2022-11-29_SPADE_scan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e6ede3a-e8c0-4753-8bde-c5ac49d2d276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !python3 -m pip install --user neo elephant viziphant"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurentperrinet/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from brainhack import ground_truth_spike_synchronous\n",
    "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": [
    "## using the tutorial\n",
    "\n",
    "There are different tutorials such as this one on [ASSET](https://elephant.readthedocs.io/en/latest/tutorials/asset.html) - or this more directly related about [3D SPADE](https://elephant.readthedocs.io/en/latest/tutorials/spade.html) from which we take these first cells..\n",
    "\n",
    "We start by buiding $10$ spike trains with some synchrony\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dc0dff6-aaae-4075-aede-83597b4b9bf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mcompound_poisson_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mrate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mamplitude_distribution\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mt_stop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mshift\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mt_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Generate a Compound Poisson Process (CPP; see\n",
      ":cite:`generation-Staude2010_327`) with a given `amplitude_distribution`\n",
      ":math:`A` and stationary marginal rates `rate`.\n",
      "\n",
      "The CPP process is a model for parallel, correlated processes with Poisson\n",
      "spiking statistics at pre-defined firing rates. It is composed of\n",
      "`len(A)-1` spike trains with a correlation structure determined by the\n",
      "amplitude distribution :math:`A`: A[j] is the probability that a spike\n",
      "occurs synchronously in any `j` spike trains.\n",
      "\n",
      "The CPP is generated by creating a hidden mother Poisson process, and then\n",
      "copying spikes of the mother process to `j` of the output spike trains with\n",
      "probability `A[j]`.\n",
      "\n",
      "Note that this function decorrelates the firing rate of each SpikeTrain\n",
      "from the probability for that SpikeTrain to participate in a synchronous\n",
      "event (which is uniform across SpikeTrains).\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "rate : pq.Quantity\n",
      "    Average rate of each spike train generated. Can be:\n",
      "      - a single value, all spike trains will have same rate rate\n",
      "      - an array of values (of length `len(A)-1`), each indicating the\n",
      "        firing rate of one process in output\n",
      "amplitude_distribution : np.ndarray or list\n",
      "    CPP's amplitude distribution :math:`A`. `A[j]` represents the\n",
      "    probability of a synchronous event of size `j` among the generated\n",
      "    spike trains. The sum over all entries of :math:`A` must be equal to\n",
      "    one.\n",
      "t_stop : pq.Quantity\n",
      "    The end time of the output spike trains.\n",
      "shift : pq.Quantity, optional\n",
      "    If `None`, the injected synchrony is exact.\n",
      "    If shift is a `pq.Quantity`, all the spike trains are shifted\n",
      "    independently by a random amount in the interval `[-shift, +shift]`.\n",
      "    Default: None\n",
      "t_start : pq.Quantity, optional\n",
      "    The `t_start` time of the output spike trains.\n",
      "    Default: 0 pq.ms\n",
      "\n",
      "Returns\n",
      "-------\n",
      "list of neo.SpikeTrain\n",
      "    A list of `len(A) - 1` neo.SpikeTrains with specified firing rates\n",
      "    forming the CPP with amplitude distribution :math:`A`.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spike_train_generation.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "from elephant.spike_train_generation import compound_poisson_process\n",
    "compound_poisson_process?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fa289d",
   "metadata": {},
   "source": [
    " From the tutorial:\n",
    " \n",
    " > to a distribution A[n] indicating the likelihood of finding synchronous spikes of a given order n. In our example, we construct the distribution such that we have a small probability to produce a synchronous event of order 10 (A[10]==0.02). Otherwise spikes are not synchronous with those of other neurons (i.e., synchronous events of order 1, A[1]==0.98). Notice that the length of the distribution A determines the number len(A)-1 of spiketrains returned by the function, and that A[0] is ignored for reasons of clearer notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9db8f573",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [0]+[0.98]+[0]*8+[0.02]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9206ad9-0b99-44ae-b0e2-dbd008fcce03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spiketrains = elephant.spike_train_generation.compound_poisson_process(\n",
    "   rate=5*pq.Hz, A=A, t_stop=10*pq.s)\n",
    "len(spiketrains)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "701bb4b4",
   "metadata": {},
   "source": [
    "https://viziphant.readthedocs.io/en/latest/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62ff8fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Time (s)'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from viziphant.rasterplot import rasterplot\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(10,3))\n",
    "rasterplot(spiketrains, axes=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6bff7d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0.98, 0, 0, 0, 0, 0, 0, 0, 0, 0.02]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3c67024",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rasterplot?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72e4c67",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdf73f44-d49f-444c-ad55-e892624b5702",
   "metadata": {},
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e97c566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3428428 , 0.36964823, 1.22303783, 1.28863725, 1.63905792,\n",
       "       2.88270894, 3.03516076, 3.03817857, 3.41886987, 3.46763254,\n",
       "       3.83237276, 3.85699453, 4.17892833, 4.18912792, 4.50264362,\n",
       "       5.11709083, 5.25194019, 5.32478839, 6.39516735, 6.50198542,\n",
       "       6.92936517, 6.98985003, 7.10209661, 7.15895706, 7.56589684,\n",
       "       7.78118491, 8.24170386, 8.31116287, 8.49874386, 8.70570514,\n",
       "       8.91675734, 8.97507227, 9.03423901, 9.03853449, 9.15468654,\n",
       "       9.39298729, 9.52941963, 9.61571811, 9.9134528 ]) * s"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = spiketrains[0]\n",
    "st.times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59482683-e0b6-46fe-be2f-31dad246b199",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurentperrinet/opt/anaconda3/envs/brainhack/lib/python3.9/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: 10.402588844299316\n",
      "Time for pvalue spectrum computation: 1075.283191204071\n"
     ]
    }
   ],
   "source": [
    "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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b629cfc8-605b-45c8-baa0-a991ec0ad23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'itemset': (0, 1, 2, 3, 4, 5, 6, 7, 8, 9),\n",
       "  'windows_ids': (369, 8498, 4178, 1223, 9038),\n",
       "  'neurons': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "  'lags': array([0., 0., 0., 0., 0., 0., 0., 0., 0.]) * ms,\n",
       "  'times': array([ 369., 1223., 4178., 8498., 9038.]) * ms,\n",
       "  'signature': (10, 5),\n",
       "  'pvalue': 0.0}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9db6dc55-3e48-4353-814a-b4aabfa07678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Time (s)', ylabel='Neuron'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viziphant.patterns.plot_patterns(spiketrains, patterns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320647d5",
   "metadata": {},
   "source": [
    "## scanning some parameters\n",
    "\n",
    "the above as one function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "717a2fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "A_sync = 0.02\n",
    "shift = 1.0\n",
    "\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",
    "    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",
   "id": "2a0336ae",
   "metadata": {},
   "source": [
    "\n",
