{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# iminuit: fitting models to data\n", "\n", "**Hans Dembinski** | TU Dortmund\n", "\n", "**PyPI** https://pypi.org/project/iminuit\n", "\n", "**Source** https://github.com/scikit-hep/iminuit\n", "\n", "**Documentation** http://iminuit.readthedocs.org\n", "\n", "**Latest release** v2.16.0\n", "\n", "![](iminuit_logo.svg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "### iminuit is (not only) a frontend to the Minuit2 C++ library\n", "\n", "- Minimize statistical cost functions with box constraints\n", "- Compute **parameter uncertainties** under these constraints (unique)\n", "- Robust technology from 1975 that stood the tests of time\n", "- Minuit or Minuit2 are behind virtually every HEP analysis\n", "- iminuit is Python frontend to Minuit2 C++ code in ROOT\n", " - iminuit contributed several patches to Minuit2 C++\n", " - For history of Minuit and iminuit, see [PyHEP 2020 talk](https://github.com/hdembinski/pyhep-2020-iminuit)\n", "- Used as backend by many HEP/astroparticle fitting packages (zfit, pyhf, gammapy, ctapipe, ...)\n", "- Common **cost functions** have been added to the library" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### iminuit or pyhf or zfit?\n", "\n", "- iminuit is low-level, lightweight, hackable, easy to learn, easy to extend\n", "- Supports all kinds of fits: unbinned and binned, templates, penalized, simultaneous\n", "- Simplicity is useful for teaching, attractive for beginners and experts\n", " - [Glen Cowan](https://inspirehep.net/literature?sort=mostrecent&size=25&page=1&q=f%20a%20g%20cowan%20and%20ac%20%3C%2010) is using iminuit in lectures on statistics\n", "- Out of scope\n", " - Building statistical models: you need to write the pdf/cdf by hand\n", " - Automatic differentiation of model\n", " - Making sophisticated limits\n", " - Storing likelihoods\n", "- Where the other fitting tools shine\n", " - [pyhf](https://github.com/scikit-hep/pyhf): Complex binned fits a la HistFactory, limit setting, storing the likelihood\n", " - [zfit](https://github.com/zfit/zfit): Building statistical models, easy sampling from fitted model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A simple line fit" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from argparse import Namespace\n", "\n", "# make 10 data points with scatter on y-coordinate\n", "\n", "rng = np.random.default_rng(1)\n", "\n", "x = np.linspace(0, 1, 10)\n", "ye = np.ones_like(x) * 0.2\n", "y = rng.normal(2 * x + 1, ye)\n", "\n", "data = Namespace(x=x, y=y, ye=ye)\n", "\n", "# draw that\n", "\n", "plt.errorbar(data.x, data.y, data.ye, fmt=\"o\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get parameters of the line:\n", "\n", "- Need to provide **model** and **cost function**\n", "- Model predicts $y = f(x; \\vec p)$ for some parameters $\\vec p$\n", "- Cost function computes some \"distance\" between model and observations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- No need to write cost function, `iminuit.cost` contains all common ones\n", "- Model can be any vectorized Python function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 0.0 0.1
1 b 0.0 0.1
" ], "text/plain": [ "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a │ 0.0 │ 0.1 │ │ │ │ │ │\n", "│ 1 │ b │ 0.0 │ 0.1 │ │ │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from iminuit import Minuit, cost\n", "\n", "# line model\n", "def model(x, a, b):\n", " return a + b * x\n", "\n", "# see docs for iminuit.cost to pick correct cost function\n", "lsq = cost.LeastSquares(data.x, data.y, data.ye, model)\n", "\n", "# - initialize Minuit object by passing cost function\n", "# - Minuit uses a local minimizer, we need to set starting values\n", "m = Minuit(lsq, a=0, b=0)\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- iminuit detected that `lsq` has parameters named `a` and `b`\n", "- `Minuit`: stateful fitting object\n", " - Represents current state of fit\n", " - Central object to interact with fitting process" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 3.959 (chi2/ndof = 0.5) Nfcn = 34
EDM = 2.65e-23 (Goal: 0.0002) time = 0.2 sec
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 1.05 0.12
1 b 1.99 0.20
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a b
a 0.0138 -0.0196 (-0.843)
b -0.0196 (-0.843) 0.0393
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 3.959 (chi2/ndof = 0.5) │ Nfcn = 34 │\n", "│ EDM = 2.65e-23 (Goal: 0.0002) │ time = 0.2 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a │ 1.05 │ 0.12 │ │ │ │ │ │\n", "│ 1 │ b │ 1.99 │ 0.20 │ │ │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───┬─────────────────┐\n", "│ │ a b │\n", "├───┼─────────────────┤\n", "│ a │ 0.0138 -0.0196 │\n", "│ b │ -0.0196 0.0393 │\n", "└───┴─────────────────┘" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# call Migrad minimizer (for other options, see docs)\n", "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Printed values and errors are rounded to **PDG rounding rules**\n", "- *Hesse error*: standard symmetric error estimate (based on asymptotic theory)\n", "- *Minos error*: standard asymmetric error estimate (based on likelihood profiling)\n", "- *EDM*: estimated distance to minimum, must be smaller than \"Goal\"; shows that Migrad converged\n", "- *Nfcn*: How many function calls were used by Migrad\n", "- *Pos. def.*: Hessian matrix (matrix of second derivatives) is positive definite; must be for valid minimum\n", "- *chi2/ndof*: Computed at no additional cost whenever possible if iminuit's cost functions are used\n", "\n", "See docs for more details on Hesse, Minos, EDM.\n", "\n", "Output by parts:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 1.05 0.12
1 b 1.99 0.20
" ], "text/plain": [ "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a │ 1.05 │ 0.12 │ │ │ │ │ │\n", "│ 1 │ b │ 1.99 │ 0.20 │ │ │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.params" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 3.959 (chi2/ndof = 0.5) Nfcn = 34
EDM = 2.65e-23 (Goal: 0.0002) time = 0.2 sec
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 3.959 (chi2/ndof = 0.5) │ Nfcn = 34 │\n", "│ EDM = 2.65e-23 (Goal: 0.0002) │ time = 0.2 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.fmin" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
a b
a 0.0138 -0.0196 (-0.843)
b -0.0196 (-0.843) 0.0393
" ], "text/plain": [ "┌───┬─────────────────┐\n", "│ │ a b │\n", "├───┼─────────────────┤\n", "│ a │ 0.0138 -0.0196 │\n", "│ b │ -0.0196 0.0393 │\n", "└───┴─────────────────┘" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.covariance" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 3.959 (chi2/ndof = 0.5) Nfcn = 60
EDM = 2.65e-23 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 1.05 0.12 -0.12 0.12
1 b 1.99 0.20 -0.20 0.20
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a b
Error -0.12 0.12 -0.2 0.2
Valid True True True True
At Limit False False False False
Max FCN False False False False
New Min False False False False
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a b
a 0.0138 -0.0196 (-0.843)
b -0.0196 (-0.843) 0.0393
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 3.959 (chi2/ndof = 0.5) │ Nfcn = 60 │\n", "│ EDM = 2.65e-23 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a │ 1.05 │ 0.12 │ -0.12 │ 0.12 │ │ │ │\n", "│ 1 │ b │ 1.99 │ 0.20 │ -0.20 │ 0.20 │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌──────────┬───────────────────────┬───────────────────────┐\n", "│ │ a │ b │\n", "├──────────┼───────────┬───────────┼───────────┬───────────┤\n", "│ Error │ -0.12 │ 0.12 │ -0.2 │ 0.2 │\n", "│ Valid │ True │ True │ True │ True │\n", "│ At Limit │ False │ False │ False │ False │\n", "│ Max FCN │ False │ False │ False │ False │\n", "│ New Min │ False │ False │ False │ False │\n", "└──────────┴───────────┴───────────┴───────────┴───────────┘\n", "┌───┬─────────────────┐\n", "│ │ a b │\n", "├───┼─────────────────┤\n", "│ a │ 0.0138 -0.0196 │\n", "│ b │ -0.0196 0.0393 │\n", "└───┴─────────────────┘" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Minos Errors are not automatically computed, you need to run *Minos* explicitly\n", "m.minos()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Hesse and Minos errors identical in this case, because asymptotic theory is correct here\n", "- Prefer reporting Hesse errors unless Minos errors differ a lot" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('a', True, -0.11755076272896135, 0.11755076272914197)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Minos errors can be read-out like this\n", "\n", "me = m.merrors[\"a\"] # returns a data struct with many fields\n", "\n", "me.name, me.is_valid, me.lower, me.upper" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# draw data and fitted model\n", "\n", "plt.errorbar(data.x, data.y, data.ye, fmt=\"o\", label=\"data\")\n", "plt.plot(data.x, model(data.x, *m.values[:]), label=\"model\")\n", "\n", "# m.fval is only chi2 if cost function supports that\n", "# telltale sign: m.ndof returns something finite and not None\n", "chi2 = m.fval\n", "ndof = m.ndof\n", "\n", "title = [\n", " f\"χ²/ndof = {chi2:.1f}/{ndof} = {chi2/ndof:.1f}\",\n", "]\n", "for par in m.parameters:\n", " title.append(\n", " f\"{par} = {m.values[par]:.2f} +/- {m.errors[par]:.2f}\"\n", " )\n", "\n", "plt.legend(title=\"\\n\".join(title))\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\");" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# or use builtin visualization (only for 1D data)\n", "lsq.visualize(m.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Simple plotting of cost function around minimum is provided\n", "* Useful for debugging, if fit fails or looks fishy (we will see examples later)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_contour(\"a\", \"b\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `Minuit.draw_mncontour` draws a 68 % confidence region\n", "* In general, more expensive to compute than `Minuit.draw_contour`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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ebXbt2kWjRo3YuXMnBw4cwN3dnSpVqnD79u3PylKxcWkAti3a/VnPI2LJnBM4U7ZBCfov82L5/dkMWNGNct+UxCm+Iw9uPmJ444msm7bZ6JgiIp/Fok5LPXz4kOTJk+Pr60uZMmUitE1oaCiJEiVi6tSpNGvW7KPrv++wlv+TAL5J3YqQ4NfMPDmWjHnTf/LPIRLbBL0MYnavxaydugmA74Y2opF3HZ2mEhGLEWtOS/0vP783Mx0nTpw4wtu8ePGCkJCQ924TFBSEv7//W493cU3sQvGahQDYutA3kslFYjdHZ0faTfqOJv3rATCv36/M7rVId1aJSKxkMeUmLCyMLl26ULJkSXLnzh3h7Xr16kXq1KmpVKnSO78/YsQI3Nzcwh/u7u7vfa5KTcsCsGPJHo3wKlbHZDLR3Kchrce+OQK6fOxvTGrzM6Gh+rcgIrGLxZSb9u3bc+bMGZYuXRrhbUaOHMnSpUtZs2YNTk5O71zH29sbPz+/8MetW7fe+3xFvsiPW1IXntx7xvFtf0b6ZxCJC+p51cRrVhtsbExsmLWNkU2n8DrktdGxREQizCLKTYcOHfj999/ZuXMnadOmjdA2Y8eOZeTIkfzxxx/kzZv3ves5Ojri6ur61uN97B3sKf9NKQD+0KkpsWLVWlakz69dsbO3ZdfSffSo6MORLScJCwszOpqIyEcZWm7MZjMdOnRgzZo17NixgwwZMkRou9GjRzNkyBA2b95M4cKFozRT5eZvTk3tX3uYQL/337UlEteVre+Jz9peODo7cGbvBfpUG8YPub347actvHz+0uh4IiLvZWi5ad++PYsWLWLJkiW4uLhw79497t27x8uX//eLs1mzZnh7e4d/PWrUKPr378/cuXPx8PAI3+b58+dRkilLwYykz5mW4Fch7F6pmcLFuhWtVoBZp8dTu2M14rk4c+vCbaZ0mM03aVszw2u+JucUEYtk6K3g77vNdN68ebRo0QKAcuXK4eHhwfz58wHw8PDgxo0b/9lm4MCBDBo06KOvGZFbyZaOWssc78XkKZOD8bsGR+hnEYnrAv1fsHWBL2unbuL2pbvAm3/Dxb4sSJ2O1SlQMY9uHReRaKPpFz4gIjvn0e3HNE7XFrPZzMIrU0mVIUUMpxSxXGFhYRzdcoq1UzZyZPPJ8OXpc6alVodqVG1RDgcnB+MCikicFGvHubEUSdMkoUClPABs+0UjFov8m42NDUWrFWD4xr7MPT+RWu2/wDmBEzfO/c3kdrPoWLwPf///IzsiIkZQuXmPyv9/zJs/Fuwi+F8zKovI/3HPloYOU1ry660ZtJ3QgoTJ3bj65w3aF+7F7pUHjI4nIlZK5eY9StYpSsJkrty79oBfBi03Oo6IRYvvFp+vO9dg+vHR5CmdgxcBLxnSYDzTOs8lJFh/HIhIzFK5eQ/n+E50ntEKgOVj1nFm3wWDE4lYvqSpEzNm+0Aa9qwFwNopm/AqO5AHNx8anExErInKzQeUqlOMys3LEhZmZnTzqbwI0NgeIh9ja2fLDyObMHhdLxIkjM+FQ5doU7AnhzedMDqaiFgJlZuPaD/xO5KnS8rdq/eZ4bXA6DgisYZnzcL8dGwUWQplJODJc/rWGM7UjnN0sbGIRDuVm4+I7xafngs6YDKZ2DRnOwfWHzU6kkiskSpDCibuHUrNtlUBWDdtM99l60T3CoPYsWQPwa+CjQ0oInGSxrmJoJndF7Jy/HoSJndj1ulxJEzmFo0pReKeI5tPsGbKJo5uPsk/v3ZcEiegUpMyVP+xEh653A1OKCKWTIP4fcCnlpvgV8G0L9qb62duUbJ2EQau6qHRWEU+wYObD9k8dyeb5+3g4a3H4ctzemal2g+VKNvAE+f4TgYmFBFLpHLzAZ9abgAun7xGx2LevA4JpfvcdlRtUT6aUorEfaGhoRzdcopNs7dxYP0xwkLfzDge3y0eCy9PxTWJi8EJRcSSqNx8wOeUG4ClI9cwp88S4rk4M/PUWFJ6JI+GlCLW5fHdp/wxfxeb5mwnmXsSxu30ee+6QS+DuPrnTU7vPseLgJfU7lhNp4lFrIDKzQd8brkJDQ2lW7mBnN13kTxlcjBm+0BsbW2jIamI9QkLC8P/ccAHy8q4H6ZzZu957B3tSZwqEef2X6T73HaUqecZg0lFJKZpbqloZGtrS8/5HXCK78jp3edZPWGD0ZFE4gwbG5sPFpulo9aye8UBGvepy6R9Qxm5uR81WlVm1/L9hIWFxWBSEbFkKjefIHWmlLQd3wKAef1+5dqZm8YGErECN87dYuGg5TQb1ICyDUvgnMAZgDylc3Bw/TECnjw3OKGIWAqVm09U7YeKFP+yECHBr5nWaS5WdnZPJMbN7L6Q7EUz80XLCjg42ocvP3/oEpnye+DgZK9/hyICqNx8MpPJRPvJ3+PgZM+pXWfZu/qQ0ZFE4qxLx69y4dAlvu5Sg3guzuHLb164za0Lf5M+R1rsHOzeOzyDSo+IdVG5+QwpPZJTv/tXAPzcY6FGWxWJJgFPnpMwuRtpMqcMLzCvQ16zf+1h/v7rLqXrFsPewf6t627+XWheh7yO8cwiYhyVm8/UsFdtkqZJzL3rD1k5/nej44jESfFcnQl48hxbe7vwZTuX7uPQxuPkLpWDYjUKAW8uSP6n4LwIeMmRLScZVHcMI5tOwafeWK6fvWVIfhGJWSo3n8k5vhM/jmoCwK8jVvPozhODE4nEPakypiB9Lnd+7rGQw5tO8IvPCmb3XkwKj2Q0HVgfeHMbeVhYGDY2NoQEhzC3zxKmd51Pcvek5C+fG9fECWhXuBdLhq82+KcRkeimchMFyjcqRU7PrLwKDGJunyVGxxGJc9ySujJodQ9sbG2Y4TWffesOU6v9F7Qe04wkqRKFr/fPKasVY9dzYP1R8pfLReVmZanZpgpdf25D31+7EPDkOQ//fvy+lxKROECD+EWRi0cu06GYNwCTDwwnR7EsUfbcIvJ/Ht15QoKE8XGK50hoaCiHNhynWPWC2Nq9GUzz6p836FHRh4Anz8lXPhevAoN4FfiKfsu8SJ8jLXev3id5+qQafFMkltEgfgbIViQzVVqUA+CnLvM0oJhINEmaOjFO8RwBWDNpI5PbzWLPqoPh3980ezsOTvZ0m9OWYb97M3xjH7IVzsyaSRsBSJkhOba2tvg/DuDUrrOsnrSBvWt0t6NIXKJyE4W+H9YY5wROXDh0ie2L9xgdRyTOK/V1Mar/UIncpXMAEBwUwp2r98hZIhtVW5THwckBl0QJSJ05Jaf3nMPvkX/4qatffFYw5rtp7Ft7mJndFtC5ZF/u33ho5I8jIlFE5SYKJUmViMZ9vgZgjvdiXj5/aXAikbgtpUdymg1qQNLUiQGwsTFx/cwtMuZNH76O2WzGJVF8Xge/JizszVn4+zcecuHwJSo1LcOITX356dhoUmdOyfSu8wj0CzTkZxGRqKNyE8W+7lKDVBlT8PjOU5aOXGt0HBGrYmdvR9n6nlw/eyt8nJsrJ6+zYtx68pXLTaLkb+atSpE+Ge7Z05A2a+rwozul6xbnzN4L+D/WNA4isZ3dx1eRyHBwcqD12GYM+noMK8at54uWFUiVIYXRsUSsRpn6nvjUHUu38gNJmNyNa3/eIL5bPLxmtQFg7dRNnNp1lvvXH7Bv7WGcEzjh+VVhjm/9k1QZUxDw9Dmp0L9ZkdhMd0tFA7PZTK8qQzix/TQ5PbMyZvtAHJwcouW1ROTdFg9bRfDLYNLncidH8SykypCC9TP+YF7fJdTqUI385XNzes95Fg5aTt6yOXnh/4IqzctTu2M1o6OLyDtE5vNb5Saa3L58l47FvAl4GkiFxqXo/Uun9857IyLRw2w2vzVdQ7+aI0mUwo0e89pjY/PmrPzyMeu4cOQyPed3CL8L69/biYhl0K3gFiBN5lT0X9ENWztbdizZq1FRRQzwT0Exm83Y2duRJnNKHv3PAH6JUibk7pX7vAp89Z/tRCR2UrmJRgUq5KHDlJYAzO+/lN0rDxicSMQ6/VNW8pfPzcO/n7B5zg5CX4dy+eQ1rpy8zqvAVzjFdzI4pYhEFV1QHM2+bF2Zm+f/Zs3kjYxuPpUUHsnJVjiT0bFErFLpusUJfR3KtM7z2LJgF/evP8AlcQKaDmyAUzzH8Lmp/hEaGqqRjEViIV1zEwNCQ0Pp/9Uojmw6QZLUiZh6aARJ0ySJkdcWkf8KDQ1l+6I9OLs4kzZrKjLkTvefdcLCwuhTfThZCmSg+eCG2Nnrb0ERI+mC4g8wotwABPq/oHOJvtw49zdZCmVkvO/g8IsXRcR4/3sR8ZHNJ+hTfTgA2YtmxntxZ1JnSmlUPBGrpwuKLVB813gMWd8bt6QuXDp2ldHNp2j+KREL8r8XERf5ogD9l3uRIGF8Lhy+TNuCPdm2aLdB6UQkMlRuYlCqDCkYtLoH9g527Fl1iAUDlhkdSUQ+oEw9T2aeHEOe0jl4EfCSUc2mMLLZZAL9XxgdTUQ+QOUmhuUulYMuM1sDsGT4ak2wKWLhkqdLxpjtA2k2qAE2Nia2L9pD24I9uXD4ktHRROQ9VG4MUKV5ORr2rAXAuB+mc+7ARYMTiciH2NrZ0nRAfcbt8iF5uqTcvXqfLqX6s3TkGp1eFrFAKjcG+X54Y0rWLkJIUAgD64zh/o2HRkcSkY/IXSoHM06MoUx9T0JfhzKnzxJ6Vx3KoztPjI4mIv+icmMQGxsbei3sSKb8Hjx74MfQbybwOuS10bFE5CNcEiWg39KueM1qg1M8R05sP03rfN05sP6o0dFE5P9TuTGQcwJnfNb0fHM3xqFLLBi43OhIIhIBJpOJai0rMu3oKDLl98