diff --git a/src/constantinople_lab_to_nwb/mah_2024/mah_2024_notes.md b/src/constantinople_lab_to_nwb/mah_2024/mah_2024_notes.md
index 81d3f7a..d2ff9c7 100644
--- a/src/constantinople_lab_to_nwb/mah_2024/mah_2024_notes.md
+++ b/src/constantinople_lab_to_nwb/mah_2024/mah_2024_notes.md
@@ -165,4 +165,4 @@ Example task arguments:
 
 The following UML diagram shows the mapping of the raw Bpod output to NWB.
 
-![nwb mapping](mah_2025_uml.png)
\ No newline at end of file
+![nwb mapping](mah_2024_uml.png)
\ No newline at end of file
diff --git a/src/constantinople_lab_to_nwb/mah_2024/mah_2025_uml.png b/src/constantinople_lab_to_nwb/mah_2024/mah_2024_uml.png
similarity index 100%
rename from src/constantinople_lab_to_nwb/mah_2024/mah_2025_uml.png
rename to src/constantinople_lab_to_nwb/mah_2024/mah_2024_uml.png
diff --git a/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb b/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb
new file mode 100644
index 0000000..152609e
--- /dev/null
+++ b/src/constantinople_lab_to_nwb/mah_2024/tutorials/mah_2024_example_notebook.ipynb
@@ -0,0 +1,1547 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7a36ffb3-ce49-49af-853c-61d2fd3d364c",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "# Example notebook\n",
+    "\n",
+    "This tutorial demonstrates how to access an NWB file from the [Mah et al. 2023]((https://doi.org/10.1038/s41467-023-43250-x)) dataset using `pynwb`.\n",
+    "\n",
+    "The dataset from [“Distinct value computations support rapid sequential decisions”](https://doi.org/10.1038/s41467-023-43250-x) includes behavioral data from rats performing various decision-making tasks, collected using a Bpod system (Bpod State Machine r2, Sanworks).\n",
+    "\n",
+    "The behavioral task and data is stored using the [ndx-structured-behavior](https://github.com/rly/ndx-structured-behavior) extension for NWB.\n",
+    "\n",
+    "## Overview of NWB\n",
+    "\n",
+    "This schematic shows an overview of the data types added to NWB. \n",
+    "\n",
+    "![NWB mapping](../mah_2024_uml.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "173c17e4-7ee4-4e58-884e-2353a4ee46d5",
+   "metadata": {},
+   "source": [
+    "## Reading an NWB file\n",
+    "\n",
+    "This section demonstrates how to access an NWB file using `pynwb.NWBHDF5IO`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "52d493c5-1c7a-487f-971c-4e3ef6442abe",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-28T11:41:17.442258Z",
+     "start_time": "2024-08-28T11:41:15.123900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import ndx_structured_behavior\n",
+    "\n",
+    "nwbfile_path = \"/Users/weian/data/nwbfiles/sub-J076_ses-RWTautowait2-20230721-130627.nwb\"\n",
+    "\n",
+    "io = NWBHDF5IO(nwbfile_path, \"r\")\n",
+    "nwbfile = io.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750d82d9-13df-404e-8512-960613255b88",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-28T08:55:08.865637Z",
+     "start_time": "2024-08-28T08:55:07.711552Z"
+    }
+   },
+   "source": [
+    "## Accessing the task metadata\n",
+    "\n",
+    "The task-related general metadata is stored in a `Task` object which can be accessed as `nwbfile.lab_meta_data[\"task\"]`.\n",
+    "\n",
+    "The `EventTypesTable` is a column-based table to store the type of events that occur during the task (e.g. port poke from the animal), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].event_types`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "662468a8-23c6-4d90-8070-0575579b7e44",
+   "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>event_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>left_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>right_port_poke</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          event_name\n",
+       "id                  \n",
+       "0        state_timer\n",
+       "1     left_port_poke\n",
+       "2   center_port_poke\n",
+       "3    right_port_poke"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].event_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8035e39-8405-42c2-92cf-758b51845c77",
+   "metadata": {},
+   "source": [
+    "The `ActionTypesTable` is a column-based table to store the type of actions that occur during the task (e.g. sound output from the acquisition system), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].action_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2c1a5c0e-e4b6-4c16-af8a-fdaef0ffa353",
+   "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>action_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     action_name\n",
+       "id              \n",
+       "0   sound_output"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].action_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99b05bc7-d23e-4a19-8e5f-2a228702dc59",
+   "metadata": {},
+   "source": [
+    "The `StateTypesTable` is a column-based table to store the type of states that occur during the task (e.g. while the animal is waiting for reward), one type per row.\n",
+    "This table can be accessed as `nwbfile.lab_meta_data[\"task\"].state_types`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3ecfb7c9-e58f-4856-8916-e73d16bb326e",
+   "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>state_name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>go_cue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wait_for_side_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>announce_reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>opt_out</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>stop_sound</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            state_name\n",
+       "id                    \n",
+       "0        wait_for_poke\n",
+       "1       nose_in_center\n",
+       "2     punish_violation\n",
+       "3               go_cue\n",
+       "4   wait_for_side_poke\n",
+       "5      announce_reward\n",
+       "6              opt_out\n",
+       "7               reward\n",
+       "8           stop_sound"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].state_types[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12176f7b-b6f4-4f16-b8d0-37bde6132ec5",
+   "metadata": {},
+   "source": [
+    "The arguments for the task is stored in a `TaskArgumentsTable` which can be accessed as `nwbfile.lab_meta_data[\"task\"].task_arguments`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68998e44-3eb6-484e-8548-04de7a3816bd",
+   "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>argument_name</th>\n",
+       "      <th>argument_description</th>\n",
+       "      <th>expression</th>\n",
+       "      <th>expression_type</th>\n",
+       "      <th>output_type</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>reward_volume_ul</td>\n",
+       "      <td>The volume of reward in microliters.</td>\n",
+       "      <td>20</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nose_in_center</td>\n",
+       "      <td>The time in seconds when the animal is require...</td>\n",
+       "      <td>0.9483330026671162</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>time_increment_for_nose_in_center</td>\n",
+       "      <td>The time increment for nose in center in seconds.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>target_duration_for_nose_in_center</td>\n",
+       "      <td>The goal for how long the animal must poke cen...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>training_stage</td>\n",
+       "      <td>The stage of the training.</td>\n",
+       "      <td>9</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>reward_delay</td>\n",
+       "      <td>The delay in seconds to receive reward, drawn ...</td>\n",
+       "      <td>0.606090503076149</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>target_reward_delay</td>\n",
+       "      <td>The target delay in seconds to receive reward.</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>time_increment_for_reward_delay</td>\n",
+       "      <td>The time increment during monotonic increase o...</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>violation_time_out</td>\n",
+       "      <td>The time-out if nose is center is not satisfie...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>block_type</td>\n",
+       "      <td>The block type (High, Low or Mixed). High and ...</td>\n",
+       "      <td>High</td>\n",
+       "      <td>string</td>\n",
+       "      <td>string</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>num_trials_in_mixed_blocks</td>\n",
+       "      <td>The number of trials in each mixed blocks.</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>num_trials_in_adaptation_blocks</td>\n",
