{ "cells": [ { "cell_type": "markdown", "id": "709c49bd", "metadata": {}, "source": [ "# Showcasing of AMBER utilizing synthetic neutron scattering data" ] }, { "cell_type": "code", "execution_count": 1, "id": "d8b17015", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from AMBER.background import background\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "63aaf7ad", "metadata": {}, "source": [ "## Generate signal data\n", "The data for this tutorial is generated using the expected neutron scattering signal in an inelastic exmperiment measuring MnF$_2$. The dispersion relation, i.e. the energy at a give (H,K,L) position is given by the analytical formula [Yamany et al. 2010](https://www.doi.org/10.1139/P10-081):\n", "\n", "$$\\omega(H,K,L) = \\sqrt{\\left(2S z_2 J_2+D+2 z_1 S J_1 \\sin{L \\pi}^2\\right)^2-\\left(2 S z_2 J_2 \\cos(H \\pi)\\cos(K \\pi) \\cos(L \\pi)\\right)^2}$$\n", "\n", "In the above $J_1$, $J_2$, and $D$ are the magnetic coupling strengths and single-ion anisotropy which determine the amplitude of the dispersion. The parameters $S = 5/2$, $z_1 = 2$, and $z_2 = 8$ corresponds the the size of the magnetic spin, and the number of nearest and next-nearest neighbours in the MnF$_2$ lattice. \n", "\n", "From the above equation, the imporant fact is that it depends on the 3D vector $Q = (H,K,L)$. For simplicity, we assume that $K$ is zero and thus reduce the data to 3D, i.e. $(H,L,\\omega)$\n", "\n", "Due to how neutron scattering experiments function, the above dispersion will not be infinitely thin but rather extended. Here, this is replicated by a simple gaussian smearing along $\\omega$\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "4c67ae0c-684b-4d85-92ba-44907594cce6", "metadata": {}, "outputs": [], "source": [ "# Define parameters, dispersion, and Smearing\n", "S = 5/2\n", "z1 = 2\n", "z2 = 8\n", "\n", "def SpinWave(Q,J1,J2,D):\n", " return np.sqrt(np.power(2*S*z2*J2+D+2*z1*S*J1*np.sin(Q[2]*np.pi)**2,2.0)-\n", " \n", " np.power(2*S*z2*J2*np.cos(Q[0]*np.pi)*np.cos(Q[1]*np.pi)*np.cos(Q[2]*np.pi),2.0))\n", "\n", "def Intensity(H,K,L,E):\n", " sigmaE = 0.25\n", " omega = SpinWave(np.array([H,K,L]), J1=0.0354,J2=0.1499,D=0.131 )\n", " I = np.exp(-np.power(omega-E,2.0)/(2*sigmaE**2))\n", " \n", " return I" ] }, { "cell_type": "markdown", "id": "b8cbd985", "metadata": {}, "source": [ "## Define 3D grid domain\n", "\n", "The input to AMBER is a 3D cube of intensities which is define below" ] }, { "cell_type": "code", "execution_count": 4, "id": "b1e107a4-306c-49dd-95d9-732ba4284c1e", "metadata": {}, "outputs": [], "source": [ "# Data will ge simulated along H and L but with K = 0\n", "\n", "h = np.linspace(-0.1,2.1,101)\n", "k = 0\n", "l = np.linspace(-0.1,2.1,101)\n", "\n", "# Choose a sufficient energy range\n", "e = np.linspace(0.5,8,61)\n", "\n", "# Generate the grid upon which the dispersion is calculated\n", "\n", "H,L,E = np.meshgrid(h,l,e)\n", "K = np.zeros_like(H)\n", "\n", "# Intensities are calculated with the smearing and scaled\n", "I = 30*Intensity(H,K,L,E)\n", "\n", "# Poisson noice is added to the intensity\n", "I = np.random.poisson(I).astype(float)" ] }, { "cell_type": "markdown", "id": "5c087a9d-e273-4be6-8151-87169990074b", "metadata": {}, "source": [ "### Introduction of NaN-values\n", "\n", "Triple axis instruments cannot measure all lengths of $Q$. this we can mimic by exchaning the intensity at these points by NaNs" ] }, { "cell_type": "code", "execution_count": 5, "id": "9180b624-f76b-4d62-b87c-bda5388ef3f9", "metadata": {}, "outputs": [], "source": [ "QLength = np.linalg.norm([H,L],axis=0)\n", "\n", "I[QLength<0.35] = np.nan\n", "I[QLength>2.5] = np.nan\n" ] }, { "cell_type": "markdown", "id": "e1bfcdd4-2525-44f9-9f75-3b9e81c2491f", "metadata": {}, "source": [ "### Add background\n", "The main feature of AMBER is the background segmentation, which requires the data to have a background. As describe in the [article](???), background is assumed to:\n", "\n", "- Rotation independence of the background.\n", "- Smooth change of background along energy and $|\\vec{Q}|$.\n", "- The signal is sparse but continuous in energy and $|\\vec{Q}|$.\n", "\n", "In the following, a background is generated with a higher amplitude for low and high $|\\vec{Q}|$ mimicing instrumental background artefacts from triple axis neutron experiments - these corresponds to the direct beam contribution and to increased background at larger scattering angles. " ] }, { "cell_type": "code", "execution_count": 6, "id": "99d07fc8-250c-4870-bfa8-a68dd212ad95", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Background definition\n", "def background_simulation(q,amplitude,gamma,mu,amplitude2,gamma2):\n", " return amplitude*((gamma/(q**2+gamma**2))+amplitude2*np.exp(-np.power(q-mu,2.0)/(2*gamma2**2)))\n", "\n", "\n", "Q = np.linalg.norm([H,K,L],axis=0)\n", "\n", "# Choose suitable valies\n", "gamma = 0.5\n", "mu = 3.0\n", "amplitude = 20\n", "amplitude2 = 1.0\n", "gamma2 = 0.5\n", "\n", "# Generate an example of the background for visual inspection\n", "q = np.linspace(0.25,Q.max(),201)\n", "bg_test = background_simulation(q,amplitude,gamma,mu,amplitude2,gamma2)\n", "\n", "# display background amplitude\n", "fig,ax = plt.subplots()\n", "ax.plot(q,bg_test)\n", "ax.set_xlabel('|Q|')\n", "ax.set_ylabel('Background intensity')\n", "\n", "\n", "## Add background to data\n", "bg_tmp = background_simulation(Q,amplitude,gamma,mu,amplitude2,gamma2)\n", "\n", "I+=np.random.poisson(bg_tmp)" ] }, { "cell_type": "markdown", "id": "0d7f0e5e-6f8f-450d-b1ff-afce5699f64a", "metadata": {}, "source": [ "## Plot a constant energy plot\n", "The now background affected data is plotted to show a \"before\" picture. This is done by choosing a specific energy ($\\omega \\sim 4.5$ meV) and plotting the intensity as a color map" ] }, { "cell_type": "code", "execution_count": 27, "id": "456b2e68-9d32-4e78-9575-e6a2bcf7e9e5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'L [r.l.u.]')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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63vla9DzMXKuvu/GCXGU7M22z7ef1ccN130A4fjBBa4BSQTqCjPW6bw0D0L+p9XM49QqtFfrkL7eBYwk5enAHiBBCCCFRBxdAhBBCCIk6unUBNG/ePDnllFMkKSlJMjMzZcaMGVJcXHzA41566SUZNmyYxMbGyujRo+XNN+0FOS3LkrvuuktycnIkLi5OioqKZPPmzZ2cjRBCCCHRRrdqgN577z25+eab5ZRTTpFQKCS/+MUv5Pzzz5evv/5aEhJwSYGPP/5YLr/8cpk3b558//vfl8WLF8uMGTNk7dq1MmrUKBERefDBB+V3v/udPPfcc5Kfny933nmnTJ06Vb7++muJBYU6O8MVDNoKdKI8QOG8bGU71LIMUFMB9C6eRqDtAdoTlN8GrXiR3gVpZZAGKgYUsF+88xRlC2TbdUuBNH3NtA/K9cmQjgmUDEEgTZGAfDko/wx61qgvztIXSO+BKrr7yuuUrWmkHldcpc4rhJ4N0srEfLZF2Wr/TedymtBb56166p1zbT8P+QLMJVBWBGnWkD4Jj1XPCZgbCD0vcD8Ty0D5GWc7UGrFBZ6zHpVIGsjRVXO6/h54G/U1kCQukKavEl+lNWB7xupxvVc1WNl+POB928/toCpA8latO0N5m5DuCuXFSg3qZ+iv1e2CCfoZTrzmEWVbvXCOshFypOjWBdDSpUttPy9cuFAyMzNlzZo1ctZZZ8FjHn/8cZk2bZrcfvu3NWfuv/9+efvtt+X3v/+9LFiwQCzLkscee0x++ctfykUXXSQiIosWLZKsrCx57bXXZObMmUd3UIQQQgjp8fQoDVB9/bf/yuzVq/PolZUrV0pRkT0iZerUqbJy5UoRESkpKZHKykpbm5SUFCksLOxoQwghhJDopseEwUciEbn11lvl9NNP73BlISorKyUry572PisrSyorKzv+fp+tszZOAoGABAL73VYNDf8XUtvYLOLev32L3F2eUnxOBajq7dx+d4PteIkxc3chNxYMxwdAlwpwFSE3Q0y9dvnsadDuGCco3b60g/GDMZhWPvd/uUOfD9xP6BbbA8Kjxw5RNue983+pUwCgEH1E4lfa3WlVg/QG4HywGjoov+IGt3jZxgJt7GVviMp5IFA75