load_mat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load  channel name and cell type info\n",
    "\n",
    "<!-- \tON\tON/OFF\tOFF\tUnknown\tSum\n",
    "count\t32\t10\t13\t52\t107 -->\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = '20201216'\n",
    "# dataset = '20201209'\n",
    "\n",
    "data_path = os.path.join('data', dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = '20180828'\n",
    "\n",
    "data_path = os.path.join('data', dataset)\n",
    "\n",
    "\n",
    "width = 8\n",
    "height = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cloud stim\n",
    "# ln -s ~/data/cloud_stim data_cloud_stim\n",
    "data_folder = 'data_cloud_stim'\n",
    "dataset = '20210203_contrast100'\n",
    "# dataset = '20210203_contrast50'\n",
    "width = 52\n",
    "height = 52\n",
    "\n",
    "# dataset = '20210113'\n",
    "# width = 26\n",
    "# height = 26\n",
    "\n",
    "data_path = os.path.join(data_folder, dataset)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel</th>\n",
       "      <th>cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12a</td>\n",
       "      <td>ON-OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12b</td>\n",
       "      <td>ON-OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13a</td>\n",
       "      <td>ON-OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13b</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14a</td>\n",
       "      <td>ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>85b</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>86a</td>\n",
       "      <td>OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>86b</td>\n",
       "      <td>ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>87a</td>\n",
       "      <td>ON-OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>87b</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    channel cell_type\n",
       "0       12a    ON-OFF\n",
       "1       12b    ON-OFF\n",
       "2       13a    ON-OFF\n",
       "3       13b   Unknown\n",
       "4       14a        ON\n",
       "..      ...       ...\n",
       "97      85b   Unknown\n",
       "98      86a       OFF\n",
       "99      86b        ON\n",
       "100     87a    ON-OFF\n",
       "101     87b   Unknown\n",
       "\n",
       "[102 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the csv file\n",
    "info = pd.read_csv(os.path.join(data_path, 'cell_type.csv'))\n",
    "\n",
    "info.replace('-', 'Unknown', inplace=True)\n",
    "\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# info.channel.replace('ch_','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ON         40\n",
       "ON-OFF     33\n",
       "Unknown    21\n",
       "OFF         8\n",
       "Name: cell_type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info['cell_type'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ON         0.392157\n",
       "ON-OFF     0.323529\n",
       "Unknown    0.205882\n",
       "OFF        0.078431\n",
       "Name: cell_type, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info['cell_type'].value_counts()  / len(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load stim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data_cloud_stim/20210203_contrast100/StimInfo_52pix_33um_30Hz_contrast100%.mat'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim_info_path = glob(os.path.join(data_path,'StimInfo*.mat'))[0]\n",
    "stim_info_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read stimulus\n",
    "import scipy.io as sio\n",
    "stim = list()\n",
    "for stim_info in sio.loadmat(stim_info_path)['StimInfo']:\n",
    "    stim.append(stim_info[0][0])\n",
    "    # print(stim_info[0][0].shape)\n",
    "stim = np.array(stim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36000, 2704)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc7700055d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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95vpjBleLusiksXbcyfAI8D3gv4C/mXWnx4bYvgRcA/6XwVHIUeDdDDpwrlT3O2Yc4x8yKH2+A1yobo8UGOf7gBerOC8Cf1vNLyrOoXgf4kYHXXExAr8DvFTdLq3nzqSxerisWRIeQWeWhJPdLAknu1kSTnazJJzsZkk42c2ScLKbJfF/pOt7SV4pVEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(stim[0,:].reshape(height,width), cmap='gray', vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load spike counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_spikes(spike_times, bins, timestamp_start=0):\n",
    "    num_bins = bins.shape[0]\n",
    "    spike_count = np.zeros_like(bins)\n",
    "    for i in range(num_bins):\n",
    "        if i == 0:\n",
    "            continue\n",
    "\n",
    "        spike_count[i] = np.sum(np.logical_and(spike_times >= timestamp_start + bins[i-1], spike_times < timestamp_start + bins[i]))\n",
    "    return spike_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/102 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_cloud_stim/20210203_contrast100/SpikeTrain_20210203/SpikeTrain_ND2_52pix_33um_30Hz_contrast100%/A1a.mat\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 102/102 [05:07<00:00,  3.02s/it]\n"
     ]
    }
   ],
   "source": [
    "# read spike counts\n",
    "path = glob(os.path.join(data_path, 'SpikeTrain_'+dataset.split('_')[0],  'SpikeTrain_*'))[0]\n",
    "\n",
    "# load time stamps\n",
    "filename = os.path.join(path, 'A1a.mat')\n",
    "print(filename)\n",
    "ts = sio.loadmat(filename)['A1a'].ravel()\n",
    "# print(ts[0], ts[-1], ts.shape)\n",
    "\n",
    "# load spike times\n",
    "# channel_names = list()\n",
    "spike_counts = list()\n",
    "for i, channel_name in enumerate(tqdm(info['channel'])):\n",
    "    filename = os.path.join(path, 'ch_' + channel_name)\n",
    "    # print(filename)\n",
    "    spike_time = sio.loadmat(filename)['ch_' + channel_name].ravel()\n",
    "\n",
    "    # count spikes\n",
    "    spike_counts.append(count_spikes(spike_time, ts))\n",
    "\n",
    "spike_counts = np.array(spike_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 36000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spike_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2317700.,  908847.,  297527.,   96688.,   33691.,   11779.,\n",
       "           4070.]),\n",
       " array([-0.5,  0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5]),\n",
       " <BarContainer object of 7 artists>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(spike_counts.ravel(), [-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save to npz and csv\n",
    "\n",
    "put these files under `data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed(dataset, dataset=dataset, stim=stim, spike_counts=spike_counts)\n",
    "# if len(set(stim.ravel()))==2: # binary stim\n",
    "#     np.savez_compressed(dataset, dataset=dataset, stim=stim.astype(int), spike_counts=spike_counts)\n",
    "# else:\n",
    "#     np.savez_compressed(dataset, dataset=dataset, stim=stim, spike_counts=spike_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "info.to_csv(dataset+'_info.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}