SortDriftStudy.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script recorded running each spike sorting algorithms on different drift condition recordings.\n",
    "\n",
    "Recordings:\n",
    "- recording_toy\n",
    "- recording_onlydrift_slow\n",
    "- recording_onlydrift_fast\n",
    "\n",
    "Algorithms:\n",
    "- kilosort2.5 (KS25)\n",
    "- kilosort3 (KS3)\n",
    "- herdingspikes (HS2)\n",
    "- spykingcircus (SC)\n",
    "- spykingcircus2 (SC2)\n",
    "- tridesclous (TC)\n",
    "- tridesclous2 (TC2)\n",
    "- mountainsort5 (MS5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from compare.compare import *\n",
    "from utils import *\n",
    "\n",
    "import numpy as np\n",
    "import pickle as pkl\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import MEArec as mr\n",
    "import MEAutility as mu\n",
    "\n",
    "import spikeinterface as si\n",
    "import spikeinterface.extractors as se \n",
    "import spikeinterface.preprocessing as spre\n",
    "import spikeinterface.sorters as ss\n",
    "import spikeinterface.postprocessing as spost\n",
    "import spikeinterface.qualitymetrics as sqm\n",
    "import spikeinterface.comparison as sc\n",
    "import spikeinterface.exporters as sexp\n",
    "import spikeinterface.widgets as sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "algo_list = ['KS25','KS3','HS2','SC','SC2','TC','TC2','MS5']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## recording_toy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('studies/mouse_VISp_L5_128ch/recordings/recording_toy/sorting_results')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recording_path = Path().joinpath('studies','mouse_VISp_L5_128ch','recordings','recording_toy')\n",
    "create_empty_sortings(recording_path,algo_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'KS25','KS3' accept rawdata bin file. Extract the rawdata as bin file.\n",
    "extract_rawdata_bin(recording_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KS25 into Extractor\n",
    "sorting_KS25 = se.KiloSortSortingExtractor((recording_path / 'sorting_results' / 'KS25').as_posix(),keep_good_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KS3 into Extractor\n",
    "sorting_KS3 = se.KiloSortSortingExtractor((recording_path / 'sorting_results' / 'KS3').as_posix(),keep_good_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load recording and GT\n",
    "recording,sorting_GT = se.read_mearec((recording_path / 'recordings.h5').as_posix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! The recording is already filtered, but herdingspikes filter is enabled\n",
      "# Generating new position and neighbor files from data file\n",
      "# Not Masking any Channels\n",
      "# Sampling rate: 32000\n",
      "# Localization On\n",
      "# Number of recorded channels: 128\n",
      "# Analysing frames: 960000; Seconds: 30.0\n",
      "# Frames before spike in cutout: 10\n",
      "# Frames after spike in cutout: 58\n",
      "# tcuts: 42 90\n",
      "# tInc: 100000\n",
      "# Detection completed, time taken: 0:00:10.601363\n",
      "# Time per frame: 0:00:00.011043\n",
      "# Time per sample: 0:00:00.000086\n",
      "Loaded 8722 spikes.\n",
      "Fitting dimensionality reduction using all spikes...\n",
      "...projecting...\n",
      "...done\n",
      "Clustering...\n",
      "Clustering 8722 spikes...\n",
      "number of seeds: 127\n",
      "seeds/job: 16\n",
      "using 8 cpus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done   3 out of   8 | elapsed:    9.0s remaining:   15.0s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    9.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    9.6s finished\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\herdingspikes\\clustering\\mean_shift_.py:242: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  unique = np.ones(len(sorted_centers), dtype=np.bool)\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\herdingspikes\\clustering\\mean_shift_.py:255: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  labels = np.zeros(n_samples, dtype=np.int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated units: 26\n",
      "Saving to studies\\mouse_VISp_L5_128ch\\recordings\\recording_toy\\sorting_results\\HS2\\sorter_output\\HS2_sorted.hdf5\n",
      "herdingspikes run time 27.98s\n"
     ]
    }
   ],
   "source": [
    "# HS2\n",
    "output_folder= (recording_path / 'sorting_results' / 'HS2').as_posix()\n",
    "sorting_HS2 = ss.run_sorter('herdingspikes', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! The recording is already filtered, but spykingcircus filter is enabled\n",
      "RUNNING SHELL SCRIPT: studies\\mouse_VISp_L5_128ch\\recordings\\recording_toy\\sorting_results\\SC\\sorter_output\\run_spykingcircus.bat\n",
      "\n",
      "\n",
      "(si_env) g:\\JHU\\Study\\Biomedical_Data_Design\\neurosim>spyking-circus studies\\mouse_VISp_L5_128ch\\recordings\\recording_toy\\sorting_results\\SC\\sorter_output\\recording.npy -c 4 \n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\n",
      "\n",
      "##################################################################\n",
      "\n",
      "#####             Welcome to the SpyKING CIRCUS              #####\n",
      "\n",
      "#####                 (1.1.0+16.g38bd5b7f)                   #####\n",
      "\n",
      "#####             Written by P.Yger and O.Marre              #####\n",
      "\n",
      "##################################################################\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "File          : g:\\JHU\\Study\\Biomedical_Data_Design\\neurosim\\studies\\mouse_VISp_L5_128ch\\recordings\\recording_toy\\sorting_results\\SC\\sorter_output\\recording.npy\n",
      "\n",
      "Steps         : filtering, whitening, clustering, fitting, merging\n",
      "\n",
      "Number of CPU : 4/8\n",
      "\n",
      "Parallel HDF5 : False\n",
      "\n",
      "Shared memory : True\n",
      "\n",
      "Hostfile      : C:\\Users\\IBM\\spyking-circus\\circus.hosts\n",
      "\n",
      "\n",
      "\n",
      "##################################################################\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "| Number of recorded channels : 128\n",
      "\n",
      "\n",
      "\n",
      "| Number of analyzed channels : 128\n",
      "\n",
      "\n",
      "\n",
      "| File format                 : NUMPY\n",
      "\n",
      "\n",
      "\n",
      "| Data type                   : float32\n",
      "\n",
      "\n",
      "\n",
      "| Sampling rate               : 32000 Hz\n",
      "\n",
      "\n",
      "\n",
      "| Duration of the recording   : 0 min 30 s 0 ms\n",
      "\n",
      "\n",
      "\n",
      "| Width of the templates      : 3 ms\n",
      "\n",
      "\n",
      "\n",
      "| Spatial radius considered   : 100 um\n",
      "\n",
      "\n",
      "\n",
      "| Threshold crossing          : negative\n",
      "\n",
      "\n",
      "\n",
      "------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "| Using only 4 out of 8 local CPUs available (-c to change)\n",
      "\n",
      "\n",
      "\n",
      "------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mFiltering with a Butterworth filter (order 3) in [300, 15200] Hz\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:16<00:00, 16.93s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:16<00:00, 16.93s/it]\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mAnalyzing data to get whitening matrices and thresholds...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mFound 7.5s to compute the whitening matrix...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mBecause of whitening, need to recompute the thresholds...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching spikes to construct the PCA basis...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.21s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.21s/it]\n",
      "\n",
      "\u001b[37mFound 11465 waveforms over 102400 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| A basis with 5 dimensions has been built\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mSearching isolated random spikes to sample amplitudes...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.82s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.82s/it]\n",
      "\n",
      "\u001b[37mFound 10942 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mEstimating amplitudes distributions...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSmart Search of good isolated spikes for the clustering (1/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.64s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.64s/it]\n",
      "\n",
      "\u001b[37mFound 10938 isolated spikes over 1024000 requested (0 rejected)\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mComputing density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching random spikes to refine the clustering (2/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.46s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.46s/it]\n",
      "\n",
      "\u001b[37mFound 4 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mRefining density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching random spikes to refine the clustering (3/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.39s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.39s/it]\n",
      "\n",
      "\u001b[37mFound 0 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| No more spikes in the recording, stop searching\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mRefining density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mRunning density-based clustering...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  3%|█▏                                  |[00:00<00:05,  5.51it/s]\n",
      "\n",
      " 38%|█████████████▌                      |[00:00<00:00, 49.40it/s]\n",
      "\n",
      " 59%|█████████████████████▍              |[00:00<00:00, 53.21it/s]\n",
      "\n",
      " 88%|███████████████████████████████▌    |[00:00<00:00, 63.22it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 53.29it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of clusters found : 35\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of local merges   : 1 (method nd-bhatta, param 2)\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Not enough spikes gathered: -put safety_space=False?\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m|                             -remove smart_search?\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mEstimating the templates with the median-raw procedure ...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  9%|███▍                                |[00:00<00:01, 28.92it/s]\n",
      "\n",
      " 19%|██████▊                             |[00:00<00:00, 28.13it/s]\n",
      "\n",
      " 31%|███████████▎                        |[00:00<00:00, 32.12it/s]\n",
      "\n",
      " 47%|████████████████▉                   |[00:00<00:00, 35.32it/s]\n",
      "\n",
      " 59%|█████████████████████▍              |[00:01<00:01,  9.99it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:01<00:00, 12.16it/s]\n",
      "\n",
      " 81%|█████████████████████████████▎      |[00:01<00:00, 15.36it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:01<00:00, 17.53it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:01<00:00, 17.71it/s]\n",
      "\n",
      "\u001b[37mMerging similar templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 50%|█████████████████▌                 |[00:00<00:00, 117.62it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 235.23it/s]\n",
      "\n",
      "\u001b[37mRemoving mixtures of templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 50%|█████████████████▌                 |[00:00<00:00, 113.77it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 227.54it/s]\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 225.61it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of global merges    : 0\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of mixtures removed : 0\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mComputing optimal amplitudes for the templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 11%|████                                |[00:00<00:02,  3.24it/s]\n",
      "\n",
      " 22%|████████                            |[00:00<00:02,  2.82it/s]\n",
      "\n",
      " 33%|████████████                        |[00:01<00:03,  1.62it/s]\n",
      "\n",
      " 44%|████████████████                    |[00:02<00:03,  1.47it/s]\n",
      "\n",
      " 56%|████████████████████                |[00:03<00:03,  1.33it/s]\n",
      "\n",
      " 67%|████████████████████████            |[00:03<00:01,  1.63it/s]\n",
      "\n",
      " 78%|████████████████████████████        |[00:04<00:01,  1.70it/s]\n",
      "\n",
      " 89%|████████████████████████████████    |[00:04<00:00,  1.57it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  1.73it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  1.68it/s]\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:00<00:02, 10.55it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:02,  9.94it/s]\n",
      "\n",
      " 19%|██████▊                             |[00:00<00:02, 10.07it/s]\n",
      "\n",
      " 25%|█████████                           |[00:00<00:02,  9.71it/s]\n",
      "\n",
      " 28%|██████████▏                         |[00:00<00:02,  9.75it/s]\n",
      "\n",
      " 34%|████████████▍                       |[00:01<00:02,  9.78it/s]\n",
      "\n",
      " 41%|██████████████▋                     |[00:01<00:01,  9.87it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:01<00:01,  9.69it/s]\n",
      "\n",
      " 50%|██████████████████                  |[00:01<00:01,  9.95it/s]\n",
      "\n",
      " 53%|███████████████████▏                |[00:01<00:01,  9.73it/s]\n",
      "\n",
      " 59%|█████████████████████▍              |[00:01<00:01,  9.82it/s]\n",
      "\n",
      " 62%|██████████████████████▌             |[00:02<00:01,  9.54it/s]\n",
      "\n",
      " 66%|███████████████████████▋            |[00:02<00:01,  9.52it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:02<00:01,  9.53it/s]\n",
      "\n",
      " 72%|█████████████████████████▉          |[00:02<00:00,  9.46it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:02<00:00,  9.62it/s]\n",
      "\n",
      " 84%|██████████████████████████████▍     |[00:02<00:00, 10.12it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:02<00:00, 10.44it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:03<00:00, 11.04it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:03<00:00, 10.11it/s]\n",