E5E4dcJwHWM3JjIpYRcgCj9QCDP/qzDPlBqBIR8t2breR6/XZlgGQ1U4iJ+g3b3esE8RK6nhArtKivbpN3Tv2r4nu3ntGJdzgL1DYX3o5QCyD2HSmbUjAL3rkq7hd1BfexJNz+qbBHH8L947CeqDSEm9JgdoJtvvlk2bNggS5YsOebXnjdvnqSkpHT86dev3zHvAyGEEEKOHT1iATR79mz5+9//LsuXL5e+fXVByO+SnZ0tu3fvttl2794t2dnZHX+/z9ZZGydz586V+vr6jj87d4LMbYQQQgg5YejWBZBlWTJ79mx59dVX5d1335X8/PwDHjNp0iRZtmyZzfb222/LpEmTREQkPz9fsrOzbW0aGhpk1apVHW2c+P1+SU5Otv0hhBBCyIlLt2qAbr75Zlm8eLG8/vrrkpSU1KHRSUlJkbi4b/3GV199tfTp00fmzZsnIiK33HKLTJ48WR5++GG54IILZMmSJfLZZ5/Jk08+KSIiLpdLbr31VvnVr34lQ4YM6QiDz83NlRkzZhxU/4JDcsXy7veRe9dv023GDNQ2EOIZv7ZU2SyHLshZVkFEh1mLdBJ+3I4CdTW4fIHWj6BQfqSzaNdyJHli/F+U7boPr7H9nLhLaxtMtTKtA/RF0f1F54OlRcBYXSlJ+ljQzghwfxEWCslGYwBzwlWjtUfo2DZduURce7Xoo3CiPR/XjkytE0oCpTu863VIfbigv7Ih/Yi1TWu2/I3pyoZLzYB7DPRezqfvB6kiULqH+O36/C0D9P1F7RDoGdYN0+eLq9J6t9be4N+tPt0uUGN/1xv7a91dHMj2gLU9WrMTidH9iMToa/iatbYnYaeeO2j8vdfo8jNOPdb5p96n2vzzk7uUjRAn3boAeuKJJ0REZMqUKTb7s88+K9dcc42IiOzYsUPc7v0v2mmnnSaLFy+WX/7yl/KLX/xChgwZIq+99ppNOH3HHXdIc3Oz3HjjjVJXVydnnHGGLF269KByABFCCCHkxKVbF0CWpf9l4GTFihXKdskll8gll1zS6TEul0vuu+8+ue8+/S8DQgghhJAeIYImhBBCCDmW9Jg8QD0Rb02LeD37tSpIU4FynPhQPhsDLKBjANlycNkLUDIjVKVt3r599AmBVgJpSkK5WsuBNEBPVU5WtthNdveju92sjALSRcVtB/U3UH4fmFfJ7Bow5xPKtePQcrjaQd4mpLsCOq5IHshlA/JMubQsApYHQdfI+Vjbtv2rzt2zqdZ+vkim/rdS8jqtFUH6HDSGwPg8ZXNPHK5sLqC7s9L1HEY6NtSXpsH2AIckkFPKC7RNKM9U4icloG8631ckRj//9lytMev1qc49hdq1AVnUFYU6yevfSkfbz/+q1h15glrbg/IsoZIkTX30/UW5fEwJpOqvXcMg3edAqn0uutu1tGH0T3X+oC8fYr4gYoc7QIQQQgiJOrgAIoQQQkjUQRdYFzirwcNSDYBggU7miFxloQz79q6vxqx0BXJtIDzDhyqb6Qa1q0aPtXaodgME+ul2TveJiEjKVvtWOwqhRa4oVJLCDZ4Dcj242rWLBrn2IvXapwQKi+CyHKDPTmCJE9QOhdkj1x4YPywPAlxvTX31M4zEaTfI3hq7O67vLn1+lBZBUEkKUGolfnO1skEXaAaoCwjGilyWJhXMne+giIg7oM+PQNeE7UC5DeR62nuK9m2lfQHC9FP0fVryzQRli4+zzxMUog7/DQzKj6CyMqkBPX5UWgON1V2sz5cl2sWOSqE09rfP4dZM/WssBnjJkVss50P97jOEPnrgDhAhhBBCog4ugAghhBASdXABRAghhJCogxqgrgi0i7i/4zcHeoxwrtYooDBahFMXhLQo7r45uF9OQIg+0sCYlgxAYdX+OuDLbwBTKEubgvH2tXbMZ1v0Ndu0BsYNSlLAkG+QBgDqQsoqdOcA6LkiTYmr2q5vQZqVCLimK1ZrcSyQ3sCFyj4gLVr5bmVy5eoHkbxVz81Aqr7HDWfZr7HzAn3JgS+CUhPVILwfjatUl8xA9y6Ym6psngY9T5BWxF+jtUd+A/0c0jYhbYt/b5uyoTniazYLtY/zgZwSgLQhelyIps+cdU90P2IrdD/acrQuqvbcQcqGSmagsbZm6rnuBiWEEEh36EzlgOYDKlOS/qV+9qj8xumXPKxsH710W5f9JMcn3AEihBBCSNTBBRAhhBBCog4ugAghhBASdVAD1BXtQZsGCGlKUO4WlDYfrTSdOWmQ3gXmRkEaIENtD9IKIcLJegx7/kVrHiI1ul37Mqf2QCT3szrbz1YeKGeA9BlgXEjvg8aFSldIb51rBWpUgJZDNuqyDC7nM0PlTIDeB2t7QP4ZWM4D5CNCzxXlVUJ5b8AlnDlkGsFz3jNW23p/oc8FdUEFOucLKpnhDuhxoXni9evMTUh7pc4FNHamOh707qPSFTHlWtsH9XmAqtNSlS0jTufQ2bw5V9mSHLlwfM16XEgrg9qlLStVtqZT85UtmKm/V0h3hu5d85BUZcv8uE7Zasfa39fUjfrZBFL1fPDX6l93cVVmuqiJ1zyibKsXzlE2cnzBHSBCCCGERB1cABFCCCEk6qALrAus9naxvhMFj7boEW7RZRmsbXrb2uXYfreqdXgr3Co3LMmBykiEQcXtYIKeBpWT9Fb2wCyw9V4DXFnAk2M5XBSoPAIKPXcjFyAoZwHvHQirdlZvF8Eh2dBFB1yUTkzLoKCyGlaMfg4u9KxR2gJQ9iIMQshRX3pX63uyO8buxvSc1aLapAzV9QaaqrWbGDld3e06pYIHjAu6mMFYkbszPHbIAdsF8vQcQS4rXzlwzwL3ZEy5boZc4hG/vudVJ+u5Hj9JlwzZ2xqvbOl965TNt8I+NuSK9DZqV1Frtr6/egQiiV9pVzRKW4BA9yRhc53uywDtnnZWnIfuanRNUH4nlACL3iiSS7T7f/hdurTGN/ex4vzxBHeACCGEEBJ1cAFECCGEkKiDCyBCCCGERB0uy7KsAzeLLhoaGiQlJUWKBv6neN37fdUoJB3qNgzT/DvDdGEYfLrWEyGQLgZpRZAGpuR+5OHXBGq0NiBtnR5/1gqtW1D9Q31DIeQoXNxwXC4QBo6A2qNirXdCOMO5kRYFaVbcNVo/g8YAUyqAvqEweDQuBNJQ1I9Itv1cVaiPS8hrULamBl0yIvd1oO0Jag1Q/Hbw/FFqAADWT+ljnXMC3SOkTzOdX6b3fOf5+l3ve45+rrP7L1e2W5ZdoWyeZK1HGvBHu77F/6U+P9KsuQP6vqFSI0hT5NT6ieD5hcLvUeh6MMGlbOkb7Ndt6mv2/XKW4xERSSkF5XeAPq12qH6HE3eZpTJ47807jNqRI8O+39/19fWSnJzcZVvuABFCCCEk6uACiBBCCCFRBxdAhBBCCIk6qAEC7PMhnltwm3g9+/3LKM098qvDEhQw74tdUxT+ZpNq4+2r89GgPEDIl9+aqX3juy7Q/v2+fXW+oD0N2kcf8zHQLfx5s+6fiUanfLdqA/U+QNuCShy4BurSCkcapDNxluWwQG4YpM+B5TeAZgXOiUG6BAEsmQHmCdKUoVxTzrnZOEJrW6ov1xqY9hatkxuTpzVxGz4ZqGzZq/SnKOlrrcc5nLmj7hM4DuWyQboYBMqh05Cn501TXz3W1JH6PUT4/qyfRXyVfoZBgxw3cZUgvxEg4kdj0N+XxDKtqfE0aFswA+jigPYG5e7xF9vzsTUU9lNt9g7Xx/X6Rp8fadHc7frZoHxBban6GtQFdT/UABFCCCGEdAEXQIQQQgiJOlgKowvCD7eIK2F/CGf9X3SV8xTRrpemPmA7HpC4y74dHxhxqmpTcaY+Lq5Cr1ub++lQ06KJXypbRpt2C2zbqyukxy/T7q7ea3SJAOjuA64Xl/NQ5J5ALhtUCgNVV0clQ5BbCLlKQDsUkm+BCubO8eugXVySBP7LA/QNuUBhWD3oLwKmBjAoy5GwUx/n/aMOed/+A30HWkL6GeaM02VldmVr95y7UM/NmAb9HsboiHyJ2wPKbTgetRdUPm/qo/vbrj2W4gXeo4xLtEt8Z6kuDyJBPQNqN2vXVsZafT9T1+n5hNJgxBik0EBpFmpG63c/bZOZqwyBQuORe84PXGCoLIlzviI3qb9OjyGQqp8rom6obpf+FXDjJej3Bs2dkM6gIOec94Cyvfv2z436R44c3AEihBBCSNTBBRAhhBBCog4ugAghhBASdTAMHrAvjC7v/v8Sd+x+nQMKU21q1T70W0bq9PUf1A5RtpaQ3Yecl6DPv6ZGh3jGebXeZUCi1gAs21igbInrtG4j50PtZ3c3ap830o8grQzCGQoOj8vN0rYjHC7v7q01JQjTMHXUPydWm76XbWePVra47bo8Bjp/aIwOIUehxu4mwxBncJ/UuUCZlsBos9QDLZlaF9Gco//t1Vig7+8z5/1R2TYGcpVtV0DrXZZ8M0HZrh/9ke3nT/bqe/nFFv3OTRq2Vdk21fZWtprdXYfd7iP9Iz2HUVmGmM+2KFv7yYOVzduodWzOkh5oToeTQakVw5B/VOICnQ+FsiNtTyhDi2WCCVqm6t/bZvsZpR5AoezO40RweD/qb9gHwvHB+RBIU4WI32O/7x+8drvRccTOcRMG//7778uFF14oubm54nK55LXXXuuy/TXXXCMul0v9GTlyZEebe+65R/39sGHDjvJICCGEEHI80a0LoObmZhk7dqzMnz/fqP3jjz8uFRUVHX927twpvXr1kksuucTWbuTIkbZ2H3744dHoPiGEEEKOU7o1DH769Okyffp04/YpKSmSkrJ/G/e1116T2tpaufbaa23tvF6vZGeD0FNCCCGEEDnO8wA9/fTTUlRUJAMGDLDZN2/eLLm5uRIbGyuTJk2SefPmSf/+nesVAoGABAL7/e8NDd8mFUlfb4nXt9+X7Fml83S0gJTrf3j/X5St4WTtL44psetxNsRpPQIitkbnBmn7RusidHEMkfjtdcrmKtWlCkx1Ni7RNqTvCefa752nFOToQTlqDPMFCbgmyhcEAedz1wA9Drqu4z6hUhhIdwT1A4Z6J19xmT4W3CeYQwncY1RGxNkO6UeQPsWVod8REW3z1+r3pu/r1cr2ixU3Kltjf5AHa6DWrXjqda6ZP687z35cgX5e/mStxVn5pdbwpa3Tn890MEWQtsdXXadsbTlaA4PKqPiqzbRdLaPs3wSkMfMV71G2cJ7+xyPS+6D+onntrdbjR3ofqGMz0COZ6n0CvbT+EbVr6qv71tobOUv0+5W4S+cy8rUcOB+ViB7H2FsfVW2+eOwnoB/kUDluo8DKy8vlH//4h1x//fU2e2FhoSxcuFCWLl0qTzzxhJSUlMiZZ54pjY0godb/MW/evI7dpZSUFOnXT4sgCSGEEHLicNwugJ577jlJTU2VGTNm2OzTp0+XSy65RMaMGSNTp06VN998U+rq6uTFF1/s9Fxz586V+vr6jj87d+48yr0nhBBCSHdyXLrALMuSZ555Rq666iqJQaUYvkNqaqoMHTpUtmzR2/X78Pv94vdrd0nq+lpbNXi0bZvZfOAwTRGR9K+AK8OxDe6sLC4i0nSqrvztr9Vh8KZh0KjStSdPl1twgb5AUNkL4AbxlDpKHyDXFnKdge14Z3hvZ1j1etcPVmbP0eHM0AWGruEoN4BKEiDQGCyQBiCEqsGD8hjQBQiua1oN3vlcUTkDVBoEjcs05Nmq1semvgls4D615+pQ4yAoQeCsft76jQ6h9jXrdzWmvE7Z0Bi81drFiMpNoPsJ0yAAFyhyPcVvKFc2p8MHlkEB70MoCYy/ix3074LcTLHAfYbuE5qHweRUZXO6xdD3FqXxQA5x1N9gvN4XQKUwUHqHYIJ+63zN2kWHqtA7K86nbtL34/RLHla2j166TdmIGcflDtB7770nW7Zskeuuu+6AbZuammTr1q2Sk5NzDHpGCCGEkOOBbl0ANTU1ybp162TdunUiIlJSUiLr1q2THTu+LSg4d+5cufrqq9VxTz/9tBQWFsqoUaPU3/30pz+V9957T0pLS+Xjjz+WH/zgB+LxeOTyyy8/qmMhhBBCyPFDt7rAPvvsMzn77LM7fp4zZ46IiMyaNUsWLlwoFRUVHYuhfdTX18vLL78sjz/+ODxnWVmZXH755VJTUyO9e/eWM844Qz755BPp3Vu7OQghhBASnbAUBmBfKu2i3teJ1/0df7hhqLEVo9eVqIyCSagx0qKgMHPTsHUIKksBtFVWEhBVAKCmxOSaCNQPNH6gO0KaElSWwt1Xu0dNQ8iVVgaEwZte0/SeoGug5w/1PkCjhM6n2oAxmITPi4gE8zOVzVdSdcBriohE9ujyMDC9gWEZFXUuNG/Qs0dzGt03oIkz1Zg5U0WImOvdAgVaK+dut+tMTO+5yblEcJkKp45FRCTpa7MxmJbaiTi0ZyhEv3JyqrIhHQ8iCMYQv133A2mx6obquZNcqr/9SMfpvJ+RGK0nqpqg73naRv1sPvlL9OqCjptSGIQQQggh3QEXQIQQQgiJOrgAIoQQQkjUQQ0QYJ8PcYpcJF7Xfp+uZ/hQ1RbqQpAew0SPMkyXwoBlKpAWyVAXhHQhCDQuqMcAOhCogXKMw+nHF8F6B6SLQvcEaUBQP1CuJQTSbaDcLaYaDXV+w3tppKcSgfohE22PiBjpZ0znnAVySqF8VOh9QMDSGobjCpXpeeIdZM+rZarhQ2OA9xzprsC7BPU+zlxZnZzP2rZD2Uz0WEjDh+YXuicolxHS3jQN1poLf52+hr9UP/9wOii1A67RMsDeLvEr/U5HEnV+J0Q4WY+rIR+UzKjT/UAgbQ8q59EwCORyqrIfi/IMJW/VcwnlMkK89+YdRu2Od6gBIoQQQgjpAi6ACCGEEBJ1HJelMI4V3j454nV/Z4sUbBcjVwYChe46bS60zQ7cWHj7HIRfG27HwyrnAFTV3EIVzMGxTnecpwaEfIOxwjQA4PzQLQDuOToWugqRuwu5KEzcMcDF5Ebh0mAMsL/A3SGH4dpsGaDdjPEGrjdXu75vqIQKnIfIpYbSLCAXDXL3gvOhkiFO9w5KC+ACqRecldVFROLXgjkM3ofgGO3a9hWX6WMByEUVmjjc6HzIjWt0TXBP3AJcccBViNxd3kYw14GLqrmffv4ohN7p8moYp9+vhJ3gOwdA7qnmHFTOQu8VJG5pUDYUGq8dWSKJZTok3xkG72vW4e1b/k2fP2Ot