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+{
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "59864afa46c343bf",
+   "metadata": {},
+   "source": "# Pagan Lab — Behavioral Data\n\nThis notebook demonstrates how to access behavioral data from\n**[DANDI:001550](https://dandiarchive.org/dandiset/001550)**, a dataset of\n16,113 sessions from Long-Evans rats performing multi-sensory decision-making tasks.\n\nSessions are streamed directly from the DANDI Archive — no local download needed.\n\n**Reference:** Pagan et al., *Nature* 639, 421–429 (2025).\ndoi:[10.1038/s41586-024-08433-6](https://doi.org/10.1038/s41586-024-08433-6)"
+  },
+  {
+   "cell_type": "code",
+   "id": "setup",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:27:46.980788Z",
+     "start_time": "2026-04-14T13:27:46.104723Z"
+    }
+   },
+   "source": "import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom pynwb import NWBHDF5IO\nfrom ndx_structured_behavior.plot import (\n    plot_events,\n    plot_actions,\n    plot_states,\n    compute_state_transition_matrix,\n)\nfrom dandi.dandiapi import DandiAPIClient\nimport remfile\nimport h5py\n\nDANDISET_ID = \"001550\"\nASSET_PATH  = \"sub-P100/sub-P100_ses-TaskSwitch6-190423a.nwb\"",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60ce2b44d97a4e74",
+   "metadata": {},
+   "source": "## 1. Stream the NWB file from DANDI\n"
+  },
+  {
+   "cell_type": "code",
+   "id": "load-nwb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:32:50.556931Z",
+     "start_time": "2026-04-14T13:32:46.555502Z"
+    }
+   },
+   "source": [
+    "with DandiAPIClient() as client:\n",
+    "    asset = client.get_dandiset(DANDISET_ID, \"draft\").get_asset_by_path(ASSET_PATH)\n",
+    "    s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n",
+    "\n",
+    "file_system = remfile.File(s3_url)\n",
+    "h5_file = h5py.File(file_system, \"r\")\n",
+    "io = NWBHDF5IO(file=h5_file)\n",
+    "nwbfile = io.read()"
+   ],
+   "outputs": [],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12db6bf7734b422e",
+   "metadata": {},
+   "source": "## 2. Session and subject metadata"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:31:00.181767Z",
+     "start_time": "2026-04-14T13:31:00.158064Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(f\"Session ID         : {nwbfile.session_id}\")\n",
+    "print(f\"Session start time : {nwbfile.session_start_time}\")\n",
+    "print(f\"Subject ID         : {nwbfile.subject.subject_id}\")\n",
+    "print(f\"Experimenter       : {nwbfile.experimenter}\")\n",
+    "print(f\"Session description : {nwbfile.session_description}\")"
+   ],
+   "id": "372c5e7cf2a51864",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Session ID         : TaskSwitch6-190423a\n",
+      "Session start time : 2019-04-23 11:30:00+01:00\n",
+      "Subject ID         : P100\n",
+      "Experimenter       : ('Pagan, Marino', 'Tang, Vincent D.')\n",
+      "Session description : This session contains behavioral data from a rat performing a context-dependent decision-making task.\n",
+      "The task required the animal to accumulate sensory evidence over time and select an action based on the context\n",
+      "cue for that trial. This dataset includes event timings, trial structure, stimulus parameters, and subject responses.\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "code",
+   "id": "7ca8c49c58514c93",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:31:01.527367Z",
+     "start_time": "2026-04-14T13:31:01.502478Z"
+    }
+   },
+   "source": [
+    "print(\"Subject ID:    \", nwbfile.subject.subject_id)\n",
+    "print(\"Species:       \", nwbfile.subject.species)\n",
+    "print(\"Strain:        \", nwbfile.subject.strain)\n",
+    "print(\"Sex:           \", nwbfile.subject.sex)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Subject ID:     P100\n",
+      "Species:        Rattus norvegicus\n",
+      "Strain:         Long Evans\n",
+      "Sex:            M\n"
+     ]
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "id": "task-metadata-header",
+   "metadata": {},
+   "source": "## 3. Task metadata\n\nThe `Task` object in `nwbfile.lab_meta_data[\"task\"]` stores the vocabulary of states, events, and actions used in this session."
+  },
+  {
+   "cell_type": "code",
+   "id": "event-types",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:32:57.345977Z",
+     "start_time": "2026-04-14T13:32:57.153887Z"
+    }
+   },
+   "source": "task = nwbfile.lab_meta_data[\"task\"]\n\nprint(\"=== Event types (port pokes) ===\")\ndisplay(task.event_types[:])\n\nprint(\"=== Action types (sounds / outputs) ===\")\ndisplay(task.action_types[:])\n\nprint(\"=== State types ===\")\ndisplay(task.state_types[:])",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Event types (port pokes) ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   event_name\n",
+       "id           \n",
+       "0           C\n",
+       "1           L\n",
+       "2           R"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</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>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Action types (sounds / outputs) ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "       action_name\n",
+       "id                \n",
+       "0    direct_reward\n",
+       "1   stimulatorwave\n",
+       "2      cpoke_timer"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</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>direct_reward</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>stimulatorwave</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cpoke_timer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== State types ===\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "                state_name\n",
+       "id                        \n",
+       "0                  state_0\n",
+       "1         sending_trialnum\n",
+       "2   check_next_trial_ready\n",
+       "3           wait_for_cpoke\n",
+       "4     wait_for_cpoke_wait2\n",
+       "5       wait_for_cpoke_dir\n",
+       "6      wait_for_cpoke_freq\n",
+       "7       wait_for_cpoke_bis\n",
+       "8              nic_prestim\n",
+       "9                    cpoke\n",
+       "10                cpoke_in\n",
+       "11               cpoke_out\n",
+       "12           wait_for_cout\n",
+       "13          wait_for_spoke\n",
+       "14               hit_state\n",
+       "15              hit_state2\n",
+       "16             error_state\n",
+       "17              wait_state\n",
+       "18         nic_error_state\n",
+       "19        nic_error_state2\n",
+       "20           timeout_state\n",
+       "21          clean_up_state"
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
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+       "\n",
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+       "        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>state_0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sending_trialnum</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>check_next_trial_ready</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>wait_for_cpoke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wait_for_cpoke_wait2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>wait_for_cpoke_dir</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>wait_for_cpoke_freq</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>wait_for_cpoke_bis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>nic_prestim</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>cpoke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>cpoke_in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>cpoke_out</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>wait_for_cout</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>wait_for_spoke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>hit_state</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>hit_state2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>error_state</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>wait_state</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>nic_error_state</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>nic_error_state2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>timeout_state</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>clean_up_state</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behavioral-data-header",
+   "metadata": {},
+   "source": [
+    "## 4. Behavioral data — Events, States, Actions\n",
+    "\n",
+    "The `TaskRecording` in `nwbfile.acquisition[\"task_recording\"]` holds the full time-series of events (port pokes), states, and actions (sounds/rewards). Each table row links back to the corresponding type table via a `DynamicTableRegion` index column.\n",
+    "\n",
+    "### Events\n",
+    "\n",
+    "The `EventsTable` stores one row per port poke (enter + exit time). Each event links to `EventTypesTable` via an integer index.\n",
+    "\n",
+    "### States\n",
+    "\n",
+    "The `StatesTable` stores one row per state occurrence (`start_time` / `stop_time`). Each state links to `StateTypesTable`.\n",
+    "\n",
+    "### Actions\n",
+    "\n",
+    "The `ActionsTable` stores one row per sound or output event (onset time + duration). Each action links to `ActionTypesTable`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "events-table",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:35:01.029909Z",
+     "start_time": "2026-04-14T13:35:00.825108Z"
+    }
+   },
+   "source": [
+    "task_recording = nwbfile.acquisition[\"task_recording\"]\n",
+    "events       = task_recording.events\n",
+    "states       = task_recording.states\n",
+    "actions      = task_recording.actions\n",
+    "event_types  = task.event_types\n",
+    "state_types  = task.state_types\n",
+    "action_types = task.action_types\n",
+    "\n",
+    "print(\"\\n--- Events (first 5) ---\")\n",
+    "display(\n",
+    "    pd.merge(\n",
+    "        events[:5], event_types[:],\n",
+    "        left_on=\"event_type\", right_on=\"id\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(\"--- States (first 5) ---\")\n",
+    "display(\n",
+    "    pd.merge(\n",
+    "        states[:5], state_types[:],\n",
+    "        left_on=\"state_type\", right_on=\"id\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "print(\"--- Actions (first 5) ---\")\n",
+    "display(\n",
+    "    pd.merge(\n",
+    "        actions[:5], action_types[:],\n",
+    "        left_on=\"action_type\", right_on=\"id\",\n",
+    "    )\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Events (first 5) ---\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   timestamp  event_type value  duration event_name\n",
+       "0  52.709765           2   out  0.024506          R\n",
+       "1  52.757265           2   out  0.048500          R\n",
+       "2  52.817280           2   out  0.058997          R\n",
+       "3  52.890275           2   out  0.054990          R\n",
+       "4  52.951767           2   out  0.067498          R"
+      ],
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>event_type</th>\n",
+       "      <th>value</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>event_name</th>\n",
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+       "      <th>0</th>\n",
+       "      <td>52.709765</td>\n",
+       "      <td>2</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.024506</td>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52.757265</td>\n",
+       "      <td>2</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.048500</td>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>52.817280</td>\n",
+       "      <td>2</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.058997</td>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>52.890275</td>\n",
+       "      <td>2</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.054990</td>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>52.951767</td>\n",
+       "      <td>2</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.067498</td>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- States (first 5) ---\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   start_time  stop_time  state_type            state_name\n",
+       "0   52.710269  59.287265           0               state_0\n",
+       "1   52.710269  52.726272           1      sending_trialnum\n",
+       "2   52.726272  53.726775           3        wait_for_cpoke\n",
+       "3   53.726775  53.727276           4  wait_for_cpoke_wait2\n",
+       "4   53.727276  54.727770           5    wait_for_cpoke_dir"
+      ],
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>52.710269</td>\n",
+       "      <td>59.287265</td>\n",
+       "      <td>0</td>\n",
+       "      <td>state_0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52.710269</td>\n",
+       "      <td>52.726272</td>\n",
+       "      <td>1</td>\n",
+       "      <td>sending_trialnum</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>52.726272</td>\n",
+       "      <td>53.726775</td>\n",
+       "      <td>3</td>\n",
+       "      <td>wait_for_cpoke</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>53.726775</td>\n",
+       "      <td>53.727276</td>\n",
+       "      <td>4</td>\n",
+       "      <td>wait_for_cpoke_wait2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53.727276</td>\n",
+       "      <td>54.727770</td>\n",
+       "      <td>5</td>\n",
+       "      <td>wait_for_cpoke_dir</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Actions (first 5) ---\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   timestamp  action_type value  duration     action_name\n",
+       "0  56.146765            1   out  0.010000  stimulatorwave\n",
