2022-11-29_SPADE_protopattern.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e607479",
   "metadata": {},
   "source": [
    "#  Spike PAtterns DEtection using SPADE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 + a shift in time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "717a2fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_stop = 400*pq.ms\n",
    "slope = 4.\n",
    "A_sync = 0.15\n",
    "shift = 0.0\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=t_stop)\n",
    "                             \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf629ff",
   "metadata": {},
   "source": [
    "Add a shift:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1746b8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4542.91570369]) * ms"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spiketrains[0].times + 4.2*pq.s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf629ff",
   "metadata": {},
   "source": [
    "Wrap around time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e2d1a6e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([347.11570369]) * ms"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(np.mod(spiketrains[0].times + 4.2*pq.ms, 1000.*pq.ms))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf629ff",
   "metadata": {},
   "source": [
    "Sort in time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29276eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([347.11570369]) * ms"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(np.mod(spiketrains[0].times + 4.2*pq.ms, 1000.*pq.ms))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a909c2",
   "metadata": {},
   "source": [
    "https://neo.readthedocs.io/en/stable/core.html?highlight=spiketrain#example-spiketrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f5f5ae6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpikeTrain"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neo.SpikeTrain(np.sort(np.mod(spiketrains[0].times + 4.2*pq.s, 1000.*pq.ms)), t_start=0*pq.ms, t_stop=1000.*pq.ms)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5cc658fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i_st in range(10):\n",
    "    delay = (i_st*slope)*pq.ms\n",
    "    spike_times = np.sort(np.mod(spiketrains[i_st].times + delay, t_stop))\n",
    "    spiketrains[i_st] = neo.SpikeTrain(spike_times, t_start=0*pq.ms, t_stop=t_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8339fe87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0 max delay= 40.0 ms\n"
     ]
    }
   ],
   "source": [
    "print(slope, 'max delay=', 10*slope, 'ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5e834692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.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": null,
   "id": "c6612aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from brainhack import printfig\n",
    "printfig(fig, 'raster_plot_linearpattern')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf629ff",
   "metadata": {},
   "source": [
    "All in one function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "79922ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_stop = 10000*pq.ms\n",
    "def generate_and_test(A_sync=A_sync, shift=shift, winlen=1, slope=slope, t_stop=t_stop):\n",
    "\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=t_stop)\n",
    "    for i_st in range(10):\n",
    "        delay = (i_st*slope)*pq.ms\n",
    "        spike_times = np.sort(np.mod(spiketrains[i_st].times + delay, t_stop))\n",
    "        spiketrains[i_st] = neo.SpikeTrain(spike_times, t_start=0*pq.ms, t_stop=t_stop)\n",
    "\n",
    "    for i in range(90):\n",
    "        spiketrains.append(elephant.spike_train_generation.homogeneous_poisson_process(\n",
    "            rate=5*pq.Hz, t_stop=t_stop))\n",
    "    patterns = elephant.spade.spade(\n",
    "                                spiketrains=spiketrains, binsize=1*pq.ms, winlen=winlen, min_spikes=3,\n",
    "                                n_surr=100, dither=5*pq.ms,\n",
    "                                psr_param=[0,0,0],\n",
    "                                output_format='patterns')['patterns']\n",
    "    return patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5488bfdb",
   "metadata": {},
   "source": [
    "\n",
    "* changing the synchrony probability to check when SPADE will begin to fail:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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: 28.213847875595093\n",
      "Time for pvalue spectrum computation: 1253.4751369953156\n",
      "0.1 max delay= 40.0 ms:  []\n",
      "Time for data mining: 16.484561920166016\n",
      "Time for pvalue spectrum computation: 1284.2360949516296\n",
      "0.16681005372000587 max delay= 40.0 ms:  [{'itemset': (33, 3, 4), 'windows_ids': (1538, 8695), 'neurons': [33, 3, 4], 'lags': array([0., 0.]) * ms, 'times': array([1538., 8695.]) * ms, 'signature': (3, 2), 'pvalue': 1.0}]\n",
      "Time for data mining: 11.799942970275879\n",
      "Time for pvalue spectrum computation: 1188.7150299549103\n",
      "0.2782559402207124 max delay= 40.0 ms:  [{'itemset': (2, 3, 4), 'windows_ids': (6308, 2574, 7984, 2962, 6237, 5975, 2461, 5758), 'neurons': [2, 3, 4], 'lags': array([0., 0.]) * ms, 'times': array([2461., 2574., 2962., 5758., 5975., 6237., 6308., 7984.]) * ms, 'signature': (3, 8), 'pvalue': 0.0}, {'itemset': (4, 5, 6), 'windows_ids': (636, 8292, 3206, 7464, 1352, 3304, 3947, 1741, 3886, 1904, 9361, 3952, 6393, 1978, 7580), 'neurons': [4, 5, 6], 'lags': array([0., 0.]) * ms, 'times': array([ 636., 1352., 1741., 1904., 1978., 3206., 3304., 3886., 3947.,\n",
      "       3952., 6393., 7464., 7580., 8292., 9361.]) * ms, 'signature': (3, 15), 'pvalue': 0.0}, {'itemset': (0, 1, 2), 'windows_ids': (8291, 3205, 1351, 1223, 3303, 3946, 1740, 3885, 1006, 1903, 9360, 3951, 1977, 635), 'neurons': [0, 1, 2], 'lags': array([0., 0.]) * ms, 'times': array([ 635., 1006., 1223., 1351., 1740., 1903., 1977., 3205., 3303.,\n",
      "       3885., 3946., 3951., 8291., 9360.]) * ms, 'signature': (3, 14), 'pvalue': 0.0}, {'itemset': (8, 9, 7), 'windows_ids': (8293, 1353, 1225, 3305, 3948, 1742, 1008, 3953, 9362, 1979), 'neurons': [8, 9, 7], 'lags': array([0., 0.]) * ms, 'times': array([1008., 1225., 1353., 1742., 1979., 3305., 3948., 3953., 8293.,\n",
      "       9362.]) * ms, 'signature': (3, 10), 'pvalue': 0.0}, {'itemset': (50, 6, 7), 'windows_ids': (7464, 1008), 'neurons': [50, 6, 7], 'lags': array([0., 0.]) * ms, 'times': array([1008., 7464.]) * ms, 'signature': (3, 2), 'pvalue': 0.95}, {'itemset': (65, 3, 4), 'windows_ids': (1978, 2461), 'neurons': [65, 3, 4], 'lags': array([0., 0.]) * ms, 'times': array([1978., 2461.]) * ms, 'signature': (3, 2), 'pvalue': 0.95}]\n",
      "Time for data mining: 9.15893030166626\n"
     ]
    }
   ],
   "source": [
    "for slope_ in np.logspace(-1, 1, 10, base=10):\n",
    "    patterns = generate_and_test(slope=slope_)\n",
    "    print(slope_, 'max delay=', 10*slope, 'ms: ', patterns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc9a171",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "(ValueError('The input must have the same units.'), 'If you want to bin over the shared [t_start, t_stop] interval, provide shared t_start and t_stop explicitly, which can be obtained like so: t_start, t_stop = elephant.utils.get_common_start_stop_times(spiketrains)')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/conversion.py:517\u001b[0m, in \u001b[0;36mBinnedSpikeTrain._resolve_input_parameters\u001b[0;34m(self, spiketrains)\u001b[0m\n\u001b[1;32m    516\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 517\u001b[0m     check_neo_consistency(spiketrains,\n\u001b[1;32m    518\u001b[0m                           object_type\u001b[39m=\u001b[39;49mneo\u001b[39m.\u001b[39;49mSpikeTrain,\n\u001b[1;32m    519\u001b[0m                           t_start\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_t_start,\n\u001b[1;32m    520\u001b[0m                           t_stop\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_t_stop,\n\u001b[1;32m    521\u001b[0m                           tolerance\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtolerance)\n\u001b[1;32m    522\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m er:\n\u001b[1;32m    523\u001b[0m     \u001b[39m# different t_start/t_stop\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/utils.py:210\u001b[0m, in \u001b[0;36mcheck_neo_consistency\u001b[0;34m(neo_objects, object_type, t_start, t_stop, tolerance)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[39mif\u001b[39;00m neo_obj\u001b[39m.\u001b[39munits \u001b[39m!=\u001b[39m units:\n\u001b[0;32m--> 210\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mThe input must have the same units.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    211\u001b[0m \u001b[39mif\u001b[39;00m t_start \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mabs\u001b[39m(neo_obj\u001b[39m.\u001b[39mt_start\u001b[39m.\u001b[39mitem() \u001b[39m-\u001b[39m start) \u001b[39m>\u001b[39m tolerance:\n",
