playground/demo_convert.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/Users/oruebel/Devel/nwb/arcontrol/ArControl-convert2-nwb\")\n",
    "import arcontrol2nwb\n",
    "from ndx_beadl.plot import *\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert example data to NWB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arcfile = './2022-1113-224711.txt'\n",
    "nwb_filename = arcontrol2nwb.convert(\n",
    "    arc_data_filename=arcfile,\n",
    "    # arc_taskprogram_filename=arcfile  # TODO: The example is missing the ARControl XML task program\n",
    "    use_behavioral_time_series=True, \n",
    "    use_ndx_beadl=True, \n",
    "    append_to_nwb_file=False, \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data from NWB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pynwb import NWBHDF5IO\n",
    "\n",
    "io = NWBHDF5IO(nwb_filename, mode='r')\n",
    "nwb = io.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic visualizatons of the data in NWB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the events data\n",
    "fig = plot_states(states=nwb.get_acquisition(\"states\"),\n",
    "                  state_types=nwb.get_lab_meta_data('task').state_types,\n",
    "                  figsize=(18,4),\n",
    "                  marker_size=500)\n",
    "plt.title(\"States\", fontsize=18)\n",
    "plt.show()\n",
    "\n",
    "# Plot the actions data\n",
    "fig = plot_actions(actions=nwb.get_acquisition(\"actions\"),\n",
    "                   action_types=nwb.get_lab_meta_data('task').action_types,\n",
    "                   show_action_values=True,\n",
    "                   figsize=(18,4),\n",
    "                   marker_size=500)\n",
    "plt.title(\"Actions\", fontsize=18)\n",
    "plt.show()\n",
    "\n",
    "# Plot the events data\n",
    "fig = plot_events(events=nwb.get_acquisition(\"events\"),\n",
    "                  event_types=nwb.get_lab_meta_data('task').event_types,\n",
    "                  show_event_values=True,\n",
    "                  figsize=(18,4),\n",
    "                  marker_size=500)\n",
    "plt.title(\"Events\", fontsize=18)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the file with NWBWidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a766fe5d6ba14de2b9af6cf77bbba3bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Label(value='session_description:', layout=Layout(max_height='40px', max_width='…"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nwbwidgets import nwb2widget\n",
    "nwb2widget(nwb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print data parsed from the ARControl data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C1': array([[    0, 37681],\n",
      "       [58668,  8000]]),\n",
      " 'C1S1': array([[    0,  1002],\n",
      "       [14682,  1000],\n",
      "       [26261,  1000],\n",
      "       [58668,  1000]]),\n",
      " 'C1S2': array([[ 1002,  5000],\n",
      "       [15682,  2205],\n",
      "       [27261,  5000],\n",
      "       [59668,  5000]]),\n",
      " 'C1S3': array([[ 6002,  2000],\n",
      "       [32261,  2000],\n",
      "       [64668,  2000]]),\n",
      " 'C1S4': array([[17887,  2000]]),\n",
      " 'C1S5': array([[ 8002,  6680],\n",
      "       [19887,  6374],\n",
      "       [34261,  3420],\n",
      "       [66668,     0]]),\n",
      " 'C2': array([[37681, 20987]]),\n",
      " 'C2S1': array([[37681,  2000],\n",
      "       [45577,  2000]]),\n",
      " 'C2S2': array([[39681,  1421],\n",
      "       [47577,  5000]]),\n",
      " 'C2S3': array([[41102,  1000]]),\n",
      " 'C2S4': array([[52577,  2000]]),\n",
      " 'C2S5': array([[42102,  3475],\n",
      "       [54577,  4091]]),\n",
      " 'IN1': [[17887, 1220], [41102, 1313]],\n",
      " 'OUT1': [[0, 1002], [14682, 1000], [26261, 1000], [58668, 1000]],\n",
      " 'OUT2': [[37681, 2000], [45577, 2000]],\n",
      " 'OUT3': [[17887, 500]],\n",
      " 'OUT4': [[41102, 1000]],\n",
      " 'info': {'C0': {'label': 'End session'},\n",
      "          'C0S0': {'label': 'End session'},\n",
      "          'C1': {'label': 'Go-Trial'},\n",
      "          'C1S1': {'label': 'GoCue'},\n",
      "          'C1S2': {'label': 'Response window'},\n",
      "          'C1S3': {'label': 'Miss'},\n",
      "          'C1S4': {'label': 'Reward'},\n",
      "          'C1S5': {'label': 'Inter'},\n",
      "          'C2': {'label': 'NoGo-Trial'},\n",
      "          'C2S1': {'label': 'NoGo Cue'},\n",
      "          'C2S2': {'label': 'Resp. Win'},\n",
      "          'C2S3': {'label': 'Punish'},\n",
      "          'C2S4': {'label': 'Correct Rej.'},\n",
      "          'C2S5': {'label': 'Inter'},\n",
      "          'IN1': {'label': 'Lick'},\n",
      "          'IN2': {'label': ''},\n",
      "          'IN3': {'label': '#3'},\n",
      "          'IN4': {'label': '#4'},\n",
      "          'IN5': {'label': '#5'},\n",
      "          'IN6': {'label': '#6'},\n",
      "          'OUT1': {'label': 'GoCue'},\n",
      "          'OUT2': {'label': 'NoGoCue'},\n",
      "          'OUT3': {'label': 'Reward'},\n",
      "          'OUT4': {'label': 'Punish'},\n",
      "          'OUT5': {'label': ''},\n",
