Gg2am5v5/ExMTHhDJ29vbzg7O3N9AHJgRCVCMm7kEJF2v/i5YukfzZ8UDWksALhzJkZZyHATlyArKwvOzs5wcHDgEQ49PT0olUpONm1ra+NkOkdHRy3WPwCt/hLk5Im9C0RuhLh2un0LRL5JamoqlwIWiaBixKe/IfII5HI5FixYAA8PDwQGBiIpKQlffPEF5s2b1+/jkHDzQHICesBnn32G5cuXY+vWrXjooYcAgJf7JCYmIjExEWVlZZgxYwZiY2Nx33338Z7j/QkqAfTy8oK3tzeqq6tRWVmJqqoqLsrj7Ow8IGMR0dLSgtTUVFhYWPCOaCLEnLh4QxSNAt0wdVMCBDGnLjoVRKijtIFMJuNG0M7ODmfPnoVKpeKtWsVKAzE8TGFk+iwxhExGWQz1i04LnUMkihH5TawmaGtr4+mPiooKVFRUYMSIEVydkNaFavRFgRp6jnb0tEa0i29ra0NTUxPq6+uhUCjQ0dHBVfrs7Oy0RJbENsDknJBTplQqUVhYiMDAQF66ScaReBbkBNB60VqJ3w85f7R+FEGj5xobG7WqGcTdMqVb6PttbGzkZE4PDw+4ubnxc1GkRby+1Go1mpqaUFtbi7a2NlhbW8PW1ha2trYwNDTkIkV0LVJUQqx0oGtCd1wiGZO+l7S0NJiamiIsLEzLOdC93gcaOTk5+OCDD7B9+3YAQHh4OGJjYxETE8PHKuH2huQE9ID9+/fDxsYGEydO7PZ1xhiys7OxY8cOJCUloaCgANOnT0dcXBxmz56t1Xe9r3C1EkCNRqPlEFAZmrOzM2xsbPr1JtTQ0IDU1FQ4Oztj+PDh1z1vUWhIdAZ0b56igRX/pSgB5bgbGhogl8tRXV3NWeDDhg3T0i8nw0ZjpagBlaKRgaedJoXnKXJAY6Y0hlghQEaPStHEXDPl3svKyngjIOqiR7K5ZEDJCTA1NeVGiXbZNAfaUbe2tmrp4tN6KJVKVFdXo76+nldciJESxhiX6jU0NER1dTWKi4sxfPhw2Nvb87mIyoJUEidWW9Aun4yxuAZiREC3pE7s3khODAAu2UvXR319PbKzs+Hr6wtXV1fufDQ2NvKICaVuiFxoZmbGKzFqa2s5v8bExASOjo48mqCn19mkiSJB4njECgFAW2WRvnNyAEaNGqXluNDxg2lojx07hj//+c94//33MXfuXBw4cADJyck4ePAgHn74YfzrX/8atLFJGBqQnIA+AGOdCmoUIcjKysKUKVMQGxuLOXPmwMnJ6YZuBIwxnD9/HqWlpQgNDeVCKz1Bo9GgtraWlx4yxvhNr6/7GVRXVyMzMxM+Pj7w8fHp1TxFQw5cuXGK4xR3/wT6LDFVQP9vbm7mBC0bGxvU1taisbGRt0F2dHTktcVknMkJ0CUWUrhZNPz0IAeBdtHAFSNJToM4bpVKxRsBhYeHw8LCgofjAXDSJzkCBgYGPERORpfmSg4BtUYmQyV23yNiXGtrK0+f1NTUwMLCAtbW1rCwsOClrtXV1Zyb4OjoqBWVERUi6d/29nYolUqelqKyTJHvQc4MnUOs3CDHQPxuqZcDOSYyWWd/h8zMTHh5eXHnl6I2VBFAny3yDyjtQ9cYzYdSSNXV1TAzM4ObmxssLS15tESMDIl6AqJEMF1/ohAQXY/9lfu/Xpw8eRJxcXF455138PTTT2v9NltbW1FTU8MjKhJuX0hOQB+DMYaioiIkJiYiKSkJZ86cwcSJExEbG8vbWV6PobzREkDGOmvw5XI5KisroVb3XT8DikwEBwf3yc1E9wYrPi+WFNJzYjhelNBtaGhAeno6b/dKvAKlUskNYX19Pa8yENn4Yi6adpiiYSDDRYQ9Io6JxlKUKaYxieqEKpUKI0eOhLm5OR+7aPzEULSenp5WoxmxqoBC+iSLS5EA2oG3tbXBysqK71wp7E4GgBrbmJmZwczMDI2NjQgMDISlpSVMTU21tBzo/9RwiL4j2umT0ddoNGhqatLic6hUKn4esaQSAN+Fkz4AVRkQd6GhoQHFxcXw8PDgjgmtDcke07VBYyKDTQ4QOU6mpqY80kPpgpaWFq7myRjjDa9Ik4H4DLoiQRpNZ7MpcgDEaoih4ASkpqZizpw5WLlyJZ599tnrds4/+ugjzvIPCwvD5s2bERkZ2ePxCQkJeP311znLf+3atd2y/P/1r3+hrq4OkyZNwieffIKAgIAu51KpVIiKikJGRoYWy7+1tRWLFy/GmTNncPbsWURHR/M+AxJ6D8kJ6EcwxlBWVobExETs3LkTJ06cQGRkJJcv9vT0vOqPs69LAIk0RA5Bb/sZMMZQWlqK4uJihIWF/WFk4kZBO0XdCIaYRiCD3dDQgMzMTLi6usLf318rwiCWATY2NkIul0OhUHBNBnt7ezg7O/OQvK6ansgLEIl7ALSMBEUSxEhFa2srsrKyAACjRo3i56cdPrH5qcSOjBsZT1EtkJwAALwCgkLZRGwkiV9TU1OtEDcRARljaGpqQmtrK8rLyzmnxMDAgMtZW1hY8PGIHAAx5UHzJKPd1tbGJXlFmV2RSCmmRoiZT3X7Yo6+vr4e+fn5CAgIgLOzcxc+A3CFlEnfCTkfon4COSeWlpZa8sM0HjMzM7S3t6Oqqoo7R42NjbC1tYW9vT3c3Ny0Sj5lMhmPMoWGhmqtxWDm/wkZGRmYPXs2Xn75Zbz00kvX7QBs27YN8fHxWi2BExISrtoSeMqUKVotgdeuXdul3p/aFFO9f1ZWVrf1/suWLcO5c+dw4MABLSegqakJL7zwAkaPHo3ExESYmJhITkAfQHICBgiMMVy+fBlJSUlISkrCr7/+irCwMN7gyM/PT+vHWl1djXPnzsHQ0BBhYWF93nCIyFbkELS0tFxTPwPGOlUTKyoqEBERwWvU+xM9OQG6qm0KhQI5OTnw9fWFh4eHltZAdzdn2tFqNBqeOlEoFDA3N+cOgZWVFTfU4i5cJA7S/0VRIRobGcczZ87AyMgIISEhXXavYvUE3RBpbFT7LgrviOWURD6klIBoYMXIBI2JXgM6d1ZlZWWorKxEUFAQF9Spr69HTU2N1s6YNBXo86ktsZjCodC/KLAkagmQQ0OllcTGb2tr49ECIt5Rbw5/f3+4uLholZeSw9XU1MQdOzHSQdET8TfT3NzMyYAi8U9fXx8WFhbcERMjM9XV1VAoFGhubuYOgZ2dHQoKCmBqaqqVAqDvfbAdgJycHMyaNQtLly7F66+/3qv03M3QEvixxx5DXV2d5AT0ASQnYBDAGENlZSV27dqFxMREHD16FMHBwdwhUCqVeOihh/DWW29h3rx5A3JjoX4GlZWVPHeu289Ao9EgOzsbDQ0N/aaa2B10mfiANkmwo6MDcrkcZ8+eRWBgIFxcXPjuUzQEujdE2k3S+YBO40ukwtraWhgbG8PR0RH29vZ8vt3JxpLTRAaGjGVbWxtSU1Nhbm6OESNGaJVFkuEnch1VKxDjnULYlI8XUwtidQX1AaDPF/UW6DNEjQI6f2lpKRQKBYKDg7nin6j6R6I8VHJH7HpbW1uethBTJbodCel5mh99l2KVglimSByHxsZG5OTkICAgAE5OTl2kmmmtxXUTI0IymYzzKMTyQVovckZkMhlfX/r+KfpD60xjJoegtrYW+vr68PLy4poEYoXIYCIvLw+zZs3CokWL8NZbb/VqPG1tbTAzM8OOHTu0xIQWLFiAuro6JCcnd3mPl5cXli9fjmeffZY/t2rVKuzatQsZGRkoLi6Gv79/F4M+depUhIeH48MPPwTQ2RNgzJgx2LVrFxwcHLqI/oiQnIC+wy3bQGgoQyaTwdnZGU899RSefPJJ1NTUIDk5GYmJiVi9ejU0Gg3GjBmD0NDQARuThYUFbw1L/QwuX76MvLw8ziqvqqoCYwzjxo27oUZH1wvRAIo+K92wy8rKUFhYqJWaEMmFIn+AQDt6kdFPLHd3d3e4u7tDo9FAoVCguroaGRkZYIzxSAl1RhSNrUhmJNZ+amoqbG1tMWzYMC0tAd1SRFECWQwri4ZcTAOIPRHIeaD8vEhmFFMaogjRuXPnoFQqERkZyVsFkzNDqQ5TU1N4eHjA29sbDQ0NqKmpQUVFBc6fP89L7kjCmML0KpWKkwSpdFFUZDQ0NOSpDwA8UkHrUVVVpaWcKGobMMY4cZIcA5onzYvkkUlxkCC2RhajBaJssugc0Hqr1WqYm5vDxMQEcrmck0pra2tx5syZAev490c4d+4coqOjER8fjzfffLPXDsnN1BJYQt9AcgIGGTJZZ037E088AQMDAxw9ehSPPPIIKisrceedd8LDwwOxsbGIi4tDWFjYgNxkzMzMONu/tbUVly5d4uJEVlZWuHz5MpydnXvsZ9AfoB20aHgBoKioCGVlZRg7dixvP0uGU1R102XpA1fCt2RM6XV6Tk9PDy4uLnBxcUFQUBBqampQWVmJ3Nxc7hBQiJgcAHpvfX090tLS4OrqCl9fXz4eXflgkW1PpDiRWyDu+AFt+WExLUChdJL+pTmTcaS/ZTIZCgsL0djYiLFjx3JFPdpVi2kOcT2srKxgaWkJDw8PLeeouLgYVlZWsLOz49K9NBdyAGjupG1AjgARKInkWFNTg3PnziEwMJAbVLEyg8ZJhD+xQoMiC8AV+WRxHqLqHx0r7vppTcmBouuBxkbNgEJCQgCAN6bpruMfXRd9ncLrCefPn0d0dDQeeOABrF27dtBTEr3B9bYEltB3kJyAIQDGGN555x2sX78eycnJmDFjBoBOktu+ffuQmJiIe++9Fw4ODoiJicHcuXMxduzYAfmxd3R04PLly3BxcYGfnx+qq6shl8tRWFjYbT+D/oSuZsDZs2ehUCgwbtw4rU6Duo6CGBEQXxN3SyL5j3bCYmhdX18fLi4ucHR0xMiRIzmjvLS0FAUFBVqVBo2NjcjIyICHhwdvbSsaPF3ZWtHY0q6WyG89RUDIwJAhpDp9MrRiNYWYYsjPz0dbWxtCQ0O1mPM0ZzpWrISgXbgom0vtf9VqNSebnj9/HkZGRrzbHwkgyWQybvhF9USRL1BbW4uCggL4+fnB3Ny8S5c/jUbDKx7E8D9jV8oQKc0g1uqLayHu8kmWmJ4nx4EeBLVajYyMDK4DQNcKPRwcHODg4ICgoCAolUouAZ2dna3V8U+31W1foaysDLNnz0Z0dDQ2bNhww/eEm6ElsIS+hcQJGCJ46623EBsb22MKoKmpCQcPHkRSUhL27dsHKysrzJkzB3FxcRg/fny/lCXV1tYiPT0dXl5eXYiLxKam/LmZmRl3CK6no1lvoFarkZ2djaamJowePZp31BNJauK/V4OY+xWdBLHBjBhR0CUZEsGyoqICcrmcN+5xcXGBt7c3N2Q0HnFnTPLCNCcyviK5T2xURMZQdxdM7XPFuYjseRpnbm4u1OrOLphA16ZGlJ+nsLlY19/S0qK1m6f3iVULLS0tUCgUUCqVKC8vh0wmg6OjI6ysrLRC5STCQ+WBVAYYHBwMa2trXu1gYmLCyyhFQiOlPsixa29v55wGsUKCHA4y9roKkRqNRkvNUUwvUDSDegFQFcC15v+bmpp4vw+lUsn7Ojg6OvaZw0xtc6dPn47PPvusz+4BQ7klMEHiBPQdJCfgJkRLSwt++OEHJCUlYffu3TA2NsacOXMwd+5cTJo0qU/CkHK5HDk5OQgMDOzyA9QFNfahkjtjY+N+62fQ3t6OjIwMaDQahIeH9wk3QSTqdbeTEsP8ZCgAbadBJpNBLpcjKysLTk5OaG5uRl1dHWfW29vbazWzESsKRK4AKdeJUrrUGlfULaC1IOdBNPa6rXwZY8jJyYGBgQFntIv8AlFylxwLkb1PxpqY/bptdSnKIZIDqcqgqamJ7xJJypkcI8qzFxYWIiQkBHZ2djA2NoZSqURzczOvFBAb/4gVGKQvIBpzckrENAsdQ1oEFFUQSzLpWLGqgVIAoaGhWoqB1wvqCEoliKamplyro7e/j4qKCsyaNQtRUVH497//3aebgKHaEhgAcnNz0dbWhpUrV6KhoQEbNmwAgG7JgxKuDZITcJOjra0NR44cwY4dO5CcnAzGGKKjoxEXF4epU6f2ykheuHAB586dw6hRo7qtC74a1Go1TxkoFAped+7k5AQbG5sbcghUKhXS0tJgZGSEsLCwPr3xiU6AuOsnw6ObWhD1BwDg0qVLnJzo4OAAjUaDxsZG3vGwrq6O586pBp9y+iQkRLtP+s7E6Ib4PZLRIsEi0YBThEEkAWZnZ8PMzAwjR47kJEQiwZHxpsoB3agF/UvOhbhrphI7MSpBeXuxdNLQ0JBXGsjlcjQ3N8PFxQVmZma4dOkShg0bBltbW62qgZaWFq0oCkUg6HP09fW5WiAZfvqeyKmj91AZIvVxECsxSENBJE+2tbUhLS0NxsbGPALQV9daR0cHl/emzoeUMrhWNc+qqircd999CAkJwbffftsv3IOh2BIYAHx8fFBaWtrleMmM9R6SE3ALoaOjAz///DMSEhKQnJyMlpYWREdHIzY2Fnfeeecfig0xxlBYWIhLly4hPDycy8n2FlRapdvPwMnJ6bqZ1M3NzUhNTYW1tTXv096X6E6LQMwnk3Mg1tvTsSUlJTh//jyvTiBjJZLr2trauEJfbW0tTE1NOYnMwsICHR0dXDqYFALpIcru0rnJCJMxEwl9FB6n3aylpSWGDx/OywRFR0N0HHRvBfQ5tAbGxsZabHrS+aeQu1jKSI4KRTPomJaWFqjVat75UK1W80oDBwcHHi0Ru/UZGBigtbWVpzyAK9UXNC6x+Y+YuqDxiaWKlDKgtaMHzTc9PR1GRkY8Nddf9f8aTae8N6UN1Go11+qwt7fvVqujuroas2fPRkBAAL7//vsh00Jcws0LyQm4RaFWq3Hs2DHs2LEDu3btQn19PWbNmoXY2FjcfffdXWr81Wo1zp49i9raWowePbrPiX50w9PtZ0A3vKvdZKlBkYuLCwIDA/uFbyAaVzG0LjoABJE4VlhYiMuXLyMiIoJzIcRzkDMAgO8oOzo6uGNUVVUFIyMjrkNgYWGh1eGOogO0u6dxkbGncjzayYqEwrS0NFhZWSEgIEArzE27ZuBKF0axiyCx+sX5i8aTztPa2goAXPxHzNkTg58xxqMcJMgjl8tx8eJF3nCnuroa1dXVUCqVsLS05DtjanxFxD9qNkROGBl1ik7QLp40E4grIs6FSgdpXmL6QK3ubFNMDoBIrOxPjgt9p0Q2raqqQlNTE+zs7LhYlJ+fH+rq6hAdHQ13d3ckJiYOaJmuhFsXkhNwG0Cj0eDkyZPcIZDL5bjnnnsQFxeHe++9Fx0dHfjzn/+MmJgYLF68uN+YzATGOvsZkENALW+dnZ279DOg1sk30qDoesYllhH2RAIjI5ufn4/q6mpERETA3Nxc670icVA3jC3urolgqVAocPnyZchkMri6umqFh4nBTztXOi/pAoid+FQqFZqamnD27Fk4ODggMDCwS4UBGTb6v1haR90UdTkCZCjFBkA0DjoHpQGoxwCdQ1RHLCsrQ1FREUaOHAl7e3vOISD+AgnyKJVKLtREXAFSKSTHipwLcjhIpEm3rJDmTsJA5ChQTwAiV2ZnZ8PU1JSnAOh77G8HoDs0NzejqqoKu3fvxquvvophw4ahoaEBfn5++OGHHwa0PFfCrQ3JCbjNoNFokJqayhsclZWVwcDAAA4ODti3bx+8vb0H9KbHGOOlVXK5HCqVijsEVAY4fPjwLq2T+3tMQM8kMMqzNzY28uoEkYTXneMgVhWQU0DGVwy70664srISAPiO2M7OTquREABOjBM76DU1NSErKwuurq7w8fHhjHlxHDQWsYKAiHWtra08WiCmGES+BH0eQWwtTEx8atRD6QAjIyOUlZXh/PnzCAoK4oQ8Muo0Top8tLa28kqD6upq6Ovrw83NDTY2NrC2tuaRC9oNi5wOOo+YugHAexOILYLpmIyMDBgZGSEiIoKnXQbD+HeHc+fOIS4uDg0NDWhqaoKnpydXF+2vyiAJtw8kJ+A2RkFBAe666y5YW1tDo9GgqKgId955J2JjYxEdHQ1bW9sBdwhIvvjSpUtQqVSwsrKCp6fnVfsZDCTUajXS09OhUqm0qhPI6JCB1+UWiHX2ADgZT0xD0K6UQvJ1dXWoqamBXC5HW1sbD5NTa2qxVXFHRwfq6uqQn58PT09PeHl5cWNOBpfGT+MTiY+iUiDNQ1TcE48V8+e60sFiTT45CzKZDBcvXsTFixf5mlG9PxEjKedPjgNVJxA5sLa2Fs3NzaioqOC5c+rvIHIPAO0GTqIwEI1dtxlSVlYWDA0NMXLkSB5VGCpobm7Gn//8ZwDAvn37IJPJkJKSguTkZOzZswdJSUmYOnXqII9Sws0MyQm4TfH7779j9uzZeOyxx7B27VrIZDLk5eVhx44d2LlzJ7KzszFlyhTExsZizpw5cHR0HBCHgDGGkpISlJSUYPjw4TyH3FM/g4EEKccB6NLUiXb/utwC+nnpaguI4XRx502GisLkZCTr6uo4h6C1tZX3M3B2doahoSEqKyuRnZ0NLy8v+Pv78x01yfiK5Dcy9OJuX+RBAOChffE95CiIpD+CqGFA2gY0t3PnzuHy5csYM2YM38WLqYXW1tYuhpkcCtHBoOOpwZFCoUB7ezvs7OxgZWUFR0dHmJmZcedDJE6KJZ7kPMlkMmRmZsLIyAgjR47kztBQUdxraWnBgw8+iJaWFhw8eLBLsy5Ry0KChN5CcgJuUyxduhR+fn5aTT8IjDEUFRVxhyA1NRUTJ05EbGwsYmJi4Orq2m/kPOpQOHr0aFhaWvLXWlpaeMdDpVIJGxsb7hDcaIvlawE1AqKysavdeK+Wgwe0Q/IiD0HUDtAtWaSdOBHySLa3ubkZFhYWUCqV3AGgiENzczMXEqLnxLI9UfxG5C4QL0BUHdQV1hEJe6LSoXhOtVqNwsJClJeXIyQkhAv/kANCBp9y9Gq1Wkvoh6oc6BhxzWke1DyKOmFSKolaYxPxT9R0ADq5ExkZGTA2NsaIESP4HMSyxMGESqXCww8/jJqaGqSkpPSqUuejjz7iZX5hYWHYvHkzIiMjezw+ISEBr7/+Oi/zW7t2bbdlfv/6179QV1eHSZMm4ZNPPkFAQEC344+KikJGRkaXMr/MzEw8/fTTOHXqFBwdHfHMM8/gpZdeuu75SegbSE7AbYprzXkyxlBaWorExETs3LkTv/32GyIjIxEbG4vY2Fh4enr2iUOg0WiQk5OD+vp6jB49+qodCltbWzmpkOrvnZyc+q2fQUtLC1JTU2FpaYlRo0bdsJHQLTGkv3WdAKCrAyEaZz09PVRUVCA/Px8mJiZobm6GpaUlnJyc4ODgwI27+H4ydLqpFTL0xOYnJ0RsC0zvpVuG6MAQyY6epwhAVVUVrzYh4y46ClT+SCQ/Ojft/GlXL86ZohP0N0VNSH2wvr4edXV1sLCw4PLGYrc/tVqN06dPw9DQkH+ftEZi5cRgoa2tDfPnz8elS5dw6NAh2NnZXfc5tm3bhvj4eHz66aeIiorCxo0bkZCQgPz8/G61P44fP44pU6ZgzZo1iI6Oxn/+8x+sXbu2i+DPmjVrtAR/srKyuhX8WbZsGc6dO4cDBw5oOQFKpRKBgYGYMWMGXnnlFWRlZeGJJ57Axo0b8eSTT17/Ykm4YUhOgIRrBmMMly5dQlJSEpKSknDs2DGEh4dzkpKvr2+vHALSZ29ra0NERMR1VSe0tbVxh6Cmpobf+J2dnfukzLGpqQmpqalcH74vHB6xOoDOJza6EdMKgHalAfEIgM6Ob3l5eQgODoajoyNaWlq0iIUk5UxhcuBKq2NxHuL5yUiK7HrgiqqgqNtPjoNoNCkNkJeXx3egFhYWWk15dI03kfRo3vScmK6g9aCUgMjepwgJOU6UmqCyQ6VSyVX6bGxsUFhYyJUAe1KIHCxSYHt7Ox5//HEUFhbixx9/hIODQ6/OExUVhXHjxmHLli0AOtfd09MTzzzzTI/Sv01NTdi7dy9/bvz48QgPD9eS/n3++efxwgsvAOhMyzg7O3PpX8KBAwewfPlyJCYmYuTIkVpOwCeffILXXnsNFRUVPKX3j3/8A7t27eqxS6GE/sXgx72uER999BF8fHxgYmKCqKgo/P7771c9PiEhAUFBQbzz1/79+7VeZ4xh5cqVcHV1hampKWbMmIFz5851ey4igclkMp4T1kVhYSEsLS1vWGBnKEMmk8HDwwNLly7FkSNHcOHCBSxcuBBHjx7F6NGjMWnSJKxbtw75+fnXrODV1taGM2fOQKPRYOzYsdddnmhkZAQPDw+MHj0aU6dOhZeXF+rr6/Hbb7/h+PHjKCwsRENDQ68UxZRKJU6dOsW7CPaVYdAlEZJhFEvWRAKeGCmg91dVVeHs2bMIDg6Gk5MTL4Hz9PREeHg4Jk+eDB8fHx72PnPmDEpLS1FTU8Nr+sWGOWKlgFgOSCF+MpZkcMV50GtkiPPz81FZWYlRo0ZplQqKaRJi8RsZGfFKAuDKTlyMVBgYGMDY2Jjn+2lddNX+6GFoaAhjY2N4eHhg1KhRmDRpEvz8/NDS0oL09HQ0NTXByMgIdXV1PVZzDAY6Ojrw1FNPIS8vDz/88EOvHQD6TVEjMqDTsZkxYwZOnDjR7XtOnDihdTwAzJw5kx9//vx5VFRUaB1jbW2NqKgorXPK5XIsWrQI33zzTbfRvBMnTmDKlClanJ6ZM2ciPz8ftbW1vZqvhBvDTeEEbNu2DcuXL8eqVauQmpqKsLAwzJw5k5dR6eL48eN4+OGHsXDhQqSlpSEuLg5xcXHIzs7mx6xbtw6bNm3Cp59+ipMnT8Lc3BwzZ87kAigiXnrpJbi5ufU4vvb2djz88MO44447bnyyNwlkMhlcXFywePFipKSkoLy8HM888wx+//13jB8/HlFRUXjnnXeQk5OjVT8vorW1FadPn4axsTEvzboRGBoaws3NDREREZg6dSr8/PzQ3NyM33//HceOHcO5c+dQX19/TQ4B9Yv38fFBQEBAnxsJka0u/p8+h4wx7axFB+Dy5csoKChAaGgolygWowaklufs7IwRI0ZwI0i18CdOnEBxcTE3gmLKQKztF1MCFAUQqxEAaO3wNRoNzp07h5qaGowZM4Yr+dF5KM8vqinS3KkigjoFEulQrLYQnSeZTMYVCFUqFZ87VULo9hCws7NDa2sr7O3tuRBQdnY2fv75Z+Tk5EAul2sRBgcaarUaTz/9NFJTU3Ho0CE4Ozv3+lykxKh7DmdnZ1RUVHT7noqKiqseT/9e7RjGGB577DEsXrwYY8eOva7PET9DwsDipmgl/MEHH2DRokV4/PHHAQCffvop9u3bh61bt3Yb2vrwww9x77334sUXXwTQ2aHvhx9+wJYtW3hoa+PGjVixYgViY2MBAF9//TWcnZ2xa9euLqGtlJQUJCYm4sCBA92Ob8WKFQgKCsJdd92F48eP9/X0hzxkMhns7e2xcOFCPPHEE6ivr8fu3buRlJSEDz74AF5eXrwFMoVgU1NT8e233+Kvf/0rgoOD+/zGa2BgABcXF7i4uPD6+8rKSqSmpv5hP4OqqipkZWVdU/OkG8EfGRzajZPhA4DS0lIUFRUhLCyMl3CKKQLdMDkZeAcHB9ja2iIwMBANDQ2orq5GXl4eNBoNly8m5UYxKiDyF8RySIoeiBGNgoICNDU1ISIiAoaGhmhtbeWqflRtQGQ+0WkR0xxilYJYQim2ORYrD0RnSSxhFMff3t6OnJwcGBkZYdSoUdDX14ezszOvulAoFCgsLERra6sWsXCgKlA0Gg2effZZHD9+HEeOHLnqhmMoY/PmzWhoaMArr7wy2EORcB0Y8k4AhbbEC+taQlvLly/Xem7mzJm87eQfhbbICaDQ1q5du3okqv34449ISEhAeno6kpKSbmSqtwRkMhlsbGwQHx+P+Ph4KJVK7Nu3D0lJSbjnnnvg5OSEiIgIHDx4EI8++mi/OAC60NfX50Zf7GeQkZEBmUy7n0FlZSVycnIwcuTIHvunDzTIUBYXF6O0tBQRERG8coLSBGIZnFhqJ56D1pm6Gg4fPhx1dXWQy+XIz89HW1sbr7+3t7fXyr+T4aUwvmiEgU7NiebmZu4AkMGnigB6iARAem9rays0Gg3vbUB8AKoWIMdBTJVQOsDQ0JA7CLqOD/EOsrOzYWJiwh0AUfTIzs4OdnZ2CAgI4Cp9ZWVlyM3N5RUojo6O/abQp9Fo8OKLL+Lw4cM4cuQIvLy8bvicDg4O0NfX590bCXK5vMdr2sXF5arH079yuRyurq5ax1C+/8cff8SJEye6pPTGjh2LRx55BF999VWPnyN+hoSBxZB3Aq4W2uqJSNIfoa2SkpIun1NdXY3HHnsM3377bZcaXgmdsLKywsMPP4yHH34YTU1NWLNmDdauXQs9PT0cOHAAhoaGiIuLQ1RU1ICwsvX09Ljojkaj4UYwOzubGyBfX9/r7p7Yn2CM4dy5cygvL8e4ceO0mNhi9YCY5iBHQMx5086YoNFoYG5uDl9fX/j7+/NOfxcuXEBBQQHs7Ozg4OAAGxsb3pFQrCKgKMXZs2fR0tKC0aNH83JEsReByCfQVU6ksL84XnpNt74fgFakgv4vEgeJXyF2UDQxMUFYWJhWVQJ9puiAWlpawtLSkvMHqLFPQUEBJ5w6OTlxx+ZGodFo8Oqrr2LPnj04evQofH19b/icQCdPZsyYMTh8+DDi4uL4Zx0+fBhLlizp9j0TJkzA4cOHtUqGf/jhB0yYMAEA4OvrCxcXFxw+fFiL6X/y5En87W9/AwBs2rQJb7/9Nn//5cuXMXPmTGzbto13IJwwYQJee+01LttMnzN8+HDY2tr2yfwlXB+GvBMwWLiW0NaiRYswb948TJkyZQBHdvNi165d2LBhA7755hvExsbihx9+QGJiIh588EGYmJhgzpw5mDt3LiZOnNgv7VF1Qbli0qYvKSmBo6MjLl++jLKyMh4mp53VYICY9gqFAmPHjoW5ublWuJ6OEdsAE3QrAURHQGxsRMbc2toaVlZW8PPz41oE5eXlyM3NhZWVFezt7bWUGzUaDfLy8tDe3o4xY8ZwIyvu8kVlQiLtiY2QRGNKEQbRMRBD+jRukYdAcyDdAyotpJy/qakpwsPDr7us09TUFF5eXvDy8kJbWxsUCgUqKytx/vx5mJiY8AiBtbV1rxwCjUaDf/7zn0hISMDRo0cxbNiw6z7H1bB8+XIsWLAAY8eORWRkJDZu3IimpiaeUo2Pj4e7uzvWrFkDoLOkb+rUqXj//fcxe/ZsfP/99zh9+jQ+//xzAJ3fxbPPPou3334bAQEBvETQzc2NOxq6UQwLCwsAgL+/P0+rzZs3D2+88QYWLlyIl19+GdnZ2fjwww+xYcOGPp2/hGvHkHcChnJo68cff8Tu3bvx3nvvAbhCYjIwMMDnn3+OJ554ovcTv8WQlpaGv/3tb9i5cyfuueceAEBMTAxiYmLQ1taGw4cPIzExEfPnz4dMJkN0dDTi4uK6MIn7GoxdaZ88btw4WFpaavUzKCwsRHZ2NncIHB0dB8RBAToNRW5uLurq6jB27Fgektbd0YtzoX91IwAEXWOoq2RIRpuMoIeHB5dyrqmpQXFxMSwtLWFvb4/6+nrIZDIuBUw5eVIVpF0/RRAoekC/Ed02zWI/Al01PHI8xGZG5CzQazJZZ1thknY2NjbmEYAbgZGREdzc3ODm5ga1Wg2FQoGqqiqkpaVBX1+fSzlfa3tsxhjWrFmDr7/+GkeOHMHw4cNvaHzd4aGHHkJVVRVWrlyJiooKhIeH4+DBgzz6WVZWpjXWiRMn4j//+Q9WrFiBV199FQEBAdi1axfXCAA6CdJNTU148sknUVdXh8mTJ+PgwYPXJdZlbW2NlJQUPP300xgzZgwcHBywcuVKSSNgEHFT6ARERUUhMjISmzdvBtB5c/Ty8sKSJUt6rHltbm7Gnj17+HMTJ05EaGioVs3rCy+8gOeffx5AZ2jLycmJ17yWlZVBqVTy91Noa8eOHYiKioKHhwfOnj2rdeNKTk7G2rVrcfz4cbi7u0vhLR1cvHjxD4l2HR0d+Omnn5CQkIDk5GSoVCpER0cjNjYWd955Z592OGSss0FRdXV1j+2TGbvSz0Aul6O5uZnL9fZnPwONRsObFBHTXvd1XRU8UWynOwdAd16iVgE5sLp1+VQZQKH6trY2VFVV4fz581Cr1TA3N+dhcgsLCy2ngnbqlKensYmNk8gJoNw/VQhQzl+MJJDzJTb+oeiCqCCYlpbGK076U/1Po9Fujy2SLHuKHjHG8N5772HTpk348ccfERYW1m/jkyDhWnBTOAHbtm3DggUL8Nlnn/HQ1vbt25GXlwdnZ+cuoa3jx49j6tSpePfdd3loa/Xq1V3Ur959910t9avMzMxu1a8AoKSkBL6+vl0kMEV8+eWXePbZZ1FXV9dfS3FbQa1W49dff+VqhQ0NDbj33nsRFxeHGTNmXFVV8I9ARrahoQFjxoy55t1MU1MTdwgaGxtha2vLHYK+clDUajUyMzOhUql4nr0nXKvyY0+fI5YHisp8YrSBjiGHNysrC2q1GiNGjEB9fT0XKKLWv92FyelcxAugc1GfATqGQv9irwA6XlfWlyINdBxpIhgaGiIkJGRAJYDF6FFlZSUvR3R0dISJiQns7e3BGMOmTZuwfv16pKSk9FhGJ0HCQOKmcAIAYMuWLVwHOzw8HJs2beJkk2nTpsHHxwdffvklPz4hIQErVqzgOtjr1q3rVgf7888/56Gtjz/+GIGBgQC66m6/8sormDt3bo9OQEJCApYuXYqKigqMGjWqX3S3yRHRxYkTJzB+/PjeLOtNA41Gg99++407BFVVVbjnnnsQFxeHmTNn8vzjtUBUKPwjI3s16PYzsLa2hrOz8w31M+jo6EBGRgbUajVn2vcXxN2/CN2KA/qbdAb09fUxcuRIreoBmUyG2tpayOVyVFVVAQBfC4qI6X4e7eJp3uQYkAaB2GSJ/iVBJd1GSBqNhjcDGjVqFB+brjriQICxzpbOVVVVKC4uxp///GeMGjUKzs7OOHbsGFJSUm7536uEmwc3jRMwkBiqutvkBBw6dAgjR47kx9vb2w+JNrsDBY1Gg9TUVOzYsQNJSUm4ePEiZsyYgbi4OMyaNQtWVlY93vjFToDh4eF9tm499TNwcnK65ohFe3s70tLSoKenh/Dw8AHjHuhyCHR7G5ADkJaWBgMDA4SEhPBQt6gaSO9Xq9U8TF5VVcXD5I6Ojlp5czqedAPoXBQN0NUM0NUBoM9VqVTIysqCqakpQkJCtNIFYnpisFBYWIiXX34ZP/zwA4DO627u3LmYO3fugJTISpBwNUhOQDcYqrrb15KSuN2g0XT2gyeHoKioCHfeeSdiY2Mxe/ZsLqgDAJcuXUJOTg4cHR3/sBPgjaCnfgaUN+/pPdfapbC/oesEkHNiYGCAUaNG8Rw8Ge4/MmINDQ283E4Mk5MsbltbGzfcwBUxIiozFEsCyTkgqWHGGG8GRE4djWcoOAGMMXzzzTd48cUXsWfPHoSEhGDv3r3YuXMn/ve//2HZsmV49913B218EiRIToAO2traYGZmhh07dvDSFwBYsGAB6urqkJyc3OU9Xl5eWL58uVaN7apVq7Br1y5kZGSguLgY/v7+XYz31KlTER4ejg8//BBAZ3XCmDFjsGvXLjg4OHQx+OQEeHp6orW1FYGBgXjppZcQExPTH0tx04GIftQCOScnB1OnTkVsbCxGjRqF+fPn47777sP7778/YIahvb2dG8Dq6mqYmpryBkfU2U6lUuHMmTMwNzdHSEjIoO9cxbA9OQDUg4OiE7oNkABoSQmLu3jgiuAR5c0VCgUaGxthY2MDKysruLm58T4D9B6VSqVFDqS1kslkvJthRkYGDAwMMHz4cJiammp1MqQqhcHaaTPG8P3332PZsmXYuXMn7r77bq3Xm5qa0NDQIInkSBhU3BS9AwYSQ1l328LCAu+//z4SEhKwb98+TJ48GXFxcdi9e/f1T/QWhEwmw4gRI7By5UqkpqYiJycHd955Jz777DNMnz4dKpUKAQEBkMvlvWoo1BtQP4Pw8HCtfganTp3CsWPHkJubi5MnT8LS0nJIOADAlRB/R0cHUlNTYWZmhtDQUK1Of3SciO6ep+PpNQsLCwwbNgzjx49HZGQkbGxsUFVVhV9++QVpaWm4cOECVxCk0D81MhJ7B2g0GmRkZGhxAETBIOI0DGaoPSkpCcuWLcP27du7OAAAYG5u/ocOwGA0TouJiYGXlxdMTEzg6uqK+fPn4/Lly1rHbN++HeHh4TAzM4O3tzfWr19/1XGJ+Prrr2Fvb897PhDi4uIwf/78az6PhL7B4N9xJAC4NnEiBwcHLF++nKcr3n33XTz66KPX9QO8XSCTyRAQEICZM2eioqICf//73/HSSy9h586dGD58OO655x5s2bIFFy5cGDCHgPoZhIaGYurUqfD29kZ5eTna2tpQW1uLgoIC1NbWDth4rgZKT1hZWWk5AH+Eno4RywXJSJubm8PHxwdjx45FVFQU7OzsUF1djRMnTiA1NRUXL15ES0uLVp8B4nCkp6fD0NAQI0eO5AqBNEaRpzBY2L17NxYvXozvvvtOiyB8PRisxmnTp0/H9u3bkZ+fj8TERBQVFeH+++/nrx84cACPPPIIFi9ejOzsbHz88cfYsGEDT5/+ER544AGo1WqtzUtlZSX27dsnaasMAqR0gA4GKx0QFxeHPXv2aN1EiQhF4kTd4aOPPsLbb7+N8vLyG577rYZff/0V0dHReO2113gzKcYYLl26hKSkJCQmJuL48eOIiIhAXFwcYmNj4ePjMyC7x8bGRpw5cwaurq7w9/fXYtbLZDI4OjrC2dn5mgVo+hKtra04c+YMrK2tuZEVoasxoPucyGcQn6e/dVX/KHdPr7W3t6O2thbV1dVQKBQwMzPjKRQjIyPOnRB7AQxm2F8X+/fvx4IFC/DVV19pGc/rxWByk0Ts3r0bcXFxPD0zb948tLe3IyEhgR+zefNmrFu3DmVlZdf0Pfz9739HSUkJj1R88MEH+Oijj1BYWDhkvsfbBVIkQAei7jaBdLdJR1sXpLstoifdbQLpbtMxmzZtQkZGBtLT05Gens5/HNu2bcM777zT43jT09O1VA8lXIGFhQU2bNjAHQCgc6fq4eGBpUuX4ujRo7hw4QIef/xx/Pjjj4iIiMDkyZOxfv16FBQU9NuOXKlU4vTp0/Dw8EBAQAD09fXh4OCAkSNHYsqUKQgJCeGyt9Tqllj2/Y2WlhacPn0atra23ToAALqE5WlnTwaZntNtcSw2AKKIgDgnciAsLCzg4+ODMWPGYPr06VzL//Tp0/j111+hVqvh5+fHNQaGkgNw6NAhPPbYY/jiiy9uyAGgxmlik7NraZwmHg90Nk6j4/+ocVp3qKmpwXfffYeJEyfyKIxKpepS0WRqaoqLFy+itLT0mua3aNEipKSk4NKlSwA6NVYee+yxIfM93k4Y8rLBg4Ghqrv91VdfwcjICBEREQA6c45bt27FF1980e9rcjMiPDz8qlUUMpkMLi4u+Nvf/obFixejuroaycnJSExMxOrVqxEYGIjY2FjExcX1WSlXXV0d0tLS4OvrCx8fny6vi/0MgoKCUF9fD7lczjX6SaK2P/oZkKF1cHBAUFDQVecr9gkArpT7Uf5ffF5X1ZAiG6IgEADuSIjkPkNDQ7i6usLBwQGpqamQyWSwsLBAVlYWZDLtDpCDzaf46aefMG/ePHz00Uc97qqvFYPVOI3w8ssvY8uWLWhubsb48eO1ogszZ87Ec889h8ceewzTp09HYWEh3n//fQBAeXl5t9e1LiIiIhAWFoavv/4a99xzD3JycrBv374/fJ+EvocUCegGDz30EN577z2sXLkS4eHhSE9P76K7LYbfSXf7888/R1hYGHbs2NGt7vYzzzyDJ598EuPGjUNjY6OW7rYuAYhq2XXx1ltvYcyYMRg9ejTeeust6Ovr44MPPugVAYigUqkQHh4OmUzW5XNJ5jQwMBDGxsZwd3e/amTiZoVMJoODgwMWLlyIffv2oaKiAi+88AKysrJwxx13YMyYMfjnP/+JjIyMXu/Ia2pqkJqaimHDhl3TjVIm62zLPHz4cEyePJkrGxYWFuLo0aPIyMhAeXk5Z+XfCIis6OTk9IcOgDg+USNAfE7U+O/ufZQCEKMD1HeAIgzEIVCpVDwFEBkZiVGjRmlFTHJycvDTTz8hOzsblZWVWlLeA4Vff/0VDz74IDZs2ID4+Pibfkf74osvIi0tDSkpKdDX10d8fDx37BYtWoQlS5YgOjoaRkZGGD9+PHd6rscR++tf/4ovv/wS//73vzFjxgx4enr2y1wkXB0SJ2AIYKiKEwHA0qVLkZKSgnXr1iEkJAQ1NTWoqanplu18q0KpVGLv3r1ISkrizmBMTAzmzp2L0aNHX9ONr6qqCllZWQgKCoKbm9sNjYcU6UitsKmpCfb29rzB0fUqIDY1NeH06dNwdXVFQEBAnxowsdxQFB8S1f5E6KYHqELByMgIoaGh3Yo7McZQX1+PqqoqyOVyqFQqLQ3//hbSOnnyJOLi4rB69Wr8/e9/75P1G8xSZV1cvHgRnp6eOH78uFZKVK1Wo6KiAo6Ojjh8+DDuu+8+VFZWwtHR8ZrmWF9fDzc3N3R0dODrr7/GQw89dE3vk9C3kJyAIYChKk509uxZhIaGIjs7u186nd2MaGpqwoEDB5CYmIh9+/bB1tYWMTExiIuLQ2RkZLcherlcjuzsbC4d2x9jInGihoYG2Nra8jD5H/UzIIKiu7s7/P39+2UH2508MaUIRAdKV4CImgEZGRnxXf8f5f/Fhk/kINnZ2fH16OuOlGfOnEFMTAxWrVqFZcuW9en6DUbjtO5QVlYGb29vHDlyBNOmTev2mPj4eBQWFuL48ePXNcf4+Hjs27cPly9f7tPmYBKuHRInYJBBBCCxNPBaCEDLly/Xem7mzJnYtWsXgD8mANGPXS6XY9GiRdi1a1e30rZ79uyBn58f9u7di3vvvReMMcyYMQPr1q2DnZ3djU79poS5uTnuv/9+3H///WhpaUFKSgoSExPxwAMPwNTUFHPmzEFcXBwmTpwIAwMDfPTRR8jPz8frr79+zTuk3ozJ19cXvr6+aGlpQWVlJcrLy5Gfn3/VfgYNDQ04c+YMPD094e/v3y9jA7SJhPR3d10LReliknemCAARDq/lsywtLWFpaQl/f380NzejsrISly9fRl5eHmxsbLhD0Nv+DoSMjAzExMTgH//4R587AMDgcJNOnjyJU6dOYfLkybC1tUVRURFef/11+Pv78yiAQqHAjh07MG3aNLS2tuLf//43EhIS8NNPP133HC9duoRHHnlEcgAGEZITMMgYLAKQrjhRSUlJl88pLi5GaWkpEhIS8PXXX0OtVuO5557D/fffjx9//LFX872VYGpqitjYWMTGxqKtrQ2HDh1CYmIi5s+fD5lMBh8fH2RkZGDr1q395gB0NyZvb294e3ujtbWVh8gLCgpgaWnJHQIKs3t7e3fblKqvoasuqFsySM9RLwBqB0wOQG+7JZqZmcHHxwc+Pj58PSorK/l6kEPQXRvpqyEnJwdz5szB8uXL8dJLL/VLBOWhhx5CVVUVVq5cyRun6XKTxEgKcZNWrFiBV199FQEBAd1yk5qamvDkk0/yxmkiN8nMzAxJSUlYtWoVmpqa4OrqinvvvRcrVqzQMtRfffUVXnjhBTDGMGHCBBw9ehSRkZHXPLfa2locPXoUR48exccff3yjSyXhBiA5AbcprkWcSKPRQKVS4euvv+bdFf/v//4PY8aMQX5+vpQiEGBkZIT77rsP9913H9rb27FkyRJ8+eWXMDc357yK2NhYTJ8+fcB2PSYmJvD09ISnpyfa2tq4Q1BYWAjGGOzs7AbMOekOYnQAAI8AkBIglShSlOBGFQB110OhUEAul6O4uJjLOTs5OcHS0vKqn5OXl4fo6GgsXrwYK1as6FcS4JIlS7BkyZJuXzt69GiX5x544AE88MADPZ5PJpPhzTffxJtvvtnt6yEhIX/o4Ds4OPQYpbxWREREoLa2FmvXrpXuI4MMyQkYZFCpl1wu13peLpf3KCnq4uJy1ePpX7lcrqUhIJfLeb7/xx9/xIkTJ7oYpLFjx3JxIldXVxgYGHAHAACCg4MBdO5CpB9vVzDGsHr1aiQmJuLEiRMICwvDL7/8gsTERCxduhSNjY2YNWsW4uLiMGPGDJiamg7IuIyMjODu7g5zc3PU1dXB0dERarUaJ0+evC4D2NcQP4v6FJAUsLjL7WsFQCMjI7i5uXFimkKhQGVlJW9GROJE1tbWWmM8d+4coqOjsWDBArz55ps3fRXAYKG7yKOEwYFEDBwCGAwCUFlZGZRKJX//5cuXMXPmTOzYsQNRUVHw8PBASkoKZs6cicLCQp4zzsjIQHh4OPLz87WcAwmd+OGHHxAfH9+l3TPQyab+7bffkJiYiJ07d0KhUGDmzJmIi4vDzJkzrzskfb2ora1FWloaAgICeDmWaAAVCgWMjIy4Q6BrAPsT5AAYGhpqdVHsbRqgt1Cr1aipqeFtkNvb2/Htt99yrYjY2Fj86U9/woYNGwZdl0CChL6A5AQMAWzbtg0LFizAZ599xglA27dvR15eHpydnbsQgI4fP46pU6fi3Xff5QSg1atXdykRfPfdd7VKBDMzM7stEQS6b1Os0Wgwbtw4WFhYYOPGjdBoNHj66adhZWWFlJSUAVufmwmMMSgUij8Ms2s0Gpw5c4Z3PLx06RJmzJiBuLg4zJo1C1ZWVn06rurqamRkZGD48OFwd3fv9hi1Wo3q6mpuAPX19bXEePrLGPfkAAw2NBoNiouLsXr1aqSkpKCurg4BAQFYs2YN7r333m7JtBIk3GyQnIAhgi1btmD9+vWcALRp0yZERUUBAKZNmwYfHx98+eWX/PiEhASsWLECJSUlCAgIwLp167QalTDGsGrVKnz++eecAPTxxx/3uHvvzgkAOiMEzzzzDFJSUmBubo5Zs2bh/fffv22rA/oDGo0GmZmZ3CEoKirCXXfdhdjYWMyePRs2NjY3ZIAVCgUyMzOvS6NAo9HwHXFlZWW/9TMYqg6AiMuXL+Oee+7ByJEjERwcjKSkJJSXl2PWrFlYvHhxF6leCRJuJkhOwG2Kjz76iDsdYWFh2Lx581XZvQkJCXj99de507F27dpunY5//etfqKurw6RJk/DJJ58gICCgy7lUKhWioqKQkZGh5XT885//xBtvvNHleDMzMzQ1Nd34pG8CMMZw9uxZ7NixA0lJScjNzcW0adMQGxuL6OhoODg4XJdDUFVVhczMTIwYMaLXPSY0Gg3q6uq4Q6BWq7lDYGdn12vDfTM4ABUVFZg1axaioqLw73//m6saZmdnIykpCYGBgXj44YcHe5gSJPQaUlLrNsRgtSglvPTSS93uSF944QWUl5drPUaMGHFVtvOtBplMhhEjRmDlypVIS0tDTk4Opk+fji+//BLDhg1DdHQ0Pv/8c1RUVPxhg6PKykpkZmZi1KhRN9RkivoZBAUF4Y477kBERASMjIyQl5eHn376CZmZmZDL5dcl10sOgIGBwZB1ACorKxEdHY3Ro0dj69atfIwymQwhISFYtWrVHzoAunLgv//++1WPT0hIQFBQEExMTBASEtIrOfCYmBh4eXnBxMQErq6umD9/Pi5fvqx1zP/+9z+MHz8elpaWcHR0xJ///GeJrHe7gkm47RAZGcmefvpp/rdarWZubm5szZo13R7/4IMPstmzZ2s9FxUVxZ566inGGGMajYa5uLiw9evX89fr6uqYsbEx++9//6v1vv3797OgoCCWk5PDALC0tLQex5mens4AsJ9//vl6p3jLQaPRsOLiYrZ+/Xo2YcIEpq+vzyZNmsTWrVvH8vPzWWNjI2tqauKPkydPst27d7Pz589rPd+Xj8bGRlZeXs4yMjJYSkoK2717Nzt+/DgrLCxkdXV1Pb6vrq6OHTlyhP3yyy9MqVT22/hu5FFWVsZCQkLYn/70J9bW1tar7+z7779nRkZGbOvWrSwnJ4ctWrSI2djYMLlc3u3xx44dY/r6+mzdunUsNzeXrVixghkaGrKsrCx+zLvvvsusra3Zrl27WEZGBouJiWG+vr6spaWFH/PBBx+wEydOsJKSEnbs2DE2YcIENmHCBP56cXExMzY2Zq+88gorLCxkZ86cYVOmTGERERG9mqeEmxuSE3CbQaVSMX19fbZz506t5+Pj41lMTEy37/H09GQbNmzQem7lypUsNDSUMcZYUVFRtwZ9ypQpbOnSpfzviooK5u7uzk6dOsXOnz//h07AkiVLWGBg4DXP7XaBRqNhZWVlbOPGjWzKlCnMwMCARUZGstWrV7OcnBz2wQcfMAsLC5aenj5gRrOxsZHJ5XKWmZnJDh06xJKTk9mvv/7KCgoKWG1t7U3lAFy8eJFFRESw6OhoplKpev09DaazLSI5OZnJZDLuzCQkJDADAwOmVqv5Mbt379Y6RsLtAykdcJvhagqFuu1ECf2hUPhHaG1txXfffYeFCxde28RuI8hkMnh6emLZsmU4evQoysrKsGDBAhw6dAghISF4/vnnOV+DDRDlh1r8krzshAkTYGNjg4sXL+Lnn3/GmTNnUFJSgjNnzsDAwABhYWFDMgWgVCoxd+5cODk5ISEhode9BkgOXCQNXoscuC7JcObMmfz4P5ID7w41NTX47rvvMHHiRN5IacyYMdDT08O///1vqNVq1NfX45tvvsGMGTP6vdmShKEHyQmQMCC4FoVCETt37kRDQwMWLFjQzyO7uSGTyeDq6oq///3v+Mtf/gJTU1M899xzUCqViIyMxIQJE7BmzRrk5uYOmEMAXOlnEBUVhUmTJsHW1hbFxcVoaGhAR0cHLl68iJaWlgEbz7WgsbERf/rTn2BpaYmdO3feUG+BwXK2CS+//DLMzc1hb2+PsrIyra6Dvr6+SElJwauvvgpjY2PurG3fvr13k5VwU0NyAm4z9LdCYU/HiAqFBgYGGDZsGIBOhcLuDP0XX3yB6Ojofum6dyvik08+wfPPP48DBw7g/fffx/79+1FRUYHnn38eGRkZuOOOOzB27Fi88cYbyMzMvKZmPH0FAwMDKBQK2NjYYNKkSXB1dYVCocCxY8dw8uRJnD9/Hs3NzQM2nu7Q1NSE+++/H4aGhkhOTh4wJcf+wosvvoi0tDSkpKRAX18f8fHx3AmsqKjAokWLsGDBApw6dQo//fQTjIyMcP/99w+ooyhhaECSDb7NYGRkhDFjxuDw4cO8c5hGo8Hhw4d71CifMGECDh8+rNWn/IcffuBdxXx9feHi4oLDhw/zcj+lUomTJ0/ib3/7GwBg06ZNePvtt/n7SaFw27ZtXA+BcP78eRw5cgS7d+/uo1nf2mCM4dSpUzh48CAmTpwIoDNCYGtriwULFmDBggVQKpXYu3cvEhMTcdddd8HV1RUxMTGYO3cuIiIi+k39jtoBiykAMzMzrX4GlZWVKCoqgrm5OZfrNTc3HzClwJaWFvzlL3+BWq3GgQMHYGFhccPnHCw5cPHzHRwcEBgYiODgYHh6euK3337DhAkT8NFHH8Ha2hrr1q3jx3/77bfw9PTEyZMnMX78+F7PW8LNB8kJuA0xGC1Kvby8tMZAN1p/f394eHhovbZ161a4urpi1qxZ/bkMtwxkMhm2bt161WOsrKwwb948zJs3D42NjThw4AASExMxe/Zs2NnZYc6cOZg7dy7GjRvXZ7l66lTYEweA+hm4u7ujvb2dN/QpKSmBiYkJ73jYn/0MVCoVHnnkETQ0NCAlJaXPlBoHy9nuDhT1UalUAIDm5uYuTh99NwMZIZIwRDCYrEQJg4fNmzczLy8vZmRkxCIjI9lvv/3GX5s6dSpbsGCB1vHbt29ngYGBzMjIiI0cOZLt27dP63WNRsNef/115uzszIyNjdldd93F8vPztY7ZsmUL8/b2ZsbGxiwsLKzb6gC1Ws08PDzYq6++yrZv386GDx/OjI2N2ahRo3r8TBcXF2ZiYsLuuusuVlBQ0O18W1tbe/zMgwcPsqioKGZhYcEcHBzYn/70J3b+/Pk/XsRbAE1NTWznzp3s0UcfZTY2NszNzY0tXryYHTx4kNXX1/eaYV9fX9/rKoD6+npWXFzMTpw4wfbs2cP+97//sbS0NHbp0qUupZA38qitrWXR0dEsIiKCVVdX9/nafv/998zY2Jh9+eWXLDc3lz355JPMxsaGVVRUMMYYmz9/PvvHP/7Bjz927BgzMDBg7733Hjt79ixbtWpVtyWCNjY2LDk5mWVmZrLY2FitEsHffvuNbd68maWlpbGSkhJ2+PBhNnHiRObv789aW1sZY4wdPnyYyWQy9sYbb7CCggJ25swZNnPmTObt7c2am5v7fB0kDG1IToCEAcFg1UwTli5dymbNmtXFCZBqpq+gpaWF7d27lz3xxBPM3t6eOTk5sYULF7I9e/Zcte6/Lx0A3YdSqWTnz59nJ0+eZHv37mUHDhxgZ86cYRcuXLghh6Curo7NnTuXhYSEsKqqqn5b04F2tjMzM9n06dOZnZ0dMzY2Zj4+Pmzx4sXs4sWLWuf573//yyIiIpi5uTlzdHRkMTEx7OzZs32/ABKGPCTZYAkDgqioKIwbNw5btmwB0Bl29PT0xDPPPNNjp8Smpibs3buXPzd+/HiEh4drdUp8/vnn8cILLwAA6uvr4ezszDslEg4cOIDly5cjMTERI0eO1JIq3rFjBx5++GGoVCoeIt2zZw9iY2OhUqlu25Kp9vZ2HD16FImJidi1axfa29sxZ84cxMbGYtq0aV1aUBP+KAVwI9DtZwCANziys7O7Zl5DR0cHFi1ahKysLBw5ckQin0q4rSFVB0jodwxmzbRcLseiRYvwzTffdNv1TaqZ7h6Ghoa4++678emnn+LixYtITEyEhYUFnnnmGfj6+uKvf/0r9u7dq1XmV1NTg08++aTfdAD09PTg4OCAESNGYOrUqQgNDYWenh5yc3Px008/ITs7m/c26AlqtRpPP/000tLScPjwYckBkHDbQ3ICJPQ7hrJAkVQz/ccwMDDAtGnTsGXLFpSWlmLfvn1wdnbGyy+/DF9fXyxYsADffPMN7rnnHiQnJw9ILwCZTNZtP4P8/HytfgYdHR38PRqNBsuWLcPx48dx6NChG+qnIEHCrQLJCZBwy+JaBIqkmunrg76+PiZNmoQNGzagqKgIhw4dgpubG5YtW4b8/HzY2dkhKSkJSqVywMYkk8lgY2ODwMBATJ48GWPHjoWZmRkKCwvxj3/8A7NmzcLHH3+MJUuW4Mcff8ShQ4e6VKtIkHC7QnICJPQ7hrJAkVgzHRERgSlTpuDbb7/F4cOHcfLkyRuc+a0NPT09BAUF4dSpU5g6dSqOHTuGsLAwrFu3Dj4+PnjwwQfx3Xffoa6ubkDli62srDBs2DBMmjQJCxYswKhRo/D222/jq6++go+PD3788UcoFIoBGY8ECUMdkhMgod8h1kwTqGaaaqB1QTXTInqqmSZQzTQds2nTJmRkZCA9PR3p6em8Leu2bdvwzjvvAJBqpm8EjY2NuPfee2FlZYXk5GSMHz8e77zzDnJycnDmzBlOBPXx8cGf/vQnfPnll1AoFAMaYRk5ciRMTExgamqKgwcP4r777sPnn38OV1dX3HXXXSgoKBiwsUiQMCQxiJUJEm4jDEbNtC6661wo1Uz3Hh0dHWzjxo09rjdjnSVt+fn57J133mFjxoxhBgYGbPr06Wzjxo2sqKioT+v+dR+NjY3slVdeYU5OTiw7O1trXNSFsba2tp9XSYKEoQ3JCZAwYBgMgSIRPbUvlmqmBwYajYYVFRWxdevWsfHjxzMDAwM2efJktn79elZQUNCnDkFjYyNbtWoVs7e3Z+np6b0esyhwFRkZyU6ePHnV4/tC4GrOnDnM09OTGRsbMxcXF/boo4+yS5cu8ddXrVrFAHR5mJmZ9XqeEm5fSE6AhFsag3ETJ1xNpXDbtm0sLCyMmZqaMi8vL7Zu3bobmufNBo1Gw0pLS9mGDRvYlClTmL6+PouKimKrV69mubm5N+QQNDY2stWrVzNbW1t2+vTpXo9xsASuPvjgA3bixAlWUlLCjh07xiZMmMAmTJjAX29oaGDl5eVajxEjRnRxoiVIuBZIToCEWxZDVaVw//79zMDAgH3yySesqKiI7d27l7m6urLNmzf3+RrcDNBoNOzSpUtsy5Yt7M4772QGBgZs9OjR7M0332QZGRnX5RA0Njay9evXM2tra61IU28QGRnJnn76af63Wq1mbm5ubM2aNd0e/+CDD7LZs2drPRcVFcWeeuopPk8XFxe2fv16/npdXR0zNjZm//3vf3scR3JyMpPJZKytra3b19PT0xkA9vPPP1/z3CRIIEhOgIRbFoN5E9+/fz8LCgpiOTk5XZyAhx9+mN1///1ax2/atIl5eHgwjUbTq7neKtBoNKyyspJ99tlnbObMmczQ0JCFhISw119/nZ0+ffqqDkFjYyP78MMPmZWVFfvll19uaBwqlYrp6+uznTt3aj0fHx/PYmJiun2Pp6cn27Bhg9ZzK1euZKGhoYwxxoqKirqNCk2ZMoUtXbq023NWV1ezBx98kE2aNKnHsS5ZsoQFBgZefUISJPQAqTpAwi2JoaxSqFKpYGJiovWcqakpLl68iNLS0uuf7C0EmUwGR0dHPPnkkzhw4AAqKiqwfPlypKWlYdKkSRg3bhzefPNNZGVlaVVvMMbwzTff4LXXXkNycjImT558Q+MYLIErwssvvwxzc3PY29ujrKwMycnJ3X5ma2srvvvuOyxcuPDaJydBggDJCZBwS2IoqxTOnDkTSUlJOHz4MDQaDQoKCvD+++8DAMrLy69zprcuSBXwsccew549eyCXy/Haa6+hoKAAd955J8LDw/H6668jNTUV//3vf/HCCy9g586dmDZt2mAP/Ybx4osvIi0tDSkpKdDX10d8fHy3pZU7d+5EQ0MD172QIOF6YTDYA5Ag4VbCtagULlq0CEVFRYiOjkZ7ezusrKywbNky/POf/7zmJji3I6ytrfHII4/gkUceQWNjI/bv34/ExETMnDkTra2t2L17d5dITm/R3wJXomSxXC7nDa3Ez3dwcEBgYCCCg4Ph6emJ3377rYuuxhdffIHo6GipB4KEXkO640i4JTGUVQplMhnWrl2LxsZGlJaWoqKiApGRkQAAPz+/G5n2bQMLCws8+OCD2LZtGyoqKvDNN99g9uzZfXb+wRK46g6U9lCpVFrPnz9/HkeOHJFSARJuDIPMSZBwi6CyspI5Ozuzd955hz937NgxZmhoyA4dOjQoY4qMjGRLlizhf6vVaubu7n5VYmB0dLTWcxMmTOhCDHzvvff46/X19VrEwNLSUpaVlcUf//vf/xgAtmPHDnbhwoUexzp//nytMjAJg4/BELj67bff2ObNm1laWhorKSlhhw8fZhMnTmT+/v6stbVVa3wrVqxgbm5urKOjYwBWQ8KtCskJkNBn2LdvHzM0NGSnTp1iSqWS+fn5seeee27QxjNUVQqrqqrYJ598ws6ePcvS0tLY0qVLmYmJyR9qGEgYeAy0wFVmZiabPn06s7OzY8bGxszHx4ctXryYXbx4Ues8arWaeXh4sFdffbXvJy3htoLkBEjoU/z9739ngYGBbN68eSwkJKTL7mWgMRRVCquqqtj48eOZubk5MzMzY3fdddcN17RLkCBBQm8gOQES+hTNzc3Mz8+PGRoasszMzMEezqBhMJQKvb29u0jJ6qY+MjIy2OTJk5mxsTHz8PBga9eu7ZsJS5Ag4aaERAyU0KcoKirC5cuXodFoUFJSMtjDGRRs27YNy5cvx6pVq5CamoqwsDDMnDkTlZWV3R5//PhxPPzww1i4cCHS0tIQFxeHuLg4ZGdn82PWrVuHTZs24dNPP8XJkydhbm7OWfEi3nzzTZSXl/PHM888w19TKpW455574O3tjTNnzmD9+vX45z//ic8//7x/FkKCBAlDH4PthUi4daBSqVhYWBhbsGABW716NXNycupRovdWxmApFXp7e3dRrBPx8ccfM1tbW6ZSqfhzL7/8Mhs+fPh1zU+CBAm3DqRIgIQ+w2uvvYb6+nps2rQJL7/8MgIDA/HEE08M9rAGFIOpVAgA7777Luzt7REREYH169ejo6ND63OmTJkCIyMjrc/Jz89HbW1t7yctQYKEmxaSWJCEPsHRo0exceNGHDlyBFZWVgCAb775BmFhYfjkk0/wt7/9bZBHODC4mlJhXl5et+/pK7nZpUuXYvTo0bCzs8Px48fxyiuvoLy8HB988AE/j6+vb5dz0Gu2trbXO10JEiTc5JCcAAl9gmnTpqG9vV3rOR8fH9TX1w/SiG4/LF++nP8/NDQURkZGeOqpp7BmzRoYGxsP4sgkSJAwVCGlAyRI6EMMllJhd4iKikJHRwcnaPb0OeJn3Gr46KOP4OPjAxMTE0RFReH333+/6vEJCQkICgqCiYkJQkJCsH//fq3XGWNYuXIlXF1dYWpqihkzZuDcuXNax8TExMDLywsmJiZwdXXF/Pnzcfny5S7nee+99xAYGAhjY2O4u7vjnXfe6ZtJS5BwHZCcAAkS+hBDSW42PT0denp6cHJy4p/z888/a0VsfvjhBwwfPvyWTAUMVpXG9OnTsX37duTn5yMxMRFFRUW4//77tT5r2bJl+OKLL/Dee+8hLy8Pu3fv5tLREiQMKAabmShBwq2GwVAqPH78ONuwYQNLT09nRUVF7Ntvv2WOjo4sPj6en6Ouro45Ozuz+fPns+zsbPb9998zMzMz9tlnnw3QygwsBqtKQxfJyclMJpOxtrY2xhhjubm5zMDAgOXl5fV6bhIk9BUkJ0CChH7AQCsVnjlzhkVFRTETExMGgMlkMubh4cF++eUXrfOIYkHu7u5s3rx5/S5S1NLSwhYsWMBGjRrF9PX1WWxsbG+W9LqgUqmYvr4+27lzp9bz8fHxLCYmptv3eHp6dimxXLlyJQsNDWWMMVZUVNRF/ZExxqZMmcKWLl3a7Tmrq6vZgw8+yCZNmsSfW7t2LQsMDGTvvfce8/HxYd7e3mzhwoWsurr6+iYpQUIfQHICJEi4RfD9998zIyMjtnXrVpaTk8MWLVrEbGxsetRqOHbsGNPX12fr1q1jubm5bMWKFd1GIKytrdmuXbtYRkYGi4mJ6dIrwdvbm7355pusvLycPxobG/nrjY2NbPHixezzzz9nM2fOHBAn4NKlSwwAO378uNbzL774IouMjOz2PYaGhuw///mP1nMfffQRc3JyYox1rhcAdvnyZa1jHnjgAfbggw9qPffSSy8xMzMzBoCNHz+eKRQK/tpTTz3FjI2NWVRUFPv555/ZkSNHWHh4OJs+fXqv5ytBQm8hcQIkSLhF8MEHH2DRokV4/PHHMWLECHz66acwMzPD1q1buz3+ww8/xL333osXX3wRwcHBeOuttzB69Ghs2bIFQCd5bePGjVixYgViY2MRGhqKr7/+GpcvX8auXbu0zmVpaQkXFxf+MDc356+Zm5vjk08+waJFi25ZAqIuXnzxRaSlpSElJQX6+vqIj48HYwxAJ0dEpVLh66+/xh133IFp06bh//7v/3DkyBHk5+cP8sgl3G6QnAAJEm4BDGWRosHAYFdpODg4IDAwEHfffTe+//577N+/H7/99hsAwNXVFQYGBggMDOTHBwcHAwDKysqud6oSJNwQJCdAgoRbAFcTKRIFhUT0pUjR999/jyNHjuCpp57C6tWr8dJLL93wnG4EQ6lKQ6PRAABUKhUAYNKkSejo6EBRURE/pqCgAADg7e19PdOUIOGGIYkFSZAg4YYwVEWKli9fjgULFmDs2LGIjIzExo0b0dTUhMcffxwAEB8fD3d3d6xZswZAZ9ne1KlT8f7772P27Nn4/vvvcfr0ad5gSSaT4dlnn8Xbb7+NgIAA+Pr64vXXX4ebmxvi4uIAACdPnsSpU6cwefJk2NraoqioCK+//jr8/f25ozBjxgyMHj0aTzzxBDZu3AiNRoOnn34ad999t1Z0QIKEgYAUCZAg4RbAYIe/ReiKFA0WHnroIbz33ntYuXIlwsPDkZ6ejoMHD/LIRllZGcrLy/nxEydOxH/+8x98/vnnCAsLw44dO7Br1y6MGjWKH/PSSy/hmWeewZNPPolx48ahsbERBw8ehImJCQDAzMwMSUlJuOuuuzB8+HAsXLgQoaGh+Omnn7hDpKenhz179sDBwQFTpkzB7NmzERwcjO+//34AV0eChE7IGLFVJEiQcFMjKioKkZGR2Lx5M4DOMLSXlxeWLFmCf/zjH12Of+ihh9Dc3Iw9e/bw5yZOnIjQ0FB8+umnYIzBzc0NL7zwAp5//nkAneFvJycnfPnll/jLX/7S7Ti+++47xMfHQ6FQdBEheuyxx1BXV9eFWChBgoTBgZQOkCDhFsFghL9PnDiBkydPYvr06bC0tMSJEyfw3HPP4dFHH9VyAHJzc9HW1oaamho0NDQgPT0dABAeHj5g6yNBgoRuMKgFihIkSOhTDJZIkbW1NTMxMWHBwcFs9erVrLW1Ves83QkKSbcfCRIGH1I6QIIECRIkSLhNIREDJUiQIEGChNsUkhMgQYIECRIk3KaQnAAJEiRIkCDhNoXkBEiQIEGCBAm3KSQnQIIECRIkSLhNITkBEiRIkCBBwm0KyQmQIEGCBAkSblNIToAECRIkSJBwm0JyAiRIkCBBgoTbFJITIEGCBAkSJNymkJwACRIkSJAg4TaF5ARIkCBBggQJtykkJ0CCBAkSJEi4TSE5ARIkSJAgQcJtCskJkCBBggQJEm5T/D/vVDznlng1lwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import PlotNetwork\n", + "\n", + "pn = PlotNetwork(network_path, snudda_data=snudda_data)\n", + "pn.plot_populations(unmarked_alpha=0)\n", + "pn.plot_populations(unmarked_alpha=0.02)\n", + "\n", + "# The commented code below also plots the network, but with the option to plot more detail of the neurons:\n", + "# pn = PlotNetwork(network_path)\n", + "# pn.plot(plot_axon=False, plot_dendrite=False, plot_synapses=False, colour_population_unit=True)\n", + "\n", + "pn.close() # Close the hdf5 file so others can use it (or better shut down kernel after)" + ] + }, + { + "cell_type": "markdown", + "id": "92036f89", + "metadata": {}, + "source": [ + "# Plot neuron input" + ] + }, + { + "cell_type": "markdown", + "id": "b30b2bab-ff00-4548-b907-7ad7938726a3", + "metadata": {}, + "source": [ + "from snudda.plotting import PlotInput\n", + "input_file = os.path.join(network_path, \"input-spikes.hdf5\")\n", + "\n", + "spi = PlotInput(input_file, network_file)\n", + "\n", + "spi.plot_input_population_unit(population_unit_id=0, num_neurons=2, neuron_type=\"dSPN\", fig_size=(15,5))\n", + "spi.plot_input_population_unit(population_unit_id=0, num_neurons=2, neuron_type=\"iSPN\", fig_size=(15,5))\n", + "spi.plot_input_population_unit(population_unit_id=0, num_neurons=2, neuron_type=\"FS\", fig_size=(15,5))\n", + "spi.plot_input_population_unit(population_unit_id=0, num_neurons=2, neuron_type=\"LTS\", fig_size=(15,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "db7643f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Cleanup\n", + "pn = None\n", + "spi = None" + ] + }, + { + "cell_type": "markdown", + "id": "004a683c", + "metadata": {}, + "source": [ + "# Plot neuron activity\n", + "\n", + "## With lateral inhibtion" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ba6db56f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading ../networks/sten_5_SPN_FS_LTS/simulation/output-with-synapses-sten_5A.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "238: (LTS_0, p94d54b1c, m803558b5)\n", + "239: (LTS_0, p8c493214, m803558b5)\n", + "240: (LTS_1, pbae91695, ma4dacccf)\n", + "249: (dSPN_0, p7aa400d6, m37886c78)\n", + "262: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "272: (dSPN_0, p8bf90d1f, mf702205f)\n", + "273: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "572: (LTS_0, p7f931884, m803558b5)\n", + "573: (LTS_1, pe675a3d7, m872fbb26)\n", + "574: (LTS_1, p54dfea77, m8ded5e00)\n", + "579: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "595: (dSPN_0, pb0529fb9, mf702205f)\n", + "600: (dSPN_0, pe6ec2d4b, mf702205f)\n", + "849: (LTS_0, padd48e2a, m803558b5)\n", + "850: (LTS_0, p119533eb, m803558b5)\n", + "851: (LTS_0, p8c493214, m803558b5)\n", + "855: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "861: (dSPN_0, p7aa400d6, m37886c78)\n", + "872: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "878: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "883: (dSPN_0, p7aa400d6, mf702205f)\n", + "1125: (LTS_1, p3f21fd29, mf4ba6a4e)\n", + "1130: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "1144: (dSPN_0, pd01ac450, m22be6817)\n", + "1159: (dSPN_0, p7aa400d6, m37886c78)\n", + "1497: (LTS_0, p1886c37b, m803558b5)\n", + "1498: (LTS_1, p8b1585a0, mf4ba6a4e)\n", + "1499: (LTS_1, pb823efb9, mda52699c)\n", + "1515: (dSPN_0, pb0529fb9, m22be6817)\n", + "1517: (dSPN_0, p7aa400d6, mf702205f)\n", + "1526: (dSPN_0, p7517a0e9, mc710c1a4)\n", + "1805: (LTS_0, p1fd33c8c, m803558b5)\n", + "1806: (LTS_1, pf83691b7, m872fbb26)\n", + "1814: (dSPN_0, p1863c9a5, m22be6817)\n", + "1848: (dSPN_0, p7aa400d6, m22be6817)\n", + "2110: (LTS_0, pb66357bd, m803558b5)\n", + "2111: (LTS_0, p1886c37b, m803558b5)\n", + "2112: (LTS_0, p8c493214, m803558b5)\n", + "2113: (LTS_1, p266c7fb8, mf4ba6a4e)\n", + "2114: (LTS_1, p881c7f54, m8ded5e00)\n", + "2120: (dSPN_0, pb0529fb9, m37886c78)\n", + "2121: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "2135: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "2395: (dSPN_0, p8bf90d1f, m22be6817)\n", + "2404: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "2408: (dSPN_0, pb0529fb9, m37886c78)\n", + "2417: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "2425: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "2714: (LTS_0, p8c493214, m803558b5)\n", + "2715: (LTS_1, pbd0a2290, m803558b5)\n", + "2716: (FS_0, pb1ef6b01, m86da4874)\n", + "2721: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "2741: (dSPN_0, p7aa400d6, m22be6817)\n", + "2992: (LTS_0, p1886c37b, m803558b5)\n", + "2994: (LTS_1, pfa505758, m803558b5)\n", + "2996: (FS_0, p59a48310, me486b19e)\n", + "3016: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "3029: (dSPN_0, p1863c9a5, m37886c78)\n", + "3037: (dSPN_0, p8bf90d1f, mf702205f)\n", + "3348: (LTS_0, p7f931884, m803558b5)\n", + "3349: (LTS_0, p1fd33c8c, m803558b5)\n", + "3350: (LTS_0, pb66357bd, m803558b5)\n", + "3356: (dSPN_0, pe6ec2d4b, m37886c78)\n", + "3357: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "3400: (dSPN_0, p1863c9a5, m22be6817)\n", + "3708: (LTS_1, p54dfea77, m8ded5e00)\n", + "3709: (LTS_1, p881c7f54, m8ded5e00)\n", + "3710: (LTS_1, p973cbb84, mf4ba6a4e)\n", + "3714: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "3716: (dSPN_0, pb0529fb9, m22be6817)\n", + "3738: (dSPN_0, pd01ac450, mf702205f)\n", + "3742: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "3747: (dSPN_0, p7517a0e9, mf702205f)\n", + "4063: (LTS_0, p1886c37b, m803558b5)\n", + "4064: (LTS_0, p7f931884, m803558b5)\n", + "4065: (LTS_1, paf75a0ec, m265f1bc4)\n", + "4067: (FS_0, pb1ef6b01, m15ae4048)\n", + "4106: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "4114: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "4117: (dSPN_0, pb0529fb9, m37886c78)\n", + "4124: (dSPN_0, p1863c9a5, mf702205f)\n", + "4514: (LTS_0, p7f931884, m803558b5)\n", + "4515: (LTS_0, p8c493214, m803558b5)\n", + "4516: (LTS_1, pcec5cf27, m8ded5e00)\n", + "4517: (LTS_1, p1cd5defe, m8ded5e00)\n", + "4519: (FS_1, pf9439e45, m48f576bb)\n", + "4554: (dSPN_0, p7aa400d6, m22be6817)\n", + "4555: (dSPN_0, p1863c9a5, m22be6817)\n", + "4860: (LTS_1, pe675a3d7, m872fbb26)\n", + "4861: (LTS_1, p413c8889, m872fbb26)\n", + "4900: (dSPN_0, p1863c9a5, m22be6817)\n", + "4910: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "4913: (dSPN_0, p7517a0e9, mbb8e5b24)\n", + "5292: (LTS_0, pe9c8b984, m803558b5)\n", + "5305: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "5318: (dSPN_0, p8bf90d1f, m22be6817)\n", + "5331: (dSPN_0, p1863c9a5, m37886c78)\n", + "5333: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "5684: (LTS_0, pe9c8b984, m803558b5)\n", + "5685: (LTS_0, p1886c37b, m803558b5)\n", + "5686: (LTS_1, p57337648, mf4ba6a4e)\n", + "5698: (dSPN_0, pe6ec2d4b, mf702205f)\n", + "5706: (dSPN_0, pe1ec8fbd, m22be6817)\n", + "5710: (dSPN_0, p1863c9a5, m37886c78)\n", + "5715: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "6095: (LTS_1, pe5eef847, mda52699c)\n", + "6096: (LTS_1, pfddd6423, m265f1bc4)\n", + "6113: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "6501: (LTS_0, p1fd33c8c, m803558b5)\n", + "6512: (dSPN_0, pb0529fb9, m37886c78)\n", + "6516: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "6527: (dSPN_0, pb0529fb9, mf702205f)\n", + "6538: (dSPN_0, p1863c9a5, m37886c78)\n", + "6540: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "6885: (LTS_0, p1886c37b, m803558b5)\n", + "6886: (LTS_0, p1fd33c8c, m803558b5)\n", + "6887: (LTS_1, pb5a5193d, mda52699c)\n", + "6891: (dSPN_0, p1863c9a5, m22be6817)\n", + "6892: (dSPN_0, pb0529fb9, m22be6817)\n", + "6902: (dSPN_0, pb0529fb9, m37886c78)\n", + "6910: (dSPN_0, pd01ac450, m22be6817)\n", + "6913: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "7229: (LTS_1, p116c624b, mf4ba6a4e)\n", + "7230: (LTS_1, p116c624b, mf4ba6a4e)\n", + "7262: (dSPN_0, p7aa400d6, m22be6817)\n", + "7669: (LTS_0, p94d54b1c, m803558b5)\n", + "7670: (LTS_1, p7e8d7d6f, mda52699c)\n", + "7672: (LTS_1, p290dc260, mf4ba6a4e)\n", + "7943: (LTS_0, p119533eb, m803558b5)\n", + "7944: (LTS_1, p53b70df2, ma4dacccf)\n", + "7945: (LTS_1, p238d51d1, mda52699c)\n", + "7956: (dSPN_0, p1863c9a5, m22be6817)\n", + "7960: (dSPN_0, p7aa400d6, m22be6817)\n", + "7968: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "7973: (dSPN_0, pb0529fb9, mf702205f)\n", + "7976: (dSPN_0, p510bab86, mf702205f)\n", + "7978: (dSPN_0, pd01ac450, m37886c78)\n", + "8355: (dSPN_0, p1863c9a5, mf702205f)\n", + "8711: (LTS_0, p94d54b1c, m803558b5)\n", + "8712: (LTS_0, p1886c37b, m803558b5)\n", + "8717: (dSPN_0, p7aa400d6, mf702205f)\n", + "8737: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "8752: (dSPN_0, pe6ec2d4b, mc710c1a4)\n", + "8758: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "9080: (LTS_1, p047a6bb7, mda52699c)\n", + "9097: (dSPN_0, pe6ec2d4b, m22be6817)\n", + "9101: (dSPN_0, pd01ac450, mc710c1a4)\n", + "9123: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "9464: (LTS_0, p94d54b1c, m803558b5)\n", + "9467: (LTS_1, p53b70df2, ma4dacccf)\n", + "9818: (LTS_1, pe5eef847, mda52699c)\n", + "9819: (LTS_1, pdd9466e2, m8ded5e00)\n", + "9838: (dSPN_0, p1863c9a5, mf702205f)\n", + "9848: (dSPN_0, pb0529fb9, m37886c78)\n", + "9866: (dSPN_0, p7aa400d6, m22be6817)\n", + "10246: (LTS_0, p1fd33c8c, m803558b5)\n", + "10247: (LTS_1, p881c7f54, m8ded5e00)\n", + "10259: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "10267: (dSPN_0, pe1ec8fbd, m22be6817)\n", + "10286: (dSPN_0, p1863c9a5, m22be6817)\n", + "10309: (dSPN_0, pe6ec2d4b, mbb8e5b24)\n", + "10311: (dSPN_0, p1863c9a5, mf702205f)\n", + "10650: (FS_0, pb1ef6b01, m4cd420e2)\n", + "10684: (dSPN_0, pc8cbdb24, mf702205f)\n", + "10698: (dSPN_0, pe6ec2d4b, m37886c78)\n", + "11038: (LTS_1, paf75a0ec, m265f1bc4)\n", + "11039: (LTS_1, p733ec61e, mda52699c)\n", + "11056: (dSPN_0, p7aa400d6, m37886c78)\n", + "11057: (dSPN_0, p8bf90d1f, m37886c78)\n", + "11059: (dSPN_0, pb0529fb9, m37886c78)\n", + "11077: (dSPN_0, pc8cbdb24, mbb8e5b24)\n", + "11086: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "11094: (dSPN_0, p8bf90d1f, m37886c78)\n", + "11492: (LTS_0, p1886c37b, m803558b5)\n", + "11493: (LTS_0, p8c493214, m803558b5)\n", + "11495: (LTS_0, p1fd33c8c, m803558b5)\n", + "11496: (LTS_0, p94d54b1c, m803558b5)\n", + "11497: (LTS_1, p53b70df2, ma4dacccf)\n", + "11498: (LTS_1, p793a3d75, ma4dacccf)\n", + "11499: (LTS_1, p047a6bb7, mda52699c)\n", + "11523: (dSPN_0, p1863c9a5, m22be6817)\n", + "11529: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "11543: (dSPN_0, p1863c9a5, m22be6817)\n", + "11547: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "11927: (LTS_0, p7f931884, m803558b5)\n", + "11928: (LTS_1, p413c8889, m872fbb26)\n", + "11958: (dSPN_0, pb0529fb9, mf702205f)\n", + "11960: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "11962: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "11965: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "11969: (dSPN_0, p1863c9a5, m22be6817)\n", + "12360: (LTS_1, pb8351fef, m803558b5)\n", + "12407: (dSPN_0, p1863c9a5, m22be6817)\n", + "12415: (dSPN_0, pb0529fb9, m37886c78)\n", + "12824: (LTS_0, p1886c37b, m803558b5)\n", + "12825: (LTS_0, p94d54b1c, m803558b5)\n", + "12826: (LTS_1, p72cfe937, m872fbb26)\n", + "12835: (dSPN_0, pb0529fb9, m37886c78)\n", + "12842: (dSPN_0, pd01ac450, mc710c1a4)\n", + "12862: (dSPN_0, p1863c9a5, m22be6817)\n", + "12870: (dSPN_0, pe6ec2d4b, m37886c78)\n", + "12990: (dSPN_3, pf1939474, m17af04cc)\n", + "13246: (dSPN_0, pb0529fb9, mf702205f)\n", + "13262: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "13622: (LTS_0, p94d54b1c, m803558b5)\n", + "13623: (LTS_0, p1fd33c8c, m803558b5)\n", + "13624: (LTS_1, p8bcdee47, m803558b5)\n", + "13625: (LTS_1, p3011abd8, m872fbb26)\n", + "13626: (LTS_1, pb823efb9, mda52699c)\n", + "13627: (LTS_1, pb7358f32, mf4ba6a4e)\n", + "13640: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "13642: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "13645: (dSPN_0, p7aa400d6, mf702205f)\n", + "13670: (dSPN_0, p1863c9a5, m37886c78)\n", + "13681: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "13690: (dSPN_0, pb0529fb9, m37886c78)\n", + "14048: (LTS_1, p47d379ce, mf4ba6a4e)\n", + "14049: (LTS_1, pe675a3d7, m872fbb26)\n", + "14057: (dSPN_0, p1863c9a5, m22be6817)\n", + "14059: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "14070: (dSPN_0, pb0529fb9, mf702205f)\n", + "14081: (dSPN_0, p1863c9a5, mf702205f)\n", + "14100: (dSPN_0, pb0529fb9, m22be6817)\n", + "14108: (dSPN_0, pe1ec8fbd, mbb8e5b24)\n", + "14427: (LTS_0, padd48e2a, m803558b5)\n", + "14436: (dSPN_0, p1863c9a5, m37886c78)\n", + "14448: (dSPN_0, p8bf90d1f, m37886c78)\n", + "14458: (dSPN_0, p1863c9a5, m22be6817)\n", + "14814: (LTS_1, pd2b66278, m872fbb26)\n", + "14815: (LTS_1, p08df4357, m872fbb26)\n", + "14820: (dSPN_0, pb0529fb9, m22be6817)\n", + "14824: (dSPN_0, p1863c9a5, m22be6817)\n", + "14825: (dSPN_0, p7517a0e9, m37886c78)\n", + "14856: (dSPN_0, p1863c9a5, m37886c78)\n", + "14858: (dSPN_0, pe1ec8fbd, mbb8e5b24)\n", + "14874: (dSPN_0, p8bf90d1f, mf702205f)\n", + "15257: (LTS_1, p272f9557, mda52699c)\n", + "15258: (FS_0, p4feff98b, m9175e580)\n", + "15259: (FS_0, p59a48310, m4fdebda2)\n", + "15271: (dSPN_0, pb0529fb9, mf702205f)\n", + "15282: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "15297: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "15298: (dSPN_0, p1863c9a5, m37886c78)\n", + "15309: (dSPN_0, p7aa400d6, m37886c78)\n", + "15655: (LTS_0, p1886c37b, m803558b5)\n", + "15656: (LTS_1, p3f21fd29, mf4ba6a4e)\n", + "15657: (LTS_1, pa44a3f3f, mf4ba6a4e)\n", + "15674: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "15675: (dSPN_0, p7aa400d6, m22be6817)\n", + "15688: (dSPN_0, pb0529fb9, m22be6817)\n", + "16038: (LTS_0, pe9c8b984, m803558b5)\n", + "16040: (LTS_0, pb66357bd, m803558b5)\n", + "16041: (LTS_1, pe5eef847, mda52699c)\n", + "16077: (dSPN_0, p8bf90d1f, m22be6817)\n", + "16079: (dSPN_0, p1863c9a5, m37886c78)\n", + "16098: (dSPN_0, p1863c9a5, m37886c78)\n", + "16489: (LTS_0, p1886c37b, m803558b5)\n", + "16490: (LTS_0, p8c493214, m803558b5)\n", + "16491: (LTS_1, pe5eef847, mda52699c)\n", + "16492: (LTS_1, p4d93b0c3, m8ded5e00)\n", + "16507: (dSPN_0, p1863c9a5, m22be6817)\n", + "16512: (dSPN_0, p1863c9a5, m22be6817)\n", + "16520: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "16524: (dSPN_0, pb0529fb9, m22be6817)\n", + "16527: (dSPN_0, pb0529fb9, m37886c78)\n", + "16538: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "16825: (LTS_1, pbae91695, ma4dacccf)\n", + "16844: (dSPN_0, p1863c9a5, mf702205f)\n", + "16848: (dSPN_0, pe6ec2d4b, m22be6817)\n", + "16849: (dSPN_0, pe6ec2d4b, m22be6817)\n", + "16861: (dSPN_0, pc8cbdb24, mc710c1a4)\n", + "16874: (dSPN_0, pe6ec2d4b, m22be6817)\n", + "17185: (LTS_0, p94d54b1c, m803558b5)\n", + "17187: (LTS_0, pe9c8b984, m803558b5)\n", + "17188: (LTS_0, p1886c37b, m803558b5)\n", + "17189: (LTS_1, paf75a0ec, m265f1bc4)\n", + "17190: (LTS_1, p881c7f54, m8ded5e00)\n", + "17191: (LTS_1, pc2d8a8e6, mda52699c)\n", + "17192: (LTS_1, pd2b66278, m872fbb26)\n", + "17223: (dSPN_0, p7aa400d6, m37886c78)\n", + "17230: (dSPN_0, p1863c9a5, m37886c78)\n", + "17244: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "17577: (LTS_0, p7f931884, m803558b5)\n", + "17579: (LTS_1, p272f9557, mda52699c)\n", + "17580: (LTS_1, p3c6b5232, ma4dacccf)\n", + "17606: (dSPN_0, p1863c9a5, m22be6817)\n", + "17609: (dSPN_0, pb0529fb9, m22be6817)\n", + "17626: (dSPN_0, p1863c9a5, mf702205f)\n", + "18031: (LTS_0, p94d54b1c, m803558b5)\n", + "18033: (LTS_1, p4d93b0c3, m8ded5e00)\n", + "18047: (dSPN_0, p1863c9a5, m22be6817)\n", + "18050: (dSPN_0, p7aa400d6, m22be6817)\n", + "18064: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "18072: (dSPN_0, pe6ec2d4b, mf702205f)\n", + "18429: (LTS_0, p1fd33c8c, m803558b5)\n", + "18430: (LTS_0, pe9c8b984, m803558b5)\n", + "18431: (LTS_0, p8c493214, m803558b5)\n", + "18432: (LTS_1, pfa505758, m803558b5)\n", + "18471: (dSPN_0, p1863c9a5, m22be6817)\n", + "18476: (dSPN_0, p1863c9a5, mf702205f)\n", + "18820: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "18826: (dSPN_0, p1863c9a5, m37886c78)\n", + "18830: (dSPN_0, p1863c9a5, mf702205f)\n", + "18834: (dSPN_0, p1863c9a5, m37886c78)\n", + "18836: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "18851: (dSPN_0, pb0529fb9, mf702205f)\n", + "18858: (dSPN_0, p7517a0e9, mf702205f)\n", + "19133: (LTS_0, p1886c37b, m803558b5)\n", + "19134: (LTS_1, pb7358f32, mf4ba6a4e)\n", + "19135: (LTS_1, p54dfea77, m8ded5e00)\n", + "19143: (dSPN_0, p1863c9a5, m22be6817)\n", + "19153: (dSPN_0, p8bf90d1f, m22be6817)\n", + "19158: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "19172: (dSPN_0, pb0529fb9, m37886c78)\n", + "19498: (LTS_0, p8c493214, m803558b5)\n", + "19499: (LTS_1, p607c0a42, mf4ba6a4e)\n", + "19511: (dSPN_0, p1863c9a5, mf702205f)\n", + "19513: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "19522: (dSPN_0, p1863c9a5, m37886c78)\n", + "19525: (dSPN_0, p1863c9a5, m22be6817)\n", + "19529: (dSPN_0, p7aa400d6, m22be6817)\n", + "19537: (dSPN_0, p1863c9a5, mf702205f)\n", + "19548: (dSPN_0, pb0529fb9, mf702205f)\n", + "19930: (LTS_1, p266c7fb8, mf4ba6a4e)\n", + "19932: (LTS_1, p7e33f26e, m803558b5)\n", + "19933: (LTS_1, pcec5cf27, m8ded5e00)\n", + "19945: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "19946: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "19959: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "19964: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "19974: (dSPN_0, p7aa400d6, mf702205f)\n", + "20348: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "20356: (dSPN_0, p1863c9a5, m37886c78)\n", + "20360: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "20373: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "20375: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "20390: (dSPN_0, pb0529fb9, m22be6817)\n", + "20400: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "20850: (LTS_0, p1fd33c8c, m803558b5)\n", + "20852: (LTS_1, p1a0c46fe, m8ded5e00)\n", + "20865: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "20885: (dSPN_0, p510bab86, m22be6817)\n", + "20889: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "21278: (LTS_1, p4d93b0c3, m8ded5e00)\n", + "21290: (dSPN_0, p8bf90d1f, m37886c78)\n", + "21300: (dSPN_0, pb0529fb9, m22be6817)\n", + "21303: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "21307: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "21329: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "21330: (dSPN_0, p1863c9a5, mf702205f)\n", + "21702: (LTS_1, pb823efb9, mda52699c)\n", + "21703: (LTS_1, p96bb9239, m872fbb26)\n", + "21704: (LTS_1, p1cd5defe, m8ded5e00)\n", + "21742: (dSPN_0, pb0529fb9, mf702205f)\n", + "22092: (LTS_0, p1886c37b, m803558b5)\n", + "22093: (LTS_1, p7e33f26e, m803558b5)\n", + "22135: (dSPN_0, p1863c9a5, m22be6817)\n", + "22511: (LTS_0, padd48e2a, m803558b5)\n", + "22512: (LTS_0, p94d54b1c, m803558b5)\n", + "22513: (LTS_0, p1886c37b, m803558b5)\n", + "22514: (LTS_1, p3900bef0, mda52699c)\n", + "22530: (dSPN_0, p7517a0e9, mc710c1a4)\n", + "22538: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "22560: (dSPN_0, pe6ec2d4b, mbb8e5b24)\n", + "22564: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "22933: (LTS_0, p1fd33c8c, m803558b5)\n", + "22934: (LTS_1, pc2d8a8e6, mda52699c)\n", + "22935: (LTS_1, p290dc260, mf4ba6a4e)\n", + "22936: (LTS_1, pc2d8a8e6, mda52699c)\n", + "22945: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "22947: (dSPN_0, p1863c9a5, m37886c78)\n", + "22948: (dSPN_0, pd01ac450, m22be6817)\n", + "22970: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "23264: (LTS_1, pda92cc47, m872fbb26)\n", + "23272: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "23285: (dSPN_0, p7517a0e9, mc710c1a4)\n", + "23289: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "23702: (LTS_1, p7e8d7d6f, mda52699c)\n", + "23716: (dSPN_0, p1863c9a5, m37886c78)\n", + "23720: (dSPN_0, p7aa400d6, mf702205f)\n", + "23724: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "23725: (dSPN_0, p1863c9a5, m37886c78)\n", + "23740: (dSPN_0, pb0529fb9, mf702205f)\n", + "24126: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "24129: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "24133: (dSPN_0, pd01ac450, mf702205f)\n", + "24159: (dSPN_0, p7aa400d6, m22be6817)\n", + "24164: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "24511: (LTS_1, p272f9557, mda52699c)\n", + "24512: (LTS_1, pe2b0b6c2, ma4dacccf)\n", + "24515: (FS_0, p4feff98b, m4cd420e2)\n", + "24849: (LTS_0, p94d54b1c, m803558b5)\n", + "24852: (LTS_0, p1fd33c8c, m803558b5)\n", + "24873: (dSPN_0, pb0529fb9, mf702205f)\n", + "24880: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "24899: (dSPN_0, p7517a0e9, mf702205f)\n", + "24907: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "25249: (LTS_1, p3762fe01, ma4dacccf)\n", + "25250: (LTS_1, pd2b66278, m872fbb26)\n", + "25284: (dSPN_0, pd01ac450, m22be6817)\n", + "25291: (dSPN_0, p1863c9a5, mf702205f)\n", + "25293: (dSPN_0, pe6ec2d4b, m37886c78)\n", + "25787: (LTS_1, p3f9dfe00, ma4dacccf)\n", + "25788: (LTS_1, pe675a3d7, m872fbb26)\n", + "25800: (dSPN_0, p1863c9a5, m22be6817)\n", + "25832: (dSPN_0, pe6ec2d4b, m22be6817)\n", + "26229: (LTS_0, p94d54b1c, m803558b5)\n", + "26230: (LTS_1, p047a6bb7, mda52699c)\n", + "26231: (LTS_1, p116c624b, mf4ba6a4e)\n", + "26249: (dSPN_0, p1863c9a5, mf702205f)\n", + "26259: (dSPN_0, p1863c9a5, mf702205f)\n", + "26261: (dSPN_0, p7aa400d6, mf702205f)\n", + "26545: (LTS_1, p973cbb84, mf4ba6a4e)\n", + "26546: (LTS_1, pe5eef847, mda52699c)\n", + "26551: (dSPN_0, p1863c9a5, m37886c78)\n", + "26554: (dSPN_0, p7aa400d6, mf702205f)\n", + "26563: (dSPN_0, p7aa400d6, m22be6817)\n", + "26886: (LTS_0, p1886c37b, m803558b5)\n", + "26887: (LTS_1, pbd0a2290, m803558b5)\n", + "26888: (LTS_1, p57337648, mf4ba6a4e)\n", + "26895: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "26914: (dSPN_0, pb0529fb9, m37886c78)\n", + "26919: (dSPN_0, p8bf90d1f, m22be6817)\n", + "27323: (LTS_0, pe9c8b984, m803558b5)\n", + "27324: (FS_0, p4feff98b, m4fdebda2)\n", + "27335: (dSPN_0, pb0529fb9, m22be6817)\n", + "27343: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "27361: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "27362: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "27376: (dSPN_0, p7517a0e9, mbb8e5b24)\n", + "27378: (dSPN_0, p1863c9a5, mf702205f)\n", + "27702: (LTS_1, p57337648, mf4ba6a4e)\n", + "27736: (dSPN_0, pb0529fb9, m37886c78)\n", + "28211: (LTS_0, p94d54b1c, m803558b5)\n", + "28213: (LTS_0, p1fd33c8c, m803558b5)\n", + "28214: (LTS_1, p7e33f26e, m803558b5)\n", + "28215: (LTS_1, pe268b744, m872fbb26)\n", + "28216: (FS_0, pa09d12c1, mb1b67bcc)\n", + "28241: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "28255: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "28628: (LTS_0, p1886c37b, m803558b5)\n", + "28630: (LTS_0, p94d54b1c, m803558b5)\n", + "28632: (LTS_1, p290dc260, mf4ba6a4e)\n", + "28644: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "28645: (dSPN_0, pb0529fb9, m37886c78)\n", + "28669: (dSPN_0, pb0529fb9, m37886c78)\n", + "29108: (LTS_0, pe9c8b984, m803558b5)\n", + "29111: (LTS_1, pfddd6423, m265f1bc4)\n", + "29114: (FS_0, pb1ef6b01, m15ae4048)\n", + "29139: (dSPN_0, p7517a0e9, m9fda9b20)\n", + "29140: (dSPN_0, pc8cbdb24, mc710c1a4)\n", + "29157: (dSPN_0, pe1ec8fbd, mbb8e5b24)\n", + "29176: (dSPN_0, pb0529fb9, mf702205f)\n", + "29592: (LTS_0, pb66357bd, m803558b5)\n", + "29593: (LTS_0, pb66357bd, m803558b5)\n", + "29594: (LTS_1, p290dc260, mf4ba6a4e)\n", + "29595: (LTS_1, p3832aa3a, m803558b5)\n", + "29596: (LTS_1, p664ecb6e, m872fbb26)\n", + "29597: (LTS_1, p57337648, mf4ba6a4e)\n", + "29598: (LTS_1, p2ecc7334, ma4dacccf)\n", + "29636: (dSPN_0, pe6ec2d4b, m22be6817)\n", + "29641: (dSPN_0, pb0529fb9, m22be6817)\n", + "29643: (dSPN_0, pe1ec8fbd, m22be6817)\n", + "30020: (FS_0, pa09d12c1, m127c582f)\n", + "30033: (dSPN_0, p7517a0e9, mf702205f)\n", + "30035: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "30042: (dSPN_0, pd01ac450, m37886c78)\n", + "30045: (dSPN_0, p1863c9a5, m22be6817)\n", + "30057: (dSPN_0, pb0529fb9, m37886c78)\n", + "30516: (LTS_0, pe9c8b984, m803558b5)\n", + "30517: (LTS_1, p1cd5defe, m8ded5e00)\n", + "30518: (LTS_1, p3f21fd29, mf4ba6a4e)\n", + "30547: (dSPN_0, p1863c9a5, m37886c78)\n", + "30910: (LTS_0, pe9c8b984, m803558b5)\n", + "30911: (LTS_0, p7f931884, m803558b5)\n", + "30912: (LTS_0, p1fd33c8c, m803558b5)\n", + "30913: (LTS_1, pdd9466e2, m8ded5e00)\n", + "30914: (FS_0, pb1ef6b01, m4cd420e2)\n", + "30921: (dSPN_0, p1863c9a5, m37886c78)\n", + "30932: (dSPN_0, pb0529fb9, mf702205f)\n", + "30948: (dSPN_0, p1863c9a5, m22be6817)\n", + "30951: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "31341: (LTS_1, pfddd6423, m265f1bc4)\n", + "31355: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "31380: (dSPN_0, pe1ec8fbd, m22be6817)\n", + "31383: (dSPN_0, p8bf90d1f, m37886c78)\n", + "31761: (LTS_0, p8c493214, m803558b5)\n", + "31763: (LTS_1, p2ecc7334, ma4dacccf)\n", + "31767: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "31774: (dSPN_0, p1863c9a5, mf702205f)\n", + "32144: (LTS_0, pe9c8b984, m803558b5)\n", + "32145: (LTS_1, p47d379ce, mf4ba6a4e)\n", + "32146: (LTS_1, pbae91695, ma4dacccf)\n", + "32156: (dSPN_0, p7aa400d6, mf702205f)\n", + "32189: (dSPN_0, pb0529fb9, m22be6817)\n", + "32608: (LTS_0, p7f931884, m803558b5)\n", + "32609: (LTS_0, pe9c8b984, m803558b5)\n", + "32610: (LTS_0, p7f931884, m803558b5)\n", + "32611: (LTS_1, p607c0a42, mf4ba6a4e)\n", + "32612: (LTS_1, p57337648, mf4ba6a4e)\n", + "32613: (LTS_1, p3900bef0, mda52699c)\n", + "32627: (dSPN_0, pd01ac450, m37886c78)\n", + "33000: (LTS_1, pc2d8a8e6, mda52699c)\n", + "33036: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "33037: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "33038: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "33046: (dSPN_0, pb0529fb9, m37886c78)\n", + "33439: (LTS_0, padd48e2a, m803558b5)\n", + "33441: (LTS_1, p4d93b0c3, m8ded5e00)\n", + "33442: (LTS_1, p266c7fb8, mf4ba6a4e)\n", + "33443: (LTS_1, pe675a3d7, m872fbb26)\n", + "33460: (dSPN_0, pb0529fb9, m37886c78)\n", + "33463: (dSPN_0, pd01ac450, m22be6817)\n", + "33885: (dSPN_0, p1863c9a5, mf702205f)\n", + "33894: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "33896: (dSPN_0, p1863c9a5, m22be6817)\n", + "33901: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "33906: (dSPN_0, p8bf90d1f, mc710c1a4)\n", + "33908: (dSPN_0, pb0529fb9, mc710c1a4)\n", + "34255: (LTS_1, p72cfe937, m872fbb26)\n", + "34256: (FS_0, p4feff98b, m86da4874)\n", + "34283: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "34299: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "34301: (dSPN_0, pe6ec2d4b, mf702205f)\n", + "34318: (dSPN_0, p1863c9a5, mf702205f)\n", + "34324: (dSPN_0, p1863c9a5, m37886c78)\n", + "34330: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "34336: (dSPN_0, p7aa400d6, mf702205f)\n", + "34343: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "34888: (LTS_0, p8c493214, m803558b5)\n", + "34889: (LTS_0, p1fd33c8c, m803558b5)\n", + "34890: (LTS_0, p1fd33c8c, m803558b5)\n", + "34891: (LTS_0, p1886c37b, m803558b5)\n", + "34892: (LTS_1, p1a0c46fe, m8ded5e00)\n", + "34909: (dSPN_0, p510bab86, mc710c1a4)\n", + "34932: (dSPN_0, p1863c9a5, m37886c78)\n", + "35282: (LTS_0, p119533eb, m803558b5)\n", + "35285: (LTS_0, pe9c8b984, m803558b5)\n", + "35286: (LTS_1, pb5a5193d, mda52699c)\n", + "35307: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "35312: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "35333: (dSPN_0, p1863c9a5, m22be6817)\n", + "35342: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "35773: (LTS_0, p94d54b1c, m803558b5)\n", + "35774: (LTS_0, pe9c8b984, m803558b5)\n", + "35775: (FS_0, pb1ef6b01, m86da4874)\n", + "35785: (dSPN_0, pd01ac450, mf702205f)\n", + "35798: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "35800: (dSPN_0, p7aa400d6, m22be6817)\n", + "35804: (dSPN_0, p7517a0e9, m9fda9b20)\n", + "35815: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "36241: (LTS_0, p94d54b1c, m803558b5)\n", + "36242: (LTS_0, p94d54b1c, m803558b5)\n", + "36245: (LTS_1, pb7358f32, mf4ba6a4e)\n", + "36255: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "36259: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "36271: (dSPN_0, p7aa400d6, m22be6817)\n", + "36282: (dSPN_0, p8bf90d1f, m37886c78)\n", + "36302: (dSPN_0, p1863c9a5, m22be6817)\n", + "36696: (LTS_0, p1fd33c8c, m803558b5)\n", + "36697: (LTS_1, pe2b0b6c2, ma4dacccf)\n", + "36698: (LTS_1, pdc4da746, ma4dacccf)\n", + "36699: (LTS_1, p57337648, mf4ba6a4e)\n", + "36700: (FS_0, p59a48310, m86da4874)\n", + "36728: (dSPN_0, pb0529fb9, m37886c78)\n", + "36744: (dSPN_0, p1863c9a5, mf702205f)\n", + "36754: (dSPN_0, pb0529fb9, mf702205f)\n", + "36768: (dSPN_0, p1863c9a5, m22be6817)\n", + "37116: (LTS_1, p96bb9239, m872fbb26)\n", + "37117: (LTS_1, p47d379ce, mf4ba6a4e)\n", + "37169: (dSPN_0, p1863c9a5, mf702205f)\n", + "37172: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "37511: (LTS_1, pbae91695, ma4dacccf)\n", + "37527: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "37530: (dSPN_0, p8bf90d1f, m37886c78)\n", + "37547: (dSPN_0, p1863c9a5, m37886c78)\n", + "37550: (dSPN_0, p7aa400d6, m22be6817)\n", + "37551: (dSPN_0, p1863c9a5, m22be6817)\n", + "37552: (dSPN_0, p8bf90d1f, m9fda9b20)\n", + "37997: (LTS_0, p1886c37b, m803558b5)\n", + "37998: (LTS_1, p290dc260, mf4ba6a4e)\n", + "38025: (dSPN_0, p7aa400d6, mc710c1a4)\n", + "38357: (LTS_0, p1fd33c8c, m803558b5)\n", + "38358: (LTS_1, p2ecc7334, ma4dacccf)\n", + "38384: (dSPN_0, pb0529fb9, mbb8e5b24)\n", + "38394: (dSPN_0, p7aa400d6, m22be6817)\n", + "38808: (LTS_0, p1fd33c8c, m803558b5)\n", + "38825: (dSPN_0, p7aa400d6, m37886c78)\n", + "38841: (dSPN_0, p7aa400d6, m9fda9b20)\n", + "38864: (dSPN_0, p7517a0e9, m37886c78)\n", + "38866: (dSPN_0, p7aa400d6, mbb8e5b24)\n", + "38867: (dSPN_0, pb0529fb9, m9fda9b20)\n", + "39251: (dSPN_0, p1863c9a5, mc710c1a4)\n", + "39271: (dSPN_0, pb0529fb9, m37886c78)\n", + "39275: (dSPN_0, p7aa400d6, m22be6817)\n", + "39278: (dSPN_0, pb0529fb9, m37886c78)\n", + "39615: (LTS_0, p1fd33c8c, m803558b5)\n", + "39651: (dSPN_0, pb0529fb9, m37886c78)\n", + "39652: (dSPN_0, p8bf90d1f, mbb8e5b24)\n", + "39657: (dSPN_0, p7aa400d6, m37886c78)\n", + "39997: (LTS_1, p54dfea77, m8ded5e00)\n" + ] + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "fig_file_raster = f\"spike-raster.png\"\n", + "\n", + "time_range_zoom = (0,duration)\n", + "spr = SnuddaPlotSpikeRaster2(network_path=network_path, network_file=network_file, simulation_file=simulation_file_with_synapses)# \n", + "\n", + "# spr.plot_spike_raster(fig_file=fig_file_raster, time_range=time_range_zoom)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "19f4345a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spr.plot_spike_histogram_type(neuron_type=[\"dSPN\", \"iSPN\", \"FS\", \"LTS\"])" + ] + }, + { + "cell_type": "markdown", + "id": "aa220317", + "metadata": {}, + "source": [ + "# Summarising difference in activity (with and without lateral inhibition)" + ] + }, + { + "cell_type": "markdown", + "id": "3ca2dc04-d87f-4078-9702-15957dbbb994", + "metadata": {}, + "source": [ + "ax = spr.plot_spike_histogram_type(neuron_type=[\"dSPN\", \"iSPN\", \"FS\", \"LTS\"], label_text=\"lateral inhibition \", show_figure=False, fig_size=(10,8))\n", + "spr_no.plot_spike_histogram_type(ax=ax, neuron_type=[\"dSPN\", \"iSPN\", \"FS\", \"LTS\"], label_text=\"no inhibition \", show_figure=True, fig_file=\"spike-histogram-SPN-FS-LTS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8cb318e7-38ba-41df-b39d-2a8f89597780", + "metadata": {}, + "outputs": [], + "source": [ + "time_ranges = [(1,3), (4,6), (7,9), (10,12), (13,15), (16,18), (19,21), (22,24)]\n", + "# time_ranges = [(1,2), (4,5), (7,8), (10,11), (13,14), (16,17), (19,20), (22,23)]\n", + "# time_ranges = [(2,3), (5,6), (8,9), (11,12), (14,15), (17,18), (20,21), (23,24)]\n", + "\n", + "\n", + "dspn_id = spr.snudda_load.get_neuron_id_of_type(\"dSPN\")\n", + "ispn_id = spr.snudda_load.get_neuron_id_of_type(\"iSPN\")\n", + "fs_id = spr.snudda_load.get_neuron_id_of_type(\"FS\")\n", + "lts_id = spr.snudda_load.get_neuron_id_of_type(\"LTS\")\n", + "\n", + "\n", + "with_lat_freq_table_dspn = spr.snudda_simulation_load.get_frequency(neuron_id=dspn_id, time_ranges=time_ranges)\n", + "with_lat_freq_table_ispn = spr.snudda_simulation_load.get_frequency(neuron_id=ispn_id, time_ranges=time_ranges)\n", + "with_lat_freq_table_fs = spr.snudda_simulation_load.get_frequency(neuron_id=fs_id, time_ranges=time_ranges)\n", + "with_lat_freq_table_lts = spr.snudda_simulation_load.get_frequency(neuron_id=lts_id, time_ranges=time_ranges)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "267dbb95-2bb2-4623-9acd-85bf54b49467", + "metadata": {}, + "outputs": [], + "source": [ + "with_lat_freq_dspn_mean = np.mean(with_lat_freq_table_dspn, axis=0)\n", + "with_lat_freq_ispn_mean = np.mean(with_lat_freq_table_ispn, axis=0)\n", + "with_lat_freq_fs_mean = np.mean(with_lat_freq_table_fs, axis=0)\n", + "with_lat_freq_lts_mean = np.mean(with_lat_freq_table_lts, axis=0)\n", + "\n", + "n_dspn_cells = with_lat_freq_table_dspn.shape[0]\n", + "n_ispn_cells = with_lat_freq_table_ispn.shape[0]\n", + "n_fs_cells = with_lat_freq_table_fs.shape[0]\n", + "n_lts_cells = with_lat_freq_table_lts.shape[0]\n", + "\n", + "\n", + "# Standard error of the mean\n", + "with_lat_freq_dspn_std = np.std(with_lat_freq_table_dspn, axis=0) / np.sqrt(n_dspn_cells)\n", + "with_lat_freq_ispn_std = np.std(with_lat_freq_table_ispn, axis=0) / np.sqrt(n_ispn_cells)\n", + "with_lat_freq_fs_std = np.std(with_lat_freq_table_fs, axis=0) / np.sqrt(n_fs_cells)\n", + "with_lat_freq_lts_std = np.std(with_lat_freq_table_lts, axis=0) / np.sqrt(n_lts_cells)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6d8e0e81-c74a-41d3-b436-de7e828a8c29", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With lateral inhibition\n", + "Freq dSPN: 4.61 +/- 0.03\tFreq iSPN: 5.95 +/- 0.03\tFreq FS: 0.00 +/- 0.00 \tFreq LTS: 0.00 +/- 0.00\n", + "Freq dSPN: 3.60 +/- 0.02\tFreq iSPN: 4.95 +/- 0.02\tFreq FS: 69.79 +/- 1.79 \tFreq LTS: 0.00 +/- 0.00\n", + "Freq dSPN: 3.61 +/- 0.02\tFreq iSPN: 5.05 +/- 0.02\tFreq FS: 0.00 +/- 0.00 \tFreq LTS: 15.79 +/- 1.01\n", + "Freq dSPN: 3.44 +/- 0.02\tFreq iSPN: 4.86 +/- 0.02\tFreq FS: 69.53 +/- 1.80 \tFreq LTS: 8.94 +/- 0.94\n", + "Freq dSPN: 8.68 +/- 0.05\tFreq iSPN: 11.13 +/- 0.05\tFreq FS: 0.00 +/- 0.00 \tFreq LTS: 0.00 +/- 0.00\n", + "Freq dSPN: 7.78 +/- 0.04\tFreq iSPN: 10.35 +/- 0.05\tFreq FS: 69.49 +/- 1.79 \tFreq LTS: 0.00 +/- 0.00\n", + "Freq dSPN: 7.92 +/- 0.04\tFreq iSPN: 10.53 +/- 0.05\tFreq FS: 0.00 +/- 0.00 \tFreq LTS: 7.73 +/- 0.91\n", + "Freq dSPN: 7.63 +/- 0.04\tFreq iSPN: 10.22 +/- 0.05\tFreq FS: 69.42 +/- 1.80 \tFreq LTS: 6.73 +/- 0.85\n" + ] + } + ], + "source": [ + "print(\"With lateral inhibition\")\n", + "for freq_dspn_mean, freq_dspn_std, freq_ispn_mean, freq_ispn_std, freq_fs_mean, freq_fs_std, freq_lts_mean, freq_lts_std in zip(with_lat_freq_dspn_mean, with_lat_freq_dspn_std, with_lat_freq_ispn_mean,with_lat_freq_ispn_std,with_lat_freq_fs_mean,with_lat_freq_fs_std, with_lat_freq_lts_mean, with_lat_freq_lts_std):\n", + " print(f\"Freq dSPN: {freq_dspn_mean:.2f} +/- {freq_dspn_std:.2f}\\tFreq iSPN: {freq_ispn_mean:.2f} +/- {freq_ispn_std:.2f}\\tFreq FS: {freq_fs_mean:.2f} +/- {freq_fs_std:.2f} \\tFreq LTS: {freq_lts_mean:.2f} +/- {freq_lts_std:.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "34dd454d-fc2e-4a52-84d2-5c5c8330770c", + "metadata": {}, + "outputs": [], + "source": [ + "with_lat_n_firing_dspn = np.sum(with_lat_freq_table_dspn > 0, axis=0)\n", + "with_lat_n_firing_ispn = np.sum(with_lat_freq_table_ispn > 0, axis=0)\n", + "with_lat_n_firing_fs = np.sum(with_lat_freq_table_fs > 0, axis=0)\n", + "with_lat_n_firing_lts = np.sum(with_lat_freq_table_lts > 0, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9d99918b-287c-43b8-a1c3-20d2a1f16759", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[18010 17177 17031 16862 18670 18439 18394 18281] dSPN, [18782 18493 18472 18363 18938 18865 18868 18840] iSPN, [ 0 501 0 497 0 495 0 497] FS, [ 0 1 288 80 0 0 68 58] LTS\n" + ] + } + ], + "source": [ + "print(f\"{with_lat_n_firing_dspn} dSPN, {with_lat_n_firing_ispn} iSPN, {with_lat_n_firing_fs} FS, {with_lat_n_firing_lts} LTS\")" + ] + }, + { + "cell_type": "markdown", + "id": "9f188d49-c4c3-42b7-864a-2ffc6165eed8", + "metadata": {}, + "source": [ + "## Plot frequency histogram for individual neuron types" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "60b7df7e-c235-42a4-9904-01e703b00207", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 19587 neurons of type ['dSPN']\n", + "Saving figure ../networks/sten_5_SPN_FS_LTS/figures/spike-frequency-pop-units0-1-2.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax_dspn = spr.plot_spike_histogram(label_text=\"lateral inhibition (dSPN) \", neuron_type=\"dSPN\", show_figure=True, save_figure=True, fig_size=(10,8))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2441e889-55ff-4650-84a9-359c40ea84e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 19587 neurons of type ['iSPN']\n", + "Saving figure ../networks/sten_5_SPN_FS_LTS/figures/spike-frequency-pop-units0-1-2.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax_ispn = spr.plot_spike_histogram(label_text=\"lateral inhibition (iSPN) \", neuron_type=\"iSPN\", show_figure=True, save_figure=True, fig_size=(10,8))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "70606e32-edf6-4cec-9b2f-e24befd7662d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 536 neurons of type ['FS']\n", + "Saving figure ../networks/sten_5_SPN_FS_LTS/figures/spike-frequency-pop-units0.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax_fs = spr.plot_spike_histogram(label_text=\"lateral inhibition (FS) \", neuron_type=\"FS\", show_figure=True, save_figure=True, fig_size=(10,8))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0b083221-3f0d-4f11-8cc9-116f62fc7556", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 288 neurons of type ['LTS']\n", + "Saving figure ../networks/sten_5_SPN_FS_LTS/figures/spike-frequency-pop-units0.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax_lts = spr.plot_spike_histogram(label_text=\"lateral inhibition (LTS) \", neuron_type=\"LTS\", show_figure=True, save_figure=True, fig_size=(10,8))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7040ac16-33dd-47b9-a45e-6c10a80ce5fb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9ee1532d-4e52-4838-873c-a394f0246e23", + "metadata": {}, + "source": [ + "# Double check if there is depolarisation block for LTS??\n", + "\n", + "Now we look at 2 seconds of stim, before we just analysed the last second" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f336d8ca-dac2-4f51-b8fe-1744ff79c9e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_runSnudda_lateral.job b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_runSnudda_lateral.job new file mode 100644 index 000000000..3a1cf12be --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_runSnudda_lateral.job @@ -0,0 +1,83 @@ +#!/bin/bash -l +#SBATCH --partition=main +#SBATCH -o log/runSnudda-%j-output.txt +#SBATCH -e log/runSnudda-%j-error.txt +#SBATCH -t 3:59:00 +#SBATCH -J Snudda +#SBATCH -A naiss2024-5-306 +#SBATCH --nodes=2 +#SBATCH -n 256 +#SBATCH --cpus-per-task=2 +#SBATCH --mem-per-cpu=930M +#SBATCH --mail-type=ALL +module load snic-env + + +#.. +#export OMP_STACKSIZE=128G +ulimit -s unlimited + + +#let NWORKERS="$SLURM_NTASKS-2" +#let NWORKERS="100" +let NWORKERS="40" + +# REMEMBER TO CREATE THE "log" DIRECTORY + + +export IPNWORKERS=$NWORKERS + + +export IPYTHONDIR="/cfs/klemming/scratch/${USER:0:1}/$USER/.ipython" +rm -r $IPYTHONDIR +export IPYTHON_PROFILE=default +source $HOME/Snudda/snudda_env/bin/activate + + +#.. Start the ipcontroller +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n 1 -N 1 -c 2 --exact --overlap --mem=0 ./../../ipcontroller_new.sh & + + +echo ">>> waiting 60s for controller to start" +sleep 60 + +#.. Read in CONTROLLERIP +CONTROLLERIP=$(>> starting ${IPNWORKERS} engines " +#srun -n ${IPNWORKERS} -c 2 --exact --overlap ipengine --location=${CONTROLLERIP} --profile=${IPYTHON_PROFILE} --mpi \ +#--ipython-dir=${IPYTHONDIR} --timeout=30.0 --log-level=DEBUG \ +#--BaseParallelApplication.verbose_crash=True --IPEngine.verbose_crash=True \ +#--Kernel.stop_on_error_timeout=1.0 --IPythonKernel.stop_on_error_timeout=1.0 \ +#Session.buffer_threshold=4096 Session.copy_threshold=250000 \ +#Session.digest_history_size=250000 c.EngineFactory.max_heartbeat_misses=10 c.MPI.use='mpi4py' \ +#1> ipe_${SLURM_JOBID}.out 2> ipe_${SLURM_JOBID}.err & + +#srun -n ${IPNWORKERS} -c 2 --exact --overlap valgrind --leak-check=full --show-leak-kinds=all \ +#ipengine --location=${CONTROLLERIP} --profile=${IPYTHON_PROFILE} --mpi \ +#--ipython-dir=${IPYTHONDIR} --timeout=30.0 c.EngineFactory.max_heartbeat_misses=10 c.MPI.use='mpi4py' \ +#1> ipe_${SLURM_JOBID}.out 2> ipe_${SLURM_JOBID}.err & + +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n ${IPNWORKERS} -c 2 -N ${SLURM_JOB_NUM_NODES} --exact --overlap --mem=0 ipengine \ +--location=${CONTROLLERIP} --profile=${IPYTHON_PROFILE} --mpi \ +--ipython-dir=${IPYTHONDIR} --timeout=30.0 c.EngineFactory.max_heartbeat_misses=10 c.MPI.use='mpi4py' \ +1> ipe_${SLURM_JOBID}.out 2> ipe_${SLURM_JOBID}.err & + + +echo ">>> waiting 60s for engines to start" +sleep 30 + +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n 1 -N 1 --exact --overlap --mem=0 ./Dardel_runSnudda_lateral.sh + + +echo " " + +echo "JOB END "`date` start_time_network_connect.txt + +wait + diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_runSnudda_lateral.sh b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_runSnudda_lateral.sh new file mode 100755 index 000000000..be2c54a83 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_runSnudda_lateral.sh @@ -0,0 +1,122 @@ +#!/bin/bash + + + +SNUDDA_DIR=$HOME/Snudda/snudda +JOBDIR=../networks/sten_5_SPN_FS_LTS + +# SIMSIZE=50000 + +# If the BasalGangliaData directory exists, then use that for our data +#/cfs/klemming/scratch/${USER:0:1}/$USER/BasalGangliaData/data +#BasalGangliaData/Parkinson/PD0 +if [[ -d "$HOME/BasalGangliaData/data" ]]; then + export SNUDDA_DATA="$HOME/BasalGangliaData/data" + echo "Setting SNUDDA_DATA to $SNUDDA_DATA" +else + echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA" +fi + +mkdir -p $JOBDIR + +echo "Dardel_runSnudda.sh should be started with srun -n 1, to only get one process" + +echo "SLURM_PROCID = $SLURM_PROCID" + +if [ "$SLURM_PROCID" -gt 0 ]; then + mock_string="Not main process" +else + + # For debug purposes: + echo "PATH: "$PATH + echo "IPYTHONDIR: "$IPYTHONDIR + echo "PYTHONPATH: "$PYTHONPATH + echo "LD_LIBRARY_PATH: "$LD_LIBRARY_PATH + + echo ">>>>>> Main process starting ipcluster" + echo + + echo "Start time: " > start_time_network_connect.txt + date >> start_time_network_connect.txt + + echo ">>> Init: "`date` + # snudda init ${JOBDIR} --size ${SIMSIZE} --overwrite --randomseed 1234 + python setup_sten_5_SPN_FS_LTS.py ${JOBDIR} + + if [ $? != 0 ]; then + echo "Something went wrong during init, aborting!" + ipcluster stop + exit -1 + fi + +# WE NOW START IPCLUSTER USING ipcontroller.sh INSTEAD... +# +# echo "SLURM_NODELIST = $SLURM_NODELIST" +# let NWORKERS="$SLURM_NTASKS - 1" +# +# echo ">>> NWORKERS " $NWORKERS +# echo ">>> Starting ipcluster `date`" +# +# #.. Start the ipcluster +# ipcluster start -n ${NWORKERS} \ +# --ip='*' \ +# --HeartMonitor.max_heartmonitor_misses=1000 \ +# --HubFactory.registration_timeout=600 \ +# --HeartMonitor.period=10000 & +# +# +# #.. Sleep to allow engines to start +# echo ">>> Wait 120s to allow engines to start" +# sleep 120 #60 + + echo ">>> Place: "`date` + snudda place ${JOBDIR} --verbose --ipython_timeout 600 + + if [ $? != 0 ]; then + echo "Something went wrong during placement, aborting!" + # ipcluster stop + exit -1 + fi + + echo ">>> Detect: "`date` + snudda detect ${JOBDIR} --hvsize 50 --parallel --ipython_timeout 600 + + if [ $? != 0 ]; then + echo "Something went wrong during detection, aborting!" + # ipcluster stop + exit -1 + fi + + echo ">>> Prune: "`date` + snudda prune ${JOBDIR} --parallel --ipython_timeout 600 + + if [ $? != 0 ]; then + echo "Something went wrong during pruning, aborting!" + # ipcluster stop + exit -1 + fi + + # Disable input generation at the moment + + # echo ">>> Ablate: "`date` + # python ../ablate_network.py ${JOBDIR} + + echo ">>> Input: "`date` + # snudda input ${JOBDIR} --parallel --time 18 --input input.json --networkFile ${JOBDIR}/network-synapses-minimal.hdf5 + snudda input ${JOBDIR} --parallel --time 25 --input input.json --ipython_timeout 600 + + + #.. Shut down cluster + # ipcluster stop + #.. Shutdown ipcontroller + echo "Shutting down ipcontroller" + + python ../../ipcontroller_shutdown.py + + + date + #echo "JOB END "`date` start_time_network_connect.txt + + echo "EXITING Dardel_runjob_lateral.sh" + +fi diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_simulate_lateral.job b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_simulate_lateral.job new file mode 100644 index 000000000..ab080bad7 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_simulate_lateral.job @@ -0,0 +1,99 @@ +#!/bin/bash -l +#SBATCH --partition=main +#SBATCH -o log/Simulate-%j-output.txt +#SBATCH -e log/Simulate-%j-error.txt +#SBATCH -t 9:59:00 +#SBATCH --time-min=9:59:00 +#SBATCH -J Simulate +#SBATCH -A naiss2023-5-231 +#SBATCH --nodes=60 +#SBATCH --tasks-per-node=50 +#SBATCH --mail-type=ALL + +# 2024-02-16: 40 nodes worked, had 28% free... trying increasing to 45 nodes +# 2024-03-01: 60 nodes, 50 workers worked... + +# You need to point this as the directory where you created the network in +#NETWORK_DIR=/cfs/klemming/home/${USER:0:1}/$USER/Snudda/examples/parallel/KTH_PDC/networks/test_10k +NETWORK_DIR=../networks/sten_5_SPN_FS_LTS + +SIMULATION_CONFIG_WITH_SYNAPSES=experiment_config_sten_5-with-synapses-A.json +SIMULATION_CONFIG_NO_SYNAPSES=experiment_config_sten_5-no-synapses-A.json + + +# NETWORK_WITH_SYNAPSES_OUTPUT=$NETWORK_DIR/simulation/output-with-synapses-sten_1.hdf5 +# NETWORK_NO_SYNAPSES_OUTPUT=$NETWORK_DIR/simulation/output-no-synapses-sten_1.hdf5 + + +export N_WORKERS=$SLURM_NTASKS + +module load snic-env +source $HOME/Snudda/snudda_env/bin/activate +SNUDDA_DIR=/cfs/klemming/home/"${USER:0:1}"/$USER/Snudda + +# If the BasalGangliaData directory exists, then use that for our data +if [[ -d "/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" ]]; then + export SNUDDA_DATA="/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" + echo "Setting SNUDDA_DATA to $SNUDDA_DATA" + rm mechanisms + ln -s $SNUDDA_DATA/neurons/mechanisms/ mechanisms +else + echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA" + rm mechanisms + ln -s ../../../../snudda/data/neurons/mechanisms/ +fi + + +NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses.hdf5 +# NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses-minimal.hdf5 +NETWORK_INPUT_FILE=$NETWORK_DIR/input-spikes.hdf5 +# NETWORK_VOLTAGE_FILE=$NETWORK_DIR/simulation/voltage-trace-${SLURM_JOBID}.txt + + + +echo "Network dir: "$NETWORK_DIR + +export PATH=$SNUDDA_DIR/snudda_env/bin/:$PATH +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CRAY_LD_LIBRARY_PATH +export PYTHONPATH=$SNUDDA_DIR/snudda_env/lib/python3.11/ + +############## + +export CXX=CC +export CC=cc +export FC=ftn +export MPICC=cc +export MPICXX=CC + +CC --version + +pushd $SNUDDA_DIR/examples/parallel/KTH_PDC/ + +rm -r x86_64 + +echo "About to run nrnivmodl" +which nrnivmodl + +# srun -n nrnivmodl mechanisms/ + +srun -n 1 nrnivmodl -incflags "-lltdl=/usr/lib64/libltdl.so.7 -lreadline=/lib64/libreadline.so.7 -lncurses=/lib64/libncurses.so.6.1" -loadflags "-DLTDL_LIBRARY=/usr/lib64/libltdl.so.7 -DREADLINE_LIBRARY=/lib64/libreadline.so.7 -DNCURSES_LIBRARY=/lib64/libncurses.so.6.1" mechanisms/ + +popd + +# GJ disabled +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/x86_64/special -mpi -python $SNUDDA_DIR/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --disableGJ --time 3.5 --voltOut $NETWORK_VOLTAGE_FILE + +# GJ active +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 18 --outputFile $NETWORK_WITH_SYNAPSES_OUTPUT + +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 18 --disableSyn --outputFile $NETWORK_NO_SYNAPSES_OUTPUT + + +# Changed to using the simulation_config + +srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py dummy_file dummy_file --simulation_config $SIMULATION_CONFIG_WITH_SYNAPSES + +srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py dummy_file dummy_file --simulation_config $SIMULATION_CONFIG_NO_SYNAPSES + + +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 5 --noVolt diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_simulate_lateral_memory.job b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_simulate_lateral_memory.job new file mode 100644 index 000000000..9c74e15c1 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/Dardel_simulate_lateral_memory.job @@ -0,0 +1,101 @@ +#!/bin/bash -l +#SBATCH --partition=main +#SBATCH --mem=440GB +#SBATCH -o log/Simulate-%j-output.txt +#SBATCH -e log/Simulate-%j-error.txt +#SBATCH -t 4:59:00 +#SBATCH --time-min=4:59:00 +#SBATCH -J Simulate +#SBATCH -A naiss2024-5-306 +#SBATCH --nodes=30 +#SBATCH --tasks-per-node=100 +#SBATCH --mail-type=ALL + +# 2024-02-16: 40 nodes worked, had 28% free... trying increasing to 45 nodes +# 2024-03-01: 60 nodes, 50 workers worked... +# 2024-05-30: 60 nodes, trying 100 workers per node, with memory 440GB + +# You need to point this as the directory where you created the network in +#NETWORK_DIR=/cfs/klemming/home/${USER:0:1}/$USER/Snudda/examples/parallel/KTH_PDC/networks/test_10k +NETWORK_DIR=../networks/sten_5_SPN_FS_LTS + +SIMULATION_CONFIG_WITH_SYNAPSES=experiment_config_sten_5-with-synapses-A.json +SIMULATION_CONFIG_NO_SYNAPSES=experiment_config_sten_5-no-synapses-A.json + + +# NETWORK_WITH_SYNAPSES_OUTPUT=$NETWORK_DIR/simulation/output-with-synapses-sten_1.hdf5 +# NETWORK_NO_SYNAPSES_OUTPUT=$NETWORK_DIR/simulation/output-no-synapses-sten_1.hdf5 + + +export N_WORKERS=$SLURM_NTASKS + +module load snic-env +source $HOME/Snudda/snudda_env/bin/activate +SNUDDA_DIR=/cfs/klemming/home/"${USER:0:1}"/$USER/Snudda + +# If the BasalGangliaData directory exists, then use that for our data +if [[ -d "/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" ]]; then + export SNUDDA_DATA="/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" + echo "Setting SNUDDA_DATA to $SNUDDA_DATA" + rm mechanisms + ln -s $SNUDDA_DATA/neurons/mechanisms/ mechanisms +else + echo "SNUDDA_DATA environment variable not changed (may be empty): $SNUDDA_DATA" + rm mechanisms + ln -s ../../../../snudda/data/neurons/mechanisms/ +fi + + +NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses.hdf5 +# NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses-minimal.hdf5 +NETWORK_INPUT_FILE=$NETWORK_DIR/input-spikes.hdf5 +# NETWORK_VOLTAGE_FILE=$NETWORK_DIR/simulation/voltage-trace-${SLURM_JOBID}.txt + + + +echo "Network dir: "$NETWORK_DIR + +export PATH=$SNUDDA_DIR/snudda_env/bin/:$PATH +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CRAY_LD_LIBRARY_PATH +export PYTHONPATH=$SNUDDA_DIR/snudda_env/lib/python3.11/ + +############## + +export CXX=CC +export CC=cc +export FC=ftn +export MPICC=cc +export MPICXX=CC + +CC --version + +pushd $SNUDDA_DIR/examples/parallel/KTH_PDC/ + +rm -r x86_64 + +echo "About to run nrnivmodl" +which nrnivmodl + +# srun -n nrnivmodl mechanisms/ + +srun -n 1 nrnivmodl -incflags "-lltdl=/usr/lib64/libltdl.so.7 -lreadline=/lib64/libreadline.so.7 -lncurses=/lib64/libncurses.so.6.1" -loadflags "-DLTDL_LIBRARY=/usr/lib64/libltdl.so.7 -DREADLINE_LIBRARY=/lib64/libreadline.so.7 -DNCURSES_LIBRARY=/lib64/libncurses.so.6.1" mechanisms/ + +popd + +# GJ disabled +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/x86_64/special -mpi -python $SNUDDA_DIR/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --disableGJ --time 3.5 --voltOut $NETWORK_VOLTAGE_FILE + +# GJ active +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 18 --outputFile $NETWORK_WITH_SYNAPSES_OUTPUT + +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 18 --disableSyn --outputFile $NETWORK_NO_SYNAPSES_OUTPUT + + +# Changed to using the simulation_config + +srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py dummy_file dummy_file --simulation_config $SIMULATION_CONFIG_WITH_SYNAPSES + +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py dummy_file dummy_file --simulation_config $SIMULATION_CONFIG_NO_SYNAPSES + + +# srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py $NETWORK_INFO_FILE $NETWORK_INPUT_FILE --time 5 --noVolt diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/experiment_config_sten_5-no-synapses-A.json b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/experiment_config_sten_5-no-synapses-A.json new file mode 100644 index 000000000..bbcc27acb --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/experiment_config_sten_5-no-synapses-A.json @@ -0,0 +1,11 @@ +{ + "network_file": "../networks/sten_5_SPN_FS_LTS/network-synapses.hdf5", + "input_file": "../networks/sten_5_SPN_FS_LTS/input-spikes.hdf5", + "output_file": "../networks/sten_5_SPN_FS_LTS/simulation/output-no-synapses-sten_5A.hdf5", + "log_file": "../networks/sten_5_SPN_FS_LTS/log/network-simulation-no-synapses-log_5A.txt", + "sample_dt": 0.01, + "time": 25.0, + "disable_synapses": true, + "record_all_soma": true, + "record_all_compartments": [0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500] +} diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/experiment_config_sten_5-with-synapses-A.json b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/experiment_config_sten_5-with-synapses-A.json new file mode 100644 index 000000000..5dce06f86 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/experiment_config_sten_5-with-synapses-A.json @@ -0,0 +1,10 @@ +{ + "network_file": "../networks/sten_5_SPN_FS_LTS/network-synapses.hdf5", + "input_file": "../networks/sten_5_SPN_FS_LTS/input-spikes.hdf5", + "output_file": "../networks/sten_5_SPN_FS_LTS/simulation/output-with-synapses-sten_5A.hdf5", + "log_file": "../networks/sten_5_SPN_FS_LTS/log/network-simulation-with-synapses-log_5A.txt", + "sample_dt": 0.01, + "time": 25.0, + "record_all_soma": true, + "record_all_compartments": [0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500] +} diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/input.json b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/input.json new file mode 100644 index 000000000..1c47f6e98 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/input.json @@ -0,0 +1,37 @@ +{ + "dSPN": { + "cortical" : { + "generator" : "poisson", + "start" : [1, 4, 7, 10, 13, 16, 19, 22], + "end" : [3, 6, 9, 12, 15, 18, 21, 24], + "frequency" : [4, 4, 4, 4, 6, 6, 6, 6] + } + }, + + "iSPN": { + "cortical" : { + "generator" : "poisson", + "start" : [1, 4, 7, 10, 13, 16, 19, 22], + "end" : [3, 6, 9, 12, 15, 18, 21, 24], + "frequency" : [4, 4, 4, 4, 6, 6, 6, 6] + } + }, + + "FS": { + "cortical" : { + "generator" : "poisson", + "start" : [4, 10, 16, 22], + "end" : [6, 12, 18, 24], + "frequency" : [10, 10, 10, 10] + } + }, + + "LTS": { + "cortical_background" : { + "generator" : "poisson", + "start" : [7, 10, 19, 22], + "end" : [9, 12, 21, 24], + "frequency" : [2, 2, 2, 2] + } + } +} diff --git a/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/setup_sten_5_SPN_FS_LTS.py b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/setup_sten_5_SPN_FS_LTS.py new file mode 100644 index 000000000..1e51ec8c0 --- /dev/null +++ b/examples/parallel/KTH_PDC/lateral_inhibition/sten_5_FS_LTS/setup_sten_5_SPN_FS_LTS.py @@ -0,0 +1,62 @@ +import os +import sys +import numpy as np + +if len(sys.argv) > 1: + network_path = sys.argv[1] +else: + sys.exit("No network path specified!") + network_path="../networks/sten_5_SPN_FS_LTS" + +# network_path = "networks/lateral_1" +# snudda_data = "$HOME/BasalGangliaData/data" +snudda_data = "../../../../../../BasalGangliaData/data/" + +print(f"Network_path = {network_path}, snudda data = {snudda_data}") + + +duration=18 + +import snudda.init + +n_total = 40000 +f_dSPN=0.475 +f_iSPN=0.475 +f_FS=0.013 +f_ChIN=0 #0.011 +f_LTS=0.007 + +f_total = f_dSPN + f_iSPN + f_FS + f_ChIN + f_LTS + + +n_DSPN = int(n_total * f_dSPN / f_total) +n_ISPN = int(n_total * f_iSPN / f_total) +n_FS = int(n_total * f_FS / f_total) +n_LTS = int(n_total * f_LTS / f_total) +n_ChIN = int(n_total * f_ChIN / f_total) + +# Be careful with density if SPNs are not included... + +print("Starting SnuddaInit") +si = snudda.init.SnuddaInit(network_path=network_path, snudda_data=snudda_data, random_seed=12345, honor_stay_inside=False) +si.define_striatum(num_dSPN=n_DSPN, num_iSPN=n_ISPN, num_FS=n_FS, num_LTS=n_LTS, num_ChIN=n_ChIN, + volume_type="cube") + +print("Adding population units") + +# si.add_population_unit_random(structure_name="Striatum", neuron_types=["dSPN", "iSPN"], +# fraction_of_neurons=0.5, unit_id=1) +# si.add_population_unit_random(structure_name="Striatum", neuron_types=["dSPN", "iSPN"], +# fraction_of_neurons=0.5, unit_id=2) + +# The centre of the cube is [0.00475, 0.004, 0.00775]. num_neurons is optional +si.add_population_unit_density(structure_name="Striatum", neuron_types=["dSPN", "iSPN"], + unit_centre=np.array([0.00475, 0.004, 0.00775]) -np.array([0, 0, 100e-6]), + probability_function="(d < 300e-6) * 1", num_neurons=4000) +si.add_population_unit_density(structure_name="Striatum", neuron_types=["dSPN", "iSPN"], + unit_centre=np.array([0.00475, 0.004, 0.00775]) -np.array([0, 0, -100e-6]), + probability_function="(d < 300e-6) * 1", num_neurons=4000) + +print("Writing json") + +si.write_json() diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_analyse_dspn.ipynb b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_analyse_dspn.ipynb new file mode 100644 index 000000000..d9c328394 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_analyse_dspn.ipynb @@ -0,0 +1,23471 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7afb8f8f-84c4-44e5-8d6c-2c4e64bece08", + "metadata": {}, + "source": [ + "# DSPN neuromodulation\n", + "\n", + "This jupyter notebook analyses ```Dardel_simulate_neuromodulation_dspn.job```." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e363da89-25b4-4087-b05a-eb91099bca43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading ../networks/dspn_modulation/simulation/dspn-output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "43: (dspn_str_dspn_e_p1863c9a5_m22be6817, p1863c9a5, m22be6817)\n", + "44: (dspn_str_dspn_e_p1863c9a5_m37886c78, p1863c9a5, m37886c78)\n", + "45: (dspn_str_dspn_e_p1863c9a5_mc710c1a4, p1863c9a5, mc710c1a4)\n", + "46: (dspn_str_dspn_e_p1863c9a5_mf702205f, p1863c9a5, mf702205f)\n", + "50: (dspn_str_dspn_e_p510bab86_mc710c1a4, p510bab86, mc710c1a4)\n", + "51: (dspn_str_dspn_e_p510bab86_mf702205f, p510bab86, mf702205f)\n", + "52: (dspn_str_dspn_e_p7517a0e9_m22be6817, p7517a0e9, m22be6817)\n", + "61: (dspn_str_dspn_e_p7aa400d6_mbb8e5b24, p7aa400d6, mbb8e5b24)\n", + "62: (dspn_str_dspn_e_p7aa400d6_mc710c1a4, p7aa400d6, mc710c1a4)\n", + "64: (dspn_str_dspn_e_p8bf90d1f_m22be6817, p8bf90d1f, m22be6817)\n", + "65: (dspn_str_dspn_e_p8bf90d1f_m37886c78, p8bf90d1f, m37886c78)\n", + "67: (dspn_str_dspn_e_p8bf90d1f_mbb8e5b24, p8bf90d1f, mbb8e5b24)\n", + "70: (dspn_str_dspn_e_pb0529fb9_m22be6817, pb0529fb9, m22be6817)\n", + "71: (dspn_str_dspn_e_pb0529fb9_m37886c78, pb0529fb9, m37886c78)\n", + "75: (dspn_str_dspn_e_pb0529fb9_mf702205f, pb0529fb9, mf702205f)\n", + "76: (dspn_str_dspn_e_pc8cbdb24_m22be6817, pc8cbdb24, m22be6817)\n", + "81: (dspn_str_dspn_e_pd01ac450_m22be6817, pd01ac450, m22be6817)\n", + "85: (dspn_str_dspn_e_pd01ac450_mf702205f, pd01ac450, mf702205f)\n", + "91: (dspn_str_dspn_e_pe6ec2d4b_mbb8e5b24, pe6ec2d4b, mbb8e5b24)\n", + "92: (dspn_str_dspn_e_pe6ec2d4b_mc710c1a4, pe6ec2d4b, mc710c1a4)\n" + ] + } + ], + "source": [ + "import os\n", + "network_path = os.path.join(\"..\", \"networks\", \"dspn_modulation\")\n", + "network_simulation_path = os.path.join(network_path, \"simulation\", \"dspn-output.hdf5\")\n", + "\n", + "from snudda.utils import SnuddaLoadSimulation, SnuddaLoad\n", + "\n", + "sl = SnuddaLoad(network_path)\n", + "sls = SnuddaLoadSimulation(network_path=network_path, network_simulation_output_file=network_simulation_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "46e9acfa-2584-4c08-82d3-71a8a27a1239", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to ../networks/dspn_modulation/figures/spike_raster.