tutorial_part1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q --force-reinstall airavata-python-sdk[notebook]\n",
    "import airavata_jupyter_magic\n",
    "\n",
    "%authenticate\n",
    "%request_runtime hpc_cpu --file=cybershuttle.yml --walltime=60 --use=NeuroData25VC1:cloud,expanse:shared,anvil:shared\n",
    "%switch_runtime hpc_cpu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring `temporaldata`\n",
    "\n",
    "Notebook author: Mehdi Azabou | Contributions: Vinam Arora, Shivashriganesh Mahato, Eva Dyer\n",
    "\n",
    "***\n",
    "\n",
    "In this notebook, we will go through an example for preparing a dataset using\n",
    "data objects from **temporaldata**, which we will learn to manipulate. Then, we will learn how to use samplers from **torch_brain** as part of a deep learning workflow.\n",
    "\n",
    "The notebook is organized around three main concepts:\n",
    "- **Part 1: Data**\n",
    "- **Part 2: Dataset and slicing**\n",
    "- **Part 3: Samplers**\n",
    "\n",
    "Documentation:\n",
    "- [**temporaldata**](https://temporaldata.readthedocs.io)\n",
    "- [**torch_brain**](https://torch-brain.readthedocs.io)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Part 1: Data\n",
    "\n",
    "In part 1, we will learn about temporal data objects, and go through the process of preparing a dataset.\n",
    "\n",
    "***\n",
    "\n",
    "## Table of contents:\n",
    "* [1.1 Overview of **temporaldata**](#a)\n",
    "* [1.2 Downloading a recording from DANDI](#c)\n",
    "* [1.3 Data Objects in temporaldata](#d)\n",
    "* [1.4 Bringing it all together: Data](#e)\n",
    "* [1.5 Saving data to disk](#f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## 1.1 Overview of **temporaldata** <a class=\"anchor\" id=\"a\"></a>\n",
    "\n",
    "<img src=\"https://temporaldata.readthedocs.io/en/latest/_static/temporaldata_logo.png\" width=\"150\" height=\"150\" alt=\"temporaldata Logo\">\n",
    "\n",
    "[Documentation](https://temporaldata.readthedocs.io)\n",
    "\n",
    "**temporaldata** is a Python package designed for deep learning workflows. It provides data abstractions for multi-modal, multi-resolution temporal data that are both intuitive and efficient. You can install it using pip.\n",
    "\n",
    "\n",
    "#### Motivation:\n",
    "- Temporal data in the real world is messy, and usually involves multiple multi-modal streams of data, recorded across different sensors. An example of this is neural recordings, where brain activity and behavior are typically recorded simultaneously. Such datasets often consist of a combination of **regular** and **irregular** time series.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! dandi download dandi://dandi/000688@draft/sub-T/sub-T_ses-CO-20130819_behavior+ecephys.nwb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a sessions from [1]. The monkey is performing a center-out task, where it makes a reach to one of eight fixed targets after receiving a go cue, and then returning to the center.\n",
    "\n",
    "<img src=\"https://ik.imagekit.io/7tkfmw7hc/tbrain_notebooks/mp_image.png?updatedAt=1744132572759\" width=\"300\" alt=\"temporaldata Logo\">\n",
    "\n",
    "---\n",
    "\n",
    "[1] Perich, Matthew G.; Miller, Lee E.; Azabou, Mehdi; Dyer, Eva L. (2025) Long-term recordings of motor and premotor cortical spiking activity during reaching in monkeys (Version 0.250122.1735) [Data set]. DANDI Archive. https://doi.org/10.48324/dandi.000688/0.250122.1735"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define plotting utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.layouts import column\n",
    "from bokeh.models import ColumnDataSource, RangeTool\n",
    "import numpy as np\n",
    "\n",
    "from bokeh.models import Button\n",
    "from bokeh.models.callbacks import CustomJS\n",
    "from bokeh.layouts import row, column\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "def plot_spikes(spikes, add_range_tool=False, x_range=None, width=800, height=400):\n",
    "    \"\"\"\n",
    "    Plots an IrregularTimeSeries object defined by spikes.timestamps and spikes.unit_index.\n",
    "\n",
    "    Parameters:\n",
    "    spikes: An object containing 'timestamps' and 'unit_index' attributes.\n",
    "    \"\"\"\n",
    "    if x_range is None:\n",
    "        if add_range_tool:\n",
    "            x_range = (spikes.timestamps[0] * 1e3, spikes.timestamps[0] * 1e3 + 20_000)\n",
    "        else:\n",
    "            x_range = (spikes.timestamps[0] * 1e3, spikes.timestamps[-1] * 1e3)\n",
    "\n",
    "    # Create a figure\n",
    "    p = figure(x_axis_label='Time', y_axis_label='Unit Index', width=width, height=height, x_axis_type=\"datetime\", x_range=x_range, title=\"Spikes\")\n",
    "\n",
    "    # Prepare data for plotting\n",
    "    x_values = spikes.timestamps * 1e3\n",
    "    y_values = spikes.unit_index\n",
    "\n",
    "    # Create a ColumnDataSource\n",
    "    source = ColumnDataSource(data=dict(x=x_values, y=y_values))\n",
    "\n",
    "    # Add scatter points to the plot\n",
    "    p.scatter('x', 'y', source=source, size=5, color=\"navy\", alpha=0.5, marker=\"dash\", angle=np.pi/2)\n",
    "\n",
    "    if add_range_tool:\n",
    "        select = figure(height=height//5, width=width, tools=\"\",\n",
    "                        toolbar_location=None, background_fill_color=\"#efefef\", x_axis_type=\"datetime\",\n",
    "                        title=\"Average Population Activity\")\n",
    "        select.xaxis.visible = False\n",
    "        # select.yaxis.visible = False\n",
    "\n",
    "        range_tool = RangeTool(x_range=p.x_range)\n",
    "        range_tool.overlay.fill_color = \"navy\"\n",
    "        range_tool.overlay.fill_alpha = 0.2\n",
    "\n",
    "        spike_times_int = spikes.timestamps.astype(int)\n",
    "        population_activity =np.bincount(spike_times_int-spike_times_int[0])\n",
    "        source = ColumnDataSource(data=dict(x=(np.arange(len(population_activity)) + spike_times_int[0])* 1e3, y=population_activity))\n",
    "\n",
    "        select.line('x', 'y', source=source)\n",
    "        select.ygrid.grid_line_color = None\n",
    "        select.add_tools(range_tool)\n",
    "        p = column(select, p)\n",
    "\n",
    "    return p\n",
    "\n",
    "def plot_time_series(data, field, index=None, x_range=None, add_range_tool=False, y_axis_label=None, width=800, height=200, include_control_panel=False, shade_area_list=[]):\n",
    "    # Create a figure\n",
    "    if x_range is None:\n",
    "        if add_range_tool:\n",
    "            x_range = (data.timestamps[0] * 1e3, data.timestamps[0] * 1e3 + 20_000)\n",
    "        else:\n",
    "            x_range = (data.timestamps[0] * 1e3, data.timestamps[-1] * 1e3)\n",
    "\n",
    "    if y_axis_label is None:\n",
    "        y_axis_label = field\n",
    "\n",
    "    p = figure(x_axis_label='Time', y_axis_label=y_axis_label, width=width, height=height, x_axis_type=\"datetime\", x_range=x_range)\n",
    "\n",
    "\n",
    "    domain_start = data.domain.start * 1e3\n",
    "    domain_end = data.domain.end * 1e3\n",
    "    # Prepare data for plotting\n",
    "    x_values = data.timestamps * 1e3\n",
    "    y_values = getattr(data, field)\n",
    "\n",
    "    x_values = np.concatenate([x_values, domain_start, domain_end])\n",
    "    y_values = np.concatenate([y_values, np.nan * np.ones((len(data.domain), * y_values.shape[1:])), np.nan * np.ones((len(data.domain), *y_values.shape[1:]))])\n",
    "\n",
    "    # sort x_values and reorder y_values\n",
    "    sort_indices = np.argsort(x_values)\n",
    "    x_values = x_values[sort_indices]\n",
    "    y_values = y_values[sort_indices]\n",
    "\n",
    "    if y_values.ndim == 1:\n",
    "        # Add a line to the plot\n",
    "        source = ColumnDataSource(data=dict(x=x_values, y=y_values))\n",
    "    elif y_values.ndim == 2:\n",
    "        assert index is not None, \"Index must be provided for 2D data\"\n",
    "        source = ColumnDataSource(data=dict(x=x_values, y=y_values[:, index]))\n",
    "    else:\n",
    "        raise ValueError(f\"Field {field} has {y_values.ndim} dimensions, expected 1 or 2\")\n",
    "\n",
    "    p.line(x='x', y='y', source=source, line_width=2, color=\"green\")\n",
    "    x_range = p.x_range\n",
    "\n",
    "    colors = ['red', 'blue', 'green', 'yellow', 'purple', 'orange', 'brown', 'pink', 'gray', 'black']\n",
    "    for i, shade_area in enumerate(shade_area_list):\n",
    "        start = shade_area.start * 1e3\n",
    "        end = shade_area.end * 1e3\n",
    "        rect_x = (start + end)/2\n",
    "        rect_width = end - start\n",
    "        rect_y = np.ones_like(start) * (y_values.min() + y_values.max())/2\n",
    "        rect_height = np.ones_like(start) * (y_values.max() - y_values.min())\n",
    "        p.rect(x=rect_x, y=rect_y, width=rect_width, height=rect_height, fill_color=colors[i], fill_alpha=0.4)\n",
    "        # legend_items.append((f\"Shade Area {i+1}\", [p.renderers[-1]]))  # Add the rectangle to the legend items\n",
    "\n",
    "    # p.legend.items = legend_items\n",
    "    # p.legend.location = \"top_left\"  # Set the legend location\n",
    "    # p.legend.click_policy = \"hide\"  # Allow clicking to hide the legend items\n",
    "\n",
