{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fluxonium" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we calculate Fluxonium qubit properties such as energy spectrum, eigenfunctions, and decoherence rates for different loss channels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Circuit Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following Fluxonium qubit consists of an inductor with $E_L=0.46~\\text{GHz}$ and Josephson Junction with $E_J=10.2~\\text{GHz}$. The capacitor with $E_{C_J}=3.6~\\text{GHz}$ is assigned to Josephson Junction rather than the inductor. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firstly, we import the SQcircuit and the relavant libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import SQcircuit as sq\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the single inductive loop of the circuit via `Loop` class" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "loop1 = sq.Loop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The elements of the circuit can be defined via `Capacitor`, `Inductor`, and `Junction` classes in SQcircuit, and to define the circuit, we use the `Circuit` class. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# define the circuit elements\n", "C = sq.Capacitor(3.6, 'GHz',Q=1e6 ,error=10)\n", "L = sq.Inductor(0.46,'GHz',Q=500e6 ,loops=[loop1])\n", "JJ = sq.Junction(10.2,'GHz',cap =C , A=1e-7, x=3e-06, loops=[loop1])\n", "\n", "# define the circuit\n", "elements = {\n", " (0, 1): [L, JJ]\n", "}\n", "\n", "cr = sq.Circuit(elements, flux_dist='all')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By creating an object of `Circuit` class, SQcircuit systematically finds the correct set of transformations and basis to make the circuit ready to be diagonalized.\n", "\n", "Before setting the truncation numbers for each mode and diagonalizing the Hamiltonian, we can gain more insight into our circuit by calling the `description()` method. This prints out the Hamiltonian and a listing of the modes, whether they are harmonic or charge modes, and the frequency for each harmonic in GHz (the default unit)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\hat{H} =~\\omega_1\\hat a^\\dagger_1\\hat a_1~~+~E_{L_{1}}(\\hat{\\varphi}_1)(\\varphi_{\\text{ext}_{1}})~-~E_{J_{1}}\\cos(\\hat{\\varphi}_1)$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$------------------------------------------------------------$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\text{mode}~1:~~~~~~~~~~~\\text{harmonic}~~~~~~~~~~~\\hat{\\varphi}_1~=~\\varphi_{zp_{1}}(\\hat a_1+\\hat a^\\dagger_1)~~~~~~~~~~~\\omega_1/2\\pi~=~3.63978~~~~~~~~~~~\\varphi_{zp_{1}}~=~1.99e+00$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$------------------------------------------------------------$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\text{parameters}:~~~~~~~~~~~E_{L_{1}}~=~0.46~~~~~~~~~~~E_{J_{1}}~=~10.2~~~~~~~~~~~$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\text{loops}:~~~~~~~~~~~~~~~~~~~~\\varphi_{\\text{ext}_{1}}/2\\pi~=~0.0~~~~~~~~~~~$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cr.description()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We determinte the size of the Hilbert space and truncation number for the only mode of the circuit." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cr.set_trunc_nums([60])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate the spectrum and decoherence rates of the circuit, firstly, we need to change and sweep the external flux of `loop1` by the `set_flux()` method. Then, we need to find the eigenfrequencies and decherence rates of the circuit that correspond to that external flux via `diag()` and `dec_rate()` method. In the following lines of code the `spec` is a 2D NumPy array that each column of it contains the eigenfrequencies with respect to its external flux. The `decays` is a Python dictionary that contains the decoherence rates for each type of loss channel." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "n_eig = 5\n", "\n", "phiExt = np.linspace(0, 1, 500)\n", "\n", "decays = {'capacitive':np.zeros_like(phiExt),\n", " 'inductive':np.zeros_like(phiExt),\n", " 'cc':np.zeros_like(phiExt),\n", " 'quasiparticle':np.zeros_like(phiExt)}\n", "\n", "spec = np.zeros((n_eig, len(phiExt)))\n", "\n", "for i, phi in enumerate(phiExt):\n", " loop1.set_flux(phi)\n", " spec[:, i], _ = cr.diag(n_eig)\n", " for dec_type in decays:\n", " decays[dec_type][i]=cr.dec_rate(dec_type=dec_type, states=(1,0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Circuit Spectrum" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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TtaFa7RfR3WqVCoXYeBe549LIHZfW/Zz4heb6Dur3tNLS0EFrUydtjZ20N3fh8wl+nx/xgyvWQUyci5g4F0lpsXgy4/FkxpGSFd+vYwucblulNTwQs4jQVNNOfWUrzXUdNNW109bUha/TR1enH7/Pj8vtwOl24I51kZASQ2JKDIkpsSRnxOHJitcGanVcNGGoQcs4DMnpcVEzFbsxJpCsQt8grhSAXm4opZTqE00YSiml+kQThlJKqT7RhKGUUqpPNGEopZTqE00YSiml+kQThlJKqT7RhKGUUqpPNGEopZTqE00YSiml+kSnBlHqOIgIdHUhPh/i82PcLozbjXEE71pMREAkqK+p1LEI5Yp7S4AFwF4RmRR47ufA94CqwG4/FZHlBzn2AuAewIldie83IQlaDWoignfPHto3f01ncTGdxcV07dqFt64WX20dvro6pKPj4Ac7nTZxxMTYn4dIIiICPh/i94PXa3/6fIjPt/95nw8AExeHMyUFp8eDMz0dd+5wYkbkEVOQT9zEibhzcyNutUE1sISyhPEEcB/wVK/nfysi/3Oog4wxTuB+4FygHFhljFkmIpv6K1A1OIkIHV9/Tcunn9K2Zg1ta9birarq3u5MS8Odm4s7K5u4seNwpqfjiI/HuF02QTictqTR1Yl0dSGdXfZnVyfS2QV+/zff1BhwOjBOF8bpAIcT43Ta13M6ul8XhwN/ayu+hnp8DQ34qmto/nAFvurq/fGlphI3aRLx06aROPs04idPxri0EkEFT8g+TSKywhhTcAyHzgK2i8hOAGPMs8ClgCYMddx8zS20fPwxzStW0PLRR90Jwp2fR8IppxA/dSpxEycQW1iIMzU1vMEehL+lhY6iYto3bqR9w3ra1m+g+v77qb7vPhzJySSecjJJ884i+ax5ERm/ii6RcPlxhzHmBuAL4B9EpK7X9uFAWY/H5cDJB3shY8xiYDFAXl5eP4SqBgJ/ezvNH3xI4/LlNH/4IdLRgcPjIen02SSeMYek02fjysoKd5h94khMJH7SROInTYSrFgHgq6+nZeXntHzyCc2ffEzT2++w2+Ui8eSTST7/PJLPPRdXWtoRXlmpbwrpinuBEsbrPdowhgDVgAC/AoaKyM29jlkInC8i3w08vh6YJSJ3Hu69dMU91ZP4/bR89hkNf36V5nffxd/aijMzE88FF+C54Hzip00bkNU3IkL7xk00vfkGjW++RVdpKbjdJJ05h9TLLiNpzhxMjK64p/aL2BX3RKRy331jzCPA6wfZrRwY0eNxLlDRz6GpAaJr714aXn6F+hdfpKu8HEdKCp6L5uO56CISTjrJthcMYMaY7hJI1t//PR2bN9Pw2us0vPYaze+8izMtDc+CBaRcdinxEyeGO1wV4cJdwhgqIrsD938MnCwiV/c6xgVsBc4GdgGrgGtEZOPh3ktLGIOX+Hw0f/QR9S+8SPMHH4DPR8KsWaQuWkTyuefgiI0Nd4hhJ14vzR9/3F3ikq4u4iZMIHXRIjwLFuBMSgx3iCpMDlfCCFnCMMY8A8wFMoFK4O7A42nYKqli4PsistsYMwzbfXZ+4Nj5wO+w3WqXiMi/H+n9NGEMPl0VFdS/9DL1L72Ed88enBkZpH7rMlKvvJKYgoJwhxexfA0NNLz+OvXPv0DHli04EhLwLFhA6qJFtm1EDSoRkTBCTRPG4CBdXTR/+CF1zz9Py0cfA5B42mmkLlxI8lnztH7+KIgI7evWUffc8zQuX460txM3cSKpVy3CM/8iLXUMEpow1IDTWVZG/QsvUv/Ky/iqqnFlZZFyxeW2NJGbG+7wop6vsZGG116j/rnn6di61ZY6Lr6YtKsWETdhQrjDU/1IE4YaEPydnTS/8w71L75Iy6efgcNB0pw5pC5aaHv7DMBeTuEmIrStWUP9c8/T+Ne/Ih0dttSxcCGeBRfhTEoKd4gqyDRhqKjWsW0b9S++RMOrr+Krr8c1bCipV1xB6hVX4M7JCXd4g4avoYGGZa9R/8ILdGzdiomPx3PBBaQuvJL46dN1WpIBQhOGijr+lhYa33iD+hdepG3NGnC7ST7rLFIXLiTx1FMGfHfYSCYitG/YQP0LL9L4+uv4W1uJGTmS1CuvJOWyS3Glp4c7RHUcNGGoqCA+H62ff07Da6/T9NZb+Fta9n8RXXoJroyMcIeoerGJ/U3qX3yRtq++sol93jxSLr2ExDPOwKGdDqKOJgwVsfaNRG587TUaly/HW1WFIzGR5PPPJ/XKK7SqI4p0bN9uqw6XLcNXW4vD48Fz/nl4LlpAwkkztVQYJTRhqIgifj9ta9bS/N67NL39Dp0lJXa6ijlzSLl4AUlz5+KIiwt3mOoYSVcXLStX0vj66zS9/Q7+1lZc2dkkn3ceyWfNI2HmTO3uHME0Yaiw8zU20rpqFc0ffEjT++/babldLhJnzSL5/PPxnH+ezqY6APnb2mj+4AMaXv8LLR9/bCd6TEoi8YzTST7rLBJPO02rGiOMJgwVct66OtrXr6d11SpaPltJ+6ZN4PfjSEwk6cw5JJ11NklzzsDp8YQ7VBUi/rY2Wj5bSfP779H0/gfda3nEjhlNwkmzSJg1i4QTZ0TNTMEDlSYM1W/E66Vr1y46ioro3LGDtg0baF+/ga7ycruD20381CkknnwKiaecTPzUqVodoRC/n/YNG2hZ+Tmtf/sbrV9+ibS2AuDKziZu4kTiJkwgbsJ4YgoLicnN1c9NiGjCOAri8yFer21o3XdzOMCYQdP4KiJIRwe+xkb8zc34Gxvx1tfj3bsXb+VevHsr6aqspGtXBZ2lpdDV1X2se/hw4iZPJn7SROImTSZ+ymQcCQlhPBsVDaSry15srFtH28aNtG/aROfOov2rFDocuIcPJyY/H1fOEFxZWbiysnBnZ+PKzMTh8eBISsKZnIyJixs0/6v7iIj9Xfn9SGD992MdyBqx05tHosa//IWKf7rr0Dv0TCT7kkjvxALd9w/YZ99zDoMxju7nzOG2OQwQ2L5v3337OwymxzYM9th924wB02sbBJYPDSwj2mMJUenqwt/ejr+pCemRBHqfvzMjA3d2NjEFBSSfNY+YgkJiCguJHRmZq9KpyGfcbhKmTydh+vTu5/ytrXRs20ZnSQmdxSV2XfWSEjq2bMFbU3PwJW8BXC6cSUmYhHiM22279rrdONwx3Wus43YR+E+1/0+H+xn4Mhbxgz/wxSx+xH/gl/QB94/lmIMcf8T9Avd7y7ztNrJ+eNglg46JJoxeYk84gawf/9j+waTnHy/wE0H8fju/bmAfRAL7Efhg7NsvcMy+1+p1XPfjnh+mnh+0ntv2xdC9bf/j7vcKvLf4vNAl3R+o7g9WIJZ9/zSOxMTu+8bttvdjY3F6knEke3AmJ9mfnmScHg+u7GxcWVn2H06pfuZISCB+6lTip079xjbx+fDW1OCtqsJXXY2vsQl/cxO+pmb8TU34mpuQtvYDL44CP/0tLYjXG3ghW8MiBGpa9lW47Kt5ETnwou4Q982+izvT68Jv336O/Rdvh7p/4MWiwTgcfT9m3/3AMQkzptMfNGH0Ejd2LHFjx4Y7DKXUYRinE3d2Nu7s7HCHMqg4wh2AUkqp6KAJQymlVJ+ELGEYY5YYY/YaYzb0eO6/jTFfG2PWGWNeMcakHuLYYmPMemPMGmOM9pVVSqkw6HPCMMa8G1gqtedzDx/Fez0BXNDrubeBSSIyBbtu9z8f5vh5IjLtUN29lFJK9a+jKWEUAncZY+7u8Vyfv7xFZAVQ2+u5t0Qk0F2BlYAulaaUUhHqaBJGPXA2MMQY85oxJiXIsdwM/PUQ2wR4yxiz2hizOMjvq5RSqg+OplutCZQGbjPGfAf4GEgLRhDGmH8BvMDSQ+wyW0QqjDHZwNvGmK8DJZber7MYWAyQl5cXjNCUUkoFHE3CeHDfHRF5whizHrj9eAMwxtwILADOlkPMUyIiFYGfe40xrwCzgG8kDBF5GHgY7NQgxxubCg0Rod3XTmtXK4LgDwxU3DeYKs4ZR5wrjlhn7KCb8iGSiQht3jbafe34xd99ExEcxtH9N9O/28DR54QhIg/1erwaW410zIwxFwB3AWeKSOsh9kkEHCLSFLh/HvDL43lfFTpev5eypjJ21O9gV/Mu9rTsobK1kj0te6htr6W5q5mWzha83U1Zh7cveSTHJJMWm0ZKbAppcWmkxqaSGptKZnwmQxKHkJOQw5DEISS6E/v5DAeO1q5WdrfsZm/rXmrba6lpq7E/22uoaauhrr3O/r26WmjpaqHN27Z/hPQRxLviSY9LJyMug/T4dDLjMxmeNJx8Tz55yXnkefKId8X38xmq43XEhGGM+T0c+lMhIj/syxsZY54B5gKZxphy4G5sr6hYbDUTwEoRudUYMwx4VETmA0OAVwLbXcDTIvJGX95ThZZf/Oys38maqjWsrVrL5prN7GzYSZd//7xU8a54hiQMIScxhzxPHknuJJJjkklyJxHvisflsB9Jh3HgMA784qfD10Gbt40OXwft3nbavG00djZS315PdVs12+u3U99RT5u37RsxJbmTGJIwhCGJQ8hNyiXPk0duci4jkkeQm5RLgnvwTIzY5euirKmMosYiKporqGiuYHfL7u6f9R313zjG5XDt/6KPSyc32f7OElwJJLoTSXQnEuuMxWmcGGNwGicO48ArXjq8HbT72unwddDc2Uxtey217bVUNFewdu9a6jrqut/HYRwUegoZnzGeCRkTmJo1lQkZE7o/DyoyHHG22kCV0T6/wH7RdxORJ/shruOm05uHxp6WPXyy6xM+qfiElbtX0tTZBEBqbCoTMycyNnUso1JHMTp1NLnJuXhiPP1WPdHubae6rZrK1koqWyrtz8D93S27KW8up6Gj4YBjsuKzGJE8ghHJIxiZOpKRKfY2PGk4Tkd0Lina0NFAUUORvTXan8UNxZQ1leETX/d+8a54hiUOY2jS0AN+Zidkkx5vk0R//r2aO5spbSqltKmUHfU72FyzmU01m6hqqwIg0Z3IjOwZzMqZxZzcORSmFGrVVggEbXpzY8xXItI/s1oFmSaM/iEibK3bypvFb/J+2ftsr98OQHZCNrOHzWbGkBlMy5pGvic/Iv+5GzoaKG8qp7SplLKmMsqayihttF9a1W3V3fvFOGLIT8nvTiD7kkmBp4AYZ/jXZfD5fexu2f2NxFDUUERt+/7e626Hm3xPPoUphftvnkKGJw0nJTYlIv9Ge1v38uXeL1m1exWrKldR1FAEQL4nn7m5c5mXN49pWdOiNqFHumAmjC9FZEbQIutHmjCCa2f9TpYXLefN4jcpbizGYRzMHDKTM4afwenDT2dU6qiI/PI5Go2djeys30lRQxE7G3baW/1OdjXv6q6rdxgHuUm5FKYUku/JpyClgAJPAfmefLLis4L+O2jsbKSkoYTixmJKGku6k0NJQwmd/s7u/dJi0w5MCoHEMCxpWNR/se5p2cOHZR/yfvn7/G333+jyd5EVn8X8wvksGLWAcWnjov6zF0k0Yahj0tLVwhtFb/Dy9pdZV7WuO0mcX3A+5+SfQ3pcerhDDIl2bzvFjcXsrN/ZnUiKG4spbSylw9fRvV+CK8EmEU8BQ5OGkhWfRWZCJlnxWaTFphHjjCHWGUuMMwaf+GwPI287LV0tVLVVsbd1L1WtVVS2VlLWVEZJY8kBpQWHcTA8aTgjU0YekBgKPAWkxQWlh3vEa+5s5uOKj1m+czkf7foIr9/L6NTRLBi5gEtGXUJWgi7veryOK2EYY5rY3+idAOzrzWQAEZGIXJRZE8axERHWVq3lpW0v8Wbxm7R52yhMKeTy0ZezYNQCMuMzwx1ixPCLnz0te7qv/ksaSyhuKKa4sZjKlso+9/zqyWmcZMZnkpuc211y2VeSyU3KjYjqsEhR317PWyVv8frO1/lq71e4jIt5efNYNG4Rs3Jm4TA6t+qx0CVa1RF1+DpYvnM5SzcvZUvdFuJd8VxQcAGXj7mcqVlTtch/lPzip6Gjgaq2Kqrbqqlvr6fT30mnz96cDidxzjji3fEkuBLIiM9gSMIQ0mLTor4KKRxKG0t5ceuLvLL9Feo76sn35LNw7EIuHXUpqXGp4Q4vqmjCUIdU2VLJc1ue48WtL1LXUcfo1NFcM/4a5hfO1zEMKup0+Dp4u+RtXtjyAl/u/ZJYZyyXjLqE6ydcT2FKYbjDiwrHWyV1KZArIvcHHn8O7KsovEtEXghmsMGiCePwNlRv4MmNT/JOyTv4xMfcEXO5bvx1nJRzkpYm1ICwrW4bSzcv5bUdr9Hp7+TM3DO5YcIN+hk/guNNGJ8AV4tIWeDxGuwkhInA4yJydnDDDQ5NGN8kInxW8RlLNizh8z2fk+xO5vIxl3P1CVeTm6wTBauBqaathue3PM+zW56ltr2W8enjuX7C9VxQcAFup65P39vxJoxVInJSj8f3icgdgfsrReSUoEYbJJow9vP6vbxT8g5LNixhc+1msuOzuWHiDVw59kqtdlKDRru3nb/s/AtPbXqKnQ07yUnM4aaJN3H5mMuJc8WFO7yIcbwJY7uIjD7Eth0iMioIMQadJgz7D7JsxzIe3/A45c3lFHgKuHnSzVw08iLtbaMGLb/4+XjXxzy6/lG+2vsV6XHp3DDhBq4adxVJMUnhDi/sjjdhLAU+EJFHej3/fWCuiHw7aJEG0WBOGK1drTy35Tme3PgkNe01TM6czC2TbmFe3jztaqhUgIiwunI1j6x/hE8rPiU5JplrTriGa8dfO2jGtRzM8SaMbODPQAfwZeDpE7GTBl4mIpXBCzV4BmPCaOlq4Zmvn+GpjU9R11HHqUNP5XtTvsfMITO1kU+pw9hYvZFH1z/KO6XvEO+KZ+HYhdw48UayE7LDHVrIBaVbrTHmLGBi4OFGEXkvSPH1i8GUMJo7m3nm62d4ctOTNHQ0MHv4bG6dcivTsqeFOzSlosqO+h08tv4xlhctx2EcXDHmCr47+bsMSRwS7tBCJqjjMAJrUrSL9Jj2MgINhoTR1NnE0s1L+eOmP9LY2cgZw8/g1qm3MiVrSrhDUyqqlTWV8dj6x3h1+6s4jIMrx17JLZNvGRQljuOtknIAVwPXAjOBTmx1VBWwHHhYRLYFNeIgGMgJo7GzkaWblvLHzX+kqbOJublzuXXqrUzMnHjkg5VSfVbeVM4j6x/h1e2v4jROFo5byC2TbhnQc1Ydb8L4EHgHeBXYICL+wPPpwDzgGuAVEflTUKM+TgMxYTR2NvLHTX9k6aalNHU1MW/EPG6deisTMiaEOzSlBrSypjIeWfcIy3Ysw+VwsXDsQm6ZfMuAnFvteBOGW0S6jnefUBtICaO1q5Wlm5fy+MbHaeps4py8c/j+1O9zQvoJ4Q5NqUGlrLGMh9Y9xOs7X8flcLFo3CJunnTzgEocwW7DuAa4BPBhZ6x9TUSe6cNxS4AFwF4RmRR4Lh14DigAioFFIlJ3kGMvAO4BnNilW39zpPcbCAmj3dvOc1ue47H1j1HXUcfc3LncMf0OxqWPC3doSg1qpY2l3YkjxhHDVeOu4ubJNw+IKf+DnTAeEpHv93h8v4jc3ofj5gDNwFM9EsZ/AbUi8htjzE+ANBG5q9dxTmArcC5QDqwCvi0imw73ftGcMLp8Xby87WUeXvcwe9v2curQU7lj+h3amK1UhClpLOHhdQ/z+s7XiXPGcd2E67hx4o14YiJy1Yc+CXbCeAJ4ASgDcoErReTmPh5bALzeI2FswQ7+222MGYodIDiu1zGnAj8XkfMDj/8ZQER+fbj3isaE4fV7eX3n6zy49kF2Ne9ievZ07px+JyflnHTkg5VSYbOzYSf3f3U/b5W8hSfGw02TbuKaE64hwZ0Q7tCOWrATRgJwOTZZlGEbvFsPf1T3sQUcmDDqRSS1x/Y6EUnrdcyVwAUi8t3A4+uBk/fNZ9Vr38XAYoC8vLwTS0pKjurcwsUvft4sfpM/rPkDxY3FTMiYwJ3T72T2sNk64E6pKLK5ZjP3rbmPFeUryIjL4HtTvsfCsQujaiqeiFkP4xgTxkLg/F4JY5aI3Hm494qGEoaI8H7Z+9y35j621W1jdOpo7ph+B2eNOEsThVJRbM3eNdz71b2s2rOKoYlDuXXqrVwy6hJcDle4QzuiwyWMPkdvjLkHGI9drnUtsFRE1h5nbJXGmKE9qqT2HmSfcmBEj8e5QMVxvm9Y7Ztm/Pdf/Z4NNRvI9+Tzn2f8JxcUXqBzPSk1AEzLnsZj5z3Gyt0r+f1Xv+fuT+9myYYl3Db1tqj+Pz+aqDcD/43trbQXWGqM+Ua10FFaBtwYuH8jdqxHb6uAMcaYQmNMDHYQ4bLjfN+wWV25mu+88R2+/873qW2v5Zen/ZI/X/pn5o+cH7UfIqXUNxljOHXYqSydv5R7591LjDOGuz66iytfu5L3S98nGlc7PeYqKWNMHLBKRCb3cf9ngLlAJlAJ3I2d1PB5IA8oBRaKSK0xZhi2++z8wLHzgd9hu9UuEZF/P9L7RVqV1Maajfz+y9/zScUnZMVnsXjKYi4fc3lU1W0qpY7dvrbK+9fcT0ljCZMzJ3Pn9Ds5ZegpEVUFHexG71uB0UAyMElEZh9/iMEXKQljZ/1O7ltzH2+XvE1qbCq3TLqFq064inhXfLhDU0qFgdfvZdmOZTyw9gH2tOxhVs4s7px+Z8RMFhrshJGHHRNxOfCAiLx+/CEGX7gTRkVzBQ+sfYBlO5YR54zjxok3csOEG3SBFqUUAJ2+Tl7Y+gKPrHuEmvYa5uTO4c7pd4Z9BodgTW/+PHC3iGwOPHYAX4nI1KBFGkThShg1bTU8sv4Rnt/yPAbD1SdczS2TbxkQI0CVUsHX2tXKM18/w5INS2jsbOS8/PO4ffrtjEwZGZZ4gpUwLgH+DTsdyGogCRglItODFWgwhTphNHY28sSGJ/jT5j/R6evkstGXcevUW8lJzAlZDEqp6NXU2cRTm57iqY1P0e5rZ8HIBfxg6g/ITc4NaRzBrpKaAkzD9rBaLiIH6wobdqFKGG3eNp7e/HT31cGFBRdy27TbKEgp6Pf3VkoNPLXttSxZv4RntzyLT3xcMeYKFk9ZHLK1OCJm4F4o9XfC2Dff00PrHqKqrYozhp/BD2f8MOz1j0qpgaGypZJH1j/CS1tfwulwcvU4W73d3+uNa8IIIp/fx/Ki5fxhzR8oby5nRvYMfjTjR8wYMiPo76WUUuVN5Tyw9oHuCQ6vn3A9N068keSY5H55v2BXSV0sIq8FJbJ+FOyE4Rc/75faaTy2129nfPp4fjjjhzrfk1IqJHbW7+T+Nf0/wWGwE8Y6EYn4ebaDlTD84ue90vd4YO0DbK3bSoGngNun3855+efpyGylVMj19wSHwU4Y6/s6ujucjjdhtHa18teiv7L066Vsq9tGgaeAxVMWc2HhhVExgZhSamDrOcFhTmIO142/jotHXXzcXfi1hHEUVpSv4J4v76G4oZhOfyejU0dzy+RbuLDgQpwOZz9EqpRSx0ZEWLl7JQ+ufZAv936J0zjJ8+Rx3fjrWDRu0TG9ZlBmqx0sElwJZCdkc+rQUzkn/xymZk3VNgqlVETaN8HhqcNOZXvddv5a/Fe21W3rt6mHNGH0MrOliZlFWyHbAZ49kDkZjJYslFIRTITRdbu4s6IE9nwNnv4ZT30sCaMy6FFEGnc8fP0X+OpP4MmF2T+CGTeAOy7ckSml1H5+H6x/ET65B/ZuBHcCDJsBrv75rtJxGIfi64Itf4WVD0Dpp5A8FE7/e00cSqnwE4HNy+D9/4CqryF7Apx6B0y64ri/n3Tg3vEQgaIV8MFvbOLwDIe5/wxTvw1OrdFTSoWQCGx/F977FexeA5ljYd5PYfyl4AhON//DJQwdSHAkxsDIM+Gm5XDDq5CcA8vugAdOg82v2T+gUkr1t+JP4PELYekV0FYLl/4BfvAZTPxW0JLFkRz1JbIx5pfYle/WAGtEZNvxBGCMGQc81+OpkcDPROR3PfaZi12+tSjw1Msi8svjed+jZgyMnAuFZ9pE8e4v4bnrIPckOOfnUHB6SMNRSg0Su9fBOz+HHe9CUg5c9L8w/QZwhX61zmOqkjLGDAGmY2etHSUi3wtKMMY4gV3AySJS0uP5ucA/isiCvr5Wv89W6/PC2qfh/V9DUwWMPgfOvhuGRvwQFaVUNKgvhff+DdY9D/Gptg111vdsp5x+FPRxGCJSCbwRuAXT2cCOnskiYjldtgF88kL42yPw0f/CQ2fYx/P+BdILwx2hUioatdba75O/PQzGAaf/Hcz+O5s0wuxoFlC6BxgPCLAWeFpE1gQ1GGOWAF+KyH29np8LvASUAxXY0sbGgxy/GFgMkJeXd2JJSQjzTlu97dq28gHwe2HmTTDnnyApK3QxKKWiV1c7/O0hmyzaG2HatTDvnyElChdQMsbcCuwA3MAE4DvAg72/3I8jyBhsMpgYKMH03OYB/CLSbIyZD9wjImMO93phW9O7cTes+C9Y/aQtOp72Qzj1dojVtbyVUgfh99lqp/f/HRrKYMx5tl10yMSwhNMv3WqNMXHAqmBNRGiMuRS4XUTO68O+xcBMEak+1D5hSxj7VG+Dd39hG8iThsDcn9iGKu2Kq5SC/V1k37kbKjfAsOlw7i+hcE5YwwpqG0agpDEaSAYajzO2nr4NPHOI98wBKkVEjDGzsN2Ba4L43sGXOQau+hOU/Q3e/hm8/mP47A/2yuGEi2yvK6XU4FSxxn4vFH0Iqflw5RKYELruscfqWKJbDmwGcoFfByMIY0wCcC7wco/nbg0kJ4ArgQ3GmLXAvcDVEi0jDkfMgpv+Clc/bZPEc9fCkvOh9PNwR6aUCrW6Ynjpu/DwmbBnPVzwn3DHF3aEdoQnCzi6NozngbtFZHPgsQP4SkSm9mN8xyzsVVIH4/PCV3+ED34NzZVwwgLbFTdrbLgjU0r1p9ZaWPE/sOoRO5npqbfZOeriUsId2TcEq0rqT8Bzxs71vRpIAvxBiG/wcLps76kpi2z11Ce/s/NVzbjBtnEk54Q7QqVUMHW12Z6TH/8OOpsCPZ9+Cp5h4Y7smByxhGGMMT2rf4wxU4Gp2Oqs5SKyt/c+kSAiSxi9NVfZHlVfLAFnDJx2p73F9s/i7kqpEPH7YO0zdnLAxl0w9kI4527IHh/uyI7ouHpJGWM+wI6BeFVESns8HwOcDtwIvC8iTwQr4GCIioSxT80OO9XIpj9DYhaceRec+B1wusMdmVLqaIjAtrdtz6e9m2D4iXDur6Bgdrgj67PjTRhxwM3AtUAhUA/EYeeTegu4P9gD+IIhqhLGPuWrbc+Jko8hfRSc/TOYcKn2qFIqGuz60v7/Fn8E6SMD/7+XRd3/b9DGYRhj3EAm0CYi9cEJr39EZcKAwBXKW/D23VC1GYbPtH2zo+gKRalBpbbITje+4SVIyNxfQxCGyQGDIWjjMESkC9gdlKjUwRkDY8+3kxmuedqO/nxifqAO9OeQfUK4I1RKAbTU2DbIVY/Z6uM5/5+d2SHOE+7I+o0OO45UDifMuN72z/480MvigVNh+nUw96fgGRruCJUanDpbYeUf7Nxxnc2BXo7/PCh6OeqKe9GipQZW/DesehQcrojux63UgNS9pMF/QNNuGHeR7fmUNS7ckQWVLtE6kNQW2TnyN7wICRl2RtyZN0dtfalSEU8Etr5pez5VfW0XTTv3V5B/argj6xe6ROtAkl4IVz4G33vfzmb5xl1w/0m2wc2v4yiVCqryL+CJi+CZq+yyBYv+CLe8PWCTxZFowohWw2fADcvg2pfAnQgv3gyPngVFK8IdmVLRr2YHPH8jPHo2VG+1y6LethImXBJ13WSDSRu9o5kxMOYcGDUP1j1nq6qevBhGnwvn/iJs8+krFbUOmH0hFs78CZx2h86+EKAJYyBwOGHaNTDxW/D5Q/DR/8EDs+1z834a8hW7lIo6nS3753fraoMTb7TJInlIuCOLKNroPRD1XhP45Fvh9B9HxJrASkWU7hmkfwPNe+wM0uf83K5nM0hpL6nBqq7EDvxb9xzEp9mBRSd9F1yx4Y5MqfASgS3L4Z2f2zaKEafYGRXyTg53ZGGnvaQGq7R8uPxh+P4KGDoN3vwp3DcT1r2gParU4FW2Ch6/EJ69xiaOq5bCzW9osuiDiEgYxphiY8x6Y8waY8w3igXGutcYs90Ys84YMyMccUatoVPhhj/DdS9DbAq8/F14ZC7seD/ckSkVOtXb4bnr4bFzoHYnLPit7fk0fsGg7vl0NCKp0XueiFQfYtuFwJjA7WTggcBPdTRGnw0j58H6F+xkaX+8DEadbXtU5UwOd3RK9Y/mvbaNYvUT4I6Hef8Cp9wGsUnhjizqRFLCOJxLgacCizStNMakGmOGiohOhHi0HA6YepWdNn3VI3bZyAfPgKlX23+k1BHhjlCp4Ohohs/ug0/uBV+HnRHhzLsgKSvckUWtSEkYArxljBHgIRF5uNf24UBZj8flgecOSBjGmMXAYoC8vLz+i3YgcMfZ1f2mX2e74X7+EGx4GU5eDGf8g20kVyoa+brgy6dsqaJlr704OvtuyBgV7siiXkS0YQCzRWQGturpdmPMnF7bD1bB+I3uXSLysIjMFJGZWVl6FdEn8Wlw3q/gztUw6XL49D64Z5q9KutqD3d0SvWdCGx+Df5wCvzl7yFjNNzyDix6SpNFkEREwhCRisDPvcArwKxeu5QDPetKcoGK0EQ3SKSOgG89CLd+BLkz4e3/Z3tUrX1We1SpyFe6EpacD89dB8YJ334WbloOI04Kd2QDStgThjEm0RiTvO8+cB6wodduy4AbAr2lTgEatP2in+RMhuteghtehYR0eOX78PAc2P5uuCNT6puqtsKz19pkUVcCF98LP/gUxl2oPZ/6QSS0YQwBXjH2j+sCnhaRN4wxtwKIyIPAcmA+sB1oBW4KU6yDx8i58L0P7Cy47/0S/nS5fe7cX9puukqFU9Me20bx5VPgToCz/tX2fIpJDHdkA5qO9FZH5u2wCzet+G9oq4PJi+w/aFp+uCNTg01HE3z6e3vzdcLMW+DMf4LEzHBHNmAEbU1vNUi5YuHU22HatXZytpUPwKY/w6xAj6qE9HBHqAY6X5cdR/Hhf0JLlZ1o86z/p43ZIaYlDHX0Gsrh/V/DmqUQ64Ez/h5O/r4dFKVUMInAplfh3V/Y0dkFZ9iBpsNPDHdkA5bOJaWCKyUXLrsffvCJnX/nnbvh9yfCV0vB7wt3dGqgKP4EHj0HXrjRrk1xzQtw42uaLMJIE4Y6dkMmwrUvwI2vQ1I2vHqbHTW+7W17ZajUsdj7NTx9NTwxHxor4NLAxcnY87TnU5hpwlDHr/AM+O57cOUS6GqBpVfCU5dAxVfhjkxFk8bdsOxOeOBUKPnEjs6+c7WdjcDhDHd0Cm30VsHicMCkK+CEi+3yliv+Cx6eC5OuhLP/H6QVhDtCFanaG+CTe+yKd36vXfDrjH+ExIxwR6Z60YShgssVA6fcCtO+vf9LYNOrMOt7+iWgDuTt3H9x0VpjLy7O+ldILwx3ZOoQtJeU6l+NFfD+f9geVTFJcPrfwck/gJiEcEemwsXvh02vwLu/hLpiKJxjB4QOmx7uyBTaS0qFk2cYXHqfna4hf7b9kvj9ifDlH7VH1WBUtAIePQtevNleQFz7EtywTJNFlNCEoUIjezxc8yx8Zzl4hsKyO+CB2bD1Te1RNRhUboSlC+HJi6G5Ci57wC4dPOYc7fkURTRhqNAqmA3ffRcWPmkXtXl6ETyxAMpXhzsy1R8adsGfb7cXB6Wf26qnO7+Aaddoz6copI3eKvSMgYmXwQkX2ekePviNraaY+C04+2eQPjLcEarj1VYPH/8WPn8QxG+nltFpZKKeJgwVPk637T015So7mdxn99kFcHRCuejVe6LKKVfZpX91osoBQXtJqcjRtAc++LVtEHcnwOk/0imro4Xfv38q/PpSGDnPzvmkU+FHHe0lpaJDcg5cfA/c9pntavnev8G9M2D1k+Dzhjs6dSg7P4BH5sLL34W4FLj+Fbjhz5osBiBNGCryZI2Dbz8NN70BqXnw2g/hgdPg6+XaoyqS7F4Hf7wcnroUWuvgWw/D4hUw6qxwR6b6SdgThjFmhDHmfWPMZmPMRmPMjw6yz1xjTIMxZk3g9rNwxKpCLP9UuOUtuOpPID549tvw+Hwo16rGsKrdCS/eAg+dAbtWw3n/BnesgqlX2Sli1IAVCY3eXuAfROTLwNreq40xb4vIpl77fSQiC8IQnwonY2D8xTD2Arsc5we/gUfPhvGX2MnpMkeHO8LBo3kvfPhfsPpxcLjh9L+H2T+C+NRwR6ZCJOwJQ0R2A7sD95uMMZuB4UDvhKEGM6cbTrrF9rr57D745F7YshxO/A6ceZedXl31j/ZG+PReOy+Ytx1m3GB/556h4Y5MhVjYE0ZPxpgCYDrw+UE2n2qMWQtUAP8oIhtDGZuKELFJMPcncOJNdrnOLx6Htc/CaXfavv6xyeGOcODoaocvHoMV/wNttXaczLx/1VLdIBYx3WqNMUnAh8C/i8jLvbZ5AL+INBtj5gP3iMiYg7zGYmAxQF5e3oklJSUhiFyFVfU2u3zn5tcgIcMODpt5C7jjwh1Z9PL7bBL+4NfQUGa7yJ79Mxg+I9yRqRA4XLfaiEgYxhg38Drwpoj8Xx/2LwZmikj1ofbRcRiDzK7VdmLDnR+AJxfm3gVTrwFnRBWiI5sIbPmr/T1WbbYTAp7zcxg5N9yRqRCK6HEYxhgDPAZsPlSyMMbkBPbDGDMLG3dN6KJUEW/4iXDDq3bm0+QhduW2P5wMG162g8rU4ZV8BksusD3RfJ2w8An43vuaLNQBIuHyazZwPbDeGLMm8NxPgTwAEXkQuBL4gTHGC7QBV0skFI1U5Bl5JhS+C1//Bd77Fbx4E+T81vaoGn22zozaW+VGeOcXsO1NSMqBBb+F6dfbTgZK9RIRVVL9QaukFH4frHsePvgPO11F/mybOPJODndk4Vezwy5steEliPUEFra6VRe2UoetkoqEEoZS/cPhtEvFTroCvnzSjiFYcp4d03HWv0LO5HBHGHp1JXZJ1DXPgCvWjqOY/SOdRVb1iZYw1ODR2WKn2/7kHju2YNIVMO+nkDEq3JH1v8bd8NH/2Hm5jMOOaTn9xzp+RX1DxPeS6g+aMNQhtdXZpLHyQfB32cV8zvgHSCsId2TB11Jt16VY9Sj4vbZ9Ys4/QkpuuCNTEUoThlIH07QHPvo/u4iT+AZW4mirt2uMrHwAvG12hPyZd0F6YbgjUxFOE4ZSh9NYYa/CVz9hV4ebdg2c8Y/RuehPay2s/AN8/hB0NNrR2XP/2c4ArFQfaMJQqi++kTiuDZQ4oiBxNFfBZ7+HVY9BZ7OdsHHOP8HQKeGOTEUZTRhKHY2GXfDJ7/Ynjqnftj2JMr8xG034Ne62EwN+8bidGHDS5bZ0NGRCuCNTUUoThlLHomGXLXF89Ue7VvUJF8Hsv4MRJ4U7MjuH1mf3w5qnbWP2lEW2NBSJSU1FFU0YSh2P5ir420Pwt0egvd4OAJz9Ixh9bmgXDBKBkk/g0/tg61/BGQtTr7aD7tJHhi4ONaBpwlAqGDqa7SJOn90PjeX2S/rEm2D6df078K2zFTa+AqsegYqv7Ky8J33X3nQchQoyTRhKBZOvCzb+2a4VUfqZvdKf+C2bOPJPsyPMj5cI7F5rE9T6F2yPp4wxcOpttk3FHX/876HUQejUIEoFk9MNUxbaW+Um+GKJXT9i3bN2Ar+Jl8EJC2DELDv9Rl/5fbB7jV3bY9Ordu1sZ6x9vRk32mSkkyeqMNIShlLB0NkCW9+0k/ltext8HeBOgBEn266tQybZ0dXxafb5rjbb/bWu2DZg715jpxjvaADjhMI5MOESmHCZzvOkQkpLGEr1t5hE26V10uV2nqrij+1iTiWf2rWw/V2HOdjY+awmXmYb1Mecq0lCRSRNGEoFW5wHTphvb2DbPGq226lI2mpt6cIdDzFJttSRPlLbJFRU0IShVH9zuiF7vL0pFcXCvkSrUkqp6BARCcMYc4ExZosxZrsx5icH2W6MMfcGtq8zxswIR5xKKTWYhT1hGGOcwP3AhcAE4NvGmN4T4VwIjAncFgMPhDRIpZRSEdGGMQvYLiI7AYwxzwKXApt67HMp8JTYPsArjTGpxpihIrI72MH84rWNbKpoDPbLKqVUyEwY5uHuiycG/XXDXsIAhgNlPR6XB5472n0wxiw2xnxhjPmiqqoq6IEqpdRgFgkljIMNXe09mrAv+yAiDwMPgx24dyzB9EdWVkqpgSASShjlwIgej3OBimPYRymlVD+KhISxChhjjCk0xsQAVwPLeu2zDLgh0FvqFKChP9ovlFJKHVrYq6RExGuMuQN4E3ACS0RkozHm1sD2B4HlwHxgO9AK3BSueJVSarAKe8IAEJHl2KTQ87kHe9wX4PZQx6WUUmq/SKiSUkopFQU0YSillOoTTRhKKaX6RBOGUkqpPhmwK+4ZY6qAkmM8PBOoDmI40UDPeXDQcx4cjuec80Uk62AbBmzCOB7GmC8OtUThQKXnPDjoOQ8O/XXOWiWllFKqTzRhKKWU6hNNGAf3cLgDCAM958FBz3lw6Jdz1jYMpZRSfaIlDKWUUn2iCUMppVSfDOqEYYy5wBizxRiz3Rjzk4NsN8aYewPb1xljZoQjzmDqwzlfGzjXdcaYT40xU8MRZzAd6Zx77HeSMcZnjLkylPH1h76cszFmrjFmjTFmozHmw1DHGGx9+GynGGNeM8asDZxzVM96bYxZYozZa4zZcIjtwf/+EpFBecNOpb4DGAnEAGuBCb32mQ/8Fbvi3ynA5+GOOwTnfBqQFrh/4WA45x77vYedNfnKcMcdgr9zKrAJyAs8zg533CE4558C/xm4nwXUAjHhjv04znkOMAPYcIjtQf/+GswljFnAdhHZKSKdwLPApb32uRR4SqyVQKoxZmioAw2iI56ziHwqInWBhyuxqxtGs778nQHuBF4C9oYyuH7Sl3O+BnhZREoBRCTaz7sv5yxAsjHGAEnYhOENbZjBIyIrsOdwKEH//hrMCWM4UNbjcXnguaPdJ5oc7fncgr1CiWZHPGdjzHDgW8CDDAx9+TuPBdKMMR8YY1YbY24IWXT9oy/nfB8wHru883rgRyLiD014YRH076+IWEApTMxBnuvdx7gv+0STPp+PMWYeNmGc3q8R9b++nPPvgLtExGcvPqNeX87ZBZwInA3EA58ZY1aKyNb+Dq6f9OWczwfWAGcBo4C3jTEfiUhjP8cWLkH//hrMCaMcGNHjcS72yuNo94kmfTofY8wU4FHgQhGpCVFs/aUv5zwTeDaQLDKB+cYYr4j8OSQRBl9fP9vVItICtBhjVgBTgWhNGH0555uA34it4N9ujCkCTgD+FpoQQy7o31+DuUpqFTDGGFNojIkBrgaW9dpnGXBDoLfBKUCDiOwOdaBBdMRzNsbkAS8D10fx1WZPRzxnESkUkQIRKQBeBG6L4mQBfftsvwqcYYxxGWMSgJOBzSGOM5j6cs6l2BIVxpghwDhgZ0ijDK2gf38N2hKGiHiNMXcAb2J7WCwRkY3GmFsD2x/E9piZD2wHWrFXKFGrj+f8MyAD+EPgitsrUTzTZx/PeUDpyzmLyGZjzBvAOsAPPCoiB+2eGQ36+Hf+FfCEMWY9trrmLhGJ2mnPjTHPAHOBTGNMOXA34Ib++/7SqUGUUkr1yWCuklJKKXUUNGEopZTqE00YSiml+kQThlJKqT7RhKGUUqpPNGEopZTqE00YSh0jY8xjxpjbgvA6DxljZgf7dZUKNh2HodRRMsZcCvwWSAa6gF3Ad0Rk4zG+3hrsvE4Lgvm6SgXboB3prdSxMMaMAh7AjrD9IfAF0Ai8aIyZJCK+o3y98dj5mwqC+bpK9QetklLq6JwH/LnnPFsi8jJ2eo0xfXkBY0xaj4cXAm8E43WV6m+aMJQ6egerxzUcfDrpg/ltj/vnYxPGYV/XGJNojHnSGPOIMebavoeqVPBowlDq6LwDfCtQNQV0t2m4gJ3GmP8wxvzOGPOHwLYsY8zjxpjcwBrMFwMnGGP+MTBLbKqIVBzhdbcClwMvisj3gEtCdbJK9aRtGEodBRHZFpgV9V3swkOXYpfJvAL4buC5euza0ohIlTGmFPhf7IJUJwB/EpH7jDEXAe8f6XUDCzvlYleJA9D2DBUWmjCUOkoi8rIx5hVgCfCliPwewBjzI+B2EenYt68xJgmbPLwi0hxYnGptYPOF2PU3Dvu6AeXYBXDWoDUDKky0W61SQRKobroGu47ye9hqpoeBXwCLsIv8eLClkd8AS4GTRaSrD6+diF2Tuh34WESW9sc5KHU4mjCUUkr1iRZtlVJK9YkmDKWUUn2iCUMppVSfaMJQSinVJ5owlFJK9YkmDKWUUn2iCUMppVSfaMJQSinVJ5owlFJK9cn/D3bNMowM5e4lAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "for i in range(n_eig):\n", " plt.plot(phiExt, (spec[i, :] - spec[0, :]))\n", "\n", "plt.xlabel(r\"$\\Phi_{ext}/\\Phi_0$\")\n", "plt.ylabel(r\"($\\omega_i-\\omega_0$)GHz\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decoherence Rates" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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m1hu4t1poH7r79xKJzfnufrmZvaAkJc9UVYaCfuJ9oX81hBvgPU4LF4c4rfFRVgK3fTMU/Kf/EUYfmZnjPPFHeO9B2PMMOPbH8bqRWDU/JJKTHkvc9LQINz17nxmmSJScoSSlCWa9A3deHGpWT/9jvP5Ha1JRBlNeDP+7yYqGHUbB/l8N41c0dkUKUVkJfPQEvP3fMM7DmocW0bEnh7Emcf+/qCyHf30dVsyGSx8KM5mmQWySFDNrBywCit19Yx3b7QY8BJzk7p+YWVvgMeAFd/9TLZ+ZS0qSYmY9gelAd3evMDMDlhC6cM2t49jfBYYDpcCpwKXu/lgN22VuYKSkX1lJuNF9++4w1WazonCzu/upYfaquBb6G1bAP88MF4dv3Zv+pt/PJoQa2gF7wFevz9jsHU1WXgpTX9r2pqffbqHVZ+iB2e+XLw2mJKWRqqrg5rPDgPlvPxyf7h/1tXw2TLw3tFhXlIYbm33Pht1O1CKSUhhKNsA7/w3JyZb1YS2x3U+Fvb4UpgnOJXPeDePiRh8Fp/8hLbuMU5KyM/A/4AHgcKAEuMLdX6xh2wOAO4Bzgd8BL7n7FXXsey7bJim7A3e6+8iUbSYCP3T3V+sZr1pSct2GlTDx/jBV55b1YazJnuNhrzMyPvd32kx/De75XrgpP+em9NW2bFgBN3wpdNO4+L6wpksuWDYjrA3x8dNh+tVu/cNNzy7H6qYnxpSkNFLypmCfs8JkFrlq42p49/5wPS5ZF1a5PvC80I2zsVOmisRZyXp4+56QnJRu3Jqg73J8w5YqiJu7vg0z34LvPgZddmjy7rZXNmSzCrIFsBMwxd33AC4F7jWzL8w56u6vA98CJgCT60pQ6lA9+2pQdfn2EhSJsfUr4Kmr4JoT4LVbQjeu4y6H7z8Jh34rdxIUCC0Fe30prCT93kPp2+8zV4ebhRN/kTsJCkCvIXDyFfC9J0Lf2E2r4YnfwzUnhuSlrCTqCKXAmdl4M7u/pCQNf4uTEg35e5zW9H1FqX3XMLbu+0/C0T8MlSNPXwXXnhyuaxXl292FSE4o3QwTboZrjg8LIrfrCif/Bi59OFSQ5nKCAqHCBIcP/peVw2WzJaU7sAxo6R5Ww0u0bvzY3SfUsO2zwPPAGcB51beptv1cvtjdawbQrSHdvRpKLSkxs34FvHEbvPcwVJaFWTIOPDdMHxz3/p51Kd0M158eamMueQg6NnEtkWTt7LCD4cyr0xNjVEo3w6RH4I07QutQ+26w/zmwx6lqWYkRtaQ0wpYN8H9HQZ/hcP6t6QssDspKwli4128LM5d17QdHfjdck+La/VakLpUVoVJhwo1hFr6u/cLEN6OPjG9X6saoqoK/HRfGiH730Sb/v8amJcXdVwIvEmb2wsz6AwOBaanbmVmvxHY3uPvlwPHArWZ2VAOOtRyYBCRnDjsNmJvOBEViZMNKePov8PcTw4wZvQbDWdfABXfkx0DNVm3h2J+EudOf/WvT9lVZDk/9Jcy8k8vdR5JatQ01O995FI75cVhr5dm/hr+Ft+4J41lEctH018I4jl2OjzqS9GvZJowp++7jcOjFsHFlGB93+0VhlXuRXDL9dfjnl0Orvjsc91O45P7QDTmfEhQIY0BHHQFrF8HS6Rk/XLZ/ehcREo4/E6b5/Wb12b2A1sCV7v4AgLtPMbOjgZHVtsPMrgdOAnoDL5jZRncfnHj7QuA2M/sZsB74ekbOSKJTVgJv3hlq0ctLoM8IGPfN0EUq32rjhh8c1g2Z/HwYdNfYleAn3g8rZsHB30zb7Byx0KIV7P2lMFPKB4/A6/8Jycrb94S1X0YfpQH2kltmTwzPQ/aPNo5MatUWDjofdjsJXv4nfPBoWA9m7Elw+Le1zorE29oloYJ02iuh5f7gC0Ly3apd1JFl1uB9w0REs9+BPsMyeigt5tgE6u4VkarKsPjRS/8MNXDd+ocCbfi4/EtOUq1ZHLp9dd4BvvXfhi+otGEl/OPUMEbnkgfyuztUeWkYqPvqLaHbTJ8RYR2YgXtEHVlBUnevBnKHq48NN/GXpnEsWtwtnQ7P/DWs29S2Mxz5fdjluPy+rkvuqSgLCxy++u/Q2jnqCDjqstwa39kU5VvgT4fAgLFhZtAmiE13L5G0mPkm3HgmPPa7kKwcd3mYnWrEIflfkHXZAQ44J6wM/859Df/8C9dC2aZwMc3nBAVCy8p+Xw3dwPb5SpgV7PYL4Z7vh+lcReJs5VzYsBx22jvqSLKr91D4+o1w6pWh6+Yjv4Y7vgUr523/syLZsODjcA/y0vXQqTd89QYY/6fCSVAg3D/02wXmTcp4l2olKZIb1i4JfZbv+jasXggHng/feSRMKdzQFoVctv/XQkvKhJtDy0h9zf8QPnoSBu0bWpwKRdtOcPRloTZ61JEw/dXQd/j5azUTmMRXsqtXoSUpECqbdj4GLnkQxp4SJvr455dD196qyqijk0JVvgWeuwZuPT/0ajj04rB+2aAC/B+FcG2qKIUFH2X0MEpSJN4qyuDVW+G608Lig6OOCIuaHXYxtG4fdXTZ16J1aAkp2wQv/KN+n6mqDFMyNyuCY36U/y1ONelaDOP/COf/B3oOgjduD39TU14MXWtE4mTue6ElYcDYqCOJTttOYYr0c/8daqyfuyYMrF+tLtaSZQs+hhvPConyDiPgonvCWKpCXuNn4J7hee77GT2MkhSJr1nvhAUHqzerduoddWTRGj4utIh89ATMr0ctxjv3wdJpsO9XoHv/jIcXa313hm/eGWZLK9sM9/8Y7roUVi2IOjLJE01eJ8Ud5n0Quj617pDe4HJR/93CTeFeXwo/l39+OUwzr8oFybTKCnj5xtB6snYJHP4dOO9W6DEw6sii12c4tGwb/iczSEmKxE/JenjkN3DnxaFf9mGXwrfuK9xm1erM4JgfhpaRp6+quwvEmkUhyeuyY5h5RMKU1HudERbX2vUEmPU2/PNLYZY4dSeRJnL3B9z9jDZt2jRuByvmhLVD+u+e1rhyWss2cOyPQ0VVm45hqtf7fwwlG6KOTPLV2iVw2zfDgoy9hsCFd8MBX8+/KYUbq3kR9N0FFn6a0XEpSlIkXqa+HGaw+vAxGLQPXPxAWJCxqIDGndRH9wGhZWTJZ2FBtJpUVcHjvw99aU/4RSjoZav2XcPq9ef8Kwx6fP7v8O9zYdnMqCOTQjYv0X2ikLt61WbQ3qHCatQRMPWlMF3xoilRRyX5ZspLYXD8go/COlzfuA167hR1VPEzYGxYOHvRpxk7hJIUiYeNq+GBy+G+H4ZxKCf9Gs6+LsxoJTUb903oORhevikMLq3ulX+Fecx3O6nx66oUggFjwwDI/b4GS6aGG58JN4eFL0WyyT2MvQPot1ukocRWmw5w+h/DgnkbVsAt58Lb/1X3L2m6yvKw7sn9Pwot7mddExY9LuSxJ3VJtvZOfTljh1CSItGb/lrobjP5eRh+SFjDY7cTC3OAd0O0aB0K6xat4b+XwdxE31B3eP12eOXm0G/0mB9FG2cuaNEajvxuqDHr3h8m3BRaVVbOjToyKSRTXw7dD0cfFQaOS83MYM/T4Rv/gc594Jn/gwd/phn7pPE2roY7LoF37oX+Y+Gie8PC0FK74tHhHmPi/bB0RkYOocUcm0CLOTZRWUmYseW9B8MA0eN+CqOPVHLSUPMmhamZK0pDi8nmdaFFoOcg+Mo/oFOvqCPMLRXlIcF77T+hBu2oy2CP0/R32QRazLGeNq6Cl24I05u275aZwPLNlo3w6G9C96/ew+DMqzW5ijTMoimhF8f6ZbD3l8PCv4W0tEFTLJ4Knz4L4y5sVJfy7ZUNSlKaQElKEyyeCg//ItRUD9