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "spsr = SnuddaPlotSpikeRaster2(snudda_load=sl, snudda_simulation_load=sls, network_path=None)\n", + "spsr.plot_spike_raster()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "be19d431-751f-4e6e-b2a6-57729b4c1baf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from ../networks/dspn_modulation\n", + "Loading input info from ../networks/dspn_modulation/input-spikes.hdf5\n", + "Loading ../networks/dspn_modulation/simulation/dspn-output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "43: (dspn_str_dspn_e_p1863c9a5_m22be6817, p1863c9a5, m22be6817)\n", + "44: (dspn_str_dspn_e_p1863c9a5_m37886c78, p1863c9a5, m37886c78)\n", + "45: (dspn_str_dspn_e_p1863c9a5_mc710c1a4, p1863c9a5, mc710c1a4)\n", + "46: (dspn_str_dspn_e_p1863c9a5_mf702205f, p1863c9a5, mf702205f)\n", + "50: (dspn_str_dspn_e_p510bab86_mc710c1a4, p510bab86, mc710c1a4)\n", + "51: (dspn_str_dspn_e_p510bab86_mf702205f, p510bab86, mf702205f)\n", + "52: (dspn_str_dspn_e_p7517a0e9_m22be6817, p7517a0e9, m22be6817)\n", + "61: (dspn_str_dspn_e_p7aa400d6_mbb8e5b24, p7aa400d6, mbb8e5b24)\n", + "62: (dspn_str_dspn_e_p7aa400d6_mc710c1a4, p7aa400d6, mc710c1a4)\n", + "64: (dspn_str_dspn_e_p8bf90d1f_m22be6817, p8bf90d1f, m22be6817)\n", + "65: (dspn_str_dspn_e_p8bf90d1f_m37886c78, p8bf90d1f, m37886c78)\n", + "67: (dspn_str_dspn_e_p8bf90d1f_mbb8e5b24, p8bf90d1f, mbb8e5b24)\n", + "70: (dspn_str_dspn_e_pb0529fb9_m22be6817, pb0529fb9, m22be6817)\n", + "71: (dspn_str_dspn_e_pb0529fb9_m37886c78, pb0529fb9, m37886c78)\n", + "75: (dspn_str_dspn_e_pb0529fb9_mf702205f, pb0529fb9, mf702205f)\n", + "76: (dspn_str_dspn_e_pc8cbdb24_m22be6817, pc8cbdb24, m22be6817)\n", + "81: (dspn_str_dspn_e_pd01ac450_m22be6817, pd01ac450, m22be6817)\n", + "85: (dspn_str_dspn_e_pd01ac450_mf702205f, pd01ac450, mf702205f)\n", + "91: (dspn_str_dspn_e_pe6ec2d4b_mbb8e5b24, pe6ec2d4b, mbb8e5b24)\n", + "92: (dspn_str_dspn_e_pe6ec2d4b_mc710c1a4, pe6ec2d4b, mc710c1a4)\n" + ] + } + ], + "source": [ + "from snudda.plotting import PlotTraces\n", + "pt = PlotTraces(output_file=network_simulation_path, network_file=network_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "80f5d167-7400-4264-8cde-89f3f3e23f4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting traces: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130]\n", + "Plotted 131 traces (total 131)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/parallel/KTH_PDC/neuromodulation/networks/figures/Network-voltage-trace--dspn-traces.pdf\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.plot_traces(fig_size=(12, 15), mark_spikes=False)" + ] + }, + { + "cell_type": "markdown", + "id": "61cacb2a-0c22-45c8-8ca3-076828c97c4e", + "metadata": {}, + "source": [ + "## Check that neurons receive input" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b4738fd-cfc0-48ec-af4d-794af48de723", + "metadata": {}, + "outputs": [], + "source": [ + "from snudda.plotting import PlotInput\n", + "input_file = os.path.join(network_path, \"input-spikes.hdf5\")\n", + "spi = PlotInput(input_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "45882ff5-1f38-48c4-8da2-35075d972061", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3.63318749, 3.63318749, 3.63318749, ..., 3.63318749,\n", + " 3.63318749, 3.63318749],\n", + " ...,\n", + " [ 0.98784482, 0.98784482, 496.21047288, ..., 85.48800167,\n", + " 0.98784482, 600.30996535],\n", + " [ 0.98711958, 0.98711958, 494.59934108, ..., 83.87752159,\n", + " 0.98711958, 600.28975947],\n", + " [ 0.98639923, 0.98639923, 492.97283025, ..., 82.28478887,\n", + " 0.98639923, 600.26908276]])}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sls.get_data(\"PKAc\", 9)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "affa345a-10c2-46fd-b549-6a7f25e79ed2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.title(\"Dopamine\")\n", + "# plt.legend()\n", + "fig_path = os.path.join(network_path, \"figures\", \"DA_figure.png\")\n", + "plt.savefig(fig_path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "43bf9dd7-6e44-450b-abaa-3b462ba10fd3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_setup_neuromodulation_network.py b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_setup_neuromodulation_network.py new file mode 100644 index 000000000..4c746fb32 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_setup_neuromodulation_network.py @@ -0,0 +1,25 @@ +import os +from snudda.input.input_tuning import InputTuning + +# Create a separate dir for Neuromodulation Basal Ganglia Data while tuning +snudda_data = os.getenv("SNUDDA_DATA") + +network_path = os.path.join("..", "networks", "dspn_modulation") + +print(f"Creating network in {network_path}") + +input_tuning = InputTuning(network_path, snudda_data=snudda_data) + +neurons_path = os.path.join("$DATA", "neurons", "striatum") + +input_tuning.setup_network(neurons_path=neurons_path, + num_replicas=1, + neuron_types="dspn", + reaction_diffusion_file="reaction_diffusion_D1_with_DA_decay.json", + network_random_seed=1234) +input_tuning = None + + +from snudda import Snudda +snd = Snudda(network_path=network_path) +snd.setup_input(input_config="input.json") diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_setup_neuromodulation_network.sh b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_setup_neuromodulation_network.sh new file mode 100755 index 000000000..de46b5962 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_setup_neuromodulation_network.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# This is to prevent NEURON from trying to open display +unset DISPLAY + +export SNUDDA_DATA="$HOME/BasalGangliaData/data" + +JOBDIR=../networks/dspn_modulation + + +echo "Running Dardel_setup_neuromodulation_network: $JOBDIR" + +echo "SLURM_PROCID = $SLURM_PROCID" + +if [ "$SLURM_PROCID" -gt 0 ]; then + mock_string="Not main process" +else + echo "Running" + python Dardel_setup_neuromodulation_network.py +fi diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_simulate_neuromodulation_dspn.job b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_simulate_neuromodulation_dspn.job new file mode 100644 index 000000000..a02589fea --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/Dardel_simulate_neuromodulation_dspn.job @@ -0,0 +1,72 @@ +#!/bin/bash -l +#SBATCH --partition=main +#SBATCH -o log/Simulate-%j-output.txt +#SBATCH -e log/Simulate-%j-error.txt +#SBATCH -t 0:59:00 +#SBATCH --time-min=0:59:00 +#SBATCH -J Simulate +#SBATCH -A naiss2024-5-306 +#SBATCH --nodes=1 +#SBATCH --tasks-per-node=51 +#SBATCH --mail-type=ALL + +ulimit -s unlimited +module load snic-env + +source $HOME/Snudda/snudda_env/bin/activate +SNUDDA_DIR=/cfs/klemming/home/"${USER:0:1}"/$USER/Snudda + +export SNUDDA_DATA="/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" + +# Create the network + +export N_WORKERS=$SLURM_NTASKS + +# This will stop NEURON from failing with "can't open DISPLAY" +unset DISPLAY + +NETWORK_DIR=../networks/dspn_modulation +echo "Calling Dardel_setup_neuromodulation_network.sh" +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n 1 -N 1 --exact --overlap --mem=0 Dardel_setup_neuromodulation_network.sh + + +NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses.hdf5 +NETWORK_INPUT_FILE=$NETWORK_DIR/input-spikes.hdf5 + +echo "Network dir: "$NETWORK_DIR + +export PATH=$SNUDDA_DIR/snudda_env/bin/:$PATH +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CRAY_LD_LIBRARY_PATH +export PYTHONPATH=$SNUDDA_DIR/snudda_env/lib/python3.11/ + +export CXX=CC +export CC=cc +export FC=ftn +export MPICC=cc +export MPICXX=CC + +CC --version + +pushd $SNUDDA_DIR/examples/parallel/KTH_PDC/neuromodulation + +rm mechanisms +ln -s $SNUDDA_DATA/neurons/mechanisms/ mechanisms + +rm -r x86_64 + +echo "About to run nrnivmodl" +which nrnivmodl + +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n 1 nrnivmodl -incflags "-lltdl=/usr/lib64/libltdl.so.7 -lreadline=/lib64/libreadline.so.7 -lncurses=/lib64/libncurses.so.6.1" -loadflags "-DLTDL_LIBRARY=/usr/lib64/libltdl.so.7 -DREADLINE_LIBRARY=/lib64/libreadline.so.7 -DNCURSES_LIBRARY=/lib64/libncurses.so.6.1" mechanisms/ + +popd + +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/neuromodulation/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py dummy_file dummy_file --simulation_config dspn_experiment_config.json + +# TODO, we need to write an experiment config update, so that we use sim.add_rxd_internal_concentration_recording_all(species="PKA", neuron_id=0) + + + diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/dspn_experiment_config.json b/examples/parallel/KTH_PDC/neuromodulation/dspn/dspn_experiment_config.json new file mode 100644 index 000000000..8ccfa0a5e --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/dspn_experiment_config.json @@ -0,0 +1,18 @@ +{ + "network_file": "../networks/dspn_modulation/network-synapses.hdf5", + "input_file": "../networks/dspn_modulation/input-spikes.hdf5", + "output_file": "../networks/dspn_modulation/simulation/dspn-output.hdf5", + "log_file": "../networks/dspn_modulation/log/network-simulation-log.txt", + "sample_dt": 0.01, + "time": 4.5, + "record_all_soma": true, + "record_density_mechanism": { + "kir_ms.modulation_factor": { + "neuron_id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], + "section_id": [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], + "section_x": [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] + } + }, + "record_rxd_species_concentration_all_compartments": [["PKAc", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]], ["DA", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]]] + +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/input.json b/examples/parallel/KTH_PDC/neuromodulation/dspn/input.json new file mode 100644 index 000000000..bd67e1d11 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/input.json @@ -0,0 +1,40 @@ +{ + "dspn": { + "cortical": { + "generator": "poisson", + "start": [0.5, 2, 3.5], + "end": [1.5, 3, 4.5], + "frequency": [10, 10, 10], + "parameter_file": "tmglut_DA_parameters.json", + "mod_file": "tmGlut" + }, + + "GABA": { + "generator": "poisson", + "type": "GABA", + "start": [0.5, 2, 3.5], + "end": [1.5, 3, 4.5], + "frequency": [5, 5, 5], + "num_inputs": 100, + "conductance": 5e-10, + "mod_file": "tmGabaA", + "parameter_file": "$DATA/synapses/striatum/PlanertFitting-DD-tmgaba-fit.json" + }, + "Dopamine": { + "generator": "poisson", + "type": "GABA", + "start": [2, 3.5], + "end": [2.5, 4], + "frequency": [5, 10], + "num_inputs": 100, + "conductance": 0.5e-9, + "mod_file": "DASyn", + "RxD": { + "species_name": "DA", + "flux_variable": "open", + "region": "internal", + "weight_scale": 1e18 + } + } + } +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn/reaction_diffusion_D1.json b/examples/parallel/KTH_PDC/neuromodulation/dspn/reaction_diffusion_D1.json new file mode 100644 index 000000000..7c016e181 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn/reaction_diffusion_D1.json @@ -0,0 +1,668 @@ +{ + "species": { + "GaolfGDP": { + "initial_concentration": 0.0100831208954662, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 0.0100831208954662, + "boundary_condition": false + }, + "Gbgolf": { + "initial_concentration": 29.8851246006536, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" + ], + "concentration": 29.8851246006536, + "boundary_condition": false + }, + "GaolfGTP": { + "initial_concentration": 0.00891348109605658, + "diffusion_constant": 0, + "charge": 0, + "regions": [ + "soma_internal", + "dend_internal" 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"mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.2, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + }, + "mid": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1.15, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.3, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + }, + "high": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1.3, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.6, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + } + +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_analyse_dspn_bath.ipynb b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_analyse_dspn_bath.ipynb new file mode 100644 index 000000000..8545e9d85 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_analyse_dspn_bath.ipynb @@ -0,0 +1,636 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7afb8f8f-84c4-44e5-8d6c-2c4e64bece08", + "metadata": {}, + "source": [ + "# DSPN neuromodulation\n", + "\n", + "This jupyter notebook analyses ```Dardel_simulate_dspn_DA_bath.job```.\n", + "\n", + "\n", + "Here we use the data from Planert: Planert H, Berger TK, Silberberg G. Membrane properties of striatal direct and indirect pathway neurons in mouse and rat slices and their modulation by dopamine. PLoS One. 2013;8(3):e57054. doi:10.1371/journal.pone.0057054\n", + "\n", + "Bath application of 60 micromolar DA. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e363da89-25b4-4087-b05a-eb91099bca43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading ../networks/dspn_DA_bath/simulation/dspn-output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "43: (dspn_str_dspn_e_p1863c9a5_m22be6817, p1863c9a5, m22be6817)\n", + "44: (dspn_str_dspn_e_p1863c9a5_m37886c78, p1863c9a5, m37886c78)\n", + "45: (dspn_str_dspn_e_p1863c9a5_mc710c1a4, p1863c9a5, mc710c1a4)\n", + "46: (dspn_str_dspn_e_p1863c9a5_mf702205f, p1863c9a5, mf702205f)\n", + "50: (dspn_str_dspn_e_p510bab86_mc710c1a4, p510bab86, mc710c1a4)\n", + "53: (dspn_str_dspn_e_p7517a0e9_m37886c78, p7517a0e9, m37886c78)\n", + "54: (dspn_str_dspn_e_p7517a0e9_m9fda9b20, p7517a0e9, m9fda9b20)\n", + "58: (dspn_str_dspn_e_p7aa400d6_m22be6817, p7aa400d6, m22be6817)\n", + "59: (dspn_str_dspn_e_p7aa400d6_m37886c78, p7aa400d6, m37886c78)\n", + "61: (dspn_str_dspn_e_p7aa400d6_mbb8e5b24, p7aa400d6, mbb8e5b24)\n", + "62: (dspn_str_dspn_e_p7aa400d6_mc710c1a4, p7aa400d6, mc710c1a4)\n", + "65: (dspn_str_dspn_e_p8bf90d1f_m37886c78, p8bf90d1f, m37886c78)\n", + "66: (dspn_str_dspn_e_p8bf90d1f_m9fda9b20, p8bf90d1f, m9fda9b20)\n", + "67: (dspn_str_dspn_e_p8bf90d1f_mbb8e5b24, p8bf90d1f, mbb8e5b24)\n", + "68: (dspn_str_dspn_e_p8bf90d1f_mc710c1a4, p8bf90d1f, mc710c1a4)\n", + "69: (dspn_str_dspn_e_p8bf90d1f_mf702205f, p8bf90d1f, mf702205f)\n", + "70: (dspn_str_dspn_e_pb0529fb9_m22be6817, pb0529fb9, m22be6817)\n", + "71: (dspn_str_dspn_e_pb0529fb9_m37886c78, pb0529fb9, m37886c78)\n", + "73: (dspn_str_dspn_e_pb0529fb9_mbb8e5b24, pb0529fb9, mbb8e5b24)\n", + "74: (dspn_str_dspn_e_pb0529fb9_mc710c1a4, pb0529fb9, mc710c1a4)\n", + "79: (dspn_str_dspn_e_pc8cbdb24_mc710c1a4, pc8cbdb24, mc710c1a4)\n", + "81: (dspn_str_dspn_e_pd01ac450_m22be6817, pd01ac450, m22be6817)\n", + "87: (dspn_str_dspn_e_pe1ec8fbd_mbb8e5b24, pe1ec8fbd, mbb8e5b24)\n", + "88: (dspn_str_dspn_e_pe6ec2d4b_m22be6817, pe6ec2d4b, m22be6817)\n" + ] + } + ], + "source": [ + "import os\n", + "network_path = os.path.join(\"..\", \"networks\", \"dspn_DA_bath\")\n", + "network_simulation_path = os.path.join(network_path, \"simulation\", \"dspn-output.hdf5\")\n", + "\n", + "from snudda.utils import SnuddaLoadSimulation, SnuddaLoad\n", + "\n", + "sl = SnuddaLoad(network_path)\n", + "sls = SnuddaLoadSimulation(network_path=network_path, network_simulation_output_file=network_simulation_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "46e9acfa-2584-4c08-82d3-71a8a27a1239", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to ../networks/dspn_DA_bath/figures/spike_raster.pdf\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "spsr = SnuddaPlotSpikeRaster2(snudda_load=sl, snudda_simulation_load=sls, network_path=None)\n", + "spsr.plot_spike_raster()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "be19d431-751f-4e6e-b2a6-57729b4c1baf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading network info from ../networks/dspn_DA_bath\n", + "Loading input info from ../networks/dspn_DA_bath/input-spikes.hdf5\n", + "Loading ../networks/dspn_DA_bath/simulation/dspn-output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "43: (dspn_str_dspn_e_p1863c9a5_m22be6817, p1863c9a5, m22be6817)\n", + "44: (dspn_str_dspn_e_p1863c9a5_m37886c78, p1863c9a5, m37886c78)\n", + "45: (dspn_str_dspn_e_p1863c9a5_mc710c1a4, p1863c9a5, mc710c1a4)\n", + "46: (dspn_str_dspn_e_p1863c9a5_mf702205f, p1863c9a5, mf702205f)\n", + "50: (dspn_str_dspn_e_p510bab86_mc710c1a4, p510bab86, mc710c1a4)\n", + "53: (dspn_str_dspn_e_p7517a0e9_m37886c78, p7517a0e9, m37886c78)\n", + "54: (dspn_str_dspn_e_p7517a0e9_m9fda9b20, p7517a0e9, m9fda9b20)\n", + "58: (dspn_str_dspn_e_p7aa400d6_m22be6817, p7aa400d6, m22be6817)\n", + "59: (dspn_str_dspn_e_p7aa400d6_m37886c78, p7aa400d6, m37886c78)\n", + "61: (dspn_str_dspn_e_p7aa400d6_mbb8e5b24, p7aa400d6, mbb8e5b24)\n", + "62: (dspn_str_dspn_e_p7aa400d6_mc710c1a4, p7aa400d6, mc710c1a4)\n", + "65: (dspn_str_dspn_e_p8bf90d1f_m37886c78, p8bf90d1f, m37886c78)\n", + "66: (dspn_str_dspn_e_p8bf90d1f_m9fda9b20, p8bf90d1f, m9fda9b20)\n", + "67: (dspn_str_dspn_e_p8bf90d1f_mbb8e5b24, p8bf90d1f, mbb8e5b24)\n", + "68: (dspn_str_dspn_e_p8bf90d1f_mc710c1a4, p8bf90d1f, mc710c1a4)\n", + "69: (dspn_str_dspn_e_p8bf90d1f_mf702205f, p8bf90d1f, mf702205f)\n", + "70: (dspn_str_dspn_e_pb0529fb9_m22be6817, pb0529fb9, m22be6817)\n", + "71: (dspn_str_dspn_e_pb0529fb9_m37886c78, pb0529fb9, m37886c78)\n", + "73: (dspn_str_dspn_e_pb0529fb9_mbb8e5b24, pb0529fb9, mbb8e5b24)\n", + "74: (dspn_str_dspn_e_pb0529fb9_mc710c1a4, pb0529fb9, mc710c1a4)\n", + "79: (dspn_str_dspn_e_pc8cbdb24_mc710c1a4, pc8cbdb24, mc710c1a4)\n", + "81: (dspn_str_dspn_e_pd01ac450_m22be6817, pd01ac450, m22be6817)\n", + "87: (dspn_str_dspn_e_pe1ec8fbd_mbb8e5b24, pe1ec8fbd, mbb8e5b24)\n", + "88: (dspn_str_dspn_e_pe6ec2d4b_m22be6817, pe6ec2d4b, m22be6817)\n" + ] + } + ], + "source": [ + "from snudda.plotting import PlotTraces\n", + "pt = PlotTraces(output_file=network_simulation_path, network_file=network_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "80f5d167-7400-4264-8cde-89f3f3e23f4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting traces: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130]\n", + "Plotted 131 traces (total 131)\n", + "Saving to figure /home/hjorth/HBP/Snudda/examples/parallel/KTH_PDC/neuromodulation/networks/figures/Network-voltage-trace--dspn-traces.pdf\n" + ] + }, + { + "data": { + "image/png": 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FCxeic+fOiIqKgtVqRXR0NIYNG4bDhw/fUsxlZWVYuHAhHnroIdSsWRPe3t7w9PREXFwcBg8ejM8++wy8a5+IiOj3jZ02ExEREd2i7OxsDBo0CP/5z3/cPs/MzMSKFSvw2Wef4V//+hdGjBgBADh27BgefPDBSomdM2fO4MMPP8SSJUuwdOlS9OnT54bTXr9+PUaNGoXjx49X+u7UqVM4deoUlixZghYtWmD58uWIiIj45TNKRERE9ywmfIiIiIhuQXl5Ofr164eNGzfCarWiffv2iI6OxsWLF/H9998jNzcXIoJRo0YhISEBtWvXRqdOnXDmzBn4+PigXbt2CAsLw4ULF/Ddd9+huLgYDocDQ4YMwcGDBxEXF3fNaS9btgyPPPIIysrKAAA2mw0tWrRAbGws9Ho9UlNTsXXrVpSXl2Pbtm1o2bIldu7ciRo1atypxUNERER3CSZ8iIiIiG7B8uXLUVpait69e+P9999HSEiI+u7SpUvo3bs3Nm7cCJfLhZdffhm+vr44c+YMRo8ejRkzZsDb21sNf/bsWXTt2hWHDx9GSUkJXnnlFcybN++q0z148CCGDRuGsrIy6HQ6PP3003jhhRfg5+fnNtyJEycwbNgwbNq0CWfOnMGIESOwZs2a27IsiIiI6O7FPnyIiIiIbkFpaSk6dOiATz/91C3ZAwD+/v5YuHAhDAYDAGDdunVYuXIlhg0bhn/+859uyR4AiIyMxAcffKD+X758OcrLy6863SeeeAIlJSUAgFmzZuHvf/97pWQPANSsWRNff/01kpKSAABfffUVtm/f/ovnl4iIiO5NTPgQERER3aI33nhDJXWuFBMTg1atWqn/LRYLZsyYcc1xtW7dGlFRUQCAgoICHDlypNIwP/30k+ovqFGjRnjyySevG5+npydeeukl9f9HH3103eGJiIio+mHCh4iIiOgW1KpVCw0bNrzuMPXr11fv27ZtW6kl0JWSk5PV+5MnT1b6vuItWYMHD4ZOp7thnJ06dVLvN23adMPhiYiIqHphHz5EREREt6BicuZa/P391ft69erdcPiAgAD1Pj8/v9L3FR/zvm7dOqSlpd1wnBUfy37mzJkbDk9ERETVCxM+RERERLfA19f3hsMYjf+tYt3q8NoTuCo6d+6cev/VV1/dcHxXunTp0i3/hoiIiO5tvKWLiIiI6BbczO1Uv2b4q8nLy/tVv3c6nb86BiIiIrq3MOFDREREdJfz9PRU71esWAERueUXERER/b4w4UNERER0l6tRo4Z6n5GRUYWREBER0b2CCR8iIiKiu1zz5s3V+82bN1dhJERERHSvYMKHiIiI6C7Xq1cv9X7FihW4cOFCFUZDRERE9wImfIiIiIjucs2aNUOHDh0AACUlJXj00UfhcDhu6rcOh4NP6SIiIvodYsKHiIiI6B7wzjvvwMvLCwDw7bffol27dti+ffs1h09NTcUrr7yC2NhY3gZGRET0O2Ss6gCIiIiI6MaSk5Px8ccfY9CgQSguLsb27dvRokUL1KpVC40bN0ZAQADsdjsyMzOxb98+pKenV3XIREREVIWY8CEiIiK6R/Tq1QtbtmzByJEjsWvXLgDA8ePHcfz48Wv+JjY2FpGRkXcqRCIiIrpLMOFDREREdA+577778OOPP2Lt2rVYuXIlNm/ejHPnziE3NxcWiwXBwcGoU6cOmjdvjm7duqFly5bQ6XRVHTYRERHdYToRkaoOgoiIiIiIiIiIfjvstJmIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJphwoeIiIiIiIiIqJr5fyS0J1qC2xkMAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pt.plot_traces(fig_size=(12, 15), mark_spikes=False)" + ] + }, + { + "cell_type": "markdown", + "id": "61cacb2a-0c22-45c8-8ca3-076828c97c4e", + "metadata": {}, + "source": [ + "## Check that neurons receive input" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1b4738fd-cfc0-48ec-af4d-794af48de723", + "metadata": {}, + "outputs": [], + "source": [ + "from snudda.plotting import PlotInput\n", + "input_file = os.path.join(network_path, \"input-spikes.hdf5\")\n", + "spi = PlotInput(input_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "45882ff5-1f38-48c4-8da2-35075d972061", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spi.plot_input(\"dspn\", 2, fig_size=(15,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4c57333-42fb-4597-906a-e1926775ea01", + "metadata": {}, + "outputs": [], + "source": [ + "time = sls.get_time()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2db4273a-04f8-4346-aa6e-3de4726f660c", + "metadata": {}, + "outputs": [], + "source": [ + "data_pka = sls.get_data(\"PKAc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "37a6190c-594b-4176-ae75-4b49ba5e0285", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3.66078058, 3.66078058, 3.66078058, ..., 3.66078058, 3.66078058,\n", + " 3.66078058],\n", + " [2.43455679, 2.43455679, 2.43455679, ..., 2.43455679, 2.43455679,\n", + " 2.43455679],\n", + " [1.84552244, 1.84552244, 1.84552244, ..., 1.84552244, 1.84552244,\n", + " 1.84552244],\n", + " ...,\n", + " [0.8793405 , 0.8793405 , 0.8793405 , ..., 0.8793405 , 0.8793405 ,\n", + " 0.8793405 ],\n", + " [0.8793405 , 0.8793405 , 0.8793405 , ..., 0.8793405 , 0.8793405 ,\n", + " 0.8793405 ],\n", + " [0.8793405 , 0.8793405 , 0.8793405 , ..., 0.8793405 , 0.8793405 ,\n", + " 0.8793405 ]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_pka[0][9]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6ebd51ef-5818-443d-8a5e-71f8ae1a2840", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n", + " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,\n", + " 50, 51, 52, 53, 54, 55, 56, 57]),\n", + " array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", + " 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]))" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_pka[1][9]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "df1ecb85-f2bd-4739-8dd5-abad6d5c9015", + "metadata": {}, + "outputs": [], + "source": [ + "time = sls.get_time()\n", + "data_pka = sls.get_data(\"PKAc\", 9)[0][9]\n", + "data_da = sls.get_data(\"DA\", 9)[0][9]\n", + "data_kir_mod = sls.get_data(\"kir_ms.modulation_factor\", 9)[0][9]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "982dc471-1d42-407f-8132-e3aa444f8c03", + "metadata": {}, + "outputs": [], + "source": [ + "# sls.get_data(\"PKAc\", 9)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "affa345a-10c2-46fd-b549-6a7f25e79ed2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_da)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.title(\"Dopamine\")\n", + "# plt.legend()\n", + "fig_path = os.path.join(network_path, \"figures\", \"DA_figure.png\")\n", + "plt.savefig(fig_path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "43bf9dd7-6e44-450b-abaa-3b462ba10fd3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_pka, label=\"PKAc\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "#plt.legend()\n", + "plt.title(\"PKAc\")\n", + "fig_path = os.path.join(network_path, \"figures\", \"PKAc_figure.png\")\n", + "plt.savefig(fig_path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a8919b06-8df3-43b3-803d-0c226267b195", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure()\n", + "plt.plot(time, data_kir_mod)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Concentration\")\n", + "plt.title(\"Kir modulation factor\")\n", + "# plt.legend()\n", + "fig_path = os.path.join(network_path, \"figures\", \"Kir_mod-figure.png\")\n", + "plt.savefig(fig_path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ef2bab29-67fa-4bb5-b229-959741e43193", + "metadata": {}, + "outputs": [], + "source": [ + "# data_mf = sls.get_data(\"kir_ms.modulation_factor\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "26b00c5f-7219-4692-90f8-9a3fe57d612d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading ../networks/dspn_DA_bath/simulation/dspn-output.hdf5\n", + "WARNING. Depolarisation block in neuron - neuron_id: (name, parameter_key, morphology_key):\n", + "43: (dspn_str_dspn_e_p1863c9a5_m22be6817, p1863c9a5, m22be6817)\n", + "44: (dspn_str_dspn_e_p1863c9a5_m37886c78, p1863c9a5, m37886c78)\n", + "45: (dspn_str_dspn_e_p1863c9a5_mc710c1a4, p1863c9a5, mc710c1a4)\n", + "46: (dspn_str_dspn_e_p1863c9a5_mf702205f, p1863c9a5, mf702205f)\n", + "50: (dspn_str_dspn_e_p510bab86_mc710c1a4, p510bab86, mc710c1a4)\n", + "53: (dspn_str_dspn_e_p7517a0e9_m37886c78, p7517a0e9, m37886c78)\n", + "54: (dspn_str_dspn_e_p7517a0e9_m9fda9b20, p7517a0e9, m9fda9b20)\n", + "58: (dspn_str_dspn_e_p7aa400d6_m22be6817, p7aa400d6, m22be6817)\n", + "59: (dspn_str_dspn_e_p7aa400d6_m37886c78, p7aa400d6, m37886c78)\n", + "61: (dspn_str_dspn_e_p7aa400d6_mbb8e5b24, p7aa400d6, mbb8e5b24)\n", + "62: (dspn_str_dspn_e_p7aa400d6_mc710c1a4, p7aa400d6, mc710c1a4)\n", + "65: (dspn_str_dspn_e_p8bf90d1f_m37886c78, p8bf90d1f, m37886c78)\n", + "66: (dspn_str_dspn_e_p8bf90d1f_m9fda9b20, p8bf90d1f, m9fda9b20)\n", + "67: (dspn_str_dspn_e_p8bf90d1f_mbb8e5b24, p8bf90d1f, mbb8e5b24)\n", + "68: (dspn_str_dspn_e_p8bf90d1f_mc710c1a4, p8bf90d1f, mc710c1a4)\n", + "69: (dspn_str_dspn_e_p8bf90d1f_mf702205f, p8bf90d1f, mf702205f)\n", + "70: (dspn_str_dspn_e_pb0529fb9_m22be6817, pb0529fb9, m22be6817)\n", + "71: (dspn_str_dspn_e_pb0529fb9_m37886c78, pb0529fb9, m37886c78)\n", + "73: (dspn_str_dspn_e_pb0529fb9_mbb8e5b24, pb0529fb9, mbb8e5b24)\n", + "74: (dspn_str_dspn_e_pb0529fb9_mc710c1a4, pb0529fb9, mc710c1a4)\n", + "79: (dspn_str_dspn_e_pc8cbdb24_mc710c1a4, pc8cbdb24, mc710c1a4)\n", + "81: (dspn_str_dspn_e_pd01ac450_m22be6817, pd01ac450, m22be6817)\n", + "87: (dspn_str_dspn_e_pe1ec8fbd_mbb8e5b24, pe1ec8fbd, mbb8e5b24)\n", + "88: (dspn_str_dspn_e_pe6ec2d4b_m22be6817, pe6ec2d4b, m22be6817)\n", + "Saving figure to ../networks/dspn_DA_bath/figures/spike-raster.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from snudda.plotting import SnuddaPlotSpikeRaster2\n", + "fig_file_raster = f\"spike-raster.png\"\n", + "\n", + "spr = SnuddaPlotSpikeRaster2(network_path=network_path, simulation_file=network_simulation_path)\n", + "\n", + "spr.plot_spike_raster(fig_file=fig_file_raster)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "aa61b1c1-fb4d-4b31-ae62-588d53321cdd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spr.plot_firing_frequency_distribution(bins=30)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b7ae8f1a-9b1e-4645-b94d-1e3ce3df100e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure ../networks/dspn_DA_bath/figures/spike-frequency-pop-units0.pdf\n" + ] + }, + { + "data": { + "image/png": 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", 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"d72bcced-3fb5-4199-ae52-75d7db9d8b52", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_setup_neuromodulation_network.py b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_setup_neuromodulation_network.py new file mode 100644 index 000000000..c4d7518cc --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_setup_neuromodulation_network.py @@ -0,0 +1,25 @@ +import os +from snudda.input.input_tuning import InputTuning + +# Create a separate dir for Neuromodulation Basal Ganglia Data while tuning +snudda_data = os.getenv("SNUDDA_DATA") + +network_path = os.path.join("..", "networks", "dspn_DA_bath") + +print(f"Creating network in {network_path}") + +input_tuning = InputTuning(network_path, snudda_data=snudda_data) + +neurons_path = os.path.join("$DATA", "neurons", "striatum") + +input_tuning.setup_network(neurons_path=neurons_path, + num_replicas=1, + neuron_types="dspn", + reaction_diffusion_file="reaction_diffusion_D1_bath.json", + network_random_seed=1234) +input_tuning = None + + +from snudda import Snudda +snd = Snudda(network_path=network_path) +snd.setup_input(input_config="input.json") diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_setup_neuromodulation_network.sh b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_setup_neuromodulation_network.sh new file mode 100755 index 000000000..0e8cef364 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_setup_neuromodulation_network.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# This is to prevent NEURON from trying to open display +unset DISPLAY + +export SNUDDA_DATA="$HOME/BasalGangliaData/data" + +JOBDIR=../networks/dspn_DA_bath + + +echo "Running Dardel_setup_neuromodulation_network: $JOBDIR" + +echo "SLURM_PROCID = $SLURM_PROCID" + +if [ "$SLURM_PROCID" -gt 0 ]; then + mock_string="Not main process" +else + echo "Running" + python Dardel_setup_neuromodulation_network.py +fi diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_simulate_dspn_DA_bath.job b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_simulate_dspn_DA_bath.job new file mode 100644 index 000000000..dda7df1c8 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/Dardel_simulate_dspn_DA_bath.job @@ -0,0 +1,71 @@ +#!/bin/bash -l +#SBATCH --partition=main +#SBATCH -o log/Simulate-%j-output.txt +#SBATCH -e log/Simulate-%j-error.txt +#SBATCH -t 0:59:00 +#SBATCH --time-min=0:59:00 +#SBATCH -J Simulate +#SBATCH -A naiss2024-5-306 +#SBATCH --nodes=1 +#SBATCH --tasks-per-node=51 +#SBATCH --mail-type=ALL + +ulimit -s unlimited +module load snic-env + +source $HOME/Snudda/snudda_env/bin/activate +SNUDDA_DIR=/cfs/klemming/home/"${USER:0:1}"/$USER/Snudda + +export SNUDDA_DATA="/cfs/klemming/home/${USER:0:1}/$USER/BasalGangliaData/data" + +# Create the network + +export N_WORKERS=$SLURM_NTASKS + +# This will stop NEURON from failing with "can't open DISPLAY" +unset DISPLAY + +NETWORK_DIR=../networks/dspn_modulation +echo "Calling Dardel_setup_neuromodulation_network.sh" +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n 1 -N 1 --exact --overlap --mem=0 Dardel_setup_neuromodulation_network.sh + + +NETWORK_INFO_FILE=$NETWORK_DIR/network-synapses.hdf5 +NETWORK_INPUT_FILE=$NETWORK_DIR/input-spikes.hdf5 + +echo "Network dir: "$NETWORK_DIR + +export PATH=$SNUDDA_DIR/snudda_env/bin/:$PATH +export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CRAY_LD_LIBRARY_PATH +export PYTHONPATH=$SNUDDA_DIR/snudda_env/lib/python3.11/ + +export CXX=CC +export CC=cc +export FC=ftn +export MPICC=cc +export MPICXX=CC + +CC --version + +pushd $SNUDDA_DIR/examples/parallel/KTH_PDC/neuromodulation + +rm mechanisms +ln -s $SNUDDA_DATA/neurons/mechanisms/ mechanisms + +rm -r x86_64 + +echo "About to run nrnivmodl" +which nrnivmodl + +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n 1 nrnivmodl -incflags "-lltdl=/usr/lib64/libltdl.so.7 -lreadline=/lib64/libreadline.so.7 -lncurses=/lib64/libncurses.so.6.1" -loadflags "-DLTDL_LIBRARY=/usr/lib64/libltdl.so.7 -DREADLINE_LIBRARY=/lib64/libreadline.so.7 -DNCURSES_LIBRARY=/lib64/libncurses.so.6.1" mechanisms/ + +popd + +export FI_CXI_DEFAULT_VNI=$(od -vAn -N4 -tu < /dev/urandom) +srun -n $N_WORKERS $SNUDDA_DIR/examples/parallel/KTH_PDC/neuromodulation/x86_64/special -mpi -python $SNUDDA_DIR/snudda/simulate/simulate.py dummy_file dummy_file --simulation_config dspn_bath_experiment_config.json + + + + diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/dspn_bath_experiment_config.json b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/dspn_bath_experiment_config.json new file mode 100644 index 000000000..3083763ae --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/dspn_bath_experiment_config.json @@ -0,0 +1,27 @@ +{ + "network_file": "../networks/dspn_DA_bath/network-synapses.hdf5", + "input_file": "../networks/dspn_DA_bath/input-spikes.hdf5", + "output_file": "../networks/dspn_DA_bath/simulation/dspn-output.hdf5", + "log_file": "../networks/dspn_DA_bath/log/network-simulation-log.txt", + "sample_dt": 0.01, + "time": 5, + "record_all_soma": true, + + "rxd_enable_extracellular": false, + "bath_application": { + "DA": { + "time": [0, 1.99, 2, 3, 3.01, 10], + "concentration": [0, 0, 60e-6, 60e-6, 0, 0] + } + }, + + "record_density_mechanism": { + "kir_ms.modulation_factor": { + "neuron_id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], + "section_id": [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], + "section_x": [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] + } + }, + "record_rxd_species_concentration_all_compartments": [["PKAc", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]], ["DA", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]]] + +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/input.json b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/input.json new file mode 100644 index 000000000..dbb7834b2 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/input.json @@ -0,0 +1,24 @@ +{ + "dspn": { + "cortical": { + "generator": "poisson", + "start": [0.5, 2, 3.5], + "end": [1.5, 3, 4.5], + "frequency": [10, 10, 10], + "parameter_file": "tmglut_DA_parameters.json", + "mod_file": "tmGlut" + }, + + "GABA": { + "generator": "poisson", + "type": "GABA", + "start": [0.5, 2, 3.5], + "end": [1.5, 3, 4.5], + "frequency": [5, 5, 5], + "num_inputs": 100, + "conductance": 5e-10, + "mod_file": "tmGabaA", + "parameter_file": "$DATA/synapses/striatum/PlanertFitting-DD-tmgaba-fit.json" + } + } +} 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+ "products": "PP1_Target1p", + "forward_rate": 0.001, + "backward_rate": 10.0, + "regions": [ + "soma_internal", + "dend_internal" + ] + }, + "revreaction_16": { + "reactants": "Target1 + PKAc", + "products": "PKAc_Target1", + "forward_rate": 0.08, + "backward_rate": 10.0, + "regions": [ + "soma_internal", + "dend_internal" + ] + }, + "irrevreaction_14": { + "reactants": "PKAc_Target1", + "products": "PKAc + Target1p", + "forward_rate": 10.0, + "backward_rate": null, + "regions": [ + "soma_internal", + "dend_internal" + ] + }, + "irrevreaction_15": { + "reactants": "PP1_Target1p", + "products": "PP1 + Target1", + "forward_rate": 5.0, + "backward_rate": null, + "regions": [ + "soma_internal", + "dend_internal" + ] + } + } +} diff --git a/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/tmglut_DA_parameters.json b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/tmglut_DA_parameters.json new file mode 100644 index 000000000..9f4fd28d5 --- /dev/null +++ b/examples/parallel/KTH_PDC/neuromodulation/dspn_DA_bath/tmglut_DA_parameters.json @@ -0,0 +1,39 @@ +{ + "low": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.2, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + }, + "mid": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1.15, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.3, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + }, + "high": { + "synapse": { + "mod_pka_g_ampa_min": 1, + "mod_pka_g_ampa_max": 1.3, + "mod_pka_g_ampa_half": 12.5, + "mod_pka_g_ampa_slope": 1, + "mod_pka_g_nmda_min": 1, + "mod_pka_g_nmda_max": 1.6, + "mod_pka_g_nmda_half": 12.5, + "mod_pka_g_nmda_slope": 1 + } + } + +} diff --git a/requirements.txt b/requirements.txt index fa06518d1..539d8ef83 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,9 @@ -bluepyopt +bluepyopt >= 1.14.15 h5py >=3.2.1 ipyparallel>=6.3.0 matplotlib>=3.3.4 mpi4py>=3.0.3 -numba>=0.56.4 # Optimisation +numba==0.59.1 # Optimisation, 0.60 requires numpy 2.0 and neuron is not yet compatible # numpy==1.23.5 # -- numpy requirement satisfied through numba's numpy dependence scipy>=1.6.3 sonata>=0.0.2 diff --git a/setup.py b/setup.py index ce6c5608e..02d1b70b1 100644 --- a/setup.py +++ b/setup.py @@ -33,7 +33,8 @@ "psutil", "numexpr>=2.7.3", "numba>=0.56.4", - "wheel" + "wheel", + "open3d" # "igraph" ] diff --git a/snudda/__init__.py b/snudda/__init__.py index dd54d5b8b..3479630f2 100644 --- a/snudda/__init__.py +++ b/snudda/__init__.py @@ -1,6 +1,6 @@ from .core import Snudda -__version__ = "2.0.2" +__version__ = "2.1.2" from .init import SnuddaInit from .place import SnuddaPlace diff --git a/snudda/analyse/analyse.py b/snudda/analyse/analyse.py index 9f93dcbb1..d8c5d266b 100644 --- a/snudda/analyse/analyse.py +++ b/snudda/analyse/analyse.py @@ -7,6 +7,7 @@ import time import timeit from collections import OrderedDict +from copy import deepcopy from glob import glob import h5py @@ -81,7 +82,7 @@ def __init__(self, self.snudda_data = self.network["snudda_data"] if "config" in self.network: - self.config = json.loads(self.network["config"], object_pairs_hook=OrderedDict) + self.config = deepcopy(self.network["config"]) self.side_len = side_len self.low_memory = low_memory diff --git a/snudda/analyse/analyse_gap_junction_coupling.py b/snudda/analyse/analyse_gap_junction_coupling.py index 284c7d3b1..4c8229a4a 100644 --- a/snudda/analyse/analyse_gap_junction_coupling.py +++ b/snudda/analyse/analyse_gap_junction_coupling.py @@ -4,7 +4,7 @@ from snudda.simulate.pair_recording import PairRecording from snudda.utils.load import SnuddaLoad -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation class AnalyseGapJunctionCoupling: @@ -32,9 +32,9 @@ def load_simulation_data(self): output_file = None self.network_simulation = \ - SnuddaLoadNetworkSimulation(network_simulation_output_file=os.path.join(self.network_path, + SnuddaLoadSimulation(network_simulation_output_file=os.path.join(self.network_path, "simulation", - output_file)) + output_file)) def get_gap_junction_connection_matrix(self): diff --git a/snudda/analyse/analyse_spike_trains.py b/snudda/analyse/analyse_spike_trains.py index 7c76e46a5..095a7fdc5 100644 --- a/snudda/analyse/analyse_spike_trains.py +++ b/snudda/analyse/analyse_spike_trains.py @@ -9,7 +9,7 @@ # from neo import SpikeTrain as NeoSpikeTrain # import quantities as pq -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation from snudda.utils.load import SnuddaLoad @@ -48,7 +48,7 @@ def __init__(self, network_file=None, output_file=None, input_file=None, network self.input_file = input_file if output_file is not None: - self.output_data = SnuddaLoadNetworkSimulation(network_simulation_output_file=self.output_file) + self.output_data = SnuddaLoadSimulation(network_simulation_output_file=self.output_file) else: self.output_data = None diff --git a/snudda/analyse/analyse_topology.py b/snudda/analyse/analyse_topology.py index 84d471f20..b93996b99 100644 --- a/snudda/analyse/analyse_topology.py +++ b/snudda/analyse/analyse_topology.py @@ -3,7 +3,7 @@ import numpy as np from snudda.utils.load import SnuddaLoad -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation from snudda.utils.export_connection_matrix import SnuddaExportConnectionMatrix from collections import OrderedDict diff --git a/snudda/analyse/analyse_topology_activity.py b/snudda/analyse/analyse_topology_activity.py index 54993def9..9414cc7d6 100644 --- a/snudda/analyse/analyse_topology_activity.py +++ b/snudda/analyse/analyse_topology_activity.py @@ -3,7 +3,7 @@ import numpy as np from snudda.utils.load import SnuddaLoad -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation from snudda.utils.export_connection_matrix import SnuddaExportConnectionMatrix from collections import OrderedDict @@ -19,7 +19,7 @@ def __init__(self): self.mapping_dictionary = dict() def load_simulation_data(self, data_key, simulation_output=None): - self.simulation_data[data_key] = SnuddaLoadNetworkSimulation(network_simulation_output_file=simulation_output) + self.simulation_data[data_key] = SnuddaLoadSimulation(network_simulation_output_file=simulation_output) self.load_mapping_file(data_key) def load_mapping_file(self, data_key): diff --git a/snudda/cli.py b/snudda/cli.py index a8546838a..099321e10 100644 --- a/snudda/cli.py +++ b/snudda/cli.py @@ -138,22 +138,19 @@ def snudda_cli(): help="Exclude voltage data, to save time and space.") simulate_parser.add_argument("-randomseed", "--randomseed", "--seed", default=None, help="Random seed", type=int) - simulate_parser.add_argument("--neuromodulation", type=str, default=None, - help=('replay plays back a vector of modulation level, ' - 'adaptive sets modulation based on spiking activity')) - simulate_parser.add_argument("--disableSyn", "--disableSynapses", action="store_true", dest="disable_synapses", default=None, help="Disable synapses") simulate_parser.add_argument("--disableGJ", "--disableGapJunctions", action="store_true", dest="disable_gj", default=None, help="Disable gap junctions") - simulate_parser.add_argument("-mechdir", "--mechDir", dest="mech_dir", + simulate_parser.add_argument("--mechdir", "--mechDir", dest="mech_dir", help="mechanism directory if not default", default=None) simulate_parser.add_argument("--profile", help="Run python cProfile", action="store_true") simulate_parser.add_argument("--verbose", action="store_true") simulate_parser.add_argument("--exportCoreNeuron", action="store_true") simulate_parser.add_argument("--recordALL", dest="record_all", type=str, default=None) + simulate_parser.add_argument("--disable_rxd_neuromodulation", dest="use_rxd_neuromodulation", action="store_false", default=True) export_parser = sub_parsers.add_parser("export") export_parser.add_argument("path", help="Location of network") diff --git a/snudda/core.py b/snudda/core.py index d48d6610a..3c6d8d57b 100755 --- a/snudda/core.py +++ b/snudda/core.py @@ -166,6 +166,68 @@ def init_config(self, ############################################################################ + def init_tiny(self, neuron_paths, neuron_names, number_of_neurons, + morphology_key=None, parameter_key=None, + connection_config=None, random_seed=None, density=80500, d_min=15e-6): + + """ + network_path : Network path + + """ + + from snudda.init.init import SnuddaInit + from snudda.place import create_cube_mesh + + n_total = np.sum(number_of_neurons) + + si = SnuddaInit(network_path=self.network_path, + random_seed=random_seed) + + si.define_structure(struct_name="Cube", + struct_mesh="cube", + d_min=d_min, + struct_centre=(0.0, 0.0, 0.0), + side_len=(n_total/density)**(1/3)*1e-3, + num_neurons=n_total, + n_putative_points=n_total*5) + + if connection_config is not None: + si.replace_connectivity(connection_file=connection_config) + + if isinstance(neuron_paths, str): + neuron_paths = [neuron_paths] + + if isinstance(neuron_names, str): + neuron_names = [neuron_names] + + if isinstance(number_of_neurons, int): + number_of_neurons = [int(number_of_neurons / len(neuron_paths)) for x in neuron_names] + + if isinstance(morphology_key, str): + morphology_key = [morphology_key] + + if isinstance(parameter_key, str): + parameter_key = [parameter_key] + + assert (morphology_key is None and parameter_key is None) or \ + len(morphology_key) == len(parameter_key) == len(neuron_paths) + + for idx, (path, name, cnt) in enumerate(zip(neuron_paths, neuron_names, number_of_neurons)): + if name in si.network_data["regions"]["Cube"]["neurons"]: + raise ValueError(f"neuron name {name} defined more than once") + + si.add_neurons(name=name, neuron_dir=path, region_name="Cube", num_neurons=cnt) + + if morphology_key is not None: + si.network_data["regions"]["Cube"]["neurons"][name]["parameter_key"] = parameter_key[idx] + si.network_data["regions"]["Cube"]["neurons"][name]["morphology_key"] = morphology_key[idx] + + si.write_json() + + return si + + ############################################################################ + def import_config_wrapper(self, args): self.import_config(network_config_file=args.config_file, @@ -194,10 +256,10 @@ def import_config(self, network_config_file, snudda_data=None, overwrite=False): ############################################################################ - def create_network(self): + def create_network(self, honor_morphology_stay_inside=True): # This is a helper function, to create the full network - self.place_neurons() + self.place_neurons(honor_morphology_stay_inside=honor_morphology_stay_inside) self.detect_synapses() self.prune_synapses() @@ -235,7 +297,7 @@ def place_neurons(self, ipython_timeout=120, h5libver="latest", verbose=False, - honor_morphology_stay_inside=False): + honor_morphology_stay_inside=True): if parallel is None: parallel = self.parallel @@ -618,17 +680,21 @@ def compile_mechanisms(mech_dir=None, snudda_data=None): try: if os.path.exists("nrnmech.dll"): - h.nrn_load_dll("nrnmech.dll") + mech_path = "nrnmech.dll" elif os.path.exists("x86_64"): - h.nrn_load_dll("x86_64/.libs/libnrnmech.so") + mech_path = "x86_64/.libs/libnrnmech.so" elif os.path.exists("aarch64"): - h.nrn_load_dll("aarch64/.libs/libnrnmech.so") + mech_path = "aarch64/.libs/libnrnmech.so" elif os.path.exists("arm64"): - h.nrn_load_dll("arm64/.libs/libnrnmech.so") + mech_path = "arm64/.libs/libnrnmech.so" else: print(f"Could not find compiled mechanisms. Compile using 'nrnivmodl {mech_dir}' " f"and retry simulation.") sys.exit(-1) + + print(f"Loading mechanisms from '{os.path.abspath(mech_path)}'") + h.nrn_load_dll(mech_path) + except: import traceback print(f"Error while loading mechanisms:\n{traceback.format_exc()}") @@ -650,7 +716,7 @@ def simulate_wrapper(self, args): Example: snudda simulate [--networkFile NETWORK_FILE] [--inputFile INPUT_FILE] [--time TIME] - [--spikesOut SPIKES_OUT] [--neuromodulation NEUROMODULATION] [--noVolt] [--disableGJ] + [--spikesOut SPIKES_OUT] [--noVolt] [--disableGJ] [-mechdir MECH_DIR] [--profile] [--verbose] [--exportCoreNeuron] path """ @@ -660,13 +726,14 @@ def simulate_wrapper(self, args): output_file=args.output_file, snudda_data=args.snudda_data, time=args.time, mech_dir=args.mech_dir, - neuromodulation=args.neuromodulation, + # neuromodulation=args.neuromodulation, disable_synapses=args.disable_synapses, disable_gj=args.disable_gj, record_volt=args.record_volt, record_all=args.record_all, simulation_config=args.simulation_config, export_core_neuron=args.exportCoreNeuron, + use_rxd_neuromodulation=args.use_rxd_neuromodulation, verbose=args.verbose) sim.clear_neuron() @@ -678,18 +745,27 @@ def simulate(self, snudda_data=None, time=None, mech_dir=None, - neuromodulation=None, + # neuromodulation=None, disable_synapses=None, disable_gj=False, record_volt=True, record_all=False, + sample_dt=None, simulation_config=None, export_core_neuron=False, + use_rxd_neuromodulation=True, verbose=False): start = timeit.default_timer() from mpi4py import MPI # This must be imported before neuron, to run parallel + + # Initialize MPI + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + size = comm.Get_size() + print(f"MPI Rank: {rank}, Size: {size}") + from neuron import h pc = h.ParallelContext() @@ -697,7 +773,9 @@ def simulate(self, network_file = os.path.join(self.network_path, "network-synapses.hdf5") if input_file is None: - input_file = os.path.join(self.network_path, "input-spikes.hdf5") + putative_input_file = os.path.join(self.network_path, "input-spikes.hdf5") + if os.path.isfile(putative_input_file): + input_file = putative_input_file if output_file is None: output_file = os.path.join(self.network_path, "simulation", "output.hdf5") @@ -721,15 +799,6 @@ def simulate(self, mech_dir = os.path.realpath(snudda_path.snudda_parse_path(os.path.join("$DATA", "neurons", "mechanisms"), snudda_data)) - if neuromodulation is not None: - # read neuromod file and determine if it is replay or adaptive, then if and import the correct one - with open(neuromodulation, "r") as f: - neuromod_dict = json.load(f, object_pairs_hook=OrderedDict) - - if "adaptive" in neuromod_dict["type"]: - mech_dir = os.path.realpath(snudda_path.snudda_parse_path(os.path.join("$DATA", "neurons", - "mechanisms_ptr"), - snudda_data=snudda_data)) self.compile_mechanisms(mech_dir=mech_dir) save_dir = os.path.join(os.path.dirname(network_file), "simulation") @@ -757,66 +826,21 @@ def simulate(self, print(f"Creating directory {log_dir}") os.makedirs(log_dir, exist_ok=True) - if