{ "cells": [ { "cell_type": "markdown", "id": "subject-wagon", "metadata": {}, "source": [ "# EasyVVUQ - Vector Quantities of Interest" ] }, { "cell_type": "markdown", "id": "elect-tract", "metadata": {}, "source": [ "It is often the case that simulation outputs are vector valued and represent changes over time in whatever phenomenon that is simulated. Here we examine how to analyse these cases with EasyVVUQ. As an example we use the following model from epidemiology - [SIR](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model). It compartmentalises the population into the following groups: ($S$)usceptible, ($I$)nfected and ($R$)emoved. There are four input parameters to out model. They are initial number of susceptible people $S_0$, initial number of infected people $I_0$, transmission rate $\\beta$ and recovery rate $\\gamma$. The system is governed by three differential equations below. The number of suspectible people is reduced by them getting infected (and thus moving to $I$) at rate specified by $\\beta$. The number of infected people increases similarly. Finally the number of recovered people increases depending on the recovery rate and the number of people currently infected." ] }, { "cell_type": "markdown", "id": "neither-sierra", "metadata": {}, "source": [ "$${\\frac {dS}{dt}}=-{\\frac {\\beta SI}{N}}$$\n", "\n", "$${\\displaystyle {\\frac {dI}{dt}}={\\frac {\\beta SI}{N}}-\\gamma I}$$\n", "\n", "$${\\displaystyle {\\frac {dR}{dt}}=\\gamma I}$$" ] }, { "cell_type": "markdown", "id": "scientific-shareware", "metadata": {}, "source": [ "The simulation is run as shown below, where ```input_file``` is a JSON file of the following format: ```{\"outfile\": \"$outfile\", \"S0\": $S0, \"I0\": $I0, \"beta\": $beta, \"gamma\": $gamma, \"iterations\": $iterations}```" ] }, { "cell_type": "markdown", "id": "german-upper", "metadata": {}, "source": [ "```sir ```" ] }, { "cell_type": "markdown", "id": "religious-problem", "metadata": {}, "source": [ "Lets try and run it." ] }, { "cell_type": "code", "execution_count": 1, "id": "pressed-recipe", "metadata": {}, "outputs": [], "source": [ "!echo \"{\\\"outfile\\\": \\\"output.csv\\\", \\\"S0\\\": 997, \\\"I0\\\": 3, \\\"beta\\\": 0.2, \\\"gamma\\\": 0.04, \\\"iterations\\\": 100}\" > input.json" ] }, { "cell_type": "code", "execution_count": 2, "id": "bearing-force", "metadata": {}, "outputs": [], "source": [ "!./sir input.json" ] }, { "cell_type": "markdown", "id": "organizational-framework", "metadata": {}, "source": [ "The simulation code outputs a CSV file with a specified name. We can try opening it up and printing the first 10 lines." ] }, { "cell_type": "code", "execution_count": 3, "id": "according-timer", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "S,I,R,r0,t\r\n", "996.4018,3.47784108,0.1391136432,4982.009,1\r\n", "995.7087475755611,4.0312978050411745,0.300365555401647,4978.543737877805,2\r\n", "994.905980318425,4.672165937353743,0.4872521928957967,4974.5299015921255,3\r\n", "993.9763679472093,5.4140230672294996,0.7038131155849767,4969.8818397360465,4\r\n", "992.9001871310173,6.27247777694467,0.9547122266627635,4964.500935655087,5\r\n", "991.654756898651,7.2654467105291545,1.2453300950839297,4958.273784493255,6\r\n", "990.2140324288114,8.413460157063296,1.5818685013664615,4951.070162144058,7\r\n", "988.548155936764,9.739995672478827,1.9714683282656145,4942.740779683821,8\r\n", "986.6229648025937,11.27183768247915,2.4223418355647803,4933.11482401297,9\r\n" ] } ], "source": [ "!head output.csv" ] }, { "cell_type": "markdown", "id": "japanese-canvas", "metadata": {}, "source": [ "Finally lets plot the results." ] }, { "cell_type": "code", "execution_count": 4, "id": "moral-booking", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "df = pd.read_csv('output.csv')" ] }, { "cell_type": "markdown", "id": "introductory-cambodia", "metadata": {}, "source": [ "As we can see it shows the number of infected people increasing