diff --git a/agent_based_epidemic_sim/learning/risk_score_tuner_public.ipynb b/agent_based_epidemic_sim/learning/risk_score_tuner_public.ipynb
index 9f51ea1..ecb502e 100644
--- a/agent_based_epidemic_sim/learning/risk_score_tuner_public.ipynb
+++ b/agent_based_epidemic_sim/learning/risk_score_tuner_public.ipynb
@@ -10,7 +10,7 @@
         "\n",
         "Kevin Murphy (kpmurphy@google.com), Stelios Serghiou (serghiou@google.com), Adam Pearce (adampearce@google.com)\n",
         "\n",
-        "Last update:  **12 November 2020**\n",
+        "Last update:  **2020-11-13**\n",
         "\n",
         "**Legal disclaimer**:\n",
         "COVID Tuner is for modeling and research purposes only, and all pre-populated data are examples provided solely for reference. Health authorities remain responsible for determining and setting their own specific configuration parameters for their own apps.\n",
@@ -28,33 +28,11 @@
         "\n",
         "This Colab computes the probability of COVID transmission from a transmitter to a receiver, as a function of distance (estimated from bluetooth attenuation), duration, and infectiousness of the transmitter (estimated based on days since symtom onset), using a [standard exponential dose response model](http://qmrawiki.canr.msu.edu/index.php/Dose_response_assessment). The parameters of this model are derived from recent papers on the epidemiology of COVID, and on empirical bluetooth attenuation data (details below). \n",
         "\n",
-        "In addition, the Colab computes the risk score, as defined by the [Google/ Apple Exposure Notification System](https://en.wikipedia.org/wiki/Exposure_Notification). This risk score approximates the probability of COVID transmission between two people. The score has [various parameters](https://enconfig.storage.googleapis.com/enconfig_fixed.html) that need to be set by the public health authority. This Colab lets the user visualize the effect of changing these parameters, as compared to the above model. \n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "at-FyNpWPyT5"
-      },
-      "outputs": [],
-      "source": [
-        "import itertools\n",
-        "from dataclasses import dataclass\n",
-        "import collections\n",
-        "from collections import namedtuple\n",
-        "\n",
-        "import numpy as np\n",
-        "import scipy.stats\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "import matplotlib.cm as cm \n",
+        "In addition, the Colab computes the risk score, as defined by the [Google/ Apple Exposure Notification System](https://en.wikipedia.org/wiki/Exposure_Notification). This risk score approximates the probability of COVID transmission between two people. The score has [various parameters](https://enconfig.storage.googleapis.com/enconfig_fixed.html) that need to be set by the public health authority. This Colab lets the user visualize the effect of changing these parameters, as compared to the above model. \n",
         "\n",
-        "import sklearn\n",
-        "from sklearn import metrics\n",
+        "There is also an [interactive web-app](https://risk-score-tuner.appspot.com/) version of this colab; the javascript code for this is at the bottom of this colab.\n",
         "\n",
-        "import IPython\n",
-        "from IPython.display import display, HTML"
+        "More details on this tool can be found in the [official public documentation](https://docs.google.com/document/d/1JvbzL_RjyNOQjsFMmyMkjKVi9Uta2Vp_I7BnzfLQHKM/edit).\n"
       ]
     },
     {
@@ -82,6 +60,32 @@
         "\n"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "at-FyNpWPyT5"
+      },
+      "outputs": [],
+      "source": [
+        "import itertools\n",
+        "from dataclasses import dataclass\n",
+        "import collections\n",
+        "from collections import namedtuple\n",
+        "\n",
+        "import numpy as np\n",
+        "import scipy.stats\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.cm as cm \n",
+        "\n",
+        "import sklearn\n",
+        "from sklearn import metrics\n",
+        "\n",
+        "import IPython\n",
+        "from IPython.display import display, HTML"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -186,9 +190,9 @@
           "height": 350
         },
         "executionInfo": {
-          "elapsed": 4354,
+          "elapsed": 4415,
           "status": "ok",
-          "timestamp": 1605160576043,
+          "timestamp": 1605287298685,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -197,16 +201,16 @@
           "user_tz": 480
         },
         "id": "6vCis2fc9_WZ",
-        "outputId": "b2470cfb-b80c-4c58-d3cd-f8b231c22547"
+        "outputId": "a3b5d119-b132-42a1-a47b-47c9029f02fa"
       },
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "\u003cmatplotlib.legend.Legend at 0x7fd4d95e5c50\u003e"
+              "\u003cmatplotlib.legend.Legend at 0x7fb84b6c2e10\u003e"
             ]
           },
-          "execution_count": 245,
+          "execution_count": 53,
           "metadata": {
             "tags": []
           },
@@ -253,9 +257,9 @@
           "height": 350
         },
         "executionInfo": {
-          "elapsed": 4843,
+          "elapsed": 4983,
           "status": "ok",
-          "timestamp": 1605160576562,
+          "timestamp": 1605287299288,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -264,16 +268,16 @@
           "user_tz": 480
         },
         "id": "CC6CSCkxYEzg",
-        "outputId": "7485926a-9ae9-44ec-b78d-86818a314b40"
+        "outputId": "da57314a-5675-4dad-bbd6-4369d36942cc"
       },
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "\u003cmatplotlib.lines.Line2D at 0x7fd4d87748d0\u003e"
+              "\u003cmatplotlib.lines.Line2D at 0x7fb84b98fc18\u003e"
             ]
           },
-          "execution_count": 246,
+          "execution_count": 54,
           "metadata": {
             "tags": []
           },
@@ -424,9 +428,9 @@
           "height": 350
         },
         "executionInfo": {
-          "elapsed": 5717,
+          "elapsed": 4932,
           "status": "ok",
-          "timestamp": 1605160577480,
+          "timestamp": 1605287299290,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -435,7 +439,7 @@
           "user_tz": 480
         },
         "id": "CGEL6qrH9MnZ",
-        "outputId": "9d053ae3-2790-4de4-b686-047925c31078"
+        "outputId": "e796ef38-c14d-48f4-f1df-46c29b015401"
       },
       "outputs": [
         {
@@ -508,9 +512,9 @@
           "height": 333
         },
         "executionInfo": {
-          "elapsed": 5919,
+          "elapsed": 5556,
           "status": "ok",
-          "timestamp": 1605160577719,
+          "timestamp": 1605287299960,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -519,7 +523,7 @@
           "user_tz": 480
         },
         "id": "OlDNuNWNMCw3",
-        "outputId": "87ddb264-bf99-4af8-c41f-3c4096ab5576"
+        "outputId": "e930df32-129e-4923-a408-a2046dfdb7a8"
       },
       "outputs": [
         {
@@ -599,9 +603,9 @@
           "height": 892
         },
         "executionInfo": {
-          "elapsed": 8233,
+          "elapsed": 7708,
           "status": "ok",
-          "timestamp": 1605160580080,
+          "timestamp": 1605287302163,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -610,7 +614,7 @@
           "user_tz": 480
         },
         "id": "_SSljeFVJGGQ",
-        "outputId": "417700f4-f8ed-4ba2-af3a-fc5a43a8958f"
+        "outputId": "a1b63cda-6ba2-4c23-d729-f330e5967cf1"
       },
       "outputs": [
         {
@@ -661,9 +665,9 @@
           "height": 682
         },
         "executionInfo": {
-          "elapsed": 9949,
+          "elapsed": 8931,
           "status": "ok",
-          "timestamp": 1605160581832,
+          "timestamp": 1605287303428,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -672,7 +676,7 @@
           "user_tz": 480
         },
         "id": "psJhjV3SVu-7",
-        "outputId": "7a5c4188-a0c0-482e-84df-4cb14bf8f6e3"
+        "outputId": "34edf121-3c06-4783-9efb-359fbcf99d69"
       },
       "outputs": [
         {
@@ -819,12 +823,11 @@
         "  correction: float = 2.398\n",
         "\n",
         "\n",
-        "ble_params_lognormal_new = BleParams(slope = 0.127, intercept = 4.23, sigma = np.sqrt(0.49), model = 'log-normal', name='log-normal-new') # Mark Briers\n",
-        "ble_params_lognormal_old = BleParams(slope = 0.21, intercept = 3.92, sigma = np.sqrt(0.33), model = 'log-normal', name='log-normal-old') # Lovett paper\n",
-        "ble_params_lognormal = ble_params_lognormal_old\n",
-        "ble_params_normal = BleParams(slope = -8.69, intercept = -67.9, sigma = np.sqrt(97.03), model = 'normal', name='normal-lovett') # Lovett paper\n",
+        "ble_params_lognormal_briers = BleParams(slope = 0.127, intercept = 4.23, sigma = np.sqrt(0.49), model = 'log-normal', name='log-normal-new') # Mark Briers\n",
+        "ble_params_lognormal_lovett = BleParams(slope = 0.21, intercept = 3.92, sigma = np.sqrt(0.33), model = 'log-normal', name='log-normal-old') # Lovett paper\n",
+        "ble_params_normal_lovett = BleParams(slope = -8.69, intercept = -67.9, sigma = np.sqrt(97.03), model = 'normal', name='normal-lovett') # Lovett paper\n",
         "ble_params_normal_sklearn = BleParams(slope = -5.422, intercept = -66.696, sigma = 10.323, model = 'normal', name='normal-sklearn') # sklearn.ridge on MIT matrix data\n",
-        "ble_params_default = ble_params_lognormal_old"
+        "ble_params_default = ble_params_lognormal_lovett"
       ]
     },
     {
@@ -917,9 +920,9 @@
           "height": 333
         },
         "executionInfo": {
-          "elapsed": 10439,
+          "elapsed": 9803,
           "status": "ok",
-          "timestamp": 1605160582370,
+          "timestamp": 1605287304350,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -928,12 +931,12 @@
           "user_tz": 480
         },
         "id": "v2SynTaRfqEI",
-        "outputId": "ce281d10-2d1b-476a-ca4e-fa513e799373"
+        "outputId": "483483f1-fdbd-4304-ccc1-c953875c0020"
       },
       "outputs": [
         {
           "data": {
-            "image/png": "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\n",
+            "image/png": "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\n",
             "text/plain": [
               "\u003cFigure size 720x360 with 2 Axes\u003e"
             ]
@@ -947,7 +950,7 @@
       ],
       "source": [
         "\n",
-        "ble_params_list = [ble_params_normal, ble_params_lognormal, ble_params_lognormal_new]\n",
+        "ble_params_list = [ble_params_normal_lovett,  ble_params_lognormal_briers]\n",
         "distances  = np.arange(1, 10, 0.01)\n",
         "attens = np.arange(40,80,1)\n",
         "n = 2\n",
@@ -979,9 +982,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 10406,
+          "elapsed": 9766,
           "status": "ok",
-          "timestamp": 1605160582371,
+          "timestamp": 1605287304351,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -990,7 +993,7 @@
           "user_tz": 480
         },
         "id": "b_-Kl4qrhgHi",
-        "outputId": "8b9812cc-84c9-4a70-cb8b-7cb521014e6f"
+        "outputId": "ab58d085-eb78-4eee-fc4c-c0417f7562a7"
       },
       "outputs": [
         {
@@ -1013,12 +1016,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 682
+          "height": 350
         },
         "executionInfo": {
-          "elapsed": 11410,
+          "elapsed": 9729,
           "status": "ok",
-          "timestamp": 1605160583413,
+          "timestamp": 1605287304351,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1027,22 +1030,9 @@
           "user_tz": 480
         },
         "id": "RK0sKVnMHkf-",
-        "outputId": "994c7fc5-e79a-4ead-aaf7-2df8240e251e"
+        "outputId": "a52dfc67-235d-4773-85e6-cf559c4af6b8"
       },
       "outputs": [
-        {
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "\u003cFigure size 1080x360 with 3 Axes\u003e"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light",
-            "tags": []
-          },
-          "output_type": "display_data"
-        },
         {
           "data": {
             "image/png": "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\n",
@@ -1079,12 +1069,9 @@
         "    ax.set_title('sigma={:0.3f}, noise={}'.format(sigma, ble_params.name))\n",
         "    ax.set_ylim(20,120)\n",
         "\n",
-        "sigma_list = [0, 0.02, np.sqrt(0.33)]\n",
-        "ble_params = ble_params_lognormal\n",
-        "plot_curves(sigma_list, ble_params)\n",
         "\n",
         "sigma_list = [0, 1, np.sqrt(97.03)]\n",
-        "ble_params = ble_params_normal\n",
+        "ble_params = ble_params_normal_lovett\n",
         "plot_curves(sigma_list, ble_params)"
       ]
     },
@@ -1094,12 +1081,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 1000
+          "height": 682
         },
         "executionInfo": {
-          "elapsed": 13041,
+          "elapsed": 10438,
           "status": "ok",
-          "timestamp": 1605160585078,
+          "timestamp": 1605287305098,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1108,22 +1095,9 @@
           "user_tz": 480
         },
         "id": "raDyXhEH4nXF",
-        "outputId": "8baabe8e-da43-4ebb-cfe7-88148124a5a0"
+        "outputId": "2bf56ea7-2729-41b8-db28-c186367cb57c"
       },
       "outputs": [
-        {
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "\u003cFigure size 1080x360 with 3 Axes\u003e"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light",
-            "tags": []
-          },
-          "output_type": "display_data"
-        },
         {
           "data": {
             "image/png": "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\n",
@@ -1169,18 +1143,13 @@
         "\n",
         "\n",
         "\n",
-        "ble_params = ble_params_lognormal\n",
-        "sigma_mle = ble_params.sigma\n",
-        "sigma_list = [0, 0.1*sigma_mle, sigma_mle]\n",
-        "plot_curves(sigma_list, ble_params)\n",
-        "\n",
         "\n",
-        "ble_params = ble_params_normal\n",
+        "ble_params = ble_params_normal_lovett\n",
         "sigma_mle = ble_params.sigma\n",
         "sigma_list = [0, 0.1*sigma_mle, sigma_mle]\n",
         "plot_curves(sigma_list, ble_params)\n",
         "\n",
-        "ble_params = ble_params_lognormal_new\n",
+        "ble_params = ble_params_lognormal_briers\n",
         "sigma_mle = ble_params.sigma\n",
         "sigma_list = [0, 0.1*sigma_mle, sigma_mle]\n",
         "plot_curves(sigma_list, ble_params)"
@@ -1201,12 +1170,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 1000
+          "height": 682
         },
         "executionInfo": {
-          "elapsed": 14770,
+          "elapsed": 11637,
           "status": "ok",
-          "timestamp": 1605160586845,
+          "timestamp": 1605287306321,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1215,22 +1184,9 @@
           "user_tz": 480
         },
         "id": "vX2S5oDc4s1X",
-        "outputId": "b48e2023-8aed-4fc8-d25c-c779fd17858f"
+        "outputId": "f8cadc1e-24a3-4b7f-dd0e-39be94bef32d"
       },
       "outputs": [
-        {
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAFNCAYAAABSRs15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3hUVfrA8e87kx5IAgRICIQaQHqTJiigqNiw97r2vqvub9dd17W7q+uufe1rLyiKiA0LiApIL9I7oZNAgPQy5/fHvYQhhuQmmcnNTN7P8+TJlHPvfWcyeeeec08RYwxKKaWUUkoppUKfx+0AlFJKKaWUUkoFhlbwlFJKKaWUUipMaAVPKaWUUkoppcKEVvCUUkoppZRSKkxoBU8ppZRSSimlwoRW8JRSSimllFIqTGgFrxZE5C8i8orbcTRmIpIrIp3q6VgbReSE+jhWQyEiV4rIT27HoWpGc5P7RGSZiIyqp2NNF5Fr6uNYDYWIjBKRLW7HoWpGc5P7NDcFV0PLTVrBqwVjzCPGmAb9wRWR40VkpYjki8g0EWlfRdkOdpl8e5sTKjz/BxHZISL7ReQ1EYl2um2wGGOaGGPW18exlAoVjSk32Y0QZXZjz8GfUfZz6RUezxURIyJ3Bvv1GWN6GmOmB/s4SoWSMMxNG0WkwC+/TPV77oUKuadIRA5Uso8MESkUkbeD9Zr8aW5qXLSCF4ZEJBn4GPgb0ByYB3xQxSbvAQuBFsBfgY9EpKW9r5OAPwPHA+2BTsD9TrZV9UdEItyOQanqBDI32WbZjT0Hf6YDGGM2+z8O9AZ8wMSAvyhVJc1NKhTUIjcBnO6XZ048+KAx5oYK+ec94MNKtn8OmBuYV6BqKtxzk1bwqiAifxKRrSJyQERWicjx9uP3+be4iMjlIrJJRLJF5G/+Xfrssh+KyNv2fpaKSFcRuVtEdolIpoic6Levq0RkhV12vYhcX4vQzwaWGWM+NMYUAvcBfUWkeyWvsSswAPi7MabAGDMRWAqcYxe5AnjVGLPMGLMXeBC40uG2VbLfp7tEZImI7BORD0Qkxu/5a0VkrYjsEZHJItLG7zkjIl3s26eIyHL7PdsqInf5lTtNRBaJSI6IzBSRPg7fwyPFHC0iT4rINvvnyQpXNP9PRLbbz13jH2cl+xolIltE5E77s7BdRK7yez5RRN4Ukd325+seEfHYz10pIj+LyH9EJBu4T0ReF5HnReRLu9XwZxFJsWPca7dM9vfb/59FZJ39vi0XkbPq8t6o+qO5qcYuB2YYYzY6KSxW96IH7f+hAyIyVawTwIPPnyFWd6ccu+xRfs/5v8eDRWSeWL0fdorIv/3KDbVzUo6ILJY6dp0SEY+dIzbZf783RSTR7/kjfhYq2VcHO3ddISKbRSRLRP7q9/wR86BfXvuTiOwA/ufSZ025oDHkphq+H/FYOeuNCo9fCOQA39Vwf5qbNDc5ohW8IxCRbsAtwNHGmKbAScDGSsr1AJ4HLgFSgUQgrUKx04G3gGZYrdFfY733acADwIt+ZXcBpwEJwFXAf0RkgH2sdPsf7kg/F9v76AksPrhDY0wesM5+vKKewHpjjH/3gcV+ZQ/bl327tYi0cLCtE+cDJwMdgT4cqjyOAR61n08FNgHvH2EfrwLX23+nXsD39j76A68B12NdAXgRmOz3zz6livdyyhGO9VdgKNAP6AsMBu6x93cycAdwAtAFGOXg9adw6DNzNfCciDSzn3vGfq4TcBzWSepVftsOAdYDrYGH7cfOt+NJBoqAWcAC+/5HwL/9tl8HjLSPcT/wtoikOohZuUhz02Fl+9tf8KvtE4PftMiKiGD977xR8blqXGy/zlZAFHCXvb+uWC3yvwdaAl8An4lIVCX7eAp4yhiTAHQGJtj7SAM+Bx7CulpwFzBRDvWceL6K93LJEeK90v4ZjZUzmgDP2vtz8lmozAigG1YPjnv9ThaPmAdtKfbrag9cZz8WkM+aargaUW466B2xGmCnikjfI5Q5B9gNzPB7/Qn2a7ijin1XRXOT5qbqGWP0p5IfrBP0XVgn65EVnrsPeNu+fS/wnt9zcUAxcIJf2W/8nj8dyAW89v2mgAGSjhDHJOD2Gsb+KvCPCo/9DFxZSdnLgNkVHnsYeN2+vQ442e+5SDveDtVt6yDOjcClfvcfA17wew2P+T3XBCgBOtj3DdDFvr0ZqxKXUGH//wUerPDYKuC4Gr6fG/3+nuuAU/yeOwnYaN9+DXi0wmeoPM5K9jsKKAAi/B7bhZWcvPbnqIffc9cD0+3bVwKbK+zvdeBlv/u3Aiv87vcGcqp4nYuA8X77/ylY/1/6U/sfNDe9bt/uhNUw5LE/28uBuyvZz0j7dTWpQZzTgXv87t8EfGXf/hswwe85D7AVGGXf988XM7AaT5Ir7P9PwFsVHvsauKKG7+d04Br79nfATX7PdcPKmRHVfRYq2W8H+2/f1u+xOcCF9u2q8uAoe98xFT6XAfms2fvfUh//a/pTsx8aSW6ynzsGiLVjvxvYUVk89v/lfRUeewr4U8X3xWGc09HcpLnJwY9ewTsCY8xarFaQ+4BdIvK++HUR9NMGyPTbLh/IrlBmp9/tAiDLGFPmdx+sCgwiMk5EZovVLTEHOAXr6ktN5GK1LvhLAH4zyNdB2YrPH7x9oIbHOZIdfrfzsd8HrPd108EnjDG5WO9rZS0752C9T5tE5AcRGWY/3h6407+FCWhn77u2DovLvt3G77lMv+fKb0uFSR/8ymQbY0r97h98D5KxKtMVj+X/+v2PdVDFz1rF+wff34PdIhb5vTe9qPlnTdUzzU1WWWPMemPMBmOMzxizFKul9dxK9nMFMNHOITXhNDf5sN7nynLT1UBXYKWIzBWR0+zH2wPnVchNI7BasGurstwUgXWFv8rPghw+IUS63z4cvQccngcBdhurm5u/+v6sqXrWiHITxpifjdV1PN8Y8yhWd8uR/mXs/6VRwJt+j/XDqgD/p4bx+dPcpLmpWlrBq4Ix5l1jzAisD7wB/llJse1A24N3RCQWqztgjYnVdXAi8C+gtTEmCesSu9jPVzYznP/PJfaulmFdmj6433isS/DLKjnsMqCTiDT1e6yvX9nD9mXf3mmMyXawbV1sw3rfgfLX0AKrNeowxpi5xpjxWN0VJmF3NcBKGg8bY5L8fuKMMe/Z+/yyivfySydxAen2Y1Dhs4BVmTwYY8VJH6qThdXCVfFY/q/fONhPpcSaHexlrO40LezP2q/YnzXVsGluqpShwufXfs3nUfPumVWpmJsE63+9sty0xhhzEVZu+ifWJDHxWLnprQq5Kd4Y8w97nxVn4fP/OdLrryw3lWKdvFT5WTCHT1SzuabvAYfnQahbbqrys6YatkaSmyrzm/yD1QvhZ3P4jN+jsK5CbRZrHNhdwDkissD5qz4izU2am8ppBe8IRKSbiIyx/6CFWLV4XyVFPwJOF5HhYvVzvo/a/7GjgGis/tqlIjIO8J+ZaXOFD3vFn3fsop8AvUTkHLEmLbkXWGKMWVnxgMaY1Vhd8/4uIjFiTbTRh0Ozzb0JXC0iPUQkCasv8+tOthVrQGtt/5neA64SkX723+AR4BdTYZIEEYkSkUtEJNEYUwLs59Df6WXgBhEZIpZ4ETn14AmjMWZcFe/luCriukdEWoo1sPle4ODA8Ql2zEeJSBxWd4lasVuPJgAPi0hTu0J2h9+x6ioeK9HtBmvgMNYVPNXAaW4qzy/jRKS1fbs71v/bpxV2cxawF5jm/6AcGqjfoRbvxQTgVLGmVI8E7sQa7zqzYkERuVREWtot6Tn2wz6s/+PTReQkEfHar2+UiLS1X/sNVbyXRxoT9B7wBxHpKCJNsHLmB3YPgUB+Fg4e60h5sK6q/Kyphqux5Ca70niMff4RIyJ/xLqK83OFopdjny/5eQmr4tjP/nkBa8zbSfa+NTdpbgoIreAdWTTwD6wrKTuwWjnurljIGLMMa6zT+1gtEblYfdCLanpAY00mcBvWP+lerIG0k2uxn91Y3RYftvczBLjw4PN2C8wLfptcCAyyy/4DONfeB8aYr7DGxk3DGuu2Cfi7k22xWo5+k1gcvoZvsU7YJmK9r539X0MFlwEbRWQ/cAPWYF2MMfOAa7EG8+4F1mJP4lIHD2FNn7wEa0a/BfZjGGO+BJ7Geq/WArPtbWr8WbDdCuRhTaTyE/Au1ji/OjPGLAeewJqEZSfWGKaKX06qYdLcZDkeWCIieVitqB9jnTj4uwKrNbpiQ1M7rFz2m5ZtB69hFXAp1iRIWVhjNk43xhRXUvxkYJlY3bKfwhonUmCMyQTGA3/BOlnIBP5I3b6TX8OaKGAGsAHrBPtWO+aAfRZsR8yDdRWoz5pyRWPJTU2xxvjvxcohJwPjjNWz6WD5YVhXpg5bHsFYXTp3HPzBeu2FFc6bNDdpbqoz+e33nqoLu3UiB8gwxmxwOx43icgrwIfGmK/djsUNYs3q9CsQbQ4fZ6dUvdPcdIiI3IM1FuPFaguHIf0sqIZEP4+HaG7Sz0KgaAUvAETkdKxZggTrqsgQYEAlrcYqzNndyL7AmgnqDcBnjDnT3ahUY6W5SR2knwXVkOjnUR2kn4Xg0C6agTEeaxDnNiAD61K3fjAbp+uxuhesA8qAG90NRzVympvUQfpZUA2Jfh7VQfpZCAK9gqeUUkoppZRSYUKv4CmllFJKKaVUmNAKnlJKKaWUUkqFiYhg7VhEXgNOA3YZY36zvpaICNbUrKdgrUJ/pTGm2oUek5OTTYcOHQIcrVLKTfPnz88yxrR0O4660NykVHjS/KSUaoiqyk1Bq+BhLe74LNZC2ZUZhzWYMgNrxpz/2r+r1KFDB+bNmxegEJVSDYGIbKrn4wW8AUpzk1Lhqb7zUzBoflIq/FSVm4LWRdMYMwPYU0WR8cCbxjIbSBKR1GDFo5RSfl7HWuj1SPwboK7DaoBSSimllGrw3ByDlwZk+t3fYj+mlFJBpQ1QSimllApXITHJiohcJyLzRGTe7t273Q5HKRX+tAFKKVXvROQ1EdklIr8e4XkRkadFZK2ILBGRAfUdo1Kq4XOzgrcVaOd3v6392G8YY14yxgwyxgxq2TKkxzkrpcKINj4ppQLsdbT7uFKqjtys4E0GLrdbo4YC+4wx212MRymlDnLUAKWNT0qpQNLu40qpQAjmMgnvAaOAZBHZAvwdiAQwxrwAfIE1Q91arFnqrgpWLEopVUOTgVtE5H2s2X21AUop1RAcqfu45ielVLmgVfCMMRdV87wBbg7W8ZVS6ki0AUopFe5E5Dqsbpykp6e7HI1Sqj4Fcx08pZRqkLQBSikVomo0fwHwEsCgQYNM8ENTSjUUITGLplJKKaWU0vkLlFLVC98K3vbFTP3yYX6c/wmUlbgdjVJKldu1v5Bvlu9kf6HmJqXUIXb38VlANxHZIiJXi8gNInKDXeQLYD1W9/GXgZsCHcMnC7cwb+OheV4+W7yNxZk55fe/XLqdFdv3l9//dvlO1u7KBcAYw09rssjckw+Az2eYv2kPO/YVAlDmM6zYvp89ecXl9zdn53PAzoVlPsOevGKKSsvK91dS5sPqVKGUcip8K3jLJvH25td5ed7/wSNp8PLxPP2/M3l78t9gX6W9GZRSql4sXzSLm9+cVX7S892KnZz1/M9szSkArArg2l25lPn0pEapxsQYc5ExJtUYE2mMaWuMedUY84I9Nhh79sybjTGdjTG9jTHzAh3DQ1NWMGnRofOkv3yy9LD7v/9g0WH3r3trHp/a98t8hktf/YVPFlr3C0vLOOe/s8rL5xaWMu6pH8uf35NXzLGPT2OSfX/n/kIGPPhN+f1N2flk/PVLPl5g3V+7K5ce937FV79aFy1X7zzA8Ee/Y8Zqa5maVTsOcNozP5ZXUFftOMAVr81h2bZ9AKzZeYC7PlzM+t1WhXTd7lwe/3ol2/dZuTdzTz7v/LKpvAK6c38hP6zeTV5RKQD7C0vYmJVHSZkPsCqwWvlUDVH4VvBG/5UbhjzJFR2uhsHXQkQ0U8pWs2rz2/CfHvBkH+55+QQ++vx+yK9qRmKllAqg/D2M+v5MVsVeTZeJJ8Gkm2m77l26+9aTFC0AfLxwKyf8+wdyC62Tipnrsnhz1sbykwqllAqWL24fyR9P7H7o/m0juXVMRvn9z28bwTUjOpXf//TmEVwypD0AHhE+vGEY5wxsC0CU18MbvxvMqb2tlRxio7z895IBjOneCoCmMRH867y+DOucXH7/vtN7MCC9GQBJcZHcMbYrPdMSAEiIjeDiwem0ax5n7S/Sy/AuyTSPjwLA6xFaNY0hOsILQEmZj5z84vLGsj15xcxcm0WuXWHblJ3Hf6evI+uAVaFbunUff/3kV3YfKAJg9vpsrnhtDjv3W41xX/+6g1H/ml7eOPf+3Ew6/eWL8vufLNzCif/5gX351hXJb5bv5Nb3FlJQbF2RnLdxDy/NWEepncs3ZecxZ8Oe8kpicakPnzbsqQCQUGt5GDRokJk3r3YNVoVF+WRvnEva3uXsXTuNU4uXcc2+ffxufx5FaQN5LKo15wy7mR4ZwwMctVKqKiIy3xgzyO046sJxbirOo2TVF5gdS4nauQy2LYT8bOu5qCbQ9mj2th7Kr1F9GXncieDx8vdPf2Xy4m0s+NtYRIS3Zm1k14Ei7jyxW1Bfk1KqkeWnRsoYg4hQVFrGvvwSmsVHEen1sCevmA1ZefRsk0BMpJdN2XnM37SXk3ulEBcVweLMHL5dsZMbR3UmLiqCaSt38cHcTJ68sB8xkV7en7OZF2es5+vfH0tUhId/f7OaZ75fw7qHT8HjER77aiUvzVjPmofHISI8+sUK3py1iRUPWmvdvzxjPbPXZ/PqlUcDVnfYbfsKuHxYBwC27M3HGMorvKpxqSo3NapZNGOi40jrdhxwHM2G3sj04iIKMufA5h9ZuPxTJkTu5JhPzqZH0lHsOWo8OR3G0qldT7fDVkqFk6h4fkxswd1LvuatU9+ia1IG5GyGLXNh82zYNJNm6x9lJMCcZtD5eO7rehK3jzweEesK3+qduayzuxgB/OvrVSQ3ieLKYzq685qUUuFh5eeQtxtSekOrHhAZ63ZE9eJgbo2O8NIqwVv+ePP4qPKrgwDtW8TTvkV8+f2+7ZLo2y6p/P7o7q0YbV+dBLhwcDoXDj60RMXvj8/gumM74fFYx7tocDojMpLLjz8iI5lmfsfzeIQIr5Tf/2LpduZt2ltewfvHlytZtm0/0+4aBcDfP/2VrLxinrt4AABTlmxDEE7tY11BzS0qJT7KW348Fb4aVQWvoqioaKI6j4TOIxk6+i+8sfwHuu6eCysm8fWcx3ls3StMiOxFxojbIH0Y6D+EUioA2jRpw/gu4+mY0BFEeG/nTOZmz+XRkx8l2hsNubthww+w9jtY+y3y60c090ZB5zHQ+zwePOUUjN+J15Kt+2jb7ND956ev5diMlvRKS3Tj5SmlQtWid2HlFOu2eK1KXlp/aDsY0odCiy56LlQHHo/QJPrQqXe75nGHXX0bmdGSkRkty+9fPaIjV4841HD3xPl9KSzxHfZ8Tv6hybpaJcQQHXmogvrGzI1EeDzlFbwrXptDXJSXt64eAsCz36+hdUIM5w2yVt7Yk1dMUmxkeQVUha5GXcGraEAP6+oex91Fh4WTOXfhS2Rsngn/+5I3WmVQkHoCN4x/GDzeavellFJH0r15d/4y5C/l90t9pRSVFVmVO2DOgQ10yRhD897ngs9nXd1bMRmWfQKrv4Kopkif82DQ7yClN2/+bnD5uI3s3CKe+nYNER6hV1oiZT7D1r0FpLfQLjxKqWpc8DbkbIIdS2H7Yti6AJZPhgVvWs/Ht4IOI6DTcdBlLCSmuRtvIyMixEYdOgftb49VPOjm0V0Ou//utUPJLyorv3/x4HSiIg5Nv/HN8p10S2laXsE77ekfGdY5mSfO7wvAc9PW0r9dEsO7JAf8tajgalRj8GqlOB/fove4cNF/aFNawJO+FjD6bjhqPHjCd44apeqTjnE5pKSshDEfjmFo6lAeP+7xw5/0+WDTT1Yr+7JPoLQQ0ofD8Fuh68nlOSmvqBSfMTSNieSH1bu54rU5vHvtEIZ31i9ppWqq0ecnnw+y11hdyDf+BBt/hAP20nutekL3U+Go061unXp1L+QcHH8I8N6czbRtFsvIjJaUlvnoc/9UrhnRkTtO7EZpmY9jH5vGTaO7cOnQ9hhjWLp1H11bNyUmUi98uKGq3KQVPIeKi4vYs+RDUn55hqw9q7mldTtu6P0nRg27pN5jUSrcNPoTqArW56wHgU6JncguyObFJS9yfZ/raRHb4lCh/D1WRe+XF2BfJrQ8ymp86n76YY1Puw8U8eH8TK4Z0YmoCA/fLN/J3rxizhnYFq92w1GqWpqfKjAGdq+ENVNh9deweRYYH7TIgD7nQ58LoFn7wBxLuarMZygu9REb5WV/YQkPTVnOuN6pjO7Wisw9+Yx8bBoPndmLS4e2Z39hCZ8u3MrYHimkJMa4HXqjUFVu0ktQDkVFRZMy6FK4cSbzBt5BtqeMjt/dBp/coMssKKUCqlNSJzolWtOQL9q1iI/XfMy+on2HF4prDsNvgdsWwdmvgCmDCZfDS8dZrey2lk2juWlUl/JuOZ8u2sprP29Aq3ZKqVoRgVZHwTG3w1VfwJ2r4bQnoUlrmPYwPNUX3j7HmrDFV1b9/lSD5fUc6hKaEBPJY+f2ZXQ3axKZZvFR/PeSAeWTyizJ3MffPl1Wvsbg2l0H+OdXK8uXmFD1S6/g1VJxfg5RM5+CmU/zRLNkmrQ7h+vPfMTtsJQKSdpCXrW9hXtpFmONtXhz2Zv0admHfq36HV7IVwZLP4TvH7Ku6PUYDyc9+psxMsYY9uQV06JJNIUlZdz23kJuGt2Ffn4zwSmlDtH8VAM5m2HhO9aYvQPboHknGHoT9L8MIvWqTjgzxrA1p4DkJtHERHr5dNFW7pywmB//NJrUxFh+WpPFj2t3c8voLjSNiXQ73LCgV/CCICouCU74O/uv+Iofo6PJ2fwOTL4VivPdDk0pFWYOVu4KSgt4d+W7TFk/5beFPF7oeyHcPAdG/QVWT4Xnh8L8N6wuVTYRoUUTazKXzXvy+XXrPvLtRX+VUqpOktKtruK/XwrnvQ6xzeCLu+Dp/jDnZSgtdjtCFSQiQttmceXj8cb3S+PX+08iNdGa4XnZtn1MnL+FWPv5z5ds561ZGwm1C02hQq/gBUB+YR6eGY8RM/Mp1iR3IevYhxjW52S3w1IqZGgLuXP5JfkYDPGR8WzN3YoxhrZN2/62YPY6+Ox2a0KEjBPhzBcgvsVvihWX+sq7b743ZzNNoiM4vW+bYL8MpUKG5qc6MAY2zIDpj1pj9Zp3ghMfgm6n6IQsjVBJmY9Ir/V9c/O7C9icnc9nt44ArEXcOyTH06VVEzdDDCl6BS/I4mLiiTnxfrh0Io9E5HL/nDsoWvu922EppcJQXGQc8ZHWQrsPzX6Ia6ZeQ4mv5LcFW3SGyyfDuMdh/Q/wwjGw8effFDtYuTPGMHnRNj5esEVbVJVSgSFiLalw1ZdwyUfgiYT3L4a3z4a9m9yOTtWzg5U7gGcv6s/b11jr8fl8hj9/vJSnvltT/vyePL3aWxdawQukLsdz67Evcl9BDNHvnmf1Q1dKqSC5e/Dd/H3Y34n0HGE8g8cDQ66Da76FyDh48wyY/3qlRUWEt64ezFMX9UdE2FdQwjp7sLxSStWJCGSMhRt/hpP/CZlz4Plh8MuL1jIMqtERERJjre8uj0f4/LYR3Dm2K2DN/jz44W955xdtBKgtreAF2IAexzH02mnQYQSffvdHHn3nSrdDUkqFqfSEdIa1GQbA1I1T+d+v/6u8YGofuG4adBplddv86i+VnlRFeD0k2IPf75u8jPNemEWujs9TSgWKNxKG3gA3zYb2w+DL/4P3LoC8LLcjUy5rnRBDh2Srd0qER7h1TEb52q3Lt+3noSnLyc4tcjPEkKIVvGCIScR34ft82rQtGw78RMmPT7odkVIqzP2w5QemZ06vvLsmQEwiXPQBDLkBZj8Hk26EsiNX3u46qRsPn9mLJtERQYpYKdVoJbWzumye8i+rC/l/j7Gu6imFtQTD7Sdk0NGu8C3M3MsH8zKJsLt47i8s0aEE1dBv7iDxRMXy7BXTKJt0I5Hf/R1fdFM8g692OyylVJh6YPgDFJUVEemJxBiDVDaBgTcCTv4HxCXDtIegtMBaQy8i6jdF05JiSUuyZj/7ZX028zfv5cbjOle+X6WUqikRGHwtpA+FDy6D10+FM56Fvhe4HZlqYC4Z0p4z+6URbzc43vLuQiI9wqtXHu1yZA2XXsELoriYeJqe9yq5nU/g9nmP8tInd7sdklIqTHk9XuIi4yguK+auH+5iwqoJlRcUgeP+CCc9Ass/hUk3VDsGZsqS7Uycv4X8Yl20WCkVYCm94drvod0Q+OQ6mP7Pw5Z2UQoor9wZYzi5Zwon9mxdfn/muiy9oleBVvCCzRtJyRkvsDMinsT1b8PmX9yOSCkVxkSEorIiCksLqy447GY44X74dSJ89acqT6juP6MnH90wnPjoCP0SVUoFXlxzuPRj6HsxTH8Ept6jlTxVKRHh4iHpXHB0OgAz12Vz8cu/MHnxNpcja1i0i2Y9aJbYkrcv/paoV8fChMvguh8gIdXtsJRSYSjSE8nTY57GIw7a70b8HvKzYOYz0DQFRt5ZaTGPR2gWH4XPZ3hgynLaNY/j6hEdAxy5UqpRi4iC8c9BdFOY9SyUFMCpT+h6eapKQzo258kL+jGul3VevXDzXprHR9G+RbzLkblLr+DVk6gmLeHCd5nnK+Dad8dxIC/H7ZCUUmHqYOVuWdYyrpl6DQeKDxy58NgHoff58N2DsOqrKvdrgF0HCtmWU6BX8pRSgefxwLh/wjG3w7xX4bv73Y5INXARXg9n9k8rX9P1vsnLuP6t+Y3+O0orePWpdQ/mZ1zKDm8hW7/6s9vRKKXCXEFpATvzdrIjb8eRC4nAGU9bSyl8fC3sXn3Eol6P8MxFA/jbaT10shWlVHCIWN3HB/0OfvoP/PyU2xGpEPLS5YP413l9EasJTdMAACAASURBVBFKy3zM37TH7ZBcoRW8enb9WY8yMfVUui99D9Z973Y4SqkwNihlEJPGTyKjWUbVBSNj4YJ3wBsFH1wCxflHLOr1WBW7zdn5XPbqL+w+oOsSKaUCTMRaQqHn2fDNvdaEUEo50Dohhl5piQC8PzeTc/47i4Wb97ocVf3TCp4Lok56iJLkrrzw+U1s2nbk1nKllKorr8dLma+M1399nW25VQxCT2oH574KWavhm79Vu9/colLW7cpl8568AEarlFI2jxfOegHaHg2f3Ag7fnU7IhVizh3Yln+f35d+7ZIAa/28xkIreG6IjGXWkD/zYkIEH39+k9vRKKXC3O6C3byw5AUmrZ1UdcFOo2DYLTD3FVg9tcqiPdokMP2PoxnYvnnA4lRKqcNERMMFb0NMArx/EeQ3zu52qnZiIr2cPaAtIsKu/YWMfnw678/Z7HZY9UIreC459uhzeLvFqfxh61xY+53b4SilwlhKfAqTxk/ipn4OGpSOvxda9YRPb6r2ZCoqwoMxhgnzMhvtOAelVJA1TbG6kO/fDp/dpssnqFqJj47gxJ6tObpj42iU1Aqei3qOewBadGHH538g50CW2+EopcJYSnwKAHsK91Q9q2ZENJz9EhTstca+VKOo1MfT363h3V8yAxWqUkodru1Aq/FpxWew4E23o1EhKD46gkfP7kPnlk0AeH76WhZnhu+M9lrBc1NENGtH3M3ZTct47KOr3Y5GKRXmDhQfYPyk8Ty36LmqC6b0shZCX/gWbJpVZdGYSC8fXD+Mx8/tE8BIlVKqgmG3QMfj4Ks/Q9Yat6NRIWx/YQnv/rKZSYu2uh1K0GgFz2Vd+p/L2WUpXLv9F8hpHP2ClVLuaBrVlJv73cz5Xc+vvvBxf4LEdjDlD1BW9cD0tKRYPB4ht6iUjVk66YpSKgg8HmvSlYhomHwr+HxuR6RCVEJMJJ/dMoK/nHIUAPsKSsJu3Tyt4DUAd53/Bh3LfDDtUbdDUUqFuQu7X0inpE7VF4yKh3GPwe4VMOflaosbY7j81V+49b2FYfdFqZRqIBLawIkPweZZVg8DpWqpWXwUkV4PBcVlXPDiLP4+eZnbIQWUVvAagsS27Oh3OQ9s+Yppcye6HY1SKsztL97Po788ytLdS6su2P0U6DQaZjwOhfuqLCoi3H5CV+47o6cugq6UCp5+l0D7Y6zlXHJ3uR2NCnExkR7G9UplbI/WbocSUFrBayBKBl3PN/FxrFv4jNuhKKXCnFe8fLPpG5ZkLam+8An3QcEe+OnJaose17UlA9s3q3N8Sil1RCJw2pNQnA9T73E7GhXirMbJDEZmtARg5ros8otLXY6q7rSC10C0S81gSodLuWbbQsic43Y4SqkwFh8Zz5SzpnDJUZdUX7hNP+h9Hsz+L+yvYqF0mzGGp75dw7+nrgpApEopVYmWXWH4rbDkA9i20O1oVJjIyi3id6/P5bGvQv/7Syt4DUjiiNsgtjlbv3/E7VCUUmEuLjIOgKwCB0u0jLkHfKXww2PVFhURtubkk7m3QMfiKaWCZ8TvIa4FTP2bro2nAiK5STT/vWQgfxjb1e1Q6kwreA1JVDxvpI/mVLNWx+IppYLuu03fccKHJ7BqTzWtlc06wIDLYJG92HA1/nF2H/5zQT8di6eUCp6YRGu2340/wtpv3Y5GhYnR3VuRGBtJaZmP137aQHFpaM7WqhW8BmbMqLu5fH8B3dZNcjsUpVSYG5QyiMt7Xk6L2BbVFx5+m3UVb9az1Rb1eKyK3Y59hezaX1jXMJVSqnIDr4JmHeGbe8FX5nY0Koz8vC6bB6Ys5/uVO90OpVa0gtfAtEvN4I5uF9Jm1eewd6Pb4SilwlhidCJ3DLyD5Njk6gs37wi9zoV5/4P8PdUWzy8uZex/fuCJqasDEKlSSlUiIsrqQr5rOaz4zO1oVBg5rmtLptw6gpN7pbodSq1oBa8hGnYzi6KjeWXyXW5HopRqBJZnL+eL9V9UX3DEH6AkD+a8VG3RuKgIHj6rN7eM6RKACJVS6gh6ngUtusCPT+hYPBVQvdISAdiQlcfHC7a4HE3NaAWvIUpow3PNO/J+6XKK86tee0opperqlaWv8NSCp/CZasYatO4BXcfBLy9CSUG1+z2jbxvaNY8LUJRKKVUJj9dqfNqxBNZ843Y0Kgw9N20tj3yxktyi0Fk+IagVPBE5WURWichaEflzJc+ni8g0EVkoIktE5JRgxhNK/jjkz0zeso2oFToWTykVXP939P8x8YyJeMTBV8LQG6118ZZ94mjfmXvyufvjpeTkF9cxSqWUOoI+F0BiO/jxX3oVTwXcA+N78slNw2kSHeF2KI4FrYInIl7gOWAc0AO4SER6VCh2DzDBGNMfuBB4PljxhJqufU4nrlVPmPuyJiulAkwbnw6XEp9Ck6gmzgp3PBaSu8LcVxwVzy0qZdLCrSzKzKlDhEopVQVvpDURVOYvsGmm29GoMBMXFVHeG+XTRVvZX1jickTVC+YVvMHAWmPMemNMMfA+ML5CGQMk2LcTgepX0W0sRFiScQa/YyefznjV7WiUChva+FS5dTnruGbqNWzYt6HqgiJw9DWwdT5sXVDtfo9KTWDOX49nVLdWAYpUKaUq0f9SiG0Gc150OxIVpjZm5XHnhMX876eNbodSrWBW8NKATL/7W+zH/N0HXCoiW4AvgFuDGE/ISR14Gfs8Xlg/2e1QlAon2vhUicToRLbnbmdH3o7qC/e9ECLjYa6zxqemMZEAFJXqNOZKVUd7GNRSVJxVyVsxBfZtdTsaFYY6JMfzwfVDQ2LyMLcnWbkIeN0Y0xY4BXhL5LeDQETkOhGZJyLzdu/eXe9BuqVlszZMTD2Z8VvnQNEBt8NRKlxo41MlkmOTmXLWFIa1GVZ94ZhE6HM+/PqRoyUTAO6ZtJSLX/6ljlEqFd60h0EdDboajA/mv+52JCpMDWzfHK9HOFBYwpqdDffcPJgVvK1AO7/7be3H/F0NTAAwxswCYoDfLMhkjHnJGDPIGDOoZcuWQQq3gep7Eb7SArbMe8ftSJRqTBpl45OIYIwhvyS/+sKDroLSQlj2saN992vXjGMzWlLm0zHFSlVBexjURfOO0PUkmP8/KC1yOxoVxm56ZwFXvzGPkrJqZp92STAreHOBDBHpKCJRWK1MFfsabgaOBxCRo7AqeKF/lhRI7QZzRWpb7lqmfcqVChBtfDoCYwwXfn4hD//ycPWFU/pAqx6w+H1H+z53YFtuPyEDr0fqGKVSYU17GNTV4Gshbzcs1+EtKnj+76TuPHF+XyK9bneGrFzQojLGlAK3AF8DK7C6EywTkQdE5Ay72J3AtSKyGHgPuNIYnTLyMCIcGz+QS/fthH2htciiUg2UNj4dgYhwasdTGZE2wklhayzelrmQvc7R/o0x/LI+m4JiHYunVB00yh4GjnUaA806woI33I5EhbHebRM5ukNzAPKLG976eEGtdhpjvjDGdDXGdDbGPGw/dq8xZrJ9e7kx5hhjTF9jTD9jzNRgxhOqrj3lPk7Ly4MlE9wORamQp41PVbu85+WM6zjOWeHe54N4HF/FW7A5hwtems3nS7fXIUKlwpr2MKgrjwf6XQwbf4SczW5Ho8LcpIVbOfax6ezYV+h2KIdpmNcV1eGad2J/2iA+XfQWvjJt+VaqrrTxqWr5JfnM3zm/+oIJqdBpFCx5H3zVj0MYkJ7EMxf155TeKXWOUakwpT0MAqHPBdbvxR+4G4cKe33bJTEyI5noiIZVpWpY0agjej6+A/cklPLjos/cDkUpFeaeWfgM1029jtzi3OoL97nQaiXfPKvaoiLC6X3bEBcVEYAolQo/2sMgQJq1h/YjYPF7oG+NCqKOyfH854J+NIuPcjuUw2gFL0RcNPIPvLVtJ8fsW+F2KEqpMHdR94t4ceyLxEbEVl/4qNMgItbxbJrGGCbO38KUJTrxn1KV0R4GAdLvItizzhonrFSQ7dhXyB0fLGJPXrHboQBawQsZ7dv1ol+r/kSs+tztUJRSYS49IZ1BKYPwerzVF46Kh4wTrMWFHXTTFBHenbOZifN10iilVBD1GA+RcbDoXbcjUY1ATkEx36zYyeItOW6HAmgFL6Rs7HAczxVtZsHy6W6HopQKc1tzt/Lq0lcp8ZVUX/io8ZC7A7bMcbTvly8fxGtXHl3HCJVSqgrRTaH7abB8EpQ5yGNK1UH3lARm3X08o7u1cjsUQCt4ISWn7bG8lJTAnEVvuR2KUirMrchewZMLnmRl9srqC3c9CbxRjtedah4fhYiuh6eUCrKeZ0LBXmtGTaWCrEm0Nb58/qY9ri+ArhW8ENKv2wi+L0jihryNboeilApzI9JG8P1539O7Ze/qC8ckQKfRsOIzxxMafLpoKxe8OAufTydAUEoFSecxENUEln/qdiSqkViyJYdz/juL9+e4u0SHVvBCTIueZ8LWebCv4rI4SikVODERMbSMq8HaWT3OgH2bYdtCR8VFhEivh5wC7TqllAqSyFirh8GKKVDW8BajVuGnd1oi/zqvL+cObFd94SDSCl6I2dNxNP/XsgXPfvWI26EopcLclgNb+OtPf2V9zvrqC3c7BcQLK5x10zyjbxvevmYIzRvY1NJKqTDTYzzkZ8HmmW5HohoBEeHcgW2JjfLi5uolWsELMUlt+rExMhb2LXM7FKVUmIvyRjFjyww27t9YfeG45tDhGFj9dY2OUVhS5uqXoFIqzHU5wVrKxeEYYaUCYd3uXM56fiYrd+x35fhawQsxHq+XCW1O4pbdq6G0yO1wlFJhrFVcK3644AfGpI9xtkHGSbBrOeRkOio+bdUu+j/wDWt3OVhQXSmlaiMqHjLGWr0LfGVuR6MaiRbxURSWlJF1wJ118bSCF4oyxkJJHqUbfnY7EqVUmPNIDb4mMsZav9d+46h4j9QEzh3YlqgI/SpSSgXRUWdA7k7HY4SVqqukuCi+vH0kIzKSXTm+fquGoNL0Y7g4NYU//fBPt0NRSoW5zP2ZXPLFJczePrv6wsldISkd1nzraN+tE2J48MxetG8RX8colVKqCl2OB/HUuAu5UnUhIvh8hm+W76z3GaO1gheCImIT6GySGFC4w+1QlFJhrkVsC7zipcxJ1yYRyDgR1k+vURfyjVl57M1zpxuLUqoRiGsObQfDmqluR6IamanLd3Dtm/OYtmpXvR5XK3gh6sG+l3NJ1kbYu8ntUJRSYSwuMo43x73JMWnHONugi9WFnE3OZqzblJ3HqH9NZ8rS7XWIUimlqpExFrYvggPaOK7qz9geKbx42UBGd2tVr8fVCl6o6jKWEmDjoo/cjkQp1Qj4jI8Sn4M16zqOBG80rHXWTTO9eRz/OLs3Y7rX75efUqqR6XqS9dthblIqELwe4aSeKXg8Uq/H1QpeqErO4Ny0tty/eoLbkSilwlzmgUyO++A4pm500L0pKh46jHDcFUpEuHBwOmlJsXWMUimlqtC6FzRto900lSumLtvBRS/NpqTMVy/H0wpeqBJhvLcTl+Vmga9+PixKqcapTXwbxrYfS1qTNGcbdDkeslbDvq2OiheX+pi2chdrdx2oQ5RKKVUFEaub5rppUOagN4JSAeT1CAUlZWTl1s8SZ1rBC2G/G3gJYw5kwc5f3Q5FKRXGvB4v9w67l36t+jnboOOx1u+NPzoqXurzcf1b8/lw3pZaRqiUUg5knAhF+2HzLLcjUY3MmO6t+OSm4aQm1k9vFa3ghbIOI9kW4WXOAu2mqZQKvuyCbPJL8qsv2KonxDaHDTMc7TcuKoKPbhzGH8Z2rWOESilVhU7HgSfCmulXqXokIogI+cWlrNkZ/N4qWsELZYlp/C6lDc9s+crtSJRSYW7VnlWMmjCKGVscVNo8Hmsc3oYZYJyt/dOnbRIxkd46RqmUUlWIbgppA2H9D25Hohqpa9+cx/Vvzw/6unhawQtxt0f34m85O6Gs1O1QlFJhrHNSZ+4YeAc9WvRwtkHHY2FfJuzd4Kh4SZmPV35cz7SV9btWkFKqkel4LGxbAIX73I5ENUK3jcngsXP6BH1WTa3ghbhx/c+na8F+a20XpZQKkghPBFf1uor0hHRnG3Q8zvrtsJtmhEd47acNTK/nxWCVUo1Mx+PA+Byv1alUIA3p1IJBHZoH/ThawQt1HUYyKyaaz2a/4XYkSqkwV1xWzIKdC8gryau+cHIGNElxXMETEb7+w7HcP75XHaNUSqkqtBsMEbHaTVO5JreolH9PXcX8TXuCdgyt4IW6Ji15uEVr3sv+2e1IlFJhbsnuJVzx1RXM3TG3+sIiVleoGozDaxoTWccIlVKqGhHRkD4UNmgFT7nDK8K7czYze71W8FQVHk0aykvZO3QcnlIqqHq37M2To59kQOsBzjboeCzk7YbdKx0VLynzcffHS/l4gS6XoJQKoo7Hwq7lkKtdwlX9i43yMu2uUdw8ukvQjqEVvDDQu/tJNCnO0/XwlFJBFe2N5vj040mISnC2Qfvh1m+Ha05Fej0s27aPrXsLahmhUko50KlmY4SVCrSDPVYKS8qCsn+t4IWB4rSBvJPQhA9m6Tg8pVRw7crfxSdrPqGwtLD6ws07QXwr2PyL4/1/evMx3Hp8Rh0iVEqpaqT2g5hEreApV02cv4Whj37H3rzigO9bK3hhIKp5R15NTGJWtoNxMUopVQdLs5Zy78x7WbnHQbdLEUgf4vgKnrVJcKeOVkopPF5oNxQ2z3Y7EtWI9UpLZFyvVErKfAHft1bwwsSEuP48eSDb8WQGSilVG0NTh/LpmZ/Sp2UfZxukD4OcTbB/u6PixaU+LnxpFq/8uL4OUSqlVDXaD4OsVZCX7XYkqpHqltKUR8/uTauEmIDvWyt4YSK507FwYJu1sLBSSgVJfGQ8nRI74RGHXx/thlq/M521lEdFeEhuEk3TmIhaRqiUUg6kD7N+O8xNSgXL+t25LN2yL6D71ApemNjR4igeatGMd2e85HYoSqkwt3T3Ul5e8rKzwql9rDWnatAV6tmLB3DB0Q4XVFdKqdpo0x+80TXqQq5UoBlj+N3rc3n4i+UB3a9W8MJEUruBfBcXx/asRW6HopQKcwt2LeD5xc+zr8hBi6M3EtoOqvFJlDEmKOMSlFIKsNbDSxsAm7SCp9wjIvz7gn48fVH/gO5XK3hhIiY6ju8iunBnUY7boSilwty5Xc9l9sWzSYxOdLZB+lDYsRSKDjgqvq+ghMGPfMdbszbVIUqllKpG+lDYvgiK892ORDViA9Kb0appYMfhaQUvjHjSh1lr4RXudzsUpVQYi4+MJ9ob7XyD9KFgfLBlnqPiibGRnNo7lYzWTWoZoVJKOZA+DHylsHW+25GoRm7ljv3c9t5CcotKA7I/reCFkQXRqVybkswnM99yOxSlVJj7bN1nPLfoOWeF2w4GBDLnON7/fWf0ZGRGy9oFp5RSTrSzc5Mul6BcVlBcxow1u1m1w1lPl+poBS+MtOx4DPs8Xkqzl7kdilIqzC3ZvYQfMn/AOFmaJSYBWnaDrc6u4B20r6CEvAC1Ziql1G/ENoNWPWDzTLcjUY1cv3ZJzL77eAa2bxaQ/WkFL4y0S81gQmEc55UFdqpVpZSq6M+D/8yE0yc4X5g8bZDVDcrhWp0bsvLo98BUvljqbP08pZSqlfQhVvdxn07qpNwjIsREegO2P63ghZu0gbB1gdtRKKXCnNdTwy+itAGQn20teu5A++Zx/PGkbvRPT6pFdEop5VDaICjaD1mr3Y5EqYDRCl6Yed/Ec2ISLFtXs65QSilVU4/88ghvLHvDWeG2g6zfDida8XiEm0Z1oUurprWMTimlHDiYm2rYhVyphqzKCp6IeEVkWn0Fo+quRepA+hYWUbJdr+Kp8Kb5yX3b87aTVZDlrHCrHhARU6MeBsWlPhZn5gRsVjGl6oPmphDTIgOiEx03PikVCqqs4BljygCfiDhc7Ei5bezg83g8ex/9Cre5HYpSQaX5yX3PjHmGOwfd6aywNxJS+9WolXzB5r2Mf+5n5m7YU8sIlap/mptCjMcDaf31Cp4KKxEOyuQCS0XkGyDv4IPGmNuq21BETgaeArzAK8aYf1RS5nzgPsAAi40xFzsLXVUqMhZa96JoyzxqsEqVUqGq1vlJuSBtIMx7FcpKrApfNfq2TeK5iwfQr52Ow1MhR3NTKEkbBD/9x1rwPCrO7WiUqjMnFbyP7Z8aEREv8BwwFtgCzBWRycaY5X5lMoC7gWOMMXtFpFVNj6N+635vLF/7NjCjtISIiOpPopQKYbXNT9r4FABZBVncOf1OLutxGSe0P6H6DdIGwOznYNdySO1bbfHYKC+n9kkNQKRK1bta5SblkraDwJTB9kXQfrjb0ShVZ9VW8Iwxb4hIFNDVfmiVMabEwb4HA2uNMesBROR9YDyw3K/MtcBzxpi99rF21SR4VbmjWg6kxYbl5G7/laR2/d0OR6mgqU1+0sanwEmMTsQjHudLJfhPtOKgggewa38hs9Znc3qfNng8Do+jlMvqcO6k3JDml5u0gqfCQLWzaIrIKGAN1gnR88BqETnWwb7TgEy/+1vsx/x1BbqKyM8iMttuVVd1dP6wS7klZx9J2SvcDkWpoKplfipvfDLGFAMHG5/8aeOTA5GeSP538v84Pv14ZxsktYe4FtZ6eA79sHo3t7+/iPVZubWMUqn6V4dzJ0TkZBFZJSJrReTPRyhzvogsF5FlIvJuwAJvrJq0hKR0HYenwoaTLppPACcaY1YBiEhX4D1gYICOnwGMAtoCM0SktzEmx7+QiFwHXAeQnp4egMOGueSulEbEsnPjbNL6aa8yFdZqk58qa3waUqFMV3t/P2N147zPGPNVoIION8ZevLzaK3ki1ji8bYsc7/uEo1rz5e0j6ZjcpC4hKlXfanXupD0MXJQ2CDLnuB2FUgHhZB28yIMJCsAYsxpwMrBrK9DO735b+zF/W4DJxpgSY8wGYDVWhe8wxpiXjDGDjDGDWrZs6eDQjZzHy6UtW/P7XTPcjkSpYKttfqqOf+PTRcDLIvKbmT5E5DoRmSci83bv3h2Aw4aen7b+xKgJo9hyYIuzDVL7wu6VUFLgqHiz+CiOSk3Aq90zVWipbW7SHgZuaXs07N8CB3a4HYlSdeakgjdPRF4RkVH2z8uAk2vYc4EMEelo90O/EJhcocwkrBMoRCQZq9V8vePo1RGdEdOVqw7sBZ/P7VCUCqba5CdtfAqg1PhURqaNxGAcbtDXmsxg5zLHx1iUmcObszbWKj6lXFLbc6eADW/RBqgaamPPWVCDHgZKNVROKng3Yk2Mcpv9sxy4obqNjDGlwC3A18AKYIIxZpmIPCAiZ9jFvgayRWQ5MA34ozEmu+YvQ1V0cZ/xnHIgB/ZofVmFtdrkJ218CqDOSZ15aMRDpCc47D6f2s/6vd35SdT3K3by0JQVFJaU1SJCpVxRq3Mnhxz1MNAGqBpK6Q1IjXKTUg2VkzF4Nxhj/g38++ADInI71hTjVTLGfAF8UeGxe/1uG+AO+0cFkK91b3Z6vexf+S3dRnRxOxylgqXG+ckYUyoiBxufvMBrBxufgHnGmMn2cyfajU9laONTtYrLionyRlVfMLEtxDavUSv570Z05PrjOhMT6a1DhErVq9qeOzntYfCLPSvnBhE52MNgbp2jbsyim0ByV9i20O1IlKozJ1fwrqjksSsDHIcKsMLmXTitbRteWjnJ7VCUCqZa5SdjzBfGmK7GmM7GmIftx+61K3cYyx3GmB7GmN7GmPcDG3Z4+eecf3LqJ6c6KywCbfrB9sWO958UF0V8tJP2SKUajNqeO2kPAze16addNFVYOOI3pohcBFwMdBQR/+TSFNgT7MBU3cTFxPPX/Di6RmmXJhV+ND81LENSh9Asphk+48MjDtoNU/vCzGehtAgioh0d48N5meQXl3HF8A51C1apIKprbtIeBi5L7QdLPrAmWmma4nY0StVaVU2iM4HtQDLWdL8HHQCWBDMoFRhnpw+HZR+DMVaruVLhQ/NTAzKq3ShGtRvlfIPUvuArgV3LD01sUI3vVuwiO69IK3iqoatzbtLhLS5qY48R3rYIuunSzCp0HbGCZ4zZBGwSkUuAbcaYQgARicXqE76xXiJUtZaT3J2VFNJm40LSOw5wOxylAkbzU8NTXFZMfkk+STG/mevht8onWlnsuIL31EX9iI7QMXiqYdPcFOJS+lA+0YpW8FQIczIGbwLgP9d+GfBhcMJRgTSzLJ5rU1vz5eKP3A5FqWDR/NRAjPt4HE/Mf6L6ggDNOkB0Yo3GumjlToUYzU2hKLoJJGfoODwV8pxU8CLsxTYBsG87mCpNuW1k/9N4YUc25zob4qJUKNL81EDc0u8WTul4irPCIpDap0YTrZT5DH/9ZCkT5ztcUF0pd2luClWp/XSpBBXynFTwdvutW4eIjAeygheSCpSm8c04pmkHWuxZ43YoSgWL5qcG4qyMsxjWZpjzDdr0sxY7LytxVNzrERZvyWHznvxaRqhUvdLcFKra9IMD2+HATrcjUarWHK2DB7wjIs8CAmQClwc1KhUwy5I6sHzXAs5zOxClgkPzUwNR5isj80AmzWObkxCVUP0GKX2grAiy1kDrHo6OMeXWkXWMUql6o7kpVJWPEV4ETU9yNxalaqnaK3jGmHXGmKFAD+AoY8xwY8za4IemAmFiKTzQLJL1mcvdDkWpgNP81HCszVnL6ZNO56ctPznboHUv6/fOX4MXlFIu0dwUwlL7WL+364TMKnQ5WjlWRE4FegIxYk+3b4x5IIhxqQC5os+5XD3lK1KLdmJ9zygVXjQ/NQydEjvxwPAHGNDa4Yy9yRngjYIdS6DP+Y42ydyTz58mLuGWMV0Y3jm5DtEqFXyam0JUdFNo3snKTUqFqGqv4InIC8AFwK1Y3QzOA9oHOS4VIO0zjiWttAzPrmVuh6JUwGl+ajgiaXhT6gAAIABJREFUvZGclXEWKfEOFwf2RkKro2CH8yt4SXGRHCgspajEV31hpVykuSnEpfSGHUvdjkKpWnMyycpwY8zlwF5jzP3AMKBrcMNSAROfzFeJrXl75dduR6JUMGh+akCyCrKYtW2W8w1a2ydRxjgq3jQmks9uHcHo7q1qGaFS9UZzUyhL6Q17N0DhfrcjUapWnFTwCu3f+SLSBigBUoMXkgq0t+KaMrF0o9thKBUMmp8akElrJ3HdN9eRW5zrbIOU3pCfBbk6W50KO5qbQlmKPQ5vl85foEKTkwreZyKSBDwOLAA2Au8GMygVWI+njWHCju1QWlx9YaVCi+anBmRcx3G8fvLrRHsdLr6ZYk+0UoNuml8u3c7wR78jJ1/zmWrQNDeFspTe1m/tpqlC1BEreCJycGb9t40xOcaYiVj9x7sbY+6tl+hUQLRpP4RIXwlkrXI7FKUCQvNTw5TWJI2BrQcS6Y10tkHrntbvGkxm0CohmkEdmpNfXFaLCJUKLs1NYaJpKsS10IlWVMiq6gre3fbviQcfMMYUGWP2BTckFWh7kjryXFIiH8/90O1QlAoUzU8N1IKdC5i3Y56zwrHNIDG9RkslDGzfnKcv6k+bpNhaRqhUUGluCgci1lIuegVPhaiqlknIFpGpQEcRmVzxSWPMGcELSwVSXKujeCOxKaftXsjZbgejVGBofmqg/jXvX8RFxPFKyivONkip3UlUaZmPCK+TUQZK1SvNTeEipTfMeRnKSsHraFUxpRqMqj6xpwIDgLeAJ+onHBUMMdFx/FicTHSM25EoFTCanxqoB495kISoBOcbpPSG1V9BSQFEOrsq97dJvzJnwx6+/sOxtYxSqaDR3BQuUvpAWRFkr7GWdFEqhByxgmeMKQZmi8hwY8zueoxJBUF0Sm9Y9YU1Hbm94KpSoUrzU8PVOalzzTZo3QuMD3Yuh7YDHW1ydMfmJDeJxhiD/D97dx4fV1X/f/x1ZsnMJJmsk31r2qRLum+UAi3Slh3ZRAQEEUVEvxpc+Km4ACpuoFRQVNzZVAQLFC0WqIWWQktXui9p0iX7nkz2THJ+f0xaC3a5N83kTqaf5+ORx/RO5uS+ScIn93Pn3HOlnokwIrUpghy70Io0eGKEOeX8FilQkWGlLY6vx0DJYZlPLiKH1Kfw09rTyj/2/oOyljJjA44stFK7w/A+rpyayV2LCqW5E2FLalME8BWC3SULrYgRSS5gOEPUulPZ5HZxoMzETYiFEMKk7kA3979zP29Xvm1sQGI+OKOhxniDB8Fr8Fo6eweRUAghDLA7IXW8LLQiRiRp8M4QHzvnZl47XMkipxwQCSFCx+fxsezaZdw4/kZjA2y24PQnkw3e/AdX8sN/7RpEQiGEMChtcnD6uBAjzCkbPKXUWKXUCqXU9oHtKUqpb4c+mhhSMT6ITYNaKVQickh9Cj9KKXK8OdiUifOHaRODDZ7Whod8/oICLpmcPoiEQoSe1KYIkVYE7bXQVmt1EiFMMfIX+HcE7+vSC6C13grcEMpQIjR+GZvCl6vWWB1DiKEk9SkMbavbxqObHkUbbdhSJ0JnI7TVGN7HzWfnccG41EEmFCLkpDZFgiPXCJucYSCE1Yw0eNFa63c/8FwgFGFEaNU5E+mkg/6ATNMUEUPqUxja0bCDP+34Ew1dDcYGDOIgSmvN4cYOWruknomwJLUpEqRNCj5KgydGGCMNXr1SagygAZRS1wFVIU0lQuK7M27kNzW12JoPWB1FiKEi9SkMXV1wNe9+/F18Hp+xAYNo8Epq25j34Epe32n8XT8hhpHUpkhw5PIWafDECHOyG50f8X/Ab4HxSqkKoAy4OaSpRGgcexDlK7Q2ixBDQ+pTGHI73OYGRCeBN8PUNcL5vhgeuHoSs0clmUwnxLCQ2hQp0iZCzXarUwhhyikbPK11KbBIKRUD2LTW/tDHEqEQSCrgzvRURq1/im9PvNrqOEKcNqlP4evJHU/idri5ftz1xgakFpk6iHLYbdx8dt4g0wkRWlKbIkjaRFj3OPQFwG7kfREhrGdkFc0fKqUStNbtWmu/UipRKfXAcIQTQ8vhjsWjXST3NFkdRYghIfUpfL1Z/iZrq9YaH5A2Eer2BA+iDGrp6OXtknrji7kIMUykNkWQtEnQ1wMNJVYnEcIwI9fgXaq1bj6yobVuAi4LXSQRSr9InsnnOhutjiHEUJH6FKZ+e+FvefhDDxsfkDbR9EHUi1squOn366hp7R5EQiFCSmpTpDh6eYtM0xQjh5EGz66Uch3ZUEp5ANdJXi/CWdpEaDoA3W1WJxFiKEh9ClN2m93cgCMHUbXGFzO4sCiNZ26fQ0K009y+hAg9qU2RwjcWbA5ZaEWMKEYavGeAFUqpTyulPg28BjwR2lgiVF7uUizKyWDZ+iVWRxFiKEh9ClNVbVXcs/oettZtNTZgEAdRmQkezi3w4XaabCaFCD2pTZHC4QrWJ2nwxAhiZJGVnyiltgILB576vtZ6eWhjiVDJyZ3N7JJfk9BRbnUUIU6b1Kfw5bQ7WV+9noW5C0/9YggeRCUXQO0uU/vZXtFCY3sP88emDCKlEKEhtSnCpBbBIRPXFAthMUPLAWmtXwFeCXEWMQymjZvHtH90QL9M0RSRQepTePJ5fLz+0dfNDUqdAJWbTQ355X9K2FPjZ+XdHzK3LyFCTGpTBEkrgu3PQ2czeBKsTiPEKRlZRfNapdQ+pVSLUqpVKeVXSrUORzgRAjY7pIyjW6YaiAgg9SnCpJq/Rvjrl47n6dvnhC6TEIMgtSnCpB65RtjcDAMhrGLkGrwHgSu11vFa6zittVdrHRfqYCJ0vmGzcUlAlvsVEUHqUxh7ef/LfPa1zxq/jUHqhOBj3R7D+8j3xZCV4BlEOiFCSmpTJEkrCj6aWARKCCsZafBqtNZyyiKCTI4v4np/Kz3+WqujCHG6pD6FsT7dR09fD52BTmMDBnEQFejr5x8by9l4UG7/IsKK1KZIEp8Drjio2Wl1EiEMMXIN3gal1LPAi8DRmw1prWUZxhHq4zOugZ1PQ8Ne8KZaHUeI0yH1KYxdXXA1VxdcbXxAwihwRpuaBmW3Kb778g6umpbFzLwk8yGFCA2pTZFEqeAMg1pp8MTIYKTBiwM6gIuOeU4DUqRGqtQieoGmQxtJHXWe1WmEOB1SnyKJzQYp400tR66UYtld80iPc4cwmBCmSW2KNKlFsH0JaB1s+IQIY0Zuk3DbcAQRw8ibwaKcbCbveZFfzr/L6jRCDJrUp/B313/uYnTCaO6aYbDWpBbBvldN7SM7MXoQyYQIHalNEShtImz8E7RWQnyW1WmEOKlTNnhKKTfwaWAicPQUqdb6UyHMJUJJKW7u8TJK9VqdRIjTIvUp/CV5kvBGeY0PSCuCLU9Dez3E+AwNOdzYwXMbDnPjnFwy4mXBFWE9qU0RKPXINcI7pcETYc/IIitPAenAxcCbQDbgD2UoEXqfyTmHC5sOBacaCDFySX0Kc/fNvY9PTTJxTHtkJU0T17o0dfTwy5Ul7KmWH70IG1KbIs2RRaDkNlNiBDDS4BVorb8DtGutnwAuBwzddEgpdYlSao9SqkQp9Y2TvO4jSimtlJplLLY4XQHfeA73tdFYs9fqKEKcjkHXJxGmjtxvysRqdUUZcez83iV8aJwsGiXChtSmSONJBG+mLLQiRgQjDd6ReXzNSqlJQDxwyr+iSik78BhwKVAE3KiUKjrO67zAXcA6o6HF6XuxvZfLcrL456Z/WB1FiNMx2PokJ5+GyY6GHVz0/EVsqN5gbEBsKniSTB1EOew23E77IBMKERKDqk0izKUVya0SxIhgpMH7rVIqEfg2sBTYCfzEwLizgBKtdanWugf4G3DVcV73/YGv12UsshgK502+jPvqGzjHLtfhiRHNdH2Sk0/DK8WTwvTU6UQ7DS6EolRwMQOTZ8lf31nDvS9tH0RCIUJisMdOcgIqnKUWQf0e6JNjJxHejDR4K7TWTVrrVVrr0VrrVMDIEmdZwOFjtssHnjtKKTUDyNFa/8twYjEk0lPzuU57KeiosjqKEKdjMPVJTj4No9ToVH4y/ycUJf9PD32SQROgdrepa4T31baxYlctXb19g0gpxJAb1LGTnIAKc2kToa8HGvZbnUSIkzLS4B1vDt/zp7tjpZQNeBj4qoHX3qGU2qCU2lBXV3e6uxYDanyFrK3abHUMIU7HYOqTnHyyQKA/YPzFqROgxw/NhwwPufP80az5xgKZqinCxWCPneQEVDg7upKmLLQiwtsJb5OglBpPcHnfeKXUtcd8Ko5jlvw9iQog55jt7IHnjvACk4A3VPCGkenAUqXUlVrr912sobX+LfBbgFmzZsmyj0PkJ4FeVnnaebunm6gol9VxhDBsCOrTyb72kZNPnzTw2juAOwByc3NPZ7cR7eEND/PKgVd47brXjA04stBK7S5IzDM0RMmNh0UYGILadLwTUO9bnOXYE1BKqf93mpGFGSnjQNmD1+FN+ojVaYQ4oZPdB28ccAWQAHz4mOf9wGcMfO31QKFSKp9gY3cDcNORT2qtW4CjNzlSSr0B3P3B5k6Ezg15C7hh48+guQxSx1sdRwgzTqc+ycmnYTYtdRouh4t+3Y9NGZg4cqQe1e6AcZcY3s+Plu0izuPk/y4oGGRSIU7b6R47nZScgLKYwwXJBbKSpgh7J2zwtNYvAS8ppeZqrd8x+4W11gGl1BeA5YAd+KPWeodS6nvABq310kGnFkPirKILYc0PoX6vNHhiRDnN+iQnn4bZgtwFLMhdYHyAOx7ic4Lv4JlQ3txJslyDJyx0usdOyAmo8JdWBBWbrE4hxEkZuQbvGqVUnFLKqZRaoZSqU0rdbOSLa62Xaa3Haq3HaK1/MPDcvcdr7rTWH5IDqGGWMo5NLhdv7l5hdRIhBst0fdJaB4AjJ592AX8/cvJJKXXlcIQ+EwX6A7T3thsfkDrB9HLkj900g+9dNclkMiFCYrDHTkdPQCmlogiegDp6zKS1btFa+7TWo7TWo4C1wP80dyKEUidC80HolvvWi/BlpMG7SGvdSnDKwQGgAJA535EgKoZvpKTyRJ0swiVGrEHVJzn5NLy01sx/dj6/2vIr44NSi4KzC2Q5cjEyDbY2yQmocJd2ZKGV3dbmEOIkTnYN3hHOgcfLgee01i1yMXvk+L49n5xOuVWCGLGkPo0ASik+N/VzFCSYuDYutQj6e6GhJPhungG1/i6+8JfNfOrcfC6ZlD7ItEIMiUHXJq31MmDZB5679wSv/dBpZBSDcexKmjmzrc0ixAkYeQfvZaXUbmAmsEIplYIsyxsx5uTNIbOxFALdVkcRYjCkPo0QtxTdwtzMucYHHGnqTCxmkOCJQmsNyOVGwnJSmyJVQh44Y0xPIRdiOJ2ywdNafwM4B5ilte4F2jn+PVnECFQRk8WLMW62737D6ihCmCb1aeTo1/1UtFXQ09djbIBv7H+XIzcoymHjuTvP4ZJJGYNMKcTQkNoUwWy24AkoWUlThLGT3Qdvgdb6P8fex+UD0wuWhDKYGB4ljiS+k5LMZ3b9m0mTLrY6jhCGSH0aed48/CbFK4t5+rKnmZoy9dQDnG5IHmN6JU0hrCS16QyRVgS7/glag1wWIMLQya7BOx/4D++/j8sRGilSEWHOxEUsXX4H2TO9VkcRwgypTyPM5JTJ3Dv3XrJis4wPSi2Cqi2m9vPv7dXcv3QHL3/xPFK8LpMphThtUpvOBKkTYdOT0FYDXrneV4Sfk90H776Bx9uGL44Ybm53DPkJY6BeVoMSI4fUp5HH5/Hx0bEfNTcotQh2vgjdbeCKNTQkI97NOWOS6enrH0RKIU6P1KYzxJGVNGt2SIMnwtLJpmh+5WQDtdYPD30cYYXVMRlsbdzG/1kdRAiDpD6NTA2dDdR11jE+abyxAWkTg491uyF7lqEhU3MSePhj0waZUIjTI7XpDJE6UJtqd0LBQmuzCHEcJ1tkxTvwMQv4HJA18HEnMCP00cRweR0Hv/Paqa47aHUUIYyS+jQCPbThIYr/U2x8wLFnyU3qlXfwhDWkNp0JYpIhNn1QtUmI4XCyKZrfBVBKrQJmaK39A9v3A/8alnRiWNw1/Ua+teRWojoqgDyr4whxSlKfRqZbJtzCNQXXGB+QMGpgOXJzB1HffGEbGw80sfzL880FFOI0SW06g6RPgprtVqcQ4riM3Og8DTh2XeuegedEhEjKG7hRZ80OyDvH2jBCmCP1aQSZ6JtoboDNFnwXz2SDd/boZLISPGitP7iCoRDDRWpTpEubCGWroK8X7M5Tv16IYWSkwXsSeFcp9cLA9tXAn0OWSAy/uEyeSPDRueNl7jzrM1anEcIMqU8jSF9/H+/VvUeiO5H8+Hxjg1KLYNdSU8uRXzk18zRSCjEkpDZFurRJ0NcDDSXB++IJEUaM3Oj8B8BtQNPAx21a6x+FOpgYRkrxr2gvm7oPW51ECFOkPo08n3n1MyzZZ2Kl+LRJ0NkE/ipT++nt66elo9dkOiGGhtSmM8CRRaDkOjwRhoy8g4fWehOwKcRZhIWezLgA97bn5KadYsSR+jRy2G12Hr/wcXLjco0POrrQyk6IM/bOnNaauT9awcUT0/nBNZMHkVSI0ye1KcIlF4LNGbwOb/J1VqcR4n1O+Q6eODO4MyZDjx+aD1kdRQgRwWalzyI1OtX4gNQjDZ7xxQyUUty1aCyXTJL7UwkhQsQRBSnj5B08EZakwRMA7HOl8h1fEi+uf87qKEKICFbbUctLJS/R3ttubEB0EsRlBe83ZcItZ+cxrzBlEAmFEMKgtInS4ImwJA2eACAuczpveTzU1MmSv0KI0NnVsItvr/k2+5r2GR+Uan4lzf5+zcGGdlq75Do8IUSIpE2C1groaLQ6iRDvIw2eACAtOYuVfjufjZLr74QQoTMrfRZLr17KJN8k44PSJkLdnuBy5AbtqfFz/kNvsHJ37SBSCiGEAUcWWjE5w0CIUJMGT/xX2iSZaiCECKkYZwz58fk4bIbW+ApKmwT9vVBv/F2/gtRYfnTtZGbmJQ4ipRBCGJA2cKKqWmY/ifAiDZ446u8qmludzTT7662OIoSIYOuq1pm8VcLAWfLqbYaHOO02bjwrl+zEaJPphBDCoNhUiPZBjfHaJMRwkAZPHNURnY1GU1G2zuooQogItqxsGY9uetT4AF8h2F2mD6JaOnpZtbcOrbXJhEIIYYBSkC6zn0T4kQZPHPXJ827lyapaJvbKO3hCiND58owvs/y65cYH2J2QOsHUO3gAS9+r4BN/fJeK5k6TCYUQwqD0ycH7dJq4RliIUDNxEYSIeAmjIMpr+iBKCCHMSHAnmB+UPhn2LAOtg2fNDbiwKJ2CVC++WJf5/QkhhBHpU6GvG+r3/nc6uRAWk3fwxH/ZbNybmM7t5SusTiKEiGCB/gC/3/Z73qp4y/ig9CnQ0QD+KuND4t3MHZOM22kfREohhDAgfXLwUU6OizAiDZ54H3dUGnk9fujvtzqKECJCOWwOntr5FOuqTFzvO8iDqB2VLSzfUW1qjBBCGOYrBIcHqrZanUSIo2SKpnifb067Hl4uhuYDkDTa6jhCiAi1/CPLcTvcxgccu5Lm2IsND3vi7QOs2FXLRUVpKINTO4UQwjCbHdKKoFoaPBE+5B088X4DZ8n7K9+zOIgQIpKZau4A3HGQmG/6HbzihYUsu2ueNHdCiNBJnxJs8GTFXhEmpMET79OTVMgVWRl8690/Wx1FCBHB9jfv594191LZVml8UPpk0w1edmI0aXEmm0khhDAjfTJ0tUDLYauTCAFIgyc+IMrjZVZPFNMD7VZHEUJEsK5AF2+Wv0lVu/FFU0ifAo2l0O03ta8XNpezYleNyYRCCGFQxtTgo1yHJ8KENHjif9yffhbXt5k46BJCCJOKkot44/o3mJk20/ig9MmADt5zyoTH3yzlb+vlzLoQIkRSi0DZ5Do8ETakwRP/K30y3f5yOlpk5TkhRGgopcxfF3d0JU1zB1FPfXoOj99sopEUQggzoqIhuVBulSDChjR44n+83O3g7LwcXlz7jNVRhBARbOn+pXzlja8YHxCXCZ4kqNpiaj8pXhc2myyyIoQIofTJMkVThA1p8MT/mFZ0Kbe2tDK+t97qKEKICObv8VPXUUdPX4+xAUpB5nQwucpva1cvDy3fzbrShkGkFEIIAzKmQms5tMuxk7CeNHjif+Skj+ZLJDKjo8LqKEKICPbxCR/nqcueIsoeZXxQ5nSo2wW9nYaHuBw2/rTmANsqWgaRUgghDMiaEXys3GxtDiGQBk+cQF/6NPZXbLI6hhBCvF/mNOgPQM0Ow0NcDjtb7r2I2+eNDmEwIcQZLX0KoKTBE2FBGjxxXD/o7ubqJMXByr1WRxFCRLBvrv4mP13/U+MDMqcHH00eREU55M+dECKE3HHgKwQ5OS7CgPzFE8d1UcFlfK+uAU/jbqujCCEiWIwzhmhntPEBcVkQk2K6wSupbeNzT29kb425e+gJIYRhmTPkHTwRFqTBE8d19vQruaatndRGeQdPCBE63zr7W3x+2ueNDzi60Iq5gyinXbG9soXa1m6TCYUQwqDM6dBWDa2VVicRZzhp8MTxeRKoThzFutJVVicRQpwBtNbGX5w5Hep2Q0+74SF5yTGs/toCziv0DSKdEEIYMMgp5EIMNWnwxAl9yxPNN/v2Wx1DCBHB6jvruXzJ5by0/yXjgzKng+6H6u2hCyaEEGalTwZllwZPWE4aPHFCt6bP56HaOvr9tVZHEUJEqCR3EhOTJ5LiSTE+KGNa8NHkQdTrO2u49JHV+Lt6TY0TQghDoqIhdYIstCIsJw2eOKH5ky9nRnc3tuqtVkcRQkQom7Lx4PkPcm7WucYHxWWAN8N0gxfjcpDiddHcIQ2eECJEMqcFa5OZaedCDLGQNnhKqUuUUnuUUiVKqW8c5/NfUUrtVEptVUqtUErlhTKPMCljGhtdLl5970WrkwghIlxPXw99/X3GB2ROh4qNpvYxd0wyT37qLHKSTKzaKYQQZmTOgM5GaD5odRJxBgtZg6eUsgOPAZcCRcCNSqmiD7xsMzBLaz0FeB54MFR5xCC447jPl8YTDe9YnUSIISUnn8LLmoo1zPnLHHY3mbgtS/YsaNgHHY2m99ffL2fWRfiS+jTCZc0MPpZvsDaHOKOF8h28s4ASrXWp1roH+Btw1bEv0Fqv1Fp3DGyuBbJDmEcMwv0x03mkuQ76+62OIsSQkJNP4WdMwhhuLbqVuKg444Ny5gQfTR5E/X51KXN+tII+afJEGJL6FAHSJoEzGg6/a3UScQYLZYOXBRw+Zrt84LkT+TTwSgjziEGYNeEifJ0tUC/3wxMRQ04+hZn0mHS+NPNL5HhzjA/KnB5cre7wOlP7KkzzcvnkDDp6AiZTCjEspD6NdHZH8F08k7VJiKEUFousKKVuBmYBD53g83copTYopTbU1dUNb7gzXEf6NP4RG8PL7z5jdRQhhoqcfApD/bqfirYK4wOiYoJLkpebO0t+/tgU7r9yIl6302RCIYaF1KdIkDMHqrdBd5vVScQZKpQNXgVw7OnY7IHn3kcptQj4FnCl1rr7eF9Ia/1brfUsrfWslBQTS2mL0+ZOHc/DSYmsqFptdRQhhp2cfBo+v3nvN1y+5HI6A53GB+WcBeUboc/cu3Faaxrbe0wmFCK8SH0KYzlzQPdBpdwuQVgjlA3eeqBQKZWvlIoCbgCWHvsCpdR04HGCzZ3cbC0M2ewOnrUX8HBPx6lfLMTIICefwtCC3AXcN/c+tJmlxXPmQG871O40ta/vvLSdS36+yty+hBgeUp8iQc7s4KNM0xQWCVmDp7UOAF8AlgO7gL9rrXcopb6nlLpy4GUPAbHAc0qpLUqppSf4csJC2aPOw1a3BzqbrI4ixFCQk09haHzSeK4pvIZop4lbGGQP7iDqkokZfHFBgSy0IsKR1KdI4EmElPGy0IqwjCOUX1xrvQxY9oHn7j3m34tCuX8xNA4lFPJ8YgLj33mKyxYUWx1HiNOitQ4opY6cfLIDfzxy8gnYoLVeyvtPPgEc0lpfecIvKoZEQ2cDVe1VTPJNMjYgIRdi06B8PZz1GcP7Oa/Qx3mFvkGmFCJ0pD5FkJyzYOfS4CrktrBY8kKcQULa4InI4MqezV/jYvl4+TtchjR4YuSTk0/h6cfv/pjNtZt5/aOvGxugVPAgahDToNq6Axxu7GBCholbMwgxDKQ+RYicObDpyeD9OlPGWZ1GnGHklII4pbTkLNZ0JfAlLatBCSFC57ZJt/HT839q/jq8pgPgrza1r7v//h63P7FBrsMTQoTGkXt1HlprbQ5xRpIGTxgSlT8/OJc8cNxruYUQ4rQVJRcxLXUaA9POjMk7N/h44C1T+7rj/NE89NEppsYIIYRhyQUQ7YODb1udRJyBpMEThmyNK+SLSbEsfetPVkcRQkSwrXVbebvCxAFRxlRwxUPZKlP7mZGbyDljfOaaSSGEMEopyJ8frE0yU0AMM2nwhCFJBQsoczppr1pvdRQhRAR7bMtj/Gzjz4wPsNlh1LmmGzyAfTV+Xt1hbmqnEEIYlj8f/JXQsN/qJOIMI4usCEOyU0fxz/406KqyOooQIoLdc9Y9xLlMLnwyah7sWQYt5RCfbXjYb1eV8urOGhZNSMNmk3fyhBBDLH9+8LHsTfAVWJtFnFHkHTxhXP48KF9Pf3e71UmEEBFqVPwoktxJ5gblzws+lq02Nax4YSHLvzRfmjshRGgkjYa47EHNMBDidEiDJwxb48nhw+nJ/P3Nx62OIoSIYEv3L+X1gwZvlQCQOhE8SaYPonKSokmPd5tMJ4QQBh25Du/A6uD98IQYJtLgCcMKiq4gNxAgqXmP1VGEEBHsr7v+ykslLxkfYLPBqPOCB1EmFzNYva+O360qNZlQCCEMyp8PHQ1Qu9MVuFUKAAAgAElEQVTqJOIMIg2eMCwtOYvHHHlc1CoHQ0KI0Pnlwl/y6IJHzQ3Knw8th4P3xDNh9b56Hl9VSk9Azq4LIULg6HV4Mk1TDB9p8IQ5o8+no3ITLU2VVicRQkSoZE+y+dsX5J8ffNz/H1PDvrCggHfuWUCUQ/4cCiFCID4reE+80jesTiLOIPIXTZjydmwh83PSeXrlYqujCCEi2G/e+w1P7HjC+ABfISTkwb5XTe0nzu3EaZc/hUKIEBqzMPgOXk+H1UnEGUL+qglTZk2/luvbejm745DVUYQQEWxHww72NJq43lcpGHdp8Cy5yYOo/+yu4ZY/rCPQJ9M0hRAhMO4SCHQGb5cgxDCQBk+YEhXl4mtZFzCzYh3091kdRwgRoR654BF+OO+H5gaNvRgCXaavdekJaJo7eqn1d5vbnxBCGJF3HkR5Yc8rVicRZwhp8IRp/YUXURrws/O9l62OIoSIUDY1iD9PeedCVCzs/bepYZdMSuflL55HZoLH/D6FEOJUHFFQsAD2LpfbJYhhIQ2eMK02fRYfycrgz5t+Z3UUIUQEe2DtA9z/9v3GBzhcMOaC4EGUydslAPT1a/QgxgkhxCmNvRTaqqFqi9VJxBlAGjxhWnpKHvd1JfHFzlqrowghIpg3yos3ymtu0NhLwF8J1dtMDVtb2sBZP3idXVV+c/sTQggjCi8CZTM9w0CIwZAGTwzK1VOuJ6d+r+l7TgkhhFF3zbiLr876qrlBhRcByvRB1Ng0L3PHJGO3mbw9gxBCGBGTDNlnwZ5lVicRZwBp8MTgTLiCVR43f3n1x1YnEUJEuJbuFuMvjk2F3LNh+xJT0zSTYqL45U0zGJdu8h1DIYQwavxlwdkFDfutTiIinDR4YnASR/FIYjovN6+xOokQIoL9bMPPuPqlq+kzs2rv5OugbhfUbDe9vzp/N+VNcq8qIUQITPoIoGDb81YnERFOGjwxaA+MvpEnqw5BzQ6rowghItS5Wedy28TbCOiA8UFF14DNAVv/bmpfgb5+Llz8Jotf22cypRBCGBCfDaPOg63PDmohKCGMkgZPDNqEubfjVHbTB1FCCGHU2Rln84mJn8BldxkfFJMMBYtg+z9MLUnusNv40TWTufP80YNIKoQQBkz5GDTuh4pNVicREUwaPDF4MT6WZkznzpLnCAR6rU4jhIhQvX29vF3xNv3axP2jJn8UWivgoLlp5JdOzqAwTa7DE0KESNGVYHcF38UTIkSkwROn5WDiFBrsfZRtl1WhhBCh8erBV/ns659lS62J+0eNuyx40/Nt5mcY7Kvx89jKEtPjhBDilNzxMO6S4AyDPjk5LkJDGjxxWv7vw9/lufo2Ckv/ZXUUIUSEuiDnAn7+oZ8zJWWK8UFR0VB0FWx/AbpaTe1vTUk9v/jPPiqaO00mFUIIA6Z8DDrqYd+rVicREUoaPHFabO44mPZxerYvob5GzngLIYZetDOahXkLcdgc5gbOvh16/LDlGVPDrp+dw9p7FpKV4DG3PyGEMKLwYojLhrW/tjqJiFDS4InTVjv5Ji7NSuHhf91tdRQhRITq1/08s+sZlpWamA6eNQNy5sC6x8HEbRaioxwkREcBoGWlOyHEULM7YM4dcGA1VG21Oo2IQNLgidOWmjONRb1JXNG8AwLdVscRQkQgm7KxrHQZb5S/YW7gnDuhqcz0VKhAXz+3P7Geh1/ba25/QghhxIxPgDMG1v3G6iQiAkmDJ4bEPYu+xzmttbDjBaujCCEi1G8u/A0Pzn/Q3KAJH4a4LNNToRx2G75YF0kxUeb2J4QQRngSYdpNsO058NdYnUZEGGnwxNAYs4BW31h+u/pH9PTIu3hCiKHnjQrevqAr0GV8kN0ZvBav7E0o32hqfz/+yBRuOzff1BghhDBszp3Q1yPv4okhJw2eGBpK8besRfzC288///19q9MIISLUltotLHp+EdvqthkfNPt2iPbB6/fBIK6pW1NST6WsqCmEGGq+Aph0XXCGQWul1WlEBJEGTwyZ2698gGc6vFxbsgR65WBICDH0ChMLOSfzHKKd0cYHuePg/K8FFzQoed3U/ur83dz25/X84a0yk0mFEMKAhd+B/gCs/KHVSUQEkQZPDBmb3c6US34MrRV0vPMrq+MIISJQjDOGB+c/yJiEMeYGzrwNEvPhtftMraiZ4nXx1KfO4v9dPM5kUiGEMCBxFJx1R/B2LjU7rU4jIoQ0eGJo5c/nqYxpXL7vj+wqNXe9ixBCGNXU1cQzu56hX/cbG+CICp4pr90Bm54wta85o5NxO+30BPrp6jXeHAohhCHz74YoLyz/5qCmkQvxQdLgiSFXMPceZnV1k7Tqu1KohBAh8cbhN3hw/YNsr99ufFDRNZB/Piz/NjSam3LZ1dvHVY+t4afL95hMKoQQpxCdFDwBVboSNvzB6jQiAkiDJ4bc3CmX8NCsr5F24E3Y+Ger4wghItDVBVez5MolTEmZYnyQzQZX/wpsDnjhTlNTNd1OOxcWpXH26ORBpBVCiFOYfTuMWRg8AVVfYnUaMcJJgydCY/btVOWew1fe/SGrNy21Oo0QIsIopY5eh1fWUmZ8qmZ8Nlz2IBxeC6t/ZmqfX7lwLIuK0oDgjdCFEGLIKAVXPQYOFyz5DPSauB2MEB8gDZ4IDZuNunn38Z4ripbV34DWKqsTCSEi0I6GHVz70rU8tfMp44OmfAwmfxRW/gC2/t30Pv+1tYrLHl1NU3uP6bFCCHFCcRlw5S+gchO8cIepWQZCHEsaPBEyUwrPZsmCP3BFWzM881H6O5utjiSEiDBFSUV8/ayvc23htcYHHTlTPmoevPh52L/S1D7T490kx7hwOeVPqBBiiBVdCRf9AHa+BK98XdYyEIMif51ESMXnnw3XP8nGlv1c/9R89pRtsjqSECKCKKW4YfwNeKO89Pb3sqZijbGBDhd87GnwjYW/3gi7Xja8z5l5ifzlM3OIjnLQE+invKljkOmFEOI4zvkCzP0CrP8d/PPL0NdrdSIxwkiDJ0KvcBE7p3wR6CHlhY9D1XtWJxJCRKC/7PoLd75+J3saDa506UmAT7wE6ZPg2VvgrZ8bPluulALguy/v4IpfvEVzh0zXFEIMoQu/D+d+CTb+CZ65DjqbrE4kRhBp8MSwuOWyb/K3y/9GEorA7xby8JMfw98uUzaFEEPnpvE38fMP/ZxxScGbknf3dZ96UGwK3PoyTLwaXr8PnvgwNOw3vM/Pzh/D1y4eT0J0FACtXXKmXQgxBGw2uPC7cNWv4MAaeOxs2PGiTNkUhkiDJ4aNI2safHY1y3LP4096J6v/MA82Pw0BOfMthDh9TruThXkLATjsP8xFz1/E2qq1BgZ64CN/hA8/AlVb4dfnwPJvQUvFKYfmJkdz05xcAEpq/Zz9wxW8trPmtP47hBDiqOkfh9tfg9hUeO5WeOoaOPCWNHripELa4CmlLlFK7VFKlSilvnGcz7uUUs8OfH6dUmpUKPOIMBCTzJWffJGf5t7JJU4vvPR/PPvYFL7+h8vpr9wiBUsMC6lNkc9pczIlZQqj4kYBUNpcyvb67egT1RibDWZ+Er7wLky8Btb+Gh6ZCs99Mnh9Xm/nKfcZ63Jy1bQsZuQmALCtvIUlm8rpCcgtFYRxUp/E/8icDp9ZCRf/EKq3wZ8vh98tCNap1kqr04kwpE74x+50v7BSdmAvcCFQDqwHbtRa7zzmNZ8Hpmit71RK3QBco7X+2Mm+7qxZs/SGDRtCklkMM62hZAXFK75JB038vroWvBn81JtFQvxYbp99A6SMoz/ah83usDqtCCGl1Eat9axh2pfUpjPQvWvu5bWDr7HqY6tw2p1srduK3WZnYvLE4w9oOghrfwXbnoOOBnC4IXs25M4NXrOXMgES84KLtZzAPUu28sr2ajZ++0LsNsW/t1fT1dvH1dOzQvRfKUJB6pMIK72dsOUZ2PAnqNkefC5lAuTNhcwZkDoBkguC1xiLiHay2hTKo+azgBKtdelAiL8BVwE7j3nNVcD9A/9+HvilUkrpUHWdIrwoBYWLeLRwET3+Gtj7byh7k1VNa5laVwpP/gWAy7Mzmd9l5x53BsT4+JK/gpnuDG7JmggODz849B4z47O4JGMs/crOo6UbmZmYy7z0fHq15vf7NzErKZvZKdl09vXxVOlmZiflMN2XSXugh7+WvcdZyblMSU6ntaeb5w9u5SxfHpMS02ju7mTJoe3M9Y1iQmIK9d0dLD20g/NS8xkb76O2q41/Ht7F/LTRFMQlU9Xu55XK3VyQVkB+XCIV7a0sr9zDwoxC8mITONjWzIqqfVycOY6smDjKWptYWVPCpZnjyYjxUtLawKqaUq7ImUCqO5a9LfW8VVvGlbkT8bmi2dVUxzv1B7g2dxIJLg/bm2p4t/4Q142aQpzTxbbGatY3HOajo6bgdbrY0lDJpsYKbsyfhsfhZFNDBVsaK/n46Gm47E421JWztbmKT4yZgcNmZ23tYXa2VPOpwtkAvFNzkD2ttXxyYPutmgOU+hv4RMFMAFZVl3GovYmbx8wAYGXVfqo6W7lp9HRweSF/ngW/WKcktekM9NVZX+XKMVfitDsBWLxxMT19PTxz+TMA3Pf2fXidXu6efTcAz1S/Rfy4eVxx0Q+g7E2Wb3+S+Lp9nL36p6D7ecPjIbG/j6nORPCms8rjwedOoig6HaJiWRVo4hNxiXztPB/2jYdY1X6ILTs6cHXGgX08b7aWsGZrB8kqkc+eX8DKpt0cPKzJcaVwwTgfbzbvobnOyShPCpNz4ljdvIeuJjejo1MYnRrNWy176Wv1MDomlaykKN5u2Ud/awwFXh+p8U7WtpagW2MpjPORHGdnXet+aI2lMD6FhFjFen8pqtXL2Hgf3ljY4C9DtcQxNtFHTEw/G1vLsLcmMC4pGbenj42tZTj9SYxLTsLhDrC59QDutmTGJSWh3D1sbj1IdFsyY5OTIKqbza2HiG33Mc6XRMDZyZbWw8S1pzAuJZEeewdbWg+T0JHG+NQEOmztvNdaTnJnOmNT4mlTbWxrrcDXlcG41HhaaGVbayWpXZmMS4unUTezvaWSjO5sxqbFUdffxM6WKjJ7chib5qWmv4FdrTVk9eQyNj2Wyt569rbWkdmbw7gML047OPLOhegky34fT8Ly+vS1VV9jRuoMbhh/AwBL9i1hTMIYpqZMBeC1g6+RH5dPQWIBWmtWHl7JqPhRjI4fTV9/H6vKVzE6YTR5cXn09vfyVvlbFCQUkBOXQ09fD2sq1lCYWEi2N5uuQBfvVL7DuKRxZMZm0tHbwbqqdUxInkB6TDrtve28W/UuRclFpMWk4e/xs6F6A5N8k0iJTqGlu4VNNZuYnDIZn8d3dHtKyhSSPck0dTWxpXYL01KnkehOpKGzga11W5mRNoN4Vzz1nfVsq9vGzPSZxEXFUdtRy476HcxOn01sVCzV7dXsatjFnIw5RDujqWqrYnfjbs7OPBuPw0NFWwV7G/dyTtY5uOwuDvsPU9JUwnlZ5+G0OznUeoj9zfuZlz0Ph83BgZYDlLWUcX7O+diUjdKWUg62HOSC3AsA2N+8n3J/OefnnA/AvqZ9VLVXMT97PgB7GvdQ11nHebNvh9m3s3v/chpLlnNO3UHY+hw7tz5Fi83G3K5ucMWxIyGNNk88czyZ4I5nm62PLruT2bG54HDzXm8jvShmxeWDsrOls4o+NDO9o0DZ2Ow/BEox3TsKlGKjvww7dqZ580Ap1reW4rI5mBIbnKb+bksp0fYoJsXmALC2pQSvw83EmGwA3m7ZS6IjhgkxwZNca5r34HN6GReTCcDq5t2kRcUzNjoDgFVNu8h0JVIQnQ7AG007yXUnM9qThtaaN5p3McqdQr4nhT7dz6rm3Yz2pJLn9tHb38dbLXso8KSR406mpz/Ampa9FHrSyXYn0dXfyzst+xgXnUGmK5GOvh7WtZYwITqTdFcC7X3dvNu6n6KYLNKi4vEHOtngL2NSTDYpUXG0BDrY5D/A5NgcfE7v0e0psbkkO2Np6m1nS9tBpsXmkeiMoaG3ja1th5jhHUW8I5r6Hj/b2g8z05tPnMNDbU8rJR3VnJP7Icicdtr/H4eywcsCDh+zXQ7MOdFrtNYBpVQLkAzUH/sipdQdwB0Aubm5ocorLBTlTYOZt8LMW3mxr4/25sPQXAYNJUze9gI5Hg0OBzQdZFdUCwXNlVD2Glr38bf8XOJLV8GmFvqBP+Tn4q5sZt6aVgJK8atROXzpQBOzW/z02hS/yMvha/ubmN7qp8tm45G8bL61r5Ep/jY67HYW52Zx/54GJrW143c4WJyTScqeeia0ddDqdLA4O5PM3fWMbe+gMcrJ4qwM8nbVUdDRSX1UFIuz0inYWUt+Zxc1LheLM9OYsKOGvK5uqtwuFmekMXV7DVnd3ZR73CxOT2XW9moyuns4GO1hcVoK52yrIrWnl9KYaBan+jh/66P4egOUDGwveu9REgIB9sbGsDglmcu2PEJcXx87vbEs9iVx5eaf4+3vZ1ucl8XJiVy3eTGefs2WeC+LkxK5cdNi0JqNCXH8MjGBT2x8GID1CfH8PiGOTw1sv5OYwF/iYvnkwPaapAReio3lExt+CsCq5ERei4nm5vUPAfCGL4m3PG5uevdBSJsMn3tr+H+ZTk1q0xko3hXPrPT/nuh84LwH8Pf4j2677C7sNvvR7ZdKXiLbm80Vo6+AgoX8YttiJow9l7NvfR3q9/DjN7/ITGcyU53Z0FbLd3v3Mq+5kvsPvAvdfr6d5eOi9g6+3RBc+e7redlc3dfGl9uaYQl8JS+Hm3tb+WxTC/wd7hqVwx3NrSxsbqH3PSjOz+WLjc1c09JKh1IUj8rhK41NXNrip8WmKM7L4esNTSxq9VNvt1Gcm8136htZ6G+jym6nODeL79U1kNPWzkGHg+KcTH5UW092ewclTifF2Rn8tKaOzI5OdkU5Kc7K4JGB7fdcUdyVmc6vq2tJ7+xig9vFlzLS+H1VDWld3bztdvPljFSerKwmpbuHVR43X0lP5a8V1aT09LAi2sNX01J4vqKK5J5eXomJ5mupPl4qrySpN8DS2Bi+lZLMssMVJAT6eN0bw3d9ybx+qIKEvj5e8cbyQ18Sbx4sJ66/nxfjvDyUnMjbBw7j1Zpn4+N4JCmBDQcO4dLwVEIcv0pM4L2yQ9iAfybG86f4ODYfCP5v/lJiAs/GxfLuwXIAOpXC8alXIfqsYfjNM83y+tTY2Uh7b/vR7R+s/QEfn/Dxow3e/3vz//GpSZ+iOLEYjeaulXfx+Wmf53NTP0dAByheWcxdM+7i9sm30x3opnhlMXfPuptbJ95KW28bxSuL+eacb3Lj+Btp6W6heGUx9829j+vGXkdjVyPFK4t54NwHuKrgKmo6aiheWcyD8x/k0vxLqWyrpHhlMYs/tJhFeYs47D9M8cpiHlv4GPOz51PaUkrxymIev/BxzvGcw76mfRSvLOaPF/+R2emz2dW4i+KVxTx92dNMTZnKtrptFK8s5tkrnqUouYjNtZu5+827eeHKFyiIKmBDzQbuWX0P/7zmn+Q581hbtZZ7376X5R9ZjifWw5qKNXx/7fdZef1KXB4Xq8pX8eN3f8xbN7xFvD2eFYdW8PDGh1l30zocNgevHnyVX2z+BZtu2YRN2Xil7BV+895v2HbrNgCW7l/K0zufZuMtGwF4oeQFluxbwtqbgtcSP7f3OV498CqrblgFwN9q3mF1y3usuHkF9PfxzMovs7FuC//OuQ5aK/hz/Rp29zXycl0rdPv5Q4zmkB2WVFQD8HhaCo12G3+rDF43/Fh6Cl3KxlNVwe1H0lOxAX+srgXg4Yw0vP39/KamDoCHMtNJCwT4RW3wV++HWemM7g3w8MD297MzmNTdw0/qGgC4PyeTszq7eKC+EYBv5WSxoKODewfq5Ddys7iirYN7GoPbd+dl87HWNr7aFFyU78ujcritpZXiphYgWCc/39TM55pbCSgoHpXLXY3N3N7SSvdA3by7oYlbW/202WwU52XzzfpGbvS30TJQJ++rb+A6fzuNDjvFOVk8UNfAVW3t1DgdFGdn8mBtPZe2d1A5UDcX19SxqKOTw1FRFGel81h1LfM7uyh1RVGcmc7jVbWc09XFPreL4ow0/lhVw+yubnZ53BSnp/J0ZTVTu3vYFu2hOC2FZyuqKOrpZXO0h4eSE3m9cidc+1vD/7+eSCinaF4HXKK1vn1g+xZgjtb6C8e8ZvvAa8oHtvcPvKb+eF8TZJqBeL/+3m5qW2pxAYlRDvr7etlUWUp6dAzZ3gQCgR7WVJSQG5tAfnwyPX0BVpXvY0x8MvnxPjoCvbxVvpfCxDTy45Pp6O1ldcVexielk+dNoq23mzWV+ylKyiDHm0hLdxdrq0uZlJRFljee5u5O1lWXMdmXTWZMHI3dHayvPsDUlGzSo+No6GxnQ+1BpqfkkBrtpbbDz+a6w8xKzSPZE0N1Ryvv1ZUzO30USa5oKttb2VZfzpz0fBJcHir8LWxvrODs9NHEu9xU+JvY3ljF3PTRxLncHPQ3sruxmnmZhUQ7nZS1NrC3qYZ5WYVEO5yUtjSwr7mG87PG4nY42NdcR2lLHQtzxuGw2dnXVEtpaz0X5o7HpmzsaarhQGsDF+cVAbCroYbDbY1clDcBgB0NVVS3t7IwN7hK4baGSuo6/CzICW5vra+goaudC7LHBheuSBln6Oc4zFOgpDYJQ7TWR2+H0NDZgF3ZSXAHpz0dbj2My+EiNToVCJ55j3HGkB4TPNO8t2E3cbYo0t2JEOhhT9Me4h0xpLsSQGt2Ne8jKSqOtIHtna1l+KLiSXUnovv72eU/QJwtjmRXPFEOxR7/QRyBWFJc8XjdNva2HUZ3e0j1JJAQbWdfWzl9nR7SY/673dvhITM2gTiPYn97Jb0d0WTGxuP1KErbK+lp95DtTSTGoylrr6K3PZrsuAQ87n7K2qsIdMSS443H5eqjrL0a3eklOy4eZ1SAsrZq6PKSHR+Pw9FLWXsNtq44suPjsDl6KGuvwdGdQE68F23v5kBbLc6eRHLiY+mzdXGwvY6onkSy470EbJ0cbK/D1ZNEdkIsvaqDg+11eHp9ZCfE0KXbOdReT0wghZzEGDr6/Rxsq8fbl0pOYgxt/X4OtdUT15dGdlI0/kArh9oaSNDp5CRG0xRopqKtiXidRk5iNE472HxjwRVr6PfgTK9P9Z31uOwuvFFeAEqaSkhwJ+Dz+NBas7txNz6Pj5ToFPp1P7sbd5ManYrP46Ovv489TXuObvf297KvaR/pMekkuZPo7etlX/M+MmIySHQn0tPXQ0lzCZkxmSS4E+ju62Z/836yYrOId8XTFeiitKX06HZHbwcHWg+Q483BG+U9up3rzSU2Kpb23nYOth4kLy6PGGcM/h4/h/2HGRU3imhnNK09rZT7y8mPz8fj8NDS3UJFWwWj40fjdriPbo9JGIPL7qK5q5nK9koKEgqIskfR2NVIdXs1hYmFOG1OGjobqOmoYWziWBw2B/Wd9dR21DIucRx2m526jjrqOuuYkDQBpRS1HbXUd9ZTlBz8m1vTXkNjVyMTkoN/c6vbq2nubmZ80ngAqtqqaO1pPbpScGVbJW29bYxNHAtARVsFHb0dFCYWAlDuL6cr0EVBYgEQXHyqJ9DNGG8O9HZyqPUggUAno2MyoT/AQX8F/f0B8mMyAM2BtnLQMGqgrpW2VeBQNnKj045uO20OcqLTQGv2t1UQZXOSM1AXS9rKcdujyPYEt/e1HSba7ibLkwLAHv8h4hzRZHh8A9sHiXfGku5OBmC3/yAJTi/p7uC77TtbD/y3TmrNLv9BUlwJpLgSgr97/kOkuhLwuRLo0/3s8R8i1ZWIzxVPb3+AfW3lpLuTSIqKO7qd4U4mMcpLT38vJW0VZLqTSYjy0t3Xw/72SrI8PuKdsXT19VDaXkmWJ4V4ZwwdfV0caK8mx5OK1xlNR6CLAx3V5EanEevw0B7o5GBHDXnRacQ4PPh7OzjcWcuomHSi7W5ae9sp76wjPyYDj91FS287NV2NjE2dEpz+b8DJalMoG7y5wP1a64sHtu8B0Fr/6JjXLB94zTtKKQdQDaScbJqBHEQJEXmG+QBKapMQwjCpT0KIcHSy2hTKVTTXA4VKqXylVBRwA7D0A69ZCtw68O/rgP/INS5CiBCT2iSECFdSn4QQpy1k1+ANzAv/ArAcsAN/1FrvUEp9D9igtV4K/AF4SilVAjQSLGRCCBEyUpuEEOFK6pMQYiiEdO15rfUyYNkHnrv3mH93AR8NZQYhhPggqU1CiHAl9UkIcbpCeqNzIYQQQgghhBDDRxo8IYQQQgghhIgQ0uAJIYQQQgghRISQBk8IIYQQQgghIoQ0eEIIIYQQQggRIaTBE0IIIYQQQogIIQ2eEEIIIYQQQkQIpbW2OoMpSqk64KDBl/uA+hDGGaxwzCWZjAnHTBCeucxkytNap4QyTKiZrE0w8n9mwyUcM0F45pJMxpjNdKbVp0j4mQ2XcMwlmYwLx1xDcuw04ho8M5RSG7TWs6zO8UHhmEsyGROOmSA8c4VjpnASjt8fyWRcOOaSTMaEY6ZwEo7fn3DMBOGZSzIZF465hiqTTNEUQgghhBBCiAghDZ4QQgghhBBCRIhIb/B+a3WAEwjHXJLJmHDMBOGZKxwzhZNw/P5IJuPCMZdkMiYcM4WTcPz+hGMmCM9cksm4cMw1JJki+ho8IYQQQgghhDiTRPo7eEIIIYQQQghxxoioBk8pZVdKbVZK/XNgO18ptU4pVaKUelYpFWVBpgNKqW1KqS1KqQ0DzyUppV5TSu0beEwc5kwJSqnnlVK7lVK7lFJzwyDTuIHv0ZGPVqXUl8Ig15eVUjuUUtuVUn9VSrmt/r1SSt01kGeHUupLA88N+/dJKfVHpVStUmr7Mc8dN4cKenTge7ZVKU45Od4AAAkLSURBVDUj1PnCTbjVp3CsTQMZwqo+SW0ylUlq0wgUbrVpIEPY1SepTaaySX06cY5hqU8R1eABdwG7jtn+CbBYa10ANAGftiQVXKC1nnbMsqffAFZorQuBFQPbw+kR4N9a6/HAVILfM0szaa33DHyPpgEzgQ7gBStzKaWygGJgltZ6EmAHbsDC3yul1CTgM8BZBH92VyilCrDm+/Rn4JIPPHeiHJcChQMfdwC/HoZ84SYc61O41SYIs/oktclwJqlNI1c41iYIv/oktckAqU+n9GeGoz5prSPiA8ge+KYsAP4JKII3CnQMfH4usNyCXAcA3wee2wNkDPw7A9gzjHnigTIGrr8Mh0zHyXgRsMbqXEAWcBhIAhwDv1cXW/l7BXwU+MMx298BvmbV9wkYBWw/1e8R8Dhw4/FedyZ8hGN9CrfaNLDPsK5PUptOmklq0wj8CMfaNLDfsKpPUptMZZH6dOo8Ia9PkfQO3s8J/rD6B7aTgWatdWBgu5zgL91w08CrSqmNSqk7Bp5L01pXDfy7Gkgbxjz5QB3wp4EpGb9XSsVYnOmDbgD+OvBvy3JprSuAnwKHgCqgBdiItb9X24F5SqlkpVQ0cBmQQ/j8/E6U40jBP8Kq/x+tEo71KdxqE4R/fZLadGJSm0amcKxNEH71SWqTQVKfBmXI61NENHhKqSuAWq31RquzHMd5WusZBN9m/T+l1PxjP6mDLflwLmXqAGYAv9ZaTwfa+cBb0hZkOmpgTvaVwHMf/Nxw5xqYA30VwcKeCcTwv2+rDyut9S6C0xxeBf4NbAH6PvAay35+4ZjDamFcn8KtNvH/27vXUCmrPY7j318pZmWZRvcOO6MLvShBKbsiacE50OEQvukqFAT1IhAqqF5kb0qwNxUVFUUXim50EeN0OmGSXazM9t5amV20G6Yl3eyG1b8Xa00+7PboTA0za2Z+H1jMmmeey39mj7/Nep71bCk4n5xN2+ds6j4FZxOUl0/OpsbrcT79Da2qoycGeMCJwL8lrQceIk01uBGYKGlMXucg4LN2F5bPZBARm0hzo48FNkraHyA/bmpjSZ8Cn0bEq/n5Y6TQ6mRNVf8EVkbExvy8k3XNBtZFxBcRsRV4nPRd6+j3KiLuiohpEXEKaR77Wsr5+dWr4zPS2bKajvx77JAi86nAbIKy88nZtAPOpq5TZDZBkfnkbGqc86l5Lc+nnhjgRcSVEXFQRAyQLlMviYhzgOeBOXm1ucBT7axL0m6SJtT6pDnSq4FFuZ621xURnwOfSDoiL5oFvN3JmkY4i23TDKCzdX0MzJC0qySx7bPq9Pdqn/z4D+BM4EHK+fnVq2MRcH7+i1AzgG8q0xF6Won5VGI2QfH55GzaAWdTdykxm6DMfHI2NcX51LzW51MjN+p1UwNmAotzfwrwGvA+6dL1uDbXMgUYyu0t4Oq8fDLppub3gOeASW2uayqwAhgGngT26nRNua7dgM3AnpVlnf6srgXWkH653A+MK+B7tYwUlkPArE59TqRfKBuAraSzmxfWq4N04/4twAfAKtJf12rr96uEVko+lZpNuYbi8snZ1HBNzqYubaVkU+X4xeWTs6mpupxP9etoSz4p78DMzMzMzMy6XE9M0TQzMzMzMzMP8MzMzMzMzHqGB3hmZmZmZmY9wgM8MzMzMzOzHuEBnpmZmZmZWY/wAM/MzMzMzKxHeIBnfyLpqkp/oqRL2nz8AUlnV55Pl3RTi/YtSUsk7THKa/MlXZb790haJ2lQ0hpJ11TWe0jSYa2ox8ya43xyPpmVyNnkbCqJB3g2mqsq/YlAW0MKGAD+CKmIWBERl7Zo3/8ChiLi2wbWvTwippL+c9O5kg7Jy28DrmhRPWbWHOdT4nwyK4uzKXE2FcADvD4m6UlJb0h6S9JFedkCYHw++/IAsAA4ND9fmNe5XNLrkoYlXZuXDUh6R9KdeX/PShqfX1sqaXru7y1pfWWbZZJW5nZCLm0BcHI+5jxJMyUtzttMynUPS1ou6ei8fL6ku/OxPpRUL9TOAZ6qfAZXS1or6UXgiDrb7JIfv8+Py4DZksY08XGbWROcT84nsxI5m5xNXSEi3Pq0AZPy43hgNTA5P99SWWcAWF15fjpwByDSCYLFwCl5vV+AqXm9R4Bzc38pMD339wbW5/6uwC65fxiwIvdnAosrx/zjOXAzcE3unwoM5v584GVgXD7GZmDsKO/5I2BC7k8DVuU69gDeBy7Lr90DrAMGgS3AdSP2839gWqd/hm5uvdqcT84nN7cSm7PJ2dQNzVfw+tulkoaA5cDBpKDYkdNzexNYCRxZ2W5dRAzm/huk4NqescCdklYBjwJHNXD8k4D7ASJiCTBZ2+aEPx0RP0fEl8AmYN9Rtp8UEd/l/snAExHxQ6RpB4tGrFubZrAfMKtyloy8/wMaqNfM/hrnk/PJrETOJmdT8XyZtE9JmgnMBo6PiB8kLWXb5fTtbgpcHxG3j9jfAPBzZdGvpLNbkM5O1U4mVI8xD9gIHJNf/6mZ9zCKkccf7fv9i6SdIuK3RncaEVvy53MS6UwXpPfx418t1Mzqcz45n8xK5GxyNnULX8HrX3sCX+WAOhKYUXltq6Sxuf8dMKHy2v+ACyTtDiDpQEn77OBY60mX9AHmjKhhQw6M84Cd6xyzahlpLngtaL+Mxm76rXkXmJL7LwD/kTRe0gTgjNE2yPPFjwM+qCw+nDQ1w8xaz/nkfDIrkbPJ2dQVPMDrX88AYyS9Q7oxd3nltTuAYUkPRMRm4CVJqyUtjIhngQeBV/L0gMeoHyg1NwAXS3qTNMe75lbSX1gaIk1XqN2IOwz8KmlI0rwR+5oPTJM0nOue29zb5mnSvHQiYiXwMDAE/Bd4fcS6CyUN5npWAY8DSNoX+DEiPm/y2GbWGOeT88msRM4mZ1NXUKSbHs36gqT9gfsi4rS/sY95wLcRcVfrKjOzfud8MrMSOZu6j6/gWV+JiA2km5P/9J91NuFr4N4WlWRmBjifzKxMzqbu4yt4ZmZmZmZmPcJX8MzMzMzMzHqEB3hmZmZmZmY9wgM8MzMzMzOzHuEBnpmZmZmZWY/wAM/MzMzMzKxH/A7uV07z/JLwfgAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "\u003cFigure size 1080x360 with 3 Axes\u003e"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light",
-            "tags": []
-          },
-          "output_type": "display_data"
-        },
         {
           "data": {
             "image/png": "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\n",
@@ -1279,17 +1235,14 @@
         "    ax.set_title('sigma={:0.3f}, noise={}'.format(sigma, ble_params.model))\n",
         "\n",
         "\n",
-        "ble_params = ble_params_lognormal\n",
-        "sigma_mle = ble_params.sigma\n",
-        "sigma_list = [0, 0.1*sigma_mle, sigma_mle]\n",
-        "plot_curves(sigma_list, ble_params)\n",
         "\n",
-        "ble_params = ble_params_normal\n",
+        "\n",
+        "ble_params = ble_params_normal_lovett\n",
         "sigma_mle = ble_params.sigma\n",
         "sigma_list = [0, 0.1*sigma_mle, sigma_mle]\n",
         "plot_curves(sigma_list, ble_params)\n",
         "\n",
-        "ble_params = ble_params_lognormal_new\n",
+        "ble_params = ble_params_lognormal_briers\n",
         "sigma_mle = ble_params.sigma\n",
         "sigma_list = [0, 0.1*sigma_mle, sigma_mle]\n",
         "plot_curves(sigma_list, ble_params)"
@@ -1324,9 +1277,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 14731,
+          "elapsed": 11631,
           "status": "ok",
-          "timestamp": 1605160586846,
+          "timestamp": 1605287306322,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1335,7 +1288,7 @@
           "user_tz": 480
         },
         "id": "9DTATuPNx6QA",
-        "outputId": "1d3bfde6-5e1a-4123-cae4-1873dd624466"
+        "outputId": "810ad66e-cad6-4f98-8d2c-952d6a410124"
       },
       "outputs": [
         {
@@ -1408,9 +1361,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 14699,
+          "elapsed": 11624,
           "status": "ok",
-          "timestamp": 1605160586847,
+          "timestamp": 1605287306323,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1419,7 +1372,7 @@
           "user_tz": 480
         },
         "id": "hki9yoKOsxC3",
-        "outputId": "e411f798-47a7-471e-f3fc-a6fde2404af4"
+        "outputId": "099f8bfa-4005-45fc-b6f9-930e623e3378"
       },
       "outputs": [
         {
@@ -1448,9 +1401,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 14658,
+          "elapsed": 11617,
           "status": "ok",
-          "timestamp": 1605160586848,
+          "timestamp": 1605287306324,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1459,7 +1412,7 @@
           "user_tz": 480
         },
         "id": "nlmuqd7_DI2D",
-        "outputId": "652484b5-b7de-4ff1-c6c8-42ff6663f926"
+        "outputId": "90c590a2-0caf-4251-e6c8-d66ec8951ad4"
       },
       "outputs": [
         {
@@ -1499,9 +1452,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 14622,
+          "elapsed": 11610,
           "status": "ok",
-          "timestamp": 1605160586849,
+          "timestamp": 1605287306324,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1510,7 +1463,7 @@
           "user_tz": 480
         },
         "id": "ZuuzgiYLsyej",
-        "outputId": "47f91d0b-e3aa-4365-b125-4f2f49a4ba77"
+        "outputId": "78c78d99-d8fe-42ef-9172-b2ba9a3b7837"
       },
       "outputs": [
         {
@@ -1670,9 +1623,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 14571,
+          "elapsed": 11585,
           "status": "ok",
-          "timestamp": 1605160586852,
+          "timestamp": 1605287306327,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1681,7 +1634,7 @@
           "user_tz": 480
         },
         "id": "nxoHiE31XneK",
-        "outputId": "702a6373-93b1-4995-f1be-6a1b920b9c56"
+        "outputId": "7b2aaa8b-710a-447f-a04d-43e495de2957"
       },
       "outputs": [
         {
@@ -1774,9 +1727,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 16431,
+          "elapsed": 15331,
           "status": "ok",
-          "timestamp": 1605160588754,
+          "timestamp": 1605287310085,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1785,7 +1738,7 @@
           "user_tz": 480
         },
         "id": "W6QgHPSsLgRS",
-        "outputId": "a6c63d11-b6f1-4a5b-ff73-65725af08b8a"
+        "outputId": "838868e9-7bdb-425f-da15-aa92281c1d2e"
       },
       "outputs": [
         {
@@ -1936,9 +1889,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 16378,
+          "elapsed": 15313,
           "status": "ok",
-          "timestamp": 1605160588756,
+          "timestamp": 1605287310087,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -1947,7 +1900,7 @@
           "user_tz": 480
         },
         "id": "FLf9PlP1rKc2",
-        "outputId": "cb7c298c-259d-4862-f735-f17dc6ca4fd7"
+        "outputId": "1bbb7dde-30a1-4449-dd5b-eb9e08db74dc"
       },
       "outputs": [
         {
@@ -2048,9 +2001,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 16342,
+          "elapsed": 15308,
           "status": "ok",
-          "timestamp": 1605160588756,
+          "timestamp": 1605287310088,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2059,7 +2012,7 @@
           "user_tz": 480
         },
         "id": "1iTksnSByF_C",
-        "outputId": "cee2b4c9-97ea-4b89-e282-b89e7edc8d75"
+        "outputId": "eddd15f9-a55a-419a-e8a1-2db03af2a25a"
       },
       "outputs": [
         {
@@ -2100,12 +2053,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 782
+          "height": 773
         },
         "executionInfo": {
-          "elapsed": 17201,
+          "elapsed": 18271,
           "status": "ok",
-          "timestamp": 1605160589652,
+          "timestamp": 1605287313057,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2114,7 +2067,7 @@
           "user_tz": 480
         },
         "id": "zFHW0F09ofPc",
-        "outputId": "345a929b-f4d1-4e93-b28e-b4205ff41c22"
+        "outputId": "ee90e7b6-e827-4c00-e6b3-b770377bf6a9"
       },
       "outputs": [
         {
@@ -2186,9 +2139,9 @@
           "height": 862
         },
         "executionInfo": {
-          "elapsed": 18640,
+          "elapsed": 18266,
           "status": "ok",
-          "timestamp": 1605160591129,
+          "timestamp": 1605287313058,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2197,7 +2150,7 @@
           "user_tz": 480
         },
         "id": "qbdaUAguDeTP",
-        "outputId": "4b780536-72e4-4060-89cb-3b70f04a72e2"
+        "outputId": "2178c012-f142-40a7-a866-97c4207dc222"
       },
       "outputs": [
         {
@@ -2355,9 +2308,9 @@
           "height": 336
         },
         "executionInfo": {
-          "elapsed": 18785,
+          "elapsed": 18254,
           "status": "ok",
-          "timestamp": 1605160591316,
+          "timestamp": 1605287313059,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2366,7 +2319,7 @@
           "user_tz": 480
         },
         "id": "uriwchu0vS2b",
-        "outputId": "466f42ba-46b7-46c6-dc5c-1eb12fa1cbe9"
+        "outputId": "0b853ef2-9ed9-4dda-eb52-a7b497e6cf80"
       },
       "outputs": [
         {
@@ -2445,9 +2398,9 @@
           "height": 336
         },
         "executionInfo": {
-          "elapsed": 19754,
+          "elapsed": 21639,
           "status": "ok",
-          "timestamp": 1605160592323,
+          "timestamp": 1605287316458,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2456,7 +2409,7 @@
           "user_tz": 480
         },
         "id": "X47wVnn_vx3w",
-        "outputId": "89482597-eae8-48e3-ac62-52a994c2fd1e"
+        "outputId": "a092269c-894c-4c6d-e83c-28070b5063e2"
       },
       "outputs": [
         {
@@ -2494,30 +2447,22 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "WuQyMnAKpSbm"
+        "id": "xz6B76Z8krrB"
       },
       "source": [
-        "## Infectiousness levels"
+        "## v1 configurations"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "oJ7-dYZspoty"
+        "id": "6VwmAAPyzd6u"
       },
       "source": [
         "To specify the mapping from symptom onset to infectiousness level,\n",
         " most health authorities consider the simple \"step\" infection function shown below, where they use weight 100\\% on the standard level.\n",
         "\n",
-        "\u003cimg src=\"https://github.com/probml/covid19/blob/master/Figures/gaen-onset-mapping-step.png?raw=true\"\u003e\n",
-        "\n",
-        "\n",
-        "However we can get better results  using the \"bell\" infection function shown below.\n",
-        "(In the code, the 5 intervals are called  pre-drop, pre, mid, post, post-drop.)\n",
-        "The corresponding weights for the standard and high levels depend on the configuration.\n",
-        "\n",
-        "\u003cimg src=\"https://github.com/probml/covid19/blob/master/Figures/gaen-onset-mapping.png?raw=true\"\u003e\n",
-        "\n"
+        "\u003cimg src=\"https://github.com/probml/covid19/blob/master/Figures/gaen-onset-mapping-step.png?raw=true\"\u003e\n"
       ]
     },
     {
@@ -2528,9 +2473,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 19718,
+          "elapsed": 21635,
           "status": "ok",
-          "timestamp": 1605160592324,
+          "timestamp": 1605287316460,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2538,16 +2483,15 @@
           },
           "user_tz": 480
         },
-        "id": "E4rD2SGSqBE1",
-        "outputId": "e879e577-fcf5-4cb5-d8e8-e4c0148740e1"
+        "id": "POnvpnFYzjEN",
+        "outputId": "02807270-a94b-4fc4-a0d3-d7e54c5d242b"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "[0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
-            "[0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0]\n"
+            "[0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
           ]
         }
       ],
@@ -2567,23 +2511,7 @@
         "inf_levels_step[post+14] = 1\n",
         "inf_levels_step[post_drop+14] = 1\n",
         "print(inf_levels_step)\n",
-        "inf_weights_step = np.array([0, 100, 0])/100 # high is ignored\n",
-        "\n",
-        "inf_levels_bell = np.zeros(n, dtype=int)\n",
-        "inf_levels_bell[pre+14] = 1\n",
-        "inf_levels_bell[mid+14] = 2\n",
-        "inf_levels_bell[post+14] = 1\n",
-        "print(inf_levels_bell)\n",
-        "inf_weights_bell = np.array([0, 100, 250])/100"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xz6B76Z8krrB"
-      },
-      "source": [
-        "## Official configurations"
+        "inf_weights_step = np.array([0, 100, 0])/100 # high is ignored"
       ]
     },
     {
@@ -2616,6 +2544,139 @@
         "\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F4bGSTUFzXC5"
+      },
+      "source": [
+        "## LFPH configurations"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "44GCcGPmjQkJ"
+      },
+      "source": [
+        "\n",
+        "[LFPH doc](https://docs.google.com/document/d/1IlQHPZGi3QbD-LDUo27lH6KA-zlQMic6EofB6gl2aGA/edit?ts=5fada80e#)\n",
+        "\n",
+        "Infectiousness config narrow.\n",
+        "\n",
+        "\n",
+        "\u003cimg src=\"https://github.com/probml/covid19/blob/master/Figures/gaen-inf-narrow.png?raw=true\"\u003e\n",
+        "\n",
+        "Infectiousness config wide.\n",
+        "\n",
+        "\n",
+        "\u003cimg src=\"https://github.com/probml/covid19/blob/master/Figures/gaen-inf-wide.png?raw=true\"\u003e\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "executionInfo": {
+          "elapsed": 21625,
+          "status": "ok",
+          "timestamp": 1605287316462,
+          "user": {
+            "displayName": "Kevin Murphy",
+            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
+            "userId": "18199961579456458596"
+          },
+          "user_tz": 480
+        },
+        "id": "iFIsDarHwoW-",
+        "outputId": "0721bd15-d9fd-4ca8-c7ac-df5ed08066ef"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0]\n",
+            "[0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 0 0 0 0 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "inf_levels_narrow = np.zeros(29, dtype=int)\n",
+        "inf_levels_narrow[-3+14] = 1\n",
+        "for t in [-2, -1, 0, 1, 2, 3]:\n",
+        "  inf_levels_narrow[t+14] = 2\n",
+        "inf_levels_narrow[4+14] = 1\n",
+        "print(inf_levels_narrow)\n",
+        "inf_weights_narrow = np.array([0, 30, 100])/100\n",
+        "\n",
+        "inf_levels_wide = np.zeros(29, dtype=int)\n",
+        "for t in [-5, -4]:\n",
+        "  inf_levels_wide[t+14] = 1\n",
+        "for t in [-3, -2, -1, 0, 1, 2, 3, 4]:\n",
+        "  inf_levels_wide[t+14] = 2\n",
+        "for t in [5, 6, 7, 8, 9]:\n",
+        "  inf_levels_wide[t+14] = 1\n",
+        "print(inf_levels_wide)\n",
+        "inf_weights_wide = np.array([0, 75, 250])/100\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V-zltvT386nt"
+      },
+      "outputs": [],
+      "source": [
+        "ble_thresh_wide = np.array([55, 70, 80])\n",
+        "ble_weights_wide = np.array([200, 100, 25, 0])/100\n",
+        "ble_thresh_narrow = np.array([53, 62, 70])\n",
+        "ble_weights_narrow = np.array([150, 100, 40, 0])/100"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Af6CYoQM9bKd"
+      },
+      "outputs": [],
+      "source": [
+        "config_lfph_ble_narrow_inf_narrow = RiskConfig(\n",
+        "    ble_thresholds = ble_thresh_narrow,\n",
+        "    ble_weights = ble_weights_narrow,\n",
+        "    inf_levels = inf_levels_narrow,\n",
+        "    inf_weights = inf_weights_narrow,\n",
+        "    name= 'LFPH-BleNarrow-InfNarrow')\n",
+        "\n",
+        "config_lfph_ble_wide_inf_narrow = RiskConfig(\n",
+        "    ble_thresholds = ble_thresh_wide,\n",
+        "    ble_weights = ble_weights_wide,\n",
+        "    inf_levels = inf_levels_narrow,\n",
+        "    inf_weights = inf_weights_narrow,\n",
+        "    name= 'LFPH-BleWide-InfNarrow')\n",
+        "\n",
+        "config_lfph_ble_narrow_inf_wide = RiskConfig(\n",
+        "    ble_thresholds = ble_thresh_narrow,\n",
+        "    ble_weights = ble_weights_narrow,\n",
+        "    inf_levels = inf_levels_wide,\n",
+        "    inf_weights = inf_weights_wide,\n",
+        "    name= 'LFPH-BleNarrow-InfWide')\n",
+        "\n",
+        "config_lfph_ble_wide_inf_wide = RiskConfig(\n",
+        "    ble_thresholds = ble_thresh_wide,\n",
+        "    ble_weights = ble_weights_wide,\n",
+        "    inf_levels = inf_levels_wide,\n",
+        "    inf_weights = inf_weights_wide,\n",
+        "    name= 'LFPH-BleWide-InfWide')"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -2625,6 +2686,73 @@
         "## Augmented configurations"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AcDuHOyQ708-"
+      },
+      "source": [
+        "We can get better results  using the \"bell\" infection function shown below.\n",
+        "\n",
+        "\n",
+        "\u003cimg src=\"https://github.com/probml/covid19/blob/master/Figures/gaen-onset-mapping.png?raw=true\"\u003e\n",
+        "\n",
+        "This is based on the following recent paper:\n",
+        "\n",
+        "* [The timing of COVID-19 transmission](https://www.medrxiv.org/content/10.1101/2020.09.04.20188516v1.abstract), Luca Ferretti et al, Sept. 2020"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "executionInfo": {
+          "elapsed": 21610,
+          "status": "ok",
+          "timestamp": 1605287316466,
+          "user": {
+            "displayName": "Kevin Murphy",
+            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
+            "userId": "18199961579456458596"
+          },
+          "user_tz": 480
+        },
+        "id": "LOxv2klY7-yI",
+        "outputId": "6a131112-ca78-4dad-8039-6727efd6e924"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = np.arange(-14, 14+0.01)\n",
+        "n = len(x)\n",
+        "assert (n==29)\n",
+        "\n",
+        "pre_drop = np.array([-14, -13, -12, -11, -10, -9, -8, -7, -6])\n",
+        "pre = np.array([-5, -4, -3])\n",
+        "mid = np.array([-2, -1, 0, 1, 2])\n",
+        "post = np.array([3, 4, 5])\n",
+        "post_drop = np.array([6, 7, 8, 9, 10, 11, 12, 13, 14])\n",
+        "\n",
+        "\n",
+        "\n",
+        "inf_levels_bell = np.zeros(n, dtype=int)\n",
+        "inf_levels_bell[pre+14] = 1\n",
+        "inf_levels_bell[mid+14] = 2\n",
+        "inf_levels_bell[post+14] = 1\n",
+        "print(inf_levels_bell)\n",
+        "inf_weights_bell = np.array([0, 100, 250])/100"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -2692,10 +2820,10 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "xbX4IsQLUlqj"
+        "id": "97_dtD-n8nK-"
       },
       "source": [
-        "## Other configurations"
+        "## Arizona configurations"
       ]
     },
     {
@@ -2786,6 +2914,15 @@
         "                            name = 'Arizona(Inf2,Ble2)')"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kWPatMyF8rQV"
+      },
+      "source": [
+        "## Other configurations"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -2794,9 +2931,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 19641,
+          "elapsed": 21590,
           "status": "ok",
-          "timestamp": 1605160592329,
+          "timestamp": 1605287316470,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2805,7 +2942,7 @@
           "user_tz": 480
         },
         "id": "Q2r1jZv-UE3e",
-        "outputId": "0eae6aac-138f-4273-a0fa-59f0023c1174"
+        "outputId": "69cd12f3-04d8-4d72-f80d-c79c0d2e3394"
       },
       "outputs": [
         {
@@ -2872,45 +3009,6 @@
         "\n"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "executionInfo": {
-          "elapsed": 19931,
-          "status": "ok",
-          "timestamp": 1605160592653,
-          "user": {
-            "displayName": "Kevin Murphy",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
-            "userId": "18199961579456458596"
-          },
-          "user_tz": 480
-        },
-        "id": "hsnjnZ_pyaef",
-        "outputId": "28bd5748-461d-49bd-d8fd-2e17468c7610"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "RiskConfig(ble_thresholds=array([54, 61, 65]), ble_weights=array([1.  , 0.5 , 0.1 , 0.01]), inf_levels=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 4, 5, 6, 6, 6, 6, 5, 4, 3, 2, 2,\n",
-            "       1, 1, 0, 0, 0, 0, 0]), inf_weights=array([0.        , 0.1       , 0.15848932, 0.25118864, 0.39810717,\n",
-            "       0.63095734, 1.        ]), name='Baseline(Inf1,Ble2)', beta=0.00031)\n",
-            "RiskConfig(ble_thresholds=array([54, 61, 65]), ble_weights=array([1.  , 0.5 , 0.1 , 0.01]), inf_levels=array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 0, 0,\n",
-            "       0, 0, 0, 0, 0, 0, 0]), inf_weights=array([0. , 0.5, 1. ]), name='Baseline(Inf2,Ble2)', beta=0.00031)\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(config_baseline_inf1_ble2)\n",
-        "print(config_baseline_inf2_ble2)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -2953,9 +3051,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 19896,
+          "elapsed": 21584,
           "status": "ok",
-          "timestamp": 1605160592654,
+          "timestamp": 1605287316471,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -2964,7 +3062,7 @@
           "user_tz": 480
         },
         "id": "85LXR--U9P9Z",
-        "outputId": "26addab1-9205-432f-a026-cc137a1c32b6"
+        "outputId": "88af4616-0d4b-489e-e671-a7101b7d8de6"
       },
       "outputs": [
         {
@@ -3030,9 +3128,9 @@
           "height": 581
         },
         "executionInfo": {
-          "elapsed": 19860,
+          "elapsed": 21572,
           "status": "ok",
-          "timestamp": 1605160592657,
+          "timestamp": 1605287316473,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -3041,7 +3139,7 @@
           "user_tz": 480
         },
         "id": "3u_gXk48zNK9",
-        "outputId": "26cc9713-aa35-4c4c-ae6f-82dd71022c50"
+        "outputId": "8aa62593-4668-4760-b434-626ec65481fa"
       },
       "outputs": [
         {
@@ -3285,9 +3383,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 19841,
+          "elapsed": 21566,
           "status": "ok",
-          "timestamp": 1605160592658,
+          "timestamp": 1605287316473,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -3296,7 +3394,7 @@
           "user_tz": 480
         },
         "id": "CdnLme8I1ME-",
-        "outputId": "1cd58282-31b3-4ae1-934a-dc2067b1eae5"
+        "outputId": "4398fb58-aa9d-466f-d086-5e0be7b55731"
       },
       "outputs": [
         {
@@ -3914,9 +4012,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 22967,
+          "elapsed": 24250,
           "status": "ok",
-          "timestamp": 1605160595812,
+          "timestamp": 1605287319170,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -3925,7 +4023,7 @@
           "user_tz": 480
         },
         "id": "ltbwzcPznkOo",
-        "outputId": "665a44ee-3bb6-4705-8859-34ab8378c048"
+        "outputId": "74712b38-5963-4271-e864-0070a8951ce7"
       },
       "outputs": [
         {
@@ -4011,12 +4109,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 620
+          "height": 612
         },
         "executionInfo": {
-          "elapsed": 30494,
+          "elapsed": 32586,
           "status": "ok",
-          "timestamp": 1605160603361,
+          "timestamp": 1605287327513,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -4025,7 +4123,7 @@
           "user_tz": 480
         },
         "id": "cif7inRq0oSy",
-        "outputId": "a8bffd01-761c-4d67-becf-ff8d108f6db7"
+        "outputId": "fe5804fa-823c-4ada-9eac-73fa0cd80829"
       },
       "outputs": [
         {
@@ -4144,9 +4242,9 @@
           "height": 862
         },
         "executionInfo": {
-          "elapsed": 31581,
+          "elapsed": 42072,
           "status": "ok",
-          "timestamp": 1605160604474,
+          "timestamp": 1605287337012,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -4155,7 +4253,7 @@
           "user_tz": 480
         },
         "id": "Z1Zo8PG7l4_n",
-        "outputId": "e52e6363-c1b9-4f00-c990-2c666ca10773"
+        "outputId": "83017382-d0a1-49c6-9145-642ce6dd5fed"
       },
       "outputs": [
         {
@@ -4254,9 +4352,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 33854,
+          "elapsed": 42061,
           "status": "ok",
-          "timestamp": 1605160606773,
+          "timestamp": 1605287337014,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -4265,7 +4363,7 @@
           "user_tz": 480
         },
         "id": "Bd4bSySMBNRh",
-        "outputId": "ebf2c19d-0f26-4fbc-96a1-053490b3c9f6"
+        "outputId": "1b8f9e3d-42c5-4940-99b5-e6bf7fe43c86"
       },
       "outputs": [
         {
@@ -4400,12 +4498,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 371
+          "height": 367
         },
         "executionInfo": {
-          "elapsed": 34041,
+          "elapsed": 42049,
           "status": "ok",
-          "timestamp": 1605160606986,
+          "timestamp": 1605287337015,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -4414,7 +4512,7 @@
           "user_tz": 480
         },
         "id": "FcVkAEQUgGgo",
-        "outputId": "ce9fe4d2-2356-438c-d95e-b561d361c6a4"
+        "outputId": "9ae4a8d5-5a72-4fb1-e292-d9826ad2f151"
       },
       "outputs": [
         {
@@ -4529,11 +4627,13 @@
         "  grid_nfp = nneg * grid_fpr\n",
         "  grid_ntp = npos * grid_tpr\n",
         "  npop = npos + nneg\n",
-        "  grid_nabove = (grid_nfp + grid_ntp)/npop * popsize\n",
+        "  frac_notified = (grid_nfp + grid_ntp)/npop\n",
+        "  nabove = frac_notified * popsize\n",
         "  stats = {'thresh_table': grid_thresh,\n",
         "           'fpr_table': grid_fpr,\n",
         "           'tpr_table': grid_tpr,\n",
-        "           'nabove_table': grid_nabove, \n",
+        "           'nabove_table': nabove, \n",
+        "           'frac_table': frac_notified,\n",
         "           'npos': npos,\n",
         "           'nneg': nneg }\n",
         "  return stats"
@@ -4717,9 +4817,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 35487,
+          "elapsed": 42015,
           "status": "ok",
-          "timestamp": 1605160608483,
+          "timestamp": 1605287337019,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -4728,7 +4828,7 @@
           "user_tz": 480
         },
         "id": "8aXBkSb2oNkz",
-        "outputId": "3c6bc5eb-289f-477f-ab8f-62d81cc2bcc0"
+        "outputId": "b0603762-6f30-458c-c984-b1303ccc1f69"
       },
       "outputs": [
         {
@@ -4977,23 +5077,23 @@
         "#distances, durations, symptoms = uniform_input_data_grid(max_dist=10, ngrid_dist = 25, ngrid_dur=25, max_dur=60, onset=14)\n",
         "\n",
         "params_lognormal_old = ModelParams()\n",
-        "params_lognormal_old.ble_params = ble_params_lognormal_old\n",
+        "params_lognormal_old.ble_params = ble_params_lognormal_lovett\n",
         "pthresh = compute_exposure_threshold(params_lognormal_old)\n",
-        "data = make_input_data(distances=distances, durations=durations, symptoms=symptoms, ble_params=ble_params_lognormal_old)\n",
+        "data = make_input_data(distances=distances, durations=durations, symptoms=symptoms, ble_params=ble_params_lognormal_lovett)\n",
         "compare_roc_curves(data, config_list, params=params_lognormal_old, pthresh=pthresh)\n",
         "plot_roc_curves(data, config_list, params=params_lognormal_old, pthresh=pthresh)\n",
         "\n",
         "params_lognormal_new = ModelParams()\n",
-        "params_lognormal_new.ble_params = ble_params_lognormal_new\n",
+        "params_lognormal_new.ble_params = ble_params_lognormal_briers\n",
         "pthresh = compute_exposure_threshold(params_lognormal_new)\n",
-        "data = make_input_data(distances=distances, durations=durations, symptoms=symptoms, ble_params=ble_params_lognormal_new)\n",
+        "data = make_input_data(distances=distances, durations=durations, symptoms=symptoms, ble_params=ble_params_lognormal_briers)\n",
         "compare_roc_curves(data, config_list, params=params_lognormal_new, pthresh=pthresh)\n",
         "plot_roc_curves(data, config_list, params=params_lognormal_new, pthresh=pthresh)\n",
         "\n",
         "\n",
         "params_normal = ModelParams()\n",
-        "params_normal.ble_params = ble_params_normal\n",
