Merge pull request #3 from pqz317/main

add scaffold for format spikes

Author: pckuo

format_spikes.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from spike_tools import (\n",
+    "    general as spike_general,\n",
+    "    analysis as spike_analysis,\n",
+    ")\n",
+    "from scipy.ndimage import gaussian_filter1d\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def firing_rate(spData, channelData, bins, smoothing):\n",
+    "    bin_size = np.abs(np.diff(bins)[0])\n",
+    "    # spData is pandas dataframe with at least TrialNumber, UnitId, and SpikeTimeFromStart columns\n",
+    "    trial_unit_index = pd.MultiIndex.from_product([np.unique(spData.TrialNumber), np.unique(channelData.UnitID).astype(int), bins[:-1]], names=[\"TrialNumber\", \"UnitID\", \"TimeBins\"]).to_frame()\n",
+    "    trial_unit_index = trial_unit_index.droplevel(2).drop(columns=[\"TrialNumber\", \"UnitID\"]).reset_index()\n",
+    "    \n",
+    "    groupedData = spData.groupby([\"TrialNumber\", \"UnitID\"])\n",
+    "\n",
+    "    fr_DF = groupedData.apply(lambda x: pd.DataFrame(\\\n",
+    "                            {\"SpikeCounts\": np.histogram(x.SpikeTimeFromStart/1000, bins)[0],\\\n",
+    "                             \"FiringRate\": gaussian_filter1d(np.histogram(x.SpikeTimeFromStart/1000, bins)[0].astype(float)/bin_size, smoothing),\\\n",
+    "                             \"TimeBins\": bins[:-1]}))\n",
+    "    #print(\"Trial\", np.unique(trial_unit_index.UnitID))\n",
+    "    #print(\"FR\", np.unique(fr_DF.droplevel(2).reset_index().UnitID))\n",
+    "    all_units_df = trial_unit_index.merge(fr_DF.droplevel(2).reset_index(), how='outer', on=[\"TrialNumber\", \"UnitID\", \"TimeBins\"])\n",
+    "    #for unit in np.unique(all_units_df.UnitID):\n",
+    "    #    unit_df = all_units_df[all_units_df.UnitID == unit]\n",
+    "    #    print(unit_df)\n",
+    "    #    print(unit, len(unit_df))\n",
+    "    all_units_df.FiringRate = all_units_df.FiringRate.fillna(0.0)\n",
+    "    all_units_df.SpikeCounts = all_units_df.SpikeCounts.fillna(0)\n",
+    "    return all_units_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spike_times = pd.read_pickle(\"/data/sub-SA_sess-20180802_spike_times.pickle\")"
+   ]
+  }
+ ],
+ "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": "3.10.6"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}