{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7c1065dd", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pyerrors as pe" ] }, { "cell_type": "code", "execution_count": 2, "id": "20f67709", "metadata": {}, "outputs": [], "source": [ "plt.style.use('./base_style.mplstyle')\n", "plt.rc('text', usetex=True)" ] }, { "cell_type": "markdown", "id": "e5764fd0", "metadata": {}, "source": [ "We can load data from a preprocessed file which contains a list of `pyerror` `Obs`:" ] }, { "cell_type": "code", "execution_count": 3, "id": "fbfa65f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data has been written using pyerrors 2.0.0.\n", "Format version 0.1\n", "Written by fjosw on 2022-01-06 11:11:19 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n", "\n", "Description: Test data for the correlator example\n" ] } ], "source": [ "correlator_data = pe.input.json.load_json(\"./data/correlator_test\")" ] }, { "cell_type": "markdown", "id": "ae93c7c2", "metadata": {}, "source": [ "With this list a `Corr` object can be initialised" ] }, { "cell_type": "code", "execution_count": 4, "id": "33a8fdec", "metadata": {}, "outputs": [], "source": [ "my_correlator = pe.Corr(correlator_data)" ] }, { "cell_type": "code", "execution_count": 5, "id": "5f954607", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Corr T=96 N=1\n", "x0/a\tCorr(x0/a)\n", "------------------\n", "8\t 548(13)\n", "9\t 433(11)\n", "10\t 343.1(8.6)\n", "11\t 273.2(6.6)\n", "12\t 217.5(5.6)\n", "13\t 172.9(4.9)\n", "14\t 137.6(4.6)\n", "\n" ] } ], "source": [ "my_correlator.print([8, 14])" ] }, { "cell_type": "markdown", "id": "b00d670b", "metadata": {}, "source": [ "The `show` method can display the correlator" ] }, { "cell_type": "code", "execution_count": 6, "id": "b71529d0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "my_correlator.show()" ] }, { "cell_type": "markdown", "id": "c659557e", "metadata": {}, "source": [ "## Manipulating correlators" ] }, { "cell_type": "markdown", "id": "416cf39a", "metadata": {}, "source": [ "`Corr` objects can be shifted" ] }, { "cell_type": "code", "execution_count": 7, "id": "e8d65dd5", "metadata": {}, "outputs": [], "source": [ "shifted_correlator = my_correlator.roll(20)\n", "shifted_correlator.tag = r'Correlator shifted by $x_0/a=20$'" ] }, { "cell_type": "markdown", "id": "634dd613", "metadata": {}, "source": [ "or symmetrised" ] }, { "cell_type": "code", "execution_count": 8, "id": "127a661d", "metadata": {}, "outputs": [], "source": [ "symmetrised_correlator = my_correlator.symmetric()\n", "symmetrised_correlator.tag = 'Symmetrised correlator'" ] }, { "cell_type": "markdown", "id": "3d733872", "metadata": {}, "source": [ "We can compare different `Corr` objects by passing `comp` to the `show` method" ] }, { "cell_type": "code", "execution_count": 9, "id": "8e264aed", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shifted_correlator.show(comp=symmetrised_correlator, logscale=True)" ] }, { "cell_type": "markdown", "id": "232e88af", "metadata": {}, "source": [ "## Effective mass" ] }, { "cell_type": "markdown", "id": "83dc751c", "metadata": {}, "source": [ "The effective mass of the correlator can be obtained by calling the `m_eff` method" ] }, { "cell_type": "code", "execution_count": 10, "id": "c686f7e0", "metadata": {}, "outputs": [], "source": [ "m_eff = symmetrised_correlator.m_eff()\n", "m_eff.tag = 'Effective mass'" ] }, { "cell_type": "markdown", "id": "4a9d13b2", "metadata": {}, "source": [ "We can also use the priodicity of the lattice in order to obtain the cosh effective mass" ] }, { "cell_type": "code", "execution_count": 11, "id": "5acde8cf", "metadata": {}, "outputs": [], "source": [ "periodic_m_eff = symmetrised_correlator.m_eff('periodic')\n", "periodic_m_eff.tag = 'Cosh effective mass'" ] }, { "cell_type": "markdown", "id": "c658b000", "metadata": {}, "source": [ "We can compare the two and see how the standard effective mass deviates form the plateau at the center of the lattice" ] }, { "cell_type": "code", "execution_count": 12, "id": "1d6ea22a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "periodic_m_eff.show([4,47], comp=m_eff, ylabel=r'$am_\\mathrm{eff}$')" ] }, { "cell_type": "markdown", "id": "e3762e68", "metadata": {}, "source": [ "Arithmetic operations and mathematical functions are also overloaded for the `Corr` class. We can compute the difference between the two variants of the effective mass as follows." ] }, { "cell_type": "code", "execution_count": 13, "id": "e56d164c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "difference_m_eff = np.abs(periodic_m_eff - m_eff)\n", "difference_m_eff.show([0, 47], logscale=True)" ] }, { "cell_type": "markdown", "id": "472ab97b", "metadata": {}, "source": [ "## Derivatives" ] }, { "cell_type": "markdown", "id": "d99414fe", "metadata": {}, "source": [ "We can obtain derivatives of correlators in the following way" ] }, { "cell_type": "code", "execution_count": 14, "id": "03007f8a", "metadata": {}, "outputs": [], "source": [ "first_derivative = symmetrised_correlator.deriv()\n", "first_derivative.tag = 'First derivative'" ] }, { "cell_type": "code", "execution_count": 15, "id": "c0311739", "metadata": {}, "outputs": [], "source": [ "second_derivative = symmetrised_correlator.second_deriv()\n", "second_derivative.tag = 'Second derivative'" ] }, { "cell_type": "code", "execution_count": 16, "id": "165550d9", "metadata": {}, "outputs": [ { "data": { "image/png": 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sf3+/jUQiqTrDw8Op7Wg0agOBQElxhUKhjM85evSolWSj0WhqX0dHh+3vP3WtgsGgHRwczBtbvvOIRqMT9oXD4YxY0q+J3++3R48ezXh/rvOazLEKnRPyGx0dtZKspBZbJNegZQoAprO5ZzpfD/8qd7m7361XI3v27FFvb6+6u7u1c+fOkt/n8/k0NDSkYDCoQCCgcDgsServ708NXvf7/VqzZk1Z8bgtQW7LmHusQCCQ2o7H4xoYGFBHR0dq3/r16xWJRArGlkswGNTIyEhGi1N6F2c8Hs+YRej3+yc9q7DQsYqdE6YGY6YAYDpb9k7J91rp/i9njpmSpPFx6f6bJN8yp14NrVmzRqFQSJK0du3ast6bPcOuo6NDkUhE8+fPVyAQ0MaNG1OfXapYLJaRzBSqk57UDA8PZ4xdKmf2X2dnpyKRiCKRiGKxmDZs2JAq6+/vl+QkefF4XCMjIxoZGSn5s0s9VinnhMqRTAHAdDZjpnTJPzmz9m77sDNGatGbnBap+2+Snrhb2vDvdR18Xu6g6VxJTzQa1dDQkGKxWKpVJVdCFY/H8yY8+WYZuhKJRGpZB1d27MUSsnRdXV1avXq1IpGI4vF4xmcNDQ1py5Ytam9v14YNG4omaYXOq9CxSjknVI5uPgCY7lZ+wEmYDv9SurVd2nKW8/XwPmf/yg/UNTyfz1dWEpLNXW8pEAgoFAppcHBQO3bsyFk3vasrXSAQKNoak69O+tpY5fD7/Wpra9PAwEBGIpdIJLRu3Tr19PSos7NTPp8vdYx8MeY7r2LHmupzQm4kUwDQCFZ+QPrEXun9/+Jsv/9fpE88XJdEqpLuqlwSicSEBSzdVhq/359KFuLxeMYYqOz6nZ2dGZ+TSCQ0NDSUSiyCwaDWrFkzYbZbOWO+snV1dem6667LaA1yZ/Olx+peMzdpKvW8ih2rGueEiSru5jPG+CRtkLTeWtueozwkKZHc9Flre6eyHACQNGOm9Jq3Ot+/5q0179pzBzunT/0PBAI5u5Xcbi53+n8oFFIsFlM4HE7t6+jokN/vT7VquQlBPB7Xtm3bJJ1Kkrq7u7V8+fKMAebZIpFIamkA9zMDgYC2bNkin8+nYDCoaDSq7u5ujYyMpFp4Ojs788aW6zzSdXZ2anh4OKNlzm1h6+7uVnu782uzv79f3d3dqQU5c53XZI4lKe85YeoY6ywfMLk3GxOQtEaST9JGa+3qrPKQJLkJkDEmKCfp6pqK8hJjbJE0Ojo6qpaWlkmfKwBMC7/dK/VdJHXex4OOgQocO3ZMra2tktRqrT1WqG5FyVTqQ4zpkNSTI5k6Kukca20ibZ+11pqpKC8xNpIpAI3r+LPOy3XkCel710l/tk1aeO6p/fMWOy8AJSknmarabD5jjF9Ot1wiR1lQUryScmstj/kGgD3flO770sT937suc/uiT0nv6alNTECTqebSCPnmcCbkdAtWWp6TMWaWpFlpu+blqwsA096av5TecFnxerRKAVVTj3WmRiS16dSg8smW59Mj6bOTjA0Aphe674C6q8fSCIVXTau8fIuk1rTXWSXGBQAAULZqtkzlWx3NlyyrtDwna+3Lkl52t40peaw6AABA2aqWTFlr48aYhDHGb62NZ5XFJKnScgBods+/9Lye/93zReud8aozdMacM2oQEdB8piqZytf1tkVSUFKflFpCoW8KywGgqfU/0a+v//fXi9a7/vzr9bELPlaDiIDmU+minX5JHZI2SgpI6pW021o7kFYnpFPdcmuttd1Zn1FReQkxss4UgIaV3TIVH42r5/4ebblwi/ytpyZF0zIFlKfmi3Z6GckUgGay74V92vjDjdrx/h1auWBlvcMBpq1ykikedAwAmFK9vb2pV3d3d+pZcl7U1dWl+fPnKxYrfyhuJe9FY6nHOlMAgCoYGx/TL4/8UpL0yyO/1Bvmv0Eza/yw466uLnV1dSkQCKT2rV+/vqYxlCMSiWjPnj01f286N+Hs7++v+LPS9fX1TXigcbWO1exomQKABhB7OqbLv3+5Pv/g5yVJn3/w87r8+5cr9nRtW0127tyZkUhJ0rZt22oaw3TT3t6ujRs3TvnnRqPRmh2r2dEyBQDTXOzpmDb/dLMuOusihd8d1grfCu1P7Nctj9yizT/drJv+5CYFlwVrEksikVA8Hpfff2rwu8/n09q1a2ty/OkoGJz6f5u+vj7F4xOXZKzGsUAyBQDT2tj4mLbu2aqLzrpIN198s2YYp8Ph/DPO180X36xNuzZp656tes/Z76lJl18gEFB7e7sikUjGL+5QKJRRr7e3V36/P5V4dXR0pMr6+jJXwEnvqnLfJ0nxeDz1ubFYTN3dzmTvbdu2KR6PKx6P64UXXlA4HJ5wbJ/Pp7a2Yg/UmKiU9+Y6Nzc+v9+vrq6uVKvRxo0bU11vw8PDkqSBgQFt2bIl1R0XDAYVj8fV3t4uv9+vSCSitrY29fX1ye/3KxqNZnStxmIxRaPRjLFqoVBIQ0NDkzqW3+8v+O8FSdbahn5JapFkR0dHLQA0mocOPWRXfWuV3Xt4b87yh5972K761ir70KGHahLP8PCw9fv9VpKVZIPBoI1Goxl1Ojo6bH9/f2o7GAzawcFBa6214XDYhkKhVFl/f3+qbkdHR8ZnDQ8P22AwmNqORqPW7/dn1PH7/anPttbaUChkI5FIavvo0aNW0oQYcynlvYXOrb+/3wYCARuNRu3g4GDqPAcHB63f7884lnsu6cLhcEYsw8PDGed59OjRjPcHAoEJ5zCZYxU6p0Y2Ojrq/hy32CK5BmOmpoGxcaufD7+gH+w9qJ8Pv6Cx8cZezgJA6Z5/yVljaoVvRc7yFfNXZNSrNr/fr+HhYUWjUYVCIY2MjKi9vV0DA87yg/F4XAMDAxktG+vXr1ckElEikVB3d7d6enpSZTt27FA8HtfQ0JBisVhGa5ff79fIyEhqNl1bW5vi8fiEOm53VyKRUG9vb0ZLl8/nmzDGK5dS3lvo3Nz6Q0NDCgaDCgQCE1rM0gWDQY2MjGhoaCjjeOnHSp9F6Pf7Jz2rsNCxip0THHTzedThYyd1+PjLeuDXR9R3f1xHTrySKls493R1XujXO1+/UIvmzdKiltl1jBRAPbkLce5P7Nf5Z5w/oXz/0f0Z9WolGAymkpru7m5dd911qe4un8+X8Yt/eHhY8Xhce/bskc/ny0ga3FlnbpdWNrebyz1Wdh2fz6eRkRFJSh17Mkp5b6FzS4+3VJ2dnYpEIopEIorFYtqwYUOqzL0u7hi1kZGR1HlORr5jlXJOIJnyrO/84je6+d79OcuOnHhFX7zrcUnSpnUr9Lft59YyNAAeElgU0NK5S3XLI7dkjJmSpHE7rlsfvVVL5y5VYFHx1pdKJRIJxWKxCeNpwuGwent7lUgklEgk5Pf7M1qP3O/d1qt8nz0VJjNOqtT3Fjo3VznJXFdXl1avXq1IJDKhxW1oaEhbtmxRe3u7NmzYUDRJy54UUOqxSjknsDSCZ31o7dlaOPf0gnUWzj1dH1p7do0iAuBFM2fM1I1rbtR9B+7Tpl2btPfwXr34+xe19/Bebdq1SfcduE83rrmxZutN7d69O+d+v9+f6hbL1aqRSCQUCARyJk2JRCI1MDpbPB4veaZgvmNP1XsLndtk+P1+tbW1aWBgICORSyQSWrdunXp6etTZ2Smfz5c6Rr4Y07vwyjnWVJ9ToyKZ8qinXngpo2svlyMnXtFTL7xUo4gAeFVwWVA3/clN2p/Yr6vvulpv/+7bdfVdV2t/Yn9Nl0WQnO647LE76a1VwWBQa9asmdAKtXPnztQssfTV0hOJRGrtqmAwmPHZboJQaGZZ+i99v9+vzs7OjNmCiURCQ0NDRZODUt5b6Nwmq6urS9ddd11Ga1A8Hk8lny63i8+9JuljxeLxeEnjwnIdqxrn1Ih4Np9H/WDvQW26bW/Rejd/6AL96QVLqx8QAM8bGx/T9/Z/T59/8PP6P2//P/qzFX9W0xXQ3cTH7/dPaAnJXhqhu7tby5cvT7WCpCdE3d3dWrBgQWqAefqgb/d9kjN2xx3E7XZ7DQwMKBwOKxQKqbe3V1u2bJHf71dPT0/qGO40f7fLzV1SIRwOF+3CKuW9uc4tFospHA5rz549qVjc65Qdd/Y17e7unjDg2z1ue3u7JCd56u7u1saNG1Pn6dZZvny5Ojs7J32sfOfU6HjQcZrpmkz9fPgF/fm2B4vW+4/r3q53LF9Qg4gATAc86BiYGuUkUwxA96jXLZijhXNPL9jVt3Du6Xrdgjk1jAqA1zz/0vN6/nenlj2Ij8YzvrrOeNUZNZ/RBzQLkimPum33MyWNmbpt9zPM5gOaWP8T/fr6f399wv6e+3sytq8//3p97IKP1SosoKnQzedRrDMFoBTZLVP50DIFlIcxU2mmazKVbmzc6qEnR3T4+EktmjdbbzunTTNnmHqHBQBAw2LMVIOZOcMwyBwAAI9inSkAAIAKkEwBAABUgGQKAACgAiRTAAAAFSCZAgAAqADJFAAAQAVqsjSCMaZTkk9SQtJySVustYm08lCyTJJ81trerPcXLAcAAKiXqidTyUSoz02ejDE+SdskrU8rl7W2L7kdNMZErLVdpZQDAADUU9VXQDfGRK217fn2GWOOSjonq6XKWmtNKeUlHH/ar4AOAABqq5wV0GsxZiphjIkmW6RkjPFLiqd970tPlFzJFqiC5dUMGgAAoBS1GDN1naRBSUeNMb2ShtO66Px53pOQM8aqWPkExphZkmal7ZpXVrQAAABlqHrLVLJVKSxpQFJI0nq3laqAEUltkyzvkTSa9jpQRrgAAABlqXoyZYwJS4pba9fLmcnXJqelqpBCiVSx8i2SWtNeZ5UYKgAAQNmq2s2XNuYpJknW2rik1caYQWNMh6ShPG/