From 9f8b1aeab92882f7a0bc043e467942a3ef51359b Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Thu, 16 Nov 2023 22:30:33 +0000 Subject: [PATCH] adding fix for occurrence typo Signed-off-by: Nathaniel --- .../survival_analysis/frailty_models.ipynb | 668 +++++++++--------- .../survival_analysis/frailty_models.myst.md | 14 +- 2 files changed, 341 insertions(+), 341 deletions(-) diff --git a/examples/survival_analysis/frailty_models.ipynb b/examples/survival_analysis/frailty_models.ipynb index 40abf2eca..a59ce0d03 100644 --- a/examples/survival_analysis/frailty_models.ipynb +++ b/examples/survival_analysis/frailty_models.ipynb @@ -65,7 +65,7 @@ "\n", "### Survival Regression Models\n", "\n", - "The emphasis here is on the generality of the framework. We are describing the trajectory of state-transitions within time. Anywhere speed or efficiency matters, it is important to understand the inputs to time-to-event trajectories. This is the benefit of survival analysis - clearly articulated models which quantify the impact of demographic characteristics and treatment effects (in terms of speed) on the probability of state-transition. Movement between life and death, hired and fired, ill and cured, subscribed to churned. These state transitions are all tranparently anc compellingly modelled using survival regression models. \n", + "The emphasis here is on the generality of the framework. We are describing the trajectory of state-transitions within time. Anywhere speed or efficiency matters, it is important to understand the inputs to time-to-event trajectories. This is the benefit of survival analysis - clearly articulated models which quantify the impact of demographic characteristics and treatment effects (in terms of speed) on the probability of state-transition. Movement between life and death, hired and fired, ill and cured, subscribed to churned. These state transitions are all tranparently and compellingly modelled using survival regression models. \n", "\n", "We will see two varieties of regression modelling with respect to time-to-event data: (1) Cox's Proportional Hazard approach and (2) the Accelerated Failure time models. Both models enable the analyst to combine and assess the impacts of different covariates on the survival time outcomes, but each does so in a slightly different manner. \n", "\n", @@ -295,7 +295,7 @@ }, { "data": { - "image/png": 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UL18eTZo0wVdffYX27dsz0ZRICaquLXlJSEjA1atXhVhLS0up65d9+/bBx8cHAFCrVi2MHTtWpfmkpqYiKChIiCtUqKDUmlGpUiUhuT0pKQnBwcFqu9YjKmnOnTsnilu3bp1tOxMTE1EcExOj8BjybZkEQVTyKbq2ZKcg7olcXV3h7++PoKAgxMbGwsTEBObm5mjQoAFatWqFHj16wMDAQKUx8qLKnxFRaaXJnxtV74lyo67nLbnR9GarRFQyZN5IEMh4fqIoS0tL0UbIX567UNHH5HYiIiIiIiIiIiIiIiIiIio1bt++jbCwMCH+6quvYGFhAQBwcHBA9erVhZceHj9+jHfv3qFatWqFMVWlBAcHY+LEiXj16pVa+01LS4OLi0uWl/NykpiYiJs3b6JChQpqSW5PSUmBi4sL3NzchGNt2rTB9u3bsyQwlBSBgYFCsjYANG7cWPg7qir5Fwn79Omj9gTOM2fOiGJFXlaUTyCRr7KRm8zJ7UDGC1CNGzdW+HyikkwmkyEuLg5+fn64ePEijh8/jpSUFACAkZERtm3bBktLS4X7y1zJC8hY86OiovDu3TtcuXIFGzduxIABAzBv3rw8N7VQlrpehI6OjsYff/whOpaYmIiQkBB4eXnh4MGDsLOzw8KFC9G+fft8z5eoJFP32pKXffv2iSqHtmzZUuFro/fv32PHjh0AAIlEghUrVkBXV1el+Xz+/BlpaWlCrOxmS/IVFkNCQpjcTpQNd3d3YUM2ICOBPad/m+UTSV+/fg0HB4c8x5DJZHj9+rXo2MePH/MxWyIqLpRZW7JTEPdEf/75pyhOSkpCWFgY/Pz8cOrUKfz000+YOHEiRowYoZFNuaKiorJsgtmzZ0+1j0NUkqi6tuRFlXuivGg68TwqKgr//vuvEGuiMjwRlQyhoaGiuEKFCgqfK5FIYG1tLWxGKN8XFV1MbiciIiIiIiIiIiIiIiIiolJD/mUtJycnUdynTx/8/PPPovazZ8/OsT9tbW1oa2tDJpNBKpUKx7W0tHJ8wVRLS0v5ieciOTkZ06dPFxLbq1atipYtW8LS0hJxcXHw9fXN95g7d+7Mkthev3591K9fHxYWFtDW1kZ8fDyCgoLw4sWLbKvY51dUVBSmTZsGDw8P4Vjfvn2xatUqlZOSiqqYmBjMnTsX6enpwrEZM2aope+0tDScP39edEzdLyv6+fnh+fPnQmxjY6NQlSL5ihzKJInJV+9gcjuVZm/evEHv3r2FWCqVQiaTZWnn6OgIFxcXVK9eXa3jp6Sk4Pfff8f9+/exa9cuVK1aVS39vnr1Kl9rS375+flh3LhxmDZtGqZNm6axcYiKi8JcW169eoVff/1VdEyZn8ulS5ciKSkJANC/f380b95c5TllTioBMhL6lSGf6CbfHxFlJIsuWbJEdGz06NE5Joo2bdoUhw4dEuIbN26gX79+eY7j6emJ2NhY0TH+TBKVXMquLfmhqXuizMLDw7F27Vrcu3cPW7ZsgaGhoVr7X7NmDaKiooS4devWarmGIiqpNL22qHpPlFffmn7e8vfff2u8MjwRlQzqfN6SlpaG5ORk6Ovrq2VupDlMbiciIiIiIiIiIiIiIiIiolIhOjoa169fF2JTU1N06tRJ1KZPnz7YunWrkLBz7tw5zJo1K8dE9StXrgAAHjx4gBEjRgjHDxw4gFatWqn7I2Tr0qVLSE9PR9myZbFq1apsK598qaapjKSkJOzfv1+Iq1atiq1bt6JevXo5nvPu3TtRlfX8CgwMxPjx4/H27Vvh2LRp0zB9+nSV+y5qkpKS8O7dO9y+fRsHDx5EWFiY8L358+ejXbt2ahnn9u3b+Pz5sxA3b94cVapUUUvfX+S3MnzmKho6OjqwsrJSeEz56h0hISEKn0tU0shkMtHmGPK0tLQwbNgwjBs3TqnKN3Xq1EHXrl3RrFkz1KpVC2ZmZpDJZPj8+TOePn2KM2fO4NatW0L7N2/eYPz48fjjjz9gbm6u0mcCgFOnToliRdeWzIyMjNCxY0d06NAB9erVQ6VKlWBkZITY2FgEBATgzp07+P333xEeHg4g489y+/btMDExwciRI1X+DETFmabWlrwkJCRg9uzZouvYvn37okWLFgqd/9dff+HevXsAAHNzc8yfP18t84qPjxfFyr4oLd+eibREWa1YsQLv3r0T4ho1amDcuHE5tm/Tpg0MDAyEzSyuXbuGV69eoXbt2rmO4+rqmuUYfyaJSi5l15bMNH1PpKWlhdatW6Njx45wcHBA1apVYWJiguTkZISEhMDd3R3Hjh2Dn5+fcM6///6LBQsWYNu2bWqr4H7q1CnRJpf6+vpYunSpWvomKqlUWVvyouo9UV7U8bxF2THUvdkqEZUc8vdiqj5viY+PZ3J7McDkdiIiIiIiIiIiIiIiIiIiKhX+/vtv0YtgPXr0gJ6enqhNpUqV0KJFC7i7uwMAPnz4gPv376NNmzYFOldlpKenQ1dXF/v370eDBg2ybSP/ORXx+PFj0cska9euzTWxHQCqVaumcuWYp0+fYvLkyUKCoa6uLlatWlViXnzr0KGDkGQuk8kglUqztLGzs8P8+fPRoUMHtY0rn3iu7j/P9PT0fFeGz/z3zNDQUKmXKFn9lEhxUqkUhw8fxvHjxzF8+HDMmTMn138fzMzMcPz4cTRp0iTb79va2sLW1ha9evXCzZs3MX/+fERHRwMAAgICsHr1amzatEmlOauytnzx/fffY/HixdlWBjM3N4e5uTkaN26M0aNHY9GiRbh48aLw/Q0bNqB9+/aoWbNm/j4AUSmg7NqiCJlMBmdnZ7x69Uo4VqVKFfz4448KnR8eHo6ffvpJiJ2dnWFmZqbSnL6Q3zRKV1dXqfPl/2y+JOMSUYaDBw+K7l309PTwv//9L9ekBDMzM/Tv3x9Hjx4FkFGlb9asWTh48CAsLS2zPWf79u24e/duluP8mSQqmfKztgAFc0/UuHFjXLlyBba2tlm+p6uri1q1aqFWrVoYMmQI9uzZg82bNwubcl6+fBknT57EoEGDch1DEU+fPsXy5ctFxxYuXMh7IaJc5HdtUYSq90R5Ucfzlry8fv0az549E2JNVIYnopIjOTlZFKv6vEW+PyqatAp7AkRERERERERERERERERERAUhu6rS2XFycsr1vKJozJgxOSa259eX5PIvGjZsqNb+s3P16lWMGDFCGNvExAR79uwpMYntQMaLg1/+yy6xvX379li/fr1aE9ujo6Px77//CrGhoSF69Oihtv6BjMrwmavOK1MZPnNCurIvf8q/sMTkdirNatasCV9fX+E/b29v3L59G7t370a/fv2EFwJTU1Oxf/9+TJo0KUuSZmbGxsY5JnHIc3R0xM6dO6Gj83+1Vv7++29RZcH8uHPnTr7Xli8aN26cbWK7vDJlymDLli1wdHQUjqWnp2Pbtm1KjUdU0qh7bVHEmjVrcOnSJSE2MTHBjh07YGJiotD5q1evRlRUFACgZcuWWa7vVSF/7ZGamqrU+fJ/NgYGBirPiaikuHjxItavXy86tnLlSoXudadPn46KFSsK8evXr9GnTx8cOXIEHz58QHp6OuLi4vDff/9h/Pjx2LFjB4Csm2XJx0RU/KmythTEPVGNGjWyTWyXJ5FIMGHCBMyZM0d0/JdfflH52uvt27eYOHGiKAmsX79+GDZsmEr9EpVkqqwtilD1nigv6njekpfsfh+j7srwRFRyyP9uSNXnLazaXjwwuZ2IiIiIiIiIiIiIiIiIiEo8f39/eHl5CXHlypXRrFmzbNt2795dlGRy5coVxMfHa3yOqvj+++/V3qeRkZEo9vHxUfsYmR08eBDTp08XKuVVrFgRv//+O9q0aaPRcYuaO3fuoF+/fpg4cSI+ffqklj7d3NxEL/Z069ZNoURPZahSGT7zy9OsfkqkPnp6eihfvjwcHR2xbt06/Pnnn6Kkr7t37wqJXerQvHlz9O/fX4hlMhn++ecflfpUZW3JDy0tLSxfvly0Fl2/fh2JiYkaHZeoONH02rJz504cPnxYiPX19bFz507UqVNHofNv3LiBCxcuCHNdsWJFvueSHfnEV2Urgcm3l7/mJiqt7t27h/nz54s2AJs7d67C//abm5tjx44dMDc3F459/vwZq1atQqdOnWBvb49mzZph9OjRuHXrFoCMe4+VK1eK+lFXwhgRFQ2qri3K0sQ9kbxx48ahVq1aQhwSEoKHDx/mu7+QkBCMHTsWkZGRwrFOnTph1apVKs2TqCTT9Nqi6j2RIjT9vCU9PR3nzp3T6BhEVLLIPx9R9XkLNy4rHpjcTkREREREREREREREREREJZ6iVduBjKpMXbt2FeKEhARRlZSipnLlyqhUqZLa+23QoIGoksqCBQtEGwSoi1QqxerVq7F27VrhhUB7e3ucOHECdnZ2ah+vsN29e1eofOrj44P79+/j6NGjmDRpkigR48aNGxg8eDCCg4NVHvPMmTOiWN0vEsbExOD69etCrGxl+MwVNFj9lEhz6tatiz179ogStw8ePChKYlDVgAEDRPG9e/fy3Zeqa0t+VapUCW3bthXilJQUPHr0SOPjEhVX6lxbjh49iq1btwqxjo4OtmzZgpYtWyp0fnx8PJYvXy7EEyZMQI0aNZSeR27kX7ZWdhMs+fZMbicCvLy8MGXKFNG1/dixYzFhwgSl+mnQoAFOnjyJFi1a5Nm2UqVK+O2330QJogBgamqq1JhEVHSpa21RljrvibKjpaWV5bnO/fv389VXREQExowZI3r21KJFC2zdulVUgZ6I/o+m1xZV74kUURDPW+7evSvauFUTleGJqGRR5/MWHR0dVm4vJpjcTkREREREREREREREREREJZpUKs1SJSS35Pbsvi+fHF+UqDth54vy5cujV69eQvz+/XsMHDgQffr0wZYtW3D79m3ExsaqNEZiYiKmTZsmqkTToUMHHDlyBOXLl1ep7+JAW1sb5ubmaN68OWbPno0LFy6IKtV/+PAB8+bNg0wmy/cY/v7+ok0JbGxs0Lp1a5XmLU/VyvCZX1pSthqHfHI7E8SIcle7dm188803QpyUlISbN2+qrf8GDRqIElw/fPiQ774uXLggWhOUXVtU0bhxY1GsyucgKg3UsbacPXtWVCFUIpFgzZo16NKli8J9HDx4EB8/fgQAVKtWDRMnTlRqDoqwtLQUJXuFhIQodb58+woVKqhlXkTFlZ+fH8aPH4+EhATh2MCBA7FgwYJ89Ve5cmUcOXIEBw8exNChQ2FnZwczMzPo6uqiQoUKaNOmDVatWgU3Nze0atUKQUFBovNr1qyp0uchoqJB3WuLMtR5T5QT+fuV/GyMGBcXh3HjxsHf3184Vr9+fezatYvJYEQ50PTaoo57IkUUxPMWTW+2SkQlj7W1tSgODQ1V+FyZTCZqL98XFV3cTomIiIiIiIiIiIiIiIiIiEq0u3fvil5qaNKkSZ5VQtq1awcrKyuEhYUBADw8PBAUFARbW1uNzjU/TExMNNb38uXLERwcjMePHwvHXr58iZcvXwLIqBZVp04dtG7dGj169Mjycm1eLl68KIqdnJywdu1aaGtrqzz34sjCwgKurq7o06cPAgICAACPHj3CrVu34OjomK8+5Tdm6NOnDyQSicpzzUzVlxWNjIwQHh4OIGPDA6lUCi0txeo1sPopkfLatm2Ls2fPCrGvr6/a+tbS0oKZmZnw76cqVeHl16+CfBG6XLlyolid1e2JSipV1pZr165h0aJFog19Fi9eDCcnJ6XmkPlnNSAgQKFr0/T0dFHcrVs3UXzlyhXY2NgIsa6uLmxtbfHu3TsAGcnqMplM4eurL8n3QEaFxMx9E5U279+/x5gxYxAVFSUc69GjB1auXKly361bt1ZoU6/MG4EBQMOGDVUem4gKlybXFkWo854oJ6reryQlJWHSpEl4/vy5cKxGjRrYu3dvgW0oRlTcaHptUdc9kSI0/bwlNjYWV69eFWJNVIYnopJHfqMxZTbv+fz5M1JTU4VYU5uCk/qxcjsREREREREREREREREREZVo8om3irwQpq2tjd69ewuxTCbL0k9RkbkalLqZmJjgyJEjWLZsGapVq5bl+1KpFC9evMD+/fsxePBg9OvXD+7u7gr3L18JytvbG58/f1Z12sWaoaEhJkyYIDp26dKlfPUllUpx7tw50TF1v6z45s0bPH36VIgrVaqkdGX4zFU00tLSlPo7kDlBDGD1UyJFWFpaiuK4uDi19p+UlCR8nd+Kf2/fvsWTJ0+EOD9riyoSExNFsZ6eXoGNTVRc5XdtuXfvHmbNmoW0tDTh2KxZszB8+HCV5iOTyZCenp7nf/Lkv585ueSLzC9cJyUl4c2bNwrNKS4uTtjACMioLq/uTYeIiovQ0FCMGjVKSP4EAEdHR2zcuFHhja7U4dGjR8LXOjo6qF+/foGNTUTqV1TWFnXcEynav7JjpKamYsaMGfDw8BCO2djY4MCBA7CwsFDbHIlKEk2vLZq6J8pOQTxvka8M//XXX3PjDCLKU/ny5UWbeb948ULhc318fEQxk9uLDya3ExERERERERERERERERFRiRUXFyeqEgIAK1euhL29fZ7/HTx4UHTemTNnsk1uKel0dHQwdOhQXLp0CWfPnsWiRYvQs2dPUULyF8+fP8fIkSPx559/KtR3z549MWzYMCH29/fHsGHDEBQUpLb5F0ft2rUTxfmtqnz37l2EhoYKcbNmzVClShWV5iZPHZXh5StyfPjwQeFz5ZPb5fsioqzkE07Lli2rtr4jIyMRGxsrxPIVBRUlv6FMftYWVcj/O5Tfz0FUmuRnbXn69CmmTJmClJQU4di4ceMwefJktc9PnZo1ayaKMyfH5sbLywtSqVSImzdvrtZ5ERUXERERGDVqlKgSX8uWLbFt2zaNbt4m7+PHj3j48KEQd+zYkYlXRMVYUVlb1HVPlJvAwEBRrOgYUqkUCxYswM2bN4VjVlZWOHDgQLbP+YhI82tLQd8TFcTzFvnnxf369VNr/0RUcmV+3hIeHo73798rdJ78c5kWLVqodV6kOUxuJyIiIiIiIiIiIiIiIiKiEuvChQtZqhkpUsExPT1dlHgCZLw4mvnF99Kobt26GDlyJH7++WfcunULly5dgrOzs6gKglQqxfLly7O8aJsdiUSCpUuXiiqVBwYGYtiwYQpXwCyJ5F9Kzm9VZfmXFdVdtV1dleHlq2jIV9nIDStyEClP/uemYsWKauv71q1borhu3bpK9yGVSnH27FnRMXWvX7mRyWS4ffu26Fi9evUKbHyi4krZtcXPzw/jx49HQkKCcGzIkCGYP39+vufw448/wtfXV6n/WrZsKerj2rVrou/b2tpmGadLly6i+NKlSwrN7+LFi7n2Q1QaxMXFYdy4caL7PQcHB7i6usLAwKBA53Ls2DHRff/AgQMLdHwiUp+itLao454oL5mT05UZY9myZbhw4YIQm5mZYf/+/WrfCJGopND02qKJe6LcFMTzlnfv3uHx48dCrInK8ERUcsk/J5F/jpKTzM9l9PX1s2wgTUUXk9uJiIiIiIiIiIiIiIiIiKjEkq8SUtT6K+6qVauG0aNHw83NTfQiXGpqqlJ/VnPnzsXs2bOFOCQkBMOHD8fLly/VOt/iQh1VlePi4nD16lUhNjQ0RM+ePVWeW2b37t1DSEiIEDdr1gxVq1ZVuh/56qeZX4DMTVJSkujvSJ06dWBiYqL0+ESlSVJSEs6fPy861rZtW7X0nZqaij179oiOtW/fXul+7t+/j48fPwpxfteW/HJzc8O7d++EuGLFiqhVq1aBjU9UHCm7trx//x5jxoxBdHS0cKxPnz5YtmyZxuaoTtWqVRMlkd29excBAQG5nhMdHS1KJrO0tGQlMSp1kpKSMGnSJDx//lw4Zmdnh7179xZ4xfT379/jwIEDQlynTh189dVXBToHIlKPorS2qOueKDeBgYFZNhpUZP366aefcOLECSE2MTHBb7/9htq1a6t1fkQlhabXlsK4JyqI5y3yvxPQRGV4Iiq5OnfuDF1dXSE+efIkUlNTcz3n3r17ePv2rRA7OjrCyMhIY3Mk9WJyOxERERERERERERERERERlUgBAQF49OiRENvY2ODly5dKVXL08vISJctevHgRiYmJWcbS0dERxfJV30s6bW1tLF68GFpa//cagrKJ6ZMmTcLixYuFl93Cw8MxYsQIPH36VK1zLQ7UUVX5woULSEpKEuKvv/5a7S91nzp1ShTnt9JPgwYNYG1tLcQ3btxAcnJynuddvXpV9GITq59SaZKSkqL0OiuVSrFs2TJ8+PBBONaoUSPUqFEjS9vQ0FBERUUp1ffSpUvx6tUr4ZiVlRV69eql1ByBrC9Cq1JFzNfXV6n2L168wPLly0XHRo4cme/xiYobTa8tQMb6Mnr0aISFhQnHunXrhnXr1hWrpIfJkycLX0ulUixfvhwymSzH9uvXr0dsbKwQjx8/Pss9BFFJlpaWhpkzZ8LDw0M4Vq1aNezfvx+mpqYFOpfw8HBMmDBBuOfQ0tLCqlWroK2tXaDzICLVaXJtKYh7In9//zwTtjKLjo7GtGnTkJKSIhzr1KlTnpXXf/31V/z2229CbGhoiF9//RUNGjRQeGyi0kTT1y2FdU+kzuct2ZHJZFk231D3GERUsllaWmLQoEFC/P79e+zevTvH9snJyVi9erUQSyQS0fMaKvqY3E5ERERERERERERERERERCWS/MtaTk5OSr8cpq+vjx49eghxXFwcrly5kqVdmTJlRHHmiiulhbGxMczNzYVYmZdzv/jhhx+wZs0aIakgOjoao0ePhru7u9rmWRxkrqQF5K+qsvzf/379+qk0J3nqrAwvkUjQvXt3IY6JiYGbm1ue5x0/flwUZ+6DqKRLSkqCk5MTZsyYgX///VeU3JCdp0+fYsSIEThz5oxwTEtLCz/++GO27b29vdG5c2ds2LAhy4Yb8l6+fIlRo0Zl2fBizpw5SlfJkf93VpW1BQAWLFiA77//HmfPnkVcXFyO7ZKSknDw4EEMGTJE1K569eoYNmxYvscnKm40vbZER0dj3LhxCAoKEo599dVX2LRpU7FLKu3evTsaNmwoxP/99x/mzZuH+Ph4UbuUlBSsW7dOtEba2NhgyJAhBTZXosImk8ng7OyMGzduCMdsbGxw4MABWFpaqm2cESNG4OTJk4iJicn2+1KpFJcvX0a/fv1Elf3GjBmDRo0aqW0eRFQwNL22FMQ90fHjx9G9e3fs27cPISEhObaTyWS4fv06+vbtK9qIyNDQEPPmzct1bn/88Qc2b94sxHp6eti5cyeaNWuW63lEpZWm15bCuidS9/OW7Ny/f1+06ZkmKsMTUck3ceJE0e9ct2/fjv3792fZVDwiIgLjxo3D69evhWPffPMN7O3tC2yupDpu+0hERERERERERERERERERCVOdlVCnJyc8tWXk5MTTp48KcRnzpzBd999J2pjY2MDiUQiVGv09vYWJcUXR/7+/rC2tla42re/vz8iIiKE2MbGJl/j9u/fH0ZGRpg/fz5SU1MRHx+P8ePHY9u2bXB0dMxXn4XFy8sLDg4OSp1z8uRJXLp0SYiNjIzw9ddfK9XH+/fv8ejRIyGuVKkSWrdurVQfefnnn3/UWhl+7NixOH78uJBEt2nTJnTu3Fm0YUJmZ86cEVVP6tKlC+rWrZvv8YmKI5lMhkuXLuHSpUswNDRE3bp1UatWLZiamsLQ0BDx8fEICQmBt7c3AgMDRedKJBKsXr0610Su+Ph47Nu3D/v27YOVlRXs7e1RuXJlmJiYQCaTISIiAk+fPs22OvrYsWPztanGxYsXkZiYKMSqri0A8PjxYzx+/Bi6urqws7ODnZ0dzM3NYWRkhPj4eLx79w6enp6iispARqWgPXv2QE9PT6XxiYobTa4t169fh5+fn+jY3bt30bhxY6Xm6OTkhLVr1yp1jrpJJBJs27YNAwYMQHh4OADAzc0Nt2/fRufOnWFtbY3Pnz/j5s2booqMRkZG2LlzJ/T19Qtr6kQF7sOHDzh//nyWY126dFGqHxsbm2w3m/vC19cXDx48wIoVK9CgQQPUqVMHFhYWSE5OxqdPn3D//n3RzyMAfPfdd5g7d67Cc1i0aJFoQ48vvjwL+OLHH3/E4sWLs7Rr0aIFDh48mOsYX3/9NYKDg7Mcl0/m6NatW7bnT5kyBdOmTct1DKKSoCDWloK4JwoODsaGDRvw008/oWrVqqhXrx7Kly8PY2NjpKSk4OPHj3j48CE+fvwoOk9bWxubN29GrVq1cu3/119/FcUpKSkYN25cnvOSd+XKlXw/6yMqTjS9thTWPZEmnrfI03RleCIqHaytrbF582ZMnjwZUqkUMpkM69evx/Hjx9G6dWuYmZkhICAA//77r+h3VLVq1cLKlSsLceaUH0xuJyIiIiIiIiIiIiIiIiKiEuf+/fuil8GbNWuGKlWq5Kuv5s2bo3LlykLyzr179xASEoIKFSoIbUxMTFCzZk2hQsCxY8dgb28PR0dHtb8kVlAuXLiAgwcPwsnJCT179kTTpk0hkUiybevr64vZs2eLXujP6UV7RfTs2RNGRkaYPn06kpOTkZSUhKlTp2LTpk3Fqjr3kiVLoKenh8GDB6Nbt24oW7Zsjm2DgoLw66+/ijZSAIBp06ahXLlySo0rXymsT58+Of6/yy91V4avUKEChg8fjn379gEAwsPDMWLECLi6usLW1lbU9ty5c6JEEV1dXcycOVOl8YmKu8TERCGJOy/W1tZYsWIFOnXqpHD/YWFhuHnzZp7tDAwM4OzsnO+KxPLJYqquLZmlpqbi+fPneP78eZ5tW7ZsiZ9++gkVK1ZU2/hExZG61xb55E8ga7KmIvJzjiZUqlQJu3fvxowZM4R7j+jo6CzXSV+Ym5tj06ZN3JCHSp3sfvZlMhnS09OV6kfR9qmpqXmuXdra2pgyZQqmTp2q1L2SVCpVaB45rVOKrF/p6ekKjZFTm+z+vIlKooJeWzR9TySTyfDu3Tu8e/cuz7aVKlXC//73PzRv3lypMb5Q9s8I4NpCpYem15bCuifS5PMWIGMzEE1Xhiei0qNjx45Yt24dli9fLmzMkdt1Ur169bBjx45i+/vY0ozJ7UREREREREREREREREREVOKou0pInz59sGPHDgAZL46dPXsWEydOFLUZNGiQUC0lPj4ec+bMAQDo6+tDW1tbaLdixYosld+LqtjYWBw+fBiHDx+Gqakp7O3tUb16dZQtWxba2tqIiIjA8+fP4e3tLXoxr0uXLmjbtq1KYzs6OmLPnj2YNGkSEhISkJqaitmzZ2Pt2rVwcnJS8ZMVHC8vL3h5eWHZsmWoUaMG6tati3LlysHY2BhJSUkIDw/Hy5cv8eLFiywvN/br1w9jxoxRajyZTIZz586Jjqm7Sk5gYCAePnwoxOqqDD979mx4eXnB09MTAODn54cePXrA0dERNWrUQEJCAjw8PLJURFu2bBnq1Kmj8vhExUmZMmWwYcMG3L59Gx4eHggNDc3zHHt7e/Tt2xf9+vXL80W/OnXqYOjQoXB3d8ebN2/yfGnaysoKffv2xbBhw0SbvygjMDBQ+PkHgIoVK6JVq1b56uuL4cOH48qVK3j8+DFiYmJybaujo4MWLVpg+PDh6Ny5M7S0tFQam6g40vTaUhI1aNAAZ8+exdatW3Hu3DlER0dnaWNoaIju3btj3rx5sLKyKoRZEpUOgwcPxs2bN+Hr65tjEqa+vj66deuGSZMm5VntmIhKt4K4J+ratSsiIiLg6emJkJCQPNvb2dlh8ODB6Nu3L8qUKaPQGEREmnjeIu/SpUtISEgQYk1Uhiei0sXJyQkODg7YtGkTbt68idTU1CxtrKysMGjQIEyaNAl6enqFMEtSlUTGLZSIiIiIiIiIiIiIiIiIiKgEiY+PR/v27YWXqQwMDHDnzh2YmJjku8/AwEB07dpViKtXr46LFy+K2qSnp2P27Nm4dOlSrn2tW7dO5coonTt3FqpD9u3bF+vXr1fq/B9++AHu7u4AMqrTHj58OEub7du3Cwn9yujUqRO2bNkCQ0PDbL+v7NyfPn2K8ePHC4lCEokES5cuxdChQ5WeW0Hr06cPXr58qfR5+vr6mDZtGsaNG6d0cuX9+/cxcuRIIW7WrBl+//13peeQm23btuGXX34R4smTJ2PWrFlq6TsiIgIzZ84U/n7mRkdHB7Nnz8a4cePUMjZRcfbp0yf4+/sjKCgI0dHRSE5OhpGREYyNjWFra4v69eujbNmy+eo7ISEBr1+/RlBQED5//oyEhARIJBKYmJjAwsIC9evXR+XKldX8idQvMDAQ7969Q0hIiOjPqGzZsqhcuTIaNmyY479dRKWVJteWkiglJQWenp4IDg5GREQETE1NUbFiRTRv3pwJaEQFKDIyEs+ePUNgYKCwuU3ZsmVRvXp1NG7cmP/eE5HSCuKeKCIiAq9fv8aHDx8QGRmJxMRE6OrqwtTUFOXLl4eDgwMsLCzU9ImIiIiIio/IyEg8evQIISEhiI+Ph6WlJSpXroymTZuKNhen4oeV24mIiIiIiIiIiIiIiIiIqESRrxLSpUsXlRLbAaBy5cpo1qyZUK367du3ePLkCRo3biy00dbWxrZt23Dz5k1cuHABPj4++PjxIxITE5GWlqbS+IVh0KBBMDExwY0bN/D06VPRn6k8iUSCxo0bY+TIkejZs6da59GoUSMcOnQIY8eOxefPnyGTybBixQokJCQU+aRmZ2dnXL16Fffv31eoylfFihXx7bffYvDgwbC1tc3XmGfOnBHF6q7aLpPJcPbsWY2NYWFhgYMHD+LQoUM4cuQIAgMDs7TR0tJC69atMXv2bDg4OKhtbKLirHz58ihfvrxG+jYyMoKDg0Ox/3mrXLlysUjCJypKNLm2lER6enpo27ZtYU+DqNQzNzfHV199VdjTIKISpCDuiSwsLNCyZUuN9U9ERERUXJmbm6NLly6FPQ3SAFZuJyIiIiIiIiIiIiIiIiIiolylpaXh9evXCAgIQGhoqFCh6kvVTnt7e1hZWRX2NIu0uLg4vHr1CoGBgQgPD0diYiL09PRgbGwMKysr2Nvbo2LFioU9zSJFJpPB29sbb9++xadPn2BgYABra2s0atQI1tbWhT09IiIiIiIiIiIiIiIiIsoHJrcTERERERERERERERERERERERERERERERERERERERGRxmkV9gSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIio5GNyOxEREREREREREREREREREREREREREREREREREREREWkck9uJiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhI45jcTkRERERERERERERERERERERERERERERERERERERERBrH5HYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLSOCa3ExERERERERERERERERERERERERERERERERERERERkcYxuZ2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIg0jsntREREREREREREREREREREREREREREREREREREREREpHFMbiciIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKNY3I7ERERERERERERERERERERERERERERERERERERERERaZxOYU+AiIiIiIiIiIiIiIiIiIiIiIiICABSU1Ph5+cHX19fREdHIzk5GcbGxrCyskKDBg1gY2NT2FMkIsqWl5cX3r17h9DQUBgaGsLa2hoODg6wtrYu7KkRlTpRUVHw8/NDQEAAoqKiIJPJYGpqikqVKqFx48YwMTEp7CnmKj09He/evcOrV6/w6dMnxMfHw8jICGZmZrC3t0etWrUgkUgKe5pEpU5BrC3x8fF49uwZAgICEB0dDZlMBhMTE9jY2MDBwQFmZmb57ptrC1HRI5VK8f79ewQEBCA0NBQxMTFISUkRfjbr1q2L2rVrQ1tbu7CnqhDeExEprrjfsxSG2NhYPHnyBAEBAYiNjYW2tjbMzc1RuXJl1KtXD6ampoU9RVISk9uJiIiIiIiIiIiIiIiIiIiIiIioUIWGhmLPnj04e/YsYmJicmxXu3ZtDB06FIMHD1bq5e7w8HB4eXnBy8sL3t7e8Pb2RlRUlPD9vn37Yv369ap8BCJSUnx8PHx8fEQ/m8HBwcL3bWxscP36dbWPm56ejgEDBsDHx0d0fN26dejXr59SfUmlUhw+fBiHDx9GYGBglu9raWmhTZs2mDVrFhwcHFSaNxHlTCqVwtPTE1euXMH9+/fh5+eXY1uJRII2bdpg1KhRcHR0VHosTV1TfP78GZcvX8atW7fg4eGBuLi4HNtaWFhg0KBBGDlyJCwsLJQei4gUU5Bri7e3N/bs2YNr164hLS0t1zHGjBmDr776SqF+ubYQFT0RERH47bff8OjRI7x48QKJiYm5tjc1NcV3332HsWPHomLFinn2HxQUhC5duuRrbtbW1rh165ZS5/CeiEgxBXldkRN1Pg8JCAiAt7e3cG/04sULJCUlqdRnTjw9PbFnzx7cvn0b6enp2baRSCSoXbs2+vfvj1GjRqllXNI8iUwmkxX2JIiIiIiIiIiIiIiIiIiIqGiQyWTw9fVFUFAQIiIiEBkZCT09PZiYmKBixYqoX7++ShWCiJT1ww8/wN3dHQDQsmVLHD58uJBnRAXJz88Pr169QmhoKLS0tFChQgXY29ujSpUqhT01IlKjq1evYtGiRYiOjlb4HAcHB/zyyy8oX758ru0WLFgAT09PUcJsdpjcTlRw9u/fj1OnTuH169eQSqU5ttNUcvuePXuwcePGLMeVffE6MjISs2bNwv379/Nsq6urizlz5mDMmDFKzZWIFNOtWzcEBAQofV6vXr2wcuVKGBsb59lWk9cUZ86cwaJFi3JM1MhJuXLlsHbtWnTs2FGp84hIMQWxtgDA9u3b4erqqtQa0K9fP6xatQo6OjnXu+TaQlQ0eXt7Y8CAAUqfZ2RkhCVLluR5z1KQye28JyJSXEFdV+RG1echISEh+PHHH/Hs2TPRBl/ZUUdye2JiIlauXIlTp04pfE6jRo1w4sQJlcalgsPK7UREREREREREREREREREBA8PD5w4cQJ3795FeHh4rm2rV6+Ob775Bn369EHVqlULaIZEVJqcPXsWe/fuzbF6SZMmTTBlyhR06NChgGdGROp2584dzJo1C6mpqcIxHR0dtG7dGnZ2djA0NERkZCQeP36MFy9eCG28vLwwcuRInDx5MteXO69fv47Y2FiNfgYiUo6Hh0euFco0KTAwEDt27FC5n9TUVEyfPh0eHh7CMR0dHTg6OqJmzZqIj4+Hp6cnfH19hfYbNmyAiYkJBg4cqPL4RCQWERGR5Vi1atXg4OAAS0tL6OvrIyQkBPfu3UNISIjQ5u+//8anT5/w22+/QV9fP9cxNHlNERUVlSX51MjICE2aNEGVKlVgbm6OxMREvHjxAh4eHkLb8PBwTJ06Fdu2bct3EhsR5awg1pZt27bhl19+ER0rW7Ys2rZtiypVqkBbWxshISG4f/8+Pn78KLQ5deoUUlJSsGnTphz75tpCVDxYWlrCzs4OVatWhampKbS1tREVFYUXL17gyZMnwoZgCQkJcHFxQUpKCr7//nuF+9fS0oJEIlGobW4bZsjjPRGRcgriuiI36ngeEhkZiTt37qjUh6Li4uIwbtw4PH78WHS8SpUqaN68OaysrAAAnz9/xosXL/Dy5ctcN1CkoonJ7UREREREREREREREREREpdjr16/x008/4ebNmwqf8/btW/zyyy9wdXXFt99+i1mzZqFSpUoanCURlRbJyclYuHAh/vnnn1zbPX78GBMmTMDIkSPh7Oys8AuaRFS0JCUlYcmSJaLE9hYtWuCnn37K9tri3r17mD9/PsLCwgAAb968wbZt27Bo0SKlxrW0tESNGjXg7u6u2gcgIrUxMjJC/fr18fz5cyQkJGhsnKVLlyIpKQkAYGVlJawnytq8ebMoicPOzg6urq6wtbUVtTt37hwWLVokrHMrVqyAg4MD6tSpk89PQES5sbGxwcCBA9G3b19UqFAhy/fT09Nx4sQJrFu3DsnJyQAyNtz4+eefsXDhQqXHU/c1hY6ODjp16oTBgwejdevW0NXVzdImMDAQixYtEsZMS0vDvHnzcOnSJZQvX14t8yAiMU2tLb6+vti1a5fo2LBhwzBv3jwYGRllGeP48eNYt26dcF3h5uaGnj17omvXrrnOn2sLUdGira2NFi1aoHv37mjXrh1q1KiRY9vg4GCsXLkSN27cEI6tXbtW2ABDEWvWrFG5enJ2eE9ElD8Ffc/yhbqeh2SnSpUqMDY2ho+Pj1r6k8lkmDVrliix3c7ODsuWLUPz5s2zPefTp0+4cOECvLy81DIHKhhMbiciIiIiIiIiIiIiIiIiKqVu3ryJWbNmZUke0dfXR9OmTWFjYwNLS0ukpqYiLCwMgYGBePr0qbDzvVQqxdmzZ/Hx40ccPny4MD4CEZUwixYtEiW2SyQStGvXDnXq1EFqaiq8vb2FF5pkMhkOHDgAQ0NDzJo1q5BmTESquH79Oj58+CDE1apVw+7du7MkcnzRpk0b7N27F/3790daWhoA4M8//8S8efOgp6eX7Tlly5ZFgwYN0LBhQzRs2BANGjRApUqVEBQUxCqERIVEX18fDg4Ows9kw4YNUbNmTWhpaaFz584aS24/deoU/vvvPwAZL0V37tw5S0KZIkJCQnDkyBEhLleuHA4dOgRzc/Msbb/77jtIpVLhBfTU1FRs3boVO3fuzOenIKLsVKpUCSNHjoSTkxO0tbVzbKetrY0hQ4agUqVKmDRpkvB84/Dhwxg1ahSsra1zPFeT1xTa2tro3bs3Zs2ahcqVK+fatnLlyvjtt98wZswYIaEsISEBrq6uWLZsmUrzICIxTa8tv//+u6iyeu/evbF06dIcxxg2bBgAYOXKlcLxY8eO5ZjczrWFqGiyt7cX3U/kxsbGBjt37sT48eNx9+5dABmbgx49ehQuLi6anGaueE9EpLyCuGfJibqeh0gkElhbWwv3RF/ui0xNTXHq1Cm1rUsnT57E7du3hbh169bYs2dPjs9/AaB8+fIYNWqUWsangsPkdiIiIiIiIiIiIiIiIiKiUujcuXNwdnYWvUBZuXJlzJo1C126dIGhoWG250VERODatWvYtWsXgoKCCmq6RFQKHDlyBG5ubkJcoUIF7Nq1C/Xq1RO1u3v3LmbOnInY2FgAgKurK5o2bYoOHToU6HyJSHX37t0TxaNGjcoxsf2LunXrokuXLrh06RIAID4+Ht7e3mjWrFm27a9fv66eyRKR2mzZsqXAxwwPD8eGDRsAZLyMvXLlSty5cydffe3duxcpKSlCPHfu3GyTOL5wcnLCn3/+KSSKXbt2DS9fvkTdunXzNT4RZXXq1Cno6Cj+WryjoyN69eqF8+fPA8hIsrp27RqGDh2a4zmavKYYMmQIfvjhB4Xb6+npYdWqVejZsydkMhkA4PLly1i6dCkkEommpklU6mh6bbl//74onjJlSp5jfP/993B1dRWqrT548ABSqRRaWlpZ2nJtISoZtLW1MXfuXCG5HQBu375dqMntvCciUl5B3LNkR53PQ+rWrYtbt27l61xFRUREYOPGjUJcoUIF/PLLL7kmtlPxlfUKloiIiIiIiIiIiIiIiIiISjQ/Pz8sWbJElNg+cOBAXLhwAb17984xsR0ALCwsMHDgQPzzzz9wdnbmywREpBYJCQmiaj36+vo4cOBAlsR2AGjXrh127twperF68+bNwovXRFR8hIaGiuLGjRsrdF7Tpk1F8adPn9Q1JSIqoVavXo2oqCgAwKBBg9CkSZN89SOTyXD58mUhNjU1Re/evfM8b8iQIaL4ywYdRKQeyiSJfNGrVy9R7O3tra7pKC0/869evTrs7e2F+PPnz/jw4YM6p0VU6ml6bcl8P2RiYoKaNWvm2b+2tjYaNmwoxKmpqcI1jjyuLUQlR/369UWbAX78+LHQ5sJ7IqL8Kax7FnU9Dykox48fR3R0tBDPnTsXxsbGhTgj0iQmtxMRERERERERERERERERlSIpKSmYNWsWkpKShGNDhw7F6tWrlUpU19PTw+jRo3H48GFYWVlpYqpEVIqcOHEC4eHhQjxu3DhUr149x/YtW7bEd999J8QvXrzAv//+q9E5EpH6SaVSUWxgYKDQefIb8bCKIBHl5saNG7hw4QIAwNLSEvPmzct3X8+ePRMlonXs2BH6+vp5ntelSxfo6uoK8bVr1/I9ByJSjypVqojiz58/F9JM8q9q1aqiOPM9FREVDmXWlsz3Q4reCwEQJbhqAtcWoqKpTJkywteFuckn74mICo6q9yzqfB5SEGQyGf766y8htrCwwDfffFOIMyJNY3I7EREREREREREREREREVEpcubMGfj7+wtx7dq14eLiku/+GjdujPXr16tjakRUil28eFH4WltbG4MHD87znKFDh+bYBxEVD7a2tqJY0cpjwcHBolg++YKI6Iv4+HisWLFCiF1cXFC2bNl89/fw4UNRrGjFMwMDA9SrV0+IfX19ERMTk+95EJHq4uPjRXF+KikWtpLwGYhKGmV+LjPfD0VFRYk2I81N5vshU1NTmJubKznL3HFtISp6EhMThcrLAFC5cuVCmwvviYgKjir/Jqv7eUhBePLkCYKCgoS4e/fuvA4p4fh/l4iIiIiIiIiIiIiIiIiolJDJZNi/f7/o2I8//qhUxfbsKHt+SEgInjx5gvDwcMTGxqJs2bKwtrZG8+bNYWpqqtJcMnv58iX8/PwQGhoKXV1dlC9fHq1atUK5cuXy1d/nz5/x/PlzBAUFIS4uDjKZDIaGhrCyskKVKlVgZ2eXrz/L2NhYeHp6IjQ0FNHR0ShTpgzKlSuHJk2aoEKFCvmaa3aSk5Ph6emJjx8/Ijw8HPr6+nB0dMy1OjaQ8ZKZn58fwsPDkZKSAnNzc1SuXBlNmzZV6e9Oeno6PDw8EBAQgOjoaFhYWMDGxgYtWrTgyyqlTHh4OJ4+fSrETZo0gbW1dZ7nNW7cGBUqVEBISAgA4ObNm0hLS+PfH6Ji5KuvvsKxY8eE+J9//kHbtm1zPSc9PR2XLl0SYhsbG9SpU0djcySi4m3Lli348OEDAKBdu3bo3bu3Sv1l3igMAOzt7RU+t169evDy8hLiN2/eoHHjxirNh4jyz9fXVxSr8/67IMhkMrx69Up0rLh9BqKSSJm15auvvhKuLVJTU3HlyhV8++23ufYfGBgIb29vIXZ0dIREIlFhxmJcW4iKposXLyI1NVWIO3bsWGhz4T0RUcFR5Z5F3c9DCkLm3xMBQPPmzQtpJlRQ+Ns8IiIiIiIiIiIiIiIiIqJSwtPTE2/evBHiatWqoU2bNgUytlQqxdmzZ7Fv3z74+fll20ZbWxtt2rTBzJkz4eDgkGefzs7OOH36NICMxLbr168DAK5du4Zt27bh5cuXWc6RSCT4+uuvsWDBAoWrm9y7dw87d+6Eh4cHZDJZju10dXXRpEkTDB06FD179syzX29vb2zduhX37t1DWlpatm3s7e0xZcoUfP311wrNNXNy37Rp0zB9+nRER0dj06ZN+PvvvxEXF5flnOyS2+Pi4rBv3z6cPHkSnz59ynYsQ0NDfPvtt5g6dapSL9Skp6fjwIED2Lt3LyIiIrJ8v1y5chg6dCgmTZrEJOVS4tGjR5BKpUKsaLWfL23/+ecfABlVzl6/fo26deuqfY5EpBkdO3ZEnTp1hBc1//rrL3Tu3BmdOnXKtr1MJsNPP/2Ed+/eCcemTp0KLS2tgpguERUzT548wdGjRwEA+vr6WLZsmcp9Zr6fAoCKFSsqfG6lSpWy9MVEDqLCc+7cOVHcunXrQppJ/nh4eAjJKgBgZ2cHCwuLQpwREQHKrS0jRozAiRMnkJCQAADYsGEDGjdunOMzy4SEBCxcuFB4jqinp4dJkyapaeYZuLYQFT0vX77Ehg0bhNjMzAwjR45U+PxLly7hwoUL8Pf3R2RkJPT19WFmZobatWujRYsW6NmzJ8qXL69wf7wnIio4+b1n0cTzkIKQeQMfAGjYsCGAjN/9uLm54dKlSwgMDERUVBTKli2LihUronXr1vjmm2+4+Wkxxd8CExERERERERERERERERGVEu7u7qK4oHbpDwkJwZQpU/D8+fNc26Wnp+POnTu4e/cuZs6cicmTJys91tq1a3Hw4MEcvy+TyXD58mU8fPgQ+/fvz/Nlh40bN2LPnj0KjZ2amgp3d3fo6enlmdy+ZcsW/Prrr7kmywOAj48Ppk2bhs6dO2PLli0wMDBQaC5fvHjxApMnT8bHjx8VPsfd3R0zZ87MNvE8s8TERJw4cQJ///03tmzZAkdHxzz7jo+Px8SJE+Hh4ZFjm/DwcGzfvh337t3Drl27FJ43FV+qVPuxt7cXktu/9MXkdqLiQ1tbG1u2bMHQoUMRFRWF9PR0TJkyBYMHD4aTkxPs7OxgaGiIyMhIPHnyBAcOHMCDBw+E87///nv079+/ED8BERVVqampWLJkibCBzqRJk1C1alWV+w0NDRW+1tHRgZWVlcLnym8IFRISovJ8iCh/3N3dRc9ITExM0L59+0KckfJ27Nghinv06FFIMyGiL5RdW2xsbLB69WrMmzcPUqkUYWFh6N+/P8aOHYuuXbuicuXK0NbWRmhoKO7evYvdu3fj/fv3ADLupdasWYOaNWuq9TNwbSEqfDKZDHFxcfDz88PFixdx/PhxpKSkAACMjIywbds2WFpaKtzfjRs3RHFiYiKioqLw7t07XLlyBRs3bsSAAQMwb948lClTJs/+eE9EVDDye8+iqechBSHz74okEglsbGxw6dIlLFu2DJGRkaK2iYmJCA0NxZMnT/Drr7+iV69eWL58OUxMTAp62qQCJrcTEREREREREREREREREZUSDx8+FMWKVEdX1du3bzFixAhR9W8jIyM0bdoUtWrVQpkyZRAbGwtvb288efIEMpkMMpkMP//8M1JTUzFjxgyFx9qxY4eQ2G5ubo62bdvCxsYGEokEr1+/xu3bt4WXwMLDwzFnzhycPn0aenp62fb3119/ZUlsr1mzJho1aoTy5ctDV1cXcXFxCAkJga+vb5aKJTlZvXo1Dh8+LDpWoUIFtG/fHlZWVoiNjcXDhw/x4sUL4fvXr1/HmDFjcPDgQejq6io0TmRkJKZMmSIkttvZ2aFZs2YwNzdHVFQUvL29IZFIROdcuXIFs2fPRmpqqnDMysoKzZo1g42NDfT19REeHo4HDx4IVXPj4+MxefJk7N27F23bts1xPunp6Zg0aVKWxHZ7e3s0a9YMxsbGCAkJwe3bt/H582d4enpi8eLFCn1WKt5UqfYj31bRn0MiKjpq1qyJkydP4scff4S7uzukUimOHTuGY8eO5XiOpaUlZsyYgcGDBxfgTImoONm9ezf8/PwAZKwz48aNU0u/XyqrAoChoWGW6+ncyCeKZO6LiApOYmIilixZIjo2evRohZK5ioqTJ0+KNvwxMzPD8OHDC3FGRJTftaVXr14wMzPDsmXLEBgYiOjoaGzevBmbN2/O8Zw6depgyZIlaNGihVrm/gXXFqLC8ebNG9FGxFKpNNtNcR0dHeHi4oLq1aurdfyUlBT8/vvvuH//Pnbt2pVnEizviYg0T5V7Fk09DykI0dHRwtcWFhY4ffq0Qr8nlMlkcHNzw4sXL7B//35YW1trcpqkRkxuJyIiIiIiIiIiIiIiIiIqJV6+fCmK69evr9HxkpOTMXPmTCGxXVdXF+PHj8eYMWOy3Tn/5cuXWLhwoTBPV1dXtGrVCq1atcpzrE+fPmHHjh3Q0tLCzJkzMWbMmCxJ64GBgZg8eTJevXoFAHj9+jXOnTuHAQMGZNunq6ur8LWFhQW2bNmC1q1b5ziHjx8/4tKlSwgICMixzdWrV0WJ7bq6unBxccHQoUOzvAR2584dODs7IywsDEDG5gRbt27FvHnzcuw/s+PHjyM9PR2VKlXCunXrsp37l2R/IGMjggULFgiJ7RYWFnB2dkavXr2go5P19YIrV65g8eLFQqXdefPmwc3NDRYWFtnOZ//+/aIqE5aWlvjpp5/Qrl07UbvU1FS4urril19+wcWLF3PcfIBKjszVfgDlkttZ7YeoZKhSpQoOHz6MU6dOYf369aIXGeXZ29tj+fLlaNSoUQHOkIiKkzdv3mDXrl1CvGLFCrVdU2ZOvtDX11fqXPk5MJGDqHCsWLFC2KwNAGrUqFGsEj5evXqFNWvWiI4tXLgQpqamhTQjIgJUW1vatWuHCxcuYMuWLTh48CDS09NzbNuzZ084OztneR6iKq4tRIVHJpPl+nOvpaWFYcOGYdy4cUr97NepUwddu3ZFs2bNUKtWLZiZmUEmk+Hz5894+vQpzpw5g1u3bgnt37x5g/Hjx+OPP/6Aubl5jv3ynohI8/J7XaHJ5yEFISYmRvg6MTERy5cvF+LevXvj+++/R926daGrq4vAwEBcuHAB+/fvR2JiIoCMyu8zZ87EkSNHsv29JhU9WoU9ASIiIiIiIiIiIiIiIiIiKhiZE8W0tbVhaWmp0fF2794NX19fABkvYP3888+YOXNmtontAFC3bl0cOXIENWrUAJBRoWTbtm0KjZWamgqZTIZ169Zh0qRJ2b6sUblyZfzyyy+i7509ezbb/gIDAxEYGCjEzs7OuSa2AxkJuaNGjcKyZcuy/b5UKsW6detEx/73v/9h2LBh2VY3ad++Pfbt2yeqxLBv3z4EBQXlOo8v0tPTYWZmhiNHjuQ498x/FkuXLhVeJrO0tMTx48fRp0+fHF8A+frrr3HgwAEYGBgAAMLDw7NUpP8iJiYGO3bsEGJDQ0Ps27cvS2I7kJHwP2PGDMyaNQuAOAGfSib5lxiNjIwUPpfVfohKBn9/f4wZMwYuLi65JrYDgI+PDwYNGoQJEyZwQwsiykImk2HJkiXCNWS/fv3UWtU0OTlZ+FpXV1epc+XvUZKSktQyJyJS3MGDB3H69Gkh1tPTw//+9z+lE7MKS2RkJKZOnSokbwBA9+7d0a9fv0KcFRGpurbcuHEDffr0wb59+3JNcAWAf/75B127dsXatWtF1yWq4NpCVLRJpVIcPnwYXbt2xfr16/N8Xm5mZobjx4/j3LlzmDFjBtq1awdra2vo6+vDwMAAtra26NWrF/bs2YPdu3eLNrEICAjA6tWrc+2f90REmpXf6wpNPw8pCJl/v5OQkIC0tDRIJBJs2LABmzZtQosWLWBiYgIDAwPUrl0bM2fOxPHjx0Ubcjx+/BjHjh0rjOlTPjC5nYiIiIiIiIiIiIiIiIioFIiNjUVaWpoQGxsba3S8xMREHDlyRIgHDx6Mrl275nmeiYkJXFxchNjT01OotJ6Xzp07w8nJKdc2VatWRadOnYTY29s725dGP3/+LIobNGig0Bxyc/36dVFi+jfffIOePXvmeo6dnR1mzJghxOnp6Th69KjCY86ZMwc2NjZ5tnv69KmoqvqPP/6IqlWr5nlevXr18MMPPwjx8ePHIZPJsrQ7e/as6AXZ8ePHo06dOrn2PWHCBNStWzfPOVDxJ5+QrkxiiXxbJrcTFT93795F//79cffuXQAZL0YPHToUR48ehYeHB549e4Y7d+5g586d6NChg3DezZs30bdvX/j7+xfW1ImoCDp+/Dg8PT0BZCR1LFiwQK39Z772SE1NVepc+SSUL5tEEVHBuHjxItavXy86tnLlSrXc7xeExMRETJo0CQEBAcKxunXrYu3atYU4KyJSdW3ZuXMnJk6ciDdv3gAALCwsMGfOHJw7dw6PHj2Cl5cXrl69ijVr1qB27doAMq5BDh48iBEjRiA+Pl6l+XNtISp8NWvWhK+vr/Cft7c3bt++jd27d6Nfv35CAnlqair279+PSZMm5ZrgbmxsjCZNmig0tqOjI3bu3Cna4Pbvv/+Gn59fjufwnohIc1S5rtD085CCkN3vhoYNG5br737r1q2bZVOO/fv3QyqVqnt6pAFMbiciIiIiIiIiIiIiIiIiKgXkX3Q0NDTU6Hj//vsvoqKihDhzAnRe2rdvL6oW8uDBA4XOGz58uELtWrZsKXydmJiIDx8+ZGkjXzn6xYsXCvWdm2vXroni0aNHK3Te4MGDRdWp5fvJiZGREb777juF2mauAlG+fHn06NFDofOAjCT9LyIiIrLdjODKlSvC1zo6OhgyZEie/Wprayv8/5SKN/lKY8pU/GG1H6Li7f3795g2bZqwAUrZsmVx9OhRLFu2DM2bN0fZsmWhq6sLKysrdOnSBXv27MHy5cuF8yMiIjB58mTRBipEVHqFhoZi06ZNQrxw4UJR9S51yHyfoGy1VPlEDvl7DiLSnHv37mH+/PmiBIe5c+eib9++hTgrxaWmpmL69Ol48uSJcMzW1hZ79uzR+OaFRJQzVdeWCxcuYOvWrUJcr149nDt3DhMnTkSdOnVQpkwZ6Ovro3LlyhgwYABOnTolSu568uQJli5dmu/5c20hKpr09PRQvnx5ODo6Yt26dfjzzz9RsWJF4ft3797Fjh071DZe8+bN0b9/fyGWyWT4559/cmzPeyIizVDluqIgnocUBPk1QUdHBxMmTMjzvK5du4o2yw4ODoavr6/a50fqx+R2IiIiIiIiIiIiIiIiIqJSIHNyNACNJ4F5eHgIX5ubm6NmzZoKn6ulpQVbW1shViSxXFtbG02bNlWof/lK5tHR0Vna1KhRQ/Rntnr1aqGibH5lflHUysoKDg4OCp1naGiIr776SogDAgIQERGR53kODg4Kb2KQ+f9XkyZNoKWl+OsE8hXe5f9/SaVSeHt7C3GjRo1gYWGhUN+dO3dWeB5UfMlX41Cm4g+r/RAVbxs3bkRCQoIQr1y5Eo0aNcr1nCFDhog2SQkICMDvv/+usTkSUfGxcuVKxMbGAsjY0Kpfv35qHyPzi9aJiYlKVQKT33CMiRxEBcPLywtTpkwR3TuMHTtWoSSJokAqlWLBggW4ffu2cMzKygr79+9H+fLlC3FmRKWbqmtLamqqqDq6vr4+du7cCSsrqxzP0dPTw+rVq1GnTh3hmJubm+i5m6K4thAVH3Xr1sWePXtEG4IePHgQkZGRahtjwIABovjevXs5tuU9EZH6qXpdURDPQwqC/O+y7e3tYW1trdC5nTp1EsUPHz5U27xIc3QKewJERERERERERERERERERKR5JiYm0NHRQVpaGgAgLi5Oo+NlTnCOjIyEvb29Uuenp6cLX2euAJ+TsmXLKpzILd8uc1LdF7q6uhg+fDh+/fVXABmfYcyYMahWrRo6deqEFi1aoEmTJgonactkMgQEBAhxvXr1FDrvi/r16+PixYtC/ObNmzzHrlGjhkJ9p6SkwN/fX4gvX76s9P+vzOT/f338+FH0Z5y5ekJeypUrBysrK4SFheV7PlT0yb/EmJycnCXhPSfy1YH4QiRR8REbG4urV68KcZUqVdCjRw+Fzp0wYQKOHTsmxGfPnsXYsWPVPkciKj48PT2FNUVXVxfLly/XyDjW1tYIDAwEAKSlpeHz588KJ4B9/PhRFFeoUEHt8yMiMT8/P4wfP150Tzpw4EAsWLCgEGelnOXLl+PChQtCbGpqir1796JKlSqFOCui0k0da8udO3dEz7t69+6NSpUq5Xmerq4uxowZg4ULFwrHzp49i4YNGyo8NsC1hai4qV27Nr755hucPXsWAJCUlISbN2/CyclJLf03aNAAurq6wqajHz58yLEt74mI1EvV64qCeh5SECpUqID3798Lce3atRU+187OThSHhoaqbV6kOUxuJyIiIiIiIiIiIiIiIiIqJUxNTREeHg4gI3k8PDwc5cqV08hY8lVDMierK+tLtYHcKJPQKpFIRLFMJsu23fTp0/HmzRtcuXJFOPbu3Tvs378f+/fvB5CRQN6qVSt0794drVq1yrHieVxcnOjPwNLSUuH5Ztc+u2rz8sqWLatQ35GRkaI/A5lMptb/XzExMaJY0Q0BvrC0tGRyewkn//MbHx+v8N9fVvshKr68vb1F/960aNEiy7/ROalUqRJsbW0RFBQEAHj16pVSG2MQUcmTeYOl1NRUfPvtt3meI19h8Mcff8TixYuFeO3atVmSRWrWrAlPT08h/vDhQ74TOWrWrKnQeUSUP+/fv8eYMWNE60OPHj2wcuXKwpuUkn766Sf88ccfQmxkZITdu3crtWkcEamXutaWJ0+eiOKWLVsqfK5822fPnik1NtcWouKpbdu2QnI7APj6+qqtby0tLZiZmQnP4XOrCs97IiL1Ucd1RUE9DykINWvWhLu7uxCbmpoqfK58W0V+j0qFL/vfqhMRERERERERERERERERUYkj/4Kisi8+KkM+oVkVOSWfa5quri62b9+OzZs3o379+tm2efPmDY4dO4ZRo0ahZ8+eokT4zFRNwM0u+TcvOjqK7XevyOYBypD//6XqZzc0NFR5TlS0WVtbi+KQkBCFz2W1H6Li68uGO19YWVkpdX7m9lKpVPQiJxFRenp6nv/JX7dKpVLR9+Vf9gYyNrfKzMfHR+E5ybeV74uI1Cc0NBSjRo0SbZTm6OiIjRs35rgpXVHj6uqK3377TYj19PSwc+dONG7cuPAmRVTKqXNtiYiIEMXK3A/Jb4KZWxKqPK4tRMWX/M9+XFycWvtPSkoSvs5t80DeExGph6buWTT1PKQgyFdqT0lJUfhc+bZ6enpqmRNpFiu3ExERERERERERERERERGVEs2aNcPdu3eF2MvLC46OjhoZK3NCctOmTXHs2DGNjKNpEokEvXr1Qq9evfDu3Tvcv38fDx8+xOPHjxEYGChq++7dO0ybNg3Tpk3D9OnTRd8rU6aMKE5ISFBqHvLt5ftThYGBgSieNGkSZs+erbb+Vf3siYmJapsLFU3yFXo+fPiAJk2aKHSufCI8q/0QFR/yL0pnfolaEfL/Pii7eQoRUX40a9ZMFD9+/BhDhw7N87ykpCS8fPlSiOvUqQMTExO1z4+IMhJGR40aheDgYOFYy5YtsW3bNujq6hbizBR39OhR/Pzzz0Kso6ODn3/+GW3atCm8SRGVcupeW1S5H5Jvq+jGkFxbiIo3+WT2smXLqq3vyMhI0Sa45cqVy7Et74mIVFcS7lk0oUWLFqI4NDRU4XPlf1dkYWGhljmRZjG5nYiIiIiIiIiIiIiIiIiolGjZsqUodnNzy5KErS5mZmbCSxnySeDFVbVq1VCtWjV8//33ADJelPj333/x119/wdvbW2i3Y8cOtGnTBs2bNxeOGRsbQ1tbG+np6QCAz58/KzW2fHtTU9P8fowszM3NRbG6/3/Jv2QnX5kqL8r+WVHxI5+Q7uPjg169eil07vPnz0Uxq/0QFR/yLxj6+/srfG5qairev38vxHp6enwhmqiU69q1K3x9fZU6Z/v27dixY4cQr1u3Dv369cv1nAYNGsDa2lp4wfrGjRtITk7OtbIhAFy9ehWpqalC3KVLF6XmSkSKiYuLw7hx4/DmzRvhmIODA1xdXbNs7FZUnT17FqtWrRJiLS0tbNiwgesGUSHSxNqS3f2Qoj/nr1+/FsW5JaF+wbWFqPiTr3pesWJFtfV969YtUVy3bt0c2/KeiEg16r6uKKjnIQXBzs4OVatWRUBAAADgyZMnkEqlClWyf/z4sSi2t7fXyBxJvfL+P0tERERERERERERERERERCVC8+bNUb16dSH+UolcE2rVqiV8HRYWhrdv32pknMJUoUIFDBkyBH/++SemTJki+t7JkydFsUQiQdWqVYX4xYsXSo2lyQTeMmXKiF6E8/DwUFvfQMZLdpmr6WauzpKXiIgIhIWFqXU+VPQ0adJE9HKS/EtIuXny5InwtZmZmWjtIaKizd7eXlSJyN3dXeE1/9q1a0hISBDixo0bq3t6RETZkkgk6N69uxDHxMTAzc0tz/OOHz8uijP3QUTqkZSUhEmTJonun+3s7LB3714YGxsX4swUd+3aNSxatAgymUw4tnLlSvTu3bsQZ0VUumlqbXFwcBDF//zzj8Lnyl975HU/xLWFqPhLSkrC+fPnRcfatm2rlr5TU1OxZ88e0bH27dvn2J73RET5VxLuWTTtm2++Eb4OCwvDzZs38zwnKioKV65cEWJdXV3RBuRUdDG5nYiIiIiIiIiIiIiIiIiolJBIJBg9erTo2Jo1a5CSkqJSv9md36ZNG1GsyMtNxdmMGTNEFZeyS+Bu0qSJ8HVYWBi8vLwU6jspKQl37twR4qpVq2ap7qSqzP+/Pn36hAcPHqitby0tLTRs2FCIvby8FK7efu3aNbXNg4qucuXKoVGjRkL85MkTofJPbh4/foyQkBAhdnR0hI6OjkbmSETqZ2RkhFatWglxcnIyVq9ened5kZGR+Omnn0THOnfurPb5ERHlZOzYsdDT0xPiTZs2ITIyMsf2Z86cEW0g1aVLl1wrIRKR8tLS0jBz5kzRz1q1atWwf/9+mJqaFuLMFHf//n3Mnj0baWlpwjEXFxcMHDiwEGdFVLppcm1p0aIFypYtK8Q+Pj44evRonuc9fvwYJ06cEB3r1KlTju25thAVLSkpKUpt/goAUqkUy5Ytw4cPH4RjjRo1ynYD3NDQUERFRSnV99KlS/Hq1SvhmJWVFXr16pXrebwnIlJeSbhnKQijRo2CiYmJEK9btw5xcXG5nrN69WrRRqj9+vXjZgHFBJPbiYiIiIiIiIiIiIiIiIhKEScnJ9FLT35+flkSxJTx+PFjODs7ZzneqVMnlClTRogPHTpUoitwSyQS2NraCnFqamqWNl26dBHF+/fvV6jvEydOiF7c6Nq1az5nmTP5Sk1btmxBenq62vrPPOfU1FT88ccfeZ4jlUoVeqmXSoYePXoIX6enpyv0d+TYsWM59kFExcPUqVNF8cWLFzFjxowcrxm8vLwwdOhQBAcHC8csLS0xePBgjc6TiCizChUqYPjw4UIcHh6OESNGICgoKEvbc+fOYfHixUKsq6uLmTNnFsg8iUoLmUwGZ2dn3LhxQzhmY2ODAwcOwNLSsvAmpoRnz55hypQpSE5OFo7NmDEDo0aNKrxJEZVyml5b9PX1MXbsWNGx1atX45dffkFSUlK28zl//jzGjRsneu7YqVMnNGjQINsxuLYQFT1JSUlwcnLCjBkz8O+//+a58fDTp08xYsQInDlzRjimpaWFH3/8Mdv23t7e6Ny5MzZs2AAfH59c+3758iVGjRqFU6dOiY7PmTMHRkZGuZ7LeyIi5ZSEe5aCYmZmhsmTJwtxQEAARo4ciXfv3mVpGxcXBxcXF5w/f144VrZsWdH5VLRJZDKZrLAnQUREREREREREREREREREBefly5cYNGiQ6MXGwYMHY8mSJdDV1VWoj5SUFBw5cgRbtmxB48aNcfjw4SxtNm/ejF9//VWImzRpgj179oh23M/LvXv3slSB/8LZ2RmnT58GkPESyPXr1xXq88GDBxgxYoQQHzp0SFQ5FgACAwNRpkwZhSukR0VFoVOnTkJlgA4dOmDPnj2iNlKpFF9//bXoBa+tW7fmmpDr7++PQYMGCcnt2trauHLlCmxsbLJtX6dOHeHradOmYfr06QrNHwC+//57PH78WIiHDRuGJUuWQCKRKHR+cnIynj17hmbNmmX5XkxMDDp06IDExEQAGdV6T5w4gdq1a+fY3+7du7Fp0ybRsZYtW2b7d42Kv4SEBHTt2hXh4eEAMl7yPnv2LKpXr55te3d3d4wYMQJfXnupV68eTp8+rfDfVyIqOrZs2YJdu3aJjunp6aFly5aws7ODkZERoqKi8OTJEzx79kzUTldXF3v27MnxWgHIqA62aNGibL+XeSMXiUQCLa3sa8Xk9UI4ESknODgYX3/9dbbfk99gSVtbO9t2Bw4cQMuWLVWax/bt27Fjxw4hXrduHfr166fQuSkpKRg9ejQ8PT2FY7q6unB0dESNGjWQkJAADw8P+Pr6is5bvXo1K6USqVlwcDA6d+4sOpbbv+s5sbGxwZUrV3L8viavKVxcXLIkluW0/uVm7dq1cHJyUvo8IsqqINaWlJQUjB07Fu7u7qLjZmZmaNu2LWxtbaGtrY1Pnz7h/v37ok2+AKBixYr4448/YG1tnW3/XFuIip6YmBi0aNFCiA0NDVG3bl3UqlULpqamMDQ0RHx8PEJCQuDt7Y3AwEDR+RKJBGvWrEH//v2z7f/q1auijQStrKxgb2+PypUrw8TEBDKZDBEREXj69GmWexUgoyL7ggULFPosvCciUlxB3bMoSpXnIYsWLRJtuPGFTCaDVCoVYi0trWx/X9OiRQscPHgw1zFkMhlmz56Nf/75Rzimq6uLVq1aoU6dOtDV1UVgYCBu376NmJgYoY22tjZ27tyJjh07KvRZqPDpFPYEiIiIiIiIiIiIiIiIiIioYNWtWxcrV67EokWLhBew//jjD9y7dw+zZs1Cly5dYGBgkO25ERERuHbtGnbt2pVtFY7MJk2ahBs3bggvLz1+/BgDBgyAs7MzOnbsmGMS6qdPn3Dp0iWcPHkSHz58EL0cVVA8PDywfPly9OrVC7169ULr1q2ho5P9r9iDgoIwf/58IbEdALp165alnZaWFlxcXEQvl82bNw8REREYMmRIlj+P//77DwsWLBBVbR8zZkyOie2qWr16NQYNGoT4+HgAwNGjRxEQEID58+ejbt26OZ7n5+eHCxcu4OTJk2jUqFG2ye1ly5bF1KlTsXHjRgAZicyjR4/G//73vywJiWlpadi1a5fwYo2enl6eFWyo+DMyMsKUKVOwatUqABmbJYwePRqurq6oV6+eqO1///2HGTNmIHM9hzlz5jCxnaiYmj17NnR1dbFz507huiQlJQV37tzBnTt3cjzPwsIC69evzzWxHcjYXEY+WTY7MplMoXZEpDplft5yalfYdZ309PSwfft2zJw5U0hIS01NxdWrV7Ntr6Ojg9mzZzOJg0gDslsP8vPvel7tNXlNkd1nyM91SeZkEiJSTUGsLXp6eti5cydcXFxEiWpRUVG4cOFCrv3Wq1cPP//8c46J7V/mq8x8csK1hUhzEhMT8fjxY9GmszmxtrbGihUr0KlTJ4X7DwsLw82bN/NsZ2BgAGdnZwwZMkThvnlPRKS4grpnKQiK3hfldP2gyHWFRCLBhg0boK2tDTc3NwAZ60tuz4uNjY2xefNmODo65tk/FR1MbiciIiIiIiIiIiIiIiIiKoWcnJxgamqKOXPmCEnZ79+/x5w5c6Cvr4/mzZvDxsYGFhYWSEtLQ1hYGN6/fw8vL68sLy3kVFnAyMgIO3fuxPDhw/Hx40cAwLt37zBp0iRYW1ujRYsWsLa2hqGhIWJjYxEWFgYfHx8EBAQIL3ooU+Vd3ZKTk3Hq1CmcOnUKZcqUQb169VCzZk2YmZlBV1cX0dHR8PX1xaNHj5CWliacV79+ffTt2zfbPrt27YoffvhBqD6empqKFStWYPfu3Wjfvj2srKwQGxuLR48e4fnz56JzmzVrhpkzZ2rs89aqVQubN2/GjBkzkJycDADCiyK1a9dGo0aNYGlpCYlEgtjYWAQHB+P58+f49OmTQv2PGTMGt27dEl50CwsLw6hRo1C/fn00bdoUxsbGCA0Nxe3btxEWFgYA6N69OyIjI7NUsKKSafjw4Xj06BH+/vtvAMDHjx/Rt29ftGvXDnXq1EFaWhq8vLyyvOw5adIkdOjQoTCmTERqMm3aNHTu3Bn79+/HpUuXhH+HslOuXDkMGDAAI0eORLly5QpwlkREYhYWFjh48CAOHTqEI0eOZKmqCGTcK7Vu3RqzZ8+Gg4NDIcySiIiIijITExPs2LEDV69exaFDh+Du7p7rJj61atXC0KFDMXDgQOjp6RXgTIlIHcqUKYMNGzbg9u3b8PDwQGhoaJ7n2Nvbo2/fvujXrx+MjY1zbVunTh0MHToU7u7uePPmTZ5JpFZWVujbty+GDRuGChUqKPVZAN4TEZHm6OvrY9OmTfj666+xd+9