mirror of
https://github.com/fjosw/pyerrors.git
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* feat: corr_matrix kwargs as input for least squares fit * feat/tests: inverse covariance matrix and correlation matrix kwargs as input for least squares function * feat/tests/example: reduced new kwargs to 'inv_chol_cov_matrix' and outsourced the inversion & cholesky decomposition of the covariance matrix (function 'invert_corr_cov_cholesky(corr, covdiag)') * tests: added tests for inv_chol_cov_matrix kwarg for the case of combined fits * fix: renamed covdiag to inverrdiag needed for the cholesky decomposition and corrected its documentation * examples: added an example of a correlated combined fit to the least_squares documentation * feat/tests/fix(of typos): added function 'sort_corr()' (and a test of it) to sort correlation matrix according to a list of alphabetically sorted keys * docs: added more elaborate documentation/example of sort_corr(), fixed typos in documentation of invert_corr_cov_cholesky()
727 lines
205 KiB
Text
727 lines
205 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import pyerrors as pe\n",
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"import scipy.optimize\n",
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"from scipy.linalg import cholesky\n",
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"from scipy.stats import norm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use('./base_style.mplstyle')\n",
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"import shutil\n",
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"usetex = shutil.which('latex') not in ('', None)\n",
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"plt.rc('text', usetex=usetex)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Read data from the pcac example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data has been written using pyerrors 2.0.0.\n",
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"Format version 0.1\n",
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"Written by fjosw on 2022-01-06 11:27:34 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n",
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"\n",
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"Description: SF correlation function f_P on a test ensemble\n"
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]
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}
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],
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"source": [
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"fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding=[1, 1])\n",
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"fP.gamma_method()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now define a custom fit function, in this case a single exponential. __Here we need to use the autograd wrapped version of np__ (imported as anp) to use automatic differentiation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import autograd.numpy as anp\n",
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"def func_exp(a, x):\n",
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" y = a[1] * anp.exp(-a[0] * x)\n",
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" return y"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fit single exponential to f_P. The kwarg `resplot` generates a figure which visualizes the fit with residuals."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Fit with 2 parameters\n",
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"Method: Levenberg-Marquardt\n",
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"`xtol` termination condition is satisfied.\n",
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"chisquare/d.o.f.: 0.0023324250917750268\n",
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"fit parameters [ 0.20362603 16.25660947]\n",
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"\n",