-        "data = make_input_data(distances=distances, durations=durations, symptoms=symptoms, ble_params=ble_params_normal)\n",
+        "params_normal.ble_params = ble_params_normal_lovett\n",
+        "data = make_input_data(distances=distances, durations=durations, symptoms=symptoms, ble_params=ble_params_normal_lovett)\n",
         "compare_roc_curves(data, config_list, params=params_normal)\n",
         "plot_roc_curves(data, config_list, params=params_normal)\n",
         "\n",
@@ -5013,9 +5113,9 @@
           "height": 260
         },
         "executionInfo": {
-          "elapsed": 35650,
+          "elapsed": 42009,
           "status": "ok",
-          "timestamp": 1605160608667,
+          "timestamp": 1605287337020,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5024,7 +5124,7 @@
           "user_tz": 480
         },
         "id": "pAefqPYfySXW",
-        "outputId": "b01445e4-a3a9-4469-bd46-263c4e843d47"
+        "outputId": "e8fe7b2c-8303-40a4-9509-8d97f11e63be"
       },
       "outputs": [
         {
@@ -5123,9 +5223,9 @@
           "height": 260
         },
         "executionInfo": {
-          "elapsed": 35834,
+          "elapsed": 42003,
           "status": "ok",
-          "timestamp": 1605160608874,
+          "timestamp": 1605287337020,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5134,7 +5234,7 @@
           "user_tz": 480
         },
         "id": "g6BV_CuhxuAA",
-        "outputId": "03e273d1-2ca5-4b92-acfd-d26a137edddd"
+        "outputId": "a7c23a4a-132e-4507-c016-1da50ae8da67"
       },
       "outputs": [
         {
@@ -5233,9 +5333,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 38350,
+          "elapsed": 41998,
           "status": "ok",
-          "timestamp": 1605160611412,
+          "timestamp": 1605287337021,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5244,7 +5344,7 @@
           "user_tz": 480
         },
         "id": "bMZUKCUH1aVE",
-        "outputId": "61c74d94-1f1b-4748-fb26-8e27ff4d61e1"
+        "outputId": "630dbbbb-e244-4a38-bc4c-408045db8c56"
       },
       "outputs": [
         {
@@ -5386,9 +5486,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 40583,
+          "elapsed": 45535,
           "status": "ok",
-          "timestamp": 1605160613667,
+          "timestamp": 1605287340564,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5397,7 +5497,7 @@
           "user_tz": 480
         },
         "id": "iEb3Q3TpehK_",
-        "outputId": "42ed3090-e37b-46d4-c9da-c093616d5649"
+        "outputId": "b65a6d22-591e-45d0-dece-fa7d1a6f7051"
       },
       "outputs": [
         {
@@ -5539,9 +5639,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 42290,
+          "elapsed": 45530,
           "status": "ok",
-          "timestamp": 1605160615396,
+          "timestamp": 1605287340565,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5550,7 +5650,7 @@
           "user_tz": 480
         },
         "id": "p13qwpt1h6i_",
-        "outputId": "98397ca3-0631-4219-e2cc-df1fd8c80332"
+        "outputId": "5fb305c1-7591-465a-ee56-9cc0ff11d9df"
       },
       "outputs": [
         {
@@ -5681,9 +5781,9 @@
           "height": 683
         },
         "executionInfo": {
-          "elapsed": 42878,
+          "elapsed": 45523,
           "status": "ok",
-          "timestamp": 1605160616008,
+          "timestamp": 1605287340565,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5692,7 +5792,7 @@
           "user_tz": 480
         },
         "id": "vW1Do8OpSqvl",
-        "outputId": "d41a0965-cfda-4575-b11b-ab0e149875e1"
+        "outputId": "e09dcd99-823f-4f74-b25f-19569d4a277a"
       },
       "outputs": [
         {
@@ -5753,9 +5853,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 44772,
+          "elapsed": 45518,
           "status": "ok",
-          "timestamp": 1605160617925,
+          "timestamp": 1605287340566,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5764,7 +5864,7 @@
           "user_tz": 480
         },
         "id": "moJM-CP8sr-q",
-        "outputId": "bc4b1b34-2650-463d-de18-4bf61e71c3b4"
+        "outputId": "784fe251-038a-4ac2-966a-b3017698d1a0"
       },
       "outputs": [
         {
@@ -5979,9 +6079,9 @@
           "height": 366
         },
         "executionInfo": {
-          "elapsed": 45626,
+          "elapsed": 45495,
           "status": "ok",
-          "timestamp": 1605160618832,
+          "timestamp": 1605287340568,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -5990,7 +6090,7 @@
           "user_tz": 480
         },
         "id": "RgFPrLDofH_G",
-        "outputId": "bee1452c-1761-4722-9993-8fa7d70fc042"
+        "outputId": "8a3ab2fb-40ad-4c47-f301-43a2dd2f5b33"
       },
       "outputs": [
         {
@@ -6162,9 +6262,9 @@
           "height": 667
         },
         "executionInfo": {
-          "elapsed": 45923,
+          "elapsed": 45470,
           "status": "ok",
-          "timestamp": 1605160619177,
+          "timestamp": 1605287340570,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -6173,7 +6273,7 @@
           "user_tz": 480
         },
         "id": "wE-mu4IQP7_X",
-        "outputId": "6bb3f384-77f1-4897-f726-bdb88d741646"
+        "outputId": "c913b74c-54a0-4743-a992-e813394993b9"
       },
       "outputs": [
         {
@@ -6314,10 +6414,13 @@
         "  # interpolate to common grid \n",
         "  interp_lower = interpolate_roc(thresholds, fpr_lower, tpr_lower, thresholds_lower, npos, nneg)\n",
         "  interp_upper = interpolate_roc(thresholds, fpr_upper, tpr_upper, thresholds_upper, npos, nneg)\n",
+        "\n",
+        "  interp_fpr = fpr # matches thresholds\n",
+        "\n",
         "  interp_tpr_lower = interp_lower['tpr_table']\n",
         "  interp_tpr_upper = interp_upper['tpr_table']\n",
         "  interp_tpr = (interp_tpr_upper + interp_tpr_lower)/2\n",
-        "  interp_fpr = fpr # matches thresholds\n",
+        "  \n",
         "  interp_auc_lower = metrics.auc(interp_fpr, interp_tpr_lower)\n",
         "  interp_auc_upper = metrics.auc(interp_fpr, interp_tpr_upper)\n",
         "  interp_auc = metrics.auc(interp_fpr, interp_tpr)\n",
@@ -6341,15 +6444,6 @@
         "\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QuVb5Fd5pu8g"
-      },
-      "source": [
-        ""
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -6470,9 +6564,9 @@
           "height": 683
         },
         "executionInfo": {
-          "elapsed": 46536,
+          "elapsed": 45433,
           "status": "ok",
-          "timestamp": 1605160619865,
+          "timestamp": 1605287340573,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -6481,7 +6575,7 @@
           "user_tz": 480
         },
         "id": "r4XzekleFis9",
-        "outputId": "1f4b7715-29c8-4bf4-900e-ae5841afdbe1"
+        "outputId": "927e395a-f787-4e52-c737-5d8dd3041847"
       },
       "outputs": [
         {
@@ -6538,9 +6632,9 @@
           "height": 350
         },
         "executionInfo": {
-          "elapsed": 46912,
+          "elapsed": 45428,
           "status": "ok",
-          "timestamp": 1605160620268,
+          "timestamp": 1605287340574,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -6549,7 +6643,7 @@
           "user_tz": 480
         },
         "id": "_uyq6WJCLuKs",
-        "outputId": "d6b754c2-0b0c-42b0-c06b-c9e0a956b4b9"
+        "outputId": "44742aeb-fa7e-4066-cb23-a41dfda7bcf4"
       },
       "outputs": [
         {
@@ -6588,9 +6682,9 @@
           "height": 667
         },
         "executionInfo": {
-          "elapsed": 47472,
+          "elapsed": 45422,
           "status": "ok",
-          "timestamp": 1605160620856,
+          "timestamp": 1605287340575,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -6599,7 +6693,7 @@
           "user_tz": 480
         },
         "id": "gv6ryx5lJ9qf",
-        "outputId": "e31aa5f5-4e98-46f6-8f4e-afd60c2ca110"
+        "outputId": "77ff4578-9d3b-413f-d54c-c49813cfec4a"
       },
       "outputs": [
         {
@@ -6652,9 +6746,9 @@
           "height": 368
         },
         "executionInfo": {
-          "elapsed": 47611,
+          "elapsed": 45415,
           "status": "ok",
-          "timestamp": 1605160621024,
+          "timestamp": 1605287340575,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -6663,7 +6757,7 @@
           "user_tz": 480
         },
         "id": "YlRVA5c4VAHG",
-        "outputId": "db779ee4-a6db-45a8-df48-37a106a4abaa"
+        "outputId": "0a8b4fb8-b552-4896-e8fb-91aaac57c901"
       },
       "outputs": [
         {
@@ -6679,7 +6773,7 @@
               "Text(0.5, 1.0, '0.855')"
             ]
           },
-          "execution_count": 328,
+          "execution_count": 139,
           "metadata": {
             "tags": []
           },
@@ -6722,51 +6816,6 @@
         "## ROC stats at thresholds with confidence interval"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8TSqtWwAaZV6"
-      },
-      "outputs": [],
-      "source": [
-        "def compute_stats_at_roc_thresh(risk_thresh, table):\n",
-        "  all_thresh = table['thresh_table']\n",
-        "  ndx = np.argmin(np.abs(all_thresh - risk_thresh))\n",
-        "  nearest_thresh = all_thresh[ndx]\n",
-        "  fpr = table['fpr_table'][ndx]\n",
-        "  tpr = table['tpr_table'][ndx]\n",
-        "  nabove = table['nabove_table'][ndx]\n",
-        "  npos = table['npos']\n",
-        "  nneg = table['nneg']\n",
-        "\n",
-        "  nfp = fpr * nneg\n",
-        "  ntp = tpr * npos\n",
-        "  nfn = (1-tpr) * npos\n",
-        "  ntn = (1-fpr) * nneg\n",
-        "  npred_pos = nfp + ntp \n",
-        "  npred_neg = ntn + nfn\n",
-        "\n",
-        "  prec = ntp / npred_pos\n",
-        "  recall = ntp/ npos # sensitivity\n",
-        "  spec = ntn / nneg # specifity\n",
-        "  fdr = nfp / npred_pos\n",
-        "  npv = ntn / npred_neg\n",
-        "\n",
-        "  stats = {\n",
-        "    'thresh': nearest_thresh,\n",
-        "    'nabove': nabove,\n",
-        "    'sens': recall,\n",
-        "    'spec': spec,\n",
-        "    'ppv': prec,\n",
-        "    'npv': npv,\n",
-        "    'fpr': fpr,\n",
-        "    'tpr': tpr, \n",
-        "  }\n",
-        "\n",
-        "  return stats"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -6776,15 +6825,16 @@
       "outputs": [],
       "source": [
         "def compute_vals(field, stats_noiseless, stats_low_recall, stats_high_recall):\n",
-        "  val = stats_noiseless[field]\n",
+        "  val_clean = stats_noiseless[field]\n",
         "  val_range = np.sort([stats_low_recall[field], stats_high_recall[field]])\n",
         "  val_low = val_range[0]; val_high = val_range[1]; val_mid = np.mean([val_low, val_high]) \n",
-        "  return val_low, val_mid, val_high\n",
+        "  return val_low, val_mid, val_high, val_clean\n",
         "\n",
-        "def insert_vals(stats_bounds, field, val_low, val_mid, val_high):\n",
+        "def insert_vals(stats_bounds, field, val_low, val_mid, val_high, val_clean):\n",
         "  stats_bounds['{}_low'.format(field)] = val_low\n",
         "  stats_bounds['{}_mid'.format(field)] = val_mid\n",
         "  stats_bounds['{}_high'.format(field)] = val_high\n",
+        "  stats_bounds['{}_clean'.format(field)] = val_clean\n",
         "  return stats_bounds"
       ]
     },
@@ -6792,12 +6842,10 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "W1fJh-YklYVA"
+        "id": "2DuPN5YFN5Fx"
       },
       "outputs": [],
       "source": [
-        "# deprecated\n",
-        "  \n",
         "def compute_stats_at_roc_thresh_with_bounds(risk_thresh, roc):\n",
         "  thresh = roc['thresholds']\n",
         "  all_thresh = np.linspace(np.min(thresh), np.max(thresh)+1e-3, 100)  \n",
@@ -6807,59 +6855,77 @@
         "  # atten-sigma means contact appears closer means higher recall (sensitivity)\n",
         "  table_lower = interpolate_roc(all_thresh, roc['fpr_lower'], roc['tpr_lower'], roc['thresholds_lower'], roc['npos'], roc['nneg'])\n",
         "  stats_high_recall = compute_stats_at_roc_thresh(risk_thresh, table_lower)\n",
-        "  \n",
+        "\n",
         "  # atten+sigma means contact appears further means lower recall\n",
         "  table_upper = interpolate_roc(all_thresh, roc['fpr_upper'], roc['tpr_upper'], roc['thresholds_upper'], roc['npos'], roc['nneg'])\n",
         "  stats_low_recall = compute_stats_at_roc_thresh(risk_thresh, table_upper)\n",
         "\n",
         "  stats_bounds = {}\n",
-        "  for field in ['nabove', 'sens', 'spec', 'ppv', 'npv']:\n",
-        "    val_low, val_mid, val_high = compute_vals(field, stats_noiseless, stats_low_recall, stats_high_recall)\n",
-        "    stats_bounds = insert_vals(stats_bounds, field, val_low, val_mid, val_high)\n",
-        "\n",
+        "  for field in ['thresh', 'fpr', 'tpr', 'frac', 'nabove', 'sens', 'spec', 'ppv', 'npv']:\n",
+        "    val_low, val_mid, val_high, val_clean = compute_vals(field, stats_noiseless, stats_low_recall, stats_high_recall)\n",
+        "    stats_bounds = insert_vals(stats_bounds, field, val_low, val_mid, val_high, val_clean)\n",
+        "\n",
+        "  stats_bounds['auc_mid'] = roc['interp_auc']\n",
+        "  stats_bounds['auc_low'] = roc['interp_auc_lower']\n",
+        "  stats_bounds['auc_high'] = roc['interp_auc_upper']\n",
+        "  stats_bounds['auc_clean'] = roc['auc']\n",
         "  return stats_bounds\n",
         "\n",
-        "def compute_stats_at_roc_with_bounds(roc):\n",
-        "  thresh = roc['thresholds']\n",
-        "  all_thresh = np.linspace(np.min(thresh), np.max(thresh)+1e-3, 100)  \n",
-        "  table = interpolate_roc(all_thresh, roc['fpr'], roc['tpr'], roc['thresholds'], roc['npos'], roc['nneg'])\n",
-        "\n",
-        "  # atten-sigma means contact appears closer means higher recall (sensitivity)\n",
-        "  table_lower = interpolate_roc(all_thresh, roc['fpr_lower'], roc['tpr_lower'], roc['thresholds_lower'], roc['npos'], roc['nneg'])\n",
-        "  \n",
-        "  # atten+sigma means contact appears further means lower recall\n",
-        "  table_upper = interpolate_roc(all_thresh, roc['fpr_upper'], roc['tpr_upper'], roc['thresholds_upper'], roc['npos'], roc['nneg'])\n",
+        "def compute_stats_at_roc_thresh(risk_thresh, table):\n",
+        "  all_thresh = table['thresh_table']\n",
+        "  ndx = np.argmin(np.abs(all_thresh - risk_thresh))\n",
+        "  nearest_thresh = all_thresh[ndx]\n",
+        "  fpr = table['fpr_table'][ndx]\n",
+        "  tpr = table['tpr_table'][ndx]\n",
+        "  nabove = table['nabove_table'][ndx]\n",
+        "  frac_notified = table['frac_table'][ndx]\n",
+        "  npos = table['npos']\n",
+        "  nneg = table['nneg']\n",
         "\n",
-        "  def calc_thresh_stats(risk_thresh):\n",
-        "    stats_high_recall = compute_stats_at_roc_thresh(risk_thresh, table_lower)\n",
-        "    stats_noiseless = compute_stats_at_roc_thresh(risk_thresh, table)\n",
-        "    stats_low_recall = compute_stats_at_roc_thresh(risk_thresh, table_upper)\n",
+        "  nfp = fpr * nneg\n",
+        "  ntp = tpr * npos\n",
+        "  nfn = (1-tpr) * npos\n",
+        "  ntn = (1-fpr) * nneg\n",
+        "  npred_pos = nfp + ntp \n",
+        "  npred_neg = ntn + nfn\n",
         "\n",
-        "    stats_bounds = {}\n",
-        "    for field in ['nabove', 'sens', 'spec', 'ppv', 'npv', 'thresh', 'fpr', 'tpr']:\n",
-        "      val_low, val_mid, val_high = compute_vals(field, stats_noiseless, stats_low_recall, stats_high_recall)\n",
-        "      stats_bounds = insert_vals(stats_bounds, field, val_low, val_mid, val_high)\n",
+        "  prec = ntp / npred_pos\n",
+        "  recall = ntp/ npos # sensitivity\n",
+        "  spec = ntn / nneg # specifity\n",
+        "  fdr = nfp / npred_pos\n",
+        "  npv = ntn / npred_neg\n",
         "\n",
-        "    return stats_bounds\n",
+        "  stats = {\n",
+        "    'thresh': nearest_thresh,\n",
+        "    'nabove': nabove,\n",
+        "    'frac': frac_notified,\n",
+        "    'sens': recall,\n",
+        "    'spec': spec,\n",
+        "    'ppv': prec,\n",
+        "    'npv': npv,\n",
+        "    'fpr': fpr,\n",
+        "    'tpr': tpr, \n",
+        "  }\n",
         "\n",
-        "  return list(map(calc_thresh_stats, thresh))\n"
+        "  return stats"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "MzRtjK5fxDmd"
+        "id": "DZkpF-dlFebU"
       },
       "outputs": [],
       "source": [
-        "def compute_stats_at_threshold(risk_thresh, thresh_table, fpr_table, tpr_table, nabove_table, npos, nneg):\n",
+        "def compute_stats_at_threshold(risk_thresh, thresh_table, fpr_table, tpr_table, nabove_table, frac_table, npos, nneg):\n",
         "  ndx = np.argmin(np.abs(thresh_table - risk_thresh))\n",
         "  nearest_thresh = thresh_table[ndx]\n",
         "  fpr = fpr_table[ndx]\n",
         "  tpr = tpr_table[ndx]\n",
         "  nabove = nabove_table[ndx]\n",
-        " \n",
+        "  frac_notified = frac_table[ndx]\n",
+        "\n",
         "  nfp = fpr * nneg\n",
         "  ntp = tpr * npos\n",
         "  nfn = (1-tpr) * npos\n",
@@ -6876,6 +6942,7 @@
         "  stats = {\n",
         "    'thresh': nearest_thresh,\n",
         "    'nabove': nabove,\n",
+        "    'frac': frac_notified,\n",
         "    'sens': recall,\n",
         "    'spec': spec,\n",
         "    'ppv': prec,\n",
@@ -6883,240 +6950,232 @@
         "    'fpr': fpr,\n",
         "    'tpr': tpr, \n",
         "  }\n",
-        "  return stats\n",
+        "  return stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MzRtjK5fxDmd"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
         "\n",
         "def compute_stats_from_roc(roc):\n",
         "  # interpolate to common grid\n",
         "  thresh = roc['thresholds']\n",
-        "  fpr = roc['fpr']\n",
+        "  fpr = roc['fpr']; tpr = roc['tpr']\n",
         "  npos = roc['npos']; nneg = roc['nneg']\n",
-        "  interp_lower = interpolate_roc(thresh, roc['fpr_lower'], roc['tpr_lower'], roc['thresholds_lower'], roc['npos'], roc['nneg'])\n",
-        "  interp_upper = interpolate_roc(thresh, roc['fpr_upper'], roc['tpr_upper'], roc['thresholds_upper'], roc['npos'], roc['nneg'])\n",
+        "  interp_lower = interpolate_roc(thresh, roc['fpr_lower'], roc['tpr_lower'], roc['thresholds_lower'], npos, nneg)\n",
+        "  interp_upper = interpolate_roc(thresh, roc['fpr_upper'], roc['tpr_upper'], roc['thresholds_upper'], npos, nneg)\n",
+        "  interp_clean = interpolate_roc(thresh, fpr, tpr, thresh, npos, nneg)\n",
+        "  \n",
+        "  interp_fpr_lower = interp_lower['fpr_table']\n",
+        "  interp_fpr_upper = interp_upper['fpr_table']\n",
+        "  interp_fpr_mid = (interp_fpr_upper + interp_fpr_lower)/2\n",
+        "  interp_fpr_clean = interp_clean['fpr_table']\n",
+        "  assert np.allclose(fpr, interp_fpr_clean)\n",
+        "\n",
         "  interp_tpr_lower = interp_lower['tpr_table']\n",
         "  interp_tpr_upper = interp_upper['tpr_table']\n",
-        "  interp_tpr = (interp_tpr_upper + interp_tpr_lower)/2\n",
+        "  interp_tpr_mid = (interp_tpr_upper + interp_tpr_lower)/2\n",
+        "  interp_tpr_clean = interp_clean['tpr_table']\n",
+        "  assert np.allclose(tpr, interp_tpr_clean) \n",
+        "  \n",
+        "  interp_frac_lower = interp_lower['frac_table']\n",
+        "  interp_frac_upper = interp_upper['frac_table']\n",
+        "  interp_frac_mid = (interp_frac_upper + interp_frac_lower)/2\n",
+        "  interp_frac_clean = interp_clean['frac_table']\n",
+        "  \n",
         "  interp_nabove_lower = interp_lower['nabove_table']\n",
         "  interp_nabove_upper = interp_upper['nabove_table']\n",
-        "  interp_nabove = (interp_nabove_upper + interp_nabove_lower)/2\n",
-        "  interp_fpr = fpr # matches thresholds\n",
+        "  interp_nabove_mid = (interp_nabove_upper + interp_nabove_lower)/2\n",
+        "  interp_nabove_clean = interp_clean['nabove_table']\n",
         "\n",
         "  def calc_thresh_stats(risk_thresh):\n",
-        "    stats_high_recall = compute_stats_at_threshold(risk_thresh, thresh, interp_fpr, interp_tpr_lower, interp_nabove_lower, npos, nneg)\n",
-        "    stats_mid = compute_stats_at_threshold(risk_thresh, thresh, interp_fpr, interp_tpr, interp_nabove, npos, nneg)\n",
-        "    stats_low_recall = compute_stats_at_threshold(risk_thresh, thresh, interp_fpr, interp_tpr_upper, interp_nabove_upper, npos, nneg)\n",
+        "    stats_high_recall = compute_stats_at_threshold(risk_thresh, thresh, fpr, interp_tpr_lower, interp_nabove_lower, interp_frac_lower, npos, nneg)\n",
+        "    stats_mid = compute_stats_at_threshold(risk_thresh, thresh, interp_fpr_lower, interp_tpr_mid, interp_nabove_mid, interp_frac_mid, npos, nneg)\n",
+        "    stats_low_recall = compute_stats_at_threshold(risk_thresh, thresh, interp_fpr_upper, interp_tpr_upper, interp_nabove_upper, interp_frac_upper, npos, nneg)\n",
+        "    stats_clean = compute_stats_at_threshold(risk_thresh, thresh, interp_fpr_clean, interp_tpr_clean, interp_nabove_clean, interp_frac_clean, npos, nneg)\n",
         "    stats_bounds = {}\n",
-        "    for field in ['nabove', 'sens', 'spec', 'ppv', 'npv', 'thresh', 'fpr', 'tpr']:\n",
-        "      val_low, val_mid, val_high = compute_vals(field, stats_mid, stats_low_recall, stats_high_recall)\n",
-        "      stats_bounds = insert_vals(stats_bounds, field, val_low, val_mid, val_high)\n",
+        "    for field in ['nabove', 'frac', 'sens', 'spec', 'ppv', 'npv', 'thresh', 'fpr', 'tpr']:\n",
+        "      val_low, val_mid, val_high, val_clean = compute_vals(field, stats_clean, stats_low_recall, stats_high_recall)\n",
+        "      stats_bounds = insert_vals(stats_bounds, field, val_low, val_mid, val_high, val_clean)\n",
         "    return stats_bounds\n",
         "\n",
-        "  return list(map(calc_thresh_stats, thresh))"
+        "  return list(map(calc_thresh_stats, thresh))\n",
+        "\n",
+        "\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "executionInfo": {
-          "elapsed": 48370,
-          "status": "ok",
-          "timestamp": 1605160621842,
-          "user": {
-            "displayName": "Kevin Murphy",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
-            "userId": "18199961579456458596"
-          },
-          "user_tz": 480
-        },
-        "id": "eL3zDLK-2iPQ",
-        "outputId": "2f490efd-948b-45d5-b08a-f2be1204f69f"
+        "id": "RSweVEWcFZWX"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n",
-            "121\n",
-            "{'nabove_low': 0.16285937645247708, 'nabove_mid': 6.777224406584598, 'nabove_high': 13.39158943671672, 'sens_low': 0.012818766512376381, 'sens_mid': 0.3133767620211527, 'sens_high': 0.6139347575299291, 'spec_low': 0.9359450224405053, 'spec_mid': 0.9679725112202526, 'spec_high': 1.0, 'ppv_low': 0.5824472857635501, 'ppv_mid': 0.791223642881775, 'ppv_high': 1.0, 'npv_low': 0.8743763848806453, 'npv_mid': 0.9088718490656655, 'npv_high': 0.9433673132506857, 'thresh_low': 39.15203030303031, 'thresh_mid': 39.15203030303031, 'thresh_high': 39.15203030303031, 'fpr_low': 0.0, 'fpr_mid': 0.03202748877974744, 'fpr_high': 0.06405497755949488, 'tpr_low': 0.012818766512376381, 'tpr_mid': 0.3133767620211527, 'tpr_high': 0.6139347575299291}\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in double_scalars\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "data = make_input_data() \n",
-        "params = ModelParams()\n",
-        "config  = config_baseline\n",
-        "pthresh = pthresh_cdc\n",
-        "risk_thresh = 15\n",
-        "expo_dist = expo_dist_rnd\n",
-        "ble_params = ble_params_default\n",
-        "sigma_mle = ble_params.sigma\n",
-        "sigma = sigma_mle\n",
-        "roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist)\n",
-        "stats = compute_stats_at_roc_with_bounds(roc)\n",
-        "print(len(stats))\n",
-        "print(stats[5])"
+        "\n",
+        "def compute_stats_from_roc_old(roc):\n",
+        "  # interpolate to common grid\n",
+        "  thresh = roc['thresholds']\n",
+        "  fpr = roc['fpr']; tpr = roc['tpr']\n",
+        "  npos = roc['npos']; nneg = roc['nneg']\n",
+        "  interp_lower = interpolate_roc(thresh, roc['fpr_lower'], roc['tpr_lower'], roc['thresholds_lower'], npos, nneg)\n",
+        "  interp_upper = interpolate_roc(thresh, roc['fpr_upper'], roc['tpr_upper'], roc['thresholds_upper'], npos, nneg)\n",
+        "  interp_clean = interpolate_roc(thresh, fpr, tpr, thresh, npos, nneg)\n",
+        "  \n",
+        "  interp_fpr_lower = interp_lower['fpr_table']\n",
+        "  interp_fpr_upper = interp_upper['fpr_table']\n",
+        "  interp_fpr_mid = (interp_fpr_upper + interp_fpr_lower)/2\n",
+        "  interp_fpr_clean = interp_clean['fpr_table']\n",
+        "  assert np.allclose(fpr, interp_fpr_clean)\n",
+        "\n",
+        "  interp_tpr_lower = interp_lower['tpr_table']\n",
+        "  interp_tpr_upper = interp_upper['tpr_table']\n",
+        "  interp_tpr_mid = (interp_tpr_upper + interp_tpr_lower)/2\n",
+        "  interp_tpr_clean = interp_clean['tpr_table']\n",
+        "  assert np.allclose(tpr, interp_tpr_clean) \n",
+        "  \n",
+        "  interp_frac_lower = interp_lower['frac_table']\n",
+        "  interp_frac_upper = interp_upper['frac_table']\n",
+        "  interp_frac_mid = (interp_frac_upper + interp_frac_lower)/2\n",
+        "  interp_frac_clean = interp_clean['frac_table']\n",
+        "  \n",
+        "  interp_nabove_lower = interp_lower['nabove_table']\n",
+        "  interp_nabove_upper = interp_upper['nabove_table']\n",
+        "  interp_nabove_mid = (interp_nabove_upper + interp_nabove_lower)/2\n",
+        "  interp_nabove_clean = interp_clean['nabove_table']\n",
+        "\n",
+        "  def calc_thresh_stats(risk_thresh):\n",
+        "    stats_high_recall = compute_stats_at_threshold(risk_thresh, thresh, fpr, interp_tpr_lower, interp_nabove_lower, interp_frac_lower, npos, nneg)\n",
+        "    stats_mid = compute_stats_at_threshold(risk_thresh, thresh, fpr, interp_tpr_mid, interp_nabove_mid, interp_frac_mid, npos, nneg)\n",
+        "    stats_low_recall = compute_stats_at_threshold(risk_thresh, thresh, fpr, interp_tpr_upper, interp_nabove_upper, interp_frac_upper, npos, nneg)\n",
+        "    stats_clean = compute_stats_at_threshold(risk_thresh, thresh, fpr, interp_tpr_clean, interp_nabove_clean, interp_frac_clean, npos, nneg)\n",
+        "    stats_bounds = {}\n",
+        "    for field in ['nabove', 'frac', 'sens', 'spec', 'ppv', 'npv', 'thresh', 'fpr', 'tpr']:\n",
+        "      val_low, val_mid, val_high, val_clean = compute_vals(field, stats_clean, stats_low_recall, stats_high_recall)\n",
+        "      stats_bounds = insert_vals(stats_bounds, field, val_low, val_mid, val_high, val_clean)\n",
+        "    return stats_bounds\n",
+        "\n",
+        "  return list(map(calc_thresh_stats, thresh))\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "executionInfo": {
-          "elapsed": 48346,
-          "status": "ok",
-          "timestamp": 1605160621845,
-          "user": {
-            "displayName": "Kevin Murphy",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
-            "userId": "18199961579456458596"
-          },
-          "user_tz": 480
-        },
-        "id": "0JCepLEuv-lk",
-        "outputId": "bd75eec1-27dc-4818-9f41-e858e892324c"
+        "id": "c2bMg3vApOdK"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n",
-            "121\n",
-            "{'nabove_low': 0.16438693776956326, 'nabove_mid': 6.834719576920545, 'nabove_high': 13.505052216071526, 'sens_low': 0.012939001848428892, 'sens_mid': 0.31566123340615043, 'sens_high': 0.618383464963872, 'spec_low': 1.0, 'spec_mid': 1.0, 'spec_high': 1.0, 'ppv_low': 1.0, 'ppv_mid': 1.0, 'ppv_high': 1.0, 'npv_low': 0.8743897635087835, 'npv_mid': 0.910886228796356, 'npv_high': 0.9473826940839283, 'thresh_low': 38.81578947368421, 'thresh_mid': 38.81578947368421, 'thresh_high': 38.81578947368421, 'fpr_low': 0.0, 'fpr_mid': 0.0, 'fpr_high': 0.0, 'tpr_low': 0.012939001848428893, 'tpr_mid': 0.31566123340615043, 'tpr_high': 0.618383464963872}\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "data = make_input_data() \n",
-        "params = ModelParams()\n",
-        "config  = config_baseline\n",
-        "pthresh = pthresh_cdc\n",
-        "risk_thresh = 15\n",
-        "expo_dist = expo_dist_rnd\n",
-        "ble_params = ble_params_default\n",
-        "sigma_mle = ble_params.sigma\n",
-        "sigma = sigma_mle\n",
-        "roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist)\n",
-        "stats = compute_stats_from_roc(roc)\n",
-        "print(len(stats))\n",
-        "print(stats[5])"
+        "def make_string_float(stats, field):\n",
+        "  str = '{:0.3f} ({:0.3f}-{:0.3f})'.format(\n",
+        "      stats['{}_mid'.format(field)], \n",
+        "      stats['{}_low'.format(field)],\n",
+        "      stats['{}_high'.format(field)])\n",
+        "  return str\n",
+        "\n",
+        "def make_string_int(stats, field):\n",
+        "  str = '{:d} ({:d}-{:d}) %'.format(\n",
+        "      int(np.round(100*stats['{}_mid'.format(field)])), \n",
+        "      int(np.round(100*stats['{}_low'.format(field)])),\n",
+        "      int(np.round(100*stats['{}_high'.format(field)])))\n",