at first and then decreasing as the number of people who have recovered rises. Number of susceptible and recovered people also changes in a way that makes intuitive sense." ] }, { "cell_type": "code", "execution_count": 5, "id": "sudden-boring", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(df['S'], label='S')\n", "plt.plot(df['I'], label='I')\n", "plt.plot(df['R'], label='R')\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "collectible-bread", "metadata": {}, "source": [ "So let us see what EasyVVUQ can tell us about the distribution of the number of infected people over time. We will not go into the details of the EasyVVUQ API this time. Only where it differs from the [basic concepts](./basic_tutorial.ipynb) tutorial." ] }, { "cell_type": "code", "execution_count": 6, "id": "flexible-password", "metadata": {}, "outputs": [], "source": [ "import easyvvuq as uq\n", "import chaospy as cp\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "id": "victorian-dallas", "metadata": {}, "outputs": [], "source": [ "params = {\n", " \"S0\": {\"type\": \"float\", \"default\": 997}, \n", " \"I0\": {\"type\": \"float\", \"default\": 3}, \n", " \"beta\": {\"type\": \"float\", \"default\": 0.2}, \n", " \"gamma\": {\"type\": \"float\", \"default\": 0.04, \"min\": 0.0, \"max\": 1.0},\n", " \"iterations\": {\"type\": \"integer\", \"default\": 100},\n", " \"outfile\": {\"type\": \"string\", \"default\": \"output.csv\"}\n", "}" ] }, { "cell_type": "markdown", "id": "meaning-berry", "metadata": {}, "source": [ "Encoder is the same as in the basic concepts tutorial, essentially. However, since the simulation outputs a CSV file with the evolution of output variables over time we want the ```SimpleCSV``` decoder. The arguments to it should be self explanatory." ] }, { "cell_type": "code", "execution_count": 8, "id": "owned-preliminary", "metadata": {}, "outputs": [], "source": [ "encoder = uq.encoders.GenericEncoder(template_fname='sir.template', delimiter='$', target_filename='input.json')\n", "decoder = uq.decoders.SimpleCSV(target_filename='output.csv', output_columns=['I'])" ] }, { "cell_type": "code", "execution_count": 9, "id": "neutral-marina", "metadata": {}, "outputs": [], "source": [ "campaign = uq.Campaign(name='sir', params=params, encoder=encoder, decoder=decoder)" ] }, { "cell_type": "markdown", "id": "searching-interpretation", "metadata": {}, "source": [ "We assume that the infection rate $\\beta$ is uniformly distributed between 0.15 and 0.25 and the recovery rate $\\gamma$ is normally distributed with mean 0.04 and small variance." ] }, { "cell_type": "code", "execution_count": 10, "id": "native-order", "metadata": {}, "outputs": [], "source": [ "vary = {\n", " \"beta\": cp.Uniform(0.15, 0.25),\n", " \"gamma\": cp.Normal(0.04, 0.01),\n", "}" ] }, { "cell_type": "markdown", "id": "engaging-spoke", "metadata": {}, "source": [ "For this tutorial we will use Polynomial Chaos Expansion method. However, both [SCSampler](https://easyvvuq.readthedocs.io/en/dev/easyvvuq.sampling.html#module-easyvvuq.sampling.stochastic_collocation) and [QMCSampler](https://easyvvuq.readthedocs.io/en/dev/easyvvuq.sampling.html#module-easyvvuq.sampling.qmc) would work as well and might be preferable depending on the case." ] }, { "cell_type": "code", "execution_count": 11, "id": "cosmetic-stake", "metadata": {}, "outputs": [], "source": [ "campaign.set_sampler(uq.sampling.PCESampler(vary=vary, polynomial_order=5))" ] }, { "cell_type": "code", "execution_count": 12, "id": "governing-warrant", "metadata": {}, "outputs": [], "source": [ "execution = campaign.sample_and_apply(action=uq.actions.ExecuteLocalV2(\"sir input.json\"), batch_size=8)" ] }, { "cell_type": "code", "execution_count": 13, "id": "comparative-porcelain", "metadata": {}, "outputs": [], "source": [ "execution.start();" ] }, { "cell_type": "code", "execution_count": 17, "id": "junior-proxy", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ready': 