Plot_LickToGetReward_batch_2023-04-19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\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_#2/SessionData_LickToGetReward/untitled folder/')\n",
    "\n",
    "# Get a list of all .mat files in the current directory that end with \"adjusted_data.mat\"\n",
    "mat_files = [f for f in os.listdir() if f.endswith('adjusted_data.mat')]\n",
    "\n",
    "for mat_file in mat_files:\n",
    "    # Load the original data\n",
    "    mat_contents = loadmat(mat_file)\n",
    "    SessionData = mat_contents['adjusted_SessionData']\n",
    "\n",
    "    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]\n",
    "\n",
    "    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",
    "        if not np.isnan(licks).all():\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.tick_params(axis='both', labelsize=24)\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\",fontsize=36)\n",
    "    plt.xlabel(\"Time (s)\", fontsize=36)\n",
    "\n",
    "    # beautify the axes\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "\n",
    "    ax.spines['bottom'].set_position(('axes', -0.01))\n",
    "    ax.spines['left'].set_position(('axes', -0.01))\n",
    "\n",
    "    ax.spines['bottom'].set_linewidth(2)\n",
    "    ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "    plt.tick_params(axis='both', width=2)\n",
    "\n",
    "    # Get used in the Session\n",
    "    LickThreshold = SessionData['TrialSettings'][0, 0][0, 0]['GUI'][0, 0]['LickThreshold'][0, 0]\n",
    "\n",
    "    # count non-nan UnconditionedReward\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",
    "    unconditioned_reward_count = 0\n",
    "\n",
    "    for trial in range(num_trials):\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",
    "        if state_info is not None and \"UnconditionedReward\" in state_info.dtype.fields.keys():\n",
    "            if not np.isnan(state_info[\"UnconditionedReward\"][0][0][0]).any():\n",
    "                unconditioned_reward_count += 1\n",
    "\n",
    "    # count Reward\n",
    "    reward_count = 0\n",
    "\n",
    "    for trial in range(num_trials):\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",
    "        if state_info is not None and \"Reward\" in state_info.dtype.fields.keys():\n",
    "            if not np.isnan(state_info[\"Reward\"][0][0][0]).any():\n",
    "                reward_count += 1\n",
    "\n",
    "\n",
    "    # Extract the filename without the extension and add the current date to the end\n",
    "    file_name = os.path.splitext(mat_file)[0] + \"_\" + str(datetime.date.today()) + \".pdf\"\n",
    "\n",
    "    success_rate = (reward_count/(num_trials - unconditioned_reward_count))*100\n",
    "\n",
    "    # Add success_rate and LickThreshold information to the plot\n",
    "    info_text = f\"{file_name}\\nLickThreshold: {LickThreshold}\\nSuccess rate: {success_rate:.2f}%\"\n",
    "    plt.annotate(info_text, xy=(0, 1.05), xycoords='axes fraction', fontsize=24,\n",
    "                 horizontalalignment='left', verticalalignment='bottom', transform=plt.gca().transAxes)\n",
    "\n",
    "    # Save the figure with dpi = 1200\n",
    "    plt.savefig(file_name, dpi=1200)\n",
    "\n",
    "    # Close the figure to free up memory\n",
    "    plt.close(fig)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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