2022-11-29_SPADE_shifts.ipynb

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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# to install all dependencies use, uncomment the following line and restart the kernel\n",
    "# %pip install -U -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e607479",
   "metadata": {},
   "source": [
    "#  Spike PAtterns DEtection using SPADE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c0163f5-b685-412d-b5c6-8ff1670e9998",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import quantities as pq\n",
    "import neo\n",
    "import elephant\n",
    "import viziphant\n",
    "np.random.seed(4542)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5488bfdb",
   "metadata": {},
   "source": [
    "## introducing heterogeneous delays\n",
    "\n",
    "the function developed in the scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "717a2fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from brainhack import ground_truth_spike_synchronous\n",
    "\n",
    "A_sync = 0.02\n",
    "shift = 0.\n",
    "\n",
    "def generate_and_test(A_sync=A_sync, shift=shift):\n",
    "    A = [0]+[1.-A_sync]+[0]*8+[A_sync]\n",
    "    spiketrains = elephant.spike_train_generation.compound_poisson_process(\n",
    "                             rate=5*pq.Hz, A=A, shift=shift*pq.ms, t_stop=10*pq.s)\n",
    "    common_spikes =  ground_truth_spike_synchronous(spiketrains)\n",
    "\n",
    "    for i in range(90):\n",
    "        spiketrains.append(elephant.spike_train_generation.homogeneous_poisson_process(\n",
    "            rate=5*pq.Hz, t_stop=10*pq.s))\n",
    "            \n",
    "    patterns = elephant.spade.spade(\n",
    "                                spiketrains=spiketrains, binsize=1*pq.ms, winlen=1, min_spikes=3,\n",
    "                                n_surr=100,dither=5*pq.ms,\n",
    "                                psr_param=[0,0,0],\n",
    "                                output_format='patterns')['patterns']\n",
    "    \n",
    "    idx_detect = pattern[0].get('windows_ids')\n",
    "    common_spikes = np.array(list(common_spikes))*1000\n",
    "    idx_real = common_spikes.astype(int)\n",
    "\n",
    "    tp = len(list(set(idx_detect).intersection(real_idx)))\n",
    "    fp = len(idx_detect) - tp\n",
    "    fn = len(real_idx) - tp\n",
    "\n",
    "    return tp, fp, fn    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* changing the synchrony probability to check when SPADE will begin to fail:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8bc9a171",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/elephant/spade.py:679: UserWarning: Optimized C implementation of FCA (fim.so/fim.pyd) not found in elephant/spade_src folder, or not compatible with this Python version. You are using the pure Python implementation of fast fca.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for data mining: 37.92006206512451\n"
     ]
    }
   ],
   "source": [
    "for A_sync_ in np.logspace(-2, -.5, 10):\n",
    "    patterns = generate_and_test(A_sync=A_sync_, shift=shift)\n",
    "    print(patterns)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "filename = 'results_shift.json'\n",
    "if os.path.isfile(filename):\n",
    "    df = pd.read_json(filename)\n",
    "else:\n",
    "    df = pd.DataFrame([], columns=['shift', 'tp', 'tf', 'fn']) \n",
    "    for shift_ in np.logspace(-1, 1, 10, base=10, endpoint=True):\n",
    "        tp, fp, fn = generate_and_test(shift=shift_)\n",
    "        df.loc[i_trial] = {'shift':shift_, 'tp':tp, 'tf':tf, 'fn':fn}\n",
    "        print(tp, fp, fn)\n",
    "    df.to_json(filename)    "
   ]
  }
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