{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Large Scale Recordings\n",
    "Now that we are familiar with the structure of an NWB file as well as the groups encapsulated within it, we are ready to work with the data. In this chapter you will learn to search the NWB file for its available data and plot visualizations such as raster plots.\n",
    "\n",
    "Below, we'll import some now familiar packages and once again work with the dataset we obtained in Chapter 3. This should look very familiar to you by now!\n",
    "\n",
    "<mark>**Note**: The code below will only work if you downloaded this NWB dataset in Chapter 3.</mark>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NWB file found and read.\n",
      "<class 'pynwb.file.NWBFile'>\n"
     ]
    }
   ],
   "source": [
    "# import necessary packages\n",
    "from pynwb import NWBHDF5IO\n",
    "from nwbwidgets import nwb2widget\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# set the filename (slightly modified to ensure it looks in chapter 03 for the data)\n",
    "filename = '../Chapter_03/000006/sub-anm369962/sub-anm369962_ses-20170310.nwb'\n",
    "\n",
    "# assign file as an NWBHDF5IO object\n",
    "io = NWBHDF5IO(filename, 'r')\n",
    "\n",
    "# read the file\n",
    "nwb_file = io.read()\n",
    "\n",
    "print('NWB file found and read.')\n",
    "print(type(nwb_file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first group that we will look at is `units` because it contains information about spikes times in the data. This time, we will subset our dataframe to only contain neurons with `Fair` spike sorting quality. This means that the researchers are more confident that these are indeed isolated neurons.  \n",
    "\n",
    "> Need a refresher on how we subset a dataframe? Review the Pandas page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depth</th>\n",
       "      <th>quality</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>spike_times</th>\n",
       "      <th>electrodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>665.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>unidentified</td>\n",
       "      <td>[329.95417899999956, 330.01945899999953, 330.0...</td>\n",
       "      <td>x      y      z     imp  \\\n",
       "id         ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>715.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>unidentified</td>\n",
       "      <td>[331.09961899999956, 332.14505899999955, 333.3...</td>\n",
       "      <td>x      y      z     imp  \\\n",
       "id         ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>715.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>unidentified</td>\n",
       "      <td>[329.91129899999953, 329.92869899999954, 330.0...</td>\n",
       "      <td>x      y      z     imp  \\\n",
       "id         ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>765.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>unidentified</td>\n",
       "      <td>[330.26357899999954, 330.3849389999996, 330.60...</td>\n",
       "      <td>x      y      z     imp  \\\n",
       "id         ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>815.0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>unidentified</td>\n",
       "      <td>[329.8969389999996, 329.94389899999953, 329.95...</td>\n",
       "      <td>x      y      z     imp  \\\n",
       "id         ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    depth quality     cell_type  \\\n",
       "id                                \n",
       "2   665.0    Fair  unidentified   \n",
       "5   715.0    Fair  unidentified   \n",
       "6   715.0    Fair  unidentified   \n",
       "7   765.0    Fair  unidentified   \n",
       "10  815.0    Fair  unidentified   \n",
       "\n",
       "                                          spike_times  \\\n",
       "id                                                      \n",
       "2   [329.95417899999956, 330.01945899999953, 330.0...   \n",
       "5   [331.09961899999956, 332.14505899999955, 333.3...   \n",
       "6   [329.91129899999953, 329.92869899999954, 330.0...   \n",
       "7   [330.26357899999954, 330.3849389999996, 330.60...   \n",
       "10  [329.8969389999996, 329.94389899999953, 329.95...   \n",
       "\n",
       "                                           electrodes  \n",
       "id                                                     \n",
       "2           x      y      z     imp  \\\n",
       "id         ...  \n",
       "5           x      y      z     imp  \\\n",
       "id         ...  \n",
       "6           x      y      z     imp  \\\n",
       "id         ...  \n",
       "7           x      y      z     imp  \\\n",
       "id         ...  \n",
       "10          x      y      z     imp  \\\n",
       "id         ...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the units data frame\n",
    "units_df = nwb_file.units.to_dataframe()\n",
    " \n",
    "# Select only \"Fair\" units\n",
    "fair_units_df = units_df[units_df['quality']=='Fair']\n",
    "\n",
    "# Print the subsetted dataframe\n",
    "fair_units_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `spike_times` column contains the times at which the recorded neuron fired an action potential. Each neuron has a list of spike times for their `spike_times` column. Below, we'll return the first 10 spike times for a given neuron. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[331.099619 332.145059 333.362499 333.395059 334.149659 334.178019\n",
