2022-11-28_SPADE_tutorial.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": "ba513d84-e9f9-48bd-a613-96dab228c19d",
   "metadata": {},
   "source": [
    "# SPADE tutorial\n",
    "## Generate correlated data"
   ]
  },
  {
   "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": "code",
   "execution_count": 3,
   "id": "c9206ad9-0b99-44ae-b0e2-dbd008fcce03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spiketrains = elephant.spike_train_generation.compound_poisson_process(\n",
    "   rate=5*pq.Hz, A=[0]+[0.98]+[0]*8+[0.02], t_stop=10*pq.s)\n",
    "len(spiketrains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "id": "06a9237c-85ff-46aa-b742-4bf97a31404b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spiketrain_0 type is <class 'neo.core.spiketrain.SpikeTrain'>\n",
      "spiketrains_last type is <class 'neo.core.spiketrain.SpikeTrain'>\n"
     ]
    }
   ],
   "source": [
    "print(\"spiketrain_0 type is\", type(spiketrains[0]))\n",
    "print(\"spiketrains_last type is\", type(spiketrains[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e4e1b4d-47ea-476f-9875-8c6bf13037e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spiketrain_0 has 39 spikes:\n",
      "  t_start: 0.0 s\n",
      "  t_stop: 10.0 s\n",
      "  spike times: [0.3428428  0.36964823 1.22303783 1.28863725 1.63905792 2.88270894\n",
      " 3.03516076 3.03817857 3.41886987 3.46763254 3.83237276 3.85699453\n",
      " 4.17892833 4.18912792 4.50264362 5.11709083 5.25194019 5.32478839\n",
      " 6.39516735 6.50198542 6.92936517 6.98985003 7.10209661 7.15895706\n",
      " 7.56589684 7.78118491 8.24170386 8.31116287 8.49874386 8.70570514\n",
      " 8.91675734 8.97507227 9.03423901 9.03853449 9.15468654 9.39298729\n",
      " 9.52941963 9.61571811 9.9134528 ] s\n"
     ]
    }
   ],
   "source": [
    "print(f\"spiketrain_0 has {len(spiketrains[0])} spikes:\")\n",
    "print(\"  t_start:\", spiketrains[0].t_start)\n",
    "print(\"  t_stop:\", spiketrains[0].t_stop)\n",
    "print(\"  spike times:\", spiketrains[0].times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "459d206e-319e-4463-8b74-3d9580ff5ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "ax.eventplot([spiketrains[i].magnitude for i in range(len(spiketrains))], linelengths=0.75, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=16)\n",
    "plt.ylabel('Neuron')\n",
    "plt.title(\"Raster plot\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d232188",
   "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 printfig\n",
    "printfig(fig, 'raster_plot_for_spade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e2b78b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "permutation(x)\n",
      "\n",
      "Randomly permute a sequence, or return a permuted range.\n",
      "\n",
      "If `x` is a multi-dimensional array, it is only shuffled along its\n",
      "first index.\n",
      "\n",
      ".. note::\n",
      "    New code should use the ``permutation`` method of a ``default_rng()``\n",
      "    instance instead; please see the :ref:`random-quick-start`.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "x : int or array_like\n",
      "    If `x` is an integer, randomly permute ``np.arange(x)``.\n",
      "    If `x` is an array, make a copy and shuffle the elements\n",
      "    randomly.\n",
      "\n",
      "Returns\n",
      "-------\n",
      "out : ndarray\n",
      "    Permuted sequence or array range.\n",
      "\n",
      "See Also\n",
      "--------\n",
      "random.Generator.permutation: which should be used for new code.\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> np.random.permutation(10)\n",
      "array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) # random\n",
      "\n",
      ">>> np.random.permutation([1, 4, 9, 12, 15])\n",
      "array([15,  1,  9,  4, 12]) # random\n",
      "\n",
      ">>> arr = np.arange(9).reshape((3, 3))\n",
      ">>> np.random.permutation(arr)\n",
      "array([[6, 7, 8], # random\n",
      "       [0, 1, 2],\n",
      "       [3, 4, 5]])\n",
      "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
     ]
    }
   ],
   "source": [
    "np.random.permutation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "459d206e-319e-4463-8b74-3d9580ff5ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "\n",
    "plt.eventplot([spiketrains[i].magnitude for i in np.random.permutation(len(spiketrains))], linelengths=0.75, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=16)\n",
    "plt.ylabel('Neuron')\n",
    "plt.title(\"Raster plot (shuffled indices)\")\n",
    "printfig(fig, 'raster_plot_for_spade_permute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f354e41-742c-4101-9ce8-0e7bf94ad0d7",
   "metadata": {},
   "source": [