eeHt7Z9vOyMgITk5Owu+eqXiRyAp7+0oiIiIiIiIiIiIiIiIiIio0fn5+2LBhQ66VUXOio6OD/v37Y8aMGbC0tMyxXUREBObOnYv//vtP6THKlSuX43nOzs44ffo0AMDGxgbXr19XqM8HDx5gxIgRQnzo0CG0atVK1ObUqVNwcXFRer6NGjXCrl27YGFhkWu7LVu24Ndff1W44mSnTp2wZcsWGBoa5tquTp06wtfTpk3D9OnTFeo/s+fPn2PGjBkICgpS+tyePXvi559/zvH78fHxmDBhAjw9PfPsq2nTpti9ezemTJkiJLe3bNlS2BiASqbk5GQsWLAAFy9ezLOtRCLBiBEj4OLiwqrtRCVISkoKfHx84O/vj5iYGCQlJaFMmTIwNzeHvb09atSowZ95IipyZDIZvL298fbtW3z69AkGBgawtrZGo0aN+HI1ERERKSw2NhZeXl4ICgpCTEwMpFIpjI2NYW1tjYYNG/K6gqiE+fTpE/z9/REUFITo6GgkJyfDyMgIxsbGsLW1Rf369VG2bNl89Z2QkIDXr18jKCgInz9/RkJCAiQSCUxMTGBhYYH69eujcuXKavssvCciIk0KCAiAj48PPn36hOTkZJibm6Nq1apo0qQJdHV1C3t6lE9MbiciIiIiIiIiIiIiIiIiIri7u+OPP/7Af//9h4iIiFzb1qpVC71794aTkxMqVqyo8Bi3bt3Cvn374OnpidTU1BzbGRoaolmzZujWrRu++eabHKu3azK5PSoqCv/88w9u3ryJhw8fIiYmJtc+69Spg2HDhmHAgAHQ1tZWaB5eXl7YunUr7t+/L6r8nlm9evUwefJkdO/eXaE+1ZHcDmQkFp4+fRpHjhyBn59frm3LlSuHNm3aoGfPnujYsSN0dHRybZ+eno4DBw5g79692f5dMzc3x/fff49p06ZBR0cHP/zwA5PbS6EzZ87gt99+y/HvX+PGjTF16lRWbCciIiIiIiIiIiIiIiIqZpjcTkREREREREREREREREREAplMhpcvXyIoKAgRERGIioqCrq4uypYti4oVK6JBgwYwNTVVaYyEhAQ8efIEHz58QFRUFNLS0mBkZARLS0vUqFEDNWrUgJ6enpo+kepkMhnevHmDd+/eISQkBHFxcZDJZChTpgwqVqwIe3t7VKpUKd/9x8TEwNPTE6GhoYiOjkaZMmVQrlw5NGnSRKnNAzQlPDwcjx8/xufPnxEdHQ2JRIIyZcqgUqVKqFGjBqpUqZKvCrppaWnw8PDAu3fvEBMTAwsLC9ja2qJ58+asskACX19fvHr1CqGhodDW1kb58uVRv359VK1atbCnRkRERERERERERERERET5wOR2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi0jitwp4AERERERERERERERERERERERERERERERERERERERERlXxMbiciIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKNY3I7ERERERERERERERERERERERERERERERERERERERERaRyT24mIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEjjmNxOREREREREREREREREREREREREREREREREREREREREGsfkdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItI4JrcTERERERERERERERERERERERERERERERERERERERGRxjG5nYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiDSOye1ERERERERERERERERERERERERERERERERERERERESkcUxuJyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIo3TKewJEBERERERERERERERERERERERERFR6SKVSvH+/XsEBAQgNDQUMTExSElJgZGREczMzFC3bl3Url0b2traKo2TmJiIhw8fIjg4GFFRUTA2Noa1tTUcHBxQvnx5NX0awMvLC+/evUNoaCgMDQ2FMaytrdU2BhHlz8ePH+Ht7Y0PHz4gISEB+vr6sLS0RPXq1VG3bl3o6ekV9hSJqJiQSqXw9vaGv78/IiIioKOjg/Lly8POzg61atXKd78pKSnw9/dHcHAwQkNDER8fj/T0dJiYmMDS0hL169dH5cqV1fhJMsb88lmioqIglUphYmICW1tb2NnZoWLFimodj4hKptDQULx9+xYhISGIjIxEYmIi9PX1UbZsWdSoUQP29vYwNDQs7GkSlRqFcU1BlF9MbiciIiIiIiIiIiIiIiIiIiKiIolJYkRFQ3x8PHx8fODl5QUvLy94e3sjODhY+L6NjQ2uX7+e7/7Dw8NFfXt7eyMqKkr4ft++fbF+/XpVPkKOPnz4gF69eiEhIUF0/Nq1a7C1tdXImESlWUREBH777Tc8evQIL168QGJiYq7tTU1N8d1332Hs2LFKJ1i9ffsWO3bswOXLl5GSkpLl+9ra2mjdujWmTp2KZs2aKdX3F1KpFIcPH8bhw4cRGBiY5ftaWlpo06YNZs2aBQcHh3yNQUT5I5VKce7cORw6dAjPnz/PsZ2uri6aNGmCCRMm4Kuvvsr3eOnp6RgwYAB8fHxEx9etW4d+/frleu6DBw8wYsSIfI+dma+vr1r6ISKx6Oho7Nu3D8eOHUN0dHS2berUqYORI0eif//+CvXp4+OD48eP4/Hjx/D390d6enqu7atUqYKBAwfihx9+UClRNDAwELt374abm1uW+6DMrK2t0aFDB6xYsULlzYaIiiM+C8nZnj174O7uDi8vL9Gcs6Orq4vOnTtjzJgxaNy4sQqzJioZNLG2FMQ1xalTp+Di4qLUvL749ttvsXHjxnydK+/KlSuYNm2a6Jiq6zEVHia3ExERERERERERERERERFRseXs7IzTp0+r3M8vv/yCrl27qmFGRKQqJokRFR379+/HqVOn8Pr1a0ilUrX3v2DBAnh6eope4Cxoy5cvzzWhg4jUKzg4GHv37lW4fXR0NA4fPoy//voLS5YsyTNB9IuTJ09ixYoVSE1NzbFNeno67t69i/v372PChAmYNWuWwvMCgMjISMyaNQv379/PsY1UKsXdu3fh7u6OOXPmYMyYMUqNQUT5ExQUhLlz5+LJkyd5tk1NTYW7uzsaNWqkUnL7vn37siS2FzQjI6NCHZ+opPLy8sKUKVMQFhaWaztfX18sWrQIV65cwcaNG2FsbJxr+3v37uGPP/5QeB7v37/Hpk2bcOLECWzevDlfz0QOHjyIjRs3Zrvxj7zQ0FCcPHkSS5YsYXI7lSp8FpK3nTt3Knx+amoqLl26hMuXL2P48OFwcXHhmkKlkibXlsK4pigMsbGxWLlyZWFPg9SIye1ERERERERERERERERERESUL1evXsWLFy8AACYmJhg1alThToiKPSaJERUtHh4e8PPz01j/169fR2xsrMb6z4ubmxtu3rxZaOMTEWBpaQk7OztUrVoVpqam0NbWRlRUFF68eIEnT54IL3wnJCTAxcUFKSkp+P7773Pt88iRI1i1apXomJWVFdq3b48KFSogMTERT58+xZMnTyCTyZCeng5XV1fo6upi6tSpCs07NTUV06dPh4eHh3BMR0cHjo6OqFmzJuLj4+Hp6SlUUE5NTcWGDRtgYmKCgQMHKvNHRERKevXqFUaPHi1KQpVIJHBwcEDdunVRrlw5JCcn4+PHj/D29s52Qy1lBQYGYseOHfk+XyKR5CvJSyqVQiaTCXG3bt3yPQciyp6XlxeGDx+O5ORk4ZiRkRHatWuH6tWrQyqVwt/fH//995/Q5t9//8XMmTPx66+/QkdH8ZSdqlWrombNmrC1tYWJiQnS09MRFhaGJ0+ewN/fX2gXGBiI0aNH48CBA2jYsKHC/a9atQpHjhwRHTMzM0Pr1q1RsWJFGBoaIioqCn5+fnj27BmSkpIU7puoJOGzEOUYGRmhbt26qF69OszNzWFoaIi4uDi8efMG7u7uSExMBADIZDIcPnwYkZGR2LRpk9rGJyouNL22ZKbpawoASt2/qGtDi//973/49OmTWvqiooHJ7UREREREREREREREREREVGJoaWlBIpEofV5+zqGM5PbTp08DAGxsbJjcTiphkhhR8WBkZIT69evj+fPnGql4bmlpiRo1asDd3V3tfWcWFRWFtWvXAsi4frCwsMDnz581OiYRZbzQ3KJFC3Tv3h3t2rVDjRo1cmwbHByMlStX4saNG8KxtWvXom3btqhSpUq253h7e2P9+vWiY5MnT8aUKVOgp6cnOv748WPMnj0bHz9+BABs27YNDRs2RIcOHfL8HJs3bxZds9jZ2cHV1RW2traidufOncOiRYuECvIrVqyAg4MD6tSpk+cYRKS8iIgIjBs3TpTY3rVrV7i4uGT5+fzCz88Pp06dgomJSb7HXbp0qZAEamVllWd1Z3ktW7bMV9X37777Trg/AoC+ffsq3QcR5SwuLg4zZ84UJbZ36tQJa9asQbly5URtP378iIULF+LBgwcAgDt37mDr1q2YO3dujv0bGhqiW7du6NatG9q0aQNLS8sc23p6emLx4sV4+/atMDcXFxecPn0aurq6eX6WX3/9VZTYbmZmBmdnZ3z77bfZJuAnJCTg33//xeHDh/Psm6g04LMQsZo1a6J9+/bo1KkT6tevn+NGHnFxcdi1axf27NkjHHNzc0PHjh3x7bff5u9DEJUg6lpbCvKa4ovLly/neI+lCR4eHjhx4gSA/N1zUdHE5HYiIiIiIiIiIiIiIiIiIioxDhw4gFatWhX2NIgoH5gkRlT06Ovrw8HBAQ0bNkSDBg3QsGFD1KxZE1paWujcubPKL3SXLVtW6PfLGJUqVUJQUBC6dOmipk+RvfXr1yM8PBwAMHjwYPj7+zO5nagA2NvbZ6kYmhMbGxvs3LkT48ePx927dwEAycnJOHr0KFxcXLI9Z8uWLcI1AgBMnToVM2bMyLZtkyZNcOjQITg5OSE+Ph5ARhWw9u3bQ0tLK8d5hYSEiD5DuXLlcOjQIZibm2dp+91330EqlWLhwoUAMjbn2bp1K3bu3JnHpyei/FizZg1CQkKEeNKkSZg9e3au59jZ2cHZ2TnfY546dQr//fef0Ffnzp2xa9eufPenKB8fH1Fiu42NDZ+HEKnZkSNH8OHDByFu3bo1duzYkW0SZ8WKFbFnzx4MGTIEz58/BwAcPHgQw4YNQ4UKFbLtf+jQoRg6dKhCc2nevDmOHj2KPn36CMlcr169wq1bt/K8d3r9+jW2b98uxNbW1vj9999zTUgzMjJCr1690KtXL4XmR1SS8FlI3v7880+F2hkbG2PevHkwNDTEtm3bhOP79u1jcjuVOppcWwrqmqKwpKSkYMmSJZDJZACARYsW5XmfR8VDzk/fiIiIiIiIiIiIiIiIiIiIiIgKQE5JYtm9aP3dd99h9erVQvwlSYyI1G/Lli04efIkli5din79+qF27dq5Jnwq6/r16zhw4ADmzp2Lbt26oVKlSmrrOzf//fcfTp8+DSCj0k9u1RSJqHBpa2tn+Rm9fft2tm3fvHkjJMEDGYmeU6ZMybX/KlWqYNKkSULs5+eHy5cv53rO3r17kZKSIsRz587NNrH9CycnJ7Ro0UKIr127hpcvX+Y6BhEp77///oObm5sQd+nSReMJD+Hh4diwYQMAQCKRYOXKlTlWLlW3L9cyXzg5OUEikRTI2ESlxdGjR4WvJRIJli9fnuvPuL6+PpYtWybEycnJ2L17t9rmU65cOYwdO1Z0LKfrosyWLFkibP6jpaWFnTt3FmilVaLihs9C1G/ChAkwMzMTYh8fH24wSKWOptcWZeT3mqKw/PLLL0Kl+a+++grffPNNIc+I1IXJ7URERERERERERERERERERERUqJgkRkQFJTExEUuXLhViFxcXmJiYFOKMiCgv9evXh5GRkRB//Pgx23b37t0TxU5OTgolmQ4YMECUEHrhwoUc28pkMlHyu6mpKXr37p3nGEOGDBHFly5dyvMcIlJO5gRSXV1duLi4aHzM1atXIyoqCgAwaNAgNGnSRONjAkBaWpookV8ikaBv374FMjZRafHq1St8+vRJiJs3b47q1avneV6jRo1gZ2cnxBcvXoRUKlXbvJo3by6KQ0JCcm3v6emJR48eCXH//v3RoEEDtc2HiIqHwn4Woquri8aNG4uO5bV+EZFmKXtNUVh8fX3x22+/AQAMDAxEGwlR8cfkdiIiIiIiIiIiIiIiIiIiIiIqNEwSI6KCtG3bNgQGBgIA2rdvj169ehXyjIhIEWXKlBG+lslk2bZ5/vy5KFY0ydTCwgLVqlUT4tu3byM5OTnbts+ePUNoaKgQd+zYEfr6+nmO0aVLF+jq6grxtWvXFJobESkmMDAQ9+/fF2JHR0dUrlxZo2PeuHFD2AzD0tIS8+bN0+h48mNHREQIcfPmzTX+eYlKm/xeV8i3DQ8PFyWXq8rY2FgU55U4/+eff4ri4cOHq20uRFR8FIVnIcquX0SkWcXhZ1IqlWLJkiVITU0FAEydOpX3PSVM3ltSEhERERERERERERERERERUYGJi4vDw4cPERoaisjISBgYGMDS0hKNGjWCra2t2sYJCQnBkydPEBISgrS0NJQrVw6NGjVCjRo11DaGopKTk/HixQu8fv0a0dHRSE5OhoGBAUxNTWFjYwM7OztYWFgU+LyoYKiaJPblxaZr165h5syZGpsnERV/z58/x8GDBwFkVPpZvnx54U6IiBSSmJgoVEYGkOOLzJGRkaLY2tpa4TGsra3x9u1bAEBCQgKePXuGZs2aZWn38OFDUaxoopuBgQHq1asHLy8vABmVx2JiYlC2bFmF50hEObtw4YJo4wtNJ2zFx8djxYoVQuzi4lKgP89nzpwRxU5OTgU2NlFpkfnaA1D+uiIzDw+PLNVR8+vDhw+iOLcEr7S0NNFGgDVr1kTdunXVMg8iKj6KyrOQ4OBgUazO33UQkfKUuaYoLIcPH8bTp08BALVr18bo0aMLeUakbkxuJyIiIiIiIiIiIiIiIiIikrNx40bs2bNHiM+fPw87OzuFz09PT0fHjh3x6dMnAEDDhg2zVEqS5+npiR07dsDDwwNpaWnZtqlduzamTJmCnj17QiKR5NrfqVOn4OLiIsTXrl2Dra0tXr9+jfXr1+Pu3bvZVmKoX78+nJ2d0bJly2z7ffDgAUaMGJHleHBwMOrUqZPjfHx9fbMcCwsLw/bt2/H3338jLi4u189TrVo1dOnSBTNnzlQo8ZmKDyaJEVFBSE9Px+LFi5Geng4AmDx5cpF8aZOIsrp48aKwmQ2QsRFOduSrrevp6Sk8hvz1pb+/f7bJ7f7+/qLY3t5e4TEyX7cAwJs3b9C4cWOFzyeinD158kQUqyuJNCdbtmwRkkHatWuH3r17a3S8zCIjI3Hjxg0hNjQ0RI8ePQpsfKLSIikpSRSrcl3x+vVrtcwJANzc3ERxTtdFAODn54eEhAQh1vTaSERFT1F5FhIYGCi6F2rcuDE3syUqZMpcUxSGDx8+4OeffwYASCQSLF++HLq6uoU7KVI7JrcTERERERERERERERERERHJ6d+/vyi5/fTp01i4cKHC59+5c0dIbAeAfv365dg2JSUFixcvxtmzZ/Ps99WrV5g9ezbc3NywadMmGBoaKjwnIONllcWLFyMxMTHHNs+fP8fo0aOxfv16fPvtt0r1r4zHjx9j4sSJiI6OVqj9u3fv8Ntvv2H06NGwsrLS2Lyo4DFJjIgKwr59++Dj4wMAqFWrFsaOHVvIMyIiRbx8+RIbNmwQYjMzM4wcOTLbtiYmJqI4JiZG4XHk28pfn3zx5s0bUVyxYkWFx6hUqVKWvnjdQqQez549E762trZG+fLlAQABAQE4deoU7ty5g48fPyIxMRHm5uaoXr062rVrhz59+qBcuXJKjfXkyRMcPXoUQEYC67Jly9T3QRTg5uYm2vCjW7duMDY2LtA5EJUG6ryukL9+yK9z587hzJkzQtysWTO0b98+x/aZ10YgY/NNAEhNTcXVq1fh5uaGV69eISwsDIaGhrC0tETTpk3RpUsXfPXVV2qZMxEVrqLwLCQmJgZz584VEuwBYMaMGQU+DyL6P8peU2TH1dUV/v7+CAoKQmxsLExMTGBubo4GDRqgVatW6NGjBwwMDPI9x+XLlwub9AwYMICb9JRQTG4nIiIiIiIiIiIiIiIiIiKSU716dTRp0gSPHz8GkPGix9y5c6Gjo9iv2U+fPi18ra+vn2MVt+TkZIwZMwaenp7CMS0tLTRq1Aj16tWDmZkZkpOT4e/vj/v37wtVo65du4aJEydi//790NbWVmhO9+7dw/Lly5GWlgZ9fX20atUKNWvWhKGhIT58+ICbN28iMjISAJCWlobFixejQYMGqF69uqgfiUQijCmVSiGTyYTvKTqXiIgITJo0SZTYbmpqihYtWqBKlSooU6YMkpKSEBUVhdevX+PFixdZKmZRycEkMSLStPfv32PHjh0AMv4dW7FiBSv9EBVRMpkMcXFx8PPzw8WLF3H8+HGkpKQAAIyMjLBt2zZYWlpme+6XZNYvXr9+DQcHB4XGlK+o+vHjx2zbhoaGCl/r6OgotelShQoVRHFISIjC5xJRzmJjY0Wby1WuXBlpaWnYtWsXXF1dkZaWJmqfkJCA4OBg3LlzB9u3b8e4ceMwZcoUSCSSPMdKTU3FkiVLIJVKAQCTJk1C1apV1fuB8pD5eQMA9O3bt0DHJyotrK2tRbEy1df9/PxEcU7XFXlJT09HZGQkvL298ddff+HKlSvC96pVq4YtW7bkunbJz9nW1ha+vr5YsGABXr58KfpeQkICwsPD4evri2PHjqFRo0ZYs2YNateuna+5E1HhK8xnIUlJSXj37h1u376NgwcPIiwsTPje/Pnz0a5duwKZBxFlUPWaIjt//vmnKE5KSkJYWBj8/Pxw6tQp/PTTT5g4cSJGjBihdN/nz5/HzZs3AQAWFhaYN2+eUudT8cHkdiIiIiIiIiIiIiIiIiIiomz0799fSG7//Pkzbt68iS5duuR5XnR0NK5duybEX3/9NcqWLZtt21WrVokS27t3744FCxbA1tY2S9vw8HCsXr0aFy5cAAA8ePAArq6umDZtmkKfZ8WKFUhLS0OvXr2waNGiLElB8fHxWLx4sdB/UlISXF1d8dNPP4natWzZUqj24uzsLLxYb2Njg+vXrys0l99//x1RUVFCPGnSJEyZMgX6+vrZtk9KSsJ///2HY8eOQUtLS6ExqPhgkhgRadrSpUuFTVL69+/PSj9ERcibN29EG0HJb570haOjI1xcXLJsvJRZ06ZNcejQISG+ceMG+vXrl+ccPD09ERsbKzr2pTqYvMzHDQ0NlXpBu0yZMgqNQUTKyXxvCQCWlpZYtGgRzp49m+e5CQkJ2LZtG16+fIktW7bkuaHd7t27haTVmjVrYty4cfmed368evUKz58/F2IbGxu0bt26QOdAVFo0adJEFN+5cwdpaWl5rhPx8fFwd3cXHVP03/w//vgDK1asEOLMVY6/0NPTw8CBAzFnzhwYGxvn2l/mTSWBjOebU6dORXx8fJ5zefr0Kb7//nvs3LkTrVq1Umj+RFS0FOSzkA4dOuDz588AMjYP+7IRUGZ2dnaYP38+OnTooLF5EFEGdV9T5Ed4eDjWrl2Le/fuYcuWLTA0NFTovKioKKxdu1aInZ2dYWZmpvb5UdHA5HYiIiIiIiIiIiIiIiIiIioxRo0apXQFgC5dumD79u1Zjvfs2RNr1qxBYmIigIzqaIokt7u5uQnVJQHkmFDz4MEDnDx5UohHjx4NZ2fnHPstV64ctmzZAi0tLbi5uQEA9u3bhxEjRuSYPJ9Zamoq+vfvL3opJLMyZcpgw4YNePnypVBJ+9KlS1i5ciUMDAzy7F8Z9+7dE75u06YNZs+enWt7AwMDdO7cGZ07d1brPKhoYJIYEWnSX3/9Jfy7Y25ujvnz5xfyjIgoM5lMlu1L1l9oaWlh2LBhGDduXJZNbeS1adMGBgYGQgLHtWvX8OrVqzwrjrq6umY5pkhye04bM+VET09PoTGISDnym1PcvXtXOGZgYIBRo0bhm2++QdWqVZGamgpfX1+cOHEC586dEzbTuHz5MjZv3owFCxbkOM6bN2+wa9cuIV6xYkWWn2tNO3XqlCju06eP0s9AiEgx5cqVQ6NGjfD06VMAQFhYGP788098//33uZ536NChLMnjycnJSE9Ph7a2dq7nSqXSXK+LTExMMG3aNAwaNAhGRkZ5foaYmBhRvGLFCmFujRo1wvjx49G0aVOYmJjg8+fPuH37NlxdXYVK83FxcZgxYwbOnDmDihUr5jkeERUdBf0sJD09Pdf1q3379pgzZw7q/7/27jzKqurMG/BbBQWWFFOhzKIBmRVRUcGYD2cSXQoUjXTjlGCCdATSRNvYEpxHbLXjlBXtYHBYzkLA0FFRUcBGQUBQBISIMqoMxTxXfX+4uF23qOEW1qVAnmetWrnvqb3P2dcsDvtezm/vjh3TOg7gO5U9p9gjMzMzunbtGmeeeWZ06tQpjj766Khdu3Zs3749Vq1aFR9++GE899xziQXBIiLeeeeduP766+Ohhx5K6bPL3XffHWvXro2IiK5du0bPnj1THh8HH8uZAwAAAAAAAPCDseeBjYr+lCQnJyd69OiRqCdNmpR4oKIsRR82b9KkSXTr1q3Edo8//njidZs2beK6665L6T0OHz488bDJ5s2bY/z48Sn1O/LII+Omm24qs02NGjXisssuS9Tbtm2Lzz77LKXzV8SeXVwiIo477rhKPz8HFyExIF3WrFkTI0eOTNR2+oGDT0FBQTz99NNx7rnnxj333JO0iFRx9erViz59+iTqXbt2xb/9278lzT2Le/jhh2Pq1Kl7Hd8TkC9u+/btiddZWVmpvIWE4vOW0q4BVEzxzwB7gu316tWL559/PoYNGxZt27aNww47LGrXrh1dunSJkSNHxsiRIyMz8/8epR81alTMnz+/xGsUFhbGiBEjEvegvLy8OOWUU9L0jkq2e/fuvT7/9+7de7+OAQ41AwYMSKpHjhwZM2fOLLX95MmT49FHHy3xd5Xx9/7GjRvj7rvvjrPOOiteeeWVctuXdn+85JJL4oUXXojzzjsvGjRoEDVq1IimTZtGv379Yty4cXHCCSck+hTfPRU48B2I34VMmTIl8vLy4uqrr45vvvmmSscCVHxOERHRuXPnePPNN+PJJ5+MK6+8Mk488cTIzc2NrKysyMnJiWOPPTb69+8f48aNi2uvvTYpyP7GG28kLfZdmvfffz/Gjh0bEd99h3LLLbfsy9vjICLcDgAAAAAAAAClKLrr+s6dO2PcuHFltv/888/jk08+SdS9e/dOelh+j9WrV8eUKVMSdf/+/aN69eopjSk3Nze6du2aqD/44IOU+vXp0yelHdiLP6C/ePHilM5fEUV3205HeJ6Di5AYkC533HFH5OfnR0TEqaeeGr169arS8QB7a9WqVSxYsCDxM3fu3Jg8eXI8/vjjkZeXl5gb7Ny5M5588skYNGhQmQH3IUOGJO0sumjRoujZs2c888wzsWLFiti9e3ds2rQp3n///fjVr34VjzzySEQkz09LqvcouhDPzp07K/Rei487lbk5UL7Sdk+/7bbbon379qX2u/jii+Pyyy9P1IWFhTFq1KgS2z7//PMxY8aMiPguNF/WDu/pMmXKlPj2228TdZcuXaJFixb7fRxwKOnRo0eceeaZiXrz5s1x5ZVXxsiRI2PBggWxY8eO2L59e8ybNy/uuOOOGDRoUGJ+UHQX1IyMjMjOzi73ev/yL/+SNC+aNWtWTJw4Mf7zP/8zTj/99ES7/Pz8uPHGG+Ohhx4q83wlLSDYvn37uPnmm0vdObVOnTrxhz/8IWkuNHHixFiyZEm54wcODFXxXcjUqVMT96558+bFtGnT4tlnn41BgwZF/fr1E+0mTZoU/fr1i+XLl6d9THAoq+w5RUREy5Yto3nz5uW2y8jIiIEDB8Zvf/vbpOOPPvpomd/nbN26NWmB7quvvjp+9KMflXs9Dm7C7QAAAAAAAAD8YDz11FNJD2yk8vPYY4+Ver5TTz01jjrqqEQ9ZsyYMq9fdNf2jIyMpHB8UdOnT0+qu3TpksrbSzjmmGMSr1MNh6e6q1yzZs2S6g0bNqQ8rlQdf/zxiddTpkyJBx54wI7bhzAhMSAdJk2aFBMmTIiI70Jvt956axWPCEhFjRo1omHDhtG9e/e4++674+WXX04Kq0+dOjURSC9J/fr145FHHkkKUKxevTpuv/32OOuss6JDhw5x8sknxy9+8Yt47733IuK7xXVuu+22pPPUrl27xPMXDaoVXaAnFcXnLUXPBey7khajaNWqVfTo0aPcvgMHDkxaYGvSpElRUFCQ1Obrr7+O+++/P1H/7ne/S7rH7C/Fv4+wazukX0ZGRtx7773RsWPHxLEdO3bEn//857j44ovj+OOPj06dOkXv3r3j6aefjl27dkVExNChQ5PmLzk5OSUuflmeww8/PI466qi46KKL4sknn4wHH3ww6Z716KOPxuTJk8vsX9zAgQPLXWCzSZMm0adPn0RdUFAQkyZNqvD4gf3vQPgupFq1alG/fv3o0qVLDBs2LCZMmBDdunVL/H7FihVx3XXXRWFh4X4fGxyqvu+cYl/88pe/jGOPPTZRr1q1Kj766KNS2z/00EOxdOnSiPju30AHDhxYqePhwCTcDgAAAAAAAAClyMjISHpgfP78+TFv3rwS2+7atSvGjx+fqE855ZSkYHxRxQPpPXv2jA4dOqT88+STTyb67tmFpTyNGjVKqV3xB1/TETq/7LLLkh6c+dOf/hRnnHFGDBs2LF588cVYvHixh9sOIUJiQGXbvHlz3HLLLYl64MCB0bJly6obELD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" ] @@ -378,7 +378,7 @@ "source": [ "## Data Preperation for Survival Regression\n", "\n", - "The idea behind Cox Proportional Hazard regression models is, put crudely, to treat the temporal component of risk seriously. We imagine a latent baseline hazard of occurence over the time-interval. Michael Betancourt [asks](https://betanalpha.github.io/assets/case_studies/survival_modeling.html) that we think of the hazard as \"the accumulation of some stimulating resource\" that precedes the occurence of an event. In the context of a failure modelling it can be imagined as sporadic increasing wear and tear. In the context of HR dyanamics it could be imagined as increasing frustration is the work-environment. This term is often denoted:\n", + "The idea behind Cox Proportional Hazard regression models is, put crudely, to treat the temporal component of risk seriously. We imagine a latent baseline hazard of occurrence over the time-interval. Michael Betancourt [asks](https://betanalpha.github.io/assets/case_studies/survival_modeling.html) that we think of the hazard as \"the accumulation of some stimulating resource\" that precedes the occurrence of an event. In the context of a failure modelling it can be imagined as sporadic increasing wear and tear. In the context of HR dyanamics it could be imagined as increasing frustration is the work-environment. This term is often denoted:\n", "\n", "$$ \\lambda_{0}(t)$$\n", "\n", @@ -386,7 +386,7 @@ "\n", "$$ \\lambda_{0}(t) \\cdot exp(\\beta_{1}X_{1} + \\beta_{2}X_{2}... \\beta_{k}X_{k}) $$\n", "\n", - "and represents the baseline hazard at each point in time when the predictor variables are set at their baseline/reference levels. Which is to say any unit increase over 0 to a covariate in the regression model changes the baseline hazard. In our case we are looking at data with granularity of monthly entries. So we need to understand how the risk of attrition changes over the next 12 months subsequent to the date of the annual survey and how the covariate profile of each individual changes the baseline hazard.\n", + "and represents the baseline hazard at each point in time when the predictor variables are set at their baseline/reference levels. Which is to say any unit increase over 0 to any covariate $X_{i}$ in the regression model changes the baseline hazard. In our case we are looking at data with granularity of monthly entries. So we need to understand how the risk of attrition changes over the next 12 months subsequent to the date of the annual survey and how the covariate profile of each individual changes the baseline hazard.\n", "\n", "These models can be estimated using the approach of Bayesian estimation outlined by Austin Rochford in {ref}`survival_analysis`. In what follows we build on his examples.\n", "\n", @@ -988,7 +988,7 @@ "\n", "
\n", " \n", - " 100.00% [8000/8000 01:01<00:00 Sampling 4 chains, 0 divergences]\n", + " 100.00% [8000/8000 01:00<00:00 Sampling 4 chains, 0 divergences]\n", "
