septum-mec/actions/stimulus-spike-lfp-response-other-tetrode/data/10-calculate-stimulus-spike-lfp-response-other-tetrode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate spike-lfp coherence using other tetrode in same drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import spatial_maps as sp\n",
    "import septum_mec.analysis.data_processing as dp\n",
    "import septum_mec.analysis.registration\n",
    "import expipe\n",
    "import os\n",
    "import pathlib\n",
    "import scipy\n",
    "import scipy.signal\n",
    "import numpy as np\n",
    "import exdir\n",
    "import pandas as pd\n",
    "import optogenetics as og\n",
    "import quantities as pq\n",
    "import shutil\n",
    "from distutils.dir_util import copy_tree\n",
    "import elephant as el\n",
    "import neo\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.interpolate import interp1d\n",
    "from matplotlib import mlab\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from tqdm._tqdm_notebook import tqdm_notebook\n",
    "tqdm_notebook.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = dp.Data()\n",
    "actions = data_loader.actions\n",
    "project = data_loader.project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pathlib.Path('output/stimulus-spike-lfp-response-other-tetrode')\n",
    "(output / 'data').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "identify_neurons = actions['identify-neurons']\n",
    "# sessions = pd.read_csv(identify_neurons.data_path('sessions'))\n",
    "units = pd.read_csv(identify_neurons.data_path('units'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lim(action_id):\n",
    "    stim_times = data_loader.stim_times(action_id)\n",
    "    if stim_times is None:\n",
    "        return [0, np.inf]\n",
    "    stim_times = np.array(stim_times)\n",
    "    return [stim_times.min(), stim_times.max()]\n",
    "\n",
    "def get_mask(lfp, lim):\n",
    "    return (lfp.times >= lim[0]) & (lfp.times <= lim[1])\n",
    "\n",
    "def zscore(a):\n",
    "    return (a - a.mean(0)) / a.std(0)\n",
    "\n",
    "def compute_stim_freq(action_id):\n",
    "    stim_times = data_loader.stim_times(action_id)\n",
    "    if stim_times is None:\n",
    "        return np.nan\n",
    "    stim_times = np.array(stim_times)\n",
    "    return 1 / np.mean(np.diff(stim_times))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signaltonoise(a, axis=0, ddof=0):\n",
    "    a = np.asanyarray(a)\n",
    "    m = a.mean(axis)\n",
    "    sd = a.std(axis=axis, ddof=ddof)\n",
    "    return np.where(sd == 0, 0, m / sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_energy(p, f, f1, f2):\n",
    "    if np.isnan(f1) or np.all(np.isnan(p)):\n",
    "        return np.nan\n",
    "    mask = (f > f1) & (f < f2)\n",
    "    df = f[1] - f[0]\n",
    "    return np.sum(p[mask]) * df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_theta_peak(p, f, f1, f2):\n",
    "    if np.all(np.isnan(p)):\n",
    "        return np.nan, np.nan\n",
    "    mask = (f > f1) & (f < f2)\n",
    "    p_m = p[mask]\n",
    "    f_m = f[mask]\n",
    "    peaks, _ = find_peaks(p_m)\n",
    "    idx = np.argmax(p_m[peaks])\n",
    "    return f_m[peaks[idx]], p_m[peaks[idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_half_width(p, f, m_p, m_f):\n",
    "    if np.isnan(m_p):\n",
    "        return np.nan, np.nan\n",
    "    m_p_half = m_p / 2\n",
    "    half_p = p - m_p_half\n",
    "    idx_f = np.where(f <= m_f)[0].max()\n",
    "    idxs_p1, = np.where(np.diff(half_p[:idx_f + 1] > 0) == 1)\n",
    "    if len(idxs_p1) == 0:\n",
    "        return np.nan, np.nan\n",
    "    m1 = idxs_p1.max()\n",
    "    idxs_p2, = np.where(np.diff(half_p[idx_f:] > 0) == 1)\n",
    "    m2 = idxs_p2.min() + idx_f\n",
    "    assert p[m1] < m_p_half < p[m1+1], (p[m1], m_p_half, p[m1+1])\n",
    "    assert p[m2] > m_p_half > p[m2+1], (p[m2], m_p_half, p[m2+1])\n",
    "    \n",
    "    f1 = interp1d([half_p[m1], half_p[m1 + 1]], [f[m1], f[m1 + 1]])(0)\n",
    "    f2 = interp1d([half_p[m2], half_p[m2 + 1]], [f[m2], f[m2 + 1]])(0)\n",
    "    return f1, f2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# p = np.load('debug_p.npy')\n",
    "# f = np.load('debug_f.npy')\n",
    "# compute_half_width(p, f, 0.01038941, 30.30187709636872)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(f, p)\n",
    "# plt.xlim(29.9,30.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_stim_peak(p, f, s_f):\n",
    "    if np.isnan(s_f):\n",
    "        return np.nan\n",
    "    return interp1d(f, p)(s_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_spike_lfp_coherence(anas, sptr, NFFT):\n",
    "\n",
    "    sigs, freqs = el.sta.spike_field_coherence(anas, sptr, **{'nperseg': NFFT})\n",
    "    return sigs, freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def butter_bandpass(lowcut, highcut, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = scipy.signal.butter(order, [low, high], btype='band')\n",
    "    return b, a\n",
    "\n",
    "\n",
    "def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
    "    b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
    "    y = scipy.signal.filtfilt(b, a, data)\n",
    "    return y\n",
    "\n",
    "# def compute_spike_phase_func(lfp, times, return_degrees=False):\n",
    "#     x_a = hilbert(lfp)\n",
    "#     x_phase = np.angle(x_a)\n",
    "#     if return_degrees:\n",
    "#         x_phase = x_phase * 180 / np.pi\n",
    "#     return interp1d(times, x_phase)\n",
    "\n",
    "\n",
    "def vonmises_kde(data, kappa=100, n_bins=100):\n",
    "    from scipy.special import i0\n",
    "    bins = np.linspace(-np.pi, np.pi, n_bins)\n",
    "    x = np.linspace(-np.pi, np.pi, n_bins)\n",
    "    # integrate vonmises kernels\n",
    "    kde = np.exp(kappa * np.cos(x[:, None] - data[None, :])).sum(1) / (2 * np.pi * i0(kappa))\n",
    "    kde /= np.trapz(kde, x=bins)\n",
    "    return bins, kde\n",
    "\n",
    "\n",
    "def spike_phase_score(phase_bins, density):\n",
    "    import math\n",
    "    import pycircstat as pc\n",
    "    ang = pc.mean(phase_bins, w=density)\n",
    "    vec_len = pc.resultant_vector_length(phase_bins, w=density)\n",
    "    # ci_lim = pc.mean_ci_limits(head_angle_bins, w=rate)\n",
    "    return ang, vec_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_clean_lfp(anas, width=500, threshold=2):\n",
    "    anas = np.array(anas)\n",
    "    idxs, = np.where(abs(anas) > threshold)\n",
    "    for idx in idxs:\n",
    "        anas[idx-width:idx+width] = 0 # TODO AR model prediction\n",
    "    return anas, idxs\n",
    "\n",
    "\n",
    "def compute_clean_spikes(spikes, idxs, times, width=500):\n",
    "\n",
    "    for idx in idxs:\n",
    "        t0 = times[idx-width]\n",
    "        stop = idx + width\n",
    "        if stop > len(times) - 1:\n",
    "            stop = len(times) - 1 \n",
    "        t1 = times[stop]\n",
    "        mask = (spikes > t0) & (spikes < t1)\n",
    "        spikes = spikes[~mask]\n",
    "    spikes = spikes[spikes <= times[-1]]\n",
    "    return spikes\n",
    "\n",
    "\n",
    "def prepare_spike_lfp(anas, sptr, t_start, t_stop):\n",
    "    t_start = t_start * pq.s if t_start is not None else 0 * pq.s\n",
    "    sampling_rate = anas.sampling_rate\n",
    "    units = anas.units\n",
    "    times = anas.times\n",
    "    if t_start is not None and t_stop is not None:\n",
    "        t_stop = t_stop * pq.s\n",
    "        mask = (times > t_start) & (times < t_stop)\n",
    "        anas = np.array(anas)[mask,:]\n",
    "        times = times[mask]\n",
    "    \n",
    "    # take best channel from other drive\n",
    "    best_channel = np.argmax(signaltonoise(anas))\n",
    "#     best_channel = np.random.choice(anas.shape[1])\n",
    "    \n",
    "    cleaned_anas, idxs = compute_clean_lfp(anas[:, best_channel])\n",
    "    \n",
    "    cleaned_anas = neo.AnalogSignal(\n",
    "        signal=cleaned_anas * units, sampling_rate=sampling_rate, t_start=t_start\n",
    "    )\n",
    "    \n",
    "    spike_units = sptr.units\n",
    "    spike_times = sptr.times\n",
    "    spike_times = compute_clean_spikes(spike_times, idxs, times)\n",
    "\n",
    "    sptr = neo.SpikeTrain(\n",
    "        spike_times[(spike_times > t_start) & (spike_times < times[-1])], units=spike_units,\n",
    "        t_start=t_start, t_stop=times[-1]\n",
    "    )\n",
    "\n",
    "    return cleaned_anas, sptr, best_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_spike_phase_func(lfp, times, return_degrees=False):\n",
    "    from scipy.fftpack import next_fast_len\n",
    "    x_a = scipy.signal.hilbert(\n",
    "        lfp, next_fast_len(len(lfp)))[:len(lfp)]\n",
    "#     x_a = hilbert(lfp)\n",
    "    x_phase = np.angle(x_a, deg=return_degrees)\n",
    "    return interp1d(times, x_phase)\n",
    "\n",
    "\n",
    "def compute_spike_phase(lfp, spikes, flim=[6,10]):\n",
    "    \n",
    "    sample_rate = lfp.sampling_rate.magnitude\n",
    "    \n",
    "    # sometimes the position is recorded after LFP recording is ended\n",
    "    times = np.arange(lfp.shape[0]) / sample_rate\n",
    "     \n",
    "    spikes = np.array(spikes)\n",
    "    spikes = spikes[(spikes > times.min()) & (spikes < times.max())]\n",
    "    \n",
    "    filtered_lfp = butter_bandpass_filter(\n",
    "        lfp.magnitude.ravel(), *flim, fs=sample_rate, order=3)\n",
    "\n",
    "    spike_phase_func = compute_spike_phase_func(filtered_lfp, times)\n",
    "    \n",
    "    return spike_phase_func(spikes), filtered_lfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AFsVikLZup5s0LghAAQAR43y8Z+Rc07SNsWpAxFFthlm1NdWqramOdzFizl9wyURF8UUACgCIGIthtZiqMtou2l8Qb4cfTFP7B/vHuxgxwyWX+AhAAQBhcW9lNgpAqQwAiLX6ulrlvveI6utq412UuOujQ/EuAuCBABQAEJRgx8yQgouWoq7BprWFLWO5H4au+bds3sNKX3+/ls17JN5FQZxwiSQuAlAAQMS4h5rjknbErRxAKO7/fIPOeGqxtpVWxLsoAYU6s2ebCVwrD0qS7NWH41wQxJqZRk9mxo0vAlAAQFgCPcbN9H9mv3KL8h86ORLFAUKWv9uRqnigovWnbZKXgNbOTGMLGTrxQQAKAIiq8vVfS1mp2lWwxuc+mbte0uTqpTEsFdCytZmeTCBMXCuJhwAUAGAotIZh7yf90Xs+lyTtWfNdWOVBbJGiFinR6WHhrwOY47xW6OxMHASgAICIMXq+211bqTIDEUO3DuBX8yuE+DNxEIACAILir97bzVLlvX/jY99ChRlAjNCDH11Hyg7o8KH98S4GWqiUeBcAANDKNTY7B7t8C9Da1DfYVFNvU5cOVL9ihrzLqOj27xGOH7ISd9kiM395nkvxQQ8oACAsgZ/fVABhzNbQIOuyhfEuRsyMvuMLjb/rq4ici2oz4J+/a8TCcymuCEABAOExXROmygxPeXPvVtpn52jpJ8/GuyiSojOs0r0DLpLnp+MGMEand+IjAAUAhC1F9bo8+XPD11x1AWrMaKbv9k8lSTNX/j3OJfHUEiqwpA4C/nGNJC4CUABA2C5O/kp3tnvL8DU7qU7wIclui3cRWqxQq9ZMzoPWjvTaxEcACgAwpbOq9Xi7Z1V2YJ/Xa90NZr91sjd2J9EajUS373C1JGn+6j1xLkn0RbuXd+3iT7X9nomqqa6M7hv5wv0GjXj2JB4CULQaBypq9cmqwngXA2i1fpf8jc5NXqyN790V4hmoBMDTCNuOeBfBQ1GZIwB9d+muOJek5eu28O8abtup4u0b410UQJJkaQm59W0EAShajavmLNcN76xS0SHfPTFAW1NZXqYVj56hfYXbovYejpQ+38FlO3tt444EoECkcDnFnt1m06qHT9OaRZ/Euyjwgzgz8RGAotXYU+YIPOsbeCoDTusWvK5pFT9o+7zbwziLXYNUIoszyDR4uFv8BKCj5ehNsh3cHkYZoq+yvEwrF8yJdzEAU3jSxV5V5RFNqcrRyG/+GPa5ln7yrHKfviwCpQJaHgJQAGjFIjH25cLk/2lJxxs0OWmrj/cwd56kmsNhlyVasl+6Ufv+daym/nittq1fGu/iJJQ9O6yquKu/dlhXxbsoQELw1+Bm1syVf1d66QcRKE0MZaVqy71T4l0K08gSSFwEoGg1uNEgGkqLd2rZv85VVcWReBclRBGoKCVZJUnDLcWSDDtAW/Scg/V1tcosfE3DbY6e2pqKsjiXKDE476k7fpirLpZq7Vn4fFTfb+VXb0T1/EGJ8hc6Eg1DTKwSexaLo9ociQA0Vg6VFkfkPGu+/1CSNLIhesM5IqUlP4/aCgJQtDrxzP3fv3e31v7A2JDWZNs7f9OMw//TmgWvxbsooXFWUkO4MJxT2TsrW84ql3vVq662RkfKSk1VyBK10uasVLpQsY+Luo1fxbsIaFUifx07J7Fx3k337LAq96mLEzpromD2nyNynorNiyNynlhiyaHERQAapIojh7T88bO1f+/ueBcFCaj8hdM04ZuL4l0MwEDkWmbWFTWl0q79z6809MVjEja4NKMlzYxYWV6m2rt6acVXb8bwXZ2fT8v9Gwcr2t8I2jhiKJLXd7NzVb/xa6Xv/1gj3jvZa9fK8jLlPnWxyg8fjNz7h8Biq4vr+8eDmT95C7rtt0oEoEFa89mzmn7kWxXM+2e8i4JmEuGBPswW/4aJwq3rVLh1Q7yLgQQRiTS95sGlze3XqRWLG/eJjtUPeVfsIq0lBaB7d25Se0uDeuU+ErP3tDfEpgLbruaQqivLI3a+sv17ZWtoCOnYitrQjjMSrW9XIjzzEllm4etRO7fzntjB7nvW/fwPHlH6/o+15t27o1aOWGrJvYktt+StFwEoEKKibRuV8/YDqq4s16qv58a7OC6DZv9Eg2ZnxLsYrVZp0Q4t+/yleBcjCKGn4Do19X+FWZUOocY8qTr6qW2MpfMvc9vTkqSUqtKovs/UyiXa/fjxYZ3DbrMp/9t5Ki3aodSnxij39VtDOk+DLbrfiUicPVYBQUH+kojNDt1QX6+62pqInCsemhqrPIclSI7hCLnvPKj6OseyU5bG+0qnA/FtEG4J2Snb1uW6PrdIan5rbzlNja0fAWjIEv+CRpTNPlMZ1oe1/tnfasqSq7V55aJ4l8gD65RFx5GXz9SMpbfo8KH98S6KOXZnRSmcR2/gc5ir5CTmfXPtDx/HuwimFWe/K0kabtsZ9LFlB0pUtn9vyO/dri76E3GNatgS1vGrvn5Tk7+/QtvevUWS1K/of5EoVqsSbHvLqI9+oak/XitJKt5VoH1ZI1S4dV1I773loUy1e6BfSMdGSjjXQfPx4u73xHYP9FP6xoe0/IPHHa81vjSlKkdrF38aWmHbgJ2bVmnEvFO17JUbI3bO5lktzu/8/orIB7kIDQEo2oTsl26QslIjes4udkeqWGqVc+bMQxE9vxmr/ve28j54wvC1il0smRANvW0lkqT1c/8e55IEKQJppv7qrS25Zbm+KnGXh2muR/GSkI9NfXKUUp8aE/qb222hHxsjNQcKJUkzyxbEuST+RWYW3PCODyX1fNvCV9VPB7Tz69BmRB5Tvymk4yIp7OtAjvtd9uu3abDde4ZZe/URt70cKvZsDuv9EkKUMkWOlBZJkrofWB2V8yMxEYCGrCVXt9qeaI4Fiacpi6/SrDV3Gb9IWmFUZex7N95FMCnyY0BDlmDfyfq6Wu3cZNBQk2DlTBSRfOr9+vlsvbo48ss5tJTxvJFJwY0H5+cbn8aI5fNfUXHWqODSNW2RG8/r1M7SoMztzxm+5uz5lFtvaXy/l9H9phzIGhKZE0XhvsudPHERgIaMr3WiivV93jnOo3zN57F94wCGFURmzE6slRRtV85bd8tuM1fByX7hOm1cGrs0u+6qiNl7RYTroR7chXFgX6H25b7nsa2dHBU547tf4t8Tyw6UeKznarmvn4bOPUE1JVvjWKrgRPL2tuKL11RxJJjMjcj9jfO2H9CDn0Whx8PrAWD8iTXU1yt79p1B/v/btoL8xa6gqsvB+IxrHJ13hwaoRBXl5rMWMne+GLG1MM3xXKrFXc6cu1R092jHZ9lK9FJ4GSTRCM4TtRmqsrxMR+4aoPyF7wXeuZlV37yjsoPRHYcfSwSgobLbWvDC9E1WfPGazxTOeKmqOBLWJAWbf/xUNXf1ifgYPbvNpryPnlR1lXEAkrH3nYi+Xyhy5z3u+vkolcSxJKHb/+oFytj8L+0qMFc5zdwzW2M/PzfKpWrJQgsaej07Tn8uvUcTLFvVSY7ehvFJOyQZP9yTgnyfDfdnKvulyI35MSP1yVHq9OhgFe8qkCQlWxxlTjpc6LGfzdYQ8uypURdiL8HerKM9fi/IX6JpuTdqw0tXmD6HJcIpuJcmfxHR80lSyu5cU/utWvCGMrc+qbVv3BTxMjRnVMFe+eXrKrx7TFgTr4TaYVRRUy9JKjzkewZXI/W11a5rf1L18rCukfyHT3Vdh7FgXei9bFH+t/OiU49z9ny6/90bf84oeEID7fs06qNfRv59WziL7Mp5/hqt/vb9iJ0z0ZJZirasVTdLlboueTCo4/YVbtOUH/6kHS/8Okoliz0C0CD1LXC0WqTv/1idHh0ccP8dG5Yr57mrTPfmxNq03Bt9p3DGSadHB2vHw5mOQftZqdq2zlyFwjmmpuey/6iDpU6FG/K89wnj77D6u/c1K/9OrXrtRuUvfEepUewJ25D7lXYXrA36uKTdOVEojW9b1kR+coVONsfYWnuCBADDDvn+/jXU14d9/l0FawzXFc6ZkyVlpSr36UvDfg9JHulgwehmqVJni2eDUAfVhnQtzTo03/XzMXXrlVn4WtDnWPXNO8p5K7xlDXZ81Px4z1rKuK8u0MaHwpuNNVr61he5fg5mfcH+amqQy/92nmorHb0WXaqKfB3ixRLhtMsulsjPhmp2si1brSP4SqqLfUbDUdqvcXm3a5B9ryqC+BsWbbdqX2FT2rJdduW8dY+W/Su4Brj5ax29gXNzg5/IKlIpRpOrclX52rlRmfnUSNeC/3qX4fsrtPZF8w0wZrk+oWafla/nRUnWcO3YsDzi5XCJWBDWdKJg/m411ZVa/d0Hvndwm1k4o/gtTfr+8hDL537O8E+RSGqqHPWiXjWFAfZsOQhAgzSyIbgxK53ePU8Ze99WyZ4dUSpR6zSqYYsqVn0kSeo47/eu7WsWfaKct+/3edw4y3YNtPmuUG1/6Xchl6mu0pGq1a6qVJMX/cm1vZMtvAqMraHB68F0zBe/1uA5x5o+R/7Dp0iSxh/63vD8h0qLlfue/3UDc56/JuiJmkZ+cJom/O8PQR0TLeGk0h0s2aMjZQcMX+snx/ZZ+Xdqw/2ZHq9VV4W/ZuGQOcep27OTvLZnFPxbkpRe+qFKirYbHltStF0HS/ZIklZ/94HWPPh/3r0SjQ0z3cpCm/yji6q9tl2c8rU2vnixx7Yhln0hnb8596CqsrzM6/UpP/xJGZv/FZH38mdcXfANQLHQW02fSdd/DQ/pHD1/uCukQGJCTeJPbDbjyDcB93HPjuleEdtncy8dVnbH69TVElzvoyQNfH2W+r00xWNbxubHNeOw8RCEhvp6rfzqDVdj0dJ/nx92g1bnwh9N7+scO95Q71hHtnn20NG27Vr62i0hlWP9J48F2Xtp3Hgys+xLj/Nsu2ey1jz4fyGVySXJWbV2v8Ysqqv1vpdKUl8dVPHXkc9EW/74OVr/wHERP68krV/iHdD7surlazXpu8t8rhQQi/GxibKGaeUXnh0+NdWV2rLGRMdBY/ZJS1hSxywC0ChzflkSfWKE7Ff/Fu8i+HSUSrQ+25GqNXHhRcqwOgKp3Pce8Urhmd/hdvWz+A5ERuzxn/K1+tv3tcMaXCUr3FRX60PHKfm+3pKk7Ddu1+b7ZgR9jslVeSot3unVU5W/8B0l3dtLtU9nKn39/V43urKsgVJWqtYt+VwZxW+F/p9otHLBHK3639thn6e5Nd9/qOKsUX7HTXR8bHjI5+/5zFjZ/z3BY1vZwVLlPHeVx7Zj6tZ7/L7zyV+E/J7u2lv89/aWvfIrKStVa5s99Pu+OFk9nxkrSRr17dWaWLNCeXM9e/ecmQGhBlQdVKdRFu9W12OKPXu+T082v16n4cQ/jcr/1fT9L/p3mBVBH1LqjngG6i1gdtdIGmpz/3u2rApNzuw7tfIR89ede4/ovsJtUlaquj9xtDps/FCSlFa/0eexxbsKgn4eBNLD4r/RylnG5fN9Zwccl7RGUuD0wqXvPaip2ddr2X8dM9bOLFug9NIPQ+4cqquqCGld3qPfP1WS1PHhgV6vdTkQ2n0pc/tzyn/jZtP7O//POW/+0+u1jc9e4Pp5hG27Jtas8HmeVc+b6Z1rfDe3ep/FYvE783FSQ7VXtte+wm3KfSe4VE1JWvXIz6WsVE0/slDjatcEfbwZtcvN1xc6H9kuSaoq819XikYtOVECT6em68dRrtXPX6aRH5ym0iJzDWGD7KEvo5VoCEAhScrc+YJhSl3O7Du14rEzI/IexTs3e43trKutUdmBwAHcuK8u8Pi9tHiX0tffr+rXzvF9UJB3s7raGk36/nINe/uE4A70wW6zaWPugoCpiu5BTea2ZzS6PrTp2usNxs1Wr3VMjOTsxWuo89zHmUY8esFFIb1nc1N/vFZTFl/ltX3FF69JWalan/Ol12vZL90YsOd14reXaoBKNHnRH33uk2yxa8u9U3y+vnLBHGW/cJ3P15tPLrRxzl+Usdd/MH1Mnbm18Dbmfa29u0Nf39C5NmLDD/+R5JgJ0v0zc5+YIGPLfzy3l3mn9za3r3Cb9u/drZ2bVqng3mke57PIrj4W40kmtqw23xvidGBfoYbO9djWV/UAACAASURBVH2NDVCpbA0N2nnPuJDXhNy/d7erZ9jI9CPfKvfl612/Tyj1/l7GU3VluRrq62W32Xz2fpux8qs3tPTfxmOGasocFRlft0mLbOp4fy+/11S4plushturK8uV/cJ1hj1cGVuf1NTKJVr343yDI72NatiiQ1mDdKTsgMpeO9+1fVK1Z8rjvsJtHsFmH5VpwCvTNeztE1RbU60VX73p815euHWDln78jKnyNE8R3v7S75W/sGn+gD2blkmS2q32XcGf095kUFLmaGhoOOJZaTXbHr5j4wrtzxrq+r39ogfMHRgUz8KUFu80PTY0ucb7vrT9nola/dBJ3u/S2Mjkfn90GlVuPv111sHPTO/r/n9rqPGfKTWzbIFGzDvVY/3uw6+eq/SND2nPDuPrRHLUWwrunaZdBU2B5pTKbI99plVEaI1ytwDaTKaB6zDnF85HAG6JQugZjXNGUju7I4W5/2HHXBcVR/yn4kdi2aZEQwAaZS2pu9yS5P11yNj6pKaVe6d1Buvwof0a8OoMrXzuMo/ta584W6lPjvJVIo/f3BeOrm9MZelsa2pNNvtJ57x1t7bfM9ERFDWqqa40XBw798k/6MhdAzy2jTtsbva6pR89qbFfnK+VX73hsd1usyn33YcN0wvDsX3JPK9t6QfMjc+sU4rf18sOlBgGj2ZNy3VMNlOR7dmqX5C/2GAcYOjXzMiGbR5jUyqOHJKyUrXyqzc09cdrlblndsAZcw8f2q+ct+5WUr1xulQoxs4/T/1fnhb2OJ8UW7XKDpRoep5ny3/5wb0evd/O73bNf2aa6tnu99IU9X5uvEr+e7dGNWzR5iUful7zN7lQyeLgx3D2enacx+8b87722qe6qrxZL52De2C86uu5Pt+j93PjXT3DkmPs3PYNyzz2Gb7nK9fPXSz+/9YF+Uu04f5MVVeGn3JtRsdHBmndYz+X5Z6e6vviZI/KZTCmZl+vmWVfacWXrxu+JslnpfDMJEfjQvNhJ4Fmb7TbbKbT4Y9LNm7AWfn+w8rcM1ur3rvP57HjF/zW1HtIUg+Va8eaxT4b94q2bVS/l6Zo2NsnaHvH3+mmlPe1rOPVrteXv/5XTcv+s9b+8LEkad2Sz5X7VFOjXZfZJ2vmqttNl8fd5OqlHkM6nCZVL1XRdt+BhxmWBuMxtmYq56u/+0D7vnjII+W7vc04bXjLmhwV7/TfcOq7IbapLOWHD6rP8xM14JXphnuu+OI1dVdlU3lqm4ZNLJ//inKe+5OG23Z6NS5I/hsLIx6qGExClGF9WHYTmRaVe5pmF3bWb2wNjuN2bc53NLxkpbrShFfNf1mjGrZoyJzQUm33FW7TphXm6ni+vk+SZL0v3edrzr+HrT7AuNFWGGT50vz5duAT/+uKt8aPhgA0RhK9NSZYKx/9pZZ+6GhJNFPhqK5wtFTOOviZct66x7V9aqX5HhT3haMHvOpI0/M34YSvVzI2/0vDbTtdQZGjfMZjSdIPfKpuzcbqdLIEHny/ZfWPal/gSPetLd2msgMljrWyslK1/qHjlb7hAa157XqPYCnvoyf9nnPtkv9KWak6sM94EHqG9eGA5fKl1tLOa9u2dbla9Y2jZb7df8Zr3Je/UXVVherralVStF2Hs47y2N/9/7Jm0Sc+AgS7bA0Nyn7tVh0s2aP+H57nu1B+mumLtm1Uztz7tOb7D71eS7m/r5bPf03VVRWuGT5dlW1Jh5b6Xr9zxZevq/w/GY7vSJm5ya+CsWdJUzBYWV7m0YvpnHTLX5A6vnaNYYNNxSHPqdmd321nz3ew6g81fcf8TTyTsS/4qeSbGzvf+zvQ4GMCqqJNTZ/NlCVXG+6T+7RbI1dWqpSVqoGvz9Lwd3/msZ/dxKRMyz5/SZJU99ktOqZuvTYs/lgbl/5PNdWV2rt7i46UHVBB/hKVZg1zjW/asXGF395Xs9xTHQ8V+V4mxm6zKee5q/z2Rk/LucHna74aSVMtxj027lkIOc/+UTmvewZeuXPuUpfHhwVMKRtq8Z1Klrm18V5Y31Thra2plnXZQo/9Gu7qoaqKI6YaBmpzXvb52sA3PCvPlzWbnbd9+S5JUs0Rx9jR8V//Tun7m3qqesjx/jmz7zQ8v/udLJh65M4v/u1zNvdOqg7YK5Je6rg/ZhR4ji80urXW19W6xmju3LRKk767TDPLvvLYp6Ot0vtAOeYCcD6T3blXsi339DQ8dnC1VZtXLlL2C9dp73b/S7uMyv2Hx++Tq/JcvYPT8272mIm+6q6+fs/lrqulyjA7S1mpXt9vM+wVxlldnR8barjd82Cjv6njHjzkreNdDS8Ta1Yo+6Ub1VAaemaNJPV4cbrGfGoyy83P981fKrtT3RHjz6WmwlF/HFsf+aV9Ihm41dZUR3TZpuxX/uK6RgLWhVvhMBECUJNy33lQef8JfQKblmDPDquUleoaJ5j73qOu11Y98nPZbTZtWvGdct78p6ZWLNbM1Y6xFM4KR87z10hypNgtfeICj0qB+xjY6ZtMDrY3kSdki9RXOCnZ87wGFeCGCvOzFY788HRNqXJ8jvaGWq3/4H7XWlnjG8dkpNQc0rJXmoLgWfnGlRfJkVJoX+SYeGXnakdFN/epi33u78+mFd979P76MmLeqZryg6Nl3tm7Zmuo17IXr1HfFyd7tETnvHyz1i1uqpBNXHiRpiy52rBVfP2Pnylzx/Pq+cxYj+D+YMkeKStVw2xNKaM11ZUq2ub9YLPPPlMZmx7VyIXe6b6SZN/wX5U/PE4zDnv3rmXse0/Zr90qyTHGy73iPi3nBg20OybT6WwPfpKQQNzHozSvjDgncyr64XXlPHtlUOc1qkC4N/S4W/P9h9qbdbTrO+7eM9G52hEUuFdY49F0tuNZ49T63l8Y/73dpZf6mW3RzUATY2lmLL1F65Z87kqTn/rjtRr7+bnq8NBR6v/yNB164lgdXvCQ+uiQRn/y/1S4dZ2GvfN/0jOzJDkaGZy9tvv37vYaT7hz0ypHD/2COVr22Yvas8PqNdGVJMnue4zw4UP7lbH3bY388HRJ0vYNy1RavEtL/32+z2OanVySo8Ld2W3CKTOzyWbse08Z25tST63LFqr/dsd9YNcc/3+rrjJ/fVVXVWjVc5cq7TPP70Wyxa5Ojw5Wx0cG+VweyymYTJ7mjY6uWmyzj2TzykUeDW8ZW/03Iu46UBnwc6074pZOv/dt7X3qVMP9MpI2eFSu62prtG6JY8jFphXfGTbOOTWVwK51D/xUSz9+Rin391XHhwdq+z0TdaTEuJGz+XwH/ua22JD7lc/X3PXSYY3+5P8pc89slaz71rW9cKu5YOSo12Z5LTEk+Wgo9jPUY9lbnmNDqxrrLxO2vWqqHO4yi2Y3zqsR/N3TvVHB+ZPdZhxFZRa+pozdr7h+L9y6IWDKfvO6TaD5B9y1q4rMRHPN9V8UWvaAP9GYdqXgsZ+py+PDIna+zF2+G8W8tMIuUP95d3BJ3/hQvIsQdTt+/EBHSar99CZpYrbS1zelP02pzFbeh09o1lrPCU5Ks4ZpqKWDJDlS/bLe0rauJ2pm+XfKef1mZVzzoiTP9N52lgbZbTavlF9bQ4PyXrhKGc4NJi449wfinrJqqaP3PlUVR9TJx/EH9hWqV79BXg/SpHt7ac1JszXRbVv6htDGv2Ruf85w+/Ty77SlyvvB6W7zqh/UoXM3DZ17gno3bqttbEVM3/9xSOVxBSunN82G2KCmALy6qkIbnjpPU51luG+GRjf+vPbLVzS01Lsil7H7Fa0aMctr++78hSpav0TTGn/vd2S99pYaT/3vnjIpSQd3W7X/s7s0rdxz/EruM5crvTF4aD7pkovFoj7y3VKZueN57b77Ew22++6pCpSW6VRdVaHk5BRVVZarW/eeXt/rou1WOafeyNz9qvLeH6L2PQfK18i6zKLZpt43kIzNj3ttqywv08RvG//u9/ZSyZX56vviZNfrRmlqSRFeesNI7jOXy70Pytdsq817dPMfOln17bpp+l8+Un1drZZ//JR8J4KFZvzXvhseh9iLNKSiadbtsn27NEhSTx2WslJVre6OhqesMvV+brx6S1pz0uvqNXis+g0+WsVrvtNQOQJbSSpd1kPHGHxvu3//T9Vl/lLt2nfwem3Du3e67pkrvnxd03JuUIW9o2aa/P5aZFfZgRIVP/tLre9o1dk192i7vb/Pcb+S4740+uMzXL/nvP2AMqwPK81tn+Yt+tWV5aqvr1PX7sY9YcYcz4CODw+U993F055Hf6KBf8uW9ycUvunl30lyZDLt2LhCziro6E/+n5Z9e7Lc+/5Wf/eB2i15TEm63dVA+vX6vTp5XH/97tF31F7GFf7dd6dp0J0bNGPFbR7bRzcUaO3iTzVg1BT1aXaM+xNy+cvXKWPfu9r/darGyHt4R/638zS52bZRlkKNr10trWpac3m4badyduYbfxDN5Lz2V/WYcKqOcdu27sf56tClu2rKzTfYOjknGJSkhvoaVVeWa++uAg1L8z8O2X2JoVBl7nheUlMW0Y787zVRCmm2Yskxr0ZOWgiTOx5oynZwTjhTY3K29UGzMwLuk/vGbcq87NGA+xmZfuTbwDuFoKu93HSsvn/vbnXr0UftOxhU9qIsnJnR8xe+ownfX6VkP//Phvp6JacYh2WJNplSJBCAuqmtqZbFYjF8yIeiurK8qRKcFPjqWrlgjiYt+bOqb9muLt16RKQMoWg+06dTl03eLap9dMgrp8j5sM7Y965Ki+9QnwFDvcZ/5M17VF0GjpVz3tGCex0hSobbpCNmezJKi3eqa2pv/S3lHY/tFou0Y+NyDXvHe0ICp5IXz1HqbT9qw9eve1VcJy5sGuOT88wVCnxrjzz3Sp7TrPw7lVO8PqTy2GzG65C5B2tlD0/UVLcHuvu4qeYNEO6G//AXr20zlns+gIfZdmlY/h2mymo0mZEkpZcEXqTa17IE7vwFn8HY8NR56lO1XUPsRcoecoXGnXObNi16V90Gpmns/PO0c8wtcp/70d9nGG3Ne10Pv3KOAiWqpSXtil6BGpn5mxqZXL1Uzk67ZfMeVsamxyJYqhA0azRzZj24m7jwEtfPzYMqX40mw2y7pAf6Kf/4F7yCCPeJspyptmYbT5xSnxwlZ9/Qxx3+qU22QRqT5Hu9ueb3pUkbn/BbgTx8aL+6P+FobLP/86B2FazWKUkmxkMH0eo/wrZdy579g4KfQ9w8uyyOHm43ze81Q767QT11RHenvK476y9VF1XrotUXatWWo/RDhxzZ7MYf1GB7sbLn3CWD/m9N+N8ftOd/nleqRXaVbWwKCDL2OYYW9DYIPiXHmpdOP9b8Slcl3ajn2xtnI5mdtT9z18uSWy9OdWW5K0V01bHGDa/B2PD0+Zpa+aPyJtylWeeZn/E2Eio2e871UFq0Q4dKdsnXjBXGgu+GyyiZp8q7PlXZZT/IOchl5AenacXuJ1wNueHoV+idFWRGQf6SIP/vDmsWfeRqyPeVMm42yLfbbOr93Hgt73qipt/yid99/X3ye3dvUfWRQ5LPronI6/Tj40q2+L+frX/0ZE38+3eGr7XGSYgIQN20f7C/dlsGaPBd4Q38d1r55q2uh0lF2QGV7dutkZN+4nP/HjmPKNli194dVh09wTMcKi3epcI3LtPwP85Vai/zYxv8aX5DCTSGcHwI03n3eX6ico+53av3sOfm9zVmQ9O6hKHOdul8D0m6xuDb7C/4lKS0equsDx2r9ADjFzJKvCf4iYQ+DaFNqR1odlZfxn72q4D7hNqa7BwH1da49/Rk7npZevJlzZSkxk68uAdFfoxs8D220OlnSStjUJLwLPvsxYT4nOsWGa9PuvTf5zu+E2EymqwmXJ0bvMe/+ws+Dc/hKxNBjl7ZETlNjU5rHjlVk6qX6ibvYeeGSot3evX8+TKu7Ieo5oyPXHpXwH16yvF5/iHlf3qz4RT9I+UtjU/aIVU5xsQm+amEjtw6x+drRst93Vny14Dl8cVX8ClJtqrQxrl1fGSQ6+fmM64Ha8+Xjyu98d46a+3dyqkuU4bCW3M7ILcUXffU1uKskRqgUtPfQyd7eWjP986WGm14/1a5z7JQvym0wLG5YfU7lPfRk5p1zvWBd3Yz6qPQlhyrWum77rRz0yoNHjnR1ECqnLn3uu7xzk6OUPV/uTGUn5Htf0cfqiqOqFOXbkEdY2ZC0ok1xs9au82m6iOhzeeQyBgD2sxge3HEzpXkNkV4l7fPco3R8cU52cfQeT/3eq3g4wc1uSpPGz5/KmLl83dDWX//sRF7H6PU1TH1mwz2jKy0Ly4IvJPMDZ6PltRoP1D9aD6hB2Ckc5C9afEwY1noFfFImlyVZ7h9ZtmCGJfEvEhlAhjKStW0nBtcQZmkoNaRzCx6w9XAaIa/QDgS3P8fZiTJrolJgRt5nEKdNCzSZmx/KexzTM+7MfBOfjSfwb35REqxNEClgXcykFn4esjv6ZXuGqEesBSLzXC+ieaz9UeMj3JvW5eroXNP0JrHvOu7RkJtYHT1HAb4+EqKtgdcMs9p1Vv/CLyTd0GCP6ZR7jv365gvjJfTaskIQMPka4Y6SR6joP2NR3Pt3niFpFhsHhMbSE3rKLl3w+8qWON3ooFwhJPrjpah+YQegJGBlsSoFAMtjUV29bK0vMyQdkFMTIPYGHDY3Lhcs5yzZzu5zxTfXN5H5jo+nLOGO61b8rlmHWpar7f7xqYZ6Ks+dqRU+2q0C2Tpv89XbY2jcTR33uNat+RzlRbv1LZ1jtnrndXv6rISZb9wnUqLtvk6lQ5tXa6+L05W3vve8yYYySyKUrDuQ/ftnhN6NdQbD6VqaQhAA7DbbH6nk+/+xNHK++gpw3WUkhq8Z2Gz3peuZZ8+r4b6euW8dY/HzH3us3+m3N/XM7htXDIgZX9TevCQOcdp4reXauMy4wWBt6z+UWVZA7Xiy9ddFyoAAIi+EZbo9S73sgTXG4uWzWhd5HBsXvWD6aE8s0zO29B1pSNtueLIIa185HSvCdzG1q1XzpuO2YYDdXIU5C9R8c7Nqq1xrH3d3MyyBap4cLRWfT1X6evu0fivf6c+z0/UiHmnevRkTl5xhzL3zNa4dzK8lhS7c1mm/pryjmylBZKkdjt/MPX/dJf94vWmVsgY3VAQ1HnX53zZFGg26z3Nm2Pu75HoCEAN5L3fNI5n6SfPqM+Lk/zuPyv/Do359EytXNA0fmP5/Fc089AXXvum1W/UjBW3asVTv1fG5se1co5j+mmjKce7P3G08r+dp5zXb1dKhWMswcyyr3SkzLNHYuxnvzJcquLAN08oVRWalnODVrwSXL4/AAAI3bPt/S/LEo7H2r0QtXOj9fO19uf67C+krFQtnx94qbbmOtgqlffEb7Xt6bN8rmuZseU/WvuD/wmEJMcQsQGvztDeh6Yarn0tOWYbN1oPevkTTemq7ss93ZjinTF4bcqnrgxDya6G+nrHGt0mZRa9oVkHPzd8LXfeY8qZe6/pc0mSslK1+d7pGvflb1Rx31Blv3KLkpotw5VcZrySQEtDANrIPeV11tq7XWtYmm35kaTBP96hhvp61dXWaHqe/xnbZpZ9KckxRqC6qkK75z9iuN/k769QxvZnPBaEXv/lS67Fl52cC2kfKTugnDlZjp5Vt4XWM/a9K2WlqvzwQa1b8rmyX7nF9P8LAAAArVfZgRKN+8oxd8aApQ86NvpZO7W5EbYdmnVovs9ltJwmfHOR39fdDbEXBd6pmRmHv5alcTayzOSmVR1uMAhAJbnGhyY31Cj5vt4es8U37/BxOlRarOxXm+YeOFiyR5tXNfWgrs/+QunrHBMnbV65yOgUPjl7S7urQpm7XtLYes8OqnY1oU0SlmiYBbdRyv2eM8t2fGSQitVHA4I4Rwc5vrzJgXf1fK+HBwa1dl36hgckg4l9cp++VCk1h5RxZKH08L8NZ13cX7TN77p2AAAAaFvcexoH2fcGFXwmmiObFqmrvJczHGnxTmW2Nwark6tym7bZbFqf86XGL/itVmT8x7XElVOPp9M8lkzq+cxY9ZSkKY7eU2cgL0lH/mfcwRSqqZVLInq+eCEA9SPYmc+6qzJKJTEnvTTwhETN1zADAAAAWou799+i/2vXfNVkaYTFe6WLIQdzvaIhyz091S5ljCR5BZ/+5D75Bx3187/IfcXtaRXBjy1tC0jBlWP6ZQAAAAAt34nJ3jMHv9zee6bb36cYT+QZynKB6Qc+1dC5JwR9XFtEACqp74verSQAAAAAgMgiAAUAAAAAxAQBKAAAAAAgJghAAQAAAAAxEfIsuGlpaUmSnpU0WVKNpCusVmtBpAoGAAAAAGhdwukBPVtSR6vVminpNkneU0sBAAAAANAonAD0OElfSpLVas2RNCMiJQIAAAAAtErhBKDdJZW5/d6QlpYWckovAAAAAKB1CycAPSypm/u5rFZrfZjlAQAAAAC0UuEEoEsk/UKS0tLSMiStiUiJAAAAAACtUjgpsx9JOiUtLe1HSRZJl0amSAAAAACA1ijkANRqtdokXRXBsgAAAAAAWrFwUnABAAAAADCNABQAALjcV/f7eBcBAGJiv71b4J0QcQSgAACfXqz/ZbyLgBh7uYG/OYC2YUbNc/EuQptEACppdccZ8S4CACSkOQ0nx7sIAABEhZ1QKC741CUN+9O7ET3fuvYTI3q+1i5n5A3K6ffreBcDgIEk2eJdhISVO+6OeBcBABCiV+pPlyQVWfoHfezqjjMjXZw2hQBUUmrPPhE5T/agS6SsMo2/fbHyJt7td9+VP3km7PcrsvRz/bzrwsXanjTU1HFl120ytV/5zdtN7Wc94yNT+xmx//OgMv5wj6Zc+oSp/bMHXxbye7UG1fZ2UT1/Xs8zlNPvN1F9j1hamfmkDlyzPt7FaNHK7Z3jXYS4qfjLDuV3muWxrcjSX/uvXqelUx/UhNP/qEPqGqfSRccG25B4FwEAJEkbU47RyswnwzpHpb2Dz9furf+DJKlByZKk3L7nuV7bMPJyKatMG0+fp2UzHvU4zv7Pg5p02/88tm1LGq7Ci37U8pmPS1llEamv5Qy/NuxzJCoC0EaFzVo/lnU/WfX2YD8ei+unWefe6PVq/vEvuX6eeuqFUlaZ36Bx81n/NdyeO/Y2rTlptgbetVnKKpOyyjRk1EQN/+ca1z7Lu50kSdp94RJtPvsz5fY+W5J0QN2V2ttcS0+nzuYGZvcfPs7Ufs3t+M1CWZIcn3HHTl1cZfSl2t5O6Zc+5vo995i/h/S+RqwpY5V/wssRO1+0lFz4jXIGXx72efaqtzaljPHYVnzZMs264S1lXPNi2Of3J6/nGVE9v9O69pM19bSL1avfoJi8X7CK1dTwlX/Cy45rOQGVKjXeRfBrl2Vg1M7dpVsPVfZy3N82pYxR2XWb1POW5erdf7BmnnWNunTroR5ZhbKmpEXl/QvOmR+V8/pzQ92fo3Zua8rYqJ07kV1YG7lnlVlmG6QjLVr39xVdjo/KeeGwosvxWvmTZ1z3skp7B+WOvzPi77NXvYPa/+i/fqepp12sjSnHmD5mZ9IgDa+eq2J7T0lS4fnzdU3t9ZpV7afj53fvKKffbzTr6pd06NqNyu11pkacd68kaWz6qZpxxpUeuzvrrtYzPtKOC75VzW17NOTvSzXo6PGa/ssrJEmHrshR/vEvBPPf9VByZb4yLnkgas+XeCMAbbSrj+PmtuM3C7Xn0jzNuPkD7f/jsuBOYrEE/frwf67R6o7TVW7v5NqWN/k+1f+jRKOnNt1wl6b+3PVz2smXauLxZxm+RYW9oyRp3FWzZT3jIw0eNUGjp/xUw866Qw12iw6d934Q/x3v8hrdBHr0GeDzHOvaTzLcvrbDFA07ZrrHtvTr3lBer//n81y1N1mVlJysZd0dY9Jmnf835U3M0sFrNxruv189fJ6rubQ7cmWxuF0OMQgGQgl4bQ0NyrjiX1re9cSw3rt/1laNuWOp6/dl0x7SgKGjXb+vyHw6pPNmD7xIG9uN08YzPjR8fftvvpE9uX1I53a35qTZAf9GvS98xfXz1vMWhP2eoaq7fZ92/Gah6/ema8Lt+rI3xLZQLV1WmatCWm8J//vkz8xLHtWyGY9q9O25Su3dX526GDXMBbj3S8rvOFNFl+QF3C97yBWun0eMT1durzODKW5CWdUpw+P3g8NOC+r4Led+pdLG+3jplasjVq7mNrQz14i6sstxhttzBvze9Vpu77O1fNa/9G3DZA2vnqvh1XO12DZRqztONzw2ktae/Kbr54qTHzZ93NLU4P4uvhz6s1WzbnhLxZcFV3fap14B9+l0fOCGkZWdfxLU+/qSKPOC5PQ9P6TjmneoNJc94lqv+ofF3qCpp16otDvytOak13X48iWa8PMrfJwhdP2ztirn6OtN7buy80/UvoOjTls5M3BPoLP+WDjYMYlaTWMPZPsO7TXflqF96qnX6081PHbI6MnKuOZFWZKS1KPvUUq//k117NQl4HumzThJw8ZOU4eOnZXSzvNZNGDIKE0+6QIVJI8MeJ7mcvuep74Dh0uSLLIHfXxLQADaaMaVz2rfH1dp2DHTddQwR2tD/8GBvzTLu52kwot+VLW9nQaf6D891OKjkjLptoXqenexNp/1X+VNzNKsc65zfZGLL1umXb9fpIl/aqpMp7T3nU5w+LJFyj/+JXXq0k1pM05ybR8wdLSS7z6koyekB/w/ucprEIA2vxByxvzV7znG3/6D4fb+F79huH3W9XO0+8Ilhq917+FoOZtx8wdSVpksSUmade5N6tn3KMP9C3/6oN+yNdell/F5omXy/wX/cLHZHIHKtJuDT3vO6f9br225Y2/T1vMWaMaZV3tsn3baH7Qi3ZEWvbGxcrau/SRHBdpH4Hfw2o1Kv/wJjf1HtsbO+JnhPsOP8X6wV99apIPqHtT/kYMtSwAAIABJREFUxfnV9Fdxcg+ou/dx/G1Xdc50NWDESrv2HTwaW8q7jZAkVSQ3BTLDJ/+fJKngnM+jXh5nL9Su3y/Sll99kbA9r744ezwn3/SRqv9WGPDRvPrEV1X7970hv19Ku/aaccaVrhZvI/YAAWhez19q0t8WaODwwC3ZQ064VCVX5ktZZUpOSdHMa18PeEz9P0pUetVaLe1xumtbTtqtAY8zEk5lx/09G+wWTbn1K4/X03+fFdT5+g0d4/psbfbojUVOMnnuqX/9XHkT7vLYln3URUq/8mlN+ct/1XDHfqVf94am/+JyXVrn+fnXdOgbVhn9pRE6TTjuTK3MfFJl1xeoWx9zmQE5fc/TuCsik/XibIx2v/f64/w/FXb23zN+UN10TPpp2nj6PJ/75E2+Vw3jf2WypL6V2ztp0m3fqPCiHOUMiM9yRLnj75SyyjTsDM/6lXsDQ5W9vdadMtfw+M7XfO/3/JkXP+BV/7C4jfWfePw5GjB0tLp06xFUJuBe9da6U+YGzGabcp65e1NDStPwD6P6qNf+nRqziho7ExqcIY6t6f9WYA8/I6rBHrgs4diWNFzp174SeMcWjgC0UUq79uo3aITp/Q+riw7fuFWTr39Xg44er453l2rIqACTDwW4gEZPPV6zzr3JY9uAoaM1ZPRkdezcVZW37FTBOZ+ra/eePs9x1LA0TT4p8IQ+ZsbFGVW4LM0mJGmX6njgrPqp+TSD6luLXC07RgaPmqA98nxYr+jyU7/nXNthivfGxp62QIPLnTeTkZM8W083nfmJcvucq/xO5oN2X0qvWhv2OSQpKckxTsFfZdiXjKuf18FrN3r0xqVf8HefjRJ9Rk6VJJWNOU+rfvqCBl/1od8KdM++RykpOdn1+7Lup3i8XnRxruFxHTt1Ub2SDV/zZcSUEyRJM296z1RqTp8BQ7Xjgm817oaPXduCT7H3reIvO1R5y07V2lP87ndIXTX5iue0+sRXtb+bo9KVN/FuV1r8qMnHadnMx/ydQlJwaadbk4Z7/D7m9mzV/6NEQ0ZP9vrOO+X0v8D0+WPNGSAlp6SoY+euqk7xP1Rg/HFnuVrRo6XTec/6vUfNumGu65otuiRP5Tdv1/r2E73GFUnSoKPHedwf3a8pX1LatVefAUM04/qmCqklJbo9w+72X71OyipTxm9vV/nN27V0yv3ae0mOxz5rTpotS1KSbEFW3iqTAvdCSI5Mg1A1f675M+u8mz2uv8w/PSVLUpIsSUlKTvF//YdjR3tzvShTT7tYqb36augYg2eigYxrX1GXbp7ZQktTjXuJjOSMvMH0vs1t7uTIBkmacbHq7N7f86Zeb8d3Zmy6cbnWnTJXM8/6s6acenHIZXG+X8ONjmf1oKOPUcZVz+rITdvCOmco0s+/RZLU56jhrm2bk0dpwnFnuobgWGTX+GN/qR1JQzx6bOvtST4b5f2pTzEeyx6occ3dttEXa/yxv5StSz+/+3XsHHjcfPbAizX6kucD7rf9N9+4fk7q7qiPpqQ6rs876i9TgW2gBgwbo/YpkXveB/OZSJLNElz9prm6pMCNTy0RAWiIqtVB3Xv09upy98dMC44/nbumatRk4xSgYHXrEZmJl9R4YU35mfkKq5nPocq90pFVpml//czv/oOueEe1zR5glqR22nD6e+ryZ+NeWKelA5om3dn1+0VaOukeSdKYaScq/c+vavKtC1R8+fKAZfalWH3UZ0D4E3vkjvuHho2dFtY5evY9yiv12ZehY6ao7IYtmnXuTZryswuU2iu4FvxpN7yr6luLJDkabAaO8N3KvaW/+RSwdae+7VFhKhtjrtV72NhpHoFIfvcTTL9nIMnJKercNVXt797vc59Vxz2v6su+U8dOXTTpxHN97jfjl38M+H52k/eSNSfN1r4Rnun6lqQkr/tWTv/fevSemUkpjZfibuM9fu932VzljPIcc7+/cdzqxpRjQg4KtjUL3P0ZNnaapv31M+WO/2fAfQcOT1PX7j017vbFXuOKJOPGJTMpir6ONWO3ZYC+/+nb+qJhZkg9BL37D3b93LV7T808+89e17tz2EhBO3O9Y07tLvpQOUdfH7CBuJ2fzKBADncK7v5cmRxcxoakwEN0Ah0e1tHBaehp/m/UaVBoc0BI7v+nJG1tN8p7h6TG3u8A//vxx/7S8L4WjFp7ivoMHOY1KWW31F4eafHRtKz7KTrglg3Urn0H7bpwsSTpYDfHnA1Tfueonzg/kWH/XKtJt32j1Se+Kkla2cOz4dcfMx0RweVDWJr9G7qZlz7mUefo2td4THPX1L4quTJfhRflaOZ5f9XKzCc14+zrJEk/2ibo5NrH1KFjZyU3Xn8bIzDJWrAB6KHxFwV5fk/9LpkT1PEtBQFoDB097aTAO8VIqMGwpVmqUlJK8LN8eYy19KHzJebHqkqOwGpMzZse244aNUXHpJ9metIlyTEOYOavvFt0BwwxeDiaFpmqQ/qv/xbysdkDQ2sZTu3ZJ+RKbVJysjp26qKt5y1Q7VXGvZ9O9vbmZxIdNiHTc0PIrYuxDbKmnPxbj9S0Yefdp/xOszTuFO+/zdKp/tPHi0YGnqm4wW7RxOPPkt0WeHxpxtXPa+aN7zRtMHGNxsPmsz/ThKtne2zrM2CoMi68W2s6OBpncsf9Q/tOd0z4Vt0u9AmUel0b/Ljh9qnBT+V/UI4e3LIbtvgcq9ztr6uV0/+3QU1K1CfN/Hi4wXdZdcLPfqGr625yzQYZSe4TbvW/Zr5+VZNl6ji73a6Bw9OUcdG9hq/XNJtlMtSxe0kTzvE5bt1I78veC/o9gq20SlLO6JuDPsaMPXbvBo1t5zu+e8EOUZh8UgSyJSwWDbjGe/iBvTF1MpTPLlh18t1QlXn541F/f0macfP76pW1y2PbkFETte7UtzXpT41jNhvrbs1T5SedeK7Kri/Q1D+bD1Y8J+gzDjW3twti/KKzbJ3Cn7iueT1x9NTjDb+bKe07qO/A4Rp09DFKSk7W1NMuNqyzJDc2Ziyzhz8RWrCDFGaceU1Q+5f28MymbJ4xmNfjF0GWIDElZi2jlWqe5hJPgYJAXwGL103Pz8PH7MQORsyOIfGnz8Bh5na0J+4A771XrFDxZcu0fJa5ZWp8Se41PDIFCsHRE9Ij0gPs1DwF3ZIUfoV5WfdTTE1UZLvzQNjv5dR/8EhNvvVrw5T6mWf5f2BZUjxTSv1N6mAJ4ftt7+hohV9mGxNgz8gwk/Jbbu+k0VN+6nNiiMrGsbU9R83S2JmnKHfsbRp+ufFY80C2nb8gqIYrJ6P7aqB06aTrV6rokjyl9uzjMx2+U5duyrj6eY2afKzpsnTp6T8NTnKk9UViVm1/1v5stlKubEr7T+3VVxoyy88R5mSPuFabfvaSx7b2Pwt9tllf49aNmH62hKl972FNy/z4uI6zR5hfpmHplAdU8Zcdmnf8Ap1Y87iuqm3KHBgxPl11t+/TtBuCD64jwSjDpmuqY96HzUN9N7htPtt/dpS77CG+s0v2Jwc3O2sgeROzgtp/o5/60vif/MJ133MOw6m0eA8rSO3VN/ReYB/fr64XBtP75gjyZpznf24QM5IMgsj6Ad5p5f4mwYye4BpEzAyjcCo4Z74mX+l/TPbM698K6v0TFQEoDPWcYjwbbUV7z/QUf+ltdr5ekvy3lpXJ//im/oNHasDQ0Zr+i0vDK0OEg+ycfoHHGUfK+nYT/O8QYg+te6u6behPTE3Q5fNBEmZ6XdCaZSIkd/cdLFk6B9/w1W30sVo27SE9M8h7jGJUmJgEJtDkOFOueFr5J7ysMdNOlCUpSekX/D3kysnwY0JcYNzge1DUy3+wldqrr6nJiXxpvqzBQXV3bAtwzW9KGaPRdy5XxhX/Mnw9UmOkJ/z0LK+AzezdqEtX3z0pw0+8WF16eQb3ScnRXSfZnXOJs2iyWCzaONgRfNUldzLcJ/PiB5TT7zc+J6TxOF9Ssrp066HkJIu224/SlzbP72a79h2Cqiz7E4lx5J26dJPtzgPKuOQhn/uMnuJ/fgh3mZc/5nNioY6XR3YCuObzeURK+w4dlTP6Lzrye//ZECXyPVeIu0BLrQTVcdJ4/0tp196V2REqw8yrMOoxg3t6Xz8FttCW8Ipmj/yoyceqQ0f/a2+HmpWWaFrH/yIOOtur4l2EsARKwe3YzfjmNfyP4be8hDsW1oz8ICZF6jH9vMA7hWD1Cb5nMXNObLSjY+i9xM35Wytq1PGBUzaDYekbeoV5+Nl3BLX/2NsW+S9LqOmibt/D3mMy/OyYeHqM8gyWU/9/e/cd50Z953/8LWlX24vXZd0LLuPevW5gjIPp1RDAhGKKIXRIgHAJxXCXcrlcDpKQcgkHl3KXC+Ryv8clPy49pJxtMMVgyhAbML2DqcZldX9o5dWuRprRaJqk1/Px4IFXOzvzlUYz8/18y+c7OjdI36WeyngRQ5Szl02af8wFStQ7SwBTqv5D+92oq28smFm6f/bSwPg8nPm5uX17G9qv364h12+1/Tu7SpSflSyn9Ui7+bvFVMS8Xs4ms8SZYy6ee52TFmrh2V9R93Vv6sPJx+fdbtGF/6xpS4+03V+q5zqza5DMzhP12KE/zr9hAXPPvsV2m5316V7P2oZ0sLJ+xJqcbeKJhKcV7lRDbkB1f8uKopJQ2skecu5UMaHVok9cr1ETZxXcZvvcaxztK1Gf/uzzNvC5HGGU/PQW7bjkSV/Xo93yse/bb9RjcEvuHPGP77Kfs28ljDFzj6y4I4Sj+osA1KXmWGUHoFbLZUjp1voHG50NA3u31XrepNMAdP3o81yvTzbLaVKkdTvyZtbzilXCmFmf8X5dylSBQCM7SYg3B+t7C94ed77/zpHj9deEszm1ez73mm2LfPMwd8O1U2N719nNlw02it771DN9lliS0gmzsq0fd5FePtn5fMH8gunZ7a6xz1Jb6lpoXSf2zqezyrjZ51huK7xW9zYflw+RJCX6BmmZjKwp2+MWPrd/6HaWRdWNmnjp36tYLK54v+C+c9y0PFsXNnrW8pLLY6/49zx83GTF4nHFEwl1HX9pn4yfruwLQAtvNumw3ikATkfP9M9c7yTz9PRzv6375nxRU7rSz+DFa28par1Xuwz3VmJBRA/n/NJ+mxze3mubO/dzeNjCxy2uw6B326aWdrUN7PS0IWvQ9L7D5KcfcGyeLXMdPSu3t7PY5d8ygpiT3F9je/Hf9agjALWRWYdz87Lv6Mlj/t++15+sCWZulF8KVbCsFs01j/rZvvUCh63+mqNjzF77LcvX4w5b1Baf/Q9acEU481G8MGHBSr2jJr288HNhF8UHfZ/iu46xT5fuRv/5LJlF6bNNWVhsI0XPwyNRq/XDztCWlaVnmAuiVz+j0DJMGYvP/ILGTc0MIy2mxtV326DeVuOk5bbbeLkY9+ZW++O5EfRI7ELs4oaUTc/shm775Y3cWrvMYeXYTr8PvH8GUyfubznI0znqeXnw5UjWO0/WZsVpMNmnwdLB3zxxxF1KXvCHosvT2NymBcde2Kc+Mmj4GH1w5bPacdk227+3yo5rN3Q8VcSSO9k2DHa2bvcjdXNcJS3cE/d22STHDev7vpfW57mYZ5vfz8Fi5sD3F7coW3ujuyH7bgLQ52OlzVX1ehpVFBCA2siswzlrxSl9ehl2NHn0AI2gIZf8Oue10VlzopzeXPuPY39Zg/Xy2Zs8m2MSdY3NbWpd96LmHrYm7KJ4r9/NsKihaMoNJhpGO1saxmuLz/+6pi+1nu/8dNxZopG/1ky0nbMBGw4erl4GoP5FilbzlnzuAXXJ7tP0s5V/3KD8Q7u3JZwPhSy2p9pqfcu5VzjPflvuEsn0PLhi1kRMpeyzaE/uWunpKJvG5jZHjQlvJXPXu7T73sYsMq7varevzy266Hva0LnadrsPWtzVDT9sdDcfsVSxzOflRYBjeV+NRqucVQA3cUj6uzC01d91oiXp/USpmYEJQNFj2trCWarKWWt7bjY4++FcfT3YmDukcVc86Ul2W69s6rAOPMpV7NDPB3g0b2+Gs1eeuu/fhR7y+YKQzLqPYXh9hPPsmcXYvMz5PGbvhFNZaB/WO+piW2I/7exZXiN7aLe3JfPpfYbQBWrsn2dNWZsK5Y6OmQV/76dJnfkTlLwx/WxnO4nFiupx6a4foL2dufPmgkroEcawvf7mHJZOZlebKOI9+9Tz4sXamomDnM1zlHrvJfMsMrQuOKPwslfFaFtkneTIXjDfj0eT1nNH8z1b3Q7BtfLgkluL2Je3rL7GmXmhf3NEcUuzROFargQEoC41FsjOl+G0ByVK7JYMcGrO1Xfv+3dmnP0Lo/wP+G7es0pX7c5d3N1Kd8BrHb6bss5imE93qribXP95geWqZWb+70k8z0PyqTnOKyJOvTpgTkl//7aatXGy+3J1TvC+V/iB5mX2G7n0WLf7+92YKb3vdfx1D+qV1ek50p72emaxmpftF7/egyRtGHuRWtpy13R0Yt45hadShNXevuDYi7W5oUsP7W8zrD+VUsxhgq2NU6/V3DPyZ1K1c3/zctd/Wwy7e35NMjeRSjGKGX304JJbtXnZdz2LP/vP+/dibc3xs53fz/bE0o1atRafoeOlS/rVGR6py31GFLOcTxgmfrpf/gkPh+AOnrY857XsvdbUl5YZ1y81RTZCuQlAS30OMAQXRXltRu+aU+uHnxFiSZz54MpnNeQz91v+znWmUUmv1qSD2gHTilvg2o2b95yoO/cud7Rtd60/GT5f0UA9fdJvcl4v5pa1bdXdev28B4s+9v0Lglkw249W8XfUpA0TrrDJ5mh93HaLLLClio3sm4jr8driEpy0r3tBC09xvy6hH71pqVjhrKI5c0DzbPVOkY0pXvA2kPMnALW6T9ZNP8qXY0lSzeD8w1XtKixWFfFs3XmqB14twXTLnlWWr8ficc36zK81++DCwx1TqW7FepIZFUpGs6l1pRaedFXPMPm+n8nD9c6W25l56U8czUksyOZ63nHJk3ptrfXzN8PLTK2StGbJ2Ly/m3PIaZq14iS1DXXfsJSdT+Ktqae73k8+YU99KJT4r1iti9d4tq9CcpND9VzneW4XTgPQhxoXO1rKLCwpD58flRcKhoMA1Eddx1+y79+DF3q7DIYfGpvb8t7QnQzBfWDxNwpvEFCvwwETnSWimHma+xbxQjrXPZWVAKZ4Gydfo/Ezl9hWNh5ampvkqa7NfvH5qGpd96IWnbau4DZ+9ib1N+/oC/Yl3krLc2y/Wia92m+f/Xizz8wSL48lZ5S8r+2n/D7v71KK6aOe4bjezgH159FnVVmbtaL09RDzmba80L5Lm3ua79M2Pn5Tn5+dJmfp74VU7lSPYqRSyuoBzf/d2Dsi617c75p6f7CzTL+1yTpXCY6K0TawU50jcxMAlmJXyq7Byd7oSe6zIWf3FAWZqM1KzJf7tHf7nNy10rN9FaP3tFi/l5pah73ueT7fvqNNSp8P/0Stu6Xrlk7w7voNcgRN70ErL+wlAA3IhFn7h10E3809NF8LZ7AXTl2Ns1ZJJ8Ooi1VoLuLOmLMbudNes7rW0ipwpRjZdVxJf//6oC77jbJk1vB7IZmngubDA8FuDcJyZL/eprPP8cWe4OGdAb09z24DxDGT5xb8/Z4rt5a0fyuVMoen2Ln5XhgwuDfxy+YDv6eFF4SUDyGV2jekdLfDe6vFTrwrj63837lXzn3Ap2P2vr+HGhf7dAxngqq0P95Q2tSJQvq/B7ug1mpFgcixOS/JuvqCjYQZu+vss7N74YOku0ByYFNpw9ezvXLsf3iynxdjfTsMNhhXe7LfckEACknSDhUejurJ+PMorVEQgleS/s8Jbmj1t5U+Y8R+U/r1DhZn/rnOlvLJSE5OL7Wys67wfLetifHasvKH2r76Htdlyyf/tzeclsli5xRL0kcdxSVbyHfJ7lTPnKkA5lFnRmV4GYDu7ZigDZNyk5GULEL3uFR3iXOOHATp8USN6yQ+pTYCJOsa1DlyvDaMvUi1p99V8Ej5+NMrliuT7CQfr3s+MzKf8CMrvq/ZV/+PL8cofPzg740TLv5PPdCUm5XdiyszVldco/Vro/xdY9xLhc6VXSOhVGhZp+jcE70ycY4/uRSS7flzsASVLC1IlfeO4MrTB3y14O8bm9wt2CtJ79el5+eUuoZZOShUqdq98MK8v/PK+Jm52YejqKY2qfu7btb64Wc62n7GQSdpQ+dqjTvdOote84B0S+IbHXM0fenRGmO4HzaWVwSGwLyZtXD2UwfeXPTfD196qv1GFt5O9W2gaqnzdymlIaMm6sNUUm8svGbfvEovqzFdp96gRade6yqILyzYR2qiJv86dqXOeXISINY2un8ulBycxGOKxeNatOYLGj7OWcNK44i+87i9nBdWyFWHGqqdsLyov8m+1ks1edFhnu3LvWACkcbmNiUXWyUiLP1cz119vef7lLQv63cYPBsa7eHzsdDc7JoFayQVnvddLp4zHGb8ljR+RnnU7YpBABqwjYNWhZqK2kr3dW9q9ses5xI9XjtVH6VqS2p9mbD2Dm2a9+UAJ6j7X6l4ZMX3981NcyrfZyxJr6r4TJZjpxe+Ie1ORXu91XlHnKXF51n3hL6mAXo+1jvUr6Y2qUUXfFsdQ0ZYbj9sjKHtJ/9Oc9d+05eyFuRTYGo1vPL5+txljDY3OB/OXFtXOOBqOfHrerBpf02cVziT43uN3q35Z6WhqUUNN77WZ1h/PObd5+zX8OqgO0DDTsISdMbPPanSqixT86z567eaeExzDz+rqL95KTm25ONmgny/5l8+Hxvqy35LVetTY3f/BD5e9fA+tvgrnuzHjWFT0vWI2JzTStpP9xDruZlOGrKyhyo/XD9P067M31s/c/kJeuiA76jp0v8tuE+7UQdBe6s9N1FiznSrAtcpPaAo2cKLb9ecQ0q70IM05XPrVXfj6yXto7V9oOYffb5HJbJnFw9sPf4X2r76DyUdo87j+aMvLfv7ov+moalwSvPdKt85jIPXPaORNzxR1N+MmTLPNrOnJA0+4rN6Nj5CE5daZ+Es1aMr/03rR6zxZd9uhgw2j+itGAwfaxTcdvSk2Zpz1S9yApvsSsTCnd/Q3s70GpKpmuIf8hsnX6N7BzjPDBt28pKiBFDWt5z2jPk4P/T50/6iJ476z5L28Xaq+CDh1bM2ZP3k8LMudM1EYFRDfqV/l3oDUH+qeiNvMH3Zb6mM+Su0YUjfxI9hDAd2Ksx73NDRE6V1O2yzTtsZt8zd6BpJ2lnTW5cZcOIttg2Esz92im1SsGSetW6Tibh2XLZNOy7dGujlP2ftt7RxYOHcGYlkuA2KQSMAhaub35aDf2C/TltETZi1v8YYpScq8PTeVU6V7DI3ZvJcjb7+MbUP8qr1vu83YdrSI7V47S0l79WrSmPhZW1symBRCX5FHZqz6tNaP/xMzV59k8VfFdY0clqB+UJlLoDr+NWaYfYb+WzkhOkl934+lSr+fWQ3oLh5btldU8/ERxe9T2fH9WW39scN57BZx++9NwYdZA1a6mx6R1AeTc7K+7tKXOPRSsfI3qHymcSC/cX97umLSW0DBqmtY3DvSwF8NZN19YoNy/8dkKSZy0/0vyARUqG1ABTDTdf+9P2PKbnFrJx1DN/P8vViE2sMOuVWbWo9WFOWeD80LKWYNs37sjZOvsbzfVejHe1TJEkvabDNlv55f6z/a+k6UVffqMXnfc22F95Sev0Mx5uXVQ9oAJz25CR6eqe9WIojY5eHw/ojcVbjfd/Ph4nKylPQ3fMp97+GqiHcGdtvKbQ9MW/nWb6XaihqRMp744/U+tFWc1OlgWNKX84q6ras/GE6eWGPhZf+IGebzfULNHzslJzX3WhIpq/teCRuNGlTVq7RBynrUUMbhn6iIofZFlJd7xbwwGsaoEFDvWkpHz7W0PxP/dRicejSfRRLav7R5zte1gU2Bk2SJDVc/Oc+LweVSXNXqkYLT7Y/l5vmFj+c245f73Dj1M/ZblNOD+VYv0fqkzWTPD/Gjibrxq/+ho+brI1Tr9XjB+auF+yEVWPaW2vvc7UvPzhtmMjXu7RhyMmadXLfpDIf1vVtXNpy8A/0wMLik33lY7V2cz5eLFvyzKr/1vqRZ+9brqY/p4e4d8a6nPVe1w87w8FfZn/2wUYCNbXJPj+/n7Retuxxl+tKWrFbTSCfWJ7zUwky95G6RvtpS4mll3h2vx87MD2c9R9PKtzrGKSWtg49e+SPLH+36JP2+Su2JfbTppZg5937qXwniQF5+B0O7O5pSY3qWoI7U7Wqj+3WWyfc6SK1Efrb1PIxzX/3t/t+7j90169Mmv2TEL0ba9LArIdz8+CxkqQPBvfL+FsWlZmeaydiQ88erl+gtuO/IrcLJhldK6Vfp//93qee0VibpE+uFBGYLDzpKm3d/Gf7DUNQ6pw8N0PUs4PWRRfmrl+6p6ZvADFl0RGeJqyavfJU/fGP39SyxCN5t3lg8Tc0d/3Fnhxvwqz9PVmDvOuEK9L/WHdn74tFBsiJBu+y+nopFXN3v0x/f/t+h99f8/sCK4FXN6uGoIfr5+mD0QepeftvfDtuc513Pd/rR5ylRMdYFbeKuXfGX/dgSEf2R/k0LaMs3N91sx5sKv2BV4r2Bn9Tmmd6vKwCUCdB6V8TEyxffyo+1tPhciPG52ZdQ2GP107LeW1vZu23iA0FHT9jkZ4+6TfqOuOLvh2juLfsLKAYOSWobNjF213TVNISPtm9Ls2tA3wZ2RC0R+p61wD0cq5aqQFost5pcF/8cf529yd03Ec3+ZIt+flUOnnKhklX6oUz/lfbT/l9n9/XRjRQy7YzVasRy+2XkMgeHTJn5Sf8LFJR8s0/LF7f75Zdkrd8vcCdo70fKRG09kF55nQXeIjMvOZ3WnTqddqTSF/LsUQaGaGnAAAdyklEQVR4y9E4sXjtzeo64fLSdtLzeWxLOBvJUskIQIu0YdKV2jiFIY35zDviLM256hehHHu/wenW63MOGBfK8Z3YcelWjbryj5a/2+/6zUre+EbJx9h3u49YwFQOLBsQbLKJ1g71Zs6KHasK+7ipC3IqyVaJg9za0933mHN3lp54rG8PcrR6QL1w3+wvyDzqZ/4dIKBe48y18GGT9dJHYWtqaS/6b5wO231XjXooZd1Q6IbVNRlPNmrEftM0ZvJci7/Ib8vHvq+/3R1uMFd/4+saPcl5Q83GaddHaih99vzDVASqwX4tp7T1+GDqYg8t/ZaD95D/vjV6zW1aP/p8TY3EerXB2BNL2m9U4cK/8srMolOv08KTSeoSRfGeykVtnvTbkvTGhY+VfJy9LofsSFJbx2DVN7ibJ4IAFKyg5v7u/pYVmnfEuT4VJfzb868fe0WSD8PNI/De/LLguItkzF8RdjGyuDt3+6qLqZRe0cB9//ZKlJvHwi1b4Qav6Qccq9v2us9sHaTM5zho8tI+r78lF8nLSvRBe99exg3G1el/LHdfn2s7qdgGuWAb3LwYfu3ErAJrnDt5dnQMGaHFZ385kEaK6Nx30t8F86if6bnTojlNwm+VWwuIiA3G1TmT9+GvQjeY9oGlL73xWsN4+41QFXYNnu7bQ7P/HFDHPOz5njrMn+GAbV3pNeOGzz3cl/079UrN8D4/R3mtwIza6ccGcpzsimNU57s7UkzQ7NOoEavd2hWrrD/zfvzIZP3IQbcXtf38s77a5+dFqz+nV9c+pGlLjnBdhpETmOYiOUsUl+r2b13istLvWjDmr9CoCZWfBdkKAajPFq3+nBZd9L2wiwEv9dxAMhWE7Cx6H8bDX0i4d/HxyqnARJbbILFMjB3U9/tcOEBz/n2b3LVSWrdDoyb2ZijcvvoebV99T7FFLEr/OdYd5/+3r8fzw+yVxS34Hot7l5TKdaOIBbfB/stnb3L9PXHaWFQODRFheDHWqQ2d+Xu7cvX9HB895N8lSdtaS58HPnr6UvuNstQmc5e/GDIi6Ok6PJORxj2GLLiAY/d33ax5916e0zKd+WlL3Wx1rM7NrBi8TABK+1Kx3m2dJL2eJ0NlyAG986MHe97vnXGjHtj2og587/+XtJ9Skv84teP8+7XjteeVmd3XMaT//MbKqxTsN61L+mkpe0jJj4qz2wrY0NETPS5JtuCu8bzvP2KZobMNv+FJDbffLEfm1jltyRF6cdhGzRjmfhmzd9SoVn2Qs4ZreQj+3K4fda4WPxd+J4hf2eLL5fgZXuZoKHfUUKvYtkR0k/X4pZRLP2/80VNh6F50iYMseIiyOWut1uLKfXA9FR8rSYrV+p/l9MNU+MkKCg0H7DrhcnWsuCTA0rg3ePjYwOZFRUUsHte9s/6u6L+zzPIdjTpckYovtL+9E06fQpVZUR0+bnJJSXeCWne5WH6sv+yFsD+vqA0lD3tgWGN7eq3ht1upKxKAVrFBF/06sCxpQXC2RIAXd5++Q3BROQovm9F7vl8duCDntbA90LRMUkgP2KxL77CPvhRCATwSzbptybqOL6GBIOVP30F0rpwsAV48FfpV68PPQD5qU0yGz1he8PfNI6t8vmjAgXC+w4XdAzlqwgw9ccRdmrn2O6GWIwoIQKtYW8fgiuwNiNhzKXCZtx+1B3TZCqkFubE5vczEE03zJUkv1RYettZ6yDV6WYM1vst9Ug07lfiNuq/t0KyfqiEscKYaP4n6GcdJkh7o9nOYb2FerrUaGR5OB3FzD1o/0n7NUj/d37xc05aWR+Zi75XL1JXgTO5ayWoIIgAFHMvfohatCgNJiHyS/Xkm0sNi/Vw4u21gp7avvkdTL/2pHllxh0Zc8F8Ft58wa6mGrtuqtoGdnpel2LEFfxp3WZ/fPXnszz0tj5cWXPETPbDolrCLUR48DY4Cum8WUeaZB52osTv/TWZqdOgNmSmbAnxnTzkENNXTA1rIrraxoR077LmPbw4/UJLU3jkm1HIgeghAUVU8eWb17KP3th5+APrWRU+EXYSqMuMTX9D6YWdoznGX2W9cgjHGbNXVN2rGsuPV1jHY12N5qX+jTLmMtIhaY1JU2AVDKKyoYMlhwPzFPZ+Q1u1wWaJgeRksvnTcT7Rx0Co1t7R7ts9SPB8bJklK1jWEXJJoWnjmF/X6eQ/7nDwM5YgAFPBKiJW0AYOH9RaDLLiesgpKmlratfj8r9vMGa1MLfW5ydNT/drZyy1cKafelKBYJyHyLkDfmuqfgVh6LuYmx2phsdpoBQbPpwZJkpJtdmtSl/930o93MHHOMi28+Hbf1l8uVutlf9HDB96mQcOd9PCFcE49umafPfUe3dte/PSOeCLh8LNBtYnGFQx4wNFttoSK5ogZB0iSahec1XeXEes1YQ5o6famrD676vs8M3WXH+5ZKUl6Vw2Ku/heRe0ayasS5995wvvv/kfKze78Uvscz/b/ZM0krR93keYed6mrv/frav/23mN07q5Pa/bBxa3n6qWg55mGnfjFz+O3tg/UzINOdLZxTe5apOVi9KTZ2tvc22hy76y/DbE0xePWHj0EoKhA+R82pQRlQ0dNkNbt0IwDV0liWFql2nzg9/Tymet7X6jiJ1fmrd+yd5XG7vyRZdCQkX01ZK6ze4eeqr3XvuFJb8X6UeeWvI98UmX2KHz4wNt8X/bhN3vn6rnYcHUe/pmsV7s9Pca/7znI0/1le7tlohaf+QXVJvtW+teP+aSj7O9+NeB1K67fdM/Le03Ut6V7SD9sdr9WplN+B4Yvdqbn/zV3eD8vHeFKtg4JuwiuUG2LjtyxVABQxWYd9HHL16uxR7l3YG3ve7frPUn1+3eixoPHzLodWlz6Xhwoj8YGxz0uJXhLrRp1w+OSpBd8OsY39x6j1TW/z3rFu2usO9li+friswoH7rWJmHbvTamjybv1d4t5V5PmLtfmt7+r2UuO8uz4Yek65xa9/vo1GtQ5MuyiRNKG8Zcp3jhAXT4eY+iS1dKdt3uzsypujIX3yqvZF4igqGWd3dwUTFVdkl44Y4O2rbo7sOMhWKXWN8pllEBUrl0/bVt1t5468Veu/vbt2nRvR6LW2yGEz6Wi1zM2d/QASdLnjwtv3cZZK05SXX1jaMf3SqKmRoOGjgq7GJ7eh+7tONr13yZaensNH2pYpEWn36SuE67wolh5jZu20PNkVetHn6dEjXcNNKhOBKCoCve3+DHUK5qV1imX3KWXzrpX8UTC92ON2G+Kxs9c4vtxwkWrr53+geq+KyPr9fXjLtYTh98ZVJHQz/iZS7Tf9IWu/nb42ju1af4/pKchVIlmi2RbwNQ1X3f9t/OPuVCbWg+WVEbz4q3E4pq2/7FhlwJljjssKkeB+/nMS/5Db779ujqCK01o6huaNGyMEXYxKk8Z95K9cu4DStQkNcin/fe/9GIWEejiMz/v09HhtwGDh2n+Uef5fyAvr7ESu+/DTpyDaIr3zN19O96mpmL/NpFQzayPS3/6jfcFC0CsMV2Dijd2BNLA7aUyDvcrFj2gqDhWdZjaZJ06huSm/fcE8yIqWwWc386R410NhSvmnX9vbzpF/4upQZpx3Kd0X/vhmnLSTUUfE/56Jj5KrysaaygC5aaxuU33zbxJibPsE1lFyavq0PoRZ9lvWMD8k67RvTPWaf4Jn/aoVMGjWSk66AEFXOpNzxJugPJw/QLNDLUE1YJHVyF37T1Qd+1NZ71samnXgst/HHKJihf2tRyEsddvCbsIVaeMB09UCG9PwIJVl3m6vyAMWfe0Ss1bW1Ob9H3OKqoHASjglRBqGXuvfUMzIrIgN1CuYjGuocrjrjGh8psgEJhYephqLFZew1WBIPDUBcpYoqbGkzUWYa8aMqX2VwGjj4tTdW84WlIRGmVQhZc7PDb7xGu0cfCJmnHy9Ra/5V6D6kbNFRUj6Nt5lCpLQFCsrjPiNkTFfe2Hh10EQJLU0NSihRfdpqaW3jnXNGxEB8+tcBGAouIEfX+vhnlj1axx3imSpGEzPxZySYJXNZWlqnmjlW/vkGnpf0SodklG3ZBxfVe9VCr/eu18O8LBHFDApd4e0ExFh/acSjT9gGOlA3ZoZNgFibBUhTTC0JgUMk8ChRL34cNXgPgHCFdv0khEBTVmAEB1I0IIzaZ5X9aWutlhFyMH3wgA8A8BKFCiWISGegFeqpavdn3LQEnSziaf1gpGXvOPPl/vjz/Ks/0lmjokSanGgZ7tE+Vpw8hzev4VneaEeE2DJGl3TUvIJQHCxRBcVIxU4LXl6DzUAH84u6bKPVCd3LVSD759q2YdcHzYRalK4/c/SW8/drM6D7605H3NO+p83du9R/OPPN+DknmDJwUypu9/tDY8e7mmHHlJ2EUBQkUAiooT9HIZVC5QuZwlbCjz+FOSNOeQ08IuQtUaNHyMtO4FtdtvaiueSKjr+NID2WrAyPPgxeJxLTrtxrCLUXXKvZG0EjEEFyhZJrtayMUAAsBzHJWsUhJqQaoZOlmSlBxqhFwSRAb1tMigBxRwiWoKkMYzHZUm6JE08N68I87VX0dO0bzZB4RdFAD90AMKAACAihKLxzWR4BOIJAJQVIygeyS3N06XJO2J1QZ8ZABAuaAzFQD6YgguKk5Qz/rJF/1EW7c+rF2/+Btpr6hloGoxHB2VgmQlgDMPH3ibuvfsUvRW8UU5IAAFXGpoatGEWUv16C/CLgngF4e1cWrtqDC0JwKFzTzoxLCL4ByPqMhhCC4AoCQ82wEAUUe7UnQQgAIAnCPaBIpEtRcAshGAomKENQrw3bGHSJI6RrLWGCqNs4oz1WtUCtpXAMB/zAFFxQl67s7CUz6rd9+9UMPbOoI9MADAFzSqAIB/6AEFShSLx9VC8IkKV1eT/3FBrxEAAHCKABQAYGv6iLawiwAAQNFSNJNGjqshuIZhtEn6oaRWSUlJnzJNc72XBQMAhM3ZQ5tVWFApUj58mVnSBQhX5rKOcTFGhtse0E9J+q1pmgdKWiPpVs9KBLhECxcQDj8q7UCYKrmeyuWKalXJ13W5cZuE6J8kfZS1j53eFAcoXYz0EQAAFMSTEkBYbANQwzDOkXRFv5fPMk3zPsMwhio9FPdyPwoHAIgGejkBAIAXbANQ0zRvk3Rb/9cNw5gh6ceSrjRN8x4fygYAKDOP33RY2EUAXBvYXCdJqqtJeLZPehqBcNF8Gj1ukxBNlXSnpJNN09zsbZEAAOWqIeldxR0I2ldOnKW7t7zkadbn7MQnbQ21umD5eM/2DcA5GoOiw+0c0C9Kqpd0i2EYkrTDNM1jPSsV4MLEIS167s0PVV/L6kJAkGhdRqVoa6zVKV2jfdv/5hsO8W3fAFAuXAWgBJuIoltOma2Hn9+hIa31YRcFqAjZ0z4JMuGlb582TxOGNIddDABACNz2gAKR01Jfq6UTBoVdDKBiZAed5CCClw6bPjTsIgCoYizdFy7GKgIASkJwCuTHvDMgulgbNBwEoAAAS1bPZWJNAECU9X9OsYxY9BCAAgBs0UoMACgn/Z9bMR5kkUEACgCwRQMyAADwAgEoAMCS05iT2BTIj04XAOiLABQAAAAAEAgCUAAAgCrB8hOoNnzjo4cAFABgKTtzIA9woLIwNBjVhq98dBCAAgAcI509AAAoBQEoAMCS05T1BKVAfvQ0AuHiERU9BKAAAAAAKhqNQdFBAAoAsNSnZ7NAEzKtywAAwCkCUACArUyM6XRYLgAAUUDm5+ghAAUAAPBJjNybQERwLUYFASgAoCR0igIAAKcIQAEAtpjnCQAAvEAACgBwjCVXAABAKQhAAQCWskPNQsNsiUmB/GaNag+7CEB14xkVOTVhFwAAUN66iUABS8986ciwiwCgB/kKooMeUAAAAABAIAhAAQC2bjh6athFAAAAFYAAFABgKXtkrTG0Ne92DGsCygcj5gGEjQAUAACgysRoOQIQEpIQAQAsWdVPrTpP6FGpLLeeOld/MF8NuxgA4AkeUdFDAAoAsJQdWBbqK+HhXlmOnDlMR84cFnYxAMBT9PlHB0NwAQAAAACBIAAFAAAAAASCABQAYKmhNhF2EQAAKIlVngJyF4SLABQAYGnCkGZJ0toDxhWc58mDHAAQVameJ5h14mdmhoaBABQAUFBrfe2+f1s9qlOkIQIARFyMYDMyCEABAI4RagIAgFIQgAIAbNFuDAAAvEAACgAAAAAIBAEoAAAAACAQBKAAAAAAKhKZ2qOHABQAUBIe7gCAqLNehgVhIAAFAACoErQXAQgbASgAoCAqrEDloTMIQFgIQAEAlqyGKzHcFgAAlIIAFABgq9DcmRRRKQAgonhCRQ8BKAAAAICKxrDz6CAABQCUZOF+AyVJyRoeKQAAoDBqCwCAkjTUJiRJE4c0h1wSAAAQdQSgAAAAAIBAEIACAEpCDiIAAOAUASgAwFZNPP24WDFlSM7vUj05BgtlygUAIAwdjUlJ0gXLx4dcEmTUhF0AAED0JWvi+ss1KzSoOZl3mxg5BgEAITtt0Wj97olXNWNEmySpIZnQM186MuRSIRsBKADAkRHtDWEXAQCAglZM7iTgjDiG4AIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAEuHTx8mSTpkWmfB7ViGBSgfq+aO0OShLTpjydiwiwKgSpGECABgaerw1qISObAMCxB9Q1rq9T+XLwu7GACqGD2gAICS0AEKAACcIgAFAHiCDlAAQDlYMXmIJGnCkOaQS1KdGIILAAAAoGqcvGCUjpg5TK31tWEXpSrRAwoAKEmKLEQAgDISi8UIPkNEAAoA8AZZiAAAgA0CUAAAAABAIAhAAQAAAACBIAAFAAAAAASCABQAUBJSEAEAAKcIQAEAJckkwSUFEQAAsEMACgDwBElwAQCAHQJQAAAAAEAgCEABAAAAAIEgAAUAAAAABIIAFAAAAAAQCAJQAECJWIgFAAA4QwAKAPAESXABAIAdAlAAQElSdIACAACHakr5Y8MwJkvaKKnTNM2d3hQJAFCOYiwECgAAbLjuATUMo1XSP0r6yLviAAAAAAAqlasA1DCMmKR/lvRZSR94WiIAQFlhBG5lGNJSF3YRAABVwHYIrmEY50i6ot/L2yX92DTNzYZh+FIwAEB5YQBuefvDVcu1ey/NCQAAf9kGoKZp3ibptuzXDMPYKumcnuB0qKRfSVrmSwkBAIDvGpMlpYUAAMARV08b0zQnZP5tGMYzkg7xqDwAAAAAgArFMiwAAAAAgECUPN7GNM2xHpQDAAAAAFDh6AEFAJRk/OBmSdLpi8eEXBIAABB1ZBwAAJSkoympZ750ZNjFAAAAZYAeUAAAAABAIAhAAQAAAACBIAAFAAAAAASCABQAAAAAEAgCUAAAAABAIAhAAQAAAACBIAAFAAAAAASCABQAAAAAEAgCUAAAAABAIAhAAQAAAACBIAAFAAAAAASCABQAAAAAEAgCUAAAAABAIAhAAQAAAACBIAAFAAAAAASCABQAAAAAEIiasAsAAABQSa4+zFBrfW3YxQCASCIABQAA8NCFyyeEXQQAiCyG4AIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAUCYmD20JuwgAUJKasAsAAAAAZ+785GK9+f6usIsBAK4RgAIAAJSJlvpatdTXhl0MAHCNIbgAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIBAEoAAAAACAQBKAAAAAAgEAQgAIAAAAAAkEACgAAAAAIRI2bPzIMIyHpq5LmS6qTtM40zZ97WTAAAAAAQGVx2wN6uqRa0zSXSjpW0gTvigQAAAAAqESuekAlHSppi2EYv5AUk3SJd0UCAAAAAFQi2wDUMIxzJF3R7+XXJO2UdJSkZZJu7/k/AAAAAACWbANQ0zRvk3Rb9muGYfxY0s9N00xJuscwjEk+lQ8AAAAAUCHcDsH9s6QjJP3UMIxZkp61+4NHH330dcMwtrs8HgAAAAAgul53slEslUoVvWfDMOokfUvSVKXngF5gmuYDRe8IAAAAAFA1XAWgAAAAAAAUy+0yLAAAAAAAFIUAFAAAAAAQCAJQAAAAAEAgCEABAAAAAIFwuwxLxTAMIy7pm5JmSfpI0rmmaW4Nt1QwDGOhpL83TXO5YRgTJN0hKSVpi6SLTNPsNgzjBklHStoj6XLTNO8NrcBVxjCMWkn/ImmspDpJfyfpMXGeIsUwjISk70oylD4vn5S0U5ynSDIMY4ik+yWtVPo83CHOU6QYhvGApHd6fnxa0nck3aL0+fiVaZo3Uq8In2EYfyPpGElJpc/FPeJ6ihTDMNZIWtPzY72k2ZKWi+spMnrqev+qdF1vr6S18ujZRA+odJyketM0F0u6RtI/hlyeqmcYxtWSvqf0DUmSvirpWtM0D1B62Z9jDcOYK+lASQslnSLp1jDKWsVOk/RGzzk5TNI3xHmKoqMlyTTNpZKulfR5cZ4iqedB/x1JH/a8xHmKGMMw6iXFTNNc3vPfWZK+LelUSftLWmgYxhxRrwiVYRjLJS2RtFTp62WUuJ4ixzTNOzLXktINb5eK6ylqjpBUY5rmEkk3ycM6BAFo+kv+P5JkmuYGSfPDLQ4kbZO0KuvneUq3XkrS3ZIOVvq8/co0zZRpms9KqjEMY3Cwxaxqd0q6ruffMaVbvDhPEWOa5n9JOq/nxzGS3hbnKaq+onTl68WenzlP0TNLUqNhGL8yDON3hmEsk1RnmuY20zRTkn6p3vNEvSI8h0p6RNLPJP23pJ+L6ymyDMOYL2mapB+L6ylqnlT6uohLapW0Wx5dSwSg6Q90R9bPew3DqPqhyWEyTfOnSn/JM2I9NyNJeldSm3LPW+Z1BMA0zfdM03zXMIwWSXcp3bvGeYog0zT3GIbxr5K+LulH4jxFTs9QtNdM0/xl1sucp+j5QOmGgkOVHs5+e89rGfnOE/WKYA1SOkj5uNLn6UeS4lxPkfVZSTcqfT7eyXqd6yl87yk9/PYJpafzfE0ePZsIQNNf9pasn+Omae4JqzCw1J317xale3H6n7fM6wiIYRijJP1e0g9M0/w3cZ4iyzTNMyVNUvoB0pD1K85TNJwtaaVhGH9Qeh7U9yUNyfo95ykanpT0w55W/ieVrnB1ZP0+33miXhGsNyT90jTNXaZpmkrPe8+uDHM9RYRhGO2SDNM0f6/854PrKTxXKH0tTVJ6BMi/Kj2vOsP1tUQAKv1F6THOMgxjkdLDNhAtD/bM6ZCkwyX9SenzdqhhGHHDMEYrfUN6PawCVhvDMDol/UrSZ0zT/JeelzlPEWMYxuk9yTikdE9Nt6RNnKdoMU1zmWmaB/bMhXpI0hmS7uY8Rc7Z6pl/ZhjGcEmNkt43DGO8YRgxpXtGM+eJekV4/izpMMMwYj3nqUnSb7meImmZpN9Kkmma70jaxfUUKW+pt2fzTUm18qiuRxd2eo7ASsMw/lfpuWxnhVwe5Pq0pO8ahpGU9Liku0zT3GsYxp8krVe6IeWiMAtYhT4raYCk6wzDyMwFvUzS1zhPkfKfkm43DOOPSj84Llf63HA9RR/3vei5TdIdhmH8WekMkGcr3ajzI0kJpedAbTQM4z5RrwiNaZo/75mfe696r5OnxfUURYakp7J+zgyZ5nqKhn+S9C8910lS6brfJnlwLcVSqZTdNgAAAAAAlIwhuAAAAACAQBCAAgAAAAACQQAKAAAAAAgEASgAAAAAIBAEoAAAAACAQBCAAgAAAAACQQAKAAAAAAgEASgAAAAAIBD/B2QMX7YmIB69AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "lfp = data_loader.lfp('1833-200619-2', 6)\n",
    "# lfp = data_loader.lfp('1834-220319-3', 6)\n",
    "# lfp = data_loader.lfp('1849-010319-4', 6)\n",
    "times = np.arange(lfp.shape[0]) / lfp.sampling_rate.magnitude\n",
    "clean_lfp, _ = compute_clean_lfp(lfp.magnitude[:, 0], threshold=2)\n",
    "plt.plot(times,lfp[:,0])\n",
    "plt.plot(times,clean_lfp)\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.psd(lfp[:,0].ravel(), Fs=1000, NFFT=10000)\n",
    "plt.psd(clean_lfp, Fs=1000, NFFT=10000)\n",
    "plt.xlim(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(16,9))\n",
    "\n",
    "# plt.plot(times,lfp[:,0])\n",
    "# # plt.plot(clean_lfp*100)\n",
    "# plt.plot(times[:-1], np.diff(lfp[:,0].magnitude.ravel()))\n",
    "# plt.xlim(64.5,65.5)\n",
    "# # plt.ylim(-250,250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive_0_channel_groups = [0, 1, 2, 3]\n",
    "drive_1_channel_groups = [4, 5, 6, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbc6cf0ef60>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# action_id_0, channel_0, unit_0 = '1833-200619-1', 6, 163\n",
    "# action_id_1, channel_1, unit_1 = '1833-200619-2', 6, 28\n",
    "action_id_0, channel_0, unit_0 = '1834-220319-3', 2, 46\n",
    "action_id_1, channel_1, unit_1 = '1834-220319-4', 2, 60\n",
    "    \n",
    "\n",
    "# lfp_0 = data_loader.lfp(action_id_0, channel_0)\n",
    "# lfp_1 = data_loader.lfp(action_id_1, channel_1)\n",
    "\n",
    "# select best channel among other tetrodes in same drive\n",
    "if channel_0 in drive_0_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_0_channel_groups:\n",
    "        if channel_0 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "elif channel_0 in drive_1_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_1_channel_groups:\n",
    "        if channel_0 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "lfps_0 = np.hstack(lfps)\n",
    "\n",
    "if channel_1 in drive_0_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_0_channel_groups:\n",
    "        if channel_1 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_1, ch))\n",
    "elif channel_1 in drive_1_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_1_channel_groups:\n",
    "        if channel_1 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "lfps_1 = np.hstack(lfps)\n",
    "\n",
    "sample_rate_0 = lfp_0.sampling_rate\n",
    "sample_rate_1 = lfp_1.sampling_rate\n",
    "\n",
    "lim_0 = get_lim(action_id_0)\n",
    "lim_1 = get_lim(action_id_1)\n",
    "\n",
    "sptrs_0 = data_loader.spike_trains(action_id_0, channel_0)\n",
    "\n",
    "sptrs_1 = data_loader.spike_trains(action_id_1, channel_1)\n",
    "\n",
    "cleaned_lfps_0, sptr_0, best_channel_0 = prepare_spike_lfp(lfp_0, sptrs_0[unit_0], *lim_0)\n",
    "\n",
    "cleaned_lfps_1, sptr_1, best_channel_1 = prepare_spike_lfp(lfp_1, sptrs_1[unit_1], *lim_1)\n",
    "\n",
    "coher_0, freq_0 = compute_spike_lfp_coherence(cleaned_lfps_0, sptr_0, 4096)\n",
    "\n",
    "coher_1, freq_1 = compute_spike_lfp_coherence(cleaned_lfps_1, sptr_1, 4096)\n",
    "\n",
    "spike_phase_0, filtered_lfp_0 = compute_spike_phase(cleaned_lfps_0, sptrs_0[unit_0], flim=[6,10])\n",
    "\n",
    "spike_phase_1, filtered_lfp_1 = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[6,10])\n",
    "\n",
    "spike_phase_1_stim, filtered_lfp_1_stim = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[10.5,11.5])\n",
    "# spike_phase_1_stim, filtered_lfp_1_stim = compute_spike_phase(cleaned_lfps_1, sptrs_1[unit_1], flim=[29.5,30.5])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(freq_0, coher_0.ravel())\n",
    "plt.plot(freq_1, coher_1.ravel())\n",
    "plt.xlim(0,20)\n",
    "\n",
    "plt.figure()\n",
    "bins_0, kde_0 = vonmises_kde(spike_phase_0, 100)\n",
    "ang_0, vec_len_0 = spike_phase_score(bins_0, kde_0)\n",
    "plt.polar(bins_0, kde_0, color='b')\n",
    "plt.polar([ang_0, ang_0], [0, vec_len_0], color='b')\n",
    "\n",
    "bins_1, kde_1 = vonmises_kde(spike_phase_1, 100)\n",
    "ang_1, vec_len_1 = spike_phase_score(bins_1, kde_1)\n",
    "plt.polar(bins_1, kde_1, color='r')\n",
    "plt.polar([ang_1, ang_1], [0, vec_len_1], color='r')\n",
    "\n",
    "bins_1_stim, kde_1_stim = vonmises_kde(spike_phase_1_stim, 100)\n",
    "ang_1_stim, vec_len_1_stim = spike_phase_score(bins_1_stim, kde_1_stim)\n",
    "plt.polar(bins_1_stim, kde_1_stim, color='k')\n",
    "plt.polar([ang_1_stim, ang_1_stim], [0, vec_len_1_stim], color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(1.) * mV"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfp_0.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2\n",
      "1 2\n",
      "3 2\n"
     ]
    }
   ],
   "source": [
    "if channel_0 in drive_0_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_0_channel_groups:\n",
    "        if channel_0 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "elif channel_0 in drive_1_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_1_channel_groups:\n",
    "        if channel_0 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "lfps_0 = np.hstack(lfps)\n",
    "\n",
    "if channel_1 in drive_0_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_0_channel_groups:\n",
    "        if channel_1 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_1, ch))\n",
    "elif channel_1 in drive_1_channel_groups:\n",
    "    lfps = []\n",
    "    for ch in drive_1_channel_groups:\n",
    "        if channel_1 != ch:\n",
    "            lfps.append(data_loader.lfp(action_id_0, ch))\n",
    "lfps_1 = np.hstack(lfps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(908442, 12)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2725326, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO fix artefact stuff from phase precession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "NFFT = 8192\n",
    "theta_band_f1, theta_band_f2 = 6, 10 \n",
    "drive_0_channel_groups = [0, 1, 2, 3]\n",
    "drive_1_channel_groups = [4, 5, 6, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "coherence_data, freqency_data = {}, {}\n",
    "theta_kde_data, theta_bins_data = {}, {}\n",
    "stim_kde_data, stim_bins_data = {}, {}\n",
    "\n",
    "def process(row):\n",
    "    action_id = row['action']\n",
    "    channel_group = row['channel_group']\n",
    "    unit_name = row['unit_name']\n",
    "    \n",
    "    name = f'{action_id}_{channel_group}_{unit_name}'\n",
    "    \n",
    "    # select best channel among other tetrodes in same drive\n",
    "    if channel_group in drive_0_channel_groups:\n",
    "        lfps = []\n",
    "        for ch in drive_0_channel_groups:\n",
    "            if channel_group != ch:\n",
    "                lfps.append(data_loader.lfp(action_id, ch))\n",
    "    elif channel_group in drive_1_channel_groups:\n",
    "        lfps = []\n",
    "        for ch in drive_1_channel_groups:\n",
    "            if channel_group != ch:\n",
    "                lfps.append(data_loader.lfp(action_id, ch))\n",
    "    \n",
    "    # merge lfp of other tetrodes into a signle AnalogSignal\n",
    "    lfp_arrays = np.hstack(lfps).as_array()\n",
    "    lfp = neo.AnalogSignal(signal=lfp_arrays * lfps[0].units, sampling_rate=lfps[0].sampling_rate,\n",
    "                           t_start=lfps[0].t_start)\n",
    "    \n",
    "    sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
    "       \n",
    "    lim = get_lim(action_id)\n",
    "    \n",
    "    cleaned_lfp, sptr, best_channel = prepare_spike_lfp(lfp, sptr, *lim)\n",
    "       \n",
    "    p_xys, freq = compute_spike_lfp_coherence(cleaned_lfp, sptr, NFFT=NFFT)\n",
    "    \n",
    "    p_xy = p_xys.magnitude.ravel()\n",
    "    freq = freq.magnitude\n",
    "    \n",
    "    theta_f, theta_p_max = find_theta_peak(p_xy, freq, theta_band_f1, theta_band_f2)\n",
    "    \n",
    "    theta_energy = compute_energy(p_xy, freq, theta_band_f1, theta_band_f2) # theta band 6 - 10 Hz\n",
    "    \n",
    "    theta_half_f1, theta_half_f2 = compute_half_width(p_xy, freq, theta_p_max, theta_f)\n",
    "    \n",
    "    theta_half_width = theta_half_f2 - theta_half_f1\n",
    "    \n",
    "    theta_half_energy = compute_energy(p_xy, freq, theta_half_f1, theta_half_f2) # theta band 6 - 10 Hz\n",
    "    \n",
    "    theta_spike_phase, _ = compute_spike_phase(cleaned_lfp, sptr, flim=[theta_band_f1, theta_band_f2])\n",
    "    theta_bins, theta_kde = vonmises_kde(theta_spike_phase)\n",
    "    theta_ang, theta_vec_len = spike_phase_score(theta_bins, theta_kde)\n",
    "    theta_kde_data.update({name: theta_kde})\n",
    "    theta_bins_data.update({name: theta_bins})\n",
    "\n",
    "    # stim\n",
    "    \n",
    "    stim_freq = compute_stim_freq(action_id)\n",
    "    \n",
    "    stim_p_max = compute_stim_peak(p_xy, freq, stim_freq)\n",
    "    \n",
    "    stim_half_f1, stim_half_f2 = compute_half_width(p_xy, freq, stim_p_max, stim_freq)\n",
    "    stim_half_width = stim_half_f2 - stim_half_f1\n",
    "    \n",
    "    stim_energy = compute_energy(p_xy, freq, stim_half_f1, stim_half_f2)\n",
    "    \n",
    "    if np.isnan(stim_freq):\n",
    "        stim_spike_phase, stim_bins, stim_kde, stim_ang, stim_vec_len = [np.nan] * 5\n",
    "    else:\n",
    "        stim_spike_phase, _ = compute_spike_phase(cleaned_lfp, sptr, flim=[stim_freq - .5, stim_freq + .5])\n",
    "        stim_bins, stim_kde = vonmises_kde(stim_spike_phase)\n",
    "        stim_ang, stim_vec_len = spike_phase_score(stim_bins, stim_kde)\n",
    "        stim_kde_data.update({name: stim_kde})\n",
    "        stim_bins_data.update({name: stim_bins})\n",
    "        \n",
    "    coherence_data.update({name: p_xy})\n",
    "    freqency_data.update({name: freq})\n",
    "    \n",
    "    result = pd.Series({\n",
    "        'best_channel': best_channel,\n",
    "        'theta_freq': theta_f,\n",
    "        'theta_peak': theta_p_max,\n",
    "        'theta_energy': theta_energy,\n",
    "        'theta_half_f1': theta_half_f1, \n",
    "        'theta_half_f2': theta_half_f2,\n",
    "        'theta_half_width': theta_half_width,\n",
    "        'theta_half_energy': theta_half_energy,\n",
    "        'theta_ang': theta_ang, \n",
    "        'theta_vec_len': theta_vec_len,\n",
    "        'stim_freq': stim_freq,\n",
    "        'stim_p_max': stim_p_max,\n",
    "        'stim_half_f1': stim_half_f1, \n",
    "        'stim_half_f2': stim_half_f2,\n",
    "        'stim_half_width': stim_half_width,\n",
    "        'stim_energy': stim_energy,\n",
    "        'stim_ang': stim_ang, \n",
    "        'stim_vec_len': stim_vec_len\n",
    "    })\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d041e5a55f04f7aaac43018c589217a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/scipy/signal/spectral.py:1577: RuntimeWarning: invalid value encountered in true_divide\n",
      "  Cxy = np.abs(Pxy)**2 / Pxx / Pyy\n",
      "/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:28: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    }
   ],
   "source": [
    "results = units.merge(\n",
    "    units.progress_apply(process, axis=1), \n",
    "    left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coher, freqs = {}, {}\n",
    "# for i, row in tqdm(units.iterrows(), total=len(units)):\n",
    "#     action_id = row['action']\n",
    "#     channel_group = row['channel_group']\n",
    "#     unit_name = row['unit_name']\n",
    "    \n",
    "#     name = f'{action_id}_{channel_group}_{unit_name}'\n",
    "    \n",
    "#     lfp = data_loader.lfp(action_id, channel_group) # TODO consider choosing strongest stim response\n",
    "    \n",
    "#     sptr = data_loader.spike_train(action_id, channel_group, unit_name)\n",
    "       \n",
    "#     lim = get_lim(action_id)\n",
    "\n",
    "#     p_xys, freq, clean_lfp = compute_spike_lfp(lfp, sptr, *lim, NFFT=NFFT)\n",
    "    \n",
    "#     snls = signaltonoise(clean_lfp)\n",
    "#     best_channel = np.argmax(snls)\n",
    "#     snl = snls[best_channel]\n",
    "#     p_xy = p_xys[:,best_channel].magnitude\n",
    "#     freq = freq.magnitude\n",
    "    \n",
    "#     coher.update({name: p_xy})\n",
    "#     freqs.update({name: freq})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(coherence_data).to_feather(output / 'data' / 'coherence.feather')\n",
    "pd.DataFrame(freqency_data).to_feather(output / 'data' / 'freqs.feather')\n",
    "pd.DataFrame(theta_kde_data).to_feather(output / 'data' / 'theta_kde.feather')\n",
    "pd.DataFrame(theta_bins_data).to_feather(output / 'data' / 'theta_bins.feather')\n",
    "pd.DataFrame(stim_kde_data).to_feather(output / 'data' / 'stim_kde.feather')\n",
    "pd.DataFrame(stim_bins_data).to_feather(output / 'data' / 'stim_bins.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save to expipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = project.require_action(\"stimulus-spike-lfp-response-other-tetrode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action.modules['parameters'] = {\n",
    "    'NFFT': NFFT,\n",
    "    'theta_band_f1': theta_band_f1,\n",
    "    'theta_band_f2': theta_band_f2\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "action.data['results'] = 'results.csv'\n",
    "results.to_csv(action.data_path('results'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_tree(output, str(action.data_path()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "septum_mec.analysis.registration.store_notebook(action, \"10-calculate-stimulus-spike-lfp-response-other-tetrode.ipynb\")"
   ]
  }
 ],
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