1yRlXFS9SPoG19mVJL6fFMKnYAQAASlHtlim/Tq0PlS4ipZKrRDLpymCtjRUrn+JYAQAAylbVZCqZ8ARyjJFaba0dSH6/RVJqZl6yxaovrW6xcgAAgLqpxTpTPjmDwl/QqVl4fXbiCuhut91aa2131mcULC9yfNaZAgAAZSlnnamqJ1P1RjIFAADK5bVFOwEAABoWyRQAAEAFSKYAAAAqQDIFAABQAZIpAACACpBMAQAAVIBkCgAAoAJVfTYfGsfYuNVDT47o8PGTWjRvtt52TptmzuC5hwAAkEwhr8PHTurw8Zf1wK+PqO/+uI6ceCVVtnDu6eq80K93vn6hFs2bpUUts+sYKQAA9UMyhby+84vf6OZ79+csO3LiFX3xrsclSZvWrdDftp9by9AAAPAMxkwhrw+tPVsL555esM7CuafrQ2vPrlFEAAB4D8kU8nrqhZcyuvZyOXLiFT31wks1iggAAO8hmUJeh4+fnNJ6AAA0IpIp5LVoXmmDykutBwBAIyKZQl6vWzCnpDFTr1swp0YRAQDgPSRTyOu23c+UNGbqtt3P1CgiAAC8h6URkNdH/vi1al95ZknrTAEA0KyMtbbeMVSVMaZF0ujo6KhaWlrqHc60xQroAIBmcuzYMbW2tkpSq7X2WKG6tEyhJDNnGL1j+YJ6hwEAgOcwZgoAAKACJFMAAAAVIJkCAACoAMkUAABABUimAAAAKkAyBQAAUIGaLY1gjAlLGk5ujlhrB9LKQpISyU2ftbY3670FywEAAOql6smUMcYn6V5J66y1CWNMQNKgJJMsD0mStbYvuR00xkSstV2llAMAANRT1VdAN8ZEJA2ntyYZY4LW2ljy+6OSzrHWJtLKrbXWlFJewvFZAR0AAJSlnBXQazFmqlPSgDHGb4wJSlJaIuWX022XyH5TsgWqYHmugxljZhljWtyXpHlTdyoAAACZqppMJZMhSQpI8kmKG2MiaYmQP+cbnfFRvhLKc+mRNJr2OlBGyAAAAGWpdsuUmwwlrLVD1tq4pG5J/UXeNyKpbZLlWyS1pr3OKj1cAACA8tRqNt8e95vkIHRfvm66pEKJVMFya+3Lkl52t40paWgVAADApFS7ZSqeZ39CTqtVvnJfsqxYOQAAQF1VNZlKduvFNXHsk0/SnmR5Im1sVfp7Y8XKqxAyAABAWWoxm69b0kZ3wxjTISlmrR1K7toiKZhV3pf2/mLlaBJj41Y/H35BP9h7UD8ffkFj49Vd1gMAgFJUfZ0pSTLGdOrU7LsF1trurPKQTnXbrS23vMixWWeqAdz92CF97s59OjR6MrVvSetsffaKlbp01ZI6RgYAaETlrDNVk2SqnkimprfDx07q9ocP6ot3PZ63zqcve6OufOtSLWqZXcPIAACNzGuLdgKT9u0Hny6YSEnSF+96XN9+8OkaRQQAQCaSKXjayiWltSaWWg8AgKlGMgVPe2VsfErrAQAw1Uim4GmL5pU2DqrUegAATDWSKXja6xbM0cK5pxess3Du6Xrdgjk1iggAgEwkU/C023Y/oyMnXilY58iJV3Tb7mdqFBEAAJlq9Ww+YFI+8sevVfvKM/XAr4+o7/54RmK1cO7p6rzQr3e+fqEWzZtVxygBAM2MdaYwbYyNWz305IgOHz+pRfNm623ntGnmDB5kDQCYeuWsM0XLFKaNmTOM3rF8Qb3DAAAgA2OmAAAAKkAyBQAAUAGSKQAAgAqQTAEAAFSAZAoAAKACJFMAAAAVIJkCAACoAMkUAABABUimAAAAKsAK6EAZeKQNACAbyRRQorsfO6TP3blPh0ZPpvYtaZ2tz16xUpeuWlLHyAAA9UQ3H1DE4WMn1XffsP739qGMREqSDo2e1P/ePqS++4Z1+NjJPJ8AAGhkJFNAEd9+8Gl98a7HC9b54l2P69sPPl2jiAAAXkIyBRSxcknLlNYDADQWkimgiFfGxqe0HgCgsdR8ALoxJmqtbc/aF5KUSG76rLW95ZQD1XT6zNL+5ii1HgCgsdT07m+M6ZAUzNoXkiRrbZ+1tk/SkDEmUmo5UG37Dh2b0noAgMZirLW1OZAxPkkbJEWstSZt/1FJ51hrE2n7rFunWHkJx22RNDo6OqqWFsa0oHyHj53U7Q8fLDgI/dOXvVFXvnWpFrXMrmFkAIBqOXbsmFpbWyWp1Vpb8K/lWrZMbZC0M32HMcYvp9sukV3ZGBMsVp7rIMaYWcaYFvclad5UBI/mtahltjovWq5/vSqgJa2ZydKS1tn616sC6rxoOYkUADSpmoyZSiY+sRxF/jxvSUjylVCeS4+kz5YcHFCiS1ctUfvKxayADgDIUKsB6D5rbTzZ1VeKEUltOjXoPF95Llsk3ZS2PU/SgRKPCxQ0c4bRO5YvqHcYAAAPqXoyZYzpTA4cL0e+RKloubX2ZUkvpx2/zEMDAACUrqpjpowxAUl7ClSJ59nvS5YVKwcAAKirardMtUkKpA0WXy6lljuIW2sHjDEJY4zfWpuRHFlrY8m6BcsBAADqqWZLI0iplqrBrKURQpISbldgci2qdmttVynlJRyTpREAAEBZylkaoWYroCeToI3J78OSotbamLW21xgTSpZL0tr0RKlYOdBsxsYtMwoBwENq2jJVD7RMoZHc/dghfe7OfTo0ejK1b0nrbH32ipW6dNWSOkYGAI3Fq4t2AqjA3Y8d0vXbhzISKUl6dvSkrt8+pLsfO1SnyACguZFMAdPAocTv9Pe3P6Zc7cg2+fr72x/TocTvahwZAIBkCpgGtt7zPzpy4pWCdY6ceEVb7/mfGkUEAHCRTAHTwAVn+6a0HgBg6pBMAdPAwrmzprQeAGDqkEwB08C+QwUnkpRdDwAwdUimgGng6rcv06cve2PBOp++7I26+u3LahQRAMBVs0U7AUzeopbZ6rxouV67YA7rTAGAx7BoJzDNsAI6AFSfJx8nA2BqzJxh9I7lC+odBgAgiTFTAAAAFaBlCkDZ6GoEgFNIpgCUhYctA0AmuvkAlIyHLQPARCRTAErCw5YBIDeSKQAl4WHLAJAbyRSAkiw/Y+6U1gOARkEyBaAkw8+fmNJ6ANAoSKYAlOTGS96ghXNPL1hn4dzTdeMlb6hRRADgDSRTAEqyxPcq/eOVq2QkZa8o5e77xytXaYnvVbUPDgDqiGQKQMkuXbVEX78qoMWtszP2L26dra9fFWCdKQBNiQcdFzE2Pqahw0N6/qXndcacMxRYFNDMGTOnPlBgGvHCCuheiAFA4+JBx1Mk9nRMW/ds1cETB1P7ls5dqhvX3KjgsmAdIwPqq94PW2YVdgBeQjdfHrGnY9r8081a4Vuh7e/brl98+Bfa/r7tWuFboc0/3azY07F6hwg0JVZhB+A1NenmM8aEkt8ulyRrbVeO8kRy02et7S2nvMixy+7me/bFZ/WRH39Eb2p7k75y8Vc0w5zKOcftuD6x6xN6fORxbX/fdi1+9eJSQwFQoUOJ3+mKr/1XwcVDF849XXfe8C4GwgOoSDndfFVvmTLGhK21vclXV3JfNK08JEnW2j5rbZ+kIWNMpNTyavjqw1/V4ZcO67q3XJeRSEnSDDND1553rZ576Tl99eGvVjMMAFlYhR2AF1U1mTLG+CQFkl9dEUlBY4w/ud0jqc8ttNbGJHWm1S9WPuXOaTlHkrTCtyJn+Yr5KzLqAagNVmEH4EW1GDO1RpI/bTue/OpLJlQ+a20i+03GmGCx8irEKkl68tiTkqT9if05y/cf3Z9RD0BtsAo7AC+qajJlrU1Ya+dba4fSdrtJUFyZSVa6hCRfCeUTGGNmGWNa3JekeWWGrb9+619r0ZxF2vbINo3b8YyycTuuWx69RWfOOVN//da/LvejAVSAVdgBeFE9ZvP1SOrK1dqUZkRS2yTLeySNpr0OlBvg4lcvVs/bevSzAz/Tpl2btPfwXr34+xe19/Bebdq1ST878DN96m2fYvA5UGNeW4V9bNzq58Mv6Ad7D+rnwy9obLyx1+0DkFtNF+00xoQlveDOxkt21UWttSar3lFJ3XJar/KWJwekZx9jlqRZabvmSTowmUU7WWcK8CYvrDPlhRgAVE85s/lqlkwZYzoktaUnQMkxUcM5kiUrqV1OMpW3PDkYvdhxWQEdaED1XAHdXesq++7pHp1H6wDTn+eSqWQLlM9aO5Dc9slJrOLJVqbV1tp4Wn3rJlDFyks4dkXJFACkY60roDl4bZ2pgKSAnPWh/MnWqE45454kaYtODUp3W7DSu++KlQNAzbDWFYBsVX02X7IF6l45M+/C6WXuuClrba8xJpRMkiRpbfoK6cXKAaCWWOsKQLaqJlPJGXvzS6iX/niYgXLLAaBWvLbWVT3HjgFwVDWZAoBGc+Mlb9B9TzxfdMxULda6YkYh4A31WGcKAKYtr6x15c4oTE+kJOnZ0ZO6fvuQ7n7sUFWPD+AUkikAKNOlq5bo61cFtLh1dsb+xa2za7IswqHE7/T3tz82YWkGSbLJ19/f/pgOJX5X1TgAOOjmA4BJuHTVErWvXFyX8UrlzCj88oYLqh4P0OxIpgBgkmbOMHrH8gU1P67XZhQyCB7NjmQKAKYZL80oZBA8wJipaWFsfEy7n92tH8d/rN3P7tbY+Fi9QwJQRzde8gYtnHt6wTq1mFHIIHjAQcuUx/GwZQDZ3BmF128fkqSMgehu51q1ZxQWGwQvOYPgzz/Lx2N10PBomfKw2NMxbf7pZq3wrdD2923XLz78C21/33at8K3Q5p9uVuzpos95BtCg6j2j0GuP1Rkbt/r58Av6wd6D+vnwCxobr/5zZwEXLVMe9eyLz2rLQ1v07rPerZsvvlkzjJP3nn/G+br54pv1iV2f0Jce+pJWLVylxa9eXOdoAdRDPWcUemkQPOO2UG+0THnUP+/+Zx1+6bCue8t1qUTKNcPM0LXnXavnXnpO/7z7n+sUIQAvcGcU/ukFS/WO5QtqNovOK4PgGbcFLyCZ8qjf/cFZbG+Fb0XO8hXzV2TUA4Ba8sIgeK8tXkpXY/MimfKoD77+g5Kk/Yn9Ocv3H92fUQ8AaskLj9Xx0ritux87pHeFd+nPtz2oTbft1Z9ve1DvCu+iZaxJkEx51MWvvVhL5y7VLY/conE7nlE2bsd166O3auncpbr4tRfXKUIAza7eg+C9Mm6LrkYwAN2jZs6YqRvX3KjNP92sTbs26ZrzrtGK+Su0/+h+3frorbrvwH266U9u0swZM2sSz9j4mIYOD+n5l57XGXPOUGBRoGbHBuBd9RwE74VxW2PjVp+7c1/erkYj6XN37lP7ysU1uSasRl8fxtrG7tM1xrRIGh0dHVVLS0u9wymbF9aZ8kIMAJDtUOJ3uuJr/1Wwq2/h3NN15w3vqlp3412PHtL13xkqWu/rHwnosvOq21LHrMapdezYMbW2tkpSq7X2WKG6JFPTQD1bhdy1ri466yJd+5ZrtcK3QvsT+3XLI7ekWsdIqADUi9vFJuVevLTa3Y1/+c2H9J//83zReu95wxn65l++rWpxuNch+zd6ra5DukZpHSsnmaKbbxqYOWOm1i5eW/PjstYVAK9zx21lt8gsrlGLzKv+qLQ/bEutNxleWo3eK61jtU7oaJlCXp/86Sd1z9P3aPv7tuv8M86fUL738F5dfdfVumTZJfryn3y56vEwbgtAPvVqDfFCV+PHtg/qx489W7Te+1Yt1v931eqqxCB5p3VsqhI6WqYwJby01pVXxm2R0AHe5C5eWmteeE7i734/NqX1JsMrrWP5Ejp3ZmW1EjqWRkBeXlnryivPKIw9HdPl379cf/WTv1L3/d36q5/8lS7//uU1f0bi2PiYdj+7Wz+O/1i7n92tsfHq3SABFFfvJSI2rDl7SutNxhd+uK+kNb++8MN9VYuh2MxKyZlZWY3FVGmZQl7pa12lj5mSarfWlVfGbaUPxA+/O5wxEH/zTzfXbCA+LXSAN9VziYhL3rxYS1pn69nRkzkTCSMnsbvkzdW7R3qhdeyeXz47Ya2vdFbSodGTuueXz075zEpappCXu9bVfQfu06Zdm7T38F69+PsXtffwXm3atUn3HbhPN665saq/RL3wjMKx8TFt3bNVF511kW6++Gadf8b5mvNHc1IJ3UVnXaSte7ZWvYWIFrpMXmih80IM8I56PSdx5gyjz16xUlLu1egl6bNXrKxqPF5oHdu555kprVcOWqZQUHBZUDf9yU3aumerrr7r6tT+pXOX1qQ1xgvjtnb9ZpcOnjio8LvDORO6a867RlffdbV2/WaX2l/XXpUYshO67Ba6Tbs2aeuerXrP2e+panJLC523YpC80UpIDPWPwe1q/L93Pqbnf/8rmdOOy/5hns74ozfp/16xqupdjV5oHavnzMppkUwZY0KSEslNn7W2t47hNJ3gsqDec/Z76nKT+ODrP6j7D96v/Yn9OWcU1mLc1vd//X1JxRO67//6+1VLpkjoTvFCQueFGNw46p3QEYN3Yjht3i/VumKrXnzxt6l9ra9+jU6b93eSqptMua1j128fktG4Zsx5MpXQjb90jqQZVW8d+4f3r9RDT40kx26Na2ZaDGPJGBbOPV3/8P6VU35sz3fzJRMpWWv7rLV9koaMMZE6h9V03LWu3ud/n9YuXluzv7a88IzCV53mzDwpNhDfrVcN5SR01XL7/tt18MRBXfuWa/MmdAdPHNTt+2+vWgxe6HL1QgySN7p9icF7MZw7/9yMGM6df27NYrh01RJ97PKX5HvDlzVn2Ta9aultmrNsm3xv+LI+dvlLVW8dc2dWnjbvMbWu2JoRQ+uKrTpt3mPVm1lprfX0S9JROa1R6ftsGe9vkWRHR0ctpqfoU1F73rfOszfEbrAPP/ewPfHKCfvwcw/bG2I32PO+dZ6NPhWt6vEPnThkL955sf147ON2bHwso2xsfMx+PPZxu27nOnvoxKGqxbD5PzfbVd9aZfce3puz/OHnHrarvrXKbv7PzVWL4YrvXWFXfWuVffGVF3OWn3jlhF31rVX2iu9dUbUYBh4fKOk6DDw+0NAx/GHsD/a9A++1N8RuyPkzeUPsBvvegffaP4z9gRiIoSYxWHvqXv3x2Mft3sN77YuvvGj3Ht5rPx77eE3u1ZkxfCwzhujHyo5hdHTUyhm33mKL5BqebpkyxvjlJFKJHGU8w6RJuOO29if26+q7rtbbv/t2XX3X1dqf2F+T7pTFr16snrf16GcHfpZzIP7PDvxMn3rbp6o6m/Dv1v6dFs1ZpG2PbMvZQnfLo7fozDln6u/W/l3VYviLlX8hqXgLnVuvGv5t379JKt5C59Zr1Bi80EpIDN6JwR0GUCyGXb/ZVbUY0mdef+Xir2S02H7l4q/o3We9W1966Et69sXii4tO1tj4mLb+/Au66KwL9ZWLv5oZw7qv6qKlF2rrz79QlVZjTydTkvx59ick+XIVGGNmGWNa3JekeVWKDTUUXBbUjz74I33jvd9Q+MKwvvHeb+hHH/xRzcYikNBJV664sqQu1ytXXFm1GLyQ0HkhBi8kdMTgnRi8MAzACzOvb3/i/+ngyyO69i2duZPKt1yngy+P6PYn/t+UH9vryVQ+I5La8pT1SBpNex2oVVCornqN23I1e0LnhaUyvJDQeSEGLyR0xOCdGObMnFVSDG69avjd71+UVMLM62S9aog+dHNJMbj1ptJ0TabyJVKStEVSa9rrrJpEhKZAQkdC54UYvJDQXbniSi2d1aZb/rsvdwyPbNPSWW2NH8PyK7TUztQtj+SJ4dFtWmpn6srlV1Qthp5Z52jpmHRLnmEAtz56i5aOOfWqZf0M59dysYTOrVcNH3jxREkxuPWmkqcfdJwcMzVsrTVZ+62kdmtt0ekJPOgYmHr1XtPHC9PQY0/HtHX3P+tg2jT0WsaQWp5h6YW65i3XacX8Fdp/dL9ufWSb7jt4f/WT2/Exxb5+gTbPlS466yJdc961p2J49Bbdd+A+3XRCCl6/V6rWz8boQcX+PajNLaflj+HYmIIfjUqtS6sTw+C/KXZvtzafuSgZwzVpMdzqxPDcYQXXhaXVVWqd+tpaxV56pngMc86WbthdlRDGvrZGl895WSuWXaSbL/7KhCdmbNr1Ce1/+j796KVZmnnDnurEsOebuvyRm4rH8JbNmrnmL4t+XjkPOvZ0MiVJxpijklZba+Np+2x2glXg/SRTQAOqd0KnfXdo7J7PaOjkc3p+5kydMTamwOwzNfOSf5JWfqAmx4/9sEtbl5ylg+OnHqGxdMZs3XjogILvj1Q3jn0/kHZ+VLErwtr61O0TE9vX/amCd35K2vDv0so/rU4MO/9C2nd78RhWXiltqNKYpa+tlY48odhV39HW//7axBjO/7iC26+SFp5btURGg/8m3fmJ4tfhiq9UL6HzQlI5xQl+oyVTIUkJ66wxJWNMh5xWqa4S308yBTSi8THp6QekE89Jc8+Ulr2zei0g2fbdIe38qHTupdKFn5QWvUk6/Cvp/i9LT9ydTCCqmMiMHpRuWSctOV9jG7Zr6MjeU0nlwgs0c+dV0qFHpGtj1WuR+c56af89Us9Bjf3RqyYmtr9/SdpylrTiEukj/dWJYft66dclxPD6S6SrqhRDMpHRNTGNLQ1MjOHgoHRre3UTmfEx6SsXSIverLEN387x83C1dHif9ImHq/d/JBlDbOHZ2jp7bGKL7e9mKHjkQHVjkKb0j4yGSqakVELltkyttdZ2l/Fekimg0ey7Q7rnM1LiN6f2+V4r1aJVKO0Xlz70XWlG2tDT8XHptg9X/xdXskVG18Sks9dOLH/mIecXeDVbZHb8hfSrEmJ405XSxirFkGwdKxpDNVvHvPDzIGUl+JvTEvybapPgp8UwtuK9Gjrvcj0/+9U64+SLCjz6I83c/5PaxJCMYypajRsumaoEyRRQBc3cKpTWElHwF3g1WyLSWmQ0a+7E8pePV79FJq11TB/6jxxJxJ9Xv3WMRGZiHBP+yFgmXfKPtTm+V2KQpuQeRTKVhmQKmGLN3iqUHCNTNJGp5hgZL7TISN5IIrwQgxtHgyQRDRHDFCCZSkMyBUwhWoW8EYMXkkqXF5IIL8QgNUwSAQfJVBqSKTScet2wvfAL3AutQl64DpJ3WmQkbyQRXogBDaWcZOq02oQEYErUs4vt4e3Ocf/XNzITCMnZvnCz0yLz8Pbqtci84wanVejwr3K3Ch3+1al61TJjpnO9d37USZzyJTLV/kW+8gPOce75jHPdXb5ltU2kJOdcz7mwdsfzagxoWrRMAdNFvbvYaBXKRNcS0NDo5ktDMoWG4IUkwgtjhSS6twDUBMlUGpIpTKl6/fL0wuwtLyR0Lq+0CgFoWIyZAqqh3uOVJKcVJhd3/8Pbq5dMeWWskORc7zdeTqsQAE8gmQJKkd619L++kTleaedHq9+1dNoc52uxgdduvWph0DMATEA3H1CMF7q3vLDadDrGCgFocOV0880oVAhA0uM/dLr2Lvxk/iUBEk879aqldal0Wa/0xE+c5O2Zh5zZc8885Gw/8RPpsnBtEinpVKvQeR3OVxIpAE2Mbj6gGC+MV5K81cUGAEghmcL0Ua+uJa+MV5IYeA0AHkQyhemhnjPpLv2idOAX0v1bc49Xuv/L0rzXOPVqgYHXAOApjJmC97kz6Ra92Vlnqeeg83XRm539++6o7vG9Nl4JAOApzOaDt3lpFhsLRQJA02DRTjSOXV+Qjh+SNnw7z0y6TzqDsXd9Qfrgv1Y3FsYrAQByIJmCty081/labCadW6/aGK8EAMjCmCl425EnnK/ujLls7n63HgAANUYyBW+7+B+keUucmXTj45ll6TPpLv6H+sQHAGh6JFMozfiY9OT90qMDztfxsdocl5l0AACPYzYfiqvnGk8FY2AmHQCgOsqZzUcyhcLcNZ7OvdSZObfoTc44pfu/LD1xd20fY8LDdQEANUIylYZkqgJeWuMJAIAaKieZYswU8nPXeLrwxvxrPB3/rVMPAIAmVfV1powxoeS3yyXJWtuVozyR3PRZa3vLKUcVeW2NJwAAPKiqLVPGmLC1tjf56krui6aVhyTJWttnre2TNGSMiZRajipjjScAAIqq2pgpY4xPUr+k9dbaRHJfQNKgpOXW2rgx5qikc9zyZB1rrTXJ7wuWlxgHY6YmizFTAIAm5aUxU2sk+dO248mvPmOMX063XSL7TcaYYLHyfAc0xswyxrS4L0nzKjmBpsYaTwAAFFW1MVPJJGh+1m43CYrLSbRySUjyKTMJy1WeT4+kzxaPECVZ+QFn+YN7PuM8UNjlW1bbZREAAPCoWj/ouEdSl7U2YUzenroRSW06Neg8X3k+WyTdlLY9T9KB8sJEhpUfkN54OWs8AQCQQ8nJlDGmQ9LGEqpusdYO5Xh/WNKO5EDyQgolSkXLrbUvS3o57bhFPg4lmTFTOufCekcBAIDnlJxMWWsHJA1M