      " 334.183059 334.612499 340.767066 340.916546]\n"
     ]
    }
   ],
   "source": [
    "# Return the first 10 spike times for your neuron of choice\n",
    "unit_id = 5\n",
    "print(units_df['spike_times'][unit_id][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A **spike raster plot** can be created using the function `plt.eventplot`. A spike raster plot displays the spiking of neurons over time. In a raster plot, each row corresponds to a different neuron or a different trial, and the x-axis represents the time. Each small vertical line in the plot represents the spiking of a neuron. Spike raster plots are useful as they reveal firing rate correlations between groups of neurons. For more information on `plt.eventplot` please visit the <a href = 'https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.eventplot.html'> matplotlib documentation</a>. \n",
    "\n",
    "Below, we'll create a function called `plot_raster()` that creates a raster plot from the `spike_times` column in `units`. `plot_raster()` takes the following arguments:\n",
    "\n",
    "- `unit_df`: dataframe containing spike time data\n",
    "- `neuron_start`: index of first neuron of interest \n",
    "- `neuron_end`: index of last neuron of interest\n",
    "- `start_time`: start time for desired time interval (in seconds)\n",
    "- `end_time`: end time for desired time interval (in seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function for creating raster plots for Units group in NWB file \n",
    "def plot_raster(units_df,neuron_start,neuron_end,start_time,end_time): \n",
    "    '''\n",
    "    Takes a dataframe containing the units group of a nwb file (units_df) and creates \n",
    "    a raster plot with a given set of neurons (indexed by neuron_start, neuron_end) and a start and end time.\n",
    "    '''\n",
    "    \n",
    "    neural_data = units_df['spike_times'][neuron_start:neuron_end] # Select the data\n",
    "    num_neurons = neuron_end - neuron_start # Calculate # of neurons\n",
    "    my_colors = ['C{}'.format(i) for i in range(num_neurons)]  #Generate a list of colors (C0, C1...)\n",
    "    \n",
    "    plt.eventplot(neural_data,colors=my_colors)  # Plot our raster plot \n",
    "\n",
    "    plt.xlim([start_time,end_time]) # Set axis limits to only include points in our data\n",
    "    \n",
    "    # Label our figure \n",
    "    plt.title('Spike raster plot')\n",
    "    plt.ylabel('Neuron #')\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.yticks(np.arange(0,num_neurons))\n",
    "    plt.show()\n",
    "\n",
    "# Use our new function\n",
    "plot_raster(units_df, neuron_start = 2, neuron_end = 11, start_time = 330, end_time = 335)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above is only contains neural spikes from nine neurons in a five second time interval. While there are many spikes to consider in this one graph, each neuron has much more than five seconds worth of spike recordings! To summarize these spikes over time, we can compute a **firing rate**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning Firing Rates "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Raster plots are great for seeing individual neural spikes, but difficult to see the overall firing rates of the units. To get a better sense of the overall firing rates of our units, we can bin our spikes into time bins of 1 second using the function below, `plot_firing_rates()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_firing_rates(spike_times, start_time = None, end_time = None):\n",
    "    '''\n",
    "    plots the binned firing rate for the spike times in the list it is given in 1 second time bins.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    spike_times : list\n",
    "                  spike times, in seconds\n",
    "\n",
    "    start_time  : int\n",
    "                  start time for spike binning in seconds; sets lower-most bound for x axis\n",
    "                  \n",
    "    end_time    : int\n",
    "                  end time for spike binning in seconds ; sets upper-most bound for x axis\n",
    "    '''\n",
    "    \n",
    "    # Assign total number of bins \n",
    "    num_bins = int(np.ceil(spike_times[-1]))\n",
    "    binned_spikes = np.empty(num_bins)\n",
    "  \n",
    "    # Assign the frequency of spikes over time\n",
    "    for j in range(num_bins):\n",
    "        binned_spikes[j] = len(spike_times[(spike_times>j)&(spike_times<j+1)])\n",
    "          \n",
    "    plt.plot(binned_spikes)\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel('Firing Rate (Hz)')\n",
    "        \n",
    "    if (start_time != None) and (end_time != None):\n",
    "        plt.xlim(start_time, end_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the function we just created to plot our data. Below, we will store all of the spike times from the same unit as above as `single_unit` and plot the firing rates over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our data\n",
    "single_unit = units_df['spike_times'][unit_id]\n",
    "plot_firing_rates(single_unit,300,500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">Note: I'm considering making this a 'problem set' activity rather than an example here.</font>\n",
    "\n",
    "## Compare firing rates at different depths of cortex\n",
    "\n",
    "The units in our data were recorded from various cortical depths, therefore we can compare the firing units from differing cortical depths to test for differing firing rates. Let's first take a look at the distribution of depth from our units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of neuron depth \n",
    "plt.hist(units_df['depth'], bins=30)\n",
    "plt.title('Distribution of depth')\n",
    "plt.xlabel('Depth (\\u03bcm)') # The random letters in here are to create a mu symbol using unicode\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compare one unit recorded from 1165 microns to one unit recorded from 715 microns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c3lQQAOsJpQhi6UJvosUkCMLjVwrCJZRSEHvAUlAJgBVg2UbCK6AA1jf20BTqQqvqIEQMwhvk+1cX6pKGJYupkQoiy4gBUBaTiyhVB7FO3CeEvEApvbA5h6TgClqZxeTxsotUPg6FpYl8s2IQOWUxNQCV9lVyLGZTWMAQyqFVMQgRMFQEsbTRLItJzyqLqQFQ7b4XK1quIAKt2b/CwkKzgtR5TVlMDUCpIPXN0sMh22NQSr/RsKNSqB+tbrUhZnNKQSxt6E2spDbSXJXF5BZKBak7pfv/YnussNDR0lYbymJS4LAUyjU6zZUPZx4/+92rGpy6USpI/dVmHoiCy2ilxeTzmXJfSf2ljWa12sjbCuXEvlWKdV0otR7ElwkhRSumCSFv5x1bFRYiWlVJbaS5KotJAc1rtaHLMQgR/1KTk3pRymJ6BcBDhJA0gOcBTAAIAdgE4AIAPwfw9YYf4VJAI6Sw0Wa5xUFqpSCWNprZaiMQZfc98uQk2rh9LnTQ+pNPiyoISukDlNLLAHwGwGsAvADmAfw7gIsppX9IKZ2o+wiWOvJ54JvnAC/c6e52W91qQxGEAmDLYmpSmqtX2ZtIzQJ/OVz3Zsr2YqKUjgAYqXtPCs7QksD8CWD2qLvbbVkMQgSplcWkAFsdRCNjEDlTOQQ62G02DmCocftcyEhO8fOvD6oOotXQkuzW7dlOq9t9qywmBaC5rTbEpCTIEy4zscbtb6HDpfFEEUSrkU2wW7cH8pYXyimZrwBbFlOj6yAUQRhwKUBfSbtvhUZCrLrmOkG0UkEoi0mBw6hwpk2og+CqNdTFbtPzjdvfQkezFAQhZDNfl/pV/vh8QsiXXdm7gmkxLRaCKGi1oRTEkkaeN9HzBhs7WchrgIfPd4OcIJa0gnDnuq/EYvoXAH8CQAMASunLYGtMK7iBxWYxqSwmBRk6Dx77As1TEIbFpBREvaiEICKU0mdtz6kObG5BWEyNClI3fT0IqrKYFEzkeQsMb6AJS46qGISBJsYgJgkhG8BbfhNCPgDglCt7VwA0oSB0d7fbyiA18diKlRSWLESFc6MJQraYfCG2z6VMEC5NDCshiN8DcDuALYSQE2BLjv62K3tXALIiBuGygmh5qw1lMSnAjEE03GLKmr85QpiKWMoWUxMVBKWUvoMQEgXgoZTGCCHryr5LoTI0Iospnze31+wgsQhSe7wAiFIQSx06n9k3Mkid11kXYWExAZwglrKCaF4M4j4AoJQmKKXiE7/Xlb0rmBaTmzNtmRTctq7KQQSpCeG2glIQSxrC+vH6G0cQ4jdmIYiupU0QLk04Sy0YtAXAOQC6CSHvl/7VBda0T8ENGBaTiwO5fCG2qtUGoAhCwQwe+4KNs5jEhMijFISBRhMEgDMB/AaAHgDvkZ6PAbjJlb0rNKYOQh6UW5XmCjR21qjQHsjnuIJoYJDaUBDS2g+hLiC2hHNpXJqYlVow6AEADxBCLqWUPl3thgkhIQCPAwjy/dxLKf0Kj1/cDaAPrI3471BKl+4oojUgSG1pb9CKQjnuXCqCUJAVRDLRuH0A5kJBAFMQE/sbs792QBOD1C8QQn4PzG4yrCVK6cfLvC8D4O2U0jghxA/gSULIwwBuBvBNSundhJDbAHwCwD/XdviLAA21mEiLFYSymJY88nKaa4N+C3kHBbHULaYmBqnvALAcwNUAHgMwDGYzlQRlEP1m/fyPAng7zCD3DwG8r8pjXlxoRDdXQRD+cOtabQBs5qhabSxt6KLVRqBx3VzF713FIEw0sdXGRkrp/waQoJT+EMC1AM6rZOOEEC8h5EUA4wB2AHgDwCylVBz9KIBVRd77KULIbkLI7omJRbwuUSNjEP7IAlAQymJa0hBZTI0MUgsb1Z7FpGcaW3uxkNFEBSH2NEsIORdAN4C1lWycUqpTSi8AUx0XAzjL6WVF3ns7pXQbpXTb4OBgJbtrT2QbQRBCQTSZIPJ5disrCGUxLW0Yzfoa+FsQv3c7QQBApv5Fc9oSLin3SgjidkJIL4AvA3gQwOsA/rqanVBKZwE8CuASAD2EEBH7GAZwspptLTo0VEE02WKinCCEgvCoIPWSh2jW5w02zmIqluYKAJm5xuxzocOl5JSyBEEp/S6ldIZS+jildD2ldIhSelu59xFCBgkhPfx+GMA7AOwF8EsAH+AvuxHAA7Uf/iJAIwgi32SC2PsQ8M+Xm2RACLtVFpOCaNbnCwK5LPDS3cD33unuPpzSXJd6w75mKAgeQxiQHgcIITcRQvZWsO0VAH5JCHkZwHMAdlBKHwJwC4CbCSEHAfQD+F7th78IkG1kkLpJFtPII8DYK+YauMpiUhCwNOvLAIcfB44/4276dbE0V2DpEkSj6yAIIR8C8B0ACULICID/A5bR9BwqaNbH14240OH5Q2DxCAWgQRaTlMVE87y6uYGry4p8c3EucpBa9JpSWJowWm1wNTlzlD2fjQHhXnf2YcQgbIVywNIliCZUUn8ZwEWU0oOEkDcBeBrAhyil97uyZwUGzcU6iNHdwPheIMpFnz/Mt50DPIHi76sHlAITXFBqaXbrWcRZTC/dDfSuA1b/WquPpD2g57jFxH9/02+w22zCPYIQg6HHIUi9VJcdbUIWU5ZSehAAKKXPAzisyMFl5HUgxwdVNzzD574LPPJlc1AORPm2G2gzxU4DaR4IzHG1QBaxxbTza8Du77f6KNoHRqFckD0W7S/czC5ybNa3xFeVy+cAX7juzZRSEEOEkJulxx3yY0rpN+re+1KHbL+4MYinZtgFkZMsJre2XQwT+8z74nw8i7gXk5ZcfOfUSMitNmRk3SQIpzTXJR6DyGvcZhurazOlCOJfAHSWeKxQL4S95PG5E7RLzbCYQ2qGPfa1iCCI6MXU5q025kbZQBPqNp/T0oogqkFepLn6rc+7OXA7WUxLfVU5PWeNydSIUs36vlr31hVKI8ublwW73Kn4TE6z2wSvPG+GghiXEtqcFEQ7t9r4t/cBm94JvOvr7DGlzEZrdnV6O0MXa1I3WUEYq8otUYKQl2CtAw1MbVEoCzGghrrcs5gAkyACEXbbUAWx31QMIp6yWFptJKeAxLj5WJB4O59TsyFiEAUWk4udXZ1iEMDSXnZUWHt1QhFEKyEspmBX/TNtSgsJwt9gghAZTP2b2OMCBdHmFpOuWeNEIgjfzufUTOTz5lKgwu7w8YbQbs7sndJcgaW9qpyw9uqEIohWQsyiQt1mvUKtyMyzRnkAm/kCpsXUqAEtPsYymFaczx4XKIg2D1LrWfOcAJMs2vmcmgmjBYbPHLwHz2S3blpMRgzCZqmEuoADPwP+ai1wcKd7+2sHCGuvTpTdgi2TSWAOwB5K6Yt1H8FShhhwRM52PfUKQj0ADgqiQetSi/jD8vOBV+4pDFK3cy8mStmxa4ogaoZs/Yg6iIHNwOlXGpTmart23noLsO8h4NnbgbHXgI3b3dvnQoew9upEJQpiG4DPgLXlXgXgUwCuBPAvhJAv1n0ESxmaUBASQdQKJ4IQvm+jLCaRwSQUhGExSVlMNN84gmok8joAatpKgKkmlMVUGeTsIhGk7lkDBDobH6QGgPVvBa76c34sS+w7a2IMoh/AmyilX6CUfgGMMAYBvAXAR+s+gqWMrBSDANwjiPQcG5zFDKKRBBHuBbqG2WOnQjmgPQdUMehYFISLBDF9GNhVtudl++HV+4BTL7H74ncn10H0rAaCHfURxMGfA0eeNB87pbkKGL/BJZZ5JtYCrxOVEMRqALKm1gCsoZSmwJYVVagVRhYTz7OvZyAXKa4C3oD5A2nU7Gl8HzB4lmkfOAWpgfa0ZERr6pxTkNqF8/npF4Cf3bK41ivI54EHPgc8czt7LIjU4wX61gOrtgFrLwcCHfWd9y/+L/DoreZjPcsmJU79xjxeZnm242+wHujupLlWsoX/ALCLECLacr8HwF2EkCjY2hAKtaIRFpNILfX6JYJogMUjMpjOvd4kAs2uIARBtKOC4McsZzG5pSCOPwu8wYOmi2ngmjvOftNGfzFpnYZIH3ATP+dAtD4FkUub+wDK2ymeNq/HqQX5JllMlNI/B4s7zIIFpz9DKf0apTRBKS3b1VWhBITFJHom1TPwpGbZbc9qdusNmFkMjbCYRAbT4BaTCHL2Zn18//UOgvMnzfYhzYJhMTVAQciz38VUdCdiUpotHbigPqEGBTF7zMzyy6WBxKT5P10rXTXs9S89i0lvbprrCwDuAfAjAOOEkNV171mBzYL8EfPHXa+CCHQAUb48q0dSEI2YwYsMJpkgnNp9A/UNqLks8A8XAy/cUfs2aoE4ZrfTXOdPMvXQu67+bS00iN+EINJi6afVBqljp4G/uxA48DDffoalcgtlXK5quN3TrWtBPte0NNfPAfgKWNcnHQABW0f6/Lr3vtQhCMKNYHJqmgWMRTzDYjE1YPYkZotDZ0kEIRSElMVU7/4z82ztAJGZ1SwIUs2lmZ1GSOHMuBaI9tMDm4GZw4tr4DLWBbFZcU4KohqCmBtlv6E4bzyXSwPghaHRAW6pllAQS9VickFBVEIxnwdwJqV0qu69KViRFQTBZ9z1Kohwj0QQgcbGIEQGk1AsgEMMQmSQ1DEIikrYZg+k8v5yaVZ06EZrdrEN0W20HeMzxTBhVxAOa0UDzFKtxmISkwNBPKLlSWKSE0SutN++VC2mJqW5HgeLPSi4DS3J+iV53VAQM0C4rwhB8O0mJtl6wHMnat+PwMR+Zi8Rwv68AXNgcDOLScw0W0kQgvhki4lS5/eN7AD+8yPltxvsKNxPOyOfd1AQUpqrjECVCkLEGwS5iltBHOUCskvSYmpeFtMhAI8SQn4KKa1VrQfhArJxNptyI1aQnAaWnVPEYuLbHd/L1gM+/QrQvar2fYn9DW0xH3sD5sBAbBZTPecl2pE0e6Yt788+MAHc43UYlA49Cuz9CVNtgihliG0EBEEsEgUxd5xNeIjHQUHYhplgJ/sc9Ap98qREEHrOnPCI5/VsaTtlKVpMLhXKVUIQx/hfgP8puIVsgg0UblhBqZnyMQghzeUUwVqhZ6wtnGUFISwmjwtZTJkFqCDE/50uQFGPomcBj8OKXiIba7FZTCImNbjFnPEXi0EIcszGmS1aDrKC0DOFz5db+2AxrmxYDs2KQah1IRqITJx5+PUGk0UnVwtBOMQgxOxVHuhqRS5jbeEsKwhXLaYFEIMQn5esIPQsgGjh+0Q9ip41myXKKIhBLBLrQ2QwrbiA9T8CSscggOoJQktb101JSAqilBJZigThUgyi6KdKCPkWpfQPCCE/ActasoBS+pt1732pIxuzWky1yuBMjHVyjfRZFYS9DsIgCBcUhJ0gfAEzQ6fAYqoni0koiAVgMVkURJFzMgiiyPGKGfBiI4i540CoB+hcZv6+jBiE3WLiCqLSQLVsMckkLZ4vN1teihZTE2IQIvH8b+vei4IzMnEmt+sNUqe4rVFMQRgpm8JicktBhMzH3oBDoZwLWUxGDKLJA6k8Uy1mMTkhNV36/2K7iy0GkYmzjgC+MPsdy7ECpzoIoPJAtQhG2wlCtrKUxWRFo2MQlNI9hBAvgJsopSXSMhRqRjbBZlPGQF4rQfBZa9kYhEOwtVboGetFKROEm4VyhsW0ABRErhKCmCn8v54z7ZTFajFl+WRH2Gq5VHGLyVAQFS7mk+AZ9lqqiMWkmasnOsHrb34lfishOhE3upKaUqoDGCSEVB2cJoScQQj5JSFkLyHkNULI5/nzfYSQHYSQEX7bW+Oxtzd0jQ2ygc766yBEm42QVAchV1IbMQiXgtR5nR2rXUEIFCiIOgb3BRWklmMQDuckr+on//+FfwP+7gJGFGKgEgpisVgfmZiVILR0iTRXEYOoYNlRSiWLKWMSrDdgzWJShXImjOSA5qS5HgHwFCHkQQDGN1pBmmsOwBcopc8TQjoB7CGE7ABrEb6TUnorIeRLAL4E4JZaDr6tIWZPwY76K6nFgB+IMJIAnOsg3ApSC6Lx2RSEgJuFcsKGyDW5cbC9UE6+tf9fIBs3P2v5/3OjjDi05OItlMsmuMXEJw0WBWG3mKQsprLbjVsVnPgddK00FUS55TWXmsVU7HOvAZUUyp0E8BB/baccYikeAAAgAElEQVT0VxKU0lOU0uf5/RiAvWALDr0XwA/5y34I4H3VH/YigLg43AhSiwHfFzbXlnCqg3BLQYgLVlYQcsDazSymhRCkrjQGIbdctygQiWAWa6Gc3WLS0iVabfDhoxKLSW7KJyuIrmGzH1M5v32pEYRexNqrAU1JcyWErAVwIYBnACyjlJ7i2z5FCBmqd/ttCSGvLUHqGusgxEXjDzNZGehorIJwWiRevkDtWUz1VIgvhEpqWUH4wnx27HBO8qJNlhiGRDC5NFNYYjnYxUIQmTgb+B0VRJE6iOnDwB3vB675G6B/g/N2xfrqHp81BtE9DKMfU7GaFIGlZjGJcaQd0lwJIR0A7gPwB5TSeUJIRQdGCPkUWJtxrF69CJvHiplxUIpB1DrLEQO+mL2988+BFVsLicetLCYnBVEyBrGIWm2EuoB4yvl4ZIKwZEFJBCPSg9t5tT0niJRtP/9NlIpB+MNsErH7e+wzOfF8cYIQCqJrpVVBiE4AiUllMdnhosXU0DRXQogfjBzupJT+iD89RghZwdXDCgDjTu+llN4O4HYA2LZtW5HGN20MkZ1jqaSucaZtH7C3fZzdin5BbtdBiECrvVBOoKAOYhFZTMEu1lXUkSCKWEwWBcEJwuMCeS4UUGqmbAtlVCoGQQhfVY7XzWglgtUixbX7DFZrYcQgVpn/L6cglhpBFLP2akApgpgAAErpY7VsmDCp8D0Ae20B7QcB3AjgVn77gMPbFz8MBeFGkFqymGQQwi7OgjqIOtNcDUIqRhCi1YYLs+RWKgjiZZ+fGOBzKaBrBf+/wzkVs5gsMQjeoqSdV9uzI5dhhZrBDnOSoqVKD1QyQWRLTFhEplLXKmDqoKQgzjD/r1aUs6LUGt1VolSQ+sfiDiHkvhq2fRmA3wHwdkLIi/zvGjBiuIoQMgLgKv546cGIQUTrVxBakg1mTheJx+e+gjBiEHIltUOQ2uMFQFwqlGu2gsiyc/KHzAFeS5tJAOUsJotFlTRvF6PFZCRcdEpB6lTpgWrVm4A3f5K/tsTvMTHJVEmkz9pqo2ul+X9VKGdFk9Jc5WDB+mo3TCl90rYNGdur3d6ig3xR1bs0qFivwAkefwNjELKCkIPUnCBEG/C66iBa1YuJz0pFUJpSdhsqRRCz0vsdgtyaFIMghH03i8FiklO2jSC1lMXk1NX2Q3eyz3T3v5YniMgA265cSd3JlVx6toIV5er8DbYbiiUH1IBSCoIWua/gBix1EHW2+9ZSJQjC28A6iCIWk8djfb7W86K0hRYTrxQXCkKcs6EgHMjckubqEMPISTEIgH82Zc6LUndaozQScsq2RUHwgbtYYgoh7D3lLKYoJwg9Y34WoS7AHwWSIoupVKGcb2lZTC7GIEoRxFZCyDwhJAbgfH5/nhASI4TM173npY5snM20faH6YxAi/dIJ8sXhWh1EGYIg0oyxnsVachmp8KwFFpM3YCoIEYcoZzFF+gv/LysIXeph5fWVP699DwF/u7m6FdiaDXFsAQcFUWrgBhihlPo9JqfYZyqyo9JzLAnC42O2U3IKoPnKgtTFFnlabHAxBlGqF5ODLlRwDaIPkwgkA7XXQWgp8wKywzEG4ZLFZF8PwtinSwQhZqb+SOssJqEgRBxCtDIpRhAdy9ig5VgoxxWEtwoFMXuMBXOTU2Zx3UKDiBMFbTGI9Kz5eRWDP1KaILJJoCdqEk96lt0nhPW2SvAkyJIEEQBA2fXlgi+/4OFiDKKSSmqFRkCkBQJSL6YaZ8m5tLUmQYbXKQaRrG82pTukucr3LQqiDotJEESkHyWX+WwEhILwR9jnKwYxIwbhlMU0zQjC/n8jzTVdaDGV+87dXMOjUZBTtr0BvqpcmsVkwmVarZWzmIR96pMUhPj8wr1AnBNEyXbfdXYqaDc0KQah4DZyWeBHnwaOPcMLizhBCBVRcxZTmRiEkeYq0ltpfTPyckFqtxSEsC7CvWAzwCo+n9d+DDz217XtFzDtEV/I1kOpjMVkEEQJBWEQRAXZNaLmpFStQKuRkWIQhDBbTkuZ66SXQjmLKZdi34EgiNSseV8miHIWE1DbROW//xg4tqv697USxQoUa4AiiGbipbuAl+8G9v2EtyaQLANPBX50MZRSEBaLSa7urSMOYRTKFamktiuIWmduhoLgg0w1RPPa/cAzt9W2X7Evr58PYLLFxAnCfk6ik2vHUOGxyoVyepVBarHAUKlZdquRlboCANyWEwRRZsW4chaTxjP0/E4Kos8spCtrMaH66yuXBZ69Hdj/39W9r9VocrM+BTeQywJP8KL02WPmetQCcjpqtdCSJRSEPQbBM0rqsSzklssCcjzCksVURw66oSBqIIhsnPn2tXaBNYLUIVuQukgXVtHJNToIS+2Hrpmfv8bTNC0xiEoVxAK2mOQgNcAD+2mW1VWvxWRXEGmbghAJlo2wmIRqS89V975WQ1cE0X546S5GDOFeThBxc7ABrOmo1ULMspxgr4MQM7p6Bhwxq7UoCH6BEttPqpJZcjFkZYsJ1RGNGLTiY7XtW1hM9iC1P8LVnu2cRIprpM96zvLnnEuxAV/MgJ22U3AcIm60gC2mbJyTKZ8wWBREGYIoZTGJVelKxSAEyhXKAdVPVMR3124EkVcWU/th30+B/o3AWb/JM1Ni5sIpQH252iXTXL1SmmvavKjqspj4oCX/AMVFS2zJb95A7bP4eiwm8d5YrQSRtRbKCQXhCzmTXlpatElWBvIaEkJB+KpREG1iMclq2BdmAXs9Y353xVDKYhKfuT9sToDS8yZZyNsu12oDqH6iIj7zdiWIZrT7VnAJ2QQLYPauYdaHlrJeVF5/nUHqMjEIStkFaxBEPRZTxkw1FBAzOHvVrC9Y++BWr8UEALFTte1bzzIryB6D8IedbTNR+Bjqsgbm7QpCz1ZHEMZ2FjBBZGwE4Q8B8/xzr8di0iRSNhIiaBEFUUGQutrrq90tJpXm2kYQXmrPGvZYSzoEqRtVKJczZ6JuKQg5gwmQLCY7QYRhWcu5Goj0SUNBNNti4kHqXMr8vPxh5xYZRmV8J/tsxP+dFIRXzmIqQ3puFTc2EllbwoU/DMyfZPfrsZjkNvby79sSg+Ao1+4bWEIWk0pzbT+IOEGPtLZFQI5B1JvmWqYOQgxUBkHU0dFVl4q9jP0IX91BQdRqMWW4ty2suGYqiFzGDFIDpoXkCzvP/NO8uUCwy6owZAWRjbOqX6OSupIsJv7/hWwxZWKFFpMg97IEEWUDmtPgLbexd6q5kVNoy61JDdRgMbW7glAE0T7IpQoJwq4gaiEIXWOtlkvFIHTNHKTFmtV1KwgbIRULUguLphZkE4wcql1XQs+Zg0vsdG37NoLU/HMVnVr9oSIWkyCITuvALysI0cxPBHMrqoNwqQNvIyG6AgjIk5VydRABvn6E0/lpDjEIwFlBlLJTal3ZUFYQ7dSmw4hBKIupfaBxiyk6ZM623QhSGxdRmRhEgYKoNwZhm7GVikHkaiWIOO92W6VFkJXWOq6ZIESQWhRocYIwFEQJi8kpiynYLakQoSAqsZjaIAbhFKQWqMRiApwVUlkFIdVYlLSYamyGKT5zPVv7b7gVUAqiDSGqnT0eoIcvdmIJUvtqq4OQ16N2QsNiEDZCKpbFJHLia0EmxmamglDFYEopsOPP2FKVAMsQ2yUVxWWllNC6CMJUENMTp5Ajfvb9FSMIDycUJ4sp0mtaFdW02miHQjmnILVAJRYTUF5BOMUgfEHz/Y20mID2splUDKINIVc7C5spaItB1FJQJi6skkFqpxhEnXUQ9gtSzFbcVBCZmDkjB6xFY099m3U6BYAX7gSe+pb0Pql+Iu6OxdRx/FG8qq/G2HzauQurOFaxBoYgZBGgD/eZNpQlSF1hmuuCVhAxq8Ukfou+kGkhFUMpi6mogpAISCQwNNJiAtqLIFSrjTYDpdZFfQRBFFRS1xCDMNIvS1lMUgzCjUI5xxhEEQXhD7OZWy3qKD3H6wpsM0BxsYoZXjbOspXEPkSAun8jr6auoVBProMAEKAZ/F3uOuw6NFVEQcybhC8HsTUbMQNVttpY4BaTvB61gN8hRlAMpSwmI3Mswns8ScpBQPyeSxbK1WoxtbOCIM4LNVUJRRDNgDwTAiQF4UKQ2ijgKmcx8WMQQd9aU0+BkjGInH0RQXEx15LJlJ5j7aLtQWpxsQqlILKDEnz9YhEP6N/EbitJdT31MhDnfX0olSwm9p294duEX+QvxK5D09wasn1XmZjZyE+OLRgKwoEgKllRbqEXysnrUQv4uSqoiCCExeRQKW6f/IjPzedgYZVstSFNMGaOAlNvlD8uwPqZtxNBlFujuwoogmgGZC8VAFZdxAb0zpXma2pttVFOQYgCvJzUHkN026wVTs0B+Q9yOmk7B0FctdhM6TleeGZrtmYoCE4EGVtKq1AQAxv58xXYTHd+EHj8b9j9vA6Asv12DwMeP77jvwEAwTOHppyDy8JiAmxB6lIKogKLaaErCHnpXAGnLKNiMCwmh9+jffJj3MoKQlhMFTTry+eAh28BHvi98sdlP6Z2Ioh8zpX4A6AIojmwB5LXXwn8yXEg2m++ptZK6rIKwmuNQfhEdXAdA47DEo8aYT9ILV9MQVRJEJRKCqKIxSQrCMBUCsJ66hcEUUEtRGrafL/Yj9cP9K0H/vQEHsmci4DXg0OTCWSoQw+lzLzZ6dXSasNBQVSzYNBCj0EItSZn5InfeUUKghOEo8VUhYIoSRCSxZSati4NWwpawrRM07OlX7uQoGuuVFEDiiCaA81hELf/oGsOUlcQg9A1q83ld19BHJ9n5FZAEPIKY9Ugm2DWRSmLSRCDXUEYFhMniHIWUy7Ltp2atu6HD0g54sdsUsNbNg8AAKZT+eJBaqBQQRCPNSFBLpQTbVCKHlsZgmh1fr7R6lsOUlcTgxAKwsFisk9+/E4KQhBEhVlM2YQ1O6kUskmgczm733YKQhFE+6BcrQJQe7tvo6FZkWwR4XPL60j7I3USRLYgBnFwig2IWUpA5UGr1hiEuCAtBCEsJj6byybYAGlUTXMrSTzuWcMulHIKQgxOotbBlkc+nWTndvnGAXQGfZhKw6FZnz1ILWVc+SO2Qi+pUE7enxNKpbn+9I+YNdZKkhCDbcDWagOo32IS5Co+JycFER20/s8JhsWkcYKocH1vLcUsLG+wzQhCUxZTW8GYvRexgYD6YxDFFgyK9LEft7goDAVRTx1EoYLYP8EGRJ16EM9I52HEIKokJAtBFLOYYuxYKCdWQRCZOBtYAlGWBZUqYw+IwVe8TgzKfGCZTrD9DnaGMNQVRFL3lFEQfqvFJD5zAVlByOdlB6WlYxCnXwEO7gAOP1b6/BoJIwbhoCDKdXIFJIvJKUidYr8f0RTSKQZx4W8DN/xX6bWvjd9Pju2n0t++lmDfW6i7vQhCz6kgdVuhIgVRayW11ETOCZ3LAVDWYhyQYhD11EFkC3ox7Z9g29PhwVRcGvBcVRAOFlNGmg3KCiLAaxIqubjF4JS0WUyCIPj59EUDiAZ9yOS9VoLIZRipFLOY5PUMpO0WEJ8d4nmPz3kdcVFX8eitrVMRhoKoMQbhDTCf37EOwtZjzIhFSM+FuoHNV5feh7BbhMWkZyuzc7NJpnDajSDymrKY2gqVKIiag9RlFETnCnY7e9R8Xd0WU7pA0o+MJ5ClPkYQCWnAqzUGUdJikoLUcluNuEwQUfP95S5uYTFpCT7YWy2mSX4+Ax0BhP1epKnXOqgLkgryWaw3gGw2g1/uHy+jIMpYTAX9s2yfYSbG0kSPPQ0cfrz0OTYKBkFIFmc1BEEI+66KWUyydepUB1EJ5AmGICK7YqEU+OVfAmOvSftPsc+33QiiHdJcCSHfJ4SME0JelZ7rI4TsIISM8NsKfkGLAJUqiFrafWu2DCk7Opax25kj7NYbNFf8qgV5nRGZdJFqeh6HJuPQPX7k4cFUXFILdSuIHtbeQl59TfxPz5hxg1C31WISQdNqFATAbKYCBcGOXSiIdN5OEFKjPgDw+pHPZfD9Jw/zQS5kJXC5UA4oryCKtUfJzAPnfYDZO68/UPocGwWnGMSyc4AzrwXOuKSybfjDzhaTIFcBn4OCqARisMzMw1ii1L6/5DTw2K3AS3ebz7WrxdQmaa4/APAu23NfArCTUroJwE7+ePHDSHMt0XagnkI54i0+YxAKYuYYG5A8nvoUhBzs5jg6lYSms7oBHR7Ds2evcyEGAVhtG/lijY+z2/6N7H5etzaPq5ogZgoJIpEFIUBPJIBIwIuUPQZRQBAB+KFhdIavROezdyO1EUQxa7FU/yxKmYKIDgKrLwWOPFn6HBsFJ4sp1A18+D+AzmWVbaPYqnKCXAVqVRAeLwBijUXZCUIobGHFAu1rMbVDmiul9HEA9oTj9wL4Ib//QwDva9T+FxSMfknlYhA1KohSxBMdZAHbzJy5f3uQ+vhzlXvYRgCXXaS/OjiJe/eMsqf8wUKLqYSCeO3kHNJakcwtcUE6VSfLF6tQDf0bWbA6MVmngph2tJj6IgF4PQSRgNdBQUidXAFQrx9eUJyaSYCKQU4mCLkXE1DcYtJt7VHkTCYtyarHg53A2suByf0mWbqJxBQwsb/4/8v1AqsExSwmQa4CTjGISuENWGsZ7Gm1ghhkgnDDYqIUOLaruTGiNs5iWkYpPQUA/Hao2AsJIZ8ihOwmhOyemJho2gE2BOVsIIDHIGoIUtsDeQXb9RWmAvoj5qA49hrwvXcAB/6nwv2ZCuL4dBI3fPcZ3PbYG4gEvPAFggCxBamLxCDmUhre+w9P4f4XTjjvJz3LjtNICbUpCBGEMwiCt9WIneLrSLioIOJZ9EXZ/UjAx7KY8pp50dsIQuMr+VI9i1wmyc+Dfw7Ea87uynUZzdktJrk3kKRa1l7O7jdCRdxzI3DnB4r/P5tgg6injqGkmMVUVEHUQhD+MgrCiSBsFlMtg/yxp4HvXw28+B/Vv7dWtEMMol5QSm+nlG6jlG4bHBxs9eHUB3nB+2IQXVerhVZiuVEBUexjFDD18GU00+bSkKdfrmx/EkGMzTPiu/X95+HxL74NHl8QPq8X0wk5BhGyvo9jNplFLk8xGSsSmxBV1AJ2ghBtSkSNQ/8GdhsfY4FrmSByqdIxEFlNFbGYTILwIpHjl41QfMZ61Ox403lWfRtADrlMggepHQa3cjGIUh14DVLqAlZsZefrNkEceRI48gT7jRQbHOWEgFpR1GJKFm/zXS08PquCKEYQyUn2v1yWfb/CYqp1TYg5PgF6/G9qizHWgjYulBsjhKwAAH7bAE28AFGuVgEwV36rFuUUBAB0CIKwLdWYnjWDvBP7Ktyf2dNpkiuFc1d1Y6AjCHgD8Hh9NotJEIRVQcTS7GKJZ4tcNAUEwWsLRAuOLkEQXEEMSArCbjEB5ozbCXLhVNLJYsqw8wNYmivl7RfEwG6LQaTy7OL0IweaTVnXM5ALDMtaTDYFIVtMBinxJU5XXwIcfar4OdaCR29lt/lc8VYTwqevB4Fo8XbfrimIgFVJFiMIAJg9LqWPR6XfUA02U4K7HzOHgZf/s/r314I2VhAPAriR378RQItSL5qMXIr5zqVkeM3tvlPlL5gCBcEHnOS0SRDjlRIEJztvwAhG93fwQc8fBvEFK6qDEMV0iUylBMEVhJZi9k73Kr4hThB969nt/ElbkLrH3F4xZBMsTuPx2RQEO3a7gshByqsHrHYPgGSefc9+5MyiwpIKotIgtTSo2QPjay9nJJ+YKn6e1eDE80w9rLqIPS62XdnOqxX+cPFeTHJ8TSiVRllMIuNv9pi1vkj8DssVXDohOclsxeXnA7/6u+rfXy0oZb+NUq1HqkAj01zvAvA0gDMJIaOEkE8AuBXAVYSQEQBX8ceLH3Yv1QkeHwuyVutzipXqSkFkMhkKgg84qRmzOGxqpDIJbPQpChlWkhg88a5b8csVn7RmMYk+/rYYRDwtCKJEkLqAIDRzoO/iBBEbYwN5IAoMnc0831zaHDgrmf1lk2yQC/dyghAKIoCcnsdsUrMQRNYgCP66TIx9f3zgSnILyk9y8Ohpq4KQL9yyaa4l6iBscQ/0rmW3tS6QZMeJPex22yfYbXLS+XXZeOkkiUpQLKvOnua69cPAB39g7ftUKTw+62/AnhE2e8yM5cweNQkrEDWvl1oa9iUmgUg/sOFtwPThxgerDz8OTB4ANr7Dlc25Y1Q5gFL64SL/2t6ofS5Y2LMxnCACl/kqy+SdWm/bIdIN7S0QUjNS/6Esk8HCqim1PwDwBTAZz6Iz6EPQxy2XMy5GerAT0/uPgFIKYrRICNWmIORjEVlM4iLvHma3iXFzAF17ObD7++y+XCgHlL64hY8e7LQpCL/Rh2mgwwxSa3YFIa8mB7AgNoD+EODL88WVvH6mUiwKolyhnN1iKqEg6rFBnDC+lxX+LT+XPU4USRTRkvUriEC0+HoQ8uSnYxA457ra9uENwKiBAAptRS0BrNoG7H3IpiAi1glVtUhMAtEBtg09w4iwXkuuGChltmDnSuDC33Flkws2SL2oUMks3yMRRCU4+jQbDEUzuFIopSDkH/343vL7lWIQ04ks+jqsUrY/GkBWzyNm6ccUcohBsEExUXEMImAlCKEgaN6cUa693Pz8AvYYRImBU0uaA0Fq2jxHyUbri4oYhBeaEYOQFIRIxwUQz7H/nz3ghw86++4JsWZlAQZBvDbKZ+czR4An/n9zlqmXqIOQg9SVnudT37ZWCpfCxD5gaIuZAZfgwdudX7PO9rOJmoPUOT2PbzyyH2kEnS0mm4I4NpXEdx57w9oMslLYJ10y2YoaiN61bL14u8UkW7LVIikIQkzKathGpTjyBHDsV8AVN5d3LCqEIohmoBqCqDRQ/ex3gIf+kMnWskFqm4KwEMQ0MHAme1xJoNo2eBr2Ekc/H0inLamuhQpCEEjcyWKS14IQsFtMHcvMXv1isZo1l5mvLwhSl4lBCCvBZjHNJNj93ggbYMJ+WUEUIQiNKYmz+Mecl4OrDjGIHS8fZ4+fv4MNwCIzy1AQFVhM5WItWgrY8WfATz5fmc0xsQ8Y3MLsEYARxBu/YAQmB8PrIIjXTs7j735xEAfnCYsraVKWkK4xspeumwdfOoG/fHgfZpM1JHPIWT2BDishiQB1z2r2N3vMWgAoK+5qkZgEIgP1qZBKcXAni2W6pB4ARRDNQSU2kMiJr1RBGI3pYhWkudoURKCDB2R5kLp7mF0YlRCELmcxZQxCEBCKoiCTqWgMwuF85bUgBOwKItxjqgRBBtEBFocATNKomCA62CzP0mrDj3mudLrC7PuJBr0OFpPU6htAPMcIYnWUvTeu8+/WH7Y2OeSz2mSKfzbi8xffrfRZwxsstJh8YXNmXO48xcA0+hzwxs7inwXAll5NTjGC8AUZ+SUnzYFUDljXQRCzKfb5zOaj1mMEpDVUzOtmnv9mxPuqgtEyPMy+K9liMgjiDJMgjPY4EXb+xFvb4J6ULCagsQQxe4ydg0vqAVAE0RzYvNR8nuLgeMz6GrHAeMUEccqcQZf7QYhqanGxEcIHQ24xhXuBwbMqy2SS6iCmE1n0FygIThBxWy2ErCDiE6B8/eekIIjEJAs4A4VtNgCJIGbN/wlikD