    "    if add_range_tool:\n",
    "        select = figure(height=height//5, width=width, y_range=p.y_range, tools=\"\",\n",
    "                        toolbar_location=None, background_fill_color=\"#efefef\", x_axis_type=\"datetime\",)\n",
    "        select.xaxis.visible = False\n",
    "        select.yaxis.visible = False\n",
    "\n",
    "        range_tool = RangeTool(x_range=p.x_range)\n",
    "        range_tool.overlay.fill_color = \"navy\"\n",
    "        range_tool.overlay.fill_alpha = 0.2\n",
    "\n",
    "        select.line('x', 'y', source=source)\n",
    "        select.ygrid.grid_line_color = None\n",
    "        select.add_tools(range_tool)\n",
    "        p = column(select, p)\n",
    "\n",
    "        if include_control_panel:\n",
    "            # Add a button to control the range tool\n",
    "            play_button = Button(label=\"Play\", button_type=\"success\")\n",
    "            pause_button = Button(label=\"Pause\", button_type=\"warning\")\n",
    "            speed_buttons = [Button(label=f\"{2**i}x\", button_type=\"primary\") for i in range(5)]\n",
    "\n",
    "            # Create a shared ColumnDataSource to store the interval ID\n",
    "            shared_data = ColumnDataSource(data=dict(interval_id=[None], step_size=[1000]))\n",
    "\n",
    "            # Update the CustomJS to use the shared data source\n",
    "            play_button.js_on_click(CustomJS(args=dict(range_tool=range_tool, x_values=x_values, shared_data=shared_data), code=\"\"\"\n",
    "                // Clear previous interval if it exists\n",
    "                if (shared_data.data.interval_id[0]) {\n",
    "                    clearInterval(shared_data.data.interval_id[0]);\n",
    "                }\n",
    "\n",
    "                // Create new interval and store it in the shared data\n",
    "                let new_interval = setInterval(() => {\n",
    "                    if (range_tool.x_range.end < x_values[x_values.length - 1]) {\n",
    "                        range_tool.x_range.start += shared_data.data.step_size[0] / 10;\n",
    "                        range_tool.x_range.end += shared_data.data.step_size[0] / 10;\n",
    "                    } else {\n",
    "                        clearInterval(shared_data.data.interval_id[0]);\n",
    "                        shared_data.data.interval_id[0] = null;\n",
    "                        shared_data.change.emit();\n",
    "                    }\n",
    "                }, 100);\n",
    "\n",
    "                shared_data.data.interval_id[0] = new_interval;\n",
    "                shared_data.change.emit();\n",
    "            \"\"\"));\n",
    "\n",
    "            pause_button.js_on_click(CustomJS(args=dict(shared_data=shared_data), code=\"\"\"\n",
    "                if (shared_data.data.interval_id[0]) {\n",
    "                    clearInterval(shared_data.data.interval_id[0]);\n",
    "                    shared_data.data.interval_id[0] = null;\n",
    "                    shared_data.change.emit();\n",
    "                }\n",
    "            \"\"\"));\n",
    "\n",
    "            for button in speed_buttons:\n",
    "                button.js_on_click(CustomJS(args=dict(button=button, shared_data=shared_data), code=\"\"\"\n",
    "                    shared_data.data.step_size[0] = 1000 * parseInt(button.label.replace('x', ''));\n",
    "                    shared_data.change.emit();\n",
    "                \"\"\"));\n",
    "\n",
    "            # Add the buttons to the layout\n",
    "            button_layout = row(play_button, pause_button, *speed_buttons)\n",
    "            return p, x_range, button_layout\n",
    "\n",
    "    return p\n",
    "\n",
    "\n",
    "def plot_intervals(*interval, x_range=None, title=None, width=800, height=200):\n",
    "    colors = [\"red\", \"blue\", \"green\", \"yellow\", \"orange\", \"purple\", \"pink\", \"brown\", \"black\"]\n",
    "    # Create a figure\n",
    "    if x_range is None:\n",
    "      p = figure(title=title, x_axis_label='Time', y_axis_label='Intervals', y_range=(-len(interval), 1), width=width, height=height, x_axis_type=\"datetime\",)\n",
    "    else:\n",
    "      p = figure(title=title, x_axis_label='Time', x_range=x_range, y_axis_label='Intervals', y_range=(-len(interval), 1), width=width, height=height, x_axis_type=\"datetime\",)\n",
    "\n",
    "    p.yaxis.visible = False\n",
    "\n",
    "    for i in range(len(interval)):\n",
    "        # Prepare data for plotting\n",
    "        centers = (interval[i].start + interval[i].end) / 2.  * 1e3\n",
    "        durations = (interval[i].end - interval[i].start)  * 1e3\n",
    "        y_values = np.zeros_like(centers) - i  # y-values for the intervals\n",
    "\n",
    "        # Create a ColumnDataSource\n",
    "        source = ColumnDataSource(data=dict(x=centers, width=durations, y=y_values))\n",
    "\n",
    "        # Add rectangles to the plot\n",
    "        p.rect(x='x', y='y', width='width', height=0.8, source=source, fill_color=colors[i % 10], line_color=\"black\", alpha=0.5)\n",
    "\n",
    "    return p\n",
    "\n",
    "def make_plot(data, add_play_controls=False):\n",
    "    include_control_panel = add_play_controls\n",
    "    if include_control_panel:\n",
    "      p_cursor_pos_x, x_range, button_layout = plot_time_series(data.cursor, 'vel', y_axis_label='cursor position x', index=0, add_range_tool=include_control_panel, include_control_panel=include_control_panel, shade_area_list=[data.domain])\n",
    "    else:\n",
    "      p_cursor_pos_x = plot_time_series(data.cursor, 'vel', y_axis_label='cursor position x', index=0, add_range_tool=include_control_panel, include_control_panel=include_control_panel, shade_area_list=[data.domain])\n",
    "      x_range=p_cursor_pos_x.x_range\n",
    "\n",
    "    p_cursor_pos_y = plot_time_series(data.cursor, 'vel', y_axis_label='cursor position y', index=1, add_range_tool=include_control_panel, x_range=x_range, shade_area_list=[data.reach_intervals])\n",
    "    p_cursor_vel_x = plot_time_series(data.cursor, 'vel', y_axis_label='cursor velocity x', index=0, add_range_tool=include_control_panel, x_range=x_range, shade_area_list=[data.reach_intervals])\n",
    "    p_cursor_vel_y = plot_time_series(data.cursor, 'vel', y_axis_label='cursor velocity y', index=1, add_range_tool=include_control_panel, x_range=x_range, shade_area_list=[data.reach_intervals])\n",
    "    p_spikes = plot_spikes(data.spikes, add_range_tool=include_control_panel, x_range=x_range, height=480)\n",
    "    p_reach_intervals = plot_intervals(data.reach_intervals, x_range=x_range, height=120, title=\"Reach intervals\")\n",
    "\n",
    "    if include_control_panel:\n",
    "      return column(button_layout, row(p_spikes, column(p_reach_intervals, p_cursor_vel_x, p_cursor_vel_y)))\n",
    "    else:\n",
    "      return row(p_spikes, column(p_reach_intervals, p_cursor_vel_x, p_cursor_vel_y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we can visualize the data. We can see the simultanously recorded spiking activity and the corresponding cursor velocity. Press \"Play\" to start playing the recording back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Visualizing the data\n",
    "\n",
    "def plot_data():\n",
    "  from pynwb import NWBHDF5IO\n",
    "  import numpy as np\n",
    "  from temporaldata import ArrayDict, IrregularTimeSeries, RegularTimeSeries, Interval, Data\n",
    "\n",
    "  input_file = \"sub-T_ses-CO-20130819_behavior+ecephys.nwb\"\n",
    "  io = NWBHDF5IO(input_file, \"r\")\n",
    "  nwbfile = io.read()\n",
    "\n",
    "  spike_train_list = nwbfile.units.spike_times_index[:]\n",
    "\n",
    "  unit_ids = []\n",
    "  unit_brain_areas = []\n",
    "  for i in range(len(spike_train_list)):\n",
    "      unit_ids.append(f\"unit_{i}\")\n",
    "      unit_brain_areas.append(nwbfile.units.electrodes[i].location.item())\n",
    "  units = ArrayDict(id=np.array(unit_ids), brain_area=np.array(unit_brain_areas))\n",
    "\n",
    "  spike_timestamps = np.array([])\n",
    "  spike_unit_index = np.array([])\n",
    "\n",
    "  for i in range(len(spike_train_list)):\n",
    "      spike_train = spike_train_list[i]\n",
    "      spike_timestamps = np.concatenate([spike_timestamps, spike_train])\n",
    "      spike_unit_index = np.concatenate([spike_unit_index, np.full_like(spike_train, fill_value=i)])\n",
    "\n",
    "  spikes = IrregularTimeSeries(\n",
    "      timestamps=spike_timestamps,\n",
    "      unit_index=spike_unit_index,\n",
    "      domain=\"auto\",\n",
    "  )\n",
    "  spikes.sort()\n",
    "\n",
    "  timestamps = nwbfile.processing[\"behavior\"][\"Position\"][\"cursor_pos\"].timestamps[:]\n",
    "  cursor_pos = nwbfile.processing[\"behavior\"][\"Position\"][\"cursor_pos\"].data[:]\n",
    "  cursor_vel = nwbfile.processing[\"behavior\"][\"Velocity\"][\"cursor_vel\"].data[:]\n",
    "  cursor_acc = nwbfile.processing[\"behavior\"][\"Acceleration\"][\"cursor_acc\"].data[:]\n",
    "\n",
    "  sampling_rate = 100 # Hz\n",
    "  assert np.allclose(np.diff(timestamps), 1/sampling_rate)\n",
    "\n",
    "  cursor = RegularTimeSeries(\n",
    "      pos=cursor_pos,\n",
    "      vel=cursor_vel,\n",
    "      acc=cursor_acc,\n",
    "      sampling_rate=sampling_rate,\n",
    "      domain_start=timestamps[0],\n",
