FNIRS_connectivity_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhPm_3tHt4mQ",
        "outputId": "ec04be9c-0357-4625-8ad6-f6d590417219"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "## for google colab\n",
        "\n",
        "# If encounter google drive issue, remount\n",
        "# !rm -rf /content/drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11eC2IiTuLL6"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install mne ipywidgets ipyevents nibabel trame trame-vtk trame-vuetify pyvista nilearn PyWavelets seaborn statsmodels mne_nirs snirf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8463p6_t4mT"
      },
      "source": [
        "## 1. Load Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bjepRqht4mU",
        "outputId": "96035fda-c5ab-42fd-b5d2-c51adf0e3db7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pysnirf2:Library loaded by process 4317\n"
          ]
        }
      ],
      "source": [
        "# Import common libraries\n",
        "from collections import defaultdict\n",
        "from copy import deepcopy\n",
        "from itertools import compress\n",
        "from pprint import pprint\n",
        "\n",
        "# Import Plotting Library\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "# Import StatsModels\n",
        "import statsmodels.formula.api as smf\n",
        "from mne import Epochs, events_from_annotations, set_log_level\n",
        "from mne.preprocessing.nirs import (\n",
        "    beer_lambert_law,\n",
        "    optical_density,\n",
        "    scalp_coupling_index,\n",
        "    temporal_derivative_distribution_repair,\n",
        ")\n",
        "\n",
        "# Import MNE processing\n",
        "from mne.viz import plot_compare_evokeds\n",
        "\n",
        "\n",
        "# Import MNE-NIRS processing\n",
        "from mne_nirs.channels import get_long_channels, picks_pair_to_idx\n",
        "from mne_nirs.datasets import fnirs_motor_group\n",
        "from mne_nirs.signal_enhancement import enhance_negative_correlation\n",
        "\n",
        "# Set general parameters\n",
        "set_log_level(\"WARNING\")  # Don't show info, as it is repetitive for many subjects\n",
        "from itertools import compress\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import mne\n",
        "import snirf\n",
        "\n",
        "from scipy import signal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdmfGB71t4mU"
      },
      "source": [
        "## 2. Define function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThZGa0b4t4mV"
      },
      "source": [
        "#### 2.0 Remove close channels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zkbin2zlt4mV"
      },
      "outputs": [],
      "source": [
        "def remove_close_channels(raw_intensity):\n",
        "# Remove channels that are too close to each other (less than 1cm)\n",
        "    picks = mne.pick_types(raw_intensity.info, meg=False, fnirs=True) # [0 ... 111]\n",
        "    dists = mne.preprocessing.nirs.source_detector_distances(\n",
        "        raw_intensity.info, picks=picks\n",
        "    )\n",
        "    raw_intensity.pick(picks[dists > 0.01]) # Number of channels with distance > 1 cm: 96\n",
        "\n",
        "    return raw_intensity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzFo5Ctxt4mW"
      },
      "source": [
        "#### 2.1 Annotation function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XHeqs7m6t4mW"
      },
      "outputs": [],
      "source": [
        "def process_annotations(raw_intensity):\n",
        "\n",
        "    # Define wanted stimuli\n",
        "    wanted_stimulus = [\"2\", \"3\"]\n",
        "    unwanted = np.nonzero(~np.isin(raw_intensity.annotations.description, wanted_stimulus))\n",
        "    # The ~ operator is used to negate the result of np.isin\n",
        "    raw_intensity.annotations.delete(unwanted)\n",
        "    raw_intensity.annotations.rename(\n",
        "        {\"2\": \"Reappraise\", \"3\": \"view negative\"}\n",
        "    )\n",
        "\n",
        "    return raw_intensity\n",
        "\n",
        "# Usage:\n",
        "# raw_intensity = process_annotations(raw_intensity)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Eva31tat4mW"
      },
      "source": [
        "#### 2.2 Wavelet motion Correction function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EGTuhG-Ct4mW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pywt\n",
        "import mne\n",
        "\n",
        "def hmr_motion_correct_wavelet_standard(raw_od, iqr=1.5, turnon=1, wavename='db2'):\n",
        "    if iqr < 0 or turnon == 0:\n",
        "        return raw_od\n",
        "\n",
        "    data = raw_od.get_data()\n",
        "    dod_wavelet = np.zeros_like(data)\n",
        "    signal_length = data.shape[1]\n",
        "    n_levels = max(int(np.ceil(np.log2(signal_length))) - 4, 4) # decomposition level, L = 4 is the lowest wavelet scale used in the analysis\n",
        "\n",
        "    # Identify bad channels\n",
        "    bads = raw_od.info['bads']\n",
        "    bad_indices = [raw_od.ch_names.index(bad) for bad in bads if bad in raw_od.ch_names]\n",
        "\n",
        "    # Determine good channels\n",
        "    all_indices = set(range(data.shape[0]))\n",
        "    bad_indices_set = set(bad_indices)\n",
        "    good_indices = sorted(all_indices - bad_indices_set)\n",
        "\n",
        "    if not good_indices:\n",
        "        print(\"All channels are marked as bad. No motion correction applied.\")\n",
        "        return raw_od.copy()\n",
        "\n",
        "\n",
        "    for idx, channel_no in enumerate(good_indices):\n",
        "        # Remove DC component\n",
        "        dc_val = np.mean(data[channel_no, :])\n",
        "        data_no_dc = data[channel_no, :] - dc_val\n",
        "\n",
        "        # Normalize\n",
        "        med_dev = np.median(np.abs(data_no_dc))\n",
        "        if med_dev == 0:\n",
        "            norm_coef = 1\n",
        "            data_normalized = data_no_dc\n",
        "        else:\n",
        "            norm_coef = 1 / (1.4826 * med_dev)\n",
        "            data_normalized = data_no_dc * norm_coef\n",
        "\n",
        "        # Wavelet Transform\n",
        "        coeffs = pywt.wavedec(data_normalized, wavename, mode='periodization', level=n_levels)\n",
        "\n",
        "        # Thresholding (Artifact Removal)\n",
        "        for i in range(1, len(coeffs)):\n",
        "            c = coeffs[i]\n",
        "            q1, q3 = np.percentile(c, [25, 75])\n",
        "            iqr_val = q3 - q1\n",
        "            threshold = iqr * iqr_val\n",
        "            coeffs[i] = np.where((c > (q3 + threshold)) | (c < (q1 - threshold)), 0, c) # np.where(condition, value if true, value if false)\n",
        "\n",
        "        # Inverse Wavelet Transform\n",
        "        reconstructed = pywt.waverec(coeffs, wavename, mode='periodization')\n",
        "\n",
        "        # Denormalize and add DC component\n",
        "        reconstructed = reconstructed / norm_coef + dc_val\n",
        "\n",
        "        # Truncate to original signal length\n",
        "        dod_wavelet[channel_no, :] = reconstructed[:signal_length]\n",
        "\n",
        "    # For bad channels, retain the original data\n",
        "    if bad_indices:\n",
        "        dod_wavelet[bad_indices, :] = data[bad_indices, :]\n",
        "        print(f\"Retained original data for {len(bad_indices)} bad channels.\")\n",
        "\n",
        "    raw_od_corrected = mne.io.RawArray(dod_wavelet, raw_od.info)\n",
        "    return raw_od_corrected"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qj5fBMxt4mX"
      },
      "source": [
        "#### 2.3 hmr_Bandpass_Filter function\n",
        "(Auxiliary --> accelerometer/gyroscope data).\n",
        "Both optical density and auxiliary data uses the same bandpass filter function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xjys4sr3t4mX"
      },
      "outputs": [],
      "source": [
        "def hmr_Bandpass_Filter(data, hpf=0.01, lpf=0.4):\n",
        "    fs = data.info[\"sfreq\"] # sampling frequency = 5.09 Hz\n",
        "    if hpf > (fs/2) or lpf > (fs/2): # if bandpass filter > nyquist frequency\n",
        "        print(\"Warning: Bandpass filter is larger than the Nyquist frequency\")\n",
        "        return data\n",
        "\n",
        "    # Get the data array\n",
        "    x = data.get_data()\n",
        "    filtered_data = x.copy()\n",
        "\n",
        "    # low-pass filter\n",
        "    lpf_norm = lpf / (fs/2) #  The normalized lpf_norm = 0.157 indicates that the low-pass cutoff is at 15.7% of the Nyquist frequency.\n",
        "    if lpf_norm > 0:\n",
        "        # Create filter\n",
        "        filter_order = 3\n",
        "        # Design an 3rd-order digital Butterworth filter and return the filter coefficients.\n",
        "        b, a = signal.butter(filter_order, lpf_norm, 'low')  # butter(N, Wn, btype='low')\n",
        "            # N: The order of the filter\n",
        "            # For digital filters, if fs is not specified, Wn units are normalized from 0 to 1, where 1 is the Nyquist frequency (Wn is thus in half cycles / sample and defined as 2*critical frequencies / fs). If fs is specified, Wn is in the same units as fs.\n",
        "            # btype {‘lowpass’, ‘highpass’, ‘bandpass’, ‘bandstop’}, optional, The type of filter. Default is ‘lowpass’.\n",
        "\n",
        "        for ch in range(x.shape[0]):\n",
        "            filtered_data[ch, :] = signal.filtfilt(b, a, filtered_data[ch, :]) # b : The numerator coefficient vector; a : The denominator coefficient vector\n",
        "\n",
        "    # High-pass filter\n",
        "    hpf_norm = hpf / (fs/2)\n",
        "    if hpf_norm > 0:\n",
        "        filter_order_h = 5\n",
        "        b_h, a_h = signal.butter(filter_order_h,hpf_norm, 'high')\n",
        "        for ch in range(x.shape[0]):\n",
        "            filtered_data[ch, :] = signal.filtfilt(b_h, a_h, filtered_data[ch, :])\n",
        "\n",
        "    data_filtered = data.copy()\n",
        "    data_filtered._data = filtered_data\n",
        "\n",
        "    return data_filtered\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YP7c-yE4t4mX"
      },
      "source": [
        "#### 2.4 Define MotionCorrectCbsi function\n",
        "\n",
        "This function assumes that the true psychological signals of HbO and HbR are negatively correlated\n",
        "\n",
        "Page 5 of https://doi.org/10.3390/s23083979"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g-7VC372t4mY"
      },
      "outputs": [],
      "source": [
        "def hmrR_MotionCorrectCbsi(raw_haemo):\n",
        "\n",
        "    hbo_channels = [ch for ch in raw_haemo.ch_names if 'hbo' in ch.lower()]\n",
        "    hbr_channels = [ch for ch in raw_haemo.ch_names if 'hbr' in ch.lower()]\n",
        "\n",
        "    hbo_indices = [idx for idx, ch in enumerate(raw_haemo.ch_names) if 'hbo' in ch.lower()] # [0-47]\n",
        "    hbr_indices = [idx for idx, ch in enumerate(raw_haemo.ch_names) if 'hbr' in ch.lower()] #[48-95]\n",
        "\n",
        "    haemo_data = raw_haemo.get_data() # (96, 2396)\n",
        "    n_timepoints = haemo_data.shape[1] # 2396\n",
        "    n_channels = len(hbo_channels) # 48\n",
        "\n",
        "    data_dc = np.zeros((n_timepoints, 3 ,n_channels)) # (2396, 3, 48)\n",
        "    data_dc[:,0,:] = haemo_data[hbo_indices,:].T\n",
        "    data_dc[:,1,:] = haemo_data[hbr_indices,:].T\n",
        "\n",
        "    # avoid changing original data\n",
        "    dc = data_dc.copy()\n",
        "\n",
        "    # Copy data for correction\n",
        "    dcCbsi = dc.copy()\n",
        "\n",
        "    for idx_ch in range(n_channels):\n",
        "\n",
        "        # Mean-center the HbO and HbR signals\n",
        "        dc_oxy = dc[:, 0, idx_ch] - np.mean(dc[:, 0, idx_ch])\n",
        "        dc_deoxy = dc[:, 1, idx_ch] - np.mean(dc[:, 1, idx_ch])\n",
        "\n",
        "        # Calculate standard deviations\n",
        "        sd_oxy = np.std(dc_oxy, ddof=0) #Delta Degrees of Freedom = 0: divides by n\n",
        "        sd_deoxy = np.std(dc_deoxy, ddof=0)\n",
        "\n",
        "        # Compute alpha, avoiding division by zero\n",
        "        if sd_deoxy != 0:\n",
        "            alfa = sd_oxy / sd_deoxy\n",
        "        else:\n",
        "            alfa = 0\n",
        "\n",
        "        # Apply CBSI correction\n",
        "        dcCbsi[:, 0, idx_ch] = 0.5 * (dc_oxy - alfa * dc_deoxy)\n",
        "        if alfa != 0:\n",
        "            dcCbsi[:, 1, idx_ch] = - (1 / alfa) * dcCbsi[:, 0, idx_ch]\n",
        "        else:\n",
        "            dcCbsi[:, 1, idx_ch] = 0  # Handle division by zero\n",
        "\n",
        "        dcCbsi[:, 2, idx_ch] = dcCbsi[:, 0, idx_ch] + dcCbsi[:, 1, idx_ch]\n",
        "\n",
        "    raw_copy = raw_haemo.copy()\n",
        "\n",
        "    # Step 4: Extract and replace HbO and HbR data\n",
        "    corrected_hbo = dcCbsi[:, 0, :].T  # Shape: (n_channels, n_timepoints)\n",
        "    corrected_hbr = dcCbsi[:, 1, :].T  # Shape: (n_channels, n_timepoints)\n",
        "\n",
        "    # Replace data in-place\n",
        "    raw_copy._data[hbo_indices, :] = corrected_hbo\n",
        "    raw_copy._data[hbr_indices, :] = corrected_hbr\n",
        "\n",
        "    # Step 5: Add HbT channels\n",
        "    corrected_hbt = dcCbsi[:, 2, :].T  # Shape: (n_channels, n_timepoints)\n",
        "\n",
        "    # Define HbT channel names\n",
        "    hbt_channels = [ch.replace('hbo', 'hbt') for ch in hbo_channels]\n",
        "\n",
        "    # Define channel types for HbT\n",
        "    hbt_channel_types = ['misc'] * n_channels  # 'misc' since 'hbt' is not standard in MNE\n",
        "\n",
        "    # Create Info object for HbT channels\n",
        "    hbt_info = mne.create_info(ch_names=hbt_channels, sfreq=raw_copy.info['sfreq'], ch_types=hbt_channel_types)\n",
        "\n",
        "    # Create RawArray for HbT channels\n",
        "    raw_hbt = mne.io.RawArray(corrected_hbt, hbt_info)\n",
        "\n",
        "    # Add HbT channels to the Raw copy\n",
        "    raw_copy.add_channels([raw_hbt], force_update_info=True)\n",
        "\n",
        "    return raw_copy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yM9IFOo3t4mY"
      },
      "source": [
        "#### 2.5 Define get ROI channel function\n",
        "\n",
        "take away 'hbr' data to reduce redundancy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YlKvv7j-t4mY"
      },
      "outputs": [],
      "source": [
        "def get_ROI_channels(raw_copy):\n",
        "    # Get channel names from your fNIRS dataset\n",
        "    channel_names = raw_copy.ch_names\n",
        "\n",
        "    # Define the selection criteria\n",
        "    selection_criteria = ['S2', 'S4', 'S5', 'S6']\n",
        "\n",
        "    # Create a list to store selected channel names\n",
        "    selected_channels = []\n",
        "\n",
        "    for channel in channel_names:\n",
        "        if any(criterion in channel for criterion in selection_criteria) and ('hbo' in channel or 'hbr' in channel or 'hbt' in channel): #HbO channels: 760nm HbR channels: 850nm\n",
        "            selected_channels.append(channel)\n",
        "\n",
        "    # Now, get the indices of these selected channels\n",
        "    picks = [channel_names.index(name) for name in selected_channels]\n",
        "\n",
        "    return picks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LpOWhJht4mY"
      },
      "source": [
        "#### 2.6 Define get evoked metadata for Reappraise / view negative"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jGOJhHu8t4mY"
      },
      "outputs": [],
      "source": [
        "def get_evoked_metadata(raw_copy, events, event_dict, picks):\n",
        "    # Rejection criteria for bad epochs\n",
        "    reject_criteria = dict(hbo=70e-7, hbr=70e-7)\n",
        "\n",
        "    epochs = mne.Epochs(raw_copy,\n",
        "                       events,\n",
        "                       event_id=event_dict,\n",
        "                       tmin=-2,\n",
        "                       tmax=20,\n",
        "                       picks=picks,\n",
        "                       baseline=(None, 0),\n",
        "                       reject=reject_criteria,\n",
        "                       preload=True,\n",
        "                       verbose=False)\n",
        "\n",
        "    # Get rejected epochs information\n",
        "    drop_log = epochs.drop_log\n",
        "    dropped_indices = [i for i, drops in enumerate(drop_log) if drops]\n",
        "\n",
        "    print(f\"\\nNumber of rejected epochs: {len(dropped_indices)}\")\n",
        "    print(\"\\nRejected epochs event types:\")\n",
        "    for i in dropped_indices:\n",
        "        event_type = events[i][2]\n",
        "        print(f\"Epoch {i}: Event type {event_type}\")\n",
        "\n",
        "    # Get HbT channels by name instead of type\n",
        "    hbt_picks = [idx for idx, ch in enumerate(epochs.ch_names) if 'hbt' in ch.lower()]\n",
        "\n",
        "    # Separate epochs by condition and channel type\n",
        "    epochs_dict = {\n",
        "        \"Reappraise\": {\n",
        "            \"HbO\": epochs[\"Reappraise\"].get_data(picks=\"hbo\"),\n",
        "            \"HbR\": epochs[\"Reappraise\"].get_data(picks=\"hbr\"),\n",
        "            \"HbT\": epochs[\"Reappraise\"].get_data(picks=hbt_picks)\n",
        "        },\n",
        "        \"view_negative\": {\n",
        "            \"HbO\": epochs[\"view negative\"].get_data(picks=\"hbo\"),\n",
        "            \"HbR\": epochs[\"view negative\"].get_data(picks=\"hbr\"),\n",
        "            \"HbT\": epochs[\"view negative\"].get_data(picks=hbt_picks)\n",
        "        }\n",
        "    }\n",
        "\n",
        "    # Get channel names for each type\n",
        "    hbo_ch_names = [ch for ch in epochs.ch_names if 'hbo' in ch.lower()]\n",
        "    hbr_ch_names = [ch for ch in epochs.ch_names if 'hbr' in ch.lower()]\n",
        "    hbt_ch_names = [ch for ch in epochs.ch_names if 'hbt' in ch.lower()]\n",
        "\n",
        "    # Remove frequency encoding from channel names\n",
        "    hbo_ch_names = [ch[:-4] for ch in hbo_ch_names]\n",
        "    hbr_ch_names = [ch[:-4] for ch in hbr_ch_names]\n",
        "    hbt_ch_names = [ch[:-4] for ch in hbt_ch_names]\n",
        "\n",
        "    # Create metadata DataFrames\n",
        "    metadata_df_reappraise = pd.DataFrame({\n",
        "        'epoch': range(len(epochs[\"Reappraise\"])),\n",
        "        'condition': 'Reappraise',\n",
        "        'times': [epochs.times] * len(epochs[\"Reappraise\"]),\n",
        "        'hbo_ch_names': [hbo_ch_names] * len(epochs[\"Reappraise\"]),\n",
        "        'hbr_ch_names': [hbr_ch_names] * len(epochs[\"Reappraise\"]),\n",
        "        'hbt_ch_names': [hbt_ch_names] * len(epochs[\"Reappraise\"])\n",
        "    })\n",
        "\n",
        "    metadata_df_view_negative = pd.DataFrame({\n",
        "        'epoch': range(len(epochs[\"view negative\"])),\n",
        "        'condition': 'view_negative',\n",
        "        'times': [epochs.times] * len(epochs[\"view negative\"]),\n",
        "        'hbo_ch_names': [hbo_ch_names] * len(epochs[\"view negative\"]),\n",
        "        'hbr_ch_names': [hbr_ch_names] * len(epochs[\"view negative\"]),\n",
        "        'hbt_ch_names': [hbt_ch_names] * len(epochs[\"view negative\"])\n",
        "    })\n",
        "\n",
        "    return epochs_dict, metadata_df_reappraise, metadata_df_view_negative, epochs.times"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQhf4AI9t4mY"
      },
      "source": [
        "#### 2.7 Define interpolation function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_23Htii7t4mY"
      },
      "outputs": [],
      "source": [
        "# identifies bad fNIRS channels via scalp coupling index, interpolates them\n",
        "def interpolate_bad_channels(raw_od, sci_value):\n",
        "    # Make a copy for preprocessing\n",
        "    raw_od_copy = raw_od.copy()\n",
        "\n",
        "    # Set montage\n",
        "    montage = raw_od_copy.get_montage()\n",
        "    raw_od_copy.set_montage(montage)\n",
        "\n",
        "    # Calculate scalp coupling index\n",
        "    sci = mne.preprocessing.nirs.scalp_coupling_index(raw_od_copy)\n",
        "\n",
        "    # Mark bad channels based on SCI\n",
        "    raw_od_copy.info[\"bads\"] = list(compress(raw_od_copy.ch_names, sci < sci_value))\n",
        "    # print(f\"Bad channels before interpolation: {raw_od_copy.info['bads']}\") #####################################################\n",
        "    # Interpolate bad channels\n",
        "    raw_od_copy.interpolate_bads(\n",
        "        reset_bads=False,      # Keep bad channels in the bads list after interpolation\n",
        "        mode='accurate',       # Use high-quality interpolation\n",
        "        method=\"nearest\"       # Specify interpolation method for fNIRS channels\n",
        "    )\n",
        "    # print(f\"Bad channels after interpolation: {raw_od.info['bads']}\") #########################################################\n",
        "    # Create a RawArray with processed data and original info\n",
        "    processed_data = raw_od_copy.get_data()\n",
        "    original_info = raw_od.info.copy()\n",
        "\n",
        "    # Create new Raw object\n",
        "    raw_od = mne.io.RawArray(processed_data, original_info)\n",
        "\n",
        "    return raw_od"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9zYNQmGt4mZ"
      },
      "source": [
        "## 3. Subject Level Analysis\n",
        "\n",
        "Each person has 12 metadata, every metadata is a 2D matrix of shape {39 channels [HbO, HbR, HbT], 113 timepoints [-2 to 20s]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FpLBaWAft4mZ"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import mne\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import mne_nirs\n",
        "import logging  # Import the logging module\n",
        "\n",
        "import warnings\n",
        "\n",
        "# Suppress specific RuntimeWarning about missing nasion\n",
        "warnings.filterwarnings(\"ignore\",message=\"Fiducial point nasion not found, assuming identity unknown to head transformation\",category=RuntimeWarning)\n",
        "\n",
        "# Suppress specific RuntimeWarning about no bad channels to interpolate\n",
        "warnings.filterwarnings(\"ignore\",message=\"No bad channels to interpolate. Doing nothing...\",category=RuntimeWarning)\n",
        "\n",
        "base_dir = r\"/content/drive/MyDrive/fNIRS data/neurofeedback\" # google colab: r\"/content/drive/MyDrive/fNIRS data/neurofeedback\"\n",
        "participant_id = 1 # For participant p01\n",
        "\n",
        "def subject_wise_analysis_1_to_6(base_dir, participant_id, ROI=True):\n",
        "\n",
        "    \"\"\"\n",
        "    Process fNIRS data for a given participant across multiple runs.\n",
        "\n",
        "    Parameters:\n",
        "    - base_dir (str): Base directory containing participant data.\n",
        "    - participant_id (int): Participant identifier.\n",
        "\n",
        "    Returns:\n",
        "    - all_run_results (list): List of dictionaries containing run data.\n",
        "    \"\"\"\n",
        "\n",
        "    participant_dir = os.path.join(base_dir, f\"p{participant_id:02d}\") # 0 means add zeros if needed # 2 specifies a minimum field width of 2 characters # d indicates that the valueshould be formatted as a decimal integer(base-10 number system)\n",
        "\n",
        "    # Find all directories that have at least one underscore in their name\n",
        "    date_dirs = glob(os.path.join(participant_dir, \"????-??-??_???\")) # ['C:\\\\Users\\\\user\\\\Desktop\\\\fNIRS data\\\\p01\\\\2024-08-09_001', 'C:\\\\Users\\\\user\\\\Desktop\\\\fNIRS data\\\\p01\\\\2024-08-09_002', 'C:\\\\Users\\\\user\\\\Desktop\\\\fNIRS data\\\\p01\\\\2024-08-09_003', 'C:\\\\Users\\\\user\\\\Desktop\\\\fNIRS data\\\\p01\\\\2024-08-09_004', 'C:\\\\Users\\\\user\\\\Desktop\\\\fNIRS data\\\\p01\\\\2024-08-09_005', 'C:\\\\Users\\\\user\\\\Desktop\\\\fNIRS data\\\\p01\\\\2024-08-09_006']\n",
        "    # glob : find all file system paths matching a specified pattern\n",
        "    # the pattern \"*_*\": * indicates any number of characters, _ matches an underscore,\n",
        "    if not date_dirs:\n",
        "        print(f\"No data directories found for participant {participant_id}\")\n",
        "        return None\n",
        "\n",
        "    # Initialize dictionaries to store epochs for all runs\n",
        "    all_epochs_dict = {\n",
        "        \"Reappraise\": {\n",
        "            \"HbO\": [],\n",
        "            \"HbR\": [],\n",
        "            \"HbT\": []\n",
        "        },\n",
        "        \"view_negative\": {\n",
        "            \"HbO\": [],\n",
        "            \"HbR\": [],\n",
        "            \"HbT\": []\n",
        "        }\n",
        "    }\n",
        "    # Extract run numbers and create a dictionary mapping run numbers to full paths\n",
        "    run_dict = {int(run[-3:]): run for run in date_dirs}\n",
        "\n",
        "    # Sort run numbers\n",
        "    all_runs = sorted(run_dict.keys())\n",
        "\n",
        "    # Get practice run (first run)\n",
        "    neurofeedback_training = all_runs[:] # get all runs\n",
        "\n",
        "    # Create list of selected runs in order\n",
        "    selected_runs = [run_dict[training_no] for training_no in neurofeedback_training]\n",
        "    for run in selected_runs:\n",
        "        print(\"\\nRun\", run[-3:])  # Gets last 3 characters (e.g., '001')\n",
        "        snirf_files = glob(os.path.join(run,\"*.snirf\"))\n",
        "\n",
        "        if not snirf_files:\n",
        "            print(f\"No SNIRF file found in {run}\")\n",
        "            continue\n",
        "\n",
        "        fnirs_snirf = snirf_files[0]  # Take the first (and hopefully only) SNIRF file\n",
        "\n",
        "        # Load raw intensity info from .snirf file\n",
        "        raw_intensity = mne.io.read_raw_snirf(fnirs_snirf, preload=True)\n",
        "        raw_intensity.load_data()\n",
        "\n",
        "         # montage is a description of the sensor positionin 3D space\n",
        "\n",
        "        # drop unwanted events (only leave 2 and 3), and annotate event names\n",
        "        raw_intensity = process_annotations(raw_intensity)\n",
        "\n",
        "        # Remove channels that are too close to each other (less than 1cm)\n",
        "        raw_intensity = remove_close_channels(raw_intensity)\n",
        "\n",
        "        # Convert signal to optical density\n",
        "        raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)\n",
        "\n",
        "        # identifies bad fNIRS channels via scalp coupling index, interpolates them\n",
        "        raw_od = interpolate_bad_channels(raw_od, sci_value = 0.9)\n",
        "\n",
        "        # Motion Correction Wavelet\n",
