{ "cells": [ { "cell_type": "markdown", "id": "3f35916d-73d6-41b4-9b24-e32170a33578", "metadata": {}, "source": [ "(mix_propagation)=\n", "# Mix uncertainty propagation\n", "\n", "Different uncertainty models exist to represent uncertainty. It is suggested that aleatory and epistemic uncertainties should have been characterised and propagated distinctly due to their nature and implication. P-box manifests both uncertainties in one structure. In practice, engineering anlyses typically involve uncertainties in various kinds, and therefore deal with *mix uncertainty propagation* which is typically seen in two forms: pbox propagation or propagation of a mixure of variables of different uncertainty types.\n", "\n", "uncertain numbers are most suitable for working with mixed uncertainty problems since the they allow a consistent interface to handle various uncertainties. This notebook will demonstrate two implementations: (i) a low-level one for extra controls; and (ii) a high-level one for simplicity.\n", "\n", "You will discover that calling signature is the same." ] }, { "cell_type": "markdown", "id": "b68090e8", "metadata": {}, "source": [ "```{seealso}\n", "There is an increasing awareness, among the scientific computation community, of the differentiation of aleatory and epistemic uncertainty and that different methods are needed for characterisation and propagation. See {ref}`epistemic_propagation` for the propagation of epistemic uncertainty and {ref}`aleatory_propagation` for propagation of aleatory uncertainty.\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "id": "0743d71e-b268-4a29-94d0-8a2cc5c16c7d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pyuncertainnumber as pun\n", "from pyuncertainnumber import pba" ] }, { "cell_type": "markdown", "id": "d9899d58-94a6-4c95-b519-54149818e410", "metadata": {}, "source": [ "## P-box propagation\n", "\n", "#### low-level implementation" ] }, { "cell_type": "code", "execution_count": 2, "id": "61ded8f9-1c61-497e-b845-bb86e9f36393", "metadata": {}, "outputs": [], "source": [ "def f(x):\n", " \"\"\"a universal signature that takes both iterable (1d) and matrix (2d) inputs \"\"\"\n", "\n", " if isinstance(x, np.ndarray): # foo_vectorised signature\n", " if x.ndim == 1:\n", " x = x[None, :]\n", " return x[:, 0] ** 3 + x[:, 1] + x[:, 2]\n", " else:\n", " return x[0] ** 3 + x[1] + x[2] # foo_iterable signature" ] }, { "cell_type": "markdown", "id": "15c3471e-0fa4-4275-8f05-cdcb3de2ab25", "metadata": {}, "source": [ "```{note}\n", "the importance of inter-variable dependency\n", "```" ] }, { "cell_type": "code", "execution_count": 3, "id": "53dd12b4-16f3-417f-a828-01ea398f497b", "metadata": {}, "outputs": [], "source": [ "a = pba.normal([2,3], [1])\n", "b = pba.normal([10,14], [1])\n", "c = pba.uniform([4,5], [10,11])\n", "\n", "with pba.dependency('p'): # perfect dependency\n", " z_p = f([a,b,c])\n", "\n", "with pba.dependency('f'): # Frechet dependency\n", " z_f = f([a,b,c])" ] }, { "cell_type": "code", "execution_count": 4, "id": "298f4fb8-36d1-4f7a-b26c-94717df1da66", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "z_p.plot(nuance='curve', ax=ax, fill_color='salmon', label='perfect')\n", "z_f.plot(nuance='curve', ax=ax, label='Frechet')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f45588c0-805e-4a50-820b-d6f3ba8d2acf", "metadata": {}, "source": [ "```{tip}\n", "intrusive signature: using interval Monte Carlo, consider Gaussian copula. \n", "```" ] }, { "cell_type": "code", "execution_count": 5, "id": "057d0de6-59c0-419f-bd20-cb082eb60a02", "metadata": {}, "outputs": [], "source": [ "''' interval Monte Carlo '''\n", "corre_matrix = np.array([[1, 0.5, 0.3], [0.5, 1, 0.4], [0.3, 0.4, 1]])\n", "de = pba.Dependency(family='gaussian', corr=corre_matrix)\n", "\n", "t_gau_copula = pun.interval_monte_carlo(\n", " vars=[a,b,c], \n", " func=f, \n", " dependency=de,\n", " interval_strategy='direct', \n", " n_sam=30_000\n", ") " ] }, { "cell_type": "code", "execution_count": 6, "id": "e68a4279-f19c-44f5-9fe0-e79f19906c03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pbox ~ (range=[11.129, 252.204], mean=[34.942, 60.992], var=[457.970, 1668.588])\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pba.inspect_pbox(t_gau_copula)" ] }, { "cell_type": "code", "execution_count": 7, "id": "1e5cbbab-cd8f-4f42-b40c-a7854889d04a", "metadata": {}, "outputs": [], "source": [ "''' slicing '''\n", "a = pba.normal([2,3], [1])\n", "b = pba.normal([10,14], [1])\n", "c = pba.uniform([4,5], [10,11])\n", "\n", "test_ = pun.slicing(\n", " vars=[a,b,c], \n", " func=f, \n", " interval_strategy='direct', \n", " dependency = de,\n", " outer_discretisation=True,\n", " n_slices=100\n", ") " ] }, { "cell_type": "code", "execution_count": 8, "id": "7183275f-ba07-4cdb-a982-2f09b11de266", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pbox ~ (range=[12.995, 172.590], mean=[32.912, 58.935], var=[224.453, 1333.884])\n" ] }, { "data": { "image/png": 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mbIQECU/2eBJ/T/o7Fwb0QxkZBsycacGXX/7RUNylix1//avoxzEiJIRVHCI9Y9ghXdpydAvuWXYPjp49ijBLGNL6peHmVjfzA83PZGYaMGPGHyHHYHDh9tttePRRsTeVGUY2pRP5Bf6mk+6IxQFFI3KRvQjxEfF457Z30KZeGwYdP3LggITnngvEBx/8EXKGDLHhiSeA9u1ZxSHyNww7pCvztszDuFXj5D2u+jXvhwU3L0BkcKTap0Vekp8PzJ5tQVpaAGw2SV78b/BgG556iiGHyJ8x7JBuZlw9ue5JTNswTf76/vb346W+LyHAFKD2qZEXOBzA22+b8fzzFpw5o8yuuvFGG2bNciIpKYAhh8jPMeyQLrZ9GPPJGLyx7Q3564lJE/F4j8fZiOwnvvvOiMmTA0sWAmzd2oGZM224884AGI1c4ZiIGHbIx1kdVtz3wX1I/zld3ttqzo1zMOKaEfyXvB84dEjCU08FyruPC2FhTjzxhA3jxhkRFBSo9ukRkYYw7JBPb/0wKH0QPtv/GcwGMxbctAD3tL2HQUfnzp0DXn7ZgldfDZBXPBbNxyNGWPH88wY0asQhKyK6FMMO+aTz1vO4c+mdWHdgHQKNgXjzljc5tdwPtnZYvtyMZ5+14PhxpS+nVy8bXnrJhYQEhhwiqhzDDvlk0Lntvduw/tB6hJhDsOTWJejdsjc/7HTel/PEE4HYvl3py2neXPTl2JGSItbK+WO7ByKiijDskE+5YLsgV3TcQSf9znT0iOnBoKNT+/YZ8PTTFqxapfTl1K3rxD/+YcM//iFWPebmnERUNQw75FM9OnctvUseuhJBZ9kdyxh0dOrUKUle+Vhs1ulwSDAaXRg50opnnpHQpAmHrIioehh2yCeI1ZDvXnY31mSvQR1zHbx/+/u4vtn1/NDTmaIi4L//DcBLL1lw9qzyd3vLLVbMmOFCx45mGAwcsiKi6mPYIZ/YuXzAsgH4KusrBJuC8d7t76Fn854MOjprPl650oSpUwORk2Mo2ahzxgw7bropgCGHiGqEYYc0ze604y8r/4JVv65CkClIDjo3Nr+RQUdHNm5Umo8zM5Xm4yZNHHjuORuGDTPDbOZ6OURUcww7pFlif6uRH42UN/YMMAbgnVvfQe8WnHWlF4cPS3LI+fhjpfm4Th2l+fif/zSibl2GHCKqPQw7pNm9rh75/BG8s+MdeWXk1/u/juTYZAYdnfTl/Oc/Sl/OxYvKooDDh1sxdaqE6Gg2HxNR7WPYIU2asnYK/rv1v5AgYW7fubgj/g5+CPo4lwv44gsTHn88EAcPKj04119vw7//7eSigETkUQw7pDnTv5uOGd/PkI9n3zgbQzsO5Qehj8vOljBpUiC++koZsmrc2IHp0224914zTCZu1klEnsWwQ5ry5rY3MWXdFPn42euexeiuoxl0fJjNBsydGyCvmVNUJMFsduGvf7Xi6acNiIhgXw4ReQfDDmmGWENnzCdj5OPxCeMxPmk8g44Py8gw4NFHg7Brl7FkH6u5c8V6ORyyIiLvYtghTdh5cicGLh8oTzUf0HoAnrnhGa6t4qPOngX+9S8L0tIC4HJJiIhwYsYMG0aONMFkUoIPEZE3MeyQ6o6dPSZv7FlYXIjrmlyH1/q9BqORH4q+6JtvjBg3LqhkYcDBg6146SWxdg6rOUSkHoYdUtXZ4rNy0MkpzEHriNZ457Z3EGwJVvu0qJrOnweeeSYQCxcGyF83a+bAq6/acPvtXP2YiNTHsEOqLho4/MPh2HZiG+oH1cfSO5aiXkg9tU+LqmnTJiMefDAIBw4ooWbUqGLMns0GZCLSDoYdUs2MDTPwvz3/K1kduVVUK7VPiaq5OKDozRGzrURvjtjmYf58G267jdUcItIWhh1SxZe/fokn1z0pH8/oOQN/ivmT2qdE1Zxp9dBDQdi7V+mt+r//s+KVVyTUr89qDhFpD8MOeV12XjaGrhwKF1y4v/39GHnNSDav+giHA3jxRWXdHIdDQoMGTrz2mg0DBphZzSEizVIt7GRnZ2PFihWIjY2Vj1NTUxEeHl7pc9esWYPIyEj5OCUlRf7vyPdcsF3AgGUDkFeUh24Nu2FW71kMOj7i6FEJqalB2LBBedu45x4rXntNrIZsUfvUiIi0GXYGDRqEjIwM+VgEmDFjxiA9Pb3C54pQNHHixJKvx44di7S0NK+dK9Uesbnn9pPb5YbkN259gzOvfMQnn5gwblwg8vMN8u7kL79sxahR7M0hIt+gyjuVCDeliSqNqNxUZtmyZV44K/K0pbuWYvG2xfLmngtuWoDm4c3VPiWqQhPyY48F4r77guWgc801dmzebMPo0RYGHSLyGaq8W7mHpEoTX2dmZlb4fPG9bt26lQxn9evXz0tnSrXlQN4BjP10rHz898S/488t/6z2KdEVHDwooX//Onj9dWXtnPHji/H990D79hYOPRKRT1El7OTn51f4eG5uboWPu4e34uLi5GPRs1OZ4uJiFBYWlrmRumwOG/7vg/+TV0hOapSEyT0m88NS47780oQbb6yDbduMiIx04sMPi/DSS2YEB3NOAxH5Hk29c1UWgkQ1Z+bMmXJlR/TrCJX17EyfPh1Tp0716HlS9UxdPxWbjmxCaECoPHwVYFIqBaTN2VbTplkwZ47SdJyQYMfSpQ60asVqDhH5LlUqO2LWVfkqjvi6otlYIuBs2bIFycnJ8oytrKwsLF++/JK+H7fJkyejoKCg5JaTk+Ox66Ar23B4A6Z9N00+funPL6FlZEu1T4kqceqUhHvuCS4JOmPGFOPbb4HWrRl0iMi3qRJ2RHCpSGJi4iWPiT6epKSkMs3MItBUVgWyWCwIDQ0tcyN1nLeex4iPRsjr6QxtOxQp7VL4oalRW7YY0atXHaxfb0JwsAuLFxdh/nwz6tTRVPGXiMh3wk75NXJElUYEHXdlRwQcd+UmISFBruyUdubMGflx0rYpa6fg19xf0SSkCab1msago1HvvWfGrbcG49gxA1q3dmDDBiseeICzrYhIPySXy+VS4w8WYUb03YiqjQgzolrjDjtiDR7xuHttHdGzIwKQ+/uiMlTVRQVFg3JYWJg8pMUqj/esP7gevd/qLR8vu2MZbm51s9qnRBX05zzzjAWvvqoMW912mxVvvy0hMtKs9qkREaE2P79VCzvewrDjfees59Blfhd5WwixHcSrN73Kqo7GiEmKo0YF4auvlGAzYUIxXnjBBLNZ2euKiEhPn98ckKdaJzb4FEEnum40nu/5PIOOxmRlSRg6NFjexDMw0IX584tx//1cDZmI9Ithh2pVxrEMvLr5Vfn4xRtfRERwhNqnRKV8+60Rw4YFIS/PgMaNHUhPt+G66zjbioj0jf+Uo1rjcDrkVZKdLicGtB6A/q36q31KVMqyZWYMGBAsBx2xfs4PPzhw/fWBDDpEpHsMO1RrXtvyGjKOZ8iLB/6r57/4IaoRoitv9uwAecdym02spWPF+vVA8+Zc3JGI/AOHsahWHCk8IvfqCE/1eApNQpuofUoEwG5XNvJ86y0l2Dz6aDHmzGEjMhH5F4YdqhWPffkYzlrPyntfjewyklUdDTh3DhgxQplxZTC4MGtWMf7+dzYiE5H/YdihGvvm4DdI/zkdBsmAWTfOgsnIl5XaTp6UMGhQMLZvNyIoyIU33ijG4MFsRCYi/8RPJapxU/L4L8bLx8PaD0PXxl3VPiW/d+iQhLvuqoMDBwyIinJi5UorevVi0CEi/8WwQzWyKHMRdpzcgXBLOJ7o8QQ/UFW2d68Bd9+tbP3QrJkDn39uR4cOgWqfFhGRqjh4T1ct72Ienlj3hHw8MWki6ofUV/uU/FpmpgE336wEnTZt7Pj6axF0lK0giIj8GcMOXbXnv30eZy6eQdvIthjTdQyrOirasMGIO++sg9xcZQ2dr792IjaWQYeISGDYoatyMP+gvK6O8Nz1zyHAxDVb1PLllyYMHBiMs2cl9Oxpw+rVLjRuzL8PIiI3hh26Kk9//TSsDit6RfdCv9h+ap+O3/rkExPuvTcIRUUSbrnFhs8/567lRETlMexQtYmG5CU7lpQsIMh1W9Tx4YcmPPCAsirywIFWrFwpISSEcw6IiMrjpxRV2+S1k+GCC3fF3YWkpklqn45fWrnShJEjg2C3Sxg82Ir33hPr6TDoEBFVhGGHqmX9wfX4fP/nMBlMmNJjCpuSVZCebsLo0UFwOCQMHWrFO+8YERDA7R+IiCrDsENV5nK5MGnNJPn43nb3ok29Nmqfkt9ZutQsb+jpdEq4/34r3nqLQYeI6EoYdqjKPtn3CX48+iOCTcGYeO1EVnW8bMUKEx58MFAOOg88UIzXXzdyQ08ioipg2KEqV3WeW/+cfDyq0yg0DW2q9in53awrUdFxuZSgs2ABdy4nIqoqhh2qklW/rkLG8Qy5qjOu2zhWdbxo9WqTvHu56NH5y1+sDDpERNXEsENVqupMXT9VPn6g4wNoGNJQ7VPyG+vXG3Hffcr08nvuseLNNzl0RURUXQw7dEVfZX2FzUc3I8gUhEe6PcKqjpds2mTE0KHBJQsGvvuuARYLgw4RUXUx7FCVqzrDOgxD47qN1T4lv7BrlwGDBgXj/HkJffrYsHy5xHV0iIiuEsMOXda6A+vww5EfYDFa8Gi3R1nV8YKDByUMGBCMwkIJf/qTDR98AK6MTERUAww7dFnTNkyT7+9vfz9nYHnBqVNK0Dl50oD27e34+GMgLIx7XRER1QTDDlUq83imXNkxSkaMS+AMLE87exby0FVWlhExMQ589pkT9esz6BAR1RTDDlVqzsY58v1dre5Ci4gWap+OrlmtwH33BeOnn4yIinLis88caNEiQO3TIiLSBYYdqtCh/ENYvnu5fPxIAmdgeZLTCTz4YBC++caE4GAXPvjAik6dGHSIiGoLww5V6JVNr8DhcuDG6BvRtXFXtU9Ht1wuYMoUC1auNMNkcuG994rRs6dF7dMiItIVhh26RN7FPCzMXCgf/7XrX1nV8aD58wMwb54SbubPL8add1r4/5uIqJYx7NAl0jLScN52Hh2iOiC5ZbLap6Nbq1aZMHmyEnSee64YI0YEMOgQEXkAww6VYXfa8dqW1+Tjh655CEYjV+z1hO3bDRg1StnYc9gwK6ZMMcNg4K8jEZEn8N2Vyvhoz0c4UngE9YPqY2CbgWqfji4dOyY29FRWR+7d24a0NCOMRv4qEhF5Ct9hqYxXN79asohgsCVY7dPRnXPngCFDgnHsmAHx8Q4sXw4EBrJ6RkTkSQw7VGLnyZ1Yf2i9vIjgA50fUPt0dMfhAMaMCcKOHUbUq+fERx/ZuWggEZEXMOxQibmb58r3t8beimZhzdQ+Hd15+mkLPv/cDIvFheXLrWjbllPMiYi8gWGHSqabL9m5RD4e3Xk0ZwXVsvffN2PuXCXcpKUVo3dvBh0iIm9h2CHZm9vexAXbBbSPao+eMT3VPh1dycw0YPz4QPl4woRi3H8/p5gTEXkTww7B5XKVLCI4ouMITjevRb/9Jsl7XhUXS+jf34YXXjBxijkRkZfxXZewMWcjfjn9C4JNwRjUdpDap6OrzT2HDQvC0aMGtGrlwJIlgNnMIElE5G0MO1RS1RG7m4cHhat9OroxaVIgfvjBhLp1nfjgAwfq1ePMKyIiNTDs+LmCooKS3c2HdRjGXpJa8sYbZixeLHpzXHjjDSs6dmTQISJSC8OOn3tv53u4aL+INhFtcG3Ta9U+HV3YssWICROUhuSnnrJiwABu7klEpCaGHT+36KdF8v19He5jY3ItOHNGwgMPBMFmk3DnnVY89ZSJQYeISGUMO34s83imfAswBGBI2yFqn47PczqVFZKPHDEgNtaBxYslmEwMkEREamPY8WMLM5TG5Ntib0ODkAZqn47PmzMnAGvXmhAU5MKyZXZERbFPh4hICxh2/FSRvQjv73pfPr6v/X0caqmhr782