    "      domain=\"auto\",\n",
    "  )\n",
    "\n",
    "  trial_table = nwbfile.trials.to_dataframe().dropna()\n",
    "  reach_intervals = Interval(\n",
    "      start=trial_table.go_cue_time.values,\n",
    "      end=trial_table.stop_time.values,\n",
    "      result=trial_table.result.values,\n",
    "      target_id=trial_table.target_id.values,\n",
    "  )\n",
    "\n",
    "  data = Data(\n",
    "    spikes=spikes,\n",
    "    cursor=cursor,\n",
    "    reach_intervals=reach_intervals,\n",
    "    domain=\"auto\",\n",
    "  )\n",
    "\n",
    "  p = make_plot(data, add_play_controls=True)\n",
    "  show(p)\n",
    "\n",
    "plot_data()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was loaded using **temporaldata** objects. In this part of the tutorial, we will walk through how to prepare the data using various abstractions, that make data manipulation much more natural and efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Download the data from DANDI as NWB file*\n",
    "\n",
    "The data is stored in an NWB file. The Neurodata Without Borders (NWB) format is a standardized, open-source file format designed to store neurophysiology data, including electrophysiology, and behavioral information.\n",
    "We will use the `pynwb` library to load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pynwb import NWBHDF5IO\n",
    "\n",
    "input_file = \"sub-T_ses-CO-20130819_behavior+ecephys.nwb\"\n",
    "io = NWBHDF5IO(input_file, \"r\")\n",
    "nwbfile = io.read()\n",
    "\n",
    "# we can access the spike times and unit indices directly from the NWB file\n",
    "spike_train_list = nwbfile.units.spike_times_index[:]\n",
    "print(f\"Number of units found: {len(spike_train_list)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3. Data Objects in temporaldata\n",
    "\n",
    "We now introduce the main data objects in temporal data:\n",
    "- ArrayDict\n",
    "- IrregularTimeSeries\n",
    "- RegularTimeSeries\n",
    "- Interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.1 Using **ArrayDict** to define the unit table <a class=\"anchor\" id=\"c\"></a>\n",
    "\n",
    "The `ArrayDict` is a simple container for numpy arrays that share the same first dimension.\n",
    "We can use this object to store the list of units including their identifiers (`id`) and brain areas (`brain_area`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from temporaldata import ArrayDict\n",
    "\n",
    "unit_ids = []\n",
    "unit_brain_areas = []\n",
    "\n",
    "# loop over the units\n",
    "for i in range(len(spike_train_list)):\n",
    "    unit_ids.append(f\"unit_{i}\")  # we will name the units as unit_0, unit_1, etc.\n",
    "    unit_brain_areas.append(nwbfile.units.electrodes[i].location.item())\n",
    "\n",
    "# create the ArrayDict object for the units\n",
    "units = ArrayDict(id=np.array(unit_ids), brain_area=np.array(unit_brain_areas))\n",
    "\n",
    "print(f\"Number of units: {len(units)}. Brain areas: {np.unique(units.brain_area)}\")\n",
    "print(f\"units={units}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.2 Using **IrregularTimeSeries** for spikes\n",
    "\n",
    "Next, we will create the `spikes` object which will store the spiking neural data.\n",
    "\n",
    "- The `spike_train_list` we extracted from the NWB file is a list of numpy arrays, with each array containing the spike times for a single unit.\n",
    "In **temporaldata**, we combine all spikes into a large array. Each spike is defined by two values: the timestamp and the unit index.\n",
    "\n",
    "- Since the spiking data is irregular and sparse, we use the `IrregularTimeSeries` object to represent it. Instead of repeating the unit identifier (stored in `units.id`),\n",
    "we will use unit indices, where the unit index corresponds to a number between `0` and `num_units - 1` and maps to the unit id in `units.id`.\n",
    "\n",
    "- We will create the `spikes` object by looping over the spike trains and adding them to the `spike_timestamps` and `spike_unit_index` arrays. Then build our `IrregularTimeSeries` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from temporaldata import ArrayDict, IrregularTimeSeries\n",
    "\n",
    "# initialize the arrays\n",
    "spike_timestamps = np.array([], dtype=np.float64)\n",
    "spike_unit_index = np.array([], dtype=np.uint32)\n",
    "\n",
    "# loop over the spike trains and add them to the arrays\n",
    "for i in range(len(spike_train_list)):\n",
    "    spike_train = spike_train_list[i]\n",
    "    spike_timestamps = np.concatenate([spike_timestamps, spike_train])\n",
    "    spike_unit_index = np.concatenate([spike_unit_index, np.full_like(spike_train, fill_value=i, dtype=np.uint32)])\n",
    "\n",
    "# create the IrregularTimeSeries object\n",
    "spikes = IrregularTimeSeries(\n",
    "    timestamps=spike_timestamps,\n",
    "    unit_index=spike_unit_index,\n",
    "    domain=\"auto\",\n",
    ")\n",
    "# sort the spikes by timestamps\n",
    "spikes.sort()\n",
    "\n",
    "print(f\"Number of spikes: {len(spikes)}\")\n",
    "print(f\"spikes={spikes}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plot_spikes(spikes, width=1200)\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.3 Using RegularTimeSeries for cursor data\n",
    "\n",
    "But next, we will create a `RegularTimeSeries` object to store the cursor position, velocity, and acceleration.\n",
    "A `RegularTimeSeries` represents uniformly sampled time series data. Unlike the `IrregularTimeSeries`, we do not need\n",
    " to provide timestamps as they are inferred from the sampling rate and the first timestamp in the recording.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from temporaldata import RegularTimeSeries\n",
    "\n",
    "timestamps = nwbfile.processing[\"behavior\"][\"Position\"][\"cursor_pos\"].timestamps[:]\n",
    "cursor_pos = nwbfile.processing[\"behavior\"][\"Position\"][\"cursor_pos\"].data[:]\n",
    "cursor_vel = nwbfile.processing[\"behavior\"][\"Velocity\"][\"cursor_vel\"].data[:]\n",
    "cursor_acc = nwbfile.processing[\"behavior\"][\"Acceleration\"][\"cursor_acc\"].data[:]\n",
    "\n",
    "sampling_rate = 100 # Hz\n",
    "assert np.allclose(np.diff(timestamps), 1/sampling_rate)\n",
    "\n",
    "cursor = RegularTimeSeries(\n",
    "    pos=cursor_pos,\n",
    "    vel=cursor_vel,\n",
    "    acc=cursor_acc,\n",
    "    sampling_rate=sampling_rate,\n",
    "    domain_start=timestamps[0],\n",
    "    domain=\"auto\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the cursor position.\n",
    "\n",
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plot_time_series(cursor, field=\"pos\", index=0, width=1200)\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3.4 Using Interval for the trial table\n",
    "\n",
    "The last object we will learn about in this tutorial is the `Interval` object.\n",
    "\n",
    "An `Interval` is a set of time intervals each defined by a start time and an end time, and that can have additional fields describing the intervals.\n",
    "\n",
    "We can extract the reach intervals, which are the time intervals between the go cue time and the time the movement stops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from temporaldata import Interval\n",
    "\n",
    "trial_table = nwbfile.trials.to_dataframe().dropna()\n",
    "reach_intervals = Interval(\n",
    "    start=trial_table.go_cue_time.values,\n",
    "    end=trial_table.stop_time.values,\n",
    "    result=trial_table.result.values,\n",
    "    target_id=trial_table.target_id.values,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plot_intervals(reach_intervals, width=1200)\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Bringing it all together: Data\n",
    "\n",
    "All streams of data can be stored in a `Data` object, which is a container for multiple **temporaldata** objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from temporaldata import Data\n",
    "\n",
    "data = Data(\n",
    "    spikes=spikes,\n",
    "    units=units,\n",
    "    cursor=cursor.to_irregular(),\n",
    "    reach_intervals=reach_intervals,\n",
    "    domain=\"auto\",\n",
    "    # add metadata\n",
    "    brainset=Data(id=\"perich_miller_population_2018\"),\n",
    "    session=Data(id=\"t_20130819_center_out_reaching\"),\n",
    ")\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show(make_plot(data, add_play_controls=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Saving data to disk\n",
    "**temporaldata** allows you to save files in hdf5 format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import os\n",
    "\n",
    "recording_id = \"perich_miller_population_2018/t_20130819_center_out_reaching\"\n",
    "os.makedirs(\"perich_miller_population_2018/\", exist_ok=True)\n",
    "\n",
    "# Save to file to h5\n",
    "with h5py.File(f\"{recording_id}.h5\", \"w\") as f:\n",
    "    data.to_hdf5(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Slicing data\n",
    "\n",
    "***\n",
    "\n",
    "## Table of contents:\n",
    "* [2.1 Slicing an IrregularTimeSeries object](#aa)\n",
    "* [2.2 Slicing a Data object](#bb)\n",
    "\n",
    "***\n",
    "\n",
    "Training models often requires repeatedly accessing small segments of data randomly scattered across large datasets—extracting just a few seconds from hours of recordings. **temporaldata** has the following features:\n",