        "        raw_od_wavelet = hmr_motion_correct_wavelet_standard(raw_od, iqr=1.5, turnon=1, wavename='db2')\n",
        "\n",
        "        # Apply Bandpass Filter\n",
        "        raw_od_wavelet_filtered = hmr_Bandpass_Filter(raw_od_wavelet, hpf=0.01, lpf=0.4)\n",
        "\n",
        "        # Convert to haemoglobin, and apply further data cleaning techniques\n",
        "        raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od_wavelet_filtered, ppf=6.0) # default in Homer\n",
        "\n",
        "        # Apply Motion Correct Cbsi\n",
        "        raw_copy = hmrR_MotionCorrectCbsi(raw_haemo) # OR use library function: mne_nirs.signal_enhancement.enhance_negative_correlation(raw_haemo)\n",
        "\n",
        "        if ROI:\n",
        "            picks = get_ROI_channels(raw_copy) # get 13 ROI channel indices for each condition(HbO/HbR/HbT)\n",
        "            hbo_ch_names = [ch for ch in np.array(raw_copy.ch_names)[picks] if 'hbo' in ch.lower()]\n",
        "            hbr_ch_names = [ch for ch in np.array(raw_copy.ch_names)[picks] if 'hbr' in ch.lower()]\n",
        "            hbt_ch_names = [ch for ch in np.array(raw_copy.ch_names)[picks] if 'hbt' in ch.lower()]\n",
        "        elif not ROI:\n",
        "            picks = list(range(len(raw_copy.ch_names)))  # get all channel indices\n",
        "            hbo_ch_names = [ch for ch in raw_copy.ch_names if 'hbo' in ch.lower()]\n",
        "            hbr_ch_names = [ch for ch in raw_copy.ch_names if 'hbr' in ch.lower()]\n",
        "            hbt_ch_names = [ch for ch in raw_copy.ch_names if 'hbt' in ch.lower()]\n",
        "\n",
        "        # Find event epochs (8 view_negative / 8 reappraise)\n",
        "        events, event_dict = mne.events_from_annotations(raw_intensity)\n",
        "\n",
        "        epochs_dict, metadata_df_reappraise, metadata_df_view_negative, epochs_times = get_evoked_metadata(raw_copy, events, event_dict, picks)\n",
        "\n",
        "        # Append epochs from this run to all_epochs_dict\n",
        "        for condition in [\"Reappraise\", \"view_negative\"]:\n",
        "            for signal in [\"HbO\", \"HbR\", \"HbT\"]:\n",
        "                all_epochs_dict[condition][signal].extend(epochs_dict[condition][signal])\n",
        "\n",
        "    # Convert lists to numpy arrays\n",
        "    for condition in all_epochs_dict:\n",
        "        for signal in all_epochs_dict[condition]:\n",
        "            all_epochs_dict[condition][signal] = np.array(all_epochs_dict[condition][signal])\n",
        "            array_shape = all_epochs_dict[condition][signal].shape\n",
        "            print(f\"Participant {participant_id} - {condition}/{signal}: {array_shape}\")\n",
        "\n",
        "    return all_epochs_dict, epochs_times, hbo_ch_names, hbr_ch_names, hbt_ch_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffgV8f-Kt4mZ",
        "outputId": "50d7b12d-dd7c-4fa5-a195-763006d6bba5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 1 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 1 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 1 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 1 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 1 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 1 - view_negative/HbT: (48, 13, 113)\n"
          ]
        }
      ],
      "source": [
        "all_epochs_dict, epochs_time, hbo_ch_names, hbr_ch_names, hbt_ch_names =subject_wise_analysis_1_to_6(base_dir, participant_id, ROI=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJ9xtAWEt4ma"
      },
      "source": [
        "## 4. get all participants' data\n",
        "\n",
        "1. If a channel is bad in any run, it might be unreliable in other runs too\n",
        "2. For group analysis, we need consistent channel layouts across all runs\n",
        "3. So, we remove channels marked as bad in any run from all runs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iwm2Jeht4ma",
        "outputId": "5602a5b0-98af-49d8-9f79-9114a63631fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Processing participant 1 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 1 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 1 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 1 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 1 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 1 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 1 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 3 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 3 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 3 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 3 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 3 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 3 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 3 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 6 in Neurofeedback group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 6 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 6 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 6 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 6 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 6 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 6 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 7 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 7 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 7 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 7 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 7 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 7 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 7 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 10 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 10 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 10 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 10 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 10 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 10 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 10 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 11 in Neurofeedback group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 11 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 11 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 11 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 11 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 11 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 11 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 13 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 13 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 13 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 13 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 13 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 13 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 13 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 15 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 15 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 15 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 15 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 15 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 15 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 15 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 16 in Neurofeedback group\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 16 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 16 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 16 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 16 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 16 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 16 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 17 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 17 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 17 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 17 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 17 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 17 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 17 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 20 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 20 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 20 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 20 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 20 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 20 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 20 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 21 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 21 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 21 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 21 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 21 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 21 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 21 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 24 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 24 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 24 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 24 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 24 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 24 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 24 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 28 in Neurofeedback group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 28 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 28 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 28 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 28 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 28 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 28 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 30 in Neurofeedback group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 30 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 30 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 30 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 30 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 30 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 30 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 31 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 31 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 31 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 31 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 31 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 31 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 31 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 33 in Neurofeedback group\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 33 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 33 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 33 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 33 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 33 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 33 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 37 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 37 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 37 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 37 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 37 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 37 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 37 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 38 in Neurofeedback group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 38 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 38 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 38 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 38 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 38 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 38 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 40 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 40 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 40 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 40 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 40 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 40 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 40 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 41 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 12: Event type 1\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 8: Event type 1\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 41 - Reappraise/HbO: (46, 13, 113)\n",
            "Participant 41 - Reappraise/HbR: (46, 13, 113)\n",
            "Participant 41 - Reappraise/HbT: (46, 13, 113)\n",
            "Participant 41 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 41 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 41 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 43 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 43 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 43 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 43 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 43 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 43 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 43 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 45 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 45 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 45 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 45 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 45 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 45 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 45 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 46 in Neurofeedback group\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 46 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 46 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 46 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 46 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 46 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 46 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 48 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 48 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 48 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 48 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 48 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 48 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 48 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 50 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 50 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 50 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 50 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 50 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 50 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 50 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 55 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 55 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 55 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 55 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 55 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 55 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 55 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 56 in Neurofeedback group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 12: Event type 2\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 56 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 56 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 56 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 56 - view_negative/HbO: (47, 13, 113)\n",
            "Participant 56 - view_negative/HbR: (47, 13, 113)\n",
            "Participant 56 - view_negative/HbT: (47, 13, 113)\n",
            "\n",
            "Processing participant 2 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 2 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 2 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 2 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 2 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 2 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 2 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 5 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 5 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 5 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 5 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 5 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 5 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 5 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 9 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 9 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 9 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 9 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 9 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 9 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 9 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 12 in Sham group\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 12 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 12 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 12 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 12 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 12 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 12 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 14 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 14 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 14 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 14 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 14 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 14 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 14 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 18 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 18 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 18 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 18 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 18 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 18 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 18 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 19 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 19 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 19 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 19 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 19 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 19 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 19 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 22 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 10: Event type 1\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 7\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 6: Event type 1\n",
            "Epoch 10: Event type 2\n",
            "Epoch 11: Event type 1\n",
            "Epoch 12: Event type 2\n",
            "Epoch 13: Event type 2\n",
            "Epoch 14: Event type 1\n",
            "Epoch 15: Event type 2\n",
            "Participant 22 - Reappraise/HbO: (41, 13, 113)\n",
            "Participant 22 - Reappraise/HbR: (41, 13, 113)\n",
            "Participant 22 - Reappraise/HbT: (41, 13, 113)\n",
            "Participant 22 - view_negative/HbO: (42, 13, 113)\n",
            "Participant 22 - view_negative/HbR: (42, 13, 113)\n",
            "Participant 22 - view_negative/HbT: (42, 13, 113)\n",
            "\n",
            "Processing participant 23 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 23 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 23 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 23 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 23 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 23 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 23 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 25 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 25 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 25 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 25 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 25 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 25 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 25 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 26 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 26 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 26 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 26 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 26 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 26 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 26 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 27 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 27 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 27 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 27 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 27 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 27 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 27 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 29 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 29 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 29 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 29 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 29 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 29 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 29 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 34 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 4: Event type 1\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 34 - Reappraise/HbO: (47, 13, 113)\n",
            "Participant 34 - Reappraise/HbR: (47, 13, 113)\n",
            "Participant 34 - Reappraise/HbT: (47, 13, 113)\n",
            "Participant 34 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 34 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 34 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 35 in Sham group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 35 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 35 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 35 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 35 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 35 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 35 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 36 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 8: Event type 1\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 11: Event type 2\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 3\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 11: Event type 1\n",
            "Epoch 12: Event type 1\n",
            "Epoch 15: Event type 2\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 2\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 4: Event type 2\n",
            "Epoch 15: Event type 2\n",
            "Participant 36 - Reappraise/HbO: (45, 13, 113)\n",
            "Participant 36 - Reappraise/HbR: (45, 13, 113)\n",
            "Participant 36 - Reappraise/HbT: (45, 13, 113)\n",
            "Participant 36 - view_negative/HbO: (44, 13, 113)\n",
            "Participant 36 - view_negative/HbR: (44, 13, 113)\n",
            "Participant 36 - view_negative/HbT: (44, 13, 113)\n",
            "\n",
            "Processing participant 39 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 4\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 0: Event type 1\n",
            "Epoch 7: Event type 2\n",
            "Epoch 14: Event type 1\n",
            "Epoch 15: Event type 1\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 4: Event type 2\n",
            "Participant 39 - Reappraise/HbO: (45, 13, 113)\n",
            "Participant 39 - Reappraise/HbR: (45, 13, 113)\n",
            "Participant 39 - Reappraise/HbT: (45, 13, 113)\n",
            "Participant 39 - view_negative/HbO: (46, 13, 113)\n",
            "Participant 39 - view_negative/HbR: (46, 13, 113)\n",
            "Participant 39 - view_negative/HbT: (46, 13, 113)\n",
            "\n",
            "Processing participant 42 in Sham group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 42 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 42 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 42 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 42 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 42 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 42 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 44 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 44 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 44 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 44 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 44 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 44 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 44 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 47 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 47 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 47 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 47 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 47 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 47 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 47 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 49 in Sham group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 013\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 49 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 49 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 49 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 49 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 49 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 49 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 51 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 51 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 51 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 51 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 51 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 51 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 51 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 52 in Sham group\n",
            "\n",
            "Run 001\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 002\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 003\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 004\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 005\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 006\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 52 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 52 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 52 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 52 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 52 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 52 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 53 in Sham group\n",
            "\n",
            "Run 007\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 008\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 009\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 010\n",
            "\n",
            "Number of rejected epochs: 1\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 12: Event type 1\n",
            "\n",
            "Run 011\n",
            "\n",
            "Number of rejected epochs: 2\n",
            "\n",
            "Rejected epochs event types:\n",
            "Epoch 6: Event type 1\n",
            "Epoch 8: Event type 1\n",
            "\n",
            "Run 012\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 53 - Reappraise/HbO: (45, 13, 113)\n",
            "Participant 53 - Reappraise/HbR: (45, 13, 113)\n",
            "Participant 53 - Reappraise/HbT: (45, 13, 113)\n",
