Generate_Spiking_Motif.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45c3d8d0-dd19-4ab2-a9c7-7ea6b175e654",
   "metadata": {},
   "source": [
    "# Generate simple spiking motif with a define function (cosine, linear, vonmises)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4d84f201-9442-4093-8715-a82c3ced5683",
   "metadata": {},
   "outputs": [],
   "source": [
    "from brainhack import make_spiketrains_motif, cospattern, linear, vonmises, printfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85cd66d6-cecf-4e93-afe1-b740777f4d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_neuron = 50\n",
    "noise_density = 5e-3\n",
    "simulation_time = 5e2 # in ms\n",
    "motif_temporal_amplitude = 100\n",
    "t_true = [200] # has to be a list\n",
    "function = linear # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "18b13bf0-83dc-4ce1-a87b-c185de8e31ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(15, 9))\n",
    "\n",
    "function = linear # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)\n",
    "ax1 = plt.subplot(2, 3, 1)\n",
    "ax1 = plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "ax1 = plt.scatter(aer_pattern[1], aer_pattern[0], color='blue', alpha=.3)\n",
    "ax1 = plt.xlabel('Time (ms)', fontsize=16)\n",
    "ax1 = plt.ylabel('Neuron', fontsize=16)\n",
    "#ax1 = plt.title(\"Raster plot\");\n",
    "function = cospattern # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)\n",
    "ax1 = plt.subplot(2, 3, 2)\n",
    "ax1 = plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "ax1 = plt.scatter(aer_pattern[1], aer_pattern[0], color='blue', alpha=.3)\n",
    "ax1 = plt.xlabel('Time (ms)', fontsize=16)\n",
    "\n",
    "function = vonmises # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)\n",
    "ax1 = plt.subplot(2, 3, 3)\n",
    "ax1 = plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "ax1 = plt.scatter(aer_pattern[1], aer_pattern[0], color='blue', alpha=.3)\n",
    "ax1 = plt.xlabel('Time (ms)', fontsize=16)\n",
    "\n",
    "function = linear # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function, discard_spikes=35)\n",
    "\n",
    "ax1 = plt.subplot(2, 3, 4)\n",
    "ax1 = plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "ax1 = plt.scatter(aer_pattern[1], aer_pattern[0], color='blue', alpha=.3)\n",
    "ax1 = plt.xlabel('Time (ms)', fontsize=16)\n",
    "ax1 = plt.ylabel('Neuron', fontsize=16)\n",
    "#ax1 = plt.title(\"Raster plot\");\n",
    "function = cospattern # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function, sd_temp_jitter=10)\n",
    "ax1 = plt.subplot(2, 3, 5)\n",
    "ax1 = plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "ax1 = plt.scatter(aer_pattern[1], aer_pattern[0], color='blue', alpha=.3)\n",
    "ax1 = plt.xlabel('Time (ms)', fontsize=16)\n",
    "\n",
    "function = vonmises # can be 'linear' or 'vonmises' as well\n",
    "spike_trains, aer_pattern = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function, discard_spikes=35, sd_temp_jitter=10)\n",
    "ax1 = plt.subplot(2, 3, 6)\n",
    "ax1 = plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "ax1 = plt.scatter(aer_pattern[1], aer_pattern[0], color='blue', alpha=.3)\n",
    "ax1 = plt.xlabel('Time (ms)', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb4353ea-30aa-4780-b3c5-5c164a4dc81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "printfig(fig, 'synthetic patterns.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "033f4d5d-128c-45ef-ac7b-ac83e32095c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "function = linear\n",
    "\n",
    "spike_trains = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], 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": 16,
   "id": "19795fb6-f6a1-4718-a0f9-e43af3adc5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "function = cospattern\n",
    "\n",
    "spike_trains = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], 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": 17,
   "id": "ffa16d19-e247-43ab-b876-8e75880ac2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discard_neurons = 10 # if not None, allows to discard some neurons from the pattern, they'll have a noisy activity\n",
    "\n",
    "spike_trains = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function, discard_spikes=discard_neurons)\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], 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": 18,
   "id": "3c71ac2a-b620-406f-9781-011a70a9e85b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temporal_jitter = 10 # if not None, adds a temporal jitter to the spikes of the pattern\n",
    "\n",
    "spike_trains = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function, sd_temp_jitter=temporal_jitter)\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], 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": 19,
   "id": "bd1c9853-adc4-43ad-b4ab-92077f13443c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_density = 1e-2 # change noise density\n",
    "\n",
    "spike_trains = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function)\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], linelengths=0.75, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=16)\n",
    "plt.ylabel('Neuron')\n",
    "plt.title(\"Raster plot\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9ffb10-85eb-4380-9a5e-8c9981e94ee3",
   "metadata": {},
   "source": [
    "# Using spade to detect the pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71a26a85-8ff3-46a7-b23f-97bf8535a792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import elephant\n",
    "import quantities as pq\n",
    "\n",
    "nb_neuron = 30\n",
    "noise_density = 1e-3\n",
    "simulation_time = 1e4 # in ms\n",
    "motif_temporal_amplitude = 50\n",
    "nb_stim = 10\n",
    "discard_neurons = 10\n",
    "temporal_jitter = 5\n",
    "\n",
    "## generate a spiking motif with make_spiketrains_motif function\n",
    "nb_neuron = 10\n",
    "noise_density = 5e-3\n",
    "simulation_time = 100 # in ms\n",
    "motif_temporal_amplitude = 5\n",
    "nb_stim = 3\n",
    "discard_neurons = 1\n",
    "function = linear #tune here the type of function that you want to represent\n",
    "\n",
    "t_true = [motif_temporal_amplitude + int(np.random.rand()*(simulation_time-3*motif_temporal_amplitude)) for i in range(nb_stim)]\n",
    "spike_trains = make_spiketrains_motif(nb_neuron, noise_density, simulation_time, motif_temporal_amplitude, t_true, function= function, discard_spikes = discard_neurons, sd_temp_jitter=temporal_jitter)\n",
    "\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spike_trains[i].times for i in range(len(spike_trains))], 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": 21,
   "id": "746ab0c9-4038-4fa2-a1cd-4c58835fb23d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.8/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: 4.878205060958862\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/c0/lpsp1b8964g8z8vcc8rps0b00000gn/T/ipykernel_5471/555892783.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m patterns = elephant.spade.spade(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mspiketrains\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspike_trains\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwinlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmotif_temporal_amplitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_spikes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mn_surr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdither\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mpsr_param\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     output_format='patterns')['patterns']\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0m_rename_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maliases\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/spade.py\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    379\u001b[0m         \u001b[0;31m# Compute pvalue spectrum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0mtime_pvalue_spectrum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\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;32m--> 381\u001b[0;31m         pv_spec = pvalue_spectrum(\n\u001b[0m\u001b[1;32m    382\u001b[0m             \u001b[0mspiketrains\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwinlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdither\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdither\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_surr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_surr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mmin_spikes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_spikes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_occ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_occ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_spikes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_spikes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0m_rename_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maliases\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/spade.py\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   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m         \u001b[0;31m# Find all pattern signatures in the current surrogate data set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1338\u001b[0;31m         surr_concepts = concepts_mining(\n\u001b[0m\u001b[1;32m   1339\u001b[0m             \u001b[0mbinned_surrogates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwinlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_spikes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_spikes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1340\u001b[0m             \u001b[0mmax_spikes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_spikes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_occ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_occ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_occ\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_occ\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0m_rename_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maliases\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/spade.py\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    683\u001b[0m         'of fast fca.')\n\u001b[1;32m    684\u001b[0m     \u001b[0;31m# Return output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m     mining_results = _fast_fca(\n\u001b[0m\u001b[1;32m    686\u001b[0m         \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m         \u001b[0mmin_c\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_occ\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/spade.py\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[0;31m# Mining the spiketrains with fast fca algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \u001b[0mfca_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfast_fca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFormalConcepts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m     \u001b[0mfca_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeLattice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1149\u001b[0m     \u001b[0mfca_concepts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfca_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcepts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m     fca_concepts = [concept for concept in fca_concepts\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/spade_src/fast_fca.py\u001b[0m in \u001b[0;36mcomputeLattice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    272\u001b[0m             \u001b[0mprogress_bar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumberConceptsAndComputeIntroduced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.8/site-packages/elephant/spade_src/fast_fca.py\u001b[0m in \u001b[0;36mnumberConceptsAndComputeIntroduced\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintroducedObjects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mln\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlowerNeighbours\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m                 \u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintroducedObjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifference_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mln\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m             \u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintroducedAttributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mun\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcurConcept\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupperNeighbours\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "patterns = elephant.spade.spade(\n",
    "    spiketrains=spike_trains, binsize=1*pq.ms, winlen=motif_temporal_amplitude, min_spikes=5,\n",
    "    n_surr=100,dither=1*pq.ms,\n",
    "    psr_param=[0,0,0],\n",
    "    output_format='patterns')['patterns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8462119a-9568-455d-a22f-43f15e752acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import viziphant\n",
    "viziphant.patterns.plot_patterns(spike_trains, patterns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a80f69d5-8eef-4501-8039-6af2a1b878d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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