      "\n",
      "\u001b[37mPre-computing the overlaps of templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 50%|█████████████████▌                 |[00:00<00:00, 104.83it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 209.66it/s]\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mHere comes the SpyKING CIRCUS using 4 CPUs and 35 templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  3%|▉                                   |[00:00<00:03, 18.74it/s]\n",
      "\n",
      "  5%|█▉                                  |[00:00<00:04, 17.14it/s]\n",
      "\n",
      "  8%|██▉                                 |[00:00<00:04, 16.19it/s]\n",
      "\n",
      " 11%|███▊                                |[00:00<00:04, 15.98it/s]\n",
      "\n",
      " 13%|████▊                               |[00:00<00:04, 15.69it/s]\n",
      "\n",
      " 16%|█████▊                              |[00:00<00:04, 15.47it/s]\n",
      "\n",
      " 19%|██████▋                             |[00:00<00:03, 15.54it/s]\n",
      "\n",
      " 21%|███████▋                            |[00:00<00:03, 16.01it/s]\n",
      "\n",
      " 24%|████████▋                           |[00:01<00:03, 15.05it/s]\n",
      "\n",
      " 27%|█████████▌                          |[00:01<00:03, 15.45it/s]\n",
      "\n",
      " 29%|██████████▌                         |[00:01<00:03, 16.13it/s]\n",
      "\n",
      " 32%|███████████▌                        |[00:01<00:03, 15.39it/s]\n",
      "\n",
      " 35%|████████████▍                       |[00:01<00:03, 14.78it/s]\n",
      "\n",
      " 37%|█████████████▍                      |[00:01<00:03, 15.10it/s]\n",
      "\n",
      " 40%|██████████████▍                     |[00:01<00:02, 15.58it/s]\n",
      "\n",
      " 43%|███████████████▎                    |[00:02<00:02, 15.39it/s]\n",
      "\n",
      " 45%|████████████████▎                   |[00:02<00:02, 15.73it/s]\n",
      "\n",
      " 48%|█████████████████▎                  |[00:02<00:02, 16.21it/s]\n",
      "\n",
      " 51%|██████████████████▏                 |[00:02<00:02, 15.77it/s]\n",
      "\n",
      " 53%|███████████████████▏                |[00:02<00:02, 15.63it/s]\n",
      "\n",
      " 56%|████████████████████▏               |[00:02<00:02, 15.35it/s]\n",
      "\n",
      " 59%|█████████████████████               |[00:02<00:01, 15.70it/s]\n",
      "\n",
      " 61%|██████████████████████              |[00:02<00:01, 15.80it/s]\n",
      "\n",
      " 64%|███████████████████████             |[00:03<00:01, 15.95it/s]\n",
      "\n",
      " 67%|████████████████████████            |[00:03<00:01, 15.60it/s]\n",
      "\n",
      " 69%|████████████████████████▉           |[00:03<00:01, 15.34it/s]\n",
      "\n",
      " 72%|█████████████████████████▉          |[00:03<00:01, 14.68it/s]\n",
      "\n",
      " 75%|██████████████████████████▉         |[00:03<00:01, 13.99it/s]\n",
      "\n",
      " 77%|███████████████████████████▊        |[00:03<00:01, 14.18it/s]\n",
      "\n",
      " 80%|████████████████████████████▊       |[00:03<00:01, 14.34it/s]\n",
      "\n",
      " 83%|█████████████████████████████▊      |[00:04<00:00, 14.28it/s]\n",
      "\n",
      " 85%|██████████████████████████████▋     |[00:04<00:00, 14.76it/s]\n",
      "\n",
      " 88%|███████████████████████████████▋    |[00:04<00:00, 14.56it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:04<00:00, 15.05it/s]\n",
      "\n",
      " 93%|█████████████████████████████████▌  |[00:04<00:00, 15.77it/s]\n",
      "\n",
      " 96%|██████████████████████████████████▌ |[00:04<00:00, 15.92it/s]\n",
      "\n",
      " 99%|███████████████████████████████████▌|[00:04<00:00, 16.59it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00, 15.55it/s]\n",
      "\n",
      "\u001b[37mGathering spikes from 4 nodes...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 39.32it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 39.32it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of spikes fitted : 12967\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Automatic merging with a threshold of 0.75\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 11%|████                                |[00:00<00:07,  1.07it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00,  9.23it/s]\n",
      "\n",
      "\u001b[37mDeleting 0 noisy templates\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 115.69it/s]\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 143.24it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| We kept 35 templates out of 35 after merging\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| As meta merging is still experimental, you need\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| to use the extension option to see its results\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| >> circus-gui-matlab mydata.dat -e merged\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| or\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| >> spyking-circus mydata.dat -m converting -e merged\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "spykingcircus run time 125.72s\n"
     ]
    }
   ],
   "source": [
    "# SC\n",
    "output_folder= (recording_path / 'sorting_results' / 'SC').as_posix()\n",
    "sorting_SC = ss.run_sorter('spykingcircus', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "detect peaks with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03b2a120fc374c3c9f9b49487004493d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "detect peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 12305 peaks in total\n",
      "We kept 12305 peaks for clustering\n",
      "features_from_peaks with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5dc2350e9de45df8814ca10beba4007",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "features_from_peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 29 raw clusters, starting to clean with matching...\n",
      "extract waveforms memmap with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95c013e68dd349a0b3d8c5df25bf7b17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We kept 25 non-duplicated clusters...\n",
      "extract waveforms memmap with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54969f76e3e14499b936bbf1e7f5ac00",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find spikes (circus-omp) with n_jobs = 1 and chunk_size = 3200\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "557a79765f0b44e29bf41fba609ae5ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 15383 spikes\n",
      "spykingcircus2 run time 160.80s\n"
     ]
    }
   ],
   "source": [
    "# SC2\n",
    "output_folder= (recording_path / 'sorting_results' / 'SC2').as_posix()\n",
    "sorting_SC2 = ss.run_sorter('spykingcircus2', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local copy of recording\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8e25a1219c244b493f8b7d1e2216248",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "write_binary_recording:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataIO <id: 1821786428416> \n",
      "  workdir: studies\\mouse_VISp_L5_128ch\\recordings\\recording_toy\\sorting_results\\TC\\sorter_output\n",
      "  sample_rate: 32000.0\n",
      "  total_channel: 128\n",
      "  channel_groups: 0 [ch0 ch1 ch2 ch3 ... ch124 ch125 ch126 ch127]\n",
      "  nb_segment: 1\n",
      "  length: 960000\n",
      "  durations: 30.0 s.\n",
      "catalogue_nested_params\n",
      "{'chunksize': 32000,\n",
      " 'clean_cluster': {'apply_auto_merge_cluster': True,\n",
      "                   'apply_auto_split': True,\n",
      "                   'apply_trash_low_extremum': True,\n",
      "                   'apply_trash_not_aligned': True,\n",
      "                   'apply_trash_small_cluster': True},\n",
      " 'clean_peaks': {'alien_value_threshold': -1.0, 'mode': 'extremum_amplitude'},\n",
      " 'cluster_kargs': {'adjacency_radius_um': 50.0,\n",
      "                   'high_adjacency_radius_um': 30.0,\n",
      "                   'max_loop': 2560,\n",
      "                   'min_cluster_size': 20},\n",
      " 'cluster_method': 'pruningshears',\n",
      " 'duration': 30.0,\n",
      " 'extract_waveforms': {'wf_left_long_ms': -2.5,\n",
      "                       'wf_left_ms': -1.0,\n",
      "                       'wf_right_long_ms': 3.5,\n",
      "                       'wf_right_ms': 1.5},\n",
      " 'feature_kargs': {'adjacency_radius_um': 50.0, 'n_components_by_channel': 3},\n",
      " 'feature_method': 'pca_by_channel',\n",
      " 'make_catalogue': {'inter_sample_oversampling': False,\n",
      "                    'sparse_thresh_level2': 3,\n",
      "                    'subsample_ratio': 'auto'},\n",
      " 'memory_mode': 'memmap',\n",
      " 'mode': 'sparse',\n",
      " 'n_jobs': -1,\n",
      " 'n_spike_for_centroid': 350,\n",
      " 'noise_snippet': {'nb_snippet': 300},\n",
      " 'peak_detector': {'adjacency_radius_um': 200.0,\n",
      "                   'engine': 'numba',\n",
      "                   'method': 'geometrical',\n",
      "                   'peak_sign': '-',\n",
      "                   'peak_span_ms': 0.7,\n",
      "                   'relative_threshold': 5,\n",
      "                   'smooth_radius_um': None},\n",
      " 'peak_sampler': {'mode': 'rand_by_channel',\n",
      "                  'nb_max': 20000,\n",
      "                  'nb_max_by_channel': 1000},\n",
      " 'preprocessor': {'common_ref_removal': False,\n",
      "                  'engine': 'numpy',\n",
      "                  'highpass_freq': 400.0,\n",
      "                  'lowpass_freq': 5000.0,\n",
      "                  'pad_width': -1,\n",
      "                  'smooth_size': 0},\n",
      " 'sparse_threshold': 1.5}\n",
      "peeler_params\n",
      "{'chunksize': 32000, 'engine': 'geometrical'}\n",
      "estimate_signals_noise 5.781720200000109\n",
      "run_signalprocessor 18.55864009999982\n",
      "clean_peaks 0.008393800000021656\n",
      "sample_some_peaks 0.20693740000001526\n",
      "extract_some_noise 0.06372829999986607\n",
      "extract_some_features 2.5085771000001387\n",
      "find_clusters 4.008585200000198\n",
      "cache_some_waveforms 0.6768551999998635\n",
      "compute_all_centroid 11.176211500000136\n",
      "auto_split_cluster 32.22604230000002\n",
      "trash_not_aligned 0.17360009999993053\n",
      "auto_merge_cluster 2.5207969999999023\n",
      "trash_low_extremum 0.15708559999984573\n",
      "trash_small_cluster 0.17710039999997207\n",
      "make_catalogue_for_peeler 26.186940399999912\n",
      "CatalogueConstructor\n",
      "  chan_grp 0 - ch0 ch1 ch2 ... ch126 ch127\n",
      "  nb_peak_by_segment: 14295\n",
      "  some_features.shape: (13890, 384)\n",
      "  cluster_labels 44 [-11 -1 0 ... 42 43]\n",
      "\n",
      "peeler.tun 16.376312400000188\n",
      "tridesclous run time 120.97s\n"
     ]
    }
   ],
   "source": [
    "# TC\n",
    "output_folder= (recording_path / 'sorting_results' / 'TC').as_posix()\n",
    "sorting_TC = ss.run_sorter('tridesclous', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0824dc64d9740828ee720a67e321e8a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "detect peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 14060 peaks in total\n",
      "We kept 14060 peaks for clustering\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7be7425d8bdc43069886354c98137557",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "localize peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29f589928c33467abfffdd28b852d41e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:24: DeprecationWarning: The spikeinterface.postprocessing.template_tools is submodule is deprecated.Use spikeinterface.core.template_tools instead\n",
      "  _warn()\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:25: DeprecationWarning: The 'get_template_channel_sparsity()' function is deprecated. Use 'compute_sparsity()' instead\n",
      "  return tt.get_template_channel_sparsity(*args, **kwargs)\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:19: DeprecationWarning: The spikeinterface.postprocessing.template_tools is submodule is deprecated.Use spikeinterface.core.template_tools instead\n",
      "  _warn()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24715e57e1f44c8385c5fd485758446a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find spikes (tridesclous):   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 10239 spikes\n",
      "tridesclous2 run time 182.92s\n"
     ]
    }
   ],
   "source": [
    "# TC2\n",
    "output_folder= (recording_path / 'sorting_results' / 'TC2').as_posix()\n",
    "sorting_TC2 = ss.run_sorter('tridesclous2', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of channels: 128\n",
      "Number of timepoints: 960000\n",
      "Sampling frequency: 32000.0 Hz\n",
      "Channel 0: [ -16.  -472.5]\n",
      "Channel 1: [ -16.  -457.5]\n",
      "Channel 2: [ -16.  -442.5]\n",
      "Channel 3: [ -16.  -427.5]\n",
      "Channel 4: [ -16.  -412.5]\n",
      "Channel 5: [ -16.  -397.5]\n",
      "Channel 6: [ -16.  -382.5]\n",
      "Channel 7: [ -16.  -367.5]\n",
      "Channel 8: [ -16.  -352.5]\n",
      "Channel 9: [ -16.  -337.5]\n",
      "Channel 10: [ -16.  -322.5]\n",
      "Channel 11: [ -16.  -307.5]\n",
      "Channel 12: [ -16.  -292.5]\n",
      "Channel 13: [ -16.  -277.5]\n",
      "Channel 14: [ -16.  -262.5]\n",
      "Channel 15: [ -16.  -247.5]\n",
      "Channel 16: [ -16.  -232.5]\n",
      "Channel 17: [ -16.  -217.5]\n",
      "Channel 18: [ -16.  -202.5]\n",
      "Channel 19: [ -16.  -187.5]\n",
      "Channel 20: [ -16.  -172.5]\n",
      "Channel 21: [ -16.  -157.5]\n",
      "Channel 22: [ -16.  -142.5]\n",
      "Channel 23: [ -16.  -127.5]\n",
      "Channel 24: [ -16.  -112.5]\n",
      "Channel 25: [-16.  -97.5]\n",
      "Channel 26: [-16.  -82.5]\n",
      "Channel 27: [-16.  -67.5]\n",
      "Channel 28: [-16.  -52.5]\n",
      "Channel 29: [-16.  -37.5]\n",
      "Channel 30: [-16.  -22.5]\n",
      "Channel 31: [-16.   -7.5]\n",
      "Channel 32: [-16.    7.5]\n",
      "Channel 33: [-16.   22.5]\n",
      "Channel 34: [-16.   37.5]\n",
      "Channel 35: [-16.   52.5]\n",
      "Channel 36: [-16.   67.5]\n",
      "Channel 37: [-16.   82.5]\n",
      "Channel 38: [-16.   97.5]\n",
      "Channel 39: [-16.  112.5]\n",
      "Channel 40: [-16.  127.5]\n",
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      "Channel 43: [-16.  172.5]\n",
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      "Channel 45: [-16.  202.5]\n",
      "Channel 46: [-16.  217.5]\n",
      "Channel 47: [-16.  232.5]\n",
