{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import time\n", "import matplotlib.pyplot as plt\n", "from lmfit import Model, Parameters\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting model and fitting" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "# def mollow_convol(x, x_0, gamma_0, gamma_1, rabi, I0, I1, bg):\n", "# result = np.zeros(len(x))\n", "# i = 0\n", " \n", "# for x_cur in x:\n", "# con_list = np.arange(-50e6,50e6,5e5)\n", " \n", "# total = ((1/(1+ (4*((con_list)/gamma_1)**2))) *\n", "# (I0*(0.5*gamma_0/((x_cur-x_0+con_list)**2 + (0.5*gamma_0)**2)) +\n", "# I1*3*(0.375*gamma_0/((x_cur-x_0+rabi+con_list)**2 + (0.75*gamma_0)**2)) + \n", "# I1*3*(0.375*gamma_0/((x_cur-x_0-rabi+con_list)**2 + (0.75*gamma_0)**2))))*0.5e5\n", "# temp = np.sum(total)\n", " \n", "# result[i] = temp\n", "# i = i+1\n", "# temp = 0\n", " \n", "# return result + bg\n", "\n", "def mollow_convol(x, x_0, gamma_0, gamma_1, gamma_l, rabi, I0, I1, bg):\n", " result = np.zeros(len(x))\n", " i = 0\n", " \n", " s = 2*((rabi/gamma_0)**2)\n", " rabi_t = np.sqrt((rabi**2)-(0.25*gamma_0)**2)\n", " \n", " for x_cur in x:\n", " con_list = np.arange(-50e6,50e6,5e5)\n", " \n", " total = ((1/(1+ (4*((con_list)/gamma_1)**2))) *\n", " I0 * (((s/((1+s)**2))*((0.5*gamma_l)/((x_cur-x_0+con_list)**2 + (0.5*gamma_l)**2))) +\n", " (I1*((s)*((0.5*gamma_l)/((x_cur-x_0+con_list)**2 + (0.5*gamma_l)**2)))) +\n", " ((0.125*s/(1+s))*(gamma_0/((x_cur-x_0+con_list)**2 + (0.5*gamma_0)**2))) +\n", " ((0.0625*s/((1+s)**2))*(((1.5*gamma_0*(s-1))+((-1+(5*s))*(0.5*gamma_0/rabi_t)*(x_cur-x_0+rabi_t+con_list)))/((x_cur-x_0+rabi_t+con_list)**2 + (0.75*gamma_0)**2))) +\n", " ((0.0625*s/((1+s)**2))*(((1.5*gamma_0*(s-1))+((-1+(5*s))*(-0.5*gamma_0/rabi_t)*(x_cur-x_0-rabi_t+con_list)))/((x_cur-x_0-rabi_t+con_list)**2 + (0.75*gamma_0)**2)))))*0.5e5\n", "\n", " temp = np.sum(total)\n", " \n", " result[i] = temp\n", " i = i+1\n", " temp = 0\n", " \n", " return result + bg\n", "\n", "def fit_mollow_convol(freq, power,power_err):\n", " mod = Model(mollow_convol)\n", " gmod = mod\n", " \n", " #set parameter and make init guess\n", " p = Parameters()\n", " p.add('x_0', 134.5e6)\n", " p.add('gamma_0', 6.2e6)\n", " p.add('gamma_1', 3.92e6, vary=0)\n", " p.add('gamma_l', 0.4e6, vary=0)\n", " p.add('rabi', 50e6)\n", " p.add('I0', 5)\n", " p.add('I1', 1.5e-5)\n", " p.add('bg', 0.0001)\n", " \n", " return gmod.fit(power,x=freq,params=p, weights = 1/(power_err))" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7827746868133545\n" ] } ], "source": [ "filename = '455amp'\n", "start = time.time()\n", "data = np.genfromtxt('data_spectrum/'+filename)\n", "freq_data = data[:,0]*1e6\n", "transmission = data[:,1]/np.max(data[:,1])\n", "trans_err = np.sqrt(data[:,2])/(data[:,5]*np.max(data[:,1]))\n", "\n", "fit_convol = fit_mollow_convol(freq_data,transmission,trans_err)\n", "end = time.time()\n", "print(end - start)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[Model]]\n", " Model(mollow_convol)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 73\n", " # data points = 93\n", " # variables = 6\n", " chi-square = 169.874730\n", " reduced chi-square = 1.95258310\n", " Akaike info crit = 68.0289463\n", " Bayesian info crit = 83.2245433\n", "[[Variables]]\n", " x_0: 1.3446e+08 +/- 125284.433 (0.09%) (init = 1.345e+08)\n", " gamma_0: 7835484.65 +/- 332251.900 (4.24%) (init = 6200000)\n", " gamma_1: 3920000 (fixed)\n", " gamma_l: 400000 (fixed)\n", " rabi: 64118372.2 +/- 239991.526 (0.37%) (init = 5e+07)\n", " I0: 28.6603047 +/- 0.87023567 (3.04%) (init = 5)\n", " I1: 1.5865e-04 +/- 5.1659e-05 (32.56%) (init = 1.5e-05)\n", " bg: 0.00750047 +/- 0.00307719 (41.03%) (init = 0.0001)\n", "[[Correlations]] (unreported correlations are < 0.100)\n", " C(I0, bg) = -0.813\n", " C(gamma_0, bg) = -0.629\n", " C(x_0, I1) = -0.582\n", " C(gamma_0, I0) = 0.462\n", " C(I0, I1) = -0.435\n", " C(gamma_0, rabi) = 0.345\n", " C(rabi, bg) = -0.290\n", " C(rabi, I0) = 0.234\n", " C(I1, bg) = 0.180\n", " C(gamma_0, I1) = 0.175\n" ] }, { "data": { "image/png": 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MTpR0qaSTJM2R9FMzO8E5N/rMkBJRB4Z8TTeqmcmvmCTMFDmRmQbjbN5S7LkL8liJcibGQrOfdbQ06eDh4aqPu7Ez0U10lrU4Z7YL+j2YFFHOSpnv9St2nEtS95ZeHRoa1rJr757QTJ+lvhgHeT6s5r1b7NwR5GtUaubL7JeGIJ7zicj3fxZ6DW95cKuGnBu3vJzXLwmzZRZ7zuMsVy3PilnRJDtm9jIzm5f9m+C+3yDpSefc0865Q5JulTeaSq6LJH3Tv367pDebmfnLb3XOHXTOPSPpSX97daHS3u5JqPEKuv1XEmog662W+PneTKDHykSfnyA6CF914Ul1W+tWaR+FJLTBrEeFjvOr7nhsQrWZY1/fYu/bODo1jlXq3BFkn5pCv7Am4ReAYgq9HmODd6n1yxHV50mSf4Uudb5O8i/X5gocFKNWMrtQ0hfk1TK/KOk4SU84506qesdm75B0nnPuA/7td0s6zTn34Zx1HvXX2ebffkrSaZKukvRfzrl/85ffJOmHzrnbi+1z6tSp7nWve121RQ7c4897LXdOnDNtZFn/zFfrxTlnylo7NOnQPs3Ycp/ad/2+6H39M1+tXa84T67xyAA0NjSomU//aOSxcf5P1di6ZKWGpkwft7zx4F4d231jYPsp5tnTPi6Zjb/DOc1/8LqKthXla1TsuZux5b6i5ajkeS3n+Sm2vUpfw2LvjULbKvaYQo+r5j1Y7f9U7WOq2V6Qr0W126u2DFE9r/keU+w4z7c8e44qdayMVex9KynQ82Gp926h90WUnzP5ylDqc6HQ4woJ+n8qVD65YcnG13WWU+64X4tynvNCKj2/FlLs/5WU2Fx07733PuycW5rvvqLNTnJ8VtIfSfqpc26Jmb1J3rCDiWdmKyWtlKQpU6bEXJrisgdLg3+wDE2Zrl2vOG/k/kL3ZQ+kSg/opMj3Bi0UEmdsuU/9M1+t5pO9/3VriP9r46F9+U86hyrv7rBn3htH/S+S5BqbtGfeGwMve7HnbiLHytjXKcjnpxztu36vLZt+PaoMWfk+aIu9n4r9v4X2U+32UNsKHeeFDE2eVtWxUux9K6nofZWq5r0b9edMvvf00OT8gTq7vNLPhqDPy4Vew7YXf6f9LzslsNcvys+TUs95IfleC6l0jsmn2P97bPeNeR8b5XNUjXJrvjc655aa2W8lLXHODZvZb51zp1a9Y7PTJV3lnFvu314jSc65a3LW2eCv84CZTZK0Q9JsSatz181dr9g+ly5d6jZu3FhtkQPV1d0zrk3b2g2b87Zf6vR/1it03y9XnyMpGW3DCilWtkL35XuOJOVt1xrGz+vZn9uC2NeC1Xcq3zvNJD1z7Z9OrKB55Hvucstc6Dmv5HUq5/mp5LUttxNdofKNtezau4u+Z4qVId9+Sm2v0vJV8z9NZHvVvAer2U8YZYjqeS3W5nvscd7c1JB3tJ1yz9f5FDsmKz1eiyn13g36tQhKsfdgoT4Pxc7XYZyXC71OQb62UX6elHPeG6ua90yx90U1/2/Un7n5mFnBmu9y23z3mlm7pPskfcfMviRp/wTL9ZCk481sgZlNlteB8o4x69wh6T3+9XdIutufafMOSZf6o6EskHS8pF9PsDyRKdSGKt8BLpXf5q/exrHO1+4v6pFdgmpDG/XwWVEMAVXq+QljuMNKjvFi75lqysAMr+lU6Di/8oKTCrY3rbaNdrH3bbF20JUed7XaPyDouSbC6C9V6HUKsh17OeUOapSPavrBBD00bDWvUxL6whVTbvi+SFJG0v+U9CNJT0m6YCI7ds4dlvRhSRskPSHpNufcY2b2Gb+NuSTdJGmmmT0p6WM6UuP9mKTbJD3ul+d/1NJIJ4UOzMZ8bfDkHSxRH0hJHZ4n6o6YQYXYJHXkk/KH2Go/xCv9QInqC1Sx90ypMuR7fpI0wyuKq+ZYLvaYfMd5sQAb1fm6nOOu0Lm82jBfTSVPUJ8nE5lrIl8ZknBeruZ8GGW5q/miNpGhYfOp5v9NwmtbTFltvp1zubXc3yy4YoWcc3dJumvMsr/PuT4g6Z0FHnu1pKuDKkuUivWIbmlqLDhUWD0OI1apWh1aqNTwWWGo5AOy1NBjlQ41FdVwh8UUG3rvf657pOIyFNteEobGhKeaYfSqHTaz0BB7Exn2sdj7dux9QR93UQ4fWq1Cz3k1nw1xnJfHquZ8GHW5Kx1KMuihYav5f5Pw2hZTVs33mMl2BsxsKIhJdtKgkqEBs98o833DTMpkE0GrtIYq6d9mi4l7NrBiin2IB91EI6pawaBrJsOY4ZWmKsGrpiYx6F9jomrWMdEvsmNrsWt5xsBqPxviPi9Xez6Mu9zFhDE0bDX/b6WzeEep3Jrvkcl2csbZ/qOwClXvitWKFPuGGeVkE0EqVGtaTS1L0r/N1qpiH+LV1K4Vq4WKcjKYoGsmg6x1q4VaxqSo5HxXTSAtt09NJaI4X5c67iqdkCYJ8ytUq1Y/G4qdi5IyoZBU2TFc6rWo9n1RS7mnlHKHGhzhd3jsMrMr5bfBRn6F3jilDsxKfnaMstzVbqtQwCgn1OX7f2v1S0i1ojgBF/sQr+YDudQXTCneD8mgy1BNmA+rqUq+90QYx1BS33vVfBGaSHO2OJ+HUsGt0i93YTTri/LYq8XPhkLnIkk1/eW8Fl+LKJUVvs3s4pybDZKWShoIpUR1otSJLzeEh12OSk58QdfGJaHtb1iiGHKr1OsRVBlKtWcOui1llCfmKD6oqwnzUR3/aathr+aLUJS/xgSp2HG37Nq7K/5yF/TzkJRjL0k1yPnkOxdV8/pNVFTDSBLGy6/5zh3Z5LCkZzV+KnjkSEIHrGpOfEGXu1Tb31rsPBmlqI6jUuExyCYatS6oMB/V8Z+Ec1GUktQ5K4rjvtBxl4SOfEk49sr5HAz6dQpiexP5cp6ULxv1dN4PWrltvv8y7ILUmyTU6lZz4gu63Elp+zsRQZ5AKt1WlMdRoQ/xWg4mSS5DVMd/Es5FUavmi1+9fVms9stdkM9DHMde2CPCTEQlz2e1r19Sfm1AcUVHOzGzfzazLxf6i6qQtSgJA7xXc+ILutzFeqBHNSJALUvCcSRV37O+3iZ+ClJUx39SjiFEKwkjQyXh2KvVL5/Vvn61PGJNmpQaanCjpIclNUt6raQ/+H+LJU0Ot2i1rVZPfEGXu1TASPJwSVFLwiQQhOVoRXH8J+FcJBUfVpEhFycm3/t2Il/ugjoPJOHYm8gXgDiH6S3n9cv3OtXql420KdrsxDn3TUkysw9JOtOflVJm9lVJ94dfvNqVhBEdqvlZO4xy19vPuVEq9npE3a6P1660JD5HcZyL8s2eWuincKm2R3VIsrjPvbX6OZgU1bx+9KWqDeV2uJwhaZqk3f7tdn8ZiqjVE1/c5S5HUssVhnyvB+36UIli7+kovsSV+im8Ftvkojxxf54k4QtAlKr9spGUTpppUW74vlZSt5n9XJJJeqOkq8IqVD2J+2Qe94kP4UhSJyLUrqi+xAU58Q1QqTR9DlbzZYPKnOiVO9rJ183sh5JO8xd90jm3I7xiod7U2wkv7loC2vUhCFF9iSv1Uzg/k4cnCefeJJQh6YJ8jir9skFlTvSKhm8ze7Vz7vdm9lp/0Vb/co6ZzXHO/Sbc4iENau3EnIRaAtr11Y84j/+ovsSV+ik8jDa5tXZeAeJCZU70StV8f0zSSklfyHOfk3RO4CVC4Kr5EOKDq7AwaglK1aSPfT1quRMRkiOqL3Hl/BSelja5QNJQmRO9UqOdrPQv3xRNcYDkC7qWoJqa9LR1IkI4ovwSV+yn8DS1yUU8Kj2u4m5aGCUqc6JXapxvSZKZvdPMpvrXP21m681sSbhFA5Ip6Ikjqp0UgTHSMVFMdAWMV6hCpF7Hn+c8EL2ywrek/+2c6zOzMyWdK+kmSV8Nr1hAcgU9cQTt7RAnvsQBo9XzLJGFJg7iPBCtcocazB6FfyrpRufcnWb2DyGVCUi0oJt80N4OSZb9oKY5CNIiSRUi1b7veL8mW7nhu8fMbpD0FkmfN7MpKr/WHKg7QbZRnUh7u3o8wRL2AMSJChGErdwA/S5JGyQtd871SjpK0qrQSgWkCO3tEIV1HzydLzRAGYJuWgiMVe4kOwfM7EVJZ0r6g6TD/iWAADDaAwAkQ1pHk+KzJzplhW8zu1LSUkkLJX1dUpOkf5O0LLyiAQAARI8KEYSp3GYnb5d0oaT9kuSce17S1LAKBQAAANSjcsP3IeeckzerpcysLbwiAUir7MQWDz6zW8uuvbtux9UFgKhxfk2Oks1OzMwk/cAf7aTDzP5K0vskfa3anZrZUZLWSZov6VlJ73LO7cmz3nskfdq/+Q/OuW/6y++RdIykbHfkP3HOvVhteYAkSPtPm9XM9AkAKI3za7KUrPn2a7zfKel2Sf8hr9333zvn/nkC+10t6WfOueMl/cy/PYof0K+UdJqkN0i60sxm5KxyuXNusf9H8EbkGD0iWPU8sQUAxInza7KUO873byT1OueCGl7wIkln+9e/KekeSZ8cs85yST9xzu2WJDP7iaTzJN0SUBkAJEiSJrZA9PgiC4SH82uylBu+T5N0uZk9J7/TpSQ5506pcr9HO+e2+9d3SDo6zzqdkrbm3N7mL8v6upkNyauN/we/hn4cM1spaaUkzZs3r8riAggbE1sASJJ6+kLI+TVZyu1wuVzSKyWdI+mCnL+CzOynZvZonr+LctfL7chZgcudcydL+mP/792FVnTO3eicW+qcWzp79uwKdwMgKkxsAQDh4PyaLOVOsvNcpRt2zp1b6D4ze8HMjnHObTezYyTla7PdoyNNUyRprrzmKXLO9fiXfWb2XXltwr9VaRkBJEdaJ7ZIgqhq+OqpJhGoJZxfk6XcZidBu0PSeyRd619+L886GyR9LqeT5Z9IWmNmkyR1OOdeMrMmSW+T9NMIygwgZExsAQDh4PyaHOU2OwnatZLeYmZ/kHSuf1tmttTM/lWS/I6Wn5X0kP/3GX/ZFEkbzOx3kh6RV0Ne9bCHAAAAQFRiqfl2zu2S9OY8yzdK+kDO7Zsl3Txmnf2SXhd2GQEAAICgxVXzDQAAAKROXG2+ASAv2iICAOoZNd8AgLy6unvUvaVXDz6zW8uuvVtd3T1xFwkAah413wCAcbq6e7Rm/SYdGhqWJPX0ZrRm/SZJYngyoEbxy2IyUPMNABhn7YbNygwOjVqWGRzS2g2bYyoRANQHwjcAYJzn80xFXWw5AKA8hG8AwDhzOloqWg4AKA/hGwAwzqrlC9XS1DhqWUtTo1YtXxhTiQCgPtDhEgAwTrZT5Sdu/50ODQ2rs6NFq5YvpLMlAEwQ4RsAkNeKJZ265ddbJDFKAgAEhWYnAAAAQEQI3wAAAEBECN8AAABARAjfAAAAQEQI3wAAAEBECN8AAABARAjfAAAAQEQI3wAAAEBECN8AAABARAjfAAAAQEQI3wAAAEBECN8AAABARAjfAAAAQERiCd9mdpSZ/cTM/uBfziiw3o/MrNfMfjBm+QIze9DMnjSzdWY2OZqSAwAAANWLq+Z7taSfOeeOl/Qz/3Y+ayW9O8/yz0v6onPuVZL2SHp/KKUEAAAAAhRX+L5I0jf969+UtCLfSs65n0nqy11mZibpHEm3l3o8AAAAkCSTYtrv0c657f71HZKOruCxMyX1OucO+7e3SeostLKZrZS0UpLmzZtXRVEBIL3WffD0uIsAAHUltPBtZj+V9PI8d/2v3BvOOWdmLqxyOOdulHSjJC1dujS0/QAAAAClhBa+nXPnFrrPzF4ws2Occ9vN7BhJL1aw6V2SOsxskl/7PVdSzwSLCwAAAIQurjbfd0h6j3/9PZK+V+4DnXNO0s8lvaOaxwMAAABxiSt8XyvpLWb2B0nn+rdlZkvN7F+zK5nZ/ZL+XdKbzWybmS337/qkpI+Z2ZPy2oDfFGnpAQAAgCqYV5GcDma2U9JzcZcjJrMkvRR3IeoEz2VweC6DwfMYHJ7L4PBcBoPnMThRPpfHOedm57sjVeE7zcxso3NuadzlqAc8l8HhuQwGz2NweC6Dw3MZDJ7H4CTluWR6eQAAACAihG8AAAAgIoTv9Lgx7gLUEZ7L4PBcBoPnMTg8l8HhuWFIfp8AACAASURBVAwGz2NwynouzexmM3vRzB4tY915ZvZzM+s2s9+Z2VtLPoY23wAAAIDHzN4oqV/St5xzrymx7o2Sup1z/2JmJ0q6yzk3v9hjqPkGAAAAfM65+yTtzl1mZq80sx+Z2cNmdr+ZvTq7uqRp/vXpkp4vtf3QZrgEAAAA6sSNkv7aOfcHMztN0v+TdI6kqyT92Mw+IqlN3vw1RRG+AQAAgALMrF3SGZL+3cyyi6f4l5dJ+oZz7gtmdrqkb5vZa5xzw4W2R/gGAAAACmuQ1OucW5znvvdLOk+SnHMPmFmzvMl8Xiy2MQAAAAB5OOf2SXrGzN4pSeY51b97i6Q3+8sXSWqWtLPY9hjtBAAAAPCZ2S2SzpZXg/2CpCsl3S3pXyQdI6lJ0q3Ouc/4I5x8TVK7vM6Xn3DO/bjo9gnfAAAAQDRodgIAAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEhPANAAAARITwDQAAAESE8A0AAABEZFLcBYjSrFmz3Pz58+MuBgAAAOrYww8//JJzbna++1IVvufPn6+NGzfGXQwAAADUMTN7rtB9NDsBAAAAIkL4BgAAACJC+AYAAAAikqo23wAAAKjc4OCgtm3bpoGBgbiLkijNzc2aO3eumpqayn4M4RsAAABFbdu2TVOnTtX8+fNlZnEXJxGcc9q1a5e2bdumBQsWlP04mp0AAACgqIGBAc2cOZPgncPMNHPmzIp/DSB8AwAAoCSC93jVPCeEbwAAACAihG8AAAAk2rPPPqvXvOY1E97OPffco1/96ld57zt48KDOPfdcLV68WOvWrdMHPvABPf7445Kkz33ucxPedxYdLgEAAJAK99xzj9rb23XGGWeMu6+7u1uS9Mgjj0iSLrnkkpH7Pve5z+lTn/pUIGWg5hsAAACJd/jwYV1++eVatGiR3vGOd+jAgQOSpIcfflhnnXWWXve612n58uXavn27JOnLX/6yTjzxRJ1yyim69NJL9eyzz+qrX/2qvvjFL2rx4sW6//77R7b94osv6oorrtBDDz2kxYsX66mnntLZZ5+tjRs3avXq1cpkMlq8eLEuv/zyCf8f5pyb8EZqxdKlS93GjRvjLgYAAEBNeeKJJ7Ro0SLvxg9XSzs2BbuDl58snX9twbufffZZLViwQL/4xS+0bNkyve9979OJJ56oj370ozrrrLP0ve99T7Nnz9a6deu0YcMG3XzzzZozZ46eeeYZTZkyRb29vero6NBVV12l9vZ2ffzjHx+3j3vuuUfXXXedfvCDH0iSzj77bF133XVaunSp2tvb1d/fn7dso54bn5k97Jxbmm99mp0AAAAg8Y499lgtW7ZMknTFFVfoy1/+ss477zw9+uijestb3iJJGhoa0jHHHCNJOuWUU3T55ZdrxYoVWrFiRWzlHovwDQAAgPIVqaEO09hh/cxMzjmddNJJeuCBB8atf+edd+q+++7T97//fV199dXatCng2voq0eYbAAAAibdly5aRkP3d735XZ555phYuXKidO3eOLB8cHNRjjz2m4eFhbd26VW9605v0+c9/Xnv37lV/f7+mTp2qvr6+ivfd1NSkwcHBQP4PwjcAAAASb+HChbr++uu1aNEi7dmzRx/60Ic0efJk3X777frkJz+pU089VYsXL9avfvUrDQ0N6YorrtDJJ5+sJUuW6G/+5m/U0dGhCy64QP/5n/85rsNlKStXrhxpxjJRdLgEAABAUfk6FcJTaYdLar4BAACAiBC+AQAAgIgQvgEAAICIEL4BABW75IYHdMkN44f2AgAUR/gGAAAAIkL4BgAAQOD4hSw/wjcAoGINbkiT3KG4iwEgxa666ipdd911Be/v6urS448/HmGJykP4BgBU7LK+m3XVrk/EXQwACdXV3aPuLb168JndWnbt3erq7om+DIRvAEC9mHN4mxYMPikND8VdFAAJ09XdozXrN+nQ0LAkqac3ozXrNwUSwK+++mqdcMIJOvPMM7V582ZJ0te+9jW9/vWv16mnnqo/+7M/04EDB/SrX/1Kd9xxh1atWqXFixfrqaeeyrteHAjfAICKtbn9mqTDUt/2uIsCIGHWbtiszODoL+aZwSGt3bB5Qtt9+OGHdeutt+qRRx7RXXfdpYceekiSdPHFF+uhhx7Sb3/7Wy1atEg33XSTzjjjDF144YVau3atHnnkEb3yla/Mu14cJsWyVwBATWsd7veu7HlOmj433sIASJTnezMVLS/X/fffr7e//e1qbW2VJF144YWSpEcffVSf/vSn1dvbq/7+fi1fvjzv48tdL2zUfAMAKtY2vN+70vtcvAUBkDhzOloqWj5R733ve/WVr3xFmzZt0pVXXqmBgYEJrRc2wjcAoGKtzg/fewjfAEZbtXyhWpoaRy1raWrUquULJ7TdN77xjerq6lImk1FfX5++//3vS5L6+vp0zDHHaHBwUN/5zndG1p86dar6+vpGbhdaL2qEbwBAZYYOq9X5HZWo+QYwxoolnbrm4pM1udGLmZ0dLbrm4pO1YknnhLb72te+VpdccolOPfVUnX/++Xr9618vSfrsZz+r0047TcuWLdOrX/3qkfUvvfRSrV27VkuWLNFTTz1VcL2omXMutp1HbenSpW7jxo1xFwMAatuB3dL/XeBdn3eG9L4fxlseAKF74okntGjRoooek51gZ90HTw+jSImR77kxs4edc0vzrZ/IDpdmdrOkt0l60Tn3mjz3m6QvSXqrpAOS3uuc+020pQSAdOr6zXNaO/AlPa9ZmvNkr1Z190y4RgtA/an30F2tpDY7+Yak84rcf76k4/2/lZL+JYIyAUDqdXX3aM2PtqtHs+Vk6hmeoTXrfxfLBBoAUIsSGb6dc/dJ2l1klYskfct5/ktSh5kdE03pACC91m7YrMzh0c0VM4PDEx6/F0DypampcrmqeU4SGb7L0Clpa87tbf6yccxspZltNLONO3fujKRwAFCvwhq/F0CyNTc3a9euXQTwHM457dq1S83NzRU9LpFtvoPknLtR0o2S1+Ey5uIAQE2b09GinjxBO6zxewEkw9y5c7Vt2zZRkTlac3Oz5s6tbKKxWg3fPZKOzbk9118GAAjRquULteb23ygzdOSH05aGoQmP3wsg2ZqamrRgwYK4i1EXarXZyR2S/sI8fyRpr3Nue9yFAoB6t2JJp65ZtEVz5NV+dTbu0TXzNjLaCQCUKZE132Z2i6SzJc0ys22SrpTUJEnOua9KukveMINPyhtq8C/jKSkApM+Ko57TuS3f0ftffrvWtXxNOrgv7iIBQM1IZPh2zl1W4n4n6X9EVBwAQK6BvTrQ0OZd75gn/f7OeMsDADWkVpudAADiMtCr/dbuXZ9xnHTgJelgf7xlAoAaQfgGAFRmVM33cd5l75b4ygMANYTwDQCozMDenJrv+d5l73OxFQcAagnhGwBQmXw133sI3wBQDsI3AKAyA3u1v8Gv+W6bJTW1UvMNAGUifAMAyjd0WDrUrwPm13ybebXf1HwDQFkI3wCA8vljeo/UfEveiCfUfANAWQjfAIDyDfRK0pEOl5JX8927RXIupkIBQO0gfAMAyjewV5K0P9vhUvIm2jm4T8rsialQAFA7EjnDJQAgofzw/YmLTpPmn+4tm5Ed6/s5qfWomAoGALWBmm8AQPn88K3m6UeWMdwgAJSN8A0AKF++8J1b8w0AKIrwDQAoX77w3Txdau6g5hsAykD4BgCUb2CvZA3S5PbRyxluEADKQvgGAJRvYK80ZZrUMObjg4l2AKAshG8AQPkG9o5ucpI1wx/re3g4+jIBQA0hfAMAypfpzR++O46Thg5K/S9EXyYAqCGEbwBA+QrWfM/3Lmn3DQBFEb4BAOUrFL475nmXtPsGgKII3wCA8g3s9YYVHCsbvnu3RFseAKgxhG8AQPkK1Xw3tUjtR0u9z0ZeJACoJYRvAEB5hgalwf35w7fEcIMAUAbCNwCgPAP7vMtC4ZuJdgCgJMI3AKA8A73eZUueNt+SV/O9t0caOhxdmQCgxhC+AQDlGdjrXRar+XZD0r5t0ZUJAGoM4RsAUJ5S4bvjOO+Sdt8AUBDhGwBQnnJqviXafQNAEYRvAEB5SoXvaZ2SNWj93b/UJTc8EF25AKCGEL4BAOUpFb4bm6Rpc/Wywy9EVyYAqDGEbwBAeQb2StYgTW4vvM6M4zR7aEd0ZQKAGkP4BgCUJzu7pVnhdTqO08sI3wBQEOEbAFCeQlPL55pxnGYM71GTOxhNmQCgxhC+AQDlGegtHb794QZnD70YQYEAoPYQvgEA5Smz5luSXnaYpicAkA/hGwBQnnLCt1/zTbtvAMiP8A0AKE854bv9aA3L1DG8O5oyAUCNIXwDAMozsFdq7ii+TkODMtaq1uH90ZQJAGoM4RsAUNrhQ9LggdI135L2N7SpzRG+ASAfwjcAoLSD+7zLEuG7q7tHFxy4Uu/Z8z4tu/ZudXX3RFA4AKgdk+IuAACgBpSaWl5e8F6zfpMyboYkqac3ozXrN0mSVizpDL2IAFALqPkGAJQ20OtdFmnzvXbDZmUGh0YtywwOae2GzWGWDABqCuEbAFBaGTXfz/dmKloOAGlE+AYAlFZG+J7T0VLRcgBII8I3AKC0MsL3quUL1dLUOGpZS1OjVi1fGGbJAKCm0OESAFBaGeE726nyyn9/QPuGp2jO9BatOm8RnS0BIAfhGwBQ2sBeyRqlyW1FV1uxpFM77tmkv977T9KHHpU6CN4AkItmJwCA0rJTy5uVXHW/tR95DABgFGq+AQAFXXLDA5KkdbP2ljW7pSRlGlq9K4RvABiHmm8AQGmZ3rLD90jNd3ZWTADACMI3AKC0gfJrvg80tB15DABgFMI3AKC0CsL3fiN8A0AhhG8AQGlV1XzT7AQAxiJ8AwBKqyB8D1mTDmqKNNAbcqEAoPYQvgEARU1yh6TDGam5o+zH7G9oo8MlAORB+AYAFNU6fMC7UmbNtyQdaGinzTcA5EH4BgAU1eb6vStlhu91Hzxdc19+NOEbAPIgfAMAimod3u9daSm/2Ymap9PhEgDyIHwDAIqqtOZbkjRlGjXfAJAH4RsAkFdXd4+6t/Tqa8/P17KBL6nrmQo+Mpqn0+ESAPIgfAMAxunq7tGa9Zt0aGhYkqlHs7XmJy+pq7unvA00T/dqvp0LtZwAUGsI3wCAcdZu2KzM4NCoZZnDTms3bC5vA83TpKFD0uGBEEoHALWL8A0AGOf53kxFy8fJtg+n0yUAjEL4BgCMM6ejpaLl40zJhm86XQJALsI3AGCcVcsXqqWpcdSylqZGrVq+sLwNNBO+ASAfwjcAYJwVSzp1zcUna3Jjg0xOnZP26pqLT9aKJZ3lbSAbvg8SvgEgF+EbAJDXiiWdWjKvQxumfla/PGFd+cFb8jpcStR8A8AYhG8AQFFtw/srm2BHosMlABRA+AYAFNXq+icQvqn5BoBchG8AQFFV1Xw3tUrWSPgGgDEI3wCAgprcIU3WocrDtxlTzANAHoRvAEBBrcP7vSvNHZU/uHkaNd8AMAbhGwBQUKvr965UWvOdfQwdLgFgFMI3AKCgtpGa72rDNzXfAJCL8A0AKKhtpOa7imYnU2h2AgBjEb4BAAW1Tqjmu4MOlwAwRmLDt5mdZ2abzexJM1ud5/55ZvZzM+s2s9+Z2VvjKCcA1LO/PfNl3pWqwjc13wAwViLDt5k1Srpe0vmSTpR0mZmdOGa1T0u6zTm3RNKlkv5ftKUEgBTIhudq23wf6peGDgdbJgCoYYkM35LeIOlJ59zTzrlDkm6VdNGYdZykaf716ZKej7B8AJAOA3ulhiapqaXyx2YDO01PAGBEUsN3p6StObe3+ctyXSXpCjPbJukuSR/JtyEzW2lmG81s486dO8MoKwDUr4G9Xog2q/yxU6Yd2QYAQFJyw3c5LpP0DefcXElvlfRtMxv3/zjnbnTOLXXOLZ09e3bkhQSAmpYN39Wg5hsAxklq+O6RdGzO7bn+slzvl3SbJDnnHpDULGlWJKUDgLTI9E48fFPzDQAjkhq+H5J0vJktMLPJ8jpU3jFmnS2S3ixJZrZIXvimXQkABGlCNd80OwGAsRIZvp1zhyV9WNIGSU/IG9XkMTP7jJld6K/2d5L+ysx+K+kWSe91zrl4SgwAdSqIZidMMQ8AIybFXYBCnHN3yetImbvs73OuPy5pWdTlAoBUmUj4psMlAIyTyJpvAEBCBBG+6XAJACMI3wCA/AYHpKGD1YfvxknS5KnUfANADsI3ACC/icxumcUU8wAwCuEbAJDfSPjuqH4bzdMJ3wCQg/ANAMgviJrvKdR8A0AuwjcAIL+BXu9yQs1OptPhEgByEL4BAPll9niXrUdVvw2anQDAKIRvAEB+2fDdMqP6bdDhEgBGIXwDAPLLBNTsZGCfxATEACCJ8A0AKCSzxxunu7Gp+m00T5fckHRof3DlAoAaRvgGAOSX2TOxJicSs1wCwBiEbwBAfpk9UssExviWjjRZod03AEgifAMAChnonXjNd7Nf8034BgBJhG8AQCGB1Hz7jx+g2QkASIRvAEAhQbT5ptkJAIxC+AYAjOdcsB0us7NlAkDKEb4BAOMd2i8NHw6u5pvRTgBAEuEbAJBPdnbL5gm2+W5qlhon0+wEAHyEbwDAeEFMLZ+VneUSAED4BgDkEXj4puYbACTCNwAgnyDD95RphG8A8BG+AQDjZUcnCarmmw6XACCJ8A0AyGek5nuCHS4lmp0AQA7CNwBgvMweb5SSptaJb6t5Gh0uAcBH+AYAjJedYMds4tui5hsARhC+AQDjZXqDae8tSVOmS4cz0uFDwWwPAGoY4RsAMF4QU8tnMcslAIwgfAMAxsv0Tnx2y6xs+KbpCQAQvgEAeQRa8z3NuyR8AwDhGwCQx0CAbb6p+QaAEYRvAMBohw9Jh/oD7HBJzTcAZBG+AQCjjcxuGXCbbzpcAgDhGwAwxsjsljQ7AYCgEb4BAKMFObW8JE1ul2SEbwAQ4RsAMFYm2+wkoJrvhgammAcAH+EbADBa0M1OJKaYBwAf4RsAMFo2fAc1yY7kTTFPh0sAIHwDAMbI7JFkRzpKBqF5uh5/ZqsuueGB4LYJADWI8A0AGG2g1wveDY3BbbN5mtqG+4PbHgDUKMI3AGC0IKeWz2qerha3P9htAkANInwDAEYLKXy3DRO+AYDwDQAYLbMnuDG+s6ZMU4s7IHPDwW4XAGoM4RsAMFqmN5Sa7wY5NbtMsNsFgBpD+AYAjBZKs5NpkkSnSwCpR/gGABwxPOyNdhJCzbcktdLpEkDKEb4BAEcc3Ce54WAn2JFGwjedLgGkHeEbAHBEGFPLS+p6dpKWDXxJ33h+jpZde7e6unsC3T4A1IpJcRcAAJAgA73eZYDhu6u7R2vu3qeMZkuSenozWrN+kyRpxZLOwPYDALWAmm8AwBEh1Hyv3bBZmcNu9G4Gh7R2w+bA9gEAtYLwDQA4IoTw/Xxv/uEFCy0HgHpG+AYAHDESvoPrcDmno6Wi5QBQzwjfAIAjMn6b7wBHO1m1fKFamhpHLWtpatSq5QsD2wcA1Ao6XAIAjsjskZpapabmwDaZ7VR5zW0/14uuQ3M6WrVq+UI6WwJIJcI3AOCIMKaWlxfAF931FR20Zp2y+p7Atw8AtYJmJwCAI8KYWt53oKGNGS4BpB7hGwBwxEBv8LNb+g5Ym1qZ4RJAyhG+AQBHZPYEOtJJrv0N7Wpz/aFsGwBqBeEbAHBEmM1OrE2twwck50qvDAB1ivANADgi5DbfTRqUDg+Esn0AqAWEbwCAZzDjBeOwmp1Yu3clO5Y4AKQQ4RsA4MmG4pBqvvsapnlXDuwKZfsAUAsI3wAAz8jU8uGE730N070rB14KZfsAUAsI3wAAT8jh+6pLz/Ku7Cd8A0gvwjcAwBNy+FbbLO+SZicAUozwDQDwDPhtvkOaZEctMyRrkPbvDGf7AFADCN8AAE/YNd8NjVLLUTQ7AZBqhG8AgCezR7JGacrU8PbRNosOlwBSjfANAPBkJ9gxC28frbOk/bT5BpBehG8AgCfTG16Tk6y2mdR8A0g1wjcAwJPZE9rsliPaZtPhEkCqEb4BAJ5ss5Mwtc7y9jN0ONz9AEBCEb4BAJ4ownd2rO/M7nD3AwAJRfgGAHiiaPPdOtO7ZLhBAClF+AYASMND0sG94U2wk5Wt+abdN4CUInwDAKSBvd5l6M1OZnuXjHgCIKUSGb7N7Dwz22xmT5rZ6gLrvMvMHjezx8zsu1GXEQDqStizW2a1Zmu+GesbQDpNirsAY5lZo6TrJb1F0jZJD5nZHc65x3PWOV7SGknLnHN7zOxl8ZQWAOpEZOH7KElGzTeA1EpizfcbJD3pnHvaOXdI0q2SLhqzzl9Jut45t0eSnHMvRlxGAKgvmV7vMuzw3dDo7YMOlwBSKonhu1PS1pzb2/xluU6QdIKZ/dLM/svMziu0MTNbaWYbzWzjzp108AGAsS654QF9+c5fezfCnmRHYqIdAKmWxPBdjkmSjpd0tqTLJH3NzPJ+YjjnbnTOLXXOLZ09e3aERQSA2tE+3OddCbvmW/JGPDlAm28A6ZTE8N0j6dic23P9Zbm2SbrDOTfonHtG0n/LC+MAgCqMhO+whxqUvLG+aXYCIKWSGL4fknS8mS0ws8mSLpV0x5h1uuTVesvMZslrhvJ0lIUEgHrS5vqlKdOkxgj64bfNosMlgNRKXPh2zh2W9GFJGyQ9Iek259xjZvYZM7vQX22DpF1m9rikn0ta5ZzjN0wAqFL7cF807b0lb7jBA7u9iX0AIGUSN9SgJDnn7pJ015hlf59z3Un6mP8HAJig9uG+aJqcSP5EO84L4O30xQGQLomr+QYARM+r+Y6gs6Uktc30Lml6AiCFCN8AgGjD98gsl4RvAOlD+AaAFOvq7lH3ll6duf9aLXvsAnV1jx1cKgRtfvim5htACiWyzTcAIHxd3T1as36TDg0NSzL1HGzRmvWbJEkrloyd2yxA1HwDSDFqvgEgpdZu2KzM4OgRRzKDQ1q7YXO4O27123wTvgGkEOEbAFLq+d5MRcsD0zjJa19OsxMAKUT4BoCUmtPRUtHyQLXOouYbQCoRvgEgpVYtX6iWpsZRy1qaGrVq+cLwd942SzrA3GgA0ofwDQAptWJJp665+GRNbTgk07A6pzbqmotPDrezZVbbLGn/zvD3AwAJw2gnAJBiK5Z06oV7uvXBvV+SPvy4ND2C4C35zU4eiGZfAJAg1HwDQMq1D/d7V6KaZEfyar4zu6Xh4ej2CQAJQPgGgJRrG+7TITVJTRF0tMxqnSW5YSmzJ7p9AkACEL4BIOXah/vU3zBVMotup8xyCSClCN8AkHLtrk/7G9qj3Wk2fNPpEkDKEL4BIOXah/vUb1Oj3SlTzANIKcI3AKRc23C/1+wk0p3S7ARAOhG+ASDlpg7v1f6ow3frTO9yPxPtAEgXwjcApNnwsGapV2e/7uRo99vYJDVPp803gNQhfANAmmV2S8OHpanHRL/vttk0OwGQOoRvAEizvu3e5dSjo9936yw6XAJIHcI3AKRZ3wveZfvLo9932yzpAG2+AaQL4RsA0myk5juG8N06k5pvAKlD+AaANOvf4V22x9DspG22V/M9PBz9vgEgJoRvAEizvhek5g6pqTn6fbfNktyQNNAb/b4BICaEbwBIs77t8Yx0IjHLJYBUCjV8m9lHzGxGmPsAAExA/wvxjHQiSW3+RDsMNwggRcKu+T5a0kNmdpuZnWdmFvL+AACV6HshnpFOJGq+AaRSqOHbOfdpScdLuknSeyX9wcw+Z2avDHO/AIAyOOd1uIxjpBPJ63ApMcslgFQJvc23c85J2uH/HZY0Q9LtZvZ/w943AKCIzB5p6FB84bs12+yEsb4BpMekMDduZh+V9BeSXpL0r5JWOecGzaxB0h8kfSLM/QMAiuiLcZhBSZo0WZoynWYnAFIl1PAt6ShJFzvnnstd6JwbNrO3hbxvAEAxIxPsxDTaieR1uqTDJYAUCTV8O+euLHLfE2HuGwBQQr8/tXxco51IXqdL2nwDSBHG+QaAtBppdhJTm2/J63S5nzbfANKD8A0AadW3w2tzPbk1vjLQ7ARAyhC+ASCt+nfE2+RE8pqdHNjlDXsIAClA+AaAtOp7Ib6RTrLaZknDh/W+r/5El9zwQLxlAYAIEL4BIK36tsc70ok0MtHOtOHeeMsBABEhfANAGjnnjXYSe7MTb6KdacN74y0HAESE8A0AaTTQKx0eiHekE8lrdiLCN4D0IHwDQBr1Zcf4jjl8txK+AaQL4RsA0qjfH+M77vCdrfkeInwDSAfCNwCkUXaCnbg7XE6aIk2ZRodLAKlB+AaANBqZ3TLmDpeSunSO/veet+rBZ3Zr2bV3q6u7J+4iAUBoJsVdAABADPpfkCa3S1PaYy1GV3eP1ux7uzKuSZLU05vRmvWbJEkrlnTGWTQACAU13wCQRn3b42/vLWnths0jwTsrMziktRs2x1QiAAgX4RsA0qjvhfiHGZT0fG+mouUAUOsI3wCQRv074p9gR9KcjpaKlgNArSN8A0DaOOd1uIx7pBNJq5YvNNvyeAAAIABJREFUVEvj8KhlLU2NWrV8YUwlAoBwEb4BIG0O9kmDBxIx0smKJZ265g0H1amdkpw6O1p0zcUn09kSQN0ifANA2vQnZHZL34oTO/TL5o/q3Z0v6JerzyF4A6hrhG8ASJu+7d5lQsL3yCyXTLQDIAUI3wCQNn1+zXcCRjuRlBO+mWIeQP0jfANA2vRnp5aPv823JKmV8A0gPQjfAJA2fTukplZpyrS4S+JpalbGWgjfAFKB8A0AadO3wxvpxCzukozY1zBd04Zo8w2g/hG+ASBt+l9ITmdL396GDnUM74m7GAAQOsI3AKRN3/bEhe8TTlikk9v3xV0MAAgd4RsA0qbvheSMdJLVMU/au00aHi69LgDUMMI3AKTJwX7pUF9yRjrJ6pgnDR06MgEQANQpwjcApMnI7JbHxFuOsabP8y73bo23HAAQMsI3AKRJnz/Gd3sCa74lqXdLvOUAgJARvgEgTZI2tXxWx7HeJeEbQJ0jfANAmow0O0lY+J7cJrXOJHwDqHuEbwBIk74dUuMUqbkj7pKMN/1Y2nwDqHuEbwBIk74d3kgnCZrdckTHPGq+AdQ9wjcApEn/juSNdJLVMU/q3So5F3dJACA0hG8ASJO+F5I30klWxzzpcEba/1LcJQGA0EyKuwAAgPBdcsMDkqR1fTukV5wVc2kKmJ4z4kn77HjLAgAhoeYbAFKiyR2UDu5N3kgnWdmxvvfS7htA/SJ8A0BKzBja7V1pT2r4ZqxvAPWP8A0AKTFjeJd3ZWpC23w3T/f+ehluEED9InwDQEqM1HwndbQTSZrOcIMA6hvhGwBSomM44c1OJK/dNxPtAKhjhG8ASIkZQ7ulhiap9ai4i1JYx7FezTdjfQOoU4RvAKhzXd096t7Sq7/ddZGWDfyTuh55Pu4iFdYxTzrUL2X2xF0SAAhFYsO3mZ1nZpvN7EkzW11kvT8zM2dmS6MsHwDUgq7uHq1Zv0mHhoYlmXqGZ2jN+k3q6u6Ju2j5TWfEEwD1LZHh28waJV0v6XxJJ0q6zMxOzLPeVEkflfRgtCUEgNqwdsNmZQaHRi3LDA5p7YbNMZWohJGxvmn3DaA+JTJ8S3qDpCedc0875w5JulXSRXnW+6ykz0saiLJwAFArnu/NVLQ8dtnwTc03gDqV1PDdKSm32mObv2yEmb1W0rHOuTuLbcjMVtr/b+++46Oq8/2Pv75pEGqoQkIREQEFNIBYsDfsjljQVdetus3dvXtvVrN379Z7b9xFt93dta3+XMu6WEfWhgUrSgdpSlFqogIBQkshyff3x5lAJplJJmEyp8z7+XjkkXDmzOSTw5nvfM73fL+frzELjTELt23blvxIRUQ8LD8vt03bXZfbC3K6qda3iASWV5PvFhljMoDfAf/e2r7W2vuttROttRP79evX8cGJiHhI0ZSR5GZnRm3Lzc6kaMpIlyJqhTHOuG/1fItIQHk1+S4FBjf696DItgbdgTHAW8aYDcDJwExNuhQRiRYqLKBk6li6ZtZiqKegK5RMHUuosKD1J7slbwhUKPkWkWDyavK9ABhhjBlmjMkBrgNmNjxora2w1va11h5prT0SmAtcbq1d6E64IiLeFSos4Gv91rC+843M+cZgbyfecKjWt4hIAHky+bbW1gLfA2YBHwFPWmtXGmN+ZYy53N3oRET8p1e9D5aWb5A3BKoqnC8RkYDJcjuAeKy1LwEvNdn2szj7npWKmERE/Kpv3RfUkE1Olz5uh9K6g7W+N8OAnu7GIiKSZJ7s+RYRkeQaWFvKF1n5kOGDZj9vqPNdtb5FJIB80AqLiMjhyq8tpSxrkNthJCZPq1yKSHAp+RYRCbq6Wgrs55w0cZLbkSSmaz/I6qzkW0QCScm3iEjQ7doI9Qegzwi3I0mMan2LSIAp+RYRCbryT5zvfY52N462yBuiMd8iEkhKvkVEgq58rfPdV8m3er5FJJiUfIuIBF35OsjtBV19UGawQd4Q2F8ONfvcjkREJKmUfIuIBF35On/1egP0HOJ836WhJyISLEq+RUSCbrsPk++8SPKtcd8iEjBKvkVEgqx6L+wp82Hy3VDre6O7cYiIJJmSbxGRINvhw0onAN0GQEa2hp2ISOAo+RYRCbLydc73vj6p8d0gIwN6DlLFExEJHCXfIiJB1lDju/dR7sbRHqr1LSIBpORbRCTItq91VovMznU7krZTrW8RCSAl3yIiQebHMoMN8obC3i/gQJXbkYiIJI2SbxGRoLLWGXbi1+S7Z6TiScUWd+MQEUkiJd8iIkG1bxtUV/g3+T5Y61tDT0QkOJR8i4gE1cFKJ35NvhtqfSv5FpHgUPItIhJU29c63/3a8909H0yman2LSKAo+RYRCarydZDZ6dDYab/JzIIeBer5FpFAUfItIhJU5Z849b0zMt2OpP1U61tEAkbJt4hIUJWvhT7D3Y7i8OQNZvuWtUy77wO3IxERSQol3yIiQVRXCzvW+29Z+abyhtC7fgeZ9oDbkYiIJIWSbxGRIKrYBPUH/DvZskHPwWRQT5+67W5HIiKSFEq+RUSCaHukzKDfk+9Ire9+dV+4HIiISHIo+RYRCaKGGt99fD7spPdRAOTXapVLEQkGJd8iIkFUvg4650GX3m5Hcnh6DmKv6caRtZ+4HYmISFIo+RYRCaLytc5kS2PcjuSwhJeWcXbVdL6540Ym3zmb8JJSt0MSETksSr5FRIKo/BPfj/cOLyml+NnlbLM9sRhKd1VS/OxyJeAi4mtKvkVEgqZmH+wu9X2N7+mzVlN5oC5qW+WBOqbPWu1SRCIdY9p9H6iWfRpR8i0iEjTlkfHRPp9sWbarsk3bRUT8QMm3iEjQlAejzGB+Xm6btouI+IGSbxGRoGlIviNl+vyqaMpIcrMzo7blZmdSNGWkSxGJiBw+Jd8iIkFTvg56DoacLm5HclhChQWUTB1LTmYGYCnI2EHJlWMIFRa4HZpISmgseDAp+RYRCZrydb6fbNkgVFhA4ZA8ft7/PebkfI/Q0ZmtP0nER8JLSlmyaRfz1u9QOc00oeRbRCRIrHWWlvf5ZMumNmRHLiY+X+5uICJJ1FBOs6auHkDlNNOEkm8RkSDZtx2qK3w/2bKpjVnDnB8+X+ZuICJJpHKa6UnJt4hIkASk0klTlRldodeRSr4lUFROMz0p+RYRCZB7n33F+aFvcJLvGbeewoxbT4EB4zTsRAKlpXKaGgseXEq+RUQCZGBtKQfIdqqdBM2AcbDjU6je43YkIm0Wq3JJ0ZSR5GaZqG252ZmcPaqfxoIHmJJvSR/19fDZMtiyCDYvgM3zYdNc2PgBbJijD3QJhPzaLXyeNRAyAlgVZMBY5/sXK92NQyRJQgN3UtLpYfLNdgz1FHSupmTqWN78eJvGggdYltsBiKTMwgfhpf+I/3j/Y+HWdyFTbwvxr4F1pZRlDiKA/d6Hku/Pl8OQk92NReRwlS6GR68k1LUrb/W+hkv3Ps55lS+DuZd/29Uj5lM0FjwYlGVIeqitgfd+DwUT4MzbwWQABoxxft66Cmb9BJY+DhNudjtakfapr2NAbRkLOwU0Me2RD7m94bMP3Y5E5PBsmguPXwO5veDmmXz25Gc81PO7nDdgH8y8jfxuj1K6t77Z0+KNERd/0bATSQ8fPgG7S+Hsn8AxU2DE+TDiPDj6XBh+Npz8HRh8ErxVAjX73Y5WpF3C7y3mzKq7+WH5FcGcoGUMDNSkS/G5T9+GR6+Ebv3hqy87VXyAOpMF1z4CvYZSVP9QzLHgRVNGuhCwJJuSbwm+ulp473eQXwjDz429jzFw3i9hz2cw757UxieSBOElpRTP+oJS+gEmuBO0BoyFrR9B3QG3IxFJ2KHKJeVMfmAD4exLnMS7Z0H0jrm94EtPEuq0gJLuT9MpMnWjIC+XkqljCRUWNH9x8R0l3xJ8K5+FnRuYXnkZ0+6fG3+/oafAyIvhvT/AvvLUxSeSBNNnraayLrqnLJATtAaMg7pq2L7W7UhEEhK9iqWh1PaleM9VhNceuoA8WE4ToM9wmPYYoeqZPNn1Lk49sgdz7jhHiXeAKPmWYKuvh3fvhv7HsiiRcbDn/hxq9sK7d3V8bCJJlDaLdTSedCniA7FXsaxv+cL4yNPgsj9wfM1ivrL7HrC2g6OUVFLyLcH28Quw7WM4/d+xJoHTvf8oKLwR5j8AOzcAsWuzinhNfs+c2NuDNkGrzwjI6qyVLsU32n1hXHgjM7tezQX7X4QN73VAZOIWJd8SXNbCO9Oh93A47srEn3dWMWRkwez/7rjYRJKsaFwNuVRHbQvkBK3MLKcsqJJv8YmWVrFszVPdb6TS5MKyfyY7LHGRkm8JrnWvOx/Qp/+o1QVHonq3e+TDyd+G5U+ppJn4RihnHiXZD9Ep0xn3HegJWgPGOsNOdCtefKDozCPafWH86LfOJnfclbBqJhyo6qgQJcWUfEswWQtv/9ZZYnvctEYzzXckVoLttB869YRf+3lq4hU5XJvmERpaxQlDenHSsN7BnqA1YCxU7nTKh4p4XKhqJiVZfyM306nb3eYL43HXQvVuWPNKB0YpqaTkW4Jpw7uwZT5M/gHhZVsbzTQnsRJsnXvCGUWE11SzctPWmEm7xoKLZ9RWQ9kSp1Z9OhgwzvmuSZfidbXVsPAhQqO7MW5I3/ZdGA87A7oNgGVPdlycklJKviWY3rkLuh0BhTfFmWneegm2cPbFFNfewt46ZyHYwNZNFv/77EOn/F66LLl+xHGAUfIt3rcyDPu2wkm3tv81MjJh7NWw9lXYvyN5sYlrlHxL8GxeAOvfhlNvg+zO7Z5pPv31T6kkuoJEIOsmi/9tnud8T5ee707dnFrImpMhXmats2hb32Ng+DmH91rjroX6A7AqnJzYxFVKviV43r3LGa894atA6zPN440HT5u6yeJ/m+ZCr2HQrX/0Yh1B1jDpUsSrtix0hoNNusVZRflwDBgHfUdq6ElAKPmWYNld5kxKmXSL0zsGFE0ZSW52dLWThpnm0SuPRQ8taSlpb/METpGOYq3T850uvd4NBoyFXRuhcpfbkYhEOTgfaN690KkHHH89wOFdGBvj9H5v+gB2bkxitOIGJd8SLB+94Hwfe/XBTaHCAkqmjiUn0zndG880b2k8eLyk/exR/do+gVOko+xcD/u2wZB0S74jky6/WOluHCIx9Kord4aIFN50sCPosI29xvm+/KnkvJ64Rsm3BMtHM6HfKOg7ImpzqLCAwiF5zWaatzS0pHHSbrAUZOykJHQcb368rV0TOEU6xKaG8d5pMtmygZaZFw87b/+LUF8Hk76RvBftNRSGnOIMPVGNe19T8i3BsW87bJwDoy9L+CmtjQdvSNq/P2Alc3K+S6jnGo0FF2/ZPBc69XQuOtNJtyOgaz+tdCmek2VrOG//S3DMFOh9VHJffNy1sH01fL5M5W59TMm3BMfHL4Kt58erjky4QWppPHhjCzuf7EziXPLoYS0VLJJ0m+fD4BMhI82ac2OcoSdKvsVDwktKWb6pnML9f2bypzcnfzjisSHIyNbES59Ls9ZaAu2jf0HeUDZmxe5piDXZpaXx4I3VmhwYNw0+fpGiswsSSthFOlzlLtj6UfpNtmyQf4Lz91ftdjsSkcgE/mXsru+EJYPSvTb584G69IYRF8DypzG2rvX9xZOUfEswVO6CT9+CYy9vc0mneOPBmxl/E9TVELKzE0rYRTrclgWATd/ke/g5UF/r1PUXcZkzgb8+aluHzAcady3s/ZwxNapz71dZbgcgkhRrX3UWIBh9OaxPbm9AVG95/nhY/Cihb3+LJ+Zvav64SCptngcmEwomuB2JOwaf5JRyW/tam+Z6iHSElM0HOuZCwuZcnvu8L+X1Trnboikj1QHkI+r5lmBY9Tx0H0h468COrb89/ibYuhLKFif3dUXaY9NcGDAmeaXM/CYzG44600m+Vf1BXJbfs1Ps7UmeDxReUU5x9c2U1zvve5W79R8l3+J/Nftg3RuEe3+N4vDKdtXfTnjxgzFXQVYuLH70cKMWOTx1tVC6KP1KDDZ19Pmwpwy2rnI7EklzRaN3kkt11LaOmA80fdZqKuujBy6o3K2/KPkW/1v3BtRWMr10TMfX3+7cE44LwYpnyLFVyXtdkbb6Yjkc2J9+i+s0dfR5zve1r7kbh6S90M6HKcl7vsPnA6ncrf8p+RZfiqpv+tFMyO1N2d7Yt52T3iAV3gTVu3n05M813lvcc3BxnTRPvnsWQP/jYN3rbkci6az8E9j4HqHJ4xKbwH8YVO7W/5R8i7/VVsOaWTDqktQ1SENPhd7DYYmGnoiLNs+DHoOg5yC3I3HfiPNh0wcqOSjuWfIYmAw4/voO/1WJrk8h3qXkW/zt07ehejeMvjx1DZIxUHijs5rm9nXJfW2RRG2eB4MnuR2FN4w4XyUHxT11tfDhE878gx75Hf7rDq1PYTBYCnL2qdytzyj5Fn/7aKZTauyoMxNeMCcpTviSU+JNvd/ihl2bYXcpDEnzyZYNGpccFEm1T96APZ85nTIp4qxP0YsHej/GnM7/RmhMn5T9bjl8qvMtvhNeUsqSTbuoqatn8sZJFA3tSyjLKfEUKixITf3t7gOcVcY+fALO+alT8kwkVTZrvHeUhpKD6153Sg62caEtkcOy+BHo0heOuRBI7doP8ztN5rz9LzuLzI28MGW/Vw6Per7FV5zle5cfKidY34fiTRPdqW864WbY+wWsfin1v1vS2+Z5kN0VjhjjdiTecfT5zt2ArR+5HYmkk73bYM0rcPx1kJWT8l+/otPxzl2fj/+V8t8t7afkW3zFWb63STnBOuNOfdMRFzgT3hY+lPrfLenLWmd4xZCTIVM3Lw9qKDm4zhl6ElURSaQdEjqHlv3TmW9QeFNqgmqizmTDMVNg9cvO2PMInf/epuRbfCWR+qYJL5hzuDIyYcJXnNt95Z90/O8TAShbAjvXw3FXuh2JtzSUHNS4b0kVa50qJ4NOhP6jUv7rD37WjboU9pfD5rkpj0HaR8m3+Irn6puOvwkystT7Lamz4hnIyIbRl7odifeMOE8lByV1tiyEbR+71ut90NHnQWYn+OgFd+OQhHky+TbGXGiMWW2MWWeMuSPG4z8yxqwyxiwzxrxhjBnqRpySep6rb9p9AIy6BJY+Dge04qV0sPp6WPmc82Gb28vtaLxnxAUqOSips+QRyO7i/l2oTt1g+Dnw8QtOb7x4nueSb2NMJvAX4CLgWOB6Y8yxTXZbAky01o4DngZ+m9ooxS2hwgJKrhzDALPDqW/akeUEEzXxa1C5E1Y9714Mkh42z3MmFY65yu1IPKHZuNZIycHw+8tZsmkX89bvYPKds92ZkC2+1lBVK+45VLMPVjzrJN6de7gTZGOjL4WKzfDZ0tZjF9d5LvkGJgHrrLWfWmtrgH8CVzTewVr7prV2f+SfcwEt8ZZGQv2/YG6n71F0xKIOW763TYadCX2OhoUPuhuHBN+KZyArF0Ze5HYk3pSZTTjvyxSvHX2oItKuSoqfXa4ERBLWrKpWrHNoZRhq9ro/5KTBMReBySQ8e07rsYvrvJh8FwCbG/17S2RbPF8HXo73oDHmFmPMQmPMwm3btiUpRHHVqueoJYuFnVNXS7VFxsCErzq9kp+vcDsaCaq6WlgVdiobdOrmdjSeNX3bSVQSXfKt8kCdOxWRxJdiVtVqeg4tetjpdPHKQldd+8DQU5n+Ua8WY1cVFG/wYvKdMGPMjcBEYHq8fay191trJ1prJ/br1y91wUnHsBZWPs+yToXsy+judjSHnPAlZ8LLov/ndiQSVBvehX3bNOQkIt6t9bL9mTH3j1cpSaSpVqtqbVkIW+bDid/01oJOoy+jrC72EBid/97ixeS7FBjc6N+DItuiGGPOA/4TuNxaW52i2MRtpYuhYhPjL/xqSlcRa1WX3jBmKnw4A6r3uh2NBNGKZyCnO4w4P+17r1oaFuC5ikjiO62eQx/8xVnYpvCGFEaVgFGXkE95zId0/nuLF5PvBcAIY8wwY0wOcB0ws/EOxphC4D6cxHurCzGKW1Y955RZG3Wx25E0N/FrULMHlj/ldiQSNLU18NFMZ1JVtj5EWxoWUDRlJLkZ0Y+5WhFJfKfFqlq7NsOq55mZdT7THvbYMMOegyjqN49ccyBqc0PsmojpHZ5Lvq21tcD3gFnAR8CT1tqVxphfGWMuj+w2HegGPGWMWWqMmRnn5SRIIkNOGH62N8usDTrRWe574UMq9yTJ9ckbUFWhIScRLQ0LCBUWUHJ2NwrYBl6piCS+EiosoGTqWHIynRQp6hyafx8Ar3S9oqWXcE1o0tGUZN5H50znM6ghdkATMT3Ek2sTW2tfAl5qsu1njX4+L+VBifvKnCEnnNWs9Ls3GOP0fr/4IyhdBIMmuh2RBMWKZ5wLzqPOOth7VVNXz+Q7Z1M0ZWTaJZb5ebmUxkjAG26th845nfPfH8riTpM4/Y5wqsOTAAgVFvDE/E0Ah4Y4Vu+BRY/AsVdQvqO/i9G1YNRlhN74FeU9TuDVrpcfjH3ynbPj3i1Kt/bDCzzX8y0S10oPDzlpMO5ayOmmFS8leWr2w8cvwbFXEF62Vb1XJLDYVmY27+Sey8lV78FejUyUJFnyOFRXEO79Ne8O3+h3DPQ9hklV70dtbnUSqaSUkm/xB68POWnQqbuTgK94xll4R+RwrZ0FB/bBmKsSK4GWBlocFhAxq+tlZHMAFv3drTAlSOrrYN49hHveRPFb+7x9ATzqUo6tWUa3+t0HN2kisrco+RZ/aBhycmzI7UhaN/FrUFsFS//hdiQSBCuegW5HwNDJ6r1qJFRYQOGQPE4a1jvmYlt/+O61zpLbCx+CugNxXkUkvhm3nnJoyMnql2DnBqbvvdD7F8CjLyOTeh48efvBTa3eLZKUUvIt/uCHIScNBoyFIafC+3+GWlXBlMNQtRvWvOosYZ2Rqd6rtpp0C+wpg49fdDsS8bsP/gJ5QyjbF7uut6cugPMLodeRsGzGwU2J3C2S1FHyLd7nlyEnjZ11u/Ohv/gRtyMRP1v9EtRVH6xyot6rNhpxAeQNhfn3ux2J+FnpItj0AZz0bX9cABsDx18P69+Bii0HN7d2t0hSR8m3eJ+fhpxETHs1h4+yj4N3fwcHqtwOR/xqxTPQc4hTxhL1XjUVNSwgloxMOPEbsHEOfO6xmsziHx/81VngqvBG/1wAj5sG2Kjeb/EOJd/ifSvD/hly0sAYnup+k9P7veRRt6MRP9q/Az6ZDWOujFrCWr1XbVR4I2Tlqvdb2qdiC6wKw4SboXMP/1wA9x7mDH9c+oTWnfAgJd/iadPufZ+t8/4JR53lnyEnEStzjncav3fvTqj3O92XDJcmljwG9bUw5mq3I/G3Lr1h3DWw7ElVIJK2m38/2Hpn/kCEby6AT7geytc6w2YiWr1bJCmh5Fs8bfiBNfSv2+pMOPOJg0v4btjJ5LLbCO8cprHf0jY1+6h44y4+zBkPA8e5HY3/nfhNqK106jSLJKp6Lyx6GEZfDr2Guh1N2x0bgqzO8OETbkciTSj5Fk87uepdasnyzZCT8JLS6EVQ9lqK624l/PqbcKBKvdvSTMxzYsHf6FlfwdPdb4z5HPVetdHAcTDkFFjwgFOvWSQRCx+Eqgo45bvNHvLFe7BzDxh1KSx/WpW3PEbJt3hWePEW/nfHWYyo+juT/7jEW4sYxBFzERSbzfQ9U2Bx/MU+DvaWe3HFNEmt6r0w5498mDOeNTnHuh1NcEy6BXZugHWvux2J+MHebfDOXU7FnMGT3I6m/U64Hqp2wZpX3I5EGlHyLZ7k9CB/yOe2NxbjzVXEYoi7CAp94d3fkW1rmj3WrLfcJ3+rdJAFDxDePZIv7fs3XYwl0+jLoNsATbyUxLz1v1CzDy74b7cjOTxHne2c90s19MRLlHyLJ02ftZrK2uhtnltFLIa4NWC7ZRDedRTLN21vllBpyfD01eyOx/x1hGe/T3Hdreyrc8qZ6WIsSTKzndVn170O29e5HY142RernLHeJ34d+nmshGBbZWTCuGth3WtOb754gpJv8SS/LqMdrwbs2WMGU1x3KxX1nYHohMqvf6scnph3PJ7/iF/un0qlzY7aVxdjSTLhK07Z0gV/czsS8Spr4dX/hE7d4axit6NJjhO+5FROWvG025FIhJJv8aT83AOxt3tpFbEY4tWAffPjbXETKl+smCZJF/OOR10GO+kec39djCVB9yPguBAsfRyqdrsdjXjRuted+vpn3u6UqQyC/qNh4Amw9B9uRyIRSr7Fe+rrKMp+hlwTnYB7chWxGGLVgG2pd9s3K6ZJUsVPpk3MrboYS5JTvgvVu+Ht37gdiXjEwYpDdQdg1n9C7+FOecogOf56+HwZfLHS7UgEJd/iRWtfI1Qzk5JTjfdXEYujaRmqlnq3fbNimiRVvHMiLzdbF2MdKb8Qxt8Mc+9RIiLRFj0M21fDBb+GrBy3o0musVdDRpZqfnuEkm9xXbM6xwv+Bt0GELr4Yn+sIpaAmL3bWRxMqHyzYpokTexzwvCLy4/TxVhHO+8XkJsHL/wI6usPblYd/uBq7f+2a/0eePN/4cjTYaQ/1pVok659YcQUZ6XXutrW95cOleV2ACJRdnzqjLk783anOkFANCROP356GTV19RRk7KCox9uExp53cB/PL9ggSdVwTtz+9FJq6iz52fsomnrawe1PzN8E6LzoEF16w/m/gue/Cx/+AwpjL2YkwddQcaimro7J/JyiSSMJmdhDv3zv+Otg9Yvw6Zsw4vxmDzdcnKjN6Xjq+RZXNSu19sLzYDKcqgT4ZBWxBEX1bt/cj9D+p+Ddu90OS1wUKizg171eZn3nG5jz7ZHq3U6l478Eg0+GV/8L9u/QQldpKLrikKGUfhTPrgju//0xUyC3l4aeeICSb3FNzFJrq4YS7v8d6DHQ5eg62MgLYdxXJOgnAAAgAElEQVQ0J/n+fLnb0Yhbdm3i0n3PsKDTKZB/QtRDQbrw9KSMDLjkbqiqIPzEfVroKsDiXVil3RoLWZ1gzNXw8YuwrzzqIV18ppaSb3FNzIaPTkwvP9WliDpeVEJ14Z1OL0T4O84se0kvdbXwzDfpkp3Jibfe43Y0aSNq7O+AMXDyt5m+dmB6JWFppKUVhNNyjYVJ34Taapj714ObtMpy6in5FtfEbfj22hRH4pIuvZ2et8+XwZw/trirJoIF0Lt3w+a5zjnQe5jb0aSvs+6gjL4xHwp0EpYmWurdzu+eGfM5gS7r2W8kHHs5zL8fKncBaXgHwAOUfItrtLgMcOwVztfbv4GtH7sdjaTKpnnO//nYa+H4aW5HkzZi3lrv1J38rrH3T6u2KKBaXGMhcwa51ERtT4uynmcUObXu598P+HdFaT9T8i2uib24TEbwG76mLr4Lcro5lRfq65o93NJYPPWI+1BVBTz7DehZAJfc5XY0aaOlW+tFl5zg20W9pGVxO3ly9hOqDlNyTvf0K+s5YCwcc5Ez9KR6jzrCXKDkW1zTsLhM50wwWAo6VVIydVzwG76muvWHi34LpQvhg79EPaSxeAH04n9ARSlc9SB07ul2NGmjpVvrofGDKLl4CPlsd9qivM7pkYSlgZidPJmWovqH4KyfELrgvPRcY+GMIqjcCQse1CrLLlCdb3FVqLCAvbPv5sY9D8K334cj0qTha2rs1bDyOXjjl3DEsXC0U/+7tbF4Tn3aeibfOZuiKSpV53kfzoDlT8LZ/wmDJ7kdTVpp7dZ66PTx7Jn/D27a8wBM+jEUnpvK8KSDNFtjoXsmRdV/JTSiM5z+70Ca1rUeNAGGnwMf/JnQD24Bxh46Rnm5+jzpYOr5FncdqORGMwuGnApHHOd2NO4xBq68F/qNhhlfhrKlQPyEoaEHPIg94oEdSrNjPbz47zDklIMf+pI6idxaf6HrVGbnToF3fgtL/5Gq0KSDNayxcNbQTszpXkyo51qY+oBTbjKdnfFj2LcNFv9dqyynWJqfeeK6uX+F3Vvg7GK3I3Ff5x5ww1NOFZTHr4GdG+ImDJnGaHa6B8W9cKg7AM9+01lAaur9kBG7yoJ0nERurc/41qmc8x+Pw7AzYeb3Yf27qQ5TOsiMW07m4T6Pws6NcPVDznLr6W7oKTD0NKfaVm2129GkFSXfkhIxk5K9W+Hd38HIi2HYGe4E5jU9BsINT0NdNTx2NUVnF8RMGOps7HKMfp+dHsiFHqyFV38KWxbAZb+HvCFuR5SWGuaYtDq5LjMbrn0E+gyHGTfAtjVAgO/IBEzc/6f3/wSrwnDuz5ykUxxnFsGez2DJY1rYK4WUfIt73vwfqK2C83/ldiTe0n8UXP9P2LWJ0PLvUXLFyGYJQ0EAZ6f7fXJpzAuHhsR73r1w8ndgzFVuh5nWEr61npsHX3oSMnPgH9fAvu2pDVSS65274LWfOWVdT/2+29F4y7AzYdCJ8N4ftNhbCin5lg4XMyn5YhUsfgRO/Ab0HeF2iN4z9FS46gHYsoDQup8yfnD3qIQhiLPT/bzQQ+wLh2WEH/kDfPBnmHQrTPlfl6MUIPHevV5DnYvgPZ8Tvu8XLN20M1h3ZAKo2WfN4i3w+i9g9q+dmvpXPaRx3k0Z44