0, 'active': 0, 'finished': 36, 'failed': 0}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "execution.progress()" ] }, { "cell_type": "code", "execution_count": 18, "id": "regulated-semester", "metadata": {}, "outputs": [], "source": [ "result = campaign.analyse(qoi_cols=['I'])" ] }, { "cell_type": "markdown", "id": "descending-resident", "metadata": {}, "source": [ "We can now see the results of the analysis. One thing to try would be to plot the first order sobol indices over time. This shows us how much influence the two parameters (```beta``` and ```gamma```) have over the number of people infected over time ```t```. The ```higher orders``` line is meant to represent the influence of the interactions between the input variables. However it is negligible in this case." ] }, { "cell_type": "code", "execution_count": 19, "id": "pressing-sister", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HptR8hsWHOnaHUsKxXyEhue0GobGHS/6ijVmw+M8Q3gM6Jjo7IveVcwjWvAq7/wcISLweRt8HoQnOjqyOC/3mKoptrh/emUAjzF15xPE7yzkIpdkQf7Hj92VPei+Y/jH4hcMXM7TqCsW+MnfA/26CucO0h/mGzoZ7d2oNwe0oCYANiUAIEdnAvJ6OCUdRLpzJoGdinIFfD+WwJ6PQsTtLXaW9uloiAPCPhBlfaN1mfzFDG2JTuTBSQtpa+O80eHes1n405gG4fw9c9oLWGWA7ZMsVwWohRF0lohDiQUA9q660a+NiDQSYvHgzxcFXBWmrIKgzhMQ5dj+O0qE/THsPMrfDN3doT7MqLVdTo92a+8Ek+Ohy7Wrg0qe1BHDpU9qVVztmSxtBMvCuEOL3QBSwHxjmyKAU5UL5GgSzRsYxN+UIR7JL6BbpgPv7a2q0biV6XmH/bbelXlfAhGdh+ZOwJAiueq1Nb110aRaz1g3E2tcgZ7/2peDylyDxBjD6Ojs6mzV7RSClPAX8AIwE4oCPpZQlDo5LUS7YLaPi8PbS8bajnjbO2gPlZyB+jGO235ZG/Vm7g2Xbx9qtjaobiqZVFsP6N+G1RFh0BwgdXPOO1g3EsNkulQTAhisCIcTPQCbQD4gF5gkhVkkpH3J0cIpyIcL8vZkxrDOfrj/OQxN70iHIZN8d1LYPxLlBIgCtCqOqFNa/oY2ydsn/qSuDcxWdgk3vwJYPtLaVLqPgyleh+wSX/r+ypWroDSnlIuvPBUKIkcD/OS4kRbGfWy6K56N1aXyx6QT3T7BzlwppqyG0a7ttAGwxIWDyC1oyWPUvqCqBiX9zrdtiHeXULtjwplYNJC3amNQX3QudkpwdmV00mwiklIuEEKOB7lLKD4EQQPVapbiEzmG+jO0RwRebTnD3uG4Y9HY6qVmqtbtD+l9rn+21FzodTPmP1mfShje1kc2mzgUvo7Mja3s1Fq2Tvg1vaUnf4AdDboURd7S72z8vlC1VQ08DQ4CewIeAES0RjHJsaIpiHzeO6MJtH2/h531ZXNY/2j4bPbUTqordo33gXDqddmXgFwG/PAdleXDtB+AT7OzI2kb5GdjxOWx6F86kQWAnGP9XSJoFPiHOjs4hbKkaugYYBGwDkFJmCiECHBqVothRcs9IYoJ9+HTDcfslgjQ3ax84lxBw8UPaswZL7tfuiZ/+KUQPcHZkjnNqJ2x6T6v+qS6HziO1BNDrSu0BPDdmS+mqpJRSCCEBhBB+Do5JUexKrxNcP7wzL/540H63kqaugoje2onSnQ2+CcJ7ar2VzpsAV7yidZHgwg2jZ6ksgb1fw5YPIXOb1kg+YDoM/aN7J71z2FJh+j8hxDtAsBBiNvAz8J5jw1IU+7puaCwGveCzjccvfGMWs9ZPjztWCzWk83C4fTV0Ggrf3glf3qDdPeOqpLT2s3QPvNxLezWXa9VhD+zTuoDwoCQAtjUWvySEmAAUobUTPCWlXO7wyBTFjsL9vbmsXzQLtqbzyKRe+Bj1rd9Y5nYwl7lvtVBD/CPgxkVaA/LKv2n950x4Vrti0F3A/2VbyjuqVfvsmg/5R7XG375Xw+BZEDvMfa5yWsGmii/riV+d/BWXdv3wzizemclP+04zNfECbvlMW629dvGw+yX0XtqDZ72ugO/uhSX3aQ2q457UxtJtjyfSghOwb7HW6VvGFm1el9Fa/z99rm7fI8q1oUYTgRCiGGj08UIpZaBDIlIUBxkWF0pMsA/fbM+4wESwBiL7gl+Y/YJzJWFdYdZ3sGehdnXw5Qyt2uiie6Dn5Q4dW7dZUsLp3XDoRzj4vXb1BlqfSuP/qt3uG9TJefG1U40mAillAIAQ4jngFPApIIAbADvdeqEobUenE0xN7Mg7q46RU1xJRIB3yzdS2z4w6Eb7B+hKhNBOqn2mwo7PYNXLWpfLAdGQdIv2WVjXNgnFWJkPu/6njQtxbCUUZWgfxCTB+Geg95Q2i8VV2VI1NEVKObDe+7eEEDuBpxwUk6I4zDWDYngz5ShLdmVyy6j4lm+grn1gtP2Dc0V6AyTdrCXGQz9qg7Cn/F2bInpDr8u1tpSYJDDZoRKhsgRyDmjHIX0LpG/iovxj2memYK078Ev+D7pNgICoC9+fh7AlEZQKIW4AvkSrKpoBlDo0KkVxkO5RAfTtGMii7RmtSwSe2j7QHJ1eO+n3uhwKTsKB7+HAEljzb1j9MiAgsg9E9tK67A6J1269NQWBd6CWUGSNNlWVaQ91ledD8Smtnr/gBOQehjOpv+3TLxJih3E0eAxdx98CHQa4TsN1O2NLIrgeeM06SWCtdZ6iuKRrBsXw/Pf7OZpTQteIFjYWpq3RTmie2j5gi+BYrRuGEXdAeQFkbIX0zdo3+IytsHeR1l+PrUzBENwZogdqzzBE9tHq/IM7gxCcTEmha8dBDiqMZ7Dl9tE0YGprNi6EmIyWQPTA+1LKFxpYZjrwDFqS2SmlVElGcairBnbk70v38+32DB6Y2ILB9uraB2Y6Ljh34xMM3S7VploWMxSehLJ8rQfPyiKtXx8hAAFGP60rB5+Q364aFIeypa+hCGA22lgEdctLKW9tZj09MBeYAKQDm4UQi6WU++ot0x14HBglpTzT0LCYimJvUYEmRnUL55sdGdw/oQfC1tseVfuAfegNWqdtbtZxmyuz5cnib4EgtCeKv683NWcYcERKeUxKWYXWxnDulcVsYK6U8gyAlDLb1sAV5UJcnRjDyfxytp0osH0l1T6guClb2gh8pZSPtmLbMcDJeu/TgeHnLNMDQAixFq366Bkp5Q/nbkgIMQeYAxAVFUVKSkorwoGSkpJWr+vKPLHczZXZxyzRC3hv2WaKe9nWxfKAnYsx+nVhy+Y9dorS/tSx9hz2LLctiWCJEOJyKeVSu+zx/P13RxsXuROwSgjRX0pZUH8hKeW7wLsAQ4YMkcnJya3aWUpKCq1d15V5YrltKfP8k5vYn1fK2LFjm68esphh7WFIvL5d/1+qY+057FluW6qG7