      "Channel 48: [-16.  247.5]\n",
      "Channel 49: [-16.  262.5]\n",
      "Channel 50: [-16.  277.5]\n",
      "Channel 51: [-16.  292.5]\n",
      "Channel 52: [-16.  307.5]\n",
      "Channel 53: [-16.  322.5]\n",
      "Channel 54: [-16.  337.5]\n",
      "Channel 55: [-16.  352.5]\n",
      "Channel 56: [-16.  367.5]\n",
      "Channel 57: [-16.  382.5]\n",
      "Channel 58: [-16.  397.5]\n",
      "Channel 59: [-16.  412.5]\n",
      "Channel 60: [-16.  427.5]\n",
      "Channel 61: [-16.  442.5]\n",
      "Channel 62: [-16.  457.5]\n",
      "Channel 63: [-16.  472.5]\n",
      "Channel 64: [  16.  -472.5]\n",
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      "Channel 69: [  16.  -397.5]\n",
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      "Channel 74: [  16.  -322.5]\n",
      "Channel 75: [  16.  -307.5]\n",
      "Channel 76: [  16.  -292.5]\n",
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      "Channel 78: [  16.  -262.5]\n",
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      "Channel 80: [  16.  -232.5]\n",
      "Channel 81: [  16.  -217.5]\n",
      "Channel 82: [  16.  -202.5]\n",
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      "Channel 86: [  16.  -142.5]\n",
      "Channel 87: [  16.  -127.5]\n",
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      "Channel 89: [ 16.  -97.5]\n",
      "Channel 90: [ 16.  -82.5]\n",
      "Channel 91: [ 16.  -67.5]\n",
      "Channel 92: [ 16.  -52.5]\n",
      "Channel 93: [ 16.  -37.5]\n",
      "Channel 94: [ 16.  -22.5]\n",
      "Channel 95: [16.  -7.5]\n",
      "Channel 96: [16.   7.5]\n",
      "Channel 97: [16.  22.5]\n",
      "Channel 98: [16.  37.5]\n",
      "Channel 99: [16.  52.5]\n",
      "Channel 100: [16.  67.5]\n",
      "Channel 101: [16.  82.5]\n",
      "Channel 102: [16.  97.5]\n",
      "Channel 103: [ 16.  112.5]\n",
      "Channel 104: [ 16.  127.5]\n",
      "Channel 105: [ 16.  142.5]\n",
      "Channel 106: [ 16.  157.5]\n",
      "Channel 107: [ 16.  172.5]\n",
      "Channel 108: [ 16.  187.5]\n",
      "Channel 109: [ 16.  202.5]\n",
      "Channel 110: [ 16.  217.5]\n",
      "Channel 111: [ 16.  232.5]\n",
      "Channel 112: [ 16.  247.5]\n",
      "Channel 113: [ 16.  262.5]\n",
      "Channel 114: [ 16.  277.5]\n",
      "Channel 115: [ 16.  292.5]\n",
      "Channel 116: [ 16.  307.5]\n",
      "Channel 117: [ 16.  322.5]\n",
      "Channel 118: [ 16.  337.5]\n",
      "Channel 119: [ 16.  352.5]\n",
      "Channel 120: [ 16.  367.5]\n",
      "Channel 121: [ 16.  382.5]\n",
      "Channel 122: [ 16.  397.5]\n",
      "Channel 123: [ 16.  412.5]\n",
      "Channel 124: [ 16.  427.5]\n",
      "Channel 125: [ 16.  442.5]\n",
      "Channel 126: [ 16.  457.5]\n",
      "Channel 127: [ 16.  472.5]\n",
      "Loading traces\n",
      "Detecting spikes\n",
      "\n",
      "Adjacency for detect spikes with channel radius None\n",
      "[[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], [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], [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], [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, 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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], [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], [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], [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], [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], [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], [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], [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], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 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117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127], [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], [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], [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], [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], [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], [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, 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[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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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]]\n",
      "\n",
      "m = 0 (nbhd size: 128)\n",
      "m = 1 (nbhd size: 128)\n",
      "m = 2 (nbhd size: 128)\n",
      "m = 3 (nbhd size: 128)\n",
      "m = 4 (nbhd size: 128)\n",
      "m = 5 (nbhd size: 128)\n",
      "m = 6 (nbhd size: 128)\n",
      "m = 7 (nbhd size: 128)\n",
      "m = 8 (nbhd size: 128)\n",
      "m = 9 (nbhd size: 128)\n",
      "m = 10 (nbhd size: 128)\n",
      "m = 11 (nbhd size: 128)\n",
      "m = 12 (nbhd size: 128)\n",
      "m = 13 (nbhd size: 128)\n",
      "m = 14 (nbhd size: 128)\n",
      "m = 15 (nbhd size: 128)\n",
      "m = 16 (nbhd size: 128)\n",
      "m = 17 (nbhd size: 128)\n",
      "m = 18 (nbhd size: 128)\n",
      "m = 19 (nbhd size: 128)\n",
      "m = 20 (nbhd size: 128)\n",
      "m = 21 (nbhd size: 128)\n",
      "m = 22 (nbhd size: 128)\n",
      "m = 23 (nbhd size: 128)\n",
      "m = 24 (nbhd size: 128)\n",
      "m = 25 (nbhd size: 128)\n",
      "m = 26 (nbhd size: 128)\n",
      "m = 27 (nbhd size: 128)\n",
      "m = 28 (nbhd size: 128)\n",
      "m = 29 (nbhd size: 128)\n",
      "m = 30 (nbhd size: 128)\n",
      "m = 31 (nbhd size: 128)\n",
      "m = 32 (nbhd size: 128)\n",
      "m = 33 (nbhd size: 128)\n",
      "m = 34 (nbhd size: 128)\n",
      "m = 35 (nbhd size: 128)\n",
      "m = 36 (nbhd size: 128)\n",
      "m = 37 (nbhd size: 128)\n",
      "m = 38 (nbhd size: 128)\n",
      "m = 39 (nbhd size: 128)\n",
      "m = 40 (nbhd size: 128)\n",
      "m = 41 (nbhd size: 128)\n",
      "m = 42 (nbhd size: 128)\n",
      "m = 43 (nbhd size: 128)\n",
      "m = 44 (nbhd size: 128)\n",
      "m = 45 (nbhd size: 128)\n",
      "m = 46 (nbhd size: 128)\n",
      "m = 47 (nbhd size: 128)\n",
      "m = 48 (nbhd size: 128)\n",
      "m = 49 (nbhd size: 128)\n",
      "m = 50 (nbhd size: 128)\n",
      "m = 51 (nbhd size: 128)\n",
      "m = 52 (nbhd size: 128)\n",
      "m = 53 (nbhd size: 128)\n",
      "m = 54 (nbhd size: 128)\n",
      "m = 55 (nbhd size: 128)\n",
      "m = 56 (nbhd size: 128)\n",
      "m = 57 (nbhd size: 128)\n",
      "m = 58 (nbhd size: 128)\n",
      "m = 59 (nbhd size: 128)\n",
      "m = 60 (nbhd size: 128)\n",
      "m = 61 (nbhd size: 128)\n",
      "m = 62 (nbhd size: 128)\n",
      "m = 63 (nbhd size: 128)\n",
      "m = 64 (nbhd size: 128)\n",
      "m = 65 (nbhd size: 128)\n",
      "m = 66 (nbhd size: 128)\n",
      "m = 67 (nbhd size: 128)\n",
      "m = 68 (nbhd size: 128)\n",
      "m = 69 (nbhd size: 128)\n",
      "m = 70 (nbhd size: 128)\n",
      "m = 71 (nbhd size: 128)\n",
      "m = 72 (nbhd size: 128)\n",
      "m = 73 (nbhd size: 128)\n",
      "m = 74 (nbhd size: 128)\n",
      "m = 75 (nbhd size: 128)\n",
      "m = 76 (nbhd size: 128)\n",
      "m = 77 (nbhd size: 128)\n",
      "m = 78 (nbhd size: 128)\n",
      "m = 79 (nbhd size: 128)\n",
      "m = 80 (nbhd size: 128)\n",
      "m = 81 (nbhd size: 128)\n",
      "m = 82 (nbhd size: 128)\n",
      "m = 83 (nbhd size: 128)\n",
      "m = 84 (nbhd size: 128)\n",
      "m = 85 (nbhd size: 128)\n",
      "m = 86 (nbhd size: 128)\n",
      "m = 87 (nbhd size: 128)\n",
      "m = 88 (nbhd size: 128)\n",
      "m = 89 (nbhd size: 128)\n",
      "m = 90 (nbhd size: 128)\n",
      "m = 91 (nbhd size: 128)\n",
      "m = 92 (nbhd size: 128)\n",
      "m = 93 (nbhd size: 128)\n",
      "m = 94 (nbhd size: 128)\n",
      "m = 95 (nbhd size: 128)\n",
      "m = 96 (nbhd size: 128)\n",
      "m = 97 (nbhd size: 128)\n",
      "m = 98 (nbhd size: 128)\n",
      "m = 99 (nbhd size: 128)\n",
      "m = 100 (nbhd size: 128)\n",
      "m = 101 (nbhd size: 128)\n",
      "m = 102 (nbhd size: 128)\n",
      "m = 103 (nbhd size: 128)\n",
      "m = 104 (nbhd size: 128)\n",
      "m = 105 (nbhd size: 128)\n",
      "m = 106 (nbhd size: 128)\n",
      "m = 107 (nbhd size: 128)\n",
      "m = 108 (nbhd size: 128)\n",
      "m = 109 (nbhd size: 128)\n",
      "m = 110 (nbhd size: 128)\n",
      "m = 111 (nbhd size: 128)\n",
      "m = 112 (nbhd size: 128)\n",
      "m = 113 (nbhd size: 128)\n",
      "m = 114 (nbhd size: 128)\n",
      "m = 115 (nbhd size: 128)\n",
      "m = 116 (nbhd size: 128)\n",
      "m = 117 (nbhd size: 128)\n",
      "m = 118 (nbhd size: 128)\n",
      "m = 119 (nbhd size: 128)\n",
      "m = 120 (nbhd size: 128)\n",
      "m = 121 (nbhd size: 128)\n",
      "m = 122 (nbhd size: 128)\n",
      "m = 123 (nbhd size: 128)\n",
      "m = 124 (nbhd size: 128)\n",
      "m = 125 (nbhd size: 128)\n",
      "m = 126 (nbhd size: 128)\n",
      "m = 127 (nbhd size: 128)\n",
      "Extracting 8286 snippets\n",
      "Clustering snippets\n",
      "14 clusters in branch\n",
      "6 clusters in branch\n",
      "7 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "Found 32 clusters\n",
      "Computing templates\n",
      "Determining optimal alignment of templates\n",
      "Template alignment converged.\n",
      "Align templates offsets:  [-1  1  4  2  0 -1  0 -1  0 -2  0 -1  0  1 -1 -1  0  0  0  0  0  0  0  0\n",
      "  1  0 -2  1  0  1  1  2]\n",
      "Aligning snippets\n",
      "Clustering aligned snippets\n",
      "11 clusters in branch\n",
      "7 clusters in branch\n",
      "8 clusters in branch\n",
      "4 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "Found 33 clusters\n",
      "Computing templates\n",
      "Offsetting times to peak\n",
      "Offsets to peak: [-1  1 -2  0  4  0  2 -1  0 -1 -1 -1 -1 -2  0  1  1  2 -2  0  0  1  0  0\n",
      "  0  0  0  0  0  1 -1 -1  1]\n",
      "Reordering units\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NpzFolderSorting: 33 units - 1 segments - 32.0kHz"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MS5\n",
    "\n",
    "import mountainsort5 as ms5\n",
    "\n",
    "# Make sure the recording is preprocessed appropriately\n",
    "# lazy preprocessing\n",
    "MS5_recording_filtered = spre.bandpass_filter(recording, freq_min=300, freq_max=6000)\n",
    "MS5_recording_preprocessed: si.BaseRecording = spre.whiten(MS5_recording_filtered)\n",
    "\n",
    "# use scheme 1\n",
    "sorting_MS5 = ms5.sorting_scheme1(\n",
    "    recording=MS5_recording_preprocessed,\n",
    "    sorting_parameters=ms5.Scheme1SortingParameters()\n",
    ")\n",
    "\n",
    "sorting_MS5.save(folder=(recording_path / 'sorting_results' / 'MS5' / 'ms5').as_posix())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## recording_onlydrift_slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('studies/mouse_VISp_L5_128ch/recordings/recording_onlydrift_slow/sorting_results')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recording_path = Path().joinpath('studies','mouse_VISp_L5_128ch','recordings','recording_onlydrift_slow')\n",
    "create_empty_sortings(recording_path,algo_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'KS25','KS3' accept rawdata bin file. Extract the rawdata as bin file.\n",
    "extract_rawdata_bin(recording_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KS25 into Extractor\n",
    "sorting_KS25 = se.KiloSortSortingExtractor((recording_path / 'sorting_results' / 'KS25').as_posix(),keep_good_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KS3 into Extractor\n",
    "sorting_KS3 = se.KiloSortSortingExtractor((recording_path / 'sorting_results' / 'KS3').as_posix(),keep_good_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load recording and GT\n",
    "recording,sorting_GT = se.read_mearec((recording_path / 'recordings.h5').as_posix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! The recording is already filtered, but herdingspikes filter is enabled\n",
      "# Generating new position and neighbor files from data file\n",
      "# Not Masking any Channels\n",
      "# Sampling rate: 32000\n",
      "# Localization On\n",
      "# Number of recorded channels: 128\n",
      "# Analysing frames: 960000; Seconds: 30.0\n",
      "# Frames before spike in cutout: 10\n",
      "# Frames after spike in cutout: 58\n",
      "# tcuts: 42 90\n",
      "# tInc: 100000\n",
      "# Detection completed, time taken: 0:00:10.435207\n",
      "# Time per frame: 0:00:00.010870\n",
      "# Time per sample: 0:00:00.000085\n",
      "Loaded 6988 spikes.\n",
      "Fitting dimensionality reduction using all spikes...\n",
      "...projecting...\n",
      "...done\n",
      "Clustering...\n",
      "Clustering 6988 spikes...\n",
      "number of seeds: 74\n",
      "seeds/job: 10\n",
      "using 8 cpus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done   3 out of   8 | elapsed:    8.6s remaining:   14.3s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    8.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    8.8s finished\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\herdingspikes\\clustering\\mean_shift_.py:242: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  unique = np.ones(len(sorted_centers), dtype=np.bool)\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\herdingspikes\\clustering\\mean_shift_.py:255: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  labels = np.zeros(n_samples, dtype=np.int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated units: 15\n",
      "Saving to studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_slow\\sorting_results\\HS2\\sorter_output\\HS2_sorted.hdf5\n",
      "herdingspikes run time 25.46s\n"
     ]
    }
   ],
   "source": [
    "# HS2\n",
    "output_folder= (recording_path / 'sorting_results' / 'HS2').as_posix()\n",
    "sorting_HS2 = ss.run_sorter('herdingspikes', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! The recording is already filtered, but spykingcircus filter is enabled\n",
      "RUNNING SHELL SCRIPT: studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_slow\\sorting_results\\SC\\sorter_output\\run_spykingcircus.bat\n",
      "\n",
      "\n",
      "(si_env) g:\\JHU\\Study\\Biomedical_Data_Design\\neurosim>spyking-circus studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_slow\\sorting_results\\SC\\sorter_output\\recording.npy -c 4 \n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\n",
      "\n",
      "##################################################################\n",
      "\n",
      "#####             Welcome to the SpyKING CIRCUS              #####\n",
      "\n",
      "#####                 (1.1.0+16.g38bd5b7f)                   #####\n",
      "\n",
      "#####             Written by P.Yger and O.Marre              #####\n",
      "\n",
