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871 lines
182 KiB
871 lines
182 KiB
{
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"cells": [
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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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"Exercise 3: Least square fit with a 3rd order polynomial with iminuit"
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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": 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.pyplot as plt\n",
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"import math"
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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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"# Data x,y and dy\n",
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"x = np.array([0.2 , 0.4 , 0.6 , 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2., 2.2, 2.4, 2.6, 2.8 , 3., 3.2 ,3.4, 3.6, 3.8,4.],dtype='d')\n",
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"dy = np.array([0.04,0.021,0.035,0.03,0.029,0.019,0.024,0.018,0.019,0.022,0.02,0.025,0.018,0.024,0.019,0.021,0.03,0.019,0.03,0.024 ], dtype='d')\n",
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"y = np.array([1.792,1.695,1.541,1.514,1.427,1.399,1.388,1.270,1.262,1.228,1.189,1.182,1.121,1.129,1.124,1.089,1.092,1.084,1.058,1.057 ], dtype='d')"
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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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"source": [
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"# Define fit functions - a 3rd order polynomial\n",
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"def pol3(a0, a1, a2, a3):\n",
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" return a0 + x*a1 + a2*x**2 + a3*x**3"
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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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"# least-squares function = sum of data residuals squared\n",
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"def LSQ(a0, a1, a2, a3):\n",
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" return np.sum((y - pol3(a0, a1, a2, a3)) ** 2 / dy ** 2)"
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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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"source": [
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"# import minuit2 fitting library\n",
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"from iminuit import Minuit"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# create instance of Minuit and use LSQ function to minimize\n",
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"LSQ.errordef = Minuit.LEAST_SQUARES\n",
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"m = Minuit(LSQ,a0=0.01, a1=0.05 ,a2=0.01 ,a3=0.001)"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table>\n",
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" <tr>\n",
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" <td></td>\n",
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" <th title=\"Variable name\"> Name </th>\n",
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" <th title=\"Value of parameter\"> Value </th>\n",
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" <th title=\"Hesse error\"> Hesse Error </th>\n",
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" <th title=\"Minos lower error\"> Minos Error- </th>\n",
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" <th title=\"Minos upper error\"> Minos Error+ </th>\n",
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" <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
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" <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
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" <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 0 </th>\n",
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" <td> a0 </td>\n",
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" <td> 10.0e-3 </td>\n",
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" <td> 0.1e-3 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 1 </th>\n",
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" <td> a1 </td>\n",
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" <td> 50.0e-3 </td>\n",
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" <td> 0.5e-3 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 2 </th>\n",
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" <td> a2 </td>\n",
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" <td> 10.0e-3 </td>\n",
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" <td> 0.1e-3 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 3 </th>\n",
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" <td> a3 </td>\n",
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" <td> 1.00e-3 </td>\n",
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" <td> 0.01e-3 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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"</table>"
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],
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"text/plain": [
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"┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
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"│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n",
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"├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
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"│ 0 │ a0 │ 10.0e-3 │ 0.1e-3 │ │ │ │ │ │\n",
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"│ 1 │ a1 │ 50.0e-3 │ 0.5e-3 │ │ │ │ │ │\n",
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"│ 2 │ a2 │ 10.0e-3 │ 0.1e-3 │ │ │ │ │ │\n",
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"│ 3 │ a3 │ 1.00e-3 │ 0.01e-3 │ │ │ │ │ │\n",
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"└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"m.params"
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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": 8,
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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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"Chi2/ndof = 0.8205035668806628\n"
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]
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}
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],
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"source": [
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"# run migrad for minimization\n",
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"m.migrad()\n",
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"chi2 = m.fval / (len(y) - len(m.values))\n",
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"print (\"Chi2/ndof =\" , chi2)"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table>\n",
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" <tr>\n",
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" <th colspan=\"5\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 13.13 </td>\n",
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" <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 120 </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 7.21e-17 (Goal: 0.0002) </td>\n",
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" <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\"> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