            "Participant 53 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 53 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 53 - view_negative/HbT: (48, 13, 113)\n",
            "\n",
            "Processing participant 54 in Sham group\n",
            "\n",
            "Run 013\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 014\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 015\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 016\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 017\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "\n",
            "Run 018\n",
            "\n",
            "Number of rejected epochs: 0\n",
            "\n",
            "Rejected epochs event types:\n",
            "Participant 54 - Reappraise/HbO: (48, 13, 113)\n",
            "Participant 54 - Reappraise/HbR: (48, 13, 113)\n",
            "Participant 54 - Reappraise/HbT: (48, 13, 113)\n",
            "Participant 54 - view_negative/HbO: (48, 13, 113)\n",
            "Participant 54 - view_negative/HbR: (48, 13, 113)\n",
            "Participant 54 - view_negative/HbT: (48, 13, 113)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "def process_fnirs_data():\n",
        "    base_dir = r\"/content/drive/MyDrive/fNIRS data/neurofeedback\"\n",
        "    base_dir_sham = r\"/content/drive/MyDrive/fNIRS data/sham\"\n",
        "\n",
        "    participant_ids = [1, 3, 6, 7, 10, 11, 13, 15, 16, 17, 20, 21, 24, 28, 30, 31, 33, 37, 38, 40, 41, 43, 45, 46, 48, 50, 55, 56]\n",
        "    participants_id_sham = [2, 5, 9, 12, 14, 18, 19, 22, 23, 25, 26, 27, 29, 34, 35, 36, 39, 42, 44, 47, 49, 51, 52, 53, 54]\n",
        "\n",
        "    # Initialize group-level epochs dictionary\n",
        "    group_epochs_dict = {\n",
        "        \"Neurofeedback\": {\n",
        "            \"Reappraise/HbO\": [],\n",
        "            \"view_negative/HbO\": []\n",
        "        },\n",
        "        \"Sham\": {\n",
        "            \"Reappraise/HbO\": [],\n",
        "            \"view_negative/HbO\": []\n",
        "        }\n",
        "    }\n",
        "\n",
        "    for participant_id in participant_ids:\n",
        "        print(f\"\\nProcessing participant {participant_id} in Neurofeedback group\")\n",
        "        participant_epochs, _, _, _, _ = subject_wise_analysis_1_to_6(base_dir, participant_id, ROI=True)\n",
        "\n",
        "        for condition in [\"Reappraise\", \"view_negative\"]:\n",
        "            for signal in [\"HbO\"]:\n",
        "                key = f\"{condition}/{signal}\"\n",
        "                if participant_epochs[condition][signal].size > 0:\n",
        "                    group_epochs_dict[\"Neurofeedback\"][key].extend(participant_epochs[condition][signal])\n",
        "\n",
        "    for participant_id in participants_id_sham:\n",
        "        print(f\"\\nProcessing participant {participant_id} in Sham group\")\n",
        "        participant_epochs, _, _, _, _ = subject_wise_analysis_1_to_6(base_dir_sham, participant_id, ROI=True)\n",
        "\n",
        "        for condition in [\"Reappraise\", \"view_negative\"]:\n",
        "            for signal in [\"HbO\"]:\n",
        "                key = f\"{condition}/{signal}\"\n",
        "                if participant_epochs[condition][signal].size > 0:\n",
        "                    group_epochs_dict[\"Sham\"][key].extend(participant_epochs[condition][signal])\n",
        "\n",
        "    # Convert group-level lists to numpy arrays and create grand averages\n",
        "    grand_averages = {}\n",
        "    for group_label, conditions in group_epochs_dict.items():\n",
        "        for key, data_list in conditions.items():\n",
        "            if data_list:  # Check if the list is not empty\n",
        "                data = np.array(data_list)\n",
        "                grand_average = np.mean(data, axis=0)\n",
        "                grand_averages[f\"{group_label}/{key}\"] = grand_average\n",
        "\n",
        "    return group_epochs_dict, grand_averages\n",
        "\n",
        "# Execute the function\n",
        "participants_results, grand_averages = process_fnirs_data()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zuBhKzDZt4ma"
      },
      "outputs": [],
      "source": [
        "# print(grand_averages)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65h7RWPkt4ma"
      },
      "source": [
        "### 4.1 Plot grand average"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "fAT6DJfdcHgD",
        "outputId": "614ef742-eebc-45e3-f5fa-1be26d811d04"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_grand_averages(grand_averages, times):\n",
        "    # Plotting setup\n",
        "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(17, 5))\n",
        "\n",
        "    # Define colors and styles\n",
        "    color_dict = {\n",
        "        \"Neurofeedback/Reappraise/HbO\": \"#AA3377\",\n",
        "        \"Sham/Reappraise/HbO\": \"#87CEEB\",\n",
        "        \"Neurofeedback/view_negative/HbO\": \"#AA3377\",\n",
        "        \"Sham/view_negative/HbO\": \"#87CEEB\"\n",
        "    }\n",
        "\n",
        "    linestyles = {\n",
        "        \"Neurofeedback/Reappraise/HbO\": \"solid\",\n",
        "        \"Sham/Reappraise/HbO\": \"dashed\",\n",
        "        \"Neurofeedback/view_negative/HbO\": \"solid\",\n",
        "        \"Sham/view_negative/HbO\": \"dashed\"\n",
        "    }\n",
        "\n",
        "    y_min = -8e-8\n",
        "    y_max = 8e-8\n",
        "\n",
        "    # Plot Reappraise HbO\n",
        "    ax_reappraise = axes[0]\n",
        "    for group in [\"Neurofeedback\", \"Sham\"]:\n",
        "        key = f\"{group}/Reappraise/HbO\"\n",
        "        if key in grand_averages:\n",
        "            data = grand_averages[key]\n",
        "            mean_data = np.mean(data, axis=0)\n",
        "            std_data = np.std(data, axis=0) / np.sqrt(data.shape[0])\n",
        "\n",
        "            ax_reappraise.plot(times, mean_data,\n",
        "                               color=color_dict[key],\n",
        "                               linestyle=linestyles[key],\n",
        "                               label=group)\n",
        "\n",
        "            ax_reappraise.fill_between(times,\n",
        "                                       mean_data - 1.96 * std_data,\n",
        "                                       mean_data + 1.96 * std_data,\n",
        "                                       color=color_dict[key],\n",
        "                                       alpha=0.2)\n",
        "\n",
        "    ax_reappraise.set_title(\"Reappraise - Grand Average HbO Response\")\n",
        "    ax_reappraise.set_xlabel(\"Time (s)\")\n",
        "    ax_reappraise.set_ylabel(\"Change in Hemoglobin Concentration (mol/L)\")\n",
        "    ax_reappraise.grid(True)\n",
        "    ax_reappraise.legend()\n",
        "    ax_reappraise.set_ylim(y_min, y_max)\n",
        "    ax_reappraise.axvline(x=0, color='k', linestyle='--', alpha=0.5)\n",
        "    ax_reappraise.axvspan(-2, 0, color='gray', alpha=0.2, label='Baseline')\n",
        "\n",
        "    # Plot view_negative HbO\n",
        "    ax_view_negative = axes[1]\n",
        "    for group in [\"Neurofeedback\", \"Sham\"]:\n",
        "        key = f\"{group}/view_negative/HbO\"\n",
        "        if key in grand_averages:\n",
        "            data = grand_averages[key]\n",
        "            mean_data = np.mean(data, axis=0)\n",
        "            std_data = np.std(data, axis=0) / np.sqrt(data.shape[0])\n",
        "\n",
        "            ax_view_negative.plot(times, mean_data,\n",
        "                                  color=color_dict[key],\n",
        "                                  linestyle=linestyles[key],\n",
        "                                  label=group)\n",
        "\n",
        "            ax_view_negative.fill_between(times,\n",
        "                                          mean_data - 1.96 * std_data,\n",
        "                                          mean_data + 1.96 * std_data,\n",
        "                                          color=color_dict[key],\n",
        "                                          alpha=0.2)\n",
        "\n",
        "    ax_view_negative.set_title(\"View Negative - Grand Average HbO Response\")\n",
        "    ax_view_negative.set_xlabel(\"Time (s)\")\n",
        "    ax_view_negative.set_ylabel(\"Change in Hemoglobin Concentration (mol/L)\")\n",
        "    ax_view_negative.grid(True)\n",
        "    ax_view_negative.legend()\n",
        "    ax_view_negative.set_ylim(y_min, y_max)\n",
        "    ax_view_negative.axvline(x=0, color='k', linestyle='--', alpha=0.5)\n",
        "    ax_view_negative.axvspan(-2, 0, color='gray', alpha=0.2, label='Baseline')\n",
        "\n",
        "    for ax in axes:\n",
        "        ax.set_xlim(times[0], times[-1])\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Plot with the actual time points\n",
        "plot_grand_averages(grand_averages, epochs_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2Z7UCfVt4ma"
      },
      "source": [
        "## 5. Correlation Matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_correlation_matrices(grand_averages, ch_names_hbo):\n",
        "    \"\"\"\n",
        "    Plot correlation matrices comparing Neurofeedback and Sham groups for Reappraise and View Negative conditions.\n",
        "\n",
        "    Parameters:\n",
        "    - grand_averages (dict): Dictionary containing numpy arrays of grand average data\n",
        "    - ch_names_hbo (list): List of HbO channel names\n",
        "    \"\"\"\n",
        "    # Create a figure with 2x2 subplots for comparison\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
        "\n",
        "    # Conditions and groups to compare\n",
        "    conditions = ['Reappraise/HbO', 'view_negative/HbO']\n",
        "    groups = ['Neurofeedback', 'Sham']\n",
        "\n",
        "    for row_idx, condition in enumerate(conditions):\n",
        "        for col_idx, group in enumerate(groups):\n",
        "            key = f'{group}/{condition}'\n",
        "            data = grand_averages[key]\n",
        "            corr_matrix = np.corrcoef(data)\n",
        "\n",
        "            # Plot correlation matrix in subplot\n",
        "            ax = axes[row_idx, col_idx]\n",
        "            im = ax.imshow(corr_matrix, interpolation='none', aspect='auto',\n",
        "                           cmap='RdBu_r', vmin=-1, vmax=1)\n",
        "            fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)\n",
        "\n",
        "            # Set title and labels\n",
        "            ax.set_title(f'{group} - {condition.split(\"/\")[0]}')\n",
        "            ax.set_xlabel('Channel')\n",
        "            ax.set_ylabel('Channel')\n",
        "\n",
        "            # Set tick labels\n",
        "            ax.set_xticks(range(len(ch_names_hbo)))\n",
        "            ax.set_xticklabels(ch_names_hbo, rotation='vertical')\n",
        "            ax.set_yticks(range(len(ch_names_hbo)))\n",
        "            ax.set_yticklabels(ch_names_hbo)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Example function call\n",
        "plot_correlation_matrices(grand_averages, hbo_ch_names)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "NxoTd5c_ZJxC",
        "outputId": "a478ae26-1110-46c1-dd25-4864813af3c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 8 Axes>"
            ],
            "image/png": 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4QVXXL7/8gtzcXIwaNUot15mYmMDPz6/K3Pvo0SPcu3cPL7zwAgBUev2TJk1S+6x8Seuzedbf3x89e/ZUfXZ2dsaQIUOwb98+lJeXq7WNiIioME5N61JS5t5///vfKCsrq7TNd999Bzs7OwwYMEDt59OzZ0/I5XLmXiISHZdtISKt5s6di02bNmHx4sVYuXJlheNXr16FIAho3759pf31eczO1dVV7fP169cBAB06dKjQ1tPTE7/++qvG8129ehUANE4c5+XloaysDLm5uVi7di3Wrl1baTvl+t3Xr1+Hh4dHhcnqymoEgNatW1d48c5zzz0H4MljnsogmpycjOXLl+PKlStqYfPpn0l6ejpat26Npk2bVnk9Sl9++SUKCwuxevVqjBo1Smt7ALh//z5KS0tVny0tLWFnZ6exT7t27bBu3TooFAqkp6fjo48+wt27d9V+2VGTvzM3btzA/PnzsWvXrgrrHubl5al9lkqlcHNzU9v39M/2aZWN/dxzz+Hbb79V22dqaoq2bdtWaFuTup72xhtv4IsvvsDEiRMxe/ZsBAYG4rXXXsPrr7+uehnu1atXkZaWhhYtWlR6DmN5CRcREREZtueffx47duxAaWkpLly4gO+//x4rVqzA66+/jvPnz8PLy0vV1tnZuUL/Jk2aqOWohw8fYtGiRdi4cSNu3ryptk55Zfnq2XMqc6qTk1OF/drWz87Ly1N7d425ubnWjG1hYYHdu3cDePIOpPj4eNy5c0dtsln5/UN5E8SzbG1tVf98//59xMbG4uuvv66Q9yq7/mfzrLu7O6RSabVzb3FxMe7evQtHR0fV/me/f+lSl1KfPn0wbNgwxMbGYsWKFejbty+GDh2K0aNHQyaTAXjy88nLy6tynXzmXiISGyfPiUgrNzc3vPXWW1i7di1mz55d4bhCoYBEIsFPP/1U6dvV5XK56p+rWg+vvLy80r5PB8/aoLzTd+nSpWrrhj9NLperXn7z1ltvVTnR3qVLl1qt7WmbN29GaGgohg4dipkzZ8LBwQEmJiZYtGgR0tPTdTpnQEAAzp8/j88++wwjRoyo1oT7a6+9hv/85z+qz+PGjVO7g7wy1tbW6N+/v9q4PXr0wJw5c5CYmAig+n9nysvLMWDAANy/fx/vv/8+PD09YW1tjZs3byI0NLTKO7drk0wmU01qK+lTl6WlJQ4dOoSDBw9iz5492Lt3L7755hu89NJL+Pnnn2FiYgKFQoHOnTvjk08+qfQcz34hJCIiIhKTubk5nn/+eTz//PN47rnnMH78eHz33XdqT0dWlvkAqE2QT548GRs3bsR7770Hf39/2NnZQSKRYOTIkZXmq6rOWdl+QcsLQ6dOnYrk5GTV5z59+iA1NVVjHxMTE7XcGxQUBE9PT7zzzjuqu++VdW/atEltklrJ1PR/0zIjRozA0aNHMXPmTHTr1g1yuRwKhQLBwcHVyr1Vfdeqicq+f+lal0QiwbZt23D8+HHs3r0b+/btw4QJE7B8+XIcP35cdR4HBwd89dVXlZ6jqptJiIjqCyfPiaha5s6di82bN2PJkiUVjrm7u0MQBLi6uqru8q1KkyZNkJubW2H/9evXK9wxXBkXFxcAwO+//17h7o3ff/9ddbwqykdFbW1t1YLus1q0aAEbGxuUl5drbKes6fLlyxAEQS2w/v7775W2v3XrFoqKitTuPv/jjz8AQPWyoG3btsHNzQ07duxQO+ezy7O4u7tj3759uH//vtbJcA8PD8THx6Nv374IDg5GSkoKbGxsNPZZvny52l06ml4aW5UuXbrgrbfewr/+9S/MmDEDzs7O1f47c+nSJfzxxx9ITk5We8HTL7/8Uml7hUKBv/76S+2cz/5slZR3AT3tjz/+qNCuNup6llQqRWBgIAIDA/HJJ5/g448/xgcffICDBw+qHmm+cOECAgMDa+VLEBEREVF9Ub6wvqolHzXZtm0bxo0bh+XLl6v2PXr0qNLvD7Vt1qxZeOutt1Sfn37hZ3W1atUK06ZNU73A9IUXXlB9/3BwcND4veLBgwdISUlBbGws5s+fr9pfWWZ9+tjTd4r/+eefUCgU1c69VlZWWiendanrWS+88AJeeOEFfPTRR9iyZQvefPNNfP3115g4cSLc3d2xf/9+BAQE1PqNU0REtYFrnhNRtbi7u6smQLOystSOvfbaazAxMUFsbGyFOzoEQVDdxa08z/Hjx9WWAvn3v/+NzMzMatXh4+MDBwcHrFmzBiUlJar9P/30E9LS0hASEqKxf8+ePeHu7o5ly5ahsLCwwvG7d+8CeHIXybBhw7B9+3Zcvny5ynYAMGjQINy6dQvbtm1T7SsuLq5yuZfHjx/jX//6l+pzaWkp/vWvf6FFixaqtQiVd8s8/fM8ceIEjh07pnauYcOGQRAExMbGVhinsrtrunTpgh9//BFpaWl45ZVX1B5NrUzPnj3Rv39/1fb0o7c1MWvWLJSVlanupK7u35nKfg6CIFS6fJDSZ599ptb2s88+g5mZGQIDA9Xa7dy5Ezdv3lR9PnnyJE6cOIGBAwdqvR5d6lK6f/9+hX3KpyCUf6dHjBiBmzdvYt26dRXaPnz4EEVFRVrHISIiIqpLBw8erDRvKtfbrmoJQ01MTEwqnPPTTz+tsC53XfDy8lLLvU+vEV4TkydPhpWVFRYvXgzgyd3otra2+Pjjjytd9/vp7x9AxQyfkJBQ5VirVq1S+/zpp58CQIU8e+zYMbW1yTMzM/HDDz/g5ZdfrvLufSVd6lJ68OBBhX6V5d7y8nIsXLiwQv/Hjx/Xyy9OiIg04Z3nRFRtH3zwATZt2oTff/8d3t7eqv3u7u748MMPER0djYyMDAwdOhQ2Nja4du0avv/+e7z99tuYMWMGAGDixInYtm0bgoODMWLECKSnp2Pz5s1qLw/SxMzMDEuWLMH48ePRp08fjBo1CtnZ2Vi5ciXatWuHadOmaewvlUrxxRdfYODAgfD29sb48ePRpk0b3Lx5EwcPHoStra1q3cLFixfj4MGD8PPzQ3h4OLy8vHD//n2cPXsW+/fvV02ChoeH47PPPsPYsWNx5swZtGrVCps2bYKVlVWlNbRu3RpLlixBRkYGnnvuOXzzzTc4f/481q5dq1rr+x//+Ad27NiBV199FSEhIbh27RrWrFkDLy8vtUn/fv36YcyYMUhMTMTVq1dVj04ePnwY/fr1U3t5ptILL7yAH374AYMGDcLrr7+OnTt36rUufXV4eXlh0KBB+OKLLzBv3rxq/53x9PSEu7s7ZsyYgZs3b8LW1hbbt2+vcs1KCwsL7N27F+PGjYOfnx9++ukn7NmzB3PmzKlwV42Hhwd69eqFyMhIlJSUICEhAc2aNcOsWbO0Xk9N63paXFwcDh06hJCQELi4uODOnTv4/PPP0bZtW/Tq1QsAMGbMGHz77beIiIjAwYMHERAQgPLycly5cgXffvst9u3bp7qri4iIiEgMkydPRnFxMV599VV4enqitLQUR48exTfffIN27dph/PjxNT7nP/7xD2zatAl2dnbw8vLCsWPHsH//fjRr1qwOrqBuNGvWDOPHj8fnn3+OtLQ0dOzYEatXr8aYMWPQo0cPjBw5Ei1atMCNGzewZ88eBAQE4LPPPoOtrS169+6N+Ph4lJWVoU2bNvj5559x7dq1Kse6du0aBg8ejODgYBw7dgybN2/G6NGj0bVrV7V2nTp1QlBQEKZMmQKZTIbPP/8cACq9AedZutSllJycjM8//xyvvvoq3N3dUVBQgHXr1sHW1haDBg0C8GR5nHfeeQeLFi3C+fPn8fLLL8PMzAxXr17Fd999h5UrV+L111/XOhYRUZ0RiIiesXHjRgGAcOrUqQrHxo0bJwAQvL29Kxzbvn270KtXL8Ha2lqwtrYWPD09hUmTJgm///67Wrvly5cLbdq0EWQymRAQECCcPn1a6NOnj9CnTx9Vm4MHDwoAhO+++67SGr/55huhe/fugkwmE5o2bSq8+eabwt9//13t6zh37pzw2muvCc2aNRNkMpng4uIijBgxQkhJSVFrl52dLUyaNElwcnISzMzMBEdHRyEwMFBYu3atWrvr168LgwcPFqysrITmzZsLU6dOFfbu3SsAEA4ePKhq16dPH8Hb21s4ffq04O/vL1hYWAguLi7CZ599pnY+hUIhfPzxx4KLi4sgk8mE7t27C//+97+FcePGCS4uLmptHz9+LCxdulTw9PQUzM3NhRYtWggDBw4Uzpw5o2oDQJg0aZJavx9++EEwNTUV3njjDaG8vLzSn3NNKa+vMqmpqQIAISYmRrWvOn9n/vvf/wr9+/cX5HK50Lx5cyE8PFy4cOGCAEDYuHGjqt24ceMEa2trIT09XXj55ZcFKysroWXLlkJMTIza9V27dk0AICxdulRYvny54OTkJMhkMuH//u//hAsXLqjVrDxnZapbV0xMjPD0/92mpKQIQ4YMEVq3bi2Ym5sLrVu3FkaNGiX88ccfaucvLS0VlixZInh7ewsymUxo0qSJ0LNnTyE2NlbIy8ur8s+AiIiIqD789NNPwoQJEwRPT09BLpcL5ubmgoeHhzB58mQhOztbrW1lWVQQBMHFxUUYN26c6vODBw+E8ePHC82bNxfkcrkQFBQkXLlypUK7qnK+MnfdvXtXbb+mTKcrTedMT08XTExM1Go+ePCgEBQUJNjZ2QkWFhaCu7u7EBoaKpw+fVrV5u+//xZeffVVwd7eXrCzsxOGDx8u3Lp1q0KGVl7nf//7X+H1118XbGxshCZNmghRUVHCw4cP1WpR/uw3b94stG/fXvXd4unvKE+f89mfXU3qUv65XLt2TRAEQTh79qwwatQowdnZWZDJZIKDg4Pwj3/8Q+2aldauXSv07NlTsLS0FGxsbITOnTsLs2bNEm7dulXFnwARUf2QCIKWt2YQERE1AKGhodi2bVuly/E8LSMjA66urli6dKnqiQgiIiIiooZiwYIFiI2Nxd27d9G8eXONbSUSCSZNmqS2tCEREVUf1zwnIiIiIiIiIiIiInoGJ8+JiIiIiIiIiIiIiJ7ByXMiIiIiIiIiIiIiomdw8pyIiBqFpKQkreudA0C7du0gCALXOydqhA4dOoRXXnkFrVu3hkQiwc6dO7X2SU1NRY8ePSCTyeDh4YGkpKQKbVatWoV27drBwsICfn5+OHnyZO0XT0REVE0LFiyAIAha1zsHAEEQuN45EYmisWRzTp4TERERUaNQVFSErl27YtWqVdVqf+3aNYSEhKBfv344f/483nvvPUycOBH79u1Ttfnmm28wffp0xMTE4OzZs+jatSuCgoJw586duroMIiIiIqIGr7Fkc4kgCEKdnZ3qhUKhwK1bt2BjYwOJRCJ2OURERNQACIKAgoICtG7dGlJp3dxP8ejRI5SWlup1DkEQKuQbmUwGmUymsZ9EIsH333+PoUOHVtnm/fffx549e3D58mXVvpEjRyI3Nxd79+4FAPj5+eH5559X3bWnUCjg5OSEyZMnY/bs2TpeFTVmzOZERERUU3WdzWsjlwPGmc1N6+SsVK9u3boFJycnscsgIiKiBigzMxNt27at9fM+evQIzSzlKEa5XueRy+UVlmSKiYnBggUL9DovABw7dgz9+/dX2xcUFIT33nsPAFBaWoozZ84gOjpadVwqlaJ///44duyY3uNT48RsTkRERLqqi2xeW7kcMM5szsnzRsDGxgYA8CbawNyAV+JZlHVK7BI0Wu/+otglaFXeAJ4T6enZVOwSNMrKyBe7BK18RnUTuwStMg5eFbsErQLWfCB2CRrdbPuC2CVo5Xh2m9glaHVlww9il6DRqWkrxS6hSo+KCjF36IuqHFHbSktLUYxyvfJJKRT4qvAmMjMzYWtrq9qv7c6W6srKykLLli3V9rVs2RL5+fl4+PAhHjx4gPLy8krbXLlypVZqoManoWTzd97oKHYJGuVm5Ildgla+az4WuwStbtl7il2CRvcfPha7BK3c/vO52CVo9fiR/neT1jUr9w5il6CRiXs3sUvQSjAxE7sErS48Nuz5gC7WZWKXUKWCgkK4delRJ9m8NnI5YLzZnJPnjYDycQlzSA06oD/9L5YhspCYiF2CVuUw/NlzaxPD/s+KldTw/5xtZIYfiqxNDP/naCu3ErsEjfJtDPu/iQBga20pdglayc0M+785ltZ1MzFdm+p6WQlLSGEu0S2fmPz//9uztbU1+BxBpNRQsrnc3LDzRpmpYf/3HQBsbazFLkGrAgPPG6Wmhj95bmtZO5NCdelxA1giysrAc6WJjVzsErRqCJPn8seG/d8cW7nh/6KpLrO5PrkcMN5sbviJhIiIiIgaLBOJBCY6fgkwgQR1+XtjR0dHZGdnq+3Lzs6Gra0tLC0tYWJiAhMTk0rbODo61l1hRERERES1TJ9cDhhvNjfcWyGIiIiIqMGTSgATHTdpHd9I5+/vj5SUFLV9v/zyC/z9/QEA5ubm6Nmzp1obhUKBlJQUVRsiIiIiooZAn1xuzNmck+dERERE1CgUFhbi/PnzOH/+PADg2rVrOH/+PG7cuAEAiI6OxtixY1XtIyIi8Ndff2HWrFm4cuUKPv/8c3z77beYNm2aqs306dOxbt06JCcnIy0tDZGRkSgqKsL48ePr9dqIiIiIiBqSxpLNuWwLEREREdUZvZdtqYHTp0+jX79+qs/Tp08HAIwbNw5JSUm4ffu2KqwDgKurK/bs2YNp06Zh5cqVaNu2Lb744gsEBQWp2rzxxhu4e/cu5s+fj6ysLHTr1g179+6t8KIiIiIiIiJDVivLttRAY8nmnDwnIiIiojqjfMxTp741bN+3b18IQtULMSYlJVXa59y5cxrPGxUVhaioqBpWQ0RERERkOPTJ5YDxZnMu20JERERERERERERE9AxOnushKSkJ9vb2GtuEhoZi6NCh9VIPERERkaFRPh6q60ZUHczlRERERJrpm8uNNZuLOnl+9+5dREZGwtnZGTKZDI6OjggKCsKRI0cAAPfv38fkyZPRoUMHWFpawtnZGVOmTEFeXl61zp+RkQGJRKLabGxs4O3tjUmTJuHq1atqbXfs2IEBAwagRYsWsLW1hb+/P/bt21fr10xERERkTJSPh+q6Uf1gLiciIiJq3PTN5caazUWdPB82bBjOnTuH5ORk/PHHH9i1axf69u2LnJwcAMCtW7dw69YtLFu2DJcvX0ZSUhL27t2LsLCwGo2zf/9+3L59GxcuXMDHH3+MtLQ0dO3aFSkpKao2hw4dwoABA/Djjz/izJkz6NevH1555RWt6+wQERERUdV4d0vDwFxORERE1LjxznPdiDZ5npubi8OHD2PJkiXo168fXFxc4Ovri+joaAwePBgA0KlTJ2zfvh2vvPIK3N3d8dJLL+Gjjz7C7t278fjx42qP1axZMzg6OsLNzQ1DhgzB/v374efnh7CwMJSXlwMAEhISMGvWLDz//PNo3749Pv74Y7Rv3x67d+/Wev59+/ahY8eOkMvlCA4Oxu3btyu0iY2NVd09ExERgdLSUtWxkpISTJkyBQ4ODrCwsECvXr1w6tSpal8fEREREZGumMuZy4mIiIiocqJNnsvlcsjlcuzcuRMlJSXV7peXlwdbW1uYmprqPLZUKsXUqVNx/fp1nDlzptI2CoUCBQUFaNq0qcZzFRcXY9myZdi0aRMOHTqEGzduYMaMGWptUlJSkJaWhtTUVGzduhU7duxAbGys6visWbOwfft2JCcn4+zZs/Dw8EBQUBDu379f6ZglJSXIz89X24iIiIgMkQRPAqcum3He21L/mMt1z+UAszkRERE1DPrkcmPO5qJNnpuamiIpKQnJycmwt7dHQEAA5syZg4sXL1bZ5969e1i4cCHefvttvcf39PQE8GT9xcosW7YMhYWFGDFihMbzlJWVYc2aNfDx8UGPHj0QFRWl9tgpAJibm2PDhg3w9vZGSEgI4uLikJiYCIVCgaKiIqxevRpLly7FwIED4eXlhXXr1sHS0hLr16+vdMxFixbBzs5OtTk5OdX8B0BERERUD/hoqOFjLtc9lwPM5kRERNQwcNkW3Yi+5vmtW7ewa9cuBAcHIzU1FT169EBSUlKFtvn5+QgJCYGXlxcWLFig99iCIAAAJJX8wW/ZsgWxsbH49ttv4eDgoPE8VlZWcHd3V31u1aoV7ty5o9ama9eusLKyUn329/dHYWEhMjMzkZ6ejrKyMgQEBKiOm5mZwdfXF2lpaZWOGR0djby8PNWWmZmp/YKJiIiIRMCXEjUMzOW65XKA2ZyIiIgaBr4wVDeiTp4DgIWFBQYMGIB58+bh6NGjCA0NRUxMjFqbgoICBAcHw8bGBt9//z3MzMz0HlcZgF1dXdX2f/3115g4cSK+/fZb9O/fX+t5nq1FIpGovgDUFZlMBltbW7WNiIiIiEgfzOW6YTYnIiIiarxEnzx/lpeXF4qKilSf8/Pz8fLLL8Pc3By7du2ChYWF3mMoFAokJibC1dUV3bt3V+3funUrxo8fj61btyIkJETvcZQuXLiAhw8fqj4fP34ccrkcTk5OcHd3h7m5OY4cOaI6XlZWhlOnTsHLy6vWaiAiIiISw5O7VHR9NFTs6o0bczlzORERETUe+uVy483mur/dR085OTkYPnw4JkyYgC5dusDGxganT59GfHw8hgwZAuB/Ab24uBibN29WewFPixYtYGJiUu2xsrKyUFxcjMuXLyMhIQEnT57Enj17VOfYsmULxo0bh5UrV8LPzw9ZWVkAAEtLS9jZ2el1raWlpQgLC8PcuXORkZGBmJgYREVFQSqVwtraGpGRkZg5cyaaNm0KZ2dnxMfHo7i4GGFhYXqNS0RERCQ2fR7xrF7SI30xlzOXExERUeOn79IrxprNRZs8l8vl8PPzw4oVK1TrCzo5OSE8PBxz5swBAJw9exYnTpwAAHh4eKj1v3btGtq1a1etsZSPeVpZWcHFxQX9+vXD2rVr1c65du1aPH78GJMmTcKkSZNU+8eNG1fpWo81ERgYiPbt26N3794oKSnBqFGj1NaHXLx4MRQKBcaMGYOCggL4+Phg3759aNKkiV7jEhERERFpw1y+QHWcuZyIiIiIniYR6mMhQKpT+fn5sLOzw3g4wdzwVuJRWVFc9YuWDMFqh65il6BVeQP419XXq7nYJWh0+1qe2CVo5Te2h9glaPXXL7+LXYJWvZPjxC5Bo7+deoldglatTm8VuwSt/rtmm9glaHT8/bVil1Clh0UFmDGgC/Ly8upkjWZlPlksd4eFRLf7VB4J5ZhdmF5nNRLVhYaSzSeP6SR2CRo9+CtX7BK08k9eLnYJWt209xa7BI1yHj4WuwStPFJWiF2CVo8flYpdglZW7TuKXYJGJu17il2CVoKJ/u8ZqWvnHhv2fEA3ueH+u5KfX4AWrs/VSe6tjVwOGG82F+3OcyIiIiJq/KR6PB5quNOOREREREQNiz65HDDebN6grzsiIgJyubzSLSIiQuzyiIiIiIyefi8lMtK3EjVAzOVEREREhk3fXG6s2bxB33keFxeHGTNmVHrMmB4fICIiIiISE3M5ERERETVGDXry3MHBAQ4ODmKXQURERERVMNHj8VDdV2Sk+sZcTkRERGTY9MnlgPFm8wY9eU5EREREho2T50RERERE4uPkuW44ed6ILMo6ZdCPxU6zMuy3a396epXYJWh1u10fsUvQytTA36TQoqRc7BK0utr3JbFL0Oql7+LFLkGrxzfTxS5Bo3sfDBW7BK2cls4VuwStShO+FrsEjd4pSRO7hCrlmxeh8kU2apc+6yOawDjXVaTG4Z03OkJubiZ2GVX6dNNlsUvQyKwB/OvvtjVJ7BK0spyyTOwSNPpw5+9il6BVp0mbxS5BK5+mlmKXoJVH0G9il6CRZYv9YpegVesZcWKXoNUvaffELkGj7vZXxS6hSpLC4jofQ991y401mxv4NBcRERERERERERERUf3jnedEREREVGdMoMeyLUKtlkJEREREZLT0yeWA8WZzTp4TERERUZ2R6vF4qFSPx0qJiIiIiOh/9Mnlyv7GiMu2EBERERERERERERE9g3eeExEREVGdMZHosWyLcd7cQkRERERU6/TJ5cr+xoiT50RERERUZ0z0eDxUn8dKiYiIiIjof/TJ5cr+xoiT50RERERUZ3jnORERERGR+HjnuW645jkRERERERERERER0TN45zkRERER1Rku20JEREREJD4u26Ib3nmuh6SkJNjb22tsExoaiqFDh9ZLPURERESGRiqR6LURVQdzOREREZFm+uZyY83mok6e3717F5GRkXB2doZMJoOjoyOCgoJw5MgRAMD9+/cxefJkdOjQAZaWlnB2dsaUKVOQl5dXrfNnZGRAIpGoNhsbG3h7e2PSpEm4evWqWttff/0VAQEBaNasGSwtLeHp6YkVK1bU+jUTERERGROJiUSvjeoHczkRERFR46ZvLjfWbC7qsi3Dhg1DaWkpkpOT4ebmhuzsbKSkpCAnJwcAcOvWLdy6dQvLli2Dl5cXrl+/joiICNy6dQvbtm2r9jj79++Ht7c3iouLcenSJaxcuRJdu3bF7t27ERgYCACwtrZGVFQUunTpAmtra/z666945513YG1tjbfffrtOrp+IiIiIyBAwlxMRERERVSTanee5ubk4fPgwlixZgn79+sHFxQW+vr6Ijo7G4MGDAQCdOnXC9u3b8corr8Dd3R0vvfQSPvroI+zevRuPHz+u9ljNmjWDo6Mj3NzcMGTIEOzfvx9+fn4ICwtDeXk5AKB79+4YNWoUvL290a5dO7z11lsICgrC4cOHtZ5/37596NixI+RyOYKDg3H79u0KbWJjY9GiRQvY2toiIiICpaWlqmMlJSWYMmUKHBwcYGFhgV69euHUqVPVvj4iIiIiQyU1kei1Ud1jLmcuJyIiosZP31xurNlctMlzuVwOuVyOnTt3oqSkpNr98vLyYGtrC1NT3W+al0qlmDp1Kq5fv44zZ85U2ubcuXM4evQo+vTpo/FcxcXFWLZsGTZt2oRDhw7hxo0bmDFjhlqblJQUpKWlITU1FVu3bsWOHTsQGxurOj5r1ixs374dycnJOHv2LDw8PBAUFIT79+9XOmZJSQny8/PVNiIiIiKDZCKFRMcNJnw9T31gLtc9lwPM5kRERNRA6JHLjTmbi3bVpqamSEpKQnJyMuzt7REQEIA5c+bg4sWLVfa5d+8eFi5cWCuPa3p6egJ4sv7i09q2bQuZTAYfHx9MmjQJEydO1HiesrIyrFmzBj4+PujRoweioqKQkpKi1sbc3BwbNmyAt7c3QkJCEBcXh8TERCgUChQVFWH16tVYunQpBg4cCC8vL6xbtw6WlpZYv359pWMuWrQIdnZ2qs3JyUn3HwQRERFRHZJI9VhXUarb3S2rVq1Cu3btYGFhAT8/P5w8ebLKtn379lVbi1u5hYSEqNqEhoZWOB4cHKxTbYaIuVz3XA4wmxMREVHDoFcu1zGbN4ZcLuqvDIYNG4Zbt25h165dCA4ORmpqKnr06IGkpKQKbfPz8xESEgIvLy8sWLBA77EFQQAASJ55U+zhw4dx+vRprFmzBgkJCdi6davG81hZWcHd3V31uVWrVrhz545am65du8LKykr12d/fH4WFhcjMzER6ejrKysoQEBCgOm5mZgZfX1+kpaVVOmZ0dDTy8vJUW2ZmZvUumoiIiKiR++abbzB9+nTExMTg7Nmz6Nq1K4KCgirkM6UdO3bg9u3bqu3y5cswMTHB8OHD1doplwBRbtoyYkPDXK5bLgeYzYmIiIgq01hyuagvDAUACwsLDBgwAAMGDMC8efMwceJExMTEIDQ0VNWmoKAAwcHBsLGxwffffw8zMzO9x1UGYFdXV7X9ys+dO3dGdnY2FixYgFGjRlV5nmdrkUgkqi8AdUUmk0Emk9XpGERERES1QZ/1EaV40u/ZZTA0ZaFPPvkE4eHhGD9+PABgzZo12LNnDzZs2IDZs2dXaN+0aVO1z19//TWsrKwqhHSZTAZHR0edrqOhYC7XDbM5ERERNQT6rlte02zeWHK5wS1W4+XlhaKiItXn/Px8vPzyyzA3N8euXbtgYWGh9xgKhQKJiYlwdXVF9+7dNbarybqPVblw4QIePnyo+nz8+HHI5XI4OTnB3d0d5ubmOHLkiOp4WVkZTp06BS8vL73HJiIiIhKTRCrVawMAJycntWUxFi1aVOlYpaWlOHPmDPr376/aJ5VK0b9/fxw7dqxa9a5fvx4jR46EtbW12v7U1FQ4ODigQ4cOiIyMRE5Ojo4/kYaDuZy5nIiIiBoPfXN5TbJ5Y8rlot15npOTg+HDh2PChAno0qULbGxscPr0acTHx2PIkCEA/hfQi4uLsXnzZrUX8LRo0QImJibVHisrKwvFxcW4fPkyEhIScPLkSezZs0d1jlWrVsHZ2Vm15uKhQ4ewbNkyTJkyRe9rLS0tRVhYGObOnYuMjAzExMQgKioKUqkU1tbWiIyMxMyZM9G0aVM4OzsjPj4excXFCAsL03tsIiIiooYuMzMTtra2qs9V3eV77949lJeXo2XLlmr7W7ZsiStXrmgd5+TJk7h8+XKF9a2Dg4Px2muvwdXVFenp6ZgzZw4GDhyIY8eOVTuPGjLmcuZyIiIiouqqTjZvTLlctMlzuVwOPz8/rFixQrW+oJOTE8LDwzFnzhwAwNmzZ3HixAkAgIeHh1r/a9euoV27dtUaS/lbDisrK7i4uKBfv35Yu3at2jkVCgWio6Nx7do1mJqawt3dHUuWLME777yj97UGBgaiffv26N27N0pKSjBq1Ci19SEXL14MhUKBMWPGoKCgAD4+Pti3bx+aNGmi99hEREREYqqNZVtsbW3VAnpdWb9+PTp37gxfX1+1/SNHjlT9c+fOndGlSxe4u7sjNTUVgYGBdV5XXWMuX6A6zlxOREREjVVtLdtSH9nckHK5aJPnMpkMixYtqvKxW+DJW1b1WaewXbt21e4/efJkTJ48uUbnDw0NVVsDEgCGDh2qNubTL1mKjY2t9DwWFhZITExEYmJijcYnIiIiMnQSEwkkOoZ0CWrWr3nz5jAxMUF2drba/uzsbK3rIhYVFeHrr79GXFyc1nHc3NzQvHlz/Pnnn41i8py5/H+Yy4mIiKix0ieXAzXL5o0plxvcmudERERE1Hg8CelSHbeahXtzc3P07NkTKSkpqn0KhQIpKSnw9/fX2Pe7775DSUkJ3nrrLa3j/P3338jJyUGrVq1qVB8RERERkVj0y+U1y+aNKZc36MnziIgIyOXySreIiAixyyMiIiKiejZ9+nSsW7cOycnJSEtLQ2RkJIqKijB+/HgAwNixYxEdHV2h3/r16zF06FA0a9ZMbX9hYSFmzpyJ48ePIyMjAykpKRgyZAg8PDwQFBRUL9fUEDCXExEREdHTGksuF23ZltoQFxeHGTNmVHqsPtbFJCIiIiLNamPN85p44403cPfuXcyfPx9ZWVno1q0b9u7dq3pZ0Y0bNyCVqt8/8vvvv+PXX3/Fzz//XOF8JiYmuHjxIpKTk5Gbm4vWrVvj5ZdfxsKFC6t8cakxYi4nIiIiMmy1teZ5dTWWXN6gJ88dHBzg4OAgdhlEREREVAWJRAKJVMc1zxW69YuKikJUVFSlx1JTUyvs69ChQ5XrcVtaWmLfvn061WFMmMuJiIiIDJs+uRzQLZs3hlzeoCfPiYiIiMiwSU2kkJrotlKgVGjQKwwSERERERkMfXI5YLzZnJPnjch69xdhITERu4wqfXp6ldglaDTZZ5LYJWg16nnDfzGZ68sdxS5Bo7Lfb4ldglYtvJqLXYJWv4Z+IHYJWgWsnSd2CRp1XzpX7BK0UuTliF2CVh77PxK7BI3+M2i22CVUqUhaIHYJRI1abkYeykwN9+uWme43ntWLsspvOjMoxXceiF2CVm2lj8UuQaMuzvZil6BVK0szsUvQqqhMIXYJWtm3dxK7BI1yr2aKXYJW0rvXxC5Bq/bNnxO7BI0e3zDcn+Hj4odil0BVMNw0R0REREQNnsREAomOaytKBAOf3SMiIiIiaiD0yeWA8WZzTp4TERERUZ3h5DkRERERkfg4ea4b41yshoiIiIiIiIiIiIhIA955TkRERER1hi8MJSIiIiISH18YqhtOnhMRERFR3dHn8VAjfTSUiIiIiKjW6blsi7Fmc06eExEREVGdkUokkEp1C9pSiXEGdCIiIiKi2qZPLlf2N0bGeb89EREREREREREREZEGvPOciIiIiOqMxEQKiY5rK0oUvM+DiIiIiKg26JPLAePN5pw8JyIiIqI6IzWRQKrj2opShXE+GkpEREREVNv0yeWA8WZzTp4TERERUZ2R6PFiIomRBnQiIiIiotqmTy4HjDebG+f99rUkKSkJ9vb2GtuEhoZi6NCh9VIPEREREZExYi4nIiIiorog6uT53bt3ERkZCWdnZ8hkMjg6OiIoKAhHjhwBANy/fx+TJ09Ghw4dYGlpCWdnZ0yZMgV5eXnVOn9GRgYkEolqs7Gxgbe3NyZNmoSrV69W2e/IkSMwNTVFt27dauMyiYiIiIyWcm1FXTeqH8zlRERERI2bvrncWLO5qMu2DBs2DKWlpUhOToabmxuys7ORkpKCnJwcAMCtW7dw69YtLFu2DF5eXrh+/ToiIiJw69YtbNu2rdrj7N+/H97e3iguLsalS5ewcuVKdO3aFbt370ZgYKBa29zcXIwdOxaBgYHIzs6u1eslIiIiMjZSE+ix5nktF0NVYi4nIiIiatz0yeWA8WZz0X5lkJubi8OHD2PJkiXo168fXFxc4Ovri+joaAwePBgA0KlTJ2zfvh2vvPIK3N3d8dJLL+Gjjz7C7t278fjx42qP1axZMzg6OsLNzQ1DhgzB/v374efnh7CwMJSXl6u1jYiIwOjRo+Hv71/t8+/btw8dO3aEXC5HcHAwbt++XaFNbGwsWrRoAVtbW0RERKC0tFR1rKSkBFOmTIGDgwMsLCzQq1cvnDp1qsrxSkpKkJ+fr7YREREREemCuVz3XK7sw2xORERE1DiJNnkul8shl8uxc+dOlJSUVLtfXl4ebG1tYWqq+03zUqkUU6dOxfXr13HmzBnV/o0bN+Kvv/5CTExMtc9VXFyMZcuWYdOmTTh06BBu3LiBGTNmqLVJSUlBWloaUlNTsXXrVuzYsQOxsbGq47NmzcL27duRnJyMs2fPwsPDA0FBQbh//36lYy5atAh2dnaqzcnJqYY/ASIiIqL6IZFK9Nqo7jGX657LAWZzIiIiahj0zeXGms1Fmzw3NTVFUlISkpOTYW9vj4CAAMyZMwcXL16sss+9e/ewcOFCvP3223qP7+npCeDJ+osAcPXqVcyePRubN2+u0ReAsrIyrFmzBj4+PujRoweioqKQkpKi1sbc3BwbNmyAt7c3QkJCEBcXh8TERCgUChQVFWH16tVYunQpBg4cCC8vL6xbtw6WlpZYv359pWNGR0cjLy9PtWVmZur2QyAiIiKqY1KpFFITHTepca6rWN+Yy3XP5QCzORERETUMeuVyI87mol71sGHDcOvWLezatQvBwcFITU1Fjx49kJSUVKFtfn4+QkJC4OXlhQULFug9tiAIAACJRILy8nKMHj0asbGxeO6552p0HisrK7i7u6s+t2rVCnfu3FFr07VrV1hZWak++/v7o7CwEJmZmUhPT0dZWRkCAgJUx83MzODr64u0tLRKx5TJZLC1tVXbiIiIiAyRxESi10b1g7lct1wOMJsTERFRw6BvLjfWbC76rwwsLCwwYMAAzJs3D0ePHkVoaGiFxzMLCgoQHBwMGxsbfP/99zAzM9N7XGUAdnV1RUFBAU6fPo2oqCiYmprC1NQUcXFxuHDhAkxNTXHgwIEqz/NsLRKJRPUFgIiIiIiooWAuJyIiIiJSJ/rk+bO8vLxQVFSk+pyfn4+XX34Z5ubm2LVrFywsLPQeQ6FQIDExEa6urujevTtsbW1x6dIlnD9/XrVFRESgQ4cOOH/+PPz8/PQa78KFC3j48KHq8/HjxyGXy