    "- easily slice through multi-modal data using only start and end times.\n",
    "- efficiently load just the data you need when you need it.\n",
    "\n",
    "Let's start with the first feature: slicing. Every temporal object in **temporaldata** has a `slice` method that can be called with two arguments: `start` and `end`. It returns an object of the same class but restricted to the data points within the `[start , end]` range.\n",
    "\n",
    "<center>\n",
    "<img src=\"https://ik.imagekit.io/7tkfmw7hc/tbrain_notebooks/slicing.png?updatedAt=1744132610041\" width=\"1000\"/>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 2.1 Slicing an IrregularTimeSeries object\n",
    "Let's take a look at what this looks like for `spikes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_sliced = spikes.slice(start=0., end=10.)\n",
    "\n",
    "print(f\"Before slicing:\\nspikes={spikes}\")\n",
    "print(f\"After slicing:\\nspikes_sliced={spikes_sliced}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returned object is another `IrregularTimeSeries` object, but only contains spike events between `start` and `end`.\n",
    "\n",
    "Let's plot the sliced object.\n",
    "\n",
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plot_spikes(spikes_sliced, width=1200)\n",
    "fig.title = f\"Spikes between {spikes_sliced.domain.start[0]}s and {spikes_sliced.domain.end[0]}s\"\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### 2.2 Slicing a Data object\n",
    "\n",
    "More generally, a recording is represented by a multi-modal `Data` object. When `slice` is called, all of its children are sliced accordingly, making the slicing and object manipulation seemless.\n",
    "\n",
    "<img src=\"https://ik.imagekit.io/7tkfmw7hc/tbrain_notebooks/full_data_obj.png?updatedAt=1744132750893\" width=\"1000\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sliced = data.slice(start=20., end=100.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show(make_plot(data_sliced))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Dataset and Samplers\n",
    "\n",
    "<img src=\"https://torch-brain.readthedocs.io/en/latest/_static/torch_brain_logo.png\" width=\"150\" height=\"150\" alt=\"torch_brain Logo\">\n",
    "\n",
    "[Documentation](https://torch-brain.readthedocs.io)\n",
    "\n",
    "**torch_brain** provides several tools on top of the **temporaldata** package to make it easier to work with temporal data in deep learning workflows. In this section, we will learn about the `Dataset` and `samplers` classes.\n",
    "\n",
    "***\n",
    "\n",
    "## Table of contents:\n",
    "* [3.1 Overview of **torch_brain.data.Dataset**](#cc)\n",
    "* [3.2 Overview of **torch_brain.data.samplers**](#aa)\n",
    "* [3.3 Using sampling intervals](#bb)\n",
    "* [3.4 Samplers in action](#cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### 3.1 torch_brain.data.Dataset\n",
    "\n",
    "`torch_brain` provides a Dataset interface that allows to access a single or multiple recordings.\n",
    "\n",
    "Data is never fully loaded into memory, but rather lazy-loaded on-the-fly from disk.\n",
    "The lazy loading is done both in:\n",
    "- time: only the requested time interval is loaded, without having to load the entire recording into memory, and\n",
    "- attributes: attributes are not loaded until they are requested, this is useful when only a small subset of the attributes are actually needed.\n",
    "\n",
    "The dataset can be indexed by a recording id and a start and end times using the get method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screenshot 2025-03-24 at 9.11.06 PM.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_brain.data import Dataset\n",
    "\n",
    "recording_id = \"perich_miller_population_2018/t_20130819_center_out_reaching\"\n",
    "dataset = Dataset(\"./\", recording_id=recording_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sliced = dataset.get(recording_id, start=20, end=100)\n",
    "show(make_plot(data_sliced))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Overview of torch_brain.data.samplers\n",
    "\n",
    "The advanced sampling capabilities in torch_brain enable flexible and customizable data loading by allowing users to define arbitrary sampling intervals and window lengths for their neural data. This design makes it easy to handle complex experimental protocols with non-contiguous recording periods, while providing a simple interface that automatically handles the complexities of sampling from multiple intervals or sessions.\n",
    "\n",
    "torch_brain provides a number of samplers that can be used to generate samples for training, or evaluation:\n",
    "\n",
    "| Sampler | Description|\n",
    "| - | - |\n",
    "| SequentialFixedWindowSampler | A Sequential sampler, that samples fixed-length windows. |\n",
    "| RandomFixedWindowSampler | A Random sampler, that samples fixed-length windows. |\n",
    "| TrialSampler | A sampler that randomly samples a full contiguous interval without slicing it into windows. |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Using sampling intervals\n",
    "\n",
    "Sampling intervals are the intervals from which a data sampler is allowed to sample data. The sampling intervals do not have to be contiguous, and can be of any length. They are defined as Interval objects with start and end times.\n",
    "\n",
    "torch_brain provides a number of samplers that can be used to generate samples for training, or evaluation.\n",
    "\n",
    "The most common sampler used in practice is the RandomFixedWindowSampler, which randomly samples windows of a fixed length from the data. We provide the sampling intervals in order to restrict where the sampler can sample from."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screenshot 2025-03-24 at 4.14.48 PM.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAADu4AAAOQCAYAAABsWGYIAAAKuGlDQ1BJQ0MgUHJvZmlsZQAASImVlwdUU9kWhs+96SGhJURASugd6QSQEkILoCAdRCUkAQIhhBQU7MrgCI4FFREsIzoqomCjig1RbIOigt0BGQSUcbBgQ+VdYBFm5q333no766z9rZ19/rPPWffctS8AZGOOWCyEVQHIFMkkEYG+9Lj4BDpuEMDIjwJsAMzhSsXM8PBQgNiU/7t96ALQuL9rM6717///V1Pj8aVcAKBwhJN5Um4mwqcBsjhXLJEBgEIYGC2Wicf5AcJUCVIgwkPjnDrB6HEdavIkUydyoiJYCJsDgCdxOJJUAEhOSJyew01FdEhRCNuJeAIRwvkIe2VmZvEQbkXYHMkRIzyuz0j+i07q3zSTFZocTqqCJ/cyYXg/gVQs5OT+n8fxvy1TKJ9awwwZpDRJUATidZBz+z0jK0TBouS5YVMs4E3kT3CaPCh6irlSVsIUS4WR7CnmcfxCFDrCuaFTnCIIUOQIZOyoKeZL/SOnWJIVoVg3RcJiTjFHMl2DPCNaEU/jsxX6eWlRsVOcI4iZq6gtIzJkOoeliEvkEYq98EWBvtPrBijOIVP6l70L2Iq5srSoIMU5cKbr54uY05rSOEVtPL6f/3ROtCJfLPNVrCUWhivy+cJARVyaE6mYK0Mezum54YozTOcEh08xiAQyIAc8IABZgA78EC8FYiAEHJAr4y+RjW+IlSXOlQhS02R0JnLr+HS2iGtrTXewc3AGYPwOTz4i72gTdxOiXZ+Ora0EwLNxbGzszHQs+DYAJ5IAINZOx8wXAKDaD8DVZq5ckjMZm7hrGEAEKoAKtIAeMALmyFvCAbgAD+AD/EEwCANRIB4sBFyQBjKBBCwGy8BqUACKwGawHZSBvWA/OAyOgZOgHjSDi+AKuAFug07wGHSDPvAKDIMPYBSCIBxEhiiQFqQPmUBWkAPEgLwgfygUioDioSQoFRJBcmgZtBYqgoqhMmgfVAmdgBqhi9A1qAN6CPVAg9Bb6AuMgkkwFdaFTeFZMANmwiFwFLwAToWz4Tw4H94Il8IV8FG4Dr4I34A74W74FTyCAiglFA1lgLJBMVAsVBgqAZWCkqBWoApRJagKVDWqCdWGuovqRg2hPqOxaAqajrZBe6CD0NFoLjobvQK9AV2GPoyuQ7ei76J70MPo7xgyRgdjhXHHsDFxmFTMYkwBpgRzEFOLuYzpxPRhPmCxWBrWDOuKDcLGY9OxS7EbsLuxNdgL2A5sL3YEh8Np4axwnrgwHAcnwxXgduKO4s7j7uD6cJ/wSnh9vAM+AJ+AF+HX4EvwR/Dn8Hfw/fhRgirBhOBOCCPwCLmETYQDhCbCLUIfYZSoRjQjehKjiOnE1cRSYjXxMvEJ8Z2SkpKhkpvSPCWB0iqlUqXjSleVepQ+k9RJliQWKZEkJ20kHSJdID0kvSOTyaZkH3ICWUbeSK4kXyI/I39SpijbKrOVecorlcuV65TvKL9WIaiYqDBVFqrkqZSonFK5pTKkSlA1VWWpclRXqJarNqreVx1Ro6jZq4WpZaptUDuidk1tQB2nbqrur85Tz1ffr35JvZeCohhRWBQuZS3lAOUypY+KpZpR2dR0ahH1GLWdOqyhruGkEaOxRKNc46xGNw1FM6WxaULaJtpJWhftywzdGcwZ/BnrZ1TPuDPjo+ZMTR9NvmahZo1mp+YXLbqWv1aG1hateq2n2mhtS+152ou192hf1h6aSZ3pMZM7s3DmyZmPdGAdS50InaU6+3Vu6ozo6ukG6op1d+pe0h3So+n56KXrbdM7pzeoT9H30hfob9M/r/+SrkFn0oX0UnorfdhAxyDIQG6wz6DdYNTQzDDacI1hjeFTI6IRwyjFaJtRi9Gwsb7xHONlxlXGj0wIJgyTNJMdJm0mH03NTGNN15nWmw6YaZqxzfLMqsyemJPNvc2zzSvM71lgLRgWGRa7LW5bwpbOlmmW5Za3rGArFyuB1W6rDmuMtZu1yLrC+r4NyYZpk2NTZdNjS7MNtV1jW2/7epbxrIRZW2a1zfpu52wntDtg99he3T7Yfo19k/1bB0sHrkO5wz1HsmOA40rHBsc3TlZOfKc9Tg+cKc5znNc5tzh/c3F1kbhUuwy6Grsmue5yvc+gMsIZGxhX3TBuvm4r3ZrdPru7uMvcT7r/6WHjkeFxxGNgttls/uwDs3s9DT05nvs8u73oXkleP3t1ext4c7wrvJ/7GPnwfA769DMtmOnMo8zXvna+Et9a348sd9Zy1gU/lF+gX6Ffu7+6f7R/mf+zAMOA1ICqgOFA58ClgReCMEEhQVuC7rN12Vx2JXs42DV4eXBrCCkkMqQs5HmoZagktGkOPCd4ztY5T+aazBXNrQ8DYeywrWFPw83Cs8PPzMPOC59XPu9FhH3Esoi2SErkosgjkR+ifKM2RT2ONo+WR7fEqMQkxlTGfIz1iy2O7Y6bFbc87ka8drwgviEBlxCTcDBhZL7//O3z+xKdEwsSuxaYLViy4NpC7YXChWcXqSziLDqVhEmKTTqS9JUTxqngjCSzk3clD3NZ3B3cVzwf3jbeIN+TX8zvT/FMKU4ZSPVM3Zo6mOadVpI2JGAJygRv0oPS96Z/zAjLOJQxJowV1mTiM5MyG0XqogxRa5Ze1pKsDrGVuEDcne2evT17WBIiOSiFpAukDTIq0izdlJvLf5D35HjllOd8Whyz+NQStSWiJTdzLXPX5/bnBeT9shS9lLu0ZZnBstXLepYzl+9bAa1IXtGy0mhl/sq+VYGrDq8mrs5Y/esauzXFa96vjV3blK+bvyq/94fAH6oKlAskBffXeazb+yP6R8GP7esd1+9c/72QV3i9yK6opOjrBu6G6z/Z/1T609jGlI3tm1w27dmM3Sza3LXFe8vhYrXivOLerXO21m2jbyvc9n77ou3XSpxK9u4g7pDv6C4NLW3Yabxz886vZWllneW+5TW7dHat3/VxN2/3nT0+e6r36u4t2vvlZ8HPD/YF7qurMK0o2Y/dn7P/xYGYA22/MH6pPKh9sOjgt0OiQ92HIw63VrpWVh7RObKpCq6SVw0eTTx6+5jfsYZqm+p9NbSaouPguPz4yxNJJ7pOhpxsOcU4VX3a5PSuWkptYR1Ul1s3XJ9W390Q39DRGNzY0uTRVHvG9syhZoPm8rMaZzedI57LPzd2Pu/8yAXxhaGLqRd7Wxa1PL4Ud+le67zW9sshl69eCbhyqY3Zdv6q59Xma+7XGq8zrtffcLlRd9P5Zu2vzr/Wtru0191yvdVw2+12U8fsjnN3vO9cvOt398o99r0bnXM7O7qiux7cT7zf/YD3YOCh8OGbRzmPRh+veoJ5UvhU9WnJM51nFb9Z/FbT7dJ9tsev5+bzyOePe7m9r36X/v61L/