Yto0ZVXkV16xolu3ALVPiYiIfsew46c+2/cZCooL0CSkCXq36K326fi0Y8ckjB4dBJdLwrBhVowebWZ4JCLSEIYdP+XeB2tg64EwGU1qn47PstmAESOCcPq0AZ062TF3roErJBMRaQzflf3QmQtn5MqOMLjtYLVPx6e98IIFmzYpCwcuXepA3boMjkREWsOw44fSf06HzWlDx3od0alhJ7VPx6f7dF55RenNmTfPinbt2KdDRKRFDDt+aMkOZQhrUPwg9pZcpVOnJIwdq/TpjBhRjKFDuZM5EZFWMez4mey8bHyf8z0MkgED2wxU+3R8dj2dhx4KxMmTBrRta8e//21knw4RkYbxHdrPvLvjXfm+V3QvRIdFq306PmnevACsXm1GYKALS5awT4eISOsYdvyIy+XCuzvfLZmFxWGX6tu2zYBnnlHW05kxoxgJCezTISLSOoYdP7Lzt53Ye2YvLEYL7mh9h9qn43POnQNGjlT2vbr9divGjWOfDhGRL1Ct/p6dnY0VK1YgNjZWPk5NTUV4eHilz1+zZo38PPF8ITk52Ytnqw/pu9Pl+z83+zPCgyr/f00VmzIlEFlZRjRt6sDChYDRyH8rEBH5AtXCzqBBg5CRkSEfixAzZswYpKcrH8YVBR3xvbS0NPm5/fr1Q1ZWlpfP2PeHsMSUc+GuuLtYkaimVatMeOstUclxYdEiGxo1ClT7lIiISMthRwSW0kS1RgSayowdO7YkGInnrl692uPnqDe7fttVMoR1S9wtap+Oz00zf+QRJdyMG2dF//5Kzw4REfkGVerwIthERkaWeUx8nZmZWWEwys3NlYe4xPfz8/NLhrKo6txVnT/HcAirOlwuYPz4QJw6ZUD79nZMn25kVYyIyMeoEnZEYKmICDXliYAjgpC7v2fBggXycWWKi4tRWFhY5ubvSg9h3dnqTn5YV8O775rx2WdmmM0uvPGGA3XqcJo5EZGv0dQ7d0UhSAQgUd0RDcmiuiMamSMiIuQP8IpMnz4dU6dO9cLZ+o7dp3Zjz+k9CDAE4Na4W9U+HZ9x8KCESZOU4aunnrIiKYnTzImIfJEqlR0RWspXcdxDVeWJao543P09931FQ17C5MmTUVBQUHLLycmBv+MsrKtdJTkI585J6NHDhkmTTKyIERH5KFXCTmXTxhMTEy95rLr9ORaLBaGhoWVu/u7DvR/K93fE3cEP7CpKSwvAxo0m1KnjxOLFTgQEGNU+JSIi8tYw1sGDB+Vp4GJGVF5eXsnjoq9GTAlPSUlBixYtLvszygcYMUwlgk7pqo04Fs8TN/E9McQlHnOvtZOQkFDdU/dLB/MPYsfJHTBKRvRv2V/t0/EJWVkSpk5VZlxNm2ZFmzacfUVE5Ddh5/HHH5crA4MHD8aECRMu+f5PP/2E+fPny88RvTOXIwLTpEmTkJSUhC1btpRZY0f8t+LxiRMnlnlut27d5CnonHpedR/v/Vi+v7bxtagfUl/t0/GJ4atHHgnCxYsSevWy4eGHzayGERH5OMlVWadvObNnz5abg8PCwq74XNErM2PGjCsGHm8Qs7HEOYtz8schreS3k7H2wFo8d/1zGN99vNqno3kLF5rxz38GITjYhYwMK9q2ZVWHiMjXP7+rHHZ8lT+HnfyifNSfXR92px1b7t+C+Hrxap+S5mdfXXddCM6flzBnTjEee4x7XxER6eHz21CTkxD9O6Rdq/avkoNOm4g2aB3VWu3T0TQR+cXwlQg6111nw6OPcvYVEZFeXHXYmTZtmtyQLIjUtWjRoto8L6oFH+9T+nX6t+jPD+4reOMNM7791oSgIBcWLnTCbObsKyIi+HvYEQ3E+/fvl49FmWn06NEMPBpidVjlyo5wSyz3wrqcw4clPPWUsnjg008Xo107Lh5IRKQnVx12xNRvEXjmzJlTMpyl8/Yfn7Lh8AYUFBegflB9dG/aXe3T0Szxkv3HPwLlxQO7d7fjscc4+4qISG+uOuyIParEjCsRcMTaOlFRUYiLi6vds6Or9sWvX8j3fZr1gdHAIZnKfPihCV99ZUZAgAsLFji4eCARkQ5dddgRC/v17dtXXm9n69at8k7mlW3wSeqFnb7N+7JSUYmCArF2lDJ89dhjVnTuzOErIiI9MtRkywfRo+PeVXz58uUV7lpO3ne08Ch2/rYTEiT0ad5H7dPRrOefD8SJEwbExTnwxBNGhkIiIp266l3PW7ZsKTclu7m3diD1fZn1pXyf0DAB9erUU/t0NGnrViMWLTLLx6++akNIiFLhISIi/bnqsFPemDFjautHUS0NYf055s+sVlTAZgPGjw+EyyVh6FAr+vfn8BURkZ6psus5eY5YRHB1trJ3WHKLineX93fz5gVg1y4jIiOdmDMHMBj4a0BEpGd8l9eZH4/8KG8TEW4JR2LjRLVPR3OOH5cwY4ay39ULL9jQuLEylEVERPrFsKPTIazeMb1hMtbaKKVuPPOMRd4SIinJjlGjuCUEEZE/YNjRmS+y/lhfhx/kZW3aZMSyZWJzTxdeftnBLSGIiPxEtcLOunXrKv2eWEXZPQ39cs8jzzl94TQyjmXIx31b9FX7dDTF4QAmTlRmXN1/vw3XXcemZCIif1GtsCPW0qnMzJkzS7ZgX71aaZAl7/rm4DdwwYV2ke3QNLSp2qejKe+8Y8b27UaEhjrxwgtg1YuIyI9UK+xMmjRJ3gurvG3btpVZY4d7ZKlj3QGlotYzuic/zEvJywOee05pSp4yxYqmTdmUTETkT0zVXUjw119/veTxtLQ0zJs3r+RrftCqY+2BtSVhh/4wc6YFZ84Y0KaNHY8+yqZkIiJ/U+0G5fLVHdGrw5WT1Xek8Aj2ndkHg2TADTE3qH06mpGVZcDChUp/zuzZdgQFcYYaEZG/qfY7f/nqjujVKV3VIXV8feBr+b5L/S6ICIpQ+3Q049lnLbDbJSQn23DbbWxKJiLyR1c19Xzs2LFydYdVHe1Yd1Dp17mh6Q0cpvndjz8a8fHHZhgMLsyc6eJKyUREfuqq3v27du0qV3dEVWfChAmXfJ8Nyt4l/n+7m5N7xfRS+3Q0QbwEn3xSaUq+7z4bunZlUzIRkb+66n/qiupOQkJChd+Li4uryTlRNWXnZeNwwWGYDWb0aNpD7dPRhI8+MmHzZhOCg1147jk2zRMR+bOr7tYU1R1xqwh3QPcud1UnsVEiQiwh8HdWq+jVURYQfOQRK5o1Y68OEZE/YxODDrBfp6zXXw/AgQMGNGjgxOOPG/n/hIjIzzHs+Dj265SVny9mCCqVnCeftCE8nFPNiYj8HcOOj/vl9C/47fxvCDIFIalxEvzdSy9ZkJdnQHy8A6mpDDpERMSw4/O+O/SdfJ/YMBGBZqVPxV/l5EiYP1+p6kybZoPFwl3NiYioGmHngw8+8OyZ0FXZkLNBvu/euLvf96bMmGFBcbGEG26w4e672ZRMRETVDDvLli2r6lNJhcrOdU2vgz/bv9+A995T1tL517+cMBpZtCQiIkWVPxFWr16NRYsWXfY5V/o+1a6cghwcKjgEo2T0+36dF16wwOmUcMstNvTsyQUEiYjoKsLO2rVr0bdv3woDTWFhIWbPni1vEkres+GwMoTVqV4nhAaGwl9t327A//5nhiSJBQS5LQQREZVV5U8FsYCg2AR00KBBJbuer1u3DkOGDEFERATS0tIQGRlZ1R9HteC7w8oQ1rVNrvXrfp1//UvZFiIlxYZu3VjVISKiqww727Ztk+/Fh6rYF8toNKJfv35y0Nm6dav82PLly6v646gWKzt/avwn+KsffjDiq6/MMBpdePZZbgtBRESXqvJCJGILCFG5WbNmjbwn1vz585Gfny/vkRUaqgyhVLZ9BNW+vIt52PXbLvn4uujr/Hazz+ef/2Ozz3btWNUhIqIahJ2srCx069ZNruKUDjULFy5EUlISrrnmmqr+KKoFG3M2wgUX4sLj0DCkIfzRunVGfP+9CRaLC089xaoOERHVMOxMnjwZEyZMqLDis3LlSuTm5spf9+nTp6o/kmqhX0cMYfnjh7yo6jz3nLKI4ujRNsTGsqpDREQ17NmpKOi4DRw4UA47ooeHvNyc3Pha+KNPPjFh2zYj6tRxYcoUyS8DHxERVU2tzdFNSUmRQw95XrG9GFuPbZWPezTtAX/jcPwxA+uvf7WicWPugUVERJWr1QVJxFAXed72k9thdVgRFRiFuIg4+Jvly83Yu9eI8HAnJkwwsKpDREQ1DzsFBQU4ePDgFZ9XunFZLDQoblT7Nh3ZJN8nNEzwuwX0rFaxyadS1XnsMRvq1WOvDhERXV6VPinDwsLk7SKquhmoaFgWa+64p6RT7frx6I/yfbeG3fyuqvHWW2YcPmxAgwZOjB/PXc2JiKiW19n56aefMHjwYMTFxcnTzWNjYxEeHi6vt5OdnY3NmzfjwIED8to77N/xfGUnsVEi/MmFC8CcOUpVZ9IkG0JDlWMiIqLLqVZnpximEhUbMawl7kW4EUFHBB4RgETIEVtKkOecOn8K2XnZ8nG3xt3gTxYuDMCJEwY0a+bAgw+yqkNERFVzVdNYxLCWqPSQekNY8RHxiAiKgL8oKABefjlAPn7iCTuCg1nVISKiqvGv7lYd+PHIjyXNyf7Ur/Paaxbk5RkQH+/AAw9wqjkREXkh7Iienao2LFPt2XT0936dhv7Tr3P6tITXXlOqOs88Y0dAAIewiIjIC2EnNTUVAwYMKPPYunXrrvbHURU4XU5sPrpZPk5s7D9hRwxfnTsnoUsXOwYPZlWHiIiq56o/OcQQykMPPSQ3JotZWWK7iPT0dO6N5UF7Tu9BYXEhgk3B6FC/A/zB8eMSFi1SqjrPPmuHyaTsh0VEROTxsDNjxgwkJyfj9OnT8k1wbwZKnu3X6dKgC8xGs99UdYqKJFx7rR133KGEHiIiIq+EnbS0NPTt27fMY2vXrr3aH0fVWF/HXxYTPHpUwhtvKAHn6aftMBpZ1SEiIg+HnW3btmHZsmVo1aoVRo0adcn3y4cf8tzKyf7gxRctsFolXH+9Df37s6pDREQeDjuiatOvX7+S/hyxfcTSpUuv8o+l6iqyF2H3qd3ycbdG+g87hw9LePttZajumWecMPrJsB0REak4G2vBggXIy8vDr7/+KoedFi1aVGlzUKodO0/uhN1pR72geogOjYbeiW0hbDYJN95oQ9++DDpEROSFsCO2gRArJ7tNnjwZmZmZNfijqToyjmfI953qddL9TucHDkh4910l4Dz9tFP310tERJ5V5U8RMcW8NBF8XC7XJT095BmZx5Vg2bl+Z903J4uqjt0uoW9fG3r3ZlWHiIi8FHbEruZnz55FYWFhyU3scO5+TAxpiRla1fl5s2bNwooVK+R7saFoVUyaNKnKz9VjZeeaBtdAz7KyDHj/fVZ1iIio9kiu8uWZSogPnfIVBfGfuh9zHzscjir9wd26dUNGRkZJ8BEhRixKeDli2Ez8d6J3SOy0XhUiiIkqlNipPTQ0FL7I6rAiZFoIbE4bModlIi6qbJVNT8aODcTSpQG46SYbVq0yMuwQEfmpwlr8/DZVZ3uImTNnVvp9EXbEQoNVIcJNaWKG15o1a6r034nn+ptdv+2Sg064JRwtwltAr/btM2D58j9mYBkMHMIiIqKaq/I/m8eOHSsnrMpuotIyZMiQKv0sEWwiIyPLPCa+vlzDsxjuSklJgT/363Sqr+/m5JkzLXA6Jdx6qw1/+hODDhER1Y4qV3a6du1aK88RKuu5qWy7CfH8qg5bFRcXy7fSZTC9hJ0u9bvotjl5/34DVq40lerVYdghIqLaoakyQWUhaPny5fI+XFUxffr0MhWnmJgY6KU5WczE0qsXXwyAyyXhlltsSEpi0CEiIh8PO6JKU76KI76uqHojhrwGDx5c5Z8t1v8RzUzuW05ODnyZzWHD9hPb5eNrGl6j23V13L06U6ZwBhYREWlkI9CaEFWaiqapJyYmVlrZKd2kLKo3oj8oISHhkudaLBb5phe/nP4FxY5i1A2oi7gIfc7CeuklCxwOZV2d665jVYeIiHQQdsrPqBIBRgQdd2VHNCqLY/G88sNXolFa3PxlVlZJc3K9TjAajNCbnBypZF0dparDsENERLVLtfECsaaOWFtHzLISVZ7Sa+yIyo14vHw/j1h8UBBT4P1lq4qfjv9UMhNLj83J//63sgdWz55cLZmIiFReVNBX+fqign3e6oOvD36N//T5D4Z3GQ49OXFCQufOISgulrBqlRU33xyg9ikREZEOP7/ZCaphIofuOLlDPu5YvyP05j//CZCDTvfudvTrp8qIKhER+QGGHQ07fu44zlw8A4NkQNuottCT06clLF6sVHKmTHHAaORLkYiIPIOfMBrmruq0Cm+F4IBg6MncuQG4eFFC16523H47e3WIiMhzGHZ8IOy0j2qvq+ZkscTSwoVKVefxx1nVISIiz+KnjI+EHT1JS7Pg3DkJHTrYcc897NUhIiLPYtjxgbDToV4H6MW5cyLsKMNWEyc6YDbrb+0gIiLSFoYdjbI6rNhzeo/uws7bbwcgL8+Ali0d+MtfWNUhIiLPY9jRqL2n98LmtMnbRDQLawY9sFqVxmTh73+3IyCAVR0iIvI8hh0f6NfRy8aYK1aYcfSoAQ0aODFiBIMOERF5hz4+RXVIbzOxnE7glVeUqs5f/2pDSAiHsIiIyDsYdjRq96nd8n27qHbQg1WrTNi714i6dZ14+GG+7IiIyHv4qaNRP5/6Wb5vE9kGvk7svvbyy0pVZ8wYG6KiWNUhIiLvYdjRoIu2iziYf1A+1sM2ERs3GrFliwkWiwvjx0u6GJYjIiLfwbCjQXvP7IULLkRYIlA/uD58nbuqc++9VsTEcGsIIiLyLoYdDQ9hxUfG+/xMrJ07DVi92gyDwYV//AOs6hARkdf59iepTv1y6hf5Pj4i3ufDwb//bZHv777bhnbtlAoPERGRNzHsaNAvp38PO5Hx8GUHDkhYuVJpRp44kVUdIiJSB8OOlmdiRfj2TKy5cy1wOiX07WtDUhJnYBERkToYdjTG5rBhf+5++bhtPd+diXX6tIQlS5Rm5H/+0+nzvUdEROS7+AmkMVl5WbA77ahjroPoutHwVa+/bkZRkYQuXezo148zsIiISD0MOxodwmod3tpnqyFFRcDChUoz8t/+5oDR6JvXQURE+sBPIY3OxGod0dpnG3qXLzfj1CkDmjZ1YMgQbvhJRETqYtjR6EwsX90mQmwN8dpr7g0/7QgKYmMyERGpi2FHY3x92vmaNSbs2WNESIgTY8bw5UVEROrjp5GGuFwu7D+jzMRqFdEKvujVV5WqzvDhdm74SUREmsCwoyG/nf8NZ61nYZAMaBnWEr5mxw4D1q83wWgUG35yEUEiItIGhh0Nca+vEx0SjaCAIPgad6/OXXfZ0KoVp5sTEZE2MOxoiHsIS1R1fK0qcuyYhBUrlIDz2GOs6hARkXYw7GjIvjP75PvY8Fj4mgULAmC3S+jRw4YePdirQ0RE2sGwo8FhLF8LO+fOAYsXuxcR5NYQRESkLfxU0mDYiQuPgy95910zCgokxMY6cPfdrOoQEZG2MOxoaNr5r7m/+lzYcTiA//7XIh+PG2dHQABXTCYiIm1h2NGIY2eP4YLtAoySES3DfWfa+aefmnDwoAEREU6MGsWgQ0RE2sOwo7EhrJi6MQgwKf0vvmDuXOVcR42yITSUQ1hERKQ9DDsam3YumpN9Zdr25s1GbN5sQkCAC4884hvnTERE/odhR2szscJ8ZybWf/+rVHVSUmyIieEigkREpE0MOxrha9POjxyR8PHHyrDV3/7GRQSJiEi7GHY0NozlKzOxFi0KgMMh4frrbejWjb06RESkXQw7GuB0OZGVlyUfx0VoP+xcuAC8+aYybDVuHBcRJCIibeOnlAYcKTyCInsRTAYTmoc1h9alp5uRl2dAs2YODBjAqg4REWkbw46GhrCahzaH2ajtRl+XC5g/X2lMfvBBLiJIRETax7CjsZlYWm/0/fZbI37+2Yg6dVwYPZovHyIi0j5+WmmAe5uIlmHaXznZXdUZOtSGevU4hEVERNrHsKMB7uZkrYed7GwJq1YpAefRRzndnIiIfAPDjoYqO1pfY2fhwgC4XBL69rWhY0dt9xYRERG5MexoYLfz7Lxs+VjLG4CePQssWaIMYT3yiItVHSIi8hkMOyo7ce6EvNu5QTKgWWgzaNX775tRWCghLs6BW2/lDCwiIvIdDDsa6deJDolGoDkQWuR0/tGY/PDDdpjNDDtEROQ7GHY0NBNLq0NDa9aYkJVlRGioEyNHMugQEZFvYdhRWVauUtlpEdYCWjVvnlLVGTbMhrAwhh0iIvItDDsaGcYSqydr0d69BqxbZ4LB4MK4cZJmq09ERESVYdjRyDCWVnc7T0tTqjq33GJDfDynmxMRke9h2NHKgoIanHZeWAgsXaoEnEce4SKCRETkmxh2VJR3MQ+5F3M127OzbJkZ589LiI93oG9f9uoQEZFvYtjRQFWnQXAD1LXUhdZ2N1+0SBnCSk21w2Ri2CEiIt/EsKOFmVihLTQ3RLRxoxF79hgRHOzC8OF8mRARke9Sbdvq7OxsrFixArGxsfJxamoqwsPDK3xuZmYm1qxZIx9v2bIFCxcurPS5vljZEUNYWgs77qrO4ME2REWxMZmIiHyXamFn0KBByMjIkI9F2BkzZgzS09MrfK4IOhMnTpSPZ82ahb59+5b8t7rYADRMWxuAnjwp4eOPlZfGQw+xMZmIiHybKuMTItyUJqo77spNRVWd6dOnl3ydkpIiP1b+Z/h6ZUdL3n7bDLtdQlKSHUlJrOoQEZFvUyXsiGATGRlZ5jHxtQgx5SUkJMjDVm75+fklz9dLz47YKkIrHA7gzTeVIawHH3SyqkNERD5PlWEsd2ApLzdXmYZdnqjmuC1btgzJycmV9uwUFxfLN7dCsViMBl20XcTRs0fl49gI7QxjffmlCUeOGBAZ6cSQIZyBRUREvk9T02wqC0Glvy+amivr7RHEkFdYWFjJLSYmBlqUnacMw4UGhCIqKApaa0wePtyOOnUYdoiIyPepEnZEVaZ8FUd8faUZVpMmTcLq1asv+7zJkyejoKCg5JaTkwOt9+sYDNrInFlZEtauNUGSXBg7lsNXRESkD6p8yophqIokJiZW+t+IWVgi7IhmZlHhqawKZLFYEBoaWuamRVpcY+eNN5SqTnKyHfHxqk3UIyIi8v2wIwJLaWJmlQg67opN+dlWYuhKNCq7g87y5ct9fp0d97RzrTQnX7wILFmizLzidHMiItIT1f75LvpuRKUmKSlJXiiwdB+O6LsRj4u1dUToEWvylCaCjliE0Jdpbdr5//5nRl6eATExDtx+O3t1iIhIP1QLO6JKM3PmzEtmWwmlg494nkts1KTX3c41Utl5/XWlqjNqlANmszKcRUREpAfa6Iz1M3anHQfzD2pm2vm2bQZs3WqC2ezCmDF8SRARkb7wk00FhwsOy4HHYrSgad2map8OXn9dqeTcdZcdTZqwMZmIiPSFYUcF+8/sL5mJZTSo2x8jJrWlpytDWA8/rOqpEBEReQTDjgr2ndkn38eFx6k+6+n99wNw8aKE9u0duPFGVnWIiEh/GHZUDjtqEn3f7sbk1FQHDAZONyciIv1h2FHBvlxthJ1vvzVi/34j6tRxYfhwTjcnIiJ9YthRsbLTKqKVJvbBGjrUhvBwhh0iItInhh0vK7IX4VD+IdXDzvHjEj77TOnRefhhDl8REZF+MeyosCeWCy55t/MGdRqodh5vvWWGwyGhRw87rrmGjclERKRfDDsqNiertdu5zQa8+aYyhPXgg07VZ4QRERF5EsOOl+09s1f1aeerVplw/LgB9es7MWgQe3WIiEjfGHZUquzEhsWq3pg8fLgdQUEMO0REpG8MO342E2v/fgPWrzdBklwYO5bDV0REpH8MO34WdhYvVhYR7N/fjrg4NiYTEZH+Mex4Ud7FPJy6cEo+jovw/oKCFy4A776rDGE99JCLjclEROQXGHa8aH+usgFoozqNEGoJ9fqfv3KlGQUFElq0cOCWW9irQ0RE/oFhx482AHU3Jo8e7YDZzLBDRET+gWHHi3459UvJTCxvh52MDAO2bTPCYnFh5EgOXxERkf9g2PGin0//LN+3jWzr9T/79deVqs7dd9vQqBEbk4mIyH8w7HjR7t92y/dto7wbdnJzlX4dgY3JRETkbxh2vLgBaFZeliph5733AlBUJKFzZztuuEEJPURERP6CYcdL9pzeA6fLiXBLOBqHNPban+t0iiEsJeCMGeOA0ci/ciIi8i/85POSn08p/TrxEfFe3QD0m2+MyM42om5dJ+67j3/dRETkf/jp5+V+nTaRbbzaM+NuTB461IawMDYmExGR/2HY8ZLdp3Z7fSbWkSMSPv9cCTgPPyyxMZmIiPwSw46Xh7G82Zz85psBcDol9OplQ6dOrOoQEZF/YtjR6UwsqxV46y2lMXnsWKdX+4SIiIi0hJ+AOp2J9fHHJvz2mwGNGjlxzz3cGoKIiPwXw44Xm5O9ORPL3Zg8YoQNQUEcwiIiIv/FsOMFO07ukO/bRbXzSpPw7t0GbNxogtHoQmoqm5KJiMi/Mex4wbaT2+T7zvU7e3V389tvt6F5c66YTERE/o1hx8NcLhd+Ov6TfNypfieP/3mFhcCyZdwHi4iIyI1hx8OOnzuOUxdOwSAZ0L5ee4//eSLonD8vIT7egb592atDRETEsONh204oQ1itw1sjxBLi0T/L5fpjCCs11Q6TibOwiIiIGHY8zD2E1aFeB48PKW3YYMSePUYEB7swfDj/aomIiAR+IuqoOXnhQqWqM2SIFVFRHMIiIiISGHa8NIzl6ebkQ4ckfPKJEnDGjeM+WERERG4MOx6UX5SPX3N/lY87N/BsZWfBAmUfrN69bbjmGlZ1iIiI3Bh2PGjz0c3yfYvQFqgXXM9jf87Zs8DbbytDWI8+6uA+WERERKXwU9GDfjzyo3yf0DDBowFkyZIAFBZKaN3ajttv5yKCREREpTHseNCPR5Ww061hN4/9GQ4HMG+eUtUZN84Os5nTzYmIiEpj2PHgysmbjmySj5MaJ3nsz/n8cxMOHTIgIsKJ4cMZdIiIiMpj2PGQ7LxsnLl4BgGGAI/NxBKLCL7yilLVGTnShtBQNiYTERGVx7DjIe6qjlhfJyggyCN/xrp1RmzdakJQkAvjx3O6ORERUUUYdjzku8PfyfeJjRI9EkJEVWfmTIt8PGKEFdHRbEwmIiKqCMOOh6w7sE6+v6HpDR75+evXG/HjjyYEBrowYQJY1SEiIqoEw44HHCk8gv25++Wdzq+Pvt4jf8asWUpVZ/hwK5o3V/p2iIiI6FIMOx7w9YGv5fsu9bsgIjii1n/+6tUmfP+9CQEBLkycyKoOERHR5TDseMC6g38MYdV2ELHZgClTlKrO2LFWtGzJqg4REdHlMOzUMqfLiS9+/UI+7hnds9Z//uLFZuzbZ0S9ek489RRnYBEREV0Jw04t23psK06cO4EQcwh6xtRu2Dl5UsK0aUpV54knrKhXjzOwiIiIroRhp5Z9vPdj+b5Psz61ur6OmGr+t78FIj/fgE6d7HjwQROrOkRERFXAsFPLW0Ss+HmFfNy/Zf9aDSNLl5rx+edmmM0uvP66HYGBXC2ZiIioKhh2atHGnI3Ye2Yvgk3BuDXu1lr7uTk5EiZODJSPH3+8GImJylAWERERXRnDTi1a/NNi+f7OVnciIqh2ppxfuADce28wCgslJCTYMWUKh6+IiIiqg2GnluQX5WPZ7mXy8b3t7q2VQGK3A2PGBGH7diOiopx4/30Hh6+IiIiqiWGnlrz8w8s4bzuPdpHtcH3M9bUSdMaODcKnn5rlxQPffdeK1q25pg4REVF1qVYmyM7OxooVKxAbGysfp6amIjw8vMbPVcP+M/sx54c58vGE7hNgNBpr9PN++03CyJFB+O47E0wmF5YsKcZNN1k4fEVERORLYWfQoEHIyMiQj0WAGTNmDNLT02v8XG8Ta+oMXTkUF2wX5BWT74q/66p/VmEhkJ5uxuzZFhw/bkCdOk4sXmxFSgqDDhERkU+FHRFYShMVmzVr1tT4ud4i1rw5f96F93e9j4mrJyK/KA+hlsaYfd1/UVxkQnE1f1ZmplGeWv6//5lx4YISauLj7Vi+3IHOnRl0iIiIfC7siLASGRlZ5jHxdWZmJhISEq76uUJxcbF8cysU5ZJadvacA2GhYqjq/36/AeJP6fFMzX92fLwDI0fakZpqQEQEp5gTERH5ZINyfn5+hY/n5ubW6LnC9OnTERYWVnKLiYlBbTMaataTU5GUFCvWri3G7t3ApEkWRERwKwgiIqLaoKl5zJUFm+o8d/LkyXjsscfKVHZqO/AEBwMncs8hJz8H8VHxNfpZR48qPy8mxgyDgcNVREREugg7YiZV+cqM+LqiGVbVea5gsVjkmyeJFpqGESFoGNGuxj8rNLRWTomIiIi0NIyVnJxc4eOJiYk1ei4RERGRJsKOmFFVfsaVCC/uao1oPnbPwrrSc4mIiIg02bMj1smZNGkSkpKSsGXLljLr5ogmY/H4xIkTr/hcIiIiosuRXC6x0ot+iQZlMSuroKAAoWyQISIi8rvPb+6NRURERLrGsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREuqbadhHe4l4gWqzESERERL7B/bldGxs96D7snD17Vr6PiYlR+1SIiIjoKj7HxbYRNaH7vbGcTieOHTuGunXrQpIkaDnBikCWk5Oj2z289H6Ner8+f7hGvV+fP1yj3q/PH66x8PfrO3z4sPy53aRJExgMNeu60X1lR/wPio6Ohq8QL1w9vnj96Rr1fn3+cI16vz5/uEa9X58/XGNYWFitXR8blImIiEjXGHaIiIhI1xh2NMJiseCZZ56R7/VK79eo9+vzh2vU+/X5wzXq/fr84RotHrg+3TcoExERkX9jZYeIiIh0jWGHiIiIdI1hh4iIiHRN9+vsaFFmZibWrFkjH2/ZsgULFy5EeHi4/HV2djZWrFiB2NhY+Tg1NbXke75q0qRJmDx5su6uUfwdivMX1yEkJyfr6vrEuYtrjIyMlI9TUlJKrtVXr1H87o0ZMwYZGRllHr/c9fjStVZ2fXp6z6nsGvXynnO569PLe07mZX4PPfaeIxqUybtmzpxZ5jghIaHk69LHWVlZrpSUFJcvy8jIEA3wrry8PF1d4+rVq12pqakl1xAbG6ur6yv/OhXc1+ur15ienl7yeizvctfjK9d6uevTy3vO5a5RD+85l7s+vbznpFfxdVrb7zkMO14m/pLDw8PL/KWJv3RxL26l/0KF0s/1ReKFLX4p3W88ernG0tfkvi49XZ9Q/jpKv9H68jWWf5O93PX44rWWvz49vudcLuzo4T2nouvT23sOrvCPjtp+z2HPjpclJCTIJWS3/Px8+V6U7dzlu9LE16Lk54tEyVGUIUvTwzWKEmpubq5cQhXnLf4O3aVWPVxf6fPu1q1bSWm5X79+urvGK12PHq6V7zm+f418z1lT42tk2FFB6V/GZcuWyeOu4kXsfhMqT7zIfY24lorGU/VwjeIXTPyiucePFyxYIB/r5frc0tPT5fu4uDj52P261dM1Xul69HKtfM/x7Wvke05+ja+RDcoqEn+B4gV7uUY79/N8zfLly+UGsqrypWsUv2DiXx7uDwxxnREREaImq4vrcxP/mpo5c6Z8rWPHjpUfS0tL09U1Xs7lrsdXr5XvOb55jXzPqfk1srKjIjFjYPXq1SX/GhH35ZOqu3Tpay/YwYMHV/g9PVyj+JeVON/Sf2/uf33p4foE8WYjZu2IN1fxxpqVlSV/mIjH9XKNbpe7Hr1dK99zfPMa+Z4TXuNrZNhRyaxZs+Q3HvEiFulU3NzTCMtLTEyErxEvUlFqFTfxYp0+fbr8i6mHa3SPlVdED9cniL+rpKSkMtcspvLq7XUqXO569HStfM/x3Wvke05yja+Rw1gqEGVk0TToftNxl1/Lp1TxCyv+Mn0toZd/YYpypLhV9Avri9corkOcs7tHwL3uhfg71cP1CeJaRPm4dK/HmTNndHONpfs7yr8uS1+Pr/5Olu9f0eN7Tulr1ON7TvnXqB7fc/JLXaPH33Ouas4YXTX3tM/St/LTQidOnChPnxT3paca+hpx7mLdBHGNYgqhmAKrl2sU5yyuKS0tTb53TwPVy/W51/UQf3/iGsXN169RXI84V/F6dJ97Va7HV661suvT03vO5f4O9fCec7nr08t7zurLXKMn33O46zkRERHpGnt2iIiISNcYdoiIiEjXGHaIiIhI1xh2iIiISNcYdoiIiEjXGHaIiIhI1xh2iIiISNcYdoiIiEjXGHaIiIhI1xh2iIiISNcYdoiIiEjXuOs5EfkcsevxmjVrkJWVJe9unZmZiS1btmDy5Mk+sdszEXkXKztE5HNE0ElNTUW/fv0waNAgpKSkYMWKFcjNzVX71IhIg1jZISKfM3jwYPleVHSGDBkiH4sqDxFRRVjZISKf4x6qWrZsmVzVEfLz81U+KyLSKoYdIvIpCxYswKRJk+SqjujdiY2NlR9fvny52qdGRBoluVwul9onQURUnX4dEXIiIyPlCo84FkQPDxFRRRh2iIiISNc4jEVERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDREREusawQ0RERLrGsENERES6xrBDRERE0LP/B19THkpZy7hlAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pba.inspect_pbox(test_)" ] }, { "cell_type": "markdown", "id": "d50ce765-91f3-4dad-902d-74bcaa4c8076", "metadata": {}, "source": [ "## A mixture of uncertainties \n", "\n", "`pyuncertainnumber` allows you to compute with uncertainty without worrying about the type. This makes the calculation involving a mixture of uncertainty kinds very easy. Below illustrate the example when one has an interval, a probability distribution and also a p-box." ] }, { "cell_type": "markdown", "id": "33fc8330", "metadata": {}, "source": [ "```{tip}\n", "In terms of a non-intrusive context, one can also use the [double Monte Carlo](https://pyuncertainnumber.readthedocs.io/en/latest/autoapi/pyuncertainnumber/propagation/mixed_uncertainty/mixed_up/index.html#pyuncertainnumber.propagation.mixed_uncertainty.mixed_up.double_monte_carlo) (a.k.a nested Monte Carlo).\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "id": "f1b5436e-e71b-4b70-90e1-13f900525e92", "metadata": {}, "outputs": [], "source": [ "# the example in the readme.\n", "\n", "a = pba.I(1, 5) # interval\n", "b = pba.normal([10,14], [1]) # pbox\n", "c = pba.uniform(4, 11) # distribution" ] }, { "cell_type": "code", "execution_count": 10, "id": "bcc7085c-e1ed-405b-b783-31e923630a1a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f([a, b, c]).display()" ] }, { "cell_type": "markdown", "id": "13ddabad-b619-4a63-8d0f-1460b3f32383", "metadata": {}, "source": [ "## high-level implementation with `uncertain numbers`" ] }, { "cell_type": "markdown", "id": "e2bbc56f-8cc2-4951-b8ce-305039cd91b7", "metadata": {}, "source": [ "As described in {ref}`what_is_uncertain_number`, the concept of uncertain numbers encompass intervals, distributions, p-boxes, Dempster Shafer structures, and real numbers. Therefore, the framework of uncertain numbers can natively handle mixed uncertainty problem, and it is no longer mixed any more." ] }, { "cell_type": "code", "execution_count": 11, "id": "c287ad23-17ee-4a8d-a874-437d4557c715", "metadata": {}, "outputs": [], "source": [ "a = pun.I(1, 5) # interval\n", "b = pun.normal([10,14], [1]) # pbox\n", "c = pun.uniform(4, 11) # distribution" ] }, { "cell_type": "code", "execution_count": 12, "id": "3c69449a-2da8-4951-8d50-b3007dd7ecdc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: mixed uncertainty propagation\n" ] } ], "source": [ "p = pun.Propagation(\n", " vars = [a,b,c],\n", " func = f,\n", " method = \"slicing\",\n", " interval_strategy='endpoints'\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "id": "5055cc10-fb71-40de-91c9-a13557aa2efc", "metadata": {}, "outputs": [], "source": [ "un_p = p.run(n_slices=50)" ] }, { "cell_type": "code", "execution_count": 14, "id": "cfc3aad1-b41c-4ca6-ad0d-06700868fc72", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "un_p.display()" ] }, { "cell_type": "code", "execution_count": 15, "id": "98d9d467-cd6f-4aca-af4b-3da06387c47b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "UncertainNumber(essence='pbox', _construct=Pbox ~ (range=[13.062, 151.938], mean=[20.587, 147.927], var=[1.280, 4356.734]), nominal_value=84.257)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "un_p" ] }, { "cell_type": "code", "execution_count": 16, "id": "cf952b2f-ac44-4182-8a4b-2219ebff20e1", "metadata": {}, "outputs": [], "source": [ "# surroaget to propagte pbox is udnerway" ] } ], "metadata": { "kernelspec": { "display_name": "pbox_nn", "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }