{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8960e56",
   "metadata": {},
   "source": [
    "# Motor Imagery Decoder — Train OFFLINE, Evaluate ONLINE (FES vs NOFES)\n",
    "\n",
    "For each subject × offline-session, train a CSP + LDA classifier on the OFFLINE recording, then apply it to the two matched ONLINE sessions.\n",
    "\n",
    "**Pair 1:** train on `S001 OFFLINE_FES` → test on `S002 ONLINE_FES` and `S003 ONLINE_NOFES`\n",
    "**Pair 2:** train on `S004 OFFLINE_NOFES` → test on `S006 ONLINE_FES` and `S005 ONLINE_NOFES`\n",
    "\n",
    "**Metrics reported per (subject × pair × condition):**\n",
    "1. **Classification accuracy** — fraction of cued trials correctly classified\n",
    "2. **Classification amplitude** — mean |LDA decision-function value|\n",
    "3. **SNR** — (a) Fisher ratio of the LDA projection on online data, and (b) mu-band power ratio REST / MI over motor channels C3/Cz/C4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "578c9128",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:26:50.521758Z",
     "iopub.status.busy": "2026-04-22T19:26:50.521477Z",
     "iopub.status.idle": "2026-04-22T19:26:50.528154Z",
     "shell.execute_reply": "2026-04-22T19:26:50.527272Z"
    }
   },
   "outputs": [],
   "source": [
    "# Install dependencies if needed\n",
    "# !pip install pyxdf mne scipy numpy matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "857b22c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:26:50.530924Z",
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     "shell.execute_reply": "2026-04-22T19:26:51.595934Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "import pyxdf\n",
    "from scipy.signal import welch, butter, filtfilt, iirnotch\n",
    "from scipy.linalg import eigh\n",
    "from scipy.stats import wasserstein_distance\n",
    "\n",
    "plt.rcParams.update({'font.size': 11, 'figure.dpi': 120})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe68bf0e",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc4b2c55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:26:51.597901Z",
     "iopub.status.busy": "2026-04-22T19:26:51.597789Z",
     "iopub.status.idle": "2026-04-22T19:26:51.600886Z",
     "shell.execute_reply": "2026-04-22T19:26:51.600373Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_DIR = os.path.join(os.path.dirname(os.path.abspath('__file__')), 'Group 2 - Glove')\n",
    "\n",
    "# Marker codes (from experiment trigger table)\n",
    "MI_BEGIN       = 200\n",
    "MI_END         = 220\n",
    "MI_EARLYSTOP   = 240   # online only: live classifier fired → successful MI detection\n",
    "REST_BEGIN     = 100\n",
    "REST_END       = 120\n",
    "REST_EARLYSTOP = 140   # online only: live classifier fired → successful REST detection\n",
    "ROBOT_BEGIN    = 300\n",
    "ROBOT_END      = 320\n",
    "\n",
    "TARGET_MARKERS = [100, 120, 140, 200, 220, 240]\n",
    "\n",
    "T_PRE  = -1.0\n",
    "T_POST =  5.0\n",
    "\n",
    "# ── Preprocessing ────────────────────────────────────────────────────────────\n",
    "NOTCH_FREQ     = 60.0\n",
    "NOTCH_Q        = 30\n",
    "BP_LO, BP_HI   = 8.0, 30.0\n",
    "USE_CAR        = True\n",
    "PTP_REJECT_UV  = 100.0\n",
    "\n",
    "N_CSP = 4\n",
    "\n",
    "NON_EEG = {'AUX1', 'AUX2', 'AUX3', 'AUX7', 'AUX8', 'AUX9', 'TRIGGER'}\n",
    "RENAME  = {'FP1':'Fp1','FPZ':'Fpz','FP2':'Fp2','FZ':'Fz','CZ':'Cz',\n",
    "           'PZ':'Pz','POZ':'POz','OZ':'Oz'}\n",
    "\n",
    "MOTOR_CH = ['C3', 'Cz', 'C4']\n",
    "MU_BAND  = (8, 13)\n",
    "\n",
    "PAIRS = [\n",
    "    {'name':  'Pair1 (train=OFFLINE_FES)',\n",
    "     'train': 'S001', 'online_fes': 'S002', 'online_nofes': 'S003'},\n",
    "    {'name':  'Pair2 (train=OFFLINE_NOFES)',\n",
    "     'train': 'S004', 'online_fes': 'S006', 'online_nofes': 'S005'},\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a40df3",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e798b039",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:26:51.602206Z",
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     "shell.execute_reply": "2026-04-22T19:26:51.616072Z"
    }
   },
   "outputs": [],
   "source": [
    "# ── XDF loading + session parsing ─────────────────────────────────────────────\n",
    "\n",
    "def get_channel_names_from_xdf(eeg_stream):\n",
    "    ch_desc  = eeg_stream['info']['desc'][0]\n",
    "    channels = ch_desc.get('channels', [{}])[0].get('channel', [])\n",
    "    return [ch['label'][0] for ch in channels]\n",
    "\n",
    "\n",
    "_SESSION_RE = re.compile(r'ses-(S\\d+)(O[A-Z]*LINE)_(FES|NOFES)')\n",
    "_SUBJ_RE    = re.compile(r'SUBJ_(\\d+)')\n",
    "\n",
    "def parse_session(path):\n",
    "    base   = os.path.basename(path)\n",
    "    m_subj = _SUBJ_RE.search(base)\n",
    "    m_ses  = _SESSION_RE.search(base)\n",
    "    if not (m_subj and m_ses):\n",
    "        return None\n",
    "    ses_id, raw_kind, stim = m_ses.group(1), m_ses.group(2), m_ses.group(3)\n",
    "    kind = 'OFFLINE' if 'OFF' in raw_kind else 'ONLINE'\n",
    "    return m_subj.group(1), ses_id, kind, stim\n",
    "\n",
    "\n",
    "def load_xdf_file(filepath):\n",
    "    streams, _ = pyxdf.load_xdf(filepath)\n",
    "\n",
    "    eeg_stream = marker_stream = None\n",
    "    for s in streams:\n",
    "        stype = s['info']['type'][0].lower()\n",
    "        if stype == 'eeg':       eeg_stream    = s\n",
    "        elif stype == 'markers': marker_stream = s\n",
    "    if eeg_stream is None or marker_stream is None:\n",
    "        eeg_stream    = streams[0]\n",
    "        marker_stream = streams[1] if len(streams) > 1 else None\n",
    "\n",
    "    eeg_timestamps = np.array(eeg_stream['time_stamps'])\n",
    "    eeg_data       = np.array(eeg_stream['time_series']).T\n",
    "    channel_names  = get_channel_names_from_xdf(eeg_stream)\n",
    "    sfreq          = float(eeg_stream['info']['nominal_srate'][0])\n",
    "\n",
    "    valid_idx     = [i for i, ch in enumerate(channel_names) if ch not in NON_EEG]\n",
    "    channel_names = [channel_names[i] for i in valid_idx]\n",
    "    eeg_data      = eeg_data[valid_idx, :]\n",
    "    channel_names = [RENAME.get(ch, ch) for ch in channel_names]\n",
    "\n",
    "    ts_arr      = np.asarray(marker_stream['time_series'], dtype=float)\n",
    "    marker_data = ts_arr[:, 0].astype(int)\n",
    "    marker_ts   = ts_arr[:, 1]\n",
    "    keep        = np.isin(marker_data, TARGET_MARKERS)\n",
    "    return eeg_data, eeg_timestamps, marker_data[keep], marker_ts[keep], channel_names, sfreq\n",
    "\n",
    "\n",
    "# ── Preprocessing primitives ─────────────────────────────────────────────────\n",
    "\n",
    "def notch_filter(data, freq, sfreq, Q=NOTCH_Q):\n",
    "    b, a = iirnotch(freq, Q, fs=sfreq)\n",
    "    return filtfilt(b, a, data, axis=-1)\n",
    "\n",
    "\n",
    "def car(data):\n",
    "    return data - data.mean(axis=0, keepdims=True)\n",
    "\n",
    "\n",
    "def bandpass(data, lo, hi, sfreq, order=4):\n",
    "    nyq  = sfreq / 2.0\n",
    "    b, a = butter(order, [max(lo, 0.5) / nyq, min(hi, nyq - 0.1) / nyq], btype='band')\n",
    "    return filtfilt(b, a, data, axis=-1)\n",
    "\n",
    "\n",
    "def reject_by_ptp(X, thresh_uv=PTP_REJECT_UV):\n",
    "    if X.size == 0:\n",
    "        return np.zeros(0, dtype=bool)\n",
    "    ptp = X.max(axis=-1) - X.min(axis=-1)\n",
    "    return ptp.max(axis=-1) < thresh_uv\n",
    "\n",
    "\n",
    "def extract_epochs(eeg_data, eeg_ts, marker_data, marker_ts, sfreq, begin_code,\n",
    "                   t_pre=T_PRE, t_post=T_POST):\n",
    "    epochs = []\n",
    "    n_pre  = int(abs(t_pre) * sfreq)\n",
    "    for bi in np.where(marker_data == begin_code)[0]:\n",
    "        t_start = marker_ts[bi]\n",
    "        i0 = np.searchsorted(eeg_ts, t_start + t_pre)\n",
    "        i1 = np.searchsorted(eeg_ts, t_start + t_post)\n",
    "        if i0 < 0 or i1 > eeg_data.shape[1]:\n",
    "            continue\n",
    "        ep = eeg_data[:, i0:i1].copy()\n",
    "        if ep.shape[1] > n_pre:\n",
    "            ep -= ep[:, :n_pre].mean(axis=1, keepdims=True)\n",
    "        epochs.append(ep)\n",
    "    if not epochs:\n",
    "        return np.empty((0, eeg_data.shape[0], 0))\n",
    "    min_len = min(e.shape[-1] for e in epochs)\n",
    "    return np.stack([e[:, :min_len] for e in epochs])\n",
    "\n",
    "\n",
    "# ── Trial-level parsing from markers ─────────────────────────────────────────\n",
    "\n",
    "def trial_events(marker_data, marker_ts):\n",
    "    \"\"\"Parse marker stream into ordered per-trial records.\n",
    "    Returns list of dicts with keys: cls ('MI'|'REST'), success (bool),\n",
    "    latency (seconds from BEGIN to EARLYSTOP, or None on failure), t_begin.\n",
    "    \"\"\"\n",
    "    begin_codes = {MI_BEGIN: 'MI', REST_BEGIN: 'REST'}\n",
    "    early_of    = {'MI': MI_EARLYSTOP, 'REST': REST_EARLYSTOP}\n",
    "\n",
    "    begin_idx = np.where(np.isin(marker_data, list(begin_codes.keys())))[0]\n",
    "    trials = []\n",
    "    for i, bi in enumerate(begin_idx):\n",
    "        cls     = begin_codes[int(marker_data[bi])]\n",
    "        t_begin = float(marker_ts[bi])\n",
    "        # Search until next BEGIN (or end of stream) for EARLYSTOP of matching class\n",
    "        end    = begin_idx[i + 1] if i + 1 < len(begin_idx) else len(marker_data)\n",
    "        window = marker_data[bi + 1:end]\n",
    "        wts    = marker_ts[bi + 1:end]\n",
    "        hit    = np.where(window == early_of[cls])[0]\n",
    "        if len(hit):\n",
    "            trials.append(dict(cls=cls, success=True,\n",
    "                               latency=float(wts[hit[0]] - t_begin), t_begin=t_begin))\n",
    "        else:\n",
    "            trials.append(dict(cls=cls, success=False, latency=None, t_begin=t_begin))\n",
    "    return trials\n",
    "\n",
    "\n",
    "def marker_stats(marker_data, marker_ts):\n",
    "    \"\"\"Online performance summary from markers. Returns None for offline sessions.\n",
    "    Fields: mk_acc, mi_acc, rest_acc, mi_latency, rest_latency, trials (ordered).\n",
    "    \"\"\"\n",
    "    trials = trial_events(marker_data, marker_ts)\n",
    "    if not trials or not any(t['success'] for t in trials):\n",
    "        return None   # offline or no successful trials\n",
    "    mi   = [t for t in trials if t['cls'] == 'MI']\n",
    "    rest = [t for t in trials if t['cls'] == 'REST']\n",
    "\n",
    "    def _acc(ts): return sum(t['success'] for t in ts) / len(ts) if ts else None\n",
    "    def _lat(ts):\n",
    "        lats = [t['latency'] for t in ts if t['success']]\n",
    "        return float(np.mean(lats)) if lats else None\n",
    "\n",
    "    n_correct = sum(t['success'] for t in trials)\n",
    "    return dict(mk_acc = n_correct / len(trials),\n",
    "                mi_acc = _acc(mi),  rest_acc = _acc(rest),\n",
    "                mi_latency = _lat(mi), rest_latency = _lat(rest),\n",
    "                trials = trials)\n",
    "\n",
    "\n",
    "def load_session_epochs(filepath):\n",
    "    \"\"\"Preprocessing pipeline: notch → CAR → bandpass → epoch → PTP-reject.\n",
    "    Returns X, y, ch_names, sfreq, n_rejected, stats\n",
    "    where `stats` is the marker_stats() dict (None for offline sessions).\n",
    "    \"\"\"\n",
    "    eeg, eeg_ts, mk, mk_ts, ch_names, sfreq = load_xdf_file(filepath)\n",
    "\n",
    "    stats = marker_stats(mk, mk_ts)\n",
    "\n",
    "    eeg = notch_filter(eeg, NOTCH_FREQ, sfreq)\n",
    "    if USE_CAR:\n",
    "        eeg = car(eeg)\n",
    "    eeg_bp = bandpass(eeg, BP_LO, BP_HI, sfreq)\n",
    "\n",
    "    mi   = extract_epochs(eeg_bp, eeg_ts, mk, mk_ts, sfreq, MI_BEGIN)\n",
    "    rest = extract_epochs(eeg_bp, eeg_ts, mk, mk_ts, sfreq, REST_BEGIN)\n",
    "\n",
    "    n_pre = int(abs(T_PRE) * sfreq)\n",
    "    if mi.shape[-1]   > n_pre: mi   = mi[..., n_pre:]\n",
    "    if rest.shape[-1] > n_pre: rest = rest[..., n_pre:]\n",
    "\n",
    "    n = min(mi.shape[-1], rest.shape[-1]) if (mi.size and rest.size) else 0\n",
    "    mi, rest = mi[..., :n], rest[..., :n]\n",
    "\n",
    "    n0_mi, n0_rest = len(mi), len(rest)\n",
    "    mi   = mi[reject_by_ptp(mi)]\n",
    "    rest = rest[reject_by_ptp(rest)]\n",
    "    n_rejected = (n0_mi - len(mi)) + (n0_rest - len(rest))\n",
    "\n",
    "    X = np.concatenate([mi, rest], axis=0) if (len(mi) or len(rest)) else np.empty((0, len(ch_names), 0))\n",
    "    y = np.concatenate([np.ones(len(mi), int), np.zeros(len(rest), int)])\n",
    "    return X, y, ch_names, sfreq, n_rejected, stats\n",
    "\n",
    "\n",
    "# ── CSP + LDA (2-class, numpy/scipy only) ────────────────────────────────────\n",
    "\n",
    "def _mean_cov(X):\n",
    "    covs = np.einsum('ijk,ilk->ijl', X, X)\n",
    "    covs /= np.trace(covs, axis1=1, axis2=2)[:, None, None]\n",
    "    return covs.mean(axis=0)\n",
    "\n",
    "\n",
    "class CSPLDA:\n",
    "    \"\"\"CSP log-var features + LDA. Ramoser 2000; Blankertz 2008.\n",
    "    Ledoit-Wolf shrinkage keeps the generalized eigenproblem well-posed after CAR.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n_csp=N_CSP, cov_shrink=0.05, lda_reg=1e-4):\n",
    "        self.n_csp      = n_csp\n",
    "        self.cov_shrink = cov_shrink\n",
    "        self.lda_reg    = lda_reg\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        assert set(np.unique(y)) == {0, 1}\n",
    "        C1 = _mean_cov(X[y == 1])\n",
    "        C0 = _mean_cov(X[y == 0])\n",
    "        n_ch = C1.shape[0]\n",
    "        s = self.cov_shrink\n",
    "        C1 = (1 - s) * C1 + s * (np.trace(C1) / n_ch) * np.eye(n_ch)\n",
    "        C0 = (1 - s) * C0 + s * (np.trace(C0) / n_ch) * np.eye(n_ch)\n",
    "        evals, evecs = eigh(C1, C0 + C1)\n",
    "        order  = np.argsort(evals)\n",
    "        k      = self.n_csp // 2\n",
    "        self.filters_ = np.concatenate([evecs[:, order[:k]],\n",
    "                                        evecs[:, order[-k:]]], axis=1).T\n",
    "        F = self._features(X)\n",
    "        mu1, mu0 = F[y == 1].mean(0), F[y == 0].mean(0)\n",
    "        Sw = np.cov(F[y == 1].T, ddof=1) + np.cov(F[y == 0].T, ddof=1)\n",
    "        Sw += self.lda_reg * np.eye(Sw.shape[0])\n",
    "        self.coef_      = np.linalg.solve(Sw, mu1 - mu0)\n",
    "        self.intercept_ = -self.coef_ @ ((mu1 + mu0) / 2)\n",
    "        return self\n",
    "\n",
    "    def _features(self, X):\n",
    "        Z   = np.einsum('fc,ncs->nfs', self.filters_, X)\n",
    "        var = Z.var(axis=-1, ddof=1)\n",
    "        return np.log(var / var.sum(axis=1, keepdims=True))\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return self._features(X) @ self.coef_ + self.intercept_\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) > 0).astype(int)\n",
    "\n",
    "\n",
    "# ── Evaluation metrics ───────────────────────────────────────────────────────\n",
    "\n",
    "def evaluate(clf, X, y):\n",
    "    margin = clf.decision_function(X)\n",
    "    pred   = (margin > 0).astype(int)\n",
    "    amp    = np.abs(margin).mean()\n",
    "    m1, m0 = margin[y == 1], margin[y == 0]\n",
    "    fisher = (m1.mean() - m0.mean()) ** 2 / (m1.var(ddof=1) + m0.var(ddof=1) + 1e-30)\n",
    "    return dict(amp=amp, fisher=fisher, margin=margin, y=y, pred=pred)\n",
    "\n",
    "\n",
    "def spectral_snr(X, y, ch_idx, sfreq, band=MU_BAND):\n",
    "    def band_pwr(sig):\n",
    "        f, p = welch(sig, fs=sfreq,\n",
    "                     nperseg=min(int(sfreq * 2), sig.shape[-1]),\n",
    "                     noverlap=int(sfreq), axis=-1)\n",
    "        m = (f >= band[0]) & (f < band[1])\n",
    "        return np.trapezoid(p[..., m], f[m], axis=-1).mean()\n",
    "    return band_pwr(X[y == 0][:, ch_idx, :]) / (band_pwr(X[y == 1][:, ch_idx, :]) + 1e-30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d225db",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d266216b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:26:51.617805Z",
     "iopub.status.busy": "2026-04-22T19:26:51.617727Z",
     "iopub.status.idle": "2026-04-22T19:27:24.179747Z",
     "shell.execute_reply": "2026-04-22T19:27:24.179038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24 XDF file(s).\n",
      "Preprocessing: notch 60 Hz → CAR → bandpass 8–30 Hz → baseline-correct → PTP-reject @ 100 µV\n",
      "\n",
      "  002/S001  OFFLINE FES    n= 85  (MI=43, REST=42)  rej=5\n",
      "  002/S002  ONLINE  FES    n= 53  (MI=27, REST=26)  rej=7  acc=0.883 (MI=0.87, REST=0.90)  lat(MI=2.74s, REST=2.46s)\n",
      "  002/S003  ONLINE  NOFES  n= 52  (MI=26, REST=26)  rej=8  acc=0.833 (MI=0.87, REST=0.80)  lat(MI=2.26s, REST=2.29s)\n",
      "  002/S004  OFFLINE NOFES  n= 90  (MI=45, REST=45)  rej=0\n",
      "  002/S005  ONLINE  NOFES  n= 60  (MI=30, REST=30)  rej=0  acc=0.850 (MI=0.83, REST=0.87)  lat(MI=2.33s, REST=2.15s)\n",
      "  002/S006  ONLINE  FES    n= 56  (MI=27, REST=29)  rej=4  acc=0.917 (MI=1.00, REST=0.83)  lat(MI=2.26s, REST=1.76s)\n",
      "  003/S001  OFFLINE FES    n= 89  (MI=44, REST=45)  rej=1\n",
      "  003/S002  ONLINE  FES    n= 59  (MI=29, REST=30)  rej=1  acc=0.750 (MI=0.70, REST=0.80)  lat(MI=2.59s, REST=2.12s)\n",
      "  003/S003  ONLINE  NOFES  n= 38  (MI=17, REST=21)  rej=0  acc=0.763 (MI=0.76, REST=0.76)  lat(MI=2.15s, REST=2.74s)\n",
      "  003/S004  OFFLINE NOFES  n= 86  (MI=42, REST=44)  rej=4\n",
      "  003/S005  ONLINE  NOFES  n= 43  (MI=19, REST=24)  rej=17  acc=0.717 (MI=0.67, REST=0.77)  lat(MI=2.58s, REST=2.53s)\n",
      "  003/S006  ONLINE  FES    n= 52  (MI=23, REST=29)  rej=8  acc=0.767 (MI=0.77, REST=0.77)  lat(MI=2.31s, REST=2.56s)\n",
      "  005/S001  OFFLINE FES    n= 90  (MI=45, REST=45)  rej=0\n",
      "  005/S002  ONLINE  FES    n= 60  (MI=30, REST=30)  rej=0  acc=0.800 (MI=0.67, REST=0.93)  lat(MI=2.77s, REST=2.13s)\n",
      "  005/S003  ONLINE  NOFES  n= 59  (MI=29, REST=30)  rej=1  acc=0.933 (MI=0.97, REST=0.90)  lat(MI=1.86s, REST=2.23s)\n",
      "  005/S004  OFFLINE NOFES  n= 89  (MI=44, REST=45)  rej=1\n",
      "  005/S005  ONLINE  NOFES  n= 58  (MI=28, REST=30)  rej=2  acc=0.783 (MI=0.67, REST=0.90)  lat(MI=2.12s, REST=2.73s)\n",
      "  005/S006  ONLINE  FES    n= 59  (MI=30, REST=29)  rej=1  acc=0.917 (MI=0.90, REST=0.93)  lat(MI=2.31s, REST=2.27s)\n",
      "  009/S001  OFFLINE FES    n= 57  (MI=33, REST=24)  rej=33\n",
      "  009/S002  ONLINE  FES    n= 42  (MI=21, REST=21)  rej=18  acc=0.717 (MI=0.80, REST=0.63)  lat(MI=2.56s, REST=2.09s)\n",
      "  009/S003  ONLINE  NOFES  n=  1  (MI=1, REST=0)  rej=59  acc=0.717 (MI=0.63, REST=0.80)  lat(MI=2.36s, REST=2.47s)\n",
      "  009/S004  OFFLINE NOFES  n= 86  (MI=42, REST=44)  rej=4\n",
      "  009/S005  ONLINE  NOFES  n= 60  (MI=30, REST=30)  rej=0  acc=0.850 (MI=0.80, REST=0.90)  lat(MI=2.22s, REST=2.00s)\n",
      "  009/S006  ONLINE  FES    n= 50  (MI=26, REST=24)  rej=10  acc=0.817 (MI=0.83, REST=0.80)  lat(MI=2.64s, REST=1.69s)\n",
      "\n",
      "Loaded 4 subject(s): ['002', '003', '005', '009']  |  total artifact-rejected epochs: 184\n"
     ]
    }
   ],
   "source": [
    "xdf_files = sorted(glob.glob(os.path.join(DATA_DIR, '*.xdf')))\n",
    "print(f'Found {len(xdf_files)} XDF file(s).')\n",
    "print(f'Preprocessing: notch {NOTCH_FREQ:.0f} Hz → '\n",
    "      f'{\"CAR → \" if USE_CAR else \"\"}bandpass {BP_LO:.0f}–{BP_HI:.0f} Hz → '\n",
    "      f'baseline-correct → PTP-reject @ {PTP_REJECT_UV:.0f} µV\\n')\n",
    "\n",
    "sessions  = {}\n",
    "total_rej = 0\n",
    "\n",
    "for fp in xdf_files:\n",
    "    meta = parse_session(fp)\n",
    "    if meta is None:\n",
    "        print(f'  SKIP (unparsed): {os.path.basename(fp)}')\n",
    "        continue\n",
    "    subj, ses_id, kind, stim = meta\n",
    "    try:\n",
    "        X, y, ch_names, sfreq, n_rej, stats = load_session_epochs(fp)\n",
    "    except Exception as e:\n",
    "        print(f'  ERROR {os.path.basename(fp)}: {e}')\n",
    "        continue\n",
    "\n",
    "    sessions.setdefault(subj, {})[ses_id] = dict(\n",
    "        X=X, y=y, kind=kind, stim=stim,\n",
    "        ch_names=ch_names, sfreq=sfreq,\n",
    "        stats=stats, file=os.path.basename(fp))\n",
    "    total_rej += n_rej\n",
    "\n",
    "    if stats is not None:\n",
    "        info = (f'  acc={stats[\"mk_acc\"]:.3f} '\n",
    "                f'(MI={stats[\"mi_acc\"]:.2f}, REST={stats[\"rest_acc\"]:.2f})  '\n",
    "                f'lat(MI={stats[\"mi_latency\"]:.2f}s, REST={stats[\"rest_latency\"]:.2f}s)'\n",
    "                if stats['mi_acc'] is not None and stats['rest_acc'] is not None else '')\n",
    "    else:\n",
    "        info = ''\n",
    "    print(f'  {subj}/{ses_id}  {kind:<7} {stim:<5}  '\n",
    "          f'n={len(y):3d}  (MI={int(y.sum())}, REST={int((1-y).sum())})  '\n",
    "          f'rej={n_rej}{info}')\n",
    "\n",
    "subjects = sorted(sessions.keys())\n",
    "print(f'\\nLoaded {len(subjects)} subject(s): {subjects}  |  '\n",
    "      f'total artifact-rejected epochs: {total_rej}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c8bea",
   "metadata": {},
   "source": [
    "## Verify Session Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "611baf23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:24.182586Z",
     "iopub.status.busy": "2026-04-22T19:27:24.182447Z",
     "iopub.status.idle": "2026-04-22T19:27:24.188463Z",