    "## Mining patterns with SPADE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62aa4417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0melephant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspade\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mspiketrains\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mbin_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mwinlen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmin_spikes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmin_occ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mmax_spikes\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[0mmax_occ\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[0mmin_neu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mapprox_stab_pars\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[0mn_surr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdither\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15.\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[0mspectrum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0malpha\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[0mstat_corr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'fdr_bh'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msurr_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'dither_spikes'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mpsr_param\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[0moutput_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'patterns'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0msurr_kwargs\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",
      "Perform the SPADE :cite:`spade-Torre2013_132`,\n",
      ":cite:`spade-Quaglio2017_41`, :cite:`spade-Stella2019_104022` analysis for\n",
      "the parallel input `spiketrains`. They are discretized with a temporal\n",
      "resolution equal to `bin_size` in a sliding window of `winlen*bin_size`.\n",
      "\n",
      "First, spike patterns are mined from the `spiketrains` using a technique\n",
      "called frequent itemset mining (FIM) or formal concept analysis (FCA). In\n",
      "this framework, a particular spatio-temporal spike pattern is called a\n",
      "\"concept\". It is then possible to compute the stability and the p-value\n",
      "of all pattern candidates. In a final step, concepts are filtered\n",
      "according to a stability threshold and a significance level `alpha`.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "spiketrains : list of neo.SpikeTrain\n",
      "    List containing the parallel spike trains to analyze\n",
      "bin_size : pq.Quantity\n",
      "    The time precision used to discretize the spiketrains (binning).\n",
      "winlen : int\n",
      "    The size (number of bins) of the sliding window used for the analysis.\n",
      "    The maximal length of a pattern (delay between first and last spike) is\n",
      "    then given by winlen*bin_size\n",
      "min_spikes : int, optional\n",
      "    Minimum number of spikes of a sequence to be considered a pattern.\n",
      "    Default: 2\n",
      "min_occ : int, optional\n",
      "    Minimum number of occurrences of a sequence to be considered as a\n",
      "    pattern.\n",
      "    Default: 2\n",
      "max_spikes : int, optional\n",
      "    Maximum number of spikes of a sequence to be considered a pattern. If\n",
      "    None no maximal number of spikes is considered.\n",
      "    Default: None\n",
      "max_occ : int, optional\n",
      "    Maximum number of occurrences of a sequence to be considered as a\n",
      "    pattern. If None, no maximal number of occurrences is considered.\n",
      "    Default: None\n",
      "min_neu : int, optional\n",
      "    Minimum number of neurons in a sequence to considered a pattern.\n",
      "    Default: 1\n",
      "approx_stab_pars : dict or None, optional\n",
      "    Parameter values for approximate stability computation.\n",
      "\n",
      "    'n_subsets': int\n",
      "        Number of subsets of a concept used to approximate its stability.\n",
      "        If `n_subsets` is 0, it is calculated according to to the formula\n",
      "        given in Babin, Kuznetsov (2012), proposition 6:\n",
      "\n",
      "        .. math::\n",
      "               n_{\\text{subset}} = \\frac{1}{2 \\cdot \\epsilon^2}\n",
      "                \\ln{\\left( \\frac{2}{\\delta} \\right)} +1\n",
      "\n",
      "    'delta' : float, optional\n",
      "        delta: probability with at least :math:`1-\\delta`\n",
      "    'epsilon' : float, optional\n",
      "        epsilon: absolute error\n",
      "    'stability_thresh' : None or list of float\n",
      "        List containing the stability thresholds used to filter the\n",
      "        concepts.\n",
      "        If `stability_thresh` is None, then the concepts are not filtered.\n",
      "        Otherwise, only concepts with\n",
      "\n",
      "        * intensional stability is greater than `stability_thresh[0]` or\n",
      "        * extensional stability is greater than `stability_thresh[1]`\n",
      "\n",
      "        are further analyzed.\n",
      "n_surr : int, optional\n",
      "    Number of surrogates to generate to compute the p-value spectrum.\n",
      "    This number should be large (`n_surr >= 1000` is recommended for 100\n",
      "    spike trains in `spiketrains`). If `n_surr == 0`, then the p-value\n",
      "    spectrum is not computed.\n",
      "    Default: 0\n",
      "dither : pq.Quantity, optional\n",
      "    Amount of spike time dithering for creating the surrogates for\n",
      "    filtering the pattern spectrum. A spike at time `t` is placed randomly\n",
      "    within `[t-dither, t+dither]` (see also\n",
      "    :func:`elephant.spike_train_surrogates.dither_spikes`).\n",
      "    Default: 15*pq.ms\n",
      "spectrum : {'#', '3d#'}, optional\n",
      "    Define the signature of the patterns.\n",
      "\n",
      "    '#': pattern spectrum using the as signature the pair:\n",
      "        (number of spikes, number of occurrences)\n",
      "    '3d#': pattern spectrum using the as signature the triplets:\n",
      "        (number of spikes, number of occurrence, difference between last\n",
      "        and first spike of the pattern)\n",
      "\n",
      "    Default: '#'\n",
      "alpha : float, optional\n",
      "    The significance level of the hypothesis tests performed.\n",
      "    If `alpha is None`, all the concepts are returned.\n",
      "    If `0.<alpha<1.`, the concepts are filtered according to their\n",
      "    signature in the p-value spectrum.\n",
      "    Default: None\n",
      "stat_corr : str\n",
      "    Method used for multiple testing.\n",
      "    See: :func:`test_signature_significance`\n",
      "    Default: 'fdr_bh'\n",
      "surr_method : str\n",
      "    Method to generate surrogates. You can use every method defined in\n",
      "    :func:`elephant.spike_train_surrogates.surrogates`.\n",
      "    Default: 'dither_spikes'\n",
      "psr_param : None or list of int or tuple of int\n",
      "    This list contains parameters used in the pattern spectrum filtering:\n",
      "\n",
      "    `psr_param[0]`: correction parameter for subset filtering\n",
      "        (see `h_subset_filtering` in :func:`pattern_set_reduction`).\n",
      "    `psr_param[1]`: correction parameter for superset filtering\n",
      "        (see `k_superset_filtering` in :func:`pattern_set_reduction`).\n",
      "    `psr_param[2]`: correction parameter for covered-spikes criterion\n",
      "        (see `l_covered_spikes` in :func:`pattern_set_reduction`).\n",
      "\n",
      "output_format : {'concepts', 'patterns'}\n",
      "    Distinguish the format of the output (see Returns).\n",
      "    Default: 'patterns'\n",
      "surr_kwargs\n",
      "    Keyword arguments that are passed to the surrogate methods.\n",
      "\n",
      "Returns\n",
      "-------\n",
      "output : dict\n",
      "    'patterns':\n",
      "        If `output_format` is 'patterns', see the return of\n",
      "        :func:`concept_output_to_patterns`\n",
      "\n",
      "        If `output_format` is 'concepts', then `output['patterns']` is a\n",
      "        tuple of patterns which in turn are tuples of\n",
      "            1. spikes in the pattern\n",
      "            2. occurrences of the pattern\n",
      "\n",
      "        For details see :func:`concepts_mining`.\n",
      "\n",
      "        if stability is calculated, there are also:\n",
      "            3. intensional stability\n",
      "            4. extensional stability\n",
      "\n",
      "        For details see :func:`approximate_stability`.\n",
      "\n",
      "    'pvalue_spectrum' (only if `n_surr > 0`):\n",
      "        A list of signatures in tuples format:\n",
      "\n",
      "        * size\n",
      "        * number of occurrences\n",
      "        * duration (only if `spectrum=='3d#'`)\n",
      "        * p-value\n",
      "\n",
      "    'non_sgnf_sgnt': list\n",
      "        Non significant signatures of 'pvalue_spectrum'.\n",
      "\n",
      "Notes\n",
      "-----\n",
      "If detected, this function will use MPI to parallelize the analysis.\n",
      "\n",
      "Examples\n",
      "--------\n",
      "The following example applies SPADE to `spiketrains` (list of\n",
      "`neo.SpikeTrain`).\n",
      "\n",
      ">>> from elephant.spade import spade\n",
      ">>> import quantities as pq\n",
      ">>> from elephant.spike_train_generation import compound_poisson_process\n",
      "\n",
      ">>> np.random.seed(30)\n",
      ">>> spiketrains = compound_poisson_process(rate=15*pq.Hz,\n",
      "...     amplitude_distribution=[0, 0.95, 0, 0, 0, 0, 0.05], t_stop=5*pq.s)\n",
      "\n",
      ">>> bin_size = 3 * pq.ms # time resolution to discretize the spiketrains\n",
      ">>> winlen = 10 # maximal pattern length in bins (i.e., sliding window)\n",
      "\n",
      ">>> result_spade = spade(\n",
      "...                     spiketrains, bin_size, winlen) # doctest: +ELLIPSIS\n",
      "Time for data mining: ...\n",
      "\u001b[0;31mFile:\u001b[0m      ~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "elephant.spade.spade?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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: 10.631471872329712\n",
      "Time for pvalue spectrum computation: 1126.5950603485107\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": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afed34c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m\n",