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" Goodness of fit:\n",
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"χ²/d.o.f. = 0.002332\n",
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"p-value = 1.0000\n",
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"Fit parameters:\n",
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"0\t 0.2036(92)\n",
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"1\t 16.3(1.3)\n",
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"\n"
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]
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},
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{
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"data": {
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",
|
|
"text/plain": [
|
|
"<Figure size 800x494.438 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"start_fit = 9\n",
|
|
"stop_fit = 18\n",
|
|
"\n",
|
|
"fit_result = fP.fit(func_exp, [start_fit, stop_fit], resplot=True)\n",
|
|
"fit_result.gamma_method()\n",
|
|
"print(\"\\n\", fit_result)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The covariance of the two fit parameters can be computed in the following way"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Covariance: 0.009831165600263978\n",
|
|
"Normalized covariance: 0.8384671240792649\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cov_01 = pe.fits.covariance([fit_result[0], fit_result[1]])[0, 1]\n",
|
|
"print('Covariance: ', cov_01)\n",
|
|
"print('Normalized covariance: ', cov_01 / fit_result[0].dvalue / fit_result[1].dvalue)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Effective mass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculate the effective mass for comparison"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"m_eff_fP = fP.m_eff()\n",
|
|
"m_eff_fP.tag = r\"Effective mass of f_P\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Calculate the corresponding plateau and compare the two results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 1 parameter\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 0.13241808096938082\n",
|
|
"fit parameters [0.20567587]\n",
|
|
"\n",
|
|
"Effective mass:\t 0.2057(68)\n",
|
|
"Fitted mass:\t 0.2036(92)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"m_eff_fP.gamma_method()\n",
|
|
"m_eff_plateau = m_eff_fP.plateau([start_fit, stop_fit])\n",
|
|
"m_eff_plateau.gamma_method()\n",
|
|
"print()\n",
|
|
"print('Effective mass:\\t', m_eff_plateau)\n",
|
|
"print('Fitted mass:\\t', fit_result[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now visualize the effective mass compared to the result of the fit"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"m_eff_fP.show(plateau=fit_result[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Fitting with x-errors"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We first generate pseudo data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(Obs[-0.47(35)], Obs[-0.26(25)])\n",
|
|
"(Obs[2.44(35)], Obs[1.15(25)])\n",
|
|
"(Obs[3.68(35)], Obs[-1.23(25)])\n",
|
|
"(Obs[6.50(35)], Obs[-1.86(25)])\n",
|
|
"(Obs[7.91(35)], Obs[-0.32(25)])\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ox = []\n",
|
|
"oy = []\n",
|
|
"for i in range(0,10,2):\n",
|
|
" ox.append(pe.pseudo_Obs(i + 0.35 * np.random.normal(), 0.35, str(i)))\n",
|
|
" oy.append(pe.pseudo_Obs(np.sin(i) + 0.25 * np.random.normal() - 0.2 * i + 0.17, 0.25, str(i)))\n",
|
|
"\n",
|
|
"[o.gamma_method() for o in ox + oy]\n",
|
|
"[print(o) for o in zip(ox, oy)];"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"And choose a function to fit"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def func(a, x):\n",
|
|
" y = a[0] + a[1] * x + a[2] * anp.sin(x)\n",
|
|
" return y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can then fit this function to the data and get the fit parameter as Obs with the function `odr_fit` which uses orthogonal distance regression."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 3 parameters\n",
|
|
"Method: ODR\n",
|
|
"Sum of squares convergence\n",
|
|
"Residual variance: 0.49296554803718634\n",
|
|
"Parameter 1 : 0.72(56)\n",
|
|
"Parameter 2 : -0.43(16)\n",
|
|
"Parameter 3 : 2.33(84)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"beta = pe.fits.total_least_squares(ox, oy, func)\n",
|
|
"\n",
|
|
"for i, item in enumerate(beta):\n",
|
|
" item.gamma_method()\n",
|
|
" print('Parameter', i + 1, ':', item)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For the visulization we determine the value of the fit function in a range of x values"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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wvQnDc1SCnOKUq6EiAJ+9YDqJiEjU0OxXqV+3U2D8POhzoekkEi6desKg8RCsNp1ERCRqqAjJD1VWQKsEOOFHppNIuF3+kOkEIiJRRec85IfmjIGCP5lOIXbxb4H1i02nEBGJCipCUtemd2HjUm2s6mTvPwDPZkPlAdNJRESMUxGSupbkQtcBcMLFppOIXTKuhW+/gbX/M51ERMQ4FSE5ZOPbsOltGP47XSnmZF37WSN+RU+YTiIiYpye7eSQb76EY0+DEzUa5HinXGPNEyrdZjqJiIhRumpMDhl6o7WVhnYod76TL7d2pK8+aDqJiIhRGhESCAbhk/lwYJ9OiblFohd+cj90PN50EhERo/SsJ7DxLXh2ovVe3KPqIBQ9CTvWmE4iImKMipDbBYOw5E44ZjD01QKKruKJs/7tl//HdBIREWNUhNxu41LY8h4Mn665QW4TFw+DfgprnoWD+02nERExQkXIzWpGg7plQN9RptOICYPGQ0UpfPmK6SQiIkboqjG3O3sKJHg1GuRWnXtD2jBY9TT0v9J0GhGRiFMRcjOPRyNBAuf8FspLTacQETFCp8bcasMb8OTlegIU6HshDBxjOoWIiBEqQm5UMzeoPGCdFhPZsRpe+4P1tyEi4iIqQm604Q3YukxXiskhe4vh/fthW5HpJCIiEaUi5DY1o0HdT4XeI0ynkWiRPhySusHHT5tOIiISUSpCbrNnIxR/BudrNEi+Jy4eBo2D1flQWWE6jYhIxKgIuU2ndJiyBnppNEgOM2g8lPth3eumk4iIRIyKkJt8s966SqxtR40GyQ91OQEuu886bSoi4hJaR8gtgkF4LtsqQVcvNJ1GolXGtaYTiIhElEaE3GLta7BtJZzxS9NJJJoFg1B4u7X/mIiIC6gIuUF1NbzxN+hxJqSfbzqNRDOPB75epR3pRcQ1VITc4LPnYedqGPFHzQ2Spg0cB5vfBf8W00lERGynIuQGyWlw1q1w3Jmmk0gsOPESaN0OVi8wnURExHYqQm6QNhQybzedQmJFQgc48WL4ZL623BARx1MRcrLKA7Dgevj6E9NJJNacPRVGP2A6hYiI7VSEnOyjJ+DT5yG+tekkEmu69oPuQzSnTEQcT0XIqQ58C0v/AQPHQupJptNILNr0DjyVBdVVppOIiNhGRciplv8Hvv0Ghv/OdBKJVa3bwvoC2LjUdBIREduoCDlRdTUUPQ6nXG3tLSbSEt0y4Kje1qRpERGHUhFyorg4mPgGXPAn00kklnk8MGAsfP6SdapVRMSBVISc5tsS2FsMicnQ/ijTaSTWDRwDB/bCl6+YTiIiYgsVIadZehfMHg5VB00nESfolA7XvQQnXWY6iYiILSKy+3xeXh4pKSkA+P1+pk2bFonDuk+JD5b/F86frkvmJXx6nms6gYiIbWwfEcrLywMgOzub7OxsMjIymDRpkt2HdafFf4X2XWDYzaaTiJNUV8O8a6DoCdNJRETCzhMM2ruGfseOHdm4cWPtiBCAx+Mh1MMGAgGSk5MpLS3F6/XalNIBtq2Ehy+Ay+6DjGtNpxGneepKa8L0Df8znUREJCSh9gdbR4R8Ph9+v79OCapRWFhY7/dUVFQQCATqvEkIWreHIdfDoKtMJxEn6p8FW96D0q9MJxERCStb5wj5fL56P5+SkoLf76/3a7m5udx+uzYIPVxxoJzisopG7nEMZPyF1H2VpHojMvVL3OTEi6FVIqxZCGfdYjqNiEjYGHnG7NSpEyUlJfV+bfr06UydOrX2diAQIC0tLVLRotacZVu4d/G6Ju93y4g+TMnsG4FE4iqJXug7ytq7TkVIRBzESBFqqAQBJCQkkJCQEME0sWHCsB5k9utae3t98V5unbeKe4bto/dHd8Doh6BrP1KT9LsTm4y8HRI0T0/EtKbPEFhSkxJI9SZGIFFss7UIpafXv72D3+9v8GtSv1RvYr1/0L2/fIj+AzIg4wwDqcRVOvU0nUBE0BmCcLO9CKWkpODz+X5QfEaOHGnnod2jogwy/2I6hbjFqrmw8jG44VVrCw4RibgGzxCMG0zv1A61n9cZgtDYfmps+vTpFBYWkp2dDUB+fn7tx3IEalaOHjQeUjSHSiKkQxfY+gF8/TF0G2w6jYgrNXiGILUD/bsnG0gU22xfUHHatGn4/X7y8/PJz89n+fLlzJo1y+7DOl/NytGDJ5jNIe7Sczi06wxr8k0nEREJi4hMlv7+lhpZWVmROKSz7fyUue9Y6yvN/aiYO47v2sQ3iIRJfCs4eTSseRZG/gXitF2hiMQ2PYoZUhwo5+6CtRQHypv3jdVVzHz0CeZ8ZF15N2fZFmaGMGkubMcX6Z8FgW3w9Uemk4iIHDEVIUOKyyq4d/G6kC6B/L6Zc55lhr/uJpgzCtY2uwy19PgipA2DX62A7kNMJxEROWIqQjFk5qufMOPTdvV+rSVlSKRF4uKgcx8IBq03ETFq7odb6ryX5lERihEzF69jxpKtjd5HZUgiZt9uuH8orK9/z0ARCd2RTFWYuXgdc5ZZBUhTJVpGm1IZtr54b5P3mfvhlto/9KbMKFjLzkA540/rccTHFWlQu07W+9X50CfTbBaRWHZwP8Vffsi9i/eSuf1BUs+8GPqMhM9egILboEMqJB8LRw+AHmdCj2G13zpz8TpmFKyt8+Nqbk8e0SfkCDVTJTL7dXXlStQqQobdOm9V2H/mnGWhFyeRFvF4YEAWvHcfHNwPrduaTiQSe5bcBe/eCxVdgL/DtyVQ9d28zY7Hw0mXwr5vYM9GWPuaNT/vmmeti2ZeeJsZy/bV+2NbUobcTEXIsMNXAq3P3EWvMmdj4/f5vgnDeoQ0ImRHCRMX6Z8FS3KtB+iTR5tOIxL9Kitg1dPQ4wxIPRGO6gXnTIWU8+HpHXDZTKhZEPGYQdZbjepq2L8HgJn5Bcwoqmr0UCpDoVMRMqzJlUDLdnJHyW/p2uO3zNjSu8mfNzWzr/7wJTI694ZjBsO6AhUhkcYEg/DFy/D6H2DPZrgozypCA75bV29bKbAjhCkLrZj7+mrmNFGCamiqRGhUhKLdK7+F+DZMvv4aeH/3D84Hf59KkETc+LnQQQt6ijQosB1e+BVsWAy9RsD4eVYJqoemSpihIhTNPnsBPn8Rsh6Fdp2YPMKaoFpfGVIJEiO83az3VZXWqtMiUldlBZRutQpQ31GNblYc0lSJZlw8A5oqEQo9chmSmpTALSP6NL47cGIyDJ0IJ19e+6masvP9MtSSEhTS8UVC8dof4Ju1MGGB6SQi