+        "  return str\n",
+        "\n",
+        "def make_df_stats_with_bounds(names_list, stats_list, use_float=False):\n",
+        "  if use_float:\n",
+        "    fun = make_string_float\n",
+        "  else:\n",
+        "    fun = make_string_int\n",
+        "  X = {'names': names_list, \n",
+        "      'notified': [fun(stats, 'frac') for stats in stats_list], \n",
+        "      'sens': [fun(stats, 'sens') for stats in stats_list],\n",
+        "      'spec': [fun(stats, 'spec') for stats in stats_list],\n",
+        "      'ppv': [fun(stats, 'ppv') for stats in stats_list],\n",
+        "      'npv': [fun(stats, 'npv') for stats in stats_list],\n",
+        "      'auc': [fun(stats, 'auc') for stats in stats_list],\n",
+        "  }\n",
+        "  df = pd.DataFrame(data = X)\n",
+        "  return df"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "executionInfo": {
-          "elapsed": 48322,
-          "status": "ok",
-          "timestamp": 1605160621847,
-          "user": {
-            "displayName": "Kevin Murphy",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
-            "userId": "18199961579456458596"
-          },
-          "user_tz": 480
-        },
-        "id": "wCQ4DrVNcJzW",
-        "outputId": "7d0d4bdb-9404-4344-d364-d2a07fae75ac"
+        "id": "UOM4-Y8I_VcK"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n",
-            "\n",
-            "config Baseline, sigma 0.00\n",
-            "{'nabove_low': 11.403417740770674, 'nabove_mid': 11.403417740770674, 'nabove_high': 11.403417740770674, 'sens_low': 0.8245328431347604, 'sens_mid': 0.8245328431347604, 'sens_high': 0.8245328431347604, 'spec_low': 0.989370276015803, 'spec_mid': 0.989370276015803, 'spec_high': 0.989370276015803, 'ppv_low': 0.9186275284146141, 'ppv_mid': 0.9186275284146141, 'ppv_high': 0.9186275284146141, 'npv_low': 0.9748379859218989, 'npv_mid': 0.9748379859218989, 'npv_high': 0.9748379859218989}\n",
-            "\n",
-            "config Baseline, sigma 0.01\n",
-            "{'nabove_low': 11.403417740770678, 'nabove_mid': 11.932183103507077, 'nabove_high': 12.460948466243476, 'sens_low': 0.8245328431347604, 'sens_mid': 0.8519231976970189, 'sens_high': 0.8793135522592773, 'spec_low': 0.9852285282302324, 'spec_mid': 0.9872994021230177, 'spec_high': 0.9893702760158029, 'ppv_low': 0.896518379106364, 'ppv_mid': 0.907572953760489, 'ppv_high': 0.9186275284146139, 'npv_low': 0.9748379859218989, 'npv_mid': 0.978661229672378, 'npv_high': 0.9824844734228573}\n",
-            "\n",
-            "config Baseline, sigma 0.06\n",
-            "{'nabove_low': 8.035269548001962, 'nabove_mid': 11.913695976511082, 'nabove_high': 15.792122405020201, 'sens_low': 0.5963827826237499, 'sens_mid': 0.78239219620164, 'sens_high': 0.9684016097795302, 'spec_low': 0.9600343546714785, 'spec_mid': 0.9773917861880126, 'spec_high': 0.9947492177045468, 'ppv_low': 0.7790790601094849, 'ppv_mid': 0.8610172578441093, 'ppv_high': 0.9429554555787338, 'npv_low': 0.9442410082406066, 'npv_mid': 0.9697368196255123, 'npv_high': 0.995232631010418}\n",
-            "\n",
-            "config Baseline, sigma 0.57\n",
-            "{'nabove_low': 0.4169652953259385, 'nabove_mid': 17.19748474037587, 'nabove_high': 33.9780041854258, 'sens_low': 0.029712604723083742, 'sens_mid': 0.5122977265174008, 'sens_high': 0.9948828483117178, 'spec_low': 0.7555622169079339, 'spec_mid': 0.8775550152121696, 'spec_high': 0.9995478135164052, 'ppv_low': 0.37199800323626137, 'ppv_mid': 0.6386644546050523, 'ppv_high': 0.9053309059738431, 'npv_low': 0.8762111400526919, 'npv_mid': 0.9376132174927088, 'npv_high': 0.9990152949327257}\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
-        "\n",
-        "\n",
-        "data = make_input_data() \n",
-        "params = ModelParams()\n",
-        "config  = config_baseline\n",
-        "pthresh = pthresh_cdc\n",
-        "risk_thresh = 15\n",
-        "expo_dist = expo_dist_rnd\n",
-        "ble_params = ble_params_default\n",
-        "sigma_mle = ble_params.sigma\n",
-        "sigma_list = [0, 0.01*sigma_mle, 0.1*sigma_mle, sigma_mle]\n",
-        "#sigma_list = [0.05*sigma_mle]\n",
-        "for sigma in sigma_list:\n",
-        "  print('\\nconfig {}, sigma {:0.2f}'.format(config.name, sigma))\n",
-        "  roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist)\n",
-        "  stats = compute_stats_at_roc_thresh_with_bounds(risk_thresh, roc)\n",
-        "  print(stats)\n",
-        "\n",
-        "\n"
+        "def eval_configs_df(config_list, sigma_factor = 1, risk_thresh = 15, show_float=False, w=None):\n",
+        "  data = make_input_data() \n",
+        "  params = ModelParams()\n",
+        "  pthresh = pthresh_cdc\n",
+        "  expo_dist = expo_dist_unif\n",
+        "  sigma_mle = ble_params_default.sigma\n",
+        "  sigma = sigma_mle * sigma_factor\n",
+        "  names_list = []\n",
+        "  stats_list = []\n",
+        "  for config in config_list:\n",
+        "    name = '{}'.format(config.name, sigma)\n",
+        "    names_list.append(name)\n",
+        "    roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist, w=w)\n",
+        "    stats = compute_stats_at_roc_thresh_with_bounds(risk_thresh, roc)\n",
+        "    stats_list.append(stats)\n",
+        "  #print(stats)\n",
+        "  df = make_df_stats_with_bounds(names_list, stats_list, show_float)\n",
+        "  #print(df)\n",
+        "  from IPython.display import display, HTML\n",
+        "  display(HTML(df.to_html(index=False)))"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "c2bMg3vApOdK"
+        "id": "_8oNx8vcHixo"
       },
       "outputs": [],
       "source": [
-        "def make_string(stats, field):\n",
-        "  str = '{:0.3f} ({:0.3f}-{:0.3f})'.format(\n",
-        "      stats['{}_mid'.format(field)], \n",
-        "      stats['{}_low'.format(field)],\n",
-        "      stats['{}_high'.format(field)])\n",
-        "  return str\n",
-        "\n",
-        "def make_df_stats_with_bounds(names_list, stats_list):\n",
-        "  X = {'names': names_list,\n",
-        "      'alerts': [make_string(stats, 'nabove') for stats in stats_list], \n",
-        "      'sens': [make_string(stats, 'sens') for stats in stats_list],\n",
-        "      'spec': [make_string(stats, 'spec') for stats in stats_list],\n",
-        "      'ppv': [make_string(stats, 'ppv') for stats in stats_list],\n",
-        "      'npv': [make_string(stats, 'npv') for stats in stats_list],\n",
-        "  }\n",
-        "  df = pd.DataFrame(data = X)\n",
-        "  return df"
+        "def eval_configs_roc_plots(config_list, sigma_factor = 1, w=None):\n",
+        "  data = make_input_data() \n",
+        "  params = ModelParams()\n",
+        "  pthresh = pthresh_cdc\n",
+        "  expo_dist = expo_dist_rnd\n",
+        "  ble_params = ble_params_default\n",
+        "  sigma_mle = ble_params.sigma\n",
+        "  sigma = sigma_factor*sigma_mle\n",
+        "  n = len(config_list)\n",
+        "  fig, axs = plt.subplots(1,n, figsize=(5*n,5), sharex=True, sharey=True)\n",
+        "  for i, config in enumerate(config_list):\n",
+        "    ax = axs[i]\n",
+        "    roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist, w=w)\n",
+        "    stats_list = compute_stats_from_roc(roc)\n",
+        "    fpr = [stats['fpr_mid'] for stats in stats_list]\n",
+        "    tpr = [stats['tpr_mid'] for stats in stats_list]\n",
+        "    tpr_lower = [stats['tpr_low'] for stats in stats_list]\n",
+        "    tpr_upper = [stats['tpr_high'] for stats in stats_list]\n",
+        "    auc = roc['interp_auc']\n",
+        "    auc_lower = roc['interp_auc_lower']\n",
+        "    auc_upper = roc['interp_auc_upper']\n",
+        "    #name = '{}, sigma {:0.3f}, auc: {:0.3f} ({:0.3f}-{:0.3f})'.format(config.name, sigma, auc, auc_lower, auc_upper)\n",
+        "    name = '{}'.format(config.name)\n",
+        "    ax.plot(fpr, tpr)\n",
+        "    ax.fill_between(fpr, tpr_lower, tpr_upper,  alpha=0.4)\n",
+        "    ax.set_title(name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3pYo4y20klDg"
+      },
+      "source": [
+        "## Ravi evals"
       ]
     },
     {
@@ -7125,12 +7184,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 468
+          "height": 265
         },
         "executionInfo": {
-          "elapsed": 48519,
+          "elapsed": 45360,
           "status": "ok",
-          "timestamp": 1605160622084,
+          "timestamp": 1605287340580,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7138,8 +7197,8 @@
           },
           "user_tz": 480
         },
-        "id": "RX1U624nxFGa",
-        "outputId": "93633382-aff8-4271-acaf-61591ee58875"
+        "id": "qRV0tPIil97g",
+        "outputId": "bbf027a3-7037-4217-9446-883623b88bc0"
       },
       "outputs": [
         {
@@ -7156,45 +7215,77 @@
               "  \u003cthead\u003e\n",
               "    \u003ctr style=\"text-align: right;\"\u003e\n",
               "      \u003cth\u003enames\u003c/th\u003e\n",
-              "      \u003cth\u003ealerts\u003c/th\u003e\n",
+              "      \u003cth\u003enotified\u003c/th\u003e\n",
               "      \u003cth\u003esens\u003c/th\u003e\n",
               "      \u003cth\u003espec\u003c/th\u003e\n",
               "      \u003cth\u003eppv\u003c/th\u003e\n",
               "      \u003cth\u003enpv\u003c/th\u003e\n",
+              "      \u003cth\u003eauc\u003c/th\u003e\n",
               "    \u003c/tr\u003e\n",
               "  \u003c/thead\u003e\n",
               "  \u003ctbody\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwitzerland-sigma0.03\u003c/td\u003e\n",
-              "      \u003ctd\u003e22.657 (20.152-25.161)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.741 (0.711-0.772)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.848 (0.824-0.873)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.419 (0.390-0.448)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.958 (0.954-0.961)\u003c/td\u003e\n",
+              "      \u003ctd\u003eravi1\u003c/td\u003e\n",
+              "      \u003ctd\u003e27 (5-50) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e64 (29-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e78 (58-98) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e48 (26-71) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e95 (90-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e72 (46-97) %\u003c/td\u003e\n",
+              "    \u003c/tr\u003e\n",
+              "    \u003ctr\u003e\n",
+              "      \u003ctd\u003eravi2\u003c/td\u003e\n",
+              "      \u003ctd\u003e27 (4-50) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e60 (20-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e78 (58-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e48 (26-70) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e95 (89-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e71 (43-98) %\u003c/td\u003e\n",
+              "    \u003c/tr\u003e\n",
+              "    \u003ctr\u003e\n",
+              "      \u003ctd\u003eravi3\u003c/td\u003e\n",
+              "      \u003ctd\u003e27 (4-50) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e60 (20-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e78 (58-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e48 (26-70) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e95 (89-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e81 (64-98) %\u003c/td\u003e\n",
+              "    \u003c/tr\u003e\n",
+              "    \u003ctr\u003e\n",
+              "      \u003ctd\u003eSwitzerland\u003c/td\u003e\n",
+              "      \u003ctd\u003e20 (2-38) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e37 (6-68) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e83 (66-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e37 (23-51) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e91 (88-93) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e74 (59-90) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Ble2)-sigma0.03\u003c/td\u003e\n",
-              "      \u003ctd\u003e19.514 (17.009-22.019)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.698 (0.629-0.766)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.878 (0.859-0.897)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.456 (0.442-0.470)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.953 (0.943-0.962)\u003c/td\u003e\n",
+              "      \u003ctd\u003eSwiss(Ble2)\u003c/td\u003e\n",
+              "      \u003ctd\u003e20 (2-38) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e37 (6-68) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e83 (66-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e37 (23-51) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e91 (88-93) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e74 (56-92) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Inf2)-sigma0.03\u003c/td\u003e\n",
-              "      \u003ctd\u003e23.367 (20.851-25.882)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.994 (0.989-1.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.877 (0.849-0.905)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.547 (0.491-0.603)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.999 (0.998-1.000)\u003c/td\u003e\n",
+              "      \u003ctd\u003eSwiss(Inf2)\u003c/td\u003e\n",
+              "      \u003ctd\u003e21 (2-40) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e54 (10-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e84 (69-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e54 (31-77) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e94 (88-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e82 (65-99) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Inf2,Ble2)-sigma0.03\u003c/td\u003e\n",
-              "      \u003ctd\u003e21.687 (18.992-24.382)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.983 (0.966-1.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.895 (0.866-0.923)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.584 (0.521-0.646)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.997 (0.995-1.000)\u003c/td\u003e\n",
+              "      \u003ctd\u003eSwiss(Inf2,Ble2)\u003c/td\u003e\n",
+              "      \u003ctd\u003e21 (2-40) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e54 (10-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e84 (69-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e54 (31-77) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e94 (88-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e87 (74-100) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "  \u003c/tbody\u003e\n",
               "\u003c/table\u003e"
@@ -7207,6 +7298,72 @@
             "tags": []
           },
           "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "config_ravi = RiskConfig(\n",
+        "    ble_thresholds = np.array([55, 80]),\n",
+        "    ble_weights = np.array([200, 100, 0])/100, \n",
+        "    inf_levels = inf_levels_bell,\n",
+        "    inf_weights = np.array([0 , 100, 200])/100,\n",
+        "    name='ravi1', beta=0.00031)\n",
+        "\n",
+        "config_ravi2 = RiskConfig(\n",
+        "    ble_thresholds = np.array([55, 70, 80]),\n",
+        "    ble_weights = np.array([200, 100, 25, 0])/100, \n",
+        "    inf_levels = inf_levels_bell,\n",
+        "    inf_weights = np.array([0 , 100, 200])/100,\n",
+        "    name='ravi2', beta=0.00031)\n",
+        "\n",
+        "config_ravi3 = RiskConfig(\n",
+        "    ble_thresholds = np.array([55, 70, 80]),\n",
+        "    ble_weights = np.array([200, 100, 25, 10])/100, \n",
+        "    inf_levels = inf_levels_bell,\n",
+        "    inf_weights = np.array([0 , 100, 200])/100,\n",
+        "    name='ravi3', beta=0.00031)\n",
+        "\n",
+        "config_list = [config_ravi, config_ravi2, config_ravi3, config_swiss, config_swiss_ble2, config_swiss_inf2, config_swiss_inf2_ble2]\n",
+        "eval_configs_df(config_list, sigma_factor = 1, risk_thresh = 15)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o4PUi4e6-gnP"
+      },
+      "source": [
+        "## LFPH evals"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 740
+        },
+        "executionInfo": {
+          "elapsed": 45353,
+          "status": "ok",
+          "timestamp": 1605287340580,
+          "user": {
+            "displayName": "Kevin Murphy",
+            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
+            "userId": "18199961579456458596"
+          },
+          "user_tz": 480
+        },
+        "id": "WIDL1aELKZRy",
+        "outputId": "83675a4e-fd86-4a11-9782-34bb762c2736"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n"
+          ]
         },
         {
           "data": {
@@ -7215,45 +7372,50 @@
               "  \u003cthead\u003e\n",
               "    \u003ctr style=\"text-align: right;\"\u003e\n",
               "      \u003cth\u003enames\u003c/th\u003e\n",
-              "      \u003cth\u003ealerts\u003c/th\u003e\n",
+              "      \u003cth\u003enotified\u003c/th\u003e\n",
               "      \u003cth\u003esens\u003c/th\u003e\n",
               "      \u003cth\u003espec\u003c/th\u003e\n",
               "      \u003cth\u003eppv\u003c/th\u003e\n",
               "      \u003cth\u003enpv\u003c/th\u003e\n",
+              "      \u003cth\u003eauc\u003c/th\u003e\n",
               "    \u003c/tr\u003e\n",
               "  \u003c/thead\u003e\n",
               "  \u003ctbody\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwitzerland-sigma0.06\u003c/td\u003e\n",
-              "      \u003ctd\u003e23.180 (17.123-29.237)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.713 (0.631-0.795)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.838 (0.781-0.896)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.407 (0.346-0.468)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.953 (0.943-0.963)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleNarrow-InfNarrow\u003c/td\u003e\n",
+              "      \u003ctd\u003e13 (3-23) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e52 (25-79) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e93 (86-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e72 (44-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e93 (90-97) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e83 (69-98) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Ble2)-sigma0.06\u003c/td\u003e\n",
-              "      \u003ctd\u003e20.038 (15.028-25.047)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.679 (0.562-0.795)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.869 (0.829-0.910)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.439 (0.403-0.475)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.950 (0.934-0.965)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleWide-InfNarrow\u003c/td\u003e\n",
+              "      \u003ctd\u003e17 (5-28) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e64 (39-90) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e90 (81-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e69 (41-98) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e95 (92-98) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e90 (84-97) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Inf2)-sigma0.06\u003c/td\u003e\n",
-              "      \u003ctd\u003e24.015 (17.687-30.343)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.962 (0.924-1.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.865 (0.798-0.932)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.541 (0.419-0.664)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.994 (0.988-1.000)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleNarrow-InfWide\u003c/td\u003e\n",
+              "      \u003ctd\u003e35 (11-59) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e76 (53-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e71 (47-95) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e42 (21-62) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e97 (93-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e80 (61-99) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Inf2,Ble2)-sigma0.06\u003c/td\u003e\n",
-              "      \u003ctd\u003e22.359 (16.719-28.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.960 (0.920-1.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.884 (0.825-0.942)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.576 (0.454-0.699)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.994 (0.988-1.000)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleWide-InfWide\u003c/td\u003e\n",
+              "      \u003ctd\u003e40 (15-65) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e87 (73-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e67 (40-93) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e40 (20-60) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e98 (96-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e86 (74-98) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "  \u003c/tbody\u003e\n",
               "\u003c/table\u003e"
@@ -7267,6 +7429,13 @@
           },
           "output_type": "display_data"
         },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n"
+          ]
+        },
         {
           "data": {
             "text/html": [
@@ -7274,94 +7443,102 @@
               "  \u003cthead\u003e\n",
               "    \u003ctr style=\"text-align: right;\"\u003e\n",
               "      \u003cth\u003enames\u003c/th\u003e\n",
-              "      \u003cth\u003ealerts\u003c/th\u003e\n",
+              "      \u003cth\u003enotified\u003c/th\u003e\n",
               "      \u003cth\u003esens\u003c/th\u003e\n",
               "      \u003cth\u003espec\u003c/th\u003e\n",
               "      \u003cth\u003eppv\u003c/th\u003e\n",
               "      \u003cth\u003enpv\u003c/th\u003e\n",
+              "      \u003cth\u003eauc\u003c/th\u003e\n",
               "    \u003c/tr\u003e\n",
               "  \u003c/thead\u003e\n",
               "  \u003ctbody\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwitzerland-sigma0.57\u003c/td\u003e\n",
-              "      \u003ctd\u003e26.049 (2.004-50.095)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.451 (0.076-0.825)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.767 (0.546-0.988)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.347 (0.209-0.484)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.918 (0.880-0.956)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleNarrow-InfNarrow\u003c/td\u003e\n",
+              "      \u003ctd\u003e13 (3-23) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e52 (25-79) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e93 (86-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e72 (44-99) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e93 (90-97) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e83 (69-98) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Ble2)-sigma0.57\u003c/td\u003e\n",
-              "      \u003ctd\u003e26.049 (2.004-50.095)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.451 (0.076-0.825)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.767 (0.546-0.988)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.347 (0.209-0.484)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.918 (0.880-0.956)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleWide-InfNarrow\u003c/td\u003e\n",
+              "      \u003ctd\u003e17 (5-28) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e64 (39-90) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e90 (81-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e69 (41-98) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e95 (92-98) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e90 (84-97) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Inf2)-sigma0.57\u003c/td\u003e\n",
-              "      \u003ctd\u003e24.279 (1.869-46.690)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.550 (0.100-1.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.802 (0.611-0.993)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.475 (0.272-0.678)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.942 (0.883-1.000)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleNarrow-InfWide\u003c/td\u003e\n",
+              "      \u003ctd\u003e35 (11-59) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e76 (53-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e71 (47-95) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e42 (21-62) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e97 (93-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e80 (61-99) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "    \u003ctr\u003e\n",
-              "      \u003ctd\u003eSwiss(Inf2,Ble2)-sigma0.57\u003c/td\u003e\n",
-              "      \u003ctd\u003e24.279 (1.869-46.690)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.550 (0.100-1.000)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.802 (0.611-0.993)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.475 (0.272-0.678)\u003c/td\u003e\n",
-              "      \u003ctd\u003e0.942 (0.883-1.000)\u003c/td\u003e\n",
+              "      \u003ctd\u003eLFPH-BleWide-InfWide\u003c/td\u003e\n",
+              "      \u003ctd\u003e40 (15-65) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e87 (73-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e67 (40-93) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e40 (20-60) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e98 (96-100) %\u003c/td\u003e\n",
+              "      \u003ctd\u003e86 (74-98) %\u003c/td\u003e\n",
               "    \u003c/tr\u003e\n",
               "  \u003c/tbody\u003e\n",
               "\u003c/table\u003e"
             ],
             "text/plain": [
-              "\u003cIPython.core.display.HTML object\u003e"
+              "\u003cIPython.core.display.HTML object\u003e"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "  app.launch_new_instance()\n",
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in double_scalars\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "\u003cFigure size 1440x360 with 4 Axes\u003e"
             ]
           },
           "metadata": {
+            "needs_background": "light",
             "tags": []
           },
           "output_type": "display_data"
         }
       ],
       "source": [
+        "config_list = [config_lfph_ble_narrow_inf_narrow, config_lfph_ble_wide_inf_narrow, \n",
+        "               config_lfph_ble_narrow_inf_wide, config_lfph_ble_wide_inf_wide]\n",
+        "sigma_factor = 0.5\n",
         "\n",
-        "      \n",
-        "data = make_input_data() \n",
-        "#params = ModelParams()\n",
-        "params = ModelParams(distance_fun='sigmoid', distance_inflection=2.0)\n",
-        "pthresh = pthresh_cdc\n",
-        "risk_thresh = 10\n",
-        "expo_dist = expo_dist_unif\n",
-        "\n",
-        "ble_params = ble_params_default\n",
-        "sigma_mle = ble_params.sigma\n",
-        "\n",
-        "sigma_lists = [ [0.05 * sigma_mle], [0.1*sigma_mle], [sigma_mle],  ]\n",
-        "for sigma_list in sigma_lists:\n",
-        "    \n",
-        "  config_list = [config_swiss, config_swiss_ble2, config_swiss_inf2, config_swiss_inf2_ble2]\n",
-        "\n",
-        "  #sigma_list = [0.1*sigma_mle]\n",
-        "  #config_list = [config_swiss_inf2_ble2]\n",
-        "\n",
-        "  names_list = []\n",
-        "  stats_list = []\n",
-        "  for config in config_list:\n",
-        "    for sigma in sigma_list:\n",
-        "      name = '{}-sigma{:0.2f}'.format(config.name, sigma)\n",
-        "      names_list.append(name)\n",
-        "      roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist)\n",
-        "      stats = compute_stats_at_roc_thresh_with_bounds(risk_thresh, roc)\n",
-        "      stats_list.append(stats)\n",
-        "\n",
-        "  #print(stats)\n",
-        "  df = make_df_stats_with_bounds(names_list, stats_list)\n",
-        "  from IPython.display import display, HTML\n",
-        "  display(HTML(df.to_html(index=False)))"
+        "eval_configs_df(config_list, sigma_factor = sigma_factor, risk_thresh = 15, show_float=False, w=1)\n",
+        "eval_configs_df(config_list, sigma_factor = sigma_factor, risk_thresh = 15, show_float=False, w=None)\n",
+        "eval_configs_roc_plots(config_list, sigma_factor = sigma_factor, w=w)"
       ]
     },
     {
@@ -7378,13 +7555,12 @@
       "execution_count": null,
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 417
+          "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 48645,
+          "elapsed": 46308,
           "status": "ok",
-          "timestamp": 1605160622240,
+          "timestamp": 1605287341542,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7392,8 +7568,8 @@
           },
           "user_tz": 480
         },
-        "id": "jULopTIg6Mlh",
-        "outputId": "84ead831-5ba5-4187-878f-bc7b57372f0d"
+        "id": "c6MM1yay605w",
+        "outputId": "a9902f3a-a0c4-44d0-b998-5161a0964675"
       },
       "outputs": [
         {
@@ -7401,71 +7577,162 @@
           "output_type": "stream",
           "text": [
             "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n",
-            "BleParams(slope=0.21, intercept=3.92, sigma=0.5744562646538028, model='log-normal', name='log-normal-old', tx=0.0, correction=2.398)\n"
+            "{'thresh_low': 15.03078787878788, 'thresh_mid': 15.03078787878788, 'thresh_high': 15.03078787878788, 'thresh_clean': 15.03078787878788, 'fpr_low': 0.018005967596541456, 'fpr_mid': 0.17871583141032846, 'fpr_high': 0.3394256952241155, 'fpr_clean': 0.07410545957061339, 'tpr_low': 0.12647412455388465, 'tpr_mid': 0.40477359582834843, 'tpr_high': 0.6830730671028122, 'tpr_clean': 0.48139924583162963, 'frac_low': 0.03178658868045534, 'frac_mid': 0.2074359321468655, 'frac_high': 0.38308527561327566, 'frac_clean': 0.12585116536796534, 'nabove_low': 3.178658868045534, 'nabove_mid': 20.74359321468655, 'nabove_high': 38.308527561327566, 'nabove_clean': 12.585116536796534, 'sens_low': 0.12647412455388465, 'sens_mid': 0.40477359582834843, 'sens_high': 0.6830730671028122, 'sens_clean': 0.48139924583162963, 'spec_low': 0.6605743047758845, 'spec_mid': 0.8212841685896716, 'spec_high': 0.9819940324034586, 'spec_clean': 0.9258945404293867, 'ppv_low': 0.22653652420348833, 'ppv_mid': 0.3660200839036061, 'ppv_high': 0.5055036436037238, 'ppv_clean': 0.4859758573979487, 'npv_low': 0.8853771478948365, 'npv_mid': 0.9100545561123929, 'npv_high': 0.9347319643299493, 'npv_clean': 0.9246272620369899, 'auc_mid': 0.7207618524371647, 'auc_low': 0.5414521583124359, 'auc_high': 0.9000715465618936, 'auc_clean': 0.8546559689825608}\n",
+            "thresh_low: 15.03078787878788\n",
+            "thresh_mid: 15.03078787878788\n",
+            "thresh_high: 15.03078787878788\n",
+            "thresh_clean: 15.03078787878788\n",
+            "fpr_low: 0.018005967596541456\n",
+            "fpr_mid: 0.17871583141032846\n",
+            "fpr_high: 0.3394256952241155\n",
+            "fpr_clean: 0.07410545957061339\n",
+            "tpr_low: 0.12647412455388465\n",
+            "tpr_mid: 0.40477359582834843\n",
+            "tpr_high: 0.6830730671028122\n",
+            "tpr_clean: 0.48139924583162963\n",
+            "frac_low: 0.03178658868045534\n",
+            "frac_mid: 0.2074359321468655\n",
+            "frac_high: 0.38308527561327566\n",
+            "frac_clean: 0.12585116536796534\n",
+            "nabove_low: 3.178658868045534\n",
+            "nabove_mid: 20.74359321468655\n",
+            "nabove_high: 38.308527561327566\n",
+            "nabove_clean: 12.585116536796534\n",
+            "sens_low: 0.12647412455388465\n",
+            "sens_mid: 0.40477359582834843\n",
+            "sens_high: 0.6830730671028122\n",
+            "sens_clean: 0.48139924583162963\n",
+            "spec_low: 0.6605743047758845\n",
+            "spec_mid: 0.8212841685896716\n",
+            "spec_high: 0.9819940324034586\n",
+            "spec_clean: 0.9258945404293867\n",
+            "ppv_low: 0.22653652420348833\n",
+            "ppv_mid: 0.3660200839036061\n",
+            "ppv_high: 0.5055036436037238\n",
+            "ppv_clean: 0.4859758573979487\n",
+            "npv_low: 0.8853771478948365\n",
+            "npv_mid: 0.9100545561123929\n",
+            "npv_high: 0.9347319643299493\n",
+            "npv_clean: 0.9246272620369899\n",
+            "auc_mid: 0.7207618524371647\n",
+            "auc_low: 0.5414521583124359\n",
+            "auc_high: 0.9000715465618936\n",
+            "auc_clean: 0.8546559689825608\n"
           ]
+        }
+      ],
+      "source": [
+        "data = make_input_data() \n",
+        "params = ModelParams()\n",
+        "pthresh = pthresh_cdc\n",
+        "expo_dist = expo_dist_unif\n",
+        "sigma_mle = ble_params_default.sigma\n",
+        "sigma = sigma_mle * sigma_factor\n",
+        "config = config_swiss\n",
+        "name = '{}'.format(config.name, sigma)\n",
+        "roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist, w=1)\n",
+        "risk_thresh = 15\n",
+        "stats = compute_stats_at_roc_thresh_with_bounds(risk_thresh, roc)\n",
+        "print(stats)\n",
+        "for k in stats.keys():\n",
+        "  print('{}: {}'.format(k, stats[k]))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "executionInfo": {
+          "elapsed": 46303,
+          "status": "ok",
+          "timestamp": 1605287341543,
+          "user": {
+            "displayName": "Kevin Murphy",
+            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
+            "userId": "18199961579456458596"
+          },
+          "user_tz": 480
         },
+        "id": "s7XM9v5NE2mQ",
+        "outputId": "af380819-6f52-4cb3-8607-d8b50037666e"
+      },
+      "outputs": [
         {
-          "name": "stderr",
+          "name": "stdout",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in double_scalars\n"
+            "Making grid of 25 distances x 20 durations x 21 onsets = 10500 points\n",
+            "34\n",
+            "{'nabove_low': 3.4666666666666663, 'nabove_mid': 21.69761904761905, 'nabove_high': 39.92857142857143, 'nabove_clean': 13.37142857142857, 'frac_low': 0.034666666666666665, 'frac_mid': 0.2169761904761905, 'frac_high': 0.3992857142857143, 'frac_clean': 0.1337142857142857, 'sens_low': 0.1386806596701649, 'sens_mid': 0.4224512743628186, 'sens_high': 0.7062218890554723, 'sens_clean': 0.5044977511244377, 'spec_low': 0.9202487453632991, 'spec_mid': 0.9503600261837224, 'spec_high': 0.9804713070041458, 'spec_clean': 0.9202487453632991, 'ppv_low': 0.5082417582417582, 'ppv_mid': 0.5356641222025837, 'ppv_high': 0.5630864861634093, 'ppv_clean': 0.47934472934472927, 'npv_low': 0.8866416732438832, 'npv_mid': 0.9211216500445476, 'npv_high': 0.9556016268452119, 'npv_clean': 0.9273306948109059, 'thresh_low': 14.342105263157896, 'thresh_mid': 14.342105263157896, 'thresh_high': 14.342105263157896, 'thresh_clean': 14.342105263157896, 'fpr_low': 0.019528692995854243, 'fpr_mid': 0.04963997381627755, 'fpr_high': 0.07975125463670085, 'fpr_clean': 0.07975125463670085, 'tpr_low': 0.1386806596701649, 'tpr_mid': 0.4224512743628186, 'tpr_high': 0.7062218890554723, 'tpr_clean': 0.5044977511244377}\n",
+            "nabove_low: 3.4666666666666663\n",
+            "nabove_mid: 21.69761904761905\n",
+            "nabove_high: 39.92857142857143\n",
+            "nabove_clean: 13.37142857142857\n",
+            "frac_low: 0.034666666666666665\n",
+            "frac_mid: 0.2169761904761905\n",
+            "frac_high: 0.3992857142857143\n",
+            "frac_clean: 0.1337142857142857\n",
+            "sens_low: 0.1386806596701649\n",
+            "sens_mid: 0.4224512743628186\n",
+            "sens_high: 0.7062218890554723\n",
+            "sens_clean: 0.5044977511244377\n",
+            "spec_low: 0.9202487453632991\n",
+            "spec_mid: 0.9503600261837224\n",
+            "spec_high: 0.9804713070041458\n",
+            "spec_clean: 0.9202487453632991\n",
+            "ppv_low: 0.5082417582417582\n",
+            "ppv_mid: 0.5356641222025837\n",
+            "ppv_high: 0.5630864861634093\n",
+            "ppv_clean: 0.47934472934472927\n",
+            "npv_low: 0.8866416732438832\n",
+            "npv_mid: 0.9211216500445476\n",
+            "npv_high: 0.9556016268452119\n",
+            "npv_clean: 0.9273306948109059\n",
+            "thresh_low: 14.342105263157896\n",
+            "thresh_mid: 14.342105263157896\n",
+            "thresh_high: 14.342105263157896\n",
+            "thresh_clean: 14.342105263157896\n",
+            "fpr_low: 0.019528692995854243\n",
+            "fpr_mid: 0.04963997381627755\n",
+            "fpr_high: 0.07975125463670085\n",
+            "fpr_clean: 0.07975125463670085\n",
+            "tpr_low: 0.1386806596701649\n",
+            "tpr_mid: 0.4224512743628186\n",
+            "tpr_high: 0.7062218890554723\n",
+            "tpr_clean: 0.5044977511244377\n"
           ]
         },
         {
-          "data": {
-            "text/plain": [
-              "\u003cmatplotlib.legend.Legend at 0x7fd4e129f470\u003e"
-            ]
-          },
-          "execution_count": 338,
-          "metadata": {
-            "tags": []
-          },
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "\u003cFigure size 360x360 with 1 Axes\u003e"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light",
-            "tags": []
-          },
-          "output_type": "display_data"
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "  app.launch_new_instance()\n",
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in double_scalars\n"
+          ]
         }
       ],
       "source": [
         "data = make_input_data() \n",
         "params = ModelParams()\n",
-        "config  = config_swiss\n",
         "pthresh = pthresh_cdc\n",
+        "expo_dist = expo_dist_unif\n",
+        "sigma_mle = ble_params_default.sigma\n",
+        "sigma = sigma_mle * sigma_factor\n",
+        "config = config_swiss\n",
+        "name = '{}'.format(config.name, sigma)\n",
+        "roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist, w=1)\n",
         "risk_thresh = 15\n",
-        "expo_dist = expo_dist_rnd\n",
-        "ble_params = ble_params_default\n",
-        "print(ble_params)\n",
-        "sigma_mle = ble_params.sigma\n",
-        "sigma = 0.25*sigma_mle\n",
-        "\n",
-        "roc = compute_weighted_ROC_curve_with_sigma(data, params, config, sigma, pthresh, expo_dist)\n",
-        "stats_list = compute_stats_at_roc_with_bounds(roc)\n",
-        "\n",
-        "fpr = [stats['fpr_mid'] for stats in stats_list]\n",
-        "tpr = [stats['tpr_mid'] for stats in stats_list]\n",
-        "tpr_lower = [stats['tpr_low'] for stats in stats_list]\n",
-        "tpr_upper = [stats['tpr_high'] for stats in stats_list]\n",
-        "auc = roc['interp_auc']\n",
-        "auc_lower = roc['interp_auc_lower']\n",
-        "auc_upper = roc['interp_auc_upper']\n",
-        "name = '{}, sigma {:0.3f}, auc: {:0.3f} ({:0.3f}-{:0.3f})'.format(config.name, sigma, auc, auc_lower, auc_upper)\n",
-        "plt.figure(figsize=(5,5))\n",
-        "plt.plot(fpr, tpr, label=name)\n",
-        "plt.fill_between(fpr, tpr_lower, tpr_upper,  alpha=0.4)\n",
-        "plt.legend()"
+        "stats_list = compute_stats_from_roc(roc)\n",
+        "print(len(stats_list))\n",
+        "stats = stats_list[10]\n",
+        "print(stats)\n",
+        "for k in stats.keys():\n",
+        "  print('{}: {}'.format(k, stats[k]))"
       ]
     },
     {
@@ -7530,9 +7797,9 @@
           "height": 350
         },
         "executionInfo": {
-          "elapsed": 49311,
+          "elapsed": 46291,
           "status": "ok",
-          "timestamp": 1605160622947,
+          "timestamp": 1605287341544,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7541,7 +7808,7 @@
           "user_tz": 480
         },
         "id": "0IxPKNmDypOv",
-        "outputId": "02e50fa1-5320-4870-84f8-c904fa1efa65"
+        "outputId": "6ed668ad-24b8-46ba-c674-7538d1a2050e"
       },
       "outputs": [
         {
@@ -7580,9 +7847,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 52232,
+          "elapsed": 46283,
           "status": "ok",
-          "timestamp": 1605160625900,
+          "timestamp": 1605287341545,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7591,7 +7858,7 @@
           "user_tz": 480
         },
         "id": "ErO_TXMuVQ6i",
-        "outputId": "fd37b847-a92d-4e23-9bad-f6eaf0a4e76f"
+        "outputId": "ce168695-d576-46be-ea34-e2c61a05c5e1"
       },
       "outputs": [
         {
@@ -7690,9 +7957,9 @@
           "height": 660
         },
         "executionInfo": {
-          "elapsed": 52398,
+          "elapsed": 46277,
           "status": "ok",
-          "timestamp": 1605160626097,
+          "timestamp": 1605287341546,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7701,7 +7968,7 @@
           "user_tz": 480
         },
         "id": "AOtNQUW8V2Km",
-        "outputId": "3378aed0-ae53-4a56-c6ee-9756ae71887e"
+        "outputId": "d28c98d2-8e69-4913-e266-b95a2d0bb075"
       },
       "outputs": [
         {
@@ -7841,12 +8108,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 793
+          "height": 788
         },
         "executionInfo": {
-          "elapsed": 56213,
+          "elapsed": 50861,
           "status": "ok",
-          "timestamp": 1605160629960,
+          "timestamp": 1605287346150,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7855,7 +8122,7 @@
           "user_tz": 480
         },
         "id": "Y6hcPKXPUH4M",
-        "outputId": "e53b6266-7ad2-4ef2-ef3c-542b203699fe"
+        "outputId": "1ebab5d3-d4d5-46d6-be89-85fc884305d1"
       },
       "outputs": [
         {
@@ -7898,7 +8165,7 @@
         "\n",
         "#config_list = [config_baseline, config_swiss, config_swiss_inf2_ble2, config_ireland, config_ireland_inf2_ble2]\n",
         "config_list = [config_baseline, config_swiss, config_swiss_inf2_ble2, config_ireland, config_ireland_inf2_ble2]\n",
-        "param_list = [ModelParams(ble_params = ble_params_lognormal_old), ModelParams(ble_params = ble_params_lognormal_new)]\n",
+        "param_list = [ModelParams(ble_params = ble_params_lognormal_lovett), ModelParams(ble_params = ble_params_lognormal_briers)]\n",
         "for params in param_list:\n",
         "  print(params)\n",
         "  n = len(config_list)\n",
@@ -7920,9 +8187,9 @@
           "height": 415
         },
         "executionInfo": {
-          "elapsed": 57309,
+          "elapsed": 50856,
           "status": "ok",
-          "timestamp": 1605160631082,
+          "timestamp": 1605287346151,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -7931,7 +8198,7 @@
           "user_tz": 480
         },
         "id": "-djQjUm0ap7z",
-        "outputId": "b1468c01-cc4f-4287-c757-9c939e231c5c"
+        "outputId": "3485e4bf-635b-40f5-9ce7-663fa0b7e936"
       },
       "outputs": [
         {
@@ -7988,64 +8255,6 @@
         "  ax.set_title(config.name)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 386
-        },
-        "executionInfo": {
-          "elapsed": 58142,
-          "status": "ok",
-          "timestamp": 1605160631943,
-          "user": {
-            "displayName": "Kevin Murphy",
-            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
-            "userId": "18199961579456458596"
-          },
-          "user_tz": 480
-        },
-        "id": "kWz2-C6cjwx5",
-        "outputId": "5257d90f-0b8d-40bf-8a24-ef9d7229d4f0"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "ModelParams(ble_params=BleParams(slope=0.21, intercept=3.92, sigma=0.5744562646538028, model='log-normal', name='log-normal-old', tx=0.0, correction=2.398), distance_fun='sigmoid', distance_Dmin=1.0, distance_slope=2.0, distance_inflection=2.0, infectiousness_fun='skew-logistic', beta=0.0008)\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAFNCAYAAABSRs15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZyN5f/H8dc1Y8a+q8iSsvyismSEFkvZ15KdsUaR9hSVFpWolEKlsmSnIUW2SpEtS8lXJFsIITvZZly/P+6ZjG2cmTnn3Gd5Px+P88h1zn1f12fUfLqu+1yLsdYiIiIiIiIiwS/C7QBERERERETEOzTAExERERERCREa4ImIiIiIiIQIDfBERERERERChAZ4IiIiIiIiIUIDPBERERERkRChAZ4EDWNMR2PMomTlY8aYG9yMSUS8zxjzkTGmr8sx1DHGTE/jvf9njFltjDlqjHnUB7FZY0xxL9QzyBjT3RsxiYQD5aYr1q/cFCA0wJM0M8b8aYw5kTjQOmiM+doYU9hf7Vtrs1lrt/irPRFJH2PMD4m5ImNK11lrH7LWvuqvuC7jdWBAUiGVHZdngO+ttdmtte8bY2oYY743xhw2xvyZ0o3GmOrGmLOJefWYMWanMeaVtPwAxphoY0xcYq62xpjqF1zyNvCcMSY6LfWLhArlJr/npsrGmG+MMQeMMfuMMZ8bYwoku0S5KZ00wJP0amStzQYUAPYAQ1yOR0QCkDGmKHAXYIHGKVwX6aeQLssYUxHIaa1dlsYqrgN+S1Y+DowEenl4/67EB1jZgDuBLsaYe9MYyyKgHfD3hR9Ya3cDv5PCvw+RUKfc5Epuyg18DBRNjOkoMCrpQ+Wm9NMAT7zCWnsSiANKAxhjGhhjfjHGHDHG7DDGvJx0rTEmkzFmnDFmvzHmkDFmhTHmmsTPchpjRhhjdic+HXrtckk1+VMrY8xoY8ywxG8RjxpjfjLGFEt27Y3JnhZtMMa08OFfh4hcrD2wDBgNdEh6M/F390NjzCxjzHGgRuJ7ryV+PiPZE+NjiU+QOyZ+dnti/jic+M/bk9X7gzHmVWPM4sScMM8Yky/Z558bY/5OvHehMeamZLHWAxZc7gcxxrxsjJlijBmTWPdvxpiYxM/mAzWAoYnxlrTWLrfWjgVSPePAWrsVWEJibr1ELBmNMW8bY7YbY/YYZwpZ5sR7T1trB1trFwEJl2niB6BBauMSCSHKTf7PTbOttZ9ba49Ya/8FhgJ3XFDFDyg3pZkGeOIVxpgsQEucJAnOU6H2QC6cX9DuyZ7ydAByAoWBvMBDwInEz0YD8UBxoDxQG3jAwzBaAa/gPBnahDONAWNMVuAbYAJwdeJ1HxhjLpmURMQn2gPjE191kh7qJGqD8/uaHecbp/9Yaxsle2LcHOebqO+MMXmAr4H3cfLIO8DXxpi8F9TbCef3Php4Otlns4ESiZ/9nBhXkluADVf4eRoDk3By3Fc4HRSstXcDPwI9E+P+4wr1pMgYUwKn43O5J/YDgJJAOZy8WRB4MRVNrAfKpidGkSCn3JQGXs5NVTn/m0VQbkoXDfAkvaYbYw4Bh4FawFsA1tofrLX/s9aetdauASYC1RLvOYOT9IpbaxOstaustUcSk2p94HFr7XFr7V7gXZwBmSe+SHwaFY+TEMslvt8Q+NNaO8paG2+t/QWYipOQRcTHjDF34kzDmWKtXQVsxungJPnSWrs4MV+cvEwdJYHPgBbW2h04D442WmvHJv5eT8SZ0tMo2W2jrLV/WGtPAFM4lxOw1o601h611p4CXgbKGmNyJn6cC2fKUEoWWWtnWWsTgLF4tyNybeLshiPAH8BPXNC5BDDGGKAb8IS19oC19ijQH89zJjg/Zy4vxCwSdJSbUs3ruckYUwZn4HfhNFHlpnTQAE/S615rbS4gE9ATWGCMyW+MqWSchbv7jDGHcb6lS5qCMBaYC0wyxuwyxrxpjInCSbJRwO7EBHIIGI7zFMsTydeY/AtkS/zzdUClpDoT620L5E/7jy0iqdABmGet/SexPIFkU6GAHSndnNi5+RJ4IXG6IcC1wLYLLt2G85Q4ySVzgjEm0hgzwBizObGj8mfiNUk56iDOE/uUXFh3JmNMhivc46ld1tpc1tocOB2cEzgdyAtdBWQBViXLbXMS3/dUduBQegMWCVLKTanj1dxknGU2s4HHrLU/XlCHclM6eOtfuIS5xCdF04wxw3EW3g7EmRZQz1p70hgzmMQEZa09gzOV8hXjLG6ehTPlYBZwCsiX+C2ct+wAFlhra3mxThHxQOKaixZApDEmqeOREchljEl6smxTuD8Cp9P1vbX242Qf7cJ5eJNcEZxOxJW0AZoANXE6UDlxOk4m8fM1OFOLXGetPWyMmQBMvsTH/+B0sG6y1u5MYxOlgF/TGp9IsFJuSp/05iZjzHXAt8CriesAL6TclA76Bk+8wjia4Kx/W4/z5OVA4uDuNpJNeTDOtry3GGfzlCM4UzbPJu6aNA8YZIzJYYyJMMYUM8ZUu7jFVJkJlDTGxBpjohJfFY0xpdJZr4hc2b04G3yUxpmGVA7nf9w/4qx9uZLXgazAYxe8Pwvn97qNMSaDMaZlYhszPagzO87DpP04T5n7X6Lu9Oad/yTmskw4MxSMcTaaik72+Q8m2UZUF9ybDWda04XrU7DWngU+Ad41xlydeH1BY0ydZPdnTGwbIDqxbZOsmmo4T9BFwo1yk0u5yRhTEJgPDLXWfnSZ8JSb0kEDPEmvGcaYYzgDtdeBDtba34AeQD9jzFGcudVTkt2TH2fHzSM4g8EFONM2wUmq0cA6nKdWcThHMKRZ4tzv2jiJaBfO9IWBOE/qRMS3OuCsN9lurf076YXzDX9brjyTpDVQGThozu1W19Zaux9nfe1TOJ2hZ4CGyaZapWQMzpSpnTi55rxNAqy1PwOHjTGVPP8xU1QV52n2LJwn+SdwHmYlKQwsTla+NulnTYwzD87f1aU8i7Op1LLEKV3fAv+X7PMNie0VxJkaf4LEbxeMc+5UaSBNhyaLBDnlJvdy0wPADcDLyf7ujiXdqNyUfsbay377LCIiEpaMMbWBHtbatJ4/52k7hXA2eLj9ihd7v+1BwGZr7Qf+bltE0ka5STyhAZ6IiIiIiEiI0BRNERERERGREKEBnoiIiIiISIjQAE9ERERERCREaIAnIiIiIiISIoLuoPN8+fLZokWLuh2GiHjRqlWr/rHWXuV2HOmh3CQSmpSfRCQQpZSbgm6AV7RoUVauXOl2GCLiRcaYbW7HkF7KTSKhSflJRAJRSrlJUzRFRERERERChAZ4IiIiIiIiIUIDPBERERERkRChAZ6IiIiIiEiI0ABPREREREQkRGiAJyIiIiIiEiI0wBMREREREQkRPhvgGWNGGmP2GmPWXuZzY4x53xizyRizxhhzq69iERFJTvlJRAKRcpOIeIMvv8EbDdRN4fN6QInEVzfgQx/GIiKS3GiUn0Qk8IxGuUlE0slnAzxr7ULgQAqXNAHGWMcyIJcxpoBXg/j6a/jqq3PlGTOc95JMnw6zZ58rT5sG8+adK3/+OXz77bny5Mnw/ffnyhMmwMKF58rjxsGiRefKn30GS5acK48cCT/9dK786aewYoXz57Nn4ZNP4OefnXJ8vFP+9VenfPq0U/7f/5zyiRNOed06p3zsmFOeOzflvxMRcT8/bd3q/L4eSAxh0yanfOiQU/7jD6d89KhTXr/eKR8/7pR/+80pnzzplNesccqnTzvl1audckKCU161yiknWbECRow4V162DEaNOldesgTGjPHajysinnE9N8Gl+zJLl54rX6ovs3Kl8+cr9WVOnfKsL/P77075yBGnvHGjUz540Clv3uzVH1kk5FhrffYCigJrL/PZTODOZOXvgJjLXNsNWAmsLFKkiPXYPfdYe8cd58p33mnt3XefK992m7V1654rlytnbePG58qlSlnbvPm5crFi1rZte65cqJC1nTufK199tbUPPXSunCuXtY8+eq6cObO1vXqdK0dGWvv8886f4+OtBWv79XPK//7rlAcMcMqHDjnld95xynv2OOVhw5zyjh3W5shh7aBB1iYkpPz3IhJggJXWh7noUi9v5Kc056a4OOf3d80apzxhglP+/XenPGqUU9661SkPH+6Ud+50ykOGOOV9+5zy22875SNHnHL//k755Emn/NJLTjlJnz7WRkWdKz/1lLVZs54r9+xpbZ48nv88IiHM3/nJ9b7ThX2ZnDmtfeyxc+UL+zIREda+8ILz5yv1ZQ4edMrvvuuUL+zLbN/ulD/91Clv2uSUxySOadetc8qTJnn+84iEqJRyk3E+9w1jTFFgprX25kt8NhMYYK1dlFj+DnjWWrsypTpjYmLsypUpXnLOP/+AtXDVVU553z4wBvLlc8p790JExPnlyEjIm9cp79kDUVGQJ49T/vtviI4+v5wxI+TO7ZR374bMmSFXLqe8axdkyXJ+OWtWyJnTKe/cCdmzQ44cTpy7dl1czpHDee/sWaf+nDkhWzbnyfzff19czpfPiUkkiBhjVllrY/zcZlG8mJ9SlZtOnHC+vbv6aifH/Puv82Q6qXz8uPNt3jXXQIYM58r58zs56tgxOHz4XPnoUedJd4ECTk5LKl97rZPzjhxx3itY0Gn/8GGnjuTl48ed68Fp68QJ594qVaB/f2jd2sO/WZHQ4u/85Hrfyc2+THy80/fKlctpI6mcO7cTw5kzTl8td2547DGn7uQzs0TCSEq5KYO/g0lmJ1A4WblQ4nvekzRwS5I00Ety9dUpl6+55vxy/vwplwtcMEsiqbN0uXJS5wqcjlRK5YiI88uRkReX8+Z1Omp58zplEUkr3+anzJnP//3NksV5Jcma1Xldrpwtm/NKkj2787pcOUcO55UkZ85znbNLlXPlcl6HDkHVqhfnOhFxi+/7Tm72ZTJkSLkcFXWufNNNF8cqIoC7xyR8BbRP3BGqMnDYWrvbW5XHx8fjy28nA9LYsc6gdLfX/hpFwpXP81NQyJXLWX9To4bbkYiIQ7kpyeOPQ79+bkchEpB89g2eMWYiUB3IZ4z5C3gJiAKw1n4EzALqA5uAf4FO3mx/yJAhDBgwgIoVKxITE0OVKlWoVKkSuZKmGISiO+6AoUPPfxIvIhdxMz8dP36cq6++mptuuomYmBgqVarE7bffTvHixTHGeKsZEQlCbvedunXrxvz586lYsSIVK1akSpUqxMTEkDlzZm82IyI+5rMBnrU2xQUbiYsDH/ZV+zfffDP16tVj5cqVzJo1y1lwaAxlypShWrVq3H333dSoUYMcyactBbvSpZ2XiKTIzfx06tQpHn74YVasWMG4ceP48ENnl/Orr76aqlWrUr16dWrVqkWJEiXcH/AdOwYlS0LfvtC9u7uxiIQBt/tONWrU4Pjx46xcuZK4uDgAoqKiqFixItWrV+fuu+/mzjvvJGMgrPV/9FFYvtzZBVhEzuPTTVZ8IVULhRMdPXqU5cuXs3jxYhYuXMjixYs5efIkkZGR3H777TRq1IjGjRvzf//3fz6K2k9OnHA2lrnmGmczGJEg4cYmK96Wltx09uxZ1q9fz5IlS1i4cCELFixgx44dABQtWpT69evTqFEjatSo4U6H6vRpePhhaNYM6tTxf/siASBc89O+fftYtmwZixYtYuHChaxYsYKEhAQyZ85MjRo1aNy4MQ0bNqRg8jVy/jRypHPETP/+7rQv4rKUclNYDPAudOrUKZYuXcq8efOYNWsWvyaez1K6dGmaN29Oq1atuPHGG70Rrn9Nnep0xH79FcqUcTsaEY+FawfqQtZaNm/ezDfffMOcOXP49ttv+ffff8mRIweNGzemefPm1K1bl2g9wBHxG+Unx9GjR1mwYAFz585l1qxZbNmyBYAqVarQvHlzWrZsybUXbsAiIj6jAd4VbN++na+++oq4uDgWLlyItZaYmBg6duxI27Ztg2fd3p9/Ogez33ffuaMeRIKAOlCXduLECebPn8+0adP44osvOHjwIHnz5qVVq1Y88MADlCtXzqvticjFlJ8uZq1l/fr1fPHFF8TFxbF69WoiIiK455576NixI/fff39gTOMUCWEp5SY3d9EMGEWKFKFnz5788MMP7Ny5k0GDBnHmzBl69uzJtddeS5cuXf77li+gFS0KDzygwZ1IiMicOTMNGjRgxIgR7Nmzh5kzZ1KzZk0+/fRTypcvT6VKlRg7diynT5/2XRDXXKMpUCJyHmMMpUuX5vnnn+eXX35hw4YNPP/882zcuJG2bdtSsGBBevXqxbZt23wXxLPPQokSvqtfJIhpgHeBAgUK8OSTT7J69WpWrlxJbGwskyZNoly5ctx9993MmTMncI9fOHkStmxx1uKJSEiJioqiQYMGTJo0iV27djF48GCOHDlC+/btue666+jfvz+HDx/2fsMtWmjKt4ikqGTJkvTr14/Nmzczb948qlevzrvvvkuxYsVo0aIFP//8s/cbjYmBpk29X69ICNAALwUVKlRg+PDh/PXXX7z55pts3LiRevXqUbFiRWbMmBF4A71ly6BYMe0oJRLi8uTJw2OPPca6deuYM2cOZcuW5fnnn+e6666jb9++HDp0yHuNDRkCDRt6rz4RCVkRERHUqlWLuLg4tm7dylNPPcW8efOoUKEC9evXZ/ny5d5rrHlzGDjQe/WJhBAN8DyQO3duevXqxebNm/nkk084dOgQjRs35s4772ThwoVuh3dOqVIwZgwE4wYxIpJqxhjq1KnDnDlzWLVqFTVr1uS1117jhhtuYODAgZzQt/ki4pLChQszcOBAtm3bRv/+/VmxYgWVKlWiadOmrF+/3u3wREKaBnipEB0dzQMPPMD69esZPnw4f/75J9WqVaN58+b8+eefbofnrJWJjYUCBdyORET87NZbbyUuLo6ff/6ZypUr07t3b0qVKkVcXFz6ZhsUKQK9enkvUBEJKzlz5qRPnz5s2bKFV155hW+//ZZbbrmFxx57jIMHD6a94n79IHdu7wUqEkI0wEuDqKgounXrxqZNm+jXrx9ff/01pUqV4o033uDMmTPuBXbqFKxfD0eOuBeDiLiqfPnyzJo1i/nz55MjRw6aN29O7dq12bx5c9oq7NABbr/du0GKSNjJnj07L774Ips3b6Zr164MHTqUkiVLMm7cuLQ9hKpY0dlYTkQuogFeOmTOnJm+ffuyYcMG6tevz3PPPUeFChXw9lbpHvvjDyhdGubNc6d9EQkYNWrU4Oeff2bo0KH89NNP3Hzzzbz55pskJCSkrqJXX3WOXhER8YKrrrqKDz/8kJ9//pnixYsTGxtLnTp1Ur/jZr168NZbvglSJMhpgOcFhQsXZurUqUyfPp39+/dTpUoV+vXrR3x8vH8DKVIEJk6ESpX8266IBKQMGTLw8MMPs379eurWrcuzzz5LtWrV/jugWETELWXLlmXRokUMGzaMpUuXUqZMGT777LPA28BOJAhpgOdFTZo0Ye3atbRo0YKXXnqJqlWrsmPHDv8FkDMntGoFhQv7r00RCXgFCxZk2rRpjB07lrVr11K2bFkmTpzo2c0lS0KXLr4NUETCUmRkJD169ODXX3+lTJkydOzYkVatWnHEk6UmgwZBdDT8+6/vAxUJMhrgeVnu3LkZP348EyZMYO3atZQrV45Zs2b5p/HTp2H1avjnH/+0JyJBwxhDu3btWLNmDeXKlaNNmzY89NBDnDx5MuUbH3gA6tb1T5AiEpZuuOEGfvjhB/r378/UqVOJiYnh119/TfmmmBh4+mmIjPRPkCJBRAM8H2ndujWrVq2icOHCNGzYkDfeeMP30w727YPy5WHaNN+2IyJBq0iRIsyfP59nn32W4cOHU716dXbv3n35G555xjlvSkTEhyIjI+nTpw/ff/89x48fp0qVKnz++eeXv6FaNejfHzJm9F+QIkFCAzwfKlGiBEuXLqVVq1Y899xztG3b9spPy9Mjb15ncFe7tu/aEJGgFxUVxYABA5g2bRpr164lJiaGVatWXf4GrYkRET+56667+PnnnylfvjwtWrTgxRdfvPwDcmuVn0QuQQM8H8ucOTPjx4+nf//+TJw4kdq1a3PgwAHfNJYpk7PbXdGivqlfRELKfffdx5IlS4iKiqJatWrMnj374ovKloWmTf0fnIiErWuuuYb58+fTqVMnXn31VTp27HjxMVQffQQREbBnjztBigQwDfD8wBhDnz59mDRpEj/99BN33nmnbzZfOXsWVqyAXbu8X7eIhKQyZcqwbNkySpYsSaNGjRg9evT5Fzz0ELRs6UpsIhK+MmbMyIgRI+jXrx9jxoyhYcOGHDt27NwFMTHw0kuQJYt7QYoEKA3w/Khly5bMmTOHnTt3UrVqVbZu3erdBk6fhttug88+8269IhLS8ufPzw8//ECNGjXo1KkTw4YNO/dh9+7O7rwiIn5mjKFv376MGDGC7777jjp16nD48GHnw5gYePllyJHD1RhFApEGeH5Wo0YNvvvuOw4fPkzVqlX5448/vFd5dDTMnAktWnivThEJCzly5GDmzJk0btyYnj17MmjQIOeD+Hjn4ZGIiEs6d+7MpEmTWL58ObVq1XKWupw9C6dOOf8UkfNogOeCmJgYvv/+e06dOkWNGjW8d+hwRAQ0aADFinmnPhEJKxkzZiQuLo7mzZvz9NNP895770GNGjomQURc16xZM6ZNm8avv/5KnTp1+Hf0aGfvgY0b3Q5NJOBogOeSsmXL8t1333HixAlq1qzJX3/95Z2KlywBbw0YRSTsREVFMWHCBJo2bcrjjz/ODzroXEQCRKNGjYiLi2P16tU8OGwYp19+2dlBXETOowGei2655Rbmzp3LP//8Q61atdi/f3/6K61VCz78MP31iEjYypAhAxMmTKBu3brcPWoUn0dHux2SiAjgDPLGjRvH+F9+ofHSpZzJmdPtkEQCjgZ4LqtYsSIzZ85k69atNGnSJP3n5M2cCd26eSc4EQlbGTNmZOrUqVSvVImH2rXjxx9/dDskERHA2bTu4w8+YPHcuXTv2vXy5+SJhCkN8AJA1apVGTNmDIsXLyY2Npaz6VkwXKMGlCjhveBEJGxlyZKFOdmzs8AYmjRpwvr1690OSUQEgAcKF+YosOazz+jXr5/b4YgEFA3wAkSLFi14++23iYuL48UXX0x7RYsXw7p13gtMRMJadPfuXNO/P9HR0TRq1MjZvU5ExG2lSmHfeovbW7Tg5ZdfZtKkSW5HJBIwMrgdgJzz5JNP8vvvv/P6669TpkwZWqTluIPWraFmTRg50vsBikj4ue8+rgK+qFKF6tWr07JlS2bPnk2GDPrfh4i46IYbME8/zZunT7Nq1y46d+5MyZIlufXWW92OTMR1+gYvgBhjGDZsGHfccQcdO3bkl19+SX0ln38OvXt7PzgRCU/HjsGBA1SpUoWPPvqIb7/9ll69erkdlYiEu/h42L+faGDq1Knky5ePe++9l71797odmYjrNMALMNHR0UydOpW8efPSrFkzDh8+nLoKKlWCkiV9E5yIhJ9u3Zy8AnTq1IlHH32UwYMH8/nnn7scmIiEtWXLIF8+WLiQq6++mi+//JJ9+/bRtm1bEhIS3I5OxFUa4AWga665hsmTJ7N9+3Y6d+6cut2hFi2ClSt9F5yIhJcOHSDZuuC33nqLypUr06VLFzbqgGERcUuxYvD++/891C5fvjxDhw7l22+/5bXXXnM5OBF3aYAXoG6//XYGDBjAtGnTGDJkiOc39uwJr77qu8BEJLzUqQOxsf8Vo6OjmTx5MlFRUTRr1iz9R7uIiKRFgQLwyCNQpMh/b3Xu3Jn27dvzyiuv8O2337oYnIi7NMALYE8++SSNGjWiV69erFmzxrObxo6Ft9/2bWAiEj4OH4bdu897q0iRIowdO5Y1a9bQW2t+RcQNZ87Arl1w4sR/bxlj+OCDD7jxxhuJjY3ln3/+cTFAEfdogBfAjDGMGDGC3Llz07ZtW8+elN9yi87BExHv6d0bypa96O369evzyCOP8N577zFv3jwXAhORsPb771CwIMyadd7bWbNmZcKECezfv58HH3xQh6BLWNIAL8BdddVVjBo1irVr19KnT58r37B4MSxc6PvARCQ8tGlz2VkBAwcOpHTp0nTs2JH9+/f7OTARCWsFC8JHH0H58hd9VK5cOV5//XWmTZvGqFGjXAhOxF0a4AWBevXq0bNnTwYPHsyCBQtSvvjFF+G55/wTmIiEvrvugvbtL/lR5syZGT9+PP/88w+PPPKInwMTkbCWJw88+CDccMMlP37qqaeoUaMGjz/+ONu3b/dzcCLu0gAvSAwYMIAbbriBLl268O+//17+wuHDYfRov8UlIiHu4EHYuvWyH5crV46+ffsyceJEvvzySz8GJiJhLT4e/vwTjh695McRERGMGDGCs2fP0q1bN03VlLCiAV6QyJo1KyNGjGDz5s288MILl7+weHHnJSLiDQMHQqlSKV7Su3dvypYtS/fu3Tl48KCfAhORsLZzJ1x/PaRwJuf111/PgAEDmDt3LqP18FvCiAZ4QaR69ep0796dwYMHs2zZsktftGTJRQuORUTSrHlz+OSTFC+Jiopi5MiR7N27l6eeespPgYlIWMuXD0aNcqaRp6BHjx7cddddPPHEE+y+YEdgkVClAV6QGThwINdeey0PPfQQ8fHxF18weDCogyUi3lKhwnnn4F3OrbfeytNPP82oUaNYqI2eRMTXsmaFjh2vuHN4REQEn376KSdOnNADKAkbGuAFmezZs/Pee+/x66+/MnTo0IsveOcd+Ppr/wcmIqHpwAFnO3IP1q/07duX6667ju7du3P69Gk/BCciYSshATZscHLUFZQsWZI+ffowceJEHYAuYUEDvCDUtGlT6tevT9++ffnrr7/O/7BQocvuKCUikmrDhztr8DwYsGXNmpWhQ4eybt063n33XT8EJyJh6+hRuPFGGDPGo8t79+5N8eLF6dGjh2fnCosEMZ8O8IwxdY0xG4wxm4wxvS/xeRFjzPfGmF+MMWuMMfV9GU+oMMYwdOhQ4uPjefrpp8//cNkymDLFncBEgoRyUyo0aQITJkBkpEeXN8Sy+KoAACAASURBVGzYkHvvvZdXXnmFHTt2+Dg4kdCj/OShrFlh/HioW9ejyzNlysSwYcPYuHEjgwYN8nFwIu7y2QDPGBMJDAPqAaWB1saY0hdc9gIwxVpbHmgFfOCreELN9ddfz7PPPsvkyZP58ccfz30wejToPCqRy1JuSqXSpaF1a8iQweNb3n33Xc6ePUvv3hf1TUUkBcpPqRAVBW3aON/ieah27drcd999vPHGG+zcudOHwYm4y5ff4N0GbLLWbrHWngYmAU0uuMYCORL/nBPY5cN4Qs4zzzxDoUKFeOyxx0hISHDe7NcPli93NzCRwKbclBr798OaNc6ZUx4qWrQovXr1YsKECSxevNiHwYmEHOUnT1kL//sf/P13qm57++23OXPmDH369PFRYCLu8+UAryCQfH7OX4nvJfcy0M4Y8xcwC9BXT6mQJUsW3nrrLX755Zdz57tcfTVcd52rcYkEOOWm1Jg0CcqW9Wgjg+R69+5NwYIFeeyxxzh79qyPghMJOcpPnrIWypRx1gmnwg033MBTTz3F2LFjL3/klEiQc3uTldbAaGttIaA+MNYYc1FMxphuxpiVxpiV+/bt83uQgaxly5bccccdPPfccxw7dgxWroQRI9wOSyTYKTclqVsXpk6FHDmufG0yWbNmZcCAAaxatYrx48f7KDiRsKT8BBAR4Rxy3rx5qm997rnnKFCgAE8++STWgx2CRYKNLwd4O4HCycqFEt9LrgswBcBauxTIBOS7sCJr7cfW2hhrbcxVV13lo3CDkzGGt99+m7179zqLhr/4Ah580O2wRAKZclNqFCsGTZtCpkypvrVNmzZUqFCBF154QbvWiXhG+Sk1mjVz1gmnUrZs2ejXrx9Lly5l+vTpPghMxF2+HOCtAEoYY643xkTjLAT+6oJrtgP3ABhjSuEkqRB8zORblStX5v777+ett95ib4cOsG2b2yGJBDLlptTYvx9WrIA0DNAiIiIYOHAg27dvZ9iwYT4ITiTkKD+lxi+/wPbtabq1Y8eOlCpVij59+hCfijXGIsHAZwM8a2080BOYC6zH2fHpN2NMP2NM48TLngK6GmN+BSYCHa2+K0+T/v37c/LkSV557z0oeOF0fRFJotyUSrNnw223wYVnbnronnvuoW7durz++uscPHjQy8GJhBblp1SqWhUGD07TrRkyZGDAgAFs2LCBEVraIiHGBFtOiImJsStXrnQ7jIDUo0cPfho+nDkvvMBVL7zgbCEsEgSMMaustTFux5EeIZub/voLVq+G6tUhW7Y0VbFmzRrKlSvHM888w4ABA7wbn4iPKT8FsNmzoUgRuOmmNN1uraVatWps3LiRzZs3kyVLFi8HKOI7KeUmtzdZES/q27cvtSIiuKpfPzhxwu1wRCQUFCoEDRumeXAHUKZMGVq3bs2QIUPYs2ePF4MTkbBWr16aB3fg7GPw+uuv8/fff/PBB+F5nKCEJg3wQkiBAgWI6NGDq41h/Y4dV75BRORKDhyAxYvh2LF0VfPSSy9x8uRJfYMnIt6zciVs3JiuKu666y7q1KnDgAEDOHr0qJcCE3GXBngh5okXXuDfLFl4uV8/t0MRkVCweDHceSds2JCuakqWLEmHDh348MMP2bnzwk0BRUTS4N57YeDAdFfTr18/9u/fz/vvv++FoETcpwFeiLlq716mVKnC7ClTWLNmjdvhiEiwq1wZ5s2D4sXTXVXfvn1JSEjg9ddf90JgIhL2xo+HJ55IdzW33XYbjRs35q233uLQoUNeCEzEXRrghZoVK6j/7bcUzZaN1157ze1oRCTYXXUV1KoFOXOmu6rrr7+ezp07M2LECH2LJyLpV61autbgJffKK69w+PBhhgwZ4pX6RNykAV6oad0aDh2i8SOPEBcXx/r1692OSESC2aFD8P334KUjDnr37k1CQgJvvfWWV+oTkTC2fDmsXeuVqsqVK0ejRo0YPHiw1uJJ0NMAL9RkzAg5c/L4k0+SOXNmTYUSkfRZswbuvts5UNgLrr/+emJjYxk+fLh21BSR9OnUCV5+2WvVvfDCCxw4cEA7akrQ0wAv1GzcCG+8Qb74eLp3787EiRPZmM4dpkQkjJUpAz/8AOXLe63KPn36cPr0aQYNGuS1OkUkDI0eDa+84rXqbrvtNmrXrs2gQYM4fvy41+oV8TcN8ELN77/Dc8/Bzp08/fTTREdHM9ALO0yJSJjKlctZ55I7t9eqLFmyJC1btuSDDz7gwIEDXqtXRMJMxYpeW4OXpG/fvuzbt49PP/3Uq/WK+JMGeKGmXj3nkPPy5cmfPz+dO3dm7Nix7Nq1y+3IRCQYHTkCc+eCl6dT9unTh+PHj2sqlIik3U8/wYoVXq3yzjvv5K677uKdd97hzJkzXq1bxF80wAs1GTJApkwQ4fyrfeqpp4iPj2fw4MEuByYiQWnrVqhbF5Ys8Wq1t9xyC/Xq1eP999/nxIkTXq1bRMLEE0/A8897vdpnnnmG7du3M2nSJK/XLeIPGuCFmu3boV8/2LIFgBtuuIHmzZvz0UcfcfjwYZeDE5GgU7y4M7irVs3rVT/77LPs27eP0aNHe71uEQkDw4fDe+95vdr69etz00038eabb2Kt9Xr9Ir6mAV6o+esveOklZ7OVRM888wxHjx7lo48+cjEwEQlKWbNClSqQJ4/Xq65atSqVKlXi7bffJj4+3uv1i0iIu+UWKFXK69VGRETwzDPPsHbtWmbPnu31+kV8TQO8UFOlCiQkQJ06/7116623UrNmTd577z1Onz7tYnAiEnSOH4evvnJmB3iZMYZnn32WLVu28MUXX3i9fhEJccuWwcKFPqm6VatWFCpUSGd2SlDSAC/UGPPf+rvknnrqKXbv3s3kyZNdCEpEgta+fdCkCcyf75PqGzduTLFixXj33Xd9Ur+IhLBXXoFevXxSdXR0NI8++ig//PADv3jpHFARf9EAL9Ts3essOF6z5ry369SpQ+nSpXnnnXc0n1xEPFegAKxcCY0a+aT6yMhIHn/8cZYuXcqyZct80oaIhKj334cxY3xWfdeuXcmaNaseQEnQ0QAv1Bw8CAMHwvr1571tjOGJJ55g9erVLFiwwKXgRCToZMwIFSpA3rw+a6Jjx47kypVLnSgRSZ0SJeD//s9n1efKlYvOnTszadIkHTclQUUDvFDzf/8H8fHQsuVFH7Vt25Z8+fLxzjvvuBCYiASlU6cgLu68jZu8LVu2bHTt2pW4uDi2bdvms3ZEJMT89BPMmePTJh577DHi4+MZNmyYT9sR8SYN8MJI5syZ6dGjBzNnzmTTpk1uhyMiweD4cWjeHGbN8mkzjzzyCMYYhg4d6tN2RCSEvPsuPP64T5soVqwYTZo04aOPPtKZnRI0NMALNUePwtNPX/ZQ4oceeojIyEg9iRIRz+TMCf/7H7Rr59NmChcuTNOmTRkxYgT//vuvT9sSkRDx5pswY4bPm3n00Uc5cOAAEydO9HlbIt6gAV6oOXUKPvwQ1q695McFChSgefPmjBw5kmPHjvk5OBEJOpGRcPPNPl2Dl+SRRx7h4MGDjB8/3udtiUgIKFLEWYfnY9WrV+fmm29myJAh2qhOgoIGeKEmXz5nSlW3bpe95JFHHuHIkSOMHTvWj4GJSFA6exbGj3e+xfOxO++8k7Jly6oTJSKe+eknmDrV580YY+jZsyerV69myWVmSIkEEg3wwlDlypWpUKECQ4cOVSdKRFJmrTM9c/p0nzdljOGRRx7hf//7Hwt9dHixiISQESOgZ0+/NNWuXTty5crFkCFD/NKeSHpogBdqEhLgscdS3FUq6UnUunXrmO+jw4tFJERERMCGDfDww35prk2bNuTJk0edKBG5sldfveyeA96WNWtWOnfuzNSpU3VkggQ8DfBCjTHw2WdXnE7VqlUr8ubNy4cffuinwEQkKBkDJUtCnjx+aS5z5sx06dKF6dOnqxMlIim75hq4/nq/NdejRw/i4+P59NNP/damSFpogBdqIiLg0CHo1SvFyzJlykSnTp3UiRKRKxs7FpYv91tz3bp1IyEhQZ0oEUnZihUwZozfmitWrBh16tTh448/Jj4+3m/tiqSWBnhh7MEHH1QnSkSurHt3mDLFb80VL16c2rVrqxMlIimbMsXJT37UvXt3du7cycyZM/3arkhqaIAXih5/3KPOmDpRIuKR336D55/3a5PqRInIFfXpA7//7tcmGzRoQKFChbTERQKaBnihaNasy56Dd6GHHnqInTt38vXXX/s4KBEJWtddB7lz+7XJhg0bqhMlIinLkwcKF/ZrkxkyZKBr167MmzePTZs2+bVtEU9pgBeK/vgD+vXz6NJGjRpRsGBBPvroIx8HJSJBa8wYWLDAr00m70Rt3rzZr22LSJBYtQo+/NA5zsWPHnjgASIjI/n444/92q6IpzTAC3MZMmSgS5cuzJ07l23btrkdjogEot69Ydw4vzfbuXNnIiIiGDFihN/bFpEgMHs29OjhHBHlR9deey2NGzdm9OjRnD592q9ti3hCA7xQ9Mwz8MknHl/euXNnAHWiROTSfvkFBg3ye7OFChWifv36jBw5kjNnzvi9fREJcI89Bn//DZGRfm+6a9eu7Nu3jy+//NLvbYtciQZ4oWjJEli3zuPLr7vuOurWrcuIESO02YqIXOyaayBHDlea7tatG3v27NFmKyJysezZnfxkjN+brl27NkWKFOGTVDxQF/GXFAd4xphIY8z3/gpGvGTRInj33VTd0q1bN3bt2sWsWbN8FJSIdyk/+dHYsc5UKBfUq1ePggULaq2LBA3lJj9avRreew9OnvR705GRkTzwwAN88803bNmyxe/ti6QkxQGetTYBOGuMyemneMQlDRo0IH/+/OpESdBQfvKj/v1h9GhXms6QIQOdO3fWOmEJGspNfrRwoXM01PHjrjTfqVMnIiIidJ6wBBxPpmgeA/5njBlhjHk/6eXrwCQdXngB3norVbdERUXRqVMnZs+ezc6dO30UmIjXKT/5w9KlMHKka8136dIFgFGjRrkWg0gqKTf5Q9eucOCA349xSZK0Tnj06NFa4iIBxZMB3jSgL7AQWJXsJYHqt99g48ZU39a5c2fOnj3LZ5995oOgRHxC+ckfcuWCrFlda/66666jZs2ajBo1igQ/75YnkkbKTf6QObMzuItwb0uJLl26sHv3bubMmeNaDCIXMtaDs0OMMdFAycTiBmuta9uZxcTE2JUrV7rVfMirXr06f/31F3/88QcRLiZMCS/GmFXW2pg03hsQ+Smkc9O4cZApEzRr5loIkydPplWrVsydO5fatWu7FoeEn7Tmp0DJTRDC+WntWpg1Cx58EHK6MyP2zJkzFC5cmCpVqvDFF1+4EoOEp5Ry0xV78MaY6sBGYBjwAfCHMaaqVyOUgNGlSxc2b97Mjz/+6HYoIlek/OQnQ4eCy2tM7r33XvLkycNIF6eKinhKuclPVq2CZ5+F/ftdCyEqKor27dszc+ZM9uzZ41ocIsl58hXNIKC2tbaatbYqUAdI3RaN4l+vvQbPPZemW++//35y5MihM/EkWCg/+cP8+eDyWU8ZM2akXbt2fPHFF+x3sTMn4iHlJn9o08bZYKVoUVfD6Ny5M/Hx8YwZM8bVOESSeDLAi7LWbkgqWGv/AKJ8F5Kk244d8Oefabo1S5YstG7dmri4OA4fPuzduES8T/nJH7JkgYwZ3Y6CLl26cPr0acaPH+92KCJXotzkD1FRTn5yeUnJjTfeyO23386IESPwZOmTiK958hux0hjzqTGmeuLrE8CjidzGmLrGmA3GmE3GmN6XuaaFMWadMeY3Y8yE1AQvlzF8OExI+19l586dOXHiBJMnT/ZiUCI+kab8pNyUShMnunZMQnJlypShQoUK2k1TgoH6Tv6wYYMzaykApkZ27tyZDRs2sGzZMrdDEfFogNcdWAc8mvhaBzx0pZuMMZE4c8/rAaWB1saY0hdcUwLoA9xhrb0JeDxV0YtPVKxYkdKlSzM6ADp0IleQ6vyk3JQGo0Y5D44CQKdOnVi9ejWrV692OxSRlKjv5A/r10PfvrB7t9uR0KJFC7JkyaK+kwQETwZ4D1lr37HWNk18vYuTuK7kNmCTtXaLtfY0MAlocsE1XYFh1tqDANbavakJXi7j7bedHaXSyBhDp06dWLp0Kb///rsXAxPxurTkJ+Wm1Pr6a1i0yO0oAGjdujXR0dHqREmgU9/JHxo1gjNnoGxZtyMhe/bsNGvWjEmTJvHvv/+6HY6EOU8GeB0u8V5HD+4rCOxIVv4r8b3kSgIljTGLjTHLjDF1L1WRMaabMWalMWblvn37PGg6zB08mO7pCu3atSMyMlJn4kmgS0t+Um5KragoiIx0OwoA8uTJQ5MmTRg/fjynT592OxyRy1HfyR8iIyFDBjDG7UgA6NixI0eOHGH69OluhyJh7rIDPGNMa2PMDOB6Y8xXyV7fAwe81H4GoARQHWgNfGKMyXXhRdbaj621MdbamKuuuspLTYew11+HdCaX/PnzU69ePcaMGaODhSXg+CE/KTclN3kyDBnidhT/6dSpE//88w8zZ850OxSR86jv5GdbtjhTNNO4sZy3VatWjaJFi2qdsLguQwqfLQF2A/lwtvtNchRY40HdO4HCycqFEt9L7i/gp8TDP7caY/7ASVorPKhffKxjx47MnDmTefPmUa9ePbfDEUkuPflJuSm14uKctS6PPOJ2JADUqlWLAgUKMGrUKJo2bep2OCLJqe/kT3/+6WyyUquW60clAERERNChQwf69evH9u3bKVKkiNshSZi67Dd41tpt1tofgLY4iWSBtXYBsB4n4VzJCqCEMeZ6Y0w00Ar46oJrpuM8gcIYkw9n2sGW1P4QcoGhQ6FZs3RX06hRI/LkyaNzXSTgpDM/KTel1pQpsHat21H8J0OGDMTGxjJ79mz27g3f5UcSeNR38rMaNcBaqBo4Z8h36NABay3jxo1zOxQJY56swZsCnE1WTgA+v9JN1tp4oCcwFyexTbHW/maM6WeMaZx42VxgvzFmHfA90MtaqxNs0+vUKTh2LN3VREdH07p1a6ZPn64z8SRQpTo/KTelQYCsb0muQ4cOJCQkMCEdR8KI+JD6Tv4QgLnp+uuvp2rVqnz22Wc6E09cY670H58xZrW1ttwF7/1qrXVly6KYmBi7cqVHR8mIF6xYsYLbbruNjz/+mK5du7odjoQoY8wqa21MGu4LmPwU0rkpLg7++AOee87tSM5TsWJF4uPj+eWXX9wORUJYWvJTIOUmCOH89NdfzqylDh2gVCm3o/nPyJEj6dKlC0uXLqVy5cpuhyMhKqXc5Mk3ePuSPTXCGNME+MdbwUlgi4mJoVSpUtpNUwKV8pM/fPcdjBjhdhQXad++PatXr2bNGk+WNon4lXKTP+zbB4MHO5utBJBmzZqROXNm9Z3ENR6dgwc8Z4zZbozZATwLpP2QNfG9UaOgenWvVGWMoUOHDixevJhNmzZ5pU4RL1J+8ocPP4TNm92O4iKtW7cmKipKnSgJRMpN/lC+PJw8CQ0auB3JeXLkyEHTpk2ZNGkSJ0+edDscCUNXHOBZazdbaysDpYFS1trbrbXq6Qc6Y5yFx17Qrl07IiIitNmKBBzlp/CWL18+GjRowPjx44mPj3c7HJH/KDdJhw4dOHToEDNmzHA7FAlDnnyDhzGmAdADeNIY86Ix5kXfhiXp0qkTfP+91xYfFyxYkJo1azJ27FjOnj175RtE/Ej5yQ+mT4fevd2O4pI6dOjAnj17+Oabb9wOReQ8yk1+sG8fPPEEBOD6wrvvvpuCBQsyduxYt0ORMHTFAZ4x5iOgJfAIYIDmwHU+jksCTGxsLH/++SeLFy92OxSR/yg/+cny5RCgW37Xr19fx7lIwFFu8pOjR2HkyICcQh4ZGUnbtm2ZPXs2+/btczscCTOefIN3u7W2PXDQWvsKUAXnzBUJVJMnQ+XKTuLzkvvuu4+sWbPqSZQEGuUnf+jf39mtLgBFR0fTqlUrpk+fzpEjR9wORySJcpM/3HADHD4MLVu6HcklxcbGEh8fz6RJk9wORcKMJwO8pNWh/xpjrgXOAAV8F5KkW3Q05MzptTV4AFmzZuX+++9nypQpnDhxwmv1iqST8pPQvn17Tp48SVxcnNuhiCRRbhJuvvlmypcvrxkG4neeDPBmGGNyAW8BPwN/AjpZNpDddx/MnQs5cni12tjYWA4fPqwFwxJIlJ/84euvoXt3t6O4rNtuu40SJUpohoEEEuUmfzhyBB5+GBYudDuSy4qNjWXlypWsX7/e7VAkjFx2gGeMaZ74x3HW2kPW2qk488dvtNZqoXAYqlGjBgULFtSTKHGd8pOfrVsHU6dCgG6yZIyhffv2/PDDD2zbts3tcCSMKTf52ZkzMGUKBPAxTq1btyYyMlJ9J/GrlL7B65P4z6lJb1hrT1lrD/s2JEm3WbPg1lthxw6vVpu0YHjOnDns3bvXq3WLpJLykz/16gV790KERxsvu6Jdu3YAjB8/3uVIJMwpN/lT3rzOTpqdO7sdyWXlz5+f2rVrM378eO1ELn6T0v+t9xtj5gHXG2O+uvDlrwAlDbJkgYIFfdIZi42NJSEhQQuGxW3KT3KeokWLctdddzF27FisF9cfi6SScpNcJDY2lh07drBgwQK3Q5EwkSGFzxoAtwJjgUH+CUe8onp15+UDN998M+XKlWPcuHE8+uijPmlDxAPKT/70zTcwaRIMHQqZM7sdzWXFxsbSrVs3fv75ZypUqOB2OBKelJv86fRp6NHD2XugQQO3o7msJk2akD17dsaNG0eNGjXcDkfCwGW/4rHWnrbWLsPZ6nfBhS8/xigBJjY2lhUrVrBhwwa3Q5EwpfzkZ9u2wbx5TmcqgDVr1ozo6GhttiKuUW5ywZw5sHWr21GkKEuWLNx///3ExcVpJ3LxiyvO4bPW6nTGYLNwIdx0E/zvfz6pvnXr1kRERKgTJa5TfvKTBx5w1vTmzOl2JCnKnTs3jRo1YuLEicTHx7sdjoQx5SY/iY52zujs2dPtSK4oNjaWI0eO8NVXmqkrvhe4K+Yl7bJlg1KlIGNGn1RfoEABatWqxbhx47RgWEQCSmxsLHv37mXevHluhyIi8p/q1atTqFAhPRwXv9AALxTdeivExUHJkj5rol27dmzbto1Fixb5rA0RCRALF0JsLOzf73YkV1SvXj3y5MmjTpRIuOjc2TkqIcBFRETQpk0b7UQufnHFAZ4xpqQx5jtjzNrEchljzAu+D00C2X333UfWrFkZN26c26FIGFN+8pM9e2DJEjh50u1Irig6OpqWLVsyffp0jhw54nY4EqaUm/xo6VL480+3o/BI0k7kkydPdjsUCXGefIP3Cc65LmcArLVrgFa+DErSafVq59s7H27HmzVrVpo2bcqUKVM4GQSdPglZyk/+0Lw5bN7sHL8SBGJjYzl58iTTpk1zOxQJX8pN/rJ+PTzzjNtReCT5TuQivuTJAC+LtXb5Be9p9Xogy5YNKlSAHDlg9mzo3fvcZzNmwPPPnytPnw4vvniuHBcH/fqdK0+eDK+/fq48fjwMHAg40zQbHz7MH926nft81CgYPPhc+ZNPYMiQc+WPPoIPPzxXHjrUuSbJe+/ByJGp/IEljCk/yUUqV65MsWLF1IkSNyk3ySW1a9eO5cuXaydy8SlPBnj/GGOKARbAGNMM2O3TqCR9iheHiROhfHlYtswZVCVZuvT8AdWiRecPqBYsgNGjz5Xnz4fka1m+/RYmTADgnnvu4d5Mmcg8Y8a5z2fNOn8u/IwZkPwp+pdfOoPKJNOmQfIdpT7/HL7+OnU/r4Qz5Sd/+OknaNECtm93OxKPGGNo164d8+fP56+//nI7HAlPyk3+0rUrfPqp21F4LGkn8vHjx7sdioQwY61N+QJjbgA+Bm4HDgJbgXbW2j99Ht0lxMTE2JUrV7rRtFzC008/zfvvv8/u3bvJmzev2+FIkDLGrLLWxqThvoDJTyGdm+bNg8cfhy++gP/7P7ej8cimTZsoUaIEAwcO5Jkgmb4lgSkt+SmQchOEeH66806oVQteesntSDxWu3ZtNm3axObNmzHGuB2OBKmUcpMn5+BtsdbWBK4CbrTW3ulWgpLA065dO86cOePdBcNDhsCTT3qvPglZyk9+Urs2rFsXNIM7gOLFi1O5cmXtpimuUG7yo0WLgmpwB07faevWrSxevNjtUCREebKLZn9jTC5r7XFr7VFjTG5jzGv+CE4CX9myZbn55pu9u9Zl61b47Tfv1SchS/lJUhIbG8vatWv59ddf3Q5Fwoxyk6SkadOmZMmSReuExWc8WYNXz1p7KKlgrT0I1PddSBJMkta6LF26lM2bN3un0nfegblzvVOXhDrlJ39YvRruvdfZrS6ItGzZkgwZMuhbPHGDcpO/9Ojh9BuCSLZs2bjvvvuYMmUKp06dcjscCUGeDPAijTEZkwrGmMxAxhSulzDTtm1bjDF6EiVuUH7yh5MnnXOmTpxwO5JUyZs3L/Xr12fChAkkJCS4HY6EF+Umf9mxA/btczuKVGvXrh0HDx7ka20sJz7gyQBvPPCdMaaLMaYL8A3wmW/DkmBSqFAhatSowdixY7nSpj0eGTkSYmPTX4+EA+Unf6hc2fkW79Zb3Y4k1WJjY9m9ezffffed26FIeFFu8pcZM+CNN9yOItVq1qzJNddcoxkG4hOebLIyEHgdKJX4etVa+6avA5Pg0q5dOzZv3sxPP/2U/sr27YNNm9Jfj4Q85Se5koYNG5IzZ07NMBC/Um6SK8mQIQNt2rTh66+/5sCBA26HIyHGk2/wsNbOttY+nfjS4ii5yP3330+mTJm88yTq2Wed8/pEPKD85AcbNkD9+rBihduRpFqmTJlo0aIF06ZN49ixY26HI2FEuclPHn0UXn7Z7SjSJDY2ljNnzjAl+fnB0aZGYwAAIABJREFUIl7gyS6aTY0xG40xh40xR4wxR40xR/wRnASPHDlycO+99zJp0iROnz7tdjgSJpSf/CQhAf75B4L0dzs2Npbjx48zffp0t0ORMKHc5EeHD8OR4PyrLVeuHDfddJOmaYrXefIN3ptAY2ttTmttDmttdmttDl8HJsEnNjaWAwcOMGvWrPRV9Pnn0KABxMd7JzAJZcpP/lC6NCxfDnfc4XYkaXLHHXdQtGhRxowZ43YoEj6Um/zls8+CbhfNJMYYYmNjWbJkCZu0NEW8yJMB3h5rbXDtjS2uqF27NldffXX6n0QdOwZ792qAJ55QfpIrioiIIDY2lu+++45du3a5HY6EB+Um8Yh2Ihdf8GSAt9IYM9kY0zpxykFTY0xTn0cmQSdpwfCMGTPSt2C4UydnrU+mTN4LTkKV8pM/bN8O99wD8+e7HUmaxcbGcvbsWSZMmOB2KBIelJv85emn4fHH3Y4izQoVKsTdd9/tvZ3IRfBsgJcD+BeoDTRKfDX0ZVASvLRgWPxM+ckfjIFTp5y1eEGqRIkSVKpUSWtdxF+Um/zlzBnnFcRiY2PZsmULS5YscTsUCREm2J4WxMTE2JUrV7odhlyGtZZbbrmFHDlypD1RzZkDb78NEybA1Vd7N0AJSMaYVdbaGLfjSA/lpsD3wQcf8PDDD7N69WrKli3rdjgSJJSfxNeOHj1K/vz5adeuHcOHD3c7HAkSKeUmT3bRzGSMedgY84ExZmTSy/thSigwxtC+fXuWLl3Kxo0b01ZJQgKcPBnU3xaIfyg/SWq0bNmSqKgobbYiPqfcJKmRPXt2mjZtypQpUzh58qTb4UgI8GSK5lggP1AHWAAUAo76MigJbm3btiUiIiLtU6EaNIBFi6BAAe8GJqFI+ckf/vkH7roLgvyYgbx589KwYUPGjx9PvDZxEt9SbvKX556Djh3djiLd2rdvz6FDh5gxY4bboUgI8GSAV9xa2xc4bq39DGgAVPJtWBLMChYsSM2aNRkzZgxnz551OxwJbcpP/hAZCdHRzj+DXIcOHdizZw/z5s1zOxQJbcpN/pIhA0RFuR1Fut19990ULFiQzz77zO1QJAR4MsBLWrl6yBhzM5AT0MIoSVH79u3Ztm0bP/74Y+pvXroUqlWD33/3fmASapSf/CF3bvjuO2jUyO1I0q1evXrkzZtXnSjxNeUmf+nXDz75xO0o0i0yMpJ27doxZ84c9uzZ43Y4EuQ8GeB9bIzJDbwAfAWsAwb6NCoJevfddx/ZsmVLWycqIsJ56ds/uTLlJ0mV6Oho2rRpw5dffsmhQ4fcDkdCl3KTpFr79u1JSEj4//buPM7G8v/j+OuaMczYd2Lskq0sTaLkq00So8VSUZZU+o6UQklZU6RFSd+IkshWkuxL+slWCGXfIyVjX2fMcP3+uGdTjME55z7nzPv5eJyH+Zz7Pvd5zzTz6b7uc9/Xrdu5yFXLyABvgbX2sLV2kbW2rLW2MKBzWyRd2bNnp3nz5kyePJlTp05d3otvvhkWLoTKlb0TToKJ+pMvnD4NtWpBkExO8vjjjxMfH6/buYg3qTf5St++0LSp2yk8onLlykRFRWkiKLlqGRngfX2B577KyMaNMQ2NMZuNMduMMS+ns95DxhhrjAnoaYjlfG3atOHEiRNMmTLF7SgSvK6oP6k3XabQUChQACIi3E7iETfeeCOVK1dm9OjRbkeR4KV9J1/Jlcs5jTxItGnThjVr1rB27Vq3o0gAu+gAzxhT0RjzEJDHGPNgmkdbIPxSGzbGhALDgHuBysAjxph/fSRjjMkFPAf8dIXfg/ip2267jTJlylz+aZobN0Lt2s5MmiIXcDX9Sb3pCmTNCrNmQfPmbifxCGMMbdu2ZdmyZWzZssXtOBJEtO/kghdegCA6WPPII48QFham64TlqqT3Cd51QGMgL9AkzaMm8GQGtl0L2Gat3WGtPQNMAC70GXp/nPPSdeOPIBMSEkKbNm1YsGABu3fvzvgLw8Igb96gmLFPvOZq+pN6k9C6dWtCQkK0EyWepn0nuSoFChQgOjqasWPHkpCQcOkXiFzARQd41tpvrbXtgMbW2nZpHp2ttUszsO3iwJ409R9Jz6UwxtQESlhrZ1xJePF/jz/+ONbay7snXvnyMHs21KnjvWAS0K6yP6k3XYmoKBg61O0UHnPNNdfQsGFDxowZw9mzZ92OI0FC+04uGDTIuU9nEGnbti2xsbHMmjXL7SgSoDJyDd4DxpjcxpgwY8wCY0ysMab11b6xMSYEeBd4MQPrPmWMWWmMWRkbG3u1by0+VKZMGerXr8/o0aOx1rodR4KPx/uTetNFlCoVVNe5gLMT9ccff7BgwQK3o0jw0b6TrxQo4PSnIHLPPfdQpEgRPvvsM7ejSIDKyACvgbX2GM4pB7uA8kC3DLxuL1AiTR2Z9FyyXEBV4AdjzC6gNjDtQhcLW2tHWGujrLVRhQoVysBbiz9p27Yt27ZtY8mSJRl7wb59ULMmfPONd4NJMLiS/qTedCW+/hpaX/X+qV9p0qQJ+fLl02Qr4g3ad/KVDh1g7Fi3U3hUWFgYrVu3Zvr06QT14Fy8JiMDvLCkf+8DJltrj2Zw2yuAa40xZYwxWYGHce4FA4C19qi1tqC1trS1tjSwHIi21q7MeHwJBM2aNSNnzpx8+umnGXtBWBhERkKOHN4NJsHgSvqTepMAEB4ezqOPPsqUKVM4fPiw23EkuGjfSa5Ku3btSExMZGyQDV7FNzIywPvOGLMJuBFYYIwpRAYu6rXWJgKdgDnARmCStXa9MaafMSb6akJLYMmRIwctW7Zk0qRJHD9+/NIvKFAApk2DBg28H04C3WX3J/WmK1SnDrz+utspPK59+/bEx8czYcIEt6NIcNG+k6988AFUqeJ2Co+rUqUKtWrV4rPPPtMlLnLZTEZ+aYwx+YGj1tqzxpjsQG5r7T6vp7uAqKgou3KlDlQFmqVLl3LrrbcyatQo2rdv73Yc8TPGmFXW2iu6l5O/9Keg700dOsAtt0CQ/f1aa6lRowZZsmQhqP/7yRW70v7kL70Jgrw/TZzoXNIRhAdphg8fTseOHfn555+56aab3I4jfia93pTeffDuSPr3QaA+0DTp64bALV7IKUGsTp06VKxYkVGjRl165TNnoGpVGD7c+8EkIKk/uWDkyKAb3IFzT7z27duzatUq3VhYrpp6kwtatgzKwR3Aww8/TERERMYvcRFJkt4pmv9J+rfJBR6NvZxLgkzyTtTSpUvZtGlT+itnyQIVK0LBgr4JJ4FI/Uk8plWrVmTNmlUz1oknqDeJx+TJk4dmzZrx5ZdfcurUKbfjSABJ7z54vZP+bXeBR/AdxhWve/zxxwkNDb30kaiQEPjqK3joId8Ek4Cj/uSCevWgUSPn67g4uO8+5+8U4Phxp/72W6c+fNipZyTdpmv/fqeeO9ep//zTqb//3ql373bqH3906u3bnXr5cqfevNmpk08xW7fOqZM/cVu92qnXr3fqFSucevNmp1661Kl37HDqRYuceo9zu7ECa9awrEAB5n7+OfHx8TBnjrP8wAFn/RkznPrIEaeeOtWpT5506smTnTo+3qm//NKpz51z6jFjoEmT1J/lp5/Cgw+m1sOHO59CJBs2DFq1Sq2HDIG2bVPrt992TplN9uab0LFjat2vHzz7bGrdqxd06ZJa9+gB3bun1t26Qc+eqXWXLtC7d2rdqdP5118+/TQMHJhaP/EEvPNOav344/D++6n1o48631Oy5s1hxIjU+oEHIO3gunHj1FkRz551fpbJn9Bc7HevXz/8gXqTCz75BIyB5AHQu++ef7bBoEHw1FOp9euvQ0xMat2nDzz3XGrdsyd07Zpav/QSvPxyav3ii/Daa6l1587n//7997/wxhup9ZNPwuDBqXXbtvDee6l169bn32O0ZUv4+OOU8p3ff6fZsWNMmTLlgt++yIVkudgCY8wL6b3QWvuu5+NIMCtSpAiNGzfm888/5/XXXydr1qxuR5IApf7kgttug7JlU+v9+1N3qKw9vz53zqlPnz6/jkuaY+LsWadOHhAlJp6/PLlOXp6Q4NRnzjj1mTPn18nLExLOX56Y6NTx8enXcXGUy5mTo3/9xdSpU2mZLZuzPPkG6KdOOXXygC25Tr6G/WJ1spMnIe1U5ydOnL/8xInzlx8/njq4BDh27Pz66FE4ePD85YcOnb88bX3kiLNO2jrtzd0PH4a0/fjQodTvNbmOiDi/zp8/tT5w4PztHzzofA9pl584kVrHxqZf79+fWif/biUPppOXp/3dO3QIoq7oEl6PU29yQbVqEB2d+jt67Ni//z4u9feSdhbdI0dSexE4y0JCzq+Tew8428qSZnf64EHIlev8+p9/H2nrAwfS/XspaC1lChRg5MiRtA6yW9WI91x0khVjTPLhu+uAm0idprcJ8LO11pXfsqC+UDgTmDFjBo0bN+arr77iofQ+oYuKco54pz2KLEHrcicx8Mf+pN4U2M6dO0fZsmUpX7488+fPdzuO+JHL6U/+2JtA/SnQDRw4kB49erB582YqVKjgdhzxE1c0yYq1tq+1ti/OTTZrWmtftNa+iDPlb0nvRJVg17BhQyIjI/nkk0/SX/HGG6F0aZ9kksCj/iSeFhISwhNPPMGCBQvYvn2723Hkcvz5JyxZkvoJqovUm8Qbnlu5kr7GMHLkSLejSIDIyH3wigBpPovmTNJzIpctNDSU9u3bM3fuXHbt2nXxFYcPhzZtfJZLApb6k3hMu3btCAkJydhsv+I/xoyBunVTT+n1D+pN4jEROXNS4brrGD16NGfSnh4qchEZGeCNAX42xvQxxvQBfgJGezOUBLfk++Bp2l/xAPUn8ZjIyEgaNWrEZ599RkLy9Xzi/1q0cCbwyXLRaQXcoN4knjN6NHnefZfY2FimTZt26fUl07vkAM9aOwBoBxxOerSz1r7p7WASvEqVKkWDBg349NNPSUye5OCf7rjj/FnhRC5A/Uk87cknn2Tfvn3MSJ4BVPxf2bJw991+NcBTbxJPa9CgASVKlLj0JS4iZOwTPKy1v1hr3096rPZ2KAl+Tz/9NHv37mXmzJkXXuGWW5yZsUQuQf1JPKlRo0YUL16c4cOHux1FMmr/fli48PyZNv2AepN4TJcuhD75JB06dGDu3LnsSL7li8hFZGiAJ+JpjRs3plixYnyc5l4v53n9dXjmGd+GEpFML0uWLDz55JPMmTNHO1GBYuFC56yP3393O4mId+TMCTlz8sQTTxAaGqoDUHJJGuCJK8LCwujQoQOzZ89m586dbscREUnRoUMHQkJCGJH2Ztziv26/HX74AUqVcjuJiHf07w8ffEDx4sWJjo7m008/Jd6/JhUSP6MBnrjmySefxBhz4fPJH3wQ7r/f96FEJNMrXrw4TZo00U5UoChcGP7zH8iRw+0kIl7XsWNHDhw4wJQpU9yOIn5MAzxxTWRkJE2aNGHUqFH/nvb3ttuch4iICzp27EhsbCzffPON21HkUo4ehTlzIDbW7SQi3jFwoPNJNXDXXXdRtmzZi1/iIoIGeOKyjh07sn//fr7++uvzF3TpAi++6E4oEcn07r77bsqWLcuwYcPcjiKXsmULNGwIP/3kdhIR78iXDyIjAQgJCeHpp59m0aJFrFu3zuVg4q80wBNXNWjQgHLlymknSkT8SkhICM888wyLFy9m7dq1bseR9FSqBEuXwq23up1ExDuefhq++CKlbN++PdmyZdO+k1yUBnjiqpCQEGJiYliyZAmrV6eZRbp9e7jpJveCiUim1759eyIiIrQT5e9y5oQ6dZxPOUQygYIFC/Loo48yZswYjhw54nYc8UMa4Inr2rZtS/bs2c/fifrPf+CBB9wLJSKZXv78+WnVqhXjxo3j8OHDbseRi4mPh+++A93WQoLVZ59BxYpw+nTKU506deLUqVN8/vnnLgYTf6UBnrguX758tG7dmnHjxnHo0CHnyTZt4JVX3A0mIpleTEwMp06dYvTo0W5HkYs5fhyio2HGDLeTiHhHwYJQvTqcO5fyVM2aNalTpw7Dhg3jXJrnRUADPPETMTExxMXFMWrUKLejiIikqF69OnXr1mXYsGGcPXvW7ThyIXnzwooV8PDDbicR8Y4mTWDChH/dCqRTp05s3bqVuXPnuhRM/JUGeOIXbrjhBurXr8+HH35IYmIidOsG11zjdiwRETp37sz27duZoU+I/FOWLBAVBYUKuZ1ExKeaNWtG0aJFGTJkiNtRxM9ogCd+4/nnn2f37t3OzTtvuw2eesrtSCIiPPDAA5QsWZL33nvP7ShyMVOnwpo1bqcQ8Y7Zs6FsWdi8+byns2bNSkxMDHPmzGHDhg0uhRN/pAGe+I3GjRtTrlw550hUdDT07et2JBERsmTJwrPPPssPP/zAGg0i/NPjj8OYMW6nEPGOggWhbl0ID//Xoqeffprw8HDef/99F4KJv9IAT/xGaGgonTt3ZtmyZfz0009grfMQEXFZhw4dyJEjh06F8lfLlsFLL7mdQsQ7oqKcAxilSv1rUaFChWjdujVjxozh4MGDLoQTf6QBnviVdu3akTt3brY/9RSEhkJCgtuRRETImzcv7dq1Y/z48ezbt8/tOPJPVapAkSJupxBxxfPPP09cXBzDhw93O4r4CQ3wxK/kypWLp556ik/WreNwp05gjNuRREQAeO6550hISGDo0KFuR5F/mjEDfvjB7RQi3vHbbxAZCReZLbNKlSrcc889DB06lLi4OB+HE3+kAZ74neeee47FISH0BggLczuOiAgA5cuX58EHH+Sjjz7i+PHjbseRtF55BXT6rASrvHnhnnvSnSm2W7du7Nu3j7Fjx/owmPgrDfDE70RGRtLq0Uf5fORIDh444HYcEZEU3bp148iRI4wcOdLtKJLWt9/Cxx+7nULEO0qUgFGjoEaNi65yxx13ULNmTQYPHqwbn4sGeOKfBpQty9HTpxk7cKDbUUREUtx8883Uq1eP9957jwRdI+w/SpeGokXdTiHiGmMM3bp1Y8uWLUybNs3tOOIyDfDELxVv2pQxFSrwwWefcerUKbfjiIik6N69O3v27GHChAluR5Fk8+fDlClupxDxjiNHoHBhuMQkKs2aNaN06dIMGjQIq1nIMzUN8MQ/Va9O6U8+YcehQzoVSkT8yr333kvVqlUZOHCgToXyF8OGQZ8+bqcQ8Y6ICHjoIShfPt3VsmTJQteuXVm+fDk/aNKhTE0DPPFPZ89S78Yb+U/dugwePJj4+Hi3E4mIABASEkLPnj3ZsGEDU6dOdTuOgPPJxrx5bqcQ8Y5s2eB//4M777zkqu3bt6do0aIMGDDAB8HEX2mAJ/5p1izImZOBLVvyxx9/MGbMGLcTiYikaN68Oddeey2vv/66ToXyB4UL6z54IkBERARdu3ZlwYIFLF++3O044hIN8MQ/Va4Mb73FzQ88QFRUFAMHDiQxMdHtVCIiAISGhtKjRw9Wr17N7Nmz3Y4jS5bAJ5+4nULEe0qWhO7dM7Tq008/Tf78+fUpXiamAZ74p7JloVs3TPHivPrqq+zYsYNx48a5nUpEJEXr1q0pWbIk/fr106d4bvv6a3jhBbdTiHjPY49BnToZWjVnzpx06dKF6dOns2rVKi8HE3+kAZ74p7Nn4fBhOHOG6OhoatSoQf/+/TUtuYj4jbCwMHr27Mny5cv1KZ7beveGnTvdTiHiPQMGwAMPZHj1Z599lnz58tFHkw9lShrgiX/65RfInx/mzcMYQ9++fdm+fTtffPGF28lERFK0bduW0qVL06tXL32K56Y8eaBgQbdTiPiNPHny0LVrV6ZPn87PP//sdhzxMQ3wxD+VKgVDhkClSgA0btyYqKgo+vfvz5kzZ1wOJyLiyJo1K6+99horV65k+vTpbsfJvNasgXfeAc24LMGqTp3L+gQPnE/xChQoQO/evb0USvyVBnjinwoXhueec67FA4wx9OvXj127dvHZZ5+5HE5EJNVjjz1GuXLl6NWrl+6L55bFi6FrVzh+3O0kIt7x6KNw//2X9ZJcuXLRvXt3Zs+ezZIlS7wUTPyRBnjin86ehb//hpMnU55q2LAht9xyC3379uXUqVMuhhMRSRUWFkafPn1Ys2YNkyZNcjtO5vTEE3D0KBQo4HYSEe949llo0+ayXxYTE0ORIkXo0aOHTiPPRDTAE/+0dy8ULQoTJ6Y8ZYxh4MCB/PXXX3zwwQcuhhMROd+jjz5KtWrV6Nmzp04jd0NEBOTODca4nUTEe86eveyX5MiRg969e/Pjjz8yc+ZML4QSf+TVAZ4xpqExZrMxZpsx5uULLH/BGLPBGPOrMWaBMaaUN/NIAClQAD766F9TAt922200btyYgQMHcujQIZfCSaBTbxJPCwkJ4c0332THjh18ovux+d7WrTBwIOzb53aSq6b+JBfUrBnUqHFFL+3QoQPly5fn5Zdf5uwVDBIl8HhtgGeMCQWGAfcClYFHjDGV/7HaaiDKWnsD8BXwlrfySIDJkQOeeSZlkpW03nzzTY4dO8Ybb7zhQjAJdOpN4i0NGzakfv369O3bl+O6Fsy3tmyBHj1gzx63k1wV9Se5qObN4emnr+ilYWFhDBgwgHXr1jF27FgPBxN/5M1P8GoB26y1O6y1Z4AJQNO0K1hrF1prky+mWg5EejGPBBJrYfduOHLkX4uqVq1KmzZtGDp0KDt27HAhnAQ49SbxCmMMgwYNIjY2lkGDBrkdJ3O55x44fRqiotxOcrXUn+TCWraEmJgrfnmzZs2IioqiZ8+enEwzv4EEJ28O8IoDaQ+l/ZH03MU8AczyYh4JJAkJzq0SPvrogosHDBhAWFgY3bp183EwCQLqTeI1tWrVolWrVrz99tvs2rXL7TiZR5YsEB4eDNfgqT/JhZ075xzEuEIhISG899577N27l8GDB3swmPgjv5hkxRjTGogCLvgbZ4x5yhiz0hizMjY21rfhxB1hYTByJNx33wUXFytWjB49ejBlyhR++OEH32aTTEO9Sa7Em2++SUhICC+99JLbUTKPv/+Gfv1gwwa3k/iM+lMm0707FCp0VZuoW7cuLVq04K233mJPgJ/OLOnz5gBvL1AiTR2Z9Nx5jDF3AT2BaGvtBe9Qaq0dYa2NstZGFbrKX24JEMY4015Xq3bRVV544QVKlixJly5ddNGwXA71JvGqEiVK0L17dyZNmsSPP/7odpzM4dAh6N0bfvvN7SRXS/1JLqxRI+d3/CoNGjSIc+fO8fLL/5q/R4KINwd4K4BrjTFljDFZgYeBaWlXMMbUAIbjNKj9XswigWjHDth/8V+LiIgIBg8ezJo1axg+fLgPg0mAU28Sr+vevTuRkZF06tSJxMREt+MEv4oVITHRuU4psKk/yYXdcQd44LKU0qVL07VrV7788ksWLVrkgWDij7w2wLPWJgKdgDnARmCStXa9MaafMSY6abXBQE5gsjFmjTFm2kU2J5nRjTfCgAHprtK8eXPuuusuXnnlFf7++28fBZNApt4kvpA9e3aGDBnCr7/+yrBhw9yOE/yMgdBQt1NcNfUnuajERDh61LkW7yq98sorlCpViv/+978kJCR4IJz4GxNod7WPioqyK1eudDuG+MLkyVCmzCVnRdu8eTPXX389jzzyCJ9//rmPwoknGWNWWWsDevo79Sb5J2stjRo1YsmSJWzatIlixYq5HSl4xcdD//7QoAHUq+fRTas/iV8YNgw6dXLObPLAKbfTpk2jadOmDB48mK5du3ogoPhaer3JLyZZEbmg5s0zNOX1ddddR7du3RgzZowmXBERv2GMYejQoZw5c4YXXnjB7TjBzVp48034+We3k4h4R9268O67EBHhkc1FR0fTpEkT+vTpw+7duz2yTfEfGuCJ/9q2DX7/PUOr9uzZk7Jly/Lkk09y+iqmERYR8aTy5cvzyiuvMHHiRL777ju34wSvbNng7FnQJxESrKpVgy5dIGdOj23ygw8+AKBjx44E2hl9kj4N8MR/NW7sTAucAdmzZ+eTTz5h27Zt9OnTx7u5REQuw8svv0zVqlV55plnOHr0qNtxglPg3/9OJH0JCRAb6/zrIaVLl+aNN95g1qxZjBs3zmPbFfdpgCf+a8gQ52hVBt1xxx106NCBt99+G11rICL+ImvWrIwaNYq//vqL7hk8aCVXoE8f+Oort1OIeMe8eVC4MKxe7dHNxsTEUKdOHZ577jlNVhdENMAT/9WwIdSufVkvGTx4MEWLFqVNmzbExcV5KZiIyOWpVasWXbp0YcSIEcyZM8ftOMFpzBjQfQclWFWtCh9+CCVLenSzoaGhjBo1ihMnTuhUzSCiAZ74ry1bYNOmy3pJ3rx5+fTTT9mwYQOvvPKKl4KJiFy+/v37U7lyZdq1a8fBgwfdjhN8duyA9993O4WId5QsCTExULSoxzddqVIl3njjDaZOncro0aM9vn3xPQ3wxH899RR07HjZL7vnnnuIiYnhvffeY8GCBV4IJiJy+SIiIhg7diwHDhzgmWee0ZFyEcm4xETYuxdOnvTK5rt06UL9+vXp3LkzO3fu9Mp7iO9ogCf+a8AAeOONK3rpW2+9RYUKFWjTpg0HDhzwcDARkStTo0YN+vbty+TJk3XfTk975x3nXmEiwWjrVoiMBC/NxhsSEsLo0aMJCQmhVatWugF6gNMAT/zXrbfCLbdc0UuzZ8/O+PHjiY2NpU2bNpw7d87D4URErkz37t25/fbbiYmJYcOGDW7HCR7z5oHuhSrBqlgxGDEiQ/cHvlKlSpVi+PDhLFu2jNdee81r7yPepwGe+K+tW69qtqiaNWvyzjvvMHPmTN555x0PBhMRuXKhoaGMGzeOnDlz0qJFC06dOuV2pOAwezZMnux2ChHvyJMHnnwSypf36ts8/PDDPPXUUwwaNIhZs2Z59b3EezTAE//16qvwyCNBJREsAAAez0lEQVRXtYmYmBgeeughevTowaJFizwUTETk6lxzzTWMHTuWDRs2aOY6Ebm0c+dg5044dMjrbzVkyBBuuOEGHnvsMXbt2uX19xPP0wBP/FePHjBy5FVtwhjDqFGjKF++PM2aNWPPnj0eCicicnXuvvtu+vTpwxdffMEHH3zgdpzA98knzoFBkWCUkABly8LHH3v9rSIiIvjqq69ITEzkgQce0FkGAUgDPPFf1atD3bpXvZk8efIwdepU4uLieOCBBzh9+rQHwomIXL1XX32V+++/nxdffJGFCxe6HSewrVkD+hlKsMqaFT77DJo08cnbXXvttYwfP561a9fSoUMHnWUQYDTAE/+1bZvHblpbsWJFxo4dy6pVq2jXrp0mXRERvxASEsLnn39OhQoVaNasGVu2bHE7UuAaNgyWLHE7hYh3GANt28L11/vsLe+9914GDBjA+PHjGTBggM/eV66eBnjiv4YOhehoj20uOjqat956i4kTJ9KzZ0+PbVdE5Grkzp2b6dOnExoaSqNGjYiNjXU7koj4o23bnHvh+dDLL7/MY489xmuvvcbYsWN9+t5y5TTAE//VqZPH7/fStWtXOnbsyMCBA/nYB+exi4hkRNmyZZk2bRp79+4lOjpa17xcia++gnbt3E4h4j3/+Q/06ePTtzTGMHLkSOrXr0/79u11KnmA0ABP/Ne113rkGry0jDEMHTqU++67j//+979MnDjRo9sXEblStWvXZty4cfz88888+OCDnDlzxu1IgWXXLue0fl0rJMHqo4/gqad8/rZZs2ZlypQpXHvttURHR7NixQqfZ5DLowGe+K8dO5z7Gnn4f9ZZsmRh0qRJ1K1bl9atWzNz5kyPbl9E5Eo9+OCDfPLJJ8yZM4dWrVqRmJjodqTA0bWrcwqbMW4nEfGOpk3hpptceet8+fIxd+5cChUqRMOGDVm/fr0rOSRjNMAT/zVhAtx7rzM18Pbt8NZb8NdfzrItW5x6/36n3rjRqQ8edOp165z6yBGnXrvWqY8fByD7pk3MuesualWtyoMPPsjS9993lsfHO+svX+7UyTtXS5Y4tY4Mi4iXtW/fnnfffZevvvqKxx9/XIM8EXFs2wZbt7r29sWLF2f+/Plky5aNO++8kw0bNriWRdKnAZ74r8cfh2XLIDQUNm2Cl16CP/5wlq1b59T79jn12rVOnTw5wS+/OHXyDUF//tmpjx1z6mXLiOjdm+8mTKBSpUpMffFFZ3nyAO+HH5w6ecdqwQKn1gBPRHygS5cuvPnmm4wfP55WrVqRkJDgdiT/9/330KKFT24ELeKKtm3hmWdcjVC2bFm+//57QkJCqF+/PuvWrXM1j1yYCbT7WkRFRdmVK1e6HUN87exZZ/CVLZsz4EtMhDNnIDwcQkIuv05IcB4RERw6fJh7776bTb/+yidjx9KiZcvzlmPMv2vxKGPMKmttlNs5roZ6k3jD4MGD6d69O02bNmX8+PFERES4Hcl/ffUVvPaac0CuWDGPbVb9SfzGokXOPtCtt7qdhC1btnD77bcTHx/PzJkzqVWrltuRMp30epM+wZPAEBoK2bM7/wJkyeLUISFXVoeFObUx5M+fnzkLFnBD7do8/MgjDBs27LzlKeuHhMD998P48b77vkUkU+vWrRtDhw5l2rRpNGjQgMOHD7sdyX81a+acrr91K8TEpJySz4IFTn36tLv5RK5WvXqQK5fz+7xnj/PcL7849Z9/OvWKFU6dfAnL0qVOnfzJ9o8/OnXyGU0LFzp18sy98+Y5dfIZTbNnO/XZs049fTp06kSFChVYtGgRzbJkYeWttzJ79mxn+VdfwYsvpmaeONE5AyrZuHHwyiup9eefQ69eqfWnn0K/fqn1iBHwxhup9UcfwaBBqfXQofDOO6n1e+/B+++n1oMHw4cfptZvvglpZ1F//XUYOTK17tPHuaF8sldfhbS3h3j55fP3A7t2hcmTU+suXeCbb1Lrbt3gwAF8TQM8ESBv3rzMnTuXJk2a0KlTJ7p3787Z5GaWLFs25/4zJ0+6E1JEMqVOnToxYcIEfvrpJ+rWrcvOnTvdjuTftm2DSZNSd1C3bHFqXcsowWDvXuf3OXmOgT17nDp5wPb770594oRT79rl1MkDuO3bnTr5gMfWrU6dPGvv5s3n/71s3OjU58459bp1Tg2UK1eOwY8/zkPG0KRJE0aOHAlr1pw/wPnlF/j229R65crzb4H188+QdrK7Zctg1qzUeskSmDs3tf7xR5g/P7X+v/9zTs9O9v33zmU2yRYscF6TbO5cZ5vJZs923jPZzJnw00+p9fTpTsZk330Hq1al1lOnwurVqfWUKc5lQ+D8N3j77fMHgD6iUzRF0khMTKRz587873//o0mTJowbN45cuXK5HSvo6RQokUtbuHAhDz30EKGhoUyZMoXbbrvN7UiZgvqTSPqOHTtG8+bNmTt3Ls8//zxvv/02oclnXGVm1jozwpct65XLe3SKpkgGZcmShY8++ogPP/yQmTNnUrt2bTZu3Oh2LBERbr/9dn766Sfy58/PnXfeyYcffkigHaQVkeCTO3duZsyYQefOnRkyZAgNGzYkNnnSu8zMGChXzpW5GzTAE7mAmJgY5syZQ2xsLDfddBMTJkxwFrz1lnOdh4iIC6699lqWL1/OPffcw7PPPssjjzzCseRTs0REXJIlSxbef/99Ro0axeLFi6lRowZL0p4KmVlt3uzKdXga4IlcxJ133snq1aupXr06jzzyCG3atCEu+ZoOERGX5MuXj2+//ZaBAwcyefJkqlevrh0pEfEL7du3Z9myZYSHh1OvXj169+6duW/z8vffzkQwmzb59G01wBNJR/HixVm4cCG9e/dm7NixVPr0U+Z37Oh2LBHJ5EJCQnjppZf4MWnygHr16tGjRw9Oa6ZIEXFZ9erV+eWXX3jsscfo168ft956a+a9X94tt8DRo1C3rk/fVgM8kUsICwujT58+LF68mLCwMO6++27at2/PwYMH3Y4mIpncLbfcwtq1a2nXrh0DBw6kWrVq/N///Z/bsUQkk8udOzejR49m0qRJ7Ny5k5o1a9K7d2/i4uLcjuZbWbI4s7D7mAZ4IhlUp04dfh07lj+KFGHL559ToUIFPv7443/fTkFExIdy5crFyJEjmT9/PmfPnqV+/fo8+uij7N271+1oIpLJNW/enI0bN9KiRQv69etHlSpVmDZtWuaaIGr2bGjRIvVWEz6gAZ7IZQgvVIjiN97I6DFjuOGGG3jmmWeoWbMms2bNylzNSkT8zp133slvv/1Gr169mDJlChUqVKBXr16ahEVEXFWwYEHGjh3LvHnzyJYtG02bNqVBgwasSns/uWC2fz/8+qtzPZ6PaIAncjnKlIEZMyjfqhXff/89kyZN4uTJkzRq1Ij69euzcOFCDfRExDXZs2enb9++bNy4kcaNG9O/f3/nZsSDB3Mi+cbHIiIuuOuuu1i7di1Dhgxh9erVREVF0bJly+C/Pu+xx5xJVkJCnJuoJ086s2+fUyffVN6DM21qgCdyhYwxNG/enA0bNvDhhx+ydetW7rjjDurVq8d3333HOR9+FC8iklaZMmWYOHEiK1eupGbNmnTv3p3SpUvTv39/Dvh4um4RkWRhYWE899xzbN++nVdffZVZs2Zx/fXX8+CDD7Js2TK343lH8n3wpkyB2rXh0CGnnjjRqY8fd+p58zz2lhrgiVyuBx6AZ55JKbNmzUpMTAw7duzgww8/ZPfu3URHR1OpUiWGDh3K0aNHXQwrIpnZjTfeyJw5c1i2bBm1a9emV69elCxZkqeffpq1a9e6HU9EMqk8efLQv39/du3aRa9evVi4cCG33HILderU4csvvyQ+GG9Ldd99MHMm5M3r1E2bOnXOnE79n/947K00wBO5XNddB2XL/uvp8PBwYmJi2L59O+PHjydv3rx07tyZYsWK0b59e/7v//5Pn+qJiCtq167N9OnTWb9+Pa1atWLMmDFUr16dOnXqMGLECI4cOeJ2RBHJhPLnz0/fvn3Zs2cPQ4cO5cCBA7Rq1YrIyEhefPFFfv31V7cjek7JknDvvamzapYu7dRhYU5drJjH3soE2vVCUVFRduXKlW7HEMmQVatW8b///Y+JEydy4sQJSpcuTYsWLWjZsiU1atTAJH9sn8kZY1ZZa6PcznE11JskkBw6dIgxY8YwYsQINm7cSHh4OPfddx8tWrTgvvvuI0eOHG5H9BvqTyK+c+7cORYsWMDHH3/Md999R0JCAtWqVaNly5a0aNGCcuXKuR3Rb6TXmzTAE/GBkydP8s033zBu3Djmz59PYmIipUqVIjo6mvvuu4969eoRERHhdkzXaAdKxB3WWlauXMmYMWOYPHkyf//9N+Hh4dx9991ER0dzzz33UKJECbdjukr9ScQdBw4cYPz48Xz55ZcsX74cgKpVq9K0aVMaNWpErVq1yJIli8sp3aMBnognjRkD3brB5s2p51FfhoMHDzJ16lS+/fZb5s2bR1xcHOHh4dSrV4/bb7+d+vXrU7NmTbJmzeqF8P5JO1Ai7jt79iyLFi1K6U+///47AJUrV+aOO+7g9ttv57bbbqNQoUIuJ/Ut9ScR9/3+++9MmTKFb7/9lh9//JFz586RN29ebr/99pR9pypVqhASknmuPtMAT8STFi2CceNgwAAoWPCqNnXq1CkWLVrE7NmzmT9/PuvXrwec6/lq1arFzTffTK1atbjxxhspXbp00J7SqR0oEf9irWX9+vXMmTOHuXPnsnjxYk6dOgVAhQoVqFOnDrVq1SIqKorrr78+qM9AUH8S8S+HDx9m/vz5zJ49m++//55du3YBkDdvXmrXrs3NN9/MTTfdRM2aNbnmmmvcDetFGuCJBIj9+/ezaNEilixZwtKlS1mzZg1nzpwBnBmnbrjhBq6//nqqVq1KpUqVqFixIkWKFAn4gZ92oET825kzZ1ixYgVLlixh8eLFLF++nNjYWABCQ0OpWLFiSm+qXLkyFStWpFy5ckFxJoL6k4h/27lzJz/++GPKvtP69etT7klcpEgRqlWrRtWqValSpQqVKlXiuuuuI3/+/C6nvnquDfCMMQ2B94FQYKS1duA/lmcDxgA3AgeBltbaXeltU01K/EJcHPz9N5Qq5dTbtkHWrM4MSQBbt0J4OCRfu7JlC2TPDpGRTr15szMtbvHiTr1pE+TOnTqD0saNkDcv8fnz8+uvv7JzxgxW7dzJ4m3bWLduHZHHjhELxAI5c+SgQWQkucuXp0DFipQuWZIqISEUqFyZolWrUjBfPkK2bIGiRaFAAeeGmps3wzXXQP78cOaMk7dYMciXD+Ljne+neHHnFNS4ONi+3cmeJw+cPg07djjfW+7ccOoU7NzpfO+5csHJk7BrF1SsCKGhGfpx+noHSr1J5OpYa9m9ezcrV65kzZo1rFmzhnXr1qUcSQcICQmhVKlSlCtXjjJlylCmTBlKlSpFiRIliIyMpFixYmRLnk3Oj6k/iQSWEydO8Msvv7B69WpWr17Nb7/9xoYNG4iLi0tZJ3/+/JQvX56yZctSunRpSpcuTcmSJSlRogTFixcnb968fn/w3JUBnjEmFNgC3A38AawAHrHWbkizzn+BG6y1HY0xDwMPWGtbprddNSlxXXw8lCnjDIaSTqmkWjXn1gnffOPUlSrBDTc4N7EEZ1ndus71e+AMlho2hJEjnbpwYWjWDD76yKnz5IH27eG995w6IgI6d4ZBg5yjUqGh7Hz0UWbWrs32zZt578MPeb9gQV4+cQITF8cpoDswGCgcFsbfCQm8W7IkP1SrRvlcuXj3yy+Z/9BD/BkdTbHERO564gn+7N8f88QT5D1wgIgbboAvvoDWrWHDBqhSxfleWrSA1auhZk2YOtW5h8tPPzk36pw505nud9Ei514uhw9n+BpFX+5AqTeJeM/x48fZvHkzmzZtYsuWLWzbto1t27axa9eulE/80ipQoADXXHMNRYsWpXDhwhQuXJhChQpRsGBBChQoQP78+cmXLx/58uUjT5485M6d2+fX2Kg/iQS+s2fPsnPnzpT+tG3bNrZu3crOnTvZvXs3iYmJ560fERGR0puKFClCkSJFzutNBQoUSOlNefPmJU+ePD4/YOXWAK8O0Mdae09S3QPAWvtmmnXmJK2zzBiTBdgHFLLphFKTEr+wfDkkJMBttzn13LmQIwfceqtTz57tDNLq1HHqmTOd6/Vq1XLq6dOdT9Sikv4up01zBn01azr1N984g8jq1Z3666/h2mudQSPA5MnOILJqVTh3zllepQq2UiX+3ruXE198wY4cOdhsDPt276bYihWsTExk9YkTHP37b27ev58V586xA4gA7gNWAruAHEDjkBA25c7N0bx5uSZHDu5ISGBX0aKcLliQgqGh3Hj4MH+VKUNC4cLkt5Zr9+zhYKVK2KJFyR0fT/GtWzlz773Uu+uuDP04fbwDpd4k4oKTJ0+yZ88efv/9d/bu3cvevXv5888/2bdvH/v27SM2Npa///6bEydOpLudXLlykTt3bnLlypXyyJEjR8oje/bsZM+enYiIiJRHeHg44eHhZMuWjfDwcOrWrZvhU7TUn0SC29mzZ/nrr7/YvXs3e/bsSelP+/bt46+//krpTQcPHkz3fsbZsmUjd+7cKf0pZ86cKf0pe/bsKf+m7U3J/Sm5N5UuXZoaNWpkKLdbA7xmQENrbYek+jHgZmttpzTrrEta54+kenvSOgcutl01KZGrZ63l2LFjxMbGcvjwYQ4ePMiRI0c4fPgwR48eTXkcP36cY8eOcfLkSY4fP87Jkyc5efIkp06dSnlcrNlVr16d1atXZyiPj3eg1JtE/FhcXBwHDhzg0KFDKY8jR45w5MiRf/WmEydOpDzS9qeTJ0+SkJBw0fdYunQpdZIPwF2C+pOIgDMQPHLkyHn9Ke2+07FjxzK873Qxjz32GGOSz/a6hPR6U0DcPMIY8xTwFEDJ5GucROSKGWPIkycPefLkuartWGtJSEjg9OnTxMfHp/wbHx9PaAavvwtk6k0inhceHk5kZCSRydcsX6GzZ88SFxd33iM+Pp64uDgqVKjgobT+S/1JxLNCQ0NTTs+8Gtba8/aZ0vam3LlzeySrNwd4e4G0d0eNTHruQuv8kXSaQR6cC4bPY60dAYwA5yiUV9KKyGUzxpA1a9ZAmylPvUkkEwgNDU05bTOAqD+JBDljTMpp497izSuVVwDXGmPKGGOyAg8D0/6xzjSgTdLXzYDv0zuHXETEA9SbRMRfqT+JyFXz2id41tpEY0wnYA7OVL+fWmvXG2P6ASuttdOAUcAXxphtwCGcRiYi4jXqTSLir9SfRMQTvHoNnrV2JjDzH8/1SvN1HNDcmxlERP5JvUlE/JX6k4hcLd/eTEZERERERES8RgM8ERERERGRIKEBnoiIiIiISJDQAE9ERERERCRIaIAnIiIiIiISJDTAExERERERCRIa4ImIiIiIiAQJY611O8NlMcbEAr9ncPWCwAEvxvEGZfaNQMscaHnh8jKXstYW8mYYb7vM3gSB99800PKCMvtKoGW+3LyZrT8F2n9PUGZfCLS8EPyZL9qbAm6AdzmMMSuttVFu57gcyuwbgZY50PJCYGb2pUD7+QRaXlBmXwm0zIGW19cC8eejzN4XaHkhc2fWKZoiIiIiIiJBQgM8ERERERGRIBHsA7wRbge4AsrsG4GWOdDyQmBm9qVA+/kEWl5QZl8JtMyBltfXAvHno8zeF2h5IRNnDupr8ERERERERDKTYP8ET0REREREJNMI2gGeMaahMWazMWabMeZlt/NcijHmU2PMfmPMOrezZIQxpoQxZqExZoMxZr0x5jm3M12KMSbcGPOzMWZtUua+bmfKKGNMqDFmtTFmuttZMsIYs8sY85sxZo0xZqXbefyJepP3qT/5jnpTcFF/8i71Jt8JtN4Enu1PQXmKpjEmFNgC3A38AawAHrHWbnA1WDqMMfWAE8AYa21Vt/NcijHmGuAaa+0vxphcwCrgfj//GRsgh7X2hDEmDFgMPGetXe5ytEsyxrwARAG5rbWN3c5zKcaYXUCUtTbQ7j/jVepNvqH+5DvqTcFD/cn71Jt8J9B6E3i2PwXrJ3i1gG3W2h3W2jPABKCpy5nSZa1dBBxyO0dGWWv/stb+kvT1cWAjUNzdVOmzjhNJZVjSw++PcBhjIoH7gJFuZ5Grpt7kA+pPvqHeFHTUn7xMvck31JuCd4BXHNiTpv4DP/8DCmTGmNJADeAnd5NcWtJH9muA/cA8a63fZwaGAN2Bc24HuQwWmGuMWWWMecrtMH5EvcnH1J+8Sr0puKg/+ZB6k1cFYm8CD/anYB3giY8YY3ICXwPPW2uPuZ3nUqy1Z6211YFIoJYxxq9P6TDGNAb2W2tXuZ3lMtW11tYE7gVikk6jEfEp9SfvUW8SuXLqTd4TwL0JPNifgnWAtxcokaaOTHpOPCjpXOyvgXHW2ilu57kc1tojwEKgodtZLuFWIDrpvOwJwB3GmLHuRro0a+3epH/3A9/gnPoj6k0+o/7kdepNwUf9yQfUm7wuIHsTeLY/BesAbwVwrTGmjDEmK/AwMM3lTEEl6aLbUcBGa+27bufJCGNMIWNM3qSvI3AuJN/kbqr0WWt7WGsjrbWlcX6Pv7fWtnY5VrqMMTmSLh7HGJMDaAAExAxnPqDe5APqT96n3hSU1J+8TL3J+wKxN4Hn+1NQDvCstYlAJ2AOzgWsk6y1691NlT5jzHhgGXCdMeYPY8wTbme6hFuBx3COjKxJejRyO9QlXAMsNMb8ivM/snnW2oCZPjeAFAEWG2PWAj8DM6y1s13O5BfUm3xG/UkuRL0pHepPPqHeJBfj0f4UlLdJEBERERERyYyC8hM8ERERERGRzEgDPBERERERkSChAZ6IiIiIiEiQ0ABPREREREQkSGiAJyIiIiIiEiQ0wBMREREREQkSGuBlYsaYPsaYrklf9zPG3JXOuvcbYyr7Lt0F37+Xh7b1vDEmu4e21dgY088T2xIRh3qTR7al3iTiBepPHtmW+pOXaYAnAFhre1lr56ezyv2Aa00K6A585KFtPQ9cVpMyxoReZNEMoImnmp6InE+9KX3qTSLuUX9Kn/qTezTAy2SMMT2NMVuMMYuB69I8P9oY0yzp64HGmA3GmF+NMW8bY24BooHBxpg1xphyxpgnjTErjDFrjTFfJ/+RJm3nA2PMUmPMjuRtJi17yRjzW9JrBiY9V84YM9sYs8oY86MxpuIFMlcA4q21B9K8x/+MMcuT3qO+MeZTY8xGY8zoNK9rYIxZZoz5xRgz2RiT0xjTGSgGLDTGLLzYeknP7zLGDDLG/AI0N8Z0TvNzmQBgrbXAD0Bjj/1HEsmE1JvUm0T8lfqT+lPAsdbqkUkewI3AbzhHYHID24CuSctGA82AAsBmwCQ9nzft8jTbKpDm69eBZ9OsNxnn4EFlYFvS8/cCS4HsSXX+pH8XANcmfX0z8P0FcrcD3klTjwYmAAZoChwDrk96z1VAdaAgsAjIkfSal4BeSV/vAgomfX2p9bqned8/gWxpfy5JX7cChrr931cPPQL1od6k3qSHHv76UH9SfwrERxYkM7kN+MZaewrAGDPtAuscBeKAUcaY6cD0i2yrqjHmdSAvkBOYk2bZVGvtOWCDMaZI0nN3AZ8lv7e19lDS0Z5bgMnGmOTXZrvAe10DxP7jue+stdYY8xvwt7X2t6TvaT1QGojEaZJLkradFVh2gW3XvsR6E9N8/SswzhgzFZia5vn9OEe2ROTKqDf9m3qTiH9Qf/o39Sc/pwGenMdam2iMqQXciXNUqhNwxwVWHQ3cb61da4xpC9RPsyw+zdeGiwsBjlhrq18i1mkgzz+eS36Pc/94v3M4v9dngXnW2kcusW1zifVOpvn6PqAe0AToaYy53lqbCIQnZRQRL1Fv+hf1JhE/of70L+pPLtM1eJnLIuB+Y0yEMSYXzh/beZKODOWx1s4EugDVkhYdB3KlWTUX8JcxJgznY/ZLmQe0S3O+eX5r7TFgpzGmedJzxhhT7QKv3QiUz9B3mGo5cKsxpnzStnMknY/+z+8lvfVSGGNCgBLW2oU4pyLkwTn6BlABWHeZ+UQklXrTv78X9SYR/6D+9O/vRf3Jz2mAl4lYa3/B+dh8LTALWHGB1XIB040xvwKLgReSnp8AdDPGrDbGlANeA34ClgCbMvDes4FpwEpjzBqga9KiVsATxpi1wHqc88L/aRFQw6Q5FyED7xcLtAXGJ30vy4Dki5BHALONMQsvsV5aocDYpNMaVgMfWGuPJC27HWdGKBG5AupN6k0i/kr9Sf0pECVfDCri14wx7+OcO57edMQ+l3Se/JfW2jvdziIivqfeJCL+Sv0p89IATwJCUjO42Vp7oYubXWOMuQlIsNaucTuLiPieepOI+Cv1p8xLAzwREREREZEgoWvwREREREREgoQGeCIiIiIiIkFCAzwREREREZEgoQGeiIiIiIhIkNAAT0REREREJEj8P6NDRWSBcUxxAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "\u003cFigure size 1080x360 with 3 Axes\u003e"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light",
-            "tags": []
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "config_list = [config_baseline, config_arizona_inf1_ble1, config_arizona_inf1_ble2]\n",
-        "params = ModelParams()\n",
-        "print(params)\n",
-        "n = len(config_list)\n",
-        "fig, axs = plt.subplots(1,n, figsize=(5*n,5))\n",
-        "axs = np.reshape(axs, (n,))\n",
-        "for i in range(n):\n",
-        "  config = config_list[i]\n",
-        "  ax = axs[i]\n",
-        "  plot_risk_vs_distance(params, config, ax)\n",
-        "  ax.set_title(config.name)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -8064,9 +8273,9 @@
           "height": 366
         },
         "executionInfo": {
-          "elapsed": 58499,
+          "elapsed": 50850,
           "status": "ok",
-          "timestamp": 1605160632329,
