format_spikes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "        bin_idx  unit_0  unit_1  unit_2  unit_3  unit_4  unit_5  unit_6  \\\n",
       "0             0       0       0       0       0       0       0       0   \n",
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       "2             2       0       0       0       0       1       1       0   \n",
       "3             3       0       0       0       0       0       1       0   \n",
       "4             4       1       0       0       0       0       0       0   \n",
       "...         ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "283532   283532       0       0       0       0       1       0       0   \n",
       "283533   283533       1       0       0       0       0       0       0   \n",
       "283534   283534       1       0       0       0       0       0       1   \n",
       "283535   283535       1       1       0       0       0       0       0   \n",
       "283536   283536       0       0       0       0       0       0       0   \n",
       "\n",
       "        unit_7  unit_8  ...  unit_49  unit_50  unit_51  unit_52  unit_53  \\\n",
       "0            0       0  ...        0        0        0        0        0   \n",
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       "3            0       0  ...        0        0        0        0        0   \n",
       "4            0       0  ...        0        0        0        0        1   \n",
       "...        ...     ...  ...      ...      ...      ...      ...      ...   \n",
       "283532       0       0  ...        0        0        0        0        0   \n",
       "283533       0       0  ...        0        0        0        0        0   \n",
       "283534       0       1  ...        0        0        0        0        0   \n",
       "283535       0       0  ...        0        0        0        0        1   \n",
       "283536       0       0  ...        0        0        0        0        0   \n",
       "\n",
       "        unit_54  unit_55  unit_56  unit_57  unit_58  \n",
       "0             0        0        0        0        0  \n",
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       "2             0        0        0        0        0  \n",
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       "...         ...      ...      ...      ...      ...  \n",
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       "\n",
       "[283537 rows x 60 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hyper parameters\n",
    "bin_size = 50\n",
    "\n",
    "# load data\n",
    "all_spike_times = pd.read_pickle(\"/data/sub-SA_sess-20180802_spike_times.pickle\")\n",
    "\n",
    "units = np.unique(all_spike_times.UnitID.values)\n",
    "time_stamp_max = int(all_spike_times.SpikeTime.max()) + 1\n",
    "\n",
    "num_time_bins = int(time_stamp_max/bin_size) + 1\n",
    "bins = np.arange(num_time_bins) * bin_size\n",
    "\n",
    "df = pd.DataFrame(data={'bin_idx': np.arange(num_time_bins)[:-1]})\n",
    "for unit in units:\n",
    "    unit_spike_times = all_spike_times[all_spike_times.UnitID==unit].SpikeTime.values\n",
    "    unit_spike_counts, bin_edges = np.histogram(unit_spike_times, bins=bins)\n",
    "    df[f'unit_{unit}'] = unit_spike_counts\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<StemContainer object of 3 artists>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot spike counts\n",
    "unit = 33\n",
    "plt.figure(figsize=(8,4), dpi=300)\n",
    "plt.stem(bins[:-1], df[f'unit_{unit}'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save as pickle\n",
    "df.to_pickle('/data/processed/sub-SA_sess-20180802_spike_counts_binsize_50.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# generate design matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "df_spk_cts = pd.read_pickle('/data/processed/sub-SA_sess-20180802_spike_counts_binsize_50.pickle')\n",
    "df_behav = pd.read_pickle('/data/processed/sub-SA_sess-20180802_behavior_binsize_50.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CIRCLE', 'SQUARE', 'STAR', 'TRIANGLE', 'CYAN', 'GREEN', 'MAGENTA',\n",
       "       'YELLOW', 'ESCHER', 'POLKADOT', 'RIPPLE', 'SWIRL', 'CORRECT',\n",
       "       'INCORRECT', 'bin_idx'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_behav.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "[283537 rows x 60 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spk_cts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df_spk_cts.shift(periods=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "1            0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2            1.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3            2.0     0.0     0.0     0.0     0.0     1.0     1.0     0.0   \n",
       "4            3.0     0.0     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "...          ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "283532  283531.0     1.0     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "283533  283532.0     0.0     0.0     0.0     0.0     1.0     0.0     0.0   \n",
       "283534  283533.0     1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "283535  283534.0     1.0     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "283536  283535.0     1.0     1.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "        unit_7  unit_8  ...  unit_49  unit_50  unit_51  unit_52  unit_53  \\\n",
       "0          NaN     NaN  ...      NaN      NaN      NaN      NaN      NaN   \n",
       "1          0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "2          0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "3          0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "4          0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "...        ...     ...  ...      ...      ...      ...      ...      ...   \n",
       "283532     0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "283533     0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "283534     0.0     0.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "283535     0.0     1.0  ...      0.0      0.0      0.0      0.0      0.0   \n",
       "283536     0.0     0.0  ...      0.0      0.0      0.0      0.0      1.0   \n",
       "\n",
       "        unit_54  unit_55  unit_56  unit_57  unit_58  \n",
       "0           NaN      NaN      NaN      NaN      NaN  \n",
       "1           0.0      0.0      0.0      0.0      0.0  \n",
       "2           0.0      0.0      0.0      0.0      0.0  \n",
       "3           0.0      0.0      0.0      0.0      0.0  \n",
       "4           0.0      0.0      0.0      0.0      0.0  \n",
       "...         ...      ...      ...      ...      ...  \n",
       "283532      0.0      1.0      0.0      0.0      0.0  \n",
       "283533      0.0      0.0      0.0      0.0      0.0  \n",
       "283534      0.0      0.0      0.0      0.0      1.0  \n",
       "283535      0.0      0.0      0.0      0.0      0.0  \n",
       "283536      0.0      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[283537 rows x 60 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameter\n",
    "tau_pre = 20\n",
    "tau_post = 0\n",
    "\n",
    "unit_id = 0\n",
    "df_design_matrix = pd.DataFrame()\n",
    "for bin_idx in [500, 502]:\n",
    "    r_t = df_spk_cts[df_spk_cts['bin_idx's]=bin_idx][f'unit_{unit}']\n",
    "    for tau in range(tau_pre):\n",
    "        bin_idx_tau = bin_idx - tau\n",
    "        for var in [var in df_behav.columns if var != 'bin_idx']:\n",
    "            df_behav_tau = df_behav[df_behav['bin_idx']=bin_idx_tau]\n",
    "            \n",
    "        if tau > 0:\n",
    "            r_tau = df_spk_cts[df_spk_cts['bin_idx']=bin_idx][f'unit_{unit}']"
   ]
  }
 ],
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