      "\u001b[0mviziphant\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatterns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_patterns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mspiketrains\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mpatterns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mcircle_sizes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m70\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mcolors\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[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Raster plot with one or more chosen SPADE or CAD patterns ot top shown in\n",
      "color.\n",
      "\n",
      "Overlapping patterns (patterns that share neurons at a particular spike\n",
      "time) are represented as pie charts of individual pattern colors.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "spiketrains : list of neo.SpikeTrain\n",
      "    List of spike trains that were used as the input.\n",
      "patterns : dict or list of dict\n",
      "    One or more patterns from a list of found patterns returned by\n",
      "    :func:`elephant.spade.spade` or\n",
      "    :func:`elephant.cell_assembly_detection.cell_assembly_detection`\n",
      "    pattern detectors.\n",
      "circle_sizes : tuple of float, optional\n",
      "    A tuple of 3 elements:\n",
      "      1) raster plot neurons size that don't participate in the patterns;\n",
      "\n",
      "      2) patterns circle size;\n",
      "\n",
      "      3) pie chart (overlapped patterns) size.\n",
      "\n",
      "    Default: (3, 50, 70)\n",
      "colors : list of str or None\n",
      "    A user-defined list of pattern colors. If None, the HSV colormap will\n",
      "    be used to pick a different color for each pattern.\n",
      "    Default: None\n",
      "\n",
      "Returns\n",
      "-------\n",
      "axes : matplotlib.axes.Axes\n",
      "\n",
      "Examples\n",
      "--------\n",
      "In the plot below, two SPADE patterns are shown with pie chars representing\n",
      "the overlapping patterns, which happen to have at least one common neuron\n",
      "active at the same time across two patterns found by SPADE analysis,\n",
      "letting the user to explore pattern sizes (the number of neurons\n",
      "participating in a pattern) versus the number of occurrences.\n",
      "\n",
      "The bin size is set to a large value (``400ms``) in order to find\n",
      "\"patterns\" from 10 realizations of homogeneous Poisson process.\n",
      "\n",
      ".. plot::\n",
      "    :include-source:\n",
      "\n",
      "    import numpy as np\n",
      "    import quantities as pq\n",
      "    import matplotlib.pyplot as plt\n",
      "    from elephant import spade\n",
      "    from elephant.spike_train_generation import homogeneous_poisson_process\n",
      "    import viziphant\n",
      "\n",
      "    np.random.seed(5)\n",
      "    spiketrains = [homogeneous_poisson_process(rate=1 * pq.Hz,\n",
      "                   t_stop=10 * pq.s) for _ in range(10)]\n",
      "    patterns = spade.spade(spiketrains, bin_size=400 * pq.ms,\n",
      "                           winlen=1)['patterns']\n",
      "\n",
      "    axes = viziphant.patterns.plot_patterns(spiketrains, patterns[:2])\n",
      "    plt.show()\n",
      "\n",
      "CAD example:\n",
      "\n",
      ".. plot::\n",
      "    :include-source:\n",
      "\n",
      "    import matplotlib.pyplot as plt\n",
      "    import numpy as np\n",
      "    import quantities as pq\n",
      "    from elephant.cell_assembly_detection import cell_assembly_detection\n",
      "    from elephant.conversion import BinnedSpikeTrain\n",
      "    from elephant.spike_train_generation import compound_poisson_process\n",
      "    import viziphant\n",
      "\n",
      "    np.random.seed(30)\n",
      "    spiketrains = compound_poisson_process(rate=15 * pq.Hz,\n",
      "        amplitude_distribution=[0, 0.95, 0, 0, 0, 0, 0.05], t_stop=5*pq.s)\n",
      "    bst = BinnedSpikeTrain(spiketrains, bin_size=10 * pq.ms)\n",
      "    bst.rescale('ms')\n",
      "    patterns = cell_assembly_detection(bst, max_lag=2)\n",
      "\n",
      "    viziphant.patterns.plot_patterns(spiketrains, patterns=patterns[:2],\n",
      "                                     circle_sizes=(3, 30, 40))\n",
      "    plt.show()\n",
      "\n",
      "Additionally, one can add events to the returned axes:\n",
      "\n",
      ".. code-block:: python\n",
      "\n",
      "    event = neo.Event([0.5, 3.8] * pq.s, labels=['Trig ON', 'Trig OFF'])\n",
      "    viziphant.events.add_event(axes, event=event)\n",
      "\u001b[0;31mFile:\u001b[0m      ~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/viziphant/patterns.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "viziphant.patterns.plot_patterns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9db6dc55-3e48-4353-814a-b4aabfa07678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots()\n",
    "ax = viziphant.patterns.plot_patterns(spiketrains, patterns)\n",
    "fig = ax.get_figure()\n",
    "fig.set_figwidth(13)\n",
    "fig.set_figwidth(7)\n",
    "printfig(fig, 'raster_plot_with_spade_patterns')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('brainhack')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "f76abcfae7407d101ef34ba86f468988314045e4e2c214982f718a8acd3606b2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}