0aHyALxzN5zxC+jUE36xLKTtaELZNPWOywfQ1ZvY6NmBGnqBHBoVIUNSvYlMyezb8B2CQUgfbr0dZvKIPuwMlDNn2RYmDOvRoj/0Jo8vEqpO6fDBg9bVLe07m04jYlbZTph/LWxbCWlDodcFYd+Tr74XxIeb2moBk9sNBVSEmqIFFaPRugJ46kqoKGvwLjVDnU0NeYrYrt9o6/2nzxmNIWLcN+vg4Qtgzyb42StWCbLJ5BF9mNrAi9mpI/sw+bzj4LXfw6q5tmVwCo0IRZv9fnjpVuuKnDahXzIvYkz7o6DX+bBmIZw20XQaETP274FHL4J2R8HVz0Jy95C/taVTFRqfKnE7dO0PJ15s2/GdQiNC0eaV/4OKAFx2X6OT6kSiSv8s+PqT2nVORFynbUcY9Xf42f+aVYLg0FSFlqzqPHlEHyYMs84M/GCqxMAx0KYd7PwU3rijwb0Bj+T4TqAiFE1W58Pq+XDxvyBFp7wkhpx8Ofx2rfVkIOImW5fDstnWxwPHHtp+JoKanCqxbSW8lQeLb49gqtihU2PRpNwPgyfAgDE/+FJxoJzisora2zULYB2+EFZqUoJrW70Y1Pq7v7mD5Yc+FnG6rz+BOVdC6slw6g3Ru4RExrVQHrAWdPR21ynsw0Tpv5rLBIPWabChN8KpwXpPic1ZtoV769lV+PC1H24Z0UdXg4kZ21bCY5fCpKXQWVeqiMPtWgtPjraumrxqXvSWoBpn/gpKv4L/TYOU46DvhaYTRY0o/5dziaV5cPBbyLy9wXlBE4b1ILNf0yv4unWym0SB1H7gibMmTQ//nek0IvbZuwvmZEH7VGtidKLXdKLQjLrDmoOaoAtxvk9FyLT1i62NK4dPb/Ruqd5EnfKS6Na6LZx0iVWEzsvRZH9xrvhW0D0DMv9iZE5Qi6dKxMXD6Aesj6ur4OB+lSJUhMwq/QoW3mitNXHu/5lOI3Lk+mfBx3Nhx2o4ZqDpNCLhVV0N+3ZBUlcY85ixGGGZKvHsRKjYC+OfCfuCj7FGRciUygOw4Hpo3Q6ueNj1f4jiEOnnQbvOsHWZipA4zzv/gg//A79eAQlJxmKEZarEoPHW6b1374ZzfhPGdLFHRciU6kro3BdG5VoL0ok4QXxruGWV0ScJEVusX2ytxXNejvG/77BMleiTaZ2JeONvcOxQ6HlueMLFIE8w2MAKS1EiEAiQnJxMaWkpXm+MTEhryn4/tE0xnULEPtXVcHCf8ScMkbDYsxlmnwfdT4Wr5jtnBL+6Cp74CZT44BfvWxt9O0io/cEh/5oxZF0B3DMQdn5mOomIfR65EAq1eJs4QDAIz2ZDgheumO2cEgTfTZ5+EM6eAm3c+6LFQf+iMeDrT2DBz+C4M6HLCabTiNgnbRh89jxUVZpOInJkPB74US6Me8rIFWK2S0mzFliMi4NvS0ynMUJFKFK+WQ9PXWFtpnrlw1YTF3Gq/ldaV9dsest0EpGWK/4cqg5al8o7ffL/x8/AfUOgbIfpJBGnIhQJwaB1qWLbjjBhoeZNiPN1O8VacXf1QtNJRFqmbAc8djEs/ovpJJHR50KIa2Vt/O0yKkKR4PFY55aveV5XiIk7eDzWqFDxZw3ueC0Staqr4fmbrWJw1i2m00RGu05w0Z3w+Yvwxcum00SUipCd/FvguZvgwD5r76Xk7qYTiUTOuf8HE9/QCtMSe5Y9BBvesCYSt+9sOk3knHyFNTL08m+tTVpdQkXILrs3wKM/hs3vwf49ptOIRF6rBKsE7d1lOolI6Eq3weLbYdhN0HuE6TSR5fHAxTPgxItNJ4koFSE7bFkG/70QWiXCDa9C8rGmE4mYsfJxuHegNSoqEgu83aypDCNuM53EjJQ0uPifsbORbBioCIXbns3w+KXWqtE3vGb9TyXiVunnwcFv4cv/mU4i0rQ9m61RkX4/gTbtTKcxa81CmDPWFXP8VITCpfKA9QfT8Tjr1cS1z2titEjH463l+1fnm04i0rgSHzxwOnw0x3SS6NCuM6x7DVYvMJ3EdipC4bDzM3j4Alj5qHX75NHW/AgRsXakX1/o2sXaJAZUV8MLv4b2XazRILFGc0+6DAr+ZO1S72AqQkfi4H5rjYlZ50DVAeuVr4jUdfJoa77Bri9MJxGpX9HjsPkduOw+SOhgOk30uPBv1sU+78wwncRWtu8+7/f7mT9/PgsWLKCgoMDuw0XOnk3w6MWwrxjOnQZn36pRIJH6JB0Nv12n1dQlOu3dBYW3weCrrVEQOaTjcXDmZNjyvjVq5qR91r7H1iJUVFTEihUr8Pv9lJQ4YFh83zfW2hIDx0JyD+h/BWRcZ22bISINi4u3To3FtXLV1SgSA9q0h9N/AUMnmk4Snc6bZv1/6+D1wGwtQhkZGWRkZJCfH8UTJSsroLzUOjd8+D90MAjbi2DLB7Duddj0rvWAnj4cOqTChX81Elkk5hz4Fu7uDyP+BKffZDqNiKW6yro6bPjvTCeJXvGtrfeb3rW2ieraz2weG9h+aizqbSuCR38EbZKsIfzWidDhaLj6u/L25OVwsBx6ngOj/g4Dspy5A7GIndq0g57nwpr8FhWh4kA5xWUVTd4vNSmBVG9iSxKK21QegP9mWgsnDh5vOk10q66CRbdaa+Jd85zpNGEXdUWooqKCiopDD3iBgM3LfHc5AcbNgZINsLfYmgDdtqP1NY8Hfl5oLTDVuq29OUScbkAWLPy5Nb+u4/HN+tY5y7Zw7+J1Td7vlhF9mJLZt2X5xF0++DfsWA1H9zedJPrFxVujufOuBt8S66yIg0RdEcrNzeX222+P3AHbdYKTLmn46130oCoSFidcBK3bwZpn4ZypzfrWCcN6kNmva+3t9cV7uXXeKu4ZN5jeqYeu8klN0gULEgL/FliaZ40GHT3AdJrYcOIl1pXRBbfBxDcdNXE65CKUn5/PvHnzmrzf9OnTycjIaHGg6dOnM3XqoQfJQCBAWlpai3+eiESJNu2tNVr2fdPsb031JtZ7yqt3agf6d08ORzpxk9f/HyQmw/nTTSeJHR4PjLwdHvsxfPa8dbGQQ4RchLKyssjKyrIzCwAJCQkkJOhVnYgjjX7Q0VefSAyoPGC9jbwdEpJMp4ktx58Fox+CXuebThJWUXdqTEQczOOxnoRKt8JRvUynETdq1QauesYVe2jZomZieTDomBc1ETnJ54g1hEQkPF79HTx1pZ6IJPI+ewE+f8n62CFP4kZ8Mh/+M9K6mswBbC1CPp+PvLw8Zs2aRVFRETk5OdG9ppCI2O+kS2DPRtj+UYt/xNwPt9R5L9Kk8gC8/Bv47EXTSWLfUb1g2wr41BmX0nuCweh+WRYIBEhOTqa0tBSvVyvSisS8qkqK/3kac5KzmXDV9c1e92fm4nXMKFhbe3tqZl8mj+jTrJ9RHChnzrItTBjWQ+sOuUXBn2DZbPj1Cms9HDkyc8ZaL2h+8UHUbp8Tan9wzvVvIhIb4ltRfPxPuHdTGsWl+5v1rYeXIIAZBWuZGcIaQ99XXFbBvYvXhbRIozhAiQ8+eNDaE1IlKDyG58A3ax0xKqQiJCKR13uk9X5/6PMH6ytBNVpShsRF3rvf2kbpzMmmkzhH9yHQZxRsett0kiOmq8ZEJPKO7g+8A+07h3T3xkpQjZqvN/c0mbjAj3LhtInWVi8SPmMfd8SuCypCImLM+i/XQNWJEN/wQ9HcD7cwZ1lok6JnFKxlZ6Cc8af1aPy4xXublVNiVDBoLdWQ0gNSTzKdxnlat7V+x1uXQdqwmL0ST0VIRIy59XU/vP5BWH/mnGWhFydxuM9fgvyfwS8/1LpVdtn4FjxxGVz/irXgYgxSERIRY+5Jyaf3salw/u8bvE9zRoTA2pcslBGhW+etCvlnSgyqOgiLb4ee56kE2annuZDaD965W0VIRKS5ep80iP6f/hNS/9zgXIM7Lh9AV29ik3OEoGWX0otDffQk7N4AWY+aTuJsHg+cPQWenQg7VsfkJra6akxEzOk9Eg6UwbrXG73