      "\u001b[0;31mValueError\u001b[0m: The input must have the same units.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [47], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mfor\u001b[39;00m slope_ \u001b[39min\u001b[39;00m np\u001b[39m.\u001b[39mlogspace(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m10\u001b[39m, base\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m):\n\u001b[0;32m----> 2\u001b[0m     patterns \u001b[39m=\u001b[39m generate_and_test(slope\u001b[39m=\u001b[39;49mslope_, winlen\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m     \u001b[39mprint\u001b[39m(slope_, \u001b[39m'\u001b[39m\u001b[39mmax delay=\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m10\u001b[39m\u001b[39m*\u001b[39mslope, \u001b[39m'\u001b[39m\u001b[39mms: \u001b[39m\u001b[39m'\u001b[39m, patterns)\n",
      "Cell \u001b[0;32mIn [45], line 15\u001b[0m, in \u001b[0;36mgenerate_and_test\u001b[0;34m(A_sync, shift, winlen, slope, t_stop)\u001b[0m\n\u001b[1;32m     12\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     13\u001b[0m     spiketrains\u001b[39m.\u001b[39mappend(elephant\u001b[39m.\u001b[39mspike_train_generation\u001b[39m.\u001b[39mhomogeneous_poisson_process(\n\u001b[1;32m     14\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---> 15\u001b[0m patterns \u001b[39m=\u001b[39m elephant\u001b[39m.\u001b[39;49mspade\u001b[39m.\u001b[39;49mspade(\n\u001b[1;32m     16\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;49mwinlen, min_spikes\u001b[39m=\u001b[39;49m\u001b[39m3\u001b[39;49m,\n\u001b[1;32m     17\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     18\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     19\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 \u001b[39mreturn\u001b[39;00m patterns\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:351\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    348\u001b[0m time_mining \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[1;32m    349\u001b[0m \u001b[39mif\u001b[39;00m rank \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m compute_stability:\n\u001b[1;32m    350\u001b[0m     \u001b[39m# Mine the spiketrains for extraction of concepts\u001b[39;00m\n\u001b[0;32m--> 351\u001b[0m     concepts, rel_matrix \u001b[39m=\u001b[39m concepts_mining(\n\u001b[1;32m    352\u001b[0m         spiketrains, bin_size, winlen, min_spikes\u001b[39m=\u001b[39;49mmin_spikes,\n\u001b[1;32m    353\u001b[0m         min_occ\u001b[39m=\u001b[39;49mmin_occ, max_spikes\u001b[39m=\u001b[39;49mmax_spikes, max_occ\u001b[39m=\u001b[39;49mmax_occ,\n\u001b[1;32m    354\u001b[0m         min_neu\u001b[39m=\u001b[39;49mmin_neu, report\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ma\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m    355\u001b[0m     time_mining \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m time_mining\n\u001b[1;32m    356\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mTime for data mining: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(time_mining))\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:644\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    641\u001b[0m \u001b[39m# if spiketrains is list of neo.SpikeTrain convert to conv.BinnedSpikeTrain\u001b[39;00m\n\u001b[1;32m    642\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(spiketrains, (\u001b[39mlist\u001b[39m, SpikeTrainList)) \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    643\u001b[0m         \u001b[39misinstance\u001b[39m(spiketrains[\u001b[39m0\u001b[39m], neo\u001b[39m.\u001b[39mSpikeTrain):\n\u001b[0;32m--> 644\u001b[0m     spiketrains \u001b[39m=\u001b[39m conv\u001b[39m.\u001b[39;49mBinnedSpikeTrain(\n\u001b[1;32m    645\u001b[0m         spiketrains, bin_size\u001b[39m=\u001b[39;49mbin_size, tolerance\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m)\n\u001b[1;32m    646\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(spiketrains, conv\u001b[39m.\u001b[39mBinnedSpikeTrain):\n\u001b[1;32m    647\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[1;32m    648\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mspiketrains must be either a list of neo.SpikeTrain or \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m    649\u001b[0m         \u001b[39m'\u001b[39m\u001b[39ma conv.BinnedSpikeTrain object\u001b[39m\u001b[39m'\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/conversion.py:356\u001b[0m, in \u001b[0;36mBinnedSpikeTrain.__init__\u001b[0;34m(self, spiketrains, bin_size, n_bins, t_start, t_stop, tolerance, sparse_format)\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39munits \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m  \u001b[39m# will be set later\u001b[39;00m\n\u001b[1;32m    355\u001b[0m \u001b[39m# Check all parameter, set also missing values\u001b[39;00m\n\u001b[0;32m--> 356\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_resolve_input_parameters(spiketrains)\n\u001b[1;32m    357\u001b[0m \u001b[39m# Now create the sparse matrix\u001b[39;00m\n\u001b[1;32m    358\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msparse_matrix \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_create_sparse_matrix(\n\u001b[1;32m    359\u001b[0m     spiketrains, sparse_format\u001b[39m=\u001b[39msparse_format)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/conversion.py:524\u001b[0m, in \u001b[0;36mBinnedSpikeTrain._resolve_input_parameters\u001b[0;34m(self, spiketrains)\u001b[0m\n\u001b[1;32m    517\u001b[0m     check_neo_consistency(spiketrains,\n\u001b[1;32m    518\u001b[0m                           object_type\u001b[39m=\u001b[39mneo\u001b[39m.\u001b[39mSpikeTrain,\n\u001b[1;32m    519\u001b[0m                           t_start\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_t_start,\n\u001b[1;32m    520\u001b[0m                           t_stop\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_t_stop,\n\u001b[1;32m    521\u001b[0m                           tolerance\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtolerance)\n\u001b[1;32m    522\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m er:\n\u001b[1;32m    523\u001b[0m     \u001b[39m# different t_start/t_stop\u001b[39;00m\n\u001b[0;32m--> 524\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(er, \u001b[39m\"\u001b[39m\u001b[39mIf you want to bin over the shared \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    525\u001b[0m                          \u001b[39m\"\u001b[39m\u001b[39m[t_start, t_stop] interval, provide \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    526\u001b[0m                          \u001b[39m\"\u001b[39m\u001b[39mshared t_start and t_stop explicitly, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    527\u001b[0m                          \u001b[39m\"\u001b[39m\u001b[39mwhich can be obtained like so: \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m                          \u001b[39m\"\u001b[39m\u001b[39mt_start, t_stop = elephant.utils.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    529\u001b[0m                          \u001b[39m\"\u001b[39m\u001b[39mget_common_start_stop_times(spiketrains)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    530\u001b[0m                      )\n\u001b[1;32m    532\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_t_start \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    533\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_t_start \u001b[39m=\u001b[39m spiketrains[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mt_start\n",
      "\u001b[0;31mValueError\u001b[0m: (ValueError('The input must have the same units.'), 'If you want to bin over the shared [t_start, t_stop] interval, provide shared t_start and t_stop explicitly, which can be obtained like so: t_start, t_stop = elephant.utils.get_common_start_stop_times(spiketrains)')"
     ]
    }
   ],
   "source": [
    "for slope_ in np.logspace(-1, 1, 10, base=10):\n",
    "    patterns = generate_and_test(slope=slope_, winlen=10)\n",
    "    print(slope_, 'max delay=', 10*slope, 'ms: ', patterns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc8766c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}