+          "timestamp": 1605287346152,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -8075,7 +8284,7 @@
           "user_tz": 480
         },
         "id": "TqyLdFA8lS3i",
-        "outputId": "da04f7e1-1348-4927-f1e3-ce079505a532"
+        "outputId": "293e5982-eea5-4ded-d651-449a19029fa6"
       },
       "outputs": [
         {
@@ -8125,9 +8334,9 @@
           "height": 366
         },
         "executionInfo": {
-          "elapsed": 59154,
+          "elapsed": 50845,
           "status": "ok",
-          "timestamp": 1605160633010,
+          "timestamp": 1605287346153,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -8136,7 +8345,7 @@
           "user_tz": 480
         },
         "id": "Mo317Wnfzn_t",
-        "outputId": "81718d43-0fff-494a-9680-007009c4aadb"
+        "outputId": "ab09e02f-9ef8-427f-e336-c132eda6a8d4"
       },
       "outputs": [
         {
@@ -8244,9 +8453,9 @@
           "height": 591
         },
         "executionInfo": {
-          "elapsed": 61206,
+          "elapsed": 51753,
           "status": "ok",
-          "timestamp": 1605160635110,
+          "timestamp": 1605287347081,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -8255,7 +8464,7 @@
           "user_tz": 480
         },
         "id": "TwLUG-JZeyIX",
-        "outputId": "7b56a68c-f601-485c-e068-80473fdf5630"
+        "outputId": "8cd3c091-dc32-4698-ed21-ede0c9a84b32"
       },
       "outputs": [
         {
@@ -8324,9 +8533,9 @@
           "height": 591
         },
         "executionInfo": {
-          "elapsed": 62745,
+          "elapsed": 52657,
           "status": "ok",
-          "timestamp": 1605160636683,
+          "timestamp": 1605287347999,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -8335,7 +8544,7 @@
           "user_tz": 480
         },
         "id": "B73Fust47RtB",
-        "outputId": "9ce4f8bf-0ca9-4349-de9a-1688723d4e59"
+        "outputId": "06d76b08-d0ed-486d-9895-2811572fcf65"
       },
       "outputs": [
         {
@@ -8381,9 +8590,9 @@
           "height": 591
         },
         "executionInfo": {
-          "elapsed": 64153,
+          "elapsed": 54037,
           "status": "ok",
-          "timestamp": 1605160638115,
+          "timestamp": 1605287349385,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -8392,7 +8601,7 @@
           "user_tz": 480
         },
         "id": "xJ6wz2hQLzY3",
-        "outputId": "4f461af2-4ae3-4ebd-c02c-7cb8a035ea13"
+        "outputId": "4b644a3e-91ce-4e47-9988-044b1311f232"
       },
       "outputs": [
         {
@@ -8438,9 +8647,9 @@
           "height": 872
         },
         "executionInfo": {
-          "elapsed": 66899,
+          "elapsed": 55912,
           "status": "ok",
-          "timestamp": 1605160640887,
+          "timestamp": 1605287351267,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -8449,7 +8658,7 @@
           "user_tz": 480
         },
         "id": "rZ9iS1dcL4sh",
-        "outputId": "a8b9b15c-fa2d-4589-8aa2-1b035f575d29"
+        "outputId": "46ab172f-1e54-41d1-8c1e-ff6220d3a66a"
       },
       "outputs": [
         {
@@ -8863,11 +9072,13 @@
         "}\n",
         "\n",
         ".how-to{\n",
-        "  top: 90px;\n",
-        "  left: 200px;\n",
+        "  top: 40px;\n",
+        "  left: 38px;\n",
         "  position: absolute;\n",
         "  font-weight: 200;\n",
         "  pointer-events: none;\n",
+        "  opacity: 1 !important;\n",
+        "  font-size: 14px;\n",
         "}\n",
         "\n",
         ".expose{\n",
@@ -8905,7 +9116,9 @@
         "  font-weight: 300;\n",
         "  cursor: pointer;\n",
         "}\n",
-        "\n",
+        ".param-label:hover{\n",
+        "  color: #000;\n",
+        "}\n",
         ".info {\n",
         "  content: \"ⓘ\";\n",
         "  float: right;\n",
@@ -8941,19 +9154,33 @@
         "  font-weight: 600;\n",
         "}\u003c/style\u003e\n",
         "\u003cscript\u003ewindow.presetConfigs = [\n",
-        "   {\n",
-        "    name: 'MLE',\n",
-        "    ble_thresholds: [51, 55, 60],\n",
-        "    ble_weights: [200, 85.7, 43.3, 9.8], \n",
-        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
-        "    inf_weights:  [0 , 16, 86],\n",
+        "  {\n",
+        "    name: 'LFPH-BleNarrow-InfNarrow',\n",
+        "    ble_thresholds: [53, 62, 70],\n",
+        "    ble_weights: [150, 100, 40, 0], \n",
+        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "    inf_weights:  [0, 30, 100],\n",
         "  },\n",
-        " {\n",
-        "    name: 'MLE-old',\n",
-        "    ble_thresholds: [48, 55, 68],\n",
-        "    ble_weights: [85.6, 48.7, 9.9, 1], \n",
-        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
-        "    inf_weights:  [5 , 38, 209],\n",
+        "  {\n",
+        "    name: 'LFPH-BleWide-InfNarrow',\n",
+        "    ble_thresholds: [55, 70, 80],\n",
+        "    ble_weights: [200, 100, 25, 0], \n",
+        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "    inf_weights:  [0, 30, 100],\n",
+        "  },\n",
+        "  {\n",
+        "    name: 'LFPH-BleNarrow-InfWide',\n",
+        "    ble_thresholds: [53, 62, 70],\n",
+        "    ble_weights: [150, 100, 40, 0], \n",
+        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
+        "    inf_weights: [0, 75, 250],\n",
+        "  },\n",
+        "  {\n",
+        "    name: 'LFPH-BleWide-InfWide',\n",
+        "    ble_thresholds: [55, 70, 80],\n",
+        "    ble_weights: [200, 100, 25, 0], \n",
+        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
+        "    inf_weights: [0, 75, 250],\n",
         "  },\n",
         "  {\n",
         "    name: 'Ireland',\n",
@@ -9011,6 +9238,20 @@
         "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
         "    inf_weights: [0, 100, 250],\n",
         "  },\n",
+        "  {\n",
+        "    name: 'MLE',\n",
+        "    ble_thresholds: [51, 55, 60],\n",
+        "    ble_weights: [200, 85.7, 43.3, 9.8], \n",
+        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
+        "    inf_weights:  [0 , 16, 86],\n",
+        "  },\n",
+        "  {\n",
+        "    name: 'MLE-old',\n",
+        "    ble_thresholds: [48, 55, 68],\n",
+        "    ble_weights: [85.6, 48.7, 9.9, 1], \n",
+        "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
+        "    inf_weights:  [5 , 38, 209],\n",
+        "  },\n",
         "\n",
         "]\n",
         "\n",
@@ -9198,45 +9439,48 @@
         "  ]\n",
         "\n",
         "  var rvSliders = addSliders(sel.append('div').st({marginLeft: 30}), sliders, d =\u003e renderFPR(sliders[0].v))\n",
-        "  c.svg\n",
-        "    .on('mousemove', function(){\n",
-        "      var fpr = d3.mouse(this)[0]/c.width\n",
+        "  // c.svg\n",
+        "  //   .on('mousemove', function(){\n",
+        "  //     var fpr = d3.mouse(this)[0]/c.width\n",
         "\n",
-        "      auc.fpr = fpr\n",
-        "      auc.renderFPR()\n",
-        "    })\n",
-        "    .append('rect').at({width: c.width, height: c.height, fillOpacity: 0})\n",
+        "  //     auc.fpr = fpr\n",
+        "  //     auc.renderFPR()\n",
+        "  //   })\n",
+        "  //   .append('rect').at({width: c.width, height: c.height, fillOpacity: 0})\n",
         "\n",
         "\n",
         "  var line = d3.line()\n",
         "    .x(d =\u003e c.x(d.fpr_mid))\n",
         "    .y(d =\u003e c.y(d.tpr_mid))\n",
         "\n",
+        "  var area = d3.area()\n",
+        "    .x(d =\u003e c.x(d.fpr_mid))\n",
+        "    .y0(d =\u003e c.y(d.tpr_low))\n",
+        "    .y1(d =\u003e c.y(d.tpr_high))\n",
+        "\n",
         "  c.xAxis.ticks(5)\n",
         "  c.yAxis.ticks(5)\n",
         "  d3.drawAxis(c)\n",
         "\n",
         "  addAxisLabel(c, 'FPR = 1 - Specificity', 'TPR = Sensitivity')\n",
         "\n",
-        "  var areaSel = c.svg.append('path').at({fill: '#ddd'})\n",
-        "\n",
-        "  var altPathSel = c.svg.append('g').appendMany('path', d3.range(256))\n",
-        "    .st({stroke: '#999', fill: 'none'})\n",
-        "\n",
-        "  var pathSel = c.svg.append('path').at({stroke: '#000', strokeWidth: 2, fill: 'none'})\n",
+        "  var areaSel = c.svg.append('path.area')\n",
+        "    .at({fill: '#ddd'})\n",
+        "  var pathSel = c.svg.append('path.line')\n",
+        "    .at({stroke: '#000', strokeWidth: 2, fill: 'none'})\n",
         "\n",
         "  var fmt = d3.format('.2f')\n",
+        "  // var fmt = d3.format('.0%')\n",
         "\n",
         "  function dragEnd(auc, roc_array){\n",
         "    rv.data = []\n",
         "\n",
         "    rv.auc = auc\n",
         "    rv.roc_array = roc_array\n",
+        "    areaSel.at({d: area(rv.roc_array)})\n",
         "    pathSel.at({d: line(rv.roc_array)})\n",
         "    aucTextSel.html('\u003cb\u003eAUC: ' + fmt(auc.mid) + '\u003c/b\u003e [' + fmt(auc.low) + ' - ' + fmt(auc.high) + ']')\n",
         "\n",
-        "    altPathSel.at({opacity: 0})\n",
-        "\n",
         "    renderFPR(15, true)\n",
         "  }\n",
         "\n",
@@ -9290,7 +9534,7 @@
         "      return f(d[key + '_mid']) + ' [' + f(d[key + '_low']) + ' - ' + f(d[key + '_high']) + ']'\n",
         "    }\n",
         "\n",
-        "    alertText.text('Alerts: ' + printKey('nabove', d3.format(',.0f')))\n",
+        "    alertText.text('Alerts: ' + printKey('nabove', d =\u003e fmt(d/100)))\n",
         "    tprText.text('Sensitivity (Recall): ' + printKey('sens'))\n",
         "    fprText.text('Specificity: ' + printKey('spec'))\n",
         "    ppvText.text('PPV (Precision): ' + printKey('ppv'))\n",
@@ -9972,9 +10216,11 @@
         "\n",
         "  \n",
         "window.initViral = function(){\n",
+        "  var isInit = true\n",
+        "\n",
         "  var sel = d3.select('.sim-params-sliders').html('').classed('viral', 1)\n",
         "\n",
-        "  var selectSel = sel\n",
+        "  var distanceSelect = sel\n",
         "    .append('select')\n",
         "    .on('change', function(){\n",
         "      updateDistanceFun(this.value)\n",
@@ -10021,6 +10267,18 @@
         "      r: [0, .5]\n",
         "    },\n",
         "  ]\n",
+        "  var rv = addSliders(sel, sliders)\n",
+        "\n",
+        "  var bleSelect = sel\n",
+        "    .append('select')\n",
+        "    .on('change', function(){\n",
+        "      updateBleFn(this.value)\n",
+        "      dragEnd()\n",
+        "    })\n",
+        "    .appendMany('option', ['ble_params_lognormal_lovett', 'ble_params_normal_lovett',  'ble_params_lognormal_briers', 'ble_params_normal_sklearn'])\n",
+        "    .text(d =\u003e d)\n",
+        "    .at({value: d =\u003e d})\n",
+        "\n",
         "\n",
         "  function updateDistanceFun(fnStr){\n",
         "    rv.distance_fun = fnStr\n",
@@ -10030,14 +10288,24 @@
         "    //   rv.sliderSel.filter((d, i) =\u003e !i).st({display: '', opacity: 0, pointerEvent: 'none'})\n",
         "    // }\n",
         "    rv.sliderSel.filter(d =\u003e d.s == 'sigma').st({display: ''})\n",
+        "\n",
+        "    if (!isInit) dragEnd()\n",
         "  }\n",
         "\n",
-        "  var rv = addSliders(sel, sliders)\n",
-        "  updateDistanceFun(selectSel.data()[0])\n",
+        "  function updateBleFn(str){\n",
+        "    rv.ble_params = str\n",
+        "\n",
+        "    if (!isInit) dragEnd()\n",
+        "  }\n",
+        "\n",
+        "  updateDistanceFun(distanceSelect.data()[0])\n",
+        "  updateBleFn(bleSelect.data()[0])\n",
         "\n",
         "  rv.render = function({x, y}){\n",
         "  }\n",
         "\n",
+        "  isInit = false\n",
+        "\n",
         "  return rv\n",
         "}\n",
         "\n",
@@ -10161,18 +10429,19 @@
         "async function dragEnd(){\n",
         "  window.dragStartData = null\n",
         "\n",
-        "  var js_config = generateJSConfig()\n",
+        "  var js_settings = generateJSSettings()\n",
         "\n",
         "  if (window.google \u0026\u0026 window.google.colab){\n",
-        "    google.colab.kernel.invokeFunction('py_drag_end', [js_config], {})\n",
+        "    google.colab.kernel.invokeFunction('py_drag_end', [js_settings], {})\n",
         "  } else{\n",
-        "    var pyData = await post('drag_end_data', js_config)\n",
+        "    var pyData = await post('drag_end_data', js_settings)\n",
+        "    console.log()\n",
         "    window.pyData = pyData\n",
         "    window.js_drag_end(pyData)\n",
         "  }\n",
         "}\n",
         "\n",
-        "function generateJSConfig(name='js'){\n",
+        "function generateJSSettings(name='js'){\n",
         "  function normalize(array){\n",
         "    var sum = d3.sum(array)\n",
         "    return array.map(d =\u003e d/sum)\n",
@@ -10188,6 +10457,7 @@
         "    distance_inflection: viral.distance_inflection.v,\n",
         "    distance_Dmin: viral.distance_Dmin.v,\n",
         "    distance_fun: viral.distance_fun,\n",
+        "    ble_params: viral.ble_params,\n",
         "    distance: expose.distance.v,\n",
         "    duration: expose.duration.v,\n",
         "    onset: expose.onset.v,\n",
@@ -10239,6 +10509,7 @@
         "    .on('change', function(d){\n",
         "      d.v = +this.value\n",
         "      render('change')\n",
+        "      if (d.s == 'threshold') return\n",
         "      dragEnd()\n",
         "    })\n",
         "\n",
@@ -10308,9 +10579,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "executionInfo": {
-          "elapsed": 69814,
+          "elapsed": 57912,
           "status": "ok",
-          "timestamp": 1605160643866,
+          "timestamp": 1605287353294,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -10319,7 +10590,7 @@
           "user_tz": 480
         },
         "id": "9S4AdZRfmQPg",
-        "outputId": "8b2963e5-b060-48c4-9d7c-e558e9148fd6"
+        "outputId": "27e99339-099b-4f53-8234-0329171dc8ae"
       },
       "outputs": [
         {
@@ -10425,9 +10696,9 @@
           "height": 1000
         },
         "executionInfo": {
-          "elapsed": 70140,
+          "elapsed": 57901,
           "status": "ok",
-          "timestamp": 1605160644227,
+          "timestamp": 1605287353296,
           "user": {
             "displayName": "Kevin Murphy",
             "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjOnTbqCknID-qc_oKUjv90pW3mUH4EIZatyk9VIpw=s64",
@@ -10436,7 +10707,7 @@
           "user_tz": 480
         },
         "id": "6Xcq1rjD1qQI",
-        "outputId": "f771e41e-02d1-4607-b893-96d96b28e2eb"
+        "outputId": "fe629b27-8a24-424f-ba44-297916fbe1da"
       },
       "outputs": [
         {
@@ -10775,11 +11046,13 @@
               "}\n",
               "\n",
               ".how-to{\n",
-              "  top: 90px;\n",
-              "  left: 200px;\n",
+              "  top: 40px;\n",
+              "  left: 38px;\n",
               "  position: absolute;\n",
               "  font-weight: 200;\n",
               "  pointer-events: none;\n",
+              "  opacity: 1 !important;\n",
+              "  font-size: 14px;\n",
               "}\n",
               "\n",
               ".expose{\n",
@@ -10817,7 +11090,9 @@
               "  font-weight: 300;\n",
               "  cursor: pointer;\n",
               "}\n",
-              "\n",
+              ".param-label:hover{\n",
+              "  color: #000;\n",
+              "}\n",
               ".info {\n",
               "  content: \"ⓘ\";\n",
               "  float: right;\n",
@@ -10853,19 +11128,33 @@
               "  font-weight: 600;\n",
               "}\u003c/style\u003e\n",
               "\u003cscript\u003ewindow.presetConfigs = [\n",
-              "   {\n",
-              "    name: 'MLE',\n",
-              "    ble_thresholds: [51, 55, 60],\n",
-              "    ble_weights: [200, 85.7, 43.3, 9.8], \n",
-              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
-              "    inf_weights:  [0 , 16, 86],\n",
+              "  {\n",
+              "    name: 'LFPH-BleNarrow-InfNarrow',\n",
+              "    ble_thresholds: [53, 62, 70],\n",
+              "    ble_weights: [150, 100, 40, 0], \n",
+              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+              "    inf_weights:  [0, 30, 100],\n",
               "  },\n",
-              " {\n",
-              "    name: 'MLE-old',\n",
-              "    ble_thresholds: [48, 55, 68],\n",
-              "    ble_weights: [85.6, 48.7, 9.9, 1], \n",
-              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
-              "    inf_weights:  [5 , 38, 209],\n",
+              "  {\n",
+              "    name: 'LFPH-BleWide-InfNarrow',\n",
+              "    ble_thresholds: [55, 70, 80],\n",
+              "    ble_weights: [200, 100, 25, 0], \n",
+              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+              "    inf_weights:  [0, 30, 100],\n",
+              "  },\n",
+              "  {\n",
+              "    name: 'LFPH-BleNarrow-InfWide',\n",
+              "    ble_thresholds: [53, 62, 70],\n",
+              "    ble_weights: [150, 100, 40, 0], \n",
+              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
+              "    inf_weights: [0, 75, 250],\n",
+              "  },\n",
+              "  {\n",
+              "    name: 'LFPH-BleWide-InfWide',\n",
+              "    ble_thresholds: [55, 70, 80],\n",
+              "    ble_weights: [200, 100, 25, 0], \n",
+              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
+              "    inf_weights: [0, 75, 250],\n",
               "  },\n",
               "  {\n",
               "    name: 'Ireland',\n",
@@ -10923,6 +11212,20 @@
               "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
               "    inf_weights: [0, 100, 250],\n",
               "  },\n",
+              "  {\n",
+              "    name: 'MLE',\n",
+              "    ble_thresholds: [51, 55, 60],\n",
+              "    ble_weights: [200, 85.7, 43.3, 9.8], \n",
+              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
+              "    inf_weights:  [0 , 16, 86],\n",
+              "  },\n",
+              "  {\n",
+              "    name: 'MLE-old',\n",
+              "    ble_thresholds: [48, 55, 68],\n",
+              "    ble_weights: [85.6, 48.7, 9.9, 1], \n",
+              "    inf_levels: [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,  1, 1, 0, 0, 0, 0, 0],\n",
+              "    inf_weights:  [5 , 38, 209],\n",
+              "  },\n",
               "\n",
               "]\n",
               "\n",
@@ -11110,45 +11413,48 @@
               "  ]\n",
               "\n",
               "  var rvSliders = addSliders(sel.append('div').st({marginLeft: 30}), sliders, d =\u003e renderFPR(sliders[0].v))\n",
-              "  c.svg\n",
-              "    .on('mousemove', function(){\n",
-              "      var fpr = d3.mouse(this)[0]/c.width\n",
+              "  // c.svg\n",
+              "  //   .on('mousemove', function(){\n",
+              "  //     var fpr = d3.mouse(this)[0]/c.width\n",
               "\n",
-              "      auc.fpr = fpr\n",
-              "      auc.renderFPR()\n",
-              "    })\n",
-              "    .append('rect').at({width: c.width, height: c.height, fillOpacity: 0})\n",
+              "  //     auc.fpr = fpr\n",
+              "  //     auc.renderFPR()\n",
+              "  //   })\n",
+              "  //   .append('rect').at({width: c.width, height: c.height, fillOpacity: 0})\n",
               "\n",
               "\n",
               "  var line = d3.line()\n",
               "    .x(d =\u003e c.x(d.fpr_mid))\n",
               "    .y(d =\u003e c.y(d.tpr_mid))\n",
               "\n",
+              "  var area = d3.area()\n",
+              "    .x(d =\u003e c.x(d.fpr_mid))\n",
+              "    .y0(d =\u003e c.y(d.tpr_low))\n",
+              "    .y1(d =\u003e c.y(d.tpr_high))\n",
+              "\n",
               "  c.xAxis.ticks(5)\n",
               "  c.yAxis.ticks(5)\n",
               "  d3.drawAxis(c)\n",
               "\n",
               "  addAxisLabel(c, 'FPR = 1 - Specificity', 'TPR = Sensitivity')\n",
               "\n",
-              "  var areaSel = c.svg.append('path').at({fill: '#ddd'})\n",
-              "\n",
-              "  var altPathSel = c.svg.append('g').appendMany('path', d3.range(256))\n",
-              "    .st({stroke: '#999', fill: 'none'})\n",
-              "\n",
-              "  var pathSel = c.svg.append('path').at({stroke: '#000', strokeWidth: 2, fill: 'none'})\n",
+              "  var areaSel = c.svg.append('path.area')\n",
+              "    .at({fill: '#ddd'})\n",
+              "  var pathSel = c.svg.append('path.line')\n",
+              "    .at({stroke: '#000', strokeWidth: 2, fill: 'none'})\n",
               "\n",
               "  var fmt = d3.format('.2f')\n",
+              "  // var fmt = d3.format('.0%')\n",
               "\n",
               "  function dragEnd(auc, roc_array){\n",
               "    rv.data = []\n",
               "\n",
               "    rv.auc = auc\n",
               "    rv.roc_array = roc_array\n",
+              "    areaSel.at({d: area(rv.roc_array)})\n",
               "    pathSel.at({d: line(rv.roc_array)})\n",
               "    aucTextSel.html('\u003cb\u003eAUC: ' + fmt(auc.mid) + '\u003c/b\u003e [' + fmt(auc.low) + ' - ' + fmt(auc.high) + ']')\n",
               "\n",
-              "    altPathSel.at({opacity: 0})\n",
-              "\n",
               "    renderFPR(15, true)\n",
               "  }\n",
               "\n",
@@ -11202,7 +11508,7 @@
               "      return f(d[key + '_mid']) + ' [' + f(d[key + '_low']) + ' - ' + f(d[key + '_high']) + ']'\n",
               "    }\n",
               "\n",
-              "    alertText.text('Alerts: ' + printKey('nabove', d3.format(',.0f')))\n",
+              "    alertText.text('Alerts: ' + printKey('nabove', d =\u003e fmt(d/100)))\n",
               "    tprText.text('Sensitivity (Recall): ' + printKey('sens'))\n",
               "    fprText.text('Specificity: ' + printKey('spec'))\n",
               "    ppvText.text('PPV (Precision): ' + printKey('ppv'))\n",
@@ -11884,9 +12190,11 @@
               "\n",
               "  \n",
               "window.initViral = function(){\n",
+              "  var isInit = true\n",
+              "\n",
               "  var sel = d3.select('.sim-params-sliders').html('').classed('viral', 1)\n",
               "\n",
-              "  var selectSel = sel\n",
+              "  var distanceSelect = sel\n",
               "    .append('select')\n",
               "    .on('change', function(){\n",
               "      updateDistanceFun(this.value)\n",
@@ -11933,6 +12241,18 @@
               "      r: [0, .5]\n",
               "    },\n",
               "  ]\n",
+              "  var rv = addSliders(sel, sliders)\n",
+              "\n",
+              "  var bleSelect = sel\n",
+              "    .append('select')\n",
+              "    .on('change', function(){\n",
+              "      updateBleFn(this.value)\n",
+              "      dragEnd()\n",
+              "    })\n",
+              "    .appendMany('option', ['ble_params_lognormal_lovett', 'ble_params_normal_lovett',  'ble_params_lognormal_briers', 'ble_params_normal_sklearn'])\n",
+              "    .text(d =\u003e d)\n",
+              "    .at({value: d =\u003e d})\n",
+              "\n",
               "\n",
               "  function updateDistanceFun(fnStr){\n",
               "    rv.distance_fun = fnStr\n",
@@ -11942,14 +12262,24 @@
               "    //   rv.sliderSel.filter((d, i) =\u003e !i).st({display: '', opacity: 0, pointerEvent: 'none'})\n",
               "    // }\n",
               "    rv.sliderSel.filter(d =\u003e d.s == 'sigma').st({display: ''})\n",
+              "\n",
+              "    if (!isInit) dragEnd()\n",
               "  }\n",
               "\n",
-              "  var rv = addSliders(sel, sliders)\n",
-              "  updateDistanceFun(selectSel.data()[0])\n",
+              "  function updateBleFn(str){\n",
+              "    rv.ble_params = str\n",
+              "\n",
+              "    if (!isInit) dragEnd()\n",
+              "  }\n",
+              "\n",
+              "  updateDistanceFun(distanceSelect.data()[0])\n",
+              "  updateBleFn(bleSelect.data()[0])\n",
               "\n",
               "  rv.render = function({x, y}){\n",
               "  }\n",
               "\n",
+              "  isInit = false\n",
+              "\n",
               "  return rv\n",
               "}\n",
               "\n",
@@ -12073,18 +12403,19 @@
               "async function dragEnd(){\n",
               "  window.dragStartData = null\n",
               "\n",
-              "  var js_config = generateJSConfig()\n",
+              "  var js_settings = generateJSSettings()\n",
               "\n",
               "  if (window.google \u0026\u0026 window.google.colab){\n",
-              "    google.colab.kernel.invokeFunction('py_drag_end', [js_config], {})\n",
+              "    google.colab.kernel.invokeFunction('py_drag_end', [js_settings], {})\n",
               "  } else{\n",
-              "    var pyData = await post('drag_end_data', js_config)\n",
+              "    var pyData = await post('drag_end_data', js_settings)\n",
+              "    console.log()\n",
               "    window.pyData = pyData\n",
               "    window.js_drag_end(pyData)\n",
               "  }\n",
               "}\n",
               "\n",
-              "function generateJSConfig(name='js'){\n",
+              "function generateJSSettings(name='js'){\n",
               "  function normalize(array){\n",
               "    var sum = d3.sum(array)\n",
               "    return array.map(d =\u003e d/sum)\n",
@@ -12100,6 +12431,7 @@
               "    distance_inflection: viral.distance_inflection.v,\n",
               "    distance_Dmin: viral.distance_Dmin.v,\n",
               "    distance_fun: viral.distance_fun,\n",
+              "    ble_params: viral.ble_params,\n",
               "    distance: expose.distance.v,\n",
               "    duration: expose.duration.v,\n",
               "    onset: expose.onset.v,\n",
@@ -12151,6 +12483,7 @@
               "    .on('change', function(d){\n",
               "      d.v = +this.value\n",
               "      render('change')\n",
+              "      if (d.s == 'threshold') return\n",
               "      dragEnd()\n",
               "    })\n",
               "\n",
@@ -12213,45 +12546,32 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in double_scalars\n",
+            "  app.launch_new_instance()\n",
             "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "  from ipykernel import kernelapp as app\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars\n",
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: RuntimeWarning: invalid value encountered in double_scalars\n"
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in double_scalars\n"
           ]
         }
       ],
       "source": [
         "np.seterr(divide = 'ignore')\n",
-        "ble_params_gui = ble_params_lognormal_old\n",
-        "#ble_params_gui = ble_params_lognormal_new\n",
+        "ble_params_gui = ble_params_lognormal_lovett\n",
+        "#ble_params_gui = ble_params_lognormal_briers\n",
         "\n",
         "def extract_params_from_js(js_settings):\n",
+        "  ble_dict = {\n",
+        "    'ble_params_normal_lovett': ble_params_normal_lovett,\n",
+        "    'ble_params_lognormal_lovett': ble_params_lognormal_lovett,\n",
+        "    'ble_params_lognormal_briers': ble_params_lognormal_briers,\n",
+        "    'ble_params_normal_sklearn': ble_params_normal_sklearn,\n",
+        "  }\n",
+        "\n",
         "  params_js = ModelParams(\n",
         "    distance_fun = js_settings['distance_fun'],\n",
         "    distance_Dmin = js_settings['distance_Dmin'],\n",
         "    distance_slope = js_settings['distance_slope'],\n",
         "    distance_inflection = js_settings['distance_inflection'],\n",
-        "    ble_params = ble_params_gui,  ### MAKE ADJUSTABLE\n",
+        "    ble_params = ble_dict[js_settings['ble_params']],\n",
         "    infectiousness_fun = 'student'\n",
         "  )\n",
         "  sigma = js_settings['sigma']\n",
@@ -12415,7 +12735,7 @@
         "  rv['actual_onset'] = infectiousness_student(np.arange(-14, 14+1, 0.1))\n",
         "  rv['risk_v_distance'] = calc_risk_v_distance(params_js, config_js)\n",
         "\n",
-        "  outjson = json.dumps([rv], cls=NumpyEncoder)\n",
+        "  outjson = json.dumps([rv], cls=NumpyEncoder).replace('NaN' , 'null')\n",
         "  output.eval_js('js_drag_end({})'.format(outjson))\n",
         "output.register_callback('py_drag_end', py_drag_end)\n",
         "\n",
@@ -12469,12 +12789,12 @@
       "name": "risk_score_tuner_public.ipynb",
       "provenance": [
         {
-          "file_id": "1woRr99F1EMGZIs8ppwQk2T3Uj2PETmzQ",
-          "timestamp": 1605161480512
+          "file_id": "1rP1x2DSdAOja7p_OXT1SfKXI2z7kwIfv",
+          "timestamp": 1605288952189
         },
         {
           "file_id": "1xjVnhG-aNKtFVqZ1h31VqYajPswDOfzp",
-          "timestamp": 1605160808540
+          "timestamp": 1605288212728
         }
       ],
       "toc_visible": true