2023-06-23_dataset2_laurent_loading_raw_clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1fec7f6-4c47-4c18-90ec-11e430dab517",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "854af98e-6f5d-4b74-8c3a-7e3e258f2aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_path = Path.home()/'quantic'/'grants'/'polychronies'/'RSG-spikepatterns'/'Data'/'G_Ready-Set_bin1ms_alignedReady.mat'\n",
    "H_path = Path.home()/'quantic'/'grants'/'polychronies'/'RSG-spikepatterns'/'Data'/'H_Ready-Set_bin1ms_alignedReady.mat'\n",
    "path = G_path\n",
    "# data = scipy.io.loadmat(path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "64c9982d",
   "metadata": {},
   "source": [
    "The data inside each file is organized as follows:\n",
    "\n",
    "\\- D [1xS cells] is the spiking data, where S is the number of sessions. Each cell contains data from a given session in the form of a 3-D tensor [TxNxK] where T is the number of time points within a trial (sampled at 1-ms resolution), N is the number of neurons, and K is the number of trials. The entries of the tensor are 0 (no spike), or 1 (spike). Note that the data has been NaN-padded to accommodate trials of different durations. The data provided is between the Ready and the Set cues.\n",
    "\n",
    "\\- t_s [1xS cells] is the sample interval presented to the animal on every trial (duration of the Ready-Set epoch).\n",
    "\n",
    "\\- t_p [1xS cells] is the interval produced by the animal on every trial (duration of the Set-Go epoch).\n",
    "\n",
    "\\- id_eye [1xS cells] is an indicator of whether the monkey used the eye (1) or the hand (0) to respond at Go.\n",
    "\n",
    "\\- id_left [1xS cells] is an indicator of whether the target was located to the left (1) or right (0) to  fixation point.\n",
    "\n",
    "\\- id_short [1xS cells] is an indicator of whether the color cue was red (1) or blue (0) respectively corresponding to the Short and Long distribution.\n",
    "\n",
    "\\- id_trial [1xS cells] is the trial number within each session. Some trials were excluded offline due to the animal not responding or breaking fixation early.\n",
    "\n",
    "\\- id_neuron [1xS cells] is the neuron identity for each session in the form of a matrix [Nx2] where N is the number of neurons. The first column is the neuron label, the second column indicates 1 for putative single units and 0 for multi units.\n",
    "\n",
    "\\- wbin is the size of the bin (in ms) used to discretize the data\n",
    "\n",
    "\\- sp_times [1xS cells] is the spike times for each session in the form of a series of cells [Nx1] where N is the number of neurons, containing the spike times [N_times x 1]. Note here that we include ALL spikes, even those outside of trial periods. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2b8d69c1",
   "metadata": {},
   "source": [
    "## trying mat73"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1dca5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mat73\n",
    "mat73data_dict = mat73.loadmat(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f9413fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = mat73data_dict['wbin']\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1cbfc605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['D', 'id_eye', 'id_left', 'id_neuron', 'id_short', 'id_trial', 'sp_times', 't_p', 't_s', 'wbin'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat73data_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7a82965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of sessions = 12\n"
     ]
    }
   ],
   "source": [
    "N_session = len(mat73data_dict['D'])\n",
    "print('number of sessions =', N_session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa917105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1200, 107, 1498)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_session = 0\n",
    "data = mat73data_dict['D'][i_session]\n",
    "N_time, N_neurons, N_trial = data.shape\n",
    "N_time, N_neurons, N_trial"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75bc541b",
   "metadata": {},
   "source": [
    "## displaying some raster plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "014bf315",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 4.391640246799251 0.6562914299858119\n"
     ]
    }
   ],
   "source": [
    "print(i_session, np.mean(np.nanmean(data, axis=(0, 1))*1000), np.std(np.nanmean(data, axis=(0, 1))*1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a0d65bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100, 107)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_trial = 0\n",
    "data_trimT = data[np.isnan(data[:, :, i_trial]).sum(axis=1)<N_neurons, :, i_trial]\n",
    "data_trimT.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "025e2420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100, 85)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_trim = data_trimT[:, np.isnan(data_trimT).sum(axis=0)==0]\n",
    "data_trim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58fdd143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 8))\n",
    "ax.imshow((data_trim>0)[:300, :].T, cmap='gray');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "087cad23",
   "metadata": {},
   "source": [
    "### checking we have the good t_s on each trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6855dc76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts= 1100\n",
      "ts= 1200\n",
      "ts= 800\n",
      "ts= 900\n",
      "ts= 900\n",
      "ts= 1200\n",
      "ts= 900\n",
      "ts= 1200\n",
      "ts= 1000\n",
      "ts= 900\n",
      "ts= 1100\n",
      "ts= 800\n"
     ]
    }
   ],
   "source": [
    "for i_trial in range(N_trial):\n",
    "    print('ts=', (np.isnan(data[:, :, i_trial]).sum(axis=1)<N_neurons).sum())\n",
    "    if i_trial>10: break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "457ead92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1100., 1200.,  800., ...,  640.,  800.,  560.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat73data_dict['t_s'][i_session]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ad7e7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i_trial, t_s in enumerate(mat73data_dict['t_s'][i_session]):\n",
    "    t_s_obs = (np.isnan(data[:, :, i_trial]).sum(axis=1)<N_neurons).sum()\n",
    "    if not(t_s_obs == t_s): print('trial', i_trial)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fa47e0fb",
   "metadata": {},
   "source": [
    "### checking we have the same neurons on each trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0a70300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(560, 107)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i_trial, t_s in enumerate(mat73data_dict['t_s'][i_session]):\n",
    "    data_trimT = data[:int(t_s), :, i_trial]\n",
    "data_trimT.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "290e3e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, False, False,  True,  True,  True,  True,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "       False,  True,  True, False,  True, False, False,  True, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        True,  True, False,  True, False,  True, False, False,  True,\n",
       "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False,  True, False])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_neurons = np.isnan(data_trimT).sum(axis=0)==0\n",
    "active_neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4a9873f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85\n",
      "87\n",
      "91\n",
      "92\n",
      "97\n",
      "97\n",
      "97\n",
      "99\n",
      "99\n",
      "99\n",
      "99\n",
      "99\n"
     ]
    }
   ],
   "source": [
    "for i_trial, t_s in enumerate(mat73data_dict['t_s'][i_session]):\n",
    "    data_trimT = data[:int(t_s), :, i_trial]\n",
    "    print((np.isnan(data_trimT).sum(axis=0)==0).sum())\n",
    "    if i_trial>10: break\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c4241f12",
   "metadata": {},
   "source": [
    "### saving all to numpy arrays (generates huge files!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0702eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/laurentperrinet/quantic/grants/polychronies/RSG-spikepatterns/Data/G_Ready-Set_bin1ms_alignedReady.npy'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(path).replace('.mat', '.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4b4f81e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/laurentperrinet/quantic/grants/polychronies/RSG-spikepatterns/Data/G_Ready-Set_bin1ms_alignedReady.mat'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEBUG = True\n",
    "for path in [G_path, H_path]:\n",
    "    import mat73\n",
    "    mat73data_dict = mat73.loadmat(path)\n",
    "    N_session = len(mat73data_dict['D'])\n",
    "    for i_session in range(N_session):\n",
    "        data = mat73data_dict['D'][i_session]\n",
    "        data[np.isnan(data)] = 255\n",
    "        data = data.astype(np.uint8)\n",
    "        np.savez(str(path).replace('.mat', f'_session={i_session}.npz'), \n",
    "                data=data, \n",
    "                id_neuron=mat73data_dict['id_neuron'][i_session],\n",
    "                id_short=mat73data_dict['id_short'][i_session],\n",
    "                t_s=mat73data_dict['t_s'][i_session],\n",
    "                t_p=mat73data_dict['t_p'][i_session],\n",
    "                )\n",
    "        if DEBUG and i_session>0: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "78f9e8d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 9139104\n",
      "-rw-r--r--  1 laurentperrinet  staff   211M Jun 14 10:42 H_Ready-Set_bin1ms_alignedReady.mat\n",
      "-rw-r--r--  1 laurentperrinet  staff   407M Jun 14 10:55 G_Ready-Set_bin1ms_alignedReady.mat\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   2.0K Jun 15 12:29 README.md\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   1.7G Jun 26 16:39 G_Ready-Set_bin1ms_alignedReady_session=1.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   1.4G Jun 26 16:39 G_Ready-Set_bin1ms_alignedReady_session=0.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   545M Jun 26 16:40 H_Ready-Set_bin1ms_alignedReady_session=1.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   138M Jun 26 16:40 H_Ready-Set_bin1ms_alignedReady_session=0.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    27K Jun 27 15:43 G_Ready-Set_bin1ms_alignedReady_session=0.npz.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    34K Jun 27 15:43 G_Ready-Set_bin1ms_alignedReady_session=1.npz.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    17K Jun 27 15:44 H_Ready-Set_bin1ms_alignedReady_session=0.npz.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    25K Jun 27 15:44 H_Ready-Set_bin1ms_alignedReady_session=1.npz.npy\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    28K Jun 27 15:46 G_Ready-Set_bin1ms_alignedReady_session=0.npz\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    34K Jun 27 15:46 G_Ready-Set_bin1ms_alignedReady_session=1.npz\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    17K Jun 27 15:46 H_Ready-Set_bin1ms_alignedReady_session=0.npz\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    26K Jun 27 15:46 H_Ready-Set_bin1ms_alignedReady_session=1.npz\n"
     ]
    }
   ],
   "source": [
    "%ls -ltrh /Users/laurentperrinet/quantic/grants/Polychronies/RSG-spikepatterns/Data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eeb243be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m       \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavez\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mCall signature:\u001b[0m  \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavez\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[0;31mType:\u001b[0m            _ArrayFunctionDispatcher\n",