5iDJRGw4K5GK56nuS5YVKwcAAKi7qi/a6Q4WdxMpY4zPGOO31sYlJZIDzTNYa2PFyqsdNwAAQCmqvc5UQFJAzvpQ/mRi1Cln3JPkjG8KptXvkJTeelWsvDmMj0lP3i89OuB8HR+rd0QAACCp2utMPakcM+/S14lKLszpdtuttdZ2Z31OwfIS4pje60ztu8OZSZf4zal9vtdKl/wTM+kAAKgSHnScZlonU/vukHZ+VDr3Uuc5eIve5Kw6fv+XpSfuZmkCAACqhGQqzbRNplh9HACAuvHSCuiYrF1fkI4fki68MTORkpztCz8pHf+tUw8AANQNyZRXLTzX+broTbnL3f1uPQAAUBckU1515Ann6+Ff5S5397v1AABAXZBMedXF/yDNWyLdv9UZI5VufNwZhD7vNU49AABQNyRTXtW6VLqsV3riJ9JtH5aeeUh6+bjz9bYPO/svCzP4HACAOmM2n9flXGdqmXTJP7IsAgAAVVLObL6Sn82HOln5AemNl0tPPyCdeE6ae6a07J3Og4cBAEDdkUxNBzNmSudcWO8oAABADoyZAgAAqADJFAAAQAVIpgAAACpAMgUAAFABkikAAIAKkEwBAABUgGQKAACgAiRTAAAAFSCZAgAAqADJFAAAQAVIpgAAACpAMgUAAFABkikAAIAKkEwBAABUgGQKAACgAqfV8mDGmKi1tj1rX0hSIrnps9b2llMOAABQTzVrmTLGdEgKZu0LSZK1ts9a2ydpyBgTKbUcAACg3oy1tvoHMcYnaYOkiLXWpO0/Kukca20ibZ916xQrL/HYLZJGR0dH1dLSUn7w42PS0w9IJ56T5p4pLXunNGNm+Z8DAACmjWPHjqm1tVWSWq21xwrVrVU33wZJOyWltzr55XTbJbIrG2OCkuKFyq21sVwHMsbMkjQrbde8SUe97w7pns9Iid+c2ud7rXTJP0krPzDpjwUAAI2j6t18ycQoV+Ljz/OWhCRfCeX59EgaTXsdKB5lDvvukHZ+VFr0ZumamNRz0Pm66M3O/n13TOpjAQBAY6nFmCmftTZeRv0RSW0VlG+R1Jr2OquMYztGD0p3haRz3yt96LvS2WulWXOdrx/6rrP/rm6nHgAAaGold/MlB5BvLKHqFmvtUPI9ncmB4+UolCgVLbfWvizpZXfbmJKHV52y6wvS8UPShm9LM7LyzRkzpAs/Kd3a7tT74L+W//kAAKBhlJxMWWsHJA2UWt8YE5C0p0CVfK1VvmRZsfLqWXiu83XRm3KXu/vdegAAoGlVcwB6m6RAcsyUJC2XUssdxK21A8aYhDHGn90N6A4uL1ZeNUeecL4e/pXTtZft8K8y6wEAgKZVk6URpFRL1WDW0gghSQm3KzDZldhure0qpbzE45a/NMLoQemWddKS86UP/UdmV9/4uHTbn0uHHpGujUmtS0sNBQAATBPlLI1Qk0U7k0lQT/L7sNtalVzN3GeM6UjWWZueKBUrr5rWpdJlvdITP5Fu+7D0zEPSy8edr7d92Nl/WZhECgAA1K5lql4qWrQz5zpTy6RL/pF1pgAAaGBeXLRzelr5AemNl7MCOgAAyItkqpgZM6VzLqx3FAAAwKNq9qBjAACARkQyBQAAUAGSKQAAgAqQTAEAAFSAZAoAAKACJFMAAAAVIJkCAACoAMkUAABABZpm0c5jxwquBA8AAJBSTt7QDM/mWyrpQL3jAAAA09JZ1tqDhSo0QzJlJL1G0vF6x1KheXKSwrM0/c+lElwHB9fBwXVwcB0cXAcH18ExFddhnqTf2iLJUsN38yUvQMGMcjpwckJJ0vFiT69uZFwHB9fBwXVwcB0cXAcH18ExRdehpPcxAB0AAKACJFMAAAAVIJmaPl6W9Lnk12bGdXBwHRxcBwfXwcF1cHAdHDW7Dg0/AB0AAKCaaJkCAACoAMkUAABABUimAAAAKkAyBQAAUIGGX7SzERhjwpKGk5sj1tqBesZTD8aYTkk+SQlJyyVtsdYm6hhS1RljfJI2SFpvrW3PUR6Scz0kyWet7a1ddLVT4nWQnJ8LWWu7ahdd7RS7Dll1o8XqTFelXIdmuGeW8P+iae6Zxe4BtbhXkkx5WPI/y72S1llrE8aYgKRBSabgGxtM8j9Cn3sjSF6XbZLW1zGsqkr+W6+RczNsy1EekiRrbV9yO2iMiTRaIlHCdQhba7vTtiONmEgUuw5ZdTskBWsQVs2V8PPgUxPcM0u8PzTFPbPYPaBW90q6+bwtLGmH+x/CWjskqaF+SZSoPf0vquT3vnoFUwvW2qHkf/54nio9kvrS6sckddYitloqdB2SvyACya+uiKSgMcZfmwhro4SfB0mpa1Iw2ZrOSrgOTXHPLOE6NMU9s8R7QE3ulSRT3tYpacAY4zfGBKXUD0KzSRhjou5/mOR/koK/VBpZ8vx9uZrs3Z+TJrJGUnri5P5c+GofiidskLSz3kHUEfdMRzPdM/PeA2p5rySZ8qi0rDog5xdDPNl82Wy/LCXpOjn/WY4mx0IEG607q0z5Wl0SaqIkwlqbsNbOT7Y+uNz/H436iyOv5L2hGRMHSdwzszTFPbOEe0DN7pUkU97l/hAkkk26cUndkvrrGFNdJP+qCEsakBSStD6rWReOETVwF0+JeiR1NepA2yJ8yftEs+KemdTk98xS7gFTfq8kmfK+Pe43br93s/2llfzLKm6tXS9ntkabnEGlyNTUiVTy52SHO9C0mRhjOhtxxtokcc9s0ntmGfeAKb9Xkkx5V76/MBPK33TZcNL6vGOSZK2NW2tXyxkT0FHf6Oom38+Gr0BZQ0v+LAw36vIQhSRndu0pWrHxcc9U894z89wDanavZGkEj7LWxo0xbp9ven+wT8114/Tr1Pog6SI1jsMzkj8bCWOMP7tbpxkH26YNNHanPvsktTVRl1ebnBlNbuvLcik1JTzeLC1W3DNTmu6eWegeUKt7JS1T3tYtaaO7kcy8Y1mD7Rpa8gc+e+qrJK1ukl8S+ZqjtyhtLaHkz0Yjd2/lvA7JVpmApKHkDC6/nBldI7UMroYmXAdrbcxa2+u+lPylmdxu1P8j+f5fNNs9M+fPg5ronlnCPaAm90pjrZ3qz8QUSlvFVpIWpC9O1iySN4UeSS/o1CyM1IJ0jSh5Q+iQ84shIKlX0u70m6Hb8pDcXNuIPxuFrkPy5+JJ5ZiVY61ttEUai/48JOu5dTqSdaKN1FpZ4v+Lhr9nFrsOzXLPLPUeUIt7JckUAABABejmAwAAqADJFAAAQAVIpgAAACpAMgUAAFABkikAAIAKkEwBAABUgGQKAACgAiRTAJCDMcaXYxVpAJiAZAoAcutREz0gF8DkkUwBQG6BBn6mG4ApRDIFAFmST6GP1jsOANMDyRQATLRe0kDRWgAgkikAyMVvrY0XrwYA0mn1DgAAJssYE5C0RtJySbslxSR1JosT1tq+SXxmh6T+AmVrJQ1LiidfI9baRNnBA2gYtEwBmJaSyxYErbV91tpuSdsk9Vhre5NVuif50Rsl7cxxvE5J7dba7mSS5pOTVK2Z5HEANAhapgBMV51piZNrOPl1SFLXJD/Xl93SZIzxSwpLOidtd0KSrLWxSR4HQIMgmQIwXaUGiCeTHZ+SLUrZCU6yvENOt9xaSZFcY6KSrU+RHMeKSIplJVntcpI2AE2OZArAtJSVDAUlxQuMXeq31q6WJGNMTNK9klbnqLfeWtueY39Qzgy/dAE5Y7QANDnGTAFoBO3KWsrAfRRMcpB6SjLh8iVbq7LrJ7I/OK1edisUa1EBkEQyBWCaSnbJuTrkzOZLlaW1UuUbIB7I2s7XxScpsyUsuainrLUxY0wgO2ED0FxIpgBMO8lEKpz8vkNp3W05Hk7skzSStS8hqS1rX3uuweTJJCruJkzJz++SM/5KcmYUMnYKaGKMmQIwHcUk9SWTqj1ykptuY4wktWWtL5XQxMTJp7QEK9mVV2iRzvWSuowxg5JkrV1vjOlPHp9ECmhyxlpb7xgAoGqSLUrb3AHoyX1HJa12u+6MMWFJO2hhAjAZdPMBaGjJBMnnbie76eJZswEDJFIAJotuPgDNYH2y9Wm3nHWmUsscJFuuSKQATBrdfACamjEmIinMg40BTBbdfACaXRuJFIBK0DIFAABQAVqmAAAAKkAyBQAAUAGSKQAAgAqQTAEAAFSAZAoAAKACJFMAAAAVIJkCAACoAMkUAABABf5/deDvK0KYRKcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "symmetrised_correlator.show([5, 20], comp=[first_derivative, second_derivative], y_range=[-500, 1300])" ] }, { "cell_type": "markdown", "id": "18c75d20", "metadata": {}, "source": [ "## Missing Values \n", "\n", "Apart from the build-in functions, there is another reason, why one should use a **Corr** instead of a list of **Obs**. \n", "Missing values are handled for you. \n", "We will create a second correlator with missing values. " ] }, { "cell_type": "code", "execution_count": 17, "id": "1db86a4c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Corr T=96 N=1\n", "x0/a\tCorr(x0/a)\n", "------------------\n", "0\t 62865(41)\n", "1\t 23756(32)\n", "2\t 6434(25)\n", "3\t 2886(20)\n", "4\t 1735(21)\n", "5\t 1213(21)\n", "6\n", "7\t 699(17)\n", "8\n", "9\n", "10\t 343.1(8.6)\n", "11\t 273.2(6.6)\n", "12\n", "13\t 172.9(4.9)\n", "14\n", "15\n", "16\t 88.0(3.9)\n", "17\t 70.6(3.2)\n", "18\t 56.6(2.6)\n", "19\t 45.3(2.1)\n", "20\n", "21\t 29.2(1.4)\n", "22\t 23.4(1.2)\n", "\n" ] } ], "source": [ "new_content=[(my_correlator.content[i] if i not in [6,8,9,12,14,15,20] else None ) for i in range(my_correlator.T) ] # We reuse the old example and replace a few values with None\n", "correlator_incomplete=pe.Corr(new_content)\n", "\n", "correlator_incomplete.print([0, 22]) # Print the correlator in the range 0 - 22" ] }, { "cell_type": "markdown", "id": "602d81fa", "metadata": {}, "source": [ "We see that this is still a valid correlator. It is just missing some values. \n", "When we perform operations, which generate new correlators, the missing values are handled automatically." ] }, { "cell_type": "markdown", "id": "6479a999", "metadata": {}, "source": [ "Some functions might also return correlators with missing values. We already looked at the derivative. \n", "The symmertic derivative is not defined for the first and last timeslice. \n", "\n", "The important thing is that, whatever you do, correlators keep their length **T**. So there will never be confusion about how you count timeslices. You can also take a plateau or perform a fit, even though some values might be missing." ] }, { "cell_type": "code", "execution_count": 18, "id": "f3c4609c", "metadata": {}, "outputs": [], "source": [ "assert first_derivative.T == my_correlator.T == len(first_derivative.content) == len(my_correlator.content)\n", "assert first_derivative.content[0] is None\n", "assert first_derivative.content[-1] is None" ] }, { "cell_type": "markdown", "id": "7fcbcac4", "metadata": {}, "source": [ "There is a range of addtional methods of the `Corr` class which can be found in the documentation." ] } ], "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": 5 }