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" <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
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" <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
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" </tr>\n",
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"</table><table>\n",
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" <tr>\n",
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" <td></td>\n",
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" <th title=\"Variable name\"> Name </th>\n",
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" <th title=\"Value of parameter\"> Value </th>\n",
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" <th title=\"Hesse error\"> Hesse Error </th>\n",
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" <th title=\"Minos lower error\"> Minos Error- </th>\n",
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" <th title=\"Minos upper error\"> Minos Error+ </th>\n",
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" <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
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" <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
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" <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 0 </th>\n",
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" <td> a0 </td>\n",
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" <td> 1.884 </td>\n",
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" <td> 0.031 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 1 </th>\n",
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" <td> a1 </td>\n",
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" <td> -0.56 </td>\n",
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" <td> 0.06 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 2 </th>\n",
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" <td> a2 </td>\n",
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" <td> 0.136 </td>\n",
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" <td> 0.030 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 3 </th>\n",
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" <td> a3 </td>\n",
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" <td> -0.012 </td>\n",
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" <td> 0.005 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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"</table><table>\n",
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" <tr>\n",
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" <td></td>\n",
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" <th> a0 </th>\n",
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" <th> a1 </th>\n",
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" <th> a2 </th>\n",
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" <th> a3 </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a0 </th>\n",
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" <td> 0.000952 </td>\n",
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" <td style=\"background-color:rgb(131,131,250);color:black\"> -0.0016 <strong>(-0.918)</strong> </td>\n",
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" <td style=\"background-color:rgb(250,127,127);color:black\"> 0.000752 <strong>(0.822)</strong> </td>\n",
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" <td style=\"background-color:rgb(153,153,250);color:black\"> -0.000105 <strong>(-0.746)</strong> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a1 </th>\n",
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" <td style=\"background-color:rgb(131,131,250);color:black\"> -0.0016 <strong>(-0.918)</strong> </td>\n",
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" <td> 0.00317 </td>\n",
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" <td style=\"background-color:rgb(123,123,250);color:black\"> -0.00163 <strong>(-0.975)</strong> </td>\n",
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" <td style=\"background-color:rgb(250,110,110);color:black\"> 0.00024 <strong>(0.930)</strong> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a2 </th>\n",
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" <td style=\"background-color:rgb(250,127,127);color:black\"> 0.000752 <strong>(0.822)</strong> </td>\n",
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" <td style=\"background-color:rgb(123,123,250);color:black\"> -0.00163 <strong>(-0.975)</strong> </td>\n",
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" <td> 0.000879 </td>\n",
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" <td style=\"background-color:rgb(122,122,250);color:black\"> -0.000134 <strong>(-0.988)</strong> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a3 </th>\n",
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" <td style=\"background-color:rgb(153,153,250);color:black\"> -0.000105 <strong>(-0.746)</strong> </td>\n",
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" <td style=\"background-color:rgb(250,110,110);color:black\"> 0.00024 <strong>(0.930)</strong> </td>\n",
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" <td style=\"background-color:rgb(122,122,250);color:black\"> -0.000134 <strong>(-0.988)</strong> </td>\n",
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" <td> 2.1e-05 </td>\n",
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" </tr>\n",
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"</table>"
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],
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"text/plain": [
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"┌─────────────────────────────────────────────────────────────────────────┐\n",
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"│ Migrad │\n",
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"├──────────────────────────────────┬──────────────────────────────────────┤\n",
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"│ FCN = 13.13 │ Nfcn = 120 │\n",
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"│ EDM = 7.21e-17 (Goal: 0.0002) │ │\n",
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"├──────────────────────────────────┼──────────────────────────────────────┤\n",
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"│ Valid Minimum │ No Parameters at limit │\n",
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"├──────────────────────────────────┼──────────────────────────────────────┤\n",
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"│ Below EDM threshold (goal x 10) │ Below call limit │\n",
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"├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
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"│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n",
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"└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
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"┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
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"│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n",
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"├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
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"│ 0 │ a0 │ 1.884 │ 0.031 │ │ │ │ │ │\n",
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"│ 1 │ a1 │ -0.56 │ 0.06 │ │ │ │ │ │\n",
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"│ 2 │ a2 │ 0.136 │ 0.030 │ │ │ │ │ │\n",
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"│ 3 │ a3 │ -0.012 │ 0.005 │ │ │ │ │ │\n",
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"└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
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"┌────┬─────────────────────────────────────────┐\n",
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"│ │ a0 a1 a2 a3 │\n",
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"├────┼─────────────────────────────────────────┤\n",
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"│ a0 │ 0.000952 -0.0016 0.000752 -0.000105 │\n",
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"│ a1 │ -0.0016 0.00317 -0.00163 0.00024 │\n",
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"│ a2 │ 0.000752 -0.00163 0.000879 -0.000134 │\n",
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"│ a3 │ -0.000105 0.00024 -0.000134 2.1e-05 │\n",
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"└────┴─────────────────────────────────────────┘"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# run covariance \n",
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"m.hesse()"
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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": 10,
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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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"┌────┬─────────────────────────────────────────┐\n",
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"│ │ a0 a1 a2 a3 │\n",
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"├────┼─────────────────────────────────────────┤\n",
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"│ a0 │ 0.000952 -0.0016 0.000752 -0.000105 │\n",
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"│ a1 │ -0.0016 0.00317 -0.00163 0.00024 │\n",
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"│ a2 │ 0.000752 -0.00163 0.000879 -0.000134 │\n",
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"│ a3 │ -0.000105 0.00024 -0.000134 2.1e-05 │\n",
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"└────┴─────────────────────────────────────────┘\n"
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]
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}
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],
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"source": [
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"#get correlation matrix\n",
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"cov = m.covariance\n",
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"print (cov)"
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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": 11,
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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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"-0.0015953258466008735\n",
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"0.0007515996501623164\n"
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]
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}
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],
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"source": [
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"# access elements of the numpy arrays\n",
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"print(cov[0, 1])\n",
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"print(cov[0, 2])"
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table>\n",
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" <tr>\n",
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" <th colspan=\"5\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 13.13 </td>\n",
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" <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 280 </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 7.21e-17 (Goal: 0.0002) </td>\n",
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" <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\"> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
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" <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
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" <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
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" <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
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" </tr>\n",
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"</table><table>\n",
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" <tr>\n",
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" <td></td>\n",
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" <th title=\"Variable name\"> Name </th>\n",
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" <th title=\"Value of parameter\"> Value </th>\n",
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" <th title=\"Hesse error\"> Hesse Error </th>\n",
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" <th title=\"Minos lower error\"> Minos Error- </th>\n",
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" <th title=\"Minos upper error\"> Minos Error+ </th>\n",
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" <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
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" <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
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" <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 0 </th>\n",
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" <td> a0 </td>\n",
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" <td> 1.884 </td>\n",
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" <td> 0.031 </td>\n",
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" <td> -0.031 </td>\n",
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" <td> 0.031 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 1 </th>\n",
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" <td> a1 </td>\n",
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" <td> -0.56 </td>\n",
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" <td> 0.06 </td>\n",
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" <td> -0.06 </td>\n",
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" <td> 0.06 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 2 </th>\n",
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" <td> a2 </td>\n",
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" <td> 0.136 </td>\n",
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" <td> 0.030 </td>\n",
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" <td> -0.030 </td>\n",
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" <td> 0.030 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> 3 </th>\n",
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" <td> a3 </td>\n",
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" <td> -0.012 </td>\n",
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" <td> 0.005 </td>\n",
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" <td> -0.005 </td>\n",
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" <td> 0.005 </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" <td> </td>\n",
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" </tr>\n",
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"</table><table>\n",
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" <tr>\n",
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" <td></td>\n",
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" <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> a0 </th>\n",
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" <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> a1 </th>\n",
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" <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> a2 </th>\n",
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" <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> a3 </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th title=\"Lower and upper minos error of the parameter\"> Error </th>\n",
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" <td> -0.031 </td>\n",
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" <td> 0.031 </td>\n",
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" <td> -0.06 </td>\n",
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" <td> 0.06 </td>\n",
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" <td> -0.03 </td>\n",
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" <td> 0.03 </td>\n",
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" <td> -0.005 </td>\n",
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" <td> 0.005 </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th title=\"Validity of lower/upper minos error\"> Valid </th>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th title=\"Did scan hit limit of any parameter?\"> At Limit </th>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th title=\"Did scan hit function call limit?\"> Max FCN </th>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th title=\"New minimum found when doing scan?\"> New Min </th>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
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" </tr>\n",
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"</table><table>\n",
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" <tr>\n",
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" <td></td>\n",
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" <th> a0 </th>\n",
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" <th> a1 </th>\n",
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" <th> a2 </th>\n",
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" <th> a3 </th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a0 </th>\n",
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" <td> 0.000952 </td>\n",
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" <td style=\"background-color:rgb(131,131,250);color:black\"> -0.0016 <strong>(-0.918)</strong> </td>\n",
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" <td style=\"background-color:rgb(250,127,127);color:black\"> 0.000752 <strong>(0.822)</strong> </td>\n",
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" <td style=\"background-color:rgb(153,153,250);color:black\"> -0.000105 <strong>(-0.746)</strong> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a1 </th>\n",
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" <td style=\"background-color:rgb(131,131,250);color:black\"> -0.0016 <strong>(-0.918)</strong> </td>\n",
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" <td> 0.00317 </td>\n",
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" <td style=\"background-color:rgb(123,123,250);color:black\"> -0.00163 <strong>(-0.975)</strong> </td>\n",
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" <td style=\"background-color:rgb(250,110,110);color:black\"> 0.00024 <strong>(0.930)</strong> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a2 </th>\n",
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" <td style=\"background-color:rgb(250,127,127);color:black\"> 0.000752 <strong>(0.822)</strong> </td>\n",
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" <td style=\"background-color:rgb(123,123,250);color:black\"> -0.00163 <strong>(-0.975)</strong> </td>\n",
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" <td> 0.000879 </td>\n",
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" <td style=\"background-color:rgb(122,122,250);color:black\"> -0.000134 <strong>(-0.988)</strong> </td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th> a3 </th>\n",
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" <td style=\"background-color:rgb(153,153,250);color:black\"> -0.000105 <strong>(-0.746)</strong> </td>\n",
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" <td style=\"background-color:rgb(250,110,110);color:black\"> 0.00024 <strong>(0.930)</strong> </td>\n",
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" <td style=\"background-color:rgb(122,122,250);color:black\"> -0.000134 <strong>(-0.988)</strong> </td>\n",
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" <td> 2.1e-05 </td>\n",
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" </tr>\n",
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"</table>"
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],
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"text/plain": [
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"┌─────────────────────────────────────────────────────────────────────────┐\n",
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"│ Migrad │\n",
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"├──────────────────────────────────┬──────────────────────────────────────┤\n",
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"│ FCN = 13.13 │ Nfcn = 280 │\n",
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"│ EDM = 7.21e-17 (Goal: 0.0002) │ │\n",
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"├──────────────────────────────────┼──────────────────────────────────────┤\n",
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"│ Valid Minimum │ No Parameters at limit │\n",
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"├──────────────────────────────────┼──────────────────────────────────────┤\n",
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"│ Below EDM threshold (goal x 10) │ Below call limit │\n",
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"├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
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"│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n",
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"└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
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"┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
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"│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n",
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"├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
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"│ 0 │ a0 │ 1.884 │ 0.031 │ -0.031 │ 0.031 │ │ │ │\n",
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"│ 1 │ a1 │ -0.56 │ 0.06 │ -0.06 │ 0.06 │ │ │ │\n",
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"│ 2 │ a2 │ 0.136 │ 0.030 │ -0.030 │ 0.030 │ │ │ │\n",
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"│ 3 │ a3 │ -0.012 │ 0.005 │ -0.005 │ 0.005 │ │ │ │\n",
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"└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
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"┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n",
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"│ │ a0 │ a1 │ a2 │ a3 │\n",
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"├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n",
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"│ Error │ -0.031 │ 0.031 │ -0.06 │ 0.06 │ -0.03 │ 0.03 │ -0.005 │ 0.005 │\n",
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"│ Valid │ True │ True │ True │ True │ True │ True │ True │ True │\n",
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"│ At Limit │ False │ False │ False │ False │ False │ False │ False │ False │\n",