b5BF9mJrZt9H7qARJrQP7YMmdMHAsHDPQdBrnO/kKax7WO/eYTtIiGhESEXNS0qDvRXDg2ybvWlNy6hsZUgmSOg7uh/TzGz3lKmEU3wrO/wPs22U6SYuoCIlIxKUmJXDLiD6kJiVYG2CGqL4y1JISVOf44jztO8MVs0yncJdBPzWdoMV0akxEIi7Vm8iUzL6Htrco3QbFX4T0vZNH9GHCMGsy9IRhPVo0EvSD44tzvPVPWK09LY3Yuwte/yPs95tO0iwqQiJi3sIb4bXQT2PUXBXW1NVh4jJ7Nllzg/ZsMp3EnYLVsOwhKHrCdJJmURESEfMGjgHfm1C203QSiWVv/QPadoTTbzadxJ2SukL/LPhwNlRVmk4TMhUhETHv5MshrhWs0SkNaaESH6yaa22s2qa96TTudfrN1mreX7xkOknIVIRExLy2HaHvKPg49InTInWsfMyaJH3qDaaTuNsxA+H4c+D9B0wnCZmuGhOR6HDKNfDJfDhYDq3rTmIuDpRTXFZRe7tmr7DD9wxLTUrQBGi3uuBPMPhqR2wCGvMy/wJx8aZThMwTDAaDpkM0JhAIkJycTGlpKV6v13QcETHg7oK13Lt4XZP3u2VEH6Y0sfCiOFDZDkg62nQKiTKh9geNCIlI9DiwD9a+Bv1GQ9yhM/cThvUgs1/XJr9d6wK50Dfr4IHT4ap51krlEh2Kv7C23Rj/DCR3N52mUSpCIhI9dqy2dgu/rgv0PKf206neRJ3ykvotvQs6dLXmpUj0SO5uTWBf+Rhc8AfTaRqlydIiEj3ShkHKcfCJJk1LCHZ9aS2eeM5UaKXRwKiSkGStNr3yMag8YDpNo1SERCR6eDwwcBx89qK1X5RIY5beBd7u1kR7iT5Db4R9xVF/Kb2KkIhEl4HjoCIAX/7PdBKJZtXV1rILw3+n0aBolXoSHHc2rHnWdJJGaY6QiESXzr3h1J9DYrLpJBKF6iylMPhP1vttpT+4n5ZSiBJXzIYOqaZTNEpFSESizyUzTCeQKDVn2RYtpRBLaq4Yqyiz5g1FIRUhEYlOm9+DqoOQfp7pJBJFJgzrQeZX98M3a2HcHNbvLufWeau4Z9xgeqd2qL2fllKIIqvz4eWpcOsaSIy+9QBVhEQkOr07E/btgvTFppNIFEn9dh2pmx+Hy+6HHkdBvHVarHdqB/p31+nUqHTcmVCxFz6ZB6dNNJ3mBzRZWkSi08CxsG0F7N5gOolEkyV3Qsee1qXZEhu83eDEi2H5fyAKN7NQERKR6HTCRZDgtV5FigDs/Ay+WATnTYP41qbTSHMMvRF2fQGb3zWd5AdUhEQkOrVuCyePhlVzrUulRbqcaG3ZMGCs6STSXD3Pha79ofhz00l+QEVIRKLXkJ/BKVdDVUXT9xVnqzxg7T93wkUQf2h669wPt9R5L1HK44HspZojJCLSLN0zYHiONTokjlMcKOfugrUUB8qbvvP8a+B/v6vzqZmL1zFnmVWA5izbwswQLqs/ogxyZOJbWSvGf9P8fyc7qQiJSHTbuwveuAPKA6aTSJgVl1Vw7+J1hxZIbMhXK2Htq1Yx/s7MxeuYUbC2zt1mFKxtdhkKOYOEx6IpMO+aqJo0rSIkItGt6gC89Q/49DnTScSUJX+Hzn2h/5VA/SWoRkvKkETQwLGw63P4arnpJLVUhEQkuiV3h17nw6qnTScRE7Z+COsL4bwciItvtATVUBmKYj2HQ8px1q70UUILKopI9Bs8ARb+HL5Zb+1FJo6yvnhvw19cuw6OGgUpFzD3udW1c4KaMqNgLTsD5Yw/rUfLjy3hFxcHGdfAW/+CH+VGxZ6CKkIiEv1OvAQSkmHVHBh5m+k0Ema3zlvVyFdTgOvg3+83++fOWbYl5OIkETT4alhXCGU7VYRERELSOhEu/icc1ct0ErHB4fuE1VpXAMedBW3aAdYl8s0pNhOG9QhpRKjxIiZh5z0Gfv6a6RS1bC9CeXl5AGzYYC2TP2vWLLsPKSJONFCL6DlVvfuEbX4Plvwcxj0FPS8F4I7LB9DVm9jkHCGAqZl9mTyijx1xJRyCQdjyAXRINf4Cx9YilJOTw1133VV7e9KkSWRmZlJQUGDnYUXEqT570VqZdniO6SRitzf/Dl0HwAkX1/l0TblprAypBMWAYDWUbICj+5tOYt9VY36/n6KiIvx+f+3nJk2aRGFhIT6fz67DioiT7dkIb/8L9u8xnUTstOkd2PQ2DP+dNbn2MJNH9GFqZt96v1UlKEbExVurxickmU5i7+XzK1asqFN60tPTAeqUIxGRkA0cB9WVsGah6SQSBqlJCdwyog+pSQl1v/BmLhw90NqxvAH1laGWlKAGM4hr2HZqLCUlhT176r5qKywsBA4VovpUVFRQUXFohc9AQKvJish3ko6GPpnw0VPWbtYS01K9iUw5fGQnGISzp1jbqng8jX7/5BF92BkoZ86yLUwY1qNFI0H1ZhBXieiCirm5ucyaNYuUlJRG75OcnFz7lpaWFrmAIhL9Mq6F7R/B1x+bTiJ28Higz0g4/qyQ7l5zVVhTV4eJNCTkEaH8/HzmzZvX5P2mT59ORkbGDz6fk5PDuHHjyM7ObvL7p06dWns7EAioDInIIX1GQdaj0PkE00kk3Da8Ce/cDWOfgLYpptOIS4RchLKyssjKymrRQfLz8+nVq1eTJQggISGBhASdqxWRBsS3gv5XmE4h4RYMWleKBauiYpE9cQ/bT43VzAuqKUF+v19XjYnIkamugvwb4OOmR6klRmxYDF99CMN/3+TcIJFwsnUdoaKiIoqKisjKyqotP/n5+SGNDImINCgu3rqEfvl/YNA402nkSAWD1pVixw6F3iMavWtxoJziskMX1NTsFXb4nmGpSQmkehPDn1UcxxMMBoN2/GC/30/Pnj3rvVS+OYcMBAIkJydTWlqK1+sNY0IRiWmfPg8LroOb34eu/UynkSNR4oOHzoWxjzdZhO4uWMu9Iewsf8uIProazOVC7Q+2FaFwURESkXpVHoC7+0H/K+Giu5q+v0S3/X5rblATp8UOHxFqiEaEJNT+oE1XRSQ2tWoDg6+CVU/DhXdYk6gl9uz60tpvqm3HkO6e6k1UwZGwiug6QiIiYXXGr+Gmd1SCYlUwCM9mW28ihujRQ0RiV4cu1vuqSpWhWPTlK/D1KrjuJdNJxMU0IiQisW3HaphxIuxqeDdyiULV1daVYsefAz3PNZ1GXExFSERiW+e+EKyGFY+YTiLN8fmLsHM1nP8H00nE5VSERCS2tUqw9h9b9TRU7G36/hIdUtKszVWPO8N0EnE5FSERiX2n3gAHymD1fNNJJFTdh8DIP5tOIaIiJCIOkNID+l4Em983nUSaUlUJC2+EbUWmk4gAumpMRJziyoehdTvTKaQpqxdYb2f8ynQSEUAjQiLiFG3aW+9LtKlz1Ko6CEvvhBMvgW6DTacRAVSERMRJPpkP9w+Fsh2mk0h9Pp4LezbB8Ommk4jUUhESEefoOwri28DKx0wnkcMFg7D8v9BvNBzd33QakVoqQiLiHG1TYOA4WPGodRpGoofHA9cv0ga5EnVUhETEWU6bCHt3wBeLTCeRGgfLYW8xJCRB0tGm04jUoSIkIs7S9WQ46TItrhhNVj4GMzNgv990EpEf0OXzIuI84540nUBqVJTBW/+AfpdZpy5FooxGhETEmfYWw5qFplPIBw9CRUBXiknUUhESEWf67AVrBeM9m00nca99u+G9+2DoRGtvMZEopCIkIs40+CpI8MKyWaaTuNeBvZA+HM6ZajqJSINUhETEmdq0tzZjLXoCygOm07hTx+Os+VrtO5tOItIgFSERca7TsqGyHD7S5OmIW/oPWFdoOoVIk1SERMS5vMfAqDsg7XTTSdxl11pY8nfYvd50EpEm6fJ5EXG2YZNMJ3CfN/4K3mPh1J+ZTiLSJI0IiYjzbXoXnv+ltd+V2GvbSvj8RTh/OrRKMJ1GpEkqQiLifFUHYNVT4HvTdBLne/8B6HKiteebSAzQqTERcb704dAtA96eAb0uMJ3G2X5yP5Rug7h400lEQqIRIRFxPo8HzvkNbHobtiwzncaZqg7Cnk3Qui107m06jUjIVIRExB1O+LF1ymb5w6aTONPKx+Dfw2DvLtNJRJpFp8ZExB3i4mDsk5B8rOkkzlNeCktyoX8WdOhiOo1Is2hESETco0tfaNPOeuKW8Hn7X3BwP1zw/0wnEWk2FSERcZdN78I/T4BvtNhfWOzZbO0wf+ZkawFLkRijIiQi7tJ9CLRNgbfyTCdxhrh4GPRTOGuy6SQiLaIiJCLu0jrRuoLsk/mw60vTaWJf8rFw2X3WJrciMUhFSETcJ+Na6wl8Sa7pJLGrugqemQAb3jCdROSIqAiJiPu0SoDzcqyPqyrNZolVRU/AF4ugTQfTSUSOiC6fFxF3yrjGepPm+7YEFt8OgydA2mmm04gcEY0IiYh7VVfDJwtgx2rTSWLLG3+zTo2N/LPpJCJHzNYRIb/fz/z58wHYsGEDPp+Phx9+mJSUFDsPKyISmmA1LL0LUtLgmudMp4kNB8th87tw/u+hQ6rpNCJHzNYRoZycHEaOHEl2djZ33XUXnTp1YsyYMXYeUkQkdPGtYORt1oTf9YtNp4kNrRNh0tswdKLpJCJhYWsR8vl85Ofn197u1asXK1assPOQIiLNc+IlkHY6FPzJOt0jDVtfCMVfQKs2VokUcQBb/5ILCgrq3F6+fDkjR45s9HsqKiqoqKiovR0IBGzJJiICWDvTX/g3+O9I+PQ5GJBlOlF02rsLFt4IfUbBFbNMpxEJm4hNls7Pz8fv9/Pww43v/Jybm0tycnLtW1paWoQSiohrpQ2FCQuh309MJ4ler/3een/h38zmEAkzTzAYDNp5gJoJ036/n5SUFLKzsxu9f30jQmlpaZSWluL1eu2MKiIC+/3WFhxyyPpCeOpKGP0gDL7KdBqRkAQCAZKTk5vsDyGfGsvPz2fevHlN3m/69OlkZGTU3v5++Zk9ezYdO3Zk48aNDV45lpCQQEJCQqixRETC59Pn4MVb4BfvQ3J302miQzAIBX+GnufBoPGm04iEnW0jQn6/n9zcXKZPn15benw+H7169WLBggVkZYV2Hj7URicicsTKS+G+U+H4s2HMo6bTRI/AdqiuhJQeppOIhCzU/mDbHCGfz0deXh4lJSW1n/P7/QBaR0hEolNiMlz4V/j0WfAtMZ3GvF1rrVOF3m4qQeJYthWhjIwMpk2bRnp6eu3n5s2bR0ZGRpNXjomIGDNwHPQ4A16ZBpUHTKcx5+B+eOYqeOGXppOI2MrWy+enT59OXl5e7W2/38/ixVq0TESimMcDP/4nfPAgVJZba+a4UeHtULoVfvq06SQitrL9qrEjpTlCIiIRtvEtePxSGJULZ/zCdBqRFjE+R0hEJOZ9+jzk32BdOeUWVQfhhV/B8efAsJtMpxGxndZIFxFpSOu2sGYh9BoBp0wwnSYy4lvD5Q+BtzvE6bWyOJ/+ykVEGtJ3lDV5+rXpULbDdBr7ff2xtd/acWdCx+NMpxGJCBUhEZHG/OhOiG8Di6Y4+xRZ8efw31HwwQOmk4hElIqQiEhj2nWCS+6xFhUsLzWdxh4HvoUF10PH4+HUn5tOIxJRmiMkItKUky6BEy6CuHjTScIvGISXfwN7NkP2m9CmnelEIhGlESERkVDExcOONdbISWVFk3ePGesL4eOn4dJ7IPUk02lEIk5FSEQkVMFq+OJleP2PppOET++RcN1LMOinppOIGKEiJCISqmMGwqi/w4ez4ONnTKc5Mns2wRevWCtp9zzXdBoRYzRHSESkOYbeCNs/gpdugS4nQrfBphM137cl8FQWBKug9wholWA6kYgxKkIiIs3h8cDFM2DvTqgImE7TfAf3w9zxsL8Efl6gEiSupyIkItJcrRPh6oXWx1WVUH3QWoU62lVXwbPZ1sKJ1y+Co3qZTiRinOYIiYgciedvhvnXWYUo2pWXwu71kPUIHHuq6TQiUUFFSETkSAz6KWxYDItuid6Vp6urrXlB7TrBpLfhxB+bTiQSNVSERESORO8R8JMH4KOnrIUJq6tNJ6qrugpe+jU8+mNrZ/l4zYgQ+T79HyEicqQGjYOqCnhxMvQ8B06+3HQiy4FvYeGNsPZVGP2gtbO8iNShIiQiEg4Z10KXkw7NvQkGrSvMTNm3G+aOg52fwvi50HeUuSwiUUynxkREwiVtqFV+vngFHrvEKiOmfP2RtX/Y9YtUgkQaoSIkIhJu7bvAri/g4eHw1crIHTcYhA1vWu97j4Rfr4TuQyJ3fJEYpCIkIhJuaUNh4htWIXrkQnjvPmvSsp3KdsK8q+HJ0bB+sfW5RK+9xxRxABUhERE7dDwOfvYqnH4zFD0J1TatM1RVCSsegQeGwZYPYOwT0GekPccScSAVIRERu7RqAxf+DbKXWFtZ7FgDz/8SSnzhO8anz8KiKdD3R/DLD6HfT8L3s0VcQFeNiYjYrU07631gG6wvgI/nWoVl8ATodT7ExYf+s0q3wafPWXudXfhXOPkKa/PXYwbak13E4VSEREQipe8ouOVjWPk4rHwU5lwJI/8MZ0+BwNdQsgFSjrNWgPbEW7vDt2kPe4vh/fth83vw1XKIaw0DsqxJ0fGtVIJEjoCKkIhIJLVuC6ffBMMmwfYiaNvR+vzHT8Piv9S970mXwrinwBMHqxfCsUPg8tlwwo8gMTny2UUcyBMMRuvmOJZAIEBycjKlpaV4vboCQkQc6uB+8G8F/2Zrc9TqSkjpAcedaTqZSEwKtT9oREhEJBq0bgtd+lpvIhIxumpMREREXEtFSERERFxLRUhERERcS0VIREREXEtFSERERFxLRUhERERcS0VIREREXEtFSERERFxLRUhERERcS0VIREREXEtFSERERFxLRUhERERcK+o3XQ0Gg4C1i6yIiIhIKGp6Q02PaEjUF6GysjIA0tLSDCcRERGRWFNWVkZycnKDX/cEm6pKhlVXV7N9+3aSkpLweDym4zheIBAgLS2NrVu34vV6TcdxPP2+I0+/88jS7zvy9Du3BINBysrK6NatG3FxDc8EivoRobi4OI499ljTMVzH6/W6+n+gSNPvO/L0O48s/b4jT79zGh0JqqHJ0iIiIuJaKkIiIiLiWipCUkdCQgK33XYbCQkJpqO4gn7fkaffeWTp9x15+p03T9RPlhYRERGxi0aERERExLVUhERERMS1VIRERETEtVSERERExLWifkFFiZy8vDxSUlIA8Pv9TJs2zWwgh8vLywNgw4YNAMyaNctkHNfJzMykoKDAdAzHy8nJoVevXgB06tSJrKwsw4mcbfbs2fj9flJSUtiwYQPTp0+vfVyX+umqMQEOPSnXlJ/CwkIWLFigJ2eb5OTkcNddd9XenjRpEj6fT0/MEZKfn8+YMWOa3IxRWs7v9zNixAgWL15MSkoKRUVFDBkyRL9zG+Xl5ZGdnV3nBe3EiRNZsGCB2WBRTqfGBIDc3Fyys7Nrb48cOZLZs2cbTORcfr+foqIi/H5/7ecmTZpEYWEhPp/PXDCX8Pv9lJSUmI7heDk5OYwbN672STkjI0NF32YFBQV1Rn9SUlLqPM5I/VSEBJ/PVzuUerjCwsLIB3KBFStW1Ck96enpAHrQioD58+czduxY0zEcb/bs2WRlZeHz+WofR0aOHGk4lbOlpKSQmZlZ+zji8/lqH1ukYSpC0uAohF5N2CMlJYU9e/aQkZFR+7maJwo9aNmrsLBQT8YRUPOYUjPymZ6eXjvqKfZ5+OGH8fl8dOzYkZycHAoLCzW9IQQqQtKgTp066RRChOTm5jJr1ixNarRZzZOy2KumCKWkpJCRkUF6ejp33XUXY8aMMZzM2VJSUsjJySErK4u8vDwWLFigF7MhUBGSBqkERUbNXIrvz9GS8Ks5VSORc+qpp9Z+XDPCrFEh++Tk5JCens6CBQvYsGEDJSUlDBkyxHSsqKciJA2+QtarZ/vl5+fTq1cvLVVgs6KiojpPymKvhh43UlJSdEGATWrmetac+k1PT2flypWkpKSQn59vOF100zpCQnp6eu0D1OEPYJpPYZ+aV8Y1I0E1VzOpfIZfSUkJRUVFtb/zmrWb8vLySE9P10hRmKWnp5Oeno7P56szF87v96uQ2sTn89V7an3SpEmRDxNjtI6QAIcWU6x5Us7Pz6egoEAT7WxS86T8/Sfg/Pz8OmuAiH20po398vPzWb58ee16Wfn5+cyaNUuX0NsoMzOTBQsW1HkMmTRpkh7Hm6AiJLVqXh0DdR7AJLz8fj89e/asdxKj/ne0X35+PvPmzSM/P59p06aRmZmpkU+b1KxyDLB79249ptjM7/eTm5vLUUcdVTsnSy+umqYiJCIiIq6lydIiIiLiWipCIiIi4loqQiIiIuJaKkIiIiLiWipCIiIi4loqQiIiIuJaKkIiIiLiWipCIiIi4loqQiIiIuJaKkIiIiLiWipCIiIi4lr/H20Bu6H55/B/AAAAAElFTkSuQmCC",