z9rtgEy2a4HU3aMDbO7esgmjhxol24cKHbYaSVhqSkcVKVm51JSf/XCe17Er6/1BnjLLHNuw9e/rFzkXLxXU5DGRFeUhqo2enD7niRWK2RAdbfeUmqw2mTyXfOpjTGkJ8CtjHnjFVw0W+i/u/EP8IvzKT4vVoq6XRwW252pkoRekzMz5qMOkoy7iF00jFwye+VeMdjLdx/JlTvge8t1JyUJDHGLLLWToz1mM5E6VBxezNLx8CZtyvxbs1Jtzq3SRf8DV65A+pqDz4UtNnpfl7oIW4ZO/oq8fa56Su6RSXe4J87Mukk5mdNfSbTM78Bl/5BiXdLjHHqfu/4FFY863Y0aUFno3SoFpOSE7+Z4mh86rxfOuOF590Lj10J+8oPPhSkCTJ+HkrT4oWDEm9fa60+uCZiekPc/6fqznoPJmLkJXDEGGeYTtVut6MJPCXf0qHiJiVdgayc1AbjVxkZcGEJhO6BTfPg/rPgsw/djirpEq5G4bJYyZZz4RDdnOZmZ1A0ZVTc54g/xG3DemiNOi/x850zT8jIgMv+CHvKnEni0qGUfEuHitmbaQ5QdMkJLkXkYyd8Cb72Mtg6eHAKLH/a7YiSzq9DaUIjcykZ8A4FbIODy5OP8038El/MNowaig7cT3jWa8ErjekDMS+AJ+WQa2qitvnlzplnDJoIp94Gi/8O695wO5pAU/ItHSq6N9NSwDZKzu9LaPwgt0Pzp4IJcMtbkF8Iz3zd6aFoNA48CLw8lCZm5Z4Nc+De0wiVP8At/Vdx0pG9mXPHuQcT70DWLk8jMe/IXHoU5OZR/OYe35bGDIz6epjzR0LvhSjp9jRdM5320Kt3zjzvrJ9A35Ew8zaoqnA7msBStRNJjZ0b4c8nwnFXwtT73I7G/2prYNZPYMEDTp3WK/6sxVs6WOxqCvWUZN5DqN9nzqp5+YWtP0eVMnypoae14cJwcsnrlFY0XxWwIC+XOXecE/M56SqZxyGqylOPHIq6vUJox99g1KVw+f8x7dHVSftdaWvLInjwPDjhBuezRdpF1U7EXXUH4NlbnPJF5/6X29EEQ1YOXHIXXP5nZ+XEv5ykRRI6WOxqChlMN1+FW99plnjHfY4qZfhS0zsyZTESbzg08U93PJKvWT393TUUl00mPO4emPYYdOnt6TtnvjFoAkz+ASx5FNa+7nY0gaTkWzreaz+DzXPh8v+DnhpuklTjb4Lvzofh58DrP4d7T4eNTi9Teyb5aWJgfHGrKdR0gU7d2/acONvFP+JO8MvZT3jux75erTWZknkREvNilk5MXzNAFU2S7axi6DcK/vV9qKrQZ0OSKfmWjrXiWZj7VzjpWzD2arejCaa8wXDd43DdE1CzF/7fhfD8d+len9zxekFsfNvyN+V3i91ctlRNQRUYgivmRMyMOors35k+c77ueBBv5dd2XoTU7KNs1/6YD+litgNkdYLQX50VXh/7P93FSTIl39Jxtn4Mz38PBp8E5//a7WiCb9TF8N15MPmHhBduZOWmrcxbX87kktcTaizT7TZ5wn/v9rUw40aKqv5ELm2rpuDn2uXSspgTMa+ZQOi2uyirj714mBtJopsXzUkZdlVXC4sehj+NJ5/tMXfRxWwHKZhA+KhfUrzuWN3FSTIVKpWOUbUbZtwIOV3gmodV0ztVcroS7nsLxfZDKiOTqUsrqil+ajHU7CV0UuykL14PFThJRkOiWlNXz+Q7Z/t+KfvW/t5p931AXl059w1+HRY/Atm5hM7/AeSO48fPr3Eme+XltnocGh47OEEsgeeIf4QKC3hi/iYgeoJffl4ppbuqmu2fn9c5ZbEBrr9vD2vYlbWw+iV4/ZewfTUMPomiE46k+O2qZhOYdTHbcaZvOZZKos/lhgsotWPtp+Rbks9aeP67zlK1X34eeuS7HVFamT5rNZW10VWMKuszmB6eS2jnw3Dyd5g2YwtwKGForYeqpUS1JV6o9hArhhb/3orNrN24mZ31XZi8uZCiYwoIXfMV6NaPEBCaNLxNvz9UWKAPqQCLdW4XTRnVvMoN1RRl/QtWVMDoyyEzeR+/sc7x1i4w2/p67ZGfl0tpjES7xZ7qqgr4cAYsfAi2fQR9RsC0x2HUJYSMgT6luphNobKK5heRoKE+h0vDTiT5PvgzfDQTzvsFDDvd7WjSTtzeJtsH5t1H+O5vs27jRmdIyp1vEF5S2mIPVXtvHXthGEu8GOL9vaW79lP8Shk76rtiMZTavhR/Opbw2pqY+4vE0mxISs/OlEyqJtRpATz9VfjzBJj/AFTuPOzfFe8c98L7tk3DrkoXO8MU7x4FLxexblcdf+35I/jOXBh96cEJlaHCAtb8z0VsuPMSXy3E5Veat9IxVOdbkmvDHPj7ZTDqErj2Ec1Ad8HkO2fH7G0qyMul6PQ+FL+4kcr6Qx+IuRl1dM7JYmdV87agIC+Xsl2VxGolDLD+zktixpBIfeuO7hVvKYbps1bHPEaZ1FFHZrPtjWs3i7RbfZ0zlOK9P0DpQsjIdioVjZkKIy+Gzj3a9L5o6Rz/txlLO+R921ZRdbmb9lTv3Qofv+iM6f5sKWR3gbHXEO4+jR+/vlu92x4Q85wwtZRcPZ7QhMEuRuZ9qvMtbdauSTpbP2bXI1+iNGMgXPEXJd4uaam3afq75VGJN0BlfSa2qiLuZML29Hy01uuWil7xuDG8sJSi/vOb/71ZxEy8QbdYJUkyMmH0ZfCN1ynu8yf+lXs5fLESnrsVph9N+C/FrNr4edz3RdN2uaX3WUe8b9sjqqf69rMJDSiHt6fDA+fAXSPghR9CXQ1cfBf8+8eEB99O8WytHOoVze7i5B6gJPNeQp/9zhliKu2i5FuaaVditGUR4Xt/ykWVv2byvt8w+Q8L1Vi6JGYVhkjPVbwksoLulBS8Rz7bMNRTYMopGfA2oaowRSd1Jjc7uqlofOs41oVaS8NYklp+rAVxY9hXT+izP1EyZAE9MpyFUgrycim56gQKdItVUiC8tIxnPu/PbeVXMbnmT4RP/5eTdG6exJ56Z3J66a5Kip9aRPjll6CqIma73NL7rD2VdjqkLn1FKayaCS/8CH4/Bu47Hd78H8DA2T+FW9+Fb78Pk74JnXtqYSoPChUWUDgkj5OG9WbOz0OEzpzkjMmfrSpm7aUJlxKlXZN0PnmT8CN/pLj6ZirJSfx50mHiTfKLPwGqC6Hb7ia0dxt8Mhs2zIcN78Gs9YQAss7jv2uvZrvtTkFXKDr7CELHD4xbTaGliVYtfbge1rliLezcAGWLoXQx+VljKK3t0TyGbplw+3pCWZ2cv62JWLfdVU1BkqV5G1tF8VsH6Jw9lkqiV6itrM9k+ttb4f3rKa69hRqbHXlOJcXPLiOvSzY79zdf1TY/L7fVSjuxhoO0a4JkY1W7oWwJlC469LXnM+ex7K4w/Gw4uxhGXADd+sd8CS1M5U1Rw6DO+4UzX+HduyG3F5x6m1th+ZbGfEuUlsYLxxzzumomPPN1Jlf9gdK6vMSfJ65o85jOilLYOAc2vOsk4zs+PfRa9myKa75CZSQhAGfoRsmF+dA5j+LnVydtLGqD6+6dQ6/6cu65uLeTaO/cADvWs3b1CgbWbqGb3evsmNmJcNerKd5+YfT49gTGr7Y4RlXkMMVrY+MxQH7nGkqrmpdrzWMv1aYzlfZQP1pulqHksuGEJh0Td+hfvHbgqgkFPLOoNG77EF60mdufXUZ1nXUuwkd8Rij3Q9ix3mkb9pQd+iW9h0PBBBg00fk+YKyzcEsr2vwZJO6or4Onvwarws7q1eO/3GwXL1S7clNLY76VfEuUYXe8mHhitPhRZ+nZgokMW/fDdidUklqHlVxW74Vtq2HrKiY/34XS6uZ1iwvYxpzOPyCccQEllVew1eaRn72PokGrCA3cweRFZ8V+Xudq5py2zGnUa6udnpVGX+Fdw/nt/kv4jD7kU05R1gxC2fMIZ1/C/+y5iG22BwVd6ik6vQ+h00+ErBwl0uI58drYeFqe9Gz5fe8wv91xOp/R+9D7Iut9yOwE3Y+A7gOd3snsXGdCY3Yuk+efGvc9WDRsI79aXcCO+i4UZO+nqM97zvtsx5EU75tGJYcS6FyqKek6g1D+Luh9FPQ5CvILIX88dIm90FBrOmLSp3SQ2mp44jrnbunkH8I5/3WwhKbaXiXfB6Vr8t2Wq8+Eex3m/Ale+y9npv60x5j8u3nqrUgz8S/ULOsv+sjpBdv9mZM8H9gHNfugZj/hvcdSXHVT8w/x7L8Ryp4PGVnOoky5vQ5+hfePpXjjxOhe7CzDVeMLeGbJZ/qgFt+I18bm5WZTXVvfpuo8B9vXulrYtRH2fuEM89gT+d7w76oKOFAZ+drPsB1/wtK8V9xgWd/9FieByuoMXfo4X7m9mLzqCkqrmw8/aUiskploKXHzkdpqeOUOZwz4sDPg6v9HeE21LqBoOfnWmG+JUjRlZMtjXg9Uwuu/gHn3wnFXwpX3Q1ZO68+TwGlp/DhnFsV9Xghg8RZufyZy+zqvM0UXHE9o/NS4z5l+52wq66N/V2Wt5YkFpdQ16UDQ6mviZfHayl9cfhwQf4x2i+1rZhb0Ge58JSA/zgVAfl4XuKMsxjOg7I4XY25vmN/TnsV84tHCVP4x7cHFwLXMuGIivPgjuO8MplfeTeWB+qj91C5HU/IdcG1dXrjFSTpbFkH4W7B9DZz0bZjyP07prNaeJ4F0OBdcofGDCI0flPDvijfZqmni3dr+Im5rra2M1WYmu31tz3s33sV2pjGHNYE63ccF+1lUfrFzIEWTnyP04a2U7amDGHdW1C4fouQ7wNq7vHCosIAn5m8CIg1ibQ288Wt47/fQfQDc+CwcfW7M5ynZTh+pvOBq6YM/VgKu0oDiZe1pK5PZvrbnvRsvYW+aeDdIJNFqa+eQeEfM/GJ2DVz6NPkvLKX0QLdmz2lol3XBpTHfgZaUWeOfL4fnvgVfrIATbiCc/0N+/Pwn6t2WlGpvdQYRSZ5YY7FbHY/ewmtpXLB/tbiS8vkjKH5mKZX1h9aHyM3OoGTqOCB97pBrzHeaOqx6qbU18P4f4a3fOJPernuCcOXxSR/bJ5KIlnrqJg7tnTaNuYib4vW+t2f4WYfV+5eUaCm/CE0YDBkZ/OfTC9lfl0k+2ynquxQq8ih+o0I5BEq+A61dCybU1sDSx+Cdu2H3FjhuKlxyN3Tp7Ux6U2MpLon3wa/hTiLuae/wMy2m42+t5RcNw1czbB1PTPgcZj/P5FknUGn7Re2frjmEku8Aa9OkmroDsPRxJ+mu2AQFE+HyP8LR5x3cRY2liIg01Z4L4MNeTVNclUh+cWhM92kw5irKfjkn5mulYw6R0fou4lehwgJKpo4lJ9P5by7Iy20+nq7uACx+BP5vPPzrB9CtH9zwDHzj9ajEG+I3imosRUSkLYqmjCQ3OzNqm8rT+kdC+UVjub2cUpYx5PfIjrk9yDThsgN5eqGAHeth6T+cr91bnFXJzvoJjDi/zUsSa4KMiIi0VXhJKdNnraZsVyX5XvuMlKSLmUM0LLB2XC846Vtw5GkxcxBP51NxaIXLiFQm355MVGv2