neuromodulation is not None: - - # read neuromod file and determine if it is replay or adaptive, then if and import the correct one - - with open(neuromodulation, 'r') as neuromod_f: - neuromod_dict = json.load(neuromod_f, object_pairs_hook=OrderedDict) - - if 'type' not in neuromod_dict: - print(f"Neuromodulation is not defined correctly in {neuromodulation} : 'type' is missing. " - f"Did you specify the correct file?") - sys.exit(-1) - - elif 'replay' in neuromod_dict['type']: - from snudda.neuromodulation.neuromodulation import SnuddaSimulateNeuromodulation - - sim = SnuddaSimulateNeuromodulation(network_file=network_file, - input_file=input_file, - output_file=output_file, - disable_gap_junctions=disable_gj, - disable_synapses=disable_synapses, - log_file=log_file, - simulation_config=simulation_config, - verbose=verbose) - - sim.setup() - sim.add_external_input() - sim.apply_neuromodulation(neuromod_dict) - sim.neuromodulation_network_wide() - - elif 'adaptive' in neuromod_dict['type']: - from snudda.neuromodulation.neuromodulation_synapse import SnuddaSimulateNeuromodulationSynapse - - sim = SnuddaSimulateNeuromodulationSynapse(network_file=network_file, - input_file=input_file, - output_file=output_file, - disable_gap_junctions=disable_gj, - disable_synapses=disable_synapses, - log_file=log_file, - neuromodulator_description=neuromod_dict, - simulation_config=simulation_config, - verbose=verbose) - - sim.setup() - sim.add_external_input() - - else: - - from snudda.simulate.simulate import SnuddaSimulate - - # Simulate is deterministic, no random seed. - sim = SnuddaSimulate(network_file=network_file, - input_file=input_file, - output_file=output_file, - disable_gap_junctions=disable_gj, - disable_synapses=disable_synapses, - log_file=log_file, - simulation_config=simulation_config, - verbose=verbose) - sim.setup() - sim.add_external_input() + from snudda.simulate.simulate import SnuddaSimulate + + # Simulate is deterministic, no random seed. + sim = SnuddaSimulate(network_file=network_file, + input_file=input_file, + output_file=output_file, + disable_gap_junctions=disable_gj, + disable_synapses=disable_synapses, + log_file=log_file, + simulation_config=simulation_config, + sample_dt=sample_dt, + use_rxd_neuromodulation=use_rxd_neuromodulation, + verbose=verbose) + sim.setup() + sim.add_external_input() sim.check_memory_status() @@ -842,16 +866,22 @@ def simulate(self, return # We do not run simulation when exporting to core neuron sim.check_memory_status() - print(f"Running simulation for {t_sim} ms.") - sim.run(t_sim) # In milliseconds + if t_sim is None or t_sim > 0: + print(f"Running simulation for {t_sim} ms.") + sim.run(t_sim) # In milliseconds - print("Simulation done, saving output") - sim.write_output() + print("Simulation done, saving output") + sim.write_output() + else: + print(f"Time set to {t_sim} ms. No simulation run.") stop = timeit.default_timer() if sim.pc.id() == 0: print(f"Program run time: {stop - start:.1f}s") + # OBS! You want to do sim.clear_neuron() after the simulation if you need + # to setup another neuron simulation afterwards. + # sim.plot() return sim @@ -895,7 +925,6 @@ def setup_parallel(self, ipython_profile=None, timeout=120): # self.rc = Client(profile=ipython_profile, url_file=u_file, timeout=120, debug=False) self.rc = Client(profile=ipython_profile, connection_info=u_file, timeout=timeout, debug=False) - self.logfile.write(f'Client IDs: {self.rc.ids}') # http://davidmasad.com/blog/simulation-with-ipyparallel/ diff --git a/snudda/data/input_config/external-input-dSTR-scaled-v2.json b/snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v2.json similarity index 100% rename from snudda/data/input_config/external-input-dSTR-scaled-v2.json rename to snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v2.json diff --git a/snudda/data/input_config/external-input-dSTR-scaled-v3.json b/snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v3.json similarity index 100% rename from snudda/data/input_config/external-input-dSTR-scaled-v3.json rename to snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v3.json diff --git a/snudda/data/input_config/external-input-dSTR-scaled-v3b.json b/snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v3b.json similarity index 100% rename from snudda/data/input_config/external-input-dSTR-scaled-v3b.json rename to snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v3b.json diff --git a/snudda/data/input_config/external-input-dSTR-scaled-v4-cluster-test.json b/snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v4-cluster-test.json similarity index 100% rename from snudda/data/input_config/external-input-dSTR-scaled-v4-cluster-test.json rename to snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v4-cluster-test.json diff --git a/snudda/data/input_config/external-input-dSTR-scaled-v4.json b/snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v4.json similarity index 100% rename from snudda/data/input_config/external-input-dSTR-scaled-v4.json rename to snudda/data/input_config/legacy_format/external-input-dSTR-scaled-v4.json diff --git a/snudda/data/input_config/external-input-dSTR-scaled.json b/snudda/data/input_config/legacy_format/external-input-dSTR-scaled.json similarity index 100% rename from snudda/data/input_config/external-input-dSTR-scaled.json rename to snudda/data/input_config/legacy_format/external-input-dSTR-scaled.json diff --git a/snudda/data/input_config/input-tinytest-ChIN.json b/snudda/data/input_config/legacy_format/input-tinytest-ChIN.json similarity index 100% rename from snudda/data/input_config/input-tinytest-ChIN.json rename to snudda/data/input_config/legacy_format/input-tinytest-ChIN.json diff --git a/snudda/data/input_config/input-tinytest-Channel-Activation.json b/snudda/data/input_config/legacy_format/input-tinytest-Channel-Activation.json similarity index 100% rename from snudda/data/input_config/input-tinytest-Channel-Activation.json rename to snudda/data/input_config/legacy_format/input-tinytest-Channel-Activation.json diff --git a/snudda/data/input_config/input-tinytest-channel-1.json b/snudda/data/input_config/legacy_format/input-tinytest-channel-1.json similarity index 100% rename from snudda/data/input_config/input-tinytest-channel-1.json rename to snudda/data/input_config/legacy_format/input-tinytest-channel-1.json diff --git a/snudda/data/input_config/input-tinytest-channel-2.json b/snudda/data/input_config/legacy_format/input-tinytest-channel-2.json similarity index 100% rename from snudda/data/input_config/input-tinytest-channel-2.json rename to snudda/data/input_config/legacy_format/input-tinytest-channel-2.json diff --git a/snudda/data/input_config/input-tinytest-v7.json b/snudda/data/input_config/legacy_format/input-tinytest-v7.json similarity index 100% rename from snudda/data/input_config/input-tinytest-v7.json rename to snudda/data/input_config/legacy_format/input-tinytest-v7.json diff --git a/snudda/data/input_config/input-tinytest-v8.json b/snudda/data/input_config/legacy_format/input-tinytest-v8.json similarity index 100% rename from snudda/data/input_config/input-tinytest-v8.json rename to snudda/data/input_config/legacy_format/input-tinytest-v8.json diff --git a/snudda/data/input_config/input-tinytest-v9-freq-vectors.json b/snudda/data/input_config/legacy_format/input-tinytest-v9-freq-vectors.json similarity index 100% rename from snudda/data/input_config/input-tinytest-v9-freq-vectors.json rename to snudda/data/input_config/legacy_format/input-tinytest-v9-freq-vectors.json diff --git a/snudda/data/input_config/input_output_dspn_template_IC.json b/snudda/data/input_config/legacy_format/input_output_dspn_template_IC.json similarity index 100% rename from snudda/data/input_config/input_output_dspn_template_IC.json rename to snudda/data/input_config/legacy_format/input_output_dspn_template_IC.json diff --git a/snudda/detect/detect.py b/snudda/detect/detect.py index 3fdee42d2..a7e5f293e 100644 --- a/snudda/detect/detect.py +++ b/snudda/detect/detect.py @@ -2142,6 +2142,9 @@ def read_prototypes(self, config_file=None): if "cluster_spread" not in con_def[key]: con_def[key]["cluster_spread"] = 20e-3 + if "RxD" in con_def[key] and "weight_scale" not in con_def: + print(f"Connection {key} uses RxD, but does not specify weight_scale set, will use default scaling 1.") + self.connectivity_distributions[pre_type, post_type] = con_def ############################################################################ diff --git a/snudda/init/init.py b/snudda/init/init.py index 69de8c39b..69ba0943b 100644 --- a/snudda/init/init.py +++ b/snudda/init/init.py @@ -434,7 +434,7 @@ def add_neurons(self, name, rotation_mode="random", stay_inside=False, k_dist=30e-6, - n_random=5, + n_random=5, # Used for bending morphologies max_angle=0.1): if num_neurons is not None and num_neurons <= 0: @@ -517,32 +517,59 @@ def add_neurons(self, name, # TODO: We should force users to use same name as the directory name # ie, fs/FS_0 directory should be named FS_0 - # Find which neurons are available in neuron_dir - # OBS, we need to sort the list of neuron directories, so every computer gets the same order - dir_list = sorted(glob.glob(os.path.join(snudda_parse_path(neuron_dir, self.snudda_data), "*"))) - neuron_file_list = [] - assert len(dir_list) > 0, f"Neuron dir {snudda_parse_path(neuron_dir, self.snudda_data)} is empty!" + full_neuron_path = snudda_parse_path(neuron_dir, self.snudda_data) + has_morphology_dir = os.path.isdir(os.path.join(full_neuron_path, "morphology")) - ctr = 0 + if has_morphology_dir: + # The folder specified has a morphology directory, use those morphologies + neuron_file_list = [(name, full_neuron_path)] - for fd in dir_list: + else: + # Assume each subdirectory in current folder contains a neuron + + # Find which neurons are available in neuron_dir + # OBS, we need to sort the list of neuron directories, so every computer gets the same order + dir_list = sorted(glob.glob(os.path.join(snudda_parse_path(neuron_dir, self.snudda_data), "*"))) + neuron_file_list = [] + + assert len(dir_list) > 0, f"Neuron dir {snudda_parse_path(neuron_dir, self.snudda_data)} is empty!" + + ctr = 0 - d = snudda_simplify_path(fd, self.snudda_data) + for fd in dir_list: - if snudda_isdir(d, self.snudda_data): - # We want to maintain the $SNUDDA_DATA keyword in the path so that the user can move - # the config file between systems and still run it. - sd = snudda_parse_path(d, self.snudda_data) + d = snudda_simplify_path(fd, self.snudda_data) - neuron_file_list.append((f"{name}_{ctr}", sd)) - ctr += 1 + if snudda_isdir(d, self.snudda_data): + # We want to maintain the $SNUDDA_DATA keyword in the path so that the user can move + # the config file between systems and still run it. + sd = snudda_parse_path(d, self.snudda_data) + + neuron_file_list.append((f"{name}_{ctr}", sd)) + ctr += 1 # First check how many unique cells we hava available, then we # calculate how many of each to use in simulation n_ind = len(neuron_file_list) + + if n_ind == 0: + # Check if the current neuron_dir path contains a single swc file... + full_neuron_path = snudda_parse_path(neuron_dir, self.snudda_data) + + has_morphology_dir = os.path.isdir(os.path.join(full_neuron_path, "morphology")) + dir_list2 = sorted(glob.glob(os.path.join(full_neuron_path, "*.swc"))) + + if not has_morphology_dir and len(dir_list2) != 1: + raise ValueError(f"The directory neuron_dir should either contain directories with neurons, " + f"or point to a neuron directory directly (with a morphology subdirectory, " + f"or exactly one SWC file). {neuron_dir = }, {dir_list2 = }") + + neuron_file_list = [(name, full_neuron_path)] + n_ind = len(neuron_file_list) + assert n_ind > 0, \ - f"No swc morphologies found in {neuron_dir}.\nObs, each morphology should have its own subdirectory." + f"No swc morphologies found in '{neuron_dir}'.\nObs, each morphology should have its own subdirectory." # Add the neurons to config neuron_dict = dict() @@ -833,6 +860,34 @@ def add_population_unit_random(self, structure_name, neuron_types, fraction_of_n self.network_data["regions"][structure_name]["population_units"]["unit_id"].append(unit_id) self.network_data["regions"][structure_name]["population_units"]["neuron_types"].append(neuron_types) + def add_population_unit_mesh(self, structure_name, neuron_types, mesh_file, fraction_of_neurons=1.0, unit_id=None): + + if type(neuron_types) != list: + neuron_types = [neuron_types] + + unit_id = self.setup_population_unit(region_name=structure_name, unit_id=unit_id) + + if "method" not in self.network_data["regions"][structure_name]["population_units"]: + self.network_data["regions"][structure_name]["population_units"]["method"] = "mesh" + self.network_data["regions"][structure_name]["population_units"]["mesh_file"] = [mesh_file] + self.network_data["regions"][structure_name]["population_units"]["fraction_of_neurons"] = [fraction_of_neurons] + self.network_data["regions"][structure_name]["population_units"]["unit_id"] = [unit_id] + self.network_data["regions"][structure_name]["population_units"]["neuron_types"] = [neuron_types] + self.network_data["regions"][structure_name]["population_units"]["structure"] = structure_name + else: + old_method = self.network_data["regions"][structure_name]["population_units"]["method"] + + if old_method != "mesh": + raise ValueError(f"{structure_name} population unit, expected method 'mesh' found '{old_method}', " + f"you cant mix methods for a structure.") + + self.network_data["regions"][structure_name]["population_units"]["mesh_file"].append(mesh_file) + self.network_data["regions"][structure_name]["population_units"]["fraction_of_neurons"].append(fraction_of_neurons) + self.network_data["regions"][structure_name]["population_units"]["unit_id"].append(unit_id) + self.network_data["regions"][structure_name]["population_units"]["neuron_types"].append(neuron_types) + + + # Helper function, returns next free unit_id and sets up data structures (user can also choose own unit_id # but it must be unique and not already used def setup_population_unit(self, region_name, unit_id=None): diff --git a/snudda/init/init_config.py b/snudda/init/init_config.py index 3efbccaf5..f3e62d9ef 100644 --- a/snudda/init/init_config.py +++ b/snudda/init/init_config.py @@ -66,13 +66,7 @@ def skip_item(self, item): return isinstance(item, str) and len(item) > 0 and item[0] == '!' - def substitute_json(self, putative_file, key=None, parent_file=None): - - if not (isinstance(putative_file, str) and putative_file.endswith(".json")): - return putative_file - - # First we look for files in the same directory as the parent directory - # after that we do SNUDDA_DATA substitution if no match + def get_putative_path(self, putative_file, parent_file=None): # This allows us to exclude parsing of certain json files if os.path.basename(putative_file) in self.exclude_parse_values: @@ -96,6 +90,19 @@ def substitute_json(self, putative_file, key=None, parent_file=None): if putative_path is None: raise ValueError(f"File not found {putative_file}") + return putative_path + + def substitute_json(self, putative_file, key=None, parent_file=None): + + if not (isinstance(putative_file, str) and putative_file.endswith(".json")): + return putative_file + + # First we look for files in the same directory as the parent directory + # after that we do SNUDDA_DATA substitution if no match + + putative_path = self.get_putative_path(putative_file=putative_file, + parent_file=parent_file) + with open(putative_path) as f: print(f"Loading {putative_path}") sub_tree = json.load(f) @@ -140,11 +147,10 @@ def parse_subtree(self, config_dict, parent_file=None): def setup_random_seeds(self): - if "random_seed" in self.config_data: - if "master_seed" in self.config_data["random_seed"]: - master_seed = self.config_data["random_seed"]["master_seed"] - else: - master_seed = None + master_seed = None + + if "random_seed" in self.config_data and "master_seed" in self.config_data["random_seed"]: + master_seed = self.config_data["random_seed"]["master_seed"] from snudda.init import SnuddaInit diff --git a/snudda/input/input.py b/snudda/input/input.py index a8a0ac685..f06519d7a 100644 --- a/snudda/input/input.py +++ b/snudda/input/input.py @@ -150,6 +150,9 @@ def __init__(self, self.neuron_name = [] self.neuron_type = [] + self.virtual_spike_file_cache = dict() + self.virtual_row_mapping_cache = dict() + if self.hdf5_network_file: self.load_network(self.hdf5_network_file) else: @@ -334,11 +337,15 @@ def write_hdf5(self): if "parameter_list" in neuron_in and neuron_in["parameter_list"] is not None: # We only need to save the synapse parameters in the file syn_par_list = [x["synapse"] for x in neuron_in["parameter_list"] if "synapse" in x] + if len(syn_par_list) > 0: it_group.attrs["parameter_list"] = json.dumps(syn_par_list) it_group.attrs["parameter_id"] = neuron_in["parameter_id"].astype(np.int32) + if "RxD" in neuron_in: + it_group.attrs["RxD"] = json.dumps(neuron_in["RxD"]) + else: # Input is activity of a virtual neuron @@ -347,6 +354,26 @@ def write_hdf5(self): try: if "spike_file" in self.neuron_input[neuron_id][input_type]: spike_file = self.neuron_input[neuron_id][input_type]["spike_file"] + + if spike_file in self.virtual_spike_file_cache: + spike_file_data = self.virtual_spike_file_cache[spike_file] + else: + + float_pattern = re.compile(r'^[-+]?[0-9]*\.?[0-9]+$') + + s_data = [] + with open(spike_file, "rt") as f: + for row in f: + s_data.append(np.array([float(x) for x in row.split(" ") + if len(x) > 0 and float_pattern.match(x)])) + + self.virtual_spike_file_cache[spike_file] = s_data + + spike_file_data = s_data + + else: + spike_file_data = None + except: import traceback print(traceback.format_exc()) @@ -360,44 +387,43 @@ def write_hdf5(self): if spike_row is None: - if "row_mapping_file" in self.neuron_input[neuron_id][input_type]\ - and "row_mapping_data" not in self.neuron_input[neuron_id][input_type]: - + if "row_mapping_file" in self.neuron_input[neuron_id][input_type]: row_mapping_file = self.neuron_input[neuron_id][input_type]["row_mapping_file"] - row_mapping_data = np.loadtxt(row_mapping_file, dtype=int) - row_mapping = dict() - for nid, rowid in row_mapping_data: - if nid in row_mapping: - print(f"Warning neuron_id {nid} appears twice in {row_mapping_file}") - row_mapping[nid] = rowid - - # Save row mapping so we dont have to generate it next iteration - self.neuron_input[neuron_id][input_type]["row_mapping_data"] = row_mapping - - if "row_mapping_data" in self.neuron_input[neuron_id][input_type]\ - and neuron_id in self.neuron_input[neuron_id][input_type]["row_mapping_data"]: + + if row_mapping_file in self.virtual_row_mapping_cache: + row_mapping = self.virtual_row_mapping_cache[row_mapping_file] + else: + + row_mapping_data = np.loadtxt(row_mapping_file, dtype=int) + row_mapping = dict() + for nid, rowid in row_mapping_data: + if nid in row_mapping: + print(f"Warning neuron_id {nid} appears twice in {row_mapping_file}") + row_mapping[nid] = rowid + + # Save row mapping so we dont have to generate it next iteration + self.virtual_spike_file_cache[row_mapping_file] = row_mapping + + if neuron_id in row_mapping: + spike_row = row_mapping[neuron_id] + + elif "row_mapping_data" in self.neuron_input[neuron_id][input_type]\ + and neuron_id in self.neuron_input[neuron_id][input_type]["row_mapping_data"]: spike_row = self.neuron_input[neuron_id][input_type]["row_mapping_data"][neuron_id] + else: spike_row = neuron_id - if "spike_data" not in self.neuron_input[neuron_id][input_type]: - float_pattern = re.compile(r'^[-+]?[0-9]*\.?[0-9]+$') - - s_data = [] - with open(spike_file, "rt") as f: - for row in f: - s_data.append(np.array([float(x) for x in row.split(" ") - if len(x) > 0 and float_pattern.match(x)])) + if "spike_data" not in self.neuron_input[neuron_id][input_type]\ + and spike_file_data is not None: - self.neuron_input[neuron_id][input_type]["spike_data"] = s_data - - try: - spikes = self.neuron_input[neuron_id][input_type]["spike_data"][spike_row] - except: - import traceback - print(traceback.format_exc()) - import pdb - pdb.set_trace() + try: + spikes = spike_file_data[spike_row] + except: + import traceback + print(traceback.format_exc()) + import pdb + pdb.set_trace() # Save spikes, so check sorted can verify them. # TODO: Should we skip this, if there are MANY virtual neurons -- and we run out of memory? @@ -813,6 +839,9 @@ def make_neuron_input_parallel(self): else: synapse_density = "1" + if "RxD" in input_inf: + self.neuron_input[neuron_id][input_type]["RxD"] = input_inf["RxD"] + rng_master = np.random.default_rng(self.random_seed + neuron_id + 10072) if "dendrite_location" in input_inf: @@ -834,9 +863,16 @@ def make_neuron_input_parallel(self): input_loc = [(x, y, z), np.array(sec_id), np.array(sec_x), dist_to_soma] else: # Automatically generate dendrite locations - cluster_size = None - cluster_spread = None - + if "cluster_size" in input_inf: + cluster_size = input_inf["cluster_size"] + else: + cluster_size = None + + if "cluster_spread" in input_inf: + cluster_spread = input_inf["cluster_spread"] + else: + cluster_spread = None + if "num_soma_synapses" in input_inf: n_soma_synapses = input_inf["num_soma_synapses"] else: @@ -868,8 +904,11 @@ def make_neuron_input_parallel(self): # Done for CSV input continue - # These parameters are shared between "poisson" and "frequency_function" + # RxD info is not needed for generation, but important for simulation + if "RxD" in input_inf: + self.neuron_input[neuron_id][input_type]["RxD"] = input_inf["RxD"] + # These parameters are shared between "poisson" and "frequency_function" neuron_id_list.append(neuron_id) input_type_list.append(input_type) @@ -896,6 +935,7 @@ def make_neuron_input_parallel(self): mod_file = None parameter_file = None parameter_list = None + synapse_density = None else: assert "location" not in input_inf, \ "Location in input config has been replaced with synapse_density" @@ -958,10 +998,10 @@ def make_neuron_input_parallel(self): else: parameter_list = None - if "synapse_density" in input_inf: - synapse_density = input_inf["synapse_density"] - else: - synapse_density = "1" + if "synapse_density" in input_inf: + synapse_density = input_inf["synapse_density"] + else: + synapse_density = "1" synapse_density_list.append(synapse_density) num_inputs_list.append(n_inp) @@ -2144,7 +2184,7 @@ def make_input_helper_serial(self, # TODO: Calculate the correct x,y,z and distance to soma x = y = z = dist_to_soma = np.zeros((len(sec_id),)) - input_loc = [(x, y, z), np.array(sec_id), np.array(sec_x), dist_to_soma] + input_loc = np.array([(x, y, z), np.array(sec_id), np.array(sec_x), dist_to_soma]) else: diff --git a/snudda/input/input_tuning.py b/snudda/input/input_tuning.py index c5b64f347..affbd7c33 100644 --- a/snudda/input/input_tuning.py +++ b/snudda/input/input_tuning.py @@ -6,11 +6,12 @@ import sys import timeit +# Now locally importing matplotlib.pyplot in functions, since Dardel (parallel computer) could not handle it + # Must be run before NEURON import to run in parallel from mpi4py import MPI import h5py -import matplotlib.pyplot as plt import numpy as np import copy @@ -20,7 +21,7 @@ from snudda.neurons.neuron_prototype import NeuronPrototype from snudda.place.create_cube_mesh import create_cube_mesh from snudda.simulate.simulate import SnuddaSimulate -from snudda.utils import SnuddaLoadNetworkSimulation +from snudda.utils import SnuddaLoadSimulation from snudda.utils.load import SnuddaLoad from snudda.utils.snudda_path import snudda_isdir, snudda_parse_path, snudda_simplify_path, get_snudda_data @@ -81,7 +82,8 @@ def __init__(self, network_path, snudda_data=None, rc=None, input_seed_list=None def setup_network(self, neurons_path=None, num_replicas=10, neuron_types=None, parameter_key=None, morphology_key=None, modulation_key=None, - single_neuron_path=None): + reaction_diffusion_file=None, + single_neuron_path=None, network_random_seed=None): if not morphology_key and not parameter_key and not modulation_key: all_combinations = True @@ -96,11 +98,13 @@ def setup_network(self, neurons_path=None, num_replicas=10, neuron_types=None, snudda_data=self.snudda_data, num_replicas=num_replicas, neuron_types=neuron_types, + reaction_diffusion_file=reaction_diffusion_file, single_neuron_path=single_neuron_path, parameter_key=parameter_key, morphology_key=morphology_key, modulation_key=modulation_key, - all_combinations=all_combinations) + all_combinations=all_combinations, + random_seed=network_random_seed) print(f"Writing network config file to {self.network_config_file_name}") with open(self.network_config_file_name, "w") as f: @@ -508,6 +512,8 @@ def plot_background_info(self, neuron_id, neuron_info, best_neuron_id, spike_cou max_time, skip_time=0, label="background-inputs", show_plot=True, depol_block_flag=None): + import matplotlib.pyplot as plt + n_inputs_total = np.zeros((len(neuron_id),), dtype=int) fig_dir = os.path.join(self.network_path, "figures") @@ -548,6 +554,8 @@ def plot_signal_info(self, neuron_id, neuron_info, best_config, spike_count, inp label="background-inputs", show_plot=True, depol_block_flag=None): + import matplotlib.pyplot as plt + n_inputs_total = np.zeros((len(neuron_id),), dtype=int) fig_dir = os.path.join(self.network_path, "figures") @@ -750,9 +758,9 @@ def load_data_helper(self, idx=None, load_input=True, quiet_load=False): if load_input and os.path.isfile(self.input_spikes_file[idx]): input_data = h5py.File(self.input_spikes_file[idx], "r") - output_data_loader = SnuddaLoadNetworkSimulation(network_path=self.network_path, - network_simulation_output_file=output_file, - do_test=True, quiet_load=quiet_load) + output_data_loader = SnuddaLoadSimulation(network_path=self.network_path, + network_simulation_output_file=output_file, + do_test=True, quiet_load=quiet_load) spike_data = output_data_loader.get_spikes() # cell_id = output_data_loader.get_id_of_neuron_type() @@ -797,6 +805,8 @@ def is_depolarisation_blocked(self, neuron_id, time_range, depolarisation_blocks def plot_depolarisation_blocked_neurons(self, freq_bin=10): + import matplotlib.pyplot as plt + spike_data = dict() volt = dict() depolarisation_blocks = dict() @@ -872,6 +882,8 @@ def plot_depolarisation_blocked_neurons(self, freq_bin=10): def plot_voltage_trace(self, morphology_key, parameter_key, mp_idx=None, time_range=None): + import matplotlib.pyplot as plt + # Find all neurons with the morphology key, and parameter key # If idx is given pick the n:th trace specified to plot, if not plot all traces. @@ -1049,6 +1061,8 @@ def load_spike_data(self, file_name, n_cells): def plot_volt_data(self, volt_data, show_plots=True, input_type_name=''): + import matplotlib.pyplot as plt + for neuron_name in volt_data: fig, ax = plt.subplots() legend_text = [] @@ -1091,6 +1105,8 @@ def plot_volt_data(self, volt_data, show_plots=True, input_type_name=''): def plot_volt_vs_ninputs(self, volt_data, show_plots=True, input_type_name=''): + import matplotlib.pyplot as plt + for neuron_name in volt_data: fig, ax = plt.subplots() legend_text = [] @@ -1137,6 +1153,8 @@ def plot_volt_vs_ninputs(self, volt_data, show_plots=True, input_type_name=''): def plot_frequency_data(self, frequency_data, show_plots=True, input_type_name=''): + import matplotlib.pyplot as plt + for neuron_name in frequency_data: fig, ax = plt.subplots() legend_text = [] @@ -1178,6 +1196,8 @@ def plot_frequency_data(self, frequency_data, show_plots=True, input_type_name=' def plot_frequency_data_alt(self, frequency_data, show_plots=True, input_type_name=''): + import matplotlib.pyplot as plt + _freq_data = dict() _all_num_inputs = [] _all_input_freq = [] @@ -1258,6 +1278,8 @@ def plot_frequency_data_alt(self, frequency_data, show_plots=True, input_type_na def plot_verify_frequency_distribution(self, input_type="cortical"): + import matplotlib.pyplot as plt + network_info, input_config, input_data, neuron_id_lookup, neuron_name_list, \ spike_data, volt, time, depolarisation_blocks = self.load_data_helper() @@ -1272,9 +1294,6 @@ def plot_verify_frequency_distribution(self, input_type="cortical"): input_freq = np.array(input_config[neuron_type][input_type]["frequency"]) output_freq_list = [] - #import pdb - #pdb.set_trace() - depol_blocked_freqs = [] depol_blocked_lookup = dict() @@ -1282,8 +1301,6 @@ def plot_verify_frequency_distribution(self, input_type="cortical"): out_freq = [] for start_t, end_t, in_freq in zip(start_times, end_times, input_freq): n_spikes = np.sum(np.logical_and(start_t <= spike_data[neuron_id], spike_data[neuron_id] < end_t)) - # import pdb - # pdb.set_trace() f = n_spikes / (end_t - start_t) out_freq.append(f) @@ -1371,6 +1388,7 @@ def get_neuron_info(self, neuron_path): # The below line is wrong, but it is FRIDAY evening so will fix it monday... neuron_info["neuron_path"] = snudda_simplify_path(neuron_path, self.snudda_data) + # OBS, these are not used by snudda when placing neurons, it is just for internal bookkeeping of input tuning neuron_info["morphology"] = snudda_simplify_path(neuron_morph, self.snudda_data) neuron_info["parameters"] = snudda_simplify_path(parameter_file, self.snudda_data) neuron_info["mechanisms"] = snudda_simplify_path(mechanism_file, self.snudda_data) @@ -1429,11 +1447,11 @@ def gather_all_neurons(self, neuron_types=None, all_combinations=True): param_key = ni["parameter_key"] morph_key = ni["morphology_key"] short_name = n_name[:min(10, len(n_name))] - neuron_name = f"{neuron_type}_{short_name}_{param_key}_{morph_key}".replace("-", "_") + neuron_name = f"{neuron_type.replace('-','_')}_{short_name}_{param_key}_{morph_key}".replace("-", "_") all_neurons[neuron_name] = ni neuron_ctr += 1 else: - neuron_name = os.path.basename(os.path.dirname(neuron_info["parameters"])) + neuron_name = os.path.basename(os.path.dirname(neuron_info["parameters"])).replace("-", "_") neuron_ctr += 1 all_neurons[neuron_name] = neuron_info @@ -1490,6 +1508,7 @@ def create_network_config(self, num_replicas=10, random_seed=None, neuron_types=None, + reaction_diffusion_file=None, single_neuron_path=None, parameter_key=None, morphology_key=None, @@ -1540,6 +1559,10 @@ def create_network_config(self, else: neuron_def = self.gather_all_neurons(neuron_types=neuron_types, all_combinations=all_combinations) + if reaction_diffusion_file is not None: + for neuron_key in neuron_def.keys(): + neuron_def[neuron_key]["reaction_diffusion"] = reaction_diffusion_file + # Just generate a set of points region_def[vol_name]["volume"]["n_putative_points"] = max(len(neuron_def.keys())*5, 10000) @@ -1704,6 +1727,8 @@ def add_input(self, input_target, input_type, input_frequency, input_correlation def plot_generated_input(self, num_bins=50): # This function just checks that we have reasonable spikes generated + import matplotlib.pyplot as plt + input_spike_data = h5py.File(self.input_spikes_file, 'r') network_data = h5py.File(self.network_file, 'r') diff --git a/snudda/neuromodulation/__init__.py b/snudda/neuromodulation/__init__.py deleted file mode 100644 index 9a43363b1..000000000 --- a/snudda/neuromodulation/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from snudda.neuromodulation.neuromodulation import SnuddaSimulateNeuromodulation -from snudda.neuromodulation.neuromodulation_synapse import SnuddaSimulateNeuromodulationSynapse diff --git a/snudda/neuromodulation/modulation.py b/snudda/neuromodulation/modulation.py deleted file mode 100644 index 55f69389e..000000000 --- a/snudda/neuromodulation/modulation.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -import numexpr - - -def alpha_sub_function(t_step, tau, tstart, gmax): - - return gmax * ((t_step - tstart) / tau) * np.exp(1 - ((t_step - tstart) / tau)) - - -def alpha_repetition(parameter=None): - - time_step_array = parameter['time_step_array'] - tstarts = parameter['tstart'] - gmax = parameter['gmax'] - tau = parameter['tau'] - magnitude = np.zeros_like(time_step_array) - - for tstart in tstarts: - - index = np.where(time_step_array > tstart) - start_index = index[0][0] - - magnitude[start_index:] = alpha_sub_function(np.take(time_step_array, index), tau, tstart, gmax) - - return magnitude - -def alpha(parameter=None): - - time_step_array = parameter['time_step_array'] - tstart = parameter['tstart'] - gmax = parameter['gmax'] - tau = parameter['tau'] - - magnitude = np.zeros_like(time_step_array) - index = np.where(time_step_array > tstart) - start_index = index[0][0] - - magnitude[start_index:] = alpha_sub_function(np.take(time_step_array, index), tau, tstart, gmax) - - return magnitude - - -def step(parameter=None): - - time_step_array = parameter['time_step_array'] - tstart = parameter['tstart'] - step_stop = parameter['duration'] + parameter['tstart'] - gmax = parameter['gmax'] - - magnitude = np.zeros_like(time_step_array) - - start_index = np.where(np.logical_and(time_step_array > tstart, time_step_array < step_stop))[0][0] - - magnitude[start_index:] = gmax - - return magnitude - - -def bath_application(parameter=None): - - time_step_array = parameter['time_step_array'] - gmax = parameter['gmax'] - - magnitude = np.ones_like(time_step_array) * gmax - - return magnitude - - -def alpha_background(parameter=None): - - time_step_array = parameter['time_step_array'] - tstart = parameter['tstart'] - if 'gmax_decrease' in parameter.keys(): - gmax_shift = parameter['gmax_decrease'] * (-1) - elif 'gmax_increase' in parameter.keys(): - gmax_shift = parameter['gmax_increase'] - else: - raise ValueError('Include a gmax increase or decrease') - - tau = parameter['tau'] - tonic = parameter['tonic'] - - magnitude = np.ones_like(time_step_array) * tonic - - index = np.where(time_step_array > tstart)[0] - start_index = np.where(time_step_array > tstart)[0][0] - - magnitude[start_index:] = tonic + alpha_sub_function(np.take(time_step_array, index), tau, tstart, gmax_shift) - - if min(magnitude) < 0 or max(magnitude) > 1: - raise ValueError(' Modulation is outside the range (0,1). Modify parameters') - - return magnitude - - -def time_series(parameter=None): - - magnitude = numexpr.evalute(parameter['array']) - - return magnitude diff --git a/snudda/neuromodulation/modulation_network.py b/snudda/neuromodulation/modulation_network.py deleted file mode 100644 index ece2f3411..000000000 --- a/snudda/neuromodulation/modulation_network.py +++ /dev/null @@ -1,191 +0,0 @@ -import json -import os -import numpy as np -import copy - - -class Neuromodulation: - - def __init__(self): - - self.network_wide = dict() - self.name_to_key = dict() - self.dt = None - self.type = 'replay' - - def set_modulation(self, neurotransmitter, neurotransmitter_key): - - """ - neurotransmitter_key is equivalent to the parameter used in the mod files, which marks the - level, mod and maxMod parameters eg. neurotransmitter_key, ACh, would have parameters levelACh, - modACh and maxModACh in modulated mod files. - """ - - if neurotransmitter_key in self.network_wide.keys(): - raise KeyError('neurotransmitter already defined') - - else: - self.name_to_key.update({neurotransmitter: neurotransmitter_key}) - - self.network_wide.update({neurotransmitter_key: {'name': neurotransmitter}}) - - self.network_wide[neurotransmitter_key].update({'ion_channels': dict(), - 'receptors': dict(), - 'presynaptic': dict()}) - - def transient(self, neurotransmitter, method, duration, parameters): - - self.network_wide[self.name_to_key[neurotransmitter]].update({'method': method, - 'duration': duration, - 'parameters': parameters}) - - def set_timestep(self, dt): - - self.dt = dt - - def ion_channel_modulation(self, neurotransmitter, cell_type, section, ion_channels): - - if cell_type not in self.network_wide[self.name_to_key[neurotransmitter]]['ion_channels'].keys(): - self.network_wide[self.name_to_key[neurotransmitter]]['ion_channels'].update({cell_type: dict()}) - - self.network_wide[self.name_to_key[neurotransmitter]]['ion_channels'][cell_type].update({section: ion_channels}) - - def receptor_modulation(self, neurotransmitter, cell_type, receptor, modulation): - - if cell_type not in self.network_wide[self.name_to_key[neurotransmitter]]['receptors'].keys(): - self.network_wide[self.name_to_key[neurotransmitter]]['receptors'].update({cell_type: dict()}) - - self.network_wide[self.name_to_key[neurotransmitter]]['receptors'][cell_type].update({receptor: modulation}) - - def presynaptic_receptor_modulation(self, neurotransmitter, cell_type, receptor, modulation): - - if cell_type not in self.network_wide[self.name_to_key[neurotransmitter]]['presynaptic'].keys(): - - self.network_wide[self.name_to_key[neurotransmitter]]['presynaptic'][cell_type].update({receptor: modulation}) - - def plot_transient(self, neurotransmitter): - - import snudda.neuromodulation.modulation as modulation - import matplotlib.pyplot as plt - - temp = copy.deepcopy(self.network_wide[self.name_to_key[neurotransmitter]]) - duration = np.arange(0, temp['duration'], self.dt) - temp['parameters'].update({"time_step_array": duration}) - method = getattr(modulation, temp['method']) - modulation_vector = method(temp['parameters']) - - plt.figure() - plt.title(f" Transient for the modulation using {neurotransmitter} ") - plt.plot(duration, modulation_vector) - plt.ylabel("Modulation") - plt.xlabel("Time (ms)") - plt.show() - - def save(self, dir_path, name): - - cell_types = list() - sections = ["axon", "dendrite", "soma"] - modulation_types = ["ion_channels", "receptors", "presynaptic"] - - if not self.dt: - raise ValueError(' Set time step for simulation') - else: - for neurotransmitter_type in self.network_wide.keys(): - self.network_wide[neurotransmitter_type].update({'dt': self.dt}) - - # Add all the types of modulation, if they do not exist - - for m in modulation_types: - - for neurotransmitter_type in self.network_wide: - if m not in self.network_wide[neurotransmitter_type]: - self.network_wide[neurotransmitter_type].update({m: dict()}) - - # Add all cell types to all the types of modulation - - for neurotransmitter_type, modulation_information in self.network_wide.items(): - - for m in modulation_information: - if m in modulation_types: - for c in modulation_information[m]: - if c not in cell_types: - cell_types.append(c) - - for neurotransmitter_type, modulation_type in self.network_wide.items(): - - if "ion_channels" in modulation_type: - - for cell_type in cell_types: - - if cell_type in modulation_type["ion_channels"]: - - for section in sections: - if section in modulation_type["ion_channels"][cell_type]: - pass - else: - modulation_type["ion_channels"][cell_type].update({section: list()}) - else: - modulation_type["ion_channels"].update({cell_type: dict()}) - - for section in sections: - modulation_type["ion_channels"][cell_type].update({section: list()}) - - if "receptors" in modulation_type: - - for cell_type in cell_types: - - if cell_type in modulation_type["receptors"]: - pass - else: - modulation_type["receptors"].update({cell_type: dict()}) - - if "presynaptic" in modulation_type: - - for cell_type in cell_types: - - if cell_type in modulation_type["presynaptic"]: - pass - else: - modulation_type["presynaptic"].update({cell_type: dict()}) - - temp = dict() - temp.update({'type': self.type}) - temp.update({'description': self.network_wide}) - with open(os.path.join(dir_path, name), 'w') as out_file: - json.dump(temp, out_file, indent=4, sort_keys=True) - - -if __name__ == "__main__": - neurotransmitter = "dopamine" - neurotransmitter_key = "DA" - tstart = 300 - tonic = 0.2 - gmax_increase = 0.8 - tau = 500 - duration = 3000 - dt = 0.025 - nl = Neuromodulation() - nl.set_timestep(dt=dt) - name = "dopamine_modulation.json" - dir_path = "" - nl.set_modulation(neurotransmitter=neurotransmitter, neurotransmitter_key=neurotransmitter_key) - nl.transient(neurotransmitter=neurotransmitter, - method="alpha_background", - duration=duration, - parameters={"tstart": tstart, - "tonic": tonic, - "gmax_increase": gmax_increase, - "tau": tau}) - - nl.ion_channel_modulation(neurotransmitter=neurotransmitter, - cell_type="dSPN", - section="soma", - ion_channels=["kas_ms", "kaf_ms", "can_ms"]) - nl.ion_channel_modulation(neurotransmitter=neurotransmitter, - cell_type="dSPN", - section="dendrite", - ion_channels=["kas_ms", "kaf_ms"]) - - nl.save(dir_path=dir_path, name=name) - - diff --git a/snudda/neuromodulation/modulation_synapse.py b/snudda/neuromodulation/modulation_synapse.py deleted file mode 100644 index 13440df93..000000000 --- a/snudda/neuromodulation/modulation_synapse.py +++ /dev/null @@ -1,110 +0,0 @@ -import json -import os - - -class NeuromodulationSynapse: - - def __init__(self): - self.synapse_modulation = dict() - self.weight = None - self.type = 'adaptive' - - def set_weight(self, weight): - self.weight = weight - - def set_connection_type(self, neuromodulation_key, connector): - """ - neurotransmitter_key is equivalent to the parameter used in the mod files, which marks the - level, mod and maxMod parameters e.g. neurotransmitter_key, ACh, would have parameters levelACh, - modACh and maxModACh in modulated mod files. - """ - - if connector in self.synapse_modulation.keys(): - raise KeyError('connector is already defined') - - self.synapse_modulation.update({neuromodulation_key: {'connector': connector, - 'cells': dict()}}) - - def add_cell_modulation(self, neuromodulation_key, cell, ion_channels=None, receptors=None, extrinsic=None, - type_connection=None): - - if ion_channels is None: - ion_channels = dict(soma=list(), dendrite=list(), axon=list()) - - if receptors is None: - receptors = dict() - - if extrinsic is None: - extrinsic = dict() - - self.synapse_modulation[neuromodulation_key]['cells'].update({cell: { - 'ion_channels': ion_channels, - 'receptors': receptors, - 'extrinsic': extrinsic, - 'type': type_connection}}) - - def save(self, dir_path, name): - - cell_types = list() - - for neurotransmitter_type, modulation_information in self.synapse_modulation.items(): - - for c in modulation_information["cells"]: - if c not in cell_types: - cell_types.append(c) - - for neurotransmitter_type, modulation_information in self.synapse_modulation.items(): - for c in cell_types: - if c not in modulation_information["cells"]: - modulation_information["cells"].update({c: {"ion_channels": dict(soma=list(), dendrite=list(), axon=list()), - "receptors": None, - "extrinsic": None, - "type": None}}) - temp = dict() - temp.update({'type': self.type}) - temp.update({'description': self.synapse_modulation}) - temp.update({'weight': self.weight}) - with open(os.path.join(dir_path, name), 'w') as out_file: - json.dump(temp, out_file, indent=4) - - -if __name__ == "__main__": - sw = NeuromodulationSynapse() - sw.set_weight(weight=0) - - # Acetylcholine - - sw.set_connection_type(connector="concACh", neuromodulation_key="ACh") - - sw.add_cell_modulation(neuromodulation_key="ACh", - cell="dSPN", - ion_channels={ - "soma": ["kir_ms", "cal12_ms", "cal13_ms", "can_ms", "Im_ms"], - "dendrite": ["kir_ms", "cal12_ms", "cal13_ms"]}, - type_connection="spiking-concentration") - - sw.add_cell_modulation(neuromodulation_key="ACh", - cell="iSPN", - ion_channels={ - "soma": ["kir_ms", "cal12_ms", "cal13_ms", "can_ms"], - "dendrite": ["kir_ms", "cal12_ms", "cal13_ms"]}, - type_connection="spiking-concentration") - - # Dopamine - - sw.set_connection_type(connector="concDA", neuromodulation_key="DA") - - sw.add_cell_modulation(neuromodulation_key="DA", - cell="dSPN", - ion_channels={ - "soma": ["kas_ms", "kaf_ms", "can_ms"], - "dendrite": ["kaf_ms", "kas_ms"], - "axon": []}, - receptors={"tmGabaA": {"maxMod": 0.8}, - "tmGlut": {"maxMod_AMPA": 1.2, - "maxMod_NMDA": 1.3, - "failRate": 0.7}}, - extrinsic=["CorticalBase", "CorticalSignal", "Thalamic"], - type_connection="spiking-concentration") - - sw.save(dir_path="", name="DA-ACh-control.json") diff --git a/snudda/neuromodulation/neuromodulation.py b/snudda/neuromodulation/neuromodulation.py deleted file mode 100644 index 6921c05b6..000000000 --- a/snudda/neuromodulation/neuromodulation.py +++ /dev/null @@ -1,162 +0,0 @@ -from snudda.simulate.simulate import SnuddaSimulate -import snudda.neuromodulation.modulation as modulation -import snudda.neuromodulation.translator as translator -import json -import numpy as np -import os - - -class SnuddaSimulateNeuromodulation(SnuddaSimulate): - - """ - - Class for simulating neuromodulation using the replay mode - - """ - - def __init__(self, - network_path=None, - network_file=None, - input_file=None, - output_file=None, - verbose=False, - log_file=None, - disable_gap_junctions=False, - disable_synapses=False, - simulation_config=None): - - self.neuromodulation = dict() - - super(SnuddaSimulateNeuromodulation, self).