{ "cells": [ { "cell_type": "markdown", "id": "8ef12ff3", "metadata": {}, "source": [ "# Conditional variable\n", "\n", "In this example, we will demonstrate how to define a conditional variable. This is a variable which parameters depend on a value of another variable.\n", "\n", "First, let's import the necessary packages:" ] }, { "cell_type": "code", "execution_count": 1, "id": "4989b056", "metadata": {}, "outputs": [], "source": [ "from probabilistic_library import DistributionType, ReliabilityMethod, ReliabilityProject, ConditionalValue\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "63e20ba7", "metadata": {}, "source": [ "\n", "Consider a variable $h$ (kPa/m), which represents the pore water pressure in a clay layer of a levee. The variable follows a log-normal distribution, but its mean and standard deviation depend on the overtopping discharge $q$ (l/s/m), which varies per day. We assume the following relationships between the overtopping discharge $q$ and the mean and standard deviation of $h$:" ] }, { "cell_type": "code", "execution_count": 2, "id": "1bce4bec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Values (kPa/m)')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q = [0, 0.1, 1.0, 10.0, 30.0, 50.0, 75.0]\n", "mu = [0, 1.42, 2.24, 3.63, 6.53, 6.31, 6.48]\n", "sigma = [0, 0.32, 1.10, 1.32, 1.70, 2.15, 2.65]\n", "\n", "plt.plot(q, mu, '-o', label=\"mean of h\")\n", "plt.plot(q, sigma, '-o', label=\"deviation of h\")\n", "plt.grid()\n", "plt.legend()\n", "plt.xlabel(\"q (l/s/m)\")\n", "plt.ylabel(\"Values (kPa/m)\")" ] }, { "cell_type": "markdown", "id": "77701dcf", "metadata": {}, "source": [ "Let's assume that on a certain day the overtopping discharge $q$ is normally distributed with a mean of $50.0$ l/s/m and a standard deviation of $1.0$ l/s/m. The goal is to find the probability that the pore water pressure $h$ exceeds a level of $8.0$ kPa/m:\n", " \n", "$\\int P(h>8.0|q) \\cdot f(q) dq$ and $q \\sim N(50.0, 1.0)$\n", "\n", "To compute this probability, we define the following limit state function, in which both $h$ and $q$ are included as inputs:" ] }, { "cell_type": "code", "execution_count": 3, "id": "9458d0c5", "metadata": {}, "outputs": [], "source": [ "def pore_water_pressue(q, h, level):\n", " return level-h" ] }, { "cell_type": "markdown", "id": "3ce19488", "metadata": {}, "source": [ "Next, we define a reliability project that uses the above limit state function as its model:" ] }, { "cell_type": "code", "execution_count": 4, "id": "3f61efa7", "metadata": {}, "outputs": [], "source": [ "project = ReliabilityProject()\n", "project.model = pore_water_pressue" ] }, { "cell_type": "markdown", "id": "ca3a800b", "metadata": {}, "source": [ "Then, we define the variable $q$ and the exceedance level:" ] }, { "cell_type": "code", "execution_count": 5, "id": "cd0eecac", "metadata": {}, "outputs": [], "source": [ "project.variables[\"q\"].distribution = DistributionType.normal\n", "project.variables[\"q\"].mean = 50.0\n", "project.variables[\"q\"].deviation = 1.0\n", "\n", "project.variables[\"level\"].distribution = DistributionType.deterministic\n", "project.variables[\"level\"].mean = 8.0" ] }, { "cell_type": "markdown", "id": "b55ce7d1", "metadata": {}, "source": [ "The conditional variable $h$ is defined as follows. It is necessary to specify the source of the variable - namely, the parameter $q$ - and to define the relationship between the values of $q$ and the parameters of $h$. In the case of a conditional variable, the distribution parameters will be interpolated between the grid points. When extrapolating, the first and last points from the grid will be used." ] }, { "cell_type": "code", "execution_count": 6, "id": "14a5c172", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "gallery", "statistics" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable h:\n", " distribution = log_normal\n", "Definition:\n", " conditional source = q\n", " q = [0.0, 0.1, 1.0, 10.0, 30.0, 50.0, 75.0]\n", " mean = [0.0, 1.42, 2.24, 3.63, 6.53, 6.31, 6.48]\n", " deviation = [0.0, 0.32, 1.1, 1.32, 1.7, 2.15, 2.65]\n" ] }, { "data": { "image/png": 