+       "1  56.157265            1   out  0.009501  stimulatorwave\n",
+       "2  57.546765            2   out  0.000000     cpoke_timer\n",
+       "3  64.004271            1   out  0.010004  stimulatorwave\n",
+       "4  64.014766            1   out  0.009999  stimulatorwave"
+      ],
+      "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>duration</th>\n",
+       "      <th>action_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>56.146765</td>\n",
+       "      <td>1</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.010000</td>\n",
+       "      <td>stimulatorwave</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56.157265</td>\n",
+       "      <td>1</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.009501</td>\n",
+       "      <td>stimulatorwave</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>57.546765</td>\n",
+       "      <td>2</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>cpoke_timer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>64.004271</td>\n",
+       "      <td>1</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.010004</td>\n",
+       "      <td>stimulatorwave</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>64.014766</td>\n",
+       "      <td>1</td>\n",
+       "      <td>out</td>\n",
+       "      <td>0.009999</td>\n",
+       "      <td>stimulatorwave</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plot-behavioral-header",
+   "metadata": {},
+   "source": "### Plot events, states, and actions\n\nThe `ndx_structured_behavior.plot` module provides helpers to visualise each table."
+  },
+  {
+   "cell_type": "code",
+   "id": "plot-events",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:35:12.037098Z",
+     "start_time": "2026-04-14T13:35:11.969443Z"
+    }
+   },
+   "source": "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)\nplt.title(f\"Events — {nwbfile.session_id}\", fontsize=16)\nplt.tight_layout()\nplt.show()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "cell_type": "code",
+   "id": "plot-actions",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:35:16.463144Z",
+     "start_time": "2026-04-14T13:35:16.396068Z"
+    }
+   },
+   "source": "fig = plot_actions(\n    actions=actions[20:100],\n    action_types=action_types,\n    figsize=(18, 4),\n    marker_size=500,\n)\nplt.title(f\"Actions — {nwbfile.session_id}\", fontsize=16)\nplt.tight_layout()\nplt.show()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x400 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 31
+  },
+  {
+   "cell_type": "code",
+   "id": "plot-states",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:35:32.933307Z",
+     "start_time": "2026-04-14T13:35:32.828254Z"
+    }
+   },
+   "source": "plot_states(\n    figsize=(14, 7),\n    states=states[20:100],\n    state_types=state_types,\n    marker_size=500,\n)\nplt.title(f\"States — {nwbfile.session_id}\", fontsize=16)\nplt.tight_layout()\nplt.show()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ],
+      "image/png": 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fi4CkGbhFENfM4nUmJCREbt++rXVrn3vuuUiBW2T+mp8tBDwdQXDX5CxrF1CjGsz98sYFLywsTB+TJo3b/5SqVq3q9NxQzP4LiHODMjd3795lkxH7CrmE1xRiXyF343XFOQySbAYvAv07saN+Yts+jgR6mwVqn3LlmhJdn7KVUNvPFbFpP2+3rzt+95jbiIsYRUcQMMKASai3aUKmIWpzokxCTLCTkr9A9ipg4K7YiqrEALaHwK2ZTYoB/ACB2ajgwoDAraPUegRtf/nlF3nzzTelefPmmimLjGBbWNdUpUqVaI8hrtmusYFSEFFx9s+g6CDbmQFH16EN2Y7EvkLuwmsKsa+Qu/G6EpntXX/8Thy5nzi6KzJi+/H7b+D2qbhcU6LrUxFfm5DbLy5i036+0r7e+t0To8HJjh49qreAI5hiTmD7c1TTgwcPtLYmkT9dQNxd58YM1GJwMzA/8LaB1agCqY4uEBjADAOeoWYsfPfdd5EG9jKP59q1azH6vKLOLREREREREREReVeM70ceOHCgVfcSwR0MaHTgwAGHNTVN//33n2YZ4rZwBG+J/AH6KwbHQomBRYsWRVqO2/jXrVsnly5dkg8++EAHMTNhHUfp/igXYgZuUR8Xjhw5oo/lypVzui8oW5AmTRp9fvz48UjLZ86caX0+GzVqJLlz59bgbYsWLez+8QII6KKcwuXLlx2+FzLor1+/rvWrHdX2JSIiIiIiIiIiH8u4BQRgN27cqNOGDRvkzJkzGngy5zmaNm/eLPv27WPQlvzKsmXL9LFNmzaSLFmySMtfeeUVLTlQv379SEHQ9u3bO7zNv3v37vqIQcjOnTtnN0BXhQoVpGLFig73pV+/flaNXPyjJKrPJwYxA5RMeOONN6xl+JyaQd/evXs7XL9SpUr6eT18+LC89NJLTt+HiIiIiIiIiIh8LHAbUb58+fQWbaKEBmUHUFagUKFCOvhXhgwZrGXly5eXyZMnW6+LmEmO5SgrkipVKv0ZQdyePXtaAdhBgwZZr922bZv8+eef+hyZvdWqVbOWJU+eXIYPHy5dunTRn99///1o93v58uUyZ84chyUThg4dqo8oWYJt2QakkWlr1sVFYHn9+vWxai8iIiIiIiIiIvKhwG1s634yyEv+4urVq9K0aVO5efOmZq5euHBBdu3aJSEhIRpszZkzp6xYsUI++uijSOsiK7ZTp05aRmH79u06auCECRN0Wd++fSMFRdu2bStbtmyRHDly6LKTJ0/qeqiDO2zYMB3878MPP9QByGLi3Xff1f3PmjWrVfcWsD6Ctwgkf/HFF7p9vA/eD5m2eH9k5jZu3Njl9iMiIiIiIiIiIi8HbkuWLCnz5s2TQ4cOaZ3P06dP200IWiH4dfv2bZk1a5YbdpcofqBOLerVfvPNN9qXixYtqvVhd+zYoVmwDRo0kIcPH0Zab+zYsfpPCtSVLVGihBiGobWhUYoAyyLC56N69erSuXNnLUGCgcjwuULG79SpUzWDF4HWmEKNWgSI4c0339TJNHLkSC3JMHv2bLlz546UKlVKsmTJInv27JEhQ4bICy+8oEFfIiIiIiIiIiLyo8HJIipYsKBm6qVOndphTc+IUKOTyJ8g07Z///46xQayW2OaIQvIqkWQFlNMIJAc1WcO5R0wOYLAM7J8Pa1GjRqx3m+zBAsREREREREREbkQuEUwCyUQLl68KJMmTZKwsDD58ssvtc7m4sWL5ZlnnpFWrVppndBVq1bpgE5ERERERERERERE5MHAbc2aNfU28EaNGumt1jBgwAC91XvatGn68+eff661QOvUqSP16tWTlStXxvXtiIiIiIiIiIiIiAJGnAO3GKAJ5Q/MoC3s3btXb5FOnDix/PfffxIeHi7du3eXgwcPSteuXRm4JfIT48aNkzJlysR6PWTgN2/e3CP7RERERERE5IqMKVI5fE7kjj4Vm2X0/wQlTe6wzaL6rGYMsHaNc+A2adKkcvnyZbt5x44d08xalEfA4Exw+PBhHbgMAx8RkX/AwGpVqlSJ9XqnTp0Sb7p7965X35+IiIiIyBtSpkxpPed3Yuft0+DZQg6TVpCAQuxT7upTEfEz6bz9SmbMZjffUXs2iKaNE3r7xjlwi9HrM2fObDfv5MmT+lisWDErcGu+tmTJkq7sJ5FPS2iDajkaXMzXtWzZUkJCQry9G0RERERE8Q53u0Ljxo35nTiK9mndurUcOXLELuDDvyHYp9zZpyJiH4u+/a5cuWLNv3Hjhl3bQXBwsEQVrE3on+E4B25RFqFBgwZ6OzWemxm3GDW+QoUK8uuvv+q8JEmSaFDr3r177ttrIqIIzp8/zzYhIiIiooCGcobkHAJsZvyC2KfcgX2K7edpieO64vz58zVIu3z5cunWrZs+37Ztmzx+/Fh69OghVatWlaCgIBk9erRkypQpwUfAiYiIiIiIiIiIiLweuP35559l/fr1kiVLFq0Jg8Dt7du3Zc6cOZI6dWpZt26d/ty/f38xDEOmTJnitp0mIu/LkyePfrYxFShQwNu7Q0RERERERESUoMQ5cPvff/9pqYRPPvlEM23xM/Tp00cDugjkmtOCBQtk+vTp7txvIiIiIiIiIiIiogQrzjVu4eHDh/Lxxx/rZLp//77UqlVLXnzxRa1ti3of//zzjzv2lYiIiIiIiIiIiCggxDlwixq2KIWwf/9+h8t37NihE9SpU0cKFiwokyZNivueEhEREREREREREQWIOJdKQDkE1LaNiZEjR8qnn34a17ciIiIiIiIiIiIiCigxCtymSZNGyx7YTpAqVapI822n/PnzS40aNaRIkSKSLFkyTx8LUYKULVs2LUeCkiN37tyRe/fuye7du2XAgAGSPHlyu9eGhobqYGGlSpXSGtSbN2/Wda5evSp//fWXNG7c2On7JE6cWDp06CBr166VGzduSHh4uJw+fVpmzpwpZcqUidU+o1TKrVu3dF9mzZql27ZVsmRJmTFjhm4f73Pt2jVZsWKFNG3aNJatQ0RERERERESUcBnRTTlz5jTu3btnPHnyJM7Tpk2bon0fTmwD9gH7PlCpUiXj0qVLBjx69MjYt2+fcfjwYePp06c6b+3atUayZMms14eGhur8sWPH6mNYWJixc+dO4+zZs/qzuSxiOwcHBxubN2+2XnPy5Eljx44dxq1bt/RnfIb79u1rt06ePHms1xcoUMCaX65cOePmzZs6f/r06UaiRIns1uvRo4fx+PFjXX7nzh1j9+7dxqlTp6xtzZ4920icOHGM+wL2HapUqcL+4+I1xGxLPPKzyOsx+wr7AK8p7APx9buAv3/Y19hXXO8DZcqU0e9xeAz073GOrilsH/Ypd//+YZ8KnGtWsBv+TnZlGzHKuL1w4YKMGTNGEiVKZE1g+3NU05kzZ6RPnz6eDkATJSjp06eXBQsWaMbtsmXL5Nlnn5XSpUvL888/rxmtV65c0Yz2YcOGRVoXn7fVq1dL7ty5pXz58rpu586d5fHjx7qsZcuWdq+fM2eOVK5cWS5evCjVq1fXbHm8R9asWeWTTz6RJEmSyDfffCNNmjSJcp+xf6tWrdJ9//777zWDF1m3pvr168v48ePl6dOnuh94Xbly5SRv3rw6qOHly5elTZs2Mnz4cDe2JBERERERERFRAh6cbMSIETJt2jR9jmDsyZMnZefOndK8eXOn6/z33396WzdumSai2OnSpYvkzJlTyx80a9ZMSwqYUCqhb9++8vPPP0u7du1k6NChdgFSlCBAkPX+/fvWvKlTp2oJk0GDBmmwd968eTq/QoUK8tprr+lzvM/ff/9trfPo0SN9LQK43bp1k88//1wWL17scH9R/mDNmjWSMWNGDc46+mfNqFGjtGzC//73P32NLZRoeOedd2T58uXy3nvvybfffqslG2IqderUEhwcHOPXU2Rm+7EdKTrsKxQT7CcUU+wrxL7iuqCgIOsx0L/LObqmsH1iLxDazJXfP4HQPp7kT+0X7Ia/k11ZF6mz/xftiYV169ZpzU0Ej4jI/TZt2iRVqlTRjFdHWbWoG120aFE5duyYhIWF6TwEeZG9OnjwYA2SRoTMW2TAw3PPPafrYvBAvH779u3y0ksvOdyXggULyvHjx/V58eLF5dChQ5InTx45deqUznv99ddlypQpkiVLFqdBW9vX43Woa+sIMomxHP8UQsZxTC6AqONLREREREREROSr0qZNK3fv3vVMxm1EuEWbiDwHwVLAP0gcQdkDZ8t27NjhcP7Zs2c1Ax4lCgoXLqyBWwweaGbxOhMSEiK3b9+WdOnSacAXgVtbyPxFxiugtIMjCPianGXtQsqUKfXR3K+Yqlevnl22MMUeguDnz5+XXLlyxfqXCQUW9hViPyFeU4i/f3wLBidG4kfVqlWd/o0QyN9T2D6xFwht5sp32kBoH0/yp/YLdsPfyeY24iLOgduoGh+BHYxiv23bNisTkIhiJ1OmTPqIciOxFVWJAWwPgVtM5n98AIHZqOAChcCtoxR/BG1/+eUXefPNN61M2YULF9q9BuuakEkcHXP/YurBgwcMNroJzjUDt8S+QrymUHzj7x9iX4k7s0QaHvk9LvI1he3DPuXu3z/sU67xx/a766W/k2M0OJkJtSsxaBAGSooYVMmRI4ds3rxZs/aQfYeBkVBns1OnTu7eZ6KAupC5Um/HETNQi5IEYF54bAOrjpifeUcXKpRzwIBnEydO1J+/++47yZw5s8PjQYmEmAxqiDq3RERERERERESBKsaBW4wW/++//8qQIUOkbt26drdD49bmDRs2aH1MBFxwC/fNmzc1Y3Dy5MkyYMAAT+0/UYKFMgYRSwzYSpo0qd5agOxWDDpmy9k6+fPntwK3+DzDkSNH9LFcuXJO9wVlC9KkSaPPzVq3tmbOnKmPAwcO1Bq6GMwMwVtbR48e1UcEdJ2VU4DKlSvr+5klE4iIiIiIiIiIAlGMArcpUqSQJUuW6IBBKIGAQYhsb8XGKPUFChTQ53PnztXADF5bv359HTQI2XhmvU4iihlktkObNm10ILKIXnnlFS05gM/Z5cuX7Za1b99e/4kSUffu3fURtWDPnTunz3///Xd9rFChglSsWNHhvvTr18+qkXvgwIEoyzB069ZNn6NkwhtvvGEtQ4DYDPr27t3b4fqVKlXSzP3Dhw87HSiNiIiIiIiIiChQGNFNPXr0MJ4+fWps3rzZyJQpU6TlFy9e1OUXLlwwkidPbrfsnXfe0WWjRo2K9n04sQ3YB/6vD2TJksW4evWqAQsWLDAyZMhgLStfvrxx/vx5Xfb5559b80NDQw3TlClTjFSpUun8RIkSGT179jSePHmiy6pXr27X1n/88YfOx2e4WrVq1nx8nocPH25ts0WLFtayPHnyWPMLFChgt73Zs2fr/MuXLxuZM2e25mN9wH68//77RrJkyaxllStX1veHrVu3xrgvBAcH6zpVqlRh/3HxGmK2JR75WeT1mH2FfYDXFPaB+PpdwN8/7GvsK673gTJlyuj3ODwG+vc4R9cUtg/7lLt//7BPBc41K9gNfye7uI3oX/Tnn39qoKVw4cKRliGAhMAsltsGkGwDPw8ePDD27Nnj9cbmxDbwtz5QtWpV48aNG/oBDwsLM3bt2mWEhIToZw6WL19upEiRIlLgdv/+/fp4+/ZtY/v27VZAFJ/Td999N9L7ICiMf8yYTp48qevdunVLf378+LHxwQcf2K0TVeAW/+C5cuWKLps/f77dsiFDhlj7f/PmTX0fvJ/p8OHDGrSOaRsxcOu+/sY/nL3/mfeXiX3F++fAHyb2E++fA3+Z2Fe8fw78ZWJfSRhBEG/0E7ZP7NsxENqMgVvvtb0/9a9gLwduY1QqAfUyUbfSrLlpq0aNGtbzlStXRlr+6NEjvT06d+7cLiYGEwUe1LDF5++bb77Rwf6KFi2q9WF37NghXbp0kQYNGsjDhw8jrTd27FgdLAx1ZUuUKCGGYWgtXJQiwLKIUJO6evXq0rlzZ61XjYHISpYsqQOJTZ06VcqXLy9ffPFFjPf7+vXr0rdvX33+5ptv6mQaOXKklmSYPXu2llIpVaqUllbZs2eP1tB+4YUXtCQLEREREREREVEgSxqTFyGosnfvXofLqlatqo8YkGzLli0OXxMWFhblKPdE5NyFCxekf//+OsXGL7/8olNMPXnyRIO0mGICgWRHdXRNP//8s06OIPDctm3bGO8bEREREREREVGgiVHGbXh4uCRPnjzSfARtMDgSsvl27tzpMPMPkCFoO5gZEREREREREREREbkYuD1//rwUKFAg0vzKlStL2rRp9fnq1asdrosSCZiwDSIiIiIiIiIiIiJyU+B27dq1GqB9/fXX7eZ36NDBer5kyRKH66LOJTJy161bF5O3IiIiIiIiIiIiIgp4MQrcTpkyRYOvP/30k3Tq1EkHLRo0aJC8/fbbOn/z5s1y4MCBSOuhhmWvXr30NfPmzQv4xiYiIiIiIiIiIiJy2+BkBw8elE8//VSGDh0qkydPtqtxe/fuXenatavd6//3v/9pdi5Gh8drZsyY4XRwMyJyn3z58gVsc166dMnbu0BERERE5BVPnz6V9evX6yOxfdin+JnzdbxmuTnjFoYPHy4dO3aU0NBQDcZi2rJli1SrVk2OHj1q99r27dtL+fLl9TULFy6ULl26xGKXiIhi7/Lly2w2IiIiIgpISZIkkerVq+sjsX3Yp/iZ83W8Zrk549aEzFlM6dKlk8ePH8uDBw+c1sTdunWrzJ49W/7666/YvAURERERERERERFRwItV4NZ0+/btKJd379494BuWiIiIiIiIiIiIyOOlEoj8zbBhw3RgvE2bNnl7V/xekSJFIs1bt26dtu+IESO8sk9ERERERERERAkZA7dE5FRwcLCMHz9e9u/fz3pZRERERERERES+XiqByB9MmDBB5s2b57QWM0WvbNmy0qtXL4fL3n77bUmdOrVcu3aNTUlERERERERE5GYM3FKCdf36dZ3IM86ePcumJSIiIiIiIiLyEJZKICIiIiIiIiIiIvIxDNySX2jXrp0OhDV37ly9Pf+TTz6RI0eOSFhYmN6qv3TpUqlSpUqsBidr2LChLFmyRDNHHz58KOfPn9fSCigP4M59Xrx4saRNm1bGjRun74XSDceOHZOvvvpKsmXLFmk9c79Hjx4tjRs31uMMDw+X0NBQadGihfW65MmTS58+fWTr1q1y69Yt3S5eO2bMGMmePbvDfUqfPr2MHDlSa9bi9ffv35fjx4/LlClTpHjx4navxfutX7/e+vnJkye6X3ny5HE6OBmWYR6OM1GiRNK9e3fZs2ePvs+VK1fk119/tQY6y5Qpk7bJ6dOntf3PnDmj9XTRVkREREREREREgY6BW/IrCDxu27ZNhg4dKmnSpJF///1XgoKC5LXXXtNAYoMGDaLdRuLEieWnn36S33//XQOjSZIk0UBmqlSpNDCK7devX99t+4xA5ObNm6V3797y9OlTOXz4sAY433vvPdm9e3ekgKmpWrVqsnDhQsmQIYOukzNnTtm7d68uQ2AW+zl27FipUKGC3LhxQ9sid+7c0r9/fzl48KBUqlTJbnvYzo4dO2Tw4MFSqFAhOXnypBw9elS31alTJ9m1a5fUq1fPev3OnTvlwIED1s84BkwIIsekjRcsWCATJ06ULFmyaHA4Xbp00qRJE9myZYu89NJL8s8//0iPHj00gIzg7bPPPqv1dJcvX+5CaxMRERERERERJRwGJ7aBr/eBdu3aGaYLFy4YderUsZZlz57d2Ldvny7bu3evNX/YsGE6b9OmTXbb+vDDD3X+vXv3jBYtWljzU6RIYXz33Xe67M6dO0b69Ondts9hYWFGy5YtrWU5c+Y0tmzZossOHDhgJEmSJNJ+w6JFi4zkyZPr/MyZM1uv2bx5sy7fuHGjUbhwYWt+2rRpjalTp+qyK1euGNmyZbOWjR492mqPTJky2a2zcOFCXXb48GG7Y6hWrZq1L7b7iGndunU6f8SIEda8PHnyWK9/9OiR8fbbb1vLihUrpm0OT548MXbv3m0ULFjQWv7OO+9Y67788ssxbufg4GBdB4/e7qf+PrEtvX8O/GViX/H+OfCHif3E++fAXyb2Fe+fA3+Z2Fect02ZMmX0OzEevX2efLGfsH1i346B0GauXFMCoX08OflT+wW7IebgyjY4OBn5nZ49e8rq1autny9duiQff/yx3oZfunRpzcDFrfmOJEuWTP73v//p8wEDBsgvv/xiLcPt+sj4rFmzpt7O37x5c/nhhx/css/vv/++lmEwXbhwQbN9kYmKjNs33njDbl9M2MdHjx7pc5SEgEaNGknlypW1tAMyg5Gxarpz545mzxYrVkyzWvv162cdb6lSpfQRWby2g7ZhHZRcQGYwyiOkTJkyRlm10Zk6darMnDnT+vnQoUPy22+/SevWrbWMwptvvqlZv6YZM2bIRx99JPny5dNyFRs3bozV+wUHB7u8z4HObEO2JbGvEK8pxN8/5Iv4XcU5/A1kPgb6dzlH/YTtE3uB0GauXFMCoX08yZ/aL9gNfye7si4Dt+RXUGfV0a30KCVgwi35zgK3L7/8spZbQGASgcKIUJ/11Vdf1WDpuXPn3LLPd+/e1RqyESEQi2Bzhw4dNIgbMXCL4C4CqRGh3ACgPq9t0NbW7NmzNXCLEhJm4BZB4ldeeUU+/PBDuXr1qvzxxx8atDXfq27duuJOf/75Z6R5p06d0keUaLAN2pqwHwjcxqXOLQLZ5B5sS2JfIXfiNYXYV8jdeF1xztn4HoHIUT9h+8ReILSZK9eUQGgfT/Kn9jvvpZgDA7fkV1DL1VE2KAYpMyVN6rxbFyxY0ApiOssqdRRQdAXq5yKb19kyKFy4cKRlFy9edLiOWRMXQVkzizYi1LONuN0vv/xSM3tRK3fOnDny+PFjrWOL7GUEw7dv3y7uhAHKIjKzhxE4dgT7BMjIja1cuXJpkJzEpf8C4pcR25LYV8gdeE0h9hVyN15XnMPfBQiAVK1aVceSCGSO+gnbJ/YCoc1cuaYEQvt4kj+1X7Ab/k42txEXDNySXzEDf1GJKuiXKVMmfbx3757EZ7DZGXM/kAUcVTDaFjKKAQORYYoKgti4QODiggxilJIYOHCglil45plndAAzTMOGDdPs3r59+8rSpUvFHZxlPcN///0n7oZjZOCWbUnxi587Yj8hXlPIG/j7x/l3bzzyO3HkfsL2ib1AarO4XFMCqX08wR/b766XYg6J4/0diXzg4hCfNVTM2i1RBWGvXLkS62NAPV4EqaObbC8syHTt37+/PPvss1KiRAnp3bu3LF68WEsuoETBokWLpHz58i4dLxERERERERERuY6BWwoox44ds0ompEiRwuFrunbtquUD3nvvPbe8Z9GiRZ0uQwasOXBXTKE+LGAAMmeQTVuhQgXJkSOHNQ8lEqpXr66Dj8HBgwdlwoQJ0rRpUw3aov4sMnRbtWoV430hIiIiIiIiIiLPYOCWAgpqqCADFcHL1q1bR1qODNVOnTpJ7dq1JU2aNG55z+zZs0vDhg0dzn/99df1+YIFC2K8vd9//10fW7ZsKVmyZHH4munTp8u2bdtk7ty5+nOSJElk7969sm7dOh18LSJk/B44cMB6raOSBnGpO0tERERERERERHHDwC0FFNSU/eabb/Q5Hm0Dqgjmjh07Vl544QW5deuWfP/9925732nTpknlypWtn/PkyaO1ZFGyAcHUVatWxXhbv/zyiw5qhgHIVq5caZfRi2Dzd999J3Xq1NGg6+jRo3X+06dPZd68efrcPEZbTZo0kXr16unzZcuWWfNtawFjn4mIiIiIiIiIKH5wcDIKOJ988ok899xz0qJFC81ePXPmjNZ+LVSokKRNm1brvaJcwKVLl9zyfggCh4eHy+bNm+XIkSM66Bjqy6Iswb59+6Rt27ax2t6TJ080U3f58uVSpkwZLbOA7WK/CxcubGUK9+vXTwO7pkGDBkmVKlWkbNmysnPnTi2NgONGCQWMjggTJ060WwelJRC8xTa3b98uJ0+elI4dO1rZuURERERERERE5BnMuKWAg+xTlBlo3ry5Zrpi8LCSJUvKnTt35Mcff9S6sytWrHDb+yHw+eKLL8qUKVM0SxbBVQRb33//fc3CPX/+fKy3GRoaqgHYAQMGaEkE1LJFMBjHgLILL7/8sowbNy7SoGbVqlWToUOHyq5duyRjxox6rIkTJ5YlS5ZoCYWePXtGWueNN97QADPaKX/+/FoPl4iIiIiIiIiIPM/gxDZgH3B/H2jXrp0BZ8+eZft68DMWHBys7YxH9mO2JftA/Pw+4+eO3xvYT9gHeE1hH/DG71z+/nHeNmXKlNHvxHgM9O9DjvoJ2yf27RgIbebKNSUQ2seTkz+1X7AbYg6ubIMZt0REREREREREREQ+hoFbIiIiIiIiIiIiIh/DwcmInKhfv74MHjw4Tu3Tu3dvtiv5jIIFC0pwcHC0r0MdYyhVqpTWNyZiXyFX8JpC7CvkbryuOFekSBF2OCKiBIiBWyInsmXLJlWqVIlT+6RLl47tSj4TtD1+/His1tm0aZPH9ocSFvYVYj8hXlOIv398S8qUKb29Cz47QPX69ev1kdhm7FPex89kzDFwS+TETz/9pFNcbdiwwaX1idzBzLRddva43HgYxkYlIiIiSoAypkglDZ4tJOHh4d7eFZ+UJEkSqV69uj4S24x9yvv4mYw5Bm6JiAIAgrZXwh94ezeIiIiIiIiIKIY4OBkRERERERERERGRj2HgNoEYNmyYGIbhtN5