      "          'OUT6': {'label': ''},\n",
      "          'OUT7': {'label': '#7'},\n",
      "          'OUT8': {'label': '#8'},\n",
      "          'session_start_time': datetime.datetime(2022, 11, 13, 22, 47, 11, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=57600), 'PST')),\n",
      "          'task': 'MyGoNoGo_task',\n",
      "          'task_program_xml': None,\n",
      "          'task_xml_schema': None}}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "arcdata = arcontrol2nwb.arc2dict(\n",
    "    arc_data_filename=arcfile,\n",
    "    arc_taskprogram_aconf=None)\n",
    "pprint.pprint(arcdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and plot the States table form the NWB file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>state_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.002</td>\n",
       "      <td>6.002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.002</td>\n",
       "      <td>8.002</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.002</td>\n",
       "      <td>14.682</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.682</td>\n",
       "      <td>15.682</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.682</td>\n",
       "      <td>17.887</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.887</td>\n",
       "      <td>19.887</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.887</td>\n",
       "      <td>26.261</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26.261</td>\n",
       "      <td>27.261</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27.261</td>\n",
       "      <td>32.261</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32.261</td>\n",
       "      <td>34.261</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34.261</td>\n",
       "      <td>37.681</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37.681</td>\n",
       "      <td>39.681</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39.681</td>\n",
       "      <td>41.102</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41.102</td>\n",
       "      <td>42.102</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42.102</td>\n",
       "      <td>45.577</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45.577</td>\n",
       "      <td>47.577</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>47.577</td>\n",
       "      <td>52.577</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>52.577</td>\n",
       "      <td>54.577</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>54.577</td>\n",
       "      <td>58.668</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>58.668</td>\n",
       "      <td>59.668</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>59.668</td>\n",
       "      <td>64.668</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>64.668</td>\n",
       "      <td>66.668</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>66.668</td>\n",
       "      <td>66.668</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start_time  stop_time  state_type\n",
       "id                                   \n",
       "0        0.000      1.002           1\n",
       "1        1.002      6.002           2\n",
       "2        6.002      8.002           3\n",
       "3        8.002     14.682           5\n",
       "4       14.682     15.682           1\n",
       "5       15.682     17.887           2\n",
       "6       17.887     19.887           4\n",
       "7       19.887     26.261           5\n",
       "8       26.261     27.261           1\n",
       "9       27.261     32.261           2\n",
       "10      32.261     34.261           3\n",
       "11      34.261     37.681           5\n",
       "12      37.681     39.681           7\n",
       "13      39.681     41.102           8\n",
       "14      41.102     42.102           9\n",
       "15      42.102     45.577          11\n",
       "16      45.577     47.577           7\n",
       "17      47.577     52.577           8\n",
       "18      52.577     54.577          10\n",
       "19      54.577     58.668          11\n",
       "20      58.668     59.668           1\n",
       "21      59.668     64.668           2\n",
       "22      64.668     66.668           3\n",
       "23      66.668     66.668           5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwb.acquisition['states'].to_dataframe(index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the StatesTable we can merge the definition of the references states by using ``to_hierarchical_dataframe`` instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>source_table</th>\n",
       "      <th colspan=\"3\" halign=\"left\">states</th>\n",
       "      <th colspan=\"3\" halign=\"left\">state_types</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th>id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>id</th>\n",
       "      <th>state_name</th>\n",
       "      <th>state_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.002</td>\n",
       "      <td>1</td>\n",
       "      <td>C1S1</td>\n",
       "      <td>GoCue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.002</td>\n",
       "      <td>6.002</td>\n",
       "      <td>2</td>\n",
       "      <td>C1S2</td>\n",
       "      <td>Response window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6.002</td>\n",
       "      <td>8.002</td>\n",
       "      <td>3</td>\n",
       "      <td>C1S3</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.002</td>\n",
       "      <td>14.682</td>\n",
       "      <td>5</td>\n",
       "      <td>C1S5</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14.682</td>\n",
       "      <td>15.682</td>\n",
       "      <td>1</td>\n",
       "      <td>C1S1</td>\n",
       "      <td>GoCue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>15.682</td>\n",
       "      <td>17.887</td>\n",
       "      <td>2</td>\n",
       "      <td>C1S2</td>\n",
       "      <td>Response window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>17.887</td>\n",
       "      <td>19.887</td>\n",
       "      <td>4</td>\n",
       "      <td>C1S4</td>\n",
       "      <td>Reward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>19.887</td>\n",
       "      <td>26.261</td>\n",
       "      <td>5</td>\n",
       "      <td>C1S5</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>26.261</td>\n",
       "      <td>27.261</td>\n",
       "      <td>1</td>\n",
       "      <td>C1S1</td>\n",
       "      <td>GoCue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>27.261</td>\n",
       "      <td>32.261</td>\n",
       "      <td>2</td>\n",
       "      <td>C1S2</td>\n",
       "      <td>Response window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>32.261</td>\n",
       "      <td>34.261</td>\n",
       "      <td>3</td>\n",
       "      <td>C1S3</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>34.261</td>\n",
       "      <td>37.681</td>\n",
       "      <td>5</td>\n",
       "      <td>C1S5</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>37.681</td>\n",
       "      <td>39.681</td>\n",
       "      <td>7</td>\n",
       "      <td>C2S1</td>\n",
       "      <td>NoGo Cue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>39.681</td>\n",
       "      <td>41.102</td>\n",
       "      <td>8</td>\n",
       "      <td>C2S2</td>\n",
       "      <td>Resp. Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>41.102</td>\n",
       "      <td>42.102</td>\n",
       "      <td>9</td>\n",
       "      <td>C2S3</td>\n",
       "      <td>Punish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>42.102</td>\n",
       "      <td>45.577</td>\n",
       "      <td>11</td>\n",
       "      <td>C2S5</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>45.577</td>\n",
       "      <td>47.577</td>\n",
       "      <td>7</td>\n",
       "      <td>C2S1</td>\n",
       "      <td>NoGo Cue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>47.577</td>\n",
       "      <td>52.577</td>\n",
       "      <td>8</td>\n",
       "      <td>C2S2</td>\n",
       "      <td>Resp. Win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>52.577</td>\n",
       "      <td>54.577</td>\n",
       "      <td>10</td>\n",
       "      <td>C2S4</td>\n",
       "      <td>Correct Rej.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>54.577</td>\n",
       "      <td>58.668</td>\n",
       "      <td>11</td>\n",
       "      <td>C2S5</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>58.668</td>\n",
       "      <td>59.668</td>\n",
       "      <td>1</td>\n",
       "      <td>C1S1</td>\n",
       "      <td>GoCue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>59.668</td>\n",
       "      <td>64.668</td>\n",
       "      <td>2</td>\n",
       "      <td>C1S2</td>\n",
       "      <td>Response window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>64.668</td>\n",
       "      <td>66.668</td>\n",
       "      <td>3</td>\n",
       "      <td>C1S3</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>66.668</td>\n",
       "      <td>66.668</td>\n",
       "      <td>5</td>\n",
       "      <td>C1S5</td>\n",
       "      <td>Inter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "source_table states                      state_types             \\\n",
       "label            id start_time stop_time          id state_name   \n",
       "0                 0      0.000     1.002           1       C1S1   \n",
       "1                 1      1.002     6.002           2       C1S2   \n",
       "2                 2      6.002     8.002           3       C1S3   \n",
       "3                 3      8.002    14.682           5       C1S5   \n",
       "4                 4     14.682    15.682           1       C1S1   \n",
       "5                 5     15.682    17.887           2       C1S2   \n",
       "6                 6     17.887    19.887           4       C1S4   \n",
       "7                 7     19.887    26.261           5       C1S5   \n",
       "8                 8     26.261    27.261           1       C1S1   \n",
       "9                 9     27.261    32.261           2       C1S2   \n",
       "10               10     32.261    34.261           3       C1S3   \n",
       "11               11     34.261    37.681           5       C1S5   \n",
       "12               12     37.681    39.681           7       C2S1   \n",
       "13               13     39.681    41.102           8       C2S2   \n",
       "14               14     41.102    42.102           9       C2S3   \n",
       "15               15     42.102    45.577          11       C2S5   \n",
       "16               16     45.577    47.577           7       C2S1   \n",
       "17               17     47.577    52.577           8       C2S2   \n",
       "18               18     52.577    54.577          10       C2S4   \n",
       "19               19     54.577    58.668          11       C2S5   \n",
       "20               20     58.668    59.668           1       C1S1   \n",
       "21               21     59.668    64.668           2       C1S2   \n",
       "22               22     64.668    66.668           3       C1S3   \n",
       "23               23     66.668    66.668           5       C1S5   \n",
       "\n",
       "source_table                   \n",
       "label             state_label  \n",
       "0                       GoCue  \n",
       "1             Response window  \n",
       "2                        Miss  \n",
       "3                       Inter  \n",
       "4                       GoCue  \n",
       "5             Response window  \n",
       "6                      Reward  \n",
       "7                       Inter  \n",
       "8                       GoCue  \n",
       "9             Response window  \n",
       "10                       Miss  \n",
       "11                      Inter  \n",
       "12                   NoGo Cue  \n",
       "13                  Resp. Win  \n",
       "14                     Punish  \n",
       "15                      Inter  \n",
       "16                   NoGo Cue  \n",
       "17                  Resp. Win  \n",
       "18               Correct Rej.  \n",
       "19                      Inter  \n",
       "20                      GoCue  \n",
       "21            Response window  \n",
       "22                       Miss  \n",
       "23                      Inter  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hdmf.common.hierarchicaltable import to_hierarchical_dataframe\n",
    "to_hierarchical_dataframe(nwb.acquisition['states']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the transition count and probablity matrix\n",
    "state_transition_count_df, state_transition_probability_df = compute_state_transition_matrix(\n",
    "    states=nwb.acquisition['states'].to_dataframe(index=True).sort_values(by='start_time'), \n",
    "    state_types=nwb.get_lab_meta_data('task').state_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ig = plot_state_transition_graph(\n",
    "    state_transition_probability_df,\n",
    "    figsize=(13,7),\n",
    "    edge_color='darkblue',\n",
    "    edge_font_color='darkblue',\n",
    "    node_font_color='red',\n",
    "    fontsize=10\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and plot the TrialsTable from the NWB file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>states</th>\n",
       "      <th>events</th>\n",
       "      <th>actions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>37.681</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.681</td>\n",
       "      <td>58.668</td>\n",
       "      <td>[13, 14, 15, 16, 17, 18, 19]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[4, 5, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.668</td>\n",
       "      <td>66.668</td>\n",
       "      <td>[20, 21, 22, 23]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[7]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    start_time  stop_time                                      states events  \\\n",
       "id                                                                             \n",
       "0        0.000     37.681  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]    [0]   \n",
       "1       37.681     58.668                [13, 14, 15, 16, 17, 18, 19]    [1]   \n",
       "2       58.668     66.668                            [20, 21, 22, 23]     []   \n",
       "\n",
       "         actions  \n",
       "id                \n",
       "0   [0, 1, 2, 3]  \n",
       "1      [4, 5, 6]  \n",
       "2            [7]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the TrialsTable from file\n",
    "trials = nwb.trials\n",
    "display(trials.to_dataframe(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot trials\n",
    "plot_trials(\n",
    "    trials=trials,\n",
    "    states=nwb.acquisition['states'], \n",
    "    state_types=nwb.get_lab_meta_data('task').state_types,\n",
    "    actions=nwb.acquisition['actions'], \n",
    "    action_types=nwb.get_lab_meta_data('task').action_types,\n",
    "    events=nwb.acquisition['events'], \n",
    "    event_types=nwb.get_lab_meta_data('task').event_types,\n",
    "    figsize=None,\n",
    "    fontsize=18,\n",
    "    rectangle_height=1,\n",
    "    marker_size=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
  },
  "vscode": {
   "interpreter": {
    "hash": "aa5ae87545a60a58007824a570656f601d64201491a277889a0f31a3543334bd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}