mirror of
https://github.com/fjosw/pyerrors.git
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454 lines
129 KiB
Text
454 lines
129 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 pyerrors as pe\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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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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"plt.rc('text', usetex=True)"
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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_front=1, padding_back=1)"
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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 numpy__ (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.: 0.0023324250917749687\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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"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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"image/png": 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SamkPGGMMqWMmApNjjIOrPW5ipcN7VV5gJ43nbf0j3RU2r4UHLoU3/wYf/SGM+mymK5IkSWrzWuX0kpbWpkI3wM7t8OgN8OxP4ZiJcMZNkJvNDWokSZJat3RDt4msLcntCGf/EPocCn/6Brz/Goy/1wssJUmSMixr5nSrCY36HHz6d/Dui8k87/dfz3RFkiRJ7Zqhu60a9GG4Yh7k5iXB+43Zma5IkiSp3TJ0t2U9B8Hlf4UDT4BfXwDz70wW1pEkSVKLMnS3dZ3z4cJfwwlfgb/+Jzz8JdhR98I8kiRJanpeSNke5OTC2G9D0SHw+y/D6sXJCpbdijJdmSRJUrvgSHd78qEL4bI/wZqlMP00ePv5TFckSZLULhi625v+R8PEx5NR7p+dCf+6P9MVSZIktXmG7vaooC989s8w/Hz47ST48/XJwjqSJElqFs7pbq86doZzfwwHHAWPXg/vLUoW0unaO9OVSZIktTmOdLdnIcAxV8Bnfg/vvwrTTnGetyRJUjMwdAsGnug8b0mSpGZk6FaioF9qnvc453lLkiQ1Med0a7eOneHcO+GAI5N53u++lMzz7rZvpiuTJElq1RzpVlUV87wv/QOseg2mnwqlCzNdlSRJUqtm6FbtDjwBJj4B3fvAz86ABT+HGDNdlSRJUqtk6FbdKvp5H/VpeOSr8LsrYdsHma5KkiSp1TF0a8865MHHboFPToOXfwv3jIXVSzJdlSRJUqti6FZ6PnQhXPEYbN8M00+DV/+Y6YokSZJaDUO30tfnMJg4DwZ9GO6/GOZ8C3buyHRVkiRJWc/QrfrpXAATfgVjvwN/uwP+7xOwcWWmq5IkScpqhm7VXwhw4lfg0t/D+6/BtJPhrX9kuipJkqSsFWI7bgMXQsgH1q1bt478/PxMl9M6bXgXZl4GK56F078Lx34hCeVpWLl+Cys3bK1zf1H3PIryOzdRoZIkSU1v/fr1FBQUABTEGNfXdZwrUqpxuu+XLKQz51vJKpZv/g3OuRO6FO71ofc98xa3P/ZGnfuvHj2Ua8YOa7paJUmSMsSRbke6m86/H4GHr4TOhTD+59B35B4PrzzSvXjlRr464wVum3AkQ4q6AY50S5Kk7OdIt1reIR+D/Q6HWZ+Fe86A07+zx+kmRfmda4TqIUXdGN63oCWqlSRJajFeSKmm1eNA+OyjcMzEZLrJjE/B5rWZrkqSJCmjDN1qeh06wZn/Axf+GpY9lXQ3WfFcpquSJEnKGEO3ms/BZ8Okp6BrEfzsDPj7XdCOryGQJEntl6FbzavHgfDZP8Oxk+AvN8D9lzjdRJIktTuGbjW/Dp3gjO/Bhb9JWgrefTKsWJDpqiRJklqMoVst5+CPwheegu59kukmf7sdystZumoT985fBsC985exdNWmzNYpSZLUxOzTbZ/ulrdzO8z9Dvztdt7tfRznln6a9+lBeYScVHfBKecfwfhR/TNbpyRJ0l6k26fbkW61vNyOMPa/eefc+8l5/1X+1Ol6Tg0LASiPyW3ygy+yzBFvSZLURhi6lTG/fG8QZ2+fwgvlQ/hZpx9yY4dfkMc2AEIIzFiwPMMVSpIkNQ1DtzJmxdrNrI7duXz7tdy4/VIuzp3L7zp9k8GhlBgjK9ZuznSJkiRJTcLQrYzp16MLIQQg8IudZ3Dutu/QgZ080uk/uSh3Lv0KO+/1HJIkSa2BoVsZc8Go/lS+kPfVOICPb/suD+38MN/r8L9c9f5/wwdrMlihJElS0zB0K2MG9e7KlPOPICfs7lqyLeTx/3ZezvyRt9L17flw90mw7OnMFipJktRItgy0ZWDGLVu1iTvnLWbWcysYN7IfV502hIG9u8K6FfDQRHjr73DSNXDK9clCO5IkSVnCloFqNQb27splJwwE4LITBiaBG6CgH1z6BzjtP5KFdO4ZC++/lrlCJUmSGsjQreyWkwsnfwMunw3bNsG0k+GZ6dCO/4VGkiS1PoZutQ59R8CkJ+GoT8OfvwG/Og/Wv5PpqiRJktJi6Fbr0WkfOPuHcMmD8N4r8JPj4eXfZboqSZKkveqQ6QIqCyFcl/rjYIAY46Q0H1OWulsYY5zaPNUpawwdA1f+Hf5wNcy8FF6/CM6aAp0LMl2ZJElSrbJmpDuEMCXGODV1m5TaNnsvj7kOIMY4PcY4HVgYQpjWAuUq0/bpCRf8Ej5xN/z7EfjJSbDsb5muSpIkqVZZEbpDCIXAiNTPCtOAMSGE4j089AZgesWdGOMcYGJz1Kimt3L9FhaVrmNR6ToWr9wIwOKVG3dtW7l+y55PEAIceRF88W9Jp5N7z4bZ34QdW1ugekmSpPRlRZ/uVNheCoyOMS6stG0tMLJiW7XHFANLYoyh2vYIjE0F8OqPyQPyKm3qDqywT3dm3Dr7dW5/7I069189eijXjB2W3snKd8L8H8Hc78K+B8EnfgL7H9FElUqSJNUu3T7dWTGnO8ZYBvSotnlM6mdJHQ+rawS8DCisY98NwI31KE3N6JJjBzD20D517i/qnlfnvhpycuGkr8Lgj8Dvvgg/PQ1OmZwsqpPbsfHFSpIkNUJWhO463ABMSgXy+lgD9Kxj303ALZXudwdW1L80NYWi/M4U5Xdu2pPufwRcMQ+enAqPfx9e/SN88m4oOqRpn0eSJKkesmJOd3UhhCnAjNTFkfVVV+Amxrg1xri+4gZsaHCRyl4dOsFH/gs+Pxu2b04W1Hn61mQKiiRJUgZkXegOIYwjmau9t9Z/dU07KdzDPrUnfUcmC+oc+wWY82342Rmwqu455JIkSc0lq0J3CGEMJC0AU/cL6+peEmMsAcpq21/bRZRqpzp2htO/A5/7C3ywBu4+Cf7+Yygvz3RlkiSpHcma0B1CGAGMIOm1XZwK0xNJ5miT2nZdtYfdxO4LLitGyRsyJUVt3YBj4QtPw8jPwl/+I2kvuMZ/EJEkSS0j21oGFlbfV9ESMIQwEZgcYxxc7bHXsXs6ydExxsn1eN58YJ0tA9uZZU/D766ETe/DmG/D0Z+HnKz5/ilJklqRdFsGZkXozhRDdzu2dWOykM6Ce2DACXDOj6D3kExXJUmSWpl0Q7fDe2qf8rrBx26BSx+BDe/A3SfC07fBzh2ZrkySJLVBhm61b4M+DF+cn0wxeezb8L+j4d1Fma5KkiS1MYZuqdM+cMb34PLZsGMrTD8F5n4v+bMkSVITMHRLFfqNgklPwIe/Dk/fkiyqs/zZTFclSZLaAEO3VFmHPDjtP2DiE9ChM9wzFh79D9i2KdOVSZKkVszQLdVmv+Hw+cdg7LeTDic/OQFKnsh0VZIkqZUydEt1ye0AJ14NX/gbdD8AfnkO/O5LycqWkiRJ9WDolvam9xC47I/wsVvh33+AO4+Gf82AdtzjXpIk1Y+hW0pHTg6M+hxc9c+kzeBvJ8L/fdKl5CVJUloM3VJ9dN8Pxt8LF8+E1UvgruPhqZth5/ZMVyZJkrKYy8C7DLwaatsmmPc/8I+fwL4Hwcdvh/7H1Ps0K9dvYeWGunuCF3XPoyi/c2MqlSRJzSTdZeAN3YZuNdY7/4I/XA1vv5BMQRlzI3QuSPvht85+ndsfe6PO/VePHso1Y4c1QaGSJKmpGbrTYOhWkynfCf+cDnO/C526wVlT4NBzIYS9PrTySPfilRv56owXuG3CkQwp6gY40i1JUjZLN3R3aLmSpDYsJxeO+yIc8nH40zdg5qUw7MwkfPcYuMeHFuV3rhGqhxR1Y3jf9EfLJUlSdvNCSqkpFfSDC38NF/wfvPMi/PhYePIHsKPuOduSJKntM3RLTS0EOPScpL3gMVfA499PVrRcMjfTlUmSpAwxdEvNJa87nP5d+MLT0K1P0td75mWw/u1MVyZJklqYoVtqbkWHJCtafnI6LPtbsqLl/B/Z21uSpHbE0C21hBDgQxPgqmfhyEtg9jfh7g8nIVySJLV5hm6pJXUphI9OhYmPQ143uPej8NAk2LiSpas2ce/8ZQDcO38ZS1dtymSlkiSpCdmn2z7dypTycnjhVzD7RrZt3873Np/Hr8vHsD3mkpNq7z3l/CMYP6p/ZuuUJEl1SrdPtyPdUqbk5MCIz/DmxU8ya+vR3Njhl/y+439wXM4rlEcojzD5wRdZ5oi3JEmtnqFbyrD7X97E/9t5Beds+w4f0Jn7O32XOzveTl/eJ4TAjAXLM12iJElqJEO3lGEr1m4mxsiiWMz5277FV7ddydE5r/FY3rV8OWcW760uy3SJkiSpkVwGXsqwfj26EEKAGIHA78pPYvbWkXypw8N8Mfdhtrw5H17+Phx6btIFRZIktTqOdEsZdsGo/lS/