+       "      <td>The number of trials in each high reward (20, ...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>punish_sound_enabled</td>\n",
+       "      <td>Whether to play a white noise pulse on error.</td>\n",
+       "      <td>True</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>catch_percentage</td>\n",
+       "      <td>The percentage of catch trials.</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>is_catch</td>\n",
+       "      <td>Whether the trial is a catch trial.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>current_trial</td>\n",
+       "      <td>The current trial number.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>cumulative_reward_volume_ul</td>\n",
+       "      <td>The cumulative volume received during session ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>is_warm_up</td>\n",
+       "      <td>Whether the trial is warm-up.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>override_nose_in_center</td>\n",
+       "      <td>Whether the required time for maintaining cent...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>trials_in_stage</td>\n",
+       "      <td>The cumulative number of trials in the stages.</td>\n",
+       "      <td>50400</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>min_reward_volume_ul</td>\n",
+       "      <td>The minimum volume of reward in microliters. (...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>high_ITI</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>0</td>\n",
+       "      <td>double</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>auto_change_catch_probability</td>\n",
+       "      <td>Whether to change the probability automaticall...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>previous_was_violation</td>\n",
+       "      <td>Whether the previous trial was a violation.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>changed</td>\n",
+       "      <td>Whether a block transition occurred for the tr...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>num_trials_in_stage_3</td>\n",
+       "      <td>Determines how many trials occur in stage 3 be...</td>\n",
+       "      <td>400</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>num_trials_in_stage_8</td>\n",
+       "      <td>Determines how many trials occur in stage 8 be...</td>\n",
+       "      <td>250</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>numeric</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>center_port_cue</td>\n",
+       "      <td>Task parameter.</td>\n",
+       "      <td>False</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>boolean</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         argument_name  \\\n",
+       "id                                       \n",
+       "0                     reward_volume_ul   \n",
+       "1                       nose_in_center   \n",
+       "2    time_increment_for_nose_in_center   \n",
+       "3   target_duration_for_nose_in_center   \n",
+       "4                       training_stage   \n",
+       "5                         reward_delay   \n",
+       "6                  target_reward_delay   \n",
+       "7      time_increment_for_reward_delay   \n",
+       "8                   violation_time_out   \n",
+       "9                           block_type   \n",
+       "10          num_trials_in_mixed_blocks   \n",
+       "11     num_trials_in_adaptation_blocks   \n",
+       "12                punish_sound_enabled   \n",
+       "13                    catch_percentage   \n",
+       "14                            is_catch   \n",
+       "15                       current_trial   \n",
+       "16         cumulative_reward_volume_ul   \n",
+       "17                          is_warm_up   \n",
+       "18             override_nose_in_center   \n",
+       "19                     trials_in_stage   \n",
+       "20                min_reward_volume_ul   \n",
+       "21                            high_ITI   \n",
+       "22       auto_change_catch_probability   \n",
+       "23              previous_was_violation   \n",
+       "24                             changed   \n",
+       "25               num_trials_in_stage_3   \n",
+       "26               num_trials_in_stage_8   \n",
+       "27                     center_port_cue   \n",
+       "\n",
+       "                                 argument_description          expression  \\\n",
+       "id                                                                          \n",
+       "0                The volume of reward in microliters.                  20   \n",
+       "1   The time in seconds when the animal is require...  0.9483330026671162   \n",
+       "2   The time increment for nose in center in seconds.                   0   \n",
+       "3   The goal for how long the animal must poke cen...                   1   \n",
+       "4                          The stage of the training.                   9   \n",
+       "5   The delay in seconds to receive reward, drawn ...   0.606090503076149   \n",
+       "6      The target delay in seconds to receive reward.                 1.5   \n",
+       "7   The time increment during monotonic increase o...               0.025   \n",
+       "8   The time-out if nose is center is not satisfie...                   1   \n",
+       "9   The block type (High, Low or Mixed). High and ...                High   \n",
+       "10         The number of trials in each mixed blocks.                  40   \n",
+       "11  The number of trials in each high reward (20, ...                  40   \n",
+       "12      Whether to play a white noise pulse on error.                True   \n",
+       "13                    The percentage of catch trials.                0.25   \n",
+       "14                Whether the trial is a catch trial.               False   \n",
+       "15                          The current trial number.                   0   \n",
+       "16  The cumulative volume received during session ...                   0   \n",
+       "17                      Whether the trial is warm-up.               False   \n",
+       "18  Whether the required time for maintaining cent...               False   \n",
+       "19     The cumulative number of trials in the stages.               50400   \n",
+       "20  The minimum volume of reward in microliters. (...                   5   \n",
+       "21                                    Task parameter.                   0   \n",
+       "22  Whether to change the probability automaticall...               False   \n",
+       "23        Whether the previous trial was a violation.               False   \n",
+       "24  Whether a block transition occurred for the tr...               False   \n",
+       "25  Determines how many trials occur in stage 3 be...                 400   \n",
+       "26  Determines how many trials occur in stage 8 be...                 250   \n",
+       "27                                    Task parameter.               False   \n",
+       "\n",
+       "   expression_type output_type  \n",
+       "id                              \n",
+       "0          integer     numeric  \n",
+       "1           double     numeric  \n",
+       "2           double     numeric  \n",
+       "3           double     numeric  \n",
+       "4          integer     numeric  \n",
+       "5           double     numeric  \n",
+       "6           double     numeric  \n",
+       "7           double     numeric  \n",
+       "8           double     numeric  \n",
+       "9           string      string  \n",
+       "10         integer     numeric  \n",
+       "11         integer     numeric  \n",
+       "12         boolean     boolean  \n",
+       "13          double     numeric  \n",
+       "14         boolean     boolean  \n",
+       "15         integer     numeric  \n",
+       "16          double     numeric  \n",
+       "17         boolean     boolean  \n",
+       "18         boolean     boolean  \n",
+       "19         integer     numeric  \n",
+       "20          double     numeric  \n",
+       "21          double     numeric  \n",
+       "22         boolean     boolean  \n",
+       "23         boolean     boolean  \n",
+       "24         boolean     boolean  \n",
+       "25         integer     numeric  \n",
+       "26         integer     numeric  \n",
+       "27         boolean     boolean  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwbfile.lab_meta_data[\"task\"].task_arguments[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7511f54-0f3e-4bc1-971c-1100cb1902d7",
+   "metadata": {},
+   "source": [
+    "## Accessing the behavioral data\n",
+    "\n",