\n", " " ], @@ -1003,7 +1003,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 62 seconds.\n", + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 61 seconds.\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", @@ -1274,7 +1274,7 @@ "MutableData\n", "\n", "\n", - "\n", + "\n", "lambda_\n", "\n", "lambda_\n", @@ -1282,7 +1282,7 @@ "Deterministic\n", "\n", "\n", - "\n", + "\n", "X_data_obs->lambda_\n", "\n", "\n", @@ -1296,7 +1296,7 @@ "Gamma\n", "\n", "\n", - "\n", + "\n", "lambda0->lambda_\n", "\n", "\n", @@ -1315,14 +1315,6 @@ "\n", "\n", "\n", - "\n", - "\n", - "obs\n", - "\n", - "obs\n", - "~\n", - "Poisson\n", - "\n", "\n", "\n", "mu\n", @@ -1331,23 +1323,31 @@ "~\n", "Deterministic\n", "\n", - "\n", - "\n", - "mu->obs\n", - "\n", - "\n", - "\n", "\n", "\n", "lambda_->mu\n", "\n", "\n", "\n", + "\n", + "\n", + "obs\n", + "\n", + "obs\n", + "~\n", + "Poisson\n", + "\n", + "\n", + "\n", + "mu->obs\n", + "\n", + "\n", + "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -1609,7 +1609,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is the baseline stimulus - the growing, sporadically shifting hazard that spurs the occurence of attrition. We build regression models incorporating a slew of control variables and treatment indicators to evaluate what if any effect they have on changing the baseline hazard over time. Survival regression modelling is a transparent tool for analysing the impact of demographic and behavioural features of risk over time. Note the sharp increase at the end of an annual cycle." + "This is the baseline stimulus - the growing, sporadically shifting hazard that spurs the occurrence of attrition. We build regression models incorporating a slew of control variables and treatment indicators to evaluate what if any effect they have on changing the baseline hazard over time. Survival regression modelling is a transparent tool for analysing the impact of demographic and behavioural features of risk over time. Note the sharp increase at the end of an annual cycle." ] }, { @@ -2432,7 +2432,7 @@ "\n", "Next we examine a parametric family of regression based survival models called accelerated failure time models (AFTs). These are regression models that seek to describe the survival function of interest with appeal to one or other of the canonical statistical distributions that can be neatly characterised with a set of location and scale parameters e.g. the Weilbull distribution, the Log-Logistic distribution and the LogNormal distribution to name a few. One advantage of these family of distributions is that we have access to more flexible hazard functions without having to explicitly parameterise the time-interval. \n", "\n", - "See here for example how the log-logistic distribution exhibits a non-monotonic hazard function whereas the Weibull hazard is necessarily monotonic. This is an important observation if your theory of the case allows for rising and falling risks of event occurence.\n" + "See here for example how the log-logistic distribution exhibits a non-monotonic hazard function whereas the Weibull hazard is necessarily monotonic. This is an important observation if your theory of the case allows for rising and falling risks of event occurrence.\n" ] }, { @@ -2522,11 +2522,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_14223/1248891023.py:63: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", + "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_16636/1248891023.py:63: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", " idata = pm.sample_prior_predictive()\n", "Sampling: [beta, mu, s, y_obs]\n", - "/Users/nathanielforde/mambaforge/envs/pymc_examples_new/lib/python3.9/site-packages/pymc/distributions/continuous.py:2397: RuntimeWarning: overflow encountered in multiply\n", - " return np.asarray(beta * rng.weibull(alpha, size=size))\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", @@ -2566,7 +2564,7 @@ "\n", "
\n", " \n", - " 100.00% [8000/8000 01:30<00:00 Sampling 4 chains, 0 divergences]\n", + " 100.00% [8000/8000 01:29<00:00 Sampling 4 chains, 0 divergences]\n", "
\n", " " ], @@ -2582,7 +2580,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 90 seconds.\n", - "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_14223/1248891023.py:67: UserWarning: The effect of Potentials on other parameters is ignored during posterior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", + "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_16636/1248891023.py:67: UserWarning: The effect of Potentials on other parameters is ignored during posterior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", " idata.extend(pm.sample_posterior_predictive(idata))\n", "Sampling: [y_obs]\n" ] @@ -2635,7 +2633,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_14223/1248891023.py:63: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", + "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_16636/1248891023.py:63: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", " idata = pm.sample_prior_predictive()\n", "Sampling: [beta, mu, s, y_obs]\n", "Auto-assigning NUTS sampler...\n", @@ -2692,8 +2690,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 66 seconds.\n", - "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_14223/1248891023.py:67: UserWarning: The effect of Potentials on other parameters is ignored during posterior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 65 seconds.\n", + "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_16636/1248891023.py:67: UserWarning: The effect of Potentials on other parameters is ignored during posterior predictive sampling. This is likely to lead to invalid or biased predictive samples.\n", " idata.extend(pm.sample_posterior_predictive(idata))\n", "Sampling: [y_obs]\n" ] @@ -3002,7 +3000,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "In this case we could think of the relative change in acceleration factor between the individuals as representing a 19.89% increase\n" + "In this case we could think of the relative change in acceleration \n", + " factor between the individuals as representing a 19.89% increase\n" ] } ], @@ -3010,7 +3009,7 @@ "diff = reg.iloc[1000] - reg.iloc[0]\n", "pchange = np.round(100 * (diff / reg.iloc[1000]), 2)\n", "print(\n", - " f\"In this case we could think of the relative change in acceleration factor between the individuals as representing a {pchange}% increase\"\n", + " f\"In this case we could think of the relative change in acceleration \\n factor between the individuals as representing a {pchange}% increase\"\n", ")" ] }, @@ -3485,7 +3484,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "In this case we could think of the relative change in acceleration factor between the individuals as representing a 9.44% increase\n" + "In this case we could think of the relative change in acceleration \n", + " factor between the individuals as representing a 9.44% increase\n" ] } ], @@ -3493,7 +3493,7 @@ "diff = reg.iloc[1000] - reg.iloc[0]\n", "pchange = np.round(100 * (diff / reg.iloc[1000]), 2)\n", "print(\n", - " f\"In this case we could think of the relative change in acceleration factor between the individuals as representing a {pchange}% increase\"\n", + " f\"In this case we could think of the relative change in acceleration \\n factor between the individuals as representing a {pchange}% increase\"\n", ")" ] }, @@ -4031,7 +4031,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -4120,7 +4120,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 139 seconds.\n", + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 140 seconds.\n", "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n", "The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n" ] @@ -4217,7 +4217,7 @@ "Deterministic\n", "\n", "\n", - "\n", + "\n", "X_data_f->lambda_f\n", "\n", "\n", @@ -4237,7 +4237,7 @@ "\n", "\n", "\n", - "\n", + "\n", "lambda0->lambda_f\n", "\n", "\n", @@ -4279,7 +4279,7 @@ "\n", "\n", "\n", - "\n", + "\n", "lambda_\n", "\n", "lambda_\n", @@ -4287,7 +4287,7 @@ "Deterministic\n", "\n", "\n", - "\n", + "\n", "frailty->lambda_\n", "\n", "\n", @@ -4319,21 +4319,27 @@ "\n", "\n", "\n", - "\n", + "\n", "lambda_f->lambda_\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "mu\n", "\n", "mu\n", "~\n", "Deterministic\n", "\n", + "\n", + "\n", + "lambda_->mu\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "outcome\n", "\n", "outcome\n", @@ -4346,17 +4352,11 @@ "\n", "\n", "\n", - "\n", - "\n", - "lambda_->mu\n", - "\n", - "\n", - "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 33, @@ -4502,7 +4502,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n" + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 72 seconds.\n" ] } ], @@ -4607,7 +4607,7 @@ "Deterministic\n", "\n", "\n", - "\n", + "\n", "X_data_f->lambda_f\n", "\n", "\n", @@ -4627,7 +4627,7 @@ "\n", "\n", "\n", - "\n", + "\n", "lambda0->lambda_f\n", "\n", "\n", @@ -4669,7 +4669,7 @@ "\n", "\n", "\n", - "\n", + "\n", "lambda_\n", "\n", "lambda_\n", @@ -4677,7 +4677,7 @@ "Deterministic\n", "\n", "\n", - "\n", + "\n", "frailty->lambda_\n", "\n", "\n", @@ -4709,21 +4709,27 @@ "\n", "\n", "\n", - "\n", + "\n", "lambda_f->lambda_\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "mu\n", "\n", "mu\n", "~\n", "Deterministic\n", "\n", + "\n", + "\n", + "lambda_->mu\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "outcome\n", "\n", "outcome\n", @@ -4736,17 +4742,11 @@ "\n", "\n", "\n", - "\n", - "\n", - "lambda_->mu\n", - "\n", - "\n", - "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 35, @@ -5037,7 +5037,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_14223/3074884118.py:4: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n", + "/var/folders/__/ng_3_9pn1f11ftyml_qr69vh0000gn/T/ipykernel_16636/3074884118.py:4: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n", " temp.groupby([\"Male\", \"sentiment\", \"intention\"])[[\"frailty\"]]\n" ] }, @@ -5045,130 +5045,130 @@ "data": { "text/html": [ "\n", - "\n", + "
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\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 40, @@ -6349,11 +6349,11 @@ "pytensor: 2.11.1\n", "\n", "arviz : 0.15.1\n", - "pandas : 1.5.3\n", - "matplotlib: 3.7.1\n", + "pymc : 5.3.0\n", "pytensor : 2.11.1\n", "numpy : 1.23.5\n", - "pymc : 5.3.0\n", + "pandas : 1.5.3\n", + "matplotlib: 3.7.1\n", "\n", "Watermark: 2.3.1\n", "\n" diff --git a/examples/survival_analysis/frailty_models.myst.md b/examples/survival_analysis/frailty_models.myst.md index ffdd05bde..ed3d12b79 100644 --- a/examples/survival_analysis/frailty_models.myst.md +++ b/examples/survival_analysis/frailty_models.myst.md @@ -56,7 +56,7 @@ We will demonstrate how the concepts of survival based regression analysis, trad ### Survival Regression Models -The emphasis here is on the generality of the framework. We are describing the trajectory of state-transitions within time. Anywhere speed or efficiency matters, it is important to understand the inputs to time-to-event trajectories. This is the benefit of survival analysis - clearly articulated models which quantify the impact of demographic characteristics and treatment effects (in terms of speed) on the probability of state-transition. Movement between life and death, hired and fired, ill and cured, subscribed to churned. These state transitions are all tranparently anc compellingly modelled using survival regression models. +The emphasis here is on the generality of the framework. We are describing the trajectory of state-transitions within time. Anywhere speed or efficiency matters, it is important to understand the inputs to time-to-event trajectories. This is the benefit of survival analysis - clearly articulated models which quantify the impact of demographic characteristics and treatment effects (in terms of speed) on the probability of state-transition. Movement between life and death, hired and fired, ill and cured, subscribed to churned. These state transitions are all tranparently and compellingly modelled using survival regression models. We will see two varieties of regression modelling with respect to time-to-event data: (1) Cox's Proportional Hazard approach and (2) the Accelerated Failure time models. Both models enable the analyst to combine and assess the impacts of different covariates on the survival time outcomes, but each does so in a slightly different manner. @@ -158,7 +158,7 @@ Here we've used the Kaplan Meier non-parametric estimate of the survival curve w ## Data Preperation for Survival Regression -The idea behind Cox Proportional Hazard regression models is, put crudely, to treat the temporal component of risk seriously. We imagine a latent baseline hazard of occurence over the time-interval. Michael Betancourt [asks](https://betanalpha.github.io/assets/case_studies/survival_modeling.html) that we think of the hazard as "the accumulation of some stimulating resource" that precedes the occurence of an event. In the context of a failure modelling it can be imagined as sporadic increasing wear and tear. In the context of HR dyanamics it could be imagined as increasing frustration is the work-environment. This term is often denoted: +The idea behind Cox Proportional Hazard regression models is, put crudely, to treat the temporal component of risk seriously. We imagine a latent baseline hazard of occurrence over the time-interval. Michael Betancourt [asks](https://betanalpha.github.io/assets/case_studies/survival_modeling.html) that we think of the hazard as "the accumulation of some stimulating resource" that precedes the occurrence of an event. In the context of a failure modelling it can be imagined as sporadic increasing wear and tear. In the context of HR dyanamics it could be imagined as increasing frustration is the work-environment. This term is often denoted: $$ \lambda_{0}(t)$$ @@ -166,7 +166,7 @@ It is combined multiplicatively in the Cox Regression with a linear covariate re $$ \lambda_{0}(t) \cdot exp(\beta_{1}X_{1} + \beta_{2}X_{2}... \beta_{k}X_{k}) $$ -and represents the baseline hazard at each point in time when the predictor variables are set at their baseline/reference levels. Which is to say any unit increase over 0 to a covariate in the regression model changes the baseline hazard. In our case we are looking at data with granularity of monthly entries. So we need to understand how the risk of attrition changes over the next 12 months subsequent to the date of the annual survey and how the covariate profile of each individual changes the baseline hazard. +and represents the baseline hazard at each point in time when the predictor variables are set at their baseline/reference levels. Which is to say any unit increase over 0 to any covariate $X_{i}$ in the regression model changes the baseline hazard. In our case we are looking at data with granularity of monthly entries. So we need to understand how the risk of attrition changes over the next 12 months subsequent to the date of the annual survey and how the covariate profile of each individual changes the baseline hazard. These models can be estimated using the approach of Bayesian estimation outlined by Austin Rochford in {ref}`survival_analysis`. In what follows we build on his examples. @@ -349,7 +349,7 @@ ax.set_xlabel("Time") ax.set_title("Expected Baseline Hazard", fontsize=20); ``` -This is the baseline stimulus - the growing, sporadically shifting hazard that spurs the occurence of attrition. We build regression models incorporating a slew of control variables and treatment indicators to evaluate what if any effect they have on changing the baseline hazard over time. Survival regression modelling is a transparent tool for analysing the impact of demographic and behavioural features of risk over time. Note the sharp increase at the end of an annual cycle. +This is the baseline stimulus - the growing, sporadically shifting hazard that spurs the occurrence of attrition. We build regression models incorporating a slew of control variables and treatment indicators to evaluate what if any effect they have on changing the baseline hazard over time. Survival regression modelling is a transparent tool for analysing the impact of demographic and behavioural features of risk over time. Note the sharp increase at the end of an annual cycle. +++ @@ -545,7 +545,7 @@ In the HR context we might be interested in the time-to-attrition metrics under Next we examine a parametric family of regression based survival models called accelerated failure time models (AFTs). These are regression models that seek to describe the survival function of interest with appeal to one or other of the canonical statistical distributions that can be neatly characterised with a set of location and scale parameters e.g. the Weilbull distribution, the Log-Logistic distribution and the LogNormal distribution to name a few. One advantage of these family of distributions is that we have access to more flexible hazard functions without having to explicitly parameterise the time-interval. -See here for example how the log-logistic distribution exhibits a non-monotonic hazard function whereas the Weibull hazard is necessarily monotonic. This is an important observation if your theory of the case allows for rising and falling risks of event occurence. +See here for example how the log-logistic distribution exhibits a non-monotonic hazard function whereas the Weibull hazard is necessarily monotonic. This is an important observation if your theory of the case allows for rising and falling risks of event occurrence. ```{code-cell} ipython3 fig, axs = plt.subplots(2, 2, figsize=(20, 7)) @@ -725,7 +725,7 @@ axs[1].legend(); diff = reg.iloc[1000] - reg.iloc[0] pchange = np.round(100 * (diff / reg.iloc[1000]), 2) print( - f"In this case we could think of the relative change in acceleration factor between the individuals as representing a {pchange}% increase" + f"In this case we could think of the relative change in acceleration \n factor between the individuals as representing a {pchange}% increase" ) ``` @@ -775,7 +775,7 @@ axs[1].legend(); diff = reg.iloc[1000] - reg.iloc[0] pchange = np.round(100 * (diff / reg.iloc[1000]), 2) print( - f"In this case we could think of the relative change in acceleration factor between the individuals as representing a {pchange}% increase" + f"In this case we could think of the relative change in acceleration \n factor between the individuals as representing a {pchange}% increase" ) ```