D/cQADao1iasc5BL8KNjqeiNVTuTFYivTJ6PrzmwuMl41ay4kdpw1OJCIRlS57GiYfGE7dLjUAWDdtMx2KeXPj3C2Dk4lYN5UbC1CmnifVf6iI2WxmVLMp+D3yNzqSiESQg6M9bca3YNiGPiRM5sq10zdpV7gXG2dvNzqaiNVSubEQbSa0wD17Gh7fecrYlj9hZQNHi8R6RasVYOapsRSqko/gVyFMaDWD2b0X6W4qEQOo3FgI5/hO9P21C/YOdhxcf4zfftpidCQRiaTEKRMxfGMfmvs0BGDZ6HWMajaF4KAQg5OJWBeVGwuSKZ8HP45uCsDM7gu5+ucNgxOJSGTZ2NjQpH89esxrj62dLTuW7KVv9WE8fxZodDQRq6FyY2Fqd6xGsRoFCQkKYXjjibx6EWR0JBH5BFWal2PYBm/iuThzcudZupbpz4Nbj4yOJWIVVG4sjMlkovvcdiROmZAb5/5mhtcCoyOJyCcqVDkf43x9SJwqEdfP3KJzib5cO60jsiLRTeXGAiVM5kavhR0xmUxs+Hkre1YfMjqSiHyizPkzMOXAMNLlSMOj20/oUro/66Zt1ng4ItFI5cZCFayUlwY9vgJgwo/TdThbJBZLni4ZE/cOJU+ZHLzwf8nUjnNomqkDqydtIOilTj2LRDWVGwvWYsg3ZCuSiYCngYxsMpnQ0FCjI4nIJ3JJlIBRf/Sn07QfSOaehCd3nzK963yaZmzPyvHreRn4yuiIInGGyWxlA6r4+/vj5uaGn58frq6uRsf5qDtX7tG2YE9eBLzki+8r0PXn1tjYqJOKxGYhwSFsXeDLryNWc+/6m0lzEyZzpV63r/iqXRWcEzgbnFDE8kTm89vQT8kRI0ZQpEgRXFxcSJ48ObVr1+bixYsf3Obs2bPUrVsXDw8PTCYTEydOjJmwBkmdKSU95rfHxsbE5rk7mN51vgb4E4nl7B3sqf5jJeZdnEy3Oe1InSkFzx76M7v3IppkaM+S4asJ9H9hdEyRWMvQcuPr60v79u05ePAgW7duJSQkhCpVqhAY+P7xIF68eEHGjBkZOXIkKVOmjMG0xilVpxjd57YHYO2UTczr96vBiUQkKtjZ2/HFd+WZe34SPed3IE2WVPg/DmBev19plqkDhzedMDqiSKxkUaelHj58SPLkyfH19aVMmTIfXd/Dw4MuXbrQpUuX964TFBREUND/XbDn7++Pu7t7rDkt9W/rp29hcvvZAHw3tBGN+3xtcCIRiUqhoaH4LtvPoiEruXXxDiaTiaYD6vNt/7o6HS1WL9aclvpffn5+ACROnDjKnnPEiBG4ubmFP9zd3aPsuWNazbZVaTWmGQDz+v3K6kkbDE4kIlHJ1taWCo1LM+PkWL5sXRmz2cxCn+X0+3IE/o8DjI4nEmtYTLkJCwujS5culCxZkty5c0fZ83p7e+Pn5xf+uHXrVpQ9txHqd6tJs4ENAJjedb5mHhaJgxwc7ek8vRU953fAwcmeI5tP0q5wL/46dsXoaCKxgsWUm/bt23PmzBmWLl0apc/r6OiIq6vrW4/YrsmAetTvVhOAia1nsmPJHoMTiUh0qNysLJMPDCd1phTcv/GQLqX66w8akQiwiHLToUMHfv/9d3bu3EnatGmNjmPxTCYTP45uSs02VTCbzYxqPpV9aw8bHUtEokGmfB5MOzKK4jULERIUwoRWMxjX8icN/ifyAYaWG7PZTIcOHVizZg07duwgQ4YMRsaJVUwmEx2mtqRys7KEhYYx7JsJHNly0uhYIhINEiSMj8+annw/rPGbYSHm7aRLqf7cvXrf6GgiFsnQctO+fXsWLVrEkiVLcHFx4d69e9y7d4+XL1+Gr9OsWTO8vb3Dvw4ODubkyZOcPHmS4OBgbt++zcmTJ7l8+bIRP4KhbGxs6Da7LaXrFSck+DU+X4/hz93njI4lItHAxsaGRt51GLGlPwmTuXL5xDXaFe7FoQ3HjI4mYnEMvRXcZDK9c/m8efNo0aIFAOXKlcPDw4P58+cDcP369Xce4Slbtiy7du366GvGthGKIyIkOASfumM5tOE4zgmcGL1tANmLZjE6lohEk4d/P2ZIg3GcP3gJgG/71aXpwPrY2toanEwk+kTm89uixrmJCXGx3AAEvQyiX82RnNxxhgQJ4zPe14cMedIbHUtEoklIcAgzvBbw209bAChUJR/eizrhljTu/F4T+bdYO86NfDpHZ0cGr+1JTs+sPH8WSN8vR/Dk3lOjY4lINLF3sKfj1B/o/UsnHJ0dOPbHKdoV7sXFI9Z3il7kf6ncxCHOCZwZ+rs3abOm4uGtxwysM0Z3VIjEcRW/Lc2Ug8NJkyUVD24+omvp/uz4da/RsUQMpXITx7gkSsCQ9d64JIrPhUOXGNtyuibaFInjMuRJz7TDIyhZpyghwa8Z2WQyG37eanQsEcOo3MRBabOkYsDK7tja2bJr6T4WDV5pdCQRiWbx3eIzYEW38PGvJrb5mRXj1hsdS8QQKjdxVP7yuen0048ALPRZzq5l+wxOJCLRzcbGho7TfqBhz1oA/NxjIQsHLdfRW7E6KjdxWPUfKlK365cAjPluGhcOXzI4kYhEN5PJxA8jm/Dd0EYA/DJ4BTO7LVDBEauichPH/Ti6CcW/LETwqxAG1BrFg5sPjY4kIjGgcZ+vaTfxOwBWTdzAhFYzCQ0NNTiVSMxQuYnjbG1t8V7cmQx50vH0vh/9a43i5fOXH99QRGK9Op2q021OO2xsTGyas50upfpzyves0bFEop3KjRWI5+LMkN96kzC5G1dP3WBEk8n6C07ESnzxXXn6/NoVp3iOXDh0ie7lB9H3y+Fc/fOG0dFEoo3KjZVIkT4ZPmt7Yu9oz4HfjjLXe4nRkUQkhpSt78mCy1Oo2aYKtna2HN54gjYFejCy2WTuXX9gdDyRKKdyY0VyFs9K9zltAVg+9jc2zdlucCIRiSmJUyai008/MufcBMo1LIHZbGb7oj18l60TP3WZx7OHfkZHFIkymlvKCi0YuIxFQ1Zia2fL0N+9KVwln9GRRCSG/XXsCnO8F3N822ngzenret1qUs/rS5wTOBucTuS/NHHmB6jcgNlsZnjjiexath97Bzv6LfeixFdFjI4lIgY4vu1PZnsv5tKxqwCkypiCIb/1In1Od4OTibxNE2fKB5lMJnrM7xA+VLtP3bGai0bEShWslJeph0bQ99cuJE+XlLtX79OpRF8ObzphdDSRT6ZyY6UcHO3pv8yLik1KExYaxsgmk9k4W9fgiFgjGxsbyjUsyU9HR5GndA5e+L+kf80RrJ64QYP/SaykcmPFbO1s6Tm/A1+2rozZbGZCqxmsmvC70bFExCBuSV0ZtbU/X3xXnrAwM9O95jOh1UxCgkOMjiYSKSo3Vs7GxoZOP/1I/W41AZjRbQGLhqzUX2siVsrewR6v2W1pPbYZJtObwf96Vx2K3yN/o6OJRJjKjWAymfhxdFOa+zQE3txNNbvXIhUcEStlMpmo51WTIet7E8/FmT99z9GxeB9unLtldDSRCFG5EeDNL7Mm/evRdnwL4M04OJPbzSIsLMzYYCJimGLVCzJp/zBSZkiuC40lVlG5kbd83aUGXX9ug8lk4veZWxndYiqhrzVVg4i18sjlztRDI9660HjVhN91ZFcsmsqN/Ef1Hyrivbgztna2bF+0hyENxxMcpAsKRazV/15oPKPbAsb/OEMXGovFUrmRdyr/TUkGruqOvaM9+9YcZkCtUbx6EWR0LBExyL8vNLaxMbF57g56VRmiC43FIqncyHt51izM0N+9cYrnyLE/TuH9xVAC/QKNjiUiBvnnQuPBv7250Pj07vN0LN6H62d1obFYFpUb+aCCFfMw8o/+xHeLx5m9F+hTYwQvA18ZHUtEDPS/Fxp3LtGXvWsOGR1LJJzKjXxUrhLZGLtjEAkSxufc/osM+nqMrsERsXLhFxqXycGLgJf41B3LT13m6TocsQgqNxIhmQtkYNjGPjjFd+T41j8Z3nii7qISsXJuSV0ZvXUA9bzeDAK6ZvJGupbuz73rDwxOJtZO5UYiLGfxrPis7YW9gx371hxm3I/TNQ6OiJWzs7ej9dhm+KztSYKE8bl45AptC/Zk/7ojRkcTK6ZyI5FSsGIe+i3zwsbWhq0LfJneZb7GuxARSnxVhOnHR5O9aGaePwtkYJ3RzPCar9NUYgiVG4m0ErWK0GNeewDWTt3EggHLDE4kIpYgpUdyxu8eTN0uNQBYNXEDXmUHcv/GQ4OTibVRuZFPUqlJGTpO/QGAxcNWsWLsbwYnEhFLYO9gT5vxLRi0ugfx3eJx4dAl2hbswcHfjxkdTayIyo18sq/aVaXl8MYA/NzzFzbO2mZwIhGxFCVrF2X68dFkLZyJgKeB9P9qJD/3WMjrkNdGRxMroHIjn+Wb3nVo2LMWABPb/MzOpfsMTiQiliJVhhRM2DOEOp2qA7Bi3Hq6lRvIg1uPDE4mcZ3KjXy2liO+pWabKpjNZkY1m8KhDTr8LCJvODja027idwxY2Z34bvE4d+Av2hTowaGNx42OJnGYyo18NpPJRIepLanQuBShr0MZXH8cp3zPGh1LRCxI6a+LMf3YaLIUykjAk+f0+3IEs3sv0mkqiRYqNxIlbGxs6DGvPZ5fFSb4VQj9a47k3MG/jI4lIhYkVcYUTNw7lFrtvwBg2eh19KoyhICnzw1OJnGNyo1EGTt7O/ot7Ur+Crl5+fwVvSoNZv9vGshLRP6Pg6M9Haa0pN8yL+K5OPOn7zm8ygzg4d+PjY4mcYjKjUQpBycHBq/tSZEv8vPqRRA+X49h3bTNRscSEQtTtr4nE/YMIXGqRFw/e4vOJfpqdnGJMio3EuWcEzgz5LfeVP+hImFhZqZ2nMPM7gs1VYOIvCVj3vRM3j8M9+xpePj3Y7qW7s/pPeeNjiVxgMqNRAtbO1u6zGzN98PejIOzcvx6hn4zgaCXQQYnExFLkiJ9MibuGUJOz6w8fxZIrypD2LP6kNGxJJZTuZFoYzKZaORdB+9FnbB3sGPPyoP0rDwEv0f+RkcTEQvimsSFUVsH4PlVYUKCQhhSfxy//bTF6FgSi6ncSLSr0Lg0I7b0I0HC+Jzbf5FOJfpy+/Jdo2OJiAVxiufIwJXdqfFjJcxmM1M6zGZev181Ma98EpUbiRH5yuZi4r6hpPRIxp3L9+jk2ZdzBy4aHUtELIitnS2dZ7Si2aAGACwZvprxP0zXWDgSaSo3EmPS50jLpP3DyFo4E/6PA+hR0Yc9qw4aHUtELIjJZKLpgPp0/bkNNjYmNs/bycA6o3kZ+MroaBKLqNxIjEqcMhFjdw6ieM1CBL8KYUiD8aya8LsOPYvIW6r/UJFBa3ri6OzA4Y0n6FnRh2cP/YyOJbGEyo3EOOf4Tgxa3YOv2lXFbDYzo9sCfuo8j9DQUKOjiYgF8axZmNHbBuCSOAEXDl+mS6n+3L163+hYEguo3IghbG1t6TClJa3GNANg7dRNDK43ToeeReQtOT2zMXHvUFKkT8btS3fpXLIvfx27YnQssXAqN2IYk8lE/W416b/cC3tHe/avO0KfasMI9H9hdDQRsSDpsqdh4r6hZMyXnqf3/ehauj/bF+8xOpZYMJUbMVyZep6M3jaA+G7xOLP3Ar2rDuX5s0CjY4mIBUmaOjHjd/lQrEZBgl+FMLLpZH7usVCns+WdVG7EIuQumZ0x2we+Obd+6BI9K/ng/zjA6FgiYkHiu8XHZ21PGnnXAWDFuPX0rTEC/yf6XSFvU7kRi5GlYEbG7RxEwmSuXDp+je4VBvH0ge6OEJH/Y2try/fDGtNvmRdO8Rw59scpOhbz5tqZm0ZHEwtiaLkZMWIERYoUwcXFheTJk1O7dm0uXvz4wG4rVqwge/bsODk5kSdPHjZu3BgDaSUmZMiTnrG7fEicKhHXTt+kW7mBPLrzxOhYImJhytb3/L+BQa/cp5NnH/au0ZxU8oah5cbX15f27dtz8OBBtm7dSkhICFWqVCEw8P3XW+zfv59GjRrRsmVLTpw4Qe3atalduzZnzpyJweQSndLnSMt4Xx+SuSfh1oXbdCs3kAe3HhkdS0QsTKZ8Hkw9PJL8FXLzKjAIn7pjWThoOWFhYUZHE4OZzBY0etrDhw9Jnjw5vr6+lClT5p3rNGzYkMDAQH7//ffwZcWLFyd//vzMmDHjo6/h7++Pm5sbfn5+uLq6Rll2iXr3rj+gR4VB3Lv+kJQeyRi9fSCpMqQwOpaIWJjQ16HM7L6QNZPfHMUvUasIPRd0IL5rPIOTSVSKzOe3RV1z4+f35vqKxIkTv3edAwcOUKlSpbeWVa1alQMHDrxz/aCgIPz9/d96SOyQ0iM543wHkzpzSu5df0i3sgP5+5Im3BSRt9na2dJu4nd0n9sufFiJTp599PvCillMuQkLC6NLly6ULFmS3Llzv3e9e/fukSLF23+9p0iRgnv37r1z/REjRuDm5hb+cHd3j9LcEr2SuydlvO9g0uVIw8O/H9Ot7ABunLtldCwRsUBVW5RnvK8PSVIn4ub523Qs5s2RzSeMjiUGsJhy0759e86cOcPSpUuj9Hm9vb3x8/MLf9y6pQ/G2CZJqkSM3elDhjzpeHLvGd3LD+LqnzeMjiUiFih70SxMOzKKnJ5Zef4skH5fjmDZ6HWav87KfHa5MZvNn/2m6dChA7///js7d+4kbdq0H1w3ZcqU3L//9twi9+/fJ2XKlO9c39HREVdX17ceEvskSu7G2B2DyFIwA88e+tO9wiANwS4i75QkVSLG7BhEtZYVCQszM7v3IkY0mUTQyyCjo0kM+eRyM2fOHHLnzo2TkxNOTk7kzp2b2bNnR+o5zGYzHTp0YM2aNezYsYMMGTJ8dBtPT0+2b9/+1rKtW7fi6ekZqdeW2Mc1iQujtw0ke7EsBDx5Ts9Kg/lz9zmjY4mIBXJwtKfrz63pOPUHbO1s2fnrPvp/NUrz11mJTyo3AwYMoHPnztSsWZMVK1awYsUKatasSdeuXRkwYECEn6d9+/YsWrSIJUuW4OLiwr1797h37x4vX74MX6dZs2Z4e3uHf925c2c2b97MuHHjuHDhAoMGDeLo0aN06NDhU34UiWUSJIzPyC39yF0qO4F+L+hZaTC//bRFh5xF5D9MJhNftavKyC39cE7gxIntp+lbfTgvAl5+fGOJ3cyfIGnSpOYlS5b8Z/mSJUvMSZIkifDzAO98zJs3L3ydsmXLmps3b/7WdsuXLzdnzZrV7ODgYM6VK5d5w4YNEX5NPz8/M2D28/OL8DZieV4GvjIPbTTBXMlUz1zJVM887ofp5qBXwUbHEhELdXb/BfNXbk3NlUz1zB09vc0BT58bHUkiKTKf3580zk3ChAk5cuQIWbJkeWv5X3/9RdGiRXn27Nnndq5oo3Fu4g6z2czyMb8xx3sxZrOZnCWyMWBFN5KkSmR0NBGxQBePXsG76hACngaSpVBGRm7ph2tiF6NjSQRF+zg3TZs2Zfr06f9Z/vPPP/Ptt99+ylOKRJrJZKJhz1oM2+BNfLd4nNt/kfZFenHh8CWjo4mIBcpWOBNjdgzCLakLl45dpUcFH5491Px1cVGEj9x4eXmF///Xr18zf/580qVLR/HixQE4dOgQN2/epFmzZkyZMiV60kYBHbmJm/6+dJeBtUdx8/xt7B3t6TKjFVWalzM6lohYoBvnbtGz0mCe3HtG+pxpGbV1gI74xgKR+fyOcLkpX758hF7cZDKxY8eOCK1rBJWbuCvQ/wWjmk3hwG9HAajTqTqtxzbD1s7W4GQiYmn+/usOPSr68Oj2E9JkScWY7QNJljaJ0bHkA6Kl3MQVKjdxW1hYGL/4rGDRkJUA5K+Qm35Lu+KWVP+tReRtd6/ep0dFH+7feEjKDMkZs30gKT2SGx1L3iPWzi0l8rlsbGxo7tOQASu74xTfkZM7ztChaG+NaCwi/5EqYwrG7fIhdaYU3Lv2gG7lBnL7suajigtUbiROKv11MSYfGE6qjCm4d/0hnUv0xXfFuydXFRHrlSJ9Msbt8sE9W2oe3HxEt3IDuXnhttGx5DOp3EiclSF3OqYeHkHBynl59SKIoQ3HM7fvEkJDQ42OJiIWJGmaJIzb5YNHbnce33lKt3IDuXbmptGx5DOo3Eic5prYheEb+lDPqyYAv45Yw8DaozVCqYi8JVGKhIzdMYhM+T149sCP7uUHce7gX0bHkk+kciNxnq2dLa3HNqPXwo7YO9pzaMNxupcfyNP7z4yOJiIWxC2pK2O2DyR70cz4Pw6gR4VB7F1zyOhY8glUbsRqVGpShvG+Pm8G8Dp+jc4l+/H3JV08KCL/xyVRAkZvG0CxGgUJfhXC4HrjWD1xg9GxJJJUbsSqZC+ahYn7hpEyQ3LuXr1Pl5J9uXjkstGxRMSCOCdwxmdNT75sXRmz2cx0r/lM6zxX1+vFIio3YnXSZknF5P3DyFIwA36PAuhefhCHN50wOpaIWBBbO1s6/fQjP4xsAsDaKZsYUn8cr14EGZxMIkLlRqxSohQJGbvTJ/xOqv5fjWTL/J1GxxIRC/LP/HV9f+2CvYMd+9YeoUdFH54+0HxUlk7lRqxWPBdnhq7vTcUmpQkLDWPs9z+xZPhqrGzQbhH5iHINSzJ62wBcEifgwqFLdC7Rl1sXNRaOJVO5Eatm72BPz/kdaNCjFgDz+v3K1I5zdG5dRN6Su1QOJu0bGn69XueS/Tiz97zRseQ9VG7E6tnY2PDjqCa0ndACk8nEbz9tYdg3Ewh+FWx0NBGxIO7Z0jD5wHCyF81MwJPn9Kw8hF3L9hkdS95B5Ubk//u6c43wc+t7Vh2i9xdDCXj63OhYImJBEiV3Y8yOQZSsXYSQoBCGNZrIstHrdDrbwqjciPxL2QYlGL6pL/FcnTm9+zxeZQbw8O/HRscSEQviFM+R/iu6UadTdQBm917E5HazCH2t09mWQuVG5H/kL5+bCbuHkDhVIq6fvUXnEn25fvaW0bFExILY2trSbuJ34aezf5+5lYF1RhP0UreKWwKVG5F3yJg3PZP3D8M9W2oe/v2YziX66ty6iPzH151rMGBlNxyc3kztMqTBeF6HvDY6ltVTuRF5jxTpkzFx71DylMnBi4CXDGs0kcntZulCYxF5S6k6xRixuV94wRnVfKruuDSYyo3IB7gmcWHMtoF807sOAOtn/EHnkv24c+WewclExJLkLZOTgat6YGtny66l+5jcdpYuMjaQyo3IR9ja2dJyeGOGb+yDaxIXLp+4RttCPdm98oDR0UTEghStVgDvRZ2wsTGxcfZ2ZvX8RQXHICo3IhFU5IsCzDgxhlwls/HC/yVDGoxnasc5BAeFGB1NRCxE2QYl6DKzNQArxq1nyfDVBieyTio3IpGQLG0Sxu4YRMOeb0Y0XjdtM11L9+fu1fsGJxMRS1GtZUXajGsOwPz+S1k7ZZPBiayPyo1IJNnZ2/HDyCYMXd8bl8QJ+OvoFdoW6sneNYeMjiYiFqJu1y9p0r8eANM6z+WPBbuMDWRlVG5EPlGxGoWYcWIMOT2zEuj3Ap+6Y/mpyzxCgnWaSkSg2aAG4QP9jWv5E3tW6w+gmKJyI/IZkrsnZdwuHxp0/wqANZM30rV0f+5df2BwMhExmslkos345lRtUZ6wMDMjGk/k2NZTRseyCio3Ip/Jzt6OH0c3ZfC6Xrgkis/FI1doW7An+9cdMTqaiBjMxsaGrj+3pnTdYoQEv2ZQnTGc2XfB6FhxnsqNSBTxrFmY6cfHkL1YFp4/C2RgndHM8Jqv0UpFrJytnS29F3WmcNV8vHoRRL8vR3D55DWjY8VpKjciUShF+mSM9/WhbtcvAVg1cQO9qw7F/3GAwclExEgOjvYMXNWD3KWyE+j3Au+qQ7l2+obRseIslRuRKGbvYE+bcc3xWdOTeC7OnNp1lo7Fvblx/m+jo4mIgZziOTJ0fW+yFMzAs4f+dCs/iItHLhsdK05SuRGJJiVqFWHS/mGkzJCcO1fu08mzD0c2nzA6logYKL5bfEZtHUD2YlkIePKcnpUG8+fuc0bHinNUbkSikUcud6YeGkGe0jl44f+Sfl+OYPWkDRqSXcSKuSRKwKg/+pO/fC5eBLykT7Vh+sMniqnciEQzt6SujNrany++e3M76PSu85nYeqbGwxGxYvFcnBn6uzfFahQk6GUwA2qNYs+qg0bHijNUbkRigL2DPV6z29J6bLPwSfV0obGIdXN0dmTgqu6Uqe/J65BQhjYcz9ZffI2OFSeo3IjEEJPJRD2vmgz+rTfxXJz50/ccHYp5c+PcLaOjiYhB7B3s6bOkc/hAf6ObT2X99C1Gx4r1VG5EYlix6gXDLzS+e/U+nUr05fAmnW8XsVa2trZ4zW5D7Q7VAJjcfjbLx6wzOFXspnIjYoDwC43LvLnQuH/NEayeqAuNRayVjY0N7SZ9RyPvOgDM6rWI+f2X6nfCJ1K5ETGIW1JXRv3Rny++r/DmQmOv+UxopQuNRayVyWTi+2GNaTm8MQCLh61ietf5KjifQOVGxED2DvZ4zWpDm3HNsbExsWnOmwuN/R75Gx1NRAzyTe86dJjSEngzGe/4H2cQGhpqcKrYReVGxGAmk4m6Xb9kyHrv8AuNW+fvzsbZ2wl9rV9oItaoVvsv6DGvPTY2JjbP3cHIJpM1T10kqNyIWIii1Qow+cAwUmdOyeM7T5nQagY/5u3GntWHdFhaxApVaV6Ovku7Ymdvy65l+5naYY5+F0SQyo2IBUmf051Zf46jzbjmuCZx4daF2wyuN5ZOJfpyatdZo+OJSAwrU8+T/su7YTKZ2DBrG2smbTQ6UqxgMltZDfT398fNzQ0/Pz9cXV2NjiPyXoF+gSwf8xurJ27g1YsgAIpUK0DL4Y3JlM/D2HAiEqNWjl/PzO4LsbExMXhdL4rVKGR0pBgXmc9vlRsRC/f47lMWD1kZfg2OyWSiQuNSNB/ckFQZUhgdT0RigNlsZkKrmWyasx3nBE5M2jeUDHnSGx0rRkXm81unpUQsXJJUiej004/MOTeBcg1LYDab2b54Dy1zdmX/uiNGxxORGGAymeg4rSX5y+fi5fNX9P9qFE8f+Bkdy2Kp3IjEEmkyp6Lvr12ZdmQkecvmJCQoBJ96Y9mxZI/R0UQkBtg72NN/RTfSZEnF/RsPGVRnNMGvgo2OZZFUbkRimayFMjF66wAqNS1DWGgYI5tOYcPPW42OJSIxwDWxC0PX9yZBwvicO/AX43+coTuo3kHlRiQWsrWzpce89tRsWxWz2czENj+zYuxvRscSkRiQNmtq+q/ohq2dLdsX72HJsNVGR7I4KjcisZSNjQ0dp7akYc9aAPzc8xcWDFymv+JErEDBinnoOPXNKMbzByzFd8UBgxNZFkPLze7du6lZsyapU6fGZDKxdu3aj24zbdo0cuTIgbOzM9myZWPhwoXRH1TEQplMJn4Y2YTvh72Zi2bRkJXM8FpAWFiYwclEJLrVaFWZrzvXAGBMi6lcPHLZ4ESWw9ByExgYSL58+Zg2bVqE1p8+fTre3t4MGjSIs2fP4uPjQ/v27Vm/fn00JxWxbI2869B+0vcArJ60AZ+6Ywn0CzQ4lYhEt1Zjm1K0egGCXgYzoPZoHv792OhIFsFixrkxmUysWbOG2rVrv3edEiVKULJkScaMGRO+rFu3bhw6dIi9e/dG6HU0zo3EZVsX+jKh1QxCgl+TOnNKBq7sTsa81jUWhoi1CfR/QZdS/bh+5haZ8nswYfdgnBM4Gx0rysXZcW6CgoJwcnJ6a5mzszOHDx8mJCTkvdv4+/u/9RCJqyo3K8uEPUNIni4pdy7fo5NnH7Yt2m10LBGJRvFd4zHkt94kTObKlZPXGVBbt4jHqnJTtWpVZs+ezbFjxzCbzRw9epTZs2cTEhLCo0eP3rnNiBEjcHNzC3+4u7vHcGqRmJWtSGZ+OjqKQlXyEfQymFHNpjC53SyCg979B4CIxH4pPZIzZH1vnBM4cXLHGQbXH0dIsPX+m49V5aZ///5Uq1aN4sWLY29vT61atWjevDnw5s6Rd/H29sbPzy/8cevWrZiMLGIIt6SuDNvgTZP+9QBYP+MPupUdwINb7/4jQERiv+xFszD0d28cnR04tOE4I5tOIfR1qNGxDBGryo2zszNz587lxYsXXL9+nZs3b+Lh4YGLiwvJkiV75zaOjo64urq+9RCxBra2tjT3acjQ371xSRSfC4cv065QT45v+9PoaCISTfKWycnA1T2ws7dl94oDjPthulXePRmrys0/7O3tSZs2Lba2tixdupQvv/zyvUduRKxdseoFmXZ0FJkLZMDvUQC9qw5l8bBVVvkLT8QaFKman37LvLCxtWHrQl+mdphjdeNfGdoInj9/zsmTJzl58iQA165d4+TJk9y8eRN4c0qpWbNm4ev/9ddfLFq0iEuXLnH48GG++eYbzpw5w/Dhw42ILxJrpMqQgol7h/DFd+Uxm83M77+UgXVG8/yZbhcXiYtK1i5KrwUdMJlMrJ/xB7N6/mJVBcfQcnP06FEKFChAgQIFAPDy8qJAgQIMGDAAgLt374YXHYDQ0FDGjRtHvnz5qFy5Mq9evWL//v14eHgYEV8kVnF0dqTbnHZ4zWqDvaM9B9cfo13hXlw+ec3oaCISDSo0Lk2Xma0BWDFuPYsGrzQ4UcyxmHFuYorGuRGBv45dYUj9cdy7/hAHJ3s6T29FlebljI4lItFg9aQNTO86H4BWo5tSv/tXxgb6RHF2nBsRiRpZC2Vi2tFRFK1egOBXIYz5bhoTW8+0+rExROKirzvX4LuhjYA3c9Ctn77F4ETRT+VGxEq5JnZhyG+9ae7TEJPJxIZZ22iRrRO//bRFJUckjmnc52u+6V0HgMntZ7N1oa/BiaKXTkuJCEe2nGRcy594fOcpAElSJ6Jhz9pU/7Eijs6OBqcTkahgNpv5qfM81k7dhI2Nib5Lu1KmnqfRsSIsMp/fKjciAkDwq2A2z93J0pFrwiffS5wyIQ161KJG68o4xVPJEYntwsLCmPDjDDbP24mtnS3DNnhTqHI+o2NFiMrNB6jciHxYcFAIf8zfxa8jVvPg5psRjRMmd6N+t5rUbFslTk7IJ2JNQkNDGdl0CruW7iNhcjd+PjWWRCkSGh3ro1RuPkDlRiRiQoJD2PbLbpYMX829aw8AcE3iQv1uNanr9SX2DvYGJxSRTxX8KpgOxby5dvomhavmY9iGPhY/GK7ulhKRz2bvYE+1lhWZd2ES3ee2I3XmlPg/DmBOnyX0qTZMAwCKxGIOTg70/bULjs4OHN1yilUTNhgdKUqp3IjIB9nZ21G1RXnmnptIj3nt38w6vPMsXcv010ScIrFY+pzutBnfAoC5fRbz17ErxgaKQio3IhIhtna2VGlejvG7B5M4VSKun7lF5xJ9uXb6htHRROQT1WhViVJfF+N1SCjDG0/i5fOXRkeKEio3IhIpmfNnYPL+YaTLkYZHt5/QpXR/Tuw4bXQsEfkEJpOJrj+3JlnaJNy+dJepneYaHSlKqNyISKSlSJ+MCXuGkKd0Dl74v6RPtWHsWLLH6Fgi8glcE7vQe1EnbGxM/DF/Fzt+3Wt0pM+mciMin8Q1sQsjt/SjTH1PXoeEMqLJZBYPXUVYWJjR0UQkkvKWyUnjvnUBmNT2Z+5eu29wos+jciMin+yfOy7qdv0SgPkDluL9xVAe331qcDIRiawm/euRq2Q2Xvi/ZMS3k3gd8troSJ9M5UZEPouNjQ1txjWn2+y2OMVz5Pi207TO142Dvx8zOpqIRIKtnS3eizoT3y0e5w9eYuGg5UZH+mQqNyISJb74vgLTjo4iU34P/B4F0P+rkUzrNFeTcIrEIinSJ6Prz20AWDpyLSd3njE40adRuRGRKJMuexomHxhO3S41AFg7dRMdi/fhxrlbBicTkYgqW9+TL76vgNlsZmTTyTx76Gd0pEhTuRGRKOXgaE+b8S0YtqEPCZO5cvXPG7Qv0pvfZ27FymZ7EYm12k36DvdsqXl85yk+dccSHBRidKRIUbkRkWhRtFoBZp4aS6Eq+Qh6Gcyktj8zuP44/J8EGB1NRD7COb4TA1f3IL5bPM7svcDENjNj1R8nKjciEm0Sp0zE8I19aDWmGXb2tuxdfYg2+Xtwyves0dFE5CPS50hLv2Ve2NjasHWBL8tGrzM6UoSp3IhItLKxsaF+t5pM2j+MNFlS8fDvx/Ss6MP8AUsJfR1qdDwR+YDCVfLRdkILAOb2WcK+tYeNDRRBKjciEiOyFsrE9GOjqNqiPGFhZhYPXYVXuYHcu/7A6Ggi8gG1O1SjZtuqby4wbjKZyyevGR3po1RuRCTGOCdwpvvcdvRZ0oV4rs6c23+RNgV6sGvZPqOjicgHtJ/0HQUr5eHViyAGfDXK4gfqVLkRkRhX/puSzDgxhhzFsxDo94JhjSYy9JvxXDl13ehoIvIOtna29F/eDfdsqXn492MG1RlN0Msgo2O9l8qNiBgiVYYUjPcdTOM+X2MymfBdfoA2BXrQs/JgDm08rjmqRCxMgoTxGbK+Ny6JE3Dh8GXGfv+Txd5BZTJbarJo4u/vj5ubG35+fri6uhodR0SAv45dYcXY39i98iBhoW9KjXv2NNTtUoNKTcvg6OxocEIR+cepXWfpVWUIoa9DaTawAU0H1o+R143M57fKjYhYjPs3HrJ2yiY2ztrGi4CXALgldaFm26p81a4qiVIkNDagiACwac52xv84A4C+v3ahXMOS0f6aKjcfoHIjYvkC/V+wec4O1kzeyP0bDwGwd7Cj4relqetVE49c7gYnFJEZ3RawasLvODjZM26XD9mLZonW11O5+QCVG5HYI/R1KHvXHGbl+PVcOHQJABsbE63HNadOp+qYTCaDE4pYr9DQUAbVGcPB34+RKIUb04+PIUmqRNH2epH5/NYFxSJisWztbClb35PJ+4cxce9Qin9ZiLAwM9O7zmf8jzNi3Xw3InGJra0t3os745Hbnaf3/ZjTZ7HRkcKp3IiIxTOZTOQqkY3B63rRemwzbGxMbJ67g56VfHj6IPbNWCwSV8Rzcabb7LYAbF3gy4XDlwxO9IbKjYjEGiaTiXpeNRmy3pt4rs6c3XeRDkV7x4oRU0XiquxFs1C5eVkAfuoyzyJuD1e5EZFYp2i1Akw5OII0WVLx4OYjupbqz55VB42OJWK1Wg7/FucETpw/eIkdS/YaHUflRkRip3TZ0zDl4HAKVs7LqxdBDK4/jrl9l/Ay8JXR0USsTpJUiWjk/TUAs3r9wsvnLw3No3IjIrGWS6IEDN/Qh6871wDg1xFraJqhHb+OWEOg/wuD04lYl7pda5AyQ3Ie33nKslHrDM2iciMisZqtnS1tJ7Sgz5IupMqYAr9HAcztu4SmGdrxi88KAp4+NzqiiFVwcHKg9dhmACwf+xv3rj8wLIvKjYjECeW/Kcm8C5PouaAD7tlSE/A0kIU+y2ni0Y45fZbg98jf6IgicV7J2kXJXyE3IUEhzOq1yLAcGsRPROKc0NBQ9q46xOJhq7h2+iYATvEc+bJNFep3r0nilNE30JiItbt2+gZtCvQgLMzM2J2DyFc2V5Q8rwbxExGrZmtrS9kGJZhxYgyDVvcgS8EMvHoRxMrx62mSoT3z+y/VrOMi0SRDnvTUaFUZeHNreGhoaIxnULkRkTjLxsaGkrWLMu3IKIb+7k2O4lkICQph8bBVTGz9swqOSDRpPrghCRLG5+qpG2yesyPGX1/lRkTiPJPJRLHqBZm0bxjdZrfFxsbEpjnbGfv9T4b8VSkS17kldaXZoAYAzO+/lOfPAmP09VVuRMRqmEwmvvi+Ar0XdcbG1oatC30Z2XQKr0NeGx1NJM6p2bYK6XKk4dlDf2Z2Xxijr61yIyJWp/w3Jem/3As7e1t2Ld3H0G8mEBKsSThFopKdvR2dp7fCZHozF9z+347E2Gur3IiIVSpVpxgDV/XA3sGOfWsO41N3LMGvgo2OJRKn5C2Tk3peXwIwodVMnj2MmYluVW5ExGoV/7IQg3/rjYOTPYc2HKdbuYHsWraP4CAdxRGJKi2GfINHbneePfBjYuuZMTKxpsqNiFi1wlXyMXxjX5ziO3Lh8GWGNZpIY/fWzOi2gJsXbhsdTyTWc3ByoPcvnbCzt2Xf2iNsmb8r2l9Tg/iJiAAPbj1i46xtbJm3k0e3n4Qvz10qO9V/qETpesVxiudoYEKR2G3pqLXM8V5MPBdnZpwcQ6oMKSK1fWQ+v1VuRET+JfR1KEc2n2Tj7G0c2nCcsNA3Y+HEd4tHxW9LU/3HSmTK52FsSJFYKDQ0lO7lB3Fm7wXylM7BmB0DsbW1jfD2KjcfoHIjIhH16M4TtszbyeY527l3/WH48qyFM/FNr9qUrlvcwHQisc/dq/dpnb87L5+/4sdRTWjQo1aEt1W5+QCVGxGJrLCwME5sP83G2dvZv/Ywr0PeDPz3ZevKtJ3QAgcnB4MTisQem+ZsZ/yPM7B3sGPq4ZFkzJs+QttpbikRkShkY2NDocr56L/MiyW3ZtKwZy1MJhO/z9xKl1L9uHvtvtERRWKNL76vQPGahQgJfs3IppOj5e5ElRsRkUhIlNyNH0Y2YdgGb1yTuHDp+DXaFeoVowOUicRmJpMJr5/bkDCZK9dO32TBgGVR/hqGlpvdu3dTs2ZNUqdOjclkYu3atR/dZvHixeTLl4948eKRKlUqvv/+ex4/fhz9YUVE/qXIFwWYfnw0OYpn4fmzQAbWHs2sXosIfa25qkQ+JlGKhHSZ2RqAFWN/48/d56L0+Q0tN4GBgeTLl49p06ZFaP19+/bRrFkzWrZsydmzZ1mxYgWHDx/mxx9/jOakIiL/ldw9KeN2+fB15xoALB+zjh4VfXh058lHthSRkrWLUrVFecxmM5Pa/hylg/sZWm6qVavG0KFDqVOnToTWP3DgAB4eHnTq1IkMGTJQqlQpWrduzeHDh6M5qYjIu9k72NN2Qgv6L/cinoszp/ecp3W+7iwYuIzHd58aHU/EorWd0ByneI7cPH+b8wf/irLnjVXX3Hh6enLr1i02btyI2Wzm/v37rFy5kurVq793m6CgIPz9/d96iIhEtTL1PJl2dBQZ86XH/3EAi4as5Nv0bRnRZBLnD10yOp6IRYrvFp9SdYsBsHWhb5Q9b6wqNyVLlmTx4sU0bNgQBwcHUqZMiZub2wdPa40YMQI3N7fwh7u7ewwmFhFrkjZLKqYdHkm/ZV7kLpWd0Neh7Fiyl06efehQrDfbFu3WvFUi/6Ny07IA7Fq2P8r+fcSqcnPu3Dk6d+7MgAEDOHbsGJs3b+b69eu0adPmvdt4e3vj5+cX/rh161YMJhYRa2Nnb0fZ+p5M2D2En46OokqLctg72HHxyBVGNZtCE4+2LBy0nCf3dMpKBCBf+VwkS5uE588COfT7sSh5TosZxM9kMrFmzRpq16793nWaNm3Kq1evWLFiRfiyvXv3Urp0ae7cuUOqVKk++joaxE9EYtrTB35snLWN9dO38PjOm1JjZ29L2QYl+KZ3HTxy6YiyWLc53otZOmotxWsWYsi63u9cJ84O4vfixQtsbN6O/M+8FBbS0URE/iNRcje+7VuXRdd+ou+vXchZIhuvQ0LZvngPHYt7c3z7aaMjihiqUtMyABzZdJKnD/w++/kMLTfPnz/n5MmTnDx5EoBr165x8uRJbt68Cbw5pdSsWbPw9WvWrMnq1auZPn06V69eZd++fXTq1ImiRYuSOnVqI34EEZEIs7O3o1zDkkzaO5Sph0eSr1wuXgUG0a/GcPav0yCAYr3S53Qna+FMhL4OZdfSfZ/9fIaWm6NHj1KgQAEKFCgAgJeXFwUKFGDAgAEA3L17N7zoALRo0YLx48czdepUcufOTf369cmWLRurV682JL+IyKfKVjgTwzf1pWSdooQEv8an3li2/hJ1d4uIxDb/XFgcFf8OLOaam5iia25ExJKEvg5l3I/T2brgzS/0DlNaUqv9FwanEol5fo/8aZi6FaGvQ5l1evx/rkWLs9fciIjENbZ2tnSf047aHaoBMLXjHJYMX63rCMXquCV1pViNgsDnj3mjciMiYjAbGxvaTfqOJv3rATCv369M7zqfp/efGRtMJIZV+v+nprYv3k1o6KfP06ZyIyJiAUwmE819GtJ67JubKNZM3sg3aVrRvcIg1k3brKkcxCoUq1EQl0TxeXznKSd3nPnk51G5ERGxIPW8atJvmRfZimQiLMzMqV1nmdpxDo3StqZrmf6snrSBh38/NjqmSLRwcLSnXMOSAGxfsueTn0cXFIuIWKj7Nx6yZ9VBdq88wPmDb89PldMzK6XrFqdMveIkT5fMoIQiUe/P3efoVm4g8d3iseL+bOwd7IHIfX6r3IiIxAIPbj1i7+pD7Fl1kLP7Lr51wXH2opmp1LQsX7apHD6wqUhsFRoaSuN0bXly9ylD1/emWI1CgO6WEhGJc5K7J+XrzjWYsHsIS27NoP3k78lbNicmk4kLhy8zteMcupUbyN1r942OKvJZbG1tKf31m5nCfVce+KTn0JEbEZFY7Mm9p+xaup8FA5fxIuAl8VycaT/5eyo3K4vJZDI6nsgnOb3nPF5lBxDfLR7L783GwdFeR25ERKxF4pSJ+LpLDWaeGkvuUtl5EfCSMd9NY0iDcfg/DjA6nsgnyVUyG4lTJSLQ7wUntv0Z6e1VbkRE4oCUHskZu3MQLYc3xtbOlj2rDvFj3m4c/eOU0dFEIs3GxoYydYsD4Lsi8qemVG5EROIIW1tbvuldhykHh+OePQ1P7j7F+4uhTOs8l6CXQUbHE4mUsg08Adi39jDBQSGR2lblRkQkjslSMCM/HR0VPkfV2imbaJWvO9sX7/msUV9FYlLOEtlIkjoRL/xfcnxr5E5NqdyIiMRBTvEc6TClJcM29CFxyoTcuXyPkU0n82NuL3YsUckRy2djY0Pp8FNT+yO3bXQEEhERy1C0WgHmXpjEd0Mb4ZIoPrcu3mFEk8n8mKcbO37dq5IjFq1sgxIA7F93JFKnplRuRETiuPiu8Wjc52t+ufYTLYZ886bkXLjNiG8n0SpvN3Yu3aeSIxYpp2dWkqZJzAv/l5zccTrC26nciIhYifiu8fi2b11+uTqNFoO/IUHC+Nw8f5vhjSfSOl93di3bR1hYmNExRcL9+9TU/nVHIr5ddAUSERHLFN8tPt/2q8uia9No7tOQBAnjc+Pc3wxrNJFOnn24cuq60RFFwhWqnBeAa6dvRngblRsRESsV3y0+TfrX45er02g2qAHxXJ25eOQK7Qr3YnbvRbp9XCxC6swpAbh77UGEt1G5ERGxcgkSxqfpgPrMOTeR0vWKExYaxrLR62iVtxvHP2F0WJGolMIjOSaTiaDAiJdtlRsREQEgaerEDFjeDZ+1PUmaJjF3rtynV5UhjG4xFb9H/kbHEyvl4GhP0rSJI7WNyo2IiLylxFdFmH12ArU7VMNkMrF1oS8tc3Zh26LdWNlcy2IhUmVMEan1VW5EROQ/4rvGo/3k75m0fxgZ8qTD71EAo5pNoUupfiweuoo/d58j+FWw0THFSqTKELlyYxdNOUREJA7IUSwLPx0dxYqx6/ll8ArOHfiLcwf+AsDe0Z7sxTKTt3RO8pTJQU7PrDgncDY4scRFqTKp3IiISBSys7ejkXcdKn5bigPrj3F6zzn+9D3H0/t+nN59ntO7z8MwsLG1IWuhjOQpnYM8ZXKSu1R2XBIlMDq+xAGRPS1lMlvZCVR/f3/c3Nzw8/PD1dXV6DgiIrGS2Wzm9uV7/Ol7jtN7znF693nu33j41jomk4kMedJRsFJeGvSsRaLkbgalldju/KFLtCveg12si9Dnt8qNiIhEifs3HnJ6z3lO7z7H6T3nuXXxTvj3XBInoNWYZlRtUQ6TyWRgSomN/B75UztZM5Wb91G5ERGJGU/vP+NP33P8OnINV05eByB/+Vx0ntGatFlSGRtOYhWz2UwNt0ZsClgWoc9v3S0lIiLRIlGKhJRtUIKph0bw46gmODo7cHLnWVrl7cbiYasICY74LM9i3UwmEykzJI/w+io3IiISrezs7WjQoxazzoynUJV8hASFML//UtoV7sW5AxeNjiexRLK0SSK8rsqNiIjEiFQZUjBiU196/9KJhMlcuX7mFl1K9Wdy+9kE+gUaHU8snP+T5xFeV+VGRERijMlkouK3pZlzbiJVW5THbDazfvoWmmbqwPz+S3n6wM/oiGKh7kdi4kxdUCwiIoY5seM0U9rPDr+zysHJnsrNylGvW01ddCzhXga+onqCbyJ8t5SO3IiIiGEKVMjDrDPjGbCiG9mLZib4VQgbft7K99k741NvLOcPXTI6oliAe1fvR2p9lRsRETGUra0tpesWZ/KB4Yzb5UOxGgUxm83sXX2ITp598Co3gIO/HyMsLMzoqGKQO1ciV240/YKIiFgEk8lE3jI5yVsmJ9fP3mLFuN/YsXhP+BQP6XOm5esuX1K2gSfxXeMZHVdiUGTLja65ERERi/Xo9mPWTNrI7zO38iLgJfDmuhzPrwpToXFpinyRH3sHe4NTSnSb3G4Wa6Zv0AjF76NyIyIS+wT6BbLh521snreTWxduhy93SZyAsvU9qfhtaXKWyIaNja62iIt6fzGUQ1uOqty8j8qNiEjsZTabuXziGtsX7Wbn0n08ufcs/Hsp0iejQuNSVPy2NOlzuhsXUqLU65DXNMnQjvu3H6jcvI/KjYhI3BAaGsrJHWfYvmQP+1YfDj9tBZApvwcVvy1DpSalSZQioXEh5bP9PnMrk9r+TLzEjvz2eLHKzbuo3IiIxD2vXgRxcP1Rti/Zw5FNJwl9HQqAnb0tpesVp2abquQulV0zkscyLwNf0SJLR57ce8Z3I7/h2171VG7eReVGRCRu83vkz+4VB/hjoS8X/jVOjkdud75qW5WKTcoQz8XZwIQSUYuHrmL+gKWkzJCciYcGkzRZUpWbd1G5ERGxHpeOX2X99D/YsWQPQS+DAXBO4ESlpmWp2bYKGXKnMzihvM+zh340z9yRFwEv8V7cmcI18kb481vlRkRE4rznzwL5Y8Eu1k/fwt9/3Q1fnqdMDr5qW5WSdYrqlnIL81OXeayZvJEsBTMw9fBInj9/rnLzPio3IiLWy2w2c2LHGdZP38L+dUcIC30z6nHC5G541ixMkWoFKFgpjwYJNNjda/f5PntnXoeEMuqP/hSslDdSn98qNyIiYpUe/v2YjbO2sXHWtrduKbe1syVXyWwU+aIARasVIEOedLoQOYaNbDqZ7Yv3ULByXkZt6Q9E7vNb5UZERKza65DXnNh+msObTnBk80luX7r71veTpklM4ar5KfrPUR23+AYltQ6XT16jbcGeAPx0dBRZCmYEVG4+SOVGREQ+5M6Ve/+/6Jzg1M6z4Rciw5ujOjlLZKXoFwXwrFWE9DnSGpg07tm98gBTOszh2QM/yjcqSZ/FXcK/p3LzASo3IiISUUEvg/hz93mO/P+y8++LkQGyFMpI5aZlKfdNSRIldzMoZez3+O5Tpnacw97VhwBInzMtIzb3I1naJOHrqNx8gMqNiIh8qrtX73Nk80kObTzGsT/+DB8s0MbWhqLVClCpaVk8axbCwcnB4KSxg9lsZsv8XczstoDnzwKxtbPlm961ady3Lg6Ob9+9pnLzASo3IiISFZ499GPXsv1s+8WXi0euhC+P7xaPsvU9qdysLLlKalTk97l3/QETWs/k+NY/gTdHwbrPaUfGvOnfub7KzQeo3IiISFS7eeE2237xZdui3Ty89Th8ecoMyanUpAyVmpYhTeZUBia0HKGhofw2bQtz+y7hVWAQDk72NPdpSN2uX2JrZ/ve7WJNudm9ezdjxozh2LFj3L17lzVr1lC7du33rt+iRQsWLFjwn+U5c+bk7NmzEXpNlRsREYkuYWFhnN59nq0Lfdm98gAvn78K/17ilAnxyO2OR650b/43dzrS50xrVVNB3Dj/N+N/nMG5/ReBN4Moes1qS9osHy9+sabcbNq0iX379lGoUCG+/vrrj5YbPz8/Xr78v1lfX79+Tb58+ejYsSODBg2K0Guq3IiISEx49SKI/euOsO0XX479cYqwsHd/3Kb0SIZH7nR45HpTeDxyu+OePc1/rjmJzV6HvGbZ6HUsHrKSkODXxHNx5odRTajRqhI2NjYReo5YU27+zWQyfbTc/K+1a9fy9ddfc+3aNdKnf/c5uqCgIIKCgsK/9vf3x93dXeVGRERizMvnL7lx7m+un7nF9TM3uXb2FtfP3OLJ3afvXN/G1oY0WVLhkdudnMWzkrdsTjLl98DW9v2nbSxRWFgYp/ec56cu87h66gYARaoVoMuMViR3Txqp57KaclOzZk2CgoL4448/3rvOoEGD8PHx+c9ylRsRETGa/+MArv//onP9zE2un73FtdM3ef4s8D/rxnN1Jnep7OQtk4u8ZXOSpWAG7OztDEj9YWFhYZw78Be7Vxxgz6qDPLr9BADXJC60m/gdFRqX+qSLrK2i3Ny5c4d06dKxZMkSGjRo8N71dORGRERiE7PZzOO7T7l+5hZXTl7n9J5znN5znhf+L99azym+I7lKZidvmZzkLZuTbEUyGTb5Z1hYGOf2X8R3xQH2rj4UXmgA4rk4U6a+J98Pb/xZYwFFptxYXuWLoAULFpAwYcKPliFHR0ccHR1jJpSIiMhnMplMJE2dmKSpE1O4Sj4a9qxFaGgoV0/d4E/fc/y5+xynd58j4Gkgx/44xbE/TgHg6OxADs+s5C2Tk/Q50/7n6Mi7jmVE9PDGv5/q388bFmbmzJ7z7Fl9kMd3/u8UWzxXZzy/KkyZep4UrpIvxsf9iZXlxmw2M3fuXJo2bYqDQ+R22D//cf39/aMjmoiISLRIkTkplTOXoXLLMoSFhXHj3N+c3XuRM/vPc3bvBfwfP+fo9hMc3X7CkHzOrk4Uq16QkrWLkr9CnvALol8Fv+JV8KuPbP1x/3xuR+SEU6wsN76+vly+fJmWLVtGetuAgAAA3N3dozqWiIiI9fKHTUuXwdLofZmAgADc3D58esvQcvP8+XMuX74c/vW1a9c4efIkiRMnJl26dHh7e3P79m0WLlz41nZz5syhWLFi5M6dO9KvmTp1am7duoWLi4vFjhr5z3VBt27d0nVB/6L98m7aL++m/fJu2i/vpv3ybpa0X8xmMwEBAaROnfqj6xpabo4ePUr58uXDv/by8gKgefPmzJ8/n7t373Lz5s23tvHz82PVqlVMmjTpk17TxsaGtGljxyyurq6uhr+ZLJH2y7tpv7yb9su7ab+8m/bLu1nKfvnYEZt/GFpuypUr98FzZ/Pnz//PMjc3N168eBGNqURERCQ2i9iwgCIiIiKxhMqNBXJ0dGTgwIG6hf1/aL+8m/bLu2m/vJv2y7tpv7xbbN0vFjOIn4iIiEhU0JEbERERiVNUbkRERCROUbkRERGROEXlRkREROIUlZsYMm3aNDw8PHBycqJYsWIcPnz4veuGhIQwePBgMmXKhJOTE/ny5WPz5s2f9ZyWKqr3y6BBgzCZTG89smfPHt0/RpTZvXs3NWvWJHXq1JhMJtauXfvRbXbt2kXBggVxdHQkc+bM7xwfKra/V6Jjv8T29wpEfr/cvXuXxo0bkzVrVmxsbOjSpcs711uxYgXZs2fHycmJPHnysHHjxqgPH42iY7/Mnz//P+8XJyen6PkBoklk98vq1aupXLkyyZIlw9XVFU9PT7Zs2fKf9Szx94vKTQxYtmwZXl5eDBw4kOPHj5MvXz6qVq3KgwcP3rl+v379mDlzJlOmTOHcuXO0adOGOnXqcOLEiU9+TksUHfsFIFeuXNy9ezf8sXfv3pj4caJEYGAg+fLlY9q0aRFa/9q1a9SoUYPy5ctz8uRJunTpwg8//PDWL6C48F6Jjv0Csfu9ApHfL0FBQSRLlox+/fqRL1++d66zf/9+GjVqRMuWLTlx4gS1a9emdu3anDlzJiqjR6vo2C/wZpTef79fbty4EVWRY0Rk98vu3bupXLkyGzdu5NixY5QvX56aNWvGjs8is0S7okWLmtu3bx/+dWhoqDl16tTmESNGvHP9VKlSmadOnfrWsq+//tr87bfffvJzWqLo2C8DBw4058uXL1ryxjTAvGbNmg+u07NnT3OuXLneWtawYUNz1apVw7+OC++Vf4uq/RKX3itmc8T2y7+VLVvW3Llz5/8sb9CggblGjRpvLStWrJi5devWn5nQGFG1X+bNm2d2c3OLslxGi+x++UfOnDnNPj4+4V9b6u8XHbmJZsHBwRw7doxKlSqFL7OxsaFSpUocOHDgndsEBQX953Cns7Nz+F+Vn/KcliY69ss/Ll26ROrUqcmYMSPffvvtf+Yni0sOHDjw1j4EqFq1avg+jAvvlU/xsf3yD2t6r0RURPedNXr+/Dnp06fH3d2dWrVqcfbsWaMjxaiwsDACAgJInDgxYNm/X1RuotmjR48IDQ0lRYoUby1PkSIF9+7de+c2VatWZfz48Vy6dImwsDC2bt3K6tWruXv37ic/p6WJjv0CUKxYMebPn8/mzZuZPn06165do3Tp0gQEBETrz2OUe/fuvXMf+vv78/LlyzjxXvkUH9svYH3vlYh6376Ly++XiMiWLRtz585l3bp1LFq0iLCwMEqUKMHff/9tdLQYM3bsWJ4/f06DBg0Ay/4sUrmxQJMmTSJLlixkz54dBwcHOnTowHfffYeNjXX/54rIfqlWrRr169cnb968VK1alY0bN/Ls2TOWL19uYHKxRHqvSGR4enrSrFkz8ufPT9myZVm9ejXJkiVj5syZRkeLEUuWLMHHx4fly5eTPHlyo+N8lHV/WsaApEmTYmtry/37999afv/+fVKmTPnObZIlS8batWsJDAzkxo0bXLhwgQQJEpAxY8ZPfk5LEx375V0SJkxI1qxZuXz5cpTmtxQpU6Z85z50dXXF2dk5TrxXPsXH9su7xPX3SkS9b9/F5ffLp7C3t6dAgQJW8X5ZunQpP/zwA8uXL3/rFJQl/35RuYlmDg4OFCpUiO3bt4cvCwsLY/v27Xh6en5wWycnJ9KkScPr169ZtWoVtWrV+uzntBTRsV/e5fnz51y5coVUqVJFWXZL4unp+dY+BNi6dWv4PowL75VP8bH98i5x/b0SUZ+y76xRaGgop0+fjvPvl19//ZXvvvuOX3/9lRo1arz1PYv+/WLo5cxWYunSpWZHR0fz/PnzzefOnTO3atXKnDBhQvO9e/fMZrPZ3LRpU3Pv3r3D1z948KB51apV5itXrph3795trlChgjlDhgzmp0+fRvg5Y4Po2C/dunUz79q1y3zt2jXzvn37zJUqVTInTZrU/ODBg5j+8T5JQECA+cSJE+YTJ06YAfP48ePNJ06cMN+4ccNsNpvNvXv3Njdt2jR8/atXr5rjxYtn7tGjh/n8+fPmadOmmW1tbc2bN28OXycuvFeiY7/E9veK2Rz5/WI2m8PXL1SokLlx48bmEydOmM+ePRv+/X379pnt7OzMY8eONZ8/f948cOBAs729vfn06dMx+rN9jujYLz4+PuYtW7aYr1y5Yj527Jj5m2++MTs5Ob21jqWL7H5ZvHix2c7Ozjxt2jTz3bt3wx/Pnj0LX8dSf7+o3MSQKVOmmNOlS2d2cHAwFy1a1Hzw4MHw75UtW9bcvHnz8K937dplzpEjh9nR0dGcJEkSc9OmTc23b9+O1HPGFlG9Xxo2bGhOlSqV2cHBwZwmTRpzw4YNzZcvX46pH+ez7dy50wz85/HPfmjevLm5bNmy/9kmf/78ZgcHB3PGjBnN8+bN+8/zxvb3SnTsl9j+XjGbP22/vGv99OnTv7XO8uXLzVmzZjU7ODiYc+XKZd6wYUPM/EBRJDr2S5cuXcL/DaVIkcJcvXp18/Hjx2Puh4oCkd0vZcuW/eD6/7DE3y8ms9lsjpZDQiIiIiIG0DU3IiIiEqeo3IiIiEiconIjIiIicYrKjYiIiMQpKjciIiISp6jciIiISJyiciMiIiJxisqNiIiIxCkqNyIiIhKnqNyIiIhInKJyIyIiInGKyo2IxAmbN2+mVKlSJEyYkCRJkvDll19y5coVo2OJiAFUbkQkTggMDMTLy4ujR4+yfft2bGxsqFOnDmFhYUZHE5EYplnBRSROevToEcmSJeP06dPkzp3b6DgiEoN05EZE4oRLly7RqFEjMmbMiKurKx4eHgDcvHnT2GAiEuPsjA4gIhIVatasSfr06Zk1axapU6cmLCyM3LlzExwcbHQ0EYlhKjciEus9fvyYixcvMmvWLEqXLg3A3r17DU4lIkZRuRGRWC9RokQkSZKEn3/+mVSpUnHz5k169+5tdCwRMYiuuRGRWM/GxoalS5dy7NgxcufOTdeuXRkzZozRsUTEILpbSkREROIUHbkRERGROEXlRkREROIUlRsRERGJU1RuREREJE5RuREREZE4ReVGRERE4hSVGxEREYlTVG5EREQkTlG5ERERkThF5UZERETiFJUbERERiVP+H81lPO38/upLAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_mncontour(\"a\", \"b\");" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
a b
a 0.0138 -0.0196 (-0.843)
b -0.0196 (-0.843) 0.0393
" ], "text/plain": [ "┌───┬─────────────────┐\n", "│ │ a b │\n", "├───┼─────────────────┤\n", "│ a │ 0.0138 -0.0196 │\n", "│ b │ -0.0196 0.0393 │\n", "└───┴─────────────────┘" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tilt of ellipse indicates anti-correlation\n", "m.covariance" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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7xJshMlV2iF4B0X9+4NkhrrztafqyVcDh+P3aueLMg+htiU6KiwcaKiPNjnnfZ0dBxIggCIIgaKgAs4GBAbLZLJZlkUwmaWlpIRQK4ff7G8LC9IkZaPRn6EJBVSd0EdLMw2EYBkO5Clfd8Qz3PDMIGPQkQ3z+jAM5aVFPw3m6D0R/bb0a4j1nqmmamUTEiCAIgiD8HcuyGBoaYnh4mHw+TyAQoL29nUQi0TCyq4/p6m0ZoGG3jL5Xplmcu/c5ddvhhj+8wr//9nnyFYuA3+D8w3fjohP3JxbyN7RidEGhjumjv97qTLM2jIgRQRAEQdiBUN6QbDZLrVYjFouRyWQIhUJuRUQJCT1fSx/F1Y2qKsAMaBAizcZ2HcdhVX+Ozy9/iqfWTwBw8LxWrjpzEYvmtEzaPeN9rnptJUL0CR1vuJouQGZiQ28zRIwIgiAIuzSWZTE8POxWQ2zbpq2tjWQy2XTLLkz9Ia5XOrxiQX+O3jLJlWv8x29f5Gd/WEPdtklFwnzy7fvy7iN2x2dM3sQLNG3TqPfXhYiO109i2zamaTY8PlOIGBEEQRB2WQqFAgMDA4yPj1OpVIjFYrS2thIOh4lEIsBkIaIbVRV69UEFmCmxoCZvdN+G8p3cu3qIL9+xiv7xIhgGpx48l8tOPoCudPQ1qyH6e+rVEL1dpAsW74SN2n3jbSnNFCJGBEEQhF0O27YZHR1lcHCQXC6H4zhkMhnS6TShUMgNLtMnZbyZIAo9xExPOtX30KgPfjU5s3a0wJfveo7fruoHHOa3J/jCaQtZun9XQw6ItxoylfnUW/XQz1HPU0JJGWDr9XpDrP2sSWAVBEEQhNlOuVxmYGCA0dFRisUi4XCYjo4OwuGw6w3xVkP0ULJmyaXKpKqP+OoCRbVoTKvOzx59lWvvf55StUbAH+Cfl+zFvx63F/FIcFI1Y6pqiBe9EuM9R03WKGPrVO0YqYwIgiAIwnbGtm3Gxsbckd16vU4mkyGRSBCNRgkGg+6kjF7t8CajKoGhWLFiBUuXLiUYDE4ys6o2Tb1eZ+W6Cb54+2pW9Y0BBofv1ckXTlvA/j1p93zvBMxU1ZBmWSHe6oi6Xsuy3Ov1jgXrf4f+83Qz2eEiCIIgCDsZ1WqVtWvX8sorrzA+Po7P56O7u5uWlhZ3bFeJCaBBgOhBZADLly/nzW9+s/vaZ599NosWLeK2225rqGCo548XK1x522re9YNHWLV+lJZYhGvOPpifvf8w9u9JNx3T1Q2p3seASUKk2Y4Zy7IwTdMVGbrYUI/pU0EziVRGBEEQhGmjWCySSCSAjebReDy+Xd/PcRxyuRx9fX1MTExgWRapVIpkMkksFnMFiGqjqIqC8ovolQSAW2+9lfe85z2TWiH9/f285z3v4Re/+AXvfOc73dbIXc8McfVdq9mQKwEGZ79ldz5z0n60J8Lu9Sl04aFXV7wJqs3wRs2rdox6vjpHiRDHcSiXy2SzWfd5tVptq+/zG0XEiCAIgrBTUqvVGBwcZHh4mEKhQCAQoLOzk1gsRiQSwefzud4Q77RMs+qEbdt8+tOfbioIlGD5zGc+w5lnnsn68QpfuP0ZHn5+CAyDvTpSXHH6Qhbv1dY0il0XIt5gs2bne/NMdOGhvCv6eLESKJZlUavVyOVyDe0bgFAotO1u/hYiYkQQBEHY6chms+5emUqlQjqdJp1OE4vFGqZl9IqC7hHRUR/oK1asoK+vb8r3dByH9evX86lv/x/3jrVQMW3CwQAfPnZvPnTsnkSCgUmVDv25+nU0a9Oo89QxFSGvhIieGaISXw3DoFar4TgOlUqFQqGAaZr4fD6SySS9vb2USiUikYgYWAVBEARhW2CaJhs2bGBoaIh8Po9hGPT09BCNRgmHwwQCAQKBwKRttV5hotBzOQYHBzfrGv7voaeI7X80i/dq48ozFrFnR6JpDkgzP4guQrzoplrAnZDRx3P12PlarYZt25TLZSqVCqVSiUAgQDQaJZlMkk6nicfjMypCFCJGBEEQhJ2CQqFAX18fY2NjVKtVotEora2tRKNRIpGIO7Kro++V8WZ76COxhmHQ3d29WdfRmungy/9wCGce3DvlrhiY7P/QxUqztowSUO4em3q9YUpG/T3VatUVUIVCgVKphGEYxGIx4vE4LS0tJJPJHUKEKESMCIIgCLOaer3Ohg0bGBgYoFAouAFmqVTKzQ4BGiZd9EAzdcyb6WFZVsNUzJIlS5gzZw79/f1TGkmTmS5+/+//Slsy2hCCpldDFN6qyGuZVPVNwSrG3esLUddrWRaFQoFKpYJt20QiESKRCJlMhmQyucPso9ERMSIIgiDMWkqlEv39/YyMjFAqldw4d2VS1Q2q6sNeHdOrFPrIrPqA9waYBQIBvv71r/Oe97wHDAO8osEw+OF3r6U9FXMrE6oy4n0/b8tG4TWmqnOVAVX5PdRkjBumZppYlkWpVKJcLlOr1QiHw6RSKdLpNKlUimAwuH3+R9gGSM6IIAiCMOuwbZsNGzbwwgsvMDAwQLlcpqOjg66uLlKpFLFYzPWG6JMyujdEzw9RIkCvhujTNUpcnHrGWbz7s9/AH880XM+cuXP53xtu4KyzznIrGHpmiV55ea12jDofNo31qmqH8oaoSHe1PbharVIsFhkeHmZiYgKAdDpNV1cXc+fOpa2tbYcWIiCVEUEQBGGWoeLc1chuOBymp6fHnZQJh8OumNCrEaoiotCrEOoDXx/rVf4StVNmxd828MXlT/OquQfdH/weuT/dSv7Pt7H8/37OCSec4Ca3NquG6AZW/T29eAWS8qzoaalqQsa2bWq1Gvl8nlqths/ncyshLS0thMPhHcoX8lqIGBEEQRBmBY7jMDo6ysDAABMTE9TrdVpbW90PXjUto3/4N6twqNcCXF+HEhFKeASDQVc8jJVMrr5zJbeu7Mfn89EeC/CpE/bjPde+C4Cjjz66oQqivr/WhEyzxNRm4kO/PthUFbEsi2w2S6VSwTAM4vE48Xic1tbWHWZCZksQMSIIgiDs8FSrVfr7+9mwYQOlUolgMEh3d7c7suvdCwNMMqk2m2hRH/jqd9XO2NgegV8+/ipfu/c5cpU6hgH/+JY5fPLkBfisTTHqzXbKqOP6e3nPVdeoL97Tw8v046pyU61WKZVKFItFfD6fO6a7I07IbAkiRgRBEIQdFsdxGBsbo7+/n2w2S61WI5FI0N7eTiQScbfs6rkhuulUfx31XVVAlMFUtWR0kfC3oQJXLH+Kv6ybAAwO6Epw1TsP4k3zW3Ech6ImRryTMN7pGP399WsAGiZkdCGi+1hqtRqmaVKpVMjlcgCEw2ESiQQtLS20tLTMWhGiEDEiCIIg7JCYpklfXx8bNmygWCwC0N3dTTwedydlmrVhvMFiOmoKRX8sFAq5H/zlWp3v/u5F/vuRNZh1h1jYz0VL9+GCo/fEZzSKGh29iuEdF9a/67kjujdEXZO+C0eJEFUNqVarhEIhEomEO6a7oxtTNxcRI4IgCMIOx/j4uOsNKZVKDdUQ5Q1plpehBEqzSoQKCdMna9TjAL97fgNX3f4M68fLYBi8fUEXnz9tIb0tUWBTTHuzqkezHTLelpCqwgANYkTtklHju4A7pqvyQiKRiGtMVR6ZnQkRI4IgCMIOg4pzHxwcJJ/PA7jjuqFQiGAwOCknRH3pkzDeJFW9GuI1mQ5MlFj2m+e5++l+HKA3FeGyUxdw0qKehqqG93V1plpmp6em6hUR1SZS56mfVTsmn8+70z0qJ6S9vZ1QKDTrWzLNEDEiCIIg7BBks1n6+/sZGxujUqkQjUbp7OwkGo26IkQJDl1QTLVXRgkHNfaqBIN6DdOq879/XMu3fvs3clWTgM/H+Yfvxsffvi/xcMB9jdfaKaN+1ysu+nFvJQRwhZESJT6fr6Edo+Lbo9Eo6XSaTCZDNBrdKUWIQsSIIAiCMKPU63WGhoYYHBwkl8th2zYdHR1uOyIYDDZUQLyVEB3vpIyKflcEg0Hq9TpPr5/gqjtW8+TacQAOmd/KF09fwKI5LZNaPN4xXe97NjumnqsEh/pdb8mo6yuVSlQqFbLZLIZhEIlE3LyQdDq9U4sQhYgRQRAEYcbI5XL09/czOjpKuVwmGo3S1dXV4A3RqxLe6oaOLiL0FFTHcQgGgziOQ65U5T/uf5GfPvYydRzS0RAXn7AP5x42n4C/eViZPtkylTDwZph4x3X1loxpmq4wKRaLZLNZHMchEom45tR0Oj2p0rIzI2JEEARBmBFUJUQFmLW3t5NKpdwtu9BoMm2WpNpsZNc0zYY0VGVevXf1EMvufo6+iRI4cMrBvVx60n50t8QarksXJLrvZKpJGt2Yqvwf6nzl+9BDzGq1GuVy2W3JqG26bW1txOPxnc6cujmIGBEEQRBmhFdeeQXbtgmHw8yZM8fNDfFu2YVGb8hU/hC1RE4JGJWium6syJfufJYHnh3EAXZri3P5KQdw3H6drpBotshOr8C8VpS7NytEXYs6t1arud6VSqXiipBgMEhbW9tOOyGzJYgYEQRBEKYF27YZGhpyf8/lcsyfP59MJkM4HHYDzIAGn0ezEDOYPLKr0kzV+TXT4uePvcp1D7xAsWoR9Bt88Oi9+PCxe5KIhhoCyppFt081OaMe1/8u5f/wjuyqiojyhCgzrUpN7ejo2GknZLYEESOCIAjCdqdYLDI4OMgrr7ziHtt9993p6OggGAy6wWO6CFAmVSVEvCbSZiO76vwn145zxa3P8OzgxsTSw/bIcMXpC9m3K/ma1RD1WgpdkHirJjqqKgI0iJBqtUqhUHBD26LRKC0tLbS1tRGLxXZ5EaIQMSIIgiBsN2zbZnx8nHXr1jExMUE2m3UfU/4IZS5VW3KBptMy3vAw1ZbRzynU6nzzN6v437+sw7EhHQny2XfszzvfNIdgMPC61RB1zbpHRF1Ls1wQwA0rUyLEtm2q1Sr5fJ5isYjjOO6YbktLyy4zIbMliBgRBEEQtgu1Wo3+/n6GhobI5XKEQiHmzp3rPr5y5UqOP/74pvkcqk2jG0n1toweKKbGd2//az9X37WakUINcDjzkDl89h3705GMNIgOhXeRndesqh9XQkP/UliW5U7IlMtlKpUKhUIB0zQJh8Ok02laW1tJpVINgkvYhNwVQRAEYZszPj7O2rVrmZiYoFKpkMlk+NOf/sSXvvQl95xzzz2X3t5errnmGs4888zXzA+BjT4S1ZbRI93XjZW5YvlTrHhpFIA9MlGuOGMRR+7d3jDp4k1S9fo+pmrP6MZUmFwZMU2TQCBAPp+nUChQrVaJRCJuO6alpcU15QrNETEiCIIgbDPUcrvh4WGy2SzBYJB58+bx8MMP85GPfGSS12JgYIALLriAn//857zzne983STVQCDgCoO6Y/DD373Id373AqYNQR/8y7F78y/H7kUkuLGyontD9MqH4rWqIV4RordplCgC3B0y5XIZv99PS0sLra2ttLW17dITMluCiBFBEARhm6C8IdlslnK57FYGIpEIV155ZdPJFCUAPve5z3HmmWc2mFVh014Z1bqxbZtgMMifXhnjCzf/lRdGSgAcvlsrV77zQPZsj08KLdPHdNV7wiaPiHezrh7hrs5XwkR5Q9ToLsCGDRuIx+OkUil3h0wkEhFfyBYgYkQQBEF4Q5imyfr16xkeHiaXy+H3++nt7XWX2/3xj3+kv79/yuc7jsP69et55JFHOPbYY10DqR5gVq/XCQQC5CoWX13+V375xFp8Pj+tUT+XnLKQs940p+H1phrT1dszymuiiw/dV6LOr9frrvhQO2TGx8fd86LRKD09PbS1tZFIJESEbAUiRgRBEIStZmxsjL6+PiYmJigWi7S2tpLJZEgkEgQCAQKBAMPDw5v1WgMDA64A0Ed21aTN8if7uOau1YyVLcDgnEN7+ezJC0hHgwBNqxtez4iOEhpAgwfEu0umXq9jWRaWZbkTMhMTE+75u+++Oz09PbtUfPu2RsSIIAiCsMWYpsnAwAADAwPkcjl8Ph9dXV20tra6O2XUlt3u7u7Nes2enh43QEx9sPt8Pl4ZLfGF257hj2tGAYN92qNcdfYhvHX3TEPmhxIXevXDO0GjUM/RfSC6mFHjuqZpYpom5XKZbDaLZVkEg0EymYz7Wi0tLSJE3iAiRgRBEIQtIpvNsn79esbGxiiXyyQSCdrb24lGo4RCIQKBQEOC6lFHHUVvb69b+fBiGAZz5szhyCOPdFs0hmFQqzv88Hcv8sMVL1G1bMJBHx9Zui//fMxe+I3GsdypfCEKdVyJE12EKPRKiBIienKq3+8nk8nQ0tJCNBrdhndUEDEiCIIgbBZ6NSSfz+M4Dp2dnbS0tBCJRPD5fA1bdvWvr33ta5x33nmTItaVmPjqV78KbIqB/8OaMa68fRUvDecxfD6W7NvBlacvYn5brOkiO12UNAtKUxUTfUJGn7TRU1OVCMnlcpRKJfx+v7vIrqOjw52QmSoqXthyRIwIgiAIr0sul3OrIcVikUQiQUdHB/F4HL/f77ZlAHdjLmwyiZ599tn4fD4+9alPNZhZ58yZw7JlyzjzzDMBGCvW+Oo9z7P8yfUbxU4qyqWnHMBpB/U2CA9vYBk0ChHvJA3ghqWp64LJ0e31ep18Ps/ExAR+v59EIkEmkyGTyRCPx8Wcup0wnFkg7XK5HOl0mmw2SyqVmunLEQRB2GUwTZOhoSH6+/vJ5/PU63U6OztJpVLu5IheDdHzOtTvsKlCkcvlXA/Jr3/9a44//nhCoRCWVWf5ygG+es9zTJQq+Hx+3nPYbnzixH1JRYJTRrd7BYr+mBIi3sAy9ZhlWW47plqtUiqVyOVyDfHtbW1tpFIp8YRsJZv7+S2VEUEQBKEphUKBtWvXMjo66lZD2tvbicfj7qbZYDA4aWeMz+dr8Izo4kA/vmTJEvx+P38bynPVHav545oRABbOyXDlGQt50/zWpqO23p+9v+siRL2/XhFRkzFKkORyOQqFArVajUgkQjKZdAWXxLdPD3KXBUEQhAZs22ZwcJD169eTy+UwTZOuri7XGxIMBl3B0Sy3QxlQvRMtepw7QKlm8f1HXuTHD6/Bcmzi4SAXHb8v7z1iPsFAY/6HV4Q084UoAaJ7SfSfVZumVqtRrVap1WqMjo66O2RaW1vp6Oigra1NRMg0I3dbEARBcCkWi7zyyitMTEyQz+eJRqPMmzePaDRKOBxuEBt6S0ZNsnij1vX2Sq1W27RTZu6B/H///STrJsrgOLx9US+Xnbw/8zLxBiGh3sdb5fCmp061xE69jkpNVS2ZbDZLqVQiFAq52Sjt7e0S3z5DiBgRBEEQsG3b9YZks1lM02yYlPH7/W5LRo9m1xfW6f6QZmbTQCDAhlyFllMuIrH/0awbL9HbEuOyUxdw4oIuV3R498V4J2TUcfWlJmXU36EeV+bUWq3mBpcpAy5AKpWipaXFNeIKM4eIEUEQhF2cQqHAunXrGBsbo1AoEIlE6OnpIRaLuSO7uhDRKyHNtuzqW3FVa8TB4H/+8Crf+u3zxPc7Gseuc/4Re/CJk/YnFQ01bML1ZoLoVRDvdEyz91SmVL0lUygUXHNqLBajpaWFzs5O4vG4mFN3AESMCIIg7KKoasjAwIAb7NXe3k4mkyEUCk3yhnhRZlRvy0RhmiY+n49nB/Nccdsqnu7LAlAbeImRe7/LxZf9hWQ05IoKXUzor6t+1v0g+lSM93HLsqjVatRqNQqFAvl8HtM0icVirjk1nU43mGmFmUXEiCAIwi5IoVBg/fr1jIyMuNWQ+fPnk0wm3ZaM8oDo0zKAO87rpV6vu+0b27ap1uHau5/lf/60lroNiXCAjx63FxeedE7Dc9Rre1syXj+IXgnR20AqQl4FlikhMjIy4sa3t7e309HRQSaTEXPqDoj8LyIIgrALYds2GzZsoK+vj2w2S7VadashalIGcD+wm02ueMd21S4Zn8/nxqbft3qIq+98lsFCDXB4x6IeLj91AQl/nQv//lzvOK73vfSFdcoXolde1DE1qmuaprvErlqtEgwGSaVSdHR0iDl1B0fEiCAIwi5CsVhk7dq1DA8PUyqVCIfD7LHHHu5OGWVC1ZNUoXGqxeuvUNkhamR3Q8HkS7c9yW+fH8bn8zEnHeaLZx3Ecft24DgOhULBfW6z8dxm1RCvYNEfr1ar7oTMxMQEpVIJwzBIJpNufHssFtt+N1XYJogYEQRB2MmxbZuRkRHWrl1LPp+nUqm4EefhcLhhrwwwyZCqt2uaTa2YpomDwU8fXsN/PPgC1bqPoN/gA0fvyceO35dwoLkPRKHMsHpOiPru9ZColoxeDcnlcoyOjro7ZJLJJD09PW5CrLDjI2JEEARhJ6ZUKrF27VpGRkYol8v4/X522203YrHYpGqIN8BMtWS8SadAwxjuU305vnjLX3l2uIxhGBw6N8mXzj6EfbsSDaKiWQaI97X05FT9/VRVpFaruUJkYmKCbDaLbdtufLsKZ5MJmdmFiBFBEISdEFUNWb9+vesNyWQytLa2EolEXCGip6jqH+B6S6bZpt16vU6uYvKte57jf/+yDsMIkI4F+PTb9+fcw3bDtutNd8J4WzNeY6o33EyfkFFf+Xye8fFxarUa0WiURCLhZqKEQqHtd1OF7YaIEUEQhJ2McrnM+vXrGR4eJp/PEwgEmDdvHolEgkgk4lY89JwQaPSGeNsbutnUNE3ufmaIZXetYrhoAQZnHNzNpacuJBML4ji2u7nXG36mv66agFGPKyOsEiFqd4xlWdTrdUqlEuPj4xSLRUKhkNtq6uzsFHPqLEfEiCAIwk6C4ziMjIywbt06stks5XK5YVJGtWICgYArONQorhIJzTJFdJPqqyMFvnT3s6x4YQQDH3u0x7ni9EUcvU/7JG+HdzpGvZb+szf6XYkQwB3RrVQqrjnV5/ORTqfJZDJ0dXWJOXUnYYuaasuWLeOtb32rGxpz1lln8fzzz7/mc66//vqG1D7DMIhEIm/oogVBEIRGKpUKL774Ii+88ALDw8MA7LHHHnR2drptmUAg4H5XlRE9SVWJFdiU+6F+LpTK/ODBFznjuw/z0PMjhP1+Ljxub27/6BKO2rutwYTqrYBAo/dDoYsgJUzq9Tq1Wo1isUipVGJsbIz169dTLBaJRCJ0d3ez9957s/vuu4sQ2YnYosrI73//ey688ELe+ta3YlkWl156KSeeeCKrV69+zVz/VCrVIFrE3SwIgrBtcByHsbExXnnlFXenTGtrq7t5VokPvSLiRflDvLkfgUCAWq3Gn14e5ct3PctzQ3nAYPHe7Xzx9IXs3ZlsqGjogkS/PtgUbtbMe6KOKXNqtVoln8+Ty+VcX4iKb89kMvIZshOyRWLkN7/5TcPv119/PZ2dnfzlL3/hmGOOmfJ5hmHQ3d29dVcoCIIgNKVarbJ+/XoGBwcplUoEg0Hmzp1LNBolEAgQDofdEDO9/aJ/9waY6YJkcCzHtQ+8yP89vhZ80J6I8dmT9uesN/U2iJpmRlX9ux5YposRfYy3Vqu5oWWjo6NUq1UikQjt7e10dXWRyWTcv0XY+XhDnpFsduOegUwm85rnFQoFdtttN2zb5tBDD+Xqq69m4cKFU55frVapVqvu77lc7o1cpiAIwk6F4ziMjo6ydu1aJiYmqNfrJJNJ1xui9spA8/0x+vSMt5qhqhW3PLGOb9z7N4YLZTD8vOst8/nU2/elLRlxr0E3vHrHd72BZc1QYWWWZbktGRVN39raSkdHh5hTdxG2WozYts1FF13EUUcdxaJFi6Y8b7/99uO///u/Oeigg8hms3zjG9/gyCOPZNWqVcydO7fpc5YtW8aVV165tZcmCIKw01Iul+nv72dwcJBisUgwGKSnp4d4PN4gQmBjpLvjOG6YmXdaplnU+wuDWa66cxWPvjAMhsF+PRm+cOoBHLZHpmFCRhcfCm9gmcoPUe+tAssUtVqNer3O+Pg4+XwewzBoaWmhtbWVrq4uEonE9r6dwg6C4TRLoNkM/vVf/5W7776bhx9+eEpR0QzTNDnggAN497vfzZe+9KWm5zSrjMybN49sNksqldqayxUEQZjV6NUQ5aVQwwTBYJBgMNgQYKbaKF4jqndsVz1Wtep8/4EX+MFDL1Gr14mEQnxk6T6878jdiIY3CpxmS+y823R18aHOU8fq9TqFQoHdd98dgBUrVmBZFrZtE4/HSafTdHd3k06nxReyk5DL5Uin06/7+b1VlZGPfOQj3HHHHTz00ENbJEQAgsEgb3rTm3jxxRenPCccDktZThAE4e+Uy2X6+vrYsGED+XyecDhMb2+vWw1R4sPv9zcsuIPJe2W87Rifz8fDL2zgylufYs1IEcPn45gDevnCKfuze3sCn8+HZVkNlRTdD6ILkWb/batv1K3X6+TzefexsbEx2tvbSSaT9Pb20tLS0uBhEXYdtkiMOI7DRz/6UW655RYefPBB9thjjy1+w3q9ztNPP80pp5yyxc8VBEHYlXAch+HhYdatW0c+n6dardLa2kprayvhcBifz9dQEfGO6ip086oevT5eMvnynU9x28oBDAzakzEuPXUBpx3YTSAQcIWEas/oQkSfglGiRL2/blhVlY9yuUy1WmVgYMC9rvb2dnbbbTe3uiPsumyRGLnwwgu54YYbuPXWW0kmkwwODgKQTqeJRqMAnH/++cyZM4dly5YBcNVVV3HEEUew9957MzExwde//nVeffVVPvjBD27jP0UQBGHnoVQqsW7dOoaHhykUCoRCIbcaotoywWCwoerhbW3ooWaqbbPRs2Hwy8df4ev3PUu2VMfA4d2H7cYnTzqAlljIPU+fmPEKD/1n9f56lcQ0Tfd7uVxmYmKC8fFxQqEQTzzxBO3t7bS3t7ufHcKuzRaJke9973sAHHfccQ3Hf/KTn/C+970PgLVr1zb8Ax4fH+ef//mfGRwcpLW1lTe/+c08+uijLFiw4I1duSAIwk6IvlNGTcqoxFE1sqv2r+hCxNuC0cWJ3+93qxUvDRf5/K//wp/W5jAMg/07E1x19sEculvGfX9dXKjvqq0DjcJEFyGqkqJi3FVy6tjYGH6/363qdHR0kE6np/vWCjswW21gnU421wAjCIIwm9GrISo3JJPJkEgkCAaDbmtG+UP0ZXZebwhsMpk6jkOt7vAf9z3Hjx9+kbrjI+x3+PjxB/CBY/bCZzQaUnVjqhIhehvGmyWiL7Or1+uUy2VyuRy5XA7TNInH4ySTSebMmUMqlZKNursQ29XAKgiCIGw7bNtmeHiYvr4+d8Nua2srLS0t7k4ZPcL99Uyq+sZbwzBY8eIoV/z6SdZlKziOw9v2a+OL7zyE3nSkwQei/6zHs+vobRrA9YTYtk2lUqFUKrmhZeFwmPb2djo7O+ns7BRzqjAlIkYEQRBmkGKxyPr16xkdHXU37KoUVSVEVCVERbor8eFdbuetXAznq3zpjme4+6k+HAy6kyEuP+NgTj6wZ9KCumYGVf1xPbpdb8soEVKtVhkbGyOXyxGJRMhkMnR0dNDT0+OKJ0GYCvkXIgiCMAM0q4a0tLTQ1tbWsMzOm6QKNAgRZVLVRYhtO9zwx1f5xj2ryVdMfMD5R+zBp05dSMS/SWAomk3I6Me9qaqqJaOSU3O5HOPj4/j9fjKZjISWCVuMiBFBEIRpplAo0NfXx+joKIVCgWAwSG9vL8lk0hUh4XB4kgdE91roJlV9/HZVX5Yv3Po0K18dBQMOmpPmS2e/iYW9KddwqtBbMnrrRRcrXhFSr9cbltmNjIzgOA6JRIJMJkNXV5eElglbjIgRQRCEacK2bTZs2MDAwADj4+Puht1UKkUsFsPn8xEKhVxhobbt+v1+CoWCu3B0w4YNpFIpVzQEAgGyxQrfeeBFfvLoS9TrNolIgE8cvx/vPWpP/L5Nu2igsdKhRIhuWFXXqk/IGIbhjukWi0XGx8cplUqkUimSySQ9PT1kMhkxpwpbhYgRQRCEaSCfz9PX19fgq5gzZ46bG6LHuPv9/qZBYwo93Kxer3Pf6iG+fMcq+sbyYBi846BeLn3HAnpbY+75wKR8EIUSId7odm81JJ/PMz4+TrFYJBKJ0NnZSVdXl4SWCW8YESOCIAjbEdM0GR0dpb+/n4mJCUzTpL29nVQqRTgcJhAIuOFlSowoAaJXGbyeEYC1I3m+cvdz3PtMPzg289oSXHbKAo5f0N0w3quLkGbR7d79MpZlARsFSa1Wo1arMTo6Si6XIxAI0NLS4ppTI5HIdr+Hws6PiBFBEHZqisWia6QsFArE4/Fpe+9cLkd/fz8jIyPk83lisRhdXV1EIhG3HaOqIvpYrtcb4t2uW7Ms/u/hl7nu/ufJV6oEDIP3H7U3Hzl+HxLRUIPnQwkRXWzo6O0a/cs0TarVKtlslrGxMRzHIZlM0traSk9Pj2Q+CdsUESOCIAjbGNM0GR4epr+/n2w2i2madHR0kEqlCIVCrhDRKyHqS2/P6PtkFL72PTjvJ0+weiAHjsOhu7VyxRmLWDindVLiKmwSIepnffxXN6eq82u1mtuSGR0dxTRNotEora2tdHd3k8lkxJwqbHNEjAiCIGxDstms6w0pFAqEw2HmzZtHKBQiHA67i+1U9sZUce6wqTXjOA6Fap3kce8nefBJrB7MkY4G+OQJ+/Ouw+YTCgUb/CUqMdWblOqNdNeDzer1OpVKhVqtxuDgIJVKhWg0SkdHB11dXbS3t4svRNhuiBgRBEHYBpimydDQEENDQ2SzWer1Om1tbSQSCbcto7bsAg1BZqpCoVdCdHFy19MDfOm2p0gecjKGY/OOBR1cdtpB9LTGXb+Hd0eMN1lVb9N4WzLVatUNLVPBa5lMhvb2dnp7ewmHwzNzU4VdBhEjgiAIb5BsNsv69evJZrMN3pBwOOxupVUVEV2E6BUQJUbUzwDrx8tcsfyv/P6FEcDBHO1n7P4f8pVLHiCd3uh9Uc9T1RCFGsf1JqbqYkT5QnK5HCMjI/h8PuLxOJ2dnfT09Eyrv0bYtRExIgiCsJXUajWGhoYYGBigUChg2zYdHR3EYjF3w66qhijfhxIi0LhbRp+iqTvwX797ge88+DwVEwLUueDw3bjq/zsXcAiFNplU9cAyJUqmCixzHAfTNN0pmWKxyMjISMMyu/nz50tomTDtiBgRBEHYCsbHx+nv73fDv2KxGK2trUQiESKRiJsbou+N0U2qgJucqloyhmHw51dG+cLNT/H8cBHHcThsfpIr3/kmumNwFZuMqOr5emXEmxWiAssAN77dtm03OVX5QlpaWujt7aW9vV2W2QkzgogRQRCELaBSqTAwMMDw8DD5fB6goRqi8kJUcBngJqkq0aE8Huo4QK5s8dXfrOaXf14HGLREDD5z0kL+31vmYRgGlUrFvQbdoKr7TfQ8EW81xDRNarUaY2NjjI+PEw6H3fj27u5uQqHQ9N5IQdAQMSIIgrCZjIyMMDAwQDabpVgskkwmSafTDbkh3mqIXhFRXhA9T8RxHJY/sZ5rfvMsI4UqDnD2IV187uRFtMSC7pZe7+4Y7+96S0YJFVUNqdVqjI+Pk81mAWhpaaG1tZU5c+bIMjthh0DEiCAIwutQqVRYv3494+Pj5HI5HMdxDapeIaLaMKoq4g0s09s0L23Ic+Udq3nkhQ0A7NUW44ozFnHEXu3ARn+Jbjr14t0xo++SqVar2LZNoVBgZGQEy7LclszcuXPFFyLsUIgYEQRBmALbthkbG3Oj3MvlMslkkpaWFkKhEMFg0B3Z1adkvEFmsGnqxe/3UzEtfvjgGr6/4iWqpknI7+ffluzBB4/bh6B/UwKr3mrx7pLx5oQoI2u9XncX2m3YsIFarUYwGKS9vZ2enh46OjrEFyLscIgYEQRBaEKxWHQNqtlsFr/f7+5iUSmq+lI7b3iZbibVR3YfeWGYq+5czYtDWcDHUXu1c8XpC9m9PeF6SZTpVM8Q0asYSnSo796WjPKzhEIh0uk03d3d4gsRdmhEjAiCIGiYpsn4+DhDQ0NMTExQqVRIpVIkk0nC4bDbktH3ygCuIIFN8e2O47jVktFCla/e8zw3/2UtODZtiSife8d+nH5w76QYdyVIlKDxZogoEaKqII7jUK1WmZiYYHx8HMMwSKVStLW10dPTQzKZnOa7KAhbhogRQRCEv5PNZtmwYYObRBoKhejs7CQejxMMBl3xoce5q+kZPe1UVUZUleTXf+njq/c8y3ixgs/n49y37MFFx+9NS2xTXoh3KkZPVAXcaglszDdRj9frdbd6Y1kWkUiEdDrNvHnzaGlpEV+IMCsQMSIIwi6PinIfGxtjYmKCWq1GKpVyF9sFg8GGcV3VNtGrIV6TKsBLw0WuuO0ZHn95FMdxOKCnhStOX8Cb5rW4AkSJClUNUa/nTUvVKyOmabrjvsPDw5TLZXeZ3Zw5c2hvb3fFkiDMBuRfqyAIuzQTExMMDg66Ue7BYJDOzk5isRiBQMBty6gKiFeEQKMQcRwHyzH47gMv8qOHX6RmOcRCfj72tn14z+HzCAcDro9Ef763GuIVIpZlueeXy2XGx8cb9sh0dHQwZ84c8YUIsxIRI4Ig7JKoKPfR0VG3xeGthgSDQVeEqPYM0DApo4/X+v1+Hn5xlCtve5q14xXA4fgFXVx28v7Madm4o8a2bdcbohtcm+2OUZMylmVRrVbda1+7di2xWIxkMkl7eztdXV2kUqmZuI2CsE0QMSIIwi7H2NiYa1AtFotEIhEymQyxWMz1hQQCAVeENItyV14O2ChOxsoWX7n9Ke58ZhCA7mSYy05byPH7tbkVD/VcVQVRr6FXQPSsEFURqdVqbmAZQDgcpquri7lz59La2iq+EGHWI2JEEIRdBuWxUIZP27ZpbW0lkUi447p6aJn60rNDdBGy0XDq44Y/vso373ueYs3GZzicd/jufOyEfYgFfW51Q/eG6Lkh3iwRNSXjOA6VSoVqtcrY2BgbNmxw33efffZh/vz5BIPBab+HgrA9EDEiCEJTisWiGxVeKBR2inXyL730EpZluYvtUqkUsVisYamd/gU0iBC9AuHz+Vjdn+OKW5/hqf6NqayLepJceeaBLOhJuOJCiRv1XH2Jnd6KAahWq+7zSqUS2WyWXC6H3++npaXFfe/u7m4RIsJOhYgRQRB2akqlkvvz0NAQiUSCTCbjjuv6/X7XpNosRVVNvOj+jrJZ5zsP/I3r//Aqtr3RoPqJ4/fhvYt3p26ZruDQw8r0aoi3JWOapnusUqlQKBQYHx/HsixisRhtbW0NYvChhx7ixBNPlCRVYadBxIggCDslpmkyOjrKK6+84h6Lx+N0dXW54iMcDrsTMsofAjR4RFR7Rf18/7Mb+PKdq+nPVsAweMeCTi4/bRHt8QB1y2wIMNPNrYCblKqCzBzHoVaruSbVYrHI6Ogo1WqVWCxGe3s73d3dPPTQQ1x00UXu33HKKacwd+5crrvuOs4+++xpu6eCsL0QMSIIwk6HCi9T3xUtLS1EIhF3p4wSGHqcuz4po1c4BrJlvnL3c9y3eggc6E1HueKMBSzdr9MVFGqiBja1Y3QviPrdNE3XG1Kv1ykWi+TzeSYmJohEIrS3t9PZ2UlXVxd33HEH73rXuxq8KgB9fX2cc8453HTTTSJIhFmPiBFBEHYaTNNkcHDQ3a5bq9Vc3wvAU089xdve9raGvTJ6a0ZvqcBGEWJadW54fD3X3f83irU6fr+P9y/ejY+8bW9Cvo3vqaoggcCmDBFvVgiAZVkN1ZBqtUqhUGBsbAzHcWhtbSWTydDb20s8Hqder/Pxj398khBR12gYBhdddBFnnnmmtGyEWY2IEUEQdgqy2WxDeFk0GmXlypV89atfdc95//vfT09PD8uWLeOMM85wRYQuRAB34uXpvixfvH0Vz/TnAIND529MUN23M/H3Ssem9FV9VFdVSfRJGeULqdVq2Lbt+kKUYEqn0/T29jZEuK9YsYL169dP+Tc7jsO6detYsWIFxx133Da/p4IwXYgYEQRhVlOtVhkaGnLHdVWF4eGHH+ZTn/rUpKrC4OAg73//+/npT386qaKgKhq5co1v3/8Sv/jTWizHoSUS5JMn7Ms/vGUujmO7gWWqLeOtgugBZpZluW0aNao7MTFBqVRyd990dXXR2dk5qboxMDCwWfdgc88ThB0VESOCIMxKbNtmfHycDRs2kM/n3f0s0WiUQCDANddc85rtjUsuuYQzzjgD2Gg2VYLh3meHWXb3swzmqhjAGYf08tkT96M9EXLNp7q3RL2md1JGhZbV63VqtRrVapVcLsfExATBYJC2tjYymQw9PT1EIpGmf2NPT89m3YvNPU8QdlREjAiCMOsoFots2LDB9YYEg0FaWlqIRqP4/X6eeOKJ16wWOI5DX18fjz32GEuWLMG2bfomKlx1xyp+/8Io4LBne5zLTz2Ao/dudyscqhWjG1x1o6ryhKhqiGmaVKtV8vk8uVwO27ZJpVKuCHm9CPclS5Ywd+5c+vr6mgorwzCYO3cuS5YseUP3UxBmGhEjgiDMGkzTZGxsjNHRUXK5HJVKhXQ67e6SiUQiGIbByMjIZr3ewMAAlu3w00de4T8efJFKrU4o4ONDR+/FPx+zB9FQwJ2CUWO/qoqih5Xp0e1KjFSrVarVKiMjI1QqFeLxOKlUijlz5pDJZBoW7U2F3+/nuuuu45xzzpmU/qqqMtdee62YV4VZj4gRQRBmBRMTE4yMjLgG1XA4THt7u7tZNxwOYxgGwWCQ7u7uzXrNgj/B//vPR3luQwHDgCP2yPCF0xewV0eiIRNEBZ8BDWO6ejtGVUJM06RWq7lbdaPRKB0dHbS1tTFnzpwtTk49++yzuemmm/jYxz5GX1+fe3zu3Llce+21MtYr7BSIGBEEYYemWq2yYcMGJiYmyOfzmKZJOp0mGo26m3VVfLvKCjn66KPp7e1lYGBgyvZGvLWDa5508PuLtEYDfO6k/Tnr0DkADUFlsKkaoioisLFKAxu3/6rrNE2TfD7vGmnb2tpobW2lp6eHWCy21ffg7LPP5oQTTiCdTgNw1113SQKrsFMhYkQQhB0SZVAdGRlxWzLBYJD29nZXhKgNu8FgsGGjbjAY5Ktf/Srnn3/+pPYGbPw9fNQFGIafsw7p5jMnHUBLdOP/HapxXD19Va9+qPaMOk/5QqrVKuPj41Sr1SlHdd8IuvA45phjRIgIOxUiRgRB2OEolUps2LCBXC5HLpcDIJVKEYlE3F0y+nI7PT1VtVPOOuss/ud//odPf/rT9Pf3u6/tT2ZoOe4DLDz8OL541kEcsWdbwxiunheih5Wp70qcWJaFZVmUy2Wy2WzDqG5nZycdHR2yzE4QNhMRI4Ig7DDoBtVCoUC1WiUSiZBIJNx9Mno1BGhYbAeNy+ne+c53suTYpexz/P9HbPeD8CfbScw/gH87bj8+tHRvAsYm34c+IaNv2NWrIurnSqWCZVluS8bv95NOp+no6KCrq+sNtWQEYVdExIggCDsEuVzObcnkcjlCoZDrDfH5fA0R7qpFob7rYkT/+dEXR/jirU/RfvJHwDA4YrcWvnT2IezWtlEseBfXKUGijkNjhLtKUC2VSu7Ejt6SUZ4OQRC2DBEjgiDMKLVajeHhYcbHxykUCpimSSqVcg2qhmG4VZFAYOP/ZXmFhx5A5vf7GSvWWHbXam7+y1oMn0G9mGP0dz/me4/eSjy+UYioiRjYWAFRSap6WJn6XU3IKF9IuVwmFouRSqXo6Ohomp4qCMLmI2JEEIQZwXEcd6uuSlANh8Mkk0m3CqJMqsqcqpbRAe53wPWMAPzq8Vf56j2rmSiZGD4f57yph+vefx7Uq5Mi29XIrqqMqCqIEiSqGlKtVslmsxSLRfx+P+3t7WQyGbq6uohGozNy/wRhZ0LEiCAI006pVGJ4eJhsNkuhUHA9F+Fw2G3DqGqIbkzVv9u2TSAQcEXIC0M5rrx9FX9YM4IB7N+d5oozFrGgM8J19SpAQ8VDvZaajNEfV8KkUqlQLpeZmJigXq+TSCRobW2lt7eXZDI5U7dPEHY6RIwIgjBtmKbJ+Pg4o6OjlEolKpUKsViMSCTiTsbo1RBV8VAmVb0Vo6ZeKmad7z3wIj98+AUsyyYaCvGxpXvzviV7Egr4yefz7vsroaFaKqoSoosQ0zSp1+uUy2V3q24kEnEnZNra2qQlIwjbGBEjgiBMC9lsltHRUXdjbSAQoKWlhXA47IoOVQ0BGqoi6ktvxwCseGGEq257ildGi4DB0gO6ueL0RczLxBvyQBS2bU/KDlHnWZZFvV6nWCy647rhcJhMJkNbWxtdXV1TLrQTBOGNIWJEEITtSrVaZWhoiFwuR6lUwrIskskk4XC4YUxX94LobRRdhKjKyEixxtV3rub2J9fiYNCbjnLZ6Ys4aWG36//w7o8BJrVllAAxTdMd1S0Wi1iWRTqdJp1O09nZSUtLy7TfN0HYlRAxIgjCdqFerzM+Ps7Y2Bi5XI5areZu11XiQ1VDYJMJ1XEcty2jt2c2jtzCjY+v5Rv3rCJbMQn4DM5bvBcXnbgf8ZDfFSJ6gJkKLoONbSL1frVajXq93jAlU61WiUajbiVkcxfaCYLwxhAxIghCU/SKwkMPPbRFu1CKxSLDw8PkcjmKxSKGYZBKpQiFQq4IUVURlaSqJlv0Kojenlndn+WK5X/lyXUTYBgcPKeVq955CIvmpBqqIeq6DcNwM0QUajJGVUPK5bI7KRMIBOjs7CSdTtPT0+OKFkEQtj8iRgRBmMTNN9/Mxz72Mff3U045hblz53Lddde95pbYWq3G2NiYu7HWcRzXoKpyQvTgMlX5gI3tGa8vxOfzUayaXHffs/z0sTVYtkMqHOCitx/Aexfvjs9gUkAZ4Kaoets0lUoFn89HqVSiWq2Sy+VwHIdkMklraysdHR3SkhGEGUDEiCAIDdx8882cc845k7bd9vX1cc4553DTTTdNEiSO4zAxMcHY2FhDZkgsFnNFh75HRt8nAzT1hRiGwb3PDPCVO1bRly0BcNKiHr5w2oF0pcKu/0PPDlEtGf0xtVUXNool0zRdk6oKLuvs7KStrU1aMoIwQ4gYEQTBpV6v8/GPf3ySEAFcU+lFF13EmWee6bZsSqUSo6Oj5HI5CoUChmGQTqddAaLvkFEmVd0Lok/NwEYR0j9R4it3rube1YNg28xpifGFMw/kbft3NcS167khalJGn6JRxlTF+Pg4sHGrb1tbG+3t7XR0dBAOh7frfRUE4bURMSIIgsuKFStYv379lI87jsO6detYsWIFRx11FKOjo+TzefL5PLVajWg0SjQaJRAINEzH6Dtk1M96JUT5Ray6zc8fWcO1D/yNcs3C7zd4/1F789ET9iUa3Pg8PRNEpaiqioiaoKnVajiOQ7VapVgsutdfLBbdwLKenh4SicR2uY+CIGwZIkYEQXAZGBjYrPPWrFnDbrvtRqFQoFAoEAqFaG1tdffIwCbhocSH8oToLRlVyQBYuXaMK297mmf6cxiGwSHzWrnyrINY0Jt2z1EmVaDhuxI0KrBMpadaluVWQwC6u7uZN28emUxGgssEYQdCxIggCC49PT2bdV4gEGB0dBTbtkmlUq5BVRce3owQ2Nge0ffBOI5DvmJy3X0v8IvHX8bBIBkJ8ukT9+fcw3bD5zPcaocSH6rtom/ZtSwL0zQ3JrJWKpimSalUolgsNhhY99xzTzKZzLa8ZYIgbANEjAiC4LJkyRLmzp1LX19fU9+IYRh0dnay7777EgwG3ZaM8oB4BYhabqeOKSECG8XEPauGuObuVQzma+AYnHZwD597xwK60huXz+ltGL01A40BZpZluV/lcpl8Pu+Gq+nTMeINEYQdExEjgiC4+P1+rrvuOs455xy3cqFQv3/qU5+ira3NrX4oAaJPyjTbrqtEiOM4rB8v86U7V/HQCyM4DuzeGuHy0w/kmH07gU2tFyVC1A4ZJWaUKNFDyyzLcsPVotEomUxGzKmCMEsQMSIIQgNnn302N910Ex/96Efp7+93j3d2dnLppZdy6qmnTpqGcRyHYDDoigjvYjslLCzb4WePvsp3fvc3SqZDOAAfOHJP/nXp3kRDAff5gOv/UCJEVViUMKnVatRqNdcfooLLWltbyWQydHZ2Eg6HGwysgiDsmGzRUP2yZct461vfSjKZpLOzk7POOovnn3/+dZ/3q1/9iv33359IJMKBBx7IXXfdtdUXLAjC9qVer3Psscdy++23u8e+853v8OCDD3LGGWe4bRk9OyQcDmPbNsFgsGFUV1VDbNvmibUT/MP3HuHr9z5H2bI5fPcWbvm3o/nEifsRj4Qaqh6q5aIMq66Y+bs3pFQquempY2NjZLNZ4vE4nZ2d7L777sybN08qIoIwi9iiysjvf/97LrzwQt761rdiWRaXXnopJ554IqtXryYejzd9zqOPPsq73/1uli1bxmmnncYNN9zAWWedxRNPPMGiRYu2yR8hCMK2oVAouMFlhULBPb5kyRKi0Y0+Dn08NxQKufkezfJCHMchV7b4998+z41/WodhQGsswGdOWsBZb+p1qyr6Phk9O0T3i6god9u2qVQqFAoFarWaO8nT1tZGJpNxc012NuLxeFMfjyDsDBjOG/jXPTw8TGdnJ7///e855phjmp5z7rnnUiwWueOOO9xjRxxxBIcccgjf//73N+t9crkc6XSabDZLKpXa2ssVBGEKqtUqo6OjFItFCoWCKwLe+ta3AvDss88Sj8cnLa5T1RHVQtH3wADc+dQAX73nOUaLNWzH4Zw3z+NTJ+5PayzYIFp0EaKmZFRFRFVJ9LZMNpvF7/cTi8XclowSS16KxaKbJ1IoFKb8DydBELY9m/v5/YY8I9lsFuA1R+Uee+wxLr744oZjJ510EsuXL5/yOdVqlWq16v6ey+XeyGUKgjAF9XqdbDZLNpttqDREo1HK5XLDuWosVwkSZUyFjSZVNXprGAavjpb40l2reeTFEQzDYK/2BJeftoDD92jcgqvGdr0mVXVctWXq9TqVSsXdKZNIJEgkEnR1dZFOpyeJIEEQZhdbLUZs2+aiiy7iqKOOes12y+DgIF1dXQ3Hurq6GBwcnPI5y5Yt48orr9zaSxMEYTPI5/NMTExQKpUoFAr4/X43xt3n8zXEqKu2jD4xowsAVc2o1W3+e8Ur/ODhl6haNkG/jwuX7MX7luxOOOB3x3u9gkMVaFXLR8W4K99IoVCgXC4TCARob2+nvb19s1sy0t4QhB2frRYjF154Ic888wwPP/zwtrweAC655JKGakoul2PevHnb/H0EYVekVqsxOjrqfsDX63USiYRrPgUmiY1gMEgwGGwwpSrU739YM8qX7ljFmtESOA5H7t3OF05dyG5tsQYjK9DgD1GVEZ/Ph23b1Go1d69MrVZjYmLC3XejzPPSahGEnYutEiMf+chHuOOOO3jooYeYO3fua57b3d3N0NBQw7GhoSG6u7unfE44HBYnvCBsY0zTpFAoMD4+TqVSoVKpEA6HicfjDS0YNdWix6U32yOjvo+XTL5+z3Msf3I9NtCRCPG5dyzglAO7G56je0D0nBD1+no1pFarUSwWqVarRKNRkskk7e3ttLa2ymZdQdgJ2SIx4jgOH/3oR7nlllt48MEH2WOPPV73OYsXL+b+++/noosuco/dd999LF68eIsvVhCErSOXy7m+kGq1SiAQIJ1O4/P5mm7SBRpaIEqY6O0On8/HzX9ZzzfufZaxch0fcO5b5nHxCfvREg+57Rj1PH0qxhvlrm/YLZfLFAoFgsEgra2ttLa20t7eLv+BIgg7MVskRi688EJuuOEGbr31VpLJpOv7SKfTrpP9/PPPZ86cOSxbtgyAj3/84xx77LF885vf5NRTT+XGG2/kz3/+Mz/84Q+38Z8iCIIX1ZIpFotuS0ZVQpTp1DsF4/P5CAaDmKbpHvOet2akyFW3PcPjr04ADgd0JbnizIM4eE4SwJ2uUdUPfXRX94uo7BC9GlKr1Ugmk65BNZlMTuctEwRhBtgiMfK9730PgOOOO67h+E9+8hPe9773AbB27dqGMuqRRx7JDTfcwOWXX86ll17KPvvsw/LlyyVjRBC2I/V6nVwu5xpU6/U6fr/f/WD3RrUroaGqIfoOGfU4QNWy+cGDL/GjR9ZQtx0iAR8ffdv+nLd4PgHfxnNUC0cJENu2sW0b0zQxDMOtjqgtuyrErFKpEAwG6ejoIJPJkMlkGiZ2BEHYeXlDOSPTheSMCMLmk8/nXV9IuVwmGAwSiUQaWjLeSojaM6N+h42ZHPPnzwdgYGCAJwfKXLn8r6zLVnEch+P26+SyUxYwtzXa4CVRUziqTWOaptuaUcdVS6ZarVIqlXAcx80MaW9vnzIzRBCE2cW05IwIgrDjUKlUmJiYoFAoUKlUsG2bRCJBIBBomILRv3uNqipsTD+PYIxLlq/i3udHMBzoTIS45JSFnLSwyz1fVUMsy3KPKcEBm9o2SphUKhXy+Tz1ep1oNEoqlaKzs5NkMimZIYKwCyJiRBBmOfV6nYmJCXK5HJVKhVqtRiQScashzZJRdYGiqhrqu8oSqVSrhBccT8vR7+HeZ0cwfA7vOXx3Pnb8PiQjQbcFo/bJKEFimqbbplFeEyVM1OhuoVAgEAiQSqVoaWmho6Njp41xFwTh9RExIgizGD24rFar4fP53CkZXWyoDA8lQmCjIFGtE4Vq4zw7kOPK5X+l7e0fBAwO6Ixz1dmHcNDcFtdwqqZvlP+jVqu5FRF9kkYlqJqmSbFYxDRN4vE46XSaTCYjrVdBEESMCMJspFwuMz4+7n6427ZNNBolFAq57RdobLvoI7yqcuH3+7Ft223VlGoW3/3tC/z0j69QdwzsSpWJx27k+t/8mGQ8jmmabmtHVT30cV01pqvEj5qSUf6VUChEe3s7bW1ttLS0NLSIBEHYdRExIgizCNM03byQcrmMbduEw2E3HdW7yA42hgiqKoUSJ0oE6Dtmfvf8MFffuYr12TJO3eH4/dv5xSfPhmoew5MZor6r1osSHno1RAmRUqkEQDKZJJ1O09nZSSQSmb6bJgjCDo+IEUGYBTiOQ6FQYGJignK57I7Bqk26Xm+IvtBO+TqAhnOUR2RDvsZX7l7Ffc8MggFz0zEuOXUBh8+L84uP5N3X09FTVFUeiWrXqJaNqoYog6oKMBMEQfAiYkQQdnBKpRLZbNaNRzcMg1QqRSgUclstSmAoUQI0GFPV7/qXg8ENf3iV6x54nnzVJuAzuOCIPfjICfsQDfopFovuNajnqHaM3pJRAkS1fsrlMqVSCZ/PR0tLC21tbaTTaUlQFQRhSkSMCMIOimmajI2NuYFgjuMQjUbdTBDHcQgGg653Q6FEiL5DRhchfr+fp9ZNcNXtT/PMQB5wePNurVx+6iIO6Em6Ee16AqsSILZtU61WG6ogalqmXq+Tz+exLItEIkEikaCjo0OW2gmC8LqIGBGEHQzbtsnn82SzWXdUNxQKEY1GXeOoMqnqZlJvYqr6XV9+V6ha/MdvV/OLx9di25CKBvjkCfvzD2+dh9/va9gbo5tLHcdxRYge5Q6411gulwkEAm56amtrqxhUBUHYLESMCMIORKFQIJfLub4Q1ZJR4sBxHAKBQEPVwxvdrm/U1Y/95pkBvnb3swwWqgCcenAPn33HAXQkNrZPVECZer4SG7Bxx00gEHBDzYCGzBDHcYjH46RSKTo6OsSgKgjCFiFiRBB2AGq1GmNjY5TLZarVjXHryWSywZiqqh2q2qBXQ3TxoQsXwzBYN1bkK3eu4vd/G8XwGcxviXHZaQtZsk97Q06IEhmqMqKbVtUOGXVMGVRLpRLxeJxYLEZ7ezvpdFoSVAVB2GJEjAjCDGJZFvl8nlwuR7VaxTRNIpEI4XC4IR1VN6nqHhD1sxIo6rvjOFi2w88eeZn//P0LlC2boN/gA4v34F+P34dI0O8GnunmVD1FtVKpNFyrqpaoaohhGGQyGZLJJO3t7YRCoWm8c4Ig7EyIGBGEGUCN6uZyOUqlEpZlufHoemgZ0BCTrlcwdJGip6z6fD5Wrs9y5fK/8vxwCRw4bH4LXzjrQPbu3Li1V6+GKAOsMq6qvJBoNMrLL7/svk+1WiWfz2OaJrFYjEQiQVtbmySoCoLwhhExIgjTTKlUavCF+Hw+Ny9Er2yo/BDl3fBOxzRLVC3UbL517yp++cR6HNuhJRrg028/gHe+ZR6GQcNr6RUWVSXRN+wqkaImaPL5PKFQiGQySSqVIpPJyD4ZQRC2CSJGBGGaME2TXC5HoVCgWq1Sr9cJh8NuhLt3UZ2qXij09FR1rlpSB3D3M0N89TerGClYGD4465A5fPodB5CJh/4uPjZVRNT76ZMxajxXvZca2S0UCtTrdRKJBPF4nPb2dmKx2IzcQ0EQdk5EjAjCdqZer1MsFhu26obDYeLxeMMIrvpZn2TR98co86oecGbbNuvGy1x1x9M89uI4GLBnR5zLT13E4r3aGhbW6ZUQNaKr3kNPUfX5fFiWRalUcvfJpNNpWlpaaGlpaWghCYIgbAtEjAjCdkSlp5bLZXc8VvlCgIYsEH0KRU20qMf1oDMlQmqWzX8/9BI/eHgNtbpDKGjwoaP24oPH7UXIv0kweP0herunVqsBjUJFGVQBUqmUWw2RcV1BELYXIkYEYTtQrVZdc6qaSolGowSDwUmVENjk4VBVDO9Irx7pbts2j788xlfuXMWLw0UcB47cu5XPn3YQu7fFmo7pekd1vQFmgFsNqVarRKNR4vE46XRa9skIgrDdETEiCNsQy7LcSoga1Q2Hw0QikQZRoCZmlHBQrRmvANGFi+M4jBVrfOu+57n5yT4AOhJBPvX2Azj9TXMahIyepKqLEL0lo7eDlDfE5/ORTqdJJpO0trbKPhlBEKYFESOCsA1wHMf1hZTLZUzTxO/3k0wmCQaDDdUKJURUVUKZRvUld3rFRE273L5ygK/f9yxjxRqG4eddb5nHxW/fj2SkMYNELa/Tk1IBd2QXcM+xbZtSqUStViMejxONRt3sEEEQhOlCxIggvEFKpRL5fN6thhiGQTQaJRwON1RCvFMsSiQ0M7GqqRnHcXhhKM/Vd63i8VcmcGyH/bqSfP6MRRw6v7VpaqpebQEaJmb0ny3LolgsEggESKfTbktGjQwLgiBMF/L/OoKwldRqNXeZndqqG4lEGkZ1lQiBjSZVVZHQjyl0A6vP56Ncs/jBAy/wkz++TM2CaMjHvy7ZlwuO3p2gf1N1RaFaM3rVxXEcLMua5A1R1RvlDclkMrJdVxCEGUPEiCBsIaZputWQWq3WsFVXn5LRI9YBtx3jjXcHGtosACv+NsyyO5/mlfGNS+2O2a+DS99xALu1J9zzlPBQYsPv9xMIBNzAMvVdvbealCmXy/h8PlKpFK2traTTadmuKwjCjCJiRBA2ExXhXiwW3bwQ5QsJBAINxlN9Z4wSIQrVBtHbNrBRkGzIVfjab1Zx59MbAIfuVIhPn3gA7ziwZ5JgUVUPaNy0q4SKXoVRO2Wq1SqRSIRkMklbW5uM6wqCsEMgYkQQNoNarcb4+LhbWTAMg1gsRiAQaNgPo+d46K0R7yZbXYCodNRf/fEVvvXA8+RrNoYB7z1sNy58296kYxsnWvTpF+X/UGJGVUnUZl31eL1ep1aruYbadDpNJpMhlUrJdl1BEHYYRIwIwmtgmmZDaJlt2wSDQWKx2KSWjBIEqjLh/bD3Tsgonh/K8+Vbn+LJ/gKO47CwO86lpy7i0N3bGqogwCTTqxoHho3ZIYBbFbFtm3K57HpZ1HZd2ScjCMKOhogRQWiCinBXe2RM0yQYDBKNRt2WjC5CFLpYUN+9rRv1WNms853fPscv/rQOsw7xkI+PHLsv7z1qDwL+TUJDN6Pq16euQTeoqrZNtVqlWq26XhbZrisIwo6MiBFB0FB5IbovxOfzkUgkMAyDYDDYUNXQx2j1ioc+LaPaJvr47u+e28BXbn+KvpyJ4Ti8bUEHl56ykDmt8Yb4dmBSu8cb566P7DqOQ6lUAmhYbCfVEEEQdmREjAjC3ymXyxQKBUqlkhsQFo1GCYVCGIZBIBBo8Gx40cd49ZYMbKqUDBdqfOXOp7h31QYMw8ecdIjPnryQty/sdsWEvonXK3KUGFFtGD1GvlKpUK1WCYfDEl4mCMKsQsSIsMtjmib5fN7NC6nX6wQCAXdUV02/6B/+egWkWWXEi4PBLx5ew388+AIl08HvMzj/8Pn829v2JRkNTXod3SPiDTBTxlQ1LVOv1ymXy8DGakgqlaKlpUWqIYIgzBpEjAi7LPV6nUKhQLlcplKpYFmW25Lx+/34/X632qGPyHr9H82EiKpwADzTl+XqO57mr/15AA7pTXH5GQexoDc16Xp0kaPySPTXtyzLrYro47rhcJhYLCbhZYIgzEpEjAi7HLZtU6lU3GpIrVZzJ2SUJ0QXAbqHQwkM2NSCARqOK0GRr5h894EX+J8/vIKDj2TEx8eX7se7Dt+NYMA/qeWjpmN0E6x+jjKqAm7YmhJPKsBMwssEQZiNiBgRdinK5bJrTq1UKgBEo1GCwSCBQKDBFwKbUlO97Re9WqE8HkqQ2LbNb58d5mt3P01/zgQHTj6ki8+eeACdycikvBA9oVU3p6r31E2q9Xrdne6JRqPEYjFaW1uJxWLb98YJgiBsR0SMCLsEpmm6LZlarYZlWUQiETe0TI3rKvGhT7DoIgMaJ2WUgPD7N1Y6+ibKLLvnWX7/3DCObbNbW4xLTj2QJXu3Tdolo4ej6aO66nutVmvYrmuaJrVaDcMwSCaT7nI7qYYIgjDbETEi7BAUi0USiY17VwqFwjbzPai8kFKp1JBEGo/HCQQC+Hw+98Nc94bonhA9WEyvXOi+kJpV538efYX/fPBFirU64YDB+5fsxYeO25toaFP8u9ekalmWO6mjcBwH0zRdIWJZltuWCYfD7mK7aDS6Te6RIAjCTCNiRNgpUemjxWLRDQBTEzKqGqKP3+qjtLo3RJ9i8e6RUd+fWDvO1XesYvVADgyHI/bIcOlpi9i7M+FWTHR/iXo9JXbUOfq16KO7pVLJ3YGjqiG6R0UQBGG2I2JE2OmoVCoUCgXXnAq4IqRZaJneOplKhOjHFLmKxX888Dz/9/h6HMcmEw9x8fH7c+ahcwgE/A2JqKqlo5tS9ffztmXq9TqWZVGtVl1vSCaTIRwOb+vbJQiCMOOIGBF2GpQvRIV/1et1wuEwoVDIbcfovg29GqKmZ9TvSoToQWYKn8/HHX/t5xv3rmI4v3Ex3ZlvmsOnT1pAayzY0NrRU1TVe+oR8eoxFbKmvCGmaU7yhshiO0EQdlZEjAizHsuyGnwhtm3j9/sJh8NuJUS1WLyhYvoYLzS2T5Qg0Vs3r4wU+Mqdz/DIC6MYhsGeHXEuOXUhi/dsA2h4bd3gqr+f3mLRt+xalkWpVHIX28XjcVpbW6UaIgjCTo+IEWHWYts2pVKJUqnUEFqmklNVAqk+DdMsM0Qfs202wuv3+6nULK5f8SI/ePglahaEQgE+dOSefHDpXgSMxnPV66nqhleI6OJDVWf0vJNwOExrayvJZFKqIYIg7BKIGBFmHY7jUC6XKZfLrjlV7ZFR5lT9g1/fqgs0VEl0caILB3UewB9eGuHq25/ipdEq+ODwPdJcdtqB7NmxcfpHb8t4x3/V43qWCOAGmClBBRCLxdwNuxLlLgjCroSIEWFW4c0LAQiFQm5oGUyuQKhjettFb50ADX4RJRzGClW+ed9qlq8cwHB8tEb9fOak/Tn90HkN5+vVC+92Xq9nRJlaVQpsrVYjFAoRiURIp9NSDREEYZdExIgwK1B+CrVHpl6vuyIkFAq55+khYso7ohtSFXpFBBpHdR3H4dYn+vj3+59lvGQDBucc2sNFJx5Aazw8aT+N/l29vy6I9Fh51Zopl8vYtk0sFnO9IVINEQRhV0XEiLBDU6/X3VFdNWXiDS3TA8l0kaH8G/o5qjWiVx/05780XODqO57m8VdzOA7s1x7h0jMO4i27ZyaNAXvRqy96y0flhtTrdTdF1e/3k0gk3GqIIAjCroyIEWGHRPeFlMtlTNPE5/MRiUQIBoMNsenNJmS8Ue5e74heFXEch6pl88MHX+T6x16hZjtEgvAvR+/D+5bsQdDvc0WGtw3jnbgJBALuBI1hGJim6VZqyuXypEkZqYYIgiCIGBF2QJQptVKpYJomgDumq8emq10yeqsEmNQ+US0b72SLEjCPrRlj2V3P8PJIBcMwOHrvDJeduoj5bXG30qLaPWpcVxckugFWXY+qhChRVa1WCYfDRCIRWlpa3Oh7QRAEQcSIsAMyPj5OKBSiXq/j9/uJRCLuhIzX3On1bHhR47XQGOler9cZKVT5xn3PcedTg+A4dCZCfOYdCzlxYae7iVeJkGZZId5dM+oxJaDUXhzHcYjFYm6AmVRDBEEQGhExIsw49XqdfD7v/l4ul4lEIq5B1VuFgMbpF2/FAxozRZSxdFOVxOHWJ/v55v3PkS1Z+P1+3v3WOVx4/L6kIkH3dVRbxptL4q3I6NemKiJqUkZVQ1pbW4nFYtNwNwVBEGYfIkaEGUMtsyuXy+RyOfd4PB4nEolMimOfatmcPr3STISocwH+NlTgq3ev4vFXshg+WNid4JLTFnHIvJZJlRf1HFWV8WaJqMfUuK5lWZimSblcxjAM4vE48XhcqiGCIAivg4gRYdpxHIdarUY+n6dWqzUkkQLuDhldiDTL61DH9EAxXSjoLZRyrc4Pfv8iP/3Dq9Qdh0Q0wL8dvSf/uHg3ggH/JJHRTJh4KyHqdzXlozwulmVx8MEHA1AoFESICIIgvA4iRoRppVqtulkhao9MOBwmHo+75/j9foCGioc+VtvMI6KbVIEGcfHg88N89e5VrJ+o4gBvO6Cdz520gDmtsUmTN97MEB39etTvSnwUi0V8Pl/D3yEIgiBsHiJGhGlBtS+UCLEsy/WE6PtcFM0i1FWIGWwSJGpsFphkNh3KVfjavc9y76oNAPSmNxpUjz+gc1L8uzcATa+MqJ036vWVSdU0TXfyJxKJEA6HaWlp2b43UhAEYSdExMgsp1gsumOihUJhh/svc+ULURt1LctyA7+U2PBWILzmUdgU065XP/Q8D/W7YRhYdZtf/nEt3/n9i2TLJn6fj/cePo9/W7oPiUiwqfFUf1/1s2VZ7jivugZlUK3VapTLZQASiQSxWMz1hhSLxe15SwVBEHY6RIwI2wW1e0XtkFGpo9FolGBwkyDwejWgMUn1taohumiBjQLimb4sy+54mr8OFAGHN81Ncclpi1jQk5p0jboIUVUPdVxVQ9QUjmEYblupWCxSqVSIRqNEo1FSqZTkhgiCILwBRIwI25xqtUqpVKJSqbjVBZUVokezq+/quEL3h3irIZZlNUS9q/OKtTrfvv9v/O/ja6k7kAwafPxt+/EPh++Gz5jsB9ErHt7odqAhXM1xHKrVqrukz+fzkU6niUajMikjCIKwDRAxImwzVOtC+UIcx2nIClEVBm+kOtBQHdErHQrVktF/V+fct3qIr9/9DP15CxybkxZ18dmTFtCZirjn6OJGFyLe9/RO8OiTMpVKxY1yj8fjslNGEARhGyFiRHjDqL0rxWLRHdMNhUJTLrJTlRC9GqELDX2fDGzasKvniBiGQX+2wpfvfIYVfxsFHOa3hPjMyYs4dt+OBoOq7itRr+99f2j0jMDGCk+tVqNUKuHz+UilUlINEQRB2A6IGBG2GpU0qnwhlmURCASIxWINlRBdDKgAMa8QsCzLfd1HH32UpUuXTtqC6+59sR1+/sjL/OdDL1CqOQR9cMFRe/DhY/cmGtrUetGrHeqY/js0juvqKapqSkZNykSjUZLJpHhDBEEQtgMiRoQtxrZt1xdimia1Ws3dqOv3+ycJDu+orL5B13Ecbr/9di655BL39c8991x6e3v5yle+wmmnndbQtlm5boIv3/Y0z20oYhgOh+2W5pLTDmSfzkTTvTVeAaJEir7cThdNymyrqiHJZNJdbifVEEEQhO2DiBFhi1DeCdXCcByHaDTqGj69YWS6GNCNqWp09s477+T973//pKj3gYEBPvCBD/DjH/+Y0047jXzV4rr7nuP//rwex7ZJRYN86oT9OPPQufj9jaPBasldIBBoCDRTwWj6rhl1XUpUqRRVVeGRSRlBEITtj+/1T2nkoYce4vTTT6e3txfDMFi+fPlrnv/ggw9O+i9QwzAYHBzc2msWZgDTNMlms+TzeYrFIrVajVAoRDKZnFQxUB/0qkKiCxHl4VCVkUsvvbTptl117PLLL+fOJ9dz+rd/z/89vg7HcTjj4F5u++gxvPMt8/D5Jm/rdRyHQCDQ8O9NHwXWRYhqyagsFNM03UpIR0eHCBFBEIRpYIsrI8VikYMPPpgPfOADnH322Zv9vOeff55UalPWQ2dn55a+tTAD1Ot1isWiO9qqDKiRyKZJFf17swAzbziZMqOuWLGC/v7+Kd/bcRz6+/v5xHd+RXju/uzeFuXS0w7kyL3bXa+H7g1p1nZRr6N7V/RrVFWeSqVCKBQikUiIN0QQBGGa2WIxcvLJJ3PyySdv8Rt1dnZKVPYsQqWMFotFt4URCoWIxWKT9r/oZlV1HGiIZvdu3nUch6Ghoc26FqOa5cPH7MU/H7c3QZ/RIETq9XpDOJkSS3q1RIkQ1bZRO2XUyG69XicWixGPx0mlUuINEQRBmGamzTNyyCGHUK1WWbRoEV/84hc56qijpjxXTTEo9PXywvZFBXwpEaLi29WEjB4eBpumY2BTQJl6HT07RD1PeTcA2tvbN+uavvKPS3jX2/eftFXXtm2CweCk69HPU5ki6nwlRFQ1JBgMkkgkSKVSxGKxN3DnBEEQhK1liz0jW0pPTw/f//73+fWvf82vf/1r5s2bx3HHHccTTzwx5XOWLVtGOp12v+bNm7e9L1NgowjMZrNks1nK5TKWZbkL4Px+v1tZUFUIb5qqEhm6UVQXJ+p39bV48WLXezQVvb29nP2Ope5r6yjTrD6W630//TElQgqFApVKxd0n097eLkJEEARhBjGcZu7BzX2yYXDLLbdw1llnbdHzjj32WObPn8/Pf/7zpo83q4zMmzePbDbb4DsRts2iPNM0KZVKVKtVLMvCtm1XgHgNqHpgWTNfhj6yqx5X1RD1Gvrx22+/nQ984J+Axn+GSqD8+Mc/5tRTT3WPNRvVnSrYDDYFpqmR3UqlQiAQcL0h20OE7OjLCwVBEKaLXC5HOp1+3c/v7V4ZacZhhx3Giy++OOXj4XCYVCrV8CVseyzLIp/Pk8vl3PRU1ZLR01OV4FDHlJDQhYiaTNGzRRTekVr1tWakyPKRNlInXYgv3tpwbT09Pfz4xz/m9NNPB2ioxqgv9d7qcYVeJbEsy02HLZfLRCIRWltbyWQyUg0RBEHYQZiRnJGVK1fS09MzE28tsCk5Ve2QsSyrIb7du59F/67wJqiqY/r5KlVVmVzV4xWzzg9/9wLX/+EVqnVo3e9wLjrvbK5679sAuOGGGzjuuOMmtYW8Ase7XM8brKYyQyqVirvcLplMEo/HX7M1JAiCIEwvWyxGCoVCQ1Xj5ZdfZuXKlWQyGebPn88ll1xCX18fP/vZzwC49tpr2WOPPVi4cCGVSoUf/ehHPPDAA9x7773b7q8QNguVnKpEiGma+P1+N7Ss2VZb/UNbFyh6FcQrXHRviN7mAXjkhWGW3fkMa8YqYNdZsl8Hl51yIJmwzVV/f5/Fixe7xlSvabZZJQQ2GVfVtau2U71ed5fbpVIpQqHQdru/giAIwtaxxWLkz3/+M0uXLnV/v/jiiwG44IILuP766xkYGGDt2rXu47VajU9+8pP09fURi8U46KCD+O1vf9vwGsL2R49vN03TjW9vNiGjG1O9eKPcobF6okSIt5UzUqjyzXue5fanBnCArlSQT739IE45qBefz0c+n3ffw5sFMhXqtfUJGtM0KZfLVKtV/H6/6w2Jx+Ob9ZqCIAjC9POGDKzTxeYaYHZFXs8sqcypqh0DEAwGG7I5pqo2qFFd3cCqT6vAZG+Il3rd5ubHX+Vbv3uBibKFz2fw7rfM5aNv359keJMWLhQK7LnnngCsXbvWFQ9er4m6Lr0qo65JBZipKaB4PE4ymZz2aogYWAVBEDayuZ/fsptmJ8WyLHejrjKQKk+IGomFTWmo6mdVZZiq2qHO00WI3h5RAsIwDJ4byLLsjmf487osGLCwO8Elpy3iTfNbJ2WS+P3+huv3Tsl4k1XVtdi2Tb1el2qIIAjCLEbEyE6Gbk41TdNdGBeNRoHmqan6h7w+egs0VEP06HV9lNfrKylVLb734N/4+R/WYdXrREMBPnLMXrz36D3xGUx6XjNvij5G7E10VQJDpcSWy2V3p4zKDplJb4heIXrooYc48cQTJ4ktQRAEYRPyn407EeVy2R3TrVQqAEQiEUKhUEObw+/3uwJAHdMFhxIilmVNqkIADZUUb9z7A88N8f++9zA/eeRVzHqdpQd0cutHlvC+Y/cm4Pc1tH6U2GjmUVEf3t7HlJBR7adCoYDjOMTjcTKZDJlMZkaFyM0338yCBQvc30855RR23313br755hm7JkEQhB0dqYzMcnTLTz6fJxwOYxgG0Wh00jisHhSmvuttEK/oUHiFgvovf72dsyFf5Zq7V3HPqg0YjkNvKsSn37GAkw6a407e6N4U1Q7Sp2CaXac3Al4FmKmEWLUvZ6arIbBRiJxzzjmTtgj39fVxzjnncNNNN23RcklBEIRdBamMzGJM02R8fNz9/dFHH51ySsZbadCrHPqHpy4c1HneD1ddIFh1m/999GXO+PZD3Ld6AwG/j/OPmM/NHzmGEw/sdasg6r0CgYArQPT3brbt1yuU6vU6pVKJYrGIbdvEYjHa2tpmvBoCG+/bxz/+8Un3CjYJxosuuqipyVcQBGFXR8TILMQ0TXK5HDfccAOHHXaYe/y9730vb37zm7n99tvdY83i21VFAjYJi3q93vBBqVdKFOq5qtWyqj/HBT96lC/f8xyFWp0De5Pc8MHD+dxpB5KMhiaNC+uCQ12TNz9EPaabYlWSaj6fp1QqEQgESKVStLW1kUgkdgg/xooVK1i/fv2UjzuOw7p161ixYsU0XpUgCMLsQNo0swh9auTWW2/lgx/84CTBMDAwwPve9z5+/vOfc/rppzdNTvXulNG/VCVCJZyq51uW5YqEsmnz3Qf+xs//+Cr1OiQjfj6xdB/OOXw3Av5NIgM27ZPxTs+ox2BT20bh3bpbLpcpl8v4fD7i8TiJRMKNrN9RGBgY2KbnCYIg7ErsOP9vLkxJvV53lweapkmtVuPzn//8lC0BwzD47Gc/y2mnnTYpzt2bkuqNcG82saIEhW3b/Pa5Yb5+9zMM5Go4wMkHdfHZkxbQmYpMugZ9TFgJDFWV0WPedfGiUFM8hUIBy7IIBoPE43Hi8fgOuVNmc9cbyBoEQRCEyYgY2YFxHIdqteqaNS3LwjAM/vKXv9Df3/+az+vr6+PRRx/l6KOPBhrbLt59Mt58EHVcVUfq9TpD+RpfvvNpHnx2BAyDeS0hPnvKIpbu39UgJPQId90volAVEDXN4w1Tg41CxDRNisUiALFYjFgsRjKZ3KGqITpLlixh7ty59PX1NRWJhmEwd+5clixZMgNXJwiCsGOzY/4/u+DukFHx7YZhEAqF8Pv9DA8Pb9Zr6C0BvRKiftdFiJ4von+Y1kyLXzz2Kv/50AuUTIdgwOCCI3bjX47bh2jI71ZBdPHhjZeHyVt19WvytpIKhQLBYJBgMEg0GiWVSrk5KTsqfr+f6667jnPOOWeSB0b9fddee+0O4W8RBEHY0RAxsoNRq9XcloyabAkGg64R1bZturu7N+u1uru73baIygzxJpt6PST63pmn+/N8eflKVm8o49g2h+2e4nOnHsh+3Sm3YqL7SrxL8bwjw81SXfWfFZZlkUwm3STVHbUa4uXss8/mpptu4mMf+xh9fX3u8blz53LttdfKWK8gCMIUzI7/l98F0M2pSjj4fD63GqI+uP1+P0cffTS9vb0MDAxM2RLo7e3lyCOPxDAMdycNMKkSoYeeKSFSqNW57p5nufEv63GAlliAT7ztAM46dC6BwKYpHL0KAo1haN7j+u/6Ij0lREqlkntNiUSC9vZ2IpHIlAv7dlTOPvtsTjjhBNLpNAB33XWXJLAKgiC8DiJGZhgV316r1Rri24PBYMM5ehCY3+/na1/7Guedd96ULYFrrrlmUuy6d8+LEgVqUgbg7qcH+dpvnmG4UAccTjuom0+/YyGt0QCBQKBBFKn308d0YZMo0dNV9UkaPcPEsiwKhQLVatX9GzKZzA7flnktdOFxzDHHiBARBEF4HUSMzBC2bbu+EMuyqNfr+P1+txKi0CdO1Ad/vV7nzDPP5Oc//zmf/vSnG7whvb29XHPNNZx66qkNbRD1XNg0Wqtnjrw6UuTLdz7No2vGwXHYqz3C5045kMV7tQGNY7jeZFc9Sl6do373Tu2oc5QIK5fL+P1+ksmk+3q6EBMEQRB2fkSMTDNqUkRVQ9QHeSgUahAe3p0sektDVSLOOOMMli5dypw5cwC46aabeNvb3uY+Rxcx0ChCLMvC7/dTs2yuX/EiP3h4DTXLIeiHfzp6Lz60dG9C/ub+j2bfFd4qgP58NaFjWRalUgnbtgmHw8RiMdmuKwiCsAsjYmQa0XeqKF+IMqfqxlKVw6H7OfSqgv4Br3+IH3HEEQ1eDHWO/lrqff1+P4+/PMrVtz/NC6NlHMdh8V4ZLjl5AXt2JNzX1Md01e/NWjLen9V1e82yqhrk8/nc8LJEItHgGREEQRB2LUSMTANqQsY0TSzLwrZttxICmwSDqirooWOwSQA0i2j3ppVOVbVQ7+v3+xkvVvn3+57n5if7MXDIxIN86oT9Of1Nc5pWObzVjWbXp/BOx6jnqmpIvV5vGNmd6Z0ygiAIwswjYmQ7onwRakxXiQG1LA4ahYd3MZx3LLZZy2OqaRpdFOhVjeV/Xsc3fvsc48UaDgb/7y1z+MTx+9EaDzdMwagJHoW3haTO0a9X3yWjo4LbfD4f0WiURCJBPB6X1owgCIIAiBjZLti2TblcdishalolFAo1tFr0ZXWBQMD9IPe2QtT5+hSKt/IAk5fbKbFiGAZrhgtcfdcqHntpDAPYpzPBpact4vA9293z9DwPvTWkv5c6pq632aSMun61Zdc0TbcakkwmCYfD2+fGC4IgCLMSESPbEGVO1X0hfr+fSGTT3hb1Qa1/16sQektG35Crqgi6DwNoaKvoVQr1elXL5ke/X8OPH1mDWa8TCQX48NF7csHRexLwbWyfBAKBSW0dXYjo1+SdyNF3zijU314qlTAMg0gkQiKRIJFITFkNicfjTas8giAIws6PiJFtgOM41Go1d0y3WSVE/0D3mlGbmVO9vg9VDfF+YOu/e9syj700yjV3PsNLY2UMDI7at4NL37GA3drjU4oLHT0gTc8V8VZAvHtlyuUytVqNYDBIJBIhlUpJNUQQBEGYEhEjbxBlTq3Vau4iOzUh4x2L1Q2r3mwOmNzmUOgR7eo5XjEBm6oTYyWTr9+zmtv/OgAOdCRDfPrt+3HKwXMaxJF3a24z86t3xFh/TPlgdA9LoVBoqIbE43EJ/RIEQRBeExEjW4llWW4FQH0QBwIB90MeGsO/vJUP9bhXlHjP8/o1mlVC3Guq17np8bX8+2+fY6JSxwD+8a3z+OiJ+5MMB6aszOjvpV97s9h4XRjpbZ1KpUKlUiEQCBCNRonH48RisTdwhwVBEIRdBREjW4jaIaPaMSpuPRKJTPJxNPN/eFsv6nz9uaodoz/uraDoGSQAxDu48H+f5In1RQwDFnYluPT0Azl4btp9vt5qUc/Vp3l0oaL7T9R7qmvSn6/2yti2TSQScUd2pRoiCIIgbC4iRjaTer3utmRUiqjXnOptxXiPe9NQ9YAy/QNeVR+8Y7PqeQ0L5moW4beeTfrQ03iir0Q87OPCY/bhvUftgd83eZuuLoKAhnaSV6x4J3r0eHkVYFar1fD5fMRiMZLJpFRDBEEQhC1mlxUjxWKRRGJj0mihUCAejzc9T5+Q0UWICi3zeiu88ejqw9tb3dBfX2+BeLNGvGmnjrNpsd1DL4xw9W0rSb/1nRjAkn1a+MIZb6I7uena1KSMei/v9alr9Ioo/T1146y6xkqlgmmahMNhIpEI6XRaqiGCIAjCVrHLipHXQ03IqORU9YGsT8gADZtsm72Gd2pGH5XV80RUu0cXDl4hY1kWABtyFa75zWruXb0Bx4F6fpix3/2Ur69YTjy+aceNqnrovhPdHOs11nozS/QqjbpO5ZVRAWapVEqqIYIgCMIbQsRIE9SYrqqE2LZNMBhsmrehUlW9bRegoQXjrW6o5+rVCq9YUMFoSgQ4GNz42Mv8x+9eIFetE/DBuW/p5VvvPw/Y2Gbx+/1NQ9O8lRlvtoj3OnV/iLpGFeQWDAaJxWJSDREEQRC2CSJGNNR/9StjqhIHaqW9t5Wh52wogeL1f+hTMs0mYbz7aXS/iLomgGf6Jlh212r+2pcHHA7uTXD5GQexW8rPt/7+mvo1NNtno34OBoMN5th6vY7f72+ogqhrr9frbpKqYRhuNSQSiUicuyAIgrBNEDHCRh9EsVikVqu5H76BQMCNR/eOw+px56ol4hUbzX7WWx+6qRRoqKboIWfFqsV3fvs8v/jTOqy6TSri5+Nv259/OGw+fp9BuVxu+FuaVTj062+W6KpEh3fCR03KmKZJKBQiFosRi8UaTLuCIAiC8EYRMQLk83nC4bD7wR0Oh90PdLUMTv9QVx/m+iRKs0Ay/TjQUG1R6O0daIxVv/fpfr5x3/P0ZSuAj3cc2M1n37GAzmS4IbRMR6/O6H+Dujb9fb3H9MqIMu0ahuGKEAkwEwRBELYHIkbY6BEJhUIN6+z1XA5oHlDWzLjaLMlUHfcKFz17RH9+/0SZr9y1igefGwYD5rVG+Ow7FnDcfp0NfhXv1l69vaJeU19op7+3XgXxBp+pMLdQKEQkEiEejxONRrf+BguCIAjCayBiBFwholcRFN7WhfeY7s/Qz1GPKbxBZXo1RImBmmnxi0de5rsPr6FUs/D7/FxwxHz+dek+xCPBBoOrz+cjEAg0XK+3neR9rFlYmi5I6vU6lUoF27aJRqPuXhmphgiCIAjbExEjbPqgVh4RbyXBuytGf9xbBfFWQJpt3NXbO+r4ynUTfPn2Z3h2IA8GvHleC5eetogDetMADRMy+vWo1op+req61DH9u9dToo7VajVM03RNqslkUqohgiAIwrQgYoRNI7GKZt6KZpkh+uPec9V3v9/fML7rbY9MlKp8+7fPc+Nf+nAch3Q8yMVL9+Hst84nENjk9dBbOt6sEkUwGGyad6JQz1PtHSWWVJZKKBSSOPdtQDweb/h3IQiCILw2Ikb+jjc11ZsLoj/uHeH1tm10VOXC+zqWZfGbpwf52j2r2ZCvAQanHdzNJ9++P90t8YbX1ash3mh3r3Dy+lS8f48uREzTpFarAbhx7lINEQRBEKYbESMevG2XqUyq+nclGJr917C3JVKv11k7WuTLdz3Dwy+MArB7Jsolpy5kyb6dDc/Tv09VdfGKqKn+Bj3ATFVDLMsiGAwSjUZJJBJunoogCIIgTCciRpi6LTMVug/EK1i8qaeA2xox6zY/eeglvrfiJSrVOuGgnw8cuQcfPHZPYuHGhFfv6+i/q7Fex3GIx+Pk8/mmbSRdJKnfLcuiWq263pBYLObu6BEEQRCEmUDEyOvQbApFT171xql7w8TUlMqfXxnjy7f9lb+NVABYvFeGS05dyN6dySmD07yTL/p5zZJem5ln1TmmaWJZFrVajWAwSCgUIp1OSzVEEARBmHFEjDC5CtLMa6F7SPQttt69Lkqc+P1+LMsiWzb55r2r+dWf+8CBtniAT594AGccOndSnLqe6KrQjatTxa83yz1RAkZ5RKrVKo7jEA6HicfjxONxiXMXBEEQdghEjDC5TaMHnHnNq3rQmDrXayhV7ZDbnlzP13+zitGyjc9ncPYhvXzi7fvTGg+5472qhaMW7qnX9b5+s2v2Bqx5hZIa2a3VagQCASKRCMlkknA4vH1upCAIgiBsBSJGmDo11Rurrn/Qw6Ylc3q2h23brBku8OXb/8qjayYA2Lc9xqWnH8jhe7Y15IXoO2GUENFbMqoi4r0+/ZoVypiqHrNtm0ql4lZDlDdEqiGCIAjCjoaIkb/jDQHT80H0D3qvt0OdB1AxLX7wwAv8+NE11OoGkYCPDy/Zi/cfsxehwKZdNrq5VB3TBYreqvEGrDUTJV5xo0yqfr9fqiGCIAjCDo+IEWj4MG/WptHNqcqQqlcYfD4fD7+wga/c9lfWjFXBcThqnwxfOP0g5rVGGyocsLHVoyoh6rVeS4h4/Sl6O0b/UiO7tm27W3aj0aiYVAVBEIQdGhEjNO500dsx3ikWvaWihMJwrsxX736G258axDB8dCQCfPYdizj5wG53s6+qrKjnBAKBSQJH954ovEFpzbJG1Jfyhvh8vgaTqiAIgiDs6IgY0fBWQ/SKhj7Su1GY2Pz6T6/w7/c/x3ixjuGD/+/Nc7nopANIhv3utlzdZ6K3ZNT76H6RqbwrujlWbxupx9VemWAwSDgcJplMSjVEEARBmDWIGNFoJhyabd5d3TfOV25/ij+tzYHjcEB3kstPX8Shu2Xc9o2qogBN2y7eKRyv8PC+p7clo1o25XIZQKohgiAIwqxFxAi4pk9vu0Zvmfj9foqVGt+9/3l+8sgrWHWbaNjPR4/bl/OP3oOA39cwKQONW3SbjeHC5Nh3L/q16MKoUqlgmiaBQIBwOEwikSAUCm2X+yMIgiAI25NdVoyoBXYAjzzyCCeccMIkMQGbfBu/XdXPsjufYd3ExnHZ4w7o5PLTFjEvs7ESoYytzcSFXu3YnG243lRXvWJTr9epVDamuKqR3Xg8/prbegVBEARhR2aXDJ24+eabWbBggfv7u971LhYtWsStt97aIEb8fj8D40UuuvHP/OvP/8TasTI9qTDfOvdQvn/eYa4QgUZfh17B0I2q+gROs5RXHX1UV4mRarVKqVTC7/cTjUZpaWkhkUiIEBEEQRBmNYbT7JNwByOXy5FOp8lms6RSqTf0WjfffDPnnHPOJAGgPtB/9rOfccYZZ2Badf7v0TV863cvkK9a+AyD9x6+Gx89YT/SsXBDBLw+busd4/UGl8FkQ6qOLkL0aoiKcw8Gg+7IbiCwyxa2BEEQhFnA5n5+71JipF6vs/vuu7N+/fqmjxuGQW9vL7+46/dcfdez/HXtBBgGB81Ncflpizh4fgZoHKlVRleFHuuuXlP/rp7vHePVR4fVtIxhGO7Irt/vd7NDYrHYVt8DQRAEQZguNvfze5f6T+sVK1ZMKURgo0jo6+vjnCt/gr9nPxLRIBct3Zd3H7kHoWDANbnqI7m6T0SJEz07RBccunnVWynR/SGwUZxUq1UsyyIUCrkmVamGCIIgCDsbu9Qn28DAwGadZ+bHOfXtvXz2HQcwJ5Nwp230Voxt264QaWZO9W7xVce8rRklPnSTqmVZ1Go1DMMgEolINUQQBEHYqdmlxEhPT89mnfeJMw7none/BdgU167vklFVDMVUBlKvqVX/rhtYLctyz1cBZlINEQRBEHYVdqlpmiVLljB37tzXnD7p7Z3Dx95z+qTWSbMwM5/P5wqFZuLEmzGiH9OX2sHGaki5XMa2bXe5XTqdFiEiCIIg7PTsUmLE7/dz3XXXAU2qGX+vYFxzzTLtkOF6PPQ9Neq19EqH/preHTL6z6rCoqZllEm1Uqng9/sJh8Ok02lisZiM7AqCIAi7BLuUGAE4++yzuemmm+jt7W04Pqe3l5/+9KecfvrpTUdxYfJkTLMo92YeEfWzN0nVcRxKpRL1et1tybS0tEiSqiAIgrBLsUuN9jZ7TYBf/epXLF261K12wCZhAbiBZd5KSDOmWnQHjYv4lDckEAg0ZIcIgiAIws7C5n5+b3Fl5KGHHuL000+nt7cXwzBYvnz56z7nwQcf5NBDDyUcDrP33ntz/fXXb+nbbnP0yZcjjjiiQYgo1Piu14TaDO+kjFfjqbaMbdvUajV3ZFclqYoQEQRBEHZVtliMFItFDj74YL773e9u1vkvv/wyp556KkuXLmXlypVcdNFFfPCDH+See+7Z4ovdXqiqB2zaMQM0xLjrS+/UeVN5RPT2jJ6kqrbs2rZNOBx2Taqbs69GEARBEHZWtnhU4+STT+bkk0/e7PO///3vs8cee/DNb34TgAMOOICHH36Yb33rW5x00klb+vbbBd3P4fP5CAaD7mN6CJn3u97G0Z+vKiB6nLtpmliW1bBlVyZlBEEQBGEaDKyPPfYYJ5xwQsOxk046iccee2zK51SrVXK5XMPX9kTPEPFWSVSVQxcsXj8JbKqi6AZV9dqqGhIKhVyTqggRQRAEQdjIdhcjg4ODdHV1NRzr6uoil8tRLpebPmfZsmWk02n3a968edv7Mhu8IVNt0fWiskLU4yqVVbV6KpUK5XLZrYa0tLQQj8cnvY4gCIIg7MrskKO9l1xyCdls1v1at27ddn2/QCDQ0GpRNDO06ngFiqqw2LZNpVLBcRx3uV06nW5o/wiCIAiCsJHt3ivo7u5maGio4djQ0BCpVGrKCZJwOEw4HN7el9aAblD1igy9RaMe170jlmW5j6u9MsFg0B3ZjUQi0/q3CIIgCMJsYruLkcWLF3PXXXc1HLvvvvtYvHjx9n7rzUYPK9MnYnTRoePNIlHPMU2Ter3esFdGJmUEQRAE4bXZ4jZNoVBg5cqVrFy5Etg4urty5UrWrl0LbGyxnH/++e75H/7wh1mzZg2f+cxneO655/jP//xPfvnLX/KJT3xi2/wF2wjVpvGmpir046oVo74cx6FSqQAbqzqpVEpGdgVBEARhM9niysif//xnli5d6v5+8cUXA3DBBRdw/fXXMzAw4AoTgD322IM777yTT3ziE1x33XXMnTuXH/3oRzvMWK8X74iuXiHRE1RVAFqtVqNer7sm1Xg8LpMygiAIgrAF7LJx8MVikUQiAcDAwEDDlIu3XaMnr1qW5R6rVqvu5l6JcxcEQRCERjb381v+E/7vNNsp462K6NUQ0zQJBoNudohUQwRBEARh65BPUJqnquptGT3ErFwuYxgGkUiESCRCNBptiIkXBEEQBGHLEDHyd7zTM95Nu3o1RI3sTvf4sSAIgiDsjIgYodEXoosQZWatVqvAxkmZUChEPB6XSRlBEARB2EaIGKEx5MwwDEzTdMPMTNPE7/e7JlUJMBMEQRCEbYuIkb+j9syotoyKcw8GgwQCATGpCoIgCMJ2Qj5dwY1xVz/XajUMw3C9ITKyKwiCIAjbDxEj4G7eNU0Ty7IIBAIEAgGSyaRUQwRBEARhOyOftGw0q5bLZXw+H8FgkEgkQiwWk5FdQRAEQZgGRIwA1WqVSCRCIBAgHo8TCoVm+pIEQRAEYZdBxAgQDAaJRqNEo1EZ2RUEQRCEaWaXFSPxeJxisUi1WiUajcrIriAIgiDMELusGAGIRCKEQiExqQqCIAjCDLJLfwr7fD4xqQqCIAjCDCOfxIIgCIIgzCgiRgRBEARBmFFEjAiCIAiCMKOIGBEEQRAEYUYRMSIIgiAIwowiYkQQBEEQhBlFxIggCIIgCDOKiBFBEARBEGYUESOCIAiCIMwoIkYEQRAEQZhRRIwIgiAIgjCjiBgRBEEQBGFGETEiCIIgCMKMMiu29jqOA0Aul5vhKxEEQRAEYXNRn9vqc3wqZoUYyefzAMybN2+Gr0QQBEEQhC0ln8+TTqenfNxwXk+u7ADYtk1/fz/JZBLDMLbZ6+ZyOebNm8e6detIpVLb7HWFRuQ+Tx9yr6cHuc/Tg9zn6WF73mfHccjn8/T29uLzTe0MmRWVEZ/Px9y5c7fb66dSKfmHPg3IfZ4+5F5PD3Kfpwe5z9PD9rrPr1URUYiBVRAEQRCEGUXEiCAIgiAIM8ouLUbC4TBXXHEF4XB4pi9lp0bu8/Qh93p6kPs8Pch9nh52hPs8KwysgiAIgiDsvOzSlRFBEARBEGYeESOCIAiCIMwoIkYEQRAEQZhRRIwIgiAIgjCj7PRi5Lvf/S677747kUiEww8/nMcff/w1z//Vr37F/vvvTyQS4cADD+Suu+6apiud3WzJff6v//ovlixZQmtrK62trZxwwgmv+7+LsIkt/TetuPHGGzEMg7POOmv7XuBOwpbe54mJCS688EJ6enoIh8Psu+++8v8fm8GW3udrr72W/fbbj2g0yrx58/jEJz5BpVKZpqudnTz00EOcfvrp9Pb2YhgGy5cvf93nPPjggxx66KGEw2H23ntvrr/++u17kc5OzI033uiEQiHnv//7v51Vq1Y5//zP/+y0tLQ4Q0NDTc9/5JFHHL/f73zta19zVq9e7Vx++eVOMBh0nn766Wm+8tnFlt7nf/zHf3S++93vOk8++aTz7LPPOu973/ucdDrtrF+/fpqvfPaxpfda8fLLLztz5sxxlixZ4px55pnTc7GzmC29z9Vq1XnLW97inHLKKc7DDz/svPzyy86DDz7orFy5cpqvfHaxpff5F7/4hRMOh51f/OIXzssvv+zcc889Tk9Pj/OJT3ximq98dnHXXXc5l112mXPzzTc7gHPLLbe85vlr1qxxYrGYc/HFFzurV692vv3tbzt+v9/5zW9+s92ucacWI4cddphz4YUXur/X63Wnt7fXWbZsWdPz3/Wudzmnnnpqw7HDDz/c+Zd/+Zftep2znS29z14sy3KSyaTz05/+dHtd4k7D1txry7KcI4880vnRj37kXHDBBSJGNoMtvc/f+973nD333NOp1WrTdYk7BVt6ny+88ELnbW97W8Oxiy++2DnqqKO263XuTGyOGPnMZz7jLFy4sOHYueee65x00knb7bp22jZNrVbjL3/5CyeccIJ7zOfzccIJJ/DYY481fc5jjz3WcD7ASSedNOX5wtbdZy+lUgnTNMlkMtvrMncKtvZeX3XVVXR2dvJP//RP03GZs56tuc+33XYbixcv5sILL6Srq4tFixZx9dVXU6/Xp+uyZx1bc5+PPPJI/vKXv7itnDVr1nDXXXdxyimnTMs17yrMxGfhrFiUtzWMjIxQr9fp6upqON7V1cVzzz3X9DmDg4NNzx8cHNxu1znb2Zr77OWzn/0svb29k/7xC41szb1++OGH+fGPf8zKlSun4Qp3DrbmPq9Zs4YHHniA97znPdx11128+OKL/Nu//RumaXLFFVdMx2XPOrbmPv/jP/4jIyMjHH300TiOg2VZfPjDH+bSSy+djkveZZjqszCXy1Eul4lGo9v8PXfayogwO7jmmmu48cYbueWWW4hEIjN9OTsV+Xye8847j//6r/+ivb19pi9np8a2bTo7O/nhD3/Im9/8Zs4991wuu+wyvv/978/0pe1UPPjgg1x99dX853/+J0888QQ333wzd955J1/60pdm+tKEN8hOWxlpb2/H7/czNDTUcHxoaIju7u6mz+nu7t6i84Wtu8+Kb3zjG1xzzTX89re/5aCDDtqel7lTsKX3+qWXXuKVV17h9NNPd4/Ztg1AIBDg+eefZ6+99tq+Fz0L2Zp/0z09PQSDQfx+v3vsgAMOYHBwkFqtRigU2q7XPBvZmvv8+c9/nvPOO48PfvCDABx44IEUi0U+9KEPcdlll+HzyX9fbwum+ixMpVLbpSoCO3FlJBQK8eY3v5n777/fPWbbNvfffz+LFy9u+pzFixc3nA9w3333TXm+sHX3GeBrX/saX/rSl/jNb37DW97ylum41FnPlt7r/fffn6effpqVK1e6X2eccQZLly5l5cqVzJs3bzovf9awNf+mjzrqKF588UVX7AH87W9/o6enR4TIFGzNfS6VSpMEhxKAjqxZ22bMyGfhdrPG7gDceOONTjgcdq6//npn9erVzoc+9CGnpaXFGRwcdBzHcc477zznc5/7nHv+I4884gQCAecb3/iG8+yzzzpXXHGFjPZuBlt6n6+55honFAo5N910kzMwMOB+5fP5mfoTZg1beq+9yDTN5rGl93nt2rVOMpl0PvKRjzjPP/+8c8cddzidnZ3Ol7/85Zn6E2YFW3qfr7jiCieZTDr/+7//66xZs8a59957nb322st517veNVN/wqwgn887Tz75pPPkk086gPPv//7vzpNPPum8+uqrjuM4zuc+9znnvPPOc89Xo72f/vSnnWeffdb57ne/K6O9b5Rvf/vbzvz5851QKOQcdthhzh/+8Af3sWOPPda54IILGs7/5S9/6ey7775OKBRyFi5c6Nx5553TfMWzky25z7vttpsDTPq64oorpv/CZyFb+m9aR8TI5rOl9/nRRx91Dj/8cCccDjt77rmn85WvfMWxLGuar3r2sSX32TRN54tf/KKz1157OZFIxJk3b57zb//2b874+Pj0X/gs4ne/+13T/89V9/aCCy5wjj322EnPOeSQQ5xQKOTsueeezk9+8pPteo2G40htSxAEQRCEmWOn9YwIgiAIgjA7EDEiCIIgCMKMImJEEARBEIQZRcSIIAiCIAgziogRQRAEQRBmFBEjgiAIgiDMKCJGBEEQBEGYUUSMCIIgCIIwo4gYEQRBEARhRhExIgiCIAjCjCJiRBAEQRCEGUXEiCAIgiAIM8r/D3JylvDmM4iIAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lsq.visualize(m.values)\n", "\n", "ab = m.mncontour(\"a\", \"b\")\n", "for a, b in ab:\n", " plt.plot(data.x, model(data.x, a, b), color=\"k\", alpha=0.01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Don't draw error bands this way, this is just for illustration\n", "* [iminuit docs have a tutorial on how to make error bands](https://iminuit.readthedocs.io/en/stable/notebooks/error_bands.html)\n", "* One way is to use [jacobi](https://github.com/HDembinski/jacobi), another is to use [resample](https://github.com/scikit-hep/resample/)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import jacobi\n", "\n", "lsq.visualize(m.values)\n", "\n", "y, ycov = jacobi.propagate(lambda p: model(x, *p), m.values, m.covariance)\n", "ye = np.diag(ycov) ** 0.5\n", "\n", "plt.fill_between(x, y - ye, y + ye, alpha=0.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **New**: matrix of profiled likelihoods ⚡️\n", "\n", "* Handy (but costly) way to inspect the profiled likelihood for all pairs of parameters" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_mnmatrix();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Why fits fail and how to fix it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Starting values far away" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Fits can fail when starting values are poor\n", "- Numerical reasons\n", " - Cost function uses log(pdf) and pdf may return 0 when data are far away from prediction\n", " - Curvature of cost function may become so small that Minuit thinks the fit has converged\n", " (stopping criterion is based on gradient and curvature)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 1.487e+05 Nfcn = 137
EDM = nan (Goal: 0.0002)
INVALID Minimum No Parameters at limit
ABOVE EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 loc 5e1 nan
1 scale 1 nan
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loc scale
loc nan nan
scale nan nan
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 1.487e+05 │ Nfcn = 137 │\n", "│ EDM = nan (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ INVALID Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ ABOVE EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ loc │ 5e1 │ nan │ │ │ │ │ │\n", "│ 1 │ scale │ 1 │ nan │ │ │ │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬─────────────┐\n", "│ │ loc scale │\n", "├───────┼─────────────┤\n", "│ loc │ nan nan │\n", "│ scale │ nan nan │\n", "└───────┴─────────────┘" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# numba_stats has faster versions of statistical distributions in scipy.stats \n", "from numba_stats import norm\n", "\n", "rng = np.random.default_rng(1)\n", "\n", "x = rng.normal(size=100)\n", "\n", "nll = cost.UnbinnedNLL(x, norm.pdf)\n", "\n", "# uh oh, starting value for loc far away\n", "m = Minuit(nll, loc=50, scale=1)\n", "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Mental note: \"Hesse ok\" is wrong, a bug in C++ Minuit2!\n", "* If you find things like this, don't 🤷‍♂️, [please report a minimal reproducer in iminuit issues](https://github.com/scikit-hep/iminuit/)\n", "* Also great way to contribute to Hackashop" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(x)\n", "xm = np.linspace(30, 60, 1000)\n", "ym = norm.pdf(xm, *m.values) * 50\n", "plt.plot(xm, ym);" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2.41970725e-01, 7.69459863e-23, 0.00000000e+00, 0.00000000e+00])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm.pdf([1, 10, 100, 1000], 0, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* By using a good starting value for `loc` we fix the issue" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 251.6 Nfcn = 31
EDM = 2.2e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 loc -0.07 0.09
1 scale 0.85 0.06
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loc scale
loc 0.00725 2.67e-06
scale 2.67e-06 0.00362
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 251.6 │ Nfcn = 31 │\n", "│ EDM = 2.2e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ loc │ -0.07 │ 0.09 │ │ │ │ │ │\n", "│ 1 │ scale │ 0.85 │ 0.06 │ │ │ │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────┐\n", "│ │ loc scale │\n", "├───────┼───────────────────┤\n", "│ loc │ 0.00725 2.67e-06 │\n", "│ scale │ 2.67e-06 0.00362 │\n", "└───────┴───────────────────┘" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = Minuit(nll, loc=0, scale=1)\n", "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- How to fix this\n", " - Find better starting values, e.g. use values from simulation\n", " - Use least-squares fit followed by maximum-likelihood fit; least-squares is more robust" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* This error also appears in more practical situations: fit of a signal+background model" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Generate signal and background mixture\n", "\n", "rng = np.random.default_rng(1)\n", "\n", "s = rng.normal(0.5, 0.1, size=1000)\n", "b = rng.exponential(0.8, size=2000)\n", "x = np.append(s, b)\n", "x = x[x < 1]\n", "\n", "data = Namespace(x=x, range=(0, 1))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Bin and draw data\n", "\n", "import boost_histogram as bh\n", "# or use numpy.histogram ...\n", "\n", "h = bh.Histogram(bh.axis.Regular(50, 0, 1))\n", "h.fill(data.x)\n", "\n", "data.edges = h.axes[0].edges\n", "data.n = h.values()\n", "\n", "plt.stairs(data.n, data.edges, fill=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Let's do an extended binned fit now" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "cost.ExtendedBinnedNLL?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = nan Nfcn = 441
EDM = nan (Goal: 0.0002)
INVALID Minimum No Parameters at limit
ABOVE EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 1 nan
1 mu 5 nan
2 sigma 1e-1 nan
3 nb 1 nan
4 slope 1 nan
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ns mu sigma nb slope
ns nan nan nan nan nan
mu nan nan nan nan nan
sigma nan nan nan nan nan
nb nan nan nan nan nan
slope nan nan nan nan nan
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = nan │ Nfcn = 441 │\n", "│ EDM = nan (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ INVALID Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ ABOVE EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 1 │ nan │ │ │ │ │ │\n", "│ 1 │ mu │ 5 │ nan │ │ │ │ │ │\n", "│ 2 │ sigma │ 1e-1 │ nan │ │ │ │ │ │\n", "│ 3 │ nb │ 1 │ nan │ │ │ │ │ │\n", "│ 4 │ slope │ 1 │ nan │ │ │ │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────┤\n", "│ ns │ nan nan nan nan nan │\n", "│ mu │ nan nan nan nan nan │\n", "│ sigma │ nan nan nan nan nan │\n", "│ nb │ nan nan nan nan nan │\n", "│ slope │ nan nan nan nan nan │\n", "└───────┴───────────────────────────────┘" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numba_stats import truncnorm, truncexpon\n", "\n", "# scaled cdf\n", "def model(x, ns, mu, sigma, nb, slope):\n", " s = ns * truncnorm.cdf(x, *data.range, mu, sigma)\n", " b = nb * truncexpon.cdf(x, *data.range, 0, slope)\n", " return s + b\n", "\n", "nll = cost.ExtendedBinnedNLL(data.n, data.edges, model)\n", "\n", "m = Minuit(nll, ns=1, mu=5, sigma=0.1, nb=1, slope=1)\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nll(1, 5, 0.1, 1, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model parameters outside mathematical domain\n", "\n", "* Minuit can place limits on parameters, this is often necessary\n", "* Examples\n", " - σ parameter of normal distribution must be positive\n", " - μ parameter of Poisson distribution must be non-negative\n", "* Without limits on such parameters, fit is likely to fail" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 203.9 (chi2/ndof = 4.5) Nfcn = 411
EDM = 1.41e-06 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 919.1 2.0 0
1 mu 0.484 0.005 0 1
2 sigma 0.094 0.004 0
3 nb 1.5179e3 0.0020e3 0
4 slope 1.3171e3 0.0020e3 0
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ns mu sigma nb slope
ns 3.99 -5.53e-05 (-0.006) 0.000141 (0.017) -0.00482 (-0.001) 1.86e-07
mu -5.53e-05 (-0.006) 2.47e-05 -5.01e-06 (-0.246) 3.36e-05 (0.003) -3.24e-08
sigma 0.000141 (0.017) -5.01e-06 (-0.246) 1.68e-05 -8.58e-05 (-0.010) 1.19e-08
nb -0.00482 (-0.001) 3.36e-05 (0.003) -8.58e-05 (-0.010) 3.99 -1.13e-07
slope 1.86e-07 -3.24e-08 1.19e-08 -1.13e-07 4
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 203.9 (chi2/ndof = 4.5) │ Nfcn = 411 │\n", "│ EDM = 1.41e-06 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 919.1 │ 2.0 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.484 │ 0.005 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.094 │ 0.004 │ │ │ 0 │ │ │\n", "│ 3 │ nb │ 1.5179e3 │ 0.0020e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope │ 1.3171e3 │ 0.0020e3 │ │ │ 0 │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────────────────────────┤\n", "│ ns │ 3.99 -5.53e-05 0.000141 -0.00482 1.86e-07 │\n", "│ mu │ -5.53e-05 2.47e-05 -5.01e-06 3.36e-05 -3.24e-08 │\n", "│ sigma │ 0.000141 -5.01e-06 1.68e-05 -8.58e-05 1.19e-08 │\n", "│ nb │ -0.00482 3.36e-05 -8.58e-05 3.99 -1.13e-07 │\n", "│ slope │ 1.86e-07 -3.24e-08 1.19e-08 -1.13e-07 4 │\n", "└───────┴───────────────────────────────────────────────────┘" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = Minuit(nll, ns=1, mu=0.5, sigma=0.1, nb=1, slope=1)\n", "\n", "m.limits[\"ns\", \"nb\", \"sigma\", \"slope\"] = (0, None)\n", "m.limits[\"mu\"] = (0, 1) # not strictly necessary\n", "\n", "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Fit converged but chi2/ndof = 4.5 looks bad, should be around 1" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nll.visualize(m.values)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_profile(\"slope\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Fit \"converged\", although slope is not in a minimum\n", "* Fix this by making limit on slope more tight" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 40.6 (chi2/ndof = 0.9) Nfcn = 744
EDM = 3.19e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 950 50 0
1 mu 0.498 0.005 0 1
2 sigma 0.093 0.005 0
3 nb 1.49e3 0.05e3 0
4 slope 0.81 0.07 0 2
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ns mu sigma nb slope
ns 2.45e+03 -0.0228 (-0.097) 0.108 (0.484) -1.5e+03 (-0.554) -0.716 (-0.216)
mu -0.0228 (-0.097) 2.26e-05 -2.86e-06 (-0.134) 0.0228 (0.088) -6.01e-05 (-0.188)
sigma 0.108 (0.484) -2.86e-06 (-0.134) 2.02e-05 -0.108 (-0.438) -4.48e-05 (-0.149)
nb -1.5e+03 (-0.554) 0.0228 (0.088) -0.108 (-0.438) 2.99e+03 0.716 (0.195)
slope -0.716 (-0.216) -6.01e-05 (-0.188) -4.48e-05 (-0.149) 0.716 (0.195) 0.0045
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 40.6 (chi2/ndof = 0.9) │ Nfcn = 744 │\n", "│ EDM = 3.19e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 950 │ 50 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.498 │ 0.005 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.093 │ 0.005 │ │ │ 0 │ │ │\n", "│ 3 │ nb │ 1.49e3 │ 0.05e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope │ 0.81 │ 0.07 │ │ │ 0 │ 2 │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────────────────────────┤\n", "│ ns │ 2.45e+03 -0.0228 0.108 -1.5e+03 -0.716 │\n", "│ mu │ -0.0228 2.26e-05 -2.86e-06 0.0228 -6.01e-05 │\n", "│ sigma │ 0.108 -2.86e-06 2.02e-05 -0.108 -4.48e-05 │\n", "│ nb │ -1.5e+03 0.0228 -0.108 2.99e+03 0.716 │\n", "│ slope │ -0.716 -6.01e-05 -4.48e-05 0.716 0.0045 │\n", "└───────┴───────────────────────────────────────────────────┘" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.limits[\"slope\"] = (0, 2)\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_mnmatrix(figsize=(10, 7));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model parameters are not constrained by data\n", "\n", "- Data may not be precise enough to constrain all model parameters\n", "- Model may be ambiguous, e.g. $f(x) = a + b + c \\, x^2$\n", "- Leads to parameters that are perfectly (anti)correlated\n", "- Cannot compute covariance matrix (inverse of Hessian), if Hessian does not have full rank" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 694.8 (chi2/ndof = 16.2) Nfcn = 1118
EDM = 3.37e-10 (Goal: 0.0002)
INVALID Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse FAILED APPROXIMATE NOT pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 4.8263e-9 0.0000e-9 0
1 mu 8.5436e-1 0.0000e-1 0 1
2 sigma 1.8477e5 0.0000e5 0
3 nb1 7.7481e2 0.0000e2 0
4 slope1 1.3316 0.0000 0 2
5 nb2 1.6622e3 0.0000e3 0
6 slope2 1.3317 0.0000 0 2
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 694.8 (chi2/ndof = 16.2) │ Nfcn = 1118 │\n", "│ EDM = 3.37e-10 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ INVALID Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse FAILED │APPROXIMATE│NOT pos. def.│ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 4.8263e-9 │ 0.0000e-9 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 8.5436e-1 │ 0.0000e-1 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 1.8477e5 │ 0.0000e5 │ │ │ 0 │ │ │\n", "│ 3 │ nb1 │ 7.7481e2 │ 0.0000e2 │ │ │ 0 │ │ │\n", "│ 4 │ slope1 │ 1.3316 │ 0.0000 │ │ │ 0 │ 2 │ │\n", "│ 5 │ nb2 │ 1.6622e3 │ 0.0000e3 │ │ │ 0 │ │ │\n", "│ 6 │ slope2 │ 1.3317 │ 0.0000 │ │ │ 0 │ 2 │ │\n", "└───┴────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add another background component to model to create ambiguity\n", "def model(x, ns, mu, sigma, nb1, slope1, nb2, slope2):\n", " s = ns * truncnorm.cdf(x, *data.range, mu, sigma)\n", " b1 = nb1 * truncexpon.cdf(x, *data.range, 0, slope1)\n", " b2 = nb2 * truncexpon.cdf(x, *data.range, 0, slope2)\n", " return s + b1 + b2\n", "\n", "nll = cost.ExtendedBinnedNLL(data.n, data.edges, model)\n", "\n", "m = Minuit(nll, ns=1, mu=0.5, sigma=0.1, nb1=1, slope1=1, nb2=1, slope2=0.1)\n", "m.limits[\"ns\", \"nb1\", \"nb2\", \"sigma\"] = (0, None)\n", "m.limits[\"mu\"] = (0, 1)\n", "m.limits[\"slope1\", \"slope2\"] = (0, 2)\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nll.visualize(m.values)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 40.6 (chi2/ndof = 0.9) Nfcn = 1808
EDM = 3.68e-07 (Goal: 0.0002)
INVALID Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse FAILED APPROXIMATE NOT pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 9.5004e2 0.0000e2 0
1 mu 4.9776e-1 0.0000e-1 0 1
2 sigma 9.3454e-2 0.0000e-2 0
3 nb1 1.487e3 0.000e3 0
4 slope1 8.1152e-1 0.0000e-1 0 2
5 nb2 0 0 0 yes
6 slope2 1e-1 0e-1 0 2
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 40.6 (chi2/ndof = 0.9) │ Nfcn = 1808 │\n", "│ EDM = 3.68e-07 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ INVALID Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse FAILED │APPROXIMATE│NOT pos. def.│ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 9.5004e2 │ 0.0000e2 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 4.9776e-1 │ 0.0000e-1 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 9.3454e-2 │ 0.0000e-2 │ │ │ 0 │ │ │\n", "│ 3 │ nb1 │ 1.487e3 │ 0.000e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope1 │ 8.1152e-1 │ 0.0000e-1 │ │ │ 0 │ 2 │ │\n", "│ 5 │ nb2 │ 0 │ 0 │ │ │ 0 │ │ yes │\n", "│ 6 │ slope2 │ 1e-1 │ 0e-1 │ │ │ 0 │ 2 │ │\n", "└───┴────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# resolve ambiguity by fixing nb2 to 0... but forget to fix slope2, too\n", "m.values = [p.value for p in m.init_params]\n", "m.values[\"nb2\"] = 0\n", "m.fixed[\"nb2\"] = True\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nll.visualize(m.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Fit converged (we were lucky), but errors are wrong since Hesse failed\n", "* `slope2` not constrained by data now, since `nb2 = 0`\n", "* Fixing `slope2` resolve the issue" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 40.56 (chi2/ndof = 0.9) Nfcn = 2136
EDM = 2.02e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 950 50 0
1 mu 0.498 0.005 0 1
2 sigma 0.093 0.005 0
3 nb1 1.49e3 0.05e3 0
4 slope1 0.81 0.07 0 2
5 nb2 1 0 0 yes
6 slope2 1e-1 0e-1 0 2 yes
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ns mu sigma nb1 slope1 nb2 slope2
ns 2.45e+03 -0.0228 (-0.097) 0.108 (0.484) -1.5e+03 (-0.554) -0.716 (-0.215) 0 0
mu -0.0228 (-0.097) 2.26e-05 -2.87e-06 (-0.134) 0.0229 (0.088) -6.05e-05 (-0.189) 0 0
sigma 0.108 (0.484) -2.87e-06 (-0.134) 2.02e-05 -0.108 (-0.439) -4.48e-05 (-0.148) 0 0
nb1 -1.5e+03 (-0.554) 0.0229 (0.088) -0.108 (-0.439) 2.99e+03 0.715 (0.194) 0 0
slope1 -0.716 (-0.215) -6.05e-05 (-0.189) -4.48e-05 (-0.148) 0.715 (0.194) 0.00454 0 0
nb2 0 0 0 0 0 0 0
slope2 0 0 0 0 0 0 0
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 40.56 (chi2/ndof = 0.9) │ Nfcn = 2136 │\n", "│ EDM = 2.02e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 950 │ 50 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.498 │ 0.005 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.093 │ 0.005 │ │ │ 0 │ │ │\n", "│ 3 │ nb1 │ 1.49e3 │ 0.05e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope1 │ 0.81 │ 0.07 │ │ │ 0 │ 2 │ │\n", "│ 5 │ nb2 │ 1 │ 0 │ │ │ 0 │ │ yes │\n", "│ 6 │ slope2 │ 1e-1 │ 0e-1 │ │ │ 0 │ 2 │ yes │\n", "└───┴────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌────────┬───────────────────────────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb1 slope1 nb2 slope2 │\n", "├────────┼───────────────────────────────────────────────────────────────────────┤\n", "│ ns │ 2.45e+03 -0.0228 0.108 -1.5e+03 -0.716 0 0 │\n", "│ mu │ -0.0228 2.26e-05 -2.87e-06 0.0229 -6.05e-05 0 0 │\n", "│ sigma │ 0.108 -2.87e-06 2.02e-05 -0.108 -4.48e-05 0 0 │\n", "│ nb1 │ -1.5e+03 0.0229 -0.108 2.99e+03 0.715 0 0 │\n", "│ slope1 │ -0.716 -6.05e-05 -4.48e-05 0.715 0.00454 0 0 │\n", "│ nb2 │ 0 0 0 0 0 0 0 │\n", "│ slope2 │ 0 0 0 0 0 0 0 │\n", "└────────┴───────────────────────────────────────────────────────────────────────┘" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "m.values = [p.value for p in m.init_params]\n", "m.fixed[\"slope2\"] = True\n", "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Another practical example: fit signal+background model to background-only sample\n", "* Common in limit setting to compute distribution of test statistic under null hypothesis" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 33.56 (chi2/ndof = 0.7) Nfcn = 679
EDM = 9.4e-05 (Goal: 0.0002)
Valid Minimum SOME Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 0.1e3 0.5e3 0
1 mu 0.75 0.18 0 1
2 sigma 0.3 0.6 0
3 nb 1.1e3 0.6e3 0
4 slope 0.8 0.7 0 2
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ns mu sigma nb slope
ns 3.58e+05 -49 (-0.441) 410 (0.952) -3.58e+05 (-0.998) -472 (-0.985)
mu -49 (-0.441) 0.0345 -0.0517 (-0.387) 49 (0.441) 0.0553 (0.372)
sigma 410 (0.952) -0.0517 (-0.387) 0.518 -410 (-0.951) -0.521 (-0.904)
nb -3.58e+05 (-0.998) 49 (0.441) -410 (-0.951) 3.59e+05 472 (0.984)
slope -472 (-0.985) 0.0553 (0.372) -0.521 (-0.904) 472 (0.984) 0.64
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 33.56 (chi2/ndof = 0.7) │ Nfcn = 679 │\n", "│ EDM = 9.4e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ SOME Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 0.1e3 │ 0.5e3 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.75 │ 0.18 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.3 │ 0.6 │ │ │ 0 │ │ │\n", "│ 3 │ nb │ 1.1e3 │ 0.6e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope │ 0.8 │ 0.7 │ │ │ 0 │ 2 │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────────────────────────┤\n", "│ ns │ 3.58e+05 -49 410 -3.58e+05 -472 │\n", "│ mu │ -49 0.0345 -0.0517 49 0.0553 │\n", "│ sigma │ 410 -0.0517 0.518 -410 -0.521 │\n", "│ nb │ -3.58e+05 49 -410 3.59e+05 472 │\n", "│ slope │ -472 0.0553 -0.521 472 0.64 │\n", "└───────┴───────────────────────────────────────────────────┘" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def model(x, ns, mu, sigma, nb, slope):\n", " s = ns * truncnorm.cdf(x, *data.range, mu, sigma)\n", " b = nb * truncexpon.cdf(x, *data.range, 0, slope)\n", " return s + b\n", "\n", "b = rng.exponential(1, size=2000)\n", "b = b[b < 1]\n", "\n", "data.nb = np.histogram(b, bins=data.edges)[0]\n", "\n", "nll = cost.ExtendedBinnedNLL(data.nb, data.edges, model)\n", "\n", "m = Minuit(nll, ns=10, mu=0.5, sigma=0.1, nb=200, slope=1)\n", "m.limits[\"ns\", \"nb\", \"sigma\"] = (0, None)\n", "m.limits[\"mu\"] = (0, 1)\n", "m.limits[\"slope\"] = (0, 2)\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nll.visualize(m.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Fit converged and looks ok (we are lucky), but is very unstable\n", "* Several parameters are almost perfectly correlated, Hessian does not have full rank\n", "* Fix this by adding Gaussian penalty terms for `mu` and `sigma`\n", "* Penalty terms add external pulls when data does not" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 33.89 (chi2/ndof = 0.7) Nfcn = 732
EDM = 6.68e-06 (Goal: 0.0002)
Valid Minimum SOME Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 0 15 0
1 mu 0.5 0.1 0 1
2 sigma 0.10 0.05 0
3 nb 1.255e3 0.035e3 0
4 slope 1.06 0.11 0 2
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ns mu sigma nb slope
ns 0.217 -0.000413 (-0.009) 0.000106 (0.005) -0.396 (-0.024) -0.000339 (-0.006)
mu -0.000413 (-0.009) 0.01 -1.83e-07 0.000752 5.96e-07
sigma 0.000106 (0.005) -1.83e-07 0.0025 -0.000193 -1.65e-07
nb -0.396 (-0.024) 0.000752 -0.000193 1.26e+03 0.000617
slope -0.000339 (-0.006) 5.96e-07 -1.65e-07 0.000617 0.0126
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 33.89 (chi2/ndof = 0.7) │ Nfcn = 732 │\n", "│ EDM = 6.68e-06 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ SOME Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 0 │ 15 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.5 │ 0.1 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.10 │ 0.05 │ │ │ 0 │ │ │\n", "│ 3 │ nb │ 1.255e3 │ 0.035e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope │ 1.06 │ 0.11 │ │ │ 0 │ 2 │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────────────────────────┤\n", "│ ns │ 0.217 -0.000413 0.000106 -0.396 -0.000339 │\n", "│ mu │ -0.000413 0.01 -1.83e-07 0.000752 5.96e-07 │\n", "│ sigma │ 0.000106 -1.83e-07 0.0025 -0.000193 -1.65e-07 │\n", "│ nb │ -0.396 0.000752 -0.000193 1.26e+03 0.000617 │\n", "│ slope │ -0.000339 5.96e-07 -1.65e-07 0.000617 0.0126 │\n", "└───────┴───────────────────────────────────────────────────┘" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nll_mod = nll + cost.NormalConstraint((\"mu\", \"sigma\"), (0.5, 0.1), (0.1, 0.05))\n", "\n", "m = Minuit(nll_mod, ns=1, mu=0.5, sigma=0.1, nb=1, slope=1)\n", "m.limits[\"ns\", \"nb\", \"sigma\"] = (0, None)\n", "m.limits[\"mu\"] = (0, 1)\n", "m.limits[\"slope\"] = (0, 2)\n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nll_mod.visualize(m.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Correlations are now tamed, Hessian does have full rank, and fit is stable\n", "* Make sure that penalty is weak if data actually constrains model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Be careful about warning \"Parameter at limit\"\n", "\n", "* Perfectly fine when fitting signal+background model to background-only\n", " * True value of `ns` is 0, so fitted value may be close to lower limit\n", "* In general: keep limits loose enough so that they do not interfere with error calculation" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 40.6 (chi2/ndof = 0.9) Nfcn = 144
EDM = 6.35e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 950 50 0
1 mu 0.498 0.005 0 1
2 sigma 0.093 0.005 0
3 nb 1.49e3 0.05e3 0
4 slope 0.81 0.07 0 2
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ns mu sigma nb slope
ns 2.45e+03 -0.0228 (-0.097) 0.108 (0.484) -1.5e+03 (-0.554) -0.716 (-0.216)
mu -0.0228 (-0.097) 2.26e-05 -2.86e-06 (-0.134) 0.0228 (0.088) -6.01e-05 (-0.188)
sigma 0.108 (0.484) -2.86e-06 (-0.134) 2.02e-05 -0.108 (-0.438) -4.48e-05 (-0.149)
nb -1.5e+03 (-0.554) 0.0228 (0.088) -0.108 (-0.438) 2.98e+03 0.716 (0.195)
slope -0.716 (-0.216) -6.01e-05 (-0.188) -4.48e-05 (-0.149) 0.716 (0.195) 0.00449
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 40.6 (chi2/ndof = 0.9) │ Nfcn = 144 │\n", "│ EDM = 6.35e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 950 │ 50 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.498 │ 0.005 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.093 │ 0.005 │ │ │ 0 │ │ │\n", "│ 3 │ nb │ 1.49e3 │ 0.05e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope │ 0.81 │ 0.07 │ │ │ 0 │ 2 │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────────────────────────┤\n", "│ ns │ 2.45e+03 -0.0228 0.108 -1.5e+03 -0.716 │\n", "│ mu │ -0.0228 2.26e-05 -2.86e-06 0.0228 -6.01e-05 │\n", "│ sigma │ 0.108 -2.86e-06 2.02e-05 -0.108 -4.48e-05 │\n", "│ nb │ -1.5e+03 0.0228 -0.108 2.98e+03 0.716 │\n", "│ slope │ -0.716 -6.01e-05 -4.48e-05 0.716 0.00449 │\n", "└───────┴───────────────────────────────────────────────────┘" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def model(x, ns, mu, sigma, nb, slope):\n", " s = ns * truncnorm.cdf(x, *data.range, mu, sigma)\n", " b = nb * truncexpon.cdf(x, *data.range, 0, slope)\n", " return s + b\n", "\n", "nll = cost.ExtendedBinnedNLL(data.n, data.edges, model)\n", "\n", "m1 = Minuit(nll, ns=1000, mu=0.5, sigma=0.1, nb=1000, slope=1)\n", "m1.limits[\"ns\", \"nb\", \"sigma\"] = (0, None)\n", "m1.limits[\"mu\"] = (0, 1)\n", "m1.limits[\"slope\"] = (0, 2)\n", "m1.migrad()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 40.65 (chi2/ndof = 0.9) Nfcn = 141
EDM = 2.11e-06 (Goal: 0.0002)
Valid Minimum SOME Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 955.5 1.8 0
1 mu 0.498 0.005 0 1
2 sigma 0.0945 0.0017 0.0925 0.0945
3 nb 1.4815e3 0.0018e3 0
4 slope 0.81 0.06 0 2
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ns mu sigma nb slope
ns 3.33 -1.02e-05 (-0.001) 4.54e-11 -0.00281 (-0.001) -0.000644 (-0.005)
mu -1.02e-05 (-0.001) 2.24e-05 -7.37e-13 6.55e-06 -6.95e-05 (-0.226)
sigma 4.54e-11 -7.37e-13 1.97e-13 -2.93e-11 -3.28e-12
nb -0.00281 (-0.001) 6.55e-06 -2.93e-11 3.33 0.000416 (0.004)
slope -0.000644 (-0.005) -6.95e-05 (-0.226) -3.28e-12 0.000416 (0.004) 0.00422
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 40.65 (chi2/ndof = 0.9) │ Nfcn = 141 │\n", "│ EDM = 2.11e-06 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ SOME Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 955.5 │ 1.8 │ │ │ 0 │ │ │\n", "│ 1 │ mu │ 0.498 │ 0.005 │ │ │ 0 │ 1 │ │\n", "│ 2 │ sigma │ 0.0945 │ 0.0017 │ │ │0.0924765│0.0944765│ │\n", "│ 3 │ nb │ 1.4815e3 │ 0.0018e3 │ │ │ 0 │ │ │\n", "│ 4 │ slope │ 0.81 │ 0.06 │ │ │ 0 │ 2 │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬───────────────────────────────────────────────────┐\n", "│ │ ns mu sigma nb slope │\n", "├───────┼───────────────────────────────────────────────────┤\n", "│ ns │ 3.33 -1.02e-05 4.54e-11 -0.00281 -0.000644 │\n", "│ mu │ -1.02e-05 2.24e-05 -7.37e-13 6.55e-06 -6.95e-05 │\n", "│ sigma │ 4.54e-11 -7.37e-13 1.97e-13 -2.93e-11 -3.28e-12 │\n", "│ nb │ -0.00281 6.55e-06 -2.93e-11 3.33 0.000416 │\n", "│ slope │ -0.000644 -6.95e-05 -3.28e-12 0.000416 0.00422 │\n", "└───────┴───────────────────────────────────────────────────┘" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m2 = Minuit(nll, ns=1000, mu=0.5, sigma=0.1, nb=1000, slope=1)\n", "m2.limits[\"ns\", \"nb\"] = (0, None)\n", "# set tight limits around previously fitted value of sigma\n", "m2.limits[\"sigma\"] = m1.values[\"sigma\"] - 1e-3, m1.values[\"sigma\"] + 1e-3\n", "m2.limits[\"mu\"] = (0, 1)\n", "m2.limits[\"slope\"] = (0, 2)\n", "m2.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Minuit now warns about \"Some parameters at limit\"\n", "* Fit got slightly worse (FCN 40.65 > 40.6) and error of `sigma` is now too small\n", "* But also errors of other parameters, especially `ns`, are artificially reduced 🙁\n", "* Do not blindly trust Hesse errors when some parameters are at limit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **New**: Interactive fitting ⚡️\n", "\n", "* [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/index.html) package allows one to generate interactive widgets inside Jupyter\n", " * `ipywidgets.interact` offers an easy way to visualize how a function reacts to parameter changes\n", "* iminuit offers a similar feature to enable an interactive fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Useful\n", "* To understand what is going on\n", "* To find starting values\n", "* For teaching" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7721003645a2489e8bdde4583e60940f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Output(), VBox(children=(HBox(children=(Button(description='Fit', style=ButtonStyle()), ToggleB…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m1.interactive()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **New**: Template fits ⚡️\n", "\n", "* We often fit signal+background mixtures\n", "* Sometimes you do not have a parametric model for signal or background\n", "* If simulation is trustworthy, can generate templates for signal or background from simulation\n", "* But simulation sample is finite, need to include that uncertainty!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Barlow, Beeston, Comput.Phys.Commun. 77 (1993) 219-228](https://inspirehep.net/literature/35053)\n", " * Exact likelihood for this problem\n", " * Somewhat expensive solve exact likelihood\n", " * Very limited support for weighted simulation\n", " * [TFractionFitter in ROOT](https://root.cern.ch/doc/master/classTFractionFitter.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [C.A. Argüelles, A. Schneider, T. Yuan, JHEP 06 (2019) 030, arXiv:1901.04645 [physics.data-an]](https://inspirehep.net/literature/1713790)\n", " * Alternative Bayesian approach\n", " * Full support for weighted simulation\n", " * Faster fits that with TFractionFitter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "* [H.P. Dembinski, A. Abdelmotteleb, arXiv:2206.12346, submitted to JHEP](https://arxiv.org/abs/2206.12346)\n", " * Fast approximation to exact likelihood\n", " * Full support for weighted simulation and data" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "cost.BarlowBeestonLite?" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Migrad
FCN = 31.92 (chi2/ndof = 0.7) Nfcn = 113
EDM = 1.8e-06 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 ns 990 70
1 nb 1.45e3 0.09e3
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ns nb
ns 4.58e+03 -2.62e+03 (-0.448)
nb -2.62e+03 (-0.448) 7.47e+03
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 31.92 (chi2/ndof = 0.7) │ Nfcn = 113 │\n", "│ EDM = 1.8e-06 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ ns │ 990 │ 70 │ │ │ │ │ │\n", "│ 1 │ nb │ 1.45e3 │ 0.09e3 │ │ │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌────┬─────────────────────┐\n", "│ │ ns nb │\n", "├────┼─────────────────────┤\n", "│ ns │ 4.58e+03 -2.62e+03 │\n", "│ nb │ -2.62e+03 7.47e+03 │\n", "└────┴─────────────────────┘" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = np.random.default_rng(1)\n", "\n", "# generate templates from \"simulation\"\n", "nb = np.histogram(rng.normal(0.5, 0.1, size=1000), bins=data.edges)[0]\n", "ns = np.histogram(rng.exponential(1, size=1000), bins=data.edges)[0]\n", "\n", "nll = cost.BarlowBeestonLite(data.n, data.edges, (nb, ns), name=(\"ns\", \"nb\"))\n", "\n", "m = Minuit(nll, ns=1, nb=1)\n", "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Error on `ns` larger compared to fitting parametric model (70 vs 50), since uncertainty of template not negligible " ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nll.visualize(m.values)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b2678ead232844c48982d2a1849ec5c2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Output(), VBox(children=(HBox(children=(Button(description='Fit', style=ButtonStyle()), ToggleB…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.interactive()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `BarlowBeestonLite` also supports higher dimensional data (2D, 3D, ...)\n", "* Weighted data and weighted simulation are supported\n", "* chi2/ndof is computed automatically\n", "* Performance comparable to exact Barlow-Beeston method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Warning: still need to estimate systematic uncertainties of simulation deviating from data\n", "* No automatic way in iminuit or elsewhere to handle this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Want to learn more?\n", "\n", "* iminuit's comprehensive docs are full of [tutorials](https://iminuit.readthedocs.io/en/stable/tutorials.html) and [practical studies around fitting](https://iminuit.readthedocs.io/en/stable/studies.html)" ] } ], "metadata": { "kernelspec": { "display_name": "iminuit_tutorial", "language": "python", "name": "iminuit_tutorial" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" }, "vscode": { "interpreter": { "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" } } }, "nbformat": 4, "nbformat_minor": 4 }