+    "The `TaskRecording` object stores the data for events, states, and actions that occured during the task. The `TaskRecording` is added as acquisition which can be accessed as `nwbfile.acquisition[\"task_recording\"]`.\n",
+    "\n",
+    "The `EventsTable` is a column-based table to store the information about the events (e.g. poke times), one event per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].events`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "21dab6d0-0392-48f3-9c1b-65055bab99bb",
+   "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>timestamp</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>event_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>42.9993</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>43.1775</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.0395</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>43.3815</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>44.0395</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19161</th>\n",
+       "      <td>5270.2239</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19162</th>\n",
+       "      <td>5271.2239</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19163</th>\n",
+       "      <td>5308.2722</td>\n",
+       "      <td>2</td>\n",
+       "      <td>In</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19164</th>\n",
+       "      <td>5308.2948</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Out</td>\n",
+       "      <td>center_port_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19165</th>\n",
+       "      <td>5309.2948</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Expired</td>\n",
+       "      <td>state_timer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19166 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timestamp  event_type    value        event_name\n",
+       "0        42.9993           2       In  center_port_poke\n",
+       "1        43.1775           2       In  center_port_poke\n",
+       "2        43.0395           2      Out  center_port_poke\n",
+       "3        43.3815           2      Out  center_port_poke\n",
+       "4        44.0395           0  Expired       state_timer\n",
+       "...          ...         ...      ...               ...\n",
+       "19161  5270.2239           2      Out  center_port_poke\n",
+       "19162  5271.2239           0  Expired       state_timer\n",
+       "19163  5308.2722           2       In  center_port_poke\n",
+       "19164  5308.2948           2      Out  center_port_poke\n",
+       "19165  5309.2948           0  Expired       state_timer\n",
+       "\n",
+       "[19166 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].events[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].event_types[:],\n",
+    "    left_on=\"event_type\",\n",
+    "    right_on=\"id\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7b526b5-7aa2-47df-a733-fa458d9d28fc",
+   "metadata": {},
+   "source": [
+    "The `ActionsTable` is a column-based table to store the information about the actions (e.g. sound onset times), one action per row. This table can be accessed as `nwbfile.acquisition[\"task_recording\"].actions`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "18183143-c735-427f-a54f-43d03e81f8c3",
+   "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>timestamp</th>\n",
+       "      <th>action_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.2117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>16.4018</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>42.9994</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>43.0396</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>43.5398</td>\n",
+       "      <td>0</td>\n",
+       "      <td>On</td>\n",
+       "      <td>sound_output</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestamp  action_type value   action_name\n",
+       "0    16.2117            0    On  sound_output\n",
+       "1    16.4018            0    On  sound_output\n",
+       "2    42.9994            0    On  sound_output\n",
+       "3    43.0396            0    On  sound_output\n",
+       "4    43.5398            0    On  sound_output"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].actions[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].action_types[:],\n",
+    "    left_on=\"action_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d47f67a-a13c-4f46-a703-6ab6753fe62b",
+   "metadata": {},
+   "source": [
+    "The `StatesTable` is a column-based table to store the information about the states (e.g. the duration while nose is in center port). This table can be accessed as `nwbfile.acquisition[\"task_recording\"].states`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4fd969f-8b11-4bbd-986a-b275413c8079",
+   "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",
+       "      <th>state_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.2116</td>\n",
+       "      <td>42.9993</td>\n",
+       "      <td>0</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>42.9993</td>\n",
+       "      <td>43.0395</td>\n",
+       "      <td>1</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43.0395</td>\n",
+       "      <td>44.0395</td>\n",
+       "      <td>2</td>\n",
+       "      <td>punish_violation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>44.1310</td>\n",
+       "      <td>72.9036</td>\n",
+       "      <td>0</td>\n",
+       "      <td>wait_for_poke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>72.9036</td>\n",
+       "      <td>74.0515</td>\n",
+       "      <td>1</td>\n",
+       "      <td>nose_in_center</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   start_time  stop_time  state_type        state_name\n",
+       "0     16.2116    42.9993           0     wait_for_poke\n",
+       "1     42.9993    43.0395           1    nose_in_center\n",
+       "2     43.0395    44.0395           2  punish_violation\n",
+       "3     44.1310    72.9036           0     wait_for_poke\n",
+       "4     72.9036    74.0515           1    nose_in_center"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(\n",
+    "    nwbfile.acquisition[\"task_recording\"].states[:],\n",
+    "    nwbfile.lab_meta_data[\"task\"].state_types[:],\n",
+    "    left_on=\"state_type\",\n",
+    "    right_on=\"id\",\n",
+    ").head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d983e620-b3d4-424b-bb53-cd64c5ec6cd8",
+   "metadata": {},
+   "source": [
+    "### Plot the events, actions, and states\n",
+    "\n",
+    "The ``plot_events``, ``plot_actions``, and ``plot_states`` functions can consume both the raw table as well as a subset of the table as a pandas DataFrame created through slicing, e.g., via ``events[:100]`` will plot only the first 100 rows from the events table.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e145e47-ebd3-4eb3-93c5-6e9d036c111b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "from ndx_structured_behavior.plot import plot_events, plot_actions, plot_states, plot_trials\n",
+    "\n",
+    "# Get the events from file\n",
+    "events = nwbfile.get_acquisition(\"task_recording\").events\n",
+    "event_types = nwbfile.get_lab_meta_data(\"task\").event_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_events(\n",
+    "    events=events[20:100],\n",
+    "    event_types=event_types,\n",
+    "    show_event_values=True,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Events\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b14f720f-2e2e-423a-ac16-35940f92e775",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the actions from file\n",
+    "actions = nwbfile.get_acquisition(\"task_recording\").actions\n",
+    "action_types = nwbfile.get_lab_meta_data(\"task\").action_types\n",
+    "\n",
+    "# Plot the data\n",
+    "fig = plot_actions(\n",
+    "    actions=actions[20:100],\n",
+    "    action_types=action_types,\n",
+    "    figsize=(18,4),\n",
+    "    marker_size=500,\n",
+    ")\n",
+    "plt.title(\"Actions\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "68ecfe11-c8f4-4449-a1f9-23a331258fea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YunQpEyZMyDe+1atXs2zZMt5++21iYmKIjo7mwoULDBs2DID58+fz6aefFvs5vP7668TExHDfffexe/duMjIyiI6OJjU1lb/++osPPviA+vXr52qTlZVF7969OXbsGN7e3vzvf/8jMTGR2NhY/vnnH3r27El2djbDhw/nhx9+KHaM+XnllVdwcnJi69atJCUlkZiYyL59+6hTpw7p6ekMHTqU7OzsPLH36dOH8PBwfH19WbZsGUlJScTFxfHbb7/RsmVLBg0adN1iFhEREREREREREZFbixJNYtWuXbsA6Ny5M2+++SZ+fn6WMn9/f7p27cqCBQuoVKlSkfo9e/Ysn3zyCQBjxoxh8uTJ+Pj4AODr68u7777LyJEjAZgxYwbnz5+32k9cXBzjx49n/PjxeHl5AVC2bFk+/fRTnnrqKQAmTZpEampqkeK7mvk5fPbZZ7Rq1cqyQsrJyYnatWvz+uuv88Ybb+Rqs2LFCsvWdMuWLePJJ5+0rOiqWbMm3333HS1btsRkMvHmm28WK76CODg4sG3bNoKCgrCzs8MwDJo3b25ZkRQeHs7u3btztVm5ciUHDhwAYPny5fTp0wcHh5yFj/fccw8//vgj/v7+hY4hLS2N+Pj4XJeIiIiIiIiIiIiI3D6UaBKrzMmfS5cukZWVVWL9rly5kszMTFxcXBgzZozVOuPHj8fZ2ZmMjAxWrFhhtY6rqyujRo2yWvbWW28BEB0dzaZNm4oVr/k55Jfwssa8JWDr1q2tbk3n4ODAxIkTATh27BhHjx4tVoz5GTp0KOXKlctzv0GDBtSoUQOAI0eO5Cr79ttvAWjbti2dOnXK09bNza1IybGpU6fi7e1tuQICAooyBRERERERERERERG5ySnRJFZ16tQJFxcXfv31V9q3b8+8efM4efJksfs1r5Zp3ry5ZSXS1Xx9fWnWrFmu+ldr1qxZvu1r165NlSpVCmxfWD179gRg0KBBvP7662zfvp3k5OQC25jH7Ny5c751goKCsLe3L5EY89OyZct8y8wr0aKjo3PdN8fSsWPHfNsWVHa1sWPHEhcXZ7nOnDlT6LYiIiIiIiIiIiIicvNTokmsqlWrFl999RUeHh7s3r2b5557jpo1a1KuXDn69evHmjVrMJlMRe734sWLAFSuXLnAeuZEkbn+1a7V3lyeX/vCeu+99wgKCiIxMZEZM2YQGBiIl5cXzZo1Y+LEiZw9ezZPm8LM0cXFhTJlypRIjPnx9PTMt8y8HV5GRkau+4WJ3fzaFIazszNeXl65LhERERERERERERG5fSjRJPl68sknCQ8PZ86cOfTr14+AgAAuXbrEsmXL6NWrFx06dLjtz9zx8fFh69athIWF8eabb9K2bVscHBw4ePAgkydPpnbt2ixdurS0wxQRERERERERERERKRVKNEmB/Pz8eOGFF/j22285ffo0J06cYMyYMRiGQVhYGMHBwUXqz3xmUERERIH1zOXWzhgCrK4kslaeX/uiateuHdOnT2fnzp3ExsayZs0aGjRoQEpKCs888wwXLlyw1C3MHFNTU7l8+bLVGM1b6qWmpubbPi4uzua5FMQcS0HP91rPXkRERERERERERETuHEo0SZHUqlWLqVOnMmDAAAA2bdpkKbOzy3k7FbSl3pVnL+WXLImNjc11lpM1Bw4cIDEx0WrZiRMnLEke83glycXFhYcffphVq1YBOQmhnTt3WsrNY27ZsiXfPkJDQ8nMzATyztHX1xcg3/OMEhIS+OOPP2yfQAHMsW/bti3fOlu3br0uY4uIiIiIiIiIiIjIrUeJJrEqLS2twHJXV1fg/5JLgOX8ndjY2Hzb9e7dGwcHB1JTU5k+fbrVOlOmTCEtLQ1HR0d69+5ttU5KSgoffPCB1bJ33nkHyFmN1aVLlwLnUZDMzEyys7PzLTc/A8j9HJ544gkAdu/ezU8//WS138mTJwNw7733cu+99+Yqb9SoEQArV660Ou4HH3xwzdfHVv369QNg586dhIaG5ilPSUnh/fffvy5ji4iIiIiIiIiIiMitR4kmsWrYsGH07duXlStXcvHiRcv9xMRE5syZw6JFiwDo0aOHpcycMAkLC+P48eNW+61cuTLDhw8HYNq0aUycONGSmIqNjWXChAmWRMbIkSOpWLGi1X68vb15++23mTp1KgkJCQBERUUxfPhwFi5cCMCECRNwcXGx9REQERFB7dq1eeedd/j1118tK5AAjhw5wlNPPQWAu7s7HTp0sJT17t2bli1bAtC3b1+WLFlCRkYGACdPnqR3797s3r0bgPfeey/PuP379wdg48aNTJw40XIOVlRUFOPGjeOdd97Bx8fH5nkVpHfv3tx3332Wn1euXElWVhYAf/zxB927d+fSpUvXZWwRERERERERERERufUYpoL2OZM71uDBgy0JGwAPDw8cHBxyrVZq164dP/74I+7u7gDExMRQp04dSyKiTJkylrJvv/2WVq1aAZCens7TTz/NsmXLgJzVQN7e3sTFxVlWEPXv35+FCxfi6OhoNa5BgwaRmppKSEgI9vb2eHl5ERsba9m2b+DAgcyfPz/XSqOiOnXqFDVq1LD8bm9vj7e3N4mJiaSnpwPg5OTE4sWLefzxx3O1PXv2LA888AC//fabpZ6bm5vl+dnZ2fHRRx/xn//8J8+4WVlZdOnSxbJ9nWEY+Pj4WNq+9957rF+/nu3btzNx4sQ852QZhgHkbH8XGBhodW6BgYH5tv/3338JDAy0bN3n7OyMi4sLcXFxODk5sXz5ch555JFrjmFNfHy85bU2r4ATERERERERERERkZtLUb7L1YomsWrChAnMnDmTRx99lLp16+Lg4EBiYiLlypWjS5cufP3114SGhloSSZBzttCOHTt44oknqFy5MnFxcYSHhxMeHk5qaqqlnpOTEyEhIaxYsYLu3bvj7+9PQkIC/v7+dO/enVWrVrFkyZI8SaarLV26lM8//5wmTZqQmZmJu7s7rVu3ZtGiRSxcuLBYSSbIWX21du1aRowYQatWrahYsSKJiYk4ODhwzz338Morr3Ds2LE8SSZz2wMHDjBjxgxatWqFq6srycnJBAQE8PTTT3Pw4EGrSSbISWht2LCBSZMmUbduXZycnDAMg65du7Jp0yZGjRpVrHldS82aNTl06BAjR46kRo0amEwmXFxcePzxx9m1axcPP/zwdR1fRERERERERERERG4dWtEkt5QrVzQtWLCgtMORItKKJhEREREREREREZGbn1Y0iYiIiIiIiIiIiIiIyHXnUNoBiIiUtupjNlh+PjWtRylGIiJSMsx/1/Q3TcR2+hyJ3Dz0eZSbWfUxG/TeFLmDXfmdUn70N0LuBFrRJCIiIiIiIiIiIiIiIjbRiia57e3atYvHHnusSG3atGnDqlWrrlNEIiIiIiIiIiIiIiK3ByWa5JayYMECFixYUKQ26enpXLhwoUhtoqOji1RfREREREREREREROROpK3z5LYXGBiIyWQq0hUaGlraYd/UAgMDMQyD4ODg0g5FREREREREREREREqREk0iIiIiIiIiIiIiIiJiEyWaRERERERERERERERExCZKNImIiIiIiIiIiIiIiIhNlGgSERERERERERERERERmyjRJHeswMBADMMgODiYjIwMPvzwQ5o1a4aPjw+GYRAaGmqpe+zYMYYOHUrt2rVxc3PDw8ODhg0b8t///peoqKg8fb/66qsYhsHjjz+epywjIwNPT08Mw6Bs2bKYTKY8dR544AEMw2DChAm57p88eZLp06fTrVs37r77btzd3fHw8OCee+7htdde4/Tp0yUy36ysLD799FPuu+8+3N3d8fPzIzAwkBUrVhTiyYqIiIiIiIiIiIjIncKhtAMQKW2pqakEBgaya9cuHBwcLEkgs/fee4+xY8eSnZ0NgJubGxkZGRw9epSjR48yf/58NmzYQJMmTSxtgoKC+OyzzwgNDcVkMuXqb9++fSQmJgIQFRXF0aNHadiwoaU8IyODnTt3AtCxY8dcsQ4ZMoTt27cD4OTkhKenJzExMfzxxx/88ccfLFiwgPXr19OuXTub55uWlsYjjzzCxo0bAbCzs8PJyYkdO3awfft2Ro8eXbQHLCIiIiIiIiIiIiK3La1okjverFmzOHLkCPPnzyc+Pp7o6GguXbpEw4YNmTdvHqNHj8bNzY13332X8+fPk5SURHJyMgcOHKBjx46cP3+ehx9+2JI8gv9bPXT58mUOHz6ca7xt27YB4OXlBcDWrVtzle/du5fk5GScnZ1p3bp1rrLGjRsza9Ys/vrrL1JSUoiKiiItLY29e/fSrVs34uLi6NevHykpKTbNF2Ds2LFs3LgRwzB45513iImJISYmhsjISF566SWmT5/OoUOHbH7eIiIiIiIiIiIiInL7UKJJ7niJiYksWbKEwYMH4+rqCoC/vz+Ojo6MGjUKgBUrVjBu3DgqVKgAgL29PU2bNmXjxo00bdqUiIgIvvrqK0uffn5+NGrUCMibSDL//tprrxVY3rp1a1xcXHKVffzxx7z88svUrl0bO7ucj6+DgwMtWrRg/fr1NGzYkHPnzrFy5coiz9fPz49z587x6aefAjB+/Hj++9//WhJi5cqV4/PPP6d///7ExcVd87lCzuqo+Pj4XJeIiIiIiIiIiIiI3D6UaJI7Xv369XnooYfy3F+5ciWxsbE0adKEBx54wGpbBwcH+vfvD2DZas4sKCgIyJ1ISktLY/fu3bi7uzNy5EjLlnRZWVmWOuYVT+b2hWVvb0+3bt0ALFvvWZPffCEnoZaZmYmrq6slyXa14ODgQsc0depUvL29LVdAQECh24qIiIiIiIiIiIjIzU+JJrnjtW3b1ur9n3/+GYA//viDChUq5HtNnjwZgPDw8FztzecrhYWFWRJJu3btIjU1lXbt2uHt7U3Lli2Ji4vj4MGDQM75Sbt37wbyTzSFhYUxePBg6tati4eHB4ZhWK733nsPgIiIiCLPF+DAgQMANGvWzLKS6Wp33303lStXzrePK40dO5a4uDjLdebMmUK1ExEREREREREREZFbg0NpByBS2sqVK2f1/rlz54Cc5E9qauo1+0lOTs71+/3334+9vT3x8fHs37+fVq1aWVYrmZNQHTt2JCwsjK1bt9KiRQt27dpFWloarq6utGzZMs8Yo0ePtiSTIGcVk6+vL05OTkDOtnhJSUkkJSUVeb4AFy9eBLhmIqlKlSqcPXu2wDoAzs7OODs7X7OeiIiIiIiIiIiIiNyatKJJ7nj29vZW75tXIfXr1w+TyXTN69SpU7nae3l50bRpU+D/ts8z/+eViSZr5W3btrUkj8w2bdpkSTK9/PLLHD16lLS0NKKjo4mMjCQyMpIRI0YAYDKZijxfEREREREREREREZGiUqJJJB8VKlQA8m6JVxRXntOUlJTEvn378PHx4b777gOgVatWuLq68vPPP5Oenp4nEXWlb7/9FoAHHniAWbNmce+99+ZJGkVGRtocK/zfaqdrrVYqzGomEREREREREREREbn9KdEkkg/zWUYHDx7k/PnzNvVhTjTt2rWLLVu2kJGRQYcOHbCzy/noOTk50bZtW5KTk9m8eTP79+/P1e5K5vONmjRpYnUsk8lkSVTZqlmzZkDOWU2JiYlW6/z9998FngElIiIiIiIiIiIiIncOJZpE8tGnTx98fHzIyMhg5MiRBW5Hl52dTWxsbJ777dq1w9HRkZSUFKZMmQLkXa1kTipNnjyZzMxMPDw8LAmfK3l7ewNw+PBhqzHMmTOHf//9t1Bzy0/v3r2xt7cnJSWFDz74wGqdyZMnF2sMEREREREREREREbl9KNEkkg8fHx8+/vhjIGfbuh49erB3716ys7OBnOTSH3/8wYcffkj9+vVZv359nj7c3d1p0aIFAHv37gXyJprMv5vL27dvj4ODQ56+unXrBsAPP/zA22+/TVJSEgCxsbFMmTKFV199FX9//2LNuXLlyrzyyisAvP3220ydOpWEhAQALl26xLBhw/jf//5nSXqJiIiIiIiIiIiIyJ1NiSaRAgwaNIjZs2fj5OTEDz/8QKtWrXBzc6NMmTK4uLhwzz33MGrUKI4fP45hGFb7uDKxVK5cOe69995c5c2aNcPT09Pyu7Vt8wAGDhxI+/btAXjrrbfw9PTEz88Pf39//vvf/9KtWzdeeuml4k6Z6dOn07lzZ7Kzsxk3bhy+vr74+flRvnx5Zs2axejRo2ncuHGxxxERERERERERERGRW58STSLX8OKLL/Lnn38yatQoGjVqhLOzM7GxsZYt7l599VU2bdpE//79rba/MnFkLYnk4OBgSSDlVwfA0dGRn376iYkTJ3L33Xfj6OiIyWSiRYsWzJ49m7Vr12Jvb1/M2YKLiws//PADn3zyCY0bN8bJyQmTyUT79u1ZtmwZ06ZNK/YYIiIiIiIiIiIiInJ7MEwFHTwjIlKC4uPj8fb2Ji4uDi8vr9IOx6L6mA2Wn09N61GKkYiIlAzz3zX9TROxnT5HIjcPfR7lZlZ9zAa9N0XuYFd+p5Qf/Y2QW1VRvsvViiYRERERERERERERERGxiRJNIiIiIiIiIiIiIiIiYhNtnSciN8zNunWeiNxYV28toG0ERERERERERERuLto6T0RERERERERERERERK47h5LoJDs7m4MHDxIeHk5ycjIDBw4siW5FRERERERERERERETkJlbsFU2ffvopFStWpFWrVvTr148hQ4bkKo+JieHee++lbt26XLhwobjDiYiIiIiIiIiIiIiIyE2iWImmV155hddee41Lly7h6emJYRh56vj6+nLffffx999/s3z58uIMJyIiIiIiIiIiIiIiIjcRmxNNP/74I7Nnz8bDw4PvvvuO2NhYypYta7XugAEDMJlMbN682eZARW4lq1evJjg4mNWrV5d2KCXm1KlTBAcHExwcXNqhiIiIiIiIiIiIiMhNwuZE05w5czAMg8mTJ/PII48UWLd169YAHD161NbhRG4pq1evZtKkSbddomnSpElMmjSptEMRERERERERERERkZuEzYmmvXv3AvDMM89cs663tzdeXl5ERkbaOpyIiIiIiIiIiIiIiIjcZGxONEVHR+Pt7Y2np2fhBrKzIzs729bhRERERERERERERERE5CZjc6LJy8uL+Ph4MjIyrlk3OjqauLg4ypQpY+twIsUSGhpKnz59qFy5Ms7OzpQpU4ZOnToxf/58srKy8tQPDg7GMAwCAwMBWLZsGR06dMDPzw93d3eaNm3KZ599lqdtaGgohmGwcOFCABYuXIhhGLmu0NDQYs8nMjKSN954g/r16+Pu7o67uzv169fnzTff5MKFC/k+A3MMBbEWZ/Xq1QkKCspTx3wNHjy42HMSERERERERERERkVuPg60NGzRowPbt29m7dy/t2rUrsO7SpUsxmUw0a9bM1uFEbDZy5Eg++ugjICdB4u3tTWxsLFu3bmXr1q3873//Y/Xq1fmuzhs9ejTvvfcehmHg4+NDamoqv/zyC7/88gvr169nzZo1ODs7A+Dk5ET58uWJi4sjNTUVFxcXvL29c/Xn5ORUrPls376dXr16ERsbC4C7uzsAv//+O7///jtfffUVa9euvebnsijKli1LfHw8MTExAJQvXz5X+dVzFBEREREREREREZE7g80rmh5//HFMJhPBwcEFbol3+PBhxo8fj2EY9O/f39bhRGzy2WefWZJMQ4cO5dy5c8TExBAXF8dHH32Eg4MDW7du5fnnn7fa/tChQ7z33nsMGzaMCxcuEB0dTUxMDG+//TaGYbBx40bGjh1rqd+mTRsiIyPp168fAP369SMyMjLX1aZNG5vnc+bMGUuS6Z577mHnzp0kJiaSmJjIjh07qFOnDjExMTzyyCOcPXvW5nGutn//flatWmX5/eo5ffLJJyU2loiIiIiIiIiIiIjcOmxOND3//PPcc889bNu2jS5durB+/XrLNmJ///03mzZt4j//+Q9t2rQhLi6OVq1a0adPnxILXORaUlJSmDhxIgD9+/fniy++oEKFCkDOKqDXXnuNGTNmABASEsLBgwfz9BEXF8fTTz/Np59+StmyZYGcbSPHjx/Pf//7XwA+/fRTzp07dyOmxJQpU4iNjcXX15ctW7bQtm1bS1n79u3ZvHkzXl5eREdHM3Xq1BsSU0HS0tKIj4/PdYmIiIiIiIiIiIjI7cPmRJOjoyMbNmzg7rvvZtu2bTzyyCNcvnwZgLp169KtWzdmzZpFSkoKDRo0YOXKldc8G0akJG3atIno6Ggg58wla15++WUqVqwIwJIlS6zWeeutt6zef+ONN3B1dSUzM5OVK1cWP+BrMJlMLFu2DIAXX3zRkjS7UpUqVXjxxRcB+Pbbb697TNcydepUvL29LVdAQEBphyQiIiIiIiIiIiIiJcjmRBNAtWrVOHjwIJMmTaJq1aqYTKZcV6VKlQgODmbXrl1WvxQXuZ4OHDgAQEBAAHfffbfVOvb29nTs2DFX/SsFBARw1113WW3r5eVF06ZN821b0k6ePGlJnHXu3Dnfel26dAHg8uXLnDx58rrHVZCxY8cSFxdnuc6cOVOq8YiIiIiIiIiIiIhIyXIobgdubm5MmDCBCRMmcO7cOc6dO0dWVhYVKlSgWrVqJRGjiE0uXrwIQOXKlQusV6VKlVz1r3SttuZya21L2pVjFBSXeT7mNjVq1LiucRXE2dkZZ2fnUhtfRERERERERERERK6vYiearlSpUiUqVapUkl2KiIiIiIiIiIiIiIjITapYW+eJ3MzKlSsHQERERIH1zOXm+lc6e/ZsgW3N5dbalrQrxyhoTleWXdnGweH/8sqpqalW28bFxRUnRBERERERERERERG5wxQ70WQymVi5ciV9+vShRo0auLu74+7uTo0aNejTpw8rV64kOzu7JGIVKZJmzZoBOYmXv/76y2qdrKwstm3bBkDz5s3zlJ85c4Z//vnHatuEhAQOHjyYaywzO7ucj5bJZLIteCtq1KiBn58fAFu2bMm33ubNmwHw9/fPtW2er6+v5ef8zkrau3dvvv2a5wQlOy8RERERERERERERuXUVK9F0+vRpWrduTd++fVm1ahXh4eGkpKSQkpJCeHg4q1atom/fvrRq1Yrw8PCSilmkULp06YK/vz8AwcHBVut88cUXnDt3DoD+/ftbrfP2229bvf/hhx+SkpKCg4MDvXv3zlXm5eUFQGxsrA2RW2cYBv369QNy4o6MjMxT59y5c3zxxRdA3vncfffduLq6ArBy5co8bbOzs5k6dWq+45vnBCU7LxERERERERERERG5ddmcaIqLi6NDhw7s378fk8lE69atGT9+PLNnz2b27NmMHz+eNm3aYDKZOHDgAEFBQdqWS24oV1dXS4Jp6dKlvPjii1y4cAGA5ORkZs6cyWuvvQZAv379aNq0aZ4+vL29WbhwIcOHDycqKgrIWck0ZcoUJk+eDMArr7yS52yye++9F4CwsDCOHz9eYnMaN24cPj4+REdH07lzZ3bt2mUp+/nnn+ncuTOxsbH4+fkxZsyYXG0dHR0tCbEpU6awbNky0tPTAfjzzz959NFHOXLkSL5j33333Tg5OQHw1VdfaVWTiIiIiIiIiIiIiGCYbPy2+M033+SDDz7Az8+PkJAQOnXqZLXetm3b6NOnDzExMYwaNYrp06cXK2CRoho5ciQfffQRkLMqyMfHh4SEBDIzMwEICgpizZo1eHp6WtoEBwczadIkOnToQMuWLXnvvfcsbePj48nKygKgc+fOrFu3DhcXl1xjxsTEUKdOHS5dugRAmTJlcHd3B+Dbb7+lVatWNs9n+/btPPLII5bErbnfpKQkAHx8fFi7di3t27fP0zYiIoKWLVtaVnE5Ojri6upKfHw8np6erFu3jsDAQCDns2v+2ey5555j3rx5ALi5uVGmTBkMw+Dxxx/ngw8+uGbs8fHxeHt7ExcXl2uFlIjcWaqP2ZDr91PTepRSJCIiIiIiIiIiYk1Rvsu1eUXTd999h2EYzJkzJ98kE+R8iT9nzhzLWU4iN9qMGTPYunUrvXv3pnz58iQmJuLp6UlQUBBff/01mzZtypVkutr06dP59ttvadeuHSaTCScnJxo3bswnn3zCjz/+mCfJBDnnIe3YsYMnnniCypUrExcXR3h4OOHh4aSmphZrPh06dOCPP/7g9ddfp169emRnZ2MymahXrx6jRo3ijz/+sJpkAqhSpQp79+7lueeeo3LlygB4eHgwcOBAfvnlFzp06FDg2LNmzSI4OJgGDRoAOdtnhoeHW1Z7iYiIiIiIiIiIiMidxeYVTeazXpKSkrCzKzhflZWVhYeHBwApKSm2DCdyQ125oik0NLS0w7ltaEWTiIBWNImIiIiIiIiI3OyK8l2ug62D+Pr6kpKScs0kE4C9vT0uLi6W5JSIiIiIiIiIiIiIiIjc+mzeOq9NmzbEx8fz119/XbPuX3/9RVxcHO3atbN1OBEREREREREREREREbnJ2JxoGjNmDI6Ojrz88sukpaXlWy89PZ2XX34ZR0dHxowZY+twIiIlrvqYDXm28BKR6+/UtB65LilZ+rsmUnz6HImUPvO/1fV5lJuV3p8ior8BIv/H5kRTs2bNWLZsGQcPHqRx48bMnz+fU6dOkZGRQUZGBqdOnWL+/Pk0adKEX375hRUrVnDfffeVZOwit6SQkBAqVKhQpGv48OGlHbaIiIiIiIiIiIiISB42n9Fkb29v+Tk+Pp7nnnuuwPq9evWyet8wDDIzM20NQ+S6CA4OJjg4+Lr0nZKSwoULF4rUJi4u7rrEIiIiIiIiIiIiIiJSHDYnmkwmU0nGIXLHGDx4MIMHDy7tMEREREREREREREREis3mRNO2bdtKMg4RuYWcOnWKGjVqAHDy5EmqV69eugGJiIiIiIiIiIiISKmwOdHUoUOHkoxDREREREREREREREREbjF2pR2AiIiIiIiIiIiIiIiI3JpsTjQ988wz7N27tyRjERERERERERERERERkVuIzYmmBQsW0KZNGxo1asTnn39OfHx8ScYlIiIiIiIiIiIiIiIiNzmbE01t27bFZDJx9OhRXn31VSpVqsSzzz57061yiomJYd68efTt25cGDRrg5+eHi4sL1apVY8CAAezZs8dqu+DgYAzDIDAwEIAtW7bQo0cPypYti4uLC/Xq1WPSpEmkpqZabT948GAMw2Dw4MEArFixgsDAQPz8/HBzc6Nx48Z88sknZGdnFxh/aGgoffr0oXLlyjg7O1OmTBk6derE/PnzycrKKtTY1ixYsADDMKhevfp1i/3MmTO8+eabNG7cGG9vb1xdXalVqxaPPPIIixYtyvfZ/fzzzzz11FNUq1YNFxcXvL29adGiBdOnTycxMbHAMQvr1KlTGIaBYRicOnWKf/75h6FDh1KjRg2cnZ3zPJfs7GwWL17Mgw8+SPny5XFycqJs2bJ07dqVpUuXYjKZctXPysrCx8cHwzBYv359nvGXLl1qGX/UqFF5ys+fP28p/+eff3LFsWXLFv7zn//QqlUrqlSpgpOTE/7+/nTo0IE5c+aQkZFRInM+e/YsL7zwAgEBATg7O1OlShWGDBnCiRMnCvmURUREREREREREROR2Z3OiKSwsjD/++IMRI0bg7+9PcnJyrlVOs2fPvilWOX3yySc899xzLF++nD/++MNy//Tp0yxdupQ2bdowc+bMAvt4//336dKlCz/88AOZmZmkp6dz/PhxgoODefDBB/NN+JgNGzaMPn36EBYWhslkIiUlhcOHD/Paa68xZMiQfNuNHDmSoKAgVqxYwfnz53FzcyM2NpatW7fyzDPP0LVrVxISEor2QIrI1ti/+eYb7r77bt5//30OHz5Mamoq7u7unD59mrVr1zJo0CCOHz+eq012djbDhw+nXbt2LF68mNOnT+Po6EhSUhL79+9nzJgxNGvWjPDw8BKd465du2jcuDFz587l4sWLODo65iqPjo4mKCiIp556ih9++IGLFy/i5uZGVFQUmzZtYsCAAfTq1Yv09HRLG3t7e+6//34Atm7dmmfMK+8VVF61alVq1apluX/69Gk6d+7Mp59+yt69e4mLi8PNzY3o6Gh27NjBSy+9RKdOnUhJSSnWnH/55RcaNGjAl19+SUREBPb29sTFxbFgwQKaNm3Kvn37CuxfRERERERERERERO4MNieaAOrUqcOHH35IREQES5Yssaz+OXr0KMOGDbspVjlVqlSJiRMncuDAAZKTk4mOjiYlJYV///2X4cOHAzkJnV9//dVq+8OHDzNmzBjGjBnDxYsXiYmJITY2lrfeeguAbdu2sXDhwnzHX7t2LXPnzmXGjBnExMQQExNDVFQUzz33HACLFi2ymmj47LPP+OijjwAYOnQo586dIyYmhri4OD766CMcHBzYunUrzz//fLGeT0FsjX3Dhg0MGjSI1NRU2rZtS1hYGCkpKURFRZGUlERYWBjPP/88Tk5OudpNnDiRmTNnUq5cOWbNmsXly5dJSEggJSWFbdu20aRJE/78808ee+yxa66mKooXXniB+vXrs3//fpKSkkhMTOSnn34CclYmPfbYY+zYsYPGjRuzbt06kpKSiI2NJTExkYULF1KuXDnWrl3L6NGjc/UbFBQEWE8kbdu2DQAvLy8OHz5MdHS01XJzH2YODg48+eSTrF271vJ8YmNjSUhIYP78+VSqVImwsDD++9//2jznhIQEHn30UWJiYqhatSo//fQTSUlJJCQksGvXLgICAnjhhRcK+3hFRERERERERERE5HZmKmEnTpwwjR492lShQgWTYRgmwzBMdnZ2poYNG5o+//xzU1xcXEkPWSyvvPKKCTA9++yzue5PnDjRBJgA08SJE622feyxx0yAqXPnznnKBg0aZGk/f/58q+2bNm1qAkzPPfdcrvvJyckmPz8/E2Dq37+/1bYzZ8609H/gwAGrYw8aNMj6pE0m0/z5802AqVq1aiUae0ZGhqlGjRomwNSuXTtTWlpavjFc6eTJkyZ7e3uTq6ur6dChQ1brxMfHm6pUqWICTN99912h+i1oPPMcq1WrZkpISLBab9GiRSbAVLduXVNsbKzVOgcOHDAZhmFycnIyXbhwwXL/0KFDJsBkGIYpKirKcj88PNwEmGrVqmUaMGCACTCtXLkyV5/mZ7hgwYIizWv//v0mwOTu7m5KSUmxac7Tp083ASYnJyfT77//nqf8/PnzJl9fX0tfJ0+ezDee1NRUU1xcnOU6c+aMCbhp/g5UG73eVG30+tIOQ0SkROnvmkjx6XMkUvrM/1bX51FuVnp/ioj+BsjtLi4urtDf5RZrRZM1tWrVYtq0aZw5c4aVK1fSsmVLTCYTx44ds6xyGjp0aK5t7EpTjx49ANi5c6fVcmdnZ6tn6AA88sgjABw5ciTf/gMCAhg0aJDVsocffthq+02bNllWuAQHB1tt+/LLL1OxYkUAlixZku/4xWFL7Nu2bePkyZMAfPTRR3lWLeVnwYIFZGVl0a1bNxo1amS1jqenJ7169QJg48aNheq3MIYNG4aHh4fVsnnz5gHw0ksv4e3tbbVO06ZNqV+/Punp6ZaVSAANGzbE398fk8mU6775544dO9KxY0cg96qn8PBwyzO8ekXTtTRr1oxy5cqRlJTEoUOH8q1X0Jy//fZbAPr06UO9evXylFeoUIEXX3yxUPFMnToVb29vyxUQEFCodiIiIiIiIiIiIiJyayjxRJNZWFgYy5Yt49ChQxiGgclkAiA5OZl58+bRsGFDXnvttRLdAi0///77L6NGjaJp06b4+Phgb2+PYRgYhsGDDz4IQEREhNW29evXz/cL+UqVKgHk2fbsSs2bN8cwjCK1P3DgAJCT6Ln77ruttrW3t7ckKcz1S5otse/atQvISUY0a9as0GP9/PPPAPz0009UqFAh32v+/PkAJXpOU9u2ba3ez8rKYs+ePUBOwq+guP788888cRmGYdlO0tqZTB07drS6vZ7555o1a1K1atU8caWnpzNnzhy6du1KpUqVcHZ2tryfDcPg4sWLQP7v6YLmnJ6eztGjRy3x5aegsiuNHTuWuLg4y3XmzJlCtRMRERERERERERGRW4NDSXZ26dIl5s+fz1dffcU///xjSS41bdqUl156iccff5xNmzbx6aefsmPHDj799FPKli17zfNkiuO7776jf//+pKWlWe55eXnh4uKCYRikp6cTExNDUlKS1faenp759u3gkPP4MjMz861TmPYZGRm57psTBZUrV863LUCVKlVy1S9ptsQeGRkJQLVq1Yo01rlz5wBISkrK97W4UnJycpH6L0i5cuWs3o+Ojra8b2JiYgrV19VxBQUFsXLlylyJpCtXNJUrV45q1arxxx9/EBkZSYUKFfI9nwlyXuvOnTtbkkEALi4ulClTBnt7eyDnc5idnV3gcyxozub3c0HvP/N771qcnZ1xdnYuVF0RERERERERERERufWUyIqmTZs20adPHwICAhg7diwnTpzA1dWVZ599lv3797N//36eeeYZvLy86N27N6GhocyZMweTycSCBQtKIgSrLl++zODBg0lLS6Njx46EhoaSnJxMXFwcFy5cIDIykuXLl1+38e9E+a2AupasrCwARo8ejclkuuYVGhpaYjGbEzT5xQTwww8/FCquq7c6NK/8+fPPPzl37hwnTpzgzJkz1K9f35LsuXr7vIISTSNGjODo0aP4+/vz9ddfc/78eVJSUrh06RKRkZFERkZaVpuZE71FmbOIiIiIiIiIiIiISFHYvKIpMjKSr7/+mnnz5nHq1CnLl9r33HMPL774IgMHDsTLyyvf9kOHDmXMmDElugXa1b7//nvi4+Px9fVl3bp1uLm5WZ3HzcacgCho67Mry69enWJebZSamppv27i4uOKEmK8KFSoARd/azrz93PV8PxSVv78/Dg4OZGZm2hxXvXr1qFChApGRkWzdutWy4unKreeCgoKYP38+W7dupVmzZpbX9ert6TIyMli1ahUAn332GU888USe8bKysoiKirIpVgA/Pz/s7e3Jysri7Nmz+dYrqExERERERERERERE7hw2r2iqWrUqEyZM4OTJkzg6OtK/f3+2b9/OsWPHGDZsWIFJJjMvL69cq0ZKmvk8mDp16lhNMgFs3rz5uo1vK/PZRhEREfz1119W62RlZVlWvjRv3jxXma+vL0CB5+Hs3bu3JELNo02bNkBOAq8oZ0eZzwzavHlzgQmyG8nR0ZEWLVoAsG7dOpv7ufKcpiu3zTO7ckWTubxOnTpUrFgxVz+XLl2yPJsmTZpYHWvnzp3Fen5OTk40bNgQ+L+VVdZcuRWgiIiIiIiIiIiIiNy5bE40ZWZmUr16daZNm0ZERASLFy+mffv2ReojJCTkun5h7e3tDcBff/1l9cv3Q4cOsWTJkus2vq26dOmCv78/QJ6t2My++OILy7lG/fv3z1XWqFEjAPbv32812fTHH39YVsaUtKCgIGrWrAnkbPOWnp5eqHbPPPMMDg4OREVFMXHixALrpqenk5iYWOxYC2Po0KFAzuq477//vsC60dHRVu9fmUgKDQ3Fzs7OknyCnLOQateuzcmTJ5k/fz5gfds8Ly8vy9aEhw8fzlOemZlZIued9evXD4Dly5fz559/5im/ePEic+bMKfY4IiIiIiIiIiIiInLrsznR9MMPP/DPP//w5ptvUqZMGZv6aNmyJR06dLA1hGvq2rUrdnZ2REdH8+STT1q2+0pPT2fZsmV07doVT0/P6za+rVxdXS0JpqVLl/Liiy9y4cIFAJKTk5k5cyavvfYakJMUaNq0aa72Dz30EB4eHmRkZNC3b19LsiAjI4M1a9bQuXNn3N3dr0vs9vb2fPbZZxiGwc6dO+nUqRM7d+4kOzsbyHn2oaGhPPXUU/z++++WdrVq1WLChAkAvPfeewwcOJBjx45ZyjMzMzl06BCTJ0/mrrvu4tChQ9cl/qs99dRTdO7cGZPJxKOPPso777xjSfABJCUlsW3bNl555RVLgu1q5qRReHg4kZGRNGnSBB8fn1x1zMko80oza4kmDw8Py8qvkSNHsnXrVstzPXbsGA8++CAHDhwo9mv70ksvUaVKFdLS0ujWrRtbtmyxbI25d+9eOnfubBlXRERERERERERERO5sNiea6tWrV6RzWs6dO8fp06dtHc4mtWvX5o033gBg1apVVKlSBR8fHzw8POjXrx8eHh7MnDnzhsZUWMOGDWPEiBFAzuqlihUr4ufnh7e3N8OHDycjI4OgoCDmzp2bp623tzcff/wxhmGwZ88e6tati5eXFx4eHvTq1YuqVasyefLk6xZ79+7dWbBgAc7OzuzcuZP27dvj5uZGmTJlcHd3JygoiMWLF+dZ7TRhwgQmTJiAYRh88803NGjQwNLOxcWFJk2aMHHiRM6cOWNZ2XO92dvbs3LlSnr27El6ejoTJkygcuXKeHt74+vri6enJx07duTzzz8nKSnJah933XUXAQEBlt+vPnsJ8iaWrlzxdKWPP/4Yd3d3zp49S6dOnXBzc8PLy4sGDRqwbds25s6da3Pi18zLy4vvvvsOHx8fTp06RefOnfHw8MDT05NWrVpx6tQpvvjii2KNISIiIiIiIiIiIiK3B5sTTdWrV7ecX1MYbdu2zXfFx/U0bdo0Fi1aRIsWLXB1dSUjI4O77rqLcePG8euvv1KpUqUbHlNhzZgxg61bt9K7d2/Kly9PYmIinp6eBAUF8fXXX7Np06Z8V2Q9++yzbNiwgY4dO+Ll5UVmZiZ3330306ZNY/v27ddtRZPZwIEDOX78OK+99hr33HMPDg4OpKSkUK1aNXr16sU333xDvXr1crUxDIPJkydz5MgRXn75ZerVq4e9vT1xcXH4+vrSpk0b3njjDXbt2mVZ2XMjeHl5sW7dOr7//nv69etH1apVSUtLIzk5mcqVK9O1a1emTp1qdZs5sysTSfklmszJs/r161OuXDmr/TRt2pR9+/bRt29fypQpQ3Z2Np6envTt25ddu3bx9NNPF3O2OZo1a8aRI0d47rnnqFy5MpmZmXh7ezNo0CB++eWXIn32RUREREREREREROT2ZZjMe2IVkZ2dHRUqVMi1jVhBatSowenTp8nKyrJlOBG5DcTHx+Pt7U1cXBxeXl6lHQ7Vx2wA4NS0HqUciYhIyak+ZoP+rokUkz5HIqXP/G910L/X5eak/z0pIvo3o9zuivJdrs0rmooqNTUVBweHGzWciIiIiIiIiIiIiIiIXGc3JPNz7tw5Ll26lO92YCIitrjy/+VoC/2/Tm5NxX3dpfTdSZ+90ni/3knPV26cO+1vrz5HUlh32mfjRtLn8PZ1u3xu9B4VKZzb5TNvjf4OiPyfQieaduzYQWhoaK57iYmJTJ48Od82JpOJ2NhYvv/+e0wmEy1btrQ5UBEREREREREREREREbm5FDrRtG3bNiZNmoRhGJZ7SUlJTJo06ZptTSYTLi4ujB071rYoRaw4c+YMzZs3L1KbgIAA9u/ff50iEhERERERERERERG5sxQ60VS9enU6dOhg+X379u04OjrSunXrfNvY2dnh5eXFvffey6BBg7jrrruKF63IFbKysrhw4UKR2ri4uFynaERERERERERERERE7jx2ha04aNAgtm3bZrkA/Pz8ct27+tqyZQvfffcdb7/99m2VZKpevTqGYbBgwYIbOm5WVhYzZsygSZMmuLu7YxgGhmGwevXqGxrHjRAcHIxhGAQGBuZbp3r16phMJqvXxIkTAejQoUOu+6dOnboxE7hJBAYGYhgGwcHBpR2KiIiIiIiIiIiIiNyGCr2i6Wrz58/H1dW1JGO5LZi/0B88eDDVq1cv0b5fe+01PvvsMwCcnJwoX748oFU6IiIiIiIiIiIiIiJSOmxONA0aNKgk47il1KpVCxcXF7y9vfOUmc+sCgwMLNFEU0JCAl988QUA7733HqNGjcp1XtbtpkyZMtSpU4eqVauWdigiIiIiIiIiIiIiIpIPmxNNd7ItW7bc8DGPHz9ORkYGAC+99NJtnWQCGDZsGMOGDSvtMEREREREREREREREpACFPqNJSldycrLlZw8Pj1KMREREREREREREREREJMctkWhq0KABhmFYzie60u7duzEMA8MwePzxx/OUZ2Rk4OnpiWEYuVYi7dmzh9GjR9O+fXuqVauGi4sLPj4+tGrViunTp5OYmJhvPNWrV8cwDBYsWGC5N3jw4FyrjIKCgixxGYZh8zZ6CxYswDAMAgMDLfeu7PfK+2arVq2iZ8+elC9f3nKWU8+ePfnuu+/yHccc/+DBgzGZTHz11Ve0a9cOf3//PHO1RUhICN27d6d8+fI4Ojri4+ND7dq1efjhh5k1axapqam56gcHB+c7P7MffviBLl264OPjg4eHB40aNeK9996zrPy6llOnTvHaa69Rv359PDw8cHNzo27dugwfPpzTp08XZ7q5XPl+SUhIYOzYsdSpUwdXV1fKlClDr1692Lt3b4F9ZGVl8fXXX9OxY0fKlCmDs7MzlStXpk+fPoSGhtoc28KFC3F0dMQwDP773//mKd+wYQO9e/emcuXKODs74+vry/3338/s2bNJT0+3eVwRERERERERERERuT3cElvnBQUFcezYMbZu3ZpnO7WtW7dafg4NDcVkMuVK+Ozbt4/ExEScnZ1p27at5X7r1q0tP7u5ueHm5kZMTAx79+5l7969LFq0iG3btlGuXLlCxejt7U358uW5cOECAL6+vjg5OVnKy5YtW7RJ/3+urq6UL1+e9PR0YmJiAChfvryl3M/Pz/Jzeno6AwcOJCQkBAA7Ozu8vb2Jiopiw4YNbNiwgf79+1uSC9aYTCb69OnDypUrLe3t7IqXj3zmmWeYP3++5XcPDw8yMjI4ceIEJ06cYN26dfTo0aNIybjg4GDLeVgAPj4+/P7774wePZoNGzbkeq2tWbx4Mc8++yxpaWkAODs7Y2dnx59//smff/7J/PnzWbFiBV27di3aZAsQExND8+bN+fPPP3FycsLFxYXLly+zZs0a1q1bx9y5c3nmmWfytIuLi6NXr16WhJK9vT2enp6cP3+eFStWsGLFCkaNGsX7779fpHimTZvG2LFjsbOz47PPPuOVV16xlKWkpDBw4EBWrFhhuefl5UVcXBxhYWGEhYWxaNEivv/+e3x9fW17ICIiIiIiIiIiIiJyy7slVjQFBQUBsH37drKzs3OVbdu2Dcj5Evzy5cscPnzYanmrVq1wcXGx3H/ooYcICQnh/PnzJCUlER0dTXJyMqtWraJOnTr8/vvvvPjii4WO8ZNPPiEyMtLy+6pVq4iMjLRc+/fvL9qk/79+/foRGRnJqlWrLPeu7PfK++PGjSMkJATDMJgwYQKXL18mOjqaqKgoxo0bB8DSpUuZMGFCvuOtWrWKNWvW8MEHHxATE0N0dDRxcXE88MADNsW/c+dO5s+fj52dHdOnT+fy5cskJCSQlJREVFQUGzduZNCgQbmScteydu1aS5KpT58+nD59mpiYGOLj45k1axZ79uxh9uzZ+bbftGkTAwcOJCsrizfffJOTJ0+SkpJCUlISx48fp0+fPiQkJFj6LimTJk3i4sWLLFu2jKSkJOLi4vj999/p0KED2dnZvPDCC/zyyy952j377LOEhobi5OTEzJkziY+PJyYmhnPnzlkSUx988AFz5swpVBwmk4nhw4czduxYnJ2dCQkJyZVkAhg6dCgrVqygZs2aLF68mLi4OOLi4khOTmbNmjXUrFmTPXv2WE2MiYiIiIiIiIiIiMid45ZINAUGBmJnZ0d0dDSHDh2y3E9LS2PXrl24ubkxdOhQIPcKpyt/NyerzNauXUvfvn2pUKGC5Z6rqyuPPvooW7ZswdnZmdWrV5doouF6Onv2LJ988gkAY8aMYfLkyfj4+AA5q6veffddRo4cCcCMGTM4f/681X4SExOZMWMGr7/+Ol5eXkDOCqSKFSvaFNeuXbsA6Ny5M2+++WauFVj+/v507dqVBQsWUKlSpUL3OXbsWAA6dOjAt99+S0BAAJDz+r388svMnDmT2NhYq22zs7N55ZVXyM7OZtasWUyfPt2ytZ1hGNSpU4dly5bx8MMPEx8fz4wZM2yatzVxcXEsX76cPn364OCQs5iwXr16/PDDD9SuXZvMzMw8ScC9e/eycuVKAD799FNeffVV3NzcAKhQoQLz5s2jd+/eAEyYMCHPFoRXS09P54knnmDmzJl4e3vz448/5tlyMiwsjP/973+UK1eO0NBQBgwYYHkvuLi48PDDD7N9+3bc3d1ZvXp1rs/k1dLS0oiPj891iYiIiIiIiIiIiMjt45ZINPn6+tKoUSMgdyJpz549pKSk0LZtW7p165anPC0tjd27dwN5E00FqVy5Mo0aNcJkMlkSJTe7lStXkpmZiYuLC2PGjLFaZ/z48Tg7O5ORkZFrS7Qr+fr68sILL5RYXOZk16VLl8jKyip2f0eOHOH3338HcuZjbVu/559/nsqVK1ttv2PHDv7++2/KlCnDc889l+84AwcOBGDjxo3Fjtmsbdu2dOrUKc99V1dX3njjDQB+/PFH4uLiLGXmbRCrVKmSb7xvv/02AFFRUWzatCnf8ePj4+nWrRvLli2jYsWK7Nixw+oZWPPmzQPgySeftCTxrlalShXLZ6qgZzR16lS8vb0tV379iYiIiIiIiIiIiMit6ZZINAF07NgRyJ1IMv/csWNH2rRpg7OzM2FhYZaExq5du0hNTcXV1ZVWrVrl6i87O5slS5bw8MMPU7VqVVxdXS2rWgzDYN++fQBERETciOkV24EDBwBo3ry5ZfXJ1Xx9fWnWrFmu+ldr3rx5kbaxu5ZOnTrh4uLCr7/+Svv27Zk3bx4nT560uT9z3A4ODrRv395qHTs7O6sJFICff/4ZyFldVKlSJSpUqGD1ev755wEIDw+3Odarmd/DBZVlZ2fn2j7PPN+goKB8z8qqV6+eJbGW3+t6/vx5OnTowLZt27j77rvZtWsXDRs2tFrX/IzmzZuX7/OpUKECmzdvBgp+RmPHjrVsuxcXF8eZM2fyrSsiIiIiIiIiIiIitx6HkujkyJEjbNy4kfDwcFJSUiwrIgAyMjK4dOkShmHYvP0a5HzR/uGHHxIWFkZmZiYODg6W85c6duxoSSZt376d/fv306pVK0t5mzZtciVPkpOT6dmzp6UcwMnJCT8/PxwdHQGIjo4mIyODpKQkm2O+kS5evAiQ70oesypVquSqf7Vy5cqVaFy1atXiq6++4sUXX2T37t2WFWZly5YlKCiIAQMG8PDDD2MYRqH6M8ddpkwZnJ2d861nnufVzp07B+S8Ly9cuHDN8VJSUgoVV2EU9NpcWXbla1OU1/Xs2bP5vq5ffvklkLP13ebNmwtcWWR+RoXd6i45OTnfMmdn5wJfJxERERERERERERG5tRVrRVNcXBy9e/emSZMmjBkzhs8//5wFCxbkqpORkUGjRo0ICAjgt99+s3ms+++/HwcHBxITE9m3bx/Jycns3bsXb29vmjZtCuRd9ZTf+Uzvvvsu27Ztw9XVlY8++ojw8HBSU1O5fPkykZGRREZG0rJlSwBMJpPNMd+K7O3tS7zPJ598kvDwcObMmUO/fv0ICAjg0qVLLFu2jF69etGhQ4cbdnaPebVby5YtMZlMhbpuBz179sTb25vU1FSGDBlSYHLI/Ixmz55dqOdz9WdeRERERERERERERO4cNieaMjIy6N69O6tXr8bNzY0ePXrg4uKSp56bmxtDhgwhOzs733OBCsPT09OSUNq6dSs7d+4kPT2d+++/35IcMSeUtm7dSlJSkmX7u6u3LPv2228BeOutt3jttdeoWrVqnhU1kZGRNsdaGswrka611Z+5vKRXLl2Ln58fL7zwAt9++y2nT5/mxIkTjBkzBsMwCAsLIzg4uFD9mOOOiooiPT0933pnz561er9ChQpAyW6JV1j5xXR12ZWvTUm9rk2bNmXz5s34+vqyZcsWevToke9qvdJ8RiIiIiIiIiIiIiJya7E50TRv3jz27NlDzZo1+fPPP1m7di3e3t5W6/bu3RuAHTt22DockDuRdOW2eWYtW7bEzc2NXbt2sWXLFjIyMvDw8KB58+a5+jGfE9OkSROr45w6dYoTJ07YFKM5YXWjV8JcefZSXFyc1TqxsbG5znIqTbVq1WLq1KkMGDAAgE2bNhWqnXmemZmZhIWFWa2TnZ1NaGio1bK2bdsCOYnE/M4zul6u3KoxvzI7O7tc70vzfLdt20Z2drbVtsePH7ckqgp6XZs1a8aWLVvw8/MjNDSU7t27k5iYmKee+RmtX7/+GjMSERERERERERERkTudzYmmpUuXYhgGH330EZUqVSqwbpMmTbCzs+P48eO2Dgf8X1Jp9+7d/PDDD7nuQc45S23btiUlJYUpU6YA0K5dOxwcch9FZU6IHT582Oo4Y8aMsTlGLy8vICepcyP17t0bBwcHUlNTmT59utU6U6ZMIS0tDUdHR0vy73pLS0srsNzV1RXISbAURsOGDalXrx6QswWiteTL119/ne8KoKCgIO666y4ARowYUeCqKMg5q6uk7Ny502oCLDU1lQ8//BCABx54AB8fH0vZE088AeSsePrqq6+s9vvWW28BOedWde7cucAYmjRpwtatWylTpgxhYWF069aNhISEXHWGDh0KwLFjx5g9e3aB/SUlJV3zGYqIiIiIiIiIiIjI7cvmRNPRo0cxDIOuXbtes66TkxPe3t5cvnzZ1uGAnJUWTk5OpKamcvjwYcqWLUuDBg1y1TEnnvbu3QvkPZ8JoFu3bgC88847rFq1iszMTABOnjzJgAEDWLZsGb6+vjbFeO+99wKwePHiAs/BKWmVK1dm+PDhAEybNo2JEydakl2xsbFMmDCB999/H4CRI0dSsWLFGxLXsGHD6Nu3LytXruTixYuW+4mJicyZM4dFixYB0KNHj0L3+e677wI5q3wGDBhgSSqlpqYyZ84chg0blitZcyUHBwfmzJmDg4MDO3fu5P7777esfjP7999/mTNnDs2bN+fzzz8v6pTz5e3tTe/evVmxYoXlPXf8+HF69OjB8ePHsbe3Z/LkybnatGjRwpIUfPXVV/nss88s76vIyEief/55li9fDsDbb79tdfvKqzVq1IitW7dStmxZfv75Zx544IFcZ2R16NCBIUOGAPDKK68wYsQI/v33X0t5Wloae/bs4c0336RatWq5XlcRERERERERERERubPYnGhKTk7G09MTJyenQtXPyMjIs7KoqNzc3GjZsqXl98DAwDxnK12dWLKWaHrnnXcoX748CQkJ9O7dG1dXV3x8fKhZsyZLly7l3XffpWHDhjbF+OKLLwKwcuVKfHx8qFKlCtWrV6ddu3Y29VcUU6ZMoW/fvphMJiZPnoy/vz9+fn74+/vzzjvvANC/f3/efvvt6x6LWUZGBsuXL+fxxx+nfPnyeHp64uvri6enJy+99BLp6em0a9eO//73v4Xu89FHH7XUDwkJISAgAD8/P0ufLVq04KWXXsq3fadOnVi+fDmenp7s3buXzp074+7uTpkyZXBxcaFWrVq89NJLHDhwIM/7qzgmTpxI2bJl6dOnDx4eHvj4+FCvXj22bt2KYRjMnj3bslXelebNm0eHDh1IT0/n1VdfxdvbGz8/PypVqmRZ5TRq1CjLe68wGjRoQGhoKOXLl2f37t106dIl1yq8OXPm8Nxzz2Eymfj444+pVasWnp6e+Pn54ebmRuvWrXn//fe5fPlyiT4jEREREREREREREbm12JxoKlOmDPHx8VbPeLnayZMnSUxMvOYWe4VxZeLoym3zzJo1a2bZvs7Ly4v77rsvT51q1apx4MABnn32WUtMLi4u9OzZk40bNzJ27Fib43vqqaf45ptvaNeuHW5ubpw/f57w8PB8t3IrSU5OToSEhLBixQq6d++Ov78/CQkJ+Pv70717d1atWsWSJUtwdHS87rGYTZgwgZkzZ/Loo49St25dHBwcSExMpFy5cnTp0oWvv/6a0NBQ3N3di9TvO++8w/r16+nYsSNeXl6kpaVRr149pk2bxpYtW66ZAO3VqxcnTpxg4sSJtGjRAg8PD2JjY3F2dqZRo0Y899xzfPfdd7zxxhvFmX4uvr6+7Nu3jzFjxlC1alXS0tLw8/PjoYce4ueff+b555+32s7b25stW7Ywb948AgMD8fT0JDExkQoVKtC7d2+2bdtmWa1WFPfccw+hoaFUrFiRffv20blzZ2JiYoCc99LcuXPZtWsXgwcPplatWmRlZVleu8DAQN566y2OHDlC5cqVi/VcREREREREREREROTWZZhMJpMtDXv37s3q1atZsmQJ/fr1A6BixYpcvHiRrKysXHVHjhzJxx9/zJAhQ5g3b17xoxa5hVSvXp3w8HDmz5/P4MGDSzucUhUfH4+3tzdxcXGWhHBxVB+zoVjtT00r/JaNcvMo7usupe9O+uyVxvv1Tnq+cuPcaX979TmSwrrTPhs3kj6Ht6/b5XOj96hI4dwun3lr9HdAbndF+S7X5hVNzzzzDCaTiQkTJnDu3Ll8633xxRd88sknGIbB0KFDbR1OREREREREREREREREbjI2H5rUo0cPevfuzcqVK2nWrBkDBgwgJSUFgC+//JLw8HDWr1/PsWPHMJlMPP/887nOVxIREREREREREREREZFbm81b5wGkpqby/PPPs3jxYgzDyFNu7vqZZ55hzpw5ODjYnNcSuWVp67z/U9Jb54mIiIiIiIiIiIhIySvKd7nFyvy4uLjwzTff8MILL/DVV1+xa9cuzp07R1ZWFhUqVKBt27YMHTqU+++/vzjD3DYee+wxdu3aVaQ2q1atok2bNtcposK7lWMvCc2bN+fMmTNFarN//34CAgKuU0QiIiIiIiIiIiIiIqWvRJYYtWvXjnbt2pVEV7e16OhoLly4UKQ26enp1ymaormVYy8Jly5dKvL8s7KyADh16tR1iEhEREREREREREREpPQVa+s8EZGi0NZ5IiIiIiIiIiIiIje/onyXa2frIHZ2dlSuXLnQ9WvUqKEzmkRERERERERERERERG4jNieaAIq6GEqLp0RERERERERERERERG4fxUo0FUV6ejp2djdsOBEREREREREREREREbnObkjmJzY2losXL+Lr63sjhhMREREREREREREREZEboNCHJh05coRDhw7lupeSksKiRYvybWMymYiNjWXFihVkZ2fTpEkTmwMVERERERERERERERGRm0uhE03fffcdkydPznUvPj6eIUOGXLOtyWTCMAxGjhxZ9AhFrgOTycSCBQv48ssvOXbsGHZ2dtStW5dnn32W559/niFDhrBw4UIGDRrEggUL8rRftWoVX3/9Nfv37ycmJgZfX1+aN2/Os88+y6OPPlri8f700098/fXX7N69m4sXL+Lq6kqVKlXo0KEDAwYMoHXr1pa6wcHBTJo0iQ4dOhAaGmq1v9DQUIKCgizPwpqEhARmzZrFmjVr+PPPP0lKSqJcuXK0bduW4cOH5xpTRERERERERERERO5MhU40+fj4ULVqVcvv4eHh2NnZUaVKlXzb2NnZ4eXlxb333svQoUNp37598aIVKQFZWVk8+eSThISEAGAYBj4+Phw4cIB9+/YRGhqKk5OT1bbp6ekMHDjQ0tbOzg5vb2+ioqLYsGEDGzZsoH///ixcuBBHR8dix5qcnMzgwYNZvny55Z6npyfZ2dkcPXqUo0ePEhYWlme1YXEdOnSIhx56iIiICADs7e1xc3MjIiKCkJAQli1bxrvvvsvYsWNLdFwRERERERERERERubUU+oym4cOHc/LkScsFULZs2Vz3rr7++ecffv31V7755hslmeSm8f7771sSRSNHjuTSpUtER0cTExPDlClT+Pbbb1m7dq3VtuPGjSMkJATDMJgwYQKXL18mOjqaqKgoxo0bB8DSpUuZMGFCicQ6ZMgQli9fjp2dHaNHj+bMmTPEx8cTGxvLpUuXWLx4cYmvLDp//jwPPPAAERERPPbYYxw4cICUlBTi4+O5cOECEyZMwN7ennHjxrF69eoSHVtEREREREREREREbi2FXtF0tYkTJ+Lh4VGSsYhcd0lJSUydOhWAZ599lg8//NBS5uXlxdixY0lLS2PSpEl52p49e5ZPPvkEgDFjxuTaStLX15d3332X1NRUZsyYwYwZMxg+fDgVK1a0OdYtW7awbNkyAD777DNeeumlXOVlypRhwIABDBgwwOYxrBk/fjwXL15kwIABLF68OFdZuXLlmDx5Mr6+vowcOZLg4GB69eqVb19paWmkpaVZfo+Pjy/RWEVERERERERERESkdBV6RdPVJk6cyOuvv16SsYhcdz/99JMl2fHf//7Xap3XX38dNze3PPdXrlxJZmYmLi4ujBkzxmrb8ePH4+zsTEZGBitWrChWrF9//TUA9957b54k0/WSmprKkiVLABg9enS+9QYOHAjA4cOHuXDhQr71pk6dire3t+UKCAgo2YBFREREREREREREpFTZnGgSuRX98ssvAFStWpUaNWpYrePp6UnTpk3z3D9w4AAAzZs3x8vLy2pbX19fmjVrlqu+rXbt2gVAz549i9VPURw8eJDU1FQAunbtSoUKFaxe9evXt7QJDw/Pt7+xY8cSFxdnuc6cOXPd5yAiIiIiIiIiIiIiN47NW+ddadeuXezcuZOIiAiSkpIwmUxW6xmGwbx580piSBGbXLp0CYBKlSoVWK9y5cp57l28eDHfsitVqVIlV31bRUZGAlCtWrVi9VMU586ds/xc0EqlKyUnJ+db5uzsjLOzc7HjEhEREREREREREZGbU7ESTX///TcDBgywrBIxM5lMGIZh9Z4STXIzuPr9eTMqjRizsrIsP6ekpODi4nLDYxARERERERERERGRW4fNW+ddvnyZjh07cvDgQcqVK0efPn0wmUy4uLjw1FNP0alTJzw8PDCZTPj7+zNo0CDLuS4ipaVs2bJA7pU71pw9ezbPvXLlygEQERFRYFtzubm+rSpUqAAUvDWdNQ4OOflj8xZ41sTFxRU4pi3jioiIiIiIiIiIiMidx+ZE08cff8zZs2dp2bIl//zzD99++y0A3t7eLFq0iJ9++olz587xxhtvEBUVhaurK/Pnzy+xwEVscd999wE5SZRTp05ZrZOYmMjBgwfz3L/y7KX8EjWxsbG5znIqjjZt2gCwbt26IrXz9fUFKPA8pL1791q937x5c5ycnGwaV0RERERERERERETuPDYnmjZs2IBhGEyZMgU3Nzerddzd3Zk+fTrDhw/niy++YPny5TYHKlISunbtipeXFwBTpkyxWuejjz6yeu5Q7969cXBwIDU1lenTp1ttO2XKFNLS0nB0dKR3797FivXZZ58F4LfffmP27NmFbteoUSMgZ9WWtYTSxYsXmTt3rtW27u7uDBgwAIDp06dz+vTpAseKjo4udFwiIiIiIiIiIiIicvuxOdH0zz//YBgG7du3z3U/PT09T90xY8YA8OWXX9o6nEiJcHd3Z/To0QDMnTuXN99805IsSUhIYPr06QQHB1tWBV2pcuXKDB8+HIBp06YxceJEYmNjgZyVTBMmTOD9998HYOTIkVSsWLFYsQYFBfHEE08AMGzYMMaOHZtr276oqCi++uorS0LKrE2bNlSrVg2AQYMGceDAAUwmE9nZ2YSGhhIYGEh2dna+406ZMoVKlSoRFRVF69at+eabb0hISLCUX7p0iZUrV/Loo4/Sv3//Ys1RRERERERERERERG5thslkMtnS0M3NDXd3dy5dumS55+HhQUZGBmlpaXnq+/n54ejoyIULF2yPVqQEZGZm0r9/f1asWAGAnZ0d3t7exMfHk5WVxdNPP41hGCxatIgXXniBOXPmWNqmp6fz9NNPs2zZslxt4+LiLMmb/v37s3DhQhwdHYsda3JyMk8//TSrVq2y3PPy8sIwDMv2fY0aNeLQoUO52m3cuJGHHnqIjIwMIOfzmp2dTWpqKrVr12by5MmWJJG1PwF//PEHvXr14q+//rLM08fHh7S0NJKSkiz1OnfuzKZNmwo9n/j4eMvzMq8sExEREREREREREZGbS1G+y7V5RVOlSpXybC9Wvnx5MjMz+ffff3Pdz8jIID4+Pt9zbURuJAcHB5YtW8ZXX31FixYtcHV1JTMzk2bNmvHVV1+xaNEiy0olHx+fXG2dnJwICQlhxYoVdO/eHX9/fxISEvD396d79+6sWrWKJUuWlEiSCXISRCtXrmT9+vU8+uijVKpUidTUVBwcHGjYsCH/+c9/rK4UfOCBBwgLC6Nnz574+vqSlZVFQEAAY8aM4eDBg1SoUKHAcevVq8eRI0f44osv6Nq1K2XKlCE+Ph6TycRdd91Fnz59+PLLLy0JNxERERERERERERG5M9m8oqlTp06EhoYSHh5OlSpVAHjiiSdYvnw5EydO5K233rLUnTt3Li+88AI1atTgn3/+KZnIRa4Tk8lE1apViYiIYNGiRTz99NOlHdJtQyuaRERERERERERERG5+N2RFk/lsptDQUMu9p59+GpPJxDvvvMMrr7zC3LlzGTZsGMOGDcMwDHr16mXrcCI3zDfffENERAQODg507ty5tMMREREREREREREREblp2Zxo6tOnD1WrVmXLli2Wez169OCJJ54gMzOTOXPm8OKLLzJ79mwyMjKoW7durlVOIqXJfEZTVFSU5d6FCxeYNm0azz//PAADBw6kYsWKpRWiiIiIiIiIiIiIiMhNz+at8/JjMpn46quvCAkJ4cyZM3h7e9OtWzdef/11vL29S3IoEZv5+PhYzgxzc3PD0dEx1xli7du3Z/369drerYRp67ybR/UxG0qsr1PTepRYXyJQsu9P0Hv0ZnY7/i3S+1duVqXxebsdP+MiRaH/TpBbxe3y9/pO+MzdLq+ViFxfJfW3IjstmTMf9y3Ud7kOJTLiFQzD4Pnnn7esChG5Gc2cOZMffviBX3/9lYsXL5KYmEjZsmVp3LgxTzzxBE8//TSOjo7FHufMmTM0b968SG0CAgLYv39/sccWEREREREREREREbneSjzRJHIrGDhwIAMHDrzu42RlZXHhwoUitXFxcblO0YiIiIiIiIiIiIiIlCybz2iys7OjcuXKha5fo0YNHByU15I7S/Xq1TGZTEW6Tp06Vdphi4iIiIiIiIiIiIgUis2JJsg5j+l61peSYxgGhmEQGhp6U/Z3PQUGBmIYBsHBwSXab3BwMIZhEBgYWKL93irji4iIiIiIiIiIiIjcsCVG6enp2NkVK68lckc4dOgQq1evxsfHh9dee620wxERERERERERERERydcNSTTFxsZy8eJFfH19b8RwYkWdOnUAcHNzK+VIbryqVatSp04dypQpU9qhFMqhQ4eYNGkS1apVKzDRVKZMGerUqUPVqlVvXHAiIiIiIiIiIiIiIlcodKLpyJEjHDp0KNe9lJQUFi1alG8bk8lEbGwsK1asIDs7myZNmtgcqBTP8ePHSzuEUlPQe/RWNmzYMIYNG1baYYiIiIiIiIiIiIjIHazQiabvvvuOyZMn57oXHx/PkCFDrtnWZDJhGAYjR44seoQiIiIiIiIiIiIiIiJyUyr0oUk+Pj5UrVrVcgHY2dnlunf1Vb16dRo2bMiTTz5JaGgoDzzwwHWbyI0QGBiIYRgEBweTnp7OtGnTaNiwIe7u7vj6+tKlSxd++OEHq20Nw8AwDEJDQwvVf0HtExISGD9+PHXr1sXV1RV/f3969uzJ3r178+27oPFjYmJ46623uO+++/Dy8sLJyYkKFSrQsGFDXnzxRbZs2VLgc7ElnsLKyMigTJkyGIbBzJkzC6z79ddfYxgGXl5eJCcnW+4X9FzNVq1aRc+ePSlfvjxOTk6UL1+enj178t1339kUd3JyMkuXLmXgwIE0btyYsmXL4uzsTKVKlejVq1eB7xNz8jY8PNzyupmvK+cQHByMYRgEBgbmG8evv/7KwIEDqVatGi4uLvj6+tKmTRs+/vhj0tLSrLZZsGABhmFQvXp1AA4ePEjfvn2pWLEizs7O1KxZk5EjRxITE2PTsxERERERERERERGR20ehVzQNHz6c4cOHW363s7OjbNmynDx58roEdjNLT0+nc+fOhIWF4eDggIeHB7GxsWzevJnNmzczceLEApMaxXH+/Hnuu+8+Tpw4gYuLC3Z2dkRHR7NhwwY2bdrEunXr6Nq1a6H7i4iIoG3btpw+fRrIeV29vb2JioriwoULHD16lOPHj9OpU6cbEs/VHB0deeKJJ5g1axbffPMN//nPf/Kt+8033wDQu3fvQp9FlZ6ezsCBAwkJCQFyz3/Dhg1s2LCB/v37s3DhQhwdHQsd97JlyywJI3Pyy8HBgfPnz7NmzRrWrFnD66+/zgcffJCrXfny5UlJSSE+Pt7yGbuSh4dHoWP46KOPeP311zGZTAB4e3uTlJTE7t272b17N/Pnz+fHH3+kYsWK+faxZMkSBg8eTEZGBt7e3mRmZnLy5Ek++ugjfvrpJ/bs2VOkmERERERERERERETk9lLoFU1XmzhxIq+//npJxnLL+Pzzz9m3bx9z5swhISGBmJgYTp8+zeOPPw7ApEmTWLt27XUZ+5VXXsHJyYmtW7eSlJREYmIi+/bto06dOqSnpzN06FCys7ML3V9wcDCnT5+mevXqbN68mfT0dKKjo0lLS+PUqVPMnj2bVq1a3bB4rBk4cCAABw4cyPesqdOnT7N9+/Zc9Qtj3LhxhISEYBgGEyZM4PLly0RHRxMVFcW4ceMAWLp0KRMmTChSzL6+vowaNYqdO3eSmJhIbGwsSUlJnDt3jkmTJuHo6MiHH36Y530SGRnJJ598AkBAQACRkZG5rlGjRhVq/PXr1zNy5EhMJhOPPPII//77L7GxsSQmJrJo0SI8PT05cuQIjz/+OFlZWVb7uHTpEs888wyDBg3i9OnTxMbGkpCQwGeffYajoyO//fYb7733XpGei4iIiIiIiIiIiIjcXpRoskFcXByff/45L7zwAi4uLkBOUiAkJIT7778fwJKkKGkODg5s27aNoKAg7OzsMAyD5s2bs3z5ciBnu7Xdu3cXur9du3YBMGXKFDp16oS9vT0A9vb2VKtWjRdffJFp06bdsHisadGiBXXq1AFg0aJFVussXrwYk8lE1apVC9xK7kpnz561JHXGjBnD5MmT8fHxAXISRe+++67lXLEZM2Zw/vz5Qsf8yCOP8P7779O2bdtcq6sqVqzIW2+9xZQpUwCuuR2grd58800A2rdvz8qVK6lRowYATk5OPP300yxevBjIef3z2x4wOTmZJ554grlz5xIQEACAm5sbr7zyCq+++iqQk4QrSFpaGvHx8bkuEREREREREREREbl92Jxoyk96ejpr167l/fffZ+bMmezcubOkhyh1AQEBlm3RrmRnZ8f48eMB+O233zh69GiJjz106FDKlSuX536DBg0syYQjR44Uuj9zYqUoSZTrGU9+nn76aeD/EkpXM2+b9+STT2IYRqH6XLlyJZmZmbi4uDBmzBirdcaPH4+zszMZGRmsWLHCxujz6tGjBwC7d+/Od0WRrY4cOcIff/wB5MRvTh5e6aGHHqJFixZAwcki8/v5ao888ggAJ06cyHUe1tWmTp2Kt7e35TInrERERERERERERETk9lDoRFNCQgKLFi1i0aJFpKWlWa1z4MABateuzaOPPsqYMWMYMWIEHTp0oE2bNkRGRpZY0KUtMDAw32RG+/btcXDIOfrqwIEDJT52y5Yt8y2rVKkSANHR0YXur2fPnkDOip6hQ4fy448/FmnVSUnHk5+nn34awzBybZFndvDgQUtipSjb5plfn+bNm+Pl5WW1jq+vL82aNctVv7AuXLjAxIkTad26Nf7+/jg4OGAYBoZhcM899wA5q4ZiYmKK1O+1mON0cHCgQ4cO+dbr0qVLrvpX8/Pz46677rJaZn5tgQLjHzt2LHFxcZbrzJkz14xfRERERERERERERG4dhU40bdmyhcGDB/Pxxx/j7Oycp/zixYs8+OCDREREYDKZcl179+7l4YcfLtHAS1PlypXzLXNxccHf3x/IeSYlzdPTM98yc4IrIyOj0P298cYb9O3bl4yMDObOnUv37t3x8fGhQYMGvPHGG/z55583NJ78VK1a1ZI0Ma9eMjP/3rx5c+rWrVvoPs2vT0GvJ0CVKlVy1S+M3bt3U7duXSZPnsyePXuIjo7G1dWVcuXKUb58ecqUKWOpm5SUVOh+C8McZ5kyZax+Vs2uNa/CvLZQ8Ovr7OyMl5dXrktEREREREREREREbh+FTjSFhYUBMGDAAKvl06dPJyoqCoBBgwbx888/c/jwYUaMGIHJZOLgwYMluvWYlAxHR0dCQkI4dOgQb731Fh07dsTNzY1jx47xwQcfUL9+fT788MPSDhP4v9VKK1asICUlBYDMzEzL1m/m7fVKW2ZmJv379yc2NpbGjRvz/fffEx8fT0JCAhcuXCAyMpI9e/ZY6lvbClBERERERERERERE5FZQ6ETTvn37MAyDbt26WS1fvHgxhmHw0EMPMX/+fFq3bk2DBg348MMPGTRoECaTiZUrV5ZY4KXp7Nmz+ZalpaVx+fJlgFxnF5nPyUlNTc23bVxcXAlFWHSNGjVi0qRJbNmyhdjYWDZv3sz9999PVlYWb7zxBocPHy612Mwef/xxXF1diY+PZ82aNQD89NNPXLx4EUdHR/r371+k/syvT0RERIH1zOXWzqKyZvfu3YSHh2Nvb8/69evp3r17ntVB13MrSXOcUVFR+W5zCUWfl4iIiIiIiIiIiIjI1QqdaDp//jwODg6Ws2Wu9Ntvv1m23/rPf/6Tp3z48OEA/Prrr7bGeVPZvn17vqtQwsLCyMzMBLCc7QM5Z/0A+Z5Rk5CQYDlnqLQ5ODjQqVMnNmzYgLOzMyaTic2bN5d2WHh6etKrVy/g/7bLM/9n9+7dc21HVxhXnr2UX5IvNjY211lOhWF+jcuWLZvvtnwFPU87u5yPpa0rnczzyszMzHOelbUYCjsvEREREREREREREZGrFTrRdOHCBby8vCxfgl9p3759ADg5OdGuXbs85ffeey+GYXDu3LlihHrzOH36NAsXLsxzPzs7mylTpgBwzz330KBBA0tZo0aNAPJd1fXBBx8UuPrkeiloTGdnZ8tKLGuve2kwb5/3008/8ffff1tWNpnvF0Xv3r1xcHAgNTWV6dOnW60zZcoU0tLScHR0pHfv3oXq19vbG8j5zFy4cCFPeUREBDNnzsy3vfkco9jY2EKNd7WGDRtaEsLvvPMOWVlZeep8//337N27F6DIK8FERERERERERERERMwKnT3IysoiPj7eatnBgwcBqFevHk5OTnnKHRwc8PX1tZyrc6vz9vbmpZdeYu7cuZat8M6cOUP//v3Ztm0bkPMF/5XMX+Zv3LiRiRMnWp5lVFQU48aN45133sHHx+fGTeL/q1atGmPHjmXPnj25kk4nTpzgySefJDk5GTs7Ox544IEbHps1Xbp0oUKFCmRmZjJgwABSUlLw9fWlZ8+eRe6rcuXKltV206ZNY+LEiZbkTmxsLBMmTOD9998HYOTIkVSsWLFQ/bZr1w53d3dMJhN9+/blr7/+AnI+Qxs3biQwMBDDMPJtf++99wIQHx/PsmXLijwvwJI4CwsL4/HHH+fkyZMAZGRksHjxYsv7sU2bNpZVYiIiIiIiIiIiIiIiRVXoRFO5cuXIzMzkn3/+yVO2e/duDMMocAuuxMRE3N3dbYvyJvPyyy/TrFkzhg4dipeXF35+flStWtWSFBg/fjyPPvporjaDBw8mKCgIgMmTJ+Pj44Ofnx/lypVj2rRpTJ8+3bLq6Ua6cOEC06ZNo3Xr1ri5ueHn54erqyu1a9dm+fLlGIbBhx9+aHXLxNJgb2/PgAEDACxb2vXt2xdnZ2eb+psyZQp9+/bFZDIxefJk/P398fPzw9/f35Is7N+/P2+//Xah+/T29uaDDz4AYMeOHdSpUwdPT088PDzo1q0bcXFxzJ8/P9/2d911F506dQKgX79+eHl5Ub16dapXr87HH39cqBh69uzJjBkzMAyD1atXU7NmTXx9ffHw8OCpp54iPj6eBg0asHz5csuqNRERERERERERERGRoip0oum+++4D4Msvv8x1/++//+bQoUMAdOjQwWrb8PBw0tPTqVKlio1h3lycnJzYsmULU6ZMoU6dOqSlpeHt7W0518haUsLe3p4NGzYwadIk6tati5OTE4Zh0LVrVzZt2sSoUaNKYSY5W9CNHTuW9u3bExAQYFl1dtdddzFkyBD279/Pa6+9Viqx5efqbfJs2TbPzMnJiZCQEFasWEH37t3x9/cnISEBf39/unfvzqpVq1iyZAmOjo5F6vfFF19kw4YNBAYG4uHhQWZmJpUrV+bVV1/l8OHDubZVtGbFihWMGDGCu+++m4yMDMLDwwkPDy/SdnojRozgwIEDPPXUUwQEBJCcnIyrqyutWrXio48+Yv/+/VSqVKlI8xIRERERERERERERuZJDYSv279+f1atX89FHH1GmTBkefvhhIiIieP311zGZTHh4ePDQQw9Zbbtjxw7g/7YEux04OTkxduxYxo4dW+g2rq6uvPXWW7z11ltWy0NDQ/NtazKZrtm/Le27dOlCly5drtl3ScdTHI0aNSrU+EWJo3fv3oU+g8ksODiY4ODgfMsffPBBHnzwwXzLC5qDj48PM2bMYMaMGTaPDzkJ4m+++abAOlcbPHgwgwcPLrBO9erVi/QaiIiIiIiIiIiIiMjtqdArmvr06cP9999PZmYmY8aM4Z577qFr164cPXoUwzAYOXIknp6eVtuGhIRgGAbt2rUrscBFRERERERERERERESkdBV6RRPAmjVrePrpp1m/fr3lnmEYPPfcc/mu0vn777/58ccfAQpc3SEid457J27Eztmt2P2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",
+      "text/plain": [
+       "<Figure size 1800x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the states from file\n",
+    "states = nwbfile.get_acquisition(\"task_recording\").states\n",
+    "state_types = nwbfile.get_lab_meta_data(\"task\").state_types\n",
+    "\n",
+    "# Plot the data\n",
+    "plot_states(states=states[20:100],\n",
+    "            state_types=state_types,\n",
+    "            marker_size=500)\n",
+    "plt.title(\"States\", fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d811ac1c-771a-4fc0-a995-613065ae60fd",
+   "metadata": {},
+   "source": [
+    "## Accessing the trials\n",
+    "\n",
+    "The `TrialsTable` is a column-based table to store information about trials, one trial per row.\n",
+    "The table can be accessed from the file as `nwbfile.trials`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ca66b7b5-c6ac-405f-8297-8aeb6cc4d92e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-28T11:41:33.949842Z",
+     "start_time": "2024-08-28T11:41:33.947843Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trials = nwbfile.trials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cc9adeaf-ae23-403f-ad66-5a6ed695760f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-28T11:41:35.210373Z",
+     "start_time": "2024-08-28T11:41:35.192278Z"
+    }
+   },
+   "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",
+       "      <th>is_warm_up</th>\n",
+       "      <th>nose_in_center</th>\n",
+       "      <th>target_reward_delay</th>\n",
+       "      <th>block_type</th>\n",
+       "      <th>reward_delay</th>\n",
+       "      <th>...</th>\n",
+       "      <th>is_rewarded</th>\n",
+       "      <th>is_violation</th>\n",
+       "      <th>is_opt_out</th>\n",
+       "      <th>wait_time</th>\n",
+       "      <th>wait_time_unthresholded</th>\n",
+       "      <th>wait_time_threshold</th>\n",
+       "      <th>wait_for_center_poke</th>\n",
+       "      <th>z_scored_wait_for_center_poke</th>\n",
+       "      <th>rewarded_port</th>\n",
+       "      <th>inter_trial_interval</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>16.2116</td>\n",
+       "      <td>44.1310</td>\n",
+       "      <td>[0, 1, 2]</td>\n",
+       "      <td>[0, 1, 2, 3, 4]</td>\n",
+       "      <td>[0, 1, 2, 3, 4]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.948330</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>0.606090</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15271.6804</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>26.7877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44.1310</td>\n",
+       "      <td>75.1291</td>\n",
+       "      <td>[3, 4, 5]</td>\n",
+       "      <td>[5, 6, 7, 8, 9, 10, 11]</td>\n",
+       "      <td>[5, 6, 7, 8, 9, 10]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.149690</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15271.6804</td>\n",
+       "      <td>28.7726</td>\n",
+       "      <td>1.511654</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>28.7726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>75.1291</td>\n",
+       "      <td>77.0541</td>\n",
+       "      <td>[6, 7, 8, 9, 10, 11, 12]</td>\n",
+       "      <td>[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]</td>\n",
+       "      <td>[11, 12, 13, 14, 15, 16, 17]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.819198</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>0.551172</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.5512</td>\n",
+       "      <td>0.5512</td>\n",
+       "      <td>15271.6804</td>\n",
+       "      <td>0.2159</td>\n",
+       "      <td>-0.235383</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>0.2159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>77.0541</td>\n",
+       "      <td>81.9992</td>\n",
+       "      <td>[13, 14, 15]</td>\n",
+       "      <td>[23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 3...</td>\n",
+       "      <td>[18, 19, 20, 21, 22]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.184697</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15271.6804</td>\n",
+       "      <td>3.6389</td>\n",
+       "      <td>-0.025972</td>\n",
+       "      <td>Left</td>\n",
+       "      <td>1.3749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>81.9992</td>\n",
+       "      <td>83.2778</td>\n",
+       "      <td>[16, 17, 18]</td>\n",
+       "      <td>[48, 49, 50]</td>\n",
+       "      <td>[23, 24, 25, 26, 27]</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.946593</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>Mixed</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15271.6804</td>\n",
+       "      <td>0.1183</td>\n",
+       "      <td>-0.241354</td>\n",
+       "      <td>Right</td>\n",
+       "      <td>0.1183</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 38 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    start_time  stop_time                    states  \\\n",
+       "id                                                    \n",
+       "0      16.2116    44.1310                 [0, 1, 2]   \n",
+       "1      44.1310    75.1291                 [3, 4, 5]   \n",
+       "2      75.1291    77.0541  [6, 7, 8, 9, 10, 11, 12]   \n",
+       "3      77.0541    81.9992              [13, 14, 15]   \n",
+       "4      81.9992    83.2778              [16, 17, 18]   \n",
+       "\n",
+       "                                               events  \\\n",
+       "id                                                      \n",
+       "0                                     [0, 1, 2, 3, 4]   \n",
+       "1                             [5, 6, 7, 8, 9, 10, 11]   \n",
+       "2        [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]   \n",
+       "3   [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 3...   \n",
+       "4                                        [48, 49, 50]   \n",
+       "\n",
+       "                         actions  is_warm_up  nose_in_center  \\\n",
+       "id                                                             \n",
+       "0                [0, 1, 2, 3, 4]       False        0.948330   \n",
+       "1            [5, 6, 7, 8, 9, 10]       False        1.149690   \n",
+       "2   [11, 12, 13, 14, 15, 16, 17]       False        0.819198   \n",
+       "3           [18, 19, 20, 21, 22]       False        1.184697   \n",
+       "4           [23, 24, 25, 26, 27]       False        0.946593   \n",
+       "\n",
+       "    target_reward_delay block_type  reward_delay  ...  is_rewarded  \\\n",
+       "id                                                ...                \n",
+       "0                   1.5      Mixed      0.606090  ...        False   \n",
+       "1                   1.5      Mixed    100.000000  ...        False   \n",
+       "2                   1.5      Mixed      0.551172  ...         True   \n",
+       "3                   1.5      Mixed    100.000000  ...        False   \n",
+       "4                   1.5      Mixed    100.000000  ...        False   \n",
+       "\n",
+       "    is_violation  is_opt_out  wait_time  wait_time_unthresholded  \\\n",
+       "id                                                                 \n",
+       "0           True       False        NaN                      NaN   \n",
+       "1           True       False        NaN                      NaN   \n",
+       "2          False       False     0.5512                   0.5512   \n",
+       "3           True       False        NaN                      NaN   \n",
+       "4           True       False        NaN                      NaN   \n",
+       "\n",
+       "    wait_time_threshold  wait_for_center_poke  z_scored_wait_for_center_poke  \\\n",
+       "id                                                                             \n",
+       "0            15271.6804                   NaN                            NaN   \n",
+       "1            15271.6804               28.7726                       1.511654   \n",
+       "2            15271.6804                0.2159                      -0.235383   \n",
+       "3            15271.6804                3.6389                      -0.025972   \n",
+       "4            15271.6804                0.1183                      -0.241354   \n",
+       "\n",
+       "    rewarded_port  inter_trial_interval  \n",
+       "id                                       \n",
+       "0           Right               26.7877  \n",
+       "1           Right               28.7726  \n",
+       "2            Left                0.2159  \n",
+       "3            Left                1.3749  \n",
+       "4           Right                0.1183  \n",
+       "\n",
+       "[5 rows x 38 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials[:].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1b7a20f0-94c2-46ac-8619-f2a606cafae0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot trial 2 - 5\n",
+    "plot_trials(\n",
+    "    trials=trials[2:6],\n",
+    "    states=states, state_types=state_types,\n",
+    "    actions=actions, action_types=action_types,\n",
+    "    events=events, event_types=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,
+   "id": "6720ee9e-9bd5-4145-8631-2f5e507b5794",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}