kVLBuVCiCIhRLEQosiG9TLQhras1ck6r750YLGU0iylTAUOoSUGRXG4SX07cDyvjAOni5tfOGMbmEs9p6M5xaPYMnh9gJRSJ6X0kVIGWt/b8mTIZqC7ECJeCGEE/gAsPmeZRWhXAwghwtGqio61pACK0loT+kQhBPy493TzC7v7+AOKR2uqr6HBTa0opdzWzOfVQoi7gR/R6v8/kFLuFUI8C2yRUi62fjZRCLEPsAAPSynzWloIRWmNiABvhnQJ4Yc9p7lvfDPjGaeugqj+4BvaNsEpShtqqo3g5SY+k8C45jZubVdYes68p+r9LIEHrJOitLlJfTvw/Pf7OZ5XSpewRsZcqq6Ek5u08WoVxQ011encJW0ZiKI4Q20i+HHvaeZc3EjHZOmbobpCVQspbsuWxmJFcVuxob70iQ7kx71ZjS+UuhqEDrpc1HaBKUobUolA8XiT+3Vg6/EzZBdVNLxA2mqtQzOf4DaNS1HaikoEiseb2FfrrnjFgQYebK8q09oH1G2jihtz2F1DbclsNpOenk5FRSPf6KyCgoLYv39/G0XVfjir3CaTiU6dOmEwOHHEKhv0jAqgY5CJlIPZzBjW+ewPT26EGjPEj3VOcIrSBhx611BbSU9PJyAggLi4uCafEC0uLiYgIKANI2sfnFFuKSV5eXmkp6cTH9+Kfv/bkBCCsT0j+W5nJlXVNRi96l0op60GoYfOI5wXoKI4mFvcNVRRUdFsElDalhCCsLAwcnJynB2KTZJ7RvDFphNsOZ7PRV3Df/sgdTXEDAZvz/sCoXiOZtsIhBAGIcSfhRALrNPdQoh2d62vkkD740rHZFS3cAx6wa8H6yWuymLI3KZuG1Xcni2NxW8BScCb1inJOk9R3Ia/txdD40JJqZ8I0tZCTTUkJDstLkVpC7YkgqFSyllSyl+s0y3AUEcH5mrS0tLo16+fzct/9NFHZGZmOjAipaWSe0ZwMKuYzIJybcaxleBlgthze09XFPdiSyKwCCHqHrkUQiSg9QukXACVCNqfS3pqA+TVXRUcXak9RGYwOTEqRXE8WxLBw8BKIUSKEOJX4BfgQceG5Zqqq6u54YYb6N27N9deey1lZWVs3bqVsWPHkpSUxKRJkzh16hQLFixgy5Yt3HDDDSQmJlJeXs6zzz7L0KFD6devH3PmzEHrhklpS90i/YkJ9iHlYDYUZkDuQUhwmXsmFKXVbBm8foV1bOHaUb4PSikrHRtW6/31u73sy2x4uASLxYJer2/xNvt0DOTpq/o2u9zBgweZN28eo0aN4tZbb2Xu3Ll88803fPvtt0RERDB//nz+8pe/8MEHH/DGG2/w0ksvMWTIEADuvvtunnpK64/vxhtvZMmSJVx11VUtjlVpPe020gi+3Z5B9ZGD2h9HV5UIFPdny+D1JuBOYDTa8wOrhRBvSymbfnrLA8XGxjJqlDae7cyZM/n73//Onj17mDBhAqAloujo6AbXXblyJf/6178oKysjPz+fvn37qkTgBMk9Ivh84wnO7P6RCN9wiGz+C4CiuDpbhqr8BCgG/mN9fz3wKfB7RwV1IZr65u7oB6vOvV0yICCAvn37sn79+ibXq6io4M4772TLli3ExsbyzDPPNPuUtOIYo7qF46UDn4zV0HMc6FQvLIr7s+W3vJ+U8jYp5UrrNBtQX5MacOLEibqT/ueff86IESPIycmpm2c2m9m7dy+gJYniYm2IxNqTfnh4OCUlJSxYsMAJ0SsAft5eTI0uwN+cr6qFFI9hSyLYJoSoe75eCDEc2OK4kFxXz549mTt3Lr179+bMmTPcc889LFiwgEcffZSBAweSmJjIunXrALj55pu54447SExMxNvbm9mzZ9OvXz8mTZrE0KHq7lxnmhJwCICi6NFOjkRR2kZTnc7tRmsTMADrhBAnrB91Bg60QWwuJS4ujgMHzv9vSUxMZNWqVefNnzZtGtOmTat7//zzz/P88887NEbFNgPN2zlaE83hXBOTOzg7GkVxvKbaCK5ssygUpb2oriQoezPfizEcOJLL5H4qEyjur6lO547X/iyEGAjUdriyWkq509GBKYpTHF+HMJeREzmKtUdznR2NorQJWzqduxf4DIi0Tv8VQtzj6MAUxSkOLwe9N8F9x3Msp5RTheXOjkhRHM6WxuLbgOFSyqeklE8BI4DZjg1LUZzk8I8QN5phPWIBWHskz8kBKYrj2ZIIBGf3LWSxzlMU95J3FPKOQPeJ9OoQQKifkXVHVPWQ4v5seaDsQ2CjEOIbtAQwFZjn0KgUxRkOL9deu09ApxNc1DWMNUdykVK61NgKitJSzV4RSClfAW4B8oFc4BYp5b8dHJeitL3DP0FYNwjTOtsd1S2c7OJKjuaUODkwRXGsRhOBEMK3diQy60D1P6JdQbTvAWgVpTWqSiFtDXSfVDdrlHXIStVOoLi7pq4IfgDiAIQQ3YD1QAJwlxDiBceH5nqee+45evbsyejRo5kxYwYvvfQS7733HkOHDmXgwIFMmzaNsrIyQHuy+E9/+hMjRowgISGBlJQUbr31Vnr37s3NN99ct01/f38efvhh+vbty/jx49m0aRPJyckkJCSwePFiQBsUZ8yYMQwePJjBgwfXPb2stEDqKrBUQo+JdbM6h/kSE+zDhmMqESjurak2ghAp5WHrz7OAL6SU9wghjMBW4DGHR9cayx6D07sb/MjHUg16W5pFztGhP1zWdO7bvHkzCxcuZOfOnZjNZgYPHkxSUhK/+93vmD1bu8nqiSeeYN68edxzj3b37ZkzZ1i/fj2LFy9mypQprF27lvfff5+hQ4eyY8cOEhMTKS0tZdy4cbz44otcc801PPHEEyxfvpx9+/Yxa9YspkyZQmRkJMuXL8dkMnH48GFmzJjBli2qF5AWOfwTGP2h80VnzR6REMbKg9mqnUBxa02dFeuPjDIOeBFASlklhKhxaFQuaO3atUydOhWTyYTJZKrrQnrPnj088cQTFBQUUFJSwqRJv1U9XHXVVQgh6N+/P1FRUfTv3x+Avn37kpaWRmJiIkajkcmTJwPQv39/vL29MRgM9O/fn7S0NEDrzO7uu+9mx44d6PV6Dh061LaFd3VSwqGftLGJvYxnfTQ8IZSF29I5nF1CjyjH9VyrKM7UVCLYJYR4CcgAugE/AQghgtsgrtZr4pt7uYO7oW7IzTffzKJFixg4cCAfffQRKSkpdZ95e3sDoNPp6n6ufV9dXQ2AwWCo+yZaf7n6y7z66qtERUWxc+dOampqMJnU0IotkrUXitJh7CPnfTQyIQyADcfyVCJQ3FZTbQSz0e4SigMmSinLrPP7AC85OC6XM2rUKL777jsqKiooKSlhyZIlgDYGQnR0NGazmc8++8wh+y4sLCQ6OhqdTsenn36KxaKGlG6RA0sAAT0mn/dRpxAf1U6guL2m+hoqB877ei2lXAeo1shzDB06lClTpjBgwIC6ap6goCCee+45hg8fTkREBMOHD68bg8Ce7rzzTqZNm8Ynn3zC5MmT8fPzs/s+3Nr+76DzSAiIOu8jIQTDE0L59WCOaidQ3JeU0qWmpKQkea59+/adN68hRUVFNi3XWsXFxVJKKUtLS2VSUpLcunWrQ/dnK0eXuym2Hht7W7lypW0L5h6R8ulAKdfNbXSR+ZtOyC6PLpGHTjvv/9FWNpfbjXhimaVsebmBLbKR82orbqFRGjNnzhz27dtHRUUFs2bNYvDgwc4OSWnO/u+0196N97o+ol47QXfVTqC4oSYTgRBCD/xTSvlQG8Xj0j7//HNnh6C01P7vIDoRgjs3ukhsqA8dg0xsSM3nxpFxbRaaorSVJruYkFJagFaP1yeEmCyEOCiEOCKEaPS5AyHENCGEFEIMae2+FKXFCjMgYwv0vqrJxYQQjEgIY+OxPLQrbEVxL7b0PrpdCLFYCHGjEOJ3tVNzK1mvJuYCl6HdaTRDCNGngeUCgHuBjS2MXVEuzAHtzi76TG120eEJoeSWVKl+hxS3ZEsiMAF5aA+VXWWdbBnGchhwREp5TEpZBXyJ1nPpuZ4D/glU2BSxotjL/u8goheEd2920dp2gvXH8h0dlaK0uWYbi6WUt7Ry2zHAyXrv04Hh9RcQQgwGYqWU3wshHm5sQ0KIOcAcgKioqLMeygIICgqy6bZMi8XikNs32ztnlruiouK849UWSkpKmtyvoaqQi9LWcrzLtaTZEJ+UkhBvwZIN+4mtSLVfoHbWXLndkSeWGexc7sZuJ6qdgB7ACmCP9f0A4Akb1rsWeL/e+xuBN+q91wEpQJz1fQowpLnttsfbR1NTU2Xfvn0b/OzJJ5+Uy5cvb3L9p59+Wr744ouOCE1KaVu5myrDhWi3t49uek+7bTRzp83b/PMX2+TQ55fLmpqaCwvOgTzxVkpPLLOU9r191JaqofeAxwGzNXHsAv5gw3oZQGy9952s82oFAP2AFCFEGtoQmIvdrcH42WefZfz48Q7dR2ueJK7tnqK1LnR9p9v1P4jorXUoaKNh8aFkF1dyPK+s+YUVxYXY8hyBr5Ry0zlPVNpyFtgMdBdCxKMlgD8A19d+KKUsBMJr3wshUoCHpJQu2W2mxWJh9uzZrFu3jpiYGL799lt8fHy4+eabufLKK7n22mtZunQpDzzwAH5+fowaNYpjx47VdUWxb98+kpOTOXHiBPfddx9//vOfAfjvf//L66+/TlVVFcOHD+fNN99Er9fj7+/P7bffzs8//8zcuXMZPfq3m7t27NjBHXfcQVlZGV27duW1114jICCA5ORkEhMTWbNmDTNmzCA5OZlbb70VgIkTJ55Vlscee4yUlBQqKyu56667uP3220lJSeHJJ58kJCSEAwcOsH37dqZPn056ejoWi4Unn3yS6667rg3/11sp/xic3AiXPg0teFJ4eHwoAJtS84kLV09vt1RWUQWHsoo5llNKam4pOcWVnCmroqDMTJWlpu6OLG8vPQEmLwJ9DIT7G+kY5EPHYB86h/nSIzKAIF+Dk0vifmxJBLlCiK5YeyMVQlwLnGpuJSlltRDibrQBbfTAB1LKvUKIZ9EuURZfQNyN+uemf3Ig/0CDn1ksFvR6fYu32Su0F48Oe7TJZQ4fPswXX3zBe++9x/Tp01m4cCEzZ86s+7yiooLbb7+dVatWER8fz4wZM85a/8CBA6xcuZLi4mJ69uzJn/70J44cOcL8+fNZu3YtBoOBO++8k88++4ybbrqJ0tJShg8fzssvv3xeLDfddBP/+c9/GDt2LE899RQvvPACb775JgBVVVV1XVQPGDCAN954g4svvpiHH/6tiWbevHkEBQWxefNmKisrGTVqVF2i2LZtG3v27CE+Pp6FCxfSsWNHvv/+e0Dr88gl7PpKex0wvUWrdY3wJ8zPyMbUfKYPjW1+BQ+XV1LJiv3ZbDiWx6a0fNLPlNd95mfUExVkIsTXSHSQCZPht7/LymoLRRXVnMwvY/uJAnJLKs/ablSgN706BJIYG0xi52BKq9QtvRfKlkRwF/Au0EsIkQGkAjObXkUjpVwKLD1n3lONLJtsyzbbq/j4eBITEwFISkqq6yK61oEDB0hISCA+XhvgbcaMGbz77rt1n19xxRV4e3vj7e1NZGQkWVlZrFixgq1btzJ06FAAysvLiYyMBECv1zNt2rTz4igsLKSgoICxY8cCMGvWrLOWq/3GXlBQQEFBARdffDEAN954I8uWLQPgp59+YteuXSxYsKBum4cPH8ZoNDJs2LC6MvTv358HH3yQRx99lCuvvJIxY8a0/j+wrUgJu+ZD3BgI6tSiVYUQDIsPZWOq6oCuMWVV1Xy3M5Mlu06x7mgelhpJmJ+RYfGh3DIqnj7RgSRE+BEZ4G1zv00VZguZBeUczyvjUFYxB7OK2ZdZxH9+OUyN1AZSn7t/NRd1DeOirmGM7BqGr1F1mtASttw1dAwYL4TwA3RSynZ9201T39yLHdgNdf1upPV6PeXl5U0s3fz61dXVSCmZNWsW//jHP85b3mQyterqxpYO6aSU/Oc//zlr7ASAlJSUs9bv0aMH27ZtY+nSpTzxxBNceumlPPVUg3m+/cjYCvlHYfT9rVp9WHwoy/acJqOgnJhgHzsH57oyC8r5eH0aX2w8QVFFNXFhvtwxNoHL+0fTJzrwgjrrMxn0JET4kxDhzyW9Iuvml1RWsyu9gK9WbuN0jYFPNxxn3ppUjF46RiaEcWnvSCb0iSI6SB2n5jSaCIQQDzQyH6gb1F6xUc+ePTl27BhpaWnExcUxf/78Zte59NJLmTp1Kvfffz+RkZHk5+dTXFxMly5dGl0nKCiIkJAQVq9ezZgxY/j0008ZNWrUecsFBwcTHBzMmjVrGD169FldZE+aNIm33nqLcePGYTAYOHToEDExMedtIzMzk9DQUGbOnElwcDDvv/++jf8bTrRrPniZoM+UVq0+PF57nmBzaj4xg87/P/E0eSWVvLbiMJ9vPEGNlFzWL5pbR8cxuHOIw3tq9ff24qKu4VSdNJKcPIIKs4XNafmsPJDDyoPZPPXtXp76di+JscFc1q8DVw7sqJJ3I5q6Iqj96twTGArU1ulfBWxyZFDuyMfHhzfffLOum+ja6p6m9OnTh+eff56JEydSU1ODwWBg7ty5TSYCgI8//riusTghIYHXX3+9weU+/PBDbr31VoQQZzUW//GPfyQtLY3BgwcjpSQiIoJFixadt/7u3bt5+OGH0el0GAwG3nrrrWbL5FQWM+xZCD0vA1NQqzbRs0MAgSYvNqbmcbUHJ4LKagvvr07lrZSjlJstzBgWyx1ju9IpxNdpMZkMesZ0j2BM9wieuqoPR7JL+HHvaZbtOcU/lh3gH8sOMCwulKmDOnJl/46q0bkeUdtS3+gCQqwCrqitErJ2CfG9lPLiNojvPEOGDJHnjse7f/9+evfu3ey6jqwaskVJSQn+/v5IKbnrrrvo3r0799/fuiqKlnBmuW09NvaWkpJCcnLy2TMPLIUvZ8CML7Vk0Eq3fbSZ1LxSfnkwudll21qD5baz3emFPPjVDg5llTChTxSPXdaLrhH+Dt1nU2wp8/G8UhbvyGTRjgyO5pRi9NIxsU8U1yZ1Ykz3CPQ61xtnoqXHWgixVUrZ4O35trSoRAFV9d5XWecpLfTee+/x8ccfU1VVxaBBg7j99tudHZJn2fIB+HeAbhf2XMfwhFBWHMgmp7iSiADv5ldwE2ZLDXNXHuGNX44Q5m/kw5uHnlVn3551CfPjnku7c/e4buzNLGLB1nQW7chgya5TdAwycd3Qzkwf2slj2xNsSQSfAJuEEN9Y318NfOSogNzZ/fff3yZXAEoDzqTBkZ+1cYn1F1YlMMzaTrApNZ8rBkTbIbj2L6+kkjs/28bG1HyuGRTDM1f1dcmqFSEE/WKC6BcTxOOX92LF/my+2HSCV38+xGsrDjGuVxQ3jezC6G7h6FzwKqG1mhuPQKAlgmVA7b2Bt0gptzs6sJaSahjBdqe5asc2teVDEDoYPOuCN9W3YyC+Rj0bU/M8IhHsyyxi9idbyCmp5JXpA/nd4JbddtteeXvpubx/NJf3j+ZEXhlfbD7B/zaf5Of9WcSH+3HjiC78fkgnAkyul/BaqslEIKWUQoilUsr+wLY2iqnFTCYTeXl5hIWFqWTQTkgpycvLw2QyOTsUqK6E7Z9q7QJBF97Aa9DrSOoSwkYP6In0531Z3PPFdoJ8DCy4YyQDOgU7OySH6Bzmy6OTe3Hf+O4s232aj9en8eySfbyy/BDXJnXi5ovi3PppcluqhrYJIYZKKTc7PJpW6tSpE+np6eTk5DS5XEVFRfs4MbUxZ5XbZDLRqVM7+Pa4bzGU5cGQW+22yREJYbz440HyS6sI9TPabbvtybc7Mnjgfzvp1zGQ92YNITLA/f92vL30XD0ohqsHxbDzZAEfrUvjs43H+Xh9GhN6R/HHMQkMjXP8rbFtzZZEMByYae0YrhTtQT4ppRzgyMBawmAw1D3t2pSUlBQGDRrUBhG1L55a7jpb5kFIPCRcYrdN1o5PsCk1j8n93K96aP7mEzz29W6GxoXywc1D8ff2vCd1B8YG8+p1iTx+WS8+WX+c/248zk/7shjYKYjZFycwuW8HvPS29NvZ/tlydCc1v4iitFNZ++DEepjwHOjs90c7oFMQPgY9G47lu10i+O+G4zyxaA9je0Tw9swkfIwtf4LdnUQGmnhoUk/uuqQbC7alM2/1Me7+fDuxoT7MHpPA75NiXf7/yJZEkAD0tf68V0q50oHxKIp9rZ+rPUmceINdN2vQ6xgSF8KGY+7V79D3u07x5Ld7uLRXJG/OHIy3l2uf4OzJx6jnxhFduH5YZ5bvO807q47x1Ld7+ffPh7n5ojhuGtmFYF/XrCZs9CuSECJGCLEReAYtGSQAzwghNgkhPPeRSsV1FKZrXUoMvgn8wuy++REJYRw4XcyZ0qrmF3YB647kcv/8HSR1DuGN61USaIxeJ5jcL5qv/3QR8+eMYGCnIF5ZfohRL/zC377fR1aR642629QVwRvAW1LKj+rPFELcBLxJw+MPK0r7sX4uyBoYebdDNl87PsHG1Hwm9+vgkH20lb2Zhcz5dCtx4b7MmzXU5as62oIQguEJYQxPCGP/qSLe/vUo89ak8vG640xL6sQdYxPoEuYadxo1VWna59wkACCl/ATo5bCIFMUOvMxFsPUj6P97CGm6b6bWGtApGJNB5/LVQ7kllfzx4y0Emrz45NbhLvmgmLP1jg7ktT8MIuWhS5g+tBMLt6VzyUsp3Pvldg6ebtcdNgNNJ4IGPxNC6NAGmlGUdqtT+vdgLoPR9zlsH0YvHUO6hLp0IjBbarjzs22cKavi3ZuG0CHI/W8RdaTOYb48f3V/1jxyCbPHJPDzviwm/XsVcz7Zwq70AmeH16imEsESIcR71nEIALD+/DbnDDajKO1KZQkxGd9Dz8sh0rEd3g2PD+VgVjEFZa7ZTvC37/ezKTWff04bQL+Y1vXIqpwvMtDE45f3Zu1j47hvfHc2puYz5Y213PTBJjantb8HEZtKBI8AhcBxIcRWIcRWIA0oAh5qg9gUpXW2fIChurjVg8+0xIiuYUiptRO4moVb0/loXRq3jY5naqK6/8MRgn2N3De+B2sfG8ejk3uxL7OQ37+9nuveWc/qwzntphuWRhOBlNIspXwIiAVutk5dpJQPSSld8+uP4v7KC2DNK+SHJELsMIfvbkCnIJdsJziaU8ITi/YwIiGUxy9TTX6O5u/txZ+Su7L6kXE8dWUf0vJKuXHeJq55cx0r9mc5PSE0+4SNlLJcSrnbOpW1RVCK0mprXoXyAo4lXHjncrbw9tIzpEso6464TiKoqq7hvi934G3Q8e/rBrnN07GuwMeo59bR8ax65BKev7ofuSWV3PbxFq54fQ1Ld5+ipsY5CUH9BijuozAdNr4NA6ZTEpDQZrsd1S2cg1nFZLvI/eMvLz/I7oxC/jltgGocdhJvLz0zR3Rh5UPJvPT7gZSbLdz52TYm/nsVi7ZnUG2padN4VCJQ3MfKf2jPDVzylzbd7Zju4QCsPZrbpvttjbVHcnnn12NcP7wzk/q69rMP7sCg13FtUid+fmAsr88YhE7AffN3cOkrvzJ/8wmqqtsmITSbCIQQK2yZpyhOlbUPdn4Ow+Y47LmBxvSJDiTE18Dqw+07ERRXmHnoq510jfDjySv6ODscpR69TjBlYEd+uPdi3p6ZRKDJwKMLd5P84ko+WZ9Ghdni0P03+mSxEMIE+ALhQogQtF5HAQIBdYuB0n5ICT88CsYAGPNgm+9epxNc1C2cNYdz2/UASf9YdoCsogq+vnOUenK4ndLpBJP7dWBS3yh+PZTDG78c4alv9/L6iiPMuTieG4Z3wc8BPcE2tcXbgfuAjsBWfksERWjdTyhK+7DzS0hdBVe8Ar6hTglhTLdwvt91isPZJfSICnBKDE1ZdzSXzzeeYPaYeBJjg50djtIMIQTJPSMZ2yOCjan5vPHLEf6+9AAAcy7uavf9NZoIpJSvAa8JIe6RUv7H7ntWFHsozYMf/w9ih0PSLU4LY7S1nWDN4dx2lwjKqqp5bOFu4sJ8eWBCT2eHo7SAEIIRCWGMSAhj+4kzdIv0d8h+bGksPi2ECLAG9YQQ4mshxGCHRKMoLfXTX6CyCK78t13HG2ipTiG+xIf7seZI+2sneOnHQ5zIL+Of0waoKiEXNqhziMPGT7blL+dJKWWxEGI0MB6YB7zlkGgUpSWOpcDOL2DUvRDl/MbP0d3C2XAsr83u9LDFnoxCPlqXyg3DOzM8wf5dcSvuwZZEUNtcfQXwrpTye8A1R19Q3EdZPiy6C0IT4OKHnR0NoD1PUFZlYfuJM84OBYCaGslfFu0h1M/II5PV08NK42xJBBlCiHeA64ClQghvG9dTFMeQEhbfAyVZMG0eGHycHREAI7uGoRO0m+qhLzafYOfJAv5yRW+CfFTX0krjbDmhTwd+BCZJKQuAUKB9fAVTPNPm9+HAEhj/DMS0n+aqIB8DA2ODWXUox9mhkFtSyb9+OMiIhFCuVh3KKc1oMhEIIfTANinl11LKwwBSylNSyp/aJDpFOdfpPfDjX6DbBBhxp7OjOc/43lHsTC90+nCFLyw7QGllNc9f3a/dPtegtB9NJgIppQU4KITo3EbxKErjSvNg/kzwCYar33LqXUKNmdgnCoDl+7KcFsPW4/ks2JrOH8ck0C2yfd3KqrRPtvwlhQB7hRArhBCLaydHB6YoZ6muhPk3QFEmXPcZ+Ec4O6IGdYv0Jy7Ml5+clAhqpOSZxfvoEGjinnHdnBKD4npseVb5SYdHoShNkRK+vQtOrIdrP4DYoc6OqFFCCCb27cCHa1MprjA77L7vxqxKr2Z3Rhmv/SHRIV0RKO7JlvEIfm1osmXjQojJQoiDQogjQojHGvj8ASHEPiHELusVR9v2Fqa4hpV/h91fwbgnoN80Z0fTrAl9ojBbJCkH27bRuLDMzMJDVQyLC2XKwI5tum/FtTWaCIQQa6yvxUKIonpTsRCiqLkNWxua5wKXAX2AGUKIc5/62Q4MkVIOABYA/2ptQRQ39euLsOpfkDgTxrjGCKmDO4cQ5mds83aCV38+RIkZnp7SRzUQKy3S1BXBDQBSygApZWC9KUBKGWjDtocBR6SUx6xDW34JTK2/gJRyZb1RzzYAnVpRBsVdrXoRVj4PA66DKa+Di5zc9DrBpb0jWXkgu82eMj5wuohPNxznklgv+nZUg9ArLdNUIvim9gchxMJWbDsGOFnvfTpNd199G7CsFftR3I2U8Ou/4BdrErj6LdC5Vh85E/t0oLiymo2pjh/CUkrJM4v3EmDy4nfd1UP/Sss11ZpU/+uXQ8f9E0LMBIYAYxv5fA4wByAqKoqUlJRW7aekpKTV67oyVyq3qKmm++G36XhqOaejkjkQch2sWt3i7Ti7zDUWiVEPHy7fhiXD26H72nSqmg3HKrmpjxGqSl3mWNuLs4+1s9i13FLKBie0B8nO+9nWCRgJ/Fjv/ePA4w0sNx7YD0Tast2kpCTZWitXrmz1uq7MZcpddkbKj6dI+XSglD8/K6XF0upNtYcy3/7JFpn03HJZVd36cjSntNIsR/79Zzn536tktaWmXZS7rXlimaVsebmBLbKR82pTVUMDaxuHgQEtbSwGNgPdhRDxQggj8AfgrOcPhBCDgHeAKVLKbJuzl+J+svbCvAmQthamvgmXPtkuHxhriWuTOpFbUskvBxz3q/1WylEyCyv465S+6HWu0YaitD+N/qVJKfXyt8ZhL9nCxmIpZTVwN1o/RfuB/0kp9wohnhVCTLEu9iLgD3wlhNihHlTzQFJqfQe9ewlUFMKN38CgG5wdlV0k94wgKtCbLzadcMj203JLeWfVMaYmdmRYvHNGZlPcg0OfOJFSLgWWnjPvqXo/j3fk/pV2rvg0fP+g1oFct/Fw9dvt9onh1vDS65g+JJY3Vh4ho6CcmGD79ZIqpeSJRXsw6nX83+W97bZdxTO59rW34ppqarSrgDeGwuHlMPF5uP4rt0oCtaYPiQXgf5tPNrNkyyzemcmaI7k8MrknUYEmu25b8TwqESht6/h6rS3g+weh4yC4cz1cdI/Ltwc0JjbUl9Hdwvlqy0ksNdIu2ywsM/Pckn0M7BTEDcPVw/jKhXPPvz6l/cnaC59fBx9OhsJ0uOYduOlbCOvq7Mgc7vphncksrLDbOAX//PEA+aVV/O2a/qqBWLEL1SuV4lgnNsDa1+Hg9+AdBJc+BcP/BEZfZ0fWZi7tHUW4v5HPN53gkl6RF7StdUdz+WLTCW4dFU+/GPUEsWIfKhEo9mcuh32LtXaA9E3gE6KNKzziTvD1vLtbjF46/jC0M3NTjrA7vZD+nVp3Aj9TWsUD83cSH+bHgxN72DlKxZOpRKDYh5SQsRV2/Q92fandChoSD5f9CwbNBKOfsyN0qjljE/hy8wn++t1evrpjZIs7hZNS8vjXu8krreT9WaPwNao/XcV+1G+T0no1FkjfolX77P0GCk6A3gi9p0DSLOgy2m0bgVsq0GTgoYk9eezr3Xy361SLu4mev/kkP+w9zeOX9VJVQordqUSgtEzBSUhbDUdXwpGfoTwfdF6QcAkkPw49L9eGklTO8/shsXy64Tj/WLqfCb2j8DHa1pHenoxC/vrdPkZ1C2P2GId2+6V4KJUIlMbVWCDnIJzcCOmb4fg6OJOqfeYbBt0nQo+J0HWc1g6gNEmvEzx9VV+mv7Oed1Yd5b7xzdfzH8oq5sZ5Gwn1M/LK9ER06i4hxQFUIlA0lSXaST97H5zeDad2aq/mUu1z3zCIHQ7D5kD8xRDZR1X7tMKw+FCuGBDN3JVH6B4ZwBUDohtd9lhOCde/txGDXsfns4erB8cUh1GJwJNYzFo9/plUyE+FvCOQe1ibCuv1h2Pwg+gBMPhGiE6E2GEQmuAyA8O0d3+/uj+nCyu4+4tt5Jf25caRcects/FYHvfP34GUks/njKBLmGc3tiuOpRKBu7CYoSQLirOg+JQ2FWVAUSaJx3fDtiIozgRZb8Qsgx+Ed4POwyH8Ru1bflQfCI5T3/YdKMjXwH9vG87dn2/jyW/3kppbxiW9IogN8aW4opqXlx8k5WAOHQJNfHLbMLpFBjg7ZMXNqUTQHlVXabdfVhRor+UFWqNsWT6Un4GyXCjLg9JcKM2Bkmzt83PpDBAQDQRA/BgIioWQLtq3+5B4COigvuU7iY9Rzzs3JvHEoj18sDaVD9am1n0W5GPg8ct6MeuiOEwG1xqZTXFNKhG0hJTaN29L1W9TdaX1tUL7uboCzBVQXa69msu0B6zMZdpUVabVu1eVavXyVSVQWVxvKtK20RSfEPAN1+rtw7tDl1HgF6Gd2AM6gH8UBHXSltHp2JGSQnJycpv8Fym289LreGHaAO4d353jeWWczC+jwmxhSmIMQT4GZ4eneBDPSQS7F5C4/WU4GqDdDSNrQFq0njClBWqqtfl1r2btpH/Wz+YLDEJoD1YZfMHbX/vZ6A/+kdq3dO8AMAWCKUjrjsEnGEzB2qtPCPiEaj+72Pi9StOig3yIDvJhREKYs0NRPJTnJAJACj14mbQTqdCB0P/2s87LOum1KhWdXnuvN4LeS5unN4LeYJ28wctofbVOtT8bfLT9GHysk+9v81RVjKIo7YznJIL+17IzL1xVkSiKopxD3RqiKIri4VQiUBRF8XAqESiKong4lQgURVE8nEoEiqIoHk4lAkVRFA+nEoGiKIqHU4lAURTFw6lEoCiK4uFUIlAURfFwKhEoiqJ4OJUIFEVRPJxKBIqiKB5OJQJFURQPpxKBoiiKh1OJQFEUxcN5zsA0HqS8upz8inzOVJwhvyKfzSWbydifQWFlIcVVxZRVl1FSVUKFpYJKSyWV1ZVU11RjkRYkEiklep0evdDjpfPCpDfh7eWNj5cPAYYA/I3+BBoDCTGFEOwdTIgphHBTOOG+4fh4+Ti7+IqitJBKBC6k0lJJdlk2WaVZZJdla1N5NjllOWSXZZNXkUdueS6l5tLzV87TXvwMfnWTj5cP3npvvL288dP5oRd6dEIHEizSQo2swVxjprS6lPyK/LoEUmwuprqmusEYAwwBRPlFEeUbRZRfFB38OtDRryPRftF09O9IlF8UBp0amF1R2hOVCNoBKSVFVUW/ndzLsskqyzr7tTSLM5VnzlvXx8uHSN9Iwn3C6R3am3CfcMJ8wggzhRFiCiHEFMKhHYeYePFE/A3+6O0w8L2UkvLqcgorCzlTqV115Jbnklue+1v8pVkcPHOQ3PLcs9bVCR2RvpF09OtIjH8M0f7RWqKo9+qt977gGNualJKCygKyy7LJLc+tS8oFFQUUVhXWXY2VV5dTZi6jwlKhJduaGizSgl7oEULgpfPSkrNeuwLzNfjib/DHz+BHgDGAQGMggcZAgryDCPQOJNg7uG5SV2NKazk0EQghJgOvAXrgfSnlC+d87g18AiShfWe9TkqZ5siY2lJ5dXld9UztyTKvXDtB5JTnaK9lOeSU51BpqTxv/VBTqPbN2jeKAeEDiPKLItI3kkjfSKJ8tZ/9Df4IIZqM44zhDEHeQXYrlxACX4MvvgZfov2jm1y2ylLF6dLTZJZmcqrkFBklGWSWZJJZmsmWrC1kpWZRI2vOWqeu3NYri0jfSCJ8IrQEVy/JtUXCkFJSbC6uO261ya72KiyrLKsuWZtrzOetb9QZCfYOJtA7sO4k3sGvA0a9ES/hhZfOCyEEUkos0kJ1TbVWXWeppKK6goKKAtKL0ykxl1BcVdzg70n9fZmEichvIwn2DibIO0ibjEF1+w8wBvw2Wav5/A3++Hj5NPt7pLgvhyUCIYQemAtMANKBzUKIxVLKffUWuw04I6XsJoT4A/BP4DpHxWQrKWXdH2N5dXndVGYuo9RcSqm5tO4Ps7iqmKKqIoqqiiis1L75FVQWUFBZQHl1eYPb9zf4E+4TToRvBP0j+hPlG0WETwQRvhF1J75I30iMemMbl9z+jHojnQM70zmwc4Ofm2vMZJdla8mhJJPTpac5XXZaSx4lmezI3kFBZUGD6/p4+RDsHXzWic3H4IOvly95+Xls37odo96IQWdAJ3TahK6u2qtaVlNlqaKiWmsrqX9s6x/Lhk7w3nrvumOVGJlIpE9k3XEL9wmvuzLz9fK16wm20lJJUaX2+1b7e1b/d+5A2gF8AnwoqCzgeNFxiiq15apqqprcrkBL7n5efvgafPHx8sHHyweTl6muCtHkZdJe9SaMeqM26YwY9Ia6/+fa97VJrv6kF3r0Oj1eQvtZp9PhJbzQCV1dtaRep687Tjqd9VXoEELUza/9WSDcLnlJKeva6eq/1sgaJBIvnZdDqlYdeUUwDDgipTwGIIT4EpgK1E8EU4FnrD8vAN4QQggppbR3MN8c/oY3M9/klUWv1P3nVtdUUyNrsNRYMNeYMdeYqbJUNftHU5+X8Prt2553IBG+EXQP6V7XiBpqCiXEO4RQn1Dt5GAKw+RlsnfxXJZBZyDGP4YY/5hGl6m0VNZ9G88tz+VMxRkKKgvqXmsT8qnSU1rCri6jpKKEtfvWNngSr69+VYyfwQ9/gz++Bl+6BHap+1YdagqtuxKpTdiBxkCnnIS89d5E+GoxNCSlOIXk5OTz5ldUV2hfWCqL6r7E1L6WmcsoMZdQai797QtPdSkV1RWUVJWQXZZdd1NBuaUcs8VMhaXCsQVtId0n1sRgTQ7nvtaqP7/+POsPZ81viETWf9PoZ/VP4qCd4M+dL5EgoYaas5ZtypMjnmR6z+nNLtdSjkwEMcDJeu/TgeGNLSOlrBZCFAJhwFkVy0KIOcAcgKioKFJSUloczMmyk4SLcAxmQ93B1gu99o1Dr8NLr31L8RJeeAkvDMKAQRgwCiPeOm/tVXjjrfPGpDPhI3zw0flgEIbzTwjV1qlem+0Z67/DHG5x7BeqpKSkVf9n7ZEePeHWf3V0gMk6WZWUlODv769VuWA561tV7bfM2tdGVVmnYu1tJZWkW/+1Vy051j7Wfw3SW6dGat+klFSj3WlWLavrJgvae4u0YMGiXX1Ro33hwnLWa+0J0IIFKaW2HDVnHasaatDOl7Lus9plQZtfVVWFwWg4+8TL2SfW2vVqfz6vPDachBvSVOKo/ayxxCPq/atNQud9Vm9dHTqqjleRcioFsO/ftUs0Fksp3wXeBRgyZIhs6BtPc5JJZkDKgAa/Lbm7lJSGvyW6M08sM3hmuT2xzGDfcjvygbIMILbe+07WeQ0uI4TwAoKou9FRURRFaQuOTASbge5CiHghhBH4A7D4nGUWA7OsP18L/OKI9gFFURSlcQ6rGrLW+d8N/IhW2/iBlHKvEOJZYIuUcjEwD/hUCHEEyEdLFoqiKEobcmgbgZRyKbD0nHlP1fu5Avi9I2NQFEVRmqY6nVMURfFwKhEoiqJ4OJUIFEVRPJxKBIqiKB5OuNrdmkKIHOB4K1cP55ynlj2EJ5bbE8sMnlluTywztLzcXaSUDfZN4nKJ4EIIIbZIKYc4O4625onl9sQyg2eW2xPLDPYtt6oaUhRF8XAqESiKong4T0sE7zo7ACfxxHJ7YpnBM8vtiWUGO5bbo9oIFEVRlPN52hWBoiiKcg6VCBRFUTycxyQCIcRkIcRBIcQRIcRjzo7HEYQQsUKIlUKIfUKIvUKIe63zQ4UQy4UQh62vIc6O1d6EEHohxHYhxBLr+3ghxEbr8Z5v7QrdrQghgoUQC4QQB4QQ+4UQIz3kWN9v/f3eI4T4QghhcrfjLYT4QAiRLYTYU29eg8dWaF63ln2XEGJwS/fnEYlACKEH5gKXAX2AGUKIPs6NyiGqgQellH2AEcBd1nI+BqyQUnYHVljfu5t7gf313v8TeFVK2Q04A9zmlKgc6zXgByllL2AgWvnd+lgLIWKAPwNDpJT90Lq4/wPud7w/AiafM6+xY3sZ0N06zQHeaunOPCIRAMOAI1LKY1LKKuBLYKqTY7I7KeUpKeU268/FaCeGGLSyfmxd7GPgaqcE6CBCiE7AFcD71vcCGAcssC7ijmUOAi5GG9MDKWWVlLIANz/WVl6Aj3VUQ1/gFG52vKWUq9DGaKmvsWM7FfhEajYAwUKI6Jbsz1MSQQxwst77dOs8tyWEiAMGARuBKCnlKetHp4EoZ8XlIP8GHgHriOYQBhRIKaut793xeMcDOcCH1iqx94UQfrj5sZZSZgAvASfQEkAhsBX3P97Q+LG94PObpyQCjyKE8AcWAvdJKYvqf2YdCtRt7hkWQlwJZEsptzo7ljbmBQwG3pJSDgJKOacayN2ONYC1XnwqWiLsCPhxfhWK27P3sfWURJABxNZ738k6z+0IIQxoSeAzKeXX1tlZtZeK1tdsZ8XnAKOAKUKINLQqv3FodefB1qoDcM/jnQ6kSyk3Wt8vQEsM7nysAcYDqVLKHCmlGfga7XfA3Y83NH5sL/j85imJYDPQ3XpngRGtcWmxk2OyO2vd+Dxgv5TylXofLQZmWX+eBXzb1rE5ipTycSllJyllHNpx/UVKeQOwErjWuphblRlASnkaOCmE6GmddSmwDzc+1lYngBFCCF/r73ttud36eFs1dmwXAzdZ7x4aARTWq0KyjZTSIybgcuAQcBT4i7PjcVAZR6NdLu4Cdliny9HqzFcAh4GfgVBnx+qg8icDS6w/JwCbgCPAV4C3s+NzQHkTgS3W470ICPGEYw38FTgA7AE+Bbzd7XgDX6C1gZjRrv5ua+zYAgLtrsijwG60O6patD/VxYSiKIqH85SqIUVRFKURKhEoiqJ4OJUIFEVRPJxKBIqiKB5OJQJFURQPpxKBotiBtSfQO50dh6K0hkoEimIfwYBKBIpLUolAUezjBaCrEGKHEOJFZwejKC2hHihTFDuw9va6RGp95CuKS1FXBIqiKB5OJQJFURQPpxKBothHMRDg7CAUpTVUIlAUO5BS5gFrrQOqq8ZixaWoxmJFURQPp64IFEVRPJxKBIqiKB5OJQJFURQPpxKBoiiKh1OJQFEUxcOpRKAoiuLhVCJQFEXxcP8Pp3zcX7lVZQcAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result.plot_sobols_first('I', xlabel='t')" ] }, { "cell_type": "markdown", "id": "exposed-paraguay", "metadata": {}, "source": [ "Finally we can try and visualize certain aspects of the distribution for our quantity of interest (number of infected people) over time. In this plot we will plot the mean value, standard deviation and 0.01 and 0.99 quantiles. This is with respect to the input variable distributions we have specified in the ```vary``` dictionary." ] }, { "cell_type": "code", "execution_count": 20, "id": "corrected-titanium", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "result.plot_moments('I', xlabel='t')" ] }, { "cell_type": "markdown", "id": "overhead-wright", "metadata": {}, "source": [ "If you want to access all of this data for processing you can use the following methods. Due to large output I have not evaluated these cells." ] }, { "cell_type": "code", "execution_count": null, "id": "destroyed-darkness", "metadata": {}, "outputs": [], "source": [ "result.sobols_first('I')" ] }, { "cell_type": "code", "execution_count": null, "id": "contrary-wound", "metadata": {}, "outputs": [], "source": [ "result.describe('I', 'mean')" ] }, { "cell_type": "code", "execution_count": null, "id": "biblical-saint", "metadata": {}, "outputs": [], "source": [ "result.describe('I', 'std')" ] }, { "cell_type": "code", "execution_count": null, "id": "demanding-quick", "metadata": {}, "outputs": [], "source": [ "result.describe('I', '1%')" ] }, { "cell_type": "code", "execution_count": null, "id": "distinguished-greene", "metadata": {}, "outputs": [], "source": [ "result.describe('I', '99%')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 5 }