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aWvTWFll4TsavrFxp34xWWlmkRaVSJW1HaFm/Rb8MCQESE+3PyxBDVuNudAEo66KiojB69Gi1aWSePHlw//33Y6vjJiUAnnzySbi5uaU6Hnroods+77x58xASEqI2knz++edT3Xfy5ElUqlQJkZGROVInIlcfy3LiROpz1asDxYoB8mv78svA3LnA3r1aKDl1KvVj16wBoqO1rqSFCwHZ91VmIJUqxS4jsj62yJjQkCFDsG/fPsyZM0dt/jh37ly0adMGBw4cQIkSJVIeJ8FlliwEcZOP7MKWgfDwcPW8soFkuXLl8PDDD+PBBx9E8+bN1f3PPPMMpkyZggCucEV0T60s586lbmGRSwkg0nLiGGak6+jwYW38imMrixwSUBzJY4hcFYOMydy4cQM//fQTFi9enBIyJk6ciCVLluDzzz/H5MmTUwWXonpH+h0cP34c+fPnx6OPPqput2zZEocOHVL/h2w06eXlhe7du+dQrYisR5blDw3VgoeuXTttBdz0SGPnjRtAnjza7e++A4KCtHEtRJQxBpl0yOC59DZFk/Oyv4jjG0t6j3Xca0R/U7rdY6WPO7MSExORlJQE3zTbwkoX09+ydriDdevWoXDhwqqrqFWrVirkFMxgMYiKFSsiJiYGO3fuVF1W0lUl3VPXrl3DhAkTsHbt2swXksiFyHuDbJAo3T56S4scsqCcvAfI773eGFqmjLZ5omyWKAGnZk17S4s0pjp2A5UsaViViEzF0CCz+fhlzPjzOPb+G4GwqDh8+UR9tK9ub0Gw2WyYtuoIvt96BpE3EtCgTAFM7loTZYOz8Ml/F/z9MxpOFIgOHWxYvtx+tnBh7S+v9LRoIWHCflvexGTPkvSamzMrX758aNKkCd58801UrVoVRYoUUS0mGzduRIUKFVJ1K0kLStmyZXHs2DGMHTsWHTp0UI/z0LehdSBh59tvv0X//v1Vq49ctm/fHgMGDMCIESNw4sQJdOnSBQkJCaoFqGfPnpkvNJFFXLumBRZZIE7/NRo8GJg9O/3HS4uK7Emk/2rKuiyffAKk+TuEiMwaZGISklC1WAB6NQjBsLnbb7n/i/XHMeufk5jaqzZCgvwwdeUR9P9mM1aNaQFfr1s/jF2FjI0ZNGiQGg8joaRevXro06cPtss64zc99thjKddr1qyJWrVqoXz58qqVpnXr1uk+b7du3dShk1aY/fv3qy4rGegrgUm6qho1aqS6nKS1h8iKEhKAI0e0lhXHlhYJJULGrsgMISGtK9JSK0v4O45jkdaWtD27XB2XyGJBpmXlwupIj7TGfLPhBEa2qoB2N1tpPni0NhpMXo2VBy6iS+3i6X5dXFycOnT6LBtpSZDDkdyW/yc5OVkd9q9JvzwyWygwMB+Sk91SLUKVEWlWdnhaHD+e/uMcH5MZ0soiIeP69euqfsWKFVPBRc471sNRmTJlEBwcjCNHjqjxL3ci38Nnn30Wn332GY4ePaq6tJo1a6buk1AjLTud9R3jUtUlWX2v5HubXsuPM9N/PtL+nFiZq9dZH3y7b58bGjWyoUAB7TFvvOGOyZPT//ktVcqGc+eSULas1pQ6bBgwcmT6GyY6y7fV1V5nV6uvVeuc2bo47RiZM1du4FJUHB6oEJxyLsDXC3VCArHj1NUMg8w777yDSZMm3XJ+5cqV8Eszas7T01O1MERHRyNeVpm6AxnLkpAQlek3p6SkzL2R3cuM5rx58+L06dNYsWKFqndG06P//fdfXL58WQ3ozcwU6rfeeksFntq1a2PPnj3qB0r/utjYWPU9S+955PsoXVN//vmnCj9mtGrVKrgaV6hzbKwHTp0KwMmTATh1qib+858YnD4dgKgob3X/669vRIMGYep6fHwx5MlTF6VLR948olCmTARKlYqEv38iIiKQqovZLFzhdXbl+lqtzjJu09RB5lK0totZIf/UU4bl9qVoe4tLWq+99lqqNVDkw1bWRmnXrt0tU4flA/nMmTPw9/e/ZfBsRi0yMkZF1mQxkoQWKU/lypURGhqKV155RY2XGT58uJpdJCHjjTfeUGNkJKjJGJlXX31VjaGRrqPbTcMWMo1bZkVt27ZNtbBI15W0rixatEg9n7TQSNdSelOx5XsqA4/l/jt9T52NhDV5E2jbtq36ProCK9ZZ8rN0C0krS/36tpRdmufNc8Mrr9z6lufhYVPTl2vXboiOHW0ps4veeENaVeVn3PxLDljxdb4dV6uvVeuc2XXLnDbI3C35kE7vg1pe2LQvrsz+kVDi7u6ujtvRu2z0xxtJApUEtrNnzyIoKAg9evRQLSh6vaWee/fuxXfffadmHclaMxLkZICwhIzbkYA0bNgwfPDBByrgyQ+StGTJ+jIy6Fe6nD799FMVDtMj3xv5HqX3/TYLM5fd1eos73Oyi7OMY9HHssjKt3oD64cfAqNGadfr1NHGrNSsmQw/v+Po0qUM6tb1RNWqbjcH39rfDk34rbD063y3XK2+VqtzZuvhtEGmkL/217y0vhQOsP9lL7erFTP/X0j3onfv3urIiIQVabW5GxJC9GncjuNtOnXqpA4iI0hXzr592lGjhrbRodi/H+jQIf1uYHlcYKD9nAzEPX9e/nJNwvLl+9GxY2nLBhYiV+K0QSYkKA8K5fPBP6GXUb24tjlIVGwCdp25hn73lTa6eESUQ2Sp/cWLtRYWCS5yefq0/f5nn7UHGQkr1appl3Los4VkqYO0Dadcqp/ImgwNMtfjEnHysn2VuDNXYrD/XAQC/bxRIjAPBj1QFp+sOYoywXlVsJHp10UCfNCuWhEji01E90ga+2Q5fj2olCsH9O2r3RcbC/Trd+vXyIJxElLk0OXLp7XKEJHrMjTI7DkbgT5fbUq5PXnZQXXZo15JTO1dG8NalMON+ES89r+9iIxNQMMyBfDtwEYuvYYMkRnJ7L3p0+1jWSR8OE5IkI0R9SATHKxteOgYXGQDRVlcjojIqYJMk/IFcXLKw7cdr/F8u8rqICLnH3irj2ORQ9Zk0VdC8PSUPcG0sS46WURO7xa6uURRil9/zd2yE5F5Oe0YmdwkM3Uoe/B76VoknGzdqgUXx3EsQpbl14OMjE8ZMUJb1l9vZZH7JeAQEd0Ll34b0ad2yaI7d5qWTFlbwMgq0/9cmazHIrs3O7aySBeRDMTVLV0KOOyMoTY6dBx46+itt3Kv7ETkOlw6yMgib4GBgQgL01bzlPVSMlrsTqYiy6q1suCb0evI5Jas1FlaYiTEyPdSvqdm257AlaVtRBs7Vlu11nE9Fp3kUwkzek597jltd2d9HIu+xD8RUW5x6SAjZKVaoYeZ231Qy9L70nJj9Mq+ueVu6iwhRv+ekvO3uAwf7oGffmqfas8w2RNs9277eiwSUKSFRcKKXDrq3z93y0xElJbLBxn5gJZNF2Un59ttUCX3yf5BsvS+q3SbZLXO8hi2xJinFUY2Opw5U1rafHHyZAIq3xxTL+cff1wLLaVL37oeCxGRM3H5IKOTD+DbfQjLfbIJouwf5CpBxhXr7Crefx/44gsJ8jY8++wulCplb2rRF5sjIjIDBhkiF7NwIfDyy9r1999PRvnyp+HpmabPiIjIJNhoTORCNmywj2uRzRRHjrTvp0VEZEYMMkQuNLh3wAAgLg7o2hWYOtXoEhER3TsGGSIXIYvPyRowPXsC8+Zpi9MREZkdx8gQuRCZSr1okdGlICLKPmyRIbJ4d5LsJL1undElISLKGQwyRBZeK+aZZ7RuJBkTc+2a0SUiIsp+DDJEFiV7G331lbag3ezZsuqy0SUiIsp+DDJEFvTNN8C4cdr1jz/WWmSIiKyIQYbIYmRH6qFDteuvvAKMGGF0iYiIcg6DDJGF7NkD9O4NJCVpC9+9847RJSIiylmcfk1kIVWq2Af2fv217KVkdImIiHIWgwyRhXh7A3Pnaqv3cp9PInIF7FoiMrmrV4EpU4Dkm9smySylPHmMLhURUe5giwyRicXEAJ07a5tBnjunzVAiInIlbJEhMqmEBG1gr4QYWSPmqaeMLhERUe5jkCEyIelGGjIEWLYM8PUFliwBatY0ulRERLmPQYbIhFsPvPwy8N132g7Wsglk06ZGl4qIyBgMMkQm89//AlOn2lfw7dTJ6BIRERmHQYbIhGvF+PhoYUYWvSMicmWctURkMrLg3YEDQLlyRpeEiMh4bJEhMoE//gBOnrTfZoghItIwyBA5ub//1taKeeCB1GGGiIgYZIic2vbtwMMPAzduAHXrAiVKGF0iIiLnwiBD5KT27wfatwciI4HmzYGFC7l/EhFRWgwyRE7o2DGgbVvg8mWgYUNtwTs/P6NLRUTkfBhkiJzM2bNA69bA+fNAjRrA778DAQFGl4qIyDlx+jWRk5E1YgoUALy9gVWrgKAgo0tEROS8GGSInEyhQsDatUBUFFC0qNGlISJybuxaInICERHankk62c06JMTIEhERmQODDJHBpOWlQwegd2/gyy+NLg0RkbkwyBAZKCYG6NIF2LhRa4W57z6jS0REZC4MMkQGiY0FunUD1q0D8uUDVq4Eatc2ulRERObCIENkgLg4oEcPLbzI+jDLl2vrxRARUdYwyBDlsqQkbTyMhJc8eYBly4CmTY0uFRGROTHIEOUyDw+gfn3A1xf49VfgwQeNLhERkXkxyBAZYPx4bS+lNm2MLgkRkbkxyBDlgsRE4J13gOho+7ly5YwsERGRNTDIEOVCiBkwABg7FujaFbDZjC4REZF1MMgQ5UKImT8f8PQEnn0WcHMzulRERNbBIEOUg7OTHEOMbEEgLTJERJR9GGSIcqElZuFChhgiopzAIEOUA0aNAubNs4cYWcGXiIiyH4MMUQ4YOhQoWlTrTmKIISLKOZ45+NxELkv2TAoNBfLmNbokRETWxhYZomwQHw/06wf89Zf9HEMMEVHOY5AhyoZdrLt318bEyKXjondEROTCXUtJyTZ8uPoIft75Ly5FxaFIgC961i+Jka0qwI2LcZATiInRZiOtWqVtAClhxt/f6FIREbkOpw4yX6w/hrmbTmFq79qoWDgf9v4bgZcW7UY+X08MfKCs0cUjFyctL506AevXa91IS5dyA0giotzm1EFm+6mraFutCFpVKaJuhwT54ddd57D7zLUMvyYuLk4dusjISHWZkJCgjrulf+29PIfZuFqds1LfiAigSxcPbNzojoAAG5YsSUKTJjaY7Vvlaq+xYJ2tz9Xqa9U6Z7Yubjab8+78Mn1tKOZvPo05gxuhXCF/HDgXif7fbMbrD1dD17ol0v2aiRMnYtKkSbecnz9/Pvz8/HKh1OQK5s6tih9/rAR//3hMnLgRFSpkHK6JiCjrYmJi0LdvX0RERCAgIMCcQSY52YZ3VxzGl38eg4ebG5JsNrzYrjJGtKyQpRaZkJAQhIeH3/YbkZlkuGrVKrRt2xZeXl5wBa5W56zUV2YpDR3qgdGjk1CnDkzL1V5jwTpbv86uVl+r1lk+v4ODg+8YZJy6a2np3vNYvOtffPRYXVQqorXIvLH0QMqg3/T4+PioIy15YbPjxc2u5zETV6tzRvW9dAkoWBBwd5fHaAN7rTLxz9VeY8E6W5+r1ddqdc5sPZz6Xfid5Qcx/MHy6FK7OKoUDUD3eiUx+IGy+GxdqNFFIxcji9s1bAiMHg04bxsmEZHrceogcyMh6ZZp1u7ubvwgoVy1fz/QrBlw6hSwYgVwjcNhiIichlN3LbWuUgTT14SiRKCvmn69/1wkZv59Ar0apN+tRJTdtm4FHnoIuHIFqFlTWy+mQAGjS0VERKYIMpMeqY6pKw9j3C/7ER6tLYjXt1EpPNe6otFFIxewbh3QubO2XkzjxsDy5UBQkNGlIiIi0wQZfx9PTOhcXR1EuWnJEqBXL5kFB7RqBSxezBV7iYickVOPkSEyigQYWYvpkUeAZcsYYoiInJVTt8gQGaVnT+CPP4CmTQFP/pYQETktvkUTQZtSPW2aOwoUyJNyjvsmERE5PwYZcnnJycBzzwHTp3ugePH70bs3kD+/0aUiIqLM4BgZcmmy1UC/fhJiADc3Gzp1Og5uyUVEZB5skSGXJdOqZSyMLHIn42BmzUpCvnwnAFQ1umhERJRJbJEhlyT7Jsm0agkx0gIj060ffZRLRhMRmQ1bZMgljRihrdorm0DK9GpZ8E6mWxMRkbkwyJBL+vhj4PJl4LPPgMqVjS4NERHdLQYZchlnzgAhIdr1okW1dWKIiMjcOEaGXMIPPwAVKgDffWd0SYiIKDsxyJDlF7qbOhXo00ebav3779o5IiKyBgYZsvRCd88/D7z4onZbFr2bM0fWizG6ZERElF04RoYsKTYW6N8fWLRIu/3ee8ALLzDEEBFZDYMMWY50IbVpA2zYAHh5AbNnA337Gl0qIiLKCexaIsvx9tYWu5P9kmTBO4YYIiLrYpAhy3AcxDtpErB3L9CypZElIiKinMYgQ5bw889ad9KNG9ptGQujrxlDRETWxSBDlphe3aMHsGaNtmIvERG5Dg72JdNKTARGjgS++EK7/cwz2swkIiJyHQwyZEqRkbJbtbbAnXQjffABMGoUp1cTEbkaBhkyndOngU6dtMG8fn7A/PnAI48YXSoiIjICgwyZskvp/Hlt48clS4AGDYwuERERGYVBhkynXDlg2TKgWDHOTCIicnWctUSmmJn05ptaeNE1asQQQ0REbJEhJyfrwgweDHz/PeDvDxw5orXEEBERCQYZclrnzgFduwJbtwKentrMJIYYIiJyxCBDTmnbNm0mkoSZoCDgp5+ABx80ulRERORsOEaGnM6CBUCzZlqIqVZNa5FhiCEiovQwyJDT+ftvIDYW6NgR2LhRm6VERESUHnYtkdORsTDSEjN0KODhYXRpiIjImbFFhgx37BgwfDiQkKDd9vLSbjPEEBHRnbBFhgy1ejXQuzdw9SpQsCAwebLRJSIiIjNhiwwZtsjd++8D7dtrIaZxY233aiIioqxgkKFcFxMDPP448NJLQHIyMGgQsG4dULy40SUjIiKzYdcS5aqTJ4Fu3YBdu7RF7j78UGuJcXMzumRERGTZILNnz54sP3G1atXgKZ9URGlaY0JDgcKFgUWLgObNjS4RERGZWaaSRp06deDm5gabDGzIBHd3dxw5cgTluAAIpSHTqn/+GahcmZs+EhHRvct0k8nmzZtRqFChOz5Owk6NGjXutVxkEZGRwFNPad1HLVpo59q0MbpURETkUkGmRYsWqFChAgIDAzP1pM2bN0eePHnutWxkcocOaeNh5FJW6D16FPDxMbpURETkckFm7dq1WXrS5cuX3215yCIWLgQGDwaio4ESJbTxMAwxRERk+PTrWNkEJwPnz5+/1/KQycnqvGPGAI8+qoUY2exx+3ZtnRgiIiLDg0y9evWwS+bOpvHTTz+hVq1a2VUuMiEJLq1aaVOqxSuvAKtWAUWKGF0yIiKyqiwHmQcffBD33Xcf/vvf/6rb169fx5NPPoknnngCY8eOzYkykknkzavNRAoI0GYmTZmirRVDRESUU7L8MfPZZ5/h4YcfxpAhQ7B06VLVneTv748tW7ZwtpILkpV5pbfRz09b1G7GDODCBaBCBaNLRpkhswyvJ8jrmLmlFYiInM1d/b3coUMHdO/eHZ9//rla9G7JkiUMMS7o8mXgiSe0Qbz/+58WZPz9GWLM4nzEDQyYuQVHwjwxfsdqBPv7oFC+m4fj9TTn8vqwmY2InEeW35GOHTuGvn374sKFC1ixYgXWr1+PLl26YNSoUXjrrbfg5eWVMyUlp7J5M9CrF3DmDODrC+zfDzDLmsfFyFj0/WozToRfV7cTk224EBmrjjvJ4+WRKtwE5/NGIX/flHPB/t43L33g6+WRC7UhIleW5SAjq/xK15KEGFlXpm3btujYsSP69++PVatWYefOnTlTUnIKsrjztGnaQN7ERKBiRW1qNUOMeYRFxqLPV5tUiCkZ6IuBZaPRrk0rXLuRjLCoWIRFxSE8Kg6XouMQHh2HS1FxKeeuxyfhRkISTl+JUced5PP1TNWak1GrT1Beb3h5cA9bIsqlMTIysNfR/fffrwLM6NGj76IIZBZXrgADBwK//qrdlhaZr7/WBveSOUgo6fv1Zhy/dB0lAvNgzqAG2LNxLYoG+CKkoLSm5r/t11+PS1ThJlXYuXkpz30pOl67HRWH+KRkRMUmqkP+v9uRbskCftKyc2urTtpLCT0e7txllIjuMsikDTG6fPnyYebMmVl9OjIRWaX3zz8Bb29tivWwYdy12kwuR8fh8a83ITQsGsXy++L7p+5DsQAvZGVLWBkfI0fpgnnvOIg48kaiQ8DRAo8KQDfP6YHoyvV4JCXb1KUchy9G3fa5JcME5U0deNK2+OiXgXm84M7QQ2RpmQoyv/76qxrgm9nxL7Kyb8uWLblNgcXIjPtBg4B584C6dY0uDWWFBAcJMUcuRqNIgI8KMaUK+iFBVjDMAbLJbH4/L3VUKOx/28fKjKmrMfH20JMq7MSnun0lJh4ywUpuy3En0nJTMK936hYdP09cOueGxN3nUSzQD8E3g1B+hh4i6waZbt26qcG9mdk0Ujz22GNq0Tzufm1uly7JqryF0bGjdvu++4C9ewEPjt80FRn3IgN7pSVGDzFlgm/fopKbJDwU9PdRR5Wit39sYlKyarXRW3Yk6OghRw88+u2rMQmqpUceKwdSLTzugV9O7U313J6qHFroSTnUQGZ7CGJLD5FJg4w0E8uidz6Z3CzndtsYkDns2we0a+eJy5cboVu35JQWGIYYcw7sPXbpuhoH8/3Q+1DWiUJMVnl6uKNwgK867iQ+UQs9Ktg4dmdF3MCeIyfhHVAQ4dcT1LlrMQlq5tbFyDh1ZKWlJ1jv4nIIP45hiGN6iJwgyAwYMCBLT/r4448jIJtGgF6IiMWU3w5i3ZFLuBGfhDIF8+K9XrVQq2TmduKmu2uJ6dRJ9s5yQ8mSMXBzu/OHBjnnFOs+MzbhePh1FJcxMUPvu+PYFivx9nRH0fy+6nAk3WnLcRwdOzZM6S6X0HP5uozjkS6u2JuXqcfz6K0/127b0pPRmB7HcGMfyKwHIQlFnL1FlINBZtasWTBCREwCenz+D5qUL4jZAxupX3aZMip92ZQz4uOBnj2BU6eA8uVtmDjxL1Sv3tboYtFdLHanrxMjs5P0MTGUcegplj+POu40cyttS48MYnbs4tK7t+ScPE4b0yP3xwO4/UBmUcDPK1XIkeCjurQcWnukG07O+3iyiZTIqZfo/Hz9MRQP9MX7vWqnnAsJuv2bcVxcnDp0kZGRKX+F3cvARv1rc2pwpLOsEfP00x7480935Mtnw4IFsTh79t6+b2Zildf47NUbeGLWNnVZItAXcwc1ULOT0quXVeqcFfdaZ+kkKujngYJ+fqhc+PbvRwk3x/RIiLl8M/zI5eWbwUadl9Bz3R56ZGyPHEfDojO1To/WxeWtLtUYn7wSdLRzWujxRj4vN8QlAdeib8DDM0H9rsumFMk2W8p1GUKQ9jxSPcamypfyONvN++RBDtf18+p0qvOp/y/1XEjv/7n59WnKop9LTlMW7WtvPv/N64mJidh3yQ0x287Azd09pVz6Y1KXXS+LvVxIp4z61yffUka9LGkee4cyIp2y3KmMKd8jh/8rWX/O5GRcvOiOXy7vUIPt9f8/4zLay5J+GdN7DRy/Xw7nYMOoVhXQseYdBrllUWZ/R91seg2cUJsP1qN5xUK4EHkDm49fQZEAXzzRpDT6NCqV4ddMnDgRkyZNuuX8/Pnz4ScbAlGGFi8uj1mzasDd3Yb//GcT6tcPM7pIlEVhN4DpBzxwLd4Nwb42jKiWhKDMDW0jg8kHwvVEICoeiEpwQ1SCXKZ/PToBSLJx3A05j97lkvBAkeyNEzExMWongYiIiNsOV3HqIFPp9d/U5ZCmZdGxZjHsORuBSUv2461uNdGzfslMt8iEhIQgPDz8nsbtSDKUlYtlJWMrbsMgPwV9+njgf/9zx9SpSRg5MtnydU7L7PWVv+AHzNqmFqUrF5wX3w2sr8K/let8N6xQZ7VOT6wsTnizlSelxUcutdvSyqN3b8XEJ2XqeWVdKIlH7m5u2nW5THNershZGfuj3dQfe/PrHB7nnubr1fOlOY90n+vm87infv6U6xmUUT8nb2hXLl9GcHAwPLQnSfU1qnyOX4OM66Ie6nazjA7/v70ut5Y7ozLKbTXu2+Fr9K+/YxnT/L+pnh9ai8yhQwdRvVo1eHp43PL/pvf1cl0fh25/jEMZU77efl1/XVPXG2r8auF82ftXk3x+y2t4pyDj6ey/rDVL5MfLD1VRt2uUyI8jF6Mwb/OpDIOMzKxKb3aVvGFlx5tWdj2PM5KtBmTV3kcekV8CD5eoc3rMWN8D5yLxxDfb1IdZlaL5MHdIY9WtYOU63yuz1znY2xvBAZlrZY6KicXvv6/AQw+1h7e3l0MQcfjgtdDqlmpA9/Ll6Nixgalf4yzX+eoBdGxc2jJ1zmw9nHp4fOF8vqhYOF+qc+UL++PctRuGlclqoqK01hghf7h07crVes1mx+mreGzGRhViJPjLwN6shBiyPtm809tDu5QBwjK4Waayy7RwWQ/HSiGGXM9dtcj88ccf6ggLC1PNWY6++eab7Cob6pcugOPhqQe8nbi5Rwzduxs3ZK0YQNYtlN0lZBdrMpd/QsMx5LttqutAfl++ebIhZ/URkUvJcouMDKRt166dCjIy7uTq1aupjuw0uGlZ7Dx9DdPXhuJk+HUs3vUvvt9yGv2blMnW/8cVSf6U5YE2bQJ++w04e9boElFWrT5wEU/O3qpCTNMKwZgzuBFDDBG5nCy3yHzxxReYPXt2hptHZqfaIYH48on6ePf3w/joj6MIKZAH4ztXQ9e6JXL8/7a6sWO1MTHSBfnzz0CFCkaXiLLi193n8PyCXWo12nbViuDjPnVVtwERkavJcpCJj4/H/fffj9zSumoRdVD2+eorbQNIIV1KLVoYXSLKirmbTmHc4n1qbFO3uiXwXs9aarwDEZEryvK735AhQ9SaLGROq1YBw4dr1ydMAHKhYY2ycRbfp2uO4vVftBDT775SmNqrNkMMEbm0TLXIPP/88ynXZXDvjBkzsHr1atSqVeuW6VEffPBB9peSssX167IPFpCUBPTrpwUZMofkZBveWn4QM/8+oW4/16oCxrStxNkmROTyMhVkdu7cmep2nTp11OU+2SLZAd9UnVvevNq4mGnTgK+/5jRrs0hMSsYrP+3FTzu0EdnjO1XDoKZljS4WEZF5gszatWtzviSUK2Q8DMfEmIfs+D7y+x1YfTBMrfkh42G610t/MUgiIlfEznWLi43VupPSNJ6RCVy9Ho/Hv96kQoyPpzu+7FefIYaIyExbFFD2rBWzcCHw99/A0aOAt7fRpaLMkNWr+3+zBaFh0Qjw9VQL3TUoE2R0sYiInA6DjEXJrBYZoy0hRsZjz5rFEGMWsp9Y/5lbcCEyFsXy++LbQY1QqUjqrTqIiEjDIGNRU6YAH32kXZcQ06qV0SWizNhy4gqGfLtV7WxcobA/vhvUCMW5JQcRUYYYZCxIZiTJyr1CZsPLGBlyfkv3yGq9uxGflKz2TZo5oAEC/diMRkR0OwwyFlzw7umnteuvvgqMGWN0iSgzC93J+jCTlx1Ut9tXL4KPHuOWA0REmcEgYzGNGgHNm2s7Wr/9ttGloTtJSrZh8rIDmLXhpLo9oElpjO9cXU21JiKiO2OQsZj8+bXdrD09ueCdGdaIGbNgF37ff0HdHtuxCp5qVo4LSxIRZQHXkbGAw4ftA3uFr68WZMh5XYqKw2NfbVIhxtvDXe1ePbR5eYYYIqIs4sedyZ06BbRpA5w9q4WXESOMLhHdydGLURg4eyvOXr2BQD8vtdBd43IFjS4WEZEpMciY2MWL9hBTpQrQu7fRJaI72RAajmFztyMqNhFlCvqphe7KFfI3ulhERKbFIGNSV68C7doBoaFA6dLabKVChYwuFd3Owq1nMPbnvUhMtqFB6QKY0b8BgvJyejUR0b1gkDGhqCigY0dgzx6gSBFg9WqgJLfgceqZSf/9/RBm/Hlc3e5Suzje7VmL06uJiLIBg4zJJCUBnToBmzYBBQoAK1cCFSoYXSrKyPW4RIz6YRdWH7yobo9qXRGj21TkoF4iomzCIGMyHh7aWBhpjZEQU6uW0SWi2238OPjbbTh4PhLenu54r2ctPFKnhNHFIiKyFAYZE5KZSY8+CgQHG10Sysj2U1fx9JztCI+OQ7C/txoPU69UAaOLRURkOVxHxgQSE4HXXweuXLGfY4hxXj9uP4s+MzapEFOlaD78MuIBhhgiohzCFhkThBjZ9HHhQq0rScbGuDN+Ou2g3im/HcRXf51I2TPpg951kNeHv2ZERDmF77BOHmL69dNCjJcXMH48Q4yziriRgOe+34n1Ry6p28+1qoDRbSrBnXsmERHlKAYZJw4xTzwBLFighZifftJmK5HzCQ2LwlPfbceJ8Ovw9XLH+71qo1Ot4kYXi4jIJTDIOOkU6wEDgB9+0LYdWLQI6NzZ6FJRelbuv4DnF+5GdFwiiuf3VYN6a5TIb3SxiIhcBoOME3rlFWD+fC3ESLfSI48YXSJKKznZho/XHMWHq4+q243LBmH64/UQ7O9jdNGIiFwKR1w4oeHDgTJltG6lbt2MLg2lFRmbgKfnbk8JMU/eXwZzhzRmiCEiMgBbZJxQ+fLAoUOADz8Xnc6Ri1EYNmc7jodfV4vcvdW1Bno1CDG6WERELostMk4gLg7o1QtYutR+jiHG+Szdcw5dp29QIUbGwyx6uglDDBGRwdgiY7DYWKBHD2D5cm0H65MngcBAo0tFjhKTktWmj/r6MA9UKIiPH6uLguxKIiIyHIOMgaKjtYG8a9YAefJoU6wZYpxLWGQsRn6/E5tPaMsqP92iHF5qVxmeHmzMJCJyBgwyBrl2DejYEdi4EfD317qVWrQwulTkaNPxy3h2/k611UBebw+1PkyHmsWMLhYRETlgkDHApUtAu3bArl1AgQLA778DjRoZXSpynFr9+bpjeG/FISTbgMpF8uGzfvVQvpC/0UUjIqI0GGQM8NlnWogpXFgbF1OrltElIl1MIvDM97vwxyFtq4HudUtgcrca8PPmrwoRkTPiu7MBZCfriAjg6aeBypWNLg3pdp25hvf2eOBK3CV4e7hjYpfq6NMoBG5u3C+JiMhZMcjkkqNHtUXuZN8kDw/ggw+MLhHpbDYbvtlwUu1cnZDkhlJBefDZ4/W51QARkQlw6kUu+PtvoGFDYOBAGX9hdGnIUURMAobO2Y43lx5AQpINdQom45fh9zHEEBGZBFtkctiyZdpidzduAKdOATEx2iwlMt72U1fw3Pe78O+1G6oraWyHSggM34d8vl5GF42IiDKJLTI5aO5coGtXLcQ8/DCwYgVDjDNISrZh+tpQ9P5ykwoxpQv64X/P3I/HG5cCh8MQEZkLW2RygM0GvPeetou16NcP+OYbbXwMGb/A3egFu/DPscvqdtc6xTG5W034+3giISHB6OIREVEWMcjk0Kykt9/Wrr/wAvDuu4A7274Mt+bQRby0aA8uX49HHi8PvNm1BnrUK8FZSUREJsaP1xzQurW26ePUqcD77zPEGC02IQnjF+/DoNnbVIipWiwAS0Y2Rc/6JRliiIhMji0y2didpH8mtmoFhIYCJUsaXSo6dCESz32/E0cuRqvbgx4oi5cfqgxfLw+ji0ZERNmAbQXZ4MwZbZ+kAwfs5xhijN9mYNaGE+jy6QYVYoL9vTF7YEOM71yNIYaIyELYInOPZKsBmZF07hwwZAiwYYO9ZYaMcTEyFi8u2o2/joar2y0rF8K7PWujUD4fo4tGRETZjEHmHsTHAx06ABcuADVqAAsWMMQYbfne8xj7815ci0mAj6c7xnasiv5NSnMsDBGRRTHI3AMJMHLItOq//gICA40ukeuKjE3ApF8P4KcdZ9XtGiUC8OGjdVChcD6ji0ZERDmIQeYeyMaPQgIMQ4xx/gkNx0s/7lGL27m7Ac88WAHPta4Ib08OASMisjoGmXsQGald5ue2PIa4EZ+E//5+CLP/Oalulwrywwe9a6NBmSCji0ZERLmEQSYbWmQCAowuievZcfoqXly4G8fDr6vb/e4rhdc6VEVeH/5IExG5Er7r34OaNYGvv2aQye3F7aatPoKv/jyOZBtQJMBHzUhqUamQ0UUznaQk4KOP3BEVVRgdO966HhIRkRkwyNyDkBBg8GCjS+E6dp25pqZVh4Zpi9t1q1sCEztXR34/bmJ1N92ijz8OLF3qgTfeSE45LzPv/vMfoF49+1G3LlC4sKHFJSKyRpD5bF0o3v39MAY+UAYTOlc3ujiUS+ISk/Dh6qP4cv0x1Qoj68G83a0m2lYrYnTRTOnECaBzZ2D/ftlKw4Y8eRJT7tu+HTh+XDt+/NH+NSVKaKFG9g2rUsWYchMRmTrI7D5zDfM3n0aVos4znXbfPuDff4FKlYCyZY0ujTVtP3UVL/+4G8cuXU/ZrXpil+oI9PM2umimJMsEdO8OhIcDxYoBP/2UhLCwayn3jx0LPPQQsGOHduzcCRw5ov2cy/HJJ/bn+ugjadHRWmzq1NGCTsWKgAcXTiaiXGSKIHM9LhGjF+zClO618Mmao3AWX34JfPqpttv1m28aXRpriYlPxHsrDqsZSbabrTCTu9ZA++pFjS6aac2cCQwfDiQkAPXrA4sXS5eRDcuX2x9ToIC26akcuqgoYPduYM8eoFQp+/l164DVq7VD5+cH1K6thRvZAZ4z+ogop5kiyIxbvA8tKxdG04rBdwwycXFx6tBF3pwjnZCQoI67pX+t43NcuyZ/erojb94kJCTYxxlYRXp1zg0bj1/G2F8O4OzVG+p297rFMbZDZeTP45WjZTGqvrnVnTR8uCcSEtzQo0cyZs5MUqEjM3X29QUaN9aORHsvFMaNk9YbN+zapR179rghJsYNGzdKS44N77+fqEKTmDDBHadPu6F2bRvq1LGhVi0bggyaJW/l1zkjrlZnV6uvVeuc2bq42Wzy967z+nX3OUxfE4rFzz6gNvt79MuNqFY8IMMxMhMnTsSkSZNuOT9//nz4yTt3Nnr77UbYsqUYhg/fhfbtT2Xrc7ui6wnA4lPu2HxJW8iugLcNj5ZPRtVAp/4RNY1Vq0rh6lVf9Op1JEdmJsksqHPn/HH8eH5ERvqgc+fjKfeNGvUgTp1K3TxTqFAMypaNQPny19C7d86UiYjMKyYmBn379kVERAQCbjM92KmDzLlrN9Dl078xZ3BjVC2mVeJOQSa9FpmQkBCEh4ff9huRmWS4atUqtG3bFl6yJwGAtm09sH69O+bMScSjjzrttzFb65wT5Edw2d4LmLz8MC5fj1cfaH0bhuDFdhXhn4vrwuRWfXNzQ1N3d6BWLePr/NtvbtixQ2u52bvXDceP21NLlSo27Nljb+p57jl3Nc5GWm+k5aZaNa1VKLtY7XXODFers6vV16p1ls/v4ODgOwYZp+5a2vtvBMKj49Hpk79TziUl27Dl5BV8t/EUjkzuAA9Zk96Bj4+POtKSFzY7XlzH55GxA6JgQU+135JVZdf3Lj2yrcC4X/ZhzaEwdbtCYX/8t0dN1C8dZMn65pa5c4GhQ4EiRYCtW4HgYGPr3KWLdjguJinjbmQwsa+vW8r/nZwMzJkDXNfGdisSamSmlIy9adlS22U+O1jhdc4qV6uzq9XXanXObD2cOsg8UCEYK0Y3T3XupR93o3whfwxrUf6WEJPbuLLv3UtMSsasDSfV4nYx8Unw9nDHMy3LY/iD5eHjyWkv97Ij+wsvaIPQhQQAZ5xFJIOAmzfXDkcyBmf6dC3k6Mfly9pUcTnkjwc9yEhbcu/e2oxBCTnS8iT1tch7OBFlklMHGelWqJxmunUeLw8E+nndct4I3Gvp7he2G/u/vThwXvsGNixTQK0LU7GI8a+pmZ07B/TqBfzzj30w7oQJzhlkMuLtDQwYYL8tYUWmfeuhpkIF+33nz6de60b/eumKklAjLUA9euRe2YnIGE4dZJydLA4mfy3KYmF0ZxE3EjB15WHM2XRKfUDJLKSxHaugV/0QuBvcumaF9WGkdeLCBS1YS/eMLHpndjJeqmRJ7Xj44dT3ydh9WQJBDzkyPVxabGRskBwFC9qDzNWrQJ8+WsCpVs0NEREBqvWKrTdE5me6ILPg6SZwFk8+aXQJzEEG8y7edQ6Tlx1EeLQ2ELt73RIY+3BVBPvfOp6Jsu7997UQU6MG8L//aQvTWV1goDYOSCfja06dsoeaZs3s9+3dC6xYoR3a215LvPiiTXVFSbjp3x9o396IWhCRywUZMpfQsCi8/ss+bDp+Rd0uVygvJj9SA/dXuMPoU8qSb74BJk/WFmb094dLkhlaMl5Gjq5dU98nwU5vvdmzJxk7diQhJsZLrc4tR9Om9sdKa86oUVrAkY1h5ZCAmI89n0ROiUHmLkkTtiz8Jc3Xskoq3boa8ydrQvH1X8eRmGyDr5c7RraqiCHNynIwbzaQPZGk5UXCi3S/yM/htGlGl8p5yXYMeutNQkISli1bjlq1OuLAAS/VevPgg/bHytYMf/6pHY7KlNFCzcsvpw4+RGQsBpm7dPiw1hQtffdnzhhdGufqRlqy5zzeXnYQFyJj1bk2VQurdX9CgrJ3QUJXJGOLPvsMeP55bYaSDGyVXawpayT8ye715coBnTqlvq9tW+C777TuKemSkksZWHzypHY8+6z9sTLYWFrBHFtu5FKemwv8EeUOBpm7xBlLtzp8IQoTfrV3I5UK8sP4TtXQhrtUZ4tr14DBg7WWGCGzcjp2NLpU1iMh5IknUp+TQf0SauRwbIGV1hsJOnI4km4oCTWff65NDddXPjbTDDIis2CQuUtcQ8YuIiZBrQcjs5FkwUIfT3eMaFkBQ5uXU9tK0L3bvBl47DGtRUBm2siMORnHwb/6c4d03Un3k2MXlJDWmSZNtICjj7c5dMje9ew4rkY20ZT1fSTgOB7SqsY/iIjuHoPMXWKLjLbK8g9bT+P9FYdxNUbb3KtdtSIY16kau5Gy0YwZwIgR2mJx0hWyYAHQoIHRpSJRvLh2OE51ly6/o0e1cCPjanQScsLCgDVrtCNtK9CGDdqlkMflzasdRHR7DDJ3ydVbZDYfv4xJSw6kLGpXsbC/GgcjO5RT9pLwIt0SstjdV1+5dng2A1mUr3p17Ug7s+zFF7UVivXWGzlkwT+ZOl+0qP2xL72krQUkQUh/Lv2QKePZvP8tkakxyNwlV22ROX05Bu/8dhC/7bugbgf4emJM20rod19peHlou1bTvXP8YGvTBti0CWjYkF1JZiatK/IaypF27NPx46kX55NVmmVg94kT2rF0qf0+T08gOlr2lbN3O0p