|
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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}
|
|
],
|
|
"source": [
|
|
"x_t = np.arange(min(ox).value - 1, max(ox).value + 1, 0.01)\n",
|
|
"y_t = func([o.value for o in beta], x_t)\n",
|
|
"\n",
|
|
"plt.errorbar([e.value for e in ox], [e.value for e in oy], xerr=[e.dvalue for e in ox], yerr=[e.dvalue for e in oy], marker='D', lw=1, ls='none', zorder=10)\n",
|
|
"plt.plot(x_t, y_t, '--')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also take a look at how much the inidividual ensembles contribute to the uncetainty of the fit parameters"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameter 0\n",
|
|
"\n",
|
|
"Parameter 1\n",
|
|
"\n",
|
|
"Parameter 2\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
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"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"for i, item in enumerate(beta):\n",
|
|
" print('Parameter', i)\n",
|
|
" item.plot_piechart()\n",
|
|
" print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Correlated fits with a covariance of your own choosing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"##### generate a random data set"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def fitf(p, x):\n",
|
|
" return p[1] * anp.exp(-p[0] * x)\n",
|
|
"\n",
|
|
"num_samples = 400\n",
|
|
"N = 10\n",
|
|
"x_random = norm.rvs(size=(N, num_samples)) # generate random numbers\n",
|
|
"\n",
|
|
"r = np.zeros((N, N))\n",
|
|
"for i in range(N):\n",
|
|
" for j in range(N):\n",
|
|
" r[i, j] = np.exp(-0.8 * np.fabs(i - j)) # element in correlation matrix\n",
|
|
"\n",
|
|
"errl = np.sqrt([10.0, 2.5, 25.0, 2.8, 4.2, 4.7, 4.9, 5.1, 3.2, 4.2]) # set y errors\n",
|
|
"for i in range(N):\n",
|
|
" for j in range(N):\n",
|
|
" r[i, j] *= errl[i] * errl[j] # element in covariance matrix\n",
|
|
"\n",
|
|
"c = cholesky(r, lower=True)\n",
|
|
"y = np.dot(c, x_random)\n",
|
|
"x = np.arange(N)\n",
|
|
"\n",
|
|
"\n",
|
|
"data = []\n",
|
|
"for i in range(N):\n",
|
|
" data.append(pe.Obs([[np.exp(-(i + 1)) + np.exp(-(i + 1)) * o for o in y[i]]], ['ens']))\n",
|
|
"\n",
|
|
"data[2] = data[2]+0.05\n",
|
|
"\n",
|
|
"[o.gamma_method() for o in data]\n",
|
|
"\n",
|
|
"corr = pe.covariance(data, correlation=True)\n",
|
|
"covdiag = np.diag(1 / np.asarray([o.dvalue for o in data]))\n",
|
|
"\n",
|
|
"chol_inv = pe.obs.invert_corr_cov_cholesky(corr,covdiag)\n",
|
|
"chol_inv_keys = [\"\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 400x400 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.matshow(corr, vmin=-1, vmax=1)\n",
|
|
"plt.title('The full correlation matrix')\n",
|
|
"plt.set_cmap('RdBu')\n",
|
|
"plt.colorbar()\n",
|
|
"plt.draw()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### generate a block diagonal covariance matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"e=0\n",
|
|
"block_diag_corr_matrix = np.zeros((N,N))\n",
|
|
"for k in range(3):\n",
|
|
" if(k==0):\n",
|
|
" step = 4\n",
|
|
" block = pe.covariance(data[:4],correlation=True)\n",
|
|
" else:\n",
|
|
" step = 3\n",
|
|
" block = pe.covariance(data[:3],correlation=True) \n",
|
|
" block_diag_corr_matrix[e:e+step,e:e+step] += block\n",
|
|
" e+=step\n",
|
|
"\n",
|
|
"block_diag_chol_inv = pe.obs.invert_corr_cov_cholesky(block_diag_corr_matrix,covdiag)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 400x400 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.matshow(block_diag_corr_matrix, vmin=-1, vmax=1)\n",
|
|
"plt.title('A block diagonal correlation matrix')\n",
|
|
"plt.set_cmap('RdBu')\n",
|
|
"plt.colorbar()\n",
|
|
"plt.draw()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"#### perform a fully correlated fit and a fit with a block diagonal covariance matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 2 parameters\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 2.3597637233070254\n",
|
|
"fit parameters [0.9754457 0.28547338]\n",
|
|
"Fit with 2 parameters\n",
|
|
"inv_chol_cov_matrix handed over as kwargs.\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 2.3217921000302923\n",
|
|
"fit parameters [0.9766841 0.2933594]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"fitpc = pe.least_squares(x, data, fitf, correlated_fit=True)\n",
|
|
"fitp_inv_block_diag_cov = pe.least_squares(x, data, fitf, correlated_fit = True, inv_chol_cov_matrix = [block_diag_chol_inv,chol_inv_keys])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### generate a block diagonal covariance matrix with modified weights for particular data points + perform the fit again"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 2 parameters\n",
|
|
"inv_chol_cov_matrix handed over as kwargs.\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 0.3401961132842267\n",
|
|
"fit parameters [0.99320618 0.33488345]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"covdiag[2][2] = covdiag[2][2]/100. # weight the third data point less\n",
|
|
"block_diag_chol_inv_weighted = pe.obs.invert_corr_cov_cholesky(block_diag_corr_matrix,covdiag)\n",
|
|
"\n",
|
|
"fitp_inv_block_diag_cov_weighted = pe.least_squares(x, data, fitf, correlated_fit = True, inv_chol_cov_matrix = [block_diag_chol_inv_weighted,chol_inv_keys])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### compare the fully correlated fit to those with block-diagonal covariance matrices (and modified weights)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
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"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"x_fit = np.arange(min(x) - 1, max(x)+ 1, 0.01)\n",
|
|
"y_fit_correlated = fitf([o.value for o in fitpc.fit_parameters], x_fit)\n",
|
|
"y_fit = fitf([o.value for o in fitp_inv_block_diag_cov.fit_parameters], x_fit)\n",
|
|
"y_fit_weighted = fitf([o.value for o in fitp_inv_block_diag_cov_weighted.fit_parameters], x_fit)\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.errorbar(x,data,yerr=[o.dvalue for o in data])\n",
|
|
"plt.plot(x_fit, y_fit_correlated, '--',label = '$\\chi^2/\\mathrm{d.o.f.}$=' + str(round(fitpc.chisquare/fitpc.dof,2)) +': fully correlated')\n",
|
|
"plt.plot(x_fit, y_fit, '--',label = '$\\chi^2/\\mathrm{d.o.f.}$=' + str(round(fitp_inv_block_diag_cov.chisquare/fitp_inv_block_diag_cov.dof,2)) +': block-diag. cov matrix')\n",
|
|
"plt.plot(x_fit, y_fit_weighted, '--',label = '$\\chi^2/\\mathrm{d.o.f.}$=' +str(round(fitp_inv_block_diag_cov_weighted.chisquare/fitp_inv_block_diag_cov_weighted.dof,2)) + \n",
|
|
" ': block-diag. cov matrix + reduced weight 2. point')\n",
|
|
"plt.xlim(-0.5,10.0)\n",
|
|
"plt.ylim(-0.1,0.6)\n",
|
|
"plt.legend(fontsize=11)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"- the fully correlated fit vs. the fit with a block diagonal covariance matrix\n",
|
|
" - the fits do not differ significantly $\\rightarrow$ the block diagonal covariance matrix can be a good estimator \n",
|
|
" (if a large fraction of the off-diagonal elements are small)\\\n",
|
|
" $\\rightarrow$ sparser matrices can be more easily/cheaply inverted/saved \n",
|
|
"- the fit with a block diagonal covariance matrix vs. the fit with \" and a decreased weight for the third data point\n",
|
|
" - the $\\chi^2/\\mathrm{d.o.f.}$ improves - decreasing/increasing the weights can be used for points that are known to be less/more 'trustworthy'"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"interpreter": {
|
|
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|