      "##################################################################\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "File          : g:\\JHU\\Study\\Biomedical_Data_Design\\neurosim\\studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_slow\\sorting_results\\SC\\sorter_output\\recording.npy\n",
      "\n",
      "Steps         : filtering, whitening, clustering, fitting, merging\n",
      "\n",
      "Number of CPU : 4/8\n",
      "\n",
      "Parallel HDF5 : False\n",
      "\n",
      "Shared memory : True\n",
      "\n",
      "Hostfile      : C:\\Users\\IBM\\spyking-circus\\circus.hosts\n",
      "\n",
      "\n",
      "\n",
      "##################################################################\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "| Number of recorded channels : 128\n",
      "\n",
      "\n",
      "\n",
      "| Number of analyzed channels : 128\n",
      "\n",
      "\n",
      "\n",
      "| File format                 : NUMPY\n",
      "\n",
      "\n",
      "\n",
      "| Data type                   : float32\n",
      "\n",
      "\n",
      "\n",
      "| Sampling rate               : 32000 Hz\n",
      "\n",
      "\n",
      "\n",
      "| Duration of the recording   : 0 min 30 s 0 ms\n",
      "\n",
      "\n",
      "\n",
      "| Width of the templates      : 3 ms\n",
      "\n",
      "\n",
      "\n",
      "| Spatial radius considered   : 100 um\n",
      "\n",
      "\n",
      "\n",
      "| Threshold crossing          : negative\n",
      "\n",
      "\n",
      "\n",
      "------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "| Using only 4 out of 8 local CPUs available (-c to change)\n",
      "\n",
      "\n",
      "\n",
      "------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mFiltering with a Butterworth filter (order 3) in [300, 15200] Hz\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:19<00:00, 19.99s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:20<00:00, 20.01s/it]\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mAnalyzing data to get whitening matrices and thresholds...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mFound 7.5s to compute the whitening matrix...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mBecause of whitening, need to recompute the thresholds...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching spikes to construct the PCA basis...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.91s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.91s/it]\n",
      "\n",
      "\u001b[37mFound 10590 waveforms over 102400 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| A basis with 5 dimensions has been built\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mSearching isolated random spikes to sample amplitudes...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.90s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.90s/it]\n",
      "\n",
      "\u001b[37mFound 10231 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mEstimating amplitudes distributions...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSmart Search of good isolated spikes for the clustering (1/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.79s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.79s/it]\n",
      "\n",
      "\u001b[37mFound 10220 isolated spikes over 1024000 requested (0 rejected)\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mComputing density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching random spikes to refine the clustering (2/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.78s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.78s/it]\n",
      "\n",
      "\u001b[37mFound 5 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mRefining density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching random spikes to refine the clustering (3/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.24s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00,  2.24s/it]\n",
      "\n",
      "\u001b[37mFound 0 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| No more spikes in the recording, stop searching\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mRefining density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mRunning density-based clustering...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  3%|█▏                                  |[00:00<00:05,  5.60it/s]\n",
      "\n",
      " 38%|█████████████▌                      |[00:00<00:00, 50.27it/s]\n",
      "\n",
      " 59%|█████████████████████▍              |[00:00<00:00, 50.70it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:00<00:00, 53.24it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 54.28it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of clusters found : 27\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of local merges   : 1 (method nd-bhatta, param 2)\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Not enough spikes gathered: -put safety_space=False?\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m|                             -remove smart_search?\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mEstimating the templates with the median-raw procedure ...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:00, 34.57it/s]\n",
      "\n",
      " 25%|█████████                           |[00:00<00:00, 35.85it/s]\n",
      "\n",
      " 41%|██████████████▋                     |[00:00<00:00, 39.92it/s]\n",
      "\n",
      " 53%|███████████████████▏                |[00:01<00:01, 12.14it/s]\n",
      "\n",
      " 66%|███████████████████████▋            |[00:01<00:00, 15.86it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:01<00:00, 14.56it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:01<00:00, 17.60it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:01<00:00, 14.37it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:01<00:00, 16.58it/s]\n",
      "\n",
      "\u001b[37mMerging similar templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:00<00:00, 99.98it/s]\n",
      "\n",
      " 75%|███████████████████████████         |[00:00<00:00, 58.69it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:00<00:00, 58.29it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 63.61it/s]\n",
      "\n",
      "\u001b[37mRemoving mixtures of templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:00<00:00, 94.85it/s]\n",
      "\n",
      " 75%|███████████████████████████         |[00:00<00:00, 61.16it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:00<00:00, 61.12it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 66.29it/s]\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 242.03it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of global merges    : 0\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of mixtures removed : 0\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mComputing optimal amplitudes for the templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 14%|█████▏                              |[00:00<00:04,  1.47it/s]\n",
      "\n",
      " 29%|██████████▎                         |[00:01<00:03,  1.44it/s]\n",
      "\n",
      " 43%|███████████████▍                    |[00:02<00:02,  1.43it/s]\n",
      "\n",
      " 57%|████████████████████▌               |[00:02<00:01,  1.90it/s]\n",
      "\n",
      " 71%|█████████████████████████▋          |[00:02<00:00,  2.30it/s]\n",
      "\n",
      " 86%|██████████████████████████████▊     |[00:03<00:00,  2.02it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  1.59it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  1.69it/s]\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  3%|█▏                                  |[00:00<00:05,  5.97it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:00<00:04,  6.30it/s]\n",
      "\n",
      "  9%|███▍                                |[00:00<00:04,  5.88it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:04,  5.91it/s]\n",
      "\n",
      " 16%|█████▋                              |[00:00<00:03,  6.91it/s]\n",
      "\n",
      " 19%|██████▊                             |[00:00<00:03,  7.08it/s]\n",
      "\n",
      " 22%|███████▉                            |[00:01<00:03,  7.20it/s]\n",
      "\n",
      " 25%|█████████                           |[00:01<00:03,  6.53it/s]\n",
      "\n",
      " 28%|██████████▏                         |[00:01<00:03,  6.43it/s]\n",
      "\n",
      " 31%|███████████▎                        |[00:01<00:03,  6.83it/s]\n",
      "\n",
      " 34%|████████████▍                       |[00:01<00:02,  7.35it/s]\n",
      "\n",
      " 41%|██████████████▋                     |[00:01<00:02,  8.54it/s]\n",
      "\n",
      " 47%|████████████████▉                   |[00:02<00:01,  9.10it/s]\n",
      "\n",
      " 50%|██████████████████                  |[00:02<00:01,  8.68it/s]\n",
      "\n",
      " 53%|███████████████████▏                |[00:02<00:01,  8.82it/s]\n",
      "\n",
      " 59%|█████████████████████▍              |[00:02<00:01,  9.77it/s]\n",
      "\n",
      " 66%|███████████████████████▋            |[00:02<00:01, 10.35it/s]\n",
      "\n",
      " 72%|█████████████████████████▉          |[00:02<00:00, 10.93it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:02<00:00, 11.35it/s]\n",
      "\n",
      " 84%|██████████████████████████████▍     |[00:03<00:00, 11.46it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:03<00:00, 11.61it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:03<00:00, 11.88it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:03<00:00,  9.14it/s]\n",
      "\n",
      "\u001b[37mPre-computing the overlaps of templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 44%|███████████████▎                   |[00:00<00:00, 102.46it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:00<00:00, 62.46it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 62.17it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 65.48it/s]\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mHere comes the SpyKING CIRCUS using 4 CPUs and 27 templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  4%|█▍                                  |[00:00<00:03, 22.62it/s]\n",
      "\n",
      "  8%|██▉                                 |[00:00<00:04, 16.60it/s]\n",
      "\n",
      " 11%|███▊                                |[00:00<00:03, 17.10it/s]\n",
      "\n",
      " 15%|█████▎                              |[00:00<00:03, 18.17it/s]\n",
      "\n",
      " 17%|██████▏                             |[00:00<00:03, 18.00it/s]\n",
      "\n",
      " 20%|███████▏                            |[00:00<00:03, 16.25it/s]\n",
      "\n",
      " 24%|████████▋                           |[00:01<00:03, 17.62it/s]\n",
      "\n",
      " 27%|█████████▌                          |[00:01<00:03, 17.41it/s]\n",
      "\n",
      " 29%|██████████▌                         |[00:01<00:02, 17.67it/s]\n",
      "\n",
      " 33%|████████████                        |[00:01<00:02, 17.88it/s]\n",
      "\n",
      " 36%|████████████▉                       |[00:01<00:02, 16.21it/s]\n",
      "\n",
      " 39%|█████████████▉                      |[00:01<00:02, 15.82it/s]\n",
      "\n",
      " 41%|██████████████▉                     |[00:01<00:02, 15.85it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:01<00:02, 16.78it/s]\n",
      "\n",
      " 48%|█████████████████▎                  |[00:02<00:02, 17.97it/s]\n",
      "\n",
      " 51%|██████████████████▏                 |[00:02<00:02, 18.26it/s]\n",
      "\n",
      " 55%|███████████████████▋                |[00:02<00:01, 19.17it/s]\n",
      "\n",
      " 59%|█████████████████████               |[00:02<00:01, 19.00it/s]\n",
      "\n",
      " 61%|██████████████████████              |[00:02<00:01, 15.88it/s]\n",
      "\n",
      " 64%|███████████████████████             |[00:02<00:01, 14.91it/s]\n",
      "\n",
      " 67%|████████████████████████            |[00:02<00:01, 15.02it/s]\n",
      "\n",
      " 69%|████████████████████████▉           |[00:03<00:01, 14.85it/s]\n",
      "\n",
      " 72%|█████████████████████████▉          |[00:03<00:01, 14.69it/s]\n",
      "\n",
      " 75%|██████████████████████████▉         |[00:03<00:01, 15.00it/s]\n",
      "\n",
      " 77%|███████████████████████████▊        |[00:03<00:01, 15.12it/s]\n",
      "\n",
      " 80%|████████████████████████████▊       |[00:03<00:00, 15.88it/s]\n",
      "\n",
      " 83%|█████████████████████████████▊      |[00:03<00:00, 15.74it/s]\n",
      "\n",
      " 85%|██████████████████████████████▋     |[00:03<00:00, 13.74it/s]\n",
      "\n",
      " 88%|███████████████████████████████▋    |[00:04<00:00, 15.02it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:04<00:00, 15.53it/s]\n",
      "\n",
      " 93%|█████████████████████████████████▌  |[00:04<00:00, 16.21it/s]\n",
      "\n",
      " 96%|██████████████████████████████████▌ |[00:04<00:00, 16.20it/s]\n",
      "\n",
      " 99%|███████████████████████████████████▌|[00:04<00:00, 16.07it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00, 16.37it/s]\n",
      "\n",
      "\u001b[37mGathering spikes from 4 nodes...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 50%|██████████████████                  |[00:00<00:00, 10.03it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 16.78it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of spikes fitted : 10096\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Automatic merging with a threshold of 0.75\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 57%|████████████████████▌               |[00:00<00:00, 12.26it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 21.45it/s]\n",
      "\n",
      "\u001b[37mDeleting 1 noisy templates\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 116.98it/s]\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 82.57it/s]\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 353.65it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| We kept 25 templates out of 27 after merging\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| As meta merging is still experimental, you need\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| to use the extension option to see its results\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| >> circus-gui-matlab mydata.dat -e merged\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| or\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| >> spyking-circus mydata.dat -m converting -e merged\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "spykingcircus run time 139.51s\n"
     ]
    }
   ],
   "source": [
    "# SC\n",
    "output_folder= (recording_path / 'sorting_results' / 'SC').as_posix()\n",
