{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a33c680",
   "metadata": {},
   "source": [
    "# Optimal Transport in linear ICA\n",
    "\n",
    "#### In this notebook we test the performance scaling of a OT ICA on Stiefel Manifold versus Fast ICA over dimensions on a mixture consisting only continous laplace distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9597f81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import FastICA\n",
    "from tqdm.notebook import tqdm\n",
    "import time\n",
    "from joblib import Parallel, delayed\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from wasserstein_ica import WassersteinICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "351914b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# Thesis Theme Configuration\n",
    "# ==========================================\n",
    "def set_thesis_theme():\n",
    "    thesis_colors = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC', '#CA9161']\n",
    "    plt.rcParams.update({\n",
    "        'figure.figsize': (10, 6),\n",
    "        'figure.dpi': 300,\n",
    "        'axes.prop_cycle': plt.cycler(color=thesis_colors),\n",
    "        'axes.grid': True,\n",
    "        'grid.alpha': 0.3,\n",
    "        'grid.linestyle': '--',\n",
    "        'axes.axisbelow': True,\n",
    "        'axes.spines.top': False,\n",
    "        'axes.spines.right': False,\n",
    "        'font.size': 12,\n",
    "        'axes.titlesize': 14,\n",
    "        'axes.labelsize': 12,\n",
    "        'legend.frameon': True,\n",
    "        'legend.fontsize': 11,\n",
    "        'lines.linewidth': 2.5\n",
    "    })\n",
    "set_thesis_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91f5ec16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# 1. Helpers & Data Generation\n",
    "# ==========================================\n",
    "def amari_error(W_est, A_true):\n",
    "    if W_est is None or np.any(np.isnan(W_est)): return np.nan\n",
    "    P = np.abs(W_est @ A_true)\n",
    "    n = P.shape[0]\n",
    "    term1 = np.sum((np.sum(P, axis=1) / np.max(P, axis=1)) - 1)\n",
    "    term2 = np.sum((np.sum(P, axis=0) / np.max(P, axis=0)) - 1)\n",
    "    return (term1 + term2) / (2 * n)\n",
    "\n",
    "def generate_dataset(n_dim, n_samples, seed=None):\n",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "        torch.manual_seed(seed)\n",
    "    # Standard Laplace (Super-Gaussian)\n",
    "    sources = [np.random.laplace(0, 1, n_samples) for _ in range(n_dim)]\n",
    "    S = np.stack(sources)\n",
    "    \n",
    "    cond_num = 1000\n",
    "    while cond_num > 100:\n",
    "        A = np.random.randn(n_dim, n_dim)\n",
    "        cond_num = np.linalg.cond(A)\n",
    "    return torch.tensor(A @ S, dtype=torch.float32), A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebc34c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# 2. Parallel Worker Function\n",
    "# ==========================================\n",
    "def run_single_trial(dim, trial, n_samples, compute_level):\n",
    "    torch.set_num_threads(1)\n",
    "    trial_results = []\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    \n",
    "    X_torch, A_true = generate_dataset(n_dim=dim, n_samples=n_samples, seed=trial)\n",
    "    X_torch = X_torch.to(device)\n",
    "    X_np = X_torch.cpu().numpy()\n",
    "    \n",
    "    # Compute Parameters\n",
    "    if compute_level == 'Low Compute':\n",
    "        fast_max_iter = 1000\n",
    "        w2_restarts = min(dim * 4, 150)\n",
    "        w2_def_iter = 150\n",
    "        w2_sym_iter = 200\n",
    "    else: # High Compute\n",
    "        fast_max_iter = 5000\n",
    "        w2_restarts = min(dim * 15, 600)\n",
    "        w2_def_iter = 300\n",
    "        w2_sym_iter = 500\n",
    "\n",
    "    # --- FastICA ---\n",
    "    t0_fast = time.perf_counter()\n",
    "    try:\n",
    "        import warnings\n",
    "        from sklearn.exceptions import ConvergenceWarning\n",
    "        with warnings.catch_warnings():\n",
    "            warnings.simplefilter(\"ignore\", category=ConvergenceWarning)\n",
    "            fast_ica = FastICA(n_components=dim, max_iter=fast_max_iter, tol=1e-4, random_state=trial)\n",
    "            fast_ica.fit(X_np.T)\n",
    "            W_fast_total = fast_ica.components_\n",
    "            score_fast = amari_error(W_fast_total, A_true)\n",
    "    except Exception:\n",
    "        score_fast = np.nan\n",
    "    t_fast_total = time.perf_counter() - t0_fast\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, 'Method': 'FastICA', 'Compute Level': compute_level,\n",
    "        'Amari Error': score_fast, 'Total Time (s)': t_fast_total\n",
    "    })\n",
    "    \n",
    "    # --- OT-ICA ---\n",
    "    ica = WassersteinICA(X_torch)\n",
    "    ica.whiten()\n",
    "    W_white_np = ica.W_white.cpu().numpy()\n",
    "    \n",
    "    try:\n",
    "        t0_p1 = time.perf_counter()\n",
    "        extracted_ws = []\n",
    "        for _ in range(dim):\n",
    "            prev = torch.stack(extracted_ws) if extracted_ws else None\n",
    "            w, _ = ica.optimize_wasserstein2(\n",
    "                prev_components=prev, max_iter=w2_def_iter, \n",
    "                n_restarts=w2_restarts, dither_sigma=0.0\n",
    "            )\n",
    "            extracted_ws.append(w)\n",
    "        W_deflation_init = torch.stack(extracted_ws)\n",
    "        t_phase1 = time.perf_counter() - t0_p1\n",
    "        \n",
    "        t0_p2 = time.perf_counter()\n",
    "        W_stiefel_unmixed = ica.optimize_symmetric(\n",
    "            n_components=dim, max_iter=w2_sym_iter, lr=1.0, \n",
    "            init_w=W_deflation_init, optimizer='stiefel',\n",
    "            dither_sigma=0.0, batch_size=None\n",
    "        )\n",
    "        t_phase2 = time.perf_counter() - t0_p2\n",
    "        \n",
    "        W_wass_total = W_stiefel_unmixed.cpu().numpy() @ W_white_np\n",
    "        score_wass = amari_error(W_wass_total, A_true)\n",
    "    except Exception:\n",
    "        score_wass, t_phase1, t_phase2 = np.nan, np.nan, np.nan\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, 'Method': 'OT-ICA', 'Compute Level': compute_level,\n",
    "        'Amari Error': score_wass, \n",
    "        'Total Time (s)': t_phase1 + t_phase2 if not np.isnan(t_phase1) else np.nan\n",
    "    })\n",
    "    \n",
    "    return trial_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a19eb4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- FastICA vs. OT-ICA: Laplace Scaling ---\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ef2758c97c04232b1901db8d95182f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running Trials:   0%|          | 0/78 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 3. Main Execution\n",
    "# ==========================================\n",
    "DIMENSIONS = [n for n in range(10, 71, 5)] # Scaled up to 40 for good visualization\n",
    "N_SAMPLES = 10000\n",
    "COMPUTE_LEVELS = ['Low Compute', 'High Compute']\n",
    "N_TRIALS = 3\n",
    "\n",
    "print(f\"--- FastICA vs. OT-ICA: Laplace Scaling ---\")\n",
    "tasks = [(dim, trial, N_SAMPLES, comp) for dim in DIMENSIONS for trial in range(N_TRIALS) for comp in COMPUTE_LEVELS]\n",
    "\n",
    "results_nested = Parallel(n_jobs=12)(\n",
    "    delayed(run_single_trial)(dim, trial, n_samples, comp) \n",
    "    for dim, trial, n_samples, comp in tqdm(tasks, desc=\"Running Trials\")\n",
    ")\n",
    "df = pd.DataFrame([item for sublist in results_nested for item in sublist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c33de183",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 3600x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 4. Plot 1: Amari Error vs Dimension\n",
    "# ==========================================\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "palette = {'FastICA': '#0173B2', 'OT-ICA': '#D55E00'}\n",
    "\n",
    "for idx, comp in enumerate(COMPUTE_LEVELS):\n",
    "    sns.lineplot(\n",
    "        data=df[df['Compute Level'] == comp], x='Dimension', y='Amari Error', \n",
    "        hue='Method', palette=palette, marker='o', linewidth=2.5, markersize=8, ax=axes[idx]\n",
    "    )\n",
    "    axes[idx].set_title(f\"{comp} Regime\", fontsize=14)\n",
    "    axes[idx].set_xlabel(\"Number of Dimensions\", fontsize=13)\n",
    "    axes[idx].set_xticks(DIMENSIONS)\n",
    "    \n",
    "    # --- NEW: Lock the x-axis maximum to 65 ---\n",
    "    axes[idx].set_xlim(min(DIMENSIONS), 65)\n",
    "    \n",
    "    axes[idx].axhline(0.3, color='gray', linestyle=':', alpha=0.8, linewidth=2)\n",
    "    if idx == 0:\n",
    "        axes[idx].set_ylabel(\"Amari Error (Lower is Better)\", fontsize=13)\n",
    "        axes[idx].text(min(DIMENSIONS)+1, 0.35, 'Good Separation', color='gray', fontsize=11)\n",
    "        handles, labels = axes[idx].get_legend_handles_labels()\n",
    "        axes[idx].legend(handles=handles, labels=labels, title='Method', loc='upper left')\n",
    "    else:\n",
    "        axes[idx].set_ylabel(\"\")\n",
    "        if axes[idx].get_legend(): axes[idx].get_legend().remove()\n",
    "\n",
    "# --- Lock the y-axis strictly from 0 to 6 ---\n",
    "plt.ylim(0, 3)\n",
    "\n",
    "fig.suptitle(\"Amari Scores: FastICA vs OT-ICA\\nLaplacian Independent Sources\", fontsize=15, y=1.05)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('laplace_amari_ot_ica_scaling.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8362d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5fr2PPvro1MyZMzf6uCNGjEh16NAhT/mqVauWuueee1Lr1q3L03PKq9JyHAEAAABbhuLs9SSdD8/POaHccvz+nHp+PPfcc6natWvn6zxL7dq1U88///yvOZLO9ebnHGVentOoUaPyfF4qIlLdunVLjR07tsCvS6boG+WfvpG+Uaa+9I0K9p6cRN8o/Uvf6Gf6RgDA5sQtKgAAgIiI+PLLL6NNmzZxwQUXxA8//JCvtbNmzYoLL7ww9tlnn/jiiy/yvK569erRtm3btPj777+fr8f/5JNPYsWKFYnb8psrOzs78WqIPXr0yFeegnr88ccT42effXZUq1atWGrIrwEDBsTuu+8ed911Vyxfvjxfaz///PM48sgj4+ijj46FCxdmvLbs7Ow4/fTT4/jjj4/vv/8+T2t++umn6NOnT5x//vmRSqXStn/99dfRtm3buO2223I97n5v6NCh0bZt2/j444/zVX9eXHvttXHaaafF4sWL87Vu3rx50atXr7j++uszXtPWYMSIEdGyZcu48MILN3rl2CQ//fRT/OMf/4iWLVvGuHHjiqbAEla7du3E+IIFCwqUb9WqVXH++edH69ato3///rFu3bp8rR88eHDsu+++cfnll8f69esLVMMvXn755dhvv/3i66+/zte69evXx2233RZHHXVUrF69ulA1FIdRo0ZFixYt4qmnnsrz6/3qq69G+/btY8KECWnbUqlU9O3bNw444ID45JNP8pRv2bJlceGFF8bJJ59c6O9bROk6jgAAAAC2BJdddlmcfPLJ+T6/v3DhwjjppJPihhtuKKLK/k8qlYrrr78+OnXqlOfzUhERw4YNiw4dOsSAAQOKsLpN0zfKLH2j3OkbFY6+0cbpG6XTN9I3AgA2PwbwAACAePfdd2P//fePL7/8slB5Jk6cGB07dox33nknz2uSBtvyOzS3sf0/+uijfJ2w/vTTT9OagRUqVIjOnTvnq6aCWLFiRa7P5ZRTTinyxy+IW265JY4//vgCN0d+MWjQoGjfvn2+G1Ibs379+jj++OPjySefLND6+++/P2688cYNYhMmTIiuXbvGN998k+98y5Yti4MPPjj+97//FaieJPfff3/cdNNNhcpxww03xM0335yhirYO9913X3Tv3j0mT55cqDyTJ0+OTp06xeDBgzNUWemxcuXKxHj58uXznWvWrFnRsWPHuP/++yMnJ6fANeXk5ES/fv3iyCOPjDVr1hQox5tvvhknnHBCgddHRLz11lvRp0+fAq8vDiNHjowePXrE/Pnz87129uzZccghh8Ts2bN/jaVSqTj33HPj5ptvLtD38MUXX4zzzjsv3+t+qzQdRwAAAABbgquuuiruuOOOQuW4/vrr4//9v/+XoYqS/fnPf44bbrihQOeEsrOz48QTT4xBgwYVQWWbpm+kb5Rf+kYlQ99o0/SNcqdvpG8EAGw+ypV0AQAAQMkaPXp0HHPMMbFq1arE7eXKlYsDDjggdt5556hXr14sWLAgpkyZEsOHD4+1a9em7b9ixYo45phjYsiQIdGxY8dNPn6PHj3i1ltv3SD20UcfxZo1a6JixYp5eg4bG8BbvXp1jBo1Krp3717gXPvuu29Urlw5T+sLY+jQoYmvaePGjaNly5ZF/vj5dc8998Q111yT6/YaNWpE9+7dY4cddohq1arFnDlz4ssvv4wxY8Yk7j9lypTo3r17fPrpp1G3bt1C13fFFVekNcVr1qwZ3bp1ix122CFq1KgRP/30U4wZMyY+//zzxKuW3njjjXHYYYdF27ZtY968eXHYYYfFTz/9tME+u+22W+y3335Rv379KFOmTMyaNSuGDBkSc+bMScu3ZMmSOPfcc+ODDz4o9PP76quv4r777tsgtuOOO0bv3r3jiCOOiMaNG0eNGjVi9uzZMXHixHjhhReif//+icfYtddeG+3atYuePXsWuq4tXb9+/eLyyy/PdXv16tWjY8eO0aRJk6hTp06sWbMm5syZEx9++GFMnz49bf+VK1fGUUcdFaNHj45WrVoVYeXF68cff0yM53aF09zMmTMnDjjggPjuu+9y3Wf33XePdu3aRd26daNKlSrx008/xbfffhsffvhh4vH+SyPzv//9b75qmTVrVpx00kmJV7DMysqKww8/PP70pz9FmzZtYvvtt49169bF7Nmz48MPP4znnntug7urvvjii7Hrrrvm6/GLy4wZM+Loo49OG15v3bp1tGrVKho0aBDZ2dkxffr0eO+992LRokVpOWbPnh3nnntuvPbaaxERcdttt8XDDz+8wT6VK1eOAw44IHbaaaeoXbt2LF68OCZMmBAjR45MfI0ffvjhOOGEE/L8+/y3StNxBAAAALAleP311+Of//xn4rYqVarEn/70pzj++ONjt912iwYNGsSSJUti5syZ8dZbb8UzzzwT33777a/7X3TRRXHhhRcWSZ033HBDPPLIIxvEKlSoEB07doxdd9016tevH6tWrYoZM2bE+++/n3jHtfXr18ef//zn6NKlS9SqVatI6syNvpG+UX7oG5UMfaO80TfSN9I3AgC2CCkAAKDEDBs2LBURaV/XXXddsTz+smXLUk2bNk2soXz58qlrr702NX/+/MS1CxYsSN10002pChUqJK5v0qRJaunSpZusYdWqValKlSqlrR86dGiensPy5ctT5cuXT6zhl69//OMfeX5NunTpkrb+hhtuyPP6wrj66qsT6+/du3exPH5+jB8/Ptfvfb169VJPP/10avXq1Ylrv/vuu9RJJ52U6/erV69e+aqld+/eaTmaNWuWysrK+vXfjRs3Tr344oup7OzsxBwTJkxItWvXLrGeAw44IJVKpVIHH3zwBvEjjzwyNXHixMR8OTk5qUceeSRVtWrVxJwvvfRSvp5j48aN03KUK1dug3+ff/75qZUrV240z4QJE1ItW7bM9Wd2xYoVea4pKcewYcPy9bx+77rrrkvL2bVr12JbvykDBw7c4Lj67df++++feuONN1Lr1q3Ldf3HH3+c6tq1a+L6PfbYI7Vq1aqM1ZofScdX48aNC5Vzxx13THyeL7zwQp5zrF69OtW+ffvEPNWqVUtdccUVqVmzZuW6ftmyZalbbrklVa1atcQcTz31VL6e09FHH53rz05ejv0XX3wxVbt27Q1+zyblmzp1ap7qmTp1apGs32233X7976ysrFSfPn1S06dPT8yxdu3a1D//+c9cn8vw4cNTH330UapMmTIb/I548MEHcz3ep02blvrDH/6QmK9FixZ5em6/VdqOIwAAAGDLU5y9nqTz4fk5J5Rbjvz0IZYtW5baYYcdEuvo1q3bJmtZu3Zt6rrrrtvgnFHS+aX8nKNMek7Nmzff4DHq1KmT+ve//51atmxZYo5169al7r///lzPA11wwQV5ridT9I1+/tI3SqdvVDzrN0XfKO/0jdLpG/3f7wh9IwBgc2EADwAASlBJD+Cdc845iY9fp06d1Oeff56nHOPHj0/Vq1cvMc8ZZ5yRpxzdu3dPW3v11Vfnae3bb7+dtrZs2bIb/LtDhw55yrVixYrE5uDIkSPztL6wDj300MTX8e677y6Wx8+rNWvWpPbaa6/EWtu3b5+aN29envI888wzG5xc/+3X008/ned6cvvAwS9fPXr0yLWh/lvLli1LtWnTJjHHb5vcZcqUSf2///f/8lTbsGHD0hqeEZH6wx/+kOfnl0olN7p++5WfIdHFixen9txzz8Q8l19+eZ7zJK3fkhups2fPTtWsWTMtf7ly5VJ33HFHnvPk5OSkrrzyysTX7+KLL85IrfmV6UbqyJEjE59fVlZWasaMGXnOc8UVVyTmadOmTer777/Pc57//e9/qSZNmqTlqV69emrmzJl5yvHGG28k1rLDDjvk6zl99tlnuTbkfvkq6UbqL18VK1ZMvfLKK3nK9fzzzyfmOPLIIzf4fdGqVavUjz/+uMl8a9euTfvwyi9fo0aNylNNvyhNxxEAAACwZdraBvAuvfTSxBoOOuig1Jo1a/Kc56GHHtro+anCDuD99qt169apuXPn5inXqFGjUpUrV07LUatWrWIfhtE3+r8vfaMN6RsVz/qN0TfKO32j3Okb6RsBAJuXMgEAAGyVJk2aFI888khavHLlyvHmm29G69at85Rn7733jrfeeiuqVKmStu3xxx+PiRMnbjJHjx490mLvv/9+nh7/9/tlZWXFqaeeukHss88+iyVLlmwy14cffhhr167dIFa1atVo3759nmoprO+++y4xvvvuuxfL4+fVk08+GV9++WVavFmzZvHWW29FvXr18pTnlFNOif/85z+J26688srIzs4uVJ0REW3bto0333wzqlatusl9q1atGg8//HBkZWWlbbvlllt+/e+77rorzj333Dw9/gEHHJC47/vvvx9z5szJU45NOe644+Laa6/N8/41atSIt99+O6pVq5a27f/9v/8Xy5cvz0hdW5rLL788Fi9enBZ/6qmn4pJLLslznqysrLjtttviwgsvTNv24IMPxsKFCwtTZolbt25dXHnllYnbOnXqFDvssEOe8kyaNCnuuOOOtHjr1q3jgw8+iJ122inPNTVv3jyGDRsWNWrU2CC+dOnSuO+++/KU45577kmLlSlTJl599dU8P6eIiDZt2sTjjz+e5/1L0nPPPRdHH310nvb905/+FEcddVRa/LXXXvv190WTJk1i2LBhUb9+/U3mK1++fDzyyCOxzTbbpG175pln8lRTROk7jgAAAAA2d6tWrUrsLTVq1CgGDhwYFSpUyHOuc845J/76179msrxEu+22WwwfPjy23XbbPO2/3377xXXXXZcWX7RoUbz22muZLm+j9I3+j75R3ukbFQ99o7zRN9o4fSN9IwBg82IADwAAtlIPPPBApFKptPg111wTHTp0yFeutm3bJjYjIyLuv//+Ta5PGsAbM2ZMLF26dJNrfz+A16pVqzjhhBM2iK1fvz4++OCDfOeKiOjcuXOUL19+k2szYdasWYnxRo0aFcvj51VS8zMrKyueeOKJqFOnTr5ynXvuuXHooYemxWfPnh0DBw4scI0RERUrVoxnnnkmKlasmOc1rVu3ji5duuS6vXv37nHBBRfkq46LLrooLbZ+/fr46KOP8pUnyTbbbBN33nlnvtc1atQorrrqqrT4smXL4oUXXih0XVuab775Jp5//vm0+MUXXxwnnXRSgXLefvvtsccee2wQW7lyZeKHVzYX2dnZcc4558TIkSMTtycdc7m58cYbY/369RvEqlWrFq+99lriwPemNGnSJPH30SOPPBKrVq3a6Nrp06cn/n4466yz8jys/lvHHXdcdO/ePd/ritNpp50WvXr1yteav//97xvd/tRTT0XNmjXznK9Ro0Zx/PHHp8VHjBiR5xyl6TgCAAAAtj433HBDZGVlZfTrqaeeKtHn1L9//8QLHt5+++15Gmj6vZtuuinffYX8KFu2bPz3v/9NHCzamAsvvDDtA/URP1/IsTjpG/0ffaO80TcqHvpGeaNvlDf6RnmjbwQAlAYG8AAAYCu0YsWKxCbtTjvtFBdffHGBcl544YWx8847p8WfffbZTQ7StW3bNqpXr75BbP369TF8+PCNrluwYEGMHz9+g1iPHj0Sh+aGDBmy0VwRyQN4ScOBRWHFihWxYsWKxG15vSprcRg5cmRMmDAhLf6nP/0p9t9//wLl/Pe//x1ly5ZNi+d2ldO8OuGEE6J58+b5Xpd0Nb5f9O3bN/FKpxvTtGnT2GuvvdLivz92C6J3796x4447Fmjt3//+97Sfu4jYbK6yWJweeuihyMnJ2SBWt27dfF1B9vcqVKgQV1xxRVp8c3z9U6lUvPnmm7HffvvFE088kbjPCSecEIccckie8s2fPz/69++fFr/yyitj++23L3CdJ510Uuyyyy4bxBYuXBivvvrqRtc999xzad//iIjLLruswLVcfvnlBV5b1MqUKVOgY7tz585Rq1atxG1du3bd6IdUcpP0fjxp0qRYvXr1JteWtuMIAAAAYEvw7LPPpsV22GGHOPHEEwuUr1atWnH22WcXtqxcHXvssbHPPvvke13FihUT7/Izbty4DFSVN/pG+kYFoW9UPPSNNk7fKP/0jfJG3wgAKGkG8AAAYCs0YsSIxCuUnnHGGfm66uNvVahQIc4888y0+PLly2PYsGEbXVu2bNno2rVrWjxpIO63hg0blnYXvx49ekSVKlVi3333zVeuhQsXJja2imsAb+XKlbluK8jV1orK66+/nhj/y1/+UuCczZo1S3ydR44cGYsXLy5w3oI27ffee+/E+K677hoHHHBAgXK2atUqLTZx4sQC5fqtgl5FM+Lnq6Aec8wxafExY8a4It9vpFKpxKu7nn766YlXQM6PE044Ie0999tvv4158+YVKm8mLFmyJG6++eZcv6699tq44IIL4ogjjoj69evH4YcfHp9//nliro4dO+baYE0yYMCAyM7O3iBWvnz5OO+88wr1nMqUKROnnHJKWnxTV60eNWpUWqxdu3ZpzbT8OPDAA6N+/foFXl+UunXrljhQvylly5aNPffcM3FbQd+Pk947161bF5MmTdrk2tJ2HAEAAABs7nJycuLjjz9Oi5944on5HkD6rcKc596UwvQu2rdvnxbLxHn9vNI30jcqCH2joqdvpG+kb5Q3+kb6RgCwJSpX0gUAAADFL6lBGlH4JufJJ58cV199ddpQ3Mcff7zRq0NG/Dzo9vsm3aaG5n6/vUKFCr9eKa1Hjx4bnNT8+uuvY86cObHddtsl5ho2bFjilQpza6pl2sauylahQoViqSEvko6dxo0bR8eOHQuV95RTTonBgwdvEEulUvHJJ59Ez549852vUqVK0aFDhwLV0qxZs8R4Qa7C94tdd901LbZgwYIC54uIqF27dnTq1KlQOY4++ui0u2GuW7cuxo0bV+Ar025pxo8fH3Pnzk2LH3fccYXOXbFixejQoUOMGDFig/ioUaMSr65cnBYvXhx9+/YtVI6srKw4++yz4957783XcPc777yTFjvwwAOjZs2ahaonIvnnOKlR+ltjxoxJix188MGFqqNs2bJx0EEHxXPPPVeoPEWhoB8Yifj5/TOpoZg0ZJ8XTZo0ifLly6c1RPPy/lnajiMAAACAzd2kSZNi2bJlafHCnivba6+9Yvvtt49Zs2YVKs/vVaxYsVDnuXffffe02JIlSyInJyfKlCn6a97rG+kb5Ze+UfHQNyo4faPc6Rvljb4RAFDSDOABAMBWKKkZ1qhRo2jatGmh8u64447RpEmTmDp16iYf7/eSrmQ5ceLEmDt3bjRo0CBxze8H8Pbdd9+oXLnyr/muv/76DbYPHTo0Tj755Dzlivj5am6FuWprfpQtWzbXbatXr/71eZWk9evXx2effZYW79y5c6Ffp9xOsn/88ccFaqS2bNkyypcvX6Baqlevnhhv27ZtgfJFROIVL5cuXVrgfBE/P8fCvu65DZiOGTNGI/X/l9QcqVChQrRp0yYj+Xfddde0RurYsWNLvJFaGNtss00cddRRcdlll0Xr1q3zvX706NFpsUwdj0kfavjqq68iOzs78T1j5syZiVeWbdmyZaFradmyZalspBbm2E56/9x2222jUaNGBcpXpkyZqFq1aixatGiDeF7eP0vTcQQAAABsnbp16xbdu3fPaM6BAwfGuHHjMpozr3K7k1GmzpVlegCvZcuW+Rrw+L1atWqlxVKpVCxdujQjH9bfFH0jfaP80jcqHvpG+advlDf6RpumbwQAlDQDeAAAsBX66quv0mKtWrXKSO5WrVqlDeAlPd7v7bnnnlG/fv20E9ZDhw5NvDPfDz/8EJMnT94gduCBB/763x06dIgqVarEihUrfo29//77+RrASxoKLCrbbLNNrtuWLFlSKhqp33//faxcuTItnoljZ8cdd4zatWvHwoULN4jn5dhJUr9+/QLXUrVq1cR4vXr1Mpoz6UrB+bHXXnsVan3Ez1cJrFatWlot06dPL3TuLcWECRPSYrvuuutGP/yQH3Xq1EmLzZ8/PyO5S0qjRo3iqKOOKlATdc6cOYnPv3nz5pkoLfH1Xr9+fSxcuDC23XbbxHqStGjRotC1ZCJHUcj0+2dh3jt/yfn7Ruqm3j9L23EEAAAAbJ26dOkS11xzTUZzfvfddyU2gJd0rqx+/fpRt27dQudu0aJFvP3224XO81uFOc8VkfvQ1bJly4plAE/fSN8ov/SNioe+Uf7pG+WNvlHec+obAQAlpUxJFwAAABS/3zerIiJ22223jOROOkm5aNGiSKVSm1ybdCXYpMG43OK/HZgrX758dOnSJU+5Zs6cGd9+++1G8xW1qlWr5npVyiVLlhRbHRuTdNxEZO7YScqT22NuSmGa32XKJP+vcqZzrl+/vsD5IiIjJ+uzsrISmxy/b1pszZKayhMnToysrKyMfN1+++1p+Tf313/y5Mnxpz/9KU477bRYu3Ztvtbm1sQ//vjjM/J65/ahlNxe89zef2vXrp2v51VUOYpCpt/rCvthpIK8f5a24wgAAABgS5B0rixT57iK4lxZUZyXiij8uf280jfSN8ovfaPioW+Uf/pGeaNvVPCc+kYAQHExgAcAAFuZNWvWxOrVq9PiNWrUyEj+pBOm69evz9NVG5MG3vI6gFetWrVo3779RvPNmDEjvvvuuzw9xo477hi77LLLJmvOlPLly+d6tbfFixcXWx0bk1sdRXnsFPSkdKauMlnUOQsjt6vvZiJPaTnmSoO5c+cW+2OWhmZM48aNI5VKJX6tXbs2fvrppxg1alT069cv16vqPvPMM3HyySdHTk5Onh+3JF7viNxf89x+FqpVq1box8zUz3CmZfq9riTeO0vbcQQAAACwJUg6V5aJ82QRRXOurFy5chnPWZz0jfSN8kvfqHjoG+kbJdE3Krl8eVHajiMAYPNlAA8AALYyuZ3kK8qmzMYe97eSBvCmT58eU6ZMSYsPHTp0g3937do1rZma14G+pFjS3fiK2o477pgY/+abb4q5kmQlcew4KZ273K6kl19VqlRJiy1fvjwjubcEK1euLPbHXLNmTbE/Zn6UL18+6tSpE/vtt19ceuml8cUXX8RDDz0UFSpUSNt3wIABcf311+c5d0m83hG5v+a5xStWrFjox8xEDpKVtuMIAAAAYEuQdO4jU+e4nCtLpm+kb5Qf+kbFQ98onb6RvlFpV9qOIwBg82UADwAAiIiIrKysEs+z0047RZMmTdLiQ4YM2eDf//vf/2L27NkbxJKG7fbee++oU6fORnNFJA/gJeUranvuuWdi/LPPPivmSvKnNBw7W6NMNQpWrFiRFqtatWpGcm8J1q9fX9IllHpZWVlxzjnnxKuvvpp41cpbbrklPvroozzlKm2vd24fFMnLXV03JRM5SFbajiMAAACALUHSubJMneNyriyZvpG+UX7oGxUP5583Td+oYPwuLDql7TgCADZf5Ta9CwAAsCWpWbNmYnzp0qUZyb9kyZLEeK1atfK0vkePHvHYY49tEHv//ffjz3/+8wb//r0DDzwwLZaVlRXdunWLAQMG/BobNmxYpFKpX5t233zzTdowX0TJ3AGvTZs28eSTT6bFx4wZU+y1JCmJYyevx83WKFOve1Ke3L7XW6NKlSqlxXbcccc4++yzi+wxGzduXGS5i9IhhxwSt912W1x++eUbxHNycuLMM8+ML7/8MsqXL7/RHEmvd0REnz59Yuedd85Yrb+30047JcZz+1lYtmxZoX9OMvUzTLrSdhwBAAAAbAmSzodlaljAubJk+kb6Rvmhb1Q89I3yTt8of/wuLDql7TgCADZfBvAAAGArU6lSpahUqVKsXr16g/jixYszkj+pGVa2bNmoVq1antYnDeD9fmju9wN42267ba5XAe3Ro8cGA3gLFiyI8ePHxz777JOYKyJi9913j4YNG+ap3kzq2rVrYvyLL76IpUuX5npFveKSW1OzKI8djdTczZs3r9A5UqlUzJ8/Py1eUo3UdevWlcjjbkzSMVijRo245pprSqCa0u+SSy6JgQMHxscff7xBfNKkSXHvvffGJZdcstH1uf3MH3HEEXHsscdmrM68yq2eGTNmxA477FCo3D/88EOh1pO70nYcAQAAAGwJks65zJ49O9avX594h6P8cK4smb6RvlF+6BsVD32j/NE3yju/C4tOaTuOAIDNV5mSLgAAACh+tWvXTotNnjw5I7knTZqUFqtZs+avw3ObknTnuZ9++im++OKLiPj5injDhw/f5Jpf9OjRIy3226G7pAG8pDXFYa+99ko8Mb9mzZp47rnnSqCiDSUdNxGZO3a+/fbbtJhGau4mTJhQ6BzTpk1LvELxjjvuuMm1ST/TOTk5hapn4cKFhVpfFJJeiwULFpRAJZuHMmXKxH/+85/E4+PWW2/d5NU7czv2Suo1b9q0aeKHhzLx8/fL7zUyr7QdRwAAAABbgmbNmqXFVq9enXhuP7+cK0umb6RvlB/6RsVD3yh/9I3yzu/ColPajiMAYPNlAA8AALZCLVq0SIuNHz8+I7mT8uR2d7ok2267bWJ9vwzKff7552lXzjzwwANzzbfrrrumNSd/yZU0zBdRcgN4EREnnnhiYvyRRx4p5krS7bTTTlG5cuW0eCaOnZkzZ8ZPP/2UFs/PsbO1mTBhQqRSqULlyK2R065du02uTbqr5fLlywtVT9JVVUvaHnvskRabO3du4pV3+Vnr1q3jhBNOSIsvXLgw7rzzzo2ubdq0aVSqVCkt/s0332SsvvyoXLly4u+kTz/9tNC5M5GDZKXtOAIAAADYEuR23riw57lWrVoVX331VaFybMn0jTakb5Q7faPioW+Uf/pGeaNvVHRK23EEAGy+DOABAMBWaL/99kuLzZgxI6ZPn16ovLNmzYrvv/8+T4+3MRu7a11B7lj3++0ffvhhZGdnx9ixY2PRokUbbCtTpkwccMAB+ao3k84666zE+Lhx4+KDDz4o5mo2VK5cuWjbtm1a/MMPPyx07hEjRiTG83vsbE0WLlwYI0eOLFSOV199NS1Wrly5aN269SbXVq9ePbGmwihsY6lMmfTTHIVtNu+7775psZycnFyPWX524403Jl4B9J577kl73/2tcuXKRZs2bdLiScPSxaV9+/ZpsUGDBsXatWsLnHP27NmF/vkld6XxOAIAAADY3NWqVSt22WWXtHj//v0LlXfQoEGxevXqQuXYkukbbUjfKHf6Run0jUoPfaON0zcqWqXxOAIANk8G8AAAYCuU1BiIiHj++ecLlfe5555LbFrk9ni5SRqoGzFiRGRnZ6cN4O2yyy7RuHHjfOVbsWJFfPzxx4nDfG3atImaNWvmq95MatasWRx99NGJ284555xYtWpV8Rb0O0nfy6lTp8bo0aMLlffZZ59Ni2VlZUWHDh0KlXdLV5if2dWrV8crr7ySFm/btm3iFWt/r3bt2mmx//3vfwWuZ+rUqTFjxowCr4+IqFq1alqssD8znTt3Trxq68CBAwuVd0u36667xp/+9Ke0+JIlS+Kuu+7a6NpDDz00LTZ+/PiYOnVqxurLj0MOOSQttmjRonjttdcKnPPpp5+OnJycwpTFJpS24wgAAABgS5B0rmzw4MExe/bsAud88sknC1HRlk/f6P/oG22avtGG9I1KD32jjdM3Knql7TgCADZPBvAAAGAr1KVLl6hRo0Za/PHHH4/s7OwC5czOzo7HHnssLV6lSpXo1q1bvnJ17do17Qp4K1asiA8++CA++uijDeKbuvtdRET37t3TYu+//37iAF7SvsXt5ptvTrwC4LfffhtXX311kT72o48+GhMnTsx1+xFHHJEYf/DBBwv8mFOmTIn33nsvLd6pU6cSHYbcHDz11FPxww8/FGjt3XffHUuXLk2Ln3766Xla36JFi7TYJ598UqBaIiIeeOCBAq/9RVLDs7BXV61YsWIce+yxafHnnnuu0HcN3dL17ds38b3s3nvv3ej35cQTT0y7Km1OTk7cdtttGa8xL4444oioX79+Wvwf//hHga5mOn/+/PjXv/6VidLYiNJ2HAEAAABsCc4888y0WHZ2dlx11VUFyvfee+/Fu+++W9iytnj6Rj/TN9o0faMN6RuVLvpGyfSNikdpO44AgM2TATwAANgKValSJXr37p0W/+677+Lee+8tUM777rsvvv3227T4qaeeGtWrV89Xrho1akSbNm3S4jfddFPaVQnzMoDXsGHD2H333TeIvfXWWzFy5Mi0ffOSr6i1aNEiLr744sRtd999dzzxxBMZf8zs7Oy47LLL4uyzz97oEGanTp2iZcuWafFnnnkmPvvsswI99kUXXRTr1q1Li//1r38tUL6tyapVq+KSSy7J97qZM2fGrbfemhavWrVq4tUnkyT9jH744YcxZ86cfNfzww8/xMMPP5zvdb/XoEGDtNj06dNj9erVhcp7/vnnp8Wys7Pjr3/9q6tRbkSzZs0Sj6elS5fGnXfemeu6pk2bxmGHHZYWf/LJJxPft4ta+fLlE39nTp48Od8fbsnJyYlzzz03Fi9enKHqyE1pO44AAAAAtgR77713tGvXLi3+zDPPxBtvvJGvXD/99FOcd955mSpti6Zv9DN9o03TN9qQvlHpom+UTt+o+JS24wgA2DwZwAMAgK3UeeedF1lZWWnx66+/PsaNG5evXOPGjYvrrrsucdvf/va3AtWXNAg3YsSIDf6dlZWV5zvW/T7fmDFj0ob5KlasGJ06dcpnpUXjxhtvjL322istnkql4qyzzsroVfAmTpwYnTt3jjvuuCNP+yc1OFOpVPTp0yffzYFHH300Xn/99bT4dtttl3j1SNL1798/brrppjzvv2TJkjjssMNi2bJladv+/Oc/J14NNEmXLl3SYjk5OXHzzTfnuZaInxuSp5xySuJVVfNr7733ToutW7cuBg8eXKi8bdq0ieOOOy4t/uabb8Zll11WqNy/tXLlyo1eSXhzlNvVTO+7776NXs305ptvTrsKZXZ2dhx77LHx3XffZay+sWPHxvr16ze536WXXhq1a9dOi99xxx3xz3/+M0+PtW7duvjLX/4SAwcOzHedFExpO44AAAAAtgRJQzqpVCr++Mc/xtChQ/OUY+HChXHooYdm9BzNlk7fSN8or/SN/o++Uemjb/R/9I2KX2k7jgCAzY8BPAAA2ErttttucfbZZ6fFly9fHoccckh8+eWXecrz5ZdfxiGHHBLLly9P23bGGWdEixYtClRfXu5E16pVq6hTp07G8u23336xzTbb5ClfUatUqVK8/vrrUa9evbRtOTk5ceWVV8ZBBx0U33zzTYEfY9asWXHhhRdGq1at4pNPPsnzut69eyc2eSdOnBhHHHHERpsjv/Xf//43zj333MRt//rXv6J8+fJ5rmlrVK5cuV//+9prr40LL7wwbaj097766qvo2rVrTJgwIW1b48aN44Ybbsjz47dr1y7tzpIREQ8++GAMGjQoTzmWLVsWhx9+eNpwbUHtuOOOiVczveSSSxLv0Jkf//73v6NWrVpp8bvuuiuOOeaYQl2Zcs6cOdG3b9/YYYcd4plnnilElaVPblczXbZs2UY/vNGyZcvEq/TOnz8/2rVrF6+88kqBa8rJyYm33347evToEW3atNno1Zt/Ub9+/ejXr1/itquuuip69uy50d+bI0eOjA4dOmxwxd4999wz/8WTL6XtOAIAAADYEhx44IFx6qmnpsVXrVoVBx10UFx00UUxd+7cxLVr1qyJ5557LvbYY48YM2bMr3HnyjZN30jfaFP0jdLpG5U++kY/0zcqGaXtOAIANj/lNr0LAABQ3EaMGJHvqwFuyqmnnhqNGzfeIHbnnXfGkCFD4vvvv98g/uOPP0bbtm2jb9++cd555yVevW3RokXxwAMPxE033RRr1qxJ296kSZO4++67C1xvx44do1KlSrF69epc9znwwAPznO+AAw6IMmXKRE5OTq775GVIrzg1btw4Bg8eHAcddFD89NNPaduHDBkSLVq0iKOPPjp69+4dBx100CYHCJcuXRrDhg2L5557LgYNGhRr167Nd10VK1aMp59+Otq3b5924njkyJHRokWLuOuuu6JXr15RoUKFtPXff/99XHfddfHss88m5j/22GMTm/ds6MILL4x777331+/BvffeG4MGDYrevXvHEUccEY0bN47q1avH3Llz46uvvooXXnghXnrppVy/5w888EBUqVIlXzWcc845cdFFF20Qy8nJiT/+8Y/Rt2/fuPTSS6NSpUpp69asWRP//e9/49prr40ffvjh1/gee+wRX3/9db5q+L1TTjklrUH33Xffxe677x77779/tGzZMurUqZN4bHbp0iXxCq0REY0aNYqnnnoqjj766LT3kVdffTWGDx8ef/nLX+Kcc86JJk2abLTGnJycmDx5crz77rsxYMCA+Oijjzb63rS5u+aaa+K///1v2pUe77vvvrjkkktyHaS++eabY9SoUfHRRx9tEF+8eHEce+yx0bVr17jkkkviD3/4Q1SsWHGjNSxZsiQ++eSTGDhwYLzyyisxb968fD+PM844IwYOHBhvvvlm2rbBgwdHy5Yto0WLFtG2bdto2LBhrFu3LmbPnh0jR46M6dOnb7B/jRo14r777otu3bql5Uq6Oy0FV9qOIwAAAIAtwb///e/44IMPYsaMGRvEc3Jy4u6774577703OnXqFM2bN4/69evHsmXL4ocffoj3338/lixZssGaNm3axLnnnpt20UjnydLpG7Ex+kbJ9I1KH30jfaOSVNqOIwBg82IADwAASqFhw4bFsGHDMpqzU6dOaQN4VatWjWeffTa6d++eNui2du3a6Nu3b9x4443RrVu32HnnnaNu3bqxYMGCmDJlSgwbNizXhkylSpXi2WefjWrVqhW43kqVKsV+++230dchPwNzNWvWjDZt2mxwRdXC5CsurVq1ig8++CCOPPLImDJlStr2nJycGDhwYAwcODDKly8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a9us7p8iyjDPPPDOs9Q5swCstLcXy5cvDmpOIiIiIiIiIiIiIiIiIiIiIiKinMfwN2PPqLdh+11g0/Pxl2PPZcw5B78tfQtbMh5DQ/zBIjsQIVElEoVI8mbBnDYaQZEgOF2zp/SEUG0xdhVqWD6OxLtYlhkyyOZB6/JXIuXQh7L2GhT1f7Vf/QcET56B+62cRqC4+Kcm9ICQFpuaHXlMCAFArC2D4u0/zJVFnsAGPiKgFhmEEPn5gynAclZsSlXUm9E/BA1OGR2VuapvT6cSAAQOCxp599tmQzt20aRNuuOGGDq1XWlra4q57oTiwea1fv9a3u87NDW4YXbduXafW7Kxhw4bh3nvvDRprbGzESSedFFbz2qefforDDjsMy5Yta/HzCxcuDHp87LHHIjMzs9PrAXuvZdSoUUFjXbHrIRERERERERERERERERERERERkVXUbf4QP982EuXL7wN0rf0T2iBsCUg9+Qb0u24ZPIdPh+JOi1CVRNRRcmIy7NnDIBQ7hOKALb3/3mZY04BasQu6rzLWJXaII/sg5PzfQqQcfxWEbA9rLr1mD/a89EeUvHEHdF9VZAqMI0JWoHizAQC6r3Lvjn+GAbUsL2gjHCKrYQMeEVELdF0P/ACQaJex/JIjI96EN6F/CpZffCQS7eFtWUydd9JJJwU9njdvHpYsWdLmOR9//DGOPfZY1NV17N05PvvsM/Tt2xc333xzh3baW7BgAV555ZWgsdNPP73V48eOHRv0eP369Xjttdc6VGu4rrvuOsyaNStorKGhAVOmTMFFF12EXbt2hTzXt99+ixkzZmDixInYvn17i8d8/fXX2LRpU9DY2Wef3fHCW3DgLngrVqxAaSm3ySYiIiIiIiIiIiIiIiIiIiIiImqLVlOK3fMvxM5/ngC15Oew50sYegz6Xr8cmWf9FfaMARASf/eSKNYkewIc2QdBcrggJAVKWj9IickAAK26GFp1cbdqyBKyAu/Rs5Fz+WI4+h0W9ny+b99FwRNno27Tym719xAKyemGnLj3d+u1qiKYpg6jyQetujjGlRFFjxLrAoiI4pWu65BlGUIIeBNs+OCyo3DD0s2Y/0U+wvkZSAjgsvG5eGDKcDbfxdg111yDZ555BrquA9i78+HMmTPx1ltvYc6cORg9ejRcLhdKS0uxfv16LF68GG+++Wbg/N///vd47rnnQl6vsrISc+fOxdy5czF8+HCcfPLJGDt2LEaMGIH09HR4PB74/X7s3r0bX3/9NV566SV8+OGHQXP069cP559/fqtrHHHEEcjNzUV+fn5g7Oyzz8aYMWMwZswYeL1eyHLw1919990X8jWE6rnnnoOu61i8eHFgzDRNvPDCC3j55Zdx7LHH4pRTTsERRxyBzMxMZGRkAAAqKiqwdetWfP7551i2bFlIO/gduCudoig466yzInId5557Lm677bbAY1VV8dJLL+Haa6+NyPxERERERERERERERERERERERERWYpomqj97AXuWXA+9rjzs+WR3OtJOuwnJx8yGLSk9AhUSUSQJxQZ7r6FQy/Oh11XA5s2Brtih1ZRC91XC1PxQUnp3q6ZZe3p/ZM+ej9p1b6Dig0dh+us7PZdRX4nS126F77v3kHbazVA8maGfLCRI9kQIRyIUmxNKhgkhJJimAUUSMNVGmE31MPz1gGl0usbOkj2ZMJrqYOoqtOpi2Ly9oVUVQU7wQHK4urweomhjAx4R0QGEEJCkvRuEGoYBSZIghECiXca8343C9ENzcOl/NmJ7ecd/mBqUloj5Zx+K4wbzRWAs7ct46NChuOuuu3DHHXcEff7VV1/Fq6++2uYcRxxxBB599NEONeDtb/Pmzdi8eXOHznG73Vi8eDE8Hk+rx0iShL/+9a+YPXt20Pj69euxfv36Fs+JRgOeoihYtGgRhg8fjr/85S/QNC3wOb/fj5UrV2LlypUdnvfggw9G//79g+Y6cNfCyZMnIz09HYbx64sJIUTHLwLAoEGDMGbMmKC/uwULFrABLw5IkgSXyxX0mKyFGVsfM7Y+ZtwzMGfrY8bWx4ytjxlbHzO2PmZMFHm8r3oG5mx9zNj6mLH1MWPrY8bWx4x7BuZsfbIsIz39199pDCdj/55tKFxwGeq3rI5EaUga9ztknHkXHDkHd6vmnXjD+9j6Yp2xkCTYMwZAVRx7G7Dc6RCKA2rVbhhNPqhl+bCl9oFQ7F1aVziEJMFz+HQkDjkaZcv/gYatn4U1X/2Pn6Ahbx1ST/gjksZMg2gjIynBAyUpA1JictDvv9paOd40TRj11dBqS2E01IRVZ0cISYKS0htqWT6M+hoYziRITg/8pXm/PG/zuaYjYn0fU/vYgEdEdAAhRLMdwvZ33OB0/HjTcVj+/R48+XkeVv5U2uaOeEIAJw7NwOUT+uO0g7MgS51rBKLI2T/j22+/HXV1dZg7d27I55988sl4+eWXg37IaY/N1tqPvaE55JBD8OKLL2LMmDHtHnvRRRehpKQEt912G1RVDWvdcAghcOutt2Lq1Km47rrr8MEHH3R6rtzcXNx66624+OKLg+7Pd955BxUVFUHHnnvuue3exx1x7rnnBjXgbdy4ERs2bMDo0aMjMj91jizLSEpKinUZFEXM2PqYsfUx456BOVsfM7Y+Zmx9zNj6mLH1MWOiyON91TMwZ+tjxtbHjK2PGVsfM7Y+ZtwzMGfrkyQp7F/yNzU/yt59AGVv3w1TbQy7JlvGQGT87u/wjJ0GyZ4Q9nw9He9j64uXjG0pOZBsTvjL8iA5k2BP6w+1fBdMrQn+sjzYUvt2u3ta8fZC1syH4fvufZS/ez+MhupOz2U2+VC+7F74Nr2P9Cm3w5bWN+jzsisFyi9/hx0hhIDs8kJ2eWGojdAqC6H7KjtdZ0dI9gTI7jTodWXQqopgy0gE0Aitcnez66O2xct9TK1jAx4RUSfIksDU4b0wdXgv1DSq+GZ3DdYXVCG/sgF+3YBdlpCbkoAxfbw4rLcHHmd4zVcUXffddx+OO+44/OUvf8EXX3zR6nGHHXYYbrzxRpx33nkdXuO0007Dzz//jGXLlmHVqlVYs2YNSktL2zxHCIHf/OY3mD17Ni666CIoSujftvfV+dJLL+F///sftmzZgvLyctTV1UHX9Q7XH44RI0Zg5cqV2LBhAxYsWIC3334beXl57Z7Xp08fTJ48GRdeeCEmTZrU4i52CxYsCHpss9lw5plnRqjyvc455xzcdNNNzdZ9+OGHI7oOERERERERERERERERERERERFRd1O/bQ2Knr8UTQWbwp5LyHZ4j70U6VNvhc2bHYHqiKirye5UOBQ7/CU/A3DCljEAWuUuGP5GqGX5ULzZkBOTY11mhwgh4B51MhIGHYnydx+Ab9P7Yc3XmLcOu5+cgZRJf4Bn/HkQdifsaf0gu1KaHas31KAxbz0a89ZBLd8JU2uCUBywpfWDs/9YOPuPgZzgCRwv2ZywZw6E7quEv2wnYGhh1RoKOSkdRpMPptoAraoQtrR+0GpKICUmB9VG1N0J02xr3yYisprNmzdjxIgRgcebNm3C8OHDw55X0zRs3bo1aGzIkCEdahgiige7du3CZ599hqKiIvh8PrhcLuTm5uLwww9H376RfSeGnTt34qeffkJeXh6qqqrQ0NCAxMREeL1eDB48GIceeii8Xm9E14wXRUVF+O6775Cfn4/Kyko0NTXB7XYjJSUFmZmZGDNmDHr16hXrMqOKz5tEREREREREREQULdH6/yAiIiIiIiIiso6ONnU0O7++GiWv3YrK1U8CEfh1dGf/scia8SAShx0NIclhz0dEsWVqfvj3bIPhb4BpGtAqC2E01gIAZHc6FE9GjCvsvPofP0HZsvug15aEPVfy0XOQPftJSDZHYMw0dNRtWIaKVfPg2/xB28+xQsA1/ASkTr4C7tGnBz1/mroGf8nPMBrrwq6zPYbaBLVsB2CaUJKzILtSIWQbHL0PgZD5e7FkDfxKJiIi2k/fvn0xY8aMLlmrX79+6NevX5esFW+ys7ORnc13KCIiIiIiIiIiIiIiIiIiIiIiIuoqkWrqMDQ/Cv99CWq/fi3smqSEZKSddhNST/wjZEdi2PMRUXwQih327GHwl2yH0VADW2ofaDUl0OvKodeVwdT9UJKzISQp1qV2WOKwieiTOwYVHzyK2nVvdHoe96GnNWu+821ehcIFl0It2R7aJKYJ36aV8G1aCVvmQOTMfhqu4ZMBAEJWYM8a8ksG1Z2uMxSSzQHFkwWtuhhabQkkhwsAoJbvhD1zYFTXJuoqbMAjIjqAaZrYf3NQIQSEEDGsiCKNGVsfM7Y+0zShab9uja4oCjO2GGZsfczY+phxz8CcrY8ZWx8ztj5mbH3M2PqYMVHk8b7qGZiz9TFj62PG1seMrY8ZWx8z7hmYs3VEsqlDUuzofdmLKFAbULdxeadrco86FVkzH4Ije1in56D28T62vnjNWEgy7FmDoVUUQKspgeLJhFDs0KqLYTTUQNVU2FL7dMsd0iSnG+lTboVrxIkoW3oPtIpdHTo/8eBJ6HP164HmO6OpHntevh6VH83v9K6iasl25N9/AlImXYasGQ9CciRCSBLsmQPh37M16jvhya4UGI21MJp80KoKoaT3h+6rhF5XAdmdGtW1rSBe72P6Vfd7piIiijLTNKHreuCxLMv85mUxzNj6mLH1aZqG8vLywOO0tDTYbLYYVkSRxoytjxlbHzPuGZiz9TFj62PG1seMrY8ZWx8zJoo83lc9A3O2PmZsfczY+pix9TFj62PGPQNz7v6i1dQh2Z3oc9Vr2PngKaj/4eMOzaek9EbmOXORPH5G0M56FB28j60vnjMWQsCW1hfC5oBavgtyohdCtkGr3A1TbYBalgcltQ8kmzPWpXZKwoBx6H35ElR9/DSqP18EmEa758jJWehz1X8CzXe6rwo7HzoVDdvWhF+QaaJy9VNo3LkR/a5bAdnl/aUJbxAaCzYDhtb+HGFQvDnwl/wMw98IvbYMSlIG1PKdkJxuCMUe1bW7u3i+j2mv7rdfJxERERERERERERERERERERERERERERG1SfdVIf+fx6Ny9VOdbr4L+KWpI/+fx0P3VQHA3ia8q1+D7MkMbQ4hw/vbSzDw7o3wTjifzXdEPYjiyYS91xAISYbkcMGW3h9CscPUVahl+VHfnS2aJJsTqSdcg5xLFsCeNaTd47MvehKKOw3A3ibpiDXf7adh2xrsfOhUGE31AAAhK7Cn94voGi0RsgLFmw0A0OvKYaoNMA0d/rK8qK9NFG1swCMiIiIiIiIiIiIiIiIiIiIiIiIiIiKykK5q6lDcaci+6Ml2z3P0Ho7cm1cj5/fPQElKi2hNRNQ9yAke2LOHQSh2CMUOW0Z/SI5EwDSgVuyCXlcR6xLD4uh9CHIufREpx10OyC3vXOY58lx4xp4ZeLzn5esj/jy9T8O2Ndjz8vWBx7IrBbIrJSpr7U9O8EBK8ACmCbVyN0zTgNFQC62mJOprE0UTG/CIiIiIiIiIiIiIiIiIiIiIiIiIiIiILKQrmzo8486C58hzWzxW2BORceZfMeBv6+E6aGJU6iGi7kOyJ8CRczAkhwtCyFDS+kFK9AIAtJo90KqKYYa7Y2cMCVmBd+LF6P2HxXD0HXXAJwUyf/f3wEPf5lWo/Gh+VOup/Gg+fFtWBx4rKTlRXS+wTnIvCNkGU1Oh/9J4p1YUwPA3dsn6RNHABjwiIiIiIiIiIiIiIiIiIiIiIiIiIiIii4hFU0fGWXcDQgQdk3jIcRh49wZkTLsTkmKPaj1E1H0IWYG911DI7lQICNi82VA8mQAAvb4SWsUumIYe4yrDY88YgOw5zyLtlBshbAkAANfwE2DPGgwAMA0dhQsuBaLdbGiaKHz+0sDfp2RzQkpIiu6aAIQkQ/FmAwB0XyWMprq9O+KV7ejWDZbUs7EBj4iIiIiIiIiIiIiIiIiIiIiIiIiIiMgCYtXU4eg1BK7hxwMA5KQM5PzfAuT++UM4eg2Jbh1E1C0JSYI9Y0BgRzbZnQZbah9AEjCafFDL8mBq/hhXGR4hSfAceS56X/kqEgYdhdTJVwQ+V7dhGdSS7V1Sh1ryM+o2Lg88VpIyu2RdyeGC7EoBAGiVRTANDUZTPbSqoi5ZnyjS2IBHREREREREREREREREREREREREREREZAGxbOpImXwFkn9zIQbd9wO8R18EccCOeEREB7J5s2HPGAAIAcmZBHtafwjZBlPzw1+WB6OpPtYlhs3mzUbWrMfhHj0lMFaxal6X1rD/elJiMiC6ppVITsqEUOwwDQ1adTEAQKsqgtFY1yXrE0USG/CIiIiIiIiIiIiIiIiIiIiIiIiIiIiILCCWTR1Jh01F70sXQnGndmkNRNS9ye5UOLKHQcgKhM0JW3p/SHYnYOhQy3dCr6+OdYlhkx0uCGlv+47eUAPf5g+6dH3fppXQG2oAAEIISPaELllXSBIUb28AAkZDLfSGKgCAvywPpmF0SQ1EkcIGPCIiIiIiIiIiIiIiIiIiIiIiIiIiIqJuLvZNHfzVdCLqHMnhgiPnYEj2BAhZgZKWCykhCYAJraoQWk1prEsMi3AkBj5uzFsPmGbXFmCaaMz/Zr96XF22tGR3Qk5KBwDo1Xtg6ipMtQlqxa4uq4EoEvhTDhEREREREREREREREREREREREREREVE3Fw9NHUREnSUUO+zZwyAnJkMICbaUPpDdaQAAva4MakVBt901TbLv34C3LiY17L9uV+2At4/sToOwJ8A0DGhVhQAAvbbMErsbUs/BBjwiIiIiIiIiIiIiIiIiIiIiIiIiIiKibi4emjqIiMIhJBm2zEFQPJkAAMWTCSUlGxACRmMt1PKdMHUtxlV2nJDlwMdq+c6Y1KCW5Qc+FpLSpWsLIWDz5uzNsakeel35LzXldcs8qWdiAx4RERERERERERERERERERERERERERFRNxcPTR1EROESQsCW1he2tH6AEJATvLCn9YOQZJhqA9SyPBhqY6zL7CAR+MjUmmJSgan7f30gROsHRolQ7FA8vQAAWm0pTK0Rpq7xewh1G13btkpE1A0IISDv9y4DIgY/YFB0MWPrY8bWpygK0tLSgh6TtTBj62PG1seMewbmbH3M2PqYsfUxY+tjxtbHjIkij/dVz8CcrY8ZWx8ztj5mbH3M2PqYcc/AnLuXuGjqoLjD+9j6rJqx4smAsDmglmwH7ImwpfeHWrELpuaHWpYHW0ofSE53rMsMkRn4SCiOmFQgZPuvD0yz9QOjSHZ5YTTWwmiqg1ZVBCW9P/T6Kmh15VDcae1PYGFWvY+thIkQER1ACMFmHYtjxtbHjK1PCAGbzRbrMiiKmLH1MWPrY8Y9A3O2PmZsfczY+pix9TFj62PGRJHH+6pnYM7Wx4ytjxlbHzO2PmZsfcy4Z2DO3UtcNHVQ3OF9bH1WzlhO8EBkHwR/yTYAgC2jP7SKAhhN9VArdkHxZEF2p8a4yvaZuh742JbWLyY12NJzAx+bhhaTGgBA8WbDX7odhr8Rek0pFE8mtPJdkBxuSLbYfB+LB1a+j61CinUBRERERERERERERERERERERERERERERBSeeGjqICKKNMnuhCP7IEhON4SQoaT1g5ToBQBoNXugVRXDjNGObqEy/PWBj539x8akhv3XNfwNMakBAISsQEnOBgDodeUw/fUwDR1qWV7c50g9GxvwiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLq5eGjqICKKBiErsPcaCtmdCgEBmzcbiicTAKDXV0Kr2AXT0NuZJXbMpv0b8MYAQnRtAULAmXvYfvX4unb9A8gJSZASkgEAalUhTFOH0VgHvaYkpnURtYUNeERERERERERERERERERERERERERERETdXDw0dRARRYsQAvaMAVBScgAAsjsNttS+gCRgNPn27qCm+WNcZcsMf31gdzc5wQPX8BO6dH3XiBMhJ3gAAKZpxnQHvH2U5CwI2QZTU6FV7228Uyt3x0VtRC1RYl0AEVG8MU0ThmEEHkuSBNHVL0gpqpix9TFj69N1HfX1v74jTGJiImRZjmFFFGnM2PqYsfUx456BOVsfM7Y+Zmx9zNj6mLH1MWOiyON91TMwZ+tjxtbHjK2PGVsfM7Y+ZtwzMOf4V/vdSrgPOQ5CVgJNHb5NK7ts/f2bOig+8T62vp6Wsc2bDcnmhL8sD5LTDXt6f6jlBTA1P/xlebCl9IHkSIx1mcFMA0Z9NWSXFwCQOvmKLn2uTp18ReBjo74aMI02ju4aQpKheHOglufDqK+C4XRDciZBLd0Be/ZBEFLP2m+sp93H3REb8IiIDnBg444Qgo07FsOMrY8ZW59hGPD5ft0C3el08oWGxTBj62PG1seMewbmbH3M2PqYsfUxY+tjxtbHjIkij/dVz8CcrY8ZWx8ztj5mbH3M2PqYcc/AnOOX3lCLPUuuR9V/n0HfP76FpDFnAIhtUwfFJ97H1tcTM5ZdKXAodvj3/AwAsKX3h1ZZAMPfALV8JxRvL8iJ3tgWeQCttjTQgOcefTpsmQOhlmyP+rq2zEFwH3r6fnWURH3NUEmORMjuNOh15dCqimDLTIDhb4BWVQhbap9Yl9eleuJ93N30rJZQIiIiIiIiIiIiIiIiIiIiIiIiIiIiom7M98Mn2H7Hoaj67zMAgIpV8wKf29fU0RUObOogIupKksMFR85BkOwJELICJa0fpIQkACa0qiJoNfHTaAYARkMNDLURwN7d33JmPw1Ee3MJIZAz5+nAbnKG2gijoTa6a3aQnJQBoThgGjq0qmIAgFa9B3qc1UnEBjwiIiIiIiIiIiIiIiIiIiIiIiIiIiKiOGf4G1C8+Drk33cs1NIdgXHf5g/g37MNQOyaOoiIYkEodtizh0FOTIYQEmwpfSAnpQMA9LpyqBUFMA0jxlX+SqssDHzsGj4ZKZMui+p6KZMug+uQ41pcP14IIaCk5AAQMBproddXAQDUsjyYhh7T2oj2x594iIiIiIiIiIiIiIiIiIiIiIiIiIiIiOJYw/avsP3OMah4/1+AaQZ/0jRR8vrtgYexaOogIooVIcmwZQ6CkpwFAFCSMqCkZANib0OXWp4PU9diXOVeuq8Suq8y8DhrxoNIGHxUVNZKGDIBWTMebHXteCLZnFA8GQAAraYYpuaHqfmhlu+KcWVEv2IDHhEREREREREREREREREREREREREREVEcMjU/St64EzvuPgr+oh9aPa7my1dQ8/Ubgcdd2dRBRBRrQgjYUvvAltYPEAJyghf2tH4QkgxTbYRalgfD3xjrMgEA/rKdgYZAyZGIftetiPjzdcKQCeh33QpIjkQAgKmr8JftjOgakSa70yDZEwHDhFa1d6c+va4cuq8qtoUR/YINeERERERERERERERERERERERERERERERxprFgE3b8bTzK3r4bMPR2jy9aeDm0unIAXdfUQUQUTxRPBuxZgyEkGcKeCFt6fwjFDlNXoZbnQW+ojXWJgKHBX/IzTMMAAMguL3L//CFSjvsDIER4cwuBlOP+gNwbP4CcmLx3OX8jKj/+N2DExy6AbVG8OYCQYPgboNeVAcDeHQw1NcaVEbEBj4iIiIiIiIiIiIiIiIiIiIiIiIiIiChumIaOsuX/xI6/jEVj/jchn6fXlKDgsemBXZ6i3dRBRBSP5AQP7NkHQdgcEIodtoz+kBwuwDShVRZA/6VROZaMxjr4S7YHmvAkRyKyL3oSuX/+ELbMgZ2a05Y5CLl//hDZFz0ZaJI2/I0oeHw69iy5DmpVUcTqjxah2KAkZwEAtNoymGojTF2DWp4f48qI2IBHREREREREREREREREREREREREREREFBf8e7Yh797fouTVm2Bq/g6fX//Dxyh4/NcmvGg1dRARxTPJ7oQj+yBITjeEkKGk9YXsSgEAaDUlUKuKYJpmTGs0Gqrh37MNpv7rznSuQ47D4Lk/oe+1b8M18qT2m6eFgGvkSeh77dsYPPdHuA45LvAprbYMOx88BXUbl8NUG1C+9N6YX3Mo5EQvJGfS3obJqkKYpgG9vhpaTWmsS6MeTol1AUREREREREREREREREREREREREREREQ9mWmaqFz9JPa8fCNMf31YczUVbEJT0Q9IyB0dGNvX1FG3cTkqVs2Db9NKoK1GDCHgGnEiUidfAfehp0FIclg1ERF1NSErsPcaCrUsH3pdOZTkXhCKHVr1Hhj1VdA0P5TUPjF9fjMaa9FYsBn29H6BBkEhyUg6bCqSDpsKvaEGjfnfoDFvHdSyfJi6H0K2w5aeC2f/sXDmHgY5wdNs3pqv30DRwsuh15QExhp+XoO6b1cg6dDTuuz6OktJ7gXV3wBDbYJeWwrFkwW1sgBSggeSzRHr8qiHYgMeERERERERERERERERERERERERERERUYyo5btQ+O+L4dv8QdhzeX97CbLOewhyQlKzz0WiqYOIqDsRQsCe0R+azQm1cjdkVyqEbIdWtRuGvx5qWR5sqX0hFHvsijQ0+Eu2Q3alQEnJgWRzBj4lJ3jgOui3cB3025CmaireitI37kDNl6+0+PmK9x5C4qCjILtTI1J6tAhZgeLNhlqxC3pdBSSHG5LDBbV0B+zZwyDa2xmQKArYgEdERERERERERERERERERERERERERETUxUzTRPVnL6L4pWtg1FeHNZeS3AvZF/8bSYeeGtLxHW3qICLqzhRvLwibA/6yPEhON2zpuVDLC2BqfvjL8mBL6Q3J4YppjbqvErqvElJCEpSkTEiJySE1mpmGhtpvlqFy9Tz4Nn/Y5u6mRkM1yt97AJnT741k6VEhOd2QE1Og11dCqyqCLXMAjCYftOpi2LzZsS6PeiA24BERHeDAH1TYIW89zNj6mLH1CSGgKErQY7IWZmx9zNj6mHHPwJytjxlbHzO2PmZsfczY+pgxUeTxvuoZmLP1MWPrY8bWx4ytjxlbHzPuGZhz9Gg1JSh6/jLUrn8r7Lk8R85A9oVPxP2ORhQbvI+tjxmHRnalwKHY4d/zMwDAlt4fWmUBDH8D1PJdULy9ICd6Y1skAKOhFv6GWkBIkOwJEA4XhM0J3QSEkGCaBmQBmGojzCYfmvZsw+55M2BqTSHN79u0EvUjT0HisGOifCXhkz2ZMJrqYOrqL413vaFVFUFOSIbkSIx1eRHF+zj+CdNso72ViCxn8+bNGDFiRODxpk2bMHz48LDn1TQNW7duDRobMmRI0DcBoq6Sl5eHzZs3Iz8/HzU1NdB1HW63G71798bQoUMxYsQISJIU6zK7NVVVsWnTJmzbtg1FRUXw+XwQQsDr9SIlJQUDBw7EoYceCrs9hltyxzk+bxIREREREREREVG0ROv/g4iIiIiIiIgoMmq+fgNFCy6DXlsW1jyyKxXZs5+C54izI1QZEZH1mZof/pKfYTTVwzQNaFWFMBpqAQCyOw2KJzPGFXZc9WcvouKDR0I+XvZkoc8Vr0ByuqNYVWQY/gaoZXkAAFtqb0hOD4TNCUfOwRD8fXDqQvwNbyIisoTNmzfjmWeeweuvv46CgoI2j/V4PDjllFMwZ84cnHTSSSHN379/f+Tn50ei1JA8//zzmD17dlhzHFjzRRddhAULFnR6vvr6eixZsgSvvvoqPv30UzQ0NLR5vMPhwOjRo3HmmWfi/PPPR58+fTq99v5Gjx6NjRs3Bo2dccYZeOuttyIyPxERERERERERERERERERERERUTTovkoUL7oG1Z8vCnsu96GnIef3z0Lx9opAZUREPYdQ7LD3Gga1dDv0+mrYUvpAU0qh15ZBryuHqfmheHO6VXOXZ/x5qNu0Ev6i70M6Xq/Zg4oPH0f66TdHubLwSfYEyO506HVl0KqKYctIBNAIrXI3bGl9Y10e9SDd5xmBiIioBbt378a5556LkSNH4pFHHmm3+Q4Aampq8Morr+Dkk0/G4Ycfji+//LILKu2+VFXFAw88gNzcXFxyySVYuXJlu813ANDU1IQvv/wSN998M3JzczF16lRs2rQprFq++eabZs13ALBixQqUlpaGNTcREREREREREREREREREREREVG01H33Pn6+bWTYzXeSMwk5Fz+Hvn9ayuY7IqJOEpIEW+YgKMlZAAAlKQO2lBxACBiNtVDL82HqWoyrDJ2QFaSfcQcgySGfU/v1a2jM/yaKVUWOnJQOYXPCNHRoVYUAAK2mBHpDTYwro56EDXhERGHSG2rg+/5jlL/7IIoX/RFFC/6A4kV/RPm7D8L3/cf8xh5F77zzDoYPH45XX30Vpml2ao6vv/4aEyZMwF133dXpOaxs69atOPzww3HjjTeirKys0/MYhoGlS5fi0EMPxe23397peVrbwU9VVbz00kudnpeIiIiIiIiIiIiIiIiIiIiIiCgajMY6FC24HDsfOBla5e6w5ko86FgMvOc7eCfOgRAiQhUSEfVMQgjYUvvAlp4LCAEpIRn2tH4QkgxTbYRaugOGvzHWZYbM0Wsokn9zYYfOKXvnbhhqU5QqihwhBBTvLw2STT7ovkoAgFqaB9PQY1wd9RRKrAsgIuqOTENH3YZlqFg1D77NHwBtNW4JAdfwE5A6+Qq4R58O0YF3FqDWzZ8/H5dffnmzpjmbzYYTTjgB55xzDkaMGIGcnBw4HA4UFhZix44deOutt/DWW2+hoqIicI5hGPjrX/+K7du3Y8GCBZBa2DL68ssvR2VlZcj1zZ07N+jxb37zGxx99NEhnz9y5MiQj42WNWvW4NRTT0VVVVWzz/Xv3x+nnHIKTjzxRPTv3x8ZGRlITk6Gz+fDrl278N1332H16tV4++23UVtbGzjPMAz873//61Q9qqpi8eLFrX5+wYIFuPbaazs1NxERERERERERERERERERERERUaT5fvwUhc/Mhlq6Pax5hC0BWefORcrkKyFa+P02IiLqPCUpHUJxQC35GbAnwpbeH1plAQy1CWp5HhRvb8gJSbEuMyTeiZegfstqqOX5IR2vlu9E1SfPInXylVGuLHySzQElKRNazR5otXsgORIB7L0Ge8aAGFdHPQEb8IiIDmCaJjRt75bBQgjIshz0TjG+zatQuOBSqCUhviA2Tfg2rYRv00rYMgciZ/bTcA2fHI3Se4xXXnmlxea74447DvPnz8fgwYObnZOamooRI0ZgypQpePjhh3HLLbfgySefhGEYgWNefPFFuN1uzJs3r9n5N910U4dqPLAB7/jjj8ddd93VoTliaePGjTjppJOCmucAoF+/fvj73/+OCy64oMV3UHK73cjKysK4ceMwZ84cNDQ04LnnnsM//vEP7N4d3rs3LVu2LGgXPpfLhfr6+sDXwcaNG7FhwwaMHj066D4GAEVR+I5PFqNpWlBzqNfrhaLwR1srYcbWx4ytjxn3DMzZ+pix9TFj62PG1seMrY8ZE0Ue76uegTlbHzO2PmZsfczY+pix9THjnoE5d47hb0TpG3eg/L0H236D/xA4Bx6B3pe9CEevoRGqLhgztj5mbH3MOHxyQhJE9kHwl2wDACjpudAqdsNo8kGrLAD0TMjutJjVp+saamvrAo+TktyQ5eYZSzYH0qfejqLn/y/kuas/ewGu4SdE7ftMJMnuVBhNdXtzqSqCkp4Lva4CekIyZHdqrMsLC+/j+Me3QCAiOsC+Zp4Dm++MpnoULbwc+fefEHrz3QHUku3Iv/8EFC28HEZTfcRq7km2bNmC3//+982a72666SasWrWqxea7A7lcLvzrX//Cm2++CbvdHvS5J598EgsXLoxozd1NVVUVpk6d2qz57qSTTsKWLVswa9askJvZEhIScOWVV+KHH37AH//4x7DqWrBgQdDjc845B8cee2yLxxz49XHgY+r+9jVZ7vvDjK2HGVsfM7Y+ZtwzMGfrY8bWx4ytjxlbHzO2PmZMFHm8r3oG5mx9zNj6mLH1MWPrY8bWx4x7BubccQ071mHHX8ai/N0Hwmu+k23InH4vBtz+WVSbIpix9TFj62PGkSHZnXBkHwTJ6YYQMpS0vpBdKQAAraYEalVRzP5uTXNvE96+P22V4cw9DEnjpoc+uaGj7O27Yepa+8fGAcWbAwgJhr8Beu3ejT3U8p0wNX+MKwsP7+P4xwY8IqJW7N98p/uqkP/P41G5+qmw340GponK1U8h/5/HQ/dVhV9oD2KaJi655BLU1wc3L1599dW47777OjzfKaecgkWLFkGSgr8dXnvttSguLg6r1u7suuuuw86dO4PGzjjjDCxduhQul6tTc7rdbjz88MN4++234Xa7O3x+aWkp3n333aCxCy+8ELNmzQoaW7x4MVRV7VSNREREREREREREREREREREREREnWVqKkrf/Ct23D0eTYVbwprL0WckBt71NdKn3ALRwg5HREQUHUJWYO81FLI7DQICSnIvKMlZgBAw6qugle/sFo1qqcdfBdmTFfLx/qLvUfPFkihWFDlCVqB4swEAel05TLUBpqHDX5Yf48rI6tiAR0TUggN3vtv50Klo2LYmoms0bFuDnQ+dyp3wOuA///kP1qwJzmHkyJF46KGHOj3ntGnTcNlllwWNVVVV4e9//3un5+zONmzY0GynuZycHCxYsAA2my3s+adOndps/lAsWrQoqLGuX79++O1vf4vp06cjISEhMF5aWorly5eHXScREREREREREREREREREREREVGomnZvwY67j0LpW3cB4TRmCAnpU27FwL9+DWe/URGrj4iIQieEgD2jP2wpvQEAsisVtpQ+EJIEw18PtSwPhtoU4yrbJjndSD/t5g6dU/nRU1ArCqJUUWTJCR5ICR7ANKFWFsI0DRgNNdBqSmJdGlkYG/CIiFqw/45oe16+PuLNd/s0bFuDPS9fH5W5regf//hHs7GnnnoKihLeO/zcc889yMoKfpeHZ599FmVlZWHN2x3de++9zbYsnjdvHrxeb8TWSE9P7/A5BzbtnX/++RBCICkpCdOmTWvzWCIiIiIiIiIiIiIiIiIiIiIiomgwDR1lKx7A9r+MQWPeurDmsmcNQf/bP0Pm9HsgFHuEKiQios5SvL1gzxwESBIkpxu29FwI2QZTV/c24TX5Yl1imxKHHQPXiBNDPt7UmlC29J5mv0ccr5TkXhCSAlPzQ/+l8U6t3A3D3xjjysiquCcxdcq2bduwYcMG7Nq1Cz6fDwkJCcjJycGoUaMwfPjwqK5tGAa++eYbbNq0CSUlJWhqaoLb7caAAQMwduxY9OnTJ6rr19fXY+3atfjxxx9RWVkJ0zSRnJyMIUOG4IgjjkBycnJU16fo27fzHQD4Nq9C5Ufzo7pe5Ufz4Tn8bLgOOS6q63R369evx4YNG4LGJk6ciAkTJoQ9d3JyMi677DL87W9/C4w1NTXhxRdfxJ/+9Kew5+8uSktL8dZbbwWNDR48GFOnTo1NQb9Yv349vv3226CxCy+8MOjjJUt+3fZ6xYoVKC0tRWpqapfVSEREREREREREREREREREREREPYu/ZDsKn5mN+p8+DXuu1BOuQebZ/4DkSIxAZUREFCmyywuHMgz+PdsAALaM/tAqCmD4G6CW74KS3AuyyxvbItuQdvINaPj5SxgN1SEd37jjK9R98zaSxkyLbmERICQZijcbasUu6L5KSE43JIcbatkO2LMPCuoJIIoENuB1MxUVFfj6668Df9atW4edO3c2Oy4aXccNDQ148skn8dRTT2Hr1q2tHtenTx9cfPHFuPbaayO6Y9Lu3bvxwAMP4MUXX0R5eXmrxx122GG48sorMXv2bMiyHLH1v/rqK9x///1YunQpGhtb7opWFAUnnHACrr/+ekyePDlia1PX2vfN1jR0FC64FIh2F79povD5SzF47o8QUuS+Zq3mzTffbDY2Z86ciM0/e/Zs3H333UHPn2+99VaPasBbtmwZVFUNGrvkkkti/gPogTvajRs3DgcddFDg8QknnIBevXqhuLgYAKCqKhYvXoyrrrqqK8skIiIiIiIiIiIiIiIiIiIiIqIewDRNVH38NIqXXA8zzN2PlNR+6H3pArgOnhSh6oiIKNIkRyIcOQfDX7INRlM9lLR+0KqLYNTXQKsugqk1QUnOinWZLZLdqUg9+TqUvfmXkM+peP9hJAw5GkpSehQriwzJ6YbsSoHuq4RWWQRb5gAYTfXQqopgS8mJdXlkMWzAi3ObNm3C8uXLsW7dOnz99dfYsWNHTOr47LPPcP755yM/P7/dYwsKCvDXv/4V8+bNw7PPPhuRnZPmz5+PG264AXV1de0e+8033+CSSy7BY489hldeeQXDhg0La+2mpib86U9/wlNPPdVuY6OmaXj33Xfx7rvv4uyzz8azzz4Lj8cT1vo9gdHkQ9PuLbEuAwBgzxoM2ZUCAKjbsAxqyfYuWVct+Rl1G5cj6bC994vuqwy8U0IsOXofAsnhinUZAIAPPvgg6LEQAmeddVbE5h8wYADGjBmDdevWBcbWrFmD+vp6JCb2jHcVOvDvGEDMd79TVTVodzsAmDVrVtBjWZYxc+ZMPPTQQ4GxhQsXsgGPiIiIiIiIiIiIiIiIiIiIiIgiSq3YjcLnLobvu/fDnss78ffImvkvyAn8PVMiongnFBvsvYZBLd0Bvb4KNm9vaLIdem0ZdF8FTF2F4s2BkKRYl9qMe9Sp8H37Hhp+XhPS8UZTHcpXzEXWufdHubLIkJMyYTT5YGp+aNXFsKX0gVZVBDkxOW5+D56sgQ14ce7ZZ5/FI488EtMa3njjDcyYMaPZrkjtKS0txbRp0/D444/jiiuu6PT6119/fVBTRag2btyII444AitXrsSRRx7ZqbV9Ph9OPfVUfPLJJx0+9z//+Q+2bNmCjz76CBkZGZ1av6do2r0FO/56RKzLgHC4cNCTVYHHFavmden6FavmBRrwJGcS8u6bFPa744RrwF/WImHg4TGtAQB0XcfGjRuDxoYOHRrxBtexY8cGNeCpqopvv/0W48ePj+g68errr78OepyUlBR2E3O4li5dirKyssBjRVFw3nnnNTvuwgsvDPpesXHjRmzYsAGjR4/uijKJiIiIiIiIiIiIiIiIiIiIiMjCTNNE9ecvoXjR1TDqq8KaS/ZkIef3zyDpsCmRKY6IiLqEkCTYswZBrdgNrboYSlIGJMUOtboIRmMt1PJ82FL6QCi2WJcaRAiBtCm3YvcT58BUG0I6p/77j+DbshquQ46LcnXhE5IExdsbalkejIZaGM5qSAnJ8JfugCPnkLhsiqTuiQ141KbPP/+8xeY7SZJw+umn4+ijj0bfvn1RWlqKjRs3YsmSJaivrw8cZ5omrrrqKmRnZ+PMM8/s8Pr3339/i813LpcL5513HkaNGoX09HTs2rULn376KZYvXx60S11NTQ1OOeUUrFu3DgMGDOjQ2qZpYsaMGS023/Xu3Rvnn38+hgwZAofDgR07duCtt97CN998E3Tc5s2bcfrpp+PTTz+F3W7v0PrU9RJyD4OQ9z4t6g018G1uvhtYNPk2rYTeUAM5wQMhK3D2G42GrZ91aQ3xaseOHWhsbAwaGzduXMTXGTt2bLOxzZs394gGPL/fj59//jlo7LDDDoMU4x86FyxYEPT45JNPbrGp+dBDD8XIkSPx3XffBcZefPFFNuAREREREREREREREREREREREVFYtJpSFC38A2q/fiPsuTxHnINeF82D4k6LQGVERBQLttTeEDYH1PKdkBKSYVfsUMt3wVQboZblQUntC8nujHWZQWzebKRMvhIV7z0Q8jnlK+bCOWBct9ipVbI7ISelQ68t3bsLnj0RAKBVFsCW1i/G1ZFVsJWzm7Lb7RgzZgwuvfRSeL3eqKzh8/lw3nnnNWu+GzZsGDZt2oS3334bN954I2bMmIGrr74azz77LHbt2oWpU6cGHW+aJubMmYPi4uIOrb9+/XrceuutzcbPOOMM7Nq1C8888wyuvvpqnHfeefjzn/+MpUuXYtOmTRg6dGjQ8ZWVlZg5c2ZQY14oHn/8cSxbtqzZ+N/+9jfs2LEDc+fOxSWXXIJZs2bhzjvvxPr16/Hmm2/C7XYHHb927VrccccdHVqbYsPZ/9fmq8a89UAHv2bCZppozP+1iTNhQOQbzLqrXbt2NRvLzc2N+DotzVlQUBDxdeLR7t27YRhG0FifPn1iVM1eJSUlePfdd4PGZs2a1erxF154YdDjl19+ucO7txIREREREREREREREREREREREe1Tu/5t/HzbiLCb7yRXKnpf8TL6XPkKm++IiCxASUqHPWsIhCRD2BJgyxgAyeaAaWhQy/OgN9TGusRmPEecDUefkSEfr9eVo2LlI1GsKLJkdxqELQGmYUCrKgSwt4ler6+OcWVkFWzA6wYURcGoUaPw+9//HvPmzcPatWtRW1uLdevWYf78+UhOTo7Kuvfeey927twZNDZ48GB8/vnnOPjgg1s8JzU1FW+88QZ+97vfBY1XV1fj5ptv7tD6V199NTRNCxqbPn063njjDaSkpLR4ziGHHILPP/8cgwcPDhr/4osvsHDhwpDXLisrw+23395s/NFHH8Udd9wBm63lbWGnTZuG1atXN9vt7l//+he2bt0a8voUG87cwwIfN+ati0kN+6/r7Dc6JjXEo4qKimZj0Xjua6mhuby8POLrxKOu+jvuiEWLFgV9H0hOTm7W5L2/mTNnQpblwOPS0lKsWLEiqjUSEREREREREREREREREREREZH16L4q7H76Iux6ZBr0mpKw5nKNOhWD7t2E5CPPjVB1REQUD+SEJNizD4KwOSBkG5T0XEhON2Ca0CoLoNWWxbrEIEKSkT71dkBSQj6n7pu30bD9qyhWFTlCCNhScgAhYDTVQ6/b+zvgalk+TF1r52yi9rEBL87ddNNNqKmpwcaNG/Hvf/8bl19+OQ4//PBmDV6RVlVVhccffzxoTJIkPP/880hNTW3zXFmWMX/+fGRlZQWNL1q0CNu3bw9p/dWrV+Pzzz8PGuvVqxfmz58PSWr7yzYtLQ3PPfdcs+PuueeeZrs7tebRRx9FTU1N0NiJJ56Iq6++ut1zDz/8cNx5551BY6qq4h//+EdIa1PsyK5fv7bV8p1tHBk9all+4GPZ3fa91pM0NDQ0GwunOUwIAUmSAn+EEK3O2dLaVhTpv+NIOLBx+uyzz4bT2fqW3Dk5OZg8eXLQ2IsvvhiUMVmHJElwuVyBP+39fEDdDzO2PmZsfcy4Z2DO1seMrY8ZWx8ztj5mbH3MmCjyeF/1DMzZ+pix9TFj62PG1seMrY8Z9ww9Mee6TR/g59tHovqzF8KaR3K4kT3nGfS7bhls3uwIVRd5PTHjnoYZWx8zjh3J7oQj+yBICUkQQoaS2geya+9mQ3ptKdTKQpimGf46kkBCQkLgjyR17vdi7ZmD4J34+w6dU7b0Hhj+xk6t19WEYofi6QUA0OpKYWqNMHU16Hf04xXv4/jHROJcdnY2EhISunzdl156qVkD2mmnnYajjz46pPPT0tJw4403Bo3puo5nnnkmpPOffPLJZmM33nhju81/+xxzzDE49dRTg8a2bduGDz74oN1zW6uzIw10N9xwA9LSgrcIX7JkCaqruX1pPBPKr42tptYUkxpM3R/4WCiOmNTQXYTTUCWEgCzLgT/75mKTVrBY/n2sX78e3377bdDYrFmz2j3vwGPeffddVFRUMFsLkmUZSUlJgT/7735I1sCMrY8ZWx8z7hmYs/UxY+tjxtbHjK2PGVsfMyaKPN5XPQNztj5mbH3M2PqYsfUxY+tjxj1DT8rZaPKh6IUrsfP+E6FVFIQ1V+Kw32LgPd8h5dhL4v53l3pSxj0VM7Y+ZhxbQlZgzxoCOSkdAgJKci8oyb327sTWUA21PPwd2CRJRmKiK/BHkjqfsffoObBlDAz5eK2yAFUfz+/0el1NdnkhOdyAYUKrKoIJE3p9FbRfdsSLV7yP4x8b8KhFL7/8crOxP/zhDx2a46KLLoLDEdxAtGTJknbP8/l8WLp0adCY0+nE7NmzO7T+ZZdd1mwslPU//vhjFBcXB40dfvjhGDNmTMhrOxyOZvU2NjbizTffDHkO6nqmFvvmNyHHvgkwHrXUiByNhtaqqqqQ1rairvo7DtXzzz8f9Lh///445phj2j3vrLPOgtvtDjxWVRUvvfRSxOsjIiIiIiIiIiIiIiIiIiIiIiLrqN/6ObbfMRqVq+aFNY+wOZF1/sPIvXk17Bn9I1McERHFPSEE7Om5sKX2AQDIrhTYUvtASBJMfwPUsjwYanz8brhQbEifegeA0BvEq9e8hKbC76NXVIQp3mxAkmH4G6HXlAIAtPJdQf0CRB2lxLoAij/V1dVYs2ZN0FhSUhJOOumkDs2Tnp6OY489Fu+//35gLD8/H99//z0OPvjgVs/7+OOP0dQU/M1l0qRJIe9+t8/JJ58Mt9uNurq6wNj7778P0zTbfDeR9957r9nY9OnTO7T2vnMefPDBoLF33323w42E1HV0X0XgY1tav5jUYEvPDXys11W0cWTP0tL9H43msJbmPHA3y2h7+umnsX379pCPP/vsszF27Niw1+2qv+NQ+P3+Zg3TF1xwQUjvBJWYmIjf/e53WLhwYWBswYIFuPbaayNdJhERERERERERERERERERERERdXOG2oTSN+5E+bsPAKYR1lzOgUeg9/8thCPnoAhVR0RE3Y2SnAWhOOAv2wHJ4YYtPRdqRQFMTYValgdbah9IDlesy4Sz70h4jjwXNV8237ipRaaBsrfvRs6lL0DI8d+GJGQFSnI2tMoC6L4KyE43YE+EvywPjl5DY10edVPx/5VPXW7NmjXQdT1obPz48Z3awvKYY44JasADgE8//bTNBrxPP/202djRRx/d4bUVRcH48ePx4YcfBsaKi4uxdetWDB3a+pNmpNYfO3YsEhIS0NDQ0ObcFD8a878Bjr4IAODsH35DU2fsv27jzg0xqSEe9enTp9lYfn5+xNdpac6W1o6mxYsX47///W/Ixx900EERacDr3bs3JEmCYfz6j0gFBQVhz9sZS5cuRXl58DbPs2bNCvn8WbNmBTXgbdy4ERs2bMDo0aMjVSIREREREREREREREREREREREXVzDfnfoPDpC9FUsCm8iSQFGWfehfTTbuoWTQlERBRdsssLhzIM/j3bAAC29P7QKgpg+Buglu+CktwLsssb2yIBpBx3Bep/+C+06qKQjvfv+QnVn78I7zFzolxZZMgJSTAak2E0VEOtKoQtYyCMhlpo1XugJGfFujzqhvhTHjWzbt26ZmNHHXVUp+aaMGFCSPNHc/39G/D2zd9aA56u69i4cWPQmM1mw7hx4zq8ts1mw+GHH45PPvkkMFZUVISioiJkZ2d3eD6Kvsa8X7/2nP3HAEIAptl1BQgBZ+5hgYcNO77uurXj3IABA+B0OtHY2BgY+/rrzv/9mKYJc79shRAQQrT4/HPIIYd0ep3uxG63Y9CgQdi6dWtgbP369e3uGhoNCxYsCHrs8Xjw3HPPhXy+YRiQZTmomfz555/HI488EqkSKQ6YpglN0wKPFUXp8q9Vii5mbH3M2PqYcc/AnK2PGVsfM7Y+Zmx9zNj6mDFR5PG+6hmYs/UxY+tjxtbHjK2PGVsfM+4ZrJizqWsoW/YPlL79N0DX2j+hDY4+I9D70hfhzB0dmeJiwIoZUzBmbH3MOP5IjkQ4cg6Gv2QbjKZ6KOm50KoKYdTXQKsugqk1QfZkhpyTCRO69uvvxcqKDIHwMpYciUibciv2LLo65HOqPn4GiQdPgj29f1hrdxUlOQuqvx6mpkKr3gObNxtq5W5ICR5I9oRYlxeE93H8YwMeNfPDDz80Gxs8eHCn5ho0aFCzsR9//DFu18/Lywtq8AGAvn37wm63d3r9/Rvw9q3PBrxgjt6HYMBf1sa6DEBIgWYjOcED1/AT4Nu0ssuWd404EXKCB8Deb6C9zn8k7C3tw+XoHR/NZ4qiYNSoUVi79tevk59++gk1NTXweDwdns80zaDmLFmWW2zA27duTzF27NigBrza2lr8+OOPOOigg7qshj179uC9994LGqupqcHcuXPDmnfJkiV44IEHYLPZwpqH4oemaUE7JaalpTFfi2HG1seMrY8Z9wzM2fqYsfUxY+tjxtbHjK2PGRNFHu+rnoE5Wx8ztj5mbH3M2PqYsfUx457Bajk3FX6P3U9fhMYdX4U3kZCQduqfkXHmXZBsjsgUFyNWy5iaY8bWx4zjk1BssPcaBrV0B/T6Kti8vaEpDug1pdB9FTB1FYo3B0KS2p1L13RUV1cFHicne6Eo4bcDJQ4+Cu5Rp6Lu2xUhHW/qfpS9cw+yZ88Pqe5YE5IMxZsNtXwnjPoqGM4kSE431NIdsOccHFcNbryP4x8b8KiZvLy8ZmO5ubmdmqt3797NdiHavn17q8f7/X4UFhYGjSmKgpycnE6t369fv2Zjba0fyWtva/1jjz2203NakeRwIWHg4bEuo5nUyVd0aQNe6uQrAh8LIZAwYGyXrd0dHH/88UENeIZh4M0338RFF10Ukfnz8vKaNeAdddRRcLlcEZk/VB9//HGXrre/E088ES+//HLQ2DvvvNOlDXiLFi0KeveGSCktLcXy5csxbdq0iM9NRERERERERERERERERERERETxzTQMVKx8BCWv3QpTbWz/hDbYMgej96ULkThkQoSqIyIiqxKSBHvWIKgVu6FVF0Nxp0NS7FCrCmE01kIty4MttS+EErtGq9STr0P9tjUw6itDOr5p5zeoXfcGPIdPj3JlkSE5XJBdqdB9FdCqCmHLHAjD3wCtshC21N6xLo+6ETbgUTPFxcXNxvr27dupuWRZRnZ2NgoKCgJje/bsafX40tJSGEbwjl85OTmQZblT67dUd1vrR/LaO7N+R5WUlKC0tLRD52zbti3osaqqUFW13fOEEC12yWuaFtju1DTNoM8ZhtEsTyFEs05x0zSbnRtKPeHO09Ic+3OPPh22zIFQS1pv2owUW+YguA89vdl4pK4pEvPEOqczzzwT9957b9Dnnn/+ecyaNSusWvaf68C6pk2bFrGvYdM0A3PFKqf9a2hpntNPPx02my3oOeHZZ5/F9ddf3+47PETqmhYuXBjysR21YMECTJ06NeTjo52TYRjN5lFVtdW5W9pKWtf1FjNtq5a2nstDJUlSs+/NB259HYpwrknTtECDf0s/J3THa9rHSjnt05lr2j/jfY+7+zW1pCdfU0sZ7687XlN7eto1tZfxPt3pmkLV064plJz36S7XZMWcInFN++7tUHWHa7JiTh29ppbW7O7XZMWc+PqpZVbKaR++fmpdT74mvn7aK5Rrsuo7wcbT/weFKtZfC/tr7Zo68loG6B7XZMWc+HqmZ+XE1zPWzImvZ1pmpZz24euZ1vXka+Lrmb2sfE38/6Cec03d/f+D/KU7UPjsHNT/8N8O1dCSlOOvQtY590HYE0N6rb2/7vC1x9dP1rwmvn5qmZWuia+f2hfza0rKhBAy1PKdELZE2NNyoZYXwNSaoJblQUntA1O2obVL0nUtaA1d1yBJApJ0wDXBhK7pB57eJjkxGWmn3IDS128L+ZyKDx6FfdAEKJ5MAIAQgCy39O+wWqvX1JKIXZMiQ+DXnGRPJrTGGuj+JujlBbCl9IFWXgDDlgjJ6W5xDr5+al2s76dY/X8QG/ComYqKimZjbnfLTyqhOPBcv9+Purq6FueM9toAgrbljLf1O2revHn461//GtYc1dXVIdWkKArS09ObjVdVVUHTtKAXXfuelHVdb/aE2NqT7f7fLEIhy3KzuVtq+GtPS0++pmnubZiRZOTMfhr595+ADn3n7yghkDPn6Ra34Y3UNXX0m1k85jRu3DiMHDkS3333XWD8k08+wZo1a3DEEUe0O0dL1wTs/TqtqanB008/HTTucDgwY8aMZtfc2WsyDCNorljk1Nrn9l1TRkYGpk2bhv/85z+Bz23duhVvv/02pkyZ0m494V7T+vXrg/IFgI8++ggTJ07s1NfeK6+8gpkzZwbGVqxYgeLiYmRkZIQ8T1fltG/OyspKSK1syd3SVtL19fXw+Xwh19Lec3moXC4XkpKSgsY0Tevw97hwrknTNNTW1kKWZXg8nmaf747XtI+VctqnM9e0L+N9TNNEcnJyt76mlnT3nFoS6jW1lPH+L+674zW1p6ddU3sZ79OdrilUPemaqqurUVNTE3jcWs77dIdrsmJOkbgmwzBQU1PTbsb7i/drsmJOnbmm5OTkZmPd/ZqsmBNfP7XMSjntw9dPrevuObWEr59a19lr6tWrV4fW6S7i6f+DQhXrr4X9tXZNPp8v8O+0ofyc2x2uyYo58fVMz8mJr2f2smJOfD3TMivltA9fz7Suu+fUEr6eaV1Puyb+f1DPuKbu/P9BsixD2fwW9iy5DkZjXYfWP5CS2hc5lzwH9/DjAex9s5t4yomvn+Lray/eromvn1pmpWvi66f2xcs1mbZUoLoA3qQk2DL6Q6vYBUNtglqeD59wwbQltHieruvw1df/Oo9pwu12IzHRFXycpqO6uqpD15Sc7IVrxImo++49NPz0aWjX4a9H6dJ7kHDaXRBCQJYVeL3eZsfV1tZB10PPKSEhIWLXtP/3MiEEdGcafBU/AnW1EE06pIRkoG4D5KyhEFLz3zHn66fWxfp+itX/B7X8G97Uo7X0xZqQ0PITeShaOrd+vyf/eFk7Htan+LB/U4xr+GSkTLosquulTLoMrkOOi+oaVnHzzTcHPTZNE1dccUWHG6IOdOeddzbbAXPOnDkdatSyiltuuaVZg+E111yDqqqqiK1RVlbW4viBu99lZ2dj4sSJnV7n1FNPhcv16w/hqqpiyZIlnZ6PiIiIiIiIiIiIiIiIiIiIiIi6B6OmGNXPnoei5y8Nu/ku8cjzMeie7wLNd0RERJ0lHG4omUMgbE4I2QYlPXfvDmymCaO6CIav+YZCXVKXEEg/7WYIu6v9g3+h7fgS2tZPolhVZAmbA8K9t/nMrCuDqauA7odRVRjjyqi7YAMeNdPSlthOp7PT87XUhOb3++Nu7XhYn+LDgTtTZc14EAmDj4rKWglDJiBrxoNRmduKZsyYgXHjxgWNffvtt80a8zpi2bJlmD9/ftBYcnIybrnllk7P2Z0ddthhuOiii4LGdu/ejUsuuaTF58iOWrp0KS655JJm436/H6+88krQ2O9+97tWd4MLRWJiIk477bSgsRdeeKHT8xERERERERERERERERERERERUXwzTRNNG95E9UOT4P9hVVhzCXc63BctQNqsJyEnNt9BjIiIqDOE4oAj5yBICUkQQoaS2geyKwUAYPrKYdQUwzTNLq9LSc5C6glXd+icxk/mwWioaf/AOCESvIDNCZgGzJo9AACzvgJGQ3VsC6NuIbT9ganHO3A3pHDP7cg3hFiuHQ/rt+WKK67A2Wef3aFztm3bhmnTpgUeJycnIy0trd3zWvt78Hq9ME0TmqY12yFLlmXIcvB2rC3Ns3fb2ebbtna0HkmSOpRXa8fKsgxN0wLzSY5E9LtuBXY+dCoatq3pUJ1tSRgyAf2uWwHJkQhg79fGgTVF6poURQn7vouHnCRJwr///W8ceeSRaGxsDIw/+uijcLlc+Pvf/96hWlauXIlZs2bBMIyg8QcffBB9+vQJeZ5QrkmSpMDfX6xyau1zB87zr3/9C6tXr8bOnTsDY++88w7OOussvPbaa0hMTAypFuDXa/L5fLj99tvx2GOPYeLEic3qWL58OSoqgt+149xzz233mtozffp0vPrqq4HH3377Lb777juMHj263XO7Mqd9W0SnpKQEbRd94LoHSkxM7FCjeHvP5aFqqTFSUZSQvp8ceM6BQr0mTdPavO+64zXtY6Wc9unMNR2YcWpqKux2e4v1dZdrakl3z6kloV5TSxnvfx3d8Zra09Ouqb2M9+lO1xSqnnRNycnJQW9g0lrO+3SHa7JiTpG4JkmS4PF42s14f/F+TVbMqTPX1NKa3f2arJgTXz+1zEo57cPXT63r7jm1hK+fWheJa7KSePr/oFDF09dCa9fkcv36Ts6h/JzbHa7Jijnx9UzPyYmvZ34950A9+Zr4eqZ93f2a+HrG+tfE1zN7Wfma+P9BzVnxmrrT/wfptWUoeelq+L5+vUNrtcQ99ixkXvA45KT0bpETXz91j/uJr5+YU1v4+qllVsppHyEEhCTDnjUEavlO6LVlUJJ7wSMp0KqLARMQeg2UlN4Q0t71dD04Y4/HA5vN1mxuWZGRnOzt0DXJyq+/z5o09izUffc+mnZ+E9K5ZkM1jC8XIGnanS1+PinJjY60T0hS87zDvaZ9nE4n7HYHzCQ31NIdgGlAljXI7nQIsxb25L4Qyq9/p3z91Lp4up+6EhvwqBmbzYampqagsYaGBrjd7k7N19DQ0GyspX8s27d2KOdHY+14WL+jMjMzkZmZGdYcNputxesO1b4nPSFEi81joexe1dK5nRGpefbNpes6ZFne2yjj8iL3zx9iz8vXo/Kj+ejQTwLNJ0fKpMuQNePBQPPdvjVbqiNe/m7ipZZRo0Zh/vz5zXZp+8c//oGvvvoK8+fPx8CBA9ucY18z2OOPP96s+e7SSy/FxRdf3KGaQrkmIUS790O0cwqlBmDvD1HvvPMOjj76aNTV1QXG33//fYwYMQL33HMPZs6cGVKtjY2NWLBgAe69914UFBQExg+s48Cd6fr06YPf/OY37V5Ta/blevLJJyMpKQm1tbVBa40ZM6ZD8+0vUjm11Lhps9lC/scroOVG587oyJqtEUKE9f1kn45cU1vHdddraktPvKYDG1VbayLuTtcUip50TQdm3N51d4dr6iirX1NHM95fvF5TOKx6TeHkDMTnNYXLqtcky3KnMt5fvF2TFXPq6DW1tNt5d7+mlvT0a+Lrp47rbtfE10/h6Q7XxNdPkbum7iie/j8oHPH2tRDuaxkg/q7Jijnx9UzLrJoTX8+0rKdfE1/PdFx3uya+nglPd7gmvp6x/jXx/4OCWfWausP/B9V+sxSFz/8f9Oo9Ya0hJXqRfeET8Iw/r83fEYrHnMLF10+t6+7XxNdPHdNdr4mvnzou1tckhIA9PReazQm1ogD2pHQodie0yt0wdT/MygLIqX0h2RwAgn/3VpYVSFILr58gwvq7EZKE9Km3ofDJmTB1f0jn1H+3Ak2HnoLEwUc1+5wsRyCnMK9pH0mSIUkAFAVSag60qiKgoQqy2wshFKCmELaswe3Ow9dPLYv1/dQV2v8tfOpxWtrZKNJNaPu/o2S8rB0P61N8MU0Tuq4HurklRyKyL3oSuX/+ELbMthu8WmPLHITcP3+I7IueDGq+o4658MIL8cgjjzQb//DDD3HwwQdjypQpeOGFF/DNN9+guLgYlZWV2LJlC5YtW4b/+7//w4ABA/Doo482a74777zzMG/evK66jLh26KGHYuXKlUhOTg4az8/PxwUXXIBBgwbhqquuwjvvvINvv/0WRUVF8Pl8KC0txfr167FgwQJceOGF6NWrF6644oqg5rsDFRcX4/333w8amz59ekQaPp1OJ6ZMmRI0tnjx4hb/QYGIiIiIiIiIiIiIiIiIiIiIiLoXvb4au5+Zg10PTw27+c418mQMumcTko8K7c3JiYiIIkFJzoI9cxAgSZAcbtjS+0MoNpi6CrUsD0ZjXfuTRJA9vT+8v72kQ+eUL70XRlN9lCqKPDnRC8mZBJgmtMpCmDCh11dDqy2LdWkUx7gDHjWTlpaGysrKoLG6ujpkZGR0ar79d08C9u4A19puei1tOXng+eGs3doa8bI+xR/TNGGaZtCLadchx2Hw3J9Qt3E5KlbNg2/TyrZ3xBMCrhEnInXyFXAfehpEC+82QB13zTXXICcnB3PmzAm61/x+P5YtW4Zly5aFPJcQArfddhv+9re/8R9O9nPUUUdh7dq1OOecc7Bx48agz+3YsQNPPPEEnnjiiQ7NabPZcMIJJwSNLVq0CJqmBY2de+65nSu6BdOnT8fixYsDj0tLS7F8+XJMmzYtYmsQEREREREREREREREREREREVHX8m1ZjcJn50At3xnWPMLhQq/zHoL32P/j748REVFMyC4vHMow+Et+BgDY0vtDqyiA4W+AWlEAuNIAdN33qOTfXAjf5g/h3/NTSMdr1UWoXP0k0k65PsqVRY6S3AuqvwGG2gS9pgSKJwtqxS5IzqTAroNE+2MDHjWTlZWFbdu2BY0VFBRgwIABHZ5L13UUFRU1m781GRkZkCQpaFeqoqIiGIYRtGVqqHbt2tVsrK31W/pcW7s2RXp96j6EJCPpsKlIOmwq9IYaNOZ/g8a8dVDL8mHqfgjZDlt6Lpz9x8KZexjkBE+sS7ak6dOn44gjjsAf//hHvPXWW52aY/To0Xjsscdw9NFHR7Y4ixg6dCi++uorPPzww5g7dy7Ky8s7NY+iKDjrrLNwzz33YPDg4O2ZFy5cGPQ4NzcX48eP73TNBzrxxBPh9XpRVVUVGFuwYAEb8IiIiIiIiIiIiIiIiIiIiIiIuiGjqR4l/7kZFR88FvZcicMmIueS52HPHBiByoiIiDpPciTCkX0Q/CXbYDTVQ0nPhVZVBKO+GnrNHhi6DOFO75JmcSErSJ96OwqfnQ2YRrvHA0DNly/DNeJEOPuOjG5xESJkBYo3G2rFLuh1FZAcbkgOF9TSHbBnD2NTPjXT8Y4msryWGu3y8/M7NVdhYWGzXY3aauSz2+3IyckJGlNVFYWFhZ1af+fO5u9q0tb6kbz2zqxP3ZOc4IHroN8i7eTr0OuCR5B90ZPodcEjSDv5OrgO+i2b76KsX79+ePPNN7Fx40ZcddVVzZ5DWpKUlITp06fjnXfewdq1azFhwoQuqLT7stlsuPHGG7Fz5048++yzOOGEE+B0Ots9z+Fw4KijjsKDDz6IgoICvPLKK82a777++mts2rQpaOzss8+OaP12ux1nnHFG0NiKFStQWloa0XWIiIiIiIiIiIiIiIiIiIiIiCi66retwfY7RofdfCcUB7LOexC5N3/E5jsiIoobQrHB3msYZFcKBARs3hzIngwAgNlQBbO6CKahd0ktjt6HIPmo8ztwhomyd+6GqalRqynSJKcbUqIXAKBVFcE0dRhNPujVe2JbGMUl7oBHzQwbNqzZ2IE74oXq559/bjZ20EEHtbv+gbvObdu2DX369In6+rm5uXA6nWhsbAyM7dq1C36/H3a7PerrU3wQQkCW5aDHFP9GjRqFxx57DI899hh+/vlnbN68Gfn5+aitrYVhGHC73cjJycHQoUMxYsSIoF01o5GxaZoRn7Oj8vLyIjpfYmIiLr74Ylx88cVQVRXfffcdtm3bhsLCQvh8PkiShJSUFKSkpGDQoEE49NBDYbPZ2pxz3LhxUfm7OvA+fv7557FgwYKIr0OxoygK0tLSgh6TtTBj62PG1seMewbmbH3M2PqYsfUxY+tjxtbHjIkij/dVz8CcrY8ZWx8ztj5mbH3M2PqYcc8QLzkbahNK37wL5Sv+GfJOPK1x9h+L3pe+AEfvQyJUXfcWLxlT9DBj62PG1iIkCfbMgVArC6FVFUFxp0PINkDsAkxAaDWQRGqX1OI99jL4vv8IWmVB+wcDUEu3o+p/zyPl2EujXFnkKJ4sqE0+mLoKrXoPbN4cqFWFkBI8kByJXVcH7+O4x0SombFjxzYbW7NmTafm+vzzz5uNjRkzpt31V61a1Wz9Y489NurrK4qCUaNGYe3atYExv9+PdevW4aijjurQ2qqq4quvvgoa69WrF7Kzszs0D3U9IQSb7rq5QYMGYdCgQbEuw9JsNhvGjBnT7nN6rPA+tj4hRLsNntS9MWPrY8bWx4x7BuZsfczY+pix9TFj62PG1seMiSKP91XPwJytjxlbHzO2PmZsfczY+phxzxAPOTfu3IjdT1+Ipl3fhjeRrCBj6h1IP/0WCIVfu/vEQ8YUXczY+pixNdlSciBsDqhl+ZATkpGQZYdaXgDT0KBX74GU2vENjjpKsjuRPuU2FL9wecjnVH3yHFyHTIY9s3v8LreQJCgpvaGW5cGor4bhdENyeuAv3QFHzsEQ+236EtU6eB/Hva75SqBu5aijjgraNQjY2wCn6x3fqvR///tfs7GJEye2ec4xxxwT0jzt0XW9WeNgVlYWhg4d2iXrf/PNN6ivrw8aa+/aiYiIiIiIiIiIiIiIiIiIiIiIiIgIMHUNpUvvxfa7Dg+7+c7RezgG3PklMqbdyeY7IiLqNhR3Ghy9hkLICoQtAbb0vgAAo7EWhr+hS2pIGHg43IedEfoJhoayd/4O0+h4/0msSPYEyO50AIBWVQxT12CqjdAqd8e4MoonbMCjZrxeL8aPHx80Vltbi5UrV3ZonoqKCnz00UdBY/369cPBBx/c5nnHHnssHA5H0Njq1atRWVnZofXff/991NXVBY2ddNJJ7e6IdPLJJzcbe+211zq0dmvntDQ3ERERERERERERERERERERERERERH9qqnoR+TdczRKX7sN0NXOTyQE0k69EQPu+hoJ/cdErkAiIqIuIjndsGcfBCHJEIoTUmIyAECvKemyGlJP/CNkd1rIxzcVfIeatf+JYkWRJyelQ9icMA0dWnURAECrKYHeUBvjyihesAGPWjRjxoxmY/Pnz+/QHAsXLkRjY2PQ2HnnndfueW63G6effnrQWGNjIxYuXNih9VuqN5T1J02ahKysrKCxtWvXYsOGDSGv7ff7sWDBgqAxp9OJM888M+Q5iIiIiIiIiIiIiIiIiIiIiIiIiIh6EtMwUL7yEWy/YzQafv4yrLlsGQPR/9ZPkHXuPyHZnRGqkIiIqOtJNgcUbzYAQEnKAISA4a+H0VjXzpmRISd4kHbqTR06p3LVE1CriqJUUeQJIaB4c/b+3TbWQfft3UBKLcvrVrv5UfRYogGvoqIC27Ztw8aNG/H555/jq6++wpYtW7Bz505omhbr8rqlCy64AElJSUFjS5cuxeeffx7S+RUVFbj//vuDxmRZxv/93/+FdP7ll1/ebOyf//xnyLvgffbZZ1i2bFnQ2KBBg3DCCSe0e25rdd5yyy0hrQ0ADz30EEpLS4PGZsyYAa/XG/IcFDumaULX9cAf0zRjXRJFGDO2PmZsfbquo7a2NvBH1/nixmqYsfUxY+tjxj0Dc7Y+Zmx9zNj6mLH1MWPrY8ZEkcf7qmdgztbHjK2PGVsfM7Y+Zmx9zLhn6Mqc/WX5yP/n8djz0rUw1cb2T2hDyuQrMOjvG5E49OgIVWddvJetjxlbHzO2Pl3XUQ8n6ptUNDT5IRL27oKn1Za2c2bkuA45DokHTwr5eFNtQPnSe7vV7/BKNgeUpEwAgFa7B6bmh6n5oZbvjPravI/jnxLrAjqiqakJX375JdauXYu1a9fi+++/R35+Pnw+X6vnSJKE7OxsDBw4EGPGjMHhhx+O8ePHY8CAAV1Yeffj9Xpx5ZVX4r777guMGYaBOXPm4IsvvkBKSkqr5xqGgT/84Q8oKgruVp45cyYGDRoU0vqTJ0/G+PHj8cUXXwTGioqKcPnll2Px4sWQpNZ7RysqKjBnzhwYhhE0fuutt0KW5ZDWv+aaa/DII4+gtvbX7ULfe+89PPHEE7jyyivbPHfdunW46667gsYURcHNN98c0toUe6ZpBn39CCEghIhhRRRpzNj6mLH1GYYR9DOg0+kM+fs8dQ/M2PqYsfUx456BOVsfM7Y+Zmx9zNj6mLH1MWOiyON91TMwZ+tjxtbHjK2PGVsfM7Y+ZtwzdEXOpmmi6pPnsGfxn2A01rZ/QhuUlN7Iufg5uEeeGKHqrI/3svUxY+tjxtZnGAbqGxpg2Dww6nbBk5QCNNbAVBuh11dDTkzukjrSTr0JjTu+Dvn7dcPPa1D37QokHXpalCuLHNmdCqOxFoa/HlpVIZT0XOh1FdATvZBdrffRhIv3cfyL+x3wSkpK8NRTT2HKlClIS0vDpEmTcNNNN+H111/H5s2bUVdXB9M0W/2j6zoKCgrw6aef4pFHHsEFF1yAwYMHY9iwYfjTn/6E1atXx/oS49Ztt92GPn36BI399NNPmDBhAn744YcWz6msrMRZZ52F//znP0HjHo8nqJkvFI8//nizJ4xXXnkF06dPR1VVVYvnfP/995gwYQK2bt0aNH7EEUdg9uzZIa+dkZGBv/3tb83Gr776atxzzz2t7qz4zjvvYNKkSWhqagoav/baazFs2LCQ1yciIiIiIiIiIiIiIiIiIiIiIiIisjq1qgi7Hp6KoucuCbv5LnnCLAy6ZxOb74iIyLJEYgqEzQkhKZDdaQAAvba0y3aZU5LSkXriHzt0TsV7D0Gvq4hSRdGheHMAIcHwN0CvKwcAqGX5MDU1xpVRLMXlDni6ruPNN9/EwoUL8f777we2TmzpSSHU3WwOPHfr1q149NFH8eijj6J3796YNWsWZs+ejSFDhoR/ARF26qmnorCwsNXPt/S50aNHtznnihUrkJOT0+YxbrcbixcvxnHHHRfUcPbDDz9g+PDhmDJlCo455hj07t0bZWVl2LhxI5YsWdLijoT//ve/213vQGPHjsXf//533HLLLUHjb775Jj744APMnDkTo0aNQlpaGgoKCvDJJ59g+fLlzXa+S05ObnfXvJb88Y9/xMqVK/Huu+8GxkzTxO23346nnnoK559/PoYMGQKHw4EdO3bgrbfewvr161u9DiIiIiIiIiIiIiIiIiIiIiIiIiIi2qv6y1dRvPBy6L7wfilfTspA9uz58Iw7M0KVERERxSchBISnF6BVQXKlQtRVwtRVGL5KyO7ULqnBfdgZqPvufTTu+Cqk442GapS/9wAyp98b5coiRyg2KMm9oFUVQq8tg+xwAzYn/GV5cPSKv54j6hpx1YBXU1ODp556Co899ligqWz/xrlQm+1a0tK5++YuKCjAfffdh7lz5+K0007DDTfcgGOOOabTa0Xali1bkJ+f36FzNm7c2Obn/X5/SPMcc8wxWLx4MWbOnBnUhGcYBt5++228/fbbbZ4vhMDDDz+M6dOnh7TegW6++WYUFxfjkUceCRqvq6vD008/3e75SUlJWLFiBQYNGtThtYUQePXVV3HyySfjs88+C/pcQUEB5s6d2+4cBx98MFasWAGHw9Hh9YmIiIiIiIiIiIiIiIiIiIiIiIiIrEarK0fxC1ei5stXwp4raeyZyJ79FBRPZgQqIyIiin9SQjIkVYXQmiAnpUOrLoZWVwYpMRlCkqO+vhAC6VNuw+5558LUmkI6x7dpJepHnoLEYfHTp9MeOTEZRlMdjIYaqJW7YcsYAKOhBlpNKRRPRqzLoxjo2JZgUVJbW4s77rgDffv2xS233ILdu3fDNE2Yprm3Q/eXPwAC4/v/AYCEhASkpqaiT58+yMrKQlJSEhRFafH4fefsP7dpmjAMA8uWLcOxxx6LCRMm4KOPPorZ30k8Ofvss7Fq1Sr07du3Q+elpaXh9ddfxzXXXBPW+g8//DAef/xxJCYmdui8kSNH4osvvsCECRM6vbbb7caHH36ISy+9tMMNoGeddRbWrFmDzEy+qCMiIiIiIiIiIiIiIiIiIiIiIiIiqt2wHNtvHRF2852UkIycS19En6tfZ/MdERH1OIq3NwBAcnkhFDtg6NDrwttRtiNsqX2QMukPHTqnbPl9MBrrolRRdCieLAhJgan5odeUAADUygIY/sYYV0axENMGPFVV8cADD2DgwIG49957UVtb26zpbv+mubS0NJx00km48cYbsWjRInz22WfIy8tDU1MT6urqUFpaivz8fBQWFqKqqgpNTU0oLy/Hxo0bsXz5ctx///244IILMHLkyKC5ATRb74svvsDxxx+Pk046CRs2bIjlX1NcmDhxIn744Qfcf//9GDx4cJvH9u7dG3fccQe2bt2KM8+MzHbeV155JX788Udcc801SE1te2vUQw89FE8//TTWr1+PQw45JOy1nU4n5s+fjy+++AK/+93v2tzNTlEUnHTSSVi5ciVef/11JCcnh70+EREREREREREREREREREREREREVF3pjfUoPDfl2DXv06HVl0c1lyuESdi0L2b4P3NBR3eXIGIiMgKJKcbcqIXAiKwG5vuK4epa11Wg2f8ebBnHxzy8XrNHlSseiKKFUWekBUo3mwAgO6rhNFUBxgG1LIdgV4k6jmUWC28cuVKXH311di2bVtQExyAwGNFUXDcccfh5JNPxnHHHYdRo0Z1eJ2UlBSkpKRg5MiROOWUUwLjlZWV+O9//4tVq1bh7bffRkFBQVAN++r44IMPsHr1alx22WW45557YtJQlZeX1+VrtiQxMRE33HADbrjhBvz000/YsGEDdu3ahfr6ejidTuTk5GDUqFEYOXJkVNbv06cPHnnkETz00EP45ptvsGnTJuzZsweqqsLlcqF///4YN25ch3fqC9URRxyB1157DfX19fjyyy/x448/oqJib5d4cnIyBg8ejCOPPBJerzcq6xMRERERERERERERERERERERERERdSVZluH1egObXMiy3OE5fN9/hMJn50Atyw+rFmFPRNZ5DyJl0mVsvCMioh5PScmBXl8FyemBZK+A4W+AXlcGJblXl6wvZAXpU29H4dMXAqYe0jm1X/0H7hEnwZk7OrrFRZDkdEN2pUD3VUKrLIItcwCMpnpoVUWwpeTEujzqQjFpwDv77LPxxhtvtNp4d+yxx+KCCy7AtGnT2t3trLNSUlIwbdo0TJs2DY899hi++OILvPrqq1i0aBHKysqC6tJ1HU8++STeeOMNvPTSS5g0aVJUaupOhg4diqFDh8ZkbVmWMW7cOIwbNy4m6ycmJmLSpEn8OiAiIiIiIiIiIiIiIiIiIiIiIiIiS5MkCU6ns1PnGv4GlPznFlSsfCTsOhKG/Aa9/28h7FmDwp6LiIjICiR7AmR3GvS6cshJGTDKd0L3VUF2pUIo9i6pwZE9DMm/uRDV/3s+5HPK3rkbOX9YDMnmiGJlkSUnZcJo8sHU/NCqi2FL6QOtuhhyYjIkhyvW5VEXkWKx6Ouvvw5gb4PbvnfE8Hg8uPbaa/HDDz9g9erV+P3vfx+15ruWjB8/Hg899BB2796NRYsW4eijj4ZpmjBNM1Djnj178Omnn3ZZTURERERERERERERERERERERERERERN1N/c9fYvudh4XdfCcUOzLPvR/9b/0vm++IiIgOYEvJAYSA5HD90ghmQqst7dIavL+9BLa0fiEfr5bno+qTf0exosgTkgTF2xuAgNFQC6OhGjBN+Et3wDSMWJdHXSQmO+DtY5omMjIy8Kc//QlXXnklkpKSYlkOAMBms2HmzJmYOXMmPv/8c9xzzz149913Y10WERERERERERERERERERERERERERFRl9AbatCYtx6Neeuglu+EqTVBKA7Y0vrB2X8snP3HQE7wNDvPNAyULZ+L0tdvB8zwfiHdmTsGOZe+AGef4WHNQ0REZFVCsUPxZEKr3gMlORP+kh0wGmpguNIg2Tu3g21HSTYH0qbcjuIFl4Z8TvVnC+EefgLsvYZEsbLIkuxOyEnp0GtL9+6CZ08EAGiVBR1qQKTuK2YNeB6PB7fccguuvvpqJCQkxKqMNk2YMAHLly/H+vXr8ec//xmrV6+OdUlEREREREREREREREREREREREREREQRZxo66jYsQ8WqefBt/gAwzdYPFgKu4ScgdfIVcI8+HUKS9w5LElImXQrfppWo/+HjzhUiyUifejsyptwGodg6NwcREVEPoST3gl5bBihOSIkeGPU10GtLIHVhU1hC/zFIGvc71H79emgnGDpK37kbORc/ByHHdF+xDpHdaTAa62CqDdCqCmFLy4VWUwopIRlyYnKsy6Moi8lX6jXXXIM77rgDaWlpsVi+w8aMGYMPP/wQK1asQHV1dazLIaIoE0K0+Zi6P2ZsfczY+oQQUBQl6DFZCzO2PmZsfcy4Z2DO1seMrY8ZWx8ztj5mbH3MmCjyeF/1DMzZ+pix9TFj62PG1seMrY8ZW4tv8yoULrgUasn20E4wTfg2rYRv00rYMgciZ/bTcA2fDABQ3Gnod/27KHh8Ouo2Lu9QHfacg9H70heQMGBcRy+BOon3svUxY+tjxtbXVsZCVqAk94JauRtKUib8DbUwmnwwGusgOd1dVmPq8Vej/sdPodeWhHS8v3ALar58GckTLohyZZEjhIAtJQf+0u0wmuqh+yogu1KhluVD6n1IWM2EvI/jnzDNtt6egoisZvPmzRgxYkTg8aZNmzB8ePjbs2uahq1btwaNDRkyJOibABER/YrPm0RERERERERERBQt0fr/ICIiIiIiIrIeo6kee16+HpUfzW97x7v2CIGUSZcha8aDkByJe+f2N2Lng6eEthOeEEg96Tpk/u5uSPaEztdBRETUA5mGgaaCTTB1FVp1MXRfJYTNCXvGgC6to/7HT7BnyXUhHy8UB3pf8QpsqX2iWFXk6b5KaNXFgCRgTx8AoTggu1JgzxwY69IoiqRYF0BE1tBShzX7e4mIWmcYRrMxvlsFEREREREREREREREREREREXUV3VeF/H8ej8rVT4XXfAcAponK1U8h/5/HQ/dVAQAkuxN9rn4NsiezzVNtGQOQe/PH6HXeA2y+IyIi6gQhSVBScgAAclI6hCTBVBuhN9R0aR2JwybCNfyEkI83tSaULb2n2/UdyK4USA43YJjQqgphwoTuq4ReVxHr0iiK2IBHRBEhSc2fTjRNi0ElRETdg67rzcZaei4lIiIiIiIiIiIiIiIiIiIiIoo0o6keOx86FQ3b1kR03oZta7DzoVNhNNUDABR3GrIverLV41MmXYZBf/8WroMmRrQOIiKinkZ2p0HYnBCSAsmdBgDQa0q6vLkt7ZQbISUkh3x8446vUPfNO1GsKDoUbzYgyTD8jdBrywAAavlOmJo/xpVRtPC3vIkoIoQQUBQlaMzn88WoGiKi+Hfgc6SiKNwBj4iIiIiIiIiIiIiIiIiIiIi6xJ6Xr494890+DdvWYM/L1wcee8adBc+R5wYdo3hz0O/6d5E9+ylITndU6iAiIupJhBCwpfQGAMiuVAhJgamrMOqrurQO2Z2K1JP+1KFzKlY+DO2XJrbuQsgKlOReAAC9rhym2gDT0OEvy4ttYRQ1bMAjoohxu4NfBNfW1saoEiKi+Hfgc+SBz6FERERERERERERERERERERERNHg27wKlR/Nj+oalR/Nh2/L6sDjjLPuBn55c+rko87HoHs3wT3q5KjWQERE1NPILi8khwtCSJCT0gEAem0ZTMPo0jrch56GhEHjQz7eaKxF+Yp/RrGi6JATPHt3+zNNqJW7YZoGjIZaaNV7Yl0aRYHS/iFERKHxeDyoqqoKPG5sbERdXV23ayoxTROapgUec1cq62HG1hfvGdfV1aGxsTFozOPxxKia7knTtKDvOV6vt9lOrNS9MWPrY8bWx4x7BuZsfczY+pix9TFj62PG1seMiSKP91XPwJytjxlbHzO2PmZsfczY+phx92QaOgoXXAqYZpQXMlH4/KUYPPdHCEmGo9cQJI37HZKPPBeew6dHd23qEN7L1seMrY8ZW19HMlZSesNf/BMklxfCVw5TU6H7KqD80pDXFYQQSDv9Vuyedy5MtSGkc+q/Xw3f9x/BdfCkKFcXWUpyFlR/PUxNhVa9BzZvNtTK3ZASPJDsCSHPw/s4/vW4HfCqq6uxadMmrF27Frt27YIZ7RcQRD1IYmIiZFkOGisoKEBdXV2MKuqcA58X+DxhPczY+uI547q6OhQUFASNybKMxMTEGFXUPe1rstz3J54ypshgxtbHjK2PGfcMzNn6mLH1MWPrY8bWx4ytjxkTRR7vq56BOVsfM7Y+Zmx9zNj6mLH1MePuqW7DMqgl27tkLbXkZ9RtXB543PuyF9l8F4d4L1sfM7Y+Zmx9HclYTkiCnJgMAQHFkwkA0OvKYepaq+dEgy0lBymTr+jQOeXL50JvqI1SRdEhJBmKNxsAYNRXwWis27sjXumODt2LvI/jX49ph1yyZAn+9a9/Yf369UFfiGlpaTj33HNxyy23ICcnJ4YVEnV/QggkJyejoqIiMGaaJnbt2gWn04mkpCS4XC7IsgxJit/+X8MwoOt64LFpmnFdL3UcM7a+eMp4Xy0+nw+1tbXNdr4DgOTk5LjaoY+IiIiIiIiIiIiIiIiIiIiIrKli1bwuXy/psKkAAMnm7NK1iYiIeiolpTf0+mpITg8kezkMfyP0ujIoyb26tA7PEefA9937aNq9KaTj9boyVHzwCDKm3h7lyiJLcrggu1Kh+yqgVRXBljkAhr8BWmUhbKm9Y10eRUi3aMDTNA2///3voWm/dtxOnz4dZ511VrvnNjQ0YObMmXjnnXcANN8Bp6ysDPPmzcPixYvx/PPPY+rUqZEtnqiHyczMhKZpqKmpCRpvbGxEY2MjSktLY1RZ6FrqFmdjjLUwY+vrThl7PB5kZmbGugwiIiIiIiIiIiIiIiIiIiIisji9oQa+zR906Zq+TSuhN9RATvB06bpEREQ9mWRPgOxOg15XDjkpA0b5Lui+KsiuVAjF3mV1CElG+hl3YPdT5wNGaDvw1a1/C+6RJyNhwLgoVxdZclIGjCYfTK0JWlUxbKl9oFUXQ05MhuR0x7o8ioBusd3PqlWrsGjRIrzyyit4+eWX8eqrr2LUqFEhnXvppZfi7bffhmmaME0TQohmf0zTRGVlJc455xysWrUqyldDZG1CCOTk5MDj4YtlIqL2eDwe5OTkxG1zIBERERERERERERERERERERFZR2PeeqCFN7aOKtNEY/43XbsmERERwZaSAwgByeGG5HABMKHVdv1mOvbMQfAeM6dD55S983cY/sYoVRQdQpKgpOQAEDAaa6E3VAEA/GV5MA09prVRZHSLBry33noLAAINdJMnT8bgwYPbPW/58uV46aWXgprt9tl/Z5x9n/P7/ZgxYwaqqqoifQlEPcq+JrzU1FTIshzrcjpF07TAH7ImZmx98ZyxLMtITU1l8x0RERERERERERERERERERERdZnGvHU9al0iIqKeTCh2KEkZAADFkwkAMBpqYKhd39jmPWYObBkDQz5eqyxA1cfzo1hRdEg2J+SkdACAXr0Hpq7CVJugVhTEuDKKhG7RgPfZZ58F/XL61KlTQzrvL3/5S7Mx0zTRq1cvjB8/Hn379g1qxAOAioqKFs8joo4RQiArKwtDhgxBv3794PV6oShKrMsiIooZRVHg9XrRr18/DBkyBFlZWWy+IyIiIiIiIiIiIiIiIiIiIqIuo5bvjM26ZfkxWZeIiKinU7zZEJIMYXNCSkgCAOg1Xb8LnlDsSJ96O4DQf2+2es1LaCr8PnpFRYnsToOwJ8A0DGiVuwEAem0ZdF9VbAujsMV9N4zP58OWLVuCxk499dR2z/vqq6+wfv36wC+2m6YJt9uNBQsW4Kyzzgoc9+677+LCCy9ERUUFhBAwTRPPPfcc7rvvPiQkJET2Yoh6ICEEXC4XXC4XgL33omEYzZpf44mqqqisrAw8TklJgc1mi2FFFGnM2PriKWMhBCRJYrMdEREREREREREREREREREREcWUqTXFZl3dH5N1iYiIejohK5CTs6BVFkJJyoS/sQ5GUx2MJh8kh6tLa3H2HQXPkeei5suXQzvBNFD29t3IufQFCDnuW58ChBCweXPgL90Ow98Ava4Msjsdank+JKe7W10LBYv75LZt2wbDMAK/tJ6cnIwBAwa0e97ixYsDH5umCSEEnnjiiaDmOwA45ZRT8Oqrr2Ly5MmBNerr6/H2229jxowZEbwSIgL2fkORZTnWZbTJNE1I0q8bhCqKwt37LIYZWx8zJiIiIiIiIiIiIiIiIiIiIiIKJhRHbNaV7TFZl4iIiADFkxXY9U5O9EL3VUKrKYU9o2sb8AAg5bgrUP/Df6FVF4V0vH/PT6j+/EV4j5kT5coiSyh2KMm9oFUVQastg+R0A3BCLcuHPWtQrMujTpLaPyS28vLyAh8LITB8+PCQzluxYkXQTjO5ubm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",
      "text/plain": [
       "<Figure size 3600x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- OT-ICA Time Breakdown ---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Time (s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>75.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>126.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>188.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>266.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>386.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>493.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>555.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>616.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>677.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>685.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>641.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>520.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total Time (s)\n",
       "Dimension                \n",
       "10                  41.85\n",
       "15                  75.36\n",
       "20                 126.80\n",
       "25                 188.93\n",
       "30                 266.72\n",
       "35                 386.91\n",
       "40                 493.37\n",
       "45                 555.18\n",
       "50                 616.52\n",
       "55                 677.39\n",
       "60                 685.42\n",
       "65                 641.37\n",
       "70                 520.60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 5. Plot 2: Total Execution Time vs Dimension\n",
    "# ==========================================\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "\n",
    "palette = {'FastICA': '#0173B2', 'OT-ICA': '#D55E00'}\n",
    "\n",
    "# --- Subplot 1: Low Compute ---\n",
    "df_low = df[df['Compute Level'] == 'Low Compute']\n",
    "sns.lineplot(\n",
    "    data=df_low, \n",
    "    x='Dimension', \n",
    "    y='Total Time (s)', \n",
    "    hue='Method', \n",
    "    palette=palette,\n",
    "    marker='o', \n",
    "    linewidth=2.5,\n",
    "    markersize=8,\n",
    "    ax=axes[0]\n",
    ")\n",
    "axes[0].set_title(\"Low Compute Regime\", fontsize=14)\n",
    "axes[0].set_ylabel(\"Total Time (Seconds)\", fontsize=13)\n",
    "axes[0].set_xlabel(\"Number of Dimensions\", fontsize=13)\n",
    "axes[0].set_xticks(DIMENSIONS)\n",
    "\n",
    "handles, labels = axes[0].get_legend_handles_labels()\n",
    "axes[0].legend(handles=handles, labels=labels, title='Method', loc='upper left', fontsize=11)\n",
    "\n",
    "# --- Subplot 2: High Compute ---\n",
    "df_high = df[df['Compute Level'] == 'High Compute']\n",
    "sns.lineplot(\n",
    "    data=df_high, \n",
    "    x='Dimension', \n",
    "    y='Total Time (s)', \n",
    "    hue='Method', \n",
    "    palette=palette,\n",
    "    marker='o', \n",
    "    linewidth=2.5,\n",
    "    markersize=8,\n",
    "    ax=axes[1]\n",
    ")\n",
    "axes[1].set_title(\"High Compute Regime\", fontsize=14)\n",
    "axes[1].set_ylabel(\"\") # Shared y-axis\n",
    "axes[1].set_xlabel(\"Number of Dimensions\", fontsize=13)\n",
    "axes[1].set_xticks(DIMENSIONS)\n",
    "\n",
    "if axes[1].get_legend() is not None:\n",
    "    axes[1].get_legend().remove()\n",
    "\n",
    "fig.suptitle(\"Time Complexity: FastICA vs OT-ICA\\nLaplacian Independent Sources\", fontsize=15, y=1.05)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('laplace_time_ot_ica_scaling.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Display time breakdown table for OT-ICA\n",
    "otica_times = df[df['Method'] == 'OT-ICA'].groupby('Dimension')[['Total Time (s)']].mean()\n",
    "print(\"\\n--- OT-ICA Time Breakdown ---\")\n",
    "display(otica_times.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd35736",
   "metadata": {},
   "source": [
    "### What happens if we only do Stiefel step i.e. Phase 2, without Phase 1 with SGD? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f43297d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- FastICA vs. W-ICA (Stiefel Cold Start Only) ---\n",
      "Dimensions: 10 to 20\n",
      "Learning Rate: 0.1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "Running Cold Start Trials:   0%|          | 0/55 [00:00<?, ?it/s]"
      ]
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     "output_type": "stream",
     "text": [
      "W-ICA Error (dim 10, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 10, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 10, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 10, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 10, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 11, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 11, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 11, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 11, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 11, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 12, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 12, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 12, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 12, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 12, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 13, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 13, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 13, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 13, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 13, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 14, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 14, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 14, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 14, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 14, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 15, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 15, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 15, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 16, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 15, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 16, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 16, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 16, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 15, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 17, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 17, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 16, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 17, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 18, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 17, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 17, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 18, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 20, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 20, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 20, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 20, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 20, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 19, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 19, trial 1): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 18, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 19, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 18, trial 4): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 19, trial 2): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 18, trial 3): t() expects a tensor with <= 2 dimensions, but self is 3D\n",
      "W-ICA Error (dim 19, trial 0): t() expects a tensor with <= 2 dimensions, but self is 3D\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Method</th>\n",
       "      <th>FastICA</th>\n",
       "      <th>W-ICA (Cold Start)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.1183</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.1281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.1401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.1550</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1650</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.1771</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.1896</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.1997</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.2115</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.2220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.2324</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Method     FastICA  W-ICA (Cold Start)\n",
       "Dimension                             \n",
       "10          0.1183                 NaN\n",
       "11          0.1281                 NaN\n",
       "12          0.1401                 NaN\n",
       "13          0.1550                 NaN\n",
       "14          0.1650                 NaN\n",
       "15          0.1771                 NaN\n",
       "16          0.1896                 NaN\n",
       "17          0.1997                 NaN\n",
       "18          0.2115                 NaN\n",
       "19          0.2220                 NaN\n",
       "20          0.2324                 NaN"
      ]
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   ],
   "source": [
    "# ==========================================\n",
    "# 1. Experiment Setup\n",
    "# ==========================================\n",
    "DIMENSIONS = [n for n in range(10, 21)]  # 10 to 20 dimensions\n",
    "N_SAMPLES = 5000\n",
    "N_TRIALS = 5  \n",
    "\n",
    "print(f\"--- FastICA vs. W-ICA (Stiefel Cold Start Only) ---\")\n",
    "print(f\"Dimensions: {DIMENSIONS[0]} to {DIMENSIONS[-1]}\")\n",
    "print(f\"Learning Rate: 0.1\")\n",
    "\n",
    "# ==========================================\n",
    "# 2. Parallel Worker Function\n",
    "# ==========================================\n",
    "def run_cold_start_trial(dim, trial, n_samples):\n",
    "    torch.set_num_threads(1) \n",
    "    trial_results = []\n",
    "    \n",
    "    # Generate standard Laplace data for a clear baseline\n",
    "    X_torch, A_true = generate_dataset(n_dim=dim, n_samples=n_samples, seed=trial)\n",
    "    X_np = X_torch.numpy()\n",
    "    \n",
    "    # --- FastICA (Baseline) ---\n",
    "    t0_fast = time.perf_counter()\n",
    "    try:\n",
    "        fast_ica = FastICA(n_components=dim, max_iter=2000, tol=1e-4, random_state=trial)\n",
    "        fast_ica.fit(X_np.T)\n",
    "        W_fast_total = fast_ica.components_\n",
    "        score_fast = amari_error(W_fast_total, A_true)\n",
    "    except Exception:\n",
    "        score_fast = np.nan\n",
    "    t_fast_total = time.perf_counter() - t0_fast\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, \n",
    "        'Method': 'FastICA', \n",
    "        'Amari Error': score_fast,\n",
    "        'Time (s)': t_fast_total\n",
    "    })\n",
    "    \n",
    "    # --- W-ICA (Phase 2 Stiefel Cold Start ONLY) ---\n",
    "    t0_wass = time.perf_counter()\n",
    "    try:\n",
    "        ica = WassersteinICA(X_torch)\n",
    "        ica.whiten()\n",
    "        W_white_np = ica.W_white.cpu().numpy()\n",
    "        \n",
    "        # We completely skip optimize_wasserstein2 (Phase 1)\n",
    "        # Passing init_w=None forces optimize_symmetric to generate a random orthogonal matrix\n",
    "        W_stiefel_cold = ica.optimize_symmetric(\n",
    "            n_components=dim, \n",
    "            max_iter=300,       # Slightly higher max_iter to account for the slower 0.1 LR\n",
    "            lr=0.1,             # Your requested safe learning rate\n",
    "            init_w=None,        # Cold Start trigger\n",
    "            optimizer='stiefel',\n",
    "        )\n",
    "        \n",
    "        W_wass_total = W_stiefel_cold.cpu().numpy() @ W_white_np\n",
    "        score_wass = amari_error(W_wass_total, A_true)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"W-ICA Error (dim {dim}, trial {trial}): {e}\")\n",
    "        score_wass = np.nan\n",
    "        \n",
    "    t_wass_total = time.perf_counter() - t0_wass\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, \n",
    "        'Method': 'W-ICA (Cold Start)', \n",
    "        'Amari Error': score_wass,\n",
    "        'Time (s)': t_wass_total\n",
    "    })\n",
    "    \n",
    "    return trial_results\n",
    "\n",
    "# ==========================================\n",
    "# 3. Execute Main Loop\n",
    "# ==========================================\n",
    "tasks = [(dim, trial, N_SAMPLES) for dim in DIMENSIONS for trial in range(N_TRIALS)]\n",
    "\n",
    "results_nested = Parallel(n_jobs=12)(\n",
    "    delayed(run_cold_start_trial)(dim, trial, n_samples) \n",
    "    for dim, trial, n_samples in tqdm(tasks, desc=\"Running Cold Start Trials\")\n",
    ")\n",
    "\n",
    "results = [item for sublist in results_nested for item in sublist]\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "display(df.groupby(['Dimension', 'Method'])['Amari Error'].mean().unstack().round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "233cc84f",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 4. Plotting the Cold Start Divergence\n",
    "# ==========================================\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot the Amari Error curves\n",
    "sns.lineplot(\n",
    "    data=df, \n",
    "    x='Dimension', \n",
    "    y='Amari Error', \n",
    "    hue='Method', \n",
    "    marker='o', \n",
    "    linewidth=2.5,\n",
    "    palette=['#1f77b4', '#ff7f0e'] # Force consistent colors (FastICA Blue, W-ICA Orange)\n",
    ")\n",
    "\n",
    "plt.title(\"Amari Error vs. Dimension\\n(The Local Minimum Trap: Phase 2 Cold Start)\", fontsize=14)\n",
    "plt.ylabel(\"Amari Error (Lower is Better)\", fontsize=12)\n",
    "plt.xlabel(\"Number of Dimensions\", fontsize=12)\n",
    "plt.xticks(DIMENSIONS) # Ensure every dimension is ticked from 10 to 20\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ot_ica",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}