8F+UVJv35/5YDDQPNgwODtl/Nf9r0SvxodKvhD7Y9dr81fn/7T58+bw3HDfW8kb8bebnin9e7Qe6f3LSPhI88+ZH4Y/Vj4SevT4c+Mz21fYr/0jy7+ivta+s3iW9P3kO9PxjLHxsQcCWeiFUAhA05JAeDtIQDI8QBQkB6COH+yx54waPK7YILAf+LJPnzCXACoRtx4e8S6AMBxZJiuAkDFB4Dx1ijKB8COjoox1Q9P9O7jhkW+Yqrdo+6qzH5mXQn+aZN9/V/q/qcHCtW/+X8BBY4PppkenMkAAACKZVhJZk1NACoAAAAIAAQBGgAFAAAAAQAAAD4BGwAFAAAAAQAAAEYBKAADAAAAAQACAACHaQAEAAAAAQAAAE4AAAAAAAAAkAAAAAEAAACQAAAAAQADkoYABwAAABIAAAB4oAIABAAAAAEAAA7uoAMABAAAAAEAAAOQAAAAAEFTQ0lJAAAAU2NyZWVuc2hvdFlJ73IAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAHXaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA2LjAuMCI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjkxMjwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4zODIyPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+ChxVh+4AAAAcaURPVAAAAAIAAAAAAAAByAAAACgAAAHIAAAByAABXhuE/ommAABAAElEQVR4Aezdz24bSZ4n8Kjd7tvOMxQwvi0WsDHLWpHPMcDaLQx2BpCApi768w5764MlzwIisNJxBfIptoEetMQpzsI+7Nx84BNUYVyFcqNkaxzsTlaSIkVSTFKZGZ8EXMw/kZHx+0Saqm7UV/HV3Zct2AgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWEvgK8HdtfzcTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGAkILjrRSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAguOsdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCAgOBuAYi6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICC46x0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUICA4G4BiLogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgILjrHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQgIDgbgGIuiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrCpwe3e76i2Ftf/VV78qrC8dESBAgAABAgQIECBAgAABAgQI/CIguPuLhT0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwIMC02Hb/PHPd5/u3Xt79/O9c1U58auvfj13qL/+6j/OvTYvFDzv/NyOXCBAgAABAgQIECBAgAABAgQIVFBAcLeCk2bIBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLECWQA3+8yHcKscvi1WaTO9zQoIzwsGzwr/zjq3mZHqlQABAgQIECBAgAABAgQIECCwWEBwd7GRFgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDFBbJAbvwUyq34ZC45fIHgJaE0I0CAAAECBAgQIECAAAECBAoVENwtlFNnBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQCZwc3OT7Y4+r6+vQ6vVmjjXbDYnjtc5mBXOtVruOqK/3DvoD345eMK9Qf/bJ3x6CIObQfj1f/j1k47Bw2cL7OzszL5QwNnp761FXRb5vbboWa4TIECAAAECBAgQIECAQPkEBHfLNydGRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIESiuQhXFjCDdu/X5/YqzZ+YmTKxysGpDLuv7588/Zbu0+yxKarR2sggiUROCx33urDD8fbM6eJ2C8iqC2BAgQIECAAAECBAgQWF5AcHd5Ky0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEByAjGoG8O4MaC7big3OTwFEyBAoCICWZg3DjcL+cZzwr0VmUDDJECAAAECBAgQIECgVAKCu6WaDoMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJPJ5AP6cZRCOo+3Vx4MgECBMoiEAO8wrxlmQ3jIECAAAECBAgQIECgCgKCu1WYJWMkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIbEohh3dPT062HdBs7jXsVNZqT5xo734zbDPrfjvfX3RncDNbtYnT/oF9MP4UMRicECJRGYNb327zBTX/vzW33l+/Dh74L5323beK7Sph33kw5T4AAAQIECBAgQIAAgRAEd70FBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgMYFNhXXzYbUYRsuCt/nziVEvVW6RobqHQn1LDSbXaF4IMNdk6d0ia1z6oRrWSmCV75FVw7DLQK3y/GX6e6o28e9i9j0R/44X+Xfz+Pg4xEBvs9l8qvI8lwABAgQIECBAgAABAqUQENwtxTQYBAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2KxADOteX1+Hfr+/1uq6WXgtH8yNI8/Ob7YKvRMoRqDIsGIxI6pOL/6uV2euVhlpPtAb71s31CvEu4q+tgQIECBAgAABAgQI1E1AcLduM6oeAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQE1h3dd0Y0msfHYx6FNjLwdolQIBAAgKdN+ejKjtnnUdXK8T7aDo3EiBAgAABAgQIECBQUQHB3YpOnGETIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF5Ard3t+H09DT84+k/zmsy83wWzBXUncnjJAECBJIXWDfIG0O8JycnyTsCIECAAAECBAgQIECg3gKCu/WeX9URIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQCICMaz70+c/hX/pfzsK7A76g4WV54O62f7CmzQgQIAAAQJfBNYJ8QrweoUIECBAgAABAgQIEKizgOBunWdXbQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQa4EsrBuLvLm5Dp2z87AosBsDuo3mlz8733z506i1j+IIECBAYHsCMcjbOeus9EAB3pW4NCZAgAABAgQIECBAoCICgrsVmSjDJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECmUAW2L29+3kU1F02sNs+OhDWzRB9EiBAgMBGBAR4N8KqUwIECBAgQIAAAQIEKiQguFuhyTJUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEhbYNXAblxRV1g37XdG9QQIEHgqgRjgHdwMFq4Enx+fFXjzGvYJECBAgAABAgQIEKiqgOBuVWfOuAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGYF8YDcW/dBqhsK6ybwWCiVAgEAlBOLPrLh1zjpLj7fX64Vms7l0ew0JECBAgAABAgQIECBQJgHB3TLNhrEQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGcwHRgN17a392buXqhwG4Ozi4BAgQIlFLgoV88MT1gq+9OizgmQIAAAQIECBAgQKAqAoK7VZkp4yRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBpAQ+fPox3N79PK550B98Wa3w/F5oV2B3TGSHAAECBCoisGyAV3i3IhNqmAQIECBAgAABAgQITAgI7k5wOCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAk8rEFfZ/fDph/EgBHbHFHYIECBAoEYCy4Z3Y8kCvDWaeKUQIECAAAECBAgQSEBAcDeBSVYiAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRfIAZ2f/r8p4lVdueFmtpH7dA+PCh/UUZIgAABAgQWCMz7WTd9m/DutIhjAgQIECBAgAABAgTKKiC4W9aZMS4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSEbg4+ePX0K7Hyfq3d/dC3G13fzW2GmE9tFBiJ82AgQIECBQF4Flw7uxXgHeusy6OggQIECAAAECBAjUV0Bwt75zqzICBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqIDAh08/TqyyG8O6nbPze6Fdq+xWYDINkQABAgTWElg2wCu8uxazmwkQIECAAAECBAgQ2LCA4O6GgXVPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRmCdze3X5ZZfdPE6HdeYGli6tLq+zOQnSOAAECBGonMO9n4XShwrvTIo4JECBAgAABAgQIECiLgOBuWWbCOAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGYEY2v3w6YeJevd39+6tstvYaYT20YHQ7oSUAwIECBBIQWA6wNtqtUZlX19fj8sX3h1T2CFAgAABAgQIECBAoEQCgrslmgxDIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6C3z8/PHLSrsfx4UO+oPQOTufGdqNK+3aCBAgQIBAqgLT4d3oEAO8wrupvhHqJkCAAAECBAgQIFANAcHdasyTURIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBADQRmhXbjSrvTW1xpV2h3WsUxAQIECKQoILyb4qyrmQABAgQIECBAgEC1BQR3qz1/Rk+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRGYDu3OCiLFUoR2KzKhhkmAAAECWxOIq9NP/6ILK+9ujd+DCBAgQIAAAQIECBBYUUBwd0UwzQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwKoCQrurimlPgAABAgQmBYR3Jz0cESBAgAABAgQIECBQXgHB3fLOjZERIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQA0EhHZrMIlKIECAAIFSCAjvlmIaDIIAAQIECBAgQIAAgQUCgrsLgFwmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKPFRDafayc+wgQIECAwGyB6fBuq9UKOzs74fT0dHxDr9cLzWZzfGyHAAECBAgQIECAAAEC2xQQ3N2mtmcRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDIC06Hd6aBRBtHYaYSLq8vs0CcBAgQIECCwQGD6Z2oM78bt+vp69BmPu93uaN8/CBAgQIAAAQIECBAgsG0Bwd1ti3seAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRe4PbuNnz49MO4zumA0fjCl52379/lD+0TIECAAAECSwhM/2yNvwgjnss24d1MwicBAgQIECBAgAABAtsWENzdtrjnESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDtBb67/X6ixhfPnk8cZwfto3ZoHx5khz4JECBAgACBFQQ6b85D56wzvmM6vHt8fBxOTk7G1+0QIECAAAECBAgQIEBgGwKCu9tQ9gwCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSEbgw6cfw+3dz+N6p0NF2QWh3UzCJwECBAgQeLzA9M/Z6fBur9cLzWbz8Q9wJwECBAgQIECAAAECBFYUENxdEUxzAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwT+Dj54/hpy9/sm06TJSdF9rNJHwSIECAAIH1BfZ398KgP5jbkfDuXBoXCBAgQIAAAQIECBDYgIDg7gZQdUmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQp8d/v9ROEvnj2fOM4O3r5/l+36JECAAAECBAoQyId346q7ccvCvK1WK3S73QKeogsCBAgQIECAAAECBAgsFhDcXWykBQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWChgtd2FRBoQIECAAIGNCcSQbgzvztusujtPxnkCBAgQIECAAAECBIoWENwtWlR/BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJCcgNBuclOuYAIECBAoocCi8O5wOCzhqA2JAAECBAgQIECAAIG6CQju1m1G1UOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECWxf47vb78TPnhYYaO41wcXU5bmeHAAECBAgQKF6g8+Y8dM46Mzs+Pj4OJycnM685SYAAAQIECBAgQIAAgaIEBHeLktQPAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQpML3a7v7uXojh3ekthnZjeNdGgAABAgQIbFZg3s/i+NRerxeazeZmB6B3AgQIECBAgAABAgSSFhDcTXr6FU+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC6whMh3bnrfLXPmqH9uHBOo9yLwECBAgQILCkQPwFGjG8O2trtVqh2+3OuuQcAQIECBAgQIAAAQIEChEQ3C2EUScECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkKLAdHD3xbPnMxnevn8387yTBAgQIECAwGYE5v0yjfg0q+5uxlyvBAgQIECAAAECBAj8WUBw15tAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQeKfDd7ffjO+cFhKy2OyayQ4AAAQIEtiow7xdqWHV3q9PgYQQIECBAgAABAgSSExDcTW7KFUyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECRQgss9puY6cRLq4ui3icPggQIECAAIEVBQb9Qdjf3Zt51/HxcTg5OZl5zUkCBAgQIECAAAECBAisIyC4u46eewkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgWQGr7SY79QonQIAAgQoJxOBuDPDO2obD4azTzhEgQIAAAQIECBAgQGAtAcHdtfjcTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIpCiyz2m77qB3ahwcp8qiZAAECBAiURuChVXd7vV5oNpulGauBECBAgAABAgQIECBQDwHB3XrMoyoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYIsCVtvdIrZHESBAgACBNQU6b85D56xzr5dWqxW63e69804QIECAAAECBAgQIEBgHQHB3XX03EuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECyQlYbTe5KVcwAQIECNRAYH93L8TVd6c3q+5OizgmQIAAAQIECBAgQGBdAcHddQXdT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJJCSyz2u7b9++SMlEsAQIECBAou0AM7cbw7vRm1d1pEccECBAgQIAAAQIECKwrILi7rqD7CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCApgXxw98Wz5/dqbx+1Q/vw4N55JwgQIECAAIGnFbDq7tP6ezoBAgQIECBAgACBVAQEd1OZaXUSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwNoCHz9/DD99+RO3zpvz0Dnr3OvTarv3SJwgQIAAAQKlELDqbimmwSAIECBAgAABAgQI1F5AcLf2U6xAAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEChK4MOnH8Pt3c+j7mat2me13aKk9UOAAAECBDYj8OLZ85kdD4fDmeedJECAAAECBAgQIECAwKoCgrurimlPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAskKfHf7/bj2WcEfwd0xjx0CBAgQIFBKgc6b89A569wb2/HxcTg5Obl33gkCBAgQIECAAAECBAisKiC4u6qY9gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQpMDHzx/DT1/+xG1e6Oft+3dJ2iiaAAECBAhURWDQH4T93b2Zw7Xq7kwWJwkQIECAAAECBAgQWFFAcHdFMM0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIE2BfHA3Bn5i8Ce/WW03r2GfAAECBAiUV2DWz/E42l6vF5rNZnkHbmQECBAgQIAAAQIECFRCQHC3EtNkkAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDw1ALf3X4/HsKLZ8/H+9nOxdVlaOw0skOfBAgQIECAQEkF5q2622q