    "sorting_SC = ss.run_sorter('spykingcircus', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "detect peaks with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ea3802f9e7348ea81ed6697ee7dadb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "detect peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 11387 peaks in total\n",
      "We kept 11387 peaks for clustering\n",
      "features_from_peaks with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46d21c81aa2041e5be2849a1d54ed04f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "features_from_peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 25 raw clusters, starting to clean with matching...\n",
      "extract waveforms memmap with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "911c8feb1c244aca963bb419da373050",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We kept 22 non-duplicated clusters...\n",
      "extract waveforms memmap with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebc9058474e74e4d9779c5ae9beea28d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find spikes (circus-omp) with n_jobs = 1 and chunk_size = 3200\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e02560741b1e483b8d676a86f27abd56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 15060 spikes\n",
      "spykingcircus2 run time 151.37s\n"
     ]
    }
   ],
   "source": [
    "# SC2\n",
    "output_folder= (recording_path / 'sorting_results' / 'SC2').as_posix()\n",
    "sorting_SC2 = ss.run_sorter('spykingcircus2', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local copy of recording\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02ad6393950a48d0a10f968f7b2a650a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "write_binary_recording:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataIO <id: 1341239573520> \n",
      "  workdir: studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_slow\\sorting_results\\TC\\sorter_output\n",
      "  sample_rate: 32000.0\n",
      "  total_channel: 128\n",
      "  channel_groups: 0 [ch0 ch1 ch2 ch3 ... ch124 ch125 ch126 ch127]\n",
      "  nb_segment: 1\n",
      "  length: 960000\n",
      "  durations: 30.0 s.\n",
      "catalogue_nested_params\n",
      "{'chunksize': 32000,\n",
      " 'clean_cluster': {'apply_auto_merge_cluster': True,\n",
      "                   'apply_auto_split': True,\n",
      "                   'apply_trash_low_extremum': True,\n",
      "                   'apply_trash_not_aligned': True,\n",
      "                   'apply_trash_small_cluster': True},\n",
      " 'clean_peaks': {'alien_value_threshold': -1.0, 'mode': 'extremum_amplitude'},\n",
      " 'cluster_kargs': {'adjacency_radius_um': 50.0,\n",
      "                   'high_adjacency_radius_um': 30.0,\n",
      "                   'max_loop': 2560,\n",
      "                   'min_cluster_size': 20},\n",
      " 'cluster_method': 'pruningshears',\n",
      " 'duration': 30.0,\n",
      " 'extract_waveforms': {'wf_left_long_ms': -2.5,\n",
      "                       'wf_left_ms': -1.0,\n",
      "                       'wf_right_long_ms': 3.5,\n",
      "                       'wf_right_ms': 1.5},\n",
      " 'feature_kargs': {'adjacency_radius_um': 50.0, 'n_components_by_channel': 3},\n",
      " 'feature_method': 'pca_by_channel',\n",
      " 'make_catalogue': {'inter_sample_oversampling': False,\n",
      "                    'sparse_thresh_level2': 3,\n",
      "                    'subsample_ratio': 'auto'},\n",
      " 'memory_mode': 'memmap',\n",
      " 'mode': 'sparse',\n",
      " 'n_jobs': -1,\n",
      " 'n_spike_for_centroid': 350,\n",
      " 'noise_snippet': {'nb_snippet': 300},\n",
      " 'peak_detector': {'adjacency_radius_um': 200.0,\n",
      "                   'engine': 'numba',\n",
      "                   'method': 'geometrical',\n",
      "                   'peak_sign': '-',\n",
      "                   'peak_span_ms': 0.7,\n",
      "                   'relative_threshold': 5,\n",
      "                   'smooth_radius_um': None},\n",
      " 'peak_sampler': {'mode': 'rand_by_channel',\n",
      "                  'nb_max': 20000,\n",
      "                  'nb_max_by_channel': 1000},\n",
      " 'preprocessor': {'common_ref_removal': False,\n",
      "                  'engine': 'numpy',\n",
      "                  'highpass_freq': 400.0,\n",
      "                  'lowpass_freq': 5000.0,\n",
      "                  'pad_width': -1,\n",
      "                  'smooth_size': 0},\n",
      " 'sparse_threshold': 1.5}\n",
      "peeler_params\n",
      "{'chunksize': 32000, 'engine': 'geometrical'}\n",
      "estimate_signals_noise 5.945308699999941\n",
      "run_signalprocessor 22.500021800000013\n",
      "clean_peaks 0.008087799999884737\n",
      "sample_some_peaks 0.01334139999994477\n",
      "extract_some_noise 0.04583679999996093\n",
      "extract_some_features 2.5659861999999976\n",
      "find_clusters 3.1722391999999218\n",
      "cache_some_waveforms 0.7226865999998608\n",
      "compute_all_centroid 6.077732999999853\n",
      "auto_split_cluster 32.972946800000045\n",
      "trash_not_aligned 0.2160837999999785\n",
      "auto_merge_cluster 2.195098200000075\n",
      "trash_low_extremum 0.15936379999993733\n",
      "trash_small_cluster 0.1576734000000215\n",
      "make_catalogue_for_peeler 18.467308900000035\n",
      "CatalogueConstructor\n",
      "  chan_grp 0 - ch0 ch1 ch2 ... ch126 ch127\n",
      "  nb_peak_by_segment: 13517\n",
      "  some_features.shape: (13451, 384)\n",
      "  cluster_labels 37 [-11 -1 0 ... 35 36]\n",
      "\n",
      "peeler.tun 13.809678500000018\n",
      "tridesclous run time 109.23s\n"
     ]
    }
   ],
   "source": [
    "# TC\n",
    "output_folder= (recording_path / 'sorting_results' / 'TC').as_posix()\n",
    "sorting_TC = ss.run_sorter('tridesclous', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "649c4559aea74e2985633e2a65d779cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "detect peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 12887 peaks in total\n",
      "We kept 12887 peaks for clustering\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea3f7eeff5094cb49e244ab5caf8feb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "localize peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f557048c91bf43b7925c6e94422ab5a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:24: DeprecationWarning: The spikeinterface.postprocessing.template_tools is submodule is deprecated.Use spikeinterface.core.template_tools instead\n",
      "  _warn()\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:25: DeprecationWarning: The 'get_template_channel_sparsity()' function is deprecated. Use 'compute_sparsity()' instead\n",
      "  return tt.get_template_channel_sparsity(*args, **kwargs)\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:19: DeprecationWarning: The spikeinterface.postprocessing.template_tools is submodule is deprecated.Use spikeinterface.core.template_tools instead\n",
      "  _warn()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a2c0721455245c9a7d40ab6c565860e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find spikes (tridesclous):   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 8361 spikes\n",
      "tridesclous2 run time 171.29s\n"
     ]
    }
   ],
   "source": [
    "# TC2\n",
    "output_folder= (recording_path / 'sorting_results' / 'TC2').as_posix()\n",
    "sorting_TC2 = ss.run_sorter('tridesclous2', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of channels: 128\n",
      "Number of timepoints: 960000\n",
      "Sampling frequency: 32000.0 Hz\n",
      "Channel 0: [ -16.  -472.5]\n",
      "Channel 1: [ -16.  -457.5]\n",
      "Channel 2: [ -16.  -442.5]\n",
      "Channel 3: [ -16.  -427.5]\n",
      "Channel 4: [ -16.  -412.5]\n",
      "Channel 5: [ -16.  -397.5]\n",
      "Channel 6: [ -16.  -382.5]\n",
      "Channel 7: [ -16.  -367.5]\n",
      "Channel 8: [ -16.  -352.5]\n",
      "Channel 9: [ -16.  -337.5]\n",
      "Channel 10: [ -16.  -322.5]\n",
      "Channel 11: [ -16.  -307.5]\n",
      "Channel 12: [ -16.  -292.5]\n",
      "Channel 13: [ -16.  -277.5]\n",
      "Channel 14: [ -16.  -262.5]\n",
      "Channel 15: [ -16.  -247.5]\n",
      "Channel 16: [ -16.  -232.5]\n",
      "Channel 17: [ -16.  -217.5]\n",
      "Channel 18: [ -16.  -202.5]\n",
      "Channel 19: [ -16.  -187.5]\n",
      "Channel 20: [ -16.  -172.5]\n",
      "Channel 21: [ -16.  -157.5]\n",
      "Channel 22: [ -16.  -142.5]\n",
      "Channel 23: [ -16.  -127.5]\n",
      "Channel 24: [ -16.  -112.5]\n",
      "Channel 25: [-16.  -97.5]\n",
      "Channel 26: [-16.  -82.5]\n",
      "Channel 27: [-16.  -67.5]\n",
      "Channel 28: [-16.  -52.5]\n",
      "Channel 29: [-16.  -37.5]\n",
      "Channel 30: [-16.  -22.5]\n",
      "Channel 31: [-16.   -7.5]\n",
      "Channel 32: [-16.    7.5]\n",
      "Channel 33: [-16.   22.5]\n",
      "Channel 34: [-16.   37.5]\n",
      "Channel 35: [-16.   52.5]\n",
      "Channel 36: [-16.   67.5]\n",
      "Channel 37: [-16.   82.5]\n",
      "Channel 38: [-16.   97.5]\n",
      "Channel 39: [-16.  112.5]\n",
      "Channel 40: [-16.  127.5]\n",
      "Channel 41: [-16.  142.5]\n",
      "Channel 42: [-16.  157.5]\n",
      "Channel 43: [-16.  172.5]\n",
      "Channel 44: [-16.  187.5]\n",
      "Channel 45: [-16.  202.5]\n",
      "Channel 46: [-16.  217.5]\n",
      "Channel 47: [-16.  232.5]\n",
      "Channel 48: [-16.  247.5]\n",
      "Channel 49: [-16.  262.5]\n",
      "Channel 50: [-16.  277.5]\n",
      "Channel 51: [-16.  292.5]\n",
      "Channel 52: [-16.  307.5]\n",
      "Channel 53: [-16.  322.5]\n",
      "Channel 54: [-16.  337.5]\n",
      "Channel 55: [-16.  352.5]\n",
      "Channel 56: [-16.  367.5]\n",
      "Channel 57: [-16.  382.5]\n",
      "Channel 58: [-16.  397.5]\n",
      "Channel 59: [-16.  412.5]\n",
      "Channel 60: [-16.  427.5]\n",
      "Channel 61: [-16.  442.5]\n",
      "Channel 62: [-16.  457.5]\n",
      "Channel 63: [-16.  472.5]\n",
      "Channel 64: [  16.  -472.5]\n",
      "Channel 65: [  16.  -457.5]\n",
      "Channel 66: [  16.  -442.5]\n",
      "Channel 67: [  16.  -427.5]\n",
      "Channel 68: [  16.  -412.5]\n",
      "Channel 69: [  16.  -397.5]\n",
      "Channel 70: [  16.  -382.5]\n",
      "Channel 71: [  16.  -367.5]\n",
      "Channel 72: [  16.  -352.5]\n",
      "Channel 73: [  16.  -337.5]\n",
      "Channel 74: [  16.  -322.5]\n",
      "Channel 75: [  16.  -307.5]\n",
      "Channel 76: [  16.  -292.5]\n",
      "Channel 77: [  16.  -277.5]\n",
      "Channel 78: [  16.  -262.5]\n",
      "Channel 79: [  16.  -247.5]\n",
      "Channel 80: [  16.  -232.5]\n",
      "Channel 81: [  16.  -217.5]\n",
      "Channel 82: [  16.  -202.5]\n",
      "Channel 83: [  16.  -187.5]\n",
      "Channel 84: [  16.  -172.5]\n",
      "Channel 85: [  16.  -157.5]\n",
      "Channel 86: [  16.  -142.5]\n",
      "Channel 87: [  16.  -127.5]\n",
      "Channel 88: [  16.  -112.5]\n",
      "Channel 89: [ 16.  -97.5]\n",
      "Channel 90: [ 16.  -82.5]\n",
      "Channel 91: [ 16.  -67.5]\n",
      "Channel 92: [ 16.  -52.5]\n",
      "Channel 93: [ 16.  -37.5]\n",
      "Channel 94: [ 16.  -22.5]\n",
      "Channel 95: [16.  -7.5]\n",
      "Channel 96: [16.   7.5]\n",
      "Channel 97: [16.  22.5]\n",
      "Channel 98: [16.  37.5]\n",
      "Channel 99: [16.  52.5]\n",
      "Channel 100: [16.  67.5]\n",
      "Channel 101: [16.  82.5]\n",
      "Channel 102: [16.  97.5]\n",
      "Channel 103: [ 16.  112.5]\n",
      "Channel 104: [ 16.  127.5]\n",
      "Channel 105: [ 16.  142.5]\n",
      "Channel 106: [ 16.  157.5]\n",
      "Channel 107: [ 16.  172.5]\n",
      "Channel 108: [ 16.  187.5]\n",
      "Channel 109: [ 16.  202.5]\n",
      "Channel 110: [ 16.  217.5]\n",
      "Channel 111: [ 16.  232.5]\n",
      "Channel 112: [ 16.  247.5]\n",
      "Channel 113: [ 16.  262.5]\n",
      "Channel 114: [ 16.  277.5]\n",
      "Channel 115: [ 16.  292.5]\n",
      "Channel 116: [ 16.  307.5]\n",
      "Channel 117: [ 16.  322.5]\n",
      "Channel 118: [ 16.  337.5]\n",
      "Channel 119: [ 16.  352.5]\n",
      "Channel 120: [ 16.  367.5]\n",
      "Channel 121: [ 16.  382.5]\n",
      "Channel 122: [ 16.  397.5]\n",
      "Channel 123: [ 16.  412.5]\n",
      "Channel 124: [ 16.  427.5]\n",
      "Channel 125: [ 16.  442.5]\n",
      "Channel 126: [ 16.  457.5]\n",
      "Channel 127: [ 16.  472.5]\n",
      "Loading traces\n",
      "Detecting spikes\n",
      "\n",
      "Adjacency for detect spikes with channel radius None\n",
      "[[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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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, 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[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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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], [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]]\n",
      "\n",
      "m = 0 (nbhd size: 128)\n",
      "m = 1 (nbhd size: 128)\n",
      "m = 2 (nbhd size: 128)\n",
      "m = 3 (nbhd size: 128)\n",
      "m = 4 (nbhd size: 128)\n",
      "m = 5 (nbhd size: 128)\n",
      "m = 6 (nbhd size: 128)\n",
      "m = 7 (nbhd size: 128)\n",
      "m = 8 (nbhd size: 128)\n",
      "m = 9 (nbhd size: 128)\n",
      "m = 10 (nbhd size: 128)\n",
      "m = 11 (nbhd size: 128)\n",
      "m = 12 (nbhd size: 128)\n",
      "m = 13 (nbhd size: 128)\n",
      "m = 14 (nbhd size: 128)\n",
      "m = 15 (nbhd size: 128)\n",
      "m = 16 (nbhd size: 128)\n",
      "m = 17 (nbhd size: 128)\n",
      "m = 18 (nbhd size: 128)\n",
      "m = 19 (nbhd size: 128)\n",
      "m = 20 (nbhd size: 128)\n",
      "m = 21 (nbhd size: 128)\n",
      "m = 22 (nbhd size: 128)\n",
      "m = 23 (nbhd size: 128)\n",
      "m = 24 (nbhd size: 128)\n",
      "m = 25 (nbhd size: 128)\n",
      "m = 26 (nbhd size: 128)\n",
      "m = 27 (nbhd size: 128)\n",
      "m = 28 (nbhd size: 128)\n",
      "m = 29 (nbhd size: 128)\n",
      "m = 30 (nbhd size: 128)\n",
      "m = 31 (nbhd size: 128)\n",
      "m = 32 (nbhd size: 128)\n",
      "m = 33 (nbhd size: 128)\n",
      "m = 34 (nbhd size: 128)\n",
      "m = 35 (nbhd size: 128)\n",
      "m = 36 (nbhd size: 128)\n",
      "m = 37 (nbhd size: 128)\n",
      "m = 38 (nbhd size: 128)\n",
      "m = 39 (nbhd size: 128)\n",
      "m = 40 (nbhd size: 128)\n",
      "m = 41 (nbhd size: 128)\n",
      "m = 42 (nbhd size: 128)\n",
      "m = 43 (nbhd size: 128)\n",
      "m = 44 (nbhd size: 128)\n",
      "m = 45 (nbhd size: 128)\n",
      "m = 46 (nbhd size: 128)\n",
      "m = 47 (nbhd size: 128)\n",
      "m = 48 (nbhd size: 128)\n",
      "m = 49 (nbhd size: 128)\n",
      "m = 50 (nbhd size: 128)\n",
      "m = 51 (nbhd size: 128)\n",
      "m = 52 (nbhd size: 128)\n",
      "m = 53 (nbhd size: 128)\n",
      "m = 54 (nbhd size: 128)\n",
      "m = 55 (nbhd size: 128)\n",
      "m = 56 (nbhd size: 128)\n",
      "m = 57 (nbhd size: 128)\n",
      "m = 58 (nbhd size: 128)\n",
      "m = 59 (nbhd size: 128)\n",
      "m = 60 (nbhd size: 128)\n",
      "m = 61 (nbhd size: 128)\n",
      "m = 62 (nbhd size: 128)\n",
      "m = 63 (nbhd size: 128)\n",
      "m = 64 (nbhd size: 128)\n",
      "m = 65 (nbhd size: 128)\n",
      "m = 66 (nbhd size: 128)\n",
      "m = 67 (nbhd size: 128)\n",
      "m = 68 (nbhd size: 128)\n",
      "m = 69 (nbhd size: 128)\n",
      "m = 70 (nbhd size: 128)\n",
      "m = 71 (nbhd size: 128)\n",
      "m = 72 (nbhd size: 128)\n",
      "m = 73 (nbhd size: 128)\n",
      "m = 74 (nbhd size: 128)\n",
      "m = 75 (nbhd size: 128)\n",
      "m = 76 (nbhd size: 128)\n",
      "m = 77 (nbhd size: 128)\n",
      "m = 78 (nbhd size: 128)\n",
      "m = 79 (nbhd size: 128)\n",
      "m = 80 (nbhd size: 128)\n",
      "m = 81 (nbhd size: 128)\n",
      "m = 82 (nbhd size: 128)\n",
      "m = 83 (nbhd size: 128)\n",
      "m = 84 (nbhd size: 128)\n",
      "m = 85 (nbhd size: 128)\n",
      "m = 86 (nbhd size: 128)\n",
      "m = 87 (nbhd size: 128)\n",
      "m = 88 (nbhd size: 128)\n",
      "m = 89 (nbhd size: 128)\n",
      "m = 90 (nbhd size: 128)\n",
      "m = 91 (nbhd size: 128)\n",
      "m = 92 (nbhd size: 128)\n",
      "m = 93 (nbhd size: 128)\n",
      "m = 94 (nbhd size: 128)\n",
      "m = 95 (nbhd size: 128)\n",
      "m = 96 (nbhd size: 128)\n",
      "m = 97 (nbhd size: 128)\n",
      "m = 98 (nbhd size: 128)\n",
      "m = 99 (nbhd size: 128)\n",
      "m = 100 (nbhd size: 128)\n",
      "m = 101 (nbhd size: 128)\n",
      "m = 102 (nbhd size: 128)\n",
      "m = 103 (nbhd size: 128)\n",
      "m = 104 (nbhd size: 128)\n",
      "m = 105 (nbhd size: 128)\n",
      "m = 106 (nbhd size: 128)\n",
      "m = 107 (nbhd size: 128)\n",
      "m = 108 (nbhd size: 128)\n",
      "m = 109 (nbhd size: 128)\n",
      "m = 110 (nbhd size: 128)\n",
      "m = 111 (nbhd size: 128)\n",
      "m = 112 (nbhd size: 128)\n",
      "m = 113 (nbhd size: 128)\n",
      "m = 114 (nbhd size: 128)\n",
      "m = 115 (nbhd size: 128)\n",
      "m = 116 (nbhd size: 128)\n",
      "m = 117 (nbhd size: 128)\n",
      "m = 118 (nbhd size: 128)\n",
      "m = 119 (nbhd size: 128)\n",
      "m = 120 (nbhd size: 128)\n",
      "m = 121 (nbhd size: 128)\n",
      "m = 122 (nbhd size: 128)\n",
      "m = 123 (nbhd size: 128)\n",
      "m = 124 (nbhd size: 128)\n",
      "m = 125 (nbhd size: 128)\n",
      "m = 126 (nbhd size: 128)\n",
      "m = 127 (nbhd size: 128)\n",
      "Extracting 8176 snippets\n",
      "Clustering snippets\n",
      "5 clusters in branch\n",
      "3 clusters in branch\n",
      "3 clusters in branch\n",
      "Found 9 clusters\n",
      "Computing templates\n",
      "Determining optimal alignment of templates\n",
      "Template alignment converged.\n",
      "Align templates offsets:  [ 2 -1  0  6  2  6  0  4 -1]\n",
      "Aligning snippets\n",
      "Clustering aligned snippets\n",
      "8 clusters in branch\n",
      "3 clusters in branch\n",
      "2 clusters in branch\n",
      "3 clusters in branch\n",
      "3 clusters in branch\n",
      "3 clusters in branch\n",
      "2 clusters in branch\n",
      "2 clusters in branch\n",
      "Found 19 clusters\n",
      "Computing templates\n",
      "Offsetting times to peak\n",
      "Offsets to peak: [ 6  3  2  3  2  2  2  2  2  2  2 -1  0  2  0  6  4  2 -1]\n",
      "Reordering units\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NpzFolderSorting: 19 units - 1 segments - 32.0kHz"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MS5\n",
    "\n",
    "import mountainsort5 as ms5\n",
    "\n",
    "# Make sure the recording is preprocessed appropriately\n",
    "# lazy preprocessing\n",
    "MS5_recording_filtered = spre.bandpass_filter(recording, freq_min=300, freq_max=6000)\n",
    "MS5_recording_preprocessed: si.BaseRecording = spre.whiten(MS5_recording_filtered)\n",
    "\n",
    "# use scheme 1\n",
    "sorting_MS5 = ms5.sorting_scheme1(\n",
    "    recording=MS5_recording_preprocessed,\n",
    "    sorting_parameters=ms5.Scheme1SortingParameters()\n",
    ")\n",
    "\n",
    "sorting_MS5.save(folder=(recording_path / 'sorting_results' / 'MS5' / 'ms5').as_posix())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## recording_onlydrift_fast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WindowsPath('studies/mouse_VISp_L5_128ch/recordings/recording_onlydrift_fast/sorting_results')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recording_path = Path().joinpath('studies','mouse_VISp_L5_128ch','recordings','recording_onlydrift_fast')\n",
    "create_empty_sortings(recording_path,algo_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'KS25','KS3' accept rawdata bin file. Extract the rawdata as bin file.\n",
    "extract_rawdata_bin(recording_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KS25 into Extractor\n",
    "sorting_KS25 = se.KiloSortSortingExtractor((recording_path / 'sorting_results' / 'KS25').as_posix(),keep_good_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KS3 into Extractor\n",
    "sorting_KS3 = se.KiloSortSortingExtractor((recording_path / 'sorting_results' / 'KS3').as_posix(),keep_good_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load recording and GT\n",
    "recording,sorting_GT = se.read_mearec((recording_path / 'recordings.h5').as_posix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! The recording is already filtered, but herdingspikes filter is enabled\n",
      "# Generating new position and neighbor files from data file\n",
      "# Not Masking any Channels\n",
      "# Sampling rate: 32000\n",
      "# Localization On\n",
      "# Number of recorded channels: 128\n",
      "# Analysing frames: 960000; Seconds: 30.0\n",
      "# Frames before spike in cutout: 10\n",
      "# Frames after spike in cutout: 58\n",
      "# tcuts: 42 90\n",
      "# tInc: 100000\n",
      "# Detection completed, time taken: 0:00:09.997505\n",
      "# Time per frame: 0:00:00.010414\n",
      "# Time per sample: 0:00:00.000081\n",
      "Loaded 8280 spikes.\n",
      "Fitting dimensionality reduction using all spikes...\n",
      "...projecting...\n",
      "...done\n",
      "Clustering...\n",
      "Clustering 8280 spikes...\n",
      "number of seeds: 63\n",
      "seeds/job: 8\n",
      "using 8 cpus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done   3 out of   8 | elapsed:    7.2s remaining:   12.1s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    7.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done   8 out of   8 | elapsed:    7.8s finished\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\herdingspikes\\clustering\\mean_shift_.py:242: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  unique = np.ones(len(sorted_centers), dtype=np.bool)\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\herdingspikes\\clustering\\mean_shift_.py:255: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  labels = np.zeros(n_samples, dtype=np.int)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of estimated units: 23\n",
      "Saving to studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_fast\\sorting_results\\HS2\\sorter_output\\HS2_sorted.hdf5\n",
      "herdingspikes run time 20.67s\n"
     ]
    }
   ],
   "source": [
    "# HS2\n",
    "output_folder= (recording_path / 'sorting_results' / 'HS2').as_posix()\n",
    "sorting_HS2 = ss.run_sorter('herdingspikes', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! The recording is already filtered, but spykingcircus filter is enabled\n",
      "RUNNING SHELL SCRIPT: studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_fast\\sorting_results\\SC\\sorter_output\\run_spykingcircus.bat\n",
      "\n",
      "\n",
      "(si_env) g:\\JHU\\Study\\Biomedical_Data_Design\\neurosim>spyking-circus studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_fast\\sorting_results\\SC\\sorter_output\\recording.npy -c 4 \n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\n",
      "\n",
      "##################################################################\n",
      "\n",
      "#####             Welcome to the SpyKING CIRCUS              #####\n",
      "\n",
      "#####                 (1.1.0+16.g38bd5b7f)                   #####\n",
      "\n",
      "#####             Written by P.Yger and O.Marre              #####\n",
      "\n",
      "##################################################################\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "File          : g:\\JHU\\Study\\Biomedical_Data_Design\\neurosim\\studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_fast\\sorting_results\\SC\\sorter_output\\recording.npy\n",
      "\n",
      "Steps         : filtering, whitening, clustering, fitting, merging\n",
      "\n",
      "Number of CPU : 4/8\n",
      "\n",
      "Parallel HDF5 : False\n",
      "\n",
      "Shared memory : True\n",
      "\n",
      "Hostfile      : C:\\Users\\IBM\\spyking-circus\\circus.hosts\n",
      "\n",
      "\n",
      "\n",
      "##################################################################\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "| Number of recorded channels : 128\n",
      "\n",
      "\n",
      "\n",
      "| Number of analyzed channels : 128\n",
      "\n",
      "\n",
      "\n",
      "| File format                 : NUMPY\n",
      "\n",
      "\n",
      "\n",
      "| Data type                   : float32\n",
      "\n",
      "\n",
      "\n",
      "| Sampling rate               : 32000 Hz\n",
      "\n",
      "\n",
      "\n",
      "| Duration of the recording   : 0 min 30 s 0 ms\n",
      "\n",
      "\n",
      "\n",
      "| Width of the templates      : 3 ms\n",
      "\n",
      "\n",
      "\n",
      "| Spatial radius considered   : 100 um\n",
      "\n",
      "\n",
      "\n",
      "| Threshold crossing          : negative\n",
      "\n",
      "\n",
      "\n",
      "------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "| Using only 4 out of 8 local CPUs available (-c to change)\n",
      "\n",
      "\n",
      "\n",
      "------------------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mFiltering with a Butterworth filter (order 3) in [300, 15200] Hz\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:19<00:00, 19.36s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:19<00:00, 19.36s/it]\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mAnalyzing data to get whitening matrices and thresholds...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mFound 7.5s to compute the whitening matrix...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mBecause of whitening, need to recompute the thresholds...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching spikes to construct the PCA basis...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.44s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.44s/it]\n",
      "\n",
      "\u001b[37mFound 10552 waveforms over 102400 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| A basis with 5 dimensions has been built\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mSearching isolated random spikes to sample amplitudes...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:06<00:00,  6.26s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:06<00:00,  6.26s/it]\n",
      "\n",
      "\u001b[37mFound 10012 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mEstimating amplitudes distributions...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSmart Search of good isolated spikes for the clustering (1/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:08<00:00,  8.59s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:08<00:00,  8.59s/it]\n",
      "\n",
      "\u001b[37mFound 10011 isolated spikes over 1024000 requested (0 rejected)\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mComputing density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching random spikes to refine the clustering (2/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.10s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:04<00:00,  4.10s/it]\n",
      "\n",
      "\u001b[37mFound 19 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mRefining density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mSearching random spikes to refine the clustering (3/3)...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.59s/it]\n",
      "\n",
      "100%|████████████████████████████████████|[00:05<00:00,  5.60s/it]\n",
      "\n",
      "\u001b[37mFound 0 spikes over 1024000 requested\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| No more spikes in the recording, stop searching\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mRefining density estimations...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mRunning density-based clustering...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  9%|███▍                                |[00:00<00:01, 26.39it/s]\n",
      "\n",
      " 19%|██████▊                             |[00:00<00:01, 23.16it/s]\n",
      "\n",
      " 31%|███████████▎                        |[00:00<00:00, 23.35it/s]\n",
      "\n",
      " 41%|██████████████▋                     |[00:00<00:00, 19.98it/s]\n",
      "\n",
      " 50%|██████████████████                  |[00:00<00:01, 13.68it/s]\n",
      "\n",
      " 56%|████████████████████▎               |[00:01<00:01, 10.86it/s]\n",
      "\n",
      " 62%|██████████████████████▌             |[00:01<00:01, 11.20it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:01<00:00, 12.14it/s]\n",
      "\n",
      " 75%|███████████████████████████         |[00:01<00:00, 12.65it/s]\n",
      "\n",
      " 81%|█████████████████████████████▎      |[00:01<00:00, 12.06it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:02<00:00, 14.78it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:02<00:00, 15.76it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:02<00:00, 15.07it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of clusters found : 41\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of local merges   : 2 (method nd-bhatta, param 2)\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Not enough spikes gathered: -put safety_space=False?\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m|                             -remove smart_search?\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mEstimating the templates with the median-raw procedure ...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  3%|█▏                                  |[00:00<00:09,  3.41it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:01<00:19,  1.51it/s]\n",