1wEWgUg5Y/zMg3Pcf+Zo8XnlM2wXzhcC9PczXrIOb5YNTNCSIS8EKDQ5qrRBDzWXZZLQ+wGfE0f8wUhEwQ7LNKptPMNrEThNzWJBAptJjkRYiEgilHEADwi78ARnaw9ZmdMMGtxqEt7DbSz74fuVZAoB6C4PGdSSeCsK/CCBjfhXhfVRAJAYEo+7NYTMeYAgt1M4JITpoxF3+EXy891Q/uOT6hiQ6aKqQWm6pSzB4z17t3CYogmgGbl/rI62N45zcfx8lZaVbtrUJBUMoG060fZj9sUedQDF4fa0XRKb1O2CnJaU4Qm4Gpg+XtBz5oUWEx2WIQy7vZeb56Qhqo7DGI+z+N9x7+GgAzWI0Hf59V7QJFCMJvtZiCXSYxyAPUpncCIGadhNzye8efAT+4pvCcZItJkxaU8fqNWWtXSBCEzWLKJljsIDpgbG6eW0wDXjagT2Q4oXStNNUcgDwxK6njiTgwfYg9jjsQhN/WjiItLXEKsNiGP1KcIMTAdO4HgJMvsOrou4rkkYjWGoNnsdvoIBswDQXBCSKv8zqcWgmCDfjjGieBsgpCs7yvKgiLKRApLMybPAD0rmH3RTbYqZfM1wPm9VINklxpRfqbqCDcJYiGZTEpSNDSQKc5Ezoxm0KeAidnU1jZw5+vJkidnmMX5tBZwFVftfjfRfGJR6xWVLiXWQXpOTa7CUSZpZFNlE4j5INWLOdDLk8LFMRQZwhXnb0MP/jVEXzyLevZwGrvHjuxH32ZPAAgkdVZxtPsMTZA5vPFFUQuw/4n6grEccoz6U1XATfvBbrMgdggiKkRdhElp62L2Yh2EcLrj48zdebxGrPWjhD7fqIyQeQ14Lnvsm1f9FFjc/N8/OrRmCIazXbizQDwW/9uKkUwK6oLrF5i5thedFL2mRRYTN5gYTsKu4IA2GShWLaWGJgu+zxw2e8DT36TxRScIDKYxIQiOsA/Nz7gidm1JqWC1gAx0J/KCoKQZteOCkJYTLUoCE7GgQ4eg+CfZS7LbDfh2w9fzG4P7rTuP9xXfYBZKK1mWExaimX0KQXRhrApiDkhreVAbjVBarHGcudy9uPzVVAUE+61WlGRPp69Qdn/Kv0B80Frio9d/R2F+/789k2YT+fwg6eOsCfkGEQuC8ROolufAkCh5ykyuTwbdLQEMHe8hMWkWYPXAQeLCbCSg9hOagaYOMAe25vPaZKCABhBcEtiPq2hM+iD18NUQTjgRZby7yo1yzKDNmwHzrjY2Nxshl1Wvhhrs/BGWgTMuyyD6XyWbdOPHNInja74knXFjYAAACAASURBVMUktfwoZzGJ8yxnMUX6WGuOobP5RMNhsBUZTCJNOdJvVRCCKJw6uVYBMdCPZkLWYwSkGIBEEPUoiGIW08kX2OcqrMmeM5iKmOPnKtRRLQpCEGl00Fw0qlEEMccaZqJnjaubVQTRDNiymESQzVJQJmY4layXK7JcOstYS6UQ7jV/VNUQBJ/ZTfMJXl+0sC/Ouau6cdXZy/DdJw4hm8tbYxDzJwCaR5Bm0Ak2CMTTmjnoTOwzZ2pBu8XEYxBCMYkBslzhVKibLc8pbK4JWzqvsJhExs7BHQbpzqdyRoAaAAI+D/Liu7r3Y+y4r7R2rZ8VXytPn9wXdx5AhdLwQwcd38dUy+AWq4Lw+HmL9qiDgrApx0oIQnzP4lyTUsD56X8E/nI1cPRX7DgEooNsUiJqL4TFVCdBzPGB/ngqaD1GwHEhLDMGUT1BzGaYdar7woxsxbEfeYLdyhlwgiw8PnPyVRNBCIuJ24+1qJBKIVJ1u89wdbOKIJoBO0EkBUFIgdszLmGznN3/Wn57YgApF3sohXAvjMKhcK+Up13mIkjNAr4wJlJsdmm3mAS2bxnCfDqHiXjGGoOQet4MEbavVGzG7GQ7sY/N6vwR0xcGzCD19CFTRouBqVyhVqibyW8BORiva2y7gSjrZ3TFHwEXfxp4118B4AoiZHVij/rW49HlH2dVxtf8rUU9AMCMyNacPY6UJ4I3iozZcxn2GQZIDoHpA6xdSM8aa5qrCGp3D1uDylUTxDT7DEXNjIiZyAHnw0+wAfGS/8X+BKT4Cnxh8z11EsQMV9KjCQ8jQjmA67CUrpHFVEOQeirF7Ls4DVktpiNPAkPnANF+88Vrr+D7luqLwr3Vt9qQLaZat1Ep5FRdF6EIohmwpbnOOFlM3auAN/0u8MK/WxuHOUEQRKULsjhB+O0AIwdDQZSZ4fDK0II+TDb0d0j1EHIdxJyZRbQpzNRSZnbMfOP4PnbRrr7EWtzkDbDPcXKExV6A4haTHbJV1b/Rms4rD3L+MLD9fwPvvhW4gAVw51OaRUEAQDAYwn8PfJS97uKbLP+jlGJG8H56FsnAIE7MpJDPFwb/5zM5ZKkXPuTQFXuD2Tqdy0wLUc+Y1sjQFmB+1Oz7lJmr3mIK95m2kVEAJ1VIJyfZanDv+rqZwQSYM2CA2VNCQdQbg+CKIJ7RQSN9NovJqiAopaaCqCHNNaWz2E88HzQrt3NZ4PgzpmIQEGpCJohIH1NQ1TTUTEwwVShqVGpRIZVi9hgj2XpcBQcogmg0KOVdKc2Z0JyTxQQAl/8hu4Cf+EbpbcZOs0HRPkBUg7AUpK3GYuJ53VPSMpxOEM9PJbiCEBe8RH4bOUFoMT679wZYJej463gOZ1sG1TTlC9vn0qb9YQSpTYKIpTX806MHkdPz5sEIguhcCZzxa84EUaQafT6dMzKYBCIBLxJZ5x5SyaxutPsGAC0yhKyeZ0rKvu2Uhhx86CZp9GZG2Xl1rmAxED3HSFUoCHHOE/vN1eSqJghphV8x6Ms1DaKoyw55dr3qIjZQ5jKmHVpjFtOcZBXlAt02i8kag0hpOnL891CLxSTan8zrAXM9FHv8QUDEIQI2BQFUpwBEHybRpDPcY1VJp18BfvoF4KGbgdE9lW0zrwNPfsv8nvc+xPovzR5jKlNKgnADiiAaDQcv1bSYbATRPQyceQ1w8Oeltxk/Xf9MQR4sqiEIPohMJWx9mGww6yGy3GIyCYLyfa0JsNlwjtdEYPjNxtrZf/n6APaeNlt075uQBlgxWIpiOElB3P/CCfz1z/bjpVHpQhYEMbSFvTc+Zl6oxizYWYUwBWG1mCIBn1m/YX99WjOznAAQ/j2NzhS2kphP56DBi4sDh+BBHlh2Lq96p2z2qWdNBWEQxD7ranIySi1sk7QRhJPFlJg0lYUM8VygExjYyN835YrFNNTJCDDr7y6pIEQGE1CbxZTkxYszmp991zpXDwBTq3Zs+ziw8SrzcS1ZSMkpqz1nV0nP/gtrl7PnB8DTf1/ZNk+9CPz8K8Br97PH//3HwH2fZAkYLttLgCKIxsMhG8O0mBwWseldwwawUvUIsdOWfPqaIA8WoW6+LGakfCGPZDHZayBkCOtpOsEJQs+wFNbZY8j1b0GchrDCy2ZBomhOzOSynhBeoesxMmYG7CeSkiIY5K1BHBTErkNsRjw2L322giAGt5j2lMhkErPgIoPcfFpzVBDJIgpiPpUzu70CCPSweozRmcJupfMpDVn4sFF/gz2x5jLze42d4tYkVxC9a9nnOLGvsM2GfJ4075zoYFcQoR5mfwiLKZdhVfmyWjBOmA9yPasl5TEhdTytvvlcPk8xl9KwYZB9dylfV0kFITKYAl5PTRZTnHfYncz6zOM9sYfN8DscFra87PPANX9tPhaWbDUEkZiwEoSwmMS1HTvNLL11VzgXcDpB2Mvj+5iaiZ1k48XYK4og2hI2BZHT88ZiOQUKAmADhJ4t/UOMnTL7K9WAtKbjtVm+LGao2+wkWy6IRimbcUb6MZXIFLWXAKAj6EPAy9enFum1egaYPYZMxzDGaC+GwM6RCE+be78Hg+dAgw8Hxsxq89MJXjcRWm4WiBkxCL4WNKXYdYgR3Pi81NdHeMCDW0xyEZlMJQa5fJ4insmhK2RXECUIwqYgOgZZ11lHgkhr0OGHBxQjdBg0OmDGlWKnmUcuWnN4vKy77fjeghXszPMs0W4jNQNEJILweMwKacC8dbSYJIKwNO8T5Fp9N9dYJoc8BdYPMmJOeLucFYQgCE4Kw73hmiymBBcg4xmfORk4scearVUKtTTbs1t2RkdX/puL84meaO8BsPOOjRVuS4ATRH58L/a9shsAQEWygssproAiiMZDs8+E2C81EvBiJpkt7EwpBn6RyWKHqKKuw2L69s4RfOoeNmudzksz53JBtGwCyKWRCw/g4HgcyzqLt/gghKAvGmCxCmGvZWLA/AkkIysxgR705tnF5klNmUtneoN4mmwFAIyMx/kpU5yKswF5MrzO3EnXCgDEmAGOjMcN0h2TlzIViwutvIClAfqjZk2EYZMUDnKxTA6UoiBIHQn6inahnU9p0OEBBa9x6F6JgY6As8WUykHn9S+/0s9iVeVCQcRPs8FEbs0xeBZTPvZW3wLlCEJWEAAb+O3Fb04Wky/I4jdDWyRrqj6LScQf1nMFMYcO629v+g2WZspjQ0JBrO6PMGJ1CPqXQoxXt59KekBFzGTueBUEUYPFlJiwfp72TMHYaXa996xmWXbZJPDkN4B//vXiDgIniOSJ1/CvD/wMAHBnL884G9xc+bFVCEUQjYaNIIR/un4wCk2nBmEYMCyGIgu/iCrqOgji1RNz6BtkRDStV0EQ3K/ePeHB2HwGH7q4dM51XzRgWkwAS1GlecRCKzFGe9GRZYOSLz3F2xH0AL/3DP45xbzfEa4gJuNZo6hs1CfNkjZsBz63x0iHFfZS0OfBuGwxrb4U+OweRkCEMLtGzNhKWExi1mq3mKIBL1IlFARAQEXsoHM5VvVGiisI3m5jV/5sjMcyQHQIADEVhIUgzmSZTGId8UoJQkux34ydIEQBHFCYkmnHTTuBt/yx1N11wpwJl2sW6QBhs67ui8DnIZihHWx7Wpqd+4v/AZz/W0Ymm4hBrO6LgFLzu6kEaU03gtSzuQASVP5MG0QQqVlG5GLdEvs29Bz7DDtXmDP/ueNM1ci9oOzgbVg6shO4LHAQGQRxd+bXgc/uBrb8RmXHVgUUQTQahsXELqIZPnMS3uuUPQ4hWwxOMKqoa49BHBiLYdPwcujwYiwXNrOFxHoIxcBtiPv2pXHBGT1462aH2aaE/o4As5gEQXDffzawAmO0F8H0BAI+gkBm2hiYYpFhTKYoOoI+HJ1OIq3pGBmLGb7+QSpdcIQA/RuMh7sOTWFVTxhbVnRhPJa2vk4EVwGbpJcWhrHB7ORaGKQuttCREUw1CGIFhnvDRWMQlCuIZ/NbMDafZnZfdJB9/3KaK2DGT44/y24rJQh7kZxAdFCymGxFXXZ0rWQkasQuJs0WJaWW0i0CEUfojfiZ0tQj5rE+9W2WTnrFF4zXGwqiL2J5fyWYSmSR49XvCYSYzSQwVCFBBLtYw8tKCcKpLkFeVyIxweJFncvN18weM2t0iqW6S+PC2/EcJsNrcXw2w+xHlzOYAEUQjYehINggOcfbCwiCEJbI/tMxfPC2X2HOx2doxSwm8Ty3on7/rhfw0MtsRnnfnlH88T0vlTycuZSGsfkMNi3rQjbQg6l8B06IpoHlFAQfTA7EQ/iDd2wyV4wrgv5ogKW5+q0EMeVbhnHaA08uhWX+DELZGUOKi2O5YtMAKAXemIhjZDyOLGUzyVdzKx33RSnFM4em8Wvr+7C8K8gG22KQCaKExSQG+2JB6odfOYWb/m23xSYUhE9EBW7HMgz3hnFiJlVgi8ync8h7gsj2nYkpdOP0HD/mzmU8SG23mPhg9uzt7NapUA4wCWJ0N/Ddq9iaIUARi0kQxIT5XCl4POb7XOjk2hMJoC8aMBv2Tb/BMnu2fshC/kIxnCEIwpbJ9M0dB/D3O0cc9zUVzyDHO/AmEcTplDSQioaE5eDxsM8vPg7c+UHgtR+XOcESBJGaMRsyygQx9ipTiIBZGQ0A+/4buPu3ub18yjjmDhpHomsj5lKaQaBuQxFEo2FTELOG98ouLLFuwoMvncBzR2bwwqkMu9DjRQJVUwfZbd86zKU0PPjSSfz05VN8Gydx3/OjSGtF1nkGjDbjm5d14NSlX8H3c+8yg8H2LAs7+GCih/vLqgeAteGYjksK4sgTQLAb494hjFN2sawOzCOSmzGsi9FpRhBv28LjCmNxHBiLYU/gIvx08JN4Oulsa43HMphKZLF1uAdDnSFm1xRDz2pWaJaalQiilIIoJIhcnuLfnzmKHa+P4Y0Jto1MTsc9e0axbU0viDfAgufBDqwfiCKr53HCpiLmUxp2LrsR5OqvI+z34uVRPrD3rgOm3rCmuQKs0vqttwDnfYDNru1ZK/KiQZSy7rWjz5opkXLtC8DUgujHlJxkhVb2wLcTIgNMcYg26TVA1AL1RPwY6AjipMa389qP2TVz0ccsr59P5xD2ezHI02LtCuLePaO47/lRx31NJbKGAk3SIE4mRF1CX3lClBHuBV69Dxh5BHjxztKvNQhCskTlhYdiEkF0LGef/cjPC98PAK//GNj3EBsTYmPID78ZSW6T5fliX/bflltQBNFo2BTEbIHFxAhCZN+MjMXZD6aYghjfxwaerlWGRy8G+JGxGPJ81l0MB3jq6OZlnei/5Aa8RDcazyHSxwYlzWH5R8CYZfYNriyrHgBmMSWyOrKED3KnXwbWXoZYhmIcbDA7wzePaG7OuFBFMPeKTQPweQgOjMUwMhbH0LKVeHXDTTgdzzlWJYvPYNOyDizrCmI2qRUnyh5OMmLlMPta0RzFYhCRALeFDrPvTMQ+/mv3KE7NpfEH79jMvHMeJ9o41Gk5RmP7aQ2jg1fCf+Y7sG1tr7EdDJ3F1gdPz1sVBCHA2/4UeO8/Atv/zKyKFhDZXek5RsZi5bd9D7FbJwUBsICzKOqq4HtFlMcuxEJLNUCs1NcdZhbTyXTIPNZAB7DyQsvrRT1KDydrWUHEMzmcmE0ZlqQd0/GsoSAQ7MCxBD/HobMqO1+BcK8Zszr6NIsjFMPsMWa/RWwFqYCVIDqWM3XScwZw7Gn2HPFYCUIsBXz6VSAxgZh/EAcpU9LBlecAcM6ScwOKIBqNAgWRBSHAugGmIKYTGSSzObx0nKWXjozH2MBSLNVtYh8LVhJiZPkcmUpiJpHFSW5RHBwvThAjY3GE/V6s6gmjO+zH8q4Q2ydQdu1dmphECgGsXl7ZrEsUy81ppqTfQ87BfFrDJNiFs9FzAj7kDO97dCaFkN+D5V0hrB2I4pHXx/D6qXlsXtaBZZ1B5PLUCHDKkIlviGdXTRRTEbLnKwY5h4HCWAvCFoOIBtn5aDojql2HppDJ6finXx7EtjW9uGxjP5v5c4LYtIwNogekRaKMFFo+4F2yvh/7Tscwk8iy75fmWY67vAJdOXj90H0RnDh1ii9ev4L1FRKTjaIEMVm8SM4J0UH2Hq0OiymVRUfQB7/Xg/6OAN6IcxKOnWJJBV7rZ856YvnRG2G/KTnV9aCR7eY8OZpKmBZTV1cPjoj6S5HyXCnE53f+b7GakdMvW/+fiQO7bmPxE7E2g/y7Mjq6TnOCMDPw0LOaraLoC7HaCEEQeZ2tVwEAhx8DQDFJ+jDCY3E9a84FUFiISSnFv+8qsiBUFVAE0WiIL5oXIM2mNHSH/Qj5vegI+jCVyOL5o7PI5SnCfi8b6DqXFw9Si1bMMGekep5ix+smodhnqjJGxmPYONQBD29fvWlZh1mQViZTIz07jinahc3LK1h/Ama7jVmJIP7spV789OVTiAWHgEAHtme4rOaD0+hMCqt6wiCEYPtZQzg+nUSeUrxl0yCGutjAbymC4zg4HkNfNICBjiCGutigaglUyxCyf/YY+ysyMBprQQStg1U4YD6+bGM/dh2axn89dxyn5tL4vIjNrPl1YMPbADAFsqI7hINS4Z+RQstrLC5ZzwjzmcPTVl+8klbuEmbyYeT2/Yyph8tvBjZulw7cnsUkFb0lJ52L5JwwsJnFNcb3FW1RUg5zSQ09fJ3vN63uRcwjBdztrS/Au+qGfAahygQh/97l4kqBqUQWB8g60OXnoWvoDLw4TlnLkM3vqu6g11zG3nMVW+wKR560/v+pbwE/uwU48LPii/d0rQKmDrEYRHTA7DcmXjuwiVmMYtyYPWpOMg89CgA4le/GTv1CpJdfhJ4VGxH2ewsUxAvHZ/HlH7+KeqEIotEQ3SL5xTmb1AyZ3N8RwFQ8i12HpuD1EFx7/gocHI+DdixnPyB7LCAxxS5mPoCMjMURDbDB96FX2CwxGvCalpEDDozFjBktAGwa6sTB8TizbcoQRGpuDNO0E5uGKusBJaqpZ/iSm3HSgb10NY5NJ+EPRYFf+zTWZXlgkQ9Oo7NJDPeyQedP3n0W9v/Fu/H6196Fd5+3AstKDPwHxuLYOMTOSyiIcQciAcDOM9DB2nocfYoN5g6YT2voCPrg81ovE/GZbxiM4j3nr8RkPIO//p/9uGhNLy7fyAfd3/x7SxbOxqEOi4Iw7Cv+WzhvVQ/Cfi+zmfo3msvJVqEg5tMapnJhrMFJ5KLLWfNH0ZnUGyic7RtFb1OFRV2l8OZPstlw/HRdbTYEQbxn60rs/ur7oBNGlunhSx3PrSvsh9dD0BXyWSymkbEYAj6PYUnaMR3P4ljkXJDPPIk3bViJY3NZHL/+IWDz1dUd9OV/ANzwn2wCN7DZShDJaaYeAODIU8UJYs2l7Dc3f9Kaqi5eO3gWT6I4zq5/kdUU6DAUy9FsFx6ml8DzyZ+DeH08S86qIAy7sk4ogmgkclnQ488gucrs9TKTzKKby+S+aADHZ5J4fGQC5w93Y+sZPYhncpj39TtXU4smczybZWQ8hrdtGYKHAL86OImAz4PLNw0UtZiMDCZpgN+8rAMpTWfZQ2WqRfXYBKZol4VgSqGfrxUxnWY/s136mVgzwPbdGfIDl34WaQ/PXpEspuFe57x6p4F/PMbWcz4wFsNmflyCSIpmMhHCLsIDD7P1JcQgaoOYtdohYhCXrO/HJesZscXSuZKZXZuXSUQMKQDO4xsBn8eMQ/gCZgaPr3KC2H1kGvNg5Primo+xuNeKrWxwCfcW2mg1W0wDZhfbKglC0/PYe2oep+cz6AlL6ogQ6MEexGkIuzOFFcHzKbPlSW80YAlSj4zHsWGwA+sGoobtKmMqkTXUrPi+6h5A117OYgYiDrHrn5jt1LsO2P9TlgThRBBrr2C/uaO/srbrF6p2aAt7Xy7FvhNxzUtq52CyA8u7Qgj42HXllEYtYpr1oiUEQQh5FyFkPyHkICHkS+Xf0aY4+QKIlsQfPduF108y43MuZSqIld1hvHBsFi+PzuHXN/RjM58Bn9B5Jok9UC0tJi8G+3NXdWNNfxS5PMWGwQ6cubwLR6cSjsG653hQdctyiSD4/WcOT5dVEL70FOK+Hgx0VDZoiV5N41oIlHjxhH4uvnztWVg3EMVARwCI9OHZof8Peb6G9LGpJGaTmpHrbofIYBED/717RnHJ13fiZ6+eRiydM4ivNxKAz0Os1dR2yKmuay9zfImYtdoxwM/rik0DWNMfwaqesFU9OGDTUAfSWt64kI0UWim+IeIQR6cSZkprFQSx69A0JtGLMfThrtyV7Emvnw1KTnUzoR6mUE6+WLwPUzH8+u/zIGwVWUAA/u9P9+Ld334Ce0/NG436BLw9q7CLno1dRworwefTOeOz6o8GmNLmCntkLI5NQx3cLi1UEFOJrKFmNw11oC8aqH8AXXsFK4Q78oSpHs5+L3DBb5dem0G0Es/GrQpiYBO7Xb7VGiOb2MdsKbHmCPFgXyxomUQN2woxNT2P3UemsbK7eKeDStH0NakJIV4A/wjgKgCjAJ4jhDxIKX292cfSaGiHHocfwNP6mcjvHMFtv3MRZpOaEaD+ym+ejfdsXQkPAS7bOMCW3gTwRroTZwNmMy8BOYPpKBvENw11YNNQBw5PJrB5WQc2L+swMpnOWWmmLFJK8Q+/PIhVPWFcJg1k/6+9c4+uqrrz+Od3k5BAcgMkJAES3txEICDSyENEQKlFmCllWjogVTtrhMGWaWk7U2vHNXb6mErHUrWOspzRFl+oSxQRgSKgSLWggCjhDRohQAwEQngHkj1/nH1ubpJzL0kI916mv89aWTnn7H3u+d6dX85vP397cF4HCnL8PP7OXiYPHOYM5Xk5CGNod6GyWdMC/cmJJCUIh6rb8szABSzcCP/aO5OFM+paVB/0nMlDBwp4PTWLx/+0lTaJPiYNzvX8vJSkBDq0S6L85Hku1NTy8Krd1BqCfa1uy8bnE7L9yeG7mKDun7Bjr/qrXUMIrbWGEsjx88bsGynMTUdEWDhjOKnJCRFndgVy6mYyueEioP4MqSlfyuPR1Xv477f38pvsfrBjSf1prpdg/acVfNZ5DuvTfPy5xHmBighMesx7ZprP56w32PyMc96cl31qJ5i1rv4snUtw+MRZXtiwn9sKOzNpcC5FPeuPiSRMfY5nni3mTIPavbsXhFtWfzckj/sXF7Nuz1GG9OjIwcqzTBvajQs1huXFZZy7UENKUt2417HT5+mV6VQ6RIRhvTIuvwVRMMFpAbz7kNNtVH3SmYJ8ri4CsaeDcEOJHy+p77S7Xgcz34Eug6HcvgorP3dmMGUV1FUYUrPZX1nN0F515Z7XsW1wLUR6ShJbD57gTHUN903ox18u71vGpAUxFNhrjPnUGFMNvAhMioGOK075J6vYWduNQfl9WLGtjB2Hq6g8Ux2ciZHtT2F8YWduHdCZ1OREO8jahu1VtgbdcKDaYwZTfo6ffPvyCT1u2M20dvcRthyoZPbNfYNNU3Bept8fF+DTI6dZuv2YM+jo4SBM9SmSOU9SukfkyzCICJmpyVScOs8bX2TSPzeDtOREOrdPobOt3bRLSeGTmp7sO3KKVzaVcvvQ7sE0L7L9ziK4VzeXUnr8LKPzs4JrSdzvDpCVnhJ+kBrq/nk9BkRdQmutDRmY1z7oELpntgtukBQO13m5fzd3DKJ9SAslOz2F24d1Z9HmgxxNsTGnmtiCqDp3geKDJ+gX6Et+QT++qDpPSYV1Cqmdwkf6HPWjuhW4Te1icsns03jgOwJPvLOPWmP4t4n9GF/YuXFLtEN3+vftzcellZwJiXXl7gXhtuamFOXRtX0Kj6zeE2wxBKzte81kqjhVXe/vM7x3Jgcrz3LgWJjp3E0hKQVG/RA+/7Oz8rv/JKcylzukLvRIuOB5rs013PCr63VON6C7bejxz5wZTFn9gqvoTVoOZVXnGrUgoG4thOv8JhRe/uZBUW9BALlAaGzbUmBYpBuqD2+j5OeFV1TUlSC35hDF/ok8Ou06bpy7hjue2mBfOo1rpS59s9N4aVc59/rg2JKfUvXGr+s+r/Ywq5LGMG/eWipOVwenq7ovn77ZafTMTCXRJ/xi6XYeW7M3eG/5yfPkdmjL14c0ri2PH9CZghw/979WzAhfO9q+/zQV6+uvFE0wNXQD/BnNM7qM1DYsLy7jTPVFZt7Up1F6qu3Pn/rkenw+YdboxnlCyUlPYd2eo7y39yjX5rXniW8NYdTct6k1pt72pzn+ZNbuPsKX5631/JwbL5ziAeDBHZ1Yvc87T0nF6XrdcZdDeoozpXj+2n28urk02IfecDvTe0b34YUN+5m9+iwvAo+9u5/X13vrC+XcxRpqjTMbKsfO9pr+P+tJTb70v/ichJuZWLuSOW8cYNvySz+rpXx29DRTiroFX2heDO+dwfy1+7jtkXW0sZMD3BXoblklJybwnbF9uX9xMbOeczbaCWSnBTcUmrFgY/B7G5xNnEIjD7vjEFOfXE+7Ni0PT5FkevOsZJBZc4wZn99CibW1uSaffuziq/O3ek6fHledxb3AA2sqeH+dd3kvEj+y+rf4Ocdvtwgrtm1nkfjZdiSJmlrTwEE4x3cv2Ei7NgmUVZ0jPyftkpWWphALB+HVDm+08klEZgIzAQq6+DnWrlejm+KdoxKg7/jv0b5tEv85eSDLiw+T4PMxcWD4OEozb+rNK5va8GbFnXSpLqmXVkEftnacTCAljQDwpR4Z+HzC2GuyufvGXowKdKJNoo8f3VrA1oP1w3YHctKYen33eq0HF59P+NXkQv7wXglrKqeTf/YjT21lvgEEbvhas8pg5k29Wbm9jESfj7+/vvEq6LEF2WwefJwLNbWMzs+K2HoAuGtET/wpiYgIM0f1pl2bRP5ryiDKq87X6+K5Y0QPEhPCd/mcqBnJqmNT+CLzFgI+7xdWfo6fYvtA/QAACIdJREFUbxa13ibwP/hygLW764KwdctoV68FAU4r4hdfK2Ttzk4sO3YHZeljCCQ1bVLA6PwsinpkkJQg/NPo3k2uIa+7OJOU4+nUZg4m4GvetNrmMKBrOnPGBSLmGdEnk2lDuwdD0rgMymvPmIK61us3i7qxs6yKY6er6dK+LT0zUzHAt2/o2ajl2L9LOuNDatP5OWnMGNWrLsTMZbDw9I/JqT5AUsdC3G+29sy32VVdQqCDd+WirPYWVlWUUpV5Q1jbW5F8F33PfsIFSaa80xgCiX4Wn5jF8aRsJqfn1iuLfl3SuX1Y9+DMrkBOGn87yDskTXORRuGmrzAiMgL4mTHmK/b8PgBjzK/D3VNUVGQ2btwYJYWKoij/PxCRTcaYopbeH4sxiA+BgIj0EpE2wFRgSQx0KIqiKBGIeheTMeaiiMwG/gQkAE8bY7ZFW4eiKIoSmViMQWCMWQYsi8WzFUVRlKahK6kVRVEUT9RBKIqiKJ6og1AURVE8UQehKIqieKIOQlEURfEk6gvlWoKInAR2xVpHE+gEHI21iCZwNei8GjSC6mxtVGfrUmCMaXG8mJhMc20Buy5nNWC0EJGNqrN1uBo0gupsbVRn6yIilxWCQruYFEVRFE/UQSiKoiieXC0O4slYC2giqrP1uBo0gupsbVRn63JZOq+KQWpFURQl+lwtLQhFURQlysTcQYhIioh8ICIfi8g2EfkPe72XiGwQkT0i8pINDY6IJNvzvTa9Z4x1Pi8iu0SkWESeFpEke32MiJwQkS32599jrPOPIvJZiJ7B9rqIyKO2PD8RkSEx1rkuROMhEVlsr8ekPO2zE0TkIxFZas/jyjYj6Iwr24ygM65sM4LOeLTNEhHZap+70V7LEJG3rH2+JSId7fXml6cxJqY/ODvMpdnjJGADMBx4GZhqr88H7rHH3wHm2+OpwEsx1jnBpgmwMETnGGBpHJXnH4FveOSfACy39w0HNsRSZ4M8i4A7Y1me9tk/BF5wnx9vthlBZ1zZZgSdcWWb4XQ2SIsX2ywBOjW49hvgJ/b4J8DclpZnzFsQxsHdZTzJ/hjgZuAVe30B4O51OcmeY9NvEfHY+DVKOo0xy2yaAT4AGm/6HEUilGc4JgHP2PvWAx1EJPyeqFHSKSJ+HBtY7HF71BCRPGAi8L/2XIgz2/TSCU5Y/XiyTfDWGYGY2CZE1hkvthmBUDtsaJ/NKs+YOwgINuW2AOXAW8A+oNIYc9FmKQVy7XEucACczYeAE0BmLHQaYzaEpCUBdwArQm4ZYbtQlovIgGhovITOX9mm5e9ExN3RPFieltCyjpVOgMnAamNMVci1WJTnw8CPgVp7nkkc2qaHziDxZJuE1xlXtkmE8iR+bBOcStVKEdkkIjPttRxjzGEA+9vdwLrZ5RkXDsIYU2OMGYxTwxkK9PPKZn971ciiMhWroU4RKQxJfhx41xizzp5vBnoYY64Ffk8UaxthdN4HXANcD2QA99rs8Vqe03C6RVyiXp4i8jdAuTFmU+hlj6wxtc0wOkOJC9uMoDOubLMJ5Rlz2wxhpDFmCHAb8F0RuSlC3maXZ1w4CBdjTCXwDk7/WAcRcUOB5AGH7HEp0A3AprcHjsVI53ir4wEgC6fP0s1T5XahGGcHvSQR6RQrncaYw7ZpeR74A44jhpDytISWddR1AohIptX3ZkieWJTnSOCrIlICvIjTrfAw8WebjXSKyHNWRzzZpqfOOLTNSOUZL7bpPvuQ/V0OvGa1feF2Hdnf5TZ7s8sz5g5CRLJEpIM9bguMA3YAbwPfsNnuAl63x0vsOTZ9je1jjYXOnSJyN/AVYJoxpjYkf2e3/1lEhuKUdUUMdboGIzh9ksX2liXAnXaGw3DghNs8jYVOmzwFZ9DvXEj+qJenMeY+Y0yeMaYnzqDzGmPMdOLMNsPo/Fa82WYEnXFlm+F02uS4sE37rFQ7HoKIpAK34pRdqB02tM9mlWc8BOvrAiwQkQScgn3ZGLNURLYDL4rIL4GPgKds/qeAZ0VkL07tbGqMdV4EPgf+Ym3kVWPMz3FeEPfY9LM4s16i0XUTTucaEcnCaWZuAWbZ/MtwZjfsBc4A/xAFjWF12rSpwIMN8seqPL24l/iyzXDMJ75sMxzPx5ltRiKebDMHeM3+bROBF4wxK0TkQ+BlEflHYD+OU4MWlKeupFYURVE8iXkXk6IoihKfqINQFEVRPFEHoSiKoniiDkJRFEXxRB2EoiiK4ok6CEVRFMUTdRDKXxUikil1YZnLRORgyPn7V+iZ14lI2OB0dtHginDpihIr4mGhnKJEDWNMBeDuN/Az4JQx5qEr/NifAr+MoOmIiBwWkZHGmPeusBZFaTLaglAUi4icsr/HiMhaEXlZRHaLyIMiMl2cDY62ikgfmy9LRBaJyIf2Z6THZ/qBQcaYj+356JAWy0duqAScAG/To/RVFaVJqINQFG+uBb4PDMQJlZ1vjBmKsz/AP9s8jwC/M8ZcD3wd7z0OiqiLLQTwL8B3bRTbUTihGQA22nNFiRu0i0lRvPnQDWQmIvuAlfb6VmCsPR4H9Je6PYHSRcRvjDkZ8jldgCMh5+8B80TkeZzYSKX2ejnQtfW/hqK0HHUQiuLN+ZDj2pDzWur+b3zACGPMWcJzFkhxT4wxD4rImzhB09aLyDhjzE6bJ9LnKErU0S4mRWk5K4HZ7omIDPbIswPoG5KnjzFmqzFmLk630jU2KZ/6XVGKEnPUQShKy/keUCTOVpnbqQtTHcS2DtqHDEbPEZFiEfkYp8Ww3F4fS8gmNIoSD2i4b0W5wojID4CTxphIayHeBSYZY45HT5miREZbEIpy5XmC+mMa9bCb5cxT56DEG9qCUBRFUTzRFoSiKIriiToIRVEUxRN1EIqiKIon6iAURVEUT9RBKIqiKJ78H1h2GCEwbTLyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assign dataframes for different depths \n",
    "depth_1165 = units_df[units_df['depth']==1165]\n",
    "depth_715 = units_df[units_df['depth']==715]\n",
    "\n",
    "# Create list of containing 1 entry from each depth\n",
    "neural_list_1165 = depth_1165['spike_times'].iloc[0]\n",
    "neural_list_715 = depth_715['spike_times'].iloc[0]\n",
    "\n",
    "# Plot firing rates\n",
    "plot_firing_rates(neural_list_1165, 300, 500)\n",
    "plot_firing_rates(neural_list_715, 300, 500)\n",
    "plt.legend(['1165','715'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the neuron that's more superficial (at a depth of 715 microns) has a higher firing rate! But we'd certainly need more data to make a conclusive argument about that.\n",
    "\n",
    "<hr>\n",
    "\n",
    "## Additional resources\n",
    "\n",
    "For more ideas about how to play with this dataset, see this [Github repository](https://github.com/vathes/DJ-NWB-Economo-2018/blob/master/notebooks/Economo-2018-examples.ipynb).\n",
    "\n",
    "<font color=\"red\">Note: Reproducing the above analysis, comparing left vs right in the upper ALM PT neurons would be fantastic...</font>"
   ]
  }
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