1QrfbLemoDYsAAQIECBAgQIAAgaoICO5WZaaMkwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSeTOD27jZ8+PTD6PmdN+ehc9aZGEsM7Mbgro0AAQIECBCohsCsVXcFd6sxd0ZJgAABAgQIECBAoOwCgrtlnyHjI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEnF/j4+WP46cufuM0K+rSP2qF9ePDk4zQAAgQIECBAYDmBeavu9nq90Gw2l+tEKwIECBAgQIAAAQIECMwQENydgeIUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIC+SDuy+ePc9fGu2/ff/u3jknCBAgQIAAgXILzPplHFbdLfecGR0BAgQIECBAgACBKggI7lZhloyRAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJ5U4MOnH8Pt3c+h8+Y8dM46E2Ox2u4EhwMCBAgQIFAZgVmr7gruVmb6DJQAAQIECBAgQIBAaQUEd0s7NQZGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmUR+O72+9FQZq3MJ7hbllkyDgIECBAgsLrAi2fP793U6/VCs9m8d94JAgQIECBAgAABAgQILCMguLuMkjYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkKzA7d1t+PDph1H9s8I9b9+/S9ZG4QQIECBAoOoCs34ph1V3qz6rxk+AAAECBAgQIEDgaQUEd5/W39MJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoOQCHz9/DD99+TPoD0IM9+Q3q+3mNewTIECAAIHqCcz6+S64W715NGICBAgQIECAAAECZRIQ3C3TbBgLAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJROIAvudt6ch85ZZ2J8grsTHA4IECBAgEAlBV48e35v3L1eLzSbzXvnnSBAgAABAgQIECBAgMAiAcHdRUKuEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDSAh8+/Rhu734Os4K7b9+/S9pG8QQIECBAoA4C+7t7Ia68m98Ed/Ma9gkQIECAAAECBAgQWEVAcHcVLW0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIDmB726/H9U8K9QjuJvc66BgAgQIEKihQAztxp/z+a3VaoVut5s/ZZ8AAQIECBAgQIAAAQJLCQjuLsWkEQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAikKpAFd188ez5B0D5qh/bhwcQ5BwQIECBAgED1BAR3qzdnRkyAAAECBAgQIECgzAKCu2WeHWMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgScVuL27DR8+/RBmBXoEd590ajycAAECBAgUKhBX3I0/7/Nbr9cLzWYzf8o+AQIECBAgQIAAAQIEFgoI7i4k0oAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUhXIgrudN+ehc9aZYHj7/t3EsQMCBAgQIECgugKzfkmH4G5159PICRAgQIAAAQIECDylgODuU+p7NgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUWkBwt9TTY3AECBAgQKAwgVnB3VarFbrdbmHP0BEBAgQIECBAgAABAmkICO6mMc+qJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFHCGTB3f3dvRADPdnWPmqH9uFBduiTAAECBAgQqIHA9M97wd0aTKoSCBAgQIAAAQIECDyBgODuE6B7JAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUQ+Dj54/hpy9/Xjx7PjFgwd0JDgcECBAgQKAWAp0356Fz1pmoZTgcThw7IECAAAECBAgQIECAwCIBwd1FQq4TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLICMbj7h+t/CnEFvvz29v27/KF9AgQIECBAoAYCg/7g3s/8Xq8Xms1mDapTAgECBAgQIECAAAEC2xIQ3N2WtOcQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOUEYnD39enridX3GjuNcHF1WblaDJgAAQIECBBYLPDi2fOJRoK7ExwOCBAgQIAAAQIECBBYQkBwdwkkTQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgTQHB3TTnXdUECBAgkK7A/u5eiCvvZlur1Qrdbjc79EmAAAECBAgQIECAAIGFAoK7C4k0IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFUBT58+jH8r7M3Eyvuto/aoX14kCqJugkQIECAQK0FBHdrPb2KI0CAAAECBAgQILAVAcHdrTB7CAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUUSAGd//hN/9jYuU9wd0qzqQxEyBAgACB5QTiarsxvJvfhsNh/tA+AQIECBAgQIAAAQIEHhQQ3H2Qx0UCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSFlgVnD34uoyNHYaKbOonQABAgQI1FZgVnC31+uFZrNZ25oVRoAAAQIECBAgQIBAsQKCu8V66o0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEaiQwK7j79v27GlWoFAIECBAgQGBaIK64GwO82Sa4m0n4JECAAAECBAgQIEBgGQHB3WWUtCFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJAVicPe//PV/nqhdcHeCwwEBAgQIEKidwHRw9/j4OJycnNSuTgURIECAAAECBAgQILAZAcHdzbjqlQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqIDAd3G3sNMLF1WUNKlMCAQIECBAgME8grrYbw7vZ1mq1QrfbzQ59EiBAgAABAgQIECBA4EEBwd0HeVwkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZQFBHdTnn21EyBAgECqAoK7qc68ugkQIECAAAECBAgUIyC4W4yjXggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECghgK//+Pvw9//5u/HlbWP2qF9eDA+tkOAAAECBAjUU+DFs+cThQ2Hw4ljBwQIECBAgAABAgQIEJgnILg7T8Z5AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEhe4A9//EP4u9/83dhBcHdMYYcAAQIECNRaYH93L8SVd7NNcDeT8EmAAAECBAgQIECAwCIBwd1FQq4TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLICr1+/Dqenp+P6L64uQ2OnMT62Q4AAAQIECNRTYDq42+v1QrPZrGexqiJAgAABAgQIECBAoFABwd1COXVGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnUSmA7uvn3/rk7lqYUAAQIECBCYIxBX243h3WwT3M0kfBIgQIAAAQIECBAgsEhAcHeRkOsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkKyA4G6yU69wAgQIEEhcYDq4e3x8HE5OThJXUT4BAgQIECBAgAABAssICO4uo6QNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCQpILib5LQrmgABAgQIjARePHs+lmi1WqHb7Y6P7RAgQIAAAQIECBAgQGCegODuPBnnCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCB5AcHd5F8BAAQIECCQsIDgbsKTr3QCBAgQIECAAAECawgI7q6B51YCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqLdAPrjb2GmEi6vLehesOgIECBAgQGAssL+7Fwb9wejYirtjFjsECBAgQIAAAQIECCwQENxdAOQyAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKQrILib7tyrnAABAgQI5IO7UWM4HEIhQIAAAQIECBAgQIDAQgHB3YVEGhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAqgKCu6nOvLoJECBAgEAInTfnoXPWGVMI7o4p7BAgQIAAAQIECBAg8ICA4O4DOC4RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQNoCgrtpz7/qCRAgQCBtgUF/EOKqu9kmuJtJ+CRAgAABAgQIECBA4CEBwd2HdFwjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaQFBHeTnn7FEyBAgEDiAtPB3V6vF5rNZuIqyidAgAABAgQIECBAYJGA4O4iIdcJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIFmBfHC3fdQO7cODZC0UToAAAQIEUhR48ez5uGzB3TGFHQIECBAgQIAAAQIEHhAQ3H0AxyUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSFtAcDft+Vc9AQIECBAQ3PUOECBAgAABAgQIECCwqoDg7qpi2hMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAMgKCu8lMtUIJECBAgMBMgf3dvTDoD0bXjo+Pw8nJycx2ThIgQIAAAQIECBAgQCATENzNJHwSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEpgVevXoXr6+vR2fZRO7QPD6ZaOCRAgAABAgTqLCC4W+fZVRsBAgQIECBAgACBzQgI7m7GVa8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAMBwd0aTKISCBAgQIDAGgKdN+ehc9YZ9WDF3TUg3UqAAAECBAgQIEAgIQHB3YQmW6kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsJqA4O5qXloTIECAAIG6CQz6gxBX3Y1bq9UK3W63biWqhwABAgQIECBAgACBggUEdwsG1R0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1EcgH9y9uLoMjZ1GfYpTCQECBAgQILBQQHB3IZEGBAgQIECAAAECBAhMCQjuToE4JECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECmcDr16/D6enp6LB91A7tw4Pskk8CBAgQIEAgAQHB3QQmWYkECBAgQIAAAQIEChYQ3C0YVHcECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUB8Bwd36zKVKCBAgQIDAYwQ6b85D56wzurXVaoVut/uYbtxDgAABAgQIECBAgEBCAoK7CU22UgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgNYF8cLex0wgXV5erdaA1AQIECBAgUGkBwd1KT5/BEyBAgAABAgQIEHgSAcHdJ2H3UAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCogoDgbhVmyRgJECBAgMDmBAR3N2erZwIECBAgQIAAAQJ1FRDcrevMqosAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE1hYQ3F2bUAcECBAgQKDSAoK7lZ4+gydAgAABAgQIECDwJAKCu0/C7qEECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAWBfHA3jvft+3dVGLYxEiBAgAABAgUJ7O/uhUF/MO5tOByO9+0QIECAAAECBAgQIEBgloDg7iwV5wgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwBcBwV2vAQECBAgQSFtAcDft+Vc9AQIECBAgQIAAgccICO4+Rs09BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJCEwM3NTXj58uW41oury9DYaYyP7RAgQIAAAQL1FhDcrff8qo4AAQIECBAgQIDAJgQEdzehqk8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqIWA4G4tplERBAgQIEDg0QIvnj2fuHc4HE4cOyBAgAABAgQIECBAgMC0gODutIhjAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDwFwHBXa8CAQIECBBIW0BwN+35Vz0BAgQIECBAgACBxwgI7j5GzT0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkISA4G4S06xIAgQIECAwV0Bwdy6NCwQIECBAgAABAgQIzBEQ3J0D4zQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJgO7raP2qF9eACGAAECBAgQSERAcDeRiVYmAQIECBAgQIAAgQIFBHcLxNQVAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRLYDq429hphIury3oVqRoCBAgQIEBgpsCgPwj7u3sT14bD4cSxAwIECBAgQIAAAQIECEwLCO5OizgmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ/ERDc9SoQIECAAIF0BQR30517lRMgQIAAAQIECBBYR0Bwdx099xIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA7QW+/vrrcY1W3B1T2CFAgAABArUXmBXc7fV6odls1r52BRIgQIAAAQIECBAg8HgBwd3H27mTAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBIQENxNYJKVSIAAAQIEZgh03pyHzlln4org7gSHAwIECBAgQIAAAQIEZggI7s5AcYoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQC+eBuPPf2/bvskk8CBAgQIECgxgKCuzWeXKURIECAAAECBAgQ2KCA4O4GcXVNgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAtUXENyt/hyqgAABAgQIPEZAcPcxau4hQIAAAQIECBAgQEBw1ztAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQeEJgO7l5cXYbGTuOBO1wiQIAAAQIE6iAguFuHWVQDAQIECBAgQIAAge0LCO5u39wTCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBCAoK7FZosQyVAgAABAgUKCO4WiKkrAgQIECBAgAABAgkJCO4mNNlKJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHVBQR3VzdzBwECBAgQqIPA/u5eGPQHE6X0er3QbDYnzjkgQIAAAQIECBAgQIBAXkBwN69hnwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITAn87X//24nQzsXVZWjsNKZaOSRAgAABAgTqJiC4W7cZVQ8BAgQIECBAgACB7QgI7m7H2VMIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKIC08Hd9lE7tA8PKlqNYRMgQIAAAQLLCgjuLiulHQECBAgQIECAAAECeQHB3byGfQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkBO4vbsNL1++nFhxV3A3B2SXAAECBAjUWODFs+f3quv1eqHZbN477wQBAgQIECBAgAABAgQyAcHdTMInAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYEhDcnQJxSIAAAQIEEhIQ3E1ospVKgAABAgQIECBAoEABwd0CMXVFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUSmBXcbew0wsXVZb0KVQ0BAgQIECBwT2BWcPf4+DicnJzca+sEAQIECBAgQIAAAQIEMgHB3UzCJwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmBIQ3J0CcUiAAAECBBISENxNaLKVSoAAAQIECBAgQKBAAcHdAjF1RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1fXayCgAAQABJREFUEhDcrdd8qoYAAQIECCwrMOgPwv7u3r3mVty9R+IEAQIECBAgQIAAAQJTAoK7UyAOCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAJhCDu797/bvQOetkp0JjpxEuri7Hx3YIECBAgACB+gkI7tZvTlVEgAABAgQIECBAYFsCgrvbkvYcAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKicwMfPH8Pr09cTwd1YxNv37ypXiwETIECAAAECywsI7i5vpSUBAgQIECBAgAABApMCgruTHo4IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBYQHB3TGGHAAECBAgkJdB5c37vF3dEgOPj43BycpKUhWIJECBAgAABAgQIEFhNQHB3NS+tCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAhAcHdhCZbqQQIECBAICcguJvDsEuAAAECBAgQIECAwEoCgrsrcWlMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAikJCO6mNNtqJUCAAAECvwgI7v5iYY8AAQIECBAgQIAAgdUEBHdX89KaAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBISmBfcvbi6DI2dRkISSiVAgAABAmkJCO6mNd+qJUCAAAECBAgQIFCkgOBukZr6IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaCcTg7h+u/yns7+5N1CW4O8HhgAABAgQI1E4g/uwf9AejuuIv68j2j4+Pw8nJSe3qVRABAgQIECBAgAABAsUJCO4WZ6knAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKiZgOBuzSZUOQQIECBAYEmBfHC3fdQOnbPO6E7B3SUBNSNAgAABAgQIECCQsIDgbsKTr3QCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQeFhAcPdhH1cJECBAgEBdBeYFd1utVuh2u3UtW10ECBAgQIAAAQIECBQgILhbAKIuCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCeAh8+/Rhubq5DDO/kt7jyXvvwIH/KfoECg/6gwN6221Vjp7HdB3oaAQIECGxE4MWz5+N+L64ux/8uILg7ZrFDgAABAgQIECBAgMAcAcHdOTBOEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqgd3FwVgv735du4k/79//pe517ILD92ftfG5OYFvmt+s3fnf/Lf/unYfsYMixiL0XMhU6IQAgYIEBHcLgtQNAQIECBAgQIAAgQQFBHcTnHQlEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMByAusGd/PB2VVDrssEZ2MVq/a7XOVaEaimQBEB4nmVFxVyntd/Eec3Vf9Tf8/kvw/z85DVK/RdxNujj2kBwd1pEccECBAgQIAAAQIECCwrILi7rJR2BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJCcwDLB3SycG4Nt+XDZUwfdqjpZWRCvquPPxm3+MwmfBLYrkIV4G83GxIMbO9+E7FoIX01cC+Fu6tghgRAEd70FBAgQIECAAAECBAg8VkBw97Fy7iNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB2gvMCu5+9dVX4T/91V+FD//2b7Wvf16BMVybX/Uy325R8PaX4Fz+LvuPFciC44+9P95XVMg4H1x/7HiKGstjn+++NATi91AW7J0M9Obrj+Fegd68SEr78bt1f3dvVHJ8X9pHB+PjVqsVut1uShxqJUCAAAECBAgQIEBgRQHB3RXBNCdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBdAT+7x9/H/71X/9/+N3//F24u9t8gCsfas1CZbO0BzeD8ekigpvjzkqysyj8mx/mvABxvs1j91cZx2Ofsep92w62FhHGzde47fHnn21/swL5769ln/TQ99x0HzFgG7dB/9s/f/7le7DI78DlAr2jx/tHzQUEd2s+wcojQIAAAQIECBAgsGEBwd0NA+ueAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKojcHNzMxrs6enp6PP6+rqwwedDbVlYLQui5a8V8cDpIFsWdMv3nQ//xvPT9+Tb2idAYLMCj/kOyL5Hlh1Z9n2zfPvGsk1L0S77Dst/38Xvuez8OoNsH7VHt7cPD9bpxr0VEojvjRV3KzRhhkqAAAECBAgQIECgZAKCuyWbEMMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAge0KxLBu0UHdbNXGLCj3mFDedhXmP21W6C0fjJt153QoON9mVn/56/YJzBNY5+/RqiHXbAzZ3+HseJnPdca5TP/aPE4gfvdk313rBHqz7/c4iskg71dfzmx+ZfbHVe+uVQU6b85D56wzui0Gt+N3QRbkbbVaodvtrtql9gQIECBAgAABAgQIJCQguJvQZCuVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBH4RyAK766yqmwX0YigwC/hl5355kr1VBVYN92ZhvFWfs2z7h4LIy/ZRRLvHhk8f++zsnX7s/dP3+bsxLeL4qQWKCPPOD/I+dXWev46A4O46eu4lQIAAAQIECBAgQEBw1ztAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkkJrBPYzQJaMdAohJjUa6NYAgQSEciHebMVV1cpPa7OGrfJ1XhX6UHbMggI7pZhFoyBAAECBAgQIECAQHUFBHerO3dGToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJLCsSwblxZ9/T0dMk7/twsC+e2jw4EdVeS05gAAQL1EMiCvI8N8QrwVvM9ENyt5rwZNQECBAgQIECAAIGyCAjulmUmjIMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEChd4zOq6Mawbg7pxy4K7hQ9MhwQIECBQSYEY6BzcDEIM9C67xZ8ljeaXny2Hf/7Zsux92j2dgODu09l7MgECBAgQIECAAIE6CAju1mEW1UCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECEwKrBnazsK6g7gSjAwIECBB4QCCGO+O2ymq87aP26B4h3hFDaf+xv7s3DmdfXF2OfpHHi2fPR+NttVqh2+2WduwGRoAAAQIECBAgQIDA0wsI7j79HBgBAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBQo8Pr163B6erqwR2HdhUQaECBAgMCSAo8N8QrwLgm85WYPBXfjUIbD4ZZH5HEECBAgQIAAAQIECFRJQHC3SrNlrAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwV2CZVXaFdefyuUCAAAECBQgM+oMvK7V+u/QqvHEF3ru7EA6ODgp4ui6KEhDcLUpSPwQIECBAgAABAgTSFBDcTXPeVU2AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaCSxaZVdgt1bTrRgCBAhUQiCuwts56yw1VgHepZi21khwd2vUHkSAAAECBAgQIECglgKCu7WcVkURIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgDYG4yu7Lly/nFiuwO5fGBQIECBDYkoAA75agC3yM4G6BmLoiQIAAAQIECBAgkKCA4G6Ck65kAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVF0gBnZPT0/D9fX1vVJiWLfRbIT24cG9a04QIECAAIGnEogB3rgtswrvbw/b4ZvmNyH+TLNtX+DFs+fjh759/260nz83HA7H1+0QIECAAAECBAgQIEBgWkBwd1rEMQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUWuD169ej0O6sQbaP2gK7s2CcI0CAAIHSCMQA7+BmEAb9wcIxxQDvwZFfRLEQquAG+ZCu4G7BuLojQIAAAQIECBAgkICA4G4Ck6xEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1EVgXmg3rkh4cXVZlzLVQYAAAQIJCMQA77Kr7wrvbveFENzdrrenESBAgAABAgQIEKibgOBu3WZUPQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKipwKtXr8L19fVEdTGw2/6yEmH8tBEgQIAAgSoKCPCWb9YEd8s3J0ZEgAABAgQIECBAoEoCgrtVmi1jJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQIIC81bZbR+1Q/vwIEERJRMgQIBA3QRieHdwMwiD/uDB0n572A5W332QqJCLgruFMOqEAAECBAgsFLi5uRm1mf4lXfHkycnJwvs1IECAQFkFBHfLOjPGRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJhVmjXKrteDAIECBCoq8Cyq+9eXF1abX6DL4Hg7gZxdU2AAAECyQvEsO7p6WmYFdadxjk+Ph6dEuKdlnFMgEDZBQR3yz5DxkeAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFEBV69enXvP+QVVEr0ZVA2AQIEEhNYJsBr9d3NvRSCu5uz1TMBAgQIpCWQX1G33+/f+9/4y2rEAK/w7rJa2hEgUAYBwd0yzIIxECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBYYNbqO1bZHfPYIUCAAIFEBIR3n26is+Bu/PeP+EtD4padi/vD4TB+2AgQIECAAIEpgSyoG1fUjdsyq+pOdTE6bLVa9+4V3p0l5RwBAmUVENwt68wYFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBIUiP+R78uXLycqz4dmJi44IECAAAECNRcQ3t3+BA/6g7C/uzd6cP7fQQR3tz8XnkiAAAEC1RDIfvlWHO1jg7qzKo1B3bhlIeC4L7wbFWwECFRBQHC3CrNkjAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEhA4PXr1xP/QW4sOR+YSYBAiQQIECBA4J5ADJJ2zs5D/Jy3fdP8Jvzv/3Mx77LzKwgI7q6ApSkBAgQIJCuQhXVXCerGVXSzMG4eLvbR7/dnhn6z9vnwbq/XC81mM9+FfQIECJROQHC3dFNiQAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEhPQGg3vTlXMQECBAisJrBo9V3h3dU857WeF9yNq/Bm4enhcDjvducJECBAgEBtBdYJ6y4btJ31/w3E8G4+2BsDwN1ut7bOCiNAoB4Cgrv1mEdVECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoLICr169ureyjpV2KzudBk6AAAECGxRYFN6Nj764uhytWL/BYdS662WCu1b6q/UroDgCBAgkLRDDufktW+l2lZV14/3Z6rrLBnbzz4z70wHeGN7NxhKv+1kcFWwECJRZQHC3zLNjbAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKi5wKzQbiz57ft3Na9ceQQIECBA4HECi8K7Vt59nGt2l+BuJuGTAAECBFITmA7LPrb+GLI9OTl57O3j+x4aj1V3x0x2CBAoqYDgbkknxrAIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQd4F5oV0rBdZ95tVHgAABAusK5MOls/oS3p2lsty5vG1jpzFawTjeub+7F+K1uFnlb8TgHwQIECBQM4GHgrKx1BiW3dnZmVj5Nk9QVGA33+e8/98gthkOh/mm9gkQIFAqAcHdUk2HwRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCANgXn/QXD7qB3ahwdpIKiSAAECBAisIZAPmM7qRnh3lsric/kVjfP/XiK4u9hOCwIECBCovkD83+rZ1u/3QwzjZtvp6Wm4vr7ODsefm1799uuvvx4/K7/jF2nkNewTIFA2AcHdss2I8RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDmAkK7NZ9g5REgQIDA1gQWhXd/e9gOB0d+IcYqEyK4u4qWtgQIECBQZ4GHVruNdcfAbgz2NpvNjTLMG4fg7kbZdU6AwL+zdx9wVlSH28cPFuwtxoJRV0WxC5oFVmPBEntvu9gSpS0WirEXYq+oiAUQ0FhAVmPsxt7I30VdFaxJFHXVaNRo9LXFEn19hpzrmblnbtlb597f+Xz0Tj1z5jtzZ5fZeeYUKEBwt0BAVkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMhdgNBu7lYsiQACCCCAQC4C2cK7U6ZPNY39G3OpimV+FCC4y2mAAAIIIICAMbNmzTLNzc1einIFdu3G49pS6p5+7fb5RAABBLoiQHC3K2qsgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkLdA3MO2ChMpVERBAAEEEEAAga4JuGFTXw2z587xTWaaR8C1bB3ZalpHzOuxePABg4xC0ir08OeBYxICCCCAQM0J2J5uFZBVKUfvunGIti3ufIK7rgbDCCBQbQIEd6vtiNAeBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKBGBRoaGrx7RpjIy8JEBBBAAAEE8hJwA6fRFfs29TWTp02JTmbcI+A6Etz1ADEJAQQQQACBCgjEvQiss7OzAq1hkwgggEB2AYK72Y1YAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoEABX+84qlI97arHXQoCCCCAAAIIFC7ghk6jtQ0b0WqGj5zXe2x0HuM/CbiGBHd/cmEIAQQQQACBSgv4XgZGcLfSR4XtI4BAnADB3TgZpiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBRF4JJLLjHjxo1Lq8sNw6TNZAICCCCAAAIIdElg8AGDTMeTHd51eWGGlyU0keBuiIMRBBBAAAEEqkbA90KwtrY209TUVDVtpCEIIICAFSC4ayX4RAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKLoAod2ik1IhAggggAACWQUyhXdnz52Tdf16XsAN7rpBZ9eUkFA9nyHsOwIIIIBApQQI7lZKnu0igEBXBAjudkWNdRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBHISaGho8C7nBmG8CzARAQQQQAABBLosoB53FTT1lb5Nfc3kaVN8s5j2o4Ab0HV/X3GnE9zlVEEAAQQQQKD8ArNmzTLNzc2hDfMzOcTBCAIIVJEAwd0qOhg0BQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoJQF6262lo8m+IIAAAggkTcDtOTbadjeQGp1X7+NuQNd1cqcTEqr3s4T9RwABBBCohADB3Uqos00EEOiqAMHdrsqxHgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQKyA74Fau/DsuXPsIJ8IIIAAAgggUEIBN2zqboZed12N8LBrRnA3bMMYAggggAAClRZoaGgINYGXaYQ4GEEAgSoSILhbRQeDpiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACtSJAb7u1ciTZDw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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show\n",
    "from bokeh.models import ColumnDataSource, Button, Div\n",
    "from bokeh.models.callbacks import CustomJS\n",
    "from bokeh.layouts import layout\n",
    "\n",
    "def simulate_sampling_process(sampler):\n",
    "  assert len(sampler.sampling_intervals) == 1\n",
    "  sampling_intervals = list(sampler.sampling_intervals.values())[0]\n",
    "  # Create single figure\n",
    "  p = figure(\n",
    "      width=800,\n",
    "      height=300,\n",
    "      x_axis_label=\"Time (s)\",\n",
    "      y_range=(0.0, 1.0),\n",
    "      tools=[\"hover\", \"xwheel_zoom\", \"xpan\", \"reset\"],\n",
    "      active_scroll=\"xwheel_zoom\",\n",
    "  )\n",
    "  p.x_range.bounds = (sampling_intervals.start[0] - 10, sampling_intervals.end[-1] + 10)\n",
    "\n",
    "  # Remove horizontal grid lines while keeping vertical ones\n",
    "  p.xgrid.grid_line_color = \"#E5E5E5\"\n",
    "  p.ygrid.grid_line_color = None\n",
    "\n",
    "  # Add hover tooltip only for sampled windows\n",
    "  p.hover.tooltips = [(\"start\", \"@left{0.2f} s\"), (\"end\", \"@right{0.2f} s\")]\n",
    "\n",
    "  # Set axis limits with padding\n",
    "  p.x_range.start = sampling_intervals.start[0] - 10\n",
    "  p.x_range.end = sampling_intervals.end[-1] + 10\n",