    "* changing the synchrony probability to check when SPADE will begin to fail:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bc9a171",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurentperrinet/opt/anaconda3/envs/brainhack/lib/python3.9/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: 10.125552892684937\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [15], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame([], columns\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mA_sync\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mtp\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mtf\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mfn\u001b[39m\u001b[39m'\u001b[39m]) \n\u001b[1;32m      8\u001b[0m \u001b[39mfor\u001b[39;00m A_sync_ \u001b[39min\u001b[39;00m np\u001b[39m.\u001b[39mlogspace(\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m, \u001b[39m-\u001b[39m\u001b[39m.5\u001b[39m, \u001b[39m10\u001b[39m):\n\u001b[0;32m----> 9\u001b[0m     tp, fp, fn \u001b[39m=\u001b[39m generate_and_test(A_sync\u001b[39m=\u001b[39;49mA_sync_)\n\u001b[1;32m     10\u001b[0m     df\u001b[39m.\u001b[39mloc[i_trial] \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mA_sync\u001b[39m\u001b[39m'\u001b[39m:A_sync_, \u001b[39m'\u001b[39m\u001b[39mtp\u001b[39m\u001b[39m'\u001b[39m:tp, \u001b[39m'\u001b[39m\u001b[39mtf\u001b[39m\u001b[39m'\u001b[39m:tf, \u001b[39m'\u001b[39m\u001b[39mfn\u001b[39m\u001b[39m'\u001b[39m:fn}\n\u001b[1;32m     11\u001b[0m     \u001b[39mprint\u001b[39m(tp, fp, fn)\n",
      "Cell \u001b[0;32mIn [14], line 14\u001b[0m, in \u001b[0;36mgenerate_and_test\u001b[0;34m(A_sync, shift)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m90\u001b[39m):\n\u001b[1;32m     12\u001b[0m     spiketrains\u001b[39m.\u001b[39mappend(elephant\u001b[39m.\u001b[39mspike_train_generation\u001b[39m.\u001b[39mhomogeneous_poisson_process(\n\u001b[1;32m     13\u001b[0m         rate\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m\u001b[39m*\u001b[39mpq\u001b[39m.\u001b[39mHz, t_stop\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m\u001b[39m*\u001b[39mpq\u001b[39m.\u001b[39ms))\n\u001b[0;32m---> 14\u001b[0m patterns \u001b[39m=\u001b[39m elephant\u001b[39m.\u001b[39;49mspade\u001b[39m.\u001b[39;49mspade(\n\u001b[1;32m     15\u001b[0m                             spiketrains\u001b[39m=\u001b[39;49mspiketrains, binsize\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m\u001b[39m*\u001b[39;49mpq\u001b[39m.\u001b[39;49mms, winlen\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m, min_spikes\u001b[39m=\u001b[39;49m\u001b[39m3\u001b[39;49m,\n\u001b[1;32m     16\u001b[0m                             n_surr\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m,dither\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m\u001b[39m*\u001b[39;49mpq\u001b[39m.\u001b[39;49mms,\n\u001b[1;32m     17\u001b[0m                             psr_param\u001b[39m=\u001b[39;49m[\u001b[39m0\u001b[39;49m,\u001b[39m0\u001b[39;49m,\u001b[39m0\u001b[39;49m],\n\u001b[1;32m     18\u001b[0m                             output_format\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mpatterns\u001b[39;49m\u001b[39m'\u001b[39;49m)[\u001b[39m'\u001b[39m\u001b[39mpatterns\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m     20\u001b[0m idx_detect \u001b[39m=\u001b[39m pattern[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mwindows_ids\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     21\u001b[0m common_spikes \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(\u001b[39mlist\u001b[39m(common_spikes))\u001b[39m*\u001b[39m\u001b[39m1000\u001b[39m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/utils.py:80\u001b[0m, in \u001b[0;36mdeprecated_alias.<locals>.deco.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[39m@wraps\u001b[39m(func)\n\u001b[1;32m     78\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapper\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     79\u001b[0m     _rename_kwargs(func\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, kwargs, aliases)\n\u001b[0;32m---> 80\u001b[0m     \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade.py:381\u001b[0m, in \u001b[0;36mspade\u001b[0;34m(spiketrains, bin_size, winlen, min_spikes, min_occ, max_spikes, max_occ, min_neu, approx_stab_pars, n_surr, dither, spectrum, alpha, stat_corr, surr_method, psr_param, output_format, **surr_kwargs)\u001b[0m\n\u001b[1;32m    378\u001b[0m \u001b[39mif\u001b[39;00m n_surr \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m    379\u001b[0m     \u001b[39m# Compute pvalue spectrum\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     time_pvalue_spectrum \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 381\u001b[0m     pv_spec \u001b[39m=\u001b[39m pvalue_spectrum(\n\u001b[1;32m    382\u001b[0m         spiketrains, bin_size, winlen, dither\u001b[39m=\u001b[39;49mdither, n_surr\u001b[39m=\u001b[39;49mn_surr,\n\u001b[1;32m    383\u001b[0m         min_spikes\u001b[39m=\u001b[39;49mmin_spikes, min_occ\u001b[39m=\u001b[39;49mmin_occ, max_spikes\u001b[39m=\u001b[39;49mmax_spikes,\n\u001b[1;32m    384\u001b[0m         max_occ\u001b[39m=\u001b[39;49mmax_occ, min_neu\u001b[39m=\u001b[39;49mmin_neu, spectrum\u001b[39m=\u001b[39;49mspectrum,\n\u001b[1;32m    385\u001b[0m         surr_method\u001b[39m=\u001b[39;49msurr_method, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msurr_kwargs)\n\u001b[1;32m    386\u001b[0m     time_pvalue_spectrum \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m time_pvalue_spectrum\n\u001b[1;32m    387\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mTime for pvalue spectrum computation: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m    388\u001b[0m         time_pvalue_spectrum))\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/utils.py:80\u001b[0m, in \u001b[0;36mdeprecated_alias.<locals>.deco.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[39m@wraps\u001b[39m(func)\n\u001b[1;32m     78\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapper\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     79\u001b[0m     _rename_kwargs(func\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, kwargs, aliases)\n\u001b[0;32m---> 80\u001b[0m     \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade.py:1338\u001b[0m, in \u001b[0;36mpvalue_spectrum\u001b[0;34m(spiketrains, bin_size, winlen, dither, n_surr, min_spikes, min_occ, max_spikes, max_occ, min_neu, spectrum, surr_method, **surr_kwargs)\u001b[0m\n\u001b[1;32m   1328\u001b[0m     max_occs \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mempty(shape\u001b[39m=\u001b[39m(len_partition \u001b[39m+\u001b[39m add_remainder,\n\u001b[1;32m   1329\u001b[0m                                max_spikes \u001b[39m-\u001b[39m min_spikes \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m, winlen),\n\u001b[1;32m   1330\u001b[0m                         dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39muint16)\n\u001b[1;32m   1332\u001b[0m \u001b[39mfor\u001b[39;00m surr_id, binned_surrogates \u001b[39min\u001b[39;00m _generate_binned_surrogates(\n\u001b[1;32m   1333\u001b[0m         spiketrains, bin_size\u001b[39m=\u001b[39mbin_size, dither\u001b[39m=\u001b[39mdither,\n\u001b[1;32m   1334\u001b[0m         surr_method\u001b[39m=\u001b[39msurr_method, n_surrogates\u001b[39m=\u001b[39mlen_partition\u001b[39m+\u001b[39madd_remainder,\n\u001b[1;32m   1335\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39msurr_kwargs):\n\u001b[1;32m   1336\u001b[0m \n\u001b[1;32m   1337\u001b[0m     \u001b[39m# Find all pattern signatures in the current surrogate data set\u001b[39;00m\n\u001b[0;32m-> 1338\u001b[0m     surr_concepts \u001b[39m=\u001b[39m concepts_mining(\n\u001b[1;32m   1339\u001b[0m         binned_surrogates, bin_size, winlen, min_spikes\u001b[39m=\u001b[39;49mmin_spikes,\n\u001b[1;32m   1340\u001b[0m         max_spikes\u001b[39m=\u001b[39;49mmax_spikes, min_occ\u001b[39m=\u001b[39;49mmin_occ, max_occ\u001b[39m=\u001b[39;49mmax_occ,\n\u001b[1;32m   1341\u001b[0m         min_neu\u001b[39m=\u001b[39;49mmin_neu, report\u001b[39m=\u001b[39;49mspectrum)[\u001b[39m0\u001b[39m]\n\u001b[1;32m   1342\u001b[0m     \u001b[39m# The last entry of the signature is the number of times the\u001b[39;00m\n\u001b[1;32m   1343\u001b[0m     \u001b[39m# signature appeared. This entry is not needed here.\u001b[39;00m\n\u001b[1;32m   1344\u001b[0m     surr_concepts \u001b[39m=\u001b[39m surr_concepts[:, :\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/utils.py:80\u001b[0m, in \u001b[0;36mdeprecated_alias.<locals>.deco.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[39m@wraps\u001b[39m(func)\n\u001b[1;32m     78\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapper\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     79\u001b[0m     _rename_kwargs(func\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, kwargs, aliases)\n\u001b[0;32m---> 80\u001b[0m     \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade.py:685\u001b[0m, in \u001b[0;36mconcepts_mining\u001b[0;34m(spiketrains, bin_size, winlen, min_spikes, min_occ, max_spikes, max_occ, min_neu, report)\u001b[0m\n\u001b[1;32m    679\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    680\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mOptimized C implementation of FCA (fim.so/fim.pyd) not found \u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m\n\u001b[1;32m    681\u001b[0m     \u001b[39m'\u001b[39m\u001b[39min elephant/spade_src folder, or not compatible with this \u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m\n\u001b[1;32m    682\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mPython version. You are using the pure Python implementation \u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m\n\u001b[1;32m    683\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mof fast fca.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    684\u001b[0m \u001b[39m# Return output\u001b[39;00m\n\u001b[0;32m--> 685\u001b[0m mining_results \u001b[39m=\u001b[39m _fast_fca(\n\u001b[1;32m    686\u001b[0m     context,\n\u001b[1;32m    687\u001b[0m     min_c\u001b[39m=\u001b[39;49mmin_occ,\n\u001b[1;32m    688\u001b[0m     min_z\u001b[39m=\u001b[39;49mmin_spikes,\n\u001b[1;32m    689\u001b[0m     max_z\u001b[39m=\u001b[39;49mmax_spikes,\n\u001b[1;32m    690\u001b[0m     max_c\u001b[39m=\u001b[39;49mmax_occ,\n\u001b[1;32m    691\u001b[0m     winlen\u001b[39m=\u001b[39;49mwinlen,\n\u001b[1;32m    692\u001b[0m     min_neu\u001b[39m=\u001b[39;49mmin_neu,\n\u001b[1;32m    693\u001b[0m     report\u001b[39m=\u001b[39;49mreport)\n\u001b[1;32m    694\u001b[0m \u001b[39mreturn\u001b[39;00m mining_results, rel_matrix\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade.py:1148\u001b[0m, in \u001b[0;36m_fast_fca\u001b[0;34m(context, min_c, min_z, max_z, max_c, report, winlen, min_neu)\u001b[0m\n\u001b[1;32m   1146\u001b[0m \u001b[39m# Mining the spiketrains with fast fca algorithm\u001b[39;00m\n\u001b[1;32m   1147\u001b[0m fca_out \u001b[39m=\u001b[39m fast_fca\u001b[39m.\u001b[39mFormalConcepts(context)\n\u001b[0;32m-> 1148\u001b[0m fca_out\u001b[39m.\u001b[39;49mcomputeLattice()\n\u001b[1;32m   1149\u001b[0m fca_concepts \u001b[39m=\u001b[39m fca_out\u001b[39m.\u001b[39mconcepts\n\u001b[1;32m   1150\u001b[0m fca_concepts \u001b[39m=\u001b[39m [concept \u001b[39mfor\u001b[39;00m concept \u001b[39min\u001b[39;00m fca_concepts\n\u001b[1;32m   1151\u001b[0m                 \u001b[39mif\u001b[39;00m _fca_filter(concept, winlen, min_c, min_z, max_c, max_z,\n\u001b[1;32m   1152\u001b[0m                                min_neu)]\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade_src/fast_fca.py:274\u001b[0m, in \u001b[0;36mFormalConcepts.computeLattice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    271\u001b[0m     curConcept \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconcepts[curConceptIndex]\n\u001b[1;32m    272\u001b[0m     progress_bar\u001b[39m.\u001b[39mupdate()\n\u001b[0;32m--> 274\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnumberConceptsAndComputeIntroduced()\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade_src/fast_fca.py:239\u001b[0m, in \u001b[0;36mFormalConcepts.numberConceptsAndComputeIntroduced\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    237\u001b[0m curConcept\u001b[39m.\u001b[39mintroducedObjects \u001b[39m=\u001b[39m \u001b[39mset\u001b[39m(curConcept\u001b[39m.\u001b[39mextent)\n\u001b[1;32m    238\u001b[0m \u001b[39mfor\u001b[39;00m ln \u001b[39min\u001b[39;00m curConcept\u001b[39m.\u001b[39mlowerNeighbours:\n\u001b[0;32m--> 239\u001b[0m     curConcept\u001b[39m.\u001b[39;49mintroducedObjects\u001b[39m.\u001b[39;49mdifference_update(ln\u001b[39m.\u001b[39;49mextent)\n\u001b[1;32m    240\u001b[0m curConcept\u001b[39m.\u001b[39mintroducedAttributes \u001b[39m=\u001b[39m \u001b[39mset\u001b[39m(curConcept\u001b[39m.\u001b[39mintent)\n\u001b[1;32m    241\u001b[0m \u001b[39mfor\u001b[39;00m un \u001b[39min\u001b[39;00m curConcept\u001b[39m.\u001b[39mupperNeighbours:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "filename = 'results_A_sync.json'\n",
    "if os.path.isfile(filename):\n",
    "    df = pd.read_json(filename)\n",
    "else:\n",
    "    df = pd.DataFrame([], columns=['A_sync', 'tp', 'tf', 'fn']) \n",
    "    for A_sync_ in np.logspace(-2, -.5, 10):\n",
    "        tp, fp, fn = generate_and_test(A_sync=A_sync_)\n",
    "        df.loc[i_trial] = {'A_sync':A_sync_, 'tp':tp, 'tf':tf, 'fn':fn}\n",
    "        print(tp, fp, fn)\n",
    "    df.to_json(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ec1ae5",
   "metadata": {},
   "source": [
    "\n",
    "* adding a random shift to check robustness of SPADE:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b503f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "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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