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"│ Max FCN │ False │ False │ False │ False │ False │ False │ False │ False │\n",
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"│ New Min │ False │ False │ False │ False │ False │ False │ False │ False │\n",
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"└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n",
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"┌────┬─────────────────────────────────────────┐\n",
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"│ │ a0 a1 a2 a3 │\n",
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"├────┼─────────────────────────────────────────┤\n",
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"│ a0 │ 0.000952 -0.0016 0.000752 -0.000105 │\n",
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"│ a1 │ -0.0016 0.00317 -0.00163 0.00024 │\n",
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"│ a2 │ 0.000752 -0.00163 0.000879 -0.000134 │\n",
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"│ a3 │ -0.000105 0.00024 -0.000134 2.1e-05 │\n",
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"└────┴─────────────────────────────────────────┘"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# run minos error analysis\n",
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"# The Minos algorithm uses the profile likelihood method to compute\n",
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"# (generally asymmetric) confidence intervals.\n",
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"m.minos()"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.contour.ContourSet at 0x7f783a26f280>"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
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]
|
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},
|
|
"metadata": {},
|
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"output_type": "display_data"
|
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}
|
|
],
|
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"source": [
|
|
"# Get a 2D contour of the function around the minimum for 2 parameters\n",
|
|
"# and draw a 2 D contours up to 4 sigma of a1 and a2 \n",
|
|
"m.draw_mncontour(\"a1\", \"a2\", cl=[1, 2, 3, 4])\n"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
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{
|
|
"data": {
|
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"text/plain": [
|
|
"(array([0.07628965, 0.07748762, 0.07868559, 0.07988356, 0.08108153,\n",
|
|
" 0.0822795 , 0.08347748, 0.08467545, 0.08587342, 0.08707139,\n",
|
|
" 0.08826936, 0.08946733, 0.0906653 , 0.09186328, 0.09306125,\n",
|
|
" 0.09425922, 0.09545719, 0.09665516, 0.09785313, 0.0990511 ,\n",
|
|
" 0.10024908, 0.10144705, 0.10264502, 0.10384299, 0.10504096,\n",
|
|
" 0.10623893, 0.1074369 , 0.10863488, 0.10983285, 0.11103082,\n",
|
|
" 0.11222879, 0.11342676, 0.11462473, 0.1158227 , 0.11702068,\n",
|
|
" 0.11821865, 0.11941662, 0.12061459, 0.12181256, 0.12301053,\n",
|
|
" 0.12420851, 0.12540648, 0.12660445, 0.12780242, 0.12900039,\n",
|
|
" 0.13019836, 0.13139633, 0.13259431, 0.13379228, 0.13499025,\n",
|
|
" 0.13618822, 0.13738619, 0.13858416, 0.13978213, 0.14098011,\n",
|
|
" 0.14217808, 0.14337605, 0.14457402, 0.14577199, 0.14696996,\n",
|
|
" 0.14816793, 0.14936591, 0.15056388, 0.15176185, 0.15295982,\n",
|
|
" 0.15415779, 0.15535576, 0.15655373, 0.15775171, 0.15894968,\n",
|
|
" 0.16014765, 0.16134562, 0.16254359, 0.16374156, 0.16493953,\n",
|
|
" 0.16613751, 0.16733548, 0.16853345, 0.16973142, 0.17092939,\n",
|
|
" 0.17212736, 0.17332533, 0.17452331, 0.17572128, 0.17691925,\n",
|
|
" 0.17811722, 0.17931519, 0.18051316, 0.18171113, 0.18290911,\n",
|
|
" 0.18410708, 0.18530505, 0.18650302, 0.18770099, 0.18889896,\n",
|
|
" 0.19009693, 0.19129491, 0.19249288, 0.19369085, 0.19488882]),\n",
|
|
" array([7.76355388e+03, 7.45301172e+03, 7.14880716e+03, 6.85094020e+03,\n",
|
|
" 6.55941083e+03, 6.27421905e+03, 5.99536487e+03, 5.72284829e+03,\n",
|
|
" 5.45666930e+03, 5.19682790e+03, 4.94332410e+03, 4.69615790e+03,\n",
|
|
" 4.45532929e+03, 4.22083827e+03, 3.99268485e+03, 3.77086903e+03,\n",
|
|
" 3.55539080e+03, 3.34625016e+03, 3.14344712e+03, 2.94698168e+03,\n",
|
|
" 2.75685383e+03, 2.57306357e+03, 2.39561091e+03, 2.22449585e+03,\n",
|
|
" 2.05971838e+03, 1.90127850e+03, 1.74917622e+03, 1.60341154e+03,\n",
|
|
" 1.46398445e+03, 1.33089495e+03, 1.20414305e+03, 1.08372875e+03,\n",
|
|
" 9.69652035e+02, 8.61912920e+02, 7.60511400e+02, 6.65447475e+02,\n",
|
|
" 5.76721145e+02, 4.94332410e+02, 4.18281270e+02, 3.48567725e+02,\n",
|
|
" 2.85191775e+02, 2.28153420e+02, 1.77452660e+02, 1.33089495e+02,\n",
|
|
" 9.50639250e+01, 6.33759500e+01, 3.80255700e+01, 1.90127850e+01,\n",
|
|
" 6.33759500e+00, 8.32667268e-11, 0.00000000e+00, 6.33759500e+00,\n",
|
|
" 1.90127850e+01, 3.80255700e+01, 6.33759500e+01, 9.50639250e+01,\n",
|
|
" 1.33089495e+02, 1.77452660e+02, 2.28153420e+02, 2.85191775e+02,\n",
|
|
" 3.48567725e+02, 4.18281270e+02, 4.94332410e+02, 5.76721145e+02,\n",
|
|
" 6.65447475e+02, 7.60511400e+02, 8.61912920e+02, 9.69652035e+02,\n",
|
|
" 1.08372875e+03, 1.20414305e+03, 1.33089495e+03, 1.46398445e+03,\n",
|
|
" 1.60341154e+03, 1.74917622e+03, 1.90127850e+03, 2.05971838e+03,\n",
|
|
" 2.22449585e+03, 2.39561091e+03, 2.57306357e+03, 2.75685383e+03,\n",
|
|
" 2.94698168e+03, 3.14344712e+03, 3.34625016e+03, 3.55539080e+03,\n",
|
|
" 3.77086903e+03, 3.99268485e+03, 4.22083827e+03, 4.45532929e+03,\n",
|
|
" 4.69615790e+03, 4.94332410e+03, 5.19682790e+03, 5.45666930e+03,\n",
|
|
" 5.72284829e+03, 5.99536487e+03, 6.27421905e+03, 6.55941083e+03,\n",
|
|
" 6.85094020e+03, 7.14880716e+03, 7.45301172e+03, 7.76355388e+03]))"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"m.draw_profile(\"a2\",subtract_min=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"-0.029647391105505568\n",
|
|
"0.029647391215961143\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# access fit results by parameter name and get minos asymetric errors\n",
|
|
"print (m.merrors['a2'].lower)\n",
|
|
"print (m.merrors['a2'].upper)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<ValueView a0=1.884312126113227 a1=-0.555095569066645 a2=0.135589233702581 a3=-0.012129739293476468> <ErrorView a0=0.030846480016973075 a1=0.056345427583826234 a2=0.029649793193055433 a3=0.004578762777736633>\n",
|
|
"<ErrorView a0=0.030846480016973075 a1=0.056345427583826234 a2=0.029649793193055433 a3=0.004578762777736633>\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# more print out\n",
|
|
"print (m.values,m.errors)\n",
|
|
"print (m.errors)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Access fit results\n",
|
|
"a0_fit = m.values[\"a0\"]\n",
|
|
"a1_fit = m.values[\"a1\"]\n",
|
|
"a2_fit = m.values[\"a2\"]\n",
|
|
"a3_fit = m.values[\"a3\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# display fitted function \n",
|
|
"x_plot = np.linspace( 0.1, 4.1 , 100 )\n",
|
|
"y_fit = a0_fit + a1_fit * x_plot + a2_fit * x_plot**2 + a3_fit * x_plot**3\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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L4OVP3MFcRs7dxtG0zAJfp1rPMXLONgUcERGRUmDqmJvimjhxIh06dOCFF14AoHnz5lSoUIFOnTrx+uuvExoaesU+vr6++Pr6OrrUggJDyYl6nAlrbsTAACwFvs7bMmH5Hno2DcHTw1LYUURERMQOLnXn5syZM3h4FCzZ09MTAMNw7nErm2sNJYWqXB5s8hhAivUcm5NOOrQuERERd2NquDl9+jSJiYkkJiYCkJSURGJiIocOHQJsXUqDBg3Kbx8TE8PixYuZPn06Bw4c4LvvvuPpp58mKiqKsLAwM07BbseyfOxrl36ujCsRERFxb6Z2SyUkJNC1a9f89dGjRwMwePBgZs+eTUpKSn7QARgyZAjp6elMnTqV5557jsqVK9OtWzcmT57s8NqLq3olv1JtJyIiIoWzGM7en1PK0tLSCAoKwmq1EhgY6LCfm5Nr0HHyGlKtZzEK6ZqyACFBfmx8qZvG3IiIiFymOL+/XWrMjSvz9LAwLqYpYMFCwTyZF2XGxTRVsBEREblOCjcO1Ds8lOkDW1LDv2C4CQnyY/rAlvQOv/JpLxERESkel3oU3B30Dg+lQ8NeLP9/91PBco6qjW+j3YOxumMjIiJSSnTnxgSenp78L/dW7vLcRIcD7+FpPXTtnURERMQuCjcm2ZDbjI05t2DJyYK1b5hdjoiIiNtQuDGNhcnZD9o+/rQQUneZW46IiIibULgx0U6jAdlN+gEGfDPO7HJERETcgsKNyc53fgU8vGD/N3BgndnliIiIuDyFG5MZwQ2g9aO2lVWvQW6uuQWJiIi4OIUbZ9D5RfANhNSfYNeXZlcjIiLi0hRunEGFG6DjKNvnb/8O5zV5poiISEkp3DiLW0dCpTCwJsPmj8yuRkRExGUp3DgLnwDo9qrt84a34cxJc+sRERFxUQo3Jgjw8eLgpL4cnNSXAJ9LZsCIeACq3wLnrLDhHfMKFBERcWEKN87EwxN6/t32efNHcDLJ3HpERERckMKNs7mxOzToAjlZ8O0Es6sRERFxOQo3zsZigV6vAxbYvQQO/Wh2RSIiIi5F4cYZhTSDyIG2z1+/DIZhbj0iIiIuROHGWXV7FbwrwOEE2LXI7GpERERchsKNs6oUcvHFft9M0Iv9RERE7KRw48zaPXnhxX6H4McZZlcjIiLiEhRunJlPAHQfa/u84R3IOG5uPSIiIi5A4cbZNb8fQiMgMw3WvmF2NSIiIk5P4cbZeXjA7RdCzdZZcHSPufWIiIg4OYUbV1CvIzSJASNXj4aLiIhcg8KNq+j5D/D0gQNr4devza5GRETEaSncuIrg+tD2/2yfv34ZsrPMrUdERMRJKdy4kk7PQYXqcPI32PKx2dWIiIg4JYUbV+IXCN1fs31eN7nQR8PPZGVTb8wK6o1ZwZmsbAcXKCIiYj6FG1fT4iHb3FOZVj0aLiIiUgiFG1fj4Qm9J9k+b50FR3ebW4+IiIiTMTXcxMfHExMTQ1hYGBaLhaVLl161/ZAhQ7BYLFcst9xyi2MKdhb1OkKTO22Phv/vJT0aLiIicglTw01GRgYRERFMnTrVrvbvv/8+KSkp+UtycjLBwcH87W9/K+NKnVCv18HLDw5ugD3/zd+ck3sx6GxOOllgXUREpDzwMvOHR0dHEx0dbXf7oKAggoKC8teXLl3KqVOnGDp0aFmU59yq1IUOz8D6ybDqVbipF3G/Whm37GI31ZBZWwgN8mNcTFN6h4eaWKyIiIjjuPSYm5kzZ9KjRw/q1q1bZJvMzEzS0tIKLG6jwygIrAXWZOIWz2bknG0cTcss0CTVeo6Rc7YRtyvFnBpFREQczGXDTUpKCv/73/8YNmzYVdtNnDgx/45PUFAQtWvXdlCFDuATAL3+QY5hYcKOShTWAZW3bcLyPeqiEhGRcsFlw83s2bOpXLky/fr1u2q72NhYrFZr/pKcnOyYAh3llrvZXP1vpBjBRTYxgBTrOTYnnXRcXSIiIiYxdcxNSRmGwaeffsrDDz+Mj4/PVdv6+vri6+vroMpMYLFw7JZhkHzlC/0udyz9nAMKEhERMZdL3rlZv349+/fv59FHHzW7FKdQvfaN9rWr5FfGlYiIiJjP1Ds3p0+fZv/+/fnrSUlJJCYmEhwcTJ06dYiNjeXw4cN89tlnBfabOXMmt956K+Hh4Y4u2SlF1Q8mNNCH1LRMDCxXfG8BQoL8iKpfdNeViIiIuzD1zk1CQgKRkZFERkYCMHr0aCIjIxk7dixgGzR86NChAvtYrVYWLVqkuzaX8PSwMO5OW9CzkFvgu7yoMy6mKZ4eVwYfERERd2MxjPL1etu0tDSCgoKwWq0EBgaaXU6pivvpMOMXxpOac/G89J4bERFxB8X5/e2SA4qlcL2b16RThXr8NPtZjlGZyrfH0rH9bbpjIyIi5YpLDiiWollqt+F3owZ3eW6i085X8Lysm0pERMTdKdy4oTezH8BqBOBxdCckfGp2OSIiIg6lcOOGThLI29n32VbW/ANO/2luQSIiIg6kcONmAny8ODipL//4x7sQ0gzOWeGb8WaXJSIi4jAKN+7KwxP6vmv7nDgHft9kbj0iIiIOonDjzmpHQctBts8rRkPOeXPrERERcQCFG3fXYwL4B8OxPfDjDLOrERERKXMKN+4uIBh6/t32ee1EsB42tx4REZEypnBTHrR4CGrfCuczIG6M2dWIiIiUKYWb8sDDwza42OIJPy+DfavNrkhERKTMKNyUFyHh0Hak7fPK5+H8WXPrERERKSMKN+VJlzFQKQxOHYT4t82uRkREpEwo3JQnvpWgz5u2z9+9D8d+NrceERGRMqBwU940vgMa9YHc8/DVs5CriTVFRMS9KNyUNxYLRL8J3hXg0Cbb24tFRETciMJNeVS5NnR92fZ51WuaWFNERNyKwk15deuICxNr/gWrXjG7GhERkVKjcFNeeXrBHe8DFvhpIfy21uyKRERESoXCTXlWqxVEPWb7/NWzkHXG3HpERERKgcJNedfttQvvvkmC9ZPMrkZEROS6KdyUd36B0Pcd2+fvp0LKDnPrERERuU4KNwKN+0DTfmDkwLKnISfb7IpERERKTOFGbKLfBL8gSEmEH2eYXY2IiEiJKdyITaUa0Ot12+e1/882/5SIiIgLUriRiyIfhnqd4PwZ29NThmF2RSIiIsWmcCMXWSwQ8z54+sJva2DHfLMrEhERKTaFGymoakPoGmv7HBcL6UfNrUdERKSYFG7kSu2egtAI29QMK58zuxoREZFiUbiRK3l6wV0fgocX/Lwcdi81uyIRERG7KdxI4UKaQcdnbZ9XPg9nTppbj4iIiJ1MDTfx8fHExMQQFhaGxWJh6dKl19wnMzOTV155hbp16+Lr60vDhg359NNPy77Y8ui2F6BaY8j4E75+2exqRERE7GJquMnIyCAiIoKpU6favc99993Ht99+y8yZM9m7dy/z58+ncePGZVhlOeblC3dOBSy2J6f2rTa7IhERkWvyMvOHR0dHEx0dbXf7uLg41q9fz4EDBwgODgagXr16ZVSdAFC7DbQdCT9Mg+XPwP9tsr3JWERExEm51JibZcuW0bp1a958801q1qzJzTffzPPPP8/Zs2eL3CczM5O0tLQCixRTt9egSn1IO6zuKRERcXouFW4OHDjAxo0b2bVrF0uWLGHKlCl8+eWXPPHEE0XuM3HiRIKCgvKX2rVrO7BiN+ETAP2mARbYPkfdUyIi4tRcKtzk5uZisViYO3cuUVFR9OnTh3fffZfZs2cXefcmNjYWq9WavyQnJzu4ajdRtz3cOsL2ednTcPYvU8sREREpikuFm9DQUGrWrElQ0MUxH02aNMEwDP74449C9/H19SUwMLDAIiXUfSwEN4D0I7DqFbOrERERKZRLhZsOHTpw5MgRTp8+nb/t119/xcPDg1q1aplYWTnhEwB3qXtKREScm6nh5vTp0yQmJpKYmAhAUlISiYmJHDp0CLB1KQ0aNCi//YABA6hatSpDhw5lz549xMfH88ILL/DII4/g7+9vximUP3Xb2Z6eAnVPiYiIUzI13CQkJBAZGUlkZCQAo0ePJjIykrFjxwKQkpKSH3QAKlasyOrVq/nrr79o3bo1Dz30EDExMXzwwQem1F9udXsNghvauqfixphdjYiISAEWwzAMs4twpLS0NIKCgrBarRp/cz0O/QizeoORC/fPhSZ3mF2RiIi4seL8/napMTfiROrcCu2ftn1e/gyc/tPcekRERC5QuJGS6/oyVL8FzhyHFc9C+boJKCIiTkrhRkrOyxfungEeXvDzcvjpC7MrEhERUbgR+5zJyqbemBXUG7OCM1nZF78IbQ6dLwwqXvkCWA+bU6CIiMgFCjdy/To+CzVbQaYV/vsE5OaaXZGIiJRjCjdy/Ty9oN8M8PKHA2thy8dmVyQiIuWYwo2Ujmo3Q69/2D6vHgvHfjG3HhERKbcUbqT0tBkGDbtD9jlY8jhkZ5ldkYiIlEMKN1J6LBa460PwrwIpO2D9JLMrEhGRckjhRkpXYCjEvG/7vPE9OPSDufWIiEi5o3Ajpa/pXRAxwDY1w+LHITPd7IpERKQcUbiRshE9GSrXgb9+h5Uvml2NiIiUIwo3Ujb8AuHuj8DiATvmwc4vza5IRETKCYUbsUtO7sV5ozYnnSywXqS67aDT87bPX42Gvw6VUXUiIiIXKdzINcXtSqHHu+vz14fM2kLHyWuI25Vy7Z07vwS12tjeXrz4ccjJvvY+IiIi10HhRq4qblcKI+ds42haZoHtqdZzjJyz7doBx9ML7vkYfCrBoU2w8d0yrFZEREThRq4iJ9dgwvI9FNYBlbdtwvI91+6iCq4Pfd+2fV43CZI3F/i6yEk5RURESkDhRoq0OekkKdZzRX5vACnWc2xOOnntgzW/H8LvBSMHFj0K56ylV6iIiMglFG6kSMfSiw42xW5nscAd7154PPwQLB8Fhh2DkkVERIpJ4UaKVL2SX6m2wy8I+n8KFk/YvRi2f34d1YmIiBRO4UaKFFU/mNAgPyxFfG8BQoP8iKofbP9Ba7eBbq/aPq98UbOHi4hIqVO4kSJ5elgYF9MU4IqAk7c+LqYpnh5FxZ8idBgFDbpA9ln48hE4f/Y6KxUREblI4Uauqnd4KNMHtqR6oG+B7SFBfkwf2JLe4aHFP6iHh+3txRWqwbHdeH87tpSqFRERUbgRO/QOD+Wb0Z3z12cPbcPGl7qVLNjkqVQD7p4BgPe2T7ndY/M1dhAREbGPwo3Y5dKup6j6wcXviirMjT2g/dPkGBbu81xPMFb7p3YQEREpgpfZBUj5FhcynPHZt5CaUwmwTe0QGuTHuJim13dnSEREyi3duRHTxO1KYeS8HfnBJo/dUzuIiIgUQuFGTFFqUzuIiIhcRuFGTFGqUzuIiIhcQuFGTFGqUzuIiIhcwtRwEx8fT0xMDGFhYVgsFpYuXXrV9uvWrcNisVyx/PKL3nLrakp9agcREZELTA03GRkZREREMHXq1GLtt3fvXlJSUvKXm266qYwqlLJy7akdcgnlOFHpaxxal4iIuD5THwWPjo4mOjq62PtVr16dypUrl35BUqQAHy8OTupbasfLm9ph5JxtWKDAwGLLhf8d5/05nit2Q1gzqN641H62iIi4N5cccxMZGUloaCjdu3dn7dq1ZpcjJXTVqR0GRNL7xgA4fwa+eBgyT5tUpYiIuBqXCjehoaF89NFHLFq0iMWLF9OoUSO6d+9OfHx8kftkZmaSlpZWYBHnUeTUDs1rQv+ZUCkMjv8Ky58GQ4+Fi4jItbnUG4obNWpEo0aN8tfbtWtHcnIyb7/9Nrfddluh+0ycOJEJEyY4qkQpgSKndqhYDf42C2b3hV2LoPatcOtwk6oUERFX4VJ3bgrTtm1b9u3bV+T3sbGxWK3W/CU5OdmB1cl1q9MWev7d9vnrl+HQD+bWIyIiTs/lw8327dsJDS16DiJfX18CAwMLLOJi2v4f3HIP5GbDF4MgPdXsikRExImZ2i11+vRp9u/fn7+elJREYmIiwcHB1KlTh9jYWA4fPsxnn30GwJQpU6hXrx633HILWVlZzJkzh0WLFrFo0SKzTkEcwWKBO/8Jx36GP3+G/wyBwcvB09vsykRExAmZGm4SEhLo2rVr/vro0aMBGDx4MLNnzyYlJYVDhw7lf5+VlcXzzz/P4cOH8ff355ZbbmHFihX06dPH4bWLg/lWhAfmwkdd4NAmWPUqRE82uyoREXFCFsMoX4+gpKWlERQUhNVqVReVkziTlU3TsV8DsOfvtxPgc5XM/ctKWPCg7fPdH0HE/Q6oUEREzFac398KN+J6vv0HbHgbvPzh0a8hNMLsikREpIwV5/e3yw8olnKo68twYw/IPgsLHoKM42ZXJCIiTkThRlyPhyf0/wSCG4A12TbAOOe82VWJiIiTULgR1+RfBR6YBz4V4eAGWPWa2RWJiIiTULgR11W9Cdz9L9vnH6dD4jxz6xEREaegcCOurckd0HmM7fPyUfDHVlPLERER8ynciOvr/BI06gM5mbBgAKSlmF2RiIiYSOFGXJ+Hh617qloTOJ1qCzjnz5pdlYiImEThRtyDXyA8OB/8g+HINvjvk1C+XuEkIiIXKNyI+wiuD/d9Bh5esOtL2Piu2RWJiIgJFG7EvdTvBH3esn3+9u/wywpz6xEREYdTuBH30/oRaPOY7fOixyB1p7n1iIiIQynciHvqPZGcerfB+QwOT7+LMyf/MLsiERFxEIUbcU+e3mTePYvfckOpaTmB738GQtYZs6sSEREHKHa42bt3L+PHj6d79+40bNiQ0NBQmjdvzuDBg5k3bx6ZmZllUadI8flX5pHzL3DKqIhnynZYOgJyc82uSkREypjd4Wb79u307NmTiIgI4uPjadOmDaNGjeIf//gHAwcOxDAMXnnlFcLCwpg8ebJCjjiF340Qhmc9i+HhDXv+C2tfN7skEREpY172NuzXrx8vvPACCxcuJDg4uMh2mzZt4r333uOdd97h5ZdfLpUiRa7HZqMJWX2n4Lv8CdjwDlS9EVoMMLssEREpI3aHm3379uHj43PNdu3ataNdu3ZkZWVdV2EipSmn2QPw1wFbuFn2FATWhAadzS5LRETKgN3dUvYEG4AzZ84Uq72Iw3R9FW65B3KzYeHDcOxnsysSEZEyUKKnpbp06cIff1z5aO2PP/5IixYtrrcmkVKRk3tx+oXNSSfJwQL9pkPttpBphbl/g/SjJlYoIiJloUThJjAwkObNm7NgwQIAcnNzGT9+PLfddht33nlnqRYoUhJxu1Lo8e76/PUhs7bQcfIa4vaess1BFdwQrMkw7z7IyjCxUhERKW0WwyjZ7IIzZszg+eef58477+TgwYMcOnSI2bNn06NHj9KusVSlpaURFBSE1WolMDDQ7HKkDMTtSmHknG1c/g/bcuHP6QNb0jv0DMzsCWdOwM3R8MBc8PB0dKkiImKn4vz+LvFL/EaMGMFTTz3FggULSEhI4IsvvnD6YCPuLyfXYMLyPVcEGyB/24Tle8ip0gAeXACevvDr/2DlC5pFXETETZQo3Jw6dYr+/fszffp0/vWvf3HffffRq1cvpk2bVtr1iRTL5qSTpFjPFfm9AaRYz7E56STUjoL+HwMWSJhpe5JKRERcXonCTXh4OEePHmX79u089thjzJkzh5kzZ/Laa6/Rt2/f0q5RxG7H0osONoW2a3oXRL9p+7zmH7B9bhlVJiIijlKicDNixAji4+OpX79+/rb777+fHTt26P02YqrqlfyK3+7Wx6HDKNvnZU/Bvm9KvzAREXGYEg8odlUaUOzecnINOk5eQ6r1XKHjbixASJAfG1/qhqeH5eIXubmwZDjs/AK8K8DQFRAW6aiyRUTkGspkQPGhQ4eKVcThw4eL1V6kNHh6WBgX0xS4+HRUnrz1cTFNCwYbAA8PuOtDqN8ZzmfAnHvhxG9lXq+IiJQ+u8NNmzZteOyxx9i8eXORbaxWKx9//DHh4eEsXry4VAoUKa7e4aFMH9iS6oG+BbaHBPnZHgMPDy18Ry8fuH8OhDSHM8fh836QllJo0zNZ2dQbs4J6Y1ZwJiu7lM9ARESuh91zS/3888+88cYb9O7dG29vb1q3bk1YWBh+fn6cOnWKPXv2sHv3blq3bs1bb71FdHR0WdYtclW9w0PpcOMNNBu/CoDZQ9vQ6aZqV96xuZxfIAxcBDN7wakkmNMfhq4E/8plX7SIiJQKu+/c/PHHH0yePJkjR44wY8YMbr75Zo4fP86+ffsAeOihh9i6dSvfffedgo04hUuDTFT94GsHmzwVq8PDS6BiDTi2G+Y/AFlnyqjKgnRHSETk+tl95yYyMpLU1FSqVavGc889x5YtW6hatep1/fD4+Hjeeusttm7dSkpKCkuWLKFfv3527fvdd9/RuXNnwsPDSUxMvK46RK4QXB8GLoZZfeDQJvhyqK3LytMbuHLeKrvuComIiEPYfeemcuXKHDhwAICDBw+Sm5t73T88IyODiIgIpk6dWqz9rFYrgwYNonv37tddg0iRQsJhwALw8oNf4+C/T0JubtHzVu0qfHyOiIg4lt13bvr370/nzp0JDQ3FYrHQunVrPD0Ln4snLwRdS3R0dIm6sIYPH86AAQPw9PRk6dKlxd5fxG5128PfZsOCh+CnBcSdbsDIPeFXPGaeaj3HyDnbrj5gWUREHMLucPPRRx9xzz33sH//fp5++mkee+wxKlWqVJa1FWrWrFn89ttvzJkzh9dff/2a7TMzM8nMzMxfT0tLK8vyxB01ioa7/0XOoseZsCcUA4PLHzTP2zJh+R56Ng1RF5WIiInsDjcAvXv3BmDr1q0888wzDg83+/btY8yYMWzYsAEvL/tKnzhxIhMmTCjjysTtNf8bm/84S0p80ePMLp23ql3Dko1H01geEZHrV6LpF2bNmuXwYJOTk8OAAQOYMGECN998s937xcbGYrVa85fk5OQyrFKcSYCPFwcn9eXgpL4E+BQrxxfqWJh9Y7zsnd/qchrLIyJSOq7/v/gOkp6eTkJCAtu3b+fJJ58EIDc3F8Mw8PLyYtWqVXTr1u2K/Xx9ffH19b1iu0hxlWjeKjvF7Uph5JxtGssjIlIKXCbcBAYGsnPnzgLbpk2bxpo1a/jyyy8LTOIpUhai6gcTGuR3zXmrouoHF+u4ObkGE5bvKfSYGssjIlJ8poab06dPs3///vz1pKQkEhMTCQ4Opk6dOsTGxnL48GE+++wzPDw8CA8PL7B/9erV8fPzu2K7SFnIm7dq5JxtWKBAGLnqvFXXsDnpJCnWoruySmMsj4hIeVKiMTelJSEhgcjISCIjbbMvjx49msjISMaOHQtASkpKsSfsFClLRc5bZTnB9B4+Jeo6sneMTknH8oiIlDcWwzAKuxvutoozZbpIUdLPnafZ+FV4kMNnDdbQ7vBsPL39bPNS1etQrGNt+u0ED378wzXbzX+sre7ciEi5VZzf36beuRFxVXldT7l40vLhN/C8uSdkn4W5f4PfNxXrWHljeYrqzLIAoSUYyyMiUl4p3IhcL08fuO9zaNAVzmfA3Hvh0LXvxOTvfmEsD1z+asDrG8sjIlJeqVtKpLScPwvz7oek9eBTyTazeO02du8etyuFcct2czTt4hu1Q4P8GBfTVI+Bi0i5V5zf3wo3IqUp6wzMuw8ObgDfQFvAqdXa7t3zxvIAzB7aRm8oFhG5QGNuRMziEwADFkLdjpCZBp/fDclb7N790iATVT9YwUZEpAQUbkRKm08FW8Cp0/5iwDn0o9lViYiUGwo3ImXBtyIM/BLqdYKsdJhzD/z+vdlViYiUCwo3ImXFpwIM+ALqd4as0zCnPyRtMLsqERG3p3AjUpbyxuA07Abnz9jeg3NgndlViYi4NT0tJeII58/BwoGwfzV4+sL9n8PNt5tdlYiIy9DTUiLOxtsPHpgLje+AnExY8BDsXmp2VSIibknhRsRRvHzhb7MhvD/knocvh8KOBWZXJSLidhRuRBzJ0xvu+RgiB4KRC0tGQMKnZlclIuJWFG5EHM3DE2L+CVGPAwZ89Sx894HZVYmIuA2FGxEzeHhA9JvQYZRtffVr8M0EKF/j+0VEyoTCjYhZLBboOQF6jLetb3wXVoyG3FxTyxIRcXUKNyJm6/gs3DEFsNjG3yx+DHLOm12ViIjLUrgRcQath8K9M8HDG3Z9CfMfhKwMs6sSEXFJCjciziK8Pzy4ALz8bS/7+/edkHHC7KpERFyOwo2IM7mpBwxeDv5V4HACfHo7/HXI7KpERFyKwo2Is6ndBh75GgJrwYl9MLMXHN1jdlUiIi5D4UbEGVVrBI+ugmqNIT0FZvWGg9+ZXZWIiEtQuBFxVkE1Yej/oPatcM4Kn/eDXYvNrkpExOkp3Ig4s4BgGPTfCxNuZtnmo/p+ql72JyJyFQo3Is7O2x/u++zCdA3AqlcgLhZyc8ytS0TESSnciLgCD0/bdA09/2Fb/3E6fDEIss6YW5eIiBNSuBFxFRYLdHga+s8ETx/45SuY3RfSj5pdmYiIU1G4EXE1ze61jcPxD4Yj2+CT7npUXETkEgo3Iq6obnsY9g0ENwRrsu1lf7+tMbsqERGnoHAj4qqqNrQFnLodIDMN5twLWz4xuyoREdMp3Ii4soBgeHgJNH8AjBxY8RyseB5yss2uTETENKaGm/j4eGJiYggLC8NisbB06dKrtt+4cSMdOnSgatWq+Pv707hxY9577z3HFCvirLx84e4Z0H2sbX3LxzC3P5w9ZW5dIiImMTXcZGRkEBERwdSpU+1qX6FCBZ588kni4+P5+eefefXVV3n11Vf56KOPyrhSESdnsUCn5+D+ueBdAQ6sg096wPH9ZlcmIuJwFsNwjledWiwWlixZQr9+/Yq13z333EOFChX4/PPP7WqflpZGUFAQVquVwMDAElQq4uRSd8K8ByDtD/ALgv6f2mYbFxFxYcX5/e3SY262b9/O999/T+fOnYtsk5mZSVpaWoFFxK2FNIPH10KtKNucVPP+Bt+9rykbRKTccMlwU6tWLXx9fWndujVPPPEEw4YNK7LtxIkTCQoKyl9q167twEpFTFKxOgz5CiIfBiMXVo+FRcP0RmMRKRdcMtxs2LCBhIQEZsyYwZQpU5g/f36RbWNjY7FarflLcnKyAysVMZGXL9z5T+jzNnh4wa4vbe/D+euQ2ZWJiJQpL7MLKIn69esD0KxZM44ePcr48eN58MEHC23r6+uLr6+vI8sTcR4WC0Q9BtWb2OaiSv0J/tUZ7v0UGnY1uzoRkTLhknduLmUYBpmZmWaXIeLc6nWEx9dBaAs4exLm3AMb3tU4HBFxS6beuTl9+jT79198VDUpKYnExESCg4OpU6cOsbGxHD58mM8++wyADz/8kDp16tC4cWPA9t6bt99+m6eeesqU+kVcSuU68MjXthf9Jc6BbyfA4a3Qbzr46clBEXEfpoabhIQEuna9eGt89OjRAAwePJjZs2eTkpLCoUMXxwfk5uYSGxtLUlISXl5eNGzYkEmTJjF8+HCH1y7ias5kZdN07LdANL/2i8Rn1RjbzOIf74X7PoMaTc0uUUSkVDjNe24cRe+5kfLKFm6+BmDP328n4FgiLHwY0o+Alz/ETIGIB0ytUUSkKOXmPTcich1qtYYRG6BBV8g+C0uGw7Kn4fw5sysTEbkuCjci5URO7sWbtJuTTtrWK9wAAxdBl1jAAtv+DTN7wInfinXsM1nZ1BuzgnpjVnAmS5N2ioi5FG5EyoG4XSn0eHd9/vqQWVvoOHkNcbtSwMMTuoyBhxdDQFXb9A3/6gy7FplYsYhIySnciLi5uF0pjJyzjaNpBV+ZkGo9x8g522wBB6BhNxi+Aeq0g6x0+PIRWP4MnD9rQtUiIiWncCPixnJyDSYs30NhTw3kbZuwfM/FLqugmjD4K7jtBcACW2fDx93hz73X/Dl58ru8RERMonAj4sY2J50kxVr0AGEDSLGeY3PSyYsbPb2g26vw8BKoUB2O7YaPusDWfxf60r+rdnmJiJhA4UbEjR1Lt+/Jp0LbNewKIzZCgy5w/gwsf9o2hcOZi0HI7i4vEREHUrgRcWPVK/ldX7tKNWDgEuj5d9vkmz8vgxkd4eDG4nd5iYg4iMKNiBuLqh9MaJAfliK+twChQX5E1Q8u+iAeHtDhGXh0NQQ3hLTDMPsONi96r/hdXiIiDqBwI+LGPD0sjIuxTatwecDJWx8X0xRPj6LizyVqtoTh8RA5EDA49tMqu2qwt2tMRKS0KNyIuLne4aFMH9iS6oG+BbaHBPkxfWBLeoeH2n8w34pw14dw32dU982xaxd7u8ZEREqLqRNniohj9A4PpcONN9BsvO1uy+yhbeh0UzX77tgUpuldRNWMIvSteFKzAzAK+f9JFmwB6qpdXiIiZUB3bkTKiUuDTFT94JIHm7zjBYUy7v7bAAsWcgt8V+wuLxGRUqRZwUXkusTtSmH80h2knr7YTRXqncG4O5vTu01jEysTEXeiWcFFxGF6h4ey+vnueJBDD8tW5vtOZKPH4/Re0xt2flnoi//spQk5RaQkFG5E5Lp5eljIxZNvjFa0eGwanqHN4OwpWPQoLHgI0lPNLlFEyhGFGxEpVUb1pvDYGugSCx7esHcFfBgF2+de110cERF7KdyIyHUL8PHi4KS+HJzUlwAfL/D0hi5jYPh6CIuEc1b47//BnP5w6nezyxURN6dwIyJlp8Yt8Og30GMCePrCb9/CtLaw6UPIvfZ7cjTbuIiUhJ6WEhHHOL4Plj8Dv39nWw+LhDv/CSHNCm0etyuFcct2F5iUMzTIj3ExTYv34kERcQt6WkpEnM8NN8HgryDmffANgiPb4V+dYfVYyMoo0FSzjYvI9VC4ERHH8fCAVkPgyc3Q9C4wcuC79+HDtrA3DkCzjYvIdVO4ERHHqxQC930GDy6AoNpgPQTz74cFD7F55y+abVxErovCjYiYp1E0PPEjdBgFHl7wy1ccWxJr166abVxEiqJwIyLm8qkAPSfA8A1Qpx3Vc+x74Z9mGxeRoijciIhzqNEUhqwk6u5nCPU4dcVknHks2J6a0mzjIlIUhRsRcR4eHni2fJBx97ZDs42LSEkp3IiI0+ndsiHTB7aiRkXvAttDPE4x/bYset8SYlJlIuIKvMwuQESkML3DQ+lw4w00G/81LSz7eTHoW249uwHPzQaktoPeE20vAhQRuYzu3IiI07J1PVlING6ixVPz8Oz2Mnj5w6FN8FEXWDwcrH+YXaaIOBmFGxFxDd7+0PlFeGorNL/ftu2nBfDPVrDmdchMN7c+EXEapoab+Ph4YmJiCAsLw2KxsHTp0qu2X7x4MT179qRatWoEBgbSrl07vv76a8cUKyLOIagm3PMRPLYW6rSH7HMQ/xZ80BK2fAI55+06zJmsbOqNWUG9MSs4k5VdxkWLiCOZGm4yMjKIiIhg6tSpdrWPj4+nZ8+erFy5kq1bt9K1a1diYmLYvn17GVcqIk6nZksYuhLunwPBDSDjGKx4Dj68FXYvBZPmBFZoEjGfqQOKo6OjiY6Otrv9lClTCqy/8cYb/Pe//2X58uVERmpgoYi7CfDx4uCkvkU3sFigSQzcdDtsnQ3rJ8PJ3+A/g6FmK+gxAep3cli9IuIcXHrMTW5uLunp6QQHF/0yr8zMTNLS0gosIuJmvHzg1sfhmUTo/BJ4V4DDW+Hfd8Bn/WyfRaTccOlw884775CRkcF9991XZJuJEycSFBSUv9SuXduBFYqIQ/lWgq4vw9Pboc0w8PCGA2vh426wcCAc+yW/6aWzim9OOqlZxkXciMuGm/nz5zN+/HgWLlxI9erVi2wXGxuL1WrNX5KTkx1YpYiYolIN6PsOPJUAEQ8CFvh5OUxrC4seI+777fR4d31+8yGzttBx8hridqWYV7OIlBqXDDcLFy7k0Ucf5YsvvqBHjx5Xbevr60tgYGCBRUTKiSr14O4Z8H+boPEdgEFcYhIjlx3maFrBWcVTrecYOWebAo6IG3C5cDN//nyGDBnCvHnz6Nv3KgMNRUTyVG8CD8wlZ9g6JvA4tg6ognNT5XVKTVi+57q6qNTdJWI+U8PN6dOnSUxMJDExEYCkpCQSExM5dOgQYOtSGjRoUH77+fPnM2jQIN555x3atm1LamoqqampWK1WM8oXERezObMOKecrcHmwyWMAKdZzbE46WaLjx+1KUXeXiBMwNdwkJCQQGRmZ/xj36NGjiYyMZOzYsQCkpKTkBx2Af/3rX2RnZ/PEE08QGhqavzzzzDOm1C8iruVY+rlrNwKOJe8v9rHjdqUwcs42jqZlFtiu7i4RxzP1PTddunTBuMqLtmbPnl1gfd26dWVbkIi4teqV/Oxr9+0zcLgOdBoNddpes31OrsGE5Xso7L9mBrb7RBOW76Fn05AL82WJSFlyuTE3IiIlFVU/mNAgvyI6pWwhJNT7DFGev8K+r+HT2+HTaNi3+qpvPN6cdJIUa9F3ha63u0tEikfhRkTKDU8PC+NimgJXjrrJWx93f0c8n9oCLQfZ3pNz6HuYey9Mbw+J8yE764rj2t3dZWc7Ebk+CjciUq70Dg9l+sCWVA/0LbA9JMiP6QNb0js8FKo2hDv/Cc/sgLZPgE9FOLYHlo6A9yPguw/g3MUHGezu7rKznYhcH4txtUEvbigtLY2goCCsVqveeSNSjqWfO0+z8asAmD20DZ1uqlb0eJizpyDhU/hhhm2CTgCfStDyYbh1ODlBdek4eQ2p1nOFjruxYAtPG1/qpjE3IiVUnN/funMjIuXSpSEjqn7w1UOHfxXo9ByM2gkxH8ANN0NWOvwwDT6IxPM/gxjX1vZ8RpHdXTFNFWxEHEThRkTEXt5+0Gow/N+P8NAiaNAVjFz4eRm919/J9OpLqOGfU2CXAt1dIuIQ6pYSEbkeR3fb7uDs/BKyz5FjWFid24plOe24/94H6dhCd2xESkNxfn8r3IiIlIaME7Dt3+Ru+QSPtMMAGBZPLI2ibTOU1+8MHrpZLlJSGnMjIuJoFapCp9Gc+79tjMgaxaacpliMHPjlK/i8H0xtDZs+hDN6141IWVO4EREpTR5exOVG8eD5Vzn72HcQ9bjtyaqTv8HXL8M7jWHRY/D791d9MaCIlJzCjYhIGTGqNYY+b8Fzv0DfdyGkGeRkws4vYFY0fBgF3/8TTv9pdqkibkVjbkREHMUw4Mg2SJgFuxbB+TO27R5e0KiP7a3IDbuBh6e5dYo4IQ0ovgqFGxFxCufSYOd/YPvncGT7xe2VwiDiAWgxAG64ybz6RJyMws1VKNyIiNNJ3WULOT8ttL0NOU+tNhDxIITfY3uRYBk5k5VN07FfA7Dn77cT4ONVZj9LpKT0tJSIiCsJCYfoyfDcXvjbv+Gm28HiCX9sgRWj4e2bYeFA+Hk5ZGeaXa2I01M8FxFxFl6+cEs/25J+1DbwOHE+HNttCzY/Lwe/ynDL3dDsb1Cnnd6dI1IIhRsREWdUqQa0f8q2pO6ydVnt/A+kp8DWWbYlsObFoBMaARa9CVkEFG5ERJxfSLht6TEeDm6An/5ju4uTdhg2TbUtwQ1tQSf8Hqje1GmCjsbziBl0P1NExFV4eEKDLtDvQ3hhH9w/1xZovPxtLwnc8DZMb297f87aN2zzXtnxzEhO7sU2m5NOFlgXcUWK0CIiLuKKuyBN7oAmd0Dmafg1DnYvgX2r4fivsH6ybQluCE3vhCZ3QljkFXd04nalMG7Z7vz1IbO2EBrkx7iYpprJXFyW7tyIiLg634rQ7F54YC68sB/u+dj2UkBPX9sdnY3vwcddYUpz+N8YSNoAOdnE7Uph5JxtHE0r+ARWqvUcI+dsI25XynWXprtCYga950ZExEWknztPs/GrAJg9tA2dbqqGp8dVxtZkpsO+VbBnme3PvDciAzl+Vel45k1SsvyAK49hAUKC/Nj4Urer/4yryLsrdGl40l0hKSm950ZExM3E7Uqhx7vr89eHzNpCx8lrrn53xbcShPeH+/4NL/wGD8yHFg+BfzCbM6qTkuVPYcEGwABSrOfYnFSyWcwdcVdIpCgKNyIiTq5UgoJPADTuA/2mwfP7OHbbG3b97GNpZ67d6DI5uQYTlu+hsG6BvG0Tlu9RF5WUGYUbEREnViZBwdOL6g0i7GpafeUwWPy47fHzjBN27bM56SQp1nNFfn+9d4VErkVPS4mIOLHiBIV2Davafdyo+sGEBvmRaj1XaHCyYBBiOUVU1o/w0w+2lwhigZot4caecGN3CGsJnlf+GjmWXnS9JWknUlwKNyIiTqysgoKnh4VxMU0ZOWcbFigQcCwX/nfcg13xrLQc9q+Gfd/YpoE4vNW2rJ8EfkFQv7Mt6DToClXqAlC9kp9dNdjbztH04kHXpysmIuLEyjIo9A4PZfrAllc80RRS4ImmWlC/E/T8O6Qdgf3fwP5v4cA6OPcX/LzMtgAEN4AGXYiq14XQwABS07KKuCtk+xlR9YOLXbOIPRRuRESc2LW7j64vKPQOD6XDjTfY94h5YBi0HGRbcnPgyHb4bY0t7PyxBU4egJMH8Ez4lHE5bRjJqAt3hS4eK+/TuJimJX7EXORaNKBYRMSJ5XUfwZUPbZdWULh036j6wfYdy8MTarWGzi/Co1/DSwfhwQVw6wio1pjenluY7j2FEAoOGg7xzWJ6Dx9636z3jEnZ0Z0bEREnZ1/3kcn8AqFRtG0BSEuh98ENdN2/jq+3HcCwQHX+Iopf8NxowPfetsHJdTvYljq32t7LI1IKTL1zEx8fT0xMDGFhYVgsFpYuXXrV9ikpKQwYMIBGjRrh4eHBqFGjHFKniIjZeoeH8s3ozvnrs4e2YeNL3Zwj2BQmMBSa30fOHR/wdPZTvJV9Py3vHIlniwcgsBbknofkH2HjuzC3P0yqAx91gbiXbTOen/7zqoc/k5VNvTErqDdmBWeysh1zTuIyTL1zk5GRQUREBEOHDqV///7XbJ+ZmUm1atV45ZVXeO+99xxQoYiI8yhR95GT+MOoTk7E7dBmsG2m8r9+t81xdWgTHNxoWz+y3bb88KFtp6o3QZ22tqV2W6ja8IqJP0UKY2q4iY6OJjo62u729erV4/333wfg008/LauyRETKlQAfLw5O6uu4Y1ssUKWebWn5sG2b9Q/4/Xtb2Dn0AxzbAyf22Zbtn184WFWofSvUvhWPkFb4kkUmPqVe8+WTfV5zDi9xOm4/5iYzM5PMzIt91GlpaSZWIyJScmUZQkwXVAua32dbAM6chOTNtrCT/CMc3gZnTsDelbB3Jd6GhZneTdmWexPbvkikXbNGeNZpA5XrXNfdnbzJPvMMmbWl1Cb71PtzHMft/2YnTpzIhAkTzC5DRESKIyAYGvW2LQDZmZDyEyT/QNzOw4z/PZzU3Cq273ZB6K4TjPN+lN6Bv0OtNlCrFdRsDWGRtsHOdsibw+vyR+7z5vCaPrCl845xkgLcPtzExsYyevTo/PW0tDRq165tYkUiIlJsXr5Quw1x1lqMTCokgBDMyPOjmJ42hd57V8DeFRe+scANN0PNVrans2q2hBrhtuNd4lpzeFmwzeHVs2mIuqhcgNuHG19fX3x9fa/dUEREnNrVA4jFFkB8n6Nn19/wPLIVDifAX4fg+F7bsmOerbGnjy3g1Gxlu7MTFsnm9BvKZA6vy+vPo7E8Zcvtw42IiLgHuyYRPZ3L5pAHadfhSdvG039enA/ryDbbn2dP2T4f2Za/7zG6AI9fs4aSTvZZlmN55EqmhpvTp0+zf//+/PWkpCQSExMJDg6mTp06xMbGcvjwYT777LP8NomJifn7/vnnnyQmJuLj40PTpk0dXb6IiDhQiSYRrVit4Ngdw4BTBy8EnW1wJBFSEql+9qhdx65ewbt4RaOxPGYwNdwkJCTQtWvX/PW8sTGDBw9m9uzZpKSkcOjQoQL7REZG5n/eunUr8+bNo27duhw8eNAhNYuIiDlKZRJRiwWC69uW8AvvV8vNIerPfYTO2EfqWUuBubDydyOXEE4StaA5hNwCoc0hNAJCmkP1puBd+M901FgePYlVkKln36VLFwyjsEtuM3v27Cu2Xa29iIi4rzKbRNTDE88ajRnXP4iRc7ZdmOzz0uPaYsg4/y/xzDlrG8tzOOGS/b2gWmNb0AltbvszpBn4BdrXlXadY3nkSuU72omIiMvIm0S08ABicz2TiBY9h5e/bWxM0//aZj5P3QEpO2yPpqfsgLMn4egu25I3aBmgSj2O+dwBtL/mzy7pWB4pnMKNiIi4jLKeRLR3eCgdbryBZuNXAbY5vAo81XTDjbYlr0vLMGxvV079yRZ28v5M+wNOHaR6zrfYE27s7XIzgyt2eTl/hSIiIpe4ZgC5TsWaw8tigcq1bUvjS94efeYkpO4kKmUnoV+fJTXLr+ixPJZTRK26G0Kb2bqzQsJtj6r7Vy6V8ymPFG5ERMTlOP0kogHB0KAzng06My4o5epjebw+w/PoDji6o+AxgurYgk5IM1vYCQmHyvXAw8Nx5+GiFG5ERETK0FXH8vRtQu9aH0PqTttydJftT2syWA/Zlr0rLx7MpxLUuOXiHZ6QZrantSj+I+ruTOFGRESkjF2zKy24PjS98+IOZ0/B0d0XQs8uOLoTjv0MWemQ/INtyWPxwLvKjTzl2Zyfc+uy5TsPOraMxDMoxIFn6FwUbkRExOW44gzpxepK868C9Traljw55+H4vot3dy4scen1mXBkECnYHiX/5utcQr9exbiKS+ld17hwl+fC4+lVbwQPz2LV7YrTRliMcvbimLS0NIKCgrBarQQG2jdTrIiIlB+u9HRQ3M4URs698u3HFnIBC9O9p9Dbc8vFL7z8oUbTC4GnGYRE2NZ9KhR+/AvTRlzanWbWtBHF+f3tvFdMREREipSTazDhq6Lefuxhe/ux19P0bLkOz6M7bd1c5zMuzrWVx+Jhu6Nz6UsIQyOIO5DpstNG6M6NiIiIC9r02wke/PiHa7ab/1hb29uPc3PgZJLtXTypP9m6tVJ+goxjV+yTY1joeP5DUnKDoNBH2G3vFtr4UjeHdVHpzo2IiIibK/ZEoh6el7yE8J6LDdKPXnj54I78lxBuPu5HSm7lIo+ZP23Ezl9o17yx7X0/TkThRkRExAWVykSiAJVqQKWecFPP/E3HtuyHRXuveexjXz4LX/8KYZEQ2sL2Z1gLCKxpauBRuBEREXFBZTaRKFA9uIp97TzS4cwJ2P+NbclTKRRG7QRPc96/o9ccioiIuKC8iUThylEx1zuRaF5wKmpPC7anpqJe/gaGrYG+70DkQNublC2etkfZTQo2oDs3IiIiLqusJhK1ewZ2X3+o1cq25Dl/FtJTS/RzS4uelhIREXFx6efOl8lEonrPjYiIiJiirCYSLesZ2MuKxtyIiIhIkZx+BvZCKNyIiIiIW1G4EREREbeicCMiIiJuRQOKRUREpEgBPl4cnNTX7DKKRXduRERExK3ozo2IiIiLc8W7K2VJd25ERETErSjciIiIiFtRuBERERG3onAjIiIibkXhRkRERNyKwo2IiIi4FVPDTXx8PDExMYSFhWGxWFi6dOk191m/fj2tWrXCz8+PBg0aMGPGjLIvVERERFyGqeEmIyODiIgIpk6dalf7pKQk+vTpQ6dOndi+fTsvv/wyTz/9NIsWLSrjSkVERMRVmPoSv+joaKKjo+1uP2PGDOrUqcOUKVMAaNKkCQkJCbz99tv079+/jKoUERERV+JSY242bdpEr169Cmy7/fbbSUhI4Pz58yZVJSIiIs7EpaZfSE1NpUaNGgW21ahRg+zsbI4fP05oaOgV+2RmZpKZmZm/npaWVuZ1ioiIiHlc6s4NgMViKbBuGEah2/NMnDiRoKCg/KV27dplXqOIiIiYx6XCTUhICKmpqQW2HTt2DC8vL6pWrVroPrGxsVit1vwlOTnZEaWKiIiISVyqW6pdu3YsX768wLZVq1bRunVrvL29C93H19cXX19fR5QnIiIiTsDUOzenT58mMTGRxMREwPaod2JiIocOHQJsd10GDRqU337EiBH8/vvvjB49mp9//plPP/2UmTNn8vzzz5tRvoiIiDghU+/cJCQk0LVr1/z10aNHAzB48GBmz55NSkpKftABqF+/PitXruTZZ5/lww8/JCwsjA8++KBYj4HnjdHRwGIRERHXkfd7O+/3+NVYDHtauZE//vhDg4pFRERcVHJyMrVq1bpqm3IXbnJzczly5AiVKlUq8gmrS6WlpVG7dm2Sk5MJDAx0QIWOpfNzfe5+jjo/1+fu5+ju5wfOcY6GYZCenk5YWBgeHlcfVeNSA4pLg4eHxzUTX2ECAwPd9h8t6Pzcgbufo87P9bn7Obr7+YH55xgUFGRXO5d6FFxERETkWhRuRERExK0o3FyDr68v48aNc9t35ej8XJ+7n6POz/W5+zm6+/mB651juRtQLCIiIu5Nd25ERETErSjciIiIiFtRuBERERG3onAjIiIibqXch5tp06ZRv359/Pz8aNWqFRs2bLhq+/Xr19OqVSv8/Pxo0KABM2bMcFClJVecc1y3bh0Wi+WK5ZdffnFgxfaLj48nJiaGsLAwLBYLS5cuveY+rnQNi3t+rnb9Jk6cSJs2bahUqRLVq1enX79+7N2795r7uco1LMn5udo1nD59Os2bN89/uVu7du343//+d9V9XOX6QfHPz9Wu3+UmTpyIxWJh1KhRV23n7NewXIebhQsXMmrUKF555RW2b99Op06diI6OLjBZ56WSkpLo06cPnTp1Yvv27bz88ss8/fTTLFq0yMGV26+455hn7969pKSk5C833XSTgyounoyMDCIiIpg6dapd7V3tGhb3/PK4yvVbv349TzzxBD/88AOrV68mOzubXr16kZGRUeQ+rnQNS3J+eVzlGtaqVYtJkyaRkJBAQkIC3bp146677mL37t2Ftnel6wfFP788rnL9LrVlyxY++ugjmjdvftV2LnENjXIsKirKGDFiRIFtjRs3NsaMGVNo+xdffNFo3LhxgW3Dhw832rZtW2Y1Xq/inuPatWsNwDh16pQDqitdgLFkyZKrtnHFa5jHnvNz5etnGIZx7NgxAzDWr19fZBtXvob2nJ+rX0PDMIwqVaoYn3zySaHfufL1y3O183PV65eenm7cdNNNxurVq43OnTsbzzzzTJFtXeEalts7N1lZWWzdupVevXoV2N6rVy++//77QvfZtGnTFe1vv/12EhISOH/+fJnVWlIlOcc8kZGRhIaG0r17d9auXVuWZTqUq13DknLV62e1WgEIDg4uso0rX0N7zi+PK17DnJwcFixYQEZGBu3atSu0jStfP3vOL4+rXb8nnniCvn370qNHj2u2dYVrWG7DzfHjx8nJyaFGjRoFtteoUYPU1NRC90lNTS20fXZ2NsePHy+zWkuqJOcYGhrKRx99xKJFi1i8eDGNGjWie/fuxMfHO6LkMudq17C4XPn6GYbB6NGj6dixI+Hh4UW2c9VraO/5ueI13LlzJxUrVsTX15cRI0awZMkSmjZtWmhbV7x+xTk/V7x+CxYsYNu2bUycONGu9q5wDcvdrOCXs1gsBdYNw7hi27XaF7bdmRTnHBs1akSjRo3y19u1a0dycjJvv/02t912W5nW6SiueA3t5crX78knn+Snn35i48aN12zritfQ3vNzxWvYqFEjEhMT+euvv1i0aBGDBw9m/fr1RQYAV7t+xTk/V7t+ycnJPPPMM6xatQo/Pz+793P2a1hu79zccMMNeHp6XnEH49ixY1ck0jwhISGFtvfy8qJq1aplVmtJleQcC9O2bVv27dtX2uWZwtWuYWlwhev31FNPsWzZMtauXUutWrWu2tYVr2Fxzq8wzn4NfXx8uPHGG2ndujUTJ04kIiKC999/v9C2rnj9inN+hXHm67d161aOHTtGq1at8PLywsvLi/Xr1/PBBx/g5eVFTk7OFfu4wjUst+HGx8eHVq1asXr16gLbV69eTfv27Qvdp127dle0X7VqFa1bt8bb27vMai2pkpxjYbZv305oaGhpl2cKV7uGpcGZr59hGDz55JMsXryYNWvWUL9+/Wvu40rXsCTnVxhnvoaFMQyDzMzMQr9zpetXlKudX2Gc+fp1796dnTt3kpiYmL+0bt2ahx56iMTERDw9Pa/YxyWuoSnDmJ3EggULDG9vb2PmzJnGnj17jFGjRhkVKlQwDh48aBiGYYwZM8Z4+OGH89sfOHDACAgIMJ599lljz549xsyZMw1vb2/jyy+/NOsUrqm45/jee+8ZS5YsMX799Vdj165dxpgxYwzAWLRokVmncFXp6enG9u3bje3btxuA8e677xrbt283fv/9d8MwXP8aFvf8XO36jRw50ggKCjLWrVtnpKSk5C9nzpzJb+PK17Ak5+dq1zA2NtaIj483kpKSjJ9++sl4+eWXDQ8PD2PVqlWGYbj29TOM4p+fq12/wlz+tJQrXsNyHW4MwzA+/PBDo27duoaPj4/RsmXLAo9oDh482OjcuXOB9uvWrTMiIyMNHx8fo169esb06dMdXHHxFeccJ0+ebDRs2NDw8/MzqlSpYnTs2NFYsWKFCVXbJ++xy8uXwYMHG4bh+tewuOfnatevsHMDjFmzZuW3ceVrWJLzc7Vr+Mgjj+T/96VatWpG9+7d83/xG4ZrXz/DKP75udr1K8zl4cYVr6HFMC6MAhIRERFxA+V2zI2IiIi4J4UbERERcSsKNyIiIuJWFG5ERETErSjciIiIiFtRuBERERG3onAjIiIibkXhRkRERNyKwo2IiIi4FYUbERERcSsKNyLi8v78809CQkJ444038rf9+OOP+Pj4sGrVKhMrExEzaG4pEXELK1eupF+/fnz//fc0btyYyMhI+vbty5QpU8wuTUQcTOFGRNzGE088wTfffEObNm3YsWMHW7Zswc/Pz+yyRMTBFG5ExG2cPXuW8PBwkpOTSUhIoHnz5maXJCIm0JgbEXEbBw4c4MiRI+Tm5vL777+bXY6ImER3bkTELWRlZREVFUWLFi1o3Lgx7777Ljt37qRGjRpmlyYiDqZwIyJu4YUXXuDLL79kx44dVKxYka5du1KpUiW++uors0sTEQdTt5SIuLx169YxZcoUPv/8cwIDA/Hw8ODzzz9n48aNTJ8+3ezyRMTBdOdGRERE3Iru3IiIiIhbUbgRERERt6JwIyIiIm5F4UZERETcisKNiIiIuBWFGxEREXErCjciIiLiVhRuRERExK0o3IiIiIhbUbgRERERt6JwIyIiIm5F4UZERETcyv8Hw6gMhg3CjqoAAAAASUVORK5CYII=",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"\n",
|
|
"plt.errorbar(x, y, dy , fmt=\"o\")\n",
|
|
"plt.plot(x_plot,y_fit ) \n",
|
|
"plt.xlabel('x')\n",
|
|
"plt.ylabel('f(x)')\n",
|
|
"plt.title('iminuit exponential Fit')\n",
|
|
"#plt.axis([0,30,-1.2,1.2])\n",
|
|
"\n",
|
|
"# show the plot\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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": "3.8.16"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|