{ "cells": [ { "cell_type": "markdown", "id": "94a4daa2", "metadata": {}, "source": [ "# Example 5: Generate Synthetic Data for Two Tracers\n", "In this example, synthetic observation data is generated for two tracers. This is done by first generating time series of tracer input for both tracers. This data is then used as a model input, together with a pre-defined set of reference parameter values (a single model / a single set of model parameter values is used for both tracers as they are applied to the same hypothetical physical system / aquifer). As a result, we obtain simulated time series of tracer concentrations of both tracers at a (hypothetical) sampling location. From this time series we select a number of values as observations (measurements of both tracers at all observed time steps); those values are perturbed by noise to include measurement error." ] }, { "cell_type": "code", "execution_count": 1, "id": "b25444c9", "metadata": {}, "outputs": [], "source": [ "import PyTracerLab.model as ism\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime" ] }, { "cell_type": "markdown", "id": "ac5cff6c", "metadata": {}, "source": [ "## 1. Generate Synthetic Observation Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "47feadbf", "metadata": {}, "outputs": [], "source": [ "# make reproducible\n", "np.random.seed(42)\n", "\n", "### define first input series\n", "# define time steps\n", "n_years = 100\n", "timesteps = np.arange(0.0, n_years * 12.0, 1.0) # 50 years of monthly values\n", "\n", "# this represents the tracer input to the aquifer\n", "# we start with a constant input of 1.0\n", "input_series_1 = np.ones(len(timesteps))\n", "# we add a pulse of higher tracer input during some years, similar to the\n", "# increase in tritium in the atmosphere\n", "# this is just a bell-shaped pulse\n", "offset = 200.\n", "scale = - 0.0005 # negative; closer to zero means wider pulse\n", "input_series_1 += 1000 * np.exp(scale * ((timesteps - offset) ** 2))\n", "# add some noise to the data\n", "input_series_1 += np.random.normal(0.0, 0.02 * input_series_1, len(input_series_1))" ] }, { "cell_type": "code", "execution_count": 3, "id": "e95dd2cc", "metadata": {}, "outputs": [], "source": [ "# make reproducible\n", "np.random.seed(42)\n", "\n", "### define second input series\n", "\n", "# this represents the tracer input to the aquifer\n", "# we start with a constant input of 1.0\n", "input_series_2 = np.zeros(len(timesteps))\n", "# we add a pulse of higher tracer input during some years, similar to the\n", "# increase in tritium in the atmosphere\n", "# this is just a bell-shaped pulse\n", "offset = 200.\n", "scale = - 0.0005 # negative; closer to zero means wider pulse\n", "input_series_2 += 10 * np.log((timesteps / 100) + 1.1)\n", "# add some noise to the data\n", "input_series_2 += np.random.normal(0.0, 0.005 * input_series_2, len(input_series_2))" ] }, { "cell_type": "code", "execution_count": 4, "id": "b13aef1c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rU4Og+NrkXeOTGsuWLVPPGVkjdOuFGgMdCv5oK3PJBkwSmDlzcPdfs6ZIjRp65BUmJOzRI7j7J6KQwHRYweo+iiTo5kHxbY/rn0UIXLxXBK9Zs6YKLPDlbnZdeatXr56qcUFRcMaM1nwl//HHH4luI0MFBa8PJkHtDbrYwLswGVA7BNeuXbvhsVDA3KVLFxXY3X///RIOru264vDyEPF+A6CeJhTM/7OePXU3GRGRRVA7g1FD+PJHN9Tdd9/tkzFBADNgwAC57777ZPbs2RIdHa2+7FGfAhgFderUKTXqCfUq6K5asGCB6tbxJ3AIVrA2duxYVROE78aZM2eqgmQzI9WkSZP4AmwEbC+88ILP75cuXVplijBM/MSJEyrYS667CjVCjz76qNx+++1y9OhRdcHzDyXXBjocXh4iZr8u0rVdu4bmGN7dVfz/IyILjRs3ThX8Ysg0umMw9BtZGm+TJk1S3VkPPfSQqr9BIfA5DAcTkWLFiqlAA0FNu3btVAYIw8hR72N2G4XasGHD1IgvZGxGjx6tnpP3EHYUR2POINQJoW14jDcMSX/ppZdUQTOGx6MIOykY9XX+/HkZM2aM6pYzLz3xR2sIpTO8O95cKCYmRs2XgCFumByK0uixx0QwtHDYMJE33wzNMdau9WR1vvlG5PbbQ3McIkqVixcvqsxF2bJlJUuWLFY3h1KAjBOCF1zs9lry9/vbtRkdCpHt2/X2ev9uSGDUA2p14MCB0B2HiIhsj4EOBQ/6k80Zkc3lGkLFXA2dC3wSEVEKOOqKgtulhGnL8+bVWZdQMut0MMSciIhSZb/XCDGnYkaHgmffPr1Ft1KCuRuCDrMkQ4KJr4iIiLwx0KHgOXRIb0uUCP2xzEAHkxP++2/oj0dEAXP5WBeKkNcQAx0KDrwYn3wyfIEOVkU3zZ8f+uMRUcCz8WIoMVFamK+hhDM8B8K1NTqYFAmXcE3I5HhHj3quN28e+uN5j+r688/QH4+I/Ia1lTAPjLmmUrZs2dSEckSBZHIQ5OA1hNeS93pdgeI8OpxHJzgWLtRLP2AV23/+Cc8xX3tN5Nln9QrpmDiQH6REEQNfLZj19vTp01Y3hWwMQU6RIkWSDJT9/f52bUaHgszMqtx0U/iO2auXDnTWrRM5ccJTt0NElsMXE2a9LVSoUJoXgSR3ypQpU5oyOSYGOmS/QmRThQp6mDmGmOP4DHSIIg6+qILxZUWUWixGpuAGOsWLh/e4ZmBlHp+IiMgLAx1KO6zUu2SJtYHOnj3hPS4REdkCAx1Ku507PUsxmEszhEuTJp75dIiIiBJgoENpd/iw3lavHv46mQ4d9HbpUpFLl8J7bCIiingMdCh4gU6xYuE/du3auiD53Dk9xJyIiMgLAx1Ku1Wr9LZo0fAfG3MrNGumr69cGf7jExFRRGOgQ2mDmU8//lhfb9/emjbUqqW3LEgmIqIEXBvoYPmHatWqScOGDa1uir2huwi1MZUqifTpY00bihTRW8ynQ0RE5MW1gc7gwYNl+/btsoZ1HWmDGYmhTBnrlmBgoENERMlwbaBDQQ50Cha0rg0oRoYjR6xrAxERRSQGOpQ2J09aH+hgKQg4cMB3FXUiInI9BjqUNubSC1YGOjh23br6+m+/WdcOIiKKOAx0KPUMQ+Tnn/X1xo2tbQvm04EdO6xtBxERRRQGOpR6WPYBNToZM4q0aGFtW6pW9SxHQUREdB0DHUq97dv1tmJFkaioyAh0mNEhIiIvDHQo9cygolo1q1viCXQ2bhRZv97q1hARUYRgoENpz+hEQqBTtqzn+vz5VraEiIgiCAMdckagkyGDyJNP6uucT4eIiK5joEOpH3FlBjpmt5HVSpXSWwY6RER0HQMdSh0st3D6tEj69Hqdq0hgrp5++LDVLSEiogjh2kCHi3qmkZnNKVdOJGtWiQjFiuktMzpEROT2QIeLeqZRpHVbeWd0EOiga42IiFzPtYEOOWhoecJA59IlkX//tbo1REQUARjokP1HXJkyZxYpVEhf//xzq1tDREQRgIEOOSfQgf/+V29nzbK6JUREFAEY6FDg/vlH5Phxfb1KFYkoffvq7erVrNMhIiIGOpQK5sKZJUuK5MghEaVMGb29cEEPfyciIldjoEOBO3RIb0uXloiDoe758vm2k4iIXIuBDgXO7LYqXFgiUvHiestAh4jI9RjoUOoDHXOEU6QxA7ATJ6xuCRERWYyBDqVu+YdIzujkz+8pmiYiIldjoEOBO3DAt4so0jDQISKi6xjoUOB27dLbypUlogOdkyetbgkREVmMgQ4FPrR8//7InEPHVKCA3rJGh4jI9RjoUGDmzdPb1q1FChaUiGQuNLp2rdUtISIiizHQocCY2ZwGDSRiNWkikjGjbuu2bVa3hoiILMRAhwITHa23ZctKxMqZU6RdO3194UKrW0NERBZioEOpC3TMpRYiVYUKvkPhiYjIlRjokP+wSKbZdRXJGR0w64dYkExE5GoMdMh/mJcmNjZy17nyxkCHiIjcHOhMnDhRqlWrJg0bNrS6KfZhrh2FICJLFrFFoGMuV0FERK7k2kBn8ODBsn37dlmzZo3VTbGPSF/MM6kaHYy6unbN6tYQEZFFXBvoUCqY3UCROn+Ot+rV9eirs2dFduywujVERGQRBjrkv2XL7BPoZMiggx1goENE5FoMdMh/H3+st3nzii1UquS7NhcREbkOAx3yT1yc5/o994gtlCuntwcPWt0SIiKyCAMd8n9ouVnU27ix2EKhQnrLkVdERK7FQIf8Y84wnD+/SFSU2CrQ4Vw6RESuxUCH/HP0qH2Glps4lw4Rkesx0KHAAp0iRcQ2zKDsyBG9fAUREbkOAx0KrOvKToEO1uPKlEkvW3HggNWtISIiCzDQIed2XaGWqFo1fX3rVqtbQ0REFmCgQ/4xVy23U0bHu70YNUZERK7DQIdu7MoVkZ9+0tdbtBBbyZNHb8+csbolRERkAQY65F99zoULIhkz2mcOnYSBzunTVreEiIgswECHbuzwYb0tWlQkvc1eMgx0iIhczWbfWmR5oGM3uXPrLQMdIiJXYqBDN4Z5aKBYMbEdcwHSPXusbgkREVmAgQ45O6PTtq3e/vYbZ0gmInIhBjrk7IxO+fKeVcy3b7e6NUREFGYMdOjGDh2yb0YHqlfX2x07rG4JERGFGQMdStn58yK//66vV6wotlS6tG/ARkRErsFAh1K2e7dITIxIvnwiN90ktmTOjmwuY0FERK7BQIdSduqUJ1iw2xw6JgY6RESuZdNvLgqbf//1HaZt50Bn2TKRS5esbg0REYURAx3yL6ODriu7atVKZ6NiY0U2bLC6NUREFEYMdMj5GZ3s2UVq1dLXubgnEZGrMNChlJl1Lfnzi61xzSsiIleyfaBz8OBBadWqlVSrVk1q1aolM2fOtLpJzmJ29dSoIbbGNa+IiFwpo9hcxowZ5Z133pE6derI0aNHpX79+tKpUyfJju4KSrutW/W2bl2xNWZ0iIhcyfaBTtGiRdUFihQpIgUKFJBTp04x0AmGa9dE/vnHvss/eGOgQ0TkSpZ3Xf3666/StWtXKVasmKRLl05mz56d6DETJ06UMmXKSJYsWaRx48ayevXqJPe1bt06uXbtmpQsWTIMLXfJiCvDcEaNTsGCvguUEhGRK1ge6Jw7d05q166tgpmkTJ8+XYYOHSojR46U9evXq8e2b99ejidYiRpZnP79+8uHH36Y4vEuXbokMTExPhdKxsmTnhFXGW2e/KtcWW+53hURkatYHuh07NhRRo8eLT169Ejy5+PGjZNBgwbJwIEDVcHx5MmTJVu2bDJlyhSf4KV79+7yzDPPSLNmzVI83pgxYyR37tzxF2Z/UnDihG82xM6qVdPbnTs9WSoiInI8ywOdlFy+fFl1R7Vp0yb+vvTp06vbK1euVLcNw5B7771XWrduLf369bvhPp999lk5c+ZM/AWjtigZ0dF6W7y42F6FCiIZMoicPcvFPYmIXCSiA52TJ0+qmpvChQv73I/bGGEFK1asUN1bqO3ByCtctmzZkuw+M2fOLLly5fK5UDLM81izptheVJQOdmD7dqtbQ0REYWLzwgssqH2TxMXFWd0MZ9q717e+xQndV7t26Tqddu2sbg0REbk9o4Oh4hkyZJBjx4753I/bGEpOYSpGLlRIHKFqVb1lQTIRkWtEdKATFRWlJgBcvHhx/H3I3uB206ZN07RvjPJCcXPDhg2D0FKHBzp2H1puMruu9u+3uiVEROSWrqvY2FjZs2dP/O3o6GjZuHGj5MuXT0qVKqWGlg8YMEAaNGggjRo1UrMgY0g6RmGlxeDBg9UFw8sx+oqSYE4WWKCAOIK5AjsnDSQicg3LA521a9fKLbfcEn8bgQ0guJk6dar07t1bTpw4ISNGjFAFyCg2nj9/fqICZQoy1D05LdAxA1quYE5E5BrpDIzPdjEzo4Oh5hyB5QXD7kuV0hMFnj8vkimT2N7GjXrNLtR3HTlidWuIiCgM398RXaNDFjKHYFes6IwgB7jeFRGR6zDQoaT9+afeVqkijmEGOhcvYjptq1tDRERh4NpAh6OubuDvv/XWSUtkILWZLZu+nszCsERE5CyuDXQw4mr79u2yZs0aq5sSmcxlEpyw/IMpfXqRLl309V9/tbo1REQUBq4NdMiFgQ6ULeu7YCkRETkaAx1K2uHDzgx0zJXYGegQEbkCAx1KOaNTrJg4irmcxfHjVreEiIgiMdC577775OzZs4nux2zF+Bk5QEwMpqzW15nRISIiNwU6n376qVy4cCHR/bjvs88+E7vgqCs/sjmYSTh7dnEUZnSIiFwlYyAzEGISZVyQ0cmSJUv8z65duyZz586VQjZa5ZprXbmwENk7o4MFSzEpeLp0VreIiIgiIdDJkyePpEuXTl0qVaqU6Oe4/6WXXgp2+8gKTi1E9g50rlzRa16ZkwgSEZEj+R3oLF26VGVzWrduLd9++61aXdwUFRUlpUuXlmJOK1x1q717nRvoIBOJiQNRh4TuKwY6RESO5neg07JlS7WNjo6WUqVKqQwOOdT06Xpbvbo4EgI4BDr794skkZ0kIiIXBjqmAwcOqEtybr755rS2iayEupV9+/T1nj3FkWrUENmxQ2TLFpF27axuDRERRVKg06pVq0T3eWd3UJhMNnbunK5fgcKFxZGqVdPbnTutbgkREUXa8PJ///3X53L8+HGZP3++Gqb9888/h6aVFD6nTultVJRnAUynMRcqNYuuiYjIsQLO6CQ1FLtt27aqIHno0KGybt06scs8OrgwA5VMoINic6fWYZlF8wx0iIgcL2hLQBQuXFh27doldsHVy28Q6OTPL45ljiYz5wsiIiLHCjijs3nzZp/bGHJ+5MgRee2116ROnTrBbBtZ4Z9/9NZr+gDHZnSwDMTly7qbjoiIHCngQAfBDIqPEeB4a9KkiUyZMiWYbSOru66cCtkqBDcIco4cESld2uoWERFRpAQ6mEfHW/r06aVgwYI+S0KQjbkh0EHtEbI6mEcHdToMdIiIHCvgQAczIJODuSHQATPQYZ0OEZGjpaoYefHixdKlSxcpX768uuD6okWLgt86sq5Gx8nFyN4FyRx5RUTkaAEHOu+//7506NBBcubMKUOGDFGXXLlySadOndRwbbIx1F2tWqWvlyghjmYWJDOjQ0TkaAF3Xb366qvy9ttvy8MPPxx/36OPPirNmzdXP8OwbTvgPDpJwGre27fr6126iKMxo0NE5AoBZ3ROnz6tMjoJtWvXTs7gi9ImOI9OEk6f1tusWUXy5rW6NaHFjA4RkSsEHOjcdtttMmvWrET3f//996pWh2zs33/1Nk8ecTxmdIiIXCHgrqtq1arJK6+8Ir/88os0bdpU3ffHH3/IihUrZNiwYTJ+/HifLi2yYUbH6dkcYEaHiMgV0hkJZ/67gbJly/q343TpZN++fRLpYmJi1Ppd6HZDUbWrIVPXs6dIs2YiK1aIo8XGiuTMqa/HxHiuExGRLfj7/Z3mCQPJQdzUdZUjhwjeGAhykNWpUsXqFhERUSQv6kkOcOyY3hYoIK7AOh0iIscLOKOD4dhTp05VkwYeP35c4uLifH