+Hk5AR3d3eYm5vjyJEjquNlZWU4deoUvLy89BqXiIiISGwSE6leG4mHuZy5nIiIiBoPfXO5sWZz3d/uo6ecnBwMHz4cEyZMQJcuXWBjY4PTp08jPj4eQ4YMAfC/gF5cXIzNmzervb2+RYsWMDExqfZYWVlZKC4uxuXLl5GQkICTJ09iz549qnN06tRJrY+DgwMsLCwq7NdFaWkpwsLCMHfuXGRkZCAmJgZRUVGQSqWwtrZGZGQkZs6ciaZNm8LZ2Rnx8fEoLi5GWFiY3mMTERERiUkilUKi4/qIuvajmmEuZy4nIiKixk+fXK7sb4xEmzyXy+Xw8/PDihUrVOsLOjk5ITw8HHPmzAEAnD17FidOnAAAeHh4qPW/du0a2rVrV62x+vfvD+DJOoguLi7o168f1q5dW+GcdSUwMBDt27dH7969UVJSglGjRqmtD7l48WIoFAqMGTMGBQUF8PHxwb59+9CkSZN6qY+IiIiorihfMKRrX6p7zOULVMeZy4mIiKix0ieXK/sbI4nAhQAbvPz8fNjZ2eEjazdYSKp3148YJqeuELsEjSb7TBK7BK1GPd9K7BK0cn25o9glaPTg91til6CVotzw/7Ocdz1P7BK0Clg7T+wSNJJYWGlvJDJFXo7YJWiVf+qY2CVodHHQbLFLqFJRYQEG93BHXl5enbzgUJlPzk4YDLm5butiF5aWoceGXXVWI1FdUP7d3+vnB2tT0e5V0uqbo3+LXYJGZYYfhzBj8gtil6BV24//JXYJGi05liV2CVq1GjFE7BK0sjU1/AmtPtP6iV2CRrlXM8UuQav2M6aJXYJWOx7X7GXf9e2VG9+LXUKV8osfwnHkrDrJvbWRywHjzeaGm+aIiIiIqOHTZ31EI727hYiIiIio1um7brmRZvMGfdURERGQy+WVbhEREWKXR0RERGT0JFI9XkpkpOsqNkTM5URERESGTa9cbsTZvEHfeR4XF4cZM2ZUesyYHh8gIiIiIhITczkRERERNUYNevLcwcEBDg4OYpdBRERERFWQSHW/S8VY725piJjLiYiIiAybPrlc2d8YNejJc1JXLgDlMNw369xu10fsEjRqCC/j3HrqttglaNXut3til6BR1qPHYpeg1eKfPhC7BK2Ovb9O7BK0UngY9ku8fi9qAP8XvHCU2BVodeindLFL0Gjg6AVil1ClApPSehnnyWOeur3QXGJSXsvVENUf3zUfw9bGWuwyquS2NUnsEjQqvvNA7BK0WvbpcbFL0KrvVn+xS9Do/U3viV2Cdluni12BVt++uULsErT6a1Sc2CVodLSzYX93AIAmOyPFLkGroMm9xC5Bo4+WHBS7hCqVCIo6H0OfXP6kv3Fm8wbwzZ2IiIiIGirlGom69iUiIiIiIv3pk8uV/Y2RcV41EREREREREREREZEGvPOciIiIiOqMVCqFVMf1EXXtR0RERERE6vTJ5cr+xoiT50RERERUZ7hsCxERERGR+Lhsi26M86qJiIiIqF4oQ7qumy5WrVqFdu3awcLCAn5+fjh58mSVbZOSkiCRSNQ2CwsLtTaCIGD+/Plo1aoVLC0t0b9/f1y9elWn2oiIiIiIxKBvLtclmzeGXM7JcyIiIiJqNL755htMnz4dMTExOHv2LLp27YqgoCDcuXOnyj62tra4ffu2art+/bra8fj4eCQmJmLNmjU4ceIErK2tERQUhEePHtX15RARERERNUiNJZdz8pyIiIiI6oxEIoVEquMmeRJV8/Pz1baSkpIqx/vkk08QHh6O8ePHw8vLC2vWrIGVlRU2bNigoUYJHB0dVVvLli1VxwRBQEJCAubOnYshQ4agS5cu+PLLL3Hr1i3s3Lmz1n5ORERERER1Sa9crkM2byy5nJPnRERERFRnauPRUCcnJ9jZ2am2RYsWVTpWaWkpzpw5g/79+6v2SaVS9O/fH8eOHauyxsLCQri4uMDJyQlDhgzBb7/9pjp27do1ZGVlqZ3Tzs4Ofn5+Gs9JRERERGRIamvZlupk88aUy/nCUCIiIiKqM7XxwtDMzEzY2tqq9stkskrb37t3D+Xl5Wp3qABAy5YtceXKlUr7dOjQARs2bECXLl2Ql5eHZcuW4cUXX8Rvv/2Gtm3bIisrS3WOZ8+pPEZEREREZOhq64Wh1cnmjSmXc/KciIiIiAyara2tWkCvTf7+/vD391d9fvHFF9GxY0f861//wsKFC+tkTCIiIiKihqqusrmh5nIu26KHpKQk2Nvba2wTGhqKoUOH1ks9RERERIZGaiLVa6uJ5s2bw8TEBNnZ2Wr7s7Oz4ejoWK1zmJmZoXv37vjzzz8BQNVPn3NS3WMuJyIiItJM31xek2zemHK5qJPnd+/eRWRkJJydnSGTyeDo6IigoCAcOXKkQltBEDBw4EBIJJJqLwKfkZEBiUSi2mxsbODt7Y1Jkybh6tWram137NiBAQMGoEWLFrC1tYW/vz/27dtXG5dJREREZLQkUokeLyaS1Ggsc3Nz9OzZEykpKap9CoUCKSkpanexaFJeXo5Lly6hVatWAABXV1c4OjqqnTM/Px8nTpyo9jkbAuZyIiIiosZNv1xes2zemHK5qJPnw4YNw7lz55CcnIw//vgDu3btQt++fZGTk1OhbUJCAiSSmn2BUtq/fz9u376NCxcu4OOPP0ZaWhq6du2q9sM+dOgQBgwYgB9//BFnzpxBv3798Morr+DcuXM6Xx8RERER1a/p06dj3bp1SE5ORlpaGiIjI1FUVITx48cDAMaOHYvo6GhV+7i4OPz888/466+/cPbsWbz11lu4fv06Jk6cCACQSCR477338OGHH2LXrl24dOkSxo4di9atWzequ5iZy4mIiIioNjWWXC7amue5ubk4fPgwUlNT0adPHwCAi4sLfH19K7Q9f/48li9fjtOnT6t+21ATzZo1U92+7+bmhldeeQWBgYEICwtDeno6TExMkJCQoNbn448/xg8//IDdu3eje/fuGs+/b98+vPfee8jMzESvXr2wcePGCnXGxsbis88+Q0lJCUaPHo3ExESYm5sDAEpKSjBz5kx8/fXXyM/Ph4+PD1asWIHnn3++0vFKSkpQUlKi+pyfn1/THwkRERFRvaiNF4bWxBtvvIG7d+9i/vz5yMrKQrdu3bB3717Vi4Vu3LgBqfR/533w4AHCw8ORlZWFJk2aoGfPnjh69Ci8vLxUbWbNmoWioiK8/fbbyM3NRa9evbB3715YWFjodF2Ghrlc91yu7MNsTkRERIautl4YWl2NJZeLdue5XC6HXC7Hzp071cLms4qLizF69GisWrWq1tavkUqlmDp1Kq5fv44zZ85U2kahUKCgoABNmzbVeK7i4mIsW7YMmzZtwqFDh3Djxg3MmDFDrU1KSgrS0tKQmpqKrVu3YseOHYiNjVUdnzVrFrZv347k5GScPXsWHh4eCAoKwv379ysdc9GiRbCzs1NtTk5ONfwJEBEREdUPZUjXddNFVFQUrl+/jpKSEpw4cQJ+fn6qY6mpqUhKSlJ9XrFihaptVlYW9uzZU2GCViKRIC4uDllZWXj06BH279+P5557TqfaDBFzue65HGA2JyIiooZB31yuSzZvDLlctMlzU1NTJCUlITk5Gfb29ggICMCcOXNw8eJFtXbTpk3Diy++iCFDhtTq+J6engCerL9YmWXLlqGwsBAjRozQeJ6ysjKsWbMGPj4+6NGjB6KiotQeOwWerPOzYcMGeHt7IyQkBHFxcUhMTIRCoUBRURFWr16NpUuXYuDAgfDy8sK6detgaWmJ9evXVzpmdHQ08vLyVFtmZmbNfwBERERE9UAi0WNdRQnfbV8fmMt1z+UAszkRERE1DHrlciPO5qKveX7r1i3s2rULwcHBSE1NRY8ePVS/ddi1axcOHDhQ4dHN2iAIAgBUul7jli1bEBsbi2+//RYODg4az2NlZQV3d3fV51atWuHOnTtqbbp27QorKyvVZ39/fxQWFiIzMxPp6ekoKytDQECA6riZmRl8fX2RlpZW6ZgymQy2trZqGxERERGRrpjLdcvlALM5ERERUWMm+q8MLCwsMGDAAMybNw9Hjx5FaGgoYmJiAAAHDhxAeno67O3tYWpqClPTJ0u0Dxs2DH379tVrXGUAdnV1Vdv/9ddfY+LEifj222/Rv39/recxMzNT+yyRSFRfAIiIiIiMncTEBFIdN4mJidjlGxXmciIiIqLGS59cbszZXPTJ82d5eXmhqKgIADB79mxcvHgR58+fV23AkzVwNm7cqPMYCoUCiYmJcHV1VVs7Z+vWrRg/fjy2bt2KkJAQva7jaRcuXMDDhw9Vn48fPw65XA4nJye4u7vD3NwcR44cUR0vKyvDqVOn1BbEJyIiImqIxFjznGoHczlzORERETUeYqx53hiYijVwTk4Ohg8fjgkTJqBLly6wsbHB6dOnER8fr1pH0dHRsdKXETk7O1e4M0XbWFlZWSguLsbly5eRkJCAkydPYs+ePTD5/7812bJlC8aNG4eVK1fCz88PWVlZAABLS0vY2dnpda2lpaUICwvD3LlzkZGRgZiYGERFRUEqlcLa2hqRkZGYOXMmmjZtCmdnZ8THx6O4uBhhYWF6jUtEREQkNn2CtrEG9PrGXM5cTkRERI2fvhPgxprNRZs8l8vl8PPzw4oVK1TrCzo5OSE8PBxz5syp1bGUj3laWVnBxcUF/fr1w9q1a+Hh4aFqs3btWjx+/BiTJk3CpEmTVPvHjRun9uZXXQQGBqJ9+/bo3bs3SkpKMGrUKCxYsEB1fPHixVAoFBgzZgwKCgrg4+ODffv2oUmTJnqNS0RERESkDXP5AtVx5nIiIiIieppok+cymQyLFi3CokWLatSvJusWtmvXrtrtU1NTa1QHAISGhiI0NFRt39ChQ9XGfDrgx8bGVnoeCwsLJCYmIjExscY1EBERERkyiVQKiVTHO8917Ec1w1z+P8zlRERE1Fjpk8uV/Y2RaJPnRERERNT4cdkWIiIiIiLxcdkW3TToq46IiIBcLq90i4iIELs8IiIiIiKjwFxORERERI1Rg77zPC4uDjNmzKj0mK2tbT1XQ0RERETPkkglut95LpXUcjVUV5jLiYiIiAybPrlc2d8YNejJcwcHBzg4OIhdBhERERFVgWueGwfmciIiIiLDxjXPddOgJ89JXU/PprA2Mdw/UlMD/3fM9eWOYpegVbvf7oldglYZxWVil6CRraH/RQRg2sZd7BK0yv67QOwStJKUGHaNBSU2Ypeg1b3U62KXoNWfhaVil6CRk9Rw/x7mSwvrZRyJ1AQSqYnOfYkaqlv2niiwMdy77i2nLBO7BI3aSh+LXYJWfbf6i12CVqn3isUuQSPbiFVil6CV3cEDYpeg1T8+TBe7BK1CtpwTuwSNlvd0FLsErQ6eui12CVr9O/Gw2CVoZG7Ad04rhLqvTZ9cruxvjAx/FomIiIiIiIiIiIiIqJ4Z7m3KRERERNTwSU2ebLr2JSIiIiIi/emTy5X9jRAnz4mIiIio7kilTzZd+xIRERERkf70yeXK/kaIk+dEREREVGckJiaQmOi45rmO/YiIiIiISJ0+uVzZ3xgZ568MiIiIiIiIiIiIiIg04J3nRERERFR3uOY5EREREZH4uOa5Tjh5TkRERER1RyrVY/KcD0kSEREREdUKfXK5sr8RMs6rJiIiIiIiIiIiIiLSgHeeExEREVGdkUilkOh4l4qu/YiIiIiISJ0+uVzZ3xhx8pyIiIiI6o5Ej7UVJca5riIRERERUa3TJ5cr+xsh4/yVQS1JSkqCvb29xjahoaEYOnRovdRDREREZHCULybSdSOqBuZyIiIiIi30zeVGms1FnTy/e/cuIiMj4ezsDJlMBkdHRwQFBeHIkSMV2gqCgIEDB0IikWDnzp3VOn9GRgYkEolqs7Gxgbe3NyZNmoSrV69W2e/IkSMwNTVFt27ddLwyIiIiIqKGg7mciIiIiKgiUZdtGTZsGEpLS5GcnAw3NzdkZ2cjJSUFOTk5FdomJCRAIpHoNM7+/fvh7e2N4uJiXLp0CStXrkTXrl2xe/duBAYGqrXNzc3F2LFjERgYiOzsbJ3GIyIiIqInuOZ5w8BcTkRERNS4cc1z3Yh21bm5uTh8+DCWLFmCfv36wcXFBb6+voiOjsbgwYPV2p4/fx7Lly/Hhg0bdBqrWbNmcHR0hJubG4YMGYL9+/fDz88PYWFhKC8vV2sbERGB0aNHw9/fv9rn37dvHzp27Ai5XI7g4GDcvn27QpvY2Fi0aNECtra2iIiIQGlpqepYSUkJpkyZAgcHB1hYWKBXr144depUleOVlJQgPz9fbSMiIiIySHw01OAxl+uey5V9mM2JiIjI4HHZFp2INnkul8shl8uxc+dOlJSUVNmuuLgYo0ePxqpVq+Do6FgrY0ulUkydOhXXr1/HmTNnVPs3btyIv/76CzExMdU+V3FxMZYtW4ZNmzbh0KFDuHHjBmbMmKHWJiUlBWlpaUhNTcXWrVuxY8cOxMbGqo7PmjUL27dvR3JyMs6ePQsPDw8EBQXh/v37lY65aNEi2NnZqTYnJ6ca/gSIiIiI6olUqkdAN867W+obc7nuuRxgNiciIqIGQq9cbrzZXLSrNjU1RVJSEpKTk2Fvb4+AgADMmTMHFy9eVGs3bdo0vPjiixgyZEitju/p6QngyfqLAHD16lXMnj0bmzdvhqlp9VezKSsrw5o1a+Dj44MePXogKioKKSkpam3Mzc2xYcMGeHt7IyQkBHFxcUhMTIRCoUBRURFWr16NpUuXYuDAgfDy8sK6detgaWmJ9evXVzpmdHQ08vLyVFtmZqZuPwQiIiIiMnrM5brncoDZnIiIiKgxE/VXBsOGDcOtW7ewa9cuBAcHIzU1FT169EBSUhIAYNeuXThw4AASEhJqfWxBEAAAEokE5eXlGD16NGJjY/Hcc8/V6DxWVlZwd3dXfW7VqhXu3Lmj1qZr166wsrJSffb390dhYSEyMzORnp6OsrIyBAQEqI6bmZnB19cXaWlplY4pk8lga2urthEREREZIomJiV4b1Q/mct1yOcBsTkRERA2DvrncWLO56PfbW1hYYMCAAZg3bx6OHj2K0NBQ1eOZBw4cQHp6Ouzt7WFqaqq682TYsGHo27evXuMqA7CrqysKCgpw+vRpREVFqcaJi4vDhQsXYGpqigMHDlR5HjMzM7XPEolE9QWAiIiIyOhJpfptVG+Yy4mIiIgaMX1zuZFm8+o/B1lPvLy8sHPnTgDA7NmzMXHiRLXjnTt3xooVK/DKK6/oPIZCoUBiYiJcXV3RvXt3SCQSXLp0Sa3N559/jgMHDmDbtm1wdXXVeSwAuHDhAh4+fAhLS0sAwPHjxyGXy+Hk5ITmzZvD3NwcR44cgYuLC4Anj5yeOnUK7733nl7jEhERERHpirmcuZyIiIjI2Ik2eZ6Tk4Phw4djwoQJ6NKlC2xsbHD69GnEx8er1lF0dHSs9GVEzs7ONQrOOTk5yMrKQnFxMS5fvoyEhAScPHkSe/bsgcn/f+SgU6dOan0cHBxgYWFRYb8uSktLERYWhrlz5yIjIwMxMTGIioqCVCqFtbU1IiMjMXPmTDRt2hTOzs6Ij49HcXExwsLC9B6biIiISFTKFwzp2pfqHHM5czkREREZAX1yubK/ERJt8lwul8PPzw8rVqxQrS/o5OSE8PBwzJkzp1bH6t+/P4An6yC6uLigX79+WLt2LTw8PGp1nKoEBgaiffv26N27N0pKSjBq1CgsWLBAdXzx4sVQKBQYM2YMCgoK4OPjg3379qFJkyb1Uh8RERFRXZFITSDRMWjr2o9qhrl8geo4czkRERE1VvrkcmV/YyQRuBBgg5efnw87Ozvs6eEDaxODW4lHpf2+X8QuQaPHy6eIXYJWW1YcFrsErTKKy8QuQSNbU8Nfo+vj8+vFLkGrbX0niV2CVq/99qPYJWh0qshG7BK0uuffW+wStDp4t1jsEjSKv3NE7BKqlF9QgBaunsjLy6uTFxwq88m9A1thK7fS3qGycxQWo/lLo2pc46pVq7B06VJkZWWha9eu+PTTT+Hr61tp23Xr1uHLL7/E5cuXAQA9e/bExx9/rNY+NDQUycnJav2CgoKwd+9eHa6KGjvl3/3L127CxsZwXx5qaSYRuwSNbKSPxS5Bq52u/mKXoFXqPcP+/8nBLnZil6CV3cGq37VgKDrujRe7BK1C7g8QuwSNlv8cK3YJWh08dVvsErRqam7Yk6t3S8rFLqFKjwQFFpVcq5NsXhu5HNAtmzeGXG74s0hERERERNX0zTffYPr06YiJicHZs2fRtWtXBAUF4c6dO5W2T01NxahRo3Dw4EEcO3YMTk5OePnll3Hz5k21dsHBwbh9+7Zq27p1a31cDhERERFRg9RYcnmDnjyPiIiAXC6vdIuIiBC7PCIiIiKjp3w8VNcNeHK3zNNbSUlJleN98sknCA8Px/jx4+Hl5YU1a9bAysoKGzZsqLT9V199hXfffRfdunWDp6cnvvjiCygUCqSkpKi1k8lkqnW/HR0duYzHM5jLiYiIiAybvrm8ptm8seRyw13joxri4uIwY8aMSo/VxePHRERERFRDUqkeLwx9cp+Hk5OT2u6YmBi1daqVSktLcebMGURHRz91Cin69++PY8eOVWvI4uJilJWVoWnTpmr7U1NT4eDggCZNmuCll17Chx9+iGbNmtXwghov5nIiIiIiA6dPLlf2R/WyeWPK5Q168tzBwQEODg5il0FEREREVZFKVUFbp74AMjMz1SZgZTJZpc3v3buH8vJytGzZUm1/y5YtceXKlWoN+f7776N169aqF1sCTx4Nfe211+Dq6or09HTMmTMHAwcOxLFjx2BiYthre9YX5nIiIiIiA6dPLlf2R/WyeWPK5Q168pzUZWXkw8qA33zbwoBfzAAAZb/fErsErbIeGf6Lkwz9hZz5jxVil6BVQ3iDdRN7C7FL0Eowtxa7BM2KxC5AuxYdmmpvJDLznIdil6BRnpnhvgitwNSwXxb4NFtb23q5e3nx4sX4+uuvkZqaCguL//13buTIkap/7ty5M7p06QJ3d3ekpqYiMDCwzuuihun+w8coNTXc7Pbhzt/FLkGjLs72Ypeg1fub3hO7BK1sI1aJXYJGu67niV2CVsfXnhC7BK22TftA7BK0ypi8WewSNPLdvEbsErSymDxN7BK0+unnv8QuQaPxM/uKXUKVCkpKsSj+mthlVEt9ZHNDyuWGPctFRERERA2axMREr60mmjdvDhMTE2RnZ6vtz87OhqOjo8a+y5Ytw+LFi/Hzzz+jS5cuGtu6ubmhefPm+PPPP2tUHxERERGRWPTN5TXJ5o0pl3PynIiIiIjqjtREv60GzM3N0bNnT7WXCilfMuTv719lv/j4eCxcuBB79+6Fj4+P1nH+/vtv5OTkoFWrVjWqj4iIiIhINPrm8hpk88aUy7lsCxERERHVHR0mwdX61tD06dMxbtw4+Pj4wNfXFwkJCSgqKsL48eMBAGPHjkWbNm2waNEiAMCSJUswf/58bNmyBe3atUNWVhYAQC6XQy6Xo7CwELGxsRg2bBgcHR2Rnp6OWbNmwcPDA0FBQbpdFxERERFRfdMnlyv710BjyeWcPCciIiKiRuONN97A3bt3MX/+fGRlZaFbt27Yu3ev6mVFN27cgPSpFyWtXr0apaWleP3119XOExMTgwULFsDExAQXL15EcnIycnNz0bp1a7z88stYuHBhlS8uJSIiIiIydo0ll3PynIiIiIjqjEQqhUSq20qBuvaLiopCVFRUpcdSU1PVPmdkZGg8l6WlJfbt26dTHUREREREhkKfXK7sX1ONIZdz8pyIiIiI6o5Ej8dDJXo8VkpERERERP+jTy5X9jdCfGEoEREREREREREREdEzeOc5EREREdUdiQSQ6Hi/hkRSu7UQERERERkrfXK5sr8R4uQ5EREREdUdiVSPyXM+JElEREREVCv0yeXK/kaIk+dEREREVGcEiRSCjkFb135ERERERKROn1yu7G+MjPOqa0lqaiokEglyc3OrbLNgwQJ069at3moiIiIiIjI2zOVEREREVBdEnTy/e/cuIiMj4ezsDJlMBkdHRwQFBeHIkSMV2gqCgIEDB0IikWDnzp3VOn9GRgYkEolqs7Gxgbe3NyZNmoSrV69WaF9SUoIPPvgALi4ukMlkaNeuHTZs2KDvZRIREREZL+XjobpuVC+Yy4mIiIgaOX1zuZFmc1GXbRk2bBhKS0uRnJwMNzc3ZGdnIyUlBTk5ORXaJiQkQKLjwvT79++Ht7c3iouLcenSJaxcuRJdu3bF7t27ERgYqGo3YsQIZGdnY/369fDw8MDt27ehUCh0vj4iIiIioyeR6P5yISN9KZEYmMuJiIiIGjl9crmyvxES7VcGubm5OHz4MJYsWYJ+/frBxcUFvr6+iI6OxuDBg9Xanj9/HsuXL9f5bpNmzZrB0dERbm5uGDJkCPbv3w8/Pz+EhYWhvLwcALB371785z//wY8//oj+/fujXbt28Pf3R0BAgNbznzlzBj4+PrCyssKLL76I33//vUKbf/3rX3BycoKVlRVGjBiBvLw81TGFQoG4uDi0bdsWMpkM3bp1w969e6scr6SkBPn5+WobERERkUGSSvXbqM4xl+ueywFmcyIiImog9M3lRprNRbtquVwOuVyOnTt3oqSkpMp2xcXFGD16NFatWgVHR8daGVsqlWLq1Km4fv06zpw5AwDYtWsXfHx8EB8fjzZt2uC5557DjBkz8PDhQ63n++CDD7B8+XKcPn0apqammDBhgtrxP//8E99++y12796NvXv34ty5c3j33XdVx1euXInly5dj2bJluHjxIoKCgjB48OBKH2EFgEWLFsHOzk61OTk56fHTICIiIiJjxlyuey4HmM2JiIiIGjPRJs9NTU2RlJSE5ORk2NvbIyAgAHPmzMHFixfV2k2bNg0vvvgihgwZUqvje3p6Aniy/iIA/PXXX/j1119x+fJlfP/990hISMC2bdvUwnRVPvroI/Tp0wdeXl6YPXs2jh49ikePHqmOP3r0CF9++SW6deuG3r1749NPP8XXX3+NrKwsAMCyZcvw/vvvY+TIkejQoQOWLFmCbt26ISEhodLxoqOjkZeXp9oyMzP1+2EQERER1RFBItVro7rHXK57LgeYzYmIiKhh0DeXG2s2r/aa56+99lq1T7pjx45qtRs2bBhCQkJw+PBhHD9+HD/99BPi4+PxxRdfIDQ0FLt27cKBAwdw7ty5ao9dXYIgAIBqvUaFQgGJRIKvvvoKdnZ2AIBPPvkEr7/+Oj7//HNYWlpWea4uXbqo/rlVq1YAgDt37sDZ2RkA4OzsjDZt2qja+Pv7Q6FQ4Pfff4eVlRVu3bpV4THUgIAAXLhwodLxZDIZZDJZTS+ZiIiIqP7p83IhIw3o2jCXG04uB5jNiYiIqIHQ96WfRprNq33VTz+KqG2rCQsLCwwYMADz5s3D0aNHERoaipiYGADAgQMHkJ6eDnt7e5iamsLU9Mlc/7Bhw9C3b98ajfOstLQ0AICrqyuAJ+G6TZs2avV37NgRgiDg77//1nguMzMz1T8/HfqJiIiIiGobc3nVmMuJiIiIqDZV+87zjRs31mUdKl5eXti5cycAYPbs2Zg4caLa8c6dO2PFihV45ZVXdB5DoVAgMTERrq6u6N69O4And5R89913KCwshFwuBwD88ccfkEqlaNu2rc5jAcCNGzdw69YttG7dGgBw/PhxSKVSdOjQAba2tmjdujWOHDmCPn36qPocOXIEvr6+eo1LREREJDreeV7rmMt1x1xORERERot3nuuk2pPnz3r8+DFSU1ORnp6O0aNHw8bGBrdu3YKtra0q5GqSk5OD4cOHY8KECejSpQtsbGxw+vRpxMfHq9ZRdHR0rPRlRM7Ozqo7U6ojJycHWVlZKC4uxuXLl5GQkICTJ09iz549MDExAQCMHj0aCxcuxPjx4xEbG4t79+5h5syZmDBhgsZHQ6vDwsIC48aNw7Jly5Cfn48pU6ZgxIgRqmubOXMmYmJi4O7ujm7dumHjxo04f/48vvrqK73GJSIiIhIdJ8/rHHN59TGXExERkdHi5LlOdJo8v379OoKDg3Hjxg2UlJRgwIABsLGxwZIlS1BSUoI1a9ZoPYdcLoefnx9WrFiB9PR0lJWVwcnJCeHh4ZgzZ44uZVWpf//+AAArKyu4uLigX79+WLt2LTw8PNTq+eWXXzB58mT4+PigWbNmGDFiBD788EO9x/fw8MBrr72GQYMG4f79+/jHP/6Bzz//XHV8ypQpyMvLwz//+U/cuXMHXl5e2LVrF9q3b6/32ERERERiEiQSnV8uJPz/ZTeoaszlNcNcTkRERMZKn1yu7G+MdJo8nzp1Knx8fHDhwgU0a9ZMtf/VV19FeHh4tc4hk8mwaNEiLFq0qEZjK18oVB3t2rWrUXtPT0/88ssv1W7ft2/fCufv1q2b2r4FCxZgwYIFAIDIyMhKzyOVShETE6NaU5KIiIiIqDqYy59gLiciIiKiuqDT5Pnhw4dx9OhRmJubq+1v164dbt68WSuFEREREVEjwGVb6hRzORERERFVC5dt0YlOV61QKFBeXl5h/99//w0bGxu9i6quiIgIyOXySreIiIh6q4OIiIiIqiCR6LeRRszlRERERFQt+uZyI83mOt15/vLLLyMhIQFr164FAEgkEhQWFiImJgaDBg2q1QI1iYuLw4wZMyo9ZmtrW291EBEREVEVeOd5nWIuJyIiIqJq4Z3nOtFp8nz58uUICgqCl5cXHj16hNGjR+Pq1ato3rw5tm7dWts1VsnBwQEODg71Nh4RERERkSFhLiciIiIiqjs6TZ63bdsWFy5cwNdff42LFy+isLAQYWFhePPNN2FpaVnbNVI1+YzqBhuZmdhlVOlq35fELkGjFl7NxS5Bq8U/fSB2CVqZtnEXuwSNJFITsUvQapLXWLFL0Gp6hI/YJWh1JXyM2CVo5DvrXbFL0G7LOrEr0KqHfRuxS9DoQGah2CVUqbiwqF7GESRSCDrepaJrP2PCXG643P7zOWwtZWKXUaVOkzaLXYJGrSwN93uNytbpYlegld3BA2KXoNHxtSfELkGrFxYbfma7c9RJ7BK0Wnv1gdglaFTy632xS9BKvnab2CVote/Ls2KXoNGMsI5il1Ali/wCIH5TnY6hTy5X9jdGOk2eA4CpqSneeuut2qyFiIiIiBobiRSQctmWusRcTkRERERa6ZPLlf2NkM6T51evXsXBgwdx584dKBQKtWPz58/XuzAiIiIiItKOuZyIiIiIqG7oNHm+bt06REZGonnz5nB0dITkqbetSiQShnQiIiIieoIvDK1TzOVEREREVC18YahOdJo8//DDD/HRRx/h/fffr+16iIiIiKgx4eR5nWIuJyIiIqJq4eS5TnSaPH/w4AGGDx9e27UQERERUWPDyfM6xVxORERERNXCyXOd6HTVw4cPx88//1zbtRARERERUQ0wlxMRERER1R2d7jz38PDAvHnzcPz4cXTu3BlmZmZqx6dMmVIrxRERERFRwyZIJBB0vEtFeGr9bqocczkRERERVYc+uVzZ3xjpNHm+du1ayOVy/Oc//8F//vMftWMSiYQhnYiIiIie4LItdYq5nIiIiIiqhcu26ESnyfNr167Vdh1ERERE1BhJJE82XfuSRszlRERERFQt+uRyZX8jZJy/MiAiIiIiIiIiIiIi0kCnyfPy8nKsX78eo0ePRv/+/fHSSy+pbcYiKSkJ9vb2GtuEhoZi6NCh9VIPERERkcFRPh6q66aDVatWoV27drCwsICfnx9Onjypsf13330HT09PWFhYoHPnzvjxxx/VjguCgPnz56NVq1awtLRE//79cfXqVZ1qq23M5U8wlxMRERFpoW8u1yGbN4ZcrtM3kqlTp2Lq1KkoLy9Hp06d0LVrV7Wtuu7evYvIyEg4OztDJpPB0dERQUFBOHLkiKpN3759IZFI1LaIiIhqnT8jI0Otn42NDby9vTFp0qQKP9hff/0VAQEBaNasGSwtLeHp6YkVK1ZU+1qIiIiIqCJBItVrq6lvvvkG06dPR0xMDM6ePYuuXbsiKCgId+7cqbT90aNHMWrUKISFheHcuXMYOnQohg4disuXL6vaxMfHIzExEWvWrMGJEydgbW2NoKAgPHr0SOefS21hLiciIiKi6tA3l9c0mzeWXK7Tmudff/01vv32WwwaNEivwYcNG4bS0lIkJyfDzc0N2dnZSElJQU5Ojlq78PBwxMXFqT5bWVnVaJz9+/fD29sbxcXFuHTpElauXImuXbti9+7dCAwMBABYW1sjKioKXbp0gbW1NX799Ve88847sLa2xttvv63XdRIRERFR/fjkk08QHh6O8ePHAwDWrFmDPXv2YMOGDZg9e3aF9itXrkRwcDBmzpwJAFi4cCF++eUXfPbZZ1izZg0EQUBCQgLmzp2LIUOGAAC+/PJLtGzZEjt37sTIkSPr7+IqwVxORERERIaoseRyne48Nzc3h4eHh14D5+bm4vDhw1iyZAn69esHFxcX+Pr6Ijo6GoMHD1Zra2VlBUdHR9Vma2tbo7GaNWsGR0dHuLm5YciQIdi/fz/8/PwQFhaG8vJyAED37t0xatQoeHt7o127dnjrrbcQFBSEw4cPaz3/vn370LFjR8jlcgQHB+P27dsV2sTGxqJFixawtbVFREQESktLVcdKSkowZcoUODg4wMLCAr169cKpU6eqHK+kpAT5+flqGxEREZFBqoVHQ5/NPSUlJZUOVVpaijNnzqB///6qfVKpFP3798exY8cq7XPs2DG19gAQFBSkan/t2jVkZWWptbGzs4Ofn1+V56xPzOXq6juXK/swmxMREZHBq6VlW6qTzRtTLtdp8vyf//wnVq5cCUEQdB5YLpdDLpdj586dVX4BUvrqq6/QvHlzdOrUCdHR0SguLtZ5XODJH9bUqVNx/fp1nDlzptI2586dw9GjR9GnTx+N5youLsayZcuwadMmHDp0CDdu3MCMGTPU2qSkpCAtLQ2pqanYunUrduzYgdjYWNXxWbNmYfv27UhOTsbZs2fh4eGBoKAg3L9/v9IxFy1aBDs7O9Xm5ORUw58AERERUf0QJBK9NgBwcnJSyz6LFi2qdKx79+6hvLwcLVu2VNvfsmVLZGVlVdonKytLY3vl/9bknPWJufx/xMjlALM5ERERNQz65vKaZPPGlMt1Wrbl119/xcGDB/HTTz/B29sbZmZmasd37NihfWBTUyQlJSE8PBxr1qxBjx490KdPH4wcORJdunRRtRs9ejRcXFzQunVrXLx4Ee+//z5+//33ao2hiaenJ4An6y/6+vqq9rdt2xZ3797F48ePsWDBAkycOFHjecrKyrBmzRq4u7sDAKKiotQeZQWe3BG0YcMGWFlZwdvbG3FxcZg5cyYWLlyIhw8fYvXq1UhKSsLAgQMBAOvWrcMvv/yC9evXqx5VeFp0dDSmT5+u+pyfn8+QTkRERAZJEJ5suvYFgMzMTLU7nGUyWS1U1jgwl/+PGLkcYDYnIiKihkGfXK7sDxhfNtdp8tze3h6vvvqq3oMPGzYMISEhOHz4MI4fP46ffvoJ8fHx+OKLLxAaGgoAausadu7cGa1atUJgYCDS09NVwVgXyrtzJP//tyZKhw8fRmFhIY4fP47Zs2fDw8MDo0aNqvI8VlZWanW0atWqwsL3Xbt2VVsP0t/fH4WFhcjMzEReXh7KysoQEBCgOm5mZgZfX1+kpaVVOqZMJmv0fzGJiIiIlGxtbau1PEjz5s1hYmKC7Oxstf3Z2dlwdHSstI+jo6PG9sr/zc7ORqtWrdTadOvWrSaXUSeYy/9HjFwOMJsTERGRcalONm9MuVynyfONGzfWWgEWFhYYMGAABgwYgHnz5mHixImIiYlRhfRn+fn5AQD+/PNPvUK6MgC7urqq7Vd+7ty5M7Kzs7FgwQKNIf3Zu3skEolej80SERERNSYKQYBCx2xU037m5ubo2bMnUlJSMHTo0CfnUCiQkpKCqKioSvv4+/sjJSUF7733nmrfL7/8An9/fwBPsqGjoyNSUlJUoTw/Px8nTpxAZGRkja+ptjGX/w9zOREREVHV9Mnlyv7V1ZhyuU5rntclLy8vFBUVVXn8/PnzAKD2G4aaUigUSExMhKurK7p3766xnbZ1H6vjwoULePjwoerz8ePHIZfL4eTkBHd3d5ibm+PIkSOq42VlZTh16hS8vLz0HpuIiIhITIKeW01Nnz4d69atQ3JyMtLS0hAZGYmioiKMHz8eADB27FhER0er2k+dOhV79+7F8uXLceXKFSxYsACnT59WhXqJRIL33nsPH374IXbt2oVLly5h7NixaN26teqLQGPFXM5cTkRERI2Hvrm8ptm8seRyne48B4Bt27bh22+/xY0bN9TeUA8AZ8+e1do/JycHw4cPx4QJE9ClSxfY2Njg9OnTiI+Px5AhQwAA6enp2LJlCwYNGoRmzZrh4sWLmDZtGnr37q22/mJ1xsrKykJxcTEuX76MhIQEnDx5Env27IGJiQkAYNWqVXB2dlatuXjo0CEsW7YMU6ZMqfY4VSktLUVYWBjmzp2LjIwMxMTEICoqClKpFNbW1oiMjMTMmTPRtGlTODs7Iz4+HsXFxQgLC9N7bCIiIiIxKYQnm659a+qNN97A3bt3MX/+fGRlZaFbt27Yu3ev6sVCN27cgFT6v/tHXnzxRWzZsgVz587FnDlz0L59e+zcuROdOnVStZk1axaKiorw9ttvIzc3F7169cLevXthYWGh24XVMuby6mMuJyIiImOlTy5X9q+JxpLLdZo8T0xMxAcffIDQ0FD88MMPGD9+PNLT03Hq1ClMmjSpWueQy+Xw8/PDihUrkJ6ejrKyMjg5OSE8PBxz5swB8OQW//379yMhIQFFRUVwcnLCsGHDMHfu3BrV279/fwBP1kF0cXFBv379sHbtWnh4eKjaKBQKREdH49q1azA1NYW7uzuWLFmCd955p0ZjVSYwMBDt27dH7969UVJSglGjRmHBggWq44sXL4ZCocCYMWNQUFAAHx8f7Nu3D02aNNF7bCIiIiJjExUVVeXjoKmpqRX2DR8+HMOHD6/yfBKJBHFxcRVePmkImMtrhrmciIiIqP40hlwuEXRYCNDT0xMxMTEYNWoUbGxscOHCBbi5uWH+/Pm4f/8+Pvvss7qolaqQn58POzs7XJg0AjYyM+0dRHJ1T9UvWjIELbyai12CVh0jqv4PiKEwbaP7mqf1QSI1EbsErSZ5jRW7BK2mR/iIXYJWD+8Vil2CRl6z3hW7BO1aPyd2BVqV27cRuwSNDmQWi11ClYoLCzD8+fbIy8ur1ss4a0qZT67fvK3z+fPz8+HSplWd1dgYMJcbHuXf/b9Xvw9bS8N9keiydzaLXYJGbSwN93uN0tit08UuQaszHYaJXYJGk9aeELsErV5YbPiZbWxvJ7FL0Crn6gOxS9DopeUjxS5Bq5uB+j+BVdcmfqn9iTcx/RLWUewSqpSfXwCHdnWTzWsjlyvPY4zZXKc1z2/cuIEXX3wRAGBpaYmCggIAwJgxY7B169baq46IiIiIGjTl46G6bqQZczkRERERVYe+udxYs7lOk+eOjo64f/8+AMDZ2RnHjx8HAFy7dq1e32gfEREBuVxe6RYREVFvdRARERERiYG5nIiIiIio7ui05vlLL72EXbt2oXv37hg/fjymTZuGbdu24fTp03jttddqu8YqxcXFYcaMGZUeM6bHB4iIiIgMmZHepFIvmMuJiIiIqLqYy2tOp8nztWvXQqFQAAAmTZqEZs2a4ejRoxg8eHCtvMinuhwcHODg4FBv4xERERFRzejziKexPhpaE8zlRERERFQd+i69YqzZXKfJc6lUCqn0fyu+jBw5EiNHGv7LFRq7jINXYW1iuC9DfOm7eLFL0OjX0A/ELkGrY++vE7sErbL/LhC7BI2a2FuIXYJWDeFlnJ+sOS12CVrFLh0idgka3X8uUOwStHow/U2xS9Dq9tnbYpegUbcDKWKXUKUCy/J6GUcQBJ2XD6nPZUcaKuZyw/X4USkeSyRil1Eln6aWYpegUVGZQuwStPr2zRVil6DVPz5MF7sEjbZNM/zvYHeOGv7LOL88lCl2CVqtuvKV2CVo9KV/mNglaBXwxnmxS9Dql0WrxS5Bo5MDh4pdQpWKHj+u8zH0yeXK/sZIp8lzAMjNzcXJkydx584d1d0uSmPHjtW7MCIiIiIi0o65nIiIiIiobug0eb579268+eabKCwshK2tLSRP3VEhkUgY0omIiIgIAKD4/5uufUkz5nIiIiIiqg59crmyvzGSam9S0T//+U9MmDABhYWFyM3NxYMHD1Tb/fv3a7tGIiIiImqgBEG/jTRjLiciIiKi6tA3lxtrNtfpzvObN29iypQpsLKyqu16iIiIiKgR4QtD6xZzORERERFVB18Yqhud7jwPCgrC6dOG/7I4IiIiIqLGjLmciIiIiKjuVPvO8127dqn+OSQkBDNnzsR///tfdO7cGWZmZmptBw8eXHsVEhEREVGDJQgCBB2f8dS1X2PHXE5ERERENaVPLlf2N0bVnjwfOnRohX1xcXEV9kkkEpSXl+tVFBERERE1DnxhaO1jLiciIiKimuILQ3VT7clzhcJYf0REREREpCsBur9cyDjvbdGOuZyIiIiIakqfXK7sb4xqtOb5gQMH4OXlhfz8/ArH8vLy4O3tjcOHD9dacUREREREVBFzORERERFR3avR5HlCQgLCw8Nha2tb4ZidnR3eeecdfPLJJ7VWHBERERE1bApB0GujyjGXExEREVFN6JvLjTWb12jy/MKFCwgODq7y+Msvv4wzZ87oXVRDkZSUBHt7e41tQkNDK12XkoiIiMgYCHpuVDnmcnXM5URERESa6ZvLjTWb12jyPDs7G2ZmZlUeNzU1xd27d6t9vrt37yIyMhLOzs6QyWRwdHREUFAQjhw5omrTt29fSCQStS0iIqJa58/IyFDrZ2NjA29vb0yaNAlXr16tst+RI0dgamqKbt26VftaiIiIiIjqC3M5EREREVHdq/YLQwGgTZs2uHz5Mjw8PCo9fvHiRbRq1ara5xs2bBhKS0uRnJwMNzc3ZGdnIyUlBTk5OWrtwsPDERcXp/psZWVVk7Kxf/9+eHt7o7i4GJcuXcLKlSvRtWtX7N69G4GBgWptc3NzMXbsWAQGBiI7O7tG4xARERGROoXwZNO1L1WOuZyIiIiIakKfXK7sb4xqdOf5oEGDMG/ePDx69KjCsYcPHyImJgb/+Mc/qnWu3NxcHD58GEuWLEG/fv3g4uICX19fREdHY/DgwWptrays4OjoqNoqW9tRk2bNmsHR0RFubm4YMmQI9u/fDz8/P4SFhaG8vFytbUREBEaPHg1/f/9qn3/fvn3o2LEj5HI5goODcfv27QptYmNj0aJFC9ja2iIiIgKlpaWqYyUlJZgyZQocHBxgYWGBXr164dSpU1WOV1JSgvz8fLWNiIiIyCAJgKDjZrTPhlYDc3nl6juXK/swmxMREZHB0yOXG3M2r9Hk+dy5c3H//n0899xziI+Pxw8//IAffvgBS5YsQYcOHXD//n188MEH1TqXXC6HXC7Hzp07UVJSorHtV199hebNm6NTp06Ijo5GcXFxTcquQCqVYurUqbh+/braWpAbN27EX3/9hZiYmGqfq7i4GMuWLcOmTZtw6NAh3LhxAzNmzFBrk5KSgrS0NKSmpmLr1q3YsWMHYmNjVcdnzZqF7du3Izk5GWfPnoWHhweCgoJw//79SsdctGgR7OzsVJuTk1MNfwJERERE9UMBQa+NKsdcXpEYuRxgNiciIqKGQd9cbqzZvEaT5y1btsTRo0dVYfnVV1/Fq6++ijlz5qBTp0749ddf0bJly2qdy9TUFElJSUhOToa9vT0CAgIwZ84cXLx4Ua3d6NGjsXnzZhw8eBDR0dHYtGkT3nrrrZqUXSlPT08AT9ZfBICrV69i9uzZ2Lx5M0xNq7+aTVlZGdasWQMfHx/06NEDUVFRSElJUWtjbm6ODRs2wNvbGyEhIYiLi0NiYiIUCgWKioqwevVqLF26FAMHDoSXlxfWrVsHS0tLrF+/vtIxo6OjkZeXp9oyMzN1+yEQERERUYPEXF6RGLkcYDYnIiIiasxqtOY5ALi4uODHH3/EgwcP8Oeff0IQBLRv3x5NmjSp8eDDhg1DSEgIDh8+jOPHj+Onn35CfHw8vvjiC4SGhgIA3n77bVX7zp07o1WrVggMDER6ejrc3d1rPKaSIDz5bYlEIkF5eTlGjx6N2NhYPPfcczU6j5WVlVodrVq1wp07d9TadO3aVW09SH9/fxQWFiIzMxN5eXkoKytDQECA6riZmRl8fX2RlpZW6ZgymQwymaxGdRIRERGJQfWYp459qWrM5erEyOUAszkRERE1DPrkcmV/Y1SjO8+f1qRJEzz//PPw9fXVKaArWVhYYMCAAZg3bx6OHj2K0NBQjY9n+vn5AQD+/PNPnccEoArArq6uKCgowOnTpxEVFQVTU1OYmpoiLi4OFy5cgKmpKQ4cOFDleczMzNQ+SyQS1RcAIiIiImOnfDGRrhtpx1z+BHM5ERERUdX0zeXGms11njyvK15eXigqKqry+Pnz5wE8uZNEVwqFAomJiXB1dUX37t1ha2uLS5cu4fz586otIiICHTp0wPnz51VfDHR14cIFPHz4UPX5+PHjkMvlcHJygru7O8zNzXHkyBHV8bKyMpw6dQpeXl56jUtEREQkNn1eSsR5T3ExlzOXExERUeOhby431mxe42VbaktOTg6GDx+OCRMmoEuXLrCxscHp06cRHx+PIUOGAADS09OxZcsWDBo0CM2aNcPFixcxbdo09O7dG126dKnRWFlZWSguLsbly5eRkJCAkydPYs+ePTAxMQEAdOrUSa2Pg4MDLCwsKuzXRWlpKcLCwjB37lxkZGQgJiYGUVFRkEqlsLa2RmRkJGbOnImmTZvC2dkZ8fHxKC4uRlhYmN5jExERERFpwlzOXE5ERERElRNt8lwul8PPzw8rVqxAeno6ysrK4OTkhPDwcMyZMwfAkxf67N+/HwkJCSgqKoKTkxOGDRuGuXPn1mis/v37A3iyDqKLiwv69euHtWvXwsPDo9avqzKBgYFo3749evfujZKSEowaNQoLFixQHV+8eDEUCgXGjBmDgoIC+Pj4YN++fXo9dktERERkCBQQoIBut6no2o9qhrl8geo4czkRERE1VvrkcmV/YyQRuBBgg5efnw87Ozv84NUd1v//jh1D1OerxWKXoNGvoR+IXYJWQgNYYCr77wKxS9Coib2F2CVo5drfTewStPpkzWmxS9AqdukQsUvQLPxjsSvQ6sH0N8UuQavbZ2+LXYJGHQ6kiF1ClQry8+HZrg3y8vJga2tb6+dX5pMjV25AbqPb+QsL8hHg6VxnNRLVBeXf/YwV02BrabgvEv01drfYJWhUVKYQuwStShtANv/Hh4PFLkGj+8MM/zvYneEhYpeg1ZeHMsUuQatVV74SuwSNvvQ3/CeMAt7Q/wmsuuayaLXYJWh0cuBQsUuoUtHjxxh4+mSd5N7ayOWA8WZzg1vznIiIiIiIiIiIiIhIbA168jwiIgJyubzSLSIiQuzyiIiIiIyeQhD02qhhYC4nIiIiMmz65nJjzeairXleG+Li4jBjxoxKjxnT4wNEREREhqpc8WTTtS81DMzlRERERIZNn1yu7G+MGvTkuYODAxwcHMQug4iIiIiqoM9dKsZ6d0tDxFxOREREZNj0vXvcWLN5g162hYiIiIiIiIiIiIioLjToO89JXcCaD2ArtxK7jCo9vpkudgkaBaydJ3YJWik8XhC7BK0kJQVil6CRYG4tdglaXQkfI3YJWsUuHSJ2CVrFzPxB7BI0GvyPOWKXoFXTeRvELkGrzk1lYpeg0dDVJ8QuoUqPHxXVyzgKQUC5Ad55fv/+fUyePBm7d++GVCrFsGHDsHLlSsjl8irbx8TE4Oeff8aNGzfQokULDB06FAsXLoSdnZ2qnUQiqdB369atGDlyZJ1dCxkmK/cOsLK2FLuMKnkE/SZ2CRrZt3cSuwSt/hoVJ3YJWoVsOSd2CRplTN4sdglarb36QOwStFp15SuxS9BqkuebYpeg0fTsi2KXoFXo+lNil6DVf0d8JnYJGkXMXSd2CVUqKS4ERvjV6Rj65HJl/7piyNmck+dEREREVGcUgu5BW1GHT4a++eabuH37Nn755ReUlZVh/PjxePvtt7Fly5ZK29+6dQu3bt3CsmXL4OXlhevXryMiIgK3bt3Ctm3b1Npu3LgRwcHBqs/29vZ1dyFERERERNWgTy5X9q8rhpzNOXlORERERHXGEF8YmpaWhr179+LUqVPw8fEBAHz66acYNGgQli1bhtatW1fo06lTJ2zfvl312d3dHR999BHeeustPH78GKam/4vV9vb2cHR0rJviiYiIiIh0YKgvDDX0bM41z4mIiIjIoOXn56ttJSUlep3v2LFjsLe3V4VzAOjfvz+kUilOnKj+Ujt5eXmwtbVVC+cAMGnSJDRv3hy+vr7YsGEDBCN9uRIRERERNT7Gls155zkRERER1RmFIOixbMuTfk5O6msfx8TEYMGCBTrXlJWVBQcHB7V9pqamaNq0KbKysqp1jnv37mHhwoV4++231fbHxcXhpZdegpWVFX7++We8++67KCwsxJQpU3Sul4iIiIhIX/rkcmV/wPiyOSfPiYiIiKjOlOvxYiJlv8zMTNja2qr2y2SVvyh29uzZWLJkicZzpqWl6VTL0/Lz8xESEgIvL68KXxTmzfvfC8i7d++OoqIiLF26lJPnRERERCQqfXK5sj9gfNmck+dEREREZNBsbW3VAnpV/vnPfyI0NFRjGzc3Nzg6OuLOnTtq+x8/foz79+9rXQ+xoKAAwcHBsLGxwffffw8zMzON7f38/LBw4UKUlJRU+cWCiIiIiKihMLZszslzIiIiIqozCgAKHW9wqek7iVq0aIEWLVpobefv74/c3FycOXMGPXv2BAAcOHAACoUCfn5+VfbLz89HUFAQZDIZdu3aBQsLC61jnT9/Hk2aNOHEORERERGJSp9cruxfE40lm3PynIiIiIjqTLlCQLmOKV3Xftp07NgRwcHBCA8Px5o1a1BWVoaoqCiMHDkSrVu3BgDcvHkTgYGB+PLLL+Hr64v8/Hy8/PLLKC4uxubNm1UvSAKefDEwMTHB7t27kZ2djRdeeAEWFhb45Zdf8PHHH2PGjBl1ch1ERERERNWlTy5X9q8Lhp7NOXlORERERHVG0OPFRIIeazJq89VXXyEqKgqBgYGQSqUYNmwYEhMTVcfLysrw+++/o7i4GABw9uxZnDhxAgDg4eGhdq5r166hXbt2MDMzw6pVqzBt2jQIggAPDw988sknCA8Pr7PrICIiIiKqDn1yubJ/XTHkbM7Jcz0kJSXhvffeQ25ubpVtQkNDkZubi507d9ZbXURERESkWdOmTbFly5Yqj7dr107tC0Lfvn21fmEIDg5GcHBwrdVI1cdcTkRERNRwGXI2l+p9Bj3cvXsXkZGRcHZ2hkwmg6OjI4KCgnDkyBFVm759+0IikahtERER1Tp/RkaGWj8bGxt4e3tj0qRJuHr1qlrb1NTUCuNIJBJkZWXV6jUTERERGZNyQb+N6gdzOREREVHjpm8uN9ZsLuqd58OGDUNpaSmSk5Ph5uaG7OxspKSkICcnR61deHg44uLiVJ+trKxqNM7+/fvh7e2N4uJiXLp0CStXrkTXrl2xe/duBAYGqrX9/fff1d4Y6+DgoMOVEREREREAKPR4PFSfx0qpZpjLiYiIiBo3fXK5sr8xEu3O89zcXBw+fBhLlixBv3794OLiAl9fX0RHR2Pw4MFqba2srODo6Kjang7R1dGsWTM4OjrCzc0NQ4YMwf79++Hn54ewsDCUl5ertXVwcFAbSyrV/iPat28fOnbsCLlcjuDgYNy+fbtCm9jYWLRo0QK2traIiIhAaWmp6lhJSQmmTJkCBwcHWFhYoFevXjh16lSV45WUlKgWwn96QXwiIiIiQ6N8MZGuG9U95nLdc7myD7M5ERERGTp9c7mxZnPRJs/lcjnkcjl27tyJkpISjW2/+uorNG/eHJ06dUJ0dLRqcXhdSaVSTJ06FdevX8eZM2fUjnXr1g2tWrXCgAED1B5TrUpxcTGWLVuGTZs24dChQ7hx40aFt7ampKQgLS0Nqamp2Lp1K3bs2IHY2FjV8VmzZmH79u1ITk7G2bNn4eHhgaCgINy/f7/SMRctWgQ7OzvV5uTkpMNPgYiIiIiIuVyfXA4wmxMRERE1ZqJNnpuamiIpKQnJycmwt7dHQEAA5syZg4sXL6q1Gz16NDZv3oyDBw8iOjoamzZtwltvvaX3+J6engCerL8IAK1atcKaNWuwfft2bN++HU5OTujbty/Onj2r8TxlZWVYs2YNfHx80KNHD0RFRSElJUWtjbm5OTZs2ABvb2+EhIQgLi4OiYmJUCgUKCoqwurVq7F06VIMHDgQXl5eWLduHSwtLbF+/fpKx4yOjkZeXp5qy8zM1PvnQURERFQXlI+H6rpR3WMu1z2XA8zmRERE1DDom8uNNZuLvuZ5SEgIDh8+jOPHj+Onn35CfHw8vvjiC4SGhgIA3n77bVX7zp07o1WrVggMDER6ejrc3d11Hlv5RlaJRAIA6NChAzp06KA6/uKLLyI9PR0rVqzApk2bqjyPlZWVWh2tWrXCnTt31Np07dpVbT1If39/FBYWIjMzE3l5eSgrK0NAQIDquJmZGXx9fZGWllbpmDKZDDKZrAZXS0RERCQOfV4uZKwvJRIDc7luuRxgNiciIqKGQd+XfhprNhftznMlCwsLDBgwAPPmzcPRo0cRGhqKmJiYKtv7+fkBAP7880+9xlUGYFdX1yrb+Pr6ah3HzMxM7bNEIlF9ASAiIiIiaiiYy4mIiIiI1Ik+ef4sLy8vFBUVVXn8/PnzAJ7cSaIrhUKBxMREuLq6onv37hrH0mccpQsXLuDhw4eqz8ePH4dcLoeTkxPc3d1hbm6uto5jWVkZTp06BS8vL73HJiIiIhITHw1tuJjLmcuJiIio8eCyLboRbdmWnJwcDB8+HBMmTECXLl1gY2OD06dPIz4+HkOGDAEApKenY8uWLRg0aBCaNWuGixcvYtq0aejduze6dOlSo7GysrJQXFyMy5cvIyEhASdPnsSePXtgYmICAEhISICrqyu8vb3x6NEjfPHFFzhw4AB+/vlnva+1tLQUYWFhmDt3LjIyMhATE/P/2LvzsKjK/g3g9wzIvomJoAIiLoimuBLimgsuZaaJWyVKpuZSlpaa5lJqKr4u2VuaC1r55pKZtujrWoaaK5qFG7mQexKLoGzz/P7gx7yOLIPMDOc5zP25rnNdzlnvOcOZ+c7jM8/BmDFjoNVq4ezsjFGjRmHixInw9PSEn58f5s+fj8zMTERHR5t8bCIiIiIl6XQCOl3ZCu2ybkePh3U563IiIiKq+Eypywu2t0aKNZ67uLggNDQUixYtQmJiInJycuDr64vhw4djypQpAPJv6LN7924sXrwYGRkZ8PX1Rd++fTF16tTHOlbnzp0B5I+D6O/vj44dO2LFihWoU6eOfp3s7Gy89dZbuHbtGpycnNC4cWPs3r0bHTt2NPm5durUCXXr1kW7du2QlZWFgQMHYsaMGfrlH374IXQ6HV566SWkp6ejRYsW2LlzJypXrmzysYmIiIiUpDNhbEUrrc/LHevyGfrlrMuJiIioojKlLi/Y3hppBAcCVL20tDS4u7vj7583w83FyfgGCsm7laR0hBLZVK2hdASjdHWeUjqCUZqsdKUjlEjYOSsdwaizw19SOoJR3qH1ja+ksOkTv1U6Qol6JfyqdASjPB0qGV9JYXU85b5JX+9P5H2dcx9k4NcZvZCamgo3Nzez77+gPln+0x9wdHEt0z7u30vHiPbBFstIZAkFf/u3t30KN2dHpeMU68/PNysdoUQedX2VjmDUnwNnKR3BqAnrTyodoUSXT59XOoJRKw78W+kIRvXYt1zpCEaNDhqsdIQSvXnrtNIRjIpadVTpCEb9se9npSOUaOTr/ZWOUKyszHtYHBlqkbrXHHU5YL21uWI9z4mIiIio4jNlfERrHVeRiIiIiMjcTB233Fprc+luGPo4Ro4cCRcXlyKnkSNHKh2PiIiIyOrlCWHSROrAupyIiIhIbqbW5dZam6u65/msWbMwYcKEIpdZ088HiIiIiGTFG4ZaB9blRERERHLjDUPLRtWN515eXvDy8lI6BhERERGRVWNdTkREREQVkaobz4mIiIhIbnkA8srYSSXPrEmIiIiIiKyXKXV5wfbWiI3nFci1mk8hzVXen8X+/W5vpSOUqOmCqUpHMOpchvyXbHpW2e/cXC4ylA5gXKu3X1M6glHJ9TopHcGoXs9MUTpCibY1CFU6glHLzqxROoJRJ7RNlI5QolWJy5SOUKx72TloUQ7H4Q1DyVrZBIbAxtVF6RjFcqy6W+kIJUq5kKR0BKMOPvmU0hGMWtjcW+kIJWr1xadKRzAq65dkpSMYtS4sWukIRr1567TSEUr0r2qNlY5g1PQ6nkpHMKrdv4YpHaFESyKfUzpCsR4IncWPwRuGlo38LXFEREREpFqm3FzIWm9KRERERERkbqbe9NNaa3Ot0gGIiIiIiIiIiIiIiGTDnudEREREZDE6nUCerozDtpRxOyIiIiIiMmRKXV6wvTVi4zkRERERWUyeCUW6KcU9ERERERH9jyl1ecH21ojDthARERERERERERERPYI9z4mIiIjIYtjznIiIiIhIeex5XjZsPCciIiIii8nTlb3QztOZOQwRERERkZUypS4v2N4asfGciIiIiCyGPc+JiIiIiJTHnudlwzHPiYiIiIiIiIiIiIgewcZzE8TGxsLDw6PEdaKiotC7d+9yyUNEREQkm4IeLmWdiEqDdTkRERFRyUyty621Nle08fzOnTsYNWoU/Pz8YG9vD29vb0RERCAuLk6/TocOHaDRaAymkSNHlmr/ly9fNtjO1dUVDRs2xOjRo3HhwgWDdaOiogodR6PRoGHDhmZ9zkRERETWRGdCca6z0gJdCazLiYiIiCo2U+pya67NFR3zvG/fvsjOzsbatWtRu3Zt3Lp1C3v27MHdu3cN1hs+fDhmzZqlf+zk5PRYx9m9ezcaNmyIzMxM/Pbbb1iyZAmaNGmC7du3o1OnTgCAJUuW4MMPP9Rvk5ubiyZNmqBfv34mPEMiIiIi65YnTBjzXFhnga4E1uVEREREFZspdXnB9tZIsZ7nKSkpOHDgAObNm4eOHTvC398frVq1wuTJk9GrVy+DdZ2cnODt7a2f3NzcHutYVapUgbe3N2rXro3nnnsOu3fvRmhoKKKjo5GXlwcAcHd3NzjGsWPH8M8//2Do0KFG979z5040aNAALi4u6NatG27cuFFonZkzZ6Jq1apwc3PDyJEjkZ2drV+WlZWFcePGwcvLCw4ODmjTpg2OHj1a7PGysrKQlpZmMBERERERlQXr8rLX5QXbsDYnIiIiqpgUazx3cXGBi4sLtm7diqysrBLX/fLLL/HEE0+gUaNGmDx5MjIzM006tlarxeuvv44rV67g+PHjRa6zatUqdO7cGf7+/iXuKzMzEzExMfj888/x888/4+rVq5gwYYLBOnv27EFCQgL279+P//znP9iyZQtmzpypX/7222/j66+/xtq1a3HixAnUqVMHERERSE5OLvKYc+fOhbu7u37y9fV9zDNAREREVD44rqL8WJeXvS4HWJsTERGROnDM87JRrPHc1tYWsbGxWLt2LTw8PBAeHo4pU6bg9OnTBusNGjQIX3zxBfbt24fJkyfj888/x4svvmjy8YOCggDkj7/4qOvXr+PHH3/EK6+8YnQ/OTk5+PTTT9GiRQs0a9YMY8aMwZ49ewzWsbOzw+rVq9GwYUP07NkTs2bNwtKlS6HT6ZCRkYFPPvkECxYsQPfu3REcHIzPPvsMjo6OWLVqVZHHnDx5MlJTU/VTUlLS458AIiIionLAAl1+rMvLXpcDrM2JiIhIHdh4XjaKj3nes2dPHDhwAIcPH8aPP/6I+fPnY+XKlYiKigIAvPrqq/r1n3zySfj4+KBTp05ITExEYGBgmY8t/n+cHo1GU2hZwReH3r17G92Pk5OTQQ4fHx/cvn3bYJ0mTZoYjAcZFhaGe/fuISkpCampqcjJyUF4eLh+eaVKldCqVSskJCQUeUx7e3vY29sbzUZEREREVBqsy8tWlwOszYmIiIgqMsV6nhdwcHBAly5dMG3aNBw8eBBRUVGYPn16seuHhoYCAC5evGjScQsK4ICAAIP5QgisXr0aL730Euzs7Izup1KlSgaPNRqN/gsAERERkbXL1QmTJktJTk7G4MGD4ebmBg8PD0RHR+PevXslbtOhQwdoNBqDaeTIkQbrXL16FT179oSTkxO8vLwwceJE5ObmWux5mBPrciIiIqKKy9S63Fprc8Ubzx8VHByMjIyMYpfHx8cDyO9JUlY6nQ5Lly5FQEAAmjZtarDsp59+wsWLFxEdHV3m/T/q1KlTuH//vv7x4cOH4eLiAl9fXwQGBsLOzg5xcXH65Tk5OTh69CiCg4PNloGIiIhICbL+NHTw4MH4/fffsWvXLnz33Xf4+eefDXpWF2f48OG4ceOGfpo/f/7/nmteHnr27Ins7GwcPHgQa9euRWxsLN577z2LPQ9LYl3OupyIiIgqDpmHbZG5Nlds2Ja7d++iX79+GDZsGBo3bgxXV1ccO3YM8+fPx3PPPQcASExMxPr169GjRw9UqVIFp0+fxvjx49GuXTs0btz4sY518+ZNZGZm4syZM1i8eDGOHDmC77//HjY2Ngbrrlq1CqGhoWjUqJHZnmt2djaio6MxdepUXL58GdOnT8eYMWOg1Wrh7OyMUaNGYeLEifD09ISfnx/mz5+PzMxMs35RICIiIlKCzoRCW2ehAj0hIQE7duzA0aNH0aJFCwDARx99hB49eiAmJgbVq1cvdlsnJyd4e3sXuey///0v/vjjD+zevRvVqlVDSEgI3n//fbzzzjuYMWNGqXpPK4F1OetyIiIiqvhMqcsLtrcE2WtzxRrPXVxcEBoaikWLFiExMRE5OTnw9fXF8OHDMWXKFAD5N/TZvXs3Fi9ejIyMDPj6+qJv376YOnXqYx2rc+fOAPJPqL+/Pzp27IgVK1agTp06Buulpqbi66+/xpIlS8zzJP9fp06dULduXbRr1w5ZWVkYOHAgZsyYoV/+4YcfQqfT4aWXXkJ6ejpatGiBnTt3onLlymbNQURERKRGaWlpBo9NHWP60KFD8PDw0BfnQH69qNVq8euvv+L5558vdtsvv/wSX3zxBby9vfHss89i2rRp+jG0Dx06hCeffBLVqlXTrx8REYFRo0bh999/L9SzWhasy2fol7MuJyIiIiqZtdXmijWe29vbY+7cuZg7d26x6/j6+uKnn34q8zFq1ar1WOMcuru7IzMzs9TrR0VF6W+gVKB3794Gx4yNjdX/e+bMmUXux8HBAUuXLsXSpUtLfWwiIiIiNcgTAnllHHe6YDtfX1+D+dOnTzdo8HxcN2/ehJeXl8E8W1tbeHp64ubNm8VuN2jQIPj7+6N69eo4ffo03nnnHZw7dw5btmzR7/fh4hyA/nFJ+1Ua6/L/YV1OREREFZUpdXnB9oD11eaKNZ4TERERUcVnyviIBdslJSXBzc1NP7+4ni2TJk3CvHnzStxnwc0py+LhcReffPJJ+Pj4oFOnTkhMTERgYGCZ90tEREREZGmmjlturbW5qhvPR44ciS+++KLIZS+++CI+/fTTck5ERERERA8zR+O5m5ubQYFenLfeeqtQ7+NH1a5dG97e3rh9+7bB/NzcXCQnJxc7ZmJRQkNDAQAXL15EYGAgvL29ceTIEYN1bt26BQCPtV81Yl1OREREJDdzNZ5bW22u6sbzWbNmYcKECUUuK82LSEREREQVR9WqVVG1alWj64WFhSElJQXHjx9H8+bNAQB79+6FTqfTF92lER8fDwDw8fHR73f27Nm4ffu2/qenu3btgpubG4KDgx/z2agL63IiIiIielhFqc1V3Xju5eVVaEwcIiIiIpKHOXqem1uDBg3QrVs3DB8+HJ9++ilycnIwZswYDBgwANWrVwcAXLt2DZ06dcK6devQqlUrJCYmYv369ejRoweqVKmC06dPY/z48WjXrh0aN24MAOjatSuCg4Px0ksvYf78+bh58yamTp2K0aNHm3QTJTVgXU5EREQkN3P1PDc32WtzVTeeExEREZHc8oQOeTpdmbe1lC+//BJjxoxBp06doNVq0bdvX4ObRObk5ODcuXP6m1ba2dlh9+7dWLx4MTIyMuDr64u+ffti6tSp+m1sbGzw3XffYdSoUQgLC4OzszOGDBmCWbNmWex5EBERERGVhil1ecH2liJzba4Rj3Pbe5JSWloa3N3dcWvzYrg5Oyodp1i2vnWVjlAiXepdpSMYdXbpaqUjGHVp/xWlI5Soan1PpSMY1XL9Z0pHMCrxvUlKRzAqZZrc10uLjFNKRzBqTKOhSkcwqns1Z6UjlCiwUy2lIxTrXk4OwjbtQmpqqkWG1SioTyJX7IOdo0uZ9pF9/x42vtrRYhmJLKHgb//vs8fh5lq2v/3yIGztlI5QIu2dS0pHMCq2zSilIxh1KytX6Qgl6t61ttIRjHJZsVnpCMZNH6Z0AqOi/OS+Xqavnqh0BKO+uZisdASjOjzhpHSEEp1Jy1I6QrGyhA4xOZctUveaoy4HrLc2Z89zIiIiIrIYnQk/D9VZ6KehRERERETWxpS6vGB7a8TGcyIiIiKymDydgFayMc+JiIiIiKyNKXV5wfbWiI3nRERERGQxuTpAU8ZCO9dywyoSEREREVkVU+rygu2tkVbpAEREREREREREREREsmHPcyIiIiKyGA7bQkRERESkPA7bUjZsPCciIiIii2HjORERERGR8th4XjZsPCciIiIii2HjORERERGR8th4XjYc85yIiIiIiIiIiIiI6BHseU5EREREFqPTiTL3UtFZae8WIiIiIiJzM6UuL9jeGrHnuQn2798PjUaDlJSUYteZMWMGQkJCyi0TERERkUzy/r9IL+tEVBqsy4mIiIhKZmpdbq21uaKN53fu3MGoUaPg5+cHe3t7eHt7IyIiAnFxcQbrHTp0CE8//TScnZ3h5uaGdu3a4f79+6U6hkaj0U/Ozs6oW7cuoqKicPz48WK3uXjxIlxdXeHh4WHK0yMiIiIiUgXW5UREREREhSnaeN63b1+cPHkSa9euxfnz57Ft2zZ06NABd+/e1a9z6NAhdOvWDV27dsWRI0dw9OhRjBkzBlpt6aOvWbMGN27cwO+//46PP/4Y9+7dQ2hoKNatW1do3ZycHAwcOBBt27Y1y3MkIiIismZCCAhdGSdhnb1blMC6nIiIiKhiM6kut+LaXLHG85SUFBw4cADz5s1Dx44d4e/vj1atWmHy5Mno1auXfr3x48dj3LhxmDRpEho2bIj69esjMjIS9vb2pT6Wh4cHvL29UatWLXTt2hWbN2/G4MGDMWbMGPzzzz8G606dOhVBQUGIjIws9f6PHz+OFi1awMnJCa1bt8a5c+cKrbN8+XL4+vrCyckJkZGRSE1N1S/T6XSYNWsWatasCXt7e4SEhGDHjh3FHi8rKwtpaWkGExEREZGMdDph0kSWx7q87HU5wNqciIiI1MHUutxaa3PFGs9dXFzg4uKCrVu3Iisrq8h1bt++jV9//RVeXl5o3bo1qlWrhvbt2+OXX34x+fjjx49Heno6du3apZ+3d+9ebNq0CR9//PFj7evdd9/FwoULcezYMdja2mLYsGEGyy9evIiNGzdi+/bt2LFjB06ePInXXntNv3zJkiVYuHAhYmJicPr0aURERKBXr164cOFCkcebO3cu3N3d9ZOvr+9j5SUiIiIqL0IIkyayPNblZa/LAdbmREREpA6m1uXWWpsr1nhua2uL2NhYrF27Fh4eHggPD8eUKVNw+vRp/Tp//vkngPyb+wwfPhw7duxAs2bN0KlTpxIL2NIICgoCAFy+fBkAcPfuXURFRSE2NhZubm6Pta/Zs2ejffv2CA4OxqRJk3Dw4EE8ePBAv/zBgwdYt24dQkJC0K5dO3z00Uf46quvcPPmTQBATEwM3nnnHQwYMAD169fHvHnzEBISgsWLFxd5vMmTJyM1NVU/JSUlPf4JICIiIiIC63JT6nKAtTkRERFRRab4mOfXr1/Htm3b0K1bN+zfvx/NmjVDbGwsgPyfTQLAiBEjMHToUDRt2hSLFi1C/fr1sXr1apOOXfC/JRqNBgAwfPhwDBo0CO3atXvsfTVu3Fj/bx8fHwD5vXMK+Pn5oUaNGvrHYWFh0Ol0OHfuHNLS0nD9+nWEh4cb7DM8PBwJCQlFHs/e3h5ubm4GExEREZGMTBpX0Up/GqoE1uVlq8sB1uZERESkDqbW5dZamyvaeA4ADg4O6NKlC6ZNm4aDBw8iKioK06dPB/C/gjc4ONhgmwYNGuDq1asmHbegAA4ICACQ/9PQmJgY2NrawtbWFtHR0UhNTYWtra3RLwSVKlXS/7ug6C/4gkFERERkzTiuonqwLiciIiKquDjmedko3nj+qODgYGRkZAAAatWqherVqxe60c/58+fh7+9v0nEWL14MNzc3dO7cGQBw6NAhxMfH66dZs2bB1dUV8fHxeP7550061tWrV3H9+nX948OHD0Or1aJ+/fpwc3ND9erVERcXZ7BNXFxcoS8nRERERGojdKZNpBzW5flYlxMREVFFYGpdbq21ua1SB7579y769euHYcOGoXHjxnB1dcWxY8cwf/58PPfccwDye4tMnDgR06dPR5MmTRASEoK1a9fi7Nmz2Lx5c6mPlZKSgps3byIrKwvnz5/H8uXLsXXrVqxbtw4eHh4A8nvNPOzYsWPQarVo1KiRyc/VwcEBQ4YMQUxMDNLS0jBu3DhERkbC29sbAPTPMTAwECEhIVizZg3i4+Px5ZdfmnxsIiIiIqKSsC5nXU5ERERERVOs8dzFxQWhoaFYtGgREhMTkZOTA19fXwwfPhxTpkzRr/fGG2/gwYMHGD9+PJKTk9GkSRPs2rULgYGBpT7W0KFDAeQXyzVq1ECbNm1w5MgRNGvWzOzPqyh16tRBnz590KNHDyQnJ+OZZ57Bv//9b/3ycePGITU1FW+99RZu376N4OBgbNu2DXXr1i2XfERERESWIoTQj2ldlm3J8liXsy4nIiKiis+Uurxge2ukEdb6zCuQtLQ0uLu749bmxXBzdlQ6TrFsfeX+0qFLvat0BKPOLjXthlzl4dL+K0pHKFHV+p5KRzCq5frPlI5gVOJ7k5SOYFTKNLmvlxYZp5SOYNSYRkOVjmBU92rOSkcoUWCnWkpHKNa9nByEbdqF1NRUi9zgsKA+aTHtW9g6lO11yn2QgWPvP2exjESWUPC3//fZ43BzdVE6TrGErZ3SEUqkvXNJ6QhGxbYZpXQEo25l5SodoUTdu9ZWOoJRLitK/+saxUwfpnQCo6L85L5epq+eqHQEo765mKx0BKM6POGkdIQSnUnLUjpCsbKEDjE5ly1S95qjLgestzZXrOc5EREREVV8QicgynhzobJuR0REREREhkypywu2t0bS3TD0ccyZMwcuLi5FTt27d1c6HhERERGRVWBdTkREREQVkap7no8cORKRkZFFLnN0lHf4EiIiIiKrYUoPFyvt3aJGrMuJiIiIJGdiz3Nrrc1V3Xju6ekJT0/5xy8mIiIislY6IaAp4y12dLw1j2qwLiciIiKSmyl1ecH21kjVw7YQEREREREREREREVmCqnuek6Gzq7+FSyV5X9LsxV8pHaFEdXbPVjqCUT//mKh0BKMu3stWOkKJ7O7eVzqCUc08aigdwagbJ24oHcGoJz3tlY5QohPaJkpHMKp7tbLfib28/HgrQ+kIJVr24XylIxQrLf0esGmXxY8jhAk3DLXS3i1UMZzK9YRLrpvSMYq1K+FvpSOUqO4T9ZSOYFTE2DZKRzDqu6UHlI5Qoh//+6fSEYzaue6E0hGM2jX3E6UjGPVH5DKlI5So3b+GKR3BqH+G/VvpCEbt/ztT6QglGtLBX+kIxcrIzUXML5ctegxT6vKC7a2RvC2tRERERKR6woSxFU0ak5GIiIiIiPRMqcsLtrdGbDwnIiIiIovR6QBNGQttnc7MYYiIiIiIrJQpdXnB9taIY54TERERERERERERET2CPc+JiIiIyGKEEGUeH9Fax1UkIiIiIjI3U+rygu2tERvPiYiIiMhihC5/Kuu2RERERERkOlPq8oLtrREbz4mIiIjIYnQ6YcKY59bZu4WIiIiIyNxMqcsLtrdGHPOciIiIiKxOcnIyBg8eDDc3N3h4eCA6Ohr37t0rdv3Lly9Do9EUOW3atEm/XlHLv/rqq/J4SkREREREqiRzbc6e50RERERkMUInIMrYS6Ws25XG4MGDcePGDezatQs5OTkYOnQoXn31Vaxfv77I9X19fXHjxg2DeStWrMCCBQvQvXt3g/lr1qxBt27d9I89PDzMnp+IiIiI6HGYUpcXbG8pMtfmbDwnIiIiIouRsfE8ISEBO3bswNGjR9GiRQsAwEcffYQePXogJiYG1atXL7SNjY0NvL29DeZ98803iIyMhIuLi8F8Dw+PQusSERERESlJ1sZz2WtzDttigoKfCMTHxxe7TmxsLHsbEREREZkgLS3NYMrKyjJpf4cOHYKHh4e+OAeAzp07Q6vV4tdffy3VPo4fP474+HhER0cXWjZ69Gg88cQTaNWqFVavXg0hrHN8yPLEupyIiIiofFhbba5o4/mdO3cwatQo+Pn5wd7eHt7e3oiIiEBcXJzBeocOHcLTTz8NZ2dnuLm5oV27drh//36pjvHwmDbOzs6oW7cuoqKicPz48ULrCiEQExODevXqwd7eHjVq1MDs2bPN8lyJiIiIrJFOCJMmIP9nme7u7vpp7ty5JmW6efMmvLy8DObZ2trC09MTN2/eLNU+Vq1ahQYNGqB169YG82fNmoWNGzdi165d6Nu3L1577TV89NFHJuUtD6zLiYiIiCo2U+tya63NFR22pW/fvsjOzsbatWtRu3Zt3Lp1C3v27MHdu3f16xw6dAjdunXD5MmT8dFHH8HW1hanTp2CVlv6dv+CsW0ePHiA8+fPY8WKFQgNDcXq1avx8ssv69d7/fXX8d///hcxMTF48sknkZycjOTkZLM+ZyIiIiJrYo5hW5KSkuDm5qafb29vX+T6kyZNwrx580rcZ0JCQpmyPOz+/ftYv349pk2bVmjZw/OaNm2KjIwMLFiwAOPGjTP5uJbEupyIiIioYjPXsC3WVpsr1niekpKCAwcOYP/+/Wjfvj0AwN/fH61atTJYb/z48Rg3bhwmTZqkn1e/fv3HOtbDY9vUqlULXbt2xZAhQzBmzBg8++yzqFy5MhISEvDJJ5/gzJkz+v0HBASUav9//vknxo8fj19//RV169bFp59+irCwMIN1tm7diokTJyIpKQnt27fHypUr4evrq1/+ySefICYmBklJSQgICMDUqVPx0ksvFXm8rKwsg59EpKWlPdb5ICIiIiovQpjQeP7/vVvc3NwMCvTivPXWW4iKiipxndq1a8Pb2xu3b982mJ+bm4vk5ORSjYe4efNmZGZmGjT2Fic0NBTvv/8+srKyiv1ioTTW5WWvywHW5kRERKQOptTlBdsD1lebKzZsi4uLC1xcXLB169Zix8a5ffs2fv31V3h5eaF169aoVq0a2rdvj19++cXk448fPx7p6enYtWsXAGD79u2oXbs2vvvuOwQEBKBWrVp45ZVXStXD5d1338WECRMQHx+PevXqYeDAgcjNzdUvz8zMxOzZs7Fu3TrExcUhJSUFAwYM0C//5ptv8Prrr+Ott97CmTNnMGLECAwdOhT79u0r8nhz5841+HnEw8U+ERERkbWqWrUqgoKCSpzs7OwQFhaGlJQUg+FC9u7dC51Oh9DQUKPHWbVqFXr16oWqVasaXTc+Ph6VK1eWtuEcYF1uSl0OsDYnIiIiKkpFqc0Vazy3tbVFbGws1q5dCw8PD4SHh2PKlCk4ffq0fp0///wTADBjxgwMHz4cO3bsQLNmzdCpUydcuHDBpOMHBQUByL+5UMGxrly5gk2bNmHdunWIjY3F8ePH8cILLxjd14QJE9CzZ0/Uq1cPM2fOxJUrV3Dx4kX98pycHCxbtgxhYWFo3rw51q5di4MHD+LIkSMAgJiYGERFReG1115DvXr18Oabb6JPnz6IiYkp8niTJ09GamqqfkpKSjLpXBARERFZitAJ6Mo4mdIzpiQNGjRAt27dMHz4cBw5cgRxcXEYM2YMBgwYgOrVqwMArl27hqCgIH29VuDixYv4+eef8corrxTa7/bt27Fy5UqcOXMGFy9exCeffII5c+Zg7NixFnke5sK6vOx1OcDanIiIiNTBlLrcmmtzRW8Y2rdvX1y/fh3btm1Dt27dsH//fjRr1gyxsbEAAJ1OBwD6Hh9NmzbFokWLUL9+faxevdqkYxf81ECj0eiPlZWVhXXr1qFt27bo0KEDVq1ahX379uHcuXMl7qtx48b6f/v4+ACAwc8NbG1t0bJlS/3joKAgeHh46Mf1SUhIQHh4uME+w8PDix33x97eXv8TidL+VIKIiIhICUIIkyZL+fLLLxEUFIROnTqhR48eaNOmDVasWKFfnpOTg3PnziEzM9Ngu9WrV6NmzZro2rVroX1WqlQJH3/8McLCwhASEoLly5fjX//6F6ZPn26x52EurMvLVpcDrM2JiIhIHUyty621Nlf0hqEA4ODggC5duqBLly6YNm0aXnnlFUyfPh1RUVH6gjc4ONhgmwYNGuDq1asmHbegAC4YP9HHxwe2traoV6+ewXEA4OrVqyWO51ipUiX9vx8u+omIiIisnTluGGoJnp6eWL9+fbHLa9WqVeQXhDlz5mDOnDlFbtOtWzd069bNbBnLG+tyIiIioorLXDcMtQSZa3NFe54XJTg4GBkZGQDyT0z16tUL9TA5f/48/P39TTrO4sWL4ebmhs6dOwPI71GSm5uLxMREg+MAMPlYubm5OHbsmP7xuXPnkJKSov8S0KBBA8TFxRlsExcXV+jLCRERERFReWFdno91OREREZH1Uqzn+d27d9GvXz8MGzYMjRs3hqurK44dO4b58+fjueeeA5DfW2TixImYPn06mjRpgpCQEKxduxZnz57F5s2bS32slJQU3Lx5E1lZWTh//jyWL1+OrVu3Yt26dfDw8AAAdO7cGc2aNcOwYcOwePFi6HQ6jB49Gl26dDHo9VIWlSpVwtixY7F06VLY2tpizJgxeOqpp9CqVSsAwMSJExEZGYmmTZuic+fO2L59O7Zs2YLdu3ebdFwiIiIipel0AihjLxWdBXu30P+wLmddTkRERBWfKXW5fnsrpFjjuYuLC0JDQ7Fo0SIkJiYiJycHvr6+GD58OKZMmaJf74033sCDBw8wfvx4JCcno0mTJti1axcCAwNLfayhQ4cCyP8pao0aNdCmTRscOXIEzZo106+j1Wqxfft2jB07Fu3atYOzszO6d++OhQsXmvxcnZyc8M4772DQoEG4du0a2rZti1WrVumX9+7dG0uWLEFMTAxef/11BAQEYM2aNejQoYPJxyYiIiJSktDlQejyyrwtWR7rctblREREVPGZUpcXbG+NNMKSo71TuUhLS4O7uzt+6tERLpUUH8a+WNmLv1I6QonqfDtb6QhGffXed0pHMOrivWylI5TITqtROoJRs1JOKR3BqENtOisdwagnf9qrdIQS/ZmSpXQEo260aqt0BKN+vJWhdIQSLbv6vdIRipWWfg9VGj6F1NRUi9zgsKA+8X1pNbR2TmXahy47E0mfD7NYRiJLKPjb33PmElxc5f273XXhb6UjlKjuE85KRzAq7Gv5bwb83dIDSkcoUXK2/A0xO2etVDqCUbteaah0BKOqRS5TOkKJboytqnQEo74d9m+lIxi1/+9M4yspaEgH04Z/s6SM3Fx0+eWgRepec9TlgPXW5vK2tBIRERGR6rHnORERERGR8tjzvGyku2Ho45gzZw5cXFyKnLp37650PCIiIiKrJ3Q6faH++JNO6fhUSqzLiYiIiORmWl1uvbW5qnuejxw5EpGRkUUuc3R0LOc0RERERPQokZcHkVfGnudl3I7KH+tyIiIiIrmZUpcXbG+NVN147unpCU9PT6VjEBERERFZNdblRERERFQRqbrxnIiIiIjkJoQJY54L6+zdQkRERERkbqbU5QXbWyM2nlcgR8cvgaOzq9IxijUiK0HpCCX6qcckpSMY1X3QDKUjGOWrTVc6QolSK7krHcGovUn3lI5gVMjePUpHMKr3J78qHaFEqxKXKR3BKKdOtZSOYNSyD+crHaFEY/x6Kh2hWNkonzELecNQslaNnXPg5pKtdIxiNfW4oHSEEuVevaR0BKNmz9undASj7LQapSOUaOjEDkpHMGpCdAOlIxh1pHtvpSMYNXLqZ0pHKNGSyOeUjmDUvVz5x5se0sFf6QglWrv/itIRilUetTlvGFo2bDwnIiIiIoth4zkRERERkfLYeF42WqUDEBERERERERERERHJhj3PiYiIiMhi2POciIiIiEh57HleNmw8JyIiIiKLETqdCY3n8o/tSURERESkBqbU5QXbWyMO20JERERERERERERE9Aj2PCciIiIii9Hp8oAy9nDRWelPQ4mIiIiIzM2Uuly/vRVi4zkRERERWQzHPCciIiIiUh7HPC8bNp4TERERkcWw8ZyIiIiISHlsPC8bjnlORERERERERERERPQINp6bYP/+/dBoNEhJSSl2nRkzZiAkJKTcMhERERFJJS8PoowT8qyzdws9PtblREREREaYUJdbc22uaOP5nTt3MGrUKPj5+cHe3h7e3t6IiIhAXFycwXqHDh3C008/DWdnZ7i5uaFdu3a4f/9+qY6h0Wj0k7OzM+rWrYuoqCgcP37cYL0ZM2YYrPvwNkRERERUNkLk6X8i+tiTsM4CXQmsy4mIiIgqNpPqciuuzRUd87xv377Izs7G2rVrUbt2bdy6dQt79uzB3bt39escOnQI3bp1w+TJk/HRRx/B1tYWp06dglZb+nb/NWvWoFu3bnjw4AHOnz+PFStWIDQ0FKtXr8bLL78MAJgwYQJGjhxpsF2nTp3QsmVL8zxZIiIiIiskdDqgzGOe68ychorDupyIiIioYjOlLtdvb4UU63mekpKCAwcOYN68eejYsSP8/f3RqlUrTJ48Gb169dKvN378eIwbNw6TJk1Cw4YNUb9+fURGRsLe3r7Ux/Lw8IC3tzdq1aqFrl27YvPmzRg8eDDGjBmDf/75BwDg4uICb29v/XTr1i388ccfiI6ONrr/48ePo0WLFnByckLr1q1x7ty5QussX74cvr6+cHJyQmRkJFJTU/XLdDodZs2ahZo1a8Le3h4hISHYsWNHscfLyspCWlqawUREREREVBasy8telwOszYmIiIgqMsUaz11cXODi4oKtW7ciKyuryHVu376NX3/9FV5eXmjdujWqVauG9u3b45dffjH5+OPHj0d6ejp27dpV5PKVK1eiXr16aNu2rdF9vfvuu1i4cCGOHTsGW1tbDBs2zGD5xYsXsXHjRmzfvh07duzAyZMn8dprr+mXL1myBAsXLkRMTAxOnz6NiIgI9OrVCxcuXCjyeHPnzoW7u7t+8vX1fYxnTkRERFR+TPppqAk9Y6j0WJeXvS4HWJsTERGROphal1trba5Y47mtrS1iY2Oxdu1aeHh4IDw8HFOmTMHp06f16/z5558A8sc9HD58OHbs2IFmzZqhU6dOJRawpREUFAQAuHz5cqFlDx48wJdfflmq3i0AMHv2bLRv3x7BwcGYNGkSDh48iAcPHhjsb926dQgJCUG7du3w0Ucf4auvvsLNmzcBADExMXjnnXcwYMAA1K9fH/PmzUNISAgWL15c5PEmT56M1NRU/ZSUlPR4T56IiIionAidzqSJLI91ednrcoC1OREREamDqXW5tdbmit4wtG/fvrh+/Tq2bduGbt26Yf/+/WjWrBliY2MB5P9sEgBGjBiBoUOHomnTpli0aBHq16+P1atXm3RsIQSA/BsXPeqbb75Beno6hgwZUqp9NW7cWP9vHx8fAPm9cwr4+fmhRo0a+sdhYWHQ6XQ4d+4c0tLScP36dYSHhxvsMzw8HAkJCUUez97eHm5ubgYTERERkYzYu0UdWJeXrS4HWJsTERGROrDnedko2ngOAA4ODujSpQumTZuGgwcPIioqCtOnTwfwv4I3ODjYYJsGDRrg6tWrJh23oAAOCAgotGzlypV45plnUK1atVLtq1KlSvp/FxT9Oiv93xgiIiIiUifW5UREREREhhRvPH9UcHAwMjIyAAC1atVC9erVC93o5/z58/D39zfpOIsXL4abmxs6d+5sMP/SpUvYt29fqX8aWhpXr17F9evX9Y8PHz4MrVaL+vXrw83NDdWrV0dcXJzBNnFxcYW+nBARERGpDXu3qBfr8nysy4mIiKgiYM/zsrFV6sB3795Fv379MGzYMDRu3Biurq44duwY5s+fj+eeew5Afm+RiRMnYvr06WjSpAlCQkKwdu1anD17Fps3by71sVJSUnDz5k1kZWXh/PnzWL58ObZu3Yp169bBw8PDYN3Vq1fDx8cH3bt3N9tzdXBwwJAhQxATE4O0tDSMGzcOkZGR8Pb2BgD9cwwMDERISAjWrFmD+Ph4fPnll2bLQERERKQEnS4PmjIW2tZaoJc31uWsy4mIiKjiM6UuB6y3Nles8dzFxQWhoaFYtGgREhMTkZOTA19fXwwfPhxTpkzRr/fGG2/gwYMHGD9+PJKTk9GkSRPs2rULgYGBpT7W0KFDAeQXyzVq1ECbNm1w5MgRNGvWzGA9nU6H2NhYREVFwcbGxjxPFECdOnXQp08f9OjRA8nJyXjmmWfw73//W7983LhxSE1NxVtvvYXbt28jODgY27ZtQ926dc2WgYiIiIioKKzLWZcTERERUdE0ouAOPaRaaWlpcHd3R8yu03B0dlU6TrFGeF5TOkKJftLWUzqCUf4eDkpHMMpXm650hBKlVnJXOoJRR6/fUzqCUSHVnJWOYFTkiiNKRyjRqsRlSkcwKicjS+kIRgV9OF/pCCUa49dT6QjFyoYOa5CE1NRUi9zgsKA+cWgxAhpbuzLtQ+Rm48Gx5RbLSGQJBX/7dy6dh5ubvLW55tJxpSOUKPf6JaUjGDX7hUVKRzDKTlv4RrwyGTqxg9IRjPIaP1vpCEYd6dFH6QhGfT/1M6UjlMgj8jmlIxh1L1f+e3j0bF1T6QglWrv/itIRimXJ2twcdTlgvbW5Yj3PiYiIiKjiEyIPKOuwLcI6fxpKRERERGRuptTl+u2tkHQ3DH0cc+bMgYuLS5GTOcdGJCIiIqKy4U2JrAPrciIiIiK58YahZaPqxvORI0ciPj6+yGnlypVKxyMiIiIiSc2ePRutW7eGk5NToRtVFkcIgffeew8+Pj5wdHRE586dceHCBYN1kpOTMXjwYLi5ucHDwwPR0dG4d0/+4bhMxbqciIiIiMpK5tpc1cO2eHp6wtPTU+kYRERERFQMocsDNGUctsWCvVuys7PRr18/hIWFYdWqVaXaZv78+Vi6dCnWrl2LgIAATJs2DREREfjjjz/g4JB/X5LBgwfjxo0b2LVrF3JycjB06FC8+uqrWL9+vcWeiwxYlxMRERHJzZS6XL+9hchcm6u68ZzyFdzz9UGG3L2a0uwylI5QogzJb3QJAOk22UpHMCpNK/ffYbqt3DdNAoDMe3JfKwCQ7ij/z7VyH8h9Hu9l5ygdwaicHPkzpqXL/Z6TDXlv7FSQzdL3jhc5D8peaOfl/w2mpaUZzLa3t4e9vb1JuWbOnAkAiI2NLdX6QggsXrwYU6dOxXPP5d9UbN26dahWrRq2bt2KAQMGICEhATt27MDRo0fRokULAMBHH32EHj16ICYmBtWrVzcpM6lDwTWVLvn7k+ZeptIRSpSbeV/pCEZlCXnf4wvohNy1b3qW/N9vHNLk/56YkZurdASjsjLlfk98oILrWQ3vObL/LVp7bW5SXQ5Yb20uSPWSkpIEAE6cOHHixIkTp8eekpKSLFKf3L9/X3h7e5ucz8XFpdC86dOnmy3nmjVrhLu7u9H1EhMTBQBx8uRJg/nt2rUT48aNE0IIsWrVKuHh4WGwPCcnR9jY2IgtW7aYKzJJjrU5J06cOHHixKmskyVqc3PV5YB11ubseV4BVK9eHUlJSXB1dYVGY3rPgrS0NPj6+iIpKQlubm5mSGh+zGg62fMBzGgOsucDmNEcZM8HMKM5mDufEALp6ekW6w3t4OCAS5cuITvbtF6FQohC9Y2pPVvK4ubNmwCAatWqGcyvVq2aftnNmzfh5eVlsNzW1haenp76dajiY20uX0bZ8wHyZ5Q9H8CM5iB7PoAZzUH2fIB1ZrRkbW6uuhywztqcjecVgFarRc2aNc2+Xzc3N2nfpAowo+lkzwcwoznIng9gRnOQPR/AjOZgznzu7u5m2U9xHBwc9OMNlodJkyZh3rx5Ja6TkJCAoKCgckpE1oi1ubwZZc8HyJ9R9nwAM5qD7PkAZjQH2fMB1pfRkrV5edflQMWpzdl4TkREREQVwltvvYWoqKgS16ldu3aZ9u3t7Q0AuHXrFnx8fPTzb926hZCQEP06t2/fNtguNzcXycnJ+u2JiIiIiKxBRanN2XhORERERBVC1apVUbVqVYvsOyAgAN7e3tizZ4++IE9LS8Ovv/6KUaNGAQDCwsKQkpKC48ePo3nz5gCAvXv3QqfTITQ01CK5iIiIiIhkVFFqc63Z05Pq2dvbY/r06YqMWVRazGg62fMBzGgOsucDmNEcZM8HMKM5yJ5Pba5evYr4+HhcvXoVeXl5iI+PR3x8PO7du6dfJygoCN988w0AQKPR4I033sAHH3yAbdu24bfffsPLL7+M6tWro3fv3gCABg0aoFu3bhg+fDiOHDmCuLg4jBkzBgMGDLDY2PJU8anh2pc9o+z5APkzyp4PYEZzkD0fwIzmIHs+gBmtkcy1uUYIIcz6bImIiIiIJBcVFYW1a9cWmr9v3z506NABQH5RvmbNGv3PTYUQmD59OlasWIGUlBS0adMG//73v1GvXj399snJyRgzZgy2b98OrVaLvn37YunSpXBxcSmPp0VEREREpDoy1+ZsPCciIiIiIiIiIiIiegSHbSEiIiIiIiIiIiIiegQbz4mIiIiIiIiIiIiIHsHGcyIiIiIiIiIiIiKiR7DxnIiIiIiIiIiIiIjoEWw8JyIiIiIiIiIiIiJ6hK3SAUgOKSkpWLVqFRISEgAADRs2xLBhw+Du7q5wsv9hRtPJng9QR8bExEQsXrxYnzE4OBivv/46AgMDFU6WTw3nkBlNJ3s+QP5rBQB++uknxMTEGGScOHEi2rZtq3CyfGo4h0Rkfmp4j5c9o+z5AGY0BzV8Tsp+DmXPBzCjOajhWpG9LgfUcR7J/DRCCKF0CFLWsWPHEBERAUdHR7Rq1QoAcPToUdy/fx///e9/0axZM4UTMqM15APUkXHnzp3o1asXQkJCEB4eDgCIi4vDqVOnsH37dnTp0kXRfGo4h8xY8fMB8l8rAPDFF19g6NCh6NOnj0HGb775BrGxsRg0aJCi+dRwDonI/NTwHi97RtnzAcxoDmr4nJT9HMqeD2BGc1DDtSJ7XQ6o4zyShQiyem3atBFRUVEiJydHPy8nJ0cMGTJEtG3bVsFk/8OMppM9nxDqyBgSEiLeeeedQvPfeecd0bRpUwUSGVLDOWRG08meTwj5rxUhhAgKChL/+te/Cs1fuHChCAoKUiCRITWcQyIyPzW8x8ueUfZ8QjCjOajhc1L2cyh7PiGY0RzUcK3IXpcLoY7zSJbBxnMSDg4OIiEhodD833//XTg6OiqQqDBmNJ3s+YRQR0Z7e3tx/vz5QvPPnTsn7O3tFUhkSA3nkBlNJ3s+IeS/VoQQws7OTly4cKHQ/AsXLkiRUQ3nkIjMTw3v8bJnlD2fEMxoDmr4nJT9HMqeTwhmNAc1XCuy1+VCqOM8kmXwhqEENzc3XL16tdD8pKQkuLq6KpCoMGY0nez5AHVkrFq1KuLj4wvNj4+Ph5eXV/kHeoQaziEzmk72fID81woA+Pr6Ys+ePYXm7969G76+vgokMqSGc0hE5qeG93jZM8qeD2BGc1DD56Ts51D2fAAzmoMarhXZ63JAHeeRLIM3DCX0798f0dHRiImJQevWrQHkj9s0ceJEDBw4UOF0+ZjRdLLnA9SRcfjw4Xj11Vfx559/GmScN28e3nzzTYXTqeMcMqPpZM8HyH+tAMBbb72FcePGIT4+3iBjbGwslixZonA6dZxDIjI/NbzHy55R9nwAM5qDGj4nZT+HsucDmNEc1HCtyF6XA+o4j2QhSnd9J+VlZWWJcePGCTs7O6HVaoVWqxX29vbijTfeEA8ePFA6nhCCGa0hnxDqyKjT6cS//vUvUaNGDaHRaIRGoxE1atQQixcvFjqdTul4qjiHzFjx8wkh/7VSYMuWLSI8PFx4enoKT09PER4eLrZu3ap0LCGEes4hEZmXGt7jZc8oez4hmNEc1PA5Kfs5lD2fEMxoDmq4VoSQuy4XQj3nkcxPI4QQSjfgkxwyMzORmJgIAAgMDISTk5PCiQpjRtPJng9QR0YASE9PBwApfor3KDWcQ2Y0nez5Csh8ragFzyGR9VHDe7zsGWXPBzCjucj+OSn7OZQ9H8CM5iL7taIWPI/WhcO2kJ6TkxM8PDz0/5YRM5pO9nyAOjLevn0b586dAwAEBQWhatWqCicypIZzyIymkz0fIP+1AgDHjh1DQkICACA4OBjNmzdXOJEhNZxDIjI/NbzHy55R9nwAM5qDGj4nZT+HsucDmNEc1HCtyF6XA+o4j2RmSnd9J+Xl5OSIqVOnCjc3N/1PjNzc3MS7774rsrOzlY4nhGBGa8gnhDoypqWliRdffFHY2Njof6pla2srBg8eLFJSUpSOp4pzyIwVP58Q8l8rQgiRlJQk2rRpIzQajahcubKoXLmy0Gg0Ijw8XCQlJSkdTxXnkIjMTw3v8bJnlD2fEMxoDmr4nJT9HMqeTwhmNAc1XCuy1+VCqOM8kmWw8ZzEyJEjhZeXl/j000/FqVOnxKlTp8Snn34qvL29xciRI5WOJ4RgRmvIJ4Q6MkZGRoq6deuKHTt2iNTUVJGamip27Ngh6tevL/r37690PFWcQ2as+PmEkP9aEUKIiIgIERoaKs6ePaufd/bsWREWFiYiIiIUTJZPDeeQiMxPDe/xsmeUPZ8QzGgOaviclP0cyp5PCGY0BzVcK7LX5UKo4zySZbDxnISbm5v44YcfCs3//vvvhZubmwKJCmNG08meTwh1ZHRychIHDhwoNP/nn38WTk5OCiQypIZzyIymkz2fEPJfK0II4eDgIE6cOFFo/rFjx4Sjo6MCiQyp4RwSkfmp4T1e9oyy5xOCGc1BDZ+Tsp9D2fMJwYzmoIZrRfa6XAh1nEeyDK3Sw8aQ8uzt7VGrVq1C8wMCAmBnZ1f+gYrAjKaTPR+gjoxVqlSBu7t7ofnu7u6oXLmyAokMqeEcMqPpZM8HyH+tAICvry9ycnIKzc/Ly0P16tUVSGRIDeeQiMxPDe/xsmeUPR/AjOaghs9J2c+h7PkAZjQHNVwrstflgDrOI1kGG88JY8aMwfvvv4+srCz9vKysLMyePRtjxoxRMNn/MKPpZM8HqCPj1KlT8eabb+LmzZv6eTdv3sTEiRMxbdo0BZPlU8M5ZEbTyZ4PkP9aAYAFCxZg7NixOHbsmH7esWPH8PrrryMmJkbBZPnUcA6JyPzU8B4ve0bZ8wHMaA5q+JyU/RzKng9gRnNQw7Uie10OqOM8kmXYKh2AlNGnTx+Dx7t370bNmjXRpEkTAMCpU6eQnZ2NTp06KREPADOag+z5AHVkbNq0KTQajf7xhQsX4OfnBz8/PwDA1atXYW9vjzt37mDEiBHlnk8N55AZTSd7PkD+awUAKleubJAxIyMDoaGhsLXNL4lyc3Nha2uLYcOGoXfv3uWeTw3nkIjMTw3v8bJnlD0fwIzmoIbPSdnPoez5AGY0BzVcK7LX5YA6ziNZHhvPrdSjPzXp27evwWNfX9/yjFMkZjSd7PkAdWRU6oO6tNRwDpnRdLLnA+S/VgBg8eLFSkcokRrOIRGZnxre42XPKHs+gBnNQQ2fk7KfQ9nzAcxoDmq4VmSvywF1nEeyPI0QQigdgoiIiIiIiIiIiIhIJux5TkRUBtnZ2bh9+zZ0Op3B/IKfbxFRPtmvFZ1Oh4sXLxaZsV27dgqlMiT7OSQiIlISPyeJSkf2a0UNdTkg/3kkCxBk9W7evClefPFF4ePjI2xsbIRWqzWYZMCMFT+fEOrIeO7cOdGmTZtC2TQajRQZ1XAOmbHi5xNC/mtFCCEOHTokAgIC9LkenmTIqIZzSETmp4b3eNkzyp5PCGY0BzV8Tsp+DmXPJwQzmoMarhXZ63Ih1HEeyTLY85wQFRWFq1evYtq0afDx8TG4GYIsmNF0sucD1JFx6NChsLW1xXfffSdlRjWcQ2Y0nez5APmvFQAYOXIkWrRoge+//17KjGo4h0Rkfmp4j5c9o+z5AGY0BzV8Tsp+DmXPBzCjOajhWpG9LgfUcR7JMjjmOcHV1RUHDhxASEiI0lGKxYymkz0foI6Mzs7OOH78OIKCgpSOUiQ1nENmNJ3s+QD5rxUgP+OpU6dQp04dpaMUSQ3nkIjMTw3v8bJnlD0fwIzmoIbPSdnPoez5AGY0BzVcK7LX5YA6ziNZhlbpAKQ8X19fyP5/KMxoOtnzAerIGBwcjL///lvpGMVSwzlkRtPJng+Q/1oBgNDQUFy8eFHpGMVSwzkkIvNTw3u87Bllzwcwozmo4XNS9nMoez6AGc1BDdeK7HU5oI7zSBZSvqPEkIx27twpunbtKi5duqR0lGIxo+lkzyeEvBlTU1P10549e0RYWJjYt2+f+Pvvvw2WpaamKh1V2nP4MGY0naz51HCtnDp1Sj9t2bJFBAcHizVr1ohjx44ZLDt16pQi+dRwDonIsmR9j3+Y7BllzycEM5aV2j4nZTyHD5M9nxDMWFZquFZkr8uFUMd5JMvjsC1WqnLlygbjM2VkZCA3NxdOTk6oVKmSwbrJycnlHQ8AM5qD7PkAdWTUarUGGYUQhcY3K5iXl5dX3vFUcQ6Z0XSy5wPkv1aA/2UsrvwpWKZURjWcQyIyPzW8x8ueUfZ8ADOagxo+J2U/h7LnA5jRHNRwrchelwPqOI9kebxhqJVavHix0hGMYkbTyZ4PUEfGffv2KR2hRGo4h8xoOtnzAfJfKwBw6dIlpSOUSA3nkIjMTw3v8bJnlD0fwIzmoIbPSdnPoez5AGY0BzVcK7LX5YA6ziNZHnueExERERERERERERE9gjcMJSIiIiIiIiIiIiJ6BBvPiYiIiIiIiIiIiIgewcZzIiIiIiIiIiIiIqJHsPGciIiIiIiIiIiIiOgRbDwnspD9+/fj/v37SsdQNdnPYVZWFhITE5GVlaV0FFWT/XUm84iNjUVqaqrSMYp14cIF7NmzBxcvXlQ6ChERmRlrDfOQ+TyyLjcPmV9jMh/W5USPh43nVu7UqVP44IMP8O9//xt///23wbK0tDQMGzZMoWT/s3LlSgwZMgRr1qwBAGzYsAENGjRA7dq1MX36dIXTFa9r1664fPmy0jEAqON1LopM5zA2NhaHDh0CADx48ADR0dFwdnZGvXr14OLigpEjRyperPNaMd3t27cNHsfHx2PIkCEIDw/HCy+8gP379ysT7P89+eSTeP/995GUlKRojrJ49dVXcf36daVjAADmzp2LPXv2AAD++ecfdO7cGfXr10eXLl1Qv359dO/eHSkpKYrlc3V1RXR0NA4ePKhYBiJShhpqNjXWGzLVGoA6XueiyHIe1VCXA7xWTCV7XQ6otzZnXf54WJsTBFmtnTt3Cjs7O9GwYUPh5+cnqlSpIvbu3atffvPmTaHVahVMKMSiRYuEs7Oz6NOnj/Dx8REffPCBqFKlivjggw/EzJkzhZubm1i+fLmiGZs2bVrkpNFoRIMGDfSPlaKG11n2cyiEEAEBAeLw4cNCCCEmTJggatWqJbZs2SISEhLE1q1bRb169cTEiRMVy8drxTy0Wq24deuWEEKIuLg4UalSJdG+fXsxceJE0aVLF2Frayt++uknxfJpNBpRpUoVYWNjIyIiIsTmzZtFTk6OYnmKUrly5SInjUYj3N3d9Y+VVLNmTXHixAkhhBCvvPKKaNq0qThx4oS4f/++iI+PF0899ZSIjo5WLJ9GoxENGzYUGo1GBAUFiZiYGHH79m3F8hBR+VBDzSZ7vaGGWkMNr7Ps51H2ulwIXivmIHtdLoT8tTnrcvNgbU62Sjfek3JmzJiBCRMmYPbs2RBCYMGCBejVqxc2bdqEbt26KR0PALB8+XKsWLECgwYNwsmTJ9GqVSt8+umniI6OBgDUqFEDn3zyCV599VXFMv7222/o3LkznnrqKf08IQROnTqFjh07wsvLS7FsgDpeZ9nPIQBcv34dPj4+AIBt27bhk08+0Z+/oKAgVK5cGS+99BLmz5+vSD5eK+YhhND/e8aMGXjppZewatUq/bw33ngDM2fO1PeOUMLp06dx5MgRrF69GgMGDEDlypXx8ssvIzo6Gg0aNFAsV4GcnBy0b98e/fr1088TQuCVV17B22+/jRo1aiiYLt+dO3fg6ekJANi9ezfWrl2Lpk2bAgCaNGmCZcuW4dlnn1UyIvbu3YsbN25g5cqVmDNnDqZMmYJnnnkGr7zyCrp16waNRqNoPiIyPzXUbLLXG2qoNdTwOst+HmWvywFeK+aghrockLs2Z11uPqzNrZwSLfYkBzc3N3Hx4kWDeV9++aVwdnYW27dvl6LXg6Ojo7hy5Yr+sb29vThz5oz+8YULF4SHh4cS0fR++eUXERgYKN577z2Rl5enn29rayt+//13BZPlU8PrLPs5FEIIf39/fa+gGjVqiKNHjxos/+OPP4Szs7MS0YQQvFbMRaPR6Hu4+Pj4iEOHDhksP3PmjHjiiSeUiCaEMMwnhBDXr18Xc+bMEXXr1hVarVaEhYWJVatWKZZPiPy/tZYtW4qXX35ZpKen6+fL9DrXq1dPfPfdd0KI/N5rcXFxBstPnjwp3NzclIgmhCj8Oj948ECsX79edOrUSWi1WlGzZk0xbdo0xfIRkWWooWaTvd5QQ62hhtdZ9vMoe10uBK8Vc5C9LhdC/tqcdbl5sDYnjnluxezt7QuNHTVo0CCsXLkS/fv3xzfffKNMsIc4OTkhIyND/7hq1apwcXExWCc3N7e8YxkIDw/H8ePHcf78ebRu3RqJiYmK5nmUGl5n2c8hAAwePBjvvvsuUlJS8NJLL2HWrFm4d+8eACAzMxMzZsxAeHi4Yvl4rZhPeno60tLS4ODgAHt7e4NlDg4OyMzMVCgZCvVo8PHxweTJk3H+/Hns2bMHgYGBGDdunELp8tWpUwcHDx6Et7c3QkJCEBcXp2ieogwfPhwTJ07ExYsXMWbMGEyYMEH/93jp0iWMHz8eXbt2VSzfo6+zvb09Bg4ciN27dyMxMRFRUVGIjY1VJhwRWYwaajbZ6w011BpqeJ1lP4+y1+UArxVzkbkuB+SvzVmXmwdrc2LPcyvWpUsXsWDBgiKXrV+/XlSqVEnxXg/h4eHiq6++Knb59u3bRaNGjcoxUclWr14tvL29xfLly0WlSpWk+N9cNbzOD5PxHAohRFZWlujVq5eoXLmy6NKli3BwcBBOTk6ibt26wtnZWfj5+Ylz584plo/XinloNBqh1WqFVqsVGo1GrFixwmD5t99+K+rUqaNQusK9HoqSmppaTmmM27Nnj/Dz8xOTJ0+W6nUWQoixY8eKSpUqiaCgIOHg4CC0Wq2ws7MTWq1WtGjRQty4cUOxbKV5nXU6XTmlIaLyooaaTU31hqy1hhpe54fJeB5lr8uF4LViDrLX5UKoqzZnXV52rM2JY55bsVGjRuHnn38uctnAgQMhhMBnn31WzqkMzZs3D87OzsUuv3r1KkaMGFGOiUo2dOhQtGnTBoMHD1a8l28BNbzOD5PxHAKAnZ0dvv32W+zYsQPbt2+HjY0NdDodfHx8EB4ejkGDBpX4t2ppvFbMY9++fQaPC8bTLHDp0iVFx40fMmQIHB0dS1zHzc2tnNIY9/TTT+PEiRMYPnw4nJ2dYWNjo3QkvaVLl2LUqFH47rvv8Oeffxpcz507d1Z03MLp06cX6p32KI6rSFTxqKFmU1O9IWutoYbX+WEynkfZ63KA14o5yF6XA+qqzVmXlx1rc9II8dBdGIjILHQ6HdLT0+Hm5sY30TLiObQOfJ2JiIjIklhrmAfPY8XH15iIqGjseU4QQuD48eO4fPkyNBoNAgIC0LRpU6k+MNWYsXbt2ggJCVE6lh7PoXnIfh5lzwfwdTYH2fMBQGpqKnbt2mXwOnfq1Ema3jeA/BkfzRcQEIDOnTtLk4+ILEMN7/GyZ2StYR6yn0c1nkPZMsr+GgPyn0NA/oyy17yAOjOyNrci5TU+DMlp7969IiAgQD+OWMG4YoGBgeKnn35SOp4QghmtIZ8QzGgN+YRgRmvIJ4QQn3/+uXB3d9fnK5g8PDxKHP+zPMmeUfZ8RGQZaniPlz2j7PmEYEZryCeE/BllzycEM5qDGmpKZiTZsfHcil24cEE4OTmJjh07iq1bt4qzZ8+KhIQE8fXXX4v27dsLZ2dnkZiYyIwqzyh7Pma0jnzMaB35hBDi+PHjwtbWVgwZMkTEx8eLBw8eiPv374vjx4+Ll156SVSqVEnEx8czo4rzEZFlqOE9XvaMsudjRuvIp4aMsudjRvNQQ03JjKQGbDy3YqNHjxZPP/10kct0Op14+umnxZgxY8o5lSFmNJ3s+YRgRnOQPZ8QzGgOsucTQoioqCjxwgsvFLu8b9++YujQoeWYqDDZM8qej4gsQw3v8bJnlD2fEMxoDrLnE0L+jLLnE4IZzUENNSUzkhqw8dyKNWzYUGzbtq3Y5du2bRMNGzYsx0SFMaPpZM8nBDOag+z5hGBGc5A9nxBC1K1bV+zatavY5bt27RJ169Ytx0SFyZ5R9nxEZBlqeI+XPaPs+YRgRnOQPZ8Q8meUPZ8QzGgOaqgpmZHUQKv0mOuknKtXr+LJJ58sdnmjRo1w5cqVckxUGDOaTvZ8ADOag+z5AGY0B9nzAcD169dRr169YpfXq1cP165dK8dEhcmeUfZ8RGQZaniPlz2j7PkAZjQH2fMB8meUPR/AjOaghpqSGUkN2Hhuxe7duwcnJ6dilzs5OSEzM7McExXGjKaTPR/AjOYgez6AGc1B9nwAkJmZCQcHh2KX29vb48GDB+WYqDDZM8qej4gsQw3v8bJnlD0fwIzmIHs+QP6MsucDmNEc1FBTMiOpga3SAUhZf/zxB27evFnksr///ruc0xSNGU0nez6AGc1B9nwAM5qD7PkAYOfOnXB3dy9yWUpKSvmGKYbsGWXPR0SWoYb3eNkzyp4PYEZzkD0fIH9G2fMBzGgOaqgpmZFkpxFCCKVDkDK0Wi00Gg2K+hMomK/RaJCXl6dAunzMaDrZ8wHMaA6y5wOY0RxkzwfkZzSGGUsmez4isgy1vMfLnFH2fAAzmoPs+QD5M8qeD2BGc1BDTcmMpAbseW7FLl26pHQEo5jRdLLnA5jRHGTPBzCjOcieDwB0Op3SEYySPaPs+YjIMtTwHi97RtnzAcxoDrLnA+TPKHs+gBnNQQ01JTOSKljqTqRU8YwaNUrcuXNH6RglYkbTyZ5PCGY0B9nzCcGM5iB7PiGE6NGjh7h+/brSMUoke0bZ8xGRZajhPV72jLLnE4IZzUH2fELIn1H2fEIwozmooaZkRlICh22hUnNzc0N8fDxq166tdJRiMaPpZM8HMKM5yJ4PYEZzkD0fALi6uuLUqVPMaALZ8xGRZajhPV72jLLnA5jRHGTPB8ifUfZ8ADOagxpqSmYkJRgfuIfo/6nh/1mY0XSy5wOY0RxkzwcwoznIno+IiMpODe/xsmeUPR/AjOYgez5A/oyy5wOYkYgsh43nRERERERERERERESPYOM5EREREREREREREdEj2HhORERERERERERERPQINp4TERERERERERERET2CjedUai+++CLc3NyUjlEiZjSd7PkAZjQH2fMBzGgOsucDgClTpsDT01PpGCWSPaPs+YjIMtTwHi97RtnzAcxoDrLnA+TPKHs+gBnNQQ01JTOSEjSCt/u1ejqdDlpt4f9H0el0+Ouvv+Dn56dAqsJZmNG8nn76aaxZswb+/v5KRykWM5rm0qVLuHjxInx8fNCoUSOl4xSJGU0nW76srCxotVpUqlQJAJCYmIjVq1fj6tWr8Pf3R3R0NAICAphRxfmIyLLUUFPKnlH2fEWRuaYsIHtG2fPJVrM9SvZ8ADM+LjXUlMxIqiDIaqWmpop+/foJBwcH4eXlJaZNmyZyc3P1y2/evCm0Wq2CCZnRHL799tsiJxsbG7Fs2TL9YyUxo+lGjRol0tPThRBCZGZmir59+wqtVis0Go3QarWiY8eO+uXMqN6MsucTQoj27duLTZs2CSGE+OWXX4S9vb1o3Lix6N+/v2jatKlwcnISBw8eZEYV5yMiy5C9phRC/oyy5xNC/ppSDRllzyeE/DWb7PmY0TzUUFMyI6kBG8+t2Lhx40S9evXEpk2bxGeffSb8/f1Fz549RVZWlhAiv7jUaDTMqPKMBR/cGo2m2EnpLxHMaDqtVitu3bolhBBi8uTJombNmmLv3r0iIyND/PLLLyIwMFBMmjRJsXzMaB35hBDCzc1NnD9/XgiRX2iOHz/eYPnUqVNFeHi4EtH0ZM8oez4isgzZa0o1ZJQ9nxDy15RqyCh7PiHkr9lkz8eM5qGGmpIZSQ3YeG7F/Pz8xL59+/SP79y5I1q1aiW6du0qHjx4IEXPDGY0Xbdu3UTPnj31H+oFbG1txe+//65QKkPMaDqNRqPP1qhRI7F+/XqD5d9++62oV6+eEtH0mNF0sucTQghnZ2eRkJAghBCiWrVqIj4+3mD5xYsXhYuLixLR9GTPKHs+IrIM2WtKIeTPKHs+IeSvKYWQP6Ps+YSQv2aTPZ8QzGgOaqgpmZHUgDcMtWJ37twxGA/uiSeewO7du5Geno4ePXogMzNTwXT5mNF0P/74Izp16oQWLVrgu+++UzRLcZjRPDQaDQDg5s2baNy4scGyJk2aICkpSYlYBpjRdLLnCw0Nxfbt2wEAgYGBOHXqlMHy+Ph4xW+gI3tG2fMRkWXIXlMC8meUPR+gjppS9oyy5ysge80mez6AGU2lhpqSGUkNbJUOQMrx8/NDQkKCwY0NXF1d8d///hddu3bF888/r2C6fMxoHuPHj0fHjh0xePBgbN++HYsWLVI6UiHMaLpp06bByckJWq0W169fR8OGDfXL7t69C2dnZwXT5WNG08me74MPPkD37t2RkZGBgQMH4q233sKFCxfQoEEDnDt3DkuXLsXkyZOZUcX5iMgy1FBTyp5R9nwFZK8pAfkzyp4PkL9mkz0fwIymUkNNyYykBmw8t2Jdu3bFmjVr0KNHD4P5Li4u2LlzJ7p06aJQsv9hRvMJCQnBsWPHMH78eISEhEAIoXSkQpix7Nq1a4dz584BAIKDg3HlyhWD5T/88INBIacEZjSd7PkAICwsDD/++CPefPNN/PrrrwCA2bNnAwCqV6+OGTNm4PXXX1cyovQZZc9HRJahhppS9oyy53uYrDXlw2TPKHM+2Ws22fMBzGgOaqgpmZHUQCNk+oShcvXPP/8U+p/Rh6Wnp+PEiRNo3759OSf7H2a0jG3btmHfvn2YPHkyvLy8lI5TJGY0rz///BN2dnaoWbOm0lGKxYymky3fnTt38Oeff0Kn08HHxwe1atVSOlIhsmeUPR8RmY8aakrZM8qerzhqqCllzyh7vkfJVrM9SvZ8ADM+LjXUlMxIsmLjORERERERERERERHRI3jDUCt26NChQjdYWbduHQICAuDl5YVXX30VWVlZCqXLx4ymkz0fwIzmIHs+gBnNQfZ8ADOag+z5iMgy1HDty55R9nwAM5qD7PkA+TPKng9gRnOQPR/AjKQSgqxWt27dxIcffqh/fPr0aWFrayteeeUVsXDhQuHt7S2mT5+uXEDBjNaQTwhmtIZ8QjCjNeQTghmtIR8RWYYarn3ZM8qeTwhmtIZ8QsifUfZ8QjCjNeQTghlJHdh4bsW8vb3F0aNH9Y+nTJkiwsPD9Y83btwoGjRooEQ0PWY0nez5hGBGc5A9nxDMaA6y5xOCGc1B9nxEZBlquPZlzyh7PiGY0RxkzyeE/BllzycEM5qD7PmEYEZSBw7bYsX++ecfVKtWTf/4p59+Qvfu3fWPW7ZsiaSkJCWi6TGj6WTPBzCjOcieD2BGc5A9H8CM5iB7PiKyDDVc+7JnlD0fwIzmIHs+QP6MsucDmNEcZM8HMCOpAxvPrVi1atVw6dIlAEB2djZOnDiBp556Sr88PT0dlSpVUioeAGY0B9nzAcxoDrLnA5jRHGTPBzCjOciej4gsQw3XvuwZZc8HMKM5yJ4PkD+j7PkAZjQH2fMBzEjqwMZzK9ajRw9MmjQJBw4cwOTJk+Hk5IS2bdvql58+fRqBgYEKJmRGa8gHMKM15AOY0RryAcxoDfmIyDLUcO3LnlH2fAAzWkM+QP6MsucDmNEa8gHMSCqh9LgxpJw7d+6Itm3bCo1GI1xdXcWWLVsMlj/99NNiypQpCqXLx4ymkz2fEMxoDrLnE4IZzUH2fEIwoznIno+ILEMN177sGWXPJwQzmoPs+YSQP6Ps+YRgRnOQPZ8QzEjqoBFCCKUb8ElZqampcHFxgY2NjcH85ORkuLi4wM7OTqFk/8OMppM9H8CM5iB7PoAZzUH2fAAzmoPs+YjIMtRw7cueUfZ8ADOag+z5APkzyp4PYEZzkD0fwIwkNzaeExERERERERERERE9gmOeExERERERERERERE9go3nRERERERERERERESPYOM5EREREREREREREdEj2HhORCQxjUaDrVu3Kh2jVKKiotC7d2+lYxARERERmR3rciIi68TGcyIiBd28eRNjx45F7dq1YW9vD19fXzz77LPYs2eP0tGIiIiIiKwG63IiIiqKrdIBiIis1eXLlxEeHg4PDw8sWLAATz75JHJycrBz506MHj0aZ8+eVToiEREREVGFx7qciIiKw57nREQKee2116DRaHDkyBH07dsX9erVQ8OGDfHmm2/i8OHD+vX+/vtvPP/883ByckLdunWxbds2/bK8vDxER0cjICAAjo6OqF+/PpYsWWJwnIKfbcbExMDHxwdVqlTB6NGjkZOTo1+nVq1amDNnDoYNGwZXV1f4+flhxYoVBvtJSkpCZGQkPDw84Onpieeeew6XL1+2zMkhIiIiIionrMuJiKg4bDwnIlJAcnIyduzYgdGjR8PZ2bnQcg8PD/2/Z86cicjISJw+fRo9evTA4MGDkZycDADQ6XSoWbMmNm3ahD/++APvvfcepkyZgo0bNxrsb9++fUhMTMS+ffuwdu1axMbGIjY21mCdhQsXokWLFjh58iRee+01jBo1CufOnQMA5OTkICIiAq6urjhw4ADi4uLg4uKCbt26ITs727wnh4iIiIionLAuJyKikrDxnIhIARcvXoQQAkFBQUbXjYqKwsCBA1GnTh3MmTMH9+7dw5EjRwAAlSpVwsyZM9GiRQsEBARg8ODBGDp0aKEivXLlyli2bBmCgoLwzDPPoGfPnoXGb+zRowdee+011KlTB++88w6eeOIJ7Nu3DwCwYcMG6HQ6rFy5Ek8++SQaNGiANWvW4OrVq9i/f795TgoRERERUTljXU5ERCXhmOdERAoQQpR63caNG+v/7ezsDDc3N9y+fVs/7+OPP8bq1atx9epV3L9/H9nZ2QgJCTHYR8OGDWFjY6N/7OPjg99++63Y42g0Gnh7e+uPc+rUKVy8eBGurq4G2zx48ACJiYmlfi5ERERERDJhXU5ERCVh4zkRkQLq1q0LjUZTqpsPVapUyeCxRqOBTqcDAHz11VeYMGECFi5ciLCwMLi6umLBggX49ddfS72P0qxz7949NG/eHF9++WWhfFWrVjX6HIiIiIiIZMS6nIiISsLGcyIiBXh6eiIiIgIff/wxxo0bV2h8xZSUFIPxFYsTFxeH1q1b47XXXtPPs0SPk2bNmmHDhg3w8vKCm5ub2fdPRERERKQE1uVERFQSjnlORKSQjz/+GHl5eWjVqhW+/vprXLhwAQkJCVi6dCnCwsJKtY+6devi2LFj2LlzJ86fP49p06bh6NGjZs86ePBgPPHEE3juuedw4MABXLp0Cfv378e4cePw119/mf14RERERETlhXU5EREVh43nREQKqV27Nk6cOIGOHTvirbfeQqNGjdClSxfs2bMHn3zySan2MWLECPTp0wf9+/dHaGgo7t69a9DbxVycnJzw888/w8/PD3369EGDBg0QHR2NBw8esMcLEREREaka63IiIiqORjzO3TGIiIiIiIiIiIiIiKwAe54TERERERERERERET2CjedERERERERERERERI9g4zkRERERERERERER0SPYeE5ERERERERERERE9Ag2nhMRERERERERERERPYKN50REREREREREREREj2DjORERERERERERERHRI9h4TkRERERERERERET0CDaeExERERERERERERE9go3nRERERERERERERESPYOM5EREREREREREREdEj2HhORERERERERERERPQINp4TERERERERERERET2CjedERERERERERERERI9g4zkRERERERERERER0SPYeE5ERERERERERERE9Ag2nhMRERERERERERERPYKN50REREREREREREREj2DjORGpwo4dOxASEgIHBwdoNBqkpKSU27H3798PjUaD/fv36+d16NABjRo1KrcMBTQaDcaMGVPuxy1OrVq1EBUVpXSMCuPy5cvQaDSIjY1VOgoRERFRkWSrR8tThw4d0KFDB6VjVCgajQYzZsxQOgYRUbHYeE5UgcXGxkKj0cDBwQHXrl0rtFypBuDHdffuXURGRsLR0REff/wxPv/8czg7Oysdi6jM1q9fj8WLFysdg4iIiEjvt99+wwsvvAB/f384ODigRo0a6NKlCz766COlo5HK/fDDD2wgJyLVslU6ABFZXlZWFj788EPVFr5Hjx5Feno63n//fXTu3FnpOPSQc+fOQavl/8M+rvXr1+PMmTN44403DOb7+/vj/v37qFSpkjLBiIiIyCodPHgQHTt2hJ+fH4YPHw5vb28kJSXh8OHDWLJkCcaOHat0RCn897//VTqCKv3www/4+OOPi2xAv3//Pmxt2TRFRPLiOxSRFQgJCcFnn32GyZMno3r16krHwYMHD2BnZ1fqRtfbt28DADw8PCyYisrC3t5e6QgVSsEvRYiIiIjK0+zZs+Hu7o6jR48WqrkLanEC7OzslI5Q4bD2JSLZsbsgkRWYMmUK8vLy8OGHH5Zq/S+++ALNmzeHo6MjPD09MWDAACQlJRmsU9xY14+OA1gwXvhXX32FqVOnokaNGnByckJaWhoAYNOmTfpjPfHEE3jxxRcNhpjp0KEDhgwZAgBo2bIlNBqNwXF//fVXdOvWDe7u7nByckL79u0RFxdXKNe1a9cwbNgwVKtWDfb29mjYsCFWr15daL2//voLvXv3hrOzM7y8vDB+/HhkZWUVe66OHz+O1q1bw9HREQEBAfj0008NlmdnZ+O9995D8+bN4e7uDmdnZ7Rt2xb79u0rtC+dToclS5bgySefhIODA6pWrYpu3brh2LFjxR4fAD744ANotVqz/bLgmWeeQe3atYtcFhYWhhYtWugfF/V3kJKSgjfeeAO+vr6wt7dHnTp1MG/ePOh0Ov06zZo1Q58+fQy2e/LJJ6HRaHD69Gn9vA0bNkCj0SAhIaFU2QvGDI+JicGKFSsQGBgIe3t7tGzZEkePHi20/tmzZ/HCCy/A09MTDg4OaNGiBbZt21ZovdOnT6N9+/ZwdHREzZo18cEHH2DNmjXQaDS4fPmyfr1vv/0WPXv2RPXq1WFvb4/AwEC8//77yMvL06/ToUMHfP/997hy5Qo0Gg00Gg1q1aplkL9gzPOYmBhoNBpcuXKlUKbJkyfDzs4O//zzj35eaa8HIiIiooclJiaiYcOGRXZW8fLyKnKbrVu3olGjRvraeseOHQbLr1y5gtdeew3169eHo6MjqlSpgn79+hnUTsD/hpr85ZdfMG7cOFStWhUeHh4YMWIEsrOzkZKSgpdffhmVK1dG5cqV8fbbb0MIYa6njjFjxsDFxQWZmZmFlg0cOBDe3t76Wq6oMc+zsrIwffp01KlTB/b29vD19cXbb79t8B2iT58+aNasmcF2zz77LDQajUHt+euvv0Kj0eDHH38sdf6CMeiNvR5A6b8TXblyBb169TL4TrRz585C94E6cOAA+vXrBz8/P/1zHz9+PO7fv69fJyoqCh9//LE+a8H0cP6CHumbN2+GRqPBTz/9VCjT8uXLodFocObMGf280tbyRESmYM9zIisQEBCAl19+GZ999hkmTZpUYu/z2bNnY9q0aYiMjMQrr7yCO3fu4KOPPkK7du1w8uTJMvf+fv/992FnZ4cJEyYgKysLdnZ2iI2NxdChQ9GyZUvMnTsXt27dwpIlSxAXF6c/1rvvvov69etjxYoVmDVrFgICAhAYGAgA2Lt3L7p3747mzZtj+vTp0Gq1WLNmDZ5++mkcOHAArVq1AgDcunULTz31lL6wrFq1Kn788UdER0cjLS1NP3TG/fv30alTJ1y9ehXjxo1D9erV8fnnn2Pv3r1FPqd//vkHPXr0QGRkJAYOHIiNGzdi1KhRsLOzw7BhwwAAaWlpWLlyJQYOHIjhw4cjPT0dq1atQkREBI4cOYKQkBD9/qKjoxEbG4vu3bvjlVdeQW5uLg4cOIDDhw8bNFg/bOrUqZgzZw6WL1+O4cOHl+m1eVT//v3x8ssv4+jRo2jZsqV+/pUrV3D48GEsWLCg2G0zMzPRvn17XLt2DSNGjICfnx8OHjyIyZMn48aNG/pxvtu2bYv//Oc/+u2Sk5Px+++/Q6vV4sCBA2jcuDGA/IK8atWqaNCgwWM9h/Xr1yM9PR0jRoyARqPB/Pnz0adPH/z555/6IVF+//13hIeHo0aNGpg0aRKcnZ2xceNG9O7dG19//TWef/55APlfMjp27AiNRoPJkyfD2dkZK1euLLLXfWxsLFxcXPDmm2/CxcUFe/fuxXvvvYe0tDT9eXv33XeRmpqKv/76C4sWLQIAuLi4FPk8IiMj8fbbb2Pjxo2YOHGiwbKNGzeia9euqFy5MoDSXw9EREREj/L398ehQ4dw5syZUt0T6ZdffsGWLVvw2muvwdXVFUuXLkXfvn1x9epVVKlSBUD+0IsHDx7EgAEDULNmTVy+fBmffPIJOnTogD/++ANOTk4G+xw7diy8vb0xc+ZMHD58GCtWrICHhwcOHjwIPz8/zJkzBz/88AMWLFiARo0a4eWXXzbLc+/fvz8+/vhjfP/99+jXr59+fmZmJrZv346oqCjY2NgUua1Op0OvXr3wyy+/4NVXX0WDBg3w22+/YdGiRTh//jy2bt0KIL/2/fbbb5GWlgY3NzcIIRAXF6evfXv16gUgv/bVarUIDw9/rOdQmtejtN+JMjIy8PTTT+PGjRt4/fXX4e3tjfXr1xfZ+WfTpk3IzMzEqFGjUKVKFRw5cgQfffQR/vrrL2zatAkAMGLECFy/fh27du3C559/XuLz6NmzJ1xcXLBx40a0b9/eYNmGDRvQsGFD/d9naWt5IiKTCSKqsNasWSMAiKNHj4rExERha2srxo0bp1/evn170bBhQ/3jy5cvCxsbGzF79myD/fz222/C1tbWYL6/v78YMmRIoWO2b99etG/fXv943759AoCoXbu2yMzM1M/Pzs4WXl5eolGjRuL+/fv6+d99950AIN57770in0cBnU4n6tatKyIiIoROp9PPz8zMFAEBAaJLly76edHR0cLHx0f8/fffBlkHDBgg3N3d9bkWL14sAIiNGzfq18nIyBB16tQRAMS+ffsMnicAsXDhQv28rKwsERISIry8vER2drYQQojc3FyRlZVlcNx//vlHVKtWTQwbNkw/b+/evQKAwevz8HMtAECMHj1aCCHEW2+9JbRarYiNjS20jSlSU1OFvb29eOuttwzmz58/X2g0GnHlyhX9vEf/Dt5//33h7Owszp8/b7DtpEmThI2Njbh69aoQQohNmzYJAOKPP/4QQgixbds2YW9vL3r16iX69++v365x48bi+eefL3X2S5cuCQCiSpUqIjk5WT//22+/FQDE9u3b9fM6deoknnzySfHgwQP9PJ1OJ1q3bi3q1q2rnzd27Fih0WjEyZMn9fPu3r0rPD09BQBx6dIl/fyH/8YLjBgxQjg5ORkcp2fPnsLf37/Y/GvWrNHPCwsLE82bNzdY78iRIwKAWLdunT53aa8HIiIiokf997//FTY2NsLGxkaEhYWJt99+W+zcuVNf0z4MgLCzsxMXL17Uzzt16pQAID766CP9vKLqokOHDhnUMEL8r9Z/tI4JCwsTGo1GjBw5Uj8vNzdX1KxZ0+D7hql0Op2oUaOG6Nu3r8H8jRs3CgDi559/1s979LvO559/LrRarThw4IDBtp9++qkAIOLi4oQQQhw9elQAED/88IMQQojTp08LAKJfv34iNDRUv12vXr1E06ZNHyt/aV+P0n4nWrhwoQAgtm7dql/n/v37IigoqNB3oqJe47lz5xb6zjB69GhRXPMTADF9+nT944EDBwovLy+Rm5urn3fjxg2h1WrFrFmz9PNKW8sTEZmKw7YQWYnatWvjpZdewooVK3Djxo0i19myZQt0Oh0iIyPx999/6ydvb2/UrVu3yN4GpTVkyBA4OjrqHx87dgy3b9/Ga6+9ZjDOXc+ePREUFITvv/++xP3Fx8fjwoULGDRoEO7evavPmpGRgU6dOuHnn3+GTqeDEAJff/01nn32WQghDJ5XREQEUlNTceLECQD5N7Lx8fHBCy+8oD+Ok5MTXn311SIz2NraYsSIEfrHdnZ2GDFiBG7fvo3jx48DAGxsbPRjI+p0OiQnJyM3NxctWrTQHxcAvv76a2g0GkyfPr3QcR7+WSMACCEwZswYLFmyBF988YV+WBtzcXNzQ/fu3bFx40aDn8Ru2LABTz31FPz8/IrddtOmTWjbti0qV65scK47d+6MvLw8/PzzzwDye98A0D8+cOAAWrZsiS5duuDAgQMA8od/OXPmjH7dx9G/f399j+yHj/fnn38CyO/pvnfvXkRGRiI9PV2f8+7du4iIiMCFCxf0wwft2LEDYWFhBr8S8PT0xODBgwsd9+G/8YL9tm3bFpmZmTh79uxjP4+C53L8+HEkJibq523YsAH29vZ47rnnAJT+eiAiIiIqSpcuXXDo0CH06tULp06dwvz58xEREYEaNWoUOQxG586d9b8GBYDGjRvDzc1NX2sBhnVRTk4O7t69izp16sDDw8OgDi4QHR1tUPeGhoZCCIHo6Gj9PBsbG7Ro0cLgOKbSaDTo168ffvjhB9y7d08/f8OGDahRowbatGlT7LabNm1CgwYNEBQUZFD7Pv300wCg//7UtGlTuLi4GNS+NWvWxMsvv4wTJ04gMzMTQgj88ssvZap9jb0ej/OdaMeOHahRo4a+NzyQPy55Ub9yffg1zsjIwN9//43WrVtDCIGTJ08+9vMA8mvf27dvGwwPs3nzZuh0OvTv3x/A49XyRESm4rAtRFZk6tSp+Pzzz/Hhhx9iyZIlhZZfuHABQgjUrVu3yO0Lhrsoi4CAAIPHBWM4169fv9C6QUFB+OWXX0rc34ULFwCgxIbj1NRU5OTkICUlBStWrMCKFSuKXK/gJkhXrlxBnTp1CjVWF5URAKpXrw5nZ2eDefXq1QOQP3b1U089BQBYu3YtFi5ciLNnzyInJ0e/7sPnJDExEdWrV4enp2exz6fAunXrcO/ePXzyyScYOHCg0fWB/AIzOztb/9jR0RHu7u7Frt+/f39s3boVhw4dQuvWrZGYmIjjx4/rh10pzoULF3D69GlUrVq1yOUF57patWqoW7cuDhw4gBEjRuDAgQPo2LEj2rVrh7Fjx+LPP/9EQkICdDpdmb5APNrAX9CQXjA++MWLFyGEwLRp0zBt2rRis9aoUQNXrlxBWFhYoeV16tQpNO/333/H1KlTsXfvXv24/gVSU1Mf+3kAQL9+/fDmm29iw4YNmDJlCoQQ2LRpE7p37w43NzcApb8eHv4PBSIiIqKHtWzZElu2bEF2djZOnTqFb775BosWLcILL7yA+Ph4BAcH69ctqjNF5cqVDe7Fcv/+fcydOxdr1qzBtWvXDDplFFUXPbrPglrV19e30PyHj1OU1NRUg3G37ezsSqyz+/fvj8WLF2Pbtm0YNGgQ7t27hx9++EE/BGBxLly4gISEBKO1r42NDcLCwvSdRA4cOIC2bduiTZs2yMvLw+HDh1GtWjUkJyebpfYFDF+PO3fuPNZ3osDAwELPu6ja9+rVq3jvvfewbdu2Qq9JWWvfgvv3bNiwAZ06dQKQ/x8ZISEh+u9aj1PLExGZio3nRFakdu3aePHFF7FixQpMmjSp0HKdTqe/QU1R4/o9PC5zcUVkXl5ekds+3CvBHAp60S5YsMCgR/DDXFxccPfuXQDAiy++WGzDYsH42pbwxRdfICoqCr1798bEiRPh5eUFGxsbzJ0716An8eMIDw9HfHw8li1bhsjIyFI1uPfp08fgxjtDhgzR35SyKM8++yycnJywceNGtG7dGhs3boRWqzUYB7IoOp0OXbp0wdtvv13k8oKCFwDatGmDPXv24P79+zh+/Djee+89NGrUCB4eHjhw4AASEhLg4uKCpk2bGn1+jypuXMqCL20Ffz8TJkxAREREkesW9QWhJCkpKWjfvj3c3Nwwa9YsBAYGwsHBASdOnMA777xT5p7f1atXR9u2bbFx40ZMmTIFhw8fxtWrVzFv3jz9OqW9HoiIiIiMsbOzQ8uWLdGyZUvUq1cPQ4cOxaZNmwx+IWms1gLyxzBfs2YN3njjDYSFhcHd3R0ajQYDBgwosi4qbp9FzRdGbhj6+uuvY+3atfrH7du3N+jJ/KinnnoKtWrVwsaNGzFo0CBs374d9+/f1/d0Lo5Op8OTTz6Jf/3rX0Uuf7jhv02bNpg9ezYePHiAAwcO4N1334WHhwcaNWqEAwcOoFq1agBQpsbz0ta+5vxOlJeXhy5duiA5ORnvvPMOgoKC4OzsjGvXriEqKqrMta+9vT169+6Nb775Bv/+979x69YtxMXFYc6cOfp1LFHLExEVh43nRFZm6tSp+OKLLwwa3goEBgZCCIGAgACDRs6iVK5cGSkpKYXmX7lyBbVr1zaaw9/fHwBw7tw5/c8aC5w7d06/vDgFP0t0c3ND586di12vatWqcHV1RV5eXonrFWQ6c+YMhBAG/zlw7ty5Ite/fv06MjIyDHqfnz9/HgBQq1YtAPk/Maxduza2bNlisM9Hh2cJDAzEzp07kZycbLQxvE6dOpg/fz46dOiAbt26Yc+ePXB1dS1xm4ULFxr0BinpprEA4OzsjGeeeQabNm3Cv/71L2zYsAFt27Y1ul1gYCDu3btn9FwD+V8M1qxZg6+++gp5eXlo3bo1tFot2rRpo288b926dbFfBkxR8DdaqVKlUv1dXLx4sdD8R+ft378fd+/exZYtW9CuXTv9/EuXLhXatqQeTEXp378/XnvtNZw7dw4bNmyAk5MTnn32Wf3y0l4PRERERI+j4Kb1xQ37WJLNmzdjyJAhWLhwoX7egwcPivwOYW5vv/02XnzxRf3j0vz6LjIyEkuWLEFaWho2bNiAWrVq6X9JWpzAwECcOnUKnTp1MlrftW3bFtnZ2fjPf/6Da9eu6RvJ27Vrp288r1evnr4R3Zwe9zvRH3/8Ueg70aO172+//Ybz589j7dq1Bjdv3bVrV6F9lqX2Xbt2Lfbs2YOEhAQIIQz+I+NxankiIlNxzHMiKxMYGIgXX3wRy5cvx82bNw2W9enTBzY2Npg5c2ah3hxCCH0v7oL9HD582GAokO+++w5JSUmlytGiRQt4eXnh008/RVZWln7+jz/+iISEBPTs2bPE7Zs3b47AwEDExMQYjE1Y4M6dOwDye2H07dsXX3/9Nc6cOVPsegDQo0cPXL9+HZs3b9bPy8zMLPanjbm5uVi+fLn+cXZ2NpYvX46qVauiefPm+uMDhr1jfv31Vxw6dMhgX3379oUQAjNnzix0nKJ61jRu3Bg//PADEhIS8Oyzzxr8LLUozZs3R+fOnfXTwz+7LU7//v1x/fp1rFy5EqdOnTLa8wbI/9Jx6NAh7Ny5s9CylJQU5Obm6h8XfGGYN28eGjdurP9pbtu2bbFnzx4cO3asTD1vSsPLywsdOnTA8uXLi/wy+PDfRUREBA4dOoT4+Hj9vOTkZHz55ZcG2xT1WmdnZ+Pf//53of07Ozs/1k9Z+/btCxsbG/znP//Bpk2b8Mwzzxj8p01prwciIiKiouzbt6/ImvOHH34AUPwwhiWxsbEptM+PPvoIeXl5ZQv5GIKDgw1q34LavCT9+/dHVlYW1q5dix07diAyMtLoNpGRkbh27Ro+++yzQsvu37+PjIwM/ePQ0FBUqlQJ8+bNg6enJxo2bAggv/Y9fPgwfvrpJ4vVvo/znSgiIgLXrl0zGOv+wYMHhZ5jUbWvEKLI4UEL6tbS/sdJ586d4enpiQ0bNmDDhg1o1aqVwZCXj1PLExGZij3PiazQu+++i88//xznzp3TF21AfoP4Bx98gMmTJ+Py5cvo3bs3XF1dcenSJXzzzTd49dVXMWHCBADAK6+8gs2bN6Nbt26IjIxEYmIivvjiC4Mb1ZSkoHAcOnQo2rdvj4EDB+LWrVtYsmQJatWqhfHjx5e4vVarxcqVK9G9e3c0bNgQQ4cORY0aNXDt2jXs27cPbm5u2L59OwDgww8/xL59+xAaGorhw4cjODgYycnJOHHiBHbv3o3k5GQAwPDhw7Fs2TK8/PLLOH78OHx8fPD555/DycmpyAzVq1fHvHnzcPnyZdSrVw8bNmxAfHw8VqxYoR8f/plnnsGWLVvw/PPPo2fPnrh06RI+/fRTBAcHGzRyduzYES+99BKWLl2KCxcuoFu3btDpdPqxwMeMGVPo+E899RS+/fZb9OjRAy+88AK2bt1q0rj0j+rRowdcXV0xYcIEfcFtzMSJE7Ft2zY888wziIqKQvPmzZGRkYHffvsNmzdvxuXLl/HEE08AyO9B7+3tjXPnzmHs2LH6fbRr1w7vvPMOgLL9bLW0Pv74Y7Rp0wZPPvkkhg8fjtq1a+PWrVs4dOgQ/vrrL5w6dQpAfs+lL774Al26dMHYsWPh7OyMlStXws/PD8nJyfqeNK1bt0blypUxZMgQjBs3DhqNBp9//nmRX0SbN2+ODRs24M0330TLli3h4uJi0JP8UV5eXujYsSP+9a9/IT09vdB/ZDzO9UBERET0qLFjxyIzMxPPP/88goKCkJ2djYMHD+p7YA8dOvSx9/nMM8/g888/h7u7O4KDg3Ho0CHs3r0bVapUscAzMF2zZs1Qp04dvPvuu8jKyipVx5GXXnoJGzduxMiRI7Fv3z6Eh4cjLy8PZ8+excaNG7Fz5059730nJyc0b94chw8fxrPPPquvIdu1a4eMjAxkZGRYtPYt7XeiESNGYNmyZRg4cCBef/11+Pj44Msvv4SDgwOA//UiDwoKQmBgICZMmIBr167Bzc0NX3/9dZHj0Rf858W4ceMQEREBGxsbDBgwoNislSpVQp8+ffDVV18hIyMDMTExhdYpbS1PRGQyQUQV1po1awQAcfTo0ULLhgwZIgCIhg0bFlr29ddfizZt2ghnZ2fh7OwsgoKCxOjRo8W5c+cM1lu4cKGoUaOGsLe3F+Hh4eLYsWOiffv2on379vp19u3bJwCITZs2FZlxw4YNomnTpsLe3l54enqKwYMHi7/++qvUz+PkyZOiT58+okqVKsLe3l74+/uLyMhIsWfPHoP1bt26JUaPHi18fX1FpUqVhLe3t+jUqZNYsWKFwXpXrlwRvXr1Ek5OTuKJJ54Qr7/+utixY4cAIPbt26dfr3379qJhw4bi2LFjIiwsTDg4FU7epAAAy31JREFUOAh/f3+xbNkyg/3pdDoxZ84c4e/vL+zt7UXTpk3Fd999J4YMGSL8/f0N1s3NzRULFiwQQUFBws7OTlStWlV0795dHD9+XL8OADF69GiD7b799ltha2sr+vfvL/Ly8oo8z2U1ePBgAUB07ty5yOX+/v5iyJAhBvPS09PF5MmTRZ06dYSdnZ144oknROvWrUVMTIzIzs42WLdfv34CgNiwYYN+XnZ2tnBychJ2dnbi/v37j5X30qVLAoBYsGBBoWUAxPTp0w3mJSYmipdffll4e3uLSpUqiRo1aohnnnlGbN682WC9kydPirZt2wp7e3tRs2ZNMXfuXLF06VIBQNy8eVO/XlxcnHjqqaeEo6OjqF69unj77bfFzp07C/393Lt3TwwaNEh4eHgIAPq/hYL8a9asKZT/s88+EwCEq6trseeltNcDERER0cN+/PFHMWzYMBEUFCRcXFyEnZ2dqFOnjhg7dqy4deuWwbpF1aNCFK4L//nnHzF06FDxxBNPCBcXFxERESHOnj1baL3iav3p06cLAOLOnTsG84cMGSKcnZ1Nf9JFePfddwUAUadOnSKXP/pdR4j82nXevHmiYcOGwt7eXlSuXFk0b95czJw5U6SmphqsO3HiRAFAzJs3z2B+nTp1BACRmJj42JlL+3oIUfrvRH/++afo2bOncHR0FFWrVhVvvfWW+PrrrwUAcfjwYf16f/zxh+jcubNwcXERTzzxhBg+fLg4depUoXo2NzdXjB07VlStWlVoNBrxcFNUUTW6EELs2rVLABAajUYkJSUV+dxLW8sTEZlCI4SRO20QERFRIW+88QaWL1+Oe/fuWWRcdiIiIiIiWSxevBjjx4/HX3/9hRo1aigdh4io3LDxnIiIyIj79+/D0dFR//ju3buoV68emjVrVuRNkYiIiIiI1OrR2vfBgwdo2rQp8vLycP78eQWTERGVP455TkRE0svLyzN64x8XFxe4uLhY5PhhYWHo0KEDGjRogFu3bmHVqlVIS0vDtGnTLHI8IiIiIrJuN2/eLHG5o6Mj3N3dLXLsPn36wM/PDyEhIUhNTcUXX3yBs2fP4ssvv7TI8YiIZKZVOgAREZExSUlJ8PHxKXEq6kZC5tKjRw/88MMPGD9+PObNmwc/Pz/8+OOPaNeuncWOSUSP7+eff8azzz6L6tWrQ6PRYOvWrUa32b9/P5o1awZ7e3vUqVMHsbGxhdb5+OOPUatWLTg4OCA0NBRHjhwxf3giIqKHGKt9X3/9dYsdOyIiAnFxcZg4cSJmzpwJe3t7fPXVVxg0aJDFjklEFU9Fqc3Z85yIiKTn7e1tdHiU2rVrW+z4c+bMwZw5cyy2fyIyj4yMDDRp0gTDhg1Dnz59jK5/6dIl9OzZEyNHjsSXX36JPXv24JVXXoGPjw8iIiIAABs2bMCbb76JTz/9FKGhoVi8eDEiIiJw7tw5eHl5WfopERGRlTJW+1avXt1ix37jjTfwxhtvWGz/RGQdKkptzjHPiYiIiKjC0Wg0+Oabb9C7d+9i13nnnXfw/fff48yZM/p5AwYMQEpKCnbs2AEACA0NRcuWLbFs2TIAgE6ng6+vL8aOHYtJkyZZ9DkQEREREVUEaq7N2fO8AtDpdLh+/TpcXV2h0WiUjkNEREQqIIRAeno6qlevDq3WMiP5PXjwANnZ2SbtQwhRqL6xt7eHvb29SfsFgEOHDqFz584G8yIiIvS97bKzs3H8+HFMnjxZv1yr1aJz5844dOiQyceniom1ORERET0uS9fm5qjLAeuszdl4XgFcv34dvr6+SscgIiIiFUpKSkLNmjXNvt8HDx6giqMLMpFn0n5cXFxw7949g3nTp0/HjBkzTNovkH8ztmrVqhnMq1atGtLS0nD//n38888/yMvLK3Kds2fPmnx8qphYmxMREVFZWaI2N1ddDlhnbc7G8wrA1dUVADAYNWAn8T1gh0RYbjxiczh+6C+lIxjV7dTPSkcw6o+/M5WOUKIpq44qHcGo4V9OUzqCUQ1qeSgdwahLV1OVjlCilByd0hGMGrN/mdIRjGqy9IbSEUp0apyP0hGKlXYvE7WefkFfR5hbdnY2MpGHl02oT7Khw7p715CUlAQ3Nzf9fHP0bCGyFLXU5lUq2SgdoUQNXO2UjmBU1/efUzqCUUFfZikdQfVmnfle6QhGdRrcVOkIRiXuPK90hBJtmyp/3XvxRrrSEYxKOHBc6QglqlavvtIRipWXlYnzy4dapDY3R10OWG9tzsbzCqDg5xJ20EpdoLtUkvvPzVEj9xcIAHB96M1JVk4P5D6PNvZOSkcwSg1/i8428meU/Tw+UMEv+d1cnJWOYJTWTu5rWg3n0NLDSthBCztNGeuT/78zj5ubm0GBbi7e3t64deuWwbxbt27Bzc0Njo6OsLGxgY2NTZHreHt7mz0PVQxqqc3ty3pdlhMnrdyf4wDg5ih/Y4FG7q9gqiB7TQkArvaVlI5glLPk17Sdk4vSEYyydZC/8422koPSEUqkhvYAS9bmJtXlgNXW5nJXTERERESkajYajUmTJYWFhWHPnj0G83bt2oWwsDAAgJ2dHZo3b26wjk6nw549e/TrEBERERGpgal1ubXW5vx/aCIiIiKyGK0GsCljna0F9D1cSuPevXu4ePGi/vGlS5cQHx8PT09P+Pn5YfLkybh27RrWrVsHABg5ciSWLVuGt99+G8OGDcPevXuxceNGfP/9/36i/+abb2LIkCFo0aIFWrVqhcWLFyMjIwNDhw4t25MiIiIiIlKAKXU5YL21ORvPiYiIiMhiTOmlYoPH2+7YsWPo2LGj/vGbb74JABgyZAhiY2Nx48YNXL16Vb88ICAA33//PcaPH48lS5agZs2aWLlyJSIiIvTr9O/fH3fu3MF7772HmzdvIiQkBDt27Ch0oyIiIiIiIpmZ2nvcWmtzNp4TERERUYXQoUMHCFF8d5jY2Ngitzl58mSJ+x0zZgzGjBljajwiIiIiIqtRUWpzNp4TERERkcXYmPDzULlvLUZEREREpB6m1OWA9dbmbDwnIiIiIospz2FbiIiIiIioaOU9bEtFoVU6gJrFxsbCw8OjxHWioqLQu3fvcslDREREJJuCHi5lnYhKg3U5ERERUclMrcuttTZXtPH8zp07GDVqFPz8/GBvbw9vb29EREQgLi4OAJCcnIyxY8eifv36cHR0hJ+fH8aNG4fU1NRS7f/y5cvQaDT6ydXVFQ0bNsTo0aNx4cIFg3W3bNmCLl26oGrVqnBzc0NYWBh27txp9udMRERERCQb1uVERERERIUp2njet29fnDx5EmvXrsX58+exbds2dOjQAXfv3gUAXL9+HdevX0dMTAzOnDmD2NhY7NixA9HR0Y91nN27d+PGjRs4deoU5syZg4SEBDRp0gR79uzRr/Pzzz+jS5cu+OGHH3D8+HF07NgRzz77rNFB6omIiIioeAU/Dy3rROWDdTkRERFRxWZqXW6ttbliY56npKTgwIED2L9/P9q3bw8A8Pf3R6tWrfTrNGrUCF9//bX+cWBgIGbPno0XX3wRubm5sLUtXfwqVarA29sbAFC7dm08++yz6NSpE6Kjo5GYmAgbGxssXrzYYJs5c+bg22+/xfbt29G0adMS979z50688cYbSEpKQps2bbBmzRr4+PgYrDNz5kwsW7YMWVlZGDRoEJYuXQo7OzsAQFZWFiZOnIivvvoKaWlpaNGiBRYtWoSWLVsWebysrCxkZWXpH6elpZXqPBARERGVNw3K3lvDOsvz8se6vOx1ecE2rM2JiIhIdqbU5QXbWyPFep67uLjAxcUFW7duNSg2jUlNTYWbm1upC/SiaLVavP7667hy5QqOHz9e5Do6nQ7p6enw9PQscV+ZmZmIiYnB559/jp9//hlXr17FhAkTDNbZs2cPEhISsH//fvznP//Bli1bMHPmTP3yt99+G19//TXWrl2LEydOoE6dOoiIiEBycnKRx5w7dy7c3d31k6+v72OeASIiIiKifKzLy16XA6zNiYiIiCoyxRrPbW1tERsbi7Vr18LDwwPh4eGYMmUKTp8+Xew2f//9N95//328+uqrJh8/KCgIQP74i0WJiYnBvXv3EBkZWeJ+cnJy8Omnn6JFixZo1qwZxowZY/CzUwCws7PD6tWr0bBhQ/Ts2ROzZs3C0qVLodPpkJGRgU8++QQLFixA9+7dERwcjM8++wyOjo5YtWpVkcecPHkyUlNT9VNSUtLjnwAiIiKicsCfhsqPdXnZ63KAtTkRERGpA4dtKRvFxzy/fv06tm3bhm7dumH//v1o1qwZYmNjC62blpaGnj17Ijg4GDNmzDD52EIIAICmiBd+/fr1mDlzJjZu3AgvL68S9+Pk5ITAwED9Yx8fH9y+fdtgnSZNmsDJyUn/OCwsDPfu3UNSUhISExORk5OD8PBw/fJKlSqhVatWSEhIKPKY9vb2cHNzM5iIiIiIZGSjMW2i8sG6vGx1OcDanIiIiNTB1LrcWmtzRRvPAcDBwQFdunTBtGnTcPDgQURFRWH69OkG66Snp6Nbt25wdXXFN998g0qVKpl83IICOCAgwGD+V199hVdeeQUbN25E586dje7n0SwajUb/BYCIiIjI2uUX2mXt3aJ0euvCupyIiIio4jKtLrfe2lzxxvNHBQcHIyMjQ/84LS0NXbt2hZ2dHbZt2wYHBweTj6HT6bB06VIEBAQY3HToP//5D4YOHYr//Oc/6Nmzp8nHKXDq1Cncv39f//jw4cNwcXGBr68vAgMDYWdnh7i4OP3ynJwcHD16FMHBwWbLQERERET0OFiXsy4nIiIisnZlv7uPie7evYt+/fph2LBhaNy4MVxdXXHs2DHMnz8fzz33HID/FeiZmZn44osvkJaWpr97fdWqVWFjY1PqY928eROZmZk4c+YMFi9ejCNHjuD777/X72P9+vUYMmQIlixZgtDQUNy8eRMA4OjoCHd3d5Oea3Z2NqKjozF16lRcvnwZ06dPx5gxY6DVauHs7IxRo0Zh4sSJ8PT0hJ+fH+bPn4/MzExER0ebdFwiIiIipZnyE8/SVXpkKtblrMuJiIio4jN16BVrrc0Vazx3cXFBaGgoFi1apB9f0NfXF8OHD8eUKVMAACdOnMCvv/4KAKhTp47B9pcuXUKtWrVKdayCn3k6OTnB398fHTt2xIoVKwz2uWLFCuTm5mL06NEYPXq0fv6QIUOKHOvxcXTq1Al169ZFu3btkJWVhYEDBxqMD/nhhx9Cp9PhpZdeQnp6Olq0aIGdO3eicuXKJh2XiIiISGmm3FzIBlb629Byxrp8hn4563IiIiKqqEy96ae11uYawYEAVS8tLQ3u7u4YCl/YyTcSj97wZ+oYX0lBRw4kKR3BqGf+PK50BKN+u51hfCUFvfXpYaUjGDV6zdtKRzCqYW0PpSMYlXg5VekIJfonJ0/pCEa9eXSV0hGMCoy5rnSEEiVOqK50hGKl3cuAZ6seSE1NtcgNDgvqk/mugXDUlK2fyn2Rh7fTEy2WkcgS1FKbP2End/+xRm72Skcwqsf8vkpHMKpGbJbSEVRv/qlvlY5gVMTQ5kpHMOrCd2eVjlCirz+Qv+49fy1N6QhG/b7/iNIRSuQd1EDpCMXKy8pEwtL+Fql7zVGXA9Zbm8tbzRERERERERERERERKUTVjecjR46Ei4tLkdPIkSOVjkdERERk9Qp+HlrWidSBdTkRERGR3Eyty621NldszHNzmDVrFiZMmFDkMmv6+QARERGRrHjDUOvAupyIiIhIbrxhaNmouvHcy8sLXl5eSscgIiIiIrJqrMuJiIiIqCJSdeM5EREREcmNPc+JiIiIiJTHnudlw8bzCmRIRG24VJL3Jf3su4tKRyjR83U8lY5gVNUTm5SOYJRr7WeVjkDloM3K95SOYFT2gGlKRyhRI3/5hzE47dpY6QhGjR9WU+kIJVp7T97P5fv30svlOKaMj2gD6xxXkSqGKpVsYK+R9xZTf2fnKR2hRJczc5SOYNStY+eUjmCUZ+3nlI5Qovv/3FI6glHn0rOVjmBUq4u3lY5g1O837ykdoUTdG3orHcGoxr4eSkcw6vi33ysdoUQNGrRXOkKxcu7fQ4KFj2HquOXWWpvL+42OiIiIiFTPBib0PBdmjUJEREREZLVMqcsB663N5e0KQURERERERERERESkEPY8JyIiIiKL0Zrw81CtCT8rJSIiIiKi/zGlLi/Y3hqx8ZyIiIiILMakG4ZaZ31ORERERGR2Jt8w1EprczaeExEREZHFmHTDUCvt3UJEREREZG4m3zDUSmtzjnlORERERERERERERPQI9jwnIiIiIovhsC1ERERERMrjsC1lw8ZzIiIiIrIYDttCRERERKQ8DttSNmw8JyIiIiKL0Wo00Jax0C7rdkREREREZMiUurxge2vEMc9NEBsbCw8PjxLXiYqKQu/evcslDxERERGRNWJdTkRERESWoGjj+Z07dzBq1Cj4+fnB3t4e3t7eiIiIQFxcHAAg+f/Yu/e4qOr8f+CvGe4wXLwhaIBcvIECGsEiZhoqqJu68dNVu6GEYpJmaYmrobhlIibSupklgpa2pWb6bdMUYzW8oim6y5pSKoWgQdx1QGZ+f7AzNQEzMIfhDMzr+Xh8HitzPp/zeR/W6sWHcz6nrAwvvvgiBg4cCBsbG7i7u2PhwoWoqKho1flv3LgBiUSibvb29vDz88OCBQtw7do1jb7ffPMNwsLC0KNHD9jY2GDQoEHYuHFju18zERERkSmRmEkENeoYzOVEREREXZvQXG6q2VzUbVuioqJQV1eHzMxMeHl5oaSkBFlZWSgtLQUAFBUVoaioCCkpKfD19cXNmzcRFxeHoqIi7Nmzp9XzHD16FH5+fqitrcXly5exadMmBAQE4ODBgwgPDwcA2NnZIT4+Hv7+/rCzs8M333yDefPmwc7ODnPnzjXI9RMRERF1dVIzCaRSbtti7JjLiYiIiLo2IbkcMN1sLtqd5+Xl5Thx4gTWrVuHMWPGwMPDA8HBwUhISMDkyZMBAEOGDMHevXvxxBNPwNvbG48//jjeeOMNHDx4EA8ePGj1XD169ICLiwu8vLwwZcoUHD16FCEhIYiJiUFDQwMAYNiwYZg5cyb8/PzQr18/PP3004iIiMCJEyd0nv/w4cMYPHgwZDIZIiMjcfv27SZ9Vq9ejV69esHBwQFxcXGoq6tTH5PL5Vi4cCGcnZ1hbW2NkSNH4ty5cy3OJ5fLUVlZqdGIiIiIiPTBXK5/LleNYTYnIiIi6ppEWzyXyWSQyWTYv38/5HJ5q8dVVFTAwcEB5ub63zQvlUqxaNEi3Lx5E+fPn2+2z7fffouTJ0/iscce03qu2tpapKSkYOfOnTh+/Dhu3bqFJUuWaPTJyspCfn4+srOzsXv3buzbtw+rV69WH3/11Vexd+9eZGZm4sKFC/Dx8UFERATKysqanXPt2rVwdHRUNzc3tzZ+B4iIiIg6iJkUEj0bzPSLqps3b0a/fv1gbW2NkJAQnD17tsW+o0eP1thORNUmTZqk7hMdHd3keGRkpF61GSPmcv1zOcBsTkRERJ2EgFyubzbvCrlctMVzc3NzZGRkIDMzE05OTggLC8Py5cuRl5fX4piff/4Za9asaZfHNQcNGgSgcf/F33rooYdgZWWFoKAgLFiwAM8//7zW89TX12PLli0ICgrC8OHDER8fj6ysLI0+lpaWSE9Ph5+fHyZNmoSkpCSkpaVBoVCgpqYG7777LtavX48JEybA19cX77//PmxsbLBt27Zm50xISEBFRYW6FRYW6v+NICIiIjIgiVTAvop6PFb6j3/8Ay+//DISExNx4cIFBAQEICIiAnfu3Gm2/759+3D79m11u3LlCszMzDBt2jSNfqq7mFVt9+7den0/jBFzuf65HGA2JyIios5BUC7XI5t3lVwu6gtDo6KiUFRUhAMHDiAyMhLZ2dkYPnw4MjIymvStrKzEpEmT4Ovri1WrVgmeW6lUAgAkv9uv58SJE8jNzcWWLVuQmpqq8/8AW1tbeHt7q792dXVt8pcgICAAtra26q9DQ0NRXV2NwsJCFBQUoL6+HmFhYerjFhYWCA4ORn5+frNzWllZwcHBQaMRERERGSOpmURQa6u3334bsbGxmD17Nnx9fbFlyxbY2toiPT292f7du3eHi4uLuh05cgS2trZNQrrqJZqq1q1bN72+H8aKuVy/XA4wmxMREVHnIDSXtzWbd5VcLuriOQBYW1tj3LhxWLlyJU6ePIno6GgkJiZq9KmqqkJkZCTs7e3x2WefwcLCQvC8qgDs6emp8bmnpyeGDh2K2NhYLF68WOcPBL+vRSKRqH8AICIiIiLhfr+fdEtbi9TV1eH8+fMYO3as+jOpVIqxY8fi1KlTrZpr27ZtmDFjBuzs7DQ+z87OhrOzMwYOHIj58+erX6TZlTCXExEREZEurcnmXSmXi754/nu+vr6oqalRf11ZWYnx48fD0tISBw4cgLW1teA5FAoF0tLS4OnpiWHDhmnt15Z9H1ty6dIl3Lt3T/316dOnIZPJ4ObmBm9vb1haWiInJ0d9vL6+HufOnYOvr6/guYmIiIjEJJFKBTUAcHNz09hTeu3atc3O9fPPP6OhoQG9e/fW+Lx3794oLi7WWevZs2dx5cqVJtuDREZGYseOHcjKysK6devwr3/9CxMmTFC/4LKrYi5nLiciIqKuQ2gub0s270q5XP+3+whUWlqKadOmYc6cOfD394e9vT1yc3ORnJyMKVOmAPg1oNfW1uLDDz/UeHt9r169YGZm1uq5iouLUVtbiytXriA1NRVnz57FF198oT7H5s2b4e7urt5z8fjx40hJScHChQsFX2tdXR1iYmKwYsUK3LhxA4mJiYiPj4dUKoWdnR3mz5+PpUuXonv37nB3d0dycjJqa2sRExMjeG4iIiIiMem7/QoASNE4rrCwUGMrDCsrq3ap7fe2bduGoUOHIjg4WOPzGTNmqP88dOhQ+Pv7w9vbG9nZ2QgPDzdILR2JuZy5nIiIiLo+Ibkc6Nhsbky5XLTFc5lMhpCQEGzcuFG9v6CbmxtiY2OxfPlyAMCFCxdw5swZAICPj4/G+B9++AH9+vVr1VyqRwRsbW3h4eGBMWPGYOvWrRrnVCgUSEhIwA8//ABzc3N4e3tj3bp1mDdvnuBrDQ8PR//+/TFq1CjI5XLMnDlT47HTt956CwqFAs888wyqqqoQFBSEw4cPd7m9NImIiMj0qF4wpNfY/wX01u4j3bNnT5iZmaGkpETj85KSEri4uGgdW1NTg48//hhJSUk65/Hy8kLPnj1x/fr1LrF4zly+Sn2cuZyIiIi6KiG5HGhbNu9KuVy0xXMrKyusXbu2xcduAWD06NGC9ins169fq8e/+OKLePHFF9t0/ujoaERHR2t8NnXqVI05f/uSpdWrVzd7Hmtra6SlpSEtLa1N8xMRERHRrywtLfHwww8jKysLU6dOBdC4EJuVlYX4+HitYz/99FPI5XI8/fTTOuf58ccfUVpaCldX1/YoW3TM5b9iLiciIiISrivlcqPb85yIiIiIuo7GO1ykera23xnz8ssv4/3330dmZiby8/Mxf/581NTUYPbs2QCAZ599FgkJCU3Gbdu2DVOnTkWPHj00Pq+ursbSpUtx+vRp3LhxA1lZWZgyZQp8fHwQERGh3zeFiIiIiKiDCcvlbc/mXSWXi3bneXuIi4vDhx9+2Oyxp59+Glu2bOngioiIiIjot9pjz/O2+POf/4y7d+/i9ddfR3FxMQIDA3Ho0CH1y4pu3boFqVTz/pGrV6/im2++wVdffdXkfGZmZsjLy0NmZibKy8vRp08fjB8/HmvWrDHY3uudEXM5ERERkXFrrz3PW6ur5PJOvXielJSEJUuWNHusNftiEhEREVHXEx8f3+LjoNnZ2U0+GzhwYItbitjY2ODw4cPtWV6XxFxORERERL/XFXJ5p148d3Z2hrOzs9hlEBEREVELJBIJJFI9Xxiq0P/OGOpYzOVERERExk1ILgdMN5t36sVzIiIiIjJuUjMppGb6vWZHquTreYiIiIiI2oOQXA6Ybjbn4nkXcv7Uj7CRmIldRov+5NNd7BK0+ux6mdgl6BT4yHSxS9Bpz7ECsUvQ6u71fLFL0EnAFmQd5tvlG8QuQae6mjqxS9Aq99xtsUvQKdZOLnYJOkXt+FbsErS6mjRM7BJaVFn1ANrfc98+Gl9MpOed58pO8C9EohYMtreErdR4s/mN2nqxS9DK2OsDACefvmKXoFP3n437ZzDj/wkMcLQw/sWi+nsPxC5Bp4r6BrFL0Epmabz/vlZxtrMUu4ROz9XJWuwSWiS3NPw/x0JyOWC62dz4/ytARERERERERERERNTBeOc5ERERERkM7zwnIiIiIhIf7zzXDxfPiYiIiMhguOc5EREREZH4uOe5frh4TkRERESGI+QOFxO9u4WIiIiIqN0JvPPcVLO5af7KgIiIiIiIiIiIiIhIC955TkREREQGI5VIIJXqd5eKVGKad7cQEREREbU3IblcNd4UcfGciIiIiAxGYiaFRM+9FSUKPiRJRERERNQehORywHSzuWleNRERERERERERERGRFrzznIiIiIgMRmomgVTPFxNJFab5aCgRERERUXsTkssB083mvPNcgIyMDDg5OWntEx0djalTp3ZIPURERETGRmImEdSIWoO5nIiIiEg7obncVLO5qIvnd+/exfz58+Hu7g4rKyu4uLggIiICOTk5AICysjK8+OKLGDhwIGxsbODu7o6FCxeioqKiVee/ceMGJBKJutnb28PPzw8LFizAtWvXWhyXk5MDc3NzBAYGtsdlEhEREZks1d6K+jbqGMzlRERERF2b0Fxuqtlc1G1boqKiUFdXh8zMTHh5eaGkpARZWVkoLS0FABQVFaGoqAgpKSnw9fXFzZs3ERcXh6KiIuzZs6fV8xw9ehR+fn6ora3F5cuXsWnTJgQEBODgwYMIDw/X6FteXo5nn30W4eHhKCkpadfrJSIiIiIyRszlRERERERNifYrg/Lycpw4cQLr1q3DmDFj4OHhgeDgYCQkJGDy5MkAgCFDhmDv3r144okn4O3tjccffxxvvPEGDh48iAcPHrR6rh49esDFxQVeXl6YMmUKjh49ipCQEMTExKChoUGjb1xcHGbNmoXQ0NBWn//w4cMYPHgwZDIZIiMjcfv27SZ9Vq9ejV69esHBwQFxcXGoq6tTH5PL5Vi4cCGcnZ1hbW2NkSNH4ty5cy3OJ5fLUVlZqdGIiIiIjJHU7Nf9FdvexK7eNDCX65/LVWOYzYmIiMjYCcvlppvNRVs8l8lkkMlk2L9/P+RyeavHVVRUwMHBAebm+t80L5VKsWjRIty8eRPnz59Xf759+3Z8//33SExMbPW5amtrkZKSgp07d+L48eO4desWlixZotEnKysL+fn5yM7Oxu7du7Fv3z6sXr1affzVV1/F3r17kZmZiQsXLsDHxwcREREoKytrds61a9fC0dFR3dzc3Nr4HSAiIiLqGBKpRFAjw2Mu1z+XA8zmRERE1DkIzeWmms1FWzw3NzdHRkYGMjMz4eTkhLCwMCxfvhx5eXktjvn555+xZs0azJ07V/D8gwYNAtC4/yIAXLt2DcuWLcOHH37Yph8A6uvrsWXLFgQFBWH48OGIj49HVlaWRh9LS0ukp6fDz88PkyZNQlJSEtLS0qBQKFBTU4N3330X69evx4QJE+Dr64v3338fNjY22LZtW7NzJiQkoKKiQt0KCwv1+yYQERERGZhUKoXUTM8mNc19FTsac7n+uRxgNiciIqLOQVAuN+FsLupVR0VFoaioCAcOHEBkZCSys7MxfPhwZGRkNOlbWVmJSZMmwdfXF6tWrRI8t1KpBABIJBI0NDRg1qxZWL16NQYMGNCm89ja2sLb21v9taurK+7cuaPRJyAgALa2tuqvQ0NDUV1djcLCQhQUFKC+vh5hYWHq4xYWFggODkZ+fn6zc1pZWcHBwUGjERERERHpi7lcv1wOMJsTERERdWWi/8rA2toa48aNw8qVK3Hy5ElER0c3eTyzqqoKkZGRsLe3x2effQYLCwvB86oCsKenJ6qqqpCbm4v4+HiYm5vD3NwcSUlJuHTpEszNzXHs2LEWz/P7WiQSifoHACIiIiJTJzGTCGrUcZjLiYiIiLouobncVLO56Ivnv+fr64uamhr115WVlRg/fjwsLS1x4MABWFtbC55DoVAgLS0Nnp6eGDZsGBwcHHD58mVcvHhR3eLi4jBw4EBcvHgRISEhgua7dOkS7t27p/769OnTkMlkcHNzg7e3NywtLZGTk6M+Xl9fj3PnzsHX11fQvERERERik5hJBTUSD3M5czkRERF1HUJzualmc/3f7iNQaWkppk2bhjlz5sDf3x/29vbIzc1FcnIypkyZAuDXgF5bW4sPP/xQ4+31vXr1gplZ617zWlpaiuLiYtTW1uLKlStITU3F2bNn8cUXX6jPMWTIEI0xzs7OsLa2bvK5Purq6hATE4MVK1bgxo0bSExMRHx8PKRSKezs7DB//nwsXboU3bt3h7u7O5KTk1FbW4uYmBjBcxMRERERacNczlxORERERM0TbfFcJpMhJCQEGzduVO8v6ObmhtjYWCxfvhwAcOHCBZw5cwYA4OPjozH+hx9+QL9+/Vo119ixYwE07oPo4eGBMWPGYOvWrU3OaSjh4eHo378/Ro0aBblcjpkzZ2rsD/nWW29BoVDgmWeeQVVVFYKCgnD48GF069atQ+ojIiIiMhSJVAqJni8X0ncctQ1z+Sr1ceZyIiIi6qqE5HLVeFMkUXIjwE6vsrISjo6O2ODgDRtJ6+76EYNXLzuxS9Dqs+tlYpeg0+ryf4tdgk5vHisQuwStPtr2T7FL0GnNN9vFLkGnh0P6il2CTiWX7+juJKKbNfVil6BT7I3jYpeg0+Bl34hdglZXk4aJXUKLKquq0LN/ICoqKgzygkNVPrkwZzJklvrti11dV4/h6QcMViORIaj+7m/rPgC2UuPN5jdqjfu/Q8ZeHwCseftPYpeg0/ifx4tdglZlRcb/M1j07tfFLkGnCSPdxC5Bpy+/KRS7BK1G5p0SuwSdfrln/P9ejFm4SewStJrz0tNil9AieW013ns6zCC5tz1yOWC62Vy0O8+JiIiIyAQI2R/RRPdVJCIiIiJqd0L3LTfRbN6przouLg4ymazZFhcXJ3Z5REREREQmgbmciIiIiLqiTn3neVJSEpYsWdLsMVN6fICIiIjIWEmk+t/hYqr7KnZGzOVERERExk1ILleNN0WdevHc2dkZzs7OYpdBRERERC3gC0NNA3M5ERERkXHjC0P106kXz4mIiIjIuEnMpJCY6ffSRIlZQztXQ0RERERkmoTk8sbxppnNuXjehUReOg57I34stteFT8UuQavAR6aLXYJOiU5+YpegU0pNvtglaLW+t4/YJeimWCN2BTqFfGkvdgk6nVpaLHYJWt37w5/FLkGnh1dliV2CTl/UZYpdgla9n/u32CW0SPngvtglEHVp49dMgYONldhltKgk96rYJWjl5NNX7BJ0WvnyZ2KXoNOeO0lil6BVYaVc7BJ06lm3S+wSdKouqRG7BJ2emj1M7BK0cv9+v9gl6CSxthW7BJ1uJs0VuwStXnYuEruEFlVW1+A9sYugZnHxnIiIiIgMpvEOFz23bRGwJyMREREREf1KSC5XjTdFXDwnIiIiIoORSqWQ6rk/or7jiIiIiIhIk5BcrhpvikzzqomIiIiIiIiIiIiItODiOREREREZjOrxUH2bPjZv3ox+/frB2toaISEhOHv2bIt9MzIyIJFINJq1tbVGH6VSiddffx2urq6wsbHB2LFjce3aNb1qIyIiIiISg9Bcrk827wq5nIvnRERERGQwHR3Q//GPf+Dll19GYmIiLly4gICAAERERODOnTstjnFwcMDt27fV7ebNmxrHk5OTkZaWhi1btuDMmTOws7NDREQE7t/nS1eJiIiIqHPo6MXzrpLLuXhORERERAYjkUghkerZJG2Pqm+//TZiY2Mxe/Zs+Pr6YsuWLbC1tUV6erqWGiVwcXFRt969e6uPKZVKpKamYsWKFZgyZQr8/f2xY8cOFBUVYf/+/fp8S4iIiIiIOpygXK5HNu8quZyL50RERERk1CorKzWaXC5vtl9dXR3Onz+PsWPHqj+TSqUYO3YsTp061eL5q6ur4eHhATc3N0yZMgX//ve/1cd++OEHFBcXa5zT0dERISEhWs9JRERERNQVtSabd6VczsVzIiIiIjKY9ng01M3NDY6Ojuq2du3aZuf6+eef0dDQoHGHCgD07t0bxcXFzY4ZOHAg0tPT8fnnn+PDDz+EQqHAiBEj8OOPPwKAelxbzklEREREZGzaa9uW1mTzrpTLzQ12ZiIiIiIyeUJe/KkaV1hYCAcHB/XnVlZW7VIbAISGhiI0NFT99YgRIzB48GC89957WLNmTbvNQ0REREQkJiG5XDUeMFw2N9ZczjvPBcjIyICTk5PWPtHR0Zg6dWqH1ENERERkbKRmUkENaHxx0G9bSwG9Z8+eMDMzQ0lJicbnJSUlcHFxaVW9FhYWGDZsGK5fvw4A6nFCzkmGx1xOREREpJ3QXN6WbN6Vcrmoi+d3797F/Pnz4e7uDisrK7i4uCAiIgI5OTlN+iqVSkyYMAESiaTVm8DfuHEDEolE3ezt7eHn54cFCxbg2rVrGn337duHcePGoVevXnBwcEBoaCgOHz7cHpdJRERERB3A0tISDz/8MLKystSfKRQKZGVladzFok1DQwMuX74MV1dXAICnpydcXFw0zllZWYkzZ860+pydAXM5EREREbWXrpTLRd22JSoqCnV1dcjMzISXlxdKSkqQlZWF0tLSJn1TU1MhkUj0mufo0aPw8/NDbW0tLl++jE2bNiEgIAAHDx5EeHg4AOD48eMYN24c3nzzTTg5OWH79u144okncObMGQwbNkzQdRIRERGZKolUAolUz21bpG3Pfi+//DKee+45BAUFITg4GKmpqaipqcHs2bMBAM8++yz69u2r3psxKSkJf/jDH+Dj44Py8nKsX78eN2/exPPPP99Yg0SCl156CX/961/Rv39/eHp6YuXKlejTp0+XuouZuZyIiIioaxOSy1Xj26Kr5HLRFs/Ly8tx4sQJZGdn47HHHgMAeHh4IDg4uEnfixcvYsOGDcjNzVX/tqEtevToob5938vLC0888QTCw8MRExODgoICmJmZITU1VWPMm2++ic8//xwHDx7UGdIPHz6Ml156CYWFhRg5ciS2b9/epM7Vq1fjb3/7G+RyOWbNmoW0tDRYWloCAORyOZYuXYqPP/4YlZWVCAoKwsaNG/HII480O59cLtd4k21lZWVbvyVEREREHaI99jxviz//+c+4e/cuXn/9dRQXFyMwMBCHDh1Sv1jo1q1bkP7mh4ZffvkFsbGxKC4uRrdu3fDwww/j5MmT8PX1Vfd59dVXUVNTg7lz56K8vBwjR47EoUOHYG1trdd1GRvmcv1zuWoMszkREREZu/ba87y1ukouF23bFplMBplMhv3792uEzd+rra3FrFmzsHnz5nbbv0YqlWLRokW4efMmzp8/32wfhUKBqqoqdO/eXeu5amtrkZKSgp07d+L48eO4desWlixZotEnKysL+fn5yM7Oxu7du7Fv3z6sXr1affzVV1/F3r17kZmZiQsXLsDHxwcREREoKytrds61a9dqvNXWzc2tjd8BIiIioo6hCun6Nn3Ex8fj5s2bkMvlOHPmDEJCQtTHsrOzkZGRof5648aN6r7FxcX44osvmizQSiQSJCUlobi4GPfv38fRo0cxYMAAvWozRszl+udygNmciIiIOgehuVyfbN4Vcrloi+fm5ubIyMhAZmYmnJycEBYWhuXLlyMvL0+j3+LFizFixAhMmTKlXecfNGgQgMb9F5uTkpKC6upqTJ8+Xet56uvrsWXLFgQFBWH48OGIj4/X2HsHaNznJz09HX5+fpg0aRKSkpKQlpYGhUKBmpoavPvuu1i/fj0mTJgAX19fvP/++7CxscG2bduanTMhIQEVFRXqVlhY2PZvABERERERmMuF5HKA2ZyIiIioKxP1haFRUVEoKirCgQMHEBkZiezsbAwfPlz9W4cDBw7g2LFjTR7dbA9KpRIAmt2vcdeuXVi9ejU++eQTODs7az2Pra0tvL291V+7urrizp07Gn0CAgJga2ur/jo0NBTV1dUoLCxEQUEB6uvrERYWpj5uYWGB4OBg5OfnNzunlZVVkzfbEhERERkjiUQKiVTPJhE1qpoU5nL9cjnAbE5ERESdg6BcbsLZXPSrtra2xrhx47By5UqcPHkS0dHRSExMBAAcO3YMBQUFcHJygrm5OczNG7doj4qKwujRowXNqwrAnp6eGp9//PHHeP755/HJJ59g7NixOs9jYWGh8bVEIlH/AEBERERk6iRmZpDq2SRmZmKXb1KYy4mIiIi6LiG53JSzueiL57/n6+uLmpoaAMCyZcuQl5eHixcvqhvQuAfO9u3b9Z5DoVAgLS0Nnp6eGnvn7N69G7Nnz8bu3bsxadIkQdfxW5cuXcK9e/fUX58+fRoymQxubm7w9vaGpaUlcnJy1Mfr6+tx7tw5jQ3xiYiIiIg6EnM5czkRERGRqTMXa+LS0lJMmzYNc+bMgb+/P+zt7ZGbm4vk5GT1PoouLi7NvozI3d29yZ0puuYqLi5GbW0trly5gtTUVJw9exZffPEFzP73W5Ndu3bhueeew6ZNmxASEoLi4mIAgI2NDRwdHQVda11dHWJiYrBixQrcuHEDiYmJiI+Ph1QqhZ2dHebPn4+lS5eie/fucHd3R3JyMmpraxETEyNoXiIiIiKxCXnxp77jqG2Yy5nLiYiIqOsTkstV402