__init__(network_path=network_path, - network_file=network_file, - input_file=input_file, - output_file=output_file, - verbose=verbose, - log_file=log_file, - disable_gap_junctions=disable_gap_junctions, - disable_synapses=disable_synapses, - simulation_config=simulation_config) - - self.write_log(" Using neuromodulation module in Snudda") - - def neuron_vector(self, vector): - - return self.sim.neuron.h.Vector(vector) - - def apply_neuromodulation(self, neuromodulation_dict): - - define_neuro_modulation = neuromodulation_dict['description'] - - # Rewrite for event handling - # https://github.com/Hjorthmedh/Snudda/blob/master/snudda/simulate/simulate.py#L1529 - - for type_modulation, description_neuromodulation in define_neuro_modulation.items(): - duration = np.arange(0, description_neuromodulation['duration'], description_neuromodulation['dt']) - method = getattr(modulation, description_neuromodulation['method']) - - description_neuromodulation['parameters'].update({"time_step_array": duration}) - - modulation_vector = method(description_neuromodulation['parameters']) - - self.neuromodulation.update({ - type_modulation: - { - 'name': description_neuromodulation['name'], - 'modulation_vector': self.neuron_vector(modulation_vector), - 'ion_channels': description_neuromodulation['ion_channels'], - 'receptors': description_neuromodulation['receptors'], - 'presynaptic': description_neuromodulation['presynaptic'] - } - }) - - def neuromodulation_network_wide(self): - - for type_modulation, modulation_items in self.neuromodulation.items(): - - if 'ion_channels' in modulation_items.keys(): - self.modulate_ion_channels(modulation=type_modulation, ion_channels=modulation_items['ion_channels']) - - if 'receptors' in modulation_items.keys(): - self.modulate_synapses(modulation=type_modulation, synapses=modulation_items['receptors'], - intrinsic=True) - if 'presynaptic' in modulation_items.keys(): - self.modulate_synapses(modulation=type_modulation, synapses=modulation_items['presynaptic'], - extrinsic=True) - - def modulate_ion_channels(self, modulation, ion_channels): - - cells = dict((k, self.neurons[k]) for k in self.neuron_id if not self.is_virtual_neuron[k]) - - for index, cell in cells.items(): - - cell_type_name = cell.type - - cell_modulation = ion_channels[cell_type_name] - - for part, modulate_section in cell_modulation.items(): - - # translate, translates the neuron section into the name used in the swc file, e.g dend becomes basal - tpart = translator.translate(part) - - for comp in getattr(cell.icell, tpart): - for seg in comp: - for mech in seg: - if mech.name() in modulate_section: - - # Check that modulation value is not equal to 1.0 otherwise modulation will not work - assert getattr(mech, f"maxMod{modulation}") != 1.0 and getattr(mech, f"maxMod{modulation}") > 0, \ - f"NeuronModel has not loaded modulation.json," \ - f"neuromodulation is not turned on within the model for {modulation} in " \ - f"{mech} and value: {getattr(mech, f'maxMod{modulation}')} for " \ - f"cell type : {cell_type_name} in neuron part : {tpart}" - - setattr(mech, "mod" + modulation, 1) - self.neuromodulation[modulation]['modulation_vector'].play( - getattr(mech, "_ref_level" + modulation), - self.sim.neuron.h.dt) - - @staticmethod - def get_syn_name(syn): - return str(syn).split("[")[0] - - def modulate_synapses(self, modulation, synapses, intrinsic=None, extrinsic=None): - - if extrinsic: - for (neuronID, input_type), synlist in self.external_stim.items(): - for syntuple in synlist: - - cell_type_name = self.neurons[neuronID].type - syn_name = self.get_syn_name(syntuple[3]) - - if cell_type_name in synapses.keys() and syn_name in synapses[cell_type_name].keys(): - self.modulate_receptor(syn=syntuple[3], modulation=modulation, modulation_parameter=synapses[cell_type_name][syn_name]) - - if intrinsic: - - ## The synapse list has been removed and replaced with synapse dictionary - - for key in self.synapse_dict.keys(): - - syn = self.synapse_dict[key][0][0] - cell_type_name = str(syn.get_segment()).split("_")[0] - syn_name = self.get_syn_name(syn) - - if cell_type_name in synapses.keys() and syn_name in synapses[cell_type_name].keys(): - self.modulate_receptor(syn=syn, modulation=modulation, modulation_parameter=synapses[cell_type_name][syn_name]) - - def modulate_receptor(self, syn, modulation, modulation_parameter): - - setattr(syn, f"mod{modulation}", 1) - - """ - Adding the modulation to the receptor - """ - for key, value in modulation_parameter.items(): - setattr(syn, f"{key}{modulation}", value) - - if self.verbose: - print(f" {key}{modulation} set to {value} at {syn}") - - self.neuromodulation[modulation]['modulation_vector'].play( - getattr(syn, f"_ref_level{modulation}"), self.sim.neuron.h.dt) - diff --git a/snudda/neuromodulation/neuromodulation_synapse.py b/snudda/neuromodulation/neuromodulation_synapse.py deleted file mode 100644 index 7a1cd15c8..000000000 --- a/snudda/neuromodulation/neuromodulation_synapse.py +++ /dev/null @@ -1,445 +0,0 @@ -from typing import List, Any -from snudda.simulate.simulate import SnuddaSimulate -import snudda.neuromodulation.modulation as modulation -import snudda.neuromodulation.translator as translator -from snudda.simulate.nrn_simulator_parallel import NrnSimulatorParallel -from snudda.utils.snudda_path import snudda_parse_path -from snudda.neurons.neuron_model_extended import NeuronModel -from snudda.utils.load import SnuddaLoad -from neuron import h -import json -import numpy as np -import h5py - - -class SnuddaSimulateNeuromodulationSynapse(SnuddaSimulate): - - """ - - Class for simulating neuromodulation using the adaptive mode - - """ - - def __init__(self, - network_path=None, - network_file=None, - input_file=None, - output_file=None, - verbose=False, - log_file=None, - disable_gap_junctions=True, - disable_synapses=False, - simulation_config=None, - neuromodulator_description=None): - """ - - @type neuromodulation_weight: float - - """ - self.neuromodulation_synapse_ids = None - self.neuromodulator_description = neuromodulator_description['description'] - self.neuromodulation = dict() - self.current_cell = None - self.syn_gpcrs = list() - self.inplace_gpcrs = list() - self.cell_modulator = dict() - self.neuromodulation_weight = neuromodulator_description['weight'] - self.connector = [info['connector'] for info in self.neuromodulator_description.values()] - self.name_modulation_keys = [c.replace("conc", "") for c in self.connector] - self.module_connector = [k+'()'for k in self.connector] - self.mod_str = dict(zip(self.module_connector, self.connector)) - - super(SnuddaSimulateNeuromodulationSynapse, self).__init__(network_path=network_path, - network_file=network_file, - input_file=input_file, - output_file=output_file, - verbose=verbose, - log_file=log_file, - disable_gap_junctions=disable_gap_junctions, - disable_synapses=disable_synapses, - simulation_config=simulation_config) - - # Change the self.custom_setup from None, and execute the custom setup code within this file - self.cell_type_ion_channels_per_section = self.ion_channels_per_section() - self.key_list = self.modulation_keys() - - self.write_log(" Using neuromodulation module in Snudda") - - def setup(self): - - self.check_memory_status() - self.distribute_neurons() - self.setup_neurons() - self.check_memory_status() - self.pc.barrier() - - # Neuromodulation requires this to be run, before connect_network - - # self.synapse_parameters is loaded in self.setup_neurons, hence it is None before - - self.neuromodulation_synapse_ids = [sid for sid, synapse in self.synapse_parameters.items() if - str(synapse[0]) in self.module_connector] - self.neuromodulation_setup() - - self.connect_network() - self.check_memory_status() - self.pc.barrier() - - self.setup_parse_sim_info() - - def neuromodulation_setup(self): - - # This loops through all the synapses, and connects the relevant ones - # nextRowSet = [ fromRow, toRow ) -- ie range(fromRow,toRow) - next_row_set = self.find_next_synapse_group(next_row=0) - - while next_row_set is not None: - # Add the synapses to the neuron - self.connect_neuron_synapses_gpcr(start_row=next_row_set[0], end_row=next_row_set[1]) - - # Find the next group of synapses - next_row_set = self.find_next_synapse_group(next_row_set[1]) # 2nd number was not included in range - - def get_neuromodulator_synapses(self, synapse_type_ids): - - return [i for i, synapse_type_id in enumerate(synapse_type_ids) if - synapse_type_id in self.neuromodulation_synapse_ids] - - def modulation_keys(self): - - key_list = list() - for neuromodulation_key in self.neuromodulator_description.keys(): - key_list.append('level' + neuromodulation_key) - key_list.append('mod' + neuromodulation_key) - return key_list - - def ion_channels_per_section(self): - - cell_type = dict() - - for neuromodulator_key, info_for_neuromodulator in self.neuromodulator_description.items(): - - for cell_type_name in info_for_neuromodulator['cells'].keys(): - - cell_type.update({cell_type_name: dict()}) - - for tpart in info_for_neuromodulator['cells'][cell_type_name]['ion_channels'].keys(): - - if tpart in cell_type[cell_type_name].keys(): - cell_type[cell_type_name][tpart] = cell_type[cell_type_name][tpart] + \ - info_for_neuromodulator['cells'][cell_type_name][ - 'ion_channels'][tpart] - else: - cell_type[cell_type_name].update({tpart: info_for_neuromodulator['cells'][ - cell_type_name]['ion_channels'][tpart]}) - - return cell_type - - def get_ion_channels_per_section(self, cell_type): - return self.cell_type_ion_channels_per_section[cell_type] - - def connect_neuron_synapses_gpcr(self, start_row, end_row): - - source_id_list, dest_id, dend_sections, sec_id, sec_x, synapse_type_id, axon_distance, \ - conductance, parameter_id = self.get_synapse_info(start_row=start_row, end_row=end_row) - - gpcr_synapse_index = self.get_neuromodulator_synapses(synapse_type_id) - - # Filter away synapse_type_id not conc* connector and check if the start and end row defines the cell, - # hence you can send all information to add_gpcr_synapse - - channel_modules = [str(self.synapse_parameters[synapse_type_id[i]][0]) for i in gpcr_synapse_index] - - gpcr_synapse_info = np.take([source_id_list, dend_sections, sec_x, sec_id], gpcr_synapse_index, axis=1).transpose() - - # rewrite code as it is sorted on sec_id, jump in step of sec_id and add to dict - sort_idx = gpcr_synapse_info[:, -1].argsort() - - gpcr_synapse_info = gpcr_synapse_info[sort_idx] - - if gpcr_synapse_info.shape[0] == 0: - pass - else: - self.add_gpcr_synapse(channel_modules, gpcr_synapse_info) - - def add_gpcr_synapse(self, channel_modules, gpcr_info): - - if gpcr_info.shape[0] == 0: - self.write_log(f'add_gpcr_synapse : Empty gpcr_info for {channel_modules}. Check if the gpcr synapses are correctly defined for all cell types') - - cell = gpcr_info[0][1].cell() - postcell_type = str(cell).split('_')[0] - cell_information = dict() - current_section = gpcr_info[0][-1] - start_index = 0 - dend_section = gpcr_info[start_index][1] - - for i, dend_info in enumerate(gpcr_info): - - if current_section != dend_info[-1]: - - cell_information.update({dend_section: {'precell': gpcr_info[start_index:i, 0]}}) - cell_information[dend_section].update({'section_dist': gpcr_info[start_index:i, 2]}) - cell_information[dend_section].update({'mod': channel_modules[start_index:i]}) - - start_index = i - dend_section = gpcr_info[start_index][1] - current_section = gpcr_info[start_index][-1] - - cell_information.update({dend_section: {'precell': gpcr_info[start_index:i, 0]}}) - cell_information[dend_section].update({'section_dist': gpcr_info[start_index:i, 2]}) - cell_information[dend_section].update({'mod': channel_modules[start_index:i]}) - - self.connect_gpcr_synapse_to_ion_channels(cell_information, postcell_type) - - def connect_gpcr_synapse_to_ion_channels(self, cell_information, cell_type): - - cell_added_synapses = self.add_gpcrs_in_cell_segments(cell_information=cell_information) - - ion_channels_per_section = self.get_ion_channels_per_section(cell_type) - - for sec, sec_info in cell_information.items(): - - tpart = translator.re_translation[sec.name().split('.')[-1].split('[')[0]] - - ion_channels = ion_channels_per_section[tpart] - - for mechanism_name, values in sec.psection()['density_mechs'].items(): - - mechanism_name_ptr = mechanism_name + "_ptr" - - if mechanism_name in ion_channels: - - if self.verbose: - for name, value in sec.psection()['density_mechs'][mechanism_name].items(): - if "maxMod" in name: - for v in value: - - if self.verbose: - print(f" Value of {name} is {v} for {mechanism_name} on {cell_type}") - - assert v > 0, "Modulation value should be positive" - - level_list = [type_level for type_level in [*sec.psection()['density_mechs'][mechanism_name].keys()] if 'level' in type_level] - mod_key_list = [f"mod{n.replace('level','')}_{mechanism_name_ptr}" for n in level_list] - sec.insert(mechanism_name_ptr) - - - # Inplace of - - for syn in self.connector: - neuromodulation_key = syn.replace("conc", "") - - for segment in sec: - - fake = self.sim.neuron.h.concHld(segment) - self.inplace_gpcrs.append(fake) - pointer = fake._ref_concentration - # talk to NEURON maybe they can help change this, so you don't have to replace the mechanisms, crashes with uninitialised pointers - for neurotransmitter_level, mod_key in zip(level_list, mod_key_list): - - if neuromodulation_key in mod_key: - setattr(segment, mod_key, 1) - self.sim.neuron.h.setpointer(pointer, neurotransmitter_level, - getattr(segment, mechanism_name_ptr)) - - for syn in self.connector: - - neuromodulation_key = syn.replace("conc", "") - - for segment in sec: - seg_x_str = str(segment.x) - pointer = cell_added_synapses[sec][seg_x_str][syn]._ref_concentration - # talk to NEURON maybe they can help change this, so you don't have to replace the mechanisms, crashes with uninitialised pointers - for neurotransmitter_level, mod_key in zip(level_list, mod_key_list): - - if neuromodulation_key in mod_key: - setattr(segment, mod_key, 1) - self.sim.neuron.h.setpointer(pointer, neurotransmitter_level, - getattr(segment, mechanism_name_ptr)) - - # Parameterize the pointer version of density_mech, skip level and mod, as that would turn off modulation - for param, val in values.items(): - for i, segmentet in enumerate(sec): - if param not in self.key_list: - setattr(segmentet, '_'.join([param, mechanism_name_ptr]), val[i]) - if self.verbose: - print(f"Value {segmentet} {'_'.join([param, mechanism_name_ptr])} : {getattr(segmentet, '_'.join([param, mechanism_name_ptr]))}") - else: - if self.verbose: - print(f"Value {segmentet} {'_'.join([param, mechanism_name_ptr])} : {getattr(segmentet, '_'.join([param, mechanism_name_ptr]))}") - - for mech_name in ion_channels: - sec.uninsert(mech_name) - - def add_gpcrs_in_cell_segments(self, cell_information): - - added_synapses = dict() - # Add all the gpcrs to the segments which have been marked in this cell - - for str_mod_name, syn_in_section in self.mod_str.items(): - - for sec, sec_info in cell_information.items(): - step = 1/(sec.nseg*2) - for seg in sec: - - synapse_gpcr = getattr(self.sim.neuron.h, syn_in_section)(seg) - - self.syn_gpcrs.append(synapse_gpcr) - - str_modules = [str(k) for k in sec_info['mod']] - - if len(np.where(((seg.x - step) < sec_info['section_dist']) & ((seg.x - step) < sec_info['section_dist']))) > 0: - - for cell_id_source in np.take(sec_info['precell'], np.where((np.array(str_modules) == str_mod_name) & ((seg.x - step) < sec_info['section_dist']) & ((seg.x - step) < sec_info['section_dist'])))[0]: - nc = self.pc.gid_connect(cell_id_source, synapse_gpcr) - nc.weight[0] = self.neuromodulation_weight - nc.delay = self.synapse_delay - nc.threshold = self.spike_threshold - - #self.net_con_list.append(nc) - self.synapse_dict.update({("neuromodulation_nc", 0): nc}) - self.synapse_dict.update({("neuromodulation", 0): synapse_gpcr}) - #self.synapse_list.append(syn) - - if sec in added_synapses.keys() and str(seg.x) in added_synapses[sec].keys(): - - added_synapses[sec][str(seg.x)].update({syn_in_section: synapse_gpcr}) - - elif sec in added_synapses.keys() and str(seg.x) not in added_synapses[sec].keys(): - - added_synapses[sec].update({str(seg.x): {syn_in_section: synapse_gpcr}}) - else: - added_synapses.update({sec: {str(seg.x): {syn_in_section: synapse_gpcr}}}) - - return added_synapses - - def get_synapse(self, channel_module, dend_compartment, section_dist): - - cell_name = str(dend_compartment).split("_")[0] - - syn = None - # add lookup on channel_module to skip split - if str(channel_module).split('()')[0] not in self.connector: - - channel_module_p = eval(f"self.sim.neuron.h.{str(channel_module).split('()')[0]}_ptr") - - if self.verbose: - print(dend_compartment(section_dist).point_processes()) - - for point_process_in_section in dend_compartment(section_dist).point_processes(): - - neuromodulation_name = str(point_process_in_section).split('[')[0] - neuromodulation_name_key = neuromodulation_name.replace("conc", "") - - if neuromodulation_name in self.connector: - - syn = channel_module_p(dend_compartment(section_dist)) - - level = [x for x in dir(syn) if 'level' in x] - - for nkey in level: - - if nkey.replace("level", "") in self.name_modulation_keys: - fake = self.sim.neuron.h.concHld(dend_compartment(section_dist)) - self.inplace_gpcrs.append(fake) - pointer = fake._ref_concentration - self.sim.neuron.h.setpointer(pointer, nkey, syn) - - pointer = point_process_in_section._ref_concentration - - for neurotransmitter_key in level: - # remove this parameter by setting default 1 - - if neuromodulation_name_key in neurotransmitter_key: - setattr(syn, f"mod{neurotransmitter_key.replace('level', '')}", 1) - - modulator = neurotransmitter_key.replace('level', '') - receptor_name = str(channel_module).split('()')[0] - if modulator in self.neuromodulator_description and cell_name in self.neuromodulator_description[modulator]["cells"]: - if receptor_name in self.neuromodulator_description[modulator]["cells"][cell_name]["receptors"]: - parameters = self.neuromodulator_description[modulator]["cells"][cell_name]["receptors"][receptor_name] - - for p, v in parameters.items(): - setattr(syn, f"{p}{modulator}", v) - - if self.verbose: - print(f" Value of {p}{modulator} is {getattr(syn, f'{p}{modulator}')} at {syn} on {cell_name}") - print(f" Value mod{modulator} : {getattr(syn, f'mod{modulator}')}") - assert getattr(syn, f'{p}{modulator}') != 1.0 and getattr(syn, f'{p}{modulator}') > 0, "NeuronModel has not loaded modulation.json," \ - "neuromodulation is not turned on within the model" - assert getattr(syn, f"mod{modulator}") == 1.0 - - self.sim.neuron.h.setpointer(pointer, neurotransmitter_key, syn) - - if self.verbose: - print(f" Value of {neurotransmitter_key} is {getattr(syn, f'{neurotransmitter_key}')} at {syn} on {cell_name}") - - if syn is None: - syn = channel_module(dend_compartment(section_dist)) - - return syn - - def get_external_input_synapse(self, channel_module, section, section_x): - - cell_name = str(section).split("_")[0] - syn = None - - if self.verbose: - print(section(section_x).point_processes()) - - for point_process_in_section in section(section_x).point_processes(): - - if str(point_process_in_section).split('[')[0] in self.connector and str(channel_module).split('()')[0] not in self.connector: - - channel_module_p = eval('self.sim.neuron.h.' + str(channel_module).split('()')[0] + "_ptr") - - syn = channel_module_p(section(section_x)) - - neuromodulation_name = str(point_process_in_section).split('[')[0] - neuromodulation_name_key = neuromodulation_name.replace("conc", "") - - level = [x for x in dir(syn) if 'level' in x] - - for nkey in level: - - if nkey.replace("level", "") in self.name_modulation_keys: - fake = self.sim.neuron.h.concHld(section(section_x)) - self.inplace_gpcrs.append(fake) - pointer = fake._ref_concentration - self.sim.neuron.h.setpointer(pointer, nkey, syn) - - pointer = point_process_in_section._ref_concentration - - for neurotransmitter_key in level: - - if neuromodulation_name_key in neurotransmitter_key: - setattr(syn, 'mod' + neurotransmitter_key.replace('level', ''), 1) - self.sim.neuron.h.setpointer(pointer, neurotransmitter_key, syn) - - if self.verbose: - print(f" Value of {neurotransmitter_key} is {getattr(syn, f'{neurotransmitter_key}')} at {syn} on {cell_name}") - - modulator = neurotransmitter_key.replace('level', '') - receptor_name = str(channel_module).split('()')[0] - if modulator in self.neuromodulator_description and cell_name in self.neuromodulator_description[modulator]["cells"]: - if receptor_name in self.neuromodulator_description[modulator]["cells"][cell_name]["receptors"]: - parameters = self.neuromodulator_description[modulator]["cells"][cell_name]["receptors"][ - receptor_name] - - for p, v in parameters.items(): - setattr(syn, f"{p}{modulator}", v) - - if self.verbose: - print(f" Value of {p}{modulator} is {getattr(syn, f'{p}{modulator}')} at {syn} on {cell_name}") - print(f" Value mod{modulator} : {getattr(syn, f'mod{modulator}')}") - assert getattr(syn, f'{p}{modulator}') != 1.0 and getattr(syn, - f'{p}{modulator}') > 0, "NeuronModel has not loaded modulation.json," \ - "neuromodulation is not turned on within the model" - assert getattr(syn, f"mod{modulator}") == 1.0 - - if syn is None: - syn = channel_module(section(section_x)) - - return syn - diff --git a/snudda/neuromodulation/translator.py b/snudda/neuromodulation/translator.py deleted file mode 100644 index c7c35645e..000000000 --- a/snudda/neuromodulation/translator.py +++ /dev/null @@ -1,23 +0,0 @@ - -translation ={ - "dendrite" : "basal", - "soma" : "soma", - "axon" : "axon" -} - -re_translation ={ - "dend" : "dendrite", - "soma" : "soma", - "axon" : "axon" -} - - -def translate(word): - - return translation[word] - - -def re_translate(word): - - return re_translation[word] - diff --git a/snudda/neurons/__init__.py b/snudda/neurons/__init__.py index ec7bdda63..ba709b332 100644 --- a/snudda/neurons/__init__.py +++ b/snudda/neurons/__init__.py @@ -1,4 +1,5 @@ from snudda.neurons.neuron_morphology import NeuronMorphology from snudda.neurons.neuron_morphology_extended import NeuronMorphologyExtended from snudda.neurons.neuron_model_extended import NeuronModel +from snudda.neurons.neuron_modulation import NeuronModulation diff --git a/snudda/neurons/neuron_model_extended.py b/snudda/neurons/neuron_model_extended.py index 38e508589..b70291439 100644 --- a/snudda/neurons/neuron_model_extended.py +++ b/snudda/neurons/neuron_model_extended.py @@ -4,10 +4,12 @@ import json import os from collections import OrderedDict +import numpy as np import bluepyopt.ephys as ephys from snudda.neurons.neuron_prototype import NeuronPrototype +from snudda.neurons.neuron_modulation import NeuronModulation class NeuronModel(ephys.models.CellModel): @@ -24,12 +26,16 @@ def __init__(self, mech_file=None, param_file=None, modulation_file=None, + reaction_diffusion_file=None, parameter_id=None, morphology_id=None, modulation_id=None, parameter_key=None, morphology_key=None, - modulation_key=None): + modulation_key=None, + use_rxd_neuromodulation=True, + replace_axon_length=60e-6, + replace_axon_nseg_frequency=40e-6): """ Constructor @@ -40,8 +46,9 @@ def __init__(self, mech_file: Path to mechanism file param_file: Path to parameter file modulation_file: Path to neuromodulation parameter file + reaction_diffusion_file: Path to the RxD reaction diffusion file parameter_id: ID of parameter set - morphology_id: ID of morphology set + morphology_id: ID of morphology set -- DEPRECATED modulation_id: ID of neuromodulation parameter set parameter_key (str): parameter key for lookup in parameter.json morphology_key (str): morphology key, together with parameter_key lookup in meta.json @@ -58,6 +65,7 @@ def __init__(self, self.script_dir = os.path.dirname(__file__) self.config_dir = os.path.join(self.script_dir, 'config') + self.use_rxd_neuromodulation = use_rxd_neuromodulation if os.path.isfile(morph_path): # If morph_path is a swc file, use it directly @@ -82,7 +90,8 @@ def __init__(self, morphology_path=morph_path, parameter_path=param_file, mechanism_path=mech_file, - modulation_path=modulation_file) + modulation_path=modulation_file, + reaction_diffusion_path=reaction_diffusion_file) morph_file, _ = neuron_prototype.get_morphology(parameter_id=parameter_id, morphology_id=morphology_id, @@ -95,7 +104,10 @@ def __init__(self, self.morph_file = morph_file - morph = self.define_morphology(replace_axon=True, morph_file=morph_file) + morph = self.define_morphology(replace_axon=True, morph_file=morph_file, + replace_axon_length=replace_axon_length, + replace_axon_nseg_frequency=replace_axon_nseg_frequency) + mechs = self.define_mechanisms(mechanism_config=mech_file) params = self.define_parameters(param_file, parameter_id, parameter_key) @@ -109,6 +121,13 @@ def __init__(self, super(NeuronModel, self).__init__(name=cell_name, morph=morph, mechs=mechs, params=params) + + if reaction_diffusion_file and self.use_rxd_neuromodulation: + self.modulation = NeuronModulation(neuron=self) + self.modulation.config_file = reaction_diffusion_file + else: + self.modulation = None + self.syn_list = [] self.section_lookup = None @@ -140,8 +159,7 @@ def define_mechanisms(self, mechanism_config=None): continue seclist_loc = \ - ephys.locations.NrnSeclistLocation(section_list, - seclist_name=section_list) + ephys.locations.NrnSeclistLocation(section_list, seclist_name=section_list) for channel in channels: mechanisms.append(ephys.mechanisms.NrnMODMechanism( name='%s.%s' % (channel, section_list), @@ -156,12 +174,12 @@ def define_mechanisms(self, mechanism_config=None): # Helper function - def define_parameters(self, parameter_config=None, parameter_id=None, parameter_key=None): + def define_parameters(self, parameter_config, parameter_id=None, parameter_key=None): """ Define parameters based on parameter_config and parameter_id. If there are n parameter sets, and parameter_id is k, then the parameter set is n % k.""" - assert (parameter_config is not None) + assert parameter_config is not None # print("Using parameter config: " + parameter_config) @@ -170,7 +188,7 @@ def define_parameters(self, parameter_config=None, parameter_id=None, parameter_ parameters = [] - if type(param_configs) == OrderedDict: + if isinstance(param_configs, (OrderedDict, dict)): # Multiple parameters, pick one if parameter_key is not None: @@ -225,38 +243,49 @@ def define_parameters(self, parameter_config=None, parameter_id=None, parameter_ bounds=bounds, value=value)) elif param_config['type'] in ['section', 'range']: + if param_config['dist_type'] == 'uniform': scaler = ephys.parameterscalers.NrnSegmentLinearScaler() elif param_config['dist_type'] in ['exp', 'distance']: - scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( - distribution=param_config['dist']) - seclist_loc = ephys.locations.NrnSeclistLocation( - param_config['sectionlist'], - seclist_name=param_config['sectionlist']) - - name = '%s.%s' % (param_config['param_name'], - param_config['sectionlist']) - - if param_config['type'] == 'section': - parameters.append( - ephys.parameters.NrnSectionParameter( - name=name, - param_name=param_config['param_name'], - value_scaler=scaler, - value=value, - frozen=frozen, - bounds=bounds, - locations=[seclist_loc])) - elif param_config['type'] == 'range': - parameters.append( - ephys.parameters.NrnRangeParameter( - name=name, - param_name=param_config['param_name'], - value_scaler=scaler, - value=value, - frozen=frozen, - bounds=bounds, - locations=[seclist_loc])) + scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(distribution=param_config['dist']) + else: + raise ValueError(f"Unknown dist_type = {param_config['dist_type']}, " + f"expected 'uniform', 'exp' or 'distance'") + # 2024-07-23: Updated format, so that "sectionlist" is allowed to be either a string (of one section type) + # or a list of strings with section types. + + section_list = param_config['sectionlist'] + if not isinstance(section_list, list): + section_list = [section_list] + + for seclist_item in section_list: + + seclist_loc = ephys.locations.NrnSeclistLocation(seclist_item, + seclist_name=seclist_item) + + name = '%s.%s' % (param_config['param_name'], + seclist_item) + + if param_config['type'] == 'section': + parameters.append( + ephys.parameters.NrnSectionParameter( + name=name, + param_name=param_config['param_name'], + value_scaler=scaler, + value=value, + frozen=frozen, + bounds=bounds, + locations=[seclist_loc])) + elif param_config['type'] == 'range': + parameters.append( + ephys.parameters.NrnRangeParameter( + name=name, + param_name=param_config['param_name'], + value_scaler=scaler, + value=value, + frozen=frozen, + bounds=bounds, + locations=[seclist_loc])) else: raise Exception(f"Param config type has to be global, section or range: {param_config}") @@ -266,7 +295,9 @@ def define_parameters(self, parameter_config=None, parameter_id=None, parameter_ # Helper function - def define_morphology(self, replace_axon=True, morph_file=None): + def define_morphology(self, replace_axon=True, morph_file=None, + replace_axon_length=60e-6, + replace_axon_nseg_frequency=40e-6): # This only supported by hoc replacement """ Define morphology. Handles SWC and ASC. @@ -277,18 +308,17 @@ def define_morphology(self, replace_axon=True, morph_file=None): assert (morph_file is not None) - return ephys.morphologies.NrnFileMorphology(morph_file, do_replace_axon=replace_axon) - - # OLD BUGFIX FOR segment pop - # ,replace_axon_hoc=self.getReplacementAxon()) + return ephys.morphologies.NrnFileMorphology(morph_file, do_replace_axon=replace_axon, + axon_stub_length=replace_axon_length*1e6, + axon_nseg_frequency=replace_axon_nseg_frequency*1e6) ############################################################################## # Neuron_morphology defines sectionID, these must match what this returns # so that they point to the same compartment. # - # Soma is 0 - # axons are negative values (currently all set to -1) in Neuron_morphology + # Soma is -1 + # axons are negative values (-2, -3, ..) in Neuron_morphology # dendrites are 1,2,3,4,5... ie one higher than what Neuron internally # uses to index the dendrites (due to us wanting to include soma) @@ -323,13 +353,10 @@ def map_id_to_compartment(self, section_id): if self.section_lookup is None: self.build_section_lookup() - try: + if isinstance(section_id, int): + sec = self.section_lookup[section_id] + else: sec = [self.section_lookup[x] for x in section_id] - except: - import traceback - print(traceback.format_exc()) - import pdb - pdb.set_trace() return sec @@ -437,69 +464,134 @@ def instantiate(self, sim=None): for param in self.params.values(): param.instantiate(sim=sim, icell=self.icell) + if self.modulation: + self.modulation.load_json() + ############################################################################ - def get_replacement_axon(self): - - assert False, "Old bugfix for segment stack pop, should not be needed anymore" - - new_axon_hoc = \ - ''' -proc replace_axon(){ local nSec, D1, D2 - // preserve the number of original axonal sections - nSec = sec_count(axonal) - // Try to grab info from original axon - if(nSec == 0) { //No axon section present - D1 = D2 = 1 - } else if(nSec == 1) { - access axon[0] - D1 = D2 = diam - } else { - access axon[0] - D1 = D2 = diam - - //access soma distance() //to calculate distance from soma - soma distance() //to calculate distance from soma - forsec axonal{ - D2 = diam - //if section is longer than 60um then store diam and exit from loop - if(distance(0.5) > 60){ - break - } - } - } - // get rid of the old axon - forsec axonal{ - delete_section() - } - create axon[2] - - //access axon[0] { - axon[0] { - L = 30 - diam = D1 - nseg = 1 + 2*int(L/40) - all.append() - axonal.append() - } - // access axon[1] { - axon[1] { - L = 30 - diam = D2 - nseg = 1 + 2*int(L/40) - all.append() - axonal.append() - } - nSecAxonal = 2 - soma[0] connect axon[0](0), 1 - axon[0] connect axon[1](0), 1 - - if(nSec > 0) { - pop_section() - } - - -} - ''' - - return new_axon_hoc + def get_replacement_axon(self, axon_length=60e-6, axon_diameter=None, axon_nseg=None): + + raise DeprecationWarning("This function is now orphaned. Might need to re-update it if we switch to using hoc") + + """ + If axon_length is given as a scalar, then the code is similar to the BluePyOpt hoc, with the modification + that the total axon_length is specified by the user. + + If axon_length is a vector, then axon_diameter and axon_nseg must be given and be vectors of same length + The returned hoc will then create the user specified axon stump. + + """ + + if isinstance(axon_length, (int, float, np.integer, np.floating)): + assert axon_diameter is None and axon_nseg is None + # We have only the length specified, use two sections + + assert axon_length < 10000e-6, "Please specify replacement axon_length in SI units (meters)." + + axon_length_specified_hoc = \ + f''' + proc replace_axon(){{ local nSec, D1, D2 + // preserve the number of original axonal sections + nSec = sec_count(axonal) + + // Try to grab info from original axon + if(nSec == 0) {{ //No axon section present + D1 = D2 = 1 + }} else if(nSec == 1) {{ + axon[0] D1 = D2 = diam + }} else {{ + axon[0] D1 = D2 = diam + soma distance() //to calculate distance from soma + forsec axonal{{ + //if section is longer than 60um then store diam and exit from loop + if(distance(0.5) > {axon_length * 1e6}){{ + D2 = diam + break + }} + }} + }} + + // get rid of the old axon + forsec axonal{{ + delete_section() + }} + + create axon[2] + + axon[0] {{ + L = {axon_length * 1e6 / 2} + diam = D1 + nseg = 1 + 2*int(L/40) + all.append() + axonal.append() + }} + axon[1] {{ + L = {axon_length * 1e6 / 2} + diam = D2 + nseg = 1 + 2*int(L/40) + all.append() + axonal.append() + }} + nSecAxonal = 2 + soma[0] connect axon[0](0), 1 + axon[0] connect axon[1](0), 1 + }} + ''' + + return axon_length_specified_hoc + + assert axon_length is not None and axon_diameter is not None and axon_nseg is not None + + # In all remaining cases the user has to specify vectors for axon_length, axon_diameter, axon_nseg + assert len(axon_length) == len(axon_diameter) == len(axon_nseg), \ + f"Unequal lengths: {axon_length = }, {axon_diameter = }, {axon_nseg = }" + + user_defined_axon_hoc = \ + f""" + proc replace_axon() {{ + + // get rid of the old axon + forsec axonal{{ + delete_section() + + create axon[{len(axon_length)}] + + }} + """ + + for idx, (al, ad, an) in enumerate(zip(axon_length, axon_diameter, axon_nseg)): + + assert 0 < al < 500e-6, "Please make sure you specify axon length in SI units (meters)" + assert 0 < ad < 10e-6, "Please make sure you specify axon diameter in SI units (meters)" + assert an % 2 == 1, f"{axon_nseg = } must contain odd integer" + + user_defined_axon_hoc += \ + f""" + axon[{idx}] {{ + L = {al*1e6} + diam = {ad*1e6} + nseg = {an} + all.append() + axonal.append() + }} + """ + + user_defined_axon_hoc += f""" + nSecAxonal = {len(axon_length)} + """ + + for idx, al in enumerate(axon_length): + + if idx == 0: + user_defined_axon_hoc += f""" + soma[0] connect axon[0](0), 1 + """ + else: + user_defined_axon_hoc += f""" + axon[{idx-1}] connect axon[{idx}](0), 1 + """ + + user_defined_axon_hoc += "}" + + return user_defined_axon_hoc + diff --git a/snudda/neurons/neuron_modulation.py b/snudda/neurons/neuron_modulation.py new file mode 100644 index 000000000..bad214eed --- /dev/null +++ b/snudda/neurons/neuron_modulation.py @@ -0,0 +1,363 @@ +# TODO: How to handle conductance for DA channel, we have that DA vesicles contain 33000 DA molecules +# (watch out for overflow of integers in snudda synapse matrix) + +# TODO: How to handle co-release? + +# TODO: Move DA species to external compartment. IMPORTANT. + +import numpy as np +from mpi4py import MPI # This must be imported before neuron, to run parallel +import neuron.rxd as rxd +import json +from itertools import chain + + +class NeuronModulation: + should_update_rxd_nodes = True + + def __init__(self, neuron, config_file=None): + + self.neuron = neuron + self.compartments = dict() + self.species = dict() + self.rates = dict() + self.reactions = dict() + self.config_data = {} + self.config_file = config_file + + self.node_cache = None + + self.build = {"soma_internal": lambda neuron_dummy: self.set_default_compartments("soma", nrn_region="i"), + "dend_internal": lambda neuron_dummy: self.set_default_compartments("dend", nrn_region="i"), + "axon_internal": lambda neuron_dummy: self.set_default_compartments("axon", nrn_region="i"), + "soma_external": lambda neuron_dummy: self.set_default_compartments("soma", nrn_region="o"), + "dend_external": lambda neuron_dummy: self.set_default_compartments("dend", nrn_region="o"), + "axon_external": lambda neuron_dummy: self.set_default_compartments("axon", nrn_region="o") } + + def __del__(self): + + # Clear old rxd objects -- this avoids segfault for unittests + rxd.rxd.byeworld() + + def set_default_compartments(self, compartment, nrn_region="i"): + if compartment == "axon": + section_list = self.neuron.icell.axon + elif compartment == "dend": + section_list = self.neuron.icell.dend + elif compartment == "soma": + section_list = self.neuron.icell.soma + + return rxd.Region(section_list, nrn_region=nrn_region) + + def add_species(self, species_name, diffusion_constant, initial_conc, + charge=0, + compartment=("soma_internal", "dend_internal"), + boundary_condition=False): + + # boundary_condition is used when either a species concentration is fixed, or externally driven (specified) + + # print(f"Adding species: {species_name} to {compartment}") + + if species_name not in self.species: + self.species[species_name] = dict() + + for comp in compartment: + if comp not in self.compartments: + self.compartments[comp] = self.build[comp](self.neuron) + + if comp in self.species[species_name]: + raise ValueError(f"{species_name = } already defined for {comp = }") + + if boundary_condition: + # print(f"Fixing {species_name} concentration to constant {initial_conc}") + # The concentration is fixed + self.species[species_name][comp] = rxd.Parameter(self.compartments[comp], + name=species_name, + value=initial_conc, + charge=charge) + + else: + + # TODO: Add atol_scale etc... + self.species[species_name][comp] = rxd.Species(self.compartments[comp], + d=diffusion_constant, + initial=initial_conc, + charge=charge, + name=species_name) + + def get_species(self, *species, region_name): + """ Example usage: + a, b, c = self.neurons[123].modulation.get_species('A', 'B', 'C') + """ + return [self.species[x][region_name] for x in species] + + def add_decay(self, species_name, decay_rate): + + species = self.species[species_name] + self.rates[f"decay_{species_name}"] = rxd.rate(species, -decay_rate*species) + + def add_rate(self, species_name, left_side, right_side, region_name, overwrite=False): + + # print(f"Add rate {species_name = }, {left_side = }, {right_side = }, {region_name = }") + + if species_name not in self.rates: + self.rates[species_name] = dict() + + if not overwrite and region_name in self.rates[species_name]: + raise KeyError(f"Reaction {species_name} is already defined in neuron {self.neuron.name}") + + self.rates[species_name][region_name] = rxd.Rate(left_side, right_side, + regions=self.compartments[region_name]) + + def get_neuron_regions(self, region_list): + + regions = [] + for region in region_list: + regions.append(self.compartments[region]) + + return chain(*regions) + + def add_reaction(self, reaction_name, left_side, right_side, forward_rate, backward_rate, region_name, overwrite=False): + + # print(f"add_reaction {reaction_name = }, {left_side = }, {right_side = }, {forward_rate = }, {backward_rate = }, {region_name = }") + + if reaction_name not in self.reactions: + self.reactions[reaction_name] = dict() + + if not overwrite and region_name in self.reactions[reaction_name]: + raise KeyError(f"Reaction {reaction_name} is already defined in neuron {self.neuron.name}") + + if backward_rate is None: + backward_rate = 0 + + self.reactions[reaction_name][region_name] = rxd.Reaction(left_side, + right_side, + forward_rate, + backward_rate, + regions=self.compartments[region_name]) + + def _get_nodes(self, species, force_update=None): + if force_update is None: + force_update = NeuronModulation.should_update_rxd_nodes + + import neuron.crxd as rxd + import itertools + + if force_update: + print("Forcing rxd update...", flush=True) + NeuronModulation.should_update_rxd_nodes = False + + rxd.species._nrn_shape_update() + if rxd.initializer.is_initialized(): + print("Updating node data... (takes ≈ 1 microcentury)") + rxd.rxd._update_node_data() + else: + rxd.initializer._do_init() + print("RxD update completed.") + else: + rxd.initializer._do_init() + + species._all_intracellular_nodes = [] + if species._intracellular_nodes: + for r in species._regions: + if r in species._intracellular_nodes: + species._all_intracellular_nodes += species._intracellular_nodes[r] + # The first part here is for the 1D -- which doesn't keep live node objects -- the second part is for 3D + species._all_intracellular_nodes = [ + nd for nd in species._all_intracellular_nodes[:] if nd.sec + ] + return rxd.nodelist.NodeList( + list(itertools.chain.from_iterable([s.nodes for s in species._secs])) + + species._all_intracellular_nodes + + species._extracellular_nodes + ) + + def build_node_cache(self): + print(f"Build node cache {self.neuron.name} ({self.neuron.icell})", flush=True) + self.node_cache = {} + + for species_name, species_data in self.species.items(): + if species_name not in self.node_cache: + self.node_cache[species_name] = {} + + for region_name, species in species_data.items(): + + if region_name not in self.node_cache[species_name]: + self.node_cache[species_name][region_name] = {} + + # This row below might need to be sped up + #all_nodes = species.nodes + + all_nodes = self._get_nodes(species, force_update=None) + + for node in all_nodes: + if node._sec._sec not in self.node_cache[species_name][region_name]: + self.node_cache[species_name][region_name][node._sec._sec] = ([], []) + + self.node_cache[species_name][region_name][node._sec._sec][0].append(node) + self.node_cache[species_name][region_name][node._sec._sec][1].append(node._location) + + # Now we want to also extract the sec_x + for node_key in self.node_cache[species_name][region_name]: + node_list, node_x = self.node_cache[species_name][region_name][node_key] + self.node_cache[species_name][region_name][node_key] = (node_list, np.array(node_x)) + + print(f"Node cache built.", flush=True) + + def get_node_from_cache(self, species_name, seg, region_name): + try: + node_list, node_x = self.node_cache[species_name][region_name][seg.sec] + idx = np.argmin(np.abs(node_x - seg.x)) + except: + import traceback + print(f"get_node_from_cache failed: {species_name = }, {seg = }, {region_name = }") + print(traceback.format_exc()) + import pdb + pdb.set_trace() + return node_list[idx] + + def clear_cache(self): + self.node_cache = None + + def link_synapse(self, species_name, region: str, synapse, flux_variable: str): + # region: "soma_internal", "soma_external", "dend_internal", "dend_external" + + # print(f"link_synapses {species_name}, {region}") + + if self.node_cache is None: + self.build_node_cache() + + node = self.get_node_from_cache(species_name=species_name, seg=synapse.get_segment(), region_name=region) + node.include_flux(synapse, flux_variable) + + # self.species[species_name][region].nodes(synapse.get_segment())[0].include_flux(synapse, flux_variable) + + def load_json(self, config_path=None): + + # print(f"Parsing neuromodulation json: {config_path}") + + if config_path is None: + config_path = self.config_file + + with open(config_path, "r") as f: + self.config_data = json.load(f) + + # print(f"Parsing species") + + for species_name, species_data in self.config_data.get("species", {}).items(): + initial_concentration = species_data.get("initial_concentration", 0) * 1e3 # Convert to millimolar for RxD + diffusion_constant = species_data.get("diffusion_constant", 0) + charge = species_data.get("charge", 0) + regions = species_data.get("regions", ("soma_internal", "dendrites_internal")) + boundary_condition = species_data.get("boundary_condition", False) + + # TODO: Read atol_scale, boundary_boundary_conditions, represents parameters + + self.add_species(species_name=species_name, + diffusion_constant=diffusion_constant, + initial_conc=initial_concentration, + compartment=regions, charge=charge, + boundary_condition=boundary_condition) + + # Black magic, setup the species variables + species_name_vars = ",".join(self.species.keys()) + "," + species_name_str = "','".join(self.species.keys()) + + # print(f"Parsing rates.") + + for rate_name, rate_data in self.config_data.get("rates", {}).items(): + if rate_name not in self.species: + raise ValueError(f"Species {rate_name} is not defined. Available: {self.species.keys()}") + + rates = rate_data["rates"] + if isinstance(rates, str) and len(rate_data["regions"]) > 1: + rates = [rates for x in rate_data["regions"]] + + for region, rate in zip(rate_data["regions"], rates): + exec(f"{species_name_vars} = self.get_species('{species_name_str}', region_name=region)") + + try: + right_side = eval(rate) + except: + print(f"Problem evaluating rate {rate}") + import traceback + print(traceback.format_exc()) + import pdb + pdb.set_trace() + + self.add_rate(species_name=rate_name, + left_side=self.get_species(rate_name, region_name=region)[0], + right_side=right_side, + region_name=region) + + # print(f"Parsing reactions") + + for reaction_name, reaction_data in self.config_data.get("reactions", {}).items(): + + # BE CAREFUL, BECAUSE VALUES ARE UNSCALED AND FED DIRECTLY INTO RxD + # which uses mmolar, ms, litres as units. So usually forward rate needs + # to account for the concentration if rescaled. + # TODO: Add proper unit handling. + forward_rates = reaction_data["forward_rate"] + backward_rates = reaction_data["backward_rate"] + + if not isinstance(forward_rates, (tuple, list)): + forward_rates = [forward_rates] * len(reaction_data["regions"]) + + if not isinstance(backward_rates, (tuple, list)): + backward_rates = [backward_rates] * len(reaction_data["regions"]) + + if not ( len(reaction_data["regions"]) == len(forward_rates) == len(backward_rates)): + raise ValueError(f"{reaction_data} incompatible lengths for regions, forward and backward rates") + + for region, forward_rate, backward_rate in zip(reaction_data["regions"], forward_rates, backward_rates): + # TODO: Sanitise species_name_str before exec call + exec(f"{species_name_vars} = self.get_species('{species_name_str}', region_name=region)") + + left_side = eval(reaction_data["reactants"]) + right_side = eval(reaction_data["products"]) + + try: + n_left = sum((left_side*1)._items.values()) + n_right = sum((right_side*1)._items.values()) + except: + import traceback + print(traceback.format_exc()) + import pdb + pdb.set_trace() + + assert n_left >= 1 and n_right >= 1 + + # First 1e3 is for 1/second to 1/ms, second term is to correct for concentration + left_scale_from_SI = 1e-3 * 1e-3 ** (n_left - 1) + right_scale_from_SI = 1e-3 * 1e-3 ** (n_right - 1) + + scaled_forward_rate = forward_rate * left_scale_from_SI if forward_rate is not None else forward_rate + scaled_backward_rate = backward_rate * right_scale_from_SI if backward_rate is not None else backward_rate + + print(f"Reaction name: {reaction_name}, left: {left_side}, right: {right_side}") + print(f"k_forward: {forward_rate} (scaled: {scaled_forward_rate})") + print(f"k_backward: {backward_rate} (scaled: {scaled_backward_rate})") + + self.add_reaction(reaction_name=reaction_name, + left_side=left_side, + right_side=right_side, + forward_rate=scaled_forward_rate, + backward_rate=scaled_backward_rate, + region_name=region) + + def concentration_from_vector(self, species_name, concentration_vector, time_vector, interpolate=True): + + # Loops over all nodes in node_cache, and sets a vector to play + print(f"Playing concentration vector for {species_name} in all neurons.") + + if self.node_cache is None: + raise ValueError("node_cache not build (build_node_cache)") + + if species_name not in self.node_cache: + raise ValueError(f"{species_name} not present in node_cache, does {self.neuron.name} have RxD species?") + + for region_name, node_dictionary in self.node_cache[species_name].items(): + for node_name, node_data in node_dictionary.items(): + for nd in node_data[0]: + concentration_vector.play(nd._ref_concentration, time_vector, interpolate) \ No newline at end of file diff --git a/snudda/neurons/neuron_morphology_extended.py b/snudda/neurons/neuron_morphology_extended.py index 68f933cc0..da56e9aaf 100644 --- a/snudda/neurons/neuron_morphology_extended.py +++ b/snudda/neurons/neuron_morphology_extended.py @@ -18,6 +18,7 @@ def __init__(self, snudda_data=None, param_data=None, mech_filename=None, + reaction_diffusion=None, neuron_path=None, parameter_key=None, morphology_key=None, @@ -46,6 +47,7 @@ def __init__(self, self.param_data = param_data self.mech_filename = mech_filename + self.reaction_diffusion = reaction_diffusion self.neuron_path = neuron_path self.parameter_key = parameter_key @@ -187,6 +189,7 @@ def clone(self, snudda_data=self.snudda_data, param_data=self.param_data, mech_filename=self.mech_filename, + reaction_diffusion=self.reaction_diffusion, neuron_path=self.neuron_path, parameter_key=self.parameter_key, morphology_key=self.morphology_key, diff --git a/snudda/neurons/neuron_prototype.py b/snudda/neurons/neuron_prototype.py index 6b55619a9..d4e400875 100644 --- a/snudda/neurons/neuron_prototype.py +++ b/snudda/neurons/neuron_prototype.py @@ -17,6 +17,7 @@ def __init__(self, parameter_path=None, mechanism_path=None, modulation_path=None, + reaction_diffusion_path=None, snudda_data=None, meta_path=None, virtual_neuron=False, @@ -83,6 +84,17 @@ def __init__(self, else: self.modulation_path = None + if reaction_diffusion_path: + self.reaction_diffusion_path = snudda_parse_path(reaction_diffusion_path, self.snudda_data) + elif self.neuron_path: + self.reaction_diffusion_path = snudda_parse_path(os.path.join(self.neuron_path, "reaction_diffusion.json"), + self.snudda_data) + + if not os.path.exists(self.reaction_diffusion_path): + self.reaction_diffusion_path = None + else: + self.reaction_diffusion_path = None + self.neuron_name = neuron_name self.parameter_info = None self.meta_info = None @@ -379,6 +391,7 @@ def instantiate(self): self.morphology_cache[morph_tag] = NeuronMorphologyExtended(swc_filename=morph_path, param_data=self.parameter_path, mech_filename=self.mechanism_path, + reaction_diffusion=self.reaction_diffusion_path, neuron_path=self.neuron_path, snudda_data=self.snudda_data, name=self.neuron_name, @@ -470,6 +483,7 @@ def clone(self, parameter_id=None, morphology_id=None, modulation_id=None, snudda_data=self.snudda_data, param_data=self.parameter_path, mech_filename=self.mechanism_path, + reaction_diffusion=self.reaction_diffusion_path, neuron_path=self.neuron_path, parameter_key=parameter_key, morphology_key=morphology_key, diff --git a/snudda/place/bend_morphologies.py b/snudda/place/bend_morphologies.py index 11061a0dc..d47c3143c 100644 --- a/snudda/place/bend_morphologies.py +++ b/snudda/place/bend_morphologies.py @@ -57,9 +57,9 @@ def bend_morphology(self, morphology: NeuronMorphologyExtended, parent_dir, parent_point, parent_dist, parent_moved = parent_direction[section.section_id, section.section_type] else: if morphology.rotation is not None: - parent_dir = np.matmul(morphology.rotation, np.array([[1], [0], [0]])).T + parent_dir = np.matmul(morphology.rotation, np.array([[0], [0], [1]])).T else: - parent_dir = np.array([[1, 0, 0]]) + parent_dir = np.array([[0, 0, 1]]) if morphology.position is not None: parent_point = morphology.position @@ -145,7 +145,7 @@ def get_full_rotation_representation(self, morphology: MorphologyData): if (section.section_id, section.section_type) in parent_direction.keys(): parent_dir = parent_direction[section.section_id, section.section_type] else: - parent_dir = np.array([[1, 0, 0]]) + parent_dir = np.array([[0, 0, 1]]) try: rot_and_len, last_direction = self.rotation_representation(section=section, parent_direction=parent_dir) @@ -174,9 +174,9 @@ def apply_rotation(self, morphology: MorphologyData, rotation_representation): parent_dir, parent_pos = parent_direction[section.section_id, section.section_type] else: if morphology.rotation is not None: - parent_dir = np.matmul(morphology.rotation, np.array([[1, 0, 0]]).T).T + parent_dir = np.matmul(morphology.rotation, np.array([[0, 0, 1]]).T).T else: - parent_dir = np.array([[1, 0, 0]]) + parent_dir = np.array([[0, 0, 1]]) if morphology.position is not None: parent_pos = morphology.position @@ -223,7 +223,7 @@ def rotation_representation(self, section: SectionMetaData, parent_direction=Non if parent_direction is None: # parent_direction = np.array([[1, 0, 0]]) - parent_direction = np.array([1, 0, 0]) + parent_direction = np.array([0, 0, 1]) rotations_and_length = [] parent_direction = parent_direction / np.linalg.norm(parent_direction) @@ -335,6 +335,19 @@ def write_swc(self, morphology: MorphologyData, output_file, comment=None): swc_data[:, 6] = morphology.section_data[:, 3] + 1 # parent compartment swc_data[0, 6] = -1 + # There is a special case, when the first point after the soma is a branch point + # which could lead to a 1 point section, to handle those Snudda set section_type to 0 for that point + # https://github.com/neuronsimulator/nrn/blob/5038de0b79ddf7da9b536639989da4c10dbae7f7/share/lib/hoc/import3d/read_swc.hoc#L304 + # We need to find those points and set the section_type to that of the child + + bad_idx_list = np.where(morphology.section_data[:, 2] == 0)[0] + for bad_idx in bad_idx_list: + child_idx = np.where(morphology.section_data[:, 3] == bad_idx)[0] + s_type = morphology.section_data[child_idx, 2] + assert (s_type == s_type[0]).all(), f"Children of different type in {morphology.swc_file} {s_type}" + swc_data[bad_idx, 1] = s_type[0] + print(f"Setting {output_file} row {bad_idx} type to {s_type[0]}.") + with open(output_file, "wt") as f: if comment: f.write(f"#{comment}\n") @@ -450,4 +463,4 @@ def test_bending(): profiler.print_stats(sort='cumulative') # import pdb - # pdb.set_trace() \ No newline at end of file + # pdb.set_trace() diff --git a/snudda/place/place.py b/snudda/place/place.py index 0dc0fd1a9..baee1145e 100644 --- a/snudda/place/place.py +++ b/snudda/place/place.py @@ -25,8 +25,7 @@ from snudda.utils.snudda_path import get_snudda_data from snudda.neurons.neuron_prototype import NeuronPrototype -# from snudda.place.region_mesh import RegionMesh -from snudda.place.region_mesh_redux import NeuronPlacer +from snudda.place.region_mesh_redux import NeuronPlacer, RegionMeshRedux from snudda.place.rotation import SnuddaRotate from snudda.utils.snudda_path import snudda_parse_path, snudda_path_exists, snudda_simplify_path @@ -190,6 +189,7 @@ def add_neurons(self, param_filename=None, mech_filename=None, modulation=None, + reaction_diffusion=None, name="Unnamed", hoc=None, volume_id=None, @@ -210,6 +210,7 @@ def add_neurons(self, param_filename (str): Path to parameter file mech_filename (str): Path to mechanism file modulation (str): Path to neuromodulation file + reaction_diffusion (str): Path to RxD reaction diffusion file name (str): Name of neuron population, e.g. DSPN (which will become DSPN_0, DSPN_1, etc...) hoc (str): Path to hoc file (currently disabled) volume_id (str): ID of the volume to place neurons in @@ -235,6 +236,7 @@ def add_neurons(self, parameter_path=param_filename, mechanism_path=mech_filename, modulation_path=modulation, + reaction_diffusion_path=reaction_diffusion, load_morphology=False, virtual_neuron=virtual_neuron) @@ -465,7 +467,8 @@ def parse_config(self, config_file=None, resort_neurons=True): else: n_neurons = num_neurons - # print(f"{n_neurons = }") + # RxD reaction diffusion config file + default_reaction_diffusion = neuron_data.get("reaction_diffusion") parameter_key_list = SnuddaPlace.replicate_str(neuron_data.get("parameter_key"), n_neurons, f"{neuron_type} parameter_key") @@ -491,6 +494,13 @@ def parse_config(self, config_file=None, resort_neurons=True): if not snudda_path_exists(modulation, snudda_data=self.snudda_data): modulation = None + if default_reaction_diffusion is None: + reaction_diffusion = os.path.join(neuron_path, "reaction_diffusion.json") + if not snudda_path_exists(reaction_diffusion, snudda_data=self.snudda_data): + reaction_diffusion = None + else: + reaction_diffusion = default_reaction_diffusion + if model_type == "virtual": param = None mech = None @@ -505,6 +515,7 @@ def parse_config(self, config_file=None, resort_neurons=True): param_filename=param, mech_filename=mech, modulation=modulation, + reaction_diffusion=reaction_diffusion, num_neurons=num, hoc=None, volume_id=region_name, @@ -809,7 +820,6 @@ def get_projection_axon_location(self, source_position, proj_info, rng, patch_hu return target_centres, target_rotation, axon_swc - ############################################################################ def all_neuron_positions(self): @@ -983,6 +993,11 @@ def write_data(self, file_name=None): for n in self.neurons] mok_str_type = 'S' + str(max(1, max([len(x) for x in mok_list]))) + rd_list = [n.reaction_diffusion.encode("ascii", "ignore") + if n.reaction_diffusion is not None else "" + for n in self.neurons] + rd_str_type = 'S' + str(max(1, max([len(x) for x in rd_list]))) + neuron_param_key = neuron_group.create_dataset("parameter_key", (len(self.neurons),), pk_str_type, @@ -998,6 +1013,10 @@ def write_data(self, file_name=None): mok_str_type, compression="gzip") + reaction_diffusion = neuron_group.create_dataset("reaction_diffusion_file", + (len(self.neurons),), + rd_str_type) + neuron_pos_all = np.zeros((len(self.neurons), 3)) neuron_rot_all = np.zeros((len(self.neurons), 9)) @@ -1008,6 +1027,9 @@ def write_data(self, file_name=None): neuron_mod_key_list = [] neuron_mod_key_idx = [] + reacdiff_list = [] + reacdiff_key = [] + for (i, n) in enumerate(self.neurons): neuron_pos_all[i, :] = n.position neuron_rot_all[i, :] = n.rotation.reshape(1, 9) @@ -1024,6 +1046,10 @@ def write_data(self, file_name=None): neuron_mod_key_list.append(n.modulation_key) neuron_mod_key_idx.append(i) + if n.reaction_diffusion: + reacdiff_list.append(n.reaction_diffusion) + reacdiff_key.append(i) + neuron_position[:, :] = neuron_pos_all neuron_rotation[:, :] = neuron_rot_all @@ -1036,6 +1062,9 @@ def write_data(self, file_name=None): if len(neuron_mod_key_list) > 0: neuron_modulation_key[neuron_mod_key_idx] = neuron_mod_key_list + if len(reacdiff_list) > 0: + reaction_diffusion[reacdiff_key] = reacdiff_list + # Store input information if self.population_unit is None: # If no population units were defined, then set them all to 0 (= no population unit) @@ -1107,7 +1136,8 @@ def define_population_units(self, config): """ method_lookup = {"random": self.random_labeling, - "radial_density": self.population_unit_density_labeling} + "radial_density": self.population_unit_density_labeling, + "mesh": self.population_unit_mesh} for region_name in self.config["regions"]: if "population_units" in self.config["regions"][region_name]: @@ -1285,6 +1315,51 @@ def population_unit_density_labeling(self, population_unit_info, neuron_id): else: self.population_units[0] = remove_nid + def population_unit_mesh(self, population_unit_info, neuron_id): + + self.init_population_units() # This initialises population unit labelling if not already allocated + + unit_id = population_unit_info["unit_id"] + mesh_file = population_unit_info["mesh_file"] + fraction_of_neurons = population_unit_info["fraction_of_neurons"] + neuron_types = population_unit_info["neuron_types"] # list of neuron types that belong to this population unit + structure_name = population_unit_info["structure"] + + pos = np.vstack([self.neurons[nid].position for nid in neuron_id]) + model_neuron_types = [self.neurons[nid].name.split("_")[0] for nid in neuron_id] + + member_probability = np.zeros(shape=(pos.shape[0], len(mesh_file))) + + for idx, (mf, frac, nts) in enumerate(zip(mesh_file, fraction_of_neurons, neuron_types)): + + # This checks if neurons are of the types that are included in population unit + has_nt = np.array([n in nts for n in model_neuron_types], dtype=bool) + + rm = RegionMeshRedux(mf, verbose=self.verbose) + member_probability[:, idx] = np.logical_and(rm.check_inside(pos), has_nt) * frac + + # If the probability sums to more than 1, then normalise it, otherwise keep smaller + member_probability = np.divide(member_probability, np.maximum(1, np.sum(member_probability, axis =1).reshape(len(member_probability),1))) + + # Also, we need to add population unit 0 as an option, since choice needs P_sum = 1 + full_member_probability = np.zeros(shape=(member_probability.shape[0], member_probability.shape[1]+1)) + full_member_probability[:, :-1] = member_probability + full_member_probability[:, -1] = np.maximum(1 - np.sum(member_probability, axis=1), 0) + all_unit_id = unit_id + [0] + + # Normalise to 1 + row_sums = np.sum(full_member_probability, axis=1) + row_sums = row_sums[:, np.newaxis] + full_member_probability = full_member_probability / row_sums + + for idx, (nid, P) in enumerate(zip(neuron_id, full_member_probability)): + + uid = self.random_generator.choice(all_unit_id, p=P) + self.population_unit[nid] = uid + + if uid > 0: + self.population_units[uid].append(nid) + ############################################################################ def init_population_units(self): diff --git a/snudda/place/region_mesh_redux.py b/snudda/place/region_mesh_redux.py index 0c4afcd11..c0e37ec53 100644 --- a/snudda/place/region_mesh_redux.py +++ b/snudda/place/region_mesh_redux.py @@ -7,10 +7,11 @@ class RegionMeshRedux: - def __init__(self, mesh_path): + def __init__(self, mesh_path, verbose=False): self.mesh_path = mesh_path self.mesh = o3d.io.read_triangle_mesh(mesh_path) + self.verbose = verbose # Convert from micrometers to meters to get SI units scale_factor = 1e-6 @@ -102,7 +103,7 @@ def get_line_set(self, neurons): class NeuronPlacer: def __init__(self, mesh_path: str, d_min: float, random_seed=None, rng=None, - n_putative_points=None, putative_density=None): + n_putative_points=None, putative_density=None, verbose=False): """ Args: mesh_path (str): Path to wavefront obj file @@ -111,7 +112,8 @@ def __init__(self, mesh_path: str, d_min: float, random_seed=None, rng=None, rng: Numpy rng object, either rng or random_seed is given n_putative_points (int): Number of putative positions to place within volume (before d_min filtering)""" - self.region_mesh = RegionMeshRedux(mesh_path=mesh_path) + self.verbose = verbose + self.region_mesh = RegionMeshRedux(mesh_path=mesh_path, verbose=verbose) self.d_min = d_min self.density_functions = dict() @@ -134,7 +136,12 @@ def __init__(self, mesh_path: str, d_min: float, random_seed=None, rng=None, if putative_density: n_putative_points = int(np.ceil(np.prod(self.cube_side)*putative_density*1e9)) else: - n_putative_points = int(np.ceil(np.prod(self.cube_side) * (1/self.d_min) ** 3)) + + # n_putative_points = min(int(np.ceil(np.prod(self.cube_side) * (1/self.d_min) ** 3)), 1000000) + n_putative_points = min(int(np.ceil(np.prod(self.cube_side) * 300e3*1e9)), 1000000) + + print(f"No n_putative_points and putative_density, setting {n_putative_points = }" + f"\n(this must be larger than the number of neurons you want to place)") else: # We need to compenate n_putative_points for fact that we sample points outside volume also n_putative_points *= np.prod(self.cube_side) / self.region_mesh.volume @@ -230,13 +237,16 @@ def _remove_close_neurons_helper(self, points, remove_fraction=0.05): points = np.delete(points, remove_idx, axis=0) - print(f"n_points = {points.shape[0]}, previous close_pairs = {len(close_pairs)}") + if self.verbose: + print(f"n_points = {points.shape[0]}, previous close_pairs = {len(close_pairs)}") return points, False def remove_outside(self, points): - print(f"Filtering {points.shape[0]} points..") + if self.verbose: + print(f"Filtering {points.shape[0]} points..") + # keep_flag = self.region_mesh.point_inside(points=points) keep_flag = self.region_mesh.check_inside(points=points) @@ -259,10 +269,16 @@ def get_neuron_positions(self, n_positions, neuron_density=None): # k=2, since we don't want distance to point itself, but closest neighbour # closest_distance, _ = cKDTree(data=free_positions).query(x=free_positions, k=2) - closest_distance, _ = cKDTree(data=free_positions).query(x=free_positions, k=2) + if len(free_positions) > 1: + closest_distance, _ = cKDTree(data=free_positions).query(x=free_positions, k=2) + # Volume is proportional to distance**3, so scale probabilities to pick position by that + free_volume = np.power(np.mean(closest_distance[:, 1:2], axis=1), 3) + P_neuron = free_volume + else: + # Special case, when there is only one + P_neuron = [1] + assert neuron_density is None, "You can not specify neuron_density if there is only one free position." - # Volume is proportional to distance**3, so scale probabilities to pick position by that - free_volume = np.power(np.mean(closest_distance[:, 1:2], axis=1), 3) x, y, z = free_positions.T if neuron_density: @@ -272,8 +288,6 @@ def get_neuron_positions(self, n_positions, neuron_density=None): else: P_neuron = np.multiply(neuron_density(x=x, y=y, z=z), free_volume) # P_neuron = numexpr.evaluate(neuron_density) - else: - P_neuron = free_volume P_neuron /= np.sum(P_neuron) @@ -295,11 +309,6 @@ def get_neuron_positions(self, n_positions, neuron_density=None): return neuron_positions -class NeuronBender: - - def __init__(self): - pass - # Goal for today: # - Function that returns N positions that are not within d_min of each other # - Within a given volume diff --git a/snudda/plotting/Blender/visualisation/visualise_network.py b/snudda/plotting/Blender/visualisation/visualise_network.py index 81783a951..862695c0a 100644 --- a/snudda/plotting/Blender/visualisation/visualise_network.py +++ b/snudda/plotting/Blender/visualisation/visualise_network.py @@ -9,7 +9,7 @@ import numpy as np from snudda.utils.load import SnuddaLoad from snudda.utils.snudda_path import snudda_parse_path, get_snudda_data -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation class VisualiseNetwork(object): @@ -39,7 +39,7 @@ def __init__(self, network_path, blender_save_file=None, blender_output_image=No self.blender_output_image = blender_output_image if simulation_output_file_name: - self.slns = SnuddaLoadNetworkSimulation(simulation_output_file_name) + self.slns = SnuddaLoadSimulation(simulation_output_file_name) self.spike_times = self.slns.get_spikes() else: self.spike_times = None diff --git a/snudda/plotting/plot_cross_correlogram.py b/snudda/plotting/plot_cross_correlogram.py index 95cbae1cd..6663bb690 100644 --- a/snudda/plotting/plot_cross_correlogram.py +++ b/snudda/plotting/plot_cross_correlogram.py @@ -4,7 +4,7 @@ from numba import jit import matplotlib.pyplot as plt -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation class PlotCrossCorrelogram: @@ -14,7 +14,7 @@ def __init__(self, simulation_file=None, snudda_simulation_load=None): if snudda_simulation_load: self.sim_data = snudda_simulation_load else: - self.sim_data = SnuddaLoadNetworkSimulation(network_simulation_output_file=simulation_file) + self.sim_data = SnuddaLoadSimulation(network_simulation_output_file=simulation_file) def calculate_all_pair_cross_correlogram(self, neuron_id, time_range=None, shuffle_correct=True, n_bins=101, width=50e-3): diff --git a/snudda/plotting/plot_input_locations.py b/snudda/plotting/plot_input_locations.py index e469ad740..3177de794 100644 --- a/snudda/plotting/plot_input_locations.py +++ b/snudda/plotting/plot_input_locations.py @@ -3,6 +3,8 @@ import numpy as np import json +from copy import deepcopy + from snudda.utils.snudda_path import get_snudda_data from snudda.utils.snudda_path import snudda_parse_path from snudda.utils import SnuddaLoad @@ -215,7 +217,7 @@ def get_input_soma_distance_summary(self, neuron_type, input_name): def load_input_config(self): - self.input_config = json.loads(SnuddaLoad.to_str(self.input_data["config"][()])) + self.input_config = deepcopy(self.input_data["config"]) def get_max_dendrite_distance(self, neuron_type): diff --git a/snudda/plotting/plot_network_simulation.py b/snudda/plotting/plot_network_simulation.py index fab4faadd..cc989a150 100644 --- a/snudda/plotting/plot_network_simulation.py +++ b/snudda/plotting/plot_network_simulation.py @@ -1,7 +1,6 @@ #Plot control file. Currently used by batch scripts in examples/parallel. #Place this in snudda/plotting at a later stage import sys -from snudda.plotting import PlotSpikeRaster from snudda.plotting import SnuddaPlotSpikeRaster2 from snudda.plotting import PlotTraces diff --git a/snudda/plotting/plot_neuron_voltage.py b/snudda/plotting/plot_neuron_voltage.py index 04d0ca7bc..48d860b96 100644 --- a/snudda/plotting/plot_neuron_voltage.py +++ b/snudda/plotting/plot_neuron_voltage.py @@ -4,7 +4,7 @@ import matplotlib.pyplot as plt from snudda.neurons import NeuronMorphologyExtended -from snudda.utils import SnuddaLoad, SnuddaLoadNetworkSimulation, snudda_parse_path +from snudda.utils import SnuddaLoad, SnuddaLoadSimulation, snudda_parse_path class PlotNeuronVoltage: @@ -33,9 +33,9 @@ def __init__(self, network_path, network_file=None, simulation_file=None, snudda self.snudda_data = snudda_data print(f"Loading simulation data {self.simulation_file}") - self.simulation_data = SnuddaLoadNetworkSimulation(network_simulation_output_file=self.simulation_file, - network_path=self.network_path, - do_test=False) + self.simulation_data = SnuddaLoadSimulation(network_simulation_output_file=self.simulation_file, + network_path=self.network_path, + do_test=False) def load_morphology(self, neuron_id): morphology_file = snudda_parse_path(self.network_data["neurons"][neuron_id]["morphology"], diff --git a/snudda/plotting/plot_spike_raster_v2.py b/snudda/plotting/plot_spike_raster_v2.py index 29f68db36..8f9e44b2e 100644 --- a/snudda/plotting/plot_spike_raster_v2.py +++ b/snudda/plotting/plot_spike_raster_v2.py @@ -7,7 +7,7 @@ import matplotlib.pyplot as plt -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation class SnuddaPlotSpikeRaster2: @@ -58,7 +58,7 @@ def __init__(self, network_path, network_file=None, simulation_file=None, figure f"snudda_simulation_load refers to {snudda_simulation_load.self.network_simulation_output_file_name}," \ f" but user passed simulation_file={simulation_file}" else: - self.snudda_simulation_load = SnuddaLoadNetworkSimulation(network_simulation_output_file=self.simulation_file) + self.snudda_simulation_load = SnuddaLoadSimulation(network_simulation_output_file=self.simulation_file) spike_data = self.snudda_simulation_load.merge_spikes() diff --git a/snudda/plotting/plot_traces.py b/snudda/plotting/plot_traces.py index 9e80383be..796ae10cb 100644 --- a/snudda/plotting/plot_traces.py +++ b/snudda/plotting/plot_traces.py @@ -7,7 +7,7 @@ import h5py import numpy as np from snudda.utils.load import SnuddaLoad -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation import matplotlib.pyplot as plt import re @@ -65,8 +65,8 @@ def __init__(self, output_file, network_file=None, input_file=None, experiment_n else: self.input_info = None - self.output_load = SnuddaLoadNetworkSimulation(network_simulation_output_file=output_file, - network_path=network_path) + self.output_load = SnuddaLoadSimulation(network_simulation_output_file=output_file, + network_path=network_path) self.voltage = self.output_load.get_voltage() self.time = self.output_load.get_time() diff --git a/snudda/simulate/network_pair_pulse_simulation.py b/snudda/simulate/network_pair_pulse_simulation.py index c57adf42e..72d72e594 100644 --- a/snudda/simulate/network_pair_pulse_simulation.py +++ b/snudda/simulate/network_pair_pulse_simulation.py @@ -54,7 +54,7 @@ import numpy as np from snudda.simulate.simulate import SnuddaSimulate -from snudda.utils import SnuddaLoadNetworkSimulation +from snudda.utils import SnuddaLoadSimulation from snudda.utils.load import SnuddaLoad from snudda.utils import snudda_parse_path from snudda.utils.snudda_path import get_snudda_data @@ -321,7 +321,7 @@ def analyse(self, max_dist=None, n_max_show=10, pre_id=None, post_type=None): self.snudda_load = SnuddaLoad(self.network_file) self.data = self.snudda_load.data - ssd = SnuddaLoadNetworkSimulation(network_path=self.network_path) + ssd = SnuddaLoadSimulation(network_path=self.network_path) voltage = ssd.get_voltage() time = ssd.get_time() diff --git a/snudda/simulate/pair_recording.py b/snudda/simulate/pair_recording.py index b21af5fd9..98442fa66 100644 --- a/snudda/simulate/pair_recording.py +++ b/snudda/simulate/pair_recording.py @@ -423,7 +423,6 @@ def distribute_neurons(self): self.gap_junctions = self.gap_junctions[keep_gj_flag, :] - def run(self): """ Run simulation. """ @@ -562,7 +561,7 @@ def set_channel_rev(self, channel_name, v_rev): print(f"Setting {channel_name} reversal potential to {v_rev * 1e3} mV") - for syn in self.synapse_list: + for syn in self.synapse_dict.values(): if channel_name == syn.hname().split("[")[0]: syn.e = v_rev * 1e3 diff --git a/snudda/simulate/save_network_recording.py b/snudda/simulate/save_network_recording.py index 062fa04cd..f7badf36a 100644 --- a/snudda/simulate/save_network_recording.py +++ b/snudda/simulate/save_network_recording.py @@ -1,3 +1,4 @@ +import sys import os.path import h5py import numpy as np @@ -180,7 +181,12 @@ class SnuddaSaveNetworkRecordings: # TODO: Add saving of simulation_config file (and experiment_config_file for pair recording) - def __init__(self, output_file, network_data=None, sample_dt=None): + def __init__(self, output_file, network_data=None, sample_dt=None, node_id=0): + + # Only do this check on the first node + if node_id == 0 and not self.check_file_available(output_file): + sys.exit(-1) + self.output_file = output_file self.network_data = network_data self.header_exists = False @@ -267,6 +273,19 @@ def write_string_meta_data(self, group, name): str_type = f"S{max_len}" group.create_dataset(name, (len(string_data),), str_type, string_data, compression="gzip") + def check_file_available(self, file_name): + + if os.path.isfile(file_name): + # Try to open and close file, to make sure it is available + try: + f = h5py.File(file_name, "w") + f.close() + except BlockingIOError as e: + print(f"Unable to create file {file_name}. Is some other program using the file?") + return False + + return True + def write_header(self): self.pc.barrier() @@ -280,7 +299,26 @@ def write_header(self): os.mkdir(os.path.dirname(self.output_file)) print(f"Writing network output to {self.output_file}") - out_file = h5py.File(self.output_file, "w") + + try: + out_file = h5py.File(self.output_file, "w") + except Exception as e: + + print(e) + print(f"Trying to recover, and save to different file name.") + + ctr = 1 + temp_name = f"{self.output_file}-{ctr}" + + while os.path.isfile(temp_name): + print(f"File exists: {temp_name}") + ctr += 1 + temp_name = f"{self.output_file}-{ctr}" + + print(f"\n!!! Unable to create {self.output_file} (file locked?), using {temp_name} instead\n\n") + self.output_file = temp_name + + out_file = h5py.File(temp_name, "w") meta_data = out_file.create_group("meta_data") diff --git a/snudda/simulate/simulate.py b/snudda/simulate/simulate.py index 00788e48c..3c5f40e1e 100644 --- a/snudda/simulate/simulate.py +++ b/snudda/simulate/simulate.py @@ -28,6 +28,8 @@ import h5py import neuron import numpy as np + +from mpi4py import MPI # This must be imported before neuron, to run parallel from neuron import h # , gui import copy @@ -59,6 +61,7 @@ def __init__(self, disable_synapses=None, disable_gap_junctions=None, sample_dt=None, + use_rxd_neuromodulation=True, simulation_config=None): """ @@ -86,16 +89,24 @@ def __init__(self, elif network_file: self.network_path = os.path.dirname(network_file) else: - assert False, "You must give network_path or network_file" + self.write_log("No network network_path or network_file specified") + self.network_path = None self.snudda_data = get_snudda_data(snudda_data=snudda_data, network_path=self.network_path) - if not network_file: - self.network_file = os.path.join(self.network_path, "network-synapses.hdf5") + if not network_file and self.network_path is not None: + alt_network_file = os.path.join(self.network_path, "network-synapses.hdf5") + if os.path.isfile(alt_network_file): + self.network_file = alt_network_file + else: + self.network_file = None else: self.network_file = network_file + if self.network_file is None: + self.write_log(f"Warning: no network_file defined.", is_error=True) + if not input_file: default_input_file = os.path.join(self.network_path, "input-spikes.hdf5") @@ -124,6 +135,8 @@ def __init__(self, self.neuron_id = None self.neuron_id_on_node = None self.synapse_parameters = None + self.use_rxd_neuromodulation = use_rxd_neuromodulation + self.bath_application = dict() self.sim_start_time = 0 self.fih_time = None @@ -137,6 +150,17 @@ def __init__(self, self.disable_synapses = False self.disable_gap_junctions = False + self.current_injection_info = dict() + self.current_clamps = dict() + + node_id = int(self.pc.id()) + total_nodes = int(self.pc.nhost()) + + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + size = comm.Get_size() + print(f"MPI Rank: {rank}, Size: {size} -- NEURON: This is node {node_id} out of {total_nodes}") + if simulation_config: if type(simulation_config) == dict: @@ -180,6 +204,18 @@ def __init__(self, # Do not change this unless you know what you are doing self.snudda_data = self.sim_info["snudda_data"] + if "current_injection_file" in self.sim_info: + current_file = self.sim_info["current_injection_file"] + if not os.path.isfile(current_file): + raise ValueError(f"No such current injection file {current_file}") + + with open(current_file, "rt") as f: + self.current_injection_info = json.load(f) + + if "current_injection_info" in self.sim_info: + # This is merged with current injection info read from file (above) + self.current_injection_info |= self.sim_info["current_injection_info"] + else: self.sim_info = None @@ -224,13 +260,11 @@ def __init__(self, self.virtual_neurons = {} - self.net_con_list = [] # Avoid premature garbage collection -- todo: THIS WILL BE REMOVED - self.synapse_list = [] # todo: THIS WILL BE REMOVED, replaced by synapse_dict - self.synapse_dict = dict() + self.synapse_dict = dict() # Avoid premature garbage collection self.i_stim = [] self.v_clamp_list = [] - self.gap_junction_list = [] - self.external_stim = dict([]) + self.gap_junction_dict = dict() + self.external_stim = dict() self.t_save = [] self.i_save = [] self.i_key = [] @@ -250,10 +284,13 @@ def __init__(self, # We need to initialise random streams, see Lytton el at 2016 (p2072) - self.load_network_info(self.network_file) + if self.network_file is not None: + self.load_network_info(self.network_file) + else: + self.write_log("No network path or file specified, not loading network.") self.record = SnuddaSaveNetworkRecordings(output_file=self.output_file, network_data=self.network_info, - sample_dt=self.sample_dt) + sample_dt=self.sample_dt, node_id=node_id) self.record.add_unit(data_type="voltage", target_unit="V", conversion_factor=1e-3) self.record.add_unit(data_type="synaptic_current", target_unit="A", conversion_factor=1e-9) self.record.add_unit(data_type="spikes", target_unit="s", conversion_factor=1e-3) @@ -295,13 +332,87 @@ def setup_parse_sim_info(self): self.add_volt_recording_soma(cell_id=record_soma_cell_id) if "record_all_compartments" in self.sim_info: + raise DeprecationWarning("record_all_compartments deprecated, please use record_voltate_all_compartments") record_comp_cell_id = np.array(self.sim_info["record_all_compartments"], dtype=int) self.add_volt_recording_all(cell_id=record_comp_cell_id) + if "record_voltage_all_compartments" in self.sim_info: + record_comp_cell_id = np.array(self.sim_info["record_voltage_all_compartments"], dtype=int) + self.add_volt_recording_all(cell_id=record_comp_cell_id) + if "record_all_synapses" in self.sim_info: + raise DeprecationWarning("record_all_synapses deprecated, please use record_current_all_synapses") record_syn_cell_id = np.array(self.sim_info["record_all_synapses"], dtype=int) self.add_synapse_current_recording_all(record_syn_cell_id) + if "record_current_all_synapses" in self.sim_info: + record_syn_cell_id = np.array(self.sim_info["record_current_all_synapses"], dtype=int) + self.add_synapse_current_recording_all(record_syn_cell_id) + + if "rxd_enable_extracellular" in self.sim_info: + import neuron.rxd as rxd + rxd.options.enable.extracellular = self.sim_info["rxd_enable_extracellular"] + + if "record_rxd_species_concentration_all_compartments" in self.sim_info and self.use_rxd_neuromodulation: + + rec_info = self.sim_info["record_rxd_species_concentration_all_compartments"] + + if type(rec_info[0]) == str: + # Only one species + rec_info = [rec_info] + + for record_rx_species, record_rxd_neuron_id in rec_info: + self.write_log(f"Recording {record_rx_species} from neuron_id = {record_rxd_neuron_id}") + for rxd_neuron_id in record_rxd_neuron_id: + self.add_rxd_internal_concentration_recording_all(record_rx_species, rxd_neuron_id) + + if "record_rxd_species_all" in self.sim_info and self.use_rxd_neuromodulation: + rxd_record_neuron_id = self.sim_info["record_rxd_species_all"] + + self.add_rxd_internal_concentration_recording_all_species(neuron_id=rxd_record_neuron_id) + + if "record_rxd_species_soma" in self.sim_info and self.use_rxd_neuromodulation: + rxd_record_neuron_id = self.sim_info["record_rxd_species_soma"] + + self.add_rxd_internal_concentration_recording_all_species(neuron_id=rxd_record_neuron_id, + include_dendrites=False) + + if "record_density_mechanism" in self.sim_info: + record_info = self.sim_info["record_density_mechanism"] + + for key_name, density_data in record_info.items(): + density_mechanism_name, variable_name = key_name.split(".") + neuron_id = density_data["neuron_id"] + sec_id = density_data["section_id"] + sec_x = density_data["section_x"] + + if not (len(neuron_id) == len(sec_id) == len(sec_x)): + raise ValueError(f"neuron_id, section_id and section_x must be lists of same length in experiment config.\n" + f"{record_info = }") + + for nid, sid, sex in zip(neuron_id, sec_id, sec_x): + + self.add_density_mechanism_recording(neuron_id=nid, + sec_id=sid, sec_x=sex, + density_mechanism=density_mechanism_name, + variable=variable_name) + if "bath_application" in self.sim_info: + for species_name, bath_info in self.sim_info["bath_application"].items(): + + bath_time = np.array(bath_info["time"]) + bath_conc = np.array(bath_info["concentration"]) + neuron_id = bath_info.get("neuron_id", None) + interpolate_bath = bath_info.get("interpolate", False) + + self.add_bath_application(species_name=species_name, + concentration=bath_conc, + time=bath_time, + neuron_id=neuron_id, + interpolate=interpolate_bath) + + # Add any current injections that are specified + self.parse_current_injection_info() + # Do we need blocking call here, to make sure all neurons are setup # before we try and connect them @@ -431,21 +542,20 @@ def load_synapse_parameters(self): # We need to load all the synapse parameters self.synapse_parameters = dict() - for (preType, postType) in self.network_info["connectivity_distributions"]: + for (pre_type, post_type) in self.network_info["connectivity_distributions"]: - syn_data = self.network_info["connectivity_distributions"][preType, postType] + syn_data = self.network_info["connectivity_distributions"][pre_type, post_type] - for synType in syn_data: + for syn_type in syn_data: - synapse_type_id = syn_data[synType]["channel_model_id"] - info_dict = syn_data[synType] + synapse_type_id = syn_data[syn_type]["channel_model_id"] + info_dict = syn_data[syn_type] if synapse_type_id == 3: # Gap junctions, skip parameters continue - if ("channel_parameters" in info_dict - and info_dict["channel_parameters"] is not None): + if "channel_parameters" in info_dict and info_dict["channel_parameters"] is not None: channel_param_dict = copy.deepcopy(info_dict["channel_parameters"]) mod_file = channel_param_dict["mod_file"] @@ -454,7 +564,7 @@ def load_synapse_parameters(self): eval_str = f"self.sim.neuron.h.{mod_file}" channel_module = eval(eval_str) # If this fails, check that NEURON modules are compiled else: - self.write_log(f"Empty mod_file field for {preType} -> {postType} synapses. This channel is IGNORED.", force_print=True) + self.write_log(f"Empty mod_file field for {pre_type} -> {post_type} synapses. This channel is IGNORED.", force_print=True) channel_module = None # These are not variables to set in the mod_file @@ -465,7 +575,7 @@ def load_synapse_parameters(self): del channel_param_dict["parameter_file"] else: - assert False, (f"No channel module specified for {preType}->{postType} synapses, " + assert False, (f"No channel module specified for {pre_type}->{post_type} synapses, " f"type ID={synapse_type_id}") if "parameter_file" in info_dict["channel_parameters"] \ @@ -478,6 +588,7 @@ def load_synapse_parameters(self): # Save data as a list, we don't need the keys par_data = [] for pd in par_data_dict: + if "synapse" in par_data_dict[pd]: # Add channel parameters specified in network file, however # any values in the synapse parameter file will overwrite them @@ -486,6 +597,7 @@ def load_synapse_parameters(self): p_dict[x] = par_data_dict[pd]["synapse"][x] par_data.append(p_dict) + else: self.write_log(f"WARNING: Old data format in parameter file {par_file}") @@ -520,6 +632,9 @@ def setup_neurons(self): for ID in self.neuron_id: name = self.network_info["neurons"][ID]["name"] + neuron_type = self.network_info["neurons"][ID]["type"] + + region = self.network_info["neurons"][ID]["volume_id"] # We need to get morphology from network_info, since it can now be redefined for bent morphologies morph = snudda_parse_path(self.network_info["neurons"][ID]["morphology"], self.snudda_data) @@ -528,10 +643,57 @@ def setup_neurons(self): param = os.path.join(neuron_path, "parameters.json") mech = os.path.join(neuron_path, "mechanisms.json") - modulation = os.path.join(neuron_path, "modulation.json") - if not os.path.isfile(modulation): - modulation = None + if "modulation" in self.network_info["neurons"][ID]: + modulation = self.network_info["neurons"][ID]["modulation"] + + if not os.path.isfile(modulation): + raise ValueError(f"Missing modulation file {modulation} " + f"for neuron {self.network_info['neurons'][ID]['name']}") + + elif neuron_type in self.network_info["config"]["regions"][region]["neurons"] and \ + "modulation" in self.network_info["config"]["regions"][region]["neurons"][neuron_type]: + modulation = self.network_info["config"]["regions"][region]["neurons"][neuron_type]["modulation"] + + if not os.path.isfile(modulation): + modulation = os.path.join(neuron_path, modulation) + + else: + modulation = os.path.join(neuron_path, "modulation.json") + + if not os.path.isfile(modulation): + modulation = None + + if "reaction_diffusion_file" in self.network_info["neurons"][ID]: + reaction_diffusion_file = SnuddaLoad.to_str(self.network_info["neurons"][ID]["reaction_diffusion_file"]) + + if reaction_diffusion_file is not None and not os.path.isfile(reaction_diffusion_file): + raise ValueError(f"Missing RxD reaction diffusion file {reaction_diffusion_file} " + f"for neuron {self.network_info['neurons'][ID]['name']}") + + else: + reaction_diffusion_file = os.path.join(neuron_path, "reaction_diffusion.json") + + if not os.path.isfile(reaction_diffusion_file): + reaction_diffusion_file = None + + meta_file = snudda_parse_path(os.path.join(neuron_path, "meta.json"), self.snudda_data) + axon_length = 60e-6 + axon_nseg_frequency = 40e-6 + + if os.path.isfile(meta_file): + with open(meta_file, "r") as mf: + meta_data = json.load(mf) + + meta_parameter_key = self.network_info["neurons"][ID]["parameter_key"] + meta_morphology_key = self.network_info["neurons"][ID]["morphology_key"] + + if meta_parameter_key in meta_data: + if meta_morphology_key in meta_data[meta_parameter_key]: + if "axon_stump" in meta_data[meta_parameter_key][meta_morphology_key]: + replace_info = meta_data[meta_parameter_key][meta_morphology_key]["axon_stump"] + axon_length = replace_info.get("axon_length", 60e-6) + axon_nseg_frequency = replace_info.get("axon_nseg_frequency", 40e-6) # Obs, neurons is a dictionary if self.network_info["neurons"][ID]["virtual_neuron"]: @@ -563,6 +725,8 @@ def setup_neurons(self): # A real neuron (not a virtual neuron that just provides input) parameter_key = self.network_info["neurons"][ID]["parameter_key"] morphology_key = self.network_info["neurons"][ID]["morphology_key"] + + # TODO: Modulation key currently has no USE -- deprecated? Remove? modulation_key = self.network_info["neurons"][ID]["modulation_key"] self.neurons[ID] = NeuronModel(param_file=param, @@ -570,9 +734,13 @@ def setup_neurons(self): mech_file=mech, cell_name=name, modulation_file=modulation, + reaction_diffusion_file=reaction_diffusion_file, parameter_key=parameter_key, morphology_key=morphology_key, - modulation_key=modulation_key) + modulation_key=modulation_key, + use_rxd_neuromodulation=self.use_rxd_neuromodulation, + replace_axon_length=axon_length, + replace_axon_nseg_frequency=axon_nseg_frequency) # Register ID as belonging to this worker node try: @@ -620,6 +788,12 @@ def setup_neurons(self): self.check_id_recordings.append((ID, id_spikes)) self.record.register_spike_data(neuron_id=ID, data=t_spikes, sec_id=-1, sec_x=0.5) + # RxD is slow when doing species.nodes call, so we cache it... + for neuron_id in self.neuron_id: + if not self.network_info["neurons"][ID]["virtual_neuron"] \ + and self.neurons[neuron_id].modulation is not None: + self.neurons[neuron_id].modulation.build_node_cache() + ############################################################################ def connect_network(self): @@ -920,8 +1094,16 @@ def find_local_gap_junctions(self): # gj_idx_a = np.where([x in self.neuron_id for x in self.gap_junctions[:, 0]])[0] # gj_idx_b = np.where([x in self.neuron_id for x in self.gap_junctions[:, 1]])[0] - gj_idx_a = np.where([self.neuron_id_on_node[x] for x in self.gap_junctions[:, 0]])[0] - gj_idx_b = np.where([self.neuron_id_on_node[x] for x in self.gap_junctions[:, 1]])[0] + # We need to remove gap junctions where one or both of the neurons are virtual + + real_gj_idx = ~np.logical_or(self.is_virtual_neuron[self.gap_junctions[:, 0]], + self.is_virtual_neuron[self.gap_junctions[:, 1]]) + + if np.sum(real_gj_idx) == 0: + return np.array([]), np.array([]), np.array([]), np.array([]), np.array([]), np.array([]) + + gj_idx_a = np.where([self.neuron_id_on_node[x] for x in self.gap_junctions[real_gj_idx, 0]])[0] + gj_idx_b = np.where([self.neuron_id_on_node[x] for x in self.gap_junctions[real_gj_idx, 1]])[0] gj_id_offset = 100 * self.num_neurons gj_gid_src_a = gj_id_offset + 2 * gj_idx_a @@ -936,8 +1118,14 @@ def find_local_gap_junctions(self): seg_id_a = self.gap_junctions[gj_idx_a, 2] seg_id_b = self.gap_junctions[gj_idx_b, 3] - compartment_a = [self.neurons[x].map_id_to_compartment([y])[0] for (x, y) in zip(neuron_id_a, seg_id_a)] - compartment_b = [self.neurons[x].map_id_to_compartment([y])[0] for (x, y) in zip(neuron_id_b, seg_id_b)] + try: + compartment_a = [self.neurons[x].map_id_to_compartment([y])[0] for (x, y) in zip(neuron_id_a, seg_id_a)] + compartment_b = [self.neurons[x].map_id_to_compartment([y])[0] for (x, y) in zip(neuron_id_b, seg_id_b)] + except: + import traceback + self.write_log(traceback.format_exc(), is_error=True) + import pdb + pdb.set_trace() seg_xa = self.gap_junctions[gj_idx_a, 4] / 1000.0 seg_xb = self.gap_junctions[gj_idx_b, 5] / 1000.0 @@ -983,7 +1171,8 @@ def connect_network_gap_junctions_local(self): section_dist=s_x, gid_source_gj=gid_src, gid_dest_gj=gid_dest, - g_gap_junction=g) + g_gap_junction=g, + neuron_id=nid) gap_junction_count += 1 @@ -1026,6 +1215,10 @@ def add_synapse(self, cell_id_source, cell_id_dest, dend_compartment, section_id # The target neuron is a virtual neuron, do not add synapse return + if conductance < 0: + raise ValueError(f"Negative conductance found, this can be caused by specifying too large conductance values." + f"Remember that the synapse matrix is 32-bit int as pico siemens") + # You can not locate a point process at endpoints (position 0.0 or 1.0) if it needs an ion if section_dist == 0.0: section_dist = 0.01 @@ -1050,13 +1243,16 @@ def add_synapse(self, cell_id_source, cell_id_dest, dend_compartment, section_id syn = self.get_synapse(channel_module, dend_compartment, section_dist) + weight_scale = 1 + if par_data is not None: # Picking one of the parameter sets stored in par_data par_id = parameter_id % len(par_data) par_set = par_data[par_id] + for par in par_set: - if par == "expdata" or par == "cond": + if par in ("expdata", "cond", "RxD"): # expdata is not a parameter, and cond we take from synapse matrix continue @@ -1087,6 +1283,28 @@ def add_synapse(self, cell_id_source, cell_id_dest, dend_compartment, section_id import pdb pdb.set_trace() + if "RxD" in par_set and self.use_rxd_neuromodulation: + species_name = par_set["RxD"]["species_name"] + region = par_set["RxD"]["region"] + + if region in ("internal", "external"): + if section_id == -1: + region = f"soma_{region}" + else: + region = f"dend_{region}" + + weight_scale = par_set["RxD"].get("weight_scale", 1) * 1e-6 # (to compensate for 1e6 multiplication later) + + # If you have a RxD synapse it is good idea to set weight scale, especially + # if your channel has valence 0, then cond variable is actually flux and needs to be in + # number of molecules per second. + flux_variable = par_set["RxD"]["flux_variable"] + + self.neurons[cell_id_dest].modulation.link_synapse(species_name=species_name, + region=region, + synapse=syn, + flux_variable=flux_variable) + if axon_dist is not None: # axon dist is in micrometer, want delay in ms synapse_delay = (1e3 * 1e-6 * axon_dist) / self.axon_speed + self.synapse_delay @@ -1098,17 +1316,13 @@ def add_synapse(self, cell_id_source, cell_id_dest, dend_compartment, section_id self.synapse_dict[cell_id_source, cell_id_dest] = [] nc = self.pc.gid_connect(cell_id_source, syn) - nc.weight[0] = conductance + nc.weight[0] = conductance * weight_scale nc.delay = synapse_delay nc.threshold = self.spike_threshold # This prevents garbage collection of syn and nc self.synapse_dict[cell_id_source, cell_id_dest].append((syn, nc, synapse_type_id, section_id)) - # TODO: Johanna promised to remove these when she is ready for it. - self.synapse_list.append(syn) - self.net_con_list.append(nc) - return syn ############################################################################ @@ -1118,7 +1332,8 @@ def add_synapse(self, cell_id_source, cell_id_dest, dend_compartment, section_id def add_gap_junction(self, section, section_dist, gid_source_gj, gid_dest_gj, - g_gap_junction): + g_gap_junction, + neuron_id): """ Add gap junction. @@ -1129,12 +1344,17 @@ def add_gap_junction(self, gid_source_gj: GID of source gap junction gid_dest_gj: GID of destination gap junction g_gap_junction: Gap junction conductance + neuron_id: ID of neuron, this is for book-keeping """ # If neuron complains, make sure you have par_ggap.mod gj = h.gGapPar(section(section_dist)) - self.gap_junction_list.append(gj) + + if neuron_id not in self.gap_junction_dict: + self.gap_junction_dict[neuron_id] = [(gj, gid_source_gj, gid_dest_gj)] + else: + self.gap_junction_dict[neuron_id].append((gj, gid_source_gj, gid_dest_gj)) # If you get a "NEURON: No source_var for target_var sid = 1301" error, then # make sure the neurons on both sides of the gap junction are included in the simulation @@ -1203,8 +1423,11 @@ def add_external_input(self, input_file=None): eval_str = f"self.sim.neuron.h.{mod_file}" channel_module = eval(eval_str) - for input_id, (section, section_x, param_id, n_spikes) \ + rxd_species_name, rxd_flux_variable, rxd_region, rxd_weight_scale = self.get_rxd_external_input_parameters(neuron_input) + + for input_id, (section, section_id, section_x, param_id, n_spikes) \ in enumerate(zip(sections, + neuron_input.attrs["section_id"], neuron_input.attrs["section_x"], neuron_input.attrs["parameter_id"], neuron_input["spikes"].attrs["num_spikes"])): @@ -1240,7 +1463,7 @@ def add_external_input(self, input_file=None): nc = h.NetCon(vs, syn) nc.delay = 0.0 - nc.weight[0] = neuron_input.attrs["conductance"][()] * 1e6 # Neurons needs microsiemens + nc.weight[0] = neuron_input.attrs["conductance"][()] * rxd_weight_scale # Neurons needs microsiemens nc.threshold = 0.1 # Get the modifications of synapse parameters, specific to this synapse @@ -1248,6 +1471,11 @@ def add_external_input(self, input_file=None): syn_params = param_list[param_id % len(param_list)] # No longer need to take ["synapse"], only that part saved in hdf5 for par in syn_params: + + if par == "RxD": + # RxD specific information + continue + if par == "expdata": # Not a parameter continue @@ -1267,8 +1495,38 @@ def add_external_input(self, input_file=None): # print(f"Setting {par} to {par_value}.") setattr(syn, par, par_value) + if rxd_species_name is not None: + if section_id == -1: + region_name = f"soma_{rxd_region}" + else: + region_name = f"dend_{rxd_region}" + + self.neurons[neuron_id].modulation.link_synapse(species_name=rxd_species_name, + region=region_name, + synapse=syn, + flux_variable=rxd_flux_variable) + # Need to save references, otherwise they will be freed - self.external_stim[neuron_id, input_type].append((v, vs, nc, syn, spikes)) + self.external_stim[neuron_id, input_type].append((v, vs, nc, syn, spikes, section_id, section_x)) + + def get_rxd_external_input_parameters(self, neuron_input): + + if "RxD" in neuron_input.attrs.keys() and self.use_rxd_neuromodulation: + + rxd_dict = json.loads(neuron_input.attrs["RxD"]) + + species_name = rxd_dict.get("species_name") + flux_variable = rxd_dict.get("flux_variable") + region = rxd_dict.get("region") + weight_scale = rxd_dict.get("weight_scale", 1.0) + + else: + species_name = None + flux_variable = None + region = None + weight_scale = 1e6 # we need to do pS -> micro siemens + + return species_name, flux_variable, region, weight_scale ############################################################################ @@ -1446,6 +1704,9 @@ def add_volt_recording_all(self, cell_id=None, centre_only_flag=True, section_x= for sid, sec in enumerate(self.neurons[cid].icell.dend): + assert int(sec.name().split('[')[-1].strip(']')) == sid, \ + f"Internal error, assumed {sid} was section id of {sec.name()}" + if centre_only_flag: sec_id.append(sid) sec_x.append(0.5) @@ -1527,6 +1788,168 @@ def add_synapse_current_recording_all(self, dest_id=None, max_synapses=500): self.write_log(f"Warning: Not recording all synapse currents requested, capped at max_synapses={max_synapses}", force_print=True) + def add_density_mechanism_recording(self, density_mechanism: str, variable: str, + neuron_id: int, sec_id: int, sec_x: float): + + """ Record density mechanism: + + Args: + density_mechanism (str): Name of density mechanism, e.g. "pas" + variable (str): Variable name to record, e.g. "i" (will then read _ref_i) + neuron_id (int) : Id of neuron + sec_id (int) : Section id of compartment + sec_x (float) : Section x + """ + + if neuron_id not in self.neuron_id: + # The neuron is not on this worker + return + + segment = self.neurons[neuron_id].map_id_to_compartment(sec_id)(sec_x) + mech = getattr(segment, density_mechanism) + var = getattr(mech, f"_ref_{variable}") + data = self.sim.neuron.h.Vector().record(var) + + self.record.register_compartment_data(data_type=f"{density_mechanism}.{variable}", + neuron_id=neuron_id, + data=data, + sec_id=sec_id, + sec_x=sec_x) + + if self.record.time is None: + t_save = self.sim.neuron.h.Vector() + t_save.record(self.sim.neuron.h._ref_t) + self.record.register_time(time=t_save) + + def add_membrane_recording(self, variable, neuron_id, sec_id, sec_x): + + if neuron_id not in self.neuron_id: + # The neuron is not on this worker + return + + segment = self.neurons[neuron_id].map_id_to_compartment(sec_id)(sec_x) + var = getattr(segment, f"_ref_{variable}") + data = self.sim.neuron.h.Vector().record(var) + + self.record.register_compartment_data(data_type=f"membrane.{variable}", + neuron_id=neuron_id, + data=data, + sec_id=sec_id, + sec_x=sec_x) + + def get_internal_synapse_point_process(self, source_id, dest_id, synapse_type=None): + + synapse_list = self.synapse_dict.get((source_id, dest_id), []) + + channel_model_id = None + + if synapse_type is not None: + pre_type = self.network_info["neurons"][source_id]["type"] + post_type = self.network_info["neurons"][dest_id]["type"] + + if (pre_type, post_type) in self.network_info["connectivity_distributions"]: + channel_model_id = self.network_info["connectivity_distributions"][(pre_type, post_type)][synapse_type]["channel_model_id"] + + s_list = [synapse_info for synapse_info in synapse_list + if channel_model_id is None or channel_model_id == synapse_info[2]] + + return s_list, pre_type, post_type + + def add_synapse_variable_recording(self, source_id, dest_id, variable, synapse_type=None): + + synapse_list, pre_type, post_type = self.get_internal_synapse_point_process(source_id=source_id, + dest_id=dest_id, + synapse_type=synapse_type) + + return self.add_point_process_variable_recording(point_process_list=synapse_list, + variable=variable, + post_synaptic_id=dest_id, + pre_synaptic_id=source_id, + name=f"{pre_type}_{post_type}_{synapse_type}") + + def add_gap_junction_current_recording(self, neuron_id, gj_idx=None): + + gj_list = self.gap_junction_dict.get(neuron_id, []) + + if gj_idx is not None: + gj_list = [gj_list[gj_idx]] + + # syn, nc, synapse_type_id, sec_id + point_process = [(x[0], x[0], 3, -100) for x in gj_list] + + return self.add_point_process_variable_recording(point_process_list=point_process, + variable="i", + post_synaptic_id=neuron_id, + pre_synaptic_id=-1, # We need to add bookkeeping to track destination + name=f"gj_currents_{neuron_id}", + use_netcon_weight=False) + + def get_external_synapse_point_process(self, neuron_id, input_type): + + """ Returns point process of the external synapses on the neuron """ + + external_input = self.external_stim[neuron_id, input_type] + + syn_list = [(x[3], x[2], x[5], x[6]) for x in external_input] # sym and nc + + return syn_list + + def add_external_input_variable_recording(self, neuron_id, input_type, variable, name=""): + + # TODO: Verify this works... + + syn_list = self.get_external_synapse_point_process(neuron_id=neuron_id, input_type=input_type) + syn_ctr = 0 + + for syn, nc, sec_id, sec_x in syn_list: + data = self.sim.neuron.h.Vector() + data.record(getattr(syn, f"_ref_{variable}")) + seg = syn.get_segment() + + # They are close, but not identical... + # assert sec_x == seg.x, f"Internal error, {sec_x = } should be same as stored in {seg.x = }" + + self.record.register_synapse_data(neuron_id=neuron_id, + data_type=f"{name}{'.' if len(name) > 0 else ''}{input_type}. {variable}", data=data, + synapse_type=-1, # Check what the real number is + presynaptic_id=-1, # External input + sec_id=sec_id, + sec_x=seg.x, + cond=nc.weight[0]) + syn_ctr += 1 + + return syn_ctr + + def add_point_process_variable_recording(self, point_process_list, variable, + post_synaptic_id, pre_synaptic_id=-1, + name="", use_netcon_weight=True): + + if not isinstance(point_process_list, list): + point_process_list = list(point_process_list) + + syn_ctr = 0 + + for syn, nc, synapse_type_id, sec_id in point_process_list: + data = self.sim.neuron.h.Vector() + data.record(getattr(syn, f"_ref_{variable}")) + seg = syn.get_segment() + + if use_netcon_weight: + cond = nc.weight[0] # netcon object for synapses + else: + cond = nc.g # For gap junctions nc is a gap junction object, and g conductance + + self.record.register_synapse_data(neuron_id=post_synaptic_id, + data_type=f"{name}{'.' if len(name) > 0 else ''}{variable}", data=data, + synapse_type=synapse_type_id, + presynaptic_id=pre_synaptic_id, + sec_id=sec_id, + sec_x=seg.x, + cond=cond) + syn_ctr += 1 + + return syn_ctr + def add_synapse_current_recording(self, source_id, dest_id): assert (source_id, dest_id) in self.synapse_dict, f"No synapse between {source_id} and {dest_id}" @@ -1549,12 +1972,140 @@ def add_synapse_current_recording(self, source_id, dest_id): return syn_ctr + def add_rxd_concentration_recording(self, species: str, neuron_id: int, region, sec_id, sec_x): + + if not self.use_rxd_neuromodulation: + print(f"add_rxd_concentration_recording: not enabled, ignoring recording of {species} in neuron {neuron_id}") + return + + + if sec_id == -1: + sec_type = "soma" + neuron_sec_id = 0 + elif sec_id >= 0: + sec_type = "dend" + neuron_sec_id = sec_id + else: + sec_type = "axon" + neuron_sec_id = 0 + print("Axon recordings currently not fully supported (using sec_id=0") + + if self.neurons[neuron_id].modulation is None: + raise ValueError(f"No modulation specified for neuron {self.neurons[neuron_id].name} ({neuron_id})") + + try: + segment = getattr(self.neurons[neuron_id].icell, sec_type)[neuron_sec_id](sec_x) + except: + import traceback + print(traceback.format_exc()) + import pdb + pdb.set_trace() + + + conc_ref = self.neurons[neuron_id].modulation.species[species][region].nodes(segment)._ref_concentration + + vector = self.sim.neuron.h.Vector() + vector.record(conc_ref) + + self.record.register_compartment_data(neuron_id=neuron_id, + data_type=species, + data=vector, + sec_id=sec_id, sec_x=sec_x) + + if self.record.time is None: + t_save = self.sim.neuron.h.Vector() + t_save.record(self.sim.neuron.h._ref_t) + self.record.register_time(time=t_save) + + def add_rxd_internal_concentration_recording_all(self, species, neuron_id): + + if neuron_id not in self.neuron_id: + return + + if not self.use_rxd_neuromodulation: + print(f"add_rxd_internal_concentration_recording_all: not enabled, ignoring recording of {species} in neuron {neuron_id}") + return + + # Add soma + self.add_rxd_concentration_recording(species, neuron_id, "soma_internal", -1, 0.5) + + for sid, sec in enumerate(self.neurons[neuron_id].icell.dend): + assert int(sec.name().split('[')[-1].strip(']')) == sid, \ + f"Internal error, assumed {sid} was section id of {sec.name()}" + self.add_rxd_concentration_recording(species, neuron_id, "dend_internal", sid, 0.5) + + def add_rxd_internal_concentration_recording_all_species(self, neuron_id, include_dendrites=True): + + if self.verbose: + self.write_log(f"Recording all RxD species from neurons: {neuron_id}") + + if isinstance(neuron_id, (list, np.ndarray)): + for nid in neuron_id: + self.add_rxd_internal_concentration_recording_all_species(neuron_id=nid) + return + + if neuron_id not in self.neuron_id: + return + + if not self.use_rxd_neuromodulation: + print(f"add_rxd_internal_concentration_recording_all_species: not enabled, ignoring recording of neuron {neuron_id}") + return + + for species in self.neurons[neuron_id].modulation.species.keys(): + + # Add soma + self.add_rxd_concentration_recording(species, neuron_id, "soma_internal", -1, 0.5) + + if include_dendrites: + for sid, sec in enumerate(self.neurons[neuron_id].icell.dend): + assert int(sec.name().split('[')[-1].strip(']')) == sid, \ + f"Internal error, assumed {sid} was section id of {sec.name()}" + self.add_rxd_concentration_recording(species, neuron_id, "dend_internal", sid, 0.5) + + def add_bath_application(self, species_name, concentration, time, neuron_id=None, interpolate=True): + + if neuron_id is None: + neuron_id = self.snudda_loader.get_neuron_id(include_virtual=False) + + conc_vect = self.sim.neuron.h.Vector(concentration * 1e3) # SI to millimolar + t_vect = self.sim.neuron.h.Vector(time * 1e3) # s -> ms + + if self.verbose: + self.write_log(f"Bath application t={time*1e3}ms, conc={concentration*1e3}") + + if species_name is self.bath_application: + raise KeyError(f"Bath application already applied for {species_name}") + + self.bath_application[species_name] = (time, concentration, t_vect, conc_vect) + + for nid in neuron_id: + if nid in self.neurons: + n = self.neurons[nid] + + if n.modulation is not None: + n.modulation.concentration_from_vector(species_name=species_name, + concentration_vector=conc_vect, + time_vector=t_vect, + interpolate=interpolate) + ############################################################################ + def sanity_check_play_vectors(self, sim_end_time): + # TODO: Add additional checks that all bath application play vectors are long enough + + for species_name, bath_data in self.bath_application.items(): + bath_max_time = np.max(bath_data[0]) + + if sim_end_time > bath_max_time: + raise ValueError(f"Simulation duration {sim_end_time} is " + f"longer than time vector for bath application of {species_name}") + def run(self, t=None, hold_v=None): """ Run simulation. """ + start_time = timeit.default_timer() + if self.is_virtual_neuron.all(): print("ALL YOUR NEURONS IN THE SIMULATION ARE VIRTUAL") @@ -1564,14 +2115,12 @@ def run(self, t=None, hold_v=None): else: t = 1000.0 + self.sanity_check_play_vectors(sim_end_time=t*1e-3) + if hold_v is None: if self.sim_info is not None and "hold_voltage" in self.sim_info: hold_v = self.sim_info["hold_voltage"] - self.setup_print_sim_time(t) - - start_time = timeit.default_timer() - # If we want to use a non-default initialisation voltage, we need to # explicitly set: h.v_init # self.sim.neuron.h.v_init = -78 @@ -1589,9 +2138,16 @@ def run(self, t=None, hold_v=None): # Asked on neuron, check answer: # https://www.neuron.yale.edu/phpBB/viewtopic.php?f=2&t=4161&p=18021 + self.setup_print_sim_time(t) + # Make sure all processes are synchronised self.pc.barrier() + self.write_log(f"Running simulation for {t / 1000} s", force_print=True) + + # import pdb + # pdb.set_trace() + self.sim.run(t, dt=0.025) self.pc.barrier() self.write_log("Simulation done.") @@ -1832,6 +2388,40 @@ def create_dir(self, dir_name): ############################################################################ + def parse_current_injection_info(self): + + if self.current_injection_info and self.verbose: + self.write_log(f"Parsing current_injection_info.") + + for neuron_id, cur_info in self.current_injection_info.items(): + + if int(neuron_id) not in self.neurons: + # Neuron not on this worker. + continue + + time = np.array(cur_info["time"]) + cur_amp = np.array(cur_info["current"]) + neuron_id = int(neuron_id) + + # Default mode is to interpolate between given points (because otherwise we need a full vector) + interpolate_flag = cur_info["interpolate"] if "interpolate" in cur_info else True + + if self.verbose: + self.write_log(f"Adding current injection to neuron {neuron_id}: time = {time}, current = {cur_amp}, " + f"{'interpolate' if interpolate_flag else ''}") + + t_vec = neuron.h.Vector(time * 1e3) + amp_vec = neuron.h.Vector(cur_amp * 1e9) + + if neuron_id not in self.current_clamps: + self.current_clamps[neuron_id] = [] + + i_clamp = self.sim.neuron.h.IClamp(0.5, sec=self.neurons[neuron_id].icell.soma[0]) + i_clamp.dur = 1e9 + amp_vec.play(i_clamp._ref_amp, t_vec, interpolate_flag) + + self.current_clamps[neuron_id].append((i_clamp, t_vec, amp_vec)) + def add_current_injection(self, neuron_id, start_time, end_time, amplitude): """ @@ -1981,7 +2571,10 @@ def setup_print_sim_time(self, t_max): def _setup_print_sim_time_helper(self, t_max): """ Helper method for printing simulation time during execution. """ - update_points = np.arange(t_max / 100., t_max, t_max / 100.) + update_points = np.array([0.0, 0.01, 0.02, 0.03, 0.04, 0.05, + 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) * t_max + + # update_points = np.arange(t_max / 100., t_max, t_max / 100.) for t in update_points: h.cvode.event(t, self.print_sim_time) @@ -1989,10 +2582,16 @@ def _setup_print_sim_time_helper(self, t_max): def print_sim_time(self): """ Helper function, to print simulation time during execution. """ + + if h.t == 0: + # Starting simulation wall clock, for estimate of time left + self.sim_start_time = timeit.default_timer() + return + cur_time = timeit.default_timer() elapsed_time = cur_time - self.sim_start_time fraction_done = h.t / self.t_max - time_left = elapsed_time * ((self.t_max - h.t) / h.t) + time_left = np.abs(elapsed_time * ((self.t_max - h.t) / h.t)) # Do not print status update too often if cur_time - self.last_sim_report_time > 100 or fraction_done > 0.99: @@ -2001,8 +2600,11 @@ def print_sim_time(self): else: force_print = False - self.write_log("%.0f%% done. Elapsed: %.1f s, estimated time left: %.1f s" - % (fraction_done * 100, elapsed_time, time_left), force_print=force_print) + self.write_log(f"{fraction_done * 100:>3.0f}% done. Elapsed: {elapsed_time:.1f} s, " + f"estimated time left: {time_left:.1f} s", force_print=force_print) + + # self.write_log("%.0f%% done. Elapsed: %.1f s, estimated time left: %.1f s" + # % (fraction_done * 100, elapsed_time, time_left), force_print=force_print) ############################################################################ @@ -2026,6 +2628,12 @@ def __del__(self): if self is not None: self.clear_neuron() + if self.log_file is not None: + try: + self.log_file.close() + except: + pass + def clear_neuron(self): if self.pc is not None: @@ -2036,12 +2644,10 @@ def clear_neuron(self): self.neuron_nodes = [] # Is this used? self.virtual_neurons = {} - self.net_con_list = [] - self.synapse_list = [] self.synapse_dict = dict() self.i_stim = [] self.v_clamp_list = [] - self.gap_junction_list = [] + self.gap_junction_dict = dict() self.external_stim = dict([]) self.check_id_recordings = [] self.pc = None diff --git a/snudda/utils/__init__.py b/snudda/utils/__init__.py index f5eba405d..396494ea6 100644 --- a/snudda/utils/__init__.py +++ b/snudda/utils/__init__.py @@ -1,6 +1,7 @@ from snudda.utils.cleanup import cleanup from snudda.utils.load import SnuddaLoad -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation +from snudda.utils.load_network_simulation import SnuddaLoadSimulation +from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation # TODO: Deprecate this old name from snudda.utils.numpy_encoder import NumpyEncoder from snudda.utils.snudda_path import snudda_parse_path diff --git a/snudda/utils/export_sonata.py b/snudda/utils/export_sonata.py index 1f456dff9..f0f129af6 100644 --- a/snudda/utils/export_sonata.py +++ b/snudda/utils/export_sonata.py @@ -13,6 +13,8 @@ from shutil import copyfile from glob import glob +from copy import deepcopy + import h5py import numpy as np @@ -58,7 +60,7 @@ def __init__(self, network_path=None, network_file=None, input_file=None, out_di else: self.input_file = None - self.network_config = json.loads(self.snudda_load.data["config"]) + self.network_config = deepcopy(self.snudda_load.data["config"]) if self.input_file: print(f"Using input file: {self.input_file}") diff --git a/snudda/utils/fake_load.py b/snudda/utils/fake_load.py index 72028e57c..396168c2d 100644 --- a/snudda/utils/fake_load.py +++ b/snudda/utils/fake_load.py @@ -1,6 +1,8 @@ import json from collections import OrderedDict +from copy import deepcopy + import numpy as np from snudda.utils.load import SnuddaLoad @@ -57,4 +59,4 @@ def import_json(self, json_file_name): self.data["neurons"][idx]["rotation"] = np.array(self.data["neurons"][idx]["rotation"]) if "config" in self.data: - self.config = json.loads(self.data["config"], object_pairs_hook=OrderedDict) + self.config = deepcopy(self.data["config"]) diff --git a/snudda/utils/load.py b/snudda/utils/load.py index 77f5001e3..058768e79 100755 --- a/snudda/utils/load.py +++ b/snudda/utils/load.py @@ -186,8 +186,9 @@ def load_hdf5(self, network_file, load_synapses=True, load_morph=False): if "config" in f["meta"]: if self.verbose: print("Loading config data from HDF5") - data["config"] = SnuddaLoad.to_str(f["meta/config"][()]) + # data["config"] = SnuddaLoad.to_str(f["meta/config"][()]) self.config = json.loads(f["meta/config"][()]) + data["config"] = self.config # Added so this code can also load the position file, which # does not have the network group yet @@ -413,7 +414,8 @@ def extract_neurons(hdf5_file): axon_density_type, axon_density, axon_density_radius, \ axon_density_bounds_xyz, \ morph, neuron_path, \ - parameter_key, morphology_key, modulation_key, population_unit_id \ + parameter_key, morphology_key, modulation_key, population_unit_id, \ + reaction_diffusion_file \ in zip(hdf5_file["network/neurons/name"][:], hdf5_file["network/neurons/neuron_id"][:], hdf5_file["network/neurons/hoc"][:], @@ -430,7 +432,8 @@ def extract_neurons(hdf5_file): hdf5_file["network/neurons/parameter_key"][:], hdf5_file["network/neurons/morphology_key"][:], hdf5_file["network/neurons/modulation_key"][:], - hdf5_file["network/neurons/population_unit_id"][:] + hdf5_file["network/neurons/population_unit_id"][:], + hdf5_file["network/neurons/reaction_diffusion_file"][:] ): n = dict([]) @@ -476,6 +479,7 @@ def extract_neurons(hdf5_file): n["parameter_key"] = par_key if len(par_key) > 0 else None n["morphology_key"] = morph_key if len(morph_key) > 0 else None n["modulation_key"] = mod_key if len(mod_key) > 0 else None + n["reaction_diffusion_file"] = SnuddaLoad.to_str(reaction_diffusion_file) if len(reaction_diffusion_file) > 0 else None n["population_unit"] = population_unit_id diff --git a/snudda/utils/load_network_simulation.py b/snudda/utils/load_network_simulation.py index 95bfa5780..1431d05de 100755 --- a/snudda/utils/load_network_simulation.py +++ b/snudda/utils/load_network_simulation.py @@ -3,13 +3,12 @@ import h5py import numpy as np -from collections import OrderedDict from numba import jit from snudda.utils.load import SnuddaLoad -class SnuddaLoadNetworkSimulation: +class SnuddaLoadSimulation: def __init__(self, network_simulation_output_file=None, network_path=None, @@ -156,13 +155,35 @@ def get_frequency(self, neuron_id, time_ranges=None): return freq_table + def list_data_types(self, neuron_id): + return list(self.network_simulation_file["neurons"][str(neuron_id)].keys()) + + def get_all_data(self, neuron_id, exclude=None, include_time=False): + + data = dict() + + for data_type in self.list_data_types(neuron_id=neuron_id): + + if exclude is not None and data_type in exclude: + continue + + if data_type not in data: + data[data_type] = self.get_data(data_type=data_type, neuron_id=neuron_id) + else: + data[data_type].update(self.get_data(data_type=data_type, neuron_id=neuron_id)) + + if include_time: + data["time"] = self.get_time() + + return data + def get_data(self, data_type, neuron_id=None): """ Returns data for neuron_id """ - data = OrderedDict() - sec_id_x = OrderedDict() - syn_info = OrderedDict() + data = dict() + sec_id_x = dict() + syn_info = dict() if neuron_id is None: neuron_id = self.network_simulation_file["neurons"].keys() @@ -257,11 +278,11 @@ def check_depolarisation_block(self, threshold=-40e-3, max_duration=200e-3, quie # We should only check for depolarisation block in the soma, sec_id = -1 v_idx = np.where(sec_id_x[neuron_id][0] == -1)[0][0] - depol_block = SnuddaLoadNetworkSimulation.check_trace_depolarisation_block(neuron_id=neuron_id, - time=time, - voltage=voltage[neuron_id][:, v_idx], - threshold=threshold, - max_duration=max_duration) + depol_block = SnuddaLoadSimulation.check_trace_depolarisation_block(neuron_id=neuron_id, + time=time, + voltage=voltage[neuron_id][:, v_idx], + threshold=threshold, + max_duration=max_duration) depolarisation_block = depolarisation_block + depol_block @@ -307,9 +328,9 @@ def get_synaptic_current(self, pre_id=None, post_id=None): if pre_id is None: return current, sec_id_x, syn_info - filtered_current = OrderedDict() - filtered_sec_id_x = OrderedDict() - filtered_syn_info = OrderedDict() + filtered_current = dict() + filtered_sec_id_x = dict() + filtered_syn_info = dict() for neuron_id, info in syn_info.items(): idx = np.where(syn_info[1] == pre_id)[0] @@ -402,6 +423,13 @@ def export_to_txt(self, txt_file, neuron_id=None, time_scale=1.0): f"{','.join([str(x) for x in self.network_simulation_file['meta_data/position'][nid,: ]])}\n") +class SnuddaLoadNetworkSimulation (SnuddaLoadSimulation): + + def __init__(self, *args, **kwargs): + raise DeprecationWarning("Please use SnuddaLoadSimulation instead of SnuddaLoadNetworkSimulation") + super(*args, **kwargs) + + def load_network_simulation_cli(): from argparse import ArgumentParser @@ -414,8 +442,8 @@ def load_network_simulation_cli(): parser.add_argument("--skip_test", help="Do tests on simulation data", action="store_true") args = parser.parse_args() - slna = SnuddaLoadNetworkSimulation(network_simulation_output_file=args.data_file, verbose=args.verbose, - do_test=not args.skip_test) + slna = SnuddaLoadSimulation(network_simulation_output_file=args.data_file, verbose=args.verbose, + do_test=not args.skip_test) if args.export_spike_file is not None: slna.export_to_txt(txt_file=args.export_spike_file, time_scale=args.time_scale) diff --git a/snudda/utils/sbml_to_snudda.py b/snudda/utils/sbml_to_snudda.py new file mode 100644 index 000000000..a37a1e67b --- /dev/null +++ b/snudda/utils/sbml_to_snudda.py @@ -0,0 +1,267 @@ +# This script reads an SBML file, and creates a snudda neuromodulation file +import os +import json +import libsbml +import numpy as np + + +class ReadSBML: + + def __init__(self, filename=None, out_file=None, concentration_scale_factor=None): + + self.filename = filename + self.out_file = out_file + self.reader = None + self.data = None + + if concentration_scale_factor is None: + self.concentration_scale_factor = 1 + else: + self.concentration_scale_factor = concentration_scale_factor + print(f"Using concentration scale factor: {self.concentration_scale_factor}") + + if filename is not None: + + if not os.path.isfile(filename): + raise ValueError(f"File not found {filename}") + self.parse() + + if out_file is not None: + self.write(out_file=out_file) + + def parse(self): + + print(f"Reading {self.filename}") + + self.reader = libsbml.SBMLReader() + document = self.reader.readSBML(self.filename) + + model = document.getModel() + + species_data = {} + id_to_name = {} + + # Extract global parameters + global_parameters = {} + for param in model.getListOfParameters(): + global_parameters[param.getId()] = param.getValue() + + for species in model.getListOfSpecies(): + species_name = species.getName().replace('*', '_') + species_id = species.getId() + + if species_id in id_to_name: + raise KeyError(f"{species_id =} already defined.") + id_to_name[species_id] = species_name + + initial_concentration = species.getInitialConcentration() + charge = species.getCharge() + initial_concentration = species.getInitialConcentration() + diffusion_constant = 0 # Assuming a default value as it's not in SBML + regions = ["soma_internal", "dend_internal"] # Assuming these regions + atol_scale = None # Assuming a default value + boundary_condition = species.getBoundaryCondition() # Assuming a default value + # represents = species.getId() # Assuming species ID represents itself + + species_data[species_name] = { + "initial_concentration": initial_concentration * self.concentration_scale_factor, + "diffusion_constant": diffusion_constant, + "charge": charge, + "regions": regions, + "concentration": initial_concentration * self.concentration_scale_factor + # "represents": represents + } + + if atol_scale is not None: + species_data[species_name]["atol_scale"] = atol_scale + + if boundary_condition is not None: + species_data[species_name]["boundary_condition"] = boundary_condition + + # Using this viewer: https://sv.insysbio.com/online/ + # We have identified that the names with "*" in them are forward and backward rates + # we need to extract those, and also extract the reaction + + # Extract reactions information + reactions_data = {} + for reaction in model.getListOfReactions(): + reaction_id = reaction.getName() + reactants = " + ".join([f"{self.get_stoichiometry(reactant)} * {id_to_name[reactant.getSpecies()]}" + if reactant.getStoichiometry() != 1 else id_to_name[reactant.getSpecies()] + for reactant in reaction.getListOfReactants()]) + products = " + ".join([f"{self.get_stoichiometry(product)} * {id_to_name[product.getSpecies()]}" + if product.getStoichiometry() != 1 else id_to_name[product.getSpecies()] + for product in reaction.getListOfProducts()]) + + forward_rate, backward_rate = self.extract_rates(reaction, model, global_parameters) + + reactions_data[reaction_id] = { + "reactants": reactants, + "products": products, + "forward_rate": forward_rate, + "backward_rate": backward_rate, + "regions": regions + } + + # Combine data into the desired format + self.data = { + "species": species_data, + "reactions": reactions_data + } + + def get_stoichiometry(self, reactant): + factor = reactant.getStoichiometry() + + factor_int = int(np.round(factor)) + + if np.abs(factor_int - factor) > 1e-8: + raise ValueError(f"Error, rounding reactant incorrectly ({reactant}): {factor} -> {factor_int}") + + return factor_int + + def extract_rates(self, reaction, model, global_parameters): + + # TODO: Check that we can handle cAMP ** 2 + + compartment_list = [x.getId() for x in model.getListOfCompartments()] + species_list = [x.getId() for x in model.getListOfSpecies()] + + print(f"{reaction.getKineticLaw().getFormula()}") + + # This function takes a reaction and attempts to extract the forward and backward rates + current_expression = reaction.getKineticLaw().getMath() + operator = current_expression.getOperatorName() + + if operator == "times" and current_expression.getLeftChild().getName() in compartment_list: + print(f"Removing volume {current_expression.getLeftChild().getName()}") + current_expression = current_expression.getRightChild() + + if operator == "times" and current_expression.getRightChild().getName() in compartment_list: + print(f"Removing volume {current_expression.getRightChild().getName()}") + current_expression = current_expression.getLeftChild() + + operator = current_expression.getOperatorName() + + if operator == "minus": + # We have both forward and backward rate to extract + forward_rate = self._get_rate_helper(current_expression.getLeftChild(), + global_parameters, compartment_list, model) + backward_rate = self._get_rate_helper(current_expression.getRightChild(), + global_parameters, compartment_list, model) + + else: + forward_rate = self._get_rate_helper(current_expression, + global_parameters, compartment_list, model) + backward_rate = None + + print(f"{forward_rate =}, {backward_rate =}") + + return forward_rate, backward_rate + + def _count_reactants(self, expression, model): + + num_species = 0 + species_list = [x.getId() for x in model.getListOfSpecies()] + + if expression.getId() in species_list or expression.getName() in species_list: + # We found a species, number of reactants is 1 + return 1 + + if expression.getOperatorName() == "times": + left_child = expression.getLeftChild() + right_child = expression.getRightChild() + + num_species += self._count_reactants(left_child, model) + num_species += self._count_reactants(right_child, model) + elif expression.getName() == "power": + # libsbml.formulaToString(orig_expression) + left_child = expression.getLeftChild() + right_child = expression.getRightChild() + + base = self._count_reactants(left_child, model) + exponent = right_child.getValue() + + num_species = base * exponent + + elif expression.getOperatorName() is None: + return 0 + else: + import pdb + pdb.set_trace() + raise ValueError(f"Not a multiplication: {expression.getOperatorName()}") + + return num_species + + def _get_rate_helper(self, expression, global_parameters, compartment_list, model): + + orig_expression = expression + + # TODO: We need to rescale the rates with self.concentration_scale_factor for two-factors + # and self.concentration_scale_factor**2 for three-factors + + # TODO: How to count reactants? + + while expression.getOperatorName() == "times": + left_child = expression.getLeftChild() + + # If the left child is a volume (ie has the name of a volume in compartment_list), + # then take the right child instead, and end the loop + if left_child.getName() in compartment_list: + expression = expression.getRightChild() + break + + print(f"Removing: {expression.getRightChild().getName()}") + expression = left_child + + rate_name = expression.getName() + + print(f"rate_name = {rate_name}") + + try: + rate = global_parameters[rate_name] + except: + import traceback + print(traceback.format_exc()) + import pdb + pdb.set_trace() + + if self.concentration_scale_factor != 1: + num_species = self._count_reactants(expression=orig_expression, model=model) + + rate /= self.concentration_scale_factor ** (num_species - 1) + + return rate + + def write(self, out_file=None): + + if out_file is None: + out_file = self.out_file + + if out_file is None: + raise ValueError(f"No outfile specified.") + + print(f"Writing JSON to {out_file}") + + with open(out_file, "wt") as f: + json.dump(self.data, f, indent=4) + + +def cli(): + + import argparse + + parser = argparse.ArgumentParser(description="Convert SBML to Snudda reaction diffusion with RxD", + formatter_class=argparse.RawTextHelpFormatter) + parser.add_argument("sbml_file", help="Input SBML file to convert") + parser.add_argument("json_file", help="Snudda RxD JSON output file") + parser.add_argument("--conc_scale_factor", default=1, help="Rescale concentrations by factor", type=float) + + args = parser.parse_args() + + rs = ReadSBML(filename=args.sbml_file, out_file=args.json_file, concentration_scale_factor=args.conc_scale_factor) + + +if __name__ == "__main__": + + cli() + diff --git a/snudda/utils/swap_to_degenerated_morphologies_extended.py b/snudda/utils/swap_to_degenerated_morphologies_extended.py index e57873174..82fa926e7 100644 --- a/snudda/utils/swap_to_degenerated_morphologies_extended.py +++ b/snudda/utils/swap_to_degenerated_morphologies_extended.py @@ -1,6 +1,7 @@ import numpy as np import json +from copy import deepcopy from snudda import SnuddaLoad from snudda.utils.swap_to_degenerated_morphologies import SwapToDegeneratedMorphologies @@ -231,7 +232,7 @@ def filter_synapses(self, filter_axon=False, post_degen_pruning=None): if post_degen_pruning is None: post_degen_pruning = self.post_degen_pruning - config = json.loads(self.updated_network_loader.data["config"]) + config = deepcopy(self.updated_network_loader.data["config"]) # This needs to be made bigger! num_rows = self.old_hdf5["network/synapses"].shape[0] + self.updated_hdf5["network/synapses"].shape[0] @@ -435,4 +436,4 @@ def post_degeneration_pruning(self, synapses, old_synapse_iterator, network_conf # TODO: Update filter_gap_junctions to also handle growth on dendrites - # TODO: Also handle the new inputs that might arrive on growing morphologies... \ No newline at end of file + # TODO: Also handle the new inputs that might arrive on growing morphologies... diff --git a/tests/data/sim-test-config.json b/tests/data/sim-test-config.json index 428c23ed2..cc3bc4a06 100644 --- a/tests/data/sim-test-config.json +++ b/tests/data/sim-test-config.json @@ -7,6 +7,6 @@ "output_file": "tiny_parallel/simulation/output-cnf.hdf5", "log_file": "tiny_parallel/log/simulation-cnf.txt", "record_soma": [0, 1, 2], - "record_all_compartments": [4], - "record_all_synapses": [0] + "record_voltage_all_compartments": [4], + "record_current_all_synapses": [0] } diff --git a/tests/networks/network_testing_input/network-config.json b/tests/networks/network_testing_input/network-config.json index bba929518..5f7055549 100644 --- a/tests/networks/network_testing_input/network-config.json +++ b/tests/networks/network_testing_input/network-config.json @@ -19,26 +19,6 @@ "mesh_file": "networks/network_testing_input/mesh/Striatum-cube-mesh-5e-05.obj", "n_putative_points": 30 }, - "neurons": { - "FS": { - "num_neurons": 5, - "neuron_type": "neuron", - "rotation_mode": "random", - "volume_id": "Striatum", - "neuron_path": { - "FS_0": "/home/hjorth/HBP/Snudda/tests/validation/striatum/fs/str-fs-e161205_FS1-mMTC180800A-IDB-v20190312" - } - }, - "dSPN": { - "num_neurons": 5, - "neuron_type": "neuron", - "rotation_mode": "random", - "volume_id": "Striatum", - "neuron_path": { - "dSPN_0": "/home/hjorth/HBP/Snudda/tests/validation/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508" - } - } - }, "connectivity": { "FS,FS": { "GABA": { @@ -489,6 +469,26 @@ } } }, + "neurons": { + "FS": { + "num_neurons": 5, + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "neuron_path": { + "FS_0": "/home/hjorth/HBP/Snudda/tests/validation/striatum/fs/str-fs-e161205_FS1-mMTC180800A-IDB-v20190312" + } + }, + "dSPN": { + "num_neurons": 5, + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "Striatum", + "neuron_path": { + "dSPN_0": "/home/hjorth/HBP/Snudda/tests/validation/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508" + } + } + }, "population_units": { "method": "random", "fraction_of_neurons": [ diff --git a/tests/networks/network_testing_project/network-config.json b/tests/networks/network_testing_project/network-config.json index ac8edf820..cf8431a66 100644 --- a/tests/networks/network_testing_project/network-config.json +++ b/tests/networks/network_testing_project/network-config.json @@ -18,20 +18,6 @@ "d_min": 1.5e-05, "mesh_file": "networks/network_testing_project/mesh/volume_A.obj" }, - "neurons": { - "dSPN": { - "num_neurons": 20, - "neuron_type": "neuron", - "rotation_mode": "random", - "volume_id": "VolumeA", - "neuron_path": { - "dSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508", - "dSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521", - "dSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503", - "dSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521" - } - } - }, "connectivity": { "dSPN,iSPN": { "GABA": { @@ -97,27 +83,27 @@ } } } - } - }, - "VolumeB": { - "volume": { - "type": "mesh", - "d_min": 1.5e-05, - "mesh_file": "networks/network_testing_project/mesh/volume_B.obj" }, "neurons": { - "iSPN": { + "dSPN": { "num_neurons": 20, "neuron_type": "neuron", "rotation_mode": "random", - "volume_id": "VolumeB", + "volume_id": "VolumeA", "neuron_path": { - "iSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611", - "iSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603", - "iSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527", - "iSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529" + "dSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508", + "dSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521", + "dSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503", + "dSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521" } } + } + }, + "VolumeB": { + "volume": { + "type": "mesh", + "d_min": 1.5e-05, + "mesh_file": "networks/network_testing_project/mesh/volume_B.obj" }, "connectivity": { "iSPN,iSPN": { @@ -151,6 +137,20 @@ } } } + }, + "neurons": { + "iSPN": { + "num_neurons": 20, + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "VolumeB", + "neuron_path": { + "iSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611", + "iSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603", + "iSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527", + "iSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529" + } + } } } } diff --git a/tests/networks/network_testing_projection_detection/network-config.json b/tests/networks/network_testing_projection_detection/network-config.json index 818b21e1a..980aa0a3d 100644 --- a/tests/networks/network_testing_projection_detection/network-config.json +++ b/tests/networks/network_testing_projection_detection/network-config.json @@ -18,20 +18,6 @@ "d_min": 1.5e-05, "mesh_file": "networks/network_testing_projection_detection/mesh/volume_A.obj" }, - "neurons": { - "dSPN": { - "num_neurons": 20, - "neuron_type": "neuron", - "rotation_mode": "random", - "volume_id": "VolumeA", - "neuron_path": { - "dSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508", - "dSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521", - "dSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503", - "dSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521" - } - } - }, "connectivity": { "dSPN,iSPN": { "GABA": { @@ -86,27 +72,27 @@ } } } - } - }, - "VolumeB": { - "volume": { - "type": "mesh", - "d_min": 1.5e-05, - "mesh_file": "networks/network_testing_projection_detection/mesh/volume_B.obj" }, "neurons": { - "iSPN": { + "dSPN": { "num_neurons": 20, "neuron_type": "neuron", "rotation_mode": "random", - "volume_id": "VolumeB", + "volume_id": "VolumeA", "neuron_path": { - "iSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611", - "iSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603", - "iSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527", - "iSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529" + "dSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20190508", + "dSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20190521", + "dSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20190503", + "dSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/dspn/str-dspn-e150917_c9_d1-mWT-1215MSN03-v20190521" } } + } + }, + "VolumeB": { + "volume": { + "type": "mesh", + "d_min": 1.5e-05, + "mesh_file": "networks/network_testing_projection_detection/mesh/volume_B.obj" }, "connectivity": { "iSPN,iSPN": { @@ -140,6 +126,20 @@ } } } + }, + "neurons": { + "iSPN": { + "num_neurons": 20, + "neuron_type": "neuron", + "rotation_mode": "random", + "volume_id": "VolumeB", + "neuron_path": { + "iSPN_0": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20190611", + "iSPN_1": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20190603", + "iSPN_2": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20190527", + "iSPN_3": "/home/hjorth/HBP/Snudda/snudda/data/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20190529" + } + } } } } diff --git a/tests/test_ablate.py b/tests/test_ablate.py index ca86267ae..e9230477c 100644 --- a/tests/test_ablate.py +++ b/tests/test_ablate.py @@ -81,7 +81,7 @@ def setUp(self) -> None: if False: from snudda.plotting.plot_network import PlotNetwork - pn = PlotNetwork(modified_network) + pn = PlotNetwork(self.modified_network) pn.plot(plot_axon=False, plot_dendrite=False, plot_synapses=False) def test_mapping(self): diff --git a/tests/test_cli.py b/tests/test_cli.py index 0038d3cf6..d4543852a 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -103,8 +103,10 @@ def test_workflow(self): with self.subTest(stage="simulate"): print("Running nrnivmodl:") - mech_dir = os.path.join(os.path.dirname(__file__), os.path.pardir, - "snudda", "data", "neurons", "mechanisms") + # mech_dir = os.path.join(os.path.dirname(__file__), os.path.pardir, + # "snudda", "data", "neurons", "mechanisms") + + mech_dir = os.path.join("..", "validation", "mechanisms") if not os.path.exists("mechanisms"): print("----> Copying mechanisms") diff --git a/tests/test_input.py b/tests/test_input.py index 725e329b4..2e8a60979 100644 --- a/tests/test_input.py +++ b/tests/test_input.py @@ -4,6 +4,8 @@ import json import numpy as np +from copy import deepcopy + from snudda.detect.detect import SnuddaDetect from snudda.input.input import SnuddaInput from snudda.detect.prune import SnuddaPrune diff --git a/tests/test_neuron_modulation.py b/tests/test_neuron_modulation.py new file mode 100644 index 000000000..0e1cecce4 --- /dev/null +++ b/tests/test_neuron_modulation.py @@ -0,0 +1,173 @@ +import unittest +import os +import numpy as np +from snudda.simulate import SnuddaSimulate +from snudda import Snudda +from snudda.utils import SnuddaLoadSimulation + +# TODO: Write example that uses Anu's SBML Dopamine cascade +# https://www.ebi.ac.uk/biomodels/BIOMD0000000636#Files +# Skriv SBML --> vårt json format, converter +# https://modeldb.science/237653?tab=2&file=Beskow/speedy_reduced2.mod +# +# Use: https://github.com/sbmlteam/libsbml + + +class NeuromodulationTestCase(unittest.TestCase): + + def setUp(self): + + test_path = "test_project" + if os.path.isdir(test_path): + import shutil + shutil.rmtree(test_path) + os.mkdir(test_path) + os.chdir(test_path) + + self.neuron_path = "../validation/dspn_neurons_rxd" + self.network_path = "networks/network_rxd" + + self.snudda = Snudda(network_path=self.network_path) + self.snudda.init_tiny(neuron_paths=self.neuron_path, + neuron_names="neuron", + number_of_neurons=[10], + random_seed=123456) + self.snudda.create_network() + + # Check why file size is so large, and why it is so slow to generate! + + # mech_dir = "../validation/mechanisms_rxd" + mech_dir = "../validation/mechanisms" # Added the kirrxd and DASyn as symbolic links to mechanisms + + # self.snudda.compile_mechanisms(mech_dir=mech_dir) + self.sim = self.snudda.simulate(time=0, mech_dir=mech_dir) + + def test_reaction(self): + + n = self.sim.neurons[0] + + self.sim.add_rxd_concentration_recording(species="DA", neuron_id=0, + region="soma_internal", + sec_id=-1, + sec_x=0.5) + + self.sim.add_rxd_concentration_recording(species="B", neuron_id=0, + region="soma_internal", + sec_id=-1, + sec_x=0.5) + + self.sim.add_rxd_concentration_recording(species="PKA", neuron_id=0, + region="soma_internal", + sec_id=-1, + sec_x=0.5) + + self.sim.add_density_mechanism_recording(density_mechanism="kirrxd", + variable="modulation_factor", + neuron_id=0, + sec_id=-1, + sec_x=0.5) + + self.sim.add_density_mechanism_recording(density_mechanism="kirrxd", + variable="m", + neuron_id=0, + sec_id=-1, + sec_x=0.5) + + self.sim.add_membrane_recording(variable="PKAi", + neuron_id=0, + sec_id=-1, + sec_x=0.5) + + # Add DA synapse + + #da_syn = h.DASyn(soma(0.5)) + mod_file = "DASyn" + eval_str = f"self.sim.sim.neuron.h.{mod_file}" + channel_module = eval(eval_str) + + da_syn = self.sim.get_external_input_synapse(channel_module=channel_module, + section=self.sim.neurons[0].icell.soma[0], + section_x=0.5) + da_syn.tau = 1 + + net_stim = self.sim.sim.neuron.h.NetStim() + net_stim.number = 100 + net_stim.start = 300 + net_stim.interval = 5 + + nc = self.sim.sim.neuron.h.NetCon(net_stim, da_syn) + nc.weight[0] = 100_000_000.0 #units : molecules/ms + + self.sim.neurons[0].modulation.link_synapse(species_name="DA", + region="soma_internal", + synapse=da_syn, + flux_variable="open") + + self.sim.run(t=1000) + + output_file = os.path.join(self.network_path, "simulation", "output-2.hdf5") + self.sim.record.set_new_output_file(output_file) + self.sim.record.write() + + nd = SnuddaLoadSimulation(output_file) + time = nd.get_time() + data_a = nd.get_data("DA", 0) + data_b = nd.get_data("B", 0) + data_ab = nd.get_data("PKA", 0) + + data_kir_modulation_factor = nd.get_data("kirrxd.modulation_factor", 0)[0][0] + data_kir_m = nd.get_data("kirrxd.m", 0)[0][0] + data_voltage = nd.get_data("voltage", 0)[0][0] + data_pka = nd.get_data("membrane.PKAi", 0)[0][0] + + self.assertTrue(np.max(np.abs(data_a[0][0][0] - 0)) < 1e-7) + self.assertTrue(np.max(np.abs(data_b[0][0][0] - 0.7)) < 1e-7) + self.assertTrue(np.max(np.abs(data_ab[0][0][0] - 0.1)) < 1e-7) + + #self.assertTrue(data_a[0][0][-1] < data_a[0][0][0]) + #self.assertTrue(data_b[0][0][-1] < data_b[0][0][0]) + #self.assertTrue(data_ab[0][0][-1] > data_ab[0][0][0]) + + # Plot A, B, PKA activity + da = data_a[0][0] + db = data_b[0][0] + dab = data_ab[0][0] + + self.plot_data(time, np.hstack([da, db, dab]), legend=["DA", "B", "PKA"], + ylabel="Concentration", filename="concentration.png") + + # Plot the voltage and KIR modulation + self.plot_data(time, np.hstack([data_kir_modulation_factor, + data_kir_m, data_pka]), + legend=["kir_mod_factor", "kir_m", "membrane.pka"], + filename="kir_activation.png") + + self.plot_data(time, data_voltage, + legend=["voltage"], + filename="voltage.png") + + def plot_data(self, time, data, legend, filename=None, ylabel=None): + import matplotlib.pyplot as plt + plt.figure() + plt.plot(time, data, label=legend) + plt.xlabel("Time (s)") + plt.ylabel(ylabel) + plt.legend() + + if filename is not None: + plt.savefig(filename, dpi=300) + + plt.ion() + plt.show() + + def plot_kir_data(self): + pass + + def tearDown(self): + # Remember to clear old neuron, for next unit test! + self.sim.clear_neuron() + os.chdir("..") + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_pair_recording.py b/tests/test_pair_recording.py index f4f7eeac0..9015eb35b 100644 --- a/tests/test_pair_recording.py +++ b/tests/test_pair_recording.py @@ -4,17 +4,13 @@ import numpy as np from snudda.simulate.pair_recording import PairRecording from snudda.utils.load import SnuddaLoad -from snudda.utils.load_network_simulation import SnuddaLoadNetworkSimulation - -import neuron +from snudda.utils.load_network_simulation import SnuddaLoadSimulation class PairRecordingTestCase(unittest.TestCase): def setUp(self): - print(f"Running NEURON version {neuron.__version__}") - if os.path.dirname(__file__): os.chdir(os.path.dirname(__file__)) @@ -65,8 +61,8 @@ def test_frequency(self): sim_file = os.path.join(self.network_path, "simulation", "pair-recording-simulation.hdf5") sl = SnuddaLoad(self.network_path) - sns = SnuddaLoadNetworkSimulation(network_path=self.network_path, - network_simulation_output_file=sim_file) + sns = SnuddaLoadSimulation(network_path=self.network_path, + network_simulation_output_file=sim_file) with open(self.experiment_config_file, "r") as f: experiment_config = json.load(f) diff --git a/tests/validation/dspn_neurons_rxd/dspn_rxd/mechanisms.json b/tests/validation/dspn_neurons_rxd/dspn_rxd/mechanisms.json new file mode 100644 index 000000000..922f5731e --- /dev/null +++ b/tests/validation/dspn_neurons_rxd/dspn_rxd/mechanisms.json @@ -0,0 +1,3 @@ +{ + "all": ["pas", "kirrxd"] +} diff --git a/tests/validation/dspn_neurons_rxd/dspn_rxd/meta.json b/tests/validation/dspn_neurons_rxd/dspn_rxd/meta.json new file mode 100644 index 000000000..8a322be4f --- /dev/null +++ b/tests/validation/dspn_neurons_rxd/dspn_rxd/meta.json @@ -0,0 +1,17 @@ +{ + "p1": { + "m1": { + "morphology": "WT-0728MSN01-cor-rep-ax.swc", + "axon_stump": { + "axon_length": 50e-6 + } + }, + "m2": { + "morphology": "WT-0728MSN01-cor-rep-ax-copy.swc", + "axon_stump": { + "axon_length": 70e-6, + "axon_nseg_frequency": 20e-6 + } + } + } +} diff --git a/tests/validation/dspn_neurons_rxd/dspn_rxd/morphology/WT-0728MSN01-cor-rep-ax-copy.swc b/tests/validation/dspn_neurons_rxd/dspn_rxd/morphology/WT-0728MSN01-cor-rep-ax-copy.swc new file mode 100644 index 000000000..17982e1e9 --- /dev/null +++ b/tests/validation/dspn_neurons_rxd/dspn_rxd/morphology/WT-0728MSN01-cor-rep-ax-copy.swc @@ -0,0 +1,16181 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60.89 49.9125 7.31333 0.22 6658 +6660 2 61.62 49.9967 7.31556 0.22 6659 +6661 2 62.325 50.07 7.32 0.22 6660 +6662 2 62.59 50.13 7.33333 0.22 6661 +6663 2 62.82 49.56 7.89333 0.22 6662 +6664 2 64.56 47.914 7.89333 0.22 6663 +6665 2 65.29 46.79 7.89333 0.22 6664 +6666 2 65.426 46.344 7.89333 0.22 6665 +6667 2 65.6425 45.9075 7.89333 0.22 6666 +6668 2 65.92 45.54 7.89333 0.22 6667 +6669 2 66.255 45.26 7.89333 0.22 6668 +6670 2 66.57 45.31 7.89333 0.22 6669 +6671 2 63.4 51.46 7.45333 0.22 6662 +6672 2 64.35 52.25 7.45333 0.22 6671 +6673 2 64.37 54.77 7.01333 0.22 6672 +6674 2 63.425 57.7675 6.76 0.22 6673 +6675 2 63.55 59.1475 6.51 0.22 6674 +6676 2 64.026 60.866 6.21067 0.22 6675 +6677 2 64.524 61.85 6.112 0.22 6676 +6678 2 65.1167 63.3383 6.01333 0.22 6677 +6679 2 65.298 63.748 6.01333 0.22 6678 +6680 2 65.508 64.734 6.01333 0.22 6679 +6681 2 65.884 65.768 5.97867 0.22 6680 +6682 2 66.222 66.768 5.944 0.22 6681 +6683 2 66.424 67.732 5.90933 0.22 6682 +6684 2 66.774 68.706 5.87467 0.22 6683 +6685 2 67.075 69.15 5.84 0.22 6684 +6686 2 67.3475 70.17 5.39333 0.22 6685 +6687 2 67.7675 71.19 4.95 0.22 6686 +6688 2 68.1067 71.7033 4.65333 0.22 6687 +6689 2 69.21 73.38 3.22333 0.22 6688 +6690 2 69.855 74.39 2.80667 0.22 6689 +6691 2 70.696 75.754 2.38667 0.22 6690 +6692 2 71.102 76.634 2.20533 0.22 6691 +6693 2 71.376 77.546 2.024 0.22 6692 +6694 2 71.6417 78.835 1.78222 0.22 6693 +6695 2 71.726 79.286 1.66133 0.22 6694 +6696 2 71.74 79.7725 1.48 0.22 6695 +6697 2 71.975 81.075 1.48 0.22 6696 +6698 2 72.0633 81.6433 1.48 0.22 6697 +6699 2 72.285 82.345 1.48 0.22 6698 +6700 2 73.1167 86.1833 1.40444 0.22 6699 +6701 2 73.994 88.868 0.936 0.22 6700 +6702 2 74.482 89.782 0.712 0.22 6701 +6703 2 74.7375 90.2075 0.576667 0.22 6702 +6704 2 75.594 91.962 0.285333 0.22 6703 +6705 2 75.875 92.4175 0.273333 0.22 6704 +6706 2 76.295 93.735 0.233333 0.22 6705 +6707 2 76.7175 95.0525 0.193333 0.22 6706 +6708 2 76.8967 95.6533 0.195556 0.22 6707 +6709 2 77.6825 98.045 -0.473333 0.22 6708 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6733 +6735 2 88.2167 63.6 4.48444 0.22 6734 +6736 2 89.7167 64.87 4.18222 0.22 6735 +6737 2 92.096 65.926 3.88 0.22 6736 +6738 2 93.076 66.366 3.88 0.22 6737 +6739 2 94.048 66.878 3.91733 0.22 6738 +6740 2 94.93 67.506 3.95467 0.22 6739 +6741 2 95.846 67.996 3.992 0.22 6740 +6742 2 96.66 68.412 4.02933 0.22 6741 +6743 2 97.476 68.944 4.31733 0.22 6742 +6744 2 98.074 69.452 4.568 0.22 6743 +6745 2 98.435 69.5575 4.69333 0.22 6744 +6746 2 98.6867 69.8467 4.90222 0.22 6745 +6747 2 98.87 70.505 5.32 0.22 6746 +6748 2 98.95 71.13 5.32 0.22 6747 +6749 2 99.02 71.23 5.32 0.22 6748 +6750 2 101.786 72.924 5.32 0.22 6749 +6751 2 102.608 73.452 5.32 0.22 6750 +6752 2 103.072 73.685 5.32 0.22 6751 +6753 2 104.132 74.275 5.32 0.22 6752 +6754 2 105.155 74.85 5.32 0.22 6753 +6755 2 106.532 75.15 5.32 0.22 6754 +6756 2 108.177 75.3675 5.38667 0.22 6755 +6757 2 109.457 75.505 5.45333 0.22 6756 +6758 2 110.953 75.73 5.94333 0.22 6757 +6759 2 112.335 76.0325 6.43333 0.22 6758 +6760 2 113.4 76.5025 6.85667 0.22 6759 +6761 2 115.02 77.7117 7.28 0.22 6760 +6762 2 115.434 78.01 7.28 0.22 6761 +6763 2 116.216 78.556 7.51467 0.22 6762 +6764 2 117.044 78.986 7.74933 0.22 6763 +6765 2 117.47 79.2525 7.86667 0.22 6764 +6766 2 118.483 79.5325 8.45 0.22 6765 +6767 2 119.812 79.828 9.14933 0.22 6766 +6768 2 120.52 79.76 9.38133 0.22 6767 +6769 2 120.877 79.845 9.61333 0.22 6768 +6770 2 121.19 79.79 9.61333 0.22 6769 +6771 2 121.46 79.41 9.61333 0.22 6770 +6772 2 121.84 79.18 9.61333 0.22 6771 +6773 2 97.64 72.53 5.32 0.055 6748 +6774 2 98.035 76.31 2.34 0.055 6773 +6775 2 99.705 78.525 1.66 0.055 6774 +6776 2 101.88 79.435 0.293333 0.055 6775 +6777 2 103.22 79.09 -0.733333 0.055 6776 +6778 2 54.48 47.68 7.42667 0.165 6654 +6779 2 55.33 51.758 7.42667 0.165 6778 +6780 2 55.42 52.2925 7.42667 0.165 6779 +6781 2 55.4725 53.5875 7.42667 0.165 6780 +6782 2 55.49 54.96 7.37667 0.165 6781 +6783 2 55.4125 56.2525 7.32667 0.165 6782 +6784 2 55.37 56.8333 7.29333 0.165 6783 +6785 2 55.315 57.38 7.22667 0.165 6784 +6786 2 55.1 57.71 7.22667 0.165 6785 +6787 2 55.97 58.9 7.22667 0.165 6786 +6788 2 58.636 59.06 7.22667 0.165 6787 +6789 2 60.0967 59.1283 7.23556 0.165 6788 +6790 2 60.532 59.152 7.23733 0.165 6789 +6791 2 61.534 59.27 7.24267 0.165 6790 +6792 2 62.538 59.508 7.248 0.165 6791 +6793 2 63.614 59.856 7.09333 0.165 6792 +6794 2 64.0875 59.9375 7.05333 0.165 6793 +6795 2 65.225 60.6425 6.85333 0.165 6794 +6796 2 65.7267 61.0233 6.72 0.165 6795 +6797 2 66.89 62.15 6.12 0.165 6796 +6798 2 68.25 63.6367 5.49333 0.165 6797 +6799 2 68.9 64.31 5.01333 0.165 6798 +6800 2 71.1775 67.3675 3.89333 0.165 6799 +6801 2 72.12 68.834 3.784 0.165 6800 +6802 2 72.4375 69.185 3.81333 0.165 6801 +6803 2 73.1225 70.0475 3.92 0.165 6802 +6804 2 74.574 70.91 4.04 0.165 6803 +6805 2 75.1375 71.1325 4.06 0.165 6804 +6806 2 76.36 71.5225 4.09333 0.165 6805 +6807 2 76.9033 71.5867 4.09333 0.165 6806 +6808 2 78.5767 72.34 4.16889 0.165 6807 +6809 2 79.525 72.79 4.20667 0.165 6808 +6810 2 81.475 74.405 4.32 0.165 6809 +6811 2 83.7733 76.84 5.31556 0.165 6810 +6812 2 84.6225 79.0825 5.81333 0.165 6811 +6813 2 84.7825 80.42 5.81333 0.165 6812 +6814 2 84.9625 81.8675 5.81333 0.165 6813 +6815 2 85.36 83.345 5.81333 0.165 6814 +6816 2 85.6067 84.0267 5.81333 0.165 6815 +6817 2 85.865 84.715 5.81333 0.165 6816 +6818 2 88.155 86.295 7.06 0.165 6817 +6819 2 91.6433 87.88 8.30667 0.165 6818 +6820 2 92.8867 88.8233 8.30667 0.165 6819 +6821 2 93.8225 90.8525 8.55 0.165 6820 +6822 2 93.885 92.17 8.79333 0.165 6821 +6823 2 93.9775 93.355 9.03667 0.165 6822 +6824 2 94.04 93.9867 9.28 0.165 6823 +6825 2 94.06 94.455 9.28 0.165 6824 +6826 2 94.14 94.78 9.28 0.165 6825 +6827 2 54.02 59.32 7.2 0.165 6786 +6828 2 54.2933 62.5967 7.2 0.165 6827 +6829 2 55.1367 64.1867 7.05333 0.165 6828 +6830 2 55.6 64.745 6.98 0.165 6829 +6831 2 56.38 65.6 6.76 0.165 6830 +6832 2 56.06 67.05 6.06667 0.165 6831 +6833 2 55.552 70.7 6.06667 0.165 6832 +6834 2 55.4175 71.255 6.06667 0.165 6833 +6835 2 54.9775 72.645 6.06667 0.165 6834 +6836 2 54.76 73.29 6.06667 0.165 6835 +6837 2 54.28 75.0133 6.08444 0.165 6836 +6838 2 53.505 77.8183 6.11111 0.165 6837 +6839 2 53.6343 78.7586 6.12 0.165 6838 +6840 2 53.575 79.0833 6.12 0.165 6839 +6841 2 53.7783 79.735 6.34667 0.165 6840 +6842 2 53.974 79.93 6.392 0.165 6841 +6843 2 54.286 80.706 6.664 0.165 6842 +6844 2 54.556 81.55 6.936 0.165 6843 +6845 2 54.675 82.0575 7.14 0.165 6844 +6846 2 54.97 83.432 8.09333 0.165 6845 +6847 2 55.02 84.32 8.4 0.165 6846 +6848 2 55.09 84.66 8.63 0.165 6847 +6849 2 55.432 85.984 9.01333 0.165 6848 +6850 2 55.588 86.948 9.01333 0.165 6849 +6851 2 55.952 87.844 9.01333 0.165 6850 +6852 2 56.605 89.1333 9.01333 0.165 6851 +6853 2 56.776 89.57 9.01333 0.165 6852 +6854 2 56.955 90.05 9.01333 0.165 6853 +6855 2 57.22 90.5033 9.01333 0.165 6854 +6856 2 57.36 90.92 9.01333 0.165 6855 +6857 2 57.43 91.37 9.01333 0.165 6856 +6858 2 58.51 67.2 6.97333 0.165 6831 +6859 2 60.04 68.57 6.97333 0.165 6858 +6860 2 59.58 70.8 5.70667 0.165 6859 +6861 2 60.7033 74.2667 5.58667 0.165 6860 +6862 2 61.46 76.545 5.43667 0.165 6861 +6863 2 61.93 77.8375 5.34667 0.165 6862 +6864 2 62.1833 78.3367 5.34667 0.165 6863 +6865 2 62.6233 79.54 6.16889 0.165 6864 +6866 2 62.3975 80.9875 7.19667 0.165 6865 +6867 2 61.43 81.642 7.81333 0.165 6866 +6868 2 60.45 81.682 7.81333 0.165 6867 +6869 2 59.9625 81.6825 7.81333 0.165 6868 +6870 2 59.4467 81.6367 7.81333 0.165 6869 +6871 2 58.795 81.32 7.81333 0.165 6870 +6872 2 58.12 81.13 7.81333 0.165 6871 +6873 2 61.02 69.7 5.68 0.165 6859 +6874 2 62.635 71.97 3.73667 0.165 6873 +6875 2 63.3225 73.08 3.3 0.165 6874 +6876 2 64.06 74.1675 2.98333 0.165 6875 +6877 2 64.9125 75.11 3.35 0.165 6876 +6878 2 65.3467 75.57 3.49333 0.165 6877 +6879 2 65.88 75.975 3.78 0.165 6878 +6880 2 66.45 76.2 4.64 0.165 6879 +6881 2 66.34 77.58 4.64 0.165 6880 +6882 2 65.194 79.29 4.64 0.165 6881 +6883 2 65.098 80.052 4.51467 0.165 6882 +6884 2 65.054 80.886 4.39467 0.165 6883 +6885 2 65.048 81.802 4.27733 0.165 6884 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58.215 103.9 0.666667 0.165 6909 +6911 2 55.775 104.715 0.126667 0.165 6910 +6912 2 52.335 106.112 -1.12333 0.165 6911 +6913 2 51.1775 106.735 -1.36 0.165 6912 +6914 2 50.045 107.345 -1.20667 0.165 6913 +6915 2 49.5267 107.637 -1.15556 0.165 6914 +6916 2 47.245 107.27 -0.9 0.165 6915 +6917 2 46.5367 107.057 -0.746667 0.165 6916 +6918 2 45.855 106.675 -0.746667 0.165 6917 +6919 2 45.5 106.12 -0.746667 0.165 6918 +6920 2 68.85 88.34 5.50667 0.165 6892 +6921 2 70.956 91.006 5.65867 0.165 6920 +6922 2 71.28 91.92 5.87467 0.165 6921 +6923 2 71.726 92.734 6.09067 0.165 6922 +6924 2 72.242 93.492 6.256 0.165 6923 +6925 2 72.776 94.286 6.42133 0.165 6924 +6926 2 73.352 95.064 6.58667 0.165 6925 +6927 2 74.076 95.804 6.58667 0.165 6926 +6928 2 74.77 96.582 6.58667 0.165 6927 +6929 2 75.472 97.378 6.58667 0.165 6928 +6930 2 75.78 97.8275 6.58667 0.165 6929 +6931 2 76.6025 98.9625 6.80333 0.165 6930 +6932 2 77.03 99.4433 6.87556 0.165 6931 +6933 2 78.4233 100.543 7.16444 0.165 6932 +6934 2 80.4067 101.713 7.45333 0.165 6933 +6935 2 82.4867 102.213 7.09333 0.165 6934 +6936 2 84.98 102.89 6.64333 0.165 6935 +6937 2 86.806 103.078 6.51733 0.165 6936 +6938 2 87.34 103.27 6.55333 0.165 6937 +6939 2 88.705 103.125 6.73333 0.165 6938 +6940 2 89.3567 102.983 6.85333 0.165 6939 +6941 2 91.0633 102.527 6.58222 0.165 6940 +6942 2 92.155 102.295 6.32667 0.165 6941 +6943 2 94.415 102.16 5.56 0.165 6942 +6944 2 98.1233 102.713 5.57778 0.165 6943 +6945 2 100.087 103.22 5.58667 0.165 6944 +6946 2 101.75 104.137 5.58667 0.165 6945 +6947 2 102.84 105.46 5.58667 0.165 6946 +6948 2 104.095 107.7 5.58667 0.165 6947 +6949 2 104.362 109.14 5.58667 0.165 6948 +6950 2 104.567 109.82 5.58667 0.165 6949 +6951 2 104.495 110.61 5.58667 0.165 6950 +6952 2 104.12 111.25 5.58667 0.165 6951 +6953 2 68.75 77.05 2.36 0.165 6880 +6954 2 69.67 77.45 2.36 0.165 6953 +6955 2 69.73 77.69 2.36 0.165 6954 +6956 2 72.0475 80.98 2.68 0.165 6955 +6957 2 73.015 81.8475 2.50667 0.165 6956 +6958 2 74.21 83.326 2.33333 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183.815 112.893 6.82667 0.165 12579 +12581 2 183.423 113.223 7 0.165 12580 +12582 2 183.29 113.6 7 0.165 12581 +12583 2 183.28 113.72 7 0.165 12582 +12584 2 184.68 85.34 -2.12 0.165 12561 +12585 2 186.708 87.605 -2.75667 0.165 12584 +12586 2 187.125 88.71 -3.10333 0.165 12585 +12587 2 187.026 90.258 -3.50667 0.165 12586 +12588 2 186.978 90.7125 -3.50667 0.165 12587 +12589 2 186.86 91.2933 -3.50667 0.165 12588 +12590 2 186.493 92.66 -4.56444 0.165 12589 +12591 2 186.415 93.22 -5.09333 0.165 12590 +12592 2 186.106 96.632 -6.928 0.22 12591 +12593 2 185.966 97.746 -7.21067 0.055 12592 +12594 2 185.634 98.462 -7.65067 0.055 12593 +12595 2 185.44 98.8675 -7.82 0.055 12594 +12596 2 185.015 99.74 -8.37 0.055 12595 +12597 2 184.555 100.745 -8.98 0.055 12596 +12598 2 184.127 101.678 -9.68333 0.055 12597 +12599 2 183.86 102.843 -10.1167 0.055 12598 +12600 2 183.297 103.952 -10.7 0.055 12599 +12601 2 182.758 104.298 -11.3227 0.055 12600 +12602 2 182.583 104.5 -11.4267 0.055 12601 +12603 2 182.34 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6598 +6605 2 40.02 50.59 9.66667 0.22 6604 +6606 2 39.97 51.15 9.66667 0.22 6605 +6607 2 35.22 45.17 7.50667 0.22 6592 +6608 2 34.3375 47.9425 7.50667 0.22 6607 +6609 2 34.2062 48.5688 7.50667 0.22 6608 +6610 2 34.0611 49.4889 7.50667 0.22 6609 +6611 2 34.0237 49.8013 7.50667 0.22 6610 +6612 2 33.9886 50.1114 7.50667 0.22 6611 +6613 2 33.965 50.3967 7.50667 0.22 6612 +6614 2 33.928 50.676 7.50667 0.22 6613 +6615 2 33.8775 50.975 7.50667 0.22 6614 +6616 2 33.8333 51.2933 7.50667 0.22 6615 +6617 2 33.77 51.56 7.50667 0.22 6616 +6618 2 33.76 51.68 7.50667 0.22 6617 +6619 2 33.24 52.38 6.84 0.22 6618 +6620 2 31.7567 55.0033 6.84 0.22 6619 +6621 2 31.4967 55.875 6.84 0.22 6620 +6622 2 31.388 56.3 6.84 0.22 6621 +6623 2 30.922 57.148 6.84 0.22 6622 +6624 2 30.298 58.016 6.84 0.22 6623 +6625 2 29.462 58.784 6.84 0.22 6624 +6626 2 29.1075 59.2025 6.84 0.22 6625 +6627 2 27.348 59.722 6.84 0.22 6626 +6628 2 26.83 59.915 6.84 0.22 6627 +6629 2 26.2233 60.0067 6.84 0.22 6628 +6630 2 25.59 60 6.84 0.22 6629 +6631 2 25.26 59.78 6.84 0.22 6630 +6632 2 33.62 54.38 7.66667 0.22 6618 +6633 2 33.5125 57.56 7.66667 0.22 6632 +6634 2 33.585 58.8975 7.46667 0.22 6633 +6635 2 33.695 60.25 7.26667 0.22 6634 +6636 2 33.875 61.69 7.06667 0.22 6635 +6637 2 33.9833 62.4433 6.86667 0.22 6636 +6638 2 34.08 63.12 6.86667 0.22 6637 +6639 2 34.24 63.77 6.86667 0.22 6638 +6640 2 36.8 31.33 6.53333 0.22 6579 +6641 2 39.8767 34.4 6.53333 0.22 6640 +6642 2 41.3067 35.4167 6.53333 0.22 6641 +6643 2 41.93 35.765 6.53333 0.22 6642 +6644 2 42.43 36.09 6.53333 0.22 6643 +6645 2 43.96 36.87 6.97333 0.22 6644 +6646 2 46.5833 37.6367 6.97333 0.22 6645 +6647 2 48.07 38.8667 7.12444 0.22 6646 +6648 2 49.795 40.8875 7.31333 0.22 6647 +6649 2 50.81 41.92 7.42667 0.22 6648 +6650 2 51.8 42.9025 7.42667 0.22 6649 +6651 2 52.63 43.8575 7.42667 0.22 6650 +6652 2 53.06 44.34 7.42667 0.22 6651 +6653 2 53.445 44.91 7.42667 0.22 6652 +6654 2 53.53 45.52 7.42667 0.22 6653 +6655 2 55.32 46.91 7.06667 0.22 6654 +6656 2 57.898 49.478 7.25867 0.22 6655 +6657 2 58.3475 49.665 7.30667 0.22 6656 +6658 2 60.258 49.85 7.312 0.22 6657 +6659 2 60.89 49.9125 7.31333 0.22 6658 +6660 2 61.62 49.9967 7.31556 0.22 6659 +6661 2 62.325 50.07 7.32 0.22 6660 +6662 2 62.59 50.13 7.33333 0.22 6661 +6663 2 62.82 49.56 7.89333 0.22 6662 +6664 2 64.56 47.914 7.89333 0.22 6663 +6665 2 65.29 46.79 7.89333 0.22 6664 +6666 2 65.426 46.344 7.89333 0.22 6665 +6667 2 65.6425 45.9075 7.89333 0.22 6666 +6668 2 65.92 45.54 7.89333 0.22 6667 +6669 2 66.255 45.26 7.89333 0.22 6668 +6670 2 66.57 45.31 7.89333 0.22 6669 +6671 2 63.4 51.46 7.45333 0.22 6662 +6672 2 64.35 52.25 7.45333 0.22 6671 +6673 2 64.37 54.77 7.01333 0.22 6672 +6674 2 63.425 57.7675 6.76 0.22 6673 +6675 2 63.55 59.1475 6.51 0.22 6674 +6676 2 64.026 60.866 6.21067 0.22 6675 +6677 2 64.524 61.85 6.112 0.22 6676 +6678 2 65.1167 63.3383 6.01333 0.22 6677 +6679 2 65.298 63.748 6.01333 0.22 6678 +6680 2 65.508 64.734 6.01333 0.22 6679 +6681 2 65.884 65.768 5.97867 0.22 6680 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95.0525 0.193333 0.22 6706 +6708 2 76.8967 95.6533 0.195556 0.22 6707 +6709 2 77.6825 98.045 -0.473333 0.22 6708 +6710 2 77.99 98.73 -0.697778 0.22 6709 +6711 2 78.1375 101.085 -1.12 0.22 6710 +6712 2 77.9325 102.403 -1.10333 0.22 6711 +6713 2 77.52 103.782 -1.09333 0.22 6712 +6714 2 77.14 104.965 -1.09333 0.22 6713 +6715 2 76.5825 106.22 -1.29333 0.22 6714 +6716 2 76.3333 106.717 -1.36 0.22 6715 +6717 2 76.05 107.255 -1.49333 0.22 6716 +6718 2 75.7 107.82 -1.89333 0.22 6717 +6719 2 67.26 53.83 7.14667 0.22 6672 +6720 2 70.4367 54.29 6.22222 0.22 6719 +6721 2 72.2575 54.4475 5.12667 0.22 6720 +6722 2 73.842 54.524 4.79467 0.22 6721 +6723 2 74.778 54.766 4.57333 0.22 6722 +6724 2 75.2625 54.7975 4.57333 0.22 6723 +6725 2 76.355 55.195 4.74667 0.22 6724 +6726 2 77.4475 55.7975 4.92 0.22 6725 +6727 2 78.5475 56.3375 4.69 0.22 6726 +6728 2 79.5425 56.9825 4.46 0.22 6727 +6729 2 80.01 57.1667 4.19111 0.22 6728 +6730 2 81.27 58.06 3.65333 0.22 6729 +6731 2 82.4833 59.41 3.65333 0.22 6730 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0.22 6756 +6758 2 110.953 75.73 5.94333 0.22 6757 +6759 2 112.335 76.0325 6.43333 0.22 6758 +6760 2 113.4 76.5025 6.85667 0.22 6759 +6761 2 115.02 77.7117 7.28 0.22 6760 +6762 2 115.434 78.01 7.28 0.22 6761 +6763 2 116.216 78.556 7.51467 0.22 6762 +6764 2 117.044 78.986 7.74933 0.22 6763 +6765 2 117.47 79.2525 7.86667 0.22 6764 +6766 2 118.483 79.5325 8.45 0.22 6765 +6767 2 119.812 79.828 9.14933 0.22 6766 +6768 2 120.52 79.76 9.38133 0.22 6767 +6769 2 120.877 79.845 9.61333 0.22 6768 +6770 2 121.19 79.79 9.61333 0.22 6769 +6771 2 121.46 79.41 9.61333 0.22 6770 +6772 2 121.84 79.18 9.61333 0.22 6771 +6773 2 97.64 72.53 5.32 0.055 6748 +6774 2 98.035 76.31 2.34 0.055 6773 +6775 2 99.705 78.525 1.66 0.055 6774 +6776 2 101.88 79.435 0.293333 0.055 6775 +6777 2 103.22 79.09 -0.733333 0.055 6776 +6778 2 54.48 47.68 7.42667 0.165 6654 +6779 2 55.33 51.758 7.42667 0.165 6778 +6780 2 55.42 52.2925 7.42667 0.165 6779 +6781 2 55.4725 53.5875 7.42667 0.165 6780 +6782 2 55.49 54.96 7.37667 0.165 6781 +6783 2 55.4125 56.2525 7.32667 0.165 6782 +6784 2 55.37 56.8333 7.29333 0.165 6783 +6785 2 55.315 57.38 7.22667 0.165 6784 +6786 2 55.1 57.71 7.22667 0.165 6785 +6787 2 55.97 58.9 7.22667 0.165 6786 +6788 2 58.636 59.06 7.22667 0.165 6787 +6789 2 60.0967 59.1283 7.23556 0.165 6788 +6790 2 60.532 59.152 7.23733 0.165 6789 +6791 2 61.534 59.27 7.24267 0.165 6790 +6792 2 62.538 59.508 7.248 0.165 6791 +6793 2 63.614 59.856 7.09333 0.165 6792 +6794 2 64.0875 59.9375 7.05333 0.165 6793 +6795 2 65.225 60.6425 6.85333 0.165 6794 +6796 2 65.7267 61.0233 6.72 0.165 6795 +6797 2 66.89 62.15 6.12 0.165 6796 +6798 2 68.25 63.6367 5.49333 0.165 6797 +6799 2 68.9 64.31 5.01333 0.165 6798 +6800 2 71.1775 67.3675 3.89333 0.165 6799 +6801 2 72.12 68.834 3.784 0.165 6800 +6802 2 72.4375 69.185 3.81333 0.165 6801 +6803 2 73.1225 70.0475 3.92 0.165 6802 +6804 2 74.574 70.91 4.04 0.165 6803 +6805 2 75.1375 71.1325 4.06 0.165 6804 +6806 2 76.36 71.5225 4.09333 0.165 6805 +6807 2 76.9033 71.5867 4.09333 0.165 6806 +6808 2 78.5767 72.34 4.16889 0.165 6807 +6809 2 79.525 72.79 4.20667 0.165 6808 +6810 2 81.475 74.405 4.32 0.165 6809 +6811 2 83.7733 76.84 5.31556 0.165 6810 +6812 2 84.6225 79.0825 5.81333 0.165 6811 +6813 2 84.7825 80.42 5.81333 0.165 6812 +6814 2 84.9625 81.8675 5.81333 0.165 6813 +6815 2 85.36 83.345 5.81333 0.165 6814 +6816 2 85.6067 84.0267 5.81333 0.165 6815 +6817 2 85.865 84.715 5.81333 0.165 6816 +6818 2 88.155 86.295 7.06 0.165 6817 +6819 2 91.6433 87.88 8.30667 0.165 6818 +6820 2 92.8867 88.8233 8.30667 0.165 6819 +6821 2 93.8225 90.8525 8.55 0.165 6820 +6822 2 93.885 92.17 8.79333 0.165 6821 +6823 2 93.9775 93.355 9.03667 0.165 6822 +6824 2 94.04 93.9867 9.28 0.165 6823 +6825 2 94.06 94.455 9.28 0.165 6824 +6826 2 94.14 94.78 9.28 0.165 6825 +6827 2 54.02 59.32 7.2 0.165 6786 +6828 2 54.2933 62.5967 7.2 0.165 6827 +6829 2 55.1367 64.1867 7.05333 0.165 6828 +6830 2 55.6 64.745 6.98 0.165 6829 +6831 2 56.38 65.6 6.76 0.165 6830 +6832 2 56.06 67.05 6.06667 0.165 6831 +6833 2 55.552 70.7 6.06667 0.165 6832 +6834 2 55.4175 71.255 6.06667 0.165 6833 +6835 2 54.9775 72.645 6.06667 0.165 6834 +6836 2 54.76 73.29 6.06667 0.165 6835 +6837 2 54.28 75.0133 6.08444 0.165 6836 +6838 2 53.505 77.8183 6.11111 0.165 6837 +6839 2 53.6343 78.7586 6.12 0.165 6838 +6840 2 53.575 79.0833 6.12 0.165 6839 +6841 2 53.7783 79.735 6.34667 0.165 6840 +6842 2 53.974 79.93 6.392 0.165 6841 +6843 2 54.286 80.706 6.664 0.165 6842 +6844 2 54.556 81.55 6.936 0.165 6843 +6845 2 54.675 82.0575 7.14 0.165 6844 +6846 2 54.97 83.432 8.09333 0.165 6845 +6847 2 55.02 84.32 8.4 0.165 6846 +6848 2 55.09 84.66 8.63 0.165 6847 +6849 2 55.432 85.984 9.01333 0.165 6848 +6850 2 55.588 86.948 9.01333 0.165 6849 +6851 2 55.952 87.844 9.01333 0.165 6850 +6852 2 56.605 89.1333 9.01333 0.165 6851 +6853 2 56.776 89.57 9.01333 0.165 6852 +6854 2 56.955 90.05 9.01333 0.165 6853 +6855 2 57.22 90.5033 9.01333 0.165 6854 +6856 2 57.36 90.92 9.01333 0.165 6855 +6857 2 57.43 91.37 9.01333 0.165 6856 +6858 2 58.51 67.2 6.97333 0.165 6831 +6859 2 60.04 68.57 6.97333 0.165 6858 +6860 2 59.58 70.8 5.70667 0.165 6859 +6861 2 60.7033 74.2667 5.58667 0.165 6860 +6862 2 61.46 76.545 5.43667 0.165 6861 +6863 2 61.93 77.8375 5.34667 0.165 6862 +6864 2 62.1833 78.3367 5.34667 0.165 6863 +6865 2 62.6233 79.54 6.16889 0.165 6864 +6866 2 62.3975 80.9875 7.19667 0.165 6865 +6867 2 61.43 81.642 7.81333 0.165 6866 +6868 2 60.45 81.682 7.81333 0.165 6867 +6869 2 59.9625 81.6825 7.81333 0.165 6868 +6870 2 59.4467 81.6367 7.81333 0.165 6869 +6871 2 58.795 81.32 7.81333 0.165 6870 +6872 2 58.12 81.13 7.81333 0.165 6871 +6873 2 61.02 69.7 5.68 0.165 6859 +6874 2 62.635 71.97 3.73667 0.165 6873 +6875 2 63.3225 73.08 3.3 0.165 6874 +6876 2 64.06 74.1675 2.98333 0.165 6875 +6877 2 64.9125 75.11 3.35 0.165 6876 +6878 2 65.3467 75.57 3.49333 0.165 6877 +6879 2 65.88 75.975 3.78 0.165 6878 +6880 2 66.45 76.2 4.64 0.165 6879 +6881 2 66.34 77.58 4.64 0.165 6880 +6882 2 65.194 79.29 4.64 0.165 6881 +6883 2 65.098 80.052 4.51467 0.165 6882 +6884 2 65.054 80.886 4.39467 0.165 6883 +6885 2 65.048 81.802 4.27733 0.165 6884 +6886 2 65.125 82.2675 4.18667 0.165 6885 +6887 2 65.3375 83.3125 4.28333 0.165 6886 +6888 2 65.5875 84.3125 4.53667 0.165 6887 +6889 2 65.6933 84.8067 4.70222 0.165 6888 +6890 2 66.7867 86.0467 5.02667 0.165 6889 +6891 2 67.305 86.56 5.02667 0.165 6890 +6892 2 67.93 87.3 5.02667 0.165 6891 +6893 2 67.56 88.66 5.70667 0.165 6892 +6894 2 67.968 91.53 5.50133 0.165 6893 +6895 2 68.076 92.444 5.18933 0.165 6894 +6896 2 68.052 93.484 4.87733 0.165 6895 +6897 2 68.094 94.4 4.63733 0.165 6896 +6898 2 68.036 95.31 4.392 0.165 6897 +6899 2 67.852 96.37 4.18133 0.165 6898 +6900 2 67.692 97.37 4.216 0.165 6899 +6901 2 67.502 98.196 3.90667 0.165 6900 +6902 2 67.3925 98.6425 3.84667 0.165 6901 +6903 2 66.705 99.3875 3.18333 0.165 6902 +6904 2 66.4433 99.72 2.80444 0.165 6903 +6905 2 65.2133 100.453 1.62222 0.165 6904 +6906 2 63.6 101.307 1.14222 0.165 6905 +6907 2 62.0233 102.07 1.03111 0.165 6906 +6908 2 59.6625 103.123 0.913333 0.165 6907 +6909 2 58.9167 103.417 0.831111 0.165 6908 +6910 2 58.215 103.9 0.666667 0.165 6909 +6911 2 55.775 104.715 0.126667 0.165 6910 +6912 2 52.335 106.112 -1.12333 0.165 6911 +6913 2 51.1775 106.735 -1.36 0.165 6912 +6914 2 50.045 107.345 -1.20667 0.165 6913 +6915 2 49.5267 107.637 -1.15556 0.165 6914 +6916 2 47.245 107.27 -0.9 0.165 6915 +6917 2 46.5367 107.057 -0.746667 0.165 6916 +6918 2 45.855 106.675 -0.746667 0.165 6917 +6919 2 45.5 106.12 -0.746667 0.165 6918 +6920 2 68.85 88.34 5.50667 0.165 6892 +6921 2 70.956 91.006 5.65867 0.165 6920 +6922 2 71.28 91.92 5.87467 0.165 6921 +6923 2 71.726 92.734 6.09067 0.165 6922 +6924 2 72.242 93.492 6.256 0.165 6923 +6925 2 72.776 94.286 6.42133 0.165 6924 +6926 2 73.352 95.064 6.58667 0.165 6925 +6927 2 74.076 95.804 6.58667 0.165 6926 +6928 2 74.77 96.582 6.58667 0.165 6927 +6929 2 75.472 97.378 6.58667 0.165 6928 +6930 2 75.78 97.8275 6.58667 0.165 6929 +6931 2 76.6025 98.9625 6.80333 0.165 6930 +6932 2 77.03 99.4433 6.87556 0.165 6931 +6933 2 78.4233 100.543 7.16444 0.165 6932 +6934 2 80.4067 101.713 7.45333 0.165 6933 +6935 2 82.4867 102.213 7.09333 0.165 6934 +6936 2 84.98 102.89 6.64333 0.165 6935 +6937 2 86.806 103.078 6.51733 0.165 6936 +6938 2 87.34 103.27 6.55333 0.165 6937 +6939 2 88.705 103.125 6.73333 0.165 6938 +6940 2 89.3567 102.983 6.85333 0.165 6939 +6941 2 91.0633 102.527 6.58222 0.165 6940 +6942 2 92.155 102.295 6.32667 0.165 6941 +6943 2 94.415 102.16 5.56 0.165 6942 +6944 2 98.1233 102.713 5.57778 0.165 6943 +6945 2 100.087 103.22 5.58667 0.165 6944 +6946 2 101.75 104.137 5.58667 0.165 6945 +6947 2 102.84 105.46 5.58667 0.165 6946 +6948 2 104.095 107.7 5.58667 0.165 6947 +6949 2 104.362 109.14 5.58667 0.165 6948 +6950 2 104.567 109.82 5.58667 0.165 6949 +6951 2 104.495 110.61 5.58667 0.165 6950 +6952 2 104.12 111.25 5.58667 0.165 6951 +6953 2 68.75 77.05 2.36 0.165 6880 +6954 2 69.67 77.45 2.36 0.165 6953 +6955 2 69.73 77.69 2.36 0.165 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73.2867 73.4933 0.00888889 0.055 7029 +7031 2 74.3367 72.7967 -0.746667 0.055 7030 +7032 2 75.3333 72.0833 -1.51111 0.055 7031 +7033 2 76.69 72.0133 -2.16889 0.055 7032 +7034 2 78.3967 72.7567 -2.76444 0.055 7033 +7035 2 79.2967 73.6467 -2.70667 0.055 7034 +7036 2 79.9233 74.8133 -2.18222 0.055 7035 +7037 2 79.68 75.395 -1.4 0.055 7036 +7038 2 81.76 75.935 -0.506667 0.055 7037 +7039 2 84.435 75.795 0.68 0.055 7038 +7040 2 86.905 76.28 1.87333 0.055 7039 +7041 2 89.775 77.43 2.91333 0.055 7040 +7042 2 93.485 78.46 3.81333 0.055 7041 +7043 2 97.595 79.02 4.60667 0.22 7042 +7044 2 101.13 80.305 6.21333 0.22 7043 +7045 2 104.295 81.985 7.77333 0.22 7044 +7046 2 107.657 83.2033 8.14667 0.22 7045 +7047 2 109.717 83.4733 7.88889 0.22 7046 +7048 2 110.665 83.785 7.76 0.22 7047 +7049 2 113.065 84.655 7.63333 0.22 7048 +7050 2 116.617 86.0333 8.20889 0.11 7049 +7051 2 118.857 86.54 9.00444 0.11 7050 +7052 2 120.453 87.2067 9.78222 0.11 7051 +7053 2 122.37 87.9733 9.91111 0.11 7052 +7054 2 123.125 88.39 9.72667 0.11 7053 +7055 2 126.46 88.805 9.63333 0.11 7054 +7056 2 130.043 89.7833 9.92444 0.11 7055 +7057 2 130.93 90.245 9.84 0.11 7056 +7058 2 131.29 90.96 9.86667 0.11 7057 +7059 2 43.08 37.62 5.50667 0.22 6644 +7060 2 42.5375 39.6225 5.50667 0.22 7059 +7061 2 42.5167 40.0233 5.50667 0.22 7060 +7062 2 42.72 40.3 5.50667 0.22 7061 +7063 2 42.78 40.4 5.50667 0.22 7062 +7064 2 43.14 41.31 5.50667 0.165 7063 +7065 2 43.8725 44.995 7.10667 0.165 7064 +7066 2 43.924 46.804 7.29333 0.165 7065 +7067 2 43.98 47.928 7.4 0.165 7066 +7068 2 44.082 49.036 7.50667 0.165 7067 +7069 2 44.26 50.06 7.408 0.165 7068 +7070 2 44.3625 50.5325 7.38333 0.165 7069 +7071 2 44.615 51.9475 7.26 0.165 7070 +7072 2 44.8067 52.5933 7.17778 0.165 7071 +7073 2 45.0025 54.98 7.01333 0.165 7072 +7074 2 44.9775 56.43 7.01333 0.165 7073 +7075 2 44.9125 57.6725 6.68667 0.165 7074 +7076 2 44.98 58.95 6.36 0.165 7075 +7077 2 44.9733 59.6333 6.14222 0.165 7076 +7078 2 45.96 61.4625 5.62333 0.165 7077 +7079 2 46.995 62.2725 5.54 0.165 7078 +7080 2 47.6033 62.6267 5.48444 0.165 7079 +7081 2 48.23 62.935 5.37333 0.165 7080 +7082 2 48.95 62.8 5.37333 0.165 7081 +7083 2 41.3 40.81 4.64 0.055 7063 +7084 2 37.705 40.41 2.76 0.055 7083 +7085 2 36.4675 40.9175 2.44667 0.275 7084 +7086 2 35.9833 41.2667 2.4 0.275 7085 +7087 2 35.635 41.88 2.30667 0.275 7086 +7088 2 33.905 42.15 0.406667 0.275 7087 +7089 2 31.89 42.78 -1.77667 0.275 7088 +7090 2 31.1375 43.375 -2.08667 0.275 7089 +7091 2 30.445 44.245 -2.54 0.275 7090 +7092 2 29.775 45.105 -2.99333 0.165 7091 +7093 2 28.7525 45.9075 -3.28333 0.165 7092 +7094 2 28.3233 46.3833 -3.47556 0.055 7093 +7095 2 27.0133 47.39 -3.73333 0.165 7094 +7096 2 25.48 48.3333 -3.99111 0.165 7095 +7097 2 23.9033 49.4533 -4.64 0.165 7096 +7098 2 23.24 49.94 -4.93333 0.11 7097 +7099 2 21.885 51.66 -7.91333 0.11 7098 +7100 2 21.55 52.7 -10.0133 0.38 7099 +7101 2 20.87 54.42 -7.50667 0.33 7100 +7102 2 17.5425 57.095 -7.33667 0.275 7101 +7103 2 16.34 57.825 -7.27333 0.275 7102 +7104 2 15.0775 58.7625 -7.26667 0.275 7103 +7105 2 14.5733 59.11 -7.26222 0.275 7104 +7106 2 13.2133 60.18 -7.25333 0.275 7105 +7107 2 12.505 60.925 -7.25333 0.275 7106 +7108 2 11.59 61.56 -7.25333 0.275 7107 +7109 2 10.48 63.12 -7.22667 0.22 7108 +7110 2 9.68333 65.6633 -8.89333 0.22 7109 +7111 2 9.65 67.51 -9.07111 0.22 7110 +7112 2 9.68 68.55 -9.16 0.22 7111 +7113 2 10.21 71.43 -9.58 0.22 7112 +7114 2 11.1933 75.2967 -9.93778 0.22 7113 +7115 2 11.44 77.1467 -10.1422 0.22 7114 +7116 2 11.7467 78.7733 -10.5511 0.22 7115 +7117 2 12.07 80.5033 -10.7378 0.22 7116 +7118 2 12.59 82.5867 -10.9244 0.22 7117 +7119 2 12.845 83.745 -10.9067 0.22 7118 +7120 2 13.02 85.1 -10.9067 0.22 7119 +7121 2 12.02 86.38 -10.3067 0.22 7120 +7122 2 11.0167 88.7233 -9.46667 0.22 7121 +7123 2 10.93 90.0767 -8.61778 0.22 7122 +7124 2 10.55 91.4033 -7.73778 0.22 7123 +7125 2 10.32 92.98 -7.37778 0.22 7124 +7126 2 9.50667 94.13 -7.02667 0.22 7125 +7127 2 9.085 94.71 -6.70667 0.22 7126 +7128 2 8.3 94.81 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1 0.165 12031 +12033 2 85.1675 -29.6425 1 0.165 12032 +12034 2 85.1175 -31.06 1 0.165 12033 +12035 2 84.93 -32.39 1 0.165 12034 +12036 2 84.5625 -33.7875 1 0.165 12035 +12037 2 84.36 -34.4 1 0.165 12036 +12038 2 84.025 -35.175 1 0.165 12037 +12039 2 83.68 -35.98 1 0.165 12038 +12040 2 90.18 -18.72 1 0.055 12027 +12041 2 91.3383 -15.825 3.79333 0.055 12040 +12042 2 91.606 -15.764 3.81867 0.055 12041 +12043 2 92.454 -15.558 3.97067 0.055 12042 +12044 2 92.76 -15.5825 4.04667 0.055 12043 +12045 2 93.26 -15.46 4.17333 0.055 12044 +12046 2 94.47 -14.31 4.85333 0.055 12045 +12047 2 95.89 -13.4133 5.62222 0.055 12046 +12048 2 97.3133 -13.15 6.39111 0.055 12047 +12049 2 99.5467 -13.7567 6.51111 0.055 12048 +12050 2 100.46 -14.07 6.4 0.055 12049 +12051 2 103.35 -15.5 6.10667 0.055 12050 +12052 2 105.815 -17.645 5.66 0.055 12051 +12053 2 107.64 -20.25 5.20889 0.055 12052 +12054 2 107.945 -20.96 5.22667 0.055 12053 +12055 2 110.22 -22.72 5.02667 0.055 12054 +12056 2 114.57 -23.01 4.60889 0.055 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0.165 16068 +16070 2 130.409 -1.93647 26.524 0.165 16069 +16071 2 131.317 -1.55899 26.9505 0.165 16070 +16072 2 133.15 -1.32622 27.7044 0.165 16071 +16073 2 133.446 -1.1948 27.8452 0.165 16072 +16074 2 134.251 -0.747157 28.4146 0.165 16073 +16075 2 134.637 -0.481639 28.6595 0.165 16074 +16076 2 135.581 -0.0626845 29.3185 0.165 16075 +16077 2 137.024 0.399796 30.2209 0.165 16076 +16078 2 137.514 0.50534 30.5995 0.165 16077 +16079 2 138.741 1.25938 31.5793 0.165 16078 +16080 2 139.163 1.46133 31.8713 0.165 16079 +16081 2 139.976 1.9469 32.8335 0.165 16080 +16082 2 140.709 2.43014 33.7646 0.165 16081 +16083 2 140.988 2.62738 34.1319 0.165 16082 +16084 2 143.592 2.47706 35.4016 0.165 16083 +16085 2 144.547 2.36386 35.6991 0.165 16084 +16086 2 145.196 2.30267 35.8891 0.165 16085 +16087 2 146.209 3.38784 35.4955 0.165 16086 +16088 2 146.431 3.66379 35.3859 0.165 16087 +16089 2 146.723 4.15458 35.1798 0.165 16088 +16090 2 147.697 5.98307 35.0893 0.165 16089 +16091 2 148.496 6.68454 35.5395 0.165 16090 +16092 2 148.889 7.05389 35.7658 0.165 16091 +16093 2 150.511 7.66105 36.5139 0.165 16092 +16094 2 152.272 8.2743 37.3173 0.165 16093 +16095 2 154.308 8.93811 38.2367 0.165 16094 +16096 2 155.262 9.21018 38.6598 0.165 16095 +16097 2 156.284 9.48383 39.1092 0.165 16096 +16098 2 131.46 -0.251437 26.1767 0.165 16070 +16099 2 131.132 3.56748 27.2635 0.165 16098 +16100 2 131.051 4.18406 27.5033 0.165 16099 +16101 2 131.025 5.7687 28.3992 0.165 16100 +16102 2 131.512 7.75421 29.5741 0.165 16101 +16103 2 132.071 9.0417 30.0509 0.165 16102 +16104 2 132.641 10.2219 30.5101 0.165 16103 +16105 2 133.03 11.3631 30.8916 0.165 16104 +16106 2 133.111 12.856 31.0503 0.165 16105 +16107 2 132.992 14.2291 31.1076 0.165 16106 +16108 2 132.669 15.509 31.0676 0.165 16107 +16109 2 132.229 16.916 31.0078 0.165 16108 +16110 2 132.066 17.5169 31.0669 0.165 16109 +16111 2 131.459 18.9158 31.692 0.165 16110 +16112 2 130.831 20.3405 32.4494 0.165 16111 +16113 2 130.699 21.6922 33.3835 0.165 16112 +16114 2 131.343 22.669 33.1474 0.165 16113 +16115 2 131.872 23.1197 33.0337 0.165 16114 +16116 2 132.605 23.5457 32.1752 0.165 16115 +16117 2 120.483 -5.83204 18.0781 0.165 16055 +16118 2 120.798 -2.59546 18.2533 0.165 16117 +16119 2 120.93 -1.20029 18.0611 0.165 16118 +16120 2 121.108 0.461017 18.019 0.165 16119 +16121 2 121.136 0.999768 17.9483 0.165 16120 +16122 2 121.683 2.06041 17.8906 0.165 16121 +16123 2 121.834 2.50195 17.9892 0.165 16122 +16124 2 122.383 4.0165 18.204 0.165 16123 +16125 2 122.931 4.89044 18.3287 0.165 16124 +16126 2 122.917 5.93447 18.3877 0.165 16125 +16127 2 108.01 -39.18 9.09333 0.165 15902 +16128 2 106.14 -37.9775 9.09333 0.165 16127 +16129 2 105.787 -37.6967 9.09333 0.165 16128 +16130 2 105.44 -37.405 9.09333 0.165 16129 +16131 2 105.26 -37.27 9.09333 0.165 16130 +16132 2 104.28 -37.32 9.09333 0.165 16131 +16133 2 101.345 -36.2283 8.32667 0.165 16132 +16134 2 100.998 -36.114 8.17333 0.165 16133 +16135 2 100.22 -35.4833 7.81556 0.165 16134 +16136 2 99.7483 -35.0617 7.56 0.165 16135 +16137 2 99.4317 -34.3267 7.56 0.165 16136 +16138 2 99.278 -33.984 7.56 0.165 16137 +16139 2 99.115 -33.5925 7.56 0.165 16138 +16140 2 99.0325 -32.5375 7.56 0.165 16139 +16141 2 98.99 -32.04 7.56 0.165 16140 +16142 2 99.235 -31.245 7.56 0.165 16141 +16143 2 99.51 -30.46 7.56 0.165 16142 +16144 2 105.57 -35.72 9.17333 0.165 16131 +16145 2 107.9 -33.18 9.61778 0.165 16144 +16146 2 109.227 -32.0433 9.84 0.165 16145 +16147 2 110.293 -30.9467 10.4533 0.165 16146 +16148 2 111.2 -29.3033 11.0667 0.165 16147 +16149 2 111.55 -28.495 11.68 0.165 16148 +16150 2 111.207 -25.63 13.24 0.165 16149 +16151 2 111.332 -24.205 14.2967 0.165 16150 +16152 2 111.672 -23.0675 14.5 0.165 16151 +16153 2 112.033 -22.7167 14.4756 0.165 16152 +16154 2 112.26 -20.79 14.28 0.165 16153 +16155 2 112.235 -19.59 14.1333 0.165 16154 +16156 2 112.21 -18.39 13.9867 0.165 16155 +16157 2 77.26 -56.58 -4.06667 0.22 15853 +16158 2 75.76 -59.2225 -4.07667 0.22 16157 +16159 2 75.32 -59.7733 -4.08 0.22 16158 +16160 2 74.54 -61.3933 -4.18667 0.22 16159 +16161 2 73.6433 -62.9233 -4.28889 0.22 16160 +16162 2 72.3033 -64.3767 -3.90222 0.22 16161 +16163 2 70.79 -65.3967 -3.41778 0.22 16162 +16164 2 69.87 -65.955 -2.93333 0.22 16163 +16165 2 69.36 -66.58 -2.93333 0.22 16164 +16166 2 67.8 -68.29 -2.98667 0.22 16165 +16167 2 64.8 -72.32 -3.92 0.22 16166 +16168 2 62.7133 -74.3467 -4.04444 0.22 16167 +16169 2 62.045 -74.835 -3.8 0.22 16168 +16170 2 61.165 -75.355 -5.66667 0.22 16169 +16171 2 60.205 -77.16 -7.8 0.22 16170 +16172 2 58.56 -78.91 -9.49333 0.22 16171 +16173 2 57.155 -80.635 -11.3067 0.22 16172 +16174 2 56.7 -81.81 -11.6933 0.22 16173 +16175 2 69.43 -68.64 -1.56 0.22 16165 +16176 2 68.9467 -72.1433 -0.386667 0.22 16175 +16177 2 68.46 -73.7267 0.684444 0.22 16176 +16178 2 67.49 -74.86 1.59111 0.22 16177 +16179 2 66.11 -76.2167 2.68444 0.22 16178 +16180 2 65.205 -76.745 3.19333 0.22 16179 +16181 2 64.57 -77.67 3.48 0.22 16180 diff --git a/tests/validation/dspn_neurons_rxd/dspn_rxd/parameters.json b/tests/validation/dspn_neurons_rxd/dspn_rxd/parameters.json new file mode 100644 index 000000000..dec2bf4a0 --- /dev/null +++ b/tests/validation/dspn_neurons_rxd/dspn_rxd/parameters.json @@ -0,0 +1,57 @@ +{ "p1" : + [ + { + "param_name": "celsius", + "type": "global", + "value": 35.0 + }, + { + "param_name": "v_init", + "type": "global", + "value": -80.0 + }, + { + "dist_type": "uniform", + "param_name": "ek", + "sectionlist": "all", + "type": "section", + "value": -89.1 + }, + { + "param_name": "mod_pka_g_min_kirrxd", + "type": "range", + "sectionlist": "all", + "dist_type": "uniform", + "value": 0 + }, + { + "param_name": "mod_pka_g_max_kirrxd", + "type": "range", + "sectionlist": "all", + "dist_type": "uniform", + "value": 1 + }, + { + "param_name": "mod_pka_g_half_kirrxd", + "type": "range", + "sectionlist": "all", + "dist_type": "uniform", + "value": 0.25 + }, + { + "param_name": "mod_pka_g_slope_kirrxd", + "type": "range", + "sectionlist": "all", + "dist_type": "uniform", + "value": 0.02 + }, + { + "param_name": "gbar_kirrxd", + "type": "range", + "sectionlist": "all", + "dist_type": "uniform", + "value": 1e-3 + } + ] + +} diff --git a/tests/validation/dspn_neurons_rxd/dspn_rxd/reaction_diffusion.json b/tests/validation/dspn_neurons_rxd/dspn_rxd/reaction_diffusion.json new file mode 100644 index 000000000..b978d8eb7 --- /dev/null +++ b/tests/validation/dspn_neurons_rxd/dspn_rxd/reaction_diffusion.json @@ -0,0 +1,47 @@ +{ + "species": { + "DA":{ + "initial_concentration": 0, + "diffusion_constant": 0, + "charge": 0, + "regions": ["soma_internal", "dend_internal"], + "atol_scale": null, + "ecs_boundary_conditions": null, + "represents": "DA" + }, + "B":{ + "initial_concentration": 0.7e-3, + "diffusion_constant": 0, + "charge": 0, + "regions": ["soma_internal", "dend_internal"], + "atol_scale": null, + "ecs_boundary_conditions": null, + "represents": "DA" + }, + "PKA":{ + "initial_concentration": 0.1e-3, + "diffusion_constant": 0, + "charge": 0, + "regions": ["soma_internal", "dend_internal"], + "atol_scale": null, + "ecs_boundary_conditions": null, + "represents": "DA" + } + + }, + "rates": { + "DA": { + "rates": ["-0.001*DA", "-0.0001*DA"], + "regions": ["soma_internal", "dend_internal"] + } + }, + "reactions": { + "my_reaction_1": { + "reactants": "DA + B", + "products": "PKA", + "forward_rate": 0.01e-6, + "backward_rate": 0.0005e-3, + "regions": ["soma_internal", "dend_internal"] + } + } +} diff --git a/tests/validation/mechanisms/DASyn.mod b/tests/validation/mechanisms/DASyn.mod new file mode 120000 index 000000000..af52601c2 --- /dev/null +++ b/tests/validation/mechanisms/DASyn.mod @@ -0,0 +1 @@ +../mechanisms_rxd/DASyn.mod \ No newline at end of file diff --git a/tests/validation/mechanisms/kirrxd.mod b/tests/validation/mechanisms/kirrxd.mod new file mode 120000 index 000000000..68d13369f --- /dev/null +++ b/tests/validation/mechanisms/kirrxd.mod @@ -0,0 +1 @@ +../mechanisms_rxd/kirrxd.mod \ No newline at end of file diff --git a/tests/validation/mechanisms_rxd/DASyn.mod b/tests/validation/mechanisms_rxd/DASyn.mod new file mode 100644 index 000000000..bb1437882 --- /dev/null +++ b/tests/validation/mechanisms_rxd/DASyn.mod @@ -0,0 +1,30 @@ +NEURON { + POINT_PROCESS DASyn + RANGE quanta, tau, open +} +UNITS { + (mM) = (milli / liter) +} + +PARAMETER { + quanta = 1e-4 (mM/ms) + tau = 10 (ms) +} + +INITIAL { + open = 0 +} + +STATE { + open (1) +} + +BREAKPOINT {SOLVE state METHOD cnexp} + +DERIVATIVE state { + open' = -open/tau +} + +NET_RECEIVE(weight) { + open = open + weight +} diff --git a/tests/validation/mechanisms_rxd/kirrxd.mod b/tests/validation/mechanisms_rxd/kirrxd.mod new file mode 100644 index 000000000..a890c2a9a --- /dev/null +++ b/tests/validation/mechanisms_rxd/kirrxd.mod @@ -0,0 +1,122 @@ +TITLE Non-inactivating inwardly rectifying potassium current (Kir2.3) + + +NEURON { + SUFFIX kirrxd + USEION k READ ek WRITE ik + USEION PKA READ PKAi VALENCE 0 + + RANGE gbar, gk, ik, shift + RANGE mod_pka_g_min, mod_pka_g_max, mod_pka_g_half, mod_pka_g_slope + RANGE modulation_factor +} + +UNITS { + (S) = (siemens) + (mV) = (millivolt) + (mA) = (milliamp) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + gbar = 0.0 (S/cm2) + shift = 0.0 (mV) + q = 1 : body temperature 35 C + mod_pka_g_min = 1 (1) + mod_pka_g_max = 1 (1) + mod_pka_g_half = 0.000100 (mM) + mod_pka_g_slope = 0.01 (mM) +} + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + gk (S/cm2) + minf + mtau (ms) + PKAi (mM) + modulation_factor (1) +} + +STATE { m } + +BREAKPOINT { + SOLVE states METHOD cnexp + modulation_factor=modulation(PKAi, mod_pka_g_min, mod_pka_g_max, mod_pka_g_half, mod_pka_g_slope) + gk = gbar*m*modulation_factor + ik = gk*(v-ek) +} + +DERIVATIVE states { + rates() + m' = (minf-m)/mtau*q +} + +INITIAL { + rates() + m = minf +} + +: TODO: These parameters should NOT be hardcoded, and use function instead of procedure? +PROCEDURE rates() { + UNITSOFF + minf = 1/(1+exp((v-(-82)-shift)/13)) + mtau = 1/(exp((v-(-103))/(-14.5))+0.125/(1+exp((v-(-35))/(-19)))) + UNITSON +} + +FUNCTION modulation(conc (mM), mod_min (1), mod_max (1), mod_half (mM), mod_slope (mM)) (1) { + : returns modulation factor + modulation = mod_min + (mod_max-mod_min) / (1 + exp(-(conc - mod_half)/mod_slope)) +} + + +COMMENT + +2024-05-28 : Wilhelm Thunberg, Johannes Hjorth (KTH, Stockholm) +Adding neuromodulation using RxD + +Original model by Wolf et al (2005) [1] for the rat MSN cells from the +nucleus accumbens. The activation curve was fitted to a mouse Kir2.1 +channel expressed in HEK cells [2] and shifted to match extracellular +concentration of K in rat. Measured half-activation values are -109.3 +mV (striatonigral MSN) and -113.2 mV (striatopallidal MSN) [6, Supp +Tab.1]. Time constants were derived from Aplysia data [3] and adjusted +to match the rat experiments [1]. Time constant was further tuned [4] +to fit the rat data below -80 mV [5]. Kinetics is corrected to the body +temperature 35 C [4]. + +Non-inactivating Kir current was observed in cells expressing Kir2.2 +and/or Kir2.3 [5]. Activation variable with m^1 kinetics is used [1,4]. +Smooth fit of the time constants by Alexander Kozlov . + +[1] Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, +O'Donnell P, Finkel LH (2005) NMDA/AMPA ratio impacts state transitions +and entrainment to oscillations in a computational model of the nucleus +accumbens medium spiny projection neuron. J Neurosci 25(40):9080-95. + +[2] Kubo Y, Murata Y (2001) Control of rectification and permeation by +two distinct sites after the second transmembrane region in Kir2.1 K+ +channel. J Physiol 531, 645-660. + +[3] Hayashi H, Fishman HM (1988) Inward rectifier K+ channel kinetics +from analysis of the complex conductance of aplysia neuronal membrane. +Biophys J 53, 747-757. + +[4] Steephen JE, Manchanda R (2009) Differences in biophysical properties +of nucleus accumbens medium spiny neurons emerging from inactivation of +inward rectifying potassium currents. J Comput Neurosci 27(3):453-70 + +[5] Mermelstein PG, Song WJ, Tkatch T, Yan Z, Surmeier DJ (1998) +Inwardly rectifying potassium (IRK) currents are correlated with IRK +subunit expression in rat nucleus accumbens medium spiny neurons. J +Neurosci 18(17):6650-61. + +[6] Shen W, Tian X, Day M, Ulrich S, Tkatch T, Nathanson NM, Surmeier DJ +(2007) Cholinergic modulation of Kir2 channels selectively elevates +dendritic excitability in striatopallidal neurons. Nat Neurosci +10(11):1458-66. + +ENDCOMMENT