Raster_batch_python_20230322.ipynb

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    "# This code analyze mat files generated by \"Licking_Optogenetics_LEDCue.m\" and save raster plots as pdf.\n",
    "\n",
    "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/Licking/Analysis_Code/Analsysis_#18,#19_lickometer/')\n",
    "\n",
    "# loop over all .mat files in the directory\n",
    "for file in os.listdir():\n",
    "    if file.endswith(\".mat\"):\n",
    "        # load the data from the .mat file\n",
    "        mat = loadmat(file)\n",
    "        session_data = mat['SessionData']\n",
    "        n_trials = session_data['nTrials'][0, 0][0, 0]\n",
    "\n",
    "        raw_events = session_data['RawEvents'][0, 0]['Trial'][0, 0]\n",
    "\n",
    "        reward = {\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",
    "                reward['BNCHigh'][i] = np.nan\n",
    "                reward['BNCLow'][i] = np.nan\n",
    "            else:\n",
    "                reward['BNCHigh'][i] = trial['BNC1High'][0, 0]\n",
    "                reward['BNCLow'][i] = trial['BNC1Low'][0, 0]\n",
    "\n",
    "\n",
    "        # Remove empty cells\n",
    "        reward[\"BNCHigh\"] = [x for x in reward[\"BNCHigh\"] if not np.isnan(x).all()]\n",
    "        reward[\"BNCLow\"] = [x for x in reward[\"BNCLow\"] if not np.isnan(x).all()]\n",
    "\n",
    "        # Reward delivery time\n",
    "        trials = session_data[\"RawEvents\"][0, 0][\"Trial\"]\n",
    "\n",
    "        # Extract \"States\" for each trial\n",
    "        states_list = [trial.item()[0, 0][\"States\"][0, 0] for trial in np.nditer(trials, flags=['refs_ok'])]\n",
    "\n",
    "        # Extract \"Drinking\" states for each trial\n",
    "        drinking_list = [states['Drinking'][0] for states in states_list]\n",
    "\n",
    "\n",
    "        num_trials = len(reward[\"BNCHigh\"])\n",
    "\n",
    "        # Create a new figure with a specific width and height\n",
    "        fig = plt.figure(figsize=(12, 8))  # Adjust the width value (6) to make the figure less wide\n",
    "\n",
    "        for trial in range(num_trials):\n",
    "            licks = reward[\"BNCHigh\"][trial]\n",
    "            for lick_arr in licks:\n",
    "                for lick in lick_arr:\n",
    "                    ylicks = np.array([trial, trial + 0.2, np.nan])\n",
    "                    xlicks = np.array([lick, lick, np.nan])\n",
    "                    plt.plot(xlicks, ylicks, color='SteelBlue', linewidth=2)\n",
    "\n",
    "        y_height = num_trials + 1\n",
    "        #plt.ylim([1, y_height])\n",
    "        plt.ylim([1, 71]) # limit ylim to be consistent across days\n",
    "        plt.xlim([0, 20])\n",
    "        plt.ylabel(\"Trial\")\n",
    "        plt.xlabel(\"Time (s)\")\n",
    "\n",
    "        reward_time = np.round(drinking_list[0][0][0])\n",
    "        x_points = [reward_time[0], reward_time[0], reward_time[0] + 1, reward_time[0] + 1]\n",
    "        y_points = [0, y_height, y_height, 0]\n",
    "        color = [0, 0.4470, 0.7410]\n",
    "\n",
    "        cue = plt.fill(x_points, y_points, color, linestyle=\"none\")\n",
    "        cue[0].set_alpha(0.1)\n",
    "\n",
    "        plt.axvline(reward_time[0], linestyle=\":\", color=\"r\", linewidth=1.5)\n",
    "\n",
    "        # save the plot as a PDF file using the original .mat file name\n",
    "        filename = os.path.splitext(file)[0] + '.pdf'\n",
    "        plt.savefig(filename, dpi=1200)\n",
    "\n",
    "        plt.close()  # close the figure to free memory\n"
   ]
  }
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