    "  # Remove y-axis ticks\n",
    "  p.yaxis.visible = False\n",
    "\n",
    "  # Plot original intervals\n",
    "  intervals_data = {\n",
    "      \"left\": sampling_intervals.start,\n",
    "      \"right\": sampling_intervals.end,\n",
    "      \"top\": 0.4 * np.ones_like(sampling_intervals.start),\n",
    "      \"bottom\": 0.1 * np.ones_like(sampling_intervals.start),\n",
    "  }\n",
    "  intervals_source = ColumnDataSource(intervals_data)\n",
    "  intervals = p.quad(\n",
    "      top=\"top\",\n",
    "      bottom=\"bottom\",\n",
    "      left=\"left\",\n",
    "      right=\"right\",\n",
    "      color=\"gray\",\n",
    "      source=intervals_source,\n",
    "      alpha=0.3,\n",
    "      legend_label=\"Sampling Intervals\",\n",
    "  )\n",
    "\n",
    "  # Create data source for sampled windows\n",
    "  sampled_windows_data = {\"left\": [], \"right\": [], \"top\": [], \"bottom\": []}\n",
    "  sampled_source = ColumnDataSource(sampled_windows_data)\n",
    "  windows = p.quad(\n",
    "      top=\"top\",\n",
    "      bottom=\"bottom\",\n",
    "      left=\"left\",\n",
    "      right=\"right\",\n",
    "      source=sampled_source,\n",
    "      alpha=0.5,\n",
    "      color=\"red\",\n",
    "      line_color=\"black\",\n",
    "      line_width=0.3,\n",
    "      legend_label=\"Sampled Windows\",\n",
    "  )\n",
    "  p.legend.click_policy = \"hide\"\n",
    "\n",
    "  # Move legend outside of plot area and stack horizontally\n",
    "  p.legend.location = \"top_right\"\n",
    "  p.legend.click_policy = \"hide\"\n",
    "  p.legend.border_line_color = None\n",
    "  p.legend.background_fill_alpha = 0\n",
    "  p.legend.orientation = \"horizontal\"\n",
    "  # p.add_layout(p.legend[0], 'above')\n",
    "\n",
    "\n",
    "  def sample_windows(sampler):\n",
    "      sampled_windows = []\n",
    "      sampler_iter = iter(sampler)\n",
    "      for _ in range(len(sampler)):\n",
    "          try:\n",
    "              idx = next(sampler_iter)\n",
    "              sampled_windows.append({\"start\": idx.start, \"end\": idx.end})\n",
    "          except StopIteration:\n",
    "              break\n",
    "      return sampled_windows\n",
    "\n",
    "\n",
    "  num_samples = len(sampler)\n",
    "\n",
    "\n",
    "  # Sample windows upfront\n",
    "  sampled_windows_1 = sample_windows(sampler)\n",
    "  sampled_windows_2 = sample_windows(sampler)\n",
    "  sampled_windows_3 = sample_windows(sampler)\n",
    "\n",
    "  # Add controls\n",
    "  div = Div(\n",
    "      text=\"\"\"<p>Click the Generate samples button to start sampling windows of 1s.</p>\"\"\",\n",
    "      width=400,\n",
    "      # height=30,\n",
    "  )\n",
    "\n",
    "  # Create auto-play callback\n",
    "  auto_callback = CustomJS(\n",
    "      args={\n",
    "          \"source\": sampled_source,\n",
    "          \"windows_1\": sampled_windows_1,\n",
    "          \"windows_2\": sampled_windows_2,\n",
    "          \"windows_3\": sampled_windows_3,\n",
    "          \"current_windows\": sampled_windows_1.copy(),  # Start with first set\n",
    "          \"button\": None,  # Add button reference that will be updated later\n",
    "      },\n",
    "      code=\"\"\"\n",
    "      // Disable button while playing\n",
    "      button.disabled = true;\n",
    "\n",
    "      // Clear existing windows\n",
    "      source.data['left'] = [];\n",
    "      source.data['right'] = [];\n",
    "      source.data['top'] = [];\n",
    "      source.data['bottom'] = [];\n",
    "      source.change.emit();\n",
    "\n",
    "      // Initialize window set if not already set\n",
    "      if (!button.window_set) {\n",
    "          button.window_set = 1;\n",
    "      }\n",
    "\n",
    "      function playNextWindow() {\n",
    "          if (current_windows.length > 0) {\n",
    "              var window = current_windows.shift();  // Remove and get first window\n",
    "\n",
    "              var data = source.data;\n",
    "              data['left'].push(window.start);\n",
    "              data['right'].push(window.end);\n",
    "              data['top'].push(0.8);\n",
    "              data['bottom'].push(0.5);\n",
    "\n",
    "              source.change.emit();\n",
    "\n",
    "              setTimeout(playNextWindow, 10);\n",
    "          } else {\n",
    "              // All windows played - prepare for next seed\n",
    "              button.disabled = false;\n",
    "\n",
    "              if (button.window_set === 1) {\n",
    "                  current_windows.push.apply(current_windows, windows_2);\n",
    "                  button.window_set = 2;\n",
    "                  button.label = 'Generate samples for epoch 2';\n",
    "              } else if (button.window_set === 2) {\n",
    "                  current_windows.push.apply(current_windows, windows_3);\n",
    "                  button.window_set = 3;\n",
    "                  button.label = 'Generate samples for epoch 3';\n",
    "              } else {\n",
    "                  current_windows.push.apply(current_windows, windows_1);\n",
    "                  button.window_set = 1;\n",
    "                  button.label = 'Generate samples for epoch 1';\n",
    "              }\n",
    "          }\n",
    "      }\n",
    "\n",
    "      // Start playing windows\n",
    "      playNextWindow();\n",
    "  \"\"\",\n",
    "  )\n",
    "\n",
    "  auto_button = Button(\n",
    "      label=\"Generate samples for epoch 1\", button_type=\"primary\", width=100\n",
    "  )\n",
    "  auto_callback.args[\"button\"] = auto_button\n",
    "\n",
    "  # Enable HTML rendering for the label\n",
    "  auto_button.css_classes = [\"fa-button\"]\n",
    "  auto_button.js_on_click(auto_callback)\n",
    "\n",
    "  # Create layout and show\n",
    "  show(layout([[div], [auto_button], [p]]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Samplers in action\n",
    "\n",
    "We can visualize what the sampler is doing as we are iterating over it.\n",
    "\n",
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_brain.data.sampler import RandomFixedWindowSampler\n",
    "\n",
    "sampling_intervals = {recording_id: data.domain}\n",
    "\n",
    "sampler = RandomFixedWindowSampler(\n",
    "    sampling_intervals=sampling_intervals,\n",
    "    window_length=10.0,\n",
    ")\n",
    "\n",
    "simulate_sampling_process(sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_intervals = {recording_id: data.reach_intervals}\n",
    "\n",
    "sampler = RandomFixedWindowSampler(\n",
    "    sampling_intervals=sampling_intervals,\n",
    "    window_length=0.5,\n",
    ")\n",
    "\n",
    "simulate_sampling_process(sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spliting the data to train/val/test\n",
    "\n",
    "Typically, data is split into train, validation and test. In `torch_brain` this is also done through sampling_intervals.\n",
    "\n",
    "Below we are going to use `Interval.linspace` and `Interval.split`, please refer to the documentation of both methods.\n",
    "\n",
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the recording into 20 blocks\n",
    "grid = Interval.linspace(start=data.domain.start[0], end=data.domain.end[-1], steps=20)\n",
    "\n",
    "train_intervals, valid_intervals, test_intervals = grid.split([0.6, 0.2, 0.2], shuffle=False)\n",
    "\n",
    "p = plot_intervals(train_intervals, valid_intervals, test_intervals)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also choose to randomly shuffle the different blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_intervals, valid_intervals, test_intervals = grid.split([0.6, 0.2, 0.2], shuffle=True)\n",
    "\n",
    "p = plot_intervals(train_intervals, valid_intervals, test_intervals)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`train_intervals`, `valid_intervals` and `test_intervals` are all `Interval` objects that can be used as `sampling_intervals`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_intervals = {recording_id: train_intervals}\n",
    "sampler = RandomFixedWindowSampler(\n",
    "    sampling_intervals=sampling_intervals,\n",
    "    window_length=10.0,\n",
    ")\n",
    "\n",
    "simulate_sampling_process(sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, to restrict sampling to the training intervals and the intervals corresponding to the reaching movement, we can use interval operations.\n",
    "\n",
    "Documentation: https://temporaldata.readthedocs.io/en/latest/concepts/interval_operations.html\n",
    "\n",
    "In this case, we want the intersection of `train_intervals` and `data.reach_intervals`, which is simply: `train_intervals & data.reach_intervals`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://poyo-brain.github.io/assets/bk-tap.png\" width=\"50\" alt=\"This cell is interactive\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_reaching_intervals = train_intervals & data.reach_intervals\n",
    "\n",
    "sampling_intervals = {recording_id: train_reaching_intervals}\n",
    "\n",
    "sampler = RandomFixedWindowSampler(\n",
    "    sampling_intervals=sampling_intervals,\n",
    "    window_length=0.5,\n",
    ")\n",
    "\n",
    "simulate_sampling_process(sampler)"
   ]
  }
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