gdTvmtCpamWD4rJHyz34Gi1jDmKDjiu4U5TXO6zJsED/8S1i0JNWon/iqMY5EGU5IiGVVVFVb/nnATtOsKX/oL9Ns16qgkF3z0JDx2ZaiEOv5nsOvxUUdUUJSkZJAKoUZwh7fuDqufY3DYJbDv2dBMPQ/ToqoqXGxV658eWzaENWY+firc/Jx6pWaZawQlKZIVU16C//0qjC044eeh27BITdzDuM0JN0HHXvDlv4Y1UCSrtlc26M5QsqdkQ2hSfe5v0KUYLrg9rKCuBCV9mjVTgpJOrTvAqb+D0/4QutPdeUkYVK+pikXiZ+ShYcHWDj1CF7BX/qUB9fJFFeXh72PCTWFMxQW3K0GJKd0dSnYsTsya9NmEMCj0m3eFi4NILhhzFHzz7jBf/oSb4O7vwKY1UUclItX1HhImwOg9LCzE98Qfw6J8IhBax+/5Lnz4OAw5IExB36FH1FFJLZSkNEKTVxQuJO7w7oNhmsgNy+G4y+G030OrtlFHJtIw3fqG2YTGnhJmYLrxrDBuRUTipUN3OPdfsNPe8P5DYeFHzfwlG1fBfy4IYzf3OD108dK9SKxpTEoTqM/xdlSUwZN/gkmPQucd4Yw/q0lV8sNHT8ITfwh934/5cZgOVWpVSGNSzGw8ML5r167jV61aFXU4ha2iHB77bRhT1ncXOOvvYZ0VKTzrlsLt34LV8+GwS+GAc9Q1OgY0cD6DlKTUYeMquO9HYcXWwfuF1pM2HaOOSiR9ls2Eey+DNYtCrdwxP9S0lbUopCQlSeVDTFRVhYla3rwT+oyAr16vNWgKzaoFcMe3wkyNx/4E9joj6ogkQQPnJfuWfAY3fzUkKPt9NazaqgRF8k2vwXDBHTBgj7DWz52XaJyKSNw0axamYz/4m2H9qNsv1P9pIVk+C/5zflgD5eQrlKDkGCUpkl6Tn4dbzoNNq+Hk34RFkZo1jzoqkcxo2xm+eh3s9SWY+z786+uhhUVE4sMMDrkwTHm/bEYYl7BhRdRRSaYlf9eb18Lpf4RdT4g6ImkgJSmSHu7w+m3wwOVhAcFz/qVFkaQwNG8Bx/4Yjv85rF8akvTZE6OOSkSqO/A8OOoyWDknjE9Qi0r+WpH4HZdugi/9H4w6POqIpBGUpEjTVVXCU3+GF/4BPQfDBXdC3zFRRyWSXXucCl+9IdTa3vXtMFhXROJl369sTVTuvCSs3yX5ZdUCuP0i2LI+TNgz7KCoI5JGUpIiTVO+JUzv+O4DoW/+ef+GTr2ijkokGgP3gPNvhfbd4OFfwmv/0WJyInGz71fgkG/B0mlw97dDbbvkh7VL4I6LQpfzU6+E4eOijkiaQEmKNN6mNaE59bMJMOZoOPu6sEK3SCHrOSgsJtdrCLx4XZiGWyvUi8TLQefD/l+HhZ/Af78fKtwkt21cnZjFa1kYEzv6yKgjkiZSkiKNs24p3Ho+LPw4XOhP+R0UafpVEQA69gyLyQ3cK8z89cDlYd0gEYkHMzj821snvXj4V6pMyGWlm+Ge78HqBXDcT2CXY6OOSNJASYo03OoFcOs3YNU8OPqHcMR3wjSPIrJV6w7wlWth9FEw9SW49wda9VokTsxCGTbqSJj6Ijx7tbpn5qLKcnjgJ7B4Mhz0DdhzfNQRSZrozlIaZvnskKCsXwYn/Rr2OTPqiETiq6gFnPo7GHsyzHwT7v4ObNkYdVQiktSsGZzyG+i/O7xzL7x1V9QRSUO4w2O/C9fX3U6CQy6KOiJJIyUpUn+Lp8JtF8DmNXD6H2C3E6OOSCT+mjWHE34Be58J8z6AOy6GzeuijkpEkopawpf/D3oMgueugU+ejToiqa9X/gUfPQlDD4TjfxZaxyRvKEmR+lk0OUzpV7oZvvxXGHVE1BGJ5A4zOPoHYbDu4slh1evNa6OOSkSS2nSEs6+FDj3gkStg4adRRyTbM/l5mHAT9BkeFmtsXhR1RJJmSlJk+xZPDfPJV5bDV/4eaixEpGHM4NCLt656fcfFULI+6qhEJKlTbzjzb+F/9b4fwnqtSh9bi6fC/34dpnv/8tXQsk3UEUkGKEmRui2dHhKUirJw8d5pr6gjEsltB54X+k0vnabF5ETiZocRcNKvYMMKuO8HUF4adURS3YYV8N/LwniUL/9Va7PlMSUpUrtlM8Oc42WbQ3/dQXtHHZFIfjj4gjALzeIpcNelGkyfR8xsvJndX1Kimdxy1pij4YBzQjfnx3+vGb/ipKIM7v0hbFgOJ/0SisdEHZFkkJIUqdnKuSFB2bIRvvQXGLxf1BGJ5JdDLoIDzoVFn4ZZvzQ9cV5w9wfc/Yw2bdT9JKcdejEMOQA+fhLe+W/U0UjSs38L18z9vw47ay2UfKckRb5o3bLQDWXzOhj/J41BEckEszA+Zb+vwoKP4P4fh3FfIhK9Zs3htCuhaz947u8aSB8HnzwD794PA3YPSaTkPSUpsq3N60L3k3VLQ7/cEYdEHZFI/jKDI74b5vef+SY88huoqoo6KhGBsCDr+D+BNYMHLtfU4VFaMQceuzIMlD/tD5rJq0AoSamBmV1pZi9EHUfWlZXAPd+DFbPhyO/DrsdHHZFI/jML8/sPOxg+eVqrXovESZ9hcMyPYN0SePQ3+t+MQllJaGmuKIPT/wQdukcdkWRJJEmKmf3azNzMRjdxP9ea2dya9mVmQ8zsTTObbmYTzWxkPfc5HvigKXHlpMpyuP8nsPDj0Ndzv7OjjkikcDQvCguk9tst9H9//T9RRyQiSbufAmOOgWmvwFt3Rx1N4Xnqz6Hy9LBLYMDYqKORLMp6kmJmY4F9gPm1vN/SzAZWe621mfWvYfMHgQOAeTW8dxNws7sPBa4Cbknsq7eZTaj2uCbx3hBgd3d/uJGnl5vc4fE/wMw3YNcT4fBvRx2RSOFp0TpM891rCLx4PXz4eNQRiQhsbe3sPgBeuDas0SHZMfmFcC0cvH8YvycFJatJipm1Aq4HLgZqazMdBbxoZmMSn2kLPAGcWX1Dd3/V3RfWcJyewFjgrsRLDwEDzWyAuy9193HVHt9LbHcs0CmRtAw1sxNrOY/8mmLyjdvhw8fCReCEn4cLsohkX5sOcPY/oGOv0P963qSoIxIRgFZtw6rmGDz8SyjfEnVE+W/9cnjiD9C2cxgj20wjFApNtn/jvwXucvc5tW3g7pOArwGPmtnBwDPA6+7+pwYcpy+w2N0rEvt0QstNv7o+5O5/d/dvJZKW6e7+WC3b5c8Uk1Neghf+AT0Hhe4mGowmEq0OPeCsv0NRS7j3B7B6QdQRiQhA76Fw6Ldg5Rx44bqoo8lvVVXwyBVQsg5O/JXGoRSorCUpZrYvsCdww/a2dffXgW8BE4DJ7n5FIw5ZvaWmQc0D7n54I46ZWxZPhYd/Ae26hpui1u2jjkhEAHoPgdN+D1s2hMkstCq9SDzs91Xot2sYOzbrnaijyV/v3Auz34HdT4XhB0cdjUQkmy0pBwPDgTlmNhcoBp41s2Oqb2hm3YE/AH8GjjKzcQ081gKg2MyKEvszQutKjeNgCtL65fDf74fxKF++Gjr3iToiEUk17KAwy97KufDAT7SGikgcNGsOp/wWWrZN1PSvjzqi/LN8dujh0bUfHHVZ1NFIhLKWpLj7n9x9B3cf4O4DgIXAUe7+dOp2ZtYLeBG4wd0vB44HbjWzoxpwrOXAJCA5RdVpwFx3n9v0M8kD5aXw38tgwwo4+QroOybqiESkJvucCbufFmoUn/1b1NGICECXHeHoH8KG5fD0X6KOJr9UVcJjv4OqikQymAfd6qXR4jgKqTVwpbvfAuDuU4CjgS/8pZrZ9Wa2kNAq84KZzUx5+0LgQjObDlwOnJ/xyHPFU3+GJVPhoPNhTL1zPxHJNjM49kdhheWJ98FHT0UdkYgA7HYiDDkAPn4KZrwZdTT5Y+L9YSmEvc9UBapgroWJGq24uNgXLvzC5GLx9v7D8PjvYfB+cNY1oelaROJt4yq46ewwiPQbt4UBvDnEzBa5e3HUcWRTTpYP0jBrl8ANZ0CbjnDxA2EGMGm8NYvDz7NdV7j4PrWiFIDtlQ1xbEmRTFk0GZ66CjrvAKdeqQRFJFe07wZnXBW6Qtz3Q/WDF4mDzn3gsEth3VJ4abtzAkld3OGJ30N5SVgKQQmKoCSlcGxaA/f9CKwZfOkv0LZT1BGJSEP0HQPH/BDWLArrNFRVRR2RiOw5Hop3DrNRLfwk6mhy10dPwqy3YdcTYNDeUUcjMaEkpRC4h1lI1i+D4y6HPsOjjkhEGmOP02GX42DG6/DWXdvfXkQyq1kzOPGXoWfC438IrZ3SMJvXwbNXQ7tuYUZDkQQlKYVg4n3hpmaX48JgPxHJTWZw3E+hW3948fqw1pGIRKvnTmH9lGXT4b2Hoo4m97z8zzDe7qjvqZeHbENJSr5bOgOe+zt07QvH/iTqaESkqVq2gdP/EBKWB38GpZujjkhEDjofOvYKY1M2rYk6mtyx5LOQ2PXbFcZ8Ydk8KXBKUvJZWQk8+FPwyrB6dat2UUckIunQZzgc/m1YPV/rNIjEQcs2cOT3YMsGePG6qKPJDVVV8OSfw9fH/iRUvIikUJKSz579G6ycA4deAjuOijoaEUmnvc8MU4l/+Bh88mzU0YjIqCNg4J7wwaOw8NOoo4m/j58Ka6LsOT7nplWX7FCSkq9mvAHvPwQD9wp9ZUUkvzRrBidfEdYUePKPsGFF1BGJFDYzOObH4X/zmf8Lk9ZIzUo3wwvXQtvOcMhFUUcjMaUkJR9t2QCPXQkt28HJvw4XTBHJP+27wfE/C//zj/9BN0UZYGZdzOwWM1sQdSySA3ruFGbhW/gJTHkx6mji6627wiK1h1wUFsMUqYHuXvPRs3+DDcvhqO9Dp95RRyMimTTiEBhzNEx/FT55OupoYsHMrjWzuWbmZja62ntDzOxNM5tuZhPNbGRd+3L3Ne5+PjAto0FL/jj4glBJ+OJ1UFEedTTxs2ElvHFHmKVw7MlRRyMxpiQl38x4EyY9CjvtrX9+kUJxzI9Ct6+n/hJuAORB4ABgXg3v3QTc7O5DgauAWwDMrLeZTaj2uCZrEUv+aNcFDjgHVi8I3a5lWxNuDivLH/5taN4i6mgkxpSk5JPSTfB4opvXib/UTBkihaJtZzj+p7BlPTz5p6ijiZy7v+ruC6u/bmY9gbFAciXMh4CBZjbA3Ze6+7hqj+9lMWzJJ/ucCR16hhvyLRuijiY+VsyBDx4JUw4PHxdxMBJ3SlLyyYSbw6ryh18KnftEHY2IZNOIQ2Hk4fDZyzD9taijiau+wGJ3rwBwdwfmA/3q+pCZ3QgMN7MbzWxQLdtcZmYLk4+NGzemO3bJJS3bwKHfCosUvn571NHExwv/CMsiHPE9VaTKdilJyRfLZsDb/4UdRsIep0UdjYhE4egfQMu2odtX+Zaoo4mr6rMLbPdOyd0vcvfixPOsWra5OrFNsbsXt2/fPi3BSg7b5TjoOQje+S9sXB11NNFb+ClMewVGHAZ9x0QdjeQAJSn5wD108fAqOO6n0Kx51BGJSBQ69oRxF8LaRfD6bVFHE0cLgGIzKwIwMyO0rsyPNCrJT82ah//H8i3w5h1RRxO9CTcDBuO+GXUkkiOUpOSDj56A+R+GFpQd65yoRkTy3d5fCrW3r98Gq3TvncrdlwOTgLMTL50GzHX3uZEFJflt+CHQawhMvL+wJ7VY8DHMfANGHwm9BkcdjeSIBiUpZtbOzI40s/PM7MztTd0oWbBlAzz3d2jbBQ67JOpoRCRqzVuEFtXKcnj6L1FH02DpKGfM7HozWwgUAy+Y2cyUty8ELjSz6cDlwPnpifzzY483s/tLSkrSuVvJVc2ahdaUitIw7W6hmnATYGF6ZpF6qleSYmb9zew/wGzgJ8BhwOnAg2Y22czOzWCMUpfXb4PNa8JgeS2IJCIA/XeDnY+FmW/CrLejjqZe0lnOuPslibEhRe7e290Hp7w3zd33dfeh7r6Hu09O53m4+wPufkabNm3SuVvJZcPHQe9h8N6DsGFF1NFk3/wPw3VozNHQY2DU0UgOqW9Lyp3Ao8CO7n6Yu3/F3U9z95HAccBQM7s0Y1FKzdYtDYPlew6GXU+IOhoRiZNDL4bmLeG5a6CqMupo6kPljOQns7CyekVpYY4Vm3AzWDO1okiD1StJcfeD3P0R4Ewz6wBgZkVmZu4+191/6u7XZTJQqcFLN4SL3pHf1WB5EdlW5z5hrYZlM+CjJ6OOZrtUzkheG3pgaE15/3+waU3U0WTPoikw+x0YfRR07x91NJJjGjpw/ufARjNrA8wElprZ/ukPK1pmdqWZvRB1HHVaMg0+eiqsLD94v6ijEZE4OvBcaNMJXvonlOXMGImcLmc0JkVqZBZWoa8oDYPoC8Ubt4XnA74eaRiSmxqapGxKLH51LPA8sA/wx/p+2MyeM7OPzexDM3vNzHZt4PGr7+9aM5trZm5mo6u9N8TM3jSz6WY2sb6DL81sPPBBU+LKipdvBByO+G7UkYhIXLXuAAd/AzYsh/cfjjqa+mpSORM1jUmRWo04FLrsCBPvy6VKg8ZbtQCmvASD9w8znIk0UEOTlFIz6wN8GbjP3ecA7Rrw+TPcfWd33xX4K3Br9Q3MrKWZDaz2Wmszq6md8EHgAGBeDe/dBNzs7kOBq4BbEvvqbWYTqj2uSbw3BNjd3eNdmi+eCtNfDQsi9RkWdTQiEme7nwbtu8Mbt+fKAo9NLWdE4ql5Eex7dliFftKjUUeTeW/eCbhaUaTRGpqk/JIwx/wQ4OXEQlj1XlbX3demfNsJqKphs1HAi2Y2BsDM2gJPAGfWsL9X3X1h9dfNrCcwFrgr8dJDwEAzG+DuS919XLXH9xLbHQt0SiQtQ83sxPqeW1a98q/wfPA3oo1DROKvRatwk7BxVegPH39NKmdEYm23E8OSAW/eFaYJz1cbV8GHj8OOo6H/2KijkRzVoCTF3V9MTOe4q7tXAkOBlxuyDzO7w8wWAFcCX0iv3X0S8DXgUTM7GHgGeN3d/9SAw/QFFrt7RWKfTlhRuF9dH3L3v7v7txJJy3R3f6yWc4iuz/GSaTDtlcSUhkOzf3wRyT27nwrtuyVaU0qjjqZO6ShnRGKrRWvY+8uwbglMfj7qaDLnnXuhsgz2/3oYjyPSCE1acT4x3/xFDfzM19y9L/ALoMaVxtz9deBbwARgsrtf0Zjwqn3foP8Sdz+8jvei63P86r/Ds6byE5H6atE63CxsWAEfPBJ1NA3SmHImSho4L9u153ho0Sa0pnj1W5U8UFYC7z4IXfvB8IOjjkZyWH0Xc9x7O++3aeiqwO5+O3CImXWrYX/dgT8AfwaOMrNxDdk3sAAoNrOixP6M0Loyv4H7iZdVC2Dqy2Eqwz7Do45GRHLJ7qdC287w1t2xXDclE+VMFDRwXrarbSfY5ThYOg0WfBx1NOn3yTOwZX2YAl3LI0gT1Lcl5cdm9oKZnWtmw8ysU2IA+jgzuwp4B+hd1w7MrKOZ7ZDy/SnAKmB1te16AS8CN7j75cDxwK1mdlR9T8rdlxP6NJ+deOk0YK67z63vPmLpnf8CDvt+JepIRCTXtGwDe5wOaxeFLqPx0+RyRiRn7PWl8DzxvmjjSDf3MMVyy3YhERNpgvomKfcDPwX2B54ElgGTgV8TWif2c/eXtrOPTsAjZvaJmX0EXAIcnxgvkqo1cKW73wLg7lOAo4EvVEuZ2fVmthAoBl4ws5kpb18IXGhm04HLgfPrea7xVLIBJj0GvYbCgD2ijkZEctGe46F5C3jrnqgjqUk6yhmR3NBzJxi4J0x5IXTDzBfzP4Rl02HX46GVJuWTpimq53Y/cfexwLuNPZC7LwD2qsd286g2pbC7Twem17DtJYRkp6b9TAP2bVSwcfTB/6C8JLSiaBCaiDRGh+4w5ugw686iKbBjrHpPNbmcEckpe50Bc94Ns+6N+2bU0aRHcqHKPcdHG4fkhfq2pOiuOEpVVfDeQ9CuK4w+MupoRCSX7Z2Yzf29B6ON44tUzkhhGXoQdOwVyvd8mI54/QqY+iLstDf0GLj97UW2o75JSnczOzZ1TIlk0bz3Yc1C2PUEKGoZdTQiksv6DIMdRsGnz0HppqijSZUX5Yxm95J6a14Ee54OG1eGSXFy3fsPh0k5kuNtRJqovklKe0K/4JlmtszMnjGzP5rZGWamxToyLTll6G7xXFtSRHLM2JNC99FPn4s6klR5Uc5odi9pkLGnhHFi7z4QdSRNU1kRkpROfWDoAVFHI3mivknKXHffG+gAHAbcQxjgfglhxhXJlJL1MOUl6LcbdB8QdTQikg9GHxXWTpn0aNSRpFI5I4WnXRcYcSjM+wBW5fAqCTPfDC1CY0/WtMOSNg1dcb7S3T919zvc/fvufrC7d8lUcEKYb7yyLPzji4ikQ+v2MOoIWPgJLJ8VdTTbUDkjBWe3k8JzvCoNGmbSo4CFWb1E0qS+ScqfMxqF1O7T56CoVahpERFJl+QaBpOfjzaOrVTOSGEauCd03gE+fCJ0m8o1G1fB9Ndg0D7QSUsZSfrUK0lx93szHYjUYP2KMOf40AOgVduooxGRfNJ/bJgxcPLzYQG2iKmckYLVrFmYGGfjytBtKtd8/FQYMD/2pKgjkTzToO5ekmVTXwQcRh4edSQikm+aNYcRh8DKubHr8pXLNLuXNMquJwC2daKcXOEeFppu0wmGHRx1NJJnlKTE2eQXQlevoQdGHYmI5KNRR4TnKS9EG0ce0exe0iid+8DgfWH666H7VK5Y9CmsmA07H6MlEiTtlKTEVWpXr5Yq7EQkA2LW5UukoO16AnglfPps1JHU36THwvNu6uol6ackJa6mvwY4jDgs6khEJF81aw7DE12+cnn6U5F8MPRAaNkOPn4m6kjqp7w0JFS9h0HvnFnKSHKIkpS4mvkmWLPQ/CsikilD9g/PuThgVySftGwTxoktngwr50UdzfbNeANKN8HOx0YdieQpJSlxVFEOsydC8Rho0zHqaEQknw3cA5oVwcy3oo5ERJI3/J88HW0c9fHJM4DB6COjjkTylJKUOFr4MZRtUiuKiGReq3bQb1eY+x6Ub4k6GpHCNnAPaN89TOsb53FiWzaGbukDxkLHnlFHI3lKSUoczXo7PA/eL9o4RKQwDN4PKkph/kdRRyJS2Jo1hzFHwZpFsPDTqKOp3WcvQ2UZjDk66kgkjylJiaN5H4TBc32GRx2JiBSCAbuH53kfRBuHiMCYY8JznLt8ffJM6CaqyX0kg5SkxE15KSyaDP12CTUqIiKZ1mcYtGgD8ydFHUnO02KO0mR9hkPXvjD1JaiqijqaL9q4Cma/G1pg23aKOhrJY0pS4mbRZKgsh367RR2JiBSK5i2g786he0lFedTR5DQt5ihNZgYjD4cNK8IY1biZ+lJYz2XMUVFHInlOSUrcJGsy+ytJEZEs6rdrGJeyeErUkYjIyEQ3qikvRhtHTaa8BM1bhnVdRDJISUrczP8o1GruOCrqSESkkCQrRhZo8LxI5PoMh847hiQlTl2+Nq+Fue/DoH3CzIAiGaQkJU7cQy1mr6FQ1DLqaESkkPQZEZ4XT402DhFJdPk6DNYvC4s7xsW0V0NXrxGHRB2JFAAlKXGyYQVsXgM7aFYvEcmy1u2hW39Y8lnUkYgIbO3yNfmFaONINfUlsOYw7OCoI5ECoCQlTpI1mJp6WESi0Gc4rJ4fFmoTkWjtOAo69Q5dvuKwsOOWjWEdt4F7aFYvyQolKXGSrMFMdrsQEcmmZAXJ0mnRxiEiocvXiENh3RJYNiPqaGDG62H20RGHRh2JFAglKXGy5LOwOFLPQVFHIiKFKJmkqMuXSDwkZ9Ca9kq0cUDo6oXBcHX1kuxQkhInS6eFBEWD5kUkCkpSROKl/27Qqn0YsB6linKY+TYUj4YOPaKNRQqGkpS4KN0UZvHosVPUkYhIoWrTMdyArJwbdSQ5SyvOS1o1bwFD9g8zf65fEV0c8ydB2SYYelB0MUjBUZISFyvnhefuAyINQ0QKXLf+IUmJw0DdHKQV5yXtkjNpTX8tuhiSxx56QHQxSMFRkhIXq+aG5+79Iw1DRApc9/5Qthk2rIw6EhEBGLwfNGse7biU6a9Bx17Qa0h0MUjBUZISF8mWlG4DIg1DRApcsjV35ZxIwxCRhDYdoP/uMHsilEXQjXDlPFi9ILSimGX/+FKwlKTExcq5gEG3vlFHIiKFLFlRsmpepGGISIphB0FlWVinJNs+7+p1YPaPLQVNSUpcrJoHnftAi9ZRRyIihezzlhQlKSKxkRwLMvPN7B97+mtQ1AoG7pn9Y0tBU5ISB1VVsGp+GLAqIhKlTr3DDYlm+BKJj659w2Pmm9md1GLLhjCz18A9VYkqWackpQZmdqWZvZC1A65fBhWl0K1f1g4pIlKjZs3CzdCq+VFHIiKpBu8H65ZmtwJh1ttQVamuXhKJrCUpZtbazB4xs+lm9qGZPWNmA5q4z2vNbK6ZuZmNrvbeEDN7M3G8iWY2sp77HA980JS4Gmzd0vDcZcesHlZEpEad+4TKk6qqqCMRkaTB+4bnbHb50tTDEqFst6TcDAxz912BJxLfb8PMWprZwGqvtTazmvpCPQgcANTUefom4GZ3HwpcBdyS2FdvM5tQ7XFN4r0hwO7u/nCjz7AxkklKx15ZPayISI069YaqCti0KupIRCRpwB5hccdsJSnuYZX5noPCNUEky7KWpLj7Fnd/yv3zzpRvAzUtrz4KeNHMxgCYWVtCQnNmDft81d0XVn/dzHoCY4G7Ei89BAw0swHuvtTdx1V7fC+x3bFAp0TSMtTMTmz0CTfE+mXhuZOSFBGJgWSFybpl0cYhIlu1bAP9x8LcD7IzFfGymaGiYtC+mT+WSA2iHJPyHeDx6i+6+yTga8CjZnYw8Azwurv/qQH77gssdveKxD4dmA/UOejD3f/u7t9KJC3T3f2xmrYzs/Fmdn9JSZouEsmWFNVUiEgcJK9FyWuTiMTD4P3CVMTzstArfdZb4XnQ3pk/lkgNIklSzOxnwBDg5zW97+6vA98CJgCT3f2KRhym+vQXDVqByN0Pr+O9B9z9jDZt2jQirBqsWwbNiqBdt/TsT0SkKZJJynq1pIjEyuD9wnM2unzNfgeat4R+u2X+WCI1yHqSYmY/BE4FjnH3zbVs0x34A/Bn4CgzG9fAwywAis2sKLE/I7SuxHO6mnVLQ/eKZppsTURi4PPuXmpJEYmVHgPD/+fMtzJ7nPItMG8S9N8tdDMTiUBW74rN7DLC2JIj3H1tLdv0Al4EbnD3y4HjgVvN7Kj6HsfdlwOTgLMTL50GzHX3uY2PPoPWL9N4FBGJj449ANOYFJG4MQutKavmweovDMlNn/kfhqURdlJXL4lONqcgLgb+CnQGXk5MQ/xODZu2Bq5091sA3H0KcDTwhVTezK43s4VAMfCCmc1MeftC4EIzmw5cDpyfzvNJm7ISKFmn8SgiEh/NW0CH7rBeLSkisZPs8jUrg60psxK3Z4P2ydwxRLajKFsHSszCtd1xIe4+j2pTCrv7dGB6DdteAlxSy36mAfGfkkLTD4tIHHXsBeuWRB1FzkmstTW+a9euUYci+WrgnmDNwkKLe47PzDFmvQ1tu0CvIZnZv0g9aBBE1D6fflgtKSISI516w8ZVUFEWdSQ5Je0Tq4hU16YD7Dga5rwLlRXp3//GVbBsepjVS2NlJUL664vahpXhuUP3aOMQEUnVoUd43rQ62jhE5IsG7QOlm2DR5PTve7a6ekk8KEmJWvIGQNMPi0ictEt0V9q0Jto4ROSLkgnErLfTv+/kPjVoXiKmJCVqyRuAdl2ijUNEJFXymqSWFJH42XEUtGqX/iTFPQya7zEIOvZM775FGkhJStQ+b0nRIEsRiZHPW1KUpIjETvMiGLgXLPoUSjakb7/LZ8HGlVplXmJBSUrUNq+BolZaLElE4kXdvUTibdA+4FVhAH26JFtmNB5FYkBJStQ2rQndKmy7szOLiGTP5929lKSIxFImxqXMfiesk9R/bPr2KdJISlKitmm1unqJSPyou5dIvHUthi7FYVFH96bvr7wU5r4PfXdW7w6JBSUpUXLf2pIiIhInLduErqhqSRGJr0F7w9rFsHph0/e14COoKIVB8V8HWwqDkpQolW0OFwS1pIhI3JiFChS1pIjE1+ddvt5q+r40HkViRklKlDSzl4jEWbuuSlJE4mzAnmDN0zMuZdbb0LYz9B7W9H2JpIGSlChtWhue23aOMgoRkZq16xK6e6Wjv7uIpF+bDlA8Gua8B5Xljd/PxlWwdFpYwLGZbg0lHvSXGCW1pIhInLXrCpVloWuqiMTToH2gbBMs/LTx+5j9ztZ9icSEkpQoKUkRkTjTDF8i8ZdMLJKJRmPMUpIi8aMkJUrJWXM0u5eIxJHWShGJvx1GQqv2jR+X4h4+22MQdOyZ3thEmkBJSpQ+b0lRkiIiMdQ2maSoJUUktpoXwU57waLJULK+4Z9fPgs2rlQrisSOkpQobU7UTrZVkiIiMaSWFJHcMGgf8CqY827DP/v51MN7pzcmkSZSkhKlzWuhZVto0SrqSEREvihZgbJ5baRhiMh2fL5eSiO6fM16G5q3gP5j0xuTSBMpSYnSpjWaflhE4it5fdqslhSRWOuyI3QphplvNWzK8LISmPcB9NsVWrbJWHgijaEkJUqb16qrl4jE1+fdvdZGGoaI1MOgfWDdEli9oP6fmf0OVJTC0AMzF5dIIylJiYp7aElp1znqSEREatayDRS1UkuKSC5Idvma8Ub9P/PZK+F52MHpj0ekiZSkRKVsc1gkTS0pIhJn7boU9JgUMzvEzG4xs7vN7Lqo4xGp1U57h0qFqS/Xb/uqSpj+GvQcBF2LMxubSCMoSYmK1kgRkVzQtkvOze5lZtea2VwzczMbXe29IWb2pplNN7OJZjayrn25+8vufr67fwXob2YdMhq8SGO1agtD9g9jTDau2v72Cz8JraTDxmU8NJHGUJISlc+nH+4caRgiInVq2zkXu3s9CBwAzKvhvZuAm919KHAVcAuAmfU2swnVHtckP2RmxwNT3H1D5sMXaaQRhwJev9aUZFev4erqJfFUFHUABSvZfULdvUQkztp1gfItYRagHJn9x91fBTCzbV43s57AWODIxEsPAdeZ2QB3nwuMq2l/ZvYNoNjdf1LbMc3sMuCy5PedOnVq/AmINNbQA8N0wlNfgj1Pr307d5j6InToCX1GZC8+kQZQS0pU1N1LRHJBfq2V0hdY7O4VAO7uwHygX20fMLPTgF8Dvc3sRjPrUdN27n61uxcnH+3bt89A+CLb0bo9DNoX5rxXdzfN+R/CmkUw5mhopltBiSf9ZUZFLSkikguSMxDmR5ICUH0RCatxq+TG7g+5e193vyjxWJHB2ESabvQR4JXw8dO1b/PRk+F5l+OyE5NIIyhJicomjUkRkRyQvEbl2OD5WiwAis2sCMBCf7C+hNYUkfww4lBo0wnee7DmhR1LN8Hk56D3MOg1OPvxidSTkpSoJAeiap0UEYmzz7t75X6S4u7LgUnA2YmXTgPmJsajpIWZjTez+0tKStK1S5GGadEadj0eVs2DOe9+8f33/xcSlbrGrIjEgJKUqGxaA82KoJX6LYtIjH2+6nzuJClmdr2ZLQSKgRfMbGbK2xcCF5rZdOBy4Px0HtvdH3D3M9q0yY1JBiRP7TEerDm8fOO2rSkV5fDW3dC+G+ysrl4Sb0pSorJiDnTZEazO7tAiItHqkljkbcXsaONoAHe/JDGAvcjde7v74JT3prn7vu4+1N33cPfJUcYqkhHd+sLup8CCj2DKi1tff/tu2LAc9j0bWrSKLj6RelCSEoVNa2DtIigevf1tRUSi1KE7dOwFi3QvL5JTxl0Yems88QdYNCWsi/LSP6Fbf9jrS1FHJ7JdSlKikCzsd1SSIiI5YMfRoSWldHPUkYhIfbXvCqddGcaf/OurcO9l0LItnPFntaJITtBijlFo3QFGHg79dos6EhGR7Rt2UFh/oWwztGobdTSxZmbjgfFdu3aNOhSRsLjjN26D9x+Golawz5mhq7lIDjCvaXo6qZfi4mJfuHBh1GGIiMSamS1y9+Ko48gmlQ8iInXbXtmg7l4iIiIiIhIrSlJERERERCRWlKSIiIiIiEisKEkRERFJE604LyKSHkpSRERE0kQrzouIpIeSFBERERERiRVNQdwEZlYKrGjkx9sDG9MYTi4otHPW+eY3nW/99XD3glo9TuVDg+h885vON79lrGxQkhIRM1tYaOsGFNo563zzm85XMqXQftY63/ym881vmTxfdfcSEREREZFYUZIiIiIiIiKxoiQlOldHHUAECu2cdb75TecrmVJoP2udb37T+ea3jJ2vxqSIiIiIiEisqCVFRERERERiRUmKiIiIiIjEipKUDDKzIWb2pplNN7OJZjaylu3ON7MZZjbLzG42s6Jsx5ou9TlnMzvUzN4xsylm9qmZ/d7MLIp4m6q+v+PEtq0T5/xeNmNMpwb8TY8xswlmNtXMppnZqdmONR3q+fdsZvYXM5tsZh+b2ctmNjiKeJvKzK41s7lm5mY2uo7t8uaaFQWVDSobqm2rsiHHqGyodbv0XrPcXY8MPYCXgHMSX58OvFXDNgOBxUAvwIDHgAujjj3D57wbsFPi69bA68BZUceeqfNN2favwC3Ae1HHneHfb1tgFnBA4vsiwoJNkcefofM9CXgHaJH4/hfA/VHH3sjzPQgoBuYCo2vZJq+uWRH9nFU2qGxI3VZlQ449VDbUuE3ar1lqSckQM+sJjAXuSrz0EDDQzAZU2/R04H/uvszDb/lG4MysBZpG9T1nd5/k7rMTX28BPgR2yl6k6dGA3zFmdiAwBLgzawGmWQPO9yzCBft1AHevcPfGrrwdmYb8foFWQOtErW9HYGFWgkwzd3/V3bcXe95cs6KgsgFQ2ZC6rcqGHKOyoVZpv2YpScmcvsBid68ASPzC5gP9qm3XD5iX8v3cGrbJFfU958+ZWW/CH/ZTWYkwvep1vmbWDrgG+Fa2A0yz+v5+RwJbzOwJM/vQzO4wsx5ZjjUd6nu+jwMvA0uBJcBhwK+yGGe25dM1KwoqG1Q2ACobVDbknbRfs5SkZFb1+Z1r61vr9dgmV9T3nDGzjoR/4qvc/YOMRpU59TnfvwDXu/uiLMSTafU53xbAUcCFhO4bC4DrMxxXptTnfMcCw4EdgR2AF4HrMhxX1PLpmhUFlQ0qG0Blg8qG/JPWa5aSlMxZABQnBw0lmvr6ErLtVPOBASnf969hm1xR33PGzDoAzwCPuXuuLnxU3/M9APiVmc0F7gXGmNnkbAaaJvU933nAy+6+KFHDdDewV1YjTY/6nu85hPNd6+5VwO3AIdkMNMvy6ZoVBZUNKhuSVDaobMgnab9mKUnJEHdfDkwCzk68dBow193nVtv0IeAUM+uV+EO/iHCxyjn1PWcza08ohJ51999lNcg0qu/5uvvO7j7A3QcAXwY+cfdR2Yw1HRrwN30/sGeiNhTgaOCjrASZRg0439nAYWbWIvH9CcCnWQkyGnlzzYqCygZAZUNyO5UNKhvySfqvWY0dca9HvWZDGAa8BUwH3gNGJV7/N3BiynYXADMJf9D/JjETRC4+6nPOwM+BcsKgyOTj51HHnsnfccr248jtGVzq+zf9NWAyoQB6CiiOOvZMnS9hYOS/gM+Aj4FngQFRx97I872eMLCzgtCPemYtv9+8uWbF9e8q337OKhtUNiS+V9kQg/gbcb6RlA2W2KmIiIiIiEgsqLuXiIiIiIjEipIUERERERGJFSUpIiIiIiISK0pSREREREQkVpSkiIiIiIhIrChJERERERGRWFGSIiIiIiIisaIkRSRHmNkoM1ttZq1reG+8mf2zvtuLiEh+UNkg+UpJikjMJQqUV4AngQ7ANDO7x8y6pWx2MvBIA7YXEZEcprJB8p2SFJEYM7OOwEvAvcA4YB0wGNgC/DexTQtgf+Dl+mwvIiK5TWWDFAIlKSLxdiiw1t0/b65393LgZ8ARZtYZOAR4093L6rm9iIjkNpUNkveKog5AROrUDPAaXveU908G/lff7c2sFXATsB5o7u6XpC1aERHJBpUNkvfUkiISby8B3c3s/OQLZtYc+C0wAVgDHAU8XZ/t3X01cBrwirt/B1hjZvtm40RERCRtVDZI3lOSIhJj7r4WOAL4JvAi0BGYBfQAvgzsBUx194313B6gPzA38fXsxPciIpIjVDZIIVB3L5GYc/dJwN5mtjOhhmyUu28CMLPvk5i5pT7bJ8xja+EzgK01bSIikiNUNki+M/eauiiKSC4ws8nAIe6+vAGfaQX8E1gLtHT3SzMUnoiIREBlg+QDJSkiIiIiIhIrGpMiIiIiIiKxoiRFRERERERiRUmKiIiIiIjEipIUERERERGJFSUpIiIiIiISK0pSREREREQkVpSkiIiIiIhIrChJERERERGRWFGSIiIiIiIisaIkRURERCQHmdkEM7syomNfYWav13Pbc8xsYaZjkvxSFHUAIiIiItIopwJlmT5IIhl5wd2vSHn5/4BrM31sKVxKUkRERERykLuvzuT+zayVu5fWcuyNmTy2iLp7ieQoMysys9+a2Xwz22JmU83shMR7R5jZO4nXl5nZDVHHKyKSj8ysk5k9ZGYlZjbdzI41MzezcTV1c6reTcrMzjezD81sk5nNM7PfmVlRyvtHmNmkxP5XmtmTKe9t090rcdwLzOy1xPX/PTMbk/L+fmb2spmtNbMVZvZfM+ue8v45ZrbQzM4ys1nACjO7Ddgf+HVi/3NrOY9ay6Rafm7fMbPZZrbZzN41s3EN/NFLnlNLikju+g1wHnAJ8BEwFHAzGwk8CfwFOAdoQyhgREQk/a4BRgGHJr5vaBeoZsAPgVnAcOBWYAlwQyJZeRD4FfAI0CnlOLX5LfAdYDKhnPifmQ1z90qgPfBP4AOgcyL2G4AzUj7fHTgXOB0oBxYAI4DXCF28Kms5bo1lUk0bmtl5wHeBi4HpwHHAU2Y20t3nbuf8pEAoSRHJQWbWBvgBcLa7P5x4eVbivduB59z95ykf+SDLIYqI5D0z6wicDZzg7m8lXvsl8HR99+Hu/0r5do6Z/Z2QINxASEo6Ag+7+4LENh9vZ5e3u/sDiVi+ASwGjgGecPfnqsX/Q+A1M2ueSGIAWgEXpCYLZlYObHT3pTUdsK4yqRa/AL7j7s8kvv9HotXlbCCSiQAkftTdSyQ3DSYUJBNqeG90La+LiEh67USo8J2Y8trEWratUaIL1nNmtsjMNgJXAH0B3H0VcC/wqZnda2bnmln77ezy8+O7+zpgGjAscaxiM7sz0c1qA/BiIv7eKZ9f04jWjLrKpG0k4h8I3GdmG5MP4BDCz1MEUJIikquske+JiEj6JK+3NXZrAqr44jW5xecfNutA6J47BzgNGAv8KXUbdz8TOJKQbPyQkLB0qyOm2mIBuA3oD3wT2JPQYrNNTMDmOj5fm4aUO+0Sz2cBu6Y8RgA/bcSxJU8pSRHJTTOAUmBcDe99UsvrIiKSXrOACmCvlNf2TPl6BdDNzFKTgDEpXw8jjA35ibu/7e7TSbSipHL3d9z918Buie0PqyOmz2NJdEcbSkhwAPYBrnb3F9z9M8L4k/ooB5rX8X5dZVJ1y4GlQD93n1ntsaye8UgB0JgUkRzk7iVm9lfgWjOrAiYBQwgVD38GPjSz3wN3EprgD3T36yILWEQkD7n7ejO7B/ibmZ1LaFH4Xcom7xJaU36VGC94LHAQ8Gni/fmEBOBiM7uX0GJyMrABwMwGAt8AHiPc2B9AGPw+o46wvm5m7xMGzl8BLAOSYz9mAV81s8nAIOBn9TzVecA+ZrYjsNnd11T7OdRaJqWMO0lu62b2B+B3iW5erwJdgMOBie7+Uj1jkjynlhSR3PVr4D/AdcBU4G9Ac3efApwAHE2YYeVZEv2RRUQk7b5HaKl4BbiblCTF3VcSZso6G/gQ2IUwu1by/eWErlcXE1rBjyR090raTBhn+GjiGD8HznP3SXXEcwVwWeJ4Q4BT3b0i8d43CONHPknE+Yt6nuP/Ad2A2YQEpCY1lkk1beju/wB+nHhMBR4ntAAtqmc8UgDMva6uiyIiIiJSX4lpg8uBQ9x9QpaP7cAR7v5CNo8rkglqSRERERERkVhRkiIiIiIiIrGi7l4iIiIiIhIrakkREREREZFYUZIiIiIiIiKxonVSmqBVq1beo0ePqMMQEYm1RYsWlbl7q6jjyCaVDyIiddte2aAkpRHMbDwwvmvXrixcuDDqcEREYs3MVkQdQ7aofBARqZ/tlQ3q7tUI7v6Au5/Rpk2bqEMREZEYUfkgIpIeSlJERERERCRWlKSIiIiIiEisKEkREREREZFYUZLSCGY23szuLykpiToUEREREZG8oySlETQwssAs+QxK1kcdRf4p3QRlSvRFRDLKHea8C4unRh2JSIMoSRGpy5YNcNNX4Oazo44k//zxoPAQEZHMWTEbbr8olGPlW6KORqTelKSI1CVZ079mUbRx5CuvijoCEZH8tmXD1q8ryqKLQ6SBlKSIiIikicYsSuxUpVQGVVVGF4dIAylJERERSRONWZTYqapI+VpJiuQOJSkiIiIi+Sq1W6262EoOUZLSCGrOLyCqdRIRkVym7l6So5SkNIKa8wuIap1ERCSXeUpiojJNcoiSFJG6VOmCLiIiOSy19UQtKZJDlKSI1CV1wKGIiEiuUXcvyVFKUkTqoqZxERHJZeruJTlKSYpIXdTdKzP0cxURyQ5195IcpSRFpC6uC3pG6OcqIpId6u4lOUpJikhdVOOfGSooRUSyQ929JEcVRR2ASKzpZjozlPxJjjCzQ4CzgdbAGne/NOKQRBpmm5YUXXsld6glpRG0mGMBUbekzFDyJxEws2vNbK6ZuZmNrvbeEDN708ymm9lEMxsJ4O4vu/v57v4VoL+ZdYgkeJHG2mZMimaslNyhJKURtJhjAVGtU2Yo+ZNoPAgcAMyr4b2bgJvdfShwFXBL6ptmdjwwxd03ZDxKkXRSdy/JUUpSROqim+nMSK3Zc48uDiko7v6quy+s/rqZ9QTGAnclXnoIGGhmAxLvfwPYw91/kq1YRdJGA+clRylJEamLWlIyo0o1exIrfYHF7l4B4O4OzAf6mdlpwK+B3mZ2o5n1qGkHZnaZmS1MPjZu3Ji14EXqpJYUyVEaOC9SF7WkZMY2faSroFnz6GIRCao36RmAuz9EaFmp+8PuVwNXJ78vLi5WE6HEg9ZJkRyllhSRuuiCnhnbtKToZyyRWwAUm1kRgJkZoXVlfkN3pIlVJHbU3UtylJIUkbqou1dmVG9JEYmQuy8HJhGmGgY4DZjr7nMbsS9NrCLxou5ekqOUpIjURbX8mZFaUOpnLFliZteb2UKgGHjBzGamvH0hcKGZTQcuB86PIkaRtNM6KZKjNCZFpC66oGdG6lz96n4gWeLulwCX1PLeNGDfph7DzMYD47t27drUXYmkh9ZJkRyllhSRuqhpPDNUsyd5St29JHbU3UtylJIUkbqo1ikzNHBeRCQ7VCkkOUpJSoKZdTGzW8xsQdSxSIzogp4ZGjgveUqze0nsaAyg5KicTlLM7Fozm2tmbmajq703xMzeNLPpZjbRzEbWtS93X+Pu5wPTMhq05BY1jWeGuh9InlJ3L4kdjQGUHJXTSQrwIHAAMK+G924Cbnb3ocBVwC0AZtbbzCZUe1yTtYglt+iCnhlaXExEJDvU3UtyVE7P7uXurwKEdbe2MrOewFjgyMRLDwHXmdmAxLz347IXpeQ0r3Zxb5breX1MKEmRPKXZvSR21N1LclS+3nH1BRa7ewWAuzth5eB+dX3IzG4EhpvZjWY2qIb3LzOzhcnHxo0bMxG7xIkGeGdGlbp7SX5Sdy+JHY0BlByV0y0p2+HVvrcat0r9gPtF23n/auDq5PfFxcXVjyH5pvrFvXl0oeQVtaSIiGSHrreSo/K1JWUBUGxmRQAW+oP1JbSmiNTfNt29dHFPm226H6hmT0QkY9TdS3JUXiYp7r4cmAScnXjpNGBuYjxKk2mKyQKi7l6ZodlmJE+pfJDYUXcvyVE5naSY2fVmthAoBl4ws5kpb18IXGhm04HLgfPTdVz1OS4gaknJDHU/kDyl8kFiRy0pkqNyekyKu18CXFLLe9OAfTNxXM3eUkBUA5UZVeruJSKSFaoUkhyV0y0pUVFNWQHRxT0z9HMVEckOVbZJjlKSIlKXbbp7VdS+nTSMVpwXEckOdfeSHKUkpRE0MLKAqFtSZqglRfKUygeJHV1vJUcpSWkEdfcqIK6Le0ao0JQ8pfJBYkfdvSRHKUkRqYtupjNDK86LiGSHuntJjlKSIlKXKk1BnBFK/kREskMtKZKjlKQ0gvocFxAN8M4MrTgvIpIdqhSSHKUkpRHU57iAqCUlM1SzJyKSHV4FWOJrlWOSO5SkiNQl9YJeqSmI0yb1Z6lCU/KIWtoldqoqoXmLxNeqFJLcoSRFpC6pNf4VpdHFkW9Sf5ZqoZI8opZ2iR2vgqIWW78WyRFKUhpBNWUFJLXWSUlK+myTpKjQFBHJmKpKaN4y8bV6BEjuUJLSCKopKyCpXZHKlaSkTerPUt29REQyR929JEcpSRGpyzYtKVuiiyPfqCVFRCQ7vColSVGlkOQOJSkidUntv6uWlPQpT0n4VGiKiGROZTm0aB2+VncvySFKUkTqklrjrzEp6ZP6s6wsiy4OEZF8V1EWBs43b6lyTHKKkpRG0MD5AlKW8jtWS0r6pP4sy/R/JCKSMRVlIUEpagkV5VFHI1JvSlIaQQPnC0hqtyTVQKVP6s+yXEmKiEjGVJaHBKWopVquJacoSRGpyzYtKRo4nzbbtKTo5yr5Qy3tEjsVZSFBad5CLSmSU5SkiNSlvATadg5fqyUlfSq2bP25qiVF8oha2iV2KstCgqKWFMkxSlJE6lKWkqRoTEr6lJdu/blqTIqISGZUVYUZFItaJgbOK0mR3KEkRaQuaknJjIpSaN0BrJlaUkREMiXZctK8ZZjhq1LdvSR3KElpBPU5LhDuYbxEm07he41JSZ/yUihqBS3aqCVFRCRTki0nn09BrJYUyR1KUhpBfY4LRGU5eCW0bBNuqNXdK30qSqFFK2jZWsmfiEimVKS2pLRUS4rkFCUpIrVJ1vC3SCQpFbqZTgv3kKQUtVZLiohIJiWTkqLEwHm1pEgOUZIiUpvkWImWbUKtv1pS0iM5tqdFq/Cz1ZgUEZHMSG1Jad5Cs3tJTlGSIlKb1JaUFq01cD5dksmexqSIiGRWMilJLuZYVRkeIjlASYpIbZJjJVq2hlbtoHRTtPHki+TPsXV7taSIiGRScvHG5omB86AuX5IzlKSI1Ca1JaV1B9iyIdp48kXy59i6Q2ihKtsSxqmIiEh6bdOS0iLxmgbPS25QkiJSm9KN4blVu0SSsjEsjCVNk5qktGofZlDTDF8SU2bWxcxuMbMFUcci0mAVKUmKWlIkxyhJEalNybrw3LZzuKHGoUxdvprs8ySlPbRNrEGT/FmLZJCZXWtmc83MzWx0tfeGmNmbZjbdzCaa2UgAd1/j7ucD0yIJWqQpKqot5ghqSZGcoSSlEbSYY4EoWR+e23QMN9QQWlOkaVJbUtp0DF8nf9YimfUgcAAwr4b3bgJudvehwFXALdkMTCQjKlMWcyxqFb5WS4rkCCUpjaDFHAtEsna/TadESwoal5IOyUSvVfvwswW1pEhWuPur7r6w+utm1hMYC9yVeOkhYKCZDchieCLp9/nA+cQUxKBpiCVnKEkRqc02LSlKUtJGLSkSP32Bxe5eAeDuDswH+gGY2Y3AcDO70cwG1bQDM7vMzBYmHxs3qtVVYqD6FMSwNXERibmiqAMQia3Nidr91kpS0mqbJCXRkrJZLSkSuepTzNnnb7hftN0Pu18NXJ38vri4WFPWSfRqmoJYLSmSI9SSIlKbknXQsm3oy6skJX1qSlLU3UuitQAoNrMiADMzQuvK/IbuSGMWJVaSixBv05KiJEVyg5IUkdqUrN/aHSmZpKjGv+lK1kOz5mEhR3X3khhw9+XAJODsxEunAXPdfW4j9qUxixIf5SnrfbVoHb4uUwItuUFJikhtNq/dWtPfrmvitTWRhZM3Nq0KP0+zrVMQb14baUhSGMzsejNbCBQDL5jZzJS3LwQuNLPpwOXA+Y08hlpSJD7KEmtQtWwTHrA1cRGJOY1JEamJO2xcCT13Ct+3TyQpG1dHF1O+2Lh6a9LXqn2YFnPjymhjkoLg7pcAl9Ty3jRg3zQc4wHgAY1JkVj4vCWldWhNAbWkSM5QS4pITUrWhwWv2ncP37dqHwYebloVbVy5zh02rYb23cL3ZtChO2xQkiIiknbJhEQtKZKDImtJMbN2wP6EZvcS4CN3nxJVPCLbSNbsd0gkKWah9n+Tuns1SdnmMJCzXZetr7XvDqsaPD5Z8lSulw1mNh4Y37Vr16hDEYHyRHev1DEpyddEYi7rLSlm1t/M/gPMBn4CHAacDjxoZpPN7NxsxyTyBRtWhOcOPba+1r4bbFRLSpMku8u167b1tQ49wlifSs3dX8jypWzQwHmJlfISsGZhZq9kS0qZkhTJDVG0pNxJmEv+guTCWUmJ1X0vNLNL3f26CGITCZLdj5LdvSC0pCybEbosmdX8Oalbsrtc+5Ra5mRr1cZV0Kl39mOSuFDZIJJuZSWhFcVs65gUdfeSHJH1JMXdDwIws6+a2SPuviExN31lYrrHn2Y7pkQ8hxCmn2wNrHH3S6OIQ2KippaUDj1Cbf/mtdt2V5L6qyn5S/6M169QklLA4lo2iOS08i3QMtHNq6UGzktuiXLg/M+BjWbWBpgJLDWz/RuyAzO71szmmpmb2ehq7w0xszfNbLqZTTSzkXXty91fdvfz3f0rQH8z69DQE5I8snZxeO7cZ+trnXdIvLck+/Hki/XLwnNqMtJlx/C8ZmH245E4anLZECVNQSyxkmxJAbWkSM6JMknZ5O4OHAs8D+wD/LGB+3gQOACYV8N7NwE3u/tQ4CrgFgAz621mE6o9rkl+yMyOB6a4u5YWL2RrF4dBhu1SuiUlE5ZkAiMNt25peO7Ya+trXfuG59ULsh+PxFE6yobIaEyKxEp5ydYWlOZFYM3VkiI5I8p1UkrNrA/wZeAmd5+TmNWl3tz9VQCrNj7AzHoCY4EjEy89BFxnZgMS3QbG1bQ/M/sGUOzuP6nl/cuAy5Lfd+rUqSHhSi5Zszi0nKT+bX3ekqIkpdHWLQMMOqZ0o+tSHJ5XqyVFgDSUDSKSUFYCbRPdk81C1y/N7iU5IsqWlF8Ck4AhwMsWMo32adp3X2BxcvBlolZuPtCvtg+Y2WnAr4HeZnajmfWovo27X+3uxclH+/bpCldipaoK1i3ZtqsXQBclKU22fmkYKN+8xdbXWrcPLVZqSZEgk2VDxqm7l8RKaksKhC5fakmRHBFZS4q7vwh83jHdzIYBL6fzENW+r3M6Jnd/iNDiIoVu3dIwQD5Zw5/Urhs0b6kkpSnWLt2a7KXq2ldrpQiQlbIho7TivMSGe5huOLk+CoSERWNSJEfEZsV5d5/m7helaXcLgOLEzDAkauL6ElpTmkw1ZXluxazw3HPQtq83axZaV9YoSWmU0s1hCuLkGJRUXYvDWilbNBRMtpXmskGkcFSWg1du25LSsm1YVFckB0SxmOPe23m/zfZm4toed19O6C5wduKl04C5ifEoTaaBkXlu+ezw3GOnL77XeYfQkuKqJG2w5Oxd1VuoIGXwvMalFKpslA0iBWXLxvDcKqW3ZKt2ULopmnhEGiiKlpQfm9kLZnaumQ0zs06JGbfGmdlVwDukNPXXxcyuN7OFQDHwgpnNTHn7QsLiX9OBy4Hz03UCaknJc7W1pEBIUipKYdOa7MaUD5LduWpsSdEMX5K+skFEgNJEktI6NUlpH5IXVbRJDohiTMr9wGxCEvFzQoKxCfiYMCZkP3ffWJ8dufslwCW1vDcN2DcdAdewb/U5zmfLZ4UFBtt0/OJ7ycH0axZtu2q6bN/qZJJSQ0tK9wHhefmsrIUjsZO2siFKZjYeGN+1q64PErGaWlJat4eqilDZljpWRSSGokhSfuLuY4F3Izi2SN2qqmDFHOi/W83v9xgYnlfMgr5jshdXPkgmIMmfYaoeg8KMX0umZjcmiZO8KBtUiSWxUVtLCoQERkmKxFwU3b3qnGUrF6i7Vx5buyjUMPWooasXQM/B4XnZzJrfl9otmxlWmm/d4YvvFbUIP9vFn6kbQuHK+bJBJFZqbElJXH9LY98oKRJJktLdzI41sxrmIc0NGjifx5ZOD8+9Btf8fucdwjzzy5WkNEhlOaycuzXJq8kOI8LsXxtWZC0siZWcLxtEYiU5W2Lrat29Ut8TibEounu1JyyaOMbMNhBm4Uo+PnT36RHEJBIs+Cg8F9fSlatZs5DALJsRavxNlb/1snJe6Afda0jt2/QZEZ6XfAYde2YnLokTlQ0i6VRa0+xeKd29RGIuipaUue6+N9ABOAy4B2hNGAD/TgTxiGy14GNo0wm69a99m56DYfNa2Lgqa2HlvGUzwnNtLVQAfYaH58Ual1KgVDaIpNOWGsakJL9Wdy/JAVGuOF8JfJp43BFVHI2h2VvyVHlpuEEetE9oMalN8kZ7+Uzo0D07seW6ZPe4urp79RoMRa22tmZJQcrlsgFUPkiMqCVFclwULSl/juCYaaUxKXlqydTQJanvLnVvl+yytFS9T+pt6QxoVgTd62ihKmoJfXeG+R9CRVnWQpPYyPmyAVQ+SIyoJUVyXNaTFHe/N9vHFKmXeZPCc9+d696uzwiwZrDwk8zHlA+qqsLPaocRYZrhugzcM8yutvDT7MQmsaGyQSTNStaBNd+2JSW5/lfJ+mhiEmmAKFpSROJp5lth3vjaBs0ntWobWlMWfKTpcutj1TzYsn77yR+EJAVg7nuZjUlEJN9tXgttO207wUvbzlvfE4k5JSmNoHVS8tCWDbDgw3CT3KLV9rfvu0sYOL92ccZDy3kLPg7P2+tGB6G1pWVbmK1x0iIiTVKyLkwEk6p1B8Bg87pIQhJpCCUpjaA+x3lo9kSoqoQh+9dv++Rq8xrkvX2fT+tcj5aU5i1gp71CYrNpTWbjEhHJZ5vXbW05SWrWPHT5KlkbRUQiDaIkRQRgxhvheXB9k5REq8B8JSnbteBj6NQHOvao3/bDx4FXwfTXMhqWiEjeqqoKLSnVkxQIr6m7l+QAJSkiFeUw9eUwzqRLPRe77rwDdOwFc97NbGy5bv0KWDkH+o+t/2eGHhQGe059OXNxiYjks9KNobKnbacvvtemk7p7SU5QkiIy660wsHv0UfX/jFlYT2XVPFi7JHOx5brZb4fnwfvU/zNtO8GAsTDr7TBWSEREGibZUlJXS4omfpGYU5LSCBo4n2c+fS48jz6yYZ8blLjxnvV2euPJJ8mfzU57N+xzY46GyjKY/EL6YxLJIJUPEgvJJKX6wHkIFUFVFVC6KashiTSUkpRG0MD5PLJlA3z2cph2uMuODfvswD0BU5JSm6oqmPUO9BoK7bs17LMjDw+rz3/4eGZiE8kQlQ8SCxtXheearr3tuye2WZm9eEQaQUmKFLaPn4byLTD2lIZ/tl0X2GFkSFIqytMfW65bPAU2r4HB+zb8s63bw8jDwsxgK+emPTQRkbxWV5LSITGJyfrl2YtHpBGUpEjhcof3Hgyr8Ta0q1fSiEPCAMU5E9MbWz74LDHwffi4xn1+91PD89v/TUs4IiIF4/MkpfsX30vOtLhhRfbiEWkEJSlSuOa8C8tnwS7HQctGds0YeVh4nvJi+uLKF1NfDgXkjqMb9/l+u8IOo0KXL62ZIiJSf8muXDW2pPQMz0pSJOaUpEjhev22MNXtvl9p/D669QtjLj6bAJXq8vW5FXPCzGfDD4ZmjbzMmMF+Z0NFaWjxEhGR+tm4KrFwYw0D5zsmkhR195KYU5LSCJq9JQ8smgKz34FRhzd8wHx1Iw8Ni2bNfT89seWDj58OzyMPb9p+RhwaFoKceD+U6f9NRKReNq6Edt1qriRq1xWsmVpSJPaUpDSCZm/Jce7wwj8AgwPPbfr+kjfik59v+r7yQVUVfPxkWOxywO5N21fzItj/a7BptcamiIjU1/rlWwfIV9e8KCQqakmRmFOSIoVn1lthoPsux4VV5puqx0DoPQw+fV61/QDz3od1S2HnY0J3g6ba/RTo2hfeuE1jU0REtqeyHDashE69a9+mY0+1pEjsKUmRwlJVCc//A5q3hEMuSt9+x54MZZvUmgLw4RPheZfj07O/5i3g0IvDwmOv/js9+xQRyVfrVwAOnXrVvk2HHqFLWFVV1sISaSglKVJY3n0Qlk2Hfc6Ezn3St98xx4TFBz94JH37zEWlm8NMZzuOCi1M6TLqiDBL2MQHYMm09O1XRCTfrFsanutqSenQI1TabVqdnZhEGkFJihSOdUvhxevCQOyDvpHefbfpEMamLPgozGxVqD56AspLYLeT07tfMzj+p+Hrx68MhatIFphZKzO7zcyuNbPro45HZLvW1yNJ6ahpiCX+lKRIYXCHJ/8MZZvDzW6rtuk/xtiTw/O7D6R/37mgqgom3getO4bxKOnWZ3iYLnrxFA2il0ZJJBpzzczNbHS194aY2ZtmNt3MJprZyMRbpwGvuPt3gDVmtm/WAxdpiLVLwnOdSUqvbbcViSElKVIY3nsIpr8KY46GIftn5hj9dwtrpkx6DErWZ+YYcTb7HVg5NyRrjV0cc3vGfRO6FIcWsaXTM3MMyWcPAgcA82p47ybgZncfClwF3JJ4vT8wN/H17MT3IvG1emF47tq39m26Jf6MV9X0ryASD0pSJP8tmwnPXg2dd4DjLs/cccxgv6+G7k7vPZS548TVO/8Nc+/vOT5zx2jZBk7/A3gVPPhTzaYmDeLur7r7wuqvm1lPYCxwV+Klh4CBZjaAkNAkE5Pk9yLxtXpBaNFu07H2bbr1C8+r5mcnJpFGUJLSCFrMMYeUrIf7fwyVFXDaH6B1h8web/QR0KFn6PZUUUAr0C+dATPegOHjoMsOmT3WjqPgsEtDq82jvw1d+USapi+w2N0rANzdgflAP0LCcpCZXQ10dfe3atqBmV1mZguTj40bN2YrdpFtrVlYdysKQNtO0LazWlIk1pSkNIIWc8wRleVw/0/CRfio70PfMZk/ZvMWsPeXw2DEjx7P/PHi4rVbw/OB52XnePueDcMPgcnPwSv/ys4xJd9Vz3YNwN1L3f08d7/M3S+t9cPuV7t7cfLRvn37jAYrUqOyklD+dC3e/rbd+ofKHpGYUpIi+ckdnvlrWLRx99NC4pAte54ObTrBq7cURmvKyrlhfZjB+8MOI7JzzGbN4NTfhUU0J9wEnz6XneNKvloAFJtZEYCZGaF1pcF9YdTSLpFKJh3dB2x/2279oWQdbF6bwYBEGk9JiuSnV/4VZtkauCcc+6MwXiRbWrWD/b8epjye9Ej2jhuVV28FHA46P7vHbdkGzrwa2neD//0KZr2d3eNL3nD35cAk4OzES6cBc919biP2pZZ2ic6K2eG5x07b37Z7cvC8xqVIPClJkfzzxh2hdr3PcDjjL6ELVrbtdQa07RJu4MtLs3/8bFk2Az5+CnbaG/rtkv3jd+oNZ18HLVrDfy+DuR9kPwbJGWZ2vZktBIqBF8xsZsrbFwIXmtl04HIgy1m3SBokk5Seg7a/7eeD5zUuReJJSYrkl7fugef/Hi7QX70+LLIYhZZt4IBzYMNyeP/haGLIhhf+ATgc/u3oYug9NCQqzYrgnu/CvEnRxSKx5u6XJMaMFLl7b3cfnPLeNHff192Huvse7j65McdQdy+J1NIZoWKuPmNSkq0ty2ZkNiaRRlKSIvnBHV66AZ79K3QfCF+9IcxcEqU9ToP23UPXs5IN0caSCXPeCzN6jT4qe2NRalM8Gr7yd8Dgzktg2qvRxiMFS929JDLusOjTMFavPj0IuvYL3ZMXTcl8bCKNoCRFcl9lOTzxxzBQfcdRcN6/oUP3qKMKrSmHXxoGJr6aZzNQucPz14bWi0MvjjqaoP9ucM5N4ed+7w/DopoiIoVi7eJQ3uw4qn7bN2sGO4yEJVPDNP0iMaMkRXLb5rVw56Xw/kNhXMTXboy+BSXVzsdBnxHwzr2wMo/6/X78FCyeHGYyq0+3gmzZYQScdwt07AGP/iYs4qnCV7JI3b0kMosSPRTrm6Qkty3fAivnZCYmkSZQkiK5a9kMuPlrMPc92POM0N2nVduoo9pWs2Zw9A+gqhKeuybqaNJjywZ47u8hGRx3YdTRfFH3AXDBHdB3F3jr7jBOpWR91FFJgVB3L4nMok/Dc0OSlB0S2y5q1BAskYxSkiK5xx0+eAT+fQ6sXwrH/xyO+0k0s3jVR//dYNSRMP1VmP5a1NE03YSbYdOqMFi+Tceoo6lZ+27w9Ztg7MlhauKbvgILPo46KhGRzFk0GVq1D2NN6iuZ0Cz8NDMxiTSBkhTJLSUb4MGfwmO/CwsmnnMz7HFq1FFt31Hfh5bt4Mk/hxWBc9WymfDOfbDjaNj1xKijqVtRCzjhF3D8z2DjKrj1G2FK6KrKqCOTPKbuXhKJ0k0h0ei7S2jBr6+OPaFTn7DwsXvm4hNpBCUpCWa2i5ndaGb/NrNbEysOS5zMeBNu/HJY3XzEoXDRf6HfrlFHVT8de8Jhl8C6JfDyjVFH0zhVlfD4leBVcOxPGlYQRsUszLJ2wR2hG9hL18NtF+bX+CCJFXX3kkjMnghVFTBk/4Z9zgyG7AdrFmlRR4mdHLjLqJ2ZXWtmc83MzWx0tfeGmNmbZjbdzCaa2ci69uXuH7n7Re7+DaAD0C6TsUsDbF4bVhS/+9thbMHxP4MzroK2naKOrGH2PD00rb/9X1jyWdTRNNw798HCT2CfM2HHOv+d4qfXYPjmHbDXl2D+JPjnl+H12zSoXkTyw8w3w/OQ/Rr+2cGJxGbmG+mLRyQNcjpJAR4EDgBqqha9CbjZ3YcCVwG3AJhZbzObUO1xTeK9I8zsHmAVsDkrZyC1cw+zSF0/Hj56MlxIL74/1IznYkNXs+ah+xHA47/PrW5HqxfAi9dBlx3jM+VwQ7VoDcf+GM79N3TuExai/PfXQ+IlIpKr3MOaVV37Qde+Df/8wD3DmM4ZSlIkXnI6SXH3V919YfXXzawnMBa4K/HSQ8BAMxvg7kvdfVy1x/cS+3ve3c8CKoFds3MWUqNFk+GW8+DhX4ab+VN+F2bv6twn6siapvdQ2O9sWDwF3rgj6mjqxx0euxIqSuHEX4Z1SHJZ/91CV8EDzg2rM//7HHj4V7B+RdSRSR7QmBTJuoWfwPplMOzAxn2+VVsYsAfMeRc2rUlvbCJNkNNJSh36AovdvQLA3R2YD9Q65YWZHZXoPnYD0Bz4QvWqmV1mZguTj40bN2Yo/AK2fgX879fwr6+FRGWvL8G3/we7HJubrSc1GXchdB8YxqYsmxF1NNv3/v/CNM+7nxZq3PJBi1Zhoc1v3QuD9oWPn4R/nBIWBC3fEnV0ksM0JkWy7oNHwvOuJzR+H7ueECoEP3oyLSGJpEO+JikA1aepqPMO192fdffvuPvFibEp5TVsc7W7Fycf7du3T2vABW3TmrCOyLUnwUdPhIUZv3Vv6J6Ta2NPtqdFKzjlt6GF4n+/hoov/KnFx9ol8Pw10LEXHPGdqKNJv547wdn/gLOugQ494KUb4NqT4d0H4v17EREBKN0cJpPZYST0GtL4/QwfB607wqRHNcuXxEa+JikLgGIzKwJIzNTVl9Ca0mRqzk+jkvXw4vVwzQnw5p3QrR+c+Tf46vXhBjJf7TgSDjoPlk6DV/8VdTQ1q6oMExaUbgpjaVrnaVJuBkMPDOOdkgtvPvknuO5UmPSYBteLSHx98D8o2xzWhGqKFq1Cj4UVs2HmW2kJTaSp8jJJcfflwCTg7MRLpwFz3X1umvav5vym2rgaXvpnSE5euxU69Ybxf4IL74FhB+VP1666HHg+9B4Gr90Wz4W03rgD5n0Qutw1ZsaYXFPUAvY5C77zKBx2KWzZCI/+Bq4/Hd57GMpLo45QRGSr0s3w2n9CS/cuxzd9f/t9NQygf/mfak2RWMjpJMXMrjezhUAx8IKZzUx5+0LgQjObDlwOnB9FjFLNqgXwxB/gmuPh1X9Duy5hUPzF98GoI3Jj7Y10KWoBp/wmnPPDvwwFTlwsnhoKqh475Wc3r7q0agsHngvfewwO/iZsXgdP/B7+fkKYtnjLhqgjlBhTS7tkzWu3wuY1cND5oSWkqTr1DrNnLp4CHz/d9P2JNJG5suUGM7PxwPiuXbuOX7VqVdTh5IaFn8Abd8LUlwCHHUbB/l+DEYeEqXkL2Vt3w7NXh4GLJ18RdTRQVgI3fSUs7nXBHdBnWNQRRat0c+hS8eZdsGE5tGoHY0+BPcdD1+Koo8sJZrbI3Qvqh1VcXOwLF35h8kmR9FjwCdx6Xpgx8hu3hRaQdNi0Bm44AyrL4Vv3Qade6dmvSA22VzYoSWkCFULbUVYCnz4XBiEvmRpeG3JASE76jy2MLl31UVUF93wvLKR16pWw8zHRxvPEH+C9h+CI74bflQQV5fDJ06Eb3Mo5gMHQA0J3uJ32LqxWwAZSkiKSRmsWwy3nQsk6uPBu6Dkovfv/7BW497LQHfmcm/N3PKJETklKBqkQqsXKefDeg/Dh46FrTIs2sPOxsNcZYeVv+aKNq8Mq6OVb4KJ7oquh/2wC3PuDMNXwV2/QjXdN3GH2RJh4H0x7FXDo1h/2PD38nbftHHWEsaMkRWrlHtZgKt209VG+JbxWURZq9CtKQyVBZVl49sqtn61N8xZQ1DI8Jx/J74tahnKpVdvQMtqyXVj/KRcqzpbPgnu+D2sXw+l/gNFHZuY4E26GCTfBjqPhy3+FDt0zc5x0qqoKkwiUbU75WypJ/A0l/n4+/zr5fR0ToyT/Hpo1h6JWW/92ilpC85Zbvy5qBa3ah7+l1u3Da1IvSlIyQN29alBWAp+9DJMehzkTw2s9dgpdYnY+Blp3iDa+XDDrbbjzEthxFJx3S/qa7+trzWK46SywZmGxQzXzb9+axaGl8INHYMv68DsbPg52O0mtKymUpBSQ0k2wfnnoNrR5DWxanfh67davS9alJCUbw4x6UbNmIVFp1R5atg03m207h0ebTomvE89tOqe81xGaF2U+vvJSePd+ePmmcHN9/M9h7EmZO557mJL9tVuhXTc48nsw5qjsdc8uL038/SQfq1O+X73172vLxq1JSVlMxnU2b7k1YWnVLkzt3K4LtOuaeE5+nfi+fffcXyS5kZSkZFDBFkJJ7jD/w9BiMvkFKNsEzYrCOJM9x6tLV2M8fy28cTvs//XsDlivKIf/nB8W0PzKtTBk/+wdOx+Ub4EpL8GkR2Du++G1Tr1h1xPDWKMuO0QaXtSUpOQJd9iwAlbNDzX565fBumUpz0vDzWJdWncIN/3JG7hkDXTqo0WbUDtd1CJRY91i25rsGm+UU8oa90RNeUrryzZfl4WKteSNbTJhSr3ZLVkfkqnK7a2XZCFRads53HC27ZLy3Lna913CdvWtaS/dFAaxT3sNPnkGNq2CLjvCSVfAgLH120dTffIMPHVV+Fl06h0qYAbtC32Gh99LfZVvCYnq5rUpCWxqArJ2a/KxaU24n6hL85bh59m6fWgJS20VS/26ReuUv6OWIaH8/OsWiQQz9T6l2j2xA1UV27boVZRu+335lhDvlo0h6d6ycWsCvmVjqMDa3v9Fm05hlrZOvUMFYafe4fvOfaBrv3CueXg/pSQlg/KyEKqPtUvCqrQfPQGrF4TX+gwPN2Ojjwr/TNI4FeVw6/mweHJYL2bYQdk57jN/hbfvgQPOgcO/nZ1j5qtVC0Li/uHjYaA9BgN2hzFHw8jDwg1NgSmkJCUvWtpLN8OKWbB8NqyaB6sXwur54XpfvuWL2xe1Stxg9QrPHXturSVO3pwnb9Cz3ULcFO4hmUneXG9eCyVrt/1+09qtN9yb14TZAL+wlnQ1Ldttm8Ck/kwqy8O+Nq6CdUu2vt55hzD+bc/Tw413Nm1aAxPvD2MVNyX+pq1Z4oa6d0gqWySSyGQ3vYrScIOe/DlVbGcK92bNw99M6t/LNn9D1VohWrbNrZv2irJtW4E2rd762LBy24S/qoYuaK3ah3XkuvULSUu3ftBjIHQfmJ6Z3SKiJCUD8qIQaqg1i2DKizD1ZVj4cXitXdfQB3/X45u20q1sa81iuPls8Cr45l2ZH58y9WW474fQb1f4+k3Z6bpQCKoqQxe+SY+GmtDKstDSOGT/kLAMPbBgmvgLKUlJyolKLHdYtzS0oC6bActnwrKZ4XqfeqNtzaBTH+jWF7r2DTdJXXZM1Pb2DMlHLt0wZlJVZWiFqd5i8Plz9dfXbtvdLXmz3q5LGOvWeygM3jcMYo/6Z1xRHmbqnDMxjI1ZvTDcVCfHEAFg4aa5qFVozUh2i6v+2Cb56BpaRKI+vzioqgqJ4LqlIWlZuzhUDqyaF1owN6zYdntrDt0HhPG+vYaER5/huTGGCCUpGZUThVBTrJgTpgye8mJYGR1C7cWQA8I4k8H75latWC6Z8Sbc/Z1QQJ1/a+ZqztYsghvPConJRf8NNxySfls2hGTwk2dgzrshAW3ZFoYfEhKWnfbM6/8lJSkxUboZFn0aFo9NPm9KqWiz5qGGtteQcNPTc1C4Ue6yowYDS93cQwtAsyIlG5lUunlr0rJidqhcWDYjUbGQolMf6LszFI+G4jEhyY3h/7CSlAyKZSHUFO6wdDpMfTH0r185J7zeukPodjTiMBi0T043LeaU5Owqu50EJ/0q/fsvKwldy5ZOg7P/AYMLYFX5ONiwEiY/HxKWRZ+G11p3hOEHw4hDw/9YDAuTplCSEpGKMljwcUiM57wb/t6StfbNmodkZMfRYbKO3sNCjayu7yK5p3RTaN1aOj2MY1r4SUhikopahd4SO+0VJnXpPSwWE7soScmgWBRCTVVVFZr6p74YWk2S2Xi7rmGWohGHwsA98rqWN7aqquCe78LMN+GEX8Dup6Rv3+7w0M/h02dh3IUw7pvp27fU3+oFYS2hbVor24X1V0YeHlor86BLmJKULCpZDzNeD9OJz3gzTMEKoetN/92h/26hZrXP8Lz42xKRWpRsSLSYfhImOZo3KXQ7hjBQf+gBkVc+K0nJgJwfk1JVGf5Yp74UuqBsWB5e79grzMw14jDot4tWgo+DzWvhprPDIMpzbgrNt+nw+u3wwrWhu9EZV8WiRqXgrV4Q/h+nvLi1haVFaxi8P4w8NHSzzNFF1ZSkZFhFOUx/LYx/mvVWorXEwnV88P6hO2GfERpvJlLIyreEltXZ78DMt7btxj/0QBh7MgzYI6v3A0pSMiinWlIqykNz/9QXw2qym9eE17sUhxmHRhwamvzVlzR+Fk+F/3wjzKBywe2hf3hTzHgD7v5uWMfmG/8JNawSL+uWJsaDvRRqwPDQmrnT3uH/deiBOTWLnpKUDNm4OszK98H/QoWGNQvTw448NPyNtO+W2eOLSO5as2hrOZOcEKlrX9j9VNjjtKzcGyhJyaDYJynlW0K2PPVlmPZKmLMboMegUIiNOCwMjlRiEn9TX4b7fhSmHDz/1sYvjrloMtx2YbjhveCOMFuPxNuGlaHrztSXYM57YbVtax7WSRhxaOiWGfMJD5SkpNnmtfDKv+H9h8OsSl37hYX9dj4OOvbIzDFFJH+tmh8WJZ70WKjEbtMJ9vsq7P3ljHYLVZKSQbFMUko3hZryKS+G52R/5D4jEonJoWFwpOSeN+6A5/8e+pV/5e8Nv3Csmg+3nBcWKvvaP0NXEMktm9fB9FdD0jrzra39i4vHJBKWQ2KZeCpJSRP3cBPx/N/D4np9RsCB54VEVV02RaSpKsrho8fh1VtCi37nHeGEn4VxKxmgJCWDYpOkJG9cprwU1mWoLAMsjF9IduXq3CfqKKWp3OGZ/4N37g1dOs68uv6zQK1ZBLdfFC46X/q/MJOU5LbSzTDzjZCwTH8tJJ8QZmwacWgYX9YzHi2lhZSkZGzMYulmeOL3YVa4Dj3gyO+FxXNj8PsVkTxTUQ7v3g8v/TNUdu97dljoOc3j2pSkZEAsBs5vXhtuTiY/D3PfCwMlrXlY2XpkogtIBzX7552qKnj8yjBAdsDuIeHY3grmy2fDnReHbkMn/Qp2OzE7sUr2lJeGBdamvhy6hpWsC6937bs1YdlhVGS17YWUpCSltRJr8zq48xJYMjVc20+6Ato0ssuniEh9rV0SZgJd8FG49pz+JyhK32yvSlIyKOstKZvXwWcvw+QXYPbE0Dc9hwfTSiNVVcKzV4cWle4DwkWj95Cat53yIjz6mzA+6dQrYfSRWQ1VIlBZAfMnhYRl6ktbVyju0DMxe98h0G+3rM70pCSlCcq3wH8uCGsfHHxBmDJcrSciki0V5fDoFaEVd9QRcPof03YNUpKSQVlJUirLw9iSDx4NXTuqKsOKroP2CX8sww5WjVqheuseeO6aUDu+x2mw+2lhYH1VRZhm8M07Qzegtp3htD/AoL2jjliyraoKFk8OXUGnvgRrEterNp1CC8vYk7Myq5+SlCZ45Ar48PEw9uSwS5q+PxGRhqqqhAd+GmaIPeK7sP/X0rJbJSkZlNEkZf1ymHhfKJw2rgpTS+60d6gJHz5u+118pDAsmgxP/DF0AwFo3jK0sCVXlR51JBz1/djP/iRZ4A7LZibWR3oxrE4MYeXh3U+BnY/N2JSTSlIaacYbcPd3wlonZ12jwfEiEp3SzfDvr4dJeC6+H7r3b/IulaRkUEaSlDWL4bVbQ3JSVRFmVtjtRNj1BOjUK73HkvzgHrr/TX81LAjYrCgMnh5zdGhZEanJkmlhCtuPn4ayTaF1Zf+vw15npH3KSSUpjVBRDv/8EqxfBpc+rOu/iERv3qSwbtuQA8Iso020vbJBy8/GRWU5vHU3TLg5zHtfvDMceG74Q1DtmdTFLHTlUncuaYg+w+D4n4am+4+fgtf/Ay9cC2/fDcdeHibgkOh8/BSsmgeHXKQERUTiof9uYVbBT5+FhZ+E6e8zSHe/cbBpDdxxMbzwD+jYC86+LizYN+wgJSgiklmt2sKep8O3/wfH/iTU4N//I3j6L2EQvmSfe+ju26JNWExNRCQuDjw3PL9zX8YPpTvgRjCz8WZ2f0lJSdN3VlYSEpR5H8BeX4Jv3QuD99XsLSKSXUUtQ1evSx6A/mPD7HGP/iYMvpfsmv8hLJ0Wuvm21sQoIhIjvYbAgD3CEhjJ2SMzRElKI7j7A+5+Rps2Tey37R4GPS+bHqaWPPbH0KJVeoIUEWmMDt3hqzeEmQM/fgom3Bh1RIXn3QfC815nRBuHiEhN9v5SGDf9waMZPYySlCi99yB8/GQYd3LwN6OORkQkKGoBp/0edhgJr94aJmaQ7Ni0JszANmB3TXwhIvE09MCwYPgH/9s6m2gGKEmJyrxJ8PT/QZcd4dTfaeyJiMRLyzZhfZ2WbeB/v8p4s35cmVkXM7vFzBZk5YDvPRQmUtnj9KwcTkSkwZq3gN1OgnVL4bNXMnYY3RlH4cMn4LYLoVlz+NL/ac0TEYmnbn3DYPoNK+D68VvXVok5M7vWzOaamZvZ6GrvDTGzN81suplNNLORde3L3de4+/nAtIwGDTD15TDDY8deYT0sEZG42v0UKGoVFpxdkpnLo6YgjsLAPcLjsEuh99CooxERqd2ux4das0+fhe4Doo6mvh4ErgJer+G9m4Cb3f02MzsduAXY18x6A/dW2/ZDd/9eRiNNVTw6TPF5ws/DRAYiInHVqTec/Q944w7o1i8jh9Bijk2Q0RXnRUTyRFSLOZrZXOB4d/808X1PYDrQ3d0rzMyAJcA+7j53O/t6wd0Pr++xVT6IiNRte2WDunuJiEih6AssdvcKAA+1dPOBOqsBzexGYLiZ3Whmg2rZ5jIzW5h8bNy4Md2xi4gUFHX3EhGRQlK9+8B2F6Vy94vqsc3VwNXJ74uLi9VNQUSkCdSS0ghpXcxRRESyZQFQbGZFAInuXn0JrSlpofJBRCQ9lKQ0QtoWcxQRkaxx9+XAJODsxEunAXO3Nx6lgcdQ+SAikgZKUkREJK+Y2fVmthAoBl4ws5kpb18IXGhm04HLgfOjiFFEROqmMSkiIpJX3P0S4JJa3psG7JupY5vZeGB8165dM3UIEZGCoCmIm8DMSoHGLsPcHii06V8K7Zx1vvlN51t/Pdy9VTqDiTuVDw2i881vOt/8lrGyQUlKRMxsYRTrBkSp0M5Z55vfdL6SKYX2s9b55jedb37L5PlqTIqIiIiIiMSKkhQREREREYkVJSnRuXr7m+SdQjtnnW9+0/lKphTaz1rnm990vvktY+erMSkiIiIiIhIrakkREREREZFYUZIiIiIiIiKxoiQlg8xsiJm9aWbTzWyimY2sZbvzzWyGmc0ys5vNLGcX2azPOZvZoWb2jplNMbNPzez3ZmZRxNtU9f0dJ7ZtnTjn97IZYzo14G96jJlNMLOpZjbNzE7NdqzpUM+/ZzOzv5jZZDP72MxeNrPBUcTbVGZ2rZnNNTM3s9F1bJc316woqGxQ2VBtW5UNOUZlQ63bpfea5e56ZOgBvASck/j6dOCtGrYZCCwGegEGPAZcGHXsGT7n3YCdEl+3Bl4Hzoo69kydb8q2fwVuAd6LOu4M/37bArOAAxLfFxEWbIo8/gyd70nAO0CLxPe/AO6POvZGnu9BQDEwFxhdyzZ5dc2K6OesskFlQ+q2Khty7KGyocZt0n7NUktKhphZT2AscFfipYeAgWY2oNqmpwP/c/dlHn7LNwJnZi3QNKrvObv7JHefnfh6C/AhsFP2Ik2PBvyOMbMDgSHAnVkLMM0acL5nES7YrwO4e4W7N3bl7cg05PcLtAJaJ2p9OwILsxJkmrn7q+6+vdjz5poVBZUNgMqG1G1VNuQYlQ21Svs1S0lK5vQFFrt7BUDiFzYf6Fdtu37AvJTv59awTa6o7zl/zsx6E/6wn8pKhOlVr/M1s3bANcC3sh1gmtX39zsS2GJmT5jZh2Z2h5n1yHKs6VDf830ceBlYCiwBDgN+lcU4sy2frllRUNmgsgFQ2aCyIe+k/ZqlJCWzqs/vXFvfWq/HNrmivueMmXUk/BNf5e4fZDSqzKnP+f4FuN7dF2Uhnkyrz/m2AI4CLiR031gAXJ/huDKlPuc7FhgO7AjsALwIXJfhuKKWT9esKKhsUNkAKhtUNuSftF6zlKRkzgKgODloKNHU15eQbaeaDwxI+b5/DdvkivqeM2bWAXgGeMzdc3Xho/qe7wHAr8xsLnAvMMbMJmcz0DSp7/nOA15290WJGqa7gb2yGml61Pd8zyGc71p3rwJuBw7JZqBZlk/XrCiobFDZkKSyQWVDPkn7NUtJSoa4+3JgEnB24qXTgLnuPrfapg8Bp5hZr8Qf+kWEi1XOqe85m1l7QiH0rLv/LqtBplF9z9fdd3b3Ae4+APgy8Im7j8pmrOnQgL/p+4E9E7WhAEcDH2UlyDRqwPnOBg4zsxaJ708APs1KkNHIm2tWFFQ2ACobktupbFDZkE/Sf81q7Ih7Peo1G8Iw4C1gOvAeMCrx+r+BE1O2uwCYSfiD/jeJmSBy8VGfcwZ+DpQTBkUmHz+POvZM/o5Tth9Hbs/gUt+/6a8BkwkF0FNAcdSxZ+p8CQMj/wV8BnwMPAsMiDr2Rp7v9YSBnRWEftQza/n95s01K65/V/n2c1bZoLIh8b3KhhjE34jzjaRssMRORUREREREYkHdvUREREREJFaUpIiIiIiISKwoSRERERERkVhRkiIiIiIiIrGiJEVERERERGJFSYqIiIiIiMSKkhQREREREYkVJSkiOcLMRpnZajNrXcN7483sn/XdXkRE8oPKBslXSlJEYi5RoLwCPAl0AKaZ2T1m1i1ls5OBRxqwvYiI5DCVDZLvlKSIxJiZdQReAu4FxgHrgMHAFuC/iW1aAPsDL9dnexERyW0qG6QQKEkRibdDgbXu/nlzvbuXAz8DjjCzzsAhwJvuXlbP7UVEJLepbJC8VxR1ACJSp2aA1/C6p7x/MvC/+m5vZq2Am4D1QHN3vyRt0YqISDaobJC8p5YUkXh7CehuZucnXzCz5sBvgQnAGuAo4On6bO/uq4HTgFfc/TvAGjPbNxsnIiIiaaOyQfKekhSRGHP3tcAR/9/OHeJmEYQBGH7HtK4O3fomSNDcghOganoaTsMZUA2KptgqBOgP0T+pqSiCdP7yPGo3mU3WfXkns1t9qr5UZ9X36k31sXpXfZuZX89cX3Ve3R2ubw/3ABwJs4H/geNesLmZ+Vq9X2u97WGH7HJmfletta47/LnlOesPfvQ4fC563GkD4EiYDbx2a+apI4rAMVhr3VQfZub+L545rT5XP6uTmbn6R68HwAswG3gNRAoAALAV36QAAABbESkAAMBWRAoAALAVkQIAAGxFpAAAAFsRKQAAwFZECgAAsBWRAgAAbEWkAAAAWxEpAADAVv4AFkUvkjAsn2YAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(2, 2, figsize=(10, 6), constrained_layout=True, dpi=80)\n", "for dec_type, ax in zip(decays, axs.flat):\n", " ax.semilogy(phiExt, 1/decays[dec_type],'#ff7f32')\n", " ax.set_title(dec_type)\n", " ax.set_xlabel(r\"$\\Phi/\\Phi_0$\")\n", " ax.set_ylabel(r\"$T(s)$\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eigenfunctions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get the phase space eigenfunction of a specific eigenvector of a circuit by using the `eig_phase_coord()` method. To calculate the eigenfunction at $\\Phi_{ext} = 0.5\\Phi_0$, we set back the flux of our loop to $0.5$ and diagonalize the `cr` again." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "loop1.set_flux(0.5)\n", "_, _ = cr.diag(5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PCx3sVhQb3tz7LJe5hPuJrpyzfqPoS3s7t0iGklToTwPXmdleMxsG7gYeazjmL4FfMLNBM9sMvBc4ku5Qpe/EFW8n0xbT2Fc06QbRsSFtQyfV0LZCd/dFM3sAeBwYAB529xfM7P7o9QPufsTM/hp4DlgGHnL372U5cOkD3bRchsejC5RL4UajTi0tRNvPddJyiZccUKBLuSVpueDuh4BDDc8daPj6C8AX0hua9L244u10HjqEXwajWzs/Z6dL54J66FIZulNUilPrYtpiPDul25ku8fd1dVFUc9Gl3BToUpxupi2O9LhA19xU9D7qoUv/UaBLcRa6nIcO3V8YXWm5qIcu/UeBLsWpzQC2uhFzEnFl3W2FPh9X6J0EejQLR4EuJadAl+LU5kL1a83uXWthZRu6bit0tVykfynQpTgLs6vVb1LDvVboXVwUHRgM66erQpeSU6BLcTrZfi5WP22xG91cFAXtWiSVoECX4nS6FjrUbUPXw0XRTQOd9e0hCnRNW5RyU6BLcTpdlxzCMgG2qftwnZ8OF0Q76dtDtISuKnQpNwW6FKc229kythCCuJcldOenOpvhEhverJaLlJ4CXYpT66JCh96W0J2f7rx/DtGuRQp0KTcFuhRnfqbzHjr0toTu3FRnM1zqz6keupScAl2KU5vuLtB7WUK306VzY/E2dCIlpkCXYriHFkY3LZfh8d7moXfVQ9csFyk/BboUY2EOfLk6FbrmoUsFKNClGCubW3R5UbSbWS7uPVwUjWa5eON2uiLloUCXYsQtk27CdXQbzF7o/PsW5sKORWPbOv/eoTHwpfD9IiWlQJdi9FKhj20LLZdOw3Uu+iUw2kWgr6y4qD66lJcCXYrRa6DD6rosSc2eu/j7O6E10aUCFOhSjJWNJnoI9Digk5o9f/H3d6LXNWREcqBAl2KsVOhd9tCh8z56L4EeT3VUy0VKTIEuxYiDsasKfXt47LpC3975OVd2SuqwzSOSIwW6FGM+jR76+c6+r5ceeq9b34nkQIEuxYhvDOol0Gc7DPS5C2AD3c99B/XQpdQSBbqZ3WFmL5rZUTN7cJ3j3mNmS2b2L9IbovSl2mzYZGJgsPPvHd0aHjsN9Nlz4ZdBp2uhw2oPXYEuJdY20M1sAPgScCdwA3CPmd3Q4rj/Ajye9iClD81Ph9vpuzEwGCrmjgP9fHftFljt9auHLiWWpEK/BTjq7i+5ew14BNjf5Lh/C3wNOJni+KRf1Wa6m+ESG92Wb6BvGgh3i6pClxJLEui7gGN1Xx+PnlthZruAXwUOrPdGZnafmR02s8OTk5OdjlX6STfbz9Ub29bFRdEeAh26X0NGJCdJAr1Zw7FxhaLfBz7n7kvrvZG7H3T3fe6+b2JiIuEQpS91s0F0vbFuK/QupizGFOhSckmuSB0Hrq77ejfwWsMx+4BHLFxs2gncZWaL7v4XaQxS+lBturs1VWJj2+Dkjzr7nrnzqxdUuzGyRYEupZYk0J8GrjOzvcBPgLuBj9Uf4O5748/N7CvANxTmsq75Gdh+RfffP7a9sztFl5fCtMWeK3RdFJXyahvo7r5oZg8QZq8MAA+7+wtmdn/0+rp9c5Gmer4oujVMQ3RPNg0xXmmx1x769Onuv18kY4kmAbv7IeBQw3NNg9zd/3Xvw5K+NzfVW/tjbDssL4Y1zpNMf+xlHZeYeuhScrpTVPK3tBj10LvY2zO2OWqdzJxNdnwvt/3HFOhScgp0yV8cir0E+vil4XHqjWTHT52++Pu6EV8U1TZ0UlIKdMnfys5BPbRctlwWHpP2tKej4O8p0MfDNnQLc92/h0iGFOiSvzQCvdsKPf5F0A0t0CUlp0CX/KUZ6Ikr9NPhfIPD3Z9TgS4lp0CX/MV7gfYS6EMjoaeduEI/1Vu7BRToUnoKdMlffHPOSA8XRSG0T5JW6FOnYcvO3s63soSubi6SclKgS/7SaLlAqLinOmi5bFGFLv1NgS75m7sA2GpAdmvLpauzV9qZeiPFlosqdCknBbrkb+5CCMdNPf7nN35Zsgp9YT6EcC8zXKBuc+oO1pARyZECXfI3d6H3dguECn3uPCwurH/cdApTFiH8ErJNq3edipSMAl3yNzfV212isaQ3F02lcFMRhL8oRrd2vg67SE4U6JK/tCr08Q4DvdcKHaKNNVShSzkp0CV/abZcIMwxX08at/3HutkpSSQnCnTJX1otl3iDjHMn1j/u/CRgvc9Dh2hjDQW6lJMCXfKXZstlYBjONO6I2ODcCdi6EwaHej+nWi5SYgp0ydfycrgxp9e7RCFcpNxxJZxtE+jnf9rbdnf1VKFLiSnQJV/z04CnU6ED7LiqfaCfO5FioG8Lf2EsL6XzfiIpUqBLvtLYOaheu0B3Tz/Q8dUFxkRKRIEu+Zo5Ex4370jn/XZcGbahm59p/vrUG7A4n27LBdR2kVJSoEu+pqNAH78knfe7ZFd4bFWlnzkeHi+9Jp3zxX9Z6MKolJACXfIVb+qcxpxwCBU6tA7008fC46VXp3M+VehSYgp0yVd8V+fmlCr0HVeFx/UC3QZgR5o9dFShSykp0CVf02dgcASGx9J5v/FLYWhstRJv9MaroYofSGEOOqhCl1JLFOhmdoeZvWhmR83swSav/5qZPRd9PGlmN6U/VOkLM2fCBVGzdN7PDCbeDJM/bv76yZfg8rekcy5YnW6pCl1KqG2gm9kA8CXgTuAG4B4zu6HhsB8D73f3G4HPAwfTHqj0iemz6V0QjV3+Zjh5dO3ziwvwxsvpBvrAYFhGV4EuJZSkQr8FOOruL7l7DXgE2F9/gLs/6e7R9AWeAnanO0zpGzNn0rsgGrv8LWF6YjyDJnbq5XADUJqBDp1tfSeSoySBvguob1Aej55r5ZPAN3sZlPSx6TPpXRCNXfn28PjakYuffz36+oq3pXu+LTvbr/AoUoAkgd6s2elNDzT7ACHQP9fi9fvM7LCZHZ6cnEw+SukfM2dgfEe673nVDWEnoePPX/z8sedCz/uya9M939adcEGBLuWTJNCPA/WTeHcDa+aImdmNwEPAfndvunOvux90933uvm9iYqKb8UqV1WZhYS79Cn1kPLRVjn334uePPQe7f673vUsbqUKXkkryX/rTwHVmttfMhoG7gcfqDzCza4BHgY+7+w/TH6b0hZWbilIOdIC9t8Ar/xR+aQCc+ylM/gj27Ev/XFt3Qm2m9XIDIgVpG+juvgg8ADwOHAH+3N1fMLP7zez+6LDfAi4Dvmxmz5rZ4cxGLNUV31SU9kVRgOveB0s1+NFT4esfPhEe3/oL6Z8r3ihDVbqUzGCSg9z9EHCo4bkDdZ9/CvhUukOTvhPPQkm75QKw511hz9Bnvg5vvy08Xv4WmNib/rm2RoF+4RRcltIaMSIp0J2ikp+4ok1js+ZGA0Pwnn8JR/8RvvoZOPEi3Pqx9G5gqhePXxW6lIwCXfJz/mR43JrRBfH33QvX3BxC/e0fgHd+NJvzqOUiJZWo5SKSivMnwz6gaezt2czgMHzij+DCZPilkUV1DmHpgk2DmroopaNAl/xcOAnbMp6uagbbLs/+HFsuU4UupaOWi+Tn/EnY9qaiR5EO3VwkJaRAl/ycP5ld/zxvWydCa0ekRBToko+5qbBCYbwhRdXFm1N701UwRAqhQJd8xDsKXbLeum4VcsmusIzBtFZdlPJQoEs+4s2a+yXQ4780zrTY+k6kAAp0yceZPqzQAc7+pNhxiNRRoEs+Th8LS9nGmyxX3Y6rAGu9l6lIARToko9TP4adGayrUpThsbD5dKu9TEUKoECXfEy+nM1CWUXauSdscydSEgp0yd7seZh+IwRgP9m5N9q3dLnokYgACnTJw4loz5O0N2su2pt+Bhbn4fSrRY9EBFCgSx5e/0F4vPL6YseRtnhz6vjfJ1IwBbpk7/UjYQ2XLRnsVFSkib0wMAyvHSl6JCKAAl3ycOx5uOqGokeRvoEhuOr6tZtTixREgS7ZOvt6uPlmz7uLHkk2rr0ZXvv+6ubUIgVSoEu2Xvp/4XHPvmLHkZU974blJXjlO0WPRESBLhn7wd/D9ivCjJB+dO27YXhz+HeKFEyBLtmZOQc/egreflt228EVbWgErnsfHPlfsFgrejSywSnQJTvPPgZLNbh5f9EjydbNH4WZs/D9vyt6JLLBKdAlG7VZePJPQ0viircWPZpsvfnWcBfs/34YlhaLHo1sYAp0yca3fg+m3oDbP130SLK3aRPc/gBMvgT/8EdFj0Y2sESBbmZ3mNmLZnbUzB5s8rqZ2R9Erz9nZu9Kf6hSCYsL8M3/Coe/Bj9/L1xzU9Ejysf1H4B3/go88VAIdVXqUoDBdgeY2QDwJeCXgOPA02b2mLt/v+6wO4Hroo/3An8YPUo/cw/bsM2eg1OvhBtsnv2rsN3ce++GDz5Q9Ajz9Sv/KVwY/fYB+N634KYPw653hHbM2HYYHCp6hNLn2gY6cAtw1N1fAjCzR4D9QH2g7wf+xN0deMrMdpjZle7+euojPvok/PXv1j3RsElvkk171xzT7j0aX1/zhj2+fxNlH2Mc5ksNMzuufRd8+MEw82OjGRiEf/47cP0vwj/+Mfztf294fRhGxmHTANim0Kqx+g/LeTZQTufq1xlOvbh5P/z8r6f+tkkCfRdQvy3LcdZW382O2QVcFOhmdh9wH8A111zT6ViDkS1rV+1b8x+MtXm9iY7fo/H1NW/Y4/s3UbYxDo7A5u1hF6IdV8FVPwtjW9cMe0Mxg5/9YPg4Pwknj4ZdjeYuwNwU1KbDcrsefSwvgy+FX5Ce4zK8SYqKdE6U03kqJqN1jZIEerOkafxfKckxuPtB4CDAvn37uvtf+uobw4dI2W2bCB/8s6JHIhtEkouix4Gr677eDTRudZ7kGBERyVCSQH8auM7M9prZMHA38FjDMY8B90azXW4FzmXSPxcRkZbatlzcfdHMHgAeBwaAh939BTO7P3r9AHAIuAs4CswAn8huyCIi0kySHjrufogQ2vXPHaj73IENcAeJiEh56U5REZE+oUAXEekTCnQRkT6hQBcR6RPmud0x1nBis0nglUJOvr6dwKmiB1Ew/Qz0M9jo/34o78/gWnefaPZCYYFeVmZ22N37dAPMZPQz0M9go//7oZo/A7VcRET6hAJdRKRPKNDXOlj0AEpAPwP9DDb6vx8q+DNQD11EpE+oQhcR6RMKdBGRPqFAX4eZfdbM3Mx2Fj2WvJnZF8zsB9Gm3183sx1FjykP7TZE73dmdrWZfdvMjpjZC2b2maLHVAQzGzCzfzKzbxQ9lk4o0Fsws6sJG2O/WvRYCvI3wDvc/Ubgh8B/KHg8mavbEP1O4AbgHjO7odhR5W4R+Hfufj1wK/DpDfgzAPgMcKToQXRKgd7a7wH/ng26KaK7f8vdF6MvnyLsQtXvVjZEd/caEG+IvmG4++vu/p3o8wuEUNtV7KjyZWa7gQ8DDxU9lk4p0Jsws48CP3H37xY9lpL4N8A3ix5EDlptdr4hmdke4Gbg/xY8lLz9PqGYy3HX7nQk2uCiH5nZ3wJXNHnpN4H/CPxyviPK33o/A3f/y+iY3yT8Gf7VPMdWkESbnW8EZrYF+BrwG+5+vujx5MXMPgKcdPdnzOy2gofTsQ0b6O7+wWbPm9nPAXuB75oZhFbDd8zsFnc/keMQM9fqZxAzs38FfAS43TfGDQva7BwwsyFCmH/V3R8tejw5ex/wUTO7CxgFtpnZ/3D3Xy94XInoxqI2zOxlYJ+7l3HVtcyY2R3A7wLvd/fJoseTBzMbJFwAvh34CWGD9I+5+wuFDixHFqqYPwZOu/tvFDycQkUV+mfd/SMFDyUx9dCllS8CW4G/MbNnzexAu2+ouugicLwh+hHgzzdSmEfeB3wc+MXof/dno2pVKkAVuohIn1CFLiLSJxToIiJ9QoEuItInFOgiIn1CgS4i0icU6CIifUKBLiLSJ/4/h1UPmSdwXicAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "phi1= np.pi*np.linspace(-1.5,1.5,500)\n", "# creat the grid list\n", "grid = [phi1]\n", "\n", "# the ground state\n", "state0 = cr.eig_phase_coord(k=0, grid=grid)\n", "plt.plot(phi1, np.abs(state0)**2, '#ff7f32')" ] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }