diff --git a/notebooks/Courses/pingouin_scipy_cours.ipynb b/notebooks/Courses/pingouin_scipy_cours.ipynb
index c22d9d635fb59015c2c7efbed3c46cbddb6ed60a..4a63f7dd14ec05aa988033651d137b9547530233 100644
--- a/notebooks/Courses/pingouin_scipy_cours.ipynb
+++ b/notebooks/Courses/pingouin_scipy_cours.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "<h1 align='center'>Statistical tests with the Pingouin and SciPy libraries</h1>\n",
     "\n",
-    "<div style='text-align:center'><img width=600 src='https://pypi-camo.freetls.fastly.net/f007bb509f4d58bd383e8b7c2494a413cac7397f/68747470733a2f2f70696e676f75696e2d73746174732e6f72672f6275696c642f68746d6c2f5f696d616765732f6c6f676f5f70696e676f75696e2e706e67'/></div>\n",
+    "<div style='text-align:center'><img width=600 src='https://pingouin-stats.org/build/html/_images/logo_pingouin.png' /></div>\n",
     "<div style='text-align:center'><img width=300 src='https://docs.scipy.org/doc/scipy/_static/logo.svg' /></div>\n",
     "\n",
     "The [Pingouin](https://pingouin-stats.org/build/html/index.html) library features a selection of commonly-used statistical operations. The provided functions have verbose output and can be used independently of one another."
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "1f8a7a30-48e3-4f5a-8836-3912b049b5bb",
    "metadata": {},
    "outputs": [],
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "42342d74",
    "metadata": {},
    "outputs": [],
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "78db8576-5c83-4967-97b8-19dd4ff9b3b5",
    "metadata": {},
    "outputs": [],
@@ -154,10 +154,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "898957c8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipping\n"
+     ]
+    }
+   ],
    "source": [
     "%%script echo skipping\n",
     "\n",
@@ -183,10 +191,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "0a56b871-8335-42b1-b8e0-f1cdc68b5281",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>244.000000</td>\n",
+       "      <td>244.000000</td>\n",
+       "      <td>244.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>19.785943</td>\n",
+       "      <td>2.998279</td>\n",
+       "      <td>2.569672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.902412</td>\n",
+       "      <td>1.383638</td>\n",
+       "      <td>0.951100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.070000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>13.347500</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>17.795000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>24.127500</td>\n",
+       "      <td>3.562500</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>50.810000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       total_bill         tip        size\n",
+       "count  244.000000  244.000000  244.000000\n",
+       "mean    19.785943    2.998279    2.569672\n",
+       "std      8.902412    1.383638    0.951100\n",
+       "min      3.070000    1.000000    1.000000\n",
+       "25%     13.347500    2.000000    2.000000\n",
+       "50%     17.795000    2.900000    2.000000\n",
+       "75%     24.127500    3.562500    3.000000\n",
+       "max     50.810000   10.000000    6.000000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dataframe = pg.read_dataset('tips')\n",
     "dataframe.describe()\n",
@@ -207,13 +311,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "e60ba8f3",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scipy_material.illustration_confidence_interval(19.785943, stats.sem(dataframe['total_bill']))"
    ]
@@ -230,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "e911524f",
    "metadata": {
     "hidden": true
@@ -245,10 +360,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "2494dc66-d5bf-4235-b2f7-02daf5da7d2f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(18.668922839262997, 20.902962406638643)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "distribution_of_the_mean.interval(0.95)"
    ]
@@ -263,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "1fad2898-81e4-4814-b496-146f54d21e29",
    "metadata": {},
    "outputs": [],
@@ -281,12 +407,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "1341b01d",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2704798697499871"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# probability density function\n",
     "distribution_of_the_mean.pdf(19.0)"
@@ -294,12 +431,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "d6bbb149",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0839406210836206"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# cumulative distribution function\n",
     "distribution_of_the_mean.cdf(19.0)"
@@ -325,12 +473,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "62f75d44",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9599639845400545"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "alpha = 0.05\n",
     "z = stats.norm().isf(alpha / 2)\n",
@@ -351,12 +510,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "99fe274d",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bills are 19.79 ± 1.12 on average\n"
+     ]
+    }
+   ],
    "source": [
     "print(f'Bills are {mu:.2f} ± {z * sigma:.2f} on average')"
    ]
@@ -383,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "11cb5a3f-c48d-457d-9134-c56665b5e169",
    "metadata": {},
    "outputs": [],
@@ -405,13 +572,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "578edd4a-bcb1-4f9e-a8ba-d3e63bbabb71",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Pk5qa2uTzbrcbt9vd4vch7auoqIiqqkrG3Dqb6NSMVp3La9rY708A06R863vUnntBcIq02Hl9Erh8aCpvbcvnv//xKS/ffh6GYVhdlohIh2H5EFh2djY333wz55xzDqNHj+bpp5+moqKC6dOnAzBt2jR69OjBnDlzALjnnnsYN24cTz31FJdffjlLlixh/fr1LFq0CIDy8nIee+wxrr32WlJSUtizZw+/+MUv6NevHxMmTLDsfUrwRadmEJ8+sFXnOHiiEoqrcflK8Jd1rYnyM7+byT9zC/l47wmWby/gO0Ob/gVARCQUWX4b/JQpU/jd737HrFmzGDFiBJs3b2b58uUNE53z8vLIz/9ief+xY8eyePFiFi1axPDhw3n11VdZtmwZQ4YMAcBut7N161auvPJKBgwYwG233caoUaP48MMP1csjjZimybGyuknC4VXBmU/UkfSMC+dH3+oDwG/e3YlPt8WLiDSwvAcIYMaMGacd8lq5cuVXjk2ePJnJkyc32T4sLIx33303mOVJF1VS5cPrD+CwGbi9xVaX0ybuGNeXv6zNY19RBUs+OchN5/WyuiQRkQ7B8h4gEascPdX7kxjpwqBr7qIe6Xbw00v7AzDvn7uoqKm1uCIRkY5BAUhCks8faFgjJymqaw+NTh2dTq+EcIrKa/jTh/usLkdEpENQAJKQdKLCiwmEu+xEuDvESHCbcTls3HdqccRF/9pDUXmNxRWJiFiva//kFzmN4+VfDH91Vrm5uWfcNtU06RvnZM9JH48sWc3tI2O+0iYxMZH09PRgligi0mEpAEnI8dYGKK2umwuT0AkDUFXJccDgxhtvbNbr3OlDSZk6h7d3lvKn+39AbXF+o+fDwsLZsSNXIUhEQoICkISc46fm/kS5HbgddouraT5fZRlgMuKGB0jqndms1x7ye6m0uxj442fobi9rOF6av5+1zz1GUVGRApCIhAQFIAk5x0/NgemMvT9fFtktvdkLQbpratl2uJRy04M7uVuXn/8kInI6mgQtIaXa56e8xg9AfETnDkAtEeF2NLzvQyerLK5GRMQ6CkASUuonP8eEOXA5QvPbPy0uDICTlT7KtS6QiISo0PwEkJBVP/8nIaJrr/3zdcJc9oa73w6dUC+QiIQmBSAJGdU+P5Xe+uEvp8XVWKvnqV6g4iofZdU+i6sREWl/CkASMk6c6v2JDnPgsIf2t77HaScpSnOBRCR0hfangISUk5V1PR3x4aE3+bkpPWLDMICSqloqzdDuEROR0KMAJCHBWxug7NTih3EhPvxVr64XqG4u1HF/uMXViIi0LwUgCQnFlXXDXxFue6dc/LCt9IgLwzCgChee9GFWlyMi0m4UgCQknKjQ8FdT3A4b3U71AsWMvd7iakRE2o8CkHR5/oBJSVVdAIoLwcUPv0n3WA9g4uk1jNxjXqvLERFpFwpA0uUVV3oxAY/TRphT3/L/ye2wE2NUA7D0s7JvaC0i0jXo00C6vPq7v+LCXRiGYXE1HVO8rRIz4GdzoZfNB4utLkdEpM0pAEmXZpomxQ0BSHd/nY7TCFDx6fsAPJOzy+JqRETangKQdGnlNX5qAyZ2m0GURzuff52SNUuxGZCz4yjbD5dYXY6ISJtSAJIurf7295gwp4a/vkHtySNckOYBYP77uy2uRkSkbSkASZem4a/m+f7gSAwDln9awM4CTYgWka5LAUi6LG9tgIpTm5/GKgCdkZ7RTr47JBWA+R+oF0hEui4FIOmyik+t/RPhtuMM8c1Pm2PGJf0A+MfWI+w5Vm5xNSIibUOfCtJl1c//idPqz80yKDWabw9OxjTh/32wx+pyRETahAKQdEkB06Tk1Pyf2DANfzXXjIvreoH+tvkwh4urLK5GRCT4FICkSyqvrsVvgsNmEOHW5qfNNTwtlvP7JVAbMPmff+21uhwRkaBTAJIuqX7+T2y4bn9vqbsuqusFWvJJHsfLayyuRkQkuBSApEuq3/w0RsNfLTa2bwLDesZQ7Qvwwur9VpcjIhJUCkDS5dT6A1TU1N3+rgDUcoZhcNdFfQF4cfV+ymtqLa5IRCR4FICkyympqvugDnPacTn0Ld4alw1OoU9SBKXVtSxee8DqckREgkafDtLlfDH8pb2/WstmM/jxuLpeoD99uI+aWr/FFYmIBIcCkHQppml+EYC0+nNQTBrRg9QYD0fLanhtw2GryxERCQoFIOlSamoD1NQGMIBojwJQMLgcNn54YR8A/vivPfgDpsUViYi0ngKQdCn1t79HehzYbbr9PVimjk4jLtzJgeOVvL0t3+pyRERaTQFIuhSt/tw2wl0ObhnbG4D/t3IPpqleIBHp3BSApMswTZPS6ro7wKIVgILu5rG9CHfZyc0vZeXnx6wuR0SkVRSApMuo9PrxB0zsBkRq+4ugiw13ccPodACe1SapItLJKQBJl1F6av5PVJi2v2grP7ywD067wbr9J1i//4TV5YiItJgCkHQZDcNfHq3/01ZSYjxcc3ZPAJ5dqV4gEem8FICkS9D8n/bzo3F9MAzI2XGUnQVlVpcjItIiCkDSJVQ0zP8xiHBp/k9b6pMUyXeGpADwx1XqBRKRzkkBSLqEL+b/ODT/px3Ub4/xty1HOHSy0uJqRESar0MEoAULFpCRkYHH42HMmDGsW7fua9svXbqUzMxMPB4PQ4cO5e233z5t2x//+McYhsHTTz8d5KqlI6kf/orR/J92MaxnLOf3S8AfMPnTh/usLkdEpNksD0CvvPIK2dnZzJ49m40bNzJ8+HAmTJjA0aNHm2y/evVqpk6dym233camTZuYNGkSkyZNYvv27V9p+8Ybb/Dxxx/TvXv3tn4bYiHTNCmr0vyf9nbnuH4ALPkkjxMVXourERFpHssD0Ny5c7n99tuZPn06gwcPZuHChYSHh/Pcc8812X7evHlMnDiR+++/n0GDBvH4448zcuRI5s+f36jd4cOH+clPfsJf/vIXnE59KHZlFV4/ftPEbjMI1/yfdnN+vwSG9Iim2hfghdX7rS5HRKRZLA1AXq+XDRs2MH78+IZjNpuN8ePHs2bNmiZfs2bNmkbtASZMmNCofSAQ4KabbuL+++/nrLPOapvipcOon/8T7dH8n/ZkGEZDL9CLq/dTUVNrcUUiImfO0gBUVFSE3+8nOTm50fHk5GQKCgqafE1BQcE3tn/yySdxOBz89Kc/PaM6ampqKC0tbfSQzqO0fvhLu7+3u4lDUshICKekyseSTw5aXY6IyBmzfAgs2DZs2MC8efN44YUXzrg3YM6cOcTExDQ80tLS2rhKCRbTNCmrPtUDFKYJ0O3NbjP40ak7wv704V68tQGLKxIROTOWBqDExETsdjuFhYWNjhcWFpKSktLka1JSUr62/YcffsjRo0dJT0/H4XDgcDg4cOAAP//5z8nIyGjynDNnzqSkpKThcfCgfpPtLCpq/PhNNP/HQlef3YOkKDf5JdX8bfNhq8sRETkjlv7K7HK5GDVqFDk5OUyaNAmom7+Tk5PDjBkzmnxNVlYWOTk53HvvvQ3HVqxYQVZWFgA33XRTk3OEbrrpJqZPn97kOd1uN263u/VvSNpdabXm/wRTbm5ui143sbeL/91aw7z3PqO3cRTbqf8XiYmJpKenB7NEEZGgsHzMIDs7m5tvvplzzjmH0aNH8/TTT1NRUdEQVqZNm0aPHj2YM2cOAPfccw/jxo3jqaee4vLLL2fJkiWsX7+eRYsWAZCQkEBCQkKjr+F0OklJSWHgwIHt++akzZWdWv8nSuv/tEpVyXHA4MYbb2zR6w1XGD3vfJ5DRPKt6++mavdaAMLCwtmxI1chSEQ6HMs/NaZMmcKxY8eYNWsWBQUFjBgxguXLlzdMdM7Ly8Nm+2KkbuzYsSxevJiHH36Yhx56iP79+7Ns2TKGDBli1VsQi5jmFwFIE6Bbx1dZBpiMuOEBknpntugcRX4bJ0xIv/ZB0uzFlBXsZ+1zj1FUVKQAJCIdjuUBCGDGjBmnHfJauXLlV45NnjyZyZMnn/H59+/f38LKpCPzYqc2YGIzINyt+T/BENktnfj0lvWURtYGOHmwmGrTibNbH6I1IikiHViXuwtMQkeVWdfrE+l2NMw5Eeu4HDaSIuvm0h0prra4GhGRr6cAJJ1WfQDS/J+Oo3usB4DiKh81pnrlRKTjUgCSTqu6IQBp/k9H4XHaSYhwAXAiEG5xNSIip6cAJJ2SPTIBH3U9DJHqAepQ6nuBykw3jpjkb2gtImINBSDplNw9BwEQ4bLjsGn+T0cS4XYQE+YEDKJHX211OSIiTVIAkk7J3bNuk1vN/+mY6nuBIoZ+m+Jqv8XViIh8lQKQdEqenoMBzf/pqKI9Djz4sDndvLWrwupyRES+okUBaO/evcGuQ+SMVXgDOJMyAPUAdVSGYRBnqwRg+e7Khg1rRUQ6ihYFoH79+nHxxRfzf//3f1RXa70PaV+fn/Bh2Ow48eNyqBOzo4o0vPiOH6TCZ/LyujyryxERaaRFnx4bN25k2LBhZGdnk5KSwo9+9CPWrVsX7NpEmpR7zAtAmKFehY7MMKBk7WsA/OnDfdTUai6QiHQcLQpAI0aMYN68eRw5coTnnnuO/Px8LrjgAoYMGcLcuXM5duxYsOsUaZBbVBeAPApAHV7FpyuJD7NxtKyGNzYetrocEZEGrRo/cDgcXHPNNSxdupQnn3yS3bt3c99995GWlsa0adPIz88PVp0iAPj8AXadUA9QpxGo5coBEQAs+tde/AHT4oJEROq0KgCtX7+eu+66i9TUVObOnct9993Hnj17WLFiBUeOHOGqq64KVp0iAOTml+L1g7+qDBcaUukMvt0nnJgwJ3uLKnjv0wKryxERAVoYgObOncvQoUMZO3YsR44c4aWXXuLAgQP88pe/pHfv3lx44YW88MILbNy4Mdj1SojbeOAkAN4jO9H+p51DmNPGzVm9AHh21R5MU71AImK9FgWgZ599lhtuuIEDBw6wbNkyrrjiCmy2xqfq1q0bf/7zn4NSpEi9jXnFANQc2WFtIdIsN4/NwOO0sfVQCWv2HLe6HBGRlgWgFStW8MADD5CamtrouGma5OXV3e7qcrm4+eabW1+hyJdszKvrAVIA6lwSIt1MOScNqOsFEhGxWosCUN++fSkqKvrK8RMnTtC7d+9WFyXSlKNl1Rw6WYUB1BzZaXU50kw/vLAPdpvBh7uK2HaoxOpyRCTEtSgAnW4Mv7y8HI/H06qCRE5n06nhr7QYB6a3ytpipNnS4sO5cnh3ABaqF0hELNasfQSys7OBumXuZ82aRXh4eMNzfr+ftWvXMmLEiKAWKFKvfvhrYIKTDy2uRVrmR+P68Mamw7yzPZ99RRX0ToywuiQRCVHNCkCbNm0C6nqAtm3bhsvlanjO5XIxfPhw7rvvvuBWKHLKpgPFAAyId319Q+mwMlOiuSSzG+/vOMqif+1lzjVDrS5JREJUswLQBx98AMD06dOZN28e0dHRbVKUyH/y+QNsPVwMwMBE7QDfmd15UV/e33GU1zYc4mfj+9MtWsPmItL+WjQH6Pnnn1f4kXa1I7+Mal+AaI+D7lHaAb4zOzcjnlG94vD6Azz30X6ryxGREHXGnyTXXHMNL7zwAtHR0VxzzTVf2/b1119vdWEiX1Y//+fs9DhsWgGx07tzXF9++NJ6/vLxAe66uC/RHvXqiUj7OuMAFBMTg3HqgycmJqbNChJpyhcBKBYot7QWab1LMrsxIDmSzwvL+b+PD3DXRf2sLklEQswZB6Dnn3++yf8WaQ/1AWhkehyUKwB1djabwY/H9SX7r1t47t/7ufX83nicdqvLEpEQ0qI5QFVVVVRWVjb8/cCBAzz99NO89957QStMpN6xshoOnqjCMGBEeqzV5UiQfG94d3rEhlFUXsNrGw9ZXY6IhJgWBaCrrrqKl156CYDi4mJGjx7NU089xVVXXcWzzz4b1AJFNp3q/enfLVJzRboQp93GDy+sWzn+j6v2UusPWFyRiISSFgWgjRs3cuGFFwLw6quvkpKSwoEDB3jppZf4wx/+ENQCReo3QD07Lc7aQiToppybRly4k7wTlbyzvcDqckQkhLQoAFVWVhIVFQXAe++9xzXXXIPNZuO8887jwIEDQS1QpGH+T69YawuRoAt3ObhlbF0v0MJVe067zY6ISLC1KAD169ePZcuWcfDgQd59910uu+wyAI4ePar1gSSofP4AWw8VA6cmQEuXMy2rF2FOO58eKeXDXV/dZFlEpC20KADNmjWL++67j4yMDMaMGUNWVhZQ1xt09tlnB7VACW07C+oWQIzyOOibFGl1OdIG4iJcTB2dDsCzK7VJqoi0jxYFoO9///vk5eWxfv16li9f3nD80ksv5fe//33QihOpnwA9Ii0Wm00LIHZVP7ywNw6bwZq9x9l8sNjqckQkBLQoAAGkpKRw9tlnY7N9cYrRo0eTmZkZlMJEADYfLAHg7LRYawuRNtU9NoxJZ/cA4NmVuy2uRkRCQYs2VaqoqOCJJ54gJyeHo0ePEgg0vn117969QSlOZMup+T/DFYC6vB+P68NrGw/x7qeF7CgoJTNF8wlFpO20KAD98Ic/ZNWqVdx0002kpqY2bJEhEkyl1T72HKtb9VkBqOvr1y2K7w5J5a1t+cx/fzfzbxhpdUki0oW1KAC98847vPXWW5x//vnBrkekwbZDJZgm9IwLIzHSbXU50g5mXNKPt7bl89a2fO49Wka/blFWlyQiXVSL5gDFxcURHx8f7FpEGqmfDKven9AxKDWaCWclY5ow/33NBRKRttOiAPT4448za9asRvuBiQTbllMBaETPWEvrkPb1k0v6A/DmliPsK6qwuBoR6apaNAT21FNPsWfPHpKTk8nIyMDpbLw/08aNG4NSnIQ2TYAOTUN6xHBpZjdydhxlwQe7+d3k4VaXJCJdUIsC0KRJk4JchkhjBSXVFJbWYDNgSA/dDRRqfnJpf3J2HOWNTYf56SX9SU8It7okEeliWhSAZs+eHew6RBqpn/8zIDmKcFeLvk2lExuRFsu4AUms+vwY/2/lbp64dpjVJYlIF9PihRCLi4v505/+xMyZMzlx4gRQN/R1+PDhoBUnoat++GuEhr9C1k8v7QfAaxsPceik5huKSHC1KABt3bqVAQMG8OSTT/K73/2O4uJiAF5//XVmzpwZzPokRG3RHWAhb1SveM7vl4DPb7JwlfYIE5HgatHYQnZ2Nrfccgu/+c1viIr6Yp2O7373u9xwww1BK05CUyBgsvVQ3RYYw3UHWKeXm5vb4tdOTDP5aDcsWZfHuMRqEsLtJCYmkp6eHsQKRSQUtSgAffLJJ/zxj3/8yvEePXpQUFDQ6qIktO0tKqe8ppYwp50BydoBvrOqKjkOGNx4442tOk/y1Dl40ocy5dE/cTJnEWFh4ezYkasQJCKt0qIA5Ha7KS0t/crxzz//nKSkpGafb8GCBfz2t7+loKCA4cOH88wzzzB69OjTtl+6dCmPPPII+/fvp3///jz55JN897vfbXj+0UcfZcmSJRw8eBCXy8WoUaP41a9+xZgxY5pdm7S/+g1Qh/aIwWFv8TQ1sZivsgwwGXHDAyT1bvkmyZUBJ4cCEHPO9+jbO531f3qYoqIiBSARaZUWBaArr7yS//7v/+avf/0rAIZhkJeXxwMPPMC1117brHO98sorZGdns3DhQsaMGcPTTz/NhAkT2LlzJ926dftK+9WrVzN16lTmzJnDFVdcweLFi5k0aRIbN25kyJAhAAwYMID58+fTp08fqqqq+P3vf89ll13G7t27WxTQpH19Mf8nxtpCJCgiu6UTnz6wxa+PM02Kj5RRXlNLbeKAIFYmIqGsRb9eP/XUU5SXl5OUlERVVRXjxo2jX79+REVF8atf/apZ55o7dy63334706dPZ/DgwSxcuJDw8HCee+65JtvPmzePiRMncv/99zNo0CAef/xxRo4cyfz58xva3HDDDYwfP54+ffpw1llnMXfuXEpLS9m6dWtL3q60My2AKF9mGAY94zwAFJth2CJirS1IRLqEFvUAxcTEsGLFCj766CO2bNlCeXk5I0eOZPz48c06j9frZcOGDY3uHLPZbIwfP541a9Y0+Zo1a9aQnZ3d6NiECRNYtmzZab/GokWLiImJYfjwpleUrampoaampuHvTQ3vSfuo9vnJza+7/poALfViwpxEuu2U1/iJOe86q8sRkS6g2QEoEAjwwgsv8Prrr7N//34Mw6B3796kpKRgmiaGYZzxuYqKivD7/SQnJzc6npyczI4dO5p8TUFBQZPt/3Py9T/+8Q+uv/56KisrSU1NZcWKFSQmJjZ5zjlz5vDYY4+dcd3SdnLzS/H5TRIiXPSMC7O6HOkgDMMgLS6c3IIyokZ8h6JKv9UliUgn16whMNM0ufLKK/nhD3/I4cOHGTp0KGeddRYHDhzglltu4eqrr26rOpvt4osvZvPmzaxevZqJEydy3XXXcfTo0Sbbzpw5k5KSkobHwYMH27laqfflHeCbE6al64sOcxCGF8Ph5LXccqvLEZFOrlk9QC+88AL/+te/yMnJ4eKLL2703Pvvv8+kSZN46aWXmDZt2hmdLzExEbvdTmFhYaPjhYWFpKSkNPmalJSUM2ofERFBv3796NevH+eddx79+/fnz3/+c5MLNbrdbtxu9xnVLG2rYQK0hr/kPxiGQYK9kkN+Fzn7Kjl4opK0eO0RJiIt06weoJdffpmHHnroK+EH4JJLLuHBBx/kL3/5yxmfr/4W9ZycnIZjgUCAnJwcsrKymnxNVlZWo/YAK1asOG37L5/3y/N8pGPaUr8Aou4AkyaEGz6q9m+iNgDPvL/L6nJEpBNrVgDaunUrEydOPO3z3/nOd9iyZUuzCsjOzuZ//ud/ePHFF8nNzeXOO++koqKC6dOnAzBt2rRGvTb33HMPy5cv56mnnmLHjh08+uijrF+/nhkzZgBQUVHBQw89xMcff8yBAwfYsGEDt956K4cPH2by5MnNqk3aV3Gll31FFYB6gOT0ij/8PwBe23iY/ae+X0REmqtZQ2AnTpz4ygTkL0tOTubkyZPNKmDKlCkcO3aMWbNmUVBQwIgRI1i+fHnD18nLy8Nm+yKnjR07lsWLF/Pwww/z0EMP0b9/f5YtW9awBpDdbmfHjh28+OKLFBUVkZCQwLnnnsuHH37IWWed1azapH3Vb3/RKyGcuAiXxdVIR+U9spORqW425tcwL2cXv58ywuqSRKQTalYA8vv9OBynf4ndbqe2trbZRcyYMaOhB+c/rVy58ivHJk+efNreHI/Hw+uvv97sGsR6mv8jZ2rqWVFszK9h2ebD3H1xX/p1i/rmF4mIfEmzApBpmtxyyy2nnTCsOTbSGvULII7QAojyDfrGO7lscDLvfVbI7/+5iwU3jLS6JBHpZJoVgG6++eZvbHOmd4CJfJlpmg17gGkFaDkTP/v2AN77rJC3tuYz4+JSBqVGW12SiHQizQpAzz//fFvVISHuSEk1ReU1OGwGZ3XXB5l8s0Gp0Vw+NJW3tuXz+xWfs2jaOVaXJCKdiLbalg6hfv5PZmoUHqfd2mKk07h3fH8MA977rLDhe0hE5EwoAEmHoAnQ0hL9k6O4+uweADy5fAemaVpckYh0FgpA0iFsVgCSFsr+9gBcdhur9xznw11FVpcjIp2EApBYzh8w2X5YE6ClZXrGhXPjeb2Aul6gQEC9QCLyzRSAxHJ7jpVT4fUT7rLTr1uk1eVIJzTjkn5Euh18eqSUf2zLt7ocEekEFIDEcvXzf4b0iMFu0w7w0nzxES7u+FYfAJ56byfe2oDFFYlIR6cAJJar3wJjeE9tgCotd9sFvUmMdHPgeCWvfJJndTki0sEpAInl6leAHqYJ0NIKEW4HP720HwDzcnZTUdP8bXlEJHQoAImlamr95OaXAtoCQ1rv+nPTSY8Pp6i8huf+vc/qckSkA1MAEkvl5pfh85vEhTvpGRdmdTnSybkcNn5+2QAA/vivvZyo8FpckYh0VApAYqmtp4a/hqfFYhiaAC2t971h3TmrezTlNbUs+GC31eWISAelACSW2nJqA1TN/5FgsdkMfjExE4D/XXOAgycqLa5IRDoiBSCxVP0EaN0BJsH0rf6JjO2bgNcf4Lfv7rS6HBHpgBSAxDLlNbXsOVYOqAdIgsswDB767iAMA97ccoRNeSetLklEOhgFILHMtkMlmCb0iA0jKcptdTnSxQzpEcO1I3sC8Mu3crVRqog0ogAklmkY/krT8Je0jfsuG0iY086GAyd5Z3uB1eWISAeiACSW2aoFEKWNpcR4GrbImPNOLjW1fosrEpGOQgFILPPFHWDqAZK286NxfegW5ebgiSpeXL3f6nJEpINQABJLFJXXcLi4CsOAoT0UgKTthLsc3D9hIADPvL9biyOKCKAAJBapH/7qmxRJlMdpbTHS5V07sieDU6Mpq65l3j8/t7ocEekAFIDEEps1/CXtyGYzePjyQQD839o8dh8tt7giEbGaApBYor4HSBugSnsZ2y+R8YOS8QdMfvXWZ1aXIyIWUwCSdmeaJlsPaQsMaX8PfTcTp93gg53HyMkttLocEbGQApC0u0MnqzhR4cVpNxiUGmV1ORJC+iRFcusFvQH47398ptviRUKYApC0u/oFEAelRuN22K0tRkLOTy7pT7coNweOV/KnD/dZXY6IWEQBSNrdloPFgCZAizUi3Q4e+m7dhOj57+8mv6TK4opExAoKQNLutpya/zNc83/EIleN6M45veKo8vn59ds7rC5HRCygACTtyh8w2X74VADSHWBiEcMwePTKszAM+PuWI6zde9zqkkSknSkASbvafbScSq+fCJedvkmRVpcjIWxIjximjk4HYPabn1LrD1hckYi0JwUgaVf1E6CH9IjBbjOsLUZC3v2XDSQmzMmOgjIWr8uzuhwRaUcKQNKu6hdA1PCXdARxES7uu2wAAL97dydF5TUWVyQi7UUBSNpV/Q7wmgAtHcXU0emc1T2a0upafvVWrtXliEg7cVhdgHR9eXl5FBUV4fOb5ObXBSB7yUE2bsxv0flyc/UhFeqC/T3wk6wk7ny9lDc2HWbyqJ6M7ZcY1POLSMejACRtKi8vj8zMQVRVVeJKHUDqtLn4K4r5zreuaPW5fTXeIFQonUlVyXHA4MYbbwzqecPCwvnRon/yxvYTPLxsO+/ce6EW6RTp4hSApE0VFRVRVVXJmFtn40/O5FgAoiPD+PZ/Pd/ic+ZvW8P2NxdRW1sbxEqlM/BVlgEmI254gKTemUE5Z2n+ftY+9xjX9HPy7wNu9hZV8OzKPdw7fkBQzi8iHZMCkLSL6NQMTngSoNxLbEwM8fGpLT5Xaf7+4BUmnVJkt3Ti0wcG9ZwRLhuzrhjMT17exP/7YA9XjehB78SIoH4NEek4NAla2k15TV2PTaRHuVs6piuGpXJh/0S8/gAPL9uGaZpWlyQibUQBSNqF3zSo9tUtNBfpVgCSjskwDH45aQguh42Pdh/nzS1HrC5JRNqIApC0ixqzLvS4HTacdn3bScfVKyGCn1zcD4DH//EZJZU+iysSkbagTyJpF9WnpptFqPdHOoE7xvWhb1IEReVeHn/rM6vLEZE2oAAk7aLadAIQ6datxdLxuR12nrx2GIYBr244xKrPj1ldkogEWYcIQAsWLCAjIwOPx8OYMWNYt27d17ZfunQpmZmZeDwehg4dyttvv93wnM/n44EHHmDo0KFERETQvXt3pk2bxpEjGsu3UtWpITDN/5HO4pyMeG7OygDgode3NUziF5GuwfIA9Morr5Cdnc3s2bPZuHEjw4cPZ8KECRw9erTJ9qtXr2bq1KncdtttbNq0iUmTJjFp0iS2b98OQGVlJRs3buSRRx5h48aNvP766+zcuZMrr7yyPd+WfIk9KgE/dT0/GgKTzuQXEweSFh/G4eIqnnxnh9XliEgQWR6A5s6dy+2338706dMZPHgwCxcuJDw8nOeee67J9vPmzWPixIncf//9DBo0iMcff5yRI0cyf/58AGJiYlixYgXXXXcdAwcO5LzzzmP+/Pls2LCBvDzt9mwFd/e6BevCXXbtAC+dSrjLwRPXDAPgfz8+wNq9xy2uSESCxdIA5PV62bBhA+PHj284ZrPZGD9+PGvWrGnyNWvWrGnUHmDChAmnbQ9QUlKCYRjExsYGpW5pHldq3Yq6UVr/Rzqh8/slMnV0GgAPvLaVKq/f4opEJBgsDUBFRUX4/X6Sk5MbHU9OTqagoKDJ1xQUFDSrfXV1NQ888ABTp04lOjq6yTY1NTWUlpY2ekjw1PcAaf6PdFYzvzuIlGgP+49X8vt/fm51OSISBJYPgbUln8/Hddddh2maPPvss6dtN2fOHGJiYhoeaWlp7Vhl11YbMHGl9AUUgKTzivY4+fU1QwD404d72Xyw2NqCRKTVLA1AiYmJ2O12CgsLGx0vLCwkJSWlydekpKScUfv68HPgwAFWrFhx2t4fgJkzZ1JSUtLwOHjwYAvfkfynAyW12JwebATwOLt03pYu7pLMZK4+uwcBE7L/ullDYSKdnKWfSC6Xi1GjRpGTk9NwLBAIkJOTQ1ZWVpOvycrKatQeYMWKFY3a14efXbt28c9//pOEhISvrcPtdhMdHd3oIcHx+XEvAB6jFsPQBGjp3GZ/bzDJ0W72HqvgyeW6K0ykM7P8V/Ls7Gz+53/+hxdffJHc3FzuvPNOKioqmD59OgDTpk1j5syZDe3vueceli9fzlNPPcWOHTt49NFHWb9+PTNmzADqws/3v/991q9fz1/+8hf8fj8FBQUUFBTg9XoteY+hbNfxum0EPGg7Aen8YsNd/Pb7wwF4YfV+PtylBRJFOivLA9CUKVP43e9+x6xZsxgxYgSbN29m+fLlDROd8/LyyM/Pb2g/duxYFi9ezKJFixg+fDivvvoqy5YtY8iQuvH5w4cP8+abb3Lo0CFGjBhBampqw2P16tWWvMdQ9vmJL3qARLqCbw1IYlpWLwDuW7qF4kr9YiXSGXWIWakzZsxo6MH5TytXrvzKscmTJzN58uQm22dkZGCaZjDLkxYqrvRypKxunkSYoR4g6TpmfmcQ/95VxN6iCh7526c8M/Vsq0sSkWayvAdIuq76O2V8J45gNxRKpesIc9mZO2UEdpvB37cc4c0t2mpHpLNRAJI2Ux+Aao5osqh0PSPSYrn74n4APPzGNgpKqi2uSESaQwFI2symvGIAvPlaOE66pp9c0o9hPWMora7lvqVbCATU0ynSWSgASZswTVM9QNLlOe025l43Ao/Txr93F/HHf+21uiQROUMKQNIm9hVVUFLlw2kD79H9Vpcj0mb6dYvk0e+dBcDv3tvJhgMnLa5IRM6EApC0ifrenz5xTgjoFnjp2qacm8b3hnfHHzD56cubKKnUXY8iHZ0CkLSJ+vk/AxJc1hYi0g4Mw+BXVw8hPT6cw8VVPPj6Vi3HIdLBKQBJm6jvARqQ4LS2EJF2Eu1x8szUs3HaDd7ZXsBf1uZZXZKIfI0OsRCidC1VXj+5+aUA9I9XAJLOITc3Nyjn+cGQSF7YUsZjb26nh6uai0cODMp5RSS4FIAk6LYcKqY2YJIc7SYp3G51OSJfq6rkOGBw4403BumMBknfn0V433O5aeFK/vmAi8y+vYN0bhEJFgUgCbr6u2BG9YrTDvDS4fkqywCTETc8QFLvzKCcs9Y02O/14ojvya/e3ctLd2bo34JIB6MAJEH3RQCKB3RLsHQOkd3SiU8P3nCV78Ae8rw2Psyr5vmP9nPrBeoFEulINAlagioQMBsC0Dm94iyuRsQ6YUYtJz/4MwC/fjuXdftOWFyRiHyZApAE1d6ickqqfHicNgZ3j7a6HBFLlW34Oxeme6gNmNy9eCNHS7VfmEhHoQAkQbV+f13vz/CesTjt+vYSufOcGAYmR3GsrIa7F2/E5w9YXZKIoAAkQba+fvgrQ8NfIgAeh41nbxxJlNvBJ/tPMudt7Y0n0hEoAElQbfzSHWAiUqdPUiRPXTccgOc+2seyTYctrkhEFIAkaI6X17C3qAKAkekKQCJfdtlZKdx9cV8AfvHaVjbl6Q5JESvpNngJmvq7v/p1iyQ2XHuAiUDjFaYvSjBZ193NJ0dqmP7cx/xmfCKJzVwsNDExkfT09GCXKRJyFIAkaDbk6fZ3kXqnW2HacIWR8oPfUNytN7f8eQ0Ff/kFpq/mjM8bFhbOjh25CkEiraQAJEGzYb/m/4jU+7oVpn2mjTx/AFdyXwb/fDGptlLOZKHo0vz9rH3uMYqKihSARFpJAUiCoqbWz9bDJYACkMiXnW6F6fBqH58dKaPcdFMRlUZ6fLgF1YmELk2ClqDYfrgUb22AhAgXvRMjrC5HpMOL8jjpk1T3b+VIcTXHys58GExEWk8BSIJiw4G6Zf5HagNUkTOWFOWme6wHgL3HKiip8llckUjoUACSoFi3TxOgRVoiLS6M+AgXJvB5QTkVNbVWlyQSEhSApNUCAZNP9tf1AI3pk2BxNSKdi2EY9EuKIMrjwG+a7Cgoo6bWb3VZIl2eApC02o6CMkqqfES47AzRBqgizWazGQxMjiTMacfnN9mRX0at9gwTaVMKQNJqa/cdB2BURjwObYAq0iIOu43M1EhcdoMqX4CdheUEAqbVZYl0Wfq0klZbu/fU8FfveIsrEenc3A47malR2A2Dsupadh8rxzQVgkTaggKQtIppmqw7Nf/nvD4KQCKtFe5yMCAlEgM4UeFj77EKhSCRNqAAJK2y62g5Jyq8eJw2hvaItbockS4hJsxJv26RABwr93LgeKVCkEiQaSVoaZW1e+vm/4xMj8PlUJ4WCZaESBcBM4I9xyooKK3BZjOItLookS5En1jSKh/vq5//o9vfRYItKcpN78S6LTKOFFdzPKDtMkSCRQFIWsw0zS8mQGv+j0ibSI72kB4fBsDxQARRo660uCKRrkEBSFpsX1EFReU1uBw2RqTFWl2OSJfVPTaMnnF1ISh+/B28u7vC4opEOj8FIGmxtaeGv0akxeJx2i2uRqRr6xHrIc6oBOCPG0t54aN9Flck0rkpAEmL1U+APk/r/4i0OcMwSLRVUPLxqwA8+vfP+NOHey2uSqTz0l1g0iKmaTb0AGn/L5H2YRhQvOoFbpt+C6/mlvPLt3Lx+U3uvKhvq86bl5dHUVFRkKqsk5iYSHp6elDPKRJMCkDSInknKskvqcZpNxiZrh3gRdrTDUOjSOvRnd//83OeXL4Dnz/ATy/t36Jz5eXlkZk5iKqqyqDWGBYWzo4duQpB0mEpAEmLfLir7rfFs9PjCHNp/o9Ie7tnfH8cdoPfvruTuSs+x1sb4OeXDcAwjGadp6ioiKqqSsbcOpvo1Iyg1Faav5+1zz1GUVGRApB0WApA0iIf7a4LQBf0S7S4EpHQdffF/XDYDOa8s4P5H+zmeIWXX04agt3WvBAEEJ2aQXz6wDaoUqRj0iRoaTZ/wGT1nroJ0Bf0VwASsdKPxvXll5OGYBjw8ro87v7LRqp9fqvLEunwFICk2T49UkJJlY8ot4NhPWKsLkck5N14Xi/+3w0jcdltLP+0gFueX0dZtc/qskQ6NAUgabZ/nxr+Oq9vAg67voVEOoLvDE3lhVvPJdLt4OO9J7h+0cccK6uxuiyRDkufXtJsmv8j0jGN7ZvIkjvOIzHSxadHSrn22dXsOVZudVkiHZLlAWjBggVkZGTg8XgYM2YM69at+9r2S5cuJTMzE4/Hw9ChQ3n77bcbPf/6669z2WWXkZCQgGEYbN68uQ2rDz3VPj+f7D8JwPkKQCIdzpAeMbz647GkxYeRd6KSqxd8xOrdwV3jR6QrsDQAvfLKK2RnZzN79mw2btzI8OHDmTBhAkePHm2y/erVq5k6dSq33XYbmzZtYtKkSUyaNInt27c3tKmoqOCCCy7gySefbK+3EVI+2X8Cb22AlGgPfZMirC5HRJqQkRjBG3edz8j0WEqra5n23Dpe+STP6rJEOhRLb4OfO3cut99+O9OnTwdg4cKFvPXWWzz33HM8+OCDX2k/b948Jk6cyP333w/A448/zooVK5g/fz4LFy4E4KabbgJg//797fMmuphvWhH2tS2lAAyKN9i0adM3ni83NzdotYnImUuMdLP49vP4xatbeXPLER54bRt7iyp4YEImthbcJi/S1VgWgLxeLxs2bGDmzJkNx2w2G+PHj2fNmjVNvmbNmjVkZ2c3OjZhwgSWLVvWqlpqamqoqflismBpaWmrztdZncmKsKm3zMOV3JelzzzOC5+tPONz+2q8QahQRJrD47Qz7/oR9E6MYF7OLv64ai/7iyr4/ZQRhLu0DJyENsv+BRQVFeH3+0lOTm50PDk5mR07djT5moKCgibbFxQUtKqWOXPm8Nhjj7XqHF3BN60I6zNt7PMnACajJ92C4+qbv/Gc+dvWsP3NRdTW1ga/YBH5RoZh8LNvD6B3YgS/eHUr735ayNULVvPHm0aRkahhbAld+hUAmDlzZqOepdLSUtLS0iysyFqnWxH2aGkNFFUQ6XbQrceAMzpXaf7+IFcnIi0x6ewe9IwL486/bGRnYRnfm/9v5l0/glirCxOxiGWToBMTE7Hb7RQWFjY6XlhYSEpKSpOvSUlJaVb7M+V2u4mOjm70kK8qrqwbxooNd1lciYi0xDkZ8fzjJxcwqlccZdW13PrCel75tAzQnCAJPZYFIJfLxahRo8jJyWk4FggEyMnJISsrq8nXZGVlNWoPsGLFitO2l+AJmCYlVXUry8aGOy2uRkRaKjnaw8u3n8dN5/UC4JVPy0m69hH8pkKQhBZLh8Cys7O5+eabOeeccxg9ejRPP/00FRUVDXeFTZs2jR49ejBnzhwA7rnnHsaNG8dTTz3F5ZdfzpIlS1i/fj2LFi1qOOeJEyfIy8vjyJEjAOzcuROo6z1qbU9RKCurrsVvgtNuEKHd30U6NZfDxuOThjA8LZaHXt8C/UaT5/fjqa4lyqOZERIaLF0HaMqUKfzud79j1qxZjBgxgs2bN7N8+fKGic55eXnk5+c3tB87diyLFy9m0aJFDB8+nFdffZVly5YxZMiQhjZvvvkmZ599NpdffjkA119/PWeffXbDbfLSMsWVdb0/MWFODEO/KYp0Bd8f1ZNfXZKIr7gAH3Y+O1LKkeIqTNO0ujSRNmd51J8xYwYzZsxo8rmVK1d+5djkyZOZPHnyac93yy23cMsttwSpOqlXP/8nTsNfIl1K3zgn+c//lMHZL1Fuesg7UUVJlY++SZG4HJZvFiDSZiwPQNLxVfv8VPkCQF0PkIhYK5gLjObm5mJ6K0m1leGPi2f/8UpKqmrZdriEvkmRmvMnXZYCkHyj+uGvKI9Du7+LWKiq5DhgcOONNwb93LVeL8nRHiI9TnYVllPl87OjoIzkaDfp8eHYtXq0dDEKQPKNTlRo+EukI/BVlgEmI254gKTemUE5538uVhrusjO0RzQHTlRSWFpDYWkNJZU++naLIMqjnwHSdSgAydfy+QOUVtf9YIyP0Po/Ih1BZLf0JhcrbYmmFiu12Qx6J0YQF+5i77EKqmsDfHqkjNQYD2lxYdpLTLoEjWfI1zp5avgr3GXH49Tt7yKhJDbcybCe0SRG1v3yk19SzbbDpZRVa2sb6fwUgORrnTw1/KXeH5HQ5LDb6NctkgHJkTjtBlU+P58eKWVfUQW1Ad0uL52XApCclj9gUnxq9ef4CI39i4Sy+AgXw3rGNPQGFZbWsPVgccMcQZHORgFITqu40otpgsdhI0zDXyIhz3mqN2hQahQehw2v3+TzwnJ2FpRRU+u3ujyRZlEAktM6UVHX+xMX4dLqzyLSICbMybCeMXSP9WBQN1dwy8ESDp2sIqBhMekkdBeYNCkQMBtWf9b8HxH5TzabQXp8OImRLvYVVVJWXcuhk1UcK6shPqCfGdLxKQBJk05W+vCb4LLbiHRr+EtEmhbucjA4NYrjFV7yjldSUxsgnxi6TfklB0p8jAzi18rLy6OoqCiIZ4TExETS09ODek7pHBSApElF5TUAJEZq+EtEvp5hGCRGuokLd3GkuIojxVWEZYzg5+8Vseb4Fn727QGkxoS16mvk5eWRmTmIqqrKIFVdJywsnB07chWCQpACkHyF3zQatr9IjFJXtoicGbvNIC0+HFfZYbZ/toOIgefz1/WH+NvmI0w/vzd3XtS3xfsJFhUVUVVVyZhbZxOdmhGUekvz97P2uccoKipSAApBCkDyFWWmG5O6xQ/DXfoWEZHmcRoBipbN4Q/vreGNPSbr9p9g4ao9vLwuj7sv7stN52UQ5mrZ0Hp0akbQVsGW0Ka7wOQrygJugIb1PkREWmJggotXfnQef775HAYkR1JS5ePXb+/gwt98wP/8ay+VXq0oLdZRAJJG7NFJVFEXfBIi3RZXIyKdnWEYXDoomXfu+Ra//f4w0uLDKCqv4Vdv53Lhkx/wx1V7FITEEgpA0kjE4IsAiPY4cDv07SEiwWG3GUw+J433f34Rv7l2GOnx4Ryv8DLnnR1c8OQHPLtyD2XVPqvLlBCiTzhpYJomkUMuBSBRvT8i0gacdhvXnZtGzs/H8dvvD6NXQjgnKrw8uXwHY+e8z6/fzuVIcZXVZUoIUACSBp8e8+JM6IlBgATN/xGRNuS025h8Tho52eN4avJw+iZFUFZTy6J/7eVbv/mAn72ymU+PlFhdpnRhusVHGqzYW7e+RrRRg92mtX9EpHVyc3PPqF1vA568KIqN+S7e3FnB9mNe3th0mDc2HWZYsosr+kcQVryvjavt+LQQZHApAAkAJyq8rDlUDUCMrdriakSkM6sqOQ4Y3HjjjS16vSulH9HnXk145gVsLfSytdBLbUkN0WO+T3VNaE6Y1kKQwacAJAAsXX+Q2gDU5O/CkxZrdTki0on5KssAkxE3PEBS78yWn8cspjgQRonpwRGTTNxFt5BnmlQcLScl2k2k2xEyK9VrIcjgUwASav0BXlpzAIDyze9A2lSLKxKRriCyW3qrFy1Mpm5z5h1bN3Cs3Ie7+0COl3s5Xu4lzGknKcpFYqQbV4jctaqFIIMnNL5j5Gu991khh4uriHbbqPhspdXliIg0YrMZhNccp+B/f06yN5+kKDc2A6p8fvJOVLExr5gdBWUcL/cSME2ry5VOQj1Awp//XTe5cELfcLbVei2uRkTk9Fymlx5JEfSKD+N4hZdjZV7Ka2oprvRRXOnDYTNIiHSREOkiKoSGyKT5FIBC3Ma8k2w4cBKn3WBC33B+Z3VBIiJnwGG3kRztITnaQ5XXz7HyGo6V1eDzmxSW1lBYWoPLbiM+0klChJtIt11hSBpRAApx89/fDcCkET2ID/NbXI2ISPOFueykx4eTFhdGSZWPonIvJyt8eP0BCkpqKCipwe2wER9R1zMU4VIYEgWgkLb9cAnv7ziKzYC7L+7HibydVpckItJihmEQG+4iNtxFIGBSXOXjeLmXk5VeamoD5JdUk19SjctuIy7CiSPgBLs+BkOV/s+HsD/k7ALgyuHdyUiM4ESexQWJiASJzWYQH+EiPsKFP2BSXOnjeIWX4kovXn+AwtIaIJa0nyzmN6tPMtk8xMWZ3YiP0Cr4oUIBKERtPljMe58VYhgw45J+VpcjItJm7F+aGB0ImJRU+zhZ4eN4WSW4w/n4UDUfL92CYcCwnrF8q38iF/ZP4uz0WJz25t8s3RYrNp/pqtpy5hSAQpBpmvz67bp/TFef3YN+3aIsrkhEpH3YbAZx4S7iwl3EVB7iX3/+Jff85s9sO2kjN7+ULQeL2XKwmGfe302Ey05W30S+NSCRC/ol0jsx4hvnDrXVis31fDW6UzdYFIBCUE7uUdbtO4HLYeO+y7SgloiEJsMAb+Eerh8SxW9GjqSgpJp/7y7iw13H+PeuIo5XePlnbiH/zC0EoFuUm9G94xnTO54xfRLolxSJ7T/2TWyLFZsB8retYfubi6itDc2tQNqCAlCIqfb5G3p/bj2/N91jwyyuSESkY0iJ8fD9UT35/qieBAImn+WX8uGuukC0fv9JjpbV8I+t+fxjaz4AceFORveOZ3TvBEZnxJOZ+kVverBXbC7N3x+0c0kdBaAQ8+zKPewtqiApys1dF/e1uhwRkQ7JZjMY0iOGIT1iuPOivlT7/Gw+WMy6fSdYu+84Gw6c5GSlj3c/LeTdT+t6iDxOG71j7MRefCtlARcRtX5cdptuue+gFIBCyJ5j5Ty7cg8As783mGiP0+KKREQ6B4/Tznl9EjivTwLQH29tgG2HSxoForLqWnKLAsSMvob8AOTnleC0G0S6HUS6HUS47YS7HCGzb1lHpwAUInz+ANl/3YLXH+CigUlcPjTV6pJERDotl8PGqF5xjOoVx50X9SUQMNlbVMHf/r2ZX//xZRLP/jY1OPH5TU5W+jhZ6Wt4rdNuEO6qC0QRrrpQ5HGqp6i9KQCFiHn/3MWWg8VEeRz86uqh+ocmIhJENptBv26RXJwRzn3v/T9GnXsuMT0HUOGtpby6looaPxXeWqp9AXx+k5IqHyVVX4QimwHhLjthLjthzro/w512XA4Fo7aiABQCPtx1jAUr67a8mHPNUHpo4rOISJuz2wyiPc5G0w38AZNKb10YqjwViiq9fgImlNf4Ka9pvCWRzajb6sMf1ZvoMd+nyhZGldeP22nDpmDUKgpAndiZLLZ1uLSWB3OKME24tHcY3WsL2LixoMm2WmhLREJRMH/2fdO57DaDKI+DKM8XH7+maVLl81Pp9VPl9VPlC1Dl9VPtqwtGFTV+8CQSd9EtFAFFh0oAcDtseJw23A47HqcNj/PUnw77V27Pb03NzZWYmEh6enpQz9kWFIA6qTNZbMsWHkvKD57EGd+D6sO5PPe7h3jO7ztt+3paaEtEQkFVyXHA4MYbbwz6uZvzc9Qw6uYEhbsafyQHTJMaX4Aqn58je3dQeGA3sZlj8dtdBEyoqQ1QUxsAvro2kMtuw+204XbYcDnq/qx71A2r2W1Gm73/sLBwduzI7fAhSAGok/qmxbZqTYND/li8OHDgZ3B6EsMeXPS159RCWyISSnyVZYDJiBseIKl3ZlDOGcyfozbDqJsT5LJTWpnPp3//HZl9F9C97wh8fpNqn5/q2gA1Pj/VvkDd330B/KaJ1x/A6w9QdppzO2wGuNJImjSTuO69iIqJwUEABwHsRt2fNkyaO8pWmr+ftc89RlFRkQKQtK2mFtuq9vnZWVCO1+/HaTc4q3s8HmfSN55LC22JSCiK7JYetEUL2+PnqGEYuBwGLoeN6P94zjRNagMm1b4ANbV+vKd6iWpqA3X/fSog1QZMcEYQPnAsNUBN4Ktfx2aA017Xg+Sy23Da676m027DYTdw2m04bXV/NmfIraNQAOpiSqt8fF5YTm3AxGk3GJwajcdpt7osERFpB4Zh4DwVTqJO8xFfG6gLQgc/W8+etTlkXHID7tgkvP4Avtq63iN/wPyPYbavVx+WqI0l6dpZPLOumAEFucRHuEiIdBMf4SQ23EVsmJO4cBfRYU7sFocmBaAuIhAwOVRcxZHiaqDudsrMlCgtuCUiIo04bDYcbhsebwnlm94idtx36dGtd6M2/oCJ79Qwmq82gNdv4q0N4PPXP0xqT/1pQkNYAifh/Ubzwf4qPti/92vr+NG4Psz8zqC2e6PfoEN8Oi5YsICMjAw8Hg9jxoxh3bp1X9t+6dKlZGZm4vF4GDp0KG+//Xaj503TZNasWaSmphIWFsb48ePZtWtXW74Fy5gmFJXXsOVQSUP4SYp0cVb3aIUfERFpEbvNwOO0E+1xkhDpJjXGQ6+EcPp1i2RQajTDesYwslcco3vHcU5GLCPSYjirexTdbSUcf+cP/GBoFLdd0JtJI7pzYf9EhvaIoWdcGFHuL/pdwp3W9sFY3gP0yiuvkJ2dzcKFCxkzZgxPP/00EyZMYOfOnXTr1u0r7VevXs3UqVOZM2cOV1xxBYsXL2bSpEls3LiRIUOGAPCb3/yGP/zhD7z44ov07t2bRx55hAkTJvDZZ5/h8Xja+y22icLyWqJHX8N+fzy+oxUAuOwGGYkRxEe4LK5ORERCgWEYOAwDh61uuxCfzUv51ve4dtAcRo4c3ORrfP4AJVW+uiEzC1neRTB37lxuv/12pk+fzuDBg1m4cCHh4eE899xzTbafN28eEydO5P7772fQoEE8/vjjjBw5kvnz5wN1vT9PP/00Dz/8MFdddRXDhg3jpZde4siRIyxbtqwd31nw+PwB9hdV8I+tR/jVW59xxTMfcufbx4i7+FZ82HHYDHrGhTE8LVbhR0REOjSn3UZipJuYMGv3o7S0B8jr9bJhwwZmzpzZcMxmszF+/HjWrFnT5GvWrFlDdnZ2o2MTJkxoCDf79u2joKCA8ePHNzwfExPDmDFjWLNmDddff33w38gZ2ph3krV7T+APBKgNmPgD5hd/+k38gQC+gElZdW3DMulFZTXkl1QRMBufy2ZA5f4tpPfuQ6/0dMsnk4mIiHQmlgagoqIi/H4/ycnJjY4nJyezY8eOJl9TUFDQZPuCgoKG5+uPna7Nf6qpqaGmpqbh7yUldatslpaWNuPdfLP3t+7nDzm7W/Ral8NG/26RDO0Rw9AeMcR5C/jeZf9Fzxsf5LjvZFDqK80/AEDJ4V04HcEJVKF8zrY6r87Z8c/ZVufVOUPznG113jY5Z0EeAOXl5UH/DD2jr3/qa5qm+Q0t6xpZ5vDhwyZgrl69utHx+++/3xw9enSTr3E6nebixYsbHVuwYIHZrVs30zRN86OPPjIB88iRI43aTJ482bzuuuuaPOfs2bNNQA899NBDDz306AKPgwcPfmMGsbQHKDExEbvdTmFhYaPjhYWFpKSkNPmalJSUr21f/2dhYSGpqamN2owYMaLJc86cObPRsFogEODEiRMkJCQ0axfe0tJS0tLSOHjwINHR/7k8lbSWrm/b0zVuW7q+bU/XuO115GtsmiZlZWV07979G9taGoBcLhejRo0iJyeHSZMmAXXhIycnhxkzZjT5mqysLHJycrj33nsbjq1YsYKsrCwAevfuTUpKCjk5OQ2Bp7S0lLVr13LnnXc2eU63243b7W50LDY2tsXvKzo6usN9U3Qlur5tT9e4ben6tj1d47bXUa9xTEzMGbWz/Db47Oxsbr75Zs455xxGjx7N008/TUVFBdOnTwdg2rRp9OjRgzlz5gBwzz33MG7cOJ566ikuv/xylixZwvr161m0qG6fK8MwuPfee/nlL39J//79G26D7969e0PIEhERkdBmeQCaMmUKx44dY9asWRQUFDBixAiWL1/eMIk5Ly8Pm+2Lu/XHjh3L4sWLefjhh3nooYfo378/y5Yta1gDCOAXv/gFFRUV3HHHHRQXF3PBBRewfPnyLrMGkIiIiLSOYZpnMlVazkRNTQ1z5sxh5syZXxlSk9bT9W17usZtS9e37ekat72uco0VgERERCTkWL4StIiIiEh7UwASERGRkKMAJCIiIiFHAShIFixYQEZGBh6PhzFjxrBu3TqrS+q0/vWvf/G9732P7t27YxjGVzaxNU2TWbNmkZqaSlhYGOPHj2fXrl3WFNsJzZkzh3PPPZeoqCi6devGpEmT2LlzZ6M21dXV3H333SQkJBAZGcm11177lQVI5fSeffZZhg0b1rBOSlZWFu+8807D87q+wfXEE080LIFST9e4dR599FEMw2j0yMzMbHi+K1xfBaAgeOWVV8jOzmb27Nls3LiR4cOHM2HCBI4ePWp1aZ1SRUUFw4cPZ8GCBU0+/5vf/IY//OEPLFy4kLVr1xIREcGECROorq5u50o7p1WrVnH33Xfz8ccfs2LFCnw+H5dddhkVFRUNbX72s5/x97//naVLl7Jq1SqOHDnCNddcY2HVnUvPnj154okn2LBhA+vXr+eSSy7hqquu4tNPPwV0fYPpk08+4Y9//CPDhg1rdFzXuPXOOuss8vPzGx7//ve/G57rEtf3GzfLkG80evRo8+677274u9/vN7t3727OmTPHwqq6BsB84403Gv4eCATMlJQU87e//W3DseLiYtPtdpsvv/yyBRV2fkePHjUBc9WqVaZp1l1Pp9NpLl26tKFNbm6uCZhr1qyxqsxOLy4uzvzTn/6k6xtEZWVlZv/+/c0VK1aY48aNM++55x7TNPU9HAyzZ882hw8f3uRzXeX6qgeolbxeLxs2bGD8+PENx2w2G+PHj2fNmjUWVtY17du3j4KCgkbXOyYmhjFjxuh6t1BJSQkA8fHxAGzYsAGfz9foGmdmZpKenq5r3AJ+v58lS5ZQUVFBVlaWrm8Q3X333Vx++eWNriXoezhYdu3aRffu3enTpw8/+MEPyMur2+m9q1xfy1eC7uyKiorw+/0NK1fXS05OZseOHRZV1XUVFBQANHm965+TMxcIBLj33ns5//zzG1ZTLygowOVyfWU/PF3j5tm2bRtZWVlUV1cTGRnJG2+8weDBg9m8ebOubxAsWbKEjRs38sknn3zlOX0Pt96YMWN44YUXGDhwIPn5+Tz22GNceOGFbN++vctcXwUgkRB29913s3379kZj+xIcAwcOZPPmzZSUlPDqq69y8803s2rVKqvL6hIOHjzIPffcw4oVK7TFURv5zne+0/Dfw4YNY8yYMfTq1Yu//vWvhIWFWVhZ8GgIrJUSExOx2+1fmf1eWFhISkqKRVV1XfXXVNe79WbMmME//vEPPvjgA3r27NlwPCUlBa/XS3FxcaP2usbN43K56NevH6NGjWLOnDkMHz6cefPm6foGwYYNGzh69CgjR47E4XDgcDhYtWoVf/jDH3A4HCQnJ+saB1lsbCwDBgxg9+7dXeZ7WAGolVwuF6NGjSInJ6fhWCAQICcnh6ysLAsr65p69+5NSkpKo+tdWlrK2rVrdb3PkGmazJgxgzfeeIP333+f3r17N3p+1KhROJ3ORtd4586d5OXl6Rq3QiAQoKamRtc3CC699FK2bdvG5s2bGx7nnHMOP/jBDxr+W9c4uMrLy9mzZw+pqald53vY6lnYXcGSJUtMt9ttvvDCC+Znn31m3nHHHWZsbKxZUFBgdWmdUllZmblp0yZz06ZNJmDOnTvX3LRpk3ngwAHTNE3ziSeeMGNjY82//e1v5tatW82rrrrK7N27t1lVVWVx5Z3DnXfeacbExJgrV6408/PzGx6VlZUNbX784x+b6enp5vvvv2+uX7/ezMrKMrOysiysunN58MEHzVWrVpn79u0zt27daj744IOmYRjme++9Z5qmrm9b+PJdYKapa9xaP//5z82VK1ea+/btMz/66CNz/PjxZmJionn06FHTNLvG9VUACpJnnnnGTE9PN10ulzl69Gjz448/trqkTuuDDz4wga88br75ZtM0626Ff+SRR8zk5GTT7Xabl156qblz505ri+5Emrq2gPn88883tKmqqjLvuusuMy4uzgwPDzevvvpqMz8/37qiO5lbb73V7NWrl+lyucykpCTz0ksvbQg/pqnr2xb+MwDpGrfOlClTzNTUVNPlcpk9evQwp0yZYu7evbvh+a5wfbUbvIiIiIQczQESERGRkKMAJCIiIiFHAUhERERCjgKQiIiIhBwFIBEREQk5CkAiIiISchSAREREJOQoAImIiEjIUQASkZByyy23MGnSpDNqe9FFF3Hvvfd+bZuMjAyefvrphr8bhsGyZcsA2L9/P4ZhsHnz5hbVKiJtRwFIRCx3JkEjGK9pC5988gl33HGH1WWISDM5rC5ARKQzS0pKsroEEWkB9QCJiKVuueUWVq1axbx58zAMA8Mw2L9/P6tWrWL06NG43W5SU1N58MEHqa2t/drX+P1+brvtNnr37k1YWBgDBw5k3rx5raqvtraWGTNmEBMTQ2JiIo888ghf3kLxP4fARKRzUA+QiFhq3rx5fP755wwZMoT//u//BsDv9/Pd736XW265hZdeeokdO3Zw++234/F4ePTRR5t8TVJSEoFAgJ49e7J06VISEhJYvXo1d9xxB6mpqVx33XUtqu/FF1/ktttuY926daxfv5477riD9PR0br/99qBdAxFpfwpAImKpmJgYXC4X4eHhpKSkAPBf//VfpKWlMX/+fAzDIDMzkyNHjvDAAw8wa9asJl8DYLfbeeyxxxr+3rt3b9asWcNf//rXFgegtLQ0fv/732MYBgMHDmTbtm38/ve/VwAS6eQ0BCYiHU5ubi5ZWVkYhtFw7Pzzz6e8vJxDhw597WsXLFjAqFGjSEpKIjIykkWLFpGXl9fiWs4777xGdWRlZbFr1y78fn+Lzyki1lMAEpEuY8mSJdx3333cdtttvPfee2zevJnp06fj9XqtLk1EOhgNgYmI5VwuV6MelUGDBvHaa69hmmZD78tHH31EVFQUPXv2bPI19W3Gjh3LXXfd1XBsz549rapt7dq1jf7+8ccf079/f+x2e6vOKyLWUg+QiFguIyODtWvXsn//foqKirjrrrs4ePAgP/nJT9ixYwd/+9vfmD17NtnZ2dhstiZfEwgE6N+/P+vXr+fdd9/l888/55FHHuGTTz5pVW15eXlkZ2ezc+dOXn75ZZ555hnuueeeYLxtEbGQApCIWO6+++7DbrczePBgkpKS8Pl8vP3226xbt47hw4fz4x//mNtuu42HH374tK/Jy8vjRz/6Eddccw1TpkxhzJgxHD9+vFFvUEtMmzaNqqoqRo8ezd13380999yjhQ9FugDD/PKCFiIiIiIhQD1AIiIiEnIUgEQkJOXl5REZGXnaR2tunReRjk9DYCISkmpra9m/f/9pn8/IyMDh0I2yIl2VApCIiIiEHA2BiYiISMhRABIREZGQowAkIiIiIUcBSEREREKOApCIiIiEHAUgERERCTkKQCIiIhJyFIBEREQk5Px/pG0H/1kW8UcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot the histogram\n",
     "import seaborn as sns\n",
@@ -454,13 +632,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "5b1b8872",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7387575212859724"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "statistic, pvalue = stats.kstest(X, lognorm.cdf)\n",
     "pvalue"
@@ -511,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "8346ad8e",
    "metadata": {
     "hidden": true
@@ -528,13 +717,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "404476b6",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wcMk+trBoX3zxBTqdLtdl5syZWkfTlBRAQoiSs2+fusQFqKeFypYt8Qh6vZ7ffvsNvV5fsg/s6grLl6t9nb76Sl3sVZgtRVFISsvQ5PKwFdX/a8iQIYSFheVc+vfvT6VKlejatWsxvTrmwUbrAEIIC5GWBm+9dXe19+ef1zpRyXv6aRg2TC0C335bXSrDyUnrVKIAktMzqT12uyaPfebTtjjZ5f/r29XVFVdXVwDGjBnDjh072LNnD48U85xbpk5agIQQJWPmTHUkVLlyMHWq1mm089ln4O8PV6/ChAlapxEWZOzYsaxatYo9e/YQEBCgdRzNSQuQEKL4Xb8On36qbk+ZAh4emsbRlLOzWgy+/DJMnqx2CK9eXetUwkiOttac+bStZo9trJCQEFauXCnFzz2kABJCFL/331cXPH3ySXVyQA05OjrSoEEDHB0dtQvx0kvQti1s366uHv/zz6V3HqRSSqfTGXUaSkshISGsWLFCip//kFNgQojitXOnujiolZU66stK248dDw8POnbsiIeWrVA6HcyeDXZ2ahG0ZYt2WUSpNn78eObPn8+6detwcHAgPDyc8PBwUlNTtY6mOSmAhBDFJzUVBg9WtwcPhnr1tM0DpKenExkZSXp6urZBqleHESPU7WHD1BYyIYqQoihMmTKFqKgomjdvTvny5XMuJ0+e1Dqe5qQAEkIUnxkz4O+/wccHxo3TOg0A0dHRzJ8/n+joaK2jwEcfQcWKcO0afPGF1mlEKaPT6YiLi0NRlFyXJ554Qut4mpMCSAhRPK5dU0c8gXR8vh8nJ/jyS3V7yhS1WBRClAgpgIQQxePDDyEpCVq2hNdf1zqN6erUCdq3h/R0mSFaiBIkBZAQougdPQrr1t3t7CsjnO5Pp1OHxVtbw48/ymKpQpQQKYCEEEVLUdTWH4CePaF+fU3j5MXa2vh5VIpVjRowYIC6/eGH6msohChWJlEAzZ07l4CAABwcHGjatCmHDx++776bNm2icePGeHh44OzsTP369Vm1apXBPm+88UauRd/atWtX3E9DCAGwYweEhqpDvLP7AJmQ8uXLM3r0aMqXL691FENjx6qTJB46BN9+q3UakQdj1+ASxaOofg+aF0Dr168nODiYkJAQjh07Rr169Wjbti2RkZF57l+mTBk+/vhjDhw4wMmTJwkKCiIoKIjt2w3XZGnXrp3B4m9r164tiacjhGXLyrrb+vPOO+rq5yJ/fH3VCSNBHR2m9TB9kcPW1haApKQkjZMIuPt7yP69FJRO0bikbdq0KU888QRz5swBICsrC39/f4YMGcLIkSPzdYyGDRvywgsv8Nm/f22+8cYbxMbGsqWAk4vFx8fj7u5OXFwcbm5uBTqGEBZp9Wp1pmc3N/jnH01We3+YqKgoNm3aRJcuXShXrpzWcQwlJEDVqhAVBfPmwaBBWicS/woLCyM2NhZvb2+cnJzQSb+2EqcoCklJSURGRuLh4ZFnK64x39+azuOdlpbG0aNHGTVqVM51VlZWBAYGcuDAgYfeX1EUfvnlF86fP8+kSZMMbtuzZw/e3t54enry7LPPMn78eMre58M4NTXVYFbM+Pj4Aj4jISxYSgqMHq1ujxxpksUPQEZGBuHh4WRkZGgdJTdXV/VU2JAh6rxJvXqBi4vWqQTg6+sLcN+zE6LkeHh45Pw+CkPTAig6OprMzEx8fHwMrvfx8eHcuXP3vV9cXBwVKlQgNTUVa2tr5s2bx3PPPZdze7t27ejSpQuVK1fm0qVLfPTRR7Rv354DBw7k2flxwoQJjDORSdqEMFvz5qkrnFeoAEOHap3GfA0YoI4Ku3QJpk2DkBCtEwnUSQXLly+Pt7e39rOIWzBbW9siG8RgHiu5/YerqysnTpwgMTGR0NBQgoODqVKlCq1btwage/fuOfvWqVOHunXrUrVqVfbs2cP//ve/XMcbNWoUwffMvxEfH4+/v3+xPw8hSo3YWPj8c3V73Dh1gj9RMHZ26qzQ3brB1KkwcKA6k7YwCdbW1qY3ilAUiKadoL28vLC2tiYiIsLg+oiIiAc2b1lZWVGtWjXq16/P+++/T9euXZkwYcJ9969SpQpeXl5cvHgxz9vt7e1xc3MzuAghjDBlCsTEwKOPQp8+Wqcxf127QuPGkJgI48drnUaIUknTAsjOzo5GjRoRGhqac11WVhahoaE0b94838fJysp64Mq2N27c4Pbt26Y37FWI0uD2bZg1S93+/HOwMe2GZQ8PD7p27artavAPY2UFEyeq24sWwc2b2uYRohTSfBh8cHAwixcvZsWKFZw9e5ZBgwah1+sJCgoCoHfv3gadpCdMmMDOnTv5559/OHv2LNOmTWPVqlW8/u9U+4mJiYwYMYKDBw9y5coVQkND6dSpE9WqVaNt27aaPEchSrXp09WWivr1oXNnrdM8lKOjI4899hiOjo5aR3mwZ59VlxFJS4P/DPIQQhSe5n+qdevWjaioKMaOHUt4eDj169dn27ZtOR2jr127hpXV3TpNr9fz9ttvc+PGDRwdHalVqxarV6+mW7dugHp+9uTJk6xYsYLY2Fj8/Pxo06YNn332Gfb29po8RyFKrdu31aUuQB29ZAZDgxMTE/nrr7+oU6cOLqY8wkqnUztAP/ec2go0ciT4+WmdSohSQ/N5gEyRzAMkRD6NHq2e9qpXD44dU0/dmLiwsDAWLVrEgAEDTP+0uKJAq1bw22/w7rt3V44XQuTJmO9v0/+0EkKYppiYu31/xo41i+LH7GS3AoHaChQWpm0eIUoR+cQSQhTMjBnqzMV165pF3x+zFRgILVqoE01KXyAhiowUQEII4925I60/JeXeVqCFC6UVSIgiIp9aQgjjzZwJ8fHw+OPw0ktapzGKvb09NWrUMK9BEc89B82aqa1AU6ZonUaIUkE6QedBOkEL8QB37qirvMfHw8aN6qR9ovht3w7t2oGDA1y+rK4eL4QwIJ2ghRDFZ9asu60/XbponcZomZmZ6PV6MjMztY5inDZtoGlTaQUSoohIASSEyD+9/m7fn9GjzbLvT2RkJFOnTjW/Vb11OrW/Fah9gWJitM0jhJkzv08vIYR2vvpK/eKtWlVOfWmhfXt11J1eD/PmaZ1GCLMmBZAQIn/S09VlLwCGDwdZEbvk6XTwwQfq9qxZkJysbR4hzJgUQEKI/Fm3Dq5dA29vWfFdS926QaVKEBUFy5ZpnUYIsyUFkBDi4RQFJk9Wt4cOBVNfSLQ0s7GB999Xt6dOhYwMbfMIYaZkGHweZBi8EP/x00/wwgvg4qK2Anl6ap2owLKyskhPT8fW1tZgoWWzoterrUC3b8PatdC9u9aJhDAJMgxeCFG0spdgeOstsy5+AKysrLC3tzff4gfA2RmGDFG3J09WW+iEEEYx408AIUSJOHgQ9u0DW1t47z2t0xTa7du3Wb16Nbdv39Y6SuEMHgxOTnD8OOzapXUaIcyOFEBCiAfLbv15/XWoUEHbLEUgLS2NS5cukZaWpnWUwilbFvr1U7dlkVQhjCYFkBDi/s6dg61b1e0RI7TNInILDlanIwgNhaNHtU4jhFmRAkgIcX9Tp6r9Szp1gkcf1TqN+K9KlaBHD3U7e5SeECJfpAASQuQtMhJWr1a3pfXHdGX/br79Vh2hJ4TIFymAhBB5W7AAUlPhiSegRQut0xQZNzc32rdvX3qmuKhbF559FjIzYfZsrdMIYTakABJC5JaaenetqffeU5dgKCWcnZ1p0qQJzs7OWkcpOtmj8xYvhsREbbMIYSakABJC5LZuHUREqKO+Stmip8nJyZw8eZLk0rSO1vPPQ/XqEBcHy5drnUYIsyAFkBDCkKLAjBnq9uDB6vw/pUhsbCybN28mNjZW6yhFx8pKXaIE4MsvIStL2zxCmAEpgIQQhvbsgT//VCfZGzBA6zQiv/r0AQ8PuHgRfvhB6zRCmDwpgIQQhmbOVP/t0wfKlNE0ijCCi8vdgjX7dyiEuC8pgIQQd128CN9/r25nn1IR5mPwYHVixN274cQJrdMIYdKkABJC3PXll2ofoOefh5o1tU5TLGxtbXnkkUewLWV9mwDw97/baV1agYR4IJ2iyDLC/xUfH4+7uztxcXGlZ64QIR4mNhYeeQT0eti5EwIDtU4kCuLQIWjWDOzs4OpV8PXVOpEQJcaY729pARJCqL76Si1+Hn8c/vc/rdOIgmraFJo3h7Q0mD9f6zRCmCwpgIQQ6izCc+eq20OHlqqJD/8rLCyMcePGERYWpnWU4jNsmPpv9mzeQohcpAASQsBPP8GVK+qor549tU4jCuull8DPT13P7dtvtU4jhEmSAkgIAXPmqP/27QuOjtpmEYVnawsDB6rb2b9bIYQBKYCEsHTnz8OOHeppr0GDtE4jikr//mohdOAAHD2qdRohTI5JFEBz584lICAABwcHmjZtyuHDh++776ZNm2jcuDEeHh44OztTv359Vq1aZbCPoiiMHTuW8uXL4+joSGBgIBcuXCjupyGEecpe9PTFF6FyZW2ziKLj6wuvvKJuZ/fvEkLk0LwAWr9+PcHBwYSEhHDs2DHq1atH27ZtiYyMzHP/MmXK8PHHH3PgwAFOnjxJUFAQQUFBbN++PWefyZMnM2vWLBYsWMChQ4dwdnambdu2pKSklNTTEsI8JCTcXTxz8GBNo5SUcuXKMWTIEMqVK6d1lOKX/TtdswZu39Y2ixAmRvN5gJo2bcoTTzzBnH/PU2dlZeHv78+QIUMYOXJkvo7RsGFDXnjhBT777DMURcHPz4/333+f4cOHAxAXF4ePjw/Lly+ne/fuue6fmppK6j0jJeLj4/H395d5gETpN38+vP021KgBZ8+qi2qK0kNRoHFjOHYMJk2CDz7QOpEQxcps5gFKS0vj6NGjBN4z4ZqVlRWBgYEcOHDgofdXFIXQ0FDOnz/PU089BcDly5cJDw83OKa7uztNmza97zEnTJiAu7t7zsXf37+Qz0wIM6AodzvIvvOOxRQ/d+7cYdOmTdy5c0frKMVPp7vbCjRvnjrdgRAC0LgAio6OJjMzEx8fH4PrfXx8CA8Pv+/94uLicHFxwc7OjhdeeIHZs2fz3HPPAeTcz5hjjho1iri4uJzL9evXC/O0hDAPe/bAmTPg7KwufGohUlJS+OuvvyznlHj37ur0Blevwo8/ap1GCJNhln/yubq6cuLECf744w8+//xzgoOD2bNnT4GPZ29vj5ubm8FFiFIvu/Wnd29wd9c2iyg+jo7Qr5+6LUPihcihaQHk5eWFtbU1ERERBtdHRETg+4D1a6ysrKhWrRr169fn/fffp2vXrkyYMAEg537GHlMIi3LtGmzZom6/846mUUQJGDRIPR22cyecO6d1GiFMgqYFkJ2dHY0aNSI0NDTnuqysLEJDQ2nevHm+j5OVlZXTibly5cr4+voaHDM+Pp5Dhw4ZdUwhSrWFCyErC555Bh57TOs0orgFBECHDuq2DIkXAjCBU2DBwcEsXryYFStWcPbsWQYNGoRerycoKAiA3r17M2rUqJz9J0yYwM6dO/nnn384e/Ys06ZNY9WqVbz++usA6HQ6hg0bxvjx4/nuu+/466+/6N27N35+fnTu3FmLpyiEaUlLUxc+BYts/XFxceHpp5/GxcVF6yglK/t3vXIlJCZqm0UIE2CjdYBu3boRFRXF2LFjCQ8Pp379+mzbti2nE/O1a9ewumd0il6v5+233+bGjRs4OjpSq1YtVq9eTbdu3XL2+eCDD9Dr9QwYMIDY2FhatmzJtm3bcHBwKPHnJ4TJ2bxZXSOqfHno2FHrNCXO1dWV1q1bax2j5AUGQtWqcOkSrF2rzhQthAXTfB4gU2TMPAJCmJ3WrWHvXhg7FsaN0zpNiUtNTeX69ev4+/tjb2+vdZySNXUqjBgBDRqoy2PodFonEqJImc08QEKIEnb2rFr8WFtbbAtATEwMX3/9NTExMVpHKXlBQWBvD8ePwx9/aJ1GCE1JASSEJVmwQP23Qwd45BFts4iSV7YsvPqquj1/vrZZhNCYFEBCWAq9HlasULcHDtQ2i9BO9u9+3TqwhNmwhbgPKYCEsBTr1kFcnNoR9t+Z04UFat4c6taFlJS7BbEQFkgKICEsRfYpj7fesph1v/JibW2Np6cn1tbWWkfRhk6nTowI6ilRGQcjLJSMAsuDjAITpc6RI/DEE2BnBzdvgpeX1omElhISwM9PnQ8oNBSefVbrREIUCRkFJoQwlN3688orUvwIcHWFfyePzekYL4SFkQJIiNLuzh114ju4e+rDgkVERDBlypRc6wVanOz3wubNEBambRYhNCAFkBCl3cqVkJwMdepAixZap9FcVlYWSUlJZGVlaR1FW3Xrqu+HjAxYskTrNEKUOCmAhCjNFEVd+BTU4c8y86+4V/aQ+MWLITNT2yxClDApgIQozX77TZ392ckJevbUOo0wNV27gqcnXLsGO3ZonUaIEiUFkBClWXbrT48e4O6ubRZhehwdoU8fdTv7vSKEhZBh8HmQYfCiVIiJUYc6p6bCoUPQpInWiUxCWloaERER+Pj4YGdnp3Uc7Z09C7Vrq+vDXb0KFSponUiIApNh8EIItfNzairUr6/OASQAsLOzw9/fX4qfbI8+Cq1aqX2Ali7VOo0QJcboAmj37t3FkUMIUZQUBRYtUrcHDJDOz/eIj49n+/btxMfHax3FdAwYoP771VfSGVpYDKMLoHbt2lG1alXGjx/P9evXiyOTEKKwpPPzfen1eg4ePIher9c6iumQztDCAhldAN28eZPBgwfzzTffUKVKFdq2bcuGDRtIS0srjnxCiIK4t/Oz9GMTD+PgIJ2hhcUxugDy8vLivffe48SJExw6dIgaNWrw9ttv4+fnx7vvvsuff/5ZHDmFEPkVEwMbN6rbb72lbRZhPrJPg/3wg7penBClXKE6QTds2JBRo0YxePBgEhMTWbp0KY0aNaJVq1acPn26qDIKIYxxb+fnxo21TiPMhXSGFhamQAVQeno633zzDc8//zyVKlVi+/btzJkzh4iICC5evEilSpV45ZVXijqrEOJh7u38/NZb0vk5D05OTjRu3BgnJyeto5ie7BZD6QwtLIDR8wANGTKEtWvXoigKvXr1ol+/fjz++OMG+4SHh+Pn52e2a+3IPEDCbO3fD089Bc7OcOuW9P8RxklJUecBiomBH3+E55/XOpEQRinWeYDOnDnD7NmzuXXrFjNnzsxV/IDaT0iGywuhgezWH+n8fF/p6emEhYWRnp6udRTTc29n6Oz3khCllNEFUEhICK+88gr29vYG12dkZLBv3z4AbGxsePrpp4smoRAif+7t/Ny/v7ZZTFh0dDSLFi0iOjpa6yimKfu988MPaiuiEKWU0QXQM888Q0xMTK7r4+LieOaZZ4oklBCiAFavVjs/16snMz+Lgnv0UWjZUu0DtGyZ1mmEKDZGF0CKoqDLo2Pl7du3cXZ2LpJQQggjKQosXqxu9+8vnZ9F4WS3Ai1ZAmbal1OIh7HJ745dunQBQKfT8cYbbxicAsvMzOTkyZO0aNGi6BMKIR7u0CE4dUpd3VtmfhaF1bUrvPsuXL4MoaHw3HNaJxKiyOW7Bcjd3R13d3cURcHV1TXnZ3d3d3x9fRkwYACrV68uzqxCiPvJbv155RXw8NA0iqnT6XTY2dnl2ZIt/uXkBK+/rm5nv7eEKGWMHgY/btw4hg8fXqpPd8kweGFW4uOhfHlISoJff4Unn9Q6kSgN/vxTnUzT1hZu3ABvb60TCfFQxToMPiQkpFQXP0KYnTVr1OLn0UdBTkOLolKvHjRpAunpsGKF1mmEKHL56gPUsGFDQkND8fT0pEGDBg9sOj527FiRhRNC5IN0fjZKVFQUGzdu5JVXXqFcuXJaxzFt/fvD4cPqzNDDh8v7S5Qq+SqAOnXqlNPpuXPnzsWZRwhhjKNH4dgxsLODXr20TmMWMjIyiIqKIiMjQ+sopq97d3jvPfj7b9i3D2R+N1GK5KsACgkJyXO7qMydO5cpU6YQHh5OvXr1mD17Nk2aNMlz38WLF7Ny5UpOnToFQKNGjfjiiy8M9n/jjTdY8Z8m27Zt27Jt27Yizy6EprJbf7p0AS8vbbOI0sfFRZ1VfPFidWZoKYBEKWJ0H6Dr169z48aNnJ8PHz7MsGHDWFTAadPXr19PcHAwISEhHDt2jHr16tG2bVsiIyPz3H/Pnj306NGD3bt3c+DAAfz9/WnTpg03b9402K9du3aEhYXlXNauXVugfEKYrMREtf8PwIAB2mYRpVf2e+vbb9XZxoUoJYwugF577bWcdb7Cw8MJDAzk8OHDfPzxx3z66adGB5g+fTr9+/cnKCiI2rVrs2DBApycnFi6dGme+3/99de8/fbb1K9fn1q1avHVV1+RlZVFaGiowX729vb4+vrmXDw9Pe+bITU1lfj4eIOLECZvwwZISIBq1aB1a63TiNKqUSN1NFhqKqxapXUaIYqM0QXQqVOnck43bdiwgTp16vD777/z9ddfs3z5cqOOlZaWxtGjRwkMDLwbyMqKwMBADhw4kK9jJCUlkZ6eTpkyZQyu37NnD97e3tSsWZNBgwZx+/bt+x5jwoQJBvMa+fv7G/U8hNBE9umvfv2kc6oRPD096d69+wP/KBL30Onuzgy9eLE667gQpYDRBVB6enpOh+hdu3bRsWNHAGrVqkVYWJhRx4qOjiYzMxMfHx+D6318fAgPD8/XMT788EP8/PwMiqh27dqxcuVKQkNDmTRpEnv37qV9+/ZkZmbmeYxRo0YRFxeXc7l+/bpRz0OIEnfqFBw8CDY28MYbWqcxKw4ODtSsWRMHBweto5iPnj3VWcZPn1bfd0KUAkYXQI899hgLFixg//797Ny5k3bt2gFw69YtypYtW+QBH2TixImsW7eOzZs3G3yYde/enY4dO1KnTh06d+7MDz/8wB9//MGePXvyPI69vT1ubm4GFyFMWnbrT8eO8J8/IMSDJSYmsn//fhITE7WOYj7c3eHVV9VtmRlalBJGF0CTJk1i4cKFtG7dmh49elCvXj0Avvvuu/uO3LofLy8vrK2tiYiIMLg+IiICX1/fB9536tSpTJw4kR07dlC3bt0H7lulShW8vLy4ePGiUfmEMEkpKXf7YvTrp20WM5SQkMAvv/xCQkKC1lHMS/ZpsPXr1dnHhTBzRhdArVu3Jjo6mujoaIOOygMGDGDBggVGHcvOzo5GjRoZdGDO7tDcvHnz+95v8uTJfPbZZ2zbto3GjRs/9HFu3LjB7du3KV++vFH5hDBJmzbBnTtQsSK0aaN1GmEpWrRQZxtPSgIZVStKAaMLIABra+tcHQgDAgLwLsBaMcHBwSxevJgVK1Zw9uxZBg0ahF6vJygoCIDevXszatSonP0nTZrEmDFjWLp0KQEBAYSHhxMeHp7TnJ2YmMiIESM4ePAgV65cITQ0lE6dOlGtWjXatm1bkKcrhGnJPgXx5ptgba1tFmE5dLq7LY5yGkyUAkYXQBEREfTq1Qs/Pz9sbGywtrY2uBirW7duTJ06lbFjx1K/fn1OnDjBtm3bcjpGX7t2zaBz9fz580lLS6Nr166UL18+5zJ16lRALc5OnjxJx44dqVGjBn379qVRo0bs378/p/O2EGbrwgXYs0f9MnrzTa3TCEvTu7c66/jRo3D8uNZphCgUo1eDb9++PdeuXWPw4MGUL18+17pgnTp1KtKAWpDV4IXJ+vBDmDwZ2reHn37SOo1ZunPnDrt27SIwMFCGwhdE9+5qP6BBg2DePK3TCGHAmO9vowsgV1dX9u/fT/369QuT0aRJASRMUloa+PtDZKTaD+ill7ROJCxRaCgEBoKbG9y6Bc7OWicSIocx399GnwLz9/fHyJpJCFEUvv9eLX58fODFF7VOY7YyMzOJj4+/77xg4iGeeQaqVFFHgm3cqHUaIQrM6AJo5syZjBw5kitXrhRDHCHEfX31lfpvUBDY2mqbxYxFRkYyY8aM+643KB7Cygr69lW3s9+TQpghowugbt26sWfPHqpWrYqrqytlypQxuAghisHVq7B9u7qd/eUjhFaCgtQRiL/9BmfOaJ1GiAKxMfYOM2fOLIYYQogHWrpUXYPpmWfUxU+F0FL58upp2K1b1Vag6dO1TiSE0YwugPr06VMcOYQQ95OZqRZAcHc2XiG01r+/WgCtXAkTJoBMMyLMTIEmQrx06RKjR4+mR48eOefRf/75Z06fPl2k4YQQwLZtcOMGlCkjI7+E6WjXDh55BG7fhs2btU4jhNGMLoD27t1LnTp1OHToEJs2bcqZgfnPP/8kJCSkyAMKYfGyZ93t3RtkBfNC8/X15eOPP37oeoPiIayt707GKTNDCzNkdAE0cuRIxo8fz86dO7Gzs8u5/tlnn+XgwYNFGk4Ii3frFvzwg7otp7+KhE6nw8bGJtckrqIA3nxTnZX8l19AFpsWZsboAuivv/7ipTya4b29vYmOji6SUEKIfy1bpvYBatECatfWOk2pcPv2bZYvX87t27e1jmL+KlWC7DUWZUi8MDNGF0AeHh4Ga3NlO378OBUqVCiSUEIIICsLlixRtwcM0DZLKZKWlsbVq1dJS0vTOkrpkP3eXL4c0tM1jSKEMYwugLp3786HH35IeHg4Op2OrKwsfvvtN4YPH07v3r2LI6MQlik0FC5fBnd3eOUVrdMIkbcXX1RnJ4+IUGcrF8JMGF0AffHFF9SqVQt/f38SExOpXbs2Tz31FC1atGD06NHFkVEIy5TdsbRnT3By0jaLEPdja6tOjAjSGVqYFaMLIDs7OxYvXsylS5f44YcfWL16NefOnWPVqlVYW1sXR0YhLE9UFGzZom5L52dh6vr1U//dvl2dtVwIM2D0RIjZKlasSMWKFYsyixAi24oVan+Kxo2hfn2t05Qq7u7udOjQAXd3d62jlB5Vq8Kzz6qjwZYuhXHjtE4kxEPlqwAKDg7O9wGny5ToQhSOotw9lSCdn4uck5MTDRs21DpG6TNgwN0CaMwYsCnw39dClIh8vUOPHz9u8POxY8fIyMigZs2aAPz9999YW1vTqFGjok8ohKXZtw/+/hucnaF7d63TlDpJSUmcO3eOWrVq4SR9q4pO585Qtqw6a/m2bWrnaCFMWL4KoN27d+dsT58+HVdXV1asWIGnpycAd+7cISgoiFatWhVPSiEsSXbrT48e4OqqbZZSKC4uju+//57y5ctLAVSU7O2hTx91YdTFi6UAEibP6E7Q06ZNY8KECTnFD4Cnpyfjx49n2rRpRRpOCItz+zZ88426LZ2fhbnJfs/++CPcvKltFiEewugCKD4+nqioqFzXR0VFkZCQUCShhLBYq1ZBaqra8fmJJ7ROI4RxatWCp55SZy9fulTrNEI8kNEF0EsvvURQUBCbNm3ixo0b3Lhxg2+//Za+ffvSpUuX4sgohGVQFFi0SN0eMEBdY0kIc5Pdcf+rr9RCSAgTZXQBtGDBAtq3b89rr71GpUqVqFSpEq+99hrt2rVj3rx5xZFRCMvw669w9qw66WHPnlqnKbXs7OyoVKmSwWLOogi9/DKUKQPXrqnzAglhonSKoigFuaNer+fSpUsAVK1aFWdn5yINpqX4+Hjc3d2Ji4vDzc1N6zjCUvTqBatXQ9++srCkMG/BwTBjBnTqdHdCTyFKgDHf3wUugEozKYBEiYuJAT8/tf/PoUPQpInWiUotRVHIzMzE2toanZxmLB5nz0Lt2mBtrc4MLQtlixJizPe30afAhBDFILvzc7160vm5mIWHh/P5558THh6udZTS69FHoVUrtQ/QsmVapxEiT1IACaE1RYGFC9Xtt96Szs+idHjrLfXfxYulM7QwSVIACaG133672/n5tde0TiNE0Xj5ZfD0VDtD79ihdRohcjG6ANLr9cWRQwjLlT30vXt3kAU6RWnh4KDODA133+NCmBCjCyAfHx/efPNNfv311+LII4RliYmBDRvU7exTBkKUFtlzAn3/Pdy6pW0WIf7D6AJo9erVxMTE8Oyzz1KjRg0mTpzILXljC1Ew0vm5xHl7e/Pee+/h7e2tdZTS797O0DIztDAxRhdAnTt3ZsuWLdy8eZOBAweyZs0aKlWqxIsvvsimTZvIyMgojpxClD73dn6WmZ9LjLW1NW5ublhbW2sdxTJktwJJZ2hhYgrcCbpcuXIEBwdz8uRJpk+fzq5du+jatSt+fn6MHTuWpKSkfB9r7ty5BAQE4ODgQNOmTTl8+PB99128eDGtWrXC09MTT09PAgMDc+2vKApjx46lfPnyODo6EhgYyIULFwr6VIUoHvv3q52fnZ3h9de1TmMx7ty5w8aNG7lz547WUSxD1653Z4betk3rNELkKHABFBERweTJk6lduzYjR46ka9euhIaGMm3aNDZt2kTnzp3zdZz169cTHBxMSEgIx44do169erRt25bIyMg899+zZw89evRg9+7dHDhwAH9/f9q0acPNe1Yenjx5MrNmzWLBggUcOnQIZ2dn2rZtS0pKSkGfrhBFb8EC9d/XXgOZcLPEpKSkcObMGfk8KCkODhAUpG5nv+eFMAWKkb799lvlxRdfVGxtbZV69eops2fPVu7cuWOwz8WLFxVbW9t8Ha9JkybKO++8k/NzZmam4ufnp0yYMCFf98/IyFBcXV2VFStWKIqiKFlZWYqvr68yZcqUnH1iY2MVe3t7Ze3atXkeIyUlRYmLi8u5XL9+XQGUuLi4fGUQwmgREYpia6sooChHjmidxqLcunVL+eSTT5Rbt25pHcVynD+vvtd1OkW5ckXrNKIUi4uLy/f3t9EtQEFBQfj5+fHbb79x4sQJBg8ejIeHh8E+fn5+fPzxxw89VlpaGkePHiUwMDDnOisrKwIDAzlw4EC+8iQlJZGenk6ZMmUAuHz5MuHh4QbHdHd3p2nTpvc95oQJE3B3d8+5+Pv75+uxhSiw5cshPV3t+NyokdZphCheNWrA//6n9nuTde6EiTC6AAoLC2PhwoU88YARK46OjoSEhDz0WNHR0WRmZuLj42NwvY+PT76nqf/www/x8/PLKXiy72fMMUeNGkVcXFzO5fr16/l6bCEKJCvrbufnQYO0zSJESRk4UP33q6/U4l8IjRldALm6uubZP+f27dslPqpi4sSJrFu3js2bN+Pg4FDg49jb2+Pm5mZwEaLY7NwJ//yjTnrYrZvWaSyOq6srzz77LK6urlpHsSydOoGvL4SHw9atWqcRwvgCSLnP4vGpqanY2dkZdSwvLy+sra2JiIgwuD4iIgJfX98H3nfq1KlMnDiRHTt2ULdu3Zzrs+9XkGMKUSKyO4L26aMufyFKlIuLC61atcLFxUXrKJbF1hb69VO3pTO0MAE2+d1x1qxZAOh0Or766iuDD4/MzEz27dtHrVq1jHpwOzs7GjVqRGhoaM6osaysLEJDQxk8ePB97zd58mQ+//xztm/fTuPGjQ1uq1y5Mr6+voSGhlK/fn0A4uPjOXToEIPkdIPQ2o0b6qy4IDM/ayQlJYWrV69SqVKlQrUciwLo3x+++AJCQ+Hvv9W+QUJoJN8F0IwZMwC1BWjBggUGp7vs7OwICAhgQQGq+uDgYPr06UPjxo1p0qQJM2fORK/XE/TvsMnevXtToUIFJkyYAMCkSZMYO3Ysa9asISAgIKdfj4uLCy4uLuh0OoYNG8b48eOpXr06lStXZsyYMfj5+eV7aL4QxWbJEnUyuKeegtq1tU5jke7cucO6desYMGAA5cuX1zqOZalYEZ5/Hn74QV0fbOpUrRMJC5bvAujy5csAPPPMM2zatAlPT88iCdCtWzeioqIYO3Ys4eHh1K9fn23btuV0Yr527RpWVnfP1M2fP5+0tDS6du1qcJyQkBA++eQTAD744AP0ej0DBgwgNjaWli1bsm3bNvlrT2grI0OdDRek87OwXAMHqgXQsmUwfrw6T5AQGtAp9+vUY8Hi4+Nxd3cnLi5OOkSLorNlC7z0EpQrB9evg7291oksUlhYGIsWLZIWIK1kZkKVKurM0CtXQq9eWicSpYgx39/5agEKDg7ms88+w9nZmeDg4AfuO3369PwnFcKSzJ+v/vvmm1L8CMtlba2uDzZ6tPp/QgogoZF8FUDHjx8n/d95G44fP37f/XSymKMQebtwAXbsUBc8zV4cUmjCxsaGcuXKYWOT7x4Aoqj17QvjxsGBA3D8ODRooHUiYYHkFFge5BSYKHLBwTBjBrzwgtr/QQhL16MHrFunDo3P7hsnRCEZ8/1d4MVQhRD5lJSkdvgEePttbbMIYSqy/y98/TXcuaNtFmGR8tUG3KVLl3wfcNOmTQUOI0SptHYtxMaqHT/btdM6jcULDw9n2bJlBAUFyeSoWmrZEurUgb/+UtfGe+89rRMJC5OvAsjd3b24cwhROikKzJ2rbg8aBFbS6Ko1RVFIS0u776z2ooTodPDOO+qw+HnzYOhQ+f8hSlS+CqBl2c33QgjjHDyodvJ0cIB/J/cUQvyrZ0/44AO4eBF27YI2bbROJCyIlNtCFKfs1p/u3aFsWW2zCGFqXFzgjTfU7ez/K0KUkHy1ADVs2JDQ0FA8PT1p0KDBA4e7Hzt2rMjCCWHWIiNh40Z1+513tM0ihKkaNAhmzVJHR169CpUqaZ1IWIh8FUCdOnXC/t+J22Q9LSHyackSSEuDJk3gP4v2Cu14eXkxYMAAvLy8tI4iAGrVgv/9T10gdcEC+HfdRyGKm8wDlAeZB0gU2r3T/S9fDn36aJ1ICNO1eTN06QJeXuoyMbI+mCigEpkH6MiRI6xatYpVq1Zx9OjRgh5GiNLpxx/V4qdsWejWTes04h5xcXH8+OOPxMXFaR1FZOvQAfz9IToavvlG6zTCQhhdAN24cYNWrVrRpEkThg4dytChQ3niiSdo2bIlN27cKI6MQpifOXPUf/v2lb9mTUxSUhJHjhwhKSlJ6ygim40NvPWWup39f0eIYmZ0AdSvXz/S09M5e/YsMTExxMTEcPbsWbKysujXr19xZBTCvJw5Azt3qnOaDByodRohzEP//mBnB4cOweHDWqcRFsDoAmjv3r3Mnz+fmjVr5lxXs2ZNZs+ezb59+4o0nBBmKfsv2I4doXJlbbMIYS68vdXpIkAdFSZEMTO6APL3989ZGf5emZmZ+Pn5FUkoIcxWbCysWKFuv/uuplGEMDvZ/2c2bICwMG2ziFLP6AJoypQpDBkyhCNHjuRcd+TIEYYOHcrUqVOLNJwQZmfpUnXx08cfh9attU4j8uDs7EyzZs1wdnbWOor4r0aNoEULSE+HhQu1TiNKuXwNg/f09DSY/FCv15ORkYGNjTqNUPa2s7MzMTExxZe2hMgweFEgmZlQvTpcvgyLFql9GoQQxlm/Xj0V5uOjToz47xx0QuSHMd/f+ZoIcebMmUWRS4jS7Ycf1OKnTBl1jSNhktLS0oiIiMDHxwc7Ozut44j/6tIF/Pzg1i11JvXXX9c6kSil8lUA9ZFJ3IR4uOyOm/37g5OTtlnEfd2+fZulS5cyYMAAypcvr3Uc8V+2tvD22zB6NHz5pfrHxAOWXxKioAq1GGpKSgrx8fEGFyEs0qlT8Msv6tD3t9/WOo0Q5m3AAPXU15Ej6rB4IYqB0QWQXq9n8ODBeHt74+zsjKenp8FFCIs0e7b670svQcWK2mYRwtyVKwc9eqjbMiReFBOjC6APPviAX375hfnz52Nvb89XX33FuHHj8PPzY+XKlcWRUQjTFhMDq1ap2zL0XYiiMWSI+u/GjWp/ICGKmNEF0Pfff8+8efN4+eWXsbGxoVWrVowePZovvviCr7/+ujgyCmHaliyB5GSoVw9atdI6jXgIKysrnJycsLIqVA8AUdwaNoSWLSEjA+bP1zqNKIWM/gSIiYmhSpUqALi5ueUMe2/ZsqXMBC0sT3r63dNf774rnTXNgI+PDyNGjMDHx0frKOJhsltUFyxQ/8gQoggZXQBVqVKFy5cvA1CrVi02bNgAqC1DHh4eRRpOCJP37bdw/bo6jf9rr2mdRojS5aWXoFIldZX41au1TiNKGaMLoKCgIP78808ARo4cydy5c3FwcOC9995jxIgRRR5QCJOlKDBtmrr9zjuy6ruZiIyMZNasWURGRmodRTyMjQ0MHapuT58OWVna5hGlSr5mgn6QK1eucOzYMapVq0bdunWLKpemZCZokS/798NTT6nDda9fV0euCJMXFhbGokWLZB4gcxEfD/7+6r8//gjPP691ImHCjPn+LnQvwICAALp06VJqih8h8m36dPXf3r2l+BGiuLi53V1WJvv/nBBFoEAFUGhoKC+++CJVq1alatWqvPjii+zatauoswlhui5ehK1b1e333tM2ixCl3ZAhYG0NoaHwbxcMIQrL6AJo3rx5tGvXDldXV4YOHcrQoUNxc3Pj+eefZ+7cucWRUQjTM3Om2gfo+efh0Ue1TiNE6VapEnTtqm5LK5AoKoqRKlSooMyePTvX9XPmzFH8/PyMPZwyZ84cpVKlSoq9vb3SpEkT5dChQ/fd99SpU0qXLl2USpUqKYAyY8aMXPuEhIQogMGlZs2aRmWKi4tTACUuLs7YpyMswe3biuLkpCigKLt2aZ1GGCklJUW5cOGCkpKSonUUYYxDh9T/c7a2inLzptZphIky5vvb6Bag2NhY2rVrl+v6Nm3aEBcXZ9Sx1q9fT3BwMCEhIRw7dox69erRtm3b+47OSEpKokqVKkycOBFfX9/7Hvexxx4jLCws5/Lrr78alUuIB1q0CJKSoG5dePZZrdMII9nb21OtWjXs7e21jiKM0aSJOjFiejrI2QZRBIwugDp27MjmzZtzXb9161ZefPFFo441ffp0+vfvT1BQELVr12bBggU4OTmxdOnSPPd/4oknmDJlCt27d3/gh5eNjQ2+vr45Fy8vL6NyCXFfaWl3Jz58/32Z+NAMJSQksGfPHhISErSOIoyk/NvfTpk/HyUxUeM0wtzZ5GenWfcsRle7dm0+//xz9uzZQ/PmzQE4ePAgv/32G++//36+HzgtLY2jR48yatSonOusrKwIDAzkwIED+T5OXi5cuICfnx8ODg40b96cCRMmUPEBC1SmpqaSmpqa87Osai/ua/16dV2i8uWhe3et04gCSExMZO/evdSsWRNXV1et4wgjJLd/gUiP8gTcCSNt6TLs3h2idSRhxvJVAM2YMcPgZ09PT86cOcOZM2dyrvPw8GDp0qWMHj06Xw8cHR1NZmZmrunofXx8OHfuXL6OkZemTZuyfPlyatasSVhYGOPGjaNVq1acOnXqvh92EyZMYNy4cQV+TGEhFAWmTlW3Bw8GOztt8whhaaytWdq4I5/uWojNl1/CO2+ro8OEKIB8FUDZS1+Yg/bt2+ds161bl6ZNm1KpUiU2bNhA375987zPqFGjCA4Ozvk5Pj4ef3//Ys8qzMy2bXDyJDg7w8CBWqcRwiJtrPMcw35bS5l/LqlL0bz6qtaRhJkq1ESIiqKgFHAiaS8vL6ytrYmIiDC4PiIi4oEdnI3l4eFBjRo1uHjx4n33sbe3x83NzeAiRC6TJqn/DhgAZcpom0UIC5Vs58DKhi+oP0yapLbMClEABSqAVq5cSZ06dXB0dMTR0ZG6deuyatUqo45hZ2dHo0aNCA0NzbkuKyuL0NDQnL5FRSExMZFLly7JlPeicA4dgr17wdYW7mktFObHwcGBOnXq4CBrt5mtFQ1fJNPBEY4dUydHFKIAjC6Apk+fzqBBg3j++efZsGEDGzZsoF27dgwcODBXX6GHCQ4OZvHixaxYsYKzZ88yaNAg9Ho9QUFBAPTu3dugk3RaWhonTpzgxIkTpKWlcfPmTU6cOGHQujN8+HD27t3LlStX+P3333nppZewtramR48exj5VIe7Kbv3p2RMeeUTbLKJQPD096dKlC56enlpHEQV0x8md6O691B+y/28KYSxjJxkKCAhQVqxYkev65cuXKwEBAcYeTpk9e7ZSsWJFxc7OTmnSpIly8ODBnNuefvpppU+fPjk/X758Odckh4Dy9NNP5+zTrVs3pXz58oqdnZ1SoUIFpVu3bsrFixeNyiQTIQoDZ88qik6nTsJ25ozWaUQhpaenK7dv31bS09O1jiKMpE9NVyp9+INS6cMflBP7jytZ1tbq/8sjR7SOJkyEMd/fRq8G7+DgwKlTp6hWrZrB9RcuXKBOnTqkpKQUTWWmIVkNXhjo2xeWLoVOnWDLFq3TiEKS1eDNV1JaBrXHbgdg48BmVHv/bTw3bYBXXoENGzROJ0xBsa4GX61aNTbk8UZbv3491atXN/ZwQpi2mzchu3/bhx9qm0UIYSBq4FAAlG+/hQsXNE6jrblz5xIQEICDgwNNmzbl8OHDhb5PZmYmY8aMoXLlyjg6OlK1alU+++yzAg9+MjX5GgZ/r3HjxtGtWzf27dvHk08+CcBvv/1GaGhonoWREGZtxgx16v1WraAIO+cLIQov9dHHiP9fW9xCt6tzdC1cqHUkTWQvK7VgwQKaNm3KzJkzadu2LefPn8fb27vA95k0aRLz589nxYoVPPbYYxw5coSgoCDc3d159913S/IpFgujW4BefvllDh8+jJeXF1u2bGHLli14eXlx+PBhXnrppeLIKIQ27ty5+4E6cqS2WYQQeYoa9G8r0IoVEB6ucZridb+WF2OXlcrvfX7//Xc6derECy+8QEBAAF27dqVNmzb5al0yB0YVQOnp6bz55pt4enqyevVqjh49ytGjR1m9ejUNGjQoroxCaGP+fEhMhDp14J4JNoUQpiOpSXP0jZqgS02FL7/UOk6BxcfH8+GHH1KvXj2qVatG//79+eWXX0hKSuLSpUu88cYb/P3337nul72sVGBgYM51D1tWKr/3adGiBaGhoTmP++eff/Lrr78aTDhszowqgGxtbfn222+LK4sQpkOvV09/AXzwgSx6WoqUL1+ekJAQ6QBdWuh0RL09TN2eO1dtuTVDkyZNIioqiilTpjD13yV3unbtirOzM/Xq1aNs2bIEBATkut+DlpUKv0+LWH7vM3LkSLp3706tWrWwtbWlQYMGDBs2jJ49exby2ZoGo0+Bde7cmS0yEkaUdgsXQnQ0VK0qi54KYeISAtuRXKs2JCSYbSvQ8OHDWbp0KbVr16Zhw4YsXryYqKgobt68SXx8PJ9//jnp6eklmmnDhg18/fXXrFmzhmPHjrFixQqmTp3KihUrSjRHcTG6E3T16tX59NNP+e2332jUqBHOzs4Gt5eGjlHCwiUnw+TJ6vZHH4GN0f9NhAmLjo5m69atdOrUCS8vL63jiKJgZUXk0A+oNOgNtQAKDgYzm8IkJiaGzp07s2/fPgAee+wx+vXrxzPPPMPx48cZM2YMa9asoVatWgb3K8iyUvm9z4gRI3JagQDq1KnD1atXmTBhAn369Cn0c9aa0Z/sS5YswcPDI6f/z710Op0UQML8ffUVRERApUrQq5fWaUQRS09P58aNGyX+17QoXvHPdySlek0cLpyHOXPUP17MyNdff027du1YtmwZer2eH374gZUrV/LRRx9RrVo1Bg4cSI0aNXLd795lpTp37gzcXVZq8ODBeT5Wfu+TlJSElZXhiSJra2uysrKK5klrzOgCyJxWhhfCaKmpd6fWHzlSXftLCGH6rKyIGvI+/u8OgOnT4d13wcVF61T59tFHH2FzT2tznTp1DJaCepDg4GD69OlD48aNadKkCTNnzjRYVmrOnDls3rzZYO3Nh90HoEOHDnz++edUrFiRxx57jOPHjzN9+nTefPPNInrW2ipU2372kDyddBAVpcWyZerkhxUqwD0fBEII0xfboQveMyZhf/mSOopzxAitI+WbTSFOtXfr1o2oqCjGjh1LeHg49evXZ9u2bTmdnKOjo7l06ZJR9wGYPXs2Y8aM4e233yYyMhI/Pz/eeustxo4dW+CspsTopTBAPQ02Y8YMLvw782b16tUZNmwY/fr1K/KAWpClMCxUejpUrw5Xr8KsWTBkiNaJRDGQpTDM13+XwnDIo2jw2PA1/u+/A97ecPkyODmVdEyhoWJdCmPs2LEMHTqUDh06sHHjRjZu3EiHDh147733Sk1VKCzUqlVq8ePjA6WkmBe5eXh48NJLL+Hh4aF1FFEMYl96lTT/ihAZCYsWaR1HmDCjW4DKlSvHrFmz6NGjh8H1a9euZciQIURHRxdpQC1IC5AFysiAmjXhn39g2jR1FIkQwqTkpwUIwHPNCh75cCiUL6/+n3ZwKMmYQkPF2gKUnp5O48aNc13fqFEjMjIyjD2cEKZh7Vr1g9LLC956S+s0ohjp9XoOHz6MXq/XOoooJrFde5Dm9wiEhcGSJVrHESbK6AKoV69ezJ8/P9f1ixYtKjWzQwoLk5EBn32mbg8fDv+Z20qULvHx8fz888/Ex8drHUUUE8XO7u7s0BMmQEqKpnmEaSpQt/MlS5awY8cOmjVrBsChQ4e4du0avXv3JvieUwfTp08vmpRCFKdVq+DCBbX15+23tU4jhCgCd7r3oty8mdjdvKHO7D50qNaRhIkxugA6deoUDRs2BMgZVufl5YWXlxenTp3K2U+GxguzkJoK48ap26NGgaurtnmEEEVCsbcncugItS/QF1+oAxtKYeuuoijyfVtARhdAu3fvLo4cQmhjyRJ15Ff58jBokNZphBBF6M4rr1Fu3kzsr16G2bPVyU1LkT179tC3b18uXLiQa8Zm8XDyignLlZQE48er26NHg6OjtnlEibCzs6Nq1arY2dlpHUUUN1tbIoP/LXomT4a4OG3zFLFx48bh5eUlLUAFJAWQsFzz56ujRCpVknl/LEjZsmV5/fXXKVu2rNZRRAmI7dSVlBq14M4ddYmMUuLw4cPs2bOHDz74QAqgApICSFimhASYOFHdDgkBaQ2wGFlZWaSmppaaBR3FQ1hbE/H+v2tqzZgBpWCuOoApU6ZQrVq1nMVMhfGkABKW6csv1Q/CGjVkxXcLExERwcSJE4mIiNA6iigh8e06kPx4XfUPnylTtI5TaBcvXmTTpk28//77WFtbax3HbEkBJCzPnTswdaq6PW4cFGIRQiGEGbCyImL4xwAos2erp77N2PTp0ylbtix9+vTROopZkwJIWJ6pU9XOkI8/Dq++qnUaIUQJSHi2DfpGTdAlJ6vD4s1UZGQky5Yt491338VRBm4UihRAwrLcvKn2AwB19mcZOiqEZdDpiBjxbyvQwoXw7zx25mbu3LlYWVkxSKbtKDT59BeWJSQEkpOhRQvo1EnrNEKIEqR/8mkSWgeiS0+Hjz7SOo7R9Ho9c+bMoV+/fjKKsQhIASQsx6lTsGyZuj1lCsjQUYvk7e3N8OHD8fb21jqK0ED4R5+g6HSwYQMcOqR1HKMsW7aMuLg43nvvPa2jlApSAAnL8eGHkJUFL7+stgAJi2RtbY2zs7OMnrFQKY8+zp1XXlN/GD4cFEXbQPmUkZHBtGnTePXVVwkICNA6TqkgBZCwDL/8Aj/9pI74mjBB6zRCQzExMaxdu5aYmBitowiNRAz/iCwHR/j1V/juO63j5Ms333zDlStXGDFihNZRSg0pgETpl5UF2R8aAwdC9era5hGaSk1N5e+//yY1NVXrKEIjGeUrEN3vbfWHDz+E9HRtAz2EoihMmTKF5557jgYNGmgdp9SQAkiUfmvXwrFj6krvY8dqnUYIYQKi3h5KRpmycP48fPWV1nEe6JdffuHYsWPS+lPENC+A5s6dS0BAAA4ODjRt2pTDhw/fd9/Tp0/z8ssvExAQgE6nY+bMmYU+pijlUlLgY3XoKyNHQrly2uYRQpiELFc3It/7UP3hk0/UWaJN1OTJk6lfvz6BgYFaRylVNC2A1q9fT3BwMCEhIRw7dox69erRtm1bIiMj89w/KSmJKlWqMHHiRHx9fYvkmKKUmzMHrl6FChVg2DCt0wghTMjtnkGkVq4KkZEmu0TGn3/+yY4dO2TR02KgaQE0ffp0+vfvT1BQELVr12bBggU4OTmxdOnSPPd/4oknmDJlCt27d8fe3r5IjilKschIdbJDUP91ctI2jzAJrq6utGnTBldXV62jCK3Z2hI+Uj0trkyZov6xZGKmTJlCpUqVeOWVV7SOUupoVgClpaVx9OhRgyY9KysrAgMDOXDgQIkeMzU1lfj4eIOLKAU++gji46FRI+jdW+s0wkS4uLjQvHlzXFxctI4iTEB8+44kNnsSXUrK3cESJuLq1ausW7eO4OBgbGTNwiKnWQEUHR1NZmYmPj4+Btf7+PgQHh5eosecMGEC7u7uORd/f/8CPb4wIX/8AdmtfrNmgcz5Iv6VnJzM6dOnSU5O1jqKMAU6HWGfTkKxsoKNG2H3bq0T5Zg5cybu7u707dtX6yilkuadoE3BqFGjiIuLy7lcv35d60iiMLKyYMgQdYKzXr1k0kNhIDY2lm+++YbY2FitowgTkfLo48T0+rfIePddyMjQNhDqfFWLFy/mnXfewdnZWes4pZJmBZCXlxfW1tZEREQYXB8REXHfDs7FdUx7e3vc3NwMLsKMrVqlTnHv4gITJ2qdRghhBiLe/4gMD091yZwFC7SOw/z588nMzGTw4MFaRym1NCuA7OzsaNSoEaGhoTnXZWVlERoaSvPmzU3mmMLMxMerE5sBjBkDfn7a5hFCmIVMT08iPhij/jBmDERHa5YlJSWFWbNm8cYbb8iadcVI01NgwcHBLF68mBUrVnD27FkGDRqEXq8nKCgIgN69ezNq1Kic/dPS0jhx4gQnTpwgLS2NmzdvcuLECS5evJjvY4pS7rPPICICatSQYe9CCKPEvNaH5NqPQ2wsjB6tWY6VK1cSFRXF+++/r1kGS6Bpt/Ju3boRFRXF2LFjCQ8Pp379+mzbti2nE/O1a9ewsrpbo926dctgGvCpU6cydepUnn76afbs2ZOvY4pS7Nw5yJ4cc+ZMsLPTMo0wUTY2Nvj6+sqoGpGbtTW3Pp1M1a7PoyxahO6tt6CEl57IzMxk6tSpdOnShWrVqpXoY1sanaKYyVK4JSg+Ph53d3fi4uKkP5C5UBRo1w527IAXX4Tvv9c6kRCiiCWlZVB77HYANg5shkMxFbH+g/vhsfUbaN5cXTDVquROlmzevJkuXbpw8OBBmjZtWmKPW1oY8/0to8BE6bB2rVr82NvDjBlapxFCmLGwj8eR6ewCBw7A4sUl9riKojBp0iSefvppKX5KgBRAwvzFxNzt7zN6NEizsXiAsLAwxo8fT1hYmNZRhInKKF+BiBH/riH44YdQQu+VX3/9lUOHDsmipyVECiBh/kaMgKgoqF0bPvhA6zTCDGRmZmodQZi4228MIKluA4iLg6FDS+Qxp0yZwmOPPUb79u1L5PEsnRRAwrzt3Xt3xueFC6XjsxCiaFhbc3PylyjW1uoM0T/+WKwPd+bMGb7//ntGjBhhMPhHFB95lYX5Sk2Ft95StwcMgJYttc0jhChVUh6rS3S/t9Uf3n4bEhOL7bGmTp1KhQoV6NGjR7E9hjAkBZAwXxMmwPnz4OsLkyZpnUYIUQpFBI8k7RF/uHYNQkKK5TFu3brF6tWrGTZsGHbSil1ipAAS5uncObUAAvjyS/Dw0DSOMB9eXl4MGjQILy8vraMIM6A4OXPzi+nq9syZcOxYkT/Gl19+iaOjIwMGDCjyY4v7kwJImJ/MTOjfH9LS4Pnn4ZVXtE4kzIitrS3e3t7Y2tpqHUWYicRnniO248vosrKgXz9ITy+yY8fHx7NgwQIGDhwo886VMCmAhPn58kt1cjIXF5g7F3Q6rRMJMxIbG8t3330nq8ELo4R9MkFdLPX4cfjiiyI77qJFi0hOTmZoCY00E3dJASTMy9mz8NFH6vb06RAQoGkcYX6Sk5M5fvw4ycnJWkcRZiSjnDe3Pp8GgDJ+PBw9WuhjpqWlMWPGDHr16oWfLNxc4qQAEuYjIwN691ZHf7VrpzZFCyFECYnr2IXYF19Cl5EBffpASkqhjrdmzRpu3brF8OHDiyihMIYUQMJ8TJwIR46oHZ6/+kpOfQkhStytz6eS7lUOTp8u1KiwrKwspk6dSocOHXj00UeLMKHILymAhHk4cQLGjVO3Z8+GChU0jSOEsEyZZcpyc9KXAChTpsDvvxfoOD///DOnT5+WZS80JAWQMH2pqWpzc0YGvPQS9OypdSJhxpydnXnyySdxdnbWOoowUwltnudO1x7oFEX9bNLrjT7G5MmTadasGS1lAlfNSAEkTN+4cXDyJHh5wYIFcupLFIqbmxuBgYEy5FgUyq1PJpBWvgJcvKgumGqEQ4cOsW/fPj744AN08nmmGSmAhGn75Re17w+oxY+3t7Z5hNlLTU3lypUrpKamah1FmLEsdw9uTp2j/jB3Lnz3Xb7vO2XKFGrUqEHHjh2LKZ3IDymAhOmKjFRPdymKOuLr5Ze1TiRKgZiYGFasWEFMTIzWUYSZS3zqGaL6v6P+EBQE168/9D4XLlxg06ZNvP/++1hbWxdzQvEgUgAJ05SVpZ5bDw+H2rXVyQ+FEMLERIwMIaluA4iJUf9gy8h44P7Tp0+nXLly9O7du4QSivuRAkiYpmnTYNs2cHCA9evByUnrREIIkYtiZ8f1uUvJdHGF/fvh00/vu29ERATLli1j6NChODg4lGBKkRcpgITpOXTo7mzPX34Jjz+ubR4hhHiAtIDK3Jw4E/h3lujdu/Pcb86cOdjY2DBo0KASTCfuRwogYVpiY6F7d7UZ+dVX1UVPhShCVlZWuLq6YmUlH3+i6MR1epmYHr3VofE9e0JUlMHtiYmJzJ07l/79++Pp6alRSnEv+QQQpiO7s/OVK+oaX4sWyZB3UeR8fHwIDg7Gx8dH6yiilLk1biIp1WtCWBj06gWZmTm3LV26lPj4eN577z0NE4p7SQEkTMfEifDtt2BrC+vWgbu71omEECLfFEcnrs1bRpaDI2zfDmPGAJCRkcH06dPp3r07FStW1DilyCYFkDANP/8MH3+sbs+ZA02baptHlFoRERFMnz6diIgIraOIUii1Vm1uTJmt/jBhAmzcyMaNG7l69aose2FibLQOIAQXLkCPHuopsAED1IsQxSQrK4uEhASysrK0jiJKqbjOXYk69SflFs4mq08fJleqRNu2balXr57W0cQ9pAVIaCshATp3hrg4aNECZs3SOpEQQhRa+MgQElq1JjQ5mRPnzjHirbe0jiT+QwogoZ3syQ7PnAE/P/jmG7C31zqVEEIUno0N1+cuZZKDAw2BZ+fPN+gULbQnBZDQzvjxsHkz2NmpnZ/Ll9c6kRBCFJlTN68TmpLCcFs7dDt3wsiRWkcS95ACSGhj9WoICVG3582DZs20zSMsRpkyZejTpw9lypTROooo5ZYvmI2ff0WaTp+nXjF1qrqoszAJUgCJkvfLL/Dmm+r28OHQt6+2eYRFsbe3JyAgAHs53SqK0c3rV9nxw2b6DBiMvnNXIt4fBYDyzjvw/fcapxMgBZAoaX/9BS+9BOnp0K0bTJqkdSJhYeLj49m1axfx8fFaRxGl2Kqv5uHq5k6nV3sCEDn0A2K690KXlYXSvTv88YfGCYVJFEBz584lICAABwcHmjZtyuHDhx+4/8aNG6lVqxYODg7UqVOHn376yeD2N954A51OZ3Bp165dcT4FkR83bsDzz0N8PDz1FCxfDrIcgShher2e3377Db1er3UUUUrF3olh09pVdOvTDycnZ/VKnY6bX0wn4en/oUtKghdfhH/+0TaohdP822f9+vUEBwcTEhLCsWPHqFevHm3btiUyMjLP/X///Xd69OhB3759OX78OJ07d6Zz586cOnXKYL927doRFhaWc1m7dm1JPB1xP/Hx8MILahFUq5ba+VlWQxZClEIbVi5Bycqixxv/mdPM1pZrC5aT/HhdiIyE9u3h9m1tQgrtC6Dp06fTv39/goKCqF27NgsWLMDJyYmlS5fmuf+XX35Ju3btGDFiBI8++iifffYZDRs2ZM6cOQb72dvb4+vrm3ORxec0lJysnvY6eRJ8fdVZn6UDqhCiFEpJTubrZQvp3O11ypT1ynV7losrV5ZvIK3CI/D332pLUEKCBkmFpgVQWloaR48eJTAwMOc6KysrAgMDOXDgQJ73OXDggMH+AG3bts21/549e/D29qZmzZoMGjSI2w+oslNTU4mPjze4iCKSmgovv6x2fHZxgR9+UBc6FUKIUui7b9YSdyeG3v3fue8+GT6+XFn5DRnuHnDwIHToAElJJRdSABoXQNHR0WRmZuZaldnHx4fw8PA87xMeHv7Q/du1a8fKlSsJDQ1l0qRJ7N27l/bt25N5n0moJkyYgLu7e87F39+/kM9MAGpH5+7d1RYfR0f48Udo1EjrVMLCOTo60qBBAxwdHbWOIkqZzMxMVi6aQ+DzHfEPqPzAfVNr1OLK6k1kurjC3r1qK3lKSgklFWACp8CKQ/fu3enYsSN16tShc+fO/PDDD/zxxx/s2bMnz/1HjRpFXFxczuX69eslG7g0ysiA11+HLVvU2Z2/+07t+CyExjw8POjYsSMeHh5aRxGlzC/bf+DalX94461387V/cv2GXFm5kUwnZ9ixA155BdLSijmlyKZpAeTl5YW1tXWuVZkjIiLw9fXN8z6+vr5G7Q9QpUoVvLy8uHjxYp6329vb4+bmZnARhZCVpc7zs2ED2NrCpk3wn9OWQmglPT2dyMhI0tPTtY4iShFFUVg2fxZPNG/J4/Ub5vt+SU804+rydWTZO6hdBF57Tf0DUhQ7TQsgOzs7GjVqRGhoaM51WVlZhIaG0rx58zzv07x5c4P9AXbu3Hnf/QFu3LjB7du3KS9LLRS/zEx1NfdVq8DaGtavV4e+C2EioqOjmT9/PtHR0VpHEaXI0UO/c+rEUd4YNNTo++qbt+LqV1+Tlb0sUK9eahcCUaw0PwUWHBzM4sWLWbFiBWfPnmXQoEHo9XqCgoIA6N27N6NGjcrZf+jQoWzbto1p06Zx7tw5PvnkE44cOcLgwYMBSExMZMSIERw8eJArV64QGhpKp06dqFatGm3bttXkOVqMtDTo2ROWLFHn91m9Wj2vLYQQpdzyBbOoVrM2LVsXrLU7sfX/uLZgBYqNDaxbpw4eSU4u4pTiXpoXQN26dWPq1KmMHTuW+vXrc+LECbZt25bT0fnatWuEhYXl7N+iRQvWrFnDokWLqFevHt988w1btmzh8ccfB8Da2pqTJ0/SsWNHatSoQd++fWnUqBH79++Xqe+LU1ISdO6stvjY2qr/du+udSohhCgWqSkp6BPV4esXz59lX+h23hg4BJ1OV+BjJjzXnqtL1qinw77/Xp0nSEYlFxudoiiK1iFMTXx8PO7u7sTFxUl/oPyIi1Pnsvj1V3W01+bNIK1twkSFhYWxaNEiBgwYIKfFzUxSWga1x24HYOPAZjjY2GiWZeaETzh/5hTzV33DmOC3OfjrXn769Ti2dnaFPrbTwd8ICOqOdWICNG6sjqT1yj2nkMjNmO9vzVuAhJmLjIRnnlGLH3d32LlTih9h8qytrbWOIMxckl5PdGQ44WE3+XHLRnr1G1QkxQ9AUrMn+Wf992SUKQtHjqgjaG/eLJJji7ukABIFd+4cPPkkHD8O3t6wZ4/6sxAmrHz58owePVpaf0Sh2Ds4kJKSwtdLFuDo6EinV15jxhchzPgipEiOn1K3Pv98+zPpvn5w9iw0b67Opi+KjBRAomB27YJmzeDiRahUCfbvh/r1tU4lhBAlwt7enpTkZL75ejkduvYg+K3erFo8l2o1Hy2yx0itVoNLm34mtUo1uH4dpUULtW+QKBJSAAnjLVgA7dqpfX+aN4fDh6FGDa1TCZEvUVFRLFy4kKioKK2jCDNm7+BIfFwsqSnJ7Prpey5dOM+itVvp8HLRDv5I96/Exa27SHzyKXR6PUqnTjB9Okj33UKTAkjkX0YGDBsGgwap8/307Kmu8eXtrXUyIfItIyOD8PBwMmSyOVEI1tY2JCfpyVIUynn7sO7H3TRuVjxdALI8PLi86ltu93wDnaLA+++r863JrNGFIgWQyJ+oKHjhBfjyS/Xnzz5TJzt0cNA2lxBCaCAyQp2epXVgO5Z98xO+fo8U7wPa2nJrwgxuhXyBYmUFX30Fzz0Ht24V7+OWYtqNIRTm49df1Tl9bt5UC56VK9U1a4QQwkL1HxxMxYDK9HhjQKHm/jGKTsftfm+TVrkq/oP7Yb1vHzRoAF9/LcsNFYC0AIn7y8qCSZOgdWu1+KlZU+3vI8WPEMLClS3nzWtBb5Vc8XOPhP+15eIPv5D86GMQGYnSpg188onaNUHkmxRAIm+3b0PHjjBypPqf6rXX4I8/oE4drZMJUSgeHh507dpVVoMXZi2tanUubd1FzGt91H5B48ZBmzYQHq51NLMhBZDI7aefoG5d+PFHsLeHhQvVdb1cXbVOJkShOTo68thjj+Ho6Kh1FCEKRXF05OakL7n+5UKyHJ3UQSl166oLqoqHkgJI3BUXB2++qXZ2vnVLHdp+6JA62kCDZl4hikNiYiIHDhwgMTFR6yhCFInYLt24+ONukmvVVgesdO2qttrfvq11NJMmBZBQbd8Ojz8Oy5apxU5wMJw4AfXqaZ1MiCKVkJDAjh07SEhI0DqKEEUmtXpNLv2wm8jB76ujxNauhcceg61btY5msqQAsnRRUdC3rzqx4Y0bULUq7NsH06apC5sKIYQwC4q9PREfjuHS1l2kVK8JERHQubM6Z1tYmNbxTI4UQJYqMxPmzVNHdi1dql43ZAj8+Se0bKltNiGEEAWWXL8hF3/aS9SgoWpr0Jo1KDVrqjNIp6drHc9kSAFkiX7/HRo3hnfegTt31NNc+/fDrFng7Kx1OiGEEIWkODgQ/tE4Lm3dRVL9RugSEtQZpOvXh927tY5nEqQAsiT//AOvv66u2H7iBHh4wJw5cOSItPoIi2Fvb0+NGjWwt7fXOooQxS65fkMubd3JjcmzyPAsA2fOwLPPqvO5nT+vdTxNSQFkCcLC1NaeWrXUGUNBHe11/rx6vY1MCC4sR5kyZejRowdlypTROooQJcPKijs9evP33qPc7t1PPS32zTcojz0G/frB9etaJ9SEFECl2Z07MGqU2rF53jz13G+bNuqEhkuWyCKmwiJlZmai1+vJlFlzhYXJ9PTk1udTubhtH/GB7dBlZsKSJSjVq6sjf6OitI5YoqQAKo1u3IDhw6FiRZg4EZKToVkz9bzv9u1q/x8hLFRkZCRTp04lMjJS6yhCaCLl0ce5umwdlzZvJ7HZk+hSU2HGDJRKleDdd+HKFa0jlggpgEqTM2cgKAiqVFGHsScmqrOCfved2vG5dWutEwohhDARSY2bcnnDD1xevYmkug3QJSfD7Nko1aqpQ+f//FPriMVKCiBzl5kJ338Pzz+vTnq1fLl6quvpp9UlLU6cgA4dZCZnIYQQuel0JD79LJd++IV/1m4l4aln1VNja9aoI8aeew42bSqVw+el96u5unlT7cfz1Vd3O7DpdPDSS/DBB9C0qbb5hBBCmA+dDn3Lp9G3fBqHU39Sbv4s3H/YjG7XLti1C3x91Q7T/fur3StKAWkBMidJSbB+vbpKe6VKEBKiFj9ly6p9fs6fVxfBk+JHCCFEAaU8Xo/rc5dw/tfjRA5+n/Ry3uoq8+PHo1SuDO3bqwtkm/l6ejpFURStQ5ia+Ph43N3diYuLw83NTdswGRlq9b1mDWzebPiGa9UKBg6ELl3AwUG7jEKYkaysLNLT07G1tcXKSv4GNCdJaRnUHrsdgI0Dm+EgU3iUCF1aGq47fqLs6qW4/Lbv7g2OjtCpk9pfqE0bsLPTLuS/jPn+lnePKUpIUEdrffcd/PgjxMTcvS0gQF3lt2dPqF1bs4hCmCsrKyuZBFEIIyh2dsS/2Jn4Fztjd/kSHps34LF5I/ZX/oF169SLu7vaF7VjR7WFyN1d69gPJS1AeSjxFiBFUU9fhYbCDz/AL79AWtrd27284NVX1aKneXPp0CxEIdy+fZuff/6Z9u3bU7ZsWa3jCCNIC5AJURQc/zyOx5YNuH+3Cduou9NKKDY26Fq3hhdfhMBA9Y/1EvrekhYgc3DjBuzZo57e2rVL7dR8r6pV1abFTp2gRQuZrVmIIpKWlsalS5dIu/ePDCGEcXQ6kus3JLl+Q8LGfI7T8SO47fgJ150/43Dx77vfbaB2oP7f/9Ri6Jln1E7UJvCHvHyrloSMDDh5En77TZ2P5/ff4do1w33s7NT1uJ57Ti16atUyiTeIEEII8UDW1iQ1bkpS46aEfzQOu38u4rbjZ1z278b50O9YhYeryzBlL8VUoYL6h332pX59TfoPSQFU1FJS4PRpOHbs7uXkSfX6e1lZQYMGakUcGKguUOroqE1mIYQQooikValG9MAhRA8cgi4lBadjh3HZvxeXX/fg+NcJdDdvwsaN6gXU4qdOHWjY8O6lTp1i/06UAqig4uLg4kU4d06dgfnMGbXwuXQJsrJy7+/urvbfefJJteJt0gRcXEo+txBCCFFCFAcH9C2eQt/iKSI+HIMuSY/Tn8dxOnoYp6OHcDpyGJvYO3D0qHrJptOpqxo89pjah6h2bfXMSPXq4OFRJNmkAHqQS5fUEVhXr6qXy5fVoufixQcvGle2rGEl26CB2qdHhtwKoTk3Nzfat2+v/RQXQlggxckZffOW6Ju3/PcKBdtrV3E89SeOp06q//51Apvb0ep38KVL6ojoe5UtC9WqqZcqVdQ+RZUqqRcjRp+ZxCiwuXPnMmXKFMLDw6lXrx6zZ8+mSZMm991/48aNjBkzhitXrlC9enUmTZrE888/n3O7oiiEhISwePFiYmNjefLJJ5k/fz7Vq1fPV56cXuTAAz8ivb3VavSxx+5eatdWO3xJ/x0hhChSMgrMQigK1rejcfj7LA5/n8P+7/PY/30W+8uXsI2MeOBd4wF3MI9RYOvXryc4OJgFCxbQtGlTZs6cSdu2bTl//jze3t659v/999/p0aMHEyZM4MUXX2TNmjV07tyZY8eO8fjjjwMwefJkZs2axYoVK6hcuTJjxoyhbdu2nDlzBgdjJgx0dFTn3bm3uqxeXa06q1YF+QtSCLOTnJzMhQsXqF69Oo7S704I06PTkelVDr1XOfQtnjK4yUqfiN3Vy9hd+Qf7y/9ge+MadjdvYHvzOrY3rkOSPv8Po3ULUNOmTXniiSeYM2cOoM7S6u/vz5AhQxg5cmSu/bt164Zer+eHH37Iua5Zs2bUr1+fBQsWoCgKfn5+vP/++wwfPhxQK0EfHx+WL19O9+7dH5oppwUoNhY3M5jMSQiRf2FhYSxatIgBAwZQvnx5reMII0gLkHggRSH5xnWatqhr+i1AaWlpHD16lFGjRuVcZ2VlRWBgIAcOHMjzPgcOHCA4ONjgurZt27JlyxYALl++THh4OIGBgTm3u7u707RpUw4cOJBnAZSamkpqamrOz/Hx8QCER0SgT0rKud7BwQFPT08yMjKIyqMPUPaHaXR0NOn/WTnXw8MDR0dH9Hp9zvGz2dnZUbZsWbKysoiIyN285+3tjbW1NTExMQY5AVxdXXFxcSE5OZnY2FiD22xsbChXrhygfuj/l5eXF7a2tsTGxpKcnGxwm7OzM25ubqSmphJz70zUqL8jHx8fACIiIsj6T6fvMmXKYG9vT3x8PHq9YTXu6OiIh4cH6enpREdH58qU/RpGRUWRkZFhcFv2a5iYmEhCQoLBbfb29pQpU4bMzEwiIyP5Lx8fH6ysrLh9+3au+V/c3NxwdnbO8zW0tbXFy8sLyPs1LFeuHDY2Nty5c4eU/4z0c3FxwdXVNc/X0NraOqeFM6/XsGzZstjZ2eX5Gjo5OeHu7p7na6jT6fD19QXyfg09PT1xcHDI8zXMfn/f7zX09fVFp9Pl+Rq6u7vj5OREUlIScXFxBrdlv78VRSE8PDzXcbPf33m9htnv75SUFO7cuWNw273v7/DwcP77t1z2+zsuLo6ke/4f3/uapaWlcfv2bYP73fv+joyMJDMz0+D27Pd3QkICif9ZC0k+I1TF9RmRkn73d5EUf4c0DH/nzq7u2Nk7kJKkJznJ8Hdja2ePi5sHWVmZxMXk/uzxKFMOnZUVCXF3yEg3fH87Obti7+hEWkoK+kTD97eNjS2uHmUAuBOd+3fj5lEWaxsb9PFxpKUZvr8dnJxxdHIhPS2VxPhYg9usrKxxL6N+9sTejkJRDF9DV3dPbGztSEpMIDUlyeA2ewdHnFzcyMhIJyHW8Hej0+nwKKt+9sTfuU1mpuFnRL5ew8xM4u7k8RqW9Uan05EQe4eMjP+8hi5u2Ds4kpqSTFKi4fvbxsYOVw9PFEUh9nbuzx53Ty+srK1JjI8lPc3w/e3o5IKDkzNpqSnoE+LQKxm57n8/mhZA0dHRZGZm5vxHyebj48O5c+fyvE94eHie+2d/sGb/+6B9/mvChAmMGzcu1/XLli0zOGVWp04dunTpQnx8PIsWLcq1f0hICABbt27lxo0bBre99NJL1K1bl9OnT/Pzzz8b3Fa1alVef/110tPT8zzu8OHDcXZ2Zvv27fz9998Gt7Vp04bmzZvzzz//8M033xjc5uvry1tvvQXAkiVLcn2QDxo0CG9vb/bt28fx48cNbnvyyScJDAwkLCyMFStWGNzm6uqaU4R+/fXXub5I+/TpQ0BAAIcPH+a3334zuK1BgwZ07NiRO3fu5Hqu1tbWjB49GoBNmzbl+n117dqVxx57jL/++osdO3YY3FajRg169OhBSkpKnq/hyJEjsbe35+eff+bSpUsGt7Vv354mTZpw4cIFNm/ebHDbI488Qt++fQHyPO6QIUMoU6YMu3fv5q+//jK47emnn6Z169Zcv36dr7Pnv/iXp6cn7777LgArV640+IIGePPNN/H39+fAgQMcPHjQ4LbGjRvzwgsvEB0dnSuTnZ1dzh8UGzduzPUl3L17d2rWrMnx48f55ZdfDG6rXbs2r7zyCnq9Ps/n+vHHH2NjY8P333/P1atXDW7r0KEDDRs25Ny5c3z//fcGt1WqVIk33niDzMzMPI/73nvv4ebmxq5duzhz5ozBbc8++yytWrXi6tWrrFu3zuC2cuXK8fbbbwPq/9X/FmXZLTy//vorR44cyfW4oH45L1261OA6JycnRowYAcC6detyFV49e/akWrVqHD16lL179xrcJp8RquL8jIAGAJz5fReR/ykGu3btSu3Kj3HgwGl23eczQq/XM3XJGv4r+zNi9S/f5fkZ0aBJE06evMKuB3xGjFs8I9dx1c8INzYd3JXnZ0Sj1q25ePEiWzYbZrr3M2LK1wvz/ozw82L79vt/RoSFhbH5P8e99zNi3pbVeX9GVK7J/v1/3vczIj4+nhlLc7+G2Z8Ry3dsyvMzonbDhhw7dpFd9/mMyMjI4POvch83+zNi42/b8/yMaNiqFefPh7F187pcf0Q9iKanwG7dukWFChX4/fffad68ec71H3zwAXv37uXQoUO57mNnZ8eKFSvo0aNHznXz5s1j3LhxRERE8Pvvv/Pkk09y69Ytg+btV199FZ1Ox/r163MdM68WIH9/f86fP4+rq2vO9fLXnUpagO6SFiCVubUAbdq0iQEDBlC2bFlpATKjzwhFUfDwUv/fJMbG5PrdyGeEypI/IxISEqhZs2a+ToFpWgClpaXh5OTEN998Q+fOnXOu79OnD7GxsWzdujXXfSpWrEhwcDDDhg3LuS4kJIQtW7bw559/8s8//1C1alWOHz9O/fr1c/Z5+umnqV+/Pl9++eVDc5nUavBCiCIVHR3N1q1b6dSpU84XlxCidDDm+1vTiWns7Oxo1KgRoaGhOddlZWURGhpq0CJ0r+bNmxvsD7Bz586c/StXroyvr6/BPvHx8Rw6dOi+xxRCWA4vLy/69u0rxY8QFk7zLvTBwcH06dOHxo0b06RJE2bOnIlerycoKAiA3r17U6FCBSZMmADA0KFDefrpp5k2bRovvPAC69at48iRIznninU6HcOGDWP8+PFUr149Zxi8n5+fQSuTEEIIISyX5gVQt27diIqKYuzYsYSHh1O/fn22bduWc/742rVrWN0zg3KLFi1Ys2YNo0eP5qOPPqJ69eps2bIlZw4gUPsQ6fV6BgwYQGxsLC1btmTbtm3GzQEkhCiVZBi8EAJMYB4gUyR9gIQovaQAEqL0Mps+QEIIIYQQWpACSAghhBAWRwogIYQQQlgczTtBCyFESSpXrhxDhgyR/n1CWDgpgIQQFsXGxoYyZcpoHUMIoTE5BSaEsCh37txh06ZNuZbWEEJYFimAhBAWJSUlhb/++suoRROFEKWPFEBCCCGEsDhSAAkhhBDC4kgn6DxkT44dHx+vcRIhRFFLSEggJSWFhIQEnJ2dtY4jhChC2d/b+VnkQpbCyMONGzfw9/fXOoYQQgghCuD69es88sgjD9xHCqA8ZGVlcevWLVxdXdHpdAa3xcfH4+/vz/Xr12UekQKS17Bw5PUrHHn9Ckdev8KT17BwHvT6KYpCQkICfn5+Bgup50VOgeXBysrqoZWjm5ubvHELSV7DwpHXr3Dk9Sscef0KT17Dwrnf6+fu7p6v+0snaCGEEEJYHCmAhBBCCGFxpAAykr29PSEhIdjb22sdxWzJa1g48voVjrx+hSOvX+HJa1g4RfX6SSdoIYQQQlgcaQESQgghhMWRAkgIIYQQFkcKICGEEEJYHCmAhBBCCGFxpAAqpI4dO1KxYkUcHBwoX748vXr14tatW1rHMgtXrlyhb9++VK5cGUdHR6pWrUpISAhpaWlaRzMbn3/+OS1atMDJyQkPDw+t45iFuXPnEhAQgIODA02bNuXw4cNaRzIL+/bto0OHDvj5+aHT6diyZYvWkczKhAkTeOKJJ3B1dcXb25vOnTtz/vx5rWOZjfnz51O3bt2cyQ+bN2/Ozz//XKhjSgFUSM888wwbNmzg/PnzfPvtt1y6dImuXbtqHcssnDt3jqysLBYuXMjp06eZMWMGCxYs4KOPPtI6mtlIS0vjlVdeYdCgQVpHMQvr168nODiYkJAQjh07Rr169Wjbti2RkZFaRzN5er2eevXqMXfuXK2jmKW9e/fyzjvvcPDgQXbu3El6ejpt2rRBr9drHc0sPPLII0ycOJGjR49y5MgRnn32WTp16sTp06cLfEwZBl/EvvvuOzp37kxqaiq2trZaxzE7U6ZMYf78+fzzzz9aRzEry5cvZ9iwYcTGxmodxaQ1bdqUJ554gjlz5gDqun/+/v4MGTKEkSNHapzOfOh0OjZv3kznzp21jmK2oqKi8Pb2Zu/evTz11FNaxzFLZcqUYcqUKfTt27dA95cWoCIUExPD119/TYsWLaT4KaC4uDjKlCmjdQxRCqWlpXH06FECAwNzrrOysiIwMJADBw5omExYori4OAD5vCuAzMxM1q1bh16vp3nz5gU+jhRAReDDDz/E2dmZsmXLcu3aNbZu3ap1JLN08eJFZs+ezVtvvaV1FFEKRUdHk5mZiY+Pj8H1Pj4+hIeHa5RKWKKsrCyGDRvGk08+yeOPP651HLPx119/4eLigr29PQMHDmTz5s3Url27wMeTAigPI0eORKfTPfBy7ty5nP1HjBjB8ePH2bFjB9bW1vTu3RtLPrNo7OsHcPPmTdq1a8crr7xC//79NUpuGgry+gkhzMc777zDqVOnWLdundZRzErNmjU5ceIEhw4dYtCgQfTp04czZ84U+HjSBygPUVFR3L59+4H7VKlSBTs7u1zX37hxA39/f37//fdCNc2ZM2Nfv1u3btG6dWuaNWvG8uXLsbKy7Lq8IO8/6QP0cGlpaTg5OfHNN98Y9F3p06cPsbGx0nJrBOkDVHCDBw9m69at7Nu3j8qVK2sdx6wFBgZStWpVFi5cWKD72xRxnlKhXLlylCtXrkD3zcrKAiA1NbUoI5kVY16/mzdv8swzz9CoUSOWLVtm8cUPFO79J+7Pzs6ORo0aERoamvPFnZWVRWhoKIMHD9Y2nCj1FEVhyJAhbN68mT179kjxUwSysrIK9V0rBVAhHDp0iD/++IOWLVvi6enJpUuXGDNmDFWrVrXY1h9j3Lx5k9atW1OpUiWmTp1KVFRUzm2+vr4aJjMf165dIyYmhmvXrpGZmcmJEycAqFatGi4uLtqGM0HBwcH06dOHxo0b06RJE2bOnIlerycoKEjraCYvMTGRixcv5vx8+fJlTpw4QZkyZahYsaKGyczDO++8w5o1a9i6dSuurq45/c7c3d1xdHTUOJ3pGzVqFO3bt6dixYokJCSwZs0a9uzZw/bt2wt+UEUU2MmTJ5VnnnlGKVOmjGJvb68EBAQoAwcOVG7cuKF1NLOwbNkyBcjzIvKnT58+eb5+u3fv1jqayZo9e7ZSsWJFxc7OTmnSpIly8OBBrSOZhd27d+f5XuvTp4/W0czC/T7rli1bpnU0s/Dmm28qlSpVUuzs7JRy5cop//vf/5QdO3YU6pjSB0gIIYQQFkc6XAghhBDC4kgBJIQQQgiLIwWQEEIIISyOFEBCCCGEsDhSAAkhhBDC4kgBJIQQQgiLIwWQEEIIISyOFEBCCCGEsDhSAAkhhBDC4kgBJIQo9bKysqhVqxYff/yxwfU//vgjdnZ2bNq0SaNkQgitSAEkhCj1rKysGDVqFHPnziUuLg6AY8eO0a1bNyZNmkSXLl00TiiEKGmyFpgQwiJkZGRQo0YN+vbtS69evWjWrBkvv/wys2fP1jqaEEIDUgAJISzGwoULGT16ND4+PlStWpXNmzdjZSUN4UJYIimAhBAWIzExkXLlylGtWjUOHTqEk5OT1pGEEBqRP32EEBZj8ODBAERHR0vLjxAWTj4BhBAWYcyYMfz4448cPHiQjIwMlixZonUkIYSGpAASQpR6ixcvZtq0aXz//ffUq1ePYcOGMXnyZNLT07WOJoTQiBRAQohS7aeffmLw4MF8/fXXNGvWDFBPhcXFxbFq1SqN0wkhtCIFkBCi1Dp69CivvvoqkydP5qWXXsq53t3dnXfffZeJEyeSmZmpYUIhhFZkFJgQQgghLI60AAkhhBDC4kgBJIQQQgiLIwWQEEIIISyOFEBCCCGEsDhSAAkhhBDC4kgBJIQQQgiLIwWQEEIIISyOFEBCCCGEsDhSAAkhhBDC4kgBJIQQQgiLIwWQEEIIISzO/wEwAD5o8B0wjwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scipy_material.illustration_onesided_probabilitymass(z, N, onesided_pvalue)"
    ]
@@ -556,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "b162e92e",
    "metadata": {
     "hidden": true,
@@ -566,7 +766,20 @@
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%%html\n",
     "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>"
@@ -595,13 +808,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scipy_material.illustration_t_pdfs()"
    ]
@@ -667,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "bb6f0e09-f529-47bb-8470-66130193a791",
    "metadata": {},
    "outputs": [],
@@ -688,10 +912,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "ec4b0084-93c3-40b9-94c5-d05679a1dc45",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>0.602406</td>\n",
+       "      <td>7</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.565899</td>\n",
+       "      <td>[31.95, 80.4]</td>\n",
+       "      <td>0.212983</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.08203</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T  dof alternative     p-val          CI95%   cohen-d   BF10  \\\n",
+       "T-test  0.602406    7   two-sided  0.565899  [31.95, 80.4]  0.212983  0.391   \n",
+       "\n",
+       "          power  \n",
+       "T-test  0.08203  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.ttest(x, mu)"
    ]
@@ -710,12 +994,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "b471633d-c9ad-455e-84e1-d32af085b32a",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=0.6024056396957578, pvalue=0.5658990587680466, df=7)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.ttest_1samp(x, mu)"
    ]
@@ -754,7 +1049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "8cc7f8c2-75d3-447f-b27c-4ed6350e4075",
    "metadata": {},
    "outputs": [],
@@ -774,10 +1069,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "7dae4d48-36b9-40df-87fa-a3c73ca42a19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>-1.961743</td>\n",
+       "      <td>14</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.069989</td>\n",
+       "      <td>[-55.4, 2.47]</td>\n",
+       "      <td>0.980872</td>\n",
+       "      <td>1.42</td>\n",
+       "      <td>0.447175</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T  dof alternative     p-val          CI95%   cohen-d  BF10  \\\n",
+       "T-test -1.961743   14   two-sided  0.069989  [-55.4, 2.47]  0.980872  1.42   \n",
+       "\n",
+       "           power  \n",
+       "T-test  0.447175  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.ttest(x1, x2)"
    ]
@@ -796,12 +1151,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "6231e214-ac36-4c4f-8a16-e1551c8484b4",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TtestResult(statistic=-1.96174329619957, pvalue=0.06998888828308221, df=14.0)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.ttest_ind(x1, x2)"
    ]
@@ -854,10 +1220,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "70ee9cae-b8ad-4b51-b443-03befe14606c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>alternative</th>\n",
+       "      <th>p-val</th>\n",
+       "      <th>CI95%</th>\n",
+       "      <th>cohen-d</th>\n",
+       "      <th>BF10</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T-test</th>\n",
+       "      <td>-2.361598</td>\n",
+       "      <td>7</td>\n",
+       "      <td>two-sided</td>\n",
+       "      <td>0.050223</td>\n",
+       "      <td>[-52.96, 0.03]</td>\n",
+       "      <td>0.980872</td>\n",
+       "      <td>1.892</td>\n",
+       "      <td>0.664343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T  dof alternative     p-val           CI95%   cohen-d   BF10  \\\n",
+       "T-test -2.361598    7   two-sided  0.050223  [-52.96, 0.03]  0.980872  1.892   \n",
+       "\n",
+       "           power  \n",
+       "T-test  0.664343  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.ttest(x1, x2, paired=True)"
    ]
@@ -900,13 +1326,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8660e3d113ab4912aae489eb52e405b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.5, description='cohen_d', max=4.0), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scipy_material.illustration_cohen_d();"
    ]
@@ -941,13 +1392,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "2ba983bc-ef4c-4a15-ac23-776fffd88afb",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.980871648099785"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.compute_effsize(x1, x2)"
    ]
@@ -1004,7 +1466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "e917eacc-d454-4476-ac0e-404d67ba5f47",
    "metadata": {},
    "outputs": [],
@@ -1025,10 +1487,125 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "47010494-cac1-4af9-9490-77fd0cf0d5b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>20</th>\n",
+       "      <th>21</th>\n",
+       "      <th>22</th>\n",
+       "      <th>23</th>\n",
+       "      <th>24</th>\n",
+       "      <th>25</th>\n",
+       "      <th>26</th>\n",
+       "      <th>27</th>\n",
+       "      <th>28</th>\n",
+       "      <th>29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>response</th>\n",
+       "      <td>85</td>\n",
+       "      <td>86</td>\n",
+       "      <td>88</td>\n",
+       "      <td>75</td>\n",
+       "      <td>78</td>\n",
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+       "      <td>98</td>\n",
+       "      <td>79</td>\n",
+       "      <td>71</td>\n",
+       "      <td>80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>79</td>\n",
+       "      <td>78</td>\n",
+       "      <td>88</td>\n",
+       "      <td>94</td>\n",
+       "      <td>92</td>\n",
+       "      <td>85</td>\n",
+       "      <td>83</td>\n",
+       "      <td>85</td>\n",
+       "      <td>82</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group</th>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>...</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  \\\n",
+       "response  85  86  88  75  78  94  98  79  71  80  ...  79  78  88  94  92  85   \n",
+       "group      A   A   A   A   A   A   A   A   A   A  ...   C   C   C   C   C   C   \n",
+       "\n",
+       "          26  27  28  29  \n",
+       "response  83  85  82  81  \n",
+       "group      C   C   C   C  \n",
+       "\n",
+       "[2 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -1050,10 +1627,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "e36f108d-4674-4a1c-89d1-eca3360b1ae0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Source</th>\n",
+       "      <th>ddof1</th>\n",
+       "      <th>ddof2</th>\n",
+       "      <th>F</th>\n",
+       "      <th>p-unc</th>\n",
+       "      <th>np2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>group</td>\n",
+       "      <td>2</td>\n",
+       "      <td>27</td>\n",
+       "      <td>2.357532</td>\n",
+       "      <td>0.113848</td>\n",
+       "      <td>0.14867</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Source  ddof1  ddof2         F     p-unc      np2\n",
+       "0  group      2     27  2.357532  0.113848  0.14867"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.anova(df, dv='response', between='group')"
    ]
@@ -1070,12 +1700,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "2bf0aafa-d23f-44fe-b9e0-cf4bf13e7314",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.f_oneway(A, B, C)"
    ]
@@ -1134,7 +1775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "a00e151c-d532-49cf-bfa2-25aa003324cf",
    "metadata": {},
    "outputs": [],
@@ -1151,10 +1792,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "e9d46687-c35e-41fb-8452-087c100c8253",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Source</th>\n",
+       "      <th>SS</th>\n",
+       "      <th>DF</th>\n",
+       "      <th>MS</th>\n",
+       "      <th>F</th>\n",
+       "      <th>p-unc</th>\n",
+       "      <th>np2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>water</td>\n",
+       "      <td>15.552000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.552000</td>\n",
+       "      <td>19.117394</td>\n",
+       "      <td>0.000205</td>\n",
+       "      <td>0.443380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sun</td>\n",
+       "      <td>21.424667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10.712333</td>\n",
+       "      <td>13.168203</td>\n",
+       "      <td>0.000138</td>\n",
+       "      <td>0.523208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>water * sun</td>\n",
+       "      <td>5.694000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.847000</td>\n",
+       "      <td>3.499693</td>\n",
+       "      <td>0.046376</td>\n",
+       "      <td>0.225791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Residual</td>\n",
+       "      <td>19.524000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.813500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Source         SS  DF         MS          F     p-unc       np2\n",
+       "0        water  15.552000   1  15.552000  19.117394  0.000205  0.443380\n",
+       "1          sun  21.424667   2  10.712333  13.168203  0.000138  0.523208\n",
+       "2  water * sun   5.694000   2   2.847000   3.499693  0.046376  0.225791\n",
+       "3     Residual  19.524000  24   0.813500        NaN       NaN       NaN"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.anova(plant_data, dv='height', between=['water', 'sun'])"
    ]
@@ -1171,10 +1900,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "83732c12-c360-42ae-94cf-06d1bf02247b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.boxplot(data=plant_data, x='sun', y='height', hue='water');"
    ]
@@ -1189,11 +1929,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "id": "ccc23571-46dd-4250-9c11-feb4eeb2b2e1",
    "metadata": {},
-   "outputs": [],
-   "source": [
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.set_option('future.no_silent_downcasting', True) # hide a warning message that the next expression would generate as of pandas 2.2.2 and pingouin 0.5.4\n",
     "pg.plot_paired(data=plant_data, dv='height', within='water', subject='sun');"
    ]
   },
@@ -1289,7 +2041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "b24c3e20-6cbc-4f03-b189-780c609a3320",
    "metadata": {},
    "outputs": [],
@@ -1321,10 +2073,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "7b4317d7-248a-462f-a252-a815215f4072",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1330x410 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
     "\n",
@@ -1345,13 +2108,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "6bcaa795-3a85-4b8c-aad9-d554e244a2ec",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1330x410 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
     "\n",
@@ -1379,10 +2153,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "920395a5-7770-45ed-9bd3-80ff71bda307",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>W</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>normal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.940223</td>\n",
+       "      <td>0.092225</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          W      pval  normal\n",
+       "0  0.940223  0.092225    True"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.normality(x_not_normal)#, method='jarque_bera')"
    ]
@@ -1410,13 +2231,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/RpItmdAOuXVycpJOihBCCGEAMp0l/0g/RojCITExkdjYWGxtbeXfqhAFXG76MTJ5WgghhBBCCCGEEMKAJNkihBBCCCGEEEIIYUCSbBFCCCGEEEIIIYQwIKnZIoQQosBSFIXHjx+Tmppq6lDEc5ibm2NhYSE1WYQQQk8uLi7069cPFxcXU4dSrEjfQjzNmP0YSbYIIYQokJKTk7l16xYPHjwwdSgiG3Z2dpQuXRorKytThyKEEIWGra0ttWrVMnUYxYr0LURmjNWPkWSLEEKIAictLY3w8HDMzc3x8vLCyspKRk4UQIqikJyczJ07dwgPD6dKlSqYmckMZSGEyImEhAROnTpFnTp1cHBwMHU4RZ70LcSzjN2PkWSLEEKIAic5OZm0tDTKli2LnZ2dqcMRWbC1tcXS0pKIiAiSk5OxsbExdUhCCFEoxMfH8/fff1O+fHlJtuQD6VuIzBizHyO3n4QQQhRYMkqicJA/JyGEEIWFfGeJZxnr74T8TRNCCCGEEEIIIYQwIJlGJIQBKYr6kIS5EEIIIYR4Vloa3LihbkdHq8+RkeDhAebmpotLCGF48iuhEAYSHAxt2qhflB07wp49auJFCCGEEEIIgE6doFw59dG+vTXnz1elfXtr2rY1dWRCCEOTZIsQeXTwIHTooCZa9u5V923fDv7+0KoVbN0qSRchhOEtWLCA8uXLY2Njg5+fH4cPH87y+JkzZ9K4cWMcHR3x8PCgV69enD9/Pt0xH3/8MRqNJt2jevXqxnwbQghRbISFqX1EAGtrePDAjbVrB3Lvnht790JoqGnjE8XbxIkT6dWrl6nDKFIk2SJELt27Bz17QrNmsGMHWFrC6NFq8mX0aLCygv37oXNn9Zjbt00dsRCiqFi1ahUTJkxg6tSpHDt2DF9fXwIDA7mdxX80e/bsYezYsRw8eJDt27eTkpJCx44dSUxMTHdcrVq1uHXrlu6xb98+Y78dIYQoFv74Q33u0AEePYLExFTu3k2ke/dUAH7/3YTBiWIvJCSEevXq5es19b1xBDm7MZSbdo1Bki1C5NKbb8KGDWp9lmHD4Px5+PZb8PNTn8PDYcIEsLODQ4dg8GB1nq4QQn+KAomJpnnkZmTawYMHad++PSVKlMjQIYiLi8vz5zF79mxGjBjBsGHDqFmzJosWLcLOzo4lS5Y895ytW7cydOhQatWqha+vL8uWLePq1ascPXo03XEWFhaUKlVK93B3d89zvEIIIZ4kU/r1U59v377N119/TbduaqJ8zRoTBSYEcOLEiXxNtuTmxpFWVjeG8tKuoUmyRYhc2LEDfv4ZNBrYvRuWLIEKFdIf4+UFs2bB4cNgawvbtqk/CyH09+ABODiY5vHggX6xnjhxAn9/f+rXr8/evXvZunUrbm5utG/fnlWrVuHk5KQ7dsaMGTg4OGT5uHr1arr2k5OTOXr0KAEBAbp9ZmZmBAQEcODAgRzHGRsbC4Cbm1u6/RcvXsTLy4uKFSvy8ssvZ7i+EEII/YWHw9Gj6k26Z2dqtGmjjpA+fRrOnTNJeMVWYbqZk5aWxowZM6hSpQo2NjZ4enoydOhQ3etXr15lyJAheHp6Ymtri6+vry4JMXXqVOrUqYO9vT2enp6MHj2alJQU3bnXr18nOjoaX1/fdO299NJLuLq64ubmxssvv8y9e/fy9Hk/LTc3jrSyujGUl3YNTZItQujp4UMYNUrdHjdOrcuSlVq1YO5cdfv999XkixCi6Bo/fjx9+vTh66+/pmbNmgQGBjJw4EASExPp379/umNHjRpFSEhIlg8vL69050RHR5Oamoqnp2e6/Z6enkRGRuYoxrS0NN566y1atGhB7dq1dfv9/PxYtmwZW7duZeHChYSHh9OqVSvi4+Nz+WkIIYSAJ1OI/P3VlYee5uQE2vy59jiRPwrTzZyZM2eycuVKFi9ezPnz51m3bh2tW7cGICIigiZNmvDw4UM2bNjAyZMnGTduHE5OTiiKgqIofPfdd4SGhrJs2TL++OMPfvjhB13bISEhODs7U+G/u8dhYWE0bNiQypUr66Yfh4WFMWnSpAxxmeLG0fNuDBnqhpShyNLPQujp00/h0iUoUwY++yxn54wYoY6G+f13ePFFOH4cnJ2NG6cQRYmdHSQkmO7aORUVFcW+ffvYs2dPuv329vZoNJoMx7u5uWUYWZIfxo4dy+nTpzPUY+ncubNuu27duvj5+eHj48Pq1asZPnx4focphBBFhnaKkHYK0bP69YMtW9S+4gcf5F9covDYtm0b3bt3p+1/S1f5+PjQvHlzAEaPHk3Tpk1ZvXq17vgqVarotj/55BPdto+PDwEBAemK5IeEhKQb1TJmzBjGjBnDtGnTdPvefffdTJMto0aNynAz6Vn63Dg6l83wLu2NoWrVqnHr1i2mTZtGq1atOH36NPHx8blu1xgk2SKEHk6dgq++UrfnzwdHx5ydp9HA4sXqqJbwcHj9dVixQt0vhMieRgP29qaOIntHjx4lLS0tXYdFu79Ro0YZjp8xYwYzZszIss3Q0FDKlSun+9nd3R1zc3OioqLSHRcVFUWpUqWyjXHcuHFs3LiR4OBgypQpk+WxLi4uVK1albCwsGzbFUIIkbmrV9X6fRoN9O6d+TE9e8LIkXDiBFy8CE/9niyMqLDczAHo0aMH7733HkeOHOGFF16gb9++uLq6EhERwZYtWzh+/Him50VERPDll1+yZ88ebty4QUpKCo8ePeLzzz/XHfN0siUiIoLt27ezb98+Zj1VAyE1NZWyZctmaD+/bxxldWPo6dcKAkm2CJFDqanqCJXHj6FPH/VLUR8uLrByJbRsCatWqZXo5UaxEEVL2n9VsBMTE3H8Lxt78uRJgoODmT59eobjc3M3yMrKioYNGxIUFKRbojEtLY2goCDGjRv33HYUReGNN95g3bp17N69WzdUOCsJCQlcunSJQYMGZXusEEKIzGmnBrVqBU/nxD09PZk8eTKWlpaYmUH79vD33+rxkyebJtbiprDczAF1aeYePXqwfv165syZo0u8nDx5Eisrq0yL2965c4fGjRvTrl07Zs+ejbe3N6mpqTRq1CjdjaGQkBC6dOkCqLXn3NzcOHToUIb2bG1tM+wzxY2jpz19Y8iQ7RqCJFuEyKFFi9S7Eo6O8M03uWujaVN16tHkyTB+PHTvnnHerhCi8PLz88PW1pZJkybxwQcfcOnSJcaOHcvYsWNp2rRphuNzezdowoQJDBkyhEaNGtGkSRPmzp1LYmIiw4YN0x0zf/581q1bR1BQEKBOHfrtt9/4888/cXR01NV3cXZ21nWeJk6cSPfu3fHx8eHmzZtMnToVc3NzBg4cmJuPQwghBM+fQmRmZoa1tbXu53791GTLmjWSbBGZq1q1Ku+++y7jx4/HycmJ0NBQLC0tefz4MQ8ePMDumeEyf/31F6mpqaxYsUI3nXn+/PmkpKTokjPx8fFcvnxZ97OlpSXx8fF4eXllaC8z+XnjKDNP3xgyZLsGoYgMYmNjFUCJjY01dSiigIiOVhRHR0UBRVmwIG9tpaYqSqNGalvvv2+Y+IQoah4+fKiEhoYqDx8+NHUoevvrr7+UqlWrKpaWlkqlSpWUr776SklNTTX4debNm6eUK1dOsbKyUpo0aaIcPHgw3etTp05VfHx8dD8DmT6WLl2qO2bAgAFK6dKlFSsrK8Xb21sZMGCAEhYWlm0sWf15yXdq/pPPXIiC4/p1tc8HinLjRvrXoqOjlZ9//lmJjo5WFEVRbt9WFDMz9dhLl0wQbBFXmPsWX3zxhbJ8+XIlNDRUOXfunPL2228rpUqVUmJiYpTo6GjF1dVVGTx4sBIaGqqcOXNGWbhwoXLhwgVl/fr1ioWFhbJ+/XrlwoULyqxZsxR3d3fF29tb1/bevXsVCwsL5dGjR4qiKMrdu3eVEiVKKH379lVCQkKUixcvKlu2bFHefPNNg76nlStXKtbW1sqyZcuU0NBQZeTIkYqLi4sSGRmpO2bevHlKu3bt0p33zjvvKLt371bCw8OV/fv3KwEBAYq7u7ty+/btHLf7LGP1Y2RkixA58NtvEB8Pdeo8WYkot8zM1FWJ+vSBBQvgvffUKvRCiKKhW7dudOvWzejXGTduXJZ3aT7++GM+/vhj3c9KDtaYXLlypSFCE0II8Z+1a9XnFi3gmZv7JCcnc+nSJZKTkwEoWVJdrWjnTnUqUSa1SEUx9ejRIz777DOuXr2Kg4MDLVq0YOfOnbi6ugLqCJZJkybRuHFjrKysaNq0KQMHDqRSpUoMHz6cQYMGYWtryyuvvEL//v2JiIjQtR0SEkL16tV1o6zc3NzYvHkz7733Hq1bt0ZRFKpUqcKQIUMM+p4GDBjAnTt3+Oijj4iMjKRevXps3bo1XXHb6OhoLl26lO6869evM3DgQO7evUvJkiVp2bIlBw8epGTJkjluN79olJz0voqZuLg4nJ2diY2NxUl+CxZAo0Zw9Kg6feiNN/LeXloa1K4NZ8/C55+rCRchxBOPHj0iPDycChUqYGNjY+pwRDay+vOS71RYsGABX331FZGRkfj6+jJv3jyaNGny3OPv37/PBx98wNq1a4mJicHHx4e5c+fq5tNnRz5zIQqONm0gOBjmzIG33kr/2q1bt1i8eDEjR46kdOnSgDptffRoaNJEnb4uDEf6FuJ5jNWPMTNkkEIURadPq4kWS0swVNkCM7Mnc3HnzIGHDw3TrhBCiIJl1apVTJgwgalTp3Ls2DF8fX0JDAzk9u3bmR6fnJxMhw4duHLlCmvWrOH8+fN8//33eHt753PkQoi8unUL9u5Vt/v0ydk5vXurRVsPH4anBh8IIQohSbYIkY3ly9Xnbt3A3d1w7Q4cCD4+EBUFS5carl0hhBAFx+zZsxkxYgTDhg2jZs2aLFq0CDs7O5YsWZLp8UuWLCEmJob169fTokULypcvT5s2bTIsJy6EKPjWrVOrtfj5wVMLsWTJ0xNat1a3tYV1hRCFkyRbhMjC48fw88/qtoGnKWJpCRMnqttffaVeSwghRNGRnJzM0aNHCQgI0O0zMzMjICCAAwcOZHrOhg0baNasGWPHjsXT05PatWszY8YMUlNTn3udpKQk4uLi0j2EEKa3Y4f6/N+iKBk4OTnRuXPnDFMTevdWn/9bTE4IUUhJskWILPz9tzrypGRJyOFUeb28+qra9pUrIHUphRCiaImOjiY1NTVDUT5PT0/d0tvPunz5MmvWrCE1NZXNmzfz4YcfMmvWLKZPn/7c68ycORNnZ2fdo2zZsgZ9H0KI3Dl+XH3288v8dXt7e5o0aYK9vX26/dqSTiEhxotNCGF8kmwRIgvLlqnPL72kjkQxNDu7J8XSPv9cLZwrhBCi+EpLS8PDw4PFixfTsGFDBgwYwAcffMCiRYuee86UKVOIjY3VPa5du5aPEQshMnPvnnozDaBevcyPefjwISdPnuThM8X76tZV67bcuqXe9BNCFE6SbBHiOWJi4M8/1e2hQ413nTFj1KWfz5yBv/4y3nWEEELkL3d3d8zNzYl65relqKgoSpUqlek5pUuXpmrVqpibm+v21ahRg8jISN3ysM+ytrbGyckp3UMIYVonTqjP5cvDf6vzZnD//n3WrVvH/fv30+23t4dq1dRt7egYIUThI8kWIZ5j1SpITlbvLjzvjoQhuLioCRdQa7cIIYQoGqysrGjYsCFBTxVeSEtLIygoiGbNmmV6TosWLQgLCyPtqaGOFy5coHTp0lhZWRk9ZiGEYWiTJPXr5+58bd9TphIJUXhJskWI59BOITLmqBat8ePV5aD374cLF4x/PSGEEPljwoQJfP/99yxfvpyzZ88yevRoEhMTGTZsGACDBw9mypQpuuNHjx5NTEwMb775JhcuXGDTpk3MmDGDsWPHmuotCCFyQZtsye0NO22SRka2CFF4WZg6ACEKorNn4fBhsLCAl182/vVKl4bAQNiyRV396NNPjX9NIYQQxjdgwADu3LnDRx99RGRkJPXq1WPr1q26orlXr17FzOzJva+yZcuybds23n77berWrYu3tzdvvvkm7733nqneghAiF7QjUnI7skWSLUIUfjKyRYhMLF+uPnfuDB4e+XNN7dLSP/8shXKFENlbsGAB5cuXx8bGBj8/Pw4fPpztOR9//DEajSbdo3r16nluV2Rt3LhxREREkJSUxKFDh/B7ammS3bt3s0w7lPI/zZo14+DBgzx69IhLly7x/vvvp6vhIoQo2B49gtBQdTurZIulpSVlypTBMpNVGLQjYsLCID7e8DEKIYzPpMmW4OBgunfvjpeXFxqNhvXr12d5/O7duzN0EjUaTYblE6WjKPJCUeDXX9Xt/JhCpNWjh1ooNyICgoPz77pCiMJn1apVTJgwgalTp3Ls2DF8fX0JDAzk9u3b2Z5bq1Ytbt26pXvs27fPIO0KIYRQnT4NqalQogR4ez//OHd3d4YPH467u3uG10qWVM9VFDh50ojBCvGfiRMn0qtXL1OHUaSYNNmSmJiIr68vCxYs0Ou88+fPp+soejw19EA6iiKvQkPh+nWwsYEuXfLvura20L+/uv3TT/l3XSEKA0VRSExONMlDURS94z148CDt27enRIkSGW4QxMXF5fnzmD17NiNGjGDYsGHUrFmTRYsWYWdnx5IlS7I918LCglKlSukeT3fy89KuEEII1dNTiDSa3LcjU4lEfgoJCaGeMVcFeYa+Ay+elt3gioIy+MKkNVs6d+5M586d9T7Pw8MDFxeXTF97uqMIsGjRIjZt2sSSJUuYPHlypuckJSWRlJSk+9kQHWFReG3frj63bq0mXPLTkCHwww/w++8wb5669J8QAh6kPMBhpoNJrp0wJQF7q5z/Yzxx4gT+/v6MGzeOefPmce3aNV566SXq16/PyJEj0y3LO2PGDGbMmJFle6GhoZQrV073c3JyMkePHk1XVNXMzIyAgAAOHDiQbXwXL17Ey8sLGxsbmjVrxsyZMylXrlye2xVCCKHK6UpEt27dYvHixYwcOZLSpUtneL1ePdi4UZItIn+cOHGCcePG5dv1tAMvXn31Vfr06ZPj87SDKxYtWoSfnx9z584lMDCQ8+fP4+Hhke3r+alQ1mypV68epUuXpkOHDuzfv1+3X9tRDAgI0O3LSUdx5syZODs76x5ly5Y1avyiYNMmW576a5RvWrSAChUgIQH0SO4KIQqQ8ePH06dPH77++mtq1qxJYGAgAwcOJDExkf7a4Wv/GTVqFCEhIVk+vLy80p0THR1NamqqrsCqlqenZ4Zptc/y8/Nj2bJlbN26lYULFxIeHk6rVq2Ij4/PU7tCCCGeyOtKRFraZI0s/2w8hWnkbFpaGjNmzKBKlSrY2Njg6enJ0KdqHly9epUhQ4bg6emJra0tvr6+uqnCU6dOpU6dOtjb2+Pp6cno0aNJSUnRnXv9+nWio6Px9fVN195LL72Eq6srbm5uvPzyy9y7dy9vH/hTOnfuzPTp0+ndu7de52U3CrcgjdItVKsRlS5dmkWLFtGoUSOSkpL44Ycf8Pf359ChQzRo0CDLjuK5c+ee2+6UKVOYMGGC7ue4uDhJuBRTycmwZ4+63aFD/l9fo4HBg2HaNHUqUX6shCREYWBnaUfClASTXTunoqKi2LdvH3u0/5H8x97eHk0mY8nd3Nxwc3PLc4w59fRo0rp16+Ln54ePjw+rV6/O1UhTIYQQ6aWmPqmxktuViLS0558+DSkpkEkdXZFHhWnk7MyZM1m1ahWLFy+mYsWK3LhxQ/c7bkREBH5+frRu3ZoNGzbg5ubG7t27cXJyQlEUFEXhu+++w9vbm9DQUIYMGULdunUZPXo0oE4hcnZ2pkKFCgCEhYXRrFkzRo8ezcGDB0lISGDMmDFMmjSJH374IV1cuRmlm1vZjcItaKN0C1WypVq1alSrVk33c/Pmzbl06RJz5szh559/znW71tbWWFtbGyJEUcgdPAiJiWpRsrp1TRODNtmyYwfcuJF1YTUhiguNRqNXh8RUjh49SlpaWro7Q9r9jRo1ynB8bjoo7u7umJubExUVle64qKgoSpUqpVe8Li4uVK1albCwMIO2K4QQxVVYmNqXtLWFqlXz1lb58uDsDLGxak3BZ75aRDGzbds2unfvTtu2bQHw8fGhefPmAIwePZqmTZuyevVq3fFVqlTRbX/yySe6bR8fHwICAjh//rxuX0hISLq+y5gxYxgzZgzTpk3T7Xv33XeZNGlShrhGjRqVYeTus54dpZtb2Q2uyO3gC2MpVMmWzDRp0kQ3PEo6iiKvtFOI2rcHMxNNsqtYEVq2hH371FWR3n3XNHEIIfSX9t+67YmJiTg6OgJw8uRJgoODmT59eobjc9NBsbKyomHDhgQFBelWDUhLSyMoKEjvudYJCQlcunSJQYMGGbRdIYQorrRTiOrWhbyu2K7RqFOR9uxRpxJJssXwCsvIWYAePXrw3nvvceTIEV544QX69u2Lq6srERERbNmyhePPKe4TERHBl19+yZ49e7hx4wYpKSk8evSIzz//XHfM08mWiIgItm/fzr59+5g1a5bumNTU1Exnf+T3KN3CpNAnW0JCQnQFpaSjKPJqxw712RRTiJ42ZIiabFm+HCZNylsleyFE/vHz88PW1pZJkybxwQcfcOnSJcaOHcvYsWNp2rRphuNz20GZMGECQ4YMoVGjRjRp0oS5c+eSmJioKw4PMH/+fNatW0dQUJBu38SJE+nevTs+Pj7cvHmTqVOnYm5uzsCBA3PcrhBCiOd7eiWi7JQsWZI33ngjXeH0Z9WvryZbjh9X+4fCsArLyFlQv8N79OjB+vXrmTNnji7xcvLkSaysrDJdSejOnTs0btyYdu3aMXv2bLy9vUlNTaVRo0bpRrKEhITQ5b9lWE+cOIGbmxuHDh3K0J6trW2Gffk5jSi7wRUFbfCFSZMtCQkJhIWF6X4ODw8nJCQENzc3ypUrx5QpU7hx4wY//bcO7ty5c6lQoQK1atXi0aNH/PDDD+zcuZO///5b14Z0FEVu3b8P2lXBTJ1seeEFGDdOHTJ67Bg0bGjaeIQQOVOyZElWr17NO++8Q926dSlXrhzjxo1LVxfMEAYMGMCdO3f46KOPiIyMpF69emzdujXdsNno6GguXbqU7rzr168zcOBA7t69S8mSJWnZsiUHDx6kZMmSOW5XCCHE8+V0JSIACwuLbBPu2t+fZUUiAVC1alXeffddxo8fj5OTE6GhoVhaWvL48WMePHiAnV360TJ//fUXqamprFixQlc7bv78+aSkpOiSM/Hx8Vy+fFn3s6WlJfHx8Xh5eWVoLzP5OY0ou8EVBW7whWJCu3btUoAMjyFDhiiKoihDhgxR2rRpozv+iy++UCpVqqTY2Ngobm5uir+/v7Jz584M7c6bN08pV66cYmVlpTRp0kQ5ePCgXnHFxsYqgBIbG5uXtycKmbVrFQUUpVo1U0eiGjBAjefNN00diRD57+HDh0poaKjy8OFDU4ciciCrPy/5Ts1/8pkLYRppaYpSsqTafzt8OPvjY2JilD/++EOJiYl57jEnTqjtOTmp7YvcK8x9iy+++EJZvny5Ehoaqpw7d055++23lVKlSikxMTFKdHS04urqqgwePFgJDQ1Vzpw5oyxcuFC5cOGCsn79esXCwkJZv369cuHCBWXWrFmKu7u74u3trWt77969ioWFhfLo0SNFURTl7t27SokSJZS+ffsqISEhysWLF5UtW7Yobxr4l5L4+Hjl+PHjyvHjxxVAmT17tnL8+HElIiJCd8y8efOUdu3apTtv5cqVirW1tbJs2TIlNDRUGTlypOLi4qJERkbm6PXMGKsfY9KRLf7+/lkuebVs2bJ0P7/77ru8m4MCFuPGjZNpQ0JvplzyOTMvvQSrVsHatTBnjkwlEkIIIYQoyG7dgjt31FottWtnf/yjR484deoUzZo1e+4xNWqAlRXExUF4uFrbTxQ/jx494rPPPuPq1as4ODjQokULdu7ciaurK6COYJk0aRKNGzfGysqKpk2bMnDgQCpVqsTw4cMZNGgQtra2vPLKK/Tv35+IiAhd2yEhIVSvXl23YIybmxubN2/mvffeo3Xr1iiKQpUqVRhi4HlsR44c0RX7BXSjgIcMGaLLA2Q2Sje7UbgFaZSuRskq21FMxcXF4ezsTGxsbJZzKEXRUrUqXLwI69dDz56mjgYePlRXRUpMhH//hUwWMhGiyHr06BHh4eFUqFABGxsbU4cjspHVn5d8p+Y/+cyFMI1Nm6BbN6hVS12uOTu3bt1i8eLFjBw5UleDMjMNG6rTytesgb59DRhwMSN9C/E8xurHmGi9FSEKlogINdFibg7+/qaORmVrC506qdvr1pk2FiGEEEIIkTV96rXoQ9ue1G0RonCRZIsQPJlC5OcHzs6mjeVpvXurz5JsEUIIIYQo2LQrEWWyKEyeaNvTti+EKBwk2SIEBa9ei1bXrmBhAWfPwvnzpo5GCCGEEEI8j74jWxwcHGjTpg0ODg5ZHicjW4QonCTZIoq9tDQIClK3Tb3k87NcXKBdO3VbRrcIIYQQQhRMsbFw+bK6ndORLY6Ojvj7++Po6JjlcXXrqgsl3LwJt2/nLU4hRP6RZIso9kJC4O5dcHBQpxEVNDKVSAghhBCiYDtxQn0uVw7c3HJ2TlJSEmFhYSQlJWV5nKMjVK6sbstUIiEKD0m2iGJPO4XI3x8sLU0aSqZ69lTvZhw+DDdumDoaIYQQQgjxrFOn1Gdf35yfExMTw6+//kpMTEy2x2pHy5w8qX9sQgjTkGSLKPZ27VKfC1q9Fq3SpaFpU3V7/XqThiKEEEIIITJx9qz6XLOmcdqvUUN9lhp+QhQekmwRxVpaGhw6pG63bGnaWLIiU4mEEEIIIQquc+fU5+rVjdO+tl1tUkcIUfBJskUUaxcvwv37YGOjFh8rqLTJlt27IQcjTYUQRVxwcDDdu3fHy8sLjUbDej2GvS1YsIDy5ctjY2ODn58fhw8f1ut1IYQQGWmTLdoRKIambVd7HSFEwSfJFlGsaUe1NGhQMOu1aFWuDLVrQ2oqbNxo6miEEKaWmJiIr68vCxYs0Ou8VatWMWHCBKZOncqxY8fw9fUlMDCQ2/8tb5Hd60IIITKKi3tSV69atZyfZ25ujqurK+bm5tkeW7Wq+nz3Lty5k4sghRD5TpItoljTJlsK4ipEz5KpRKJYUxR4nGiah6LoHe7Bgwdp3749JUqUQKPRpHvExcXl+ePo3Lkz06dPp7f2P4Ycmj17NiNGjGDYsGHUrFmTRYsWYWdnx5IlS3L0uhBCiIy0dVRKlQIXl5yf5+Hhwfjx4/Hw8Mj2WDs78PFRt2V0izCGiRMn0qtXL1OHUaRIskUUa4Ux2bJtGzx4YNpYhMh3qQ9gtYNpHqn6/YM7ceIE/v7+1K9fn71797J161bc3Nxo3749q1atwsnJSXfsjBkzcHBwyPJx9epVg3yEycnJHD16lICnqoGbmZkREBDAgQMHsn1dCCFE5rR1VIw1hUhL277UbRHGEBISQj3tslf5YObMmTRu3BhHR0c8PDzo1asX53NYAbqwTImWZIsoth4+hBMn1O3CkGypV0+9o/HwoZpwEUIUTOPHj6dPnz58/fXX1KxZk8DAQAYOHEhiYiL9+/dPd+yoUaMICQnJ8uHl5WWQuKKjo0lNTcXT0zPdfk9PTyIjI7N9XQghROZyWxw3KiqKr776iqioqBwdr21fRrYIYzhx4kS+Jlv27NnD2LFjOXjwINu3byclJYWOHTuSmJiY5XmFaUq0Rb5fUYgC4vhxePwYPDyeDMssyDQa6NkTvvkGNm9+MtJFiGLB3A76J5ju2jkUFRXFvn372LNnT7r99vb2aDSaDMe7ubnh5uaW5xCFEEKYTm5HtqSlpfHgwQPS0tJydLyMbDECRdF7BKvBmNupHfwcSktL4/PPP2fp0qVcu3YNZ2dnOnfuzLJlywC4evUqH374IVu3biUuLo6qVauyYMECWrZsydSpU1m7di2XL1/GwcGBPn368M0332D5X9HK69evEx0dja+vr+56V69eZfLkyWzZsgWNRkPnzp2ZP38+rq6uBnn7W7duTffzsmXL8PDw4OjRo7Ru3fq55z095Rlg0aJFbNq0iSVLljB58uRsX89PkmwRxZZ2NJmfn17/z5lU165Pki2KUnjiFiLPNBqwsDd1FNk6evQoaWlp6Tor2v2NGjXKcPyMGTOYMWNGlm2GhoZSrly5PMfm7u6Oubl5hjuoUVFRlCpVKtvXhRBCZM7Yyz5rycgWI9BOUzaF/gl69W1mzpzJqlWrWLx4MRUrVuTGjRuc++8vQ0REBH5+frRu3ZoNGzbg5ubG7t27cXJyQlEUFEXhu+++w9vbm9DQUIYMGULdunUZPXo0oE4hcnZ2pkKFCgCEhYXRrFkzRo8ezcGDB0lISGDMmDFMmjSJH374IV1churLxMbGAmR5E0o75XnKlCm6fZlNiX7e6/lNki2i2CpM9Vq0WrdWC6TdvAknT8Izv88JIUxMe3cyMTERR0dHAE6ePElwcDDTp0/PcPyoUaMyTC16lqGmEVlZWdGwYUOCgoJ0BfDS0tIICgpi3Lhx2b4uhBAio5QUCAtTt/OrZktEhFq/zy7nAy9FEbBt2za6d+9O27ZtAfDx8aF58+YAjB49mqZNm7J69Wrd8VWqVNFtf/LJJ7ptHx8fAgIC0tVHCQkJSXejaMyYMYwZM4Zp06bp9r377rtMmjQpQ1yG6MukpaXx1ltv0aJFC2rXrv3c47Ka8nzu3LlsX89vkmwRxVZhTLbY2EC7duryz5s3S7JFiILGz88PW1tbJk2axAcffMClS5cYO3YsY8eOpWnTphmOz+00ooSEBMK0vXsgPDyckJAQ3NzcdHeO5s+fz7p16wgKCtIdN2HCBIYMGUKjRo1o0qQJc+fOJTExUTfUNrvXhRBCpHfpkjot3cEBvL2Ney13d3Bzg5gYuHBBrecn8qiQTFMG6NGjB++99x5HjhzhhRdeoG/fvri6uhIREcGWLVs4fvx4pudFRETw5ZdfsmfPHm7cuEFKSgqPHj3i888/1x3zdLIlIiKC7du3s2/fPmbNmqU7JjU1lbJly2Zo3xBToseOHcvp06fZt29fntopaCTZIoqlO3cgPFydmdC4samj0U+XLk+SLU+NkBNCFAAlS5Zk9erVvPPOO9StW5dy5coxbtw4JkyYYNDrHDlyRHdnC9C1P2TIEN3c7ejoaC5dupTuvAEDBnDnzh0++ugjIiMjqVevHlu3btXdAcrudSGEEOlp66dUr67/9O4SJUrw6quvUqJEiRwdr9Goo1v271enEkmyxQAKyTRlUJdm7tGjB+vXr2fOnDm6xMvJkyexsrLKtLjtnTt3aNy4Me3atWP27Nl4e3uTmppKo0aN0o1kCQkJoUuXLoBaKNfNzY1D2jvTT7G1tc2wL6/TiMaNG8fGjRsJDg6mTJkyWbZT2KZES7JFFEva/zuqVwdnZ9PGoq/OndXnf/6Be/fAQDWqhBAG0q1bN7p162bUa/j7+6MoSpbHfPzxx3z88ccZ9o8bNy7LaUHZvS6EEOKJvNRrsbKyynSkQFaqV1eTLVIkt3iqWrUq7777LuPHj8fJyYnQ0FAsLS15/PgxDx48wO6ZuWV//fUXqamprFixQleof/78+aSkpOiSM/Hx8Vy+fFn3s6WlJfHx8Xh5eWVoLzO5nUakKApvvPEG69atY/fu3bp6MVkpbFOiJdkiiiVtsqVJE9PGkRvly0PNmhAaCtu3Qzb/twkhhBBCCCPJ7UpEAHFxcRw4cIBmzZrh5OSUo3O015EiucXLl19+SalSpWjcuDFmZmZ89913lChRgubNm5OWloazszOjR49m8uTJKIpCcHAw7du3p0SJEsTFxbFhwwZq1qzJX3/9xcyZM/H29qZkyZKAOpLF3NycWrVqAeqUaCcnJwYPHsyHH36Ivb09YWFhbN26lblz52aILbfTiMaOHctvv/3Gn3/+iaOjI5GRkQA4OzvrRtAU9inRkmwRxVJhrNfytC5d1GTL5s2SbBFCCCGEMJW8jGxJTEzk4MGD1K1bN8fJFu11ZGRL8fLo0SM+++wzrl69ioODAy1atGDnzp26ZZj/+usvJk2aROPGjbGysqJp06YMHDiQSpUqMXz4cAYNGoStrS2vvPIK/fv3JyIiQtd2SEgI1atXx9raGlCTJ5s3b+a9996jdevWKIpClSpVGDJkiEHf08KFCwF1tO7Tli5dytChQ4HCPyVao2Q3DrkYiouLw9nZmdjY2Bz/xycKj7Q0tbhYbCwcPQoNGpg6Iv3t2qUWyvXwgFu3wMzM1BEJYViPHj0iPDycChUqYGNjY+pwRDay+vOS79T8J5+5EPlDUdTp6PHxcOaMOvJYH7du3WLx4sWMHDmS0qVL5+icS5egcmWwtobERDA3z0XgxZT0LcTzGKsfI7+iiWLnwgU10WJjA3XqmDqa3GnRAhwd4fZtOHbM1NEIIYQQQhQ/N2+qiRZzczUBkh/Kl1cTLUlJcOVK/lxTCJE7kmwRxY52ClHDhmBpadpYcsvKCjp0ULc3bzZtLEIYkwy+LBzkzyl7CxYsoHz58tjY2ODn58fhw4efe+yyZcvQaDTpHnIXVoiCRzuVp1IltW+WH8zNoWpVdVvqtghRsEmyRRQ7hb1ei5Z2VSJJtoiiyPK/TOiDBw9MHInICe2fk2VhzWAb2apVq5gwYQJTp07l2LFj+Pr6EhgYyO3bt597jpOTE7du3dI9np5fL4QoGLTJjtwUxwWws7OjUaNGOVrx5Wna60ndFiEKNimQK4od7c3EopJsOXwYoqPB3d208QhhSObm5ri4uOh+GbWzs9MtWSgKDkVRePDgAbdv38bFxQVzKR6QqdmzZzNixAjdSgiLFi1i06ZNLFmyhMmTJ2d6jkajoVSpUvkZphBCT9pkR26K44K66krXrl31Pk97PRnZIkTBJskWUaw8fAgnTqjbhT3Z4u0Nvr7q+9m2DV5+2dQRCWFY2l80s7r7LwoGFxcXSQw8R3JyMkePHmXKlCm6fWZmZgQEBHDgwIHnnpeQkICPjw9paWk0aNCAGTNm6JblfFZSUhJJSUm6n+Pi4gz3BoQQz5XXkS0pKSlER0fj7u6u18hAWf45b2Tqq3iWsf5OSLJFFCvHj8Pjx+DpCeXKmTqavOvSRU22bN4syRZR9Gg0GkqXLo2HhwcpKSmmDkc8h6WlpYxoyUJ0dDSpqakZlpz09PTk3HN+U6pWrRpLliyhbt26xMbG8vXXX9O8eXPOnDlDmTJlMhw/c+ZMpk2bZpT4hRDPl9eRLdHR0XqvRvT09c6eVVdEkoGfOfP0FGVbW1sTRyMKEmNNh5ZkiyhWnq7XUhS+mLp0gZkzYetWSE2V5f9E0WRubi6/zItipVmzZjRr1kz3c/PmzalRowbfffcdn376aYbjp0yZwoQJE3Q/x8XFUbZs2XyJVYjiKjYWbt1St3ObbMmtqlXVfmxMjDqVvGTJ/L1+YSVTlMWzjD0dWpItolg5flx9btjQtHEYStOm4OKiftn++6/6sxBCiILD3d0dc3NzoqKi0u2PiorK8dQrS0tL6tevT1hYWKavW1tbY21tnedYhRA5px2YVro0ODvn77Xt7MDHR136+exZSbboQ6Yoi8wYazq0JFtEsaKt11KvnknDMBgLCwgIgDVr4O+/JdkihBAFjZWVFQ0bNiQoKIhevXoBkJaWRlBQEOPGjctRG6mpqZw6dYouXboYMVIhhD7yWq8lr2rUUJMt585B69amiaEwkinK4lnGnA5t0mRLcHAwX331FUePHuXWrVusW7dO1xHJzNq1a1m4cCEhISEkJSVRq1YtPv74YwIDA3XHfPzxxxnmLVerVu2586JF8ZGc/GRura+vaWMxpI4dnyRbPvrI1NEIIYR41oQJExgyZAiNGjWiSZMmzJ07l8TERN3qRIMHD8bb25uZM2cC8Mknn9C0aVMqV67M/fv3+eqrr4iIiOC1114z5dsQQjwlr/VaQP3F38rKKldTWapXhy1bZPnn3JIpyiI/mDTZkpiYiK+vL6+++ip9+vTJ9vjg4GA6dOjAjBkzcHFxYenSpXTv3p1Dhw5Rv3593XG1atVix44dup8tLGQAj1C/jFJS1Gk3RaE4rlbHjurzwYPq/OH8HsoqhBAiawMGDODOnTt89NFHREZGUq9ePbZu3aormnv16lXMzMx0x9+7d48RI0YQGRmJq6srDRs25J9//qFmzZqmegtCiGdo7+PmJdlSqlSpdCuV6UOWfxai4DNpFqJz58507tw5x8fPnTs33c8zZszgzz//5K+//kqXbLGwsJAlKEUGISHqs69v0SiOq+XjA9WqwfnzsHMn9O5t6oiEEEI8a9y4cc+dNrR79+50P8+ZM4c5c+bkQ1RCiNwqCNOIQEa2CFGQmWV/SMGVlpZGfHw8bm5u6fZfvHgRLy8vKlasyMsvv8zVq1ezbCcpKYm4uLh0D1H0aOu1FKUpRFra0S1//23aOIQQQgghirrkZNDWq87LyJY7d+7w7bffcufOHb3P1V43IgISE3MfgxDCeAp1suXrr78mISGB/v376/b5+fmxbNkytm7dysKFCwkPD6dVq1bEx8c/t52ZM2fi7Oyse8hyiUWTJFuEEEIIIUReXboEqang4ADe3rlv5/Hjx9y5c4fHjx/rfW7JklCihLp98WLuYxBCGE+hTbb89ttvTJs2jdWrV+Ph4aHb37lzZ1544QXq1q1LYGAgmzdv5v79+6xevfq5bU2ZMoXY2Fjd49q1a/nxFkQ+UpQn04iKykpET/P3B0tLuHz5yZ0WIYQQQghheE/XazHl1HSp2yJEwVYoky0rV67ktddeY/Xq1QQEBGR5rIuLC1WrViUsi99Ara2tcXJySvcQRcuNGxATA+bmUBTrCzo4QIsW6raMbhFCCCGEMB5DFMc1BEm2CFGwFbpky4oVKxg2bBgrVqyga9eu2R6fkJDApUuXKF26dD5EJwoq7RSi6tXBxsa0sRiLTCUSQgghhDA+bXKjWjXTxiHJFiEKNpMmWxISEggJCSHkv/kd4eHhhISE6AraTpkyhcGDB+uO/+233xg8eDCzZs3Cz8+PyMhIIiMjiY2N1R0zceJE9uzZw5UrV/jnn3/o3bs35ubmDBw4MF/fmyhYivIUIi1tsmXnTnWJayGEEEIIYXjnz6vPeR3Z4urqyosvvoirq2uuztcme7TxCCEKFpMmW44cOUL9+vV1yzZPmDCB+vXr89FHHwFw69atdCsJLV68mMePHzN27FhKly6te7z55pu6Y65fv87AgQOpVq0a/fv3p0SJEhw8eJCSJUvm75sTBUpRLo6rVb8+uLtDfDwcPGjqaIQQQgghih5FMdw0IhsbG6pVq4ZNLodda69//jykpeUtFiGE4VmY8uL+/v4oivLc15ctW5bu5927d2fb5sqVK/MYlSiKikOyxcwMOnSAFSvUqUStWpk6IiGEEEKIoiUqCmJj1X5X5cp5ayshIYHjx49Tv359HBwc9D6/QgV1gYSHD+HaNfDxyVs8QgjDKnQ1W4TQV2LikyXxinKyBaRuixBCCCGEMWlHtVSokPc6gPHx8ezcuZP4+PhcnW9hAVWqpI9LCFFwSLJFFHmnTqlDPkuVAk9PU0djXB06qM///gt375o2FiGEEEKIoqagrESkJUVyhSi4JNkiirziMIVIy9sbatVSk0tBQaaORgghhBCiaJFkixAipyTZIoo8bbKlKK9E9LTAQPVZphIJIYQQQhhWQVn2WUuSLUIUXHonW3bt2mWMOIQwGu2yz8VhZAs8qduybZs6wkUIIYR+pK8jhHgeQ45ssbGxoWbNmrlejejpOCTZIkTBo3eypVOnTlSqVInp06dz7do1Y8QkhMGkpcHJk+p2cUm2tGoF1tZw/bq6FKAQQgj9SF9HCJGZBw/g6lV12xDJFldXV1544QVcXV1z3YZ2hE1kpLpKkhCi4NA72XLjxg3GjRvHmjVrqFixIoGBgaxevZrk5GRjxCdEnly+rK5GZG0NVauaOpr8YWcHLVqo29u3mzYWIYQojKSvI4TIzMWL6qhhNzdwd897e6mpqcTFxZGamprrNpycoHRpdVtusglRsOidbHF3d+ftt98mJCSEQ4cOUbVqVcaMGYOXlxfjx4/nhLZAhhAFgHYKUZ066vJ4xYV2VSJJtgghhP6kryOEyMzTU4g0mry3d/v2bebMmcPt27fz1I5MJRKiYMpTgdwGDRowZcoUxo0bR0JCAkuWLKFhw4a0atWKM2fOGCpGIXKtOK1E9DRtsmX3bkhJMWkoQghRqElfRwihVdBWItKSZIsQBVOuki0pKSmsWbOGLl264OPjw7Zt25g/fz5RUVGEhYXh4+PDCy+8YOhYhdBbcU221K8PJUpAfDwcOmTqaIQQovCRvo4Q4lmSbBFC6EPviRVvvPEGK1asQFEUBg0axJdffknt2rV1r9vb2/P111/j5eVl0ECFyA3tNKLisuyzlpkZtG8Pq1erU4latjR1REIIUXhIX0cIkZmCtuyzliRbhCiY9E62hIaGMm/ePPr06YO1tXWmx7i7u8uyicLkYmJAu4hE3bqmjcUUOnZ8kmyZNs3U0QghROEhfR0hxLPS0p4UoC2oI1vCwuDx4+JVp1CIgkyjKIqizwnBwcE0b94ci2f+FT9+/Jh//vmH1q1bGzRAU4iLi8PZ2ZnY2FicnJxMHY7Ipd27oW1bKF8ewsNNHU3+u3oVfHzA3Bzu3gVnZ1NHJIQojgrjd2ph7+sUxs9ciIJO26+ytFRXurS0zHubiqKQmpqKubk5mjxU3E1LA0dHdWnqCxegSpW8xyaEUOXlO1Xvmi1t27YlJiYmw/7Y2Fjatm2rb3NCGM3p0+pznTqmjcNUypVTl7tOTQW5+SqEEDknfR0hxLO0U3QqVzZMogVAo9FgYWGRp0QLqNPHtVObZCqREAWH3skWRVEy/Q/h7t272NvbGyQoIQxBu0jEU9Psix1ZAloIIfQnfR0hxLOMURz37t27LFu2jLt37+a5LanbIkTBk+MZfX369AHUDOzQoUPTzWFOTU3l5MmTNG/e3PARCpFL2mRLrVqmjcOUOnSABQsk2SKEEDkhfR0hxPMYI9mSnJxMREQEycnJeW5LRrYIUfDkONni/F/BB0VRcHR0xNbWVvealZUVTZs2ZcSIEYaPUIhcUJQn04iKc7LF31+t2XLxIkREqHONhRBCZE76OkKI5ymoyz5rycgWIQqeHCdbli5dCkD58uWZOHGiDKMVBVpkJNy7p85hLahfivnB2Rn8/OCff9TRLa+9ZuqIhBCi4JK+jhDieQrqss9a2v7u2bPqTcc8loERQhiA3jVbpk6dKp0PUeBppxBVrgw2NqaNxdSkbosQQuhH+jpCiKfFxsKtW+p2QU22VKmiJlju3YPoaFNHI4SAHI5sadCgAUFBQbi6ulK/fv0sK2YfO3bMYMEJkVsyheiJDh1g2jQIClKXBjTTO8UqhBBFn/R1hBDPc/68+lyqFLi4GK5dZ2dnunfvrpvCmBd2dup08StX1FE4JUvmPT4hRN7kKNnSs2dPXZG4Xr16GTMeIQxCiuM+0aQJODrC3btw/Dg0bGjqiIQQouCRvo4Q4nm0yRZDT023s7OjQYMGBmuvenU12XL+PLRqZbBmhRC5lKNky9SpUzPdFqKgkmWfn7C0hLZtYcMGdSqRJFuEECIj6esIIZ7HWMVxHzx4wLlz56hevTp2dnZ5bq96ddi6VYrkClFQ6D2h4Nq1a1y/fl338+HDh3nrrbdYvHixQQMTIrcURUa2PEtbt+Xvv00bhxBCFAbS1xFCPM1YyZbY2Fj++usvYmNjDdKerEgkRMGid7LlpZdeYteuXQBERkYSEBDA4cOH+eCDD/jkk08MHqAQ+rp+HeLiwMICqlY1dTQFgzbZsn8/PHhg2liEEKKgk76OEOJpZ8+qzwW1OK7W0ysSCSFMT+9ky+nTp2nSpAkAq1evpk6dOvzzzz/8+uuvLFu2zNDxCaE37aiWqlXBysq0sRQUVatCuXKQnAzBwaaORgghCjbp6wghtJKT4eJFdbugj5iuWVN9Dg+Xm2tCFAR6J1tSUlJ0BeR27NhBjx49AKhevTq3tGuiCWFCMoUoI40GOnZUt2UqkRBCZE36OkIIrQsX4PFjcHKCMmVMHU3WSpZUH4oio1uEKAj0TrbUqlWLRYsWsXfvXrZv306nTp0AuHnzJiVKlDB4gELoS5Z9zpx2KtH27aaNQwghCjrp6wghtLT9ypo11ZtXhmRlZYWPjw9WBhyKre3/auMWQpiO3smWL774gu+++w5/f38GDhyIr68vABs2bNANuRXClGRkS+bat1c7CadPw82bpo5GCCEKLmP0dRYsWED58uWxsbHBz8+Pw4cP5+i8lStXotFoZDlqIUzEmCtclihRgqFDhxo0iauNUxu3EMJ0crT089P8/f2Jjo4mLi4OV1dX3f6RI0caZMkyIfIiLQ1CQ9VtWfY5vRIl1GWfjxyBHTtg8GBTRySEEAWTofs6q1atYsKECSxatAg/Pz/mzp1LYGAg58+fx8PD47nnXblyhYkTJ9KqVatcvQ8hRN4Z8yaeoiikpqZibm6OxkDDZrRxSrJFCNPTe2QLgLm5ebrOB0D58uWz7DAIkR+uXoXERLUwbuXKpo6m4JG6LUIIkTOG7OvMnj2bESNGMGzYMGrWrMmiRYuws7NjyZIlzz0nNTWVl19+mWnTplGxYkW9rymEMAxjJlsiIyP57LPPiIyMNFibkmwRouDQO9kSFRXFoEGD8PLywsLCAnNz83QPIUxJOz+1WjV16WeRnrZuy44d6iggIYQQGRmyr5OcnMzRo0cJCAjQ7TMzMyMgIIADBw4897xPPvkEDw8Phg8fnu01kpKSiIuLS/cQQuTdo0cQFqZuF5YR09pkS0QExMebNhYhiju9fx0dOnQoV69e5cMPP6R06dIGG/ImhCEYc15tUdCsGdjbQ1QUnDoF/5UhEEII8RRD9nWio6NJTU3F09Mz3X5PT0/OnTuX6Tn79u3jxx9/JCQkJEfXmDlzJtOmTct1jEKIzJ07p96ccnWFUqVMHU3OuLlB6dJw65Y6td7Pz9QRCVF86Z1s2bdvH3v37qVevXpGCEeIvJHiuFmztoY2bWDzZnVVIkm2CCFERqbs68THxzNo0CC+//573N3dc3TOlClTmDBhgu7nuLg4ypYta6wQhSg2nu5XFqb7y7VqqcmWM2ck2SKEKek9jahs2bIoimKQiwcHB9O9e3e8vLzQaDSsX78+23N2795NgwYNsLa2pnLlyixbtizDMbmt+C8KP1n2OXtSt0UIIbJmyL6Ou7s75ubmREVFpdsfFRVFqUxulV+6dIkrV67QvXt3LCwssLCw4KeffmLDhg1YWFhw6dKlDOdYW1vj5OSU7iGEyLvC2q+U5Z+FKBj0TrbMnTuXyZMnc+XKlTxfPDExEV9fXxYsWJCj48PDw+natStt27YlJCSEt956i9dee41t27bpjtFW/J86dSrHjh3D19eXwMBAbt++ned4RcGWmgpnz6rbhe1LMT9p67YEB8PDh6aNRQghCiJD9nWsrKxo2LAhQUFBun1paWkEBQXRrFmzDMdXr16dU6dOERISonv06NFD1/eREStC5B9jT0/38PDg7bffNvgiI1IkV4iCQaPoeevG1dWVBw8e8PjxY+zs7LC0tEz3ekxMTO4C0WhYt24dvXr1eu4x7733Hps2beL0U2naF198kfv377N161YA/Pz8aNy4MfPnzwfUDk3ZsmV54403mDx5co5iiYuLw9nZmdjYWLk7VIiEhUGVKmBjAwkJIPWaM6coULYs3Lihjm7RJl+EEMIYCuN3qqH7OqtWrWLIkCF89913NGnShLlz57J69WrOnTuHp6cngwcPxtvbm5kzZ2Z6/tChQ7l//36ORgBD4fzMhSiIKlWCy5dh505o29bU0eTcgQPQvDl4e8P166aORojCLS/fqXrXbJk7d66+pxjMgQMH0lXzBwgMDOStt94CnlT8nzJliu71nFT8T0pKIikpSfezVPEvnLTZ+xo1JNGSFY1GTbAsWybJFiGEyIyh+zoDBgzgzp07fPTRR0RGRlKvXj22bt2qK5p79epVzMz0HmwshDCixEQID1e3jTVi+t69e+zYsYOAgIAMS83nRc2a6vONG3D/Pri4GKxpIYQe9E62DBkyxBhx5EhkZGSm1fzj4uJ4+PAh9+7d07viP0gV/6KisM6rNYWOHdVky/btpo5ECCEKHmP0dcaNG8e4ceMyfW337t1ZnptZfTohhHGdPauOBi5ZEgw8y0fn0aNHhIaG0rJlS4O26+wMZcqoo1rOnIEWLQzavBAih3J1G+XSpUv83//9HwMHDtTVQtmyZQtnCunEwClTphAbG6t7XLt2zdQhiVyQZZ9zTjtA7MQJdRloIYQQ6RW1vo4QQj+FfYVLqdsihOnpnWzZs2cPderU4dChQ6xdu5aEhAQATpw4wdSpUw0e4NNKlSqVaTV/JycnbG1t9a74ryVV/IuGwv6lmJ9KloT69dVtGd0ihBDpmbKvI4QoGAp7v1J781GSLUKYjt7JlsmTJzN9+nS2b9+OlZWVbn+7du04ePCgQYN7VrNmzdJV8wfYvn27rpq/vhX/RdHx+DFoZ4oV1i/F/CZLQAshROZM2dcRQhQMhT3ZIiNbhDA9vZMtp06donfv3hn2e3h4EB0drVdbCQkJumUNQV3aOSQkhKtXrwLq9J7Bgwfrjh81ahSXL1/m3Xff5dy5c3z77besXr2at99+W3fMhAkT+P7771m+fDlnz55l9OjRJCYmMmzYMH3fqihELl2C5GSwswMfH1NHUzgEBqrPf/8NaWmmjUUIIQoSQ/Z1hBCFk7YWoDGnpzs6OtKuXTscHR0N3rY22fLUIq5CiHymd4FcFxcXbt26RYUKFdLtP378ON7e3nq1deTIEdo+tY7ahAkTALUw3bJly7h165Yu8QJQoUIFNm3axNtvv83//vc/ypQpww8//ECg9rdGsq/4L4qm0FD1uUYNkAUdcqZFC7C3V2u2nDjxZFqREEIUd4bs6wghCp/4eND+CmLMkS0ODg60atXKKG1rVySKioK7d6FECaNcRgiRBb2TLS+++CLvvfcev//+OxqNhrS0NPbv38/EiRPTjULJCX9/fxRFee7rmVXf9/f35/jx41m2m1XFf1E0nT2rPmu/WET2rKygXTv46y/Ytk2SLUIIoWXIvo4QovDR3sQrVQrc3Ix3nUePHhEREYGPjw82NjYGbdvBAcqXhytX1KlErVsbtHkhRA7oPQZgxowZVK9enbJly5KQkEDNmjVp3bo1zZs35//+7/+MEaMQ2dJ+KUqyRT+dOqnPW7eaNg4hhChIpK8jRPGWH1OIAO7du8fKlSu5d++eUdqXqURCmJbeI1usrKz4/vvv+fDDDzl9+jQJCQnUr1+fKlWqGCM+IXJEki25o0227N8PcXEgC3EJIYT0dYQo7gp7cVytWrVg0yYpkiuEqeidbNEqV64c5cqVM2QsQuRKaqpMI8qtihWhcmUIC4Ndu6BnT1NHJIQQBYf0dYQonopSsgUk2SKEqeQo2aItXJsTs2fPznUwQuRGRAQ8egTW1vBMLUORA506wfz56lQiSbYIIYor6esIIbS0yQljTyMyNm38p0+DooBGY9p4hChucpRsebYg7bFjx3j8+DHVqlUD4MKFC5ibm9OwYUPDRyhENrRTiKpVA3Nz08ZSGAUGPkm2yBexEKK4kr6OEALg/n24cUPdNvaIaQsLC0qWLImFRa4nG2SpenW1X3f3Lty+DbI4qxD5K0f/snft2qXbnj17No6OjixfvhxXV1dALe40bNgwoy1dJkRWpF5L3vj7qysTXbkCFy9C1aqmjkgIIfKf9HWEEPBkVEuZMuDsbNxrlSxZkjFjxhitfTs7dcr4pUvq+5JkixD5S+/ViGbNmsXMmTN1nQ8AV1dXpk+fzqxZswwanBA5IcmWvHFwAO3vDtu2mTYWIYQoCKSvI0TxVVTqtWhJ3RYhTEfvZEtcXBx37tzJsP/OnTvEx8cbJCgh9CHFcfMuMFB9liWghRBC+jpCFGenTqnP+ZFsiYyMZObMmURGRhrtGtq6Ldr3JYTIP3pPEOzduzfDhg1j1qxZNGnSBIBDhw4xadIk+vTpY/AAhciKomQ+siVNSeNk1EmCI4K5k3iHxJREHqQ8IDElkeTUZMo6laVaiWpULVGVau7V8LT3RFOMi5V06gTvvgu7d6vFhm1sTB2REEKYjvR1hCi+tOWb6tc3/rUURSE5ORlFUYx2jXr11OdnylIJIfKB3smWRYsWMXHiRF566SVSUlLURiwsGD58OF999ZXBAxQiK9evQ0ICWFiAuXs4Cw5vZteVXey6souYhzE5bsfN1o2uVbrSp0YfOlbqiJ2lnRGjLnhq1wYvL7h5E/btg4AAU0ckhBCmI30dIYqntDQICVG3GzQwaSgGo30fp05BSgpYWpo2HiGKE42Sy1RqYmIily5dAqBSpUrY29sbNDBTiouLw9nZmdjYWJycnEwdjsjCtm3Q6ZVzOHf7jPgKv5GmpOlec7ByoFW5VlR0rYi9pT32VvbYW9pjbmbOlftXOH/3PBfuXuDK/SvpzrOztKNz5c4MqDWA3jV6Y2FmnArxBc2rr8LSpfDOO/D116aORghRVBTm79TC2tcpzJ+5EKZ0/ry6go+tLcTHG3+Vy1u3brF48WJGjhxJ6dKljXKNtDRwdYW4ODhxAurWNcplhCiy8vKdmuvfIu3t7akr/1qFCZ29c5YpRz6FsSuJ1SigQGuf1nSs2JF2FdrRyKsRlubZp+8fPX7Evzf+Zd25daw9u5aI2Aj+OPsHf5z9gwouFZjUfBJD6w3F1tI2H96V6XTqpCZbtm2TZIsQQoD0dYQobo4dU599fY2faMkvZmbqlKg9e9T3J/+lCZF/9C6QK4SpPUh5wOt/vU6tb2tx/PEK0ChUVXpwZMQR9gzdwwetP6BZ2WY5SrQA2FjY0MqnFbMDZxP+ZjhHRhxhcovJuNu5E34/nDGbx1D+f+WZsXcG9x/dN+6bM6GAAPUL+fRpdXqWEEIIIURxkp/1WgDc3d0ZOXIk7u7uRr2O9v1I3RYh8pckW0Shci76HH4/+LH42GIUFNyiesOiY0yr/icNvRrmuX2NRkNDr4bMDJhJxFsRzOs8Dx9nH24n3uaDnR9Q+ZvKfHfkO1LTUg3wbgoWNzf4rw6krEokhBBCiGJHO7Ilv+q1WFpaUrp0aSyNXEhF+360708IkT8k2SIKjV9P/kqjxY04ffs0nvae7BgUhLJyLUTWN8qyz3aWdoxrMo6Lb1zkl96/ULNkTe4+vMuoTaNo8kMTDlw7YPiLmliXLurzpk2mjUMIIYQQIj8pypORH/mVbImNjWXTpk3ExsYa9Tra9xMSotZwEULkD72TLYmJicaIQ4jnepjykJF/jeSVda+QmJJI2/JtCRkVQi27dty7p059qVrVeNe3NLfk5bovc2LUCb7p9A3O1s4cu3WM5kuaM2T9EKISoox38XzWtav6vH07JCWZNhYhhDAV6esIUfxcvQoxMeoKl7Vq5c81Hzx4wJEjR3jw4IFRr1OtGtjYqCt4hoUZ9VJCiKfonWzx9PTk1VdfZd++fcaIR4h0HqQ8oOtvXfn+2Pdo0PBR64/YPmg7pRxKERqqHlOpkvoFYmwWZha84fcGF964wKv1XgXgpxM/UXthbdafW2/8APJB/fpQujQkJkJwsKmjEUII05C+jhDFj3aKTe3aYG1t2lgMzcJCLfoLMpVIiPykd7Lll19+ISYmhnbt2lG1alU+//xzbt68aYzYRDH36PEjeq/qza4ru3CwcmDbK9uY1nYa5mZqeXhtssUYU4iy4mHvwY89f+Tg8IPU9axL9INoeq/qzfA/hxOfFJ+/wRiYRiNTiYQQQvo6QhQ/+T2FKL9p35cUyRUi/+idbOnVqxfr16/nxo0bjBo1it9++w0fHx+6devG2rVrefz4sTHiFMVMcmoy/Vb34+9Lf2NnaceWl7fQoVKHdMeYKtmi5VfGj8OvHebd5u+iQcOSkCX4LvJl/9X9pgnIQLRTiSTZIoQorqSvI0Txox3xkV8rEeU37fuSkS1C5J9cF8gtWbIkEyZM4OTJk8yePZsdO3bQr18/vLy8+Oijj4w+91AUXSmpKQxYM4BNFzdhY2HDxoEbaVmuZYbjtMmWGjXyOcCnWFtY80WHL9g1ZBflnMsRfj+c1staMz14OmlK4axAFhAAlpbqnN4LF0wdjRBCmI70dYQoPkwxssXe3p6mTZtib29v9Gs9PbJFUYx+OSEEeUi2REVF8eWXX1KzZk0mT55Mv379CAoKYtasWaxdu5ZevXoZMExRXDxOe8wr615h/bn1WJtb8+eLf9K2QttMjzX1yJantSnfhpOjTjLYdzBpShof7vqQXit7cf/RfVOHpjdHR2jTRt2W0S1CiOJM+jpCFA+RkXDzpjqdum7d/Luuk5MTgYGBODk5Gf1atWurtVvu3oVr14x+OSEEYKHvCWvXrmXp0qVs27aNmjVrMmbMGF555RVcXFx0xzRv3pwaphxuIAqtD4I+YPWZ1ViaWbJ2wFo6VuqY6XHR0XDnjrpdvXo+BpgFZxtnlvdaTutyrRm7eSx/XfiLxt83Zm3/tdTxrGPq8PTStSvs2KEmW95+29TRCCFE/pK+jhDFi3ZUS7Vq4OCQf9dNTk4mKioKT09PrKysjHota2t1laUTJ9SpROXKGfVyQghyMbJl2LBheHl5sX//fkJCQhg3bly6zgeAl5cXH3zwgaFiFMXEhvMb+PKfLwH4uffPdKnS5bnHnj2rPpcvD/kw8lIvwxsMZ9+r+yjnXI6wmDD8fvDjt1O/mTosvWiL5AYHQ3zhrvkrhBB6k76OEMWLqYrj3r17lyVLlnD37t18uZ4UyRUif+k9suXWrVvY2dlleYytrS1Tp07NdVCi+Am/F86Q9UMAeNPvTQbUHpDl8QVpClFmGnk14ujIo7z0x0tsv7ydl9e+zOnbp5nebjpmmlzP3ss3VatC5cpq3ZYdO6B3b1NHJIQQ+Uf6OkIUL/lZHDdNSdNNM499FKt79lQ8jd5HrF8fli6VIrlC5Be9/0U7Ojpy+/btDPvv3r2Lubm5QYISxUvS4yT6r+nP/Uf3aVqmKV92+DLbcwp6sgXA3c6dLS9vYUrLKQDM3DeTAWsG8DDloYkjyxlZlUgIUVxJX0eI4iU/RrZcj7vOJ3s+ofzc8pT4sgQlvixBu5/aAdDup3aUnVOWj3Z9RMT9CKPFICNbhMhfeidblOeUr05KSjL6XENRNE3YNoEjN4/gZuvGqn6rsDLP/u9RYUi2AJibmTOj/QyW91qOpZkla0LX4L/cn6iEKFOHli1tsmXzZqlaL4QoXqSvI0Txce8eXL6sbterZ9i205Q0Nl7YSI8VPfCZ68PU3VO5Fpd5ddqb8Tf5NPhTKvyvAl1+7cL6c+tJTUs1aDy+vmoR4Bs3IKrgd0WFKPRyPI3om2++AUCj0fDDDz/g8FT1qNTUVIKDg6leUCqVikJj5emVfHvkWwB+6f0L5ZxzVq2rICz7rI/BvoMp71Ke3qt6c/jGYfx+8GPjSxup7VHb1KE9V+vWaj2cW7fUOyD5PY9ZCCHym/R1hCh+QkLU5/Llwc3NcO1GJUQx8I+B7LqyS7evjU8bRjYcSc9qPbG2sCYqKorffvmNf176h0P3DrH42GJ2hu9kS9gWtoRtoWW5lqzqtwovRy+DxOTgoE4VP39e7dt16mSQZoUQz5HjZMucOXMA9W7PokWL0g2jtbKyonz58ixatMjwEYoiK/xeOCP+GgHAB60+oHOVzjk67/59dXk+KPgjW57W2qc1B4cfpOtvXbkYc5EWS1rw54t/4l/e39ShZcraGjp0gPXr1alEkmwRQhR10tcRovgxxhSivRF7GbBmALcSbmFvac+oRqN4rcFrVHdPn6z1Lu3NpEmTACjvXZ4BtQdw8e5Ffjj2AwuPLGTf1X3U/64+K/uupG2FtgaJrUEDSbYIkV9ynGwJDw8HoG3btqxduxZXV1ejBSWKPkVRGLt5LAnJCbQq14pp/tNyfO6ZM+pz2bLg5GSkAI2kSokqHHztIL1X9SY4IpjAXwL5rc9v9K3Z19ShZapr1yfJlg8/NHU0QghhXNLXEaL4MWRxXEVRmHVgFpN3TCZVSaVmyZqseWENNUrmfCh2lRJV+KLDF7zW4DX6/d6Pk1EnCfg5gE/bfsrklpPzXES3fn1YsUKK5AqRH/T+17pr1y7pfIg8WxO6hi1hW7Ayt+L77t9jbpbzgoOFpV7L87jZurHtlW30qdGH5NRkXvj9BRYdKZh3SrVLQB8+LHN7hRDFhzH6OgsWLKB8+fLY2Njg5+fH4cOHn3vs2rVradSoES4uLtjb21OvXj1+/vlng8YjhFAZamRLQnICfVb3YdL2SaQqqbxc52UOv3Y4y0TL7du3+eabbzItyF2lRBUODj/IsHrDSFPS+GDnB/RY0YO4pLg8xSlFcoXIPzka2TJhwgQ+/fRT7O3tmTBhQpbHzp492yCBiaIr9lEs47eOB2BKyylUc6+m1/nakS21ahk6svxjY2HD6n6rGbt5LN8d/Y7Rm0YTmRDJ1DZT0Wg0pg5Px8sLGjWCI0dg40YYPtzUEQkhhHEYs6+zatUqJkyYwKJFi/Dz82Pu3LkEBgZy/vx5PDw8Mhzv5ubGBx98QPXq1bGysmLjxo0MGzYMDw8PAgMD9bq2EOL5EhPh3Dl1Oy8jW5IeJ9FnVR+2X96OlbkV/+v0P15v+Hq2fbrU1FTu3btHamrmhXBtLW1Z0nMJLcu1ZOzmsWy6uIkeK3qw5eUt2Fra5ipW7fu8dEmdmu/ikqtmhBA5kKNky/Hjx0lJSdFtP09B+iVRFFzvB71PZEIkVUtUZXLLyXqfXxSSLaCuVLSw60JKO5Tm4z0fM23PNKISoljQdUGeh4gaUs+earJl/XpJtgghii5j9nVmz57NiBEjGDZsGACLFi1i06ZNLFmyhMmTM34P+vv7p/v5zTffZPny5ezbt0+SLUIY0MmTkJYGpUpB6dK5ayM1LZVB6wax/fJ27C3t+XvQ3zQv29ygcb5a/1XqeNQh4OcA9kTs4cU/XuSP/n9gYZbjihA6bm7g4wMREerolraGKQUjhMhEjv6F7tq1K9NtQ1mwYAFfffUVkZGR+Pr6Mm/ePJo0aZLpsf7+/uzZsyfD/i5durBp0yYAhg4dyvLly9O9HhgYyNatWw0eu9DPoeuHWHhkIQCLui7CxsJG7zYK+zSip2k0Gqb6T8XTwZMxm8aw6Ogi4pPjWdpzKZbmlqYOD4BevdR6Ldu3Q0KCWsleCCGKGmP1dZKTkzl69ChTpkzR7TMzMyMgIIADBw5ke76iKOzcuZPz58/zxRdfZHpMUlISSUlJup/j4vI2zUCI4uLgQfW5UaPcna8oCqM3jeb30N+xNLNk3YB1Bk+0aDX2bsyGFzfQ6ddObDi/geEbhrO059Jc3aBr3FhNthw6JMkWIYzJ5LfPtUNrp06dyrFjx/D19SUwMDDTuYugzmO+deuW7nH69GnMzc154YUX0h3XqVOndMetWLEiP96OyEJKagojN45EQWGI75BcVVUvrCsRZWdUo1Gs6LsCCzMLfj31Ky/8/gJJj5OyPzEf1KoFlSpBUhL8/bepoxFCiMIlOjqa1NRUPD090+339PQkMjLyuefFxsbi4OCAlZUVXbt2Zd68eXTo0CHTY2fOnImzs7PuUbZsWYO+ByGKqv371ecWLXJ3/vtB7/P9se8x05jxW9/f6FAp83+jhtKmfBtW91uNucacn078xDvb3kFRFL3baf5fPkj7/oUQxpGjkS19+vTJcYNr167VKwB9h9a6ubml+3nlypXY2dllSLZYW1tTqlQpvWIRxvW/Q//jZNRJ3Gzd+Lrj17lqQzuFqEyZwrcSUXYG1B6Ag5UDfVf35c/zf9J9RXfWDViHvZW9SePSaNSpRLNnq1OJ9PjvQAghCg1j9nVyw9HRkZCQEBISEggKCmLChAlUrFgxwxQjgClTpqSrMxMXFycJFyGyoSh5S7Z8/c/XfL7/c0Adrd2vZj+923Bzc+Pll1/O8PtNVrpX687SnksZvH4wcw/NpYRdCf6v9f/pdV3t+/3nH3UalZnJb78LUTTlKNni7OxslIvndWgtwI8//siLL76IvX36X0h3796Nh4cHrq6utGvXjunTp1OiRIlM25Dht8Z3O/E2H+/+GICvO3yNu517rtrRTiEq7PVanqdr1a5seXkL3Vd0Z/vl7XT8pSObXtqEi42LSePq1UtNtmzcCI8fg4X+U4SFEKJAM1Zfx93dHXNzc6KeWdItKioqy5tCZmZmVK5cGYB69epx9uxZZs6cmWmyxdraGmtra4PGLURRd+UKREaCpaX+04g2X9zMpO2TAPi8/eeMaDgiVzFYW1vr/p3rY5DvIGIexvDWtrf4cNeH1PaoTa/qvXJ8fr16YGMDMTFw4QJUr653CEKIHMjRr0xLly41ysWzGlp7TlsaPAuHDx/m9OnT/Pjjj+n2d+rUiT59+lChQgUuXbrE+++/T+fOnTlw4ADm5hmXGJ45cybTpk3L25sRWfos+DMSUxJp7NWYofWG5rqdolIcNyttK7Rlx+AddP61M/9c+4eAnwL4e9DfuNnm/K6HoTVvDu7uEB0Ne/fK/F4hRNFjrL6OlZUVDRs2JCgoiF69egGQlpZGUFAQ48aNy3E7aWlp6W4MCSHyRjuqpUEDsNVjYZ9rsdcYtG4QAKMbjea9lu/lOob4+HiOHj1Kw4YNcXR01OvcN5u+SURsBHMOzmHo+qEcf/04FVwr5OhcKyto0gSCg9XPQZItQhhHoR409uOPP1KnTp0MxXRffPFFevToQZ06dejVqxcbN27k33//Zffu3Zm2M2XKFGJjY3WPa9eu5UP0xceV+1d0RXE/D/g8T6tWaZMtRaleS2aalmnK7iG7KWlXkqO3jtJ2eVvuJN4xWTzm5tC9u7q9fr3JwhBCiEJpwoQJfP/99yxfvpyzZ88yevRoEhMTdVOoBw8enG6U78yZM9m+fTuXL1/m7NmzzJo1i59//plXXnnFVG9BiCLnn3/UZ32mEKWkpjBgzQBiHsbQsHRD5gTOyVMMCQkJ7Nmzh4SEhFyd/0XAFzQr04zYpFj6r+mvV72/p6cSCSGMI0cjWxo0aEBQUBCurq7Ur18/y1+Wjx07luOL53ZoLUBiYiIrV67kk08+yfY6FStWxN3dnbCwMNq3b5/hdRl+a1xTd08lJS2FgIoBtKvQLk9tFfVpRE/zLeXL7qG7af9Te05GncR/uT9Bg4Mo5WCaWkS9esHSpfDnnzB3rlrLRQghigpj9XUABgwYwJ07d/joo4+IjIykXr16bN26VTey9+rVq5g9VTQhMTGRMWPGcP36dWxtbalevTq//PILAwYMyN2bE0JkoB3Z0lyPxYOmBE3hwPUDOFs7s/qF1VhbmPb3B0tzS1b1W0W97+px5OYRJv49kXld5uXoXCmSK4Tx5SjZ0rNnT10yQjsE1hDyMrT2999/JykpKUd3ea5fv87du3cpXbq0IcIWejh9+zQ/n/gZgBntZuSpraK6ElFWapasyZ6he2i3vB2hd0Jps6wNOwfvxNvJO99jCQhQh9lGRMCJE+p8XyGEKCqM1dfRGjdu3HP7Ns+OvJ0+fTrTp083eAxCCFVsLJw6pW7ndGTLn+f+ZNaBWQAs7bmUiq4VjRSdfso6l+Xn3j/T9beuzP93Pq19WvNCrReyPU+bbDl/Xp0m7p67copCiCzkKNkyderUTLcNYcKECQwZMoRGjRrRpEkT5s6dm2Forbe3NzNnzkx33o8//kivXr0yFL1NSEhg2rRp9O3bl1KlSnHp0iXeffddKleuTGBgoEFjF9n7v53/h4JCv5r9aOzdOE9tFeWViLJStURVNeHyUzsu3L1A62Wt2TVkF+Wcy+VrHHZ2EBioTiNav16SLUKIosWYfR0hRMFy6JC6GlHFipCTxUvD74Uz9M+hALzl9xa9a/Q2boB66lKlC5NbTObz/Z8zfMNw6pWqR5USVbI8x80NatSAs2fhwIEn08WFEIaT65otR44c4eeff+bnn3/m6NGjuQ5gwIABfP3113z00UfUq1ePkJCQDENrb926le6c8+fPs2/fPoYPH56hPXNzc06ePEmPHj2oWrUqw4cPp2HDhuzdu1emCuWzA9cO8Of5PzHTmPFp20/z3F5xmkL0rEpulQgeGkxF14pcvncZ/2X+RNyPyPc4evZUn6VuixCiODBUX0cIUbDoM4UoNS2Vl9a+xP1H92ni3YQvOnxhsDhsbGyoU6cONjY2eW7r03af0qpcK+KT43nxjxdJSU3J9hyZSiSEcem9gOv169cZOHAg+/fvx8XFBYD79+/TvHlzVq5cSZkyZfQOQp+htQDVqlVDUZRMj7e1tWXbtm16xyAMS1EUpgSpxf6G1RtGdfe8lzkvDisRZcXHxYc9Q/fQdnlbwmLCaLOsDbuH7qa8S/l8i6FbNzAzU6cRXbkC5fPv0kIIkW+M0dcRQhQc2uRCTqYQzTowi4PXD+Jk7cTqfquxMrcyWByurq706dPHIG1ZmFmwou8K6i6qy7Fbx/h83+d82ObDLM9p0QJ+/FGSLUIYi94jW1577TVSUlI4e/YsMTExxMTEcPbsWdLS0njttdeMEaMohP6+9Dd7IvZgbW7N1DaGGY5dXFYiykoZpzLsHrKbKm5ViIiNoM2yNoTfC8+367u7Q6tW6vaff+bbZYUQIl9JX0eIouvxY3UaEWSfbAm9E8qHu9SExdzAufi4+Bg4lsfExMTw+PFjg7Tn7eTNvM5qgdxPgj/hROSJLI/Xvv9//4XkZIOEIIR4it7Jlj179rBw4UKqVaum21etWjXmzZtHcHCwQYMThZOiKHy0+yMAxjYeS1nnsgZptzhPI3qat5M3u4fupmqJqlyNvUqbZW24FHMp366vnUq0bl2+XVIIIfKV9HWEKLpOnYKEBLX+X1Y38B6nPWbo+qEkpybTpUoXhtYbavBY7ty5w7x587hz547B2hxYeyC9q/dW4/9Tjf95qlRRb6QlJYGei6wJIXJA72RL2bJlSUnJOAcwNTUVLy8vgwQlCrfdV3Zz+MZhbCxseK/lewZpsziuRJQVL0cvdg/ZTXX36lyLu4b/cn/CYsLy5draRTr27oVnVm0XQogiQfo6QhRd2ikzzZqBufnzj/tq/1f8e/NfnK2dWdxtcZbLwRckGo2GhV0XUsK2BCGRIczY+/zVQDUaqdsihDHpnWz56quveOONNzhy5Ihu35EjR3jzzTf5+uuvDRqcKJy+2K8WDnu13qt42HsYpM3iuhJRVko7lmbXkF3UcK/B9bjr+C/Ln4RLhQrQqBGkpcHatUa/nBBC5Dvp6whRdP3zj/qc1RSi07dP8/GejwH4X6f/4e3kbfzADMjTwZMFXRYA8Nnezzh+6/hzj9V+DtrPRQhhODkqkOvq6poum5uYmIifnx8WFurpjx8/xsLCgldffZVe2tveolgKiQxh26VtmGnMeKf5OwZrV6YQZa6UQyl2DdlF2+VtORt9Fv9l/uweupvKbpWNet3+/eHIEfj9dxg92qiXEkKIfCF9HSGKh+xWIkpJTdFNH+pWtRuDfQfnX3AG1L9Wf34P/Z0/zv7B0D+H8u+IfzMt7vv0yBZFUUe7CCEMI0fJlrlz5xo5DFFUfPXPV4D6H3xF14oGa1eK4z6fp4Mnu4bsot1P7Qi9E4r/Mn92DdlFlRJVjHbNF16Ad9+FPXsgMhJKlTLapYQQIl9IX0eIou/6dbh6VZ0+5OeX+TFf//M1R28dxdXGle+6fVdopg89S6PR8G3Xb9kTsYeTUSf5LPgzprWdluG4Ro3AykqdGn75MlSqZIJghSiiNMrz1lAuxuLi4nB2diY2NhYnmbOSY1fuX6HyN5VJVVI5NvIY9UvXN1jbHTrAjh3www8wfLjBmi1SohKidAkXb0dvoydc/Pzg8GFYsADGjDHaZYQQhZx8p+Y/+cyFyNzq1TBgADRoAEePZnw9LCaM2t/WJik1iZ96/cQg30H5H6SBrT6zmgFrBmBpZsmJUSeoUbJGhmOaN4cDB+Cnn2BQ4X/LQhhUXr5T9a7Z8rRHjx4RFxeX7iGKr1n/zCJVSaVDxQ4GTbSATCPKCU8HT3YO3knNkjW5EX8D/+X+XLx70WjXe+EF9fn33412CSGEMDnp6whRdGQ1hUhRFEZtHEVSahIdKnbglbqv5G9wRvJCzRfoWqUrKWkpvL7xddKUtAzHSJFcIYxD72RLYmIi48aNw8PDA3t7e1xdXdM9RAEQdxGu/wUXvoWQKbD/ZQhqB7s6wb7+cOg1ODoBTk+Ha+sgIVydpJkHdxLv8OPxHwF4r4VhViDSenolohoZk/HiKdopRbVK1uJm/E2jJly0yRbtVCIhhCgqpK8jRNGUVXHcX07+QlB4EDYWNizsujBfpg9FR0fz448/Eh0dbbRraDQaFnRZgJ2lHXuv7mXJ8SUZjpEiuUIYR45qtjzt3XffZdeuXSxcuJBBgwaxYMECbty4wXfffcfnn39ujBhFdhQFYs/A1TVw7XeIDdW/DUsncPEFt0ZQOhA824C5TY5Pn394Pg8fP6Rh6Ya0q9BO/+tn4emViJydDdp0keRh78HOITtpt7wdZ+6cwX+5WsOlaomqBr2Oj486lejQIfjjDxg71qDNCyGEyUhfR4iiJz4ejv+3KM+zI1uiH0Qz4e8JAHzU+iMqueVP4ZKUlBSuX7+e6VLzhuTj4sOnbT/lnb/fYdL2SXSv2h1PB0/d69rP4/RpiIkBNzejhiNEsaF3suWvv/7ip59+wt/fn2HDhtGqVSsqV66Mj48Pv/76Ky+//LIx4hSZSU2C89/A5SUQd+7JfjNLcK4N9j5gV/a/hzekPYaUWEiJUx+PouD+STVRkxIHd/aqj/NzwNwOPNuBV2fw7g72ZZ8bRmJyIvP/nQ+oo1oMfSdAphDpT5twaf9Te07fPk3b5W2NknDp319Ntvz+uyRbhBBFh/R1hCh6du2C1FSoXBnKlUv/2qTtk4h+EE1tj9pMbD7RNAEa2Xi/8fx66leO3TrG29ve5re+v+le8/RU+9lnzkBQ0JPRy0KIvNE72RITE0PFiuoqM05OTsTExADQsmVLRssasPnn1t9wZBzE/zdFxMxKHZFS7gU1OWLlkvO20lLUZE3McTXZcnMzPLwJNzeqjyPjwLMtVBgEZfuCpWO605ccX0LMwxgqu1WmT40+hnuP/5GViHLHw96DoMFBuoSLdlloQyZc+vWDd96B4GC4dQtKlzZY00IIYTLS1xGi6Pn7b/W5Y8f0+3eF72JZyDIAFndbjKW5Zf4Glk8szCxY3G0xTX5oworTKxjsO5hOlTvpXu/YUe1z//23JFuEMBS9a7ZUrFiR8PBwAKpXr87q1asB9S6Qi4uLQYMTmXhwHfa+ALsC1USLTSlo8j30vQNtNqgJEX0SLaCOhHGpAxUHg9/30Os6dA4B3xng3hxQIGonHBwGaz3hn1cgahcoCmlKmm5Uy9tN38bczNzQ75hTp9Tn2rUN3nSRp0241Paoza2EW/gv8+d89HmDtV+uHDRtqs5k++MPgzUrhBAmJX0dIYqebdvU58DAJ/sePX7EqE2jABjVcBTNyjYzQWT5p6FXQ8Y3GQ/A6E2jSUxO1L2m/Vy2bctzKUchxH/0TrYMGzaMEydOADB58mQWLFiAjY0Nb7/9NpMmTTJ4gOIpYT/AxupwbQ1ozKHaW9DtHFR+Ta25YigaDbj6Qq0p0HE/9AiHutPBsSqkPoQrv6oFdzfX5dyBt7kecwFHK0cG1TX8WnGKAidPqtt16xq8+WLBw96DnYN3UsejjppwWe7Puehz2Z6XU/37q8+yKpEQoqiQvo4QRcvlyxAWBhYW4O//ZP8X+77gwt0LlHIoxcyAmfkel4uLC717987XJO6n7T6lrFNZrty/wvTg6br9rVqBtTVcuwbnDXdfTohiTaMoectdXrlyhWPHjlG5cmXqFpHfhvOylrbRhH4BIZPV7ZItoNECNSGSnxQF7h6Gy8vgys/wWM2G30uF43b1aNdpvVonxoAiI9WpKWZmamEzOzuDNl+s3Em8Q8DPAZyMOomnvSc7h6jLROfVtWvqCBeNBq5fBy8vAwQrhCgyCuR3qp4KW1+nKHzmQhjSokUwejS0bq2uoghw8e5Fai+sTXJqMiv7rmRA7QGmDTIfbTi/gZ4re2JhZkHI6yHU8lALI3bsCNu3w9y58Oabpo1RiIIiL9+peo9seVb58uXp06dPoeh8FEqKAic+eJJoqfUBBATnf6IF1N+m3f2gyULodZ17NT8iLBlczaFdUghsqAT/DIb7pw12Se2olipVJNGSVyXtSxI0OIh6peoRlRhF2+VtOXP7TJ7bLVsWmjWTqURCiKJL+jpCFG7P1mtRFIWxm8eSnJpMYKVA+tfqb5K4EhMTOXz4MImJidkfbEA9qvWgR7UePE57zOhNo9Hee9d+PtrPSwiRN7lKtgQFBdGtWzcqVapEpUqV6NatGzt27DB0bEJJg6NvwZkZ6s/1vgDf6aDJc44s76xc+OpuClUjYEpqXfBsD0qqOuJlcx3Y3R3u7M/zZWQKkWG527mzY9AO6peqz+3E27Rd3pZTUafy3K52KtGKFXluSgghCgTp6whRNKSkqCvswJNkwqozq9h+eTvW5tbM7zLf4Ctp5lRcXBxbtmwhLi4u36/9TadvsLO0Y+/VvSw/sRx48vns3g1JSfkekhBFjt6/tX/77bd06tQJR0dH3nzzTd58802cnJzo0qULCxYsMEaMxVNaKhx6DS58o/7c+Fuo+a5pY3rKo8eP+P7Y9yhA40ZTof0O6HREXQ0JjbqK0faWENQebu/N9XUk2WJ4JexKsGPwDhqUbsCdB3dou7wtIZEheWrzxRfVqV4HDsDFi4aJUwghTEX6OkIUHYcPQ1wclCgBDRpA7KNY3t72NgAftPqAym6VTRyhafi4+PBxm48BmPj3RO4+uEudOlCqFDx4APvzfs9UiGJP72TLjBkzmDNnDitWrGD8+PGMHz+e3377jTlz5jBjxgxjxFg8HZ8El5eqo1ia/QRVCtZSk7+f+Z3oB9GUcSpDj2o91J1uDaHlarVob6XX1FWOonbCjta5Trpoky116hgweIGbrRs7Bu2gsVdj7j68S7vl7Th682iu2ytV6sndkF9+MVCQQghhItLXEaLo0E6JCQgAc3P4v53/R2RCJFVLVOXdFgXnRqYpvNX0LWp71Obuw7tM3jEZjUamEglhSHonW+7fv0+nTp0y7O/YsSOxsbEGCarYu7EJzs9Rt5uvUJdzLmAW/Kve2RvVcBQWZhbpX3Sqqi4h3T0MKo/KmHS5cyBH10hJgbNn1W0Z2WJ4rraubB+0nWZlmnHv0T3a/9SeQ9cP5bq9Qf/9Nf35Z1kyUAhRuElfR4ii4+kln4/cPKLrwy7suhBrC2sTRmZ6luaWLOy6EIAfjv/A/qv70y0BLYTIG72TLT169GDdunUZ9v/5559069bNIEEVaw9uwsGh6na1N8HHNAW7snL05lEO3TiEpZklrzV47fkH2pdTi+k+m3TZ3lyt6XIvJMvrXLgAycng6Ag+hl3kSPzH2caZba9so2W5lsQmxdLh5w7sv5q7caO9eoGDA4SHy9BTIUThJn0dIYqGmBj49191u137VEZtHIWCwst1XqZdhXamDQ6wsrKiUqVKWFlZmSyGluVaMrz+cABGbxpNm7YpAISEQFSUycISokiwyP4Q+Oabb3TbNWvW5LPPPmP37t00a9YMgIMHD7J//37eeecd40RZXKSlwoFBkBQNrvXUgrgFkPaOwAu1XsDTwTP7E7RJl1qT4fSn6tLRNzeqj3L9oe4n4FQtw2lPTyEyKwA1gYsqR2tHtry8he4rurP7ym4Cfwlk40sb8S/vr1c7dnbQrx8sW6aObmnZ0ijhCiGEUUhfR4iiJygI0tKgVi3489YCjt46irO1M7M6zjJ1aACUKFGCV155xdRh8EXAF6w/t55Tt0/x6+U5NGjwLseOqctAF4DwhCi0NIqS/YD/ChUq5KwxjYbLly/nOShTy8ta2nly5nM4MQXM7aDzsUwTEKYW8zAG79nePHr8iP2v7qd52eb6NxJ3EU5NhYiVgAIac6g4DOp8DHbeusOmTIHPP4dRo2DhQoO9BfEcD1Ie0HNlT3Zc3oGNhQ3rB6wnsHKgXm3s3Ant24OzM0RGgo2NkYIVQhQaJvtO1VNR6usUls9cCGMbMQJ++AFGTLjBSvcaxCfHs6jrIl5v9LqpQwMgLS2NlJQULC0tMTPxncVlIcsY9ucwbC1sGZIYyqKZ5Rk0CH76yaRhCWFyeflOzdHIlvDw8FwFJvQQfRBO/p+63Wh+gUy0APx04icePX5EvVL1aFamWe4acaoCLX6DWlPgxP/BjQ1w6Qe48gtUHQ813wNrNymOm8/sLO34a+Bf9Fvdj00XN9FjZQ9+f+H3JwWQc8DfH8qWhWvXYONGdaSLEEIUBtLXEaJoUZQndUfOln+T+Jh4mpZpyoiGI0wb2FOioqJYvHgxI0eOpHTp0iaNZYjvEJaFLGNPxB5CSo8D/uLvvzWkpckIcyFyK0//dBRFIQcDY0R2UuJg/0ugpILPi1BxqKkjypSiKPx4/EcARjQYgUajyVuDLnWgzZ/QYR+UbAmpj+Dsl7ChEoR+wYWzDwEpjpufbCxsWDtgLX1r9CU5NZm+q/uy+szqHJ9vZgYvv6xuy50QIURRIH0dIQqn8+fVmz8WNTexL+YPzDXmfNftO8w0kjnIjEajYVG3RViaWXIwZhPW9dcSFQWnTpk6MiEKr1z9b/PTTz9Rp04dbG1tsbW1pW7duvz888+Gjq34OPM5JIaDfXlovAjymsQwksM3DnP69mlsLGx4qc5Lhmu4ZAsICIY2G9UETMp9CJnMrglVGNZmCXVqpxruWiJbVuZWrOy3kpfqvMTjtMcM/GMgy0OW5/h87apEW7bAnTtGClIIIYxM+jpCFG5//w1YJmLRYywAbzd9m7qecgcvK9Xdq/Nei/fUHzqPB+s4WQJaiDzQO9kye/ZsRo8eTZcuXVi9ejWrV6+mU6dOjBo1ijlz5hgjxqLtYSSc/5+63WAOWDmbNp4saEe19KvZDxcbF8M2rtGAd1fodBya/cQj83KUcbvBkpHDcd5fF67/JesJ5yMLMwt+6vUTw+sPJ01JY+ifQ/n2329zdG7NmtCgATx+DKtWGTlQIYQwAunrCFH4bdkCtPmERzYRlHMux8f+H5s6pELh/VbvU8m1EklWN6Hth+rnKITIFb2TLfPmzWPhwoV88cUX9OjRgx49evDll1/y7bffpqvkL3LozGeQ+gBK+EGZnqaO5rkSkhNYcXoFgG55OKMwM4cKg1h65zzv/Po18UmuEBsKwT1gRxu1to3IF+Zm5izuvpg3mrwBwNjNY5m5d2aOzh08WH2WqURCiMJI+jpCFG7378OOk6eg2WwA5neej72VvWmDKiRsLW1Z2PW/lSmazGf3haMyUlmIXNI72XLr1i2aN8+4Ak3z5s25deuWQYIqNhKuQNh36rbvjAI7fQjg9zO/k5CcQGW3yrTxaWP06x0/acPsze8w9/JltWCuuQ3c2Qt/N4O9/SDugtFjEGCmMeN/nf7H/7VSize/v/N9Ju+YnG39goEDwdwc/v0Xzp3Lj0iFEMJwpK8jROG2bn0qjzu/DuaP6V29N92rdTd1SJny8PBg4sSJeHh4mDqUdDpU6sDA2gPBLA2l2wjWrH1s6pCEKJT0TrZUrlyZ1aszFsxctWoVVapUMUhQxcapjyEtBUoFQKl2po4mS9opRK/WezXvhXFzQFuMq2ptF6j3OXS/qC4PrTGDa3/Apprw7xh4GGX0WIo7jUbDp+0+5asOXwHwxf4vGLNpDGlK2nPP8fCATp3U7aVL8yNKIYQwHOnrCFG4fR38LZQ9gDWOfNO54I5GMzc3x97eHnNzc1OHksGcwDnY4gqljzP7gEyfFCI3crT089OmTZvGgAEDCA4OpkWLFgDs37+foKCgTDsm4jliQ+HKf4X26n5m2liycS76HPuv7cdcY86QekOMfr20tCfJFt1KRHZloOkSqD4BQibDzU1wcSGE/wTVJ0KNd8DS0eixFWcTm0/E2dqZ1ze+zqKji4hLjmNZz2VYmltmevzw4bBpk5ps+fRTsLLK54CFECKXpK8jROF16upVQktPAeC9Bl9QxqmMiSN6vpiYGLZt20ZgYCBubm6mDicdTwdPPm42i/cOvEpYmY84FNYbv8qVTR2WEIWK3iNb+vbty+HDh3F3d2f9+vWsX78ed3d3Dh8+TO/evY0RY9F08kNQ0qBMb3BvYuposvTjMXVUS5cqXfBy9DL69cLDITERrK0hww1El9rgvxHa74YSTeBxIpyeBn9VhgsL1JFCwmhGNBzBir4rsDCz4LdTv9FzZU8SkxMzPbZbNyhdWl2RaP36/I1TCCHyQvo6QhROiqLwyspRYJWI3Z2WTO32uqlDylJSUhIXLlwgKSnJ1KFkalKHoTjeaQeWjxjy++vZTiMXQqSnV7IlJSWFV199FVdXV3755ReOHj3K0aNH+eWXX6hfv76xYix67v4L19aqU2J8p5s6miwlpybz00m1yqlRC+M+5eRJ9blWLbB43tgrzzbQ8SC0XA0OleHRbTgyDjbWhIjVaiJLGMWA2gPY8OIGbC1s2RK2hQ4/dyDmYUyG4ywt4dVX1e3Fi/M5SCGEyCXp6whReK04vYKTD7fAYyuGlfweM43e95XFUzQaDa97L4YUW84n72RpiMwNF0Ifev0PZGlpyR9//GHwIBYsWED58uWxsbHBz8+Pw4cPP/fYZcuWodFo0j1sbGzSHaMoCh999BGlS5fG1taWgIAALl68aPC4c+3EB+pz+UHgXNO0sWRj44WN3E68TSmHUnSp0iVfrqlNttSpk82BGg2UewG6hUKjBWDjAQlhsH8AbGsCkUFGj7W46lylM0GDg3C1ceXA9QO0XtqaG3E3Mhw3YoT6xxQUBGFhJghUCCH0VBD6Ot9//z2tWrXC1dUVV1dXAgICsjxeCAHRD6IZv+VN9YfgDxnzQnXTBlREjOpfCXZ9AsCEbe8QmRBp4oiEKDz0Tvf26tWL9QacE7Bq1SomTJjA1KlTOXbsGL6+vgQGBnL79u3nnuPk5MStW7d0j4iIiHSvf/nll3zzzTcsWrSIQ4cOYW9vT2BgII8ePTJY3LkWcwwit4OZJdSZauposqUtjDvEd8hza3MYWoZ6Ldkxs4SqY6B7GNSZBhYOEHMUdgbAzo7qZy4MrlnZZuwdthcvRy/O3DlD8yXNOR99Pt0xPj5PCuV+/70JghRCiFwwdV9n9+7dDBw4kF27dnHgwAHKli1Lx44duXEjY1JbCKF6e9vb3H0YDVG1qRHzLjUL9v3MQqNSJaif9BbcbEBs0n3e2PKGqUMSotDQKHpOvps+fTqzZs2iffv2NGzYEHv79GvWjx8/Xq8A/Pz8aNy4MfPnzwcgLS2NsmXL8sYbbzB58uQMxy9btoy33nqL+/fvZ9qeoih4eXnxzjvvMHHiRABiY2Px9PRk2bJlvPjii9nGFBcXh7OzM7GxsTg5Oen1frJ1eJS63LPPQGjxm2HbNrCb8TcpO6csaUoa58edp2qJqvly3apV4eJF2L4dAgJy0cCj23D6Mwhb+KSGS7n+UPdTcMqf91CcRNyPoOMvHblw9wIlbEuw8aWNNC3TVPf6+vXQuzeULAnXr0uhXCGKG6N+pxqJqfs6z0pNTcXV1ZX58+czePDgDK8nJSWlq/kQFxdH2bJlC9VnLkRebA3bSudfO4OigR8O8vGIJkwt+Pc0SUhI4NSpU9SpUwcHBwdTh/Ncn38OU/53HM3rjVE0qaztv5beNaR+lSge8tKP0TvZUqFChec3ptFw+fLlHLeVnJyMnZ0da9asoVevXrr9Q4YM4f79+/z5558Zzlm2bBmvvfYa3t7epKWl0aBBA2bMmEGtWrUAuHz5MpUqVeL48ePUq1dPd16bNm2oV68e//vf/zK0mW+dlJQEWOcFj+Oh/U7wbGu4to3gy/1f8t6O92hRtgX7Xt2XL9dMTARHR1AUiIpSlxDOtYRwtRDxld8ABTTmUPFVqPORurqRMJg7iXfo+ltX/r35L7YWtqzst5Ie1XoA8PixOsLl5k1YuRIGDDBxsEKI/2/vrsOjuL4Gjn837u5Ggru7W3EopVCgAjUotKWl1KBK5VdoS92QCvAW9+JQtLgFCMGDhLi7Z+f948Y2AknY7Ebu53nmmWV3dvfusJmdOffcc3WqJgZb9H2uU1xycjIuLi6sW7eOESNGlHh87ty5fPLJJyXur0n7XJIqKyEjgZa/tiQ0ORSDUzNR7/iOwEBkZosWBQVBw4agemQ2So8vcbF0IfDlQJwsnPTdNEmqcg9zHlPhYUS3b98uc6nIyQdATEwMubm5uLq6atzv6upKRETp4wGbNGnCn3/+yZYtW/j7779Rq9V0796dkJAQgILnVeQ1582bh62tbcHi7e1doc9RbsFrRaDFqiG49K2a99ASRVFYdmEZIIYQ6UpgoAi0uLo+ZKAFwMoPuv8NQ8+DxwhQciFoCWxtBOfegowYbTRZApwtnTkw+QDDGg0jPSedx9Y8xqIziwBR5PiFvNrKslCuJEk1gb7PdYp799138fDwYGAZ6Z5z5swhMTGxYLl3716F2ihJNdnru14nNDkUV6NGqPf+j+bNa06gJT09ncDAQNLT0/XdlPtq0ADatQPlwFzcjZoTlRrFqzte1XezJKnae6gS3Yqi6HwKsG7dujFp0iTatm1Lnz592LhxI87OzixatKjSr6mzk5SbeVeaDfOqhlZjZ8PPcjn6MmZGZjzR4gmdve+FC2L9wOK4FWHfGvpuhUeOgHNPyM2Aq9/AP35w8WPIStTim9VdliaWbJmwhRfavYBaUTNt+zQ+OvARiqLwwgviK79/vxgiJkmSVFPo41ynqPnz57N69Wo2bdpUYkKAfKamptjY2GgsklQX/HPtH5ZfWI6ByoAGAUsh24Jx4/TdqvJLSEhg/fr1ZZZHqE7GjQNyzPA+uwxDlSFrAtewLnCdvpslSdVapYItf/zxBy1btsTMzAwzMzNatmzJ77//XuHXcXJywtDQkMjISI37IyMjcXNzK9drGBsb065dO27mTXWS/7yKvKZOTlISAiD2JKiMwE93mSKVtey8yGoZ3XQ0tma2Onvfs2fFun37Knhx5x4w8DD03QH27SAnBS59KoIul7+EnNQqeNO6xcjAiCUjl/BxHzFQ+rPDn/Hslmdx98pi6FCxjSyUK0lSTVAdznUWLFjA/Pnz2bNnD63LXTVekuqG2LRYpm6dCsCr7d/kzKbuADUq2FKT5O/Xs/905PUOcwCYvn06kSmR93mWJNVtFQ62fPTRR7z++uuMHDmSdevWsW7dOkaOHMkbb7zBRx99VKHXMjExoUOHDuzbVzhFr1qtZt++fXTr1q1cr5Gbm0tAQADu7u6AGGft5uam8ZpJSUmcPHmy3K9ZJW7mXWF6PQrmrvffVs+ycrNYdWkVoNshRABnzoh1x45V9AYqFXgMhSFnoOc6sGkGWfFwfjb8Ux+ufg851TuVs7pTqVTM7TuXJSOXYKgyZPmF5Qz+ezBPvRAPwF9/QZESSZIkSdVOdTjX+eqrr/jss8/YtWsXHavsR1GSaq5Xd75KZGokzZya0SrmU7KyoFkzyCvjKGlZw4bQti3k5kLD0A9p49qG2PRYpm+frtfsP0mq1pQKcnJyUlauXFni/pUrVyqOjo4VfTll9erViqmpqbJ06VLl8uXLytSpUxU7OzslIiJCURRFeeaZZ5TZs2cXbP/JJ58ou3fvVoKCgpSzZ88qEyZMUMzMzJTAwMCCbebPn6/Y2dkpW7ZsUS5evKg8+uijip+fn5Kenl6uNiUmJiqAkpiYWOHPU6rsNEVZa6coK1CUsN3aec0qtOnKJoW5KO4L3JXs3GydvW9GhqIYGysKKMqtWzp609wcRQlapihb6ov/nxUoykYPRbn2s6LkZOioEbXXrhu7FOsvrBXmojT5qYni2jRIAUVZvlzfLZMkSVe0/puqA/o+15k/f75iYmKirF+/XgkPDy9YkpOTy/V+NXGfS1JFrAtcpzAXxfATQ+VUyCmld29x/vjJJ/puWcWEhYUpc+fOVcLCwvTdlHL58kuxn7t0UZTz4ecV40+NFeai/H3hb303TZKqzMP8plY4syU7O7vUHpYOHTqQk5NT4WDP+PHjWbBgAR999BFt27bl/Pnz7Nq1q6CQXHBwMOHh4QXbx8fHM2XKFJo1a8awYcNISkri2LFjNC9SCeudd95hxowZTJ06lU6dOpGSksKuXbvKHOtc5e6th+wEsPQFt8rMZaxb+YVxn2r1FEYGRjp734AAyM4GBwfw9dXRmxoYQv1JMOIqdF4MFt6QHgZnXhWFdG8shFyZhlFZgxsO5sjzR/Cy8eJa7DVSJnQFrxN8+60ohCxJklQd6ftc57fffiMrK4uxY8fi7u5esCxYsKDyH0qSaonIlEimb58OwOyes7FK6sThw2BgAM8/r+fGVZCRkRFubm4YGenufPthTJ4sJj44eRKIbMNHfUSm36s7XyU0KVS/jZOkaqjCUz/PmDEDY2Njvv32W43733rrLdLT0/nll1+02kB90Po0lXt7Q/R/0PozaPnBw79eFYpJi8HjGw+y1dkETA+gpUtLnb33woUwfToMGgS7d+vsbTXlZkLQHxD4PxF0ARGAaTFHTBttaKqnhtVsYclhjFg5Av8If8g2gy1/sf/HCfSr3rOfS5KkBTVx6ueafq5TE/e5JJWHWlEzfOVwdt3cRWvX1pyecpp33zLh++9h1Cgox0zq0kN64glYt06cs//4cw7d/+jO6bDT9PPtx95n9mJoYKjvJkqSVul06mcoLBr34osv8uKLL9KqVSuWLFmCgYEBs2bNKlgkIPGKCLSoDKH+c/puzQOtClhFtjqb9u7tdRpoAR3UaykPQ1No/DKMCoIOP4K5O6Tdg9Mvw9aGcP1XMZuRVCEe1h4cfu4wIxuPBOMMGDuRl1Z/iFpR67tpkiRJpZLnOpJU/fx48kd23dyFmZEZK8esJDfLhGUiIZuXXtJv2+qK/P3899+QkWbE32P+xtLYkgN3DvD1sa/12zhJqmYqHGy5dOkS7du3x9nZmaCgIIKCgnBycqJ9+/ZcunQJf39//P39OX/+fBU0twYKypu5wGM4WHjqty3lkD+ESNeFcaGaBFvyGZpBkxkw6hZ0+AnMPSAtBM68kldI9zvISdN3K2sUKxMrNo3fxAtN3wbghsfnDP3zCVKz5CxQkiRVL/JcR5KqH/9wf979910Avh30LS1cWrBhA8THg48PDB6s5wZWQnh4OJ9//rnGMMLqrl8/USw3ORnWrIHGjo35aehPAHx44ENOhZ7Scwslqfqo8DCiukBr6bfqXNjsARlR0GcreI7QXiOrQGBUIC1/a4mRgRFhs8JwtnTW2Xunp4O1tahwHhwM3t46e+vyyc2Am7/Dla9EpguAqTM0nSUyYYxlmnZFtHtuKee9p4JhNu3c2vHPxH/wsvHSd7MkSaoCckiL7sl9LtU2qVmpdFzSkasxV3m0yaNsGr8JlUpFr15w5Ah89hl8UL1H6pcqPDycxYsXM3Xq1IKZVWuCr7+Gd96BTp3g1ClQFIUJGyawNnAtDewb4P+SP9am1vpupiRphc6HEUnlFHtCBFqM7cC9+ofbl19YDsCwRsN0GmgBuHBBBFpcXMCrOl5zG5pBk1dh5E3ovASs6kNmNFyYA5vrwYUPISNa362sMb5/9llYegBSnfGP8Kfj4o4cDT6q72ZJkiRJklQNzdo9i6sxV/Gw9uD3Ub+jUqkIDBSBFkPDmlcYt6Z79lkwMYHTp8HfH1QqFYtGLMLH1oeg+CBe3fmqvpsoSdWCDLZUpZDNYu05HAyM9dqUB8lV5/J3wN+AfoYQnT0r1h07gkql87cvP0MTaPgijLgG3ZaDTVMx01Tg57ClHpx5HVLv6buV1V7v3tDeuQcsOYUrrYhMjaTfsn4sOrNI302TJEmSJKka2XhlI4vPLUaFiuWjl+Nk4QTA4sXi8VGjwMNDjw2sg5ydYcwYcXtR3qmbnZkdK8aswEBlwPILy1kZsFJ/DZSkakIGW6qKokBIXkl0r0f125Zy2H97P2HJYdib2TO80XCdv3+1qtdSHgZG4PcMDA+EXhvAoQPkpsP1H0VNl+PPQkKgvltZbalUMGsWkOCL8vtxHm86jmx1NtO2T2Pq1qlk5sjptiVJkiSprrsdf5sX/nkBgHd6vMOA+gMAMfx8uUjIloVx9SR/v69YIeq3APT06cmHvT8EYNq2aVyPva6n1klS9SCDLVUl6Sok3wADE3Afou/WPND/Xfw/ACa0nICpke6nN65xwZZ8KgPwHgODT0O/PeDSF5QcuL0MdrSEgyMg6rAIvkkaxo0TPVFRIZaMSF/D/AHzUaFiybkl9F3Wl7DkMH03UZIkSZIkPcnIyeDxtY+TkJFAF88ufNrv04LH1q6FhATw9YVHHtFbEx+ak5MT06dPx8nJSd9NqbA+faBxY0hJgVWrCu//oPcH9PLpRXJWMo+vfVxOhCDVaTLYUlXys1pc+4Nx9S4QlZKVwoYrGwB4pvUzOn//1FS4fFncrnHBlnwqFbg/AgMPwKAT4P04oIKw7fBvH9jTDYLXgTpH3y2tNkxMYMYMcfu771S80+Nddjy1AzszO06EnKD9ovYcvHNQr22UJEmSJEk/Xt3xKv4R/jhZOLFu3DpMDE0KHssfujJlChjU4KsZY2NjXFxcMDau3uUGSqNSwdSp4vaiIqPAjQyMWDN2DW5WblyKusRL215Czsci1VU1+PBUzdWgIUSbrmwiLTuNhg4N6erVVefvf/48qNXg6Qk1qBB72Zy6QK/1oq5Lw5fAwBRiT8KRJ2BrI7j6A2Qn67uV1cLUqWBpCRcvwtatMKThEE5POU0rF1HHZcDyAXx55Ev5Iy1JkiRJdcgf5/7gD/8/MFAZsPrx1XjbFk5T6e8Px4+DkVHNL4ybkJDAP//8Q0JCgr6bUimTJ4vOs3Pn4MSJwvvdrd1ZM3YNhipDVgSs4Lczv+mvkZKkRzLYUhXSw8VMRACeo/TblnLIH0L0TOtnUOmhOm2NHUL0IDaNoPNCePQutPwQTB0h9Q6cmwmbvcH/bUi9q+9W6pWDA7yaV7D+k0/EaKuGDg058eIJJrWZhFpRM3vfbB5b8xgJGQl6baskSZIkSVXPP9yfV3a8AsBn/T4rqNOS74svxHrcOHBz03XrtCs9PR1/f3/S09P13ZRKcXKCp54St/P/X/L1rtebLwd+CcDMXTM5GXJSx62TJP1TKbLLuISHmUsbgJuL4dRL4NgZBlfvA0toUije33mjoBD0WhD17evrvA3PPAN//w2ffQYffKDzt9ednHS4vRyufgvJeQXDVAbg9Rg0eR2ce1bzqZiqRnQ0+PmJ4WRbt8KIEeJ+RVFYcm4JM3bOICs3iwb2DVg3bh3t3Nvpt8GSJFXIQ/+mShUm97lUU8Wnx9NhcQduJ9xmROMRbJmwBQNVYd/w5cvQsqXonLl0CVq00GNji8tOguSbkBYiOl7Tw8Q6I1I8lpMsMptzkiFXTAQQnu7M4lsTmVp/Fe5WSWBkLcoP5K/NXMHcHcw98taeYN0ITGz1/GELXb8OzZqJLHV/f2jbtvAxRVEYt24cG65swMvGi3NTz+Fs6ay3tkpSZTzMb6pRFbWpbqtBQ4hWBqxEQaGnT0+9BFqgFme2FGdkDo1egoZTIGwHXP0eIvfBvQ1isW8HTV6DehPA0EzfrdUZZ2d45RX46iuYOxeGDxcxJ5VKxdQOU+ng3oGx68YSFB9E1z+68s2gb3il0yt6ycKSJEmSJKlq5KhzmLhhIrcTbuNn58fy0cs1Ai0gsicURUw7rLdAS2YcxJ+HeH9IugJJ10UnWkZkxV8rO29SiuwkEZghvHzPM3MB68ZisW0G9m3FeaSpY8Xb8JAaN4bx40WR3P/9D9atK3xMpVLx56N/EhAVwPXY64xfP57dT+/G2LDm1aiRpMqQmS2leKgeoewU2OAE6kwYdgnsqlPIXZOiKLRe2JpLUZdYNGIRUztM1XkbkpLAzk78cEZFiQvvOiXhElz7Ee78H+RmiPtMHaHBi9BwGlj56rV5uhIdLWYUSEuDbdtEwKWouPQ4nt/yPFuuiUDmY00f449Rf2Bvbq/7xkqSVCEyy0L35D6XaqLXdr7GT6d+wtzInKPPHy2RyXrzJjRpIjIozp6F9u110KjcDIg9DdFHIPaUCLDcbwi4mQtY+BTJRPEQ2SnGtiJTxdgajKzyOtVUhEfFs3jlXqY+ORB3R4vC7JfsZMhJgvTIvAyZvCyZtOD7B3UsvEXQxbGzyJh27Cw6+6rYpUvQqpXoLLt0CZo313w8MCqQrn90JSUrhSntp7BoxCLZaSbVGA/zmyqDLaV4qJOU4A1wZCxYNYCRN6r1sJDzEedpt6gdpoamhL8ZrpcL10OHoG9fqFcP7tzR+dtXH5mxEPQ7XP9V/JCCGGLkMQIavSxmOlLV7hJLb78NCxZAp05w8mTJPx1FUfjp1E+8vfdtsnKzqGdbj1WPr6Kbdzf9NFiSpHKRF/66J/e5VNP8evrXgjotG57YwJhmY0ps8+KL8McfMGwYbN9eRQ3JSYOo/yByvwiwxJ0BdVbJ7Sz9wKEd2LYCm7wMk0oM70lKSuLUqVN07ty5/H+r2UmQfKMwoybhkggCpQSV3NbAGOw7gEtPMUOqS28wsqxQG8trzBjYtAmefhr+7/9KPr7t+jZGrRqFgsK3g77ljW5vVEk7JEnbZLBFyx7qJOXYJJGl0HQWtP+mahqoJW/ufpNvT3zL2OZjWTdu3YOfUAW++QbeegsefxzWr9dLE6oXda6YLvr6zxCxt/B+q/rQcCrUfx7Mamf6T2SkqN2Sng47dsDQoaVvdzbsLOPXjycoPghDlSGf9P2E2T1nY2hgqNsGS5JULvLCX/fkPpdqkr1Bexm6Yii5Si5f9P+COb3mlNgmOBgaNICcHDh2DLppq59FUSDhAoTvEUv0EZGdXpSZq8gSceoODu3FkB0TOy01QIuyEsVniTsHMcfEZ0kvNizJwASce4DbI+A+SGTBaKkz7+xZURLAwEDUcWnQoOQ23x3/jll7ZqFCxdaJWxneeHjJjSSpmpHBFi2r9A5VZ8NGV8iKh4GHRPS4mspR5+D1rReRqZFsmbCFUU30M2vSxImwejXMmwezZ+ulCdVX0jWR6XJ7GWQnivsMjMF7rKj74tK3WmdOVcZbb4kAXOfOYgrBsj5eUmYS07ZNY9WlVQD09OnJ/z32f/ja+equsZIklYu88Nc9uc+lmuJK9BW6/dGNxMxEJrWZxNJHl5Y6vOTVV+GXX6B/f9i37yHfNDcTIg9A6D8QulUUtC3KwksEI1z6iCCLVf0qO9/KzMwkPDwcd3d3TE1NtfviiiJmwYw+AlGHIHxvYfZ0PnMP8BwpZk916//QNQOHDYOdO0UW0pIlpTVJ4aVtL7Hk3BKsTKw49vwxWrm2eqj3lKSqJoMtWlbpHRp5APb1B1MneCwcDKpv/eFdN3cxdMVQnCycCJ0ViomhiV7a0aiRGIO7dy8MHKiXJlR/OWlwdw3cXCjGC+ezaggNXwS/Z8HcVW/N06ai2S07d8KQIWVvqygKf1/8m1d2vEJyVjI2pjb8MuwXnmr1lBwHLEnViLzw1z25z6WaICYthi6/d+FW/C16ePdg36R9mBqVDDiEh4tzg8xMOHBADD+vsOwUkTkcvB7Cd0FOSuFjhhZiiI37IBFksWmis86s8PBwFi9ezNSpU3F3d6/aN1MUMfwoYm/esq/kfvAYIjr1PEeI+jIVdPw4dO8Oxsbi/N7Hp+Q22bnZDP57MAfuHMDH1oeTL57EzaqGz+Et1WoP85tau4tA6FrIP2LtOaJaB1oAll9YDsCEFhP0FmiJjxcHYoAOHfTShJrByAIaPCemER9yRgwnMrKClJtwfjZs9oL/HofQHaDO0XdrH4qrK0ybJm5/+KEoglcWlUrFM22e4cK0C3T37k5SZhLPbHqGiRsmEpcep5sGS5IkSZJUYSlZKQxbMYxb8bfws/Nj0/hNpQZaQNRzy8yEHj2gT58KvEl2CtxZLc6RNrrA0Qlwb70IMJh7iIkI+u6AsbHQdys0mQG2TWtd1nABlUrUl2n8CvTeDI/HQN9dojaghRfkpsG9jXDsSdjgDIdHw+0VolhvOXXrJrKPsrPFLJOlMTY0Zv0T62nk0IjgxGCGrhhKYkaiVj6iJFU3MtiiTRF7xNqjeo8/TMxIZNPVTQBMajNJb+04cUKsGzYEezmpTPk4dIDOi0TmVJffwbELKDnix/HQcNhSD86/B0k39N3SSnv3XbCyElOCr1r14O397P049OwhPu37KYYqQ9YErqHFry3Yfr2qqudJkiRJklRZmTmZjFkzhtNhp3E0d2T7k9txtiy9Hl1wMPz2m7j9wQfliIOosyF0Oxx9UgztPzZRnCPlpovJK5rPhsGnYXQIdP4NPIY+9NCZGsvQFDwGQ6df4NFgGHIWWrwviv2qMyFkCxx/WgSqjowXncq5pRQLLuaDD8R6yRIIKqVmL4CDuQM7ntqBi6UL5yPO8+jqR8nIydDih5Ok6kEGW7QlPRISL4vbLn312pQHWXd5HRk5GTR3bk5Hj456a8ehQ2Ldu/qWtqm+jK2gwQsw+AQMuwhNZoopo9PD4PI82NYY9vaEm0tEwbQaxNUV5uTVxpszRwwpehAjAyM+7PMhx144RlOnpkSkRDBi1Qhe2PKC7C2RJEmSpGoiV53LpM2T2HtrL5bGlux4agfNnJuVuf3bb4vzgN69YfDgMjZSFIg5CadfgU0ecGgE3F0lMjWsGkDzOTDknJgltO08cOxYe7NXKkulEsV/23wOI67C0AvQ4gMReMnNgOC1cPhR2OQOp6ZB9HGx30vRty888ghkZcGbb5b9lg0dGrLrqV1Ym1hz6O4hJm6YSE4Nz9CWpOJksEVbog6KtV0bMHPSa1MeZOn5pQBMbjNZr7UtDh8WaxlseUh2raDDdzA6DHquB49horJ89FE4NRU2ucGRCRC2s8YMM3rjDfD2hnv34Pvvy/+8zp6dOTf1HLO6ikr3f57/k1a/tWJv0N4HP1mSJEmSpCqjKAozds5gbeBajA2M2TR+E509O5e5/aFDsHatmN3mxx9LiY+khULgfNjeHPZ0hRu/QmYMmLlA49dg0Mm8AMsXYprmahhgMTAwwNraGgODanRJplKBfWto85kIvAw5A03eADM3yIqDm4tgb3fY1gQCv4DUeyWe/v33YGgIW7aIuoxlaefejn8m/oOpoSmbr25m2rZpyHKiUm0iC+SWolJFcE5NEwefJjPFhW81dTPuJo1+aoSByoB7b9zDw9pDL+1ITQU7OzGF3+3b4Ourl2bUXmlhcGeFmMkoMbDwfjMXqDcRfJ8Ch+rds/P33/DMM2JI0c2bIuOlIv67+x/PbnmWW/G3AHiu7XN8M+gb7M3lmDVJ0iVZrFX35D6XqqO5B+fyyaFPUKFi9djVPNHiiTK3zc0V9fwuXBC13PKHEpGbKWYRCvpDFHlV8oq7GZqD9xjwfQbcBlT72ok1kjoXog7A7f8ThYZz0/IeUIl9Xv8F8B5dMCxr5kz44Qdo1kz8Pxobl/3Sm69u5vG1j6NW1Lzb413mD5xf1Z9GkspNFsitDiL3i7VrP/224wHyC+MOajBIb4EWENXKc3JE9kK9enprRu1l4QHN34ZhAWIMbuPXxCxZGVFw7QfY3Rm2NYWAT6ptfZcnn4SOHSElBT7+uOLP71WvFxemXeDVTq+iQsVf5/+i+a/N2XB5g/YbK0mSJElSmeb9N49PDn0CwC/DfrlvoAVEvY8LF0TH3GefAQmBcHYWbPaEI09A+G4RaHHuKWrYjYmA7n+LGiQy0FI1DAzBbSB0Wyb2d9e/xPTYKBDxr6iPs8kTzrwOCQHMnQtOTnDlCvz66/1fenTT0SwesRiAL49+ydyDc6v600iSTsjMllJUOHqVFipmhFEZwOOxYGJX5W2sDLWixu8HP4ITg1n1+ComtJygt7Z89JH48Xz6afi//9NbM+oWdTaE7xEZLyGbRbG4fPbtwXci+DwBlqXM06cn//0nhpkZGMDFi9CiReVe52jwUV7c+iJXY64C8FjTx/h52M96DThKUl0hsyx0T+5zqTr54r8veH//+wD8r///eK/Xe/fdPj4eGjWC9JRUtvy4loG+SyDmeOEG5h5Q/1mo/xxYN6zClle9yMhIVqxYwVNPPYVrRVN4q4uUW3BrKdz6C9JCCu937Myh0CkMf3kCRmZW3LgBzqXXQS7wzbFveGvvWwB81Psj5vadq9eSB5IEMrNF/yIPiLV9+2obaAE4dOcQwYnB2Jra8miTR/XblrziuBWawk96OAbG4DkceqyEMZHQbTm4DwGVIcSfA/+3xWxGe3vCtR9FEFHPevWCMWPEFNBvvVX51+nh0wP/l/x5v9f7GBkYsenqJpr90oyfT/1MrjpXew2WJEmSJKnA/w7/r0KBFoDFX57nk5GvEPGrBwOtnheBFpUReI2GPtvg0bvQ5n81PtACoFarSU5ORq1W67splWdVH1p/CqPuiKm0vR8X55yxp+hjNoXIhe7Mf3wai+effeBLvdn9TRY8sgCATw9/yscHP5Y1XKQaTQZbtCE/2FLNhxAtu7AMgPEtxmNubK63dmRkwMmT4rYsjqsnxtbg9wz02ymmke70G7j0BlSisO7Z10W21t5eeg+8fPmlGOe7axfs3l351zEzMuPz/p9zZsoZOnl0IikziRk7Z9Dl9y6cDXvwCYAkSZIkSeX3+eHP+eCAmAf4i/5f3D/QkpMKQX+StqkL77ZuxyuP/Iq1WZKYTajNPBh9D3pvEp1GcphQ9WRgKKbS7rVeTK3d7muwboSlSQrTBizi/Q4dSdvYUcyUmZ1S5su82f1Nvhn0DQCfHf6Mjw58JAMuUo0lgy3aUFCvpb9+23EfKVkprL+8HoDJbSfrtS2nTkFmJri5iTRRSc/MnKHRNBh4CEYHQ/vvwKm7eCz6SGHgZU93uPINpNzWafMaNoRXXxW3X3mlfFNB308btzYcf+E4vw77FVtTW86Gn6Xz752ZsWMGCRkJD91eSZIkSarLFEXh00Of8uGBDwGYN2Aec3rNKX3jhEtwZoao9XHyBSzST5GVY8zRe09A/39h5HVoMRvM3XT4CaSHZuYCzd6CEddgwAGOhkwkM9sEi4yzeTNlesDplyH+QqlPn9VtFt8O+haAz//7nPf3vy8DLlKNJIMtDyvlDqTeEemNzj313Zoybbi8gdTsVBo5NKKbVze9tiV/CFHv3tV6Mpy6ycILms6EQUfh0WKBl5jj4P8W/FMfdraHgM8gIQB08OM3dy54ekJQUF6hvIdkaGDI9E7TufrqVZ5s9SRqRc3Pp3+myc9N+NP/T9RKDU7nlSRJkiQ9UStqXtv5Gh8fFJXt5w2Yx+yeszU3ys2A2ytE9uyOVnD9Z8hOJD67Pu+umk/TOSF4TVgjZrhRyUuVGk2lAte++D61kubvh/LWiq+JzWoEOclw4zfY2VZ05t1aDjmavWlvdHujIOAy78g8pm2bJod+SzWOLJBbigoVwQn6C04+D07dYNAx3TSwEvov68+BOwf4vN/nvN/7fb22ZeBA2LcPfvkFXn5Zr02RyistDEI2wb2NEHWwcKpFEGN1vUaLxalblaX3btkCo0eDkRGcPQutW2vvtf+99S8zds4oKKDbyaMTPw39iS5eXbT3JpJUR8lirbon97mkD5k5mUzaPIm1gWsB+GHID7zW5bXCDZJvws1FopBqZqy4T2UInqO4qZpOi4EDyMoyYOlSmKzfJGydyczMJDw8HHd3d0xNTfXdnCq1ejVMnAhGRmou7j1IM6OFcG8TKDliAxN78HsWGr0ENk0KnrfwzEJe3v4yCgqPNX2MlY+vxMzITD8fQqqTHuY3VQZbSlGhHXpsEtz5P2jxnijWVQ3dSbiD3w9+qFBxZ+YdfGz1N9tMVpaYxi89HS5dqvzsMpIeZcRA6D9iRqPwPaDOLHzMxAE8hoHnSHAfDCa2Wn3rxx+HjRuhUycxfbihofZeOys3i59P/czcg3NJzkoGYHKbyXwx4As5a5EkPQR54a97cp9Lupacmcxjax5j3+19GBsYs/yx5WLWS3U2hPwDNxeK6YHzWXhBg6nQ4AUyDDzo0AEuXxZF8devl5nPtdWTT8KqVaKMgL8/WBpEwK0/4eZiSL1buKFrP2g4TXTkGZqw4fIGntz4JFm5WfSu15stE7ZgZ2anr48h1TEy2KJl5d6higKbvSE9VIwrdRugu0ZWwCcHP2Huobn09+vPvkn79NqW48ehe3dwdISoKDGlr1SDZadAxB64txnCtkFWfOFjBsbg3FsUs/MYDjaNH/rtwsKgWTNISoIffoDXXnvwcyoqIiWCOfvmsPT8UgAsjC14p/s7vNX9LSxNLLX/hpJUy8kLf92T+1zSpciUSIatHMa58HNYGluyafwmHnFrDDd/h6DfISMib0uVmAWx0XRRSDUvE3bWLPjuO3B1FR1xTk76+yy6lpSUxKlTp+jcuXOd+FuNj4dWrSA0FKZPh19/zXtAnQvhu8XQovAdhRnUZi5Q/3loOJWDMXd5dPWjJGUm0dq1NTuf2ik7wySdkMEWLSv3Dk26Adsag4EJjE0AI/3N8FOWXHUufj/4cS/pHivGrODJVk/qtT1ffgmzZ8Njj4kMBakWUeeImYxCt4ol+brm41YNRdaLxzBw7QOGlUsBXbhQ/EBbWopeMJ8qStQ6GXKSN3a/wfGQ4wB4WHvweb/PmdRmEoYGWkypkaRaTl74657c55KuXIi4wKjVowhODMbVwpH/Bs+hUdx+CNsJ5F1imLlCgxegwRSw8tV4/r59Yng5wPbtMGyYTpuvd+Hh4SxevJipU6fi7u6u7+boxL//wiOPiNs7dsDQocU2SA0WQbqg3yE9PO9OFbgP4rbTYHrt+ZLQlEg8rT3ZMmELHTw66LL5Uh30ML+pMq/gYUTlTfns1LVaBloAdgft5l7SPRzMHRjTbIy+m6NRHFeqZQyMRBCl/QIYeU1UoG/3DbgOEFkuKTfh+o9wcAisd4ADw8S00knXK1Rkd+pU6NEDUlNFzZ+qChd38erC0eePsnbsWvzs/AhLDuP5f56n/eL27LixQ1bFlyRJkuq0TVc20ePPHuSmBvO9pwP3GhjT6NJbELYDUMQsnT3XioL7bf5XItCSkADPPituT5tW9wItddXAgfD66+L2889DTEyxDSx9oPWn8Ohd6LUB3B4BFAjfjV/ALO74Kvzq5Yhheii9/upVUCNIkqqjahFs+eWXX/D19cXMzIwuXbpw6tSpMrddsmQJvXr1wt7eHnt7ewYOHFhi+2effRaVSqWxDBkyRPsNj6j+Uz4vObcEgEmtJ+m9mFRODhw5Im736aPXpki6YNMYms2CAf/C47HQa6Po2TL3hNx0CN8pppXe1kTMcHRyKgSvh8y4+76sgQEsXgzGxqIX7O+/q+4jqFQqxrUYx5VXrrDgkQXYmtpyMfIiw1cOp8/SPhwNPlp1by5JkiRJ1ZCiKPzv0Cf8tX0Mq5xSuesLr1vEYZwRAaaORab83Qc+48DQpMRr5OaKYqkhIdCwISxYoPvPIenPvHliWHhEBIwfD9nZpWxkYAzeY6D/Hhh5E5q/C6bOGGVGMd08ltu+sN4lnVU7xzN3//tyJkmpWtJ7sGXNmjXMmjWLjz/+mHPnztGmTRsGDx5MVFRUqdsfPHiQiRMncuDAAY4fP463tzeDBg0iNDRUY7shQ4YQHh5esKxatUq7DVcUMSsLiCJO1VB4cjhbr20FYEqHKXpuDVy4AMnJYGur3ZlkpBrA2Bq8H4Muv8PoezAsANp9nZf1YiKmTw9aAkfGwUZn2N0FLrwvApq5GSVernlz+Ogjcfvll+HmzaptvqmRKW92f5Og14J4u/vbmBmZ8V/wf/T8qyejVo3iYuTFqm2AJEmSJFUDafGX2bS2Jc/encs/HjDSCgxVgEsf6L4SRoeK3/cH1Gl7913YtQvMzWHNGjE0WKo78v/fraxg/354440HPMG6AbSdD6NDoMcacO2PgQqGWcImD3gp5As2rW5GSuwFnbRfkspL78GWb7/9lilTpvDcc8/RvHlzFi5ciIWFBX/++Wep269YsYKXX36Ztm3b0rRpU37//XfUajX79mkWfjU1NcXNza1gsbe3L7MNmZmZJCUlaSwPlHQFMiJF3QnH6jk97F/n/yJXyaWHdw+aOzfXd3MKhhD17KndWWSkGkalAruWoudrwL8wNg76bIcmM8G2uSiKFnsKAr+A/QNgvT3sfwQC50PMSVEbBpgzB3r1gpQUUd0+K6vqm+5o4chXj3zFjRk3mNJ+CoYqQ7Ze30qbhW0Yt24cl6IuVX0jJEmqcSqSwRsYGMjjjz+Or68vKpWK77//XncNlaTS5GbC3bWk7OqB2Y4WjMm9jKcRpBta5WWxXIWBB8F3Ihg+ePripUvhm2/E7WXLoH37Km19tWZubk67du0wN6+e5QiqUqtWIjtZpYJffoFFi8rxJEMTqPeEyJoaeQOav0u6oQ3uRvC4ch2r3W1J3tkF7qwstbNOknRNr8GWrKwszp49y8D8yliAgYEBAwcO5Pjx4+V6jbS0NLKzs3FwcNC4/+DBg7i4uNCkSROmT59ObGxsma8xb948bG1tCxZvb+8Hv3Fkfr2WHuX6YdE1taLm93O/AzClvf6zWqAw2CKHEEkajCzBcxh0+A6GB4pei65/ge/TYO4ufiwj/oULc2BPV1Hv5eBwDK8vYM3CszjY53L6dGGmiy542XixeORiAl8O5IkWTwCw/vJ6Wv/WmgnrJ3Al+oruGiNJUrVW0QzetLQ06tevz/z583Fzc9NxayWpiPiLcPYN2OwJR8djFXcMAxUcyjThSuOPMR8bk5fF0qTcL3nsGLz0krj90UcwblwVtb2GsLOzY9SoUdjZ2em7KXrx6KPw+efi9quvwsGDFXiydUNoOx/zsdFca/o5BzNNUStgHX8Kjj2FsskDzrwGcf5V0XRJKhe9zkYUFhaGp6cnx44do1u3bgX3v/POOxw6dIiTJ08+8DVefvlldu/eTWBgIGZmoibJ6tWrsbCwwM/Pj6CgIN577z2srKw4fvw4hqWkVGRmZpKZmVnw76SkJLy9ve9fcfjoRLi7Glp9Cq0+rOAnr3p7g/Yy6O9B2JraEvZmGBbGFnptT04OuLiIKd9OnIAu1TMZSKpuFEVkkUXsg8h9EHkIshM0NsnGhj3+PTl8tQ9jX+5Np8EdxDhfHQqIDOCTQ5+w4coGAFSIWi/v9XyPNm5tdNoWSapu6vrMOF26dKFTp078/PPPAKjVary9vZkxYwazZ8++73N9fX2ZOXMmM2fOvO92lTqPkaTSZMbB3VVw6y+IO1twd0g2/JkEN+x6sOCxDbhauVb4pYODoVMniIqCxx+HtWtFHba6LDs7m/j4eOzt7TE21u25S3WhKPDUU7BqFTg6wqlTUL9+xV8nJi2GtzaNo17cQZ63gXpFd6d9W6j/HPg+JeoKSVIF1NnZiObPn8/q1avZtGlTQaAFYMKECYwaNYpWrVoxevRotm3bxunTpzlYRrjU1NQUGxsbjeWBovMKYzr30MIn0b78wrhPt35a74EWEIVx4+PFQbSDnKFNKi+VSgwtajIDem+Gx2NgyDkxy5HHCDC2wZgkhrfbwZcT36VTfDeUtXawbwBcnCuCNDmpVd7MVq6tWP/Ees6/dJ7Hmj6GgsLawLW0XdSWEStHcPxe+TL1JEmqXbSRwVselcrQlaR86hwI3QFHxosCGGdehbizqFVG7MmyZlgo+N1VYdDmM5Y+dahSgZakJJHFEBUFbdqI4UN1PdACEBMTw2+//UZMiSl56g6VCv74Azp2hNhYGDVKXDNUlJOFE38+uQ+LdvNpdNeAwaGwPdMatcoY4s+LSRk2ucN/YyFkK6hLq8orSdql18Ock5MThoaGREZGatwfGRn5wNTZBQsWMH/+fPbs2UPrB1RbrV+/Pk5OTtzUVhXN1GBIuwcqQ3CqfikaUalRbL66Gag+Q4g2bRLrkSPByEi/bZFqMANDcGgnZjnquxUej4MhZ8lq+S37rj1KXIo9KnUaRO6HS5/A/oGwzhZ2dYKzM+HuWkgLfeDbVFYbtzZsHL+RC9MuMKHlBAxUBmy/sZ3uf3an/7L+7Lq5S04ZLUl1SExMDLm5ubi6al6curq6EhERobX3mTNnDomJiQXLvXv3tPbaUi0WfxHOvQmbveDQcAheC+pMFLvW7LIdiscthcF3k/E3cGPPM/v4oPcHGBpUvOheSgoMHQrnz4OzM2zZIgviSprMzWHzZnB3h8BAGDJEBOgqykBlwLs932X/s4cINPRkRHAyLkE5bLUehNq+nQiw3NsAh0fBJk9xbhjnL9JrJKkK6DXYYmJiQocOHTSK2+YXuy06rKi4r776is8++4xdu3bRsWPHB75PSEgIsbGxuLu7a6XdBVkt9u1EvYlqZtn5ZWSrs+ns2blaDGFQFHEABXjsMb02RaptDAzBoT0mrd/AacxmPF+PoeW7AWyL/A3qPQkW3qDkQtwZuPYDHB0vTio314MjE+DqDxBzCnK1W123tWtrVj2+iquvXOWFdi9gbGDMgTsHGLpiKK0XtmbZ+WVkafk9JUmquyqVoSvVTWkhcPlr2NEGdraBq9+KCR9MnaHJ69zqsp7OoSYMPbOTyJxcxjYfS8D0APr5VW7mzbQ00dF27BjY2cHu3VCvnnY/klQ7eHrCnj2FQ4mGDhWBusro6dOTi9MvMrHlRGJzFUad20OHYIUbnddD01lg5gqZ0eLccFd72NEaLn8JqTJQLWmX3hP4Zs2axZIlS1i2bBlXrlxh+vTppKam8txzzwEwadIk5syZU7D9l19+yYcffsiff/6Jr68vERERREREkJL315iSksLbb7/NiRMnuHPnDvv27ePRRx+lYcOGDB48WDuNjj4i1tVwCJGiKAVDiKpLVou/vxina2EBjzyi79ZItVWbNvDbbwYEhrRk5KxprA1dAaOD4dG7YjrKRq+IAKnKANKCIXgNnJsJe7rAelvY21P08N1dAyl3tNLL0cixEb+P+p2g14J4o+sbWJlYcSnqEs9ueRa/H/yYf2Q+celxD/0+kiRVTw+TwStJWpOVAEF/iSG2m33g/DuQcFHUN/MeA723kD3qDl9neNJ81VOcCTuDvZk9K8esZO3YtThZOFXqbTMyYPRoUfTU2loEWtq10+YHk2qbli1h714RmDt2TATq0tIq91oO5g6sfFx8hx3NHTkfcZ6Wq5/ki1RHskbegj7bwGccGJhA4iU4Pxu2+MC/feHm75BVibFMklSM3oMt48ePZ8GCBXz00Ue0bduW8+fPs2vXroKU2+DgYMLDwwu2/+2338jKymLs2LG4u7sXLAsWLADA0NCQixcvMmrUKBo3bswLL7xAhw4d+O+//zA11dKsQQX1Wnpq5/W0aN/tfdyIu4GViRUTWk7Qd3OAwqyWIUNEmqAkVZVnn4U33xS3J0+GM2cASx8xHWWnn2HoORibAP33QuvPwGMYmDiIGY+ij4oevqMT4B8/2OQGB0dCwKcQthMyKj+e2tvWm28Hf8u9N+4xf8B83K3cCUsOY86+OXh968XUrVMJiAzQxi6QJKkaqWwGryQ9tJw0MXT28GOw0RVOPi+G2KKAcy/ovAgei4BeGziBCx3/6MY7/75DZm4mQxoO4dLLl5jYaiIqlapSb5+VBWPHigtnS0vYtQs6d9buR6wtSpu8oy5r104E5qytRaBu9GgRuKuscS3GcenlS4xsPJKs3Cze3/8+7ZZ04kiuLfRcC2Miocvv4NJXPCHqEJyaIv5uDo2CO6t0Uv9Pqp30OhtRdXXfisNZibDBARQ1PBYmpqatRoavHM6OGzt4tdOr/DTsJ303B4BWreDSJVi+HJ55Rt+tkWq73FzRE7JzJ3h4wOnTYl0mRYHkGxBzAmJPQuwpUUhNySm5raUvOHQEx47g0EEsJvYVbmNWbharAlbx/cnvOR9xvuD+/n79ebXTq4xsMhIjA1ncSKod6vpsRGvWrGHy5MksWrSIzp078/3337N27VquXr2Kq6srkyZNwtPTk3nz5gGiqO7ly5cBGDZsGE899RRPPfUUVlZWNGzYsFzvWdf3eZ2VmwHhu0WQJXSL5gWibXMxvNb3KbDyBSA+PZ739r3HorOLUFBwMHfg60e+5rm2z1U6yAIiE2HCBNi6VXSy7dwJffo85GeT6pxjx2DQIEhNhcGDYf16sLKq/OspisKKgBXM2j2L6LRoAF5o9wJfDvwSR4u8GYpSg+HOSri7EhKKdIIZWoDnSKj3BLgPBSPZe1yXPMxvqgy2lOK+OzRsNxwcAlb1YVSQfhpYhqsxV2n2SzNUqLg+4zoNHcp3UlaVbt6ERo3A0FBUoHdw0HeLpLogMRG6dYMrV8Q0k4cOVTCrKjdDFEyLPQVxpyH2NCRfL31bq/pg3x4c2othSvbtwLx8MzUoisKR4CP8eOpHNl3ZRK6SC4CHtQdT2k9hSvspeNp4VqDhklT9yAt/+Pnnn/n666+JiIigbdu2/Pjjj3TpIgrs9+3bF19fX5YuXQrAnTt38PPzK/Eaffr0KXNWxeLkPq9DcjMgfA8Er4OQLZCTXPiYpS/UmyiyO21bimlfALWiZvmF5cz+dzaRqWKI27Ntn+XrR76u9JChfNHRosPj5EkwNYVt26DIZFySVCGHDonaLenp0L49bN8ODzsCMy49jtn/zi4ou+Bk4cQX/b/g+XbPaxaATggU06DfXQUptwrvN7ISgRefceA+RAZe6gAZbNGy++7Qix/Bpc/A9xnovlw/DSzD9G3TWXh2IaOajGLLhC36bg4ACxbA22/DgAHw77/6bo1Ul9y8CV26QFwcjB8PK1aIoF+lZSVA3FlRbDfurFiK/vgWZe4B9m0LF7u2YN1A1IspQ3BiML+d/o0//P8o6HExVBkyqskoprSfwqAGgyo1C4Qk6Zu88Nc9uc9ruewUCN8lZlUJ3a4ZYLHwAu9xogfesUtBgCXfoTuHeGP3G/hH+APQ1KkpC4cvpI/vw6ee3LghLoyDgkTn2pYt0LP6jbivVqKjo9m4cSNjxozB2dlZ382plk6eFAG86GhRXHnnTmjW7OFf92jwUaZtn8alqEsAtHJpxbeDv2Vg/WLRQUURnW731uXNahlc+JiRpRiS7v24WBtbP3zDpGpHBlu07L47dF9/iDwAnRZCo5f008BSxKbF4v2dN+k56RycfFArP5ra0LMnHD0KP/0Er76q79ZIdc2BAyIFNScHpkyBRYtKnHc+nKx4iDsnlnh/iD8HSdeBUg6rRlZg1wrsWovFvo34t7HmMSYzJ5NNVzfx25nfOHz3cMH9XjZePNvmWZ5r9xz17etr8UNIUtWSF/66J/d5LZQZC6HbIGSzCLTkFiliYe4petl9xoFT11ID+zfjbvLO3nfYdHUTADamNnzY+0NmdJ6BqdHD1zQ8flxcEMfGgp+fuCBu0uShX7bWCw8PZ/HixUydOlV7s6bWQkFBIpB344YonrtlC/Tu/fCvm52bzS+nf+GTQ5+QkJEAwIjGI1jwyAKaOJXyBVYUkfUcvFZkk6UVmb3IwBTcB4HXY+A5Asxk8Ky2kMEWLStzh6qzYZ0d5KbBsEtg10JvbSzui/++EAWf3NpxdurZhxprqy2RkeDuLo5LwcHg7a3vFkl10dq1MHEiqNXw+uvw3XdaDrgUl50CCRcg/oKo/RJ/HhIDNE+Mi7KsB7atwK6lCL7YtgSbJmBoyuXoyyw6s4i/A/7WmLWov19/JreZzJhmY7AyeYgBzJKkA/LCX/fkPq8lUm6LoUEhmyH6P1EvMJ9VfdGb7j0GHDuXmTkZkhTC/w7/jz/8/yBbnY2ByoCXOrzEJ30/wdlSOxeDq1bB88+LIqYdO4qhQ67lG01b58lgS/nFxMCoUSKwZ2ICixeLyRC0ITYtlk8PfcqvZ34lR52DocqQ59o+x4d9PsTH1qf0JymKyHa+twGCN0DKzcLHVAbg1B28RoPXo2Ct/9IOUuXJYIuWlblDY8/A7k5gbAdjY+87JECXsnKz8P3el/CUcJaPXs4zbapHFdolS2DqVPHDe/q0vlsj1WVLl0LebPK8/z58/rmOG6DOETVf4i+KQEzCRRGMSQ8tfXuVIVg3AtsWYNuSbOvG7IsL56cru9gZtA8lL3PGwtiCx5s9zjOtn6G/X385zEiqluSFv+7JfV5DqXMg5rjIYAnbBomXNR+3ayMu3LwfE7fv03MQmRLJvCPzWHhmIZm5mQAMaTiEBY8soIWLdjoLMzLgjTdg4ULx7xEjYPVqMfuQVD4y2FIx6enw1FOwSSRo8cILInteW7OdXou5xlt732Lb9W0AmBiaMLX9VN7r9R7u1vf5/1EUMX30vY0iQBrvr/m4TVOR7eIxApy7i2nXpRpDBlu0rMwdevV7OPcGeAyHvtv01r7i/r74N89segZ3K3fuzLyDiaGJvpsEwLBhIo30f/+D997Td2ukuu7XX+GVV8TtL76AOXP02x4AMuPEj3NCQOGSGAjZiaVvrzIk28KXm7mmHIgL51hSPFey4GoW2Fl5ML7FeCa0nEAnj07VIrtNkkBe+OuD3Oc1SEaUmEEobKcYHpQVX/iYylBM0+w1GrxGgVXJwsnFhSeH892J7/jl9C+kZacB0MunF5/1+0yrQ8xv3oQnngD/vGvK99+HuXPBSE6kVyEy2FJxubni2mLuXBHjaNUK1q3T7rC1Y/eO8eGBD9l/ez8AZkZmTO84nTe7vVm+iQtSgyHkH5GVFnVIc4ZLYztwHwweQ0WB3XJOqiDpjwy2aFmZO/S/cXBvPbT5AlpUhys1MZtJxyUdORd+jv/1/x/v9aoeUY2kJHB2hqwsCAyE5s313SJJgq+/hnfeKbz91lv6bU+pFEVkvCQEikBM4iVIvCJ6OIsWQSzmbjZcy4Kr2RBr5Eg9r0F0b/EcTbz7o5IZL5IeyQt/3ZP7vBpT50DsycIAS9xZNOp8mTiIQpueI8QFmYlduV72ZtxNFhxbwNLzSwsyWTp7dubzfp8zsP5ArQbgN2wQw4aSksDREf7+G4YM0drL1ynp6encunWL+vXrY66t9Iw6Yt8+ePJJMduplZUYVjRxonbfY//t/Xx44EOO3TsGgLGBMZPaTOKdHu/Q2LFx+V4kKxEi9uRlrO2AzBjNx+3b5wVeBouaSzLrpdqRwRYtK3WHKgps8oCMCBh4CFy0UJVJCw7dOUTfZX0xNzLn3hv3CueJ17M1a2DCBGjcGK5ereIaGZJUAXPnwiefiNtvvw1ffllDvp/5QZjEy2JJuiKCMElXITO6zKelKSoSjZ0xs2+DnUsnVNaNxRAl60Zg6lRDPrxUk8kLf92T+7waURRICYKIvWKK5sj9kJ2kuY19u7yLraHg1A0qECA/E3aGBccWsO7yOtR5NV26eXXjvV7vMbzRcK0GWeLiYOZM+L//E//u0UMMG/Ly0tpbSFKFhIeLgMvBg+LfEyfCDz+IDl9tURSF3UG7+eK/L/gv+D8AVKh4vPnjvNXtLbp4dSn/i6lzRbA1bCeE5wdbizCyBtd+4PaIKLZr3Uiep1UDMtiiZaXu0JRb8E8DEW0cm1ht5lQfvXo0W65tYVqHafw24jd9N6fAE0+IlL533hEXs5JUXSiKyGp5913x70mT4PffwbgmdyRkxoqgS9I1suIDiI74DyXpGq5KCsb3+402ti0MvFg1EAXcrBuK22au8gde0gp54a97cp/rWXo4ROyHyH0QsU9zqlgQ2StuAwt7s80rNnwkOzebDVc28OPJHzkecrzg/mGNhjG7x2x6+vTU+lDS9evFUNyoKPHT8Pbbov5Zjf7trAZSUlIICAigVatWWFnJgveVkZMDn34qhhap1eDkJOq4jB+v/dOYo8FH+fLol2y9vrXgvs6enXmt82uMazGu4qUc0iNFllv4LhGQLZ71YuEFrgPAbQC49geLcgxhkrROBlu0rNQdems5nJgMjl1h8PH7v4COXIi4QNtFbQG48soVmjo11W+D8oSFQb164uB39iy0b6/vFklSScuWicJqubliOsF162pfUb/k9DgOXV7GxRvrSYg+TX2jbBoZQyNj8HnQCbKRpZjtwqpB3lI/b/EDS18wfPipQqW6QV74657c5zqWHiHqMkQdgsiDIvOwKANjcOoheqrdHhGZLJUY3hmWHMYf5/5g4dmFhCWHAWJYwxMtnuDt7m/Txq2NFj6MpogIEWTZuFH8u1kz+PNP6NpV629VJ8maLdpz5owY3hYQIP49apSo1+dZBfGJS1GXWHBsAasurSIrNwsAV0tXpnWcxovtX8TLphLpXopaTJ4QsUdkwUUfAXWW5jY2TcClL7j0EYuFx8N/GOmBZLBFy0rdoadegpuLoemb0H6BfhuYZ+SqkWy7vo0JLSew6vFV+m5OgQ8/FL0dPXvCf//puzWSVLbt22HcOFHdvksX+OcfcHHRd6uqRmpWKjtv7mTLtS1sv76d9Mx4GuQFXpqYGtLLwZ3WFha4kYpxehgaNQRKUIG5R17gxa8wAJO/tvACA1klURLkhb/uyX1ehRRFZKpE/SemY446LDILNajAob3oiXYdAC49RQC7EnLUOey8sZPf/X9n+/Xt5Cq5gLiwm95xOi91fAk3K7eH/FAlZWbCzz/DZ59BYqIofDt7NnzwAZjKWLvWyGCLdmVlwfz54jokOxusrcUkHTNngpmZ9t8vKjWKxWcX89uZ3woCoAYqA4Y2HMqU9lMY1mgYxoaVTP/KSYPoo3kZcvsh/qzm9O8A1o1FaQvnXuDSS5yDyaxkrZPBFi0rdYdubylmCem1CbxH67V9INLYev7VE0OVIVdeuUIjx0b6bhIgpgH09oaYGJEpMHasvlskSfd3/DgMHw7x8WLc+fr1IvBSm2XnZnMk+Aibr27mn+v/cCfhjsbjrZyaMN63C4NcGtDa0grT9BBIvSWGU6bchpyU+7+ByhDMPcHKFyzqgWU9sPTJW9cDC28wsqiyzydVL/LCX/fkPtcidY4oVB59TPQ0R/8HaSHFNlKBfZsiPc69wdThod72asxV/u/C/7H0wtKCiziA7t7debnjy5UbslAOiiKm1X3nHQgKEvd16AB//AFttJ84U+fJYEvVuHQJXnwRTp4U//b1FWUNxo2rmlhEdm42G69s5Nczv3L47uGC+92s3JjcZjKT2kyiufNDzhaSFS+CvPkZdPH+lOgYM/cE5555S3eway07v7RABlu0rMQOzYyDDXmFZ8dEgpl+u74VRaHvsr4cvnuYKe2nsHjkYr22p6g//xRDM3x8xI+0nAJQqgmuXoXRo+HaNTH+/IcfYNq0utE5oCgKV2KusP36dnbc3MGR4CPkqAunKDQxNKGXTy8GNRjEAL8BtHVtg2F2vAi8pN4RwZfU25ByR6xTg0Gd+eA3NnUCCx8RhLHwAUtvEYSx8BaZMeYe8gShlpAX/ron9/lDyIiB2FMQe0IEWGJPlgwwq4xE5kp+b7JLbzCxf+i3jkyJZPWl1fwd8Ddnws4U3O9o7sjkNpN5sf2LNHNu9tDvU5bTp0UtlkOHxL/d3OCLL0RtM0M5qV2VkMGWqqNWw4oVMGcOhIaK+3r0EHX7unWruve9FnONP/z/YOn5pUSnFU5g0M6tHU+3fpqJLSfibq2F/+ushCIZdv9B3BnNKaYBDC3AqQs4dRczHTl2ATMtVg+uI2SwRctK7NCQLXB4tEjVGnlN381j983dDFkxBFNDU26+drNy4wKrgKJA27Zw8SJ89ZX4wZakmiIpCZ57rnBc+qRJ8NtvYFHHEjASMxL599a/7A7aze6g3QQnahZ2tDezp69vXwb4DaC/X3+aOjXVLMSoqCEjMi/4clcEZNKC827nLQ/KjAFQGYCZuwi8FF3MPUWBuPy1YRXkBUtaJS/8dU/u83LKSYf48xB3GmJOisBKSlDJ7YxtRM0+5+6ix9ipa6WHBRUXnRrN5qubWXd5Hftv7y8YJmSoMmRIwyFMajOJR5s8iqlR1Y3dOXlSzNK3c6f4t5kZvPWWKCQva7ZWrbi4OHbv3s3gwYNxcHi4bCipdKmpsGCBuDZJSxP3DRoEH30kgi9VJSs3i63XtrL84nJ23NhR0JFloDKgr29fxjUfx2NNH8PVylU7b5iTJo5hUf9BzHGxZCeW3M7STwRgHLuAYydRQ0pmG9+XDLZoWYkdevoVuPErNHoFOv2s17apFTWdlnTiXPg5ZnWdxTeDv9Fre4o6eBD69RMXpyEhYP/wnTySpFOKIn6QZ88WPSKtW8Pff0OrVvpumX4oisL12OvsDtrNv7f+5eCdgyRnJWts42LpQu96velTrw996vWhhUsLDFQG93tRyE6A1Ht5QZjgvPU9SMtb0kNBnV2+Rpo6iiyYgsVTFIwzcxf/tvAQMysZyCkz9EVe+Oue3OelyM2AhEtiqtW4syLAknCpZE8wgE1TcSHi1E0EWGyaV6qgbVnCk8PZcm0L6y+v5+CdgwUBFoCuXl15qtVTjG8xHmfLqu2BPnZMBFn27BH/NjCAp58WdVp8fKr0rSVJ50JDRYBl2TIxOQLAgAHivt69q/a9Y9JiWBe4jr8D/ubYvWMF9xuoDOhdrzdjm41ldNPReNposZqvoobEKxBzLG85WbJ4N4ih37YtRODFoSPYtwf71rIzqwgZbNGyEjv0n0aQchN6bwGvUXpt2/rL6xm3bhxWJlbcfv02ThZOem1PUaNHw5YtMH26qP4tSTXVgQMwYYKY4tLYGD7+WPTw1fVhcTnqHM6EnWH/7f3su72PY/eOkZGTobGNvZk93by70dO7Jz18etDJoxPmxuYVe6P87Ji0UFEbIS0E0kNEQCY9VNyfHgq56eV8QZVImzVzE1OsmrsX3jZzA3O3wrWRdd0YP6ZD8sJf9+r8Ps9OgviLImsl3h/iz5UdWDFzAYdO4Ng5L82+k1aGBBWlKAoXIy/yz7V/2Hp9K6fDTms83t69PeOaj2Ns87E0dGio1fcuLicHNm+G77+Ho0fFfYaGIpvzvfegYdW+vVRMbm4uGRkZmJmZYSjHaunErVswbx4sXSr+HkDMrjVzJowZU/XTmd+Ov826y+tYf3l9iWNBB/cOjGw8klFNRtHWra3Wp3AnKwFiT4sMmNhT4nZGRMnt8gMwDu1F5ot9W7BrAya22m1PDSGDLVqmsUMNYuCfBmJ87thYkUqqJznqHFr+2pJrsdf4uM/HzO07V29tKe7WLfEDrShw5Qo0rR6zUEtSpYWHi7ot//wj/t2xo/hhbtFCr82qVjJzMjkddppDdw5xOPgwR4OPkpqdqrGNsYExbd3a0s2rG129utLVqyu+dr4PfwKRnyGTFgrpYYXr9DARiEkPL1xKu8Aqi6G5yIQxc80LwrgWWVw0bxvbycBMOdT5C389qDP7XFGLulEJFwuX+AulDwUCkQln30FcQDh2EkEWC68q+TtOzEhk3+197L65m11Bu0oMyezs2ZnHmz3O2OZjqW9fX+vvX1xcHPz+O/zyCwTnNcXICCZPFkGW+lXfBKkUsmaL/ty9K4Iuf/0lZjECMU30K6/A1Kng6Fj1bbiTcIcNlzew/sp6ToacRClS8NbLxovBDQYzpOEQBvgNwN68CoYMKIo4Z4o9LZb4cyLzLzOm9O0t/fICL61F9ottK7Cqr9XMv+pIBlu0TGOHRq6C09NEEbRHDj/4yVXoL/+/eP6f53E0d+TW67ewMa0+J1CzZsF338HgwbBrl75bI0naoSiiuNqMGZCQACYmIt30zTerZgrBmi47N5sLkRc4GnyUI/eOcCT4CBEpJXtMXCxd6OTRSSyeYl1l6fKKWpw0pIdDegRkFAnCZETk3Ze3zkl+8OsVZWAMps4i8GLqIrJn8tdmLuIxU+e8+53A2LZOBmfqzIV/NVLr9rmiiL/ThEtiZqD8dWIg5KSW/hwLL7BrKy4MHNqDQwdRgLuK/gazcrM4HXqa/bf3s+fWHo7fO64xPMjcyJxHGjzCyMYjGd5ouHYKZD6AWg2HD4vJC9avh/S8ZEAnJ9GZMH06eHhUeTOk+5DBFv2LiICFC0WdvqgocZ+Zmchyef55USLB4D6jo7UlMiWS7Te2s/X6VvYE7SEtO63gMQOVAV08uzC4wWD6+/Wni1eXKpmNDMib3j6kMPASf0FkCaYFl769oYXIgrFrCbYtC2+be9Sacx4ZbNEyjR164Tm4txFafwYtP9Bbm2LSYmjxawuiUqNY8MgC3uz+pt7aUlxyspgyNylJFFcbMkTfLZIk7QoLg5degm3bxL/9/EQ1+zFjas3vSJVQFIXbCbc5GXKS4yHHORFyAv8If43ZjvLVs61He/f2GoublZtuG5yTKoYvpUeKdUZE3rrIkh4JmdGlF517EANjEXQxdcoLxDgVWRw1b5s4irWRVY3/ktW6C/8aoMbuc0Ut6jglXYXEy5B0WdQcSLwsMtlKY2Cad3LfSiz56e5mVTvMOjs3G/8Ifw7eOciBOwf47+5/JTL7mjg2YXCDwQxuOJh+vv0qPqSykoKDYfly0WN/61bh/a1bi6ESEyfKDoPqQgZbqo/MTFizRgyx8/cvvL9ePTGBwuTJYgppXcjIyeDgnYPsvikmK7gSo1lrxcLYgp4+Penn24++vn1p796+6oIv+TLjICEv8JIQIDIJEwNFPazSGNuCTTOwbS4Wm2Zg2xQs6tW4TBgZbNGygh0aH4vN3vripHrQSXDqrLc2jV8/nrWBa2nh3IKzU89WaVX6ivr6a3jnHWjSBC5f1k30V5J0TVFg1SrxXc+fQrBPH5HR1a6dfttWk6Rnp3M+4jynw06LJfQ012JLn+XN1dKVtm5taePahjZubWjj2obGjo0xNqwGxW5zMyAjGjKjCgMwmdGQESWWzOi8x6NFZk15ZmAqjYFxYeDFxCEvKFPktolD3m0HzduGFtUmSFNjL/xrsGq/z7PiIek6JF/PW1+DpGvi32WduKsMwLqRCKzYtszrRW0hZorUwTTxKVkpnAw5yZHgI/wX/B/HQ45r9DyDmKK5n18/BvgNYEjDIfja+VZ5u/KFhcG6deJi8fjxwvutrUVw5fnnoXPnanNYkPLIYEv1oyhw9iz88QesXCk6k/N16QLjx8O4caKjWVeCE4PZdXMX+27v48DtAxpTSoPInOvq1ZWePj3p5dOLLl5ddDMCQp0DyTeLZBwGitvJN6BIZp8GA1NxLLdpCjZNxDHcprFYm1bPGblksEXLCnZo0F5sTjwiCqWNidZbFG5t4FrGrx+PocqQky+epINHB720ozRBQWKmlvR0kab63HP6bpEkVa3UVDF94FdfQUaGOHF9+mkx5l3WKqqcxIxEzkec51z4Oc6Gn+Vc+DmuxlzVGLucz8TQhKZOTWnl0opWLq1o6dKSFi4t8LH1uf8sSPqWky6CLhpLXiAmM7bYfbFiUWdW/v0MjPOCL/bFlvz77DTvN7bLu89O60WCq/2Ffy2k932uKCKgkhIkTsSTb4qT75S8dVn1AAAMTPKCKs0Le0VtmomTcR3NjpGrzuVa7DVOhpzkRMgJToSe4FLUJdSKWmM7ezN7evr0pL9ff/r79aelS0udHodu34atW2HDBvjvP7HbQfz59ukjAixjxoCldmaplqqADLZUb2lpsGmTyBLbv7/wbwzEtNGPPw6jRkGDBrprk6IoBEYHsv/2fvbf3s+R4CPEpsdqbKNCRQuXFnT17FpQL6+pU1MMdXUtm5spjvWJl8UMSImXxZJ84/7nNqaOYNUIrBvmLY3AqiFYNxDnL3qKFstgi5YV7NBjs7G5PR98xkHPtXppS2RKJC1+bUFseiwf9f6IT/p9opd2lEZRxJRpBw6I8Yz79skeE6nuCA6GOXNErweI7/4TT8D779fdqaK1KTUrlYCoAC5EXOBCpFguRl4kJav0DBELYwuaOTWjuXNzmjs3p4ljE5o6NaWBQ4OqT62tCooCuWmFgZesuLx1rEjlzb8vf8mME49lxZd/2uyyqAxE8MXYtjAAkx+MMbbNu9827z7bwvsK7rcFw8LsS71f+NdBOtnnuZliyE/qbVGkNvU2JAdByi0RZHnQUDtzD9GTad1IBFJsmomeTktfnXZuqRU1QXFBnA0/y+nQ05wJP8O58HOlHmu8bbwLeo571etFc+fmOg2u5ObCqVMiwPLPPxAYqPl4t26Fve6yFkvNoFaryc7OxtjYGAOZGl6tRUSI2kdr1sCRI5qPNWsGI0eKwEuXLrqdvVKtqLkac5X/7v7Hf8H/cST4CHcT75bYztLYkvbu7eno0ZGOHh3p4N6Bhg4NdReAAVDnQtpdSLwqhosmXxMBmKTrolDv/RjbgFUDUZDXqj5Y+YmCvVZ+YFmvSoPxMtiiZQU7dGNnbNJPQecl0PBFnbdDURTGrB3D5qubaevWlpMvnqxWFw2LF4s6FubmEBCg26iuJFUXZ8/CZ5+Jac/zjR4tiuj26CEDkNqkVtTcTbjLpahLBEQFEBAVwKWoS1yPvU5WblapzzFUGVLfvj5NnJrQyKERjR0bF6w9bTyrdzZMZSiKqD2TFZ8XiIkv5Xa8mP6x6L+zE7QTqMlnYCpOjIxtScq2xHbsBRls0SGtBFtyUvOCKcHi5Dj1LqTcEbdT7oiZv0rJPtNg5iaCKSV6KRuCsXXl2vUQUrJSCIwK5GLkRc5HnOd85HkuRFwoUWsFRAC3o0fHgp7hLl5d8LDWfQQjKAj27hXL/v2iWHs+Q0Po2VNc4I0dCz4+Om+eJNVJISEio+yff0QR6pwipehsbUUn9COPiKVhQ92fC4Ynh3My9KTIzgs9wenQ02Ue51q7tqata1vaubejtWtrWji3wNpU98dnslPysh9LyYZMD3/w8809RLDesl6RdT2w9AELHzC2qnTTZLBFywp26O8G2Jir4dE74j9Lx1ZcXMHTm57G2MCYM1PP0Nq1tc7bUJZ798QUuMnJ8O238MYb+m6RJOnXxYvw+eei1yP/qNq6Nbz8Mjz1FFhV/hgvPUCOOodb8be4HH2ZwKhArsZe5WrMVa7FXCM5q+wZhkwNTalvX58GDg1oYC+W+vb18bP3w9fOFwtjCx1+impAUUS9jILgS2LeOqHIOrHY/YlF7ksstTZNUhrYTkEGW3TogSeGOemiFzEtJG+5V2R9TwRYsuIe/EaGFpq9iwU9jg3Ev4308zeUkZPBtZhrXI6+zOXoywXB2Vvxt0rd3szIjDaubQp6fDt5dNJtyn0eRRHBlcOHxXLoENy5o7mNra2YiGDUKLF2qJ4lDqRyio2NZefOnQwdOhRHXcw1LGldQoKYiXXrVjFRSHy85uM+PmJYX+/eYmnUSPfBl/yhkWfCznAm7Aynw05zIeIC6TnppW7va+dbMFy7hUuLgoxhXRX5LiEnDVLvFMmevKWZVVnWrHRFmdiLoIuFN1h6ixnrLPLW5l5g4Vnmb5YMtmhZwQ5dAjZuTWDEVZ23ISw5jJa/tiQ+I57P+33O+73f13kbyqIoMGIE7NgBXbuKVDrDmlVUWpKqzOXL8M03ophu/jSbNjYwaRI88wx06iSzXXRFURTCU8K5GnOV67HXuRF7g+tx17kee51b8bdKnRWpKDcrN/zsROClnm096tnVK7jtbeuNlYmMoJWgzhVTaGcnQnYSZCWSFBeObbMnZLBFhwrOY859i41hvMhCSQvNC7CEli+QAmJIWH6vYH5PoZWvmE3CylfMqqWnA5qiKESlRnEt9hrXYq6Jdew1rkRf4XbC7RL1VfK5WrrSyrUV7dza0datLe3c2tHIsRFGOiiyW1x6upj15MQJsRw5AuHFOnCNjMTwoPxe8o4ddTtEQapasmZL7ZKbKzKe87PRjh2D7GIJo66uIiOta1exdOggRgnovK3qXG7E3eB8xHn8w/05H3megMgAwlNKzyJRocLP3o9mTs1o4tiEJk5NaOLYhMaOjXGzckOlr5NbRRF1wFLviAzM/HXKHTFddWpw2bPZFWdiD+aeIvBi7imyZSw8SMqxw7b5kzLYoi0awZa2M6Djjzp9/4SMBPov649/hD8dPTpy/IXjejkJKMvff4uLRhMTOH9ejFOUJElTfDwsWwa//go3bhTe36ABPPmkWGRBXf3JUecQnBhMUFwQQfFB3Iy7SVB8ELfjb3M74TZJmUkPfA17M3t8bH3wsfXB28YbTxtPvGy8ChZPa08sTWRlSlmzRfc0zmPKSi4xtMjr2cvv3SvS02eZ1/tnYqvTdhenVtSEJYdxK/4Wt+JvcSP2Bjfjb3IzTiz3+zu1N7OnuXNzmjk1o5VrYUFtZ0tnHX6CQpmZosbKuXNiOX0aLlwoeSFmYiJmDcrvBe/eXcwoJNVOMthSu6WkiNnB8rPVTp4Ux4KijIygTRsRSG3fXgRfWrYEUz1NPBuTFiOGa0eKjMArMVe4HH2ZuPSyg/TWJtY0dGioseRnC3tYe+g8S7CE7CRIvSeCL0UzOVPzsjnTQ++bHfMwGboy2FIKjZOUoVvBc4TO3js5M5lBfw/iRMgJXCxdOPLcERo5NtLZ+z/InTviIBAXJ4ZMvF99Em4kqVpSq0Xx6KVLYfNmUdk+X+vWIhV8xAiR8SJr41UPiqIQnxHPrfhb3Em4w92Eu9xJuMOdxDvcSbjDvcR7JGY+oPhnHhtTGzysPfC09sTD2gN3K3fcrd0L1m5WbrhZuWFtYq2/XqEqJoMtulewz3cMw8apnuidK+ily0uXNrbVe5qdWlETmRLJ3cS7Jf7W8gOfZdVjAtHT6mvnW9DDml8Yu7lzc1wsXfT2NxUZKYaWBgSI9cWLcOlSycAKiF7u/B7url1FcU199HJL+iGDLXVLRoYocn38uAi8HD8uCu8WZ2QkyjW0aSPOFVu1EmtXV/0cthVFITotmivRV7gSc0Ujm/BOwp0yMwlBzCLpa+dbkCmcnyHsa+dLPbt6uFm56b9+nqKIgEz+8NqCbNAwSA8jKToY27HnZLBFWwpOUv4wwuaZ+IcqqFMRadlpDF85nIN3DmJvZs/BZw9Wqzotly+LFNawMGjbVhwsjI313SpJqjlSUkQxtZUrYfduzYJqLi4wbBgMHgx9+4Kbm96aKZVDYkYi95LucS/xHncT7xKaFEpIcgghSWK5l3iv1GJ0ZTEzMsPNyg1XS1dcrVxxsXARa0sXXCxdcLZwxtnSGWcLZ5wsnDA2rDkHXxls0b3qsM/VipqYtJiCv4miS3BiMMGJwYQkhZD9gKLMRgZG1LOtR337+iV6Tuvb18fMSDfTQRenVkNoKFy9CleuiHOkK1fEEh1d+nPs7UWHVfv2YunaVdRzqKVxVqkcZLClblMUUQfz5Ekx/OjcObGOKyOJxNERmjcXowqaNRO3mzYFLy/9ddhl5mRyO+G2yDzMyzq8GX+zoMPqQUO2jQ2M8bTxLMgS9rbx1sgS9rLxwtnSWa8BGVmzRcsKdujmHtg8euTBT9CCzJxMHl39KLuDdmNtYs2+Sfvo5NlJJ+9dHqdOwdCh4o+/eXPYswc8PfXdKkmquWJjYft22LZNBF6SimXDN2smgi59+4o0ck9PeUJekyiKQnJWMmHJYYQlhxGaFEpYchjhKeFiSQ4nIiWC8JTwMqezvh87MzucLJxwsnDC0dyxYO1o4YijuSMO5g44Woh1/mJpbKmXnv7qcOFf11TlPs9R5xCVGkVkSmSJ73J4SnjBdz48OfyBgRQAA5UBntaeBb2cvrZiXd++PvXt6+Nl46W3odS5uSKgEhQEt27BzZtiWOj16+J2eum1JVGpxAwk+T3SrVqJ4Eq9evI4LmlKTU0lMDCQFi1aYGkph51KhQGYc+cKs+MCAsSxR11GAomZmTjmNG4sCvA2bAj164uh615e+qutmavOJSQppGAoaEEWY946JCnkvlkx+YwMjHC3csfD2qNElrCblVtBprCzhXOVdEbJYIuWFezQEx9h0+WTKn+/9Ox0ntz4JJuvbsbC2ILdT++mp0/PKn/f8vr3XzGVbWqqGEe8Y4eIrEqSpB1ZWaIw4rZtcOCAGMdf/Mjs7i7+/jp3FkOO2rQR2TBSzZealUpkamTBxWtUapTGEpkaSXRqNDFpMcSmx5brxKQ0RgZGOJg7YG9mj52ZHfbm9tibicXWzBY7MztsTfPWZrbYmtpqrCsbrJHBFt2ryD5XK2ri0+OJSYshOi2a6NRootOiiUqNIjo1mqi0qILgSmRqJDFpMeVuhwoVblZuhfWMrEUvpY+tD9623vjY+uBh7aG3YEp2tgim3LsHwcFiqPTdu2Kdfzur7FFMGBmJi5n8Hub83uamTUFeN0uSpE1paXDtmmYW3eXLIhhc2hDFfMbGItDr6yuW/Ns+PuDtLTrzTEx09CGKyVHnEJ4cTnBiMPeS7hVkPBZdIlIiUCh/uMLR3BFXK1dcLQuzg/MzhF0sXXCycMLZUmQJO5g7lCtjRgZbtKxgh94+iI1vnyp9r0N3DjFl6xRuxN3A1NCU7U9uZ0D9AVX6nhWxbh08/bQ42RgwQNSckFPYSlLViosThdQOHhRTfwYEiB7W4lxdC8fxtmwJTZqIXg0nJ503WdKRXHUu8RnxRKdGE5seS0xajAjCpInbcelxxKbHEpseK26nxRKfEX/fuhflZaAywMbURmOxNrHG2tQaaxNrjX9bmVhhZWIlatFkqnis3WMy2KJD+ecxy08uJ8MwQ+P7kP+9iU2PLfh3RQN4BioDXCxdCnsV89b5vY75i5uVm16GvCmKmI41PFzUQwgNFUtYmFiHhIgAS0REycB2ccbG4sKkfn2xNG5cuPj6ypmBpIeTnp7OjRs3aNSoEeayWI9UCTk5IjB8/XrhcuuWWG7fvn8gBkS2naurCLx4eYngi4eHWHt6is4+NzcxDFIfmXnZudlEpkYWZE0WzZ6MSI0oyK6MTI2s1G+Zg7lDYYZwXnZwfqZw/mOmOaaMbDNSBlu0pSDYkhCPja1dlbxHQkYC7+x9hyXnlgDgYe3BstHLGFh/YJW8X0UdPgxz54pedoAxY0SdCX1Vxpakuiw1VUwPeuqUmMHi9GnxI1rW0dvBQVwINGxY2JORv3h6inRTqe5QFIX0nHTi0uOIS48jPj2ehIwE4jPiiU+PJz4jnsSMRBIyE8Q6I4GEjAQSMxNJzEgkKTOJXKWUaF95ZQDzK1fFX6qc/PMYZgPl/Hu3NbUtGJpWWm9gfk+hq5UrjuaOOp9dIicHYmJEPZT8dVSUKEZbdImIEEvxGT/KYmIiLjC8vcHPr7DXt149EVzRZwq+VPvJmi1SVcofCnnrVmHW3t27Ighz754IPFfkWOnmJhZXV83FxQWcnQsXJyfdB6LViprYtNiCTOHI1MiCDOGiWZoxaTFEp0aXe6ID4KHOY6pFPP6XX37h66+/JiIigjZt2vDTTz/RuXPnMrdft24dH374IXfu3KFRo0Z8+eWXDBs2rOBxRVH4+OOPWbJkCQkJCfTo0YPffvuNRo0qOKtPFRTiSctOY+OVjby9920iUkT56WkdpjF/4HxszfQ7xSKIoQwffwz794t/GxvDa6/B/Pmy90aS9MXSEnr2FEu+1FQxjWj+TBeXL4vejHv3RGbMiRNiKY2jo2avRdEfy/zF0VEs+kotlbRHpVJhYWyBhbEFXjZeFX6+oiikZaeRmJlIcmYySZlJBUtylvh3cmYyyVnJBeuUrJSCdUJCApe5XAWfTHqQLl5dcHFwET10ZqJ2j0bvnYUjzhbOOFo4YmKomz/2nByRdZKQAPHxYomL01xiY8USE1O4Tkio+HvZ2YkLg/xjXdHjnre3WJyd5UxwkiTVToaGYriQj0/pjyuKCFzfuyeW/CzAopmAERHiOJ2VJYZbBgeX773t7MR5pJNT4Tmlo6PoEHRwELft7TUXO7vKX28aqAzERAKWzrR0afnA7bNyswqygvMzPfNv52cJ53dSRcVGcZ3rlWqX3jNb1qxZw6RJk1i4cCFdunTh+++/Z926dVy7dg2XUgoSHDt2jN69ezNv3jxGjBjBypUr+fLLLzl37hwtW4od++WXXzJv3jyWLVuGn58fH374IQEBAVy+fBmzcnTpant8eVJmEtuvb2fDlQ3svLmTtGwx92sTxyYsGbmEXvV6PfR7VFZEBPz3X+H87xcvivuNjeH55+G998r+A5UkqfpJSxOFG69dEz0X+bUH8peyCjqWxcqq8IfRzg5sbQvXNjZgba25WFoWLhYWYm1uLhYTE1kcsi6SNVt0ryr2uVotji/JyYVLUlLhkpwMiYliSUoqvJ2YKAIl+evk5Mq3QaUSx6KivajFe1fzU97d3GQWn1T9ycwWqSbIyBCZg/lDM4tnFUZFiaBNdLQIkD9MdMHKSpxnlnbOWXydfx5a/HzU0lK7QfQaXbOlS5cudOrUiZ9//hkAtVqNt7c3M2bMYPbs2SW2Hz9+PKmpqWzbtq3gvq5du9K2bVsWLlyIoih4eHjw5ptv8tZbbwEi5cfV1ZWlS5cyYcKEB7Ypf4fuOxWIpZU1QEFhHgUFRVEK1lnqTLJyM8nMzSAzN4OU7GRCU+4SmnKXkJS7hKTc4UrcBbLUhePlvazqMb7xC7zU8u37TllY2v9M/n1FH1OrC5fcXLHOzBR/GBkZ4uIqLa3wjyQ8XEQrg4LERVlRRkaFQZZ69R64qyRJqkEURfROFO2xCA0t/NEsuk5IKLvqfWWpVIWBF1NTcSFkZiZum5qKYEzxxdi45GJkVLg2MhI9N8XXpS0GBmIperusRaUquc5fyvp3/mcsvpR2f/59RR8rfrv4Nve7r/hj97uvss8rTXm2SU5OolUrGWzRpfzzmJMnEzEwsCEtTZwHpKaKpejt/CUlRfN20SU5WdyvzTNGK6vC3sz8gG7RpWhvaNHbckiPVJvIYItU2+TmigzF/MzEolmKpWUx5mc5PkwgvjiVSgRcrK3Fb03RpWinYP6/8zsHi3cY5i+5uUm0bl258xi9DgzJysri7NmzzJkzp+A+AwMDBg4cyPHjx0t9zvHjx5k1a5bGfYMHD2bz5s0A3L59m4iICAYOLKx9YmtrS5cuXTh+/HipwZbMzEwyiwxYS8qbg/XJjf0xMCsMi6WTTgIJGGGEM84lXieccAAcccSEwnRcRxxJiHUk/fJILG6NIDfDl5WoWEl83vubEBfniEqlxs0tssTrRka6oFYbYm8fh5mZ5sC6pCRrUlOtMDNLx94+QeOx7GwjYmJEO93cwkucFEdHO6FSGdOlSwJduqTTvj106CBOcsT0czZkZmYSV2yydwMDA1xdXfPaFom62BWZg4MDpqamJCUlkZqaqvGYubk5dnZ2ZGdnExNTckaD/B+a6OhocnI052W3s7PD3NyclJQUkov9RZqamuLg4EBubi5RUVElXtfV1RUDAwNiY2PJKja1gI2NDZaWlqSnp5NQLE/Z2NgYp7xqo+Hh4SVe19nZGSMjI+Lj48nIyNB4zMrKCmtr61L3oaGhYUHmVmn70NHRERMTk1L3oYWFBba2tqXuQ5VKhZubG1D6PrS3t8fMzKzUfWhmZoa9vX2Z+9DNzQ2VSlXqPrS1tcXCwoK0tDQSEzXHQJqYmODo6IiiKERERJR4XRcXFwwNDUvdh9bW1lhZWZGRkUF8fLzGY0ZGRjg7i+93REQExePGTk5OGBsbk5iYSFpamsZjlpaW2NjYkJWVRWxsrMZjRb/fUVFR5BarTJv//U5OTiYlRXPK3vx9mJOTQ3R0dInPmv/9jomJIbtYxbL873dqamrBMShf/j5Uq9VERpY8RuTvw7i4OI1jGRTuw6LfbycnsbRvX7gPi3+/1WowMXEiMdGY0NAEYmLSCy66RI+2JcnJNqSkZJKdHVdwAZeeDqmpBoSFuZKaCpaWkRgYaH6/Y2MdiI01xdo6CSsrze93Wpo5iYl2GBll4+ys+f1WFIiIcM/7DNEYG2t+v+Pj7cjIMMfSMgUbG83vd0aGKfHxDhgY5OLqWvL7HRHhiqIY4OAQi6mp5vc7MdGGtDRLzM3TsbNL0HgsK8uY2FhxjHB3L3mMiI52JifHCDu7eMzNNb/fyclWpKRYY2qaiYOD5jEiJ8eQ6GhxjHB1LW0fOpKVZYKNTRKWlsX3oQWJibYYG2fj5FR8H6qIiBDHCGfnaIyMiu9DezIyzLCySsHauvg+NCM+3h5Dw1xcXErbh24oigpHx1hMTAr3oVqtxTMoqUK6dNH+a6pUmj2JRXsXbW01ex6L9koW76k01n3dXEmqdoyNjfHy8sJY/kFItYShYWHmYUUUHWKanwmZvxTPlkxMLD3DMiVFnL8qSmFngb7pNdgSExNDbm5uwYVNPldXV65evVrqcyIiIkrdPv8iLn99v22KmzdvHp98UnKK5+d4DrMileUuqi+xMXcbNtjzkvFLJbb/JGY1ZFvymF1/vMw15/zbcnQ0lwPb0Kb9KQYPXqLx2K1bDViz5mlMTLJ56aXFJV73++/fIj3dkmHDdtOokeZ4sf37B3HmTDeaNbvFyJHrNR6LjXXjwIGXMDODYcP+wMBA86Kxbdvp9OnjwuHDh/H39+f2bTHsAKBHjx4MHDiQ8PBwli1bpvE8a2vrgoDXihUrSly0T548GV9fX06dOsXRo0c1HmvXrh2jRo0iPj6exYs1P6uhoSEffPABABs3bizx/zV27FhatGhBQEAAe/bs0XiscePGTJw4kYyMjBKvCzB79mxMTU3ZuXMnQUFBGo8NHTqUzp07c+PGDTZt2qTxmJeXFy+88AJAqa87Y8YMHBwcOHDgAAEBARqP9enTh759+3Lv3j1WrFih8Zi9vT2vvfYaAMuXLy8RDHj++efx9vbm+PHjnChWeKNjx44MHz6cmJiYEm0yMTEpCF6uW7euxAX/hAkTaNKkCf7+/uzPL8yTp3nz5owbN47U1NRSP+v777+PkZERW7du5e7duxqPjRw5kvbt23P16lW2bt2q8Vi9evV49tlnyc3NLfV133jjDWxsbPj333+5fFmzrkP//v3p1asXd+/eZfXq1RqPOTs78/LLLwPw119/lQgA5fcSHTlyhDNnzmg81rVrVwYPHkxkZCR//vmnxmMWFha8/fbbAKxevbpEkOepp56iYcOGnD17lkOHDmk81qpVK8aMGUNSUlKpn/Xjjz8GYMuWLYSEhGg89thjj9G6dWsCAwPZuXOnxmMNGjTg6aefJjs7u9TXfeutt7C0tGT37t1cv655jBg0aBDdunXj1q1brF+veYxwc3PjpZfEseyPP/4oEViaPn06DRu6cPnyYS5d8td4bMAAcYy4c+f+x4hvvy15jOjTZzK2tr4EBJzi9m3NY4SNTTvs7EaRmhpPbKzmZ1UUQ7KzPyA7G8zMNmJoqHmMiIwcS1JSC2xsAnB11TxGxMc35sqViRgYZNC1a8l9uHfvbLKyTOnUaScuLprHiHPnhnLzZmd8fG7QtavmMSI62osdO15AUeDZZ0u+7po1M0hMdKBfvwM0aqR5jDh9ug+nTvXF2/seo0ZpHiMSEuxZvlwcIyZPXo6FheYxYvXq5wkP96Z37+N06KB5jDh/viP79w/HxSWGp5/WbFNmpgk//SSOEePHr8PJSfMYsXHjBIKCmtCpkz99+mgeI65ebc6WLeOwtk4t9bdqwYL3yc014tFHt+LjU3iMyMjIYP78EptLOmBvL3ruivbQFR/mV7R3L38p3huYf5+1tXieHA4oSdrh5ORUcJ4pSXWZkVFhZ2BlKYro9MsPxJSVpVk8q7O0zM/8kSFpaWKbYv3B5abXYURhYWF4enpy7NgxunXrVnD/O++8w6FDhzh58mSJ55iYmLBs2TImTpxYcN+vv/7KJ598QmRkJMeOHaNHjx6EhYVppOM98cQTqFQq1qxZU+I1S8ts8fb25tq1a1hbWxfcX1N6rfMV7fkvLSsjv+c/ISGB9GKFHPJ7/mVmi8xsySczW4TakNmSTx4jCsljhFAVx4jk5GSaNGlS54cRaXsygPuRdXIkSZIkSTtqbM2WrKwsLCwsWL9+PaNHjy64f/LkySQkJLBly5YSz/Hx8WHWrFnMnDmz4L6PP/6YzZs3c+HCBW7dukWDBg3w9/enbdu2Bdv06dOHtm3b8sMPPzywXfIkRZIkSZK0Q/6mVs1kAPcj97kk1QyyZoskVX8P85uq12FEJiYmdOjQgX379hUEW9RqNfv27ePVV18t9TndunVj3759GsGWvXv3FmTG+Pn54ebmxr59+wqCLUlJSZw8eZLp06dX5ceRJEmSJEkq4dtvv2XKlCk899xzACxcuJDt27fz559/ljoZwA8//MCQIUMKhjR+9tln7N27l59//pmFCxeW2L6s2nMREREamU8y+06Q2XeFZPadoK8M3XylfZdkhq4gjxGCPEYI+srQrSy9BlsAZs2axeTJk+nYsSOdO3fm+++/JzU1teCEZNKkSXh6ejJv3jwAXn/9dfr06cM333zD8OHDWb16NWfOnCmoY6BSqZg5cyaff/45jRo1Kpj62cPDQyN7RpIkSZIkqapVxWQAxZVVe+6vv/7CrMj8x7WxrpSLS2HtuaJk7TlB1p4rVB1rzz322GMAbN26tUTwQtaeE+QxQpDHCEEfx4jiQaaK0PvUzwA///xzwTjmtm3b8uOPP9Ilr4R+37598fX1ZenSpQXbr1u3jg8++KBgHPNXX32lMY5ZURQ+/vhjFi9eTEJCAj179uTXX3+lcePG5WqPTL+VJEmSJO2o67+pVVGfrjhZe072Wste65qZ2ZKTk8PixYsZM2ZMwf9TPpnZIshjhCCPEUJNqz1XLYIt1U1dPzGUJEmSJG2p67+pugi2FFfX97kk1RSyZoskVX81tmaLJEmSJElSbebk5IShoWGJIElkZGRB71xxbm5uFdpekqSaydnZmRkzZsigqCTVUgb6boAkSZIkSVJtVXQygHz5kwEUzXQpKn8ygKKKTgYgSVLtYGRkhIODg0axXEmSag8ZbJEkSZIkSapCs2bNYsmSJSxbtowrV64wffr0EpMBFC2g+/rrr7Nr1y6++eYbrl69yty5czlz5kyZMzVKklQzxcfHs3HjxhL1XCRJqh1kGFWSJEmSJKkKjR8/nujoaD766KOCyQB27dpVUIAxODgYA4PC/q/u3buzcuVKPvjgA9577z0aNWrE5s2badmypb4+giRJVSAjI4OAgACZtSZJtZQMtkiSJEmSJFWxV199tczMlIMHD5a4b9y4cYwbN66KWyVJkiRJUlWRw4gkSZIkSZIkSZIkSZK0SGa2lCJ/Nuzic8RLkiRJklQx+b+l+b+tUtWT5zGSVDMkJyeTkZFBcnIylpaW+m6OJEmleJjzGJUiz35KCAkJwdvbW9/NkCRJkqRaIygoiPr16+u7GXXCrVu3aNCggb6bIUmSJEm1RmXOY2RmSyk8PDy4d+8e1tbWqFQqfTen2kpKSsLb25t79+5hY2Oj7+bUKHLfVZ7cd5Un913lyX1XeYmJifj4+ODg4KDvptQZ+fs6ODgYW1tbPbematWVv8268jlBftbaqK58Tqg7n7WufE54uPMYGWwphYGBAV5eXvpuRo1hY2NT6//Iqorcd5Un913lyX1XeXLfVV7R2XakqpW/r21tbevM97Wu/G3Wlc8J8rPWRnXlc0Ld+ax15XNC5c5j5JmPJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYItUaaampnz88ceYmprquyk1jtx3lSf3XeXJfVd5ct9Vntx3uleX9nld+ax15XOC/Ky1UV35nFB3Pmtd+ZzwcJ9VzkYkSZIkSZIkSZIkSZKkRTKzRZIkSZIkSZIkSZIkSYtksEWSJEmSJEmSJEmSJEmLZLBFkiRJkiRJkiRJkiRJi2SwRZIkSZIkSZIkSZIkSYtksEWSJEmSJEmSJEmSJEmLZLBFuq9ffvkFX19fzMzM6NKlC6dOnSpz26VLl6JSqTQWMzMzHba2+jh8+DAjR47Ew8MDlUrF5s2bH/icgwcP0r59e0xNTWnYsCFLly6t8nZWNxXdbwcPHizxnVOpVEREROimwdXIvHnz6NSpE9bW1ri4uDB69GiuXbv2wOetW7eOpk2bYmZmRqtWrdixY4cOWlu9VGbfyeOd8Ntvv9G6dWtsbGywsbGhW7du7Ny5877Pkd85/cnMzKRt27aoVCrOnz+v7+Zo3ahRo/Dx8cHMzAx3d3eeeeYZwsLC9N0srbtz5w4vvPACfn5+mJub06BBAz7++GOysrL03TSt+9///kf37t2x0AVl/AAACk1JREFUsLDAzs5O383RqoqcY9dUlTkfrokqew5WE1Xmd782mD9/PiqVipkzZ1boeTLYIpVpzZo1zJo1i48//phz587Rpk0bBg8eTFRUVJnPsbGxITw8vGC5e/euDltcfaSmptKmTRt++eWXcm1/+/Zthg8fTr9+/Th//jwzZ87kxRdfZPfu3VXc0uqlovst37Vr1zS+dy4uLlXUwurr0KFDvPLKK5w4cYK9e/eSnZ3NoEGDSE1NLfM5x44dY+LEibzwwgv4+/szevRoRo8ezaVLl3TYcv2rzL4DebwD8PLyYv78+Zw9e5YzZ87Qv39/Hn30UQIDA0vdXn7n9Oudd97Bw8ND382oMv369WPt2rVcu3aNDRs2EBQUxNixY/XdLK27evUqarWaRYsWERgYyHfffcfChQt577339N00rcvKymLcuHFMnz5d303RqsqcY9dElT2vq2kqex5RE1X0d782OH36NIsWLaJ169YVf7IiSWXo3Lmz8sorrxT8Ozc3V/Hw8FDmzZtX6vZ//fWXYmtrq6PW1RyAsmnTpvtu88477ygtWrTQuG/8+PHK4MGDq7Bl1Vt59tuBAwcUQImPj9dJm2qSqKgoBVAOHTpU5jZPPPGEMnz4cI37unTporz00ktV3bxqrTz7Th7vymZvb6/8/vvvpT4mv3P6s2PHDqVp06ZKYGCgAij+/v76blKV27Jli6JSqZSsrCx9N6XKffXVV4qfn5++m1Flatsxt6Ln2LVBec7raovynEfUJvf73a/pkpOTlUaNGil79+5V+vTpo7z++usVer7MbJFKlZWVxdmzZxk4cGDBfQYGBgwcOJDjx4+X+byUlBTq1auHt7d3rY9yatPx48c19jXA4MGD77uvpUJt27bF3d2dRx55hKNHj+q7OdVCYmIiAA4ODmVuI793pSvPvgN5vCsuNzeX1atXk5qaSrdu3UrdRn7n9CMyMpIpU6bwf//3f1hYWOi7OToRFxfHihUr6N69O8bGxvpuTpVLTEx84DFLqh4qe44t1RzlPY+o6crzu1/TvfLKKwwfPrzEuUt5yWCLVKqYmBhyc3NxdXXVuN/V1bXMehhNmjThzz//ZMuWLfz999+o1Wq6d+9OSEiILppco0VERJS6r5OSkkhPT9dTq6o/d3d3Fi5cyIYNG9iwYQPe3t707duXc+fO6btpeqVWq5k5cyY9evSgZcuWZW5X1veuLta8yVfefSePd4UCAgKwsrLC1NSUadOmsWnTJpo3b17qtvI7p3uKovDss88ybdo0OnbsqO/mVLl3330XS0tLHB0dCQ4OZsuWLfpuUpW7efMmP/30Ey+99JK+myKVQ2XOsaWao7znETVZRX73a7LVq1dz7tw55s2bV+nXkMEWSWu6devGpEmTaNu2LX369GHjxo04OzuzaNEifTdNqqWaNGnCSy+9RIcOHejevTt//vkn3bt357vvvtN30/TqlVde4dKlS6xevVrfTalxyrvv5PGuUJMmTTh//jwnT55k+vTpTJ48mcuXL+u7WbXe7NmzSy0QXnS5evUqP/30E8nJycyZM0ffTa6U8n7OfG+//Tb+/v7s2bMHQ0NDJk2ahKIoevwE5VfRzwoQGhrKkCFDGDduHFOmTNFTyyumMp9TkmqKunAOVhd+9+/du8frr7/OihUrHmoCBCMttkmqRZycnDA0NCQyMlLj/sjISNzc3Mr1GsbGxrRr146bN29WRRNrFTc3t1L3tY2NDebm5npqVc3UuXNnjhw5ou9m6M2rr77Ktm3bOHz4MF5eXvfdtqzvXXn/xmubiuy74ury8c7ExISGDRsC0KFDB06fPs0PP/xQauBJfue058033+TZZ5+97zb169dn//79HD9+HFNTU43HOnbsyFNPPcWyZcuqsJUPr7yfM5+TkxNOTk40btyYZs2a4e3tzYkTJ2pEintFP2tYWBj9+vWje/fuLF68uIpbpz0V/Zy1jTbOsaXq6WHOI2qSivzu11Rnz54lKiqK9u3bF9yXm5vL4cOH+fnnn8nMzMTQ0PCBryODLVKpTExM6NChA/v27WP06NGASIvbt28fr776arleIzc3l4CAAIYNG1aFLa0dunXrVmL6071799aIk8Pq5vz587i7u+u7GTqnKAozZsxg06ZNHDx4ED8/vwc+p1u3buzbt09jGru6+L2rzL4rTh7vCqnVajIzM0t9TH7ntMfZ2RlnZ+cHbvfjjz/y+eefF/w7LCyMwYMHs2bNGrp06VKVTdSK8n7O0qjVaoAyv4/VTUU+a2hoKP369aNDhw789ddfGBjUnGT1h/k/rQ20cY4tVS/aOI+oye73u19TDRgwgICAAI37nnvuOZo2bcq7775brkALyGCLdB+zZs1i8uTJdOzYkc6dO/P999+TmprKc889B8CkSZPw9PQsGMf26aef0rVrVxo2bEhCQgJff/01d+/e5cUXX9Tnx9CLlJQUjR7u27dvc/78eRwcHPDx8WHOnDmEhoayfPlyAKZNm8bPP//MO++8w/PPP8/+/ftZu3Yt27dv19dH0IuK7rfvv/8ePz8/WrRoQUZGBr///jv79+9nz549+voIevPKK6+wcuVKtmzZgrW1dcG4b1tb24LsqOJ/s6+//jp9+vThm2++Yfjw4axevZozZ87UqB5SbajMvpPHO2HOnDkMHToUHx8fkpOTWblyJQcPHiyYtl5+5/TPx8dH499WVlYANGjQoFb1vJ48eZLTp0/Ts2dP7O3tCQoK4sMPP6RBgwa1LpgXGhpK3759qVevHgsWLCA6OrrgsdqWGREcHExcXBzBwcHk5uZy/vx5ABo2bFjwXa6JHnSOXVs86LyutijPeURt8aDf/drC2tq6RM2d/HpgFarFo/X5kaRa5aefflJ8fHwUExMTpXPnzsqJEycKHuvTp48yefLkgn/PnDmzYFtXV1dl2LBhyrlz5/TQav3Ln5K4+JK/vyZPnqz06dOnxHPatm2rmJiYKPXr11f++usvnbdb3yq637788kulQYMGipmZmeLg4KD07dtX2b9/v34ar2el7TdA43tU/G9WURRl7dq1SuPGjRUTExOlRYsWyvbt23Xb8GqgMvtOHu+E559/XqlXr55iYmKiODs7KwMGDFD27NlT8Lj8zlU/t2/frpVTP1+8eFHp16+f4uDgoJiamiq+vr7KtGnTlJCQEH03Tev++uuvMo9btc3kyZNL/ZwHDhzQd9Me2v3OsWuLB53X1RblOY+oLR70u1+bVWbqZ5Wi1JCqYZIkSZIkSZIkSZIkSTVAzRngKUmSJEmSJEmSJEmSVAPIYIskSZIkSZIkSZIkSZIWyWCLJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYIskSZIkSZIkSZIkSZIWyWCLJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYIskSZIkSZIkSZIkSZIWyWCLJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYIskSZIkSZIkSZIkSZIW/T/7XA/6tM56GQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1330x410 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scipy_material.illustration_skewness_kurtosis()"
    ]
@@ -1459,12 +2291,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "8a31b653-d891-4f07-ab8b-73562b2ed86d",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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f1usCAID2wa8i88///M8BuV/MRx99pIyMDN/l2z179tQbb7yhLVu2tNjPbrcrJibmsl8PAAC0L36dWlqwYIFmzZqlTZs26fjx46qpqWmxXKyhQ4eqtLTU93UHu3fv1ubNm5Went5iv02bNqlHjx7q27evHnvsMR0/frzVY9bX1/udBwAAmMWvEZm0tDRJ0vDhw1usv9TJvk899ZRqamqUkpKi0NBQNTU1KS8vT+PHj/ftM3LkSI0ePVqJiYk6dOiQnn76aaWnp6u8vPy8l3rn5+fr+eef9+dtAQAAw/hVZP76178G5MXffvttvf7661q9erX69++vXbt2afr06YqLi9OECRMkSQ8++KBv/4EDB2rQoEFKSkrSpk2bzilSkpSdna0nn3zS97impkbx8fEByQsAAIKLX0XmzjvvDMiLz5w5U0899ZSvrAwcOFAul0v5+fm+IvOPevXqpe7du+vgwYPnLTJ2u53JwAAAdBB+3xCvurpaRUVF2rt3rySpf//+mjRp0iXdWfL06dMKCWk5TSc0NPScG+z9va+++krHjx9XbGysf8EBAEC74ddk323btikpKUkvvfSSTpw4oRMnTmjx4sVKSkq6pPvIjBo1Snl5efrLX/6iw4cP6z/+4z+0ePFi/ehHP5Ik1dbWaubMmfrb3/6mw4cPq7S0VBkZGerdu7dGjBjhT3QAANCO+DUi88QTT+iHP/yhXnvtNXXq9M0hGhsbNWXKFE2fPl3vv//+RR3n5ZdfVk5Ojn72s5/p2LFjiouL06OPPqo5c+ZI+mZ0Zs+ePVq+fLmqq6sVFxene++9V7m5uZw+AgAA/hWZbdu2tSgxktSpUyfNmjVLN99880UfJyoqSgUFBSooKDjv9oiICJWUlPgTEQAAdAB+nVrq2rWr3G73OesrKioUFRV12aEAAAAuhl9FZuzYsZo8ebLeeustVVRUqKKiQm+++aamTJmicePGBTojAADAefl1aunFF1+UzWbTww8/rMbGRklSWFiYHnvsMc2fPz+gAQEAAFrjV5Hp3Lmzfv3rXys/P1+HDh2SJCUlJalLly4BDQcAAHAhft9HRpK6dOmibt26+X4GAHRsIWeqrY6AINCWnwO/ikxjY6Oef/55LVmyRLW1tZKkq666StOmTdOzzz6rsLCwgIYEAJgh4suLu/0GECh+FZlp06bp3Xff1cKFC5WamipJKi8v13PPPafjx49r6dKlAQ0JADDDmcQ71BzRzeoYsFjImeo2K7V+FZnVq1frzTffVHp6um/doEGDFB8fr3HjxlFkAKCDao7opubI7lbHQAfi1+XXdrtdPXv2PGd9YmKiOnfufLmZAAAALopfRebxxx9Xbm6u6uvrfevq6+uVl5enxx9/PGDhAAAALsSvU0s7d+5UaWmprr/+et1www2SpN27d6uhoUHDhw/X6NGjffu+++67gUkKAADwD/wqMt26dVNmZmaLdfHx8QEJBAAAcLH8KjLLli0LdA4AAIBL5tccmTNnzuj06dO+xy6XSwUFBVq/fn3AggEAAHwXv4pMRkaGVqxYIUmqrq7WkCFDtGjRImVkZHDpNQAAaDN+FZkdO3bo9ttvlyT98Y9/VExMjFwul1asWKElS5YENCAAAEBr/Coyp0+fVlRUlCRp/fr1Gj16tEJCQnTrrbfK5XIFNCAAAEBr/CoyvXv3VnFxsSoqKlRSUqJ7771XknTs2DF17do1oAEBAABa41eRmTNnjn7xi1+oZ8+eGjJkiO/7ltavX69/+Zd/CWhAAACA1vh1+fWPf/xjDRs2TEePHvXdEE+Shg8frh/96EcBCwcAAHAhfo3ISFJMTIyioqK0YcMGnTlzRpL0ve99TykpKQELBwAAcCF+FZnjx49r+PDhSk5O1n333aejR49KkiZPnqwZM2YENCAAAEBr/CoyTzzxhMLCwuR2u9WlSxff+rFjx2rdunUBCwcAAHAhfs2RWb9+vUpKSnT99de3WN+nT592e/l1yJlqqyMgSPBZAIDg4VeROXXqVIuRmG+dOHFCdrv9skMFo4gv37c6AgAA+Ad+FZnbb79dK1asUG5uriTJZrOpublZCxcu1N133x3QgMHiTOIdao7oZnUMBIGQM9UUWwAIEn4VmRdeeEH33HOPtm3bpoaGBs2aNUv/9V//pRMnTujDDz8MdMag0BzRTc2R3a2OAQAA/s4lF5mzZ8/q5z//ud577z1t2LBBUVFRqq2t1ejRozV16lTFxsZeiZwAAADnuOQiExYWpj179uif/umfNHv27CuRCQAA4KL4dWrpoYceUlFRkebPnx/oPAAMUVdXJ7fbbWmGb6+SDIarJZ1Op8LDw62OAXQ4fhWZxsZG/f73v9fGjRs1ePBgRUZGtti+ePHigIQDELzcbreysrKsjiFJysvLszqCCgsLlZycbHUMoMPxq8h8+umnuummmyRJ+/fvb7HNZrNdfioAQc/pdKqwsNDqGEHD6XRaHQHokPwqMn/9618DnQOAYcLDwxmBAGA5v780EgAAwGoUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjOXXVUsAAJxPSJ3H6ggIAm35OaDIAAAum8PhUFhnu/RFmdVRECTCOtvlcDiu+OtQZAAAly06OlqrVq6Qx9OxR2RcLpfy8vI0e/ZsJSQkWB3HUg6HQ9HR0Vf8dSgyAICAiI6ObpNfXCZISEjghpFthMm+AADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYlhaZpqYm5eTkKDExUREREUpKSlJubq68Xq9vH6/Xqzlz5ig2NlYRERFKS0vTgQMHLEwNAACChaVFZsGCBVq6dKleeeUV7d27VwsWLNDChQv18ssv+/ZZuHChlixZot/+9rf6+OOPFRkZqREjRqiurs7C5AAAIBhY+l1LH330kTIyMnT//fdLknr27Kk33nhDW7ZskfTNaExBQYGeeeYZZWRkSJJWrFih6OhoFRcX68EHH7QsOwAAsJ6lIzJDhw5VaWmp9u/fL0navXu3Nm/erPT0dEnSl19+qcrKSqWlpfme43A4dMstt6i8vPy8x6yvr1dNTU2LBQAAtE+Wjsg89dRTqqmpUUpKikJDQ9XU1KS8vDyNHz9eklRZWSlJ53ybanR0tG/bP8rPz9fzzz9/ZYMDAICgYOmIzNtvv63XX39dq1ev1o4dO7R8+XK9+OKLWr58ud/HzM7Olsfj8S0VFRUBTAwAAIKJpSMyM2fO1FNPPeWb6zJw4EC5XC7l5+drwoQJiomJkSRVVVUpNjbW97yqqirdeOON5z2m3W6X3W6/4tkBAID1LB2ROX36tEJCWkYIDQ1Vc3OzJCkxMVExMTEqLS31ba+pqdHHH3+s1NTUNs0KAACCj6UjMqNGjVJeXp6cTqf69++vnTt3avHixZo0aZIkyWazafr06Zo3b5769OmjxMRE5eTkKC4uTg888ICV0QEAQBCwtMi8/PLLysnJ0c9+9jMdO3ZMcXFxevTRRzVnzhzfPrNmzdKpU6eUlZWl6upqDRs2TOvWrVN4eLiFyQEAQDCwtMhERUWpoKBABQUFre5js9k0d+5czZ07t+2CAQAAI/BdSwAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADG6mR1AFOE1HmsjoAgwWcBAIIHReY7OBwOhXW2S1+UWR0FQSSss10Oh8PqGADQ4VFkvkN0dLRWrVwhj4e/wl0ul/Ly8jR79mwlJCRYHcdSDodD0dHRVscAgA6PInMRoqOj+aX1dxISEpScnGx1DABooa6uTm6329IMLperxb9WcjqdCg8PtzrGFUeRAQC0C263W1lZWVbHkCTl5eVZHUGFhYUd4o9OigwAoF1wOp0qLCy0OkbQcDqdVkdoExQZAEC7EB4e3iFGINAS95EBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABjL0iLTs2dP2Wy2c5apU6dKku66665ztv37v/+7lZEBAEAQ6WTli2/dulVNTU2+x59++qm+//3v6yc/+Ylv3b/9279p7ty5vsddunRp04wAACB4WVpkrr322haP58+fr6SkJN15552+dV26dFFMTMxFH7O+vl719fW+xzU1NZcfFAAABKWgmSPT0NCgVatWadKkSbLZbL71r7/+urp3764BAwYoOztbp0+fvuBx8vPz5XA4fEt8fPyVjg4AACxi6YjM3ysuLlZ1dbUmTpzoW/fTn/5UCQkJiouL0549e/TLX/5S+/bt07vvvtvqcbKzs/Xkk0/6HtfU1FBmAABop4KmyBQVFSk9PV1xcXG+dVlZWb6fBw4cqNjYWA0fPlyHDh1SUlLSeY9jt9tlt9uveF4AAGC9oDi15HK5tHHjRk2ZMuWC+91yyy2SpIMHD7ZFLAAAEOSCosgsW7ZMPXr00P3333/B/Xbt2iVJio2NbYNUAAAg2Fl+aqm5uVnLli3ThAkT1KnT/8U5dOiQVq9erfvuu0/XXHON9uzZoyeeeEJ33HGHBg0aZGFiAAAQLCwvMhs3bpTb7dakSZNarO/cubM2btyogoICnTp1SvHx8crMzNQzzzxjUVIAABBsLC8y9957r7xe7znr4+PjVVZWZkEiAABgiqCYIwMAAOAPigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigwAADAWRQYAABiLIgMAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjEWRAQAAxqLIAAAAY1FkAACAsSgyAADAWBQZAABgrE5WB8DFqaurk9vttjSDy+Vq8a+VnE6nwsPDrY4BALAYRcYQbrdbWVlZVseQJOXl5VkdQYWFhUpOTrY6BgDAYhQZQzidThUWFlodI2g4nU6rIwAAggBFxhDh4eGMQAAA8A+Y7AsAAIxFkQEAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCxLi0zPnj1ls9nOWaZOnSrpm7vZTp06Vddcc42uuuoqZWZmqqqqysrIAAAgiFhaZLZu3aqjR4/6lg0bNkiSfvKTn0iSnnjiCb333nt65513VFZWpiNHjmj06NFWRgYAAEHE5vV6vVaH+Nb06dO1du1aHThwQDU1Nbr22mu1evVq/fjHP5Ykff755+rXr5/Ky8t16623nvcY9fX1qq+v9z2uqalRfHy8PB6Punbt2ibvAwAAXJ6amho5HI7v/P0dNHNkGhoatGrVKk2aNEk2m03bt2/X2bNnlZaW5tsnJSVFTqdT5eXlrR4nPz9fDofDt8THx7dFfAAAYIGgKTLFxcWqrq7WxIkTJUmVlZXq3LmzunXr1mK/6OhoVVZWtnqc7OxseTwe31JRUXEFUwMAACsFzXctFRUVKT09XXFxcZd1HLvdLrvdHqBUAAAgmAVFkXG5XNq4caPeffdd37qYmBg1NDSourq6xahMVVWVYmJiLEgJAACCTVAUmWXLlqlHjx66//77fesGDx6ssLAwlZaWKjMzU5K0b98+ud1upaamXvSxv53LXFNTE9jQAADgivn29/Z3XZNkeZFpbm7WsmXLNGHCBHXq9H9xHA6HJk+erCeffFJXX321unbtqmnTpik1NbXVK5bO5+TJk5LEpF8AAAx08uRJORyOVrdbXmQ2btwot9utSZMmnbPtpZdeUkhIiDIzM1VfX68RI0boN7/5zSUdPy4uThUVFYqKipLNZgtU7A7p20vZKyoquJQdQYHPJIINn8nA8Xq9Onny5HfOnQ2q+8gguF3sNf1AW+EziWDDZ7LtBc3l1wAAAJeKIgMAAIxFkcFFs9vtevbZZ7lPD4IGn0kEGz6TbY85MgAAwFiMyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDC5KeXm5QkNDW3wfFmCFiRMnymaz+ZZrrrlGI0eO1J49e6yOhg6usrJS06ZNU69evWS32xUfH69Ro0aptLTU6mjtGkUGF6WoqEjTpk3T+++/ryNHjlgdBx3cyJEjdfToUR09elSlpaXq1KmTfvCDH1gdCx3Y4cOHNXjwYP3nf/6nXnjhBX3yySdat26d7r77bk2dOtXqeO0al1/jO9XW1io2Nlbbtm3Ts88+q0GDBunpp5+2OhY6qIkTJ6q6ulrFxcW+dZs3b9btt9+uY8eO6dprr7UuHDqs++67T3v27NG+ffsUGRnZYlt1dbW6detmTbAOgBEZfKe3335bKSkp6tu3rx566CH9/ve//86vVQfaSm1trVatWqXevXvrmmuusToOOqATJ05o3bp1mjp16jklRhIl5gqz/NuvEfyKior00EMPSfpmSN/j8aisrEx33XWXtcHQYa1du1ZXXXWVJOnUqVOKjY3V2rVrFRLC32ZoewcPHpTX61VKSorVUTok/q/HBe3bt09btmzRuHHjJEmdOnXS2LFjVVRUZHEydGR33323du3apV27dmnLli0aMWKE0tPT5XK5rI6GDogRamsxIoMLKioqUmNjo+Li4nzrvF6v7Ha7XnnlFTkcDgvToaOKjIxU7969fY9/97vfyeFw6LXXXtO8efMsTIaOqE+fPrLZbPr888+tjtIhMSKDVjU2NmrFihVatGiR76/fXbt2affu3YqLi9Mbb7xhdURAkmSz2RQSEqIzZ85YHQUd0NVXX60RI0bo1Vdf1alTp87ZXl1d3fahOhCKDFq1du1aff3115o8ebIGDBjQYsnMzOT0EixTX1+vyspKVVZWau/evZo2bZpqa2s1atQoq6Ohg3r11VfV1NSkIUOGaM2aNTpw4ID27t2rJUuWKDU11ep47RpFBq0qKipSWlraeU8fZWZmatu2bdyEDJZYt26dYmNjFRsbq1tuuUVbt27VO++8wwR0WKZXr17asWOH7r77bs2YMUMDBgzQ97//fZWWlmrp0qVWx2vXuI8MAAAwFiMyAADAWBQZAABgLIoMAAAwFkUGAAAYiyIDAACMRZEBAADGosgAAABjUWQAAICxKDIAAMBYFBkAAGAsigyAoNfQ0GB1BABBiiIDoM2dPHlS48ePV2RkpGJjY/XSSy/prrvu0vTp0yVJPXv2VG5urh5++GF17dpVWVlZkqQ1a9aof//+stvt6tmzpxYtWtTiuDabTcXFxS3WdevWTX/4wx8kSYcPH5bNZtObb76poUOHKjw8XAMGDFBZWdmVfssArhCKDIA29+STT+rDDz/Un//8Z23YsEEffPCBduzY0WKfF198UTfccIN27typnJwcbd++XWPGjNGDDz6oTz75RM8995xycnJ8JeVSzJw5UzNmzNDOnTuVmpqqUaNG6fjx4wF6dwDaUierAwDoWE6ePKnly5dr9erVGj58uCRp2bJliouLa7HfPffcoxkzZvgejx8/XsOHD1dOTo4kKTk5WZ999pleeOEFTZw48ZIyPP7448rMzJQkLV26VOvWrVNRUZFmzZp1Ge8MgBUYkQHQpr744gudPXtWQ4YM8a1zOBzq27dvi/1uvvnmFo/37t2r2267rcW62267TQcOHFBTU9MlZUhNTfX93KlTJ918883au3fvJR0DQHCgyAAISpGRkZf8HJvNJq/X22Ld2bNnAxUJQBCiyABoU7169VJYWJi2bt3qW+fxeLR///4LPq9fv3768MMPW6z78MMPlZycrNDQUEnStddeq6NHj/q2HzhwQKdPnz7nWH/72998Pzc2Nmr79u3q16+fX+8HgLWYIwOgTUVFRWnChAmaOXOmrr76avXo0UPPPvusQkJCZLPZWn3ejBkz9L3vfU+5ubkaO3asysvL9corr+g3v/mNb5977rlHr7zyilJTU9XU1KRf/vKXCgsLO+dYr776qvr06aN+/frppZde0tdff61JkyZdkfcL4MpiRAZAm1u8eLFSU1P1gx/8QGlpabrtttvUr18/hYeHt/qcm266SW+//bbefPNNDRgwQHPmzNHcuXNbTPRdtGiR4uPjdfvtt+unP/2pfvGLX6hLly7nHGv+/PmaP3++brjhBm3evFl//vOf1b179yvxVgFcYTbvP55QBoA2durUKV133XVatGiRJk+efMVe5/Dhw0pMTNTOnTt14403XrHXAdB2OLUEoM3t3LlTn3/+uYYMGSKPx6O5c+dKkjIyMixOBsA0FBkAlnjxxRe1b98+de7cWYMHD9YHH3zA6R0Al4xTSwAAwFhM9gUAAMaiyAAAAGNRZAAAgLEoMgAAwFgUGQAAYCyKDAAAMBZFBgAAGIsiAwAAjPX/APq1Uc33kDktAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.boxplot(df, y='response', x='group');"
    ]
@@ -1481,10 +2324,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "3b2c0d0d-0ab0-4bf7-8128-bcfa4d1a3cec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>W</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>equal_var</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>levene</th>\n",
+       "      <td>2.080216</td>\n",
+       "      <td>0.144465</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               W      pval  equal_var\n",
+       "levene  2.080216  0.144465       True"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pg.homoscedasticity(df, dv='response', group='group')"
    ]
@@ -1506,12 +2396,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "280180fa-4f20-44a6-a463-2ce9b0ad75a4",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BartlettResult(statistic=3.3024375753550594, pvalue=0.19181598314035977)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.bartlett(A, B, C)"
    ]
@@ -1552,10 +2453,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "f4a1bcc6-c691-44ff-9932-f34ac5fd9a03",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1330x410 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "scipy_material.illustration_chi2()"
    ]
@@ -1576,10 +2488,178 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "141d0591-b63d-49e1-98e7-2491a6fa5d11",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>29.03</td>\n",
+       "      <td>5.92</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27.18</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>22.67</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>17.82</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sat</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>18.78</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Thur</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>244 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     total_bill   tip     sex smoker   day    time  size\n",
+       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
+       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
+       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
+       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
+       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
+       "..          ...   ...     ...    ...   ...     ...   ...\n",
+       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
+       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
+       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
+       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
+       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
+       "\n",
+       "[244 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "tips = pg.read_dataset('tips')\n",
     "tips"
@@ -1595,10 +2675,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "d90e3c9a-e4f5-4f07-b3c2-7bbaf1d018b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>9</td>\n",
+       "      <td>28</td>\n",
+       "      <td>18</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>10</td>\n",
+       "      <td>59</td>\n",
+       "      <td>58</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Fri  Sat  Sun  Thur\n",
+       "Female    9   28   18    32\n",
+       "Male     10   59   58    30"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "(row_labels, col_labels), counts = stats.contingency.crosstab(tips['sex'], tips['day'])\n",
     "observed_counts = pd.DataFrame(counts, index=row_labels, columns=col_labels)\n",
@@ -1632,12 +2769,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "id": "fe4ce304-25ac-4734-9a0a-e20db59b742f",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Altogether</th>\n",
+       "      <td>0.077869</td>\n",
+       "      <td>0.356557</td>\n",
+       "      <td>0.311475</td>\n",
+       "      <td>0.254098</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Fri       Sat       Sun      Thur\n",
+       "Altogether  0.077869  0.356557  0.311475  0.254098"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expected_props = observed_counts.sum(axis=0) / observed_counts.values.sum()\n",
     "\n",
@@ -1656,10 +2842,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "1473b92c-5f36-47a9-a17b-e6be81d9db52",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>0.077869</td>\n",
+       "      <td>0.356557</td>\n",
+       "      <td>0.311475</td>\n",
+       "      <td>0.254098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>0.077869</td>\n",
+       "      <td>0.356557</td>\n",
+       "      <td>0.311475</td>\n",
+       "      <td>0.254098</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Fri       Sat       Sun      Thur\n",
+       "Female  0.077869  0.356557  0.311475  0.254098\n",
+       "Male    0.077869  0.356557  0.311475  0.254098"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pd.DataFrame(dict(Female=expected_props, Male=expected_props)).T"
    ]
@@ -1674,12 +2917,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "087ea780-304c-459d-a7c0-066817f3d82f",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fri</th>\n",
+       "      <th>Sat</th>\n",
+       "      <th>Sun</th>\n",
+       "      <th>Thur</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>6.77459</td>\n",
+       "      <td>31.020492</td>\n",
+       "      <td>27.098361</td>\n",
+       "      <td>22.106557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>12.22541</td>\n",
+       "      <td>55.979508</td>\n",
+       "      <td>48.901639</td>\n",
+       "      <td>39.893443</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Fri        Sat        Sun       Thur\n",
+       "Female   6.77459  31.020492  27.098361  22.106557\n",
+       "Male    12.22541  55.979508  48.901639  39.893443"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expected_counts = np.outer(observed_counts.sum(axis=1), expected_props)\n",
     "\n",
@@ -1704,12 +3004,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "d9585b87-f5dd-4cf2-ad91-0a79dcc95c86",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.222001372406606"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "j, k = expected_counts.shape\n",
     "dof = (j - 1) * (k - 1)\n",
@@ -1727,13 +3038,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "a7cd985e-eb2b-4a72-a221-30e3e0fdf8ec",
    "metadata": {
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.004180302092822262"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.chi2(dof).sf(chi2)"
    ]
@@ -1750,12 +3072,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "4cd7ea08-110a-4ff7-9521-0f12e4169d35",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13.22200137240661, 0.004180302092822257)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "chi2, pvalue, dof, expected_props = stats.chi2_contingency(observed_counts)\n",
     "(chi2, pvalue)"
@@ -1773,12 +3106,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "id": "d7e7fd75-068c-4f37-b981-498890c4a0d7",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13.22200137240661, 0.004180302092822257)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "chi2, pvalue, dof, expected_props_T = stats.chi2_contingency(observed_counts.T)\n",
     "(chi2, pvalue)"
@@ -1794,10 +3138,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "08c1de88-2f70-4bdb-9291-634c872b730d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>test</th>\n",
+       "      <th>lambda</th>\n",
+       "      <th>chi2</th>\n",
+       "      <th>dof</th>\n",
+       "      <th>pval</th>\n",
+       "      <th>cramer</th>\n",
+       "      <th>power</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pearson</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>13.222001</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004180</td>\n",
+       "      <td>0.232784</td>\n",
+       "      <td>0.876800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cressie-read</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>13.186226</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004251</td>\n",
+       "      <td>0.232469</td>\n",
+       "      <td>0.875841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>log-likelihood</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>13.194401</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004235</td>\n",
+       "      <td>0.232541</td>\n",
+       "      <td>0.876061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>freeman-tukey</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>13.270507</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.004087</td>\n",
+       "      <td>0.233211</td>\n",
+       "      <td>0.878089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>mod-log-likelihood</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>13.407679</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.003833</td>\n",
+       "      <td>0.234413</td>\n",
+       "      <td>0.881673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>neyman</td>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>13.873557</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.003082</td>\n",
+       "      <td>0.238451</td>\n",
+       "      <td>0.893170</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 test    lambda       chi2  dof      pval    cramer     power\n",
+       "0             pearson  1.000000  13.222001  3.0  0.004180  0.232784  0.876800\n",
+       "1        cressie-read  0.666667  13.186226  3.0  0.004251  0.232469  0.875841\n",
+       "2      log-likelihood  0.000000  13.194401  3.0  0.004235  0.232541  0.876061\n",
+       "3       freeman-tukey -0.500000  13.270507  3.0  0.004087  0.233211  0.878089\n",
+       "4  mod-log-likelihood -1.000000  13.407679  3.0  0.003833  0.234413  0.881673\n",
+       "5              neyman -2.000000  13.873557  3.0  0.003082  0.238451  0.893170"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expected_counts, observed_counts, test_results = pg.chi2_independence(tips, 'sex', 'day')\n",
     "test_results"
@@ -1831,12 +3285,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "84f4e9b3-a653-422c-8fc5-83b29869ba87",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "410"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expected_props  = np.array([ .24, .2, .16, .14, .13, .13 ])\n",
     "observed_counts = np.array([ 85, 79, 56, 64, 58, 68 ])\n",
@@ -1845,12 +3310,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "8bb2916a-e9a4-4bbe-b04f-14818bb68723",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([98.4, 82. , 65.6, 57.4, 53.3, 53.3])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expected_counts = expected_props * np.sum(observed_counts)\n",
     "expected_counts"
@@ -1877,12 +3353,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "id": "a8898631-a5b5-49e8-a158-3e149345391a",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.566983829178941"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "k = len(expected_counts)\n",
     "chi2 = np.sum((observed_counts - expected_counts) ** 2 / expected_counts)\n",
@@ -1891,12 +3378,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "id": "7a8af457-13dc-483a-90e3-50d3949c8edf",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1276329790529603"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pvalue = stats.chi2(k-1).sf(chi2)\n",
     "pvalue"
@@ -1914,12 +3412,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "1e246915-e9be-4826-9677-d9f30348d0df",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=8.566983829178941, pvalue=0.1276329790529603)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.chisquare(observed_counts, expected_counts)"
    ]
@@ -1972,9 +3481,9 @@
  "metadata": {
   "celltoolbar": "Aucun(e)",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "scientific_python",
    "language": "python",
-   "name": "python3"
+   "name": "scientific_python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1986,7 +3495,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.12.3"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/Courses/scipy_cours.ipynb b/notebooks/Courses/scipy_cours.ipynb
index cb300991767d2f8ed3f22b643e209bba7c0088a4..d634fa4d1a5d23ca094d6e8853f9f9d876f51ced 100644
--- a/notebooks/Courses/scipy_cours.ipynb
+++ b/notebooks/Courses/scipy_cours.ipynb
@@ -5,21 +5,14 @@
    "execution_count": null,
    "id": "8ccafd3d",
    "metadata": {
+    "scrolled": true,
     "tags": []
    },
    "outputs": [],
    "source": [
     "import sys\n",
-    "!\"{sys.executable}\" -m pip install scipy ipywidgets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf7e7b4c-6fbe-4b1c-81b7-6d5a851c2835",
-   "metadata": {},
-   "source": [
-    "<script src=\"https://polyfill.io/v3/polyfill.min.js?features=es6\"></script>\n",
-    "<script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3.0.1/es5/tex-mml-chtml.js\"></script>"
+    "!\"{sys.executable}\" -m pip install scipy ipywidgets\n",
+    "import scipy_material"
    ]
   },
   {
@@ -36,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "42342d74",
    "metadata": {},
    "outputs": [],
@@ -60,35 +53,6 @@
     "        )"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "1091dfd5",
-   "metadata": {},
-   "source": [
-    "Reminder about module loading:\n",
-    "\n",
-    "Example: how to access the `ttest_ind` function defined in the `scipy.stats` module?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "898957c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%script echo skipping\n",
-    "\n",
-    "import scipy.stats\n",
-    "scipy.stats.ttest_ind\n",
-    "\n",
-    "from scipy import stats\n",
-    "stats.ttest_ind\n",
-    "\n",
-    "from scipy.stats import *\n",
-    "ttest_ind"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "9e2fe344",
@@ -113,7 +77,7 @@
     "tags": []
    },
    "source": [
-    "## Overview"
+    "# Outline"
    ]
   },
   {
@@ -125,787 +89,601 @@
    "source": [
     "We will merely review statistical tests:\n",
     "\n",
+    "* Distributions\n",
     "* Student $t$ tests\n",
     "    * compare a sample mean against the population mean\n",
     "    * compare means of two independent samples\n",
     "    * compare the means of paired samples\n",
-    "* analyses of variance (one-way)\n",
+    "* Analyses of variance (one-way)\n",
     "    * compare more than two group means\n",
-    "* tests for other tests' assumptions\n",
+    "* Tests for other tests' assumptions\n",
     "    * normality tests\n",
     "    * homoscedasticity tests\n",
-    "* $\\chi^2$ tests for discrete variables\n",
-    "    * goodness-of-fit test\n",
+    "* $\\chi^2$ tests for categorical variables\n",
+    "    * goodness-of-fit tests\n",
     "    * homogeneity and independence tests\n",
-    "* correlation coefficient and linear regression\n",
-    "* effect sizes and test power"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1be90cf",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## What Python can do -- What Python cannot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01c6c854",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "### Experimental design\n",
-    "\n",
-    "Some vocabulary:\n",
-    "\n",
-    "| term  | description |\n",
-    "| --: | :-- |\n",
-    "| **treatment** | every procedure or experimental condition<br />that departs from a control condition<br />(and the control itself is a special-case treatment) |\n",
-    "| **population** | a set of individuals we want our conclusions to apply to |\n",
-    "| **sample** | a finite set of selected individuals<br />assumed to be representative of a population |\n",
-    "\n",
-    "Always good to get a reminder about [general considerations](https://www.coursera.org/learn/stanford-statistics/home/welcome), __prior to data collection__ and analysis.\n",
-    "\n",
-    "* Sampling from the population,\n",
-    "* identifying the sources of variability,\n",
-    "* checking the assumptions of a test are met,\n",
-    "* etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23c92344",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Workflow"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35bcd5a9-fd6f-4914-96a7-734d240efd8e",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "To determine whether there is *sufficient evidence* to conclude the treatment has an effect, we use *statistical tests*.\n",
-    "\n",
-    "However, because experimental designs are often complex and involve multiple treatments and additional sources of variability, most studies also involve multiple tests, that are usually carried out after a so-called *omnibus* test.\n",
-    "\n",
-    "In addition, every statistical test makes various assumptions that in turn needs to be checked.\n",
-    "\n",
-    "As a consequence, reaching a conclusion about the data usually involves a series of tests and procedures.\n",
-    "\n",
-    "<table style=\"text-align:left;\"><tr><th>\n",
-    "Example worflow adapted from...?\n",
-    "</th></tr><tr><td>\n",
-    "<img src=\"../images/example_anova_workflow.png\" width=\"70%\" />\n",
-    "</td></tr></table>\n",
-    "\n",
-    "Contrary to statistical software with a GUI, the tools featured in programming languages such as Python do not offer much guidance in following such a workflow.\n",
-    "\n",
-    "<![CDATA[\n",
-    "# GraphViz source for https://sketchviz.com\n",
-    "\n",
-    "digraph G {\n",
-    "\n",
-    "  graph [fontname = \"Handlee\"];\n",
-    "  node [fontname = \"freesans\"];\n",
-    "  edge [fontname = \"Handlee\"];\n",
-    "\n",
-    "  bgcolor=transparent;\n",
-    "\n",
-    "  subgraph cluster0 {\n",
-    "    color=none;\n",
-    "    n0 -> normality [label=\"yes\", constraint=false];\n",
-    "  }\n",
-    "\n",
-    "  subgraph cluster1 {\n",
-    "    style=filled;\n",
-    "    color=lightgrey;\n",
-    "    node [style=filled,color=pink];\n",
-    "    anova; robust_f; nonparam;\n",
-    "    label = \"*Omnibus test                              *\";\n",
-    "    fontsize = 20;\n",
-    "  }\n",
-    "\n",
-    "  n0 -> nonparam [label=\"no\"];\n",
-    "  normality -> H0_0;\n",
-    "  H0_0 -> homoscedasticity [label=\"not rejected\"];\n",
-    "  H0_0 -> transform [label=\"rejected\"];\n",
-    "  transform -> homoscedasticity [label=\"success\"];\n",
-    "  transform -> nonparam [label=\"failure\"];\n",
-    "  homoscedasticity -> H0_1;\n",
-    "  H0_1 -> anova [label=\"not rejected\"];\n",
-    "  H0_1 -> robust_f [label=\"rejected\"];\n",
-    "  H0_2 -> posthoc [label=\"rejected\"];\n",
-    "  anova -> H0_2;\n",
-    "  robust_f -> H0_2;\n",
-    "  nonparam -> H0_2;\n",
-    "\n",
-    "  n0 [shape=diamond, label=<n<SUB>i</SUB>&ge;20>];\n",
-    "  normality [label=\"Normality test\"];\n",
-    "  nonparam [label=\"Non-parametric\\nomnibus test;\\nK-W, Friedman\"];\n",
-    "  H0_0 [shape=diamond, label=<H<SUB>0</SUB>>];\n",
-    "  homoscedasticity [label=\"Homoscedasticity test\"];\n",
-    "  transform [label=\"Data transform\"];\n",
-    "  H0_1 [shape=diamond, label=<H<SUB>0</SUB>>];\n",
-    "  anova [label=\"Standard ANOVA\"];\n",
-    "  robust_f [label=\"Robust F test;\\nWelch, Yuen,\\nAlexander-Govern\"];\n",
-    "  H0_2 [shape=diamond, label=<H<SUB>0</SUB>>];\n",
-    "  posthoc [label=\"Post-hoc tests\"];\n",
-    "\n",
-    "}\n",
-    "]]>"
+    "* Correlation coefficient and linear regression\n",
+    "* Effect sizes"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d6b0c2ee-4f6b-49db-8c26-dd64aede72fb",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
+   "id": "4bbf8433-58db-4738-bff9-33bde44f00df",
+   "metadata": {},
    "source": [
-    "## Data exploration"
+    "# Distributions"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f911c720-5dbd-4e00-bb2b-329c4a3cd48e",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "90e1f4cf-8993-4745-bf94-5d5fdf17b181",
+   "metadata": {},
    "source": [
-    "Each measurements or variables should be checked for undesirable properties, among others:\n",
-    "\n",
-    "* completeness (undefined values?)\n",
-    "* extreme values (outliers?)\n",
-    "* modes (multiple?)\n",
-    "\n",
-    "Let us import a few more libraries and load some example data:"
+    "For this section, the related utilities are provided by `scipy.stats`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "65104f3d",
-   "metadata": {
-    "hidden": true
-   },
+   "execution_count": 3,
+   "id": "c33e4518-3d64-4b55-baf5-cc06634590b7",
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from matplotlib import pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "#pd.options.display.max_columns = None\n",
-    "\n",
-    "dataframe = pd.read_csv('../data/mi.csv', index_col=0)\n",
-    "dataframe"
+    "from scipy import stats"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ae8b5f34",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "The most important datum in a dataset is the sample size $n$. Here $n=816$.\n",
-    "\n",
-    "We also describe the sample reporting the mean value of variables of interest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b00250f8",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "age = dataframe['Age']\n",
-    "sample_mean = np.mean(age)\n",
-    "sample_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "700ff9a1-e4e9-4ef5-9559-f6d6740977ed",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Confidence intervals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b311609",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "1091dfd5",
+   "metadata": {},
    "source": [
-    "To report the value of an estimator such as the sample mean and account for the uncertainty about the estimated value, we can report a confidence interval instead. This is used mostly for the mean of an observed variable.\n",
+    "Reminder about module loading:\n",
     "\n",
-    "Reminder: the sample mean is an estimator of the population mean. We are interested in drawing conclusions about the population the sample comes from, and not about the sample itself."
+    "Example: how to access the `sem` function defined in the `scipy.stats` module?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e60ba8f3",
-   "metadata": {
-    "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
-   "outputs": [],
+   "execution_count": 4,
+   "id": "898957c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skipping\n"
+     ]
+    }
+   ],
    "source": [
-    "# you can play with the three following parameters:\n",
-    "sample_mean = m = 46\n",
-    "sem = s = 1 # standard_error_of_the_mean = sample_sd / sqrt(sample_size)\n",
-    "alpha = 0.05\n",
-    "\n",
-    "b = 3\n",
-    "grid = np.linspace(m-b*s, m+b*s, 200) # possible population mean values\n",
-    "pdf = stats.norm(m, s).pdf\n",
-    "prob = pdf(grid) # probability for the population mean\n",
-    "\n",
-    "plt.plot(grid, prob, 'r-', zorder=3)\n",
-    "plt.axhline(0, color='k', linestyle=':', linewidth=1)\n",
-    "plt.xlabel('population mean')\n",
-    "plt.ylabel('probability density')\n",
-    "plt.axvline(m, color='k', linestyle=':', linewidth=1, label='sample mean')\n",
-    "\n",
-    "u = stats.norm().isf(alpha / 2)\n",
-    "ci_low = m - u * s\n",
-    "ci_high = m + u * s\n",
-    "\n",
-    "plt.fill_between(grid, np.zeros_like(prob), prob, where=(ci_low<=grid)&(grid<=ci_high), alpha=.1)\n",
-    "plt.plot([ci_low]*2, [0, pdf(ci_low)], color='b')#, label='confidence lower bound')\n",
-    "plt.plot([ci_high]*2, [0, pdf(ci_high)], color='b')#, label='confidence upper bound')\n",
-    "\n",
-    "ml = (grid[0]+4*ci_low)/5\n",
-    "pl = (2*pdf(ci_low)+pdf(m))/3\n",
-    "plt.annotate(f'${alpha/2*100}\\%$',\n",
-    "    [ml, .1*pdf(ml)], [ml, pl],\n",
-    "    arrowprops=dict(arrowstyle=\"->\"),\n",
-    "    horizontalalignment='center')\n",
-    "\n",
-    "ml1 = (4*m+ci_high)/5\n",
-    "ml2 = (m+ci_high)/2\n",
-    "plt.annotate(f'${(1-alpha)*100:.0f}\\%$ prob. mass',\n",
-    "    [ml1, pl], [ml2, (pdf(ml2)+pdf(m))/2],\n",
-    "    arrowprops=dict(arrowstyle=\"->\"))\n",
-    "\n",
-    "line_width, head_length, height = pdf(m)/30, b*s/10, .5*pdf(ci_low)\n",
-    "t = plt.arrow(ci_low+head_length, height, ci_high-ci_low-2*head_length, 0,\n",
-    "    width=line_width, head_length=head_length, linestyle='none')\n",
-    "t = plt.arrow(ci_high-head_length, height, ci_low-ci_high+2*head_length, 0,\n",
-    "    width=line_width, head_length=head_length, linestyle='none')\n",
-    "plt.text(m, height+line_width, f'${(1-alpha)*100:.0f}\\%$ confidence interval',\n",
-    "    ha='center')\n",
+    "%%script echo skipping\n",
     "\n",
-    "plt.legend(loc='upper left')\n",
+    "import scipy.stats\n",
+    "scipy.stats.sem\n",
     "\n",
-    "plt.xlim([grid[0], grid[-1]]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2b36e29",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "As they may differ, instead of reporting the sample mean alone, we can report a range of possible values for the population mean, and this is made possible by the fact the mean estimator is known to be normally distributed.\n",
+    "from scipy import stats\n",
+    "stats.sem\n",
     "\n",
-    "Note: a normal distribution with mean $\\mu$ and variance $\\sigma^2$ can be represented in `scipy` with the `norm` class whose methods implement many distribution-related measurements:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e911524f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "mean = mu = 30\n",
-    "standard_deviation = sigma = 10\n",
-    "normal_distribution = stats.norm(mu, sigma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f19585a9",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "normal_distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6bbb149",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# cumulative distribution function\n",
-    "normal_distribution.cdf(46)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1341b01d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "# probability density function\n",
-    "normal_distribution.pdf(46)"
+    "from scipy.stats import *\n",
+    "sem"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b05f3b3",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "5be3195f-3d79-458d-94bb-5a2ef6b558cd",
+   "metadata": {},
    "source": [
-    "For example, we may report an interval around the sample mean that should include the population mean with a $1-\\alpha=95\\%$ probability:\n",
-    "\n",
-    "$$\n",
-    "\\bar{x} \\pm z_{1-\\alpha/2}\\frac{\\sigma}{\\sqrt{n}}\n",
-    "$$\n",
+    "## Confidence intervals\n",
     "\n",
-    "$z_{1-\\alpha/2}$ is calculated as follows:"
+    "Common information such as the sample mean or standard deviation are trivial to obtain. For example, we have seen Pandas' `describe`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "62f75d44",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "alpha = 0.05\n",
-    "stats.norm().isf(alpha / 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7728089e",
-   "metadata": {
-    "hidden": true
-   },
+   "execution_count": 5,
+   "id": "da212ef7-0fc2-4ecb-9225-02e41f626b25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>score</th>\n",
+       "      <th>gdp</th>\n",
+       "      <th>family</th>\n",
+       "      <th>health</th>\n",
+       "      <th>freedom</th>\n",
+       "      <th>generosity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>712.000000</td>\n",
+       "      <td>712.000000</td>\n",
+       "      <td>712.000000</td>\n",
+       "      <td>712.000000</td>\n",
+       "      <td>712.000000</td>\n",
+       "      <td>712.000000</td>\n",
+       "      <td>712.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2016.983146</td>\n",
+       "      <td>5.384433</td>\n",
+       "      <td>0.927765</td>\n",
+       "      <td>1.084706</td>\n",
+       "      <td>0.618347</td>\n",
+       "      <td>0.410976</td>\n",
+       "      <td>0.212137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.421058</td>\n",
+       "      <td>1.127515</td>\n",
+       "      <td>0.398686</td>\n",
+       "      <td>0.330334</td>\n",
+       "      <td>0.241488</td>\n",
+       "      <td>0.152395</td>\n",
+       "      <td>0.115306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2.693000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>4.511250</td>\n",
+       "      <td>0.631888</td>\n",
+       "      <td>0.877478</td>\n",
+       "      <td>0.466158</td>\n",
+       "      <td>0.310890</td>\n",
+       "      <td>0.127045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>5.328000</td>\n",
+       "      <td>0.990357</td>\n",
+       "      <td>1.128175</td>\n",
+       "      <td>0.652632</td>\n",
+       "      <td>0.430770</td>\n",
+       "      <td>0.199319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2018.000000</td>\n",
+       "      <td>6.227000</td>\n",
+       "      <td>1.230653</td>\n",
+       "      <td>1.341106</td>\n",
+       "      <td>0.805084</td>\n",
+       "      <td>0.531000</td>\n",
+       "      <td>0.273473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2019.000000</td>\n",
+       "      <td>7.769000</td>\n",
+       "      <td>2.096000</td>\n",
+       "      <td>1.644000</td>\n",
+       "      <td>1.141000</td>\n",
+       "      <td>0.724000</td>\n",
+       "      <td>0.611705</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              year       score         gdp      family      health  \\\n",
+       "count   712.000000  712.000000  712.000000  712.000000  712.000000   \n",
+       "mean   2016.983146    5.384433    0.927765    1.084706    0.618347   \n",
+       "std       1.421058    1.127515    0.398686    0.330334    0.241488   \n",
+       "min    2015.000000    2.693000    0.000000    0.000000    0.000000   \n",
+       "25%    2016.000000    4.511250    0.631888    0.877478    0.466158   \n",
+       "50%    2017.000000    5.328000    0.990357    1.128175    0.652632   \n",
+       "75%    2018.000000    6.227000    1.230653    1.341106    0.805084   \n",
+       "max    2019.000000    7.769000    2.096000    1.644000    1.141000   \n",
+       "\n",
+       "          freedom  generosity  \n",
+       "count  712.000000  712.000000  \n",
+       "mean     0.410976    0.212137  \n",
+       "std      0.152395    0.115306  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.310890    0.127045  \n",
+       "50%      0.430770    0.199319  \n",
+       "75%      0.531000    0.273473  \n",
+       "max      0.724000    0.611705  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataframe = pd.read_csv('../data/happiness.csv')\n",
+    "dataframe.describe()\n",
+    "#dataframe.describe(exclude=np.number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b0384e-b15e-44e4-a231-72dadd49d1d6",
+   "metadata": {},
    "source": [
-    "For a $95\\%$ confidence interval, we usually take $z\\approx 1.96$.\n",
-    "[isf](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.isf.html) is the inverse survival function, here of the standard normal distribution (with null mean and unit variance).\n",
+    "To report the value of the population mean and account for the uncertainty that results from the fact the true value is actually unknown (the sample mean above is our best guess), we can give a confidence interval instead.\n",
     "\n",
-    "$\\frac{\\sigma}{\\sqrt{n}}$ is the standard deviation of the sample mean and can be calculated using the `sem` function from `scipy.stats`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a2a23163",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "age = dataframe['Age']\n",
-    "sample_mean = np.mean(age)\n",
-    "sem = stats.sem(age)\n",
-    "print(sem)"
+    "Reminder: the population mean follows a normal distribution centered at the sample mean, with standard deviation equal to the standard error of the mean (or, equivalently, the standard deviation of the sample divided by the square root of the sample size)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "99fe274d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "print(f'{sample_mean:.2f} ± {1.96 * sem:.2f} years old on average')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddef9204",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "`scipy` actually offers a more straightforward way to computing confidence intervals:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7d15497",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "stats.norm(sample_mean, sem).interval(1 - alpha)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52848e98",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
+   "execution_count": 6,
+   "id": "278c2259-7812-4d81-a6b6-56ebf3938bdf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "### Outliers"
+    "scipy_material.illustration_confidence_interval(0.410976, stats.sem(dataframe['freedom']))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e1a2ea78-087a-4cd1-bdf1-ba67db7067de",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "For example, undefined values are sometimes encoded as an otherwise-meaningless value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "69922361-f828-44ad-bb36-402f355dd0cb",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [],
+   "id": "5f719439-1afd-4a21-bb80-81040010a5d9",
+   "metadata": {},
    "source": [
-    "X = 'Age'\n",
-    "\n",
-    "x = dataframe[X]\n",
-    "x_noisy = x.copy()\n",
-    "x_noisy.name = f'{X} [corrupted]'\n",
-    "x_noisy.iloc[[\n",
-    "    29, 30, 73, 76, 82, 97, 112, 117, 136, 166, 196,\n",
-    "    209, 253, 260, 266, 339, 345, 355, 414, 446, 453,\n",
-    "    469, 486, 502, 505, 517, 579, 592, 614, 618, 623,\n",
-    "    630, 664, 669, 683, 718, 786, 797, 804, 810,\n",
-    "]] = 0\n",
-    "\n",
-    "sns.histplot(x=x_noisy)\n",
-    "sns.rugplot(x=x_noisy, color='r');"
+    "Computing a confidence interval with SciPy involves instantiating the normal distribution with the `norm` function and calling the `interval` method of the returned object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ac5a7020-b903-4196-8db6-31b90070d22b",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
+   "execution_count": 7,
+   "id": "53efbf5b-03ad-4a95-a84d-1f01d7968410",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "Y = 'PhysicalActivity'\n",
-    "y = dataframe[Y].copy()\n",
-    "y[:] = np.log(1+y)\n",
-    "y.name = f'log( {Y} )'\n",
-    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
-    "ax_corrupt = axes[0]\n",
-    "sns.regplot(x=x_noisy, y=y, line_kws=dict(color='r'), ax=ax_corrupt)\n",
-    "ax_alright = axes[1]\n",
-    "sns.regplot(x=x, y=y, line_kws=dict(color='r'), ax=ax_alright)\n",
-    "ax_alright.set_xlim(ax_corrupt.get_xlim());"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4f4e471-ec89-46ef-8984-fbf7b5afe84f",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Outliers may incur large biases and strongly impact the outcome of the analyses."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91a55884-2ca8-4cb0-9190-6c42bea7047b",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
+    "X = dataframe['freedom']\n",
+    "mu = np.mean(X)\n",
+    "sigma = stats.sem(X)\n",
+    "distribution_of_the_mean = stats.norm(mu, sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c66287ce-9ae8-487b-acf2-59d80638e503",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.39978235603755674, 0.4221699825735007)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "### Modes"
+    "distribution_of_the_mean.interval(0.95)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4c483874-e9db-440a-b1c8-7dd47a06008a",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "52080112-bf86-441c-af70-85eeadef820e",
+   "metadata": {},
    "source": [
-    "In many cases, we want the values for variables of interest to exhibit a single *mode*.\n",
-    "Otherwise, estimates of central tendency (=average, mean or median) may not be representative of the overall sample."
+    "Note again that we have set the scale parameter `sigma` equal to the sem. In contrast, if variable `Proteines` followed a normal distribution, we could define its distribution as:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "578edd4a-bcb1-4f9e-a8ba-d3e63bbabb71",
-   "metadata": {
-    "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
+   "execution_count": 9,
+   "id": "4b6a2690-7a11-4fdc-a52f-af408dd91ff5",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "X = 'HoursOfSleep'\n",
-    "Y = 'Age'\n",
-    "\n",
-    "y = dataframe[Y][dataframe[X] > 8]\n",
-    "\n",
-    "sns.histplot(y, bins=20)\n",
-    "plt.axvline(y.mean(), linestyle=':', color='red');\n",
-    "plt.axvline(y.median(), linestyle=':', color='green');"
+    "normal_distribution = stats.norm(X.mean(), X.std())"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c101eeb7-5580-4a75-889d-0fd2e965c7d6",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
+   "id": "8b10833f-1da4-4781-b791-c2754f15f158",
+   "metadata": {},
    "source": [
-    "###  Another example"
+    "The objects `norm` returns (*e.g.* `distribution_of_the_mean`) feature numerous other methods:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "72a790bd",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
+   "execution_count": 10,
+   "id": "c2943fe2-0f17-44b0-860a-1c9028e89e4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11.018949544165157"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "X = 'HoursOfSleep' # variable\n",
-    "\n",
-    "x = dataframe[X] # measurements"
+    "# probability density function\n",
+    "distribution_of_the_mean.pdf(0.4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "d6ab1d43",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
+   "execution_count": 11,
+   "id": "b3d9cfed-b060-451f-b92a-0043ff1c3e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.02731194353841207"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "sns.histplot(x=x, bins=10);\n",
-    "#sns.rugplot(x=x, color='r');"
+    "# cumulative distribution function\n",
+    "distribution_of_the_mean.cdf(0.4)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "994df34d",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "a1728396-4859-4c36-917b-fb9113378baf",
+   "metadata": {},
    "source": [
-    "#### Fitting a normal distribution\n",
+    "See [scipy.stats.rv_continuous](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.html#scipy.stats.rv_continuous) for more methods.\n",
     "\n",
-    "If $X$ follows a normal distribution:\n",
+    "As another example, we can make use of the inverse survival function `isf` to re-implement the calculation of the $1-\\alpha=95\\%$ confidence interval based on the following formula:\n",
     "\n",
-    "$X \\sim \\mathcal{N}(\\mu, \\sigma^2)$\n",
-    "\n",
-    "then we can use the **empirical mean** and **unbiased variance** as estimators for $\\mu$ and $\\sigma^2$ respectively.\n",
+    "$$\n",
+    "\\bar{x} \\pm z_{1-\\alpha/2}\\frac{\\sigma}{\\sqrt{n}}\n",
+    "$$\n",
     "\n",
-    "`scipy.stats` exposes the `norm` function to define a normal distribution:"
+    "Indeed, $z_{1-\\alpha/2}$ is calculated as follows:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "92b00d5d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
+   "execution_count": 12,
+   "id": "09d17b75-cb67-47b7-acf2-68269df1daae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.9599639845400545"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "normal_distribution = stats.norm(x.mean(), x.std())"
+    "alpha = 0.05\n",
+    "z = stats.norm().isf(alpha / 2)\n",
+    "z"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5eefc50c-44fa-403b-92c6-16aec1204d26",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "be2d1285-aa22-4c82-ad7c-d1df0a064df4",
+   "metadata": {},
    "source": [
-    "If unsure about how to fit the distribution parameters to the data, you can alternatively use `fit`:"
+    "For a $95\\%$ confidence interval, we usually take $z\\approx 1.96$. Note we took the standard normal distribution, with null mean and unit standard deviation (`stats.norm()` is equivalent to `stats.norm(0, 1)`).\n",
+    "\n",
+    "$\\frac{\\sigma}{\\sqrt{n}}$ is the standard deviation of the sample mean, or standard error of mean, that we have already calculated using the `sem` function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4f114ad2-9cd2-431a-8b3e-2283006ef970",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
+   "execution_count": 13,
+   "id": "7b3c6d6b-849e-4b68-8b6b-fcfa86582d99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Freedom to make life choice is 0.411±0.011 on average\n"
+     ]
+    }
+   ],
    "source": [
-    "normal_distribution = stats.norm(*stats.norm.fit(x))"
+    "print(f'Freedom to make life choice is {mu:.3f}±{z * sigma:.3f} on average')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cef7bb4e-38b9-4abc-97e4-419a9650173b",
+   "id": "d6b0c2ee-4f6b-49db-8c26-dd64aede72fb",
    "metadata": {
-    "hidden": true
+    "heading_collapsed": true,
+    "tags": []
    },
    "source": [
-    "The object returned by `norm` features several distribution-related functions, *e.g.* `pdf`:"
+    "## Fitting"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e114e17-8edb-4674-9309-c6f566885c0a",
+   "cell_type": "markdown",
+   "id": "994df34d",
    "metadata": {
     "hidden": true
    },
-   "outputs": [],
    "source": [
-    "sns.histplot(x=x, bins=10, stat='density')\n",
-    "#sns.rugplot(x=x, color='r')\n",
-    "\n",
-    "grid = np.linspace(x.min(), x.max(), 100)\n",
-    "plt.plot(grid, normal_distribution.pdf(grid), 'g-');"
+    "We have seen how to fit a normal distribution explicitly passing a mean and standard deviation. More generally, for any distribution from `scipy.stats`, we can get the required parameters using the `stats.<distribution>.fit` method. For example, for distribution `stats.norm` with sample `X`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6eaa04b5",
+   "execution_count": 14,
+   "id": "4f114ad2-9cd2-431a-8b3e-2283006ef970",
    "metadata": {
-    "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
+    "hidden": true
    },
    "outputs": [],
    "source": [
-    "sns.histplot(x=x, bins=10, stat='density')\n",
-    "#sns.rugplot(x=x, color='r')\n",
-    "sns.kdeplot(x, clip=(x.min(), x.max()), color='g', linestyle='-');"
+    "normal_distribution = stats.norm(*stats.norm.fit(X))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ddfe2b2f",
+   "cell_type": "markdown",
+   "id": "cef7bb4e-38b9-4abc-97e4-419a9650173b",
    "metadata": {
-    "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
+    "hidden": true
    },
-   "outputs": [],
    "source": [
-    "sns.histplot(x=x, stat='density')\n",
-    "sns.rugplot(x=x, color='r')\n",
-    "sns.kdeplot(x, clip=(x.min(), x.max()), color='g', linestyle='-');"
+    "Now, unlike the population mean, there is no guarantee a sample follows a normal distribution.\n",
+    "\n",
+    "To determine what distribution a sample best follows, we can fit various distributions to the data and visually appreciate how well these distributions match with the data by plotting a scaled histogram and the probability density functions of the fitted distributions on top of the histogram."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "cb8febd8",
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8e114e17-8edb-4674-9309-c6f566885c0a",
    "metadata": {
     "hidden": true
    },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "We can check whether `HoursOfSleep` follows a normal distribution with the `normaltest` function:"
+    "# plot the histogram\n",
+    "import seaborn as sns\n",
+    "ax = sns.histplot(X, bins=20, stat='density')\n",
+    "curve_names = []\n",
+    "\n",
+    "# fit a normal distribution\n",
+    "norm = stats.norm(*stats.norm.fit(X))\n",
+    "\n",
+    "# draw the probability density function\n",
+    "from matplotlib import pyplot as plt\n",
+    "grid = np.linspace(X.min(), X.max(), 300)\n",
+    "ax.plot(grid, norm.pdf(grid))\n",
+    "curve_names.append('normal')\n",
+    "\n",
+    "# fit and overlay more distributions\n",
+    "if False:\n",
+    "    t = stats.t(*stats.t.fit(X))\n",
+    "    ax.plot(grid, t.pdf(grid))\n",
+    "    curve_names.append('t')\n",
+    "\n",
+    "    chi2 = stats.chi2(*stats.chi2.fit(X))\n",
+    "    ax.plot(grid, chi2.pdf(grid))\n",
+    "    curve_names.append('chi2')\n",
+    "\n",
+    "if False:\n",
+    "    weibull_min = stats.weibull_min(*stats.weibull_min.fit(X))\n",
+    "    ax.plot(grid, weibull_min.pdf(grid))\n",
+    "    curve_names.append('weibull minimum extreme value')\n",
+    "    \n",
+    "    weibull_max = stats.weibull_max(*stats.weibull_max.fit(X))\n",
+    "    ax.plot(grid, weibull_max.pdf(grid))\n",
+    "    curve_names.append('weibull maximum extreme value')\n",
+    "\n",
+    "if curve_names:\n",
+    "    ax.legend(curve_names)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "417bfc62",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "7948848f-1492-41ec-9d4b-714343727106",
+   "metadata": {},
    "source": [
-    "stats.normaltest(x)"
+    "Note that plotting histograms is good practice anyway, because it helps to spot data distributions with multiple modes. Multiple modes in a sample are a red flag for tests that compare estimates of central tendency (*e.g.* means)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "655b0fc0",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
+   "id": "cf25c4a2-bf73-448a-b72a-88f8c4aca157",
+   "metadata": {},
    "source": [
-    "...and with the `kstest` function:"
+    "...and we can test whether `Freedom to make life choice` follows a Weibull distribution in our sample with the one-sample Kolmogorov-Smirnov test:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5b1b8872",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "stats.kstest(x, normal_distribution.cdf)"
+   "execution_count": 16,
+   "id": "7a0035eb-9d26-414b-b5b2-e73e792e6173",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'weibull_max' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m statistic, pvalue \u001b[38;5;241m=\u001b[39m stats\u001b[38;5;241m.\u001b[39mkstest(X, \u001b[43mweibull_max\u001b[49m\u001b[38;5;241m.\u001b[39mcdf)\n\u001b[1;32m      2\u001b[0m pvalue\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'weibull_max' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "statistic, pvalue = stats.kstest(X, weibull_max.cdf)\n",
+    "pvalue"
    ]
   },
   {
@@ -916,7 +694,7 @@
     "tags": []
    },
    "source": [
-    "## Statistical testing"
+    "# Statistical testing"
    ]
   },
   {
@@ -974,14 +752,11 @@
    "id": "404476b6",
    "metadata": {
     "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "grid = np.linspace(N.ppf(.001), N.ppf(.999), 100); pdf_curve, = plt.plot(grid, N.pdf(grid), 'r-'); z_line, = plt.plot([z, z], [0, N.pdf(z)], '-', zorder=1); tail = grid[z<=grid]; plt.fill_between(tail, np.zeros_like(tail), N.pdf(tail), alpha=.2); plt.axvline(0, linestyle='--', color='grey', linewidth=1); plt.axhline(0, linestyle='--', color='grey', linewidth=1); plt.xlim(grid[[0,-1]]); plt.xlabel('$X$'); plt.ylabel('probability density'); plt.legend([pdf_curve, z_line], ['$\\mathcal{N}(0,1)$', '$z$']); plt.annotate(f'$\\\\approx {onesided_pvalue:.2f}$', (1.8, .03), xytext=(2, .13), arrowprops=dict(arrowstyle=\"->\"));"
+    "scipy_material.illustration_onesided_probabilitymass(z, N, onesided_pvalue)"
    ]
   },
   {
@@ -1006,9 +781,6 @@
    "metadata": {
     "hidden": true,
     "hide_input": true,
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -1044,30 +816,11 @@
    "id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
    "metadata": {
     "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "grid = np.linspace(-3.1, 3.1, 100)\n",
-    "\n",
-    "dfs = [1, 2, 5, 20]\n",
-    "\n",
-    "for df, color in zip(\n",
-    "    dfs,\n",
-    "    ['blue', 'green', 'orange', 'red'],\n",
-    "):\n",
-    "    t = stats.t(df)\n",
-    "    plt.plot(grid, t.pdf(grid), '-', color=color)\n",
-    "    \n",
-    "plt.axvline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "plt.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "plt.xlim(grid[[0,-1]])\n",
-    "plt.xlabel('$t$')\n",
-    "plt.ylabel('probability density')\n",
-    "plt.legend([ f'$df={df}$' for df in dfs ]);"
+    "scipy_material.illustration_t_pdfs()"
    ]
   },
   {
@@ -1102,9 +855,9 @@
     "This test compares a sample's central tendency (*sample mean*) with a reference value (*population mean*).\n",
     "\n",
     "<table style=\"text-align: center;\"><tr><td>\n",
-    "<img src='img/8mice.svg' />\n",
+    "<img src='../images/8mice.svg' />\n",
     "</td><td>\n",
-    "<img src='img/Scientific_journal_icon.svg' width=\"96px\" />\n",
+    "<img src='../images/Scientific_journal_icon.svg' width=\"96px\" />\n",
     "</td></tr><tr><td><center>\n",
     "<code>x=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
     "</center></td><td><center>\n",
@@ -1118,9 +871,6 @@
     "\\frac{\\bar{X} - \\mu}{\\mathrm{SEM}} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim t(n-1) \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
     "$$\n",
     "\n",
-    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
-    "<code>scipy.stats</code> provides a <a href=\"https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sem.html\">sem</a> function as an addition to <code>numpy</code>'s <a href=\"https://numpy.org/doc/stable/reference/generated/numpy.std.html\">std</a>. <code>sem</code> is unbiased by default.\n",
-    "</div>\n",
     "\n",
     "`scipy`'s one-sample *t* test is [ttest_1samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html):\n",
     "\n",
@@ -1152,7 +902,7 @@
    },
    "source": [
     "If we do not mind a negative difference (resp. positive difference), *i.e.* we consider the danger zone to begin only above (resp. below) the expected value, we can make the test one-sided to gain statistical power.\n",
-    "To this aim, we must choose and specify which side: here `'greater'` (resp. `'less'`):"
+    "To this aim, we must choose and specify which side passing the `alternative` argument with value `'greater'` (resp. `'less'`):"
    ]
   },
   {
@@ -1189,9 +939,9 @@
     "This test compares the means of two samples or groups, *e.g.* a control sample and a sample from a mutated population: $H_0: \\bar{X_1} = \\bar{X_2}$.\n",
     "\n",
     "<table style=\"text-align:center;\"><tr><td>\n",
-    "<img src=\"img/8mice.svg\" alt=\"sample of the control population\" />\n",
+    "<img src=\"../images/8mice.svg\" alt=\"sample of the control population\" />\n",
     "</td><td>\n",
-    "<img src=\"img/8mutants1.svg\" alt=\"sample of a mutated population\" />\n",
+    "<img src=\"../images/8mutants1.svg\" alt=\"sample of a mutated population\" />\n",
     "</td></tr><tr><td><center>\n",
     "<code>x<sub>1</sub>=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
     "</center></td><td><center>\n",
@@ -1214,145 +964,113 @@
    "source": [
     "x1 = x\n",
     "x2 = np.array([64.22723692, 96.56483856, 101.94191774, 85.31918879,\n",
-    "               66.4952999, 63.88841224, 127.63861749, 55.00527005])\n",
+    "               66.49529990, 63.88841224, 127.63861749, 55.00527005])\n",
     "\n",
     "stats.ttest_ind(x1, x2)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2703f3b1",
-   "metadata": {
-    "hidden": true
-   },
+   "id": "5ec31e3d-7650-4208-b327-1e712c9c8460",
+   "metadata": {},
    "source": [
-    "1. Had we got a more precise assumption about *e.g.* $\\bar{X_2} > \\bar{X_1}$, we could have made a one-sided test that would have successfully rejected $H_0$.\n",
-    "\n",
-    "   ...but this should be defined prior to carring out any test!\n",
-    "   \n",
-    "   More important than the *p*-value is the *effect size*. A common measure of effect size for two independent samples is [Cohen's $d$](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d): $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$\n",
-    "   "
+    "`scipy`'s implementation does not require equal numbers of observations per group, but assumes the groups have [similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$)](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2)).\n",
+    "For heterogeneous groups, `ttest_ind` also implements Welch's *t* test with `equal_var=False`."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2ba983bc-ef4c-4a15-ac23-776fffd88afb",
+   "cell_type": "markdown",
+   "id": "1083c04c-1221-446f-84a0-7084413a7722",
    "metadata": {
+    "heading_collapsed": true,
     "hidden": true,
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "def cohen_d(x1, x2):\n",
-    "    # note: there exists a corrected version\n",
-    "    #       that is less biased than this one\n",
-    "    n1, n2 = len(x1), len(x2)\n",
-    "    m1, m2 = np.mean(x1), np.mean(x2)\n",
-    "    v1, v2 = np.var(x1), np.var(x2)\n",
-    "    pooled_variance = ((n1 - 1) * v1 + (n2 - 1) * v2) / (n1 + n2 - 2)\n",
-    "    d = (m2 - m1) / np.sqrt(pooled_variance)\n",
-    "    return d\n",
-    "\n",
-    "cohen_d(x1, x2)"
+    "### *t* test for paired samples"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
+   "cell_type": "markdown",
+   "id": "4743e7fc-b964-48d4-9e13-e78146626c5a",
    "metadata": {
-    "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
+    "hidden": true
    },
-   "outputs": [],
    "source": [
-    "from matplotlib import pyplot as plt\n",
-    "import ipywidgets as widgets\n",
-    "from ipywidgets import interact\n",
-    "def plot_pdfs(cohen_d):\n",
-    "    grid = np.linspace(-3, 3+cohen_d, 100)\n",
-    "    x1 = stats.norm(0, 1).pdf(grid)\n",
-    "    x2 = stats.norm(cohen_d, 1).pdf(grid)\n",
-    "    plt.fill_between(grid, x1, alpha=.5)\n",
-    "    plt.fill_between(grid, x2, alpha=.5)\n",
-    "    plt.show()\n",
-    "slider = widgets.FloatSlider(.5, min=0, max=4, step=.5)\n",
-    "interact(plot_pdfs, cohen_d=slider);"
+    "<img src='../images/paired1.svg' />\n",
+    "\n",
+    "`scipy`'s *t* test for paired samples is [ttest_rel](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html):\n",
+    "\n",
+    "`scipy.stats.ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided')`\n",
+    "\n",
+    "This is actually a one-sample *t* test of the between-group differences against a population mean equal to zero (compare [1](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L6450-L6460) and [2](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L5647-L5656))."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f8e1face-4845-459d-95c2-8674181d39b5",
+   "id": "119c66ea-ab2c-4347-898c-ee5c0b38e89d",
+   "metadata": {},
+   "source": [
+    "### Effect sizes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2703f3b1",
    "metadata": {
     "hidden": true
    },
    "source": [
-    "1. (...)\n",
-    "\n",
-    "   We could have found a significant effect of size $0.3$, which may not be of practical interest due to the small effect size.\n",
-    "   \n",
-    "   Measurements of effect size were proposed together with [tables](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) for interpreting size values. For example, for Cohen's $d$:\n",
-    "   \n",
-    "| $|d|$ | size of effect |\n",
-    "| :-: | :-- |\n",
-    "| $0.2$ | small |\n",
-    "| $0.5$ | medium |\n",
-    "| $0.8$ | large |\n",
-    "\n",
-    "2. Had we found enough evidence to reject $H_0$, we could have concluded about an *association* between the mutation and the observed effect.\n",
-    "   To further conclude in terms of *causation*, it is necessary to rule out all possible [confounders](https://en.wikipedia.org/wiki/Confounding) (supplier, cage effect, etc).\n",
+    "Very low *p*-values are not measurements of the strength of an effect. One should consider the *effect size* instead.\n",
     "\n",
-    "3. `scipy`'s implementation does not require equal numbers of observations per group.\n",
-    "   However, it assumes the groups are normally distributed (but is relatively robust to non-«extreme non-normality») and, more importantly, have [similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$)](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2)).\n",
-    "   For heterogeneous groups, `ttest_ind` embarks various variants of the *t* test that can be selected with additional arguments:\n",
-    "    * Welch's *t* test with `equal_var=False`;\n",
-    "    * Yuen's *t* test with `equal_var=False` and `trim=0.2` (requires more data).\n",
-    "    \n",
-    "<div class=\"alert alert-block alert-info\" markdown=\"1\">\n",
-    "<em>Tip</em>: always check for the underlying assumptions in the documentation.\n",
-    "</div>"
+    "A common measure of effect size for two independent samples is [Cohen's $d$](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d): $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "1083c04c-1221-446f-84a0-7084413a7722",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
    "metadata": {
-    "heading_collapsed": true,
     "hidden": true,
     "tags": []
    },
+   "outputs": [],
    "source": [
-    "### *t* test for paired samples"
+    "scipy_material.illustration_cohen_d();"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4743e7fc-b964-48d4-9e13-e78146626c5a",
+   "id": "f8e1face-4845-459d-95c2-8674181d39b5",
    "metadata": {
     "hidden": true
    },
    "source": [
-    "<img src='img/paired1.svg' />\n",
-    "\n",
-    "`scipy`'s *t* test for paired samples is [ttest_rel](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html):\n",
-    "\n",
-    "`scipy.stats.ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided')`\n",
-    "\n",
-    "This is actually a one-sample *t* test of the between-group differences against a population mean equal to zero (compare [1](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L6450-L6460) and [2](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L5647-L5656))."
+    "With large enough sample sizes, one can find significant effects of size $0.1$ for example, which may not be of practical interest. Statistical significance does not imply practical significance.\n",
+    "   \n",
+    "Measurements of effect size were proposed together with [tables](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) for interpreting size values. For example, for Cohen's $d$:\n",
+    "   \n",
+    "| $|d|$ | size of effect |\n",
+    "| :-: | :-- |\n",
+    "| $0.2$ | small |\n",
+    "| $0.5$ | medium |\n",
+    "| $0.8$ | large |"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "234dc53a-dce0-426e-8f57-4a4f9bfa38fc",
-   "metadata": {
-    "hidden": true
-   },
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a075fc16-e6e1-4f88-8a72-7daf0f72ff28",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "#### Effect size"
+    "def cohen_d(x1, x2):\n",
+    "    n1, n2 = len(x1), len(x2)\n",
+    "    m1, m2 = np.mean(x1), np.mean(x2)\n",
+    "    v1, v2 = np.var(x1), np.var(x2)\n",
+    "    pooled_variance = (n1 * v1 + n2 * v2) / (n1 + n2 - 2)\n",
+    "    d = (m2 - m1) / np.sqrt(pooled_variance)\n",
+    "    return d"
    ]
   },
   {
@@ -1362,7 +1080,7 @@
     "hidden": true
    },
    "source": [
-    "Adjusted Cohen's $d$:"
+    "Adjusted Cohen's $d$ for dependent samples:"
    ]
   },
   {
@@ -1600,9 +1318,6 @@
    "id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
    "metadata": {
     "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -1618,7 +1333,7 @@
     "\n",
     "ax = axes[0]\n",
     "for sigma, color in zip((.25, .5, 1), colors):\n",
-    "    ax.plot(grid, skewed_dist(sigma, grid), '-', color=color, label=f'$\\sigma={sigma:.2f}$')\n",
+    "    ax.plot(grid, skewed_dist(sigma, grid), '-', color=color, label=f'$\\\\sigma={sigma:.2f}$')\n",
     "    \n",
     "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
     "ax.set_xlim(grid[[0,-1]])\n",
@@ -1690,7 +1405,7 @@
     "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
     "\n",
     "df = pd.DataFrame(data=dict(A=A, B=B, C=C))\n",
-    "plt.boxplot(df, tick_labels=df.columns);"
+    "plt.boxplot(df, labels=df.columns);"
    ]
   },
   {
@@ -1746,9 +1461,6 @@
    "id": "b8123d48-7285-4fa3-8473-04f316dedffc",
    "metadata": {
     "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -1769,7 +1481,7 @@
     "    \n",
     "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
     "ax.set_xlim(grid[0],grid[-1])\n",
-    "ax.set_xlabel('$\\chi^2$')\n",
+    "ax.set_xlabel(r'$\\chi^2$')\n",
     "ax.set_ylabel('probability density')\n",
     "ax.legend([ f'$df={df}$' for df in dfs ])\n",
     "\n",
@@ -1779,7 +1491,7 @@
     "ax.plot(grid, chi2, '-', color=color)\n",
     "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
     "ax.set_xlim(grid[0],grid[-1])\n",
-    "ax.set_xlabel('$\\chi^2$')\n",
+    "ax.set_xlabel(r'$\\chi^2$')\n",
     "ax.set_ylabel('probability density');\n",
     "\n",
     "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
@@ -2629,24 +2341,14 @@
     "plt.axhline(0.8, color='r', linestyle=':', linewidth=1)\n",
     "plt.xlim(nobservations[0], nobservations[-1]);"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "084b9635",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "celltoolbar": "Aucun(e)",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "scientific_python",
    "language": "python",
-   "name": "python3"
+   "name": "scientific_python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2658,7 +2360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/Courses/scipy_material.py b/notebooks/Courses/scipy_material.py
index bf00bf63888ec41a62698c28c5bcf2afe920af12..04d896238ec0d19b94320192d9f891e4099cf037 100644
--- a/notebooks/Courses/scipy_material.py
+++ b/notebooks/Courses/scipy_material.py
@@ -28,7 +28,7 @@ def illustration_confidence_interval(m=46, s=1, alpha=0.05):
     plt.axhline(0, color='k', linestyle=':', linewidth=1)
     plt.xlabel('population mean')
     plt.ylabel('probability density')
-    plt.axvline(m, color='k', linestyle=':', linewidth=1, label='sample mean')
+    plt.axvline(m, color='g', linestyle=':', linewidth=1, label='sample mean')
 
     u = stats.norm().isf(alpha / 2)
     ci_low = m - u * s
diff --git a/notebooks/data/happiness.csv b/notebooks/data/happiness.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2ff5f9808eda861aca723fdf2113ab0d973be093
--- /dev/null
+++ b/notebooks/data/happiness.csv
@@ -0,0 +1,713 @@
+country,region,year,score,gdp,family,health,freedom,generosity
+Switzerland,Europe,2015,7.587,1.39651,1.34951,0.94143,0.66557,0.29678
+Iceland,Europe,2015,7.561,1.30232,1.40223,0.94784,0.62877,0.4363
+Denmark,Europe,2015,7.527,1.32548,1.36058,0.87464,0.64938,0.34139
+Norway,Europe,2015,7.522,1.459,1.33095,0.88521,0.66973,0.34699
+Canada,USA/Canada,2015,7.427,1.32629,1.32261,0.90563,0.63297,0.45811
+Finland,Europe,2015,7.406,1.29025,1.31826,0.88911,0.64169,0.23351
+Netherlands,Europe,2015,7.378,1.32944,1.28017,0.89284,0.61576,0.4761
+Sweden,Europe,2015,7.364,1.33171,1.28907,0.91087,0.6598,0.36262
+Israel,Asia,2015,7.278,1.22857,1.22393,0.91387,0.41319,0.33172
+Costa Rica,Latin America/Caribbean,2015,7.226,0.95578,1.23788,0.86027,0.63376,0.25497
+Austria,Europe,2015,7.2,1.33723,1.29704,0.89042,0.62433,0.33088
+Mexico,Latin America/Caribbean,2015,7.187,1.02054,0.91451,0.81444,0.48181,0.14074
+United States,USA/Canada,2015,7.119,1.39451,1.24711,0.86179,0.54604,0.40105
+Brazil,Latin America/Caribbean,2015,6.983,0.98124,1.23287,0.69702,0.49049,0.14574
+Luxembourg,Europe,2015,6.946,1.56391,1.21963,0.91894,0.61583,0.28034
+Ireland,Europe,2015,6.94,1.33596,1.36948,0.89533,0.61777,0.45901
+Belgium,Europe,2015,6.937,1.30782,1.28566,0.89667,0.5845,0.2225
+United Arab Emirates,Asia,2015,6.901,1.42727,1.12575,0.80925,0.64157,0.26428
+United Kingdom,Europe,2015,6.867,1.26637,1.28548,0.90943,0.59625,0.51912
+Oman,Asia,2015,6.853,1.36011,1.08182,0.76276,0.63274,0.21542
+Venezuela,Latin America/Caribbean,2015,6.81,1.04424,1.25596,0.72052,0.42908,0.05841
+Singapore,Asia,2015,6.798,1.52186,1.02,1.02525,0.54252,0.31105
+Panama,Latin America/Caribbean,2015,6.786,1.06353,1.1985,0.79661,0.5421,0.24434
+Germany,Europe,2015,6.75,1.32792,1.29937,0.89186,0.61477,0.28214
+Chile,Latin America/Caribbean,2015,6.67,1.10715,1.12447,0.85857000000000006,0.44132,0.33363
+Qatar,Asia,2015,6.611,1.69042,1.0786,0.79733,0.6404,0.32573
+France,Europe,2015,6.575,1.27778,1.26038,0.94579,0.55011,0.12332
+Argentina,Latin America/Caribbean,2015,6.574,1.05351,1.24823,0.78723,0.44974,0.11451
+Czech Republic,Europe,2015,6.505,1.17898,1.20643,0.84483,0.46364,0.10686
+Uruguay,Latin America/Caribbean,2015,6.485,1.06166,1.2089,0.8116,0.60362,0.2324
+Colombia,Latin America/Caribbean,2015,6.477,0.91861,1.24018,0.69077,0.53466,0.18401
+Thailand,Asia,2015,6.455,0.9669,1.26504,0.7385,0.55664,0.5763
+Saudi Arabia,Asia,2015,6.411,1.39541,1.08393,0.72025,0.31048,0.13706
+Spain,Europe,2015,6.329,1.23011,1.31379,0.95562,0.45951,0.18227
+Malta,Europe,2015,6.302,1.2074,1.30203,0.88721,0.60365,0.51752
+Taiwan,Asia,2015,6.298,1.29098,1.07617,0.8753,0.3974,0.25376
+Kuwait,Asia,2015,6.295,1.55422,1.16594,0.72492,0.55499,0.16228
+Suriname,Latin America/Caribbean,2015,6.269,0.99534,0.972,0.6082,0.59657,0.16991
+Trinidad and Tobago,Latin America/Caribbean,2015,6.168,1.21183,1.18354,0.61483,0.55884,0.31844
+El Salvador,Latin America/Caribbean,2015,6.13,0.76454,1.02507,0.67737,0.4035,0.10692
+Guatemala,Latin America/Caribbean,2015,6.123,0.74553,1.04356,0.64425,0.57733,0.27489
+Uzbekistan,Asia,2015,6.003,0.63244,1.34043,0.59772,0.65821,0.22837
+Slovakia,Europe,2015,5.995,1.16891,1.26999,0.78902,0.31751,0.16893
+Japan,Asia,2015,5.987,1.27074,1.25712,0.99111,0.49615,0.10705
+Ecuador,Latin America/Caribbean,2015,5.975,0.86402,0.99903,0.79075,0.48574,0.11541
+Bahrain,Asia,2015,5.96,1.32376,1.21624,0.74716,0.45492,0.17362
+Italy,Europe,2015,5.948,1.25114,1.19777,0.95446,0.26236,0.22823
+Bolivia,Latin America/Caribbean,2015,5.89,0.68133,0.97841,0.5392,0.57414,0.20536
+Moldova,Europe,2015,5.889,0.59448,1.01528,0.61826,0.32818,0.20951
+Paraguay,Latin America/Caribbean,2015,5.878,0.75985,1.30477,0.66098,0.53899,0.3424
+Kazakhstan,Asia,2015,5.855,1.12254,1.12241,0.64368,0.51649,0.11827
+Slovenia,Europe,2015,5.848,1.18498,1.27385,0.87337,0.60855,0.25328
+Lithuania,Europe,2015,5.833,1.14723,1.25745,0.73128,0.21342,0.02641
+Nicaragua,Latin America/Caribbean,2015,5.828,0.59325,1.14184,0.74314,0.55475,0.27815
+Peru,Latin America/Caribbean,2015,5.824,0.90019,0.97459,0.73017,0.41496,0.14982
+Belarus,Europe,2015,5.813,1.03192,1.23289,0.73608,0.37938,0.11046
+Poland,Europe,2015,5.791,1.12555,1.27948,0.77903,0.53122,0.16759
+Malaysia,Asia,2015,5.77,1.12486,1.07023,0.72394,0.53024,0.33075
+Croatia,Europe,2015,5.759,1.08254,0.79624,0.78805,0.25883,0.05444
+Libya,Africa,2015,5.754,1.13145,1.11862,0.7038,0.41668,0.18295
+Russia,Europe,2015,5.716,1.13764,1.23617,0.66926,0.36679,0.00199
+Jamaica,Latin America/Caribbean,2015,5.709,0.81038,1.15102,0.68741,0.50442,0.2123
+Cyprus,Asia,2015,5.689,1.20813,0.89318,0.92356,0.40672,0.30638
+Algeria,Africa,2015,5.605,0.93929,1.07772,0.61766,0.28579,0.07822
+Turkmenistan,Asia,2015,5.548,0.95847,1.22668,0.53886,0.4761,0.16979
+Mauritius,Africa,2015,5.477,1.00761,0.98521,0.7095,0.56066,0.37744
+Hong Kong,Asia,2015,5.474,1.38604,1.05818,1.01328,0.59608,0.39478
+Estonia,Europe,2015,5.429,1.15174,1.22791,0.77361,0.44888,0.0868
+Indonesia,Asia,2015,5.399,0.82827,1.08708,0.63793,0.46611,0.51535
+Vietnam,Asia,2015,5.36,0.63216,0.91226,0.74676,0.59444,0.1686
+Turkey,Asia,2015,5.332,1.06098,0.94632,0.73172,0.22815,0.12253
+Kyrgyzstan,Asia,2015,5.286,0.47428,1.15115,0.65088,0.43477,0.3003
+Nigeria,Africa,2015,5.268,0.65435,0.90432,0.16007,0.34334,0.27233
+Bhutan,Asia,2015,5.253,0.77042,1.10395,0.57407,0.53206,0.47998
+Azerbaijan,Asia,2015,5.212,1.02389,0.93793,0.64045,0.3703,0.07799
+Pakistan,Asia,2015,5.194,0.59543,0.41411,0.51466,0.12102,0.33671
+Jordan,Asia,2015,5.192,0.90198,1.05392,0.69639,0.40661,0.11053
+Montenegro,Europe,2015,5.192,0.97438,0.90557,0.72521,0.1826,0.1614
+China,Asia,2015,5.14,0.89012,0.94675,0.81658,0.51697,0.08185
+Zambia,Africa,2015,5.129,0.47038,0.91612,0.29924,0.48827,0.19591
+Romania,Europe,2015,5.124,1.04345,0.88588,0.7689,0.35068,0.13748
+Serbia,Europe,2015,5.123,0.92053,1.00964,0.74836,0.20107,0.19231
+Portugal,Europe,2015,5.102,1.15991,1.13935,0.87519,0.51469,0.13719
+Latvia,Europe,2015,5.098,1.11312,1.09562,0.72437,0.29671,0.18226
+Philippines,Asia,2015,5.073,0.70532,1.03516,0.58114,0.62545,0.24991
+Morocco,Africa,2015,5.013,0.73479,0.64095,0.60954,0.41691,0.07172
+Macedonia,Europe,2015,5.007,0.91851,1.00232,0.73545,0.33457,0.22359
+Mozambique,Africa,2015,4.971,0.08308,1.02626,0.09131,0.34037,0.22269
+Albania,Europe,2015,4.959,0.87867,0.80434,0.81325,0.35733,0.14272
+Bosnia and Herzegovina,Europe,2015,4.949,0.83223,0.91916,0.79081,0.09245,0.24808
+Lesotho,Africa,2015,4.898,0.37545,1.04103,0.07612,0.31767,0.16388
+Dominican Republic,Latin America/Caribbean,2015,4.885,0.89537,1.17202,0.66825,0.57672,0.21684
+Laos,Asia,2015,4.876,0.59066,0.73803,0.54909,0.59591,0.42192
+Mongolia,Asia,2015,4.874,0.82819,1.3006,0.60268,0.43626,0.3323
+Swaziland,Africa,2015,4.867,0.71206,1.07284,0.07566,0.30658,0.18259
+Greece,Europe,2015,4.857,1.15406,0.92933,0.88213,0.07699,0
+Lebanon,Asia,2015,4.839,1.02564,0.80001,0.83947,0.33916,0.21854
+Hungary,Europe,2015,4.8,1.12094,1.20215,0.75905,0.32112,0.128
+Honduras,Latin America/Caribbean,2015,4.788,0.59532,0.95348,0.6951,0.40148,0.23027
+Tajikistan,Asia,2015,4.786,0.39047,0.85563,0.57379,0.47216,0.22974
+Tunisia,Africa,2015,4.739,0.88113,0.60429,0.73793,0.26268,0.06431
+Bangladesh,Asia,2015,4.694,0.39753,0.43106,0.60164,0.4082,0.21222
+Iran,Asia,2015,4.686,1.0088,0.54447,0.69805,0.30033,0.38086
+Ukraine,Europe,2015,4.681,0.79907,1.20278,0.6739,0.25123,0.15275
+Iraq,Asia,2015,4.677,0.98549,0.81889,0.60237,0,0.17922
+South Africa,Africa,2015,4.642,0.92049,1.18468,0.27688,0.33207,0.11973
+Ghana,Africa,2015,4.633,0.54558,0.67954,0.40132,0.42342,0.23087
+Zimbabwe,Africa,2015,4.61,0.271,1.03276,0.33475,0.25861,0.18987
+Liberia,Africa,2015,4.571,0.0712,0.78968,0.34201,0.28531,0.24362
+India,Asia,2015,4.565,0.64499,0.38174,0.51529,0.39786,0.26475
+Sudan,Africa,2015,4.55,0.52107,1.01404,0.36878,0.10081,0.19062
+Haiti,Latin America/Caribbean,2015,4.518,0.26673,0.74302,0.38847,0.24425,0.46187
+Nepal,Asia,2015,4.514,0.35997,0.86449,0.56874,0.38282,0.32296
+Ethiopia,Africa,2015,4.512,0.19073,0.60406,0.44055,0.4345,0.24325
+Sierra Leone,Africa,2015,4.507,0.33024,0.95571,0,0.4084,0.21488
+Mauritania,Africa,2015,4.436,0.45407,0.86908,0.35874,0.24232,0.219
+Kenya,Africa,2015,4.419,0.36471,0.99876,0.41435,0.42215,0.37542
+Djibouti,Africa,2015,4.369,0.44025,0.59207,0.36291,0.46074,0.18093
+Armenia,Asia,2015,4.35,0.76821,0.77711,0.7299,0.19847,0.07855
+Botswana,Africa,2015,4.332,0.99355,1.10464,0.04776,0.49495,0.10461
+Georgia,Asia,2015,4.297,0.7419,0.38562,0.72926,0.40577,0.05547
+Malawi,Africa,2015,4.292,0.01604,0.41134,0.22562,0.43054,0.33128
+Sri Lanka,Asia,2015,4.271,0.83524,1.01905,0.70806,0.53726,0.40828
+Cameroon,Africa,2015,4.252,0.4225,0.88767,0.23402,0.49309,0.20618
+Bulgaria,Europe,2015,4.218,1.01216,1.10614,0.76649,0.30587,0.11921
+Egypt,Africa,2015,4.194,0.8818,0.747,0.61712,0.17288,0.11291
+Yemen,Asia,2015,4.077,0.54649,0.68093,0.40064,0.35571,0.09131
+Angola,Africa,2015,4.033,0.75778,0.8604,0.16683,0.10384,0.12344
+Mali,Africa,2015,3.995,0.26074,1.03526,0.20583,0.38857,0.18798
+Comoros,Africa,2015,3.956,0.23906,0.79273,0.36315,0.22917,0.17441
+Uganda,Africa,2015,3.931,0.21102,1.13299,0.33861,0.45727,0.29066
+Senegal,Africa,2015,3.904,0.36498,0.97619,0.4354,0.36772,0.20843
+Gabon,Africa,2015,3.896,1.06024,0.90528,0.43372,0.31914,0.06822
+Niger,Africa,2015,3.845,0.0694,0.77265,0.29707,0.47692,0.19387
+Cambodia,Asia,2015,3.819,0.46038,0.62736,0.61114,0.66246,0.40359
+Tanzania,Africa,2015,3.781,0.2852,1.00268,0.38215,0.32878,0.34377
+Madagascar,Africa,2015,3.681,0.20824,0.66801,0.46721,0.19184,0.21333
+Central African Republic,Africa,2015,3.678,0.0785,0,0.06699,0.48879,0.23835
+Chad,Africa,2015,3.667,0.34193,0.76062,0.1501,0.23501,0.18386
+Guinea,Africa,2015,3.656,0.17417,0.46475,0.24009,0.37725,0.28657
+Burkina Faso,Africa,2015,3.587,0.25812,0.85188,0.27125,0.39493,0.21747
+Afghanistan,Asia,2015,3.575,0.31982,0.30285,0.30335,0.23414,0.3651
+Rwanda,Africa,2015,3.465,0.22208,0.7737,0.42864,0.59201,0.22628
+Benin,Africa,2015,3.34,0.28665,0.35386,0.3191,0.4845,0.1826
+Syria,Asia,2015,3.006,0.6632,0.47489,0.72193,0.15684,0.47179
+Burundi,Africa,2015,2.905,0.0153,0.41587,0.22396,0.1185,0.19727
+Togo,Africa,2015,2.839,0.20868,0.13995,0.28443,0.36453,0.16681000000000001
+Denmark,Europe,2016,7.526,1.44178,1.16374,0.79504,0.57941,0.36171
+Switzerland,Europe,2016,7.509,1.52733,1.14524,0.86303,0.58557,0.28083
+Iceland,Europe,2016,7.501,1.42666,1.18326,0.86733,0.56624,0.47678
+Norway,Europe,2016,7.498,1.57744,1.1269,0.79579,0.59609,0.37895
+Finland,Europe,2016,7.413,1.40598,1.13464,0.81091,0.57104,0.25492
+Canada,USA/Canada,2016,7.404,1.44015,1.0961,0.8276,0.5737,0.44834
+Netherlands,Europe,2016,7.339,1.46468,1.02912,0.81231,0.55211,0.47416
+Sweden,Europe,2016,7.291,1.45181,1.08764,0.83121,0.58218,0.38254
+Israel,Asia,2016,7.267,1.33766,0.99537,0.84917,0.36432,0.32288
+Austria,Europe,2016,7.119,1.45038,1.08383,0.80565,0.54355,0.32865
+United States,USA/Canada,2016,7.104,1.50796,1.04782,0.779,0.48163,0.41077
+Costa Rica,Latin America/Caribbean,2016,7.087,1.06879,1.02152,0.76146,0.55225,0.22553
+Germany,Europe,2016,6.994,1.44787,1.09774,0.81487,0.53466,0.30452
+Brazil,Latin America/Caribbean,2016,6.952,1.08754,1.03938,0.61415,0.40425,0.15776
+Belgium,Europe,2016,6.929,1.42539,1.05249,0.81959,0.51354,0.2424
+Ireland,Europe,2016,6.907,1.48341,1.16157,0.81455,0.54008,0.44963
+Luxembourg,Europe,2016,6.871,1.69752,1.03999,0.84542,0.5487,0.27571
+Mexico,Latin America/Caribbean,2016,6.778,1.11508,0.7146,0.71143,0.37709,0.11735
+Singapore,Asia,2016,6.739,1.64555,0.86758,0.94719,0.4877,0.32706
+United Kingdom,Europe,2016,6.725,1.40283,1.08672,0.80991,0.50036,0.50156
+Chile,Latin America/Caribbean,2016,6.705,1.2167,0.90587,0.81883,0.37789,0.31595
+Panama,Latin America/Caribbean,2016,6.701,1.18306,0.98912,0.70835,0.48927,0.2418
+Argentina,Latin America/Caribbean,2016,6.65,1.15137,1.06612,0.69711,0.42284,0.10989
+Czech Republic,Europe,2016,6.596,1.30915,1.00793,0.76376,0.41418,0.09929
+United Arab Emirates,Asia,2016,6.573,1.57352,0.87114,0.72993,0.56215,0.26591
+Uruguay,Latin America/Caribbean,2016,6.545,1.18157,1.03143,0.72183,0.54388,0.18056
+Malta,Europe,2016,6.488,1.30782,1.09879,0.80315,0.54994,0.56237
+Colombia,Latin America/Caribbean,2016,6.481,1.03032,1.02169,0.59659,0.44735,0.15626
+France,Europe,2016,6.478,1.39488,1.00508,0.83795,0.46562,0.1216
+Thailand,Asia,2016,6.474,1.0893,1.04477,0.64915,0.49553,0.58696
+Saudi Arabia,Asia,2016,6.379,1.48953,0.84829,0.59267,0.37904,0.15457
+Taiwan,Asia,2016,6.379,1.39729,0.92624,0.79565,0.32377,0.25495
+Qatar,Asia,2016,6.375,1.82427,0.87964,0.71723,0.56679,0.32388
+Spain,Europe,2016,6.361,1.34253,1.12945,0.87896,0.37545,0.17665
+Algeria,Africa,2016,6.355,1.05266,0.83309,0.61804,0.21006,0.07044
+Guatemala,Latin America/Caribbean,2016,6.324,0.83454,0.87119,0.54039,0.50379,0.28808
+Suriname,Latin America/Caribbean,2016,6.269,1.09686,0.77866,0.50933,0.52234,0.16665
+Kuwait,Asia,2016,6.239,1.61714,0.87758,0.63569,0.43166,0.15965
+Bahrain,Asia,2016,6.218,1.44024,0.94397,0.65696,0.47375,0.17147
+Trinidad and Tobago,Latin America/Caribbean,2016,6.168,1.32572,0.98569,0.52608,0.48453,0.31935
+Venezuela,Latin America/Caribbean,2016,6.084,1.13367,1.03302,0.61904,0.19847,0.0425
+Slovakia,Europe,2016,6.078,1.27973,1.08268,0.70367,0.23391,0.13837
+El Salvador,Latin America/Caribbean,2016,6.068,0.8737,0.80975,0.596,0.37269,0.08877
+Malaysia,Asia,2016,6.005,1.25142,0.88025,0.62366,0.39031,0.41474
+Nicaragua,Latin America/Caribbean,2016,5.992,0.69384,0.89521,0.65213,0.46582,0.29773
+Uzbekistan,Asia,2016,5.987,0.73591,1.1681,0.50163,0.60848,0.34326
+Italy,Europe,2016,5.977,1.35495,1.04167,0.85102,0.18827,0.16684
+Ecuador,Latin America/Caribbean,2016,5.976,0.97306,0.85974,0.68613,0.4027,0.10074
+Japan,Asia,2016,5.921,1.38007,1.06054,0.91491,0.46761,0.10224
+Kazakhstan,Asia,2016,5.919,1.22943,0.95544,0.57386,0.4052,0.15011
+Moldova,Europe,2016,5.897,0.69177,0.83132,0.52309,0.25202,0.19997
+Russia,Europe,2016,5.856,1.23228,1.05261,0.58991,0.32682,0.02736
+Poland,Europe,2016,5.835,1.24585,1.04685,0.69058,0.4519,0.14443
+Bolivia,Latin America/Caribbean,2016,5.822,0.79422,0.83779,0.4697,0.50961,0.21698
+Lithuania,Europe,2016,5.813,1.2692,1.06411,0.64674,0.18929,0.02025
+Belarus,Europe,2016,5.802,1.13062,1.04993,0.63104,0.29091,0.13942
+Slovenia,Europe,2016,5.768,1.29947,1.05613,0.79151,0.53164,0.25738
+Peru,Latin America/Caribbean,2016,5.743,0.99602,0.81255,0.62994000000000006,0.37502,0.14527
+Turkmenistan,Asia,2016,5.658,1.08017,1.03817,0.44006,0.37408,0.22567
+Mauritius,Africa,2016,5.648,1.14372,0.75695,0.66189,0.46145,0.36951
+Libya,Africa,2016,5.615,1.06688,0.95076,0.52304,0.40672,0.17087
+Latvia,Europe,2016,5.56,1.21788,0.95025,0.63952,0.27996,0.17445
+Cyprus,Asia,2016,5.546,1.31857,0.70697,0.8488,0.29507,0.27906
+Paraguay,Latin America/Caribbean,2016,5.538,0.89373,1.11111,0.58295,0.46235,0.25296
+Romania,Europe,2016,5.528,1.1697,0.72803,0.67602,0.36712,0.12889
+Estonia,Europe,2016,5.517,1.2796400000000001,1.05163,0.68098,0.41511,0.08423
+Jamaica,Latin America/Caribbean,2016,5.51,0.89333,0.96372,0.59469,0.43597,0.22245
+Croatia,Europe,2016,5.488,1.18649,0.60809,0.70524,0.23907,0.18434
+Hong Kong,Asia,2016,5.458,1.5107,0.87021,0.95277,0.48079,0.40097
+Turkey,Asia,2016,5.389,1.16492,0.87717,0.64718,0.23889,0.04707
+Indonesia,Asia,2016,5.314,0.95104,0.87625,0.49374,0.39237,0.56521
+Jordan,Asia,2016,5.303,0.99673,0.86216,0.60712,0.36023,0.14262
+Azerbaijan,Asia,2016,5.291,1.12373,0.76042,0.54504,0.35327,0.0564
+Philippines,Asia,2016,5.279,0.81217,0.87877,0.47036,0.54854,0.21674
+China,Asia,2016,5.245,1.0278,0.79381,0.73561,0.44012,0.04959
+Bhutan,Asia,2016,5.196,0.8527,0.90836,0.49759,0.46074,0.48546
+Kyrgyzstan,Asia,2016,5.185,0.56044,0.95434,0.55449,0.40212,0.38432
+Serbia,Europe,2016,5.177,1.03437,0.81329,0.6458,0.15718,0.20737
+Bosnia and Herzegovina,Europe,2016,5.163,0.93383,0.64367,0.70766,0.09511,0.29889
+Montenegro,Europe,2016,5.161,1.07838,0.74173,0.63533,0.15111,0.17191
+Dominican Republic,Latin America/Caribbean,2016,5.155,1.02787,0.99496,0.57669,0.52259,0.21286
+Morocco,Africa,2016,5.151,0.84058,0.38595,0.59471,0.25646,0.04053
+Hungary,Europe,2016,5.145,1.24142,0.93164,0.67608,0.1977,0.099
+Pakistan,Asia,2016,5.132,0.68816,0.26135,0.40306,0.14622,0.31185
+Lebanon,Asia,2016,5.129,1.12268,0.64184,0.76171,0.26228,0.23693
+Portugal,Europe,2016,5.123,1.27607,0.94367,0.79363,0.44727,0.11691
+Macedonia,Europe,2016,5.121,1.0193,0.78236000000000006,0.64738,0.27668,0.23507
+Vietnam,Asia,2016,5.061,0.74037,0.79117,0.66157,0.55954,0.25075
+Tunisia,Africa,2016,5.045,0.97724,0.43165,0.59577,0.23553,0.03936
+Greece,Europe,2016,5.033,1.24886,0.75473,0.80029,0.05822,0
+Tajikistan,Asia,2016,4.996,0.48835,0.75602,0.53119,0.43408,0.25998
+Mongolia,Asia,2016,4.907,0.98853,1.08983,0.55469,0.35972,0.34539
+Laos,Asia,2016,4.876,0.68042,0.5497,0.38291,0.52168,0.43079
+Nigeria,Africa,2016,4.875,0.75216,0.64498,0.05108,0.27854,0.23219
+Honduras,Latin America/Caribbean,2016,4.871,0.69429,0.75596,0.58383,0.26755,0.2044
+Iran,Asia,2016,4.813,1.11758,0.38857,0.64232,0.22544,0.38538
+Zambia,Africa,2016,4.795,0.61202,0.6376,0.23573,0.42662,0.17866
+Nepal,Asia,2016,4.793,0.44626,0.69699,0.50073,0.37012,0.3816
+Albania,Europe,2016,4.655,0.9553,0.50163,0.73007,0.31866,0.1684
+Bangladesh,Asia,2016,4.643,0.54177,0.24749,0.52989,0.39778,0.19132
+Sierra Leone,Africa,2016,4.635,0.36485,0.628,0,0.30685,0.23897
+Iraq,Asia,2016,4.575,1.07474,0.59205,0.51076,0.24856,0.19589
+Namibia,Africa,2016,4.574,0.93287,0.70362,0.34745,0.48614,0.07795
+Cameroon,Africa,2016,4.513,0.52497,0.62542,0.12698,0.42736,0.2268
+Ethiopia,Africa,2016,4.508,0.29283,0.37932,0.34578,0.36703,0.29522
+South Africa,Africa,2016,4.459,1.02416,0.96053,0.18611,0.42483,0.13656
+Sri Lanka,Asia,2016,4.415,0.97318,0.84783,0.62007,0.50817,0.46978
+India,Asia,2016,4.404,0.74036,0.29247,0.45091,0.40285,0.25028
+Egypt,Africa,2016,4.362,0.95395,0.49813,0.52116,0.18847,0.12706
+Armenia,Asia,2016,4.36,0.86086,0.62477,0.64083,0.14037,0.07793
+Kenya,Africa,2016,4.356,0.52267,0.7624,0.30147,0.40576,0.41328
+Ukraine,Europe,2016,4.324,0.87287,1.01413,0.58628,0.12859,0.20363
+Ghana,Africa,2016,4.276,0.63107,0.49353,0.29681,0.40973,0.21203
+Georgia,Asia,2016,4.252,0.83792,0.19249,0.64035,0.32461,0.06786
+Senegal,Africa,2016,4.219,0.44314,0.77416,0.40457,0.31056,0.19103
+Bulgaria,Europe,2016,4.217,1.11306,0.92542,0.67806,0.21219,0.12793
+Mauritania,Africa,2016,4.201,0.61391,0.84142,0.28639,0.1268,0.22686
+Zimbabwe,Africa,2016,4.193,0.35041,0.71478,0.1595,0.25429,0.18503
+Malawi,Africa,2016,4.156,0.08709,0.147,0.29364,0.4143,0.30968
+Sudan,Africa,2016,4.139,0.63069,0.81928,0.29759,0,0.18077
+Gabon,Africa,2016,4.121,1.15851,0.72368,0.3494,0.28098,0.06244
+Mali,Africa,2016,4.073,0.31292,0.86333,0.16347,0.27544,0.21064
+Haiti,Latin America/Caribbean,2016,4.028,0.34097,0.29561,0.27494,0.12072,0.47958
+Botswana,Africa,2016,3.974,1.09426,0.89186,0.34752,0.44089,0.12425
+Comoros,Africa,2016,3.956,0.27509,0.60323,0.29981,0.15412,0.1827
+Cambodia,Asia,2016,3.907,0.55604,0.5375,0.42494,0.58852,0.40339
+Angola,Africa,2016,3.866,0.84731,0.66366,0.04991,0.00589,0.12071
+Niger,Africa,2016,3.856,0.1327,0.6053,0.26162,0.38041,0.2097
+Chad,Africa,2016,3.763,0.42214,0.63178,0.03824,0.12807,0.18667
+Burkina Faso,Africa,2016,3.739,0.31995,0.63054,0.21297,0.3337,0.24353
+Uganda,Africa,2016,3.739,0.34719,0.90981,0.19625,0.43653,0.27102
+Yemen,Asia,2016,3.724,0.57939,0.47493,0.31048,0.2287,0.09821
+Madagascar,Africa,2016,3.695,0.27954,0.46115,0.37109,0.13684,0.2204
+Tanzania,Africa,2016,3.666,0.47155,0.77623,0.357,0.3176,0.31472
+Liberia,Africa,2016,3.622,0.10706,0.50353,0.23165,0.25748,0.24063
+Guinea,Africa,2016,3.607,0.22415,0.3109,0.18829,0.30953,0.29914
+Rwanda,Africa,2016,3.515,0.32846,0.61586,0.31865,0.5432,0.23552
+Benin,Africa,2016,3.484,0.39499,0.10419,0.21028,0.39747,0.2018
+Afghanistan,Asia,2016,3.36,0.38227,0.11037,0.17344,0.1643,0.31268
+Togo,Africa,2016,3.303,0.28123,0,0.24811,0.34678,0.17517
+Syria,Asia,2016,3.069,0.74719,0.14866,0.62994000000000006,0.06912,0.48397
+Burundi,Africa,2016,2.905,0.06831,0.23442,0.15747,0.0432,0.2029
+Norway,Europe,2017,7.53700017929077,1.61646318435669,1.53352355957031,0.796666502952576,0.635422587394714,0.36201223731041
+Denmark,Europe,2017,7.52199983596802,1.48238301277161,1.55112159252167,0.792565524578094,0.626006722450256,0.355280488729477
+Iceland,Europe,2017,7.50400018692017,1.480633020401,1.6105740070343,0.833552122116089,0.627162635326385,0.475540220737457
+Switzerland,Europe,2017,7.49399995803833,1.56497955322266,1.51691174507141,0.858131289482117,0.620070576667786,0.290549278259277
+Finland,Europe,2017,7.4689998626709,1.44357192516327,1.5402467250824,0.80915766954422,0.617950856685638,0.24548277258873
+Netherlands,Europe,2017,7.3769998550415,1.50394463539124,1.42893922328949,0.810696125030518,0.585384488105774,0.470489829778671
+Canada,USA/Canada,2017,7.31599998474121,1.47920441627502,1.48134899139404,0.83455765247345,0.611100912094116,0.435539722442627
+Sweden,Europe,2017,7.28399991989136,1.49438726902008,1.47816216945648,0.830875158309937,0.612924098968506,0.385399252176285
+Israel,Asia,2017,7.21299982070923,1.37538242340088,1.37628996372223,0.83840399980545,0.405988603830338,0.330082654953003
+Costa Rica,Latin America/Caribbean,2017,7.0789999961853,1.10970628261566,1.41640365123749,0.759509265422821,0.580131649971008,0.214613229036331
+Austria,Europe,2017,7.00600004196167,1.48709726333618,1.4599449634552,0.81532841920852706,0.567766189575195,0.316472321748734
+United States,USA/Canada,2017,6.99300003051758,1.54625928401947,1.41992056369781,0.77428662776947,0.505740523338318,0.392578780651093
+Ireland,Europe,2017,6.97700023651123,1.53570663928986,1.55823111534119,0.80978262424469,0.573110342025757,0.42785832285881
+Germany,Europe,2017,6.95100021362305,1.48792338371277,1.4725203514099101,0.798950731754303,0.562511384487152,0.336269170045853
+Belgium,Europe,2017,6.89099979400635,1.46378076076508,1.46231269836426,0.818091869354248,0.539770722389221,0.231503337621689
+Luxembourg,Europe,2017,6.86299991607666,1.74194359779358,1.45758366584778,0.845089495182037,0.59662789106369,0.283180981874466
+United Kingdom,Europe,2017,6.71400022506714,1.44163393974304,1.49646008014679,0.805335938930511,0.508190035820007,0.492774158716202
+Chile,Latin America/Caribbean,2017,6.65199995040894,1.25278460979462,1.28402495384216,0.819479703903198,0.376895278692245,0.326662421226501
+United Arab Emirates,Asia,2017,6.64799976348877,1.62634336948395,1.26641023159027,0.726798236370087,0.60834527015686,0.3609419465065
+Brazil,Latin America/Caribbean,2017,6.63500022888184,1.10735321044922,1.43130600452423,0.616552352905273,0.437453746795654,0.16234989464283
+Czech Republic,Europe,2017,6.60900020599365,1.35268235206604,1.43388521671295,0.754444003105164,0.490946173667908,0.0881067588925362
+Argentina,Latin America/Caribbean,2017,6.59899997711182,1.18529546260834,1.44045114517212,0.695137083530426,0.494519203901291,0.109457060694695
+Mexico,Latin America/Caribbean,2017,6.57800006866455,1.15318381786346,1.210862159729,0.709978997707367,0.412730008363724,0.120990432798862
+Singapore,Asia,2017,6.57200002670288,1.69227766990662,1.35381436347961,0.949492394924164,0.549840569496155,0.345965981483459
+Malta,Europe,2017,6.52699995040894,1.34327983856201,1.48841166496277,0.821944236755371,0.588767051696777,0.574730575084686
+Uruguay,Latin America/Caribbean,2017,6.4539999961853,1.21755969524384,1.41222786903381,0.719216823577881,0.57939225435257,0.175096929073334
+Guatemala,Latin America/Caribbean,2017,6.4539999961853,0.872001945972443,1.25558519363403,0.540239989757538,0.531310617923737,0.283488392829895
+Panama,Latin America/Caribbean,2017,6.4520001411438,1.23374843597412,1.37319254875183,0.706156134605408,0.550026834011078,0.21055693924427
+France,Europe,2017,6.44199991226196,1.43092346191406,1.38777685165405,0.844465851783752,0.470222115516663,0.129762306809425
+Thailand,Asia,2017,6.42399978637695,1.12786877155304,1.42579245567322,0.647239029407501,0.580200731754303,0.572123110294342
+Spain,Europe,2017,6.40299987792969,1.38439786434174,1.53209090232849,0.888960599899292,0.408781230449677,0.190133571624756
+Qatar,Asia,2017,6.375,1.87076568603516,1.27429687976837,0.710098087787628,0.604130983352661,0.330473870038986
+Colombia,Latin America/Caribbean,2017,6.35699987411499,1.07062232494354,1.4021829366684,0.595027923583984,0.477487415075302,0.149014472961426
+Saudi Arabia,Asia,2017,6.3439998626709,1.53062355518341,1.28667759895325,0.590148329734802,0.449750572443008,0.147616013884544
+Trinidad and Tobago,Latin America/Caribbean,2017,6.16800022125244,1.36135590076447,1.3802285194397,0.519983291625977,0.518630743026733,0.325296461582184
+Kuwait,Asia,2017,6.10500001907349,1.63295245170593,1.25969874858856,0.632105708122253,0.496337592601776,0.228289797902107
+Slovakia,Europe,2017,6.09800004959106,1.32539355754852,1.50505924224854,0.712732911109924,0.295817464590073,0.136544480919838
+Bahrain,Asia,2017,6.08699989318848,1.48841226100922,1.32311046123505,0.653133034706116,0.536746919155121,0.172668486833572
+Malaysia,Asia,2017,6.08400011062622,1.29121541976929,1.28464603424072,0.618784427642822,0.402264982461929,0.416608929634094
+Nicaragua,Latin America/Caribbean,2017,6.07100009918213,0.737299203872681,1.28721570968628,0.653095960617065,0.447551846504211,0.301674216985703
+Ecuador,Latin America/Caribbean,2017,6.00799989700317,1.00082039833069,1.28616881370544,0.685636222362518,0.4551981985569,0.150112465023994
+El Salvador,Latin America/Caribbean,2017,6.00299978256226,0.909784495830536,1.18212509155273,0.596018552780151,0.432452529668808,0.0782579854130745
+Poland,Europe,2017,5.97300004959106,1.29178786277771,1.44571197032928,0.699475347995758,0.520342111587524,0.158465966582298
+Uzbekistan,Asia,2017,5.97100019454956,0.786441087722778,1.54896914958954,0.498272627592087,0.658248662948608,0.415983647108078
+Italy,Europe,2017,5.96400022506714,1.39506661891937,1.44492328166962,0.853144347667694,0.256450712680817,0.17278964817524
+Russia,Europe,2017,5.96299982070923,1.28177809715271,1.46928238868713,0.547349333763123,0.373783111572266,0.0522638224065304
+Japan,Asia,2017,5.92000007629395,1.41691517829895,1.43633782863617,0.913475871086121,0.505625545978546,0.12057276815176
+Lithuania,Europe,2017,5.90199995040894,1.31458234786987,1.47351610660553,0.62894994020462,0.234231784939766,0.010164656676352
+Algeria,Africa,2017,5.87200021743774,1.09186446666718,1.1462174654007,0.617584645748138,0.233335807919502,0.0694366469979286
+Latvia,Europe,2017,5.84999990463257,1.26074862480164,1.40471494197845,0.638566970825195,0.325707912445068,0.153074786067009
+Moldova,Europe,2017,5.83799982070923,0.728870630264282,1.25182557106018,0.589465200901031,0.240729048848152,0.208779126405716
+Romania,Europe,2017,5.82499980926514,1.21768391132355,1.15009129047394,0.685158312320709,0.457003742456436,0.133519917726517
+Bolivia,Latin America/Caribbean,2017,5.82299995422363,0.833756566047668,1.22761905193329,0.473630249500275,0.558732926845551,0.22556072473526
+Turkmenistan,Asia,2017,5.82200002670288,1.13077676296234,1.49314916133881,0.437726080417633,0.41827192902565,0.24992498755455
+Kazakhstan,Asia,2017,5.81899976730347,1.28455626964569,1.38436901569366,0.606041550636292,0.437454283237457,0.201964423060417
+Slovenia,Europe,2017,5.75799989700317,1.3412059545517,1.45251882076263,0.790828227996826,0.572575807571411,0.242649093270302
+Peru,Latin America/Caribbean,2017,5.71500015258789,1.03522527217865,1.21877038478851,0.630166113376617,0.450002878904343,0.126819714903831
+Mauritius,Africa,2017,5.62900018692017,1.18939554691315,1.20956099033356,0.638007462024689,0.491247326135635,0.360933750867844
+Cyprus,Asia,2017,5.62099981307983,1.35593807697296,1.13136327266693,0.84471470117569,0.355111539363861,0.271254301071167
+Estonia,Europe,2017,5.61100006103516,1.32087934017181,1.47667109966278,0.695168316364288,0.479131430387497,0.0988908112049103
+Belarus,Europe,2017,5.56899976730347,1.15655755996704,1.44494521617889,0.637714266777039,0.295400261878967,0.15513750910759
+Libya,Africa,2017,5.52500009536743,1.10180306434631,1.35756433010101,0.520169019699097,0.465733230113983,0.152073666453362
+Turkey,Asia,2017,5.5,1.19827437400818,1.33775317668915,0.637605607509613,0.300740599632263,0.0466930419206619
+Paraguay,Latin America/Caribbean,2017,5.49300003051758,0.932537317276001,1.50728487968445,0.579250693321228,0.473507791757584,0.224150657653809
+Philippines,Asia,2017,5.42999982833862,0.85769921541214,1.25391757488251,0.468009054660797,0.585214674472809,0.193513423204422
+Serbia,Europe,2017,5.39499998092651,1.06931757926941,1.25818979740143,0.65078467130661,0.208715528249741,0.220125883817673
+Jordan,Asia,2017,5.33599996566772,0.991012394428253,1.23908889293671,0.604590058326721,0.418421149253845,0.172170460224152
+Hungary,Europe,2017,5.32399988174438,1.2860119342804,1.34313309192657,0.687763452529907,0.175863519310951,0.0784016624093056
+Jamaica,Latin America/Caribbean,2017,5.31099987030029,0.925579309463501,1.36821806430817,0.641022384166718,0.474307239055634,0.233818337321281
+Croatia,Europe,2017,5.29300022125244,1.22255623340607,0.96798300743103,0.701288521289825,0.255772292613983,0.248002976179123
+China,Asia,2017,5.27299976348877,1.08116579055786,1.16083741188049,0.741415500640869,0.472787708044052,0.0288068410009146
+Pakistan,Asia,2017,5.26900005340576,0.72688353061676,0.672690689563751,0.402047783136368,0.23521526157856,0.315446019172668
+Indonesia,Asia,2017,5.26200008392334,0.995538592338562,1.27444469928741,0.492345720529556,0.443323463201523,0.611704587936401
+Venezuela,Latin America/Caribbean,2017,5.25,1.12843120098114,1.43133759498596,0.617144227027893,0.153997123241425,0.0650196298956871
+Montenegro,Europe,2017,5.23699998855591,1.12112903594971,1.23837649822235,0.667464673519135,0.194989055395126,0.197911024093628
+Morocco,Africa,2017,5.2350001335144,0.878114581108093,0.774864435195923,0.59771066904068,0.408158332109451,0.0322099551558495
+Azerbaijan,Asia,2017,5.23400020599365,1.15360176563263,1.15240025520325,0.540775775909424,0.398155838251114,0.0452693402767181
+Dominican Republic,Latin America/Caribbean,2017,5.23000001907349,1.07937383651733,1.40241670608521,0.574873745441437,0.55258983373642,0.186967849731445
+Greece,Europe,2017,5.22700023651123,1.28948748111725,1.23941457271576,0.810198903083801,0.0957312509417534,0
+Lebanon,Asia,2017,5.22499990463257,1.07498753070831,1.12962424755096,0.73508107662200906,0.288515985012054,0.264450758695602
+Portugal,Europe,2017,5.19500017166138,1.3151752948761,1.36704301834106,0.795843541622162,0.498465299606323,0.0951027125120163
+Bosnia and Herzegovina,Europe,2017,5.18200016021729,0.982409417629242,1.0693359375,0.705186307430267,0.204403176903725,0.328867495059967
+Honduras,Latin America/Caribbean,2017,5.18100023269653,0.730573117733002,1.14394497871399,0.582569479942322,0.348079860210419,0.236188873648643
+Macedonia,Europe,2017,5.17500019073486,1.06457793712616,1.20789301395416,0.644948184490204,0.325905978679657,0.25376096367836
+Vietnam,Asia,2017,5.07399988174438,0.788547575473785,1.27749133110046,0.652168989181519,0.571055591106415,0.234968051314354
+Nigeria,Africa,2017,5.07399988174438,0.783756256103516,1.21577048301697,0.0569157302379608,0.394952565431595,0.230947196483612
+Tajikistan,Asia,2017,5.04099988937378,0.524713635444641,1.27146327495575,0.529235124588013,0.471566706895828,0.248997643589973
+Bhutan,Asia,2017,5.01100015640259,0.885416388511658,1.34012651443481,0.495879292488098,0.501537680625916,0.474054545164108
+Kyrgyzstan,Asia,2017,5.00400018692017,0.596220076084137,1.39423859119415,0.553457796573639,0.454943388700485,0.42858037352562
+Nepal,Asia,2017,4.96199989318848,0.479820191860199,1.17928326129913,0.504130780696869,0.440305948257446,0.394096165895462
+Mongolia,Asia,2017,4.95499992370605,1.02723586559296,1.4930112361908,0.557783484458923,0.394143968820572,0.338464230298996
+South Africa,Africa,2017,4.8289999961853,1.05469870567322,1.38478863239288,0.187080070376396,0.479246735572815,0.139362379908562
+Tunisia,Africa,2017,4.80499982833862,1.00726580619812,0.868351459503174,0.613212049007416,0.289680689573288,0.0496933571994305
+Egypt,Africa,2017,4.7350001335144,0.989701807498932,0.997471392154694,0.520187258720398,0.282110154628754,0.128631442785263
+Bulgaria,Europe,2017,4.71400022506714,1.1614590883255,1.43437945842743,0.708217680454254,0.289231717586517,0.113177694380283
+Sierra Leone,Africa,2017,4.70900011062622,0.36842092871666,0.984136044979095,0.00556475389748812,0.318697690963745,0.293040901422501
+Cameroon,Africa,2017,4.69500017166138,0.564305365085602,0.946018218994141,0.132892116904259,0.430388748645782,0.236298456788063
+Iran,Asia,2017,4.69199991226196,1.15687310695648,0.711551249027252,0.639333188533783,0.249322608113289,0.387242913246155
+Albania,Europe,2017,4.64400005340576,0.996192753314972,0.803685247898102,0.731159746646881,0.381498634815216,0.201312944293022
+Bangladesh,Asia,2017,4.60799980163574,0.586682975292206,0.735131740570068,0.533241033554077,0.478356659412384,0.172255352139473
+Namibia,Africa,2017,4.57399988174438,0.964434325695038,1.0984708070755,0.33861181139946,0.520303547382355,0.0771337449550629
+Kenya,Africa,2017,4.55299997329712,0.560479462146759,1.06795072555542,0.309988349676132,0.452763766050339,0.444860309362411
+Mozambique,Africa,2017,4.55000019073486,0.234305649995804,0.870701014995575,0.106654435396194,0.480791091918945,0.322228103876114
+Senegal,Africa,2017,4.53499984741211,0.479309022426605,1.17969191074371,0.409362852573395,0.377922266721725,0.183468893170357
+Zambia,Africa,2017,4.51399993896484,0.636406779289246,1.00318729877472,0.257835894823074,0.461603492498398,0.249580144882202
+Iraq,Asia,2017,4.49700021743774,1.10271048545837,0.978613197803497,0.50118046998977706,0.288555532693863,0.19963726401329
+Gabon,Africa,2017,4.46500015258789,1.1982102394104,1.1556202173233,0.356578588485718,0.312328577041626,0.0437853783369064
+Ethiopia,Africa,2017,4.46000003814697,0.339233845472336,0.86466920375824,0.353409707546234,0.408842742443085,0.312650740146637
+Sri Lanka,Asia,2017,4.44000005722046,1.00985014438629,1.25997638702393,0.625130832195282,0.561213254928589,0.490863561630249
+Armenia,Asia,2017,4.37599992752075,0.900596737861633,1.00748372077942,0.637524425983429,0.198303267359734,0.0834880918264389
+India,Asia,2017,4.31500005722046,0.792221248149872,0.754372596740723,0.455427616834641,0.469987004995346,0.231538489460945
+Mauritania,Africa,2017,4.29199981689453,0.648457288742065,1.2720308303833,0.285349279642105,0.0960980430245399,0.201870024204254
+Georgia,Asia,2017,4.28599977493286,0.950612664222717,0.57061493396759,0.649546980857849,0.309410035610199,0.0540088154375553
+Mali,Africa,2017,4.19000005722046,0.476180493831635,1.28147339820862,0.169365674257278,0.306613743305206,0.183354198932648
+Cambodia,Asia,2017,4.16800022125244,0.601765096187592,1.00623834133148,0.429783403873444,0.633375823497772,0.385922968387604
+Sudan,Africa,2017,4.13899993896484,0.65951669216156,1.21400856971741,0.290920823812485,0.0149958552792668,0.182317450642586
+Ghana,Africa,2017,4.11999988555908,0.667224824428558,0.873664736747742,0.295637726783752,0.423026293516159,0.256923943758011
+Ukraine,Europe,2017,4.09600019454956,0.89465194940567,1.39453756809235,0.575903952121735,0.122974775731564,0.270061463117599
+Uganda,Africa,2017,4.08099985122681,0.381430715322495,1.12982773780823,0.217632606625557,0.443185955286026,0.325766056776047
+Burkina Faso,Africa,2017,4.03200006484985,0.3502277135849,1.04328000545502,0.215844258666039,0.324367851018906,0.250864684581757
+Niger,Africa,2017,4.02799987792969,0.161925330758095,0.993025004863739,0.26850500702858,0.36365869641304,0.228673845529556
+Malawi,Africa,2017,3.97000002861023,0.233442038297653,0.512568831443787,0.315089583396912,0.466914653778076,0.287170469760895
+Chad,Africa,2017,3.93600010871887,0.438012987375259,0.953855872154236,0.0411347150802612,0.16234202682972,0.216113850474358
+Zimbabwe,Africa,2017,3.875,0.375846534967422,1.08309590816498,0.196763753890991,0.336384207010269,0.189143493771553
+Lesotho,Africa,2017,3.80800008773804,0.521021246910095,1.19009518623352,0,0.390661299228668,0.157497271895409
+Angola,Africa,2017,3.79500007629395,0.858428180217743,1.10441195964813,0.0498686656355858,0,0.097926490008831
+Afghanistan,Asia,2017,3.79399991035461,0.401477217674255,0.581543326377869,0.180746778845787,0.106179520487785,0.311870932579041
+Botswana,Africa,2017,3.76600003242493,1.12209415435791,1.22155499458313,0.341755509376526,0.505196332931519,0.0993484482169151
+Benin,Africa,2017,3.65700006484985,0.431085407733917,0.435299843549728,0.209930211305618,0.425962775945663,0.207948461174965
+Madagascar,Africa,2017,3.64400005340576,0.305808693170547,0.913020372390747,0.375223308801651,0.189196765422821,0.208732530474663
+Haiti,Latin America/Caribbean,2017,3.6029999256134,0.368610262870789,0.640449821949005,0.277321130037308,0.0303698573261499,0.489203780889511
+Yemen,Asia,2017,3.59299993515015,0.591683447360992,0.93538224697113,0.310080915689468,0.249463722109795,0.104125209152699
+Liberia,Africa,2017,3.53299999237061,0.119041793048382,0.872117936611176,0.229918196797371,0.332881182432175,0.26654988527298
+Guinea,Africa,2017,3.50699996948242,0.244549930095673,0.791244685649872,0.194129139184952,0.348587512969971,0.264815092086792
+Togo,Africa,2017,3.49499988555908,0.305444717407227,0.431882530450821,0.247105568647385,0.38042613863945,0.196896150708199
+Rwanda,Africa,2017,3.47099995613098,0.368745893239975,0.945707023143768,0.326424807310104,0.581843852996826,0.252756029367447
+Syria,Asia,2017,3.46199989318848,0.777153134346008,0.396102607250214,0.50053334236145,0.0815394446253777,0.493663728237152
+Tanzania,Africa,2017,3.34899997711182,0.511135876178741,1.04198980331421,0.364509284496307,0.390017777681351,0.354256361722946
+Burundi,Africa,2017,2.90499997138977,0.091622568666935,0.629793584346771,0.151610791683197,0.0599007532000542,0.204435184597969
+Central African Republic,Africa,2017,2.69300007820129,0,0,0.0187726859003305,0.270842045545578,0.280876487493515
+Finland,Europe,2018,7.632,1.305,1.592,0.874,0.681,0.202
+Norway,Europe,2018,7.594,1.456,1.582,0.861,0.686,0.286
+Denmark,Europe,2018,7.555,1.351,1.59,0.868,0.683,0.284
+Iceland,Europe,2018,7.495,1.343,1.644,0.914,0.677,0.353
+Switzerland,Europe,2018,7.487,1.42,1.549,0.927,0.66,0.256
+Netherlands,Europe,2018,7.441,1.361,1.488,0.878,0.638,0.333
+Canada,USA/Canada,2018,7.328,1.33,1.532,0.896,0.653,0.321
+Sweden,Europe,2018,7.314,1.355,1.501,0.913,0.659,0.285
+United Kingdom,Europe,2018,7.19,1.244,1.433,0.888,0.464,0.262
+Austria,Europe,2018,7.139,1.341,1.504,0.891,0.617,0.242
+Costa Rica,Latin America/Caribbean,2018,7.072,1.01,1.459,0.817,0.632,0.143
+Ireland,Europe,2018,6.977,1.448,1.583,0.876,0.614,0.307
+Germany,Europe,2018,6.965,1.34,1.474,0.861,0.586,0.273
+Belgium,Europe,2018,6.927,1.324,1.483,0.894,0.583,0.188
+Luxembourg,Europe,2018,6.91,1.576,1.52,0.896,0.632,0.196
+United States,USA/Canada,2018,6.886,1.398,1.471,0.819,0.547,0.291
+Israel,Asia,2018,6.814,1.301,1.559,0.883,0.533,0.354
+United Arab Emirates,Asia,2018,6.774,2.096,0.776,0.67,0.284,0.186
+Czech Republic,Europe,2018,6.711,1.233,1.489,0.854,0.543,0.064
+Malta,Europe,2018,6.627,1.27,1.525,0.884,0.645,0.376
+France,Europe,2018,6.489,1.293,1.466,0.908,0.52,0.098
+Mexico,Latin America/Caribbean,2018,6.488,1.038,1.252,0.761,0.479,0.069
+Chile,Latin America/Caribbean,2018,6.476,1.131,1.331,0.808,0.431,0.197
+Taiwan,Asia,2018,6.441,1.365,1.436,0.857,0.418,0.151
+Panama,Latin America/Caribbean,2018,6.43,1.112,1.438,0.759,0.597,0.125
+Brazil,Latin America/Caribbean,2018,6.419,0.986,1.474,0.675,0.493,0.11
+Argentina,Latin America/Caribbean,2018,6.388,1.073,1.468,0.744,0.57,0.062
+Guatemala,Latin America/Caribbean,2018,6.382,0.781,1.268,0.608,0.604,0.179
+Uruguay,Latin America/Caribbean,2018,6.379,1.093,1.459,0.771,0.625,0.13
+Qatar,Asia,2018,6.374,1.649,1.303,0.748,0.654,0.256
+Saudi Arabia,Asia,2018,6.371,1.379,1.331,0.633,0.509,0.098
+Singapore,Asia,2018,6.343,1.529,1.451,1.008,0.631,0.261
+Malaysia,Asia,2018,6.322,1.161,1.258,0.669,0.356,0.311
+Spain,Europe,2018,6.31,1.251,1.538,0.965,0.449,0.142
+Colombia,Latin America/Caribbean,2018,6.26,0.96,1.439,0.635,0.531,0.099
+Slovakia,Europe,2018,6.173,1.21,1.537,0.776,0.354,0.118
+El Salvador,Latin America/Caribbean,2018,6.167,0.806,1.231,0.639,0.461,0.065
+Nicaragua,Latin America/Caribbean,2018,6.141,0.668,1.319,0.7,0.527,0.208
+Poland,Europe,2018,6.123,1.176,1.448,0.781,0.546,0.108
+Bahrain,Asia,2018,6.105,1.338,1.366,0.698,0.594,0.243
+Uzbekistan,Asia,2018,6.096,0.719,1.584,0.605,0.724,0.328
+Kuwait,Asia,2018,6.083,1.474,1.301,0.675,0.554,0.167
+Thailand,Asia,2018,6.072,1.016,1.417,0.707,0.637,0.364
+Italy,Europe,2018,6,1.264,1.501,0.946,0.281,0.137
+Ecuador,Latin America/Caribbean,2018,5.973,0.889,1.33,0.736,0.556,0.114
+Lithuania,Europe,2018,5.952,1.197,1.527,0.716,0.35,0.026
+Slovenia,Europe,2018,5.948,1.219,1.506,0.856,0.633,0.16
+Romania,Europe,2018,5.945,1.116,1.219,0.726,0.528,0.088
+Latvia,Europe,2018,5.933,1.148,1.454,0.671,0.363,0.092
+Japan,Asia,2018,5.915,1.294,1.462,0.988,0.553,0.079
+Mauritius,Africa,2018,5.891,1.09,1.387,0.684,0.584,0.245
+Jamaica,Latin America/Caribbean,2018,5.89,0.819,1.493,0.693,0.575,0.096
+Russia,Europe,2018,5.81,1.151,1.479,0.599,0.399,0.065
+Kazakhstan,Asia,2018,5.79,1.143,1.516,0.631,0.454,0.148
+Cyprus,Asia,2018,5.762,1.229,1.191,0.909,0.423,0.202
+Bolivia,Latin America/Caribbean,2018,5.752,0.751,1.223,0.508,0.606,0.141
+Estonia,Europe,2018,5.739,1.2,1.532,0.737,0.553,0.086
+Paraguay,Latin America/Caribbean,2018,5.681,0.835,1.522,0.615,0.541,0.162
+Peru,Latin America/Caribbean,2018,5.663,0.934,1.249,0.674,0.53,0.092
+Moldova,Europe,2018,5.64,0.657,1.301,0.62,0.232,0.171
+Turkmenistan,Asia,2018,5.636,1.016,1.533,0.517,0.417,0.199
+Hungary,Europe,2018,5.62,1.171,1.401,0.732,0.259,0.061
+Libya,Africa,2018,5.566,0.985,1.35,0.553,0.496,0.116
+Philippines,Asia,2018,5.524,0.775,1.312,0.513,0.643,0.12
+Honduras,Latin America/Caribbean,2018,5.504,0.62,1.205,0.622,0.459,0.197
+Belarus,Europe,2018,5.483,1.039,1.498,0.7,0.307,0.101
+Turkey,Asia,2018,5.483,1.148,1.38,0.686,0.324,0.106
+Pakistan,Asia,2018,5.472,0.652,0.81,0.424,0.334,0.216
+Hong Kong,Asia,2018,5.43,1.405,1.29,1.03,0.524,0.246
+Portugal,Europe,2018,5.41,1.188,1.429,0.884,0.562,0.055
+Serbia,Europe,2018,5.398,0.975,1.369,0.685,0.288,0.134
+Greece,Europe,2018,5.358,1.154,1.202,0.879,0.131,0
+Lebanon,Asia,2018,5.358,0.965,1.179,0.785,0.503,0.214
+Montenegro,Europe,2018,5.347,1.017,1.279,0.729,0.259,0.111
+Croatia,Europe,2018,5.321,1.115,1.161,0.737,0.38,0.12
+Dominican Republic,Latin America/Caribbean,2018,5.302,0.982,1.441,0.614,0.578,0.12
+Algeria,Africa,2018,5.295,0.979,1.154,0.687,0.077,0.055
+Morocco,Africa,2018,5.254,0.779,0.797,0.669,0.46,0.026
+China,Asia,2018,5.246,0.989,1.142,0.799,0.597,0.029
+Azerbaijan,Asia,2018,5.201,1.024,1.161,0.603,0.43,0.031
+Tajikistan,Asia,2018,5.199,0.474,1.166,0.598,0.292,0.187
+Macedonia,Europe,2018,5.185,0.959,1.239,0.691,0.394,0.173
+Jordan,Asia,2018,5.161,0.822,1.265,0.645,0.468,0.13
+Nigeria,Africa,2018,5.155,0.689,1.172,0.048,0.462,0.201
+Kyrgyzstan,Asia,2018,5.131,0.53,1.416,0.594,0.54,0.281
+Bosnia and Herzegovina,Europe,2018,5.129,0.915,1.078,0.758,0.28,0.216
+Mongolia,Asia,2018,5.125,0.914,1.517,0.575,0.395,0.253
+Vietnam,Asia,2018,5.103,0.715,1.365,0.702,0.618,0.177
+Indonesia,Asia,2018,5.093,0.899,1.215,0.522,0.538,0.484
+Bhutan,Asia,2018,5.082,0.796,1.335,0.527,0.541,0.364
+Cameroon,Africa,2018,4.975,0.535,0.891,0.182,0.454,0.183
+Bulgaria,Europe,2018,4.933,1.054,1.515,0.712,0.359,0.064
+Nepal,Asia,2018,4.88,0.425,1.228,0.539,0.526,0.302
+Venezuela,Latin America/Caribbean,2018,4.806,0.996,1.469,0.657,0.133,0.056
+Gabon,Africa,2018,4.758,1.036,1.164,0.404,0.356,0.032
+South Africa,Africa,2018,4.724,0.94,1.41,0.33,0.516,0.103
+Iran,Asia,2018,4.707,1.059,0.771,0.691,0.459,0.282
+Ghana,Africa,2018,4.657,0.592,0.896,0.337,0.499,0.212
+Senegal,Africa,2018,4.631,0.429,1.117,0.433,0.406,0.138
+Laos,Asia,2018,4.623,0.72,1.034,0.441,0.626,0.23
+Tunisia,Africa,2018,4.592,0.9,0.906,0.69,0.271,0.04
+Albania,Europe,2018,4.586,0.916,0.817,0.79,0.419,0.149
+Sierra Leone,Africa,2018,4.571,0.256,0.813,0,0.355,0.238
+Bangladesh,Asia,2018,4.5,0.532,0.85,0.579,0.58,0.153
+Sri Lanka,Asia,2018,4.471,0.918,1.314,0.672,0.585,0.307
+Iraq,Asia,2018,4.456,1.01,0.971,0.536,0.304,0.148
+Mali,Africa,2018,4.447,0.37,1.233,0.152,0.367,0.139
+Namibia,Africa,2018,4.441,0.874,1.281,0.365,0.519,0.051
+Cambodia,Asia,2018,4.433,0.549,1.088,0.457,0.696,0.256
+Burkina Faso,Africa,2018,4.424,0.314,1.097,0.254,0.312,0.175
+Egypt,Africa,2018,4.419,0.885,1.025,0.553,0.312,0.092
+Mozambique,Africa,2018,4.417,0.198,0.902,0.173,0.531,0.206
+Kenya,Africa,2018,4.41,0.493,1.048,0.454,0.504,0.352
+Zambia,Africa,2018,4.377,0.562,1.047,0.295,0.503,0.221
+Mauritania,Africa,2018,4.356,0.557,1.245,0.292,0.129,0.134
+Ethiopia,Africa,2018,4.35,0.308,0.95,0.391,0.452,0.22
+Georgia,Asia,2018,4.34,0.853,0.592,0.643,0.375,0.038
+Armenia,Asia,2018,4.321,0.816,0.99,0.666,0.26,0.077
+Chad,Africa,2018,4.301,0.358,0.907,0.053,0.189,0.181
+India,Asia,2018,4.19,0.721,0.747,0.485,0.539,0.172
+Niger,Africa,2018,4.166,0.131,0.867,0.221,0.39,0.175
+Uganda,Africa,2018,4.161,0.322,1.09,0.237,0.45,0.259
+Benin,Africa,2018,4.141,0.378,0.372,0.24,0.44,0.163
+Sudan,Africa,2018,4.139,0.605,1.24,0.312,0.016,0.134
+Ukraine,Europe,2018,4.103,0.793,1.413,0.609,0.163,0.187
+Togo,Africa,2018,3.999,0.259,0.474,0.253,0.434,0.158
+Guinea,Africa,2018,3.964,0.344,0.792,0.211,0.394,0.185
+Lesotho,Africa,2018,3.808,0.472,1.215,0.079,0.423,0.116
+Angola,Africa,2018,3.795,0.73,1.125,0.269,0,0.079
+Madagascar,Africa,2018,3.774,0.262,0.908,0.402,0.221,0.155
+Zimbabwe,Africa,2018,3.692,0.357,1.094,0.248,0.406,0.132
+Afghanistan,Asia,2018,3.632,0.332,0.537,0.255,0.085,0.191
+Botswana,Africa,2018,3.59,1.017,1.174,0.417,0.557,0.042
+Malawi,Africa,2018,3.587,0.186,0.541,0.306,0.531,0.21
+Haiti,Latin America/Caribbean,2018,3.582,0.315,0.714,0.289,0.025,0.392
+Liberia,Africa,2018,3.495,0.076,0.858,0.267,0.419,0.206
+Syria,Asia,2018,3.462,0.689,0.382,0.539,0.088,0.376
+Rwanda,Africa,2018,3.408,0.332,0.896,0.4,0.636,0.2
+Yemen,Asia,2018,3.355,0.442,1.073,0.343,0.244,0.083
+Tanzania,Africa,2018,3.303,0.455,0.991,0.381,0.481,0.27
+Central African Republic,Africa,2018,3.083,0.024,0,0.01,0.305,0.218
+Burundi,Africa,2018,2.905,0.091,0.627,0.145,0.065,0.149
+Finland,Europe,2019,7.769,1.34,1.587,0.986,0.596,0.153
+Denmark,Europe,2019,7.6,1.383,1.573,0.996,0.592,0.252
+Norway,Europe,2019,7.554,1.488,1.582,1.028,0.603,0.271
+Iceland,Europe,2019,7.494,1.38,1.624,1.026,0.591,0.354
+Netherlands,Europe,2019,7.488,1.396,1.522,0.999,0.557,0.322
+Switzerland,Europe,2019,7.48,1.452,1.526,1.052,0.572,0.263
+Sweden,Europe,2019,7.343,1.387,1.487,1.009,0.574,0.267
+Canada,USA/Canada,2019,7.278,1.365,1.505,1.039,0.584,0.285
+Austria,Europe,2019,7.246,1.376,1.475,1.016,0.532,0.244
+Costa Rica,Latin America/Caribbean,2019,7.167,1.034,1.441,0.963,0.558,0.144
+Israel,Asia,2019,7.139,1.276,1.455,1.029,0.371,0.261
+Luxembourg,Europe,2019,7.09,1.609,1.479,1.012,0.526,0.194
+United Kingdom,Europe,2019,7.054,1.333,1.538,0.996,0.45,0.348
+Ireland,Europe,2019,7.021,1.499,1.553,0.999,0.516,0.298
+Germany,Europe,2019,6.985,1.373,1.454,0.987,0.495,0.261
+Belgium,Europe,2019,6.923,1.356,1.504,0.986,0.473,0.16
+United States,USA/Canada,2019,6.892,1.433,1.457,0.874,0.454,0.28
+Czech Republic,Europe,2019,6.852,1.269,1.487,0.92,0.457,0.046
+United Arab Emirates,Asia,2019,6.825,1.503,1.31,0.825,0.598,0.262
+Malta,Europe,2019,6.726,1.3,1.52,0.999,0.564,0.375
+Mexico,Latin America/Caribbean,2019,6.595,1.07,1.323,0.861,0.433,0.074
+France,Europe,2019,6.592,1.324,1.472,1.045,0.436,0.111
+Taiwan,Asia,2019,6.446,1.368,1.43,0.914,0.351,0.242
+Chile,Latin America/Caribbean,2019,6.444,1.159,1.369,0.92,0.357,0.187
+Guatemala,Latin America/Caribbean,2019,6.436,0.8,1.269,0.746,0.535,0.175
+Saudi Arabia,Asia,2019,6.375,1.403,1.357,0.795,0.439,0.08
+Qatar,Asia,2019,6.374,1.684,1.313,0.871,0.555,0.22
+Spain,Europe,2019,6.354,1.286,1.484,1.062,0.362,0.153
+Panama,Latin America/Caribbean,2019,6.321,1.149,1.442,0.91,0.516,0.109
+Brazil,Latin America/Caribbean,2019,6.3,1.004,1.439,0.802,0.39,0.099
+Uruguay,Latin America/Caribbean,2019,6.293,1.124,1.465,0.891,0.523,0.127
+Singapore,Asia,2019,6.262,1.572,1.463,1.141,0.556,0.271
+El Salvador,Latin America/Caribbean,2019,6.253,0.794,1.242,0.789,0.43,0.093
+Italy,Europe,2019,6.223,1.294,1.488,1.039,0.231,0.158
+Bahrain,Asia,2019,6.199,1.362,1.368,0.871,0.536,0.255
+Slovakia,Europe,2019,6.198,1.246,1.504,0.881,0.334,0.121
+Poland,Europe,2019,6.182,1.206,1.438,0.884,0.483,0.117
+Uzbekistan,Asia,2019,6.174,0.745,1.529,0.756,0.631,0.322
+Lithuania,Europe,2019,6.149,1.238,1.515,0.818,0.291,0.043
+Colombia,Latin America/Caribbean,2019,6.125,0.985,1.41,0.841,0.47,0.099
+Slovenia,Europe,2019,6.118,1.258,1.523,0.953,0.564,0.144
+Nicaragua,Latin America/Caribbean,2019,6.105,0.694,1.325,0.835,0.435,0.2
+Argentina,Latin America/Caribbean,2019,6.086,1.092,1.432,0.881,0.471,0.066
+Romania,Europe,2019,6.07,1.162,1.232,0.825,0.462,0.083
+Cyprus,Asia,2019,6.046,1.263,1.223,1.042,0.406,0.19
+Ecuador,Latin America/Caribbean,2019,6.028,0.912,1.312,0.868,0.498,0.126
+Kuwait,Asia,2019,6.021,1.5,1.319,0.808,0.493,0.142
+Thailand,Asia,2019,6.008,1.05,1.409,0.828,0.557,0.359
+Latvia,Europe,2019,5.94,1.187,1.465,0.812,0.264,0.075
+Estonia,Europe,2019,5.893,1.237,1.528,0.874,0.495,0.103
+Jamaica,Latin America/Caribbean,2019,5.89,0.831,1.478,0.831,0.49,0.107
+Mauritius,Africa,2019,5.888,1.12,1.402,0.798,0.498,0.215
+Japan,Asia,2019,5.886,1.327,1.419,1.088,0.445,0.069
+Honduras,Latin America/Caribbean,2019,5.86,0.642,1.236,0.828,0.507,0.246
+Kazakhstan,Asia,2019,5.809,1.173,1.508,0.729,0.41,0.146
+Bolivia,Latin America/Caribbean,2019,5.779,0.776,1.209,0.706,0.511,0.137
+Hungary,Europe,2019,5.758,1.201,1.41,0.828,0.199,0.081
+Paraguay,Latin America/Caribbean,2019,5.743,0.855,1.475,0.777,0.514,0.184
+Peru,Latin America/Caribbean,2019,5.697,0.96,1.274,0.854,0.455,0.083
+Portugal,Europe,2019,5.693,1.221,1.431,0.999,0.508,0.047
+Pakistan,Asia,2019,5.653,0.677,0.886,0.535,0.313,0.22
+Russia,Europe,2019,5.648,1.183,1.452,0.726,0.334,0.082
+Philippines,Asia,2019,5.631,0.807,1.293,0.657,0.558,0.117
+Serbia,Europe,2019,5.603,1.004,1.383,0.854,0.282,0.137
+Moldova,Europe,2019,5.529,0.685,1.328,0.739,0.245,0.181
+Libya,Africa,2019,5.525,1.044,1.303,0.673,0.416,0.133
+Montenegro,Europe,2019,5.523,1.051,1.361,0.871,0.197,0.142
+Tajikistan,Asia,2019,5.467,0.493,1.098,0.718,0.389,0.23
+Croatia,Europe,2019,5.432,1.155,1.266,0.914,0.296,0.119
+Hong Kong,Asia,2019,5.43,1.438,1.277,1.122,0.44,0.258
+Dominican Republic,Latin America/Caribbean,2019,5.425,1.015,1.401,0.779,0.497,0.113
+Bosnia and Herzegovina,Europe,2019,5.386,0.945,1.212,0.845,0.212,0.263
+Turkey,Asia,2019,5.373,1.183,1.36,0.808,0.195,0.083
+Malaysia,Asia,2019,5.339,1.221,1.171,0.828,0.508,0.26
+Belarus,Europe,2019,5.323,1.067,1.465,0.789,0.235,0.094
+Greece,Europe,2019,5.287,1.181,1.156,0.999,0.067,0
+Mongolia,Asia,2019,5.285,0.948,1.531,0.667,0.317,0.235
+Nigeria,Africa,2019,5.265,0.696,1.111,0.245,0.426,0.215
+Kyrgyzstan,Asia,2019,5.261,0.551,1.438,0.723,0.508,0.3
+Turkmenistan,Asia,2019,5.247,1.052,1.538,0.657,0.394,0.244
+Algeria,Africa,2019,5.211,1.002,1.16,0.785,0.086,0.073
+Morocco,Africa,2019,5.208,0.801,0.782,0.782,0.418,0.036
+Azerbaijan,Asia,2019,5.208,1.043,1.147,0.769,0.351,0.035
+Lebanon,Asia,2019,5.197,0.987,1.224,0.815,0.216,0.166
+Indonesia,Asia,2019,5.192,0.931,1.203,0.66,0.491,0.498
+China,Asia,2019,5.191,1.029,1.125,0.893,0.521,0.058
+Vietnam,Asia,2019,5.175,0.741,1.346,0.851,0.543,0.147
+Bhutan,Asia,2019,5.082,0.813,1.321,0.604,0.457,0.37
+Cameroon,Africa,2019,5.044,0.549,0.91,0.331,0.381,0.187
+Bulgaria,Europe,2019,5.011,1.092,1.513,0.815,0.311,0.081
+Ghana,Africa,2019,4.996,0.611,0.868,0.486,0.381,0.245
+Nepal,Asia,2019,4.913,0.446,1.226,0.677,0.439,0.285
+Jordan,Asia,2019,4.906,0.837,1.225,0.815,0.383,0.11
+Benin,Africa,2019,4.883,0.393,0.437,0.397,0.349,0.175
+Gabon,Africa,2019,4.799,1.057,1.183,0.571,0.295,0.043
+Laos,Asia,2019,4.796,0.764,1.03,0.551,0.547,0.266
+South Africa,Africa,2019,4.722,0.96,1.351,0.469,0.389,0.13
+Albania,Europe,2019,4.719,0.947,0.848,0.874,0.383,0.178
+Venezuela,Latin America/Caribbean,2019,4.707,0.96,1.427,0.805,0.154,0.064
+Cambodia,Asia,2019,4.7,0.574,1.122,0.637,0.609,0.232
+Senegal,Africa,2019,4.681,0.45,1.134,0.571,0.292,0.153
+Namibia,Africa,2019,4.639,0.879,1.313,0.477,0.401,0.07
+Niger,Africa,2019,4.628,0.138,0.774,0.366,0.318,0.188
+Burkina Faso,Africa,2019,4.587,0.331,1.056,0.38,0.255,0.177
+Armenia,Asia,2019,4.559,0.85,1.055,0.815,0.283,0.095
+Iran,Asia,2019,4.548,1.1,0.842,0.785,0.305,0.27
+Guinea,Africa,2019,4.534,0.38,0.829,0.375,0.332,0.207
+Georgia,Asia,2019,4.519,0.886,0.666,0.752,0.346,0.043
+Kenya,Africa,2019,4.509,0.512,0.983,0.581,0.431,0.372
+Mauritania,Africa,2019,4.49,0.57,1.167,0.489,0.066,0.106
+Mozambique,Africa,2019,4.466,0.204,0.986,0.39,0.494,0.197
+Tunisia,Africa,2019,4.461,0.921,1,0.815,0.167,0.059
+Bangladesh,Asia,2019,4.456,0.562,0.928,0.723,0.527,0.166
+Iraq,Asia,2019,4.437,1.043,0.98,0.574,0.241,0.148
+Mali,Africa,2019,4.39,0.385,1.105,0.308,0.327,0.153
+Sierra Leone,Africa,2019,4.374,0.268,0.841,0.242,0.309,0.252
+Sri Lanka,Asia,2019,4.366,0.949,1.265,0.831,0.47,0.244
+Chad,Africa,2019,4.35,0.35,0.766,0.192,0.174,0.198
+Ukraine,Europe,2019,4.332,0.82,1.39,0.739,0.178,0.187
+Ethiopia,Africa,2019,4.286,0.336,1.033,0.532,0.344,0.209
+Swaziland,Africa,2019,4.212,0.811,1.149,0,0.313,0.074
+Uganda,Africa,2019,4.189,0.332,1.069,0.443,0.356,0.252
+Egypt,Africa,2019,4.166,0.913,1.039,0.644,0.241,0.076
+Zambia,Africa,2019,4.107,0.578,1.058,0.426,0.431,0.247
+Togo,Africa,2019,4.085,0.275,0.572,0.41,0.293,0.177
+India,Asia,2019,4.015,0.755,0.765,0.588,0.498,0.2
+Liberia,Africa,2019,3.975,0.073,0.922,0.443,0.37,0.233
+Comoros,Africa,2019,3.973,0.274,0.757,0.505,0.142,0.275
+Madagascar,Africa,2019,3.933,0.274,0.916,0.555,0.148,0.169
+Lesotho,Africa,2019,3.802,0.489,1.169,0.168,0.359,0.107
+Burundi,Africa,2019,3.775,0.046,0.447,0.38,0.22,0.176
+Zimbabwe,Africa,2019,3.663,0.366,1.114,0.433,0.361,0.151
+Haiti,Latin America/Caribbean,2019,3.597,0.323,0.688,0.449,0.026,0.419
+Botswana,Africa,2019,3.488,1.041,1.145,0.538,0.455,0.025
+Syria,Asia,2019,3.462,0.619,0.378,0.44,0.013,0.331
+Malawi,Africa,2019,3.41,0.191,0.56,0.495,0.443,0.218
+Yemen,Asia,2019,3.38,0.287,1.163,0.463,0.143,0.108
+Rwanda,Africa,2019,3.334,0.359,0.711,0.614,0.555,0.217
+Tanzania,Africa,2019,3.231,0.476,0.885,0.499,0.417,0.276
+Afghanistan,Asia,2019,3.203,0.35,0.517,0.361,0,0.158
+Central African Republic,Africa,2019,3.083,0.026,0,0.105,0.225,0.235
diff --git a/notebooks/pingouin_scipy_cours.ipynb b/notebooks/pingouin_scipy_cours.ipynb
deleted file mode 100644
index 4a63f7dd14ec05aa988033651d137b9547530233..0000000000000000000000000000000000000000
--- a/notebooks/pingouin_scipy_cours.ipynb
+++ /dev/null
@@ -1,3516 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ccafd3d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "!\"{sys.executable}\" -m pip install scipy ipywidgets\n",
-    "import scipy_material"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d2c6ebd",
-   "metadata": {},
-   "source": [
-    "<h1 align='center'>Statistical tests with the Pingouin and SciPy libraries</h1>\n",
-    "\n",
-    "<div style='text-align:center'><img width=600 src='https://pingouin-stats.org/build/html/_images/logo_pingouin.png' /></div>\n",
-    "<div style='text-align:center'><img width=300 src='https://docs.scipy.org/doc/scipy/_static/logo.svg' /></div>\n",
-    "\n",
-    "The [Pingouin](https://pingouin-stats.org/build/html/index.html) library features a selection of commonly-used statistical operations. The provided functions have verbose output and can be used independently of one another."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1f8a7a30-48e3-4f5a-8836-3912b049b5bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pingouin as pg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b46252c6-5c09-40e2-890d-9abeccd4e7be",
-   "metadata": {},
-   "source": [
-    "In contrast, [SciPy](https://docs.scipy.org/doc/scipy/reference/#api-definition) is a collection of mathematical tools aiming at diverse fields. It is the one of the oldest Python libraries. Its functionalities are split in several modules:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "42342d74",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import (\n",
-    "    cluster,     # Clustering algorithms\n",
-    "    constants,   # Physical and mathematical constants\n",
-    "    fftpack,     # Fast Fourier Transform routines\n",
-    "    integrate,   # Integration and ordinary differential equation solvers\n",
-    "    interpolate, # Interpolation and smoothing splines\n",
-    "    io,          # Input and Output\n",
-    "    linalg,      # Linear algebra\n",
-    "    ndimage,     # N-dimensional image processing\n",
-    "    odr,         # Orthogonal distance regression\n",
-    "    optimize,    # Optimization and root-finding routines\n",
-    "    signal,      # Signal processing\n",
-    "    sparse,      # Sparse matrices and associated routines\n",
-    "    spatial,     # Spatial data structures and algorithms\n",
-    "    special,     # Special functions\n",
-    "    stats,       # Statistical distributions and functions\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a37e843-892d-4bfd-93a2-46e94c2a45e8",
-   "metadata": {},
-   "source": [
-    "We will make a brief overview of the `scipy.stats` module only, in particular basic functionalities that cannot be found in Pingouin."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d715671-e4ad-4d4d-b60f-9397e2b80b90",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Outline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5162d7b2-625a-4699-922e-92c5c2bfa769",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Descriptive statistics are well covered by Pandas and the plotting libraries.\n",
-    "This course focuses merely on statistical tests.\n",
-    "\n",
-    "* Distributions\n",
-    "* Student $t$ tests\n",
-    "    * compare a sample mean against the population mean\n",
-    "    * compare means of two independent samples\n",
-    "    * compare the means of paired samples\n",
-    "* Analyses of variance\n",
-    "    * compare more than two group means\n",
-    "* Tests for other tests' assumptions\n",
-    "    * normality tests\n",
-    "    * homoscedasticity tests\n",
-    "* $\\chi^2$ tests for categorical variables\n",
-    "    * homogeneity and independence tests\n",
-    "    * goodness-of-fit tests\n",
-    "* Effect sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d15967d-ecf9-469a-a740-c7fe9cbf04d2",
-   "metadata": {},
-   "source": [
-    "# Distributions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "164b1fe2-412d-48f8-a6f0-d2352a9d575a",
-   "metadata": {},
-   "source": [
-    "For this section, the related utilities are provided by `scipy.stats`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "78db8576-5c83-4967-97b8-19dd4ff9b3b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy import stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c99b2f0-8832-40cb-a63e-d4abdef4f257",
-   "metadata": {},
-   "source": [
-    "Reminder about module loading:\n",
-    "\n",
-    "Example: how to access the `sem` function defined in the `scipy.stats` module?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "898957c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "skipping\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%script echo skipping\n",
-    "\n",
-    "import scipy.stats\n",
-    "scipy.stats.sem\n",
-    "\n",
-    "from scipy import stats\n",
-    "stats.sem\n",
-    "\n",
-    "from scipy.stats import *\n",
-    "sem"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4dc592bf-9900-4838-ab03-98039c17cfd8",
-   "metadata": {},
-   "source": [
-    "## Confidence intervals\n",
-    "\n",
-    "Common information such as the sample mean or standard deviation are trivial to obtain. For example, we have seen Pandas' `describe`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0a56b871-8335-42b1-b8e0-f1cdc68b5281",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>total_bill</th>\n",
-       "      <th>tip</th>\n",
-       "      <th>size</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>244.000000</td>\n",
-       "      <td>244.000000</td>\n",
-       "      <td>244.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>19.785943</td>\n",
-       "      <td>2.998279</td>\n",
-       "      <td>2.569672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>8.902412</td>\n",
-       "      <td>1.383638</td>\n",
-       "      <td>0.951100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>3.070000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>13.347500</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>17.795000</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>24.127500</td>\n",
-       "      <td>3.562500</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>50.810000</td>\n",
-       "      <td>10.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       total_bill         tip        size\n",
-       "count  244.000000  244.000000  244.000000\n",
-       "mean    19.785943    2.998279    2.569672\n",
-       "std      8.902412    1.383638    0.951100\n",
-       "min      3.070000    1.000000    1.000000\n",
-       "25%     13.347500    2.000000    2.000000\n",
-       "50%     17.795000    2.900000    2.000000\n",
-       "75%     24.127500    3.562500    3.000000\n",
-       "max     50.810000   10.000000    6.000000"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataframe = pg.read_dataset('tips')\n",
-    "dataframe.describe()\n",
-    "#dataframe.describe(exclude=np.number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b311609",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "To report the value of the population mean and account for the uncertainty that results from the fact the true value is actually unknown (the sample mean above is our best guess), we can give a confidence interval instead.\n",
-    "\n",
-    "Reminder: the population mean follows a normal distribution centered at the sample mean, with standard deviation equal to the standard error of the mean (or, equivalently, the standard deviation of the sample divided by the square root of the sample size)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e60ba8f3",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_confidence_interval(19.785943, stats.sem(dataframe['total_bill']))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2b36e29",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Computing a confidence interval with SciPy involves instantiating the normal distribution with the `norm` function and calling the `interval` method of the returned object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e911524f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "X = dataframe['total_bill']\n",
-    "mu = np.mean(X)\n",
-    "sigma = stats.sem(X)\n",
-    "distribution_of_the_mean = stats.norm(mu, sigma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "2494dc66-d5bf-4235-b2f7-02daf5da7d2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(18.668922839262997, 20.902962406638643)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "distribution_of_the_mean.interval(0.95)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db72ca26-00df-45be-9700-2f9af50228d0",
-   "metadata": {},
-   "source": [
-    "Note again that we have set the scale parameter `sigma` equal to the sem. In contrast, if variable `total_bill` followed a normal distribution, we could define its distribution as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1fad2898-81e4-4814-b496-146f54d21e29",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "normal_distribution = stats.norm(X.mean(), X.std())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d77e3ca-f4af-4135-bfee-92054898eaa6",
-   "metadata": {},
-   "source": [
-    "The objects `norm` returns (*e.g.* `distribution_of_the_mean`) feature numerous other methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1341b01d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.2704798697499871"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# probability density function\n",
-    "distribution_of_the_mean.pdf(19.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d6bbb149",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0839406210836206"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# cumulative distribution function\n",
-    "distribution_of_the_mean.cdf(19.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b05f3b3",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "See [scipy.stats.rv_continuous](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.html#scipy.stats.rv_continuous) for more methods.\n",
-    "\n",
-    "As another example, we can make use of the inverse survival function `isf` to re-implement the calculation of the $1-\\alpha=95\\%$ confidence interval based on the following formula:\n",
-    "\n",
-    "$$\n",
-    "\\bar{x} \\pm z_{1-\\alpha/2}\\frac{\\sigma}{\\sqrt{n}}\n",
-    "$$\n",
-    "\n",
-    "Indeed, $z_{1-\\alpha/2}$ is calculated as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "62f75d44",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.9599639845400545"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = 0.05\n",
-    "z = stats.norm().isf(alpha / 2)\n",
-    "z"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7728089e",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "For a $95\\%$ confidence interval, we usually take $z\\approx 1.96$. Note we took the standard normal distribution, with null mean and unit standard deviation (`stats.norm()` is equivalent to `stats.norm(0, 1)`).\n",
-    "\n",
-    "$\\frac{\\sigma}{\\sqrt{n}}$ is the standard deviation of the sample mean, or standard error of mean, that we have already calculated using the `sem` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "99fe274d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bills are 19.79 ± 1.12 on average\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Bills are {mu:.2f} ± {z * sigma:.2f} on average')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91a55884-2ca8-4cb0-9190-6c42bea7047b",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "## Fitting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72a7068e-ac7c-4940-b5e1-103ecf4cec2c",
-   "metadata": {},
-   "source": [
-    "We have seen how to fit a normal distribution explicitly passing a mean and standard deviation. More generally, for any distribution from `scipy.stats`, we can get the required parameters using the `stats.<distribution>.fit` method. For example, for distribution `stats.norm` with sample `X`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "11cb5a3f-c48d-457d-9134-c56665b5e169",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "normal_distribution = stats.norm(*stats.norm.fit(X))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c483874-e9db-440a-b1c8-7dd47a06008a",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Now, unlike the population mean, there is no guarantee a sample follows a normal distribution.\n",
-    "\n",
-    "To determine what distribution a sample best follows, we can fit various distributions to the data and visually appreciate how well these distributions match with the data by plotting a scaled histogram and the probability density functions of the fitted distributions on top of the histogram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "578edd4a-bcb1-4f9e-a8ba-d3e63bbabb71",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the histogram\n",
-    "import seaborn as sns\n",
-    "sns.histplot(X, bins=20, stat='density')\n",
-    "\n",
-    "# fit a log-normal distribution\n",
-    "lognorm = stats.lognorm(*stats.lognorm.fit(X))\n",
-    "\n",
-    "# draw the probability density function\n",
-    "from matplotlib import pyplot as plt\n",
-    "grid = np.arange(1, 52, .1)\n",
-    "plt.plot(grid, lognorm.pdf(grid));\n",
-    "\n",
-    "# fit and overlay more distributions\n",
-    "#weibull = stats.weibull_min(*stats.weibull_min.fit(X))\n",
-    "#plt.plot(grid, weibull.pdf(grid));\n",
-    "#t = stats.t(*stats.t.fit(X))\n",
-    "#plt.plot(grid, t.pdf(grid));\n",
-    "#chi2 = stats.chi2(*stats.chi2.fit(X))\n",
-    "#plt.plot(grid, chi2.pdf(grid));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1fc8931-8459-46cf-b085-765298aaac0a",
-   "metadata": {},
-   "source": [
-    "Note that plotting histograms is good practice anyway, because it helps to spot data distributions with multiple modes. Multiple modes in a sample are a red flag for tests that compare estimates of central tendency (*e.g.* means)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cb8febd8",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "...and we can test whether `total_bill` follows a log-normal distribution in our sample with the one-sample Kolmogorov-Smirnov test:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "5b1b8872",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7387575212859724"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "statistic, pvalue = stats.kstest(X, lognorm.cdf)\n",
-    "pvalue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddeacee0-2062-476d-b7ec-9e0f036d6c4b",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "# Statistical testing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f2fcb2e-1097-4bb7-ba77-fceaa3b55702",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "\n",
-    "> What have we just done?\n",
-    "\n",
-    "We compared our **observations** `x` with some **expectation**.\n",
-    "\n",
-    "We actually formulated a so-called *null hypothesis*, denoted $H_0$, that models the situation such that \"nothing is going on\", *i.e.* the observations meet the expectation.\n",
-    "\n",
-    "We also implicitly defined an alternative hypothesis, usually denoted $H_1$ or $H_A$, that can simply be the opposite of $H_0$.\n",
-    "\n",
-    "For example:\n",
-    "\n",
-    "$$\n",
-    "\\left\\{\n",
-    "\\begin{array}{ l l l }\n",
-    "H_0: & X \\sim \\mathcal{N}(\\mu, \\sigma^2) & \\mbox{with } \\mu \\mbox{ assumed to be } \\bar{x} \\mbox{ and } \\sigma^2 \\mbox{ as } \\frac{1}{n-1}\\sum_{i=0}^{n-1} (x_i - \\bar{x})^2 \\\\\n",
-    "H_A: & \\mbox{not } H_0\n",
-    "\\end{array}\n",
-    "\\right.\n",
-    "$$\n",
-    "\n",
-    "A test consists in contrasting the two incompatible hypotheses.\n",
-    "\n",
-    "If we had a single observation – say $z=1.4$ – to compare with a distribution – say $\\mathcal{N}(0,1)$ – we would simply compute the probability for this value to be drawn from this distribution (or not):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "8346ad8e",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "z = 1.4\n",
-    "\n",
-    "N = stats.norm(0, 1)\n",
-    "\n",
-    "onesided_pvalue = N.sf(z) # sf= survival function\n",
-    "twosided_pvalue = 2 * min(N.cdf(z), N.sf(z))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "404476b6",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_onesided_probabilitymass(z, N, onesided_pvalue)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99a007ac",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "In practice, all tests boil down to comparing a single value with a reference distribution. Basically, a test expresses the discrepancy between the observations and the expectation in the shape of a *statistic*, and this statistic is supposed to follow a given distribution under $H_0$.\n",
-    "\n",
-    "This is used as a basis to calculate a *p*-value that estimates the probability of erroneously rejecting $H_0$.\n",
-    "\n",
-    "The experimenter also defines a significance level $\\alpha$, with common values $\\alpha=0.05$ or $0.01$, that sets the maximum tolerated risk of making a *type-1 error*, *i.e.* of rejecting $H_0$ by chance.\n",
-    "If the obtained <em>p</em>-value is lower than $\\alpha$, then s·he can conclude there is sufficient evidence to reject $H_0$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "b162e92e",
-   "metadata": {
-    "hidden": true,
-    "hide_input": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%html\n",
-    "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "446ba63e-df67-46ca-8921-eca686462c93",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## *t* tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f9602b8-5f64-438b-8051-c71f9c8b0b14",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "*t* tests derive a statistic that is supposed to follow the [Student's *t* distribution](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html) under $H_0$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_t_pdfs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "701ee597-2eec-4404-8a0e-601ae2121e19",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "At high degrees of freedom, the *t* distribution approaches the normal distribution. At lower degrees of freedom, the *t* distribution exhibits heavier tails and is less sensitive to extreme values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1606aca-1582-44a8-8f17-4804b416e005",
-   "metadata": {},
-   "source": [
-    "There exist several *t* tests. Pingouin's [`ttest`](https://pingouin-stats.org/build/html/generated/pingouin.ttest.html#pingouin.ttest) function provides several of them:\n",
-    "\n",
-    "`pg.ttest(x, y, paired=False, alternative='two-sided', correction='auto', r=0.707, confidence=0.95)`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca6bf548-cadf-4c75-8130-fe0c3ef8de9a",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### One-sample *t* test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9fdde2b-8764-4f9f-b249-6af66347b3b1",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "This test compares a sample's central tendency (*sample mean*) with a reference value (*population mean*).\n",
-    "\n",
-    "<table style=\"text-align: center;\"><tr><td>\n",
-    "<img src='img/8mice.svg' />\n",
-    "</td><td>\n",
-    "<img src='img/Scientific_journal_icon.svg' width=\"96px\" />\n",
-    "</td></tr><tr><td><center>\n",
-    "<code>x=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
-    "</center></td><td><center>\n",
-    "<code>&mu;=50</code>\n",
-    "</center></td></tr></table>\n",
-    "\n",
-    "Let us call $\\mu$ this reference value. Our expectation is that the sample mean $\\bar{X}$ is close enough to $\\mu$.\n",
-    "In other words, $H_0: \\bar{X} = \\mu$.\n",
-    "The statistic is:\n",
-    "$$\n",
-    "\\frac{\\bar{X} - \\mu}{\\mathrm{SEM}} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim t(n-1) \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "bb6f0e09-f529-47bb-8470-66130193a791",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = np.array([49.47257879, 81.93967205, 64.030398, 17.25423608, 59.80082512,\n",
-    "              94.56012514, 69.91672899, 12.39640637])\n",
-    "\n",
-    "mu = 50"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ff75a9a-9f15-4be6-9312-388fb0b62a4a",
-   "metadata": {},
-   "source": [
-    "With Pingouin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "ec4b0084-93c3-40b9-94c5-d05679a1dc45",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>T</th>\n",
-       "      <th>dof</th>\n",
-       "      <th>alternative</th>\n",
-       "      <th>p-val</th>\n",
-       "      <th>CI95%</th>\n",
-       "      <th>cohen-d</th>\n",
-       "      <th>BF10</th>\n",
-       "      <th>power</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T-test</th>\n",
-       "      <td>0.602406</td>\n",
-       "      <td>7</td>\n",
-       "      <td>two-sided</td>\n",
-       "      <td>0.565899</td>\n",
-       "      <td>[31.95, 80.4]</td>\n",
-       "      <td>0.212983</td>\n",
-       "      <td>0.391</td>\n",
-       "      <td>0.08203</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               T  dof alternative     p-val          CI95%   cohen-d   BF10  \\\n",
-       "T-test  0.602406    7   two-sided  0.565899  [31.95, 80.4]  0.212983  0.391   \n",
-       "\n",
-       "          power  \n",
-       "T-test  0.08203  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.ttest(x, mu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e96a45be-1ab1-4278-a5a3-e49574e0133a",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "For completeness, SciPy's one-sample *t* test is [ttest_1samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html):\n",
-    "\n",
-    "`scipy.stats.ttest_1samp(a, popmean, axis=0, nan_policy='propagate', alternative='two-sided')`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "b471633d-c9ad-455e-84e1-d32af085b32a",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TtestResult(statistic=0.6024056396957578, pvalue=0.5658990587680466, df=7)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.ttest_1samp(x, mu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2144869e-9e4a-4e63-ae58-81c5a6be7205",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### *t* test for independent samples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b892f33-761a-40b6-873d-ff9f7758cef3",
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-    "This test compares the means of two samples or groups, *e.g.* a control sample and a sample from a mutated population: $H_0: \\bar{X_1} = \\bar{X_2}$.\n",
-    "\n",
-    "<table style=\"text-align:center;\"><tr><td>\n",
-    "<img src=\"img/8mice.svg\" alt=\"sample of the control population\" />\n",
-    "</td><td>\n",
-    "<img src=\"img/8mutants1.svg\" alt=\"sample of a mutated population\" />\n",
-    "</td></tr><tr><td><center>\n",
-    "<code>x<sub>1</sub>=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
-    "</center></td><td><center>\n",
-    "<code>x<sub>2</sub>=[64.2 96.6 101.9 85.3 66.5 63.9 127.6 55.0]</code>\n",
-    "</center></td></tr></table>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "8cc7f8c2-75d3-447f-b27c-4ed6350e4075",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x1 = x\n",
-    "x2 = np.array([64.22723692, 96.56483856, 101.94191774, 85.31918879,\n",
-    "               66.4952999, 63.88841224, 127.63861749, 55.00527005])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16c87bf3-dd77-487f-8b96-9560a1ef3315",
-   "metadata": {},
-   "source": [
-    "With Pingouin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "7dae4d48-36b9-40df-87fa-a3c73ca42a19",
-   "metadata": {},
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>T</th>\n",
-       "      <th>dof</th>\n",
-       "      <th>alternative</th>\n",
-       "      <th>p-val</th>\n",
-       "      <th>CI95%</th>\n",
-       "      <th>cohen-d</th>\n",
-       "      <th>BF10</th>\n",
-       "      <th>power</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T-test</th>\n",
-       "      <td>-1.961743</td>\n",
-       "      <td>14</td>\n",
-       "      <td>two-sided</td>\n",
-       "      <td>0.069989</td>\n",
-       "      <td>[-55.4, 2.47]</td>\n",
-       "      <td>0.980872</td>\n",
-       "      <td>1.42</td>\n",
-       "      <td>0.447175</td>\n",
-       "    </tr>\n",
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-       "               T  dof alternative     p-val          CI95%   cohen-d  BF10  \\\n",
-       "T-test -1.961743   14   two-sided  0.069989  [-55.4, 2.47]  0.980872  1.42   \n",
-       "\n",
-       "           power  \n",
-       "T-test  0.447175  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.ttest(x1, x2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce4cf3a8-190f-4436-9044-743e985fe748",
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-   "source": [
-    "SciPy's *t* test for independent samples uses the statistic $t=\\frac{\\bar{X_1}-\\bar{X_2}}{\\sqrt{(\\frac{1}{n_1}+\\frac{1}{n_2})\\mbox{ }\\textrm{PooledVariance}}}$ with $\\textrm{PooledVariance} = \\frac{1}{n_1+n_2-2}\\sum_{j\\in\\{1,2\\}}\\sum_i (x_{ij}-\\bar{x_j})^2$ and is available as [ttest_ind](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html):\n",
-    "\n",
-    "`scipy.stats.ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "6231e214-ac36-4c4f-8a16-e1551c8484b4",
-   "metadata": {
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-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TtestResult(statistic=-1.96174329619957, pvalue=0.06998888828308221, df=14.0)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.ttest_ind(x1, x2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b579c8e-1062-4083-bcc1-49009f0c65a1",
-   "metadata": {},
-   "source": [
-    "Results are consistent.\n",
-    "\n",
-    "However, if the two samples were of different sizes, Pingouin's `ttest` would automatically use Welch's *t* test instead.\n",
-    "\n",
-    "SciPy's `ttest_ind` also provides this variant of the *t* test, passing argument `equal_variance=False`. It also provides a less common variant known as Yuen's *t* test, with `equal_var=False` and `trim=0.2` (requires more data).\n",
-    "\n",
-    "Note that SciPy's default *t* test does not require equal numbers of observations per group. However, it assumes the groups are normally distributed (but is relatively robust to non-«extreme non-normality») and, more importantly, have [similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$)](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2))."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1083c04c-1221-446f-84a0-7084413a7722",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### *t* test for paired samples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7dbe59b-7e10-4bd6-b455-37e4a517b30f",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "<img src='img/paired1.svg' />"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa3d8377-0704-4371-ab1f-e8567e66f112",
-   "metadata": {},
-   "source": [
-    "Let us now assume `x1[i]` and `x2[i]` are measurements from a same animal `i`, under two different experimental conditions.\n",
-    "\n",
-    "With Pingouin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "70ee9cae-b8ad-4b51-b443-03befe14606c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\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>T</th>\n",
-       "      <th>dof</th>\n",
-       "      <th>alternative</th>\n",
-       "      <th>p-val</th>\n",
-       "      <th>CI95%</th>\n",
-       "      <th>cohen-d</th>\n",
-       "      <th>BF10</th>\n",
-       "      <th>power</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T-test</th>\n",
-       "      <td>-2.361598</td>\n",
-       "      <td>7</td>\n",
-       "      <td>two-sided</td>\n",
-       "      <td>0.050223</td>\n",
-       "      <td>[-52.96, 0.03]</td>\n",
-       "      <td>0.980872</td>\n",
-       "      <td>1.892</td>\n",
-       "      <td>0.664343</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-       "               T  dof alternative     p-val           CI95%   cohen-d   BF10  \\\n",
-       "T-test -2.361598    7   two-sided  0.050223  [-52.96, 0.03]  0.980872  1.892   \n",
-       "\n",
-       "           power  \n",
-       "T-test  0.664343  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.ttest(x1, x2, paired=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cfc1090-95e0-4f80-8eec-9e81b21082ef",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "SciPy's *t* test for paired samples is [ttest_rel](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html):\n",
-    "\n",
-    "`scipy.stats.ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided')`\n",
-    "\n",
-    "This is actually a one-sample *t* test of the between-group differences against a population mean equal to zero (compare [1](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L6450-L6460) and [2](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L5647-L5656))."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "234dc53a-dce0-426e-8f57-4a4f9bfa38fc",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "### Effect sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed009c59-e22b-4771-bfc9-f113dc365047",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Very low *p*-values are not measurements of the strength of an effect. One should consider the *effect size* instead.\n",
-    "\n",
-    "A common measure of effect size for two independent samples is [Cohen's $d$](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d): $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8660e3d113ab4912aae489eb52e405b6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.5, description='cohen_d', max=4.0), Output()), _dom_classes=('widget…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_cohen_d();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8e1face-4845-459d-95c2-8674181d39b5",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "With large enough sample sizes, one can find significant effects of size $0.1$ for example, which may not be of practical interest. Statistical significance does not imply practical significance.\n",
-    "   \n",
-    "Measurements of effect size were proposed together with [tables](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) for interpreting size values. For example, for Cohen's $d$:\n",
-    "   \n",
-    "| $|d|$ | size of effect |\n",
-    "| :-: | :-- |\n",
-    "| $0.2$ | small |\n",
-    "| $0.5$ | medium |\n",
-    "| $0.8$ | large |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a060217-5a49-49b5-b8df-ec2609084c1a",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Pingouin's [compute_effsize](https://pingouin-stats.org/build/html/generated/pingouin.compute_effsize.html#pingouin.compute_effsize) provides various effect sizes (default is Cohen's *d*) for the comparison of two means, for dependent or independent (default) samples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "2ba983bc-ef4c-4a15-ac23-776fffd88afb",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-0.980871648099785"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.compute_effsize(x1, x2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b0fd532-87d0-4747-b3a3-322d77c2bfbd",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Analysis of variance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b03b649e-32e0-421a-8ba9-fcb81b01404a",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "### One-way ANOVA\n",
-    "\n",
-    "Comparing three or more group means reads $H_0: \\bar{X_0} = \\bar{X_1} = ... = \\bar{X_k}$ and is usually carried out with an *analysis of variance*.\n",
-    "\n",
-    "The total variance ($SS_{\\textrm{total}}$) is decomposed as the sum of two terms: *within-group* variance ($SS_{\\textrm{error}}$) and *between-group* variance ($SS_{\\textrm{treatment}}$).\n",
-    "\n",
-    "$$\n",
-    "\\underbrace{\\sum_j\\sum_i (x_{ij} - \\bar{\\bar{x}})^2}_{SS_{\\textrm{total}}} = \\underbrace{\\sum_j\\sum_i (\\bar{x_j} - \\bar{\\bar{x}})^2}_{SS_{\\textrm{treatment}}} + \\underbrace{\\sum_j\\sum_i (x_{ij} - \\bar{x_j})^2}_{SS_{\\textrm{error}}}\n",
-    "$$\n",
-    "Many statistical tools give the following detailed table:\n",
-    "\n",
-    "| Source | Degrees of<br />freedom | Sum of squares | Mean squares | $\\mbox{ }F\\mbox{ }$ | $p$-value |\n",
-    "| :- | :-: | :-: | :-: | :-: | :-: |\n",
-    "| Treatment | $k-1$ | $SS_{\\textrm{treatment}}$ | $MS_{\\textrm{treatment}}$ | $\\frac{MS_{\\textrm{treatment}}}{MS_{\\textrm{error}}}$ | $\\mbox{ }p\\mbox{ }$ |\n",
-    "| Error | $N-k$ | $SS_{\\textrm{error}}$ | $MS_{\\textrm{error}}$ | | |\n",
-    "| Total | $N-1$ | $SS_{\\textrm{total}}$ | | | |\n",
-    "\n",
-    "The statistic $F = \\frac{MS_{\\textrm{treatment}}}{MS_{\\textrm{error}}}$ follows the Fisher's [F](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f.html) distribution under $H_0$.\n",
-    "\n",
-    "More about it at: https://www.coursera.org/learn/stanford-statistics/lecture/pskeN/the-idea-of-analysis-of-variance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5329bbf-ee6c-432d-a130-dca099e18258",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "If we define three samples or groups:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "e917eacc-d454-4476-ac0e-404d67ba5f47",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# copied-pasted from https://www.statology.org/bartletts-test-python/\n",
-    "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
-    "B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]\n",
-    "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "671a6865-7bf5-4ff4-b4ff-ec8ee4454994",
-   "metadata": {},
-   "source": [
-    "To perform an ANOVA with Pingouin, we will have to represent the above data in a single DataFrame, in the so-called *long format*, with one row = one observation, and each variable (both dependent and independent) as a column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "47010494-cac1-4af9-9490-77fd0cf0d5b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "    }\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>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>20</th>\n",
-       "      <th>21</th>\n",
-       "      <th>22</th>\n",
-       "      <th>23</th>\n",
-       "      <th>24</th>\n",
-       "      <th>25</th>\n",
-       "      <th>26</th>\n",
-       "      <th>27</th>\n",
-       "      <th>28</th>\n",
-       "      <th>29</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>response</th>\n",
-       "      <td>85</td>\n",
-       "      <td>86</td>\n",
-       "      <td>88</td>\n",
-       "      <td>75</td>\n",
-       "      <td>78</td>\n",
-       "      <td>94</td>\n",
-       "      <td>98</td>\n",
-       "      <td>79</td>\n",
-       "      <td>71</td>\n",
-       "      <td>80</td>\n",
-       "      <td>...</td>\n",
-       "      <td>79</td>\n",
-       "      <td>78</td>\n",
-       "      <td>88</td>\n",
-       "      <td>94</td>\n",
-       "      <td>92</td>\n",
-       "      <td>85</td>\n",
-       "      <td>83</td>\n",
-       "      <td>85</td>\n",
-       "      <td>82</td>\n",
-       "      <td>81</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>group</th>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>...</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 30 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  \\\n",
-       "response  85  86  88  75  78  94  98  79  71  80  ...  79  78  88  94  92  85   \n",
-       "group      A   A   A   A   A   A   A   A   A   A  ...   C   C   C   C   C   C   \n",
-       "\n",
-       "          26  27  28  29  \n",
-       "response  83  85  82  81  \n",
-       "group      C   C   C   C  \n",
-       "\n",
-       "[2 rows x 30 columns]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.DataFrame(dict(\n",
-    "    response = np.concatenate([A,B,C]),\n",
-    "    group    = np.repeat(['A','B','C'], [len(A),len(B),len(C)]),\n",
-    "))\n",
-    "\n",
-    "df.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec8b9c7e-8ba4-4f47-9ff7-33d70d33145f",
-   "metadata": {},
-   "source": [
-    "With Pingouin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "e36f108d-4674-4a1c-89d1-eca3360b1ae0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Source</th>\n",
-       "      <th>ddof1</th>\n",
-       "      <th>ddof2</th>\n",
-       "      <th>F</th>\n",
-       "      <th>p-unc</th>\n",
-       "      <th>np2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>group</td>\n",
-       "      <td>2</td>\n",
-       "      <td>27</td>\n",
-       "      <td>2.357532</td>\n",
-       "      <td>0.113848</td>\n",
-       "      <td>0.14867</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Source  ddof1  ddof2         F     p-unc      np2\n",
-       "0  group      2     27  2.357532  0.113848  0.14867"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.anova(df, dv='response', between='group')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a85b93e4-596d-47c9-a0c5-e8c43150dca7",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "SciPy also provides a [one-way ANOVA](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/mstats_basic.py#L2937-L2967) with function [f_oneway](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.f_oneway.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "2bf0aafa-d23f-44fe-b9e0-cf4bf13e7314",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.f_oneway(A, B, C)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "164aa1ae-5446-4680-9571-c783edaf4101",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "The ANOVA is an *omnibus* test and does not tell which groups exhibit differing means. Specific differences are later identified using *post-hoc tests* (more about it in next session)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ea4e6c8-4851-4704-b9ca-6257239ab07b",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Size effect"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39925882-deb4-43e1-8d82-9fb1e68df0ba",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Pingouin's `anova` provides the partial $\\eta_p^2=\\frac{SS_{\\textrm{treatment}}}{SS_{\\textrm{treatment}}+SS_{\\textrm{error}}}$ in the returned table.\n",
-    "\n",
-    "You can pass argument `effsize='n2'` to get the $\\eta^2=\\frac{SS_{\\textrm{treatment}}}{SS_{\\textrm{error}}}$ instead."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b59d33f-5f79-4e57-a629-bce754da1c7b",
-   "metadata": {},
-   "source": [
-    "### Multi-way ANOVA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2194415d-c032-4133-b051-4018f1f05417",
-   "metadata": {},
-   "source": [
-    "Pingouin's [`anova`](https://pingouin-stats.org/build/html/generated/pingouin.anova.html#pingouin.anova) can take a __list__ of factors as argument `between`. Beware that it treats the designated columns as categorical variables and does not warn if the data are continuous. Pay attention to the reported numbers of degrees of freedom."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "a00e151c-d532-49cf-bfa2-25aa003324cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plant_data = pd.DataFrame({\n",
-    "    'water':  np.repeat(['daily', 'weekly'], 15),\n",
-    "    'sun':    np.tile(np.repeat(['low', 'med', 'high'], 5), 2),\n",
-    "    'height': np.array([\n",
-    "                        6.3, 6.8, 5.5, 5.1, 6.0, 6.1, 5.0, 6.1, 3.6, 5.4,\n",
-    "                        6.4, 5.7, 8.3, 7.7, 7.0, 2.9, 3.2, 2.3, 3.9, 4.1,\n",
-    "                        3.5, 5.3, 5.8, 4.6, 3.6, 5.2, 6.2, 5.1, 6.7, 7.0,\n",
-    "])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "e9d46687-c35e-41fb-8452-087c100c8253",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Source</th>\n",
-       "      <th>SS</th>\n",
-       "      <th>DF</th>\n",
-       "      <th>MS</th>\n",
-       "      <th>F</th>\n",
-       "      <th>p-unc</th>\n",
-       "      <th>np2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>water</td>\n",
-       "      <td>15.552000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15.552000</td>\n",
-       "      <td>19.117394</td>\n",
-       "      <td>0.000205</td>\n",
-       "      <td>0.443380</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>sun</td>\n",
-       "      <td>21.424667</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10.712333</td>\n",
-       "      <td>13.168203</td>\n",
-       "      <td>0.000138</td>\n",
-       "      <td>0.523208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>water * sun</td>\n",
-       "      <td>5.694000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.847000</td>\n",
-       "      <td>3.499693</td>\n",
-       "      <td>0.046376</td>\n",
-       "      <td>0.225791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Residual</td>\n",
-       "      <td>19.524000</td>\n",
-       "      <td>24</td>\n",
-       "      <td>0.813500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Source         SS  DF         MS          F     p-unc       np2\n",
-       "0        water  15.552000   1  15.552000  19.117394  0.000205  0.443380\n",
-       "1          sun  21.424667   2  10.712333  13.168203  0.000138  0.523208\n",
-       "2  water * sun   5.694000   2   2.847000   3.499693  0.046376  0.225791\n",
-       "3     Residual  19.524000  24   0.813500        NaN       NaN       NaN"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.anova(plant_data, dv='height', between=['water', 'sun'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9fbad44-1b73-411d-a861-b3c09e824c9f",
-   "metadata": {},
-   "source": [
-    "Note that Pingouin's `anova` defaults to using type-2 sums of squares. Matlab `anovan` function uses type-3 ss instead.\n",
-    "\n",
-    "In the above table, we observe an interaction effect between the two factors. If we plot the data, we should the effect of *e.g.* `water` depends on the other factor `sun`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "83732c12-c360-42ae-94cf-06d1bf02247b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(data=plant_data, x='sun', y='height', hue='water');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae728113-0020-4d50-80bc-9dcb11a3dd42",
-   "metadata": {},
-   "source": [
-    "Alternatively, Pingouin provides an interaction plot:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "ccc23571-46dd-4250-9c11-feb4eeb2b2e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.set_option('future.no_silent_downcasting', True) # hide a warning message that the next expression would generate as of pandas 2.2.2 and pingouin 0.5.4\n",
-    "pg.plot_paired(data=plant_data, dv='height', within='water', subject='sun');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bf2c9ad-68fd-4f4b-b68c-180de69d3522",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Assumptions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd484cda-1d0b-4468-a092-f362744f8aa0",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "The standard *F* test requires the data to exhibit the following properties:\n",
-    "\n",
-    "* independent observations,\n",
-    "* normally distributed residuals,\n",
-    "* all groups have equal population variance (*homoscedasticity*),\n",
-    "* at least 5 observations ($n \\ge 5$) per group (and equal number)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "305a944c-3a67-49df-9ff5-9a3b8c36cfed",
-   "metadata": {},
-   "source": [
-    "Pingouin provides [more forms of analyses of (co-)variance](https://pingouin-stats.org/build/html/api.html#anova-and-t-test)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e38c759e-8807-4975-9620-9c2100b964a8",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Checking for common assumptions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af5b8858-496a-4045-83de-db7fbb463634",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Most parametric tests make assumptions or have requirements on the distribution of the dependent variable or the residuals.\n",
-    "\n",
-    "The desired properties can be checked with dedicated statistical tests that Pingouin conveniently groups into three functions with self-explanatory names:\n",
-    "* [`normality`](https://pingouin-stats.org/build/html/generated/pingouin.normality.html#pingouin.normality)\n",
-    "* [`homoscedasticity`](https://pingouin-stats.org/build/html/generated/pingouin.homoscedasticity.html#pingouin.homoscedasticity)\n",
-    "* [`sphericity`](https://pingouin-stats.org/build/html/generated/pingouin.sphericity.html#pingouin.sphericity) (not mentioned any further in this material)\n",
-    "\n",
-    "However, visual checks for the desired properties are often preferred."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96dc6c80-6816-4088-9ceb-1ddd58759a98",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Normality"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ecd70e2-0860-4dc4-9620-b34f2f0e8c31",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Having this property is usually not critical, because most tests are fairly robust to non-normality.\n",
-    "We only need to avoid cases of \"extreme non-normality\".\n",
-    "\n",
-    "Beware however that, in the case of residuals (prediction errors of a model), a departure from normality may be an indication of systematic errors in some groups."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "b24c3e20-6cbc-4f03-b189-780c609a3320",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(1245619531)\n",
-    "\n",
-    "x_normal = 2 * stats.norm.rvs(loc=0, scale=1, size=30) # generate 30 observations from the standard normal distribution\n",
-    "x_not_normal = stats.norm.rvs(loc=[-1,1], scale=[1,3], size=(15,2)).ravel() # generate 30 observations from a mixture of normal distributions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5fa2537-0d3b-4b1d-bba4-a479cdf2c0a9",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "#### Graphical approaches"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c977f26f-df05-4ead-a6cd-5ad9f318db0f",
-   "metadata": {},
-   "source": [
-    "Pingouin provides Q-Q plots:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "7b4317d7-248a-462f-a252-a815215f4072",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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l/E5IiOwvWFCmFtsTe3p7Q51S5+D1+nD2OABRxYoxrFVXwjNm9uhAFsBiGEkNcqdNERER+Pv7Ex4eTvbs2c1ujlLp25UrULSoJHzaty/JG75pLl2C2rXh4EEoVQrWro17JOoJgaze65RKm/R3Wyk3duoUVK4M4eGwZg3UqZPkxzZuBHueYW3YkOwpHHPpkpzowAEAogMf4ZsOPbmcJZtbB7L23u90ZFYpZY6cOWHHDrnpu1Mge/Wq1Fw7eFC2b96MW+/iCYGsUkoppUyQP7/0Hxo0uG8U+qBMwxaLvH93UqcUuXwZGjaMC2RvFyrEt+1edftA1hEazCqlXMswJEDMnFlGPd1JZCQ0bQp79sh2oUKwfj0EBmogq5RSSqnkZcwoJXiSSz18hy2pU5s28tGEAe2Dkjo5JDwcGjeGO+v6b+cvwPD2PbiUzT/NBLLgQQmghg4dytNPP022bNnIly8fLVu25MiRI2Y3SynlqClT4Kmn4K+/zG5JYjdvQvPmsF0CVvLmhXXroHhxDWSVUkopdS/DgH79YPx42X5AIGvzMEmd7HLtmjyc//13AGLy5GFk+x6c98+VpgJZ8KBgdtOmTfTu3ZvffvuN4OBgbt++zXPPPcf169fNbppSyl5790KfPjKVN0cOkxuTQHS0PCLdsEG2c+SA4GAoVUoDWaWUUkolbcwYeU2aJH0JByTMNDxnjvx54oQTAtkbN6BZM9i2DYCYnDkZ1aEnYbnypLlAFjxomvGqVasSbU+fPp18+fKxe/duatWqZVKrlFJ2i4iAtm0hKgoWL5bHj+4gJgY6dYIVK2Q7a1ZYtQrKldNAVimllFJJ+/VXePttWZK0bBn4+jp8Cm9vJyV5srl1C1q2hE2bALD6+zOm42uE5smfJgNZ8KBg9m7h4eEA5MqVK9nPREVFERUVFbcdERGR6u1SSiXBMCRV/bFj8NFHsobDHcTGQvfusHChbGfKJKn0q1bVQFYppZRSSdu/Hzp0kHWyy5bdO1/YDLZZZsHBAFizZuOHDj05na9gmg1kwYOmGScUGxtL//79qVGjBmXKlEn2c0OHDsXf3z/uFRgY6MJWKqXijBsHv/wijx8/+8zs1gjDgN69pbg5yBPVRYugdm0NZJVSSimVtAsX4IUXJGnkTz9BxYpmtwhu35bg+tdfAYjNnJnxHXtwsmBAmg5kwUOD2d69e/PXX38xb968+35u4MCBhIeHx73OnDnjohYqpRKpV09ec+aAjxtMCDEMePddmDBBtr294eefoVEjDWSVUkoplbwcOaRQ7DffQKtWZrdGlku9/LI8kAdiM2bkxw49OF64SJoPZMEDpxn36dOH5cuXs3nzZgIesObOz88PPz8/F7VMKZWs0qUlM7C7+OwzGDFC/m6xyOhsy5YayCqllFLq/nx9Ydo0s1shbMulfv5ZNn19mdzhVf5+pHi6CGTBg0ZmDcOgT58+LFq0iPXr11OsWDGzm6SUuh/DgL594Y8/zG5JYsOGweefx29PmgQdO2ogq5RSSqnkvfOOFIgFeRBuZxmeVBMbC716xS2XMjJkYFq7bhwqWjLdBLLgQSOzvXv3Zs6cOSxZsoRs2bJx7tw5APz9/cmUKZPJrVNK3WP4cPjhBzhzRrIXu4OxY+GDD+K3R4+GV1/VQFYppZRSyRszRmZ0lS0rAWTGjOa2x1bfdvJk2fT2Zmabrvz1aOl0FciCB43Mjh8/nvDwcOrUqUPBggXjXj/fGVZXSrmRrVvhww8lu9+dG63ppk2TGrc2X30F/fppIKuUUkqp5C1bBv37Q8GCkmDJHQLZd9+VB/SA4eXF7BdfZu/jZdJdIAseNDJrGIbZTVBK2ePiRWjfXqbfzJ8PefKY3SJZS9KjR/z2Rx/BwIEayCqllFIqebt3S5ZgW+k+syujGAZ8/HFc3g/DYmFeq87sfqJ8ugxkwYOCWaWUB4iNhZdegtBQ+O47qF7d7BbJE9WXXpK2Abz1FgwZooGsUkoppZJ39iw0awa3bsGSJQ6V4LFaISQEwsJkQLdmTSmc8NC++EJmlt2xoHkHdpatlG4DWdBgVinlTBcvSiDbogUMGGB2a6RweJs2krYeZHR25EiCt/yugaxSSimlkpc3r9STLVdO/rRTUJA8N//33/h9AQGSpqN164doz7BhMGhQ3OaiF9qyrULVdB3IggazSilnyp8fduyQ4NHsLH8hIRJUR0fLdqdOMGGCBrJKKaWUSp5hSB8mQwb48cd7+jP3G3UNCpJn6HevjgwNlf0LFqQwoB09OlECy2WNW7K5co10H8iCByWAUkq5sbCw+BI8WbKAv7+57fn9d3j+ebh5U7ZbtYIZMwjetlsDWaWUUkolzTCgZ0/49tv4oDaBoCAoWhTq1pVn5HXrynZQkAS5b711byBrOy1IHimr1cE2TZggB96xokEz1j9TRwPZOzSYVUo9nNu3oV07WR974IDZrYE//4RGjeDaNdlu3BjmziV4+x8ayCqllFIqeZ9/DlOmyBCqbWbXHbZR14TThyF+1PXLL+99LyHDkGqFISEOtGfaNHjjjbjN4DqNCX62fvoIZO1M/qvBrFLq4bz7LmzZAi++CE88YW5bjhyBhg3hyhXZrl0bFi4keOc+DWSVUkoplbxp0+Czz6B4cUke6ecX95Y9o66jR9t3mbAwO9szZw68+mrc5oZnG7CidqP0EciePStD33bQYFYplXI//QTffw9PPQUTJ5q7TvbECahfHy5ckO2qVWHZMoJ3/6WBrFJKKaWSt2YNvPYa5M4Nq1ZBvnyJ3g4JefCo6+XL9l2qYEE7PrRgAXTpEhcph1Srw9L6z1O29KNpP5AFmDkTVqyw66MazCqlUmbfPrnx58ghc2+yZDGvLaGhEsiGhsp2+fKwciXBew9pIKuUUkqp5B09KrPLfHyklmzJkvd8xN7R1Fy5kn+ub7FImdqaNR9wkqVLoWPHuMW1259+lqDnWqT9QPbUKYiKkr+/8w5MnWrXYRrMKqVS5tNPpfba7NlQooR57bhwARo0kJFZgNKlYc0agv86qoGsUkoppe6veHF45RWZ1vvMM0l+xK7RVGQqMtwb0Nq2R416QL3ZVaugbdu4koI7Kz7DL01ap+1A1mqVWX5PPglDh8q+DBnkAYMdNJhVSqXM7NmwcCE0bWpeGy5fljWyhw/LdvHisHYtwYdPaCCrlFJKqeTZatB7e8OYMVL5IBk1a0qt2AeNun70kcwQLlw48fsBAXaU5Vm3TtpwJ/HUH09VZt4L7SjzRMm0G8geOgS1aslTgEyZUpR7RYNZpZRjbHNtsmS5740/1UVEQJMmkr0Y5Ftk3TqCj/+rgaxSSimlkhcVBc89B199ZVfWXG/v+ARPDxp1bd0aTp6EDRtksHfDBpk8dt9ANiQEmjeXGW/AvifLM7tFx7QbyN6+Lf/ty5eHbdsk2dPBg1Idw0EazCql7LdsmYx+/vyzue24cQNeeAF27pTt/PllRPbMBQ1klVJKKZW82Fjo1k2izH377C4B07q1/aOu3t5Qp44sfa1T5wFTi3/7TWa53bgBwIFSZZnZ+mWefPKxtBnIggxEfPwx5M0ra4Rnz5a/p4CPk5umlEqrjh6Fl14CLy9zS/BERcmIsK1QW65cEsiev6qBrFJKKaXu74MPYO5cmTs8Y4b0a+zUujW0aCFdkLAwWUtbs+YDgtX72b0bGjeGyEgADpcszbQ2XdNmIHvrliwPK1QIKlWCefOgUSPw93+o02owq5R6sMhIuYNHRMicmbJlzWnH7dvQvr2k0AfInl2SPV25oYGsUkoppe5v1Cj47jtJNrRkCWTM6PApbKOuD+3PP2Wqc3g4AEeLP8bUdt14oszjaS+Q3bpVaubmyiVPAry9UzSlOCk6zVgpdX+GAT16wF9/Qf/+MmfGDFar1FxbskS2M2eGFSsIvmHVQFYp5XKbN2+mWbNmFCpUCIvFwuLFix94zMaNG6lYsSJ+fn48+uijTJ8+PdXbqZS6Y8sWePttmSe8ciXkzGleWw4dkkoMd4rT/lOkOJM7vEqpsqXTViAbGSnJnWrWlBl+1arFJ95yEg1mlVL3N2OGrJGtVQuGDTOnDbGxUtN23jzZ9vODpUsJjs2ggaxSyhTXr1+nXLlyjB071q7Pnzhxgueff566deuyd+9e+vfvT48ePVi9enUqt1QpBUD16vDhh1L+JjDQvHYcPQr168PFiwCcCijKxI6v8fhTT6StQDY4WGbyff+9lE3ctg2GD5c+nBPpNGOl1P21awd798LAgVL3y9UMQ0aEbcWzfXxg4UKCfbNpIKuUMk2TJk1o0qSJ3Z+fMGECxYoVY/jw4QCULl2aLVu2MHLkSBo1apTkMVFRUURFRcVtR0REPFyjlUqPrlyRUVgvr/g6pmY5cQLq1YurDPFvoUAmdH6Nx8o/mbYC2agomVYcFgaDBsH//uf0INZGR2aVUkmzTQPJnFnWmOTPb047PvpI6r+BfBHNmUNw9rwayCqlPMr27dtp0KBBon2NGjVi+/btyR4zdOhQ/P39416BZo4mKeWJTp2CMmXg88/NbgmcOSOB7L//AhCWvxDjX3qdkhXKpp1A9swZ+dPPTzIU794NgwenWiALGswqpZISGQnPPAMTJpjbji+/TPwUdepUggsU0UBWKeVxzp07R/67Hgrmz5+fiIgIbt68meQxAwcOJDw8PO51xtZRVEo92H//Sabgs2cl8ZCdrFbYuFESHm/cKNsPLSxMAtmTJwE4n7cA415+gxIVn0obgez585Kg88kn44J1ataEp55K9UvrNGOlVGKxsfDKK/I0rWZN89oxapTUILMZN47g4qU1kFVKpRt+fn74peKIhlJp1o0b0Lw5HD4s62T79Il7y2pNvrROUJDkK7LFYyB1ZEePTlxH1iEXLsga2WPHALiYKy/jurxBscrlPT+QNQwZgX3rLUlmVbOmVJ5wIQ1mlVKJffklLFwoN95vvzWnDRMnSsZBm+++I7hMJQ1klVIeq0CBApw/fz7RvvPnz5M9e3YyZcpkUquUSoNiYmSUcNs2ePll+OqruLfuF6wCtGkj8VlCoaGyf8GCFAS0//0nWYsPHZLNnLkZ1/VNijxd0fMD2TNn4PXXYcUKyJoVxo2DXr0cqtvrDBrMKqXiLVkiC/WLFZMMxj4m3CJ++klujjaffUZwlZoayCqlPFq1atVYsWJFon3BwcFUq1bNpBYplUbNmAHLl0OTJjBlClgsgASyyQWrL74IuXPf+x7IPotFclG2aBE/ivtAV69KHdn9+wG44p+TsV3eJLBqZc8PZAE++UQC2caN4ccf4ZFHTGmGrplVSokjR+CllyBLFglqc+d2fRuCgmSKs+3b5L33CK7bWANZpZTbiYyMZO/evezduxeQ0jt79+7l9OnTgKx37dKlS9znX3/9df755x/ef/99Dh8+zLhx45g/fz5vJ5yFopR6eN26yVDrL7/EVWGwWmVENrlgFWQQNTmGIQORISF2tiEiQoK8P/4AIDxbdsZ1eZOAalU8O5C9U04IgG++gVmzJKA1KZAFDWaVUjZFisgjy5kzpS6Yq61cCR06xGdaePNNgl94kRUbfgM0kFVKuZddu3ZRoUIFKlSoAMCAAQOoUKECgwYNAiAsLCwusAUoVqwYv/76K8HBwZQrV47hw4czefLkZMvyKKUctG+f/OnlBf36ycP5O0JCEk8tTqk7FXXu7/p1eP552LEDgGtZsjGuy5sUrPGM5wayhgHTp0OJEvKQAKTKxUsvxY18m0WnGSulRMaMMG2aOdfeuFEWotiSBrzyCsHtXtZAVinlturUqYOR1DDPHdOnT0/ymD179qRiq5RKp+bMgc6dJdfHu+/e87ZdQagdChZ8wAdu3pTEU1u2AHA9U2bGd3mD/DVreG4ge/EivPYaLF4MOXKYHrzeTUdmlUrvPvxQ6rjep1OWqrZvhxdegFu3ZLtdO4K7vsaKjfJEUwNZpZRSSiVrzRro2hVy5pR1skl4YBD6ABYLBAY+oMhDVBS0agXr1wNwM2MmJrz8Bnlq1/TcQHb5cqnTu3ixJAbdv19m8bkRDWaVSs9mzpQ1Dz/+GB9MutKePfLFc/26bDdrxtrX32LFpp2ABrJKKaWUuo+dO2VmV4YMEng9+WSSH6tZU7IWJzeoaLFIqhCL5d7P2LZHjbpP8qfoaGjbFlavBuCWX0YmvNSLnPXqeG4gu2kTNGsm639Hj5aHBgEBZrfqHhrMKpVebdsGPXvKk8zFi8HVpSEOHpQsf+Hhst2gAev6vcevm38HNJBVSiml1H0cPixrU2/dgvnzoXr1ZD/q7R1ffie5YHXiRCm/U7hw4vcDAh5QlicmBjp1gmXLAIjO4MvETq/h36C+Zwaytpl6tWrBO+/A7t2yBtnFJXfspWtmlUqPTp2Cli0hNlZqyj76qGuvf+yY1F27dEm2n32Wde99zPKtkvVPA1mllFJK3deMGdKPmDZNlis9QOvWEpQmVWd21Kj4YLVFC0kYFRYm05Nr1rzPiKzVKlOcFy4E4LZPBiZ17EHWRg09L5CNjobPP4fLl6VmrMUC331ndqseSINZpdKbyEhJTnDxIkyYAHXruvb6p0/LugtbNobKldnwv8Es374X0EBWKaWUUnb46ispf1O7tt2HtG794GDV2xvq1LHjZLGx0KOHJJ8CYrx9mNKhO5maNvG8QPbgQXj5ZSklVKKETC3Ont3sVtlFg1ml0htfX3j6abn59+rl2muHhUkgaytXUaYMGz/9iqU7paC4BrJKKaWUSlZkJCxaJIGXxeJQIGtjd7B6P4YBb74p5WoAq5cX09u9gu8LL3hWIBsbCz/8AB98INO1X3sNhg+HrFnNbpndNJhVKr3x9YVJk1yfvfjSJZlafOwYABEFH2NJj2H8sfsgYH8ga7U6MP1HKaWUUmmDLVvw2rVSQzbZRaypzDCgf39JnokEsjPbdMWrRQvPCmQNQ4aply+HfPmkfqwd07XdjQazSqUXP/0EFy7A228nna4vNV29KsmeDkrgepIi9An8jpJX/wagSH77AtmgoKTXuowebd53mlJKKaVSmdUKL70kgWzr1rJcygyGIaOY338PQKzFwuxWnTFat/asQBakH1ivHvj4SParvHnNblGKuGdaKqWUc23fDq++Cl98AefPu/bakZHQtKmU4QFCKUT/yiMp2fQfALatq8aAN6sQFHT/0wQFSWmzhIEsQGio7H/Q8UoppZTyQIYBvXtL9qa6dWH2bAnAzPDZZ/Dtt3Gb85p3IKZtO88JZC9fhnffhZs3Zbt/f+lAeWggCxrMKpX2nT4dn7l4wQIoUMB11755U56ebt8OwAXy0r/SSIq9IGtmt66rxs4QGZHt318evCbFapUR2aRmRtv23e94pZRSSnmojz+WKb2VKkkpwYwZzWnHV19Jtt875r/QllsdO3lOIBscDGXLyprYiRNln6tn6qUCDWaVSstsmYsvXIAxY2Q6iatER8uQ6YYNAFwhB29XGEFAM8livHVdNX6/E8gaBpw5I2thkxIScu+IbEIPOl4ppZRSHsgw4L//4PHHYeVK8zLsDh8OH30UtxnUuBWRL3XxjEA2OlqWmD33nOQvGTYM+vQxu1VOo2tmlUqrrFbo2BH27ZOb1uuvu+7atgLiK1YAcDtjVgY8Ppx8Lf4DEgeyCdmq9di7P6WfU0oppZQHsFhg/HjJvZEzpzltGDtWpubesbRhc66+0t0zAtnTp6FdO9ixA8qUkSnaTz1ldqucSkdmlUqrDAMCA6FJExg50nXXjY2F7t3jCoiTKROr+n9LjlbXgOQDWZDsxI7sT+nnlFJKKeXGfvkFvv5a/m6xmBfITpqUaBRzRd2mXHq1p2cEsgDnzknt2G7dJKBNY4Es6MisUmmXj488Tbx923WJEmxJGmbNku0MGfhjyNesj4wCJNlTUoGsxSJZiWvWTPq0NWvK+6GhSa+bfdDxSimllPIQq1ZB586QOTN06QKFCpnTjpkzoVevuM01NRtyrtcb7h/IWq0ynTh/fqhSBf78E0qVMrtVqUZHZpVKa1avhqFDJeqzWKSurCsYhkzDmTBBtr292fvZl8yKlOizSP5q/L6lyj15Bmzbo0YlXy/W21vK7yT8vCPHK6WUUsoDbN0qpXd8fODXX80LZH/+WUYz7zxB31CtLv++2df9A9lz56BhQ1kfa8tYnIYDWdBgVqm05cABWRvx+edw/Lhrr/3ZZzBihPzdYmH//z5lxm0JpJvWq0b/16uwYAEULpz4sIAASbL8oDqxrVvzUMcrpZRSyo3t2wfPPy95N4KCoEYNc9qxaJGMDMfGAhBSpSYn+r1Nl7ZN3TuQ3bQJKlSQxJuPPSb/HdMBnWasVFpx/rx8CUREyFqTRx913bWHDUuUrv7gewOZ6i0ZB5vWq0bDmjK1uHVraNFCsg6Hhcka15o17R9RfdjjlVJKKeWGQkOhUSPpw8ybB40bm9OOX3+F9u3jav1tr1iNo/3fde9ANjZW+mEffRQ/la1vX48vuWMvDWaVSgtu3pRasqdOSR20Nm1cd+2xY+GDD+I2D/d9m0mZ8wGJA1kbb2+oUyfll3vY4x1im6qtlFJKqdRTsKA8sa5QQWaYmWHNGnjxRck1Avxe7mkOvfshXdo9776BLEiukgkTJOnnL79A1apmt8ilHnqasdVqZe/evVy5csUZ7VFKOSo2VtZ1/PYbvPIKfPih6649bVqiLH9He77Bj7mLAEkHsh7jwgX5Mp071+yWKKUcoH0SpTyMbV2nlxeMGwc9e5rTjo0bZVAgShJW/lGmAn998JH7B7IgFSRatIA9e9JdIAspCGb79+/PlClTAPnSqF27NhUrViQwMJCNGzc6u31KqQcxDHmiWbs2/Pij60YSf/4ZevSI2/zn5VcYV/hxwIMDWcOQ6U1PPilPN23lhZRSbkn7JEp5sMuXJdvul18mXarAVbZtgxdeiAus/yz1FPs+HMTL7V5wz0DWMKT27t9/y/bTT8PixZA7t6nNMovDweyCBQsoV64cAMuWLePEiRMcPnyYt99+m48++sjpDVRKPYC3t9SRXb3adZmLly6Fl16KS45wqk17xhSX+4LHBrK3bskUp44d5Qtt7FgJaJVSbkv7JEp5qGvXZF3sX3/Bf/+Z147ff4cmTeD6dQAOlHyCPz7+jJc7NHPPQPbaNemnvPlmoplx6ZnDweylS5coUKAAACtWrKBt27Y89thjdO/enf379zu9gUqpZKxeDYMHxz/N9PNzzXWDg6Ft27gsef8+34JRTz4DFguli1ajXnUPDGRB/vt5e0O9erB/v3xReGnCd6XcmfZJlPJAN29Cs2YSSPboAcOHm5OfYu9eKWETEQHAkeKPs+uTIbzUobl7BrJHj8o04p9/hlq1YMYMs1vkFhzuqeXPn5+DBw9itVpZtWoVDRs2BODGjRt4u+P/eKXSon37JKD85hs4dsx11w0JkXUZ0dEAHH66ESMq1QaLha3rqtHrlSoULSoZ9T1CaKh8iYJ8kc6YAWvXQrFi5rZLKWUX7ZMo5WGioyVJ5aZN0KGDJC4yI5D96y9o0ACuXgXgaNFH+e3TL+jcqYV7BrKrV8uU7EOH4N13Yd06WWKmHA9mu3XrRrt27ShTpgwWi4UGDRoAsGPHDkql8aK8SrmF0FApwRMZCbNnQ8mSrrnu77/Lde+sKTlWvg6TGj+H4eXF1nXV+D2kSlzz2rRx84DWMCR51ZNPypeCbW1dliyavVgpD6J9EqU8zNSpsGKFjMzOnGlObb3DhyWQvTO9+URgMbZ/9hWdO7dyz0A2OhreeEOWQ/30E3z7LfhoQRobh/9LfPbZZ5QpU4YzZ87Qtm1b/O5MbfT29uZDV2ZRVSo9unZNAsrQUBgxAlq1cs11//xT6r9duwbApSrVmPDcC8R6eycKZCG+mk3//jKI63bfC2fOwGuvwapVkD07TJkiybOUUh5H+yRKeZjXXpPSNz16QIYMrr/+sWNQvz6cPw/AqUKPsGXwUDq91No9A1mQfChBQVL7tlIls1vjdiyGkfL0Ybdu3SJjxozObE+qioiIwN/fn/DwcLJnz252c5RyTEwMNG8OK1fKov/vv3fNKOKRI7I248IFAC6Xr8jXTTtw29f3nkD2bhs2uLAmrD1mzZJ6bNeuQdOmkv05IMDsVjmd3utUeuRpfZKU0N9t5ZEMQ6bF3pk5YZpTp6Q/c/o0AP8WKMzGL76jwytt3S+QPXNGyi6OHQuPP252a0xh7/3O4WnGVquVIUOGULhwYbJmzco///wDwCeffBKXHl8plQq8vKBsWUkfP2qUawLZEyfkCeadQPbqE08yrEk7uwJZgLCw1G+iQ27elKHiGTNg+fI0GcgqlZ5on0QpN2cY8M470LChzIRyEatVVhDNnSt/Wk/9Kwke7wSyYXkLsOnzb9wzkN2yBSpXlgcA8+eb3Rq353Aw++WXXzJ9+nSGDRuGb4IyIGXKlGHy5MlObZxSKgEvL0n4tGiRa+buhoZKIBsaCkBEyccY1qwzUX4ZKV30wYEsuEFugthYWZ9zJ1MhPXvKSHOXLro2Vqk0wOw+ydixYylatCgZM2akatWq7Ny5M9nPTp8+HYvFkuiV1keSlWLQICkfWKYMtGzpkksGBUHRolC3LnTqBB3rhnHy0fpw52HX+dz52DBkGO27d3C/QHbSJAm6L1+WWrKffGJ2i9yew8HszJkzmThxIp07d06UKbBcuXIcPnzYqY272+bNm2nWrBmFChXCYrGwePHiVL2eUm5h1iwYODCupqtLFv1fuCDTgU6cACCySFGGtezCzUyZaVqvGq++VIWAgOTjQYsFAgOhZs3Ub2qy/vlHgvFXX5UvU1vD8uUzsVFKKWcys0/y888/M2DAAD799FP++OMPypUrR6NGjbhwZyZLUrJnz05YWFjc69SpU6naRqVM9fXX8MUX8NhjUikgd+5Uv2RQkCSh/Pdf2c7DRdbSgBIxfwNwMWceNgwZRrsendwrkI2OlpKAr70GOXLIqOzrr5vdKo/gcDAbGhrKo48+es/+2NhYbt++7ZRGJef69euUK1eOsWPHpup1lHIba9dC9+4wcSKcPeuaa16+LNOB7nQEI/IW5tsXu3E9S1aa1KlGw5pV8PaG0aPl43cHtLbtUaNMSv4UGyvricuWlblFrVvLwwClVJpjZp9kxIgR9OzZk27duvHEE08wYcIEMmfOzNSpU5M9xmKxUKBAgbhX/vz5U7WNSpnm++/lu7dYMQnMXPBv3WqFt96Smc0AOblMMA15koMAXPbPyaD639Dq1ZfcK5AFqVCxahVUqAC7dsnaXmUXh4PZJ554gpCQkHv2L1iwgAoVKjilUclp0qQJX3zxBa1clcFVKTP9+acEYj4+sGyZa9Z3RkRAkyZybeBCxvyM6tSdiOz+bF0nI7K2kjutW8OCBVC4cOJTBATI/tatU7+59/jnH8k49dZbkDmzFBZfsMAlX6JKKdczq08SHR3N7t2740oBAXh5edGgQQO2b9+e7HGRkZEUKVKEwMBAWrRowYEDB+57naioKCIiIhK9lHJ7hiH9iMKFJZB1UX6KkJD4EdnshLOaRpRnHwBXs/nz/rPDmLioK9u3uVEgGxUlf+bKBcHBsl72kUfMbZOHcXi+4qBBg+jatSuhoaHExsYSFBTEkSNHmDlzJsuXL0+NNqZYVFQUUbZ/JKBfAspznDkj2XYjIyUYq1499a9544bUfbuz5uu/DLmY3ONVruTMHZfsyWKR6Tu2YLV1aym/ExIiyZ4KFpSpxaY98IyOlva3bw9jxkDevCY1RCnlCmb1SS5duoTVar1nZDV//vzJTm9+/PHHmTp1Kk899RTh4eF89913VK9enQMHDhCQTGd/6NChDB482OntVypVWSwyo+zcOShUyGWXtSWdzMo1VtKEp9kFQETW7HxQ8xumrepGrNXbfZJT/vor9OolI7JlykCJEma3yCM5PDLbokULli1bxtq1a8mSJQuDBg3i0KFDLFu2jIYNG6ZGG1Ns6NCh+Pv7x70CAwPNbpJSDxYZKYFsaKgkTXDFEGdUlNSs3bwZgIgM2ZncoycX8+RLlLXYNnWnf3+ZzgMSuNapAx07yp8uD2SPHIE9e+TvpUrBX3/BvHkayCqVDnhSn6RatWp06dKF8uXLU7t2bYKCgsibNy8//vhjsscMHDiQ8PDwuNeZM2dc2GKlHDR3Lnz3nfzdy8ulgSzIA/XMXGc5L1AdmSERmTkLHz47lMmrexBr9Y77nKkMQ6ZhN28OV67EZVhWKZOiTDI1a9YkODjY2W1xuoEDBzJgwIC47YiICA1olfvLkkUy/jVsKNNlU9vt2zKSuWYNANGZsjDp5Z6cy18wyfI7hiEDxyEhJteQjYmBESMkuVPRorB/vxRgT2L9nFIq7TKjT5InTx68vb05f/58ov3nz5+nQIECdp0jQ4YMVKhQgWPHjiX7GT8/P/z8/B6qrUq5xMKF8PLLkD27VAwwIdlizco3WeXXgppR8mD+esbMDHx2KD8G9yLW6o3FIjOeTU1OGRMjfbtx4yTYX7YMKlY0sUGez+GRWU/i5+dH9uzZE72Uclu2YU+LBYYMgeHDU/+aVit07QpLlshmxoxM6Pgq/xYKfGAdWVOn6Rw4IFOvP/hA1pkMGyaBrFJKuYCvry+VKlVi3bp1cftiY2NZt24d1apVs+scVquV/fv3U9D0YSKlHtKyZdChgzyMX7PGnKoBUVF4t29DzSj5nbzpl5GPa37BuHVvxgWyYGJySoDwcHjhBQlkK1SQZVEayD40u0Zmc+bMicXOmoyXL19+qAYplS4Zhszd9fGBb7+V6TmpXQc1NlbWasydC4DV15cf23XnxCPFHxjIgknTdGJj5Zto4EBZH/vKKzI6mzOnCY1RSpnBXfokAwYMoGvXrlSuXJkqVaowatQorl+/Trdu3QDo0qULhQsXZujQoQB8/vnnPPPMMzz66KNcvXqVb7/9llOnTtGjR49Ua6NSqW71akmm4ecnaz8rV3Z9G2wzzFasAOCWrx9f1B/C96v7xU0tDgiQ7oMpySltYmIkUWXz5jB7NmTNamJj0g67gtlRo0alcjPsExkZmWg6zokTJ9i7dy+5cuXiEc38pTzZd9/J+omnnpJETKl9gzMMePttmDIFgFhvb6a+2JWjxR+jSZ1qLJgsyZ5sg8UJmTpNJzoapk6V0dgpU2RtsVIqXXGXPkn79u25ePEigwYN4ty5c5QvX55Vq1bFJYU6ffo0Xl7xE+CuXLlCz549OXfuHDlz5qRSpUps27aNJ554wqwfQamHs3+/LIvy9pZkRnbOSnAWqxW2bIyh2EedeWSHzDCL9snAuoGfMeh/b9Nwm7d7JKeMjJR+Xe7ckpskb14TG5P2WAwjqe6qe9q4cSN169a9Z3/Xrl2ZPn36A4+PiIjA39+f8PBwnXKs3MdPP8k6k0cege3bXZMw4X//gzujBbEWL2a9+DJ7y1SgaT2pI2srOg6JA1rbYIjLS+8cOxa/FvbvvyWYzZPHhQ3wLHqvUypt0t9t5VasVujTRzoELk64FhQEb/ez8kXoK7zMTwDc9vZhfs9Paf/DQPepIztvHvTtC2vXQrlyZrfGo9h7v7NrZDYiIiLuJA8qb5OaN9c6dergQbG3Ug8WHAzdusk02VWrXBPIfvllXCALMK9FB/aWqcBfv1ejZlmZWmyrIfvWW/E128CEaToREdCvn0yF3rlTvggee8xFF1dKuSN36ZMolW7Zhju9vWH8eJdfPigI2r4Yy4/0igtkY7y8+bLGR3wxaSCZGnqbO50YZCTgiy8kSaW/v2QtVqnCrpFZb29vwsLCyJcvH15eXkmuVTEMA4vFgtVWr8MN6RNN5VZOnZK6YjExUlTcFbVkR42S6cV3LGjahq1VnmXrumrs2iKBbMJRV6vVxBqyW7fKiPWJE1C1qoxga6Ziu+i9TqVlaaVPkhL6u61Mt20bNGoEn3+eqD/hKlYrFC1i8GFoH3ozTvZZvBhaYyCfbh+MEetNQIB0HUwbnL19W3KSTJsGxYvLFOxSpUxqjOdy6sjs+vXryZUrFwAbNmxwTguVSu8eeQTef1/WyboikJ00KdEXz5KGzeMCWVuyJ4tF8lC1aCFfArYasi51+7Z8SX71lWx/+il89JFmK1ZKAdonUco0O3ZA48Zw6xaUKGFKE0I2G7wd+k5cIBtrsfBttff4dPvguGRPppYPjIiAtm0lq/Mzz8DSpVr3PpXZFczWrl077u/FihUjMDDwniehhmFoMW+l7BEZKenrLRb45BPXXPOnn+Qp4R2rajdiY41692QtdosasiNGyNSc4sWl3S5OKKGUcm/aJ1HKBLt2yYjsjRswf75k5HU1wyDvqI8YwEgAYrEwssrbfLTjy7hA1sa08oFWq3SkWraUjMWZM5vUkPTD4TqzxYoV4+LFi/fsv3z5MsWKFXNKo5RKs8LDZa5uv35SZsYVgoKkhM2dFQXrq9dldZ3G9y2/4/IvAcOIzzTVt6+U3tm7VwNZpdR9aZ9EKRfYs0cSPEVGSg4LsxakDhnCk0vjc3788HQf3t817J5AFkwoH3jrlvyZMyds3ChrtjSQdQmHg1nbOpS7RUZGkjFjRqc0Sqk06dYteVK3d69sp3YdWcC6fCWx7TvIk0JgS+UaLGvYnK3rq9+3jqxLvwQuXpT/LmPHynbmzDLFOFs2FzZCKeWJtE+ilAtMnSrTZ2fNkim0ZvjmG1l2dMf4iq/z9h8j7wlkLRYIDHRx+cD162Xa9a5dsp0vn5becSG7phmDFAcHsFgsfPLJJ2RO8LTBarWyY8cOypcv7/QGKpUmWK3w0kvytK5dOxg9OtWD2c2fb6TKp63JyG0AdpZ7mqCmL7Lnt+pxyZ7u5vIasitXSjbn8+dlu3dvlwT5SinPpn0SpVxo1Cjo0AFq1DDn+qNHw4cfxm2uafEqfZf/gBF7byAL0lyXxZI//QTdu8vFz5yBypVddGFlY3cwu2fPHkCegu7fvx9fX9+493x9fSlXrhzvvvuu81uolKczDKnDtnAh1K8PM2eCl8OTIpLNLJzU/pBh26n86QtkRKa97H2iHD8378DWDTXYmSDZU1I1ZF3yJXDjhiS/GjsW/Pzkon37aiCrlLKL9kmUSmX79knm4jfekE6BWYHs+PGSmfKOXR27UG/Wj8xf4m1u+UDDgK+/hv/9T6YWL1ni4uFgZWNXaZ6EunXrxujRoz0yJbymtFemWLkSmjaFihVhwwZIwb+9oKCka7527CjLVxLufy7vHuZfqou/EQ7AgZJPMK19dzZvqsnvIVWwWCBXLsiUKfFxgYEu+hKIiJDszQcOQNmykiChbNlUvmj6ovc6lV54cp8kJfR3W7nEvn1Qrx5cvSrf1WaVlZk6FV59NW5zz4sdKfvzLHzuPHE3rXxgTIwMUvz4IxQtKv08Lb3jdPbe7xwOZj2ZfgkoUxiGPFls00bWUTgoKEgOtec3tTQH2URt8nIJgL+LPcakTj3ZtLnWPWtk166Vm74pNWR79pSMzl9/Dbquzen0XqdU2qS/2yrV7dsns8guX4YZM6Teuxlmz8Z4+WUsdzo/+1q04ckFc/HxsXtSaeoJD4dnn5WZZcuXQ4ECZrcoTXJqndmErl+/ztdff826deu4cOECsXdlZP3nn38cb61SadG+fVJD1mKBN99M0SmsVhmRtSeQLcEx1tIgLpD9J7AYUzq+mmQgC3DhgozsusTt2/Dzz9C5s/z3+PHHFE21VkqphLRPopQT/flnfCA7fbp5gewvv2B06RIXyP7VtIV7BLKGIX0Yf3+pI5stG2TNam6blOPBbI8ePdi0aRMvv/wyBQsWTDKLoFLp3sqVUoNtwADJwJdCISGJpwInJ5DTrKM+hZCaOmcKBjKp82tsCKmTbNZil2UsDguD9u3lh4mJkTJBGsgqpZxA+yRKOcmJEzK1+PJlmDYNunQxpx1LlmB06oTlzoOpQw2bUGrJAvMD2X//hRdflGRUzzxjQu0flRyH/2WsXLmSX3/9lRpmLQRXyt1t2yY3PF9fKTnzEOyp95qfc6ylAUU4DcDZfAWZ8PLrrNtaL8lA1qUZizdvluzN58/LMHCbNi64qFIqvdA+iVJO8sgjkt+jXj3o2tWcNqxcidG2LZaYGACO1G1IyV+XmB/IHjoEjRpJtuJVqySYVW7D4X8dOXPmJFeuXKnRFqU8359/wvPPy7TaZcugWrWHOt2DHvzl5hJracBjHAXgQu68THj5DYK3N0g2kAUXZCw2DBg+XFLpe3nBmDFadkcp5XTaJ1HqIV26BHnySKdgxgzzvqfXrSO2ZUu8bks5wWPP1qHEquX4ZMhgTntstm+HF16QEevvvoN33jG3PeoeDs/1GzJkCIMGDeLGjRup0R6lPNexY/Dcc5IYYOZMaNw4RaexWqUc7dy58veAgKS/W/y5ymoaUYYDAFz2z8n4Lm+yamcjdm2pQu7ccmxCAQGwYIELMhavXw/vvSdJETZtkqx/GsgqpZxM+yRKPYRdu+Cxx+CHH2TbrO/pzZuJfeEFvKKjATjxTA2Krl2FT4KSW6ZYvlzWEEdEwKxZGsi6KYdHZocPH87x48fJnz8/RYsWJcNdT0z++OMPpzVOKY8SFCTTacePtyuzUlIp5ZcsubcET+7c8TkHbImgshDJCppSCfl9u5rNn3Fde/Prrqbs2iIjshMnQosWJqWtr19f1pV06JCiDM5KKWUP7ZMolUK//w4NG8K1azIya5bt27E2bYr3rVsAnKpclcANa/Hx8zOvTQCxsTB4sMwuW7YsxQMUKvU5HMy2fMg1gEqlWe+/L9GiHVOLk6obmzs3/PffvZ+9fFn+zJVL3s/ITZbSnOpsByAycxbGd3mDpXua8XtIlXvqxdap83A/lt1mz4Z162DKFIm8+/Vz0YWVUumV9kmUSoEdO2Qm2fXrMg2sXTtz2rFrF9bnGuF9/ToAZ8pXovCm9fi4Q8k+Ly9YuhRCQ6FyZbNbo+5D68wq9TCuXZMpxW++aff0HEfqxtpYLFC4MMyYFM3jA1tReO8KAG5kzMTYrr3xr9aWbL5VXF8vFiRD8bvvykisvz/88QcUL+7CBqi76b1OqbRJf7fVQ9u+XUYZb9yQQNasxIz79mGtXRvv8HAAzpYpR77tW/Axs9SNYcAnn8h/n2efNa8dCkjFOrNKqTtu3ZJsxevXSxD30ksPPMSRurEJGQaE/RtD2aGdyHsnkL3l68ePL/WifOe2NKyZdPmdVHflipTdCQ6WmrqLFmkgq5RSSrmradMkkP35Zxck0EjGwYPE1K2Hz51A9lypJ8m3dbO5gWxMDLz+uswu27ABtmzRXB8ewuFg1mq1MnLkSObPn8/p06eJvrNY2+aybU6kUmnZ7duyHnT9enmqaccaWbC/buzdLMQyle7k3bwQgGifDEzu1JMyXTqaF8gePgzNmkniq1atZIRai4crpVxI+yRKOWjcOOje3bzyMn//TUydOvhckd/NCyUfJ8/2LfiYOdPg1i3o3FmmzlWtKtOLNZD1GA5nMx48eDAjRoygffv2hIeHM2DAAFq3bo2XlxefffZZKjRRKTcTGwvdukm2pkaN4Kef7J7Xa0/d2HsZjKU3XZgFQIyXN9Pad+fx7i+bF8iCrLk5dkym5CxYoIGsUsrltE+ilB1Wr4YJE+TvPj7mBbInTnC7dh18Ll4E4FLxR8m1fSs+OXIke0jCCg8bN8q2U127JiUVg4IkIdbatZLERHkOw0HFixc3li9fbhiGYWTNmtU4duyYYRiGMXr0aKNjx46Ons6lwsPDDcAIDw83uynKU8XGGsbrrxsGGEbNmoZx/bpDh2/YIIfa/4o1vmNA3I4Yi5cxpX13Y83mHanz8z1IbKxhREXFb+/ZY0471H3pvU6lF57cJ0kJ/d1WDlu2zDB8fQ0jc2bDOHvWvHacOmVEFS4c15/575Gixu3z5+97yMKFhhEQkLhfFBAg+52me3c5cZs2hnHrlhNPrB6Wvfc7h0dmz507R9myZQHImjUr4Xfmu7/wwgv8+uuvzouylXJXuXJBxYqSqj1zZocOrVkz+bqxSfmMz3iHEQDEYmFOq84E9u5lzohsVJSMSHfqJKPTAOXLu74dSil1h/ZJlLqPRYtkXWyGDLBihdToM8PZs0TXrIVvaCgAVwICyf7bNnzuU7rPlizz7qVZoaGyPyjISW0bOhQGDYJ588DsckAqRRwOZgMCAgi7M1eyRIkSrFmzBoDff/8dP/1HoNI6iwW+/FISA/j7O3y4t7ck/bWd6u5TQ/zslvcYxqd8Hvf+/GbtKNC/jzmB7LlzULcuzJgh3yTXrrm+DUopdRftkyiVjPnzoW1byJhRphnXrm1OO86fJ6pmLXxPnwIgvEAhsm3fhs99Auv7Jcu07evf/yGmHB89Clu3yt/z5ZN6si4tA6GcyeFgtlWrVqxbtw6Avn378sknn1CyZEm6dOlC9+7dnd5ApdzCDz/Ae+/F30UzZbL70LvXe7RoIUtMCxdO/LmAAFi4EM6fhyP9xjKMD+LeW9S4FbnfG2BOIPvHH/D005LOv0sXyfKXgkBeKaWcTfskSiUhJEQSU2bNKtUGatQwpx2XLnGrVm38/jkOwLV8+cny2zZ8AgLue9iDkmUaBpw5I59z2J9/yjS5pk3hwoUUnEC5G4ezGX/99ddxf2/fvj2PPPII27dvp2TJkjRr1sypjVPKLUydCn37QqFCUk81f367Dw0KkqeLCW/KAQEyOnvypNyIw8JIXB922jQe+75P3Od/rfc8Wf/3oTmB7OLFMq04Kgq++w4GDNAMf0opt6F9EqWSUK2aZCx+/XWoVMmcNly5wq3adcj49xEAInPnIdO2rfgUKfLAQ+1NlulwUs0dO6BJE7h6VRJi3Weas/IcD11ntlq1alSrVs0ZbVHK/cybBz16QN68kuHOwUC2TZt7p8nY1nssWJBEibeff5br3RH8bAN8PxtkXtbiyEjJfLhggTzFVEopN6Z9EpWu7dsH5crJ9/akSea1IyKCm3XqkungAQCu58xFxq1b8ClRwq7D7V3a69AS4A0bpJzgrVswe7bdJRWV+7MYRlIz0pM3c+bM+77fpUuXh2pQaoqIiMDf35/w8HCym1nPSnmGJUvgxRchWza5CTqQ7MhqhaJFk58mY7HICO2JEwmWaSxbJtFtTAwAm6vWImrYMBrWqvpQP4bDYmKkjq5tKvXFixLMK4+h9zqVXpjdJxk7dizffvst586do1y5cowZM4YqVZJ/+PjLL7/wySefcPLkSUqWLMk333xDUwceFOrvtkrWV1/BRx/BrFnw0kvmtSMykht16pJ59y4AbvrnIMPWLfg8+aTdp7D1oUJDk143m2Qf6n42bJARWYBffpGgVrk9u+93jqZJzpEjR6JXlixZDIvFYvj5+Rk5c+ZMYfJl19CU9spuBw9KKvusWQ3jt98cPtzeEjwbNtw5IDhYrnfnjW0VnzHWbHL8ug8tIsIwmjY1jObNDSMmxvXXV06h9zqVXpjZJ5k3b57h6+trTJ061Thw4IDRs2dPI0eOHMb5ZMqNbN261fD29jaGDRtmHDx40Pj444+NDBkyGPv377f7mvq7re4RG2sYH30k/YdixQzjxAnz2nL9uhFZ9Zm4vszNbNmM2yks4bdwoWFYLPJK2G+y7XOoPM+FC4bx9NOGsW5ditqizJFqpXmuXLmS6BUZGcmRI0d49tlnmTt3bkqDb6XcS6lSMHAgLF8OVe0fGbUle1q40L7Ph4UhmZFbtIDoaAB2l61E5PARrh+RDQ2FWrUkfb/VKutklVLKjZnZJxkxYgQ9e/akW7duPPHEE0yYMIHMmTMzderUJD8/evRoGjduzHvvvUfp0qUZMmQIFStW5IcffkjVdqo0zDAkl8WXX8Ljj0sijqJFzWnLrVtENmpMlh2/ARCVJQs+69fjk8ISfq1bJ58sM8llWkk5f17+zJtX1svWq5eitij35nAwm5SSJUvy9ddf89ZbbznjdEqZ58wZ+XKwWOCzzxxKZR8UJN8hdetK8mN7PBaxS9ai3rgBwJ+lynJl9Pc0rOPiNV/79knQvncv9O4tiZ8crKGrlFLuwBV9kujoaHbv3k2DBg3i9nl5edGgQQO2b9+e5DHbt29P9HmARo0aJft5gKioKCIiIhK9lAKk3vsbb8CoUVC2LGzadG/k5yrR0Vxr+jxZt0h64ehMmfAODsancuWHOm3r1pIsc8MGmDNH/jxxws5AdtQoKFkSfv9dtjV5ZZrllGAWwMfHh7NnzzrrdEq53h9/yBfCBx88+LN3Sa64d3IsFmiQfz8V/9cormbroRKluPDDOBrUre7w9R/KypXw7LNw9iyMHAljxkjyCKWU8lCp3Se5dOkSVquV/HclBcyfPz/nzp1L8phz58459HmAoUOH4u/vH/cKDAx8+MartMEw4L//JFvxhg0OJah0qtu3iWjWgmwb1sumX0a8Vq3Cx0mJ2Ly9oU4dyddUp46da2S/+greflvKCGopwTTP4R7r0qVLE20bhkFYWBg//PADNcyqY6XUw9q/H557TgJLB9PY36+4d1IsFnjMOMKyWw2whF8G4FiREpwd/yMN6j/raMsfXni4ND4oCFq2dP31lVIqhdJ6n2TgwIEMGDAgbjsiIkID2vQuNha8vCSqmz1bsvOalQzMaiW81Yv4r1kFQEwGX7x+XY53rVrmtMcw4JNPZNp1sWKwbp38qdI0h4PZlnd1di0WC3nz5qVevXoMHz7cWe1SynUOH4YGDeDyZZg+Hdq3d+jwBxX3vlu1AidYG1WfjJelWPfJwkU4/eMk6jd04c3fMGRNbMaM0KGDzI0266muUkqlkFl9kjx58uDt7c1525q8O86fP0+BAgWSPKZAgQIOfR7Az88PPz+/h2+wShsiI+Whc/v20LMn+PrKywyxsVxt254cvy4DwOrjg2XJYrzr1zenPbb1w6NGwWOPSSAbEGBOW5RLOTzNODY2NtHLarVy7tw55syZQ0GHCj4p5QaOHZOEABcuSAHtFJRxsLdod58+sHV+KFsy1ifT5VAAQvMX4sTEydRrVNfh66bY7dvw6quy6OT2bdmngaxSygOZ1Sfx9fWlUqVKrFu3LlFb1q1bl2yd22rVqiX6PEBwcLDWxVX2uXIFGjaUIG3tWvung6UGw+BKp5fIsUiyXVq9pR69t638jRmuXYM1a2S52ObNGsimI7owTqVv8+dLNPr99/Daayk6hb39pQ71LlD9fw0kewFwPk8+jk6cQt2mDR5wpBNFRkK7drJOtmpV2c6Z03XXV0qpNGLAgAF07dqVypUrU6VKFUaNGsX169fp1q0bIDVuCxcuzNChQwF46623qF27NsOHD+f5559n3rx57Nq1i4kTJ5r5YyhPcP68LIX680/o1g0mTjQvoZFhcLlrN3L9LNnCY728YN5cvFu0MKc9NtmzS5Dv6wu5c5vbFuVSDgezCdduPMiIESMcPb1SrjVwoJSjeTbla1Vr1pQHgPcr7v1kwctU/6yhTGkGLuXMzZEfp1CneeMUX9dhFy7A88/Drl3wwgvw88+asVgp5dHM7JO0b9+eixcvMmjQIM6dO0f58uVZtWpVXJKn06dP4+UVPwGuevXqzJkzh48//pj//e9/lCxZksWLF1OmTBmntkulMadPy4js339Lgo4RI2TNrBkMg/969iL3rBkAxFq8MGbNwrtNG3Pac/s29OolgxHPPGP/6IJKUyyG4dg8hbp167Jnzx5u377N448/DsDff/+Nt7c3FStWjD+xxcL69eud29qHFBERgb+/P+Hh4WQ3a7G8Ml9oKMybJ2srnPRk05bNGBIHtBYLZDMiOFGyIbmO7gTgSvYc/DVpGjXbtXTKte1y/Dg0bizTqnv2hHHjNGNxGqb3OpVeeHKfJCX0dzsd6t8fRo+GQYOkZKCJJWYu9e5LnnFSe9CwWIidOhXvV14xpzFRUbJ2eMkSWTa1cKE57VCpxt77ncO92WbNmpEtWzZmzJhBzjvTE69cuUK3bt2oWbMm77zzTspbrVRqCwuTNbJ//w1PPSVPO53AVtz7rbcSJ4N6tNANtmV/gVyHJJCNyJKNg+MnuTaQBZmadPw4DB4smf603ppSKg3QPolK84YNkylgL75oajMuvP0O+e4EsgCx48ebF8jeuiX/PVasgEaN4KefzGmHcgsOj8wWLlyYNWvW8OSTTyba/9dff/Hcc8+5da1ZfaKZzp0/L1l7Dx2Cjz+GIUOcfgmrVbIbh4VBodxR1PquOZbgNQBcz5SZP8dNotornZx+3WQZRnzgevAgPPGE666tTKP3OpVeeHKfJCX0dzud2LhRZlL16GF2SwA4/+H/yP/N0Lht6+jRePfr59RrJOw/FSwo8XuSNWVv3JCMzsHBsmTql1+kMoNKc1JtZDYiIoKLFy/es//ixYtcu3bN0dMp5RqXLkn5nUOH4IMP4PPPU+UytuLe3L4NbdvDnUD2pl9G/ho9zrWB7Pz5MGuWTL3x9dVAVimV5mifRKU5S5bI9FkvL8lzYfI60HODPqNAwkB22DCnB7JBQffObAsIkNnVrVvf9eEBAySQbdVKloyZVZpIuQ2Hg9lWrVrRrVs3hg8fTpUqVQDYsWMH7733Hq3v+RenlBuIjJRA9q+/5CY4dGjqTrO1WqFrV/lCAqIy+LJ/xPdU7dk19a55t3HjpBZQ9uwypVoTjCil0iDtk6g0Zfp0GY3NlAmWLjU9kA374isKDhkct239/HO833vvgcclN8qa1P4lSyTnyN3zRENDZf+CBXcFtJ99BtmywVdfQYYMzvlBlWczHHT9+nXjjTfeMPz8/AwvLy/Dy8vL8PX1Nd544w0jMjLS0dO5VHh4uAEY4eHhZjdFuVJsrGEMGGAY/frJ31OT1WoYr75qGHJfNqK9fYxdI39I3WsmFBtrGJ9+KtcvWNAw9u1z3bWV29B7nUovPLlPkhL6u52GDR8u39158hjG77+b3Roj9JvvDKvFEtefsX70kV3HLVxoGAEBcYcZINvvvXfv/sKFDSN37sT7Er4sFsMIDDSMmP+uGsYff6TyT6zcjb33O4fXzNpcv36d48ePA1CiRAmyZMnitAA7tehak3Tm+nUpPWOxxD/yS80RWcOQrIPffw+A1cuLP7/6lgof2F864qHExkK/fjB2LJQoIdNwihVzzbWVW9F7nUpvPLFPkhL6u51G/fKL1IAPDIQ1a6BUKVObEzpqDAUHvIXXnb5T7Dvv4PXttw/sQ9kqO6QsskhaDq5w6vHnyH7uKOzeLf0blS6k2ppZmyxZsvDUU0+l9HClUtd//8nU4qZN4YsvXJO996OP4gLZWIuFA4OGuC6QBdi5E8aPh/LlYdUquFPrUCml0jrtkyiP1rIl9O0L774LjzxialP+HTuBgu/0jw9k+/a1K5C1WmXdqzMD2ZxcZg3Pkf3Ibpl+rQ/oVRK00KRKey5flpI7e/dCjRquueaXX8pa3DsOfvgJT336P9dc2+aZZ2DRIqhdG/z9XXttpZRSStnv5k1YuxaaNZO1n3cehpvpzMQpFHyrD96xsQDEvvYaXqNHJxvIJlwDe/584gRODysX/xFMQyqyh7PNelHox3GSFEupu+i/CpW22ALZPXvgzTdhzJjUH5UdNUpK/dxx6O33KPPV4OQ/70xXr8L770vxcIDmzTWQVUoppdzZlStSH7V5c1kS5AZOT5tJgd6v42O1AhDbtSte48cn24cKCoKiRaXiYadO8PbbzmtLbi6xjvpUZA8zs7xB/oUayKrk6cisSjtsU4v37oXXX4cffkj9QHbSpER38L/f6EvpEcNS95o2Fy7Il+HevTItqU8f11xXKaWUUinz77/QpIlUWOjUSWZTmezU7HkUeq0HGWJiAIjt0AGvKVOSDSBTY21sQhm4TSZu8gN9KDTje7wzuGCpmPJY+phDpR3Tp0tg9+abUpomFQNZqxUOfjQbo1evuH3HX+nBY+NcNE3ozBnJab93L/TuLT+zUkoppdzXoUNQvXp8qcBZs0yvk3ry5wUU6N6VDDG3AYht1QqvWbOklk4SnL021mKB3LmlrizISc9RkDaFf6PQgu9p/aIGsur+7BqZXbp0qd0nbN68eYobo9RDGTBAkgO0apWqgWxQEKx8LYjx/3XFcufGO794Z3xemIhLcuwdPSoj0KdPS9KpIUNck+BKKaXcgPZJlEf6+2949llZDvXtt5LsyWQnFi6mYNeX8YuOBiD2+efxmjcPfOLDg7trw1qtzlsba+u6TJwILapd4FrjNvzWfhQZq1ekZs2cycXTSiViVzDbsmXLRNsWi4WEFX0sCTrS1jtz7ZVyiYsX4eefZXTSYrmrsrZzJLyRHz0Kv326kiWW9vgg/9YXFHyRDv/MgraWe4t7O9v167JAJTQUhg0DO4qXK6VUWqJ9EuWRiheX7+8WLeDll81uDSeWLCf/S53IGHULgNiGDfFasCDRSHFQkIzCJgxec+Vy/FqBgdChA8ydm/hcAQGSdqT1sxegXj1yHDhA45ZLoE7FFP5UKj2ya5pxbGxs3GvNmjWUL1+elStXcvXqVa5evcqKFSuoWLEiq1atSu32KhXvwgX5YujbF1avTpVL3J3gYP2nGwnyakUGQ9aVLM/3PO3C5mMgnaf+/SX4TTVZskjm5B9/1EBWKZUuaZ9EeZQDB+RPHx+pJ+sGgew/v64iX6cOZL51E4DYOnXwWrwYMmaM+4xtXezdo7CXL9t3jZEjYc4c2LABTpyQ5+8nT8p2wv2ta16E+vXlv9OAAfDZZ075GVU6YjjoySefNEJCQu7Zv3nzZqNUqVKOns6lwsPDDcAIDw83uynqYZ09axilSxsGGMa77xpGbKzTL7FwoWFYLHIJMIyqbDeue2WK2xGcp77hze24922vDRuc3hTD2LXLMG7eTIUTq7RI73UqvfDkPklK6O+2B4mNNYzPPpOOwezZZrcmzrFVa42ILNniOi2x1asbxrVriT4TE2MYAQHGPf0be14Wi2EEBso5HujCBcMoU0YOfPvtVOnLKc9l7/3O4QRQx48fJ0eOHPfs9/f35+TJkw8ZWitlh3//lex/hw7BBx/I4z4nrRm1WmHjRpg9WxIi22aulWcPwd4NyRwrTzG35KpB00srsCYxUz8szClNibd6tayz6dLFySdWSinPpn0S5ZZu34YePWSUsUQJePpps1sEwLF1G8nTtjXZrl8DwHj6aSwrV0LWrIk+FxKSsnWxtq7YqFHJ5o9KrFcvSYbVvz8MH675P1SKOBzMPv300wwYMIDz58/H7Tt//jzvvfceVapUcWrjkjJ27FiKFi1KxowZqVq1Kjt37kz1ayo38t9/EsgePQqDBsHQoU67+SWcUvzSS7IcF6A0B1nvU49s1kgAduZ4mgaX13KbpDMQFizolOaIxYulDp3FAj17OvHESinl+czukyh1j2vXoFkzmDoVqlaF7duhZEmzW8WxjVvI/WJr/K9FAGBUqIBl9WrInv2ez9r7UP7u9bMBATiWO2TMGOnHjRihgaxKMYfrzE6dOpVWrVrxyCOPEBgYCMCZM2coWbIkixcvdnb7Evn5558ZMGAAEyZMoGrVqowaNYpGjRpx5MgR8uXLl6rXVm4iVy6pz1awoGTydZLkaqaV4BibfGuTM/oqAHuzP0XdqxuIIuM957BY5EZes6aTGjV3rqytyZQJfv0VatVy0omVUiptMLNPotQ9Ll+W9Z9790qipzlzIHNms1vF0ZBt5GrdkpzhVwAwypbFsmYN5MyZ5OftfSg/f76MwNoyHdesaceI7H//wdmzULYsFC4MH37owE+i1L0shuF4pSjDMAgODubw4cMAlC5dmgYNGiTKIJgaqlatytNPP80PP/wASBKIwMBA+vbty4d2/DJERETg7+9PeHg42ZN4EqXc2KVLUojMYpGI04n/1qxWGZG9e0pNIKfZ4VeVglHnADiUtRTPRP5GBP73nMPWHKdlM546VaYo+fvLNGMdYVAO0HudSk/M6pOYQX+33ZzVCu3aQaFCDsy1TV1Ht+0kR/MXyPufTDczSpXCsnEj5M+f7DG2flFoaNL1ZG0P70+ccPBHtAX7p05JwP/II478KCqdsfd+5/DILEja++eee45atWrh5+fnki+M6Ohodu/ezcCBA+P2eXl50aBBA7Zv357kMVFRUURFRcVtR0REpHo7VSo4eFBufm+8IVOLnfzvLam1IQUIY0vGGhS8JYHsscwleDZyS5KBLCRIL++ssjwZMkC+fBLIlivnpJMqpVTaY0afRKlETp+WwMzbG2x1Wt3g3+HfO3bh37JFfCBbogSWdevuG8iC/BijR8uMNdsYgo3D62Jtrl6F556TILZPH6nXo5QTOLxmNjY2liFDhlC4cGGyZs3KiRMnAPjkk0+YMmWK0xtoc+nSJaxWK/nv+gXMnz8/586dS/KYoUOH4u/vH/cK1F8cz7Nnj6yRPXcO8uZ16qltyZ4WLky8PzeX2JL5WR65JRHu6UyBPHtjC5fJDcTfyAcPviu9vDMC2dhY+fPll6XAugaySimVLLP6JErFmTNH1sROny7bGTK4RyD7+x6ytWhB/ovSRzaKFMGyfr2MGtuhdWuZbVa4cOL9Dq+LBQgPh0aNYPduya75/fdu8d9IpQ0OB7NffPEF06dPZ9iwYfgmKKxcpkwZJk+e7NTGPayBAwcSHh4e9zpz5ozZTVKO+O03qFdPpqVMmyYjs06SMNnTnVnrAPhzlZCsz1Lixj8AnPUrSI2bWzlPgbjPBARIADxoEHTsCHXqOGkm0TffQMuWEB0t2zqFTCml7suT+iQqjTEMSV7UubOsiy1e3OwWxTnyx59kbtmCgufPAmAEBEgg6+C03tatk6kN60ggGxEBjRvDzp2yfGrsWA1klVM5PM145syZTJw4kfr16/P666/H7S9XrlzcepXUkCdPHry9vRNlLATJWligQIEkj/Hz88PPzy/V2qRS0caNkg3w1i25g7Zv77RTJ5fsKQuRbMpem9IRRwC46JuHmlEh/EsgefNKAfDChe1McOCoIUMkOi5SRNIo3/0oVCml1D3M6pOodC46WsrKTJ8u39srV0Lp0ma3CoAje/8iY4sWBJyVARyjQAGZWvyAYNtqlWVXdydz8vaWh/Ypdu2a9GteeQV+/BG8HB5HU+q+HP4XFRoayqOPPnrP/tjYWG7fvu2URiXF19eXSpUqsW7dukTXXLduHdWqVUu16yqTLF8uXxYLFzo1kLVa4a237g1kM3KTDf51KRfxJwBXfHJQO3oTJywlsFhgwgR5+Oq0UVgbw4BPPpFAtnhx2LxZA1mllLKTWX0SlY5FRsraz+nTpfTOjh1uE8ge/vMgGVq2pMi/JwEw8uSRQPaxx+57XMLZap06yZ9Fi8r+h1a4sJQnmjxZA1mVKhz+V/XEE08QEhJyz/4FCxZQoUIFpzQqOQMGDGDSpEnMmDGDQ4cO8cYbb3D9+nW6deuWqtdVJhg2DHbtkhqrTpRUsqcMRBOcqyFPh+8CIMInG3VjNnCIJ1K2NsRehiEp6b/4Qr5oNm/WzH5KKeUAM/skKp3KnBkKFJAH7Rs2PDCZkqsc/usw3q1aUfzUcQCMnDmxrF0LTzxx3+Nss9Xu7huFhsr+FAW0N2/Kf599+2Q7b163yOys0iaHpxkPGjSIrl27EhoaSmxsLEFBQRw5coSZM2eyfPny1GhjnPbt23Px4kUGDRrEuXPnKF++PKtWrbonKZTyUHPnwrFjMlLp5SU1yJzs7kLg3sSwMk8Tnr20FYDr3pl5LmYNNfuUZ9SLqTSl2GbXLvj2W3miu369fDkqpZSym5l9EpXO/POPzKDy8oKZMyVjsZuMNB4+cARat6bkP38DYGTPjiU4+IFJJJObrQbxVRD795eSuXb3haKioFUrqcaQPTtMmuTYD6OUo4wU2Lx5s9GgQQMjb968RqZMmYwaNWoYq1evTsmpXCo8PNwAjPDwcLObou42frxhWCyGkTOnYYSFpdplNmwwDLlFG4YFq7E8X9O4HTe9/IxabDRAPucSv/xiGOfPu+hiKr3Qe51KTzy1T5IS+rttkqlTDSNDBsOYONHsltzj4MG/jb9KlY3ry8RmzWoY27fbdWzCPtH9Xnb3iaKiDKNZMzmoeXPDiI5O6Y+llN33O4ceKcXExPD5559TrFgxgoODuXDhAjdu3GDLli0899xzqRFrq/Tgm28kU3G+fLBpU6qOUNasKdmILRjML9iO5y+sAOC2xYeWsYsJsdQmMFA+lyoMA6ZMic9Y3KaN/NxKKaUcYmaf5PLly3Tu3Jns2bOTI0cOXn31VSIjI+97TJ06dbBYLIleCZNWKTcUGyvLgbp3l1HGUqXMblEihw4fI7ptO548vB8AI3NmLCtWwDPPJHuMrSzh3LmQIA3Nfd09qy1JMTGy4HbZMslePH++lClSKpU5FMz6+PgwbNgwYmJiUqs9Kj0xDBg4UL4oihSBLVtSZWpxQt7eMHqUwazCL9EmTArMxuBNO2M+ayyNgRQUAreXYUDfvpKa/pNPUuECSimVfpjZJ+ncuTMHDhwgODiY5cuXs3nzZl577bUHHtezZ0/CwsLiXsOGDXNBa1WK3LgBbdvKA/dSpSTRU6o96XbcoSPHudm+I+UO7AXA8PPDsnTpfdt4d6KnL76w71oFC9rxoQ8/lKSd9erJhbSaiHIRh9fM1q9fn02bNlG0aNFUaI5KV4KC4Ouv5UsiOFiGTF2g3PLXKBE6B4BYLHRlBotpRWCABLKpkuwpNhb69IHx46F8eXj//VS4iFJKpS9m9EkOHTrEqlWr+P3336lcuTIAY8aMoWnTpnz33XcUKlQo2WMzZ86cbDlB5UYiIqB+fcltUb8+/PIL5MxpdqviHPr7H651eokqf0riSsPXF8uiRdLWZCRXlvB+LBbpmtkVw/frJyV4xo2DTJnsv4hSD8nhYLZJkyZ8+OGH7N+/n0qVKpElS5ZE7zd3cvZZlYa1aiWPBV97TTLducDfb/blsemT47aPvjuRFyp2pmfBVEz2FBsLvXtLfZ+KFSVwz5UrFS6klFLpixl9ku3bt5MjR464QBagQYMGeHl5sWPHDlq1apXssbNnz+ann36iQIECNGvWjE8++YTMmTMn+/moqCiioqLitiMiIpzzQ6j7y5ZNkjNWrAg//OBW02UPHT3BlZe7Uv2P3wAwfHywzJ8PTZrEfebumrHVqyef6Ck5Fov8ed/ZaoYBZ89K+Z1HHoEZM1L2Qyn1EBwOZt98800ARowYcc97FosFq9X68K1SadfNm7Bokcxv8fKCjz5y2aUPDXif0uN/iN8xahSPv9WDx1P7wn37SiBbqZIEsm70dFcppTyZGX2Sc+fOke+uXAc+Pj7kypWLc+fOJXtcp06dKFKkCIUKFeLPP//kgw8+4MiRIwTdp/bJ0KFDGTx4sNParh7gt99kvanFAlOnShRni+rcwKGjJ7jYtTu1dm4BwPDywjJnjqQbviMoSALXhKV28uSBS5ccu1bAg2arGQa88w7MmiUlisqUcewCSjmJw8FsbGxsarRDpQdXr0rd2JAQecrZtq3LLv3X/wZRZuS38Tu+/FLu9q5QoQJUrgxr1mggq5RSTuTMPsmHH37IN998c9/PHDp0KMXnT7imtmzZshQsWJD69etz/PhxSpQokeQxAwcOZMCAAXHbERERBAYGprgNKhkxMfDBBzBiBEycCD17SukdN3Lo6AnCXn2Nets3AmBYLFhmzkzUl0puKrG9gezHH0tZ2oL2zFYbNAhGjpQg1q5FtUqljof6Tb116xYZM2Z0VltUWnbunGS327dPRmUTPEVMbX9+9iVlvk6Q5eB//5NXajIMmefj4yMJn155xe2+GJVSKi152D7JO++8wyuvvHLfzxQvXpwCBQpw4cKFRPtjYmK4fPmyQ+thq1atCsCxY8eSDWb9/Pzw00Q6qevyZejQQWZOPfmkJDByM4eOneRMr948F7I2bp9l8mTo3Dlu+341Y+1Vvz7UqWPHB4cOlWVijz0Ga9dC7twpv6hSD8nhas9Wq5UhQ4ZQuHBhsmbNyj///APAJ598wpQpU5zeQJUGHD8ONWpIINuvn0xJ8fV1yaX3fD2cJ4cMwst2d+/Xz/70fSllGHKdDh3g9m3Zp4GsUko5nTP7JHnz5qVUqVL3ffn6+lKtWjWuXr3K7t27445dv349sbGxcQGqPfbu3QtAQR3VMs+BA1CligSyLVvC9u2QzIMFsxw6dpITb/bjuQ0r43eOHy/lghIICUk8tdgRFgv2lyUcPVoGBIoVk9o++fOn7KJKOYnDweyXX37J9OnTGTZsGL4JApIyZcowefLk+xyp0qXQUAlk//kHhgyRBRheDv+zS5HdI3+g7Mcf4G2bhtajh1w/Nde/GAb07y8JI44dgwfUHVRKKZVyZvRJSpcuTePGjenZsyc7d+5k69at9OnThw4dOsRlMg4NDaVUqVLs3LkTgOPHjzNkyBB2797NyZMnWbp0KV26dKFWrVo89dRTqdJO9QD//CPrY48fh08/lbIy2bKZ3apEDh07ydF+A2gavCx+56hRkER9YrtqwSbBrkRPNtevywcLF5ZA1kVVKJS6L8NBJUqUMNauXWsYhmFkzZrVOH78uGEYhnHo0CEjR44cjp7OpcLDww3ACA8PN7sp6UdsrGH06mUY48e79LI7f/jRuJXB1zAkvDTO1e9kzP0pxtiwwTBiYlLporGxhvH223LNcuUM49KlVLqQUven9zqVXpjVJ/nvv/+Mjh07GlmzZjWyZ89udOvWzbh27Vrc+ydOnDAAY8OGDYZhGMbp06eNWrVqGbly5TL8/PyMRx991Hjvvfcc/h3V320nio01jD59DCMoyOyWJOng0RPGwmbt4voxBhjGN9/EvR8TYxgbNhjGnDny59q1iT+a3Ctv3sTbgYGGsXChAw07fdowDh929o+r1D3svd85PPcxNDSURx999J79sbGx3LZNqVTqwAFZe2KxSCZfF7FaYcmQGTQd2g+/29EArMrYkmbrphOzTh45BgTILBmn1pM1DCkYbkuGoGtIlFIq1ZnVJ8mVKxdz5sxJ9v2iRYtiJFi8GBgYyKZNm1KtPcpO167BTz/JyKbFAmPGmN2iJB06dpL9731Eu2Xz43cOHhxXoz6pjMWFC0u34/LlpNfN2mrGHjsG27bFl+2xqyzhsmUy/fqJJ2Q+slJuxOH5nk888QQhISH37F+wYAEVKlRwSqOUh/vxR3jqKckK6EJBQfB63Tk0GvYmGaOlLt8qGtHi1jxiiK8RFxoq2f7uUw3BcTt3wrBhcqNft07y4CullEpV2idRdjt+HKpVgzffdHIHwLkOHTvJ3g8/pc3iufE7Bw6ETz4B4jMW370+9uxZ+O8/CWTvXk2VcCqxr68keerYUf58YCC7ejW8+CI0axafB0QpN+LwyOygQYPo2rUroaGhxMbGEhQUxJEjR5g5cybLly9PjTYqT2EYsu5kyBDIlw9q13bZpYOCYM5XC5h2uBdZbt4AYBO1aE0Q0STOBGm70ffvL0mVH3gjt0fVqjB3rnwz3FV/UCmlVOrQPomyy4oV8NJLcOUKvP22SysqOOLQsZPs+uhzOgfNwgsZXj3TdgBbynxJwU0WqldPPmOxrW+TKxdkypQ42H1gzdjkbNkCrVpJEsvp06WsolLuJiVzmDdv3mw0aNDAyJs3r5EpUyajRo0axurVq1NyKpfStSapKDraMLp1kwUYJUsaxp11S64QE2MYnesuMi5nzxG3CGQ7VY2sRDxw7cid5Uwpt2iRYdy+7YSfQinn0XudSk88tU+SEvq77aDbtw1j4ED5ws+Y0TBmzDC7Rck6ePSEMa1jDyPG4hXXSZmWpbcBsXF9ljx57FsXu3Zt4vW0KcoVsmuXYWTPbhgZMhjGypVO/mmVerBUWzMLULNmTYKDg50ZUytPFhUlT+5WrpQU98uXQ968Lrv83DHLGb2nBzkjrgKwl3I0YSWRPDgrYUqz/wHw3Xfw3nvymHTUqIc4kVJKqZTSPolK1ooVUhO1ZElYsECWQLmhQ8dOsu3zb+j68zS8DanAMJkevHb9eyB+zvClS/ad78IFmUacYgcPQqNGUpFh/nxo3PghTqZU6nJNjRSVtvn6QpEi8MILsH69SwPZjUtX0fDLV8l99T8ADlGK51jDVXLadXyKy/uNGSOB7COPyJQlpZRSSrkH2zzcZs2kJuuuXW4dyIZ8+R1d5kzGJ9YKwILML9OLCRgp7KY/dOnijBnB3x+mTpX1skq5MYthJDXzPrGcOXNisbM25+XLlx+6UaklIiICf39/wsPDyZ49u9nN8XwXLkjgarFIGmHDkHUVLrLh12Aee/UVCp8/C8BxilOLzZyl8AOPtWX1O3EiBWtmJ06EXr2gUCHYvNntCqwrpfc6lZallT5JSujv9gPExsI338ChQzBjRurWlXeCQ8dOsuHrUfSYMQ7fGEmudKFOOwptnI3V8bQ2D9e3udvNm7L4VimT2Hu/s+s3ZVSCKZT//fcfX3zxBY0aNaJatWoAbN++ndWrV/PJnUxrKh3YsUOeeL79tmTZc0oWJfutX72BEr16xAWyZ70DaGBdZ3cgC3YWCL/bjBmS0j9/fhmF1kBWKaVcSvskKkmXL0OXLvDrrzJr6sIF+a52U4eOnWTdsDH0mDUhLpClZUvWt/4J68aUBbKQwr4NyBzm9u2ldmGZMhrIKs/h6GLc1q1bG2PGjLln/5gxY4wWLVo4ejqX0sQJTrJggSRS8PY2jEmTXH75tWs2GUeLlIjPdJA/v7F6zBHDYjEMi+XBiREcLhCe0NixUnF8/36n/kxKOZPe61R64cl9kpTQ3+1k7NhhGEWKyJd806aGcemS2S26r4NHTxijXn/PuOGXMa5zcqlqUyPm+i1jwwb7kjzlzevEvk14uGFUqiQnGjzYmT+qUilm7/3OrmnGCWXNmpW9e/feU6T82LFjlC9fnsjISKcF2s6m03MekmHA8OFStDtrVvjlF0kQ4EJr122hcK+elD5+GICorLn4c/RGKnYty5Il9xYRDwyUJufN62CB8Pu5ehVy5HiYH0OpVKX3OpVeeHKfJCX0dzsJP/4IffvKcqchQ+DDD8HLfVPCHDp2klUjJ/Da1DFkuSWlBINpQDOWkTcgIyNGwIABEBqadAke21TiY8dg2zYn9G1u3oQmTWDTJllCNX6820/PVumDvfc7h3/bc+fOzZIlS+7Zv2TJEnLnzu3o6ZSniI2FN96QpEcBAbB1q+sD2Q3byNfnzbhANoJs1IhcTZVXy1K0qHzm5EnYsAHmzJE/T5yAtm0dLBB+t1WroFu3+GLhGsgqpZRb0D6JwsdHiquuXQv/+5/bB7K/jp5Ez+k/xAWym6hFC5YQRUZCQ2Wmry0T8d0xZcKpxL6+D9m3AenXtGsngWz79jB2rAayyuM4PCl/8ODB9OjRg40bN1K1alUAduzYwapVq5g0aZLTG6jchJcX+PlBxYqwbJkkP3IBqxVCQuCPP7dTe1Jfnjq8H4DrZKYpK9hNZUCeYLZpI5n3HS4Kfj+bNknZIYtF1ge7aTZEpZRKj7RPkk4dOCBVFLJmhe7dJeOumz9oPnTsJMvGTOH1aWPIeuM6ANuoxgss5yaZARmJtVhg3jypiPP224lnmwUESCDrtH7Oe+9JOcWmTWHmTJfnP1HKGRyeZgzyRfH9999z6NAhAEqXLk2/fv3ivkjclU7PSYFLlyB37viMxbduQZYsLrl0UJBMGy5U9DdGX3+bZ/b8BsAt/HiB5ayjQaLPOzWLH8Bvv0HDhhAdLQH8c8854aRKpT6916n0xFP7JCmR7n+3DUMSMb75pjxonj3b7BbZ5dCxkywZN43XJ48mx7VwAH6nMg1YSwT+SR6zYYNMHQ4JceIyqbudOAGffSZTizNnduKJlXp4Ts1mbHP79m169erFJ598wmwPuYGoh/DHH5Kx+LXX4NNP5Q7qwkC2TRuoXGMH397+IC6QvY0PbfnlnkAW5DvuzBm58dep85AN2LtX1pDcvAkLF2ogq5RSbkb7JOnM5cuy3Gn+fMiZM34urps7dOwkQRNm8sbUMXGB7F7K0YjVyQayIAGst7cT+jNJuXxZpmYXKyYPB5TyYA4tLMiQIQMLFy5MrbYod7J0KdSqBefOyQ3PhaxWGZGt/OxOvuRjau3YLPvxojOzWU6z+x4fFvaQDbhyRYLX8HD46Sdo0eIhT6iUUsrZtE+SjqxeDWXLSiBbsybs2QMvvGB2qx7o0LGTLJj4E72mfk+u8CsAXC/6JA0J5gr371sVLJhKjRo5Ep54AvbvT6ULKOVaDq+Sb9myJYsXL06Fpii3YBgwdCi0bCnbixdLlkAXCgmBgsV28mmGwTTcsjZu/6tM4RfaPfD4h/4CyJlTpt1MngwdOjzkyZRSSqUW7ZOkA5cuyZrYS5dg2DCZf1ukiNmteqBDx07yy6Q59Jr6PXmu/Cc7H3uMjFvWkjEgb7J5liwWqcRQs2YqNGrGDEmVnCED+Cc/KqyUJ3E4AVTJkiX5/PPP2bp1K5UqVSLLXdNO+/Xr57TGKReLjZWC47NnyxfF0qUuS3hkS/QUFgZ7D+1kYOahPL96Rdz7bzKWGbxy33PY1sym+Avg0iUJZL29ZT2OUkopt6Z9kjQsKkoST+bJA9OmweOPe0wSxkPHTvLzlLm8Pm0M+f67CMDNQsXxDV6Pd+ECjB4tS6kslsTldxJmK3Z6LqZly+DVVyUPypo18MgjTr6AUuZwOAFUsWLFkj+ZxcI///zz0I1KLek+cYI93n8ftm+XdaL58rnkkrZET//+C0/X3Mk7OUbQftnPce+/xzC+4737nsP2BZDibMaXLsm06qeekmBeM/opD6b3OpVeeHKfJCXSxe92TAx89ZXU2Nu1SzIWe5BDx04yd+rPvDZtDAHnQgE4xSPUYjOxAUUYPVr6KQn7PjaBgU7OVmyzebOUU/T2lpHtp5928gWUcr5USQAFcOLEiYdqmHJDhw/DY49J+Z2hQ2WY1NfXJZe2JXoyDAlk++YdQ9ug+XHvD2ZQkoGst7c00+ah0tWHh8tN/tAhaN7crWvUKaWUiqd9kjTm77/h5Zdh506ZIXb6tKzvdEMJZ5TZMg3/feIkc6b/Qs8ZY+MC2VAKUY/1nKYIlrvKCLZokcrZim0NfeMNmX23bJkGsirNcTiYtbl06RIAefLkcVpjlAnmzJEabe+9B0OGyF3URaOStkRPtkD29UI/0mn+HLyQyQLf8Q6f8VmiY2wjsHPnQt68TvgCuHFDkkj88Yfc7IcO1YLhSinlYbRP4uEMAyZMgHfekSoCr7wCo0eDm44+JzWqWumZkzSst4DXZo7jkbNnADhHfuqxnn8oAcTXke3fXwLZVMtWnJC3N6xYAX/9BQ3urQShlKdzaAjq6tWr9O7dmzx58pA/f37y589Pnjx56NOnD1evXk2lJqpUERsLAwdC586QKVMqZRq4v5CQ+KnFrz4yhS6/zMDbiAVgPK/zHt8CiQPLgAB5otm2rXwBdOwof6YokI2KkkejW7bASy/BDz9oIKuUUh5C+yRpyDvvSK6KLFkkUpw2za0D2TZtEgeyRR49Sa3aQbw6ewLF/j0JwEXyUJ91/M3jiY5PWEYwVZ09C0eP3mlgEXj++VS+oFLmsHtk9vLly1SrVo3Q0FA6d+5M6dKlATh48CDTp09n3bp1bNu2jZw5c6ZaY5WTRERI8LZsmSRUWLpUphm7WFiYBLJdis+k2+xp+MTKvOGZvExvxmILZD/+WGYZOX0KzqpVku6/ZUv54tTpxUop5RG0T5LGdO8Op07B2LFQoIDZrUlWwhllNkUePUmrNovoNX8ij546DsBlctKQYA7yZLLneugygvdz9So0bizlFf/8063/myr1sOwOZj///HN8fX05fvw4+fPnv+e95557js8//5yRI0c6vZHKiW7dgho1ZLpJ48YyXzdHDlOaci16J50em0vPmZPwjbkNwC+0oTtTMRJMGqhfP5Wm4bRoAUuWSE1ZnxTPuFdKKeVi2ifxcOHhMte2Tx+oVAnKlJHEk27ONqPMpsijJ2nZdjE9F0zm8X+OABBOdhqxmn2Uv++5Uq2O7M2b0KyZ1JF9+2246/dDqbTG7qGoxYsX8913393zpQFQoEABhg0bxqJFi5zaOJUKMmaUm9yAAbB8uWmBbHDITiK2/0KvnybgdzsagOU8T2dmY73zjCVVaq0Zhnxh2rJHNW8u/02UUkp5DO2TeLCNG6VywPTpMGaM2a1xSMLR1CKPnqRF2yV0XzSVJ44eBCCSLDRhJf/ketqcOrIxMdChQ/zyqe++0+VTKs2zO5gNCwvjySeTny5RpkwZzp0755RGKSeLiYGJE+MDuC+/hOHDTSs/Exyyk93zFvH6rPFkiroFwDrq0YYF3EayKKdarbWvv5bFLv/7nxNPqpRSypW0T+KBbt2StbH16sl6zi++gMmTzW7VPaxWibfnzpU/E1ZOsI2mFnn0JC3aLaHr0hk8dXg/ADfIxPP8ynaq89Zb8rm748hUrSNrGPD667J0rHFjmDpVl0+pdMHuf+V58uTh5MmTyb5/4sQJcuXK5Yw2KWc6d06y1/XqBd9/L/tMfEoXHLKTHQuW8cbMcWS9cR2A/x6vzpuFlhBF/AipLdGTU2utTZggQWzRosR90yillPI42ifxMKdPQ+XKMGKE5Or47Tf46CO3W+ITFCRdhLp1oVMn+bNoUdkPMppa6ZmTNG+/lJeW/0TFA3sAuIUfzVlKiKU2gYHyoy1YAIULJz5/qvRtbC5cgDVroEoV+OUXyJAhFS6ilPuxGEbCZezJ6969O8ePHyc4OBjfu2qQRkVF0ahRI4oXL87UqVNTpaHOkC6KjSe0ZQu0ayfzYtq3h0mTIFs205oTHLKTbUEr6Dvte3KFX5GdFSvC+vVYs/qnbq21efPkmyl/fvnvUqKEE0+ulHtJd/c6le6khT5JSnjs7/atW1ChguSo+PprqaLgZhLWvU/I9vx/wQIo/dRJJv+0lDbL5lLtj98AiCYDrVjESsvzcZ+zBatJ1aJN1Ulx//4rS6e0RJVKA+y939kdzP77779UrlwZPz8/evfuTalSpTAMg0OHDjFu3DiioqLYtWsXgYGBTvshnM1jvwQcZRgwciS8/77chUeMkCQLJo/IhixZTZ9pY8h3+aLsfPJJmcOT2jfdVatknXDWrLBpk6zVUSoNSzf3OpVupYU+SUp41O/29u1w+DB06ybb169L6R03ZLXKCGzC5E4JWSxQqepJ6jRdRvNl86n5+xYAYvCmLb+wmFYEBsr04VQZdb2fxYuhVCl5KZWG2Hu/s3t+R0BAANu3b+fNN99k4MCB2GJgi8VCw4YN+eGHH9Lcl4bH2rVL1qUULgzz50P16qY2JzhkJxuXr6X3rPHxgeyjj8Lata55evj33+DrC7/+qoGsUkqlAdoncWPXr8s82++/lxHY5s0hd263DWTh3izFd3ukxEmeqb+MpiuD4gJZw8uLIx/Npl3pVrzlilHXpAQHywy8IkXg0CG3m7atlCvYPTKb0JUrVzh6pxDzo48+6jHrUjzqiWZKGEb86OuMGdCkCeTLZ2qTgkN2sn7Fet6cMY7AsDOy85FH5JvjkUdc15CzZ6FQIdddTykTpfl7nVIJmNEn+fLLL/n111/Zu3cvvr6+XL169YHHGIbBp59+yqRJk7h69So1atRg/PjxlCxZ0u7ruv3v9tq10LMnnDwJpUtLEqJnnjG7VQ80d66sREpKkUdP0qzDcpptWspzIcGy02KRftbLL7uukXf74w+oXVuSfK5Zk0rpkZUyj733uxSlOcuZMydVqlShSpUqHhPIpnlz5kDLlvFp97p2dYtAdu2qjbw2e2J8IFuwIKxfn/qB7KlTMs06Jka2NZBVSqk0yYw+SXR0NG3btuWNN96w+5hhw4bx/fffM2HCBHbs2EGWLFlo1KgRt27dSsWWukhsrASxDRvKEOfHH8OePR4RyELyNV9tgWyTrSviA1mQChFmBrL//CMDFjduSCSugaxKx3Q+gqe7eVOmFI8fD/7+Ms2kTBmzW0VwyE7WrN5Mz7lTKHbmhOzMkwfr6rWEnClB2M5UTIZw4YIkmfj7b7lAs2ZOvoBSSqn0bPDgwQBMnz7drs8bhsGoUaP4+OOPadGiBQAzZ84kf/78LF68mA4dOiR5XFRUFFFRUXHbERERD9fw1OLlJa+KFWU0tlw5s1vkkJo1JdNwaGh8AihbIPvcb2toumFl/Id/+AF69DCnoQAXL0rpnQsXpEpDy5bmtUUpN6AFqDzZnj1QqZIEsuXKyVpZNwlkV68J4ZX503jsxN+yM0cO1n2whqJNn0g25b1TXLsGTZtKIPvJJxrIKqWUMt2JEyc4d+4cDRo0iNvn7+9P1apV2b59e7LHDR06FH9//7iXW60DvnBB6tbbor+RI2HHDo8LZEEeqo8eLX+3WOID2bq71tNs7bL4Dw4fDr17m9NIm/BwuH1b+ji9epnbFqXcgAaznmrCBKhaVUZiBwyQmm2PPmp2qwgO2cmq4C28FPQTTx49KDuzZGHDBytp+H6FexIshIZKKnynBLRRUdCqFezeLYXD7zw5V0oppcx07tw5APLnz59of/78+ePeS8rAgQMJDw+Pe505cyZV22kXw4DZs+GJJ2Q68aJFsj9zZo9OQNS6tZTVqVhVAtmaezfTetWi+A989ZX0t8z26KPSz9E+jlKABrOeq2hRqZm6dq08KcyY0ewWERyyk5Vrt9Jh6TzKH9wrOzNmxLpkOV3GPnNP7TaIf6Dbv3/8ct8UMQxZJ7xuHbz4okwDMrEUkVJKKc/y4YcfYrFY7vs6fPiwS9vk5+dH9uzZE71M9e+/MuPppZfkAfIPP6Spaa6lnzpJ3eeXU23/Ntot/yX+jUGDYOBA8xpmGJIh+uCdQYJcubSPo9QdnvsILT36+Wd49lkpudO4sUyldZPC48EhO1mxbhttVizg6X2/y84MGWDRIkK869w35b1hwJkzkuC4Tp0UNsBikUQTFy/CTz+ZkB9fKaWUJ3vnnXd45ZVX7vuZ4sWLp+jcBQoUAOD8+fMUTJBt6Pz585QvXz5F53S5OXPgjTcgIgIaNYIff5SSMB7AapU+RlhY8vk6Dh07ydR5y3lqz046Lp0X/8b778Nnn7m0vff47DMZGf7jD1i58oEfVyo90WDWE1y9Cn36yLSeVq3i5+S6WSDbfM1SauzaJju9vSX4btyYsLn2nScsLIUNsJUk6t8f+vbVQFYppZTD8ubNS968eVPl3MWKFaNAgQKsW7cuLniNiIhgx44dDmVENlXGjPL9On06dOniMSODQUHw1luJ68gGBMga2datZdsWyD6x/w86L56NxTZt7K234Ouvzf1Zf/wRPv8cSpaEmTPNa4dSbkqnGbu7zZslmcLs2VCjhkwpdiPBITtZsX47jTauou72DbLTVn+tVSsg+ZT3d7P3c4lMngzdukkyBNBAVimlVKo7ffo0e/fu5fTp01itVvbu3cvevXuJjIyM+0ypUqVYdGc9qcVioX///nzxxRcsXbqU/fv306VLFwoVKkRLd52ma7XC99/D+fOy3bo1HD8uS3o8KJBt04b75uuwBbKPH9xH14Uz8YqNlQ+9/roktTLzZ122DN58U5aVrVoFqfSwRSlPpiOz7io6Gj79FL75RgK0L76ADz5wq+QKtkC27tb1NN60Om5/7Pgf2Vy4M2FzJUCtXv3elPcJWSzyvsNl0pYulUx+uXPD2bMeM91JKaWUZxs0aBAzZsyI265QoQIAGzZsoM6d9TJHjhwhPDw87jPvv/8+169f57XXXuPq1as8++yzrFq1ioxukPPiHgcOQPfusHMn7N8PkybJ/pw5zW2XA6xWGVhNLl+HxQJDvz1JneeXU+LIX3T7ZTpetuQd3brB2LHmBrI7d0L79jILb8UKSOEUd6XSOothJPVrnjZFRETg7+9PeHi4+UkUHuTcOSmzkyuXjMo+/bTZLUrEFsjW2LmFNisWxO3f120kLwT3v2c6T8eO8N13sp3wX5zte2LBgvjpPnbZtg3q15fgfsMGqFw55T+MUmmMR93rlFJ2S/Xf7ehomVb7xRcy46lLFxmdzJXL+ddKZRs3SgnA5NjK75Q6fYhecyfjczta3ujUSabzmj3T6+hRSbY1ciQ0aWJuW5Qygb33O/cZ5lMS5Z06JZmKCxSA1auhVCnIksXsliViC2Sf3rMjUSB7oOMXVJje/56noKGhEsi++y7MnXvvupVRoxwMZA8flht8TIyMzmogq5RSSj2cgwehQwcZiQ0MlLWaHhxE3S8Phy2QLRl6lJ5zp8QHsm3ayDIpswNZkDWy+/dLMk2lVLJ0zay7OH8eXngBqlSJX59SqZLbBrLl/9pDx2U/x+2Pff9DGod8dN/yO/PmyXKbDRskKeKGDXDihIOBbGSkZHK+fBmmTYOGDR/uB1JKKaUUZM8Op0/LGs0DBzw6kIXk83DYAtni547TY9YkfG9HyRvNm0vnxMzlXDdvQtu28Oefsq2BrFIPpCOz7mDRIln7efGiBGpuyhbIPnnkL15e9BMWW5KEfv3Y3Pgr/h2W/LG28jvbtj1E+R2ArFnhvffgxg2pc6eUUkqplNm8WaYT168vU6WOHYM8ecxulVPUrHlvvg5bIFv0wkl6zphE5pib8kbjxjB/vrnBo9UKnTtLnzBvXhg3zry2KOVBPGZk9ssvv6R69epkzpyZHDlymN0c57h4Uab0tG4N165J8fEVKyRrnZuxBbKPHT9C9wUz4pIkHK/TnY0tRhJ61r4kCSkuv3P7NtiC5969JaBVSimllOMiImQEtnZtePXV+IoAbh7IWq2yFnbuXPnTlq8pKd7eUn4HJD+HLZAN/O8MPadPJOvt6/JmvXqS1tjPL7WbnzzDgAEDJJBt0EDWXyml7OIxI7PR0dG0bduWatWqMWXKFLOb4xz9+kkt1mefhalTZX2EG7i7uHi0ZScrN26n2Kl/eO2XaXjd+dKbQ0de3jiR2I1edn//paj8TmysJKHw8pKpxb6+KTiJUkoppVi5UmaDnTkDZctK/8MDprPaUy/2bq1bS4LJr749SbUGyyl0JZTXpv5I9uhr8oFnn5XcG5kypf4PcD8jR0oZpKeekgZrP0cpu3lMMDt48GAApk+fbm5DHta1a5Atm/z9m2+kbk3v3hKouYG7vyyerrmTGvW3Exh6mjd/noz3rVsALKYFXZlBLJIk4dKl+583xeV3AN5/Xxbc1q4dPzqrlFJKKfvduAFvvCGZejNkgMGD4cMPPSJwstWLTSrBZJs296+IUPqpk9R9fjk5Lpyjz6yJZIu6Uy6palX49Vfzc5MEBcE770gnacUK8Pc3tz1KeRj3iKBSSVRUFBEREYlepjEMmRdTrJhkKQZ45BHo29etAtmExcVtgWzB82d5ddpkfG7cAGA1z9Gen4kh6Se5d5dls22PGpWCBIHffw/Dh8OTT8LixeCO9fiUUkopd5cxI/zzjySa3LMHBg3yiED2QfViAfr3T3rK8aFjJ5k6bznZLl3grTk/ki3isrxRsSKsWiVJr8xWvrwE1itWQOHCZrdGKY/jHlFUKhk6dCj+/v5xr8DAQHMaEhYGrVpJ7bJbt2StrJu5+8vCFsjmvXSBVydPwj9GHgRsohatWEQ0ya8tuXvKcUBACurIgkTX/ftDoUJyk08ra6WVUkopV/PykjWZ27bJA2IPERKSeGrx3WwJJkNCEu+3BbJZL1/i7Tk/kvW/O1PIypaFNWvcp09RvDhs3y7tUko5zNRg9sMPP8Risdz3dfjw4RSff+DAgYSHh8e9zpw548TW28EwYNYs+dJYskSyBf71l1tm4U34ZWELZHNe+Y9XJ04m9+0rAOygCi+wnJtkvu+5Ro58yPI7II3p3FmyF69YIaPYSimllEq5PHlcXkPVkaRNSbE3cWTCz9kC2cxXL9N/7kSyXbwgb5QuDWvXQu7cjjXC2S5fhrp1Yfdu2b57SptSym6mrpl95513eOWVV+77meLFi6f4/H5+fviZmZ0uKEgSF2XLJsXHe/Z02xuW7UvAFsj6R1yl+49TyR8tXwB7KUdjVhFJtgeeq3Dhhyy/AzKc+/33ULQolCv3kCdTSimllKulJGnT3exNHGn7nC2QzRh+lf7zJuN/7k4H59FHYd06yJfP/h8gNURFQcuWMoqwbBlUqmRue5TycKYGs3nz5iVv3rxmNsH5DANiYiS5QsuWsqi/Xz+3H1ksWDA+kM0aeY2u46cTcCsUgEOU4jnWcJWc5M0ryZ6SWrvyUEmebMLDJfj38pLgXymllFIe52GSNiWUVL3YhBL2PWyBrO+1CN6eP4WcoXdm5BUrBuvXp7CkghMZBnTvLoFshw6yblkp9VA8Zs3s6dOn2bt3L6dPn8ZqtbJ371727t1LZGSk2U2Ld/o0NGkCAwfKtrc3fPed2weyIOV3atTfTuYb13l5/EyK3TwJwHGK04C1XLLkIzAwvoa3U5M82Vy/LvXVOnSIr3mnlFJKKY/yMEmb7nZ3vdiEEvY9/j4hgaxP5DXe/mUquU6flDcDAyWQNStvSkKffiprsGrUkFKDbpIAVClP5jG/RYMGDaJChQp8+umnREZGUqFCBSpUqMCuXbvMbprcmSdOhDJlJFPx0aOOLwoxUXCI1JH1i7pFx/FzeOz6UQDOEEAD1hJmkex6o0bFP029O+FeipM82Vit0LEj7Nolael9PKZqlFJKKaUSSGnSpuTY6sUm1/co/ZQEst7Xr/P2whnkOXFcPlCwoEwtLlo0RT+HU82eDUOGyHRnrc6glNN4TMQwffp096wxe/Ik9OghN8ucOaV+20svue3a2LsFh+xkxfrtZIiO5oMVP5Pz2gEAzpOPBqzlJMUIDJBA1haotm4NLVrIl1BYmHxX1Kz5ECOyhiGPaJctg8aNYfx4j/nvp5RSSqnEUpK06UGS63vYRmS9bt6g/6KZ5Dt2RA7Il0/6ZiVLOv4DpIZnn4XatWHSpHvLPiilUsxjglm3FB4OFSrA1atyhx0/3vz1GA6wBbLeMTG8vzaInPv2AGDkysXpb9fyWabHkw1Uvb2dkOTJZvRo+OEHSfQ0f76OyiqllFIezNGkTfa6u+9hWyNruXWTt5bMpsCRg/JGrlyStbh0accukBoMQx7QFyki6ZyVUk6lUUNK2G5M/v7wv//JOoz27T1qNNEWyHpZrby/aRl5dv4mb2TLhmX1ap6uXJanXdGQI0ckSVbhwrB8uSR/UkoppZTHciRpU0rZAlkjKoq3ls+j0IE/5Q1/fwgOdo+6rRcvQvPmMr2talWzW6NUmuQxa2bdQmysjCLWqhWfoOi99yRhkQcGspbYWN7dvoZ8IZvkjcyZpaZr5cqua8zjj8P06fDrr/LNppRSSimPZm/SppQuT0oYyPZbOZ+AfX/IG9mySe6SihVTdmJnunVLqlr89hts2GB2a5RKszSYtdfff0sQ27+/jCb+/bfZLUoRWyCLYTDgj00UXLta3vDzgyVLZE2HK1y4EJ8k6+WXtZasUkoplYY8KGlTShNG2gLZ2Ohoeq8J4pHdO+UN2wN5dxgBNQzJp7JtG3TpAh98YHaLlEqzdJrxg1it8vjw44/lKVuHDvD99+CB9XETBrJv/7WdgOVL5A0fH/jlFymL4wpXrkgShMcfl280XSOrlFJKpTnOThhpC2Stt2/Te/1Siu3cJm9kzChJJF31QP5BvvxSshc/+6xUu/Cg2XtKeRqNIh6kSxepCZY/v/zZqpXZLUqRuEAW6LJ7H48snw+A4eWFZfZsaNbMNQ2JjpZvt8OH5RtOA1mllFIqzXJWwsiEgezrm36lxNY7S6R8faXUTb16D38RZwgKgk8+geLFYdEimfmmlEo1Gkk8SK9eUtR61CjIndvs1qRIwkC27NwjVDgyPe69Af5TqenTjpSWh3WIYcDrr0s2v7Zt4auvXHFVpZRSSnkwWyAbExNDr21reGzTOnnDx0dmeDVqZG4DE6pSRaL3ceO0BI9SLqBrZh+kVi2YNStNBLKBM0/R/cj4uPfeZCyjr3alTRt5kJjqhg2DadNkPcuMGfKQQCmllFIqGQkD2R47N1Bq7Sp5w9sb5s1z3cwyewUESMIndygLpFQ6oNFEGpYokF14gQH/jIx77z2GMZ4341Lm9+8fn48pVRw5ImWMihSRRFOZMqXixZRSSinl6RIGst3+2MKTK5fKGxYLzJwJL75obgNtrl2D+vVhyxazW6JUuqPBbBqVMJBtdjac/vuHxr33GZ/yHe/FbRsGnDkjCRpSzeOPw9y5Uks2f/5UvJBSSimlPF1cIGu10uWvHTy1bGH8m1OmQKdO5jUuIasVOnaE9etl7a5SyqV0zWwalDCQ7eoTxVOTP8cLGYL9jncYzKdJHhcWlgqNuXgRcuaUdS3t2qXCBZRSSimVliQMZDsf2k2FhfPi35wwAbp1M69xd3v3Xfj1V3j+efjmG7Nbo1S6oyOzaUzCQPblLFB+8Md4xcr84XG8wXt8CySdIr5gQSc3xjbtpnlzyWKslFJKKXUfCQPZjsf+pPLPs+LfHD1aEnO6iwkTJEFo2bIy+yylNYeUUimmwWwakjCQ7ZzTl4qffAi3bwPwS+Yu9OUHkgpkLRYIDJTab04TEyM1effvh5IlJXW+UkoppVQyEgay7U4dpspPU+PfHDYM+vUzr3F327gR+vSBfPmkxm22bGa3SKl0SYNZD2e1yv10+A/xgWzH/Fmp/L/34NYt+VDbtvhMn4Jh8bqnbrdte9QoJz9QfPddWLFCpt2MGOHEEyullFLm+vLLL6levTqZM2cmR44cdh3zyiuvYLFYEr0aN26cug31IAkD2RdDj1Ft2oT4Nz//HN57L/mDzfDYY1C9uqyTLVLE7NYolW7pmlkPFhQEb70FBYvtpEZ9CWRvrs1N+T194fp1+dDzz8NPP9HK14cF3vL5f/+NP0dAgASyrZ1ZaHbiRJkKpNNulFJKpUHR0dG0bduWatWqMWXKFLuPa9y4MdOmTYvb9vPzS43meZyEgWyrC6eoMXls/JsffQSffGJe45JTqBBs2sQ9owRKKZfSYNZDBQVBmzZQ+dn4QDZ0WQHG7u6DL1flQ/XqSTHxO1N8W7eGFi0ka3FYmKyRrVnTybHmmTPQt69Ou1FKKZVmDR48GIDp06c7dJyfnx8FChSw+/NRUVFERUXFbUdERDh0PU+QMJBtfvksNSeMxmKrG/jOOzBkiLkNTMhqhVdfha5doW5dDWSVcgMazHogq1VGWBMGsieXBzJ2dx/ycgn+396dx0VZ7v8ffw0gioq4IUriApb7vqWluZBi5dFcMo8ppKfTKa1Mra/mSfSUmUdTyyztdHJLTTuJ9rPcc08zMSw3CncRNTMg3FC4f3/cMTKyCApzM/B+Ph7zkLnva+b+3Dcyc33uawP2eLal6fKVuJco4fBad3fo0CEfgwsIMBcxr1RJ3W5ERETS2bx5M5UqVaJcuXJ06tSJN998kwoVKmRZftKkSfbEuTBKn8g+lnieDrPewZaaau4cNgymTClYCePYsTB/Pvz+u5nMiojlNGbWBW3b5ti1OOarmkzfMwJ/zLV1ImlG5+Sv2fZDaecF9ccf5qRPAI8/Dg884Lxji4iIFHAhISEsWLCAjRs3MnnyZLZs2UK3bt1ISUnJ8jVjxowhISHB/jh16pQTI85f6RPZbpcv0mnmFGxp1+KZZ8zhSgUpkf30U3PpnTp1YMECq6MRkT+pZdYFRe6/mcgeXn0f737/EjU4AcB+6tOVtSTikz/rxmbmxg3o29dsMo6IgNJOTKJFRETywOjRo5l8m3VCDx06RJ06de7o/Z988kn7zw0bNqRRo0YEBQWxefNmOnfunOlrihcvXijH1aZPZLteS+Dhdydj+3P1BUJDzSVv3ApQe8t338Hf/gblysGXX4KPj9URiciflMy6mPXbdnP6NzOR3b+mHtO+G8F9/ALAz9xLMBv4jYpAPqwbm5VRo2DtWnM92ZIlnXRQERGRvDNy5EjCwsKyLRMYGJhnxwsMDKRixYrExMRkmcwWRukT2YdTLtN1xtvY0sYFP/kk/Pe/BSuRjY01e5zduAHLlpnLDYpIgaFk1oWkX0f26LcNmbTrVRqyH4ATVCOYDZyjMjabOUtxnq4bm5X//OfmzMWfflqwvoBERERyyNfXF19fX6cd7/Tp0/z2229UcdqdZ+ulT2Q725LpNm0ititXzJ2PP2523y1oKyB4eECNGjBmDAQHWx2NiNxCmYeLSJ/IPtamCZ+ceJ0WRAJwhip04htOUS3/1o3NzObN8Pzz4OurmYtFRKTIOHnyJFFRUZw8eZKUlBSioqKIiooiKSnJXqZOnTpEREQAkJSUxCuvvMKuXbs4fvw4GzdupEePHtSqVYuuXbtadRpOlT6R7VgshUffeRNb+mUEP/sMihWzNsjM+PmZS/AMG2Z1JCKSCbXMugCHRPaBZnR+ewJEfwvAb24VCU7dwFGCgHxaNzYzf/wBTzxhtsRGRGjmYhERKTLGjRvH/Pnz7c+bNm0KwKZNm+jw55IB0dHRJCQkAODu7s6PP/7I/PnziY+Px9/fny5duvDGG28UyjGxt0qfyD5Uwkb3t9/AlrbMUJcuDssIFhjTp0ODBvDwwwUzyRYRAGyGkbaYV+GXmJiIj48PCQkJlClTxupwciR9IvtouxYET5sEX31l7ixblpT137AtqWn+rRubnVWrID4ennrKSQcUkZxwxc86Ebk9V/zbTp/ItivlweNvh2P77TdzZ4cOZp2moM23sWKF2e25Vi04eFDJrIgFcvp5p5bZAswhkW3fkuAPZ9xMZEuVgtWrcW/RlA7ODCo11Zy1uFgxeOwxZx5ZREREXEj6RPZBb08enzz+ZiL7wAPmEKWClsgePAgDB5r1rOXLlciKFHAaM1tApU9kH+nQmuB5c+Dzz82dJUqYraL33+/8wN580+wSdOGC848t+e7UqVN06NCBevXq0ahRIz5P+z8nIiKSC+kT2bZlS9LrnTexnT9v7mzVCr7+uuAt5RcfDz17QlKSORlVw4ZWRyS5pHpM0aOW2QLIIZHteD8PL5l3c4HuYsXMMap/jslxqhUrIDwcAgML1kLmkmc8PDyYMWMGTZo04ezZszRv3pxHHnmEUqVKWR2aiIi4iPSJ7P0VvOkzZQK2M2fMnU2awJo1UNC6SaemmsOmfvkFxo51wuQjkh9Ujyl6lMwWMBkS2S8/NxcPB3Mw7NKlEBLi/MAOHLjZ7WblSqhQwfkxSL6rUqWKfZmIypUrU7FiRS5evKgvARERyZH0iWxrXx+eeOcNbCdPmjsbNID166FcOWuDzMwff0BiIjzyCEyYYHU0codUjyl61M24AHFIZDu14eFvVsM775g7bTaYP9+ckMDZLl6EHj3MbjcLF5pfRuKSHnroIWw2GzabDU9PT+rWrcvixYszLRsZGUlKSgoBAQF5HsesWbOoUaMGJUqUoHXr1uzevTvb8n/88QfDhw+nevXqeHl50bZtW77//nuHMikpKbz++uvUrFkTLy8vgoKCeOONN7h1jrvcHltERHImfSLbyq8c/d6dhO3oUXNn7dqwYQNUrGhtkFnx8YGNG2HJkoK31q3YFeZ6zPjx4+3nlvaoU6eOQ5mtW7fSvXt3/P39sdlsrFixIq9PzeUomS0gMiSyu7Y43hmcMwcGDHB+YIZhHvfIEbOLsRXJtOQJwzD44YcfmDp1KnFxcURHRxMSEsKgQYM4duyYQ9mLFy8yaNAgPvroozyPY+nSpYwYMYLw8HD27t1L48aN6dq1K+fTxlJl4m9/+xvr169n4cKF/PTTT3Tp0oXg4GBiY2PtZSZPnsyHH37I+++/z6FDh5g8eTL//ve/mTlz5l0dW0REbi99ItvSvyJPzpyM7eefzZ1BQWai6OdnbZCZOXjQ7PYM5lCugtb9WewKez0GoH79+sTFxdkf27dvd9h/6dIlGjduzKxZs/L8vFyWUYQkJCQYgJGQkGB1KA7Wbf3OGD5+hjF8/Axj3dbvDGPWLMMw00jzMX26tQGuWWMYYWGGkZJibRxyV6Kjow3A2L9/v33bTz/9ZADG6tWr7duuXr1qtGvXzliwYEG+xNGqVStj6NCh9ucpKSmGv7+/MWnSpEzLX7582XB3dzdWrVrlsL1Zs2bG2LFj7c8fffRRY/DgwQ5levXqZQwYMOCOj+2qCupnnYjcnYL6t33wl2PGqDdmGsPHzzAWfrzYSG3c+GYdpnp1wzhxwuoQM/f774Zx772G4eZmGD//bHU0chuFvR4THh5uNG7cOMdxAEZERESuYnclOf28U8usxTK0yB45CEOH3izw5pswfLg1waXp2hXmzgU3/XdxZZGRkZQrV4569eoBcPr0acaOHUvx4sVp1KgRYN71DAsLo1OnTgwcODDL93rrrbcoXbp0to+TaWOk0klOTiYyMpLg4GD7Njc3N4KDg9m5c2emx7px4wYpKSmUKFHCYbuXl5fDHcu2bduyceNGfv6zJWDfvn1s376dbt263fGxRUQke+lbZJtVq8yA2dOw7dtn7rznHvjmG6hWzdogM5N+wqfRo+Hee62OSG6jsNdjAH755Rf8/f0JDAxkwIABmcYgjjQBlIUyJLJnjsGQITcLjB4Nr71mTXDff28ee9EiqFTJmhgkT+3du5eEhAS8vb1JSUnh6tWreHl5MXv2bPz9/QHYsWMHS5cupVGjRvZxGAsXLqThLcsT/OMf/+CJJ57I9nhp75nehQsXSElJwe+WrmZ+fn4cPnw40/fx9vamTZs2vPHGG9StWxc/Pz+WLFnCzp07qVWrlr3c6NGjSUxMpE6dOri7u5OSksLEiRMZ8Gf3/Ds5toiIZC19Itu0ehWemjMd25495s7Klc1ENjDQ2iCzEh4OX30F3brBv/5ldTSSA4W9HtO6dWvmzZtH7dq1iYuLY8KECbRr1479+/fj7e192+tTVCmZtUiGRDb+nHmHMDXVLPDCC/DWW9YsgXP+vDkl/ZkzsH8/dOrk/Bgkz+3du5ehQ4fy4osvEh8fz6hRo3jggQcICwuzl3nwwQdJTfs/mI3y5ctTvnz5fIzW0cKFCxk8eDD33HMP7u7uNGvWjP79+xMZGWkvs2zZMhYtWsTixYupX78+UVFRDB8+HH9/f0JDQ50Wq4hIUZA+kW1S8x4Gfvwetl27zJ0VK5pjZO+7z9ogs7J8udnzrVYtWLxYEz65iMJej0nrSQbQqFEjWrduTfXq1Vm2bBlD0jd2iQP1G7VAhkT2WiL07Qs3bpgFBg+GGTOsSWRv3IB+/eD0aXj7bSWyhcjevXtp27YttWrVokWLFnzwwQdMnjyZ48eP5/q97rR7TsWKFXF3d+fcuXMO28+dO0flypWzPF5QUBBbtmwhKSmJU6dOsXv3bq5fv05gujv+r7zyCqNHj+bJJ5+kYcOGDBw4kJdffplJkybd1bFFRMSRQyIbGMDABbOxbdtm7ixXzpy1+M+uoAXS/PlQujSsWAFly1odjeRQYa/H3Kps2bLcd999xMTE5Pr8ihIls052ayLbKTWZlO494No1AFL7PQkffWTd+NQxY2DzZujTB0aNsiYGyXNHjx4lPj6eBumWVapXrx5BQUFZTmmfnX/84x9ERUVl+8ise46npyfNmzdn48aN9m2pqals3LiRNm3a3Pa4pUqVokqVKvz++++sXbuWHj162PddvnwZt1v+btzd3e13aO/22CIi4pjINg6qxsAlH+OW9rlapgysWweNG1sb5O188QVs2wb161sdieRQUajH3CopKYkjR47Y182VzKmbsRPdmsi673Tj8uhH8DYuA7CSvzB8+wLeWelOr14WBLhsGUydat5N/eQTa1qGJV9ERkZSrFgx7ruly1fnzp2JiIjgtVyOzb6b7jkjRowgNDSUFi1a0KpVK2bMmMGlS5d4+umn7WXef/99IiIi7F8Wa9euxTAMateuTUxMDK+88gp16tRxeE337t2ZOHEi1apVo379+vzwww9MmzaNwYMH5+rYIiKSufSJbKNa1Rm0bC5uq1ebO0uXNpe4adHC2iCzkpoK330HbdqAhwc0aWJ1RJILRaEeM2rUKLp370716tU5c+YM4eHhuLu7079/f3uZpKQkh5baY8eOERUVRfny5alWECdacwIls05yayJr2+NF4//rgDd/ALCWLjzBMq6fKUafPvC//+H8hLZ8eahZ0xxLooHmhcrevXu599578fT0dNgeHBzM7NmzOX36NFWrVnVKLP369ePXX39l3LhxnD17liZNmrBmzRqHyRQuXLjAkSNH7M8TEhIYM2YMp0+fpnz58vTu3ZuJEydSrFgxe5mZM2fy+uuv8/zzz3P+/Hn8/f159tlnGTduXK6OLSIiGTkksvfWIDTiU9y+/NLc6eVlTqZUkHu5jB9vjpNdssQcTiUupSjUY06fPk3//v357bff8PX15cEHH2TXrl34+vray+zZs4eOHTvan48YMQKA0NBQ5s2bl49nXXDZDMMwrA7CWRITE/Hx8SEhIYEyTlwUO0PX4opludigHb6p5uLKW2lHCGu4QknAbBCtWhWOHbNgToLr181Fw0XEZVn1WSci+cuqv22HRPa+GoR+9TluixaZO4sXh1WrIN1SJQXOl19Cjx7mhE/ff69xsiIuIKefdxozm88yTPYUUInrD3W2J7Lf0YrHWGVPZMFcZfzUKXM4R75LTYXXXzdnLgYlsiIiImKXPpFteF9NQjd8eTORLVYMIiIKdiJ75AgMGgQlS5qxKpEVKVTUzTgfZUhkA++B9u0p8etpAPbRiG6s5g8yv9sQF+eEIN96y+x2c+IELFjghAOKiIiIK3BIZGsHErZ1NW6ffGLu9PCAzz8312ktqK5cMSe0TEiATz+FdJMHiUjhoGQ2n2RIZGvXgIcegqNHAThMbR5mPb+T9eDzfJ+8bM0aGDcOatQwlwISERERIZNEdtdG3D780Nzp5mauz5rNTKwFwgcfQFQUPPccDBhgdTQikg+UzGYjJcXs6hsXZyaW7drlbAxrhkS2fi1zvdbDhwEwatZk4NWNXDhbCTIZsZw2ZrZdu7w8m1scPQp//as51mX5cnPyJxERESnyMiSyUTtwe/ddc6fNZq7T2revtUHmxEsvgacn/P3vVkciIvlEY2azsHy52WDZsaOZ83XsaD5fvjz712VIZJvUNbvg7NtnFqhaFdvGjYx5/x4g4+o3ac9nzMjHyZ8uXzanSv79d3NN26ZN8+lAIiIi4kocEtk6QYQejsRt8uSbBf7zH3jqKesCzInL5pKHeHjACy+YN+5FpFBSMpuJ5cvNIRanTztuj401t2eV0GZIZJs3gMceg927zQKVKsGGDVCzJr16mcvv3HOP43tUreqEZXliYsyTGTYMBg7MxwOJiIiIq8iQyB79CfcJE24WmDULhgyxLsCciI8315AND7c6EhFxAnUzvkVKitkrJbMFiwzDbDkdPtwcJpK+5TRDItuqsVlo61azQLlysH491K5tf02vXmaRO+nKfFcaNYIffjCTaxERESnUcjJsKkMiG/sz7q+9drPAtGnw/PPODTy3DAOefhp++SXzipyIFDpKZm+xbVvGFtn00i+b06GDuS1DInt/U3jiCVi71izg7W3+3KhRhvdzd7/5Pvlu/35zbKy/v9kELCIiIoXa8uXmTfr0dZuqVeHdd2/2AsuQyF44gfvIkTdfMGkSvPyycwO/E1OnwooV8PDDapkVKSKUzN4ip8vhpJXLkMi2bW523V2xwizg5QVffw0tW+Z9sLkRH282A1++DNHR4MTF1kVERMT50oZN3dpImTZs6n//g7qNbklk/ziL+7BhNwuHh8Po0c4N/E5s3QpjxpiZ+qJFTujmJiIFgcbM3iKny+FUqZJJIvtAC3j2WViyxCzk6QkrV8KDD+ZTtDlkGDB4sDmD8bPPKpEVEREp5G43bApg0pRbEtnk33FPP/Pv6NGu0cJ5/jz062eOBVu2DHx9rY5IRJxELbO3aNfOvKkXG5v5F0DasjnJtt2sTp/IPtjSHEz73/+aBT08zFueDz/svOCz8u67EBEBnTvD669bHY2IiIjks9sNm6oWdJz7g9MlslzCPSzsZuVn+HB4662Myy4URBUqmDftK1eGNm2sjkZEnEjJ7C3c3c3cr08f8/M7fUKb9nk+4rXdrN6cLpFt1wrGjoX33jMLuLnBp59C9+5Ojj4Tu3bBK6+YTcnqdiMiIlIkZDdsqnqt43R/chUeHimUKx1EqOd13J94ClJTzQLPPWdO+OQKiSyYdZuJE62OQkQsoG7Gmchu2ZxpH+zmxLlbEtm33jIfaf77X7O7i9UMw1x+JzUVPvsM/PysjkhERMSlHT9+nCFDhlCzZk28vLwICgoiPDyc5OTkbF939epVhg4dSoUKFShdujS9e/fm3Llz+RZnVsOm0ieyMYeC+EsyuPfrBzdumAUGD4b333eNRHbFCrMxISXF6khExCJqmc1CZsvmJNsyaZGdMcP8IE0zaxaEhVkScwY2mzlmd8sWaN/e6mhERERc3uHDh0lNTWXOnDnUqlWL/fv388wzz3Dp0iWmTp2a5etefvllvvrqKz7//HN8fHwYNmwYvXr1YseOHfkSZ2bDpm5NZL03edJ4xV/g+nWzwFNPwUcfmT3MCrqYGAgNNZPwp5+GWrWsjkhELGAzjKKzEFdiYiI+Pj4kJCRQJpeTIGWY7KldK/jPfyD9RAn//rfZpbcguHoVSpSwOgoRscDdfNaJSO5NmTKFDz/8kKNHj2a6PyEhAV9fXxYvXkyfPn0AMymuW7cuO3fu5P7778/RcXL7t502mzGYY2TTJ7JJn5divcejeCRfMQv07QuLF5tzfhR0V66YY2P37TOHUP31r1ZHJCJ5LKefdy5w6+3Ou/TklUwT2UWLzJmB04wbV3AS2S1bIDAQNm+2OhIREZFCLyEhgfLly2e5PzIykuvXrxMcHGzfVqdOHapVq8bOnTuzfN21a9dITEx0eORG2rCpZq0dE1n3LeVY5/mXm4lsjx5mvcYVElmAoUPNRPb555XIihRxLpHMpu/Sc+DAAaZPn87s2bN57bXX8v3YmSayERFm15a0Ru2RI2H8+HyPJUfOnYP+/eHCBXONWxEREck3MTExzJw5k2fT3+C+xdmzZ/H09KRs2bIO2/38/Dh79myWr5s0aRI+Pj72R0BAQK7jq9voOB0fvTnZ07gOlYi48ijFriaZBbp1g6VLoVixXL+3Jf77X5g7F1q1MiepEpEizSWS2ZCQEObOnUuXLl0IDAzkL3/5C6NGjWL58uXZvu5u72hmmsiuWWNO7pQ22cA//gFTphSMiRJSUmDAAHOQ75Qp0Lq11RGJiIi4hNGjR2Oz2bJ9HD582OE1sbGxhISE0LdvX5555pk8j2nMmDEkJCTYH6dOncrV6w/FOK4j+1pHf1q+FoItIcEs0LkzfPEFFC+e57Hnm7g4qFjRXE/WleIWkXzhIv1JMrpdlx4w72hOmDDhjt4/00R282Z4/PGbEyUMGmRO+FQQElmAN96AjRvNfkUvvmh1NCIiIi5j5MiRhN1mAsfAwED7z2fOnKFjx460bduWjz76KNvXVa5cmeTkZOLj4x1aZ8+dO0flypWzfF3x4sUpfocJ262J7KB61fHo3Bl+/90s0K6dOUmkq/Xi+uc/zW7G5cpZHYmIFAAuOQFUTEwMzZs3Z+rUqdneCb127RrXrl2zP09MTCQgIOC2A4kzTWR37YLgYLh0ySxU0CZK2LABunSBmjUhMhJu6cokIkWHJoASyV+xsbF07NiR5s2b8+mnn+J+mzXc0yaAWrJkCb179wYgOjqaOnXq5MsEUBkS2UZBeHTqBGldmu+/H9atA2/vnJ1wQbByJTz6aMGpd4lIvnKJCaDyu0tP8eLFKVOmjMPjdjJNZKOizDElaYnso4/Cp58WrA/UunXN7kKff65EVkREJJ/ExsbSoUMHqlWrxtSpU/n11185e/asw9jX2NhY6tSpw+7duwHw8fFhyJAhjBgxgk2bNhEZGcnTTz9NmzZtcpzI5lSGRLZPNzz27YPz580CzZvD6tWulcguXQo9e8Lw4VZHIiIFjKXZWH526bkTmSayhw7Bww9DfLxZqFMnc2pAT888P/5dueceWL/e6ihEREQKtfXr1xMTE0NMTAxVq1Z12JfW2e369etER0dz+fJl+77p06fj5uZG7969uXbtGl27duWDDz7I09gyTWTd3eHJJ80CU6bA2rWuddP72DFzGURvbyWzIpKBy3Qzzm2Xnsxk11ydaSJ75Ig5piQuzizUtq35JVC69F2fT5557z2oV8/sAi0igroZixRW2f1tZ5nIppeSAndQf7LM9evw4IOwe7fWkxUpYnJalylA/WSzltalp3r16vYuPWmymzghpzJNZE+fNrvtpiWyzZrB118XrER2xw54+WWoUcNsQS5orcUiIiKS73KUyIJrJbJgTva0ezeEhSmRFZFMuUQym5MuPXf83pklsgA+PuZkSidOQP36Zousj89dHStPJSSYy/DYbOb4XSWyIiIiRU6OE1lXc+QITJ0KtWvDzJlWRyMiBZRLrDMbFhaGYRiZPu5GloksmGMzvv4annvOnCm4YsW7OlaeMgxzfdsTJyA8HNq0sToiERERcbJCm8gCBAXBmjXw2WcFq1eciBQoLtEymx+yTWTTeHlBHk/OkCcWLDA/3Nu1g9deszoaERERcbJCm8imppo37d3dzQk4RUSy4RIts3ntm2/33D6RLagMw0xky5Y1uxcXhi8uERERybHoIycKZyILZtfi9HOWiIhko0i2zK7b8j3FS5RwvUQWzDGy/+//weHDUK2a1dGIiIiIky38Yg3uxTwLXyL73Xcwdiz4+WkuEBHJkSLZMgsu2CILcPy4+a+HBzRoYGkoIiIiYo0bKamFL5FNSID+/c1uxosWQYUKVkckIi6gSLXMpk0Y9WCLerRuXIfExESLI8qFTZugVy+YNMmc/ElEJAtpn20usoy4iORQ2t90YIAfPbs8wOVLlyyOKI8YBgwZAseOwejR0LQpuFIdTUTyXE7rMjajCNV2Tp8+TUBAgNVhiIg4xalTpzIsZyYirkv1GBEpam5XlylSyWxqaipnzpzB29sbm8122/KJiYkEBARw6tQpypQp44QIC56ifg10/jp/Vzx/wzD4448/8Pf3x82tyI4mESl0cluPSc9VP8+spGuWO7peuaPrlb2c1mWKVDdjNze3O2qlKFOmTJH/T1bUr4HOX+fvaufv4+NjdQgiksfutB6Tnit+nllN1yx3dL1yR9crazmpy+iWvYiIiIiIiLgcJbMiIiIiIiLicpTMZqN48eKEh4dTvHhxq0OxTFG/Bjp/nX9RPn8RKTz0eZZ7uma5o+uVO7peeaNITQAlIiIiIiIihYNaZkVERERERMTlKJkVERERERERl6NkVkRERERERFyOklkRERERERFxOUpmc+j48eMMGTKEmjVr4uXlRVBQEOHh4SQnJ1sdmtNMnDiRtm3bUrJkScqWLWt1OPlu1qxZ1KhRgxIlStC6dWt2795tdUhOs3XrVrp3746/vz82m40VK1ZYHZLTTJo0iZYtW+Lt7U2lSpXo2bMn0dHRVoclIpInVJ+5M0WtDpRbRbnOlFtFuY6VH5TM5tDhw4dJTU1lzpw5HDhwgOnTpzN79mxee+01q0NzmuTkZPr27ctzzz1ndSj5bunSpYwYMYLw8HD27t1L48aN6dq1K+fPn7c6NKe4dOkSjRs3ZtasWVaH4nRbtmxh6NCh7Nq1i/Xr13P9+nW6dOnCpUuXrA5NROSuqT5zZ4pSHSi3inqdKbeKch0rP2hpnrswZcoUPvzwQ44ePWp1KE41b948hg8fTnx8vNWh5JvWrVvTsmVL3n//fQBSU1MJCAjghRdeYPTo0RZH51w2m42IiAh69uxpdSiW+PXXX6lUqRJbtmyhffv2VocjIpLnimp95k4UhTpQbqnOdOeKeh0rL6hl9i4kJCRQvnx5q8OQPJacnExkZCTBwcH2bW5ubgQHB7Nz504LIxMrJCQkAOhvXUQKLdVn5E6pziRWUzJ7h2JiYpg5cybPPvus1aFIHrtw4QIpKSn4+fk5bPfz8+Ps2bMWRSVWSE1NZfjw4TzwwAM0aNDA6nBERPKc6jNyN1RnEqsV+WR29OjR2Gy2bB+HDx92eE1sbCwhISH07duXZ555xqLI88adnL9IUTF06FD279/PZ599ZnUoIiLZKur1mTuhOpCI6/OwOgCrjRw5krCwsGzLBAYG2n8+c+YMHTt2pG3btnz00Uf5HF3+y+35FwUVK1bE3d2dc+fOOWw/d+4clStXtigqcbZhw4axatUqtm7dStWqVa0OR0QkW0W9PnMnVAe6e6ozidWKfDLr6+uLr69vjsrGxsbSsWNHmjdvzty5c3Fzc/2G7dycf1Hh6elJ8+bN2bhxo31AfmpqKhs3bmTYsGHWBif5zjAMXnjhBSIiIti8eTM1a9a0OiQRkdsq6vWZO6E60N1TnUmsVuST2ZyKjY2lQ4cOVK9enalTp/Lrr7/a9xWVO08nT57k4sWLnDx5kpSUFKKiogCoVasWpUuXtja4PDZixAhCQ0Np0aIFrVq1YsaMGVy6dImnn37a6tCcIikpiZiYGPvzY8eOERUVRfny5alWrZqFkeW/oUOHsnjxYlauXIm3t7d9zI+Pjw9eXl4WRycicndUn7kzRakOlFtFvc6UW0W5jpUvDMmRuXPnGkCmj6IiNDQ00/PftGmT1aHli5kzZxrVqlUzPD09jVatWhm7du2yOiSn2bRpU6a/69DQUKtDy3dZ/Z3PnTvX6tBERO6a6jN3pqjVgXKrKNeZcqso17Hyg9aZFREREREREZdTNAdJiIiIiIiIiEtTMisiIiIiIiIuR8msiIiIiIiIuBwlsyIiIiIiIuJylMyKiIiIiIiIy1EyKyIiIiIiIi5HyayIiIiIiIi4HCWzIiIiIiIi4nKUzIpTbN68GZvNRnx8vNWh5IrNZmPFihV59n41atRgxowZefZ+znb8+HFsNhtRUVGA6/5eRUREcsNVv+9Uj3Gkekzho2RW7prNZsv2MX78eKtDvK3x48fTpEmTDNvj4uLo1q2b8wMqAMLCwujZs6fDtoCAAOLi4mjQoIE1QYmIiOQx1WMKJ9VjigYPqwMQ1xcXF2f/eenSpYwbN47o6Gj7ttKlS7Nnzx4rQiM5ORlPT887fn3lypXzMBrX5+7urmsiIiKFiuoxRYfqMYWPWmblrlWuXNn+8PHxwWazOWwrXbq0vWxkZCQtWrSgZMmStG3b1uHLAmDlypU0a9aMEiVKEBgYyIQJE7hx44Z9/8mTJ+nRowelS5emTJkyPPHEE5w7d86+P+3O5Mcff0zNmjUpUaIEAPHx8fztb3/D19eXMmXK0KlTJ/bt2wfAvHnzmDBhAvv27bPfhZ03bx6QsXvO6dOn6d+/P+XLl6dUqVK0aNGC7777DoAjR47Qo0cP/Pz8KF26NC1btmTDhg25upYpKSmMGDGCsmXLUqFCBV599VVCQ0Md7ixm1sWnSZMmDneOp02bRsOGDSlVqhQBAQE8//zzJCUl2ffPmzePsmXLsnbtWurWrUvp0qUJCQmxf6GPHz+e+fPns3LlSvs12bx5c4buOZnZvn077dq1w8vLi4CAAF588UUuXbpk3//BBx9w7733UqJECfz8/OjTp0+urpGIiEheUj1G9Zj0VI9xLUpmxanGjh3LO++8w549e/Dw8GDw4MH2fdu2bWPQoEG89NJLHDx4kDlz5jBv3jwmTpwIQGpqKj169ODixYts2bKF9evXc/ToUfr16+dwjJiYGL744guWL19u/7Dq27cv58+fZ/Xq1URGRtKsWTM6d+7MxYsX6devHyNHjqR+/frExcURFxeX4T0BkpKSeOihh4iNjeXLL79k3759vPrqq6Smptr3P/LII2zcuJEffviBkJAQunfvzsmTJ3N8fd555x3mzZvHJ598wvbt27l48SIRERG5vcy4ubnx3nvvceDAAebPn88333zDq6++6lDm8uXLTJ06lYULF7J161ZOnjzJqFGjABg1ahRPPPGE/YshLi6Otm3b3va4R44cISQkhN69e/Pjjz+ydOlStm/fzrBhwwDYs2cPL774Iv/617+Ijo5mzZo1tG/fPtfnJyIiYgXVY7Kneow4nSGSh+bOnWv4+Phk2L5p0yYDMDZs2GDf9tVXXxmAceXKFcMwDKNz587GW2+95fC6hQsXGlWqVDEMwzDWrVtnuLu7GydPnrTvP3DggAEYu3fvNgzDMMLDw41ixYoZ58+ft5fZtm2bUaZMGePq1asO7x0UFGTMmTPH/rrGjRtniBswIiIiDMMwjDlz5hje3t7Gb7/9lsOrYRj169c3Zs6caX9evXp1Y/r06VmWr1KlivHvf//b/vz69etG1apVjR49emT7Ho0bNzbCw8OzfN/PP//cqFChgv353LlzDcCIiYmxb5s1a5bh5+dnfx4aGupwXMMwjGPHjhmA8cMPPxiGcfP3+vvvvxuGYRhDhgwx/v73vzu8Ztu2bYabm5tx5coV44svvjDKlCljJCYmZhmriIiIVVSPcaR6jOoxBZ3GzIpTNWrUyP5zlSpVADh//jzVqlVj37597Nixw34HE8zuKlevXuXy5cscOnSIgIAAAgIC7Pvr1atH2bJlOXToEC1btgSgevXq+Pr62svs27ePpKQkKlSo4BDLlStXOHLkSI5jj4qKomnTppQvXz7T/UlJSYwfP56vvvqKuLg4bty4wZUrV3J8RzMhIYG4uDhat25t3+bh4UGLFi0wDCPHcQJs2LCBSZMmcfjwYRITE7lx44b9OpYsWRKAkiVLEhQUZH9NlSpVOH/+fK6Oc6t9+/bx448/smjRIvs2wzBITU3l2LFjPPzww1SvXp3AwEBCQkIICQnh8ccft8ckIiJSkKkekzXVY8QKSmbFqYoVK2b/2WazATh0b5kwYQK9evXK8Lq0MSM5UapUKYfnSUlJVKlShc2bN2coW7Zs2Ry/r5eXV7b7R40axfr165k6dSq1atXCy8uLPn36kJycnONj5ISbm1uGL4Xr16/bfz5+/DiPPfYYzz33HBMnTqR8+fJs376dIUOGkJycbP/ATf+7APP3kdsvm1slJSXx7LPP8uKLL2bYV61aNTw9Pdm7dy+bN29m3bp1jBs3jvHjx/P999/n6nchIiJiBdVj7p7qMZKXlMxKgdGsWTOio6OpVatWpvvr1q3LqVOnOHXqlP2u5sGDB4mPj6devXrZvu/Zs2fx8PCgRo0amZbx9PQkJSUl2/gaNWrExx9/zMWLFzO9q7ljxw7CwsJ4/PHHAfMD8fjx49m+Z3o+Pj5UqVKF7777zj7+4saNG/axMWl8fX0dZl5MTEzk2LFj9ueRkZGkpqbyzjvv4OZmDotftmxZjuNIk5NrcqtmzZpx8ODBLH+HYN6lDQ4OJjg4mPDwcMqWLcs333yT6Ze/iIiIq1A9RvUYcT5NACUFxrhx41iwYAETJkzgwIEDHDp0iM8++4x//vOfAAQHB9OwYUMGDBjA3r172b17N4MGDeKhhx6iRYsWWb5vcHAwbdq0oWfPnqxbt47jx4/z7bffMnbsWPtU+zVq1ODYsWNERUVx4cIFrl27luF9+vfvT+XKlenZsyc7duzg6NGjfPHFF+zcuROAe++91z5Zw759+/jrX/9qv1ubUy+99BJvv/02K1as4PDhwzz//PMZFvLu1KkTCxcuZNu2bfz000+Ehobi7u5u31+rVi2uX7/OzJkzOXr0KAsXLmT27Nm5iiPtmvz4449ER0dz4cIFh7umWfm///s/vv32W4YNG0ZUVBS//PILK1eutE+csGrVKt577z2ioqI4ceIECxYsIDU1ldq1a+c6PhERkYJE9RjVY8T5lMxKgdG1a1dWrVrFunXraNmyJffffz/Tp0+nevXqgNl9ZOXKlZQrV4727dsTHBxMYGAgS5cuzfZ9bTYbX3/9Ne3bt+fpp5/mvvvu48knn+TEiRP4+fkB0Lt3b0JCQujYsSO+vr4sWbIkw/t4enqybt06KlWqxCOPPELDhg15++237R/A06ZNo1y5crRt25bu3bvTtWtXhzuROTFy5EgGDhxIaGgobdq0wdvb236HNM2YMWN46KGHeOyxx3j00Ufp2bOnw5iRxo0bM23aNCZPnkyDBg1YtGgRkyZNylUcAM888wy1a9emRYsW+Pr6smPHjtu+plGjRmzZsoWff/6Zdu3a0bRpU8aNG4e/vz9gdodavnw5nTp1om7dusyePZslS5ZQv379XMcnIiJSkKgeo3qMOJ/NuNvO5SKSr8LCwoiPj3dYJ05ERETEFageI/lJLbMiIiIiIiLicpTMioiIiIiIiMtRN2MRERERERFxOWqZFREREREREZejZFZERERERERcjpJZERERERERcTlKZkVERERERMTlKJkVERERERERl6NkVkRERERERFyOklkRERERERFxOUpmRURERERExOX8fxXryReJUZ3OAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
-    "\n",
-    "pg.qqplot(x_normal, ax=axes[0])\n",
-    "pg.qqplot(x_not_normal, ax=axes[1]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0f983f0-6143-44b0-9eee-f6d54eb5cf51",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "SciPy also provides similar plots (probability plots) with [probplot](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "6bcaa795-3a85-4b8c-aad9-d554e244a2ec",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
-    "\n",
-    "stats.probplot(x_normal, plot=axes[0])\n",
-    "stats.probplot(x_not_normal, plot=axes[1]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f46e74c-ee1f-454c-a4a1-3e551899e7e7",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "#### Normality tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da10f0a4-eb46-4a63-90de-117e428b01d4",
-   "metadata": {},
-   "source": [
-    "With Pingouin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "920395a5-7770-45ed-9bd3-80ff71bda307",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>W</th>\n",
-       "      <th>pval</th>\n",
-       "      <th>normal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.940223</td>\n",
-       "      <td>0.092225</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          W      pval  normal\n",
-       "0  0.940223  0.092225    True"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.normality(x_not_normal)#, method='jarque_bera')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35c08d95-c21f-4202-8bbf-eed8b50fcdf7",
-   "metadata": {},
-   "source": [
-    "A major issue with tests of normality is they depend too much on the sample size. They are not powerful enough on small samples, and tend to see departures of normality everywhere in large samples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81a1572d-1639-42d9-834c-2ba86c7c7dd3",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "\n",
-    "Similar options can be found in Scipy:\n",
-    "* D'Agostino's test: [normaltest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html), preferably for large samples ($n>20$),\n",
-    "    * Similar test for skewness only: [skewtest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skewtest.html) ($n\\ge8$),"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/RpItmdAOuXVycpJOihBCCGEAMp0l/0g/RojCITExkdjYWGxtbeXfqhAFXG76MTJ5WgghhBBCCCGEEMKAJNkihBBCCCGEEEIIYUCSbBFCCCGEEEIIIYQwIKnZIoQQosBSFIXHjx+Tmppq6lDEc5ibm2NhYSE1WYQQQk8uLi7069cPFxcXU4dSrEjfQjzNmP0YSbYIIYQokJKTk7l16xYPHjwwdSgiG3Z2dpQuXRorKytThyKEEIWGra0ttWrVMnUYxYr0LURmjNWPkWSLEEKIAictLY3w8HDMzc3x8vLCyspKRk4UQIqikJyczJ07dwgPD6dKlSqYmckMZSGEyImEhAROnTpFnTp1cHBwMHU4RZ70LcSzjN2PkWSLEEKIAic5OZm0tDTKli2LnZ2dqcMRWbC1tcXS0pKIiAiSk5OxsbExdUhCCFEoxMfH8/fff1O+fHlJtuQD6VuIzBizHyO3n4QQQhRYMkqicJA/JyGEEIWFfGeJZxnr74T8TRNCCCGEEEIIIYQwIJlGJIQBKYr6kIS5EEIIIYR4Vloa3LihbkdHq8+RkeDhAebmpotLCGF48iuhEAYSHAxt2qhflB07wp49auJFCCGEEEIIgE6doFw59dG+vTXnz1elfXtr2rY1dWRCCEOTZIsQeXTwIHTooCZa9u5V923fDv7+0KoVbN0qSRchhOEtWLCA8uXLY2Njg5+fH4cPH87y+JkzZ9K4cWMcHR3x8PCgV69enD9/Pt0xH3/8MRqNJt2jevXqxnwbQghRbISFqX1EAGtrePDAjbVrB3Lvnht790JoqGnjE8XbxIkT6dWrl6nDKFIk2SJELt27Bz17QrNmsGMHWFrC6NFq8mX0aLCygv37oXNn9Zjbt00dsRCiqFi1ahUTJkxg6tSpHDt2DF9fXwIDA7mdxX80e/bsYezYsRw8eJDt27eTkpJCx44dSUxMTHdcrVq1uHXrlu6xb98+Y78dIYQoFv74Q33u0AEePYLExFTu3k2ke/dUAH7/3YTBiWIvJCSEevXq5es19b1xBDm7MZSbdo1Bki1C5NKbb8KGDWp9lmHD4Px5+PZb8PNTn8PDYcIEsLODQ4dg8GB1nq4QQn+KAomJpnnkZmTawYMHad++PSVKlMjQIYiLi8vz5zF79mxGjBjBsGHDqFmzJosWLcLOzo4lS5Y895ytW7cydOhQatWqha+vL8uWLePq1ascPXo03XEWFhaUKlVK93B3d89zvEIIIZ4kU/r1U59v377N119/TbduaqJ8zRoTBSYEcOLEiXxNtuTmxpFWVjeG8tKuoUmyRYhc2LEDfv4ZNBrYvRuWLIEKFdIf4+UFs2bB4cNgawvbtqk/CyH09+ABODiY5vHggX6xnjhxAn9/f+rXr8/evXvZunUrbm5utG/fnlWrVuHk5KQ7dsaMGTg4OGT5uHr1arr2k5OTOXr0KAEBAbp9ZmZmBAQEcODAgRzHGRsbC4Cbm1u6/RcvXsTLy4uKFSvy8ssvZ7i+EEII/YWHw9Gj6k26Z2dqtGmjjpA+fRrOnTNJeMVWYbqZk5aWxowZM6hSpQo2NjZ4enoydOhQ3etXr15lyJAheHp6Ymtri6+vry4JMXXqVOrUqYO9vT2enp6MHj2alJQU3bnXr18nOjoaX1/fdO299NJLuLq64ubmxssvv8y9e/fy9Hk/LTc3jrSyujGUl3YNTZItQujp4UMYNUrdHjdOrcuSlVq1YO5cdfv999XkixCi6Bo/fjx9+vTh66+/pmbNmgQGBjJw4EASExPp379/umNHjRpFSEhIlg8vL69050RHR5Oamoqnp2e6/Z6enkRGRuYoxrS0NN566y1atGhB7dq1dfv9/PxYtmwZW7duZeHChYSHh9OqVSvi4+Nz+WkIIYSAJ1OI/P3VlYee5uQE2vy59jiRPwrTzZyZM2eycuVKFi9ezPnz51m3bh2tW7cGICIigiZNmvDw4UM2bNjAyZMnGTduHE5OTiiKgqIofPfdd4SGhrJs2TL++OMPfvjhB13bISEhODs7U+G/u8dhYWE0bNiQypUr66Yfh4WFMWnSpAxxmeLG0fNuDBnqhpShyNLPQujp00/h0iUoUwY++yxn54wYoY6G+f13ePFFOH4cnJ2NG6cQRYmdHSQkmO7aORUVFcW+ffvYs2dPuv329vZoNJoMx7u5uWUYWZIfxo4dy+nTpzPUY+ncubNuu27duvj5+eHj48Pq1asZPnx4focphBBFhnaKkHYK0bP69YMtW9S+4gcf5F9covDYtm0b3bt3p+1/S1f5+PjQvHlzAEaPHk3Tpk1ZvXq17vgqVarotj/55BPdto+PDwEBAemK5IeEhKQb1TJmzBjGjBnDtGnTdPvefffdTJMto0aNynAz6Vn63Dg6l83wLu2NoWrVqnHr1i2mTZtGq1atOH36NPHx8blu1xgk2SKEHk6dgq++UrfnzwdHx5ydp9HA4sXqqJbwcHj9dVixQt0vhMieRgP29qaOIntHjx4lLS0tXYdFu79Ro0YZjp8xYwYzZszIss3Q0FDKlSun+9nd3R1zc3OioqLSHRcVFUWpUqWyjXHcuHFs3LiR4OBgypQpk+WxLi4uVK1albCwsGzbFUIIkbmrV9X6fRoN9O6d+TE9e8LIkXDiBFy8CE/9niyMqLDczAHo0aMH7733HkeOHOGFF16gb9++uLq6EhERwZYtWzh+/Him50VERPDll1+yZ88ebty4QUpKCo8ePeLzzz/XHfN0siUiIoLt27ezb98+Zj1VAyE1NZWyZctmaD+/bxxldWPo6dcKAkm2CJFDqanqCJXHj6FPH/VLUR8uLrByJbRsCatWqZXo5UaxEEVL2n9VsBMTE3H8Lxt78uRJgoODmT59eobjc3M3yMrKioYNGxIUFKRbojEtLY2goCDGjRv33HYUReGNN95g3bp17N69WzdUOCsJCQlcunSJQYMGZXusEEKIzGmnBrVqBU/nxD09PZk8eTKWlpaYmUH79vD33+rxkyebJtbiprDczAF1aeYePXqwfv165syZo0u8nDx5Eisrq0yL2965c4fGjRvTrl07Zs+ejbe3N6mpqTRq1CjdjaGQkBC6dOkCqLXn3NzcOHToUIb2bG1tM+wzxY2jpz19Y8iQ7RqCJFuEyKFFi9S7Eo6O8M03uWujaVN16tHkyTB+PHTvnnHerhCi8PLz88PW1pZJkybxwQcfcOnSJcaOHcvYsWNp2rRphuNzezdowoQJDBkyhEaNGtGkSRPmzp1LYmIiw4YN0x0zf/581q1bR1BQEKBOHfrtt9/4888/cXR01NV3cXZ21nWeJk6cSPfu3fHx8eHmzZtMnToVc3NzBg4cmJuPQwghBM+fQmRmZoa1tbXu53791GTLmjWSbBGZq1q1Ku+++y7jx4/HycmJ0NBQLC0tefz4MQ8ePMDumeEyf/31F6mpqaxYsUI3nXn+/PmkpKTokjPx8fFcvnxZ97OlpSXx8fF4eXllaC8z+XnjKDNP3xgyZLsGoYgMYmNjFUCJjY01dSiigIiOVhRHR0UBRVmwIG9tpaYqSqNGalvvv2+Y+IQoah4+fKiEhoYqDx8+NHUoevvrr7+UqlWrKpaWlkqlSpWUr776SklNTTX4debNm6eUK1dOsbKyUpo0aaIcPHgw3etTp05VfHx8dD8DmT6WLl2qO2bAgAFK6dKlFSsrK8Xb21sZMGCAEhYWlm0sWf15yXdq/pPPXIiC4/p1tc8HinLjRvrXoqOjlZ9//lmJjo5WFEVRbt9WFDMz9dhLl0wQbBFXmPsWX3zxhbJ8+XIlNDRUOXfunPL2228rpUqVUmJiYpTo6GjF1dVVGTx4sBIaGqqcOXNGWbhwoXLhwgVl/fr1ioWFhbJ+/XrlwoULyqxZsxR3d3fF29tb1/bevXsVCwsL5dGjR4qiKMrdu3eVEiVKKH379lVCQkKUixcvKlu2bFHefPNNg76nlStXKtbW1sqyZcuU0NBQZeTIkYqLi4sSGRmpO2bevHlKu3bt0p33zjvvKLt371bCw8OV/fv3KwEBAYq7u7ty+/btHLf7LGP1Y2RkixA58NtvEB8Pdeo8WYkot8zM1FWJ+vSBBQvgvffUKvRCiKKhW7dudOvWzejXGTduXJZ3aT7++GM+/vhj3c9KDtaYXLlypSFCE0II8Z+1a9XnFi3gmZv7JCcnc+nSJZKTkwEoWVJdrWjnTnUqUSa1SEUx9ejRIz777DOuXr2Kg4MDLVq0YOfOnbi6ugLqCJZJkybRuHFjrKysaNq0KQMHDqRSpUoMHz6cQYMGYWtryyuvvEL//v2JiIjQtR0SEkL16tV1o6zc3NzYvHkz7733Hq1bt0ZRFKpUqcKQIUMM+p4GDBjAnTt3+Oijj4iMjKRevXps3bo1XXHb6OhoLl26lO6869evM3DgQO7evUvJkiVp2bIlBw8epGTJkjluN79olJz0voqZuLg4nJ2diY2NxUl+CxZAo0Zw9Kg6feiNN/LeXloa1K4NZ8/C55+rCRchxBOPHj0iPDycChUqYGNjY+pwRDay+vOS71RYsGABX331FZGRkfj6+jJv3jyaNGny3OPv37/PBx98wNq1a4mJicHHx4e5c+fq5tNnRz5zIQqONm0gOBjmzIG33kr/2q1bt1i8eDEjR46kdOnSgDptffRoaNJEnb4uDEf6FuJ5jNWPMTNkkEIURadPq4kWS0swVNkCM7Mnc3HnzIGHDw3TrhBCiIJl1apVTJgwgalTp3Ls2DF8fX0JDAzk9u3bmR6fnJxMhw4duHLlCmvWrOH8+fN8//33eHt753PkQoi8unUL9u5Vt/v0ydk5vXurRVsPH4anBh8IIQohSbYIkY3ly9Xnbt3A3d1w7Q4cCD4+EBUFS5carl0hhBAFx+zZsxkxYgTDhg2jZs2aLFq0CDs7O5YsWZLp8UuWLCEmJob169fTokULypcvT5s2bTIsJy6EKPjWrVOrtfj5wVMLsWTJ0xNat1a3tYV1hRCFkyRbhMjC48fw88/qtoGnKWJpCRMnqttffaVeSwghRNGRnJzM0aNHCQgI0O0zMzMjICCAAwcOZHrOhg0baNasGWPHjsXT05PatWszY8YMUlNTn3udpKQk4uLi0j2EEKa3Y4f6/N+iKBk4OTnRuXPnDFMTevdWn/9bTE4IUUhJskWILPz9tzrypGRJyOFUeb28+qra9pUrIHUphRCiaImOjiY1NTVDUT5PT0/d0tvPunz5MmvWrCE1NZXNmzfz4YcfMmvWLKZPn/7c68ycORNnZ2fdo2zZsgZ9H0KI3Dl+XH3288v8dXt7e5o0aYK9vX26/dqSTiEhxotNCGF8kmwRIgvLlqnPL72kjkQxNDu7J8XSPv9cLZwrhBCi+EpLS8PDw4PFixfTsGFDBgwYwAcffMCiRYuee86UKVOIjY3VPa5du5aPEQshMnPvnnozDaBevcyPefjwISdPnuThM8X76tZV67bcuqXe9BNCFE6SbBHiOWJi4M8/1e2hQ413nTFj1KWfz5yBv/4y3nWEEELkL3d3d8zNzYl65relqKgoSpUqlek5pUuXpmrVqpibm+v21ahRg8jISN3ysM+ytrbGyckp3UMIYVonTqjP5cvDf6vzZnD//n3WrVvH/fv30+23t4dq1dRt7egYIUThI8kWIZ5j1SpITlbvLjzvjoQhuLioCRdQa7cIIYQoGqysrGjYsCFBTxVeSEtLIygoiGbNmmV6TosWLQgLCyPtqaGOFy5coHTp0lhZWRk9ZiGEYWiTJPXr5+58bd9TphIJUXhJskWI59BOITLmqBat8ePV5aD374cLF4x/PSGEEPljwoQJfP/99yxfvpyzZ88yevRoEhMTGTZsGACDBw9mypQpuuNHjx5NTEwMb775JhcuXGDTpk3MmDGDsWPHmuotCCFyQZtsye0NO22SRka2CFF4WZg6ACEKorNn4fBhsLCAl182/vVKl4bAQNiyRV396NNPjX9NIYQQxjdgwADu3LnDRx99RGRkJPXq1WPr1q26orlXr17FzOzJva+yZcuybds23n77berWrYu3tzdvvvkm7733nqneghAiF7QjUnI7skWSLUIUfjKyRYhMLF+uPnfuDB4e+XNN7dLSP/8shXKFENlbsGAB5cuXx8bGBj8/Pw4fPpztOR9//DEajSbdo3r16nluV2Rt3LhxREREkJSUxKFDh/B7ammS3bt3s0w7lPI/zZo14+DBgzx69IhLly7x/vvvp6vhIoQo2B49gtBQdTurZIulpSVlypTBMpNVGLQjYsLCID7e8DEKIYzPpMmW4OBgunfvjpeXFxqNhvXr12d5/O7duzN0EjUaTYblE6WjKPJCUeDXX9Xt/JhCpNWjh1ooNyICgoPz77pCiMJn1apVTJgwgalTp3Ls2DF8fX0JDAzk9u3b2Z5bq1Ytbt26pXvs27fPIO0KIYRQnT4NqalQogR4ez//OHd3d4YPH467u3uG10qWVM9VFDh50ojBCvGfiRMn0qtXL1OHUaSYNNmSmJiIr68vCxYs0Ou88+fPp+soejw19EA6iiKvQkPh+nWwsYEuXfLvura20L+/uv3TT/l3XSEKA0VRSExONMlDURS94z148CDt27enRIkSGW4QxMXF5fnzmD17NiNGjGDYsGHUrFmTRYsWYWdnx5IlS7I918LCglKlSukeT3fy89KuEEII1dNTiDSa3LcjU4lEfgoJCaGeMVcFeYa+Ay+elt3gioIy+MKkNVs6d+5M586d9T7Pw8MDFxeXTF97uqMIsGjRIjZt2sSSJUuYPHlypuckJSWRlJSk+9kQHWFReG3frj63bq0mXPLTkCHwww/w++8wb5669J8QAh6kPMBhpoNJrp0wJQF7q5z/Yzxx4gT+/v6MGzeOefPmce3aNV566SXq16/PyJEj0y3LO2PGDGbMmJFle6GhoZQrV073c3JyMkePHk1XVNXMzIyAgAAOHDiQbXwXL17Ey8sLGxsbmjVrxsyZMylXrlye2xVCCKHK6UpEt27dYvHixYwcOZLSpUtneL1ePdi4UZItIn+cOHGCcePG5dv1tAMvXn31Vfr06ZPj87SDKxYtWoSfnx9z584lMDCQ8+fP4+Hhke3r+alQ1mypV68epUuXpkOHDuzfv1+3X9tRDAgI0O3LSUdx5syZODs76x5ly5Y1avyiYNMmW576a5RvWrSAChUgIQH0SO4KIQqQ8ePH06dPH77++mtq1qxJYGAgAwcOJDExkf7a4Wv/GTVqFCEhIVk+vLy80p0THR1NamqqrsCqlqenZ4Zptc/y8/Nj2bJlbN26lYULFxIeHk6rVq2Ij4/PU7tCCCGeyOtKRFraZI0s/2w8hWnkbFpaGjNmzKBKlSrY2Njg6enJ0KdqHly9epUhQ4bg6emJra0tvr6+uqnCU6dOpU6dOtjb2+Pp6cno0aNJSUnRnXv9+nWio6Px9fVN195LL72Eq6srbm5uvPzyy9y7dy9vH/hTOnfuzPTp0+ndu7de52U3CrcgjdItVKsRlS5dmkWLFtGoUSOSkpL44Ycf8Pf359ChQzRo0CDLjuK5c+ee2+6UKVOYMGGC7ue4uDhJuBRTycmwZ4+63aFD/l9fo4HBg2HaNHUqUX6shCREYWBnaUfClASTXTunoqKi2LdvH3u0/5H8x97eHk0mY8nd3Nxwc3PLc4w59fRo0rp16+Ln54ePjw+rV6/O1UhTIYQQ6aWmPqmxktuViLS0558+DSkpkEkdXZFHhWnk7MyZM1m1ahWLFy+mYsWK3LhxQ/c7bkREBH5+frRu3ZoNGzbg5ubG7t27cXJyQlEUFEXhu+++w9vbm9DQUIYMGULdunUZPXo0oE4hcnZ2pkKFCgCEhYXRrFkzRo8ezcGDB0lISGDMmDFMmjSJH374IV1cuRmlm1vZjcItaKN0C1WypVq1alSrVk33c/Pmzbl06RJz5szh559/znW71tbWWFtbGyJEUcgdPAiJiWpRsrp1TRODNtmyYwfcuJF1YTUhiguNRqNXh8RUjh49SlpaWro7Q9r9jRo1ynB8bjoo7u7umJubExUVle64qKgoSpUqpVe8Li4uVK1albCwMIO2K4QQxVVYmNqXtLWFqlXz1lb58uDsDLGxak3BZ75aRDGzbds2unfvTtu2bQHw8fGhefPmAIwePZqmTZuyevVq3fFVqlTRbX/yySe6bR8fHwICAjh//rxuX0hISLq+y5gxYxgzZgzTpk3T7Xv33XeZNGlShrhGjRqVYeTus54dpZtb2Q2uyO3gC2MpVMmWzDRp0kQ3PEo6iiKvtFOI2rcHMxNNsqtYEVq2hH371FWR3n3XNHEIIfSX9t+67YmJiTg6OgJw8uRJgoODmT59eobjc9NBsbKyomHDhgQFBelWDUhLSyMoKEjvudYJCQlcunSJQYMGGbRdIYQorrRTiOrWhbyu2K7RqFOR9uxRpxJJssXwCsvIWYAePXrw3nvvceTIEV544QX69u2Lq6srERERbNmyhePPKe4TERHBl19+yZ49e7hx4wYpKSk8evSIzz//XHfM08mWiIgItm/fzr59+5g1a5bumNTU1Exnf+T3KN3CpNAnW0JCQnQFpaSjKPJqxw712RRTiJ42ZIiabFm+HCZNylsleyFE/vHz88PW1pZJkybxwQcfcOnSJcaOHcvYsWNp2rRphuNz20GZMGECQ4YMoVGjRjRp0oS5c+eSmJioKw4PMH/+fNatW0dQUJBu38SJE+nevTs+Pj7cvHmTqVOnYm5uzsCBA3PcrhBCiOd7eiWi7JQsWZI33ngjXeH0Z9WvryZbjh9X+4fCsArLyFlQv8N79OjB+vXrmTNnji7xcvLkSaysrDJdSejOnTs0btyYdu3aMXv2bLy9vUlNTaVRo0bpRrKEhITQ5b9lWE+cOIGbmxuHDh3K0J6trW2Gffk5jSi7wRUFbfCFSZMtCQkJhIWF6X4ODw8nJCQENzc3ypUrx5QpU7hx4wY//bcO7ty5c6lQoQK1atXi0aNH/PDDD+zcuZO///5b14Z0FEVu3b8P2lXBTJ1seeEFGDdOHTJ67Bg0bGjaeIQQOVOyZElWr17NO++8Q926dSlXrhzjxo1LVxfMEAYMGMCdO3f46KOPiIyMpF69emzdujXdsNno6GguXbqU7rzr168zcOBA7t69S8mSJWnZsiUHDx6kZMmSOW5XCCHE8+V0JSIACwuLbBPu2t+fZUUiAVC1alXeffddxo8fj5OTE6GhoVhaWvL48WMePHiAnV360TJ//fUXqamprFixQlc7bv78+aSkpOiSM/Hx8Vy+fFn3s6WlJfHx8Xh5eWVoLzP5OY0ou8EVBW7whWJCu3btUoAMjyFDhiiKoihDhgxR2rRpozv+iy++UCpVqqTY2Ngobm5uir+/v7Jz584M7c6bN08pV66cYmVlpTRp0kQ5ePCgXnHFxsYqgBIbG5uXtycKmbVrFQUUpVo1U0eiGjBAjefNN00diRD57+HDh0poaKjy8OFDU4ciciCrPy/5Ts1/8pkLYRppaYpSsqTafzt8OPvjY2JilD/++EOJiYl57jEnTqjtOTmp7YvcK8x9iy+++EJZvny5Ehoaqpw7d055++23lVKlSikxMTFKdHS04urqqgwePFgJDQ1Vzpw5oyxcuFC5cOGCsn79esXCwkJZv369cuHCBWXWrFmKu7u74u3trWt77969ioWFhfLo0SNFURTl7t27SokSJZS+ffsqISEhysWLF5UtW7Yobxr4l5L4+Hjl+PHjyvHjxxVAmT17tnL8+HElIiJCd8y8efOUdu3apTtv5cqVirW1tbJs2TIlNDRUGTlypOLi4qJERkbm6PXMGKsfY9KRLf7+/lkuebVs2bJ0P7/77ru8m4MCFuPGjZNpQ0JvplzyOTMvvQSrVsHatTBnjkwlEkIIIYQoyG7dgjt31FottWtnf/yjR484deoUzZo1e+4xNWqAlRXExUF4uFrbTxQ/jx494rPPPuPq1as4ODjQokULdu7ciaurK6COYJk0aRKNGzfGysqKpk2bMnDgQCpVqsTw4cMZNGgQtra2vPLKK/Tv35+IiAhd2yEhIVSvXl23YIybmxubN2/mvffeo3Xr1iiKQpUqVRhi4HlsR44c0RX7BXSjgIcMGaLLA2Q2Sje7UbgFaZSuRskq21FMxcXF4ezsTGxsbJZzKEXRUrUqXLwI69dDz56mjgYePlRXRUpMhH//hUwWMhGiyHr06BHh4eFUqFABGxsbU4cjspHVn5d8p+Y/+cyFMI1Nm6BbN6hVS12uOTu3bt1i8eLFjBw5UleDMjMNG6rTytesgb59DRhwMSN9C/E8xurHmGi9FSEKlogINdFibg7+/qaORmVrC506qdvr1pk2FiGEEEIIkTV96rXoQ9ue1G0RonCRZIsQPJlC5OcHzs6mjeVpvXurz5JsEUIIIYQo2LQrEWWyKEyeaNvTti+EKBwk2SIEBa9ei1bXrmBhAWfPwvnzpo5GCCGEEEI8j74jWxwcHGjTpg0ODg5ZHicjW4QonCTZIoq9tDQIClK3Tb3k87NcXKBdO3VbRrcIIYQQQhRMsbFw+bK6ndORLY6Ojvj7++Po6JjlcXXrqgsl3LwJt2/nLU4hRP6RZIso9kJC4O5dcHBQpxEVNDKVSAghhBCiYDtxQn0uVw7c3HJ2TlJSEmFhYSQlJWV5nKMjVK6sbstUIiEKD0m2iGJPO4XI3x8sLU0aSqZ69lTvZhw+DDdumDoaIYQQQgjxrFOn1Gdf35yfExMTw6+//kpMTEy2x2pHy5w8qX9sQgjTkGSLKPZ27VKfC1q9Fq3SpaFpU3V7/XqThiKEEEIIITJx9qz6XLOmcdqvUUN9lhp+QhQekmwRxVpaGhw6pG63bGnaWLIiU4mEEEIIIQquc+fU5+rVjdO+tl1tUkcIUfBJskUUaxcvwv37YGOjFh8rqLTJlt27IQcjTYUQRVxwcDDdu3fHy8sLjUbDej2GvS1YsIDy5ctjY2ODn58fhw8f1ut1IYQQGWmTLdoRKIambVd7HSFEwSfJFlGsaUe1NGhQMOu1aFWuDLVrQ2oqbNxo6miEEKaWmJiIr68vCxYs0Ou8VatWMWHCBKZOncqxY8fw9fUlMDCQ2/8tb5Hd60IIITKKi3tSV69atZyfZ25ujqurK+bm5tkeW7Wq+nz3Lty5k4sghRD5TpItoljTJlsK4ipEz5KpRKJYUxR4nGiah6LoHe7Bgwdp3749JUqUQKPRpHvExcXl+ePo3Lkz06dPp7f2P4Ycmj17NiNGjGDYsGHUrFmTRYsWYWdnx5IlS3L0uhBCiIy0dVRKlQIXl5yf5+Hhwfjx4/Hw8Mj2WDs78PFRt2V0izCGiRMn0qtXL1OHUaRIskUUa4Ux2bJtGzx4YNpYhMh3qQ9gtYNpHqn6/YM7ceIE/v7+1K9fn71797J161bc3Nxo3749q1atwsnJSXfsjBkzcHBwyPJx9epVg3yEycnJHD16lICnqoGbmZkREBDAgQMHsn1dCCFE5rR1VIw1hUhL277UbRHGEBISQj3tslf5YObMmTRu3BhHR0c8PDzo1asX53NYAbqwTImWZIsoth4+hBMn1O3CkGypV0+9o/HwoZpwEUIUTOPHj6dPnz58/fXX1KxZk8DAQAYOHEhiYiL9+/dPd+yoUaMICQnJ8uHl5WWQuKKjo0lNTcXT0zPdfk9PTyIjI7N9XQghROZyWxw3KiqKr776iqioqBwdr21fRrYIYzhx4kS+Jlv27NnD2LFjOXjwINu3byclJYWOHTuSmJiY5XmFaUq0Rb5fUYgC4vhxePwYPDyeDMssyDQa6NkTvvkGNm9+MtJFiGLB3A76J5ju2jkUFRXFvn372LNnT7r99vb2aDSaDMe7ubnh5uaW5xCFEEKYTm5HtqSlpfHgwQPS0tJydLyMbDECRdF7BKvBmNupHfwcSktL4/PPP2fp0qVcu3YNZ2dnOnfuzLJlywC4evUqH374IVu3biUuLo6qVauyYMECWrZsydSpU1m7di2XL1/GwcGBPn368M0332D5X9HK69evEx0dja+vr+56V69eZfLkyWzZsgWNRkPnzp2ZP38+rq6uBnn7W7duTffzsmXL8PDw4OjRo7Ru3fq55z095Rlg0aJFbNq0iSVLljB58uRsX89PkmwRxZZ2NJmfn17/z5lU165Pki2KUnjiFiLPNBqwsDd1FNk6evQoaWlp6Tor2v2NGjXKcPyMGTOYMWNGlm2GhoZSrly5PMfm7u6Oubl5hjuoUVFRlCpVKtvXhRBCZM7Yyz5rycgWI9BOUzaF/gl69W1mzpzJqlWrWLx4MRUrVuTGjRuc++8vQ0REBH5+frRu3ZoNGzbg5ubG7t27cXJyQlEUFEXhu+++w9vbm9DQUIYMGULdunUZPXo0oE4hcnZ2pkKFCgCEhYXRrFkzRo8ezcGDB0lISGDMmDFMmjSJH374IV1churLxMbGAmR5E0o75XnKlCm6fZlNiX7e6/lNki2i2CpM9Vq0WrdWC6TdvAknT8Izv88JIUxMe3cyMTERR0dHAE6ePElwcDDTp0/PcPyoUaMyTC16lqGmEVlZWdGwYUOCgoJ0BfDS0tIICgpi3Lhx2b4uhBAio5QUCAtTt/OrZktEhFq/zy7nAy9FEbBt2za6d+9O27ZtAfDx8aF58+YAjB49mqZNm7J69Wrd8VWqVNFtf/LJJ7ptHx8fAgIC0tVHCQkJSXejaMyYMYwZM4Zp06bp9r377rtMmjQpQ1yG6MukpaXx1ltv0aJFC2rXrv3c47Ka8nzu3LlsX89vkmwRxVZhTLbY2EC7duryz5s3S7JFiILGz88PW1tbJk2axAcffMClS5cYO3YsY8eOpWnTphmOz+00ooSEBMK0vXsgPDyckJAQ3NzcdHeO5s+fz7p16wgKCtIdN2HCBIYMGUKjRo1o0qQJc+fOJTExUTfUNrvXhRBCpHfpkjot3cEBvL2Ney13d3Bzg5gYuHBBrecn8qiQTFMG6NGjB++99x5HjhzhhRdeoG/fvri6uhIREcGWLVs4fvx4pudFRETw5ZdfsmfPHm7cuEFKSgqPHj3i888/1x3zdLIlIiKC7du3s2/fPmbNmqU7JjU1lbJly2Zo3xBToseOHcvp06fZt29fntopaCTZIoqlO3cgPFydmdC4samj0U+XLk+SLU+NkBNCFAAlS5Zk9erVvPPOO9StW5dy5coxbtw4JkyYYNDrHDlyRHdnC9C1P2TIEN3c7ejoaC5dupTuvAEDBnDnzh0++ugjIiMjqVevHlu3btXdAcrudSGEEOlp66dUr67/9O4SJUrw6quvUqJEiRwdr9Goo1v271enEkmyxQAKyTRlUJdm7tGjB+vXr2fOnDm6xMvJkyexsrLKtLjtnTt3aNy4Me3atWP27Nl4e3uTmppKo0aN0o1kCQkJoUuXLoBaKNfNzY1D2jvTT7G1tc2wL6/TiMaNG8fGjRsJDg6mTJkyWbZT2KZES7JFFEva/zuqVwdnZ9PGoq/OndXnf/6Be/fAQDWqhBAG0q1bN7p162bUa/j7+6MoSpbHfPzxx3z88ccZ9o8bNy7LaUHZvS6EEOKJvNRrsbKyynSkQFaqV1eTLVIkt3iqWrUq7777LuPHj8fJyYnQ0FAsLS15/PgxDx48wO6ZuWV//fUXqamprFixQleof/78+aSkpOiSM/Hx8Vy+fFn3s6WlJfHx8Xh5eWVoLzO5nUakKApvvPEG69atY/fu3bp6MVkpbFOiJdkiiiVtsqVJE9PGkRvly0PNmhAaCtu3Qzb/twkhhBBCCCPJ7UpEAHFxcRw4cIBmzZrh5OSUo3O015EiucXLl19+SalSpWjcuDFmZmZ89913lChRgubNm5OWloazszOjR49m8uTJKIpCcHAw7du3p0SJEsTFxbFhwwZq1qzJX3/9xcyZM/H29qZkyZKAOpLF3NycWrVqAeqUaCcnJwYPHsyHH36Ivb09YWFhbN26lblz52aILbfTiMaOHctvv/3Gn3/+iaOjI5GRkQA4OzvrRtAU9inRkmwRxVJhrNfytC5d1GTL5s2SbBFCCCGEMJW8jGxJTEzk4MGD1K1bN8fJFu11ZGRL8fLo0SM+++wzrl69ioODAy1atGDnzp26ZZj/+usvJk2aROPGjbGysqJp06YMHDiQSpUqMXz4cAYNGoStrS2vvPIK/fv3JyIiQtd2SEgI1atXx9raGlCTJ5s3b+a9996jdevWKIpClSpVGDJkiEHf08KFCwF1tO7Tli5dytChQ4HCPyVao2Q3DrkYiouLw9nZmdjY2Bz/xycKj7Q0tbhYbCwcPQoNGpg6Iv3t2qUWyvXwgFu3wMzM1BEJYViPHj0iPDycChUqYGNjY+pwRDay+vOS79T8J5+5EPlDUdTp6PHxcOaMOvJYH7du3WLx4sWMHDmS0qVL5+icS5egcmWwtobERDA3z0XgxZT0LcTzGKsfI7+iiWLnwgU10WJjA3XqmDqa3GnRAhwd4fZtOHbM1NEIIYQQQhQ/N2+qiRZzczUBkh/Kl1cTLUlJcOVK/lxTCJE7kmwRxY52ClHDhmBpadpYcsvKCjp0ULc3bzZtLEIYkwy+LBzkzyl7CxYsoHz58tjY2ODn58fhw4efe+yyZcvQaDTpHnIXVoiCRzuVp1IltW+WH8zNoWpVdVvqtghRsEmyRRQ7hb1ei5Z2VSJJtoiiyPK/TOiDBw9MHInICe2fk2VhzWAb2apVq5gwYQJTp07l2LFj+Pr6EhgYyO3bt597jpOTE7du3dI9np5fL4QoGLTJjtwUxwWws7OjUaNGOVrx5Wna60ndFiEKNimQK4od7c3EopJsOXwYoqPB3d208QhhSObm5ri4uOh+GbWzs9MtWSgKDkVRePDgAbdv38bFxQVzKR6QqdmzZzNixAjdSgiLFi1i06ZNLFmyhMmTJ2d6jkajoVSpUvkZphBCT9pkR26K44K66krXrl31Pk97PRnZIkTBJskWUaw8fAgnTqjbhT3Z4u0Nvr7q+9m2DV5+2dQRCWFY2l80s7r7LwoGFxcXSQw8R3JyMkePHmXKlCm6fWZmZgQEBHDgwIHnnpeQkICPjw9paWk0aNCAGTNm6JblfFZSUhJJSUm6n+Pi4gz3BoQQz5XXkS0pKSlER0fj7u6u18hAWf45b2Tqq3iWsf5OSLJFFCvHj8Pjx+DpCeXKmTqavOvSRU22bN4syRZR9Gg0GkqXLo2HhwcpKSmmDkc8h6WlpYxoyUJ0dDSpqakZlpz09PTk3HN+U6pWrRpLliyhbt26xMbG8vXXX9O8eXPOnDlDmTJlMhw/c+ZMpk2bZpT4hRDPl9eRLdHR0XqvRvT09c6eVVdEkoGfOfP0FGVbW1sTRyMKEmNNh5ZkiyhWnq7XUhS+mLp0gZkzYetWSE2V5f9E0WRubi6/zItipVmzZjRr1kz3c/PmzalRowbfffcdn376aYbjp0yZwoQJE3Q/x8XFUbZs2XyJVYjiKjYWbt1St3ObbMmtqlXVfmxMjDqVvGTJ/L1+YSVTlMWzjD0dWpItolg5flx9btjQtHEYStOm4OKiftn++6/6sxBCiILD3d0dc3NzoqKi0u2PiorK8dQrS0tL6tevT1hYWKavW1tbY21tnedYhRA5px2YVro0ODvn77Xt7MDHR136+exZSbboQ6Yoi8wYazq0JFtEsaKt11KvnknDMBgLCwgIgDVr4O+/JdkihBAFjZWVFQ0bNiQoKIhevXoBkJaWRlBQEOPGjctRG6mpqZw6dYouXboYMVIhhD7yWq8lr2rUUJMt585B69amiaEwkinK4lnGnA5t0mRLcHAwX331FUePHuXWrVusW7dO1xHJzNq1a1m4cCEhISEkJSVRq1YtPv74YwIDA3XHfPzxxxnmLVerVu2586JF8ZGc/GRura+vaWMxpI4dnyRbPvrI1NEIIYR41oQJExgyZAiNGjWiSZMmzJ07l8TERN3qRIMHD8bb25uZM2cC8Mknn9C0aVMqV67M/fv3+eqrr4iIiOC1114z5dsQQjwlr/VaQP3F38rKKldTWapXhy1bZPnn3JIpyiI/mDTZkpiYiK+vL6+++ip9+vTJ9vjg4GA6dOjAjBkzcHFxYenSpXTv3p1Dhw5Rv3593XG1atVix44dup8tLGQAj1C/jFJS1Gk3RaE4rlbHjurzwYPq/OH8HsoqhBAiawMGDODOnTt89NFHREZGUq9ePbZu3aormnv16lXMzMx0x9+7d48RI0YQGRmJq6srDRs25J9//qFmzZqmegtCiGdo7+PmJdlSqlSpdCuV6UOWfxai4DNpFqJz58507tw5x8fPnTs33c8zZszgzz//5K+//kqXbLGwsJAlKEUGISHqs69v0SiOq+XjA9WqwfnzsHMn9O5t6oiEEEI8a9y4cc+dNrR79+50P8+ZM4c5c+bkQ1RCiNwqCNOIQEa2CFGQmWV/SMGVlpZGfHw8bm5u6fZfvHgRLy8vKlasyMsvv8zVq1ezbCcpKYm4uLh0D1H0aOu1FKUpRFra0S1//23aOIQQQgghirrkZNDWq87LyJY7d+7w7bffcufOHb3P1V43IgISE3MfgxDCeAp1suXrr78mISGB/v376/b5+fmxbNkytm7dysKFCwkPD6dVq1bEx8c/t52ZM2fi7Oyse8hyiUWTJFuEEEIIIUReXboEqang4ADe3rlv5/Hjx9y5c4fHjx/rfW7JklCihLp98WLuYxBCGE+hTbb89ttvTJs2jdWrV+Ph4aHb37lzZ1544QXq1q1LYGAgmzdv5v79+6xevfq5bU2ZMoXY2Fjd49q1a/nxFkQ+UpQn04iKykpET/P3B0tLuHz5yZ0WIYQQQghheE/XazHl1HSp2yJEwVYoky0rV67ktddeY/Xq1QQEBGR5rIuLC1WrViUsi99Ara2tcXJySvcQRcuNGxATA+bmUBTrCzo4QIsW6raMbhFCCCGEMB5DFMc1BEm2CFGwFbpky4oVKxg2bBgrVqyga9eu2R6fkJDApUuXKF26dD5EJwoq7RSi6tXBxsa0sRiLTCUSQgghhDA+bXKjWjXTxiHJFiEKNpMmWxISEggJCSHkv/kd4eHhhISE6AraTpkyhcGDB+uO/+233xg8eDCzZs3Cz8+PyMhIIiMjiY2N1R0zceJE9uzZw5UrV/jnn3/o3bs35ubmDBw4MF/fmyhYivIUIi1tsmXnTnWJayGEEEIIYXjnz6vPeR3Z4urqyosvvoirq2uuztcme7TxCCEKFpMmW44cOUL9+vV1yzZPmDCB+vXr89FHHwFw69atdCsJLV68mMePHzN27FhKly6te7z55pu6Y65fv87AgQOpVq0a/fv3p0SJEhw8eJCSJUvm75sTBUpRLo6rVb8+uLtDfDwcPGjqaIQQQgghih5FMdw0IhsbG6pVq4ZNLodda69//jykpeUtFiGE4VmY8uL+/v4oivLc15ctW5bu5927d2fb5sqVK/MYlSiKikOyxcwMOnSAFSvUqUStWpk6IiGEEEKIoiUqCmJj1X5X5cp5ayshIYHjx49Tv359HBwc9D6/QgV1gYSHD+HaNfDxyVs8QgjDKnQ1W4TQV2LikyXxinKyBaRuixBCCCGEMWlHtVSokPc6gPHx8ezcuZP4+PhcnW9hAVWqpI9LCFFwSLJFFHmnTqlDPkuVAk9PU0djXB06qM///gt375o2FiGEEEKIoqagrESkJUVyhSi4JNkiirziMIVIy9sbatVSk0tBQaaORgghhBCiaJFkixAipyTZIoo8bbKlKK9E9LTAQPVZphIJIYQQQhhWQVn2WUuSLUIUXHonW3bt2mWMOIQwGu2yz8VhZAs8qduybZs6wkUIIYR+pK8jhHgeQ45ssbGxoWbNmrlejejpOCTZIkTBo3eypVOnTlSqVInp06dz7do1Y8QkhMGkpcHJk+p2cUm2tGoF1tZw/bq6FKAQQgj9SF9HCJGZBw/g6lV12xDJFldXV1544QVcXV1z3YZ2hE1kpLpKkhCi4NA72XLjxg3GjRvHmjVrqFixIoGBgaxevZrk5GRjxCdEnly+rK5GZG0NVauaOpr8YWcHLVqo29u3mzYWIYQojKSvI4TIzMWL6qhhNzdwd897e6mpqcTFxZGamprrNpycoHRpdVtusglRsOidbHF3d+ftt98mJCSEQ4cOUbVqVcaMGYOXlxfjx4/nhLZAhhAFgHYKUZ066vJ4xYV2VSJJtgghhP6kryOEyMzTU4g0mry3d/v2bebMmcPt27fz1I5MJRKiYMpTgdwGDRowZcoUxo0bR0JCAkuWLKFhw4a0atWKM2fOGCpGIXKtOK1E9DRtsmX3bkhJMWkoQghRqElfRwihVdBWItKSZIsQBVOuki0pKSmsWbOGLl264OPjw7Zt25g/fz5RUVGEhYXh4+PDCy+8YOhYhdBbcU221K8PJUpAfDwcOmTqaIQQovCRvo4Q4lmSbBFC6EPviRVvvPEGK1asQFEUBg0axJdffknt2rV1r9vb2/P111/j5eVl0ECFyA3tNKLisuyzlpkZtG8Pq1erU4latjR1REIIUXhIX0cIkZmCtuyzliRbhCiY9E62hIaGMm/ePPr06YO1tXWmx7i7u8uyicLkYmJAu4hE3bqmjcUUOnZ8kmyZNs3U0QghROEhfR0hxLPS0p4UoC2oI1vCwuDx4+JVp1CIgkyjKIqizwnBwcE0b94ci2f+FT9+/Jh//vmH1q1bGzRAU4iLi8PZ2ZnY2FicnJxMHY7Ipd27oW1bKF8ewsNNHU3+u3oVfHzA3Bzu3gVnZ1NHJIQojgrjd2ph7+sUxs9ciIJO26+ytFRXurS0zHubiqKQmpqKubk5mjxU3E1LA0dHdWnqCxegSpW8xyaEUOXlO1Xvmi1t27YlJiYmw/7Y2Fjatm2rb3NCGM3p0+pznTqmjcNUypVTl7tOTQW5+SqEEDknfR0hxLO0U3QqVzZMogVAo9FgYWGRp0QLqNPHtVObZCqREAWH3skWRVEy/Q/h7t272NvbGyQoIQxBu0jEU9Psix1ZAloIIfQnfR0hxLOMURz37t27LFu2jLt37+a5LanbIkTBk+MZfX369AHUDOzQoUPTzWFOTU3l5MmTNG/e3PARCpFL2mRLrVqmjcOUOnSABQsk2SKEEDkhfR0hxPMYI9mSnJxMREQEycnJeW5LRrYIUfDkONni/F/BB0VRcHR0xNbWVvealZUVTZs2ZcSIEYaPUIhcUJQn04iKc7LF31+t2XLxIkREqHONhRBCZE76OkKI5ymoyz5rycgWIQqeHCdbli5dCkD58uWZOHGiDKMVBVpkJNy7p85hLahfivnB2Rn8/OCff9TRLa+9ZuqIhBCi4JK+jhDieQrqss9a2v7u2bPqTcc8loERQhiA3jVbpk6dKp0PUeBppxBVrgw2NqaNxdSkbosQQuhH+jpCiKfFxsKtW+p2QU22VKmiJlju3YPoaFNHI4SAHI5sadCgAUFBQbi6ulK/fv0sK2YfO3bMYMEJkVsyheiJDh1g2jQIClKXBjTTO8UqhBBFn/R1hBDPc/68+lyqFLi4GK5dZ2dnunfvrpvCmBd2dup08StX1FE4JUvmPT4hRN7kKNnSs2dPXZG4Xr16GTMeIQxCiuM+0aQJODrC3btw/Dg0bGjqiIQQouCRvo4Q4nm0yRZDT023s7OjQYMGBmuvenU12XL+PLRqZbBmhRC5lKNky9SpUzPdFqKgkmWfn7C0hLZtYcMGdSqRJFuEECIj6esIIZ7HWMVxHzx4wLlz56hevTp2dnZ5bq96ddi6VYrkClFQ6D2h4Nq1a1y/fl338+HDh3nrrbdYvHixQQMTIrcURUa2PEtbt+Xvv00bhxBCFAbS1xFCPM1YyZbY2Fj++usvYmNjDdKerEgkRMGid7LlpZdeYteuXQBERkYSEBDA4cOH+eCDD/jkk08MHqAQ+rp+HeLiwMICqlY1dTQFgzbZsn8/PHhg2liEEKKgk76OEOJpZ8+qzwW1OK7W0ysSCSFMT+9ky+nTp2nSpAkAq1evpk6dOvzzzz/8+uuvLFu2zNDxCaE37aiWqlXBysq0sRQUVatCuXKQnAzBwaaORgghCjbp6wghtJKT4eJFdbugj5iuWVN9Dg+Xm2tCFAR6J1tSUlJ0BeR27NhBjx49AKhevTq3tGuiCWFCMoUoI40GOnZUt2UqkRBCZE36OkIIrQsX4PFjcHKCMmVMHU3WSpZUH4oio1uEKAj0TrbUqlWLRYsWsXfvXrZv306nTp0AuHnzJiVKlDB4gELoS5Z9zpx2KtH27aaNQwghCjrp6wghtLT9ypo11ZtXhmRlZYWPjw9WBhyKre3/auMWQpiO3smWL774gu+++w5/f38GDhyIr68vABs2bNANuRXClGRkS+bat1c7CadPw82bpo5GCCEKLmP0dRYsWED58uWxsbHBz8+Pw4cP5+i8lStXotFoZDlqIUzEmCtclihRgqFDhxo0iauNUxu3EMJ0crT089P8/f2Jjo4mLi4OV1dX3f6RI0caZMkyIfIiLQ1CQ9VtWfY5vRIl1GWfjxyBHTtg8GBTRySEEAWTofs6q1atYsKECSxatAg/Pz/mzp1LYGAg58+fx8PD47nnXblyhYkTJ9KqVatcvQ8hRN4Z8yaeoiikpqZibm6OxkDDZrRxSrJFCNPTe2QLgLm5ebrOB0D58uWz7DAIkR+uXoXERLUwbuXKpo6m4JG6LUIIkTOG7OvMnj2bESNGMGzYMGrWrMmiRYuws7NjyZIlzz0nNTWVl19+mWnTplGxYkW9rymEMAxjJlsiIyP57LPPiIyMNFibkmwRouDQO9kSFRXFoEGD8PLywsLCAnNz83QPIUxJOz+1WjV16WeRnrZuy44d6iggIYQQGRmyr5OcnMzRo0cJCAjQ7TMzMyMgIIADBw4897xPPvkEDw8Phg8fnu01kpKSiIuLS/cQQuTdo0cQFqZuF5YR09pkS0QExMebNhYhiju9fx0dOnQoV69e5cMPP6R06dIGG/ImhCEYc15tUdCsGdjbQ1QUnDoF/5UhEEII8RRD9nWio6NJTU3F09Mz3X5PT0/OnTuX6Tn79u3jxx9/JCQkJEfXmDlzJtOmTct1jEKIzJ07p96ccnWFUqVMHU3OuLlB6dJw65Y6td7Pz9QRCVF86Z1s2bdvH3v37qVevXpGCEeIvJHiuFmztoY2bWDzZnVVIkm2CCFERqbs68THxzNo0CC+//573N3dc3TOlClTmDBhgu7nuLg4ypYta6wQhSg2nu5XFqb7y7VqqcmWM2ck2SKEKek9jahs2bIoimKQiwcHB9O9e3e8vLzQaDSsX78+23N2795NgwYNsLa2pnLlyixbtizDMbmt+C8KP1n2OXtSt0UIIbJmyL6Ou7s75ubmREVFpdsfFRVFqUxulV+6dIkrV67QvXt3LCwssLCw4KeffmLDhg1YWFhw6dKlDOdYW1vj5OSU7iGEyLvC2q+U5Z+FKBj0TrbMnTuXyZMnc+XKlTxfPDExEV9fXxYsWJCj48PDw+natStt27YlJCSEt956i9dee41t27bpjtFW/J86dSrHjh3D19eXwMBAbt++ned4RcGWmgpnz6rbhe1LMT9p67YEB8PDh6aNRQghCiJD9nWsrKxo2LAhQUFBun1paWkEBQXRrFmzDMdXr16dU6dOERISonv06NFD1/eREStC5B9jT0/38PDg7bffNvgiI1IkV4iCQaPoeevG1dWVBw8e8PjxY+zs7LC0tEz3ekxMTO4C0WhYt24dvXr1eu4x7733Hps2beL0U2naF198kfv377N161YA/Pz8aNy4MfPnzwfUDk3ZsmV54403mDx5co5iiYuLw9nZmdjYWLk7VIiEhUGVKmBjAwkJIPWaM6coULYs3Lihjm7RJl+EEMIYCuN3qqH7OqtWrWLIkCF89913NGnShLlz57J69WrOnTuHp6cngwcPxtvbm5kzZ2Z6/tChQ7l//36ORgBD4fzMhSiIKlWCy5dh505o29bU0eTcgQPQvDl4e8P166aORojCLS/fqXrXbJk7d66+pxjMgQMH0lXzBwgMDOStt94CnlT8nzJliu71nFT8T0pKIikpSfezVPEvnLTZ+xo1JNGSFY1GTbAsWybJFiGEyIyh+zoDBgzgzp07fPTRR0RGRlKvXj22bt2qK5p79epVzMz0HmwshDCixEQID1e3jTVi+t69e+zYsYOAgIAMS83nRc2a6vONG3D/Pri4GKxpIYQe9E62DBkyxBhx5EhkZGSm1fzj4uJ4+PAh9+7d07viP0gV/6KisM6rNYWOHdVky/btpo5ECCEKHmP0dcaNG8e4ceMyfW337t1ZnptZfTohhHGdPauOBi5ZEgw8y0fn0aNHhIaG0rJlS4O26+wMZcqoo1rOnIEWLQzavBAih3J1G+XSpUv83//9HwMHDtTVQtmyZQtnCunEwClTphAbG6t7XLt2zdQhiVyQZZ9zTjtA7MQJdRloIYQQ6RW1vo4QQj+FfYVLqdsihOnpnWzZs2cPderU4dChQ6xdu5aEhAQATpw4wdSpUw0e4NNKlSqVaTV/JycnbG1t9a74ryVV/IuGwv6lmJ9KloT69dVtGd0ihBDpmbKvI4QoGAp7v1J781GSLUKYjt7JlsmTJzN9+nS2b9+OlZWVbn+7du04ePCgQYN7VrNmzdJV8wfYvn27rpq/vhX/RdHx+DFoZ4oV1i/F/CZLQAshROZM2dcRQhQMhT3ZIiNbhDA9vZMtp06donfv3hn2e3h4EB0drVdbCQkJumUNQV3aOSQkhKtXrwLq9J7Bgwfrjh81ahSXL1/m3Xff5dy5c3z77besXr2at99+W3fMhAkT+P7771m+fDlnz55l9OjRJCYmMmzYMH3fqihELl2C5GSwswMfH1NHUzgEBqrPf/8NaWmmjUUIIQoSQ/Z1hBCFk7YWoDGnpzs6OtKuXTscHR0N3rY22fLUIq5CiHymd4FcFxcXbt26RYUKFdLtP378ON7e3nq1deTIEdo+tY7ahAkTALUw3bJly7h165Yu8QJQoUIFNm3axNtvv83//vc/ypQpww8//ECg9rdGsq/4L4qm0FD1uUYNkAUdcqZFC7C3V2u2nDjxZFqREEIUd4bs6wghCp/4eND+CmLMkS0ODg60atXKKG1rVySKioK7d6FECaNcRgiRBb2TLS+++CLvvfcev//+OxqNhrS0NPbv38/EiRPTjULJCX9/fxRFee7rmVXf9/f35/jx41m2m1XFf1E0nT2rPmu/WET2rKygXTv46y/Ytk2SLUIIoWXIvo4QovDR3sQrVQrc3Ix3nUePHhEREYGPjw82NjYGbdvBAcqXhytX1KlErVsbtHkhRA7oPQZgxowZVK9enbJly5KQkEDNmjVp3bo1zZs35//+7/+MEaMQ2dJ+KUqyRT+dOqnPW7eaNg4hhChIpK8jRPGWH1OIAO7du8fKlSu5d++eUdqXqURCmJbeI1usrKz4/vvv+fDDDzl9+jQJCQnUr1+fKlWqGCM+IXJEki25o0227N8PcXEgC3EJIYT0dYQo7gp7cVytWrVg0yYpkiuEqeidbNEqV64c5cqVM2QsQuRKaqpMI8qtihWhcmUIC4Ndu6BnT1NHJIQQBYf0dYQonopSsgUk2SKEqeQo2aItXJsTs2fPznUwQuRGRAQ8egTW1vBMLUORA506wfz56lQiSbYIIYor6esIIbS0yQljTyMyNm38p0+DooBGY9p4hChucpRsebYg7bFjx3j8+DHVqlUD4MKFC5ibm9OwYUPDRyhENrRTiKpVA3Nz08ZSGAUGPkm2yBexEKK4kr6OEALg/n24cUPdNvaIaQsLC0qWLImFRa4nG2SpenW1X3f3Lty+DbI4qxD5K0f/snft2qXbnj17No6OjixfvhxXV1dALe40bNgwoy1dJkRWpF5L3vj7qysTXbkCFy9C1aqmjkgIIfKf9HWEEPBkVEuZMuDsbNxrlSxZkjFjxhitfTs7dcr4pUvq+5JkixD5S+/ViGbNmsXMmTN1nQ8AV1dXpk+fzqxZswwanBA5IcmWvHFwAO3vDtu2mTYWIYQoCKSvI0TxVVTqtWhJ3RYhTEfvZEtcXBx37tzJsP/OnTvEx8cbJCgh9CHFcfMuMFB9liWghRBC+jpCFGenTqnP+ZFsiYyMZObMmURGRhrtGtq6Ldr3JYTIP3pPEOzduzfDhg1j1qxZNGnSBIBDhw4xadIk+vTpY/AAhciKomQ+siVNSeNk1EmCI4K5k3iHxJREHqQ8IDElkeTUZMo6laVaiWpULVGVau7V8LT3RFOMi5V06gTvvgu7d6vFhm1sTB2REEKYjvR1hCi+tOWb6tc3/rUURSE5ORlFUYx2jXr11OdnylIJIfKB3smWRYsWMXHiRF566SVSUlLURiwsGD58OF999ZXBAxQiK9evQ0ICWFiAuXs4Cw5vZteVXey6souYhzE5bsfN1o2uVbrSp0YfOlbqiJ2lnRGjLnhq1wYvL7h5E/btg4AAU0ckhBCmI30dIYqntDQICVG3GzQwaSgGo30fp05BSgpYWpo2HiGKE42Sy1RqYmIily5dAqBSpUrY29sbNDBTiouLw9nZmdjYWJycnEwdjsjCtm3Q6ZVzOHf7jPgKv5GmpOlec7ByoFW5VlR0rYi9pT32VvbYW9pjbmbOlftXOH/3PBfuXuDK/SvpzrOztKNz5c4MqDWA3jV6Y2FmnArxBc2rr8LSpfDOO/D116aORghRVBTm79TC2tcpzJ+5EKZ0/ry6go+tLcTHG3+Vy1u3brF48WJGjhxJ6dKljXKNtDRwdYW4ODhxAurWNcplhCiy8vKdmuvfIu3t7akr/1qFCZ29c5YpRz6FsSuJ1SigQGuf1nSs2JF2FdrRyKsRlubZp+8fPX7Evzf+Zd25daw9u5aI2Aj+OPsHf5z9gwouFZjUfBJD6w3F1tI2H96V6XTqpCZbtm2TZIsQQoD0dYQobo4dU599fY2faMkvZmbqlKg9e9T3J/+lCZF/9C6QK4SpPUh5wOt/vU6tb2tx/PEK0ChUVXpwZMQR9gzdwwetP6BZ2WY5SrQA2FjY0MqnFbMDZxP+ZjhHRhxhcovJuNu5E34/nDGbx1D+f+WZsXcG9x/dN+6bM6GAAPUL+fRpdXqWEEIIIURxkp/1WgDc3d0ZOXIk7u7uRr2O9v1I3RYh8pckW0Shci76HH4/+LH42GIUFNyiesOiY0yr/icNvRrmuX2NRkNDr4bMDJhJxFsRzOs8Dx9nH24n3uaDnR9Q+ZvKfHfkO1LTUg3wbgoWNzf4rw6krEokhBBCiGJHO7Ilv+q1WFpaUrp0aSyNXEhF+360708IkT8k2SIKjV9P/kqjxY04ffs0nvae7BgUhLJyLUTWN8qyz3aWdoxrMo6Lb1zkl96/ULNkTe4+vMuoTaNo8kMTDlw7YPiLmliXLurzpk2mjUMIIYQQIj8pypORH/mVbImNjWXTpk3ExsYa9Tra9xMSotZwEULkD72TLYmJicaIQ4jnepjykJF/jeSVda+QmJJI2/JtCRkVQi27dty7p059qVrVeNe3NLfk5bovc2LUCb7p9A3O1s4cu3WM5kuaM2T9EKISoox38XzWtav6vH07JCWZNhYhhDAV6esIUfxcvQoxMeoKl7Vq5c81Hzx4wJEjR3jw4IFRr1OtGtjYqCt4hoUZ9VJCiKfonWzx9PTk1VdfZd++fcaIR4h0HqQ8oOtvXfn+2Pdo0PBR64/YPmg7pRxKERqqHlOpkvoFYmwWZha84fcGF964wKv1XgXgpxM/UXthbdafW2/8APJB/fpQujQkJkJwsKmjEUII05C+jhDFj3aKTe3aYG1t2lgMzcJCLfoLMpVIiPykd7Lll19+ISYmhnbt2lG1alU+//xzbt68aYzYRDH36PEjeq/qza4ru3CwcmDbK9uY1nYa5mZqeXhtssUYU4iy4mHvwY89f+Tg8IPU9axL9INoeq/qzfA/hxOfFJ+/wRiYRiNTiYQQQvo6QhQ/+T2FKL9p35cUyRUi/+idbOnVqxfr16/nxo0bjBo1it9++w0fHx+6devG2rVrefz4sTHiFMVMcmoy/Vb34+9Lf2NnaceWl7fQoVKHdMeYKtmi5VfGj8OvHebd5u+iQcOSkCX4LvJl/9X9pgnIQLRTiSTZIoQorqSvI0Txox3xkV8rEeU37fuSkS1C5J9cF8gtWbIkEyZM4OTJk8yePZsdO3bQr18/vLy8+Oijj4w+91AUXSmpKQxYM4BNFzdhY2HDxoEbaVmuZYbjtMmWGjXyOcCnWFtY80WHL9g1ZBflnMsRfj+c1staMz14OmlK4axAFhAAlpbqnN4LF0wdjRBCmI70dYQoPkwxssXe3p6mTZtib29v9Gs9PbJFUYx+OSEEeUi2REVF8eWXX1KzZk0mT55Mv379CAoKYtasWaxdu5ZevXoZMExRXDxOe8wr615h/bn1WJtb8+eLf9K2QttMjzX1yJantSnfhpOjTjLYdzBpShof7vqQXit7cf/RfVOHpjdHR2jTRt2W0S1CiOJM+jpCFA+RkXDzpjqdum7d/Luuk5MTgYGBODk5Gf1atWurtVvu3oVr14x+OSEEYKHvCWvXrmXp0qVs27aNmjVrMmbMGF555RVcXFx0xzRv3pwaphxuIAqtD4I+YPWZ1ViaWbJ2wFo6VuqY6XHR0XDnjrpdvXo+BpgFZxtnlvdaTutyrRm7eSx/XfiLxt83Zm3/tdTxrGPq8PTStSvs2KEmW95+29TRCCFE/pK+jhDFi3ZUS7Vq4OCQf9dNTk4mKioKT09PrKysjHota2t1laUTJ9SpROXKGfVyQghyMbJl2LBheHl5sX//fkJCQhg3bly6zgeAl5cXH3zwgaFiFMXEhvMb+PKfLwH4uffPdKnS5bnHnj2rPpcvD/kw8lIvwxsMZ9+r+yjnXI6wmDD8fvDjt1O/mTosvWiL5AYHQ3zhrvkrhBB6k76OEMWLqYrj3r17lyVLlnD37t18uZ4UyRUif+k9suXWrVvY2dlleYytrS1Tp07NdVCi+Am/F86Q9UMAeNPvTQbUHpDl8QVpClFmGnk14ujIo7z0x0tsv7ydl9e+zOnbp5nebjpmmlzP3ss3VatC5cpq3ZYdO6B3b1NHJIQQ+Uf6OkIUL/lZHDdNSdNNM499FKt79lQ8jd5HrF8fli6VIrlC5Be9/0U7Ojpy+/btDPvv3r2Lubm5QYISxUvS4yT6r+nP/Uf3aVqmKV92+DLbcwp6sgXA3c6dLS9vYUrLKQDM3DeTAWsG8DDloYkjyxlZlUgIUVxJX0eI4iU/RrZcj7vOJ3s+ofzc8pT4sgQlvixBu5/aAdDup3aUnVOWj3Z9RMT9CKPFICNbhMhfeidblOeUr05KSjL6XENRNE3YNoEjN4/gZuvGqn6rsDLP/u9RYUi2AJibmTOj/QyW91qOpZkla0LX4L/cn6iEKFOHli1tsmXzZqlaL4QoXqSvI0Txce8eXL6sbterZ9i205Q0Nl7YSI8VPfCZ68PU3VO5Fpd5ddqb8Tf5NPhTKvyvAl1+7cL6c+tJTUs1aDy+vmoR4Bs3IKrgd0WFKPRyPI3om2++AUCj0fDDDz/g8FT1qNTUVIKDg6leUCqVikJj5emVfHvkWwB+6f0L5ZxzVq2rICz7rI/BvoMp71Ke3qt6c/jGYfx+8GPjSxup7VHb1KE9V+vWaj2cW7fUOyD5PY9ZCCHym/R1hCh+QkLU5/Llwc3NcO1GJUQx8I+B7LqyS7evjU8bRjYcSc9qPbG2sCYqKorffvmNf176h0P3DrH42GJ2hu9kS9gWtoRtoWW5lqzqtwovRy+DxOTgoE4VP39e7dt16mSQZoUQz5HjZMucOXMA9W7PokWL0g2jtbKyonz58ixatMjwEYoiK/xeOCP+GgHAB60+oHOVzjk67/59dXk+KPgjW57W2qc1B4cfpOtvXbkYc5EWS1rw54t/4l/e39ShZcraGjp0gPXr1alEkmwRQhR10tcRovgxxhSivRF7GbBmALcSbmFvac+oRqN4rcFrVHdPn6z1Lu3NpEmTACjvXZ4BtQdw8e5Ffjj2AwuPLGTf1X3U/64+K/uupG2FtgaJrUEDSbYIkV9ynGwJDw8HoG3btqxduxZXV1ejBSWKPkVRGLt5LAnJCbQq14pp/tNyfO6ZM+pz2bLg5GSkAI2kSokqHHztIL1X9SY4IpjAXwL5rc9v9K3Z19ShZapr1yfJlg8/NHU0QghhXNLXEaL4MWRxXEVRmHVgFpN3TCZVSaVmyZqseWENNUrmfCh2lRJV+KLDF7zW4DX6/d6Pk1EnCfg5gE/bfsrklpPzXES3fn1YsUKK5AqRH/T+17pr1y7pfIg8WxO6hi1hW7Ayt+L77t9jbpbzgoOFpV7L87jZurHtlW30qdGH5NRkXvj9BRYdKZh3SrVLQB8+LHN7hRDFhzH6OgsWLKB8+fLY2Njg5+fH4cOHn3vs2rVradSoES4uLtjb21OvXj1+/vlng8YjhFAZamRLQnICfVb3YdL2SaQqqbxc52UOv3Y4y0TL7du3+eabbzItyF2lRBUODj/IsHrDSFPS+GDnB/RY0YO4pLg8xSlFcoXIPzka2TJhwgQ+/fRT7O3tmTBhQpbHzp492yCBiaIr9lEs47eOB2BKyylUc6+m1/nakS21ahk6svxjY2HD6n6rGbt5LN8d/Y7Rm0YTmRDJ1DZT0Wg0pg5Px8sLGjWCI0dg40YYPtzUEQkhhHEYs6+zatUqJkyYwKJFi/Dz82Pu3LkEBgZy/vx5PDw8Mhzv5ubGBx98QPXq1bGysmLjxo0MGzYMDw8PAgMD9bq2EOL5EhPh3Dl1Oy8jW5IeJ9FnVR+2X96OlbkV/+v0P15v+Hq2fbrU1FTu3btHamrmhXBtLW1Z0nMJLcu1ZOzmsWy6uIkeK3qw5eUt2Fra5ipW7fu8dEmdmu/ikqtmhBA5kKNky/Hjx0lJSdFtP09B+iVRFFzvB71PZEIkVUtUZXLLyXqfXxSSLaCuVLSw60JKO5Tm4z0fM23PNKISoljQdUGeh4gaUs+earJl/XpJtgghii5j9nVmz57NiBEjGDZsGACLFi1i06ZNLFmyhMmTM34P+vv7p/v5zTffZPny5ezbt0+SLUIY0MmTkJYGpUpB6dK5ayM1LZVB6wax/fJ27C3t+XvQ3zQv29ygcb5a/1XqeNQh4OcA9kTs4cU/XuSP/n9gYZbjihA6bm7g4wMREerolraGKQUjhMhEjv6F7tq1K9NtQ1mwYAFfffUVkZGR+Pr6Mm/ePJo0aZLpsf7+/uzZsyfD/i5durBp0yYAhg4dyvLly9O9HhgYyNatWw0eu9DPoeuHWHhkIQCLui7CxsJG7zYK+zSip2k0Gqb6T8XTwZMxm8aw6Ogi4pPjWdpzKZbmlqYOD4BevdR6Ldu3Q0KCWsleCCGKGmP1dZKTkzl69ChTpkzR7TMzMyMgIIADBw5ke76iKOzcuZPz58/zxRdfZHpMUlISSUlJup/j4vI2zUCI4uLgQfW5UaPcna8oCqM3jeb30N+xNLNk3YB1Bk+0aDX2bsyGFzfQ6ddObDi/geEbhrO059Jc3aBr3FhNthw6JMkWIYzJ5LfPtUNrp06dyrFjx/D19SUwMDDTuYugzmO+deuW7nH69GnMzc154YUX0h3XqVOndMetWLEiP96OyEJKagojN45EQWGI75BcVVUvrCsRZWdUo1Gs6LsCCzMLfj31Ky/8/gJJj5OyPzEf1KoFlSpBUhL8/bepoxFCiMIlOjqa1NRUPD090+339PQkMjLyuefFxsbi4OCAlZUVXbt2Zd68eXTo0CHTY2fOnImzs7PuUbZsWYO+ByGKqv371ecWLXJ3/vtB7/P9se8x05jxW9/f6FAp83+jhtKmfBtW91uNucacn078xDvb3kFRFL3baf5fPkj7/oUQxpGjkS19+vTJcYNr167VKwB9h9a6ubml+3nlypXY2dllSLZYW1tTqlQpvWIRxvW/Q//jZNRJ3Gzd+Lrj17lqQzuFqEyZwrcSUXYG1B6Ag5UDfVf35c/zf9J9RXfWDViHvZW9SePSaNSpRLNnq1OJ9PjvQAghCg1j9nVyw9HRkZCQEBISEggKCmLChAlUrFgxwxQjgClTpqSrMxMXFycJFyGyoSh5S7Z8/c/XfL7/c0Adrd2vZj+923Bzc+Pll1/O8PtNVrpX687SnksZvH4wcw/NpYRdCf6v9f/pdV3t+/3nH3UalZnJb78LUTTlKNni7OxslIvndWgtwI8//siLL76IvX36X0h3796Nh4cHrq6utGvXjunTp1OiRIlM25Dht8Z3O/E2H+/+GICvO3yNu517rtrRTiEq7PVanqdr1a5seXkL3Vd0Z/vl7XT8pSObXtqEi42LSePq1UtNtmzcCI8fg4X+U4SFEKJAM1Zfx93dHXNzc6KeWdItKioqy5tCZmZmVK5cGYB69epx9uxZZs6cmWmyxdraGmtra4PGLURRd+UKREaCpaX+04g2X9zMpO2TAPi8/eeMaDgiVzFYW1vr/p3rY5DvIGIexvDWtrf4cNeH1PaoTa/qvXJ8fr16YGMDMTFw4QJUr653CEKIHMjRr0xLly41ysWzGlp7TlsaPAuHDx/m9OnT/Pjjj+n2d+rUiT59+lChQgUuXbrE+++/T+fOnTlw4ADm5hmXGJ45cybTpk3L25sRWfos+DMSUxJp7NWYofWG5rqdolIcNyttK7Rlx+AddP61M/9c+4eAnwL4e9DfuNnm/K6HoTVvDu7uEB0Ne/fK/F4hRNFjrL6OlZUVDRs2JCgoiF69egGQlpZGUFAQ48aNy3E7aWlp6W4MCSHyRjuqpUEDsNVjYZ9rsdcYtG4QAKMbjea9lu/lOob4+HiOHj1Kw4YNcXR01OvcN5u+SURsBHMOzmHo+qEcf/04FVwr5OhcKyto0gSCg9XPQZItQhhHoR409uOPP1KnTp0MxXRffPFFevToQZ06dejVqxcbN27k33//Zffu3Zm2M2XKFGJjY3WPa9eu5UP0xceV+1d0RXE/D/g8T6tWaZMtRaleS2aalmnK7iG7KWlXkqO3jtJ2eVvuJN4xWTzm5tC9u7q9fr3JwhBCiEJpwoQJfP/99yxfvpyzZ88yevRoEhMTdVOoBw8enG6U78yZM9m+fTuXL1/m7NmzzJo1i59//plXXnnFVG9BiCLnn3/UZ32mEKWkpjBgzQBiHsbQsHRD5gTOyVMMCQkJ7Nmzh4SEhFyd/0XAFzQr04zYpFj6r+mvV72/p6cSCSGMI0cjWxo0aEBQUBCurq7Ur18/y1+Wjx07luOL53ZoLUBiYiIrV67kk08+yfY6FStWxN3dnbCwMNq3b5/hdRl+a1xTd08lJS2FgIoBtKvQLk9tFfVpRE/zLeXL7qG7af9Te05GncR/uT9Bg4Mo5WCaWkS9esHSpfDnnzB3rlrLRQghigpj9XUABgwYwJ07d/joo4+IjIykXr16bN26VTey9+rVq5g9VTQhMTGRMWPGcP36dWxtbalevTq//PILAwYMyN2bE0JkoB3Z0lyPxYOmBE3hwPUDOFs7s/qF1VhbmPb3B0tzS1b1W0W97+px5OYRJv49kXld5uXoXCmSK4Tx5SjZ0rNnT10yQjsE1hDyMrT2999/JykpKUd3ea5fv87du3cpXbq0IcIWejh9+zQ/n/gZgBntZuSpraK6ElFWapasyZ6he2i3vB2hd0Jps6wNOwfvxNvJO99jCQhQh9lGRMCJE+p8XyGEKCqM1dfRGjdu3HP7Ns+OvJ0+fTrTp083eAxCCFVsLJw6pW7ndGTLn+f+ZNaBWQAs7bmUiq4VjRSdfso6l+Xn3j/T9beuzP93Pq19WvNCrReyPU+bbDl/Xp0m7p67copCiCzkKNkyderUTLcNYcKECQwZMoRGjRrRpEkT5s6dm2Forbe3NzNnzkx33o8//kivXr0yFL1NSEhg2rRp9O3bl1KlSnHp0iXeffddKleuTGBgoEFjF9n7v53/h4JCv5r9aOzdOE9tFeWViLJStURVNeHyUzsu3L1A62Wt2TVkF+Wcy+VrHHZ2EBioTiNav16SLUKIosWYfR0hRMFy6JC6GlHFipCTxUvD74Uz9M+hALzl9xa9a/Q2boB66lKlC5NbTObz/Z8zfMNw6pWqR5USVbI8x80NatSAs2fhwIEn08WFEIaT65otR44c4eeff+bnn3/m6NGjuQ5gwIABfP3113z00UfUq1ePkJCQDENrb926le6c8+fPs2/fPoYPH56hPXNzc06ePEmPHj2oWrUqw4cPp2HDhuzdu1emCuWzA9cO8Of5PzHTmPFp20/z3F5xmkL0rEpulQgeGkxF14pcvncZ/2X+RNyPyPc4evZUn6VuixCiODBUX0cIUbDoM4UoNS2Vl9a+xP1H92ni3YQvOnxhsDhsbGyoU6cONjY2eW7r03af0qpcK+KT43nxjxdJSU3J9hyZSiSEcem9gOv169cZOHAg+/fvx8XFBYD79+/TvHlzVq5cSZkyZfQOQp+htQDVqlVDUZRMj7e1tWXbtm16xyAMS1EUpgSpxf6G1RtGdfe8lzkvDisRZcXHxYc9Q/fQdnlbwmLCaLOsDbuH7qa8S/l8i6FbNzAzU6cRXbkC5fPv0kIIkW+M0dcRQhQc2uRCTqYQzTowi4PXD+Jk7cTqfquxMrcyWByurq706dPHIG1ZmFmwou8K6i6qy7Fbx/h83+d82ObDLM9p0QJ+/FGSLUIYi94jW1577TVSUlI4e/YsMTExxMTEcPbsWdLS0njttdeMEaMohP6+9Dd7IvZgbW7N1DaGGY5dXFYiykoZpzLsHrKbKm5ViIiNoM2yNoTfC8+367u7Q6tW6vaff+bbZYUQIl9JX0eIouvxY3UaEWSfbAm9E8qHu9SExdzAufi4+Bg4lsfExMTw+PFjg7Tn7eTNvM5qgdxPgj/hROSJLI/Xvv9//4XkZIOEIIR4it7Jlj179rBw4UKqVaum21etWjXmzZtHcHCwQYMThZOiKHy0+yMAxjYeS1nnsgZptzhPI3qat5M3u4fupmqJqlyNvUqbZW24FHMp366vnUq0bl2+XVIIIfKV9HWEKLpOnYKEBLX+X1Y38B6nPWbo+qEkpybTpUoXhtYbavBY7ty5w7x587hz547B2hxYeyC9q/dW4/9Tjf95qlRRb6QlJYGei6wJIXJA72RL2bJlSUnJOAcwNTUVLy8vgwQlCrfdV3Zz+MZhbCxseK/lewZpsziuRJQVL0cvdg/ZTXX36lyLu4b/cn/CYsLy5draRTr27oVnVm0XQogiQfo6QhRd2ikzzZqBufnzj/tq/1f8e/NfnK2dWdxtcZbLwRckGo2GhV0XUsK2BCGRIczY+/zVQDUaqdsihDHpnWz56quveOONNzhy5Ihu35EjR3jzzTf5+uuvDRqcKJy+2K8WDnu13qt42HsYpM3iuhJRVko7lmbXkF3UcK/B9bjr+C/Ln4RLhQrQqBGkpcHatUa/nBBC5Dvp6whRdP3zj/qc1RSi07dP8/GejwH4X6f/4e3kbfzADMjTwZMFXRYA8Nnezzh+6/hzj9V+DtrPRQhhODkqkOvq6poum5uYmIifnx8WFurpjx8/xsLCgldffZVe2tveolgKiQxh26VtmGnMeKf5OwZrV6YQZa6UQyl2DdlF2+VtORt9Fv9l/uweupvKbpWNet3+/eHIEfj9dxg92qiXEkKIfCF9HSGKh+xWIkpJTdFNH+pWtRuDfQfnX3AG1L9Wf34P/Z0/zv7B0D+H8u+IfzMt7vv0yBZFUUe7CCEMI0fJlrlz5xo5DFFUfPXPV4D6H3xF14oGa1eK4z6fp4Mnu4bsot1P7Qi9E4r/Mn92DdlFlRJVjHbNF16Ad9+FPXsgMhJKlTLapYQQIl9IX0eIou/6dbh6VZ0+5OeX+TFf//M1R28dxdXGle+6fVdopg89S6PR8G3Xb9kTsYeTUSf5LPgzprWdluG4Ro3AykqdGn75MlSqZIJghSiiNMrz1lAuxuLi4nB2diY2NhYnmbOSY1fuX6HyN5VJVVI5NvIY9UvXN1jbHTrAjh3www8wfLjBmi1SohKidAkXb0dvoydc/Pzg8GFYsADGjDHaZYQQhZx8p+Y/+cyFyNzq1TBgADRoAEePZnw9LCaM2t/WJik1iZ96/cQg30H5H6SBrT6zmgFrBmBpZsmJUSeoUbJGhmOaN4cDB+Cnn2BQ4X/LQhhUXr5T9a7Z8rRHjx4RFxeX7iGKr1n/zCJVSaVDxQ4GTbSATCPKCU8HT3YO3knNkjW5EX8D/+X+XLx70WjXe+EF9fn33412CSGEMDnp6whRdGQ1hUhRFEZtHEVSahIdKnbglbqv5G9wRvJCzRfoWqUrKWkpvL7xddKUtAzHSJFcIYxD72RLYmIi48aNw8PDA3t7e1xdXdM9RAEQdxGu/wUXvoWQKbD/ZQhqB7s6wb7+cOg1ODoBTk+Ha+sgIVydpJkHdxLv8OPxHwF4r4VhViDSenolohoZk/HiKdopRbVK1uJm/E2jJly0yRbtVCIhhCgqpK8jRNGUVXHcX07+QlB4EDYWNizsujBfpg9FR0fz448/Eh0dbbRraDQaFnRZgJ2lHXuv7mXJ8SUZjpEiuUIYR45qtjzt3XffZdeuXSxcuJBBgwaxYMECbty4wXfffcfnn39ujBhFdhQFYs/A1TVw7XeIDdW/DUsncPEFt0ZQOhA824C5TY5Pn394Pg8fP6Rh6Ya0q9BO/+tn4emViJydDdp0keRh78HOITtpt7wdZ+6cwX+5WsOlaomqBr2Oj486lejQIfjjDxg71qDNCyGEyUhfR4iiJz4ejv+3KM+zI1uiH0Qz4e8JAHzU+iMqueVP4ZKUlBSuX7+e6VLzhuTj4sOnbT/lnb/fYdL2SXSv2h1PB0/d69rP4/RpiIkBNzejhiNEsaF3suWvv/7ip59+wt/fn2HDhtGqVSsqV66Mj48Pv/76Ky+//LIx4hSZSU2C89/A5SUQd+7JfjNLcK4N9j5gV/a/hzekPYaUWEiJUx+PouD+STVRkxIHd/aqj/NzwNwOPNuBV2fw7g72ZZ8bRmJyIvP/nQ+oo1oMfSdAphDpT5twaf9Te07fPk3b5W2NknDp319Ntvz+uyRbhBBFh/R1hCh6du2C1FSoXBnKlUv/2qTtk4h+EE1tj9pMbD7RNAEa2Xi/8fx66leO3TrG29ve5re+v+le8/RU+9lnzkBQ0JPRy0KIvNE72RITE0PFiuoqM05OTsTExADQsmVLRssasPnn1t9wZBzE/zdFxMxKHZFS7gU1OWLlkvO20lLUZE3McTXZcnMzPLwJNzeqjyPjwLMtVBgEZfuCpWO605ccX0LMwxgqu1WmT40+hnuP/5GViHLHw96DoMFBuoSLdlloQyZc+vWDd96B4GC4dQtKlzZY00IIYTLS1xGi6Pn7b/W5Y8f0+3eF72JZyDIAFndbjKW5Zf4Glk8szCxY3G0xTX5oworTKxjsO5hOlTvpXu/YUe1z//23JFuEMBS9a7ZUrFiR8PBwAKpXr87q1asB9S6Qi4uLQYMTmXhwHfa+ALsC1USLTSlo8j30vQNtNqgJEX0SLaCOhHGpAxUHg9/30Os6dA4B3xng3hxQIGonHBwGaz3hn1cgahcoCmlKmm5Uy9tN38bczNzQ75hTp9Tn2rUN3nSRp0241Paoza2EW/gv8+d89HmDtV+uHDRtqs5k++MPgzUrhBAmJX0dIYqebdvU58DAJ/sePX7EqE2jABjVcBTNyjYzQWT5p6FXQ8Y3GQ/A6E2jSUxO1L2m/Vy2bctzKUchxH/0TrYMGzaMEydOADB58mQWLFiAjY0Nb7/9NpMmTTJ4gOIpYT/AxupwbQ1ozKHaW9DtHFR+Ta25YigaDbj6Qq0p0HE/9AiHutPBsSqkPoQrv6oFdzfX5dyBt7kecwFHK0cG1TX8WnGKAidPqtt16xq8+WLBw96DnYN3UsejjppwWe7Puehz2Z6XU/37q8+yKpEQoqiQvo4QRcvlyxAWBhYW4O//ZP8X+77gwt0LlHIoxcyAmfkel4uLC717987XJO6n7T6lrFNZrty/wvTg6br9rVqBtTVcuwbnDXdfTohiTaMoectdXrlyhWPHjlG5cmXqFpHfhvOylrbRhH4BIZPV7ZItoNECNSGSnxQF7h6Gy8vgys/wWM2G30uF43b1aNdpvVonxoAiI9WpKWZmamEzOzuDNl+s3Em8Q8DPAZyMOomnvSc7h6jLROfVtWvqCBeNBq5fBy8vAwQrhCgyCuR3qp4KW1+nKHzmQhjSokUwejS0bq2uoghw8e5Fai+sTXJqMiv7rmRA7QGmDTIfbTi/gZ4re2JhZkHI6yHU8lALI3bsCNu3w9y58Oabpo1RiIIiL9+peo9seVb58uXp06dPoeh8FEqKAic+eJJoqfUBBATnf6IF1N+m3f2gyULodZ17NT8iLBlczaFdUghsqAT/DIb7pw12Se2olipVJNGSVyXtSxI0OIh6peoRlRhF2+VtOXP7TJ7bLVsWmjWTqURCiKJL+jpCFG7P1mtRFIWxm8eSnJpMYKVA+tfqb5K4EhMTOXz4MImJidkfbEA9qvWgR7UePE57zOhNo9Hee9d+PtrPSwiRN7lKtgQFBdGtWzcqVapEpUqV6NatGzt27DB0bEJJg6NvwZkZ6s/1vgDf6aDJc44s76xc+OpuClUjYEpqXfBsD0qqOuJlcx3Y3R3u7M/zZWQKkWG527mzY9AO6peqz+3E27Rd3pZTUafy3K52KtGKFXluSgghCgTp6whRNKSkqCvswJNkwqozq9h+eTvW5tbM7zLf4Ctp5lRcXBxbtmwhLi4u36/9TadvsLO0Y+/VvSw/sRx48vns3g1JSfkekhBFjt6/tX/77bd06tQJR0dH3nzzTd58802cnJzo0qULCxYsMEaMxVNaKhx6DS58o/7c+Fuo+a5pY3rKo8eP+P7Y9yhA40ZTof0O6HREXQ0JjbqK0faWENQebu/N9XUk2WJ4JexKsGPwDhqUbsCdB3dou7wtIZEheWrzxRfVqV4HDsDFi4aJUwghTEX6OkIUHYcPQ1wclCgBDRpA7KNY3t72NgAftPqAym6VTRyhafi4+PBxm48BmPj3RO4+uEudOlCqFDx4APvzfs9UiGJP72TLjBkzmDNnDitWrGD8+PGMHz+e3377jTlz5jBjxgxjxFg8HZ8El5eqo1ia/QRVCtZSk7+f+Z3oB9GUcSpDj2o91J1uDaHlarVob6XX1FWOonbCjta5Trpoky116hgweIGbrRs7Bu2gsVdj7j68S7vl7Th682iu2ytV6sndkF9+MVCQQghhItLXEaLo0E6JCQgAc3P4v53/R2RCJFVLVOXdFgXnRqYpvNX0LWp71Obuw7tM3jEZjUamEglhSHonW+7fv0+nTp0y7O/YsSOxsbEGCarYu7EJzs9Rt5uvUJdzLmAW/Kve2RvVcBQWZhbpX3Sqqi4h3T0MKo/KmHS5cyBH10hJgbNn1W0Z2WJ4rraubB+0nWZlmnHv0T3a/9SeQ9cP5bq9Qf/9Nf35Z1kyUAhRuElfR4ii4+kln4/cPKLrwy7suhBrC2sTRmZ6luaWLOy6EIAfjv/A/qv70y0BLYTIG72TLT169GDdunUZ9v/5559069bNIEEVaw9uwsGh6na1N8HHNAW7snL05lEO3TiEpZklrzV47fkH2pdTi+k+m3TZ3lyt6XIvJMvrXLgAycng6Ag+hl3kSPzH2caZba9so2W5lsQmxdLh5w7sv5q7caO9eoGDA4SHy9BTIUThJn0dIYqGmBj49191u137VEZtHIWCwst1XqZdhXamDQ6wsrKiUqVKWFlZmSyGluVaMrz+cABGbxpNm7YpAISEQFSUycISokiwyP4Q+Oabb3TbNWvW5LPPPmP37t00a9YMgIMHD7J//37eeecd40RZXKSlwoFBkBQNrvXUgrgFkPaOwAu1XsDTwTP7E7RJl1qT4fSn6tLRNzeqj3L9oe4n4FQtw2lPTyEyKwA1gYsqR2tHtry8he4rurP7ym4Cfwlk40sb8S/vr1c7dnbQrx8sW6aObmnZ0ijhCiGEUUhfR4iiJygI0tKgVi3489YCjt46irO1M7M6zjJ1aACUKFGCV155xdRh8EXAF6w/t55Tt0/x6+U5NGjwLseOqctAF4DwhCi0NIqS/YD/ChUq5KwxjYbLly/nOShTy8ta2nly5nM4MQXM7aDzsUwTEKYW8zAG79nePHr8iP2v7qd52eb6NxJ3EU5NhYiVgAIac6g4DOp8DHbeusOmTIHPP4dRo2DhQoO9BfEcD1Ie0HNlT3Zc3oGNhQ3rB6wnsHKgXm3s3Ant24OzM0RGgo2NkYIVQhQaJvtO1VNR6usUls9cCGMbMQJ++AFGTLjBSvcaxCfHs6jrIl5v9LqpQwMgLS2NlJQULC0tMTPxncVlIcsY9ucwbC1sGZIYyqKZ5Rk0CH76yaRhCWFyeflOzdHIlvDw8FwFJvQQfRBO/p+63Wh+gUy0APx04icePX5EvVL1aFamWe4acaoCLX6DWlPgxP/BjQ1w6Qe48gtUHQ813wNrNymOm8/sLO34a+Bf9Fvdj00XN9FjZQ9+f+H3JwWQc8DfH8qWhWvXYONGdaSLEEIUBtLXEaJoUZQndUfOln+T+Jh4mpZpyoiGI0wb2FOioqJYvHgxI0eOpHTp0iaNZYjvEJaFLGNPxB5CSo8D/uLvvzWkpckIcyFyK0//dBRFIQcDY0R2UuJg/0ugpILPi1BxqKkjypSiKPx4/EcARjQYgUajyVuDLnWgzZ/QYR+UbAmpj+Dsl7ChEoR+wYWzDwEpjpufbCxsWDtgLX1r9CU5NZm+q/uy+szqHJ9vZgYvv6xuy50QIURRIH0dIQqn8+fVmz8WNTexL+YPzDXmfNftO8w0kjnIjEajYVG3RViaWXIwZhPW9dcSFQWnTpk6MiEKr1z9b/PTTz9Rp04dbG1tsbW1pW7duvz888+Gjq34OPM5JIaDfXlovAjymsQwksM3DnP69mlsLGx4qc5Lhmu4ZAsICIY2G9UETMp9CJnMrglVGNZmCXVqpxruWiJbVuZWrOy3kpfqvMTjtMcM/GMgy0OW5/h87apEW7bAnTtGClIIIYxM+jpCFG5//w1YJmLRYywAbzd9m7qecgcvK9Xdq/Nei/fUHzqPB+s4WQJaiDzQO9kye/ZsRo8eTZcuXVi9ejWrV6+mU6dOjBo1ijlz5hgjxqLtYSSc/5+63WAOWDmbNp4saEe19KvZDxcbF8M2rtGAd1fodBya/cQj83KUcbvBkpHDcd5fF67/JesJ5yMLMwt+6vUTw+sPJ01JY+ifQ/n2329zdG7NmtCgATx+DKtWGTlQIYQwAunrCFH4bdkCtPmERzYRlHMux8f+H5s6pELh/VbvU8m1EklWN6Hth+rnKITIFb2TLfPmzWPhwoV88cUX9OjRgx49evDll1/y7bffpqvkL3LozGeQ+gBK+EGZnqaO5rkSkhNYcXoFgG55OKMwM4cKg1h65zzv/Po18UmuEBsKwT1gRxu1to3IF+Zm5izuvpg3mrwBwNjNY5m5d2aOzh08WH2WqURCiMJI+jpCFG7378OOk6eg2WwA5neej72VvWmDKiRsLW1Z2PW/lSmazGf3haMyUlmIXNI72XLr1i2aN8+4Ak3z5s25deuWQYIqNhKuQNh36rbvjAI7fQjg9zO/k5CcQGW3yrTxaWP06x0/acPsze8w9/JltWCuuQ3c2Qt/N4O9/SDugtFjEGCmMeN/nf7H/7VSize/v/N9Ju+YnG39goEDwdwc/v0Xzp3Lj0iFEMJwpK8jROG2bn0qjzu/DuaP6V29N92rdTd1SJny8PBg4sSJeHh4mDqUdDpU6sDA2gPBLA2l2wjWrH1s6pCEKJT0TrZUrlyZ1aszFsxctWoVVapUMUhQxcapjyEtBUoFQKl2po4mS9opRK/WezXvhXFzQFuMq2ptF6j3OXS/qC4PrTGDa3/Apprw7xh4GGX0WIo7jUbDp+0+5asOXwHwxf4vGLNpDGlK2nPP8fCATp3U7aVL8yNKIYQwHOnrCFG4fR38LZQ9gDWOfNO54I5GMzc3x97eHnNzc1OHksGcwDnY4gqljzP7gEyfFCI3crT089OmTZvGgAEDCA4OpkWLFgDs37+foKCgTDsm4jliQ+HKf4X26n5m2liycS76HPuv7cdcY86QekOMfr20tCfJFt1KRHZloOkSqD4BQibDzU1wcSGE/wTVJ0KNd8DS0eixFWcTm0/E2dqZ1ze+zqKji4hLjmNZz2VYmltmevzw4bBpk5ps+fRTsLLK54CFECKXpK8jROF16upVQktPAeC9Bl9QxqmMiSN6vpiYGLZt20ZgYCBubm6mDicdTwdPPm42i/cOvEpYmY84FNYbv8qVTR2WEIWK3iNb+vbty+HDh3F3d2f9+vWsX78ed3d3Dh8+TO/evY0RY9F08kNQ0qBMb3BvYuposvTjMXVUS5cqXfBy9DL69cLDITERrK0hww1El9rgvxHa74YSTeBxIpyeBn9VhgsL1JFCwmhGNBzBir4rsDCz4LdTv9FzZU8SkxMzPbZbNyhdWl2RaP36/I1TCCHyQvo6QhROiqLwyspRYJWI3Z2WTO32uqlDylJSUhIXLlwgKSnJ1KFkalKHoTjeaQeWjxjy++vZTiMXQqSnV7IlJSWFV199FVdXV3755ReOHj3K0aNH+eWXX6hfv76xYix67v4L19aqU2J8p5s6miwlpybz00m1yqlRC+M+5eRJ9blWLbB43tgrzzbQ8SC0XA0OleHRbTgyDjbWhIjVaiJLGMWA2gPY8OIGbC1s2RK2hQ4/dyDmYUyG4ywt4dVX1e3Fi/M5SCGEyCXp6whReK04vYKTD7fAYyuGlfweM43e95XFUzQaDa97L4YUW84n72RpiMwNF0Ifev0PZGlpyR9//GHwIBYsWED58uWxsbHBz8+Pw4cPP/fYZcuWodFo0j1sbGzSHaMoCh999BGlS5fG1taWgIAALl68aPC4c+3EB+pz+UHgXNO0sWRj44WN3E68TSmHUnSp0iVfrqlNttSpk82BGg2UewG6hUKjBWDjAQlhsH8AbGsCkUFGj7W46lylM0GDg3C1ceXA9QO0XtqaG3E3Mhw3YoT6xxQUBGFhJghUCCH0VBD6Ot9//z2tWrXC1dUVV1dXAgICsjxeCAHRD6IZv+VN9YfgDxnzQnXTBlREjOpfCXZ9AsCEbe8QmRBp4oiEKDz0Tvf26tWL9QacE7Bq1SomTJjA1KlTOXbsGL6+vgQGBnL79u3nnuPk5MStW7d0j4iIiHSvf/nll3zzzTcsWrSIQ4cOYW9vT2BgII8ePTJY3LkWcwwit4OZJdSZauposqUtjDvEd8hza3MYWoZ6Ldkxs4SqY6B7GNSZBhYOEHMUdgbAzo7qZy4MrlnZZuwdthcvRy/O3DlD8yXNOR99Pt0xPj5PCuV+/70JghRCiFwwdV9n9+7dDBw4kF27dnHgwAHKli1Lx44duXEjY1JbCKF6e9vb3H0YDVG1qRHzLjUL9v3MQqNSJaif9BbcbEBs0n3e2PKGqUMSotDQKHpOvps+fTqzZs2iffv2NGzYEHv79GvWjx8/Xq8A/Pz8aNy4MfPnzwcgLS2NsmXL8sYbbzB58uQMxy9btoy33nqL+/fvZ9qeoih4eXnxzjvvMHHiRABiY2Px9PRk2bJlvPjii9nGFBcXh7OzM7GxsTg5Oen1frJ1eJS63LPPQGjxm2HbNrCb8TcpO6csaUoa58edp2qJqvly3apV4eJF2L4dAgJy0cCj23D6Mwhb+KSGS7n+UPdTcMqf91CcRNyPoOMvHblw9wIlbEuw8aWNNC3TVPf6+vXQuzeULAnXr0uhXCGKG6N+pxqJqfs6z0pNTcXV1ZX58+czePDgDK8nJSWlq/kQFxdH2bJlC9VnLkRebA3bSudfO4OigR8O8vGIJkwt+Pc0SUhI4NSpU9SpUwcHBwdTh/Ncn38OU/53HM3rjVE0qaztv5beNaR+lSge8tKP0TvZUqFChec3ptFw+fLlHLeVnJyMnZ0da9asoVevXrr9Q4YM4f79+/z5558Zzlm2bBmvvfYa3t7epKWl0aBBA2bMmEGtWrUAuHz5MpUqVeL48ePUq1dPd16bNm2oV68e//vf/zK0mW+dlJQEWOcFj+Oh/U7wbGu4to3gy/1f8t6O92hRtgX7Xt2XL9dMTARHR1AUiIpSlxDOtYRwtRDxld8ABTTmUPFVqPORurqRMJg7iXfo+ltX/r35L7YWtqzst5Ie1XoA8PixOsLl5k1YuRIGDDBxsEKI/2/vrsOjuL4Gjn837u5Ggru7W3EopVCgAjUotKWl1KBK5VdoS92QCvAW9+JQtLgFCMGDhLi7Z+f948Y2AknY7Ebu53nmmWV3dvfusJmdOffcc3WqJgZb9H2uU1xycjIuLi6sW7eOESNGlHh87ty5fPLJJyXur0n7XJIqKyEjgZa/tiQ0ORSDUzNR7/iOwEBkZosWBQVBw4agemQ2So8vcbF0IfDlQJwsnPTdNEmqcg9zHlPhYUS3b98uc6nIyQdATEwMubm5uLq6atzv6upKRETp4wGbNGnCn3/+yZYtW/j7779Rq9V0796dkJAQgILnVeQ1582bh62tbcHi7e1doc9RbsFrRaDFqiG49K2a99ASRVFYdmEZIIYQ6UpgoAi0uLo+ZKAFwMoPuv8NQ8+DxwhQciFoCWxtBOfegowYbTRZApwtnTkw+QDDGg0jPSedx9Y8xqIziwBR5PiFvNrKslCuJEk1gb7PdYp799138fDwYGAZ6Z5z5swhMTGxYLl3716F2ihJNdnru14nNDkUV6NGqPf+j+bNa06gJT09ncDAQNLT0/XdlPtq0ADatQPlwFzcjZoTlRrFqzte1XezJKnae6gS3Yqi6HwKsG7dujFp0iTatm1Lnz592LhxI87OzixatKjSr6mzk5SbeVeaDfOqhlZjZ8PPcjn6MmZGZjzR4gmdve+FC2L9wOK4FWHfGvpuhUeOgHNPyM2Aq9/AP35w8WPIStTim9VdliaWbJmwhRfavYBaUTNt+zQ+OvARiqLwwgviK79/vxgiJkmSVFPo41ynqPnz57N69Wo2bdpUYkKAfKamptjY2GgsklQX/HPtH5ZfWI6ByoAGAUsh24Jx4/TdqvJLSEhg/fr1ZZZHqE7GjQNyzPA+uwxDlSFrAtewLnCdvpslSdVapYItf/zxBy1btsTMzAwzMzNatmzJ77//XuHXcXJywtDQkMjISI37IyMjcXNzK9drGBsb065dO27mTXWS/7yKvKZOTlISAiD2JKiMwE93mSKVtey8yGoZ3XQ0tma2Onvfs2fFun37Knhx5x4w8DD03QH27SAnBS59KoIul7+EnNQqeNO6xcjAiCUjl/BxHzFQ+rPDn/Hslmdx98pi6FCxjSyUK0lSTVAdznUWLFjA/Pnz2bNnD63LXTVekuqG2LRYpm6dCsCr7d/kzKbuADUq2FKT5O/Xs/905PUOcwCYvn06kSmR93mWJNVtFQ62fPTRR7z++uuMHDmSdevWsW7dOkaOHMkbb7zBRx99VKHXMjExoUOHDuzbVzhFr1qtZt++fXTr1q1cr5Gbm0tAQADu7u6AGGft5uam8ZpJSUmcPHmy3K9ZJW7mXWF6PQrmrvffVs+ycrNYdWkVoNshRABnzoh1x45V9AYqFXgMhSFnoOc6sGkGWfFwfjb8Ux+ufg851TuVs7pTqVTM7TuXJSOXYKgyZPmF5Qz+ezBPvRAPwF9/QZESSZIkSdVOdTjX+eqrr/jss8/YtWsXHavsR1GSaq5Xd75KZGokzZya0SrmU7KyoFkzyCvjKGlZw4bQti3k5kLD0A9p49qG2PRYpm+frtfsP0mq1pQKcnJyUlauXFni/pUrVyqOjo4VfTll9erViqmpqbJ06VLl8uXLytSpUxU7OzslIiJCURRFeeaZZ5TZs2cXbP/JJ58ou3fvVoKCgpSzZ88qEyZMUMzMzJTAwMCCbebPn6/Y2dkpW7ZsUS5evKg8+uijip+fn5Kenl6uNiUmJiqAkpiYWOHPU6rsNEVZa6coK1CUsN3aec0qtOnKJoW5KO4L3JXs3GydvW9GhqIYGysKKMqtWzp609wcRQlapihb6ov/nxUoykYPRbn2s6LkZOioEbXXrhu7FOsvrBXmojT5qYni2jRIAUVZvlzfLZMkSVe0/puqA/o+15k/f75iYmKirF+/XgkPDy9YkpOTy/V+NXGfS1JFrAtcpzAXxfATQ+VUyCmld29x/vjJJ/puWcWEhYUpc+fOVcLCwvTdlHL58kuxn7t0UZTz4ecV40+NFeai/H3hb303TZKqzMP8plY4syU7O7vUHpYOHTqQk5NT4WDP+PHjWbBgAR999BFt27bl/Pnz7Nq1q6CQXHBwMOHh4QXbx8fHM2XKFJo1a8awYcNISkri2LFjNC9SCeudd95hxowZTJ06lU6dOpGSksKuXbvKHOtc5e6th+wEsPQFt8rMZaxb+YVxn2r1FEYGRjp734AAyM4GBwfw9dXRmxoYQv1JMOIqdF4MFt6QHgZnXhWFdG8shFyZhlFZgxsO5sjzR/Cy8eJa7DVSJnQFrxN8+60ohCxJklQd6ftc57fffiMrK4uxY8fi7u5esCxYsKDyH0qSaonIlEimb58OwOyes7FK6sThw2BgAM8/r+fGVZCRkRFubm4YGenufPthTJ4sJj44eRKIbMNHfUSm36s7XyU0KVS/jZOkaqjCUz/PmDEDY2Njvv32W43733rrLdLT0/nll1+02kB90Po0lXt7Q/R/0PozaPnBw79eFYpJi8HjGw+y1dkETA+gpUtLnb33woUwfToMGgS7d+vsbTXlZkLQHxD4PxF0ARGAaTFHTBttaKqnhtVsYclhjFg5Av8If8g2gy1/sf/HCfSr3rOfS5KkBTVx6ueafq5TE/e5JJWHWlEzfOVwdt3cRWvX1pyecpp33zLh++9h1Cgox0zq0kN64glYt06cs//4cw7d/+jO6bDT9PPtx95n9mJoYKjvJkqSVul06mcoLBr34osv8uKLL9KqVSuWLFmCgYEBs2bNKlgkIPGKCLSoDKH+c/puzQOtClhFtjqb9u7tdRpoAR3UaykPQ1No/DKMCoIOP4K5O6Tdg9Mvw9aGcP1XMZuRVCEe1h4cfu4wIxuPBOMMGDuRl1Z/iFpR67tpkiRJpZLnOpJU/fx48kd23dyFmZEZK8esJDfLhGUiIZuXXtJv2+qK/P3899+QkWbE32P+xtLYkgN3DvD1sa/12zhJqmYqHGy5dOkS7du3x9nZmaCgIIKCgnBycqJ9+/ZcunQJf39//P39OX/+fBU0twYKypu5wGM4WHjqty3lkD+ESNeFcaGaBFvyGZpBkxkw6hZ0+AnMPSAtBM68kldI9zvISdN3K2sUKxMrNo3fxAtN3wbghsfnDP3zCVKz5CxQkiRVL/JcR5KqH/9wf979910Avh30LS1cWrBhA8THg48PDB6s5wZWQnh4OJ9//rnGMMLqrl8/USw3ORnWrIHGjo35aehPAHx44ENOhZ7Scwslqfqo8DCiukBr6bfqXNjsARlR0GcreI7QXiOrQGBUIC1/a4mRgRFhs8JwtnTW2Xunp4O1tahwHhwM3t46e+vyyc2Am7/Dla9EpguAqTM0nSUyYYxlmnZFtHtuKee9p4JhNu3c2vHPxH/wsvHSd7MkSaoCckiL7sl9LtU2qVmpdFzSkasxV3m0yaNsGr8JlUpFr15w5Ah89hl8UL1H6pcqPDycxYsXM3Xq1IKZVWuCr7+Gd96BTp3g1ClQFIUJGyawNnAtDewb4P+SP9am1vpupiRphc6HEUnlFHtCBFqM7cC9+ofbl19YDsCwRsN0GmgBuHBBBFpcXMCrOl5zG5pBk1dh5E3ovASs6kNmNFyYA5vrwYUPISNa362sMb5/9llYegBSnfGP8Kfj4o4cDT6q72ZJkiRJklQNzdo9i6sxV/Gw9uD3Ub+jUqkIDBSBFkPDmlcYt6Z79lkwMYHTp8HfH1QqFYtGLMLH1oeg+CBe3fmqvpsoSdWCDLZUpZDNYu05HAyM9dqUB8lV5/J3wN+AfoYQnT0r1h07gkql87cvP0MTaPgijLgG3ZaDTVMx01Tg57ClHpx5HVLv6buV1V7v3tDeuQcsOYUrrYhMjaTfsn4sOrNI302TJEmSJKka2XhlI4vPLUaFiuWjl+Nk4QTA4sXi8VGjwMNDjw2sg5ydYcwYcXtR3qmbnZkdK8aswEBlwPILy1kZsFJ/DZSkakIGW6qKokBIXkl0r0f125Zy2H97P2HJYdib2TO80XCdv3+1qtdSHgZG4PcMDA+EXhvAoQPkpsP1H0VNl+PPQkKgvltZbalUMGsWkOCL8vtxHm86jmx1NtO2T2Pq1qlk5sjptiVJkiSprrsdf5sX/nkBgHd6vMOA+gMAMfx8uUjIloVx9SR/v69YIeq3APT06cmHvT8EYNq2aVyPva6n1klS9SCDLVUl6Sok3wADE3Afou/WPND/Xfw/ACa0nICpke6nN65xwZZ8KgPwHgODT0O/PeDSF5QcuL0MdrSEgyMg6rAIvkkaxo0TPVFRIZaMSF/D/AHzUaFiybkl9F3Wl7DkMH03UZIkSZIkPcnIyeDxtY+TkJFAF88ufNrv04LH1q6FhATw9YVHHtFbEx+ak5MT06dPx8nJSd9NqbA+faBxY0hJgVWrCu//oPcH9PLpRXJWMo+vfVxOhCDVaTLYUlXys1pc+4Nx9S4QlZKVwoYrGwB4pvUzOn//1FS4fFncrnHBlnwqFbg/AgMPwKAT4P04oIKw7fBvH9jTDYLXgTpH3y2tNkxMYMYMcfu771S80+Nddjy1AzszO06EnKD9ovYcvHNQr22UJEmSJEk/Xt3xKv4R/jhZOLFu3DpMDE0KHssfujJlChjU4KsZY2NjXFxcMDau3uUGSqNSwdSp4vaiIqPAjQyMWDN2DW5WblyKusRL215Czsci1VU1+PBUzdWgIUSbrmwiLTuNhg4N6erVVefvf/48qNXg6Qk1qBB72Zy6QK/1oq5Lw5fAwBRiT8KRJ2BrI7j6A2Qn67uV1cLUqWBpCRcvwtatMKThEE5POU0rF1HHZcDyAXx55Ev5Iy1JkiRJdcgf5/7gD/8/MFAZsPrx1XjbFk5T6e8Px4+DkVHNL4ybkJDAP//8Q0JCgr6bUimTJ4vOs3Pn4MSJwvvdrd1ZM3YNhipDVgSs4Lczv+mvkZKkRzLYUhXSw8VMRACeo/TblnLIH0L0TOtnUOmhOm2NHUL0IDaNoPNCePQutPwQTB0h9Q6cmwmbvcH/bUi9q+9W6pWDA7yaV7D+k0/EaKuGDg058eIJJrWZhFpRM3vfbB5b8xgJGQl6baskSZIkSVXPP9yfV3a8AsBn/T4rqNOS74svxHrcOHBz03XrtCs9PR1/f3/S09P13ZRKcXKCp54St/P/X/L1rtebLwd+CcDMXTM5GXJSx62TJP1TKbLLuISHmUsbgJuL4dRL4NgZBlfvA0toUije33mjoBD0WhD17evrvA3PPAN//w2ffQYffKDzt9ednHS4vRyufgvJeQXDVAbg9Rg0eR2ce1bzqZiqRnQ0+PmJ4WRbt8KIEeJ+RVFYcm4JM3bOICs3iwb2DVg3bh3t3Nvpt8GSJFXIQ/+mShUm97lUU8Wnx9NhcQduJ9xmROMRbJmwBQNVYd/w5cvQsqXonLl0CVq00GNji8tOguSbkBYiOl7Tw8Q6I1I8lpMsMptzkiFXTAQQnu7M4lsTmVp/Fe5WSWBkLcoP5K/NXMHcHcw98taeYN0ITGz1/GELXb8OzZqJLHV/f2jbtvAxRVEYt24cG65swMvGi3NTz+Fs6ay3tkpSZTzMb6pRFbWpbqtBQ4hWBqxEQaGnT0+9BFqgFme2FGdkDo1egoZTIGwHXP0eIvfBvQ1isW8HTV6DehPA0EzfrdUZZ2d45RX46iuYOxeGDxcxJ5VKxdQOU+ng3oGx68YSFB9E1z+68s2gb3il0yt6ycKSJEmSJKlq5KhzmLhhIrcTbuNn58fy0cs1Ai0gsicURUw7rLdAS2YcxJ+HeH9IugJJ10UnWkZkxV8rO29SiuwkEZghvHzPM3MB68ZisW0G9m3FeaSpY8Xb8JAaN4bx40WR3P/9D9atK3xMpVLx56N/EhAVwPXY64xfP57dT+/G2LDm1aiRpMqQmS2leKgeoewU2OAE6kwYdgnsqlPIXZOiKLRe2JpLUZdYNGIRUztM1XkbkpLAzk78cEZFiQvvOiXhElz7Ee78H+RmiPtMHaHBi9BwGlj56rV5uhIdLWYUSEuDbdtEwKWouPQ4nt/yPFuuiUDmY00f449Rf2Bvbq/7xkqSVCEyy0L35D6XaqLXdr7GT6d+wtzInKPPHy2RyXrzJjRpIjIozp6F9u110KjcDIg9DdFHIPaUCLDcbwi4mQtY+BTJRPEQ2SnGtiJTxdgajKzyOtVUhEfFs3jlXqY+ORB3R4vC7JfsZMhJgvTIvAyZvCyZtOD7B3UsvEXQxbGzyJh27Cw6+6rYpUvQqpXoLLt0CZo313w8MCqQrn90JSUrhSntp7BoxCLZaSbVGA/zmyqDLaV4qJOU4A1wZCxYNYCRN6r1sJDzEedpt6gdpoamhL8ZrpcL10OHoG9fqFcP7tzR+dtXH5mxEPQ7XP9V/JCCGGLkMQIavSxmOlLV7hJLb78NCxZAp05w8mTJPx1FUfjp1E+8vfdtsnKzqGdbj1WPr6Kbdzf9NFiSpHKRF/66J/e5VNP8evrXgjotG57YwJhmY0ps8+KL8McfMGwYbN9eRQ3JSYOo/yByvwiwxJ0BdVbJ7Sz9wKEd2LYCm7wMk0oM70lKSuLUqVN07ty5/H+r2UmQfKMwoybhkggCpQSV3NbAGOw7gEtPMUOqS28wsqxQG8trzBjYtAmefhr+7/9KPr7t+jZGrRqFgsK3g77ljW5vVEk7JEnbZLBFyx7qJOXYJJGl0HQWtP+mahqoJW/ufpNvT3zL2OZjWTdu3YOfUAW++QbeegsefxzWr9dLE6oXda6YLvr6zxCxt/B+q/rQcCrUfx7Mamf6T2SkqN2Sng47dsDQoaVvdzbsLOPXjycoPghDlSGf9P2E2T1nY2hgqNsGS5JULvLCX/fkPpdqkr1Bexm6Yii5Si5f9P+COb3mlNgmOBgaNICcHDh2DLppq59FUSDhAoTvEUv0EZGdXpSZq8gSceoODu3FkB0TOy01QIuyEsVniTsHMcfEZ0kvNizJwASce4DbI+A+SGTBaKkz7+xZURLAwEDUcWnQoOQ23x3/jll7ZqFCxdaJWxneeHjJjSSpmpHBFi2r9A5VZ8NGV8iKh4GHRPS4mspR5+D1rReRqZFsmbCFUU30M2vSxImwejXMmwezZ+ulCdVX0jWR6XJ7GWQnivsMjMF7rKj74tK3WmdOVcZbb4kAXOfOYgrBsj5eUmYS07ZNY9WlVQD09OnJ/z32f/ja+equsZIklYu88Nc9uc+lmuJK9BW6/dGNxMxEJrWZxNJHl5Y6vOTVV+GXX6B/f9i37yHfNDcTIg9A6D8QulUUtC3KwksEI1z6iCCLVf0qO9/KzMwkPDwcd3d3TE1NtfviiiJmwYw+AlGHIHxvYfZ0PnMP8BwpZk916//QNQOHDYOdO0UW0pIlpTVJ4aVtL7Hk3BKsTKw49vwxWrm2eqj3lKSqJoMtWlbpHRp5APb1B1MneCwcDKpv/eFdN3cxdMVQnCycCJ0ViomhiV7a0aiRGIO7dy8MHKiXJlR/OWlwdw3cXCjGC+ezaggNXwS/Z8HcVW/N06ai2S07d8KQIWVvqygKf1/8m1d2vEJyVjI2pjb8MuwXnmr1lBwHLEnViLzw1z25z6WaICYthi6/d+FW/C16ePdg36R9mBqVDDiEh4tzg8xMOHBADD+vsOwUkTkcvB7Cd0FOSuFjhhZiiI37IBFksWmis86s8PBwFi9ezNSpU3F3d6/aN1MUMfwoYm/esq/kfvAYIjr1PEeI+jIVdPw4dO8Oxsbi/N7Hp+Q22bnZDP57MAfuHMDH1oeTL57EzaqGz+Et1WoP85tau4tA6FrIP2LtOaJaB1oAll9YDsCEFhP0FmiJjxcHYoAOHfTShJrByAIaPCemER9yRgwnMrKClJtwfjZs9oL/HofQHaDO0XdrH4qrK0ybJm5/+KEoglcWlUrFM22e4cK0C3T37k5SZhLPbHqGiRsmEpcep5sGS5IkSZJUYSlZKQxbMYxb8bfws/Nj0/hNpQZaQNRzy8yEHj2gT58KvEl2CtxZLc6RNrrA0Qlwb70IMJh7iIkI+u6AsbHQdys0mQG2TWtd1nABlUrUl2n8CvTeDI/HQN9dojaghRfkpsG9jXDsSdjgDIdHw+0VolhvOXXrJrKPsrPFLJOlMTY0Zv0T62nk0IjgxGCGrhhKYkaiVj6iJFU3MtiiTRF7xNqjeo8/TMxIZNPVTQBMajNJb+04cUKsGzYEezmpTPk4dIDOi0TmVJffwbELKDnix/HQcNhSD86/B0k39N3SSnv3XbCyElOCr1r14O397P049OwhPu37KYYqQ9YErqHFry3Yfr2qqudJkiRJklRZmTmZjFkzhtNhp3E0d2T7k9txtiy9Hl1wMPz2m7j9wQfliIOosyF0Oxx9UgztPzZRnCPlpovJK5rPhsGnYXQIdP4NPIY+9NCZGsvQFDwGQ6df4NFgGHIWWrwviv2qMyFkCxx/WgSqjowXncq5pRQLLuaDD8R6yRIIKqVmL4CDuQM7ntqBi6UL5yPO8+jqR8nIydDih5Ok6kEGW7QlPRISL4vbLn312pQHWXd5HRk5GTR3bk5Hj456a8ehQ2Ldu/qWtqm+jK2gwQsw+AQMuwhNZoopo9PD4PI82NYY9vaEm0tEwbQaxNUV5uTVxpszRwwpehAjAyM+7PMhx144RlOnpkSkRDBi1Qhe2PKC7C2RJEmSpGoiV53LpM2T2HtrL5bGlux4agfNnJuVuf3bb4vzgN69YfDgMjZSFIg5CadfgU0ecGgE3F0lMjWsGkDzOTDknJgltO08cOxYe7NXKkulEsV/23wOI67C0AvQ4gMReMnNgOC1cPhR2OQOp6ZB9HGx30vRty888ghkZcGbb5b9lg0dGrLrqV1Ym1hz6O4hJm6YSE4Nz9CWpOJksEVbog6KtV0bMHPSa1MeZOn5pQBMbjNZr7UtDh8WaxlseUh2raDDdzA6DHquB49horJ89FE4NRU2ucGRCRC2s8YMM3rjDfD2hnv34Pvvy/+8zp6dOTf1HLO6ikr3f57/k1a/tWJv0N4HP1mSJEmSpCqjKAozds5gbeBajA2M2TR+E509O5e5/aFDsHatmN3mxx9LiY+khULgfNjeHPZ0hRu/QmYMmLlA49dg0Mm8AMsXYprmahhgMTAwwNraGgODanRJplKBfWto85kIvAw5A03eADM3yIqDm4tgb3fY1gQCv4DUeyWe/v33YGgIW7aIuoxlaefejn8m/oOpoSmbr25m2rZpyHKiUm0iC+SWolJFcE5NEwefJjPFhW81dTPuJo1+aoSByoB7b9zDw9pDL+1ITQU7OzGF3+3b4Ourl2bUXmlhcGeFmMkoMbDwfjMXqDcRfJ8Ch+rds/P33/DMM2JI0c2bIuOlIv67+x/PbnmWW/G3AHiu7XN8M+gb7M3lmDVJ0iVZrFX35D6XqqO5B+fyyaFPUKFi9djVPNHiiTK3zc0V9fwuXBC13PKHEpGbKWYRCvpDFHlV8oq7GZqD9xjwfQbcBlT72ok1kjoXog7A7f8ThYZz0/IeUIl9Xv8F8B5dMCxr5kz44Qdo1kz8Pxobl/3Sm69u5vG1j6NW1Lzb413mD5xf1Z9GkspNFsitDiL3i7VrP/224wHyC+MOajBIb4EWENXKc3JE9kK9enprRu1l4QHN34ZhAWIMbuPXxCxZGVFw7QfY3Rm2NYWAT6ptfZcnn4SOHSElBT7+uOLP71WvFxemXeDVTq+iQsVf5/+i+a/N2XB5g/YbK0mSJElSmeb9N49PDn0CwC/DfrlvoAVEvY8LF0TH3GefAQmBcHYWbPaEI09A+G4RaHHuKWrYjYmA7n+LGiQy0FI1DAzBbSB0Wyb2d9e/xPTYKBDxr6iPs8kTzrwOCQHMnQtOTnDlCvz66/1fenTT0SwesRiAL49+ydyDc6v600iSTsjMllJUOHqVFipmhFEZwOOxYGJX5W2sDLWixu8HP4ITg1n1+ComtJygt7Z89JH48Xz6afi//9NbM+oWdTaE7xEZLyGbRbG4fPbtwXci+DwBlqXM06cn//0nhpkZGMDFi9CiReVe52jwUV7c+iJXY64C8FjTx/h52M96DThKUl0hsyx0T+5zqTr54r8veH//+wD8r///eK/Xe/fdPj4eGjWC9JRUtvy4loG+SyDmeOEG5h5Q/1mo/xxYN6zClle9yMhIVqxYwVNPPYVrRVN4q4uUW3BrKdz6C9JCCu937Myh0CkMf3kCRmZW3LgBzqXXQS7wzbFveGvvWwB81Psj5vadq9eSB5IEMrNF/yIPiLV9+2obaAE4dOcQwYnB2Jra8miTR/XblrziuBWawk96OAbG4DkceqyEMZHQbTm4DwGVIcSfA/+3xWxGe3vCtR9FEFHPevWCMWPEFNBvvVX51+nh0wP/l/x5v9f7GBkYsenqJpr90oyfT/1MrjpXew2WJEmSJKnA/w7/r0KBFoDFX57nk5GvEPGrBwOtnheBFpUReI2GPtvg0bvQ5n81PtACoFarSU5ORq1W67splWdVH1p/CqPuiKm0vR8X55yxp+hjNoXIhe7Mf3wai+effeBLvdn9TRY8sgCATw9/yscHP5Y1XKQaTQZbtCE/2FLNhxAtu7AMgPEtxmNubK63dmRkwMmT4rYsjqsnxtbg9wz02ymmke70G7j0BlSisO7Z10W21t5eeg+8fPmlGOe7axfs3l351zEzMuPz/p9zZsoZOnl0IikziRk7Z9Dl9y6cDXvwCYAkSZIkSeX3+eHP+eCAmAf4i/5f3D/QkpMKQX+StqkL77ZuxyuP/Iq1WZKYTajNPBh9D3pvEp1GcphQ9WRgKKbS7rVeTK3d7muwboSlSQrTBizi/Q4dSdvYUcyUmZ1S5su82f1Nvhn0DQCfHf6Mjw58JAMuUo0lgy3aUFCvpb9+23EfKVkprL+8HoDJbSfrtS2nTkFmJri5iTRRSc/MnKHRNBh4CEYHQ/vvwKm7eCz6SGHgZU93uPINpNzWafMaNoRXXxW3X3mlfFNB308btzYcf+E4vw77FVtTW86Gn6Xz752ZsWMGCRkJD91eSZIkSarLFEXh00Of8uGBDwGYN2Aec3rNKX3jhEtwZoao9XHyBSzST5GVY8zRe09A/39h5HVoMRvM3XT4CaSHZuYCzd6CEddgwAGOhkwkM9sEi4yzeTNlesDplyH+QqlPn9VtFt8O+haAz//7nPf3vy8DLlKNJIMtDyvlDqTeEemNzj313Zoybbi8gdTsVBo5NKKbVze9tiV/CFHv3tV6Mpy6ycILms6EQUfh0WKBl5jj4P8W/FMfdraHgM8gIQB08OM3dy54ekJQUF6hvIdkaGDI9E7TufrqVZ5s9SRqRc3Pp3+myc9N+NP/T9RKDU7nlSRJkiQ9UStqXtv5Gh8fFJXt5w2Yx+yeszU3ys2A2ytE9uyOVnD9Z8hOJD67Pu+umk/TOSF4TVgjZrhRyUuVGk2lAte++D61kubvh/LWiq+JzWoEOclw4zfY2VZ05t1aDjmavWlvdHujIOAy78g8pm2bJod+SzWOLJBbigoVwQn6C04+D07dYNAx3TSwEvov68+BOwf4vN/nvN/7fb22ZeBA2LcPfvkFXn5Zr02RyistDEI2wb2NEHWwcKpFEGN1vUaLxalblaX3btkCo0eDkRGcPQutW2vvtf+99S8zds4oKKDbyaMTPw39iS5eXbT3JpJUR8lirbon97mkD5k5mUzaPIm1gWsB+GHID7zW5bXCDZJvws1FopBqZqy4T2UInqO4qZpOi4EDyMoyYOlSmKzfJGydyczMJDw8HHd3d0xNTfXdnCq1ejVMnAhGRmou7j1IM6OFcG8TKDliAxN78HsWGr0ENk0KnrfwzEJe3v4yCgqPNX2MlY+vxMzITD8fQqqTHuY3VQZbSlGhHXpsEtz5P2jxnijWVQ3dSbiD3w9+qFBxZ+YdfGz1N9tMVpaYxi89HS5dqvzsMpIeZcRA6D9iRqPwPaDOLHzMxAE8hoHnSHAfDCa2Wn3rxx+HjRuhUycxfbihofZeOys3i59P/czcg3NJzkoGYHKbyXwx4As5a5EkPQR54a97cp9Lupacmcxjax5j3+19GBsYs/yx5WLWS3U2hPwDNxeK6YHzWXhBg6nQ4AUyDDzo0AEuXxZF8devl5nPtdWTT8KqVaKMgL8/WBpEwK0/4eZiSL1buKFrP2g4TXTkGZqw4fIGntz4JFm5WfSu15stE7ZgZ2anr48h1TEy2KJl5d6higKbvSE9VIwrdRugu0ZWwCcHP2Huobn09+vPvkn79NqW48ehe3dwdISoKDGlr1SDZadAxB64txnCtkFWfOFjBsbg3FsUs/MYDjaNH/rtwsKgWTNISoIffoDXXnvwcyoqIiWCOfvmsPT8UgAsjC14p/s7vNX9LSxNLLX/hpJUy8kLf92T+1zSpciUSIatHMa58HNYGluyafwmHnFrDDd/h6DfISMib0uVmAWx0XRRSDUvE3bWLPjuO3B1FR1xTk76+yy6lpSUxKlTp+jcuXOd+FuNj4dWrSA0FKZPh19/zXtAnQvhu8XQovAdhRnUZi5Q/3loOJWDMXd5dPWjJGUm0dq1NTuf2ik7wySdkMEWLSv3Dk26Adsag4EJjE0AI/3N8FOWXHUufj/4cS/pHivGrODJVk/qtT1ffgmzZ8Njj4kMBakWUeeImYxCt4ol+brm41YNRdaLxzBw7QOGlUsBXbhQ/EBbWopeMJ8qStQ6GXKSN3a/wfGQ4wB4WHvweb/PmdRmEoYGWkypkaRaTl74657c55KuXIi4wKjVowhODMbVwpH/Bs+hUdx+CNsJ5F1imLlCgxegwRSw8tV4/r59Yng5wPbtMGyYTpuvd+Hh4SxevJipU6fi7u6u7+boxL//wiOPiNs7dsDQocU2SA0WQbqg3yE9PO9OFbgP4rbTYHrt+ZLQlEg8rT3ZMmELHTw66LL5Uh30ML+pMq/gYUTlTfns1LVaBloAdgft5l7SPRzMHRjTbIy+m6NRHFeqZQyMRBCl/QIYeU1UoG/3DbgOEFkuKTfh+o9wcAisd4ADw8S00knXK1Rkd+pU6NEDUlNFzZ+qChd38erC0eePsnbsWvzs/AhLDuP5f56n/eL27LixQ1bFlyRJkuq0TVc20ePPHuSmBvO9pwP3GhjT6NJbELYDUMQsnT3XioL7bf5XItCSkADPPituT5tW9wItddXAgfD66+L2889DTEyxDSx9oPWn8Ohd6LUB3B4BFAjfjV/ALO74Kvzq5Yhheii9/upVUCNIkqqjahFs+eWXX/D19cXMzIwuXbpw6tSpMrddsmQJvXr1wt7eHnt7ewYOHFhi+2effRaVSqWxDBkyRPsNj6j+Uz4vObcEgEmtJ+m9mFRODhw5Im736aPXpki6YNMYms2CAf/C47HQa6Po2TL3hNx0CN8pppXe1kTMcHRyKgSvh8y4+76sgQEsXgzGxqIX7O+/q+4jqFQqxrUYx5VXrrDgkQXYmtpyMfIiw1cOp8/SPhwNPlp1by5JkiRJ1ZCiKPzv0Cf8tX0Mq5xSuesLr1vEYZwRAaaORab83Qc+48DQpMRr5OaKYqkhIdCwISxYoPvPIenPvHliWHhEBIwfD9nZpWxkYAzeY6D/Hhh5E5q/C6bOGGVGMd08ltu+sN4lnVU7xzN3//tyJkmpWtJ7sGXNmjXMmjWLjz/+mHPnztGmTRsGDx5MVFRUqdsfPHiQiRMncuDAAY4fP463tzeDBg0iNDRUY7shQ4YQHh5esKxatUq7DVcUMSsLiCJO1VB4cjhbr20FYEqHKXpuDVy4AMnJYGur3ZlkpBrA2Bq8H4Muv8PoezAsANp9nZf1YiKmTw9aAkfGwUZn2N0FLrwvApq5GSVernlz+Ogjcfvll+HmzaptvqmRKW92f5Og14J4u/vbmBmZ8V/wf/T8qyejVo3iYuTFqm2AJEmSJFUDafGX2bS2Jc/encs/HjDSCgxVgEsf6L4SRoeK3/cH1Gl7913YtQvMzWHNGjE0WKo78v/fraxg/354440HPMG6AbSdD6NDoMcacO2PgQqGWcImD3gp5As2rW5GSuwFnbRfkspL78GWb7/9lilTpvDcc8/RvHlzFi5ciIWFBX/++Wep269YsYKXX36Ztm3b0rRpU37//XfUajX79mkWfjU1NcXNza1gsbe3L7MNmZmZJCUlaSwPlHQFMiJF3QnH6jk97F/n/yJXyaWHdw+aOzfXd3MKhhD17KndWWSkGkalAruWoudrwL8wNg76bIcmM8G2uSiKFnsKAr+A/QNgvT3sfwQC50PMSVEbBpgzB3r1gpQUUd0+K6vqm+5o4chXj3zFjRk3mNJ+CoYqQ7Ze30qbhW0Yt24cl6IuVX0jJEmqcSqSwRsYGMjjjz+Or68vKpWK77//XncNlaTS5GbC3bWk7OqB2Y4WjMm9jKcRpBta5WWxXIWBB8F3Ihg+ePripUvhm2/E7WXLoH37Km19tWZubk67du0wN6+e5QiqUqtWIjtZpYJffoFFi8rxJEMTqPeEyJoaeQOav0u6oQ3uRvC4ch2r3W1J3tkF7qwstbNOknRNr8GWrKwszp49y8D8yliAgYEBAwcO5Pjx4+V6jbS0NLKzs3FwcNC4/+DBg7i4uNCkSROmT59ObGxsma8xb948bG1tCxZvb+8Hv3Fkfr2WHuX6YdE1taLm93O/AzClvf6zWqAw2CKHEEkajCzBcxh0+A6GB4pei65/ge/TYO4ufiwj/oULc2BPV1Hv5eBwDK8vYM3CszjY53L6dGGmiy542XixeORiAl8O5IkWTwCw/vJ6Wv/WmgnrJ3Al+oruGiNJUrVW0QzetLQ06tevz/z583Fzc9NxayWpiPiLcPYN2OwJR8djFXcMAxUcyjThSuOPMR8bk5fF0qTcL3nsGLz0krj90UcwblwVtb2GsLOzY9SoUdjZ2em7KXrx6KPw+efi9quvwsGDFXiydUNoOx/zsdFca/o5BzNNUStgHX8Kjj2FsskDzrwGcf5V0XRJKhe9zkYUFhaGp6cnx44do1u3bgX3v/POOxw6dIiTJ08+8DVefvlldu/eTWBgIGZmoibJ6tWrsbCwwM/Pj6CgIN577z2srKw4fvw4hqWkVGRmZpKZmVnw76SkJLy9ve9fcfjoRLi7Glp9Cq0+rOAnr3p7g/Yy6O9B2JraEvZmGBbGFnptT04OuLiIKd9OnIAu1TMZSKpuFEVkkUXsg8h9EHkIshM0NsnGhj3+PTl8tQ9jX+5Np8EdxDhfHQqIDOCTQ5+w4coGAFSIWi/v9XyPNm5tdNoWSapu6vrMOF26dKFTp078/PPPAKjVary9vZkxYwazZ8++73N9fX2ZOXMmM2fOvO92lTqPkaTSZMbB3VVw6y+IO1twd0g2/JkEN+x6sOCxDbhauVb4pYODoVMniIqCxx+HtWtFHba6LDs7m/j4eOzt7TE21u25S3WhKPDUU7BqFTg6wqlTUL9+xV8nJi2GtzaNo17cQZ63gXpFd6d9W6j/HPg+JeoKSVIF1NnZiObPn8/q1avZtGlTQaAFYMKECYwaNYpWrVoxevRotm3bxunTpzlYRrjU1NQUGxsbjeWBovMKYzr30MIn0b78wrhPt35a74EWEIVx4+PFQbSDnKFNKi+VSgwtajIDem+Gx2NgyDkxy5HHCDC2wZgkhrfbwZcT36VTfDeUtXawbwBcnCuCNDmpVd7MVq6tWP/Ees6/dJ7Hmj6GgsLawLW0XdSWEStHcPxe+TL1JEmqXbSRwVselcrQlaR86hwI3QFHxosCGGdehbizqFVG7MmyZlgo+N1VYdDmM5Y+dahSgZakJJHFEBUFbdqI4UN1PdACEBMTw2+//UZMiSl56g6VCv74Azp2hNhYGDVKXDNUlJOFE38+uQ+LdvNpdNeAwaGwPdMatcoY4s+LSRk2ucN/YyFkK6hLq8orSdql18Ock5MThoaGREZGatwfGRn5wNTZBQsWMH/+fPbs2UPrB1RbrV+/Pk5OTtzUVhXN1GBIuwcqQ3CqfikaUalRbL66Gag+Q4g2bRLrkSPByEi/bZFqMANDcGgnZjnquxUej4MhZ8lq+S37rj1KXIo9KnUaRO6HS5/A/oGwzhZ2dYKzM+HuWkgLfeDbVFYbtzZsHL+RC9MuMKHlBAxUBmy/sZ3uf3an/7L+7Lq5S04ZLUl1SExMDLm5ubi6al6curq6EhERobX3mTNnDomJiQXLvXv3tPbaUi0WfxHOvQmbveDQcAheC+pMFLvW7LIdiscthcF3k/E3cGPPM/v4oPcHGBpUvOheSgoMHQrnz4OzM2zZIgviSprMzWHzZnB3h8BAGDJEBOgqykBlwLs932X/s4cINPRkRHAyLkE5bLUehNq+nQiw3NsAh0fBJk9xbhjnL9JrJKkK6DXYYmJiQocOHTSK2+YXuy06rKi4r776is8++4xdu3bRsWPHB75PSEgIsbGxuLu7a6XdBVkt9u1EvYlqZtn5ZWSrs+ns2blaDGFQFHEABXjsMb02RaptDAzBoT0mrd/AacxmPF+PoeW7AWyL/A3qPQkW3qDkQtwZuPYDHB0vTio314MjE+DqDxBzCnK1W123tWtrVj2+iquvXOWFdi9gbGDMgTsHGLpiKK0XtmbZ+WVkafk9JUmquyqVoSvVTWkhcPlr2NEGdraBq9+KCR9MnaHJ69zqsp7OoSYMPbOTyJxcxjYfS8D0APr5VW7mzbQ00dF27BjY2cHu3VCvnnY/klQ7eHrCnj2FQ4mGDhWBusro6dOTi9MvMrHlRGJzFUad20OHYIUbnddD01lg5gqZ0eLccFd72NEaLn8JqTJQLWmX3hP4Zs2axZIlS1i2bBlXrlxh+vTppKam8txzzwEwadIk5syZU7D9l19+yYcffsiff/6Jr68vERERREREkJL315iSksLbb7/NiRMnuHPnDvv27ePRRx+lYcOGDB48WDuNjj4i1tVwCJGiKAVDiKpLVou/vxina2EBjzyi79ZItVWbNvDbbwYEhrRk5KxprA1dAaOD4dG7YjrKRq+IAKnKANKCIXgNnJsJe7rAelvY21P08N1dAyl3tNLL0cixEb+P+p2g14J4o+sbWJlYcSnqEs9ueRa/H/yYf2Q+celxD/0+kiRVTw+TwStJWpOVAEF/iSG2m33g/DuQcFHUN/MeA723kD3qDl9neNJ81VOcCTuDvZk9K8esZO3YtThZOFXqbTMyYPRoUfTU2loEWtq10+YHk2qbli1h714RmDt2TATq0tIq91oO5g6sfFx8hx3NHTkfcZ6Wq5/ki1RHskbegj7bwGccGJhA4iU4Pxu2+MC/feHm75BVibFMklSM3oMt48ePZ8GCBXz00Ue0bduW8+fPs2vXroKU2+DgYMLDwwu2/+2338jKymLs2LG4u7sXLAsWLADA0NCQixcvMmrUKBo3bswLL7xAhw4d+O+//zA11dKsQQX1Wnpq5/W0aN/tfdyIu4GViRUTWk7Qd3OAwqyWIUNEmqAkVZVnn4U33xS3J0+GM2cASx8xHWWnn2HoORibAP33QuvPwGMYmDiIGY+ij4oevqMT4B8/2OQGB0dCwKcQthMyKj+e2tvWm28Hf8u9N+4xf8B83K3cCUsOY86+OXh968XUrVMJiAzQxi6QJKkaqWwGryQ9tJw0MXT28GOw0RVOPi+G2KKAcy/ovAgei4BeGziBCx3/6MY7/75DZm4mQxoO4dLLl5jYaiIqlapSb5+VBWPHigtnS0vYtQs6d9buR6wtSpu8oy5r104E5qytRaBu9GgRuKuscS3GcenlS4xsPJKs3Cze3/8+7ZZ04kiuLfRcC2Miocvv4NJXPCHqEJyaIv5uDo2CO6t0Uv9Pqp30OhtRdXXfisNZibDBARQ1PBYmpqatRoavHM6OGzt4tdOr/DTsJ303B4BWreDSJVi+HJ55Rt+tkWq73FzRE7JzJ3h4wOnTYl0mRYHkGxBzAmJPQuwpUUhNySm5raUvOHQEx47g0EEsJvYVbmNWbharAlbx/cnvOR9xvuD+/n79ebXTq4xsMhIjA1ncSKod6vpsRGvWrGHy5MksWrSIzp078/3337N27VquXr2Kq6srkyZNwtPTk3nz5gGiqO7ly5cBGDZsGE899RRPPfUUVlZWNGzYsFzvWdf3eZ2VmwHhu0WQJXSL5gWibXMxvNb3KbDyBSA+PZ739r3HorOLUFBwMHfg60e+5rm2z1U6yAIiE2HCBNi6VXSy7dwJffo85GeT6pxjx2DQIEhNhcGDYf16sLKq/OspisKKgBXM2j2L6LRoAF5o9wJfDvwSR4u8GYpSg+HOSri7EhKKdIIZWoDnSKj3BLgPBSPZe1yXPMxvqgy2lOK+OzRsNxwcAlb1YVSQfhpYhqsxV2n2SzNUqLg+4zoNHcp3UlaVbt6ERo3A0FBUoHdw0HeLpLogMRG6dYMrV8Q0k4cOVTCrKjdDFEyLPQVxpyH2NCRfL31bq/pg3x4c2othSvbtwLx8MzUoisKR4CP8eOpHNl3ZRK6SC4CHtQdT2k9hSvspeNp4VqDhklT9yAt/+Pnnn/n666+JiIigbdu2/Pjjj3TpIgrs9+3bF19fX5YuXQrAnTt38PPzK/Eaffr0KXNWxeLkPq9DcjMgfA8Er4OQLZCTXPiYpS/UmyiyO21bimlfALWiZvmF5cz+dzaRqWKI27Ntn+XrR76u9JChfNHRosPj5EkwNYVt26DIZFySVCGHDonaLenp0L49bN8ODzsCMy49jtn/zi4ou+Bk4cQX/b/g+XbPaxaATggU06DfXQUptwrvN7ISgRefceA+RAZe6gAZbNGy++7Qix/Bpc/A9xnovlw/DSzD9G3TWXh2IaOajGLLhC36bg4ACxbA22/DgAHw77/6bo1Ul9y8CV26QFwcjB8PK1aIoF+lZSVA3FlRbDfurFiK/vgWZe4B9m0LF7u2YN1A1IspQ3BiML+d/o0//P8o6HExVBkyqskoprSfwqAGgyo1C4Qk6Zu88Nc9uc9ruewUCN8lZlUJ3a4ZYLHwAu9xogfesUtBgCXfoTuHeGP3G/hH+APQ1KkpC4cvpI/vw6ee3LghLoyDgkTn2pYt0LP6jbivVqKjo9m4cSNjxozB2dlZ382plk6eFAG86GhRXHnnTmjW7OFf92jwUaZtn8alqEsAtHJpxbeDv2Vg/WLRQUURnW731uXNahlc+JiRpRiS7v24WBtbP3zDpGpHBlu07L47dF9/iDwAnRZCo5f008BSxKbF4v2dN+k56RycfFArP5ra0LMnHD0KP/0Er76q79ZIdc2BAyIFNScHpkyBRYtKnHc+nKx4iDsnlnh/iD8HSdeBUg6rRlZg1wrsWovFvo34t7HmMSYzJ5NNVzfx25nfOHz3cMH9XjZePNvmWZ5r9xz17etr8UNIUtWSF/66J/d5LZQZC6HbIGSzCLTkFiliYe4petl9xoFT11ID+zfjbvLO3nfYdHUTADamNnzY+0NmdJ6BqdHD1zQ8flxcEMfGgp+fuCBu0uShX7bWCw8PZ/HixUydOlV7s6bWQkFBIpB344YonrtlC/Tu/fCvm52bzS+nf+GTQ5+QkJEAwIjGI1jwyAKaOJXyBVYUkfUcvFZkk6UVmb3IwBTcB4HXY+A5Asxk8Ky2kMEWLStzh6qzYZ0d5KbBsEtg10JvbSzui/++EAWf3NpxdurZhxprqy2RkeDuLo5LwcHg7a3vFkl10dq1MHEiqNXw+uvw3XdaDrgUl50CCRcg/oKo/RJ/HhIDNE+Mi7KsB7atwK6lCL7YtgSbJmBoyuXoyyw6s4i/A/7WmLWov19/JreZzJhmY7AyeYgBzJKkA/LCX/fkPq8lUm6LoUEhmyH6P1EvMJ9VfdGb7j0GHDuXmTkZkhTC/w7/jz/8/yBbnY2ByoCXOrzEJ30/wdlSOxeDq1bB88+LIqYdO4qhQ67lG01b58lgS/nFxMCoUSKwZ2ICixeLyRC0ITYtlk8PfcqvZ34lR52DocqQ59o+x4d9PsTH1qf0JymKyHa+twGCN0DKzcLHVAbg1B28RoPXo2Ct/9IOUuXJYIuWlblDY8/A7k5gbAdjY+87JECXsnKz8P3el/CUcJaPXs4zbapHFdolS2DqVPHDe/q0vlsj1WVLl0LebPK8/z58/rmOG6DOETVf4i+KQEzCRRGMSQ8tfXuVIVg3AtsWYNuSbOvG7IsL56cru9gZtA8lL3PGwtiCx5s9zjOtn6G/X385zEiqluSFv+7JfV5DqXMg5rjIYAnbBomXNR+3ayMu3LwfE7fv03MQmRLJvCPzWHhmIZm5mQAMaTiEBY8soIWLdjoLMzLgjTdg4ULx7xEjYPVqMfuQVD4y2FIx6enw1FOwSSRo8cILInteW7OdXou5xlt732Lb9W0AmBiaMLX9VN7r9R7u1vf5/1EUMX30vY0iQBrvr/m4TVOR7eIxApy7i2nXpRpDBlu0rMwdevV7OPcGeAyHvtv01r7i/r74N89segZ3K3fuzLyDiaGJvpsEwLBhIo30f/+D997Td2ukuu7XX+GVV8TtL76AOXP02x4AMuPEj3NCQOGSGAjZiaVvrzIk28KXm7mmHIgL51hSPFey4GoW2Fl5ML7FeCa0nEAnj07VIrtNkkBe+OuD3Oc1SEaUmEEobKcYHpQVX/iYylBM0+w1GrxGgVXJwsnFhSeH892J7/jl9C+kZacB0MunF5/1+0yrQ8xv3oQnngD/vGvK99+HuXPBSE6kVyEy2FJxubni2mLuXBHjaNUK1q3T7rC1Y/eO8eGBD9l/ez8AZkZmTO84nTe7vVm+iQtSgyHkH5GVFnVIc4ZLYztwHwweQ0WB3XJOqiDpjwy2aFmZO/S/cXBvPbT5AlpUhys1MZtJxyUdORd+jv/1/x/v9aoeUY2kJHB2hqwsCAyE5s313SJJgq+/hnfeKbz91lv6bU+pFEVkvCQEikBM4iVIvCJ6OIsWQSzmbjZcy4Kr2RBr5Eg9r0F0b/EcTbz7o5IZL5IeyQt/3ZP7vBpT50DsycIAS9xZNOp8mTiIQpueI8QFmYlduV72ZtxNFhxbwNLzSwsyWTp7dubzfp8zsP5ArQbgN2wQw4aSksDREf7+G4YM0drL1ynp6encunWL+vXrY66t9Iw6Yt8+ePJJMduplZUYVjRxonbfY//t/Xx44EOO3TsGgLGBMZPaTOKdHu/Q2LFx+V4kKxEi9uRlrO2AzBjNx+3b5wVeBouaSzLrpdqRwRYtK3WHKgps8oCMCBh4CFy0UJVJCw7dOUTfZX0xNzLn3hv3CueJ17M1a2DCBGjcGK5ereIaGZJUAXPnwiefiNtvvw1ffllDvp/5QZjEy2JJuiKCMElXITO6zKelKSoSjZ0xs2+DnUsnVNaNxRAl60Zg6lRDPrxUk8kLf92T+7waURRICYKIvWKK5sj9kJ2kuY19u7yLraHg1A0qECA/E3aGBccWsO7yOtR5NV26eXXjvV7vMbzRcK0GWeLiYOZM+L//E//u0UMMG/Ly0tpbSFKFhIeLgMvBg+LfEyfCDz+IDl9tURSF3UG7+eK/L/gv+D8AVKh4vPnjvNXtLbp4dSn/i6lzRbA1bCeE5wdbizCyBtd+4PaIKLZr3Uiep1UDMtiiZaXu0JRb8E8DEW0cm1ht5lQfvXo0W65tYVqHafw24jd9N6fAE0+IlL533hEXs5JUXSiKyGp5913x70mT4PffwbgmdyRkxoqgS9I1suIDiI74DyXpGq5KCsb3+402ti0MvFg1EAXcrBuK22au8gde0gp54a97cp/rWXo4ROyHyH0QsU9zqlgQ2StuAwt7s80rNnwkOzebDVc28OPJHzkecrzg/mGNhjG7x2x6+vTU+lDS9evFUNyoKPHT8Pbbov5Zjf7trAZSUlIICAigVatWWFnJgveVkZMDn34qhhap1eDkJOq4jB+v/dOYo8FH+fLol2y9vrXgvs6enXmt82uMazGu4qUc0iNFllv4LhGQLZ71YuEFrgPAbQC49geLcgxhkrROBlu0rNQdems5nJgMjl1h8PH7v4COXIi4QNtFbQG48soVmjo11W+D8oSFQb164uB39iy0b6/vFklSScuWicJqubliOsF162pfUb/k9DgOXV7GxRvrSYg+TX2jbBoZQyNj8HnQCbKRpZjtwqpB3lI/b/EDS18wfPipQqW6QV74657c5zqWHiHqMkQdgsiDIvOwKANjcOoheqrdHhGZLJUY3hmWHMYf5/5g4dmFhCWHAWJYwxMtnuDt7m/Txq2NFj6MpogIEWTZuFH8u1kz+PNP6NpV629VJ8maLdpz5owY3hYQIP49apSo1+dZBfGJS1GXWHBsAasurSIrNwsAV0tXpnWcxovtX8TLphLpXopaTJ4QsUdkwUUfAXWW5jY2TcClL7j0EYuFx8N/GOmBZLBFy0rdoadegpuLoemb0H6BfhuYZ+SqkWy7vo0JLSew6vFV+m5OgQ8/FL0dPXvCf//puzWSVLbt22HcOFHdvksX+OcfcHHRd6uqRmpWKjtv7mTLtS1sv76d9Mx4GuQFXpqYGtLLwZ3WFha4kYpxehgaNQRKUIG5R17gxa8wAJO/tvACA1klURLkhb/uyX1ehRRFZKpE/SemY446LDILNajAob3oiXYdAC49RQC7EnLUOey8sZPf/X9n+/Xt5Cq5gLiwm95xOi91fAk3K7eH/FAlZWbCzz/DZ59BYqIofDt7NnzwAZjKWLvWyGCLdmVlwfz54jokOxusrcUkHTNngpmZ9t8vKjWKxWcX89uZ3woCoAYqA4Y2HMqU9lMY1mgYxoaVTP/KSYPoo3kZcvsh/qzm9O8A1o1FaQvnXuDSS5yDyaxkrZPBFi0rdYdubylmCem1CbxH67V9INLYev7VE0OVIVdeuUIjx0b6bhIgpgH09oaYGJEpMHasvlskSfd3/DgMHw7x8WLc+fr1IvBSm2XnZnMk+Aibr27mn+v/cCfhjsbjrZyaMN63C4NcGtDa0grT9BBIvSWGU6bchpyU+7+ByhDMPcHKFyzqgWU9sPTJW9cDC28wsqiyzydVL/LCX/fkPtcidY4oVB59TPQ0R/8HaSHFNlKBfZsiPc69wdThod72asxV/u/C/7H0wtKCiziA7t7debnjy5UbslAOiiKm1X3nHQgKEvd16AB//AFttJ84U+fJYEvVuHQJXnwRTp4U//b1FWUNxo2rmlhEdm42G69s5Nczv3L47uGC+92s3JjcZjKT2kyiufNDzhaSFS+CvPkZdPH+lOgYM/cE5555S3eway07v7RABlu0rMQOzYyDDXmFZ8dEgpl+u74VRaHvsr4cvnuYKe2nsHjkYr22p6g//xRDM3x8xI+0nAJQqgmuXoXRo+HaNTH+/IcfYNq0utE5oCgKV2KusP36dnbc3MGR4CPkqAunKDQxNKGXTy8GNRjEAL8BtHVtg2F2vAi8pN4RwZfU25ByR6xTg0Gd+eA3NnUCCx8RhLHwAUtvEYSx8BaZMeYe8gShlpAX/ron9/lDyIiB2FMQe0IEWGJPlgwwq4xE5kp+b7JLbzCxf+i3jkyJZPWl1fwd8Ddnws4U3O9o7sjkNpN5sf2LNHNu9tDvU5bTp0UtlkOHxL/d3OCLL0RtM0M5qV2VkMGWqqNWw4oVMGcOhIaK+3r0EHX7unWruve9FnONP/z/YOn5pUSnFU5g0M6tHU+3fpqJLSfibq2F/+ushCIZdv9B3BnNKaYBDC3AqQs4dRczHTl2ATMtVg+uI2SwRctK7NCQLXB4tEjVGnlN381j983dDFkxBFNDU26+drNy4wKrgKJA27Zw8SJ89ZX4wZakmiIpCZ57rnBc+qRJ8NtvYFHHEjASMxL599a/7A7aze6g3QQnahZ2tDezp69vXwb4DaC/X3+aOjXVLMSoqCEjMi/4clcEZNKC827nLQ/KjAFQGYCZuwi8FF3MPUWBuPy1YRXkBUtaJS/8dU/u83LKSYf48xB3GmJOisBKSlDJ7YxtRM0+5+6ix9ipa6WHBRUXnRrN5qubWXd5Hftv7y8YJmSoMmRIwyFMajOJR5s8iqlR1Y3dOXlSzNK3c6f4t5kZvPWWKCQva7ZWrbi4OHbv3s3gwYNxcHi4bCipdKmpsGCBuDZJSxP3DRoEH30kgi9VJSs3i63XtrL84nJ23NhR0JFloDKgr29fxjUfx2NNH8PVylU7b5iTJo5hUf9BzHGxZCeW3M7STwRgHLuAYydRQ0pmG9+XDLZoWYkdevoVuPErNHoFOv2s17apFTWdlnTiXPg5ZnWdxTeDv9Fre4o6eBD69RMXpyEhYP/wnTySpFOKIn6QZ88WPSKtW8Pff0OrVvpumX4oisL12OvsDtrNv7f+5eCdgyRnJWts42LpQu96velTrw996vWhhUsLDFQG93tRyE6A1Ht5QZjgvPU9SMtb0kNBnV2+Rpo6iiyYgsVTFIwzcxf/tvAQMysZyCkz9EVe+Oue3OelyM2AhEtiqtW4syLAknCpZE8wgE1TcSHi1E0EWGyaV6qgbVnCk8PZcm0L6y+v5+CdgwUBFoCuXl15qtVTjG8xHmfLqu2BPnZMBFn27BH/NjCAp58WdVp8fKr0rSVJ50JDRYBl2TIxOQLAgAHivt69q/a9Y9JiWBe4jr8D/ubYvWMF9xuoDOhdrzdjm41ldNPReNposZqvoobEKxBzLG85WbJ4N4ih37YtRODFoSPYtwf71rIzqwgZbNGyEjv0n0aQchN6bwGvUXpt2/rL6xm3bhxWJlbcfv02ThZOem1PUaNHw5YtMH26qP4tSTXVgQMwYYKY4tLYGD7+WPTw1fVhcTnqHM6EnWH/7f3su72PY/eOkZGTobGNvZk93by70dO7Jz18etDJoxPmxuYVe6P87Ji0UFEbIS0E0kNEQCY9VNyfHgq56eV8QZVImzVzE1OsmrsX3jZzA3O3wrWRdd0YP6ZD8sJf9+r8Ps9OgviLImsl3h/iz5UdWDFzAYdO4Ng5L82+k1aGBBWlKAoXIy/yz7V/2Hp9K6fDTms83t69PeOaj2Ns87E0dGio1fcuLicHNm+G77+Ho0fFfYaGIpvzvfegYdW+vVRMbm4uGRkZmJmZYSjHaunErVswbx4sXSr+HkDMrjVzJowZU/XTmd+Ov826y+tYf3l9iWNBB/cOjGw8klFNRtHWra3Wp3AnKwFiT4sMmNhT4nZGRMnt8gMwDu1F5ot9W7BrAya22m1PDSGDLVqmsUMNYuCfBmJ87thYkUqqJznqHFr+2pJrsdf4uM/HzO07V29tKe7WLfEDrShw5Qo0rR6zUEtSpYWHi7ot//wj/t2xo/hhbtFCr82qVjJzMjkddppDdw5xOPgwR4OPkpqdqrGNsYExbd3a0s2rG129utLVqyu+dr4PfwKRnyGTFgrpYYXr9DARiEkPL1xKu8Aqi6G5yIQxc80LwrgWWVw0bxvbycBMOdT5C389qDP7XFGLulEJFwuX+AulDwUCkQln30FcQDh2EkEWC68q+TtOzEhk3+197L65m11Bu0oMyezs2ZnHmz3O2OZjqW9fX+vvX1xcHPz+O/zyCwTnNcXICCZPFkGW+lXfBKkUsmaL/ty9K4Iuf/0lZjECMU30K6/A1Kng6Fj1bbiTcIcNlzew/sp6ToacRClS8NbLxovBDQYzpOEQBvgNwN68CoYMKIo4Z4o9LZb4cyLzLzOm9O0t/fICL61F9ottK7Cqr9XMv+pIBlu0TGOHRq6C09NEEbRHDj/4yVXoL/+/eP6f53E0d+TW67ewMa0+J1CzZsF338HgwbBrl75bI0naoSiiuNqMGZCQACYmIt30zTerZgrBmi47N5sLkRc4GnyUI/eOcCT4CBEpJXtMXCxd6OTRSSyeYl1l6fKKWpw0pIdDegRkFAnCZETk3Ze3zkl+8OsVZWAMps4i8GLqIrJn8tdmLuIxU+e8+53A2LZOBmfqzIV/NVLr9rmiiL/ThEtiZqD8dWIg5KSW/hwLL7BrKy4MHNqDQwdRgLuK/gazcrM4HXqa/bf3s+fWHo7fO64xPMjcyJxHGjzCyMYjGd5ouHYKZD6AWg2HD4vJC9avh/S8ZEAnJ9GZMH06eHhUeTOk+5DBFv2LiICFC0WdvqgocZ+Zmchyef55USLB4D6jo7UlMiWS7Te2s/X6VvYE7SEtO63gMQOVAV08uzC4wWD6+/Wni1eXKpmNDMib3j6kMPASf0FkCaYFl769oYXIgrFrCbYtC2+be9Sacx4ZbNEyjR164Tm4txFafwYtP9Bbm2LSYmjxawuiUqNY8MgC3uz+pt7aUlxyspgyNylJFFcbMkTfLZIk7QoLg5degm3bxL/9/EQ1+zFjas3vSJVQFIXbCbc5GXKS4yHHORFyAv8If43ZjvLVs61He/f2GoublZtuG5yTKoYvpUeKdUZE3rrIkh4JmdGlF517EANjEXQxdcoLxDgVWRw1b5s4irWRVY3/ktW6C/8aoMbuc0Ut6jglXYXEy5B0WdQcSLwsMtlKY2Cad3LfSiz56e5mVTvMOjs3G/8Ifw7eOciBOwf47+5/JTL7mjg2YXCDwQxuOJh+vv0qPqSykoKDYfly0WN/61bh/a1bi6ESEyfKDoPqQgZbqo/MTFizRgyx8/cvvL9ePTGBwuTJYgppXcjIyeDgnYPsvikmK7gSo1lrxcLYgp4+Penn24++vn1p796+6oIv+TLjICEv8JIQIDIJEwNFPazSGNuCTTOwbS4Wm2Zg2xQs6tW4TBgZbNGygh0aH4vN3vripHrQSXDqrLc2jV8/nrWBa2nh3IKzU89WaVX6ivr6a3jnHWjSBC5f1k30V5J0TVFg1SrxXc+fQrBPH5HR1a6dfttWk6Rnp3M+4jynw06LJfQ012JLn+XN1dKVtm5taePahjZubWjj2obGjo0xNqwGxW5zMyAjGjKjCgMwmdGQESWWzOi8x6NFZk15ZmAqjYFxYeDFxCEvKFPktolD3m0HzduGFtUmSFNjL/xrsGq/z7PiIek6JF/PW1+DpGvi32WduKsMwLqRCKzYtszrRW0hZorUwTTxKVkpnAw5yZHgI/wX/B/HQ45r9DyDmKK5n18/BvgNYEjDIfja+VZ5u/KFhcG6deJi8fjxwvutrUVw5fnnoXPnanNYkPLIYEv1oyhw9iz88QesXCk6k/N16QLjx8O4caKjWVeCE4PZdXMX+27v48DtAxpTSoPInOvq1ZWePj3p5dOLLl5ddDMCQp0DyTeLZBwGitvJN6BIZp8GA1NxLLdpCjZNxDHcprFYm1bPGblksEXLCnZo0F5sTjwiCqWNidZbFG5t4FrGrx+PocqQky+epINHB720ozRBQWKmlvR0kab63HP6bpEkVa3UVDF94FdfQUaGOHF9+mkx5l3WKqqcxIxEzkec51z4Oc6Gn+Vc+DmuxlzVGLucz8TQhKZOTWnl0opWLq1o6dKSFi4t8LH1uf8sSPqWky6CLhpLXiAmM7bYfbFiUWdW/v0MjPOCL/bFlvz77DTvN7bLu89O60WCq/2Ffy2k932uKCKgkhIkTsSTb4qT75S8dVn1AAAMTPKCKs0Le0VtmomTcR3NjpGrzuVa7DVOhpzkRMgJToSe4FLUJdSKWmM7ezN7evr0pL9ff/r79aelS0udHodu34atW2HDBvjvP7HbQfz59ukjAixjxoCldmaplqqADLZUb2lpsGmTyBLbv7/wbwzEtNGPPw6jRkGDBrprk6IoBEYHsv/2fvbf3s+R4CPEpsdqbKNCRQuXFnT17FpQL6+pU1MMdXUtm5spjvWJl8UMSImXxZJ84/7nNqaOYNUIrBvmLY3AqiFYNxDnL3qKFstgi5YV7NBjs7G5PR98xkHPtXppS2RKJC1+bUFseiwf9f6IT/p9opd2lEZRxJRpBw6I8Yz79skeE6nuCA6GOXNErweI7/4TT8D779fdqaK1KTUrlYCoAC5EXOBCpFguRl4kJav0DBELYwuaOTWjuXNzmjs3p4ljE5o6NaWBQ4OqT62tCooCuWmFgZesuLx1rEjlzb8vf8mME49lxZd/2uyyqAxE8MXYtjAAkx+MMbbNu9827z7bwvsK7rcFw8LsS71f+NdBOtnnuZliyE/qbVGkNvU2JAdByi0RZHnQUDtzD9GTad1IBFJsmomeTktfnXZuqRU1QXFBnA0/y+nQ05wJP8O58HOlHmu8bbwLeo571etFc+fmOg2u5ObCqVMiwPLPPxAYqPl4t26Fve6yFkvNoFaryc7OxtjYGAOZGl6tRUSI2kdr1sCRI5qPNWsGI0eKwEuXLrqdvVKtqLkac5X/7v7Hf8H/cST4CHcT75bYztLYkvbu7eno0ZGOHh3p4N6Bhg4NdReAAVDnQtpdSLwqhosmXxMBmKTrolDv/RjbgFUDUZDXqj5Y+YmCvVZ+YFmvSoPxMtiiZQU7dGNnbNJPQecl0PBFnbdDURTGrB3D5qubaevWlpMvnqxWFw2LF4s6FubmEBCg26iuJFUXZ8/CZ5+Jac/zjR4tiuj26CEDkNqkVtTcTbjLpahLBEQFEBAVwKWoS1yPvU5WblapzzFUGVLfvj5NnJrQyKERjR0bF6w9bTyrdzZMZSiKqD2TFZ8XiIkv5Xa8mP6x6L+zE7QTqMlnYCpOjIxtScq2xHbsBRls0SGtBFtyUvOCKcHi5Dj1LqTcEbdT7oiZv0rJPtNg5iaCKSV6KRuCsXXl2vUQUrJSCIwK5GLkRc5HnOd85HkuRFwoUWsFRAC3o0fHgp7hLl5d8LDWfQQjKAj27hXL/v2iWHs+Q0Po2VNc4I0dCz4+Om+eJNVJISEio+yff0QR6pwipehsbUUn9COPiKVhQ92fC4Ynh3My9KTIzgs9wenQ02Ue51q7tqata1vaubejtWtrWji3wNpU98dnslPysh9LyYZMD3/w8809RLDesl6RdT2w9AELHzC2qnTTZLBFywp26O8G2Jir4dE74j9Lx1ZcXMHTm57G2MCYM1PP0Nq1tc7bUJZ798QUuMnJ8O238MYb+m6RJOnXxYvw+eei1yP/qNq6Nbz8Mjz1FFhV/hgvPUCOOodb8be4HH2ZwKhArsZe5WrMVa7FXCM5q+wZhkwNTalvX58GDg1oYC+W+vb18bP3w9fOFwtjCx1+impAUUS9jILgS2LeOqHIOrHY/YlF7ksstTZNUhrYTkEGW3TogSeGOemiFzEtJG+5V2R9TwRYsuIe/EaGFpq9iwU9jg3Ev4308zeUkZPBtZhrXI6+zOXoywXB2Vvxt0rd3szIjDaubQp6fDt5dNJtyn0eRRHBlcOHxXLoENy5o7mNra2YiGDUKLF2qJ4lDqRyio2NZefOnQwdOhRHXcw1LGldQoKYiXXrVjFRSHy85uM+PmJYX+/eYmnUSPfBl/yhkWfCznAm7Aynw05zIeIC6TnppW7va+dbMFy7hUuLgoxhXRX5LiEnDVLvFMmevKWZVVnWrHRFmdiLoIuFN1h6ixnrLPLW5l5g4Vnmb5YMtmhZwQ5dAjZuTWDEVZ23ISw5jJa/tiQ+I57P+33O+73f13kbyqIoMGIE7NgBXbuKVDrDmlVUWpKqzOXL8M03ophu/jSbNjYwaRI88wx06iSzXXRFURTCU8K5GnOV67HXuRF7g+tx17kee51b8bdKnRWpKDcrN/zsROClnm096tnVK7jtbeuNlYmMoJWgzhVTaGcnQnYSZCWSFBeObbMnZLBFhwrOY859i41hvMhCSQvNC7CEli+QAmJIWH6vYH5PoZWvmE3CylfMqqWnA5qiKESlRnEt9hrXYq6Jdew1rkRf4XbC7RL1VfK5WrrSyrUV7dza0datLe3c2tHIsRFGOiiyW1x6upj15MQJsRw5AuHFOnCNjMTwoPxe8o4ddTtEQapasmZL7ZKbKzKe87PRjh2D7GIJo66uIiOta1exdOggRgnovK3qXG7E3eB8xHn8w/05H3megMgAwlNKzyJRocLP3o9mTs1o4tiEJk5NaOLYhMaOjXGzckOlr5NbRRF1wFLviAzM/HXKHTFddWpw2bPZFWdiD+aeIvBi7imyZSw8SMqxw7b5kzLYoi0awZa2M6Djjzp9/4SMBPov649/hD8dPTpy/IXjejkJKMvff4uLRhMTOH9ejFOUJElTfDwsWwa//go3bhTe36ABPPmkWGRBXf3JUecQnBhMUFwQQfFB3Iy7SVB8ELfjb3M74TZJmUkPfA17M3t8bH3wsfXB28YbTxtPvGy8ChZPa08sTWRlSlmzRfc0zmPKSi4xtMjr2cvv3SvS02eZ1/tnYqvTdhenVtSEJYdxK/4Wt+JvcSP2Bjfjb3IzTiz3+zu1N7OnuXNzmjk1o5VrYUFtZ0tnHX6CQpmZosbKuXNiOX0aLlwoeSFmYiJmDcrvBe/eXcwoJNVOMthSu6WkiNnB8rPVTp4Ux4KijIygTRsRSG3fXgRfWrYEUz1NPBuTFiOGa0eKjMArMVe4HH2ZuPSyg/TWJtY0dGioseRnC3tYe+g8S7CE7CRIvSeCL0UzOVPzsjnTQ++bHfMwGboy2FIKjZOUoVvBc4TO3js5M5lBfw/iRMgJXCxdOPLcERo5NtLZ+z/InTviIBAXJ4ZMvF99Em4kqVpSq0Xx6KVLYfNmUdk+X+vWIhV8xAiR8SJr41UPiqIQnxHPrfhb3Em4w92Eu9xJuMOdxDvcSbjDvcR7JGY+oPhnHhtTGzysPfC09sTD2gN3K3fcrd0L1m5WbrhZuWFtYq2/XqEqJoMtulewz3cMw8apnuidK+ily0uXNrbVe5qdWlETmRLJ3cS7Jf7W8gOfZdVjAtHT6mvnW9DDml8Yu7lzc1wsXfT2NxUZKYaWBgSI9cWLcOlSycAKiF7u/B7url1FcU199HJL+iGDLXVLRoYocn38uAi8HD8uCu8WZ2QkyjW0aSPOFVu1EmtXV/0cthVFITotmivRV7gSc0Ujm/BOwp0yMwlBzCLpa+dbkCmcnyHsa+dLPbt6uFm56b9+nqKIgEz+8NqCbNAwSA8jKToY27HnZLBFWwpOUv4wwuaZ+IcqqFMRadlpDF85nIN3DmJvZs/BZw9Wqzotly+LFNawMGjbVhwsjI313SpJqjlSUkQxtZUrYfduzYJqLi4wbBgMHgx9+4Kbm96aKZVDYkYi95LucS/xHncT7xKaFEpIcgghSWK5l3iv1GJ0ZTEzMsPNyg1XS1dcrVxxsXARa0sXXCxdcLZwxtnSGWcLZ5wsnDA2rDkHXxls0b3qsM/VipqYtJiCv4miS3BiMMGJwYQkhZD9gKLMRgZG1LOtR337+iV6Tuvb18fMSDfTQRenVkNoKFy9CleuiHOkK1fEEh1d+nPs7UWHVfv2YunaVdRzqKVxVqkcZLClblMUUQfz5Ekx/OjcObGOKyOJxNERmjcXowqaNRO3mzYFLy/9ddhl5mRyO+G2yDzMyzq8GX+zoMPqQUO2jQ2M8bTxLMgS9rbx1sgS9rLxwtnSWa8BGVmzRcsKdujmHtg8euTBT9CCzJxMHl39KLuDdmNtYs2+Sfvo5NlJJ+9dHqdOwdCh4o+/eXPYswc8PfXdKkmquWJjYft22LZNBF6SimXDN2smgi59+4o0ck9PeUJekyiKQnJWMmHJYYQlhxGaFEpYchjhKeFiSQ4nIiWC8JTwMqezvh87MzucLJxwsnDC0dyxYO1o4YijuSMO5g44Woh1/mJpbKmXnv7qcOFf11TlPs9R5xCVGkVkSmSJ73J4SnjBdz48OfyBgRQAA5UBntaeBb2cvrZiXd++PvXt6+Nl46W3odS5uSKgEhQEt27BzZtiWOj16+J2eum1JVGpxAwk+T3SrVqJ4Eq9evI4LmlKTU0lMDCQFi1aYGkph51KhQGYc+cKs+MCAsSxR11GAomZmTjmNG4sCvA2bAj164uh615e+qutmavOJSQppGAoaEEWY946JCnkvlkx+YwMjHC3csfD2qNElrCblVtBprCzhXOVdEbJYIuWFezQEx9h0+WTKn+/9Ox0ntz4JJuvbsbC2ILdT++mp0/PKn/f8vr3XzGVbWqqGEe8Y4eIrEqSpB1ZWaIw4rZtcOCAGMdf/Mjs7i7+/jp3FkOO2rQR2TBSzZealUpkamTBxWtUapTGEpkaSXRqNDFpMcSmx5brxKQ0RgZGOJg7YG9mj52ZHfbm9tibicXWzBY7MztsTfPWZrbYmtpqrCsbrJHBFt2ryD5XK2ri0+OJSYshOi2a6NRootOiiUqNIjo1mqi0qILgSmRqJDFpMeVuhwoVblZuhfWMrEUvpY+tD9623vjY+uBh7aG3YEp2tgim3LsHwcFiqPTdu2Kdfzur7FFMGBmJi5n8Hub83uamTUFeN0uSpE1paXDtmmYW3eXLIhhc2hDFfMbGItDr6yuW/Ns+PuDtLTrzTEx09CGKyVHnEJ4cTnBiMPeS7hVkPBZdIlIiUCh/uMLR3BFXK1dcLQuzg/MzhF0sXXCycMLZUmQJO5g7lCtjRgZbtKxgh94+iI1vnyp9r0N3DjFl6xRuxN3A1NCU7U9uZ0D9AVX6nhWxbh08/bQ42RgwQNSckFPYSlLViosThdQOHhRTfwYEiB7W4lxdC8fxtmwJTZqIXg0nJ503WdKRXHUu8RnxRKdGE5seS0xajAjCpInbcelxxKbHEpseK26nxRKfEX/fuhflZaAywMbURmOxNrHG2tQaaxNrjX9bmVhhZWIlatFkqnis3WMy2KJD+ecxy08uJ8MwQ+P7kP+9iU2PLfh3RQN4BioDXCxdCnsV89b5vY75i5uVm16GvCmKmI41PFzUQwgNFUtYmFiHhIgAS0REycB2ccbG4sKkfn2xNG5cuPj6ypmBpIeTnp7OjRs3aNSoEeayWI9UCTk5IjB8/XrhcuuWWG7fvn8gBkS2naurCLx4eYngi4eHWHt6is4+NzcxDFIfmXnZudlEpkYWZE0WzZ6MSI0oyK6MTI2s1G+Zg7lDYYZwXnZwfqZw/mOmOaaMbDNSBlu0pSDYkhCPja1dlbxHQkYC7+x9hyXnlgDgYe3BstHLGFh/YJW8X0UdPgxz54pedoAxY0SdCX1Vxpakuiw1VUwPeuqUmMHi9GnxI1rW0dvBQVwINGxY2JORv3h6inRTqe5QFIX0nHTi0uOIS48jPj2ehIwE4jPiiU+PJz4jnsSMRBIyE8Q6I4GEjAQSMxNJzEgkKTOJXKWUaF95ZQDzK1fFX6qc/PMYZgPl/Hu3NbUtGJpWWm9gfk+hq5UrjuaOOp9dIicHYmJEPZT8dVSUKEZbdImIEEvxGT/KYmIiLjC8vcHPr7DXt149EVzRZwq+VPvJmi1SVcofCnnrVmHW3t27Ighz754IPFfkWOnmJhZXV83FxQWcnQsXJyfdB6LViprYtNiCTOHI1MiCDOGiWZoxaTFEp0aXe6ID4KHOY6pFPP6XX37h66+/JiIigjZt2vDTTz/RuXPnMrdft24dH374IXfu3KFRo0Z8+eWXDBs2rOBxRVH4+OOPWbJkCQkJCfTo0YPffvuNRo0qOKtPFRTiSctOY+OVjby9920iUkT56WkdpjF/4HxszfQ7xSKIoQwffwz794t/GxvDa6/B/Pmy90aS9MXSEnr2FEu+1FQxjWj+TBeXL4vejHv3RGbMiRNiKY2jo2avRdEfy/zF0VEs+kotlbRHpVJhYWyBhbEFXjZeFX6+oiikZaeRmJlIcmYySZlJBUtylvh3cmYyyVnJBeuUrJSCdUJCApe5XAWfTHqQLl5dcHFwET10ZqJ2j0bvnYUjzhbOOFo4YmKomz/2nByRdZKQAPHxYomL01xiY8USE1O4Tkio+HvZ2YkLg/xjXdHjnre3WJyd5UxwkiTVToaGYriQj0/pjyuKCFzfuyeW/CzAopmAERHiOJ2VJYZbBgeX773t7MR5pJNT4Tmlo6PoEHRwELft7TUXO7vKX28aqAzERAKWzrR0afnA7bNyswqygvMzPfNv52cJ53dSRcVGcZ3rlWqX3jNb1qxZw6RJk1i4cCFdunTh+++/Z926dVy7dg2XUgoSHDt2jN69ezNv3jxGjBjBypUr+fLLLzl37hwtW4od++WXXzJv3jyWLVuGn58fH374IQEBAVy+fBmzcnTpant8eVJmEtuvb2fDlQ3svLmTtGwx92sTxyYsGbmEXvV6PfR7VFZEBPz3X+H87xcvivuNjeH55+G998r+A5UkqfpJSxOFG69dEz0X+bUH8peyCjqWxcqq8IfRzg5sbQvXNjZgba25WFoWLhYWYm1uLhYTE1kcsi6SNVt0ryr2uVotji/JyYVLUlLhkpwMiYliSUoqvJ2YKAIl+evk5Mq3QaUSx6KivajFe1fzU97d3GQWn1T9ycwWqSbIyBCZg/lDM4tnFUZFiaBNdLQIkD9MdMHKSpxnlnbOWXydfx5a/HzU0lK7QfQaXbOlS5cudOrUiZ9//hkAtVqNt7c3M2bMYPbs2SW2Hz9+PKmpqWzbtq3gvq5du9K2bVsWLlyIoih4eHjw5ptv8tZbbwEi5cfV1ZWlS5cyYcKEB7Ypf4fuOxWIpZU1QEFhHgUFRVEK1lnqTLJyM8nMzSAzN4OU7GRCU+4SmnKXkJS7hKTc4UrcBbLUhePlvazqMb7xC7zU8u37TllY2v9M/n1FH1OrC5fcXLHOzBR/GBkZ4uIqLa3wjyQ8XEQrg4LERVlRRkaFQZZ69R64qyRJqkEURfROFO2xCA0t/NEsuk5IKLvqfWWpVIWBF1NTcSFkZiZum5qKYEzxxdi45GJkVLg2MhI9N8XXpS0GBmIperusRaUquc5fyvp3/mcsvpR2f/59RR8rfrv4Nve7r/hj97uvss8rTXm2SU5OolUrGWzRpfzzmJMnEzEwsCEtTZwHpKaKpejt/CUlRfN20SU5WdyvzTNGK6vC3sz8gG7RpWhvaNHbckiPVJvIYItU2+TmigzF/MzEolmKpWUx5mc5PkwgvjiVSgRcrK3Fb03RpWinYP6/8zsHi3cY5i+5uUm0bl258xi9DgzJysri7NmzzJkzp+A+AwMDBg4cyPHjx0t9zvHjx5k1a5bGfYMHD2bz5s0A3L59m4iICAYOLKx9YmtrS5cuXTh+/HipwZbMzEwyiwxYS8qbg/XJjf0xMCsMi6WTTgIJGGGEM84lXieccAAcccSEwnRcRxxJiHUk/fJILG6NIDfDl5WoWEl83vubEBfniEqlxs0tssTrRka6oFYbYm8fh5mZ5sC6pCRrUlOtMDNLx94+QeOx7GwjYmJEO93cwkucFEdHO6FSGdOlSwJduqTTvj106CBOcsT0czZkZmYSV2yydwMDA1xdXfPaFom62BWZg4MDpqamJCUlkZqaqvGYubk5dnZ2ZGdnExNTckaD/B+a6OhocnI052W3s7PD3NyclJQUkov9RZqamuLg4EBubi5RUVElXtfV1RUDAwNiY2PJKja1gI2NDZaWlqSnp5NQLE/Z2NgYp7xqo+Hh4SVe19nZGSMjI+Lj48nIyNB4zMrKCmtr61L3oaGhYUHmVmn70NHRERMTk1L3oYWFBba2tqXuQ5VKhZubG1D6PrS3t8fMzKzUfWhmZoa9vX2Z+9DNzQ2VSlXqPrS1tcXCwoK0tDQSEzXHQJqYmODo6IiiKERERJR4XRcXFwwNDUvdh9bW1lhZWZGRkUF8fLzGY0ZGRjg7i+93REQExePGTk5OGBsbk5iYSFpamsZjlpaW2NjYkJWVRWxsrMZjRb/fUVFR5BarTJv//U5OTiYlRXPK3vx9mJOTQ3R0dInPmv/9jomJIbtYxbL873dqamrBMShf/j5Uq9VERpY8RuTvw7i4OI1jGRTuw6LfbycnsbRvX7gPi3+/1WowMXEiMdGY0NAEYmLSCy66RI+2JcnJNqSkZJKdHVdwAZeeDqmpBoSFuZKaCpaWkRgYaH6/Y2MdiI01xdo6CSsrze93Wpo5iYl2GBll4+ys+f1WFIiIcM/7DNEYG2t+v+Pj7cjIMMfSMgUbG83vd0aGKfHxDhgY5OLqWvL7HRHhiqIY4OAQi6mp5vc7MdGGtDRLzM3TsbNL0HgsK8uY2FhxjHB3L3mMiI52JifHCDu7eMzNNb/fyclWpKRYY2qaiYOD5jEiJ8eQ6GhxjHB1LW0fOpKVZYKNTRKWlsX3oQWJibYYG2fj5FR8H6qIiBDHCGfnaIyMiu9DezIyzLCySsHauvg+NCM+3h5Dw1xcXErbh24oigpHx1hMTAr3oVqtxTMoqUK6dNH+a6pUmj2JRXsXbW01ex6L9koW76k01n3dXEmqdoyNjfHy8sJY/kFItYShYWHmYUUUHWKanwmZvxTPlkxMLD3DMiVFnL8qSmFngb7pNdgSExNDbm5uwYVNPldXV65evVrqcyIiIkrdPv8iLn99v22KmzdvHp98UnKK5+d4DrMileUuqi+xMXcbNtjzkvFLJbb/JGY1ZFvymF1/vMw15/zbcnQ0lwPb0Kb9KQYPXqLx2K1bDViz5mlMTLJ56aXFJV73++/fIj3dkmHDdtOokeZ4sf37B3HmTDeaNbvFyJHrNR6LjXXjwIGXMDODYcP+wMBA86Kxbdvp9OnjwuHDh/H39+f2bTHsAKBHjx4MHDiQ8PBwli1bpvE8a2vrgoDXihUrSly0T548GV9fX06dOsXRo0c1HmvXrh2jRo0iPj6exYs1P6uhoSEffPABABs3bizx/zV27FhatGhBQEAAe/bs0XiscePGTJw4kYyMjBKvCzB79mxMTU3ZuXMnQUFBGo8NHTqUzp07c+PGDTZt2qTxmJeXFy+88AJAqa87Y8YMHBwcOHDgAAEBARqP9enTh759+3Lv3j1WrFih8Zi9vT2vvfYaAMuXLy8RDHj++efx9vbm+PHjnChWeKNjx44MHz6cmJiYEm0yMTEpCF6uW7euxAX/hAkTaNKkCf7+/uzPL8yTp3nz5owbN47U1NRSP+v777+PkZERW7du5e7duxqPjRw5kvbt23P16lW2bt2q8Vi9evV49tlnyc3NLfV133jjDWxsbPj333+5fFmzrkP//v3p1asXd+/eZfXq1RqPOTs78/LLLwPw119/lQgA5fcSHTlyhDNnzmg81rVrVwYPHkxkZCR//vmnxmMWFha8/fbbAKxevbpEkOepp56iYcOGnD17lkOHDmk81qpVK8aMGUNSUlKpn/Xjjz8GYMuWLYSEhGg89thjj9G6dWsCAwPZuXOnxmMNGjTg6aefJjs7u9TXfeutt7C0tGT37t1cv655jBg0aBDdunXj1q1brF+veYxwc3PjpZfEseyPP/4oEViaPn06DRu6cPnyYS5d8td4bMAAcYy4c+f+x4hvvy15jOjTZzK2tr4EBJzi9m3NY4SNTTvs7EaRmhpPbKzmZ1UUQ7KzPyA7G8zMNmJoqHmMiIwcS1JSC2xsAnB11TxGxMc35sqViRgYZNC1a8l9uHfvbLKyTOnUaScuLprHiHPnhnLzZmd8fG7QtavmMSI62osdO15AUeDZZ0u+7po1M0hMdKBfvwM0aqR5jDh9ug+nTvXF2/seo0ZpHiMSEuxZvlwcIyZPXo6FheYxYvXq5wkP96Z37+N06KB5jDh/viP79w/HxSWGp5/WbFNmpgk//SSOEePHr8PJSfMYsXHjBIKCmtCpkz99+mgeI65ebc6WLeOwtk4t9bdqwYL3yc014tFHt+LjU3iMyMjIYP78EptLOmBvL3ruivbQFR/mV7R3L38p3huYf5+1tXieHA4oSdrh5ORUcJ4pSXWZkVFhZ2BlKYro9MsPxJSVpVk8q7O0zM/8kSFpaWKbYv3B5abXYURhYWF4enpy7NgxunXrVnD/O++8w6FDhzh58mSJ55iYmLBs2TImTpxYcN+vv/7KJ598QmRkJMeOHaNHjx6EhYVppOM98cQTqFQq1qxZU+I1S8ts8fb25tq1a1hbWxfcX1N6rfMV7fkvLSsjv+c/ISGB9GKFHPJ7/mVmi8xsySczW4TakNmSTx4jCsljhFAVx4jk5GSaNGlS54cRaXsygPuRdXIkSZIkSTtqbM2WrKwsLCwsWL9+PaNHjy64f/LkySQkJLBly5YSz/Hx8WHWrFnMnDmz4L6PP/6YzZs3c+HCBW7dukWDBg3w9/enbdu2Bdv06dOHtm3b8sMPPzywXfIkRZIkSZK0Q/6mVs1kAPcj97kk1QyyZoskVX8P85uq12FEJiYmdOjQgX379hUEW9RqNfv27ePVV18t9TndunVj3759GsGWvXv3FmTG+Pn54ebmxr59+wqCLUlJSZw8eZLp06dX5ceRJEmSJEkq4dtvv2XKlCk899xzACxcuJDt27fz559/ljoZwA8//MCQIUMKhjR+9tln7N27l59//pmFCxeW2L6s2nMREREamU8y+06Q2XeFZPadoK8M3XylfZdkhq4gjxGCPEYI+srQrSy9BlsAZs2axeTJk+nYsSOdO3fm+++/JzU1teCEZNKkSXh6ejJv3jwAXn/9dfr06cM333zD8OHDWb16NWfOnCmoY6BSqZg5cyaff/45jRo1Kpj62cPDQyN7RpIkSZIkqapVxWQAxZVVe+6vv/7CrMj8x7WxrpSLS2HtuaJk7TlB1p4rVB1rzz322GMAbN26tUTwQtaeE+QxQpDHCEEfx4jiQaaK0PvUzwA///xzwTjmtm3b8uOPP9Ilr4R+37598fX1ZenSpQXbr1u3jg8++KBgHPNXX32lMY5ZURQ+/vhjFi9eTEJCAj179uTXX3+lcePG5WqPTL+VJEmSJO2o67+pVVGfrjhZe072Wste65qZ2ZKTk8PixYsZM2ZMwf9TPpnZIshjhCCPEUJNqz1XLYIt1U1dPzGUJEmSJG2p67+pugi2FFfX97kk1RSyZoskVX81tmaLJEmSJElSbebk5IShoWGJIElkZGRB71xxbm5uFdpekqSaydnZmRkzZsigqCTVUgb6boAkSZIkSVJtVXQygHz5kwEUzXQpKn8ygKKKTgYgSVLtYGRkhIODg0axXEmSag8ZbJEkSZIkSapCs2bNYsmSJSxbtowrV64wffr0EpMBFC2g+/rrr7Nr1y6++eYbrl69yty5czlz5kyZMzVKklQzxcfHs3HjxhL1XCRJqh1kGFWSJEmSJKkKjR8/nujoaD766KOCyQB27dpVUIAxODgYA4PC/q/u3buzcuVKPvjgA9577z0aNWrE5s2badmypb4+giRJVSAjI4OAgACZtSZJtZQMtkiSJEmSJFWxV199tczMlIMHD5a4b9y4cYwbN66KWyVJkiRJUlWRw4gkSZIkSZIkSZIkSZK0SGa2lCJ/Nuzic8RLkiRJklQx+b+l+b+tUtWT5zGSVDMkJyeTkZFBcnIylpaW+m6OJEmleJjzGJUiz35KCAkJwdvbW9/NkCRJkqRaIygoiPr16+u7GXXCrVu3aNCggb6bIUmSJEm1RmXOY2RmSyk8PDy4d+8e1tbWqFQqfTen2kpKSsLb25t79+5hY2Oj7+bUKHLfVZ7cd5Un913lyX1XeYmJifj4+ODg4KDvptQZ+fs6ODgYW1tbPbematWVv8268jlBftbaqK58Tqg7n7WufE54uPMYGWwphYGBAV5eXvpuRo1hY2NT6//Iqorcd5Un913lyX1XeXLfVV7R2XakqpW/r21tbevM97Wu/G3Wlc8J8rPWRnXlc0Ld+ax15XNC5c5j5JmPJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYItUaaampnz88ceYmprquyk1jtx3lSf3XeXJfVd5ct9Vntx3uleX9nld+ax15XOC/Ky1UV35nFB3Pmtd+ZzwcJ9VzkYkSZIkSZIkSZIkSZKkRTKzRZIkSZIkSZIkSZIkSYtksEWSJEmSJEmSJEmSJEmLZLBFkiRJkiRJkiRJkiRJi2SwRZIkSZIkSZIkSZIkSYtksEWSJEmSJEmSJEmSJEmLZLBFuq9ffvkFX19fzMzM6NKlC6dOnSpz26VLl6JSqTQWMzMzHba2+jh8+DAjR47Ew8MDlUrF5s2bH/icgwcP0r59e0xNTWnYsCFLly6t8nZWNxXdbwcPHizxnVOpVEREROimwdXIvHnz6NSpE9bW1ri4uDB69GiuXbv2wOetW7eOpk2bYmZmRqtWrdixY4cOWlu9VGbfyeOd8Ntvv9G6dWtsbGywsbGhW7du7Ny5877Pkd85/cnMzKRt27aoVCrOnz+v7+Zo3ahRo/Dx8cHMzAx3d3eeeeYZwsLC9N0srbtz5w4vvPACfn5+mJub06BBAz7++GOysrL03TSt+9///kf37t2x0AVl/AAACk1JREFUsLDAzs5O383RqoqcY9dUlTkfrokqew5WE1Xmd782mD9/PiqVipkzZ1boeTLYIpVpzZo1zJo1i48//phz587Rpk0bBg8eTFRUVJnPsbGxITw8vGC5e/euDltcfaSmptKmTRt++eWXcm1/+/Zthg8fTr9+/Th//jwzZ87kxRdfZPfu3VXc0uqlovst37Vr1zS+dy4uLlXUwurr0KFDvPLKK5w4cYK9e/eSnZ3NoEGDSE1NLfM5x44dY+LEibzwwgv4+/szevRoRo8ezaVLl3TYcv2rzL4DebwD8PLyYv78+Zw9e5YzZ87Qv39/Hn30UQIDA0vdXn7n9Oudd97Bw8ND382oMv369WPt2rVcu3aNDRs2EBQUxNixY/XdLK27evUqarWaRYsWERgYyHfffcfChQt577339N00rcvKymLcuHFMnz5d303RqsqcY9dElT2vq2kqex5RE1X0d782OH36NIsWLaJ169YVf7IiSWXo3Lmz8sorrxT8Ozc3V/Hw8FDmzZtX6vZ//fWXYmtrq6PW1RyAsmnTpvtu88477ygtWrTQuG/8+PHK4MGDq7Bl1Vt59tuBAwcUQImPj9dJm2qSqKgoBVAOHTpU5jZPPPGEMnz4cI37unTporz00ktV3bxqrTz7Th7vymZvb6/8/vvvpT4mv3P6s2PHDqVp06ZKYGCgAij+/v76blKV27Jli6JSqZSsrCx9N6XKffXVV4qfn5++m1Flatsxt6Ln2LVBec7raovynEfUJvf73a/pkpOTlUaNGil79+5V+vTpo7z++usVer7MbJFKlZWVxdmzZxk4cGDBfQYGBgwcOJDjx4+X+byUlBTq1auHt7d3rY9yatPx48c19jXA4MGD77uvpUJt27bF3d2dRx55hKNHj+q7OdVCYmIiAA4ODmVuI793pSvPvgN5vCsuNzeX1atXk5qaSrdu3UrdRn7n9CMyMpIpU6bwf//3f1hYWOi7OToRFxfHihUr6N69O8bGxvpuTpVLTEx84DFLqh4qe44t1RzlPY+o6crzu1/TvfLKKwwfPrzEuUt5yWCLVKqYmBhyc3NxdXXVuN/V1bXMehhNmjThzz//ZMuWLfz999+o1Wq6d+9OSEiILppco0VERJS6r5OSkkhPT9dTq6o/d3d3Fi5cyIYNG9iwYQPe3t707duXc+fO6btpeqVWq5k5cyY9evSgZcuWZW5X1veuLta8yVfefSePd4UCAgKwsrLC1NSUadOmsWnTJpo3b17qtvI7p3uKovDss88ybdo0OnbsqO/mVLl3330XS0tLHB0dCQ4OZsuWLfpuUpW7efMmP/30Ey+99JK+myKVQ2XOsaWao7znETVZRX73a7LVq1dz7tw55s2bV+nXkMEWSWu6devGpEmTaNu2LX369GHjxo04OzuzaNEifTdNqqWaNGnCSy+9RIcOHejevTt//vkn3bt357vvvtN30/TqlVde4dKlS6xevVrfTalxyrvv5PGuUJMmTTh//jwnT55k+vTpTJ48mcuXL+u7WbXe7NmzSy0QXnS5evUqP/30E8nJycyZM0ffTa6U8n7OfG+//Tb+/v7s2bMHQ0NDJk2ahKIoevwE5VfRzwoQGhrKkCFDGDduHFOmTNFTyyumMp9TkmqKunAOVhd+9+/du8frr7/OihUrHmoCBCMttkmqRZycnDA0NCQyMlLj/sjISNzc3Mr1GsbGxrRr146bN29WRRNrFTc3t1L3tY2NDebm5npqVc3UuXNnjhw5ou9m6M2rr77Ktm3bOHz4MF5eXvfdtqzvXXn/xmubiuy74ury8c7ExISGDRsC0KFDB06fPs0PP/xQauBJfue058033+TZZ5+97zb169dn//79HD9+HFNTU43HOnbsyFNPPcWyZcuqsJUPr7yfM5+TkxNOTk40btyYZs2a4e3tzYkTJ2pEintFP2tYWBj9+vWje/fuLF68uIpbpz0V/Zy1jTbOsaXq6WHOI2qSivzu11Rnz54lKiqK9u3bF9yXm5vL4cOH+fnnn8nMzMTQ0PCBryODLVKpTExM6NChA/v27WP06NGASIvbt28fr776arleIzc3l4CAAIYNG1aFLa0dunXrVmL6071799aIk8Pq5vz587i7u+u7GTqnKAozZsxg06ZNHDx4ED8/vwc+p1u3buzbt09jGru6+L2rzL4rTh7vCqnVajIzM0t9TH7ntMfZ2RlnZ+cHbvfjjz/y+eefF/w7LCyMwYMHs2bNGrp06VKVTdSK8n7O0qjVaoAyv4/VTUU+a2hoKP369aNDhw789ddfGBjUnGT1h/k/rQ20cY4tVS/aOI+oye73u19TDRgwgICAAI37nnvuOZo2bcq7775brkALyGCLdB+zZs1i8uTJdOzYkc6dO/P999+TmprKc889B8CkSZPw9PQsGMf26aef0rVrVxo2bEhCQgJff/01d+/e5cUXX9Tnx9CLlJQUjR7u27dvc/78eRwcHPDx8WHOnDmEhoayfPlyAKZNm8bPP//MO++8w/PPP8/+/ftZu3Yt27dv19dH0IuK7rfvv/8ePz8/WrRoQUZGBr///jv79+9nz549+voIevPKK6+wcuVKtmzZgrW1dcG4b1tb24LsqOJ/s6+//jp9+vThm2++Yfjw4axevZozZ87UqB5SbajMvpPHO2HOnDkMHToUHx8fkpOTWblyJQcPHiyYtl5+5/TPx8dH499WVlYANGjQoFb1vJ48eZLTp0/Ts2dP7O3tCQoK4sMPP6RBgwa1LpgXGhpK3759qVevHgsWLCA6OrrgsdqWGREcHExcXBzBwcHk5uZy/vx5ABo2bFjwXa6JHnSOXVs86LyutijPeURt8aDf/drC2tq6RM2d/HpgFarFo/X5kaRa5aefflJ8fHwUExMTpXPnzsqJEycKHuvTp48yefLkgn/PnDmzYFtXV1dl2LBhyrlz5/TQav3Ln5K4+JK/vyZPnqz06dOnxHPatm2rmJiYKPXr11f++usvnbdb3yq637788kulQYMGipmZmeLg4KD07dtX2b9/v34ar2el7TdA43tU/G9WURRl7dq1SuPGjRUTExOlRYsWyvbt23Xb8GqgMvtOHu+E559/XqlXr55iYmKiODs7KwMGDFD27NlT8Lj8zlU/t2/frpVTP1+8eFHp16+f4uDgoJiamiq+vr7KtGnTlJCQEH03Tev++uuvMo9btc3kyZNL/ZwHDhzQd9Me2v3OsWuLB53X1RblOY+oLR70u1+bVWbqZ5Wi1JCqYZIkSZIkSZIkSZIkSTVAzRngKUmSJEmSJEmSJEmSVAPIYIskSZIkSZIkSZIkSZIWyWCLJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYIskSZIkSZIkSZIkSZIWyWCLJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYIskSZIkSZIkSZIkSZIWyWCLJEmSJEmSJEmSJEmSFslgiyRJkiRJkiRJkiRJkhbJYIskSZIkSZIkSZIkSZIW/T/7XA/6tM56GQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_skewness_kurtosis()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eed065d2-dd35-4fdb-ad91-8d387a06c7ea",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "* Shapiro-Wilk's test: [shapiro](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html),\n",
-    "* Generic goodness-of-fit tests: [kstest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstest.html) and [anderson](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c7a08d6-3c74-48ce-ad32-f164089b6ec7",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Equal variance (homoscedasticity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e5a893e-6475-401b-9335-b117629b8d0a",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "#### Graphical approaches\n",
-    "\n",
-    "Simple per-group box plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "8a31b653-d891-4f07-ab8b-73562b2ed86d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(df, y='response', x='group');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ecc864d-c32b-4465-b562-6b23c5f1ace2",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "#### Equality-of-variance tests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "3b2c0d0d-0ab0-4bf7-8128-bcfa4d1a3cec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>W</th>\n",
-       "      <th>pval</th>\n",
-       "      <th>equal_var</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>levene</th>\n",
-       "      <td>2.080216</td>\n",
-       "      <td>0.144465</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               W      pval  equal_var\n",
-       "levene  2.080216  0.144465       True"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pg.homoscedasticity(df, dv='response', group='group')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "348a2ae9-6644-412a-b41c-d494e4620b90",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "In SciPy, we also find:\n",
-    "* Bartlett's test: [bartlett](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bartlett.html), most basic and common test,\n",
-    "* Levene's test: [levene](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.levene.html), better for skewed or heavy-tailed distributions,\n",
-    "* ...and others: Fligner-Killeen's test ([fligner](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fligner.html)), Ansari-Bradley's test ([ansari](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ansari.html)), etc\n",
-    "\n",
-    "Example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "280180fa-4f20-44a6-a463-2ce9b0ad75a4",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BartlettResult(statistic=3.3024375753550594, pvalue=0.19181598314035977)"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.bartlett(A, B, C)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e906820a-af04-4a9d-992f-f2e07a7aed91",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "In the above example, as there is not enough evidence to reject $H_0$ ($p>0.05$), we can proceed to perform a standard one-way ANOVA, for example with Pingouin's `anova`. Otherwise, we would use Pingouin's [`welch_anova`](https://pingouin-stats.org/generated/pingouin.welch_anova.html) or SciPy's [`alexandergovern`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.alexandergovern.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c09c6222-4167-4e32-b6da-4abe3a8960de",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## χ² tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b60daa6-99f2-4b43-9b95-1bcda7114ec2",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "When the sum of the observations is known, *e.g.* observations are frequencies -- proportions that sum to $1$, we use a $\\chi^2$ test instead of an ANOVA.\n",
-    "\n",
-    "These tests are named after the $\\chi^2$ distribution. Tests based on this distribution derive a one-sided *p*-value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "f4a1bcc6-c691-44ff-9932-f34ac5fd9a03",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_chi2()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac68602f-a5ee-4b75-befc-5fc198a7b5b1",
-   "metadata": {},
-   "source": [
-    "We will use:\n",
-    "* SciPy's [`contingency.crosstab`](), [`chisquare`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html) and [`chi2_contingency`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html),\n",
-    "* Pingouin's [`chi2_independence`](https://pingouin-stats.org/build/html/generated/pingouin.chi2_independence.html#pingouin.chi2_independence).\n",
-    "\n",
-    "Scipy's functions take counts as input data, while Pingouin's only function takes a dataframe and the column names of categorical variables.\n",
-    "\n",
-    "If we have data in long format:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "141d0591-b63d-49e1-98e7-2491a6fa5d11",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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_bill</th>\n",
-       "      <th>tip</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>smoker</th>\n",
-       "      <th>day</th>\n",
-       "      <th>time</th>\n",
-       "      <th>size</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>16.99</td>\n",
-       "      <td>1.01</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10.34</td>\n",
-       "      <td>1.66</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>21.01</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>23.68</td>\n",
-       "      <td>3.31</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>24.59</td>\n",
-       "      <td>3.61</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sun</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>239</th>\n",
-       "      <td>29.03</td>\n",
-       "      <td>5.92</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sat</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>240</th>\n",
-       "      <td>27.18</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Sat</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>241</th>\n",
-       "      <td>22.67</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Sat</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>17.82</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Sat</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>18.78</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Thur</td>\n",
-       "      <td>Dinner</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>244 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     total_bill   tip     sex smoker   day    time  size\n",
-       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
-       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
-       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
-       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
-       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
-       "..          ...   ...     ...    ...   ...     ...   ...\n",
-       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
-       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
-       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
-       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
-       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
-       "\n",
-       "[244 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tips = pg.read_dataset('tips')\n",
-    "tips"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f04ebc2-0fa8-43f8-a1d9-65069f18f53c",
-   "metadata": {},
-   "source": [
-    "We can summarize the crossed counts of the categorical variables in a contingency table, using SciPy's `contingency.crosstab`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "d90e3c9a-e4f5-4f07-b3c2-7bbaf1d018b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Fri</th>\n",
-       "      <th>Sat</th>\n",
-       "      <th>Sun</th>\n",
-       "      <th>Thur</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Female</th>\n",
-       "      <td>9</td>\n",
-       "      <td>28</td>\n",
-       "      <td>18</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Male</th>\n",
-       "      <td>10</td>\n",
-       "      <td>59</td>\n",
-       "      <td>58</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Fri  Sat  Sun  Thur\n",
-       "Female    9   28   18    32\n",
-       "Male     10   59   58    30"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(row_labels, col_labels), counts = stats.contingency.crosstab(tips['sex'], tips['day'])\n",
-    "observed_counts = pd.DataFrame(counts, index=row_labels, columns=col_labels)\n",
-    "observed_counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1feb29a6-eeac-4096-8e90-c663fdeea8a8",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Homogeneity and independence"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ef858d5-ec8f-405d-924e-62f5286a94c5",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "$H_0$: men and women give tips following a similar weekly pattern.\n",
-    "\n",
-    "If we consider the proportion of each day in the total collected tips:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "fe4ce304-25ac-4734-9a0a-e20db59b742f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Fri</th>\n",
-       "      <th>Sat</th>\n",
-       "      <th>Sun</th>\n",
-       "      <th>Thur</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Altogether</th>\n",
-       "      <td>0.077869</td>\n",
-       "      <td>0.356557</td>\n",
-       "      <td>0.311475</td>\n",
-       "      <td>0.254098</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Fri       Sat       Sun      Thur\n",
-       "Altogether  0.077869  0.356557  0.311475  0.254098"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_props = observed_counts.sum(axis=0) / observed_counts.values.sum()\n",
-    "\n",
-    "pd.DataFrame(dict(Altogether=expected_props)).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f4b4727-3a9b-474f-b6ad-b632be97b04e",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "$H_0$ can be expressed in terms of proportions as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "1473b92c-5f36-47a9-a17b-e6be81d9db52",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Fri</th>\n",
-       "      <th>Sat</th>\n",
-       "      <th>Sun</th>\n",
-       "      <th>Thur</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Female</th>\n",
-       "      <td>0.077869</td>\n",
-       "      <td>0.356557</td>\n",
-       "      <td>0.311475</td>\n",
-       "      <td>0.254098</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Male</th>\n",
-       "      <td>0.077869</td>\n",
-       "      <td>0.356557</td>\n",
-       "      <td>0.311475</td>\n",
-       "      <td>0.254098</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             Fri       Sat       Sun      Thur\n",
-       "Female  0.077869  0.356557  0.311475  0.254098\n",
-       "Male    0.077869  0.356557  0.311475  0.254098"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(dict(Female=expected_props, Male=expected_props)).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6fa1de7-3da6-4853-9bac-bbd226b31014",
-   "metadata": {},
-   "source": [
-    "Or equivalently in terms of counts:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "087ea780-304c-459d-a7c0-066817f3d82f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\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>Fri</th>\n",
-       "      <th>Sat</th>\n",
-       "      <th>Sun</th>\n",
-       "      <th>Thur</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Female</th>\n",
-       "      <td>6.77459</td>\n",
-       "      <td>31.020492</td>\n",
-       "      <td>27.098361</td>\n",
-       "      <td>22.106557</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Male</th>\n",
-       "      <td>12.22541</td>\n",
-       "      <td>55.979508</td>\n",
-       "      <td>48.901639</td>\n",
-       "      <td>39.893443</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             Fri        Sat        Sun       Thur\n",
-       "Female   6.77459  31.020492  27.098361  22.106557\n",
-       "Male    12.22541  55.979508  48.901639  39.893443"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_counts = np.outer(observed_counts.sum(axis=1), expected_props)\n",
-    "\n",
-    "pd.DataFrame(expected_counts, index=row_labels, columns=col_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0f49b1a-3a7a-4ad8-8d2a-9fd3513ad2a6",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "These counts are not integers. They are theoretical counts. We can contrast them with the observed counts, and derive the following statistic:\n",
-    "\n",
-    "$$\n",
-    "\\chi^2 = \\sum_{i=1}^{k}\\sum_{j=1}^{l} \\frac{(O_{ij} - E_{ij})^2}{E_{ij}} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim \\chi^2_{(k-1)(l-1)} \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
-    "$$\n",
-    "\n",
-    "where $O_{ij}$ are the observed counts and $E_{ij}$ the expected or theoretical counts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "d9585b87-f5dd-4cf2-ad91-0a79dcc95c86",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13.222001372406606"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "j, k = expected_counts.shape\n",
-    "dof = (j - 1) * (k - 1)\n",
-    "chi2 = np.sum((observed_counts.values - expected_counts) ** 2 / expected_counts)\n",
-    "chi2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12881bb7-405f-42c0-a892-eea600084170",
-   "metadata": {},
-   "source": [
-    "The $p$-value can calculated as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "a7cd985e-eb2b-4a72-a221-30e3e0fdf8ec",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.004180302092822262"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.chi2(dof).sf(chi2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13d7ef58-e62d-48de-a3a4-2ccc2ac588cb",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "SciPy's $\\chi^2$ test for homogeneity/independence is [chi2_contingency](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "4cd7ea08-110a-4ff7-9521-0f12e4169d35",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(13.22200137240661, 0.004180302092822257)"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chi2, pvalue, dof, expected_props = stats.chi2_contingency(observed_counts)\n",
-    "(chi2, pvalue)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45da92b9-cd4e-4d1b-8996-451285ef1896",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Due to the design of the test, it doesn't matter what factor whose effect is hypothesized to be null under $H_0$. We get the exact same result after transposing the contingency table:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "d7e7fd75-068c-4f37-b981-498890c4a0d7",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(13.22200137240661, 0.004180302092822257)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chi2, pvalue, dof, expected_props_T = stats.chi2_contingency(observed_counts.T)\n",
-    "(chi2, pvalue)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cb3833b5-fc3b-4433-9a91-608d9eeb68cd",
-   "metadata": {},
-   "source": [
-    "As already mentioned, Pingouin's `chi2_independence` is suitable for data in long format:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "08c1de88-2f70-4bdb-9291-634c872b730d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>test</th>\n",
-       "      <th>lambda</th>\n",
-       "      <th>chi2</th>\n",
-       "      <th>dof</th>\n",
-       "      <th>pval</th>\n",
-       "      <th>cramer</th>\n",
-       "      <th>power</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>pearson</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>13.222001</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.004180</td>\n",
-       "      <td>0.232784</td>\n",
-       "      <td>0.876800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>cressie-read</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>13.186226</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.004251</td>\n",
-       "      <td>0.232469</td>\n",
-       "      <td>0.875841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>log-likelihood</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>13.194401</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.004235</td>\n",
-       "      <td>0.232541</td>\n",
-       "      <td>0.876061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>freeman-tukey</td>\n",
-       "      <td>-0.500000</td>\n",
-       "      <td>13.270507</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.004087</td>\n",
-       "      <td>0.233211</td>\n",
-       "      <td>0.878089</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>mod-log-likelihood</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>13.407679</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.003833</td>\n",
-       "      <td>0.234413</td>\n",
-       "      <td>0.881673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>neyman</td>\n",
-       "      <td>-2.000000</td>\n",
-       "      <td>13.873557</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.003082</td>\n",
-       "      <td>0.238451</td>\n",
-       "      <td>0.893170</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 test    lambda       chi2  dof      pval    cramer     power\n",
-       "0             pearson  1.000000  13.222001  3.0  0.004180  0.232784  0.876800\n",
-       "1        cressie-read  0.666667  13.186226  3.0  0.004251  0.232469  0.875841\n",
-       "2      log-likelihood  0.000000  13.194401  3.0  0.004235  0.232541  0.876061\n",
-       "3       freeman-tukey -0.500000  13.270507  3.0  0.004087  0.233211  0.878089\n",
-       "4  mod-log-likelihood -1.000000  13.407679  3.0  0.003833  0.234413  0.881673\n",
-       "5              neyman -2.000000  13.873557  3.0  0.003082  0.238451  0.893170"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_counts, observed_counts, test_results = pg.chi2_independence(tips, 'sex', 'day')\n",
-    "test_results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d2998ef-7712-40a6-b79d-22b855aa12eb",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Goodness-of-fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d855b1f2-3105-445a-b1a6-b99bceeb66b7",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Instead of inferring theoretical counts from the observed counts, we may want to test *vs* predefined (theoretical) counts. This is essentially the same test.\n",
-    "\n",
-    "A random example: [Color proportion of M&Ms [Coursera]](https://www.coursera.org/learn/stanford-statistics/lecture/rAwbR/the-color-proportions-of-m-ms):\n",
-    "| blue | orange | green | yellow | red | brown |\n",
-    "| :-:  | :-:    | :-:   | :-:    | :-: | :-:   |\n",
-    "| 24%  | 20%    | 16%   | 14%    | 13% | 13%   |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "84f4e9b3-a653-422c-8fc5-83b29869ba87",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "410"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_props  = np.array([ .24, .2, .16, .14, .13, .13 ])\n",
-    "observed_counts = np.array([ 85, 79, 56, 64, 58, 68 ])\n",
-    "np.sum(observed_counts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "8bb2916a-e9a4-4bbe-b04f-14818bb68723",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([98.4, 82. , 65.6, 57.4, 53.3, 53.3])"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_counts = expected_props * np.sum(observed_counts)\n",
-    "expected_counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a75bd8d7-f61e-49bc-80b4-cb1f05ccd7c1",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "|          | blue | orange | green | yellow | red  | brown |\n",
-    "| --:      | :-:  | :-:    | :-:   | :-:    | :-:  | :-:   |\n",
-    "| Expected | 98.4 | 82     | 65.6  | 57.4   | 53.3 | 53.3  |\n",
-    "| Observed | 85   | 79     | 56    | 64     | 58   | 58    |\n",
-    "\n",
-    "The statistic is again:\n",
-    "\n",
-    "$$\n",
-    "\\chi^2 = \\sum_{i=1}^{k} \\frac{(O_i - E_i)^2}{E_i} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim \\chi^2_{k-1} \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "a8898631-a5b5-49e8-a158-3e149345391a",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.566983829178941"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "k = len(expected_counts)\n",
-    "chi2 = np.sum((observed_counts - expected_counts) ** 2 / expected_counts)\n",
-    "chi2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "7a8af457-13dc-483a-90e3-50d3949c8edf",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.1276329790529603"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pvalue = stats.chi2(k-1).sf(chi2)\n",
-    "pvalue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92886c91-f6ef-419d-bfda-e28d9dfcc7bc",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "SciPy's implementation of the test is [chisquare](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "1e246915-e9be-4826-9677-d9f30348d0df",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Power_divergenceResult(statistic=8.566983829178941, pvalue=0.1276329790529603)"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.chisquare(observed_counts, expected_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81cbe581-b511-4e29-89cb-62c234c45fbb",
-   "metadata": {},
-   "source": [
-    "Pingouin's `chi2_independence` is not suitable for performing this test."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca862d48",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Two-sample goodness-of-fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b1a4872",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "The $\\chi^2$ test can also be used for comparing the distributions of a continuous variable for two samples (two groups) in a more general way than a $t$-test for independent samples.\n",
-    "\n",
-    "The procedure consists in binning the continuous variable so that the problem can be formulated as a homogeneity test, with bins as the levels of one factor, and the grouping criterion as another factor.\n",
-    "\n",
-    "As a consequence, we will use the same functions as before.\n",
-    "\n",
-    "A similar test is the two-sample Kolmogorov-Smirnov test implemented in SciPy as [ks_2samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html). This test does not require binning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e38724a8-405f-45d6-b622-c6f3caaa2025",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "celltoolbar": "Aucun(e)",
-  "kernelspec": {
-   "display_name": "scientific_python",
-   "language": "python",
-   "name": "scientific_python"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/scipy_cours.ipynb b/notebooks/scipy_cours.ipynb
deleted file mode 100644
index 2fdc4bb6ecde21bd9261885947b262834bcf4252..0000000000000000000000000000000000000000
--- a/notebooks/scipy_cours.ipynb
+++ /dev/null
@@ -1,2962 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ccafd3d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "!\"{sys.executable}\" -m pip install scipy ipywidgets\n",
-    "import scipy_material"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf7e7b4c-6fbe-4b1c-81b7-6d5a851c2835",
-   "metadata": {},
-   "source": [
-    "<script src=\"https://polyfill.io/v3/polyfill.min.js?features=es6\"></script>\n",
-    "<script async src=\"https://cdn.jsdelivr.net/npm/mathjax@3.0.1/es5/tex-mml-chtml.js\"></script>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d2c6ebd",
-   "metadata": {},
-   "source": [
-    "<h1 align='center'>Statistical tests with the SciPy library</h1>\n",
-    "\n",
-    "<div style='text-align:center'><img src='https://docs.scipy.org/doc/scipy/_static/logo.svg' /></div>\n",
-    "\n",
-    "[SciPy](https://docs.scipy.org/doc/scipy/reference/#api-definition) is a collection of mathematical tools aiming at diverse fields, with functionalities split in several modules:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "42342d74",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import (\n",
-    "        cluster,     # Clustering algorithms\n",
-    "        constants,   # Physical and mathematical constants\n",
-    "        fftpack,     # Fast Fourier Transform routines\n",
-    "        integrate,   # Integration and ordinary differential equation solvers\n",
-    "        interpolate, # Interpolation and smoothing splines\n",
-    "        io,          # Input and Output\n",
-    "        linalg,      # Linear algebra\n",
-    "        ndimage,     # N-dimensional image processing\n",
-    "        odr,         # Orthogonal distance regression\n",
-    "        optimize,    # Optimization and root-finding routines\n",
-    "        signal,      # Signal processing\n",
-    "        sparse,      # Sparse matrices and associated routines\n",
-    "        spatial,     # Spatial data structures and algorithms\n",
-    "        special,     # Special functions\n",
-    "        stats,       # Statistical distributions and functions\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e2fe344",
-   "metadata": {},
-   "source": [
-    "`scipy.stats` content (see the [official documention](https://docs.scipy.org/doc/scipy/reference/reference/stats.html#module-scipy.stats)):\n",
-    "\n",
-    "* [Probability distributions](https://docs.scipy.org/doc/scipy/reference/reference/stats.html#probability-distributions)\n",
-    "* [Summary statistics](https://docs.scipy.org/doc/scipy/reference/reference/stats.html#summary-statistics)\n",
-    "* [Frequency statistics](https://docs.scipy.org/doc/scipy/reference/reference/stats.html#frequency-statistics)\n",
-    "* [Correlation functions](https://docs.scipy.org/doc/scipy/reference/reference/stats.html#correlation-functions)\n",
-    "* [Statistical tests](https://docs.scipy.org/doc/scipy/reference/reference/stats.html#statistical-tests)\n",
-    "* ...\n",
-    "\n",
-    "`scipy.stats` features basic functionalities and we will occasionally mention the `statsmodels` and `pingouin` libraries as we will hit `scipy.stats` limitations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d715671-e4ad-4d4d-b60f-9397e2b80b90",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Outline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5162d7b2-625a-4699-922e-92c5c2bfa769",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "We will merely review statistical tests:\n",
-    "\n",
-    "* Distributions\n",
-    "* Student $t$ tests\n",
-    "    * compare a sample mean against the population mean\n",
-    "    * compare means of two independent samples\n",
-    "    * compare the means of paired samples\n",
-    "* Analyses of variance (one-way)\n",
-    "    * compare more than two group means\n",
-    "* Tests for other tests' assumptions\n",
-    "    * normality tests\n",
-    "    * homoscedasticity tests\n",
-    "* $\\chi^2$ tests for categorical variables\n",
-    "    * goodness-of-fit tests\n",
-    "    * homogeneity and independence tests\n",
-    "* Correlation coefficient and linear regression\n",
-    "* Effect sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bbf8433-58db-4738-bff9-33bde44f00df",
-   "metadata": {},
-   "source": [
-    "# Distributions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90e1f4cf-8993-4745-bf94-5d5fdf17b181",
-   "metadata": {},
-   "source": [
-    "For this section, the related utilities are provided by `scipy.stats`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c33e4518-3d64-4b55-baf5-cc06634590b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from scipy import stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1091dfd5",
-   "metadata": {},
-   "source": [
-    "Reminder about module loading:\n",
-    "\n",
-    "Example: how to access the `sem` function defined in the `scipy.stats` module?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "898957c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "skipping\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%script echo skipping\n",
-    "\n",
-    "import scipy.stats\n",
-    "scipy.stats.sem\n",
-    "\n",
-    "from scipy import stats\n",
-    "stats.sem\n",
-    "\n",
-    "from scipy.stats import *\n",
-    "sem"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5be3195f-3d79-458d-94bb-5a2ef6b558cd",
-   "metadata": {},
-   "source": [
-    "## Confidence intervals\n",
-    "\n",
-    "Common information such as the sample mean or standard deviation are trivial to obtain. For example, we have seen Pandas' `describe`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "da212ef7-0fc2-4ecb-9225-02e41f626b25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Energie</th>\n",
-       "      <th>EnergiePredite</th>\n",
-       "      <th>Eau</th>\n",
-       "      <th>Proteines</th>\n",
-       "      <th>Glucides</th>\n",
-       "      <th>Lipides</th>\n",
-       "      <th>Sucres</th>\n",
-       "      <th>Fibres</th>\n",
-       "      <th>Polyols</th>\n",
-       "      <th>Alcool</th>\n",
-       "      <th>AcidesOrganiques</th>\n",
-       "      <th>Calcium</th>\n",
-       "      <th>Cuivre</th>\n",
-       "      <th>Fer</th>\n",
-       "      <th>Magnesium</th>\n",
-       "      <th>Manganese</th>\n",
-       "      <th>Phosphore</th>\n",
-       "      <th>Potassium</th>\n",
-       "      <th>Zinc</th>\n",
-       "      <th>VitamineC</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>94.000000</td>\n",
-       "      <td>260.000000</td>\n",
-       "      <td>260.000000</td>\n",
-       "      <td>260.000000</td>\n",
-       "      <td>260.000000</td>\n",
-       "      <td>260.000000</td>\n",
-       "      <td>260.000000</td>\n",
-       "      <td>258.000000</td>\n",
-       "      <td>66.000000</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>78.000000</td>\n",
-       "      <td>240.000000</td>\n",
-       "      <td>225.000000</td>\n",
-       "      <td>234.000000</td>\n",
-       "      <td>231.000000</td>\n",
-       "      <td>223.000000</td>\n",
-       "      <td>232.000000</td>\n",
-       "      <td>239.000000</td>\n",
-       "      <td>226.000000</td>\n",
-       "      <td>238.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>61.577660</td>\n",
-       "      <td>56.468218</td>\n",
-       "      <td>84.074462</td>\n",
-       "      <td>1.872962</td>\n",
-       "      <td>9.391577</td>\n",
-       "      <td>0.645046</td>\n",
-       "      <td>6.403823</td>\n",
-       "      <td>3.030736</td>\n",
-       "      <td>1.212111</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.568796</td>\n",
-       "      <td>37.480042</td>\n",
-       "      <td>0.139627</td>\n",
-       "      <td>0.803304</td>\n",
-       "      <td>20.934589</td>\n",
-       "      <td>0.245655</td>\n",
-       "      <td>47.515474</td>\n",
-       "      <td>305.494142</td>\n",
-       "      <td>0.359111</td>\n",
-       "      <td>23.003992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>68.474914</td>\n",
-       "      <td>65.349912</td>\n",
-       "      <td>17.470638</td>\n",
-       "      <td>1.592328</td>\n",
-       "      <td>14.088675</td>\n",
-       "      <td>1.970706</td>\n",
-       "      <td>10.554115</td>\n",
-       "      <td>2.339028</td>\n",
-       "      <td>3.682805</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.902139</td>\n",
-       "      <td>37.351220</td>\n",
-       "      <td>0.366930</td>\n",
-       "      <td>0.904636</td>\n",
-       "      <td>22.936433</td>\n",
-       "      <td>0.267493</td>\n",
-       "      <td>46.449534</td>\n",
-       "      <td>343.062188</td>\n",
-       "      <td>0.578719</td>\n",
-       "      <td>32.729210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>13.100000</td>\n",
-       "      <td>13.816358</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.230000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.000190</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>31.500000</td>\n",
-       "      <td>0.025000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>25.675000</td>\n",
-       "      <td>26.818142</td>\n",
-       "      <td>84.275000</td>\n",
-       "      <td>0.927500</td>\n",
-       "      <td>2.450000</td>\n",
-       "      <td>0.190000</td>\n",
-       "      <td>1.555000</td>\n",
-       "      <td>1.700000</td>\n",
-       "      <td>0.048000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.120000</td>\n",
-       "      <td>14.450000</td>\n",
-       "      <td>0.048000</td>\n",
-       "      <td>0.282500</td>\n",
-       "      <td>9.900000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>22.875000</td>\n",
-       "      <td>158.500000</td>\n",
-       "      <td>0.140000</td>\n",
-       "      <td>4.340000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>36.050000</td>\n",
-       "      <td>35.230313</td>\n",
-       "      <td>89.600000</td>\n",
-       "      <td>1.440000</td>\n",
-       "      <td>4.900000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>2.800000</td>\n",
-       "      <td>2.485000</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.205000</td>\n",
-       "      <td>26.000000</td>\n",
-       "      <td>0.079000</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>13.600000</td>\n",
-       "      <td>0.170000</td>\n",
-       "      <td>37.900000</td>\n",
-       "      <td>225.000000</td>\n",
-       "      <td>0.230000</td>\n",
-       "      <td>11.550000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>66.450000</td>\n",
-       "      <td>53.273011</td>\n",
-       "      <td>92.000000</td>\n",
-       "      <td>2.412500</td>\n",
-       "      <td>9.740000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>6.155000</td>\n",
-       "      <td>3.390000</td>\n",
-       "      <td>0.487500</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.717500</td>\n",
-       "      <td>47.250000</td>\n",
-       "      <td>0.130000</td>\n",
-       "      <td>0.977500</td>\n",
-       "      <td>22.150000</td>\n",
-       "      <td>0.290000</td>\n",
-       "      <td>56.200000</td>\n",
-       "      <td>329.000000</td>\n",
-       "      <td>0.407500</td>\n",
-       "      <td>23.400000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>389.000000</td>\n",
-       "      <td>452.981215</td>\n",
-       "      <td>96.100000</td>\n",
-       "      <td>14.200000</td>\n",
-       "      <td>78.400000</td>\n",
-       "      <td>20.600000</td>\n",
-       "      <td>70.300000</td>\n",
-       "      <td>23.600000</td>\n",
-       "      <td>22.600000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.300000</td>\n",
-       "      <td>257.000000</td>\n",
-       "      <td>5.170000</td>\n",
-       "      <td>9.090000</td>\n",
-       "      <td>194.000000</td>\n",
-       "      <td>1.850000</td>\n",
-       "      <td>356.000000</td>\n",
-       "      <td>3430.000000</td>\n",
-       "      <td>7.660000</td>\n",
-       "      <td>228.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Energie  EnergiePredite         Eau   Proteines    Glucides  \\\n",
-       "count   94.000000      260.000000  260.000000  260.000000  260.000000   \n",
-       "mean    61.577660       56.468218   84.074462    1.872962    9.391577   \n",
-       "std     68.474914       65.349912   17.470638    1.592328   14.088675   \n",
-       "min     13.100000       13.816358    3.000000    0.230000    0.100000   \n",
-       "25%     25.675000       26.818142   84.275000    0.927500    2.450000   \n",
-       "50%     36.050000       35.230313   89.600000    1.440000    4.900000   \n",
-       "75%     66.450000       53.273011   92.000000    2.412500    9.740000   \n",
-       "max    389.000000      452.981215   96.100000   14.200000   78.400000   \n",
-       "\n",
-       "          Lipides      Sucres      Fibres    Polyols  Alcool  \\\n",
-       "count  260.000000  260.000000  258.000000  66.000000   260.0   \n",
-       "mean     0.645046    6.403823    3.030736   1.212111     0.0   \n",
-       "std      1.970706   10.554115    2.339028   3.682805     0.0   \n",
-       "min      0.000000    0.000000    0.500000   0.000000     0.0   \n",
-       "25%      0.190000    1.555000    1.700000   0.048000     0.0   \n",
-       "50%      0.300000    2.800000    2.485000   0.250000     0.0   \n",
-       "75%      0.500000    6.155000    3.390000   0.487500     0.0   \n",
-       "max     20.600000   70.300000   23.600000  22.600000     0.0   \n",
-       "\n",
-       "       AcidesOrganiques     Calcium      Cuivre         Fer   Magnesium  \\\n",
-       "count         78.000000  240.000000  225.000000  234.000000  231.000000   \n",
-       "mean           0.568796   37.480042    0.139627    0.803304   20.934589   \n",
-       "std            0.902139   37.351220    0.366930    0.904636   22.936433   \n",
-       "min            0.000000    3.000000    0.010000    0.000190    3.000000   \n",
-       "25%            0.120000   14.450000    0.048000    0.282500    9.900000   \n",
-       "50%            0.205000   26.000000    0.079000    0.600000   13.600000   \n",
-       "75%            0.717500   47.250000    0.130000    0.977500   22.150000   \n",
-       "max            6.300000  257.000000    5.170000    9.090000  194.000000   \n",
-       "\n",
-       "        Manganese   Phosphore    Potassium        Zinc   VitamineC  \n",
-       "count  223.000000  232.000000   239.000000  226.000000  238.000000  \n",
-       "mean     0.245655   47.515474   305.494142    0.359111   23.003992  \n",
-       "std      0.267493   46.449534   343.062188    0.578719   32.729210  \n",
-       "min      0.000000    6.000000    31.500000    0.025000    0.000000  \n",
-       "25%      0.100000   22.875000   158.500000    0.140000    4.340000  \n",
-       "50%      0.170000   37.900000   225.000000    0.230000   11.550000  \n",
-       "75%      0.290000   56.200000   329.000000    0.407500   23.400000  \n",
-       "max      1.850000  356.000000  3430.000000    7.660000  228.000000  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataframe = pd.read_csv('data/fruleg.tsv', sep=' ')\n",
-    "dataframe.describe()\n",
-    "#dataframe.describe(exclude=np.number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61b0384e-b15e-44e4-a231-72dadd49d1d6",
-   "metadata": {},
-   "source": [
-    "To report the value of the population mean and account for the uncertainty that results from the fact the true value is actually unknown (the sample mean above is our best guess), we can give a confidence interval instead.\n",
-    "\n",
-    "Reminder: the population mean follows a normal distribution centered at the sample mean, with standard deviation equal to the standard error of the mean (or, equivalently, the standard deviation of the sample divided by the square root of the sample size)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "278c2259-7812-4d81-a6b6-56ebf3938bdf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_confidence_interval(1.872962, stats.sem(dataframe['Proteines']))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f719439-1afd-4a21-bb80-81040010a5d9",
-   "metadata": {},
-   "source": [
-    "Computing a confidence interval with SciPy involves instantiating the normal distribution with the `norm` function and calling the `interval` method of the returned object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "53efbf5b-03ad-4a95-a84d-1f01d7968410",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = dataframe['Proteines']\n",
-    "mu = np.mean(X)\n",
-    "sigma = stats.sem(X)\n",
-    "distribution_of_the_mean = stats.norm(mu, sigma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c66287ce-9ae8-487b-acf2-59d80638e503",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.6794111748692484, 2.0665119020538287)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "distribution_of_the_mean.interval(0.95)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52080112-bf86-441c-af70-85eeadef820e",
-   "metadata": {},
-   "source": [
-    "Note again that we have set the scale parameter `sigma` equal to the sem. In contrast, if variable `Proteines` followed a normal distribution, we could define its distribution as:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4b6a2690-7a11-4fdc-a52f-af408dd91ff5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "normal_distribution = stats.norm(X.mean(), X.std())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b10833f-1da4-4781-b791-c2754f15f158",
-   "metadata": {},
-   "source": [
-    "The objects `norm` returns (*e.g.* `distribution_of_the_mean`) feature numerous other methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c2943fe2-0f17-44b0-860a-1c9028e89e4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.8912148653338976"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# probability density function\n",
-    "distribution_of_the_mean.pdf(1.9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b3d9cfed-b060-451f-b92a-0043ff1c3e2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6078814760069171"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# cumulative distribution function\n",
-    "distribution_of_the_mean.cdf(1.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a1728396-4859-4c36-917b-fb9113378baf",
-   "metadata": {},
-   "source": [
-    "See [scipy.stats.rv_continuous](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.html#scipy.stats.rv_continuous) for more methods.\n",
-    "\n",
-    "As another example, we can make use of the inverse survival function `isf` to re-implement the calculation of the $1-\\alpha=95\\%$ confidence interval based on the following formula:\n",
-    "\n",
-    "$$\n",
-    "\\bar{x} \\pm z_{1-\\alpha/2}\\frac{\\sigma}{\\sqrt{n}}\n",
-    "$$\n",
-    "\n",
-    "Indeed, $z_{1-\\alpha/2}$ is calculated as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "09d17b75-cb67-47b7-acf2-68269df1daae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.9599639845400545"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha = 0.05\n",
-    "z = stats.norm().isf(alpha / 2)\n",
-    "z"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be2d1285-aa22-4c82-ad7c-d1df0a064df4",
-   "metadata": {},
-   "source": [
-    "For a $95\\%$ confidence interval, we usually take $z\\approx 1.96$. Note we took the standard normal distribution, with null mean and unit standard deviation (`stats.norm()` is equivalent to `stats.norm(0, 1)`).\n",
-    "\n",
-    "$\\frac{\\sigma}{\\sqrt{n}}$ is the standard deviation of the sample mean, or standard error of mean, that we have already calculated using the `sem` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "7b3c6d6b-849e-4b68-8b6b-fcfa86582d99",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Proteins represent 1.87±0.19 on average\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Proteins represent {mu:.2f}±{z * sigma:.2f} on average')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6b0c2ee-4f6b-49db-8c26-dd64aede72fb",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Fitting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "994df34d",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "We have seen how to fit a normal distribution explicitly passing a mean and standard deviation. More generally, for any distribution from `scipy.stats`, we can get the required parameters using the `stats.<distribution>.fit` method. For example, for distribution `stats.norm` with sample `X`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4f114ad2-9cd2-431a-8b3e-2283006ef970",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "normal_distribution = stats.norm(*stats.norm.fit(X))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cef7bb4e-38b9-4abc-97e4-419a9650173b",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Now, unlike the population mean, there is no guarantee a sample follows a normal distribution.\n",
-    "\n",
-    "To determine what distribution a sample best follows, we can fit various distributions to the data and visually appreciate how well these distributions match with the data by plotting a scaled histogram and the probability density functions of the fitted distributions on top of the histogram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "8e114e17-8edb-4674-9309-c6f566885c0a",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the histogram\n",
-    "import seaborn as sns\n",
-    "ax = sns.histplot(X, bins=30, stat='density')\n",
-    "\n",
-    "# fit a log-normal distribution\n",
-    "lognorm = stats.lognorm(*stats.lognorm.fit(X))\n",
-    "\n",
-    "# draw the probability density function\n",
-    "from matplotlib import pyplot as plt\n",
-    "grid = np.linspace(X.min(), X.max(), 300)\n",
-    "ax.plot(grid, lognorm.pdf(grid));\n",
-    "\n",
-    "# fit and overlay more distributions\n",
-    "#weibull = stats.weibull_min(*stats.weibull_min.fit(X))\n",
-    "#ax.plot(grid, weibull.pdf(grid));\n",
-    "t = stats.t(*stats.t.fit(X))\n",
-    "ax.plot(grid, t.pdf(grid));\n",
-    "#chi2 = stats.chi2(*stats.chi2.fit(X))\n",
-    "#ax.plot(grid, chi2.pdf(grid));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7948848f-1492-41ec-9d4b-714343727106",
-   "metadata": {},
-   "source": [
-    "Note that plotting histograms is good practice anyway, because it helps to spot data distributions with multiple modes. Multiple modes in a sample are a red flag for tests that compare estimates of central tendency (*e.g.* means)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf25c4a2-bf73-448a-b72a-88f8c4aca157",
-   "metadata": {},
-   "source": [
-    "...and we can test whether `Proteines` follows a log-normal distribution in our sample with the one-sample Kolmogorov-Smirnov test:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "7a0035eb-9d26-414b-b5b2-e73e792e6173",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.968516871896989"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "statistic, pvalue = stats.kstest(X, lognorm.cdf)\n",
-    "pvalue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddeacee0-2062-476d-b7ec-9e0f036d6c4b",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "# Statistical testing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f2fcb2e-1097-4bb7-ba77-fceaa3b55702",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "\n",
-    "> What did we do?\n",
-    "\n",
-    "We compared our **observations** `x` with some **expectation**.\n",
-    "\n",
-    "We actually formulated a so-called *null hypothesis*, denoted $H_0$, that models the situation such that \"nothing is going on\", *i.e.* the observations meet the expectation.\n",
-    "\n",
-    "We also implicitly defined an alternative hypothesis, usually denoted $H_1$ or $H_A$, that can simply be the opposite of $H_0$.\n",
-    "\n",
-    "For example:\n",
-    "\n",
-    "$$\n",
-    "\\left\\{\n",
-    "\\begin{array}{ l l l }\n",
-    "H_0: & X \\sim \\mathcal{N}(\\mu, \\sigma^2) & \\mbox{with } \\mu \\mbox{ assumed to be } \\bar{x} \\mbox{ and } \\sigma^2 \\mbox{ as } \\frac{1}{n-1}\\sum_{i=0}^{n-1} (x_i - \\bar{x})^2 \\\\\n",
-    "H_A: & \\mbox{not } H_0\n",
-    "\\end{array}\n",
-    "\\right.\n",
-    "$$\n",
-    "\n",
-    "A test consists in contrasting the two incompatible hypotheses.\n",
-    "\n",
-    "If we had a single observation – say $z=1.4$ – to compare with a distribution – say $\\mathcal{N}(0,1)$ – we would simply compute the probability for this value to be drawn from this distribution (or not):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8346ad8e",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "z = 1.4\n",
-    "\n",
-    "N = stats.norm(0, 1)\n",
-    "\n",
-    "onesided_pvalue = N.sf(z) # sf= survival function\n",
-    "twosided_pvalue = 2 * min(N.cdf(z), N.sf(z))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "404476b6",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_onesided_probabilitymass(z, N, onesided_pvalue)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99a007ac",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "In practice, all tests boil down to comparing a single value with a reference distribution. Basically, a test expresses the discrepancy between the observations and the expectation in the shape of a *statistic*, and this statistic is supposed to follow a given distribution under $H_0$.\n",
-    "\n",
-    "This is used as a basis to calculate a *p*-value that estimates the probability of erroneously rejecting $H_0$.\n",
-    "\n",
-    "The experimenter also defines a significance level $\\alpha$, with common values $\\alpha=0.05$ or $0.01$, that sets the maximum tolerated risk of making a *type-1 error*, *i.e.* of rejecting $H_0$ by chance.\n",
-    "If the obtained <em>p</em>-value is lower than $\\alpha$, then s·he can conclude there is sufficient evidence to reject $H_0$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b162e92e",
-   "metadata": {
-    "hidden": true,
-    "hide_input": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%html\n",
-    "<style>table#typeoferrors { text-align: center; font-size: large; margin-left: 1px;} #typeoferrors td { text-align: center; font-size: large; border-right: solid 1px black; border-bottom: solid 1px black; } #typeoferrors td.border { font-size: small; border-left: solid 1px black; border-top: solid 1px black; } #typeoferrors td.wrong { color: orange; } #typeoferrors td.ok { color: green; } #typeoferrors span.sub { font-size: x-small; } #typeoferrors td.footnote { text-align: left; font-size: xx-small; border-right: 0px; border-bottom: 0px; } </style> <table id=\"typeoferrors\">     <tr><td rowspan=\"2\" colspan=\"2\"></td><td colspan=\"2\" class=\"border\">Conclusion about $H_0$<br />from the statistical test</td></tr>    <tr><td>accept</td><td>reject</td></tr>     <tr><td rowspan=\"2\" class=\"border\">Truth about $H_0$<br />in the population</td><td>true</td><td class=\"ok\">Correct</td><td  class=\"wrong\">Type 1 error<br /><span class=\"sub\">observe difference<br />when none exists</span></td></tr>     <tr><td>false</td><td class=\"wrong\">Type 2 error<br /><span class=\"sub\">fail to observe difference<br />when one exists</span></td><td class=\"ok\">Correct</td></tr>     <tr><td colspan=\"4\" class=\"footnote\"> <a href=\"https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting\">https://faculty.nps.edu/rbassett/_book/hypothesis-testing-one-sample.html#fig:errorsHypTesting</a>     </td></tr> </table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "446ba63e-df67-46ca-8921-eca686462c93",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## *t* tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f9602b8-5f64-438b-8051-c71f9c8b0b14",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "*t* tests derive a statistic that is supposed to follow the [Student's *t* distribution](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html) under $H_0$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "67d046f0-ca64-4a12-8e32-58b8e7e142a0",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_t_pdfs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "701ee597-2eec-4404-8a0e-601ae2121e19",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "At high degrees of freedom, the *t* distribution approaches the normal distribution. At lower degrees of freedom, the *t* distribution exhibits heavier tails and is less sensitive to extreme values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca6bf548-cadf-4c75-8130-fe0c3ef8de9a",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### One-sample *t* test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e1a462c-5c60-4438-a339-7c284ff56e15",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "This test compares a sample's central tendency (*sample mean*) with a reference value (*population mean*).\n",
-    "\n",
-    "<table style=\"text-align: center;\"><tr><td>\n",
-    "<img src='img/8mice.svg' />\n",
-    "</td><td>\n",
-    "<img src='img/Scientific_journal_icon.svg' width=\"96px\" />\n",
-    "</td></tr><tr><td><center>\n",
-    "<code>x=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
-    "</center></td><td><center>\n",
-    "<code>&mu;=50</code>\n",
-    "</center></td></tr></table>\n",
-    "\n",
-    "Let us call $\\mu$ this reference value. Our expectation is that the sample mean $\\bar{X}$ is close enough to $\\mu$.\n",
-    "In other words, $H_0: \\bar{X} = \\mu$.\n",
-    "The statistic is:\n",
-    "$$\n",
-    "\\frac{\\bar{X} - \\mu}{\\mathrm{SEM}} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim t(n-1) \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "`scipy`'s one-sample *t* test is [ttest_1samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html):\n",
-    "\n",
-    "`scipy.stats.ttest_1samp(a, popmean, axis=0, nan_policy='propagate', alternative='two-sided')`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "b471633d-c9ad-455e-84e1-d32af085b32a",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TtestResult(statistic=0.6024056396957578, pvalue=0.5658990587680466, df=7)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mu = 50\n",
-    "\n",
-    "x = np.array([49.47257879, 81.93967205, 64.030398, 17.25423608, 59.80082512,\n",
-    "              94.56012514, 69.91672899, 12.39640637])\n",
-    "\n",
-    "stats.ttest_1samp(x, mu)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dac3b6db-2ad7-46b4-937f-9dda1015b7fc",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "If we do not mind a negative difference (resp. positive difference), *i.e.* we consider the danger zone to begin only above (resp. below) the expected value, we can make the test one-sided to gain statistical power.\n",
-    "To this aim, we must choose and specify which side passing the `alternative` argument with value `'greater'` (resp. `'less'`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b732c69f-1851-4ef2-bdc2-8f80b4c5281e",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TtestResult(statistic=0.6024056396957578, pvalue=0.2829495293840233, df=7)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.ttest_1samp(x, mu, alternative='greater')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2144869e-9e4a-4e63-ae58-81c5a6be7205",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### *t* test for independent samples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e31b437-eceb-4da1-9ab3-f11d45b352c0",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "This test compares the means of two samples or groups, *e.g.* a control sample and a sample from a mutated population: $H_0: \\bar{X_1} = \\bar{X_2}$.\n",
-    "\n",
-    "<table style=\"text-align:center;\"><tr><td>\n",
-    "<img src=\"img/8mice.svg\" alt=\"sample of the control population\" />\n",
-    "</td><td>\n",
-    "<img src=\"img/8mutants1.svg\" alt=\"sample of a mutated population\" />\n",
-    "</td></tr><tr><td><center>\n",
-    "<code>x<sub>1</sub>=[49.5 81.9 64.0 17.3 59.8 94.6 69.9 12.4]</code>\n",
-    "</center></td><td><center>\n",
-    "<code>x<sub>2</sub>=[64.2 96.6 101.9 85.3 66.5 63.9 127.6 55.0]</code>\n",
-    "</center></td></tr></table>\n",
-    "\n",
-    "`scipy`'s *t* test for independent samples uses the statistic $t=\\frac{\\bar{X_1}-\\bar{X_2}}{\\sqrt{(\\frac{1}{n_1}+\\frac{1}{n_2})\\mbox{ }\\textrm{PooledVariance}}}$ with $\\textrm{PooledVariance} = \\frac{1}{n_1+n_2-2}\\sum_{j\\in\\{1,2\\}}\\sum_i (x_{ij}-\\bar{x_j})^2$ and is available as [ttest_ind](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html):\n",
-    "\n",
-    "`scipy.stats.ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "6231e214-ac36-4c4f-8a16-e1551c8484b4",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TtestResult(statistic=-1.96174329619957, pvalue=0.06998888828308221, df=14.0)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x1 = x\n",
-    "x2 = np.array([64.22723692, 96.56483856, 101.94191774, 85.31918879,\n",
-    "               66.49529990, 63.88841224, 127.63861749, 55.00527005])\n",
-    "\n",
-    "stats.ttest_ind(x1, x2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ec31e3d-7650-4208-b327-1e712c9c8460",
-   "metadata": {},
-   "source": [
-    "`scipy`'s implementation does not require equal numbers of observations per group, but assumes the groups have [similar variances ($0.5<\\frac{s_{X_1}}{s_{X_2}}<2$)](https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes,_similar_variances_(1/2_%3C_sX1/sX2_%3C_2)).\n",
-    "For heterogeneous groups, `ttest_ind` also implements Welch's *t* test with `equal_var=False`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1083c04c-1221-446f-84a0-7084413a7722",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### *t* test for paired samples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4743e7fc-b964-48d4-9e13-e78146626c5a",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "<img src='img/paired1.svg' />\n",
-    "\n",
-    "`scipy`'s *t* test for paired samples is [ttest_rel](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html):\n",
-    "\n",
-    "`scipy.stats.ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided')`\n",
-    "\n",
-    "This is actually a one-sample *t* test of the between-group differences against a population mean equal to zero (compare [1](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L6450-L6460) and [2](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/stats.py#L5647-L5656))."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "119c66ea-ab2c-4347-898c-ee5c0b38e89d",
-   "metadata": {},
-   "source": [
-    "### Effect sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2703f3b1",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Very low *p*-values are not measurements of the strength of an effect. One should consider the *effect size* instead.\n",
-    "\n",
-    "A common measure of effect size for two independent samples is [Cohen's $d$](https://en.wikipedia.org/wiki/Effect_size#Cohen's_d): $d = \\frac{\\bar{X_2}-\\bar{X_1}}{\\sqrt{\\textrm{PooledVariance}}}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "3beb1fbb-a1ac-40aa-b4ce-b97f151392f5",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b97d1ae54141479bb7b24d939f1bf1b5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.5, description='cohen_d', max=4.0), Output()), _dom_classes=('widget…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scipy_material.illustration_cohen_d();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8e1face-4845-459d-95c2-8674181d39b5",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "With large enough sample sizes, one can find significant effects of size $0.1$ for example, which may not be of practical interest. Statistical significance does not imply practical significance.\n",
-    "   \n",
-    "Measurements of effect size were proposed together with [tables](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) for interpreting size values. For example, for Cohen's $d$:\n",
-    "   \n",
-    "| $|d|$ | size of effect |\n",
-    "| :-: | :-- |\n",
-    "| $0.2$ | small |\n",
-    "| $0.5$ | medium |\n",
-    "| $0.8$ | large |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "a075fc16-e6e1-4f88-8a72-7daf0f72ff28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cohen_d(x1, x2):\n",
-    "    n1, n2 = len(x1), len(x2)\n",
-    "    m1, m2 = np.mean(x1), np.mean(x2)\n",
-    "    v1, v2 = np.var(x1), np.var(x2)\n",
-    "    pooled_variance = (n1 * v1 + n2 * v2) / (n1 + n2 - 2)\n",
-    "    d = (m2 - m1) / np.sqrt(pooled_variance)\n",
-    "    return d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "479e8f62-c653-472d-b9d5-d3bf854d09ff",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Adjusted Cohen's $d$ for dependent samples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "25adcf4f-6611-434f-b619-27f341b3caad",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.1839903712840414"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r, _ = stats.pearsonr(x1, x2)\n",
-    "cohen_d(x1, x2) / np.sqrt(1 - r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b0fd532-87d0-4747-b3a3-322d77c2bfbd",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Analysis of variance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8443a0ff-75ed-4d88-a4e8-a988e6de3716",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "### One-way ANOVA\n",
-    "\n",
-    "Comparing three or more group means reads $H_0: \\bar{X_0} = \\bar{X_1} = ... = \\bar{X_k}$ and is usually carried out with an *analysis of variance*.\n",
-    "\n",
-    "The total variance ($SS_{\\textrm{total}}$) is decomposed as the sum of two terms: *within-group* variance ($SS_{\\textrm{error}}$) and *between-group* variance ($SS_{\\textrm{treatment}}$).\n",
-    "\n",
-    "$$\n",
-    "\\underbrace{\\sum_j\\sum_i (x_{ij} - \\bar{\\bar{x}})^2}_{SS_{\\textrm{total}}} = \\underbrace{\\sum_j\\sum_i (\\bar{x_j} - \\bar{\\bar{x}})^2}_{SS_{\\textrm{treatment}}} + \\underbrace{\\sum_j\\sum_i (x_{ij} - \\bar{x_j})^2}_{SS_{\\textrm{error}}}\n",
-    "$$\n",
-    "Many statistical tools give the following detailled table:\n",
-    "\n",
-    "| Source | Degrees of<br />freedom | Sum of squares | Mean squares | $\\mbox{ }F\\mbox{ }$ | $p$-value |\n",
-    "| :- | :-: | :-: | :-: | :-: | :-: |\n",
-    "| Treatment | $k-1$ | $SS_{\\textrm{treatment}}$ | $MS_{\\textrm{treatment}}$ | $\\frac{MS_{\\textrm{treatment}}}{MS_{\\textrm{error}}}$ | $\\mbox{ }p\\mbox{ }$ |\n",
-    "| Error | $N-k$ | $SS_{\\textrm{error}}$ | $MS_{\\textrm{error}}$ | | |\n",
-    "| Total | $N-1$ | $SS_{\\textrm{total}}$ | | | |\n",
-    "\n",
-    "The statistic $F = \\frac{MS_{\\textrm{treatment}}}{MS_{\\textrm{error}}}$ follows the Fisher's [F](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f.html) distribution under $H_0$.\n",
-    "\n",
-    "More about it at: https://www.coursera.org/learn/stanford-statistics/lecture/pskeN/the-idea-of-analysis-of-variance\n",
-    "\n",
-    "The most basic [implementation](https://github.com/scipy/scipy/blob/v1.7.1/scipy/stats/mstats_basic.py#L2937-L2967) of the one-way ANOVA in SciPy is [f_oneway](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.f_oneway.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "2bf0aafa-d23f-44fe-b9e0-cf4bf13e7314",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "F_onewayResult(statistic=2.3575322551335636, pvalue=0.11384795345837218)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
-    "B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]\n",
-    "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
-    "\n",
-    "stats.f_oneway(A, B, C)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "164aa1ae-5446-4680-9571-c783edaf4101",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "The ANOVA is an *omnibus* test and does not tell which groups exhibit differing means. Specific differences are later identified using *post-hoc tests* (more about it in next session)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bf2c9ad-68fd-4f4b-b68c-180de69d3522",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Assumptions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd484cda-1d0b-4468-a092-f362744f8aa0",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "The standard ANOVA requires the data to exhibit the following properties:\n",
-    "\n",
-    "* independent observations,\n",
-    "* normally distributed residuals,\n",
-    "* all groups have equal population variance (*homoscedasticity*),\n",
-    "* at least 5 observations ($n \\ge 5$) per group (and equal number)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ea4e6c8-4851-4704-b9ca-6257239ab07b",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "#### Size effect"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39925882-deb4-43e1-8d82-9fb1e68df0ba",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Mentioned for completeness: Cohen's $f=\\sqrt{\\frac{R^2}{1 - R^2}}=\\sqrt{\\frac{SS_{\\textrm{treatment}}}{SS_{\\textrm{error}}}}$ and [$\\sqrt{F}$ root mean square effect](https://en.wikipedia.org/wiki/Effect_size#%CE%A8,_root-mean-square_standardized_effect) are suitable for one-way ANOVA but not widely used, as post-hoc tests give a more natural approach to size effects.\n",
-    "\n",
-    "`statsmodels` features [effectsize_oneway](https://www.statsmodels.org/stable/generated/statsmodels.stats.oneway.effectsize_oneway.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e38c759e-8807-4975-9620-9c2100b964a8",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Checking for common assumptions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9509dde-4edf-4d5b-bcb6-7bd6bbd87918",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Visually checking for desired properties like normality or equal variance is acceptable, especially if the data are generally known to exhibit these properties.\n",
-    "\n",
-    "### Normality\n",
-    "\n",
-    "Having this property is usually not critical, because most tests are fairly robust to non-normality.\n",
-    "We only need to avoid cases of «extreme non-normality».\n",
-    "\n",
-    "Beware however that, in the case of residuals (prediction errors of a model), a departure from normality may be an indication of systematic errors in some groups."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5fa2537-0d3b-4b1d-bba4-a479cdf2c0a9",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "#### Graphical approaches"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0f983f0-6143-44b0-9eee-f6d54eb5cf51",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Probability plots with [probplot](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html) (or [statsmodels.api.qqplot](https://www.statsmodels.org/stable/generated/statsmodels.graphics.gofplots.qqplot.html) with one sample):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "6bcaa795-3a85-4b8c-aad9-d554e244a2ec",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(1245619531)\n",
-    "\n",
-    "x_normal = stats.norm.rvs(loc=0, scale=1, size=30) # generate 30 observations from the standard normal distribution\n",
-    "x_not_normal = stats.norm.rvs(loc=[-1,1], scale=[1,3], size=(15,2)).ravel() # generate 30 observations from a mixture of normal distributions\n",
-    "\n",
-    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
-    "\n",
-    "stats.probplot(x_normal, plot=axes[0])\n",
-    "stats.probplot(x_not_normal, plot=axes[1]);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f46e74c-ee1f-454c-a4a1-3e551899e7e7",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "#### Normality tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81a1572d-1639-42d9-834c-2ba86c7c7dd3",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "* D'Agostino's test: [normaltest](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.normaltest.html), preferably for large samples ($n>20$),\n",
-    "    * Similar test for skewness only: [skewtest](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.skewtest.html) ($n\\ge8$),"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "e964be07-0696-4ff8-844a-3e7d318a7f3a",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "skewed_dist = lambda sigma, x: np.exp( -.5*(np.log(x)/sigma)**2 ) / ( x*sigma*np.sqrt(2*np.pi) )\n",
-    "heavy_tailed_dist = lambda scale, x: stats.cauchy.pdf(x, 0, scale)\n",
-    "\n",
-    "colors = ['blue', 'green', 'orange', 'red']\n",
-    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
-    "\n",
-    "grid = np.linspace(0, 3, 100)\n",
-    "grid = grid[1:]\n",
-    "\n",
-    "ax = axes[0]\n",
-    "for sigma, color in zip((.25, .5, 1), colors):\n",
-    "    ax.plot(grid, skewed_dist(sigma, grid), '-', color=color, label=f'$\\\\sigma={sigma:.2f}$')\n",
-    "    \n",
-    "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "ax.set_xlim(grid[[0,-1]])\n",
-    "ax.set_title('skewness')\n",
-    "\n",
-    "grid = np.linspace(-4, 4, 100)\n",
-    "\n",
-    "ax = axes[1]\n",
-    "for s, color in zip((.5, 1, 2), colors):\n",
-    "    ax.plot(grid, heavy_tailed_dist(s, grid), '-', color=color, label=f'$scale={s:.2f}$')\n",
-    "    \n",
-    "ax.axvline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "ax.set_xlim(grid[[0,-1]])\n",
-    "ax.set_title('kurtosis')\n",
-    "\n",
-    "for ax in axes:\n",
-    "    ax.set_ylabel('probability density')\n",
-    "    ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eed065d2-dd35-4fdb-ad91-8d387a06c7ea",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "* Shapiro-Wilk's test: [shapiro](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.shapiro.html),\n",
-    "* Generic goodness-of-fit tests: [kstest](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.kstest.html) and [anderson](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.anderson.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c7a08d6-3c74-48ce-ad32-f164089b6ec7",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Equal variance (homoscedasticity)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e5a893e-6475-401b-9335-b117629b8d0a",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "#### Graphical approaches\n",
-    "\n",
-    "Simple per-group box plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "8a31b653-d891-4f07-ab8b-73562b2ed86d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
-    "B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]\n",
-    "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
-    "\n",
-    "df = pd.DataFrame(data=dict(A=A, B=B, C=C))\n",
-    "plt.boxplot(df, labels=df.columns);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e985446d-d250-4d88-9a13-50a098cd016b",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "#### Equality-of-variance tests\n",
-    "\n",
-    "* Bartlett's test: [bartlett](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bartlett.html), most basic and common test,\n",
-    "* Levene's test: [levene](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.levene.html), better for skewed or heavy-tailed distributions,\n",
-    "* ...and others: Fligner-Killeen's test ([fligner](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fligner.html)), Ansari-Bradley's test ([ansari](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ansari.html)), etc\n",
-    "\n",
-    "Example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "280180fa-4f20-44a6-a463-2ce9b0ad75a4",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BartlettResult(statistic=3.3024375753550594, pvalue=0.19181598314035977)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# copied-pasted from https://www.statology.org/bartletts-test-python/\n",
-    "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
-    "B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]\n",
-    "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
-    "\n",
-    "stats.bartlett(A, B, C)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e906820a-af04-4a9d-992f-f2e07a7aed91",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "In the above example, as there is not enough evidence to reject $H_0$ ($p>0.05$), we can proceed to perform a standard one-way ANOVA. Otherwise, we would go for an Alexander-Govern's test or Welch's *F* test instead.\n",
-    "\n",
-    "The Alexander-Govern's test is available in `scipy` as [alexandergovern](https://docs.scipy.org/doc/scipy/reference/reference/generated/scipy.stats.alexandergovern.html), but the Welch's *F* test is not (neither in `scipy.stats` nor in `statsmodels`). Install the `Pingouin` package and try out the [welch_anova](https://pingouin-stats.org/generated/pingouin.welch_anova.html) function instead.\n",
-    "\n",
-    "The Bartlett's test statistic follows $\\chi^2_{k-1}$ with $k$ the number of groups. As most tests based on the $\\chi^2$ distribution, the *p*-value is one-sided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "b8123d48-7285-4fa3-8473-04f316dedffc",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1330x410 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "grid = np.linspace(0, 20, 200)\n",
-    "\n",
-    "dfs = [2, 3, 5, 9]\n",
-    "\n",
-    "_, axes = plt.subplots(1, 2, figsize=(13.3,4.1))\n",
-    "\n",
-    "ax = axes[0]\n",
-    "for df, color in zip(\n",
-    "    dfs,\n",
-    "    ['blue', 'green', 'orange', 'red'],\n",
-    "):\n",
-    "    chi2 = stats.chi2.pdf(grid, df)\n",
-    "    ax.plot(grid, chi2, '-', color=color)\n",
-    "    \n",
-    "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "ax.set_xlim(grid[0],grid[-1])\n",
-    "ax.set_xlabel(r'$\\chi^2$')\n",
-    "ax.set_ylabel('probability density')\n",
-    "ax.legend([ f'$df={df}$' for df in dfs ])\n",
-    "\n",
-    "ax = axes[1]\n",
-    "df, color = 2, 'blue'\n",
-    "chi2 = stats.chi2.pdf(grid, df)\n",
-    "ax.plot(grid, chi2, '-', color=color)\n",
-    "ax.axhline(0, linestyle='--', color='grey', linewidth=1)\n",
-    "ax.set_xlim(grid[0],grid[-1])\n",
-    "ax.set_xlabel(r'$\\chi^2$')\n",
-    "ax.set_ylabel('probability density');\n",
-    "\n",
-    "A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]\n",
-    "B = [91, 92, 93, 85, 87, 84, 82, 88, 95, 96]\n",
-    "C = [79, 78, 88, 94, 92, 85, 83, 85, 82, 81]\n",
-    "bartlett_statistic, bartlett_pvalue = stats.bartlett(A, B, C)\n",
-    "bartlett_statistic_line, = ax.plot([bartlett_statistic]*2, [0, stats.chi2.pdf(bartlett_statistic, df)], '-', zorder=1)\n",
-    "\n",
-    "tail = grid[bartlett_statistic<=grid]\n",
-    "ax.fill_between(tail, np.zeros_like(tail), stats.chi2.pdf(tail, df), alpha=.2)\n",
-    "\n",
-    "ax.annotate(f'$\\\\approx {bartlett_pvalue:.2f}$', (4, .02), xytext=(8, .1), arrowprops=dict(arrowstyle=\"->\"));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c09c6222-4167-4e32-b6da-4abe3a8960de",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## χ² tests"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ec71b98-8d1b-4854-b86d-df738ddc8595",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "When the sum of the observations is known, *e.g.* observations are frequencies -- proportions that sum to $1$, we use a $\\chi^2$ test instead of an ANOVA.\n",
-    "\n",
-    "### Goodness-of-fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d855b1f2-3105-445a-b1a6-b99bceeb66b7",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Example:\n",
-    "Comparing the frequencies of the different allele variants at a given locus between a reference genome and a test genome.\n",
-    "\n",
-    "Another popular example: [Color proportion of M&Ms [Coursera]](https://www.coursera.org/learn/stanford-statistics/lecture/rAwbR/the-color-proportions-of-m-ms):\n",
-    "\n",
-    "| blue | orange | green | yellow | red | brown |\n",
-    "| :-:  | :-:    | :-:   | :-:    | :-: | :-:   |\n",
-    "| 24%  | 20%    | 16%   | 14%    | 13% | 13%   |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "84f4e9b3-a653-422c-8fc5-83b29869ba87",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "410"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_props  = np.array([ .24, .2, .16, .14, .13, .13 ])\n",
-    "observed_counts = np.array([ 85, 79, 56, 64, 58, 68 ])\n",
-    "np.sum(observed_counts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "8bb2916a-e9a4-4bbe-b04f-14818bb68723",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([98.4, 82. , 65.6, 57.4, 53.3, 53.3])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_counts = expected_props * np.sum(observed_counts)\n",
-    "expected_counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a75bd8d7-f61e-49bc-80b4-cb1f05ccd7c1",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "|          | blue | orange | green | yellow | red  | brown |\n",
-    "| --:      | :-:  | :-:    | :-:   | :-:    | :-:  | :-:   |\n",
-    "| Expected | 98.4 | 82     | 65.6  | 57.4   | 53.3 | 53.3  |\n",
-    "| Observed | 85   | 79     | 56    | 64     | 58   | 58    |\n",
-    "\n",
-    "The statistic is:\n",
-    "\n",
-    "$$\n",
-    "\\chi^2 = \\sum_{i=1}^{k} \\frac{(O_i - E_i)^2}{E_i} \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\mbox{ } \\sim \\chi^2_{k-1} \\mbox{ } \\textrm{under} \\mbox{ } H_0\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "a8898631-a5b5-49e8-a158-3e149345391a",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.566983829178941"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "k = len(expected_counts)\n",
-    "chi2 = np.sum((observed_counts - expected_counts) ** 2 / expected_counts)\n",
-    "chi2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "7a8af457-13dc-483a-90e3-50d3949c8edf",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.1276329790529603"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pvalue = stats.chi2(k-1).sf(chi2)\n",
-    "pvalue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92886c91-f6ef-419d-bfda-e28d9dfcc7bc",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "`scipy.stats`'s implementation of the test is [chisquare](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "1e246915-e9be-4826-9677-d9f30348d0df",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Power_divergenceResult(statistic=8.566983829178941, pvalue=0.1276329790529603)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.chisquare(observed_counts, expected_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00614391-2cdb-4e8f-9734-a1fd2df57637",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "#### Size effect\n",
-    "\n",
-    "Cohen's $w$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "f2231df3-3ce3-40c4-ae75-f6159061e3d6",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.9269410361636843"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cohen_w = np.sqrt(chi2)\n",
-    "cohen_w"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1feb29a6-eeac-4096-8e90-c663fdeea8a8",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Homogeneity and independence"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ef858d5-ec8f-405d-924e-62f5286a94c5",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "Example:\n",
-    "Comparing the frequency of cell types in cultures that differ in the treatments:\n",
-    "\n",
-    "| Observed    | Type A cells | Type B cells | Type C cells | Type D cells |\n",
-    "| --:         | :-:          | :-:          | :-:          | :-:          |\n",
-    "| Treatment 1 | 134          | 86           | 32           | 11           |\n",
-    "| Treatment 2 | 101          | 92           | 38           | 8            |         \n",
-    "| Treatment 3 | 188          | 67           | 54           | 19           |\n",
-    "\n",
-    "$H_0$: the treatments have no effect on the frequency of the cell types.\n",
-    "\n",
-    "https://www.coursera.org/learn/stanford-statistics/lecture/78IMJ/the-chi-square-test-for-homogeneity-and-independence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "13145d41-7dc3-4eff-9f9c-38d0abed1e8d",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "observed_counts = np.array([\n",
-    "    [ 134, 86, 32, 11 ],\n",
-    "    [ 101, 92, 38, 8 ],\n",
-    "    [ 188, 67, 54, 19 ],\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "fe4ce304-25ac-4734-9a0a-e20db59b742f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.50963855, 0.29518072, 0.14939759, 0.04578313])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_props = np.sum(observed_counts, axis=0) / np.sum(observed_counts)\n",
-    "expected_props"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f4b4727-3a9b-474f-b6ad-b632be97b04e",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Under $H_0$, the expected proportions are:\n",
-    "\n",
-    "| Expected    | Type A cells | Type B cells | Type C cells | Type D cells |\n",
-    "| --:         | :-:          | :-:          | :-:          | :-:          |\n",
-    "| Treatment 1 | 51%          | 29%          | 15%          | 5%           |\n",
-    "| Treatment 2 | 51%          | 29%          | 15%          | 5%           |         \n",
-    "| Treatment 3 | 51%          | 29%          | 15%          | 5%           |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "087ea780-304c-459d-a7c0-066817f3d82f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[134.03493976,  77.63253012,  39.29156627,  12.04096386],\n",
-       "       [121.80361446,  70.54819277,  35.7060241 ,  10.94216867],\n",
-       "       [167.16144578,  96.81927711,  49.00240964,  15.01686747]])"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected_counts = np.outer(np.sum(observed_counts, axis=1), expected_props)\n",
-    "expected_counts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0f49b1a-3a7a-4ad8-8d2a-9fd3513ad2a6",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "| Expected    | Type A cells | Type B cells | Type C cells | Type D cells |\n",
-    "| --:         | :-:          | :-:          | :-:          | :-:          |\n",
-    "| Treatment 1 | 134          | 77.6         | 39.3         | 12           |\n",
-    "| Treatment 2 | 121.8        | 70.5         | 35.7         | 10.9         |         \n",
-    "| Treatment 3 | 167.2        | 96.8         | 49           | 15           |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "d9585b87-f5dd-4cf2-ad91-0a79dcc95c86",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26.7075512595244"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "j, k = expected_counts.shape\n",
-    "dof = (j - 1) * (k - 1)\n",
-    "chi2 = np.sum((observed_counts - expected_counts) ** 2 / expected_counts)\n",
-    "chi2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "a7cd985e-eb2b-4a72-a221-30e3e0fdf8ec",
-   "metadata": {
-    "hidden": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.00016426084515914902"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.chi2(dof).sf(chi2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13d7ef58-e62d-48de-a3a4-2ccc2ac588cb",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "`scipy.stats`'s $\\chi^2$ test for homogeneity/independence is [chi2_contingency](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "4cd7ea08-110a-4ff7-9521-0f12e4169d35",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Chi2ContingencyResult(statistic=26.707551259524408, pvalue=0.0001642608451591484, dof=6, expected_freq=array([[134.03493976,  77.63253012,  39.29156627,  12.04096386],\n",
-       "       [121.80361446,  70.54819277,  35.7060241 ,  10.94216867],\n",
-       "       [167.16144578,  96.81927711,  49.00240964,  15.01686747]]))"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.chi2_contingency(observed_counts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45da92b9-cd4e-4d1b-8996-451285ef1896",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Due to the design of the test, it doesn't matter what factor whose effect is hypothesized to be null under $H_0$:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "d7e7fd75-068c-4f37-b981-498890c4a0d7",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Chi2ContingencyResult(statistic=26.707551259524408, pvalue=0.0001642608451591484, dof=6, expected_freq=array([[134.03493976, 121.80361446, 167.16144578],\n",
-       "       [ 77.63253012,  70.54819277,  96.81927711],\n",
-       "       [ 39.29156627,  35.7060241 ,  49.00240964],\n",
-       "       [ 12.04096386,  10.94216867,  15.01686747]]))"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stats.chi2_contingency(observed_counts.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca862d48",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Two-sample goodness-of-fit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b1a4872",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "The $\\chi^2$ test is also used for comparing the distributions of a continuous variable for two samples (two groups) in a more general way than a $t$-test for independent samples.\n",
-    "\n",
-    "The procedure consists in binning the continuous variable so that the problem can be formulated as a homogeneity test, with bins as the levels of one factor, and the grouping criterion as another factor.\n",
-    "\n",
-    "As a consequence, we will also use the `chi2_contingency` function.\n",
-    "\n",
-    "A similar test is the two-sample Kolmogorov-Smirnov test implemented as [ks_2samp](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "141dc10b-c964-4621-9e53-32f36c11d2da",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Correlation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6efe325b",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
-   "source": [
-    "### Analyses of association (recap)\n",
-    "\n",
-    "| Test | Types of variables |\n",
-    "| :-:  | :--               |\n",
-    "| $\\chi^2$ test<br />(ind./homo.) | categorical *vs* categorical |\n",
-    "| ANOVA | categorical (*e.g.* group) *vs* continuous (response) |\n",
-    "| ? | continuous *vs* continuous |\n",
-    "\n",
-    "Note: frequencies in the $\\chi^2$ tests are summary statistics and play the role of a sample size. They are NOT treated as measurements of a variable, although they could be, at another conceptual level (e.g. population of the bags of M&Ms)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "344da42e-5707-4a0a-9b98-4cbf47781fbe",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Correlation coefficient"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c8b61ee-e2da-4dc4-b3ad-02790d407d5b",
-   "metadata": {
-    "hidden": true,
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
-   "source": [
-    "Pearson correlation coefficient of two series is the covariance of the two series normalized by the standard deviation of each series:\n",
-    "\n",
-    "$$\n",
-    "\\textrm{Var}(x) = \\frac{1}{n-1}\\sum_i (x_i - \\bar{x})(x_i - \\bar{x}) \\\\\n",
-    "\\textrm{Cov}(x, y) = \\frac{1}{n-1}\\sum_i(x_i - \\bar{x})(y_i - \\bar{y}) \\\\\n",
-    "-1 \\le \\mbox{ } \\mbox{ } \\mbox{ } r(x, y) = \\frac{\\textrm{Cov}(x, y)}{\\sqrt{\\textrm{Var}(x)\\textrm{Var}(y)}} \\mbox{ } \\mbox{ } \\mbox{ } \\le 1\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "The [pearsonr](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html) function computes the Pearson correlation coefficient together with a *p*-value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "f5a36568-d5b5-4388-b3f9-f38855c58c80",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.35186801325748274, 0.0565392063030969)"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x1 = stats.norm.rvs(loc=46, scale=30, size=30)\n",
-    "x2 = stats.norm.rvs(loc=71, scale=30, size=30)\n",
-    "\n",
-    "r, pv = stats.pearsonr(x1, x2)\n",
-    "r, pv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9fbd953",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "The correlation coefficient is a commonly-used effect size for the linear relationship between the two variables, similarly to (but not to be confused with) a regression coefficient:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "3581eeb0-f98d-4b2f-a0f1-bef073d86980",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(dict(x1=x1, x2=x2))\n",
-    "sns.regplot(x=\"x1\", y=\"x2\", data=df);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "730a1bd8-51b6-4c06-9431-9a0fce53c42c",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.4132334074789644, 0.02322460846741898)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x2_correlated = x1 + 1.5 * np.std(x1) * np.random.randn(x1.size)\n",
-    "\n",
-    "r, pv = stats.pearsonr(x1, x2_correlated)\n",
-    "r, pv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "bfd1c00e-20b3-48bd-9724-7342ae70f626",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(dict(x1=x1, x2=x2_correlated))\n",
-    "sns.regplot(x=\"x1\", y=\"x2\", data=df);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53338c9a-9345-4ead-8487-1b51254d7330",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Pearson $r$ assumes the observations are drawn from normal distributions.\n",
-    "\n",
-    "If the data are not normally distributed, Spearman $r$ ([spearmanr](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html)) is an interesting alternative, although Pearson $r$ works well enough in most cases:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "39423438-a688-4afa-b258-e17076ba1fbd",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.01098799181445502, 0.003536313065532505)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rng = np.random.default_rng()\n",
-    "x1 = rng.integers(10, size=30)\n",
-    "x2 = x1 + rng.integers(10, size=x1.size)\n",
-    "\n",
-    "# plus a few noisy observations\n",
-    "x1 = np.r_[x1, 9, 12]\n",
-    "x2 = np.r_[x2, 1, 2]\n",
-    "\n",
-    "pearson_r, pearson_pv = stats.pearsonr(x1, x2)\n",
-    "spearman_r, spearman_pv = stats.spearmanr(x1, x2)\n",
-    "\n",
-    "pearson_pv, spearman_pv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "bca6551e-9ffb-4f60-8b96-9a085b2e2282",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(dict(x=np.r_[x1, x2], series=np.r_[np.zeros_like(x1), np.ones_like(x2)]))\n",
-    "sns.countplot(x=\"x\", data=df, hue=\"series\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "a2dee6dc-0f17-4b33-9431-c6dc2d2bd418",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame(dict(x1=x1, x2=x2))\n",
-    "sns.regplot(x=\"x1\", y=\"x2\", data=df);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18902dd3-022a-447a-9681-6713b2e9296e",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "What could possibly go wrong?\n",
-    "\n",
-    "<table style=\"text-align: center;\"><tr><td><img alt=\"Pearson vs Spearman\" src=\"img/Pearson_vs_Spearman.svg\" width=\"1200px\" /></td></tr>\n",
-    "<tr><td><a href=\"http://geoinfo.amu.edu.pl/qg/archives/2011/QG302_087-093.pdf\">from Hauke &amp; Kossowski 2011</a></td></tr></table>\n",
-    "\n",
-    "Because Pearson coefficient relies on squared differences, it is very sensitive to outliers.\n",
-    "On the other side, Spearman coefficient is based on ranks and may catch less intuitive patterns."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37816425",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Linear regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8abb81b1",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "As previously said, the correlation is not directly related to the regression lines we constantly plot to illustrate the relationship a correlation coefficent is supposed to quantify.\n",
-    "\n",
-    "The linear regression also offers an approach for quantifying an association between two quantitative variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "fb9d664f",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5.700381018358158, 0.6305853827502598, 0.01098799181445498)"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slope, intercept, R, pvalue, slope_std_err = stats.linregress(x1, x2)\n",
-    "intercept, slope, pvalue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cfc5265",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "The $p$-value returned by [linregress](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html) is related to $H_0$: the slope is $0$.\n",
-    "\n",
-    "If there is no slope, there is no association between the variables.\n",
-    "\n",
-    "A linear regression is also a model that can predict the value of one variable from the value of the other variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "7f69b66e",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8.853307932109457"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x1_observed = 5\n",
-    "x2_predicted = intercept + slope * x1_observed\n",
-    "x2_predicted"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "14836378",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.818456468003295"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x2_observed = 10\n",
-    "x1_predicted = (x2_observed - intercept) / slope\n",
-    "x1_predicted"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "51bff28b",
-   "metadata": {
-    "hidden": true,
-    "jupyter": {
-     "source_hidden": true
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = sns.regplot(x=x1, y=x2)\n",
-    "x1_min, _ = ax.get_xlim()\n",
-    "x2_min, x2_max = ax.get_ylim()\n",
-    "ax.plot([x1_min, x1_observed, x1_observed], [x2_predicted, x2_predicted, x2_min], 'r:', linewidth=1)\n",
-    "ax.plot([x1_min, x1_predicted, x1_predicted], [x2_observed, x2_observed, x2_min], 'r:', linewidth=1)\n",
-    "ax.set_ylim([x2_min, x2_max])\n",
-    "ax.set_xlabel('x1')\n",
-    "ax.set_ylabel('x2');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f26feda-d0fd-4d9d-a748-b0b82d0f84b4",
-   "metadata": {
-    "heading_collapsed": true,
-    "tags": []
-   },
-   "source": [
-    "## Effect sizes and test power"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d8555e2-f91e-4ed8-b518-d95b1f46aa11",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "`scipy.stats` does not offer any helper for effect size and power calculation.\n",
-    "\n",
-    "The [`statsmodels`](https://www.statsmodels.org/dev/stats.html#power-and-sample-size-calculations) and `pingouin` ([1](https://pingouin-stats.org/api.html#effect-sizes) & [2](https://pingouin-stats.org/api.html#power-analysis)) libraries feature useful tools, therefore we will start using these libraries, which we will cover more extensively in the next session."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78c838fa-6eff-459a-867c-b9e930b0cebd",
-   "metadata": {
-    "hidden": true,
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "!\"{sys.executable}\" -m pip install statsmodels pingouin"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "5eaf887e-9310-4b89-acca-ccde9dff5f02",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": [
-    "from statsmodels.stats import power\n",
-    "import pingouin as pg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5532ddbe-202e-48f5-96fd-825c465a503f",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Effect sizes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33eb7fcf-3c42-40bc-abb7-aba678a33eda",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "An effect is worth reporting only if it can be quantified in terms of magnitude:\n",
-    "* for comparisons between group means, we need to know the actual difference(s) between these group means;\n",
-    "* for correlation or association studies, we want the strength of the correlations or associations, if any; etc.\n",
-    "\n",
-    "The significance of an effect is reported as a $p$-value, but a $p$-value in itself is not a useful estimator of the magnitude of the effect.\n",
-    "Appart from the fact a $p$-value can be compared with a gold-standard significance level, it suffers from a major flaw: the lower its value, the less reliable it is.\n",
-    "For a very small $p$-value, a slight change in the value of one or more observations may lead to a dramatically different $p$-value.\n",
-    "In particular, $p$-values are not robust across the repetitions of an experiment.\n",
-    "\n",
-    "As a side note, we may also face situations with very little measurement noise and find consistent differences (that happen to be statistically significant) that are very small and, consequently, of no practical interest.\n",
-    "\n",
-    "Instead, we report the magnitude (or size) of the effect as a [measure](https://en.wikipedia.org/wiki/Effect_size) that can be admittedly translated into common language as \"small\", \"medium\", \"large\", etc, following some convention.\n",
-    "\n",
-    "More about the [MAGIC criteria](https://en.wikipedia.org/wiki/MAGIC_criteria) for good measures of effect size.\n",
-    "\n",
-    "For example, we have seen Cohen's $d$ that takes the difference between two group means (which itself already deserves to be reported) and standardize it dividing by the within group variance. Once calculated, we can refer to a [reference table](https://core.ecu.edu/wuenschk/docs30/EffectSizeConventions.pdf) (see also the table on this [webpage](https://www.statology.org/effect-size/)).\n",
-    "\n",
-    "The most useful and comprehensive interface for effect sizes is featured by the `pingouin` library.\n",
-    "We can calculate the effect size for many different tests using [compute_effsize](https://pingouin-stats.org/generated/pingouin.compute_effsize.html) ([source](https://github.com/raphaelvallat/pingouin/blob/v0.4.0/pingouin/effsize.py#L553-L717)) and, for the tests that feature several effect size measures, we can convert from one measure to another using [convert_effsize](https://pingouin-stats.org/generated/pingouin.convert_effsize.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "d934a53e-f2b5-423a-a101-21c69e1a472b",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.980871648099785\n",
-      "0.9914036363636364\n"
-     ]
-    }
-   ],
-   "source": [
-    "x1 = np.array([49.47257879, 81.93967205, 64.030398, 17.25423608, 59.80082512,\n",
-    "              94.56012514, 69.91672899, 12.39640637])\n",
-    "x2 = np.array([64.22723692, 96.56483856, 101.94191774, 85.31918879,\n",
-    "               66.4952999, 63.88841224, 127.63861749, 55.00527005])\n",
-    "print(pg.compute_effsize(x1, x2, paired=False, eftype='cohen')) # unbiased Cohen's d\n",
-    "print(pg.convert_effsize(1.0486, 'cohen', 'hedges', nx=8, ny=8))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b30414c-6575-4dff-9637-664cb0f29a5a",
-   "metadata": {
-    "heading_collapsed": true,
-    "hidden": true,
-    "tags": []
-   },
-   "source": [
-    "### Power analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9b54a050-85cc-44c3-b406-650734d0d2ec",
-   "metadata": {
-    "hidden": true
-   },
-   "source": [
-    "Prior to collecting data, in the presence of preliminary data to roughly predict the expected effect size, one can estimate the sample size necessary for a test to detect such an effect.\n",
-    "\n",
-    "This is done in a *power analysis*. Reminder: the power of a test is the probability of detecting an effect (rejecting $H_0$) when $H_0$ is false.\n",
-    "\n",
-    "`statsmodels` features utilities for such analyses, for most of the tests previously mentioned.\n",
-    "\n",
-    "* one-sample $t$-test: `TTestPower`\n",
-    "* $t$-test for independent samples: `TTestIndPower`\n",
-    "* $t$-test for dependent samples: `TTestPower`\n",
-    "* one-way ANOVA: `FTestAnovaPower`\n",
-    "* (one-sample) goodnes-of-fit $\\chi^2$ test: `GofChisquarePower`\n",
-    "\n",
-    "All classes, once initialized, feature a `plot_power` helper method for quickly inspecting the relationship between various parameters that are key in the design of a test:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "de47ba11-f486-44e3-9f3b-9ddffacc95ee",
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [
-    {
-     "data": {
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lKyur3r7W1tYIDQ1FWFgYwsLC4OLiQvVECGlmlLAQQtolrVaLuLg43Lp1C1FRUUhNTUV9S6vJZDIEBQUhLCwM4eHh8PLyglgsbuGICWnfKGEhhLQLBoMBSUlJ/AhKQkJCvacPi8Vi+Pv78yMoAQEBNL+EEIHRJ5AQ0iYxxpCens6PoMTGxt51MT4vLy+EhoYiPDwcwcHBVK2VEDNDCQshpM3Izc01mShbUlJSb19nZ2d+BCU0NBRKpbIFIyWENBYlLISQVqukpMQkQcnNza23r62tLZ+ghIWFwdnZuQUjJYQ8KEpYCCGtRkVFBWJjY/kEJS0trd6+lpaWCA4O5ifKenh40Jk8hLRilLAQQsyWXq9HQkICn6AkJibCaDTW2dfCwgIBAQH8CIqfnx/VPyGkDaGEhRBiNoxGI9LS0vgEJTY2Flqtts6+IpEI3t7e/AhKUFAQrZ1DSBtGCQshRDCMMeTk5CAqKgq3bt1CdHQ0ysrK6u3v6upqMlHW2tq6BaMlhAiJEhZCSIsqKioymSibn59fb187Ozt+BCU0NBSOjo4tGCkhxJxQwkIIaVZqtRoxMTH8KEpGRka9fRUKhUnJezc3N5ooSwgBQAkLIaSJ6XQ6xMfH8yMoSUlJ9Za8l0qlCAwM5EdRfHx8qOQ9IaROlLAQQh6I0WhESkoKn6DExcVBp9PV2VckEsHPz48fQQkMDIRUKm3hiAkhrRElLISQRmGMITMzk09QYmJiUF5eXm9/d3d3PkEJCQmBlZVVC0ZLCGkrKGEhhNxTQUGByUTZwsLCevs6OjqaVJS1s7NruUAJIW0WJSyEkFrKysoQHR3NJyhZWVn19rW2tuYnyoaHh0OlUtFEWUJIk6OEhRACrVaLuLg4fmXj1NTUeifKymQyvuR9WFgYvLy8aKIsIaTZUcJCSDtkMBiQlJTEj6AkJCRAr9fX2VcsFsPf359PUAICAmBhQV8dhJCWRd86hLQDjDGkp6fzIyixsbGoqKiot7+XlxefoAQHB8PS0rIFoyWEkNooYSGkjcrNzTWZKFtSUlJvX2dnZ5OS90qlsgUjJYSQe6OEhZA2oqSkxCRByc3Nrbevra2tyZk8zs7OLRgpIYQ0HiUshLRSWq3W5EyetLS0evtaWlryE2XDw8Ph4eFBZ/IQQloVSlgIaWWMRiPOnDmD//3vf/WubGxhYYGAgAB+BMXPzw8SiaSFIyWEkKZDCQshrUh6ejo2btyIuLg4k3aRSARvb29+BCUoKAgymUygKAkhpOlRwkJIK6DVarF7924cOHAARqORb+/Rowd69+6N0NBQWFtbCxghIYQ0L0pYCDFz165dw+bNm5GXl8e3ubi4YPz48QgPDxcwMkIIaTmUsBBipgoKCrB161b8888/fJuFhQUef/xxPP7447TKMSGkXaGEhRAzYzAYcPToUfz+++/QaDR8e1hYGMaPHw9XV1cBoyOEEGFQwkKIGUlMTMTGjRuRmprKt9na2uLZZ59Fnz596FRkQki7RQkLIWagvLwcO3fuxIkTJ/hFB0UiEQYMGIBRo0bRhFpCSLtHCQshAmKM4fz589i+fTuKi4v5di8vL7z44osICAgQMDpCCDEflLAQIpCsrCxs3rwZt27d4tvkcjmeeuopDBo0iAq9EUJaJa2+Agm5FxGdfQZXEo432e1SwkJIC9PpdNi3bx/27dsHvV7Pt3fr1g3PP/88HB0dBYyOEEIaR2/QIin/CqKz/kJU1l9IyP0HeiN3woC23HiPazccJSyEtKBbt25h8+bNyMrK4tscHR3xwgsvoGvXrgJGRgghDWM0GpBScB1RWX8hOusM4nLOQWtQN/v9UsJCSAsoLi7G9u3bce7cOb5NLBZjyJAheOKJJyCXywWMjhBC6mdkRtwujEJ01hlEZ/+F2OxzUOuK6+3vbO2NUNdIhLr2g7tlZ6xHcJPEQQkLIc3IaDTi1KlT2LFjB8rLy/n2wMBAvPjii/D09BQwOkIIqY0xhqyS+MoRlL8QnfU3yrQF9fa3V7gi1LUf988lEs423vy+micTPChKWAhpJqmpqdi4cSMSExP5NmtrazzzzDPo168fxGKxgNERQgiHMYbcslREZ51GdNYZRGX9heKKnHr728qdEOIaiVCXSIS59oOLrX+L1IiihIWQJlZRUYE//vgDR44cMVmoMDIyEmPGjIGtra2A0RFCCFBQnsEnJzHZZ5BXllZvXyupEsEufRHm2g+hrpFwtwuBWNTyf3BRwkJIE2GM4cqVK9iyZQsKCqqHT93c3DB+/HiEhoYKGB0hpD0rrshFdNYZxGRzSUp2SWK9feUWVghW9UGIayTCXB+Ct30HiMXCl1mghIWQJpCXl4ctW7bg6tWrfJtUKsWIESMwdOhQWFjQR40Q0nLKtEWIzf6bn4eSXhRTb1+pRI4A556VIyj94OfYBRKx+S2uSt+ihDwAg8GAQ4cO4c8//4RWq+XbO3bsiHHjxkGlUgkYHSGkvajQlSI251zlJNkzSC24AQZWZ1+xyAIBzt35SbIBzt0hlVi2cMSNRwkLIfcpLi4OGzduRHp6Ot9mZ2eH559/Hj169KCFCgkhzUarVyM+9yJfrC05/yqMzFBnX5FIDF/HLvwk2UBVL8gtrFo44gdHCQshjVRaWoodO3bg1KlTfJtIJMIjjzyCp59+GgqFQsDoCCFtkd6gRWLeZX6SbELuJeiN2nr7e9l34GuhhKj6QCFTtmC0zYMSFkIaiDGGv//+G7/++itKS0v5dh8fH7z44ovw8/MTLjhCSJtiMOqRkn8N0ZWTZONyzkNnqKi3v7syqHKSbD+EuPSFjbztLfFBCQshDZCRkYFNmzYhJqZ64pqlpSVGjRqFgQMHUk0VQsgD4arJ3uInycZmn0OFvrTe/s42PtwkWZd+CHXtCzuFawtGKwxKWAi5C61Wiz179uDAgQMwGKqPD/fs2RPPPfcc7O3thQuOENJqMcaQURzLT5KNyf4bZdrCevs7WLlXJieRCHWNhJO1V8sFayYoYSGkHtevX8fmzZuRm5vLtzk7O2PcuHHo1KmTgJERQlobxhhySpO59Xiy/kJ09pl7VJN15ueghLpGwsXGr91P5KeEhZA7FBYWYtu2bbh48SLfJpFIMGzYMAwfPhwymUzA6AghrUV+2W2+mmx09hkUlKfX29dKZocQl74IrSzW5q4MbvcJyp0oYSGkktFoxLFjx7Br1y5UVFRPbgsJCcH48ePh7u4uYHSEEHNXpM6uXNGYG0XJKU2ut6/cwhrBLn0Q5sIVa/OyDzeLarLmjBIWQgAkJydjw4YNSElJ4dtsbGwwduxY9O3bl/7SIYTUUqopQEz23/w8lIzi2Hr7SiVyBDn35g/x+Dp2NstqsuaMEhbSrqnVauzatQvHjh0DY9VVIfv374/Ro0fDxsZGwOgIIeZErStBbHZVNdm/kFZ4q95qshKxFAFOPfh5KP5O3SCVyFs44rblvhKWlStXYunSpcjMzETXrl3x3//+F3369Km3/7Jly7B69WqkpKTA2dkZY8eOxZIlS2Bpaf6lgEnbxBjDxYsXsW3bNhQVFfHtHh4eePHFFxEUFCRgdIQQc6DVqxGXc4GrJpv9F1Lyr9VbTVYsknDVZCvL3QepekFmQUUkm1KjE5atW7di7ty5WLNmDSIiIrBs2TIMGzYM0dHRcHFxqdV/06ZNeO+99/Djjz+iX79+iImJwcSJEyESifD11183yYMgpDFycnKwefNm3Lhxg2+TyWR44oknMGTIEEgkdByZkPZIZ9AgMe+fylooZ5CY9w8MRl2dfUUQwcuhA1/uPsilDxRS2xaOuH0RsZrj4A0QERGB3r17Y8WKFQC4iYre3t54/fXX8d5779XqP2vWLNy6dQuHDx/m29566y2cPXvWpLT53RQXF8POzg5FRUVQKlt/eWEiDJ1Oh4MHD2LPnj3Q6aq/hLp06YIXXngBTk5OAkZHCGlpBqMOSflX+VON43MvQGfQ1NvfXRmMUNd+fDVZa7l9ywXbSjXl73ejynNqtVpcvHgRQ4YMqb4BsRhDhgzBmTNn6rxOv379cPHiRZw7dw4AkJCQgD179mDEiBH13o9Go0FxcbHJPwBAjb+IcfMmkJrKXa6oAC5dAkpKuO2sLODKleq+0dFAcuVsbZ2O61t1GCAnB/jnn+q+sbFAYiJ32WDg+hYUcNt5edx2VY4XH8/9A7i2S5e4PgB3nUuXuNsAuNuMrTEh659/uPsGuFguXeJiA7hYo6Or+165wj0mgHuMly5xjxngnoObN6v7XrsGZGRwl8vKuL5qNbd9+7bpc3jjBpCWxl1Wq7m+VSXnMzKAq1er+966BVRNSNVouL5Vr0tWFnD5cnXf6GggKYm7XPV8FxZy2zk53HaV2FggIYG7XPV85+dz2/n53LbRyG0nJABxcdXXvXQJqKqRUvV86/XcdmIiUKMqbdLOnfhm3jzs2rULkrIyeOfmwlmpxPTp0zHjiSfglFOjHsLVq0BmJne5tNT0+U5LM32+r18HqhY/LC/n+paXc9vp6dz+KjdvVj/fVe/Zquc7M9P0+Y6Kqn6+tVrT5zs72/T5jompfs/q9abv2dxc0+c7Lq76+TYa636+q96zCQmm79lLl6rfs4WFpu/ZpCTT9+zly9Xv2eJirq+m8ocgJYV7P1W5erX6PVv1fFe9Z9PSar9nb9/mLle9Z8vKuO2MDO79X4W+I7ht+o7gLhcUwHjhPFKy/8GBW9/h/7aMwaf/DcMXB5/BrqtLUXr2KCzzuc+uZbkR3olauMt9MCBwPGb4fogvwzfho5GHMK7XYnQv8IB1QeXzS98Rps93fd8RTYE1wu3btxkA9tdff5m0v/POO6xPnz71Xm/58uVMKpUyCwsLBoBNmzbtrvezcOFCBqDWvyJ39+pOnTsz9vrr3OXYWMYAxo4e5ba/+IIxB4fqvn37MvbKK9zl9HSu759/ctsrVjAmk1X3HTyYsRde4C4XFXF9t23jtn/6idvW6bjtJ5/k/jHGtQFcH8a46wDcbTDG3ebgwdX3I5Nx980YFwvAxcYYF2vfvtV9HRy4x8QY9xgB7jEzxj0HnTtX9/X0ZGzhQu7yuXNc3ytXuO333mMsMLC6b0gIY2+/zV2+fp3rW/XafvwxY25u1X27d2dsxgzucmIi1/fgQW77q68Ys7Wt7vvQQ4xNmMBdzs7m+u7axW2vWcOYRFLdd+hQxsaO5S6XlnJ9N23itn/5hduuqOC2R49mbMSI6usCjH3/PXf5f//jtvPyuO0XX2Rs4EBWXFzMfvzxR1ZhYcE29+vHpk6dypaPHMkYwNRVz+HUqYz16lV9u87OjH36KXf55EnudqOiuO05cxjr0KG6r68vY++/z12+eJHre/Eit/3++9z+Kh06cNdnjLs9gLt9xrj7c3au7turFxcXY4ylpnJ99+3jtpcvZ8zKqrrvwIHc42WMe/wA93wwxj0/NT/mI0ZwzyNj3PMKcM8zY9zzDnCvA2Pc6zJ0aPV1JRLu9WOMez0B7vVljHu9H3qouq+tLfe+YIx7nwDc+4Yx7n3UvXt1Xzc37v3GGPf+A7j3I2Pc+zMkpLpvYCD3PmaMe18D3PucMe597+lZ3Ze+I7jtdv4dkb/8Y3Y46ke29+MhjAFs9lpPNnWTD/v7ISsWFS5nUzf5sKmbfFiFXMR2TfFlP52Zy26sX8Ddbmoqdzv0HcFt38d3RFF8PPf7XfU+fwDNnrAcPXqUubq6su+//55dvXqV/fbbb8zb25stXry43vupqKhgRUVF/L/U1FTuAde83xs3GEtJ4S6r1dwboLiY287MZOzy5eq+UVGMJSVxl7Varm9hIbednc3YpUvVfWNiGEtI4C7r9Vzf/HxuOzeX2zYaue24OO4fY1zbxYtcH8a461y8yN0GY9xtxsRU38+lS9Vf9oWFXF+tlttOSqp+8zPGPZbMTO5ycTHXV63mtlNSuOeiytWr1V9qpaVc3/JybjstrfqHgDHuctUHsryc61tSwm2np1d/iTHG2M2bjCUnc5crKri+VW/AzEzG/vmnum9UVPWPU9XzXVDAbWdnV39YGeOek/h47nLV812VdOTlcdsGA7cdH1/9JcwYty8nh7tc9XxX/lAY4uLY+Y0b2ezZs9nUqVPZ4meeYW/9619syZIlLPXGDa6vRsNdNzmZsVu3qm/3yhXGMjK4yyUlps93aqrp833tGmO3b3OXy8q4vmVl3Pbt29z+KjduVD/fVe/Zquc7I8P0+b51q/r51mhMn++sLNPnOzq6+j2r05m+Z3NyTJ/v2Njq59tgqPv5rnrPxsebvmcvXqx+zxYUmL5nExNN37P//FP9ni0q4vpW/agkJ3PvpypXrlS/Z6ue76r3bGpq7fdsWhp3ueo9W/XlmZ7Ovf+r0HcEt93OviOKr/3NjsduYN+dmsG+XhrG5qzhEpTZa73Yx5+4sWm/eLOpm3zYvGUe7LMVHdn3p2exE7GbWN6p3cx453uWviMe+DuiKDe3yRKWRs1h0Wq1sLKywq+//opRo0bx7RMmTEBhYSF27dpV6zoDBgxA3759sXTpUr5tw4YNmDp1KkpLSxu0aBzNYSGNcfv2bWzcuBHxVUPxAKysrDB69Gj079+fFiokpI2p0JXhctp+nE3agVtZp8CYsc5+1jL7ymqy3DwUN2UQ1VhqZk35+92os4RkMhl69uyJw4cP8wmL0WjE4cOHMWvWrDqvU15eXusHouosjEbkSoTck0ajwZ9//olDhw7BaKz+wurTpw+effZZSnYJaUMMRj1uZZ7E2aQduJx2AFqDulYfSwsbBLtEcKsau0bC0z4cYhH9wdJaNfq05rlz52LChAno1asX+vTpg2XLlqGsrAyTJk0CALz88svw9PTEkiVLAABPPvkkvv76a3Tv3h0RERGIi4vDBx98gCeffJJOHyVN5sqVK9iyZQvyqyaHAXBxccH48eMRHh4uYGSEkKbCGENS/hWcS9qJ88l/oESTW6uPk7UX+viNQlfPx+Dj0AkSMdVHbSsa/Uo+//zzyMnJwYcffojMzEx069YN+/btg6urKwAgJSXFZERlwYIFEIlEWLBgAW7fvg2VSoUnn3wSn3zySdM9CtJu5efnY+vWrbhcY0a8hYUFhg8fjmHDhkEqpdLXhLR2OSXJOJu8E2eTdiC7JLHWfiuZHXr5PIkIv1EIdO5Fh3naqEbXYRECzWEhdzIYDDhy5Aj++OMPaDTVdRPCw8Mxbtw4PoEmhLROpZp8XEj5E2eTdiAh91Kt/RZiObp6DkEfv1Ho5P4ILCS0iro5EmwOCyHmICEhARs3bkRaVb0CAEqlEs8++yx69+5Nf10R0kpp9RW4mn4IZ5N24Hr6MRiZ3mS/CCKEuPRFhN9o9PAeDoWM/oBtTyhhIa1GWVkZdu7ciZMnT/ITtkUiER5++GGMGjUKVlZWAkdICGkso9GA6OwzOJu0E/+k7kWFvrRWH0+7MET4jUZv36fgaO0hQJTEHFDCQsweYwznzp3D9u3bUVJVqRSAt7c3XnzxRfj7+wsYHSGksRhjSCu8hbNJO3A+eRcK1Vm1+tgr3NDHbxQifEfBy4EmzhNKWIiZy8zMxKZNmxBdowy5XC7HU089hUGDBtGZZoS0Ivll6TiXvAtnk3YgvSi61n5LqS16eg9HhN9oBKsiIBbT55tUo4SFmCWdToe9e/di//790Ourj2P36NEDzz33HBwcHASMjhDSUOXaIlxK3YuzSTsQm30WDKbneYhFFujsMQgRfqPR2WMwZBaWAkVKzB0lLMTs3Lx5E5s3b0Z2djbf5uTkhHHjxqFz584CRkYIaQidQYMbGcdwNmknrt4+DL2x9grIgc49EeE3Gj19RsJG7ihAlKS1oYSFmI2ioiJs374d58+f59vEYjGGDh2KkSNHQiaj0xYJMVdGZkRC7kWcTdqBCyl/olxbVKuPq20AIvxGo4/fKKhsfASIkrRmlLAQwRmNRpw4cQI7d+6EWl1dXjsoKAgvvvgiPDzorABCzFVGUSzOJu/EuaSdyCtLq7XfVu6M3r5PIcJvNHwdO1PZAXLfKGEhgkpJScHGjRuRlJTEt1lbW2PMmDGIjIykhQoJMUNF6iycT/4DZ5N3IiX/Wq39MokC3byGIcJvNMLd+lN5fNIk6F1EBFFRUYHff/8dR44cMVkEs1+/fhgzZgxsbGwEjI4Qcqd7rYgsEokR7joAEX6j0M1rGCyl1gJFStoqSlhIi2KM4Z9//sHWrVtRWFjIt7u7u2P8+PEICQkRLjhCiImGrIjs69gFfXxHobfvk7BTuAgQJWkvKGEhLSY3NxdbtmzBtWvVQ8hSqRQjR47EY489BgsLejsSIjTGGJLzr1YWdat/ReQIv9GI8BsFN2WQAFGS9oh+IUiz0+v1OHToEP7880/odDq+vVOnTnjhhRegUqkEjI4QAgA5pSk4l7QDZ5N2IqskodZ+WhGZCI0SFtKsYmNjsWnTJqSnp/Nt9vb2eO6559CjRw/60iNEQLQiMmlNKGEhzaK0tBS//fYbTp8+zbeJRCIMGjQITz31FBQKhYDREdJ+0YrIpLWihIU0KcYYzpw5g19//RVlZWV8u6+vL1566SX4+FCxKEJamtFoQEz23zibtAOX6lkR2cMuFH39nqEVkYnZooSFNJn09HRs2rQJsbGxfJulpSVGjRqFgQMHUk0VQlqQ6YrIv6NQnVmrj73CDX18n0aE32haEZmYPUpYyAPTarXYvXs3Dhw4AKOxujZD79698eyzz8LOzk7A6AhpXxqyInIPr8cR4TcaIS59aUVk0mpQwkIeyLVr17B582bk5eXxbSqVCuPGjUPHjh0FjIyQ9oNWRCbtASUs5L4UFBRg27ZtuHSp+swCiUSCxx9/HI8//jgtVEhIM6MVkUl7QwkLaRSDwYBjx45h165d0GiqvyBDQ0Mxfvx4uLm5CRgdIW1bw1dEHoU+vqOgsvUVIEpCmgclLKTBCgsLsXLlSqSkpPBttra2GDt2LCIiIqimCiHNJLM4Dn8n7bjLishONVZE7kKfRdImUcJCGkSr1WLVqlUmycqAAQMwevRoWFvTImeENLUidXblisg7aEVkQkAJC2kAxhh+/vlnJCcnAwAcHR0xZcoUBAYGChwZIW1L9YrIO3Er6yStiExIDZSwkHvat28fzp8/DwCQy+WYNWsWPD09BY6KkLaBVkQmpGEoYSF3deXKFezatYvfnjx5MiUrhDwgWhGZkMajhIXU6/bt2/jhhx/AGFfT4emnn0a3bt2EDYqQVoxWRCbk/lHCQupUWlqKVatW8acu9+7dG8OHDxc4KkJan4asiNzFczAi/EbTisiE3AUlLKQWg8GA7777Drm53DC1j48PXn75Zfprj5AGasiKyMEuEfyKyFYyWr6CkHuhhIXUsnXrVsTExAAAlEolZsyYQZVrCbkHWhGZkOZFCQsxcfz4cRw/fhwAYGFhgenTp8PBwUHgqAgxX6WaAhyJ+Qmn47fWsyKyK/r4jqIVkQl5QJSwEF50dDS2bNnCb7/00ksICAgQMCJCzFeROhsHo77HibgN0OjLTfZZWtigh/dwWhGZkCZECQsBAOTk5OC7776D0cgVqnrssccQGRkpcFSEmJ/8stvYf2sNTsVvNVlwUCyyQCePRxDhNxpdPIbQisiENDFKWAgqKiqwatUqlJWVAQA6deqEZ555RuCoCDEvWcUJ2HdzFf5O2mEyidZCLEf/wOcxNPw1OFl7CRghIW0bJSztnNFoxI8//oj09HQAgJubG6ZMmQKxWCxwZISYh9uFUdh7YyUupP5pUipfbmGFh4NewmNhU2CncBUwQkLaB0pY2rnff/8dV65cAQBYWVlhxowZUCgUAkdFiPCS8q5gz40VuHL7gEm7QqrEoyET8WjoZNjIaUI6IS2FEpZ27Ny5c9i7dy8AQCwW49VXX4WrK/2lSNq3mOyz2HtjBW5mnjBpt5E7YkjoFDwS/C8oZEqBoiOk/aKEpZ1KSkrCzz//zG8/++yz6NChg4ARESIcxhhuZp7Anhv/RVzOeZN99gpXPBb2GgYEjYPcwkqgCAkhlLC0Q0VFRVi9ejV0Oh0AoH///hg0aJDAURHS8ozMiKu3D2L3jf8iJf+ayT5na28M6zADkf5jIJXIBYqQEFKFEpZ2RqfTYdWqVSgsLAQABAUFYdy4cVR2n7QrRqMBF1L+xN6bK5BeFGOyz00ZiOEdZqG371OQiOkrkhBzQZ/GdoQxhl9++QVJSUkAAEdHR7z22muwsKC3AWkf9AYt/k76DftvrkZ2aZLJPm/7DhjecRa6ew+HWERnyRFibuiXqh05cOAAzp49CwCQyWSYMWMGlEqaPEjaPq2+AqcTtmD/re9QUJ5usi/AuQdGdJiFTh6P0kgjIWaMEpZ24tq1a9ixYwe/PWnSJHh7ewsYESHNr0JXiuNxG3Aoah2KK3JM9oW69sPIjq8jxCWSEhVCWgFKWNqBjIwMrFu3DowxAMCTTz6JHj16CBwVIc2nTFuEI9E/4UjMjyjXFpns6+zxKIZ3nIVA554CRUcIuR+UsLRxZWVlWLlyJSoqKgAAPXr0wIgRIwSOipDmUVyRi0NR63A89hdU6Ev5dhFE6OE9AsM7zoS3Q0cBIySE3C9KWNowg8GAtWvXIieHGwr39vbGxIkTqew+aXMKyjNw4NZ3OBm/GTpDBd8uFknQx3cUhnecATdlkIAREkIeFCUsbdj27dsRFRUFALC1tcWMGTMgl1M9CdJ25JQkY9+t1TiT+CsMRh3fbiGWoV/AsxgWPg3ONj4CRkgIaSqUsLRRJ0+exNGjRwEAEokE06ZNg6Ojo8BREdI00otisO/mKpxL3mWyIKFUYomHg17EY2FT4WDlJmCEhJCmRglLGxQTE4NNmzbx2y+++CKCgmg4nLR+KfnXsOfGClxO2w8GxrdbSm0xKPhlDA59BbaWTgJGSAhpLpSwtDG5ubn47rvvYDRyf3UOHjwYDz30kMBREfJg4nLOY++NFbieccyk3Vpmj8Ghr2BQyARYyeyECY4Q0iIoYWlDKioqsHr1apSWcmdHdOjQAWPGjBE4KkLuD2MMUVmnsefGfxGT/bfJPqWlCkPDpmJA0IuwlFoLFCEhpCVRwtJGGI1G/PTTT0hLSwMAuLq6YsqUKZBIJAJHRkjjMMZwNf0w9t5YgcS8f0z2OVp5YliHaXgo4DlIJZYCRUgIEQIlLG3En3/+icuXLwMAFAoFZsyYAWtr+suTtB5GowGX0vZi740VSCu8ZbLPxdYfwzvMRITfKEjEUoEiJIQIiRKWNuDChQvYvXs3AEAkEuHVV1+FmxudIUFaB4NRh3NJu7D35ipklcSb7PO0C8PwjrPQ03sExGIaLSSkPaOEpZVLSUnB+vXr+e0xY8agY0eq5EnMn85Qgb8SfsX+W6uRV5Zmss/PqRtGdJiFzp6DaeVkQggASlhataKiIqxatQo6HVcwKzIyEkOGDBE4KkLuTqMvx4m4jTgYtRZF6myTfSEufTG8wyyEu/WnBQkJISYoYWmldDod1qxZg4KCAgBAQEAAXnzxRfqSJ2arXFuEY7E/41DUDyjTFpjs6+g+ECM6vo4gVW+BoiOEmDtKWFohxhg2btyIhIQEAICDgwOmTZsGqZQmIxLzU6rJx6HoH3A05v9QoSsx2dfNaxhGdHwdvo6dBYqOENJaUMLSCh0+fBhnzpwBAEilUsyYMQN2dlQ0i5iXwvIsHIj6DifjNkFrUPPtIpEYvX2ewvCOM+FhFyJghISQ1oQSllbmxo0b+PXXX/ntiRMnwseHFncj5iO3NBX7b63BXwnboDdq+XaJWIpI/zEYFj4dLrZ+wgVICGmVKGFpRTIzM/H999+DMW4NlZEjR6JXr14CR0UIJ7M4HvtursLZpB0wMgPfLpXI0T9wHIaGvQZHaw8BIySEtGb3db7gypUr4efnB0tLS0RERODcuXN37V9YWIiZM2fC3d0dcrkcISEh2LNnz30F3F6VlZVh1apVUKu5ofVu3brhiSeeEDgqQoDUgptYe3omPto9GGcSf+WTFbmFNYaGT8OnT57GCz0XUbJCCHkgjR5h2bp1K+bOnYs1a9YgIiICy5Ytw7BhwxAdHQ0XF5da/bVaLR577DG4uLjg119/haenJ5KTk2Fvb98U8bcLBoMB69atQ1ZWFgDAy8sLkyZNglhM9SmIcBJz/8Gemytw9fYhk3YrmR0eDZmER0MmwVpuL0xwhJA2R8Sqji80UEREBHr37o0VK1YA4Naw8fb2xuuvv4733nuvVv81a9Zg6dKliIqKuu+zWIqLi2FnZ4eioiIolcr7uo3WbNu2bTh8+DAAwMbGBvPmzYOzs7PAUZH2iDGGmOy/sefGfxGVddpkn63cGY+FvYqBwS/BUmojUISEEHPSlL/fjRph0Wq1uHjxIubNm8e3icViDBkyhD9r5U6///47IiMjMXPmTOzatQsqlQrjx4/Hv//9b1qYrwFOnz7NJytisRivvfYaJSukxTHGcD3jKPbeWIH43Ism+xys3DE0/DX0D3gBMguFQBESQtq6RiUsubm5MBgMcHV1NWl3dXVFVFRUnddJSEjAkSNH8OKLL2LPnj2Ii4vDjBkzoNPpsHDhwjqvo9FooNFo+O3i4uLGhNlmxMXFYePGjfz2+PHjERJCp4GSlmNkRlxO24c9N1YgteCGyT6VjS8e7zAdff3GwEIiEyhCQkh70exnCRmNRri4uGDt2rWQSCTo2bMnbt++jaVLl9absCxZsgSLFi1q7tDMWn5+PtasWQODgZvA+Mgjj2DAgAECR0XaC4NRj/PJv2PfzVXIKI412eeuDMaIjrPQ0+cJSMR0oiEhpGU06tvG2dkZEomEn/xZJSsrq97Vgd3d3SGVSk0O/4SHhyMzMxNarRYyWe2/zObNm4e5c+fy28XFxfD29m5MqK2aRqPBqlWrUFLCVQUNCwvDc889J3BUpD3QGTT4O/E37Lu1CrmlKSb7fBw6YUTH19HVaygtSEgIaXGNSlhkMhl69uyJw4cPY9SoUQC4EZTDhw9j1qxZdV7noYcewqZNm2A0GvmzWmJiYuDu7l5nsgIAcrkccrm8MaG1GUajEevXr0dqaioAQKVSYerUqTTfhzQrrV6NU/FbsP/WGhSqM032BTr3woiOr6Oj+0Baq4oQIphGj+fOnTsXEyZMQK9evdCnTx8sW7YMZWVlmDRpEgDg5ZdfhqenJ5YsWQIAmD59OlasWIE333wTr7/+OmJjY/Hpp5/ijTfeaNpH0kbs2bMHly5dAgBYWlpixowZsLa2Fjgq0lapdSU4HvsLDkWtQ4kmz2RfuNsAjOg4C8GqCEpUCCGCa3TC8vzzzyMnJwcffvghMjMz0a1bN+zbt4+fiJuSkmJSH8Tb2xv79+/HnDlz0KVLF3h6euLNN9/Ev//976Z7FG3EpUuX8McffwAARCIRpkyZAg8PKrZFml6ppgBHYn7C0eifUK4zndTe1fMxDO84C/5O3YQJjhBC6tDoOixCaA91WFJTU/HFF19Aq+XWXnnmmWcwbNgwgaMibU2ROhuHotbheNwv0OjL+XYRROjp8wSGd5gJL4dwASMkhLQlgtVhIc2juLgYq1at4pOViIgIDB06VOCoSFuSX3Yb+299h9MJW6AzVJcMEIssEOE3Go93mA43ZaCAERJCyN1RwiIwvV6P7777Dvn5+QAAPz8//Otf/6I5A6RJZJUkYv/NVTiT+BuMTM+3W4jleCjwOQwLnwYnay8BIySEkIahhEVAjDFs2rQJcXFxAAB7e3vMmDHjvpcwIKTK7cJo7L25AhdS/gRjRr5dJlFgYPBLeCzsVdgpXO9yC4QQYl4oYRHQ0aNHcfo0tx6LVCrF9OnTYWdnJ3BUpDVLyruKvTdX4HLafpN2hVSJQSETMDh0MmzkjgJFRwgh948SFoHcvHkT27Zt47dffvll+Pn5CRcQadVis89hz40VuJl53KTdRu6IwaGvYFDwy1DI2uaEdUJI+0AJiwCysrLw/fffo+oErccffxx9+vQROCrS2jDGcCvzJPbcWIHYnLMm++wVrngsbCoGBI2H3MJKoAgJIaTpUMLSwtRqNVatWoXycu6U0i5duuDpp58WOCrS2iTnX8PmCx8gMe8fk3Ynay883mEGIv3HQippn9WiCSFtEyUsLchoNGLdunXIzORKn3t4eOCVV14xKbRHyN2Ua4uw6+qXOB77CxiqSyi5KQPxeIeZ6OP7FCRimrRNCGl7KGFpQb/99huuX78OALC2tsaMGTNgaWkpcFSkNWCM4WzSDvz6zyco0eTy7e7KIDzZeS66ez0OsZjWmyKEtF2UsLSQM2fO4ODBgwAAsViM1157DSqVSuCoSGuQURSLTRcWICb7b75NJlHgiU6zMTh0MiwkdS8iSgghbQklLC0gISEBGzZs4LdfeOEFhIaGChgRaQ00+nLsuf5fHIz+Hgajjm/v5jUMz/dYCEdrTwGjI4SQlkUJSzMrKCjA6tWroddzVUYffvhhDBw4UOCoiLm7knYQWy99hLyyNL7N2dobL/RchM6egwWMjBBChEEJSzPSarVYtWoViou51XBDQkLwwgsvCBwVMWe5panYeukjXL19iG+TiKUYFj4NwzvMhMxCIWB0hBAiHEpYmgljDD///DNSUlIAAM7OznjttdcgkdDESFKb3qDFwajvsfvGt9AZKvj2MNeHMK7Xx7QwISGk3aOEpZns3bsX58+fBwDI5XLMmDEDNjY2AkdFzFFU5mlsvvgBMovj+TalpQrP9fgQvXyepIUwCSEElLA0i8uXL2PXrl0AAJFIhFdeeQWenjRBkpgqUmfj138+wbnknXybSCTGoOAJeKrzXCqlTwghNVDC0sRu376NH3/8kd9+6qmn0LVrVwEjIubGaDTgeNwv2Hn1S1ToSvh2f6fuGN/rP/Bx7CRgdIQQYp4oYWlCpaWlWLlyJTQaDQCgd+/eGD58uMBREXOSmHcZm86/j5SC63yblcwOz3Sdh4cCn4dYRFWPCSGkLpSwNBG9Xo/vvvsOeXl5AAAfHx+8/PLLNP+AAADKtEXYeeULnIzbaFJSv5//s3im2zzYWjoJGB0hhJg/SliaAGMMW7duRUxMDABAqVRixowZkMmoAml7xxjD34n/w/8uf4oSTR7f7mEXihd7f4IgVW8BoyOEkNaDEpYmcPz4cZw4cQIAYGFhgRkzZsDBwUHgqIjQ0otisPH8+4jLOce3yS2s8GSnOXg0dBItUkgIIY1ACcsDioqKwtatW/ntf/3rX/D39xcwIiK0Cl0Zdl9fjkPRP8DI9Hx7D+8ReK7Hh3CwchcwOkIIaZ0oYXkAOTk5WLt2LYxGIwBg6NCh6Nu3r8BREaEwxnA5bT+2XlqEgvJ0vl1l44sXei5GJ49HhAuOEEJaOUpY7pNarcbKlStRVlYGAOjUqRNGjx4tcFREKDmlKdhy8UNcTz/Kt1mIZXi8wwwMC58OmYWlgNERQkjrRwnLfTAajfjhhx+QkZEBAHB3d8eUKVMgFtMpqe2NzqDBgVvfYe/NFdAZNHx7B7eH8UKvxXC1pcODhBDSFChhuQ+7du3CtWvXAABWVlaYMWMGFApalK69uZV5CpsuLEB2SSLfZq9wxXM9FqKH9wg6pZ0QQpoQJSyNdPbsWezbtw8AIBaLMXXqVLi4uAgcFWlJheVZ2P7Px7iQ8gffJhZJ8GjIJDzZeQ4spbRmFCGENDVKWBohKSkJP//8M7/97LPPIjw8XMCISEsyGPU4Fvszfr/6FSr0pXx7gHMPjO/1CbwdOggYHSGEtG2UsDRQYWEhVq1aBb2eO021f//+GDRokMBRkZaSkHsJm86/j9TCm3ybtcwBY7rNQ2TAs1RSnxBCmhklLA2g1WqxevVqFBUVAQCCgoIwbtw4mqPQDpRqCrDjyuc4Fb/ZpL1/4AsY3fU92MipQCAhhLQESljugTGGX375BUlJSQAAJycnTJs2DRYW9NS1ZUZmxJnEX/Hb5SUo1eTz7V72HTC+938Q6NxTwOgIIaT9oV/de9i/fz/OneNKq8vlckyfPh22trYCR0WaU1rBLWy6sADxuRf4NrmFNZ7q/BYGhUyAREwfG0IIaWn0zXsXV69exc6dO/ntSZMmwdvbW7iASLOq0JXij2vf4EjMTzAyA9/ey+dJjO2+AA5WbgJGRwgh7RslLPVIT0/HDz/8AMYYAOCpp55C9+7dBY6KNAfGGC6l7sG2S4tRqM7k211s/TGu58fo4D5AwOgIIYQAlLDUqbS0FCtXrkRFRQUAoGfPnhgxYoTAUZHmkF2ShM0XPsTNzON8m4VYjhEdZ2Jo+GuQSqikPiGEmANKWO5gMBiwdu1a5ObmAgC8vb0xYcIEOiOojdEZKrDv5hrsu7kKemN1Sf1O7o/ghZ6LobL1FTA6Qgghd6KE5Q7btm1DdHQ0AMDW1hYzZsyAXC4XOCrSlG5kHMfmCx8gpzSZb3OwcsdzPRaiu9fjlJwSQsgDMhiMSL9djGtXk5rsNilhqeHEiRM4duwYAEAikWD69OlwdHQUNijSZArKM7Ht0iJcSt3Dt4lFFhgS+gpGdnoTllJrAaMjhJDWy2Aw4nZaERLi8pAQn4/EhHxoNHpoNOVNdh+UsFSKiYnB5s3VxcFefPFFBAYGChgRaSoGox5HYn7CH9e+gUZfxrcHqXpjfK9P4GkfKmB0hBDS+hgMRqSlViUoeUhMzIdWY7j3FR8AJSwAcnNzsWbNGhiNRgDA4MGD8dBDDwkcFWkKcTnnsfnCB0grvMW32cgdMbbb++jrP4YO/xBCSAPo9UakpRYiIT4fCXF5SErMh1Zbf4JiaytHQJATXN2k+HZ108TQ7hOWiooKrFy5EmVl3F/eHTp0wJgxYwSOijyoUk0+fru8BKcTtvFtIogwIGg8RnV5F9Zye+GCI4QQM6fXG5CaUoSE+DwuQUkqgO5uCYpSjsAgJwQEOiEgyAkqlTVEIhGKi4ubLKZ2nbAYjUb8+OOPSE9PBwC4urri1VdfhUQiETgycr+MzIi/Erbit8ufoUxbyLd7O3TEi70+gb8z1dIhhJA76fUGpCQX8glKclIBdDpjvf3t7CwREOSEgEBHBAQ5wdnZutlHrNt1wvLHH3/gypUrAACFQoEZM2bAyspK4KjI/UotuIGN599HYt4/fJul1BajuryNgUH/glhMiSghhACATleZoFTOQUlOLoD+bgmKvSUCK0dPAoOc4Ohk1eKH1NttwnL+/Hns2cOdLSISifDqq6/CzY1Kr7dGal0Jfr/6NY7Grgdj1R+4Pr5PY2z392GncBUwOkIIEZ5Oa0BycgGfoKQkF0Kvrz9BsXdQ8AlKQKCjIAnKndplwpKcnIz/+7//47fHjh2Ljh07ChgRuR+MMVxI+RPb/1mMInU23+5qG4jxvT5GmBtNnCaEtE9arQHJSQX8IZ6U5EIYDPUnKI6OVggIcuTnoDg6mt/RhnaXsBQVFWHVqlXQ6XQAgH79+mHw4MECR0UaK6s4AZsvfohbmSf5NqlEjhEd38BjYa9CKqFif4SQ9kOr0SOpRoKSmlIIg4HV29/Ryap6kmygIxzMMEG5U7tKWHQ6HVavXo3CwkIAQGBgIMaPHy/4MBdpOK2+AvtursT+W2ugN2r59s4eg/FCz4/gbOMjYHSEENIyNBo9khMLEF8jQTEa609QnJxrJihOsHdQtGC0TaPdJCyMMWzYsAGJiYkAAAcHB0ybNg1SqVTgyEhDXU8/is0XP0RuaQrf5mjlied7foSuno9R4kkIabMqKvRITsxHfOUclLTUorsmKM4qawQEOlUmKY6ws299Ccqd2k3CcujQIfz9998AAKlUihkzZkCpVAocFWmI/LJ0bLu0CP+k7ePbxCILDA2bihGdXofcwvyHMgkhpDEqKnRITMjnS93fTrt7guLiYgP/QEd+FEVp1/ZWmm8XCcv169fxv//9j9+eNGkSfHzo0IG5Mxh1OBT9A3ZfXw6Nvno9ihCXvhjX62N42IUIGB0hhDQdtboyQak8xHM7rQis/vwELq42JnNQbJVtL0G5U5tPWDIzM/H999+DVb7yI0eORM+ePQWOitxLbPY5bLrwPtKLYvg2W7kzxnZ/HxF+o+nwDyGkVSsv11YmKNwoSvrtuycorm62CKw8i8c/0Am2tu3vxII2nbCUlZVh5cqVqKioAAB069YNTzzxhMBRkbsprsjFb5eX4Ezir3ybCCI8HPwSRnV5B1YyOwGjI4SQ+1NexiUoVXNQMtKL75qguLnb8iMo/oGOsLFpfwnKndpswmIwGPD9998jO5urz+Hl5YVJkyZBLBYLHBmpi5EZcSp+M3Zc+Rzl2iK+3dexC8b3+gR+Tl0EjI4QQhqnrEyLxPi8ygQlH5kZ9ScoIhHg5q5EQOUcFP8AJ1jbyFo24FagzSYsv/76K27d4lbotbGxwfTp02Fp2faP8bVGKfnXsPHCAiTlXebbFFIlRnd9FwMCx1NJfUKI2Sst1SAxnpuDEh+Xh8yMknr7ikSAu4eSP4vHP8ARVtaUoNxLm0xYTp06hSNHjgAAJBIJpk2bBmdnZ4GjIndSa4ux69pXOBb7s0lJ/b5+z2BMt/lQKlQCRkcIIfUrLdFwE2Tj8xAfl4+szLsnKB6edtw6PIFO8AtwgJUVJSiN1eYSlri4OGzatInfHj9+PIKDgwWMiNyJMYbzybuw/Z//oLgih293VwZhXK//INQ1UsDoCCGktpLiCsTH5/OHebKzSuvtKxIBnl52levwcCMoCgXV/HpQbSphycvLw5o1a2AwGAAAgwYNQv/+/QWOitSUWRyHTRc+QHTWX3ybTKLAyE5vYkjoK7CQ0F8dhBDhFRdV8Id3EuPzkZ1df4IiFovg6WXHHd4JdISfPyUozaHNJCwVFRVYtWoVSkq4Ybnw8HA8++yzAkdFqmj1auy58V8ciFoLg1HHt3f1HIrney6Ek7WXgNERQtq7okI1EuKrz+LJzSmrt69YLIKXtx0/B8XX3xGWlm3m59RstYln2Gg04v/+7/+QlpYGAHBxccGrr74KiYQma5qDq7cPY8vFD5FXlsa3OVl74fmei9DVc4iAkRFC2qvCAjU/gpIQn4e83PJ6+4rFInj72PNzUHz9HSCXt4mfz1alTTzju3fvxqVLlwAAlpaWmDFjBqytrQWOiuSVpWHrxUW4cvsA3yYRSzE0/DWM6DALMovWv7YFIaR1KChQV5a555KU/Lz6ExSJ5I4Exc8BMkpQBNfqX4GLFy/izz//BACIRCJMmTIF7u7uAkfVvukNWr6kvtag5ttDXfthfK+P4aYMEjA6Qkh7kJ9fbpKgFOSr6+0rkYjh42vPT5L19XOATEYj9OamVScsqampWL9+Pb89evRodO7cWbiACKKzzmDzhQXIKI7j25SWKjzb/QP09n2KSuoTQppFSXEF4mLzEBuTi/jYXBQU1J+gWFjckaD4OkBKCYrZu6+EZeXKlVi6dCkyMzPRtWtX/Pe//0WfPn3ueb0tW7Zg3LhxePrpp7Fz5877uWtecXExVq5cCa1WCwDo27cvhg4d+kC3Se5fsToHv17+BGeTdvBtIpEYjwS/jKc7vwWFjFbGJoQ0nYoKPRLi8xAXk4u42Ny7FmqzkIrh6+vALRQY5AQfX3tIpZSgtDaNTli2bt2KuXPnYs2aNYiIiMCyZcswbNgwREdHw8XFpd7rJSUl4e2338aAAQMeKGAA0Ol0WLNmDQoKCgAA/v7+eOmll+ivdwEYjQaciN+InVeWQq0r5tv9nLrhxV7/gY8jjXgRQh6cXm9EanIBYmNyERuTi9SUQhiNdde6t5CK4efniIAgRwQGOsHb1x4WFpSgtHYixu62/FJtERER6N27N1asWAGAO0PH29sbr7/+Ot577706r2MwGPDwww9j8uTJOHnyJAoLCxs1wlJcXAw7OzsUFRXB1tYWv/zyC06fPg0AsLe3x/z582FnR4vitbSkvKvYeGE+UvKv8W1WMjuM7voe+ge+ALGI1m0ihNwfo5EhM6MEcbG5iI3JQWJ8PrRaQ519RSLA28ceQcHOCApxhq+fA42gmImav99K5YONtDdqhEWr1eLixYuYN28e3yYWizFkyBCcOXOm3ustXrwYLi4ueOWVV3Dy5Ml73o9Go4FGo+G3i4ur/3I/cuQIn6xIpVJMnz6dkpUWVq4tws6rS3EidgMYqvPdSP9n8Uy396C0pGUQCCGNl59fjtiYXP4wT1mptt6+Li42CArhEpTAICcq1NYONCphyc3NhcFggKurq0m7q6sroqKi6rzOqVOn8MMPP+Dy5csNvp8lS5Zg0aJFtdqjoqKwfft2fvvll1+Gn59fg2+XPBjGGM4m7cCv/3yCEk0u3+5hF4rxvf6DYJd7z2MihJAqZaVaxMdxh3jiYnPvWgvFVilHcIgzgkNUCAp2gp09lUVob5r1LKGSkhL861//wvfff9+oxQfnzZuHuXPn8tvFxcXw9vbG+vXrUXUEa/jw4Q2a6EuaRqkmH7+cew+X0/bzbXILKzzRaTYGh06GREx/3RBC7k6rNSAxgZsoGxuTi4z0YtQ3KcHS0gKBQU7cYZ5QZ7i42NA8xXauUQmLs7MzJBIJsrKyTNqzsrLg5uZWq398fDySkpLw5JNP8m1GI7cqr4WFBaKjoxEYGFjrenK5HHK5vFa7Wq2GTCZD165d8dRTTzUmdPIAbmacxPqzc1Gkzubbuns9jud6LISjtYeAkRFCzJnBYERaahGXoMTmIjmxAAaDsc6+EokYvv4OCA5xRlCwM7y87SCR0Dw4Uq1RCYtMJkPPnj1x+PBhjBo1CgCXgBw+fBizZs2q1T8sLAzXrl0zaVuwYAFKSkqwfPlyeHt7NzpgDw8PTJ48GWIxvZGbm86gwc4rX+BQ9Dq+zVrmgJcjPkc3r2ECRkYIMUeMMWRnlfIJSkJcHioq9HX2FYkAD087BAU7IThEBb8ARyrWRu6q0YeE5s6diwkTJqBXr17o06cPli1bhrKyMkyaNAkAN6/E09MTS5YsgaWlJTp16mRyfXt7ewCo1d4QVlZWmDlzJiwtLRt9XdI46UUx+OGvN5FWeJNv6+D2MCb2/RJ2Cte7XJMQ0p4UFqr5SbKxMbkoKdbU29fJ2QpBwc4IDnFGYJAzrG1odXbScI1OWJ5//nnk5OTgww8/RGZmJrp164Z9+/bxE3FTUlKabfRj8uTJjZoLQxqPMYbjcb/g13/+A52B++KxEMswuut7eDR0Ep2qTEg7p1brEB+Xh7iYHMTF5CE7u7TevtY2Mj5BCQpxhqOjVQtGStqaRtdhEUJTnsdN6ldckYufz76Da+lH+DYPuxC8EvktvBzCBYyMECIUnc6A5KQCfqJsWmphvRNlZTIJ/AMdERyiQnCIM1zdbCEW00TZ9kywOiyk7bqefhTr/37b5HTlQSET8UzXeZBZ0CE4QtoLo5Eh/XYRXw8lMTEfel3dE2XFYhF8fO35URRvXwdYWNAoLGkelLC0c1p9BX67sgRHY9bzbbZyZ0zouxSdPR4VLjBCSItgjCEvt6pgWw7i4/JQXq6rt7+buy1fUTYg0BGWllTSgLQMSljasduFUVj31xtIL4rm2zp5DMKEPkuhVKgEjIwQ0pxKSjSIi83lD/MU3mVlY3t7SwRVHuIJCnaCrZJGXIkwKGFph4zMiKMx6/Hb5c+gN3ITa6USOcZ0ex+PBL9MxZkIaWMqKvRIjM/jK8rebWVjhZUUQUFOfNl7Z2dr+k4gZoESlnamSJ2F9X+/g5uZx/k2L/twvNLvW3jYhQgYGSGkqRgMRqQkFyI2JgdxMblISb77ysb+/o4ICuHmoXh42tFEWWKWKGFpR66kHcTP595FqSafbxsc+gpGd/03pJLalYUJIa2D0ciQlVnCT5RNiM+768rGXt72fIJCKxuT1oISlnZAq1dj+z//wYm4DXyb0lKFSX2/Rgf3hwWMjBByvwoqVzaOjclFfGwuSu+ysrHKxZoveR8Q5AQrKyrYRlofSljauJT86/jhzBvILI7n27p6DsXLEZ/DRu4oYGSEkMYoK9MiPraBKxvbyvlibUEhzrCnlY1JG0AJSxtlZEYcivoeO68uhcHInaIolVjiuR4LMSBwHE2iI8TMabUGJCXk8yXv028X1VuwTS63QGCwE18PxcWVVjYmbQ8lLG1QQXkmfvp7DqKz/uLbfBw64ZV+y+GmDBIwMkJIfQwGI26nVRdsS7rrysYi+Po5IDhEhaAQWtmYtA+UsLQxl1L3YsO591CmLQQAiCDCY+Gv4enOb8FCQsetCTEXjDFkZ3MrG8fF5CL+LisbA4CHp5KfKOvv7wiZnL6+SftC7/g2okJXhm2XFuF0wla+zV7hhkl9v0aY20MCRkYIqVJUqEZcbB5XsC02B8VF9a9s7OhkxU+UDQx2go0NnclH2jdKWNqApLwr+OHMm8guSeTbengPx0u9P4O13F64wAhp59RqHRLiqgu2ZWfdZWVjaxk/STY42BmOTrSyMSE1UcLSihmNBuy/tQa/X/saRsYNJcstrPB8z0Xo5/8sTbojpIXp9QYkJRbwZe9TU+pf2VgqkyAgoKpgmwpu7rSyMSF3QwlLK5Vfdhs/npmD2JyzfJufY1e80u9buNj6CRcYIe0IYwyZGSWIic5BbHQuEhPyoLvLysbePtUF23xoZWNCGoUSllbofPIf2Hh+PtS6YgDcxNrHO87Ek51mQyKmlVMJaU6lpRrExuQiJioHMdE5KCmufx6Kq5ttjYJttLIxIQ+CEpZWRK0rwZYLC/F30v/4NkcrT0yK/AYhLhECRkZI22UwGJGcVICYqBxER+XctR6Knb1ldcG2IGco7WhlY0KaCiUsrURC7iX8cOZN5Jam8G29fZ7C+N7/gZXMTsDICGl78nLLEF05ghIXmwutpu51eaQyCQKDnBASqkJImAoqFa1sTEhzoYTFzBmMeuy9uRK7ry+HkXFfmpYWNhjX62NE+I2mL0dCmkBFhR7xsbmIieaSlLuVvXf3UCI0jEtQ/PwdYGFBCwcS0hIoYTFjuaUp+PHMbMTnXuTbApx7YHLkcqhsfASMjJDWzWhkuJ1WxCcoyYkFMBrrPs5jbSPjRlBCVQgJdYatkg7zECIESljM1N+Jv2HzhQ9QoefqNohEYozs+CZGdJwFiZheNkIaq6ioArGVCUpsdC7Kyupe3VgiEcHX3xGhlYd53D2UdLoxIWaAfvnMTLm2CJsvfIBzybv4Nmdrb0yOXIZAVS8BIyOkddHpDEhMyOdGUaJykJlRUm9fZ5U1Pw8lMMgJcip7T4jZoU+lGYnLOY8fz8xGXlka39bXbwxe6LUICqmtgJERYv4YY8jOKuUTlIT4+muiWFpacCsbh6oQGqaiqrKEtAKUsJgBg1GHP68vx96bK8EY9wWrkCrxYu9P0Nv3KYGjI8R8lZdrEReTi+ioHMRG56CwsKLOfiIR4OVtz4+i+Pja0+rGhLQylLAILLskCT+ceRNJeZf5tiBVH0yO/AZO1l7CBUaIGTIYjEhNKeSLtt2t9L3STo6QUBeEhKkQHOIMa2tarZyQ1owSFoEwxvBX4nZsvbgQGj13CqVYZIEnO8/B4+HTIRbTqZKEAEBBfnl1TZSYXFRU6OvsZyEVIyDACSFh3Bk9rm42dNo/IW0IJSwCKNMWYeO5ebiYuptvc7Hxw+R+y+Hv1E24wAgxA1qNHvFxefwpxznZZfX2dXWzRUiYCqGhKvgHOEIqo0SfkLaKEpYWFp11Bj/9PQcF5Rl820MBz+G5Hh/BUmotXGCECIQxhoz0Yv4wT2JCAQyGuifLWllJEVxZEyU41Bn29ooWjpYQIhRKWFqI3qDF79e+xoFba8DAHXS3ktnhpd5L0NNnpMDREdKySks0iInJqUxSclFaUvcCgmKxCD6+DggJc0ZIqApe3vZUE4WQdooSlhaQWRyPH/56AykF1/m2UJdITIr8Bg5W7gJGRkjL0OuNSE7Kr7GAYHG9fR0cFQipPN04MNgZCgWtcEwIoYSlWTHGcCp+C7ZdWgStQQ0AkIileLrL23gsbCrEIjqtkrRNjDHk5pZxIyhROYiPy4NWW/cCgrKqBQQr1+dxdqYFBAkhtVHC0kxKNfn45dx7uJy2n29ztQ3ElH7L4ePYWcDICGkearUO8bFVNVFykZ9f/wKCHp7VCwj6+jnCwoKSd0LI3VHC0gxuZpzE+rNzUaTO5tseDnoRY7svgNyCKmqStsFoZEhLLeQry6YkF9a7gKCNrRwhoc6VNVFUsLWVt3C0hJDWjhKWJqQzaLDzyhc4FL2Ob7OWOeDliC/QzWuogJER0jSKCtWVpxvnIjY6B+Xlujr7SSRi+Ac48JVl3dxpAUFCyIOhhKWJpBfF4Ie/3kRa4U2+rYPbw5jY90vYKVwFjIyQ+6fTGpCQkIeY6FzEROUgK7P+BQRVLpULCIZyCwjKaAFB0gQMBgN0uroTYyI8qVQKiaRl6h/RN8oDYozheNwv+PWf/0Bn4E7NtBDLMLrre3g0dBJNrCWtCmMMWZk1FhBMyIP+bgsIhjjzoyiOjnS4kzQdxhgyMzNRWFgodCjkHuzt7eHm5tbsk+UpYXkAxRW5+PnsO7iWfoRvc1cGY0q//8LLIVzAyAhpuLIyLWJjuBGU2OgcFBXVv4Cgt0/1AoLePrSAIGk+VcmKi4sLrKys6MwxM8QYQ3l5ObKzufma7u7NW6aDEpb7dD39KNb//TZKNLl82yPBEzCm23zILCwFjIyQuzMYjEhJrlpAMBtpqUX1LiBoZ2/J10QJCnaGFS0gSFqAwWDgkxUnJyehwyF3oVBw1aazs7Ph4uLSrIeHKGFpJK2+Ar9dWYKjMev5Nlu5Myb0XYrOHo8KFxghd5GfV7WAYDbiY/PqXUBQKhUjILB6AUEXV1pAkLS8qjkrVlZ0mLE1qHqddDodJSzm4nZhFNb99QbSi6L5tk4egzChz1IoFSoBIyPElEajR3xs9QKCuTn1LyDo5m7LJyj+AY6QSmkBQWIeKFluHVrqdaKEpQGMzIijMevx2+XPoDdyE2ulEjnGdHsfjwS/TB8qIjijkSH9djFio3MQHZ2D5MR8GAx1H+exspYiOIQ7zBMcqoKdHR3CJISYP0pY7qFInYX1f7+Dm5nH+TYv+3C80u9beNiFCBgZae9KSjT8Csex0TkoLdXW2U8sFsHXz4EfRfH0sqOaKISQVocSlru4knYQP597F6WafL5tcOgrGN31XUgl9FcpaVlVk2Wjb2UjOioHt9OK6u3r6GTFlb4PVSEw2AmWlrSAICFtzbFjxzB37lzcuHED3t7eWLBgASZOnFhv/+joaEybNg03b95EUVERPDw8MH78eCxcuBBSafV3xPbt2/HBBx8gKSkJwcHB+PzzzzFixIgWeER3RwlLHbR6Nbb/8x+ciNvAtyktVZjU92t0cH9YwMhIe1NUqEZ0VA6ib2UjNia33smyMrkEQcHOfOE2Z5V1C0dKCGlJiYmJGDlyJKZNm4aNGzfi8OHDmDJlCtzd3TFs2LA6ryOVSvHyyy+jR48esLe3x5UrV/Dqq6/CaDTi008/BQD89ddfGDduHJYsWYInnngCmzZtwqhRo3Dp0iV06tSpJR9iLSLG6juh0XwUFxfDzs4ORUVFUCqVzXpfKfnX8cOZN5BZHM+3dfUcipcjPoeN3LFZ75sQvd6IpMR8fhQlM6P+yrIenkqEhrsgJFQFXz8HWkCQtBkVFRVITEyEv78/LC1b12i20WjE559/jrVr1yIzMxMhISH44IMPMHbsWBQUFGDWrFk4cOAASktL4eXlhfnz52PSpEmNvp9///vf2L17N65fv863vfDCCygsLMS+ffsafDtz587F+fPncfLkSQDA888/j7KyMvz55598n759+6Jbt25Ys2ZNnbdxt9erKX+/aYSlkpEZcSjqe+y8uhQGI3dKnVRiied6LMSAwHE0sZY0m4L8ckTdykFMVDZiY3Oh1Rjq7GdlJUVwZU2U0DAVbJWt64uckPZgyZIl2LBhA9asWYPg4GCcOHECL730ElQqFbZv346bN29i7969cHZ2RlxcHNRqNX/djh07Ijk5ud7bHjBgAPbu3QsAOHPmDIYMGWKyf9iwYZg9e3aDY42Li8O+ffvwzDPP8G1nzpzB3Llza93uzp07G3y7zYUSFgAF5ZlY//dcRGWd5tu8HTpiSr9v4aYMEjAy0hbpdAYkJlSOotzKQXZ2aZ39RCLAy9ueS1DCXeDtY0+TZUm7NeL3/yJbXf+IY3NxUdhiz1OvN6ivRqPBp59+ikOHDiEyMhIAEBAQgFOnTuG7775DaWkpunfvjl69egEA/Pz8TK6/Z8+eu66bVFWkDeAqAbu6mq5T5+rqiuLiYqjVapO+d+rXrx8uXboEjUaDqVOnYvHixfe83czMzLs/+BbQ7hOWS6l7seHceyjTFgIARBDhsfDX8HTnt2AhoaqepGnk5ZYhqvIwT3xcHnTaukdRrG1kCA3lEpTgUGfY2MhbOFJCzFO2ugSZ5cVCh3FXcXFxKC8vx2OPPWbSrtVq0b17d3z00UcYM2YMLl26hKFDh2LUqFHo168f38/X17dF4ty6dStKSkpw5coVvPPOO/jyyy/x7rvvtsh9P4h2m7BU6Mqw7dIinE7YyrfZK9wwqe/XCHN7SMDISFug1RoQH8etzxN1Kxt5ueV19hOJAB9fB4SFuyAkjE45JqQ+Lgpbs7/f0lJutHT37t3w9PQ02SeXy+Ht7Y3k5GTs2bMHBw8exODBgzFz5kx8+eWXABp3SMjNzQ1ZWVkm+7OysqBUKu86ugIA3t7eAIAOHTrAYDBg6tSpeOuttyCRSOq9XTc3twY8A82rXSYsSXlX8MOZN5Fdksi39fAejpd6fwZrub1wgZFWizGGnOwyREdxh3kS4vOg19e9yrGtUo7QMJfKwm3OsLKikTxC7qWhh2WE1KFDB8jlcqSkpGDgwIF19lGpVJgwYQImTJiAAQMG8CMcQOMOCUVGRmLPnj0m+w8ePMgfimooo9EInU4Ho9EIiUSCyMhIHD582GQuzP3cbnNoVwmL0WjA/ltr8Pu1r2Fk3OmhcgsrPN/jI/QLeI4m1pJG0Wj0iIvNRfStHERHZaMgX11nP7FYBD9/By5JCXeBu4ctvdcIaYNsbW3x9ttvY86cOTAajejfvz+Kiopw+vRpKJVKxMfHo2fPnujYsSM0Gg3+/PNPhIeH89dvzCGhadOmYcWKFXj33XcxefJkHDlyBNu2bcPu3bv5PitWrMCOHTtw+PBhAMDGjRshlUrRuXNnyOVyXLhwAfPmzcPzzz/P12F58803MXDgQHz11VcYOXIktmzZggsXLmDt2rVN9Czdv3aTsOSX3caPZ+YgNucs3+bn2BWT+y2Hq62/gJGR1oIxhqzMEv6MnsSE+svf29lbIjTMBWHhKgQGO0OhoMJthLQHH3/8MVQqFZYsWYKEhATY29ujR48emD9/PlJTUzFv3jwkJSVBoVBgwIAB2LJly33dj7+/P3bv3o05c+Zg+fLl8PLywrp160xqsOTm5iI+vrpEh4WFBT7//HPExMSAMQZfX1/MmjULc+bM4fv069cPmzZtwoIFCzB//nwEBwdj586dgtdgAdpJHZbzyX9g4/n5UOu4CVsiiPB4x5l4stNsSMT0Q0Lqp1brEBeTyx3qicpBUWFFnf0kEhH8A5wQGq5CaJgLXN1olWNC7ldrrsPSHlEdliZQoSvF5gsf4u+k//FtjlaemBT5DUJcIgSMjJgrxrhFBKuqyyYnFcBorDund3BUICzcBaFhLggMdoJc3qY/ToQQIqg2+w2bkHsJP5x5E7mlKXxbb5+nML73f2AlsxMwMmJuysu0iInJRfStbMRE5aCkRFNnPwsLMQKCnBAWpkJIuAtUKmsaRSGEkBbS5hIWg1GPvTdXYvf15TAyrtaFpYUNxvX6GBF+o+kHhsBoZLidVoSoygQlJbkA9R0YdVZZ84XbAgKdIJNJWjZYQgghANpYwpJbmoIfz8xGfO5Fvi3AuQcmRy6HysZHwMiI0EpLNYiJykF0VA5ionJQVqats59UJkFQkBNCw7nTjp2caRFBQggxB20mYTmbtAObLnyACh1XulkkEmNkxzcxouMsSMRt5mGSBjIaGVKSC/i5KLfTiuodRXF1teETFL8AR0ilNIpCCCHmptX/kqu1xdh0YQHOJe/i25ytvTE5chkCVb0EjIy0tOKiCsREc5VlY2NyoS6vuwCTXG6B4BBnhFQe6nFwuHtVSEIIIcJr1QlLXM55/HhmNvLK0vi2vn7P4IVei6GQClPGmbQcg8GI5KQCbhHBqByk365/nRF3DyU/F8XXzwEWFuIWjJQQQsiDapUJi8Gow5/Xl2PvzZVgjCt/rpAqMb7Xf9DH72mBoyPNqbBQzR/miYvJRUWFvs5+CoUUwSHOCK1co8fOjmo5EEJIa9bqEpbskiT8cOZNJOVd5tuCVH0wOfIbOFl7CRcYaRZ6vQFJiQX8GT2ZGfUvL+/pZVdZF0UFb197SCQ0ikIIIS2JMYZSnQZZ5cXIKi9GQnZGk932fSUsK1euxNKlS5GZmYmuXbviv//9L/r06VNn3++//x4///wzrl+/DgDo2bMnPv3003r7383ZxB34I+ZzaPRlAACxSIInO8/F4+HTIRbTRMm2Ij+/nD/MExeTC63WUGc/K2spQkJV/EKCNrbyFo6UEELaDyMzQmc0Qmc0QG80QFf5T12hRo66BAsO/IhrRZko11efhWlU113X6n40OmHZunUr5s6dizVr1iAiIgLLli3DsGHDEB0dDRcXl1r9jx07hnHjxqFfv36wtLTE559/jqFDh+LGjRu1lt++l00XFkBmxf3VrLLxxSv9voW/U7fGPgRiZnQ6AxLi8/hFBHOyy+rsJxIB3j72lYsIquDlbQ+xmOrqEELap2PHjmHu3Lm4ceMGvL29sWDBAkycOLHe/klJSfD3r7123um//kKvPr35BOR/v/4PSxZ9jLSUFPgGBuCthe+j3+BBMLC6V6BnOj00Bj3SSgtMkpWm1ui1hCIiItC7d2+sWLECALc0tbe3N15//XW8995797y+wWCAg4MDVqxYgZdffrlB91m1FsHEdV6QWYnxUMBzeK7HR7CUUo2M1ionpxQxt3IQFZWNhLg86HR1fxBsbGQIDXNBSLgKIaEqWFvLWjhSQkhLo7WE7i0xMRGdOnXCtGnTMGXKFBw+fBizZ8/G7t27+QUQGWPQV46I6IwGJCQmoEd4J2z6/TcEhIZAz4zQGwywdrDjV2u+fO4CXnlqLF5f8B4eHjoYe/+3Ez/9dzW2HN6DoPCwOmNhOj1y0tKxJPE0jBZiuFopuX8KJZRGMd7o+3jLryWk1Wpx8eJFzJs3j28Ti8UYMmQIzpw506DbKC8vh06ng6OjY719NBoNNJrqYaTiYu7sD4VMiVce+gI9fUY2JmxiBrQaPeLi8hBTuYhgXm55nf3EYhF8fB34RQQ9PJU0ikIIaTWMRiM+//xzrF27FpmZmQgJCcEHH3yAsWPHoqCgALNmzcKBAwdQWloKLy8vzJ8/H5MmTWrUfTDGsGr1Kvj5+2Hhkk+gMxrw7OSXceDYEXy69HMERHTnD9vUHJHIKOd+S8U2VrB0rHuJmk1rf0C/Rx/BxFnTAAAz572Dv4+fxPYff8Z/ln0JqVjC/7Oo/N+o0UJqXY49T71e5+KHbzTq0dWvUQlLbm4uDAYDXF1dTdpdXV0RFRXVoNv497//DQ8PDwwZMqTePkuWLMGiRYtqX/exHfB1C2lMyEQgjDFkZ5dyh3luZSMhPh8GQ92jKEo7OT8PJThUBYWCVtAmhJha/tVJlNazzldzsrGV4823BjS4/5IlS7BhwwasWbMGwcHBOHHiBF566SWoVCps374dN2/exN69e+Hs7Iy4uDio1Wr+uh07dkRycrLJ7dVMOHr2jcB32zZAZzTiyMkT6PZQJBKLc6v3P/wQvlyw6J6HZWb/azI0Gg18AwIw5Y0ZGDZyBJ983Lh4GTPeeB0BSmc+KRk14gns2rULQXa1p30AQIWBQdwCy9606FlCn332GbZs2YJjx47ddZhv3rx5mDt3Lr9dXFwMb29vOFi5tUSY5D5VVOgRF1u9iGBBgbrOfmKxCH4BjgirrIvi5m5LazwRQu6qtESDoqIKocO4K41Gg08//RSHDh1CZGQkACAgIACnTp3CmjVrUFJagk5duyCwcwfojQZ0UNlDZzQgrigbeoMRX2/4EVpd/cmGXGEJrZE7CSE3OwdOKmeT/U4qFUpLSqBRq2FjbVM5EiLmkxGFiwc++fwzPPTQQ5BZWGDnbzvw+r8mY+fOnXjqqacAANlZWfD38oatrPo32s3NDZmZmU39dDVaoxIWZ2dnSCQSZGVlmbRnZWXBze3uycSXX36Jzz77DIcOHUKXLl3u2lcul0MupzM+zB1jDJkZJYi+lY2oqBwkJeTDaKx7SpS9g4Iv3BYU7AxLy1Z3Rj0hREBCnQV4r/s1MsafMXP15jWUl5djyGOPVe7lvg+1Wh3COnfEtHfm4u3JU3HuwgVEDnoYg4YPQ7c+1RXZ3bzvfSKKhUgMqUQCsUgEhYUUrgpbfiQk3po7zNPJyRNWVla1rutqpcT8d//Nb0dG9EVGRgaWLl3KJyzmrFG/GjKZDD179sThw4cxatQoANzxusOHD2PWrFn1Xu+LL77AJ598gv3796NXLyqX35qp1TrERnOLCEZHZaO4qO4hWolEjIBAR26NnnAVXFxsaBSFEHLfGnNYpikwxirngRhRpFHfcSpv9am9+hpnzsRX1hz5duNPcHE3/SNeJpfDzdMDey79jVOHjuDv4yfx2pgX8PzkCZi76ANIRGKMfmgQ0tPSAFR/V9b81uw/YAD27d0LAPD28ISmsARu1tVzUYry8qFUKutMVuoTERGBgwcP8ttubm73NSjREhr9Z+7cuXMxYcIE9OrVC3369MGyZctQVlbGTxp6+eWX4enpiSVLlgAAPv/8c3z44YfYtGkT/Pz8+GElGxsb2NjYNOFDIc3BaGRIv12M6KhsRN/KQUpyQb2jKI5OVgirnCwbGOQEmZxGUQgh5oUxxp0dU0fyUXU2jd5orDVhtSECQoMhk8uReTsdvR7iDgmJRaIak1TFUHn7otMrU/D6q6/h5x9+xIL35uOnFashFolxaP8B6HR1r4EGAApF9bpnkZGR2LNnj8n+gwcP8oeiGury5ctwd3c3ud2qM44e5HabQ6N/UZ5//nnk5OTgww8/RGZmJrp164Z9+/bxE3FTUlIgFldXGF29ejW0Wi3Gjh1rcjsLFy7ERx999GDRk2ZRVqblRlFu5SA6OqfeiW4WUjECg5wQVnnasUpFCSghRBiMMRgqC5vdmXxUX+b+b2wiUhcxRLAQi03OmHG3ssPrs9/E1x9+DFdLGzw84GGUFBfj2F9/QalUIj4+Hj179kTHjh2h0WhwcO9+hIeHQyzifjN9fX0bfP/Tpk3DihUr8O6772Ly5Mk4cuQItm3bht27d/N9VqxYgR07duDw4cMAgP/7v/+DTCZD9+7dAQC//fYbfvzxR6xbt46/zptvvomBAwfiq6++wsiRI7FlyxZcuHABa9eubYJn7cHc15/As2bNqvcQ0LFjx0y2k5KS7ucuSAsyGhnSUgv5wm2pKYWorzqPysWaL9wWEOAEqYwqDBNCmpfBZBSk9ohIVZJibIJURATwk1SllQmJRa1TecWQiMR1HuZeuuQzeLt74MsvlmLGtOmwt7dHjx49MH/+fKSmpmLevHlISkqCQqHAgAEDsGXLlvuK09/fH7t378acOXOwfPlyeHl5Yd26dXwNFoA7szc+Pt7keh9//DGSk5NhYWGBsLAwbN261WRAoV+/fti0aRMWLFiA+fPnIzg4GDt37kSnTp3uK86m1OjCcUKoKhzXFIVnCKe0RIOY6BxE3cpGbHQuysrqnpkuk0kQGOzMH+pxdGr4sVFCCLkbtV6LrPISft2ZLHUxMstLUKFWY5iNN5w93WG0EMPQBD9TXCIirjP5kIolsBBVH7ah+XaNc7dCf035+02TDNoJg8GI1JTqUZTbaUX1jqK4utnyhdv8AxxgYUGjKISQhtMY9MhRc4lIZlUyUpWYqIv5BKVIW/dpyp5yWzwS7A6t0QARu3fyYCESmyYfJklJdVtL1AohzYcSljasuKiiehQlJhfq8ronc8nlFggOdeZOOw5zgb2Dos5+hJD2TW80IEddWmNE5M6khEtM8jV1rwfWWGKIIJdY1JmA1BwhqZoDQto2SljaEIPBiOSkAn6l4/TbxfX2dfdQcod5wl3g6+cAiYQ+8IS0V0ZmRF5FGZ9wmCQg6urRkRx1KVgTzBOxlEjhVrneDPe/bfX6M1ZKOEsU0OYUINDBldYSIjxKWFq5wkI1n6DExeSiokJfZz+FQlo5iuKCkDAV7OzoS4CQto4xhkKtGpllpodiqkdFqhKREpN6IvdLJpaYJB6uiupExK1Gu61Uftd5IhUVFUjMLXzgeEjbQglLK6PXG5CUWICoyvL3mRkl9fb19LJDWGXhNm8fexpFIaSNYIyhVKepTj7UJSbJCD9nRF0MjaHuP2IaQyISw4VPPmxNko+qVXndrGxhL7eiCauk2VDC0grk55ebjKJotYY6+1lZSxESquIXEhSqlDUh5P6V67Q1RkNKaiQlpm33WuCuIUQQwVlhDVdFzQTEtnpERKGEm7USjnJrSMT0Bw8RFiUsZkinMyAxPp8fRcnOLq2zn0gEePvY83VRvLztIRbTXzeEmCONQY/sqjki6jpGQyrnixTXc+ZMYznIrWocjrG9Iynh/qkU3AJ5hLQGlLCYidycMn4UJT4uFzpd3ceTbWxk3DyUcBVCQlWwtpa1cKSEkJp0d545c0dNkartAk15k9yfUmZZI/kwnazqpuDaVApbWFpIm+T+CDEXlLAIRKs1ID4ul6+Lkpdb95eZSAT4+jlwiwiGqeDhaUejKIS0AL3RgNyKsspRkdojI9mVc0Ry1WVNcuaMwkIKNyu76kMydRymcVUoYSWlP1JI+0QJSwthjCEnu4xfRDAhPg96fd2jKLZKOTdZNkyFoBBnWFnRFxQhTaWuU3iz1abzRbLKi5FTUQpjE1RYlUssKkdEbGsdknGrcRaNzT3OnCHkTseOHcPcuXNx48YNeHt7Y8GCBZg4ceJdr7N//34sXLgQN27cgKWlJR5++GF89dVX8PPze6DbbQmUsDQjjUaPuNjqUZSCfHWd/cRiEfz8q0ZRXODuYUtfXIQ0kpEZUaApr5V4ZPFn0DTtKbwWIjFcaox8cGfP2NVKTOxlCvo8kyaXmJiIkSNHYtq0adi4cSMOHz6MKVOmwN3d3WQ9oTuv8/TTT2Pu3LnYuHEjioqKMGfOHDzzzDO4dOnSfd9uS6GEpQkxxpCVWYKoWzmIicpGYkI+DIa6/0Kzt7fkD/MEhTjD0pKONxNSl6paIjWTkGx1Sa2S79nqEuiMdZ9B1xgSkRjOCpvKs2Rqj4pUtTlaWlGFVVKL0WjE559/jrVr1yIzMxMhISH44IMPMHbsWBQUFGDWrFk4cOAASktL4eXlhfnz52PSpEmNvp81a9bA398fX331FQAgPDwcp06dwjfffFNvYnHx4kUYDAb85z//gbjyrK+3334bTz/9NHQ6HaRS6X3dbkuhhOUBqdW6ylEUbsJsUWHdM/wlEjECAh0REqZCWLgLXFxt6K8u0q4xxlCsrUB2XeXdK9uym7CWiAgiqBQ21XNCFEp+hMSNHyVRwsmSTuEl92/JkiXYsGED1qxZg+DgYJw4cQIvvfQSVCoVtm/fjps3b2Lv3r1wdnZGXFwc1OrqkfeOHTsiOTm53tseMGAA9u7dCwA4c+YMhgwZYrJ/2LBhmD17dr3X79mzJ8RiMX766SdMnDgRpaWl+OWXXzBkyBBIpdL7vt2WQglLIzHGkJFejKjKwzzJiQUwGuseRXF0tOIXEQwMdoJcTk83aR+qippl3jEqUl1llbtcYah7favGcra04ZKPGnNFqoqbVRU8UylsYEGn8LZan+x/AsXqnBa/X6VChfeH/dmgvhqNBp9++ikOHTqEyMhIAEBAQABOnTqF7777DqWlpejevTt69eoFACbzRgBgz5490Onq/0woFNXrvGVmZsLV1dVkv6urK4qLi6FWq036VvH398eBAwfw3HPP4bXXXoPBYEBkZCT27NnzQLfbUugXtAHKy7WIjc7lJ8yWlGjq7GchFSMw0IlbRDDcBc4qaxpFIW1KuU5b5yTVqrNnsivbypqgqBlQu5aIyx2n87oplHBW2EAmoa+ytq5YnYNCdabQYdxVXFwcysvL8dhjj5m0a7VadO/eHR999BHGjBmDS5cuYejQoRg1ahT69evH9/P19W3W+DIzM/Hqq69iwoQJGDduHEpKSvDhhx9i7NixOHjwoNn/XtGnvA5GI8PttCI+QUlJLkB9Jws4q6z5BCUg0AkyGf0FR1oftV5Xx5kyJbXWnynR1Z2sN5adTGFyqu6dFVZdFFRLhJhSKlRmf7+lpVyRz927d8PT09Nkn1wuh7e3N5KTk7Fnzx4cPHgQgwcPxsyZM/Hll18CaNwhITc3N2RlZZnsz8rKglKprHcUZOXKlbCzs8MXX3zBt23YsAHe3t44e/Ys+vbte1+321IoYalUVqpFTHQONxclOgdlpXX/hSiVSRAY5MSfduzkbN3CkRLScFXVVeuapFqz/HuRtu4z2BrLViqvtcaM6Vkz3CiJghIR0kgNPSwjpA4dOkAulyMlJQUDBw6ss49KpcKECRMwYcIEDBgwAO+88w6fsDTmkNCdh3IA4ODBg/yhqLqUl5fzk22rSCTcH9lGo/G+b7eltNuExWhkSE0p5CfLpqUW1juK4uJqw5/R4x/gCKmURlGIsHRGA3JMCpmVcAXOmqm6qpWFrNahmDuTEFcrW1hLaf0q0n7Z2tri7bffxpw5c2A0GtG/f38UFRXh9OnTUCqViI+PR8+ePdGxY0doNBr8+eefCA8P56/fmENC06ZNw4oVK/Duu+9i8uTJOHLkCLZt24bdu3fzfVasWIEdO3bg8OHDAICRI0fim2++weLFi/lDQvPnz4evry+6d+/e4NsVSrtKWEpKNIiJykHUrWzERuegvLzuTFYmlyA42JlPUhwcrVo4UtJeaQ16rsz7HXNCstSmp+/mVZQ1yf1ZSqTVa83cMUm15sRVG0pECGmQjz/+GCqVCkuWLEFCQgLs7e3Ro0cPzJ8/H6mpqZg3bx6SkpKgUCgwYMAAbNmy5b7ux9/fH7t378acOXOwfPlyeHl5Yd26dSanHufm5iI+Pp7ffvTRR7Fp0yZ88cUX+OKLL2BlZYXIyEjs27ePH71pyO0KRcRYE5RybGbFxcWws7NDUVERlEplg69nMBiRklw9inI7rajevm7utggLd0FImAp+/o6wsKDTGknTqUpEak5YrTkiUrUoXr6maRKRquqq1YXNaichLgpbKGWWZj/RjrQ/FRUVSExMhL+/PywtLYUOh9zD3V6v+/39rkubG2EpKlQjJjoHUbdyEBudg4qKuus3WFpaIDhUxU2YDVPBzl7YyUSkdaqZiNQ8dTe7mRIRqVhicgjGRVGVgFB1VUJI29bqExa93ojkpHy+/H1Gekm9fT08ldxk2XAX+PjaQyKhURRSN41Bjxx1iclhmLqKmjVVIiITS/g6IneWe3epvOxmZQt7uRUlIoSQdqlVJiwFBerKwzzZiIvJg0ZT9yiKlZXUZBTFVklDi+1ddSJSfNdkpKkmq9ZMRFytuGTErcY8ERdKRAghpEFaVcKyd3cU0pLLkZVVWud+kQjw8rZDaBg3iuLtYw+xmH4E2oOaiUhmjZLu1ZNWmz4RuXOC6p3l3l0VlIgQQkhTaVUJy18nkyCXm56xY20jQ2ioCiHhKoSEqmBjQ2cztCV11RHJrnFIJrNyu6kSEbnEosboh61JIlI1QkJzRAghpOW1qoQF4EZRfHwd+DV6PL3saBSlFapKRGpWU82+41BNlroEhU2YiNQc/eCTkarkhBIRQggxa60qYXl2XFf06OEPK2uZ0KGQelTodchWl3CjH+piPikxKfvejImIq4KbpFpz3RkXK1tKRAghpJVrVQlLl67ulKwIpCoRufMsGZORkWZIRKrPkqlxeKZysqqrlS3sKBEhhJB2oVUlLKTplek0/AhINn94poQfJamqKVKkrWiS+5NLLO44S8Y0Ealqo0SEEEJITZSwtEGMMZToNJWVVE0Tj5qjI9nqEpQ20eq7VYmI692SEYWSKqsSQgi5L5SwtCKMMRRq1ZXJRo1F76qSEnX1fBG1vv4VPxtDYSHla4W4VM4VqUpE3Komq1IiQgghLSojIwNvvfUWLly4gLi4OLzxxhtYtmzZXa9z5coVfPbZZzh16hRyc3Ph5+eHadOm4c033zTpd+zYMcydOxc3btyAt7c3FixYgIkTJzbfg2kgSljMgJEZUaApNylkll3z7JmqpERdAo2h7iJ5jWUjlVePhFjZVp8tozDdtpHKKREhhBAzo9FooFKpsGDBAnzzzTcNus7Fixfh4uKCDRs2wNvbG3/99RemTp0KiUSCWbNmAQASExMxcuRITJs2DRs3bsThw4cxZcoUuLu7C74AYpte/FBoBqMReRVl1RNT1XdOVOUu56hLoGfGJrlPO5nlHYlH1ZoztiYl362kNHmZEGKeWvPih0ajEZ9//jnWrl2LzMxMhISE4IMPPsDYsWNRUFCAWbNm4cCBAygtLYWXlxfmz5+PSZMmPdB9PvLII+jWrds9R1jqMnPmTNy6dQtHjhwBAPz73//G7t27cf36db7PCy+8gMLCQuzbt6/O26DFD82YzmhAjrqUL2hWfQpvjcmr5cXIrSiDoYkSEUe5NT/yUbOWiIuVEm6VyYlKYQuFhbRJ7o8QQkjjLVmyBBs2bMCaNWsQHByMEydO4KWXXoJKpcL27dtx8+ZN7N27F87OzoiLi4Nareav27FjRyQnJ9d72wMGDMDevXubNN6ioiI4Ojry22fOnMGQIUNM+gwbNgyzZ89u0vu9H5Sw1FBzwbuquiEmE1crk5G8ijIwPPjAlAgiOCusucRDUfOUXdPREJXCBjIJvVSEkPbrk08+QXFxcYvfr1KpxPvvv9+gvhqNBp9++ikOHTqEyMhIAEBAQABOnTqF7777DqWlpejevTt69eoFAPDz8zO5/p49e6DT1T//UKFQ3N+DqMdff/2FrVu3Yvfu3XxbZmYmXF1dTfq5urqiuLgYarW6yWNojHbxK6jW60xGPqoSkJrFzJqyvLtEJIazwqbGvBBb08MzlW0qhQ0sxJImuU9CCGnLiouLUVhYKHQYdxUXF4fy8nI89thjJu1arRbdu3fHRx99hDFjxuDSpUsYOnQoRo0ahX79+vH9fH19WyzW69ev4+mnn8bChQsxdOjQFrvfB9GqE5aaNUTurKaaXSMpaaoaIlKxBCqFTZ2n7nL/c5cd5daQiMVNcp+EEEIg2PzFxtxvaSm3MO/u3bvh6elpsk8ul8Pb2xvJycnYs2cPDh48iMGDB2PmzJn48ssvAbTcIaGbN29i8ODBmDp1KhYsWGCyz83NDVlZWSZtWVlZUCqVgo6uAK0sYXnr5HYUiPX8SEmZXtskt1uzvHvVaIhbjTkiLgpu216ugFhEiQghhLS0hh6WEVKHDh0gl8uRkpKCgQMH1tlHpVJhwoQJmDBhAgYMGIB33nmHT1ha4pDQjRs38Oijj2LChAn45JNPau2PjIzEnj17TNoOHjzIH+ISUqtKWHYnX4dY0fDVmBUW0hpnyZgejqk5X4SqqhJCCHlQtra2ePvttzFnzhwYjUb0798fRUVFOH36NJRKJeLj49GzZ0907NgRGo0Gf/75J8LDw/nrN/aQ0OXLlwFwIzs5OTm4fPkyZDIZOnToAADYsWMH5s2bh6ioKADcYaBHH30Uw4YNw9y5c5GZmQkAkEgkUKlUAIBp06ZhxYoVePfddzF58mQcOXIE27ZtM5nnIpRWlbBUsZXK+cTDxcqWP0vmzomrVEOEEEJIS/r444+hUqmwZMkSJCQkwN7eHj169MD8+fORmpqKefPmISkpCQqFAgMGDMCWLVvu+766d+/OX7548SI2bdoEX19fJCUlAeDOAIqOjub7/Prrr8jJycGGDRuwYcMGvr3mdfz9/bF7927MmTMHy5cvh5eXF9atWyd4DRagldVhuZKagCBXT6ohQgghbVhrrsPSHrVUHZZWNSHDT+lEyQohhBDSDrWqhIUQQggh7RMlLIQQQggxe5SwEEIIIcTsUcJCCCGEELNHCQshhBCz1ApOYiVoudeJEhZCCCFmRSrlVp0vL2+a9d1I86p6napet+bSKgvHEUIIabskEgns7e2RnZ0NALCysqIioGaIMYby8nJkZ2fD3t4eEknzLuZLCQshhBCz4+bmBgB80kLMl729Pf96NSdKWAghhJgdkUgEd3d3uLi43HVBQCIsqVTa7CMrVShhIYQQYrYkEkmL/SAS80aTbgkhhBBi9ihhIYQQQojZo4SFEEIIIWavVcxhqSpKU1xcLHAkhBBCCGmoqt/tpigu1yoSlry8PACAt7e3wJEQQgghpLHy8vJgZ2f3QLfRKhIWR0dHAEBKSsoDP2DyYIqLi+Ht7Y3U1FQolUqhw2nX6LUwH/RamBd6PcxHUVERfHx8+N/xB9EqEhaxmJtqY2dnR28+M6FUKum1MBP0WpgPei3MC70e5qPqd/yBbqMJ4iCEEEIIaVaUsBBCCCHE7LWKhEUul2PhwoWQy+VCh9Lu0WthPui1MB/0WpgXej3MR1O+FiLWFOcaEUIIIYQ0o1YxwkIIIYSQ9o0SFkIIIYSYPUpYCCGEEGL2KGEhhBBCiNkz24Tlo48+gkgkMvkXFhYmdFjtxokTJ/Dkk0/Cw8MDIpEIO3fuNNnPGMOHH34Id3d3KBQKDBkyBLGxscIE28bd67WYOHFirc/K448/LkywbdySJUvQu3dv2NrawsXFBaNGjUJ0dLRJn4qKCsycORNOTk6wsbHBmDFjkJWVJVDEbVdDXotHHnmk1mdj2rRpAkXcdq1evRpdunThC/VFRkZi7969/P6m+kyYbcICAB07dkRGRgb/79SpU0KH1G6UlZWha9euWLlyZZ37v/jiC3z77bdYs2YNzp49C2trawwbNgwVFRUtHGnbd6/XAgAef/xxk8/K5s2bWzDC9uP48eOYOXMm/v77bxw8eBA6nQ5Dhw5FWVkZ32fOnDn4448/sH37dhw/fhzp6el45plnBIy6bWrIawEAr776qsln44svvhAo4rbLy8sLn332GS5evIgLFy7g0UcfxdNPP40bN24AaMLPBDNTCxcuZF27dhU6DMIYA8B27NjBbxuNRubm5saWLl3KtxUWFjK5XM42b94sQITtx52vBWOMTZgwgT399NOCxNPeZWdnMwDs+PHjjDHucyCVStn27dv5Prdu3WIA2JkzZ4QKs12487VgjLGBAweyN998U7ig2jEHBwe2bt26Jv1MmPUIS2xsLDw8PBAQEIAXX3wRKSkpQodEACQmJiIzMxNDhgzh2+zs7BAREYEzZ84IGFn7dezYMbi4uCA0NBTTp0/nVzgnzauoqAhA9QKtFy9ehE6nM/lshIWFwcfHhz4bzezO16LKxo0b4ezsjE6dOmHevHkoLy8XIrx2w2AwYMuWLSgrK0NkZGSTfibMdvHDiIgIrF+/HqGhocjIyMCiRYswYMAAXL9+Hba2tkKH165lZmYCAFxdXU3aXV1d+X2k5Tz++ON45pln4O/vj/j4eMyfPx/Dhw/HmTNnIJFIhA6vzTIajZg9ezYeeughdOrUCQD32ZDJZLC3tzfpS5+N5lXXawEA48ePh6+vLzw8PHD16lX8+9//RnR0NH777TcBo22brl27hsjISFRUVMDGxgY7duxAhw4dcPny5Sb7TJhtwjJ8+HD+cpcuXRAREQFfX19s27YNr7zyioCREWJeXnjhBf5y586d0aVLFwQGBuLYsWMYPHiwgJG1bTNnzsT169dpbp0ZqO+1mDp1Kn+5c+fOcHd3x+DBgxEfH4/AwMCWDrNNCw0NxeXLl1FUVIRff/0VEyZMwPHjx5v0Psz6kFBN9vb2CAkJQVxcnNChtHtubm4AUGuWd1ZWFr+PCCcgIADOzs70WWlGs2bNwp9//omjR4/Cy8uLb3dzc4NWq0VhYaFJf/psNJ/6Xou6REREAAB9NpqBTCZDUFAQevbsiSVLlqBr165Yvnx5k34mWk3CUlpaivj4eLi7uwsdSrvn7+8PNzc3HD58mG8rLi7G2bNnERkZKWBkBADS0tKQl5dHn5VmwBjDrFmzsGPHDhw5cgT+/v4m+3v27AmpVGry2YiOjkZKSgp9NprYvV6Luly+fBkA6LPRAoxGIzQaTZN+Jsz2kNDbb7+NJ598Er6+vkhPT8fChQshkUgwbtw4oUNrF0pLS03+CklMTMTly5fh6OgIHx8fzJ49G//5z38QHBwMf39/fPDBB/Dw8MCoUaOEC7qNuttr4ejoiEWLFmHMmDFwc3NDfHw83n33XQQFBWHYsGECRt02zZw5E5s2bcKuXbtga2vLH4O3s7ODQqGAnZ0dXnnlFcydOxeOjo5QKpV4/fXXERkZib59+wocfdtyr9ciPj4emzZtwogRI+Dk5ISrV69izpw5ePjhh9GlSxeBo29b5s2bh+HDh8PHxwclJSXYtGkTjh07hv379zftZ6JpT2RqOs8//zxzd3dnMpmMeXp6sueff57FxcUJHVa7cfToUQag1r8JEyYwxrhTmz/44APm6urK5HI5Gzx4MIuOjhY26Dbqbq9FeXk5Gzp0KFOpVEwqlTJfX1/26quvsszMTKHDbpPqeh0AsJ9++onvo1ar2YwZM5iDgwOzsrJio0ePZhkZGcIF3Ubd67VISUlhDz/8MHN0dGRyuZwFBQWxd955hxUVFQkbeBs0efJk5uvry2QyGVOpVGzw4MHswIED/P6m+kyIGGPsQbMrQgghhJDm1GrmsBBCCCGk/aKEhRBCCCFmjxIWQgghhJg9SlgIIYQQYvYoYSGEEEKI2aOEhRBCCCFmjxIWQgghhJg9SlgIaYWSkpIgEon4UuPmICoqCn379oWlpSW6dev2wLfn5+eHZcuWPfDtmItjx45BJBLVWlOFENIwlLAQch8mTpwIkUiEzz77zKR9586dEIlEAkUlrIULF8La2hrR0dEm64bcKTU1FZMnT4aHhwdkMhl8fX3x5ptvIi8vrwWjbV6PPPIIZs+ebdLWr18/ZGRkwM7OTpigCGnlKGEh5D5ZWlri888/R0FBgdChNBmtVnvf142Pj0f//v3h6+sLJyenOvskJCSgV69eiI2NxebNmxEXF4c1a9bg8OHDiIyMRH5+/n3f/4MyGAwwGo3NdvsymQxubm7tNqEl5EFRwkLIfRoyZAjc3NywZMmSevt89NFHtQ6PLFu2DH5+fvz2xIkTMWrUKHz66adwdXWFvb09Fi9eDL1ej3feeQeOjo7w8vLCTz/9VOv2o6Ki0K9fP1haWqJTp044fvy4yf7r169j+PDhsLGxgaurK/71r38hNzeX3//II49g1qxZmD17NpydnetdMNFoNGLx4sXw8vKCXC5Ht27dsG/fPn6/SCTCxYsXsXjxYohEInz00Ud13s7MmTMhk8lw4MABDBw4ED4+Phg+fDgOHTqE27dv4/333zfpX1JSgnHjxsHa2hqenp5YuXIlv48xho8++gg+Pj6Qy+Xw8PDAG2+8we/XaDR4++234enpCWtra0RERODYsWP8/vXr18Pe3h6///47OnToALlcjnXr1sHS0rLWYZs333wTjz76KAAgLy8P48aNg6enJ6ysrNC5c2ds3ryZ7ztx4kQcP34cy5cvh0gkgkgkQlJSUp2HhP73v/+hY8eOkMvl8PPzw1dffWVyv35+fvj0008xefJk2NrawsfHB2vXruX3a7VazJo1C+7u7rC0tISvr+9d34+EtGpNtvoRIe3IhAkT2NNPP81+++03ZmlpyVJTUxljjO3YsYPV/FgtXLiQde3a1eS633zzDfP19TW5LVtbWzZz5kwWFRXFfvjhBwaADRs2jH3yyScsJiaGffzxx0wqlfL3k5iYyAAwLy8v9uuvv7KbN2+yKVOmMFtbW5abm8sYY6ygoICpVCo2b948duvWLXbp0iX22GOPsUGDBvH3PXDgQGZjY8PeeecdFhUVxaKioup8vF9//TVTKpVs8+bNLCoqir377rtMKpWymJgYxhhjGRkZrGPHjuytt95iGRkZrKSkpNZt5OXlMZFIxD799NM67+PVV19lDg4OzGg0MsYY8/X1Zba2tmzJkiUsOjqaffvtt0wikfCLqm3fvp0plUq2Z88elpyczM6ePcvWrl3L396UKVNYv3792IkTJ1hcXBxbunQpk8vlfMw//fQTk0qlrF+/fuz06dMsKiqKlZaWMldXV7Zu3Tr+dvR6vUlbWloaW7p0Kfvnn39YfHw8H9fZs2cZY4wVFhayyMhI9uqrr7KMjAyWkZHB9Ho9v4hlQUEBY4yxCxcuMLFYzBYvXsyio6PZTz/9xBQKhclCir6+vszR0ZGtXLmSxcbGsiVLljCxWMy/TkuXLmXe3t7sxIkTLCkpiZ08eZJt2rSpzueXkNaOEhZC7kNVwsIYY3379mWTJ09mjN1/wuLr68sMBgPfFhoaygYMGMBv6/V6Zm1tzTZv3swYq05YPvvsM76PTqdjXl5e7PPPP2eMMfbxxx+zoUOHmtx3amoqA8CvrD1w4EDWvXv3ez5eDw8P9sknn/x/e3cf0lQXxwH8O2fKyGRplvSmqJsvNZemQkjTjDVjSP6xlLoyLKhAS6spJUSBJumigeHsj4gSkiypgVG5XrCi1fIlVgbTiWlFohYGIUjpOs8f0n2e++hTrnpoxe8DA8+5Z797znXj/Dj3bBPUJScns4KCAr6sVCrZkSNH/jOG3W5nAJjFYpnxuMlkYgDY8PAwY2xqss7MzBS0yc3NZRs3bmSMMXbixAkml8vZp0+fpsV6+fIlE4vF7M2bN4L69evXs7KyMsbYVMICgDkcDkGb4uJilpGRwZetVivz9/fnE42ZaLVaZjAY+HJaWhorLi4WtPl3wrJ161amVqsFbUpLS1lcXBxfDgsLY3l5eXz58+fPbOHChezUqVOMMcb27NnDMjIy+CSPkD8Z3RIi5AdVV1ejvr4eTqfzu2OsWLECPj5/vx0XLVoEhULBl8ViMYKDgzEyMiJ43po1a/i/fX19kZSUxPfj6dOnaG1tRUBAAP+IiYkBMLXf5IvVq1d/tW8fPnzA4OAgUlNTBfWpqanfNWbmwQ/E/3N8X8pfzrl582aMj48jIiICO3bsgMViweTkJACgq6sLbrcbcrlcMP579+4Jxu7n54f4+HjBOTiOw927dzE4OAgAaGhogFarhVQqBTC116WiogIKhQJBQUEICAiA1WrFq1evPLoOTqdzxmva29sLt9vN1/2zfyKRCKGhofzrID8/Hw6HA9HR0SgqKsLNmzc96gMhvxNKWAj5QSqVChqNBmVlZdOO+fj4TJugJyYmprWbM2eOoCwSiWas82RT6NjYGLKysuBwOASP3t5eqFQqvt3cuXNnHfNHREVFQSQS/WeS43Q6MX/+fISEhMwq3rJly9DT04O6ujpIJBIUFBRApVJhYmICY2NjEIvF6OzsFIzd6XSipqaGjyGRSKZtgk1OTkZkZCQaGxsxPj4Oi8UCjuP448ePH0dNTQ0OHDiA1tZWOBwOaDSaH9qw/DVfex0kJiaiv78fFRUVGB8fR05ODnQ63f/SD0J+Nd9f3QFC/gRVVVVYtWoVoqOjBfUhISEYGhoCY4yfGH/md6fY7XY++ZicnERnZyd2794NYGoyu3z5MsLDw+Hr+/1v9cDAQCxevBg2mw1paWl8vc1mQ0pKyqzjBAcHQ61Wo66uDvv27YNEIuGPDQ0NoaGhAXq9XpBA2O12QQy73Y7Y2Fi+LJFIkJWVhaysLBQWFiImJgZdXV1ISEiA2+3GyMgI1q5d6/GYOY5DQ0MDli5dCh8fH2i1WsG4N23ahLy8PABTG5JdLhfi4uL4Nn5+foJVkpnExsbCZrMJ6mw2G+RyOcRi8az7GhgYiNzcXOTm5kKn0yEzMxOjo6MICgqadQxCfge0wkLIT6BQKMBxHE6ePCmoT09Px9u3b2E0GtHX1wez2YwbN278tPOazWZYLBZ0d3ejsLAQ79+/x/bt2wFMfSJndHQUW7ZsQXt7O/r6+mC1WrFt27ZvTqb/Vlpaiurqaly8eBE9PT04ePAgHA4HiouLPYpTW1uLjx8/QqPR4P79+3j9+jVaWlqgVquxZMkSVFZWCtrbbDYYjUa4XC6YzWY0NTXx5zx37hzOnDmD58+f48WLFzh//jwkEgnCwsIgl8vBcRz0ej2uXLmC/v5+tLW14dixY7h27do3+8lxHJ48eYLKykrodDr4+/vzx2QyGW7duoWHDx/C6XRi165dGB4eFjw/PDwcjx8/xsDAAN69ezfjypjBYMCdO3dQUVEBl8uF+vp61NbWoqSkZNbX02Qy4cKFC+ju7obL5UJTUxNCQ0P521eE/EkoYSHkJykvL582McXGxqKurg5msxlKpRJtbW0eTUjfUlVVhaqqKiiVSjx48ADNzc1YsGABAPCrIm63Gxs2bIBCocDevXshlUoF+2Vmo6ioCPv374fBYIBCoUBLSwuam5shk8k8iiOTydDR0YGIiAjk5OQgMjISO3fuxLp16/Do0aNpqwIGgwEdHR1ISEjA0aNHYTKZ+I9eS6VSnD59GqmpqYiPj8ft27dx9epV/jtgzp49C71eD4PBgOjoaGRnZ6O9vR3Lly//Zj+joqKQkpKCZ8+eCW4HAcChQ4eQmJgIjUaD9PR0hIaGIjs7W9CmpKQEYrEYcXFxCAkJmXF/S2JiIi5duoTGxkasXLkShw8fRnl5OfLz82d9PefNmwej0YikpCQkJydjYGAA169f9/j/S8jvQMQ82QFHCCGEEPILUBpOCCGEEK9HCQshhBBCvB4lLIQQQgjxepSwEEIIIcTrUcJCCCGEEK9HCQshhBBCvB4lLIQQQgjxepSwEEIIIcTrUcJCCCGEEK9HCQshhBBCvB4lLIQQQgjxepSwEEIIIcTr/QXbLIbVrJCtdQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "nobservations = np.arange(5, 30+1, 5)\n",
-    "power.TTestIndPower().plot_power(nobs=nobservations, effect_size=[0.3, 0.5, 0.8, 1.2])\n",
-    "plt.xticks(nobservations)\n",
-    "plt.axhline(0.8, color='r', linestyle=':', linewidth=1)\n",
-    "plt.xlim(nobservations[0], nobservations[-1]);"
-   ]
-  }
- ],
- "metadata": {
-  "celltoolbar": "Aucun(e)",
-  "kernelspec": {
-   "display_name": "scientific_python",
-   "language": "python",
-   "name": "scientific_python"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}