Plot_LickToGetReward_2023-04-19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This code plots SessionData from LickToGetReward (2023-04-19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set the working directory to the folder containing the .mat files\n",
    "os.chdir('/Users/jun/Documents/Work/Project/Intralingual parasympathetic ganglion neurons/Behavior/ILPG_silencing_#1/Session_Data/')\n",
    "# Load the original data\n",
    "mat_contents = loadmat('ILPG_silencing#1_LickToGetReward_Training_20230414_145848_2023_04_18_Cue0_adjusted_data.mat')\n",
    "SessionData = mat_contents['adjusted_SessionData']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_trials = SessionData['nTrials'][0, 0][0, 0]\n",
    "raw_events = SessionData['RawEvents'][0, 0]['Trial'][0, 0]\n",
    "\n",
    "lickometer = {\n",
    "    'BNCHigh': [np.nan] * n_trials,\n",
    "    'BNCLow': [np.nan] * n_trials\n",
    "}\n",
    "\n",
    "for i in range(n_trials):\n",
    "    trial = raw_events[0, i]['Events'][0, 0]\n",
    "    \n",
    "    if 'BNC1High' not in trial.dtype.names or 'BNC1Low' not in trial.dtype.names:\n",
    "        lickometer['BNCHigh'][i] = np.nan\n",
    "        lickometer['BNCLow'][i] = np.nan\n",
    "    else:\n",
    "        lickometer['BNCHigh'][i] = trial['BNC1High'][0, 0]\n",
    "        lickometer['BNCLow'][i] = trial['BNC1Low'][0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove empty cells\n",
    "lickometer[\"BNCHigh\"] = [x for x in lickometer[\"BNCHigh\"] if not np.isnan(x).all()]\n",
    "lickometer[\"BNCLow\"] = [x for x in lickometer[\"BNCLow\"] if not np.isnan(x).all()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x1728 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "from matplotlib.path import Path\n",
    "from matplotlib.patches import PathPatch\n",
    "\n",
    "# Define color map for drinking patches\n",
    "drinking_cmap = cm.get_cmap('Set1')\n",
    "\n",
    "lick_plots = []\n",
    "\n",
    "fig = plt.figure(figsize=(32, 24))\n",
    "\n",
    "# Define colors for plotting\n",
    "edgecolor_cue = [0.5, 0.5, 0.5]  # use edgecolor instead of color for cue patch\n",
    "color_lick = 'SteelBlue'\n",
    "\n",
    "trials = SessionData[\"RawEvents\"][0, 0][\"Trial\"]\n",
    "states_list = [trial.item()[0, 0][\"States\"][0, 0] for trial in np.nditer(trials, flags=['refs_ok'])]\n",
    "num_trials = SessionData['nTrials'][0, 0][0, 0]\n",
    "\n",
    "# Plot Cue\n",
    "cue_start = round(states_list[0]['Cue'][0][0][0][0], 2)\n",
    "cue_duration = round(states_list[0]['Cue'][0][0][0][1] - states_list[0]['Cue'][0][0][0][0], 2)\n",
    "x_points_cue = [cue_start, cue_start, cue_start + cue_duration, cue_start + cue_duration]\n",
    "y_points_cue = [0, num_trials + 1, num_trials + 1, 0]\n",
    "cue = plt.fill(x_points_cue, y_points_cue, edgecolor=edgecolor_cue, facecolor=None, alpha=0.2)\n",
    "\n",
    "for trial in range(num_trials):\n",
    "    licks = lickometer[\"BNCHigh\"][trial]\n",
    "    trial_lick_plots = []\n",
    "    for lick_arr in licks:\n",
    "        for lick in lick_arr:\n",
    "            ylicks = np.array([trial, trial + 0.4, np.nan])\n",
    "            xlicks = np.array([lick, lick, np.nan])\n",
    "            plot = plt.plot(xlicks, ylicks, color=color_lick, linewidth=2)\n",
    "            trial_lick_plots.append(plot)\n",
    "    lick_plots.append(trial_lick_plots)\n",
    "\n",
    "    # Get state information for current trial\n",
    "    state_info = None\n",
    "    if trial < len(trials.item()[0]):\n",
    "        state_info = trials.item()[0, trial][\"States\"][0, 0]\n",
    "\n",
    "    # Plot Drinking if state information is available for current trial\n",
    "    if state_info is not None and \"Drinking\" in state_info.dtype.fields.keys():\n",
    "        drinking_start = state_info[\"Drinking\"][0][0][0][0]\n",
    "        drinking_end = state_info[\"Drinking\"][0][0][0][1]\n",
    "        drinking_duration = drinking_end - drinking_start\n",
    "        x_points_drinking = [drinking_start, drinking_start, drinking_start + drinking_duration, drinking_start + drinking_duration]\n",
    "        y_points_drinking = [trial, trial + 0.4, trial + 0.4, trial]\n",
    "    if \"UnconditionedReward\" in state_info.dtype.fields.keys() and not np.isnan(state_info[\"UnconditionedReward\"][0][0][0]).any():\n",
    "        drinking_color = 'red'\n",
    "    else:\n",
    "        drinking_color = 'green'\n",
    "    path = Path(np.array([x_points_drinking, y_points_drinking]).T)\n",
    "    drinking_patch = PathPatch(path, facecolor=drinking_color, linestyle=\"none\", alpha=0.2)\n",
    "    plt.gca().add_patch(drinking_patch)\n",
    "\n",
    "y_height = num_trials + 1\n",
    "plt.ylim([1, y_height])\n",
    "plt.ylim([1, 100]) # limit ylim to be consistent across days\n",
    "plt.xlim([-10, 15])\n",
    "plt.ylabel(\"Trial\")\n",
    "plt.xlabel(\"Time (s)\")\n",
    "\n",
    "plt.show()"
   ]
  }
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