4koCTJ08uVYbIiTDI3GOLjKsEmcjYBExfE6r2R4pPSoYsxNunUSm80K4ygvJ6G108y7hxA3jlFeDbb7UNFaU1RjRqZHTJKCcX9qtXL/W5VauAS5fse0w5HkFB9hAjZAabbEshIVd+XmTMjbTc6Jdpn5vIahhk7tIjj2h/MVt9DZkkGzB/yxl8vOYYLl+PV+eaVQzG6w9Xc4r9rqxEgku/fsCBA9rt337TxsaQaypU6NYBxtJKIwOJ0w5ElnBz5Qpw7Jh2LFmi3VeqlLbase7ZZ93VzuLy+LSHvJ8x9JAZMcjcJVkQy+qLYq09fAn/3e2BizcOpqzK+/rDVdGycmG4sY8j28j4l/feA8aPl8XatA+UWbOAhx4yumTkbOTXLu24vF9/1QKODBCWECyH3noj61w5WrLEXa2Jkx5pwZGv0T3wgNbFmV7oKV3avgu5kLAkrURyn3RzEeUmBhlK17RVR/DRH0flrRMF/Lwwuk0l9G1ciuNgsplMp5Z9fmTTR9GtmzZLKZhjpimLAadIEe1o2TLjx739dhIuX/ZUrTdyyPo4sqGmXC9fPvVjZWyOhB4Zv5OWdFk5BhlZz0hvSZSxQI6hR3Ysl59px+AlgV3uK1DA/jj5Ov59RHeDQYZu8eX6YzdDDPBgsWR8MKgpgvJxmkROkJYXCTGyP9LHH2sbkfLNnHLK44/bMr3j99q1WtBxDDv6Id1ejqRFSH5u5VK6ruTQVzyXliJHr71mDz2OZBBz1aqpFxeULThkrFDa0CPXpUutUqUsfwvIghhkKJWZf5/AO78dUtdfbFsRIdEHkc83k+98lCn6m74YO1aboSJv7vrAXiJnULly5h8rwUR2H5dJEGlbexwHJguZtSVBRO7THyNr78g6SfIcjr7/Pv3Qo++fJb87OlmeIDRUe+7AQA9ER9fGn3+6q9AlLVUDB9ofK+FKyiXddPzDwfwYZCjFt/+cxJtLtXeN51pVwNPNy2L5cm18DGVPgPnhB2D2bG06rfxlLG+msjYMkRV2H5cQIUfabipH8vOf9vdCZutJ+ImLS33fyJHa+BsJO3qrkB6AHNfdEfr4oJulkW0+1ewvIYsWOgaZ7t21BQilzDJrzLHFRx4ra/7oVq/WprvrddMfK2v5MAQ5BwYZUuZuOoUJv2oj/Ua0LK/WhkmUP5EoW8igyWeeAX7+Wbs9cyYwbJjRpSIynoQBCQXpLfKXld+RuXOBixe1oHPpUhI2bTqCwoUrISLCQ3XdOoqJ0S6lBUhvQdKVKJH6sRMnaqEnLflDRB4rY4l0U6dq49700ON4SACSsUUMP9mPQYbww5bTeP2Xfer6083L4cV2lTkrKZvIX5vz5mn7IsmbpYwDGDdO2/yRiLJP3br26wkJyShd+gg6dqwAr3T2e5PNPmNj7S08ji0+ad/6ZDdzmVno+Bj5G08GLEuXmCPZ0FXW9EmPLFaoBygxYACwZUv6oUeO0aPtZTl7VrtkS1D6GGRc3IKtp/Haz3vV9cFNy+LVDlUYYrKJ9N8//bR9VVZ5o5XBvfpuyERkHF9f+15ZtyM7mDvSBzRLqHEMJmLoUG3WmGM40sOP/H+OZDyPbDCaUegZMyZ1y9SyZfaWIG0ckD30LHVY+fm339zUAOm0wUgeb9UxQQwyLkx2qx53syXmyfvLqDViGGKyz1NPAcuXa288EyYAL7+cehl6IjIfeYuUrqq03VV6K0tmSfeydDk7jv3Rj7QkPElrrt4SJIOV9dlgefIAHh72gdJffumu3nfSI2OCZDySvtbPu+8C27alDkWO1yWU6e9ZjpMUnA2DjAvPTtIH9kpLDENM9pP+clmFVf6iS7uBIBG5NtkbS47MkNYYx5Yg/ZB9umJjUz+2YUObChxpW4VkILW0CjkuWPjnn/aWnvQ4PresdyVj/NIGHrmUNYWaNYNhGGRc0OfrjuG/v2ttmsMfLI+X23NMzL2Sv5Lef18LLm+/rZ2TNyl5oyAiys6WoJCQjB/3n/8kpzsuSFpi9M2OHWeFtW+vBSLHcCSX8njHqfNyTl8jSB+zo2vXDoZikHEhNpsNH/8Rimmrj6jbo9tUxKjWFRli7tHGjdpYmL17taZb2S9JlnsnInIWefLcuju6hBg5MjsrTF/3Rw87+qXRew4yyLhQiHlr2UF8/bc2V/Cl9pUxomUFo4tlavIL/Oqr9uXXZaXRadO01UmJiKwkMFA7nBGDjAtITErGa//bi0XbtfbA8Z2qYVDTskYXy7Skr1pWHJVZBfqAu0GDtIFzEmaIiCj3MMhYXFxiEkZ9vwu/778AD3c3/LdHLfSsn2ZLXMoSmWEgi9vJcuwyDuaLL4AWLYwuFRGRa2KQsbDrcYkYOmcbNoRehreHOz7pWxftq6dZ15syRUbv6+tASKuLDOyVqZMvvXTrXjJERJR7ZEMKsqBLUXF4bMYmFWLyentg9sCGDDF32Y30449AxYqpF52S6Yavv84QQ0RkNAYZCzoRfh09Pv8He/+NQFBeb8x76j7cXyHY6GKZzv79QJs22q66Mt1QBvISEZFzYZCxmF1nrqkQc/pKDEoF+eGn4fejToiTDjV3UjKlUPY5ka0E1qzRupTGjweWLDG6ZERElBbHyFjImkMXMWLeTtxISELNEvnxzZMNUSgf+z6y4p9/iuGppzzVXiWie3dthd4yZYwuGRERpYdBxiK+23gSE3/dj2Qb0KJSIXz2eD3k9eHLm1Xe3km4dMlNrQXz8cda1xIRETkvftKZXFKyDZOXHcCsDSfV7V71S+Lt7jXh5cFew8w4ehQ4eBDo0kW7Xb9+GBYsSES3bp7c4JGIyAQYZEw+vfq573fij0NhKav1PvNgeW45kAnh4cAbb2gbOubNqwUaWbVSvnXdutkYYoiITIJBxqTOR9zA4NnbcOB8JHw83fFB7zp4uFYxo4tlivVgpMvorbfsG6g98IC2QZqzLr9NREQZY5Axoe2nruDpOTsQHh2HYH9vfNW/AeqWKmB0sZxaUhIwfz4wbhxw6pR2rk4dbWG71q3tO1gTEZG5MMiYzMKtZ/D6L/sQn5SMKkXzqRATEuRndLGc3okTwMCBWqApUQJ4+21tl2rZrZqIiMyLQcZEGz9OXnYQs//RBvU+VL0opvauzZlJdwgvZW/ujVmhAvDCC0BQEDByJODH7EdEZAn8FDSBK9fjMfL7HWq7ATGmTSWMbFUB7u4c1JuevXu1LiRZwG7XLqBmTe38f/9rdMmIiCi7Mcg4ud1nruGZeTvw77Ub8PP2UIN6H6rBPZPSc+wYMGGCNhZG9kiSbqN16+xBhoiIrIdBxknZbDb8sPUMJizer8bDlA3Oiy/61UflovmMLprTkcG7Mgtp1iwgMVE717OnNr1aFrYjIiLrYpBxQrEJSRi/eB8WbjurbrerVgTv966NAF8ubpKWBBeZPv3vv9rt9u21UFO/vtElIyKi3MAg44Q7V4+Yt0OtDyNDYF5qXwXDWpTjIncOzp0DihbVuo48PYExY4DffgMmTdJCDRERuQ5OPnUii3f9i04f/6VCTMG83pgzuDGGc6XeFKdPA888o81E+uUX+3kJMqtXM8QQEbkitsg4SVfSpCUH8P2W0+p247JB+LhPXRQJ8DW6aE4zjVpmHH3zjX3RupUrtZ2pBdeCISJyXQwyBgsNi8Kz83fi0IUotc/PyJYV8FzrivDkpo9qM8cpU4B587SF7ETLltrMpBYtjC4dERE5AwYZA2clzd18GpOXHkBcYrLaauDDR+uiacVgo4vmNAYNAjZt0q63awe8/jrQrJnRpSIiImfCIGOAy9FxeOWnvVh98KK63axiMKb2qo3CLtyVJOu+rFkD1KsHFLi5bdTYsdqUarls0MDoEhIRkTNi/0Uu+/PIJTz00V8qxHh7uGNcp2r4dmAjlw0x0mX0449Aw4ZAmzbA55/b7+vcGfjf/xhiiIgoY2yRySUx8Yl4e/lBzN2kDeitWNgfHz1WF9WKB8AVxcQAs2cDH3ygrcgr8uQB4uONLhkREZkJg0wu2HbyCl5YtBunLseo2/2blMZrHaoij7cHXNGbbwIffghcuaLdlo0cn31WOwoVMrp0RERkJgwyOSguMQkfrDqCr/48jmQbUCy/L97rWdvlB/SGhmohplw54PnngSefBPLmNbpURERkRgwyOWTH6at4+cc9CA2LVre71yuBCZ2rI38e19lmIDkZWL4cmDZNa4HRN2989VWgSxega1fAwzUbpYiIKJswyGSzG/FJmLryMGZuOKFm4gT7+2By1xoutWN1RIQ222j6dK31RUiQmTlTuy4bOXIzRyIisnyQmb42FCv2X8CxsGj