6+ZMmSYLaPwunAAb11y+zXqNPZsYN1OkREDhZwoIMJAhHodO7cWWrUqKFqccgh3BjoADM6RESOFXCgM23aNJkxY4aaCZkcxm2Bjtl1xYwOEZFjBVyjExUVJRUqVAhNa8g6GHzntkCHGR0iIscLONDBXDnvvvuuBDgqPeJg+QfMCdSwYUOrmxI5i3meP6+vlywprsCMDhGR4wU8j06PHj1k6dKlki9fPqlevbpkypTJ5+ffffed2Ann0blu3TqRBg1EihQROXJEXGH1apHGjXXA8/ffVreGiIgiYR6dPHnyqGCHHMZt3VZgTn6JrqtLl0QyZ7a6RUREFGQBBzqffPJJsNtAkcCNgQ7mC8qeXeTcOf38K1WyukVERBRknDCQNLPrxi31OYCpEcyCZHOyRCIicmdGp27dun7NmbN+/fq0tomsXNAzf35xldy59fbMGatbQkREVgY63bt3D8XxKdICHTesc5VUoIM1r4iIyL2BzsiRI0PbErKW2wMdZnSIiByJNTrk7kDHHJLIQIeIyJEY6JBvoGNmONyCGR0iIkdjoEOemZEhb15xFbP4euNGq1tCREQhwECHRP7915PRKFVKXKVjR71dssTqlhARkdWBzpUrV+TWW2+VP//8MxRtIavs3au3WP4BE+i5iblA7eXL+kJERO4NdLCu1ebNm0PXGrJ2VmRzSQQ38Q7szp61siVERBQJXVd9+/aVjz/+WOyOq5d7OXpUb4sWFdfBorTmGlexsVa3hoiIrF7r6urVqzJlyhRZtGiR1K9fX7In6OoYN26c2MHgwYPVxVz91NXMQAddV26UM6de1JOBDhGR4wQc6GzdulXq1aunru/evdvnZ/4sEUERyO2BTo4cIidPMtAhInKggAOdpUuXhqYlZB0GOnrLQIeIyHFSPbx8z549smDBArlw4YK6bRhGMNtF4cRAR29ZjExE5DgBBzr//POPGmJeqVIl6dSpkxw5ckTd/5///EeGDRsWijZSqLk90ClcWG///tvqlhAROe7rZdUqm3VdPf7442qY+V9//SVVq1aNv793794ydOhQeeutt4LdRgqluDiRY8fcHehUry7y/fcoQLO6JUREQXf2rEiWLHqQKT7yUU6LCz76o6L0FGIZMui5YytWFPn5Z5GSJfXPx4wRyZZN5MQJkcOH9WTymGcVYzjwu5idBBPrz5+vH5NU507BgiIbNogUL26TQOfnn39WXVYlSpTwub9ixYpywJyPhexj/37MBCmSMaMns+E21arp7c6dVreEiCgegpL06fWgUAQmf/yh/x6dO1ekdGmRWrV0oIGsCS6//qr/ZjPXK0bZYf36Ips2JZ4PtUwZ/fEfqOhokbVrA/udQoVEzp8XywQc6Jw7d06yIbxL4NSpU5LZnI+E7OO33/S2USMdnrsRPjHg4EGrW0JENoRAZM0aHYw0baozKNOn6yClRg2RYsVEvvwSta3670lkSfr2FfnxR736TvnyOkuClWi2bw9Om2Ji9HbNmqR/npogx1wOsXt3TDUj8ssv+mMTgQwCKawNjZ9fvKi/TvC18vTTIjffrLNJtgl0WrRoIZ999pmMGjUqfkh5XFycjB07Vm655ZZQtJFCCblIqFxZXAs5WrNGx/wTiogcCQlsvMURjCALgi/ghQtFWrXS9yHBiy4Y3IcAZtYsPWk8kt74EkcAg6xGvny6OwjdNnicCZkUM8hIyRdf+K7CM2FC4M8FgRXyCwgskjNypG4vuqSw4g0CD3jmGR2AnDqlj4+fIVt0/DimjtEfhfjZoEF6/wjKmjUTQcWK90wy6KqK9JllAg50ENCgGHnt2rVy+fJleeqpp2Tbtm0qo7NixYrQtJJCByE45MkjroU/t/BOxZ8k+IRzaxceUYRC9gCBBuo88DY1syDoDsGXOLITCFI+/1zXgyCLcu6cyJYtIhs36oGVLVuK/PRT8NqEICAp/gQ5gI4Rsztn4EAdgOF5Tpum78PHELqdBgzQWRMEIF266OwJal3wnLz/LkN3Ej7G8DhzreKk3HVX4vtatPBcR8CDgMYb5tT973+T3l+kBzmpCnRq1KihJgqcMGGC5MyZU2JjY6Vnz55qluGiblxCwO7MVcvdPDs0/qQrUEAHOfiUYKBDlKYAAH834QsQX9x4eyE4QYakcWNd6Iq/LZAJQHcOCmAXLdJJZXxx4+2HYMEsfkUAgyAH+/LHrl0iy5f73odaldQGOTg2siYInAAZH3TJIEOC+9BVhcwQPkLxnBGAoDccgQy2OPb69XrMA54zAhNkg5KDcwL+JJa9H9OgQeqenxsEHOgAlkx4/vnng98aCj8GOhr+TMInK4YZ1KxpdWuIIgKmSTO7RhYs0PUj+CLHX/zINsybJ/LsszpwwUcJHocMCkbmYFROIHUgydWSgL9BTkLI+piru9x2m8i1azrYQHdMnTq6jgSBFt725lsfBb8IlpBJQebEzFjgueFcBJrBwPlCF5G/2HMeAYHOJ598Ijly5JA77rjD5/6ZM2fK+fPnZQBeHWQf7LrS8Gfktm06o0NkY6gZwdsZX+oIVJBZwNBhZEuQgUA9Bkbq4EsbwQluo4sHtSUolsUKP+PHBxZc4PcTtgGXQKD2A+1GrQy6T9BmDFlGO9DthGAFWRHUy2DEEDIteE4IVBB8oF4GmRKU2iGAwd8u/sD58R723KtX0o/D/sklgc6YMWPkgw8+SHR/oUKF5P7772egYzfM6Gjmp6I5pxBRBEEWApmJdet0EIAgxpzzBIWjZhISb2PzLZ1aGEmTWl276r8V0A2FWhkEB3hLoV1oK7p8MCQabUYgU6lSyvvz5+sk4SDgBDOfEAUe6GCiwLIIqRMoXbq0+hnZjPlnV0qdxm4KdJjRoTAELQhG0LXz7be6gLZPH51ZQbcFggPMk4KSR3SX/P67yPUJ6G/I3yAHwcbmzb73IZODehoETcj4IMNTt66ukencWb9FcB9GHqGIFvejyyo1w4a95polirxAB5mbzZs3SxnkDr1s2rRJ8qNjluy5/IPbC3DN589Ah1KAolp0pXhnERAUoHYFf/+VKiUyerQuyEXWBRkYZDJmzEh5v3PmBK+NOB6yJfiIRvFvuXJ6ZBLqatClg7oVZD0wkgd/3+CxgXbLmN1ERI4MdPr06SOPPvqoGnF18/UKq2XLlsmQIUPkrqTGrVHkwp+L5lhIty7/YGLXleuh3uPkSV1Ii9I1JKiR9UBWA905n32mAxdABgaZjEOHRBYvTnm/GBLtD2RK8DbcsUPvG8WyqE1BRgfHffddkTZtUA+pu4jwWLx9kSFCkBVokaw5fRSR0wUc6GCiwP3796u5dDKiGkx9QMRJ//795dVXXw1FGylUzC91/Jnn9hodZnQcC/+lWFQQBaeoDZkyRXe7YNSQOZQXL/9AVrD5+mv/H4usCgIXvM1Qk4KABMETCoPxFkTAcf/9urjWH5jozcQkOlEIAp2oqCiZPn26CnjQXZU1a1apWbOmqtGxk4kTJ6rLNeSX3QrDqQFVg3aY9SmUmNGxDQQIWbPqLiQMlMMcJci4IKuBQAajiVDvgmG9GBKdHAwjTjj4MCVNmugCYAQkSF7jOLj8+aceUt2woR5GjFoXdGHZYcZYIjdIZxhJrTXqHjExMWpeoDNnzkguVNq5CWbpattWTyOacHyo26CIAcUMKD5ApSW/oSyDTyR01SADgiHFY8fqhQqbN9eZFyxciPvTqls33fWDmhUcEwsfojsIw5tRt4L78dbAWq9oy623BuPZEVG4v79TNWHg33//LT/88IMaZYVlILyNGzcuNbskK3AOncQZHdQtoV/DbUFvGJgZDgz0M4cXr14t8uSTKa/VY/J3hRkEKvhYwgy2iFsRICHbgrgeNS/ItiAj5G8sm8QgUyKykYADncWLF8ttt90m5cqVk507d6olIVCzg8RQPeRsyT6QhzerIN0ue3Z9Qb8IijoY6AQEGZZly3RAga4dZGAw7wu6hzCE2szAILgJtLcYw7ARe5owqy1W7MBMulhH+IEH/BsBhAwNEblPwIHOs88+K0888YS89NJLauTVt99+q4ac33PPPdKhQ4fQtJJCgxmdxAXJ+/bpOh3MykZqJBLmZpk0SRfNoqcTGZkHHxT57bfA95cwyEGXkHdSGPu/916R1q09pWPmlPiseSGisAQ6O3bskK+vDznAqKsLFy6oJSFefvll6datmzyIT0CyBwY6ibuvEOi4dOQVMjIo1UJAgwJfZGTeeiv16wyheBc1NUgcIvuCLiXM5YKuJMSUOA6CGX8xyCGisAQ62bNnj6/LwWrle/fulepYgEQwB8XJVDWCLIIZw8DtkwU6fOQVMiGYEwaZEQQLGGyHNYQw2+5HH+n6GAQd5svBX4iPMVwas+Oi7gUvI6ygjPlfOJCPiGwb6DRp0kSWL18uVatWlU6dOsmwYcNky5Yt8t1336mfkY1gOldA1SbZfi4ddClhhl2MHsLfIlhGAIEI5ofByKGUeAc5yMLg9zBxHYKj//xHXzAbMDI+WK8IMSFXWSYiRwY6GFUVe72yEHU6uI55dSpWrMgRV3aDCUCgShWrWxIZbLDeFbIzyMCgZnr5cpH9+0Xee0/3uCU3aj45yMRgdWtkYzDUGksHoHsJayyhaNis0fEOaDgCiYgcHehgcj0MLa+FFeGud2NNnjw5VG2jUMI3pbn8A6aMJU9Gx8KuK3OVagyLxnpJ778vcuWKyMsvp36fGG301FOYFkLPBoxJ7jCxXcJVn5PCrA0RuSrQyZAhg7Rr104VJOdhAau9mV/mmFAE43fJsowOsiZTp4o8/7xnjdVAYjMERfg9BEMDB+qh16yPISJKZdcV5s3Zt2+flGUO297Mb1RMBctvxZAXI6NmBjU0qH/B+krjx+sFIQGZFfN6SjBXDH6/e3eRfv1EqlbV2Rn+9xERBTHQGT16tJpHB2td1a9fX3VfeXPdMgpOCHQo6F1XmC8GQ7RRBIyabywkmVyiCMOvzbkKAcvGobvpppswb5XO2BARUYgDHcyTgxFWGGkFmB05ndefkpgZGbddvUimnTDQSbo615xfCFPx3qBLD/U0K1fqgt7Dh/XCkkuXeiZXvhEsDIlaGdSC16ypu5yIiMiiQAcjrB544AFZik9ysj8GOomh7gz9Q6gCxjCm2rV9fowiYQQ2M2fqZQ7Q/eQPdE1hPafHHtMBELI15qgmIiKKkEDHXOS8JVbFI/tjoJM0RCHXA53LVWurlbIXLBB5882U12lCjy0mycMoJWRqMHFeixaJlxFjDT8RUQTX6Hh3VZHNmdWvmDTF5ZCpQYCC0U8L0nWT4fKRbL29psTp2D6eGeQgm4NCYMwAjPi/fXuRPn2YpSEisn2gU6lSpRsGO6fw1zDZZ7JAFy9eiW6kp5/WQ7s9ntcbw7dGGaOmHn9c5JFHmJUhInJsoIM6ndwY30r2hhnozKl0Mde/SxJY69aJvPCCXsYgOVEZrkqFa7ukeY0Y6T+pqUp4oTeLiIhcEOjcddddUsica4TsncpAPwz6WhzYdWV2Mf32m8jChXrNplmzUv4ddEdhSYU6S8ZL5ueGidTtJ3JT07C0l4iIIiDQYX2Og2DmOsAIIwfN8Y/FK0eOFPn00xs/FqOg/ve/JLI1269XD588GZI2EhFRhI+6IgcFOvnzi5173xB7Y0I+1Ni8+67I1avJPx4jop55Rgc3Cea49GWeEwY6RETuCnTiMCSFnMHGgQ6KgjGVU+fOyQ/1hjvvFOndW6/KjRmKa9TQy3rdkDlrn3mOiIjIXUtAkAPYKNBBIvGzz0R+/llkzx6RjRt1sJMQSsdQaNy3rx4V5d3T2rBhAAc0Ax1mdIiIHIGBjpsDnQhccwCJw48/FileXOT330V++kkHNwlhyQSsEYXFLUePDmKpkRn8xcTo/rFMmYK0YyIisgIDHTcysxURltFBV9TDD4tMnpxyEfHAgXrCvpDAVMbm7IEYnYaIi4iIbIuBjhtFUNcVuqYwv81LL4nMmZN0EfGQISLVqol06RKGBAuCHCwfHh2t5xpioENEZGsMdNwoAgId1NsguPnySx3smFBbM3iwyIgROsixBGaLRqCDRmLBKiIisi1HTKLSo0cPyZs3r/Tq1cvqptiDRYEOiohRWIy6mooVRb74whPkVKmia21Ql/PeexYGOVCunN7u329hI4iIKBgckdEZMmSI3HffffKpPzPFUVgDHQQyWCPq/fd1bW9C6I5CZqdWLT07cUTA4lZw7JjVLSEiojSKlK+WNGnVqpX88ssvVjfDPsI06gq1NwMGiGzb5ns/Mjq1a+sFMqOiJPKYgQ6KkYmIyNYs77r69ddfpWvXrlKsWDG1zMTs2bMTPWbixIlSpkwZyZIlizRu3FhWr15tSVsdAUObMC47RBkdLF7fv7+eu6ZpU98g54EHdOkLuq+GDYvQIAeY0SEicgzLA51z585J7dq1VTCTlOnTp8vQoUNl5MiRsn79evXY9u3by3H+tZ06p097CmOw1lUQd9uunY6dPv9cZO1a3VXVtateLBOHnDRJpEwZiXzmwrUMdIiIbM/yrquOHTuqS3LGjRsngwYNkoGYPEUwx8pk+emnn2TKlCnyDBYvCtClS5fUxRSDieHc2G2VK1dQxmojFrj5ZpHdu33vx31PPKFrcGy3HiwzOkREjmF5Riclly9flnXr1kmbNm3i70ufPr26vXLlylTtc8yYMZI7d+74S8mSJcVVzExYGoc1XbigMzdFivgGOe3b61hq2TKdzbFdkOMd6MTGipw/b3VriIjIqYHOyZMn5dq1a1LY/OK5DrePHj0afxuBzx133CFz586VEiVKpBgEPfvss3LmzJn4y8GDB8VVzPOGCCUVzp7VsxNny6ZrcUzNm4ts3iwyf35Qe8SsgWyXWUDELlIiIluzvOsqGBYtWuT3YzNnzqwurpXKQAcrImCem5EjPfflzCny0EN6cj8EPo6BNBSCawTB6L6yRWERERHZLtApUKCAZMiQQY4lqJXA7SKpzEi4nhnoFC3q18NRRPzqq3plcG8tW+oJ/0qUEGcyAx1mdIiIbC2iu66ioqKkfv36snjx4vj74uLi1O2mGLtMIc3ooESlVKnEQQ5G92PaIscGOcCRV0REjmB5Ric2Nlb2YE2h66Kjo2Xjxo2SL18+KVWqlBpaPmDAAGnQoIE0atRI3nnnHTUk3RyFlVoYzo4LaoBcxY9AB7U2H38sMnUqRqV57sfE05jsz5YFxoHiyCsiIkewPNBZu3at3HLLLfG3EdgAgpupU6dK79695cSJEzJixAhVgFynTh2ZP39+ogLlQA0ePFhdMLwco69c4waBziefiNx3n+99t98uMm1aBC3REA4MdIiIHCFjJCzfYHgvX52Ehx9+WF0odIHOmTM6W/Pjj74BzvPPi9StK+7DZSCIiBzB8kCHwghDp8wMxfVAB3MnYlHNMWN8HzpvnkiHDuJerNEhInKEiC5GpiDDTH5Xr/p8kWO4uHeQgwwOipBdHeQAu66IiBzBtRkdVxYjm91WBQrI2k2ZZPBgPYLK5Posjjd2XREROYJrMzooRN6+fbusWbNG3BborM99izRr5glyevTQvVoMcpIIdJAFw+qkRERkS64NdFzp6FFZKU3k1gMfx393P/WUXrPKFUPGA4F1LNJff3ucOGF1a4iIKJUY6LgEBrZNmlVEmslKOX01p2C+xVOnRF5/XSR7dqtbF4EyZPAsfMo6HSIi22Kg4xKYX/GhWW3V9SZFD8jChSJ581rdqgjHOh0iIttjoONwWK6pVSs9qzG8LMNl2ZDvmMXxB0deERHZHgMdBzt0SAc5y5bp2y+VnSrDZbRElbg+RwyljHPpEBHZnmsDHQwtr1atmjRs2FCcaNs2kRYtRPbt07dnzhQZkfUNfYMrv/uHGR0iIttzbaDj5OHleEqtW2OBVD14CEFPr16BrVxOrNEhInIC104Y6FQIapo311O/IMhZt06kTJnraz1gmBUw0PEPMzpERLbHQMdBsDBnjRqe24sWXQ9yvL+sM2XicCt/sUaHiMj2GOg4BL6LO3f2xDJ79oiUKuX1ALPbClkKcyI8Shm7roiIbI+BjgMcPixSvLjn9tixCYIcYH1O2gIdrJHBAJGIyHYY6NjcuXMiTZp4bi9dqoeUJ8JAJ3DmzMhY+BX1TQUKWN0iIiIKkGv/RHXK8PI33tCTAkK5ciItWybzQLP7xaw7oRuLivLUM7FOh4jIllwb6DhhePnUqSKjRunr48eL7N2bwuKcWIUbmJUIDOt0iIhszbWBjt398otevwqlI336IHC7wS+cPKm3DHQCwyHmRES2xkDHpiuRP/SQvo45c774wo86WWZ0UoeBDhGRrTHQsaHJk0V27BDJlk3kp5/8HAxkZnTy5w9185zFrGli1xURkS0x0LEZrFllZnOeeEIkd24/f5FdV6nDjA4Rka0x0LGRf/8Vuf9+fb13b5GRIwP4ZbPrihmdwDDQISKyNQY6NoGi44cfFjl9WqRKFZHPPw9g/rrLl0ViYvR1ZnQCw0CHiMjWGOjYqC7nq688c+dgmQe/mYt5IjLKkyck7XMsc4JFTD9NRES249pAx04TBmLh8Vde0ddHjBDp0iXAHZj1OZj8LkOGoLfP0cxVURHo4D+CiIhsxbWBjp0mDHzrLf09W6yYyHPPpWIHf/+tt9gBBb4MBIa3YUz/X39Z3RoiIgqQawMdu0CMMny4vo6sTubMqdhJdLTeli0b1La5AqaaNldINQNGIiKyDQY6EQxrSfbqpQuRmzYVGTAglTvav9+3G4YCky+fZ9gbERHZCgOdCDZrlsiqVTqpgDWtkl3H6kbMQtoSJYLZPPcwC7gZ6BAR2Q4DnQg2YYLeoi7n1lvTsKOjR31HEFFgzBXMMbafiIhshYFOhFq/XmTZMj0i/H//S+POzDlgzDlhKHWBDjM6RES2w0AnAqEmZ9Agfb1nT5GSJdO4Q2Z00oaBDhGRbTHQiUDffKMzOjlzirz3Xhp3dvWqZx4dBjqpY2bCOGkgEZHtMNCJQOPH6+3jjwchNjlxQs8Bgz4wrnOVOuXK6e3evVa3hIiIAsRAJ8Js2yayYoWewDjNtTne3VaFCnFW5NQqX15v9+3TQSMREdmGawOdSF0C4sMP9fa224I0kTELkdPOHJZ/7pzI2bNWt4aIiALg2kAnEpeA2L3bE+jcf3+Qdrpnj96as/tS4LAERI4c+jpXMScishXXBjqRaOJEkYsXRRo0EGnXLoh9YVC9epB26FJmRoyBDhGRrTDQiRAopZk8WV9/9lldOxwUZgFtlSpB2qFLMdAhIrIlBjoR4qefRC5f1omX7t2DuGOzGLlo0SDu1IXM4W/m+SQiIltgoBMBLlwQGT1aX7/rriBmc4DFyMHBjA4RkS0x0IkAc+fqBcYxyuqxx4K8/DknCwwOBjpERLbEQCcCYE0r6NbNM7gnKDBZINaTQIqoQIEg7tiFGOgQEdkSAx2LXbkiMmOGvt6+fZB3bn4pI8jhZIHBmUsHqTciIrINBjoWmz9fxyOYuLhTpyDvnPU5wVO5smeyI2TJiIjIFhjoWOyzz/S2b1+RTJmCvHOuWh48Zcvq/yBUjh86ZHVriIjITwx0LIRa4YUL9fXevUNwAGZ0gidjRs+aHAx0iIhsg4GOhZYsETlzRiR3bpF69UJwADOjw0AnOBjoEBHZjmsDnUhY1HPaNL295x6dMAhZRoddV8FRvLjeHj5sdUuIiMhPrg10rF7U0zA83VZYqTwk2HUVmowOAx0iIttwbaBjNQzeOXhQJCpKpEWLEB3kyBG9ZaATHOy6IiKyHQY6FvnyS71t1UokW7YQpYyio/X1MmVCcAAXYtcVEZHtMNCxcNkHsz4nJFCIfP68nhWZgU5wMKNDRGQ7DHQsgJFWGzbo67feGqKD7N2rt6VK6f4xSjucSzhwQGfMiIgo4jHQscCKFXpy3fLlPb0hIQt0KlQI0QFcGuikS6cnDeSaV0REtsBAxwK//OKpzwmZPXv0FtEUBQcyYyVL6uv79lndGiIi8gMDHQtXK2/ZMoQH+ftv3+4WCm6dDjM6RES2wEAnzM6eFVm3LgyBzokTeovVQil4zPNpnl8iIopoDHQsqM/BGldYIzKkyRbzi7hgwRAexIXM83n8uNUtISIiPzDQcWK3FTDQCW1Gh4EOEZEtMNBxYiEyhj6bX8QMdEJTo/PXX1a3hIiI/MBAJ4xiY0XWrg1DRufff0XOndPXS5QI4YFcqFIlvd21y+qWEBGRHxjohNGqVSJXr+ranJBOVrx/v2eNq6xZQ3ggF6pc2TNPEYqtiIgoojHQCSNztFWTJiE+EGbuhdKlQ3wgl3ZdYdLAK1dE/vnH6tYQEdENMNAJo/Xr9bZu3RAfyMzocI2r4MuUSaRAAd/V4YmIKGIx0Akjc32revXClNFhoBMaRYt6Fk4lIqKIxkAnjBMF7t4dpozOjh16y66r0AY65uzTREQUsRjohMmmTZ5BUCEd8R0TI7Jokb7eunUID+RiZkHy9u1Wt4SIiG7AtYHOxIkTpVq1atKwYcOwdluFPJuD+V2wNHr+/CJVqoT4YC5Vo4bebttmdUuIiOgGXBvoDB48WLZv3y5r1qxxViHy4cO+E9tR6ObSMVeIJyKiiOXaQMexhcgMdEKvQgXP6DYMMycioojFQCcMLl3y9HKEPKNz6JBvwSwFH85tlix6wkAuBUFEFNEY6ITB1q16RmSUzZQsGaaMTvHiIT6Qi6VPL1K+vGeGZCIiilgMdMJciIxJdcOS0WGgE1pmoMM6HSKiiMZAJwy2bNHbOnXCcDAGOuEtSObIKyKiiMZAJwzM3o2KFcNwMBYjh4dZVW4OpyMioojEQCeMgU65ciE+EAqBzGUJmNEJrerV9dac7pqIiCISA50Qw9x90dG+ZR0hg0UmccAMGUQKFQrxwVwOU1zDqVMiFy5Y3RoiIkoGA50w9CRheHnGjGEYcTV1qiebg2CHQidvXs/1ceOsbAkREaWAgU6I7dvnWV8TwU5I/fGH3nboEOIDkc/wuREjrGwJERGlgIFOmOpzQt5tBbt26W2fPmE4GEnPnmGaBZKIiFKLgY5TCpHPnPEUA1WtGuKDkfLAA3qLvkkiIopIDHTC1HUV8ozO8uW6EBkHKlw4xAcjpUABvf3nH6tbQkREyWCg45SMzuzZetuuXYgPRPGwpgecPCliGFa3hoiIksBAxyk1Ojt26O0tt4T4QBTPXDgVK5h/8IHVrSEioiQw0Alx2YzZqxHSjA7mclmxwrc7hUIvUybP9QcftLIlRESUDAY6YajPKVhQJGfOEB7osccSd6dQeLz4oud6TIyVLSEioiQw0HFCIfKsWZ7rDHTCa+RIkTJl9PXVq61uDRERJcBAx+6FyLGx+mJioBN+TZrordl9SEREEYOBjt0LkZcs8VzH+lZZsoTwYJSkW2/V23nzrG4JERElwEDH7l1XW7Z4CoHMlcspvNq21dt160QuXrS6NURE5IWBTggdPKi3pUqFIdB54gnf9ZcofPAfjEDz6lWRTZusbg0REXlhoBNCx475TrcSdEeOiEyfrq/XqBGig9ANIcBs2FBf/+wzq1tDREReGOiECHowTp/W10OyIgNm4m3aVF9Pn54LS1qtQQO9ff99vRwHERFFBAY6Ic7mREWJ5MkTggNMnixy4IC+/uabIUwbkV/+9z/P9RYtrGwJERF5YaATImZdcJEiISqdefllz/WHHw7BASggxYqJdO/uuc2FPomIIoIjAp05c+ZI5cqVpWLFivJ///d/EkkZnaB3W6HgFV+iWF8CvvzSdykCsg7+L0z16olcuGBla4iIyAmBztWrV2Xo0KGyZMkS2bBhg7zxxhvyTwT8Ne2d0QkarJKNLhKsZ2V+id55ZxAPQGmSLZvIo4/q63/9JdKli8i5c4HvJy5OIsq1ayIDBoi8/rpEJNSrBXLO8Ob8++/UL54biTNgr1kj8ssv+lwsWKA/K0LxOrjR9Ak4/saNgU2zgC743bvT3Dy6wV/e51LxWWTC+wsT054965kgzkZsH+isXr1aqlevLsWLF5ccOXJIx44d5eeff46cQCdml8jnn+tMjPeHAT6U8KKBy5dFHnrIdykH02+/iRw+LPLaa3oI85Qpnp/VqiWSMWOonwoFYvhwz/8JJnPMkUNk/XqRxx/X8+2Y62FhtJz3TMr4ckIfJy4IZPFhgsVa+/bV+xk1SuS990S2bUv8JYIPoUOHRKZNE+nVS6RlSz2nz/Hj+ufYD9o1Z07Kbccx+vTxfa1u3SrStaseTfbMM/rYuCDo+eILke++E9m5U2TXLn3szZtTd97wIfr77/q9YT4n8/ojj4i0b++ZAfzTTz0F33gM3hsohlu1Kul94/zjOSDjhnOOJTtKltT7e+UV/dcIpgVAlvT77/UXuhk84Fhm4Ir/i2rVRBo31ufbhOP+8YfIpUt6JXssC3Kjc21CBrpOHR0c3Aj+uHnjDX2+vaG9jRqJ3HKL/nmHDiLNm4vcfLPuTk3NFxyOhc8nfE6Z5wPBbtasIi+8ILJnj74PzxmvO/MYM2bogRH33y9y4oTIypX6/2j/fr3w7a+/6vP43HN6gk28LvH/UbWqyLBhIhMm6Mfjj1X8n2D/uI19nT8v0qqVSOvWIlOn+mZM8XrBaw/H6N/f92f4zBw6VO8Hr220AVvcxuv2zz/1637RoqTPBQJbBLjmeUE7f/pJvzdMuI4/dNCFjVEoeC3hOd1+u35NAF4zeD3hnIwfr4+PLwq8Vsz3tPlY7/egCY/54Qf92VG7tsj27Ykfg31XrCjy44/6e+eBB3QQidf7HXfotuF84LUNM2fq1x7OLdps/r/i/g0b9PsMn0cZMugFG3PlEqlcOfGAC+w3ufc+XhPe58oKhsWWLVtmdOnSxShatCg+1YxZs2YlesyECROM0qVLG5kzZzYaNWpkrFq1Kv5nM2fONAYPHhx/e+zYscYbb7zh9/HPnDmjjottMD3U9S8DZ3e4vISXs2FkymQYO3YYxuXLhvHkk/q+1q0N48gRwxgwQN/GBc9l+HDDuP12w1iwwHN/Upd164LaZgqS6Ojk/89atjSMe+/13H7kEcPYvDnx44YMMYyHH075//6VVwzjyy9Tfo00aGAYNWt6bn/+uWFs324YTz1lGL/8Yhj16xvGzTcbxrlznse89JJh/Pe/hvHjj4aRLp3v/ubONYy33kr5mHFxhnH0qGH8849hPPusYdx9t2HUqWMYd95pGBs2GMbKlYYxYoRhxMQYxk8/+f5uo0aGMX68YWTJYhiNG+MDwvfnX3zhuZ4rV+JjDxxoGP36GUbWrPrc1K2bfDvLlEn6/tq19T7M2/PmGUaPHr6P+fhj/TwPH/bcV7iwYUye7LndsaNh3HqrYXTo4LkP5yBvXn3d+37zUqWKYWzbZhgvv2wYo0YZBj7rDhzw/T8sX17/3+Dz4epVw3j66ZT/P3B54AHDmD1bf7bgNYi24v952jTD6NJFPyZjRsP43//woWgYOXKkvL9mzfRrvW9fz33t2/s+plgxz3O6UftSe6lXL+n7q1Y1jEcfNYy2bT33/fCDYXTq5LldqFDi38Pn7sKF+tK9u2F8+KFhREXpn+H/Au+5G7WpSRP9f2/ebtpUHzul38mQwTDef9/3PrwvscX7p2HDpH/vq68Mo3dvwyhSRL