     "shell.execute_reply": "2026-04-22T19:27:24.188025Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels (32): ['Fp1', 'Fpz', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'FC5', 'FC1', 'FC2', 'FC6', 'M1', 'T7', 'C3', 'Cz', 'C4', 'T8', 'M2', 'CP5', 'CP1', 'CP2', 'CP6', 'P7', 'P3', 'Pz', 'P4', 'P8', 'POz', 'O1', 'Oz', 'O2']\n",
      "Sampling rate: 512.0 Hz\n",
      "Motor channels ['C3', 'Cz', 'C4'] → indices [14, 15, 16]\n"
     ]
    }
   ],
   "source": [
    "# Verify channel layout is consistent across sessions, locate motor channels\n",
    "ref_subj = subjects[0]\n",
    "ref_ses  = next(iter(sessions[ref_subj].values()))\n",
    "channel_names_global = ref_ses['ch_names']\n",
    "sfreq_global         = ref_ses['sfreq']\n",
    "\n",
    "mismatches = [f'{subj}/{sid}' for subj in subjects for sid, s in sessions[subj].items()\n",
    "              if s['ch_names'] != channel_names_global]\n",
    "if mismatches:\n",
    "    print('!! channel mismatch in:', mismatches)\n",
    "\n",
    "motor_idx_global = [channel_names_global.index(c) for c in MOTOR_CH\n",
    "                    if c in channel_names_global]\n",
    "\n",
    "print(f'Channels ({len(channel_names_global)}): {channel_names_global}')\n",
    "print(f'Sampling rate: {sfreq_global} Hz')\n",
    "print(f'Motor channels {MOTOR_CH} → indices {motor_idx_global}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70922abb",
   "metadata": {},
   "source": [
    "## Train CSP + LDA on OFFLINE, Evaluate on ONLINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5e80da3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:24.190995Z",
     "iopub.status.busy": "2026-04-22T19:27:24.190909Z",
     "iopub.status.idle": "2026-04-22T19:27:26.129399Z",
     "shell.execute_reply": "2026-04-22T19:27:26.128933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[009] Pair1 (train=OFFLINE_FES) / NOFES: only 1 clean epochs — acc=0.717, skipping EEG-derived metrics\n",
      "\n",
      "Subj  Pair                         Cond      n  trainAcc   mkAcc  MIacc  REacc  MIlat  RElat   |marg|   Fisher   muSNR\n",
      "----------------------------------------------------------------------------------------------------------------------\n",
      "002   Pair1 (train=OFFLINE_FES)    FES      53     0.659   0.883   0.87   0.90   2.74   2.46    0.778    0.288   1.197\n",
      "002   Pair1 (train=OFFLINE_FES)    NOFES    52     0.659   0.833   0.87   0.80   2.26   2.29    1.162    0.073   1.452\n",
      "002   Pair2 (train=OFFLINE_NOFES)  FES      56     0.811   0.917   1.00   0.83   2.26   1.76    1.012    3.133   1.665\n",
      "002   Pair2 (train=OFFLINE_NOFES)  NOFES    60     0.811   0.850   0.83   0.87   2.33   2.15    0.756    0.695   1.576\n",
      "003   Pair1 (train=OFFLINE_FES)    FES      59     0.843   0.750   0.70   0.80   2.59   2.12    0.879    0.017   1.347\n",
      "003   Pair1 (train=OFFLINE_FES)    NOFES    38     0.843   0.763   0.76   0.76   2.15   2.74    0.910    0.683   1.219\n",
      "003   Pair2 (train=OFFLINE_NOFES)  FES      52     0.907   0.767   0.77   0.77   2.31   2.56    1.166    0.000   1.273\n",
      "003   Pair2 (train=OFFLINE_NOFES)  NOFES    43     0.907   0.717   0.67   0.77   2.58   2.53    1.071    0.029   1.296\n",
      "005   Pair1 (train=OFFLINE_FES)    FES      60     0.978   0.800   0.67   0.93   2.77   2.13    3.346    2.498   2.695\n",
      "005   Pair1 (train=OFFLINE_FES)    NOFES    59     0.978   0.933   0.97   0.90   1.86   2.23    3.620    3.029   2.399\n",
      "005   Pair2 (train=OFFLINE_NOFES)  FES      59     1.000   0.917   0.90   0.93   2.31   2.27    5.740    6.291   2.911\n",
      "005   Pair2 (train=OFFLINE_NOFES)  NOFES    58     1.000   0.783   0.67   0.90   2.12   2.73    5.261    4.325   2.287\n",
      "009   Pair1 (train=OFFLINE_FES)    FES      42     0.667   0.717   0.80   0.63   2.56   2.09    0.304    0.040   1.326\n",
      "009   Pair1 (train=OFFLINE_FES)    NOFES     1     0.667   0.717   0.63   0.80   2.36   2.47       --       --      --\n",
      "009   Pair2 (train=OFFLINE_NOFES)  FES      50     1.000   0.817   0.83   0.80   2.64   1.69    4.673    6.192   1.594\n",
      "009   Pair2 (train=OFFLINE_NOFES)  NOFES    60     1.000   0.850   0.80   0.90   2.22   2.00    3.754    8.959   2.000\n"
     ]
    }
   ],
   "source": [
    "MIN_TEST_TRIALS = 10\n",
    "\n",
    "results = []\n",
    "\n",
    "for subj in subjects:\n",
    "    subj_ses = sessions[subj]\n",
    "\n",
    "    for pair in PAIRS:\n",
    "        needed  = (pair['train'], pair['online_fes'], pair['online_nofes'])\n",
    "        missing = [k for k in needed if k not in subj_ses]\n",
    "        if missing:\n",
    "            print(f'[{subj}] {pair[\"name\"]}: missing {missing} — skipping')\n",
    "            continue\n",
    "\n",
    "        train = subj_ses[pair['train']]\n",
    "        if set(np.unique(train['y'])) != {0, 1}:\n",
    "            print(f'[{subj}] {pair[\"name\"]}: training set lacks both classes — skipping')\n",
    "            continue\n",
    "\n",
    "        clf       = CSPLDA(n_csp=N_CSP).fit(train['X'], train['y'])\n",
    "        train_acc = (clf.predict(train['X']) == train['y']).mean()\n",
    "\n",
    "        for cond_key, cond_label in [('online_fes', 'FES'), ('online_nofes', 'NOFES')]:\n",
    "            te = subj_ses[pair[cond_key]]\n",
    "            st = te['stats']\n",
    "            if st is None:\n",
    "                print(f'[{subj}] {pair[\"name\"]} / {cond_label}: no EARLYSTOP markers — skipping')\n",
    "                continue\n",
    "\n",
    "            row = dict(\n",
    "                subject=subj, pair=pair['name'], condition=cond_label,\n",
    "                train_file=train['file'], test_file=te['file'],\n",
    "                train_acc=train_acc, n_test=len(te['y']),\n",
    "                acc          = st['mk_acc'],\n",
    "                mi_acc       = st['mi_acc'],\n",
    "                rest_acc     = st['rest_acc'],\n",
    "                mi_latency   = st['mi_latency'],\n",
    "                rest_latency = st['rest_latency'],\n",
    "                trials       = st['trials'],   # ordered per-trial records for trajectory analysis\n",
    "            )\n",
    "\n",
    "            # EEG-based metrics require enough clean epochs of both classes\n",
    "            if len(te['y']) >= MIN_TEST_TRIALS and set(np.unique(te['y'])) == {0, 1}:\n",
    "                res   = evaluate(clf, te['X'], te['y'])\n",
    "                snr_s = spectral_snr(te['X'], te['y'], motor_idx_global, te['sfreq'])\n",
    "                row.update(amp=res['amp'], fisher=res['fisher'], mu_snr=snr_s,\n",
    "                           margin=res['margin'], y_test=res['y'], pred=res['pred'])\n",
    "            else:\n",
    "                print(f'[{subj}] {pair[\"name\"]} / {cond_label}: only {len(te[\"y\"])} clean epochs — '\n",
    "                      f'acc={st[\"mk_acc\"]:.3f}, skipping EEG-derived metrics')\n",
    "                row.update(amp=np.nan, fisher=np.nan, mu_snr=np.nan,\n",
    "                           margin=np.array([]), y_test=np.array([]), pred=np.array([]))\n",
    "\n",
    "            results.append(row)\n",
    "\n",
    "hdr = (f'{\"Subj\":<5} {\"Pair\":<28} {\"Cond\":<6} {\"n\":>4} '\n",
    "       f'{\"trainAcc\":>9} {\"mkAcc\":>7} {\"MIacc\":>6} {\"REacc\":>6} '\n",
    "       f'{\"MIlat\":>6} {\"RElat\":>6} {\"|marg|\":>8} {\"Fisher\":>8} {\"muSNR\":>7}')\n",
    "print('\\n' + hdr)\n",
    "print('-' * len(hdr))\n",
    "for r in results:\n",
    "    fmt = lambda v, s: (f'{v:{s}}' if (v is not None and not (isinstance(v, float) and np.isnan(v))) else f'{\"--\":>{int(s.split(\".\")[0].lstrip(\">\"))}}')\n",
    "    print(f'{r[\"subject\"]:<5} {r[\"pair\"]:<28} {r[\"condition\"]:<6} {r[\"n_test\"]:>4} '\n",
    "          f'{r[\"train_acc\"]:>9.3f} {r[\"acc\"]:>7.3f} '\n",
    "          f'{r[\"mi_acc\"]:>6.2f} {r[\"rest_acc\"]:>6.2f} '\n",
    "          f'{r[\"mi_latency\"]:>6.2f} {r[\"rest_latency\"]:>6.2f} '\n",
    "          f'{fmt(r[\"amp\"], \">8.3f\")} {fmt(r[\"fisher\"], \">8.3f\")} {fmt(r[\"mu_snr\"], \">7.3f\")}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab81600",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 1 — Per-metric comparison (FES vs NOFES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d53e63b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:26.130998Z",
     "iopub.status.busy": "2026-04-22T19:27:26.130913Z",
     "iopub.status.idle": "2026-04-22T19:27:26.627725Z",
     "shell.execute_reply": "2026-04-22T19:27:26.627293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1680x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: fes_vs_nofes_metrics.png\n"
     ]
    }
   ],
   "source": [
    "METRICS = [\n",
    "    ('acc',    'Classification accuracy',                            '0–1'),\n",
    "    ('amp',    'Classification amplitude (mean |decision fn|)',      'a.u.'),\n",
    "    ('fisher', 'Fisher ratio on LDA projection (test-set SNR)',      'a.u.'),\n",
    "    ('mu_snr', 'μ-band power ratio  REST / MI  @ C3/Cz/C4',          'ratio'),\n",
    "]\n",
    "\n",
    "cond_color = {'FES': '#E05C2A', 'NOFES': '#2A7BE0'}\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 9))\n",
    "fig.suptitle('Online decoding: FES vs NOFES feedback  (per subject × offline-trained model)',\n",
    "             fontsize=13, fontweight='bold', y=1.00)\n",
    "\n",
    "for ax, (key, title, unit) in zip(axes.ravel(), METRICS):\n",
    "    labels, vals, colors = [], [], []\n",
    "    for subj in subjects:\n",
    "        for pair in PAIRS:\n",
    "            tag = pair['name'].split()[0]   # \"Pair1\" / \"Pair2\"\n",
    "            for cond in ('FES', 'NOFES'):\n",
    "                row = next((r for r in results\n",
    "                            if r['subject']==subj and r['pair']==pair['name']\n",
    "                            and r['condition']==cond), None)\n",
    "                if row is None: continue\n",
    "                labels.append(f'{subj}\\n{tag}\\n{cond}')\n",
    "                vals.append(row[key])\n",
    "                colors.append(cond_color[cond])\n",
    "    x = np.arange(len(vals))\n",
    "    ax.bar(x, vals, color=colors, edgecolor='white', zorder=2)\n",
    "    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=7.5)\n",
    "    ax.set_title(f'{title}  ({unit})', fontsize=11, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "    if key == 'acc':\n",
    "        ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "\n",
    "fig.legend(handles=[Patch(color=cond_color['FES'],   label='ONLINE_FES'),\n",
    "                    Patch(color=cond_color['NOFES'], label='ONLINE_NOFES')],\n",
    "           loc='upper right', ncol=2, bbox_to_anchor=(0.98, 1.0))\n",
    "plt.tight_layout()\n",
    "plt.savefig('fes_vs_nofes_metrics.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: fes_vs_nofes_metrics.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248740bd",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 2 — LDA decision-function distributions\n",
    "\n",
    "Visualizes classification amplitude and separability directly: wider FES vs NOFES spread between MI and REST curves = higher Fisher ratio and larger mean |margin|."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "393042a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:26.629009Z",
     "iopub.status.busy": "2026-04-22T19:27:26.628916Z",
     "iopub.status.idle": "2026-04-22T19:27:27.543034Z",
     "shell.execute_reply": "2026-04-22T19:27:27.542546Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/numpy/lib/_histograms_impl.py:897: RuntimeWarning: invalid value encountered in divide\n",
      "  return n / db / n.sum(), bin_edges\n"
     ]
    },
    {
     "data": {
      "image/png": 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0XddcAAAAgIGQVADAILTncTE8tqoIj9WsaCZUeKw9j4vh8bxpjjTEHV4PAEAv2me4qalJbr31Vunq6iptX7dunaxatUoWL14sCxcuLPzflUrJM888I5s2bSrtN2fOHJk5c6b84x//kL/85S/9AmjthfzWt76VZx0AAAADIkAGgEFoz+PhhMcqGjI8bkv6pfC4pY7wGADQX2trq1nsTttVHH300XLDDTeYfsbHHnus+T/quuuuK+27Zs0a2XfffU3P4/IF9JYvXy6+78s73vEO0+/429/+trnW72OxmHzhC1/gqQcAAMCAWEQPAAahPY+HEx6HoW0r2no88YOA8BgAUNEFF1wgM2bMMEHy5ZdfbkLfY445Rq6++mo56KCDKh6/bNkymT9/vlx//fVy2223SXd3t7m/d73rXfLZz35WDj74YF4FAAAADIgAGQAGM4rhsbat8H2RlnqbymMAQChLliwxl6FoO4sgCAZthaEXAAAAoBoEyAAwhnK+LZt39jyeVm9JXYxOQgAAAAAAYOIiuQCAMeIGlryarS/1PK6L0fMYAAAAAABMbATIADAG3MCWDjdurlkwDwAAAAAA1AoCZAAYg7YVHW5MvMCSObE0PY8BAAAAAEDNIEAGgJB6st6wwmNtW6HhcUskJ82RPM83AAAAAACoGQTIABBCZ9qT7d3+sMJjbVuh4XHCDhFABwGvBwAAAAAAmDAIkAEgRHi8qcMTp4o17/Jl4bG2rQgTHgdBINuS1YXUAAAAAAAAky5A3rFjx3g8LAAMOzyOOCJzmyOhj9uWT5TC4zBtKzQ83tjhSSpLgAwAAAAAAKZ4gLxgwQI566yz5I9//ON4PDwAhO55XAyPF02PSsSxQj9zxQXzqgmPuzO+1MeZGAIAAAAAACaOcUkqTjjhBPnBD34gixcvln333Ve+8Y1vUJUMYMLRnsfF8DgWCR8eq+nRbNXhcVPCllmNBMgAAAAAAGDiGJek4mc/+5m88sorsnz5cslms3LppZfKHnvsIR/60IfkD3/4w3icEgD04wwzPFYNjlt5pyCQ9lRQCo/nT3NErOofCwAAAAAAYLSMW6nb/Pnz5bOf/ay8+OKL8pvf/Ebe/e53y6pVq+S4446TffbZR77+9a9LW1vbeJ0eAJiex8MJj8PQyuP2lC+Z/K7w2CI8BgAAAAAAE8yEmCt94oknmvB4w4YNpjfys88+K5/61KdKvZLXrl073qcIYAqqpudxNYptKzL5QBJRwmMAAAAAADBxTYgAOZ1Oy8qVK+W0006T733vexKJRMzXevnRj34kb37zm+W///u/x/s0AWC3BYGUeh4nopa01ltUHgMAAAAAgAlrXAPkRx99VJYtWybz5s2Tc845x1Qga19k7Y/84x//WO6++27T4kID5M997nPjeaoAMCI25+pKPY9b623CYwAAAAAAMKFFxuNBv/Wtb8ltt91mAmTt+XnSSSfJhRdeKCeffLLYdu9MWxfX+/jHPy7nnnvueJwqAIyYTi8mSS8ns3f2PH5pu7bICHiGAQAAAADAhDUuAbJWHc+dO1euuOIK+djHPiaLFi0acv99993X9EIGgFptW9HpxiTjO9Lo5FkwDwAAAAAA1IxxCZB1wbxTTz3V9DoO4/DDDzcXABhPrhcMKzzWthUaHiccT+bG0rStAAAAAAAANWNceiD39PSYfseDefnll81iegAwUeTcQDZ3ucMKj5NeVBK2Jy1OTiztWgEAAAAAAFAjxiVA/uhHPyp//vOfB7394YcfNvsAwEQJj9ftyIvnDS881rYVLZFcqON6slU8CAAAAAAAwGQMkANNVoaQz+f7LaYHAOMZHrueyMym8H+X2vLxUnisbSvC6Ex7sr3b342zBQAAAAAAmAQ9kJU1yDzujo4O+eUvfynz5s0b83MCgL49j4vh8bxpjrQlw/WfcLesk2QqK3Ni7TIrkhQ/JxJk60Q/OvN7OgcNjzd1eOI4vAYAAAAAAGDiGLMy3+XLl4vjOOai4fGHPvSh0vfllxkzZsiPfvQjef/73z9WpwYAA9Kex8XwuKUufLIbuDmJ92yTWekNEiQ7xe/uFD+dkiDdI4HvDRoeRxyRuc3j9rkeAAAAAABAP2OWVLzpTW+SD3/4w6Z9hS6Qd8wxx8jrXve6XvtosNzY2ChHHHGE/PM///NYnRoADMgbRnhcNCOak0hDU+l7O50QP5fpt18q55fC40XTo7KhvbqF+gAAAAAAACZFgHzqqaeai3rggQfk0ksvlVNOOWWsHh4AqqY9j4cTHit7kDY95dI5TzpTvkRmFcLjWCRciwwAAAAAAICxMi5zpV966aXxeFgAqEpDfPQaEmvbio5UYIJmwmMAAAAAADBR0WwTAEbYj/8Rk035N8jTPbv6Hb+ab5bAc6W9e6bIw92SzPqyrcuVRMySXz3RU9qvPdW/R/Jw3L5x737bzp3/bOjjb/x9x4DbP3H8tN06LwAAAAAAUFvGJEB+7WtfK7ZtyzPPPCPRaLRf7+OBaD/kF154YSxODwDGTDZwJJ/1xbZE6mKW2PoFAAAAAADAVA6Q94YxIjMAAQAASURBVNxzTxMI60UtWrSo9DUATBWub0mPH5OEJdJSZ5sqZAAAAAAAAJnqAfL9998/5PcAMNnlfZGMODJN8iY8dqg8BgAAAAAANcAe7xMAgJoRBMM6LOuJZH1bLAmkyc4SHgMAAAAAgJoxLovoZbNZ6enpkenTp5e2tbW1yY033miuP/jBD8rhhx8+HqcGAAMKgkC2Jf1hhcdJ1xFt2pMQTxxreCE0AAAAAADAlAmQly1bJmvWrJEnn3yyFCgfccQRpUXzvvWtb8mDDz4ob3nLW8bj9ACgX3i8scOTVJU9i4vhsU71SNi+2CGyY98PTNgMAAAAAAAwZVtYaDj87ne/u/T9qlWrTHh88803y8MPPyzz5s2Tr33ta+NxagAwYHjcnfGlPh7+T2bOt0rhcXPEEyfEoZ4fSDJLhTIAAAAAAJjiFcibNm2S1772taXvf/vb38q+++4rF1xwgfn+/PPPl29/+9vjcWoAsEtZeNyUsCXmhH9y0kFEmiVfCI/1OL9yeNyZ9sUnPwYAAAAAAFO9AtnzvF7f//GPf5TFixeXvp8/f75s3bp1HM4MAHbRnsfF8Hj+NEfECt9cQhfMK4XHEj48ro/RwAIAMLB77rnHtH1raGiQ1tZWOeWUU2Tt2rVVPV1///vfZenSpTJ37lyJx+Oyxx57mPt5+eWXedoBAAAwcQLkPffc07SxUE888YSsW7euV4CsFcrNzc3jcWoAUKI9j4vhsVVFeKwabbfq8LgxbkssQoAMAOjv9ttvlzPOOMMsRH3NNdfIlVdeKY8//rgcddRRpXVFKrn77rvNQtUvvfSS/Nu//ZtpH/fJT35SotGo7Nixg6cdAAAAE6eFxfvf/3656qqrZPv27fLUU0/JtGnT5J3vfGfp9scee0xe//rXj8epAUCJ9jweTnisbCvcgnmZXCBNiUJ4nIhaks7zAgAAemtvb5dLLrlEFixYYIowioUWZ555puy3335y0UUXyb333jvk0/bss8/KOeecIx/4wAdk5cqVYtvjUkcCAACAGjQuI8dPf/rTcu6558pf/vIXcRxHvve975UGwh0dHfLzn/9c3v72t4/HqQFAyaxGe1jhcRhaeZzOBaY1cjE8BgBgIKtXr5auri4577zzes3SW7RokSxZskTuu+8+Wb9+/ZBP3ooVK8R1Xfn6179uwuNUKiW5XI4nHAAAABMzQI7FYnLrrbdKW1ubvPDCC/Lud7+7dJsOijdv3mwqlAFgXI1ieGzaVohIImIRHgMAhvTwww+ba21X0Vdx2yOPPDLkffzyl7+UffbZxxRwaNWy9lGuq6uTI488Uu6///6Kr4CuT6IzB8svzz//PK8cAADAFDAuLSyGohURLS0t430aADAqvMCS1M6exxoeR+l5DACoYMOGDeZaW1j0VdxW3GcgnZ2dZo2RbDYrp59+ulxwwQVy9dVXy3PPPWeu3/GOd8jvfvc7edvb3jbofdx0002yfPlyXisAAIApaFwDZK1a0H5sWokcBEG/2z/84Q8Pa3Xqr371q2YxEa10PuaYY+TLX/6yHHDAAVXfl/ZiPuyww8x0v+9///vyoQ99qOr7AIAi3xfp9uMS2blgXiqnNcgAAAxN202oeDze77ZEItFrn4F0d3eba10o74orrjBj46JDDz1UTjjhBLP9z3/+86D3sWzZMlm6dGm/sfxpp53GywcAADDJjUuAvG3bNjn77LPlt7/9rfl+oPBY+45WGyDr6tTaG07DYl2dOpPJyPXXX2+m9umCIwceeGDo+9LQWPs066A8mUxWdR4A0Jfni6TFkUbx6HkMAKhKfX29udYK4r50vFu+z0C0VUXRRz/60V63HX/88aaXsrbJ0BB6sPuZPXu2uQAAAGDqGZcA+V//9V9NePyxj33MTJmbMWPGhFidutzXvvY1M61PF/z73Oc+t9vnB2Dq8jyRjG+LLpnXYOfoeQwAqEp5m4p99903dHuLounTp5uexz09PTJv3rx+t+u2devWmcWshwqiAQAAMDWNS4D8m9/8Rs4//3y5+eabR3x1ag2RB1qd+o477jCrUy9cuLDifWlbDe3xpq0wmpqaRuwcAdS2nqw3rPC4y3VE51kkxJO4Vf19AACmtsMPP1y+9a1vyUMPPWSKL8rpNqVt1wajM/v0Pu677z4zHu4bQuu2SCRigmYAAACgL1vGgbaH0H5rE2116mI7DW1dcfDBB8snPvGJET1HACPj2c05eXJDtt9Ft4+WzrQn27v9YYXHelTc9iVi92/X09dALX0AAFOb9hnWooZbb73VFEwUadXwqlWrZPHixaUiCW1D8cwzz5hF88pp+zh144039tr+05/+VDZu3Gj6IBf7KQMAAADjXoGsFRA6sJ1Iq1OXrzCtYfTf/vY3se3q8vWtW7ea/s59FxcBMLKybiCPvJSRHT2eCWlnNtkSi9jypkX9FxcaqfB4U4cnjhP+GD/YFR43RjzJ+CKBVzk8TmVFmDwMACjX2toqK1askAsvvFCOPvpoueCCC0w/ZF3rQ6uLr7vuutK+a9askeOOO84ExitXrixtP+uss+Suu+4yAbKOWXWfF154QW644QZpaWmRa6+9licdwIi48fcdvb7/xPHTQu9faV8AwBQKkL/yla/IySefbPoTv/Wtb50Qq1MXqzh0BerLLrusqgX3ysNnbX0BYPRpeDy7KSLzpjnSUufIi9vyoxoeRxyRuc3h/2Qm/Yg0OK4Jj+MaPPuVw+PuTCB5jwpkAEB/GhrruiEaJF9++eUSi8XkmGOOkauvvloOOuigik+ZFkb87Gc/MwtN33nnnabyWNu+aXXzF77wBdl777152gEAADBxAmTtfazT7LSC4sgjj5TXve514vQp7dNqittvv33MVqcuDsznzJkj//Ef/yHDsWzZMlm6dGm/CmQdmAMYWVp5XAyPR7Pncc4NTHi8aHpUNrS7oY8NxNoVHku48FgfK+pYu3fSAIBJS9f10MtQtJ3FYO2QtKjiqquuMhcAAABgQgfI5dPpHnzwQXPpq9oAeXdXp/7JT35iFve75ZZberW60Cl+asuWLSYMnj9//qBB9OzZs80FwOjTthWjGR4r7Xm8cIZtwuNYpLpgt85yqw6P9TFs7ZOcHv45AwAAAAAA1Pwier7vV7x4Wl5YZV/l8pWoq12d+pVXXilVIe+1116ly6c//WmzXdta6Pd/+MMfqjovAKOjIUw6u5t0YsRwwmMVC7lgXia/KzxuSljmwzMAAAAAAIApXYE8GrRNxEUXXWRWp7744otNT7ehVqfW7bpgyLx588y2d7/73QNWKN9///1msZF//dd/NX3mDjnkkDH+yQCMF+15PJzwOAwTHudE3IDwGAAAAAAATFzjGiD39PSY6mBtD3HCCSeY/sPjtTr1G97wBnPpK5lMlqqXK/WcAzC5REapH3GxbYXra39lKo8BAAAAAMDENS4tLNS3v/1tU/F74oknyoc//GF56qmnSj2H4/G4qSSulobGWm2sPYp1deovfvGLcuCBB5oeywcffPAo/BQAUB1d16jY8zhiW5KI0rYCAAAAAABMXONSgbx69WpTKfye97xHTj31VDnvvPNKt+kidCeddJLZ5/zzzx/z1an7+shHPmIuALC79O9OTxDTvhWmNUYiVlgwFAAAAAAAYKIalwrkr371q/K2t73NhMQaIPf1lre8RdauXTsepwagVoX8UGg8Ty8bOJILHBbMAwAAAAAANWNcAuTHH39c3vve9w56+/z5801fZAAIW9m7LelP2Ccr8EWyvogrtsQsT5oStK0AAAAAAAC1Ydx6IA9l8+bNUldXN96nAaBGwuONHZ6kNKEdZa4XDCs87nEtcX1bIuJLg5WjbQUAAAAAAKgZ4xIg77///nL//fcPevtPf/pTOeSQQ8b0nADUbnjcnfGlPj66f8500bvNXe6wwuOshse2L3HLE1oeAwAAAACAWjIuAbIumqch8Te/+U3J5/Nmmy4k1d7ebhbO+/vf/y4f+9jHxuPUANSKsvC4KWHLrEZ7VMPjdTvy4nlVnJ4EpfA4ruGxHW7BPH0sAAAAAACAiSIyHg+qIfGDDz4oF198sfz7v/+7CVXOOOMM6ezsNBWFevv73ve+8Tg1ADVCex631IkJj+dPc+Sl7cGohseuJzKzKXxInfYc8U1w7EtDJJBOT0PloWXygaRygbTs9lkDAAAAAADUcICsVq5cKaeffrp8//vfl2eeeUZ835djjz1WPvzhDw+5wB4AKO15vKA1YsLjMJW9w+15XAyP501zpC0Z/nHy4kiz7Zrw2NLc2ascHiezvtij86MAAAAAAADUVoCsTj31VHMBgGppz+ORDo9z65+VIJ81X+e32fLquojMqg9kXqMvDcmotMnc0PcVFW9XeLxT4Hvm/r3udvO9n7YlsESSm5OSdB2JxBNSF4j4PV2Fc9jyiuQiEbEbCjXJ2RfXl+4rv2WduXbbt/R6XD/ZWbjOz+93Tl5nWzVPBwAAAAAAwNgHyMlkUm688Ub5xS9+If/4xz9M24qWlhbZZ5995D3veY8sW7ZMGhoaeGkADEl7Ho905bGGu9nnHtOu7JJ+NSuZ9gUyfWZW6tIiQfwNVf3FrHO8XuHxzkeQIJcTP9lhvvOzccm7gXTlO8S2AmlsqZNuLyZ+qhAge507xK1vEmdnqJ17aUPhXlxXMs8/JuL7JjC2orHSI5Tu20n1eWhfgtbqFgEEAAAAAAAY0wBZW1WcdNJJsn79etPruKmpSWbPni1dXV3ypz/9yVxuvvlm+c1vfiN77703rw6AwY1S2woNj8W2xWmaLrPj02XmHhFxt28axr1Yg999fZP50s0Ekgs807aiKW5JtLFF7KwlQaK+sK9ti2XOpdV8G50fkyCfM9XHGp5bzdPFjiV63b2f6i4cGt+1PQhEgp2hNAAAAAAAQDXCrwi1m/L5vFko79VXX5VPfepT8sILL5jqYw2T9Vq/1+36ve6n+wPAWHN3bBbxXHFaZkhTY3zUHiedzUs265kguDkuEnEqH1MMj/X8rIZpYheD5qGO0ZX78lkJfH9kThwAAAAAAEwpYxYg/8///I88/fTTcvvtt8tXvvIVee1rX9vrdv3+mmuukVtvvVWeeuopueeee8bq1ABgF98TZ8Y8sesbRzU8TvbkxbItqUtExAkTHnteKTzW83MSdVWEx9pOY8z+3AMAAAAAgElkzBKFn/70p3LQQQfJhz/84SH3+8hHPmL2+8lPfjJWpwYAJXbLTHEaC4vWjYZs4Jjw2LYtqY9HzHWY8Njv6dwVHoc4v97hsSMSiY7QTwAAAAAAAKaSMQuQH3/8cXnXu94Val/d77HHdCErABhbTt3oLeLpii09fsyExi2NMbGdEOGxLpSn4bHvVxEeB73D46i24hitntEAAAAAAGAyG7MAefPmzf3aVgxG99P9ASCsnqw3oZ+svC+S8SMmxtXwOBKm6bHvS5BNm2urvil0eBzkMr3C41FbbxAAAAAAAEx6YxYgJ5NJaWwM11O0oaHB7A8AYXSmPdnePQaLxJm+ENXTbDvr22JLIM12Jlx4rPlxPmseU8NjOxYPV3ms4bEuskd4DAAAAAAAailA9v3qwh0ThABAiPB4U4cXaiG63aF/krYl/WGFx0nXMZXHdbYrjhXib5u/cx8Nj2Px0OFxkOqWwHXFciJUHgMAAAAAgBERkTG0evVqefnllyvuR/9jANWEx1rQO7d59P6cBb7IpqQlKc8fVnisn9QlbF8sr3J47Lm+SDZlvra0/USIxe9K4XE+J1YkIhJL0LYCAAAAYyK3/lkJdObcTm577+KH7Ivrzbg2tnBvXhEAqFFjGiCvWrXKXMKwaNoJoELP45wbmPB40fSobGh3R+X50skTG9uzkk1YUl8fftJGzrfE2xkeN0c8SfkifoU2za7rSTLtSePOGRtWiLLqXuFxNLYzPKbpMQAAAMaGhsfZ5x4rFT547QskyKYkcPNmW+bZzZLY+xBeDgCoYWMWIN93331j9VAApgDtebxwhm3C41hkdAJT7STx6pYu6c6KzIgFUt8YPkBOBxFplrwJj00OXKF42fVEepI58X1brHgi1GP0DY+1V7KV7Ah9jgAAAMBI0KA4Ov91ZnxqvRKYdmp2fXNhLY/oDp5kAKhxYxYgv+1tbxurhwIwBWgoO5rhsQ5+t+3okSbflaa4yLzGQDZUUdlr6YJ5xfC4As8TE1KLHUh9XUQsL0zbCpGgu713eEzlMQAAAMaJjp+97RslyE8vtGIz41NeDgCYDMZsET0AGEna83g0w2O/Y7v0pD1paojI/NZ41YPfRl0wL0R47PuBdGW1QNmSxoaoxGORcOeXbBc/lyE8BgAAwLjT4gYNj/1UN+ExAExCBMgAalLEsUZ38JtJSUOdI3vMaRZrGH8p7RCn53uBpDOuCY8bor7UxaPhKztyGbG133HYyuNKDZgBAACAYfI7t5nw2NaxKZXHADDpjOkiegBQC4NfaWgROzFHZkUbxLa9Su2Lh0UXzEtlC4FwQzSQRNQKHR6byg4NjxuniXS2VT7Oc80iJgAAAMBIenZzTpLbbEm3JyQamyO2NVO2pXtXX/xfV4NEt9nSWJ/jyQeAGkWADABltPI4MmuBGfzao7Teh4bHncmcBH5U4vGIJKzqpgVqZYfd2KqNliUIEx7nc0IDOgAAAIy0rBvIL56LSl1qkczx5pnx8/Y+AfIzToP4Wx15yx4BU6ABoEYRIANAGTtRL87M+WLtGJ0WGV5gSTKZM72P43FHolFbxA2xYF6quxQem/Pb9FJV4bEViY3kjwEAAAAYzfFADp7rSXRaoWXak9t7j1L3rM/IqzTPBICaxp9xACj/o9gyK1xP4WEIxJIuP2HCY10wLxatvMpeMTwO8tld4XGI8+sVHkcTVCADAABgVMysD4a1ZggAoHbwZx7ApOB6lepxwxml7Fg8XyTtR0zVsIbHYRbMMzvn0iY8tqLx8OGxm+8VHlthVvQDAAAAhiHMSDPJchwAUNMIkAHUvJwbyOauCn0gxpHniWR8W3yxpMHOhQuPNQjOZ00lsYbHhdWsQ4bHuQzhMQAAACaEjh5X2lIUNABALSNABlDz4fG6HXkT0o62nmz1D6Ln1eU6ppg4YbsSt0Lcx85i6sD3xHIiO8PjEIflMjvDY5vKYwCYhO655x454ogjpKGhQVpbW+WUU06RtWvXDuu+HnvsMYlGo+bDyTvvvHPEzxUAVDqbl82dnjgkDwBQ0/gzDqDmw2PXE5nZNLp/zrqyItu7/WGFx3pU3PYlYr4amu+LBNmM+dqyHZFYXejw2E8nC+FxnLYVADDZ3H777XLGGWdIT0+PXHPNNXLllVfK448/LkcddZQ8+eSTVd2X67py7rnnSiKRGLXzBQANj5M9eYnYInMaRqbdHABgfBAgA6jZnsfF8HjeNEca4pUXpNudaXebkrY4VTyEH+wKjxsjnkRD/LXV8DiVzkvgFZrEmdYVVYfHdYXgGQAwabS3t8sll1wiCxYskAcffFD+5V/+RS677DL54x//KL7vy0UXXVTV/X3ta1+T5557Tj796U+P2jkDmNqK4bFtW7JoRjTUWBgAMHHxZxxATdKex8XwuKVuFMPjzrSZdqeVE3ObI6GPS/qRUngcJtvW8Li7Jyt51xfL2dkjucrw2G5oEcvmzzoATDarV6+Wrq4uOe+886S5ubm0fdGiRbJkyRK57777ZP369aHu69lnn5Xly5fL1VdfbQJpABjN8LilMSYx0mMAqHkkDQBqkjcG4XF3MiubtqdMeLyw2ZeIE37xj0Cs0OFx4Iv05APJ5X2JRgotKMLwM6ne4XE1JdIAgJrx8MMPm2ttV9FXcdsjjzxS8X6CIDCtKw4++GD5xCc+UdU5bN26VZ566qlel+eff76q+wAw+WnxRXl4HIkwPgWAySB8OR0ATCDa83g0w2M/lZTtnZbsUW/LghlRiVX5UHWWGy48DkSSee3nbEmiwRYnEhXJVj7Oy6TF7+kkPAaAKWDDhg3meqCK4eK24j5Duemmm0wY/be//U3sKmes6LFauQwAQ4XHWnxh23WExwAwyRAgA6hJo9nz2Et2iteZFMeeLXvOaxKnu7CoXTVidhCqT3I245rwOBbxpakhLl15rV6ufH5BT4dY2vM4bOWxJtUAgJqUSqXMdTwe73dbcSG84j6DWbdunVxxxRWmd/KBBx5Y9TksW7ZMli5d2mubViCfdtppVd8XgMlH1wzZ5qbEcWzCYwCYhAiQAaCMl+6RIJ8TsafJ3Jn1ou/V3e6Rf4q057GGx7oYYCwSSEPUkjDFYCbcbttUqDxuniFBsqPiMYEfSODmRubEAQBjrr6+3lxns/2nqGQymV77DOaCCy6QOXPmyH/8x38M6xxmz55tLgDQl86m6+70JNZsm+KLf2ygbQUATDb0QAaAMn7ndhEnIpEZcyUWHZ3Bb3HBPA2Pta9yY1SqC4+diDjNM8SKRMKFx/kMFcgAUMOGalMxVHuLop/85Cfym9/8Rj71qU+Z/bVyWC/a11ht2bLFfF+pihkABtKWssyaIRoex+PUqAHAZESADAC9/io6Ep2zKFQ4OxzaSULDY10wT8PjeCKixcQV+blsKTwOe37l4bGlvZUBADXp8MMPN9cPPfRQv9uK2w477LBBj3/llVdKVch77bVX6fLpT3/abNe2Fvr9H/7wh1H6CQBMZo4tsmhGlPAYACYxPh4EgPI/itPnihWNjcpzol2Ik0FMgrwvsagt8UhELCtceBykuneFxyHOr1d4HI1J4Loj80MAAMac9hm+6KKL5NZbb5WLL75YmpubS32NV61aJYsXL5aFCxeabVpFrNtbWlpk3rx5Ztu73/3uASuU77//frnxxhvlX//1X+WYY46RQw45ZIx/MgC7q+OeG4e8fdp7PzHqT/KchkBi2V0VEfkt63rdnt36uGQS7ZLc8VeRtmfMtq7/vVs6/lH4G5XZuPeunY9/x6ifLwCgegTIACaHEVokbjQrj7NBRHKBIw1Ru7BgXk/lBfMCN18Ij227ivDY6xUeW06EABkAalhra6usWLFCLrzwQjn66KNNJbH2Q77++uvFsiy57rrrSvuuWbNGjjvuODn77LNl5cqVZtsb3vAGc+krmUyWqpeXLFkyhj8RgGrl1j8rQb5/H3S3fUvpa8uOiNMyY8yf3GiI2XT5QHfyxuJ0AACjgAAZQM0LgkC2JX2ZqAJdMM8XcX1bYrZnwuMwPY8Dz5MglxVpssVuaBErmgnxWL4E2YxI3a7wGABQ+zQ0njFjhgmSL7/8conFYqZq+Oqrr5aDDjpovE8PwCjT8Dj73GP92pJ57dsK47+eLoku2mtCvg4535KtboPWHY/3qQAAholkAUDNh8cbOzxJaUI7ynTRu+GExz2uZcLjiO1Ko5ULFR6bY7XKxLIK4bHjhAucs2nzoITHADD5aJVwpUphbWeh/zeG8ZGPfMRcANQGDY+j81/Xa5v9j0fF7+mUIERbtPHgeSIb8vXiRyfoCQIAQmERPQA1Hx53Z3ypj4/un7OcJ7K5yx1WeJw14bEvccsN1fNY21YYliVWvC50eKxvHkx4HEtQeQwAADDJBfncrvFfvF4mYnjc5TriBbZMt9PjfToAgN1ABTKA2lQWHjclbIlVzliHLZf3ZXOXLV4VhROBBKXwOK7h8c5AuZJszpWItqDQT/iicbFClCv3C4/7TG0EAADA5AuPzWJ1gS92XaP4OgttAobHOvydE0tJl7OzQAIAUJOoQAZQk7TncTE8nj/NMdW6oyGbdWVdW15cX2RmU/g/mWnPKYXHDZEg1Omls3lJpV2zYJ5RZXisbx4IjwEAAKZIeOy5hfFfLCETNTxujHjSHGHxPACodQTIAGqS9jwuhse6Av1oyOU9eWVTtwmP5zX60hAPX+acF2dXeBziL20mH0iyJy+2bYkdcgpi4Lq9w+MJ9uYBAAAAoxceOzPmTbjxnxZflIfHVQyfAQATGAEygJqkPY9HMzzWcHbz9pR4ni9zWxxp1h4UVYiKV0V4LNKTt0143FgXFbGtcOFxVxvhMQAAwBSh47/y8NhpbJGJFh5r8QXhMQBMPgTIAGrSrEZ79MLjfE7cts3i+b7Mm1kv0xqqbxdf53ihwuN83peevCW2BNLSGBMnEqJtRT4nXlebBFVVHgfhThwAAAATkrtj84QNj3XNEA2PtfiCymMAmHxYRA9AbRrFymNT2eHXycyWhExriUj2pZfEisbEi9iSF0+yuV2r4VnRuMQW7t3/9MQK1SIjm/OkWQJpiotEIo6IV8WCKQ0tIplU5Z8pEAmyafG6dkj2xSd73WbuSyum5yzqtd1tj4vlOOI0z6h4/wAAABgDvjchw+O8L2bNkKCuUHzx9JbkeJ8SAGCEESADQJ/KjkjrbHFaZkhTWvtWeGawnvrbfZKKLZLuyGbpiGwu7Ox7Mm3JJ6t+/jS0zXoi6dw0EzM3BDvEygeS97LiegmRto1mP699m7jRWCncLe95ZzVMEztRJ36FAFnDY8lnzWJ77rZXpf3HN/aq3PaTHebabpwm9Ye8bdfzsGWmRPqEytXouOfGAbdPe+8nhn2fAAAAU5ndMnPChcdqS48lLb7IAp2511InIgTIADDZECADwACVHXa2USRdtt22xU7Uix1rFieeNZvcrh3Deu40PE66jgmP62xfHL9yewkNgHv1vOvcXrEpRSk89rWdhi1WrF4izdN7/7ipbnPtNLVKdP7rzNdeslOCfOFnBAAAwMTg1DXIROT5YtYMKYTHAIDJiAAZAMp8t+twibw4W7ambNmetuXJ7YG47XvKCfJcr+cpCAK5I/lWSfw9LpGXClW8YWQDR1KuYxrQJ2xfbCcQs9JIhfDY7+kUmR6+513v8NiRwInI97zjxensfaznvkHOjty76/tkp3htm0SsmVKNG3/f+znIbNxbzp3/bFX3AQAAgNozoz6QaS7RAgBMZiyiBwBlrGi04vOh4XGQ6pbAr5D89uGKLT1+zPzhbY544oT5CxwEhfDY96sIj4Ne4bFEtRVHZaXw2ImYxfkAAABQG8z4b5w0Vh4+F4obAAA1iwAZwKTQo30hxsTO8DifM20hqllcJONHTNsKEx47IR/Lc014bNU3hQ6Pg1ymV3gcZr1BPaYYHpuey1X8bAAAABjn8DjEwsrjxQ8CafPqx/s0AAC7gYQAQM3rTHuyvbu6auDh0FbFQSZVCI+jMZFINHTP46xviy2BNNuZkOFxoXWFhsgaHtuxeLg3Dxoe6yJ71YTHbl78dLIUHpufDQAAABNeaWacm5eJKPBFtuQSkvJpcQEAtYwAGUDNh8ebOsJW9A5f4AfSlnHM4FwDVg11xdQTV7NgniuOFWLBvFIWHohlR0KHx4U3D65YTiR0eKzVzVp9LJZNeAwAAFBDSuM/LW4IWdgwlnRM2+Na0u3FpN52x/t0AAC7YdIFyPfcc48cccQR0tDQIK2trXLKKafI2rVrQx3785//XM477zzZf//9pampSWbNmiVHHnmkfOc73xHX5T88YKKGxxFHZG5zZFQHv5u9Rkl5jhmca3hsWeHD4+KCeZYEoVaxDryc+dpUEYdoJdH7zUNErFgiXOWx50rguyY8thtaqDwGAACoETr+87Zv3DUzLlE/IcNjnYXX5ORkhjNxW2wAAKZYgHz77bfLGWecIT09PXLNNdfIlVdeKY8//rgcddRR8uSTT1Y8/vzzz5f77rtPTjrpJPnGN74hn/nMZ0xwfO6558qpp546rgsTAOjf87gYHi+aHpWIE6bctnq6Tl4yL5L0Y1LveGIl6kOFxznfKoXHYRfM044VPZ69a5URq8rwWN88mPDYChce5zWotsSKJ8Qa7RJuAAAAjGh47Ke6SzPjwoz/xiM8jtu+zIllws2MAwBMWJOmEVF7e7tccsklsmDBAnnwwQelubnZbD/zzDNlv/32k4suukjuvffeIe/jrrvukre//e29/vO9+OKLZfHixfKrX/1Kfv3rX8vJJ5886j8LgMq05/HCGbYJj2OR0QuPu3uyknMtabRzUpfQnsThHisdRKRZ8rsWzKvQotkLCoGzRsemBUUIfcNj8+Yh2RE+PLYs81im0hkAAAA1FR7bOvabYOGx3yc8bogEYk+g8wMATPEK5NWrV0tXV5dpQVEMj9WiRYtkyZIlprJ4/fr1Q97H8ccf3+8/X8dxZOnSpebrJ554YpTOHkC1NJQdzfBYk9xUOi+5vC+xSCBznaTYVTyUtqsohcdSOTzu8hMmY65z/FDVwFqkHHS39w6Pq6k81vA4mjDXAAAAmPh0/FceHjsz50+48PjVLV29wuMQE+oAADVg0lQgP/zww+Za21X0pdvuuOMOeeSRR2ThwoVV3/err75qrufMmTPkflu3bpVt27b12vb8889X/XgAKtOex5XC42c35yTr9m49s66tsEL1ohm9FxqJRyzZszyczaQk70alqc6W+kDEyluyzauXWL61dMx2v1HS7bY42Zy01O8aHbdnLG0MESo89r1CeKxn2RTxJCPRUJUnfrJd/FymX3j8oj9HchKR7V6fPniBK61Bd6/w2KomEQcAAMC48ju3iTS0TMjwONgZHnf3uITHADAJTZoAecOGDeZaW1j0VdxW3Kfa+73lllvMgnzaB3koN910kyxfvrzqxwBQvTA9jzU8/uXjSRPmzmyypSHumAA5kEC6M7uC5bwXyJsWxQvfaHisbSHcQKKRuDQ1RETXmVNeYMsL+emSCwrJcJvfKKkeSyzPk2TWl1ROp+iJ5FOWWCEWv9MF8zK+LXUi0mDnJO7USUa7ZIRZMCWXETuWkMCJ9nrzoOHxU8Ei2e427Hoc/fk1PPZdsawE4TEAAEAN8jMpicxaMOrh8Y2/H7ol2ieOn9brex1Vb2zPSo9TGDs3RLIVK4/v2banzI28ZrfPZbSPBwBMsgA5lSqs6hqP7wyByiQSiV77hJVMJk1orK0xfvzjH8v06dOH3H/ZsmWldhflFcinnXZaVY8LYORoePzm18Slpa4Q+m7sKKTBr5u1q9L3xW2FqmQVZFMi0bhYkUapt6Ni20Gv9sUR8WRRtDAQXWu3SsbSIDgQx7alpc6Sljpb1mdEdmbOQy6Yp+GxVjub8NjyqlswRRfLa5wm0tnWb7+ouNLiZErfb8snJPAL/ZtDVx7rHEQAAABMGHaiftDw2E/3FHq89ZF9cddi8lY0LrGFe1f9uF5XmwQ6eC3d567WkPlttmzZ0i11nSlpXThd9pjTLNZLvWfl+rm0+JIStzstVtf2wrZsSvxMctdOli12vM8MOgDAhDFpAuT6+sJ/Ntlstt9tmUym1z5hw2NdMO/RRx+VG264QU4//fSKx8yePdtcAEwcWnlcDI/DCNy82PXNYkXrxd6VwQ4q74vk3EBaLTHhsRMinNXxd9fOBfPiji9xE+5W1/PObmw16/n1btDRX9YTSXuFEpDCgnkh+iTnc+Knk8LSegAAABOH3TJr8MrjwBdfCws8z4zlTFGEZUv2ucfFikTMGDe+15uG9bgaHns7NovsnGGXe2lDaXya3ZiXro2NMqvBNeHxgJPwtP1aqkd8q1Okq91s8pOd4nW1ixWJmRpmu75xWOcGABgbk6al/VBtKoZqbzGQ7u5uOemkk+RPf/qT3HzzzaayGEBt0rYVFenodycrEjU9hTWcrSTrWZJ1C39IQ4fHumCe65iqZl1cJOpUioB39mROdfdZMCXM+YkkXcf8KJYdCbVgnr7hyG9Z1+s5AQAAwPirOJSzHbHjdWZBZruhxbS7iC3aW6LzX2fGuLvFtsVpajUXvb/IvNeKHYuLVdco9XGReY3WwOFx6XjLFGnoMeZnicYK3yfqTNANAJjYJk0F8uGHHy7f+ta35KGHHpJ3vOMdvW7Tbeqwww6reD+dnZ0mPF6zZo3cdtttcs4554zaOQMYf9oWYltyV7sGK14fKpztylmS9LX/sEgiZoUKjwMpLJinpb2NumBeyC4RpidzPlvVginF8FiH4w2OX7lUuTw89lzzPAAAAKB2aJWxziIz7SAaWkyQPCqPU9ZWzU7MkZl1qaHD4yI3Z/o4G5H4gDPj+i6CvaVr8MZwEduSGY1D/4xtSU9cf/CB8JMbsr0W1d57rlZEAwD6mjQf9Wmf4aamJrn11ltNz+KidevWyapVq2Tx4sWycOHCUi/kZ555RjZt2tQvPD7xxBPlkUcekZUrVxIeAzXE9YJhDX43dniSypYluSHD482ZmNgSSF1EwlUe+yJpP2JyXA2PwxRGm3P0XBMea8+6sOFxyouUwuPmiCeOHW5qYjE8dmbMEyu6m1UqAAAAGNPwWBdZHv3wuE9btWkzQxUQ6/hU+x4Xdx5sTKvh8WPrsvLUqzl5fF1WXtial+e25OXVdle2dXuly+bOoYPhIt1H9y0/tvyij6OXB59L9wquAQCTtAK5tbVVVqxYIRdeeKEcffTRcsEFF5h+yNdff735z+m6664r7avVxccdd5ycffbZJiguOuGEE+Svf/2rWThPj7nzzjt7PcZBBx1kLgAmFu1BvLnLlYOl/yKalcLj7owv9fHwn6WlXUs252MSsUSanbzkQgTOxQXzfPELC+Y5deEeTHsjB74Jj7WthmX1hHgwV3Z49TqDsRAe63uHCi2WdYE9v6ezFB47jS36rIY7RwAAAIwrDY7HKjzu11ZtR+XBcLcbNeNTrV+z65oq7h91LFnQGpF1O/LSELelMW5LItr7cdpTldcQKdJiitb6gZ8TXVi7M+3JK9t3LaoNAJjEAbLS0HjGjBkmSL788sslFovJMcccI1dffXWo4FfDY7V69Wpz6euqq64iQAYmYHisg8uyhaGrCo+bErbEQo6x064ufheRRRGRPeqz8ky3VLVgXsJ2JW6FWzDPXPu+eSNQCI9DvnnI58W2gl3hcYgHC7IZEX0TUAqPAQAAUAt0/FdsW2HFE6MWHutgdrht1bZ4jWbmnl3XKFbGDjWzUMf3ricDhscjScPjTR0hx80AMIVNqgBZLVmyxFyGou0sNEDqa6BtACZ+eKyDy5lNIauIg0DaU0EpPJ4/zZGXtgehKic6gog4ViE8DtOCohgeFxfMc7SPReXTM5XAyrJtsZxI6PC48OYhIbOdpHSHWTvQD0yLDK1y1gE94TEAAEDt8JKdZeFxnRk79pX8w08L+3a3S/bl/5P0Y3+oeL+ZjXtLYt/e6wcF2n5CZ8XtbKvW88dCwVUqO0vybbbkrO3iJzt37v3GXmtyaCmFjk/DNEpO3rdKXmmfJzOjWZkTS4vdMac0Ly625z6Dn/PTj5jrjs5ne23PvXKgyIw3DnxMPjDhccQRmds8cDTScc+Ngz7mtPd+ouLPAwCTxaTpgQxgaimvTJg3zZGGEImufkjUnvIlk98VHoepnMjkC5UTGh5Pj7mhwmMtHi6Gx9rzOBqmD7H2STZdK3ZWKWsfimorT2IJiThBuPA4nyks7RdLmAsAAABqJzz22jbtalsRahW73euxvKutWnULOi+IdIYanxZ5gWXC4+bI6LWV0PA4mfVNeLxoelQiOtAHAAyKABlATdKex8XwuKXOCd22QgeLiWj48DidzUtP3jKVE61xVxy78uBX1/NIy67wOEzgrJXHPa4led8WK2Rw3Dc81jcPYcqVS+FxEIhlR8SKsGAeAABAzYXHTiR0z2PTGm036HgxbFu18vBY26pFqwiP1fRodkzCY10HW8PjmC5uAgAYEgEygJrkDSM81rYV2kOttd4KVzmRcyXZkzc927RyIkz7Nc8PTK/kQKyqwuNkEJOsb0vU9s2bgTD8TKpXeBzuzUNZeByNhZpKCAAAgIkXHkfnLAo3/vM8CXTMuBuseH2o8LgrZ/UKj8OuyVGuwXFDHBLsdnjcUmcTHgPAVO2BDGBq0J7HYcJj6bNgXmu9LRKmp3A+J6m0SHPMksa4SFTLikOEx51p34yBExIyPPZFeoKY5AJH6rRPsp5emKA6kxa/p7O61baD3uGx9lcGAABAbfDSPWaMWgqPtRggRHhsxow66NwdIcPjzZlYVeGxKW5wtctxQ+hT0fA4lZXdDo8d/QIAEAqlZwBqUpiex2pb0u+1YF6YdNbPZSXIpMS2LWlpjIUa/BbDY82Z4xGRSIhWFxo0a9Vx1nckZnnSEAnChcfJTgl6OsSqKjzWNxBaGk14DAAAUIv8zu3DDo+1gng06YLTGh5rN4iqwuOdxQ3VhMfdmUDyXnUVyITHALB7CJABTGqpbHUL5gX5vASpbtPaobEuKhFdWaOK8Lgxrm0oKp+XFoFkfRHXtyRue9Jo5bSYuLoFU5pnhAqPPV8fT6cCVhEeD29WIAAAAEaL7QwrPLbrGkMdM1wpL2IWnNbweI/6bLjwuHxmXMj1OIrhcc4NJFrFone6P5XHALB7CJABTGr18fDhsQqyKRMe60DbiVT+E6mhcSYXlMJj7bEcqm2Fa4nr26ZSuUHDY6u6nneOhseRSKhe0T1e4ecwC+aFCI/NgF6fBwAAAEwYkelzhxcexxKjd1JuTnZ49WbBaQ2PQ04SFHGzZWtyhFnTRErhsS56Vx8Pf4qpXEDbCgDYTQTIACa1WY126PDYsCzTFiLM4nJa2asL5mlHudDhcVAIj7V1RcT2JW77ocJjbavRa8GUkOFxl6vDeQ2PnVA/k4bH3vaNErijt/I1AKB699xzjxxxxBHS0NAgra2tcsopp8jatWtDHfvzn/9czjvvPNl///2lqalJZs2aJUceeaR85zvfEdetvFgVgIkhzPhvLMPjIJcRP5sqLTgdav0PHUCbL8LPjDMLTvuRUnjclAi3IHYRPY8BYPcRIAOY3EIMLnuyXulrM9AOMe9OD+nyooUF8yJWuPBYRJJBzITHGhzHdcG8EH+FTU9mbatRRc+7YnisQ/Q6xw/1PBTDYz/VHXoqIQBg9N1+++1yxhlnSE9Pj1xzzTVy5ZVXyuOPPy5HHXWUPPnkkxWPP//88+W+++6Tk046Sb7xjW/IZz7zGRMcn3vuuXLqqaeav/8Aal/gj3F4nE6aSGG2k5SoE+LviO+Ln+4ufB2JVhce65ohwwiPVWPcYsE8ANhNIRphAkANGOab3860J9u7y1alDlGlq+Hxq6m4+GKZBfOi2vAtxOllg4jkAkfqbN8smNe5K7cenO+XejKHDY9dz5IuKYTHjRFPMhKtKjy265tGfaEVAEA47e3tcskll8iCBQvkwQcflObmZrP9zDPPlP32208uuugiuffee4e8j7vuukve/va39wpdLr74Ylm8eLH86le/kl//+tdy8skn85IANSzQMWM2I1LfNOrhsbZVM+GxrslR1yiRTBBqfGqO0V5uO1urVRUe296wwmOlC2NXPj8+SAOAoVCBDKDmafi5LVkWAlcRHm/qCLdKdJHnWyY8drXnsZ2vcsE8W2KWZ8LjMJXHeqBZ/E57Mje0hFv8JAhkq9dYCo9DTSXUnsep7lJ47MycL1L92BwAMApWr14tXV1dpgVFMTxWixYtkiVLlpjK4vXr1w95H8cff3y/0MVxHFm6dKn5+oknnuC1A2qYtq0IsukxqTwutVXT8Dhk27e8Z5nxqTZ+sxPhihQ0PN6cqyuFx422O6zwOAxtjbG5i3Y+ADAUAmQANU3Dz40dnqQ0oR1GeBxxROY2h5uMkQ9EduQiJjyem8hJPMRUvd4L5vnSqAvm2eGnIWqSa8LjECm3efOg0wnFCh0em+NyGQnyuVJ4PFqDcwBA9R5++GFzre0q+ipue+SRR4b11L766qvmes6cObw0QI0q73mswfFoh8fFtmphx6faVm2D22LGp3a8XqxoPHR4nPSiZeGxjFp4vG5H3pwnAGBwtLAAUPPhcXfGl3ptKBxSKueXwuNF06Oyod0NVTnRno2Yyl4Nj5tjgUgqXHhcXDAv6ocb/OobgQLL9IYLGx4X3jxEZXokJT2hKo91VO9K4LmmupnwGAAmng0bNphrbWHRV3FbcZ9q7/eWW24xC/JpH+RKtm7dKtu2beu17fnnn6/6cQGMHC0A6BUej+IaFkE+L+LtaquWe3Ft6DU5PLFkupMSiYSYTScibfm4NEdcaXTyoxoee34hPHY9kZlN1NYBwFAIkAHUprLwuClhSyxktW0650lnypfIrEJ4rItxhK2c8GyRlqhbCI8rnl5QCo/Ngnk7A+VKcr5IxCsE2iY4DrP4XXnlSTQm9bYbLjzOZ02/PBNSa99jKo8BYMJJpQqfVsbj/av2EolEr33CSiaTJjTW1hg//vGPZfr06RWPuemmm2T58uVVPQ6A0Q2P81vWldpW+JnU6D5eNiVWQ/POtmqZqhZ0nuMkpb2KnnFpPyLznbTMjaVHNTzuTPsmPJ43zZG2JDPwAGAoBMgAapL2PG6pExMez5/myEvbg1BtKzpSgdiWFTo8dl1PurJiKiemRV1JhPirWVgwzymFx8UF84IQi/OlPVuaSiPl6sJjffMgqUjFB9oVHnti2XahYoXwGAAmpPr6Qr/QbDbb77ZMJtNrn7DhsS6Y9+ijj8oNN9wgp59+eqjjli1bVuqZXF6BfNppp4V+bAAjHB57buiex1rcsFus8G3VysNjbavWZOWlvYqHr7PdMQmP/aAQHrfUaYBc/XoqADCVECADqEna83hBa8SEx2HCz2LPY13nY0aDHa7y2PWlM50zPdu0cqKrcjZrBucZT8SVXeGx6Xlcoa+aBs4p1zGRseWEm94XuG6v8Ljw5sGrIjx2JNDqY8JjAJiwyttU7LvvvqHbWwyku7tb3vWud8mf//xnufnmm+WCCy4IfR6zZ882FwCjI7f+WQny/T8oKsq/+oK47VslMmtBKTx2ZswT65VnwoXHu1mhbMaaIcLjbJ/w2KzJ4YVoxWEXxuauvUNa85b4yU7zvZ+bUdgp7CIiA91/pke8nS3iPF+kK2eZ8LgxGkh9R5ukX9oq2WRM8tIiWZ0OWMZt37Lz4SPitOw8FwCYggiQAdQk7XlcbXisPY9nNDjmuiLfl2Tak3o7kIZoIE1+PtTgvDsTiKttKKQsPJbK4XGPHxMdkzc4fqhjTHjc1Vb9attl4bHoIia5ylMQAQDj5/DDD5dvfetb8tBDD8k73vGOXrfpNnXYYYdVvJ/Ozk456aSTZM2aNXLbbbfJOeecM2rnDKB6Gh5nn3ts0D7G+c3rTCFDeXjsNLZUvt8gMAvfBW7lseyQtAojRHj8aireOzyueH6a6ubFT2ZEclnx8u0SeHHxs4UAOchlC23a4nX9Fr8LK/B88TM7xAtEOlK+BIEljQlLop4t+U3bxNuxRdxsk+TjbZJL9vQ61t2xVYJ0UqJ7vC704wHAZESADKAmzWq0Q4XHPVnPDDCLC+Zt1l4SFeqITVuIdFp8PyGNTVGJasHG4AUhvcJj81i2SMTSkFZCLc6n4bH+JM0RT5JSefETrdLwutokqDI81sXyysNjCo8BYOLTFhEXXXSR3HrrrXLxxRdLc3Oz2b5u3TpZtWqVLF68WBYuXFjqhazbW1paZN68eb3C4xNPPFH+9re/ycqVK+Wss84at58HwOA0PI7Of92gAbIpHpgxt/rwOJ8z9/1CrlX8nsrHbcvHJb0lZ4bMLfW2eClL/GxMbKcwuH2qLSKp7Czz9cv5VmnzA5knbbIpFRfNdQcKj7cHzfJy3pIgV+i53uEnxHHrRdy8eH6TiG2JE2+R9TFL2txmeU0iV3hOgpg5/3KZfCBtPZ7UxXa9F8jlC33in+7z83W4MenJW9IQaGV0TCQSSFNDVOrihTF3ZGZEvM7tpro6MmsPic5wez1/tvOnUvUyAExlBMgAalPI9HN7ty8LZ9ihex5r5bFpC+FHpL4uInVxR9xU+PBYHyPhiHghzi/va6WGLU0aHtsZcZyGUFP8SgumNLSEmo5oKjt8bcKs1c2ExwBQS1pbW2XFihVy4YUXytFHH23aTmg/5Ouvv958kHrdddeV9tXq4uOOO07OPvtsExQXnXDCCfLXv/7VLJynx9x55529HuOggw4yFwATU3nxwLDC42hMAtuWXODI0z2tErUG7/fb40XkhVST5LbnJRG1JOc54vdogBwVe2d1xNNtEcnmZpqvN+SbxQs6ZYPXIkFMZG4iJy8MUOzsiS0v5FvFzk8z3+9w6ySVbZC8b0vEbpCElRfHq5cXc63iDHF+Gh4ns75Ygci2bs+0pFD5XKGg4plU7+dmRy4uVqxQeazhcWNZeFzp+fO2byyF7wAw1REgA5jUtC1E6PBYc9Z0UuxEg1iJeom7kcrVyrpgXn5XeNyUsGTH/8/enYBHVpZp47/PObVn7yTd6aa7oUEYgWZxFGT5oyAKjjOyNogMi8oq6rAJjPAxPXwDzCDiMLIPy7AoMIItLQzKJwIuyCayOiIgSG9Jd5JOKqnUfs75X8+brupKpZI656QqXVW5f1x1panU8lZVuvPWXc95Hg2ODvGTIXuSM6vwWHbBLgamaE3t0EPhshO3bUsG88lXe/zQQ4eVx+boMDTDh2zxut9bC80fRGDJLuVvRG4nOgjb2norqffeyP/Zze0QEc1lEhp3dnaqIPniiy9GIBDAQQcdhKuuuspR8CvhsVi9erU6FVu5ciUDZKIaVVw84CU81iIt0GLD6nsSHm8fmtimIWck60faMtDqyyAR0KDrGjoiBkzThpXNQg+O71d3aDOR8I/f3tpMM4bNMLqhYVEojdaADUzRLcMHEwt8o+rPhpVGwM6gxbAQtkfV/lQqnH2aNeXuOxceS7vkcMH6RNo3XqU8+bHZSGSAiK1NqDx2Eh5b8dF8+E5ENNcxQCaihtbT6nMcHiu2Pb7JlkPcss4G5ln61vDYSVsNCY9jWwbmhXULhoS75ZZlmhN73kUHyg/0s4CxrKYqOzRdU4Gwo/A4FoWdSqj788khgwWb5tQ77yO4897lbyS/hizMwT55R6D+P/3+H8bPz2Zc3Q4R0Vy3YsUKdZqOtLNQw7KKlDqPiGpfqeIBT+Gxgw2ghMcb02EV4LYaGaS2DLUrt6cdSkksbKqB062BjrLXyW17ZX+aGzhtm85mhmS2hMdtYV0FyeWkLR0Ja3wPKj2P3YbHujx3BeE7EdFcxgCZiBqaz5i8+U3+6feI9Q3k/38k3oLY20PAgXtBC0agB4Jlw2NLwlnTh/RoFH6fgWB6FJktd2WO+abd/MazBiSSDekWdMOGmjRSrieztNWY52JgypbwWKqc/brcQfkpJrFfPQI7nVRV2ObIR1XgbG4JfnMSw79Fet07SLz6K/X/7cd8reztSnhstIy/ocj19Uuvf6/89YiIiIjmqMLw2HHxQAXC4+2CcfxxbLzNhJOBeTKYrs1IosXnZOA0MJgc31vK/jQ3cLrc40qZGuJGAKEt4bHhINyW8Hh9KgIb4+3lQjKkxGV4bHQtcvT8ERHNBQyQiWhOk81lf2brEDrNX74yQcLjvoRPhcGy0Q7qtqPK3ix0NTBP2mrIwDxpxVaWbW/pyey8552UduTCY6nsUPNOHNxXLjyWnbyEx9WasiebcyvaX5XbJiIiImq48Nhp24p0csbhcUAVHkwvlcqq8FgG5rX7swjIrI1yLBvRjIGQOR5BRIzx8NjRkXuWHz7YrsPjrK0jpJnwl6+jUMUX1vAALD/DYyKiUtjMh4gawpjsLl3KbS5N23lQKpUTEh7Hsj4ENRNBZB3lrDIwL2n5VNsKCY8lRHZwb7DNrAqP5U2Ao/DYtjFoRfLhsars0NyFx2o4XxXDY1XZ4WD4HxEREdFc4zU8hoTH0uqiyuFxNmvig95RFR7LwLywg5I0mcURtUNImjoiRtZxEjGS1lTbNx02WvSU6/B4QSABn+6gVZwFDCRstT9l5TERUWkMkImo7kUTJgZGnZTzlt5czvOnHF1HerZJ5YSEx82+LJoMZ+FxbmCebH7VwDxH4fF46woJkeVNgGqr4eTNQzaNuOXfGh47+Fde+hEXhsea0wW6NOGwwFCkKvdBREREVK/sbNZb5bG0rchmx+ddVDk8jsbSME1LhcdqYF659VnARrNZFVKEDAudIWdFHxIe9yUDKrCQnsxOBk5LUUhheCzDAJ0cWbghBsQzmtqfsm0FEVFpDJCJqO7D495hpxW947JFm0sJgh1VTqQNVTkh4XFPOOthYF7W0eZXNtpb/gRN9zkOj9WbB9NERM84Do9l1yzVx7MRHsv6cj3l9LbuqtwPERERUb3Kbu7zFh5L2wqfD1ogVLXw2LQsFR5blo2FXRHH4bG0VYtZAYT0LNr8phqCV04iOx4eyxxsdeSegz2thMcjpt91eLx+4whiGR0Rvw29vYs9j4mIpsAAmYjqPjz2GUBPq7OW7lnTxnA26GpzKZvfPlU5oavKCQmPdac92woG5mllR4TI5lwqj9Pqz5oMsHNwRxPePBgGOvW4s8pjMwvbyroKj23ZabtUuL6thwW6vhkiIiKixmaZrsJjObIr1/MYDsNjGQLtPjwGhuOWCo+bm/xobwu7GujcrKfRpiUd7f8SWQm4fSo83i6SclQkYlq2Co8tW3MVHo+OpTA6lkWz30JXWGN4TEQ0DQbIRFS3PY9z4fHSeX74DAc90bI2BsdMVaHgZnMZy2BL5YQ1Xjnh4F/OrJXr2eaicsKUTb2Mot4SNDtIgYunbcMXgJPSDhUeZySo1qAFQw7DYxu2tLpwKxnPr4+HBRIRERGVprd1uQqP5cguNz2PxeZM0GV4bKt2EratqfA4HPS7Co+lrdoCI+ZofyrFF8MZn9o/S3gcdBgeRxOWCo+bjYyr8DidsdDS5MOiZkfbbiKiOY3/TBJRXZKex7nwOCAlCg7C4zWbM2rD2OZLu9tcZrXxyomA6Wxgnq0jiYLw2HB42F3WUDXK0r/OieLwWN48OFlgPjzWtPFeeVLp7CQ8ziQL+2s4Jj2W3b65ISIiIpprjHCTu5kSsrdyuL8aLxyAaqfmJjweD2eB5pDmKDy2isLj8bZqDsJjW+aMGJCakHmBrMvwGCo8lvtzEx4H/Dq2W9DK8JiIyAEGyERUlwwP4XHWBNojGkK6g+EdNhBPZMY3lz4bPUbMUc822SynbJ9qV+EqPLZCkC1v2LCcVQPbgD06NCE81tyGx/6Qs8A5Hx7b0ILuh99pPj/DYyIiIqIZKg6PnR7ZJfMu7FRc/bnbn3QUHku7imTaHg9n/TZCft1RONuX8BWFx3AUHo9Z4wPzOoJZGLrtbn3B8ftzGx63NAUdHVmYC9+JiOYyZ2VuREQ1RnoeOwmPpedxLjxe2G5gIGYg6SScTcaRyfrREtYRsQEt46BywtIRM/3QYSMIZ+GxZY6Hx7JNbvGZSMLBYYG2DSs2BCuddFfZKwPzCsJjJ9UgE8JjuS9pk+FWKMLKYyIiIqJpjD79EMyhfhjvvj7pe82fOCpfPOAlPLakBdmWJNfvpPLY1pCQcFbuO6jDb0P9edr7sWwVHseyPnfhsbRwswJq4LQUX/g1VcdRUvqDt/LrG000q8sFU2PQHTwmef5iXsLjgvCdiGguYwUyEdUlJz2PRd9INh8et4UdJLqyOZe2ENk0/L7xzaWjzW8uPNZsBDVnQ/ZkIEnCkrgZaNLTjg7Vyw9MSSehy7AUp+GxtLtQA/Och8fqOoXhscPWGsXYtoKIiIjIu8LiAa/hsRZudnRfaUsfH0gnNQA+DSFJdB2sTwZOS3jc7Mu6Co+lbYUKj/Wk8yP3TD9saCqoDjoMj0eTpuvw2IxFJ4TvRERzGf8lJKKGZroJj2WDmZKBbylovgAiYb+jzWWmIDxuNTKqt5yTdSUlPLa3hMea6W5gioTHLR2O3jykLakKkdt3GR6b2RmHx0RERETkXXHxgJfwWG9qg+ZgUyvh8fpURA2kk/DY7+BoP2khMWKH1MBpCY97wllHeWtGBlXnBk5LeOxk/7wlPFbr00xH1dRSqSFBdSoL1+GxOdjrKnwnImpkDJCJqKF1teiOw+OtA9+C0EIRZ+GxravqY7fhcW5gXtCwHIbHmNDzTm92Fh7Lhjlhjj+Q8YF5Tvokm+PhsdR2MDwmIiIi2iZyA5PdFg9MCo8dlPbmwuOsrauBdI7CYwuIpg0kLZ8aOC3hsZP9s4jnwmOZGeIyPJb1+Rz0SZb9c9I21NGIQR/ch8eGz3H4TkTU6PgvIRE1tCYHfSFkc1k88E0dS1dGytS2DMyD8/BYNr9ZQx0WKIfc+Q1nm9/cm4ethy26OyxQ033OBuaZJqyx6Hh4rPtYeUxERES0DcNjmV/hpnhgpuHxgkDC8UA66XksR9SF9Oz4wGkH6cLYlrkiuZ7HjtpWyPA7K5gPj522rehLh5G1NfgMoCVkuA6P/QuWOnr+iIjmAgbIRDSnyeZyMBPM/78WlIFv5a83ktYQs8YH5oWMrKPwWHq1ycA8NZBEhoQ4/Bd4/M1DylXPu1x4LHfRZFiOAvF8eGxb44FzFastzMRY1W6biIiIqFHCYzkazGnxgBxJN9PwuNWXcRweS8/jkG6hVUs6OsotkdUwkB4fGN3sNDyWI/dUT2b34bG0mPPJfBLDUR0FrHhsYnjsZXg0EVGDYoBMRA0ha5YPcKfaXCasgv6+DsPjvmRAhccyMM/JPL/xgXk+1bZCNsxOBuapNZrZ8Z7M/qDj8Dhuyoa+4LBA3V14rEuft2qGxzKQJDpQtdsnIiIiapTw2OnAZAmPpfp4NsLj9RtH8gPz2gKmo/UlLQMjWR9yq3Ky1ZSCiPEj97TxmSEuw2MJnKVXspPwOJayYUYHGR4TEU2BATIR1b101kbfiPTsda5wcxnWnV9XKickPJa2cG4H5uU3v06PhJPBd1INLD2ZHb55gJnFZjOyNTx2cF8yYK8wPJYee9WSPyxQ5+GARERERBUJj9PJWQuPR8dSGB3L5gfmOZnNPJr1I2qG1P50fjhd/gpbwuP18eD4kXt6xvHMkMLwuCeQcFQcEk0Ag0lN7U9ZeUxEVFpB2R0RUf2R8HjN5owKad0o3Fz6HWxIRSIrVRA+LPUB20VSeGsUrgbmSX84p5tf9VV26TL5Wb15gLM3D5nM+EA/h+Gx3JmdSgLSW6/K4bGsL3dYoG9eT9Xuh4iIiKguJeOwZViey/A417ZCC4aqFh7L/jQ2lkI6Y6GlyYd5DgfmSRC80WxWR+7NC2QRSJa/jmlpKjzO2lvavmXSsDO2CpPz68lKlKHBQgKQ6msbGDOaEB1NoFmPotsXg5WWy6WhpZKw9KRqB1dsOJpAX1xTldG+zh5o4x02XEmvfVsdMTgdKQgJLNnF/Y0TEdUIBshEVPfhsUxW7mpx2lDYRtQMIGam85UJa1JNjionhm2falch4bGTKuJceJwbmGdIH4vyy1NVxEImPmuGz3F4PP7mIYT5RgyjjiqPbdUiw03lsVTGeJFf35aectn+dZ5uh4iIiKjevd2XRkrS0QLDqS5kMwkYwTZovgiQ1rA+26q+t/OgD6l4x3hBwqBvQs/eeExC0Tb4Ak3YUe8ve98Zj+HxaNJERrMQ8OvYbkErUpvgeCaHlFJ0GAkYMmOj3PpsYCjtQ5cB9ITS+HMGsNI27LSkwQVrMsNb1paClUkjZgeQ0VKIZDagO5CErWnq4nY2CysVB0KT9+GJVAa9A3HVJ9kIBvH+WBDa4MRij3iqG1Y2CD3VAd9YmzovoFvYKby1kkTC49Q7r6ph3DnvxiJIW+ObeCubRXDpLvBrpUPmoE/DLj0B1z8z03Fym0REbjBAJqK67XmcC48XthsYjDmo0LBtDMUtJE0jHx47CWeTmfHKCcMHVTnhJDyW4uFceCyVE0lrvCPFtOuTPsmm7G+3XNBhm4cJlSeBEMY7LZe7Lxt2RkpAbHUdZ+GxvBOIO1rThOtl0rDkMWk6DwskIiKiOU+CwFfXpOCXygTbRn/MwsDoMoS0FnRpPuhbujysy7SqIDQ76EM60a7OC2wJkEdjKQxENdiZXdEeNLGH9rqj57U/E0KXP+UiPLYRy2hIZYFQREdLUxC6nnA10HmxL4oBrctR8cWQ6VP7ZwmPWwM2kFuiJlW8WwNRTaIMaSWnBzCWBtK2gbBfw8I2P3R9a5CrpeVyWsnwODaWgRHRsbDFxkDSwDtDPqSyEy+bSnfBykagp5phau3oTYdx2LwNk25PwmP/oh3z/28N+vCH/vEhfht7e5HJ+NAymkB3sz5hPRnTxt5Lg+5+ZqZ5fyRt/VIZG0d8pLnsbRIRucEAmYjqkmyO5rf4VHjcFpYA2Sq7+d0wbCKZsREyTMfhsWwuxzJyWJuNjqDDgXm2hgQMNBUOzLPKh7PxrKaqQsIu+gMXhsfS8w5jKhN2Fh7bNjTdN6FaYrr1IZNSA1rcslNxaE1t4z35OM2aiIiISAWBy7p8an/aFgbaAkPoNnvhC2xts9CXbVbbuh3aTKQGxiteg22maruQSMaxXUTH/OZ+bLTay+/HpLphyz7VTXg8mrRVGBz0YUt4DFfhsbRV88uw6zJ7YVmXXEf2z23+7Hh4XPYxAWOWhpRlIKCbaPIZjtaXC491XcP2C1uAdRqQhJpxIs/1hMv6h2FlYsj6bPRpbU5aKudJeOxPDKEFcXTOs7B0WXBSW5L3+jOufmZ27PZPe2SmvD/KODjqkYjILQ7RI6K6JBUKufC4nFx4PJq0EPJraDPSjsLjVDo7vrmErSon/A7D45QtNcDa1vC47PqgDrtLWTr8Ml1aSp0dsJLxCeGxk553E8JjCXMd7LLz4bFlOgqbJ3GxPiIiIqI5oWB/2hLS0eOLQXewQZXwWNouGIauwk9He03ThC17RgDz/CnH4bEUXkgwKffREnIWzo6kx4NgNwOdZf88YoVUxtzuzyLscxAe21B7ZzkFdRPNWlq2nK7C47bmAIKSjJchrSjWZ1pV2w95/pyQ569/85gaONgSBBY2284GYs+wrZ+8P2pyPLGbiMg5BshEVJek57GT8Lh4c94R0R23XYgnZDiIhtagfOJvO9v8mn5Y0BCCw/BYKifsgDrsTvoky0Nysrc0kwlYY1F34aw9MTyW/squwmOpjA5F4JYm/ZUZHhMRERHlSduK3P50UbvhaP8XTdgTw2MH4aeEx2rPKJtOAE1Gtvx1bCCZBrKWjYBPQ7PfdrQ+CY/7kgF34bHMDLFCqtJaii/CDuoo5KGkLFmfpvbPTRIea+7DY5/PcFRNvS4VgYnxntHOnj8b1vAAxhKmGji4qCPoaH2VCI8dvT8iIvKAATIR1SWnn6wXb86d7N6sdAp2Mp7fXDqunJDw2NYQ1LLw6W4qJwwENDnsztnm3IxFYY8NQ3MVHmPLwDzn4bFSGB77Jx9254QMAyQiIiKireKpwvC4/P4qmgD64rrn8FgLOisCUEfGWT4VHvt0DS0hzdH+VAZOS3gsbSBchcdZaRQHNOlp58UXWQ1ZS1f7baf7Z6mmdh0ep7JYHw/ClIGDRsxx5bY5sEEdKdgUNtTAQSeV0V4wPCai2cR39UTU0Nxuzu1MBnZ8VLV2aA77HW0upa1bLjyW4Xx+zXJZOeH8sDsJj83B3vHK49ZOR+GxtEGzLamWcBceS+A8MTx2dDUiIiIiKiMSdL4/lbYVfXFN9dT1Eh7rcjSYgzkUEh73pcNIW4YKj6X1m5P1JW1DDZyW8Hi7SMpReFw4cFqFx5rpODyWAgyfbqnqYyf754xcL6O7Do8/6B1F1gYWBOJoMdLOw+P4KPRQBN3zmhy1/fCC4TERzTYGyETU0NxsznMD32SnJxttw6c7qjxOyrToLeGxbGRdV044POwuHx4bPhgSHvt8jio7xszxx6EG5jlqW2EDqlpZNuUMj4mIiIgqrbtZd7Q/TWQ11bZCwuPFLba38DgQchwex0y/GkgXCsDh+oCoGVIDpyU8dtp+V/oky65ZzQxxEh7bW8Nj2W8HdVVP4Sg8Tmc1NdPEbXhsmhZ6QmlVUe1kffnwONICvb0LuoP9/Zj0yHCJ4TERbQsMkImooTndnOdpmmoL4aRcQCp7xwfmwXl4bJeonHCwPGmrkQuP/QuWOg6Pc4cFqiDY0cA8W1Vgq0ndsitn5TERERFR5TkMZ0eyPtW2YomExw4mOs80PJY9bbOedbR/lp7HwxmfCo9l4LST8Di9Zbuc63nsduC06nnssG2FrE+uIxeVQXZuw+OFXRG0Bsq3pRNWtD8fHhtdixy2JTExMFr+/UMhhsdEtK0wQCaixuZg8zZmbg1j1UbbwXF3UiyQG5g3XgXhIDzGxM2v08oJ1ZNZ2mrkwmMHhyDmwmNZVdiQMFhzHh5npJ2GrqqVq9W2ws6WH0BCRERENFflwlnZKkrbioCT8NiaeXjcE0i4GphnaEC7kXA2cNrSsCkxvo+N+LwNnFbhse58ffJQQj4LDrJjZE1zQnjc3haGU9Lz2G143DvsrFf01vVxYB4RbTsMkImoMcju14ORrB+bM8GtZzio0pXwWAZqqPBYy8LvsPJYqpUnbH6dhLOWle/J7DQ8zppaPjyWyo6Ag3/pJ4THch9Oh+x5IPeR3dxXtdsnIiIiqmeF4ey8QNZR2wrZbNqp5KyEx6on85aBeR3BrGqvUY7M/dic9iHXsMHvpIgi3/O4YOC0i/BY1hfSLdX7uOz6TBsDQ0lP4bGQnsduw2MJtXtane+5+0ayyJrAwnYDbWEXyTMRUQUwQCaiuifhZ3/M3eFfufB4YzoMw8Gmt7ByQsJjGajRrLsdmKe72vzKFdXwO+nJ3NTmKDyWdwKbzOatPeUM9+GxFmlBtch9ZDauASz3/d6IiIiIGl1h+CnhrKHbjtpC2OnZCY8TqcyWnszjA/McFEYjY2oYMsNqf9oVyJS/wpbwuC/h23LknvOB04XPnxro5+A66YyNwYQGy4an8Fjobd2uw+Ol8/zwyacEDskRhgyPiWhbYYBMRHVNws8NwybiktB6CI99moVuf9LRdTI2VOWEhMcyUCPo4FC9iQPzLMeb3/HrSsg63pPZSVsN6Xknbx6kMtppeKyul05OCI9d9Yx2QdanwmMzC72tqyr3QURERFSv5AiywvDTSTgrR8b1J/0qEZbguNrhcWwso3oyOx2YJ6Hnumyb2p+2+rJo8tvO1pfwIZb1jR+553DgtDwXhc+fk/VJz+O1o5oKt9ub/Z7CY+FkfcXhcUAW6kJXi+6s8tjjkZlERNNhgExEdR8ejyYtRKShsENJy8iHx9sF445aUKjKiZRPbS7VNOaA8/A4PzBPk4EkzoLWcdp4H2KH4fF4zzsb84y444EkEubaZnZWwmO1PjMLo3MhjHBTVe6HiIiIqB6NmgFsTEdch5+5I+PUXs7nr1p4nMxYKjyWdhDSk9lpeCyhuAkNbUYSYV/5/bNUAUczhgqPmyVwdtj2LWXLdQxXz182O97zOGtraAvaiIQDVctmR1KYUXgsmhw8KK9HZhIRlcMAmYjqU0F43BLS0d3s7J+zRNpENBvIh8cBB+FxrnLCtKEqJxyFx7adD4/zA/McrE8dgmiOD5hTwbHmbtq2vHmIGFlnm99MCrZljYfUDsNjqVR2S+4jtz4VHje3ub4NIiIiokYllakbzWYYsj91ER6Ph59b2kI4nF8xmAm6Do9TWSCWtFV43NYccNSTuXCg8wIjhpBuOiq+iKYNJE1dhcc94ayjI/ckPB6zAirccBMeR2Np1fO4J2Ij4mRIoT3+/Lk1PJZFb0yfUXhczSMziYicYIBMRHVJPlnPhceL2p0FrbI5H47bquex0/BYNpdSMSCVE+3+rKPKifGBeUY+PHZcOWECCVOS5tyF3YXH0vPOyZuHreGxCU3Xxw93dBRUZ2Gn4uUfSPH1ErH8+hgeExEREU1uayBR6+Jg3FV4rMLPUBpNfmfhokhYPpeVxzZiGQ2aZqvw2CcpqIvwWNqqtfjK9z2W5UnxRdLSETIsFR47mG2t9s8SHstDaXXYwi0XHluWrXoeO+lakavclufPDTVwMGrCp89OeOz2yEwiIqf4LwsR1SX5ZD0XHrsZWCEb0XZfylnlcdYa31xCU5UTYWcDsJGCD1kUhMe688Pu5JFohoNheXJf2eyE8Nhpz7ut4bGhAmfH4bFUHztt4Dzhys7XR0RERDRXFPbE3c4/goCDgXmF4bGEn06PjENyvAggrGcdh8eZrI1YyoKuAe0R3VF4nCoKj522VYvZAVV8EdIttPnH9+xl12dq+f1zq56E4TI8bm5y1vPYsm30ZZtV5bY8f06NxlLjAwd1YEmrNSvhsZsjM4mI3Gi4f1lWrVqF/fbbD01NTejo6MARRxyBN9980/H14/E4/vEf/xE77LADgsGg+vqtb31LnU9EtUM+WXcbHsuet7NJeqM5aF5mWYglMmpzKcM+nFZOxCwZsqdDuiW7CY9zh901GZaj66jweGTQfThbGB77nR2CtzU81qBJlbNLWjDC8JiIaIa4xyVqLGOpooFqWvnihlTGnhAeOwk/JVy046Ows+N72U5/ynF4nMzaKjyWkNpwkOhKeCw9mV2FxzIzxA4gbRuq+KItYDpbnyX3p6v9swqPHezvi8PjcLB86bZlARvTIcSsgKrclufPCSsew0BUQm0dSzv9CDgcbj3T8NjpkZlERHM6QL7zzjtx7LHHYmxsDNdccw0uu+wyvPbaazjggAPwxhtvlL2+aZr43Oc+p677iU98AjfddBM+//nP49prr1VfLfntQUQ1QT5ZdxIeF2/OfYbDthCJWH5zGXJ0WOB4eJy2JKCWgXkS0jqrnCg87M5wEh5n0jBHBmG7DI9VEFwQHjsb6FcQHvtDquWFW9KXmYiIvOMel6jxDIxarnripjM21o1q3sLjTDo/YM9RWzVLHw+PAbSFdUf7U9nT5gb6uQqP1cwQAwHNVMUXupP1meNrlMei9s9OwmMTnsLjvoRPDThs1tOOK7fNWBRmdFCF7jJwMODXZy08rtZAbCIidw18atjQ0BAuuOACLF68GM8++yxaW1vV+ccffzx22203nHvuuXjqqaemvY177rkHv/zlL/GNb3wD3/ve9/LnSxXyN7/5TXz/+9/HKaecUvXHQkQOONwcyeZ8SafuvOdYbuCb5UMk7EM4aCAbdx4eB3QTQYfry1VOtOQPu2uSZsvT31cmjczGNeOVx01t+cMRy60Pljk+ZM9FeCzPxcTwmBtSIqLZxj0uUWOSdgtO96fStmLtqIasrWGpl/DYH4DtsAhgJOtXrRrk0qGABkPXym1P1brWmW2wA1A9mf9c/sA9SJny1oHTJpq0NDQ94Cg8zrWtCOuWeh7LlXlJaDwqhcO6u/B4/cYRxLI+tBgJ9Phi0LR5zsLjwV5Ab0dPVwTBIJAdRcUxPCai2dYwFcirV6/GyMgITj/99Hx4LJYuXYoVK1bg6aefxtq1a6e9jXvvvVd9vfDCCyecf8455yAcDue/T0SNuTkXUnksO0YtFEEw4GwgXaogPG7Ws44H5uUrJxwedpcPj80stKZ26KFI+etYMphPvtrj/YvdhMdWluExEdE2xj0uUWPqafU52p+alqbaVkhI2xOxPIXHWqTFUWWqhMcb02Homo3wlvC4nIwNDJthNXBawmMnPZlF3MyFx9Z4eOxw/yzhsQq3dQu6Uf6+TAtIZnU108RteDw6lkWzL4sFgaTzymMJjw0ffJ09CPjLl2FnTWfPVyGGx0S0LTRMgPzCCy+or9KuoljuvJdeemnaf4Tl+4sWLcL2228/4XsSHu+9997TXp+I6ntznmfb45tsB20X5N8NNTDP1lyHxxMqJ5yEx6aZD4+NzoUwQg7ePGyp7MhIUK1r0NTAvPLrs9PJ8fAYrDwmItrWuMclakxO2qpJOLs57VNtK3oiNtrCWtXDY2nF1mpkoDsIj2VPO5TyqfBYBk47CY+lpkE9tlx4LDNDNOcDp3UXbd9kfUlLh9xlk99yHR63NPnQE85Cd7BAMzGWD4/9C5ZC85UvRElnbfSNOB/KJxgeE9G20jAtLNatW6e+SguLYrnzcpcpZfPmzWpQ3vLly0t+X27jueeeU1XOhRXOhTZt2oT+/v4J5/3v//6v+vruu++6eDRENJ13+9J4vzeNte9pEz6937RGx7vztv6ztnZzFmMpC70fbL1c73AW6Q1r8NvBRP68pDmMfl8a63zv4Y+9UWgDoxjJGMhaOgY1WwWqzyfT2OR7T13+t9oILNj4U7oJo5pMVtaxyYgj97c/ltUhO9WMls7fhwwHsW0TCaSRwig02LA0A3KJjD3ehkLTh1R1RGJzn/r/9wZHMWQPw5d9Ta1hdcta6JEW6IFRZIfHj4Uzh6MTnpv1iRDi2Ij3tCZV1ZGFbLRtDGAzTC0GbWjiv1F2JoU/6ZuhjWw51jCbUfe1Md0MzWdAGx2ecPm3/P1A/zCMpk3q/5siPx5/rv84MXDPrH8Xb27+I0xpB5LJAPpmdX7Y90v11cpmEMwEEUg0zOeYRNTAdtppJ4RCDoeVNuAeV3CfS1TdvWxi7ShsNbg9qf4/Aw1/Tkt7Bg3NdhB9mZgKQn0bX5lwW5neAWSwGX2ZLN6HH9rGflV4oMlheH4JZ0fGL2gDG9CPZCCFtUUD+xKmD1ErpPaM83wJfJCYj0RkBLYGpNZKcUF8vB3algEfv9o4gnjvIDZnAhi0P0AT4viDNgY9Ot7s4v3sxKPknrfT2GhvAHwD+NPA+B52cHADNM2WcoUthQvje+GEMZzfb+duK2NlkTL90IwhhDUTw9r43lqktfSE6//WN5B//mR9w/afVTFJrz8LfWj8e4XksUjlcF8mAW2wHxvfHEMyC4R8wLxmP97uk+tYgJGGFpRKcB19Wgbv+EcRTq1Wt2Fu3ojMpnWAzwejuR3a/w5hXSKIuGlgjRSKWCaMTSb0NbH8/ZqmjYExU4XIT48GJryHKUXe18SSJl5/w0Yybav2Ih0RHe+WCLflfVEwFoAe5RwSIqrcHrdhAmTZGIugNBkqknsycpdxe/3i25hqc33zzTfjiiuuKPm9o446quxjIKLatWob3OdPHr52wv/fVaX7ub7kuT8tee6/Tzrntilv97slz/2h43UREdWKN998E7vvvvuc3eMK7nOJKOdHLp+KR0qc9+zD/1SVJ7TUfVXysRARzdU9bsMEyJHI+KecqZR0x58omUxOuIzb6zu9DemVfNxxx004T6o53n77beyxxx5TbtzrlVRVSzD+yCOP4EMf+tC2Xg6VwderfvC1qi98veoHX6v6Ukuvl1RnzOU9ruA+l2pVLf1bQeXx9aoffK3qC1+v+vFune5xGyZALjyEb9ddd3V86F/OvHnz1MZ5qkMA5XypypiuMmP+/PnqVGz//fdHI5Mf+G1VlUPu8fWqH3yt6gtfr/rB16q+zPXXqxb2uIL7XKp1c/3finrD16t+8LWqL3y96seH6uz3VsM0n9x3333VV+nhVix33j777DPl9WWwwMc+9jFs2LABH3zwwYTvJRIJvPrqq9Nen4iIiIio0rjHJSIiIqJtrWECZCn/bmlpwe23367aRuSsWbMGDz30EA4++GAsWbIk3+PtrbfeQm9v74TbOPnkk9XX6667bsL5t9xyiwqRc98nIiIiIpoN3OMSERER0bbWMC0sOjo6cO211+Lss8/GgQceiLPOOkv1ervhhhtUdfH1128dE/Xiiy/ikEMOwamnnoq77747f/6Xv/xl3Hvvveo60WgUn/jEJ/Daa6+poSESQJ900knb6NERERER0VzEPS4RERERbWsNU4EsJDSWamPp83bxxRfjX/7lX9TwumeffRZ77bVX2esbhoHHH38cF110EZ555hl89atfxerVq3HhhRfiscceU9+nrbq7u7Fy5Ur1lWofX6/6wdeqvvD1qh98reoLX6+tuMflzx/x34pGwX/b6wdfq/rC16t+dNdplqbZtm1v60UQERERERERERERUe1pqApkIiIiIiIiIiIiIqocBshEREREREREREREVBIDZCIiIiIiIiIiIiIqiQEyEREREREREREREZXEAJmIiIiIiIiIiIiISmKATEREREREREREREQlMUAmIiIiIiIiIiIiopIYIBMRERERERERERFRSQyQiYiIiIiIiIiIiKgkBshEREREREREREREVBIDZCIiIiIiIiIiIiIqiQEyEREREREREREREZXEAJmIiIiIiIiIiIiISmKATEREREREREREREQlMUAmIiIiIiIiIiIiopIYIBMRERERERERERFRSQyQiYiIiIiIiIiIiKgkBshEREREREREREREVBIDZCIiIiIiIiIiIiIqiQEyERERERERERERETFAJiIiIiIiIiIiIiLnWIFMRERERERERERERCUxQCYiIiIiIiIiIiKikhggE1FN2WGHHaBpGmrRM888o9Ympy996UvbZA1jY2NYsGCBWsNVV101Z58Hanyf+cxn1M/Y3/7t327rpRAREU3A/er0bNvGHnvsoX6Pn3HGGdvkp+cvf/lLfr968MEHb5M1EFWavP+Tn2l5PyjvC4lmEwNkogaVSqVw9dVXY7fddkMoFEJnZyeOOuoo/P73vy95+ZdffhlHHnmkupxcXq73r//6r0in01OGh6VO//zP/4xa2dQXnvx+P7bbbjt84QtfwCuvvDLra7ruuuvw+c9/Hl1dXfk1yTrduuGGG7Bp0yb1Gp111lkTNsny3MvpkUcewVzw61//GieccAKWLFmCYDCI9vZ27LvvvmpjNTIyMunyEnZP97O79957O/45l9Orr75a8mdOrjuVwtstfP0L3+TI6fjjj5/yevvtt5/ndZYjPz/T3ZY8x1OtudSp+GfxueeeU//OzJ8/X/2dlL8Pu+++O0488UQ88MADEy57/vnnq6+PP/44XnjhBcePgYiI6gf3q7WzX+3t7cU111yDww8/HMuWLUM4HEZLSwv2339//Nd//Zer2/rv//5vvPnmm+rP5513Xv784eHh/H717rvvxlzw+uuv4ytf+Qp23HHH/HO655574h//8R+xcePGWd2LSZCeO3+657/4dgsVnv/xj398yuv19PR4Xmc5svbC6958881TPofyPBeT5/1b3/oW9tprL/V6yOsir8+Xv/xlvPbaa5MuP5P3BaVOhX8nxLp169QHLXK9QCCAtrY2fOhDH1LvHf/v//2/Ey579tlnq/XK+8Ebb7zR8XNGVBE2ETWcTCZjH3roobb8FS8+BYNB+8knn5xw+SeeeMIOBAIlL3/YYYfZ2Ww2f9mnn3665OVyp5UrV85o7dtvv726nUrcxlQnv99v//znP3d9u8PDw/avf/1rdXr77bddXbetrW3SOmSdbl/XBQsWqOuecMIJE75X+LqceuqpdjXN5HmolG9+85vTvsZLly61//CHP0y4jjwv011nr732cvxzLqdXXnml5M+cXHcqhbdb+Pq///77E25b13X7j3/8Y8nrffzjH/e8znLk7+90tyU/x1OtudTpxz/+cf7y8u+Oz+eb8rLyb1Yhy7Ls7bbbTn3v2GOPdfwYiIioPnC/Wlv71QceeGDa9Zx77rmOb+ujH/2ous5+++034fzCvcMnP/lJu5qSyWT+eXj99dftbeE//uM/1J5uqud03rx59q9+9atZ24vJc547/7/+67+mXHfx7RYqvn15H1nqevKexes6y5G1F7+nSqfTJZ/DSy65ZMJ1f/nLX6rnfap1yOslr1uhmbwvKPd3qbe31164cOGUlzUMY9Lj/+IXv6i+19PTo/4dJZotvsrE0ERUS+RT2F/84hfqz8uXL8cVV1yhqhiuvPJKVekhlZjvvvuuqtpMJBLq09ZcpfH/+T//Bx/5yEewcuVKVTnw//7f/8Ott96Kr33ta5Pu53vf+566bKGlS5eiluTWKJ80/9M//RP+93//F5lMRn3ym6uMcEo+Df7//r//z/Hl5TnVdR0+n09Vt0pVt1TLXnrppR4eCfDTn/40X6lw7LHHerqN6dZYreeh0m666SZ85zvfUX82DEO9lp/97GfVJ/H/9m//hjfeeANr1qxRn9pLFUFzc/Ok25CfeakGKVTqckIqKB566KFJ5++8886oFsuy1BEE9957r+PrVHqdf/M3fzPpZ3W6nxOpCC8mP/M58vcvm82qP59zzjnq9ZH/f++99/DUU0+pf5sKSYWGHDUhr/ejjz6KzZs3Y968eZ4eCxER1R7uV2trvyrkCLe///u/x+c+9zn1PkFeIzkSKLfGf/iHf1CVmtORfZgc2VjJ/arsi2Sdsj6nZP3bcr/6P//zPzj33HMn7D2lsjwWi+E//uM/1L5J9jay15EqZak8r/ZerBrk/eVhhx3m6jqVXucHH3yA++67b9LevphU+srzPTQ0pP7/oIMOUq+RvAf44Q9/iLvuukv9rMnfO6kAlr8HM91vl3q/XPhay9GlUv0vDj30UPWeW9YjVdsvvvhiycrsY445Rh2519fXh5/97Gf4u7/7u2kfN1HFzFpUTUSzZtddd81/avncc8/lzz/88MPz5z/88MPqvP/+7//Onyffz5Hr5c5fvnx5yU9gp6u0rJUK5MI1PvPMMxM+0d28ebO9bt06+8tf/rK955572p2dnapCsqOjwz7kkEMmfRI+VZVvYWXr448/bl9wwQXqE2FN09Qn7oWkqrTw03I3ZJ1yPbldqS4pVU1QfMqts9waK/08SDXC5ZdfripIper9gAMOsF999dUJt/PBBx/kK0OmO7300kv56yQSCbu7uzt/P//8z/884TblNZV1575/3XXXlVxfuUr5qSqFp1LpCmQ5yWvw3nvvOa5AdvvzVEphxUa5SvbpqlNKCYVC+WqbUsbGxiad98gjj+Rv/95773XxSIiIqNZxv1pb+9X//d//tdeuXTupijd39Juc5H1DOVdccUX+8q+99lrJ+y8+5aqRC/chd955p/0v//Iv6qgyqQqVxxWLxeyzzz5bVTjPnz9fVWm3traqSuc77rjDUbVz4X3cdddd9r//+7/bO+20kzoaU57fX/ziFxNuZ+PGjY72q3IqtMcee+Tv50tf+tKk5/Wv/uqv8t//xje+MSt7sWpUIMtJqnrdVCDPVHEFspx23nnn/FGzU1Ugy/OcO1+ef3kdCsnrlPu+vH7Vfl8gPvvZz+YvW6pSvtT+eGhoKH+dr3zlK2XXQ1Qp7IFM1GDkk+w//vGP6s/SR22fffbJf++AAw6Y9Mnvb37zm5Lf/9jHPqauL6TyIfdJbSGpUJBP9zs6OtQnz08++SRqmVRkFJJKhrVr16q+bvLJ/+DgoKqIlMf69NNP4+ijj3ZVASq+/vWv47vf/a76RHh8j1U5zz77rPoqlR/Fj2Wma6z08/DVr34V//Iv/4L169erytLf/va36hP/XAWqkE/55ZP/cqcVK1ZMeA76+/vVn6VH2De+8Y0J9ys/i4XVB/XYD1r6kEsPNHmupA95o5Aec7l/o6TvnPy7Uvh3JBKJTLrOX//1X0/6+SciovrH/Wrt7Vd33XVXLF68eMLlZJ9feHRhU1NT2dvO/b7OzVTxSmZaXH755eqoMqkKFaOjo+rISKlwliPPpEpb5l48//zzOP300yf1i3VSPSszF/785z+r51me38IKVSEV2E72q3LKkduTSuycb37zm5OeV3kNclavXo16JO8Xc8/jtiLVvVJB/84776je29P58Y9/nP+zvIeQ16HQhRdemP+zvH5ylNxs7Y9zRwLLe/PCGUSl9sfSBzt3JAD3xzSbGCATNRg53KUwiJJD/HNkcFXO+++/P+nyMs218PCowsPFCy+Xs2HDBvULTgZi/PznP1chcq0OxJBNZuGmUp4LOclhSNL24Ec/+pEKwGUjfs8996C7u9vThkg2GnJ4nxxOdNttt03YFMyEvFGQjZGQQ6oKyaFPcnhU4eFu8gGBnC677DJHa6z08yBvdGQQy6pVq1TbjtzP0BNPPIGZkEM6c+QNTamWBoXD8AovX0jaujgdACmHxRVf1ssARKfk794ll1yi/iyvgRxu50Sl1yn3XXx70v5mKqWGhBT69Kc/nf+z/KzJdHYJ/I844gi14S/1gYv87MgHBdO9lkREVH+4X62P/aq8X8gN85PD6gtD0qnkClm23377Ce0WZE9aeOi/7Ndy+1XZy5ZaoxSrSCsICcjlsH8J0+T5kXYD0mZPnocHH3ww3z7g2muvnTQAvNzzIHuun/zkJ2qgWi6kvv/++zEThXsW2ceUCtIL96sSkktri2rvxSot9z5D3gdKuwWnKrnOXXbZBccdd5z6s7R/m6qAR17Xwj114fOfI4OdcwVUU+093e63DznkkEmXLxy4Xbg/lp9D+Tsmfx+l/YoMYR8bGyt5u7n3g2+//TZM05zy/okqiT2QiRpM4S+ZXPBS6v9zl3N7eQmk5Reh9F6SX1wSHssvt9/97nfqF7b0jJJf4k4qFGaDrHWqDU/uF75syq+//nr1SXM0Gp2w8ZDQViobWltbHd3fiSeeqPqaVaNSJ7cuCd0KSRAn1Sg58kZjup5vpdYoHzZU8nmQHrcXX3xxfmOTm4AsvbdzclO43ZA15OTeNBUrPF8eRz2SPnnyZlAquCWIr1QPwW1J+lbLz0KuL2Lu9ZH+xnKSN4PyxriY/LxLT8iBgYFZXjEREVUL96u1v1+VvWXh0WNyVJST28/9vi7er0rIWxjOlevVfOCBB+L73/9+yYpTKZyQYFsqhQvDMwlh33rrLey5555w4sgjj1TBvIjH4zjhhBMm7VclsJ0utC23X5U9dqmAtHgfK6/pVPM4apXMIJEqZHkfKEcelvogYDbI3xPZR/7hD39QxSvlXpOp3kfI6ySvl1Tmz9b7iNNOOw2/+tWv8IMf/CB/nnwIIpXFcrrlllvw0ksvTfr7lPt/+XdA/q4WFooRVQsDZKIGUxjcFg+lKvxEPnc5t5eXT0Vl4FXx5kE2tvJLVk7SruAzn/kMatGiRYvUJkMCTvHv//7vuOCCC6a9joTkTjfkMhis2mbaGqPUGiv9PHzyk5/M/1k2YoW3UVhtIady5BDI3CFyhfefa2VRrPD8qVp9lBqiN9UAyFLDMtwMcfFCDqm76KKL1Acyd9xxh6MhMJVeZ6nBLYVHKTgZiFL8d++5555TwyDlEMJf/vKX+SMhhGz4pZqoeBBLpVvBEBHRtsf9am3vV2Wol+zlJZATct+FLRecmOnv71KDwWSvUO5D9cK9ZiX2q1IVLh+AO5HbrxW+DhLuyXNRHCIX72NL7VkrvRerBmm7IB80PPbYY6qNiBOVXqcU08iHAdK6TlqfyNFtxYr/bsjzL9XLhXJh7HSvidv9dqkherLeHCnOkg9KpKWG3K68z5Yh4Lm2LdIORSrrpbq6eK1Es40BMlGDKTyEJtcjLXf4WO7TVLFs2bJJl5cqvxy5XuEv0OkOzZE+TFJVIJ8+TxfsbQu5X9ryHMgnzdIvqnADV/hJuVTMHn744aryWjbsud5luV/gTky3qZsJadUg65bNQql+1G6UWmOln4fCT8kLD18s3OxID2RpJVGOHAKZO9S18BBACZ/luSj+RF42XTlT9d6TsNjpZO5tNcX7zDPPVJtFeeMiVf6zvc5ylezFnFxWKo9kU5/b2EsfZDliQaqFxO9///tJAXLuTVxXV5fLR0BERLWK+9Xa3a/KIfqHHnqoCq6EHEXmZiaD/L6WVmbV2K/eeOON+T9LVbBUUofDYdXWQtooVGO/Kj2QpfDAidz1CvefUpAjrRCkPcJU+1Vp2VWq+rgae7FKkz2dBKLycyjh7bZapwTZEiBLZXrha5kjbSGkx3eujcWrr76qqtwLyeskfbWnex/hdr8tz42Ty3/84x9Xp9x7cvm7naumlv1xsdzfr1zVNNFsYA9kogYjQaMMwciFwHLIS45U/+XkepgV/kKTyuEcuV7ukLXly5fnN1iFh58XBjyFn8xXK0T1IvdLe7/99sNOO+006dN/aREg5BevtAr41Kc+pTbwufPdqlavMdkI5fq7FR5WlyPDI3LKbZxLrbHSz0O1yEYvFyTKhrzwjUTuZ1GC6RypiKhX8oYoV21U+Pe4XknlcfHPpvzbItU1OcU93ORDgtyREDMZxENERLWF+9Xa3K/+6U9/Uu8RcuGxBMduB/rm3odIEF04PLmS+9VcqC5V0jIEvNb2q/Iayh4nRwYWFirew9bzflVep1wv5G25X/3oRz+qjoqdbh2Fz/NNN900qV924eskfydzg+qqSdpXFPe/lvfSp556av7/S/U4zr0flCrqwplHRNXECmSiBnT22Wfj3HPPVX8+44wz1Kfy8smlHB4u5NPX3GFhcgibHCYnA/Hk+7IBkF/A//RP/zTh9gqn00pId8opp6j+YtLnTKojc32lJNyTjVy9kOpW6Rsn1dbSA00ek/SEk57DlQ7PpN+fPM850mvt4Ycfzlfi5No0TBeeSlAvh/1Lq5DCw6oKKyhkeq/cn3zSLpsKJz2xZut5KOSlB7IcIiY/mzL4RUgFswzFkKpVqXyXtefWLM+pVPHOtv/8z/9UQ2kKyQbUy1qk+uDb3/62o9dBWtDIa1/M6c/ATJW6b6n0zrUGkX+LpFrqC1/4Avbdd1915IJUHstU+Zx99tlnwvVzg3tEcZUIERHVN+5XnZuNfZqEx5/4xCfUkU9CBthJqF34+93JnkJ+X8t7CtmXSAuM3HC64v2qVKxKxai8dyjcL5R7HnJFK7IflErs++67r6qDdr30QBZyFFnuiCspbpCgVY66kvcDUnGeO/pKnpPc3JDZJO0ScmvIkbXkBjm7IY9r5cqV6meoEnvGmVQhF+/BC8nzLO0i5L2sDHuUDyDkPbO01JH3ZIVFKMUtI7ySn/Piimh5D5drYyHvG2RQpDyH0lJF3pdLBXLh/Rfvj2X9uTZw3B/TrLKJqOFkMhn70EMPlWOoJp2CwaD95JNPTrj8E088YQcCgZKXP+yww+xsNpu/7Cc/+cmSl5OT3++3f/zjH89o7dtvv726rUrchpyefvrpaS977bXXTnocXV1d9l/91V/l///9999Xl5Xbyp136qmn5m9D/lzu/grXVOpUeHtTefTRR/OXf/jhhye95j09PZNu97/+678crbGaz4OsIXf+ypUr7Uo477zzpn0+lyxZYr/xxhsTrlO4vnLrKHyM8tqVU+71lb83092uPLe58xcsWDDhtv/5n/95wm19/OMfL7nOqU65nwEn5Hlx+jNZuOapToXP83bbbTftZQ855BDbsqwJ9/G1r30t/+/W4OCg48dBRES1j/vV2tqvFu7XZrKnkP1X7vLf+c53Jn3/ox/96JT7hcJ9SKn7euihhyZdNxQKTbjN3GMr3Kfk9mHT3cdUz9tMXHfddbau61M+n+3t7ZNei5nsxcqZ7n1c4d50utstPD+RSOTPv/vuuyd8r3A/63bPWE7hz+oXvvCFCd87+OCDJ9zuJZdcMuH7Tz31lHrep1qHvF7f/e53Z+V9gfj7v//7aS8r7+96e3un/Hvw2GOPOX7eiGaKLSyIGpB8yimfZEofqg9/+MOqV5McKiifgkubCulrVkiqN+V8qUaWT57l8nL4mXzy+eijj044LEaa+MtgL6l8kMPo5L7kk1KZWvzCCy/U3SFY559/Pq688kpV0RCJRHDwwQer4QUyIKHWyGFZuXUVTxiW1+EnP/mJqhSRyuNGfh5yw2SeeeYZ9Wn9dtttp3rryuOW6nmZAv36669POHSwnkm1tdOhOLXswQcfVH0U999/f3UUhFQjy8/a3nvvrf6tkh6DhYerynsUqUwS8m+T/BtGRESNg/vVxtynyf4rd1Rd8X5VPPDAA2pPWzzDwokVK1bgtttuU23d5Kg0qcyUitNa3fNJKzJp/ycVzHJknLzHkmpXWa9Uw0rltLyWjUAq1nMzdralyy+/fNrvH3LIIar6WCqt5XWQ10NeF3l9pG2EzPSRv2+zRSq35WhDeT8urU9kPbJHlj9/9atfVesp/nue+3sl5+fadhDNBk1S5Fm5JyIiB+SXt/RM4z9NpUnfOwnhpD+uDCjh0ARqVBIo/+3f/q36s3w4JW0viIiIagH3q9P77//+b1VcIqSNBecYEFWGtLGRoYuJREK9L9wW7U9o7mKATEQ1hRvy6UnfNOmnK/3ppBIlN7SCaDry81I46LJWJndPR/rSPfnkkypEfuyxx7b1coiIiPK4X52eFILI0YpvvvkmTj/9dNx+++386aGyZMaL9AyejlSdS8XwXCVH7UmvZ+lFLn2Q5YgEotnCAJmIago35ESVd/fdd+PLX/7ytJdh1T8REZEz3K8SVZ60p5MWE9OR0FT+/hHR7GMPZCIiIiIiIiIiIiIqiRXIRERERERERERERFQSK5CJiIiIiIiIiIiIiAEyERERERERERERETnHCmQiIiIiIiIiIiIiKokBcpUlk0n84Q9/UF+JiIiIiBoF97lEREREcwMD5Cr785//jOXLl6uvREROyIdOmqapr0RERLWK+1wicov7XCKi+sQAmYiIiIiIiIiIiIhKYoBMRERERERERERERCUxQCYiIiIiIiIiIiKikhggExEREREREREREVFJDJCJiIiIiIiIiIiIqCQGyERERERERERERERUEgNkIiIiIiIiIiIiIiqJATIRERERERERERERlcQAmYiIiIiIiIiIiIhKYoBMRERERLQNrFq1Cvvttx+amprQ0dGBI444Am+++aaj6/70pz/FkUceiWXLlqG5uRmtra3YY489cMUVV2B4eLjkdT744AOceOKJ6O7uRjgcxt5774077rijwo+KiIiIiBqNb1svgIiIiIhorrnzzjtx+umnY/ny5bjmmmuQTCZxww034IADDsCzzz6rwuDp5ILmL3/5y1i4cCEymQxefPFFXHnllXjwwQfxu9/9TgXTOevWrVNhdTQaxXnnnaeC59WrV+OMM87A+vXrsXLlyqo/ZiIiIiKqTwyQiYiIiIhm0dDQEC644AIsXrxYhcVSPSyOP/547Lbbbjj33HPx1FNPTXsbF110kToVOuecc9T1L7nkEvzoRz/CKaeckv/epZdeir6+PnX+Mccco86T8FiqniV0Pvnkk7HjjjtW5fESERERUX1jCwsiIiIiolkklb8jIyOqAjkXHoulS5dixYoVePrpp7F27VpPt73DDjuor4VtLOLxOB5++GFVdZwLj3MkyM5ms7j//vs9Px4iIiIiamysQCaqY+m1b8POpDxdV/MHEViyS8XXRERERNN74YUX1FdpV1FMzrvnnnvw0ksvYcmSJWWfylgsptpfyNff//73qvrY7/fjsMMOy1/mjTfeQCKRwP777z/p+nKepmmq/cV0Nm3ahP7+/gnnvfvuu2XXR0RERET1jwEyUR2T8Dj22/+Bv3sxNF/5v852Novs5j7Y6RSaDzpiVtZIREREE0k/YiEtLIrlzstdppyvf/3rKnDO2X333fGTn/wEH/7whx3dXzAYRFdXV9n7u/nmm9WAPiIiopkYXnWT5+u2H/M1PvlE2wgDZKI6J+FxYGn5SmI7k0Zm4xr4OubDymZmZW1EREQ0mbSUyIW3xUKh0ITLlHPxxRfjpJNOwuDgoOqn/Otf/1r92en95e6z3P1Jf+XjjjtuUgXyUUcd5WidRERERFS/GCAT1TlHlcdbwmOYWRidC6GNTHxjSURERLMnEomor6nU5DZU0o6i8DLlyNA8OYkvfOELakie9FE2DAMnnHBC2fvL3adUIU9n/vz56kREREREcw+H6BE1uOLw2Ghu29ZLIiIimtOma1MxXbsJJ2RIXktLC2699VZH9yeh8sDAgOf7IyIiIqLGxwCZqIExPCYiIqo9++67r/r63HPPTfpe7rx99tnH022bpol0Oo2hoaH8eXvssYdqU1Hq/p5//nnYtp1fExERERFRMQbIRA2K4TEREVFtkr7BUiV8++23Y2RkJH/+mjVr8NBDD+Hggw/GkiVL1HnSm/itt95Cb2/vhNvo6+sredu33HKLqiref//98+dJC4tjjz0W77//PlatWjXh8tdddx18Ph+++MUvVvhREhEREVGjYA9kogbE8JiIiKh2dXR04Nprr8XZZ5+NAw88EGeddZYKfW+44QZomobrr78+f9kXX3wRhxxyCE499VTcfffd+fOXL1+OAw44AB/96EdV+4nNmzfjmWeeweOPP44ddtgBK1eunHCfV199NZ588kmcfPLJePnll7Fs2TKsXr0ajz32GC6//HLstNNOs/ocEBEREVH9YIBM1GAYHhMREdU+CY07OztVkHzxxRcjEAjgoIMOwlVXXYU999yz7PXPPfdc/PznP1cVx4ODgwgGg9h5551VGHz++eerkLrQ0qVLVQuLSy+9FLfddhtisRh22WUX9eczzzyzio+UiIiIiOodA2SiBsLwmIiIqH6sWLFCnaYj7SykR3ExCYrl5IZUHT/wwAOu10lEREREc1vN90CWPm377bcfmpqaVCXFEUccgTfffNPRdX/605/iyCOPVJvl5uZmtLa2qiEiV1xxBYaHh0te54MPPsCJJ56I7u5uhMNh7L333rjjjjsq/KiIKo/hMRERERERERERzakK5DvvvBOnn3666vF2zTXXIJlMqt5w0u/t2WefVWHwdHJB85e//GUsXLgQmUxG9ZG78sor8eCDD+J3v/udCqZz1q1bp8LqaDSK8847L98b7owzzsD69esn9ZIjqhUMj4mIiIiIiIiIaE4FyENDQ7jgggvUUBAJi6V6WBx//PHYbbfdVN+3p556atrbuOiii9Sp0DnnnKOuf8kll+BHP/oRTjnllPz3pCecTLSW84855hh1noTHUvUsobMMHdlxxx2r8niJvLKzWWQ2rgHMLIzOhTCa28pfZ/KRsERERERERERERPXTwkIqf0dGRlQFci48zg0AkV5xTz/9NNauXevptmUytShsYxGPx/Hwww+rquNceJwjQXY2m8X999/v+fEQVUt2c5/L8NiGFe3nC0JERERERERERPVbgfzCCy+or9Kuopicd8899+Cll17CkiVLyt6WTJmW9hfy9fe//72qPvb7/TjssMPyl3njjTeQSCSw//77T7q+nKdpmmp/MZ1Nmzahv39iMPfuu++WXR/RjFimq/DYHNgAKxnnk05ERERERERERPUbIEs/YiEtLIrlzstdppyvf/3rKnDO2X333fGTn/wEH/7whx3dXzAYRFdXV9n7u/nmm9WAPqLZpLd1uQuP46PQQ5FZWRsREREREREREdW3mg2QpaVELrwtFgqFJlymnIsvvhgnnXQSBgcHVT/lX//61+rPTu8vd5/l7k/6Kx933HGTKpCPOuooR+sk8sIIbx0E6Sg8jrQAvgCfbCIiIiIiIiIiqt8AORIZr5BMpVKTviftKAovU44MzZOT+MIXvqCG5EkfZcMwcMIJJ5S9v9x9ShXydObPn69ORLWkODw2uhbB7n1/Wy+LiIiIiIiIiIjqQM0O0ZuuTcV07SackCF5LS0tuPXWWx3dn4TKAwMDnu+PqJbCY+nnTUREREREREREVNcB8r777qu+Pvfcc5O+lztvn3328XTbpmkinU5jaGgof94ee+yh2lSUur/nn39eBXG5NRHVA4bHRERERERERETUsAGy9A2WKuHbb78dIyMj+fPXrFmDhx56CAcffDCWLFmizpPexG+99RZ6e3sn3EZfX1/J277llltUVfH++++fP09aWBx77LF4//33sWrVqgmXv+666+Dz+fDFL36xwo+SqDoYHhMRERERERERUUP3QO7o6MC1116Ls88+GwceeCDOOussFfrecMMN6hD866+/Pn/ZF198EYcccghOPfVU3H333fnzly9fjgMOOAAf/ehHVfuJzZs345lnnsHjjz+OHXbYAStXrpxwn1dffTWefPJJnHzyyXj55ZexbNkyrF69Go899hguv/xy7LTTTrP6HBB5wfCYiIiIiIiIiIgaPkAWEhp3dnaqIPniiy9GIBDAQQcdhKuuugp77rln2eufe+65+PnPf64qjgcHBxEMBrHzzjurMPj8889XIXWhpUuXqhYWl156KW677TbEYjHssssu6s9nnnlmFR8pUWUwPCYiIiIiIiIiojkTIIsVK1ao03SknYUEZ8UkKJaTG1J1/MADD7heJ9G2xvCYiIiIiIiIiIjmTA9kInKO4TEREREREREREVUDA2SiOifF9+bABljxUeiRFhhdi1SfcCIiIiIiIiIiopligExU56xov+vw2EyMzcraiIiIiIiIiIiovjFAJqpzVjLuLjyORWFFB2ZlbUREREREREREVN8YIBPVOT0UcRUem4O9gG7MytqIiIiIiIiIiKi+MUAmqnN6W7e78NjwwTevZ1bWRkRERERERERE9Y0BMlGdczIvrzA89i9YCs3nm42lERERERERERFRnWOATNTgJoXH/sC2XhIREREREREREdUJBshEDYzhMRERERERERERzQQDZKIGxfCYiIiIiIiIiIhmigEyUQNieExERERERERERJXAAJmowTA8JiIiIiIiIiKiSmGATNRAGB4TEREREREREVElMUAmahAMj4mIiIiIiIiIqNIYIBM1AIbHRERERERERERUDb6q3CoRzRozMQY7kwYMH/wLlkLzB8pex85mZ2VtRERERERERERU3xggE9U5KzoAff6SKcPjd4Z8SFsTw+PU+hTC7Tr8gZSj+wj6NOzSUz6YJiIiIiIiIiKixsIAmaje6ca0lccSHr/W70dAt5HOmOgbyCAzthg9vQbakC578+mshY9sH6rCwomIiIiIiIiIqNYxQCaqc755PWXbVkh4vDCUwgdDo+j2W+juSqG724K/2z/t9WzbxovvO6tSpsZx0y+Gq34fXzu0ver3QUREREREREQzxyF6RHVO85X/HEgqjz/oHYVpWljYFUF7U/nrSHi8YdhEPFXQ/4KIiIgqZtWqVdhvv/3Q1NSEjo4OHHHEEXjzzTcdXffRRx/F6aefjt133x0tLS3o7u7G/vvvj7vuugvZErMOvvSlL0HTtJKnb37zm3xViYiIiGhKrEAmanDS81jaVkjlsQqP28LIDgw7Co9HkxYiQX7OREREVGl33nmnCoCXL1+Oa665BslkEjfccAMOOOAAPPvss9hjjz2mvf4ZZ5yhguejjjoKu+66K6LRKB588EGcdtpp+NGPfoTHHntMhcPF7rvvvknn7bbbbhV9bERERETUWBggEzUwO5NGdnAzTKsjHx6XvU5BeNwS0hEwZmWpREREc8bQ0BAuuOACLF68WIXFra2t6vzjjz9ehbnnnnsunnrqqWlv4wc/+AE+9alPTQiJzzvvPBx88MF4/PHH8dOf/hSf+9znJl3vpJNOqsIjIiIiIqJGxtJCogYOjzMb1wCWia62kKfweFG7AZSoXiIiIiLvVq9ejZGREVWBnAuPxdKlS7FixQo8/fTTWLt27bS3ceihh06qMDYMA8cdd5z68+uvvz7l73q5b9M0+RISERERkSOsQCZq5PDYzMJo60RLIghg6xtFc3Ro0uA92wZ6YxpG0xpaAja6bRvZJGAO68jARCptQfMHEViyyzZ4RERERI3jhRdeUF+lXUUxOe+ee+7BSy+9hCVLlri+7fXr16uvCxYsKPn99vZ2FSBL2Pyxj30Ml1xyCY4++uiyt7tp0yb09/dPOO/dd991vT4iIiIiqj8MkIkaOTzuXAg91Qwkii5kmchsXKsuq65jARuGUhhNAS1BoKsjiGxs/KLZeAiZ9BhSw8MI7rz37D8gIiKiBrNu3Tr1VVpYFMudl7uM29u97bbb1EC+I488csL3JFD+xje+oUJjCZHffvtt1XP5mGOOwbe//W1cdNFF0972zTffjCuuuML1moiIiIio/jFAJmrg8NhobgNSpS+rGQZ8XQthWcD6jSMYM4Jo6/ZhuwWt0Aua2xhRA77OdmipsVl7HERERI0sHo+rr8GgHCE0USgUmnAZp2KxmAqNpbpYhujNmzdvwvdlUF+xs88+Gx/5yEdw2WWX4YQTTpi24vmcc87Jt8corECWIX5ERERE1NjYA5mokcPjMnLh8ehYFi1Nk8NjIiIiqrxIJKK+plKTP+VNJpMTLuM0PJaBea+88gpuvPFGRy0pRHNzMy688EJkMhk88cQT0152/vz52H333SecPvShDzleIxERERHVL0ZFRA2A4TEREVH9mK5NxXTtLUoZHR3FZz/7WfzmN7/BLbfcoiqF3Vi2bFm+xzERERERUSkMkInqnJ3Nuq48lp7HvTGblcdERETbwL777qu+Pvfcc5O+lztvn332KXs70WgUhx12mLrOHXfcgbPOOsv1WqQXsujp6XF9XSIiIiKaGxggE9W57OY+120rNsSAWFp31bbCTLAHMhERUSVI3+CWlhbcfvvtqmdxzpo1a/DQQw/h4IMPzvcjll7Ib731Fnp7e0uGxy+99BLuvvtufOUrX5ny/sbGxvKtMQr19/erAXrSi1mqmImIiIiISuEQPaJ6Z5muex7HMjqaA6bj8NiKx2BFByqzXiIiojmuo6MD1157rRpid+CBB6rKYemHfMMNN0DTNFx//fX5y7744os45JBDcOqpp6qgOOfTn/40fve736nBeXKd73//+xPuY88991Qn8c477+Dwww9Xl915553R3t6uKo/vuusuDA0Nqb7JixYtmsVngIiIiIjqSc0HyKtWrVKVEW+88QYCgQAOOuggXH311Vi+fHnZ6z766KNYvXq1OqxPKjpkqrUM+zjjjDNwyimnwOeb+PC/9KUv4Z577il5WzJg5Dvf+U7FHhdRpehtXa4H5jX7LSxs1hyFx6OxFMz0IKAblVkwERERqdC4s7NTBckXX3xxfp971VVX5YPf6Uh4LGSvK6diK1euzN+OtKeQAFn6JP/whz9UFcly35/4xCdw/vnnq69ERERERHUZIN955504/fTTVVh8zTXXqEPvpDLjgAMOwLPPPos99thj2utLUNzU1KQOE9x1113VoX4PPvggTjvtNPzoRz/CY489pio2it13332Tztttt90q+tiIKsUIN5W9jGVvDY+lbUW3DWgOwuPhaAIDURNYYMA3j70RiYiIKmnFihXqNB1pZ2Hb9qTzS503FQmQ7733Xk9rJCIiIiKq2QBZDqe74IIL1ARqCYtbW1vV+ccff7wKc88991w89dRT097GD37wA3zqU5+aEBKfd955aiP++OOP46c//Sk+97nPTbreSSedVIVHRLRtyBvM/s1jaLHGw2NpW5GJOwuPewfiMPQwfJ090EwHVyIiIiIiIiIiooZSs0P05FA8GSoiFci58FgsXbpUVWo8/fTTWLt27bS3ceihh06qMDYMA8cdd5z68+uvvz5l4Cb3bZpmRR4L0bYiP8vW8ADGEqargXn58NjQ0dMVgVbU7oWIiIiIiIiIiOaGmg2QX3jhBfVV2lUUy50nU6e9WL9+vfq6YMGCkt+XwSJtbW1qIvV+++2HH//4x45ud9OmTfjDH/4w4fTuu+96WiNRJcJjc2ADrGQcTWHDU3i8/cIWBPzsfUxERERERERENFfVbFnhunXr1FdpYVEsd17uMm5v97bbblPTr2USdSEJlL/xjW/gYx/7WH46tfRcPuaYY9Qgv4suumja27755ptxxRVXuF4TUdXC4/go9NACdPuboOum6/A4GPQBSb4+RERERERERERzVc0GyPH4eL9VqQIuFgqFJlzGqVgspkJjaU8hQ/TmzZs34fsyqK/Y2WefjY985CO47LLLcMIJJ2DJkiVT3v4555yTb4+RIxXIMsSPaJuEx5EW6FoX9M3wFh4TEREREREREdGcVrMtLCKRiPqaSqUmfS+ZTE64jNPwWAbmvfLKK7jxxhtx9NFHO7pec3MzLrzwQmQyGTzxxBPTXnb+/PnYfffdJ5w+9KEPOV4jUaXDY6Nr0aQ+4KUwPCYiIiIiIiIioroKkKdrUzFde4tSRkdH8dnPfha/+c1vcMstt6hKYTeWLVuW73FMVKsYHhMRERERERER0ZwJkPfdd1/19bnnnpv0vdx5++yzT9nbiUajOOyww9R17rjjDpx11lmu1yK9kEVPT4/r6xLNBobHREREREREREQ0pwJk6Rvc0tKC22+/XfUszlmzZg0eeughHHzwwfl+xNIL+a233kJvb2/J8Pill17C3Xffja985StT3t/Y2Fi+NUah/v5+NUBPejFLFTNRrbFteGpbEU3Y7HlMRERERERERETTqtkpWR0dHbj22mvVELsDDzxQVQ5LP+QbbrhBhWPXX399/rIvvvgiDjnkEJx66qkqKM759Kc/jd/97ndqcJ5c5/vf//6E+9hzzz3VSbzzzjs4/PDD1WV33nlntLe3q8rju+66C0NDQ6pv8qJFi2bxGSByxor2A01tLsNjoC+uI9TsfGCenc3yJSEiIiIiIiIimmNqNkAWEhp3dnaqIPniiy9GIBDAQQcdhKuuuiof/E5HwmOxevVqdSq2cuXK/O1IewoJkKVP8g9/+ENVkSz3/YlPfALnn3+++kpUi6xkHL7uxa4G5vXFNfg021V4nN3chyD2qtCqiYiIiIiIiIioHtR0gCxWrFihTtORdhbSA7ZYqfOmIgHyvffe62mNRNuSHoq4Co97B+IqPF7cYjsKj9MZE9nBPsAyK7RiIiIiIiIiIiKqFzXbA5mInNHbul2Fx4ahY4mEx/7y10mlsugbiKvwWG/r4ktCRERERERERDTH1HwFMhFNz0F2jNFYConkeHgsbSuwzll4/EHvKEzLD6OtE4aR4ktBRERERERERDTHsAKZqMFZ8RgGosl8eOykbUU+PDYtdLWFoEeaZ2WtRERERERERERUW1iBTNTAzFgUZjQGQ5/vKTxe2BXBMIIAsrOy3kYyvOomz9cdWdM7/vXnD2D4Twvz57cf87WKrI2IiIiIiIiIyClWIBM1cng82AvoBnq6Ip7C4/a28KyslYiIiIiIiIiIahMDZKJGDo8NH3ydPQj4jbLXYXhMRERERERERETFGCATNXB47F+wFJqPlcdEREREREREROQNA2SiRg6P/YGy12HlMRERERERERERTYUBMlGDYHhMRERERERERESVVv7YdiJqzPA4Y6OPA/OIiIiIiIiIiGgaDJCJ6pyZGIOdSbsKj9MZG+tGNWh+Cwu7ImhvC5e9jm1XaMFERERERERERFQ3GCAT1TkrOgB9/hJXPY/XjmrI2hqWOg6PbVjRftdre7svjVR2Zslz0Kdhl57yj4uIiIiIiIiIiCqPATJRvdMN1wPzJDzuiViOwmPLBqzhAVjJuOulSXj8P6/FsLjDD5+hlb181rTRN5KFaQJdLToCPh17Lw26vl8iIiIiIiIiIqoMBshEdc43r8dVeGyaFnoiNtrC5QNdywL6N49hl5Y49FDE0/okPHZSQZzO2lizOYP5LT4sbDfQFjbwXn/G030SEREREREREVFl6BW6HSLaRjRf+c+B0hkzHx5Lz2MHhccqPF6/cQRjCVOFx3pbt6f1Oak8zoXHWRP58JiIiIiIiIiIiLY9BshEDc7OZtE3EM+Hx47aVmwJj0fHsmgKG9Dbu6CVz4E9YXhMRERERERERFS72MKCqIHZmTSyg5thWh2ewuOWJh9C4SZoWrYq62N4TERERERERERU21iBTNTA4XFm4xrAMtHVFvIUHm+3oBU6K4+JiIiIiIiIiOYsBshEjRwem1kYbZ1oaQ56C4+r9C8EK4+JiIiIiIiIiOoDA2SiRg6POxdCjzSXvQ7DYyIiIiIiIiIiKoUBMlEDh8dGc1vZ6zA8JiIiIiIiIiKiqTBAJmoQDRke23Z1FkNERFQDVq1ahf322w9NTU3o6OjAEUccgTfffNPRdR999FGcfvrp2H333dHS0oLu7m7sv//+uOuuu5DNlh5++8EHH+DEE09Ulw2Hw9h7771xxx13VPhREREREVGj8W3rBRDRzDVieGzbNvpjVnUWREREtI3deeedKgBevnw5rrnmGiSTSdxwww044IAD8Oyzz2KPPfaY9vpnnHGGCp6POuoo7LrrrohGo3jwwQdx2mmn4Uc/+hEee+wxaNrWSbjr1q1TYbVc7rzzzsOyZcuwevVqdTvr16/HypUrZ+FRExEREVE9YoBMVOfsbNZ1eGxbQG/MRlKrfnicNb2FxxuGTcRTDJCJiKjxDA0N4YILLsDixYtVWNza2qrOP/7447Hbbrvh3HPPxVNPPTXtbfzgBz/Apz71qQkhsQTDBx98MB5//HH89Kc/xec+97n89y699FL09fWpcPmYY45R50l4LFXPV155JU4++WTsuOOOVXvMRERERFS/2MKCqM5lN/e5rjzeEANiad1VeGwmxjytr28k6yk8Hk1aiAT5TxQRETUeqfwdGRlRFci58FgsXboUK1aswNNPP421a9dOexuHHnrohPBYGIaB4447Tv359ddfz58fj8fx8MMPq6rjXHicI0G2tLy4//77K/ToiIiIiKjRsAKZqN5Zpuu2FbGMjuaA6Tg8tuIxWNEBT8szPYbHLSEdAQdtkomIiOrNCy+8oL5Ku4pict4999yDl156CUuWLHF929KOQixYsCB/3htvvIFEIqF6JBeT8ySIfvHFF13fF1EjebsvjVS2MvM3gj4Nu/QEKnJbREREtYABMlGd09u6XPc8bvZbWNisOQqPR2MpmOlBQPeW5na16J7C40XtBt4f4BA9IiJqPNKPWEgLi2K583KXcXu7t912mxrId+SRRzq6v2AwiK6urrL3t2nTJvT39084791333W9RqJaJeHxq2tS8BsFlf1bZnJIWzU5Mq67WQeKKv+LZUwbey8NVn/BRNvY8KqbtvUSiGgWMUAmqnNGuKnsZSx74sC8bhvQHITHw9EEBqImsMCAb16Pp/U1Bb2Fx8WH5VJjuekXw1W/j68d2l71+yAi8kJaSuTC22KhUGjCZZyKxWIqNJbWGNLneN68eY7uL3ef5e7v5ptvxhVXXOFqTUT1RsLjHbv9E/anbWFgcYfP8f70jXXJWVgpERHR7GKATNTgZPPbv3kMLdbWgXmZuLPwuHcgDkMPw9fZA82MV219DI+JiGguiUQi6msqlZr0vWQyOeEyTsNjGZj3yiuv4MYbb8TRRx/t+P5y9ylVyNM555xz8v2VCyuQjzrqKMfrJKoXXven0YSJgVEOgSYiosbDAJmowTe/1vAAxhJNWDTP+cC8fHhs6Ohpi0Dz2YBZnfUxPCYiormmsE3FrrvuOuF707WbKGV0dBR/8zd/g9/+9re45ZZbcNZZZ017f8UkVB4YGMB+++037f3Mnz9fnYga3UzC495hEwZneBARUQNyECURUb1ufs2BDbCScTSFDU/h8fYLWxDwV2cXzPCYiIjmqn333Vd9fe655yZ9L3fePvvsU/Z2otEoDjvsMHWdO+64o2R4LPbYYw/VpqLU/T3//PPqd3JuTURz2gzDY58B9LSyRouIiBpPzQfIq1atUhURTU1NaiDIEUccgTfffNPRdR999FGcfvrp2H333dHS0oLu7m41afquu+5CNpsteZ0PPvgAJ554orpsOBzG3nvvrTbkRHUZHsdHoYci6J7X5Ck8DgarswFmeExERHOZtH2Qventt9+uehbnrFmzBg899BAOPvhgLFmyRJ0nvYnfeust9Pb2lgyPX3rpJdx99934yle+MuX9SQuLY489Fu+//77aWxe67rrr4PP58MUvfrHij5Oo3sjAvJmEx0vn+eErHMJHRETUIGr649E777xTBcDLly/HNddco/qz3XDDDTjggAPw7LPPqmqK6ZxxxhkqeJZNuhweKBvtBx98EKeddpoaLvLYY49N2BTIYX0SVsvlzjvvPCxbtgyrV69Wt7N+/XqsXLlyFh41UQXD40gLdK0L+uby12N4TERENDukKOLaa6/F2WefjQMPPFBVDksrCdnnyt70+uuvz1/2xRdfxCGHHIJTTz1VBcU5n/70p/G73/1ODc6T63z/+9+fcB977rmnOuVcffXVePLJJ3HyySfj5Zdfzu9zZT98+eWXY6eddpqlR09Uu+Ipy9XAvOLwOOBjeExERI2pZgPkoaEhXHDBBapnm4TFra2t6vzjjz8eu+22G84991w89dRT097GD37wA3zqU5+a8MtfgmGp6nj88cfx05/+VA0cybn00kvR19enwuVjjjlGnSfhsVQ9X3nllWrDveOOO1btMRNVOjw2uhZB21x+I8vwmIiIaHZJaNzZ2amC5IsvvhiBQAAHHXQQrrrqqgnB71QkPBYSAsupmBQ+FN7O0qVLVQsL2e/edtttavDeLrvsov585plnVvjREdWnSNB75THDYyIiamQ1GyDLRlgO6ZMQORce5za/K1aswD333IO1a9fmD+8r5dBDD510nmEYaoL0b37zG7z++uv5AFkOD3z44YdVNUYuPM6RNUg7jPvvvx//5//8n4o+TqKqhscONr8Mj4mIiLYN2dPKaTpS+CC/44uVOq8c2ec+8MADrq9HNFd0N+sMj4mIiKoVIG/evBnz5s1DJb3wwgvqq7SrKCbnSYAsPd+mC5CnIu0oxIIFC/LnvfHGG0gkEqpHcjE5T4I4OYRwOps2bUJ/f/+E8959913X6yNyq1HD47GUWZX1EBERERFNwspjIiKikiqSFkmbCRnMIYe/yaF3lSD9iHO3Xer+Ci/j9nblUD3pPSc945zcXzAYRFdXV9n7u/nmm3HFFVe4XhPRTEgBkpfwOJqwMTgL4fFMplkPjFrVWRMRERERkUtsW0FERHOVXokbkSEeMpxODrGTYXX//u//rqqSZ0JaSuTC22KhUGjCZZySXm8SGktrDBnQV1g1Pd395e6z3P2dc845ePPNNyecHnnkEVdrJHLLivZ7CI+BvrjuKjy2s9lZn2ZtGJ7ukoiIiIioohgeExHRXFaRAPknP/kJPvjgA1V9KxOkL7zwQmy33XY46aST8Ktf/crTbUYiEfVVbq9YMpmccBmn4bH0O37llVdw44034uijj3Z8f7n7LHd/8+fPx+677z7h9KEPfcjxGom8sJJx120r+uIafJrtKjzObu7zPM3aS3gsA0l6Wmu2TTsRERERzREMj4mIaK6rSIAsFi1apAbMvffee/jZz36Gv/u7v8NDDz2EQw45BB/+8Ifx3e9+F4ODg45vb7o2FdO1myhldHQUn/3sZ9XgvFtuuUVVCru5PwmVBwYGHN8f0WzSQxHXPY8lPF7cYjsKj9MZE9nBPsAyZ32atc8ofx0iIiIiompheExERFShHsjFDjvsMHWSgXIXXXQR7r33XvX1sssuU5OmL7nkEixfvnza29h3331x66234rnnnsNnPvOZCd+T88Q+++xTdi3RaFSFxzIA74477sBXvvKVkpfbY489VJuK3G0Xev7559UQMFkTUa3R27pdD8xb2GIj4C9/nVQqi76BNHbrNKG3dXlaH6dZExEREdFcCY+zpj0rayNqZHdu2KXk+aFfDDu6/tcObUcjuMnh423054EarAK5UCKRwN13342jjjpKhcc+n0/9WU4//OEP8dd//de4//77p70NuWxLSwtuv/121bM4Z82aNaqyWfotL1myRJ0nvYnfeust9Pb2TgqPJch+6aWX1HqmCo+FtKeQQYDvv/8+Vq1aNeF71113nXoMX/ziFz0+I0TV4yA7xmgslQ+PpW2F0/D4g95RmJYFo60TRripagtkZQcRERER1RIv+9N01kbfiLe5IURERHOmAln6C0vgK+GwhL4S8Epf5NNPPx0LFy5Ul1m/fr0Kai+//HKceOKJU95WR0cHrr32Wpx99tk48MADcdZZZ6lWEjfccIOqtrz++uvzl5XqYmmVceqpp6qguHC43+9+9zs1OE+u8/3vf3/Cfey5557qlHP11VfjySefxMknn4yXX34Zy5Ytw+rVq/HYY4+p9e60006VfLqIZoUVj2EgqmG7yNaBeaU7fZcIj00LXW0h6BEDZa/kEcNjIiIiImqE8HjN5gxMb13fiIiIGj9AllYT0h5CAmQJaqVlhAS/MrRO1ycWOctwva9+9as47bTTyt6uhMadnZ0qSL744osRCARw0EEH4aqrrpoQ/E5FwmMhIbCciq1cuXLC7SxdulS1sLj00ktx2223qcF7u+yyi/rzmWee6fDZIKodZiwKMxqDoc93PDCvMDxe2BXBMIJyMF5V1sfwmIiIiIgaJTzOmkBXS1UO8iUiIqr/AFmG0vX09OBb3/qWCloliJ3Orrvuqqp8nZCeyXKajrSzkB7FxUqdV45UHT/wwAOur0dUk+HxYC+gt6OnK4Kg5MAuw+P2tjCGo9VZH8NjIiIiIqolYylThcFew+OF7QYGYxwCTUREjaciAbL0JJY2EdIn2AkZRseBdESzEB4bPvg6exCIGnKu6/C4WhgeExEREVGtGRi1sKRT9xwet4UlQLZmZa1ERESzqSLH14yNjWHdunVTfv8vf/mLGqZHRLMbHvsXLIXm4IMdhsdERERENNcZM6g8lvCYiIioUVWkAvnLX/4y7rvvPuywww4lv//CCy+oy5xyyimVuDsichoe+wNlnyuGx0RERERzT3rt27AzlZ+SrPmDCCzZBfWop9XH8JiIiKhaAXK5XsOZTGbSMD0iqqxGDI+zpvs+5kRERERUnoTHidefhdHSXv6yNmBF+2El49BDEeht3dBKbOXsbAbBnfeu26ffZ7DymIiIqGoBstBK7SAADA8P43/+53+wcOHCSt0VEVUiPM7Y6Kvh8FgOC+wbyWIvOJj+R0RERESuSXjsX7Rj2WIhc2AD0NQGX/diGF2Lpnzvl9nwXkO/CmxbQUREc5XnsuArrrgChmGok2wgTjrppPz/F546Ozvxwx/+ECeccEJlV05EipkYcx0epzM21o1qrsLjMgcaTDvN2kt4LD3lzOnn/hERERFRFeXCYys+Cj3SMm143OgYHhMR0VzmuQJ57733Vj2NZVMhA/IOOugg7LjjxE+vZXPR3NyM/fbbD1/84hcrsV4iKmJFB6DPX+KqbcXaUQ1ZW8NSx+GxrQ5bnO1p1l0tbH1DREREtC0wPN6K4TEREc11ngPkI488Up3EL3/5S1x44YU44ogjKrk2IgLwdl8aqWzp8t9Mv45EqgtB/4egjZT+6/xe1MCmuIEd2sx8z2MJj3silqPweOOYDl88htRYCOF+Hf6A82Erf96UwUjSxNJ5YU8DSQZjc7PChYiIiKgew2M5Mq7RMDwmIiKqUA/k999/n88lUZVIePzqmhT8BUM9pC2EVPZi1IdO7IBQNDTl9deMGpDstnBgXk/ERlu4/JsAywL6h5N4PxXB9sZSLBww4EO67PVy6+uPZTG/xfA8zXowZpW9HhERERHVQHgci6oj4zDXw2Ovfd+IiIjmwhA9IqoeCY937PbnB9LJZlbaQixsMaFtSsHXNnWz4A0xA1nTzIfH0vM47GBvL+Hx+o0jSKTCaA4a+FiXiUC7Bf+WdUylcH3yXkN6H5fDyg4iIiKi+g6P1UwO3cHGr0542Z/K89fPAggiImpAngLkZcuWQdd1vPXWW/D7/ZN6H5ciG48///nPXu6OiArC2cKBdFo/kCnz7Eh4PDCURHfz1oF5qQFn4fHoWBbhoI6OthA03f36+qISbE9fhcHwmIiIiKgBwmPDB9+8Hszl8HjDsIl4ikfQERFR4/EUIG+//fZqM5HbUCxdunTOTuMlmi3F4ay0hSgXHkvbisHhDCwb+fC4nMLwuKXJh45wGJrH9ZXD8JiIiIioMcJjGeic7V+HuRwejyYtRIIcAk1ERI3HU4D8zDPPTPv/RFRZ0lNYNrNuwtl8z2OrCe3NAbS3Td96olR4vN2CVqztLb8+hsdEREREczs81vwB1LusObPwuCWkI9A4XTyIiIjy+PEoUR2QgXSewmPTUuFxJBzwFB7rHtpWsPKYiIiIqD7IvDeGx1v1jWRnFB4vajekd2NVXzMiIqK6HaKXSqUwNjaGefPm5c8bHBzETTfdpL7+/d//Pfbdd99K3BXRnGR4DI+lbcVQjOExEREREU1mRfuBprY5X3mcY84wPGZbRyIialQVCZDPOeccvPjii3jjjTfygfJ+++2XH5p366234tlnn8XHPvaxStwd0ZzT0+rzFB6rnsex6a/DymMiIiKiuclKxuHrXjyn21YU6mrRGR4TERFVq4WFhMN/93d/l///hx56SIXHt9xyC1544QUsXLgQ3/nOdypxV0Rzks/wGB6jzsNjOa6SiIiIiKpCD0UYHhdoCrLymIiIqGoVyL29vVi2bFn+/5944gnsuuuuOOuss9T/n3HGGfjP//zPStwVEc2R8FgOC+yPWeUXRERERESe6G3drDx2gW0riIhorqpIgGxKs6gCv/71r/G3f/u3+f9ftGgRNm3aVIm7IqIi6YyFdS7DY9sCemM2kpq78HgkBfS7DI9nMs06nmKATERERFQJb/elkcpuPbor068jM9oE3+D0bwmteAxmNAbo7fB19kAb8SGgAzt3ZOfUC8PwmIiI5rKKBMjbb7+9amNx5pln4vXXX8eaNWtw8MEHT6hQbm1trcRdEVGBtAmsGczADrurPN4QA2JpHR0uwuPhsSw2B3SEIu7C48GYhZ42bwNJIsGKdNmhMoZX3TTh/5MbdpnxcxbadR8+70RERDVEwuOfvBJDa9hAd7MOc9BAdrQJhj71W8LRWAoDUQ2GPh89XREEogbSloa9ujOYSxgeExHRXFeRAPmEE07AypUrMTAwgD/84Q9ob2/H4Ycfnv/+q6++ip122qkSd0VEBW0h1o7oyFrAYpdtK2IZHc0B03F4HE+k0Rc3Eel017ZicMyEZduep1kHHLRJJiIiIiJnJDzed1lQta3ImBYyiSR8bROPJs0ZjiaQSMaxXUTH9gtbEAzKuSb+Ep16g2ZnG68qmeExERFRhYboXXLJJTjttNPw/PPPwzAM3HvvvfmK4+HhYTz66KP41Kc+xeebqELyPYUtoKfNcN3zuNlvYWGz5jg8Ho5l4NOBJa2Wq57Hcp9tEe/TrOFgGjgREREROSOVxxIelyPhce9AHIaRC4/L1x3ZmTSym/sa6qVgeExERFTBCuRAIIDbb79dnYpJkNzX14dIJFKJuyKa8yYMpGu2EE472NBj4sC8bhvQnLStiCYwHDOgaxqWdvodVQQXrq89oiEsTfI8hMdO3twQERERkQtVDI8zG9cAVulq5nrkdX86lmqc54CIiCin6g1GdV1HW1sb/H5/te+KaG6Fx+0GWtWhhOXD46FoMh8eS9sKp+GxevOga+jqCCHg112vL+wgcWZ4TEREc9WqVauw3377oampCR0dHTjiiCPw5ptvOrrun/70J1x00UX49Kc/jc7OThVsnX766VNe/ktf+pK6TKnTN7/5zQo+KqpnMwqPzSz0ti40Aq/702jCxMAoh0ATEVHjqUgFcs67776Lt99+G4ODg+qXbrFTTjmlkndHNKcUh7PSFiIzPP11pIXEkPSvS1to6XAxMK/gzUNnewg+w/C0vnIYHhMR0Vx15513qsB3+fLluOaaa5BMJnHDDTfggAMOUMOp99hjj2mv/9xzz+E73/kOli1bhn322QdPPPGEo/u97777Jp232267eX4c1DhmGh4bnQuhjQxiLofHvcMmHGybiYiI5maA3N/fj1NPPTW/cS0VHssvXQbIRN5kTffhbK7ncSIVRjhoeAqP5c3DwADDYyIiokoaGhrCBRdcgMWLF6uwODc75Pjjj1dh7rnnnounnnpq2tv4/Oc/j82bN6vK5b/85S8qSHbipJNOqshjoMZSifDYaG6DVe8B8gzDY58B9LRWtEaLiIioJlTkt9s//MM/qPD4zDPPxGc+8xl1GB0RVU7fSBbzW3yuw2NpWxEO6uhoC0HX01V588DKYyIiIndWr16NkZERFSLnwmOxdOlSrFixAvfccw/Wrl2LJUuWTHkbXvfbUugxOjqq2mbI8GsiT+FxNjspPG4E/TELMpvaa3i8dJ4f64ays7JWIiKiuguQf/azn+GMM87ALbfcUombI6IipofK41zP445wGE7G0TE8JiIimh0vvPCC+irtKorJeRIgv/TSS9MGyF61t7er8FrC44997GO45JJLcPTRR1f8fqg+DI9l0Z91WTyQMZEd7AOaGis8FvGUhcUdPs/hccDHIdBERNSYKhIgZ7NZfPSjH63ETRHVvfTat2FnUq6vp6o4APgXLJ14fr+O9oyByLA1qedxdmADzNEh+LoWlgyPpW3F2t7KhMdyP5o/sPVxmsDaER1ZC1jYbCEyjEnrM4fHA+9svw5f9xL2PCYiIgKwbt069TxIC4tiufNyl6mUBQsW4Bvf+IYKjSVElrkl0nP5mGOOwbe//W01kG86mzZtUm3riuefUP0aSQF9UROBVufhcSqVRd9AGrt1mg0XHotI0HvlMcNjIiJqZBUJkPfdd1+89dZblbgporon4XHqnVeh+fzq/83EGKzoAKAb8M3rgeYr/dcu07cGtvwXH1WHBWY39wGWiZSxI0LpTmSyyUnXMTdvRG5SR6nw2EvP4ynfPFgmMhvXqn536YyFNYMZFR73tBkIp33IlLiKGW2DbZqwF3cxPCYiItoiHo+rr8FgcNJzEgqFJlymUmRQX7Gzzz4bH/nIR3DZZZfhhBNOmLbi+eabb8YVV1xR0TXRtqPCz5gOnw5X4fEHvaMwLT+Mtk4YzeM/q42ku1lneExzzvCqm7b1EmiK1yO5YRfHz01o1334PFLtB8j/9m//hs997nNq8MfHP/7xStwkUV2T8Ni/aEeYsagKXPX5S1RlcWEFbzGpJha+7sWqGtnXMV9VdgRSnTAGffC1mZOuY0owPRvhce5xGQbMlm6s6x2FHbawuCuCdmkUNwU9FYA1NizzSDwNJBlLTX7MRERE9S4SiaivqdTkI5aSyeSEy1RTc3MzLrzwQnz1q19V80xOP/30KS97zjnn4LjjjptUgXzUUUdVfZ1UWfnKWR3o6fS7C49NC10yWyMiBQwN2OuXlcdERETVC5Cl97FULBx44IHYf//9seOOO04ayiFh0Z133un6tletWqUOq3vjjTcQCARw0EEH4eqrr8by5cvLXvdPf/oT7rjjDrzyyivqJJOqTzvtNHVeKV/60pdUz7lSZHP9ne98x/X6ae6S8Ngc7AUMX9nweNqBJGW6YXgNj6MJG4MuB6akMjb6trx5WFgmPC5cX29MQ0JzP816YNQq/0CIiIjqTGGbil133dVxe4tqWLZsWb5FxXTmz5+vTlTf5MN5GYAsbRcWtlrQkrqr8Fj2f8MINmZ47ADbVhAR0VxVkQD57rvvzv/52WefVadiXgJkubxUQkhYLIfdSUWG9GqT4SJyH3vssce013/uuedU6Csb43322UdVVjhx3333TTpvt912c7V2mtukbYVUHrsNj62RQaCzx3FPOVuFszaSmtvwGOiL6wg1uxmYYmPdqAbN7zw8tuW+4lmMpjV0eJhmzeHwRETUiKT926233qr2qp/5zGcmfE/OE7J3nQ3SC1n09PTMyv3RtiUfzi/p1FXPXq0fJVuQTRcey/5vOIo5ieExERHNZRUJkC0pMaywoaEhXHDBBar6QsLi1tZWdb60yZAw99xzz8VTTz017W18/vOfV1XHHR0d+Mtf/pKvsCjnpJNOqshjoLlLeh47aVuRI2GzOTII27Ych8fy125DDIildXS4bFvRF9fg02zH4XHWNLF2VEPW1rDUReXxUDSBZAZoCdieBpL0tFbknygiIqKaIm0fZC97++2347zzzsvvc9esWYOHHnoIBx98cL4fsfRClvPb2tqwcOH40Fy3xsbG1NGBuf7KOTIUT470k17Mn/3sZyvwyKjWGQUD37yEx+VI27JGxPCYiIjmuppNZ1avXo2RkREVIuc21WLp0qVYsWKFajWxdu3aaYd9dHZ2erpv27YxOjqKpqamSa04iBzRDVfhsWpbYVvQm9och8fStiKW0dEcMF33PJbweHGL7Tg8HhhKot3W0BOxHIfHsr5EKoyQH1jYbHsaSLJuaG4eHklERI1NihuuvfZaNcROWsCdddZZqh+yHGknvy+vv/76/GVffPFFHHLIITj11FMnHPUXjUbV5cXw8LD6+uqrr+LKK69Uf95rr71UMYV45513cPjhh+PII4/EzjvvjPb2dlV5fNddd6mijRtvvBGLFi2a5WeBtgX5cF7CY1QlPLZhRfvRaBgeExERVThAluoGOexu48aN+PSnP40FCxZ4vq0XXnhBfZV2FcXkPAmQX3rppWkDZK9kUy3htYTHH/vYx3DJJZfg6KOPLns96R0nlRzFw0Vo7vHN63EXHptZaE3t0EPOw1npedws7SSaNdcD8xa22Aj4nb15GBzOwLKBnoiNtrDman3hoI5W3edkHgk350RENKdIaCzFDhIkX3zxxflZH1dddRX23HPPsteX4Pfyyy+fcN7LL7+sTkIC51yALO0pJED+zW9+gx/+8Idqzy73/YlPfALnn3+++kpzg8+oXnhsDmyAlYxjrofHWbNBy7CJiGhOq1iA/J//+Z8qaJVqCKmc+PnPf64CZAlVJeSVyoYzzjjD8e1NN0CkcPBIJcl6v/GNb6jQOFeZIZUdxxxzjDq876KLLpr2+jfffDOuuOKKiq6J6pPm87kKj1XbiuiA6hnsZmBetw1oDsLjeCKN3uTWgXlY5+LNg9WE9uYA2hyEwMXr6wiHYY+VmQLI8Ni1m34xXmlWKckNu1T09oiIyBk5qk5O05F2FhLOFdthhx1Knl+KBMj33nsvXxaqbngcH4WdSiCz/s8Vf6Y1fxCBJZXbr6TXvg07M3GPmunXYQ4YyGTH2zOOpGTWiA6fvmXgoIOe0RnNj76RTuylBg0SERE1Dl+l2k3IIXhS5SCHxsnguxyZ1iw91eQybgJk6fcmpCdbsVz/ttxlKkUG9RWTx/WRj3wEl112GU444YRpK57POeccHHfccZMqkKXPHdG04bGLthW5cFbaVmTizsLj4VgGS9u3DsxLuXjzIOFxJBwAku7Xt7Z3fJDedHhYIBEREVH9h8d6pAVapBWpNX+CnU6WHTgtM0Ok7Zs6cm+a4gs7m0Fw5709PaYpbzOTQuqdV6H5/PnzMiNNyI42IZNIYngsi76oqcLjnk4/tKRevmd0MoWN7bvCdFB0QURENCcDZKnO/eQnP6lC4sHBwQkBspCK3jvvvNPVbUYiEfVV+sEVSyaTEy5TTc3Nzbjwwgvx1a9+FU888cSkx1ZIwnI5EVU8PLYnh7NO21YMxwzomuZ4YF7xm4ehWPlWHKXCYyfrY3hMRERE1BjhsdG1CFrv+4DPD/+iHae8nhmLqv2w04HTmQ3voRq0onX6gj4Yug8xxNCfjSPQurX4wsnzt7ZvHfRWoKvdwSaYiIhoLgbIr732Gv71X/91yu/LUA7pi+xGYZuKXXfd1XF7i2pYtmyZ+irtOIhmOzyWzXn/5jG0WO7DY9XzWG9FZ3sIwaDp7c1DbPrrMDwmIiIiql8VC48dDL2Q8Ngc7AUMn/OB09nZG6o8GkshUdD2zU3xhXS+2L7ZQjRozLn2aF87tL1ia6HZfy0r3c7utEVvoxHw7wXRRLPy8WhfXx/C4fIbkUL77ruv+ipD+Yrlzttnn30wG6QXcq5/HNFsh8fW8ADGEqa38NjQVXjsM4yqvHlgeExERERUv9IZy/3+z4ban85KeJxJI7u5D7PBiscwEE16Co/l+etpM9DK1sdERNSgKhIg77777njmmWem/P4jjzyi+gi7IX2DW1pacPvtt2NkZCR//po1a/DQQw+pgSK5fsTSC/mtt95Cb2+v58cg06hzrTEK9ff3qxYd0otZejkTzWZ4nJtm3RQ2PIXHsvlleExERERExdImsGYw47p4QI6Mk/3pbITHav9slT+KbqbU+qKDMHRv4bF6/poqNp+eiIio5lTkt5z0BZZhc9/73vdw/PHHq/NkIzE0NISLL74Yv//973H//fe7us2Ojg5ce+216nYPPPBAnHXWWaof8g033KBu+/rrr89f9sUXX8QhhxyCU089FXfffXf+/Gg0qi4vhofHD+V49dVXceWVV6o/77XXXmrwn3jnnXdw+OGHqyGAO++8M9rb21Xl8V133aUex4033qhacRDNangslR2hBej2N0HXTdfhsafNLyuPiYiIiBpaOmtj7Yiu2i4sdrn/G0sY0LsjMLraqx8em1nobV2opvz69Hb0dEVQYoa7o/1zdmBmbSCIiIgaPkA+44wz8Oyzz+K8887DpZdeqjYSxx57rApwJQiT73/hC19wfbsSGnd2dqogWYLoQCCAgw46CFdddRX23HPPsteX4Pfyyy+fcN7LL7+sTkIC51yALO0pJED+zW9+gx/+8IeqIlnu+xOf+ATOP/989ZVo1sPjSAt0rQv6ZszJ8Dhr2uVvmIiIiIhchcdrNmdUeCxtF9zu/5rCAejtXdA0s+rhseyftZFBVEvh+nydPQhEpe2bWfH9MxERUb2r2HE2Uvl79NFH47777lPtJCzLUqHrKaecgmOOOcbz7a5YsUKdpiPtLCR0K7bDDjuUPL8UCZDvvfdez+skqkZ4rA4L3KxVJzzO2OibpfB4JAX0uwyP5c1N30gWe4HN5IiIiIgqQT6cV+GxCSxsthBO+1zv/0LhJmhadlbCY9k/W1UKkM3EmLq//PpGqlN8QURE1Agq2qhJ2j/IiWiuk2nR7sNjwB4dcj2QJJqwMegyPE5nbKwb1aD5XUzbtoDemI2k5i48jidN9MZ0hCLuwmN5c2NWv+UdERER0ZwhH87Pb/FhYbuByDCQ8VA8sGYUsxYeV5MVHYA+f4nj9TE8JiKiuWzGQ/RisRiuueYa1Vpi/vz5aticfJXqY2k9Ia0giOYaNS3aZeWxFRuClU66DI+Bvrjuum3F2lENWVtzHh4D2BADYmndXXicSKuA26e7D4+lMqarpSJzPomIiIhIgl2pPG430BaWVg3VOfJMBuzVenis6AbDYyIiotmoQJZWFZ/97Gexdu1aFYC1tLSo8HhkZET1EpbTLbfcgp/97GfYZZddZnJXRPXFMl23rbAlPA6EHIfH0raiL67Bp9muex5LeNwTsRyHx0PRJFoyOpoDpuM3D7K+4ZihLruk1XIdHsubm8FY+esQERERkTPy4Xw1w2MzmYA1Fq398FjeCM/rYeUxERFRtQPkTCajBuWtX78eF110kRp4t2zZsvz333//fdx666347ne/qy73+9//Hn6/3+vdEdUVmRad2/y+M+RD2pqm8nh4AFYSyNiLoRktCG+e+PfkvaiBTXEDO7SZk3oeS3i8uMV2PTCvJ2KjLaw5evMwFE0gkbbQLO0umjXH4bHqyay3or3JQKD8+5RJ4bG8ufnfDWmE/DMLkYM+Dbv0lH/zQkRERNTomoJVDI9jUdhjw9A0vebDY6H5qtTz2OEMHiIiojkRID/88MP44x//qIbnyaC8YhImS2uLXXfdFV/5ylewatUqfOELX5jpeonqghFuyv9ZwuPX+v0I6BM3k5YN9G8ew1iiCU3hVrTbgJYEAoMT/1quGTVQWLxbODBvYYuNgIOANWuaEza/4QE4fvOQSIURDhpY5Ac0N+GxoaOzPQQ9mfEUHgvTAv7Ul0IyU2Ijbtvoj1mIpyxEgjq6m3WgqHI7Y9rYeymH8NE0P6+rbqrK09N+zNf4tBMRUd2ZSXis2lZoOvTWzpoPj53wEh5Ldiz7UyIiokbjOUB+5JFHsOeee5YMjwt96UtfwvXXX48f//jHDJBpzpLwuLCCOLc5b7GyWDRPNudNyLw/PpEkWHA5sSFmlAxnpW0F1jkLjweGkuhu3rr5TQ04f/MQDuroaAupcLuc4vUNDBiwPIbHOX5Dw47d/kmV2xuGTcg+fnGHD4vajZJtP97rLx9eExEREVEFwmPDB6O1E3BQ2duQ4bEaOK0hLhUQREREDcbzhKrXXnsNf/M3f+PosnK5V1991etdETUUr5vz4nDWaduKweGkqnZ2uvktXl9HWxhOmkh4WV+58LiUXHg8mrTQEtKnDI+JiIiIaPbCY9W2Yo6Gx/L8bRhKYTStqSPjiIiIGo3n3259fX0Teh5PRy4nlyea67xuzuOJtKfwWG1+LRvtzX5P4bGsj+ExERERUeOqWHhcpbYVs9lSOJ2Z2PbN1f45BbQE7PG2akRERA3G82+3WCyG5uZmR5dtampSlyeay2YSHg/HMt7CY9NCe3MAkXCgpiqjWXlMREREtO3ZHvd/Vjw2S+GxDSvaj9lgZ7PoG4h7C4/l+QsCC5tlqAmPjCMiosbjuQeyJb8tXZBf/kRzlbSQ8BrODscM6JrmKTyWze9QbO6Gx2Opif2kiYiIiCi3vxpvuzBmBF3t/0ZjKZjpQaC9+uGxObABVjJe9ZdM1pcd3AzT6vDc9q3HCDI7JiKihuU5QBarV6/GX/7yl7KXY/9jmstk89u/eUwNzPMUzuqt6GwPIRg0vfVsi83N8DiaMDEwyiEmRERERKX2VzLwTdoutHW72/8NRE1ggTE74XF8FHooUtUXML8+K4yuthDa28rvaUvtn63NY1VdJxERUd0GyA899JA6OcEhVzQXqcPuhgcwlmjConnewlkJj32GBK1mVQZ+1HJ4LKUxQ3HbU3jcO2xCPW1EREREde7tvjRS2Zkd0fnnTRlsGsliWZdv/MP5tKbaLrgvbgjD19kDzY/qh8eRFsBXPqT2asL62jrRkgiW3XNPtX/OlS1sHMki5J95G4ugT8MuPdV77ERERLMSID/99NNer0o0J2w97A5oCrdiuwVNnsLZgYHyKWjNh8cm0Ouh8ngobiGZ8RYe+wygp3VGn5ERERER1QQJj19dk4Lf2LoXypo2+kayME2gq0VHU3D6/dWawQwMveDIroCNro6g6/1fT1sEmk/C7GzVw2OjaxHs3vdRrZ7HhevTU81AYub7Z9MC/tSXQjJjT2irJkfGGVv2p76C17GUjGlj76USZhMREdUGz+nKJz/5ycquhKjBesptPexuAbr9Eh6Xb0ERTdgYdBnOpjI2+lyGxzIwpTdmI6m5C4+9rC+TtbF2RAc63LetkI13yO8tPF46z491Q9O/sSEiIiKqFxIe79jtn3Bk1/wWn+P91YahDDYXHNnVbdvIxtwXD/QmjVkLj6t5FGt2cx98HfO3ri9VueKLwtdK9qfyei3p1NX+NODTHFWcExER1RIHkRERuSXTonObX729C7qDvW80AfTFdXeVvRkb60Y115XHG2JALK27DI/dry9rmtg8ZiJreet5LIf/dUQ0T+Gxk805ERERUb3xOlNi8pFd1TnyrLiytxbDY8UyHa/P65F7Xvan8vpKZTkREVEtYYBMVAUyLdrN5lc2531xDT7Ndl55nMpi7aiGrK25blsRy+hoDliON7/xRNrT+gaHk+o+FzZbngbmdUR0hsdEREREFRhI7PbILq/hsTUyWPvhsbwRbuuqyfBYXl9pS0JERFRLGCATVeMvVijiKjyWzbmEs4tbbMfhrPQ8lvC4J2K57nnc7LewsFlzHB4PxzKe1mdaNtrCGlodtHArDo/lzY2T0hhWHhMREdFcID2PvYbHbo/s8hQeZ9IwRwZh21bNh8fCCDdVLTyOpy3P4XF2S09rIiKiWsLfTETV+IvV1u16c75EwlkHE5sLB+b1RMYD2nJkhEfh5ndRMxwPTBmOpVULDi/ra28OIBJyX3nstecx21YQERFRo5K2Bl7DYzdHdnkNj1XbCtuC3tRW8+GxE17D40TaRDRueQ6P5fUtNxCRiIhotjFAJqoCLz3lAi7DWWlb4aDwWIXHQ9HkhM2v5mbatq6hqyPkaX2RcKD8+hgeExEREZVlziA8dnpk14zCYzMLraldHYk3V8PjkRQwHLeha5rn8NjJ60tERDTbGCATbQNeNufF4azTthVD0YSqhHCz+S1cX2d7CD7DqMr6GB4TEREROSNtDbyGx1WvPM71PA6F52x4PDyWRW9MV5ftbNYZHhMRUUNhgEw0y7xszrOm6Sk8ls1vImUhHDA8hceyPobHRERERNuek7YG2zQ8boS2FbbH8FgGYkdN+CQ8bjLgM1h5TEREjYUBMlEdhMcDQ0lP4bFsfsNBHR1tIU/hcbUqo1l5TERERFRZsxoeZ7OzFh6biTHMBllf/+YxT+GxGoitA0tapfcxw2MiImo8DJCJZonXcHZwOKmqIbyEx7L57WgLw0ldRyOGxzKtnIiIiKjReQ6Ppe2Cy/1fOmMiO9g3O+FxLAorOoBqk/VZwwMYS3hv+7a004+Ag/bF7HlMRET1iAEy0SwYjaW8h7OWjfZmv6fwWA3Mm6PhsWzOZVo5ERERUSPzur+SgW/SdsHt/q9vIA5Y5qyEx+ZgL6BXd6hcfn3JOJrC3tu+Bfzlr8TwmIiI6lX5XQIRzYgVj2EgqmG7iLdwtr05gEg4IFvO6gz8qPHwOJ62PIXHMs1appUTERERTbtvWPs27Eyqok+S5g8isGSXqj/xM/pwPqartgvuixv8MNo6YTSHqh8eGz745vWgWiasL7QA3f4m6Lrpaf+cHZ3+OgyPiYionjFAJqoitfmNxmDo8z2Hs0MxCY9R5+ExPL25SaRNROMWfN3uw+OsOT6tnIiIiGjafUomhdQ7r0Lz+SftX6xov6pM1UMR6G3dcDLvzRwdRnjPA6v+pM/4yC4d6On0u97/dclsjYhUBWerHh77FyxFtn8dqkFe38L16VoX9M3V2T8zPCYionrHdIWoSgoPu+vpijjbnGds1+Gs1/A4mrDdh8ce19cb0zy9uRmO29A1zVN4vLDdcDStnIiIaFtZtWoV9ttvPzQ1NaGjowNHHHEE3nzzTUfX/dOf/oSLLroIn/70p9HZ2al+t55++unTXueDDz7AiSeeiO7uboTDYey999644447KvRo6puEx/5FO+ZPvoXLoAeC0JvaVCVxcNd9Edhu6/enOumtnbDGolVfbyWO7JKBb07aLhQXD7Q0Bx2trxLhseYvX0jhlfpwwOX6Zi08lnSbiIiohjBAJqoCmRadP+yuswcBf/mNYjpjY92o5q6yV4WztofwGOiL6y4HpnhYn9xXPIvRtOapMkYeS2ez7ik8drQ5JyIi2kbuvPNOHHvssRgbG8M111yDyy67DK+99hoOOOAAvPHGG2Wv/9xzz+E73/kO3nvvPeyzzz5lL79u3ToVVj/yyCM444wz8L3vfQ+LFy9Wf77iiisq9KgaQz307J3RkV25tmAOlum1bVmth8dCVZbXYHgsz19/zHL8OIiIiGYDW1gQVYFMi9bnLxnf/I44Oyxw7aiGrK1hqYvK3g0xIJbW0eGybUVfXINPsx1vfrOm6Wl9Q9EEkhmgJWB7GpjX2WSor+UwPCYionoyNDSECy64QAW4zz77LFpbW9X5xx9/PHbbbTece+65eOqpp6a9jc9//vPYvHmzqlz+y1/+gmXLlk17+UsvvRR9fX340Y9+hGOOOUadJ+GxVD1feeWVOPnkk7HjjjtirquHnr1e21YUH9mVqVLbMnt0qObDYyFtSWoxPJbXN55igExERLWFFchEVfmbZTje/OY25xLO9kQsV20rYhkdzQHLdc9jCY8Xt9iOw+OBoaSn9SVSFkJ+YGGz7To8ljc3PoOVx0RE1HhWr16NkZER1XIiFx6LpUuXYsWKFXj66aexdu3aaW9D2lZIeOxEPB7Hww8/rELmXHicI0F2NpvF/fffj7muYpWzvirV6Myw57GbI7u8Vh5bsSFY6WTNh8divKd1dcLjrOk9PJbXNxLk23QiIqotvnroDfftb39bHcoXCARw0EEH4eqrr8by5csd9YaTvm6vvPKKOkmVxmmnnTZtrzfpDfetb30LP//5zxGLxfBXf/VX+PrXv162pxw1Ji9TuTPr/wzZobsJj2Vz3hOx0RbWXPU8bvZbWNisuR6Yt7DFRsDv7M3D4HAGlg1P6wsHdbTqPkcDZ4rD40ZtW2FGB2Fb0w+dMUcG8l+zQ/qEn8fZmOhORETV9cILL6iv0q6imJx3zz334KWXXsKSJUsqcn+yj04kEth///0nfU/OkxDtxRdfnPY2Nm3ahP7+/gnnvfvuu2gUxQPVajH8lLYGkuN6Gpjn4siumbStsCU8DoRq8vkr5mR/6jU8HoxZ6GnzFh7L6+ukvQgREdFs8tV6bzgJbiUslt5wyWQSN9xwg9pYy+F+e+yxh6PecFJtIb3hnnjiCUe94aLRKM477zx1PakQkcP71q9fj5UrV1b4EVK9TuUu1fNY2lZI5bGdSUMLhlxvzsPjmaGrgXndNqA5CI/jiTR6k1s3v1jnovLEakJ7cwBtmvv1dYTDsMfKB/BzJTwWEh6bg30l+yPKmwck48j2bgmQo5thRrY8F5bp+sMMIiKqTbLnFNLColjuvNxlqn1/wWAQXV1dZe/v5ptvbuheyTJQDU1tNRseC2lrsLjD5yk8lv1VX9TcMqGiej2PtUAIWnN7TT5/bnlqW2ECg2MmLNv2HB7L6/v+AIfolXxNVt3k6bVsP+ZrmG31tNZ6kvzjS44uNxx9u25/Bir53BQ/D1Phzx3VdYDM3nBUa1O5p9v8Smic63mc/OOLZbbmpTfnqQF34ay0rcjEnYXHw7EMlrZv3fymXKxPwuNIOAAk3a9vbW+5tylzKzzO0w0YLR2T3jzY8VHYgRD0ljZ1ntHclr+cOTq0TZZKRESVJy0lcuFtsVAoNOEy1b6/3H2Wu79zzjkHxx133KQK5KOOOgqNQAaq+boX12x4LKStgdfwuJptKwort/XmDsBB0UFDhscZC30jutoTt0V0z+Gxk9eXiIhottVscyX2hqN64GXz62lzjsnhrNO2FcOxNHQNjje/xetT4THch8dO1jcnw+MS8uGxVK/7A9BCTdt6SUREVEWRSER9TaUmf6QrR9wVXqba95e7z3L3N3/+fOy+++4TTh/60IfQKNwMVNtW4Wd3s17T4fH48zc3w2N5/tYMZpC1gPaIhkig/EaY4TEREdUTvV57wwnpDVcplegNR3PLbIbHQ9Gkp/BYbX51DV0dIU/hsZuBeQyPKxQeR1pYeUJE1OCma1MxXbuJatyfhMoDAwMVvb965HSg2jYNP2s+PK7O82dnp58dUUmjsZSn8FgNxLZkcLSFsIMGxgyPiYio3tRsC4t67A3X6MNFaPbDYwlnh6IJJNIWWjo8hMeGjs72EHyGbGTNuRceS0/hGuxP5TU8Hn36ISRe/RWSGzhIj4ionu2777649dZb1byOz3zmMxO+J+cJmd9RKTI3RNpU5G670PPPP69+H8ma5rJGqJz1tL/KWFjndv9nA9bwACz/LITHmTSym/sQxF6oNisew0BUw3YR9+GxGojdZqBVusRkpr8Ow2MiIqpHNVuBXI+94WS4iAz8Kzw1Sl84mtnmN50xPYezidR4JYOX8Fg2v+PhMeZceCybc5lWXqtYeUxENHfJ/rClpQW33347RkZG8uevWbMGDz30EA4++GAsWbJEnSf7z7feegu9vb2e70/aUxx77LF4//33sWrVqgnfu+666+Dz+fDFL35xBo+o8TVkeGxCtV1wu//r3zymekbPRnic2bhGDRKuNrW+6CAM3Vt4rJ6/pvLXYXhMRET1qmYrkGuxN5xUIc/l4SLk/bC7voEMuv3ewtlwUEdHWwi6nq5az7ZaDo+zprfwWAaSyLTyWsTwmIhobuvo6MC1116Ls88+GwceeCDOOusstQe94YYbVBh3/fXX5y8rLdQOOeQQnHrqqbj77rvz50ejUXV5MTw8rL6++uqruPLKK9Wf99prL3z+85/PX/7qq6/Gk08+iZNPPhkvv/wyli1bpmaOPPbYY7j88sux0047zeIzUF8aMjzO2lg7oqu2C4td7v/GEgb0bukZ3V798NjMQm+b/j3YTOXXp7ejpyuCKeqJyu6fswPjfw+nwvCYiIjqWc0GyIVtKnbdddea6A233377lR0uIidqTJ4PuxvcDNPq8BzOdoTDToZZN2x4PBiTQwLdh8cyzVqmldekZBx2IOSq57H8LBERUeOQ0Lizs1MFyRdffDECgQAOOuggXHXVVdhzzz3LXn9oaEgFv4UkGJaTkMC5MEBeunSpamFx6aWX4rbbbkMsFsMuu+yi/nzmmWdW4RE2hlrv2TujI7ssqLYLbvd/TeEA9PYuaJpZ9fDY6FwIbWQQ1VK4Pl9nDwLR6rR9Y3hc3k2/mD6AF9O1cTtt0dsO7oXmijuLflZCDn6+iKhOA2T2hqNaMrPD7sLoaguhvc3nKZxd21ul8Dhjo2+WwuORFNDv4c3N4JgJy7Y9hcctIR0OZphsE3Y2A72p1Xl4nE7CTlWuZQ8REdWGFStWqNN0pJ2F/H4rtsMOO5Q8fzpSdfzAAw+4XudcVes9e2d8ZJcMfEu735+Gwk3QtOyshMdGcxusKgXIZmJs/AP63PpGqlN8wfCYiIgaQY2W57E3HNUO2Vx6qTzJb37bOtHSXP5YONsCemO2h8pe23V4nM7YWDequdv8elxfPGmiN6Z7qoyRNyxtEd1TeLyo3XA2EWcb0Hx+V+GxlYgBWs3+c01ERNRwar1n70yO7Mq1BVMD37wUD1Rp4GCp8LiarOhA1Qdiew2Px1LV/xkiIiJqiApk9oajWiGbS33+ElfhsaqU6OxRm1891QwkytyHBWyIAbG0jg5X4THQF9cRanbXtmLtqIasrWGp080vvK0vnkirgNunexuY1x7REA7ojVfZEYo4bFuRgpXQVHishZtnZWlERERzXa337PXatqL4yK7McHWOPJMBe7UeHiu6UZPhsby+A6O1OceDiIjmrpoNkAV7w1FNcLG5lM2vOTII27a2bn5Lz2WctDmPZXQ0B0zHm3NpW9EX1+DTbNc9jyU87olYjsPjoWgSLR7WNxwz1GWXtFquw2N5czMQk8pju7HCY6lAdrg+eQMGLQK9qQ1WfKTq6yIiIprrar1n70x6Hrs5sstreGwmE7DGorUfHssb4Xk9NRkey+tr1GgbNiIimrtqOkAW7A1H25rTzWV+82tbKvBzsvkt3Jw3+y0sbNYch7PStkLC48UttuuBeT0RG21hzdH6hqIJJNKWp/UZeivamwxHvYiLw2OvbStqPTx2QiqPFU1XP0sa30UQERFVXa337JW2BrJf8jQwz8WRXZ7D41gU9tgwNE2v+fBYaL7q9DyGbWMobnsOj+X17Wmt+bfpREQ0x7CpJlEFNpeFm1+tqR16KOJ6c76oGe7CWUPHEgmP/U563pkTNr9O9r659SVSFsIBw9P6OttD8HuoPJ7T4bH0PJbKY/kHOtzC8JiIiKhWZ17McvgpbQ28hseyv5L9XDXDY/X8yYffrZ01+fy55a3yGBiKW0hmvIfH8vr6jPrf0xIRUWPhR5tEMzRp8xsdKNN0ofTmPDOeGToOZ6VtBdY5C48HhpLobt66+U0NOF9fOKijoy0ELel+fQMDBsp1cGN4PPXAPM3gZ3wzcdMvyjR3BJDcsIvn2z9t0duer0tERHU+82IbhJ/GDMLjqratKKjcNlo7AZfFFw0THquB0xqSGRshv/fw2MnrS0RENNuYThDNgJfNr9fNeXE467RtxeBwEpYNx5vf4vV1tIXhZBvrZX0Mj0uHx1J5TERERLU782JbhJ/S1qCWw2P1/M3R8FgNxB5KYTStIeTX0BHRGB4TEVFDYYBMVAfhcTyR9hQeq82vZaO92e8pPJb1MTye5fBY9TzmP81EREQ1PfNiG4SfTtoabNPwuErPn7SFmC3pzMS2b672zymgJWCjI6IzPCYioobDlIKoDsLj4VjGW3hsWmhvDiASDtRUZTQrj6cLjzl2m4iIaLY1QuWsp/2Vx/2fFY/NUnhsw4r2YzbY2Sz6BuLewmN5/oLAwmYbYNsKIiJqQOyBTDQb4bHtPZwdjhnQNc1TeCyb36HY3A2PZVp5LWN4TEREVB+8hsdv96WRys68hPbPmzLYNJLFjt3+Cu6vxtsujBlBV/u/0VgKZnoQaK9+eGwObMgPF67kc5zp15EZaYJvy95VwuN31kQxmGrFngu9tX3rMYJOsuNZ7Xns9LnZOJItexmfrqGzuTYKHTZnAjCLjpP849jUP1PN61JTfi/o07BLT/mfYSKiuY4BMpELXje//ZvH0GJ5DGf1VnS2hxAMmt56tsXmZngsm3OZVl6rGB4TERHVh5lUHkt49/M/xLGozXBUmSoffsv+RQ5Ikp7HubYVawYzKnCs5P5KBr5J24W2bnf7v4GoCSwwZic8jo9CD0XKPsevrknBb2hTPn/FzAED2dEmGLpPta3oG8igN9GGzhZp+1Z2eSX3z9bmsZobmOf0569/1Mz/LMXTNnQNaA5q0OUP8nxZQI/cRo2Q8Lg3FYZf2xqOvxUv/cJlLB0jb47BNIGuFh1Nwa2PI2Pa2HtpcFbWTERU7xggEznk+bC74QGMJZqwaJ63cFbCY59qa2BWZeBHLYfHUhozFLc9hceyOa/VbhDys2RZpqu2Fba8WERERDSrKtGzV8K7HecHHO1fZL+0pFOfFC5uGJYKUbuyH86nNdV2wX1xQxi+TukZjeqHx5EWwFf+uZPwuLNZn/L5K5bJWsgkkjBDKXwwNIpuv4W0L7Kl7Vva0/7ZqrHw2M3P3+trDSQzNkzLQltYQ1tYh7ElPBZD8do7qk/C43bf1tdq+9DkAD9t6VifimB+i6/k34/3+jOzslYiokbAHshEDsz0sLumsOE5nB0Pj1Hf4bEJT29uhuIWkhlv4bFvS+VJLbJTcXfhsWnClj7JRERENGsq1rO3Sj1xZ3xkV8DGoo6g6/1fT1ekaj2ji8Njo2uRo7YQUnns+vnLWBP2z9WcGbKtwmPFwRMo4XEsZanK4+LwuF7lwuOsrTsvXiEioikxQCYqQ3qizfSwu+55TQ43l7brcDaVsV2HxzIwpTdme9j8ul9fJmtj7Yju6c2NbGZDfm/hsWzOnUwr3yZchsfWWHT8RSMiIqJZUc2evdsqPC4+sksGvmkeigcCfmMWw2NnezlpW+Hq+ZPihsHMrBRfeHl9s+bM+2Y7Jetr5PB4QSDB8JiIqAIYIBOVkd3c5/qwRXt0KL/51du71IasnGgC6Ivr7ip7MzbWjWquN78bYkAsrbvc/LpfX9Y0sXnMRNbyVhkT8mvoiGiewuNZrexwSQs3OwyPrXx4rAWn7/9HRERElVHtnr3bqvJ48pFd1TnybMbFFy7DY2G4fP5UcYPsT2swPJb19TkYalcJufU1cnjc6mObCiKiSmCATFSOZbo7bDE2BCuddLX5lc15X1yDT7OdVx6nslg7qiFra643v7GMjuaA5XjzG0+kPa1vcDip7nNhs+VpYF5HRG+48FhoTp50eb0Soyo81iVwdjCkhoiIiGamEpWzelu3o/ua7bYVbo/s8hoeWyODsxoeC2lb5ur52zIUrhbDY1mfDHyrtsL1MTwmIqJyGCATlftL0tblavNrS3gcCLkKj2VzLuHs4hbbcTgrbSskPO6JWK43v81+CwubZbKys/B4OJbxtD7TstUgjlYHw42Lw2N5c1OtnoG1TiqPx/+wJTwOhLb1koiIiBrebPbs9drWYCY9j90c2eUpPM6kYY4MwratWQ2PhZO2ZRPC92YL7U2+qoXH8bQ1ow8Hulqq+za9+OdvzlYeF0+8JCKiKTFAJirDCDe52vxK2Ke1dLjenC+RcNavuRqY1xMZD2jLrg8TN7+LmuF4YMpwLK0Oa/OyvvbmACIh95XHXnseN0Z4bI5XHss/0KEIw2MiIqLZ+P07i20XvIbHgzHL+8A8F0d2eQ2P1fMnH343tc1qeOxEceW2k+IGr+FxIm0iGnfZk7lofU3B6g188/LzZ1m1HbSatuY6PJafv/4YZ4wQETlVfjdARO4OW2zuADz0lMM6d+GstK0ID5S/H9nuDUWT6Ahs3fxm4i7Wp7eisz2EQMb9+oZiAVhj098Zw+OpB+ZpfgfvboiIiKgiMy98HfNrMjyWcHFwzIRl257DY/lw/v0BuS+7euGxmYXW1A49FK7o85cd2jjtB+qZfh3mgIGM9KWYYmBevudxs4XIMJAd2ABzdAi+roUVC4/l9kYRxuaoD7qmYWF6AFo/UC7KLLW+TcM6MjARW/sXdRn/gqXwovi5GUnJIG0dfr8PS3faztHPnxxRGEvZWIDZparZi3p5WMmY+mpnLdhZeZ1SKjwetYJI6zEsMGJoMtMo7gCS6X0v/2fN54fRtVj9/YinGCATETnFAJloBkpufnvfL7M1L705T7kMZ6VtRapMgCyb36FoAom0hZYO55UTheuT8Ngnk0ky7teH8T3elBgelw6P3QzfISIiotmfeTGb4bFUpsqeri2iew6Pq155nKvcjg6U3Qe7fv5ME6k1f0I2OggrOgDoBnzzeqD5xteXGWlCdrQJmURy0lXTGQtrBrf2PA6nfWpLa27eOD55r5I9j8dM9G1cA8vQ0d5kQNs0Uj48nmJ92XgImfQY0vE/wpb/4qMFzx9gRfthJeNqzyg9t6d6+gqfm+GxLPqiJgyYWLb3bo7D42jCwrYoQJa9sSnDzAuefCsxXphiWTpsy4fs/8/enYBHVlb5Hz/31po96fSSbrubRWXYcQMRBgVFZBwHUFYdARc2Uf8woCg6yrSKI6IOioAOoKA4qAiK4zoquCECMwraDAw0W3fTne5OOlslqe3e+3/Om67qqqSS3FuVqlQl3w9PnnRXqipvblWTt3517jmZlAw7EXFlXJZ6G6Ul7EipSDizdSKI1+dp+AUvkm27/n00xzghGwD8IkAGylTui4dyNudZx5kazs4it/kdTzVJUyxUVnis6+vrm/2FSsnweBaEx9OEx9rzeLS+TxMEAGCxz7yoZXisbQ06my1pitr1HR5X8fh52YzY4YjYy9eYatzC4cLhWFhCdljCHc6U/enmrSPiNbmyetL+1NEg2kfbtyD7ZzNwOiSybEWXKb4IL525Dd5M6wsNhSTc3SnW80+av0dW7V10/KSlQ8LLVs96/HLHJiEJ2ZEdk2i7LatjYxL10SGjMDxujs5TmzjbllBb1+41afCvFztRcdMRGXFFvJBIW9iRzvbpX3/kKs0z27fK1oQl49bEvw8/xwEAMIG33IAGCI/7BpJlhce6+W2K2dLVES8rPA40MI/weG7CYwbmAQBQ9zMvguz/RlOVhcfatqLJR9JV0/C4hj2jneT4xF4pFJ4SHs/p/nRX27dywuPCgdjmzL1qrK/M4zeSSBU9vtGIHSg8bo3ZdTdnRLu9jTq2qTZuDTvip2W0vj7aMpCSkbQVaGA3AGACATJQx+Gxbi77B5Nm81ZOeKyb366OJj8tmRdkeKwDZ+oZ4TEAAI2hkoFvfSOVDVSratuK0Wzg/V8640i2v7c24XFiSLzRQbEsu6rh8e62b05Z4XG5A7GrHR67YwnpG0oGenwnh8dxHz9TLXmuJ142ZQL/IOGxeX2UEmmLer7/fQAAdiNABgKYq3f+A4WzriedrZGywmPd/C7W8FhffPUOZ6VeER4DALDww2MVqtPwWAeqmZ64Afd/vX1jNekZreGx079VxLLFbu+uang80fbNNZXe5bZ9i9ZZeGyO31C/hOwFFh5nkqYRdFPIDRYe6+ujmA4q9AiPAaAMBMiAT7V8579wc9nZGpXmptk3zOUO/Kj38Hgs7ZZ92uekwc11w3PdwG0r9PgBAIDGCo9VT3u47sJj8+Z8wpawLWUUN7gS6uiuTXgcCktIw+NwgPUtqLZvIt7IQPnHzw5Jz9LmhREeO04+PLbCEfHREtw8vr3j4fzro1VdMbpWAECZGKIH+FDZO/8JCdnLy95cDiQWQngsZb240dMIh8ZcCS8rr3JnaVt9vkfmjSfEamoJFh4nJ6ZON4rkYw/N9xIAAJif/d/4aNHfw6E6DI/1zXlbpKc7Enj/t1RD1mZdX7bq4bG2rXB29pp2BbO11XhuoPzw2LR9a6q/tm9uYkDcdLLs4xfu7pHokD5WTsOHxxPFF56pRLckNNvTLx8eJ7JhWbLr9ZG7s/jfJgDAv/pMV4A6otldRZvfAO/8pzJeRZUTQcLjoXEv+Oa3zPXptONyXtwMjnliW1bZp322+DmvbT4ErDz2xkbM9HEAAFAblYSf7lBfoO9Vy/C48MyuNe2ur4Fqk8PPttaYr/XNRXjsp22FDvTTthqBwlmzP/Xqtu2bBrnuYJ94Gh5H4+UfPx+V2/r8a4jw2HUnwuPQ7D9TYXjcGs76fn0EAJge/xsFZuEO7QjetiI5VvzOf2T2FwLpjCebR6yKN7/+wmOR3jE74MCUMtan32ssWzTtOEhljP4s3a3+Jj+X8+Jrvlix5mDhcSZtTtUDAADVV3H4aYfqMjzOn9mVawvmY5k179kbNDzOpM1AP22rEaS4YUtCJJG26/LMPV3fjp2j5vWE7hettq6qHb/c869ew2M9GPnwuLmtrPC4pylLeAwAc4AAGZiFbt4CbX6T4xMbnQDv/OvmctOIJVnPqsnmt3fMkrDl+d78Zh2nrPXpNOtkJti048KBed0toaqd9jmf/GzolZccnQiP9frx5qqvCwCAxW4uws/wkh5f36vWbSuCntk1Lz17A4bHmW0bzUA/basR5My9RMaW1qhbl+Gxrm903BE73ix2a/XDY33+1WV4rMdjPJEPj+3o7JXvhMcAUD0EyMBs/0jizYE2v97ooFiW7Xvzlttcajjb0+zWZPOr4fHqNs93eNw3kCxrfTrNOh7xP+24MDzWFzcLMTz2IzcwT9tWmFP1tOIi4MAeAAAQ/PfvnFTO+mwbUOuex0HO7Jqvnr2Bw2Mnawb6+WmrUdj2rTXiyspWq2rhse6fK2lL19IUErtzqa+Bb2WFx44UPf/qMTw2tOcx4TEA1AWG6AGzsDuWBdv8WrbYOi06QHism8ueZk86mqyabH5XtnkS9bFR1PX1D06c1lbO+nSadbsd9rX5nRweL7S2FX6ZthXJiQEf2raC8BgAgAaYeVFm5afv/YvnycCYV/7AvPyZXdVtW1Fxz96g4XH3SrFTrSLjwWaGLPPMlr1q4bEWXyxrLX+mSbypRSwrW53nX8aV3mFbpCvYmxfz1fbNT+WxV2bbitHUzMMFAQC7UYEMzCLoO/8hDY99tq0o3Jz72FtO2VyuahVfm6Ox8XTR5tdveGzW53rS2Ropa31dHU0+ezITHpcamGfF9cVDnVaEAACwyGde1Co81v3BwJgryUz54bHfM7sq7Xlc7Z69k8PjUGtHWQOnqxUe68Dp/sGkKb6oaCB2lSqP9fHd2J+RrBvs+TeWknlhRSK+wuPRrBU4PNZ/H30j7twsFAAWASqQgQpN3rw5O3vN8Ligm/NUX/DNZWbMX3g8mMjI2s7dm99UgPV1tkaluSkqkgy+vk1bJwbp1Ut4fN2vBktentyyj8yVd696oqzblRqYR3gMAEDtZl6El62uy/BY21YkM57EI+WHx9VsW1FYua09e6VK4edchcfValuRHzgd0eKLqHR2RKq2vnLDY9M2zxXZo9X1/fwbSXqSceanAtlveJxyta1fsPBY/32EGv/kRQCoGSqQgQpUsnkLtDmX8je/g4m0qWIod+CHCY+rtPml8rhEeKw9j+Mtsx88AEDDu+uuu+Twww+XlpYW6erqkhNOOEHWr1/v+/ZjY2Py4Q9/WPbcc0+JxWLm8+WXX24un+wd73iHCR5LfXzgAx+QxS7ozItahsfatkJ71HY1Tzxe9RoeTxy/KoXH2Wxdh8eFA7HzxRdVWl+lrz96OkLSPntXiHx4rM/diI/q9VrTrhq58Dhmu77D4+GU5P999LRTTwcAfvF/TKABwuOBoaR0Rcvc/Nrt0t0Zl1jMKW99iZlvQ3g8x+GxDswbmqfzBAEANXPzzTfLOeecIwceeKBcddVVkkwm5dprr5UjjjhC7rvvPjnooINmvL3jOPLGN75RfvOb38iZZ54pr371q+WRRx6Rq6++Wh588EH5xS9+IXaJDcM3v/nNKZftv//+stgFnnkRYP+XdSofmNfVbPuq7J3f8LhKbSuyWXGH+0W6e6oeHpu2b8ng4XHhQOxEnYXH6Ywjzw3sfnxbM+lA4bE+h2zbq7vweNwRiXoT4XFL2PP3+mg0KzujtsSbJ/59bB6Yvc80AKBBKpCpzMBiDo91czkwNC7jaafsygkNj8M+zs8qd311XXk8TwM/KgqP6XkMAAvewMCAXHLJJbJ69WoTFr/vfe8zVcC/+93vxHVdueiii2a9j1tvvdWEx+9///vlG9/4hgmjNYDWMPqee+6R2267reTt3v72t0/5eNnLXiaLXbUqZ1XvcLai8FjbVvhZYFn7q4wbfP/nibiDfbUJjzNpcYb7xfPcmoTH2vatnPC43IHY1Q6PNXzv7RsL/OZAYXjcFvdX+V4rZnufzUjG3R0e++1p3TvkSNj2/+8DANAgFchUZqAezcU7/0HC2fFUkzTFQmWfdtfXtzjDY9387kjU72AMwmMAWLzuvvtuGR4eNiFye3t7/vK1a9fKKaecYsLhTZs2yZo1a6a9Dw2N1aWXXlp0+YUXXigf+9jHzNfPOuus0uHQyIhpmxGiAahv5YbH5rYVhsfValuRdsQMVPOagu3/duwclX3axsTuqH54bNpWeK7YLR1Vb1sxmAiJbVllt31rmmWeSSXrK/f4Zft3iuN2LazwOJMSz7UlEjA81tdHGh6vaXcJjwFgIQXIkyszcpvr0047zZxmp5UZWl3htzLjS1/6Uv5y7Q+nVR5amVFqY62VGMDcvvOfkWWR8sLZppgtXR1xse101Xq2VTM8dpOjku0b391zLGGbzdvKdlesHSKZab5HdmC7pLe3iBdvl6cz3sS06FZXmgdFMoPT30aFOpfL1oQlIzttybRlJZWeCJKzA6UbvrnJhOju0441z/qz52+TGpuY3DFlDdtmvJ0zvFOc8YRYdihQ5bE7Piq5SR9ucpXvdQb9uWb62arxvQBgMXrggQfMZ21XMZlepnvYhx56aNoAWUMe/fqqVatkjz32KPpaU1OTvOQlLzFfL6Wzs9OE1xoev+IVr5APfehD8uY3v3nWNW/fvl127NhRdNmGDRtkMagkPFZL2+z6C4+znmwats3+anXA/d/oeEjsZdozurP64bGTFaulU+x49Yob5qrtWzkDsf2sTwc8ln383CZZ2hGXzo7wAgqPHbOP1meEn/B4OG3Jzl2vj1Z3RyTK4DwAWFgBMpUZqDe1eud/8uayq6nJT8u7ugyPJ27gSWbbJhkYHMufNtbTHREraU8bHitn5zZJD6+QAW9Muvp2moEfTenwrLdxXU+2Pz8gIymRiDRLOp6WdGJ04uuDq0svcSwhdnOrBOK54o6OiOdkzY7WCkdE7JA4UvziesoaRwbFy6bFizYFalvhZVKS7X1O3OEBca19/J1r6zpit7QF+al2fTNXnMF+sTSw1p/L1zj1jNg+KoMAACKbN282h0ELJSbLXZa7Tik7d+40g/K0f3Ipeh/333+/CYpzRRgrVqwwRRUaGmuI/MQTT5iWF295y1vks5/9rHzwgx+c8aG5/vrrZd26dYvu4Su3eKBQS6z+wmNzZpcrZn8VdP/X0hQVu3OpWJZT9fDYtK0Y6jMzQWZcn1f5wLzdbd+cujlzz0mOizs6VP7x6+iWtvHYrD9TI4THpu3brvBY7Ij4ePqZ8Lg3GZXw1qdlZXNKvFRCxvo3SmjDX8zXx5MtknhiSAZbhqa9j863vFdqafCu6yq6fXLLPnO2loUk+VjpN1VnE9/v0DlfC9Co6jZAbsTKDCxcFW1+A7zzX2pzuWlrlcLjjCe9Ndr8Dqdt6Q81S7Td//rG+3ZIfzIkVmuTrN5jha/1ZQf6ZOu4J8m2dulYFpbmpi6JLM1KpHvihVyor6Xk7TLe01IODY81ODZBcGjiZwq1dc08cGZg+8TtgoTH6aT5EDss4WWrxU74OAXR9cQdG5ZyaXhsNbf7yqn153F1fQAAXzT8VbHY1DNj4vF40XWC3n7yfeQCZO2NPNkFF1wgL33pS+WjH/2onHHGGTO2zNDWGKeeeuqUCuSTTjpJFqqyiwd29kpMDvH9fWoZHhcN9Gt1zZvzQfd/8aYWsaxsbcJjH29Om7ZlO0elzQ3aVs2T/jpu+6YFDt7ooFiWXfbxs1OtIuOyMMLjTGoiPI7ExHIDhMeWyAuaU6Lv49RvczsAqH91GyA3YmXGYj61byFzxkfNZixo5Ungd/5dbe/gSdKqbPPrJ5xNZzzZPGKJFaCtRrnrG9WgOm1LvDVYZfSmEcts8rpbI9LZoVWws2/OtyREEmlbunatb+PI7Otz0ynxsjPVNc9AK48LwmNfA/OyGjqHA4XH7vhEiw0rFp+oCp7tNq4nXiZZ2RBBDcZ9hsfm30cdvdAAgHrX3DzR7ieVSk35WjKZLLpO0Nv7vQ/V2tpqeii/5z3vkZ///OdmEN90li9fbj4Wi8qKB3yURs5TeNyfcKWnY6Ins2kLVkb46Wd/VcvwWAf6jY63yKolQfbPIr1jwfentQqPRxIpcdL9E+3B2rvLP34pf8+/eg2PpTA8jsQmwmOrvPAYALBAA+RGrMxYrKf2LXTuUJ/Yy9cECo/d4X6R7h7f7/yXCj+rufnVcDbrWbLWb3gs5a1Pp1mPJG0Jx73Am3NdX0fMk+YmPeZpX5vzRMaW1qjjv7JDw+OxkbLDT1N9HCQ81p7H4bBINO67bYU7bu0Kj5vE8vFDFYbHpq1G2axA4bEVDtYPEgAWs8JiiP322893EUXOkiVLTDg8XTGFXq7728IBfdPZa6+98oUQmJvw0+5Y6utQ1rptRf+oI67n5Qf6TTdTYl579gYMj52+LeImRVqa2uUFK1p8t63oHbMkbHl1GR7r+vqGHJEVIQm1d4vo3rFKxy/3/KvL8FjXqHM5NDTWD1N8MfttCI8BYJEFyI1YmbEYT+1bFOxQoM2vM9wvnuf6fue/3PCzks2vhrM9za7v8HhgKCltZaxPp1nrRm91mxd4c97T7Mlg1gq0OW/ViupWK1h4bNvlh596Gl2Q8FifQz7DY7PG5JiI1Wwmj5sq5CDhcSQ6pQfjXCoKjyNxE3YDAPw57LDD5Ctf+Yo5G+71r3990df0MnXoodP3XdTfI3rG3G9/+1t57rnnitq1jY+Py8MPP1yyDVwpesad6unp4eGbo8pZSwsJ6rDnse6ZOpr9DfSbt569QcPjsRGx4ytkWUTDY8d32zfdP5ezP/V95p5UOtCvScLdPWJt2zBr/+fyjp8UPf/qMTxWepagrS3VahQe31yif3D8V7O80zINehGj1PMJaGQ+fo3Nj5naVNRrZYae1nfAAQcUfbzoRS+a9f5R38JLeoJtfj3XBH5+Nm/lhp+Vbn41nO1o8hfODgyNy3jaKWt9IduS7iZXYhEr8Obcx958youbVa0mDw4UHutjVa32C5PD4yAD8ww9bbGlI3DbCr9tNeYqPLbs+nvBAQD1TIsL2tra5MYbbzTt1HI2btwod9xxhxx99NH5s970bLnHH39ctm4tHopw5plnms+f//zniy6/4YYbTIic+7oaHR3NF08U0tZr2qZNz9g7/vjjZbGrZduFcsLjsbRb/sA8R6Sz2ZLmqF298DgxVHHP3sDhcXObGejnZytSODNkje6fy9ifBim+qGSgX8/S5omz1qpx/ExbOivw828+mLP9qhgeZ9y6jUMAoO7UbQUylRmoF08l28Xtn/mfilZ6Zvt3moF5mdALxXZjEt91m6eHQrJ9LCR7djgzbs6XeSYvDLS5XNnmSdTH5jfrOEWb36a+2b9Pbn3jqSZpioVkVST4+nSatewsb3Oe6gv+4iYzqavN9jFbYvoqq+DvutH2kikROy52U6tIypa02yp2Ni5WprjlzXB2ogVEe3hqh0Av64rr2mI7xS+OHksty11DvOSYRLK2vDA+FKznsVYe6zt8TW1lh8cDTpNk3HDJn2vW+9v1s0WciHSFxuclPN6ZiYrjo43GTELiyZLIzO1PAGA+dHV1ydVXX21apR155JFy/vnnm7PmdPaG/q645ppr8td98MEH5ZhjjpGzzz5bbrnllvzl73znO+Ub3/iGuc3Q0JC8+tWvlkceecS0VNMA+u1vf3v+uk8++aS84Q1vkBNPPFFe/OIX52d9fO1rX5OBgQH58pe/bAZPL2ZlzbyoYXisb+YPjbkSXlZeeKxtK/oSuqfwqhYem/C90p69AcPj0NJVYvnYa04eOC2bqxMe7y6+cKWtq7zwWNe3NamPVXbOj59pmzeQklTckq4Azz99Ls0HK9bsKzxOOSK9TrDwOO3asiMz0dYSANDAAbJWZlx00UWmMuPiiy/OVwpPV5mhl3d0dMjKlSvz96GVF3pqn1ZmfOlLX5q1MiMUCuV7I+dQmYG0a8mjOyIStUtvnNIZR3r7MuK4XbK0Iy4xzYnHRaK7AuSNIyGzmQkafs7V5lfD476BpCxrLS+cbYrZ0tURFysZfH06zTo7jz3lsq4lTw6EJbWrFcbzQ66MjWvhcYu0NkUklJq4UcZtFSurQ+PiUwJkRzzpz8TE9SxptrMS2fU88LIa3IbN7Qo9kV4qrifSnwzJiBOVNzQ/LlazG3hgnrJCdtmVxxq+bnfbJJwNT/m59LXjmBcyVRcR25Vmyylqeaw/W9aNSM+k/iu1rDzW9T851i4x25myvulkXEvGNDS3PInbjqyMzdJ8HADmkYbG3d3dJki+7LLLJBqNylFHHSVXXnmlHHzwwbPeXvetP/nJT+QTn/iEfOc735Hbb7/d7IO19drHP/5x8/UcbU+hAfLvf/97+e53v2v2vfq9NXT+p3/6J/N5sQs886KG4bG2rRgc88S2rLLD46q2rSio3K52z94p4bGP4zd5f6pn7qWqUXm8a+D0uLim+KLc8NicWTjLvrvc8Ng8vimR7qjn+/mXzHgylp6fAFl8hseJbEjnPwcKj59PNYvj1WflNQDUo7oNkKnMQD3R8HhyBXF+czkwIssiuc1lWFLJidHUsV3X32KqPeZmoEY5m9/+wYwJNMsNZ7uamnzVgZbc/NbBQBJ9jaWPna7PzsSlPWpJR2tUwubF18RmOG2PixVyxQ4X39FwNixpNyTtkbS0hjISs92CdaTEc1JihYqfF6tDg9I7HpY2L6wlsGLFtXJiLFB4rJXHfszWtkIrcDtCqaKfS3veJdywxFxL2iIZabWzUyo79GcbzNjz3rZCw+OVkXF/lSeuLY4XMdXi7aGMjDiVDBAEgNo45ZRTzMdMtGhCg7PpZnVoCwr9mIkGyFqtjLmbeVHL8FjbVuhep7vFruvwWI+fs7O3Sj175y48rtb+NDdwuqltovjCttNVWV9F4bE+vjGRla2e7/A4kXJ9tQiZD7nwWJ+qvsNjz5beVLNkPVuWRJjhAQANHyArKjNQz2o5jXlsPC1bk2Vuft0W6WyNSmdHpKz1bSpuudhQ4fHk9dl2067wePbdpVYkjLsawcqU8HimyhMNjxPZsLSGsxKNaLgcrPLY9DxO7KxKz+NceKyheNR2SobH9dTzWCuP/YbHCSdiKo81PA5Z81QlAwCYd0/0piUV8HT7zA5b0pEXSWR493DtTSMh8z7zmnZn2rZloY5usVPaDqv4/rLDLRLZYUskmpKntmdk+1BGYmGrooF53S0h87kewuNcizB3LCHOUELE7pwY+DYcltRYl9k75Vq5TVZ4/LaEe8Te0SxrNAWcQXK0S9yxIYmEtAPZCrGtpUVtK6ZrF1fO/nRy27cg+9PcQOyujsqLL3LHePLx9PP8K6THZttoSEJjA/nHtycU87W/KgyPLfFk5+j0j9NfN/sLYjf2Z+S5DWP5f6tux+7bbRsuPm/RGbPETUXFLjgjL+tM/BsddOIylA1LVibC4/aw47Py2JKtmXbxwrasiI7LQLbMQdoAsAjVdYCsqMxAPap1eDyYyMjazvLCWQ2Pm5t0czRzFcRcVkbXa/geJDwediLiiWWqYP2Gx6NZSxL2RHjc05SVzbMPA58aHldpYF6jhccG4TEAICANjx/emJJIaOZfIqMpR/pGXNFfu0szIbHG2iVUEHpqCzKVyFjTti1rG4+ZtmWTOSMtEu4LSUjS8lxfWkZSnnS2lB8ea9uK3iHdVHhzHh57ZeyvtEXYI1s8eTxjSchebga+RYcmvld6vNN8zrVyK1rfpOM3kGoWGS0+xpPpWXRb+ztlLNMp7c09sizSIvak99lLtYsrNzye3PatnIHYvbPeavb15dqwjRQcT7/Pv0LPDYVkJJGUtujux9fdORooPO5oss2f+xOuONNsiR99Pi1Zx5Pe4aw4jsjSNltaSiS6GiD3bp94TbJhe0ZSLSnZkXBlLOXKszuy0hzTLefEg+mOaoCsBQIFAXK2xXzemY1J2rGkLTQRHvvYPptq5c36nGuyZWV03Jy1RoAMAAsoQAbqTbnTmMsNZwcTIdPzrtxwdiAx+zvrCzk8Lgzf/2+z//BYex7HLUfCAcJjrYRtjU6Ex2Z9ThXCY8epYXjsiOfOU3jsA5XHAIBSNDzee1lkxnBWw9Y13bYJZ60drmRSSQkXVLDmWpDlqlpLtS2b7hd9NpOUSKcr4aVhefT5lCQzlYXH1WpbofsDHag2GooF3l8lRjOyd2dm1/5PL911nPqKW7nllDp+f3h+4mul2sQV7v/avFFZ2eTInnvuKbY99bqT28WVuz+ttO1bOQOxZ1qfPux7xCaOp8RTvp9/hetb/3x6ykA/N2B4HNq1/9NC4K7m0s+r1V1h8/xb3hae8fm3ZTAr4V33t7ozJLGILXqo9faDY3rm2e7nuuN44mazYsd2v3GSCSVNEJx242YvGyQ8fn4sJo6XllWhhLSHW2e/EQCgiI9fcQAqDY8HhpLlh7O2JUu74nUXztYqPK4sfE+bza/f9RWGx9q2IjzN4MTpwmOtVM6Hx1WpPHbEHR2qSXisp/h52QzhMQBgQSknnC1r/6Lh7KBjwrh4pB7DY0+2JiwzUK3a+6tK96e5yt6q709dTzpbI2Xvn61qrS/jlX38xlOuNEX9D/SbLjyeiR63oM8/tX3EDdzWJdfzWK/ZZE+cQeA3PNbONiuiY9IWmr03NQBgKiqQAZ/K3fwODI1Peec/yOayuzMuYbM7chZfeOxNhO9d0XLD93Zz/GIxx1d4nCgIj321rfA8GSsIj1vCnr/wOJspIzx2xUslRZrb/IfH4smoF5VMwPA45YVkXIcABqk8nmbAUzVQeQwAi1d60xPiZVIz9jN2+kKSyU79PT6cEtmasEVny65sd8XaIZLRiuG+LeKMDEh46co52b9khwdka7ZVxpttiSZtafc8yfZun/V2063P/NzPj5rQLNPaUnw8HJFNw7boj7uy1ZXmQZHMYOn7dwZ3tZnY4pjweHDHTml2R+UFK7Sytzr7q7nYn851ZW8jtX1LZzzZPKL7sfKOX1PM/0C/csPjoXFXUv3bpacpPePzz1x/MCTZ4YkeJMPbe2VVe6ss038fSW39UnzWgDs+Im46aYYmqvFUJj8wL267Yoe8QOFxTzwtrZpyzzFnuN+ctTeZm0yUf6f6GiG2uyc7ANT4PNLXAAC+a0lEQVQDAmTAB+05pqeNlffOf5M0xUJlby77+mYPF+s+PM540hs0PHZFBpOepC0nUPg+NO5Jf8DwXfu5JdyYeHaQ8Fhk1AlL2todHls+w2OtPharK1jbitS4OSh2U6tY4/4qj8edkHheyPRx9h0eu7aMulGxxBErHBOrxKmiJXsyZ/WFSfGL2mogPAaAxU3D47GHfmF+n9rxZrE7lhX9fssMt0h2pEUy48mi2w2OZk0fYQ1ne7ojYiXtfDjr7NwmpUoZyx2otmXIkcS2Z6VjSb+0esvFTYlktg7NeLuZ1mfud1DHmOn97Mhfls64pqeshsc9HSFpSoeLbjOZM9Rh1vfc4HZTedycGZKV7XbdFzdkJmauzf3A6Un703pr+6bPv00jlmQ9S9aWefy6mvwN9KskPNa2HxoeN+3YIJlZChyyAx0ytKPP/Dm642lZujwk2V05q5tYVvyzjI+KWVAuPB7N5Afmjc2+VZ8SHrdHtSWGzDndp2e3bZx4Y8uyzF5dnxTu+CxPXG/X/lnPLAxHROxd/w/S/X4zLTYA1B8CZMDHpqC3b6yg51j13vkvDD+rHc5uTXiStIJtfstZnw7UKKdyYktCJJm1pCXAaXdD4yK9Y7bEW/2H79msY9pWSEQHcQQIj72opDxP4qEA4XE6uSs8DtbzeKJthStWNG4+ZhuYYoqBsxnJSIu0Wxoem/2s/3BWkhOBs5+fqWCgX7URHgMAzO+ebEaia/aR0NJVU057D8fCErLDRf2MNbzbkR2TaHvp/YszNBFozdVAtUTGlta4I2tevKc8v3UilAwvnb6acLb1GZu3myAwvHT5xO/EVFY2bx0Rr8mV1T7XZ6WiMjyUlNFou3QsC8uyxEhZlb31VtxQzsDpkpW9ifoKj/X51+lZ0tPsln38Nm0VX21TKgmPW2O2tMe0P3G4ZBV/4fqGtjuSsuLm7z17vEAiy3df3x4q/hnd1FhxeGxb0pLreewGD4+rScNju6W9aH9v3piaZf+swfHkMwtdPeMQAOoQPZABH6clOW7wcDb/zn+Hv3f+c+FnOae1Ba6MSYgk0nbA8Dj4+nTz2z9um8qJcl58xcPervDd3+a8d8ySsOX5X1/WkaFEWlyxpMVO+w6PTU9hrey1nEDhca5thRWL+wyP3eLwWKsTfKxPMinTLzkijrRY6WDhseVJm53KFXz4Do/9rK0ShMcAgBytPC4VHpdS/kC1ZNkD1WrWs7estmpOXffsrcXMCz1+ucreujxzr+D519PsSUeTVbX15XpuVxIexyNWoJ7MOjhP+dlrmsroXeFxR2s0cM/jWoTHhlYe+25LV7B/9tmWDgDqAf+3AmbjurK0I75r2rH/cLYrwDv/5YSfhZvfteVUxkSdQJUd5axv29ObJZtKydLkMxJ7dnC2wlmzvt7xsCSyYWlO9UublZXMpnEZ79ucv07TIX877eZc17e6zQsWHruetNoZifl4EVo0kM5KSXNIq3RDAcPjJrH8HPRdvd+kvdOcCucmxwKFx7quppBOsw4ezlr6imCWtz0mb369bBXOCZxmfSFr9hcC5keYR4N3XSeNoPMt75XFrBqP02I/pkAtTLStsKo8UK3F9MTt7IjUbc/eWrdVq0XP3mrPvMgdv2yFlb3VH+g38fzr8LGPqzQ81oGNtQiPc2dmhtpisy/OhMcioxlbwtGJ8DgcDs3YomXewmP9f5K2mCM8BrDAUYEMzPaPpLVT2lpjAcNZN/DmMkj4WenmN0hlTO60wHLW53giHaGktIVn2+4Vh8et4aysCCV8lSYUbs7X6Pp8bGSLwuOWiMR8DOEoCo/tXGWvj119Nl08MM/HQdfK44k/7Op5rG0rAobHoqfE+ah9LyecrWXlRDnr04GIoy7vjwLAQuXn1+/cDVQLHt5VKzzOOJVV9u5uq1ad9ZVT2au/1SsNZ3f3ZJ59fbnjV+3KXtOTeZ6ef37WZwY27gqP1y6J1CQ8zp2Z6Ye2rRjNWGKLlw+PpQrh8Zx1YPOzv6fyGECDI0AGZvtHEovX5LRFv+FnOZvfyZvzVROzHQKdFljO+jojWYlbTuDwuKdJK3uDVxZFywiPm2KRwOGx34F0o5ld/duC9jzWyuNdp+iWFR5HYmVVHtdbeJxxrbLCY+1p7Xp+GscAABaikUSq4rYL1QzvyuuJ68nOcbuithB+26pVEr4HKW7Q3+oDQ8mKj1/Q8FiPn58cs9KezJVUllf1+Teala0JOx8eR8N+gmBPkmmvovDYvLky+/IKBuZ5osXKfsJjp8zwuD/jrxq6UoTHABYCAmSgQpM3R0HC2aDhZzmb31Kb80CVMbYlS7viZa2vyUe2WCo8rtqLrxqGx8NpS/rSev/lDcxTViS2aMNjNeaGyw6Pm+3qtdQAANQvdywhfUPJmsyUqGlP3HHL/M6vZH3VDo+DVvZO7slcbz2jy2sL4ZXVk7mWPaN7hxwJ2/7DY608HtfwWCoLj/2sr3Bgng7m85Edm/B4OBsKHB73pptkvAZnrBEeA1goOMcXqECpzVFmrLzNb6qMzWWqz+/m3JW2rgpPC8xUZ/Nbq/BY1zeUkEDhsabvKU/DY9d3ePzT0b8xp9AlsiEZ82KSdDvkwcTuJ4WTfZH5HBrqmDac3ebq1OY75fsjB0jP0Jppb7eQw2NVbnjcGsrU5AUB/LnuV4NVP1TvfV1n1b8H6uPx5rHGTJzEkDhDCQnZy2syU6KWPXE1vGuP13HP3lzxwCx701r3ZK5teLxr4HTcCtyTuZZvXmh4vKbd9R0em7YVIhIPWzULj7VthUyckOcrPNb1BQ2PdS/cVOWCg/L2z/M8yAMApkEFMlCm2p626FQwMMWVpmiopqcF1mN4bF58Baw8TnpazWAFqjzOhcf6o7SEXF/V3lM2l/bsP9NCD49Vi50tKzyO2bt6SAMAFld43L9VxA5Jz9LmmsyUqGU42xETaalieDcf4WyQnsxa2VuL9VXSkzk3cLqcnsy1rHxf2x2RqJ/K3oKexxoeR3y1uih/fYXhsZ+2Fa5j5cPj1rDjKzx2PU96s61mL6x7xu7IbCU85Stn/2z299nZZ8cAwHwgQAYaIDzuG0jW9cCUWoXH2haikvA9aNsKfUGpLwTKCY/bw46E7CpuLhd4eKx8tMEmPAYA7A6PQ2EJd/dINLKwBqqZnriRhVPcELQnc76ytwbhcSU9mXMDp+stPJ78/ItG7MAD8/yEx16Fz78g4bHOmx537Xx4HPMRiOvx25aOS8KNmvC4Jzrua/9cDm1LV/7+nkIIAPWJABkIqOY97waTZvNWzwNTahUe9yajFYXvQXse6wuBWLnhcWghhMcyb+GxH1QeAwAKw+PIirVihcMLbqBatXv21rq4IWhP5lxlb7WPX6U9mcsZOF2L8Djo829yeOynbYXuT7cMpCp6feQ7PHZEkq5tvmeQ8Fhff4w4UWm101UPjydmmpS7vyeiAVCf+L8TEMC8VJ64nnS2Rup6YEqtwmMtfqhm+D55YF7ccuoqPDZqFB7rcfOyKcJjAEDjhMeRaN21DajngWrlVvZWWtxQbmVvtY9fpW3fyhk4XW/Pv/LCY0+2JiwZSUmg9Q0nksVt83yGx9q2QneysZAbKDzW1x9tobT0hBPVD49dt/zikLCP0w0AYB4QIAM+zVflSWdrVJqbogui51054fF4dnd4/ILmVNXC98nhsbatkCqFx/rNygmPPScbPDz2QoHDY63s1WNRrwM/qDwGANR7eJyv7LUtWdoVr7vwuKLK3gqLG+q1srce277p2WBbE175xy/A86/c8Hgk6clI2pK2mPhen9Iq9kCV71kn3/NY511EQl7g1x8rosnahMfNbWWfWejrBQgAzAMCZMAH3UjNV+XJQgiPtdq2nPA46YZkOBveHR77CGdTGS9w+K6b31EvWhQe+6s8tsoLj51s8NPazG3cQOFx2rVk1I0GDo9H3Jh4YokVjtTdwA/CYwCAMz5ak/C43IFqhZW99TxQrdzK3loXN/ip7K1lT+aa7Z9FZEtCJJG2yz5+fp9/lYTH6awnbVFPVnXF/O3vkxN7RjtgeDyUSOd7Hvto41y6eKVa6bHrFoXHdlSD4Oq0pQOA+UKADPj45b5j52hN3vkvDD/rseddOevTzWWv0xo4PB7JRmTIiZv/SfkNj9MZTzaPWIHD91EnLGkvYHislb1uJHjbCg2CpcyBGpbte3OpVRDjXlhsCRYeDzsRccWSJisrlo8fqpYDPwiPAQDKHeqrSXhc7kC1wsreeguPC9uqlVvZW2/FDeX0ZC5V2Vtv4bE+/xIZW1qjbtnHz8/zTwPgSsLjaNiSla2e2ab6ef6NjOleWGTV0uZg4bHrBe55HPT1R7nc8QThMYAFjwAZmIWXGJTRcafscNbvO/+Tw89qntZWTs+7ctanG9Fhb9e044A9j7c5rSb8XBLN+qs8TmVl04glWc8K9OJrZDRlwuCoFSA8du2Jyl4NZ4P2PNbw2A77C4+leKCG3sZfeJwVL5sRSzxps1PBwmPPkhY7LVHbq6uBH4THAIA8O1T1thWVDFSrVc/eStuq1WPP3loNnK60srdWA/1aI66sbLWqtj4NgDfuzFQUHrfFLV/709yZmbnrRqN+3hyQ3eFxS6Quw2NDzyyk8hjAAkeADMzCTSelpSlUdjjrt/KknPCz3M1v0J53WtlRzvqG0iFJuuGJaccBB+bpeIyu0LiEfASZuc25rq+n2Q0UHqczrsQsR1qs3RtaXwPpcpW9QQfm2WHxvYvNpHef1hakT3ImLfrDaCAeNDxuDWXM8aingR9lhce1a8kMAKix8JKeqvc8rmSgWq169tbjQL+Ke/bWaOB0pZW9tRrot6pVqh4ea0hbWXhsBTozs6t99n+7uYF5OpgvFx43xSL1GR5r1+JYM20rACx4BMjAbP9IonFZtqSl6gNJgoaflWx+g1TG5E4LLGd9SdeWuJ2VnlAiUHisPY9Xh4ckHCA81s15T7MnHU1WoPA4GrGlJeS/8jg3kM5U9vr4mfSFVOHAPD8HIv9Te4UDNYKFx1Y4Zl4kViOcreXAj3LXN+b5aUgNAGhEVjhc1wPVqlbZ63mVVfbWYKDffFT2ltOTudqVvZX2ZM49/6xqhceO5MPjlZ2h2oTHu87MDPmovjAD8zQ8Fqvuw2NlRWZfHz2PATQ6AmRgFlZrp68grtLTFoOEn5VufoNUxuROCyxnfXHblXYrKZaPA1gYHmvPYz+TlSe/uPGxN58SHre1aE9hK1B47LunsIbH2VTwgXkaAivL/0CNovA44u8Fx1yFx1WbR6ItUMpYX8INS8bl1xsALFbpzAIcqOaJDCalosreWgz0C1rcUNiTuZYDp6tZ2VtOT+ZaPv90D7xp2M6Hxx1NfmZeeJLMVBYe+z0zc/fAPEtaIq6v8NgrMzzWOSi1QHgMYCHgFTYwC789vSo9bdFv+FnO5nfy5jxQZcyu0wLLWV9H1PG1uZwcHvvteVzOi5vJ4bGf41dWeOzoptQuIzzWwHlXCwm9XVnhsdXw4bEadcNlhcdpNyQRP20uAAALjpfNSm/f2IIaqKb7Fw2PU1mrosreWgz0C1LcMLkncy17RlerslcHTpfTk3nK+qr4/NvYn5GsGzA8Totk3crC46AD81oinr/KaB2InbUCh8fD2YjszPg7y68ShMcAFgoCZKBCNTttsczNb6Wb83JOC8y/uPGx+6338DhTZng8nNUuznqabSRQeKzhrKk89tkUYqGGx6rc8DhqO9Lso48zAGBh0d+H2f5ecdz6b7sQdP+XdCyJhb26XV85lb216Mlcy4F++YHTZfRkrmXPaBMet7q+w2NtW6HhcdiuXXisbSviPsZr5MJjLfYIGh5vSzf52tdXgvAYwEJCgAxUoGanLcr8DUwpOzy26ys81vUFDY+z7sSGtJzwWCPPppArlo9qn6mbS3/DRRZyeKya7WxZ4bEOD6xSS2YAQJ3S34eZbRtFXEeWdsQX1EA10xYs5ElnvHptF+alsjdIT+aMV9fhceFA7IqKL2rw+PZ0hKTdR+FtYc9jDY+1Grha4bGurzA89tu2Ihce637Rb3g84kRNeBy2XFkWSUq1lL1/1mISAKhDBMhAA4THA0PJuh6YUrPw2JGKwvegbSuSXsjkkOWEx61hR6J2mZvL2W+24MNjFfExRLFUeFyr9QEA6iw8drIS6uiWttbYghqopvsrEx5XMbyrZXFD0J7M+creGuxPK+nJnBs4XW/h8ZTnX0s48MC8eFS3nNV9/gUKj72JvXouPG4Jez4rj0OyzWk14fELYmNVa3lW7v7Z7O+zmaqsCQAqRYAMlKGWlR0DQ+MynnbqemBKrcLj58diFYXvQXseW+JJPJQtKzz28zNVtLlc4OGxH4THAICi8Lh7pdjNrQtuoJqp7LWq17O35sUNAXsy5yp7axEeV9L2rZyB07UIj4M+/yaHx77bVoxmK3p9FKTyOOVOnCmYC48tn68/tqWbRXfrGh5Hqxgee2MjFe3vAaAeESADAdW68mQ85UpTNFTXA1NqFR5nAw4kKSd8LxyY12Q7EvKxh1uQ4bG+PMxmCI8BAA0THodaO+proFoqOycD1ares7fGxQ3lVvZW+/hV2vatnIHT1T5+QZ9/5YbHwymR3iEn2PMvXXz8grStyLq2hG0vUHisrz9CWnkcGa5+eJxJlb+/D/trZQcAtUaADAQwH5UnTTFbujridd/zrlrhseNa+fC4J56uavheGB5r2wq7SuGxUVZPNLdmlcfjTojwGACw4MPjqu2vcpW9dTxQrdzK3kqLG+q2srdO274NjXvlH78Az79yw+NkxpOtCQ10/YfvakvfWODjl+t5HLZds6e1Ar7+WK2Vx1YNwuNIrOziECqQAdQrAmTAp/mqPOnqaPLVE3chhsd6atrOdDgfHrdHvaqF7ykvVBQe+2pb4ZYXHpuNYjnhsZsNFB7r827EjQUOj0e9qGQkVJcDP2hbAQDwstm6Do8LK3vreaBauZW9tS5u8FPZW8uezLUNj0V6x+yyj5/f518l4XEi5ZrweG13xNf6HK2+MFvHYMdvZDSV73kcsyVweKyvP6I+5muUZVJ4bDW3Ve3MQgCYLwTIgA8jiVRN3vkveVql1E/Pu3I3vyPZSODwOONYMuA0mXDWb3isp7VtTXiBw/eUY8moGw0WHucqe4O2rXCyZqGBT2vT8Fj8by71WOhwOVeChcdmIJ0Xkog4IuFIXQ38IDwGAKjszt6ahMflDlQrrOytt/C4sK1auZW99VbcUG5P5smVvfUWHuvzr3fMkrDllX38ahEe67Z0TbtrhlX7ef4NDKfNn7XaO0h4nM5MVB2bthVWdYpXyuWlxgiPASx4dR8g33XXXXL44YdLS0uLdHV1yQknnCDr16/3ffuxsTH58Ic/LHvuuafEYjHz+fLLLzeXl/Lcc8/J2972Nlm2bJk0NTXJS17yErnpppvm8CdCo3FTSekbSpYdzvp+539S+FnN09rK6XlX7vqSXm7acbCex5uzHSb8bA9nfVceb0mIJNJ2oPB9PJWRhBsRW4KFx6ayV8PZwD2P3YmSiaCntWl4HAr7DI898bIp8cSSFjsdLDx2QxK1HGkKOXU18IPwGMBCxD63TK5Tk/C43IFqterZW2lbtXJ79tZTeFxuT+ZKK3trNdBPw+PVbV71wnfPky2DTkXhcUeTLVE/+/tdZ2aaPbGItLfGA4XHGlAH7Xlci/BYaSEFlccAFjp/DYrmyc033yznnHOOHHjggXLVVVdJMpmUa6+9Vo444gi577775KCDDpr19Jg3vvGN8pvf/EbOPPNMefWrXy2PPPKIXH311fLggw/KL37xC7ELfoNv3rzZhNVDQ0Ny8cUXy1577SV33323nHvuufL888/LFVdcUYOfGvXGTQxKyF4VOJy1IhObt4FENFD42VXG5jfeWl5lzNoAlR3lrG88KzLkxGW1eIEH5jmSkY5QUprCLb4354mMLa1Rx//6NDwezZjwuM1OSciKB6g8TkurnZFYKBJwYJ4tokGwn3DWLTitLRT2FdKa8DiTFPFapMnKSsxyAoezzW5ahny8v1irgR+ExwAWIva55bM7lla9bcVgIiS2ZdV3z97AbdUyRW3VUn3VWZ8WN/TXsK3aeKpJmmLBejJXWtlb/YF+E8+/NW2eRCNWVcPjkaRbUXisvaN9r89tkbZmfxGEGZiX8SRrTYTHbS0xcUrXgM1reKwsPWuPthUAFri6DZAHBgbkkksukdWrV5uwuL293Vx+2mmnyf777y8XXXSR3HPPPTPex6233mrC4/e///3ypS99KX+5ViF/4AMfkNtuu03OOuus/OUf+chHpLe3V+688055y1veYi7T8Firnj/1qU+ZEHrvvfeu2s+MOmXb0rO0WWIxKS+cTVQn/Kxk8xukMiZ3WmBbGesbzIQlJFlZHR6SWGiZ7/BYex6vCCVkwO4OtDlv1dC+1QoWHtsTLR5CPsoZCgfSaWVvNOQFDI9D4mkQ7Je2hdjVE22iCtlveOyZjWzU8wKHx612VrwyerZpz7d6Co8zLj3kANQv9rmVCTW11GCgWrvpiRuLOYHDu6ZZgtk5WV9ZbdVazJlxnR2RqvfsDVrcUEk4u7snc9r38at6Za/MzfMvmqlWeCz58LgtbtcmPN51ZuZwyt/zL5ERSWctibdMhMd6/Jw6DI+VFWum5zGABa9uW1ho5e/w8LCpQM6Fx2rt2rVyyimnyL333iubNm2a8T6+8Y1vmM+XXnpp0eUXXnihaU+R+7rSlhbf+973TNVxLjzO0SA7m83Kf/zHf8zRT4dGEmrvlmgkVPXTFoOEn5VufoNUxuROCyxnfSFLpDM0LhEfQWtheKw9j9vCmcAvbla1mrw/UHjc0RqVkI/bFIbHpqewz8peDYELB+b5MdHvWHejQXoeF4THkahYPvr4lRvO1mzgR5nr0wErY27dvj8KAOxzq0yrbBfiQDWtxqykLUQtBvoFLW4o7Mlcy4HTWtkbq1Zlb5k9mWv1/JtoS2flw+NVnSFf4XEmW1l4HKQnuGlbkbUkGvby4bFUITzOuHMUh9TT/hkAFluA/MADD5jP2q5istxlDz300Iyn5OjXV61aJXvssUfR13K9jQtv/9e//lXGx8flVa961ZT70sv0l6q2vcDi4yeIm4vTFv2Gn+VsfidvzoP0vBtPudIUDZW1vq5Y1ryICBoe++15PKUnXxnhcTgcCh4e++kp7IqMO1IUHvsNZ03lsQrHygyPw40fHusbe16orPA44UTMQEQAqFfsc6tHXwPs2Dm68AaqZURGkpX17K3FQL8gxQ2TezLXcuB0tdpClNuTefL6qvn82zKQkpG0FTg8Tma9isLjoAPzNDxuifgrXtHXEkHD47Rry47M7O3r5gLhMYCFoG5LtLQfsdIWFpPlLstdp5SdO3eaqmLtn1yK3sf9999vqpy1wnmm76fD95YuXTrj91Pbt2+XHTt2FF32v//7v+bzhg0bZrwt6lN60wZ56tmN8tRYn2ycJpDKOJ70JbJmw9PZbEtvJmEqD8Lb/my+vjU5sVn/7XPpovBuZyIjyaxIPCyypDUiG0aHim6XU3j70ZQjg2Ou2UgtbQ3L8+PDJW9TaPNYRIbGXHk+lDTr27EtJE6i9Pcqtb6EFxenKST3O/2zfq/J69u2bVC8bFr+KGmx+0ufz5kRS3ZmouJ6CekIp6VvVzjrZtKyzdsiVqRP/mAN568fGf1zyeP3zDMy5eeafOwf72uVce0KIZ60xCwZ3LUjdUYGzGC7ycFrwouZ7zVgeWYgXczKyJC4u4NeTYlLvA+n19YgMy1hyVhj0heaGIJnvrYrHLb6R6ceDNc1gbOe2qye7t0hA+nwzLfzvImKZU9/hFA+5dchfxkJyYAlU34uPUYpCUvWC0nYciQmWSn8P5f+bFq9NSyObLdTU9anP4plT+rJ7Llix+Ii0d2PVSVGshEZyMRMeBy3XOnz8eowq6G9Gzbhccx2ZEc0JU549tNZy/VYdqs0uvZHH63699j67Nw8J2by6KO7z1QKYnj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      "text/plain": [
       "<Figure size 1440x1536 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: decision_margin_distributions.png\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(len(subjects), len(PAIRS),\n",
    "                         figsize=(6 * len(PAIRS), 3.2 * len(subjects)),\n",
    "                         sharex=True, squeeze=False)\n",
    "fig.suptitle('LDA decision-function distributions on ONLINE sessions\\n'\n",
    "             '(class separation ↔ classification amplitude & SNR)',\n",
    "             fontsize=12, fontweight='bold', y=1.01)\n",
    "\n",
    "for i, subj in enumerate(subjects):\n",
    "    for j, pair in enumerate(PAIRS):\n",
    "        ax = axes[i][j]\n",
    "        for cond, color in cond_color.items():\n",
    "            row = next((r for r in results\n",
    "                        if r['subject']==subj and r['pair']==pair['name']\n",
    "                        and r['condition']==cond), None)\n",
    "            if row is None: continue\n",
    "            m_mi   = row['margin'][row['y_test'] == 1]\n",
    "            m_rest = row['margin'][row['y_test'] == 0]\n",
    "            ax.hist(m_mi,   bins=15, alpha=0.5,  color=color,\n",
    "                    label=f'{cond} MI',   density=True)\n",
    "            ax.hist(m_rest, bins=15, alpha=0.25, color=color, hatch='///',\n",
    "                    edgecolor=color, label=f'{cond} REST', density=True)\n",
    "        ax.axvline(0, color='k', lw=0.8)\n",
    "        ax.set_title(f'{subj}  |  {pair[\"name\"]}', fontsize=10, fontweight='bold')\n",
    "        if j == 0:                     ax.set_ylabel('Density')\n",
    "        if i == len(subjects) - 1:     ax.set_xlabel('LDA decision function')\n",
    "        ax.spines[['top','right']].set_visible(False)\n",
    "        if i == 0 and j == 0:\n",
    "            ax.legend(fontsize=6.5, loc='upper left', ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('decision_margin_distributions.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: decision_margin_distributions.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb6d19d",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 3 — Paired Δ (FES − NOFES) per metric\n",
    "\n",
    "Within each (subject × pair), FES and NOFES sessions use the same offline-trained model. Positive bars mean FES > NOFES; negative means NOFES > FES. This removes the offline-model-quality confound and isolates the effect of feedback type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75df404b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:27.545755Z",
     "iopub.status.busy": "2026-04-22T19:27:27.545655Z",
     "iopub.status.idle": "2026-04-22T19:27:27.826448Z",
     "shell.execute_reply": "2026-04-22T19:27:27.826020Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x540 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: fes_minus_nofes_delta.png\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(16, 4.5))\n",
    "fig.suptitle('Within-pair Δ = FES − NOFES  (positive → FES better)',\n",
    "             fontsize=12, fontweight='bold', y=1.03)\n",
    "\n",
    "for ax, (key, title, _) in zip(axes, METRICS):\n",
    "    labels, deltas = [], []\n",
    "    for subj in subjects:\n",
    "        for pair in PAIRS:\n",
    "            fes = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                        and r['condition']=='FES'),   None)\n",
    "            nof = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                        and r['condition']=='NOFES'), None)\n",
    "            if fes is None or nof is None: continue\n",
    "            deltas.append(fes[key] - nof[key])\n",
    "            labels.append(f'{subj}\\n{pair[\"name\"].split()[0]}')\n",
    "\n",
    "    colors = ['#E05C2A' if d > 0 else '#2A7BE0' for d in deltas]\n",
    "    ax.bar(np.arange(len(deltas)), deltas, color=colors, edgecolor='white', zorder=2)\n",
    "    ax.axhline(0, color='k', lw=0.8)\n",
    "    ax.set_xticks(np.arange(len(deltas)))\n",
    "    ax.set_xticklabels(labels, fontsize=8)\n",
    "    ax.set_title(f'Δ {title.split(\"(\")[0].strip()}', fontsize=10, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('fes_minus_nofes_delta.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: fes_minus_nofes_delta.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9533f8",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 5 — Per-class accuracy and EARLYSTOP latency\n",
    "\n",
    "FES stimulation fires only on MI trials, so if it helps the subject/system it should\n",
    "show up in **MI-accuracy** (count_240 / count_200) specifically, not in REST-accuracy.\n",
    "A lift in MI-acc without change in REST-acc would be a genuine sensitivity gain; an\n",
    "equal-and-opposite shift would be a bias/threshold effect.\n",
    "\n",
    "**EARLYSTOP latency** (BEGIN → EARLYSTOP in seconds) is a continuous readout of how\n",
    "confidently and how fast the live classifier committed — shorter latency means a\n",
    "sharper MI signal crossing the detection threshold earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "086ef172",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:27.828111Z",
     "iopub.status.busy": "2026-04-22T19:27:27.828016Z",
     "iopub.status.idle": "2026-04-22T19:27:28.197602Z",
     "shell.execute_reply": "2026-04-22T19:27:28.197189Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/stats/_axis_nan_policy.py:430: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1680x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: per_class_acc_latency.png\n",
      "\n",
      "Per-subject paired Δ = mean(FES) − mean(NOFES) & T-Test:\n",
      "\n",
      "  MI accuracy:\n",
      "    subj 002:  FES=0.93  NOFES=0.85  Δ=+0.08   |   p = 0.5000 (t = 1.00, n = 2)\n",
      "    subj 003:  FES=0.73  NOFES=0.72  Δ=+0.02   |   p = 0.8656 (t = 0.21, n = 2)\n",
      "    subj 005:  FES=0.78  NOFES=0.82  Δ=-0.03   |   p = 0.9208 (t = -0.12, n = 2)\n",
      "    subj 009:  FES=0.82  NOFES=0.72  Δ=+0.10   |   p = 0.3743 (t = 1.50, n = 2)\n",
      "\n",
      "  REST accuracy:\n",
      "    subj 002:  FES=0.87  NOFES=0.83  Δ=+0.03   |   p = 0.7048 (t = 0.50, n = 2)\n",
      "    subj 003:  FES=0.78  NOFES=0.76  Δ=+0.02   |   p = 0.5000 (t = 1.00, n = 2)\n",
      "    subj 005:  FES=0.93  NOFES=0.90  Δ=+0.03   |   p = 0.0000 (t = inf, n = 2)\n",
      "    subj 009:  FES=0.72  NOFES=0.85  Δ=-0.13   |   p = 0.1560 (t = -4.00, n = 2)\n",
      "\n",
      "  MI EARLYSTOP latency:\n",
      "    subj 002:  FES=2.50  NOFES=2.30  Δ=+0.20   |   p = 0.6020 (t = 0.72, n = 2)\n",
      "    subj 003:  FES=2.45  NOFES=2.36  Δ=+0.09   |   p = 0.8457 (t = 0.25, n = 2)\n",
      "    subj 005:  FES=2.54  NOFES=1.99  Δ=+0.56   |   p = 0.3678 (t = 1.53, n = 2)\n",
      "    subj 009:  FES=2.60  NOFES=2.29  Δ=+0.31   |   p = 0.2213 (t = 2.76, n = 2)\n",
      "\n",
      "  REST EARLYSTOP latency:\n",
      "    subj 002:  FES=2.11  NOFES=2.22  Δ=-0.10   |   p = 0.7758 (t = -0.37, n = 2)\n",
      "    subj 003:  FES=2.34  NOFES=2.64  Δ=-0.30   |   p = 0.5264 (t = -0.92, n = 2)\n",
      "    subj 005:  FES=2.20  NOFES=2.48  Δ=-0.28   |   p = 0.3581 (t = -1.59, n = 2)\n",
      "    subj 009:  FES=1.89  NOFES=2.23  Δ=-0.34   |   p = 0.0667 (t = -9.50, n = 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adipu/ECE374N/Final Project/venv/lib/python3.13/site-packages/scipy/stats/_axis_nan_policy.py:430: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  return hypotest_fun_in(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_rel\n",
    "\n",
    "PER_CLASS_METRICS = [\n",
    "    ('mi_acc',       'MI accuracy',               '0–1'),\n",
    "    ('rest_acc',     'REST accuracy',             '0–1'),\n",
    "    ('mi_latency',   'MI EARLYSTOP latency',      'seconds'),\n",
    "    ('rest_latency', 'REST EARLYSTOP latency',    'seconds'),\n",
    "]\n",
    "\n",
    "# Restructure data to average across multiple sessions (Pairs) for each subject\n",
    "def subj_cond_metrics(key):\n",
    "    \"\"\"Return {subj: {'FES': [vals...], 'NOFES': [vals...]}} using non-null entries in `results`.\"\"\"\n",
    "    out = {s: {'FES': [], 'NOFES': []} for s in subjects}\n",
    "    for r in results:\n",
    "        v = r.get(key)\n",
    "        if v is None or (isinstance(v, float) and np.isnan(v)):\n",
    "            continue\n",
    "        out[r['subject']][r['condition']].append(v)\n",
    "    return out\n",
    "\n",
    "mi_acc_data = subj_cond_metrics('mi_acc')\n",
    "rest_acc_data = subj_cond_metrics('rest_acc')\n",
    "mi_lat_data = subj_cond_metrics('mi_latency')\n",
    "rest_lat_data = subj_cond_metrics('rest_latency')\n",
    "\n",
    "metrics_data = [mi_acc_data, rest_acc_data, mi_lat_data, rest_lat_data]\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "fig.suptitle('Per-subject average: MI vs REST, accuracy & decision latency\\n(mean across that subject\\'s sessions ± SEM)',\n",
    "             fontsize=13, fontweight='bold', y=1.03)\n",
    "\n",
    "width = 0.38\n",
    "x = np.arange(len(subjects))\n",
    "\n",
    "for ax, data_dict, (key, title, unit) in zip(axes.ravel(), metrics_data, PER_CLASS_METRICS):\n",
    "    for i, cond in enumerate(('FES', 'NOFES')):\n",
    "        means = [np.mean(data_dict[s][cond]) if data_dict[s][cond] else np.nan for s in subjects]\n",
    "        sems  = [np.std(data_dict[s][cond], ddof=1) / np.sqrt(len(data_dict[s][cond]))\n",
    "                 if len(data_dict[s][cond]) > 1 else 0.0 for s in subjects]\n",
    "        offset = (i - 0.5) * width\n",
    "        ax.bar(x + offset, means, width, yerr=sems,\n",
    "               color=cond_color[cond], label=cond, edgecolor='white', capsize=4)\n",
    "        \n",
    "        # Overlay individual session values\n",
    "        for xi, s in zip(x, subjects):\n",
    "            if data_dict[s][cond]:\n",
    "                ax.scatter(np.full(len(data_dict[s][cond]), xi + offset), data_dict[s][cond],\n",
    "                           color='k', alpha=0.5, s=14, zorder=3)\n",
    "    \n",
    "    # Calculate p-values for pairs and add them to custom labels\n",
    "    new_labels = []\n",
    "    for s in subjects:\n",
    "        label = s\n",
    "        if data_dict[s]['FES'] and data_dict[s]['NOFES'] and len(data_dict[s]['FES']) == len(data_dict[s]['NOFES']) and len(data_dict[s]['FES']) > 1:\n",
    "            try:\n",
    "                _, p_val = ttest_rel(data_dict[s]['FES'], data_dict[s]['NOFES'])\n",
    "                p_str = f'p={p_val:.3f}' if p_val >= 0.001 else 'p<0.001'\n",
    "                label = f'{s}\\n({p_str})'\n",
    "            except Exception:\n",
    "                pass\n",
    "        new_labels.append(label)\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(new_labels, fontsize=8)\n",
    "    ax.set_title(f'{title}  ({unit})', fontsize=11, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "    if 'acc' in key:\n",
    "        ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "        ax.set_ylim(0, 1.05)\n",
    "\n",
    "fig.legend(handles=[Patch(color=cond_color['FES'],   label='ONLINE_FES'),\n",
    "                    Patch(color=cond_color['NOFES'], label='ONLINE_NOFES')],\n",
    "           loc='upper right', ncol=2, bbox_to_anchor=(0.98, 1.0))\n",
    "plt.tight_layout()\n",
    "plt.savefig('per_class_acc_latency.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: per_class_acc_latency.png')\n",
    "\n",
    "# ── Paired Δ (FES − NOFES) summary per subject ─────────────────────\n",
    "print('\\nPer-subject paired Δ = mean(FES) − mean(NOFES) & T-Test:')\n",
    "for (key, title, _), data_dict in zip(PER_CLASS_METRICS, metrics_data):\n",
    "    print(f'\\n  {title}:')\n",
    "    for s in subjects:\n",
    "        if data_dict[s]['FES'] and data_dict[s]['NOFES']:\n",
    "            delta = np.mean(data_dict[s]['FES']) - np.mean(data_dict[s]['NOFES'])\n",
    "            if len(data_dict[s]['FES']) == len(data_dict[s]['NOFES']) and len(data_dict[s]['FES']) > 1:\n",
    "                t_stat, p_val = ttest_rel(data_dict[s]['FES'], data_dict[s]['NOFES'])\n",
    "                stats_str = f'p = {p_val:.4f} (t = {t_stat:.2f}, n = {len(data_dict[s][\"FES\"])})'\n",
    "            else:\n",
    "                stats_str = f'n = {len(data_dict[s][\"FES\"])} (too few paired runs)'\n",
    "                \n",
    "            print(f'    subj {s}:  FES={np.mean(data_dict[s][\"FES\"]):.2f}  '\n",
    "                  f'NOFES={np.mean(data_dict[s][\"NOFES\"]):.2f}  Δ={delta:+.2f}   |   {stats_str}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c74da761",
   "metadata": {},
   "source": [
    "### Per-subject average latency\n",
    "\n",
    "Collapses the per-(subject × pair) bars into one FES value and one NOFES value per subject\n",
    "by averaging across all that subject's ONLINE sessions of each type. Error bars are the\n",
    "session-to-session SEM within that (subject, condition). Easier to read when you just want\n",
    "the per-subject FES vs NOFES comparison without the pair split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ea9951f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.199503Z",
     "iopub.status.busy": "2026-04-22T19:27:28.199411Z",
     "iopub.status.idle": "2026-04-22T19:27:28.407015Z",
     "shell.execute_reply": "2026-04-22T19:27:28.406594Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: per_subject_latency.png\n",
      "\n",
      "Per-subject paired Δ = mean(FES lat) − mean(NOFES lat) & T-Test:\n",
      "  MI latency:\n",
      "    subj 002:  FES=2.50s  NOFES=2.30s  Δ=+0.20s   |   p = 0.6020 (t = 0.72, n = 2)\n",
      "    subj 003:  FES=2.45s  NOFES=2.36s  Δ=+0.09s   |   p = 0.8457 (t = 0.25, n = 2)\n",
      "    subj 005:  FES=2.54s  NOFES=1.99s  Δ=+0.56s   |   p = 0.3678 (t = 1.53, n = 2)\n",
      "    subj 009:  FES=2.60s  NOFES=2.29s  Δ=+0.31s   |   p = 0.2213 (t = 2.76, n = 2)\n",
      "  REST latency:\n",
      "    subj 002:  FES=2.11s  NOFES=2.22s  Δ=-0.10s   |   p = 0.7758 (t = -0.37, n = 2)\n",
      "    subj 003:  FES=2.34s  NOFES=2.64s  Δ=-0.30s   |   p = 0.5264 (t = -0.92, n = 2)\n",
      "    subj 005:  FES=2.20s  NOFES=2.48s  Δ=-0.28s   |   p = 0.3581 (t = -1.59, n = 2)\n",
      "    subj 009:  FES=1.89s  NOFES=2.23s  Δ=-0.34s   |   p = 0.0667 (t = -9.50, n = 2)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_rel\n",
    "\n",
    "# Average latency per subject across sessions (collapses the pair dimension)\n",
    "def subj_cond_latencies(key):\n",
    "    \"\"\"Return {subj: {'FES': [vals...], 'NOFES': [vals...]}} using all non-null entries in `results`.\"\"\"\n",
    "    out = {s: {'FES': [], 'NOFES': []} for s in subjects}\n",
    "    for r in results:\n",
    "        v = r.get(key)\n",
    "        if v is None or (isinstance(v, float) and np.isnan(v)):\n",
    "            continue\n",
    "        out[r['subject']][r['condition']].append(v)\n",
    "    return out\n",
    "\n",
    "mi_lat   = subj_cond_latencies('mi_latency')\n",
    "rest_lat = subj_cond_latencies('rest_latency')\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5), sharey=True)\n",
    "fig.suptitle('Per-subject average EARLYSTOP latency  (mean across that subject\\'s sessions ± SEM)',\n",
    "             fontsize=12, fontweight='bold', y=1.02)\n",
    "\n",
    "width = 0.38\n",
    "x     = np.arange(len(subjects))\n",
    "\n",
    "for ax, data, title in [(axes[0], mi_lat,   'MI trials'),\n",
    "                        (axes[1], rest_lat, 'REST trials')]:\n",
    "    for i, cond in enumerate(('FES', 'NOFES')):\n",
    "        means = [np.mean(data[s][cond]) if data[s][cond] else np.nan for s in subjects]\n",
    "        sems  = [np.std(data[s][cond], ddof=1) / np.sqrt(len(data[s][cond]))\n",
    "                 if len(data[s][cond]) > 1 else 0.0 for s in subjects]\n",
    "        offset = (i - 0.5) * width\n",
    "        ax.bar(x + offset, means, width, yerr=sems,\n",
    "               color=cond_color[cond], label=cond, edgecolor='white', capsize=4)\n",
    "        # Overlay individual session values\n",
    "        for xi, s in zip(x, subjects):\n",
    "            if data[s][cond]:\n",
    "                ax.scatter(np.full(len(data[s][cond]), xi + offset), data[s][cond],\n",
    "                           color='k', alpha=0.5, s=14, zorder=3)\n",
    "                           \n",
    "    # Calculate and format p-values for x-axis labels\n",
    "    new_labels = []\n",
    "    for s in subjects:\n",
    "        label = s\n",
    "        if data[s]['FES'] and data[s]['NOFES'] and len(data[s]['FES']) == len(data[s]['NOFES']) and len(data[s]['FES']) > 1:\n",
    "            try:\n",
    "                _, p_val = ttest_rel(data[s]['FES'], data[s]['NOFES'])\n",
    "                p_str = f'p={p_val:.3f}' if p_val >= 0.001 else 'p<0.001'\n",
    "                label = f'{s}\\n({p_str})'\n",
    "            except Exception:\n",
    "                pass\n",
    "        new_labels.append(label)\n",
    "                \n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(new_labels, fontsize=8) # slightly smaller to fit the p-vals\n",
    "    ax.set_xlabel('Subject')\n",
    "    ax.set_title(title, fontweight='bold')\n",
    "    ax.grid(axis='y', alpha=0.3)\n",
    "    ax.spines[['top','right']].set_visible(False)\n",
    "\n",
    "axes[0].set_ylabel('BEGIN → EARLYSTOP latency (s)')\n",
    "axes[0].legend(loc='lower right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('per_subject_latency.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: per_subject_latency.png')\n",
    "\n",
    "# Per-subject paired Δ summary\n",
    "print('\\nPer-subject paired Δ = mean(FES lat) − mean(NOFES lat) & T-Test:')\n",
    "for key, title in [('mi_latency', 'MI'), ('rest_latency', 'REST')]:\n",
    "    data = subj_cond_latencies(key)\n",
    "    print(f'  {title} latency:')\n",
    "    for s in subjects:\n",
    "        if data[s]['FES'] and data[s]['NOFES']:\n",
    "            delta = np.mean(data[s]['FES']) - np.mean(data[s]['NOFES'])\n",
    "            \n",
    "            # Since data arrays are appended in order (Pair 1, Pair 2), we can run a paired t-test\n",
    "            # if we have balanced matched pairs (e.g. n=2 for FES and n=2 for NOFES).\n",
    "            if len(data[s]['FES']) == len(data[s]['NOFES']) and len(data[s]['FES']) > 1:\n",
    "                t_stat, p_val = ttest_rel(data[s]['FES'], data[s]['NOFES'])\n",
    "                stats_str = f'p = {p_val:.4f} (t = {t_stat:.2f}, n = {len(data[s][\"FES\"])})'\n",
    "            else:\n",
    "                stats_str = f'n = {len(data[s][\"FES\"])} (too few or unbalanced runs for paired t-test)'\n",
    "                \n",
    "            print(f'    subj {s}:  FES={np.mean(data[s][\"FES\"]):.2f}s  '\n",
    "                  f'NOFES={np.mean(data[s][\"NOFES\"]):.2f}s  Δ={delta:+.2f}s   |   {stats_str}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2ba1928b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.408334Z",
     "iopub.status.busy": "2026-04-22T19:27:28.408256Z",
     "iopub.status.idle": "2026-04-22T19:27:28.518108Z",
     "shell.execute_reply": "2026-04-22T19:27:28.517764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: grand_avg_latency.png\n",
      "\n",
      "Grand-average latency (across-subject mean ± SEM) & Paired T-Test:\n",
      "  MI     FES = 2.523 ± 0.032 s   NOFES = 2.235 ± 0.084 s   Δ = +0.288 s   |   p = 0.0632 (t = 2.89, n = 4)\n",
      "  REST   FES = 2.135 ± 0.094 s   NOFES = 2.391 ± 0.102 s   Δ = -0.256 s   |   p = 0.0163 (t = -4.89, n = 4)\n"
     ]
    }
   ],
   "source": [
    "# Grand average across subjects: one pair of bars per class (MI, REST).\n",
    "# Each subject contributes one mean (averaged across their sessions) per condition,\n",
    "# so error bars = between-subject SEM.\n",
    "from scipy.stats import ttest_rel\n",
    "\n",
    "def subject_means(data):\n",
    "    return {cond: np.array([np.mean(data[s][cond]) for s in subjects if data[s][cond]])\n",
    "            for cond in ('FES', 'NOFES')}\n",
    "\n",
    "mi_sub   = subject_means(mi_lat)\n",
    "rest_sub = subject_means(rest_lat)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
    "classes = ['MI', 'REST']\n",
    "x       = np.arange(len(classes))\n",
    "width   = 0.38\n",
    "\n",
    "p_values = {}\n",
    "for cls, d in [('MI', mi_sub), ('REST', rest_sub)]:\n",
    "    subjects_with_both = [s for s in subjects if mi_lat[s]['FES'] and mi_lat[s]['NOFES']] if cls == 'MI' else [s for s in subjects if rest_lat[s]['FES'] and rest_lat[s]['NOFES']]\n",
    "    fes_paired = np.array([np.mean(mi_lat[s]['FES']) if cls == 'MI' else np.mean(rest_lat[s]['FES']) for s in subjects_with_both])\n",
    "    nofes_paired = np.array([np.mean(mi_lat[s]['NOFES']) if cls == 'MI' else np.mean(rest_lat[s]['NOFES']) for s in subjects_with_both])\n",
    "    \n",
    "    if len(fes_paired) > 1:\n",
    "        _, p_val = ttest_rel(fes_paired, nofes_paired)\n",
    "        p_values[cls] = p_val\n",
    "    else:\n",
    "        p_values[cls] = np.nan\n",
    "\n",
    "for i, cond in enumerate(('FES', 'NOFES')):\n",
    "    means = [mi_sub[cond].mean(),        rest_sub[cond].mean()]\n",
    "    sems  = [mi_sub[cond].std(ddof=1) / np.sqrt(len(mi_sub[cond])),\n",
    "             rest_sub[cond].std(ddof=1) / np.sqrt(len(rest_sub[cond]))]\n",
    "    offset = (i - 0.5) * width\n",
    "    ax.bar(x + offset, means, width, yerr=sems,\n",
    "           color=cond_color[cond], label=cond, edgecolor='white', capsize=5)\n",
    "    for j, arr in enumerate([mi_sub[cond], rest_sub[cond]]):\n",
    "        ax.scatter(np.full(len(arr), x[j] + offset), arr,\n",
    "                   color='k', alpha=0.6, s=20, zorder=3)\n",
    "\n",
    "# Modify X-labels to contain the P-Values below each\n",
    "new_labels = []\n",
    "for cls in classes:\n",
    "    p_val = p_values[cls]\n",
    "    if not np.isnan(p_val):\n",
    "        p_str = f'p={p_val:.3f}' if p_val >= 0.001 else 'p<0.001'\n",
    "        new_labels.append(f'{cls}\\n({p_str})')\n",
    "    else:\n",
    "        new_labels.append(cls)\n",
    "\n",
    "ax.set_xticks(x); ax.set_xticklabels(new_labels)\n",
    "ax.set_ylabel('BEGIN → EARLYSTOP latency (s)')\n",
    "ax.set_title(f'Grand-average EARLYSTOP latency across {len(subjects)} subjects\\n'\n",
    "             '(points = per-subject means, bars = across-subject mean ± SEM)',\n",
    "             fontweight='bold')\n",
    "ax.legend(); ax.grid(axis='y', alpha=0.3)\n",
    "ax.spines[['top','right']].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('grand_avg_latency.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: grand_avg_latency.png')\n",
    "\n",
    "print('\\nGrand-average latency (across-subject mean ± SEM) & Paired T-Test:')\n",
    "sem = lambda a: a.std(ddof=1) / np.sqrt(len(a)) if len(a) > 1 else 0.0\n",
    "for cls, d in [('MI', mi_sub), ('REST', rest_sub)]:\n",
    "    # Ensure arrays are aligned for t-test by taking paired subjects only\n",
    "    subjects_with_both = [s for s in subjects if mi_lat[s]['FES'] and mi_lat[s]['NOFES']] if cls == 'MI' else [s for s in subjects if rest_lat[s]['FES'] and rest_lat[s]['NOFES']]\n",
    "    \n",
    "    fes_paired = np.array([np.mean(mi_lat[s]['FES']) if cls == 'MI' else np.mean(rest_lat[s]['FES']) for s in subjects_with_both])\n",
    "    nofes_paired = np.array([np.mean(mi_lat[s]['NOFES']) if cls == 'MI' else np.mean(rest_lat[s]['NOFES']) for s in subjects_with_both])\n",
    "    \n",
    "    delta = d['FES'].mean() - d['NOFES'].mean()\n",
    "    \n",
    "    if len(fes_paired) > 1:\n",
    "        t_stat, p_val = ttest_rel(fes_paired, nofes_paired)\n",
    "        stats_str = f'p = {p_val:.4f} (t = {t_stat:.2f}, n = {len(fes_paired)})'\n",
    "    else:\n",
    "        stats_str = 'Not enough paired data for t-test'\n",
    "        \n",
    "    print(f'  {cls:<5}  FES = {d[\"FES\"].mean():.3f} ± {sem(d[\"FES\"]):.3f} s   '\n",
    "          f'NOFES = {d[\"NOFES\"].mean():.3f} ± {sem(d[\"NOFES\"]):.3f} s   '\n",
    "          f'Δ = {delta:+.3f} s   |   {stats_str}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21da9cda",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 6 — Decision-margin distribution shift (Wasserstein-1)\n",
    "\n",
    "Mean |margin| collapses the whole distribution to a scalar. If FES spreads the margin\n",
    "distribution further from zero (or sharpens it into a narrow high-confidence mode) without\n",
    "moving the mean, that shows up here but not in mean amplitude. For each (subject × pair),\n",
    "we compute W₁ between the FES and NOFES margin distributions, separately for MI and REST\n",
    "trials. Larger W₁ = greater distributional divergence between conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "41bf28d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.519611Z",
     "iopub.status.busy": "2026-04-22T19:27:28.519516Z",
     "iopub.status.idle": "2026-04-22T19:27:28.702716Z",
     "shell.execute_reply": "2026-04-22T19:27:28.702337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: margin_wasserstein.png\n",
      "\n",
      "Mean W₁ across 7 (subject × pair) comparisons:\n",
      "  MI trials:   1.207 ± 1.150\n",
      "  REST trials: 0.785 ± 0.623\n",
      "  W₁(MI) > W₁(REST) in 6/7 comparisons — FES perturbs MI margins more than REST margins\n"
     ]
    }
   ],
   "source": [
    "w_results = []   # one row per (subject, pair) — Wasserstein between FES and NOFES margin dists\n",
    "for subj in subjects:\n",
    "    for pair in PAIRS:\n",
    "        fes = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='FES'),   None)\n",
    "        nof = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='NOFES'), None)\n",
    "        if fes is None or nof is None: continue\n",
    "        if fes['margin'].size == 0 or nof['margin'].size == 0: continue\n",
    "\n",
    "        def w1(fe_mask, no_mask):\n",
    "            f = fes['margin'][fe_mask];  n = nof['margin'][no_mask]\n",
    "            return wasserstein_distance(f, n) if (f.size and n.size) else np.nan\n",
    "\n",
    "        w_results.append(dict(\n",
    "            subject=subj, pair=pair['name'].split()[0],\n",
    "            w_mi   = w1(fes['y_test'] == 1, nof['y_test'] == 1),\n",
    "            w_rest = w1(fes['y_test'] == 0, nof['y_test'] == 0),\n",
    "            w_all  = wasserstein_distance(fes['margin'], nof['margin']),\n",
    "            fes_mi_mean   = fes['margin'][fes['y_test']==1].mean() if (fes['y_test']==1).any() else np.nan,\n",
    "            nofes_mi_mean = nof['margin'][nof['y_test']==1].mean() if (nof['y_test']==1).any() else np.nan,\n",
    "        ))\n",
    "\n",
    "if w_results:\n",
    "    labels  = [f'{r[\"subject\"]}\\n{r[\"pair\"]}' for r in w_results]\n",
    "    w_mi    = np.array([r['w_mi']   for r in w_results])\n",
    "    w_rest  = np.array([r['w_rest'] for r in w_results])\n",
    "    x = np.arange(len(labels))\n",
    "\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n",
    "    fig.suptitle('Wasserstein-1 distance between FES and NOFES decision-margin distributions',\n",
    "                 fontsize=12, fontweight='bold', y=1.02)\n",
    "\n",
    "    width = 0.4\n",
    "    axes[0].bar(x - width/2, w_mi,   width, color='#E05C2A', label='MI trials',   edgecolor='white')\n",
    "    axes[0].bar(x + width/2, w_rest, width, color='#2A7BE0', label='REST trials', edgecolor='white')\n",
    "    axes[0].set_xticks(x); axes[0].set_xticklabels(labels, fontsize=8)\n",
    "    axes[0].set_ylabel('W₁(FES margin, NOFES margin)')\n",
    "    axes[0].set_title('Per-(subject × pair), by trial class', fontweight='bold')\n",
    "    axes[0].legend(); axes[0].grid(axis='y', alpha=0.3)\n",
    "    axes[0].spines[['top','right']].set_visible(False)\n",
    "\n",
    "    # Direction of the shift (FES mean − NOFES mean) on MI margins\n",
    "    fes_mi    = np.array([r['fes_mi_mean']   for r in w_results])\n",
    "    nofes_mi  = np.array([r['nofes_mi_mean'] for r in w_results])\n",
    "    mi_shift  = fes_mi - nofes_mi\n",
    "    colors_sh = ['#E05C2A' if d > 0 else '#2A7BE0' for d in mi_shift]\n",
    "    axes[1].bar(x, mi_shift, color=colors_sh, edgecolor='white')\n",
    "    axes[1].axhline(0, color='k', lw=0.8)\n",
    "    axes[1].set_xticks(x); axes[1].set_xticklabels(labels, fontsize=8)\n",
    "    axes[1].set_ylabel('mean(FES MI-margin) − mean(NOFES MI-margin)')\n",
    "    axes[1].set_title('Direction of MI-margin shift (positive = FES margin more negative is\\ncolumn below zero; see sign convention in code)', fontsize=9, fontweight='bold')\n",
    "    axes[1].grid(axis='y', alpha=0.3)\n",
    "    axes[1].spines[['top','right']].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('margin_wasserstein.png', dpi=150, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    print('Saved: margin_wasserstein.png')\n",
    "\n",
    "    print(f'\\nMean W₁ across {len(w_results)} (subject × pair) comparisons:')\n",
    "    print(f'  MI trials:   {np.nanmean(w_mi):.3f} ± {np.nanstd(w_mi, ddof=1):.3f}')\n",
    "    print(f'  REST trials: {np.nanmean(w_rest):.3f} ± {np.nanstd(w_rest, ddof=1):.3f}')\n",
    "    print(f'  W₁(MI) > W₁(REST) in {int((w_mi > w_rest).sum())}/{len(w_results)} comparisons '\n",
    "          f'— FES perturbs MI margins more than REST margins')\n",
    "else:\n",
    "    print('No comparisons available for Wasserstein analysis.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3db60ba",
   "metadata": {},
   "source": [
    "---\n",
    "## Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f27a80e",
   "metadata": {},
   "source": [
    "---\n",
    "## Figure 4 — Within-session MI-accuracy trajectory (sliding window)\n",
    "\n",
    "Refined from the original \"thirds\" analysis to address its noise floor. Each online session\n",
    "is parsed into chronological MI-only trials, then a W-trial sliding window gives a smoother\n",
    "accuracy trajectory. The slope of a linear fit to this trajectory is a per-session \"learning\n",
    "rate.\" Restricting to MI trials isolates the class that FES actually perturbs (orthotic +\n",
    "stimulation fires on MI, not REST) — slope differences between FES and NOFES sessions would\n",
    "indicate whether proprioceptive feedback accelerates within-session adaptation.\n",
    "\n",
    "Caveat: even with sliding windows, ~25–30 MI trials per session is low-N and the slope\n",
    "estimator is noisy; read this as exploratory, not confirmatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9db789c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.705422Z",
     "iopub.status.busy": "2026-04-22T19:27:28.705328Z",
     "iopub.status.idle": "2026-04-22T19:27:28.901376Z",
     "shell.execute_reply": "2026-04-22T19:27:28.900874Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1560x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: within_session_mi_trajectory.png\n",
      "\n",
      "Condition-averaged slopes (FES − NOFES paired per session):\n",
      "  FES:   mean slope = +0.0020  (n=8)\n",
      "  NOFES: mean slope = +0.0045  (n=8)\n"
     ]
    }
   ],
   "source": [
    "WINDOW = 8   # trials per sliding window\n",
    "\n",
    "lr_results = []\n",
    "for r in results:\n",
    "    mi_successes = np.array([int(t['success']) for t in r['trials'] if t['cls'] == 'MI'])\n",
    "    if len(mi_successes) < WINDOW + 2:\n",
    "        continue\n",
    "    traj = np.array([mi_successes[i:i+WINDOW].mean()\n",
    "                     for i in range(len(mi_successes) - WINDOW + 1)])\n",
    "    slope = np.polyfit(np.arange(len(traj)), traj, 1)[0]\n",
    "    lr_results.append(dict(subj=r['subject'], pair=r['pair'], cond=r['condition'],\n",
    "                           traj=traj, slope=slope, n_mi=len(mi_successes)))\n",
    "\n",
    "# ── Figure 4a: slope per condition (FES vs NOFES) ─────────────────────────────\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 4.5))\n",
    "fig.suptitle(f'MI-only within-session learning rate  (window = {WINDOW} trials)',\n",
    "             fontsize=12, fontweight='bold', y=1.02)\n",
    "\n",
    "fes_slopes   = np.array([r['slope'] for r in lr_results if r['cond'] == 'FES'])\n",
    "nofes_slopes = np.array([r['slope'] for r in lr_results if r['cond'] == 'NOFES'])\n",
    "\n",
    "ax = axes[0]\n",
    "means  = [fes_slopes.mean(), nofes_slopes.mean()]\n",
    "sems   = [fes_slopes.std(ddof=1) / np.sqrt(len(fes_slopes)),\n",
    "          nofes_slopes.std(ddof=1) / np.sqrt(len(nofes_slopes))]\n",
    "ax.bar(['FES', 'NOFES'], means, yerr=sems,\n",
    "       color=[cond_color['FES'], cond_color['NOFES']],\n",
    "       capsize=6, edgecolor='white')\n",
    "# Overlay individual points\n",
    "for i, slopes in enumerate([fes_slopes, nofes_slopes]):\n",
    "    ax.scatter(np.full(len(slopes), i) + np.random.uniform(-0.08, 0.08, len(slopes)),\n",
    "               slopes, color='k', alpha=0.5, s=20, zorder=3)\n",
    "ax.axhline(0, color='k', linestyle='--', lw=0.8)\n",
    "ax.set_ylabel('Slope of MI-accuracy trajectory\\n(Δ fraction correct / trial-step)')\n",
    "ax.set_title('Condition-averaged learning rate', fontweight='bold')\n",
    "ax.spines[['top','right']].set_visible(False)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "# ── Figure 4b: trajectories per (subject × pair) ─────────────────────────────\n",
    "ax = axes[1]\n",
    "for r in lr_results:\n",
    "    ax.plot(r['traj'], color=cond_color[r['cond']], alpha=0.6, lw=1.5,\n",
    "            label=r['cond'])\n",
    "ax.axhline(0.5, color='gray', linestyle='--', lw=0.8, alpha=0.6)\n",
    "ax.set_xlabel(f'Sliding window start (MI trial index, window = {WINDOW})')\n",
    "ax.set_ylabel('MI accuracy within window')\n",
    "ax.set_title('All session trajectories overlaid', fontweight='bold')\n",
    "# Dedup legend\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "seen = set()\n",
    "handles = [(hh, ll) for hh, ll in zip(h, l) if not (ll in seen or seen.add(ll))]\n",
    "ax.legend([h for h, _ in handles], [l for _, l in handles], loc='lower left')\n",
    "ax.spines[['top','right']].set_visible(False)\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('within_session_mi_trajectory.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved: within_session_mi_trajectory.png')\n",
    "print(f'\\nCondition-averaged slopes (FES − NOFES paired per session):')\n",
    "print(f'  FES:   mean slope = {fes_slopes.mean():+.4f}  (n={len(fes_slopes)})')\n",
    "print(f'  NOFES: mean slope = {nofes_slopes.mean():+.4f}  (n={len(nofes_slopes)})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cf55268e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-22T19:27:28.903912Z",
     "iopub.status.busy": "2026-04-22T19:27:28.903829Z",
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     "shell.execute_reply": "2026-04-22T19:27:28.914546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Aggregate across 8 complete (subject × pair) comparisons ===\n",
      "\n",
      "Metric                                      FES              NOFES  paired Δ       95% CI (bootstrap)       sign\n",
      "----------------------------------------------------------------------------------------------------------------\n",
      "Overall accuracy (markers)       0.821 ±  0.077     0.806 ±  0.075    +0.015 [ -0.040, +0.065]    4/7\n",
      "MI accuracy                      0.817 ±  0.108     0.775 ±  0.115    +0.042 [ -0.079, +0.142]    5/7\n",
      "REST accuracy                    0.825 ±  0.100     0.837 ±  0.061    -0.012 [ -0.070, +0.039]    4/7\n",
      "MI EARLYSTOP latency (s)         2.523 ±  0.204     2.235 ±  0.209    +0.288 [ +0.058, +0.530] *  6/8\n",
      "REST EARLYSTOP latency (s)       2.135 ±  0.303     2.391 ±  0.270    -0.256 [ -0.416, -0.077] *  2/8\n",
      "Classification amplitude         2.237 ±  2.063     2.362 ±  1.813    +0.151 [ -0.142, +0.476]    4/7\n",
      "Fisher ratio (test SNR)          2.307 ±  2.712     2.542 ±  3.263    +0.090 [ -1.065, +1.225]    3/7\n",
      "μ-band SNR (REST/MI)             1.751 ±  0.671     1.747 ±  0.480    +0.065 [ -0.161, +0.299]    4/7\n",
      "\n",
      "* = 95% bootstrap CI excludes zero (suggestive at this n).\n",
      "Sign column: number of (subject × pair) comparisons where FES > NOFES.\n"
     ]
    }
   ],
   "source": [
    "def boot_paired_ci(deltas, n_boot=10000, alpha=0.05, seed=0):\n",
    "    \"\"\"Bootstrap percentile CI on the mean of paired differences.\"\"\"\n",
    "    rng = np.random.default_rng(seed)\n",
    "    d = np.asarray([x for x in deltas if x is not None and not (isinstance(x, float) and np.isnan(x))])\n",
    "    if len(d) == 0:\n",
    "        return (np.nan, np.nan, np.nan)\n",
    "    boot_means = d[rng.integers(0, len(d), size=(n_boot, len(d)))].mean(axis=1)\n",
    "    lo, hi = np.percentile(boot_means, [100*alpha/2, 100*(1-alpha/2)])\n",
    "    return float(d.mean()), float(lo), float(hi)\n",
    "\n",
    "\n",
    "# Collect all (subject × pair) where both FES and NOFES exist\n",
    "paired = []\n",
    "for subj in subjects:\n",
    "    for pair in PAIRS:\n",
    "        fes = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='FES'),   None)\n",
    "        nof = next((r for r in results if r['subject']==subj and r['pair']==pair['name']\n",
    "                    and r['condition']=='NOFES'), None)\n",
    "        if fes and nof: paired.append((fes, nof))\n",
    "\n",
    "print(f'=== Aggregate across {len(paired)} complete (subject × pair) comparisons ===\\n')\n",
    "\n",
    "METRICS_SUMMARY = [\n",
    "    ('acc',          'Overall accuracy (markers)'),\n",
    "    ('mi_acc',       'MI accuracy'),\n",
    "    ('rest_acc',     'REST accuracy'),\n",
    "    ('mi_latency',   'MI EARLYSTOP latency (s)'),\n",
    "    ('rest_latency', 'REST EARLYSTOP latency (s)'),\n",
    "    ('amp',          'Classification amplitude'),\n",
    "    ('fisher',       'Fisher ratio (test SNR)'),\n",
    "    ('mu_snr',       'μ-band SNR (REST/MI)'),\n",
    "]\n",
    "\n",
    "hdr = (f'{\"Metric\":<28} {\"FES\":>18} {\"NOFES\":>18} '\n",
    "       f'{\"paired Δ\":>9} {\"95% CI (bootstrap)\":>24} {\"sign\":>10}')\n",
    "print(hdr); print('-' * len(hdr))\n",
    "\n",
    "def fmt_mean_sd(arr):\n",
    "    a = np.array([x for x in arr if x is not None and not (isinstance(x, float) and np.isnan(x))])\n",
    "    if len(a) == 0: return '       --        '\n",
    "    sd = a.std(ddof=1) if len(a) > 1 else 0.0\n",
    "    return f'{a.mean():>8.3f} ± {sd:6.3f}'\n",
    "\n",
    "for k, label in METRICS_SUMMARY:\n",
    "    fes_v = [f[k] for f, _ in paired]\n",
    "    nof_v = [n[k] for _, n in paired]\n",
    "    deltas = [f - n for f, n in zip(fes_v, nof_v)\n",
    "              if f is not None and n is not None\n",
    "              and not (isinstance(f, float) and np.isnan(f))\n",
    "              and not (isinstance(n, float) and np.isnan(n))]\n",
    "    if not deltas:\n",
    "        print(f'{label:<28} {\"(no data)\":<18} {\"\":<18}')\n",
    "        continue\n",
    "    mean_d, lo, hi = boot_paired_ci(deltas)\n",
    "    d_arr = np.array(deltas)\n",
    "    n_pos, n_neg = int((d_arr > 0).sum()), int((d_arr < 0).sum())\n",
    "    ci_crosses_zero = (lo < 0 < hi)\n",
    "    marker = '  ' if ci_crosses_zero else ' *'\n",
    "    print(f'{label:<28} {fmt_mean_sd(fes_v):>18} {fmt_mean_sd(nof_v):>18} '\n",
    "          f'{mean_d:>+9.3f} [{lo:>+7.3f},{hi:>+7.3f}]{marker}  '\n",
    "          f'{n_pos}/{n_pos+n_neg}')\n",
    "\n",
    "print('\\n* = 95% bootstrap CI excludes zero (suggestive at this n).')\n",
    "print('Sign column: number of (subject × pair) comparisons where FES > NOFES.')"
   ]
  }
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