      "\u001b[0;31mString form:\u001b[0m     <function savez at 0x10d83b6a0>\n",
      "\u001b[0;31mFile:\u001b[0m            /opt/homebrew/lib/python3.11/site-packages/numpy/lib/npyio.py\n",
      "\u001b[0;31mDocstring:\u001b[0m      \n",
      "Save several arrays into a single file in uncompressed ``.npz`` format.\n",
      "\n",
      "Provide arrays as keyword arguments to store them under the\n",
      "corresponding name in the output file: ``savez(fn, x=x, y=y)``.\n",
      "\n",
      "If arrays are specified as positional arguments, i.e., ``savez(fn,\n",
      "x, y)``, their names will be `arr_0`, `arr_1`, etc.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "file : str or file\n",
      "    Either the filename (string) or an open file (file-like object)\n",
      "    where the data will be saved. If file is a string or a Path, the\n",
      "    ``.npz`` extension will be appended to the filename if it is not\n",
      "    already there.\n",
      "args : Arguments, optional\n",
      "    Arrays to save to the file. Please use keyword arguments (see\n",
      "    `kwds` below) to assign names to arrays.  Arrays specified as\n",
      "    args will be named \"arr_0\", \"arr_1\", and so on.\n",
      "kwds : Keyword arguments, optional\n",
      "    Arrays to save to the file. Each array will be saved to the\n",
      "    output file with its corresponding keyword name.\n",
      "\n",
      "Returns\n",
      "-------\n",
      "None\n",
      "\n",
      "See Also\n",
      "--------\n",
      "save : Save a single array to a binary file in NumPy format.\n",
      "savetxt : Save an array to a file as plain text.\n",
      "savez_compressed : Save several arrays into a compressed ``.npz`` archive\n",
      "\n",
      "Notes\n",
      "-----\n",
      "The ``.npz`` file format is a zipped archive of files named after the\n",
      "variables they contain.  The archive is not compressed and each file\n",
      "in the archive contains one variable in ``.npy`` format. For a\n",
      "description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.\n",
      "\n",
      "When opening the saved ``.npz`` file with `load` a `NpzFile` object is\n",
      "returned. This is a dictionary-like object which can be queried for\n",
      "its list of arrays (with the ``.files`` attribute), and for the arrays\n",
      "themselves.\n",
      "\n",
      "Keys passed in `kwds` are used as filenames inside the ZIP archive.\n",
      "Therefore, keys should be valid filenames; e.g., avoid keys that begin with\n",
      "``/`` or contain ``.``.\n",
      "\n",
      "When naming variables with keyword arguments, it is not possible to name a\n",
      "variable ``file``, as this would cause the ``file`` argument to be defined\n",
      "twice in the call to ``savez``.\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> from tempfile import TemporaryFile\n",
      ">>> outfile = TemporaryFile()\n",
      ">>> x = np.arange(10)\n",
      ">>> y = np.sin(x)\n",
      "\n",
      "Using `savez` with \\*args, the arrays are saved with default names.\n",
      "\n",
      ">>> np.savez(outfile, x, y)\n",
      ">>> _ = outfile.seek(0) # Only needed here to simulate closing & reopening file\n",
      ">>> npzfile = np.load(outfile)\n",
      ">>> npzfile.files\n",
      "['arr_0', 'arr_1']\n",
      ">>> npzfile['arr_0']\n",
      "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
      "\n",
      "Using `savez` with \\**kwds, the arrays are saved with the keyword names.\n",
      "\n",
      ">>> outfile = TemporaryFile()\n",
      ">>> np.savez(outfile, x=x, y=y)\n",
      ">>> _ = outfile.seek(0)\n",
      ">>> npzfile = np.load(outfile)\n",
      ">>> sorted(npzfile.files)\n",
      "['x', 'y']\n",
      ">>> npzfile['x']\n",
      "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
      "\u001b[0;31mClass docstring:\u001b[0m\n",
      "Class to wrap functions with checks for __array_function__ overrides.\n",
      "\n",
      "All arguments are required, and can only be passed by position.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "dispatcher : function or None\n",
      "    The dispatcher function that returns a single sequence-like object\n",
      "    of all arguments relevant.  It must have the same signature (except\n",
      "    the default values) as the actual implementation.\n",
      "    If ``None``, this is a ``like=`` dispatcher and the\n",
      "    ``_ArrayFunctionDispatcher`` must be called with ``like`` as the\n",
      "    first (additional and positional) argument.\n",
      "implementation : function\n",
      "    Function that implements the operation on NumPy arrays without\n",
      "    overrides.  Arguments passed calling the ``_ArrayFunctionDispatcher``\n",
      "    will be forwarded to this (and the ``dispatcher``) as if using\n",
      "    ``*args, **kwargs``.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "_implementation : function\n",
      "    The original implementation passed in."
     ]
    }
   ],
   "source": [
    "np.savez?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0661b1db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e69fe2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.uint8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.uint8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "add1c82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = mat73data_dict['D'][i_session]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a754b043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1200, 41, 1451)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "745804a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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