warnYcnjsPE9wDgrVE66BY6ZEjfpbkyNpYiIiLRHsxzijH6E9j8Lix6Gyh1wxBiY8BUYfTl0P+LgczyXTyVAyXdEKpPvpJT5a6JdV3611bDhPVj5nPNVsxd6HwUnfAmOvx56Jr7QiYiIiEjSHaiE5U/BvPuc0sYYGHoqHHsFk98YRunuA82ecjj5VCqo1KALWpucGC+Rbml7wmX+Kkph7avO16dvwYH9kN3VWQ6+8AYYckpCvdwiIiIiHS47F8Z/GQpvgm0fO3fpV4bh5R9TVvUYsaYo+nmippLvDtLSTO54ifTCjTt4ZlFpzAS7xZqoI3MJvz2PX767l131uU5NzawnCfXZ7PRuHzMFjjwdcqInO/hxDJWIiIgElDHQf7TzddYdsG01+X9ZRWlVTrNd87vUQfknzt18n40T17CTDtLSGKV4K4LFWyq7IC+Xsl2VxPqfMlh+n/UXimu/SSWdDv2uLEPJ1HGExsceVuLXMVQiIiKSPmJP1KyhJOt+AKbX30BZfR75uXUUndqT0BkTCa+qcD3HaWnYiUoNdpCGMjwFebkYosvwxLtVEivxBijbtZ/8TrGfk5+9j+lZt0Ql3gCVtZbpr66JG19LPekiIiIiXhCzrOG1J8IFv6bYfofS+l5YDKWVWRS/sYPwf1/L9Kff8nSOo2EnHSjewgPxhqRkGkudbX7rJJ/tFJnnKTY3U2kP1cPMzc6gaOpp/NuMpTF/f0vjobRgjoiIiPhBrHxq8p1rqKyL7kOupBPTM79J2YHYtcO9kuOo57ujHahyxiR9+hYseQzeupOivu+Ta6Jn7uZSzfW8Si7V0dszoejSQkI/f5aSa09s0pM+jlBhQbsWv9GCOSIiIuIV4SWlTL5zNsPueJHJd84mvKS0xf3jdiJW5cRf0McjOY56vjvav74Py2ZEbQp1GwD9zmX6zrMoO9CF/C71FJ2SR+jknzBxbS3TX10Ts452vJ70Ni0jfxjPEREREUm2puO6W6zoFtFSYQuv5ziacNnR1r8DFVuceto9B0GPAsjq1Prz2qg9i99owRwRERFxW3vWRmmtcITbOY4W2YlwJfkWERERkbiG3fFinIpusP7OS+I+z+0EuyVaZEdEREREPKmlISQtiTcc1+s04VJEREREXFM0ZSS52ZlR27w0RjvZPJt8G2MuNMasNsasM8bcEePxTsaYGZHH5xljjkx9lCIiIiJyOFpaGyWIPDnsxBiTCfwFOB/YAiwwxsy01q5qtNvXgZ3W2qONMdcBvwGmpT5aERERETkcfh1C0h5e7fmeBKyz1n5qra0B/glc0WSfK4C/R35+GjjXGNN8hRoREREREY/wavJdAGxu9O8tkW0x97HW1gIVQJ+URCciIiIi0g5eTb6TxhhzizFmoTFm4bZt29wOR0RERETSmFeT71JgcKN/D4psi7mPMSYL6AmUN30ha+391tqJ1tqJ/fr166BwRURERERa59XkewEwwhgzzBiTA1wHzGyyz0zg5sjPVwOzbTqtGCQiIiIivuPJaifW2lpjzPeAWUAm8JC1dqUx5lfAQmvtTOBB4FFjzDpgB06CLiIiIiLiWZ5MvgGstS8BLzXZ9rNGP1cB16Q6LhERERGR9vLqsBMRERERkcBR8i0iIiIikiJKvkVEREREUkTJt4iIiIhIiij5FhERERFJESXfIiIiIiIpouRbRERERCRFlHyLiIiIiKSIkm8RERERkRRR8i0iIiIikiJKvkVEREREUkTJt4iIiIhIihhrrdsxpIwxZhuw0e04XNIX2O52EAGhY5k8OpbJoeOYPDqWyaNjmRw6jsmTymM51FrbL9YDaZV8pzNjzEJr7US34wgCHcvk0bFMDh3H5NGxTB4dy+TQcUwerxxLDTsREREREUkRJd8iIiIiIimi5Dt93O92AAGiY5k8OpbJoeOYPDqWyaNjmRw6jsnjiWOpMd8iIiIiIiminm8RERERkRRR8i0iIiIikiJKvgPAGHOhMWa1MWadMeaOGI9/xRizzRizNPL1jUaP3WyMWRv5ujm1kXtLAsfx942O4RpjzK5Gj9U1emxmaiP3HmPMQ8aYrcaYFXEeN8aYP0WO9TJjzPhGj+mcjEjgON4QOX7LjTHvG2OOb/TYhsj2pcaYhamL2psSOJZnGWMqGr2Pf9bosRbbhnSTwLEsanQcV0Tax96Rx3ReRhhjBhtj3jTGrDLGrDTG/CDGPmorE5DgsfROe2mt1ZePv4BM4BPgKCAH+BA4tsk+XwH+HOO5vYFPI997RX7u5fbf5NXj2GT/24CHGv17r9t/g5e+gDOA8cCKOI9fDLwMGOBkYF5ku87Jth3HUxuOD3BRw3GM/HsD0Nftv8ErXwkcy7OAF2Jsb1PbkA5frR3LJvteBsxu9G+dl4eOxUBgfOTn7sCaGJ/faiuTdyw9016q59v/JgHrrLWfWmtrgH8CVyT43CnAa9baHdbancBrwIUdFKfXtfU4Xg88kZLIfMha+w6wo4VdrgAesY65QJ4xZiA6J6O0dhytte9HjhPAXGBQSgLzoQTOyXgOp40NpDYeS7WVcVhrP7PWLo78vAf4CChospvaygQkciy91F4q+fa/AmBzo39vofmbF+CqyO2Wp40xg9v43HSQ8LEwxgwFhgGzG23ubIxZaIyZa4wJdVyYgRHveOucbL+v4/SQNbDAq8aYRcaYW1yKyW9OMcZ8aIx52RhzXGSbzsl2MsZ0wUkIn2m0WedlDMaYI4FCYF6Th9RWtlELx7IxV9vLrI7+BeIJ/wKesNZWG2NuBf4OnONyTH52HfC0tbau0bah1tpSY8xRwGxjzHJr7ScuxSdpxhhzNs6HyWmNNp8WOSf7A68ZYz6O9FhKbItx3sd7jTEXA2FghMsx+d1lwBxrbeNecp2XTRhjuuFcoPzQWrvb7Xj8LJFj6YX2Uj3f/lcKDG7070GRbQdZa8uttdWRf/4NmJDoc9NIW47FdTS5jWqtLY18/xR4C+eqW+KLd7x1TraRMWYczvv6CmttecP2RufkVuA5nOETEoe1dre1dm/k55eAbGNMX3ROHo6W2kqdl4AxJhsnWXzcWvtsjF3UViYogWPpmfZSybf/LQBGGGOGGWNycBq7qGobkfFhDS7HGQsFMAu4wBjTyxjTC7ggsi0dtXocAYwxo3Amt3zQaFsvY0ynyM99gcnAqpRE7V8zgS9HZvKfDFRYaz9D52SbGGOGAM8CN1lr1zTa3tUY073hZ5zjGLMyhTiMMQOMMSby8yScz8dyEmwbJJoxpidwJvB8o206LxuJnG8PAh9Za38XZze1lQlI5Fh6qb3UsBOfs9bWGmO+h/Omy8SpwLHSGPMrYKG1dibwfWPM5UAtziSZr0Seu8MY82ucDxeAXzW5PZg2EjyO4Hzw/tNGpkdHjAbuM8bU43xg32mtTevk2xjzBE71iL7GmC3Az4FsAGvtvcBLOLP41wH7ga9GHtM52UgCx/FnQB/gr5G8sdZaOxE4Angusi0L+Ie19pWU/wEeksCxvBr4tjGmFqgErou8z2O2DS78CZ6RwLEEuBJ41Vq7r9FTdV5GmwzcBCw3xiyNbPsJMATUVrZRIsfSM+2llpcXEREREUkRDTsREREREUkRJd8iIiIiIimi5FtEREREJEWUfIuIiIiIpIiSbxERERGRCGPMQ8aYrcaYVksOGmOGGGPeNMYsiawkfnFrz1HyLSIiIiJyyNCI5a8AAAG8SURBVMPAhQnu+1PgSWttIU454r+29gQl3yIiIiIiEZGl5aPqphtjhhtjXjHGLDLGvBtZdA/AAj0iP/cEylp7fSXfIiLSjDHmxMgt1M6RFeBWGmPGuB2XiIhL7gdus9ZOAP6DQz3cvwBujCw49RJwW2svpEV2REQkJmPMfwOdgVxgi7W2xOWQRERSwhhzJPCCtXaMMaYbsA1Y3WiXTtba0caYH+Hk03cbY07BWeZ+jLW2Pu5rK/kWEZFYjDE5OMtXVwGnWmvrXA5JRCQlmiTfPYDV1tqBMfZbCVxord0c+fenwMnW2q3xXlvDTkREJJ4+QDegO04PuIhI2rHW7gbWG2OuATCO4yMPbwLOjWwfjdNWbmvp9dTzLSIiMRljZgL/BIYBA62133M5JBGRDmeMeQI4C+gLfAH8HJgN3AMMBLKBf1prf2WMORZ4AKejwgI/tta+2uLrK/kWEZGmjDFfBq6w1l5ljMkE3geKrbWzXQ5NRMTXlHyLiIiIiKSIxnyLiIiIiKSIkm8RERERkRRR8i0iIiIikiJKvkVEREREUkTJt4iIiIhIiij5FhERERFJESXfIiIiIiIp8v8BG/+SHVutAHkAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(fit_convol.fit_report())\n", "plt.rcParams[\"figure.figsize\"] = (12,10)\n", "fit_convol.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# export simulation result" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "freq_list = np.arange(35,230,0.5)*1e6\n", "\n", "result_x_0 = fit_convol.params['x_0'].value\n", "result_gamma_0 = fit_convol.params['gamma_0'].value\n", "result_gamma_1 = fit_convol.params['gamma_1'].value\n", "result_gamma_l = fit_convol.params['gamma_l'].value\n", "result_rabi = fit_convol.params['rabi'].value\n", "result_I0 = fit_convol.params['I0'].value\n", "result_I1 = fit_convol.params['I1'].value\n", "result_bg = fit_convol.params['bg'].value\n", "\n", "sim_result = mollow_convol(freq_list, result_x_0, result_gamma_0, result_gamma_1, result_gamma_l, result_rabi, result_I0, result_I1, result_bg)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "out_text = np.c_[(freq_list*1e-6, sim_result)]\n", "file_name = 'sim_spectrum/' + filename + '.dat'\n", "np.savetxt(file_name, out_text, delimiter='\\t', fmt='%f', header=\"freq\\tnormalize_power\")" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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