g0aJF3fp+hQsXliVLlsi1a9ckLCxMTp8+LcWLF3frewSCdu3a6Xk7e/as+CrsH6ZatWrFeJ08efJY6xUoUMCj+0dERERERERElBCxVEIClz17dhkzZoxUrVpVcufO7bbRXNeuXSu5cuWSBw8eyMGDB3VeaGioW7ZPREREREREREQU6Bi4TSAmTJgg8+bN00CqrXr16knr1q3l3LlzbnsvBIERtP3vv/+kXLlycuTIEbdtmxKG8+fPS5EiRfQ5srGJiIiIiIiIiCh2GLhNIK5fv65TfMicObM+Xr58mUFbcujJkydy9OhRtg4RERERERERURyxxi3FWpIkSfTx4cOHbD0iIiIiIiIiIiIPYODWTTZv3qwDMQ0aNCjSskqVKlkDNb366quRlvfu3VuX/f7779a8l19+WWbOnCkhISFy7949CQ8P13IHCxculJo1a8ZocDL8PGPGDH3+zDPPWPsQV9WqVbPbZt68ea1tYpAtW02bNpVly5bJlStXNMB74cIF3fcaNWo43La5naxZs8qsWbPk7t27cuvWLfnrr7+sQLErSpYsKd9//722JwZTu3HjhqxZs0aaNWvmcFCtmzdvSqJEiaRv377y77//agmKM2fOyNy5c6V06dJO3yd9+vTy0UcfyZ49e/QY7t+/r+t/+eWXWm84Nrp376778vTpU30eUaNGjeSPP/7QzGe0MfrHnDlzpEyZMuIpKLuxY8cOPS4MTIc+W7t27VgNTvbcc8/JtGnTtCYy9hvnGe01cuRIyZIli8f2nYiIiIiIiIjInzBw6yZLly7Vx7p160ZaVqdOHeu5o6Brw4YN9XHJkiX6OGrUKNmwYYO0bdtWgoOD5fDhwxo0RFATgUYEMzt37hyjYPKxY8f0OQJk+BlTXN2+fdtumwgmm9tE8BCSJk0qixYt0umVV16Rx48fy759+3Q+9h2DmmGwNGewHoKDJ06c0GDppUuXNHDpCgQ9d+7cKV26dNHAIAZTQzC8Vq1aGkxGwNCRH3/8Ub755httd6yDc9GyZUvZtm2btGjRItLrS5Qooa/7+OOPNVB86tQpLRdQsGBBGTBggBw6dEiD3zGB84u6xTh2PJ80aZK1DIFsBLd/++03/UcAgqP//POPpEyZ0gqs9uzZU9xt6NChGhhG4BXHgn1D3129erUGq2PipZdekl27dkmHDh0kXbp02l6oh4u2Gzx4sAZw8U8GIiIiIiIiIqJAx8CtmwO3FStWlDRp0tgtsw3mRgzcBgUFaTAPQTBsA88HDhyoP7dv316zNMuXLy+FCxeWfPnyybp163S9ESNGaEZodIOIIQgMV69e1Z8xxRUCsLbbRFDV3OaKFSt03tdff63Ztsg2feONN3QQswoVKuhxIJiIQG7//v01k9WRF154QapXr65ZrVi3V69e4gqcj/Hjx0vy5Mll9OjRGoRFe+bOnVvbF+2MgGHErFFkziKLGJmyOXLkkBdffFEfJ0+eLClSpNCgLjKOTTjny5cv133eunWrFCpUSIORZcuW1UAkzm3GjBk1OG+7niPYL7wPBn/D8+nTp9stx7l/66235OzZszr4HNoW+4djM7O3x40b5zAT1hXom1OnTrVrD7QdIFjt6J8SESEQjrYaO3as7jcGtytWrJj2b/xDAG1lbpOIiIiIiIiIKJBxcDI3OXLkiBw/flwDdigHYJY9SJs2rQa5cLs8BvVCJiYCeLhV38zGRSAQWasIrtavX1+zY7G+WZLAhMxEZDaiHEK2bNk0UGdmuvoCBC27deumz5HdiuxZE4KQEydO1KzVzz77TEs7TJkyRW+5tzV//nyr3INZssDVLFFkqP7yyy+RyligfRHYxb4iAxSlE2xh/z/44APrZ2QYI3sXAdnKlSvLhx9+aJUwQFAax49gNrJgcfu/CeUiEMRGpinOPwKTzjKmkWWNdkFA+e2335Z58+bZLcc579evnz5v3Lix7N27166NkaWL4Cf2DZnEEY/JFTgvtvuN90MQv0iRIrrf+IcDMqqjUqpUKX1E4PvRo0fWfJRNeO+99/RcnD59Ok77h75FkeGfQ0REREQUON/9Av17sXn8tu1gfidm+8RcILSZo74SU4HQPp7kT+0X7EI/ibiNuGDg1o2QVYngEzJszcAtgrgoE4AAGup+ItiGjNJff/1Vl5s1b3HbOyD4hQnBXEdQPsCUOnVq8SUojZAsWTK5ePGiBkodQfYrsjOR0Yp2+PPPP+2Wu1LKIaJUqVJZWaCob+vIkCFD5KuvvtLAYUTIHnYE20LgFufSDNy+9tpr+vjTTz/ZBW1NyDTGsSMoi9q0jgK3rVq10oAmAs0oxWAb+LZtY5REQKkC26CtLZRRQOAWmc4oDYF/CLgDAu+O4JgQuEVGLvqkbR+NCP/cQPAaGcUIYG/cuFGePHmiy1CvF1Nc4R8bRERERESBzHbMk0Dn6O8Dtk/sBUKbufK3ZCC0jyf5U/ud91LMgYFbDwRucft6xDIJqEuLbFwE+xBMNAO3DRo0sKtva5vNWKVKFSlatKjkz59f66Qi4IVtmBIn9q1KF8i8BAQUnQ2ChqAe6r7iWFArNWLgFkFfd0E5BDMAjhqwjiCo6SiwifZHXVxH9u/fr48oFYDb/lEv1zz23bt3O90fcxmyZm2zrgEBVgxGZw7Ehtc4Urx4cX1EVq2zC5xtv8B+uStwi/qzUbUHgvbon87aGpDBjH9qoNYtPhMoqYHgLerkoi9g8Li4QsYztkeRs5z96ZchEREREcUdythF9X08ECCzDQEW278PzO/EbJ+YC4Q2c9RXYioQ2seT/Kn9gl3oJxG3ERcM3LoRskWvXbumwStk1+KWb5RCQEbh+vXrdYAxMLNAUd8zZ86cmj1pG7DCQFa4rT9Dhgx2gUQEPBHcwy30vghlIcxBzKJy584dp6niYWFhbtufTJkyWc8RXI0NlHBAlqwjtttC5jB+jsmxm8dtHrtt4BYBZpTIQPAfNYK/+OILrZmLAc5sYUAv8xGB/ehg/9zF2QXKdn50WeArV67UGsPICEa2OdoNj5i+/fZbvXB37dpVB+SLy/4xcBtZxHIkRERERJSwv/vxO3Hkvw/M78Rsn9j1pUBps7j8LRlI7eMJ/th+d70Uc/CtlE0/h+DqsmXL9DmybhG8RRAXmZYI2mFwL2Q/Pv/88zowU8QyCYAathgQC0Fb1DfFAFkYsAsBLmTfYmAqX2V2YDO46IwZkPZ0h7cNWMW2ngjKLDhje3xmNmtMjt02EB/x2FHv9fXXX5fmzZtrP0Em77Rp05we08KFC3Vwuugms2SHO0QcdM9ke8wxqUmM/6a1bt1aA+v47xrqECPrFnV98TPKivhaGRAiIiIiIiIiovjGwK2bIWPSDNyivi3glnCTOXgTljVs2NCuTAJq4SLbFlAHFjVPkWGLwK8ZsMMt8r48QBuUKVNGg4aOIIBauHBhq96pJ504ccKqn2qWGIgIWc/I8pw+fbrdfJwLBNgdKV26tD4iSxpZsrbHju05gwA8INPWNtvWHMBsxYoVGrzs2LGj7jcys80auiZkXUOxYsWiDDq//PLLki9fPreW0zDLQURUtmxZqwxGVKUOsC8FChTQ4CzgGJGljkHUUB8X8/HPD2Sh165d2237TURERERERETkjxi4dTPcCo5gHoJuZvAJGYQm83mbNm00yIcaF2Yt1cyZM1uZoc5qpXbq1MkuuBgdBMLAWSDVnXBrP8oLoPYrBtdypFevXloLFYHoDRs2eHR/UMLAHOysQ4cODl+DzE+UHECQMyIEUCNCO+JWfliwYIE138xsRYa0o/IEOOYePXpY7RRdLVlzYDSUTMibN6+1DBndCHgiqOwsuNmvXz9tW2TumiM1uoOzNuzdu7c+IvBsBsodQfAcgV388yJbtmyRlv/9999WJrJZ65eIiIiIiIiIKFAxcOuBYOG6des0ePfmm29qzdatW7day83sW5RJQAaimaFr3nZ//fp1K/hme2s9grrfffedBnxNMbmd3KzHisGwnN3q7i7nzp2TH374QZ/jsVmzZnYBz27dumkmMaDkg23NV0/B+yB4jbrAAwcOtAsIYl6fPn2sAGlE7777rhWkBQRBUb7gxRdf1HM1ZswYa9mkSZP0+FECA4Ns2QZbMfAYgrwlSpTQYx4+fHi0+z1s2DANckYsmYA6yVOmTNHnc+fOtbK2zTZGsNncPvqLO8tRoJTDp59+qkFowCPaDQPu4Z8Vn3zySZTrYxAzTPiHA/Ydhb1N2Ba2jbIL6LMonUBEREREREREFMgYuPUAMxibPHly2bJli3U7PYSGhsrJkyetn80yCYDb5IcMGWKVUjh79qxmX2LwsgsXLmjGJn4266rGpGwCAmXYLm6fx232yO5FENdT+vfvr8eEzGHUYUUwc9u2bXL58mUNbiJAN378ePn8888lPiC7E0FwtMGoUaO0JMGOHTvk4sWL8tNPP2kQEcFkR1mwGCBr8uTJmhW9fft2Xad9+/YaXH/jjTesILs5KNlrr72m56lSpUoadEXbI3MabYDgJgauw3pRlRMwhYeHS+fOnfU5srfNbF3A8SDDF8F8PGL72L9Lly7J1KlTtY0RKDb7krtgmxg0D+2ANsT7vf/++1qfF+0Sk5EgW7ZsKbdu3dL+jc8B2hhthP6BbSNjt0uXLnZtS0REREREREQUiBi49QDbLFrb+rYRyyUg2IfsXFsIFCJQt2rVKg1w4fbyrFmzavATwbsKFSpYA6AhUBiTOq/vvPOOBm0xGFTu3LntskHdDUG8Jk2a6CBbKBuRIkUKrQmL+qfIsqxevbqV5Rpfxo0bJy+99JL8/PPPmgFdqlQpDW4iMxblBpxlwKLcA4Kf2PeSJUtq0Hfs2LF6PI4yQlGaAOcLgeCDBw9K/vz5dXA6tD3quGIbq1evjvF+r1+/3squRaDbPG/4R0CjRo20jRFwxj8IUFcYQWgEqpFJjH03y2S4C84b+iACt8geRpAVA+ihdi/ObUwgUIuauBMnTtR/YmAAPwy6h0HNkFmMcxPTbRERERERERERJWQofGp4eyeIfAUCiadOndLnBQsW1MA3+TZkd6MERdq0ad1aGiKhQFAfmfqzQ/bLlfAH3t4dIiIiIvKArClTy1sFS2qSxN69ewO6jR39fWB+J2b7xFwgtJkrf0sGQvt4kj+1X7AbYg6ubIMZt0REREREREREREQ+hoFbIiIiIiIiIiIiIh+T1Ns7QPFv/vz5kiNHjlivh/T1+K5PO3DgQGnQoEGc1q1atarb98cfbdq0KU7roZby6NGj3b4/REREREREREQUPQZuA1D58uXjNEAZBqOKb4ULF5YqVarE+/smJHFtv5CQELfvC3lPxhSp2PxERERECRS/6xERJUwM3AagfPnyib9o3769TvHl9OnTkigRxuxLOBLa8VDsmIXPGzxbiE1HRERElMBxsF7HLl68KMOHD9dHihm2GdvHk9i/Yg4RHSMWryciSnAjPCZ0BQsW1HaKTlBQkJbWQJmR+/fvx8u+kX9iXyH2E+I1hfj7x/fguzDvmuPfBxRz/FuS4qufuLINZtwSESVwMf0CbwZ3//nnHwbBiX2FXMZrCrGvkLvxukJERIEmsbd3gIiIiIiIiIiIiIjsMXDrw4YNGyaGYeity44ULVrU7QOBLVmyRK5duyZhYWFa77V48eJufY9A0K5dOz1vZ8+eFV/24osvypo1a+TmzZt6W/zJkyclY8aM3t4tIiIiIiIiIiJiqQT/lD17dhkzZozWocydO7fb6hWuXbtWcuXKJQ8ePJCDBw/qvNDQULdsn3zLM888o+cb5/j27dty6NAhDTbfuHHD27tGREREREREREQM3Pq2CRMmyLx58zSQaqtevXrSunVrOXfunNveC0FgBG3/++8/KVeunBw5csRt2ybf07BhQw3aojg2Bq5CljUREREREREREfkODk7mw65fv65TfMicObM+Xr58mUHbAGCeb2TaMmhLREREREREROR7WOOWVJIkSfTx4cOHbJEAwPNNREREREREROTbGLiNwubNm7Xu56BBgyItq1Spki7D9Oqrr0Za3rt3b132+++/W/NefvllmTlzpoSEhMi9e/ckPDxcyx0sXLhQatasGaPByfDzjBkzrDql5j7EVbVq1ey2mTdvXmubGGTLVtOmTWXZsmVy5coVDfBeuHBB971GjRoOt21uJ2vWrDJr1iy5e/eu3Lp1S/766y8rcOiKkiVLyvfff6/ticHUUJ8Vg201a9bM7nV58uTR/cAgXIkSJZK+ffvKv//+qyUozpw5I3PnzpXSpUs7fZ/06dPLRx99JHv27NFjwEBeWP/LL7/UesOx0b17d92Xp0+f6vOIGjVqJH/88YdmPqON0T/mzJkjZcqUEXcOnDZ8+HD9uXr16tZ5Ql+IzXlD+6PfYBA79GVk7q5YsUL7iTNo//bt28uWLVv0fOCc/fnnn1K+fHmpVatWlIPxEREREREREREFGkT9ODlogw8++MCA9evXR1o2bNgwwzRmzJhIy1euXKnLOnbsqD+PGjXKev3ly5eNnTt3GseOHTMePXpkze/cubPD99i0aZM1D8+PHj2q88PDw/Vn2+WxnUqXLm23zbCwMGub9evX19ckTZrUWLRokbWf58+fN7Zv325cuXIlyjYwYVtPnz419u3bZ1y4cMGYM2eOy/2te/fuxsOHD3X7t2/f1vY8c+aM9Z4jR460XpsnTx6dd/PmTWPGjBn6/Nq1a8aOHTt0ntmWLVq0iPQ+JUqUMM6dO6evefLkiXHgwAFjz5491nm7fv26Ua1aNbt12rVrp8vOnj1rNx/nF+2A7XTo0MFuWZIkSYxZs2ZZ+3/p0iXdP+wnPH782OjZs6fL7YZzivNx+vRp3e6tW7es842+ENPz1qNHD90nuHPnjrF7927j1KlT1rqzZ882EidObPfeyZIlMxYvXmy95vjx49qW2A7O5Zdffhmpv8dkCg4O1vXwyGuZa/2Dbcnfhewr7APuvI7ymsL+xL7CPuDu72a8rrBPsZ+wD/Cawj4gfvi7x8Vt8KQ7a4MiRYpowyKolCZNGrtlW7ZssQJQe/futVsWFBSkgUAE6LJkyaKBPTPw98477xiJEiWyXpsrVy5j7dq1VrDOdpmjwG1UgUFXJnOboaGhkZaNGzfOCtA1a9bMmo/AHAJ4ZhCzb9++duuZEAyuWrWqzsPxZciQwaV9rVixorYlICCeIkUKaxna11xWu3Ztu8Ct6YsvvtAgIpalTJnSmDRpks5/8OCBkTdvXmtbOOdm0BbnO1++fNayrFmzGr/99psVELZdz9H5ad++vQZBEaRs27ZtpGMyA/sIPtetW9eujXv16qVtjPXNY3J1MvvWunXrIi2L7rwh+It9QR/v3bu3XYC2Zs2a2o/hk08+sdvu0KFDrUC7+U8B8/zgHwG2AePYHAu/wLvvGs625O9D9hX2AXddT3hNYV9iX2EfcOf1hNcV9if2E/YBXlPYByRAA7ccnCwKR44ckePHj0uhQoW0HIBZ9iBt2rTy4osv6u3yGOQJt4xnzJhRb/uGOnXqSIoUKbTUwtWrV6V+/fp62zvWN0sSmM6fP6+34eP28GzZsunt6bhN3lfkypVLunXrps+7dOkiixYtspb9999/MnHiRAkODpbPPvtMSztMmTJFSwnYmj9/vnX7u1mywBVDhw7VW/Z/+eWXSGUs0L4VK1bUfe3QoYOWTrCF/f/ggw+sn3GLP0oWlChRQipXriwffvihVcKgZ8+eevyXLl3SchgoF2BCuYg33nhDdu3aped/8ODB0rlzZ4f727ZtW20XlEd4++23Zd68eXbLcc779eunzxs3bix79+61a+MJEyZoWQzs28iRIyMdk6c4O2+jRo2SxIkTy//+9z8ZP3683Tpr166Vd955R5YvXy7vvfeefPvtt/q5SJ48uQwYMEBf079/fy2pYEKphYYNG+rnDZ+juEI/JNeYbci2JPYVcgdeU4h9hdyN1xViPyFeU8gff/e4+je216LW/jB99dVXGhUfP368Na9x48Y6b+zYscaSJUv0edOmTa3lU6ZM0XkDBgyw25ZtZqjtVLZsWSvb0Dar0xcybjt16qTzcau8bTaw7ZQ6dWrNvoRXX33Vmu+sBIQrU6pUqaz3qlGjhsPXIMu5UKFCWuIhYsZtpUqVHK6DLFjzOM15mzdv1nmfffaZ0/0x2wflLxydn1atWlkZwLbZyo7a/uDBg07fp1ixYtYx4PjiI+PW0XmzbcvMmTM73b5ZRuPNN9/Un+vVq2dl8Tr7HODz5ErGLRERERERERGRr2LGrQcsXbpUMwfr1atnzatbt64+YrAmZOMiSxKDi/366686v0GDBvq4ZMkSu20he7JKlSpStGhRyZ8/vxQsWFCzNbENEzIZfUmRIkX0EVmgzgZBwyBfR48e1WN57rnndLApWxcvXnTb/uTOnVuzmeGff/5x+BpkOWOKCO2/c+dOh+vs379fH3PkyCFp0qTRwePMY9+9e7fT/TGXIWvWNusasmTJooPRmQN64TWOFC9eXB+RVetsYC7bfoH9cnR87ubovJn7CosXL3a6bsqUKfXRbMMCBQroIzLYkX3uyPbt213aX2RHYyA1Epf+C4i7ANiWxL5C7sBrCrGvkLvxukLsJ8RrCvnj7x5zG3HBUgnRQLmDa9euaXA1T548els3SiE8efJE1q9fL2fOnNHXIXAL5cqVk5w5c8qhQ4ckJCTE2g5uE8dt/RkyZLALJCLgieAebqH3RSgLAbdv347ydXfu3HGa/h0WFua2/cmUKZP1HMHV2EAJh8ePHztcZrut9OnT688xOXbzuM1jtw3cIsCMICWC/02bNpUvvvhCSwicOnXKbhvp0qWzHhHYjw72Lz44Om/mvkJs9tVcL6pzdv36dXEFLqAM3LoH25LYV8ideE0h9hVyN15XiP2EeE2hQPnd41vpnT4IwdVly5bpc2TdIniLIC4yLRG027dvn2Y/Pv/885I9e3athQq//fabtQ3UsP3yyy81aIv6pu3atZMXXnhBA4PIvh0xYoT4KrNT2gbsHDED0p7uxLb1c2NbIyRVqlROl9ken5nNGpNjtw3ERzz2R48eyeuvvy7NmzfXfoJM3mnTpjk9poULF0qiRImincxay95g7iv+mRGTfUW2um3w2wyGO5I6dep4OgoiIiIiIiIiIt/HwG0MIGPSDNxikDKzTILtgEyAZRhkybZMQtKkSa1BmT7++GNp1aqVZtgi8GsGwXCLvK/CgFFQpkwZDcQ5ggBq4cKFrVvhPenEiROa7Rzxtn1byHpGyYHp06fbzce5QIDdkdKlS+sjsqTNW/nNY8f2nEEAHpBpa5ttaw5ghkG4MChZx44ddb+RmW0OfmZC1jUUK1YsyqDzyy+/LPny5fNqOQ1zXzEoHwbTcwYDvaFMglky4cCBA/qIf3o4C9A6O59ERERERERERIGIgdsYWLlypQbzEHSrXbu2zluzZo213Hzepk0bDfKhboVZSxUBLjMz1Fmt1E6dOtkFF2OSBQzOAqnuhFv7UV4AtV9btGjh8DW9evWSZMmSaSB6w4YNHt0f3GqP8hXQoUMHh69p3bq13saPIGdECKBGhHbs2rWrPl+wYIE138xsRYa0o/IEOOYePXpY7RSVPXv2yNdff63PUTIhb9681jJkdCOoi6Cy2b8i6tevn7YtMneDgoLEWxDMNoPzvXv3dviaSpUq6Tk6fPiwvPTSSzrv77//1s9F8uTJ7fq7CfN9tVwIEREREREREZG3uDxCfSBMy5cv1xHgHj58aDx48MBIkSKFtSxfvnx2o8RNnDjRWpYkSRLj2rVrOn/t2rVGhgwZrGWZM2c2vvvuO7t1y5QpYy0fNmyYztu0aZPdvjRp0kTnYz/SpEnjluNr166dbjM0NDTSsgkTJuiyO3fuGM2aNbPmJ0qUyOjWrZvx6NEjXf7hhx/arWeqVauWW89FzZo1jadPn+q2Bw4cqG1sLnv77beNx48f67JXXnlF5+XJk8faFyzr2rWr9fqgoCBj+vTpuuzKlStGpkyZrGXp0qUzzp49q8u2bNli5M2b11qWJUsWY8mSJbrs9u3bRsGCBSO1Jda13e+UKVMax48f12V//fWX3TL0Gbh69arRsGFDuzbu2LGj1cajRo1ySxuafWvdunWRlkV33lq0aKHLnzx5Yrz//vtGsmTJrGWVK1c2Lly4oMu3bt1qt16HDh2sftuyZUtrfvr06Y1FixZZ7xuxv0c3YVTGuI7OyIltyT4Qt88BP3e8frCfsA+48/rJawr7E/sK+wCvKewD3vhezt8/7HcST/3ExW3wRMWkDbp3724FllavXh1p+YkTJ6zldevWtVuG4Kbp3r17xp49e4xDhw5Zwbjdu3dr0BBee+21aAO3BQoU0KAZnD9/3ti5c6eRMWNGjwVukydPbixevNg6hnPnzhnbtm2z9hnGjRsXaT1PBW4x9enTx2qD69evGzt27DAuXrxovefw4cOt19oGbvfv32+12/bt2zUYDQiuv/zyy5Hep3Tp0vpaM1CJ9XG+EMA3A6116tRx2JYRA7eYqlevbu1Ljx49rPn4R8DSpUvt2hj7d/nyZWve/PnzjcSJE3s9cItpyJAhVvD85s2buq8nT5601j18+LAGtyOuN3r0aOs1p0+f1vN2//593dbevXsZuPXy9ZhfXPj7kH2FfYDXFPYB/v5hH/D29xF+V/F+O/v7xO+03j8H/jKxr3j/HPjDFMzArX9MuXLlsgJO//vf/yIt//7773XZrVu37DIQzalGjRrGypUrNSCHgC0Cfhs3btSAcNKkSY0ZM2bo+j/88EO0gVtMb731lnHkyBEjPDxcg3tly5b1WODWnN58801jxYoVuu9431OnThk///yzUa1aNYev92TgFtMLL7xgzJkzR9sUgVQEX//4449I72cbuH3++eeNwYMHa+ZrWFiYERISYnz77bfGM8884/R9kCWNQPC+ffs08I5gLwK4I0aMMHLkyOG0LR0FbjHhHMPdu3ftsnjNNl62bJkGxdFPEJRGdm7btm01+9Zdbedq4BbTiy++aMyaNUsDsOgPOB4EtdG+yGR2th7+sYHPAgK+WAeZ6HivqPp7VBN/2brvM8W2dP91KqFO7CvePwf+MLGfeP8c+MvEvuL9c+AvE/uK98+BP0zsJ94/B/4ysa94/xz4wxTMwK33TwKnhN0GtoFbZCt7e384OW4DBm693zf4xcX758BfJvYV758Df5jYT7x/DvxlYl/x/jnwl4l9xfvnwB8m9hPvnwN/mdhXvH8O/GEK9nLgloOTEREREREREREREfkYBm6JiIiIiIiIiIiIfExSb+8Aucf8+fMlR44csV5v79690qdPn3g9DQMHDpQGDRrEad2qVau6fX/80aZNm+K03rJly2T06NFu3x+KHwULFpTg4OBYr3f37l0JCQnxyD4REREREfnSd+KgoCB9LFWqlNy/f9+aH8jfifl3hGfaKJD7lC32L89i4DaBKF++vOTNmzfW6z158kTiW+HChaVKlSrx/r4JSVzbj79U/PuX4fHjx+O8fqFChXj+iYiIiChgvhM7SnYJxO/E/DsiegUKFNCktrgIxD5li/3L8xi4TSDy5csn/qJ9+/Y6xZfTp09LokSJJCFJaMcDH3/8sU7kmPnf32Vnj8uNh2ExbqaMKVJJg2cLxSlTl4iIiIjIl/A7MdvME9KkSRPrv7X4dxY/k/GFgVsiIj+CLxJXwh94ezeIiIiIiLyG34nZZuxXvoWfSc/h4GREREREREREREREPoaBWyIiIiIiIiIiIiIfw8BtAjVs2DAxDMNhQXYoWrSo2wccW7JkiVy7dk3CwsK0rmzx4sXd+h6BoF27dnrezp49K/5q3bp1egwjRoywm495mGrVquW1fSMiIiIiIiIi8hescRtgsmfPLmPGjJGqVatK7ty53bLNoKAgWbt2reTKlUsePHggBw8e1HmhoaFu2T4REREREREREVGgYeA2gZowYYLMmzdPA6m26tWrJ61bt5Zz58657b0QBEbQ9r///pNy5crJkSNH3LZtSjiKFCmij2fOnPH2rhARERERERER+TwGbhOo69ev6xQfMmfOrI+XL19m0JacOnr0KFuHiIiIiIiIiCiGWOOWXJYkSRJ9fPjwIVuTiIiIiIiIiIjIDRi49ZDNmzfrQEyDBg2KtKxSpUrWQE2vvvpqpOW9e/fWZb///rs17+WXX5aZM2dKSEiI3Lt3T8LDw7XcwcKFC6VmzZoxGpwMP8+YMUOfP/PMM9Y+xFW1atXstpk3b15rmxhky1bTpk1l2bJlcuXKFQ3wXrhwQfe9Ro0aDrdtbidr1qwya9YsuXv3rty6dUv++usvK1DsipIlS8r333+v7YnB1G7cuCFr1qyRZs2a2b0uT548uh83b96URIkSSd++feXff//VEhS45X/u3LlSunRpp++TPn16+eijj2TPnj16DPfv39f1v/zyS603HBvdu3fXfXn69Kk+j6hRo0byxx9/aOYz2hj9Y86cOVKmTBlxt3Tp0snQoUO1njH6I87nDz/8INmyZXO6jqPByczB2NCO+Fzs3btX+/b58+e1rYmIiIiIiIiIAhUDtx6ydOlSfaxbt26kZXXq1LGeOwq6NmzYUB+XLFmij6NGjZINGzZI27ZtJTg4WA4fPqxBQwQ1EWhEMLNz584xCiYfO3ZMnyOwh58xxdXt27fttomAm7lNBA8hadKksmjRIp1eeeUVefz4sezbt0/nY98xqBkGS3MG66Em74kTJzRYeunSJQ1cugJBz507d0qXLl0kS5YsVvARAUUEk0eOHOlwvR9//FG++eYbbXesg3PRsmVL2bZtm7Ro0SLS60uUKKGv+/jjjzVQfOrUKS0XULBgQRkwYIAcOnRIg98xgfOLusU4djyfNGmStQyBbAS3f/vtN/1HAAKh//zzj6RMmVLbbseOHdKzZ09xl2effVb+/vtv+eSTT+S5556T48ePa1/Afu3evVty5MgRp/q3K1eu1OA/2iVDhgz6SEREREREREQUqBi49XDgtmLFipImTRq7ZbbB3IiB26CgIA3mIUCHbeD5wIED9ef27dtrlmb58uWlcOHCki9fPlm3bp2uN2LECM0IjW4QMQSB4erVq/ozprhCANZ2mwiqmttcsWKFzvv666812xbZpm+88YYOYlahQgU9DgQTEcjt37+/0+zKF154QapXr65ZrVi3V69e4gqcj/Hjx0vy5Mll9OjRGoRFe+bOnVvbF+08ePBgqV27dqTMWWSHIlMWgckXX3xRHydPniwpUqTQoC6Cjiac8+XLl+s+b926VQoVKqSB3LJly2q2M85txowZNThvu54j2C+8DwZ/w/Pp06fbLce5f+utt+Ts2bM6+BzaFvuHYzOzt8eNGxfpmOJq6tSp8vzzz2twGP0QGb34Ge+J90IwN7Zwfg8cOKAZzhjgDm20evVqt+wvEREREREREZE/4uBkHnLkyBHNRETADuUAzLIHadOm1QAXbpfHoF7IxEQAD7fqm9m4CAQiaxXB1fr162t2LNY3SxKYcDs5bsNHOQTcoo5AnZnp6gsQtOzWrZs+R3YrsmdNCEJOnDhRs1Y/++wzLe0wZcoULSVga/78+Va5B7NkgStwez8yVH/55ZdIZSzQvgjsYl87dOigpRNsYf8/+OAD62dkGCN7FwHZypUry4cffmiVMEBQGsePYDayYFHmwYRyEQhi79q1S88/AsXOMqaRZY12QUD57bfflnnz5tktxznv16+fPm/cuLGWGrBtY2TpIgiKfUMmccRjii0EufGPhydPnmhAPjQ01FqGLGZk+G7cuDFO20Y73LlzR5+bn4fYQF9KyPBPHVfXj66NzOUJvS3JdewrxH5C7sRrCrGvkC99J05o2GbOmX0hderULrVvoPWpQOtfwW74O9mVdRm49SBkVb733nsa6DIDtwjiokwAAmjILkSwDRmlv/76qy43a97itndAti0mBHMdQfkAkysXG09AaYRkyZLJxYsXNVDqCLJfUUoAGa1ohz///NNuuSulHCJKlSqVleGM+raODBkyRL766iu7gKQJ2cOOYFsI3OJcmoHb1157TR9/+uknu6CtCZnGOHYEZVGb1lHgtlWrVprJi0AzSjHYBr5t2xglEVBWwDZoawtlFBC4RaYzSkPgHwJxZZbxQDD95MmTkZZjPvalWLFisdouAtMov+AK/CODnLOtd822JHfh547YT8ideE0h9hXype/EFDhthrJ9cRUI7eNJ/tR+570Uc2DgNh4Ct7h9PWKZBNSlRTYugn0IJpqB2wYNGtjVt7XNnqxSpYoULVpU8ufPr3VSka2JbZgSJ/atyheoWwoIKDobBA2BZ9R9xbHgFvuIgVsEfd0F5RDMADhu83cEQU1HgU20PzJKHdm/f78+onQCSiSgXq557Kj56oy5DFmztlnXgAArBqMzB2LDaxwpXry4PiKr1tkFz7ZfYL9cCdyaZRBQuzeqEhqxDdwiuI0MZlcgwxklORKqUqVKufRLDSVMnPV72/8C4pdRQm9Lch37CrGfkDvxmkLsK+RL34kTGrZZ9L9/ELOJa/A2EPtUoPWvYDf8nWxuIy4YuPUgZIteu3ZNg6vIrj19+rSWQsBt5uvXr9cBxsDMAkVtz5w5c2rGYkhIiLUdDGSF2/oxYJNtIBEBTwT3cAu9L0JZCMDAVVExb493lDoeFhbmtv3JlCmT9RzB1dhACQdkyTpiuy1kDuPnmBy7edzmsdsGbhFgRokMBP9RkuCLL77QmrkY4MxWunTprEcE9qOD/XOF2Qejar+4lLNwx3nGBTQhBxsjlhGJy/oxbZ+E3pbkPuwrxH5C7sRrCrGvkC99J04o2GbRs72TOS7tG2h9KlD7110v/Z3sWymaCQyCq8uWLdPn+A8OgrcI4iLTEkE7ZCYi+xEDO2FAqYhlEgA1bDEgFgJmqG+KAbIwYBcCg8i+xcBUvsrs0GZwMbpgoKc/ALYXlNjWF0GZBWdsj8/MZo3JsdsG4iMe+6NHj+T111+X5s2baz9BJu+0adOcHtPChQt1cLroJrNkR1xdv35dH83AdGzbioiIiIiIiIiIYoaBWw9DxqQZuEV9W7NMgmnt2rX6iGVm/VCzTAJq4SLbFlAHFjVPkWGLwK8ZsMMt8r48QBuUKVNGg4aOIIBauHBhfY7B3DzpxIkTmu1sW2IgImQ9I81/+vTpdvNxLhBgd6R06dL6iCxpZMnaHju25wwC8IBM24iDcWEAsxUrVmjt144dO+p+IzPbrKFrQtY1RFWaAIHUl19+WfLly+dyOQ3z/XBOnYltmQQiIiIiIiIiIoqMgVsPQ50UBPMQdKtdu7bOw8BkJvN5mzZtNMiHmhdmLdXMmTNbmaHOaqV26tTJLrgYkyxgcBZIdSfc2o/yAqj9isG1HOnVq5cOYIZA9IYNGzy6P7i93xzsrEOHDg5f07p1ay05gCBnRAigRoR27Nq1qz5fsGCBNd/MbEWGtKPyBDjmHj16WO0UlT179lgDo6FkQt68ea1lyOhGUBdBZbN/RdSvXz9tW2TuujriozlA2ksvvWQFrG2VKFFCXnzxRZfeg4iIiIiIiIiIGLj1OAQL161bp8G7N998U2t5bt261VpuZt+iTAKyIc0MXfO2e/PWdATfbG+tR1D3u+++04CvKXXq1DHaH8BgWLj93pPOnTsnP/zwgz7HY7NmzewCnt26ddNMYkDJB9uar56C90HwGnWBBw4caA3+BZjXp08fK0Aa0bvvvmsFaQFBUJQvQKAS52rMmDHWskmTJunxowQGBlyzDbZi4DEEeRHkxDEPHz482v0eNmyYZvRGLJmAOslTpkzR53PnzrWyts02RrDZ3D76i6vlKDAo2c8//6x9FQPqoRC5CaU7ENj1tUHyiIiIiIiIiIj8ESMs8cAMxiZPnly2bNli3U4PoaGhcvLkSetns0wC4Db5IUOGWKUUzp49q9mXGLzswoULmrGJn826qjEpm7B//37dLm6fx23vyO5FENdT+vfvr8eEzGHUYUUwc9u2bXL58mUNbiLzdPz48fL5559LfEBpCgTB0QajRo3SkgQ7duyQixcvyk8//aRZywgmO8qCPXz4sEyePFmzordv367rtG/fXoPrb7zxhhVkNwcle+211/Q8VapUSYOuaHtkTqMNGjdurAPXYT3bgeicCQ8Pl86dO+tzZG+b2bqA40GGL4L5eMT2sX+XLl2SqVOnahsjUGz2JVfhvZG5jKxkZPEeOHBAjw2PGAAO/6ggIiIiIiIiIiLXMHAbD2yzaG3r20Ysl4BgX8SgFwKFCNStWrVKbt26pbVZs2bNqsFPBNAqVKhgDYCGQGFM6ry+8847GrRFkC137tx22aDuhkG2mjRpooNsoWxEihQp9BZ7jNqIDNHq1atbWa7xZdy4cXqrPzJHkQGNrFEEN5EZi3IDzjJgUe4BwU/se8mSJTXoO3bsWD2ejRs3Rno9gpo4XwgEI1M1f/78Ojgd2n7kyJG6jdWrV8d4v9evX29l1yLQbZ43/COgUaNG2sYIOOMfBKhBiyA0AtXIJMa+m2UyXIV+WqtWLc1A3rt3r+5Hzpw5NTCP7GP0MSIiIiIiIiIicg0KnRouboMowcqTJ4+cOnVKnxcsWJBBSR+EbG6UnEibNq3LpSB8GYLxyLCfHbJfroQ/iPF6WVOmlrcKlpSyZctqoD0qgdKW5Dr2FWI/IXfiNYXYV8iXvhMnNGyz6H//VK1aVQcpj02/CuQ+FbHkPMUAAHBlSURBVGj9K9gNfye7sg1m3BIRERERERERERH5GAZuiYiIiIiIiIiIiHxMUm/vAHnf/PnzJUeOHLFeD+ns8V2fduDAgdKgQYM4rYvbH0j0FpC4QC3l0aNHswm9LGOKVB59PRERERGRr+N3YraZO5kDvcemX/HvLH4m4wsDtyTly5eP0wBlT548iffWK1y4sFSpUiXe3zchiWv7hYSEuH1fKOZSpkypjw2eLRSnZmPNWiIiIiLyd/xOzDbzBAycHte/tQL97yx+Jj2PgVuSfPny+U0rtG/fXqf4cvr0aUmUCGP4JRwJ7XgCRXh4uD62bt1ajhw5EusvEwy8ExEREVEgfCcOCgrSuwxxx+X9+/cl0L8T8++ImIvt31qB2qdssX95HgO3RER+BF8kfH3UTSIiIiIib30nxujt8M8//wR8NmRM24zYRp78TJJrODgZERERERERERERkY9h4NbDhg0bJoZhOB0QqmjRom6vAbtkyRK5du2ahIWF6a3+xYsXd+t7BIJ27drpeTt79qy3d8Un5cmTR9sHU4ECBSItz5Ahg2TLls3hus8995z88MMPeksJ+uidO3dk9+7dMnjwYEmTJk087D0RERERERERke9j4NZLsmfPLnPmzJEVK1a4bZuo5bN27Vpp3LixpEqVSg4ePKg1fUJDQ932HkTReffddzUo6+gfBm+88Ybs27dPOnfurCN34nXXr1+X0qVLy8iRI2XXrl2SK1cuNjIRERERERERBTwGbj1swoQJUqRIES1ybatevXo6z50DRaH4OoJe//33n5QrV07Kly+vGb22BdmJ3OH8+fParzEhq9vWt99+KxkzZnQ4CN6sWbN01MmffvpJsmbNKiVKlND5ZcuWlX///VezcefPn8+TREREREREREQBj4OTeRiyCTHFh8yZM+vj5cuXYz3qPFFsPHnyRI4ePRqrdfr06aNBWxQs79Chg/6DwYSBA5o2bapZ4pUqVZLKlSvLli1beFKIiIiIiIiIKGAx4zYBSZIkiT4+fPjQ27tCFEmNGjX0ERm1tkFbEwLB5j8ckC1ORERERERERBTIEnzgdvPmzTqA0qBBgyItQ2afOcDSq6++Gml57969ddnvv/9uzXv55Zdl5syZWpvz3r17Eh4eLufOnZOFCxdKzZo1YzQ4GX6eMWOGPkedT3Mf4qpatWp228ybN6+1TQyyZQtZjcuWLZMrV65ogPfChQu672ZQLSJzO7itHbe53717V27duiV//fWXFSh2RcmSJeX777+3Bqq6ceOGrFmzRpo1a+ZwMKybN29qeYm+ffvqrfUPHjyQM2fOyNy5c7VOqjPp06eXjz76SPbs2aPHgPIRWP/LL7/UesOx0b17d92Xp0+f6vOIGjVqJH/88YdmPqON0T9Qz7hMmTLiDp06ddL3P3bsmMPlmI/lCxYscNgOjx8/1ozZLFmyWPPRBqgxu337ds0Qf/TokT4i67V///6aKRvd4GQ//vijXT/GebTtg6h92759e/ntt9+cHptZOsQdfYuIiIiIiIiIyN8ZCXn64IMPDFi/fn2kZcOGDTNMY8aMibR85cqVuqxjx47686hRo6zXX7582di5c6dx7Ngx49GjR9b8zp07O3yPTZs2WfPw/OjRozo/PDxcf7ZdHtupdOnSdtsMCwuztlm/fn19TdKkSY1FixZZ+3n+/Hlj+/btxpUrV6JsAxO29fTpU2Pfvn3GhQsXjDlz5rh8brp37248fPhQt3/79m1tzzNnzljvOXLkSOu1efLk0Xk3b940ZsyYoc+vXbtm7NixQ+eZbdmiRYtI71OiRAnj3Llz+ponT54YBw4cMPbs2WOdt+vXrxvVqlWzW6ddu3a67OzZs3bzcX7RDthOhw4d7JYlSZLEmDVrlrX/ly5d0v3DfsLjx4+Nnj17utxu2bJl030AtIvtMrOd4OrVq5HWbdWqVaT+WKFCBePGjRs6/8GDB1b7mO0KGzZsMBInTuzwfQoUKKDzBg4cqNs17d+/364PRjcVLVrUWjem62AKDg7WdfDojmuGr05lypTR48Sjp94jUNqSE/sK+0D8fA54TeH1hn2FfYDXFfYBb3wn5u+f2LdZoE5mXzGxjTzzmfT3KdgNfye7uA3vN4InpyJFimjjIECYJk0au2VbtmyxPqB79+61WxYUFKSBQATosmTJooE9M/D3zjvvGIkSJbJemytXLmPt2rVWsM52maPAbVSBQVcmc5uhoaGRlo0bN06X3blzx2jWrJk1H8G4Hj16WEHMvn372q1nQjC4atWqOg/HlyFDBpf2tWLFitqWgIB4ihQprGVoX3NZ7dq1IwUK4YsvvjCSJUumy1KmTGlMmjTJCjzmzZvX2hbOuRm0xfnOly+ftSxr1qzGb7/9ZgWEbddzdH7at2+vAVMEYNu2bRvpmMzAPoLPdevWtWvjXr16aRtjffOYXJm2bdum79WlS5dIgWVbpUqVsls+e/Zsnf/ee+9Z+3b8+HGd9+uvvxrp06e3Xotgv/mPD2jQoEGUgduIfaZWrVoxPh7sB/65YrZ58uTJY7xuoHwxY+DW++eAU+B97ji51gbsJ+xD7CvsA+6+jvK6wj7FwG3s+0AgBNZcvaYwcBv3NgyE/hXs5cBtgh+cDDUzjx8/LoUKFdJyAGbZg7Rp08qLL76ot8tjUC/csp8xY0a9VR/q1KkjKVKk0FILV69elfr16+tt71jfLElgOn/+vN6Gj3II2bJl07ICuE3eV+TKlUu6deumz7t06SKLFi2ylqHW6MSJEyU4OFg+++wzLe0wZcoULSVgC3VJzXIPZskCVwwdOlRvh//ll18ilbFA+1asWFH3FYNY4ZZ7W9j/Dz74wPoZ5SpQsqBEiRI6qNWHH35olTDo2bOnHv+lS5e0HAbKPJhQLuKNN96QXbt26fkfPHiwdO7c2eH+tm3bVtsF5RHefvttmTdvnt1ynPN+/frp88aNG+sAXLZtPGHCBC2LgX1DSYKIxxRbS5culQoVKki9evXkhx9+sOaj38KGDRu0hAb6PAb+gsSJE2s/hiVLluhjqVKlJFOmTNqGKMFg2z4op/DFF1/oeUA5BLQvymx4wuTJk3V/4b333tNSDbGFPpyQBQUFWY+eOlZzuwm9Lcl17CvEfkLuxGsKsa+QO78T85oS+zYLVBHbg20Ue4HQv4Ld8Heyq21jJPTpq6++0sj2+PHjrXmNGzfWeWPHjjWWLFmiz5s2bWotnzJlis4bMGCA3bZsM0Ntp7Jly1r/pbHN6vSFjNtOnTrpfJQ4sM0Gtp1Sp06tGcbw6quvWvOdlYBwZUqVKpX1XjVq1HD4GmQ5FypUSLM+I2Z4VqpUyeE6yII1j9Oct3nzZp332WefOd0fs31Q/sLR+UF5ATMD2DZb2VHbHzx40On7FCtWzDoGHJ8rbVi8eHErUxglGjAP5xZlGVD2APsJS5cutdapXLmy031E1rKj90HmK8o9wMcff+z2jFtk2pqfNfPz6Op/SYmIiIiIiIiIfA0zbqPITkQWH7ITTXXr1tVHDLKFbFxkSWJwsV9//VXnN2jQwC4z0TZ7skqVKlK0aFHJnz+/FCxYULM1sQ0TMht9SZEiRfQRWaDOBkHDIF9Hjx7VY3nuuefkzz//tFt+8eJFt+1P7ty5NZsZzGzQiJDljCkitP/OnTsdrrN//359zJEjh6RJk0YHjzOPfffu3U73x1yGrFnbrGvAAF4YjM4cLAuvcaR48eL6iKxa24HobNn2C+yXo+OLqYMHD8rJkye1DyLzduvWrfLCCy9o9uzixYs14xZthcH08L543rBhQ4d9GpBxW7hwYSlbtqxm12K7xYoV0/6QKlWqSPvvDqlTp5aff/5ZP3swffp0HXQurpBZjYHnEipkR6NvVa1a1ennxlX4LyDuIEjobUmuY18h9hNyJ15TiH2F3PmdmNeU2LdZoDL7ioltFHuB0L+C3fB3csS+FhsJvlQCoNzBtWvXNLiaJ08eOX36tN5SjlvB169fL2fOnNHXIXAL5cqVk5w5c8qhQ4ckJCTE2s6AAQP0tv4MGTJY8xAQQ8ATwT3cQu+LUBYCbt++HeXr7ty54zSFOywszG37g+CiCcHV2EAJh8ePHztcZrut9OnT688xOXbzuM1jtw3cIsCMEhkI/jdt2lRLByxfvlxOnTplt4106dJZjwjsRwf75yrsEwKd+CcEArdmmQT8MwL9/cCBA3oRRUB3x44dTgO3KBkyadIkDdraQmAZpRHKlCmjgVx3QnD9jz/+sN7z66+/1n+uuAIX0IQcbDTLl+DR08eZ0NuS3Id9hdhPyJ14TSH2FXLnd2JeU2LfZoGObRS3NguUtrvrpb+TfSs11EMQXDVrcyLrFsFbBHGRaYmg3b59+zRI9fzzz0v27Nm1Fir89ttv1jZQw/bLL7/UoC3qm7Zr104DYggMIvt2xIgR4qvMjmUGF50xA9Ke7oi29XNjW+fDzP50xPb4zGzWmBy7bSA+4rGj1urrr78uzZs3136CTN5p06Y5PaaFCxdKokSJop3MWsuuBm5ts8dtA7dg1tFFnVtkOSMr+Ny5c1rT1zbzd926dRpAxT8qEDytXbu2/icJ2cWoAezObGvAfmzfvl3fEzWD+/Tp43LQloiIiIiIiIgooQmIwK1tkAuBWwSybANcsHbtWn3EsoiZiUmTJtVsW/j444+lVatWmmGLwK8ZsMMt8r48QBsgcxJBQ0cQQMWt8oDB3DzpxIkTmu1sW2IgImQ9I90et8/bwrlAgN2R0qVL6yOypJEla3vs2J4zCMADMm1ts23NAcxWrFihAcaOHTvqfiMz2xz8zISsa0B5gaiCzihdkC9fPreUHdi4caMOEle+fHn9h8NLL72kqffmMZuBW+yv2afNz4Hp3Xff1ZIFhw8f1u0g8xWfiwsXLlivcWffxrnDZ+3ZZ5/V8hzIYh4/frzbtk9ERERERERElFAETOB25cqVGsxDEAsZhbaBLdvnbdq00SAfAmBmLdXMmTNbmaHOaqV26tTJLrgYkyxgcBZIdSfc2o/yArg9vUWLFg5f06tXL0mWLJkGolEf1ZNQwgDlK6BDhw4OX9O6dWstOYAgZ0QIoEaEduzatas+X7BggTXfzGxFhrSj8gQ45h49eljtFJU9e/ZoYBNQMiFv3rzWMmR0I6iLwKTZvyLq16+fti0yd82RF12BYDL2Gf1tyJAhkjJlSrt/RiCwi4zhypUrS7NmzRyWSTDbF4FbR+UwcCzIUI9pv46qb6P9sb+oG3zr1i3ddsRAMhERERERERER/R8jUKbly5frKG4PHz40Hjx4YKRIkcJali9fPruR3iZOnGgtS5IkiXHt2jWdv3btWiNDhgzWssyZMxvfffed3bplypSxlg8bNkznbdq0yW5fmjRpovOxH2nSpHHL8bVr1063GRoaGmnZhAkTdNmdO3eMZs2aWfMTJUpkdOvWzXj06JEu//DDD+3WM9WqVcut56JmzZrG06dPddsDBw7UNjaXvf3228bjx4912SuvvKLz8uTJY+0LlnXt2tV6fVBQkDF9+nRdduXKFSNTpkzWsnTp0hlnz57VZVu2bDHy5s1rLcuSJYuxZMkSXXb79m2jYMGCkdoS69rud8qUKY3jx4/rsr/++stuGfoMXL161WjYsKFdG3fs2NFq41GjRrmtHVu0aGH1aUDb2S7fsGGD1W63bt0ykiVLZrf822+/1WV37941KleubNfnW7ZsaVy/ft1af+zYsdZy2/NRoEABu22iLQHHbDt/6tSpOv/JkydG3bp13dYGGJUxrqMz+tOE60rE64u7p0BpS07sK+wD8fM54DWF1xv2FfYBXlfYB7zxnZi/f2LfZoE6mX3FUSyHk/s+k/4+Bbvh72QXt+H9RoivqXv37tYHcvXq1ZGWnzhxwloeMbCE4Kbp3r17xp49e4xDhw5Zwbjdu3dr0BBee+21aAO3CHYhgAXnz583du7caWTMmNFjgdvkyZMbixcvto7h3LlzxrZt26x9hnHjxkVaz1OBW0x9+vSx2gABwh07dhgXL1603nP48OEOA4X79++32m379u0ajAYE119++eVI71O6dGl9rRk0xPo4X2awE4HWOnXqOGzLiIFbTNWrV7f2pUePHtZ8/CNg6dKldm2M/bt8+bI1b/78+UbixInd1oZp06a1jgNy5cplt3zo0KHWsp9//jnS+s8++6xdHzh69Kj2RTNgi7ZFwBsWLVoUo8Dt+vXrdX5YWJhuq3379kb27Nmtzwq2ic9DVBPWiWkbBMoXMwZuvX8OOAXe546Ta23AfsI+xL7CPuDu6yivK+xTDNzGvg8EQmDN1WuKiW3E/iUe+t3DwG0MGwpBLdP//ve/SMu///57p5mJmGrUqGGsXLlSA3IIQiHgt3HjRg0IJ02a1JgxY4au/8MPP0QbuMX01ltvGUeOHDHCw8M1uFe2bFmPBW7N6c033zRWrFih+473PXXqlAb0qlWr5vD1ngzcYnrhhReMOXPmaJsiAIng6x9//BHp/WwDhc8//7wxePBgzXxFcDAkJEQzR5955hmn74MsaQSC9+3bp4F3BA8RwB0xYoSRI0cOp23pKHCLCefYzFS1zeI123jZsmUaEEU/QRAU2blt27bV7Ft3t+GqVat0X9CXIi6rWLGi1W7Nmzd3uD6Cqsgax/rIAMcx4Z8SyLDFsZmB6hs3bhipU6eONnCbP39+/ZxgO5hGjx5tNG3a1IgNfG5ievyB8gWegVvvnwNOgfe54+RaG7CfsA+xr7APuPs6yusK+xQDt7HvAwzcRn9NYeA27teWQOhfwQzcev8kcPL9NogqUMgpsNsgUL7AM3Dr/XPAKfA+d5xcawP2E/Yh9hX2AXdfR3ldYZ9i4Db2fSAQAmuuXlMYuI17GwZC/wr2cuA2YAYnIyIiIiIiIiIiIvIXDNwSERERERERERER+Zik3t4B+j/z58+XHDlyxLpJ9u7dK3369InXphw4cKA0aNAgTutWrVrV7fvjjzZt2hSn9ZYtWyajR492+/5Q/ClYsKAEBwfHap0iRYp4bH+IiIiIiOIbvxOzzbzdr4KCgjyyD/6Kn0nfxMCtDylfvrzkzZs31us9efJE4lvhwoWlSpUq8f6+CUlc2y8kJMTt+0Lx+8vw+PHjcV4/ZcqUbt0fIiIiIqL4xu/EbDNf7Fd3796VQMXPpO9i4NaH5MuXT/xF+/btdYovp0+flkSJEklCktCOh2LG/O/vsrPH5cbDsBg3W8YUqaTBs4UkPDycTU1EREREfo3fidlmvtKvzL+zGjduHNBJUvxM+i4GbomIvABfJK6EP2DbExEREVHA4nditpmv9KuzZ896ZF/8DT+TvoeDkxERERERERERERH5GAZuiYiIiIiIiIiIiHwMA7fkVcOGDRPDMGTTpk0OlxctWtTtg6otWbJErl27JmFhYVo7t3jx4m59j0DQrl07PW+8nYSIiIiIiIiIyDNY45Z8Uvbs2WXMmDFStWpVyZ07t1u2GRQUJGvXrpVcuXLJgwcP5ODBgzovNDTULdsnIiIiIiIiIiJyFwZuyasmTJgg8+bN00CqrXr16knr1q3l3LlzbnsvBIERtP3vv/+kXLlycuTIEbdtm4iIiIiIiIiIyJ0YuCWvun79uk7xIXPmzPp4+fJlBm2JiIiIiIiIiMinscYtBYwkSZLo48OHD729K0RERERERERERFFi4DbAbd68WQeZGjRoUKRllSpV0mWYXn311UjLe/furct+//13a97LL78sM2fOlJCQELl3756Eh4druYOFCxdKzZo1YzQ4GX6eMWOGPn/mmWesfYiratWq2W0zb9681jYxyJatpk2byrJly+TKlSsa4L1w4YLue40aNRxu29xO1qxZZdasWXL37l25deuW/PXXX1ag2BUlS5aU77//XtsTg6nduHFD1qxZI82aNbN7XZ48eXQ/bt68KYkSJZK+ffvKv//+qyUozpw5I3PnzpXSpUs7fZ/06dPLRx99JHv27NFjuH//vq7/5Zdfar3h2Ojevbvuy9OnT/V5RI0aNZI//vhDM5/Rxugfc+bMkTJlysTqfYiIiIiIiIiIEjIGbgPc0qVL9bFu3bqRltWpU8d67ijo2rBhQ31csmSJPo4aNUo2bNggbdu2leDgYDl8+LAGDRHURKARwczOnTvHKJh87NgxfY7AHn7GFFe3b9+22yaCyeY2ETyEpEmTyqJFi3R65ZVX5PHjx7Jv3z6dj33HoGYYLM0ZrIeavCdOnNBg6aVLlzRw6QoEPXfu3CldunSRLFmy6GBqCIbXqlVLg8kjR450uN6PP/4o33zzjbY71sG5aNmypWzbtk1atGgR6fUlSpTQ13388ccaKD516pQcPXpUChYsKAMGDJBDhw5p8DsmcH5RtxjHjueTJk2yliGQjeD2b7/9pv8IQHD3n3/+kZQpU2rb7dixQ3r27OlCixERERERERERJRwM3AY4M3BbsWJFSZMmjd0y22BuxMBtUFCQBvMQoMM28HzgwIH6c/v27TVLs3z58lK4cGHJly+frFu3TtcbMWKEZoRGN4gYgsBw9epV/RlTXCEAa7tNBFXNba5YsULnff3115pti2zTN954Qwcxq1Chgh4HgokI5Pbv318zWR154YUXpHr16prVinV79eolrsD5GD9+vCRPnlxGjx6tQVi0Z+7cubV90c6DBw+W2rVrR8qcRRYxMmVz5MghL774oj5OnjxZUqRIoUFdZBybcM6XL1+u+7x161YpVKiQBnLLli2r2c44txkzZtTgvO16jmC/8D4Y/A3Pp0+fbrcc5/6tt96Ss2fP6uBzaFvsH47NzN4eN25cpGMiIiIiIiIiIgpEHJwswB05ckSOHz+uATuUAzDLHqRNm1aDarhdHoN6IRMTATzcqm9m4yIQiKxVBFfr16+v2bFY3yxJYDp//rzeho9yCNmyZdNAnZnp6gsQtOzWrZs+R3YrsmdNCEJOnDhRs1Y/++wzLe0wZcoULSVga/78+Va5B7NkgSuGDh2qGaq//PJLpDIWaF8EdrGvHTp00NIJtrD/H3zwgfUzMoyRvYuAbOXKleXDDz+0ShggKI3jRzAbWbAo82BCuQgEsXft2qXnH4FiZxnTyLJGuyCg/Pbbb8u8efPsluOc9+vXT583btxY9u7da9fGyNJFoBj7hkziiMcUEzhH/gD/9HB1fU8dq7ldf2lL8h72FWI/IV5TiL9/yFe/EyfU7ym+/HdEQmijQGifqLB/OeeOa4or6zJwS5pV+d5772mGrRm4RRAXZQIQQEP9VATbkFH666+/6nKz5i1uewdk22JCMNcRlA8wpU6d2qdaHaURkiVLJhcvXtRAqSPIfkUpAWS0oh3+/PNPu+WulHKIKFWqVFaGM+rbOjJkyBD56quvJDQ0NNIyZA87gm0hcItzaQZuX3vtNX386aef7IK2JmQa49gRlEVtWkeB21atWmkmLwLNKMVgG/i2bWOUREDZBdugrS2UUUDgFpnOKA2BfwjEBv5BEAhs60F7SqC0JbmOfYXYT8ideE0h9hVy53diXlNi32aBjO3D9vPVawoDt2QFbnH7esQyCahLi2xcBPsQTDQDtw0aNLCrb2ubPVmlShUpWrSo5M+fX+ukIlsT2zAlTuxbFTqKFCmijwgoOhsEDYFn1H3FsTz33HORArcI+roLyiGYAXDUgHUEQU1HgU20P+riOrJ//359ROkElEhAvVzz2Hfv3u10f8xlyJq1zboGBFgxGJ05EBte40jx4sX1EVm1zn4h2vYL7FdsA7fIHEapC19XqlQpl74UoMSHs37hKvwXEL+M/KUtyXvYV4j9hHhNIf7+IV/9TpxQv6f48t8RCaGNAqF9osL+5Zw7rinmNuKCgVvSbNFr165pcBXZtadPn9ZSCE+ePJH169frAGNgZoGWK1dOcubMqdmTISEhVgtiICvc1p8hQwa7QCICngju4RZ6X4SyEOYgZlG5c+eO0xT3sLAwt+1PpkyZrOcIrsYGSjggS9YR220hcxg/x+TYzeM2j902cIsAM0pkIPiPGsFffPGF1szFAGe20qVLZz0isB8d7F9s4QLqD1/MIpbZiMv6nj5Of2lL8j72FWI/IV5TiL9/yFe/Eye07yn+8HeEP7dRILRPVNi/oueta4pvpT6SVyC4umzZMn2OrFsEbxHERaYlgnYY3AvZj88//7wOKBWxTAKghi0GxELQFvVNMUAWBuxCYBDZtxiYyleZHzwzuOiMGZD29AfV9oIZ2zooKLPgjO3xmdmsMTl220B8xGN/9OiRvP7669K8eXPtJ8jknTZtmtNjWrhwoQ5OF91kluwgIiIiIiIiIgpUDNySQsakGbhFfVuzTIJp7dq1+ohlDRs2tCuTgFq4yLYF1IFFzVNk2CLwawbscIu8Lw/QBmXKlNGgoSMIoBYuXFifYzA3Tzpx4oRmO9uWGIgIWc+4BWT69Ol283EuEGB3pHTp0vqILGlkydoeO7bnDALwgExb22xbcwCzFStW6KBkHTt21P1GZrZZQ9eErGsoVqxYlEHnl19+WfLly+dz5TSIiIiIiIiIiOIboyOkVq5cqcE8BN1q166t8zAwmcl83qZNGw3yoTaHWUs1c+bMVmaos1qpnTp1sgsuxiQLGJwFUt0Jt/ajvABqv2JwLUd69eqlA5ghEL1hwwaP7g9KGJiDnXXo0MHha1q3bq0lBxDkjAgB1IjQjl27dtXnCxYssOabma3IkHZUngDH3KNHD6udorJnzx5rYDSUTMibN6+1DBndCOoiqGz2r4j69eunbYvMXVdHtCQiIiIiIiIi8ncM3JIVLFy3bp0G7958802t2bp161ardczsW5RJQDakmaFr3nZ//fp1K/hme2s9grrfffedBnxNqVOnjrbVzXqsGAwLt9970rlz5+SHH37Q53hs1qyZXcCzW7dumkkMKPlgW/PVU/A+CF6jLvDAgQOtwb8A8/r06WMFSCN69913rSAtIAiK8gUvvviinqsxY8ZYyyZNmqTHjxIYGHDNNtiKgccQ5C1RooQe8/Dhw6Pd72HDhmlGb8SSCaiTPGXKFH0+d+5cK2vbbGMEm83to78Ecm0hIiIiIiIiIiJg4JYsZjA2efLksmXLFut2eggNDZWTJ09aP5tlEgC3yQ8ZMsQqpXD27FnNvsTgZRcuXNCMTfxs1lWNSdmE/fv363Zx+zxus0d2L4K4ntK/f389JmQOow4rgpnbtm2Ty5cva3ATmafjx4+Xzz//XOIDSlMgCI42GDVqlJYk2LFjh1y8eFF++uknzVpGMNlRFuzhw4dl8uTJmhW9fft2Xad9+/YaXH/jjTesILs5KNlrr72m56lSpUoadEXbI3MabdC4cWMduA7r2Q5E50x4eLh07txZnyN728zWBRwPMnwRzMcjto/9u3TpkkydOlXbGIFisy8REREREREREQUyBm7JYptFa1vfNmK5BAT7kJ1rC4FCBOpWrVolt27d0tqsWbNm1eAngncVKlSwBkBDoDAmdV7feecdDdpmypRJcufObZcN6m4YZKtJkyY6yBbKRqRIkUJrwj548EAzRKtXr25lucaXcePGyUsvvSQ///yzZkCXKlVKg5vIjEW5AWcZsCj3gOAn9r1kyZIa9B07dqwez8aNGyO9HqUJcL4QCD548KDkz59fB6dD248cOVK3sXr16hjv9/r1663sWgS6zfOGfwQ0atRI2xgBZ/yDAHWFEYRGoBqZxNh3s0wGEREREREREVEgQwFRw9s7QUSuyZMnj5w6dUqfFyxYUAPfgQJZ0ijlkDZtWr8osYBgNTLQZ4fslyvhD2K8XtaUqeWtgiWlbNmysnfvXo/sm7+1JXkP+wqxnxCvKcTfP+Sr34kT6vcUX/47wp/bKJDaJyrsX86545riyjaYcUtERERERERERETkYxi4JSIiIiIiIiIiIvIxSb29A0QxNX/+fMmRI0esGwy3O8R3fdqBAwdKgwYN4rRu1apV3b4/5Hsypkjl0dcTEREREfk6fidmm3m7X/HvLNfag+3neQzckt8oX758nAYoe/LkicS3woULS5UqVeL9fcn3mfVsGjxbyKX1iYiIiIj8Fb8Ts818rV8F+t9Z/Ez6LgZuyW/ky5dP/EX79u11ii+nT5+WRIkw1iD5upCQEClUqJAWJ4/LL1OsT0RERETkz/idmG3mC/0qKChINm3apANzBfrfWfxM+i4GbomI4lmgfykgIiIiIuJ34thjm7m3jcwA74kTJ/iBZP/yWRycjIiIiIiIiIiIiMjHJMjAbWhoqBiGIR07dhRfNGzYMN0/pOTT/1OkSBGf2Ga7du303Jw9ezYg+qKnBfrxExERERERERHFVYIM3JL/wK0J48ePl/3790uSJEl8dptERERERERERETxiTVuyavKli0rvXr18pltLl68WLZt2yaPHz926z4RERERERERERHFBgO3RDbu3LmjExERERERERERkTexVAIRERERERERERGRj/GbwG22bNnk448/ln/++UczIu/duye7d++WAQMGSPLkyZ2uV6lSJfntt9/k6tWr8uDBAzl69KiMGjVK0qRJ43SdRo0ayR9//CGXL1+Whw8fyrlz52TOnDlSpkyZKPexTZs2smrVKrl48aKud+rUKZk2bZoULFgwxsf52Wef6WBO4eHhuh+uwrYwpUiRQl5//XVZu3at3Lx5U+7fvy979uyRvn37StKkjhOv0a59+vSRrVu3yq1bt7T9jhw5ImPGjJHs2bPbvTZz5sx63HgvtEFEKVOmlIMHD+ryFStWWANXrV+/3nrNkydPdHmePHnifLzRbXPdunX6c/369WXIkCFy6dIlbYsDBw7Ic889F+XgZDiGHj166PFhPZzj27dv67pfffWV5MiRQzypWrVqum9///23DryGwe3CwsK0n3755Zcu92Gc05EjR8r27dvl+vXr8ujRI33csmWL9O/fX4/f2Xp4f3y20EdOnz6tP6PWcESFCxe2+mRU+4Jt4TXNmzePVRsRERERERERESUkhq9PlSpVMi5dumTAo0ePjH379hmHDx82nj59qvPWrl1rJEuWzHp9aGiozt+4caO+5sGDB8aePXuM06dPG6YDBw4YKVOmtHufJEmSGLNmzbJeg/fcsWOHce3aNf358ePHRs+ePSPtX1BQkLF8+XJrvVOnThm7du0y7ty5oz/jsUyZMtbrhw0bpvM3bdpkt52RI0fqfOxv/fr13dJ2pi+++EIf7969q21htifMnTs30nrZs2fX1wHa8OTJk3pM2DdAm+C82K7ToEEDa5sdO3a0W/bdd9/p/IsXLxpZs2bVefPnzzf2799vrYP2wJQtW7Y4H29021y3bp01H44fP24cOXJE+0bixImNdu3a6fyzZ8/abTdz5szGP//8Y7XHsWPHtG9cuHDBrr/kypXLbj2zL0Zsj7hM1apV022FhIQY58+fN+7du2fs3r3buH37ttG5c2eX+nCFChWMGzduWP0Pnw+c/5s3b1rb2rBhg7aR7XolS5bUfYHw8HDtIydOnLA+Y5cvX450/Js3b9Z5X3/9tcPjrFixoi6/fv26kTx58mjbJTg4WF+Px/i6JiXUiW3p/XPgLxP7ivfPgT9M7CfePwf+MrGveP8c+MvEvuL9c+APE/uJ98+Bv0zsK94/B4HST4Jd24b3GyGqKX369FZg6M8//7QL6pUrV84KDCHoGTFYBghiZciQwVrWunVra1m3bt3s3mvUqFE6/8yZM0bdunWt+QhW9erVS4PGCNrVrl3bbr1JkybpeleuXDFq1aplzU+bNq3x66+/6jIzMOgscDt8+HCdd//+/Ujbd2Wy9emnnxopUqSwjsk8XihVqpTdemZwDcHvwoUL2x3T1KlTreONGGSdOHGiLkPAL2fOnDqvYcOGVsCzTp06DoORgKCjO445qm2agVt4//337QKzeHQWuP3xxx91PgK2BQsWtFuGvoIgqhkg93Tg1gw458iRw/rHgXle49KHsQzbA/RXfObMZUmTJjU++OAD630RnDeXoW0PHjyo89esWWMF5DHhHw+3bt2y1rM9/g4dOlhB/IiBYEyTJ0/W5RMmTIhRu/CXrfuut2xL9/7+SsgT+4r3z4E/TOwn3j8H/jKxr3j/HPjLxL7i/XPgDxP7iffPgb9M7CvePwf+MAV7OXDr84OTdenSRXLmzKm3wDdr1kxLCJhQKgG3+v/88896i/vQoUP19mrTv//+q/P/++8/a5752rp160rVqlVl8uTJOj9r1qzSr18/fd64cWPZu3evtQ7WnzBhgjzzzDPy4Ycf6u3ka9assW4Txz7C22+/LX/99Ze1Hko6vPXWW3qbeu7cuaV27doOywgMHjxYhg0bpuUfGjZsKBs2bHBzK4osXbpU38f2mFAqoGvXrpIxY0apXLmylqEwb7PHz+fPn9eSArj93faYOnXqJMWKFZOXXnpJ2+x///uftfy9996TGjVq6K38EydO1LZBuQjA7fOrV68WX4AyFrblBa5du+b0tSgl8fLLL2ub4XhDQkLsluOczps3Tzp27CglSpSQ+IByHyhNASj14EofLlWqlGTKlEk/Wzi3KIthW2riiy++0PNYoEABPb5ly5bpsqZNm2o/uHHjhrzxxht266EcRu/evWXmzJmR9v2XX36RsWPH6mcHn0OzdAagpIdZHuHHH3+MVZs4Ks1AEqc2ZFsS+wq5A68pxL5C7sbrCrGfEK8p5I+/e1xZ1+cDt6+99po+zpo1yy5oa1q4cKEGaI8dO2YXtAXUtrUN2pr279+vAaMsWbJY81555RWt4Xno0CG7gJct7AOCXhUqVNB1UTf31VdflcSJE2tdT9sAlAlBz4oVK2pQC/VGI/rggw80iIZjq1evntaT9YTff/890jy0zfHjx/V40qdPb81v0qSJPi5ZssQuaGtr9uzZGrjF+bEN3KLmKoLVOA4ED1HTFAFF1E1FoNhXxKadEbxE0BJBRdSLdcQMnqZOnVriw+bNmyPNi2sfxmsRvMe6jj5jqHWM4CzawPb48E8G83NmG7Q1zZ07V8aNG2fXt8y2WrBggbRv317/2WH7uUGfyZAhg9YNxj9mYgP/aCD3YFsS+wq5E68pxL5C7sbrCrGfEK8pFCi/e3w+cGsO7GVmg0b0+PFjp8ucNSoyWyFVqlTWvOLFi+sjMhIx6JMjCNCakFGKoJe5fwgGRzXQkiNly5aVKlWq6HMEzdKlSyee4qwtEGgF2wHKzLZAUBbZmI4guAYIzEaEgBsGkvv000/l+eef1wG8WrVqpQFQX2Fmq8YGgrYIQiPoiePOly+f9gMMsoXAZ8Q+Et/7H9c+bELQFseFfokgbf78+TWjtmTJktZnxXZ9DOYGGHTOEZxvBJGRvR3R9OnTNXCLQC0GCjQ/kwjkxiXbFnLlyiV3796N9Xpk/19AXCvYlhQd9hWKCfYTiin2FWJfIXfiNYXYV8jXrinmNhJk4Ba3cIMZ2IkNR9mDzphBUzyawdSomFmEruwfsheRqfj3339rtuSUKVM0UIZAp7s9evQoyuWJEiWK1BYo74ApKgj4ogNG7LzIwkTgFq5cuSKXLl0SX2IGrGMqW7ZsMmnSJC0jkSRJErvs0R07dmg7oPSGN/c/rn0YXnzxRT0+BG1tIbCL0ggITiOQ6yh4H1Xfv3nzptOMYWTJI1CMMgszZszQNkbWOf4Zg4zu2EIfZODWPdiWxL5C7sRrCrGvkLvxukLsJ8RrCgXK7574SQ90gXkLuqdrLprvg9ILCGJGN5mlB1zZPwS1atWqJS1btrSi999++614m3lMvXr1ilFbROy4yZIls2qbPn36VAoVKiRfffWV+CuUSFi7dq2WkECgfcSIEVYZiLRp00rNmjVl48aN3t7NOPdhZN6uW7dOg7bIkEWdYtRjRn9EhjECq44yfK9fv66PaANnbLPaIzKzatu0aaOPyMpGAPzPP/+0ywQmIiIiIiIiIgpEPh+4RVae7W3gESHQg9vCUTMTt67HlVnOABmvUQWhMEgV3se8ZTy6/YPhw4fL8uXLrdvATQiS7dmzRwf86tGjh8575513NPvWm2LSFrgdHyUDcuTIEWnZJ598okFABKNRbgHBWxyft48rrl5//XUpWrSoZoKiru9HH32kg72hPrBZQxnt4W1x7cPvvvuuZn8fPnxYypcvL19//bUOsnfhwgVrPUfHZ74fsnGdQbs589NPP2k5hWrVqmn2rllbOS5lEoiIiIiIiIiIEhqfD9yaI9gjKw+ZnBEhGIjbwuvXr+9w8K/YvA+CSKjJimxDR/r16ycbNmyQffv2SVBQkM7DwEoITObNm1dq1KgRaR3Uru3YsaPun205gogQCJw/f74+R8kET9a7jY6ZiYlMYNsB3CLWKN22bZsOQGUL5QLef/99fd6lSxcNWI8fP15/njZtmlVawmQ7eFxU7RMb7t6m+Q8BZBaHhIREWo6sVHOgLttawfEtrn3YPD4Ebh2VYMC28uTJE+n4Fi1apI8oH5EzZ85I66FNHAX2TcjixecHn2vUu61UqZJ+hpFxS0REREREREQU6Hw+cDtx4kS5du2a3m7/888/W3U1AdmBkydPtl734MGDOL/PmTNnNGAKCEaagTgz+IfgKzJn4bvvvrPKA4SGhsqcOXP0+axZszQj07aGKEoGIFvx1KlT8ssvv0S5D71795YbN254vWQC9hODraGtV65caZc1iYGkcPx16tTRAOno0aOtZSgXgeNFDVhkU5pB90GDBmnAE0G8H374we69bOujmsFBV7l7m0eOHNFHDEDWp08fu2XIOl6zZo0VkEbmqrfEtQ+bx1e3bl27gcRwHhG8t+23tseHACtq1aJPINhvm/GOf6YgUB8d/AMAsF8ICqO2Lf4RQkREREREREREIoavT1WrVjVu3LhhQFhYmLFr1y4jJCTEePr0qc5bvny5kSJFCuv1oaGhOr9jx44Otzds2DBdvmnTJrv52MbSpUsN07lz54zt27cbly9ftubNnz/fSJw4sd16adKkMdatW2e95tixY8aePXuM+/fv68/Xrl0zypYtG+37Y2rXrp21nQYNGrjcdqZatWo5XG7uN/bJdn6+fPmMI0eOWOsfPnzY2L17t3H37l1rXp8+fezWmTlzps4/f/68kT59ertl1apVs85X+/btrflBQUHWNtFOO3bsMEqUKOHSMUe1TfN4R4wY4XBds/3Pnj1rzcP53rx5s3XcOD5sE68BHNeqVav0+dWrV+22F11fjM2ENjQlSZLE4Wvi0oefffZZ48qVK9byo0ePGjt37jSuX7+uP9+5c8fYsmWLPl+0aJHd+2HdQ4cO6bLHjx9rvzf7zYkTJ/TnqI4/adKkdvtWrFixWLdLcHCwrotHV9s40Ce2pffPgb9M7CvePwf+MLGfeP8c+MvEvuL9c+AvE/uK98+BP0zsJ94/B/4ysa94/xwESj8JdmEbPp9xC6hhixqy33zzjZw+fVozQDEC/Y4dO/R2/AYNGsjDhw9dfh9sA7d9N2/eXG/xT548udbvRCYgBqdCjdoWLVrY3YpvZnjidvLOnTvrvqK8AOqMXrp0ScaNGyclSpTQWrYxgUzVVatW6XNkp3qrZAIyiVGndsCAAVoSAdmyOA7U40U9YdRJxbGZ3nzzTWnbtq0+79q1qw7iZQu350+aNEmfjx071srOxIBaGPzKvHU/f/78LtUq9sQ2cb4xiNwHH3wge/fu1W2iLVCWYN68eVoeAoOVocxA5syZ9ZZ/b4lLHz579qyULFlSs9ZRt/bZZ5/VAcvM/otlgwcP1teiHIht1i3WrVixogwdOlTXfe6557TPItsW880BzJxBGyKTHnbu3Kl1n4mIiIiIiIiISCTR/x/BJSLyioULF0qzZs10ADszuB8bKNGBfyikTZvWKv9AccO2JPYVcideU4h9hdyN1xViPyFeU8gff/e4sg2/yLglooQJmfOoxYssabNWNBERERERERERifzfEPFERPEAZTdQbgGDmqE8Q4oUKbQsCP77RERERERERERE/w8Dtz4ONXPjYtmyZTJ69GjxR6irirqssXXx4kWt7eqrSpcuLePHj4/Tup9++qmsWLFCEoJq1arJ3Llz7c7b8OHDvbpPRERERERERES+hoFbH1elSpU4rRcSEiL+CgN/xeW4T506Jb4Mg3bF9XyipEBCceTIEbl8+bLWeNm6dav07NlTbty44e3dIiIiIiIiIj9WsGBBv4+HEEXEwK2PS5QI48cFlho1akhCtGHDhoA8nxHt27dPsmfPLoEKx961a1f5/vvv5dKlS97eHSIiIiKieMfvxGwzTwRtjx8/rs8LFSrE4C0/kwkGBycjIornGr8oDYFHIiIiIqJAxO/EbDN3wx2djp5TzPAz6bsYuCUiIiIiIiIiIiLyMQzcEhEREREREREREfkYBm4p3oSGhophGNKxY0drXp48eXQepgIFCvjt2ShatGis1xk2bJge96ZNm9yyD2Y71qpVyy3bIyIiIiIiIiIi72HglsjFovpz5syRFStWsB2JiIiIiIiIiMhtkrpvU0Sxd/78eSlSpIg+P336tN81Yb169aR169Zy7ty5WK87YcIEmTdvnjx48MAj+0ZERERERERERP6LgVvyqidPnsjRo0cD8ixcv35dJyIiIiIiIiIioohYKoGIiIiIiIiIiIjIxzBw62dy5swpY8eO1SzV8PBwuXPnjhw6dEi+/fZbHejLkUaNGskff/whly9flocPH+pt/ajLWqZMmSgHuUqRIoW8/vrrsnbtWrl586bcv39f9uzZI3379pWkSZM6rfn65Zdf6v6hBADKH+Dn4OBgh693NjiZOXDX6NGjJVOmTHp8J0+e1GO+dOmSzJ07V4oXL+60nZo1a6b7feXKFbl3755s3bpVWrZsab0fBkpzFbYzY8YMff7MM89YxxFxMLYSJUro/t+4cUPu3r0ru3btkgwZMkQ5OFm6dOnkww8/lA0bNsjVq1fl0aNHeg6wLtZLnz59jPaxXbt2+h5or9SpU8snn3wiR44ckbCwMLl27ZosXbpUqlSpEuuB06pVqxbpeOHHH3+0BqArVKiQzJ49Wy5evKh9Af20X79+1mubNGmix3f79m09R5s3b5ZXXnklRsdFRERERERERJTQsVSCH8mfP78GILNly6aBLrPEQOHCheXdd9+Vd955R6pXry779u3T+UmSJNHA4ltvvaU/I3D7zz//6HZQl7V58+YahP3uu+8cvt+IESPk/fff1/c6fvy4Bo0R7MVUoUIFadWqld3rS5YsKcuXL9fXIUB88OBBDVAOGDBA6tevr4HD2EKgFceDbSIIfPjwYQ2EIgj72muvSdWqVWXv3r3W6xMlSiSTJ0+WLl266M9nzpzR9UqXLq3By0WLFom7INCYNWtWbX8c786dOx2+buLEiRocReAyKChIX4sgrDMFCxaUv/76S3Lnzi2PHz+WkJAQPQa0Rbly5XTC8b/wwgsaTI8JBHq3bdumbYe6wv/++68ULVpU2xDB0saNG8uyZcvEXSpVqqT/YECAH+cMwVy839dffy1ZsmTRnwcNGqTtgL713HPPSeXKlfUfDA0aNJCVK1e6bV+IiIiIiIiIiPwVUuY4+UEbzJ0714D58+cbQUFB1vysWbMamzdv1mXLly+35o8aNUrnnTlzxqhbt641P3HixEavXr2MR48eGU+fPjVq165t9z62Pv30UyNFihTWeuY2oVSpUtY6SZIkMQ4ePKjz16xZo/tkLqtfv75x69Yta72OHTtay/LkyWPNL1CggDV/2LBh1vzDhw8b5cqVs5YVLlxYjwkWL15st++dO3fW+ffv3zfefPNNa3769OmNBQsWWNsMDQ11yzlp166dbu/s2bORluE9TM2bN7fmZ8qUye4YN23aZLfeunXrdP7WrVuN7Nmz2y176623jCdPnujyHj16ODxvtWrVirR/cOHCBaNOnTrWMmx73759umzv3r1223K2b+ZUrVo1a7u283/88Udr/saNG41s2bLp/ESJEhnTp0/X+dh/9Lv+/fvrfCzPmDGjsXv3bl2+du3aWJ2D4OBgXQ+P3v6MxmQqU6aM7i8evb0v/t6WnNhX2Ad8+3PAa4r3z4G/TOwr3j8H/jKxryScyZPfiRNqP/HlvyN8qX1i00YJta+40n7sX+KRfuLKNphx60dKlSqljyhzYJtpiXIAyLhFhiyyGwGZoOZt6cimtM1K/e+//2TChAl6ez9uxx85cqSsWbMm0vvhNvrBgwfbrTdkyBDp2rWrZMyYUTMkkcELTZs2lWLFimk5gDfeeENu3bplrbdixQrp3bu3zJw5M07Hjexg2/0/duyYfPPNN5q9iX2wzbYdPny4Pkem8IIFC6xl2B9kCOP2fbMd48vGjRtl/vz51s9RDUiG84Z2hM6dO2tZCFsoPYDM6lq1amn2bGz07NlTVq9ebf2MbX/88cfy66+/akYysoFjmsEbHWQKo72R5Q1m2Yv27dtrJvhPP/2k58+EfoMMXcwvW7ZsnN7TWTkOX4N2Nh99bZ/N/fG1/SLfw75C7CfEawrx9w/56nfihPo9xZf/jvCl9olNGyXUvhIX7F+e7SeurMvArR/BLeXPP/+8fPbZZxoIW7VqldZ8hd27d+st5ibc/p4yZUq9Pd826Glr1qxZGrhF2QPcvo5aqrZ+//33SOsgeIv9wDq2dVYbNmyoj7/99ptd0NaEMgXjxo2LcW1WE27rd7T/ZoDadnsvvfSSllRAm0yfPj3SOk+ePNGyED/88IPEJ5RUiCkE4RG8xbkzz62txIkTa11jiE3pCRw7ylg4a0ezrq67ArcI6OPc2Tp16pT13FFZhgsXLlj7ERcR38/XOasf7Av8rS3Je9hXiP2EeE0h/v4hX/1OnFC/p/jy3xH+2kYJta/EBfuX7/UTBm79yEcffSQ1atSQIkWKaIAUgT3UvEUWJQJh+/fvt15rDtyFrFpnHzwEAU3YZsTArbNOiYGtwHaAMtQoBdS1dRY4RBDZNkM2JqLbh2TJkkU6ZgSWHQU9AYN7xTcMzhVb2H/UuC1fvrzWvEVdYgTtkRlr/qfG9vxFBxmtjtrEbEdwNuBcXJw9e9ZhFq4pYl+LuDwucuXKpYO/+TpkfOMzifrMZsa6r0DfwmfOX9qSvId9hdhPiNcU4u8f8tXvxAn1e4ov/x3hS+0DMW2jhNpX4oL9y7P9xNxGXDBw60dw4cGHaeDAgdKkSRPJnDmz1KxZUyfcho7AbY8ePWTLli1W1iIeMTBWdBxlwj569CjKdVCawIRByAADmTkT1YBczkS3D7bQHtHtg5mtGp9sg6MxgcHOMMAagvS2bt++rb+IkFWMAK6729H2fLoqusxdZG67Gy6g/vDL1mwbPPrq/vpLW5L3sa8Q+wnxmkL8/UO++p04oX1P8Ye/I7zJ9m/Q2LZRQusrccH+5bv9JOYpe+QTQkNDpUuXLno7PbIxUcsVNWQRmCtZsqSsXLlSs2zND93ChQs1IBfd5KgsQmyYdVvTpk3r9DWpUqUSTzKPOap98PXaNShZgZq4CNqePn1aawyj7EW+fPk0uP7qq69q5nJ8cRbMta0fRERERERERERE7seMWz+SJ08evW3+r7/+0hq3uO0f01dffaWDbu3cuVMzbDFQ2NGjR3Udc6ArZ4FUBH9xWzuChK5kQeL9KlasKGXKlHH6mqJFi4onHThwQB/RRs5qxMb3wGSx1aFDB8mWLZsGwsuVK+dwIDME5j0NpS0gRYoUDpcj65eIiIiIiIiIiDyHGbd+ImPGjFq7dc2aNRrQiwjLzpw5o8+TJEmiNW8RfENd1Nq1azvcZr9+/WTDhg2yb98+lzMoFy1apI+NGjVyGNTD4GU5cuQQTw8ChsG9EGxs27atw9d07tzZre9pBrvdVWYAmbWAQLqjoC3OJwLk7q5JG9G1a9f0EbV1besIm1Cqg4iIiIiIiIiIPIeBWz+BwaWWL1+uz3/88UdrMDAzaNi9e3cpUaKEPH36VEsnIIg7ZcoUXT537lwNnNq+vmPHjjJ8+HD9+bvvvnO5Tseff/6pgdM0adJo2QUzAAmosTtt2jTxNAxu9emnn+rzMWPGyGuvvWYtS506tXz//fexHhwtOmY9XQTWceyuOnLkiJUZjMxpW/Xq1dNzmzx5cuuYPAXn0jwutCn+GWBmaY8aNUoaNGjgsfcmIiIiIiIiIiIGbv1K165dNRMTAVrUOUWWLcojXL58WSZOnKivGTRokBw+fNjKqEUQFYN24fHcuXOyfft2uXTpkkydOlUzKRcsWCBDhgxxed9QuqF169by77//StmyZeXYsWOyZ88eDURiQC0EOPfu3SueNn78eA1Uo5bt0qVL5dSpU9YxozYwntuWAnAVBoRDsBwBTZSLwPlAsDOuEODGeUWgFFnMqGm8Y8cO3X8EbbNnzy7r1q3zeMkE9K/Zs2frc9RRxuiHKMtx8eJFHRwPtXeJiIiIiIiIiMhzmHHrRxC8Q03aL774QgOkKD2AAclQyxXBykqVKuky08OHD7V0QfPmzTVbF5maqEGLW+zXrl0rb7/9trRo0cKl2ra2UCsXt/EPHTpUg5jICkbNXQQjMd/Rrf/uZgaQ27dvL1u3bpUMGTJonV8EWJHB+u233+rrHjx44Jb3O3HihLzzzjt6vJkyZZLcuXNL3rx547w9ZD7jHI8ePVoOHjyog5UVL15c7ty5o+2IMhmog2tm5T777LPiKe3atZOePXtqwBbZxAUKFNDAdP369eXzzz/32PsSEREREREREdH/Y3BiGwRKH+jRo4cBK1eu9Pq+cHJPGwQHB+s5xaM/tGmZMmV0f/Ho7X3x97bkxL7CPuDbnwNeU7x/DvxlYl/x/jnwl4l9JeFMnvxOnFD7iS//HeFL7RObNkqofcWV9mP/Eo/0E1e2wYxbSjAwKBlu6d+4caPDAdLArM2KMg5ERERERERERES+ioFbSjBQGgLlJKpWraqDk6FMg21QFyUcXn31VS2TMH36dK/uKxERERERERERUVSSRrmUyM/06dNHB/Fq2bKl1vcNCQnRGr758+eXtGnTSlhYmHTs2FEHAIP58+drreDYwkBreC+iuCpSpIjPNV5QUJBVP/n+/fve3h3yYewrxH5CvKYQf/+QO74Le+I7cUL9nuLJNksIbNslpm2UUPtKXLB/ebafmNuIi0T/f80EogQjV65c8u6770rdunV1sLBkyZLJhQsXZPXq1fLdd9/J4cOHrdeGhobGaTCx9evXS40aNdy85xQXwcHBOngbAvMY3M3XYaA+DJxHRERERERERIEjbRziFgzcEpFf87fAbZkyZbTG8rKzx+XGwzBv7w4RERERUbzLmya9VMmem9+J2WZu71PrL56Sc/fvuG/DAYKfSc/KmjJI6j5TIE5xC5ZKICLyAgRtr4Q/YNsTERERUcDJmCKVPvI7MdvM3X0KQVv+ncXPpK9JlijuQ4xxcDIiIiIiIiIiIiIiH8PALZEPKlq0qF9sM67atm2rdV5xiwDKHOzcuVN69OghiRKhegsRERERERERETFwS+RDsmfPLnPmzJEVK1a4bZuFChWS5cuXy/fffy++YNy4cTJz5kwdpOvs2bNy+vRpeeGFF3TgOOxn0qSs4EJERERERERExMAtkQ+pV6+etG7d2q2Zp9he/fr1xRe0a9dOevfuLbdu3ZIqVapoFnCJEiWkXLlycuHCBT3+jz76yNu7SURERERERETkdQzcElH8XGwSJ5YhQ4bo8w8//FC2bNliLduzZ4+WT4B+/frpSItERERERERERIGMgVsiihdVq1aVggULysOHD2XWrFmRlq9du1aOHz8uadKkkUaNGvGsEBEREREREVFAY+CWyMNy5swpY8eOlaNHj0p4eLgOxnXo0CH59ttvJU+ePNbrDMOQGTNm6PNnnnlGf8ZkK126dJqtumHDBrl69ao8evRIbt68Kbt27ZJhw4ZJ+vTprddi21h/+PDh+jNKE+Dn0NDQSJmwb7/9tvz1119y/fp13ceTJ09qTVwEWt0FNW1h7969EhYW5vA1ZhZu9erV3fa+RERERERERET+iKMAEXlQ/vz5ZevWrZItWza5d++eBm+hcOHC8u6778o777yjQcp9+/bJ5s2bJWvWrLoMWak7d+602xaCqAiu5s6dWx4/fiwhISE6sBcCtKgRi6lly5Y60Nf9+/c1AItt4vWYbt++LQcOHJCLFy9a2wwKCpLFixdLnTp19Odz585p0Bb70KVLF3nrrbd0wmtcZQaBT5w44fQ1p06dstqHiIiIiIiIiCiQMeOWyIM+/fRTDdouWLBAsmfPLqVKldIJwVZklyKDdvTo0VYpgVGjRulzZNPiZ0ymKVOmaAD277//1kcM7IUgbZYsWbQ+7NOnT6VIkSI6ABhcvnxZ158+fbr+jKAtfm7evLndNhG0PXjwoLz44ovy7LPPSvny5TWAjH1PnTq1zJkzR4oVK+ZyW2Cb5rE5g4xfyJw5s8vvR0RERERERETkz5hxS+RBCNICgp/IgjVduXJFM25HjBghhw8fjlHQ0wyedu7cWS5dumS3fPbs2Zq9W6tWLSlRokSM9q1kyZLSqlUr3a969erJhQsXrGXI+MVAYsiSbdGihQwdOlSzeV2BIDAgE9gZs4SC+drYCA4OFn+ALGciIiIiIiIiougwcEvkQRhs6/nnn5fPPvtM68uuWrXKClzu3r1bGjRoEKPtINCL4G3KlCkdBj5Rpxa1c2MT9GzSpIk+rl+/3i5oawuDiCFw+8orr+h7/PfffxJXyAiOqYi1fWPi/PnzsV6HiIiIiIiIiMhXMXBL5EEfffSR1KhRQ0sY/Pbbbxp0Rc3b1atXy7Jly2T//v2x2h7WR5kElDNANixq6CIwXLp0aSvjFAHWmChevLg+otzCpk2bHL4mVapU+