RaIvnMpkMISEh2Lhxo3p/QTc3N8TGxmL58uXtOpfqMU9bW1t4eHhgzJgx2Lp1K3x8fNR9tm7digcPHmDBggVYsGCB+vPnnntO482v+ggPD0f//v0xatQoyOVyzJw5E6tWrVIff+utt6BQKPDMM8+gqqoKQUFBOHz4MLp16yZoXiIiIiKxcfHc+DGXr1IfZy4nIiKiroqL5/qRKLkRYKdXWVkJR0dH/PuHn2BvxC8o6nXhU7FL0Krikelil6BTopOf2CXolFLT8gu1jIHlxS/ELkE3hULsCnQK+dJe7BJ0OjWmWOwStLr3hz+LXYJOj67KErsEnT4tfV/sErQKux0kdgktUj64j/KstaioqDDICw5V+aRw86twsLHS7xz35HBbkGywGokM4de/+0v1/rvfEUpyr4pdglZOPn3FLkGnlS9/JnYJOi29c1nsErQqrJSLXYJOPV9/TuwSdKouqRG7BJ269+8pdglauU8ZL3YJOkmsbXV3Elma5A9il6DVy85FYpfQosrqGnQPnmiQ3NseuRww3Wxumr8yICIiIiIiIiIiIiLSolMvnsfFxUEmkzXb4uLixC6PiIiIyOSpHg/Vt1HnwFxOREREZNyE5nJTzeai7XneHpKSkrBkyZJmj5nS4wNERERExkoilei/57lU0s7VkKEwlxMREREZNyG5XDXeFHXqxXNnZ2c4OzuLXQYRERERtUAilUIi1Xfx3DTvbumMmMuJiIiIjJuQXK4ab4pM86qJiIiIiIiIiIiIiLTo1Heek6b//FwL2/tmYpfRInuvJ8QuQas9xwrELkGnlJp8sUvQaYndYLFL0Gp9xnNil6DT4eHzxC5BpwOvGP8j+P/3U6XYJWiVl3NL7BJ0Ou79jdgl6GQVEiZ2CVqVPBoldgktqqyqQs8Baw0+j0RqBolUv3yi7zgiYzDoIzkkRvzTVnevKWKXoFX3n7uLXYJOe+4kiV2CTuudh4pdglYO5sZ/P9+Xi9PELkGn+IVDxC5Bp53Z34tdglbb9v1N7BJ0Kjr3k9gl6OT245til6DV50a87UitosHgcwjJ5arxpsiI4xwRERERdXpSs8am71giIiIiIhJOSC5XjTdBxv9rXiIiIiIiIiIiIiKiDsY7z4mIiIjIcKTSxqbvWCIiIiIiEk5ILleNN0FcPCciIiIig5GYmUFipuee53qOIyIiIiIiTUJyuWq8KeLiOREREREZDvc8JyIiIiISH/c814tp3m9PRERERERERERERKQF7zwnIiIiIsORSgXcec77PIiIiIiI2oWQXK4ab4K4eE5EREREBiORSiHRM2jrO46IiIiIiDQJyeWq8aaIi+dEREREZDgSAXsrSkxzX0UiIiIionYnJJerxpsg0/yVQTvJyMiAk5OT1j7R0dGYOnVqh9RDRERERGSKmMuJiIiIyBBEXTy/e/cu5s+fD3d3d1hZWcHFxQURERHIyclp0lepVGLChAmQSCTYv39/q85/48YNSCQSdbO3t4efnx8WLFiAa9eutTguJycH5ubmCAwM1PPKiIiIiAhA490tQhp1COZyIiIioi5OaC430Wwu6rYtUVFRqKurQ2ZmJry8vFBSUoKsrCyUlpY26ZuamgqJRKLXPEePHoWfnx9qa2tx+fJlbNq0CQEBATh48CDCw8M1+paXl+PZZ59FeHg4SkpK9JqPiIiIiBpxz/POgbmciIiIqGvjnuf6Ee2qy8vLceLECaxbtw5jxoyBh4cHgoODkZCQgMmTJ2v0vXjxIjZs2ID09HS95urRowdcXFzg5eWFKVOm4OjRowgJCUFMTAwaGho0+sbFxWHWrFkIDQ1t9fkPHz6MwYMHQyaTITIyErdv327SZ/Xq1ejVqxccHBwQFxeHuro69TG5XI6FCxfC2dkZ1tbWGDlyJM6dO9fifHK5HJWVlRqNiIiIiEgfzOX653LVGGZzIiIioq5JtMVzmUwGmUyG/fv3Qy6Xt9ivtrYWs2bNwubNm+Hi4tIuc0ulUixatAg3b97E+fPn1Z9v374d33//PRITE1t9rtraWqSkpGDnzp04fvw4bt26hSVLlmj0ycrKQn5+PrKzs7F7927s27cPq1evVh9/9dVXsXfvXmRmZuLChQvw8fFBREQEysrKmp1z7dq1cHR0VDc3N7c2fgeIiIiIOggfDTV6zOX653KA2ZyIiIg6CW7bohfRFs/Nzc2RkZGBzMxMODk5ISwsDMuXL0deXp5Gv8WLF2PEiBGYMmVKu84/aNAgAI37LwLAtWvXsGzZMnz44YcwN2/9bjb19fXYsmULgoKCMHz4cMTHxyMrK0ujj6WlJdLT0+Hn54dJkyYhKSkJaWlpUCgUqKmpwbvvvov169djwoQJ8PX1xfvvvw8bGxts27at2TkTEhJQUVGhboWFhfp9E4iIiIgMTSoVENBN89HQjsZcrn8uB5jNiYiIqJMQlMtNN5uLetVRUVEoKirCgQMHEBkZiezsbAwfPhwZGRkAgAMHDuDYsWNITU1t97mVSiUAQCKRoKGhAbNmzcLq1asxYMCANp3H1tYW3t7e6q9dXV1x584djT4BAQGwtbVVfx0aGorq6moUFhaioKAA9fX1CAsLUx+3sLBAcHAw8vPzm53TysoKDg4OGo2IiIjIGEnMzAQ16hjM5frlcoDZnIiIiDoHobncVLO56L8ysLa2xrhx47By5UqcPHkS0dHR6sczjx07hoKCAjg5OcHc3Fx950lUVBRGjx4taF5VAPb09ERVVRVyc3MRHx+vnicpKQmXLl2Cubk5jh071uJ5LCwsNL6WSCTqHwCIiIiIiDoL5nIiIiIiIk2tfw6yg/j6+mL//v0AgGXLluH555/XOD506FBs3LgRTzzxhN5zKBQKpKWlwdPTE8OGDYNEIsHly5c1+vz973/HsWPHsGfPHnh6euo9FwBcunQJ9+7dg42NDQDg9OnTkMlkcHNzQ8+ePWFpaYmcnBx4eHgAaHzk9Ny5c3jppZcEzUtEREQkOqlU/0c8TfTRUGPBXM5cTkRERF2IkFyuGm+CRFs8Ly0txbRp0zBnzhz4+/vD3t4eubm5SE5OVu+j6OLi0uzLiNzd3dsUnEtLS1FcXIza2lpcuXIFqampOHv2LL744guY/e+RgyFDhmiMcXZ2hrW1dZPP9VFXV4eYmBisWLECN27cQGJiIuLj4yGVSmFnZ4f58+dj6dKl6N69O9zd3ZGcnIza2lrExMQInpuIiIhIVEJeLmSiLyXqaMzlzOVERERkAoS+9NNEs7loi+cymQwhISHYuHGjen9BNzc3xMbGYvny5e0619ixYwE07oPo4eGBMWPGYOvWrfDx8WnXeVoSHh6O/v37Y9SoUZDL5Zg5cyZWrVqlPv7WW29BoVDgmWeeQVVVFYKCgnD48GF069atQ+ojIiIiMhSJ1AwSPYO2vuOobZjLV6mPM5cTERFRVyUkl6vGmyKJkhsBdnqVlZVwdHTEp+euwVZmL3Y5LbK3NO5/yPZcKhK7BJ3eiOgvdgk6LbEbLHYJWq3PeE7sEnQ6PHye2CXoFNLX+F+GduanSrFL0CqvyLjrA4CXvt8udgk6WfXsLnYJWlk+GiV2CS2qrKpCzwHDUVFRYZAXHKryyc9ZH8HBzlb3gObOUVOLnuFPtbnGzZs3Y/369SguLkZAQADeeecdBAcHN9v3/fffx44dO3DlyhUAwMMPP4w333xTo390dDQyMzM1xkVERODQoUN6XBV1daq/+3YjFkJibiV2OS3q7hUgdglade9j3P9+B4A9L40UuwSd1jsPFbsErRzMjX8LgC8Xp4ldgk7xUcKfzDG0ndnfi12CVtsK/iZ2CToVnftJ7BJ0KvmxSuwStLKUSsQuoUW1igY88/NVg2Tz9sjlgH7ZvCvkcuP/LxURERERdV4S6a/7K7a1SdoeVf/xj3/g5ZdfRmJiIi5cuICAgABERETgzp07zfbPzs7GzJkz8fXXX+PUqVNwc3PD+PHj8dNPmj+gRkZG4vbt2+q2e/duvb4dRERERESiEJLL9cjmXSWXd+rF87i4OMhksmZbXFyc2OURERERmTzV46H6trZ6++23ERsbi9mzZ8PX1xdbtmyBra0t0tPTm+3/0Ucf4YUXXkBgYCAGDRqEDz74AAqFAllZWRr9rKys1Pt+u7i4cBuP32EuJyIiIjJuQnN5W7N5V8nlou153h6SkpKwZMmSZo8Z4vFjIiIiIup4lZWa2xxZWVnByqrpdhh1dXU4f/48EhIS1J9JpVKMHTsWp06datVctbW1qK+vR/fumltGZGdnw9nZGd26dcPjjz+Ov/71r+jRo4ceV9M1MZcTERERmYbWZPOulMs79eK5s7MznJ2dxS6DiIiIiFoilQL6vlxI2viQpJubm8bHiYmJGi95VPn555/R0NCA3r17a3zeu3dv/Pe//23VlK+99hr69OmjfrEl0Pho6JNPPglPT08UFBRg+fLlmDBhAk6dOgUzM+N+p0tHYS4nIiIiMnJCcrlqPFqXzbtSLu/Ui+dEREREZORUeyTqOxZAYWGhxt3Lzd113h7eeustfPzxx8jOzoa1tbX68xkzZqj/PHToUPj7+8Pb2xvZ2dkIDw83SC1ERERERO1KSC5XjUfHZHNjyuVcPO9Clm87BzMr/d+aa+ruXs8XuwSd1vf2EbsEndZnPCd2CVotjc7U3Ulkxxc/LHYJOlnZWIhdgk52DoZZXGsvlWX3xC5Bp3nLVopdgk6feweJXYJWgy/Eil1Ci2rud0wMlJiZQaLnXSCqcQ4ODq3a+qNnz54wMzNDSUmJxuclJSVwcXHROjYlJQVvvfUWjh49Cn9/f619vby80LNnT1y/fp2L59Rp3fulRHcnEZWJXUArFFbKxS5BJwdz437NWeUDhdgl6FRRYvx/GyvvPxC7BJ2Kvv9F7BK0cvTuK3YJOtX+XCN2CTrlXjPuf16GeTiKXUKLzBoagJ8NO4eQXK4aD7Qum3elXG7c/yUlIiIiImolS0tLPPzwwxovFVK9ZCg0NLTFccnJyVizZg0OHTqEoCDdv5D58ccfUVpaCldX13apm4iIiIioK+lKuZx3nhMRERGR4UjNBOx53vZxL7/8Mp577jkEBQUhODgYqampqKmpwezZswEAzz77LPr27Yu1a9cCANatW4fXX38du3btQr9+/VBcXAwAkMlkkMlkqK6uxurVqxEVFQUXFxcUFBTg1VdfhY+PDyIiIvS7LiIiIiKijiYkl6vGt0FXyeVcPCciIiIiw+ngxfM///nPuHv3Ll5//XUUFxcjMDAQhw4dUr+s6NatW5D+Zq/Hd999F3V1dfh//+//aZxH9eIjMzMz5OXlITMzE+Xl5ejTpw/Gjx+PNWvWGGzvdSIiIiKidtfBi+ddJZdz8ZyIiIiIDEYilUKi54uJ9B0XHx+P+Pj4Zo9lZ2drfH3jxg2t57KxscHhw4f1qoOIiIiIyFgIyeWq8W3VFXI59zwnIiIiIiIiIiIiIvod3nlORERERIYjEfB4qETAY6VERERERPQrIblcNd4EcfGciIiIiAxHIgEkej7sKJG0by1ERERERKZKSC5XjTdBXDwnIiIiIsORSAUsnnOHQSIiIiKidiEkl6vGmyDTvGoiIiIiIiIiIiIiIi24eC5AdnY2JBIJysvLW+yzatUqBAYGdlhNRERERMZEKZEKakStwVxOREREpJ3QXG6q2VzUq7579y7mz58Pd3d3WFlZwcXFBREREcjJyWnSV6lUYsKECZBIJNi/f3+rzn/jxg1IJBJ1s7e3h5+fHxYsWIBr16416S+Xy/GXv/wFHh4esLKyQr9+/ZCeni70MomIiIhMl+rxUH0bdQjmciIiIqIuTmguN9FsLuqe51FRUairq0NmZia8vLxQUlKCrKwslJaWNumbmpoKiZ4b0x89ehR+fn6ora3F5cuXsWnTJgQEBODgwYMIDw9X95s+fTpKSkqwbds2+Pj44Pbt21AoFHpfHxERERFRZ8BcTkRERETUlGi/MigvL8eJEyewbt06jBkzBh4eHggODkZCQgImT56s0ffixYvYsGGD3neb9OjRAy4uLvDy8sKUKVNw9OhRhISEICYmBg0NDQCAQ4cO4V//+hf++c9/YuzYsejXrx9CQ0MRFham8/znz59HUFAQbG1tMWLECFy9erVJn/feew9ubm6wtbXF9OnTUVFRoT6mUCiQlJSEhx56CFZWVggMDMShQ4danE8ul6OyslKjERERERkliURYI4NjLtc/lwPM5kRERNRJCM3lJprNRVs8l8lkkMlk2L9/P+RyeYv9amtrMWvWLGzevBkuLi7tMrdUKsWiRYtw8+ZNnD9/HgBw4MABBAUFITk5GX379sWAAQOwZMkS3Lt3T+f5/vKXv2DDhg3Izc2Fubk55syZo3H8+vXr+OSTT3Dw4EEcOnQI3377LV544QX18U2bNmHDhg1ISUlBXl4eIiIiMHny5GYfYQWAtWvXwtHRUd3c3NwEfDeIiIiIDEgqFdbI4JjL9c/lALM5ERERdRJCc7mJZnPRrtrc3BwZGRnIzMyEk5MTwsLCsHz5cuTl5Wn0W7x4MUaMGIEpU6a06/yDBg0C0Lj/IgB8//33+Oabb3DlyhV89tlnSE1NxZ49ezTCdEveeOMNPPbYY/D19cWyZctw8uRJ3L9/X338/v372LFjBwIDAzFq1Ci88847+Pjjj1FcXAwASElJwWuvvYYZM2Zg4MCBWLduHQIDA5GamtrsfAkJCaioqFC3wsJCYd8MIiIiIgPhS4mMH3O5/rkcYDYnIiKizoEvDNWPqFcdFRWFoqIiHDhwAJGRkcjOzsbw4cORkZEBoPGuk2PHjmkNq/pSKpUAoN6vUaFQQCKR4KOPPkJwcDAmTpyIt99+G5mZmTrvcvH391f/2dXVFQBw584d9Wfu7u7o27ev+uvQ0FAoFApcvXoVlZWVKCoqavIYalhYGPLz85udz8rKCg4ODhqNiIiIiEhfzOX65XKA2ZyIiIioK2v1C0OffPLJVp903759re5rbW2NcePGYdy4cVi5ciWef/55JCYmIjo6GseOHUNBQQGcnJw0xkRFReHRRx9FdnZ2q+f5PVUA9vT0BNAYrvv27QtHR0d1n8GDB0OpVOLHH39E//79WzyXhYWF+s+/Df1EREREJk8ibWz6jqUmmMuZy4mIiIjaTEguV403Qa2+6t/u46erCeHr64uamhoAwLJly5CXl4eLFy+qGwBs3LgR27dv13sOhUKBtLQ0eHp6YtiwYQAa7ygpKipCdXW1ut93330HqVSKhx56SP8LAnDr1i0UFRWpvz59+jSkUikGDhwIBwcH9OnTBzk5ORpjcnJy4OvrK2heIiIiItGpQrq+jZpgLtcfczkRERGZLKG53ESzeavvPBcSiptTWlqKadOmYc6cOfD394e9vT1yc3ORnJys3kfRxcWl2ZcRubu7q+9Mae1cxcXFqK2txZUrV5CamoqzZ8/iiy++gJmZGQBg1qxZWLNmDWbPno3Vq1fj559/xtKlSzFnzhzY2NgIulZra2s899xzSElJQWVlJRYuXIjp06err23p0qVITEyEt7c3AgMDsX37dly8eBEfffSRoHmJiIiIRMc7z9sdc7n+mMuJiIjIZPHOc720evH89x48eIDs7GwUFBRg1qxZsLe3R1FRERwcHCCTyXSOl8lkCAkJwcaNG1FQUID6+nq4ubkhNjYWy5cv17esZo0dOxYAYGtrCw8PD4wZMwZbt26Fj4+PRj1HjhzBiy++iKCgIPTo0QPTp0/HX//6V8Hz+/j44Mknn8TEiRNRVlaGP/7xj/j73/+uPr5w4UJUVFTglVdewZ07d+Dr64sDBw5ofSSViIiIiAhgLm8L5nIiIiIiaguJUvWGnja4efMmIiMjcevWLcjlcnz33Xfw8vLCokWLIJfLsWXLFkPUSi2orKyEo6Mj+sftgpmVrdjldFp3r7f8IihjUbTcR3cnkcmvXRa7BK2WRmeKXYJOxxeniV2CTlY2Fro7iczOwUrsErSqLNP+0jtj8OWyx8QuQaevvIPELkGrwRdOil1Ci2qqKjHW3xMVFRUGecGhKp/8/N23cLC31+8cVVXoOWCYwWrsCpjLjY/q777diIWQmBvvf4tsujV9ksCY2HTrLXYJOmWuiBC7BJ3+OShY7BK0qnxg/O8k+OezSWKXoNOLc0aIXYJO7+66JHYJWp30yha7BJ1un/lO7BJ0OvrV92KXoNUwD2Fb2hlSTUMDpl69aJDc2x65HDDdbK7X/faLFi1CUFAQfvnlF41HJ//0pz8hKyur3YojIiIiok6O+yoaFHM5EREREbUK9zzXi15XfeLECaxYsQKWlpYan/fr1w8//fRTuxTWGnFxcZDJZM22uLi4DquDiIiIiEgMzOVERERERIaj157nCoUCDQ0NTT7/8ccfYS/g9v+2SkpKwpIlS5o9ZkqPDxAREREZLYmksek7lrRiLiciIiKiVhGSy1XjTZBei+fjx49Hamoqtm7dCgCQSCSorq5GYmIiJk6c2K4FauPs7AxnZ+cOm4+IiIiI2kjII54m+mhoWzCXExEREVGrCN16xUSzuV6L5xs2bEBERAR8fX1x//59zJo1C9euXUPPnj2xe/fu9q6RiIiIiDoppUQKpZ5BW99xpoS5nIiIiIhaQ0guV403RXotnj/00EO4dOkSPv74Y+Tl5aG6uhoxMTF46qmnNF5URB0r9qOVsJGYiV1Gp2XWGZ4+UawRuwKdDg+fJ3YJWh1f/LDYJeg0auNCsUvQae2WmWKXoFPyi/8QuwStBttbiV2CTnVLz4pdgk6fr8sQuwStHrEy4v8u1xlxbdRqzOXGK+nKF0adza9W1YldglaOFsb/A3rPul1il6DTl4vTxC5Bq4qSMrFL0GnijtfFLkGnR664iF2CTnfz7ohdglY1RZfELkGn+pn1Ypeg06qZSWKXoNW69YvELqFF92qqgPEBYpdBzdBr8RwAzM3N8fTTT7dnLURERETU1UikgJTbthgSczkRERER6SQkl6vGmyC9F8+vXbuGr7/+Gnfu3IFCodA49vrrxv+bWSIiIiLqANzz3OCYy4mIiIhIJ+55rhe9Fs/ff/99zJ8/Hz179oSLiwskv3nbqkQiYUgnIiIiokZcPDco5nIiIiIiahUunutFr8Xzv/71r3jjjTfw2muvtXc9RERERETUSszlRERERESGo9fi+S+//IJp06a1dy1ERERE1NXwznODYi4nIiIiolbhned60euqp02bhq+++qq9ayEiIiKiLkYpkUApkerZJLonMHHM5URERETUGsJyuelmc73uPPfx8cHKlStx+vRpDB06FBYWFhrHFy5c2C7FERERERFRy5jLiYiIiIgMR6/F861bt0Imk+Ff//oX/vWvf2kck0gkDOlERERE1IjbthgUczkRERERtQq3bdGLXovnP/zwQ3vXQURERERdkUTS2PQdS1oxlxMRERFRqwjJ5arxJsg0f2XQTjIyMuDk5KS1T3R0NKZOndoh9RAREREZHdUdLvo2olZgLiciIiLSQWguN9FsrtdVNzQ0YNu2bZg1axbGjh2Lxx9/XKO11t27dzF//ny4u7vDysoKLi4uiIiIQE5OjrrP6NGjIZFINFpcXFyrzn/jxg2Ncfb29vDz88OCBQtw7do1jb7ffPMNwsLC0KNHD9jY2GDQoEHYuHFjq6+FiIiIiIzD5s2b0a9fP1hbWyMkJARnz57V2v/TTz/FoEGDYG1tjaFDh+Kf//ynxnGlUonXX38drq6usLGxwdixY5tkSbEwlxMRERGRseoKuVyvbVsWLVqEjIwMTJo0CUOGDIFEz9v2o6KiUFdXh8zMTHh5eaGkpARZWVkoLS3V6BcbG4ukpCT117a2tm2a5+jRo/Dz80NtbS0uX76MTZs2ISAgAAcPHkR4eDgAwM7ODvHx8fD394ednR2++eYbzJs3D3Z2dpg7d65e10dERERk6pQSKZR63qWiz7h//OMfePnll7FlyxaEhIQgNTUVERERuHr1KpydnZv0P3nyJGbOnIm1a9fij3/8I3bt2oWpU6fiwoULGDJkCAAgOTkZaWlpyMzMhKenJ1auXImIiAj85z//gbW1tV7X1l6Yy4mIiIioNYTkctX4tugquVyvxfOPP/4Yn3zyCSZOnKj3xOXl5Thx4gSys7Px2GOPAQA8PDwQHBzcpK+trS1cXFz0nqtHjx7q8V5eXnjiiScQHh6OmJgYFBQUwMzMDMOGDcOwYcPUY/r164d9+/bhxIkTOkP64cOH8dJLL6GwsBAjR47E9u3b4erqqtFn9erV+Nvf/ga5XI5Zs2YhLS0NlpaWAAC5XI6lS5fi448/RmVlJYKCgrBx40Y88sgjzc4nl8shl8vVX1dWVur1fSEiIiIyuHZ4Yejvs46VlRWsrKyaHfL2228jNjYWs2fPBgBs2bIFX3zxBdLT07Fs2bIm/Tdt2oTIyEgsXboUALBmzRocOXIEf/vb37BlyxYolUqkpqZixYoVmDJlCgBgx44d6N27N/bv348ZM2bod23thLlcU0fnctUYZnMiIiIyeu30wtDWZvOuksv1+o5ZWlrCx8dH0MQymQwymQz79+/XCJvN+eijj9CzZ08MGTIECQkJqK2tFTS3VCrFokWLcPPmTZw/f77ZPt9++y1Onjyp/gGiJbW1tUhJScHOnTtx/Phx3Lp1C0uWLNHok5WVhfz8fGRnZ2P37t3Yt28fVq9erT7+6quvYu/evcjMzMSFCxfg4+ODiIgIlJWVNTvn2rVr4ejoqG5ubm5t/A4QERERdQylRCKoAYCbm5tG9lm7dm2zc9XV1eH8+fMYO3as+jOpVIqxY8fi1KlTzY45deqURn8AiIiIUPf/4YcfUFxcrNHH0dERISEhLZ6zIzGX/0qMXA4wmxMREVHnIDSXtyWbd6Vcrtfi+SuvvIJNmzZBqVTqPbG5uTkyMjKQmZkJJycnhIWFYfny5cjLy9PoN2vWLHz44Yf4+uuvkZCQgJ07d+Lpp5/We16VQYMGAWjcf/G3HnroIVhZWSEoKAgLFizA888/r/U89fX12LJlC4KCgjB8+HDEx8cjKytLo4+lpSXS09Ph5+eHSZMmISkpCWlpaVAoFKipqcG7776L9evXY8KECfD19cX7778PGxsbbNu2rdk5ExISUFFRoW6FhYX6fyOIiIiIjFxhYaFG9klISGi2388//4yGhgb07t1b4/PevXujuLi42THFxcVa+6v+ty3n7EjM5b8SI5cDzOZERERkWlqTzbtSLtdr25ZvvvkGX3/9Nb788kv4+fnBwsJC4/i+fftadZ6oqChMmjQJJ06cwOnTp/Hll18iOTkZH3zwAaKjowFA49HMoUOHwtXVFeHh4SgoKIC3t7c+5QOA+geM3+8LeeLECVRXV+P06dNYtmwZfHx8MHPmzBbPY2trq1GHq6sr7ty5o9EnICBAYz/I0NBQVFdXq/+y1dfXIywsTH3cwsICwcHByM/Pb3ZObY8qExERERkTpbKx6TsWABwcHODg4NB+RXUhzOW/EiOXA8zmRERE1DkIyeWq8YDpZXO9Fs+dnJzwpz/9qV0KsLa2xrhx4zBu3DisXLkSzz//PBITE9Uh/fdCQkIAANevXxcU0lUB2NPTU+Nz1ddDhw5FSUkJVq1apTWk//4HFIlEIujOHyIiIqKuRKFUQqFnNmrruJ49e8LMzAwlJSUan5eUlLS4T7eLi4vW/qr/LSkp0dg7u6SkBIGBgW2qzxCYy3/FXE5ERETUMiG5XDW+tbpSLtdr8Xz79u3tXYear68v9u/f3+LxixcvAkCTF/+0hUKhQFpaGjw9PTVeRtRcP137PrbGpUuXcO/ePdjY2AAATp8+DZlMBjc3N/Ts2ROWlpbIycmBh4cHgMZHTs+dO4eXXnpJ8NxEREREpsLS0hIPP/wwsrKyMHXqVACNeS4rKwvx8fHNjgkNDUVWVpZG7jpy5AhCQ0MBNC7guri4ICsrSx3KKysrcebMGcyfP9+Ql9MqzOVtw1xOREREZHhdKZfrtXjeHkpLSzFt2jTMmTMH/v7+sLe3R25uLpKTk9VvTC0oKMCuXbswceJE9OjRA3l5eVi8eDFGjRoFf3//Ns1VXFyM2tpaXLlyBampqTh79iy++OILmJmZAQA2b94Md3d39Z6Lx48fR0pKChYuXCj4Wuvq6hATE4MVK1bgxo0bSExMRHx8PKRSKezs7DB//nwsXboU3bt3h7u7O5KTk1FbW4uYmBjBcxMRERGJSfm/pu/Ytnr55Zfx3HPPISgoCMHBwUhNTUVNTQ1mz54NAHj22WfRt29f9YuNFi1ahMceewwbNmzApEmT8PHHHyM3Nxdbt24F0Hj38ksvvYS//vWv6N+/Pzw9PbFy5Ur06dNH/YNAZ8dczlxOREREXZ+QXK4a3xZdJZfrvXi+Z88efPLJJ7h16xbq6uo0jl24cEHneJlMhpCQEGzcuBEFBQWor6+Hm5sbYmNjsXz5cgCNv6U4evSo+pvr5uaGqKgorFixok21qt7CamtrCw8PD4wZMwZbt26Fj4+Puo9CoUBCQgJ++OEHmJubw9vbG+vWrcO8efPaNFdzwsPD0b9/f4waNQpyuRwzZ87EqlWr1MffeustKBQKPPPMM6iqqkJQUBAOHz6Mbt26CZ6biIiISEwKZWPTd2xb/fnPf8bdu3fx+uuvo7i4GIGBgTh06JD6xUK3bt2CVCpV9x8xYgR27dqFFStWYPny5ejfvz/279+PIUOGqPu8+uqrqKmpwdy5c1FeXo6RI0fi0KFDsLa21u/C2hlzeesxlxMREZGpEpLLVePboqvkcolSj40A09LS8Je//AXR0dHYunUrZs+ejYKCApw7dw4LFizAG2+8YYhaqQWVlZVwdHREsr03bCRmYpfTaZlJdPcRW8z/rRG7BJ0OOI4UuwStXt+eK3YJOo3aKPzOOkNbu6XlPWeNRfKL/xC7BK0G2xv/y+UevXpW7BJ0WvzZv8UuQau//nGw2CW0qLqqEqED3VFRUWGQF/6o8snNn27rff7Kykp49HU1WI1dAXO58VH93d/gYNzZ/GpVne5OInK0kOruJLJpUYPELkGnmX3F315Km4qSMrFL0GnijtfFLkGnGcOb3z/YmPwz747uTiKaV3RJ7BJ0Kr1XL3YJOo2ZmSR2CVqtW79I7BJadK+mCq+ODzBI7m2PXK46jylmc70Syd///nds3boV77zzDiwtLfHqq6/iyJEjWLhwISoqKtq7RiIiIiIiagZzORERERGR4ei1eH7r1i2MGDECAGBjY4OqqioAwDPPPIPdu3e3X3U6xMXFQSaTNdvi4uI6rA4iIiIiap7q8VB9G2nHXE5ERERErSE0l5tqNtdrz3MXFxeUlZXBw8MD7u7uOH36NAICAvDDDz9Aj11g9JaUlIQlS5Y0e8yUHh8gIiIiMmYmmrM7BHM5EREREbUWc3nb6bV4/vjjj+PAgQMYNmwYZs+ejcWLF2PPnj3Izc3Fk08+2d41tsjZ2RnOzs4dNh8RERERtU1HvzDU1DCXExEREVFrdPQLQ7sKvRbPt27dCoVCAQBYsGABevTogZMnT2Ly5MmYN29euxZIRERERETNYy4nIiIiIjIcvRbPpVIppNJft0ufMWMGZsyY0W5FkX4G93OCnZmZ2GW0aOQHxv2W8m+XbxC7BJ1CvrQXuwSdDrxi3I9mW9lYiF2CTmu3zBS7BJ0S4jpuH119vb3PeN+kDgBXdx4SuwSdbkdNFLsEnbKtHhe7BK1uvWG8i5e1ioYOmUepVOq9fUhHbjvSWTGXG6/wp4bB3sp4c0fw9Ttil6BV/b0HYpegU3VJjdgl6BS/cIjYJWhVed/4/39+5IqL2CXo9PGFYrFL0GnulAFil6BVv+rvxC5BJw9za7FL0Cnkz1Fil6DV8/7dxS6hRZWV5njVwHMIyeWq8aZIr8VzACgvL8fZs2dx584d9d0uKs8++6zgwoiIiIio81P8r+k7lnRjLiciIiIiXYTkctV4U6TX4vnBgwfx1FNPobq6Gg4ODpBIJOpjEomEIZ2IiIiIAABKZWPTdyxpx1xORERERK0hJJerxpsiqe4uTb3yyiuYM2cOqqurUV5ejl9++UXdysrK2rtGIiIiIiJqBnM5EREREZHh6HXn+U8//YSFCxfC1ta2veshIiIioi5EoWxs+o4l7ZjLiYiIiKg1hORy1XhTpNed5xEREcjNzW3vWoiIiIioi1G9mEjfRtoxlxMRERFRawjN5aaazVt95/mBAwfUf540aRKWLl2K//znPxg6dCgsLDTfIj958uT2q5CIiIiIiNSYy4mIiIiIOkarF8+nTp3a5LOkpKQmn0kkEjQ0NAgqioiIiIi6BsX/mr5jqSnmciIiIiJqKyG5XDXeFLV68VyhMNVvERERERHpSwlA3yc8TfPBUN2Yy4mIiIiorYTkctV4U9SmPc+PHTsGX19fVFZWNjlWUVEBPz8/nDhxot2KIyIiIqLOTaFUCmrUPOZyIiIiImoLobncVLN5mxbPU1NTERsbCwcHhybHHB0dMW/ePLz99tvtVpyxy8jIgJOTk9Y+0dHRzT5aS0RERESkL+ZyTczlRERERGQIbVo8v3TpEiIjI1s8Pn78eJw/f77V57t79y7mz58Pd3d3WFlZwcXFBREREcjJyVH3GT16NCQSiUaLi4tr1flv3LihMc7e3h5+fn5YsGABrl271uK4nJwcmJubIzAwsNXXQkRERERNKQU2ah5zORERERG1hdBcbqrZvNV7ngNASUkJLCwsWj6ZuTnu3r3b6vNFRUWhrq4OmZmZ8PLyQklJCbKyslBaWqrRLzY2VuMlSLa2tm0pG0ePHoWfnx9qa2tx+fJlbNq0CQEBATh48CDCw8M1+paXl+PZZ59FeHg4SkpK2jQPEREREWlSKBubvmOpeczlRERERNQWQnK5arwpatOd53379sWVK1daPJ6XlwdXV9dWnau8vBwnTpzAunXrMGbMGHh4eCA4OBgJCQmYPHmyRl9bW1u4uLioW3OPp2rTo0cPuLi4wMvLC1OmTMHRo0cREhKCmJgYNDQ0aPSNi4vDrFmzEBoa2urzHz58GIMHD4ZMJkNkZCRu377dpM/q1avRq1cvODg4IC4uDnV1depjcrkcCxcuhLOzM6ytrTFy5EicO3euxfnkcjkqKys1GhEREZFRUja+mEifZrK3t7QCc3nzOjqXq8YwmxMREZHRE5DLTTmbt2nxfOLEiVi5ciXu37/f5Ni9e/eQmJiIP/7xj606l0wmg0wmw/79+yGXy7X2/eijj9CzZ08MGTIECQkJqK2tbUvZTUilUixatAg3b97UeJx1+/bt+P7775GYmNjqc9XW1iIlJQU7d+7E8ePHcevWLSxZskSjT1ZWFvLz85GdnY3du3dj3759WL16tfr4q6++ir179yIzMxMXLlyAj48PIiIiUFZW1uyca9euhaOjo7q5ubm18TtARERERJ0Zc3lTYuRygNmciIiIqCtr0+L5ihUrUFZWhgEDBiA5ORmff/45Pv/8c6xbtw4DBw5EWVkZ/vKXv7TqXObm5sjIyEBmZiacnJwQFhaG5cuXIy8vT6PfrFmz8OGHH+Lrr79GQkICdu7ciaeffrotZTdr0KBBABr3XwSAa9euYdmyZfjwww9hbt763Wzq6+uxZcsWBAUFYfjw4YiPj0dWVpZGH0tLS6Snp8PPzw+TJk1CUlIS0tLSoFAoUFNTg3fffRfr16/HhAkT4Ovri/fffx82NjbYtm1bs3MmJCSgoqJC3QoLC/X7JhAREREZmAJKQY2ax1zelBi5HGA2JyIios5BaC431Wzepj3Pe/fujZMnT2L+/PlISEiAUtn4TZNIJIiIiMDmzZvRu3fvVp8vKioKkyZNwokTJ3D69Gl8+eWXSE5OxgcffIDo6GgAwNy5c9X9hw4dCldXV4SHh6OgoADe3t5tKV/Db2tvaGjArFmzsHr1agwYMKBN57G1tdWow9XVFXfu3NHoExAQoLEfZGhoKKqrq1FYWIiKigrU19cjLCxMfdzCwgLBwcHIz89vdk4rKytYWVm1qU4iIiIiMagf89RzLDWPubwpMXI5wGxOREREnYOQXK4ab4ratHgOAB4eHvjnP/+JX375BdevX4dSqUT//v3RrVs3vQqwtrbGuHHjMG7cOKxcuRLPP/88EhMT1SH990JCQgAA169fFxTSVQHY09MTVVVVyM3Nxbfffov4+HgAgEKhgFKphLm5Ob766is8/vjjzZ7n9y9qkkgk6h8AiIiIiIgMhblcE3M5EREREbW3Ni+eq3Tr1g2PPPJIe9YCAPD19cX+/ftbPH7x4kUAaPULkJqjUCiQlpYGT09PDBs2DBKJBJcvX9bo8/e//x3Hjh3Dnj174OnpqfdcAHDp0iXcu3cPNjY2AIDTp09DJpPBzc0NPXv2hKWlJXJycuDh4QGg8ZHTc+fO4aWXXhI0LxEREZHYFMrGpu9Y0o25vPWYy4mIiMhUCcnlqvGmSO/Fc6FKS0sxbdo0zJkzB/7+/rC3t0dubi6Sk5MxZcoUAEBBQQF27dqFiRMnokePHsjLy8PixYsxatQo+Pv7t2mu4uJi1NbW4sqVK0hNTcXZs2fxxRdfwMzMDAAwZMgQjTHOzs6wtrZu8rk+6urqEBMTgxUrVuDGjRtITExEfHw8pFIp7OzsMH/+fCxduhTdu3eHu7s7kpOTUVtbi5iYGMFzExEREYmJ27YYP+Zy5nIiIiLq+rhti35EWzyXyWQICQnBxo0bUVBQgPr6eri5uSE2NhbLly8H0PhCn6NHjyI1NRU1NTVwc3NDVFQUVqxY0aa5xo4dC6BxH0QPDw+MGTMGW7duhY+PT7tfV3PCw8PRv39/jBo1CnK5HDNnzsSqVavUx9966y0oFAo888wzqKqqQlBQEA4fPqz3I7dERERExkLIy4VM9aVEHY25fJX6OHM5ERERdVVCX/ppqtlcouRGgJ1eZWUlHB0dcXDow7D73x07xmjkB6+LXYJW3y7fIHYJOs0LWip2CTodeOVRsUvQavKGE2KXoNMx9+Nil6BTQtxusUvQ6e19i8QuQaurOw+JXYJO90prxS5Bp4lWze99bCx2fbdf7BJaVKtowLTCf6OiogIODg7tfn5VPjl59RZk9vqdv7qqEiMGuhusRiJDUP3dvzj//8HeykL3AJH8cv2O7k4iqr/3QOwSuoRLb2SIXYJWlfeN///nR15+WuwSdPr4QrHYJeg0d0rbXgLd0QI2rhW7BJ2U5tZil6DT+M/KxC5Bq3/Oaf3Tch2tsrISvfsaJve2Ry4HTDebi3bnORERERF1fdy2hYiIiIhIfNy2RT9SsQsQIi4uDjKZrNkWFxcndnlEREREJk+hVApq1DkwlxMREREZN6G53FSzeae+8zwpKQlLlixp9pgpPT5AREREZKwaFI1N37HUOTCXExERERk3IblcNd4UderFc2dnZzg7O4tdBhERERGRSWMuJyIiIqKuqFMvnhMRERGRcRPyiKepPhpKRERERNTehG69YqrZnIvnRERERGQwCqUSDVw8JyIiIiISlZBcrhpvirh43oX8cKsCNhIzsctoUd2MlWKXoFVdTZ3YJeh0ammx2CXo9H8/VYpdglZ2DlZil6BT8ov/ELsEnd7et0jsEnR6+clNYpeg1YjuNmKXoNMj/z4jdgk6zTzxvdglaNXv0ZfFLqFF1VWVwAB3scsQTVlZGV588UUcPHgQUqkUUVFR2LRpE2QyWYv9ExMT8dVXX+HWrVvo1asXpk6dijVr1sDR0VHdTyKRNBm7e/duzJgxw2DXQsap4PB3sJMabzb/d3G12CVoVVHfIHYJOj01e5jYJei0M9u4/ztZ9P0vYpeg0928O2KXoNPcKQPELkGnrZ9/J3YJWqXs8hW7BJ1+vvdA7BJ0Orc3U+wStDoc7iN2CS2qrb4ndgmiMuZszsVzIiIiIjIYhVL/u1QUBry55amnnsLt27dx5MgR1NfXY/bs2Zg7dy527drVbP+ioiIUFRUhJSUFvr6+uHnzJuLi4lBUVIQ9e/Zo9N2+fTsiIyPVXzs5ORnuQoiIiIiIWkFILleNNxRjzuZcPCciIiIig2lQNDZ9xwJAZaXmU01WVlawstL/SaL8/HwcOnQI586dQ1BQEADgnXfewcSJE5GSkoI+ffo0GTNkyBDs3btX/bW3tzfeeOMNPP3003jw4AHMzX+N1U5OTnBxcdG7PiIiIiKi9iYkl6vGA6aXzaV6jyQiIiIi0kH1YiJ9GwC4ubnB0dFR3dauXSuoplOnTsHJyUkdzgFg7NixkEqlOHOm9dsVVVRUwMHBQSOcA8CCBQvQs2dPBAcHIz09HUoT3R+SiIiIiIyH0Fxuqtmcd54TERERkVErLCyEg4OD+mshd7YAQHFxMZydnTU+Mzc3R/fu3VFc3Lr3i/z8889Ys2YN5s6dq/F5UlISHn/8cdja2uKrr77CCy+8gOrqaixcuFBQzURERERExsDUsjkXz4mIiIjIYBqUSjToeee1apyDg4NGQG/JsmXLsG7dOq198vPz9arltyorKzFp0iT4+vpi1apVGsdWrvz1BenDhg1DTU0N1q9fz8VzIiIiIhKVkFyuGg+YXjbn4jkRERERGYwC+r9cqK1bMr7yyiuIjo7W2sfLywsuLi64c+eOxucPHjxAWVmZzv0Qq6qqEBkZCXt7e3z22WewsLDQ2j8kJARr1qyBXC4XfFcOEREREZG+hORy1fi26CrZnIvnRERERGQwDQolGvRM6W0d16tXL/Tq1Utnv9DQUJSXl+P8+fN4+OGHAQDHjh2DQqFASEhIi+MqKysREREBKysrHDhwANbW1jrnunjxIrp168aFcyIiIiISlZBcrhrfFl0lm3PxnIiIiIhMyuDBgxEZGYnY2Fhs2bIF9fX1iI+Px4wZM9CnTx8AwE8//YTw8HDs2LEDwcHBqKysxPjx41FbW4sPP/wQlZWVqKysBND4g4GZmRkOHjyIkpIS/OEPf4C1tTWOHDmCN998E0uWLBHzcomIiIiIjJaxZ3MunguQkZGBl156CeXl5S32iY6ORnl5Ofbv399hdREREREZC6VSCYWeeysqBezJqMtHH32E+Ph4hIeHQyqVIioqCmlpaerj9fX1uHr1KmprawEAFy5cwJkzZwAAPj4+Guf64Ycf0K9fP1hYWGDz5s1YvHgxlEolfHx88PbbbyM2NtZg10GNmMuJiIiItBOSy1XjDcWYs7lU4LUJcvfuXcyfPx/u7u6wsrKCi4sLIiIikJOTo+4zevRoSCQSjRYXF9eq89+4cUNjnL29Pfz8/LBgwQJcu3ZNo292dnaTeSQSSavf6kpERERETTUohTVD6d69O3bt2oWqqipUVFQgPT0dMplMfbxfv35QKpUYPXo0gMZMqlQqm239+vUDAERGRuLbb79FVVUVqqurcfHiRcybNw9SqaiRu1WYy4mIiIi6NqG53FSzuah3nkdFRaGurg6ZmZnw8vJCSUkJsrKyUFpaqtEvNjYWSUlJ6q9tbW3bNM/Ro0fh5+eH2tpaXL58GZs2bUJAQAAOHjyI8PBwjb5Xr17VeGOss7OzHldGRERERNR5MJcTERERETUl2m0w5eXlOHHiBNatW4cxY8bAw8MDwcHBSEhIwOTJkzX62trawsXFRd1+G6Jbo0ePHnBxcYGXlxemTJmCo0ePIiQkBDExMWhoaNDo6+zsrDFXa34bcfjwYQwePBgymQyRkZG4fft2kz6rV69Gr1694ODggLi4ONTV1amPyeVyLFy4EM7OzrC2tsbIkSNx7ty5FueTy+XqvXx+u6cPERERkbFR/O/xUH0bGR5zuf65XDWG2ZyIiIiMndBcbqrZXLTFc5lMBplMhv3790Mul2vt+9FHH6Fnz54YMmQIEhIS1Pvb6EsqlWLRokW4efMmzp8/r3EsMDAQrq6uGDdunMZjqi2pra1FSkoKdu7ciePHj+PWrVtNNp7PyspCfn4+srOzsXv3buzbtw+rV69WH3/11Vexd+9eZGZm4sKFC/Dx8UFERATKysqanXPt2rVwdHRUNzc3Nz2+C0RERESG16BQCmpkeMzl+udygNmciIiIOgehudxUs7loi+fm5ubIyMhAZmYmnJycEBYWhuXLlyMvL0+j36xZs/Dhhx/i66+/RkJCAnbu3Imnn35a8PyDBg0C0Lj/IgC4urpiy5Yt2Lt3L/bu3Qs3NzeMHj0aFy5c0Hqe+vp6bNmyBUFBQRg+fDji4+ORlZWl0cfS0hLp6enw8/PDpEmTkJSUhLS0NCgUCtTU1ODdd9/F+vXrMWHCBPj6+uL999+HjY0Ntm3b1uycCQkJqKioULfCwkLB3w8iIiIiQ+DdLcaPuVz/XA4wmxMREVHnwDvP9SP6nueTJk3CiRMncPr0aXz55ZdITk7GBx98gOjoaADA3Llz1f2HDh0KV1dXhIeHo6CgAN7e3nrPrXpDrEQiAQAMHDgQAwcOVB8fMWIECgoKsHHjRuzcubPF89ja2mrU4erqijt37mj0CQgI0NgPMjQ0FNXV1SgsLERFRQXq6+sRFhamPm5hYYHg4GDk5+c3O6eVlRWsrKzacLVERERERC1jLtcvlwPM5kRERERdmWh3nqtYW1tj3LhxWLlyJU6ePIno6GgkJia22D8kJAQAcP36dUHzqgKwp6dni32Cg4N1zmNhYaHxtUQiUf8AQERERGTqGpTCGnUc5nIiIiKirktoLjfVbC764vnv+fr6oqampsXjFy9eBNB4J4m+FAoF0tLS4OnpiWHDhmmdS8g8KpcuXcK9e/fUX58+fRoymQxubm7w9vaGpaWlxj6O9fX1OHfuHHx9fQXPTURERCQmPhraeTGXM5cTERFR18FtW/Qj2rYtpaWlmDZtGubMmQN/f3/Y29sjNzcXycnJmDJlCgCgoKAAu3btwsSJE9GjRw/k5eVh8eLFGDVqFPz9/ds0V3FxMWpra3HlyhWkpqbi7Nmz+OKLL2BmZgYASE1NhaenJ/z8/HD//n188MEHOHbsGL766ivB11pXV4eYmBisWLECN27cQGJiIuLj4yGVSmFnZ4f58+dj6dKl6N69O9zd3ZGcnIza2lrExMQInpuIiIhITAqFEgo9Xy6k7zhqG+Zy5nIiIiLq+oTkctV4UyTa4rlMJkNISAg2btyIgoIC1NfXw83NDbGxsVi+fDmAxhf6HD16FKmpqaipqYGbmxuioqKwYsWKNs01duxYAI37IHp4eGDMmDHYunUrfHx81H3q6urwyiuv4KeffoKtrS38/f1x9OhRjBkzRvC1hoeHo3///hg1ahTkcjlmzpyJVatWqY+/9dZbUCgUeOaZZ1BVVYWgoCAcPnwY3bp1Ezw3EREREZE2zOWr1MeZy4mIiIjotyRKbgTY6VVWVsLR0RFpjj6wkZiJXU6L3Hra6u4korqaOrFL0GncjlfELkGn/+sdIXYJWqX+879il6DToyufF7sEnVb+40WxS9Dp5Sc3iV2CViO624hdgk6P/PuM2CXolHbie7FL0OrFR1vew1ls1VWVCBngjoqKCjg4OLT7+VX55L1//Qc2Mnu9znGvugrzHvM1WI1EhqD6u7/Xyx92UuPN5v8urha7BK0q6hvELkGnp2a3vNWQsZjT7wWxS9Cq6PtfxC5Bp/+XvlTsEnSaMclHdyeRbf38O7FL0CqlpuUXQxuLn+89ELsEnYY+uUbsErT68O+LxS6hRbXVVZj1h4EGyb3tkcsB083mot15TkRERERdn5D9EU11X0UiIiIiovYmdN9yU83mRvfC0LaIi4uDTCZrtsXFxYldHhEREZHJa1AqBTXqHJjLiYiIiIyb0Fxuqtm8U995npSUhCVLljR7zJQeHyAiIiIiEhNzORERERF1RZ168dzZ2RnOzs5il0FERERELVAolFAo9Ny2Rc9x1PGYy4mIiIiMm5Bcrhpvijr14jkRERERGbcGAA165mzjf10gEREREVHnICSXq8abok695zkRERERERERERERkSHwzvMupLxegfsSsato2RAP497vMvfcbbFL0OneH/4sdgk65eXcErsErSrL7oldgk6D7a3ELkGnqzsPiV2CTiO624hdglYnO8HfxafuXRO7BJ1ycovFLkGrd0KN95/nygfVHTKPQqmEQs+XC+k7jsgYHFjxN1jaysQuo0UT/FzELkErmaWZ2CXo5P79frFL0Gnbvr+JXYJWjt59xS5Bp5qiS2KXoFO/6u/ELkGnlF2+Ypeg1RK7wWKXoJODufHf/1p2drPYJWi1NuhRsUto0X2lwuBzCMnlqvGmiIvnRERERGQwDUolGvQM2vqOIyIiIiIiTUJyuWq8KeLiOREREREZjEKhRANfGEpEREREJCohuVw13hQZ/zMfREREREREREREREQdjHeeExEREZHBNAi4w0XInTFERERERPQrIblcNd4UcfGciIiIiAyGi+dEREREROLj4rl+uHhORERERAbToNA/aDco2rkYIiIiIiITJSSXq8abIu55TkRERERERERERET0O7zznIiIiIgMhtu2EBERERGJj9u26Id3nguQkZEBJycnrX2io6MxderUDqmHiIiIyNioQrq+jag1mMuJiIiItBOay001m4u6eH737l3Mnz8f7u7usLKygouLCyIiIpCTk6PuM3r0aEgkEo0WFxfXqvPfuHFDY5y9vT38/PywYMECXLt2TaNvdHR0k3kkEgn8/Pza9ZqJiIiIiIwNczkRERERUVOibtsSFRWFuro6ZGZmwsvLCyUlJcjKykJpaalGv9jYWCQlJam/trW1bdM8R48ehZ+fH2pra3H58mVs2rQJAQEBOHjwIMLDwwEAmzZtwltvvaUe8+DBAwQEBGDatGkCrpCIiIjItCkE3KWiMNG7W8TAXE5ERETUtQnJ5arxpki0O8/Ly8tx4sQJrFu3DmPGjIGHhweCg4ORkJCAyZMna/S1tbWFi4uLujk4OLRprh49esDFxQVeXl6YMmUKjh49ipCQEMTExKChoQEA4OjoqDFHbm4ufvnlF8yePVvn+Q8fPozBgwdDJpMhMjISt2/fbtJn9erV6NWrFxwcHBAXF4e6ujr1MblcjoULF8LZ2RnW1tYYOXIkzp071+J8crkclZWVGo2IiIjIGDUoBTwaqjTNgN7RmMv1z+WqMczmREREZOwE5XITzuaiLZ7LZDLIZDLs378fcrlca9+PPvoIPXv2xJAhQ5CQkIDa2lpBc0ulUixatAg3b97E+fPnm+2zbds2jB07Fh4eHlrPVVtbi5SUFOzcuRPHjx/HrVu3sGTJEo0+WVlZyM/PR3Z2Nnbv3o19+/Zh9erV6uOvvvoq9u7di8zMTFy4cAE+Pj6IiIhAWVlZs3OuXbsWjo6O6ubm5tbG7wARERFRx+C+isaPuVz/XA4wmxMREVHnwD3P9SPa4rm5uTkyMjKQmZkJJycnhIWFYfny5cjLy9PoN2vWLHz44Yf4+uuvkZCQgJ07d+Lpp58WPP+gQYMANO6/+HtFRUX48ssv8fzzz+s8T319PbZs2YKgoCAMHz4c8fHxyMrK0uhjaWmJ9PR0+Pn5YdKkSUhKSkJaWhoUCgVqamrw7rvvYv369ZgwYQJ8fX3x/vvvw8bGBtu2bWt2zoSEBFRUVKhbYWFh278BRERERERgLheSywFmcyIiIqKuTPQ9zydNmoQTJ07g9OnT+PLLL5GcnIwPPvgA0dHRAIC5c+eq+w8dOhSurq4IDw9HQUEBvL299Z5b+b9HDSQSSZNjqh8cpk6dqvM8tra2GnW4urrizp07Gn0CAgI09oMMDQ1FdXU1CgsLUVFRgfr6eoSFhamPW1hYIDg4GPn5+c3OaWVlBSsrK521EREREYlNyF0qpnp3ixiYy/XL5QCzOREREXUOQu8eN9VsLtqd5yrW1tYYN24cVq5ciZMnTyI6OhqJiYkt9g8JCQEAXL9+XdC8qgDs6emp8blSqUR6ejqeeeYZWFpa6jyPhYWFxtcSiUT9AwARERGRqXugUApq1HGYy4mIiIi6LqG53FSzueiL57/n6+uLmpqaFo9fvHgRQOOdJPpSKBRIS0uDp6cnhg0bpnHsX//6F65fv46YmBi9z/97ly5dwr1799Rfnz59GjKZDG5ubvD29oalpSVycnLUx+vr63Hu3Dn4+vq2Ww1EREREYjDWfRXLysrw1FNPwcHBAU5OToiJiUF1dbXWMaNHj4ZEItFocXFxGn1u3bqFSZMmwdbWFs7Ozli6dCkePHhgsOswJOZy5nIiIiLqOox5z3NjzuaibdtSWlqKadOmYc6cOfD394e9vT1yc3ORnJyMKVOmAAAKCgqwa9cuTJw4ET169EBeXh4WL16MUaNGwd/fv01zFRcXo7a2FleuXEFqairOnj2LL774AmZmZhp9t23bhpCQEAwZMqTdrrWurg4xMTFYsWIFbty4gcTERMTHx0MqlcLOzg7z58/H0qVL0b17d7i7uyM5ORm1tbXt+oMCEREREf3qqaeewu3bt3HkyBHU19dj9uzZmDt3Lnbt2qV1XGxsLJKSktRf/3YLkIaGBkyaNAkuLi44efIkbt++jWeffRYWFhZ48803DXYtQjGXM5cTERERicmYs7loi+cymQwhISHYuHEjCgoKUF9fDzc3N8TGxmL58uUAGl/oc/ToUaSmpqKmpgZubm6IiorCihUr2jTX2LFjATR+Az08PDBmzBhs3boVPj4+Gv0qKiqwd+9ebNq0qX0u8n/Cw8PRv39/jBo1CnK5HDNnzsSqVavUx9966y0oFAo888wzqKqqQlBQEA4fPoxu3bq1ax1EREREHU0h4C4Vxf/GVVZWanwudI/p/Px8HDp0COfOnUNQUBAA4J133sHEiRORkpKCPn36tDjW1tYWLi4uzR776quv8J///AdHjx5F7969ERgYiDVr1uC1117DqlWrWrX1iBiYy1epjzOXExERUVclJJerxgOml80lSm4E2OlVVlbC0dERSbZesJYY3U48ao/+oa/YJWiVe+622CXo9NRP34pdgk4bc26JXYJW+48ViF2CTks+WCJ2CToNfcxd7BJ0uvwv4/67eLLsnu5OItv8/V6xS9Bp2JZisUvQ6sKLXmKX0KLKqmr08A1GRUUFHBwc2v/8/8snczK/gaWtTK9z1NVWI/25kU0+T0xM1FjwbKv09HS88sor+OWXX9SfPXjwANbW1vj000/xpz/9qdlxo0ePxr///W8olUq4uLjgiSeewMqVK9V3uLz++us4cOCAejsTAPjhhx/g5eWFCxcuNNmWhLom1d/959KP6/13vyNM8Gv+B01jIbM0091JZI9+v1/sEnS6ue+Q2CVo5eht3D8jAkDNHON9ckilX/V3Ypeg073exr391RK7wWKXoJODufGu96i8cXaz2CVotXbEi2KX0KL7SgXWyn8wSDZvj1wOmG42F+3OcyIiIiKi1igsLNT4IULInS0AUFxcDGdnZ43PzM3N0b17dxQXt/wLmVmzZsHDwwN9+vRBXl4eXnvtNVy9ehX79u1Tn7d3794aY1RfazsvEREREVFnYWrZvFMvnsfFxeHDDz9s9tjTTz+NLVu2dHBFRERERPRbQl4upBrn4ODQqjtwli1bhnXr1mntk5+fr1ctADB37lz1n4cOHQpXV1eEh4ejoKAA3t7eep+3K2AuJyIiIjJuQl/6aarZvFMvniclJWHJkua3NzDE48dERERE1DbtsXjeWq+88gqio6O19vHy8oKLiwvu3Lmj8fmDBw9QVlbW4p6JzQkJCQEAXL9+Hd7e3nBxccHZs2c1+pSUlABAm87bGTGXExERERm39lo8b62uks079eK5s7Nzk9v6iYiIiMh4dOTiea9evdCrVy+d/UJDQ1FeXo7z58/j4YcfBgAcO3YMCoVCHbpbQ7V/oqurq/q8b7zxBu7cuaPOqEeOHIGDgwN8fY17r1ehmMuJiIiIjFtHL553lWxu/G8bICIiIiJqR4MHD0ZkZCRiY2Nx9uxZ5OTkID4+HjNmzECfPn0AAD/99BMGDRqkvluloKAAa9aswfnz53Hjxg0cOHAAzz77LEaNGgV/f38AwPjx4+Hr64tnnnkGly5dwuHDh7FixQosWLBA8F6QRERERERdkbFn80595zkRERERGbcGpQINCoXeYw3lo48+Qnx8PMLDwyGVShEVFYW0tDT18fr6ely9ehW1tbUAAEtLSxw9ehSpqamoqamBm5sboqKisGLFCvUYMzMz/N///R/mz5+P0NBQ2NnZ4bnnnkNSUpLBroOIiIiIqDWE5HLVeEMx5mzOxfMuJD77b3CQ2YldRovy7P3FLkGrWDu52CXo9PCqLLFL0Om49zdil6DVvGUrxS5Bp7qlZ3V3EtntqIlil6DTI/8+I3YJWj1175rYJei0wCtK7BJ0ivnmmNglaLVn2B/FLqFFtYqGDplHIeDxUIWAx0p16d69O3bt2tXi8X79+kGp/HV+Nzc3/Otf/9J5Xg8PD/zzn/9slxqpc7t+uwrm1ob7IVMofzcnsUvQytnOUuwSdJJY24pdgk5F534SuwStan+uEbsEnepn1otdgk4e5tZil6DTz/ceiF2CVg7mxr8xQ+UD4/1vikr9jf+KXYJWd+XG+/ewzoAL0ypCcrlqvKEYczbn4jkRERERGUyDQglpB+15TkREREREzROSy1XjTZHx/2qNiIiIiIiIiIiIiKiD8c5zIiIiIjKYBwpAouddKp3g6WQiIiIiok5BSC5XjTdFXDwnIiIiIoPhti1EREREROLjti364bYtRERERERERERERES/wzvPiYiIiMhgeOc5EREREZH4eOe5frh4TkREREQGw8VzIiIiIiLxcfFcP1w8JyIiIiKDUSiUegdthYkGdCIiIiKi9iYkl6vGmyLueS5AdnY2JBIJysvLW+yzatUqBAYGdlhNRERERESmhrmciIiIiAxB1MXzu3fvYv78+XB3d4eVlRVcXFwQERGBnJwcjX6nTp3C448/Djs7Ozg4OGDUqFG4d+9eq+aQSCTqZmdnh/79+yM6Ohrnz59vccz169dhb28PJycnIZdHREREZPIa/neHi76NOgZzOREREVHXJjSXm2o2F3XxPCoqCt9++y0yMzPx3Xff4cCBAxg9ejRKS0vVfU6dOoXIyEiMHz8eZ8+exblz5xAfHw+ptPWlb9++Hbdv38a///1vbN68GdXV1QgJCcGOHTua9K2vr8fMmTPx6KOPtss1EhEREZkypVIJpULPpjTNgC4G5nIiIiKirk1QLjfhbC7a4nl5eTlOnDiBdevWYcyYMfDw8EBwcDASEhIwefJkdb/Fixdj4cKFWLZsGfz8/DBw4EBMnz4dVlZWrZ7LyckJLi4u6NevH8aPH489e/bgqaeeQnx8PH755ReNvitWrMCgQYMwffr0Vp///PnzCAoKgq2tLUaMGIGrV6826fPee+/Bzc0Ntra2mD59OioqKtTHFAoFkpKS8NBDD8HKygqBgYE4dOhQi/PJ5XJUVlZqNCIiIiJjpFAoBTUyPOZy/XM5wGxOREREnYPQXG6q2Vy0xXOZTAaZTIb9+/dDLpc32+fOnTs4c+YMnJ2dMWLECPTu3RuPPfYYvvnmG8HzL168GFVVVThy5Ij6s2PHjuHTTz/F5s2b23Suv/zlL9iwYQNyc3Nhbm6OOXPmaBy/fv06PvnkExw8eBCHDh3Ct99+ixdeeEF9fNOmTdiwYQNSUlKQl5eHiIgITJ48GdeuXWt2vrVr18LR0VHd3Nzc2lQvEREREZEKc7n+uRxgNiciIiLqykRbPDc3N0dGRgYyMzPh5OSEsLAwLF++HHl5eeo+33//PYDGl/vExsbi0KFDGD58OMLDw7UG2NYYNGgQAODGjRsAgNLSUkRHRyMjIwMODg5tOtcbb7yBxx57DL6+vli2bBlOnjyJ+/fvq4/fv38fO3bsQGBgIEaNGoV33nkHH3/8MYqLiwEAKSkpeO211zBjxgwMHDgQ69atQ2BgIFJTU5udLyEhARUVFepWWFjY9m8AERERUQdQKpWCGhkec7n+uRxgNiciIqLOQWguN9VsLvqe50VFRThw4AAiIyORnZ2N4cOHIyMjA0DjY5MAMG/ePMyePRvDhg3Dxo0bMXDgQKSnpwuaW/V/uEQiAQDExsZi1qxZGDVqVJvP5e/vr/6zq6srgMa7c1Tc3d3Rt29f9dehoaFQKBS4evUqKisrUVRUhLCwMI1zhoWFIT8/v9n5rKys4ODgoNGIiIiIjJGgfRVN9NFQMTCX65fLAWZzIiIi6hyE5nJTzeaiLp4DgLW1NcaNG4eVK1fi5MmTiI6ORmJiIoBfA6+vr6/GmMGDB+PWrVuC5lUFYE9PTwCNj4ampKTA3Nwc5ubmiImJQUVFBczNzXX+QGBhYaH+syr0q37AICIiIiLqDJjLiYiIiIg0ib54/nu+vr6oqakBAPTr1w99+vRp8qKf7777Dh4eHoLmSU1NhYODA8aOHQsAOHXqFC5evKhuSUlJsLe3x8WLF/GnP/1J0Fy3bt1CUVGR+uvTp09DKpVi4MCBcHBwQJ8+fZCTk6MxJicnp8kPJ0RERESdDV9K1HkxlzdiLiciIqKugC8M1Y+5WBOXlpZi2rRpmDNnDvz9/WFvb4/c3FwkJydjypQpABrvFlm6dCkSExMREBCAwMBAZGZm4r///S/27NnT6rnKy8tRXFwMuVyO7777Du+99x7279+PHTt2wMnJCUDjXTO/lZubC6lUiiFDhgi+Vmtrazz33HNISUlBZWUlFi5ciOnTp8PFxQUA1Nfo7e2NwMBAbN++HRcvXsRHH30keG4iIiIiMSkVjU3fsWR4zOXM5URERNT1CcnlqvGmSLTFc5lMhpCQEGzcuBEFBQWor6+Hm5sbYmNjsXz5cnW/l156Cffv38fixYtRVlaGgIAAHDlyBN7e3q2ea/bs2QAaw3Lfvn0xcuRInD17FsOHD2/362qOj48PnnzySUycOBFlZWX44x//iL///e/q4wsXLkRFRQVeeeUV3LlzB76+vjhw4AD69+/fIfURERERGYqQlwuZ6kuJOhpzOXM5ERERdX1CX/ppqtlcojTVK+9CKisr4ejoiLKz/4SDzE7sclqUZ++vu5OIfO3kYpeg08NvnBW7BJ2Oe38jdgla3ZuxUuwSdKprMP5/Ld+Omih2CTp1+8cXYpeg1YB718QuQacFXlFil6DT4G+OiV2CVr2n/lHsElpUq2hATNl3qKioMMgLDlX5JDjxAMyt9csnD+7X4OzqyQarkcgQVH/3w974Qu+/+x3h//3BXewStHK2sxS7BJ2m/PK12CXodHLxO2KXoJWTp5PYJehUn7pb7BJ0Gi65LXYJOv1oa9z/zvm7S4DYJehU+cD4b/t9e98isUvQ6rWoTWKX0KI6pQJbUWiQ3NseuRww3Wwu2p3nRERERNT1Cdkf0VT3VSQiIiIiam9C9y031WxudC8MbYs333wTMpms2TZhwgSxyyMiIiIyeUqFUlCjzoG5nIiIiMi4Cc3lpprNO/Wd53FxcZg+fXqzx2xsbDq4GiIiIiJqQkjQNtGA3hkxlxMREREZOaEL4CaazTv14nn37t3RvXt3scsgIiIiIjJpzOVERERE1BV16sVzIiIiIjJuCqUSEj3fT6/ge+2JiIiIiNqFkFyuGm+KuHhORERERAajVOr/eKjSRAM6EREREVF7E5LLVeNNERfPu5CAtNuQWtqKXUaLFs95SOwStIra8a3YJej0RV2m2CXoZBUSJnYJWn3uHSR2CTp9vi5D7BJ0yrZ6XOwSdJp54nuxS9AqJ7dY7BJ0ivnmmNgl6JQ/0rj/Ln6+aafYJbTowf0a4DXDv8hRyMuFTPWlRNQ15J84D6mFtdhltOj851+IXUKndzNprtgl6OT245til6BV7rUysUvQadXMJLFL0Cnkz1Fil6DTub3G/bNs2dnNYpegU/2N/4pdgk4vP7lJ7BK0Slg2WuwSWlQlr8PWjbsNOofQl36aajaXil0AEREREREREREREZGx4Z3nRERERGQwCgUg0fMuFYWinYshIiIiIjJRQnK5arwp4uI5ERERERmMUqnUe39EU91XkYiIiIiovQnJ5arxpojbthARERERERERERER/Q7vPCciIiIig1EqGpu+Y4mIiIiISDghuVw13hRx8ZyIiIiIDEahUArY89w0Hw0lIiIiImpvQnK5arwp4rYtRERERGQwSoVSUDOUsrIyPPXUU3BwcICTkxNiYmJQXV3dYv8bN25AIpE02z799FN1v+aOf/zxxwa7DiIiIiKi1hCay001m/POcyIiIiIyOU899RRu376NI0eOoL6+HrNnz8bcuXOxa9euZvu7ubnh9u3bGp9t3boV69evx4QJEzQ+3759OyIjI9VfOzk5tXv9RERERERdhTFnc955LoDqtxwXL15ssU9GRgZ/YCIiIiKT1R53t1RWVmo0uVwuqKb8/HwcOnQIH3zwAUJCQjBy5Ei88847+Pjjj1FUVNTsGDMzM7i4uGi0zz77DNOnT4dMJtPo6+TkpNHP2tpaUL2kG3M5ERERkXbtdee5qWVzURfP7969i/nz58Pd3R1WVlZwcXFBREQEcnJyNPqdOnUKjz/+OOzs7ODg4IBRo0bh3r17rZrjt7fl29nZoX///oiOjsb58+eb9FUqlUhJScGAAQNgZWWFvn374o033miXayUiIiIyRQqlUlADGu8scXR0VLe1a9cKqunUqVNwcnJCUFCQ+rOxY8dCKpXizJkzrTrH+fPncfHiRcTExDQ5tmDBAvTs2RPBwcFIT0+HUmn8+0MylxMRERF1bUJzualmc1G3bYmKikJdXR0yMzPh5eWFkpISZGVlobS0VN3n1KlTiIyMREJCAt555x2Ym5vj0qVLkEpbv+6vuj3//v37+O6777B161aEhIQgPT0dzz77rLrfokWL8NVXXyElJQVDhw5FWVkZysrK2vWaiYiIiEyJkP0RVeMKCwvh4OCg/tzKykpQTcXFxXB2dtb4zNzcHN27d0dxcXGrzrFt2zYMHjwYI0aM0Pg8KSkJjz/+OGxtbfHVV1/hhRdeQHV1NRYuXCioZkNjLiciIiLq2oTuW26q2Vy0xfPy8nKcOHEC2dnZeOyxxwAAHh4eCA4O1ui3ePFiLFy4EMuWLVN/NnDgwDbNpbo9HwD69euH8ePH47nnnkN8fDyeeOIJdOvWDfn5+Xj33Xdx5coV9fk9PT1bdf7vv/8eixcvxpkzZ9C/f39s2bIFoaGhGn3279+PpUuXorCwEI899hg++OADuLm5qY+/++67SElJQWFhITw9PbFixQo888wzzc4nl8s1HomorKxs0/eDiIiIqDNxcHDQCOgtWbZsGdatW6e1T35+vuB67t27h127dmHlypVNjv32s2HDhqGmpgbr16836sVz5nL9cznAbE5ERESmxdSyuWjbtshkMshkMuzfv7/FvXHu3LmDM2fOwNnZGSNGjEDv3r3x2GOP4ZtvvhE8/+LFi1FVVYUjR44AAA4ePAgvLy/83//9Hzw9PdGvXz88//zzrbrD5S9/+QuWLFmCixcvYsCAAZg5cyYePHigPl5bW4s33ngDO3bsQE5ODsrLyzFjxgz18c8++wyLFi3CK6+8gitXrmDevHmYPXs2vv7662bnW7t2rcbjEb8N+0RERETGRKkUsK9iGx+pfOWVV5Cfn6+1eXl5wcXFBXfu3NEY++DBA5SVlakXdrXZs2cPamtrNe6UbklISAh+/PFHwXtBGhJzuf65HGA2JyIios5BUC434Wwu2uK5ubk5MjIykJmZCScnJ4SFhWH58uXIy8tT9/n+++8BAKtWrUJsbCwOHTqE4cOHIzw8HNeuXRM0/6BBgwA0vlxINdfNmzfx6aefYseOHcjIyMD58+fx//7f/9N5riVLlmDSpEkYMGAAVq9ejZs3b+L69evq4/X19fjb3/6G0NBQPPzww8jMzMTJkydx9uxZAEBKSgqio6PxwgsvYMCAAXj55Zfx5JNPIiUlpdn5EhISUFFRoW6FhYWCvhdEREREhqJUKKHQs7X1sdJevXph0KBBWpulpSVCQ0NRXl6usdf2sWPHoFAoEBISonOebdu2YfLkyejVq5fOvhcvXkS3bt0EP85qSMzl+udygNmciIiIOgchudyUs7moLwyNiopCUVERDhw4gMjISGRnZ2P48OHIyMgAACgUCgBQ3/ExbNgwbNy4EQMHDkR6erqguVW/LZFIJOq55HI5duzYgUcffRSjR4/Gtm3b8PXXX+Pq1ataz+Xv76/+s6urKwBo/MbE3NwcjzzyiPrrQYMGwcnJSf1oQn5+PsLCwjTOGRYW1uKjC1ZWVupHJFr7qAQRERERNRo8eDAiIyMRGxuLs2fPIicnB/Hx8ZgxYwb69OkDAPjpp58waNAg9aKqyvXr13H8+HE8//zzTc578OBBfPDBB7hy5QquX7+Od999F2+++SZefPHFDrkuIZjL9cvlALM5ERERkRDGns1FXTwHAGtra4wbNw4rV67EyZMnER0djcTERAC/Bl5fX1+NMYMHD8atW7cEzasKwKr9E11dXWFubo4BAwZozANA51wWFhbqP/829BMRERGZOqVSKagZykcffYRBgwYhPDwcEydOxMiRI7F161b18fr6ely9ehW1tbUa49LT0/HQQw9h/PjxTc5pYWGBzZs3IzQ0FIGBgXjvvffw9ttvq7OtsWMuJyIiIuq6hOZyU83moi+e/56vry9qamoANL5EqE+fPk3uMPnuu+/g4eEhaJ7U1FQ4ODhg7NixABrvKHnw4AEKCgo05gEgeK4HDx4gNzdX/fXVq1dRXl6u/iFg8ODByMnJ0RiTk5PT5IcTIiIios5G0L6KbXw0tC26d++OXbt2oaqqChUVFUhPT4dMJlMf79evH5RKJUaPHq0x7s0338StW7cglTaN0ZGRkfj2229RVVWF6upqXLx4EfPmzWu2b2fAXN6IuZyIiIi6AqG53FSzubleV9QOSktLMW3aNMyZMwf+/v6wt7dHbm4ukpOTMWXKFACNd4ssXboUiYmJCAgIQGBgIDIzM/Hf//4Xe/bsafVc5eXlKC4uhlwux3fffYf33nsP+/fvx44dO+Dk5AQAGDt2LIYPH445c+YgNTUVCoUCCxYswLhx4zTuetGHhYUFXnzxRaSlpcHc3Bzx8fH4wx/+gODgYADA0qVLMX36dAwbNgxjx47FwYMHsW/fPhw9elTQvERERERiUyiUgJ5BW2HAgE6/Yi5nLiciIqKuT0guV483QaItnstkMoSEhGDjxo0oKChAfX093NzcEBsbi+XLl6v7vfTSS7h//z4WL16MsrIyBAQE4MiRI/D29m71XLNnzwbQ+Chq3759MXLkSJw9exbDhw9X95FKpTh48CBefPFFjBo1CnZ2dpgwYQI2bNgg+FptbW3x2muvYdasWfjpp5/w6KOPYtu2berjU6dOxaZNm5CSkoJFixbB09MT27dvb/LbFCIiIiKi9sZczlxORERERM0TbfHcysoKa9euxdq1a3X2XbZsGZYtW6bXPG3Zj6dPnz7Yu3dvq/urHhn4LScnJ43PoqOjER0dDQB48sknWzzX/PnzMX/+/FbPTURERNQZKBUNUCoa9B5Lhsdcrom5nIiIiLoiIblcNd4UibZ4TkRERERdHxfPiYiIiIjEx8Vz/XTOtxf9z5tvvgmZTNZsmzBhgtjlEREREZk8pUKhDuptbwqxy6dWYi4nIiIiMm7CcrnpZvNOfed5XFwcpk+f3uwxGxubDq6GiIiIiMg0MZcTERERUVfUqRfPu3fvju7du4tdBhERERG1QNnQAGWDntu26DmOOh5zOREREZFxE5LLVeNNUadePCciIiIi46ZUCtjzXGmaAZ2IiIiIqL0JyeWq8aaoU+95TkRERERERERERERkCLzzvAu5tNAVDjI7sctoUWa1cf91u5o0TOwSdOr93L/FLkGnkkejxC5Bq8EXYsUuQadHrMzELkGnW2/ME7sEnfo9+rLYJWj1TqiV2CXotGfYH8UuQafPN+0UuwStBix6RuwSWlQHBU50wDyqFwzpO5aos+o9YCDMrGzFLqNFgwc/JnYJWrk6WYtdgk4vOxeJXYJOn0slYpeg1TAPR7FL0Gnd+kVil6DT8/7Gv23W4XAfsUvQam3Qo2KXoNNd+QOxS9ApYdlosUvQau1b2WKX0KI6GP5lnEJyuWq8KTLu1UwiIiIi6tS4eE5EREREJD4unuuHi+dEREREZDBcPCciIiIiEh8Xz/XDPc+JiIiIiIiIiIiIiH6Hd54TERERkcEoFQoBd54bfu9HIiIiIiJTICSXq8abIi6eExEREZHBKBQNgJ4hXWGij4YSEREREbU3IblcPd4EcfGciIiIiAyGe54TEREREYmPe57rh3ueExERERERERERERH9Du88JyIiIiKD4Z3nRERERETi453n+uGd5wJkZ2dDIpGgvLy8xT6rVq1CYGBgh9VEREREZFQaGqDUs6HBNAM6tR1zOREREZEOAnK5KWdzURfP7969i/nz58Pd3R1WVlZwcXFBREQEcnJyNPqdOnUKjz/+OOzs7ODg4IBRo0bh3r17rZpDIpGom52dHfr374/o6GicP39eo9+qVas0+v52DBERERFRV8ZcTkRERETUlKjbtkRFRaGurg6ZmZnw8vJCSUkJsrKyUFpaqu5z6tQpREZGIiEhAe+88w7Mzc1x6dIlSKWtX/ffvn07IiMjcf/+fXz33XfYunUrQkJCkJ6ejmeffRYAsGTJEsTFxWmMCw8PxyOPPNI+F0tERERkgpTKBkDfbVuUpnl3ixiYy4mIiIi6NiG5XD3eBIl253l5eTlOnDiBdevWYcyYMfDw8EBwcDASEhIwefJkdb/Fixdj4cKFWLZsGfz8/DBw4EBMnz4dVlZWrZ7LyckJLi4u6NevH8aPH489e/bgqaeeQnx8PH755RcAgEwmg4uLi7qVlJTgP//5D2JiYnSe//z58wgKCoKtrS1GjBiBq1evNunz3nvvwc3NDba2tpg+fToqKirUxxQKBZKSkvDQQw/BysoKgYGBOHToUIvzyeVyVFZWajQiIiIiY6RUKNT7K7a9KcQu3yQwl+ufywFmcyIiIuochOVy083moi2ey2QyyGQy7N+/H3K5vNk+d+7cwZkzZ+Ds7IwRI0agd+/eeOyxx/DNN98Inn/x4sWoqqrCkSNHmj3+wQcfYMCAAXj00Ud1nusvf/kLNmzYgNzcXJibm2POnDkax69fv45PPvkEBw8exKFDh/Dtt9/ihRdeUB/ftGkTNmzYgJSUFOTl5SEiIgKTJ0/GtWvXmp1v7dq1cHR0VDc3N7c2XDkRERFRxxEW0E3z7paOxlyufy4HmM2JiIiocxCay001m4u2eG5ubo6MjAxkZmbCyckJYWFhWL58OfLy8tR9vv/+ewCN+x7Gxsbi0KFDGD58OMLDw7UG2NYYNGgQAODGjRtNjt2/fx8fffRRq+5uAYA33ngDjz32GHx9fbFs2TKcPHkS9+/f1zjfjh07EBgYiFGjRuGdd97Bxx9/jOLiYgBASkoKXnvtNcyYMQMDBw7EunXrEBgYiNTU1GbnS0hIQEVFhboVFha27eKJiIiIiP6HuVz/XA4wmxMRERF1ZaK+MDQqKgpFRUU4cOAAIiMjkZ2djeHDhyMjIwNA42OTADBv3jzMnj0bw4YNw8aNGzFw4ECkp6cLmlupVAJofHHR73322WeoqqrCc88916pz+fv7q//s6uoKoPHuHBV3d3f07dtX/XVoaCgUCgWuXr2K/9/enYdHVZ/9H//MJCEhy0BAIGEJhLAkgBKUihgsZUe0LlBFRGWJICiiiFShItAirYJKebSKRRaf6q/WVhFsC2WJioiymWg1bDEa+mAERJKwJARy//5IMzLZQOZMvveX+byuK9fFzAnJO9/hnLk5DGcKCwtx4MABpKWl+XzNtLQ0ZGdnV/v9wsPD4fF4fD6IiIiINCr/76EX/kF1g3P5hc3lAGdzIiIisoO/c3mwzuZGT54DQEREBAYMGICZM2fiww8/xOjRozFr1iwAPwy8nTp18vk9KSkpyMvL8+v7VgzAiYmJVbYtWbIE119/PZo1a3ZeXyssLMz764qhvyxI/0ARERERnY3/NdQenMuJiIiILl68bMuFMX7yvLJOnTrh+PHjAIA2bdqgefPmVd7oZ8+ePWjdurVf32fhwoXweDzo37+/z/25ubnIyMg47/8aej7y8vJw4MAB7+2PPvoIbrcbHTt2hMfjQfPmzbF582af37N58+YqfzkhIiIisg0HdHtxLi/HuZyIiIguBjx5fmFCTX3j7777DrfccgvGjh2Lyy67DDExMdi+fTueeuop3HjjjQDKXy0ybdo0zJo1C127dkVqaipWrFiBXbt24a9//et5f6+jR48iPz8fJSUl2LNnDxYvXoyVK1filVdeQcOGDX0+d+nSpYiPj8e1117r2M8aERGBUaNGYcGCBSgsLMTkyZNx6623Ii4uDgC8P2NSUhJSU1OxbNkyZGZm4tVXX3WsgYiIiIioOpzLOZcTERERUfWMnTyPjo5Gjx498OyzzyInJwelpaVo1aoVxo0bhxkzZng/78EHH0RxcTGmTJmCI0eOoGvXrli3bh2SkpLO+3uNGTMGQPmw3KJFC/Tq1Qtbt27F5Zdf7vN5ZWVlWL58OUaPHo2QkBBnflAA7dq1w9ChQzFkyBAcOXIE119/Pf7whz94t0+ePBkFBQWYOnUqDh48iE6dOmHVqlVo3769Yw1EREREJpSVnYHrAl+lEqyvbqlrnMs5lxMREdHFz5+5HAje2dwlFe/QQ9YqLCxEgwYNcGTrP+CJjjKdU6MVx9qYTqjVnQmnTSecU7NRfzKdcE7fLr/ddEKtdhQ3Mp1wTtHhzp0kCJS8nj81nXBObT5633RCrTqcPnDuTzLsr92GmU44p5dmLjadUKsOD9xpOqFGp1CGZdiPgoKCgLzBYcV8EtH9HrhC613Q15DTp1C8fXHAGokCoeLPfsrk1xESHmk6p0YpKU1NJ9QqvmGE6YRzeib5qOmEc3r7ZxNNJ9TKc0l90wnntOuPb5hOOKe7L9P/d5y1X58wnVCrrO7XmE44p0Ml+s9ZPPzIz0wn1Oq3v3vXdEKNAjmbOzGXA8E7mxt75TkRERERXfxEzgAX+spzCc5XtxAREREROc2fudz7+4OQujcM/THmzZuH6Ojoaj+cvDYiERERERHVjHM5EREREV2MrD55PmHCBGRmZlb7sWTJEtN5REREREFPys749REoTzzxBK6++mpERkZWeaPKGn8WETz++OOIj49H/fr10b9/f+zdu9fnc44cOYKRI0fC4/GgYcOGSE9Px7FjxwLwE+jCuZyIiIhIN3/n8mCdza2+bEujRo3QqJH+a3sRERERBSspOwO49L1h6KlTp3DLLbegZ8+eePnll8/r9zz11FNYtGgRVqxYgcTERMycORODBg3CF198gYiI8uszjxw5Et988w3WrVuH0tJSjBkzBuPHj8drr70WsJ9FA87lRERERLr5M5d7f3+AaJ7NrT55TkRERER0IebMmQMAWL58+Xl9vohg4cKFeOyxx3DjjTcCAF555RU0a9YMK1euxG233Ybs7GysWbMG27ZtQ/fu3QEA//M//4MhQ4ZgwYIFaN68eUB+FiIiIiIim2mezXny/CIgIgCAwmO637365LEi0wm1KizS/87VcrrYdMI5FRbpfpyPF1tw2DsVYrrgnE4E8F+cnXKsqNB0Qq0KT+u/jIMNj/Pp4uOmE2p1CmWmE2pU0VYxRwSKlBZf+KtUzpQCAAoLfffn8PBwhIeH+5v2o+Tm5iI/Px/9+/f33tegQQP06NEDW7ZswW233YYtW7agYcOG3uEcAPr37w+3242PP/4YN998c502kxkV+9SZEt2zeelJ3c9DJfX0z+aFx3Q/BwH6n8tDzujuA4CTx3X//QYACgv1/x3nxLGTphNqVSx6Z7YKpyxoLCo5ZTqhVsE+m/s1lwNBO5vrP8LSORX992Rlm76/MFxit0mmAy4Sl3T4rekEonIdEkwXUF14RPcbEW4yHXAeioqK0KBBA8e/br169RAXF4f8L/7i19eJjo5Gq1atfO6bNWsWZs+e7dfX/bHy8/MBAM2aNfO5v1mzZt5t+fn5aNq0qc/20NBQNGrUyPs5dPGrmM33LB5juKR22aYDLgKLTQdcDA6bDjgPA7uaLjinX5oOIPqvl579f6YTrBeI2dypuRwIztmcJ88vAs2bN8f+/fsRExMDl8vl99crLCxEq1atsH//fng8HgcKncdG/2nvA9joBO19ABudoL0PYKMTnO4TERQVFQXsUiIRERHIzc3FqVP+vQJJRKrMNzW9suXRRx/Fk08+WevXy87ORnJysl9NRLXhbK6vUXsfoL9Rex/ARido7wPY6ATtfUBwNgZyNndqLgeCczbnyfOLgNvtRsuWLR3/uh6PR+1BqgIb/ae9D2CjE7T3AWx0gvY+gI1OcLIvEK84P1tERIT3zXrqwtSpUzF69OhaP6dt27YX9LXj4uIAAN9++y3i4+O993/77bdITU31fs7Bgwd9ft/p06dx5MgR7++nix9nc72N2vsA/Y3a+wA2OkF7H8BGJ2jvA4KvMZCzeV3P5cDFM5vz5DkRERERXRSaNGmCJk2aBORrJyYmIi4uDhs2bPAO5IWFhfj4448xceJEAEDPnj1x9OhR7NixA1dccQUAYOPGjSgrK0OPHj0C0kVEREREpNHFMpu7Ha8nIiIiIlIuLy8PmZmZyMvLw5kzZ5CZmYnMzEwcO/bDmygmJyfjrbfeAgC4XC48+OCDmDt3LlatWoXPPvsMd911F5o3b46bbroJAJCSkoLBgwdj3Lhx2Lp1KzZv3oxJkybhtttuC9jlcYiIiIiIbKd5Nucrz6mK8PBwzJo1q87fKffHYKP/tPcBbHSC9j6AjU7Q3gew0Qna+2zz+OOPY8WKFd7b3bp1AwBkZGTgZz/7GQBg9+7dKCgo8H7OL3/5Sxw/fhzjx4/H0aNH0atXL6xZs8bnv8C++uqrmDRpEvr16we3241hw4Zh0aJFdfND0UXJhn1fe6P2PkB/o/Y+gI1O0N4HsNEJ2vsANgYjzbO5S0TEj5+NiIiIiIiIiIiIiOiiw8u2EBERERERERERERFVwpPnRERERERERERERESV8OQ5EREREREREREREVElPHlORERERERERERERFQJT54TEREREREREREREVUSajqAdDh69ChefvllZGdnAwA6d+6MsWPHokGDBobLfsBG/2nvA+xozMnJwcKFC72NnTp1wgMPPICkpCTDZeVsWEM2+k97H6B/XwGA9957DwsWLPBpnDZtGq655hrDZeVsWEMicp4Nx3jtjdr7ADY6wYbnSe1rqL0PYKMTbNhXtM/lgB3rSM5ziYiYjiCztm/fjkGDBqF+/fq48sorAQDbtm3DyZMn8a9//QuXX3654UI2BkMfYEfj2rVrccMNNyA1NRVpaWkAgM2bNyMrKwurV6/GgAEDjPbZsIZsvPj7AP37CgD86U9/wpgxYzB06FCfxrfeegvLly/H7bffbrTPhjUkIufZcIzX3qi9D2CjE2x4ntS+htr7ADY6wYZ9RftcDtixjhQgQkGvV69eMnr0aCktLfXeV1paKqNGjZJrrrnGYNkP2Og/7X0idjSmpqbKI488UuX+Rx55RLp162agyJcNa8hG/2nvE9G/r4iIJCcnyzPPPFPl/qefflqSk5MNFPmyYQ2JyHk2HOO1N2rvE2GjE2x4ntS+htr7RNjoBBv2Fe1zuYgd60iBwZPnJBEREZKdnV3l/s8//1zq169voKgqNvpPe5+IHY3h4eGyZ8+eKvfv3r1bwsPDDRT5smEN2eg/7X0i+vcVEZF69erJ3r17q9y/d+9eFY02rCEROc+GY7z2Ru19Imx0gg3Pk9rXUHufCBudYMO+on0uF7FjHSkw+IahBI/Hg7y8vCr379+/HzExMQaKqmKj/7T3AXY0NmnSBJmZmVXuz8zMRNOmTes+qBIb1pCN/tPeB+jfVwCgVatW2LBhQ5X7169fj1atWhko8mXDGhKR82w4xmtv1N4HsNEJNjxPal9D7X0AG51gw76ifS4H7FhHCgy+YShh+PDhSE9Px4IFC3D11VcDKL9u07Rp0zBixAjDdeXY6D/tfYAdjePGjcP48ePx5Zdf+jQ++eSTeOihhwzX2bGGbPSf9j5A/74CAFOnTsXkyZORmZnp07h8+XL8/ve/N1xnxxoSkfNsOMZrb9TeB7DRCTY8T2pfQ+19ABudYMO+on0uB+xYRwoQ0y99J/NKSkpk8uTJUq9ePXG73eJ2uyU8PFwefPBBKS4uNp0nImwMhj4ROxrLysrkmWeekRYtWojL5RKXyyUtWrSQhQsXSllZmek8K9aQjRd/n4j+faXCm2++KWlpadKoUSNp1KiRpKWlycqVK01niYg9a0hEzrLhGK+9UXufCBudYMPzpPY11N4nwkYn2LCviOiey0XsWUdynktExPQJfNLhxIkTyMnJAQAkJSUhMjLScFFVbPSf9j7AjkYAKCoqAgAV/xWvMhvWkI3+095XQfO+YguuIVHwseEYr71Rex/ARqdof57Uvoba+wA2OkX7vmILrmNw4WVbyCsyMhINGzb0/lojNvpPex9gR+PBgwexe/duAEBycjKaNGliuMiXDWvIRv9p7wP07ysAsH37dmRnZwMAOnXqhCuuuMJwkS8b1pCInGfDMV57o/Y+gI1OsOF5Uvsaau8D2OgEG/YV7XM5YMc6ksNMv/SdzCstLZXHHntMPB6P978YeTwe+dWvfiWnTp0ynScibAyGPhE7GgsLC+WOO+6QkJAQ73/VCg0NlZEjR8rRo0dN51mxhmy8+PtE9O8rIiL79++XXr16icvlktjYWImNjRWXyyVpaWmyf/9+03lWrCEROc+GY7z2Ru19Imx0gg3Pk9rXUHufCBudYMO+on0uF7FjHSkwePKcZMKECdK0aVN58cUXJSsrS7KysuTFF1+UuLg4mTBhguk8EWFjMPSJ2NF46623Svv27WXNmjVSUFAgBQUFsmbNGunYsaMMHz7cdJ4Va8jGi79PRP++IiIyaNAg6dGjh+zatct7365du6Rnz54yaNAgg2XlbFhDInKeDcd47Y3a+0TY6AQbnie1r6H2PhE2OsGGfUX7XC5ixzpSYPDkOYnH45F//OMfVe7/+9//Lh6Px0BRVWz0n/Y+ETsaIyMjZdOmTVXuf//99yUyMtJAkS8b1pCN/tPeJ6J/XxERiYiIkJ07d1a5f/v27VK/fn0DRb5sWEMicp4Nx3jtjdr7RNjoBBueJ7WvofY+ETY6wYZ9RftcLmLHOlJguE1fNobMCw8PR5s2barcn5iYiHr16tV9UDXY6D/tfYAdjY0bN0aDBg2q3N+gQQPExsYaKPJlwxqy0X/a+wD9+woAtGrVCqWlpVXuP3PmDJo3b26gyJcNa0hEzrPhGK+9UXsfwEYn2PA8qX0NtfcBbHSCDfuK9rkcsGMdKTB48pwwadIk/OY3v0FJSYn3vpKSEjzxxBOYNGmSwbIfsNF/2vsAOxofe+wxPPTQQ8jPz/fel5+fj2nTpmHmzJkGy8rZsIZs9J/2PkD/vgIA8+fPx/3334/t27d779u+fTseeOABLFiwwGBZORvWkIicZ8MxXnuj9j6AjU6w4XlS+xpq7wPY6AQb9hXtczlgxzpSYISaDiAzhg4d6nN7/fr1aNmyJbp27QoAyMrKwqlTp9CvXz8TeQDY6ATtfYAdjd26dYPL5fLe3rt3LxISEpCQkAAAyMvLQ3h4OA4dOoR77rmnzvtsWEM2+k97H6B/XwGA2NhYn8bjx4+jR48eCA0tH4lOnz6N0NBQjB07FjfddFOd99mwhkTkPBuO8dobtfcBbHSCDc+T2tdQex/ARifYsK9on8sBO9aRAo8nz4NU5f9qMmzYMJ/brVq1qsucarHRf9r7ADsaTT1Rny8b1pCN/tPeB+jfVwBg4cKFphNqZcMaEpHzbDjGa2/U3gew0Qk2PE9qX0PtfQAbnWDDvqJ9LgfsWEcKPJeIiOkIIiIiIiIiIiIiIiJN+MpzIqILcOrUKRw8eBBlZWU+91f89y0iKqd9XykrK8O+ffuqbfzpT39qqMqX9jUkIiIyic+TROdH+75iw1wO6F9HCgChoJefny933HGHxMfHS0hIiLjdbp8PDdh48feJ2NG4e/du6dWrV5U2l8ulotGGNWTjxd8non9fERHZsmWLJCYmervO/tDQaMMaEpHzbDjGa2/U3ifCRifY8DypfQ2194mw0Qk27Cva53IRO9aRAoOvPCeMHj0aeXl5mDlzJuLj433eDEELNvpPex9gR+OYMWMQGhqKd955R2WjDWvIRv9p7wP07ysAMGHCBHTv3h1///vfVTbasIZE5DwbjvHaG7X3AWx0gg3Pk9rXUHsfwEYn2LCvaJ/LATvWkQKD1zwnxMTEYNOmTUhNTTWdUiM2+k97H2BHY1RUFHbs2IHk5GTTKdWyYQ3Z6D/tfYD+fQUob8zKykK7du1Mp1TLhjUkIufZcIzX3qi9D2CjE2x4ntS+htr7ADY6wYZ9RftcDtixjhQYbtMBZF6rVq2g/d9Q2Og/7X2AHY2dOnXC4cOHTWfUyIY1ZKP/tPcB+vcVAOjRowf27dtnOqNGNqwhETnPhmO89kbtfQAbnWDD86T2NdTeB7DRCTbsK9rncsCOdaQAqdurxJBGa9eulYEDB0pubq7plBqx0X/a+0T0NhYUFHg/NmzYID179pSMjAw5fPiwz7aCggLTqWrX8Gxs9J/WPhv2laysLO/Hm2++KZ06dZJly5bJ9u3bfbZlZWUZ6bNhDYkosLQe48+mvVF7nwgbL5Rtz5Ma1/Bs2vtE2HihbNhXtM/lInasIwUeL9sSpGJjY32uz3T8+HGcPn0akZGRCAsL8/ncI0eO1HUeADY6QXsfYEej2+32aRSRKtc3q7jvzJkzdZ1nxRqy0X/a+wD9+wrwQ2NN40/FNlONNqwhETnPhmO89kbtfQAbnWDD86T2NdTeB7DRCTbsK9rncsCOdaTA4xuGBqmFCxeaTjgnNvpPex9gR2NGRobphFrZsIZs9J/2PkD/vgIAubm5phNqZcMaEpHzbDjGa2/U3gew0Qk2PE9qX0PtfQAbnWDDvqJ9LgfsWEcKPL7ynIiIiIiIiIiIiIioEr5hKBERERERERERERFRJTx5TkRERERERERERERUCU+eExERERERERERERFVwpPnRERERERERERERESV8OQ5UYC8++67OHnypOkMq2lfw5KSEuTk5KCkpMR0itW0P87kjOXLl6OgoMB0Ro327t2LDRs2YN++faZTiIjIYZw1nKF5HTmXO0PzY0zO4VxO9OPw5HmQy8rKwty5c/GHP/wBhw8f9tlWWFiIsWPHGir7wZIlSzBq1CgsW7YMAPD6668jJSUFbdu2xaxZswzX1WzgwIH46quvTGcAsONxro6mNVy+fDm2bNkCACguLkZ6ejqioqLQoUMHREdHY8KECcaHde4r/jt48KDP7czMTIwaNQppaWn4xS9+gXfffddM2H9deuml+M1vfoP9+/cb7bgQ48ePx4EDB0xnAAB++9vfYsOGDQCA77//Hv3790fHjh0xYMAAdOzYEddeey2OHj1qrC8mJgbp6en48MMPjTUQkRk2zGw2zhuaZg3Ajse5OlrW0Ya5HOC+4i/tczlg72zOufzH4WxOEApaa9eulXr16knnzp0lISFBGjduLBs3bvRuz8/PF7fbbbBQ5Nlnn5WoqCgZOnSoxMfHy9y5c6Vx48Yyd+5cmTNnjng8Hlm8eLHRxm7dulX74XK5JCUlxXvbFBseZ+1rKCKSmJgoH330kYiIPPzww9KmTRt58803JTs7W1auXCkdOnSQadOmGevjvuIMt9st3377rYiIbN68WcLCwqR3794ybdo0GTBggISGhsp7771nrM/lcknjxo0lJCREBg0aJH/961+ltLTUWE91YmNjq/1wuVzSoEED722TWrZsKTt37hQRkbvvvlu6desmO3fulJMnT0pmZqZcddVVkp6ebqzP5XJJ586dxeVySXJysixYsEAOHjxorIeI6oYNM5v2ecOGWcOGx1n7Omqfy0W4rzhB+1wuon8251zuDM7mFGr65D2ZM3v2bDz88MN44oknICKYP38+brjhBrzxxhsYPHiw6TwAwOLFi/HSSy/h9ttvxyeffIIrr7wSL774ItLT0wEALVq0wAsvvIDx48cba/zss8/Qv39/XHXVVd77RARZWVno06cPmjZtaqwNsONx1r6GAHDgwAHEx8cDAFatWoUXXnjBu37JycmIjY3FnXfeiaeeespIH/cVZ4iI99ezZ8/GnXfeiZdfftl734MPPog5c+Z4Xx1hwqeffoqtW7di6dKluO222xAbG4u77roL6enpSElJMdZVobS0FL1798Ytt9zivU9EcPfdd+OXv/wlWrRoYbCu3KFDh9CoUSMAwPr167FixQp069YNANC1a1c899xz+PnPf24yERs3bsQ333yDJUuWYN68eZgxYwauv/563H333Rg8eDBcLpfRPiJyng0zm/Z5w4ZZw4bHWfs6ap/LAe4rTrBhLgd0z+acy53D2TzImThjTzp4PB7Zt2+fz32vvvqqREVFyerVq1W86qF+/fry9ddfe2+Hh4fLv//9b+/tvXv3SsOGDU2keX3wwQeSlJQkjz/+uJw5c8Z7f2hoqHz++ecGy8rZ8DhrX0MRkdatW3tfFdSiRQvZtm2bz/YvvvhCoqKiTKSJCPcVp7hcLu8rXOLj42XLli0+2//973/LJZdcYiJNRHz7REQOHDgg8+bNk/bt24vb7ZaePXvKyy+/bKxPpPzP2k9+8hO56667pKioyHu/pse5Q4cO8s4774hI+avXNm/e7LP9k08+EY/HYyJNRKo+zsXFxfLaa69Jv379xO12S8uWLWXmzJnG+ogoMGyY2bTPGzbMGjY8ztrXUftcLsJ9xQna53IR/bM553JncDYnXvM8iIWHh1e5dtTtt9+OJUuWYPjw4XjrrbfMhJ0lMjISx48f995u0qQJoqOjfT7n9OnTdZ3lIy0tDTt27MCePXtw9dVXIycnx2hPZTY8ztrXEABGjhyJX/3qVzh69CjuvPNO/PrXv8axY8cAACdOnMDs2bORlpZmrI/7inOKiopQWFiIiIgIhIeH+2yLiIjAiRMnDJWhyisa4uPjMX36dOzZswcbNmxAUlISJk+ebKiuXLt27fDhhx8iLi4Oqamp2Lx5s9Ge6owbNw7Tpk3Dvn37MGnSJDz88MPeP4+5ubmYMmUKBg4caKyv8uMcHh6OESNGYP369cjJycHo0aOxfPlyM3FEFDA2zGza5w0bZg0bHmft66h9Lge4rzhF81wO6J/NOZc7g7M58ZXnQWzAgAEyf/78are99tprEhYWZvxVD2lpafLnP/+5xu2rV6+WLl261GFR7ZYuXSpxcXGyePFiCQsLU/GvuTY8zmfTuIYiIiUlJXLDDTdIbGysDBgwQCIiIiQyMlLat28vUVFRkpCQILt37zbWx33FGS6XS9xut7jdbnG5XPLSSy/5bH/77belXbt2huqqvuqhOgUFBXVUc24bNmyQhIQEmT59uqrHWUTk/vvvl7CwMElOTpaIiAhxu91Sr149cbvd0r17d/nmm2+MtZ3P41xWVlZHNURUV2yY2WyaN7TOGjY8zmfTuI7a53IR7itO0D6Xi9g1m3Muv3CczYnXPA9iEydOxPvvv1/tthEjRkBE8Mc//rGOq3w9+eSTiIqKqnF7Xl4e7rnnnjosqt2YMWPQq1cvjBw50virfCvY8DifTeMaAkC9evXw9ttvY82aNVi9ejVCQkJQVlaG+Ph4pKWl4fbbb6/1z2qgcV9xRkZGhs/tiutpVsjNzTV63fhRo0ahfv36tX6Ox+Opo5pz69u3L3bu3Ilx48YhKioKISEhppO8Fi1ahIkTJ+Kdd97Bl19+6bM/9+/f3+h1C2fNmlXl1WmV8bqKRBcfG2Y2m+YNrbOGDY/z2TSuo/a5HOC+4gTtczlg12zOufzCcTYnl8hZ78JARI4oKytDUVERPB4PD6IXiGsYHPg4ExERUSBx1nAG1/Hix8eYiKh6fOU5QUSwY8cOfPXVV3C5XEhMTES3bt1UPWHa2Ni2bVukpqaazvLiGjpD+zpq7wP4ODtBex8AFBQUYN26dT6Pc79+/dS8+gbQ31i5LzExEf3791fTR0SBYcMxXnsjZw1naF9HG9dQW6P2xxjQv4aA/kbtMy9gZyNn8yBSV9eHIZ02btwoiYmJ3uuIVVxXLCkpSd577z3TeSLCxmDoE2FjMPSJsDEY+kRE/vd//1caNGjg7av4aNiwYa3X/6xL2hu19xFRYNhwjNfeqL1PhI3B0Ceiv1F7nwgbnWDDTMlG0o4nz4PY3r17JTIyUvr06SMrV66UXbt2SXZ2tvztb3+T3r17S1RUlOTk5LDR8kbtfWwMjj42BkefiMiOHTskNDRURo0aJZmZmVJcXCwnT56UHTt2yJ133ilhYWGSmZnJRov7iCgwbDjGa2/U3sfG4OizoVF7HxudYcNMyUayAU+eB7H77rtP+vbtW+22srIy6du3r0yaNKmOq3yx0X/a+0TY6ATtfSJsdIL2PhGR0aNHyy9+8Ysatw8bNkzGjBlTh0VVaW/U3kdEgWHDMV57o/Y+ETY6QXufiP5G7X0ibHSCDTMlG8kGPHkexDp37iyrVq2qcfuqVaukc+fOdVhUFRv9p71PhI1O0N4nwkYnaO8TEWnfvr2sW7euxu3r1q2T9u3b12FRVdobtfcRUWDYcIzX3qi9T4SNTtDeJ6K/UXufCBudYMNMyUaygdv0NdfJnLy8PFx66aU1bu/SpQu+/vrrOiyqio3+094HsNEJ2vsANjpBex8AHDhwAB06dKhxe4cOHfB///d/dVhUlfZG7X1EFBg2HOO1N2rvA9joBO19gP5G7X0AG51gw0zJRrIBT54HsWPHjiEyMrLG7ZGRkThx4kQdFlXFRv9p7wPY6ATtfQAbnaC9DwBOnDiBiIiIGreHh4ejuLi4Douq0t6ovY+IAsOGY7z2Ru19ABudoL0P0N+ovQ9goxNsmCnZSDYINR1AZn3xxRfIz8+vdtvhw4fruKZ6bPSf9j6AjU7Q3gew0Qna+wBg7dq1aNCgQbXbjh49WrcxNdDeqL2PiALDhmO89kbtfQAbnaC9D9DfqL0PYKMTbJgp2UjauURETEeQGW63Gy6XC9X9Eai43+Vy4cyZMwbqyrHRf9r7ADY6QXsfwEYnaO8DyhvPhY21095HRIFhyzFec6P2PoCNTtDeB+hv1N4HsNEJNsyUbCQb8JXnQSw3N9d0wjmx0X/a+wA2OkF7H8BGJ2jvA4CysjLTCeekvVF7HxEFhg3HeO2N2vsANjpBex+gv1F7H8BGJ9gwU7KRrBCodyKli8/EiRPl0KFDpjNqxUb/ae8TYaMTtPeJsNEJ2vtERIYMGSIHDhwwnVEr7Y3a+4goMGw4xmtv1N4nwkYnaO8T0d+ovU+EjU6wYaZkI5nAy7bQefN4PMjMzETbtm1Np9SIjf7T3gew0Qna+wA2OkF7HwDExMQgKyuLjX7Q3kdEgWHDMV57o/Y+gI1O0N4H6G/U3gew0Qk2zJRsJBPOfeEeov+y4d9Z2Og/7X0AG52gvQ9goxO09xER0YWz4RivvVF7H8BGJ2jvA/Q3au8D2EhEgcOT50RERERERERERERElfDkORERERERERERERFRJTx5TkRERERERERERERUCU+eExERERERERERERFVwpPndN7uuOMOeDwe0xm1YqP/tPcBbHSC9j6AjU7Q3gcAM2bMQKNGjUxn1Ep7o/Y+IgoMG47x2hu19wFsdIL2PkB/o/Y+gI1OsGGmZCOZ4BK+3W/QKysrg9td9d9RysrK8J///AcJCQkGqqq2sNFZffv2xbJly9C6dWvTKTVio39yc3Oxb98+xMfHo0uXLqZzqsVG/2nrKykpgdvtRlhYGAAgJycHS5cuRV5eHlq3bo309HQkJiay0eI+IgosG2ZK7Y3a+6qjeaasoL1Re5+2ma0y7X0AG38sG2ZKNpIVhIJWQUGB3HLLLRIRESFNmzaVmTNnyunTp73b8/Pzxe12GyxkoxPefvvtaj9CQkLkueee8942iY3+mzhxohQVFYmIyIkTJ2TYsGHidrvF5XKJ2+2WPn36eLez0d5G7X0iIr1795Y33nhDREQ++OADCQ8Pl8suu0yGDx8u3bp1k8jISPnwww/ZaHEfEQWG9plSRH+j9j4R/TOlDY3a+0T0z2za+9joDBtmSjaSDXjyPIhNnjxZOnToIG+88Yb88Y9/lNatW8t1110nJSUlIlI+XLpcLjZa3ljxxO1yuWr8MP2XCDb6z+12y7fffisiItOnT5eWLVvKxo0b5fjx4/LBBx9IUlKSPProo8b62BgcfSIiHo9H9uzZIyLlg+aUKVN8tj/22GOSlpZmIs1Le6P2PiIKDO0zpQ2N2vtE9M+UNjRq7xPRP7Np72OjM2yYKdlINuDJ8yCWkJAgGRkZ3tuHDh2SK6+8UgYOHCjFxcUqXpnBRv8NHjxYrrvuOu+TeoXQ0FD5/PPPDVX5YqP/XC6Xt61Lly7y2muv+Wx/++23pUOHDibSvNjoP+19IiJRUVGSnZ0tIiLNmjWTzMxMn+379u2T6OhoE2le2hu19xFRYGifKUX0N2rvE9E/U4rob9TeJ6J/ZtPeJ8JGJ9gwU7KRbMA3DA1ihw4d8rke3CWXXIL169ejqKgIQ4YMwYkTJwzWlWOj//75z3+iX79+6N69O9555x2jLTVhozNcLhcAID8/H5dddpnPtq5du2L//v0msnyw0X/a+3r06IHVq1cDAJKSkpCVleWzPTMz0/gb6Ghv1N5HRIGhfaYE9Ddq7wPsmCm1N2rvq6B9ZtPeB7DRXzbMlGwkG4SaDiBzEhISkJ2d7fPGBjExMfjXv/6FgQMH4uabbzZYV46NzpgyZQr69OmDkSNHYvXq1Xj22WdNJ1XBRv/NnDkTkZGRcLvdOHDgADp37uzd9t133yEqKspgXTk2+k9739y5c3Httdfi+PHjGDFiBKZOnYq9e/ciJSUFu3fvxqJFizB9+nQ2WtxHRIFhw0ypvVF7XwXtMyWgv1F7H6B/ZtPeB7DRXzbMlGwkG/DkeRAbOHAgli1bhiFDhvjcHx0djbVr12LAgAGGyn7ARuekpqZi+/btmDJlClJTUyEippOqYOOF++lPf4rdu3cDADp16oSvv/7aZ/s//vEPn0HOBDb6T3sfAPTs2RP//Oc/8dBDD+Hjjz8GADzxxBMAgObNm2P27Nl44IEHTCaqb9TeR0SBYcNMqb1Re9/ZtM6UZ9PeqLlP+8ymvQ9goxNsmCnZSDZwiaZnGKpT33//fZV/GT1bUVERdu7cid69e9dx2Q/YGBirVq1CRkYGpk+fjqZNm5rOqRYbnfXll1+iXr16aNmypemUGrHRf9r6Dh06hC+//BJlZWWIj49HmzZtTCdVob1Rex8ROceGmVJ7o/a+mtgwU2pv1N5XmbaZrTLtfQAbfywbZko2klY8eU5EREREREREREREVAnfMDSIbdmypcobrLzyyitITExE06ZNMX78eJSUlBiqK8dG/2nvA9joBO19ABudoL0PYKMTtPcRUWDYsO9rb9TeB7DRCdr7AP2N2vsANjpBex/ARrKEUNAaPHiw/O53v/Pe/vTTTyU0NFTuvvtuefrppyUuLk5mzZplLlDYGAx9ImwMhj4RNgZDnwgbg6GPiALDhn1fe6P2PhE2BkOfiP5G7X0ibAyGPhE2kh148jyIxcXFybZt27y3Z8yYIWlpad7bf/nLXyQlJcVEmhcb/ae9T4SNTtDeJ8JGJ2jvE2GjE7T3EVFg2LDva2/U3ifCRido7xPR36i9T4SNTtDeJ8JGsgMv2xLEvv/+ezRr1sx7+7333sO1117rvf2Tn/wE+/fvN5HmxUb/ae8D2OgE7X0AG52gvQ9goxO09xFRYNiw72tv1N4HsNEJ2vsA/Y3a+wA2OkF7H8BGsgNPngexZs2aITc3FwBw6tQp7Ny5E1dddZV3e1FREcLCwkzlAWCjE7T3AWx0gvY+gI1O0N4HsNEJ2vuIKDBs2Pe1N2rvA9joBO19gP5G7X0AG52gvQ9gI9mBJ8+D2JAhQ/Doo49i06ZNmD59OiIjI3HNNdd4t3/66adISkoyWMjGYOgD2BgMfQAbg6EPYGMw9BFRYNiw72tv1N4HsDEY+gD9jdr7ADYGQx/ARrKE6evGkDmHDh2Sa665Rlwul8TExMibb77ps71v374yY8YMQ3Xl2Og/7X0ibHSC9j4RNjpBe58IG52gvY+IAsOGfV97o/Y+ETY6QXufiP5G7X0ibHSC9j4RNpIdXCIipk/gk1kFBQWIjo5GSEiIz/1HjhxBdHQ06tWrZ6jsB2z0n/Y+gI1O0N4HsNEJ2vsANjpBex8RBYYN+772Ru19ABudoL0P0N+ovQ9goxO09wFsJN148pyIiIiIiIiIiIiIqBJe85yIiIiIiIiIiIiIqBKePCciIiIiIiIiIiIiqoQnz4mIiIiIiIiIiIiIKuHJcyIiIiIiIiIiIiKiSnjynIhIMZfLhZUrV5rOOC+jR4/GTTfdZDqDiIiIiMhxnMuJiIITT54TERmUn5+P+++/H23btkV4eDhatWqFn//859iwYYPpNCIiIiKioMG5nIiIqhNqOoCIKFh99dVXSEtLQ8OGDTF//nxceumlKC0txdq1a3Hfffdh165dphOJiIiIiC56nMuJiKgmfOU5EZEh9957L1wuF7Zu3Yphw4ahQ4cO6Ny5Mx566CF89NFH3s87fPgwbr75ZkRGRqJ9+/ZYtWqVd9uZM2eQnp6OxMRE1K9fHx07dsTvf/97n+9T8d82FyxYgPj4eDRu3Bj33XcfSktLvZ/Tpk0bzJs3D2PHjkVMTAwSEhLw0ksv+Xyd/fv349Zbb0XDhg3RqFEj3Hjjjfjqq68CszhERERERHWEczkREdWEJ8+JiAw4cuQI1qxZg/vuuw9RUVFVtjds2ND76zlz5uDWW2/Fp59+iiFDhmDkyJE4cuQIAKCsrAwtW7bEG2+8gS+++AKPP/44ZsyYgb/85S8+Xy8jIwM5OTnIyMjAihUrsHz5cixfvtznc55++ml0794dn3zyCe69915MnDgRu3fvBgCUlpZi0KBBiImJwaZNm7B582ZER0dj8ODBOHXqlLOLQ0RERERURziXExFRbXjynIjIgH379kFEkJycfM7PHT16NEaMGIF27dph3rx5OHbsGLZu3QoACAsLw5w5c9C9e3ckJiZi5MiRGDNmTJUhPTY2Fs899xySk5Nx/fXX47rrrqty/cYhQ4bg3nvvRbt27fDII4/gkksuQUZGBgDg9ddfR1lZGZYsWYJLL70UKSkpWLZsGfLy8vDuu+86syhERERERHWMczkREdWG1zwnIjJARM77cy+77DLvr6OiouDxeHDw4EHvfc8//zyWLl2KvLw8nDx5EqdOnUJqaqrP1+jcuTNCQkK8t+Pj4/HZZ5/V+H1cLhfi4uK83ycrKwv79u1DTEyMz+8pLi5GTk7Oef8sRERERESacC4nIqLa8OQ5EZEB7du3h8vlOq83HwoLC/O57XK5UFZWBgD485//jIcffhhPP/00evbsiZiYGMyfPx8ff/zxeX+N8/mcY8eO4YorrsCrr75apa9Jkybn/BmIiIiIiDTiXE5ERLXhyXMiIgMaNWqEQYMG4fnnn8fkyZOrXF/x6NGjPtdXrMnmzZtx9dVX49577/XeF4hXnFx++eV4/fXX0bRpU3g8Hse/PhERERGRCZzLiYioNrzmORGRIc8//zzOnDmDK6+8En/729+wd+9eZGdnY9GiRejZs+d5fY327dtj+/btWLt2Lfbs2YOZM2di27ZtjreOHDkSl1xyCW688UZs2rQJubm5ePfddzF58mT85z//cfz7ERERERHVFc7lRERUE548JyIypG3btti5cyf69OmDqVOnokuXLhgwYAA2bNiAF1544by+xj333IOhQ4di+PDh6NGjB7777jufV7s4JTIyEu+//z4SEhIwdOhQpKSkID09HcXFxXzFCxERERFZjXM5ERHVxCU/5t0xiIiIiIiIiIiIiIiCAF95TkRERERERERERERUCU+eExERERERERERERFVwpPnRERERERERERERESV8OQ5EREREREREREREVElPHlORERERERERERERFQJT54TEREREREREREREVXCk+dERERERERERERERJXw5DkRERERERERERERUSU8eU5EREREREREREREVAlPnhMRERERERERERERVcKT50RERERERERERERElfx/0WbCzeNidW8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N1it288It4ma"
      },
      "outputs": [],
      "source": [
        "def plot_correlation_matrices(grand_averages, ch_names_hbo, ch_names_hbr):\n",
        "    \"\"\"\n",
        "    Plot correlation matrices for HbO and HbR channels for each condition.\n",
        "\n",
        "    Parameters:\n",
        "    - grand_averages (dict): Dictionary containing numpy arrays of grand average data\n",
        "    - ch_names_hbo (list): List of HbO channel names\n",
        "    - ch_names_hbr (list): List of HbR channel names\n",
        "    \"\"\"\n",
        "    # Create a figure with 2x2 subplots\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
        "    axes = axes.ravel()  # Flatten axes array for easier indexing\n",
        "\n",
        "    # Define which channel names to use for each condition\n",
        "    ch_names_dict = {\n",
        "        'Reappraise/HbO': ch_names_hbo,\n",
        "        'view_negative/HbO': ch_names_hbo,\n",
        "        'Reappraise/HbR': ch_names_hbr,\n",
        "        'view_negative/HbR': ch_names_hbr\n",
        "    }\n",
        "\n",
        "    # Plot each condition in a subplot\n",
        "    for idx, condition in enumerate(grand_averages):\n",
        "        if idx >= 4:  # Skip if more than 4 conditions (2x2 limit)\n",
        "            break\n",
        "\n",
        "        # Calculate correlation matrix across channels\n",
        "        # Transpose data to get channels x time points\n",
        "        data = grand_averages[condition]\n",
        "        corr_matrix = np.corrcoef(data)\n",
        "\n",
        "        # Get channel names for this condition\n",
        "        ch_names = ch_names_dict[condition]\n",
        "\n",
        "        # Plot correlation matrix in subplot\n",
        "        im = axes[idx].imshow(corr_matrix, interpolation='none', aspect='auto',\n",
        "                            cmap='RdBu_r', vmin=-1, vmax=1)\n",
        "        fig.colorbar(im, ax=axes[idx], fraction=0.046, pad=0.04)\n",
        "\n",
        "        # Set title and labels\n",
        "        axes[idx].set_title(f'Correlation Matrix - {condition}')\n",
        "        axes[idx].set_xlabel('Channel')\n",
        "        axes[idx].set_ylabel('Channel')\n",
        "\n",
        "        # Set tick labels\n",
        "        axes[idx].set_xticks(range(len(ch_names)))\n",
        "        axes[idx].set_xticklabels(ch_names, rotation='vertical')\n",
        "        axes[idx].set_yticks(range(len(ch_names)))\n",
        "        axes[idx].set_yticklabels(ch_names)\n",
        "\n",
        "    # Remove empty subplots if less than 4 conditions\n",
        "    for idx in range(len(grand_averages), 4):\n",
        "        fig.delaxes(axes[idx])\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "plot_correlation_matrices(grand_averages, hbo_ch_names, hbr_ch_names)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "q8463p6_t4mT",
        "jdmfGB71t4mU"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}