oHkTXZi640LO2j6FjgccBjMvhV98HN57OUNVSpKkxjB0Sxk2qHdXppx/BDmBXV1LKv78xfPOYJ/LHoRLZsGGd+Duk+BP34AP1mS2aEmSVC+2DLRloLLEslWbuHPeYmY9t4JxI/tx1WlDGNi76+4DdmyDf06Dx6dAbkf4yH/CiMsg11likiRlii0DpVZmYO+uXHbCQAAuO2Fg1cAN0KETnPBl+PJzcNBZ8Mevw90nwhtzWr5YSZJUL4ZuqbXp3gc+cVeyquU+veC+8+H/zoP3Xsl0ZZIkqQ6Gbqm1OuAouOyPMOFXsKYkGfV+5BrY+H6mK5MkSdUYuqXWLAQ45OPwpX/C6d+FRQ/CHUfB07fC9i2Zrk6SJKUYuqW2oEMnOP5L8JUX4KhLYO534cdHw6KHUv2/JUlSJhm6pQxbuX4Li0rXsah0HYtXbgRg8cqNu7atXF+PEet9esJZU+DKf0Cf4TDrs3DP6bBiQTNVL0mS0mHLQFsGKsNunf06tz/2Rp37rx49lGvGDmvYyUseh7/8F7z3Egw/Hz7y/6DnoIadS5Ik1ZBuy0BDt6FbGbZy/RZWbtha5/6i7nkU5Xdu+BOU74QXfg3zvgebVsHRl8PJ34CuvRt+TkmSBBi602LoVruy7QN45ifw9G3JPO+TrobjroROXff6UEmSVDtDdxoM3WqXNq2Gp34I//xp0uf71OvhqE+7sqUkSQ3gipSSate1F5x5E3x5AQw6GR75KvzkePj3I3Y6kSSpmRi6pfaqx0A4/6cw6UnI7wszLoGfnQFv/SPTlUmS1OYYuqX2bv8PwWd+B5/+LWzfnATv31wM77+W6cokSWozDN2SEoM/AhOfgPN+mrQYvOs4ePhLUPZWpiuTJKnV80JKL6SUatqxFRb8DJ66Gbasg5GfhQ9/Hbr3yXRlkiRllVbXvSSEUAhcAIyPMY5N4/gxwCRgNlACjAWejTHOqsdzGrqlPdm6EZ65G+bfATu3w7GT4ISvJCtfSpKk1tW9JIQwgiRwFwLpfpoXAmOAaanbkvoEbklpyOsGJ18LV/8LjvsiPDMdbj8SnvgBbN2Q6eokSWo1smakGyCEMA64IcY4Ms1j58QYyxrxfI50S/WxcSU8dQssuAfy8pMpJ6M+Bx0bsWJmSrOvzClJUjNId6Tb1TAkpa9bEZz1fTj+S/DkVPjrf8Hf74RTroMjL4Hcjg0+9X3PvMXtj71R5/6rRw/lmrHDGnx+SZIyqbWPdPcE1qR+Do4xTt7LY/KAvEqbugMrHOmWGmj1Epj3P7BoFvQYBKfeAIePg5zcep+q8kj34pUb+eqMF7htwpEMKeoGONItScpO7WGkeyFAjLEEIIQwMYQwM8Y4fg+PuQG4sSWKk9qFXoNh3D1w0jUw73vw24nw5A/glMkw/Lx6he+i/M41QvWQom4M71vQ1FVLktTisuJCyoaIMZZUBO6UB4BxqS4odbkJKKh069d8FUrtyH7D4aLfwBXzkiD+0OeTPt8vzYLynZmuTpKkjGu1oTs1vWSXShdUFtf1mBjj1hjj+oobYPsFqSn1HQEXz4Ar5ibTTR68HO463vAtSWr3WmXoTo1mzwwhFFfbBknPbkmZ1HckXPIAfH4uFA5IwvdPToBFD0J5eaarkySpxWVb6K61R3cIoTiEcF3F/dSo9tRq00smArMa00JQUhPrNxI+NQs+/xgU9INZn0uF74cM35KkdiUrQnelUD0JGBFCmFJt+kjF6pOV3RRCuK7iBvTay0WUkjKl3yj41INw+RzIPwBmfTYJ3y//tkb4XrpqE/fOXwbAvfOXsXTVpgwULElS08qqloEtzcVxpAxZ/k94/CZYMhf2PThZZOew83jg+Xe4/sEXASiPkBOSw6ecfwTjR/XPYMGSJNUu3ZaBhm5Dt5Q5y/8JT/4Q3vgL2/MP5P+tPp0Hd36Y7dW6meYEmPv1UxnYu2uGCpUkqXbphu6smF4iqZ3qf0xyweWkJynpUMz/dPhfHs+7hs/k/oU8tu06LITAjAXLM1ioJEmNY+iWlHn7f4g7972RM7dP4ZnyQ7ixwy95Ou9qJub+ga5sJsbIirWbM12lJEkNZuiWlBX69ejCEvrzte1Xctq2W5i9cwTXdniAp/Ou5su5DzG4+/ZMlyhJUoM5p9s53VJWWLpqE6NvfpzySm9J+7OaiR0e4aLcuXTqlEfOsVfAcV+CbvtmrlBJkipxTrekVmVQ765MOf8IcsLuriXvhV58Z+el/PX02eQcczn886dw2+Hwx2thzdLMFixJUj040u1It5RVlq3axJ3zFjPruRWMG9mPq04bsrtryQdr4Nn/hWfuhs1r4bBPwolXw/4fymzRkqR2y5FuSa3SwN5dueyEgQBcdsLAqm0C9+kJp1wHX10EZ02FFc/CtJPh/z4JJU9AOx5EkCRlN0O3pNan0z5wzBXw5efh/Htg0/vwy3Pgp6elVrncmekKJUmqwtAtqfXK7QCHj4NJT8GnHoK87jDzMrhzFCz4GWzfkukKJUkCDN2S2oIQYMhouPQPcMVc2O9weORrcNvwZMXLzWWZrlCS1M4ZuiW1LX1HwgW/hC8/Bwd/DJ6YCrceBn++HtYuy3R1kqR2qkOmC5AkgJXrt7Byw1YAFq/cWOUnQFH3PIryO6d/wl6D4eO3wak3wD+nw4J74J/TkiB+/FUw4NimLF+SpD2yZaAtA6WscOvs17n9sTfq3H/16KFcM3ZYw59g2wfwr9/AP+6C1Yuh7yg4/ktwyDnJ3HBJkhog3ZaBhm5Dt5QVKo9016beI911KS+HN/4Kf78Tlj0FBQPguC/AUZ+Gzr4PSJLqx9CdBkO31M698y/4+12waBZ06AIjL4VjJ0HhgExXJklqJQzdaTB0SwJg/duped8/g60b4dBzknnf/UZlujJJUpYzdKfB0C2piq0bd8/7XlOSzPs+dhIc+gno0CnT1UmSspChOw2Gbkm1Kt8Jr/8Fnrkblj4B3frAqM/ByM9C9z6Zrk6SlEUM3WkwdEvaq5X/Tqae/Ot+2LkdDvtkMvrdTFNPWuyCUklSkzB0p8HQLSltm8vg+V/Bsz9NFtnpOxKO/UKTTz1p9taJkqQmZehOg6FbUr2V70xaDj4zDUrmQdeiZOrJqM9C9/0affrqiwR9dcYL3DbhSIYUdQMc6ZakbJNu6HZFCEmqj5xcOOis5Pb+a8nUk/k/gqduhsM+AUdfAf2PgRAadPqi/M41QvWQom4M71vQBMVLkjIlJ9MFSFKrte9BcPbN8LVXYOx/w4oF8LPT4e6T4Nl7YOuGTFcoScoShm5JaqwuhXD8lfDlhfCpB6HwQPjTtXDzIfDI1+C9lzNdoSQpw5xeIklNJScHhoxJbutWwHO/gIW/gAX3QP/j4OjL4dBzoUNepiuVJLUwR7olqTkU9IOP/Cdc8zKM/0XS4eShK+CWQ2D2N2HN0j0+fOmqTdw7fxkA985fxtJVm1qgaElSc7F7id1LJLWU919Plpr/169hy3oYMhpGXQ7Dzkgu0Ex5YMFyrn/wRQDKI+Skrsmccv4RjB/VPxOVS5LqYMvANBi6JWXEtg9g0YPJtJO3n4eC/nDUp+GoS1i6vQejb36c8lremnMCzP36qQzs3bXla5Yk1Srd0O30EklqaZ32gRGfhomPwxXzoPhU+NvtcNvhlP9qHKfnLKADO2o8LITAjAXLW7xcSVLjeSGlJGVS3xHJ7cybYNGDdJj9E+7ueAsrOxQya+fJ3L/zNN6KfQCIMbJi7eYMFyxJaghDtyRlg7zuMPIy7n//OJ58ah7jw1wuyZ3DlR1+z/ydh3L/zo8wm6Pp16NLpiuVJDWAc7qd0y0piyxdtWnXnO48tvHRnGe4sMM8js15lbWxG7lHXkj+iZ+HokMyXaokCS+kTIuhW1I2mrlgOZOrdS8ZFN7mroMXcdC7j8AHq6Df0TDi0mTp+bzumS1YktoxQ3caDN2SstWyVZu4c95iZj23gnEj+3HVaUOSriU7tsFrf4KFv4Qlc6HjPsmCO0deDAeemCzQI0lqMYbuNBi6JWWzRaXr+NiPnuaRL5/E8L4FNQ8oWw7/uh9euA/WLk2Wnz/yYvjQhdBjYIvXK0ntkS0DJamtK+wPp3wDvvI8fPbPMOjDMP9HcPuH4N6PwQu/gW2uZClJ2cDQLUmtXQhw4Alw7o/h2tfhE3cn23/3BfjhMHj4S/DmfGjH/7IpSZlmy0BJaks6dYUjL0pua5ftnn7y/K+gx6DU9JOLklFySVKLcaRbktqqHgPh1OvhK/+CSx+BAcfD07fCbYfDLz6eBPEtdU4/lCQ1IUe6JSmLrFy/hZUbtgKweOXGKj8BirrnUZTfuX4nzclJ5nsP+jB8dCq88nAyAv7wVfDHr8NBH4UjJsCQ0ZDbscn+LpKk3exeYvcSSVnk1tmvc/tjb9S5/+rRQ7lm7LCmebJ1K+ClWfDiDFj5CuzTCw47Lwng/UYlc8UlSXtky8A0GLolZZvKI921adBIdzreXQQv3p+E8A3vQM/iJHwfPh56DW7655OkNqLVhe4QQiFwATA+xjg2zcdcB5Sl7hbGGKfW8zkN3ZJUWflOWPYUvPhAMg1l28Zk9csjJiSj4F17ZbpCScoqrSp0hxBGAKOAQmBCjHFkGo+5DqAiaIcQxpAE9kn1eF5DtyTVZdsH8Pqf4V8zYPGcZLrJkDEwfBwcdBbkdWu2p87YiL8k1VOrCt0VQgjjgBvSDN1rgUExxrJK22KMMe1JiIZuSUrTplWw6KFk/nfpAujQBYadAcPPh6FjoWOXJn26Fp3bLkmN0KZDdwihGFhSPWCHECIwNsY4J83nM3RLUn2tXQYv/xYWPQjvvgSdusPBZycBfPBpTdIBpXoXl6/OeIHbJhzJkKJkdN2RbknZIt3Q3VpbBhbXsb2MZIpKrUIIeUBepU3dm64kSWonegyEk65Jbu+/Di8/lATwF++HLj3gkHOSAD7wJMjJbdBTFOV3rhGqhxR1Y3jfgib4C0hSy2utobsua4Cee9h/A3BjC9UiSW3fvsOSBXhOmQzvLUrC96IHYeEvoFsfOPQTSQDvd3TSL1yS2qm2Frr3FLgBbgJuqXS/O7Ci+cqRpHYiBNjv8OQ2+kYoXZiE75cfgn9Og4L+cNgnk9sBR9kDXFK701pDd0kd2wv3sI8Y41Zg1+XwwTd9SWp6IUC/kcnt9O/CW39PAvgL98H8O6BgABx6TjIK3nfkHkfAl67axL3zlwFw7/xlfOm0IQzq3bVl/h6S1IRa5YWUqWPXAiNjjCWVttm9RJKy1c4d8Obfkv7f//4DbFoJ+X2TOeCHngv9j60SwB9YsJzrH3wRgPIIOal39ynnH8H4Uf0z8TeQpBpaa/eSicCk6qE71a1kXOXFbyoWxokxTk/dH0fSucQ+3ZKU7cp3wlv/SAXw3yerYHbbDw75OBx6Lku7fojRtz5FeS0fUTkB5n79VAY64i0pC7Sq0F0RqoEJwAhgKvBsjHFWav9EYHKMcXC1x13H7ukkR8cYJ9fzeQ3dkpRp5eWw4tkkgL/yMKxfwaYOPXh46wj+uPMYnik/hB2VZkPm5gQmnlzM5DMPzmDRkpRoVaE7UwzdkpRlYoTShcx5cDrD1jzGgPA+a2M3/rpzFI+WH8388sPYHjpx9hEH8KOLjsp0tZLU5vt0S5LaotRFmM8ddA2TnjyHg+NSPpr7DB/NeYYJHR5nY+zME+UfYufOs2HzIOhSmOmKJSktjnQ70i1JWWfpqk2MvvnxSnO6I0NDKafnLOCM3AUckVMCOR1g0MnJapgHfRTyD8hkyZLaKaeXpMHQLUnZa+aC5Uyuq3vJ0ACv/glefQSWPQ1xZ9J+8OCz4eCPwb4HZbBySe2JoTsNhm5Jym7LVm3iznmLmfXcCsaN7MdVpw2p2bVk81p4/a9JAF88B7Z/AL2G7g7ge+kFLkmNYehOg6FbkrLfotJ1fOxHT/PIl09ieN+CPR+8fTOUPJEE8Nf+DB+sSpajP+gsGHYmDDoFOu3TMoVLahe8kFKS1P507AIHnZncynfC8mfg1T/Ca3+C5+6FDp2T4H3QmTD0DCjom+mKJbUThm5JUtuUkwsHnpDcTv8urF4Mrz8Krz0Kf7wW4jWw3xHJCPiwM+GAo5yGIqnZGLolSW1fCNB7aHI74cvJPPDFjyUh/J/T4cmp0LUIhp0Ow86C4lMhr1umq5bUhhi6JUlZZ+X6LazcsBWAxSs3VvkJUNQ9j6L8zg1/gi494PBxyW3njmQayuuPJrfnfwW5eTDow6lR8DOgcECj/j6S5IWUXkgpSVnn1tmvc/tjb9S5/+rRQ7lm7LDmefLVS+CNvyYXYr75NyjfAfseAkPHwJCxMOA46JDXPM8tqdWxe0kaDN2SlJ0qj3TXptEj3enasg6WzIU35iTtCDe+Cx27QvEpMGR0EsJ7HNhsT581vwdJdTJ0p8HQLUlKW4zw7ktJ+F48B976R7IoT+9hMGRMcjvwROjYdCE4oyP+ktJi6E6DoVuS1GBb1iU9wRfPTkbCN7wNHbokc8GHjE2mo/QsbtRTVJ/b/tUZL3DbhCMZUpRc5OlIt5R59umWJKk5dS6AQ89JbjHCyn+nAvhs+Mt/wJ+/kYTuIWOTUfCBJ0Knrns/byVF+Z1rhOohRd32vkiQpKxj6JYkqbFCgD6HJrcTr4atG2Dpk0kAf+1P8M9pkNMxuQiz+FQYfBrsf2TSS1xSu+D0EqeXSJKaU4yw6g0omQdL5sGyp2DbxqRt4aCTofi0JIT3GFjnKZau2sSP5y1m1nMrGDeyH186bQiDetdv1FxS83BOdxoM3ZKkFrdzO6xYkArhc6H0OYjl0GNQEr6LT0vCeJdCAB5YsJzrH3wRgPIIOSE5zZTzj2D8qP4Z+ktIqmDoToOhW5KUcZvLktHvJfOSIL6mBEIOHDCCtQecxBf+ls9z5UPYUW1GaE6AuV8/lYGOeEsZZehOg6FbkpR11r65ayrK5tfn0mXHejbGzvyz/GDmlx/G38sP45U4gJycXCaeXMzkMw/OdMVSu2boToOhW5KUzb7y6wW8uejvnBhe4viclzk65zU6h+2Uxa48U34o6/c/nvHjLoZ9D04u5pTU4gzdaTB0S5Ky2ZRHX2X6kyXsLE8+qzuxnSPDYk7IfZnjc15hVO5icuMO6LovDPxwMhd80MlJq0JDuNQiDN1pMHRLkrLZ0lWbGH3z45TX8lGdE2DeV47hwA9egqVPJS0K334+WSUzv28SviuCeKEXXErNxdCdBkO3JCnbzVywnMnpdi/Zsh7e+nsSwJc+mSxbT0zaEVaE8ANPhIK+Lfp3kNoyQ3caDN2SpNZg2apN3FmpT/dVpw1Jr2vJB2vgzb/tDuHvv5psLzwwCd8DT4QDT0jaFTodRWoQQ3caDN2SpNZiUek6Pvajp3nkyyc1fBn4je8nI+Fvzoc3n4Z3FwERuu+fhO8DT4ADT4J9DzKES2lKN3S7DLwkSe1Ft33h0HOSGyQ9wpc/k4yGvzkfXnkYynfAPr1gwPG7R8P7DHfJeqmRDN2SJGWpleu3sHLDVgAWr9xY5SdAUfc8ivI7N/wJuhTCsDOSG8C2TbD8n6mR8Pkw51uwcyvk5cOA45KR8AEnwAFHQoe8hj+v1A45vcTpJZKkLHXr7Ne5/bE36tx/9eihXDN2WPMVsGNrskx9xUj4W8/A9k2QmwcHHAUDjoX+x0H/Y6Frr2Yro/KXj9o0+suH1AjO6U6DoVuSlM2yLmzu3AHvvphMSXnrH8nPDe8k+3oNrRrCew9tsnnhGf/yIe2BoTsNhm5JkhohRih7q2oIf+9lIEKXnkn4rgjiBxwFHRv2BaH6NJuvzniB2yYcyZCiboAj3cosL6SUJEnNKwTocWByO+KCZNuW9bDi2d1B/IkfpKakdIL9j0yF8NStW1FaT1OU37lGqB5S1K3hXVykDDB0S5KkptM5H4aMTm6QTEl5b9HuEL7oIZj/o2Rf4QDod/Tu236H7/ECzaWrNnHv/GUA3Dt/GV86bQiD0ulXLmUBp5c4vUSSpJZVtjwZDV+xAEoXwNsvJF1ScjvBfkekQvio5FZ4IITAAwuWc326K3NKLcg53WkwdEuSlAV2bIP3XoIVz6XC+LOwdmmyr+u+bCo6ip+8Uchz5UN5sbyYTXTZ9dCcAHO/fmp6K3RKzcDQnQZDtyRJWWrTqqRd4YpnWfavJ+hV9hLdw2bKY+D12I/ny4fwfBzCSwzltJNO4rqzDst0xWqnDN1pMHRLkpT9vvyb5/nTiysYxNsclbOYo8IbHJWzhGFhObkhsjV0Jq//iKRDSt/Uz57FLmWvFmH3EkmS1Cb069EFQi6Ly/uxeGc/ZnIqAF3ZzJG5S/n84DJO67YCXn0E/vHj5EGdC5LwfcBRcEAqiBf0M4grYxzpdqRbkqSstnTVJkbf/DjltUSWGnO6N62Gt59P3RYmPysW8Om67+4AXjEinmbbQqkuTi9Jg6FbkqTWYeaC5UxuaPeS9e/sDuGlqSC+eU2yL78f9K00Ir7/h2Cfns34N1FbY+hOg6FbkqTWY9mqTdw5bzGznlvBuJH9uOq0IQ3rWhIjlL2ZhO+KEP72C7BtQ7K/YADsf0QSwPdL/ey+n1NTVCvndEuSpDah8jLwJw3pzaznVnDSkN5s3LqDRaXr6r8MfAjQY2ByO+yTybbycli9GN59Ed55Ad55Ef7+Y9hSluzvum8SvisH8R4DDeJKmyPdjnRLkpTVbp39Orc/9kad+68ePZRrxg5r+ieOEdYth3f+lbq9mPzc+G6yP68gGRGvCOH7HwG9hkJu84xpVv7yUZt6f/lQk3B6SRoM3ZIkZb+sC5sb3kuNiKfC+Lsvwtplyb4OXaDPYakR8cOhz3Docyh0avziPRn78qE9MnSnwdAtSZKaxOa18O5Lu0fD3/kXrH4DYjkQkr7hfQ7bHcT3Gw4F/es1PaXiy0dp2WYeWLCcx/69ktGHFHHBqP70LeziSHeGtMrQHUK4DihL3S2MMU7dy/FjgEnAbKAEGAs8G2OclebzGbolSVLz2L4Z3n8V3l0E770M7y1KgnnFPPG8glQQH5787HM4FB0Cnfap85QPLFjO9Q3t4qJm0epCdypwUxG0U4F6fIxx0h4eMw74KVBIErqnxBin1+M5Dd2SJKnlxAjr394dwCvC+OrFu0fFew3ePRreJ3Ur6MfS1R+k369cLaY1hu61wKAYY1mlbTHGWOe/u6RC95zKj6nncxq6JUlS5m37IBkVf29RpZHxl2DLumR/5wKWdxzIk2X78mp5P14v789rsR9ldAcgNycw8eRiJp95cAb/Eu1Tq2oZGEIoJplOUlbLvjExxjktX5UkSVIL6bRPskpm3xG7t8UI60tTIfwl3nv274wMrzG+wzw6hZ0ArIyFvFbej8WxH3lLh8PyM6HoYMjrnqG/iOqSFaEbKK5jexnJ1JE9uSCEsAboCQyOMU6u68AQQh6QV2mTr0hJkpSdQoCCfsntoDN57INXmf5kCaF8OwPDuxwUVjAsZznDwgpOyf0XA9/7K9zzw+SxBQOS+eGVb72HQccumf07tWPZErrrUhGm67IQIMZYAhBCmBhCmBljHF/H8TcANzZtiZIkSc3vglH9mfbEEnbSgcUxGd3+Y/lxQDKne97Vx3JgLIWV/4aVryQ/Fz2Y9BoHCDlJF5WiQ6DoUNj3INj3YOg1BDrk7eGZ1RSyYk536qLJ2dXnb6fmeU9O9+LIEEIhsBboUcdUldpGulc4p1uSJLUGMxcsZ3J9u5dsWQ/vv7Y7iK98Jbltej/ZH3KS1TV7HwT7Dkv9PBh6D4XO5qO9aVUXUqbmdC+pJXRHYGxdc7pDCOOqtwdMPWZkjHFhGs/rhZSSJKlVWbZqE3fOW8ys51YwbmQ/rjptSMO6lnywJgnjq16D91/f/XPdW7uP6X5ApSA+LBXGD4KuvevVY7wta1WhG3aNao+smCqS2lZn95JKo9qDK00vqdhW60h3LecwdEuSpFah8sqci1du5KszXuC2CUcypKgb0IQrc27bBKterxTEX0vurymB8h3JMV161BwZ33cY5PeDnJzG19CKtMbQfR1QVjGVJNUOcGxFn+7UaPi4ygvmhBCmVL5wMnWOo/cwp7v6cxq6JUlSq5DxZeB3bIO1S5MQvmuE/DVY9Qbs2Jwc03Ef6Dk46TXee2gyX7zX0OR+l8ImKaPyl4/atPTKnK0udMOu0Fwx0n10tUA9kWR+9+BK2wqBiZVO0WtP3UtqeT5DtyRJahWyLWzuUl6eXKy56vXktnpxEsRXL4YN7+w+ruu+qRCeulWE8h6DoEOntJ8u418+qmmVobulGbolSZKa0daNSfiufFv1BqxeAts2JMeEHCg8sNLIeKVQ3n3/GnPHV67fwn3PvMUdc98gsPuC0kgSuC8+ZoAj3dnG0C1JkpQBMcLG96qOilfc1i7bPXe8Y9dkakqvwUm7w57FvJ17AJ/8zdu8FwuBqoE8J8Dcr5/asAtLG8jQnQZDtyRJUpbZuR3Wvgmr39gdyteUwJqlsH7FrsM+iHm8GfuwLPbhzbgfy2IflrMfJx5zDFd+/MMtdkGnoTsNhm5JkqRWZPtmvnffoyx74yUG8C4Dw3scGN5jYHiXvmEVuSGVa3PzoOegXaPju/7cYxAU9IfcplsfMt3Qne0rUkqSJEmJjl3osN8hzH09j53lVQeOO7KDATmr+MLhMH7Q9tToeAm89mcoe3P3lJWcDskc8opA3mNgpduB0Kl5pqYYuiVJktRqXDCqP9OeWFJj+3Y6sDTux9FjT4Xqc7p37kg6rKxdunuqypoSKHk8CeQ7tuw+tmvR7gBeJZAPTC7szMltUN1OL3F6iSRJUqsyc8FyJj/4IrC7ewnAlPOPYPyo/vU7WcVFnWuXpW5vVvrzMtjw9u5jcztB4YBkpDwVxDf1Opxuh3wEnNNdN0O3JElS61LRr/ztss3MWLCcx/69ktGHFDFhVH8OKOzS9P3Kt2+BsreqBvFKt61HXEznc24GQ3fdDN2SJEmtS1YtjhMj69euoqBXEXghpSRJktqKS44dwNhD+9S5v6h7XssVEwJ0SO/5DN2SJElqNYryO2dmuftGapmu4ZIkSVI7ZuiWJEmSmpmhW5IkSWpmhm5JkiSpmRm6JUmSpGZm6JYkSZKamaFbWWHr1q1861vfYuvWrZkuRVnA14Mq8/Wgynw9qLLW9HpwRUpXpMwK69evp6CgAP9bCHw9qCpfD6rM14Mqy4bXQ0UN7GVFSke6JUmSpGZm6JYkSZKamcvAAytWrPCfqDJsw4YNAJSWlrJ+fZ3/MqN2wteDKvP1oMp8PaiybHg9pPu8rXpOdwihELgAGB9jHNuAx48AnmvquiRJktTujIwxLqxrZ6sd6U4F5lFAIdCzgadZDLB8+XJHuiVJklRv69evp3///pDKlXVptaE79U1iYQhhXGPPlZ+fb+iWJElSs2m1obshQgh5QF6lTd0zVYskSZLaj3YVuoEbgBurb3z//ffZsmVLBsqRJElSa5Wbm0vHjh3TOra9he6bgFsq3e8OrNixYwc7duzIUEmSJElqjWKMhu7axBi3ArvWCQ0hZLAaSZIktRcujiNJkiQ1M0O3JEmS1MzaQuhuaI9uSZIkqUW02tAdQigOIVwHTAJGhBCmNEXPbkmSJKmptdoLKWOMJcDU1E2SJEnKWq12pFuSJElqLRo80h1C+EjFn2OMc0MI+SSLz4wAZscYf9gE9UmSJEmtXmNGui8gCdglqfvPpe5/AXg+hHBtI2uTJEmS2oTGzOl+Lsb4U4AQwmigGBgbY1wGLA0hFDdBfZIkSVKr15iR7tWV/jwWKEkF7gqxEeeWJEmS2ozGhO7K/bHHAXOq7S9sxLklSZKkNqMxoXttCOHuEMJfSQL4ZEimmoQQ/gKUNUF9kiRJUqvX4DndMcYHQwgLSS6eHB9jXB9COIpkhHs6Ti+RJEmSgEYujhNjXAosrXT/eeB5gBDC5xtXmiRJktQ2pB26K/flTkMhyfLs/1vfgiRJkqS2pj4j3bNIwnRZmscX1LcYSZIkqS2qT+heEGM8Pd2DQwh3N6AeSZIkqc2pT+ienM5BqWkoawBDtyRJkkQ9WgamLpJMx3PAOmBMgyqSJEmS2phGdS8JIQwkaRnYs9quQuDoxpxbkiRJaisaHLpTPbmfY/eFlWtSP3sCS4DxjapMkiRJaiMaM9I9ERgcY1yaCuC7pqCEEAbhMvCSJEkS0Lhl4BemFscBKCEJ4cCuRXOqTzmRJEmS2qXGhO5dy7zHGNcBR4cQDqy0f0Qjzi1JkiS1GY0J3SGE8P0QwrOp+98H5oQQTgshnIcXUkqSJElAI+Z0xxh/GkK4guSiSWKMs0IIxcBjJKPgY5umREmSJKl1CzHGvR/VRoUQ8oF1r776Kt27d890OZIkSWpFcnNz6dKlCwUFBQAFMcb1dR3bmOklkiRJktLQbKE7hDCjuc4tSZIktSaNWRznpj3sLsRl4CVJkiSgcYvjTAIWsHtFSkjCdnHqz3MacW5JkiSpzWhM6J4TY7ygth2pFSp7NOLckiRJUpvRmDndV9S1I7UcfHFd+yVJkqT2pMGhO7UKpSRJkqS9aMyFlOftYXcxyYqU/9vQ80uSJEltRWPmdP8vyYWTZbXsexb4QiPOLUmSJLUZjQndC2KMpzdZJZKkXUpKSpgxYwbLly+nf//+TJgwgeJiL5WRpNaqwcvAhxCOSl0w2Wq5DLykbDRjxgyuvfZaQgjEGHf9/OEPf8iECRMyXV6L8suHpGxWn2XgGxy624JMh24/TKSa2vv/FyUlJZxyyimUl5fX2JeTk8OTTz7JoEGDMlBZy/PLh6RslxWhO4TwkxjjF5vl5FWf5zp2zysvjDFOrcdjMxa6/TCRavL/C7jpppv4yU9+ws6dO2vsy83N5Ytf/CI33HBDBiprWX75kOrW3gcnskl9Qnfac7pDCNfWo4ZewAVAs4buVOAmxjg9dX9MCGFajHFScz5vY5WUlHDttdfW+mFy7bXXcswxx/hhonbH/y8Sy5cvp67BkBgjy5cvb+GKMmPGjBmEEGrdF0Lg/vvvbxdfPlSVYbP2wYm77rqrXQ1OtFb1uZDyP4A1VO1WMgIooWYHk2JgSWMKS9MNwK5P4RjjnBDCbJIl6rOWHyZSTf5/kejfv/8efw/9+/dv4Yoywy8fVRk2DZvg4ERrV5/QXWXZ9xDC+cDCGOPS6geGEEY3RXF7EkIoJplOUlbLvjExxjnNXUND+WFSlR8mifb+e/D/i8SECRO46667at0XY+TCCy9s4Yoywy8fuxk2DZsVHJyoqrV9btYndE+udr9HbYEbIMb4WAjh8w0vKy11/VbLSPqH1xBCyAPyKm3qDvDyyy/TtWvXXRsLCgoYMGAAW7Zs4Y033qhxnsMPPxyAxYsXs3nz5ir7+vXrR48ePVi9ejVvv/12lX1du3aluLiYvn371vmXCiGQn5/PSy+9VGX7fvvtx7777ktZWVmN8NG5c2eGDh0KwKJFi2oElyFDhtClSxdWrFjB2rVrq+zr3bs3+++/Pxs3bmTp0qr/OTt06MAhhxwCwL///W927NhRZf+gQYPo1q0b77zzDqtWraqyr0ePHvTr14/NmzezePHiGn/H4cOHA3DHHXcwderUKh8mP/7xj7n55pv5yEc+wrvvvlvlsd27d2fgwIFs376dV199tcbv79BDDyU3N5eSkhI2bdpUZd8BBxxAr169WLt2LStWrKiyr0uXLgwZMgSgxu8eYOjQoXTu3Jm33nqLdeuqLsZaVFREnz592LBhA8uWLauyr1OnThx00EEAvPLKKzXm6RYXF9O1a1emTZvGd77znVp/D+ecc84ef4evv/46W7durbJ/wIABFBQUsHLlSt57770q+/Lz8znwwAPZtm0br732Wo2/62GHHUZOTg5Llizhgw8+qLKvb9++9OzZkzVr1lBaWlpl3z777MPgwYMpLy/n5ZdfrnHegw46iE6dOvHmm2+yfn3VKW99+vTZY4iqCFl7en2XlpayZs2aKvt69erFAQccwKZNmygpKamyLzc3l0MPPRSA1157jW3btlXZP3DgQLp37857773HypUrq+xr7veIqVOn8o1vfKPG3PYpU6YwaNAgli1bxoYNG6o8tq29R+zpy0d5eTkXXngh77//fpt/jwgh1Bk2v/71r3PMMcew3377tfn3iD2FTWBX2Gzr7xH//ve/6xycKC8v58033wRoF+8RL7/8Mtdem8x8rvy5ec0113D55ZdTWFjYIu8ROTk55OXl1ThPrWKMDboB1+5l/3kNPXeazz8mKb/G9iXAxDoe8y0g7u123nnnxdLS0vj000/Xur+0tDSWlpbGESNG1Nh3xx13xNLS0vi9732vxr5TTjkllpaWxkcffbTO587JyYknnnhije3f/OY3Y2lpabz77rtr7Bs+fPiumjp16lRj/9y5c2NpaWm86KKLauy76qqrYmlpaZw5c2aNffvtt9+u8+6333419s+cOTOWlpbGq666qsa+iy66KJaWlsa5c+fW2NepU6dYWloan3rqqT3+Hmo779ixY2NpaWl88cUXa33cq6++GktLS+Mpp5xSY9/3vve9WFpaGu+4444a+0aMGLHr71rbeZ9++ulYWloazzvvvBr7vva1r8XS0tJ433331dg3cODAXeft2bNnjf0PP/zwXn8P99xzT43t3bp123XeYcOG1dj/85//PJaWlsbrr7++xr6zzz47lpaWxmeffbbW5ywpKYmlpaXx+OOPr7HvBz/4QSwtLY0/+MEPauw7/vjjY2lpaSwpKan1vM8++2wsLS2NZ599do19119/fXzqqadiCKHO38PTTz8du3XrVmPfo48+GktLS+Oll15aY98VV1wRS0tL48MPP1xjX8+ePXf9DgcOHFhj/3333RdLS0vj1772tRr7mvs94tVXX631vC+++GIsLS2NY8eOrbGvLb5H3HLLLbX+Hj7zmc/E0tLS+M1vfrPGvrb2HnH++efH3NzcWp8zhBCvuuqqWj9T2tp7xCGHHBJzcnJqfRwQzz333FhaWtrm3yP69+9f5+sBiJdffnm7eI/o2LHjHl8P//3f/93i7xGpW/6esmtj+nTfDXwjxrihjv3fjzFe36CTp/f8Y4DZMcZQbftaYHLFxZXV9tU20r3iwQcfbNGR7p07d3Lbbbdx66231hjJuvnmmzn22GPb/DfU4cOHc9NNN3HXXXfVOoKTm5vLpZdeygUXXFBle1sbxSouLuaOO+7Y4+/h85//PJ/85CerbG9ro1h9+vShqKiIe++9l//6r/+q9f+LCRMmtPlRrMrvEa+88kqN8x588MF07NixXYxiVby+582bx+9//3vee+89+vTpwxlnnMFxxx3XYqNYFTL1HvGjH/2IP//5z7W+P4QQOOecc7j55pvbxUj3L3/5y1q7+uTk5HDllVe2i5HuNWvW8KlPfarO18O8efMYOnRom3+P+PnPf86sWbPqfD187nOf49vf/naLjnSfcsop0FwtA1Nzqv8K3AQ8x+5pHaNIpqKMjzG+0KCTp//8S2oJ3REYm86c7kz36V66dCn333//rrlIF154YbuYk1bhyiuv5A9/+EOdLcE+/vGP1/nPy22Jv4eq2vv/F1JltpBM2EJyN1urZtfnZrO0DKwuxlgSQrgAeIBkfnUEAsn0ji80Z+Cu9PxlIYTiGGNJtX1ZexFlZYMGDWoXb5Z18UKphL+Hqtr7/xdSZV5YmyguLuaHP/xhnWGzvQRuSF4TxxxzTLsenGitn5tNsjhOCGEQSfAuqeviyuZQsTBOpT7d40hGudNqGZjpke72zpGLhL8HSXviyOZu/kuYILs+N7NlRcrzYowPNcvJqz7PdSS9wgGOjjFW77Kyp8caujPMD5OEvwdJe2LYlKrKls/NZgndIYSBwJqKk4UQPrKHwwuBG2KMR6dbdCYYurODHyYJfw+SJKUvGz43myt0ryG5cPHoSvcLqbkaZYWCGGNuPepucYZuSZIkNVRzXUg5nmQZ+AoLYoyn13VwqqWgJEmS1O6lHbpjjI9V27S3ixWn1b8cSZIkqe1pcMtAYFCqawkxxrmpqRo3ACNIFq35YVMUKEmSJLV2OY147AUkAbuic8hzqftfAJ4PIVzbyNokSZKkNqExI93PxRh/ChBCGE3Sp3tsjHEZsDS1YqQkSZLU7jVmpHt1pT+PJVkYZ1mlbc3TAFySJElqZRoTuntW+vM4oPrS64WNOLckSZLUZjQmdK8NIdwdQvgrSQCfDMlUkxDCX6i7f7ckSZLUrjR4TneM8cEQwkKSiyfHxRjXhxCOIhnhnt5E9UmSJEmtXmNGuiGZtz0WeCyE8JEY4/MkI9wxxvhgY4uTJEmS2oIGh+7UqPbC1N3ppOZwpxbReT6EcF6jq5MkSZLagMa0DJwYY9x1MWXlkB1jXBpCGNOoyiRJkqQ2ojHTSxbuZb8tAyVJkiQaF7oLqt0P1e6PasS5JUmSpDajMaH7+RDCsyGET4YQBgI9QggDQwjnhRDeAO5umhIlSZKk1q0xLQMfCyFMAe6h6qh3Gcl87xcaV5okSZLUNjTmQkpijLOAWSGEEcBIkqXgH2uSyiRJkqQ2olGhu0KMcSGpCytDCAXARGC2o92SJElS4xfHqSHGuC7G+APAloGSJEkSDQjdIYSPhBA+H0LI38Mx+cDgRlUmSZIktRH1Ct0hhJ8Ac0hWoFwaQjgwtT0/hHBTCOEvqc4la5u+VEmSJKl1SntOdwhhNDAWmAyUAKcD00MIk0jmcxdWOnxO6jhJkiSp3avPhZQTgbExxqWp+w+GEL4PTAMmxxh/2uTVSZIkSW1AfaaXrK0UuCtMA9YZuCVJkqS61Sd0xxobkhA+u+nKkSRJktqepmgZWCOMA4QQbmqCc0uSJEmtXn3mdBenupWEatsLQwgDqx9L1QsrJUmSpHarPqF7LEnXkuoCMKWW7dMbVJEkSZLUxtQndJeQhOs1aRw7GBjUoIokSZKkNqY+oXtOfbqUpNoJSpIkSe1efS6krO9iN15IKUmSJFGP0B1jXFefE9f3eEmSJKmtaoqWgZIkSZL2oFWH7hBCYQhhYgjBBXokSZKUtepzIWVWCSGMAEaR9APvmdlqJEmSpLq12tAdY1wILAwhjMt0LZIkSdKetOrpJZIkSVJr0GpHuhsihJAH5FXa1B0gNzeX3NzczBQlSZKkVqk++bFdhW7gBuDG6huLiorIz8/PQDmSJElqzdavX5/WcVkRulPzsiekcehNqbncDXUTcEul+92BFY04nyRJkrRXWRG6Y4yzgFkt8Dxbga0V90MIzf2UkiRJkhdSSpIkSc0tK0a6G6nRPbrTnYsjSZIkVZZujgwxxmYupXmEEIqBirngI4CpwLOpqSrpnqMvzumWJElS4/WLMZbWtbPVhu6mEJJJ3QcAGzJdi3Zd1NoP/3vI14Oq8vWgynw9qLJseT10B96OewjWbWF6SYOlfjF1fiNRy6l0UeuGGKPzfdo5Xw+qzNeDKvP1oMqy6PWw1+f2QkpJkiSpmRm6JUmSpGZm6Fa22Ap8m0p91NWu+XpQZb4eVJmvB1XWal4P7fpCSkmSJKklONItSZIkNTNDtyRJktTMDN2SJElSM2vXfbrVskIIhcAFwPgY49ha9l8HlKXuFsYYp7ZcdWppab4eAAYDxBgntVx1aml7ez1UO3b23o5R65bO6yGEMAVYkrq7pj4rUqt1SePzYiJQSJIhBgM3xRjLWq7C9Bi61SJCCCOAUST/U/SsZf91ADHG6an7Y0II0wxabVMar4cpMcbJle5PM2i1XXt7PVQ7dhwwpgXKUoak8f5QCDwGjI4xlqWOfw4I1Y9V65dmfpheEbJTr4+fAuNbrMg0Ob1ELSLGuDAVqEvqOOQGYHql4+cAE1uiNrW8Pb0eUm+YI1I/K0wDxoQQilumQrWkNN4fgF2vjT2GcrV+abwepgAzKkJWjHEh4BfyNiqN18PYyqPaqT8XNn9l9WfoVsalglRhbf8UFEJwRKt9GgVUDtgVb7aFLV+KssgFwAOZLkIZNxGYFUIorviMSA3UqH0qCyHMrhioSWWKPX6BzxRDt7JBXaOXZRiy2p0YY1mMsUdq9KpCxZevrHwjVfNLhSuDVTtX6V+7RpB8PpSkpp85QNN+XUGSI9am5vmPydapqYZuZbM1+E/JStwATMrGC2PUYgpjjH7pUkXoLktNOygBJgMzM1iTMij1uTAFmAVcB4yvNj0xaxi6lc0M3KroUDCj4iJbtT8hhIl2plA1Cyr+UDGH19Hu9in1GVESYxxP0rmkJ8mFtVnH0K1sUNfoVeEe9qkdSHWqWGL7yPYr1blgwV4PVHtR12dCGXVPVVQbVemasDkAMcaSGONIknne4zJbXU22DFTGxRhLQghlIYTi6v987MUx7VelC6Qq2kgWAj2dYtDu9CTpZlMxijkYdrUJK3EEvH1JfV6UkATsytd9FOKXs/aomN3re1Q2rYXrSIuhWy2trikjN5FcLFcRsMZRqYWg2qxaXw+p0c0RpDoUpDb7mmj7arweUl+8d335Tr02JvqvH+1CXZ8Xk4EJpEJ36vNiTrWLr9X21Pr+EEKYHEKo3gFtZDZeTBlijJmuQe1AKjiNI3mjHAFMBZ6tPEpVMXKVunt05cVR1Lbs6fWQGtFeSi2da2KMLn7RBqXz/pA6ruKYcaljZvuvYW1Pmp8XFSsQAvTy86Lt2tvrIfWZcQOwmt1dz6bHLLzw3tAtSZIkNTMvpJQkSZKamaFbkiRJamaGbkmSJKmZGbolSZKkZmboliRJkpqZoVuSJElqZoZuSZIkqZkZuiVJkqRmZuiWJEmSmpmhW5IkSWpmhm5JkiSpmRm6JUmSpGb2/wFQcVvg5ygGRAAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 800x494.438 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"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",
|
|
"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: 0.009831165592706342\n",
|
|
"Normalized covariance: 0.8384671239654656\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cov_01 = pe.fits.covariance(fit_result[0], fit_result[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 parameters\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 0.13241808096937788\n",
|
|
"\n",
|
|
"Effective mass:\t 0.2057(68)\n",
|
|
"Fitted mass:\t 0.2036(92)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"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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ht/HxcR09elTd3d16/PHHl9V5dHQ00/LktJz19/cXXJ+PO+gCAOoP3YJV8Mi+nfrGkxHtaM/u+tvR3qJvPBmp+jxXxTiX7XuRr9tMSoUD56rDfNsKPW4x4XBYHR0dGhkZWXYFY7F6+CmRSOjhhx/W4cOHM4P5nceIx+OZwfuRSET9/f06ffq0jh8/XnB9Ic6g/VzxeDwzmSsAoHYRrqrkkX079cOB39JXH9snSfrqY/v0w4HfWpVg5XRj5TMwMOA5XEWjUe3fv39ZK9WJEycyV6W5rzRMJBI6ceKEJGWu6pNSocFLS1lfX58+97nPLWuVKlaPcpQKY/F4PBMcHc5zOT4+nrmS0M25QjDf+kIikYii0WhWN6cTPpkhHQBqH92CVbSuyejeXSFJ0r27QlXvCnSmUXCCgDv4OLe/cbrnYrGYBgcHM5fw9/f369ixY4rH45n1vb29Gh0d1cDAgKanpzMtSs5YnZMnT2pgYCAzBcD09HRmWzgcVm9vrwYGBjLTOjhdbu7HdOvt7dXExETeQdXF6pEr3+McO3YsM3u500p29OjRrIH5TsvTwMBAZrJV53c8dOhQpl5OyIvH43r22WczIS93fTFOuU4AnZiY0OnTp5fV33nsQuG00L75bl4+MTGhkZERjY2NZZ4bp4UOAOBd4Le/qUXVvP3N1OyCpuYWM8tvT83rS8df0x8fuk93dG7NrO9sbebGlwAA1DCvt7+h5Spgz//4XX395beWrf/S8deylr/48J36cvddVaoVAAAICuEqYE88uFvde28uuV9na3MVagMAAIJGuApYZ1sL3X0AADQQrhYEAADwEeEKAADAR4QrAAAAHxGuAAAAfES4AgAA8BHhCgAAwEeEKwAAAB8RrgAAAHxEuAIAAPAR4QoAAMBHhCsAAAAfEa4AAAB8RLgCAADwEeEKAADAR+ur8SDGmH5JifRiyFp7zOMxktQlSdbaPte2qKQ+SaOS4pK6JZ2y1o74WG0AAICyBR6unJBkrR1OL0eNMUPusJTnmEFr7YBrecgYM2qt7U6vCkmKSupRKlwNEqwAAEAtqEbL1WFJe5wFa23MGDOqVMvTMsaYkKSIMSZkrU2kVw9JOm2MCVtr4+l1e1zbAQAAakKgY66MMWGlugETebZFixy6X1LYtewEqlCF9Wg2xrQ5P5JaKykHAACglKBbrsIF1idUICilg9i2nNVOEIu71j1ujJmW1CGpy92NmMdhSUdK1BUAAGDFVutqQScUeXVYUp+rBWxcUsxaO5IeyzVhjDlZ5PijktpdP7vKrzIAAEBpVblaMA/PwcoYMyjpuDMgXpJc464cJyQN5YzTkmv/RUmLrjLLrjAAAIAXQbdc5YYgR6jItgxjTI+kidypG9LrM1yBqlA3JAAAQFUEGq7SLUyJ9MD23G2xYsc6A95dUziEjDHh9NWEJ91lptdJHgIbAABAkKox5uqobgxId1qdhl3LYdeEoc66iKSIpPH09rCkXknT6VaqYzldg72SRpiaAQAArDZjrQ3+QVLhyQlDB3ImCO2VNGCt7UovhySdU56rCa21xrVPr2vT9hJXC+bWp01SMplMqq2trazfBQAANKbZ2Vm1t7dLUru1drbQflUJV7WGcAUAAMrlNVyt1tWCAACgiqZmFzQ1t1hyv87WZnW2tVShRmsX4QoAgAbw/I/f1ddffqvkfl98+E59ufuuKtRo7SJcAQDQAJ54cLe6994sSbq+ZPXdsxf0zCsT+vwnuvTJvTu0rik1B2Rna/NqVnNNYMwVY64AAA3kpTOTevrFs5pMLmTW7Wxv0ZFH9+qRfTtXsWa1z+uYq9W6/Q0AAKiyl85M6qnnxrOClSRdSC7oqefG9dKZyVWq2dpCuAIAoAFcX7J6+sWzytdf5ax7+sWzur7UeD1afmPMFQAANSSoq/pePTe9rMXKzUqaTC7o1XPTeqhru+dysRzhCgCAGhLUVX1Tc4WDVSX7oTDCFQAANcR9VZ8kvT01ry8df01/fOg+3dG5NbO+3Kv6Olu9tXJ53Q+FEa4AAKghnW0tme6+60tWr59PSJIuXbmme3a2ZaZMKNcDezq0s71FF5ILecddGUk72lv0wJ6OyiqODAa0AwBQg146M6nfGPyevvLCGUnSV144o98Y/F7FV/StazI68uheSakg5eYsH3l0b8XhDTcQrgAAqDFBTZnwyL6d+saTEe1oz+7629Heom88GWGeK58wiSiTiAJYZUFdHca95OrT9SWr3xj8XsEr+5zuux8O/FZZrUzu90OpGdp5P+THjZsBoE4EdXUY95JLqbeQGdSUCYXeD8+8MqFnXpnILK/190M1EK4AlFRvX071Jqirw4Iqt97UW8gMasqE3PdDIWv9/VANhCsAJdXbl1O9cV8d5nZH51btu7W95sqtN0GFzKD+6AhqyoRC7wf4j3AFrCFBnezr7csJcAsqZAb1RwdTJtQ/whWwhgR1sq+3Lycs554v6fXziRXNl1TP/Hwegvqjw5ky4annxmWkrIDFlAn1gXAFrCH1Nsam3upbry1tL52Z1NMvns0Mkv7KC2f0H773to48urehLr33+3kIarJP6caUCe76SqkWq0Z73epRQ4erubk5GUPyx9qxyUi/0nZj+rqLF1Pv751bTNZ66arm5q5W9BjXl6xeffuCJOnVty9o11ZT8ZdIUPV9f25R789fKbnfR7Zu1EfKCG7f/ME7+sYP3i2531Mf261//Zu3ey4318WLFzP/zs2tbDrC2Jsf6N/8xdll3UvOfElf++d7Fb37prLKDOr5DapcKZjnwV32v/vu2/rlXKruX3nhjL4e+5n+z0/eUXGZkvTrv7JV3/nXB/T/vTap//s7b+v/+tQd+l/u26l1TUZzc3MVl4vKeX3eG3qeqz/90z/V5s2bV7s6aECzV43mrpYOJK0brNo2VPYZXbLSqQ/X64Xzm/TYrss6sP2aVtqLcCaxXi/+olnJqze+8Ns3LOnRWxe1L3RtZYVL+sWlJv2Hn23R/3HXRd26eanickYnN+rlX5b+8n345kV17yz9Ze7Ifd2mFpp0/N1NOrT7sjpbbtS3Vl63JSsNnt2i5FWj5XNyS5JV+wargb0Xy3qMoJ7foMoN6nmQUp+J595xWindB6de/ydvXyj7s1GN9xkqc+nSJf3+7/++xDxXhW3cuJFJRLEqvv+O1bdLN4Dof94t/c7t5X+z/uQDq5NvS4n0988L5zfplSnp4B3SR2+q7Jv6Jx9YPffO8vXJq0167p1N+tzeyst2JEzqi2LLli1qa628rGiz1YFbbixfuCT9p59K//uvSjtcf0+1b2xWW7P37rvcs8WWOSu9K+3Zvkm7V1Bfh9+v288SVsmiDX5GyatGU7ZVd7V7Lz+o5zeocoN6Hpas1V+dLVymJP315Cb909ukpjJ6SQqdH46/uylrudLzAyp37Zq3oNzQ4aqlpYWWK6yKT4aX9MCtqSCxZK3Gf3ldf3Xumn5nz3pFbl6XORGHmo02t5TXLTR24ZqePbt8XFDiivTsWekL923U/h3lffSXrNXIxGUp77VLKX8RN3rotk1lfYnkPsbkB1clXdXk4gbd1bmh4rI2b5bcI1JaktclLej2jhbd3r6uojLzabmaKjd1LllZuUG8bguJa5JKjxFbULM2b/ZedlDPb1DlBvU8vPHhdSWuFJ9ramZR+vlCi+7Z7r3+7vNDMZWcH7Ayly5d8rRfQ4crYLWEWpoUakl9oT7/xhXNLKZOpH917pr+7r3reuKe8r9IpVRAef6N4t0l//nNK1kBzoufTi9l6ljI9ILVT6eXyvoSceQ+D39+9qr+68S1ip+HehPU69be7G1fr/tV05K1Ope8Lkk6l7yu3W1NFYftoJ6HZInPRLn7OZzzA+oXkRdYJWMXrulPXltcFlpmFq3+5LVFjV0ofwxTOSGoHEF9iUjBPA/1JqjX7Vc7mrStRGDoaDH61Y7KvwpyQ9CSD+N4xy5c0x/+zWX9+dlUX96fn72qP/ybyxW/F4J6Huo5vCJYVfmT0BjTLymRXgxZa495PEaSuiTJWtu30jKBciUWlpTwEBhCzUahMprng2qpCCoEBfUlEtTzUG+Cet2ajNET92zUn7xWuEvsX929seLnNogWRyds53LC9hfuU9llB/U8OKGtWDBeaXhFfQr8akEnJDnhxxgTlXQwNyzlHDNorR1wLQ9JCltruystM6f8NknJ3/3d3y055qqzs1OPPfZY1roXXnhBU1NTJR9n//79uv/++zPLV65c0Te/+U0vVdRnPvMZ7dixI7M8MTGhWCxW8rgNGzbo937v97LW/e3f/q3efPPNkseGw2F1d3dnrXvuuecyl4UX85u/+Zu65557MsvT09M6efJkyeMk6YknntDWrTfmNHr99df1ox/9qORx27Zt0+OPP5617q//+q91/vz5ksfee++9euihh7LWDQ0NLdvvrZnreiuR3Vqw9d5PakPHrZnlq9O/0M53Y7pzW+nusL6+1Fv0jQ+va/BU6fuC7b0wqvbFX2rXrl369Kc/nbXtxIkTmpmZyVqXbL5ZZ3dkv4bFyi3l4MGD6ujo0JK1+sO/uayZxSXlvdrKWm28fkmRX/yljKy2bNmiJ598MmuX0dFRxePxiup77wff05aL75XcLxqNqqurK7N84cIF/eW3/qsmmz6if5jbqH/SekU7l96XyTN27LOf/aw2btyYWT59+rTGxsYKPpaV0WTTR/TmtZv0B59+UB+/7cYYsXLPEeW+H6TyzhEfbrpN73Ts15X1WzLrOlqM/tXdqRBUyTkiE4KsldyhJP2dctf739f2yz/PrPZyjrAyGr/1n+nKus3ZZbp0tBj9r9ve1o//W/nniLEL1/SnP0nqsrnR57bx2kXdPj2WVVfJ+zniw0236Wcf+c3UQp46f+G+Zt18dVLf/va3S9ZXunGOcPzoRz/S66+/XvI4r+eIfB566CHde++9meX5+Xk9//zznurrnCMcb7zxhr7//e+XPM7rOSKfu+++Wx//+Mez1v3Zn/2Zrl4tPV1LvnPEt771rZLHSalzxOzsrA4ePCjVwNWChyXtcRastTFjzKikvEHIGBOSFDHGhKy1ifTqIUmnjTFha2283DILuXTpkpaWijez57uacGFhQfPz8yXLX1xc/leSl+MkLavXtWvXPB3r/nJweK3v5cuXl627ePGip2Nzr6BYWlry/LvmBvyrV696Ora5efkl25cvX/b1tdneZLU1PQG5lVGyZYc+WL8xa+D5L95dp++fv6T5ef9bmOauGq2bn8/72ly6dGlZnZvmL2rD9nldXb+l4JdTaKNV04dxzRcZnO5w3oc3/vJfKPhluuO9H+rifOE5YPK9NnNNOwrsnW3uapNsBe/D12eadPqWz+jqhq3aKukfJb13dV47J/9O7XPnipa1uLhY8L2UbN2jyZ2/rqsbtmqTlrfYlHuOKNkCYq02XJ3Pet3KOUc0z7+hu95/U+9v3KGJhS36/KFPZYXBcs8RWS2Oue8zYyRrdS4U0cb338wEWS/niPnNt2QFwHymF6z+8dKGis4R+3es17mLr2hs8orevtSiOzYv6CNXLsjIKrc0r+eI5vk3tHthIfN+cLjD67lz3s7f+RR7H7p5PUfkkxtKrLWBf1fl4/X8vbCw/A+Rixcv6sqV0tNzrOS7qhyBhitjTFipLrtEnm1Ra22hppj9ksKSxtPLTpQNraDMZTZv3lyy5aqlZfmowpaWlqyWlkLyffl7OU6Smpqym5HXr1/v6dgNGzYsW+e1vps2bVq2bsuW4ic6d/3cmpqaPP+uuRO5btiwwdOx+V67TZs2BfLauP/yX6/sgee7QxvV0d5a8jHdvHaftW6w2rp1a97XZvPmzXm/APbMnE79JZ0bgtJ6wtI/vOXtdXW/D/fvWK9/9pEP9O3JzVlfgBuvX0r95W/fl9LPYb73Tb7X5rrHOXpaNyxpi4fX1f0+HLtwTf/5HzcvO8tdXb9F7972yWUtK7mam5vzvic+3HSb3nVaKlzc3VblniOyuq0KhNc9iXG1br3xvFZyjlhanNVPE3Pa057dzVruOaLkGDFjdHVjq5a2hzMtbV7OEQubvd0rb9Hkf21yOecId/f+ZbWo+cr7ujYzreb167S04cZ5pGWd1Lw+9byUc47Yat/XLe99K9NC+i//p4/qsY/uzDzHXs/f+RR6H+Yq5xyRK/d7wxgT+HeV13NEPvm+l7ds2ZK3cSHXSr6ryhFot2C6u27UWmty1s9I+py1dsRjOT2STkraplTwKqtMY0yzJPcnpVXS+ZMnT+qmmyqfPReNodAYEMcX7muuaGqDVDdb8bEa//7jlU1tkDsWxinP+Ut6JZas1d/+/Kr+/OxV/W97N2S1gFRSVhDPQ72V6wjydZOkd5LX9W9/tKB/+9DKpjb4b+9d0398vfSX9h/c26x/ekt5Uxt46R4dOFDe1AYvvHVF35oo3WX0ma4NeuzO0l/Qjtwxme/NL2n4v19R7/+wUbdsvRE6yh2Tidr1wQcf1Ey3YD7Tksq5nfdhSX3W2kSR29UUK/OwpCNlPB4gKbgB10EPNN6/I9Vt6VcIcmsyRnva10m6uqwFpJKygngegpo6IugpKfx+3fJ9+bv/dZT75R/UBQ5BDRD/xG3r9dHO0q9HqMz6vvLza3lD2/B/zz5nlBvaUP9WK1x5DlbGmEFJx621wyso86ikr7mWWyWVHvWMhhfkl+n+Hev1hfsUWEuFnyEoSEE8D0FdfRfklBQOP1+3oL78gwpBQYXtoOaNCiq0of4FHa4KDfsPFdmWke4OnMgJVmWXaa1dlGt6Xm7WDK+C/jKtl5aKoPn9PATVslJv8xoF9eUfZMtr0H90+InJPlFIoO9Sa23cGJNwXeXn3lZ04Hl6vJacYJW+irBjJWXmWlhY8DyVPRpTi4cr6lL7LerSJe83kk0uWiVdu5vrzr9X9bOpGy0N7RvL+6L+boF7kuW2VJR7T7Lc+l5If2zemV6Q+8Kdcuuba2ezTf97VQuXK5889LYWq9DGG/foy2dbs3Rby4IuXfJe36DKzbWwYNP/LujShsrL2Sipc/k1LsstSeWeCve2SZ/bq6x7IUqp37+nS9rbdqWsz0Ru2f/PA1Z/Nyn9v29L//IO6dd3WjWZyssE/JDvSsV8qvEnwFFJUUlOSOpx/p9eDkvqcU8CaoyJSIpIGklvlyT3cUXL9OrKlSuanS04Hg1Qp5HaN2xR8qpR3vmdZNW+warTXFQ5b6XY5Ea9/MvlVyP9p59mLz9886K6d3r/Mrmv1ajrrtJfxq0brGZnvbe2BVXfXBcvNUnaoosXL2rWljcbea7fuWW9nnvHaVZwPyep3/vTOxc0P1d+gAuqXDc/n4cgdW2U+u+RTn24Xi+c36THdl3Wge3X1GRU1uehkJuaUs/DTU0XNT9Xu88DGoeX6R6kKkwiKmUm/XRamQ7kTBDaK2nAWtuVXg5JOqdUN18W9xWCxcr0UJ82Scnz58/nnccKcIu9+YH+zV+clZR922Lnzfi1f75X0bvLu+r0/blFvT9f+kP6ka0b9ZHW5aGm2qpV37OTczr0Zz/R8d/7qPbuLG96i3xib36gf/fdt/XLuRt139HWrIHurrJfs2qU6/D7eQhaUPWtt+cBa9/s7Kx27dol1cLVgjm3phnJ2TYsV6tTev6qbSsp06vW1la1tvKBRXGPHWjVpk0tevrFs5pM3mgS3tHeoiOP7tUj+3aWXWZra6vCpXerGUHVd2p2QVNzN8btTF60mX+3zN5oqehsbVZnW/mDWx470Krfvf92HT/1rr7ywhl99bF9OnRgt9Y1rWxMlN/lBv08BM2p45YtW1Z0Tq335wFrn9cGqdoZGQjUIOdkv2vbZv3HJ+/Xd89e0DOvTOjzn+jSJ/fu0LomozO/SHKyr9DzP35XX3/5rWXrv3T8tazlLz58p77cfVdFj7GuyejeXSFJ0r27QisOVkGUW43nwU+5Iejtqfmsfx3lfi7q7XkACiFcAUUUOtk/88qEnnllIrPMyb4yTzy4W917by65X2cNdI0Gqd6eh6BCUL09D0AhhCugCE72wepsa6HFT/X3PAT1uai35wEohHAFFMHJvj4F1W2FFD4XQHGEKwBrTlDdVoQ2AF5UZSqGWuNMxZBMJpmKAViDckNQIeWGoD8a/Vne0JaLMXjA2jQ7O6v29napxFQMhCvCFQCPggptAOqD13BFtyAAeMRYIwBe1M6dWwEAANYAwhUAAICPCFcAAAA+IlwBAAD4iAHtWBO4igsAUCsIV1gTCk0amYv5hwAAQSNcYU3IvdfZ21Pz+tLx1/THh+7THZ1bM+u5ByAAIGiEK6wJ7vmHri9ZvX4+IUm6dOWa7tnZpnVNZhVrBwBoJMzQzgzta8pLZyb19ItnNZlcyKzb2d6iI4/u1SP7dq5izQAA9c7rDO1cLYg146Uzk3rqufGsYCVJF5ILeuq5cb10ZnKVagYAaCSEK6wJ15esnn7xrPK1wzrrnn7xrK4vNV5LLQCgughXWBNePTe9rMXKzUqaTC7o1XPT1asUAKAhEa6wJkzNFQ5WlewHAEClCFdYEzpbvU0M6nU/AAAqRbjCmvDAng7tbG9RoQkXjFJXDT6wp6Oa1QIANCDCFdaEdU1GRx7dK0nLApazfOTRvcx3BQAIHOEKa8Yj+3bqG09GtKM9u+tvR3uLvvFkhHmuAABVwSSiTCK65lxfsjp+6l195YUz+upj+3TowG5arAAAK+Z1EtGq3P7GGNMvKZFeDFlrj3k4JiTpcUkHrbXdOduikvokjUqKS+qWdMpaO+JjtVFHpmYXNDW3mFnevHF95t83Jm+8/ztbmzO3yQEAIAiBh6t0sJK1dji9HDXGDFlr+4ocE5G0X1JIUr4RyCFJUUk9SoWrQYJVY3v+x+/q6y+/tWz9l46/lrX8xYfv1Je776pSrQAAjSjwbkFjzIykPdbahGudtdaW7KcxxvRIOmytvT/P+pi7zDLrRLfgGpPbclUILVcAgErVRLegMSasVDdgIs+2qLU2FuTjo3F0trUQmgAANSHobsFwgfUJpbr2VuJxY8y0Ut2GXdbagUI7GmOaJTW7VrWu8LEBAADyqsqA9jycUFSpcUmy1sYlyRjTa4w5aa09WGD/w5KOrODxAAAAPFmtcLWiabKdUOVyQtKQMSZvF6Sko5K+5lpulXR+JXVAZRgbBQBY64IOV7khyBEqsq0kY0yP++pAa23CGCOluiHHc/e31i5KWnQdX+lDY4UKXdWXi6v6AAD1KtBwZa2NG2MSxphwbmtTpYPZ0/NfnTTGdLm6BUPpzRUHNlTHEw/uVvfemyWlJvv87tkLeuaVCX3+E1365N4dmck+O1ubixUDAEDNqsbtb44qNSeVpMw0CsOu5bAzF1Yey7oP091+x3LCWq+kkUqnZkD1dLa1aN+t7To/c0l/8NxpPfPKhCTpmVcm9AfPndb5mUvad2s7XYIAgLpVldvfpMOTE4YOuK/sM8b0Shqw1na51oWVmiD0kKSIpGNyzcCebqnqdT3E9mJXC+apD/NcraKXzkzqqefGlfvOczpruQ8gAKAWeZ3ninsLEq6q6vqS1W8Mfk+TyYW8241SN1r+4cBvcT9AAEBN8RquqtEtCGS8em66YLCSJCtpMrmgV89NV69SAAD4iHCFqpqaKxysKtkPAIBaQ7hCVXW2ehuo7nU/AABqDeEKVfXAng7tbG9RodFURtLO9hY9sGdF88wCALBqCFeoqnVNRkce3StJywKWs3zk0b0MZgcA1C3CFarukX079Y0nI9rRnt31t6O9hWkYAAB1j6kYmIph1Vxfsjp+6l195YUz+upj+3TowG5arAAANcvrVAyrdeNmNKjcGzdv3rg+8+8bkzfep9y4GQBQrwhXqKpCN27+0vHXspa5cTMAoF4RrlBV7hs3F8ONmwEA9YpwharqbGuhuw8AsKZxtSAAAICPCFcAAAA+IlwBAAD4iHAFAADgI8IVAACAjwhXAAAAPiJcAQAA+IhwBQAA4CPCFQAAgI8IVwAAAD4iXAEAAPiIcAUAAOAjwhUAAICPCFcAAAA+IlwBAAD4aH01HsQY0y8pkV4MWWuPeTgmJOlxSQettd1+lAkAABC0wMNVOgTJWjucXo4aY4astX1FjolI2i8pJKnDjzJRnqnZBU3NLZbcr7O1WZ1tLVWoEQAA9cFYa4N9AGNmJO2x1iZc66y11ng4tkfSYWvt/X6Vmd63TVIymUyqra3N2y/SYP5o9Gf6+stvldzviw/fqS9331WFGgEAsLpmZ2fV3t4uSe3W2tlC+wXacmWMCSvVZZfIsy1qrY1Vo0xjTLOkZteq1nIft9E88eBude+9WX//9gca/kFcH8xfyWy7aetG9X4srF+74yZ1tjYXKQUAgMYT9ID2cIH1CaW6/KpV5mFJSdfP+Qofu2F0trXo/MwlHf3Om1nBSpI+nL+io995U+dnLtElCABAjtW6WnBaecZSBVjmUUntrp9dPj/2mnN9yerpF88qX6exs+7pF8/q+lKw3coAANSb1QpXfgeromVaaxettbPOj6S5AB5/TXn13LQmkwsFt1tJk8kFvXpuunqVAgCgDgQdruIF1oeKbFuNMpFjaq5wsKpkPwAAGkWg4cpaG5eUSA9Cz91W9mD2oMrEcp2t3sZSed0PAIBGUY1uwaOSos5CenqFYddy2Jm3Ko9iY6gKlomVe2BPh3a2t6jQ3BZG0s72Fj2wJ4geXgAA6lfg4So9c3rIGNOTDkEHcib7jErKmvzTFbj6JEWMMYPpY72WiRVa12R05NG9krQsYDnLRx7dq3VNnqYWAwCgYQQ+iWgtYhJR7146M6mnXzybNbh9Z3uLjjy6V4/s27mKNQMAoLq8TiJKuCJc5eW+/c31Javvnr2gZ16Z0Oc/0aVP7t2RabHi9jcAgEZBuCqCcFUat78BACBbTdz+BvXLuf1NKdz+BgCAbIQr5NXZ1kJ3HwAAFVitGdoBAADWJMIVAACAjwhXAAAAPiJcAQAA+IhwBQAA4CPCFQAAgI8IVwAAAD4iXAEAAPiIcAUAAOAjwhUAAICPCFcAAAA+IlwBAAD4iHAFAADgI8IVAACAjwhXAAAAPiJcAQAA+IhwBQAA4CPCFQAAgI8IVwAAAD4iXAEAAPiIcAUAAOAjwhUAAICP1lfjQYwx/ZIS6cWQtfbYSo4xxkQl9UkalRSX1C3plLV2xMdqAwAAlC3wcJUOSbLWDqeXo8aYIWtt3wqOCUmKSupRKlwNNmqwmppd0NTcoiTp+pLVP7yX1Mylq9q2eYP+yS3tWtdkJEmdrc3qbGtZzaoCANAQjLU22AcwZkbSHmttwrXOWmtNpccYY3okxdzby6xTm6RkMplUW1tbJUXUjD8a/Zm+/vJbJff74sN36svdd1WhRgAArE2zs7Nqb2+XpHZr7Wyh/QIdc2WMCSvVpZfIsy3q1zEe6tFsjGlzfiS1VlJOLXriwd36yqfuLrrPVz51t554cHeVagQAQGMLekB7uMD6hFJdeys55nFjTI8xptcYM1iiHoclJV0/50vsXze2b23WN//+nYLbjaRv/v072r61uWp1AgCgka3W1YLTkjpWcMy4Ut2CI+lxWRPGmJNFjj0qqd31s6vMx65Zr56b1mRyoeB2K2kyuaBXz01Xr1IAADSwqlwtmEe5wSrrGGttPGfbCUlDxpi83YnW2kVJi86yMQWHe9WdqbnCwaqS/QAAwMoE3XKVG4IcoSLbSh6THtCe4QpUhboU16zOVm9XAHrdDwAArEyg4SrdwpRID1LP3Rar5BhjTEjSSff29DqpcDBbsx7Y06Gd7S0q1BZnJO1sb9EDeyppLAQAAOWqxpiro0rNSSUp0+o07FoOO/NaeTkm3Up1LKdrsFfSSKVTM9SzD+cX9dlfu12FJtSwkj77a7frw/nFAnsAAAA/BT7PlZSZFNQJQwestQOubb2SBqy1XWUcE1IqUDm2u7d7qA/zXAEAgLJ4neeqKuGq1qylcMUM7QAAVIfXcLVaVwvCJ51tLVmh6X+8LbR6lQEAAKs2zxUAAMCaRLgCAADwEeEKAADAR4QrAAAAHxGuAAAAfES4AgAA8BHhCgAAwEeEKwAAAB8RrgAAAHxEuAIAAPAR4QoAAMBHhCsAAAAfEa4AAAB8RLgCAADwEeEKAADAR4QrAAAAHxGuAAAAfES4AgAA8BHhCgAAwEeEKwAAAB8RrgAAAHxEuAIAAPAR4QoAAMBHhCsAAAAfEa4AAAB8tL4aD2KM6ZeUSC+GrLXHVnpMJWUCAAAELfCWq3QIkrV22Fo7LGncGDO0kmMqKRMAAKAajLU22AcwZkbSHmttwrXOWmtNpcdUUmZO+W2SkslkUm1tbWX+RgAAoBHNzs6qvb1dktqttbOF9gu05coYE1aqyy6RZ1u0kmMqLLPZGNPm/EhqLesXAQAA8CjobsFwgfUJSaEKj6mkzMOSkq6f8wX2AwAAWJHVulpwWlKHz8cU235UUrvrZ1eZjw0AAOBJVa4WzKPcYOXlmILbrbWLkhadZWM8Dc0CAAAoW9AtV/EC60NFtpU6ppIyAQAAqiLQcGWtjUtKpAeh526LVXJMJWUCAABUSzXGXB2VlLmKzxjTI2nYtRx25q3yeoyH7QAAAKsi8HmupMykn06X3QFr7YBrW6+kAWttl9djvGwvUR/muQIAAGXxOs9VVcJVrSFcAQCActXEJKIAAACNhnAFAADgI8IVAACAjwhXAAAAPiJcAQAA+IhwBQAA4CPCFQAAgI8IVwAAAD4iXAEAAPiIcAUAAOAjwhUAAICPCFcAAAA+IlwBAAD4iHAFAADgI8IVAACAjwhXAAAAPiJcAQAA+IhwBQAA4CPCFQAAgI/Wr3YFGtH1JatXz01ram5Bna0temBPh9Y1mdWuFgAA8AHhqkqmZhc0Nbeov3/7Aw3/IK4P5q9ktt20daN6PxbWr91xkzpbm9XZ1rKKNQUAACtBuKqS53/8rr7+8lt5t30wf0Vf/c6bkqQvPnynvtx9VzWrBgAAfMSYqyr5Fwdu001bNxbd56atG/UvDtxWpRoBAIAgEK6q5J0PL2V1BebzwfwVvfPhpSrVCAAABIFwVSVTcwu+7gcAAGpT4GOujDH9khLpxZC19thKjjHGRCX1SRqVFJfULemUtXbEx2r7rrPV2yB1r/sBAIDaFGjLVTokyVo7bK0dljRujBla4TEhSVFJQ+mfiVoPVpJ0+/bNnsZc3b59c5VqBAAAghB0t+BhScPOgrU2JqnXh2P2WGuNtbYrHcBq3n859XNPY67+y6mfV6lGAAAgCIF1Cxpjwkp16SXybIumQ9OKj6kXTzy4W917b/Y0zxUAAKhfQY65ChdYn1Cqa28lxzxujJmW1CGpy1o7UKwixphmSe7U0lps/yB0trWos61F+25t1+9/LMwM7QAArFGrMYmoE4oqPWZckqy1cUkyxvQaY05aaw8WOf6wpCPlVjQo65qMHuravtrVAAAAAfAcrowxPZIOedj1qLV2vMj2coNV1jFOqHI5IWnIGJO3O9Gpk6SvuZZbJZ2voB4AAABFeQ5X6SvyyrkqLzcEOUJFtpU8xhjT47460FqbMMZIqS7FvKHOWrsoadFZTu8PAADgu8CuFky3MCXSg9Rzt+UdmF7qGGNMSNJJ9/b0OqlwMAMAAKiaoKdiOKrUnFSSMl2Lw67lsDOvlZdj0t1+x3K6BnsljRTpEgQAAKgaY60N9gFS4ckJQwfcV/YZY3olDVhru8o4JqTsea+2l7paME+d2iQlk8mk2trayjkUAAA0qNnZWbW3t0tSu7V2ttB+gYerWkS4AgAA5fIarrhxMwAAgI8IVwAAAD4iXAEAAPiIcAUAAOAjwhUAAICPCFcAAAA+IlwBAAD4iHAFAADgI8IVAACAjwhXAAAAPiJcAQAA+IhwBQAA4CPCFQAAgI8IVwAAAD4iXAEAAPiIcAUAAOAjwhUAAICP1q92BVbT7OzsalcBAADUCa+5wVhrA65K7THG3Crp/GrXAwAA1KVd1tpfFNrYqOHKSLpF0pyH3VuVCmK7PO6P2sDrVp943eoTr1t94nWrTKuk92yRANWQ3YLpJ6Rg4nRL5TBJ0py1ln7EOsHrVp943eoTr1t94nWrWMnnigHtAAAAPiJcAQAA+IhwVdqipKfT/6J+8LrVJ163+sTrVp943QLSkAPaAQAAgkLLFQAAgI8IVwAAAD4iXAEAAPiIcAUAAOCjhpxE1AtjTL+kRHoxZK09torVgQfGmKikPkmjkuKSuiWdstaOrGrFkMUYE5L0uKSD1truPNv57NWgYq8bn73alv5MSVKXJFlr+/JsT6QX+cz5gHCVh/NGtNYOp5ejxpih3Dckak5IUlRSj1In+EFO7rXFGBORtF+p16ojz3Y+ezWo1OsmPns1yxgzaK0dcC0PGWNGnYDMZy4YTMWQhzFmRtIea23Ctc5aa03ho7DajDE9kmLu1w21Kf1aHbbW3p+zns9eDSvyuvHZq0Hp1saTSrU2JtLrIpJOS+qy1sb5zAWDMVc5jDFhpZpFE3m2RatfI6Ax8NkDArFfUti1HE//G+IzFxy6BZcLF1ifUKrpG7XtcWPMtFJdF13u5nDUPD579Y3PXo1Jh6ZtOaud0BRXKnjlkxCfuRUhXHnnnDRQu8YlyVoblyRjTK8x5qS19uDqVgsrxGev9vHZqx+HJfVZaxPGFOz54zO3QnQLescbrcZZa+POyT3thKSe9LgD1C8+ezWOz159MMYMSjruDF4vgs/cChGulosXWB8qsg01ID2oNsM1jqBQdxNqC5+9OsVnr/alX6OJnGkW+MwFhHCVI/3XVyI90C93W2wVqgQPnKti3K+b669mThJ1gM9efeKzV/ucwemu6RZCxpgwn7ngEK7yO6obg/6cxF+qGRWrKP2X8rGcroleSSNcHl6TCnU78NmrbcteNz57tS099UJE0rgxJpwOUr1KjauS+MwFgnmuCkhPrOacLA5w5UvtS/+13OtatZ3XrbakT+w9kg4pdcI/ppyZvPns1Z5SrxufvdqUfl3OKc+Vf+55rPjM+Y9wBQAA4CO6BQEAAHxEuAIAAPAR4QoAAMBHhCsAAAAfEa4AAAB8RLgCAADwEeEKAADAR4QrAPAofduQ0GrXA0BtI1wBgHeHxc2IAZRAuAIA7yLW2vHVrgSA2ka4AgAPjDFRSaOrXQ8AtY9wBQDeHJQ0UnIvAA2PcAUA3oSttfHVrgSA2rd+tSsAAH4yxkQk7ZfUJemUpJik3vTmhLV2uIIyeySdLLLtgKQJSfH0z7S1NlF25QGsCbRcAVgz0tMkRK21w9baAUnPSjpsrT2W3mWgwqIPSTqR5/F6JXVbawfSoS2kVMjaX+HjAFgDaLkCsJb0uoKUYyL977ikvgrLDeW2RBljwpIGJe1xrU5IkrU2VuHjAFgDCFcA1pLMgPN0+Akp3eKUG3jS23uU6sY7IGko35iqdOvUUJ7HGpIUywld3UqFOAANjHAFYM3ICUdRSfEiY59OWmvvlyRjTEzSy5Luz7PfQWttd571UaWuIHSLKDXGC0ADY8wVgLWqWzlTJzi3rkkPes9IB7BQujUrd/9EbsGu/XJbqZgLCwDhCsDake7Cc/QodbVgZpurFavQgPNIznKhLkFJ2S1l6UlGZa2NGWMiuQEOQOMgXAFYE9LBajD9/x65uufy3Gw5JGk6Z11CUkfOuu58g9PToSruBKh0+X1Kjd+SUlcsMvYKaFCMuQKwVsQkDadD1phSYWfAGCNJHTnzWyW0PEiF5Apc6a6/YpOGHpTUZ4w5LUnW2oPGmJPpxydYAQ3MWGtXuw4AUFXpFqdnnQHt6XUzku53uvqMMYOSjtMCBaBcdAsCaDjpwBRyltPdevGcqw0jBCsAlaBbEECjOphunTql1DxXmWkV0i1bBCsAFaFbEAByGGOGJA1yo2YAlaBbEACW6yBYAagULVcAAAA+ouUKAADAR4QrAAAAHxGuAAAAfES4AgAA8BHhCgAAwEeEKwAAAB8RrgAAAHxEuAIAAPDR/w/ayL41LOgQlgAAAABJRU5ErkJggg==\n",
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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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},
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"metadata": {
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"needs_background": "light"
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|
},
|
|
"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[0.57(35)], Obs[0.49(25)])\n",
|
|
"(Obs[2.53(35)], Obs[0.56(25)])\n",
|
|
"(Obs[4.17(35)], Obs[-1.52(25)])\n",
|
|
"(Obs[5.97(35)], Obs[-1.40(25)])\n",
|
|
"(Obs[7.82(35)], Obs[-0.58(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: 0.4144435658518591\n",
|
|
"Parameter 1 : 0.26(28)\n",
|
|
"Parameter 2 : -0.228(53)\n",
|
|
"Parameter 3 : 0.98(22)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"beta = pe.fits.odr_fit(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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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"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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\n",
|
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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\n",
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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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{
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"data": {
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"image/png": 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\n",
|
|
"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": "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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|