pg2bVrJlSuXnD17VuIKNX4BwWdnzPd78OBBrLeP/bt79674Qxa2s/YmIiIiIiIiIjIxcEvkQf/8848G6gYOHKgZrqjdWrNmTZ1Q2xaB2x49emi92+hgwK7JkydrINgWBh1DIDBnzpwawI0p1NcF1ODFFJ306dO7FLi9du2aPmbKlMnpa8zatsgwji0Ebf0hcGtbMoOIiIiIiIiIyBkOTkbkYaGhodKlSxctdYBM2ffff19WrFghjx490jqzK1eulGeeeSbKbWAAso0bN2rQ9vTp0zJ48GAtX5AvXz4NqL766qty6NChOAUQv/rqK0mUKFG0EwY3c4VZyzdv3rxOX2MuO3bsmEvvRURERERERETk75hxS+RBefLk0ZIGf/31l9Zt3bVrl04IlhYqVEh27typma9NmzaVcePGOd1Ohw4dNCv2+vXrUq5cOX2MKLrgb0RHjx7VR3PQM0cyZsyoZR6QaetKti1s375dH8uWLSvJkiWTx48fR3pNpUqV9BHlJIiIiIiIiIiIAhkzbok8BEFPDE62Zs0aDbZGhGVnzpzR50mSJNFHc/AvZLjaQmYtINvWUdAWdW4rVqyoz5Mmtf9/jLNt/v777/pYu3ZtDc46gkHVUMYBA5hFXD+2/v77bw3+BgUFSdu2bSMtr1WrlgazUfph8eLFLr0XEREREREREZG/Y+CWyENu3Lghy5cv1+c//vijPPfcc9YyBEG7d+8uJUqUkKdPn2rpBNsBvBD0TZMmjfX6I0eO6CPq5SI711a9evV0/eTJk+vPqVOntltubhODd5kBYkBAFush+xUDpZmBX8C2UI6hc+fO+vPnn3+uGcOu+uSTT/Txm2++kTp16ljzy5QpIz/99JM+R+bxnTt3XH4vIiIiIiIiIiJ/xlIJRB7UtWtXDbYiQIsatKh3e+vWLS2hgLq1MGjQIKv+KwYrQyA3VapUWsrgwoULGpidNm2aDmKGjNRFixbJqVOn5OrVq5I7d24toYB6uevWrdMauBFLJuzdu9eqH4ss34sXL0rlypV1Xps2beTPP/+Ul156ScsTnDx5UgPOBQoUkAwZMuhrvv76a/nhhx/c0h5Tp06VatWqyVtvvSWrVq3SY8S+o1xD4sSJNYD88ccfu+W9iIiIiIiIiIj8GTNuiTzo0qVLOiDZF198If/++6/kyJFDByQLDw+XuXPnak1XLDOdOHFC3nnnHQ1oZsqUSQOzCLjevXtXtzN69Gg5ePCgBn2LFy+umakI6qIUA+rgAgLFzz77rLVNlDkYMGCABnuRdYuyCwj2AoK0VatW1cHT8DoMdIb1UX8WAd1GjRrJe++959Y2QZkEHCMCxTlz5tRgNI4Jg7Y1btxYA9dERERERERERIEORStdv/+ZiMhLgoODNYCdNm1aDXD7OpSF2LNnj8wO2S9Xwh94e3eIiIiIiOJdkXSZpMGzhfidmG3GPuUj+Jn0rFyp0kiLAsXjFLdgxi0RERERERERERGRj2HgloiIiIiIiIiIiMjHcHAyIooR1MVduHBhnFpr+vTp8uOPP7KliYiIiIiIiIhiiIFbIoqRlClTSpUqVeLUWmvWrImXWrf+ICgoSB+zpgySZIl40wMRERERBZ4MyVPqI78Ts83Yp3wDP5OelTll6jivy8HJiMiv5cyZU86fP+/t3SAiIiIiIiIicipXrlxy4cIFiQ0GbokoQQRvYzsyIxERERERERFRfMBdwrEN2gIDt0REREREREREREQ+hgUWiYiIiIiIiIiIiHwMA7dEREREREREREREPoaBWyIiIiIiIiIiIiIfw8AtERERERERERERkY9h4JaIiIiIiIiIiIjIxzBwS0RERERERERERORjGLglIkqgQkNDxTCMGE3r1q2zWzdjxozy9ddfy4kTJ+Thw4dy6dIlWbRokVSoUMFrx0Oeh/P++eefy5EjRyQsLExu374tW7Zskc6dO0uiRImiXI/9JXDkzp1bJk2apNcY9BM8zpkzR8qUKRPleuwngaNLly76u6Vjx45OX5MqVSoZNmyYHD58WMLDw+Xq1auyYsUKqV+/fpTbZj8KvL5iqlu3rr52xIgRUb4uSZIk8u6778revXvlwYMHcvPmTdm4caO0atXKjXtOvtZPcuXKpd9F/v33X7l//75OBw8elM8++0yyZMni9msR+W9fKViwoEydOlVOnz6tf+dcvHhRFi9eLLVr145y2/z9E5i/e2x/t2zfvl3Xa9euncT3NcXgxDZgH2AfYB9IeH1g/vz5xqZNm5xOe/fuNUw//PCDtV7WrFmN48eP6/x79+4ZO3fuNC5evKg/P3782Gjfvr3Xj42T+9sgd+7cxqlTp/Q8P3r0yDh48KD1MyxdutRImjRppPXYXwKrP9aqVcu4ffu2dT3Ys2ePcezYMf356dOnRt++fR2ux34SONMLL7xg9ZGOHTs6fE3q1KmNv//+W1/z8OFDY/fu3XbXm48++oj9yAfOpS/0FXPKly+fce7cOX3tiBEjnL4uceLExuLFi/V1T548Mfbt22ccPXrU6lvTpk3z+nFzcn8/qVKlinHjxg3rd9Phw4f1dxOew4ULF4wSJUq47VrEyX/7St26dY379+9bf+fg7yHz2gJffPGFw/X4PSaw+ok4mIYMGWL1k3bt2jl8jQevKd5vOE5sA/YB9gH2gfjvA/PmzdNfILt27TJSpkxpzV+3bp3OX7lypZE+fXqdlyhRIuODDz6wfgkVKVKE5yyBfW7XrFmj5/fAgQNGwYIFrfkNGzY0Hjx4oMsGDRoUaT32F++fu/iacuXKZdy8eVP7wtatWzXYby6rXbu2taxFixbsJwE6VatWzbh+/br1B4qzP4h+/PFHXY7A/zPPPGPNf+utt/QfR4B/EkRcj9ebwOsrmIoXL26EhoZar40qcDts2DB9Df5Qfv755635derUMe7cuaPLOnTo4PXj5+S+fpIuXTrj8uXLunzZsmVG9uzZ7QL+SFaAEydOGClSpHDLtYiTf/aVTJkyWa/5+eefjbRp01rLWrVqZQX6mzZtGmld/v4JnH4iDqZSpUrp38DRBW49eE3xfuNxYhuwD7APsA/Ebx/o1KmT/uLAHzH58+e3+2VmzseXm4jrzZw5U5fPmjWL5ywBfW7xxcJUsWLFSMuHDx9u/dFjO5/9JbCmMWPGWJlLGTJkiLS8S5cuuvz8+fN2fxyznyT8CecbATPzj96o/iDC7xy8DtmQtoE1c/rkk090XQRbbOezHwVeX0H2bI8ePazsuOgCtwjCmP9AQlZdxOUI2JpBXfxD2tttwck9/eTdd9/VZciaTJMmTaTlmTNntgI1bdq0cflaxMl/+8qHH36oy06ePGkkT5480vKJEyfq8lWrVtnN5++fwOonEmFKliyZ8c8//+i1IiwszGng1sPXFO83Iie2AfsA+wD7QPz1AWQimFkn+IPI0X8JnQVmK1WqpMvv3r1rl6XLyb/b4KWXXrK+wOAWn4jLX331VV2G/xSzvwTuhFtPAV88HS1HkAXXBnjttdes+byuJOypQIECxunTp/W84w8WZOab2ZGO/iAyMyKd/eGSM2dO63r07LPPsh8FaF/BHT8o2WMaP368lTnpLHCLP6Th7NmzTv/4xq3RgFvrvd0enNxzTVmyZIkumzx5stM2/f333/U13377rcvXIk7+21eaN29uzJkzxxgwYIDD7XXr1k3XRXkV2/n8HhNY/UQiTKNGjdLXfv3119Z6jgK3nrymcHAyIqIAM2rUKAkODpadO3fKxIkT7ZZVrFhRHzdv3uxw3R07dsjjx48lTZo08sILL8TL/pLnnTlzxnruaICpkiVL6iMGcbDF/hJY8uTJo4+7d+92uPy///6TkJAQff7SSy9Z89lPErZnnnlGB6z7+++/dQBL/I6JSnT94cKFC3Lq1Cl9Xq1atRivx99PCauvpEuXTooVK6YDTdWpU0d69+4tT548iXL70fURfH/Bdx+oXr26S8dCvnNNGTlypLRt21amT5/u9DXmAKsYXMjVaxH5b1+ZP3++tGnTRr766iuHy82/bY4fP243n79/Aquf2MLr33//fTl69KgMGjRIouLJawoDt0REAQRBOXMUzH79+kX6Ups/f359fuLECYfr44+m8+fP6/PChQt7fH8pfuCLxJIlS/T5pEmTrH4ANWrUkIEDB+pzjNZsYn8JPBhF1wx+OJMsWTJ9zJs3rz6ynyR8586dkwYNGkilSpVkz5490b4eo3lH9XsGzD9szN8z7EeB11fu3r0rrVq1khIlSsiaNWtitP249C3y/2vKrl27ZPbs2frPG0cyZcpkBeoPHTpkzWd/Cby+EtU/ij766CNp3769fsf5/PPPrWX8/RO4/SRlypTy008/aR9A3wgPD4/y9Z68pjBwS0QUQBCAS5w4sfz111+yZcsWu2UZMmSwgi5Xr151uo3r16/rY+bMmT28txSf3nrrLfnll180w+nIkSNy8OBB/eKxdu1aDdi/++67GtQ1sb8EnpMnT+pj6dKlHS5PkSKFFfRH/zAfeV1J2HCdWL58eYxfnzVr1lj/nmE/Cry+cuPGDZk3b55m8nuyb5H/X1OiM3bsWAkKCpL79+/LokWLrPnsL/7P1b7StGlTOXDggFy6dEk+/vhjDfC9/vrrsmnTJus1/P0TuP3ks88+k+eee06++eYbzdaNjievKQzcEhEF0G3OTZo0sW4riyh16tTW86j+oxgWFhbp9eT/kE35zz//6B/LCLQhgGsG4W7duiUPHjywez37S+BZunSpPvbo0UMyZswYafl7770nqVKl0ufJkyfXR/YTisjsE7H5PcN+RJ7qW5SwDR48WG+Nh08++cQuoML+Qi+++KIUL15cMyvNIG3Dhg21JFzEfgK8tgSOatWqaYmew4cPy5AhQ2K0jievKQzcEhEFiF69eknSpEn1P4br16+PtPzp06dxum2a/B9qHiOzFjWfUMcW5RHwJRa3F3bq1EnSp08vU6ZMke+++85ah/0l8CDj4OLFi5IrVy7ZsGGD1KxZUwO0WbJkkQ8//FCGDx9u/VFsllNgP6GIYtMnzN8z7Efkqb5FCRdufTcTFVAO6osvvrBbzv5C48eP12zsHDlyaCk5BNW6d++u34nNesj8/RN40qRJIz/++KPe8fHOO+/Iw4cPY7SeJ68pDNwSEQWI5s2b6+OMGTMcLr9375713PzPsyNmRl3EDEzyXyi6j+L7qF9cq1YtDezjSwqyb6dNmyZ169bVcgnItDSL6bO/BJ5r167Jq6++qv0EGSoouYJ+cuXKFb2dbOrUqfpFF+7cuaOP7CcUkdknYvN7hv2IPNW3KOFBwG3y5Ml66zusWLFCWrZsGel17C+E7zO4FqBUwsyZM6Vq1aoavC1fvryWELPtJ8BrS2D4+uuvJV++fDJmzBintbMd8eQ1hYFbIqIAUK5cOR1N89GjR7JgwQKnv2zMWzuQaemMWZMHwRpKGN58802rDtzt27cdDvzxxx9/6PPWrVvrI/tLYNq7d688//zz0r9/f/ntt99k9erVMnHiRB1JF4F9ZN+COYgh+wk5+gdAbH/PsB+Rp/oWJbw7iJYtWyZdu3bVn1EnuVGjRg4z5thfKKJjx47Jr7/+qs/NAe34+yew1K9fXzp37iz//vuvZu3HhievKQzcEhEFgMaNG+vjypUr5ebNm05v2Th69KjdiPARodRCzpw5rS83lHDqHwMGJXMGX2AA/4EG9pfAhdHeUTYBA3ggG7tnz56ybds2659EgME+gP2EIkK9uKh+z9guM3/PsB+Rp/oWJRwo44OBd/F7CVAaoVWrVlbpnojYXwIPatiWLVs2ysAaSoZB9uzZ9ZG/fwJLixYt9LFo0aL6Dx+cf9vJ/B2CO1jx87p16+LlmsLALRFRAKhSpYo+rlmzJsrXbd++XR+RPeesiD8GrsJtRMi8o4TBvK0dNb6iGynVfC2wvwTedaRfv35aTsMRBPVLliypz1FGwcR+Qrai6w8Ivpj/TNq6dSv7Ebmtb+H7ywsvvBCpb5H/w/cXlHkqUaKElnbq1q2b1l73xLWI/NfOnTtl9+7d0qFDB6evMc+5eecQ8HtM4Dh27Jhs3rzZ6WTenWq+zkxUiI9rCqricmIbsA+wD7APJNA+kChRIuP27dsGVKxYMcrX1qpVS1938+ZNI0OGDJGWz5o1S5f/+OOPXj8uTu5rg59//lnP6/bt243EiRNHWo6+cPXqVX1N9+7d2V8CtP8NGTJE+8CWLVscLp82bZouX7x4sd18XlcCbwoNDdW+0LFjx0jLChYsqMseP35sFC5cONLyESNG6PJ169axHwV4X4k4oU8A+oij5WnSpDEePHigr6lZs2ak5XgPCAkJcfi7jpN/9pNkyZIZO3fu1OXh4eHG66+/HqNtxvVaxMl/+8rEiRN12dGjR42kSZNGWp4nTx7j/v37+prGjRtb8/k9JrD6icRgvXbt2sX3NcX7jcaJbcA+wD7APuC5PlCoUCHDFBwcHO3rN23apK/dsGGDkTVrViv4+/777+v8hw8fGkWKFOE5S0Cf2+LFi+sfOzBnzhwjU6ZM1rK8efMaW7du1WUnTpwwUqZMyf4SoFO+fPn08w//+9//9LqA+SlSpDA++eQTnR8WFmYUK1aM15UAn6L7g2j27Nm6/ODBg0aBAgWs+W3atDEePXqky/CHcsT1+Psp8PpKbAK3mEaOHKmvOXfunFG2bFlrfu3atY07d+7osg4dOnj9uDm5r5+Y/1SMSxAmrtciTv7ZV/A9xgzMzp8/3+77bunSpY0jR47osvXr10dal79/AqefSBwDtx6+pni/0TixDdgH2AfYBzzXB6pXr66/JPDLIiavx5eaM2fOWEEYZDFcuHBBf3769KnRunVrnq8E+Jlt2rSp9WUWQdy9e/ca+/fvN548eaLz8EXFUcCe/SWwpp49e1p/IF+8eFGvD8jQB2S61atXj9cVHzhPvv4HUcaMGY1//vnHykzZs2ePtQ4MHDiQ/cgHzqMv9JXYBm7xj6Q1a9ZY31nwe8wMxsD333/v9WPm5L5+gmzb69evW99zEVyLaop4bYnrtYiT/15TXn31VePevXvW3zn79u2zu0YgWcE2oGtO/L4bWP1E4hi49eA1xfuNxoltwD7APsA+4Lk+8Oabb+ovCtzqHtN18IXl66+/1tsJEcS7ceOGsWzZMg0C81wl3M8r/jOM28iOHz+uX2bv3r2rXziGDh1qpEuXjv3FB86RL0y4BfnPP//UP5bxhzL+0YMyCY5uC+N1JTCnmPxBlDp1amPYsGHGoUOH9HqDbEgE5po0acJ+5APn0Jf6SmwCt5hwC3Tfvn319xcCNPinJEoBxfYPdE6+30+QVR0bjkp9xfVaxMl/ryn4vjt58mTj5MmTeicR/gG9ceNGo0uXLg5LKJgT/z4KrH4icQjceuqakuj/f0JEREREREREREREPiKxt3eAiIiIiIiIiIiIiOwxcEtERERERERERETkYxi4JSIiIiIiIiIiIvIxDNwSERERERERERER+RgGbomIiIiIiIiIiIh8DAO3RERERERERERERD6GgVsiIiIiIiIiIiIiH8PALREREREREREREZGPYeCWiIiIiIiIiIiIyMcwcEtERERERET/X3t3HpzT9QZw/Ehiq2jsVbUWLbWrqob+olRXWqpKGY1ahhoUtVXtI60WU3TKGJSKNh2dqrUoDW2pJWiIJbbaElsSkZBY0/ub55neO2+SN8mrol7x/czceV/n3HPvuec+/PE47zkGAAB4FxK3AAAAwB0UHBxsLMu65WPDhg3a3v5zy5Yt77n3NH/+fO17aGjo3e4K/hEUFOTElK+v738yLnXq1DFXrlwxo0aN+tfXKFasmImLizPffPNNrvYNAABv5ne3OwAAAADkZefOnTObNm3KVF6xYkU9rl69anbs2JGpPioq6j/qIXDn+Pn5abJV/h5Mnjz5X1/n4sWLZvTo0WbWrFlmxYoV5rvvvsvVfgIA4I3yyX/i3+1OAAAAAPebsWPHmnHjxpnjx4+bKlWqZHne448/rp8nT57UWYv3krJly5qAgACTlJRkzp49e7e7g39m3G7cuNFJqqalpd3RcRkxYoT55JNPTNeuXc2iRYtu61o+Pj5m3759Ovu2Ro0aGlcAAORlLJUAAAAAeLGDBw/qca8lbYUka6XvJG3vT6VKlTIjR440hw8fzpUlDv7++28TEhKi/yHw4Ycf5kofAQDwZiRuAQAAAAC5bujQoaZo0aJmzpw5uqZubli8eLFJSEgw/fr1MyVLlsyVawIA4K1I3AIAAABezN3mZPaGZ7LplyTGJk2aZI4ePaqzcmVJhRkzZmi5qFu3rvn+++/N+fPndT3d/fv3m4EDB2Z5P1m24csvv9RZknK9xMRE89tvv5kePXroT9VzY3My+5kKFixo2rZta8LDw/U+KSkpZteuXdo/+Rn/rShUqJAZNmyYiYiIMJcuXdK+yzIUMtMzMDAwy3YyPgsWLDAnTpzQ8YmPjzdr1qwxb7zxRrb3a9asmV5b7iHtZHyXL19uWrRo4fZ8+Xn/mDFj9Pmkf/Ks8i5k3VeZQZqR/Y7DwsLMAw88YCZMmGCio6P1uaSPci/pQ3ZLIixbtsycPn1a77Vt2zbTqVOnbJ9JluWYN2+eOXbsmLl27ZquKyv9nThxoildurS51ffRs2dPnSWb1RIJjRs31rVqY2JizPXr182FCxfMli1bzPDhw42/v7/bNnKeJG+LFCmiMQkAQF4n//XJwRgQA8QAMUAMEAPEADFADBAD/2EMjB071hLHjh3L9jxby5YtnbLg4GAtW7NmjXXw4EH9vn//fue72LRpk9WmTRvr6tWrVmpqqvXnn39aZ8+edeo//vjjTPdq166dlZKSovXyKW0OHTrktFm3bp1VpEgRj59x/vz52i40NNTtM3322Wf6eenSJWvXrl3p+hcWFubxfQoUKGBt3rxZ2924cUPHIiIiwrpw4YKWpaWlWd27d8/Urm/fvnq+SE5Otnbu3GkdP37c6cOiRYssHx+fTO1CQkL0miI+Pt7avn17ur736tUr3fl16tSxYmJitO7mzZtWVFSUPu/169e1LCEhwQoKCkrXxn7Hq1evtvbs2aPf5RrSxytXrjjP+sorr2Tq37Bhw5z+Sb+kf4mJifrn8PBwp5++vr5OmyZNmuh7EDJucp99+/Y543Pq1CmrfPnyHr+T1157TdvJddzVS6zZz3/+/Hl9XxK/dr9ljIoWLZrttWUM+Xebf7eJAWKAGCAGTN4eg7veAQ7GgBggBogBYoAYIAaIAWLgvouB3EjcitjYWKtRo0ZOXbdu3Zw6SRJK8tFOgEmibu7cuU5itlChQk67unXrakJQEmcTJkxIV1e/fn0nKbxw4cJcS9wKSYIWLFhQyyVJKgllW7169Ty6T+/evfX86Ohoq0KFCk65XPeLL77QOklc2veR46WXXtJnlcR2//790yVoW7Ro4SRiZSxc79WxY0cnaTpgwACnnXyOHDnSqatRo4aW+/v7O0lbSS5XqVLFuVaZMmWsZcuWOf2rXLmy23d8+vRpq1WrVk5d2bJlrcjISK2T5Lpr/wIDA51k9eDBg618+fI5YzF9+vR0Y++auN2yZYuWTZs2TRPhdrn01373s2bN8vjdz5gxQ9vIPTPWSZ8kbsWQIUPSjX2DBg2sc+fOad3w4cPdXrtEiRLOM5YqVequ/13mYAyIAWKAGCAGzJ0bAwaXMSAGiAFigBggBogBYoAYIAbu1cTtW2+9lamNnSg8cuRIphmjFStWdNpKQtYuX7JkiZO4c9eP2rVra0JSkmU1a9bMlcStJC0ztpH+ygxUITNiPbnPzJkz9fzPP/88U50kIWVm8rx586xy5co55TJbUwwcONDtNSWxaye4JVFol8tsXjFp0iS37dauXZtuRrMkH8WZM2esYsWKZTo/f/781u7du/WcOXPmuH3HMjs1Yzsps7nOgl61apWWffXVV277t379ereJW5mVnVWyvHXr1tby5cutESNGeBzfMmPW3exjO2FtCwgIyFTfs2dPjUf5zOr6duK3Q4cO//rvIAdjQAwQA8QAMWC8fwzuegc4GANigBggBogBYoAYIAaIgfsuBnIjcStJVHc/J7eXDZCEZsY6Pz8/55r/+9//nOSm/fN719m7GQ/5ObsYOnRoriRus0rMbd26VetlBqsn9xk0aJCz3EGfPn1ynIVZqVIlpw/ZnSs/4XdNDlatWtVp5zpz1vWQ5QSkzp7pKktWZJfolUPGQchM04zvWJLlrrOf7UNm9NrshHThwoWd92i/24zHm2++6TZxayePZeatzDiWOLmd+L58+bJe77nnnss2Ob9y5UpdpsEeL08Pe8mHrGblcjAGxAAxQAwQAyYPjMGtrfgPAAAAwGskJSXpRlfuNnAScXFxmepu3rzpfM+XL59+Vq9eXTeTEjNnztSNqdypVKmSftaoUSNX+h8bG+u2XDbgEp5uUDZ37lzdqKpWrVpm1qxZurlaZGSkWb9+vW40JpurpaWlOefXrl3b+f7jjz9meV17TOznrVatmn7KmMsGXu7IRluu7LY7d+7M8j52XZkyZUyJEiV0ky6bfJfNz7IaI9dxkvdj93nv3r1u7yXj4o5s7LZixQrTpEkT88svv+gzyritW7fOrFq1yhw5csR4SjZTk83DhGxwlpFsWCYbkM2ZM8e8+uqreshzbtiwwfz88896v6xiw2ZfV8YMAIC8isQtAAAAcI9KSUnJtl4SZJ4ICAhwvj/11FM5nl+sWDGTG+wEc1bsxHJOJMkoCcchQ4aYzp07ayK6YcOGekhC8ty5c2bUqFGa4M34vM2aNfP4eUuWLKmfly9fNp568MEHnSR7VpKTk53vRYsWTZe4zWmMXMepePHiTllWfUxMTHRbvnbtWn33klCVRKr0206qTps2zfz++++md+/e5sCBAzn2x7Ufqampbs+Rd3H48GEzePBg06pVK01Yt2/fXg+J259++sn06dMnywSuHfuu9wIAIK/xudsdAAAAAOA9CWB/f39NBGZ3SHLN20iicty4ceaxxx7TmbG9evUy3377rSYqH3roIZ3d2a5du3TPGx8fn+OzyvHBBx+kaydj5Cl7RrRrsjgj1+SjuxnUnkpISMiUMM6ocOHCWbbfvXu3Jr4lQf3ss8+a0aNHO7OV5c8yg1lm0+bEdYZwdkn+X3/91bz++uuatH3hhRdMSEiI2bFjh/Hx8TGtW7c2K1euzHHMXGceAwCQ15C4BQAAAO5zR48edZZQkOUGstKoUSNdZsD+Gby3KF26tM6ctWfEyvPIjM4uXbqYChUqmIiICC3v2rWrfh48eFA/S5UqpUndrDRt2lSXOrCXHzh06JAzK9ZeNiKjNm3a6E/+P/30U/1zdHS0fj755JPZjquQmbaus21v1cmTJ51EZoMGDdye4+79SqK0atWqmpwVEgubNm0yEydONEFBQVous2DLlStnnn/++Rz7Iclye7kNGeOM8ufPr+PauHFjJ9ErSzLIrGiZ9dupUyctr1+/vqlbt67be9jXldnUAADkVSRuAQAAgPuczFbduHGjfh8wYIDbcypXrqzJvKioKNOhQwfjTeRn/vJT/m7dumWqk1myW7du1e++vr5OMlV+pi/69+/v9pqBgYH6vLI0gCzDIOS7vbZt9+7d3baTPjRv3txZe1XWjRXBwcFuZ59KErNv3776ffXq1eZ2SAJUlhgQ7733nttzZCZyRpKMlzVsw8PD3Sayt2zZ4swEtscwO5LktdfELV++fKb6l19+WcdS+lqgQIFM9ZLEtWV1P/u6dmIcAIC86q7vkMbBGBADxAAxQAwQA8QAMUAM3G8xMHbsWEscO3Ys2/NsLVu2dMqCg4O17NSpU27bbNiwQevlHtldMygoyCkLDAy0bty4oeVTp061ihQp4tTVqlXL2r9/v9PfAgUKePSM8+fP1zahoaE5PtOt9D/jMWjQID0/OTnZevHFF9PVNW3a1EpMTNT63r17O+UdO3bUsps3b1pDhw618ufPn67N6dOntf6PP/5Id713331Xy69du2Z1797dKffx8bGGDx+uddevX7fq1Kmj5QEBAfqexObNm63KlSs7bUqXLm0tXbpU65KSkqxq1ap5/I4rVarkjKN8d31XV69e1fJJkyY5z+Xn52eNHz/ecuXr6+u02717t5aFh4dbjzzyiFMu7UNCQrTu0qVLVsmSJT16J7Nnz9Y2X3/9daY6ueb58+e1PiwszCpevLhT5+/v78TNiRMn0r0Xd89erly5u/r3mIMxIAaIAWKAGDB3dgwYYMaAGCAGiAFigBggBogBYoAYuN8Tt3J069ZNE5IiJSXFioiIsKKjo620tDQtO3PmjFW9enWPn/G/StxK0nTVqlXOdWNiYqzt27fr2NokQeqaqJRj1KhRzrNJcnfbtm3WX3/95bQ5cOCAJlcz3m/KlCnOOTImcq/4+Hj9syS/e/Toke78+vXrW7GxsU6ieM+ePdbOnTudsY6Li7NatWqVrs2/TdzK0blzZyd5m5CQoM8l9xA//PCD28RtzZo1nQS39EueXfp44cIF57nefvttj99927Zttd3Ro0fd1kvs2X1MTU21oqKirMjISE0Oi8uXL1vNmzd327ZLly56jpzPv938200MEAPEADFg8vAYsFQCAAAAALVgwQJTr149M3v2bHPmzBn9CX3FihX1Z+2yZqusN2ovMeBN5Kf5bdu2Ne+//77ZvHmzbqAl66PKpyyjIGvdSr1ssuVK1nB95plnzKJFi0xycrI+u6yXu2vXLl1vVdaejYuLy3S/IUOG6FqvS5cu1c3L5F6yLuzixYt1WYV58+alOz8yMlLHcvz48Wbv3r3m0UcfNdWrV9e1dqUPMq6uywPcLtmU7emnn9bP1NRUvf7Zs2dNv379dIzckXfcsGFDM3PmTF0OQtbwfeKJJ3S9WnkeGZuwsDCP+yDLIMhmafKssmGcu43JpI8LFy7UWJPxkE3lYmJizIwZM0zNmjWd5TvcLbUgQkNDPe4PAAD3onz/ZHABAAAAAMg1Y8aM0WT15MmTzbBhw3LlmsWLFzexsbG6nq+suywJdwAA8ipm3AIAAAAAct306dPNxYsXdWM22YQtN7zzzjumcOHCZtq0aSRtAQB5HolbAAAAAECuS0pKMlOmTDFlypTR5O3tkuTvgAEDdPkKSQoDAJDXsVQCAAAAAOCO8PPzM9u2bTMPP/ywrmEra+7+W4MHDzZTp0417du3N0uWLMnVfgIA4I2YcQsAAAAAuCNk07auXbvq2rS3s86ttP/oo490QzKStgCA+wUzbgEAAAAAAADAyzDjFgAAAAAAAAC8DIlbAAAAAAAAAPAyJG4BAAAAAAAAwMuQuAUAAAAAAAAAL0PiFgAAAAAAAAC8DIlbAAAAAAAAAPAyJG4BAAAAAAAAwMuQuAUAAAAAAAAAL0PiFgAAAAAAAACMd/k/Bwro4RgGN6kAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 32
+  },
+  {
+   "cell_type": "markdown",
+   "id": "trials-header",
+   "metadata": {},
+   "source": "## 5. Trials table\n\nEach row in `nwbfile.trials` represents one completed trial. The table always contains `start_time`, `stop_time`, and per-trial history columns. TaskSwitch-family files also include stimulus scalar and pulse-time columns."
+  },
+  {
+   "cell_type": "code",
+   "id": "trials-overview",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:34:20.670161Z",
+     "start_time": "2026-04-14T13:34:20.627247Z"
+    }
+   },
+   "source": "trials = nwbfile.trials\nprint(f\"Number of trials : {len(trials)}\")\nprint(f\"Columns          : {list(trials.colnames)}\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of trials : 900\n",
+      "Columns          : ['start_time', 'stop_time', 'states', 'events', 'actions', 'cpoke_start_time', 'left_hi', 'right_hi', 'left_lo', 'right_lo', 'crosstalk_dir', 'crosstalk_freq', 'bup_width', 'bup_ramp', 'vol_low', 'vol_hi', 'vol', 'gamma_dir', 'gamma_freq', 'duration', 'freq_lo', 'freq_hi', 'HistorySection_hit_history', 'HistorySection_side_history', 'HistorySection_quadrant_history', 'HistorySection_task_history', 'HistorySection_incoh_history', 'HistorySection_gammadir_history', 'HistorySection_gammafreq_history', 'HistorySection_result_history']\n"
+     ]
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "cell_type": "code",
+   "id": "trials-head",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:34:31.600661Z",
+     "start_time": "2026-04-14T13:34:31.573189Z"
+    }
+   },
+   "source": "pd.set_option(\"display.max_columns\", None)\n\n# Exclude ragged / region-reference columns that require special handling\nREGION_COLS = {\"left_hi\", \"right_hi\", \"left_lo\", \"right_lo\", \"states\", \"events\", \"actions\"}\nscalar_cols = [c for c in trials.colnames if c not in REGION_COLS]\n\ntrials[:5][scalar_cols]",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    start_time   stop_time  cpoke_start_time  crosstalk_dir  crosstalk_freq  \\\n",
+       "id                                                                            \n",
+       "0    52.710269   59.287265         56.146274              0               0   \n",
+       "1    59.287771   67.177265         64.003770              0               0   \n",
+       "2    67.177770  118.347265        115.138290              0               0   \n",
+       "3   118.347765  123.885765        120.366770              0               0   \n",
+       "4   123.886280  129.112281        125.904278              0               0   \n",
+       "\n",
+       "    bup_width  bup_ramp  vol_low  vol_hi   vol  gamma_dir  gamma_freq  \\\n",
+       "id                                                                      \n",
+       "0           5         2        1       1  0.15       -1.0         2.5   \n",
+       "1           5         2        1       1  0.15       -4.0         1.0   \n",
+       "2           5         2        1       1  0.15        4.0        -1.0   \n",
+       "3           5         2        1       1  0.15        2.5        -4.0   \n",
+       "4           5         2        1       1  0.15        1.0         1.0   \n",
+       "\n",
+       "    duration  freq_lo  freq_hi  HistorySection_hit_history  \\\n",
+       "id                                                           \n",
+       "0        1.3     6500    14200                         NaN   \n",
+       "1        1.3     6500    14200                         NaN   \n",
+       "2        1.3     6500    14200                         NaN   \n",
+       "3        1.3     6500    14200                         NaN   \n",
+       "4        1.3     6500    14200                         NaN   \n",
+       "\n",
+       "   HistorySection_side_history  HistorySection_quadrant_history  \\\n",
+       "id                                                                \n",
+       "0                            l                                3   \n",
+       "1                            l                                3   \n",
+       "2                            r                                1   \n",
+       "3                            r                                1   \n",
+       "4                            r                                4   \n",
+       "\n",
+       "   HistorySection_task_history  HistorySection_incoh_history  \\\n",
+       "id                                                             \n",
+       "0                            d                             1   \n",
+       "1                            d                             1   \n",
+       "2                            d                             1   \n",
+       "3                            d                             1   \n",
+       "4                            d                             0   \n",
+       "\n",
+       "    HistorySection_gammadir_history  HistorySection_gammafreq_history  \\\n",
+       "id                                                                      \n",
+       "0                              -1.0                               2.5   \n",
+       "1                              -4.0                               1.0   \n",
+       "2                               4.0                              -1.0   \n",
+       "3                               2.5                              -4.0   \n",
+       "4                               1.0                               1.0   \n",
+       "\n",
+       "    HistorySection_result_history  \n",
+       "id                                 \n",
+       "0                               3  \n",
+       "1                               3  \n",
+       "2                               3  \n",
+       "3                               3  \n",
+       "4                               3  "
+      ],
+      "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>cpoke_start_time</th>\n",
+       "      <th>crosstalk_dir</th>\n",
+       "      <th>crosstalk_freq</th>\n",
+       "      <th>bup_width</th>\n",
+       "      <th>bup_ramp</th>\n",
+       "      <th>vol_low</th>\n",
+       "      <th>vol_hi</th>\n",
+       "      <th>vol</th>\n",
+       "      <th>gamma_dir</th>\n",
+       "      <th>gamma_freq</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>freq_lo</th>\n",
+       "      <th>freq_hi</th>\n",
+       "      <th>HistorySection_hit_history</th>\n",
+       "      <th>HistorySection_side_history</th>\n",
+       "      <th>HistorySection_quadrant_history</th>\n",
+       "      <th>HistorySection_task_history</th>\n",
+       "      <th>HistorySection_incoh_history</th>\n",
+       "      <th>HistorySection_gammadir_history</th>\n",
+       "      <th>HistorySection_gammafreq_history</th>\n",
+       "      <th>HistorySection_result_history</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>52.710269</td>\n",
+       "      <td>59.287265</td>\n",
+       "      <td>56.146274</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>l</td>\n",
+       "      <td>3</td>\n",
+       "      <td>d</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>59.287771</td>\n",
+       "      <td>67.177265</td>\n",
+       "      <td>64.003770</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>l</td>\n",
+       "      <td>3</td>\n",
+       "      <td>d</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>67.177770</td>\n",
+       "      <td>118.347265</td>\n",
+       "      <td>115.138290</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>r</td>\n",
+       "      <td>1</td>\n",
+       "      <td>d</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>118.347765</td>\n",
+       "      <td>123.885765</td>\n",
+       "      <td>120.366770</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>r</td>\n",
+       "      <td>1</td>\n",
+       "      <td>d</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>123.886280</td>\n",
+       "      <td>129.112281</td>\n",
+       "      <td>125.904278</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>r</td>\n",
+       "      <td>4</td>\n",
+       "      <td>d</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 26
+  },
+  {
+   "cell_type": "markdown",
+   "id": "plot-trials-header",
+   "metadata": {},
+   "source": "### Plot trial structure (states + events + actions per trial)"
+  },
+  {
+   "cell_type": "code",
+   "id": "plot-trials",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:34:35.160825Z",
+     "start_time": "2026-04-14T13:34:35.029841Z"
+    }
+   },
+   "source": [
+    "trials_df = trials[10:16]\n",
+    "\n",
+    "fig = plt.figure(figsize=(18, 10))\n",
+    "\n",
+    "# Events (blue ticks)\n",
+    "events_index = [j for i in trials_df[\"events\"] for j in i]\n",
+    "if events_index:\n",
+    "    plot_events(events=events[events_index], event_types=event_types,\n",
+    "                show_event_values=True, marker_size=500, marker_width=2,\n",
+    "                marker_color=\"tab:blue\", y_offset=0, fontsize=14, fig=fig)\n",
+    "\n",
+    "# Actions (green ticks)\n",
+    "actions_index = [j for i in trials_df[\"actions\"] for j in i]\n",
+    "y_offset = int(np.ceil(plt.ylim()[1]))\n",
+    "if y_offset == plt.ylim()[1]:\n",
+    "    y_offset += 1\n",
+    "if actions_index:\n",
+    "    plot_actions(actions=actions[actions_index], action_types=action_types,\n",
+    "                 show_action_values=True, marker_size=500, marker_width=2,\n",
+    "                 marker_color=\"tab:green\", y_offset=y_offset, keep_yticks=True,\n",
+    "                 fontsize=14, fig=fig)\n",
+    "\n",
+    "# States (steelblue rectangles — replaces hardcoded black)\n",
+    "states_index = [j for i in trials_df[\"states\"] for j in i]\n",
+    "y_offset = int(np.ceil(plt.ylim()[1]))\n",
+    "if y_offset == plt.ylim()[1]:\n",
+    "    y_offset += 1\n",
+    "if states_index:\n",
+    "    plot_states(states=states[states_index], state_types=state_types,\n",
+    "                rectangle_color=\"steelblue\", rectangle_height=1,\n",
+    "                marker_color=\"red\", marker_size=500,\n",
+    "                y_offset=y_offset, keep_yticks=True, fontsize=14, fig=fig)\n",
+    "\n",
+    "plt.title(f\"Trial structure — {nwbfile.session_id} (trials 10–15)\", fontsize=16)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stimulus-header",
+   "metadata": {},
+   "source": "## 6. Protocol-specific: Stimulus data *(TaskSwitch family only)*\n\nFiles from the TaskSwitch protocol family include per-trial stimulus parameters:\n\n- **Scalar columns**: `gamma_dir`, `gamma_freq`, `duration`, `freq_lo`, `freq_hi`, `vol`, `vol_low`, `vol_hi`, `bup_width`, `bup_ramp`, `crosstalk_dir`, `crosstalk_freq`\n- **Ragged pulse columns**: `left_hi`, `right_hi`, `left_lo`, `right_lo` — lists of auditory pulse times, split by speaker side (left/right) and tone frequency (hi/lo)\n\n### Pulse-time reference frame\n\nPulse times are stored **relative to cpoke onset** (the moment the rat entered the centre port), exactly as they come from BControl.  \nA companion column `cpoke_start_time` gives the absolute time of cpoke onset in seconds from session start, so:\n\n```\nabsolute_pulse_time = cpoke_start_time + pulse_time\n```\n\n`cpoke_start_time` is `NaN` for trials where the rat never poked (the stimulus was still generated, so relative pulse times are preserved).\n\nNon-TaskSwitch files (PBups, ProAnti3, ProAnti3Marino) skip this section automatically."
+  },
+  {
+   "cell_type": "code",
+   "id": "check-stimulus",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:36:18.082951Z",
+     "start_time": "2026-04-14T13:36:18.038459Z"
+    }
+   },
+   "source": "STIMULUS_SCALAR_COLS = [\n    \"gamma_dir\", \"gamma_freq\", \"duration\", \"freq_lo\", \"freq_hi\",\n    \"vol\", \"vol_low\", \"vol_hi\", \"bup_width\", \"bup_ramp\",\n    \"crosstalk_dir\", \"crosstalk_freq\",\n]\nPULSE_COLS = [\"left_hi\", \"right_hi\", \"left_lo\", \"right_lo\"]\n\nhas_stimulus = any(c in trials.colnames for c in STIMULUS_SCALAR_COLS)\nhas_pulses   = any(c in trials.colnames for c in PULSE_COLS)\n\nprint(f\"Has stimulus cols : {has_stimulus}\")\nprint(f\"Has pulse cols    : {has_pulses}\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Has stimulus cols : True\n",
+      "Has pulse cols    : True\n"
+     ]
+    }
+   ],
+   "execution_count": 33
+  },
+  {
+   "cell_type": "code",
+   "id": "stimulus-scalars",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:36:20.334918Z",
+     "start_time": "2026-04-14T13:36:20.283422Z"
+    }
+   },
+   "source": "if has_stimulus:\n    avail_scalar = [c for c in STIMULUS_SCALAR_COLS if c in trials.colnames]\n    print(f\"Stimulus scalar columns: {avail_scalar}\")\n    display(trials[:5][avail_scalar])\nelse:\n    print(\"No stimulus columns in this session — skipping.\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stimulus scalar columns: ['gamma_dir', 'gamma_freq', 'duration', 'freq_lo', 'freq_hi', 'vol', 'vol_low', 'vol_hi', 'bup_width', 'bup_ramp', 'crosstalk_dir', 'crosstalk_freq']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "    gamma_dir  gamma_freq  duration  freq_lo  freq_hi   vol  vol_low  vol_hi  \\\n",
+       "id                                                                             \n",
+       "0        -1.0         2.5       1.3     6500    14200  0.15        1       1   \n",
+       "1        -4.0         1.0       1.3     6500    14200  0.15        1       1   \n",
+       "2         4.0        -1.0       1.3     6500    14200  0.15        1       1   \n",
+       "3         2.5        -4.0       1.3     6500    14200  0.15        1       1   \n",
+       "4         1.0         1.0       1.3     6500    14200  0.15        1       1   \n",
+       "\n",
+       "    bup_width  bup_ramp  crosstalk_dir  crosstalk_freq  \n",
+       "id                                                      \n",
+       "0           5         2              0               0  \n",
+       "1           5         2              0               0  \n",
+       "2           5         2              0               0  \n",
+       "3           5         2              0               0  \n",
+       "4           5         2              0               0  "
+      ],
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+       "    .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>gamma_dir</th>\n",
+       "      <th>gamma_freq</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>freq_lo</th>\n",
+       "      <th>freq_hi</th>\n",
+       "      <th>vol</th>\n",
+       "      <th>vol_low</th>\n",
+       "      <th>vol_hi</th>\n",
+       "      <th>bup_width</th>\n",
+       "      <th>bup_ramp</th>\n",
+       "      <th>crosstalk_dir</th>\n",
+       "      <th>crosstalk_freq</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>-4.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>14200</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 34
+  },
+  {
+   "cell_type": "code",
+   "id": "pulse-times",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:36:22.508666Z",
+     "start_time": "2026-04-14T13:36:22.455003Z"
+    }
+   },
+   "source": "if has_pulses:\n    trial_idx = 0\n    cpoke_t = trials[\"cpoke_start_time\"][trial_idx]\n    print(f\"Trial {trial_idx}  |  cpoke_start_time = {cpoke_t:.4f} s from session start\")\n    print()\n    print(f\"  {'column':12s}  {'n pulses':>8s}  {'first 4 (rel. to cpoke, s)':30s}  {'first 4 (abs. session time, s)'}\")\n    print(f\"  {'-'*12}  {'-'*8}  {'-'*30}  {'-'*30}\")\n    for col in PULSE_COLS:\n        if col not in trials.colnames:\n            continue\n        rel = np.array(trials[col][trial_idx])\n        abs_ = rel + cpoke_t if not np.isnan(cpoke_t) else rel\n        rel_preview  = str(np.round(rel[:4],  4).tolist()) + (\" ...\" if len(rel) > 4 else \"\")\n        abs_preview  = str(np.round(abs_[:4], 4).tolist()) + (\" ...\" if len(abs_) > 4 else \"\")\n        print(f\"  {col:12s}  {len(rel):>8d}  {rel_preview:30s}  {abs_preview}\")\nelse:\n    print(\"No pulse-time columns in this session — skipping.\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 0  |  cpoke_start_time = 56.1463 s from session start\n",
+      "\n",
+      "  column        n pulses  first 4 (rel. to cpoke, s)      first 4 (abs. session time, s)\n",
+      "  ------------  --------  ------------------------------  ------------------------------\n",
+      "  left_hi             34  [0.0236, 0.1519, 0.1672, 0.1692] ...  [56.1699, 56.2981, 56.3135, 56.3155] ...\n",
+      "  right_hi            17  [0.0536, 0.0714, 0.3894, 0.3982] ...  [56.1999, 56.2177, 56.5357, 56.5444] ...\n",
+      "  left_lo              3  [0.039, 0.0563, 0.3522]         [56.1853, 56.2025, 56.4985]\n",
+      "  right_lo             4  [0.0184, 0.2052, 0.2583, 1.1511]  [56.1647, 56.3515, 56.4046, 57.2973]\n"
+     ]
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pulse-raster-header",
+   "metadata": {},
+   "source": "### Pulse-time raster (first 10 trials)"
+  },
+  {
+   "cell_type": "code",
+   "id": "pulse-raster",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:37:10.462456Z",
+     "start_time": "2026-04-14T13:37:10.286022Z"
+    }
+   },
+   "source": [
+    "if has_pulses:\n",
+    "    N_TRIALS = 10\n",
+    "    colors = {\n",
+    "        \"left_hi\":  \"tab:blue\",\n",
+    "        \"right_hi\": \"tab:orange\",\n",
+    "        \"left_lo\":  \"tab:cyan\",\n",
+    "        \"right_lo\": \"tab:red\",\n",
+    "    }\n",
+    "\n",
+    "    fig, axes = plt.subplots(N_TRIALS, 1, figsize=(14, N_TRIALS * 0.9), sharex=True)\n",
+    "\n",
+    "    for trial_idx, ax in enumerate(axes):\n",
+    "        # Pulse times are already relative to cpoke onset — plot directly so\n",
+    "        # t=0 is cpoke entry on every trial (natural alignment point).\n",
+    "        for col, color in colors.items():\n",
+    "            if col not in trials.colnames:\n",
+    "                continue\n",
+    "            pulses = np.array(trials[col][trial_idx])\n",
+    "            if len(pulses) == 0:\n",
+    "                continue\n",
+    "            ax.vlines(pulses, 0, 1, color=color, linewidth=1.2,\n",
+    "                      label=col if trial_idx == 0 else None)\n",
+    "        ax.axvline(0, color=\"gray\", linewidth=0.8, linestyle=\"--\")  # cpoke onset\n",
+    "        ax.set_ylabel(f\"Trial#{trial_idx + 1}\", fontsize=10, rotation=0, labelpad=25)\n",
+    "        ax.set_yticks([])\n",
+    "        ax.spines[[\"top\", \"right\", \"left\"]].set_visible(False)\n",
+    "\n",
+    "    axes[0].legend(loc=\"upper right\", fontsize=9, ncol=4)\n",
+    "    axes[-1].set_xlabel(\"Time relative to cpoke onset (s)\", fontsize=12)\n",
+    "    fig.suptitle(\n",
+    "        f\"Auditory pulse raster — {nwbfile.session_id} (first {N_TRIALS} trials)\\n\"\n",
+    "        \"t=0 = centre-port entry (cpoke onset)\",\n",
+    "        fontsize=13, y=1.01,\n",
+    "    )\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No pulse-time columns in this session — skipping raster plot.\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x900 with 10 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 38
+  },
+  {
+   "cell_type": "markdown",
+   "id": "task-args-header",
+   "metadata": {},
+   "source": "## 7. Task arguments\n\n`task.task_arguments` stores all session-level parameters from the BControl file (timing thresholds, reward amounts, protocol settings, etc.)."
+  },
+  {
+   "cell_type": "code",
+   "id": "task-args",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:37:43.853631Z",
+     "start_time": "2026-04-14T13:37:43.824251Z"
+    }
+   },
+   "source": "pd.set_option(\"display.max_rows\", None)\npd.set_option(\"display.max_colwidth\", 80)\n\ntask_args = task.task_arguments[:]\nprint(f\"{len(task_args)} task arguments found\")\ntask_args[[\"argument_name\", \"expression_type\", \"expression\"]].head(20)",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "224 task arguments found\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "                            argument_name expression_type  \\\n",
+       "id                                                          \n",
+       "0          ProtocolsSection_n_done_trials         integer   \n",
+       "1       ProtocolsSection_n_started_trials         integer   \n",
+       "2     ProtocolsSection_n_completed_trials         integer   \n",
+       "3                      TaskSwitch6_sessid         integer   \n",
+       "4                 SavingSection_data_file          string   \n",
+       "5   SavingSection_settings_file_load_time         integer   \n",
+       "6              SavingSection_experimenter          string   \n",
+       "7                   SavingSection_ratname          string   \n",
+       "8                  SavingSection_hostname          string   \n",
+       "9                  SavingSection_SaveTime          string   \n",
+       "10         SavingSection_n_autosave_calls         integer   \n",
+       "11       SavingSection_autosave_frequency         integer   \n",
+       "12     SavingSection_save_all_data_to_sql         integer   \n",
+       "13                    SavingSection_title          string   \n",
+       "14   SavingSection_interactive_by_default         integer   \n",
+       "15           CommentsSection_CommentsShow         integer   \n",
+       "16           WaterValvesSection_WaterShow         integer   \n",
+       "17         WaterValvesSection_Left_volume         integer   \n",
+       "18      WaterValvesSection_LeftWValveTime           float   \n",
+       "19       WaterValvesSection_Center_volume         integer   \n",
+       "\n",
+       "                                                                         expression  \n",
+       "id                                                                                   \n",
+       "0                                                                               900  \n",
+       "1                                                                               901  \n",
+       "2                                                                               900  \n",
+       "3                                                                            689581  \n",
+       "4   C:\\ratter\\SoloData\\Data\\Marino\\P100\\data_@TaskSwitch6_Marino_P100_190423_ASV...  \n",
+       "5                                                                                 0  \n",
+       "6                                                                            Marino  \n",
+       "7                                                                              P100  \n",
+       "8                                                                         localhost  \n",
+       "9                                                              23-Apr-2019 13:52:45  \n",
+       "10                                                                              902  \n",
+       "11                                                                               20  \n",
+       "12                                                                                0  \n",
+       "13                                                                    SavingSection  \n",
+       "14                                                                                1  \n",
+       "15                                                                                0  \n",
+       "16                                                                                0  \n",
+       "17                                                                               24  \n",
+       "18                                                             0.051555780933062886  \n",
+       "19                                                                               24  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>expression_type</th>\n",
+       "      <th>expression</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ProtocolsSection_n_done_trials</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ProtocolsSection_n_started_trials</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ProtocolsSection_n_completed_trials</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TaskSwitch6_sessid</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>689581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SavingSection_data_file</td>\n",
+       "      <td>string</td>\n",
+       "      <td>C:\\ratter\\SoloData\\Data\\Marino\\P100\\data_@TaskSwitch6_Marino_P100_190423_ASV...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SavingSection_settings_file_load_time</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>SavingSection_experimenter</td>\n",
+       "      <td>string</td>\n",
+       "      <td>Marino</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>SavingSection_ratname</td>\n",
+       "      <td>string</td>\n",
+       "      <td>P100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>SavingSection_hostname</td>\n",
+       "      <td>string</td>\n",
+       "      <td>localhost</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SavingSection_SaveTime</td>\n",
+       "      <td>string</td>\n",
+       "      <td>23-Apr-2019 13:52:45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>SavingSection_n_autosave_calls</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>SavingSection_autosave_frequency</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>SavingSection_save_all_data_to_sql</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>SavingSection_title</td>\n",
+       "      <td>string</td>\n",
+       "      <td>SavingSection</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>SavingSection_interactive_by_default</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>CommentsSection_CommentsShow</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>WaterValvesSection_WaterShow</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>WaterValvesSection_Left_volume</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>WaterValvesSection_LeftWValveTime</td>\n",
+       "      <td>float</td>\n",
+       "      <td>0.051555780933062886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>WaterValvesSection_Center_volume</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 39
+  },
+  {
+   "cell_type": "markdown",
+   "id": "state-matrix-header",
+   "metadata": {},
+   "source": "## 8. State transition analysis\n\nHow often is each state followed by each other state? The transition matrix and graph below summarise the session-level flow."
+  },
+  {
+   "cell_type": "code",
+   "id": "state-matrix",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:39:09.353523Z",
+     "start_time": "2026-04-14T13:39:09.323450Z"
+    }
+   },
+   "source": [
+    "count_df, prob_df = compute_state_transition_matrix(states=states, state_types=state_types)\n",
+    "\n",
+    "print(\"State transition probability matrix (top-left corner):\")\n",
+    "display(prob_df.iloc[:10, :10])"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "State transition probability matrix (top-left corner):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "to                       state_0  sending_trialnum  check_next_trial_ready  \\\n",
+       "from                                                                         \n",
+       "state_0                 0.000000          0.735556                     0.0   \n",
+       "sending_trialnum        0.264444          0.000000                     0.0   \n",
+       "check_next_trial_ready  0.735484          0.264516                     0.0   \n",
+       "wait_for_cpoke          0.000000          0.000000                     0.0   \n",
+       "wait_for_cpoke_wait2    0.000000          0.000000                     0.0   \n",
+       "wait_for_cpoke_dir      0.000000          0.000000                     0.0   \n",
+       "wait_for_cpoke_freq     0.000000          0.000000                     0.0   \n",
+       "wait_for_cpoke_bis      0.000000          0.000000                     0.0   \n",
+       "nic_prestim             0.000000          0.000000                     0.0   \n",
+       "cpoke                   0.000000          0.000000                     0.0   \n",
+       "\n",
+       "to                      wait_for_cpoke  wait_for_cpoke_wait2  \\\n",
+       "from                                                           \n",
+       "state_0                       0.264444                   0.0   \n",
+       "sending_trialnum              0.735556                   0.0   \n",
+       "check_next_trial_ready        0.000000                   0.0   \n",
+       "wait_for_cpoke                0.000000                   1.0   \n",
+       "wait_for_cpoke_wait2          0.000000                   0.0   \n",
+       "wait_for_cpoke_dir            0.000000                   0.0   \n",
+       "wait_for_cpoke_freq           0.000000                   0.0   \n",
+       "wait_for_cpoke_bis            0.000000                   0.0   \n",
+       "nic_prestim                   0.002252                   0.0   \n",
+       "cpoke                         0.000000                   0.0   \n",
+       "\n",
+       "to                      wait_for_cpoke_dir  wait_for_cpoke_freq  \\\n",
+       "from                                                              \n",
+       "state_0                           0.000000             0.000000   \n",
+       "sending_trialnum                  0.000000             0.000000   \n",
+       "check_next_trial_ready            0.000000             0.000000   \n",
+       "wait_for_cpoke                    0.000000             0.000000   \n",
+       "wait_for_cpoke_wait2              0.546563             0.453437   \n",
+       "wait_for_cpoke_dir                0.000000             0.000000   \n",
+       "wait_for_cpoke_freq               0.000000             0.000000   \n",
+       "wait_for_cpoke_bis                0.000000             0.000000   \n",
+       "nic_prestim                       0.000000             0.000000   \n",
+       "cpoke                             0.000000             0.000000   \n",
+       "\n",
+       "to                      wait_for_cpoke_bis  nic_prestim     cpoke  \n",
+       "from                                                               \n",
+       "state_0                                0.0     0.000000  0.000000  \n",
+       "sending_trialnum                       0.0     0.000000  0.000000  \n",
+       "check_next_trial_ready                 0.0     0.000000  0.000000  \n",
+       "wait_for_cpoke                         0.0     0.000000  0.000000  \n",
+       "wait_for_cpoke_wait2                   0.0     0.000000  0.000000  \n",
+       "wait_for_cpoke_dir                     1.0     0.000000  0.000000  \n",
+       "wait_for_cpoke_freq                    1.0     0.000000  0.000000  \n",
+       "wait_for_cpoke_bis                     0.0     0.984479  0.000000  \n",
+       "nic_prestim                            0.0     0.000000  0.997748  \n",
+       "cpoke                                  0.0     0.000000  0.000000  "
+      ],
+      "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>to</th>\n",
+       "      <th>state_0</th>\n",
+       "      <th>sending_trialnum</th>\n",
+       "      <th>check_next_trial_ready</th>\n",
+       "      <th>wait_for_cpoke</th>\n",
+       "      <th>wait_for_cpoke_wait2</th>\n",
+       "      <th>wait_for_cpoke_dir</th>\n",
+       "      <th>wait_for_cpoke_freq</th>\n",
+       "      <th>wait_for_cpoke_bis</th>\n",
+       "      <th>nic_prestim</th>\n",
+       "      <th>cpoke</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>from</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>state_0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.735556</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.264444</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sending_trialnum</th>\n",
+       "      <td>0.264444</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.735556</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>check_next_trial_ready</th>\n",
+       "      <td>0.735484</td>\n",
+       "      <td>0.264516</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wait_for_cpoke</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wait_for_cpoke_wait2</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.546563</td>\n",
+       "      <td>0.453437</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wait_for_cpoke_dir</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wait_for_cpoke_freq</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wait_for_cpoke_bis</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.984479</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nic_prestim</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002252</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.997748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cpoke</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 44
+  },
+  {
+   "cell_type": "code",
+   "id": "state-graph",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:39:25.876520Z",
+     "start_time": "2026-04-14T13:39:25.605307Z"
+    }
+   },
+   "source": "import networkx as nx\nfrom matplotlib.patches import Patch\n\n# ── Build base active matrix (drop all-zero rows/cols) ────────────────────────\nmask   = (prob_df != 0).any(axis=1)\nactive = prob_df.loc[mask, prob_df.columns[(prob_df != 0).any(axis=0)]]\n\n# ── Filter out continuation states that end with \"2\" ─────────────────────────\ndrop_states = [s for s in active.index if s.endswith(\"2\")]\nfiltered = active.drop(\n    index=drop_states,\n    columns=[c for c in drop_states if c in active.columns],\n    errors=\"ignore\",\n)\nm2 = (filtered != 0).any(axis=1)\nfiltered = filtered.loc[m2, filtered.columns[(filtered != 0).any(axis=0)]]\n\nprint(f\"Dropped states : {drop_states}\")\nprint(f\"Remaining      : {filtered.index.tolist()}\")\n\n# ── Build directed graph ──────────────────────────────────────────────────────\nG = nx.DiGraph()\nfor src in filtered.index:\n    for dst in filtered.columns:\n        prob = filtered.loc[src, dst]\n        if prob > 0:\n            G.add_edge(src, dst, weight=prob)\n\n# ── Node colours by semantic role ─────────────────────────────────────────────\nREWARD_KW = {\"hit\", \"reward\", \"drink\", \"soft_drink\"}\nERROR_KW  = {\"error\", \"violation\", \"punish\", \"timeout\", \"pun\", \"warning\", \"danger\"}\n\ndef node_color(name):\n    if name == \"state_0\":                          return \"#2ecc71\"  # green\n    if any(k in name for k in REWARD_KW):          return \"#f1c40f\"  # gold\n    if any(k in name for k in ERROR_KW):           return \"#e74c3c\"  # red\n    return \"#aec6cf\"                                                  # blue-grey\n\nnode_colors = [node_color(n) for n in G.nodes()]\nnode_sizes  = [3500 if n == \"state_0\" else 2200 for n in G.nodes()]\n\n# ── Layout ────────────────────────────────────────────────────────────────────\ntry:\n    pos = nx.kamada_kawai_layout(G, weight=None)\nexcept Exception:\n    pos = nx.spring_layout(G, seed=42, k=2.0)\n\n# ── Draw ──────────────────────────────────────────────────────────────────────\nfig, ax = plt.subplots(figsize=(16, 11))\n\n# 1. Nodes first\nnx.draw_networkx_nodes(\n    G, pos, ax=ax,\n    node_color=node_colors, node_size=node_sizes,\n    edgecolors=\"white\", linewidths=1.5, alpha=0.95,\n)\n\n# 2. Edges — width and alpha scale with probability\nall_weights = [d[\"weight\"] for _, _, d in G.edges(data=True)]\nmax_w = max(all_weights) if all_weights else 1.0\n\nfor src, dst, data in G.edges(data=True):\n    w = data[\"weight\"]\n    nx.draw_networkx_edges(\n        G, pos, edgelist=[(src, dst)], ax=ax,\n        width=0.6 + 4.4 * w / max_w,\n        alpha=0.25 + 0.70 * w / max_w,\n        edge_color=\"steelblue\",\n        arrows=True,\n        arrowstyle=\"-|>\",\n        arrowsize=14,\n        connectionstyle=\"arc3,rad=0.12\",\n        min_source_margin=22,\n        min_target_margin=22,\n    )\n\n# 3. Edge labels (dominant transitions only)\nLABEL_THRESHOLD = 0.25\nedge_labels = {\n    (src, dst): f\"{data['weight']:.2f}\"\n    for src, dst, data in G.edges(data=True)\n    if data[\"weight\"] >= LABEL_THRESHOLD\n}\nnx.draw_networkx_edge_labels(\n    G, pos, edge_labels=edge_labels, ax=ax,\n    font_size=9, font_color=\"#222222\",\n    bbox=dict(boxstyle=\"round,pad=0.15\", fc=\"white\", alpha=0.65),\n)\n\n# 4. Node labels LAST — drawn on top of everything with an opaque white backing\n#    so edges passing through a node center cannot bleed into the text.\nfor node, (x, y) in pos.items():\n    ax.text(\n        x, y, node,\n        ha=\"center\", va=\"center\",\n        fontsize=10, fontweight=\"bold\", color=\"black\",\n        bbox=dict(boxstyle=\"round,pad=0.25\", fc=\"white\", ec=\"none\", alpha=0.75),\n        zorder=5,\n    )\n\n# ── Legend ────────────────────────────────────────────────────────────────────\nlegend_elements = [\n    Patch(facecolor=\"#2ecc71\", label=\"state_0  (trial start)\"),\n    Patch(facecolor=\"#f1c40f\", label=\"Reward / success\"),\n    Patch(facecolor=\"#e74c3c\", label=\"Error / punishment\"),\n    Patch(facecolor=\"#aec6cf\", label=\"Normal flow\"),\n]\nax.legend(handles=legend_elements, loc=\"lower left\", fontsize=10, framealpha=0.85)\nax.text(\n    0.99, 0.01,\n    f\"Edge labels: p ≥ {LABEL_THRESHOLD}  |  States ending in '2' removed\",\n    transform=ax.transAxes, ha=\"right\", va=\"bottom\", fontsize=8, color=\"gray\",\n)\nax.set_title(f\"State transition graph — {nwbfile.session_id}\", fontsize=15, pad=12)\nax.axis(\"off\")\nplt.tight_layout()\nplt.show()",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropped states : ['wait_for_cpoke_wait2', 'hit_state2', 'nic_error_state2']\n",
+      "Remaining      : ['state_0', 'sending_trialnum', 'check_next_trial_ready', 'wait_for_cpoke_dir', 'wait_for_cpoke_freq', 'wait_for_cpoke_bis', 'nic_prestim', 'cpoke', 'cpoke_in', 'cpoke_out', 'wait_for_cout', 'wait_for_spoke', 'error_state', 'timeout_state', 'clean_up_state']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1600x1100 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 45
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "a29b623d620cbcd"
+  }
+ ]
+}
diff --git a/001550/PaganLab/02_optogenetics_demo.ipynb b/001550/PaganLab/02_optogenetics_demo.ipynb
new file mode 100644
index 0000000..7171e70
--- /dev/null
+++ b/001550/PaganLab/02_optogenetics_demo.ipynb
@@ -0,0 +1,458 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ac85d88888d480d",
+   "metadata": {},
+   "source": "# Pagan Lab — Optogenetics Data\n\nThis notebook demonstrates how to access optogenetics data from\n**[DANDI:001550](https://dandiarchive.org/dandiset/001550)**.\n\nThe example session (TaskSwitch6, subject P131, 2019-08-15) used the **Cerebro wireless\noptogenetics system** (Karpova Lab) to bilaterally inactivate the Frontal Orienting\nField (FOF) on most trials via AAV2/5-mDlx-ChR2-mCherry.\n\n**Reference:** Pagan et al., *Nature* 639, 421–429 (2025).\ndoi:[10.1038/s41586-024-08433-6](https://doi.org/10.1038/s41586-024-08433-6)"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e339339d0c2547c9",
+   "metadata": {},
+   "source": "## 1. Stream the NWB file from DANDI\n"
+  },
+  {
+   "cell_type": "code",
+   "id": "from_pyn",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.721092Z",
+     "start_time": "2026-04-14T13:47:53.503465Z"
+    }
+   },
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import remfile\n",
+    "from dandi.dandiapi import DandiAPIClient\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pynwb import NWBHDF5IO\n",
+    "\n",
+    "DANDISET_ID = \"001550\"\n",
+    "ASSET_PATH  = \"sub-P131/sub-P131_ses-TaskSwitch6-190815a.nwb\"\n",
+    "\n",
+    "with DandiAPIClient() as client:\n",
+    "    asset = client.get_dandiset(DANDISET_ID, \"draft\").get_asset_by_path(ASSET_PATH)\n",
+    "    s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n",
+    "\n",
+    "file_system = remfile.File(s3_url)\n",
+    "h5_file = h5py.File(file_system, \"r\")\n",
+    "io = NWBHDF5IO(file=h5_file)\n",
+    "nwbfile = io.read()"
+   ],
+   "outputs": [],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b6af43d9969b238",
+   "metadata": {},
+   "source": "## 2. Session and subject metadata"
+  },
+  {
+   "cell_type": "code",
+   "id": "644057d2ad778260",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.739856Z",
+     "start_time": "2026-04-14T13:47:57.729113Z"
+    }
+   },
+   "source": "print(\"Session ID:     \", nwb.session_id)\nprint(\"Session start:  \", nwb.session_start_time)\nsub = nwb.subject\nprint(\"Subject ID:     \", sub.subject_id)\nprint(\"Species:        \", sub.species)\nprint(\"Strain:         \", sub.strain)\nprint(\"Sex:            \", sub.sex)\nprint(\"Date of birth:  \", sub.date_of_birth.date())",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Session ID:      TaskSwitch6-190815a\n",
+      "Session start:   2019-08-15 11:41:00+01:00\n",
+      "Subject ID:      P131\n",
+      "Species:         Rattus norvegicus\n",
+      "Strain:          Long Evans\n",
+      "Sex:             M\n",
+      "Date of birth:   2017-06-27\n"
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dda2e24653b44f66",
+   "metadata": {},
+   "source": "## 3. Optogenetic stimulus sites\n\nTwo `OptogeneticStimulusSite` objects describe the implant locations — one per hemisphere.\nThe virus **AAV2/5-mDlx-ChR2-mCherry** was used to express ChR2 in interneurons;\nstimulation wavelength is 473 nm. The Cerebro wireless system (Karpova Lab) delivered\nthe light."
+  },
+  {
+   "cell_type": "code",
+   "id": "for_name",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.757595Z",
+     "start_time": "2026-04-14T13:47:57.746355Z"
+    }
+   },
+   "source": "for name, site in nwb.ogen_sites.items():\n    print(f\"--- {name} ---\")\n    print(f\"  Location:          {site.location}\")\n    print(f\"  Excitation lambda: {site.excitation_lambda} nm\")\n    print(f\"  Device:            {site.device.name} ({site.device.manufacturer})\")\n    print(f\"  Description:       {site.description}\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- opto_site_left ---\n",
+      "  Location:          Frontal Orienting Field (FOF), left hemisphere\n",
+      "  Excitation lambda: 473.0 nm\n",
+      "  Device:            Cerebro_laser (Karpova Lab)\n",
+      "  Description:       Optical fiber in the left hemisphere FOF, +2 mm AP, -1.3 mm ML from bregma.\n",
+      "--- opto_site_right ---\n",
+      "  Location:          Frontal Orienting Field (FOF), right hemisphere\n",
+      "  Excitation lambda: 473.0 nm\n",
+      "  Device:            Cerebro_laser (Karpova Lab)\n",
+      "  Description:       Optical fiber in the right hemisphere FOF, +2 mm AP, +1.3 mm ML from bregma.\n"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1c45e7c796c4e74",
+   "metadata": {},
+   "source": "## 4. Structured optogenetics metadata (ndx-optogenetics)\n\nRich metadata is stored using the [ndx-optogenetics](https://github.com/rly/ndx-optogenetics)\nNWB extension inside an `OptogeneticExperimentMetadata` object.\n\nThis provides queryable fields for:\n- **Excitation source** — device model, power\n- **Optical fiber** — NA, core diameter, per-hemisphere implant coordinates\n- **Viral vector** — construct name, manufacturer\n- **Virus injections** — stereotactic coordinates per hemisphere"
+  },
+  {
+   "cell_type": "code",
+   "id": "x2r4do9al8d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.770230Z",
+     "start_time": "2026-04-14T13:47:57.758697Z"
+    }
+   },
+   "source": "opto_meta = nwb.lab_meta_data[\"optogenetic_experiment_metadata\"]\nprint(\"Stimulation software:\", opto_meta.stimulation_software)\n\n# Excitation source device (ndx-optogenetics 0.3.x: ExcitationSource has power_in_W;\n# wavelength lives on ExcitationSourceModel, or is recorded per-epoch in the epochs table)\ncerebro = nwb.devices[\"Cerebro\"]\nprint(f\"\\nExcitation source: {cerebro.name}  (manufacturer: {cerebro.manufacturer})\")\nprint(f\"  Power:      {cerebro.power_in_W * 1000:.0f} mW\")\n\n# Wavelength — stored per epoch in OptogeneticEpochsTable (ndx-optogenetics 0.3.x)\nepochs_tbl = nwb.intervals[\"optogenetic_epochs\"]\nprint(f\"  Wavelength: {epochs_tbl['wavelength_in_nm'][0]:.0f} nm  (from epochs table)\")\n\n# OptogeneticSitesTable — one row per hemisphere implant (replaces OpticalFiberLocationsTable in 0.2.x)\nsites_table = opto_meta.optogenetic_sites_table\nprint(f\"\\nOptogenetic sites ({len(sites_table.id)} implant locations):\")\nfor i in range(len(sites_table.id)):\n    fiber = sites_table[\"optical_fiber\"][i]\n    fi    = fiber.fiber_insertion\n    print(\n        f\"  Site {i}: {fiber.description}\\n\"\n        f\"           AP={fi.insertion_position_ap_in_mm:+.1f} mm  \"\n        f\"ML={fi.insertion_position_ml_in_mm:+.1f} mm\"\n    )\n\n# Virus (ndx-ophys-devices 0.3.x: ViralVector stored under .viral_vectors)\nvirus = next(iter(opto_meta.optogenetic_viruses.viral_vectors.values()))\nprint(f\"\\nVirus construct: {virus.construct_name}\")\nprint(f\"  Manufacturer:  {virus.manufacturer}\")\n\n# Injections (ndx-ophys-devices 0.3.x: ViralVectorInjection under .viral_vector_injections)\nprint(\"\\nVirus injections:\")\nfor inj in opto_meta.optogenetic_virus_injections.viral_vector_injections.values():\n    print(\n        f\"  {inj.hemisphere:6s}  \"\n        f\"AP={inj.ap_in_mm:+.1f} mm  \"\n        f\"ML={inj.ml_in_mm:+.1f} mm  \"\n        f\"→ {inj.location}\"\n    )",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stimulation software: BControl / Cerebro\n",
+      "\n",
+      "Excitation source: Cerebro  (manufacturer: Karpova Lab)\n",
+      "  Power:      25 mW\n",
+      "  Wavelength: 473 nm  (from epochs table)\n",
+      "\n",
+      "Optogenetic sites (2 implant locations):\n",
+      "  Site 0: Optical fiber implanted in the left hemisphere FOF (+2 mm AP, -1.3 mm ML from bregma).\n",
+      "           AP=+2.0 mm  ML=-1.3 mm\n",
+      "  Site 1: Optical fiber implanted in the right hemisphere FOF (+2 mm AP, +1.3 mm ML from bregma).\n",
+      "           AP=+2.0 mm  ML=+1.3 mm\n",
+      "\n",
+      "Virus construct: AAV2/5-mDlx-ChR2-mCherry\n",
+      "  Manufacturer:  unknown\n",
+      "\n",
+      "Virus injections:\n",
+      "  left    AP=+2.0 mm  ML=-1.3 mm  → Frontal Orienting Field (FOF)\n",
+      "  right   AP=+2.0 mm  ML=+1.3 mm  → Frontal Orienting Field (FOF)\n"
+     ]
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b97ec09dacea475c",
+   "metadata": {},
+   "source": "## 5. OptogeneticSeries — laser power over time\n\nTwo `OptogeneticSeries` are stored in `nwb.stimulus` — one per hemisphere.\nEach uses a **step-function** encoding:\n- Onset sample: **power = 0.025 W (25 mW)**\n- Offset sample immediately after: **power = 0 W**\n\n> **Power units:** watts (SI). The raw Cerebro internal threshold (≥ 800) was used\n> during conversion to determine laser-on vs. laser-off; all values above threshold\n> correspond to 25 mW."
+  },
+  {
+   "cell_type": "code",
+   "id": "opto_lef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.778921Z",
+     "start_time": "2026-04-14T13:47:57.771299Z"
+    }
+   },
+   "source": "opto_left  = nwb.stimulus[\"optogenetic_series_left\"]\nopto_right = nwb.stimulus[\"optogenetic_series_right\"]\n\nprint(f\"Unit: {opto_left.unit}\")  # \"watts\"\nprint()\nprint(\"Left series:\")\nprint(f\"  n samples:  {len(opto_left.data)}\")\nprint(f\"  data[:8]:   {opto_left.data[:8]}\")   # alternates 0.025 / 0.0\nprint(f\"  timestamps: {opto_left.timestamps[:8]}\")\nprint()\nprint(\"Right series:\")\nprint(f\"  n samples:  {len(opto_right.data)}\")\nprint(f\"  data[:8]:   {opto_right.data[:8]}\")",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unit: watts\n",
+      "\n",
+      "Left series:\n",
+      "  n samples:  1228\n",
+      "  data[:8]:   [0.    0.    0.025 0.    0.    0.    0.    0.   ]\n",
+      "  timestamps: [1449.866753 1451.166753 1457.688753 1458.338753 1468.262754 1468.912754\n",
+      " 1475.819754 1476.469754]\n",
+      "\n",
+      "Right series:\n",
+      "  n samples:  1228\n",
+      "  data[:8]:   [0.    0.    0.025 0.    0.    0.    0.    0.   ]\n"
+     ]
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9c89a4d08cd4147",
+   "metadata": {},
+   "source": "## 6. Reconstruct stimulation intervals\n\nEach consecutive on/off sample pair defines one stimulation interval."
+  },
+  {
+   "cell_type": "code",
+   "id": "def_get_",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.790364Z",
+     "start_time": "2026-04-14T13:47:57.780065Z"
+    }
+   },
+   "source": "def get_stim_intervals(series):\n    \"\"\"Return (on_times, off_times, powers) from a step-function OptogeneticSeries.\"\"\"\n    data = np.array(series.data[:])\n    ts   = np.array(series.timestamps[:])\n    on_mask = data > 0\n    on_times  = ts[on_mask]\n    off_times = ts[np.where(on_mask)[0] + 1]  # offset sample immediately follows\n    powers    = data[on_mask]\n    return on_times, off_times, powers\n\non_l, off_l, pwr_l = get_stim_intervals(opto_left)\non_r, off_r, pwr_r = get_stim_intervals(opto_right)\n\nprint(f\"Left:  {len(on_l)} stimulation intervals\")\nprint(f\"Right: {len(on_r)} stimulation intervals\")\nprint()\ndf_left = pd.DataFrame({\"t_on\": on_l[:5], \"t_off\": off_l[:5], \"power\": pwr_l[:5]})\nprint(\"First 5 left-hemisphere intervals:\")\nprint(df_left.to_string(index=False))",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Left:  137 stimulation intervals\n",
+      "Right: 128 stimulation intervals\n",
+      "\n",
+      "First 5 left-hemisphere intervals:\n",
+      "       t_on       t_off  power\n",
+      "1457.688753 1458.338753  0.025\n",
+      "1516.250754 1517.550754  0.025\n",
+      "1541.370253 1542.020253  0.025\n",
+      "1580.621753 1581.271753  0.025\n",
+      "1606.274253 1606.924253  0.025\n"
+     ]
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e77cec9a8a5044b2",
+   "metadata": {},
+   "source": "## 7. Visualise laser power\n\nStep-function laser power for both hemispheres over the first 500 s of the session."
+  },
+  {
+   "cell_type": "code",
+   "id": "6e23c1b8e3036242",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.872746Z",
+     "start_time": "2026-04-14T13:47:57.791326Z"
+    }
+   },
+   "source": "fig, axes = plt.subplots(2, 1, figsize=(14, 5), sharex=False)\n\nfor ax, series, label, color in zip(\n    axes,\n    [opto_left, opto_right],\n    [\"Left hemisphere (FOF)\", \"Right hemisphere (FOF)\"],\n    [\"steelblue\", \"tomato\"],\n):\n    data = np.array(series.data[:])\n    ts   = np.array(series.timestamps[:])\n    # Show first 500 s of the session\n    mask = ts < ts[0] + 500\n    ax.step(ts[mask], data[mask] * 1000, where=\"post\", color=color, lw=1.2)  # convert W → mW\n    ax.set_ylabel(\"Power (mW)\", fontsize=11)\n    ax.set_ylim(-2, 30)\n    ax.set_title(label, fontsize=12)\n    ax.set_xlabel(\"Time from session start (s)\", fontsize=11)\n\nplt.suptitle(\"Optogenetic laser power — first 500 s\", fontsize=14)\nplt.tight_layout()\nplt.show()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "cell_type": "markdown",
+   "id": "562e699376f44496",
+   "metadata": {},
+   "source": "## 8. Per-trial opto columns and stimulation epochs\n\n### Trials table opto columns\n\nTwo columns in `nwbfile.trials` describe stimulation scheduling:\n\n| Column | Content |\n|---|---|\n| `OptoSection_opto_connected` | 1 = Cerebro was connected on this trial; 0 = not connected |\n| `OptoSection_opto_type` | Window label: `'Full Trial'` (0–1.3 s), `'First Half'` (0–0.65 s), or `'Second Half'` (0.65–1.3 s) relative to cpoke onset |\n\n**Note:** Raw per-hemisphere power values are not stored as trial columns — the\nhemisphere-resolved power is fully encoded in `optogenetic_series_left` /\n`optogenetic_series_right`.\n\n### OptogeneticEpochsTable\n\n`nwb.intervals[\"optogenetic_epochs\"]` contains one row per stimulation interval with\nstructured protocol fields: `stimulation_on`, `pulse_length_in_ms`, `power_in_mW`,\n`wavelength_in_nm`, etc."
+  },
+  {
+   "cell_type": "code",
+   "id": "jira2sfr7fk",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.892105Z",
+     "start_time": "2026-04-14T13:47:57.873542Z"
+    }
+   },
+   "source": "epochs = nwb.intervals[\"optogenetic_epochs\"]\nn_epochs = len(epochs.id)\nprint(f\"Total stimulation epochs: {n_epochs}\")\n\n# Build a DataFrame from individual columns (avoids an hdmf compat issue with to_dataframe())\nimport pandas as pd\n\nepochs_df = pd.DataFrame({\n    \"start_time\":          epochs[\"start_time\"][:],\n    \"stop_time\":           epochs[\"stop_time\"][:],\n    \"stimulation_on\":      epochs[\"stimulation_on\"][:],\n    \"pulse_length_in_ms\":  epochs[\"pulse_length_in_ms\"][:],\n    \"power_in_mW\":         epochs[\"power_in_mW\"][:],\n})\n\nprint(\"\\nWindow-type distribution (by pulse_length_in_ms):\")\nprint(epochs_df[\"pulse_length_in_ms\"].value_counts().rename({1300.0: \"Full Trial (1300 ms)\", 650.0: \"Half Trial (650 ms)\"}).to_string())\nprint()\nepochs_df.head(10)",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total stimulation epochs: 614\n",
+      "\n",
+      "Window-type distribution (by pulse_length_in_ms):\n",
+      "pulse_length_in_ms\n",
+      "Half Trial (650 ms)     424\n",
+      "Full Trial (1300 ms)    190\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "    start_time    stop_time  stimulation_on  pulse_length_in_ms  power_in_mW\n",
+       "0  1449.866753  1451.166753            True              1300.0         25.0\n",
+       "1  1457.688753  1458.338753            True               650.0         25.0\n",
+       "2  1468.262754  1468.912754            True               650.0         25.0\n",
+       "3  1475.819754  1476.469754            True               650.0         25.0\n",
+       "4  1488.349256  1488.999256            True               650.0         25.0\n",
+       "5  1499.350754  1500.000754            True               650.0         25.0\n",
+       "6  1507.201258  1507.851258            True               650.0         25.0\n",
+       "7  1516.250754  1517.550754            True              1300.0         25.0\n",
+       "8  1523.471254  1524.771254            True              1300.0         25.0\n",
+       "9  1531.310753  1532.610753            True              1300.0         25.0"
+      ],
+      "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>stimulation_on</th>\n",
+       "      <th>pulse_length_in_ms</th>\n",
+       "      <th>power_in_mW</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1449.866753</td>\n",
+       "      <td>1451.166753</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1300.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1457.688753</td>\n",
+       "      <td>1458.338753</td>\n",
+       "      <td>True</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1468.262754</td>\n",
+       "      <td>1468.912754</td>\n",
+       "      <td>True</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1475.819754</td>\n",
+       "      <td>1476.469754</td>\n",
+       "      <td>True</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1488.349256</td>\n",
+       "      <td>1488.999256</td>\n",
+       "      <td>True</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1499.350754</td>\n",
+       "      <td>1500.000754</td>\n",
+       "      <td>True</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1507.201258</td>\n",
+       "      <td>1507.851258</td>\n",
+       "      <td>True</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1516.250754</td>\n",
+       "      <td>1517.550754</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1300.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1523.471254</td>\n",
+       "      <td>1524.771254</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1300.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1531.310753</td>\n",
+       "      <td>1532.610753</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1300.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-04-14T13:47:57.930986Z",
+     "start_time": "2026-04-14T13:47:57.914119Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "9480eb9c2c25db1e",
+   "outputs": [],
+   "execution_count": 22
+  }
+ ]
+}
diff --git a/001550/PaganLab/README.md b/001550/PaganLab/README.md
new file mode 100644
index 0000000..916b1c1
--- /dev/null
+++ b/001550/PaganLab/README.md
@@ -0,0 +1,49 @@
+# DANDI:001550 — Pagan Lab Behavioral and Optogenetics Notebooks
+
+These notebooks demonstrate how to access data from
+**[DANDI:001550](https://dandiarchive.org/dandiset/001550)**, a dataset of
+behavioral sessions from Long-Evans rats performing multi-sensory decision-making tasks.
+A subset of sessions (TaskSwitch6, 5 subjects) include wireless optogenetics
+(Cerebro system, Karpova Lab) used to bilaterally inactivate the Frontal Orienting
+Field (FOF) via AAV2/5-mDlx-ChR2-mCherry.
+
+**Reference:** Pagan et al., *Nature* 639, 421–429 (2025).
+doi:[10.1038/s41586-024-08433-6](https://doi.org/10.1038/s41586-024-08433-6)
+
+---
+
+## Notebooks
+
+### `01_behavior_demo.ipynb`
+
+Streams a TaskSwitch6 session (subject P100, 2019-04-23) from DANDI and demonstrates:
+
+- Session and subject metadata
+- Task vocabulary: event types (port pokes), state types (FSM states), action types (sounds)
+- Behavioral recording: events, states, and actions tables
+- Visualisations using `ndx_structured_behavior.plot`
+- Trials table: scalar columns, stimulus parameters, auditory pulse times
+- Pulse-time reference frame (relative to cpoke onset) and conversion to absolute time
+- State-transition probability matrix and graph
+
+### `02_optogenetics_demo.ipynb`
+
+Streams a TaskSwitch6 session with active laser stimulation (subject P131, 2019-08-15)
+and demonstrates:
+
+- `OptogeneticStimulusSite`, `OptogeneticSeries` step-function laser power (watts) for each hemisphere
+- Rich structured metadata via `ndx-optogenetics` 0.3.x: excitation source, optical
+  fiber implant locations, viral vector, injection coordinates
+- Reconstructing per-trial stimulation intervals
+- `OptogeneticEpochsTable`: pulse protocol parameters per epoch
+
+---
+
+## Installing the dependencies
+
+```bash
+git clone https://github.com/dandi/example-notebooks
+cd example-notebooks/001550/PaganLab
+conda env create --file environment.yml
+conda activate pagan_lab_001550
+```
\ No newline at end of file
diff --git a/001550/PaganLab/environment.yml b/001550/PaganLab/environment.yml
new file mode 100644
index 0000000..17a24f0
--- /dev/null
+++ b/001550/PaganLab/environment.yml
@@ -0,0 +1,19 @@
+name: pagan_lab_001550
+channels:
+  - conda-forge
+dependencies:
+  - python==3.12
+  - ipywidgets
+  - pip
+  - pip:
+    - dandi
+    - jupyter
+    - h5py
+    - remfile
+    - pynwb
+    - numpy
+    - pandas
+    - matplotlib
+    - networkx
+    - ndx-structured-behavior @ git+https://github.com/rly/ndx-structured-behavior.git@main
+    - ndx-optogenetics==0.3.0