4enmgXukCeLVDFZQv5A9ntOn4Zbzy056UsTDSCjO+UzUE+nnDFRw4AHz6KfDdd8D169q5/PmBoUO18S9EREQuFWQ2n7iCJ+4rjdohgUhMsuG9FYfQf+YWrHq+Ofy8nafo12Li8c7yQ1iw7Yy6LWNh3u5WEy2rFIYrTaOW6dPnz2u3q1cHRo4E+vXj+BciIso5zpMG0vHdoEapbr/fqzbqT16NvWcj0LhcQTgDaTH6z897ER6tzRt+vHEp1WqUz9fL8jtQy9YBMlhXxrnIMWwYsHOnFmBkKwHudUlERC4dZNKKik1Ul7frqomLi1OHLjIyUl0mJCSo427pX+v4HLJC77PzdyAhyYYKhfJi8iPVUL+0tiztvfxfziJtnWXw7qpVbvjqK3csW+aGpCQ3lC+fiM6dbSmDePXwkqi9VKaS3mtsdayza3C1Ortafa1a58zWxc0mm/6YQHKyDUO+24bIGwn4cfj9GT5u4sSJmDRp0i3n58+fDz8/v2wt067Lbph1xANF8tjwcq0keFp0neQrV3ywZk0prFxZGmFh9n6iqlUvo2/fQ6hZM9zQ8hERkfXExMSgb9++iIiIQEBAgPmDjHTfrDt8CT8Ob4Ji+fNkqUUmJCQE4eHht/1GZCYZrlq1Cm3btoWXl9Zt9OayQ/hu02n0axyCCZ2sNw1H6jxnzgaMGNFGtb6IwEAb+vVLxuDByWocjJWk9xpbHevMOluRq9XXqnWWz+/g4OA7BhlTdC2NX7wPaw6FYeHTtw8xwsfHRx1pyQubHS+u4/NsO3VNXd5XPtgyPzhpFS0ao9Z/kQG7Tz0F9O7thjx5ZPEX6y4Ak10/K2bCOrsGV6uzq9XXanXObD2cOshIY9GEX/erAbU/DG2CkKDs7Rq6F5GxCTh4QRt/06hMEKzi4EHgxRe1KdR6AF6zJhEFCljjF4OIiKzFqUd1jFu8Dz/v/FdtrpjXxwNhUbHqiE1IMrpo2H7yqlrwrkxBP8vsXD13rrbTtKzGO3q0/by/cy7bQ0RE5NwtMvpO0Y/N2JTq/Hs9a6FXgxAYactJbcfDhhZojYmNBUaNAmbM0G63bg28/77RpSIiIjJ5kDk55WE481YEPp7uaFjW3EHm6FEZ9wLs2qVNnR4/Hhg3TlsXxkKz+IiIyKKcOsg4s4ldquO1jlVU95JZrVsHPPywTHEDgoO1Be7atTO6VERERJnHIHMPfDzNPXOnbl2gcGGgbFlgzhygRAmjS0RERJQ1DDIuZvduoFYtrRtJNnRcv14LMNKVREREZDZOPWuJso90H8lMJGmF+fJL+/lSpRhiiIjIvNgi4wL++Qd48kltYK++VgwREZEVsEXGwmRa9csvA82aaSFGupB++w346COjS0ZERJQ92CJjUZs2Af3721thBgwAPvxQ9koyumRERETZh0HGojw9gWPHgGLFgC++ALp0MbpERERE2Y9dSxYh69ns32+/LVsNLFyojYdhiCEiIqtikLEACStt2mgzkhwH8vbooU2xJiIisioGGROLigJeeklbF2bNGm0a9fbtRpeKiIgo93CMjAklJwPz5wOvvAKcO6edk+4jGcwrq/QSERG5CgYZE46Fad8eWL1au12+PPDxx0DHjkaXjIiIKPexa8lkZGsB2djR3x94+21g3z6GGCIicl0MMk4uLEzbWmD5cvu5554DQkOB114DfH2NLB0REZGx2LXkpCIjgalTgQ8+AKKjgT/+AB56CHB3B3x8gCJFjC4hERGR8RhknHBzx88/B955B7h82b4mjNyWEENERER2DDJO5IcfgFGjtO4kUbky8NZbQPfu2tgYIiIiSo1Bxon4+WkhRqZQv/66tleSbDVARERE6ePHpEGk2+jTT4ECBbTBu6JzZ2DRIuCRRwAvL6NLSERE5PwYZHLZ2bPaAN4ZM4Dr14HgYGDwYCBvXq37qGdPo0tIRERkHgwyuWTbNmDaNG0jx8RE7VydOpxCTUREdC8YZHLBu+9q2wnoWrQAXn1VW6GXg3iJiIjuHif05oDwcOD0afvtTp20MS/9+mmbOq5bp60JwxBDRER0b9gik417IG3dCkyfDixYAPToAcybp91XrRpw/jxQsKDRpSQiIrIWBpl7cOKE1voiAUYG7+7enfq+pCTAw0O7zRBDRESU/Rhk7oGElylT7Ldl64DevYERI4BGjdh1RERElNMYZO6BtLbInkdyyBRqGQMTFGR0qYiIiFwHg8w9mDxZO4iIiMgYnLVEREREpsUgQ0RERKbFIENERESmxSBDREREpsUgQ0RERKbFIENERESmxSBDREREpsUgQ0RERKbFIENERESmxSBDREREpsUgQ0RERKbFIENERESmxSBDREREpsUgQ0RERKblCYuz2WzqMjIy8p6eJyEhATExMep5vLy84Apcrc6uVl/BOrPOVuRq9bVqnfXPbf1z3GWDTFRUlLoMCQkxuihERER0F5/j+fPnz/B+N9udoo7JJScn49y5c8iXLx/c3NzuKRlKGDpz5gwCAgLgClytzq5WX8E6s85W5Gr1tWqdJZ5IiClevDjc3d1dt0VGKl+yZMlsez75AbHKD0lmuVqdXa2+gnV2Da5WZ1errxXrfLuWGB0H+xIREZFpMcgQERGRaTHIZJKPjw8mTJigLl2Fq9XZ1eorWGfX4Gp1drX6umqdXWawLxEREVkXW2SIiIjItBhkiIiIyLQYZIiIiMi0GGSIiIjItBhkMmH69OkoU6YMfH190bhxY2zZsgVW8eeff6Jz585q5URZ+fiXX35Jdb+MBR8/fjyKFSuGPHnyoE2bNjh69CjM7J133kHDhg3Vas+FCxdG165dcfjw4VSPiY2NxYgRI1CwYEH4+/ujR48euHjxIszo888/R61atVIWymrSpAl+++03S9Y1I1OmTFE/36NHj7ZsvSdOnKjq6HhUqVLFsvUV//77L/r166fqJO9PNWvWxLZt2yz7/iWfQ2lfYzc3N/W6WvU1zgwGmTtYsGABnn/+eTWtbceOHahduzbat2+PsLAwWMH169dVnSSspefdd9/Fxx9/jC+++AKbN29G3rx5Vf3lF8as1q9fr37ZN23ahFWrVqnN1tq1a6e+F7oxY8ZgyZIlWLRokXq8bHPRvXt3mJGsbC0f5Nu3b1dv8q1atcIjjzyC/fv3W66u6dm6dSu+/PJLFeYcWbHe1atXx/nz51OOv//+27L1vXr1Kh544AG1QaIE8wMHDmDq1KkoUKCAZd+/5GfZ8fWV9y/Rq1cvS77GmSbTryljjRo1so0YMSLldlJSkq148eK2d955x2Y18uPw888/p9xOTk62FS1a1Pbee++lnLt27ZrNx8fH9v3339usIiwsTNV9/fr1KXX08vKyLVq0KOUxBw8eVI/ZuHGjzQoKFChg+/rrry1f16ioKFvFihVtq1atsrVo0cI2atQodd6K9Z4wYYKtdu3a6d5nxfq+8sortqZNm2Z4vyu8f8nPc/ny5VVdrfgaZxZbZG4jPj5e/RUrzZGOezfJ7Y0bN8LqTpw4gQsXLqSqv+x7Id1rVqp/RESEugwKClKX8ppLK41jvaWJvlSpUqavd1JSEn744QfV+iRdTFauq5CWt4cffjhV/YRV6y3dJtJNXK5cOTz++OM4ffq0Zev766+/okGDBqo1QrqI69ati6+++spl3r/k82nu3LkYNGiQ6l6y4mucWQwytxEeHq7e+IsUKZLqvNyWXxCr0+to5frL7ugybkKaqGvUqKHOSd28vb0RGBhomXrv3btX9ZnLqp/Dhg3Dzz//jGrVqlmyrjoJbNIdLGOi0rJiveUDevbs2fj999/VuCj5IG/WrJnaPdiK9T1+/LiqZ8WKFbFixQoMHz4czz33HL799luXeP+S8YzXrl3Dk08+qW5b8TXOLMvvfk10p7/Y9+3bl2osgRVVrlwZu3btUq1PP/74IwYMGKD60K3qzJkzGDVqlBpDIIP0XUGHDh1Srst4IAk2pUuXxsKFC9VAV6uRP0KkRebtt99Wt6VFRn6XZTyM/Hxb3cyZM9VrXrx4cbg6tsjcRnBwMDw8PG4Z9S23ixYtCqvT62jV+j/77LNYunQp1q5dqwbE6qRu0mwrf+1Ypd7yl1qFChVQv3591UIhA7w/+ugjS9ZVSDO7DMivV68ePD091SHBTQZ+ynX5K9WK9XYkf5lXqlQJoaGhlnydZSaStCo6qlq1akp3mpXfv06dOoXVq1djyJAhKees+BpnFoPMHd785Y3/jz/+SPVXgNyW8QVWV7ZsWfUL4Fj/yMhINfrfzPWXcc0SYqR7Zc2aNaqejuQ1l5kQjvWW6dnyBmnmejuSn+O4uDjL1rV169aqO01aofRD/nqXcSP6dSvW21F0dDSOHTumPvCt+DpLd3DaZROOHDmiWqGs/P4lZs2apcYFyfgvnRVf40wzerSxs/vhhx/UKPfZs2fbDhw4YBs6dKgtMDDQduHCBZsVyKyOnTt3qkN+HD744AN1/dSpU+r+KVOmqPouXrzYtmfPHtsjjzxiK1u2rO3GjRs2sxo+fLgtf/78tnXr1tnOnz+fcsTExKQ8ZtiwYbZSpUrZ1qxZY9u2bZutSZMm6jCjV199Vc3IOnHihHoN5babm5tt5cqVlqvr7TjOWrJivV944QX1My2v84YNG2xt2rSxBQcHq1l5Vqzvli1bbJ6enra33nrLdvToUdu8efNsfn5+trlz56Y8xorvXzJzVl5HmbWV1jCLvcaZxSCTCZ988on64fD29lbTsTdt2mSzirVr16oAk/YYMGCAul+m9Y0bN85WpEgRFehat25tO3z4sM3M0quvHLNmzUp5jLzRPfPMM2qasrw5duvWTYUdMxo0aJCtdOnS6ue3UKFC6jXUQ4zV6pqVIGO1ej/66KO2YsWKqde5RIkS6nZoaKhl6yuWLFliq1GjhnpvqlKlim3GjBmp7rfi+9eKFSvU+1V69bhhwdc4M9zkH6NbhYiIiIjuBsfIEBERkWkxyBAREZFpMcgQERGRaTHIEBERkWkxyBAREZFpMcgQERGRaTHIEBERkWkxyBAREZFpMcgQkSlNnDgRbm5u6vjwww8z/XVPPvlkytf98ssvOVpGIsp5DDJEZFrVq1fH+fPnMXTo0Ex/jez6LV9DRNbgaXQBiIjulqenp9rhOCvy58+vDiKyBrbIEJHhrl+/jv79+8Pf3x/FihXD1KlT8eCDD2L06NFZep5169bB29sbf/31V8q5d999F4ULF8bFixdzoOREZDQGGSIy3EsvvYT169dj8eLFWLlypQokO3bsyPLz6OHniSeeQEREBHbu3Ilx48bh66+/RpEiRXKk7ERkLHYtEZGhoqOjMXPmTMydOxetW7dW57799luULFnyrp5v8uTJWLVqlRo3s2/fPgwYMABdunTJ5lITkbNgkCEiQx07dgzx8fFo3LhxyrmgoCBUrlz5rp5PupbmzZuHWrVqoXTp0pg2bVo2lpaInA27lojIcv755x91eeXKFXUQkXUxyBCRocqXLw8vLy9s3rw55dzVq1dx5MiRu27hGTNmDL766ivVyiNdS8nJydlYYiJyJgwyRGQomak0ePBgNeB3zZo1alyLLFrn7p71t6ekpCT069cP7du3x8CBAzFr1izs2bNHzYIiImviGBkiMtx7772nBv127twZ+fLlwwsvvKBmHWXVW2+9hVOnTmHp0qXqtkzlnjFjBvr06YN27dqhdu3aOVB6IjISW2SIyClaZebMmaPWk7lw4YJqnbkb48ePx7lz51CwYMGUc927d0dcXBxDDJFFMcgQkWnt3btXhaDPPvss018zbNgw9TVEZA1uNpvNZnQhiIjSW9yuTp06GW4I6TgjqVChQpnediAsLAyRkZEpXU958+bNxlITUW5jkCEiIiLTYtcSERERmRaDDBEREZkWgwwRERGZFoMMERERmRaDDBEREZkWgwwRERGZFoMMERERmRaDDBEREcGs/g/XVtJF604ucQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "project.variables[\"h\"].distribution = DistributionType.log_normal\n", "project.variables[\"h\"].mean = 0\n", "project.variables[\"h\"].deviation = 1\n", "project.variables[\"h\"].conditional = True\n", "project.variables[\"h\"].conditional_source = \"q\"\n", "\n", "for ii in range(0, len(q)):\n", " conditional = ConditionalValue()\n", " conditional.x = q[ii]\n", " conditional.mean = mu[ii]\n", " conditional.deviation = sigma[ii]\n", " project.variables[\"h\"].conditional_values.append(conditional)\n", "\n", "project.variables[\"h\"].print()\n", "project.variables[\"h\"].plot()" ] }, { "cell_type": "markdown", "id": "dccbf264", "metadata": {}, "source": [ "We use the reliability method `form` and run the calculations." ] }, { "cell_type": "code", "execution_count": null, "id": "660ecf7a", "metadata": {}, "outputs": [], "source": [ "project.settings.reliability_method = ReliabilityMethod.form\n", "project.settings.relaxation_factor = 0.75\n", "project.settings.maximum_iterations = 50\n", "project.settings.epsilon_beta = 0.01\n", "\n", "project.run()" ] }, { "cell_type": "markdown", "id": "13b34ec4", "metadata": {}, "source": [ "The reliability results are as follows:" ] }, { "cell_type": "code", "execution_count": 8, "id": "143acf2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reliability (FORM)\n", " Reliability index = 0.8817\n", " Probability of failure = 0.189\n", " Convergence = 0.0053 (converged)\n", " Model runs = 15\n", "Alpha values:\n", " q: alpha = -0.0069, x = 50.0061\n", " h: alpha = -1.0, x = 7.9998\n", " level: alpha = 0.0, x = 8.0\n", "\n" ] } ], "source": [ "project.design_point.print()" ] }, { "cell_type": "markdown", "id": "291b9a24", "metadata": {}, "source": [ "On a different day, when the distribution of $q$ is normal with a mean of $20.0$ and a standard deviation of $1.5$, the probability that the pore water pressure exceeds $8.0$ kPa/m becomes:" ] }, { "cell_type": "code", "execution_count": 9, "id": "c70d641c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reliability (FORM)\n", " Reliability index = 1.7774\n", " Probability of failure = 0.0377\n", " Convergence = 0.0043 (converged)\n", " Model runs = 12\n", "Alpha values:\n", " q: alpha = -0.1078, x = 20.2873\n", " h: alpha = -0.9942, x = 7.9998\n", " level: alpha = 0.0, x = 8.0\n", "\n" ] } ], "source": [ "project.variables[\"q\"].distribution = DistributionType.normal\n", "project.variables[\"q\"].mean = 20.0\n", "project.variables[\"q\"].deviation = 1.5\n", "\n", "project.run()\n", "\n", "project.design_point.print()" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 5 }