      "\n",
      " 12%|████▌                               |[00:01<00:12,  2.21it/s]\n",
      "\n",
      " 19%|██████▊                             |[00:01<00:06,  3.75it/s]\n",
      "\n",
      " 25%|█████████                           |[00:02<00:04,  5.46it/s]\n",
      "\n",
      " 31%|███████████▎                        |[00:02<00:03,  6.91it/s]\n",
      "\n",
      " 38%|█████████████▌                      |[00:02<00:02,  8.15it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:03<00:05,  3.56it/s]\n",
      "\n",
      " 50%|██████████████████                  |[00:04<00:05,  3.02it/s]\n",
      "\n",
      " 53%|███████████████████▏                |[00:05<00:05,  2.59it/s]\n",
      "\n",
      " 56%|████████████████████▎               |[00:05<00:06,  2.28it/s]\n",
      "\n",
      " 62%|██████████████████████▌             |[00:05<00:03,  3.34it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:06<00:02,  3.37it/s]\n",
      "\n",
      " 72%|█████████████████████████▉          |[00:07<00:03,  2.25it/s]\n",
      "\n",
      " 75%|███████████████████████████         |[00:07<00:03,  2.25it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:08<00:02,  2.33it/s]\n",
      "\n",
      " 88%|███████████████████████████████▌    |[00:08<00:00,  4.26it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:08<00:00,  6.44it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:08<00:00,  3.69it/s]\n",
      "\n",
      "\u001b[37mRemoving 1 strongly shifted or noisy/mixture templates...\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mMerging similar templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:00<00:03,  9.20it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:02, 10.50it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:00<00:00, 33.26it/s]\n",
      "\n",
      " 56%|████████████████████▎               |[00:00<00:00, 18.16it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:01<00:00, 20.96it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:01<00:00, 15.30it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:01<00:00, 22.02it/s]\n",
      "\n",
      "\u001b[37mRemoving mixtures of templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:00<00:03,  8.12it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:03,  9.14it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:00<00:00, 26.87it/s]\n",
      "\n",
      " 53%|███████████████████▏                |[00:01<00:00, 16.80it/s]\n",
      "\n",
      " 62%|██████████████████████▌             |[00:01<00:00, 17.13it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:01<00:00, 15.79it/s]\n",
      "\n",
      " 75%|███████████████████████████         |[00:01<00:00, 11.92it/s]\n",
      "\n",
      " 81%|█████████████████████████████▎      |[00:01<00:00, 12.99it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:01<00:00, 18.09it/s]\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 189.19it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of global merges    : 1\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of mixtures removed : 0\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mComputing optimal amplitudes for the templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 10%|███▌                                |[00:00<00:07,  1.14it/s]\n",
      "\n",
      " 20%|███████▏                            |[00:02<00:10,  1.36s/it]\n",
      "\n",
      " 30%|██████████▊                         |[00:03<00:07,  1.13s/it]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
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      "  3%|█▏                                  |[00:00<00:05,  6.00it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:00<00:04,  6.20it/s]\n",
      "\n",
      "  9%|███▍                                |[00:00<00:04,  6.32it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:04,  6.22it/s]\n",
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      " 16%|█████▋                              |[00:00<00:04,  5.75it/s]\n",
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      " 19%|██████▊                             |[00:01<00:05,  5.09it/s]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 47%|████████████████▉                   |[00:03<00:04,  4.08it/s]\n",
      "\n",
      " 50%|██████████████████                  |[00:03<00:04,  3.77it/s]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 66%|███████████████████████▋            |[00:05<00:04,  2.72it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:05<00:03,  2.98it/s]\n",
      "\n",
      " 72%|█████████████████████████▉          |[00:05<00:02,  3.20it/s]\n",
      "\n",
      " 75%|███████████████████████████         |[00:05<00:02,  3.47it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:06<00:01,  3.57it/s]\n",
      "\n",
      " 81%|█████████████████████████████▎      |[00:06<00:01,  3.56it/s]\n",
      "\n",
      " 84%|██████████████████████████████▍     |[00:06<00:01,  3.96it/s]\n",
      "\n",
      " 88%|███████████████████████████████▌    |[00:06<00:00,  4.62it/s]\n",
      "\n",
      " 91%|████████████████████████████████▋   |[00:06<00:00,  5.31it/s]\n",
      "\n",
      " 94%|█████████████████████████████████▊  |[00:06<00:00,  5.85it/s]\n",
      "\n",
      " 97%|██████████████████████████████████▉ |[00:07<00:00,  6.31it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:07<00:00,  6.58it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:07<00:00,  4.44it/s]\n",
      "\n",
      "\u001b[37mPre-computing the overlaps of templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  6%|██▎                                 |[00:00<00:02, 11.27it/s]\n",
      "\n",
      " 12%|████▌                               |[00:00<00:02, 10.87it/s]\n",
      "\n",
      " 44%|███████████████▊                    |[00:00<00:00, 31.79it/s]\n",
      "\n",
      " 56%|████████████████████▎               |[00:01<00:00, 15.91it/s]\n",
      "\n",
      " 69%|████████████████████████▊           |[00:01<00:00, 16.78it/s]\n",
      "\n",
      " 78%|████████████████████████████▏       |[00:01<00:00, 11.50it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:01<00:00, 18.00it/s]\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[37mHere comes the SpyKING CIRCUS using 4 CPUs and 39 templates...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "  3%|▉                                   |[00:00<00:06, 11.73it/s]\n",
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      " 23%|████████▏                           |[00:01<00:05, 11.11it/s]\n",
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      " 72%|█████████████████████████▉          |[00:05<00:01, 11.23it/s]\n",
      "\n",
      " 75%|██████████████████████████▉         |[00:05<00:01, 10.20it/s]\n",
      "\n",
      " 77%|███████████████████████████▊        |[00:05<00:01,  9.50it/s]\n",
      "\n",
      " 79%|████████████████████████████▎       |[00:05<00:01,  9.53it/s]\n",
      "\n",
      " 81%|█████████████████████████████▎      |[00:06<00:01, 10.02it/s]\n",
      "\n",
      " 84%|██████████████████████████████▏     |[00:06<00:01,  9.91it/s]\n",
      "\n",
      " 87%|███████████████████████████████▏    |[00:06<00:01,  9.82it/s]\n",
      "\n",
      " 88%|███████████████████████████████▋    |[00:06<00:00,  9.65it/s]\n",
      "\n",
      " 89%|████████████████████████████████▏   |[00:06<00:00,  9.48it/s]\n",
      "\n",
      " 92%|█████████████████████████████████   |[00:06<00:00, 10.47it/s]\n",
      "\n",
      " 95%|██████████████████████████████████  |[00:07<00:00, 10.04it/s]\n",
      "\n",
      " 97%|███████████████████████████████████ |[00:07<00:00,  9.00it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:07<00:00,  9.52it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:07<00:00,  9.85it/s]\n",
      "\n",
      "\u001b[37mGathering spikes from 4 nodes...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      " 25%|█████████                           |[00:00<00:00,  8.36it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 18.55it/s]\n",
      "\n",
      "100%|████████████████████████████████████|[00:00<00:00, 16.99it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Number of spikes fitted : 12861\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll\n",
      "\n",
      "G:\\anaconda3\\envs\\si_env\\lib\\site-packages\\numpy\\.libs\\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll\n",
      "\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| Automatic merging with a threshold of 0.75\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|███████████████████████████████████|[00:00<00:00, 189.19it/s]\n",
      "\n",
      "\u001b[37mDeleting 1 noisy templates\n",
      "\n",
      "\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|██████████████████████████████████|[00:00<00:00, 9932.05it/s]\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████████████|[00:00<?, ?it/s]\n",
      "\n",
      "\u001b[37mUpdating the data...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                            |[00:00<?, ?it/s]\n",
      "\n",
      "100%|████████████████████████████████████████████|[00:00<?, ?it/s]\n",
      "\n",
      "\u001b[33m-------------------------  Informations  -------------------------\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| We kept 38 templates out of 39 after merging\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| As meta merging is still experimental, you need\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| to use the extension option to see its results\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| >> circus-gui-matlab mydata.dat -e merged\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| or\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m| >> spyking-circus mydata.dat -m converting -e merged\n",
      "\n",
      "\n",
      "\n",
      "\u001b[33m------------------------------------------------------------------\n",
      "\n",
      "\u001b[37m\n",
      "\n",
      "spykingcircus run time 171.23s\n"
     ]
    }
   ],
   "source": [
    "# SC\n",
    "output_folder= (recording_path / 'sorting_results' / 'SC').as_posix()\n",
    "sorting_SC = ss.run_sorter('spykingcircus', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "detect peaks with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b22047a66d94479ba635fbb277a5dc7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "detect peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 11115 peaks in total\n",
      "We kept 11115 peaks for clustering\n",
      "features_from_peaks with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1689c785551a41458756343b88385366",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "features_from_peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 27 raw clusters, starting to clean with matching...\n",
      "extract waveforms memmap with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b38cbcaa25e4c94bb5ef27c78e7fbb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We kept 24 non-duplicated clusters...\n",
      "extract waveforms memmap with n_jobs = 1 and chunk_size = 32000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c93ed6bb8c64b51a12af315f17a5e1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find spikes (circus-omp) with n_jobs = 1 and chunk_size = 3200\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cdaff4b03e8c4406b1b354d31b9e452c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find spikes (circus-omp):   0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 11227 spikes\n",
      "spykingcircus2 run time 201.10s\n"
     ]
    }
   ],
   "source": [
    "# SC2\n",
    "output_folder= (recording_path / 'sorting_results' / 'SC2').as_posix()\n",
    "sorting_SC2 = ss.run_sorter('spykingcircus2', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local copy of recording\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ca5553db4d044d88d8f7238c52c289b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "write_binary_recording:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataIO <id: 1341312083616> \n",
      "  workdir: studies\\mouse_VISp_L5_128ch\\recordings\\recording_onlydrift_fast\\sorting_results\\TC\\sorter_output\n",
      "  sample_rate: 32000.0\n",
      "  total_channel: 128\n",
      "  channel_groups: 0 [ch0 ch1 ch2 ch3 ... ch124 ch125 ch126 ch127]\n",
      "  nb_segment: 1\n",
      "  length: 960000\n",
      "  durations: 30.0 s.\n",
      "catalogue_nested_params\n",
      "{'chunksize': 32000,\n",
      " 'clean_cluster': {'apply_auto_merge_cluster': True,\n",
      "                   'apply_auto_split': True,\n",
      "                   'apply_trash_low_extremum': True,\n",
      "                   'apply_trash_not_aligned': True,\n",
      "                   'apply_trash_small_cluster': True},\n",
      " 'clean_peaks': {'alien_value_threshold': -1.0, 'mode': 'extremum_amplitude'},\n",
      " 'cluster_kargs': {'adjacency_radius_um': 50.0,\n",
      "                   'high_adjacency_radius_um': 30.0,\n",
      "                   'max_loop': 2560,\n",
      "                   'min_cluster_size': 20},\n",
      " 'cluster_method': 'pruningshears',\n",
      " 'duration': 30.0,\n",
      " 'extract_waveforms': {'wf_left_long_ms': -2.5,\n",
      "                       'wf_left_ms': -1.0,\n",
      "                       'wf_right_long_ms': 3.5,\n",
      "                       'wf_right_ms': 1.5},\n",
      " 'feature_kargs': {'adjacency_radius_um': 50.0, 'n_components_by_channel': 3},\n",
      " 'feature_method': 'pca_by_channel',\n",
      " 'make_catalogue': {'inter_sample_oversampling': False,\n",
      "                    'sparse_thresh_level2': 3,\n",
      "                    'subsample_ratio': 'auto'},\n",
      " 'memory_mode': 'memmap',\n",
      " 'mode': 'sparse',\n",
      " 'n_jobs': -1,\n",
      " 'n_spike_for_centroid': 350,\n",
      " 'noise_snippet': {'nb_snippet': 300},\n",
      " 'peak_detector': {'adjacency_radius_um': 200.0,\n",
      "                   'engine': 'numba',\n",
      "                   'method': 'geometrical',\n",
      "                   'peak_sign': '-',\n",
      "                   'peak_span_ms': 0.7,\n",
      "                   'relative_threshold': 5,\n",
      "                   'smooth_radius_um': None},\n",
      " 'peak_sampler': {'mode': 'rand_by_channel',\n",
      "                  'nb_max': 20000,\n",
      "                  'nb_max_by_channel': 1000},\n",
      " 'preprocessor': {'common_ref_removal': False,\n",
      "                  'engine': 'numpy',\n",
      "                  'highpass_freq': 400.0,\n",
      "                  'lowpass_freq': 5000.0,\n",
      "                  'pad_width': -1,\n",
      "                  'smooth_size': 0},\n",
      " 'sparse_threshold': 1.5}\n",
      "peeler_params\n",
      "{'chunksize': 32000, 'engine': 'geometrical'}\n",
      "estimate_signals_noise 6.2292823999998745\n",
      "run_signalprocessor 19.927072299999963\n",
      "clean_peaks 0.012341600000127073\n",
      "sample_some_peaks 0.35026670000002014\n",
      "extract_some_noise 0.2739930000000186\n",
      "extract_some_features 2.8972980000000916\n",
      "find_clusters 4.498700699999972\n",
      "cache_some_waveforms 0.4906659999999192\n",
      "compute_all_centroid 6.915445399999953\n",
      "auto_split_cluster 23.117395400000078\n",
      "trash_not_aligned 0.1309905999999046\n",
      "auto_merge_cluster 1.7605387999999493\n",
      "trash_low_extremum 0.20339749999993728\n",
      "trash_small_cluster 0.1792224000000715\n",
      "make_catalogue_for_peeler 20.265332200000103\n",
      "CatalogueConstructor\n",
      "  chan_grp 0 - ch0 ch1 ch2 ... ch126 ch127\n",
      "  nb_peak_by_segment: 13521\n",
      "  some_features.shape: (13509, 384)\n",
      "  cluster_labels 50 [-11 -1 0 ... 48 49]\n",
      "\n",
      "peeler.tun 18.330060500000172\n",
      "tridesclous run time 105.76s\n"
     ]
    }
   ],
   "source": [
    "# TC\n",
    "output_folder= (recording_path / 'sorting_results' / 'TC').as_posix()\n",
    "sorting_TC = ss.run_sorter('tridesclous', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63c5b22ae78a4a6ca646664cab0cbc7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "detect peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 12597 peaks in total\n",
      "We kept 12597 peaks for clustering\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37cd16f9c9d84adead0d43781fcb2bde",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "localize peaks:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fb7e0782da043b8b43b5c1cb25b1f5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "extract waveforms memmap:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:24: DeprecationWarning: The spikeinterface.postprocessing.template_tools is submodule is deprecated.Use spikeinterface.core.template_tools instead\n",
      "  _warn()\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:25: DeprecationWarning: The 'get_template_channel_sparsity()' function is deprecated. Use 'compute_sparsity()' instead\n",
      "  return tt.get_template_channel_sparsity(*args, **kwargs)\n",
      "g:\\anaconda3\\envs\\si_env\\lib\\site-packages\\spikeinterface\\postprocessing\\template_tools.py:19: DeprecationWarning: The spikeinterface.postprocessing.template_tools is submodule is deprecated.Use spikeinterface.core.template_tools instead\n",
      "  _warn()\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0b026bcd1fb4e13aa1ac127c6f17658",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find spikes (tridesclous):   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 8296 spikes\n",
      "tridesclous2 run time 198.78s\n"
     ]
    }
   ],
   "source": [
    "# TC2\n",
    "output_folder= (recording_path / 'sorting_results' / 'TC2').as_posix()\n",
    "sorting_TC2 = ss.run_sorter('tridesclous2', recording,\n",
    "                             output_folder= output_folder,\n",
    "                             verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of channels: 128\n",
      "Number of timepoints: 960000\n",
      "Sampling frequency: 32000.0 Hz\n",
      "Channel 0: [ -16.  -472.5]\n",
      "Channel 1: [ -16.  -457.5]\n",
      "Channel 2: [ -16.  -442.5]\n",
      "Channel 3: [ -16.  -427.5]\n",
      "Channel 4: [ -16.  -412.5]\n",
      "Channel 5: [ -16.  -397.5]\n",
      "Channel 6: [ -16.  -382.5]\n",
      "Channel 7: [ -16.  -367.5]\n",
      "Channel 8: [ -16.  -352.5]\n",
      "Channel 9: [ -16.  -337.5]\n",
      "Channel 10: [ -16.  -322.5]\n",
      "Channel 11: [ -16.  -307.5]\n",
      "Channel 12: [ -16.  -292.5]\n",
      "Channel 13: [ -16.  -277.5]\n",
      "Channel 14: [ -16.  -262.5]\n",
      "Channel 15: [ -16.  -247.5]\n",
      "Channel 16: [ -16.  -232.5]\n",
      "Channel 17: [ -16.  -217.5]\n",
      "Channel 18: [ -16.  -202.5]\n",
      "Channel 19: [ -16.  -187.5]\n",
      "Channel 20: [ -16.  -172.5]\n",
      "Channel 21: [ -16.  -157.5]\n",
      "Channel 22: [ -16.  -142.5]\n",
      "Channel 23: [ -16.  -127.5]\n",
      "Channel 24: [ -16.  -112.5]\n",
      "Channel 25: [-16.  -97.5]\n",
      "Channel 26: [-16.  -82.5]\n",
      "Channel 27: [-16.  -67.5]\n",
      "Channel 28: [-16.  -52.5]\n",
      "Channel 29: [-16.  -37.5]\n",
      "Channel 30: [-16.  -22.5]\n",
      "Channel 31: [-16.   -7.5]\n",
      "Channel 32: [-16.    7.5]\n",
      "Channel 33: [-16.   22.5]\n",
      "Channel 34: [-16.   37.5]\n",
      "Channel 35: [-16.   52.5]\n",
      "Channel 36: [-16.   67.5]\n",
      "Channel 37: [-16.   82.5]\n",
      "Channel 38: [-16.   97.5]\n",
      "Channel 39: [-16.  112.5]\n",
      "Channel 40: [-16.  127.5]\n",
      "Channel 41: [-16.  142.5]\n",
      "Channel 42: [-16.  157.5]\n",
      "Channel 43: [-16.  172.5]\n",
      "Channel 44: [-16.  187.5]\n",
      "Channel 45: [-16.  202.5]\n",
      "Channel 46: [-16.  217.5]\n",
      "Channel 47: [-16.  232.5]\n",
      "Channel 48: [-16.  247.5]\n",
      "Channel 49: [-16.  262.5]\n",
      "Channel 50: [-16.  277.5]\n",
      "Channel 51: [-16.  292.5]\n",
      "Channel 52: [-16.  307.5]\n",
      "Channel 53: [-16.  322.5]\n",
      "Channel 54: [-16.  337.5]\n",
      "Channel 55: [-16.  352.5]\n",
      "Channel 56: [-16.  367.5]\n",
      "Channel 57: [-16.  382.5]\n",
      "Channel 58: [-16.  397.5]\n",
      "Channel 59: [-16.  412.5]\n",
      "Channel 60: [-16.  427.5]\n",
      "Channel 61: [-16.  442.5]\n",
      "Channel 62: [-16.  457.5]\n",
      "Channel 63: [-16.  472.5]\n",
      "Channel 64: [  16.  -472.5]\n",
      "Channel 65: [  16.  -457.5]\n",
      "Channel 66: [  16.  -442.5]\n",
      "Channel 67: [  16.  -427.5]\n",
      "Channel 68: [  16.  -412.5]\n",
      "Channel 69: [  16.  -397.5]\n",
      "Channel 70: [  16.  -382.5]\n",
      "Channel 71: [  16.  -367.5]\n",
      "Channel 72: [  16.  -352.5]\n",
      "Channel 73: [  16.  -337.5]\n",
      "Channel 74: [  16.  -322.5]\n",
      "Channel 75: [  16.  -307.5]\n",
      "Channel 76: [  16.  -292.5]\n",
      "Channel 77: [  16.  -277.5]\n",
      "Channel 78: [  16.  -262.5]\n",
      "Channel 79: [  16.  -247.5]\n",
      "Channel 80: [  16.  -232.5]\n",
      "Channel 81: [  16.  -217.5]\n",
      "Channel 82: [  16.  -202.5]\n",
      "Channel 83: [  16.  -187.5]\n",
      "Channel 84: [  16.  -172.5]\n",
      "Channel 85: [  16.  -157.5]\n",
      "Channel 86: [  16.  -142.5]\n",
      "Channel 87: [  16.  -127.5]\n",
      "Channel 88: [  16.  -112.5]\n",
      "Channel 89: [ 16.  -97.5]\n",
      "Channel 90: [ 16.  -82.5]\n",
      "Channel 91: [ 16.  -67.5]\n",
      "Channel 92: [ 16.  -52.5]\n",
      "Channel 93: [ 16.  -37.5]\n",
      "Channel 94: [ 16.  -22.5]\n",
      "Channel 95: [16.  -7.5]\n",
      "Channel 96: [16.   7.5]\n",
      "Channel 97: [16.  22.5]\n",
      "Channel 98: [16.  37.5]\n",
      "Channel 99: [16.  52.5]\n",
      "Channel 100: [16.  67.5]\n",
      "Channel 101: [16.  82.5]\n",
      "Channel 102: [16.  97.5]\n",
      "Channel 103: [ 16.  112.5]\n",
      "Channel 104: [ 16.  127.5]\n",
      "Channel 105: [ 16.  142.5]\n",
      "Channel 106: [ 16.  157.5]\n",
      "Channel 107: [ 16.  172.5]\n",
      "Channel 108: [ 16.  187.5]\n",
      "Channel 109: [ 16.  202.5]\n",
      "Channel 110: [ 16.  217.5]\n",
      "Channel 111: [ 16.  232.5]\n",
      "Channel 112: [ 16.  247.5]\n",
      "Channel 113: [ 16.  262.5]\n",
      "Channel 114: [ 16.  277.5]\n",
      "Channel 115: [ 16.  292.5]\n",
      "Channel 116: [ 16.  307.5]\n",
      "Channel 117: [ 16.  322.5]\n",
      "Channel 118: [ 16.  337.5]\n",
      "Channel 119: [ 16.  352.5]\n",
      "Channel 120: [ 16.  367.5]\n",
      "Channel 121: [ 16.  382.5]\n",
      "Channel 122: [ 16.  397.5]\n",
      "Channel 123: [ 16.  412.5]\n",
      "Channel 124: [ 16.  427.5]\n",
      "Channel 125: [ 16.  442.5]\n",
      "Channel 126: [ 16.  457.5]\n",
      "Channel 127: [ 16.  472.5]\n",
      "Loading traces\n",
      "Detecting spikes\n",
      "\n",
      "Adjacency for detect spikes with channel radius None\n",
      "[[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], [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, 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105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127]]\n",
      "\n",
      "m = 0 (nbhd size: 128)\n",
      "m = 1 (nbhd size: 128)\n",
      "m = 2 (nbhd size: 128)\n",
      "m = 3 (nbhd size: 128)\n",
      "m = 4 (nbhd size: 128)\n",
      "m = 5 (nbhd size: 128)\n",
      "m = 6 (nbhd size: 128)\n",
      "m = 7 (nbhd size: 128)\n",
      "m = 8 (nbhd size: 128)\n",
      "m = 9 (nbhd size: 128)\n",
      "m = 10 (nbhd size: 128)\n",
      "m = 11 (nbhd size: 128)\n",
      "m = 12 (nbhd size: 128)\n",
      "m = 13 (nbhd size: 128)\n",
      "m = 14 (nbhd size: 128)\n",
      "m = 15 (nbhd size: 128)\n",
      "m = 16 (nbhd size: 128)\n",
      "m = 17 (nbhd size: 128)\n",
      "m = 18 (nbhd size: 128)\n",
      "m = 19 (nbhd size: 128)\n",
      "m = 20 (nbhd size: 128)\n",
      "m = 21 (nbhd size: 128)\n",
      "m = 22 (nbhd size: 128)\n",
      "m = 23 (nbhd size: 128)\n",
      "m = 24 (nbhd size: 128)\n",
      "m = 25 (nbhd size: 128)\n",
      "m = 26 (nbhd size: 128)\n",
      "m = 27 (nbhd size: 128)\n",
      "m = 28 (nbhd size: 128)\n",
      "m = 29 (nbhd size: 128)\n",
      "m = 30 (nbhd size: 128)\n",
      "m = 31 (nbhd size: 128)\n",
      "m = 32 (nbhd size: 128)\n",
      "m = 33 (nbhd size: 128)\n",
      "m = 34 (nbhd size: 128)\n",
      "m = 35 (nbhd size: 128)\n",
      "m = 36 (nbhd size: 128)\n",
      "m = 37 (nbhd size: 128)\n",
      "m = 38 (nbhd size: 128)\n",
      "m = 39 (nbhd size: 128)\n",
      "m = 40 (nbhd size: 128)\n",
      "m = 41 (nbhd size: 128)\n",
      "m = 42 (nbhd size: 128)\n",
      "m = 43 (nbhd size: 128)\n",
      "m = 44 (nbhd size: 128)\n",
      "m = 45 (nbhd size: 128)\n",
      "m = 46 (nbhd size: 128)\n",
      "m = 47 (nbhd size: 128)\n",
      "m = 48 (nbhd size: 128)\n",
      "m = 49 (nbhd size: 128)\n",
      "m = 50 (nbhd size: 128)\n",
      "m = 51 (nbhd size: 128)\n",
      "m = 52 (nbhd size: 128)\n",
      "m = 53 (nbhd size: 128)\n",
      "m = 54 (nbhd size: 128)\n",
      "m = 55 (nbhd size: 128)\n",
      "m = 56 (nbhd size: 128)\n",
      "m = 57 (nbhd size: 128)\n",
      "m = 58 (nbhd size: 128)\n",
      "m = 59 (nbhd size: 128)\n",
      "m = 60 (nbhd size: 128)\n",
      "m = 61 (nbhd size: 128)\n",
      "m = 62 (nbhd size: 128)\n",
      "m = 63 (nbhd size: 128)\n",
      "m = 64 (nbhd size: 128)\n",
      "m = 65 (nbhd size: 128)\n",
      "m = 66 (nbhd size: 128)\n",
      "m = 67 (nbhd size: 128)\n",
      "m = 68 (nbhd size: 128)\n",
      "m = 69 (nbhd size: 128)\n",
      "m = 70 (nbhd size: 128)\n",
      "m = 71 (nbhd size: 128)\n",
      "m = 72 (nbhd size: 128)\n",
      "m = 73 (nbhd size: 128)\n",
      "m = 74 (nbhd size: 128)\n",
      "m = 75 (nbhd size: 128)\n",
      "m = 76 (nbhd size: 128)\n",
      "m = 77 (nbhd size: 128)\n",
      "m = 78 (nbhd size: 128)\n",
      "m = 79 (nbhd size: 128)\n",
      "m = 80 (nbhd size: 128)\n",
      "m = 81 (nbhd size: 128)\n",
      "m = 82 (nbhd size: 128)\n",
      "m = 83 (nbhd size: 128)\n",
      "m = 84 (nbhd size: 128)\n",
      "m = 85 (nbhd size: 128)\n",
      "m = 86 (nbhd size: 128)\n",
      "m = 87 (nbhd size: 128)\n",
      "m = 88 (nbhd size: 128)\n",
      "m = 89 (nbhd size: 128)\n",
      "m = 90 (nbhd size: 128)\n",
      "m = 91 (nbhd size: 128)\n",
      "m = 92 (nbhd size: 128)\n",
      "m = 93 (nbhd size: 128)\n",
      "m = 94 (nbhd size: 128)\n",
      "m = 95 (nbhd size: 128)\n",
      "m = 96 (nbhd size: 128)\n",
      "m = 97 (nbhd size: 128)\n",
      "m = 98 (nbhd size: 128)\n",
      "m = 99 (nbhd size: 128)\n",
      "m = 100 (nbhd size: 128)\n",
      "m = 101 (nbhd size: 128)\n",
      "m = 102 (nbhd size: 128)\n",
      "m = 103 (nbhd size: 128)\n",
      "m = 104 (nbhd size: 128)\n",
      "m = 105 (nbhd size: 128)\n",
      "m = 106 (nbhd size: 128)\n",
      "m = 107 (nbhd size: 128)\n",
      "m = 108 (nbhd size: 128)\n",
      "m = 109 (nbhd size: 128)\n",
      "m = 110 (nbhd size: 128)\n",
      "m = 111 (nbhd size: 128)\n",
      "m = 112 (nbhd size: 128)\n",
      "m = 113 (nbhd size: 128)\n",
      "m = 114 (nbhd size: 128)\n",
      "m = 115 (nbhd size: 128)\n",
      "m = 116 (nbhd size: 128)\n",
      "m = 117 (nbhd size: 128)\n",
      "m = 118 (nbhd size: 128)\n",
      "m = 119 (nbhd size: 128)\n",
      "m = 120 (nbhd size: 128)\n",
      "m = 121 (nbhd size: 128)\n",
      "m = 122 (nbhd size: 128)\n",
      "m = 123 (nbhd size: 128)\n",
      "m = 124 (nbhd size: 128)\n",
      "m = 125 (nbhd size: 128)\n",
      "m = 126 (nbhd size: 128)\n",
      "m = 127 (nbhd size: 128)\n",
      "Extracting 7868 snippets\n",
      "Clustering snippets\n",
      "2 clusters in branch\n",
      "Found 2 clusters\n",
      "Computing templates\n",
      "Determining optimal alignment of templates\n",
      "Template alignment converged.\n",
      "Align templates offsets:  [-6  0]\n",
      "Aligning snippets\n",
      "Clustering aligned snippets\n",
      "Found 1 clusters\n",
      "Computing templates\n",
      "Offsetting times to peak\n",
      "Offsets to peak: [1]\n",
      "Reordering units\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NpzFolderSorting: 1 units - 1 segments - 32.0kHz"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MS5\n",
    "\n",
    "import mountainsort5 as ms5\n",
    "\n",
    "# Make sure the recording is preprocessed appropriately\n",
    "# lazy preprocessing\n",
    "MS5_recording_filtered = spre.bandpass_filter(recording, freq_min=300, freq_max=6000)\n",
    "MS5_recording_preprocessed: si.BaseRecording = spre.whiten(MS5_recording_filtered)\n",
    "\n",
    "# use scheme 1\n",
    "sorting_MS5 = ms5.sorting_scheme1(\n",
    "    recording=MS5_recording_preprocessed,\n",
    "    sorting_parameters=ms5.Scheme1SortingParameters()\n",
    ")\n",
    "\n",
    "sorting_MS5.save(folder=(recording_path / 'sorting_results' / 'MS5' / 'ms5').as_posix())"
   ]
  }
 ],
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