load_mat_different_resolutions.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 = '20180828'\n",
    "\n",
    "data_path = os.path.join('data1', dataset)\n",
    "\n",
    "\n",
    "width = 8\n",
    "height = 8\n",
    "fps = 10\n",
    "\n",
    "width = 8\n",
    "height = 8\n",
    "fps = 25\n",
    "\n",
    "width = 13\n",
    "height = 13\n",
    "fps = 10\n",
    "\n",
    "width = 26\n",
    "height = 26\n",
    "fps = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12b</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12c</td>\n",
       "      <td>ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12d</td>\n",
       "      <td>OFF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12e</td>\n",
       "      <td>ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>83b</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>84a</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>86a</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>86b</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>87a</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    channel cell_type\n",
       "0       12a       OFF\n",
       "1       12b   Unknown\n",
       "2       12c        ON\n",
       "3       12d       OFF\n",
       "4       12e        ON\n",
       "..      ...       ...\n",
       "103     83b   Unknown\n",
       "104     84a   Unknown\n",
       "105     86a   Unknown\n",
       "106     86b   Unknown\n",
       "107     87a   Unknown\n",
       "\n",
       "[108 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the csv file\n",
    "info = pd.read_csv(os.path.join(data_path, 'cell_type_{}pix_{}Hz.csv'.format(width, fps)))\n",
    "\n",
    "info.replace('-', 'Unknown', inplace=True)\n",
    "\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ON         0.296296\n",
       "OFF        0.277778\n",
       "Unknown    0.425926\n",
       "Name: cell_type, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info['cell_type'].value_counts()[['ON', 'OFF', 'Unknown']] / len(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load stim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data1/20180828/StimInfo_26pix_66um_10Hz.mat'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim_info_path = glob(os.path.join(data_path,'StimInfo_{}pix_*_{}Hz.mat'.format(width, fps)))[0]\n",
    "stim_info_path\n",
    "# stim_info_path = os.path.join(data_path,'StimInfo_8pix_215um_10Hz.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9000, 676)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd0e7891650>"
      ]
     },
     "execution_count": 8,
     "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.imshow(stim[0,:].reshape(height,width), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load spike counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 1/108 [00:00<00:20,  5.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data1/20180828/SpikeTrain_20180828/SpikeTrain_ND2_26pix_66um_10Hz/A1a.mat\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 108/108 [00:15<00:00,  6.86it/s]\n"
     ]
    }
   ],
   "source": [
    "# read spike counts\n",
    "path = glob(os.path.join(data_path, 'SpikeTrain_'+dataset,  'SpikeTrain_ND2_{}pix_*_{}Hz'.format(width,fps)))[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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(108, 9000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spike_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([711688., 153088.,  58199.,  25019.,  11695.,   5742.,   3005.]),\n",
       " array([-0.5,  0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5]),\n",
       " <a list of 7 Patch objects>)"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "basename = dataset+'_{}pix_{}Hz'.format(width, fps)\n",
    "\n",
    "np.savez(basename, dataset=dataset, stim=stim, spike_counts=spike_counts)\n",
    "\n",
    "info.to_csv(basename+'_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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}