+XAzl3L7+c9P0ffJDye8y84PyeP69fB97/93juf/xhGBs3GsawYZ77164N+ketv9/flgc6c+fONZ5//nnju+++SzLQmTZtmhEVFWVMmTLF2LZtmzFo0CAjT548xrFjx1IV6Fy8eFGdFPNy8ODB4Ac6f/1l9JRv1P/tBHko+G9ufDlQZNu3z/fDjhdeQnFBwGLVsR9/3Prnz4s9LkWL6j8MLAp0LO+6QlfT6NGjpUePHkn+fNy4cTJo0CAZOHCgVKtWTSZPnizZsmWTKde7cIoVKyaHvNLIuI77kjNmzBjJnTt3/KUkUnrBVrKkHC2h51UpIkFclgEp48WLRTp1Ct4+KTTKlhWZO1d3cwCH/1MoRz1Y4e23Q7fvESN0N1AoeY+SpMD07h1Y2cSYMdbO3G9EkIQZnUuXLhkZMmRIlOXp37+/cdttt6nrV65cMSpUqGD8/fffxtmzZ41KlSoZJ0+etDajYxhGuXJxKpBd/u5aw/jkE8MoXTqwCDhhNuDFF4PaPgqT/fsN48039fbvv3UXCNLnSOmiSwKpZ6Sm0U2BLpsWLXz/3995J+XXSb58vqnuixd1V5g/3RnJXe67T7fZ+77jxxM/buLElPeD7qPUHP+WW3SqH90q99xjGM8/r89PSr+zerVhFCyor6Obx7tbCN1h+fN7bqM7DvucMkV3Lb32mn/tQrcYuqBxftCdhS4gs3sm4aVAAc/1nj3xgXDj/X/6qe62Mm/j+STsvvTuwkp4MbtYXn3VMHr1MozixQ2jVSvdDZgwC4TXnfd96E5F18xNN+nu1IS/g66s557T/w9Dh/qeS+/HoRtlwgTD6NbNMLp2Tdw9iC7T9es9be3c2TAWL9bZIXSlfvONYRw6pN876Pp85hn9OHOLC849Mvo4V+jeTM1r7M8/Pe9R7C+Q38X5wznC6/M//zGMgwd1lyDuw89wLvF88Lq6ciXpfTz4oKeEARe8X/Faxz4SPrZPH9/b772nu3/xnkQXJj5H0DWc1HEGDdJtwOXff33f1/j/QZbFfP2jKw/ftTd6/g89pM/brl3+nS+c3xCxTddVSoHOoUOH1H2///67z+OefPJJVatj+v77742KFSsa5cuXNz5A/2IAQlWjkz27/j/es+f6HXhB4cMHNRN4Q+ODZvp0/eESG6sfc+GCYbRpo78IcR19nOgDxRcXXqjkDrt3G0a2bIZx1136NoKXOXMMY+RI/SGDLxrUAeF+vC7wgYsv7YTwYWh+2OA1hFowfPnh9tSp+jWGmjFArcfOnbpWx9SunX7sjBn69qZN+kseH4z4cDdhH/jAxRdcUv3wp07hrxPDePdd/UWKOhZ8yOPLAceF11/3tDUpeK45c+qfo0bIrGtDPcSJE0n/jneqHLU0+GD/v/8zDPwhZB7XdO2abgPa9cILeouaFjzebNfWrfq54LHevD/Ucc5Qi3fpkurCVvVQ5v5xbvH72A/qk/DHi/l7S5Z49ocvewQM+H3AZwBqYfCZgdcB6jXwuYHn99lnOrA5fTrpc2Cef3yR7t1rGPgsRdCN9iB4wOtmxQrP6yAhPA8E4AjSveH/H8Go97nAFyken5SvvzaMRYs8txGY4nMwEL/+6vWB6gVtwDnDdwe+/FEDhQAUr5nly3XNEZ4DuvwRIOH9lRDO8Ucf+dbXNW+uf4b/BwRfCF7xOk74/38jeK098YQ+B/j/Q50Z4P8P/3d4nZlwn3ewbLYVzwOvS2yTgu8K77oqPBfze8Ub2o5u9YTvGbz3z57V11E3OnasZ3/4v8fnkblv7/OH1xP+UNuyRdf/oaYJzwG38dzwXg1Bl1Wg39/p8I9EiHTp0smsWbOk+/WU4uHDh9Voqt9//12amssdiMhTTz0ly5Ytk1XJjbIIQExMjOrCOnPmjORCRXkQ4IxicAeyyp07i2TJEpTdkptgBARGuGA0UVqYw66xTIj54sQoGn/Szhi1FR3t6X4LJbQT3dEYUVOuXNKPMT9q8VwwUhGjSapXD31KfOxY/WbGDORJHQujeDAqDd0tqVnBFyPxgvTZQ9dft3iNYLRQamAE0tdf61FKCf9f8PoL9evt6af1SC+MxAzk/Y/3NUZGYtQxvoAw4jMtTp/W5xHnACPTZs/Wo4OzZ5dI4e/3d0QHOpcvX1b1ON988038fTBgwAA5ffq0fI+hoBEY6BAREVFo+fv9bXkxckqioqKkfv36shgFuNfFxcWp294ZntSYOHGiKm5uaK46TURERI5j+WxzsbGxssecpEiQKY+WjRs3Sr58+aRUqVJq1mNkcBo0aCCNGjWSd955R86dO6dGYaXF4MGD1cWMCImIiMh5LA901q5dK7dgRs/rENgAgpupU6dK79695cSJEzJixAg5evSo1KlTR+bPny+Fg76IFBERETlNRNXoWIE1OkRERPbjiBodIiIiorRgoENERESO5dpAh6OuiIiInI81OqzRISIish3W6BAREZHrMdAhIiIix2KgQ0RERI7FQIeIiIgcy7WBDkddEREROR9HXXHUFRERkWO/vy1f68pqZpyHE0ZERET2YH5v3yhf4/pA5+zZs2pbsmRJq5tCREREqfgeR2YnOa7vuoqLi5PDhw9Lzpw5JV26dEGNNBE8HTx4kF1iN8BzFRieL//xXPmP58p/PFeRca4QviDIKVasmKRPn3zJseszOjg5JUqUCNn+8R/LN4J/eK4Cw/PlP54r//Fc+Y/nyvpzlVImR9w+6oqIiIicj4EOERERORYDnRDJnDmzjBw5Um0pZTxXgeH58h/Plf94rvzHc2Wvc+X6YmQiIiJyLmZ0iIiIyLEY6BAREZFjMdAhIiIix2KgQ0RERI7FQCeEq6OXKVNGsmTJIo0bN5bVq1eLm4wZM0atDI8ZpwsVKiTdu3eXXbt2+Tzm4sWLMnjwYMmfP7/kyJFDbr/9djl27JjPY/766y/p3LmzZMuWTe3nySeflKtXr4qTvfbaa2qW7sceeyz+Pp4rX4cOHZK+ffuq85E1a1apWbOmrF27Nv7nGGMxYsQIKVq0qPp5mzZt5M8///TZx6lTp+See+5Rk5jlyZNH/vOf/0hsbKw4ybVr12T48OFStmxZdR7Kly8vo0aN8lkbyK3n6tdff5WuXbuqWXXxfps9e7bPz4N1XjZv3iwtWrRQ3wWYIXjs2LHipHN15coVefrpp9V7MHv27Oox/fv3VysORMy5wqgrCq5p06YZUVFRxpQpU4xt27YZgwYNMvLkyWMcO3bMcIv27dsbn3zyibF161Zj48aNRqdOnYxSpUoZsbGx8Y954IEHjJIlSxqLFy821q5dazRp0sRo1qxZ/M+vXr1q1KhRw2jTpo2xYcMGY+7cuUaBAgWMZ5991nCq1atXG2XKlDFq1aplDBkyJP5+niuPU6dOGaVLlzbuvfdeY9WqVca+ffuMBQsWGHv27Il/zGuvvWbkzp3bmD17trFp0ybjtttuM8qWLWtcuHAh/jEdOnQwateubfzxxx/Gb7/9ZlSoUMHo06eP4SSvvPKKkT9/fmPOnDlGdHS0MXPmTCNHjhzGu+++a7j9XOE98vzzzxvfffcdoj5j1qxZPj8Pxnk5c+aMUbhwYeOee+5Rn4Vff/21kTVrVuODDz4wnHKuTp8+rT53pk+fbuzcudNYuXKl0ahRI6N+/fo++7DyXDHQCQH8Jw8ePDj+9rVr14xixYoZY8aMMdzq+PHj6g2ybNmy+DdHpkyZ1AevaceOHeoxeKOYb6706dMbR48ejX/MpEmTjFy5chmXLl0ynObs2bNGxYoVjYULFxotW7aMD3R4rnw9/fTTxk033ZTsz+Pi4owiRYoYb7zxRvx9OIeZM2dWH56wfft2df7WrFkT/5h58+YZ6dKlMw4dOmQ4RefOnY377rvP576ePXuqLxPgudISfnkH67y8//77Rt68eX3eg3j9Vq5c2bArSSIoTOoPNjzuwIEDEXGu2HUVZJcvX5Z169apNKf3elq4vXLlSnGrM2fOqG2+fPnUFucIKU/v81SlShUpVapU/HnCFunQwoULxz+mffv2apG4bdu2idOgawpdT97nBHiufP3www/SoEEDueOOO1QXXd26deWjjz6K/3l0dLQcPXrU53xhPRx0IXufL6TPsR8THo/36qpVq8QpmjVrJosXL5bdu3er25s2bZLly5dLx44d1W2eq6QF67zgMTfffLNERUX5vC/Rjf/vv/+Kkz/v06VLp85PJJwr1y/qGWwnT55U/eLeXziA2zt37hS3rhCPepPmzZtLjRo11H34EMEL2nwjeJ8n/Mx8TFLn0fyZk0ybNk3Wr18va9asSfQznitf+/btk0mTJsnQoUPlueeeU+fs0UcfVedowIAB8c83qfPhfb4QJHnLmDGjCsSddL6eeeYZFewiMM6QIYP6bHrllVdUrQTwXCUtWOcFW9RHJdyH+bO8efOK01y8eFHV7PTp0yd+EU+rzxUDHQpLpmLr1q3qL0lK7ODBgzJkyBBZuHChKsKjGwfO+Mvw1VdfVbeR0cHra/LkySrQIY8ZM2bIl19+KV999ZVUr15dNm7cqP7oQMEozxUFGzLPd955pyrkxh8jkYJdV0FWoEAB9ZdTwhExuF2kSBFxm4cffljmzJkjS5culRIlSsTfj3OBbr7Tp08ne56wTeo8mj9zCnRNHT9+XOrVq6f+ysFl2bJlMn78eHUdf9XwXHlgFEy1atV87qtataoadeb9fFN6D2KLc+4NI9QwMsRJ5wsj75DVueuuu1TXZr9+/eTxxx9XoyKB5yppwTovbnpfXrke5Bw4cED90WZmcyLhXDHQCTKkz+vXr6/6xb3/AsXtpk2bilsgokeQM2vWLFmyZEmilCTOUaZMmXzOE/pi8WVlnidst2zZ4vMGMd9ACb/o7OzWW29VzxN/bZsXZCzQvWBe57nyQBdowqkKUINSunRpdR2vNXwwep8vdN+gFsD7fCFwRJBpwusU71XUYTjF+fPnVR2EN/whhucJPFdJC9Z5wWMwNBtBgPf7snLlyo7qtrpyPcjB8PtFixapaR+8WX6u0lzOTEkOL0d1/tSpU1W1+f3336+Gl3uPiHG6Bx98UA3N/OWXX4wjR47EX86fP+8zZBpDzpcsWaKGTDdt2lRdEg6ZbteunRqiPn/+fKNgwYKOHDKdkPeoK+C58h3RkTFjRjV0+s8//zS+/PJLI1u2bMYXX3zhMzQY77nvv//e2Lx5s9GtW7ckhwbXrVtXDVFfvny5GvFm9yHTCQ0YMMAoXrx4/PByDA/GtANPPfWU4fZzhVGOmIoBF3wVjhs3Tl03RwoF47xgpBaGTPfr108NmcZ3A16rdhtefjaFc3X58mU19L5EiRLqs8f78957BJWV54qBToi899576osJ8+lguDnmDnATvBmSumBuHRM+MB566CE1pBAv6B49eqg3h7f9+/cbHTt2VPMp4AN62LBhxpUrVwy3BTo8V75+/PFHFdjhD4oqVaoYH374oc/PMTx4+PDh6oMTj7n11luNXbt2+Tzmn3/+UR+0mFcGw/AHDhyoPtCdJCYmRr2O8FmUJUsWo1y5cmo+FO8vILeeq6VLlyb5GYXgMJjnBXPwYDoE7ANBJwIoJ52r6OjoZD/v8XuRcK7S4Z+05YSIiIiIIhNrdIiIiMixGOgQERGRYzHQISIiIsdioENERESOxUCHiIiIHIuBDhERETkWAx0iIiJyLAY6RERE5FgMdIgoVe69917p3r27ZcfHApXmCuZOPD9YiPOtt94KapuI3IiBDhElki5duhQvL774orz77rsydepUS9q3adMmmTt3rjz66KNitf3796tzggVYg+mFF16QV155Rc6cORPU/RK5TUarG0BEkefIkSPx16dPny4jRozwWTE8R44c6mKV9957T+644w5L2xBqNWrUkPLly8sXX3whgwcPtro5RLbFjA4RJVKkSJH4S+7cuVXGwvs+BBgJu2ZatWoljzzyiDz22GOSN29eKVy4sHz00Udy7tw5GThwoOTMmVMqVKgg8+bN8znW1q1bpWPHjmqf+B10SZ08eTLZtl27dk2++eYb6dq1q8/9ZcqUkdGjR0v//v3VvkqXLi0//PCDnDhxQrp166buq1Wrlqxdu9bn97799lupXr26ZM6cWe0jYXcR7kMX2X333aeeQ6lSpeTDDz+M/3nZsmXVtm7duuo84Tx4e/PNN6Vo0aKSP39+FbBcuXIl/mfvv/++VKxYUbJkyaKee69evXx+F89x2rRpKf5fEVHKGOgQUdB8+umnUqBAAVm9erUKeh588EGVeWnWrJmsX79e2rVrpwKZ8+fPq8efPn1aWrdurYIEBCDz58+XY8eOyZ133pnsMTZv3qy6cxo0aJDoZ2+//bY0b95cNmzYIJ07d1bHQuDTt29fdXxkSHDbXMt43bp16lioh9myZYvqkhs+fHiiLjkEPzge9vvQQw+p52VmuPBcYdGiRSoT9t1338X/3tKlS2Xv3r1qi3OD/Zr7xvNF19vLL7+s9oXnfvPNN/sct1GjRmr/ly5dSsP/CpHLBWUNdCJyrE8++cTInTt3ovsHDBhgdOvWLf52y5YtjZtuuin+9tWrV43s2bMb/fr1i7/vyJEjiDCMlStXqtujRo0y2rVr57PfgwcPqsfs2rUryfbMmjXLyJAhgxEXF+dzf+nSpY2+ffsmOtbw4cPj78NxcR9+BnfffbfRtm1bn/08+eSTRrVq1ZLdL45bqFAhY9KkSep2dHS02ueGDRsSnR/8Ls6D6Y477jB69+6trn/77bdGrly5jJiYGCM5mzZtUvvev39/so8hopQxo0NEQYOuIVOGDBlUd03NmjXj70P3DBw/fjy+qBjZDrPmB5cqVaqonyETkpQLFy6obiZ0E6V0fPNYKR1/x44dKgPkDbf//PNP1UWW1H7NbjxzHylBlxjOgwldWObvtW3bVnWvlStXTmWevvzyy/hMlylr1qxqm/B+IvIfAx0iCppMmTL53EZQ4H2fGZzExcWpbWxsrKpDwYgl7wsCjYTdOCZ0jeGL//Llyyke3zxWSsdPy/PyZx8p/R7qfdCd9vXXX6sACAXftWvXVt15plOnTqltwYIFA2ovEXkw0CEiy9SrV0+2bdumCn5RqOx9yZ49e5K/U6dOHbXdvn17mo9ftWpVWbFihc99uF2pUiWfTExKoqKi1NY7A+SvjBkzSps2bWTs2LGq9ghD1ZcsWeJTqF2iRAkV3BFR6jDQISLLYBQSshZ9+vSRNWvWqO6qBQsWqFFayQUOyG4gQFq+fHmajz9s2DBZvHixjBo1Snbv3q0KhidMmCBPPPGE3/soVKiQ6mIyC6n9nfdmzpw5Mn78eJXBOnDggHz22Wcq21O5cuX4x/z222+qgJuIUo+BDhFZplixYiqDgqAGX+iop8Hw9Dx58kj69Ml/PP33v/9VNS1phYBpxowZagg35q1B9xFGQWHofCBZGQQsH3zwgXo+GMruDzxHjNDCqDNkliZPnqy6sVDXAxcvXpTZs2fLoEGDUv38iEgkHSqSrW4EEVEgUJCMzAcmM2zatKk40aRJk2TWrFny888/W90UIltjRoeIbAddRejqSWliQbtDITNmgCaitGFGh4iIiByLGR0iIiJyLAY6RERE5FgMdIiIiMixGOgQERGRYzHQISIiIsdioENERESOxUCHiIiIHIuBDhERETkWAx0iIiISp/p/6ybD+Hw++asAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "ax.plot(\n", " timesteps,\n", " input_series_1,\n", " label=\"Tracer Input 1\",\n", " c=\"red\"\n", ")\n", "ax.plot(\n", " timesteps,\n", " input_series_2,\n", " label=\"Tracer Input 2\",\n", " c=\"blue\"\n", ")\n", "ax.set_yscale(\"log\")\n", "ax.set_xlabel(\"Time (months)\")\n", "ax.set_ylabel(\"Tracer Input\")\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 5, "id": "37eab1a7", "metadata": {}, "outputs": [], "source": [ "### combine signals of both tracers to array of shape (timesteps, 2)\n", "input_series = np.column_stack((input_series_1, input_series_2))\n", "\n", "### define model (the true system; in practice we don't know this)\n", "# get decay constant\n", "# we assume a half life of 12.3 years for tracer 1 (tritium)\n", "t_half = 12.32 * 12.0\n", "lambda_1 = np.log(2.0) / t_half\n", "\n", "# we assume a half life of 10.73 years for tracer 2 (krypton-85)\n", "t_half = 10.73 * 12.0\n", "lambda_2 = np.log(2.0) / t_half\n", "\n", "# create true observations using the model\n", "# time step is 1 month\n", "m = ism.Model(\n", " dt=1.0,\n", " lambda_=[lambda_1, lambda_2],\n", " input_series=input_series,\n", " steady_state_input=[1., 0.],\n", " n_warmup_half_lives=50\n", ")\n", "\n", "# add an exponential-piston-flow unit\n", "# define the true model parameters\n", "epm_mtt_true = 12 * 40 # 40 years\n", "epm_eta_true = 1.5\n", "m.add_unit(\n", " ism.EPMUnit(mtt=epm_mtt_true, eta=epm_eta_true),\n", " fraction=1., # 100 percent of the overall response\n", " prefix=\"epm\"\n", ")\n", "\n", "true_params = m.params\n", "\n", "# simulate\n", "output_series = m.simulate()" ] }, { "cell_type": "code", "execution_count": 6, "id": "6adb4ffc", "metadata": {}, "outputs": [], "source": [ "# make reproducible\n", "np.random.seed(11)\n", "\n", "# define observations\n", "# randomly select a number of observations from the output series\n", "n_obs = 10\n", "obs_idx = np.random.choice(len(timesteps), n_obs)\n", "# select the corresponding timesteps and values\n", "obs_timesteps = timesteps[obs_idx]\n", "obs_values = output_series[obs_idx]\n", "# add some noise to the observations (observation error)\n", "obs_error = 0.01\n", "obs_values += np.random.normal(0.0, obs_error, (n_obs, 2))\n", "\n", "# make series we can later use in the model (has to be the same length as\n", "# the input series, filled with NaN-values where we do not have any\n", "# observations)\n", "obs_series = np.full((len(input_series), 2), np.nan)\n", "obs_series[obs_idx] = obs_values" ] }, { "cell_type": "code", "execution_count": 7, "id": "a9ad8db8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### plot input series, output series, and observations\n", "\n", "# create figure\n", "fig, ax = plt.subplots(1, 1)\n", "# plot input series\n", "ax.plot(\n", " timesteps,\n", " input_series_1,\n", " label=\"Tracer 1 Input\",\n", " c=\"red\"\n", ")\n", "ax.plot(\n", " timesteps,\n", " input_series_2,\n", " label=\"Tracer 2 Input\",\n", " c=\"blue\"\n", ")\n", "# plot output series\n", "ax.plot(\n", " timesteps,\n", " output_series[:, 0],\n", " label=\"Tracer 1 Output\",\n", " c=\"coral\"\n", ")\n", "ax.plot(\n", " timesteps,\n", " output_series[:, 1],\n", " label=\"Tracer 2 Output\",\n", " c=\"lightblue\"\n", ")\n", "# plot observations\n", "ax.scatter(\n", " obs_timesteps,\n", " obs_values[:, 0],\n", " label=\"Observations Tracer 1\",\n", " color=\"darkred\",\n", " marker=\"x\",\n", " zorder=10\n", ")\n", "ax.scatter(\n", " obs_timesteps,\n", " obs_values[:, 1],\n", " label=\"Observations Tracer 2\",\n", " color=\"darkblue\",\n", " marker=\"x\",\n", " zorder=10\n", ")\n", "ax.set_xlabel(\"Time [months]\")\n", "ax.set_ylabel(\"Tracer concentration [-]\")\n", "ax.set_yscale(\"log\")\n", "ax.set_ylim(1e-1)\n", "ax.legend(ncol=3, fontsize=9)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "7c66fbbd", "metadata": {}, "source": [ "## 2. Store Values" ] }, { "cell_type": "code", "execution_count": 8, "id": "80ed0487", "metadata": {}, "outputs": [], "source": [ "# generate dummy monthly time stamps\n", "start = \"1900-01\"\n", "\n", "start_date = datetime.strptime(start, \"%Y-%m\")\n", "out = []\n", "for i in range(n_years * 12):\n", " # calculate year and month offset\n", " year = start_date.year + (start_date.month - 1 + i) // 12\n", " month = (start_date.month - 1 + i) % 12 + 1\n", " out.append(f\"{year:04d}-{month:02d}\")\n", "\n", "timestamps= np.array(out, dtype=str)" ] }, { "cell_type": "code", "execution_count": 9, "id": "fd5c4fba", "metadata": {}, "outputs": [], "source": [ "# concatenate timestamps and input series\n", "input_ = np.concatenate(\n", " (timestamps.reshape(-1, 1),\n", " input_series),\n", " axis=1,\n", " dtype=object\n", ")\n", "# store input series as CSV\n", "np.savetxt(\n", " \"example_input_series_2tracer.csv\",\n", " input_,\n", " delimiter=\",\",\n", " header=\"Date, Tracer1, Tracer2\",\n", " fmt=[\"%s\", \"%1.3f\", \"%1.3f\"]\n", ")\n", "\n", "# concatenate timestamps and observation series\n", "obs_ = np.concatenate(\n", " (timestamps.reshape(-1, 1),\n", " obs_series),\n", " axis=1,\n", " dtype=object\n", ")\n", "# store observation series as CSV\n", "np.savetxt(\n", " \"example_observation_series_2tracer.csv\",\n", " obs_,\n", " delimiter=\",\",\n", " header=\"Date, Tracer1, Tracer2\",\n", " fmt=[\"%s\", \"%1.3f\", \"%1.3f\"]\n", ")\n", "\n", "# concatenate timestamps and full output series\n", "output_ = np.concatenate(\n", " (timestamps.reshape(-1, 1),\n", " output_series),\n", " axis=1,\n", " dtype=object\n", ")\n", "# store observation series as CSV\n", "np.savetxt(\n", " \"example_output_series_2tracer.csv\",\n", " output_,\n", " delimiter=\",\",\n", " header=\"Date, Tracer1, Tracer2\",\n", " fmt=[\"%s\", \"%1.3f\", \"%1.3f\"]\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "PyTracerLab_app", "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": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 }