7m8gVdsm3KCry695Krqry3MHiBBCCCFRBxdAhBBCCIk6uAAihBBCSNTBMHjAvjC6cwtuE6/nOzoSg9T6IjgUGKFCsoH2wFSfY1oewZRIfaO+xtgh+hpfbNY2pHdyhjOD/pqWAjFNM4BAWhF0PtPyIM7nE87TZQQONVReRIz7djjPH4ZaO/QdqJQLeh+Mw9ZR30zD9tHzNy1dYtIPQKSsQp8faLFCGUADUl6nbMHcVKN2aFwImLYCvE/q/EDr5i7WWrT2kwcrm69aaxZhugB0XcPnVXtmP2VLW7bVbgDvZUOhPg7pifaclqFssXVae4O0TfV5+juXuAvoREFJC3e7/rXrCdqvi8LgkXaoNRP0A+iTys9NVbZeX+v+Hu/h8gyDJ4QQQgjpAi6ACCGEEBJ1cAFECCGEkKiDGiBAZxog07TxKI8G1I84QblhgBYH4U5J0n0z1YWgvoF2UAcB9D4I53VRjhKk94E5j5CmxlTvhHQmhjorQblrHM/fpAxIZ5jqZ5C2A+YtQrmWgH4oVKVz9zjLvsDyEChH02GUZGnP1XPY/6XWoyBM3xNnuRnjMh0gl5Op7gbq2EznHML0fTUoB4G0XajshbtRa09MywBBTHVhBrQM0ToehK9ZjxXpZ5Le+UbZ4Ltp2JdAGng3wfDdDg1Q4lr93Wgab6Yvbemt39eUUv0MEShv0z8/ucvo2J4ANUCEEEIIIV3ABRAhhBBCog6WwugCVzAorvD+NaJpSCraooZp/p1tYkDl88MIb0Yh38ilZBrKjsLgw37gGgHp5Z3uGOgWAv1F5Sfg/TV9NsC9cTjpApxuUeT+hOUskDsN9A1WVzcsNwFD+QGoZIQ4K4mDki86WFjE3TfHrB8ghN4H3GzBgr66XUmVvq6hC1icLh+UUqANuHtQCgwwb0znNewbcrOB+WRaSd75r1tTd72nXfcXhu0DF5ip+9AC7jlnlXcRkbhK7aKMOL45sRW6HzBtA8AfA/YAQH9rTtFlgNxBM/VI2gc70YWVyVnSo/LCAfowEKIfSNVjSC7Rco1Aqn7+qGQIqi5/6hUPK9snf7lN2Y43uANECCGEkKiDCyBCCCGERB1cABFCCCEk6qAGqCsC7SLu/WnMdULzTrQnSFNiUEYDnR+CNDBIZwF0Bp5ycD5D3QZKke9CGhUUHu3QQCHdTaBAl5HwFwMNEAwh1horGKaLSmGg86F24H7C++7sBwpRRuHS6FgQpo3KbaAwZYihPs1Z0sEDNEDu3loXgVIlmIK0Ta52oG1C9xP0D5WqUKD7AbRubUAXEb9Bv0zwHUbpLQp032DaAtA/lAYCppVw3E8LtIGlYcCc9labfedcfhAuDtOH6F898dtBCgWkqXLcOzRvAqP1/fWX6vsW9um3rnWAfjd9zVrvE1el3zmkf0TveluOfuec5TYSt+h3v36EDuuOr9K6oFCCDr1H5TxMqR90Yu6VnJijIoQQQgjpAi6ACCGEEBJ1cAFECCGEkKiDpTAAnZbCQKnvkTYCaWpQ3g/nuYCv+HDKKMC8H8AfD8stIE0FyA1kmkNHaRQ2blNt0H1DeUWMrwnKIyDdimlZAqSzcI4D6U5geQyQpwRqlgBQdwRylyDdmWlZCuexpqUAkH4ElqlwlKQQwVoOBMyhgzC5J4YlLkx1YqYaqGMyT4xyj4E8XqZ5psD8Qtqm1myd8weVeUD5jVBOGmdJC2+1vh+7p+iSFJkf1ykbGmvDmYOUDelnoKYoXc8JNIZIjFaL+ZrD9nMBfZJ/b5uy7ZkASsiAfEERn74mymXkrw0rG8IT1Nd49+2fGx17NGEpDEIIIYSQLuACiBBCCCFRB11ggM5cYM7QYBG8/WqKcysbVtJGYaqGoPPB8FsQVns4Vb1Ren0npiUO0DY+PL9p+RGDdAQiB1Eew3Fd6BIFZRqge84wHQE6FoZ8G44V4Zx3oTLtsvCkAJctcpUhl51hKgfj+Y9C45E7yul6Q65Y8LxgWgTDcaG+odISKITaFdDuCMuvQ5xRGgSnewtVvjcFuXZghXjkYkcgtzuQACDQ+J2gb1owP1PZ0Pc7kqRd/WisjSP0XEelJeD3FXybnOdDbremPnoupb+r05OYuhNRuDwK72/I18cm7tLPEPVv9cI5ynY0oQuMEEIIIaQLunUB9P7778uFF14oubm54nK55LXXXuuy/YoVK8Tlcqk/lZX2ZHnz58+XvLw8iY2NlcLCQlm9evVRHAUhhBBCjje6dQHU3NwsY8eOlfnz5x/UccXFxVJRUdHxJzNz/9bmCy+8IHPmzJG7775b1q5dK2PHjpWpU6dKVZV2rxBCCCEkOukxGiCXyyWvvvqqzJgxo9M2K1askLPPPltqa2slNTUVtiksLJRTTjlFfv/734uISCQSkX79+sn/+3//T37+c7MQvX0+xLMnzBWvd7/v09jnjbQBQEOgwtRR2DLSVKAwYMNQ7kPV7IiIcXkII0B/Yfp641IYAKTHAMCyFEAvcagh+aZh20jvYpxmAcw5dF2rDcxhoB9yzmuYUgHMG9OxQi1aKXjW6L1Bz+EwwvQVh1guRARrSozD6gHwfQXfnEiiDjV3YlKiprPzIw0QKt2B+hHM0LaYcq1jM76fjv7BvgHdTc05es7FVWndlTPMXkQkmABKd4BSKKicj7cR6J38h1aFKpCm38O4Sv2tCiWZfftQ31DaAmeIvohI7VCtlUK4HZdY89RPjI47VE54DdC4ceMkJydHzjvvPPnoo4867O3t7bJmzRopKirqsLndbikqKpKVK1d2R1cJIYQQ0gM5rhZAOTk5smDBAnn55Zfl5Zdfln79+smUKVNk7dq1IiJSXV0t4XBYsrLsyc2ysrKUTui7BAIBaWhosP0hhBBCyInLcVUNvqCgQAoKCjp+Pu2002Tr1q3y6KOPyp/+9KdDPu+8efPk3nvvPRJdJIQQQshxwHG1AEJMnDhRPvzwQxERycjIEI/HI7t327U0u3fvluxs7Zvdx9y5c2XOnP25ChoaGqRfv37iqawVj/s7fk7ktwd6AZinxSSVPErdj8pvGObLQL53/5c6Z4RpSn8rT99D5GuHmhpHynmkbfB9+JWytZw9WtlQOnikH0F5P3zldQfsW2f9g3ofA52Ry1CLhPLgwJw0SO9zGNouEx0bHAPShKVrjYK1Tc85pB8x1btAkPYI5QZyvJvhicNVGzRH0L30GeajgmVlkO4KnA+NH+mxwslaj+HMqwX1ZOCbhp4D0j+ieegGNj+qDgLuE/plZFK6x12s51fg5MHKllyivxso91JzP61FSl6n9ZlVUwcoW/qXIL8XuHcNg/Q1UtfZv6UV5+lyHlmfgLIygECqniNeoOOpH62/Lx5QHiNhc52yJYIcQg15+in2XmPW5+7guHKBIdatWyc5Od+KQmNiYmTChAmybNmyjr+PRCKybNkymTRpUqfn8Pv9kpycbPtDCCGEkBOXbt0Bampqki1btnT8XFJSIuvWrZNevXpJ//79Ze7cubJr1y5ZtGiRiIg89thjkp+fLyNHjpS2tjb54x//KO+++67885//7DjHnDlzZNasWXLyySfLxIkT5bHHHpPm5ma59tprj/n4CCGEENIz6dYF0GeffSZnn312x8/73FCzZs2ShQsXSkVFhezYsX97s729XW677TbZtWuXxMfHy5gxY+Sdd96xneOyyy6TPXv2yF133SWVlZUybtw4Wbp0qRJGHxKGaflRxXELhbg7z2cYVoy27dD2dgwqj2Aayg6qdUO3BdoGB/1ToDIN4Jqxy7/U50dh4GBL3VdcZtQOhj0Dt40LuaNMwpkN543Lr0O5YYgvcPfB0GjgirXGDlE25EJwuvtQan3k/nSBuQTvGwLMCQtVa0eh8Ybh3K5Yu6sIlrIxDOUPI5cweEdMvwemIf8olYGvRt9jZ/88yIWLyo+A7xAcayM4H3gf0LHWavBe+3W5BRe4d+pceX2UDYXZo/nQME7PL+TuQuH9yN0FUwMAOUGqsmgSd+lq66g0SssAPW9QGQ1ELKgaHwaPFZUHiYBq9ambwDvn6POEGx5VbY52aHxndOsCaMqUKdJVGqKFCxfafr7jjjvkjjvuOOB5Z8+eLbNnzz7c7hFCCCHkBOW41wARQgghhBwsXAARQgghJOo47sPgjyrtQRG36wBtgF4AaT5AJGCobJftZ/fYEaoNCtFG13RqG0TMSxUcThkBpMdA4ceqHzm9lQ2m6gfjMi0FAsOqQVoB53MQEfEMH6qvAY5VGiikWTEN5QYgrYRk6nuH7gnSnggqNwGel/PeIb0P0n/BVA4C9DlobgJtixuVkTCYXyJYe6HSCqAwc5NyGSLi+mKzviZoh54DTKkAbK52YEPvHPomOPUihukTrBj9a8FU/4e+JbDECZjDEfBNEPBNcOrRTEuoID1d0td6Xgfy9Bjc7frJovIYHlCCAhWMCORqzZav2l7Swh3U10R6H3e7lpGg/qL0Di0T9fsajNf7Iuj5x4FyHq2ZerTOdCy+Ft23kXO1LuireUdfF8QdIEIIIYREHVwAEUIIISTq4AKIEEIIIVGHy+oqDj1KaWhokJSUFDm34Dbxevb7NJGWI7S1RNmQlsfVrlOuw1w4BkDNDtItGJZggABNAfLRu5talQ2l6ncCyzSA/sL8M8DnD7VNqMQHKtMBdAZw/CCvjjMnERo7zNED8oq4SrUWCT5rQw0UAt4ng5IRwTNGqjb+4s4LDNsbgudqmN/IWLOG5j96/uC+q/ODdxVpx0y1QkjbhPIxmeaQQXo/lAvHqR80fX9hGRzUX6ALg3oy8E6EMrQuDGlq4jeUH/AaaC6hfDlwrAbzQQR/I+C3CTzDYG6qbteg50TTYHsFgrgq3QblrWoHeiIE0gU15OvcS70+1TnlUFkOlKcI5RBq7W3fZ/HX6SUHKlMSSNPz5oPXbtcXcLDv93d9ff0BqzpwB4gQQgghUQcXQIQQQgiJOhgG3wWupmZxuYHr6jt4++qtZ0Gh64CIY9vatCo5Ct01regOq4sjgDsKbSEjd4zLxDVgmPbfXQzOBVwgVj2qXq+fDXRFom17UFqhdpquMJ3+rr2MBEqagN1iuh/QVYJcZeA+wfBzVFoC3JMQqCTuXb/N9rP/S10uA85N5NYFKSB0HWmRUJWe/14U8g/mF3JPorIcbkf5BtMxIJ0AcsUhlzj8RgB3H3RRofcVvDsu5MZ1nsugjYjgMjiG7q6WIdpVErdyk7IFB+g0E3HbQf8MUh7AMHiAaekS5LJyV+i5FAIh76Eks5QiaP4nbmk44HGNI/T50XEoXD6uUY8h7QuzOYHcXajcBqpCH3GsMlI36nu++1Q9z1FZjSMNd4AIIYQQEnVwAUQIIYSQqIMLIEIIIYREHdQAdYVBKQzkt4dhtCDs29kOhfyitPRQo4DKCBjqB1Dqd6j5MEx9j/QzzlBwGAZtGt6NdEcgNDji1552C9g85aA8BtABpH8EdBt77CGjzrB4ERxCDcPxwbGoFEDEsGREuKC/skEth+i+qOeKnj0qIwG0SJEk/WxQP2D5EcNyLujeIU2ZUwfiLtbzHM1pGI4O7oknRd9LFC6NwrRl4zZtM9TsoXfOWYIEhZ77ist030A6BlzeB5RHWKnvObqfsRWgxAkCXKNlfJ7tZ/9e3Tekz0HfNPT9RvcEacVMdXHo/Q8UaD1SMMH+bUr8ROvJEsD3C2kz47crkwqzF8GlQPaeoku3JO7S71zYp/dPkB7J22yfd4FeOvQ+9w39fiEt1umXPKxsH710m7KZwh0gQgghhEQdXAARQgghJOrgAogQQgghUQc1QF0Q7p8pLu9+fyXKGQH1Pqj0gUkaeuArd4HcQFDbAfoG86WAvDL+UqALQblGkBwB6XFAM+d1UX9DZVp74B2Ur0+G9Cigv+5GrZVBzwbpjHzldcoG8xT1dvjLTXPUoFxJBjlPRHBZApRXxrS0hBuVwnBoXlwD9ZyDujbwHDyloOwJwNUO8jGZ5hoyxDknYK4o8M6h8cPcQGB+meqMBGnAALDUDLgnzmu4A/pbBcu7OOd0Z/1Aehek2QL9RVo8BJpPzvIYSE/nL9X3HOYeM+wHBJ0PaDZD+ZnKFvPZFmXzOr7rTq2TiIgnqPPxuAL6m450Qe4g0EoBXRjK04M0awcuePQtrZn2e4L0RJXn67mfuKvrHHz7cOqCQkFdVqMzuANECCGEkKiDCyBCCCGERB10gXWBqz0irvD+rT/jCtZgazgMyg04S1+YVr52fbFZdxZtn6Pq6qgfqMQFALnP3IauHOd10TVRCDHEsMo3TP2P3ExwKxukdAeuR6d7A7lUwgV9lc1XUmXUN9QP6O40LC2CKr8jIgH7NrLX0K2LcCEXyDbtonCD+QqTUKCQbNMq947rQncPcv+iavAw9FyDUlRAFygYP0w9gZ7hoZaMAGH28JroniPQM0RuQVAeBbo2DdJsoG8J+gYj9xRy7UC5AviWmD5/VMEdllFxhO7792pXDupvMEN/+wIg5N0N3GeobyhtBQK5D1H6gbgquysLhc+nbzD7HdSaCX5/OcaFxtkZ3AEihBBCSNTBBRAhhBBCog4ugAghhBASdVAD1AXB9FixvhMGH1OuHdcwFBT4vJGv1ekvN/Wzw7IPIFW/DBuoTCi8G+knkKYA+cGDhvoWZ3p5Z0kCEVwKA2l7UOoBqPcB4e2wBAXy76ProvIFoPSDE0+D1oAg0FiRn90V0K8t0kHAUGvD1Ajevg4tEwqfRzoWU30O0J6gZwPLMgBgSRbQP1UeBmln0D1CuhAQLo/CyhFQo4J0UYblbJA+UYXfG8xVEax3QvfJtB16/s0j9f1MBCH0UHfpKKOCykr4i5E2E5QpQZpIkC4AluQRrQFqywHlRprNwrmdID1NMFt/D+K3g/cQgLQ3zUNSlc1fC/R+oC/o96HPIFwe3aPaofobFAFTyTQ03hTuABFCCCEk6uACiBBCCCFRBxdAhBBCCIk6qAHqAv+OWvG6u86JEMxNVTakgXEBP7jTd41KUkDfPsir4kYlHoC2JZwL8goBvy3S2Qi4rlPbYwrMSQK0DchfHLcdaJFAunn0HJBuA+ZfAdobp/ZARJTmAZbaMNVYIU3JYeQkgX0BoPMhPY7JcXCsSD8B7jkcF7gu1PugXEMgJ1PE8VzR3HcjvZOh3geVzEBAvQ/ISQSfK9LeAFS+IFSmwnBuRvbUKBvMoYTOB0j8St9P1Bf0nXDqB5HeB+mC3O1meXBQOY/AaP1cfdVadxe/tlTZTPOW+Rw6PvTt91Xr+doEcv54m3U7VEYjkAbynfn077y2VL1XEpuqjw0D3U7EZ8/mFUjV2b1yX9mubC2jcpUtkKa/y87zh9vNy5twB4gQQgghUQcXQIQQQgiJOrrVBfb+++/Lb3/7W1mzZo1UVFTIq6++KjNmzOi0/SuvvCJPPPGErFu3TgKBgIwcOVLuuecemTp1akebe+65R+69917bcQUFBbJx48aD7l8kIU4inv3bgSjleMUkvV2YmaC37qrH6u3C2Gr7z/EJ2t0RAaGLdYN1aGyvb7TLyge2Qf1f6q135GZB28Aw/BaEs6Pq184yEqhSO0pVj9xdCLSVjdwH7bm6vyicE2JQbgCWnwAum0CetsV8Bo4FrkjTFPywPIihi8oZfg5TNID7EdpaomwqpL4TkNutZbw+NpCqt7id6fZFREIJup1zK78dePoSKrRLIRiv38PEXdrF6KxULiLYpTR2iLJZqGo8SuUAXG8e5GY0SKth5eg5Isj9jcqeoBI6KDWCIaExIG0HkhM4SgFFQLoP9J1DoHQE6L4heQKSP7ibzFKZoN8l4rCZfpeS1+kSIsgl3DpAT/bkraAUhl9/h+sGozB1/U6061dHWkY6yursjFVtqv6gv2lNn+kxZK/U71zdUHu7cAAW0IF06w5Qc3OzjB07VubPn2/U/v3335fzzjtP3nzzTVmzZo2cffbZcuGFF8rnn39uazdy5EipqKjo+PPhhx8eje4TQggh5DilW3eApk+fLtOnTzdu/9hjj9l+/vWvfy2vv/66/O///q+cdNJJHXav1yvZ2fpf+oQQQgghIse5BigSiUhjY6P06mV3J2zevFlyc3Nl4MCBcsUVV8iOHV1vhwYCAWloaLD9IYQQQsiJy3EdBv/QQw9JU1OTXHrppR22wsJCWbhwoRQUFEhFRYXce++9cuaZZ8qGDRskKQmklReRefPmKd2QiMjmm+LEHbffX5nUq0W1CZRqP2XT/9O6lTSf9l2mx9rPV9aofbTtQVD2QFlEdg7UvufBf9Zhjw1nDlI26EMGvvFQhvbTwhIfIOW+U/OD9D6w7APSI5Tr/qqQX8FaifCA0fq6KCQZhTOjUh2O8GBYugAAtVggbBuNH+qzAC4Qag51TEgDhMpoOEB6J+vMk5StCYTaNvXRz79JV1WRWB19LY2jdH/9yWblRmK8dl1cjE/f39pWkPriK/1ca07Sb2J2Wj9li6/S7z4KyY4gDQxIM2EaLu98X+G5lAWD3hE9Ajz/0XuI2sEyPagvDv0U0hyKwfztFPCeo/uLStxA3R1I7+BtBN8Sh/YGvefou4yuWX5uqrLFADllQ75+N5GOxz2pTtvAu/P2mGeV7bad37f9fMMZ76k2N/3p35Xt0+seVbYfnzdN2SrL7R+OcEubyALVDHLc7gAtXrxY7r33XnnxxRclM3N/Dpjp06fLJZdcImPGjJGpU6fKm2++KXV1dfLiiy92eq65c+dKfX19x5+dO3ceiyEQQgghpJs4LneAlixZItdff7289NJLUlRU1GXb1NRUGTp0qGzZsqXTNn6/X/z+rhMeEkIIIeTE4bhbAD3//PPyox/9SJYsWSIXXHDBAds3NTXJ1q1b5aqrrjroa00cWiK+hP0ug5aQdh9sA8fV7NZ7iEkb9LG1jt3H1hy9qTxojN62fmvYG8o2daO+F5svQ+HHOrdu1fgcZev3rt7eRdu2yC2GtoadrhxUvVxQBC3ajgbNYCg/2C5GYfUw2zDKtgtcRc7teAGZa+G2OAoX3qhnU/PUMcpmFgSPx4DCY5GrzBnO7knR7llnagMRkd2n6LGiUPMI+PKcfPY3yrZ6+wBl65uun2HZJp0JPK5Su1Rbku1PuzlGP31Xuw6jdYP+9h2qQ7T3ZGtXdFWZfmIRr75un+X6ut5qPYfRc0XuEp+jHXKfoNBoGMoPcPfV3w00v1AIvWXo2kbZvD01jncHuXXB+wWzqoMM3y3j85QtfnO1sqFM6yh7M8zmDTKQi8MFZvq8IjF63qB3DrmOi0bpd27ZxgJl6xOvv9dlJfp+nlYyR9lcyfb7tPFPw1Wb0FlaXvKvm/5V2TJi9dzv38s+90P+gGxVrTDdugBqamqy7cyUlJTIunXrpFevXtK/f3+ZO3eu7Nq1SxYtWiQi37q9Zs2aJY8//rgUFhZKZeW3v2zi4uIk5f8+0D/96U/lwgsvlAEDBkh5ebncfffd4vF45PLLLz/2AySEEEJIj6RbNUCfffaZnHTSSR0h7HPmzJGTTjpJ7rrrLhERqaiosEVwPfnkkxIKheTmm2+WnJycjj+33HJLR5uysjK5/PLLpaCgQC699FJJT0+XTz75RHr3Bom8CCGEEBKVdOsO0JQpU8SykAPiWxYuXGj7ecWKFQc855IlSw6zV4QQQgg50TnuNEDHknXvFogndn8YfDwoYJ5Qp3U7fdbp8ODGETosM3mVPdoMVTQP/68OKT/bd72y7ZwGKuAmaz878vk2A23TxjLtp03cZVZlNxGFszp88si/3Z6rQ2NRpWekPUEg/z66LqxqDXQFMNQ4yd7OA/Q0KJQ9WKBjvlHa/4TNdbofIIUA1FSh8HaQXT+cq+em21GWojVHj33vCBDePq5N2U4drMtjrP5wmLKtf13PuVQtvRCrTqfSL9iic3ehe+LUqJiWfEHalroN+t2UYXpT3RWn/5E3aWKxsq306fIYmQl6bibuAu8JKNWg9HNAYxML5hKcD0izhsqqgNQIbsMyKpE2rR1E77A43jFUjkfphDoBhegjvQ8s8QHedVTiB2mFIkk66MaZBgCVy0F6n/Iz9Hc5nKGfDUrjsrFO/85x7QXfvtf1XEfvXDhZj6upr92WvFXf8+QS/U63JOhv5MZM/X4502dE2vQ3qDOO2zB4QgghhJBDhQsgQgghhEQdXAARQgghJOqgBqgLeq8Lifc76b7jt2s/MNReABJ26vwFTl870oAgkBZl4Mva1pqp/bGffzRW2dovrFO2c65frWx/+0rnpOn1nvbdJgC9hOXIwYF8xb5qfS9bRuUqW2wFyIOC8oqgUhAIwzwiIaTbcfYZ6W7a9fm963XOnyAqhWBYHgBhAY1G+2itnwr79L+DnPe4bqh+ps39tf7tX0auV7Z3dw5VtuQSrWXIfnmzssHcLaC0gLMkiYiIoLIkDg0YLJcCnrM7oOdX6ptaT5fytc5bU3VaqrKtbdV6pysu+kDZ3ssfrGz1L+j8OxnVOv+Sy/EeWuAeQZ0c0vYgwHvj8puVoEBlVMIT9T1xo2+i4x1DuhsEmjehgNaLOO+biIjLUBeGcjQhTVVzP/299jv0hE19zL5fo07V35KLsz5TtjvfvljZUv+pxzWwFuSAQyWPAJEYPQ8Ty+znQ2P3Nuu8SO6g/r7Eg+mQXGI/NhQMwvx8CO4AEUIIISTq4AKIEEIIIVEHXWBdkLC1XryerkPqUJkDhDNcWkQkkOrYQgVbg8kf6KTeoQwduhiJ0WtZZ5i9iEhius6RXhvUtr+POkXZXKBsAHKf7Y7J0H0psYeCxq8tVW0CwD1j7O4ChMp2KZt3UL6yoa1s6O4y2I6HIbmgPAYCPUMUaqydR520A9v2sAo9cDPVjrXPiaZJOoTWCur+rnhuorIl7NFb2f5aHRqMws9ReZDIMO0qRP+SQ24xZ3kUVJVcVmvXlmugnpuoTANyiWc+o8fQ9P1xyva3+jOV7V+u1m6xvydoF9iOC1OVLe9Ju0vRBUpXBHP1cahqPEofIahcDAKVkAEh76iEziFjGLbu7QvKBaHUCAh0T4BbEIXku7P17w2ny6uxv37Tx31Pz82dTanKtuAXlyjbwDpQugO4v5F0IsnQBYbO5ySuyuw5tybofiApSaCXlmGYwh0gQgghhEQdXAARQgghJOowcoGtX68jOw7EiBEjxOulh40QQgghPQ+jFcq4cePE5XJ1Wbfru7jdbtm0aZMMHKh99ccTrr114nLv98siP7irVOtMkDYAhXjHlDtCcEGqeisXpNsHqHMJDr90BXS4Ya9PtVYikKp1PFNmfapsH1bqZ1yXr+dJ1gp7CCrSyiBMyhmI4NTySO+DNFuW36zERwTop5xhxOj+IlxAK4DKfiBNQcsQ/WziN5QrG9LAtJ09WtlCCXr81ePtz7BwwHbVBpWz6P25fl4whBbMdckAJRhAmRILacBQKgOgFRLn+wp0R1AXBHQhSGOFcMXquZm8To8/6Ws9r/+aonVBMefUKVvoq1RlazrVPv/jKvWzQWkWYBg8+jbpVsb3BJafQSlFUEkaZ1mdbVrXhvRO6N1HofFQiwZAJS6Q9ghp7Hwg7Dvi0M88cM0i1eZ/945TttCz+ndEMtDKINx+vQTw79W6V/R9Rdob9B1ylnhCGqNEUFYjAaSeQN9X5zVDEXMtmfEWzapVq4wqqluWJaNGjTLuACGEEELIscZoATR58mQZPHiwpKamGp30rLPOkrg4s+goQgghhJBjjdECaPny5Qd10jfffPOQOkMIIYQQciygSrkLItkZEvHs91cGM/Sulhf4RlHOH2+j9qs7y0F4RftyQxk6NxDy2yP/Nso/49QFiGBtANJyVM3U2oi+SdqHvjdb+9CdPmSUG0MEHGfot/cgfzwApf5H+UHcKI8IwKmDgJoCcH6kz7JWf6lsrolas4NyKEEtC9BBIP9+5ST9XCNeu6997du6TEEiqD4BNSWoNArIA+MC2hPT++RJ0fosV7Xun5FGBTwvp+7k23OBYw3nDcrbFEnXfcteqef19nRw4Qytl3DqTFpB7pl4oKlwxejvECrxAPMsgfuLyo2guQnnCdD3OEtmoLxF6DnAdxqVuABlOmApDFCSCOUGQt+EGKBjK5llv8atH1+m2vRfrPV6aZuBns7wXUIleWB+K/Dd9AvQcQLNViDN/lxR2QtUGglRM0HPfX9dsu3nULBN5CWj0x25MPjXX39dFi3Soi1CCCGEkJ7GEVsA/exnP5Nrr732SJ2OEEIIIeSoccRcYBs3bjxSp+oxuJtbxe3en8bfC8KlUSViP3DvoO1d57FWjH4cKIQ4DLbKkdvNX6NtsKI92N4MgbIUe36Wp2znPvGhstW0xSvb1ovt26VD/6D7gVx2QVSZuwKE6YKtV3SfPCjEF1aD1ya0De4M8YTlMtD5ATAtP7gnLePzlC12uXYLoTBw5AZJ1F4G6T3JPo7aZdplkf1enbKhed46QLun4muQ/0jjLgadG66ryyNXCQxndx6HXFso9Bq4BVzAtQFTZaCSEahkQrl2vcTrqGLxX69dGYlx+ju07dJU288DXwQVt1HouaEbz+T+iuA0ALAduE/NU8coG/qGKZArDrnT0fgRoJyHqx281+D7glxlu6foVBZWyH5sUi9dfmbviFRl8wT1fEBpV9DvKjSvUVoQ9B1GpUtQig6nxCICQu9RGSA0hsyP9TWd7rNQyDwMnpmgCSGEEBJ1HPQO0Pvvv9/l35911lmH3BlCCCGEkGPBQS+ApkyZomwu1/6qteGwWSZcQgghhJDu4qAXQLW19pTwwWBQPv/8c7nzzjvlv/7rv45Yx3oCls8nlme/pgH5UGF4KCiZ0Jaj/cBx2+0aAhhmD8LnYbkF5I4HOoP6EcnKlmboB2/qq335T71zrrLdULRM2d78vT2LONLnuIF+BGmgkD8atUOaChimmad96H5DrZC6LtAKoPmAnmHLqFxli9+sY7lRGLw1UGu2XEDblYhKizyhTLKr0q5lSQZTBGkFkC1u5SZ9MNJsgTBdD9DACEp5gHQmIJxdXRdovVDpAlNcoGQGLK2AwuANS5f0e1yHUI987BtlK02yl5uoTtUpMGLAO+dSloMAPFeklUFaMXRdE80i0l2Fv9FzDpXGgWV1CvS7hLRoLjEre+ECIe9N+hMmQ/Lser/NpTrMvGBZndH5kZ60PVf/kkC/XxBuVJYCXBd9r5zaK6S7cQPZDvpdlfK1LpnhDLMPBc03YQ56AZQC8m2cd955EhMTI3PmzJE1a9Yc7CkJIYQQQo4pR0wEnZWVJcXFxUfqdIQQQgghR42D3gFav3697WfLsqSiokIeeOABGTdu3JHqFyGEEELIUeOgF0Djxo0Tl8sllmUvAnDqqafKM888c8Q61hOIJMZIxLtfW4DKNzSN13kUEtfuUrZ4gxIMvmr9OFCKcB/ohx/kJEGairTVOm060uP4S7V+xletNUqVp2u/clHi18q2aMx5tp9j++jjkkti9TWbQYp/w3ILSHuDQH5wlLsD+dWVHgFphwAoPT6aI0jfYBnqDMJ5WkNQcYa+722f6v65EiK2n5NLgQYAlVEA/TAqPyHmmi1YqiBDz3WosymrsBtAriQ4BqA7QnPEVQOeP5oTQCuGykN4ghFlQ/MfseWNQbafM5r1GOC4THMZGYLeCXQ+mJMJlDNxlp9BoJxapiUuTN0iqMSHAJsF5lhkoM7x84OcdbafH/7sQtUGaUnjN2ihXAR8+1FeHaQ7Dfv0HXDqVUXw99BZ8kgE5/1xgnIKxVfpuYnyNvlrHWWWQmZlkUQOYQFUUlJi75DbLb1795bYWP3LixBCCCGkJ3LQC6ABAwYcjX4QQgghhBwzjlgpjM8++0xaWlpOqESIrvaIuL6T1wilUkfuLuR6QVuNzi1JVJIimKFDMptGahcIquiOwvbRdjwqoxHxp+q+JOjpkvaVDnu+xHezvu5A+3VTtupzIXcXCr1PQ6HRANMKwyiEHoHcVs6QfHSP0Paxs4SGCN4GRu4YWHEeuIrqhqUrW+8vQMmEK3QA8qRhW20/1ywGJUnAdjQsP4LC1k1LkgBQqQpYvgG5N3rb70kYlbdBqQJQ2DYaFwq9R6BSDU73nIiEQWmB2om6WvuHlbp/7Y4o4uqxuk3/DeA5gLFCDJ+XMbl6XFKuXfbK3Yncc6h0BbrnKEVFqf6mh8ZoNxYKDUff3OYB+rreTfo7sXaQfXMhtka/l7EVeqwwfQZIlYHmJirv5IOubeAqBS4w5BaPJDmlHoblRwDoPawdav/dGm53iXxsdr4jtgC66qqrZNOmTUyESAghhJAezxELg1+2bJls27btoI55//335cILL5Tc3FxxuVzy2muvHfCYFStWyPjx48Xv98vgwYNl4cKFqs38+fMlLy9PYmNjpbCwUFavXn1Q/SKEEELIic0RWwDl5uYetD6oublZxo4dK/PnzzdqX1JSIhdccIGcffbZsm7dOrn11lvl+uuvl7feequjzQsvvCBz5syRu+++W9auXStjx46VqVOnSlUVqNJNCCGEkKjEyAXW0KDTT3dGcrJOX90Z06dPl+nTpxu3X7BggeTn58vDDz8sIiLDhw+XDz/8UB599FGZOnWqiIg88sgjcsMNN8i1117bccwbb7whzzzzjPz85z83vpbIt/oLl2e/j7R1gPYXxyMdBNCexJTr8EinXzUwGqRgb9dhsInFlcqGNCVI2+Mv1b5cFPKO8AANVNXJqco2afRmZftkiz0Nva8ZhPeW6EVqouhxIS0W8tt7RWsKkC8blcJAeBv1c3WmYffXAl0I0DF4tgG9DypnYRh+HMxNVbb0d3X6/ppz9DVEdJ/Xvj3c9nPKMB183Ot1fX6PYcg31MoY6oJQmDYClaVwHmuqk0O6mNDWEmVD5RagPgmFwafoFAVIPxZK0PO1skTfk76n2DVFnke0ngiF8qP5alrOA4FSFMCQdDRP0HWd9xPMJZQCAulTkO4SXRPpBKEGDDzr4Dg9/kC67ktzyD4OL5DKoP76UUka8I6gEHV0T1A5mwB4hih9SKCXjgYPJdjPh36nIT1VzGdblK3ue8OVLXOFfZ6HIuCd7gSjBVBqaqqt4CnCsixxuVxHVQO0cuVKKSoqstmmTp0qt956q4iItLe3y5o1a2Tu3Lkdf+92u6WoqEhWrlzZ6XkDgYAEAvtv2sEs+AghhBBy/GG0AFq+fPnR7ocRlZWVkpVl/1d9VlaWNDQ0SGtrq9TW1ko4HIZtNm7c2Ol5582bJ/fee+9R6TMhhBBCeh5GC6DJkycf7X50K3PnzpU5c+Z0/NzQ0CD9+vXrxh4RQggh5GhyxMLgjwXZ2dmye7fdP717925JTk6WuLg48Xg84vF4YJvsbO0T3off7xe/H6TwdpTC8O9tO+S+hzK0r92Z0r4lU+sMEsu0PxOV33C3a40GyhkB87SAEgQwPwbISePVGd2lug3oChw09dFTL/Er7ctHvuEWkFcjMHaEsvX6tEbZUB4J5MuGegGQfydO7L7xhkF67CkN+nmhe9k4QvvZk9eh/BtgDECjgEqhhH3alZ3USz/Exha7396/Rs8vlBsFgfqG9AgIT6nWu0FnPNIUARvSBSmABgQdB8stoBw6SCsDct4Y9U1E4qqQxkGPtWyTXT83MKjnHMrlInl6XALmKywNg3JUoRIXKIcQeF5IoxT+xq73Q8/B9YXWITpzQHV2TYTps0HaI1TSwV+j5//AeHvdD11QSPDcRDoewxw9CNTOX6r1fujeeZL0+P077b83a0ZrrVviLn2PfOD7gko5OfWPoVCbiGFA+hGLAhs+fLh4PFo8dSSZNGmSLFu2zGZ7++23ZdKkSSIiEhMTIxMmTLC1iUQismzZso42hBBCCCEHtQMUCoVk8eLFMnXqVKWzmTdvntTXg9V/FzQ1NcmWLfuV3iUlJbJu3Trp1auX9O/fX+bOnSu7du2SRYsWiYjIv//7v8vvf/97ueOOO+RHP/qRvPvuu/Liiy/KG2+80XGOOXPmyKxZs+Tkk0+WiRMnymOPPSbNzc0dUWGEEEIIIQe1APJ6vfLv//7v8s0336i/mzFjxkFf/LPPPpOzzz674+d9OpxZs2bJwoULpaKiQnbs2L/1lp+fL2+88Yb85Cc/kccff1z69u0rf/zjHztC4EVELrvsMtmzZ4/cddddUllZKePGjZOlS5eqBZsJzlIYqJyFp1xvb6NqzWUX6UrPOR/at5VRaDgqrRC/HYRagr7VjtVuLBQajUKN0bHuoHaDuPXOuMR7QRVfn31sibv0gSi8Hbnn/H4dGu9rBunbUQg5GGvED9yTwPUCXSqO7eLkraDKNQgrRS4w5NqAIf+oWjnYove0A3dnUNuaGkAh43j7uHzN+tnDquTA3RMoANviIJWDcbg8cp8YhmQ7XQjQZYXCwAEWqgYOXMduFMqPzmdYNV2AO90d0o7BhDx7NGt9Xqpqkw7cv54aELaP3Fjg2SDXOQw1B6BUHr7iMt2/4UMdfQMh6rFmLlYEcru5wKNG1evRvfNoT7zITfo9fK9qsO1nZykTEfw9QKVbTEHzvz1Xu6gCgwcpW/IHW5UNhbg7q8Gnf6lvJgqfR65z1F9nuaQQ+D3VGQetAZo4caKsW7fuiBRFnTJlilhW551FWZ6nTJkin3/+eZfnnT17tsyePftwu0cIIYSQE5SDXgD9x3/8h8yZM0d27twpEyZMkIQE+2p5zJgxR6xzhBBCCCFHg4NeAM2cOVNERP7zP/+zw+ZyuY5JIkRCCCGEkCPBQS+ASkp0+vdoAZWMQKHGSLeSO3+tsjV9f5zt54Sd2ufZmq19vkhTgkK5k0uApgj42Z3lHL491izkv/kyrVuJ9+q+eDfF2352gzTy9SO00zsFxoJq0LNBY/U06P6iUPtgQV9lQ6U61PmRfgJoO9C8QZoaVOLCAxKVo9BwdGwgVbf07tT+9yxHGYXdJ2sNW5boMfi/1BqzYIKerz6UZgGUMxHTshfgHiONhkvs2gsXaIOc8ihsG5V4iGwE8begxAXS9lggNN4CeqSmvjpHWVyFDugNVafafs7apLUiqBQIKlMRBgEu3sze+nyGehRUqgHpfSJIZ5Vjvy58Nn6z8jbGGjAQfm61gXQE4HzoWce+r+fEnrPsPwf66e8SKrOE7jlKdwJTA/TV7zXCXwfKfoB7gr5hLUPsJVg8MXquxm+uVjZUoshXrcfqTDMQCoG+dsJBL4COhPaHEEIIIaQ7McoD9Le//U2CQfNV1ZtvvimtrYeuTCeEEEIIOZoYLYB+8IMfSF1dnfFJZ86cKRUVFQduSAghhBDSDRi5wCzLkmuuuQaWi0C0tR16yYieREu/BPH69usj3O06P0RTH+3zjQe6HXd7rrJ5HblrYAryWr3zhtqh8hBxlXoXrrnfgctUiGB/fNmVQ5StPaT91GOSwbFr7MfGbyhXbeJBHhiUGyVy8mBtA/kxIsDXHMzR40clQ4IoLwVKy+/wySMfPQLpjmAepNU651bwjJHK5q8BaemB3in9K31PmvrqY3884H3bz7+Kv0y1QURytC4EzUME0nIEDUrIiAjMDQR1IA7ND9KiWNu0jskF9A5I2xUJ6G+fW7TeA+bLAVqOICgHUD9IP8PT/+ULZfvmN6NtP7dm6rmZCOaIC2iRvGD8kT06wQ36FzXS8bgNS1DIMD1+VW4D5YUyBOXPMs0ig3INofOh8hgtIC1d7Mf2eTL+Yv3ulyfob58X6I6QFjE4cbiyucF3XoC2L6YcaLEATr2PiIgnaNeihn16lpgcJyLSBr7fgTS7xjAUNA/EMloAzZo1y/iEIiJXXHGFJCeDLE6EEEIIIT0AowXQs88+e7T7QQghhBByzDiuqsEfa+J2t4rX2/WG6OFUiHeGuMMQR+AqQWUqggkgFf5mvb2d9DUotwAqczedmq9sLafoquHfz9fbtH/dfpKytQ+2TzVfMyiPAELZQwWpyobCqsM+vTWKnk0MCPttHqKvgdw2e0/R1aSd6QKQ6xCBXEWwxAWoYA23o5ELCISVh3OGKlv2Kj3H78q+yPbz01ctUG1+sfFGZfOBZ5OwuU73DYWBA5eSaTp8F0irAMsGKIsGhQY3jNM+C1QKQJVpEBEBfRMULg+eNXJjtidr9+wHO7SrKFRo/yYMeEvfXxi2bppSYGB/3Q6EkMMq7MhtBWywjAZyRzowDgNHKQpQ6RIwfkSoSvfXC9x9/d7Vtu3X2103mX79nn/yb9q9k/u6dlk55RUi2KWESs2gUHNYXR7Ma/TNdaaFQSlAkKwDlXdC5w+kmck6EEesGjwhhBBCyPECF0CEEEIIiTq4ACKEEEJI1HHENEBlZWVy3333yZNPPnmkTtnttGbF2cLgE9dqTUXT+D7KFr9d+4uRLzs+YNeBoBA/kKhd0r7QaemRv9Q0JDMAQr4DqVrLER+ntQFLS3Ro5bBMXTIi8A/7zyEQ3ow0G6hMB7q/aPy7T9X+/dxXtM5IJFX3xa9fDVQexKnRqLpQpwro/bFO847KmUBA6D3SzwhIIYBCt5EOIHaLPp9r74GjOPecpHVnqcX6viVo6QXUe1jp+l0yTfMvonVsKBTYWeYB6j1AaHzi39fpSyL9iOE75xGtM0KhwDun6XlyzmnrlW35Cl2EevBfHN8JoDuyUCg3mDcwDQCYh5E8/QyRilKFsosYh7NHyuw55lA4uhuU83CB54V0ZwLSAKDnisL7w+dOUDYvKtMDtHLZf7V/60pv0dqpUwfrUlSfjdff4H7vAn1OcaWyQW0X+OYgDZAzpYSIiAt8w1UboPdB+ldUVgeW33BoaV1I69QJR2wHqKamRp5++ukjdTpCCCGEkKMGXWCEEEIIiTq4ACKEEEJI1ME8QF3gbrfEbe33YKP09SjHSd04nefAB0pVqDYgd0MwQT8ilBsEgXLNIL1P+ZlaZ3PpBR8o21+3jNPnq4xXttL3Bylb6gC7TxqVn0B+YKRF8tcCn3+79vvmvqHr0QXzM5UNaYqQHit+M9DyOHLSZHxWp9q052rtAcIXMCsZYUFdkG7XMkqXX4nbDrQXgD7v2u/nj5v/XbW55+IXlO3Oj36gbPF7UpUt8SutiTPN+eLMK9IpSN/TduB3B5bCADlvkAYGabE8pVp7ERitz7d3hH4PCyfqPFvLt2qd2aC/HngOxzdp3RHK+YPGgJ5DMDdV2XwlWv9nmvMHaUoQzmeB9Dkovw3Wu4ASH0jvAu4Tym8Ec3SBYxPeK1a21kn2HFIbl+vv6DX/+rayrfYNU7ZdZ+lv5MBScM+TtN4J5YVDmkh304F/p4no71XTYK0vTNgJyhEV9DU6v/NbEoqY/X4UOYgF0A9/+MMu//5giqUSQgghhHQnxguglBS9++H8+6uvvvqwO0QIIYQQcrQxXgBFYz2wSIxLIr79ob7ILRKJ0aHA3z1mHy299a1OKbVv1aEU5G7gFkIh1LDC7gi91Ri8UrsPRiRpt8jERB0y+1cZp2zZH+mxJpaZuXKcoJB3FHqOyiMgNxPaokfuQ7Ql25Kp76evWZ9PXRO4LJG7D5W9QMASD2XatSfARYOui0Bb3k7Xblqq3u6/a9VFyuaJ0+PaOUPfk8G1qbofMSClfyNwPYByJug+oXB5T6N9nsBQZlA127QCPXI7hkDI866zdXD4kBHa9fb1Et0Xa6B297oCDbp7jrIBzhQAnYHchKh6O7on0M3UbpZqwAIuexS67+wLSjMAy16AEifoXXIj91yGnpuHgwucz5miInulntN/bjlP2b5/6Wpl+/umUcq27Wo9/j7v62eNShJ5gBsvUKDvO8IpT0j6Wp8fhdkHsg8cUi8i4g7Y3/NwyFzZQxE0IYQQQqIOLoAIIYQQEnVwAUQIIYSQqINh8F1QP9Arnu+E/4V0xLfEaNe7ZL9Xp2xIZ1I71K69aJiqfc/xlVpjg2gcBUJXW7TO4McDPle2d6sKlO0nf79K2fzVer3sAeHnrZlaU5K8brft54ZxOvzSFOgvTgX6Icc1RURqTtd+a6QzQqUVkOahaaRdtxBXqfVPpmGfsHRDjB4X0qiYao/QvUNh2k7SvtDHhX2pylZXoOecKxtodu7VIe+1L2kdk69ZvxNxVTqVAyrxgVL/O8O528bnGZ0LzVcvSFuBQtnRu5nUq0XZSlfq8SdqGYgMXmym7XIX2zVFMOR7T40+DoR3o9QLSDuGtCLoWAQqeyJAt+PUMrkbwfmBJg6W5DA4v4iIBUp8uEp1aSRUWsUU53wN52qdUK9v9O+D//3gZGXLHaG/feUp+r3ZMxZ8q1P1XHeDdwKlgGkekqpswUz7O5Ec0DpBFGaPUqCEgb525wz7GCKtlsga1QzCHSBCCCGERB1cABFCCCEk6uACiBBCCCFRh8uyLOQajWoaGhokJSVFpi+9QXwJ+326G6t0GQX3Op1/pjVbawMQnnS7NsK9TYuM3AU6T8n43DKj82f69bHNYe3z3Vinx2X9wcwP7K/VIgWkRwmk2f25wXi99k7cpc8FtR0gT4VTiyPybSkTJ75m7X9GmqWmPrp/6V9pLQtMfe/ABUomNI3XmoLET0qUDeZLAToelBsK+dVRDqXWATrJqTOHDMq9g/pRD3JPNfbX9zJ0ir5vT4z/i7Ld/s3FylazW18DgXISDcyylzPZul6n24/E6TmX3rdO2U7qrTUg6F3aVamfYUyJ1mOkbdTXTVuttRwov5EroL85qrSIYXkIhLF+CGhqUCkIWM4F5HKCOPqM3hHjc5liOAaki0LvTihDH+t8N9F3A5UpaQE6Nuf3VkSkcgrIHwXekXOH6TIdqPyK7AR5evppHVf8p/bfa0hL6z25Ttka94Lfhw36m5M2xP77INwSkC8ufljq6+slObnrbwV3gAghhBASdfSIBdD8+fMlLy9PYmNjpbCwUFav1pkt9zFlyhRxuVzqzwUXXNDR5pprrlF/P23atGMxFEIIIYQcB3R7GPwLL7wgc+bMkQULFkhhYaE89thjMnXqVCkuLpbMTL2d/Morr0j7d7YBa2pqZOzYsXLJJZfY2k2bNs1WvsPv11uTB6L443xxx+7fqo4M1KGrj1zznLIlufU24KoWXdl3a4t9fING60rKf1g9WdlWNuhzQVr0NmhcpbblrNRbtMEEvV2KQKUlUEmLlK/t+QJMXTboXJKnw0NRyGT60i3KhrbLE0BYZuIW7VJA4ewx5faf68aBFPft2pa4RedPaDhTP1cULo8I9NIuFacbS8TM3SWinwXaxg+DMi1pH+xUtmQQzlsR1K7jn//tx8pWMxG4o/J19e/2oNmn7Oo+K+0G7YmURbsmKdvmUp0+4Z0SPZckXs+bAS+iVBbaRRO3XZekQZikLRAR7fJCVd5zzdJRhFHIOzgfKi3higXfXuR6A2425KJzhqQrV19nABcgBISyQ5cdcFF5QGX6CCjx4SvR33oTLOCKQ+ku4jfouRTx6dQAYZ9+bz7aNFbZvOAznHhytbJN6/ONsv2l5jTbz2PHlKo2SF7yzJRnlG1Zw0h9/g/t54+0orQmmG7fAXrkkUfkhhtukGuvvVZGjBghCxYskPj4eHnmGT14EZFevXpJdnZ2x5+3335b4uPj1QLI7/fb2qWlgY8VIYQQQqKSbl0Atbe3y5o1a6SoqKjD5na7paioSFauXNnFkft5+umnZebMmZKQYF+hr1ixQjIzM6WgoEBuuukmqanRgj1CCCGERCfd6gKrrq6WcDgsWVn2bdisrCzZuHHjAY9fvXq1bNiwQZ5++mmbfdq0afLDH/5Q8vPzZevWrfKLX/xCpk+fLitXrhSPR7tKAoGABAL7t/gbGkB6Z0IIIYScMHRrGHx5ebn06dNHPv74Y5k0ab/f/Y477pD33ntPVq1a1eXxP/7xj2XlypWyfv36Lttt27ZNBg0aJO+8846ce+656u/vueceuffee5V9wiW/Eq9vv7aitbfeMGsBLvRghtaU+Cv1WjOYcmCdDSo/kfmZ1g/UDdVOWn+dfrSJu7QP2duobTWjtUbD16L7G1+l+9KSqfviDOetnWimPUDnR/1FoNBgRFuO9u+jcHlUCsMZkoy0MqahsQiUWh6loEe4gB4BEc7T+hanBghpvVDpgkiiDo1Fei8E0oC5gT6ruZ9+XmEg72jO0e9OQoV9DlcV6uMy1mrNDnpvfNV6/CgNQNqyrfoiAKTvsNrAfTcMNXdqWVBZFSnXYfYo5B1qcUDfELC/6HxgDIdTWsIJ0uLAchZg/Eg/hfSEpvcTlblw4inX6T6gPgmU7jC9Zmu2fl8Tv9KaKlQKJpig35PmHG1zOz7h7VqGKBnr9e8WVGomfrPWHTn7Fgq2yerXf9nzw+AzMjLE4/HI7t32SbN7927JztYf5e/S3NwsS5Yskeuuu+6A1xk4cKBkZGTIli1aFCsiMnfuXKmvr+/4s3OnFnISQggh5MShWxdAMTExMmHCBFm2bFmHLRKJyLJly2w7QoiXXnpJAoGAXHnllQe8TllZmdTU1EhODviXiHwrmE5OTrb9IYQQQsiJS7dHgc2ZM0eeeuopee655+Sbb76Rm266SZqbm+Xaa68VEZGrr75a5s6dq457+umnZcaMGZKebq9c3NTUJLfffrt88sknUlpaKsuWLZOLLrpIBg8eLFOnTj0mYyKEEEJIz6bb8wBddtllsmfPHrnrrruksrJSxo0bJ0uXLu0QRu/YsUPcbvs6rbi4WD788EP55z//qc7n8Xhk/fr18txzz0ldXZ3k5ubK+eefL/fff/9B5wJq6usWj3//tZN2aD9lHEhBEfEhzYPW4wQT7OPK/LhOtakdqx2mSJ8SitP+/dxXdigb8gPvPlXrfVI3ae1NJEb7d8M+vYb2Nev7FMxNtf8MSmF4gqB0BdBZIM0OAvU3frvWHqB2AaCXaM3UfvC4qgPrIGB+I5C3xzQPjKmmCGkUUAp+hFN7g3Q8weRUZUOlNuD5a/RYPUjfAJ5D8iqteUB5WlK+PvDnLbEM5Lc5DL0TKl2B8tsgzU4EaLFg/iX0/Iv1u+5qd2iAgAbGnQG0KECfg7Q4znw8315Tf5sspIsB13UJeDfrdckU10B7PhukdUN6IpgvyFDvg3RySKNjKqhFuZxUn8E9h+VHeqcrGzrWU6Pvrx98m+A7t04/wwDIx5aoJVWqXBAqU4Kuib5zqAyMM1daKGSeB6jbF0AiIrNnz5bZs2fDv1uxYoWyFRQUSGfa7bi4OHnrrbeOZPcIIYQQcoLR7S4wQgghhJBjTY/YAeqpZH4eEK93v3ukqY/eVjQJ+xMRSd2it4ZF7G4FFELrr9OhgLsm6633jPX6/KjiOHIB9f5cb+9DgHejIV+7chARh6ssfo/uL+qbaQg1SgcfzDALyfaBcEtUcR6F7jtD49G2MAKFc6LzO0uIiOASFKYuNV9xmbI1nZqvbIlr7an6kesUuYrQ9jayRdK1axe5T2DVbORSAW6LZjAu57OGLlxwj1AZBTcK5QZuR5TKAM115KJAbjbkFjvkcHEUPo9KRoDzG5egMHSzwXsMQuiVSw2dP127VKArzjTkf/WX2jZ8qL4uqhC/TbsnEU7XnoBSG7CsCAI8V5PQexERK8ZsWYC+kRDn/QTPOeSQSIh0Ui4ElV9xuHZdYbMyTiLcASKEEEJIFMIFECGEEEKiDi6ACCGEEBJ1UAPUBd7GdvF6968Rawu0/zV9g/Y3RnxaF9TUR9/qdofkJ6ZBH4fOhUpheIK6H+52bUMh5K2ZoAQBCElHpQV6fW0WLu9ut5+vIV/76OMqQZ02oG1BY0XjQv3wF2u/NVIZBQp02CsMcXb42lHYPtIiIa0QSh+AQNqbSIx+NlA/Y3QFUZoPpE9xpjYQweHiKHQVlSlpHaB1QfFrS3XfkOYDkPhJibIFC/rafvZ/CfQZKDTaUO+DQKVLkC6qZUiGsiFtGzrWpM8o9QBKH4BwAT0KuiYuy6APDYzur2zOcGmRTkKmHSU49FveyXGgHQq/RuH93ho9VqTtQRod09IlzvO5UvSNg2U6EEArg0Lv3ablRwx1YUizJg4b0vqhbxqcm+X6PXRqIsMh8+pe3AEihBBCSNTBBRAhhBBCog4ugAghhBASdVAD1AX1QxPFE7NfgxKrs5BDwkC3g4h1pIIJAZcnOhfS7Piatd/TDbQyLb31sbF1ul3DAL02jqvW10A6G3SNtE12bYi7XfveUWp5v2i9ByotUZ8HShoAHdPeKwYoW7+/6Zw8u0/W5+tfqkwqnw3M5VSr9S4oRw/K5ILSwaPxIy0L0lmgHB8oJ41To9SSCdLjbzUre4H0Q0hTEwe0ARCkn0B5hYC+w5njx5lDpDPQudC9RHmm0PiRLgrqnUx1NkAX5dQoQe0QAOl9UAkGa+wQZfOgvEIANF9R+Qqk93JqarDuCGh2CvT7gHQxpjobpPeBx6JxAY2OSR4gpLsxzakE35FSXbsCadvQnEBz2ATT3G5QOzlCzwdnmQ5X5MDlifbBHSBCCCGERB1cABFCCCEk6qAL7CBALiCEr0W7lPy7QLmFvfaqtXXD9JYiKrURW61tPhDkWTVBP94IeOK+DdpmGt7uLHEhgl1PO4vs25nIndgyKlfZULi8M32AiEiMrhghaZt0VeDUjdrNsvNfdPhx36V1ytY0ElTTdoT3I3eXr1lfE5VHcJ5LRKRuqB6/F+yMp0lfZUNhxSg0HoWgOktEpK4zS3tvGsrtDI3tDORSQCVDUt/8RtlQOLM4tvdRaLhpeC8qSYLuJUp5AMPvEShMGbiFkDvCWSE+0gZCjYEbK5Srw69hAQbgskblFlClegQal4l7x7QMCKzADtw96B1xlrwREXG1g+sC1xZy7aF57XRRopIcol9peN/QtyquUqeocEOXmpkL1IPGapAaArn1kfzBBVx26Jum3pGImRtWhDtAhBBCCIlCuAAihBBCSNTBBRAhhBBCog5qgLqgNd0lHv9+3UsoXreJrQHlFkBYOdTPOMKZgwlI76Ov2aSjOaV2tLYlbdM2f53WmbT21uvgQKq2oVB7FKafuEvrh9xBe+ijB7hpkVbG1wxCvsEYTHE3ah1E0g7t84elNYDeyV974JDLhnxdzgPdy8SvdKkNT9Cs7AMqhYFKS6B7jFLTOzEN+UZ6n5YBZuGyiWt1SG4wP1O3K9P3HIY9g/5BzY/zOKRP2qhfJh8ocRApq1A2PyhpEM7TpVZQeRBUWgTpgmCqAYc2xALjQrqYSIHWk6GyBCg0Gp4PaLHguBCGZUkUQJ+CcNXUKpsPjAuWZQDPof3kwcoG9V6of049Tq7WuiGscv3dSFwL9DmonIvhNQTocQQ8Q3Q/nek4ULkgFKKPvi/o/XUeGwkHRPSnBMIdIEIIIYREHVwAEUIIISTq4AKIEEIIIVEHNUBd4G0T8Wg5j40W7cqXhrN0/pn4T7WAKOI78O1vHqj1GX3+qdetlRfra4Yq9DW9zVqzY5pXB2mU4qv0DWrqo3PXOHVBoQTt323N1Pkh3CCnUHyV1hg19QG5W4AuZs9pOk8NAmmgEncBjZIjPwjKg4N0UslV+nmh3B3+WrPyBe6A7psblMzwVgN9A9D3qL4N1pMkcYueJMhvn/jVngOeXwTrABBorCh3j0neF6RjQZooF9D7IA2Is0yDiHnJAFSWAOlnLHA+qB+qsN93t4l2RkQ8q3VOJVfvdKNjjTEt3wDyz7gdmqpIvc4N40b9NczRA3MvgWPDoLQG0vugcivoWbti7NdwoXuENFHAhvIxeWpAzh+g2YH9Be8E+tbFbdfffn+x4z00zB8E9XroOTjycYVD5hpR7gARQgghJOrgAogQQgghUQddYF2QUB4Sr2//1l9lob5d4QTtAsr4hw439dfpLcRggn39GYzX61HkJts7XPc19R3t7kIlKVBpDQQKNUfHtqXrPiftAKUwptldI2lf6uNSSnV4c81I7Rbz15qF6KMK5hmf1Slb9cmp+hoglUEgFYRlOraBkWsPpQVAoKrsCBRC3DBOh7PGVYHSB4bh7M5xIHcX2hZH5SFcIPwahcajVP3IZYdKFURyU5XNHTiwq8gCbjen60hEcFV2FBoNQO4p6GYDJQ1cKFy8RrsBAgXaF+9vsvcZueKQmxC6jwAorBqNAc1XWDIBhHM73V0iIqGqA7tU4b/sUfkJMXMzWTH6W4LKN8CyF8jdhcp+OAiV6ePQL2xYvR3MOfT8ra0lyuaGLjD9DYvfrHO0oPvkdMeh0iioPIYLfKvQu+mUIbAaPCGEEEJIF3ABRAghhJCogwsgQgghhEQd1AB1QWN/r3hAKPF3SSnWa8ggkAY054CQZINs8ClbtRalsb++ZhuI7k7QWfklol20Eg/a+eu0D7l+kB5DrI5ShcfmfGDvc8MAfVx9nvYDp3+l/bk7z9HtslcBPQoIg0d6n1ig9wmDiFEUzt6Qb7+hySVmeh9UHsNfpx9OS299zxN36Qnmbdb33NOg7x0q8RG3XYebepvt/YNp6YG2I5CmxxAP+oH0Ps7SMJ3ZjMdloB/yFZepNqhMBSo1gULoUSh//QidQiBttda7oPBrWIICtPM2alsQ6KKc6KcqMKWAVa31LghUCkSGDVQmWNKgDZQ4QekNhg+1G4B2CD0HeE0QQi+GYetQKwTSFsBSM0BT43z+3kH5+vzgOSD9DNInIawUfZ+g7gylCwBEgM7I+Q6jXRf0fiHNUhCUaXESCrWJgDJQCO4AEUIIISTq4AKIEEIIIVEHF0CEEEIIiTp6hAZo/vz58tvf/lYqKytl7Nix8t///d8yceJE2HbhwoVy7bXX2mx+v1/a2vaXFrAsS+6++2556qmnpK6uTk4//XR54oknZMiQIQfVr+Y+lrhj9+eXSSxDeXD0cSgPTlNfdKy9XWyNboP0Pqh0RVqxvmYgVZ/PDVzPtaO1Bsa3Sl/X26KPDekUL5C9w+3nQ9omlAcJ5fJxA5d63WCQMwK0Q+NvGIDyL+l2sTrthdL8oDxA/jp90bQvtB4B6VhQ3iJTHQ/K+RO/oVzZyn+oBVk5b9sH2zhC5y1JatSaDZTLCOmHUJ4SMdT7oPP5mvXDbs/VOWTc7fZ5h9L+Q90R0kAYpOUXEUneqrVCpqCcRz5QvgDdT68j3wrS06AcMmjeuGJ0nimkgUK4UF4lgDUWfJ8NtFeo8AHU7KAcRSjnESrBgPL2oDxI6Lq5+t4hnPlyQkn6efnBNY31PuBYC+iuBOTyEaQBA+NC89Dn7B+Yhwg0LhfQdjm/B+4Q+PB3QrfvAL3wwgsyZ84cufvuu2Xt2rUyduxYmTp1qlRVVXV6THJyslRUVHT82b59u+3vH3zwQfnd734nCxYskFWrVklCQoJMnTrVtkgihBBCSPTS7QugRx55RG644Qa59tprZcSIEbJgwQKJj4+XZ555ptNjXC6XZGdnd/zJytq/ErUsSx577DH55S9/KRdddJGMGTNGFi1aJOXl5fLaa68dgxERQgghpKfTrS6w9vZ2WbNmjcydO7fD5na7paioSFauXNnpcU1NTTJgwACJRCIyfvx4+fWvfy0jR44UEZGSkhKprKyUoqKijvYpKSlSWFgoK1eulJkzZ6rzBQIBCQT2bwU2NIBS6ILdPahqOnI9BVP01mD65/atuwjYGXSDnfeQrnohjf31NZEbB7mAEnaAqukn6Y3l9A3ahq7rAyHZzpIeyLWXUKHP7wHjz1iv3WcoDBy5o9pAlfc4UEYjucRst7BmlH1SoIrxKLzfB1x7vmY9LlQyIuLTY2gdYFZJPQSquqdu0pPC6XpB980LronuedLXeivbxD0lgkPIkUsJucq8KPzYAF+xdtmYhsajfqB0ARFQHgRVa0dh+giYpsDptkFlGoA7TYBbDLni0C+PCKpCDsptoMrvCFRawfnFcbWDc6Gq6Yah3LBaOXg2oTE6vB8+L3SPQf+cJSL8qMo7moeoJAdKn4DGhcL2gQswiMbqKEEhYugWBeNC74MLldUALmZfid1bZB0vpTCqq6slHA7bdnBERLKysqSyErw0IlJQUCDPPPOMvP766/LnP/9ZIpGInHbaaVJW9u3E23fcwZxz3rx5kpKS0vGnX79+hzs0QgghhPRgut0FdrBMmjRJrr76ahk3bpxMnjxZXnnlFendu7f84Q9/OORzzp07V+rr6zv+7Ny58wj2mBBCCCE9jW5dAGVkZIjH45Hdu+2ZPHfv3i3Z2XqrD+Hz+eSkk06SLVu2iIh0HHcw5/T7/ZKcnGz7QwghhJATl27VAMXExMiECRNk2bJlMmPGDBERiUQismzZMpk9e7bROcLhsHz55Zfyve99T0RE8vPzJTs7W5YtWybjxo0TkW81PatWrZKbbrrpoPoXt9slHv9+j3Pydq1RqDhTH+ev1uvKfv9EYep2W3uK1tO05Ovj3EHdLnGHMkFqTtd+4Lht2oee/79aA1NxmvbTIg3U7pO1n9YJ0iehEhpVE8zKb8SBSNvW3vo5ZH2iU9/vPlXrUVCpirgqrSlJ32DXdzT11WNHpTZQaDwK+Tcl6WuQ5h+kyPeDaNY9E/T4/XV2TUnmCl3iAOkzIKBUgRf0DYX9pnyN9XgmuEC5AdVn0DcLhPciHYupLgjdJ6QLQroYZ2i0iIi1+kt9PhDO7ywl4NSYiODQeDRvEEg/5GkH5SGA9iYEShp41+v6BZGC/voa5QY6E6DZgdoTULojEtDfPlSWAmlgoPYI9AVd151ifw9ROQ80v5rGax0PSkeBtF1ukKIApUZA2iZUlsJIAwfuB9qJQaUwWjPR3My0/RQKtYmAbASIbs8DNGfOHJk1a5acfPLJMnHiRHnsscekubm5I9fP1VdfLX369JF58+aJiMh9990np556qgwePFjq6urkt7/9rWzfvl2uv/56Efk2QuzWW2+VX/3qVzJkyBDJz8+XO++8U3JzczsWWYQQQgiJbrp9AXTZZZfJnj175K677pLKykoZN26cLF26tEPEvGPHDnG7968Pa2tr5YYbbpDKykpJS0uTCRMmyMcffywjRozoaHPHHXdIc3Oz3HjjjVJXVydnnHGGLF26VGJj9b/qCSGEEBJ9dPsCSERk9uzZnbq8VqxYYfv50UcflUcffbTL87lcLrnvvvvkvvvuO1JdJIQQQsgJRI9YAPVU4mos8cTs1+AEE7T2xgVSS7T201qRvUF9q535fFCpCaT3ifi0LqjmJK0ziasEOVk2aJ9/4yg9iG0ZQD8CSkF4gQ3hLCPRoNNKyM7z9VgTQEAeymWE8gV5gVSg4gytd+n9hfZb1w7V40dVP2qH2q1tGbpN0g79bAKpZvqZ+CozrRDKA4TKQ7gDB9YxiYgE0hzXQDqO3FRlg6UrgI4DlZvwNuprII0C0lkE8kCpggygWSu3a8BcoMRBBJWC0GeHIL0PKi3RPCRV2RK/0mOF2qOJo7UN3U+HpgbmfAH31w1KnCD9ENJKwbxCoB3S+yCMyjygnEIg5w3MgxSr33Nviv5GQJDOCPUF2GAJDoceC+rEwBhi/7Zatxs7QtuA3i0C5oSpPg2B8lFJjb3PSGOEdGeorEbCTpADzKGLch0veYAIIYQQQroDLoAIIYQQEnXQBdYFe04Niztu/zZc9gq9XgynaPcGateco8/vDF1HbiEUZt42Tm9R+jfp+hjIVYSq12e/racBCiH312nXW2uGoXPgh3YfWL9H9Dbztiv0uZJ2aNuek7QtFKf7GwOKOidU6Oe16yy9/Zq5RruKmvpo94YnaL8n6P4mGaYoQKC+5b1Sp2woRXwwQT/XiNO11QlxlfY5FijQId9hUJIjEmP2byr/l+CmoDBlEN6Ntuidri0RXL7B6SqM36x9uG5UDRyAtuiRuwvhrwUvJ8KgZIKIiAtU9baG2T8osFI5CIOH5QxQuPw24BbTR4oLuZ6A69ECFcdRSQenK8cF7hEK0XaWTBDBaQDQPUd9Q2MwLbeBSmuIcwqjKvLAxertq91YlmEZGNO53jIqV9liK/Q8cYHrOlM5ILemxyRlhYgEgVvbFbDbImHzfR3uABFCCCEk6uACiBBCCCFRBxdAhBBCCIk6XJZlIbdtVNPQ0CApKSkyfPavxePfnzzRGbYugks6oHB2Zxi4iEjNRLu/1N1qth7NHaHDGfc0aL+191Ots0mo0I875kodaltRo8OqY74CN8AQ5z1B9zJph5nGyA3c20jvZIobuO0jQBqQuOvAfvWW3loDEr8H6Yl0O3T+YILZnKgfpNuh+9nrU11HpHGE1jIkfW3300M9DUhLn7wOhNomar898uUjTVEoQYfVJm7Rwriq01KVLfPjOmVzah6C+ZmqDQKlD0BaBlS6AoFKGiD9SGSPfl6ugTqtAAo/d5aggJoVoEVBYcqm2iaYtgAAy4igkHcUzu4MyTZpI1hPhMaFdDHoOcBQdtAXVAoE6ZbUcai/QHfl7gsEpuC5tozPU7a4lZuUDZUfQboz1A7hfK5I/4RSNKDnADVhDkLhgCwrfljq6+sPWNeTO0CEEEIIiTq4ACKEEEJI1MEFECGEEEKiDuYB6gJ3SMQNMnt/lyDIAyQjdX6Emkoteonbab/9rdk6r8iQETp3x44PtO81kK6PTQS6o6opWizT5w8630RvoIFpBC7f5gLt385coYVR9YMOnC8ItclYr+9vGPStOcdsLY/yACWW6RwqqhSEmOlx2rV0SnwtKKeS7kcEaGCC8dqW8VmdssVXoUIdmrpxWgeCyog4U9OjnEJJ73yjjwO+fKT3MSV5la6FgnIS9V6j8wBBHBoNX3GZagK1EtUgNw4A5QZCuVEiZRXKFp44XNm8KF8MAmlK0h1zGOmEgN4HAccAyheYlkxA5TZgrqF08EI5z2Wq2QH6HKTFQRoVU70PfA4Bg3Ie4HzWNj0GceR2EhERw3IxKG8PApVfCZw8WNn8pWBcBpoyCzxTqB0DcwmV/HHqBEPBNpFifToEd4AIIYQQEnVwAUQIIYSQqIMusC5o7mOJO7brLAHnnPalsi3bWKBsqDJ7IMPuBknqq7fxoburn9569VXo7cKa00F17Qb9yHddoLe30flQ+HlSLx3z35qRqmzxjl1V9/e1fy70li6l3pKp1+jtILKxNUe7lHI+0O0aBujzBVK1iwalN0gu1TfAGfae9alBZXVwnIhIq676AMuPIHcEqi7vDgL3LABVWA70irX9DLfPUap+ANoqbxmin7V/b5uyobBXd7vZuNC2ujP82jnOzvrRnqtTSrjbdd+8wFUGQ61R6HKDdguhUg1h4HpyAw+g87ou4FJwgRIEKBwflm4wDINGxyKHOEpJgKrGw3D+QwSVW4Ah6sbh7eAaqEwJsDnnhAu4J12oUjt0u4G+mY4V4C/WbjHkskTpDZzuTvT9gq5TcP647cDd6XgfQiH9/nYGd4AIIYQQEnVwAUQIIYSQqIMLIEIIIYREHdQAdUEkKyASt99bXXLlXLMDTz6CnfgXs2bnrfiJspW9q330Geu1/7X8DO0bjtcVDaR+pD421Kr9tF4Qudrc367bSAjqqRcB0dLtQGaSVqx1MUk6U7tUjdfre6RjasnW53MHtUoh4gVaDsf5IjH6mlUT9HGoXAoqU5L+kfa9N43UYiFn6QoRkT2naZ1N4i6t+Wjup7UsTl1QywCtFUBh+3FVQMdSqlM5tJ6utQKxFTqEHJXgQET8+h57gL7BWYICBHLDEG0/Cg0erd8v1F+kCwrmphq1Q6D+mZR0MCkjICLiAvoRpPdC/TAt3SGNeqxQ7wN0IE7dTnhriT5u4mjdN5CiwA00NaZlL5CmyqrW76E7RQuDIvVatOU8FulzUN9QaDzU+4A0CEh7g66BUjR4gGbNqMQL0FNFcoAAEoC+Q3GVQBdlCHeACCGEEBJ1cAFECCGEkKiDCyBCCCGERB0uy7K6TnQThTQ0NEhKSorU19dLcjJIOnOCMWTeo8qGtDIxOgWDNBbohv3e0O1aflxn+zn2GZ3LA5W4aOyv1+gxDeD8WlICy4OID+hsVmv9RJuWz0ByPrb7n1HJCA/Ix7NnrFaf9P5C+9SDCTp/lLtdjwFdoyUTJDMCxFeBXDAG50r5Wj8IpPdB+hGcV0ePoamvvk+odAnSIyCcmo9wutYUIG1L4wg9X5HuygW0LbDEQ6IWvMFyAACkH0EaDV95nd2AtD0I05w3YFxIU2KsPUIaFYRDGwNz+Rg+B6TZgX1DY0UaHVDiBIF0QaZlRBSoJAcCnR/pccD8QhqollG5yha/oVyfz1H6AuUBQs8+WNBX2ZBODuUBWr5mntHvb+4AEUIIISTq4AKIEEIIIVEHw+CJbJ6rQ+gReQt/o2yeOL2duescXV5ANttdCB4Qoh47sk7Z3O+nKlsIhMvHgujQeFB+BBECO/SpW8yq0O8+xd6Z3GV1qk3VaanKhtxdyM0U8elw/ECqtiXu0v1Fri1vo9l2eSjJPljkdoJb2cgtAEK0UXmMQJ52M6WuO3Q3E3KNOF1erdl6MqHa9YlbtLsPhbz7kBsH9c0P5iZwUYRz9T3xlOpDrQDwWTvcVoEC7SeO+WyLsqFSE64YPW9MS1eEQDs36K+n0SwkWxwh06gaPCzdAcqFmALdQqiMBAhJR/0zcscB1zGc+4alMEzdXShtQWSbzjMSW6HznaD56gTdD+TuQnPkSMMdIEIIIYREHVwAEUIIISTq4AKIEEIIIVEHw+AB0RYGfzgMeuG/lC0S1Otqq9WuA0nvW6eP+7uOPa8v0NqWSJy2+Su1ziSQrX3IKOTd16LPVz8IlNEA0eIoJF/1rU6fPxivzx+/R/cXhbzHbdc+9OYhqfq6tbrDKKw8eavWFQR62XVcrZn6vqUv1fqR2nMHGZ0fla7wlVQpmykNhf2UzdsMSmuAtAJOgglaYxVXpZ8Neg4ovN9XrVP1wxIMIDQepRVoP3mwsvmLdakOp+bDSk/T5weaJRTybrVpDRgK5Q7naZ2RC5SggOkSACah9kh34vpis7bF6rkPz49AJSPSQc2fjVoDhe5TqEqnPPAOyrf9jEL0TVMPoL6he440e/B+ojIiSHsFUh6oMPUkoHUDZXUiMUDr+JW+bzWOsjrh9jZZ+/z/d/yEwc+fP1/y8vIkNjZWCgsLZfXq1Z22feqpp+TMM8+UtLQ0SUtLk6KiItX+mmuuEZfLZfszbdq0oz0MQgghhBwndPsC6IUXXpA5c+bI3XffLWvXrpWxY8fK1KlTpaoK/2twxYoVcvnll8vy5ctl5cqV0q9fPzn//PNl1y776nbatGlSUVHR8ef5558/FsMhhBBCyHFAty+AHnnkEbnhhhvk2muvlREjRsiCBQskPj5ennnmGdj+L3/5i/zHf/yHjBs3ToYNGyZ//OMfJRKJyLJly2zt/H6/ZGdnd/xJS9Pbv4QQQgiJTro1D1B7e7usWbNG5s6d22Fzu91SVFQkK1euNDpHS0uLBINB6dXL7rdcsWKFZGZmSlpampxzzjnyq1/9StLTdTpvcnhsvez/UzaUL8ip0alt1X5m37k6J4XvK+0/H7BYa0oqztDtvC16etdM1FqOtHW6XQRUkUjaofU47uCBJXStvc30RD6gWQmkgtxASVrLgI5tyNf5mJJL2pQN+fdjK+z32Nesr9l0ar6yIb0Ponao1rvEZmodT+XFur/Wbj0uV5ZuNzCrWtl2lNlz0ji1aSIivmpt21Ok71FMidasoeeaAz5l7mTwDIvLlA3NLpjLyaAcAiw1AXPIAO2JYW6YiF/nc/HVgJw84LqwZAbIe+PUt3hKgf7JUJ/kKQelMJDOBuiikI4LXRdpdNxjRyhbxKEfcvfNUW1g2Q/wXFGOLiuvj7K15uh7jvR+jf21Hqc1G+jd6rXG7nvT7BKVN5dOVG3yJum5VPaunnOBVP0MnX0LB3RfO6NbF0DV1dUSDoclK8ue8CkrK0s2btxodI6f/exnkpubK0VFRR22adOmyQ9/+EPJz8+XrVu3yi9+8QuZPn26rFy5Ujwe/YACgYAEAvtFfg0NBspWQgghhBy3HNeZoB944AFZsmSJrFixQmJj9/+rcObMmR3/P3r0aBkzZowMGjRIVqxYIeeee646z7x58+Tee+89Jn0mhBBCSPfTrQugjIwM8Xg8snv3bpt99+7dkp0Nynt/h4ceekgeeOABeeedd2TMmDFdth04cKBkZGTIli1b4AJo7ty5MmfOnI6fGxoapF8/vR1PzCi95mfKNvB3D9t+Ri4L9zq9pdo+skXZNoHyBeLTW9Rx2/TW84BXtVOh6mR9ugh4M9A2cPtI+zZ4/Kfxuh/V+poo1BoRV6XDj1EYKQK551AYvKdd2/x1dl8OCuXWR4k099Nb6tVjgNRwpHZ39s3UgQ9TknV16fsnb1C24R9fqWwjUrRrpDUEfJsOynzaVZ6ert04NSE9Lv9O/WwqJuk71esbkN4hRm/5u9t1O1T5PuIoDyEiEsywvycwVB4AQ63btW+v/YyRyhZTrp8rCu93o+ryyEUH3EfOcG4LlYwA/YXuLgBMR4BC0ttB+LlhJXVYHgO0U9cELkHk2kPvIUoLsf0H+js0drgO5a/Zpd1xAtzHkXQ9N5vD9vk/+bwvVJs9bdolevXMt5Vt8ZZTlK1vst1jE2oOyOYHdXcR3SqCjomJkQkTJtgEzPsEzZMmTer0uAcffFDuv/9+Wbp0qZx8Mvjt5aCsrExqamokJwc8RPlWMJ2cnGz7QwghhJATl26PApszZ4489dRT8txzz8k333wjN910kzQ3N8u1114rIiJXX321TST9m9/8Ru6880555plnJC8vTyorK6WyslKamr79F1pTU5Pcfvvt8sknn0hpaaksW7ZMLrroIhk8eLBMnTq1W8ZICCGEkJ5Ft2uALrvsMtmzZ4/cddddUllZKePGjZOlS5d2CKN37Nghbvf+ddoTTzwh7e3tcvHFF9vOc/fdd8s999wjHo9H1q9fL88995zU1dVJbm6unH/++XL//feL34827QkhhBASbbAUBoClMI4+xiU0GrRmI2mbjuRrB1npEUjb49YRoxJvJpdQ4fKpm7T2AKV0RyRsrlO2xhE6XYA7qHUhgVR9T5AGKL5K9w/pe5oG2+e9UxMkIlIzUv+DInJOnbI1l+p3aPT4EmWb2vsrZXvore8rm6+v1kGk/E1rCJBmywlKbVBboI+LrdHHNo7SOpaxg3cqW1mjnpxnZGudxf9+oN35mav0dX3N+vknvFesbM6w5zYQ8ozKeZhqcaxGEN4OQOUWUKg5Kv0Az+c8FvQNAlIFoPIgpoS/2aRsromjlQ2F6aNyIybjQmUqdp+qtZP1Q7XeJ6mv1iehd/OnU/+ubH944l+ULW4P+g7pb3hyif3b0ZB/YB2eiEh8lT5/3WB9/uZ+9rFGWttk50/vPH5KYRBCCCGEHEu4ACKEEEJI1EEXGIAusO4h738eUjZXsna9oOy9/nTtxol9X28NN03SYfXJ7+vQ9RCItO/9hQ7xLL3Qvp2b84E+rg1sC3uAeyqQql0vyGXnBQlo0zbpvplWWEZV4/ecZL8BKMNx7Ui9zf7jKe8q29aWTGV77+2xypaoEyGLv05vg5tWl4/EgAzcjrByd0D7P03TDCCCoNp89Rjdt/93xd+U7a09Oqz8iy06HUf6R7p/mf+rq587w6PdjXqOoLBq6NoyDDUP5qYqG7rHHpQdGl0XubccriLTqukoRB1luIaU71Ym5NpDVdhRyHtkj/apWmOH2H5G8xDNr5Yf1ynb3hqQaRukbRi6AMwJkBneFMuv++d81uF03Td0TZTNGrlnnfM8FGqT5Wvm0QVGCCGEEILgAogQQgghUQcXQIQQQgiJOro9DxAh+0B6n9hNuvI30ucEgkDHA/Q+YaAfqh2nfc1xO0EleRD2neAoYrzrfH0uX7X+d0aajlqWxF1a77LrbK0VStmk/ewIpPeJq9QCooZBOjza2ZeGAXoMj0/7s7Ld8v7lyuZu0Pcyo1iPK65K3ztnVXoRXeJBRKSpj9ZL9PpU6yycupWWIbqiu38vKNMC9DMIpB5ygxwN/y06rDgyTmtUPj7vcWU7s/qnypZSqrUs/lKDsHKglXGJ1mgEM/Qc8ZXo0iXe9Tq8H4bBI1CFeFT93PEMIwV67DD03KGxERFxAy1SaKtO0eDtqyupB/O1ts1XXqdsKPwelcewiu0fk5bvDVdtKk/X783YJK0xalin53XmWlBWpQbkdwCY6qxMcKNzGaZKQDg1RlbE7F0V4Q4QIYQQQqIQLoAIIYQQEnVwAUQIIYSQqIN5gADMA9RzGPi7h5XNlaU1Gt5NWgOEyhcg/VBChX4FqiZqf3lcJSjBkWw/NrlE625aspVJgin6/BlrzUpm+OvM8nSg8hi4nb5u21l2PcovR7+p2vz6T5cpm1fLriTrU5C4CNCaaVarD5UCSVy7S9lQ2QAnKP8IymWCcrIgjQ3KcYJy3jSN7K1sSJ+1ebbWwGwtelbZTnrgJmVLLrVrqhK/2qPaoLw9SNtjJWkNUCRJP6/DySFjSjjZfl1vtdaJIVDOo8MpyWG1aa2JO0XnHkMEC/oqW0O+Xe8Yvkx/wOJ8WidZVqb1RMN+26Bs7bm6bzHlWncGcx4hPQ7S7YASH4EC+wfQSJsmeG56GvQ9d76vzANECCGEENIFXAARQgghJOpgGDw57hiYVa1sW4I6JFVEh9Cjkg7V47ULLGGndoP0+kaHaaMyB07SNmqXTc0o/W+PxF1m4ZstmdotUj9Inw+5ANM3aDdLyYX6Pp3d3x7OnOzRbsfWfvp+ZH4EUuGDbev6EXprOrEMuBRAGYVAL91fVIIAtXOG1Tur3ouIxFWZPYeGcbo8RPIqXQ0ehUvHb9duMeR6sxr0s15Qr0OyM2fsUDbrboebDbgnkLuroVCX30j62rBSe6l2RboyDuyKFBHYP+SiUqUlUNmLRO3rdqFq8MC1ZxreHa7X4eeugJkbF9GcY3dF69kr0vpCjrL1AeViUMkIP7DBUiMoHQG6T+B8kRzt2nW62UxSG4jglAKBPD2XnC41hsETQgghhHQBF0CEEEIIiTq4ACKEEEJI1EENEOnRoJD3HR/o1PcxI3U4ZzBF+7cTdgCtTLG2+YFfvWqCfl2cOht0XCBVnz97FdC2pGrfeEumWd8y1uvzlZ+hNSURn9ZGhFP0seOTttt+nvPpJboflfp++Jp135xhyyIiHiD3QOHMzUNSlQ3pZ9pytEbBhISd+ppIOxS/WevOYoCWAelRfMVluh3QXjSN19qega/qZ/Ng/DRl69tXC77KLrNfY/ALoHQD6Bu6JyiE3I3CpQ1Do5Fmy92onysqI6E0KuA4N9ITIcAYYGg8OJ93UL7ZNUD/kL5l8AXltp9bQnp+te3R8wGlgED9tfL0/EK4m7ROEOl9oH4I4NT3wDQD4J6H83T+EBS270w9EQ6ZL2u4A0QIIYSQqIMLIEIIIYREHVwAEUIIISTqoAaI9Gi2Xvb/KVvB/Y8aHRtXodf3ESDbaNNVNAT92yBlq84XFPbZc3e09tbHxe3Rupi9w4GeSMtMxNd86JVqslfpY73NIHfPjIpDOj/KM1Q3GGmWtM3brEsm7D1Fp/RP3KW1DK52PYZIjC7ngUpL1I61a09SN2p9BtL7QN1Crs4DhHKctI/WmjVvox4X0nK0jMpVtoRifY24PJ1HxdNiv++BNH2cNz1NH1dumPMH6TZA+RFPaaWyIZ2JBfoCyzLEGOh7TLRDIiLp+p6EQYkPd7HOs4S0TShfDmpXd66eO7VVdp1VsEyfKykP5A9bqy+JtFgucM9NQc/GitHfMJjLypGnB+rEwFyC8xC0cwXs/XCF9fe2M7gDRAghhJCogwsgQgghhEQddIGR4w5UST3uU13puLmfdrM43QIiIoll2n1SO1If66vX27tON1Cb9uJIxKuviSrQ1w/S/Qik67GmbNL9iK/SLpD6PP16R0BY/T39lyvb121210vMV9pPmPWJDkmN+PU1USkM4ImUuO3atdU6QIdLo7T5KDQeldvo9anjgQEXS3DMQGXzATdGDXDZpX2hyyO42/UzdDeCqtbAfRK3HZxvRIaylb0L3Gzj7M8n8XfapYDC0QWVPQD3HLmZUDX4SL2eJ24QQu1CYfDIXeIgVKWr3Hv76pBvWIIBhHe7y4H7DLg7BVWXB/OpsWi4sgXj9XsY47XfuxiQsiPnbeCeBfcNupkQhukCXMjdB1MZ6HmtSqGgawIbKiGDSuM4XWVulsIghBBCCOkcLoAIIYQQEnVwAUQIIYSQqIMaIHLcgcpjDB6vQ7m//mCQsiWP0z70thqtqUBaocy1oDzGeHu7eJBZv11LUSSYoPU+Gev1+b2gtETdUK0BasnU+oZ4kDYfhd+vbtKal/eqBtv70aKaQL1PJEbft9bB+gYkbmlQtj2n6efQ+2PDkPRErflILAM6G0fortupTxCRYIIeV2thP2VLLtHz0LQkRyRGa9b8xTpcHGl0cj7UmpqKX2jtTYzP/vxrztE6ITQGXzkIUUdlP4BuA5Y9Gabnl9RobRMKtUYlOJyh0Ejvg/RJVgzQkwGtjFWvvyVuVAoC6WyStFbIHdTvcPuFev43ltnnhCdb6wRR6LkbhIajeeMq1WkWlD6nE9A7Z4GUB+4KrcdylsxAWrcISD2ASuOgdocDd4AIIYQQEnVwAUQIIYSQqKNHLIDmz58veXl5EhsbK4WFhbJ69eou27/00ksybNgwiY2NldGjR8ubb75p+3vLsuSuu+6SnJwciYuLk6KiItm8efPRHAIhhBBCjiO6XQP0wgsvyJw5c2TBggVSWFgojz32mEydOlWKi4slM1PnAfj444/l8ssvl3nz5sn3v/99Wbx4scyYMUPWrl0ro0aNEhGRBx98UH73u9/Jc889J/n5+XLnnXfK1KlT5euvv5bY2NhjPURyhLF262f4RcMAZfMXaK1ETQlItz9Qa2XEB7Q3g7Wv3e041F+Hyk9ovU9snT5/Ux/07xGQt2iXPjasuyZVE4BeAKRzWbz+FGXrlQ5ysjjPBXJyREB+F3gsSMufUhqnzwd8/oFJQ5UNla8IZoDzOWxekLo/tkJrD2DpBqCL8ZUAzQrCsFQD0tQgUuL189rTYD9fok/PQ6R38pnmlQHaE+/6bfpYQ50JyqFjgfw7oQy7hsT1wef6mhNHKxsqyYFwp2h9ViSnt24HdEwol5E/Q2temhr0N+zHU961/fz6f51rdE0EaofupYD8TpFE/d64A/r5e2r0WNF9coK0SFKg9WkoR1Mko69u59A7RcIBEXAJRLfvAD3yyCNyww03yLXXXisjRoyQBQsWSHx8vDzzzDOw/eOPPy7Tpk2T22+/XYYPHy7333+/jB8/Xn7/+9+LyLe7P4899pj88pe/lIsuukjGjBkjixYtkvLycnnttdeO4cgIIYQQ0lPp1gVQe3u7rFmzRoqKijpsbrdbioqKZOXKlfCYlStX2tqLiEydOrWjfUlJiVRWVtrapKSkSGFhYafnDAQC0tDQYPtDCCGEkBOXbnWBVVdXSzgclqws+9ZcVlaWbNy4ER5TWVkJ21dWVnb8/T5bZ22czJs3T+69995DGgM59uSO0Fvle1bm6IagLAUqZ4FC1xGpm/R2ce5dW2w/rxWd9j4G7Fo3Tdeukrz7dShz1WmpyobCaqvG61c5e5V2Ue28QPflx+M/ULabUzfYfj592U9UG1T2INBPu4X8dfq+BXNTlS3sA/8eSzJzqbUM0SH0CP9ee9h3CJzfK8A9BTyCTleMiIgbuOyQqwCVL2gfrV1FKDS+abwO+47z6ns8LLPK9nN1ne5vIA28D8jdhUohABt0laESFwCrTactQOHnXqerLAWEfKN7jsaArtlXf0ugCxRVfhdta80EId479Vz/o+9028+x/XWblD01yob6a1r2wxmiLiIS2Vqir5EJXFuoMjtybyHXm/O4L4BGt7f+gKOyOq52+3fOFQaShk7odhdYT2Du3LlSX1/f8Wfnzp3d3SVCCCGEHEW6dQGUkZEhHo9Hdu+2r+h3794t2dnZ8Jjs7Owu2+/778Gc0+/3S3Jysu0PIYQQQk5cunUBFBMTIxMmTJBly5Z12CKRiCxbtkwmTZoEj5k0aZKtvYjI22+/3dE+Pz9fsrOzbW0aGhpk1apVnZ6TEEIIIdFFt4fBz5kzR2bNmiUnn3yyTJw4UR577DFpbm6Wa6+9VkRErr76aunTp4/MmzdPRERuueUWmTx5sjz88MNywQUXyJIlS+Szzz6TJ598UkREXC6X3HrrrfKrX/1KhgwZ0hEGn5ubKzNmzOiuYZIjSOsL2ucNstxLTJz2F9f5tEbBO1X71duXaU3JzmlaLxF6uMD2c0qCDoNH9H5C+8/Lz9VaAaQfqhusX1tUpqMhD73eZv7xcX+9xfazP163aQIlLhJ2am1ToJcO+fU2aj1G01Adfpu4C8TtA2DoOggFdpYI8JfuUG3CeXqnuOZ0bUvcpcfgrdZzDumTfEADhYBlA4BWqnSlDiO+9AK7tuu1wbo0TL+/6fQBSLMTLNDhxyI6TYmvuEw3Q7oggBulUEA6E4cgK1Kv010IKN2AwvFdQBcEQe1QeRAQyp8IyldUjU9VtrMH2XUw2/44TJ8flBWxkO4GgMLgUWkND3j+6B6je+cCz8v5RYTlR/JAORPw/sLwfmd/LcNnKj1gAXTZZZfJnj175K677pLKykoZN26cLF26tEPEvGPHDnG797/wp512mixevFh++ctfyi9+8QsZMmSIvPbaax05gERE7rjjDmlubpYbb7xR6urq5IwzzpClS5cyBxAhhBBCRKQHLIBERGbPni2zZ8+Gf7dixQplu+SSS+SSSy7p9Hwul0vuu+8+ue+++45UFwkhhBByAsEoMEIIIYREHT1iB4iQg6FBu8ElVst4pHaz9vlnbNDtqlJSlc0PNEWurDZlCybEOX7W5QYSd2ndTSBV6wdgviAt7YBjrSzU1839UF+3PVm/8ou36FIY55z2pe3nT/88VrVB5Tdas7WOJ5CqtVO+Zt2PtE0610pTX62L8jVrvRMqe+EHuVucpSVc7aA0CiDtC/BwNuqyDxKr+xsHdCGmpSUQ6L6ffPY3yvZe1WDbz6lb9H1DGhDUN1T2BOU3ggCtSKhM61a8KNcMIFS1x37coHzVxgIaIKRtgrmM/KB0Byhd4gLSo+AY/XFC5UaSwdRJ8Nj1YzUj9Vzq/bl+hj7TUiONWidnpesPHdLjoPw+cA4DXI7rohxYaCcmmK81ZnAeltqfjUvfok7hDhAhhBBCog4ugAghhBASddAFRo473KDKe+RTXcE5aZte3zeDihn+Sv0aIHdU7EodC+4sS9GerF1RdXH6/CEQVo4qtQ94Q48VhZWLaJfarnP0+BN01LfE+PS28gc77Fv5ob46vD+5VO81x1Vqt1PCZh0ajqq8o7IUKV/runxtOaAERTty74AwZQftuXreoJIcKMw+PFGXPfFW63ZorKiMCAJVg989Xbtjmqv0xB6VWWH7uSRGh0G7K/YoGwKFRqMyCla6dimiEgzIbRVO1y4VdJ+8TneUYakN1F8B/Y1s0y8JKjeBqqb7Sqq0Dbg2y87RaRWWljjmEyjlAwFuvAgomYFwo7B1wxQCMDUAwOnyQqHy4Vx9fvQuobnkTFsRDrWJmE1r7gARQgghJPrgAogQQgghUQcXQIQQQgiJOqgBIj2avKd+q2xDhmg/8OYCrZVI+1T7mlN3aa1I3WD974A24H9H5SbKL7L7wWNKtD4Hha0n7dCamsb+Wj+EdDGRGNBOyxGkz7u6vxEgi6lu1ffu4sHrbD+v7jVAtWlZpcsj1IzWmpr0L5VJ6oZpvUfqRq3lQOU24qq0pshXXqdsSKMRibE/a38pCJcGID2RO6DD+5FGwdMOwoWBfgKF/aI0AFaDnjujBpcoW/l99jD4xGZ930w1O1A/gwAh/5E2fV1URkHfzU50XI7wa6hZMUwpgPrrAqkMICANQgSUqkAh5IP+qufE9jvsPz94xXOqzc+Ds5QtrxyUCxmo82eEMrR2zrVej8H03qFwdqgVSnJoz0A4vrsYiBNB6Q6EMx2DFdHzrTO4A0QIIYSQqIMLIEIIIYREHXSBASzr2y3mhgYdgkuOLZFWnX05BLbyUbswCI0OBbUtHND/Dojo08FjI632rfwwOC4MdmTD7dqNEQ4A11ZIHxwKamdBOABC7YM6vD0C/s0TbtGdDjTZY/LRPQ8F0T3X5w+FUDvQX9AOjRXdExfY9o6EUV/s99hjuF1uhcGzD+lnaEVAaDC6BmiHx6+vEWnVtmAzOJ/j+bhCej6gyemCY9DXRFigEjeyuSw919EzRPfdFbHPTXh+lA0YjAEdi4DzCx2L7idoF0HvRIv955ZGnQIgHABzxPC+hULAyWh878A14LEH7gtsY3ovDd6vffdj3+/xrnBZJq2ijLKyMunXr193d4MQQgghh8DOnTulb1+tU/wuXAABIpGIlJeXS1JSkrhc+l8qR4KGhgbp16+f7Ny5U5KTtdDzeOdEHt+JPDYRju94h+M7fjmRxyZybMZnWZY0NjZKbm6uuN1dq3zoAgO43e4DrhyPFMnJySfkRN/HiTy+E3lsIhzf8Q7Hd/xyIo9N5OiPLyUFVLMGUARNCCGEkKiDCyBCCCGERB1cAHUTfr9f7r77bvH7DZNuHWecyOM7kccmwvEd73B8xy8n8thEet74KIImhBBCSNTBHSBCCCGERB1cABFCCCEk6uACiBBCCCFRBxdAhBBCCIk6uAA6QsyfP1/y8vIkNjZWCgsLZfXq1V22f+mll2TYsGESGxsro0ePljfffNP295ZlyV133SU5OTkSFxcnRUVFsnnz5qM5hC45mPE99dRTcuaZZ0paWpqkpaVJUVGRan/NNdeIy+Wy/Zk2bdrRHkanHMz4Fi5cqPoeGxtra3M8P78pU6ao8blcLrngggs62vSU5/f+++/LhRdeKLm5ueJyueS111474DErVqyQ8ePHi9/vl8GDB8vChQtVm4N9n48WBzu+V155Rc477zzp3bu3JCcny6RJk+Stt96ytbnnnnvUsxs2bNhRHEXnHOz4VqxYAedmZWWlrd3x+vzQe+VyuWTkyJEdbXrK85s3b56ccsopkpSUJJmZmTJjxgwpLi4+4HE96XcfF0BHgBdeeEHmzJkjd999t6xdu1bGjh0rU6dOlaqqKtj+448/lssvv1yuu+46+fzzz2XGjBkyY8YM2bBhQ0ebBx98UH73u9/JggULZNWqVZKQkCBTp06VtjZQbfMoc7DjW7FihVx++eWyfPlyWblypfTr10/OP/982bVrl63dtGnTpKKiouPP888/fyyGozjY8Yl8m8n0u33fvn277e+P5+f3yiuv2Ma2YcMG8Xg8cskll9ja9YTn19zcLGPHjpX58+cbtS8pKZELLrhAzj77bFm3bp3ceuutcv3119sWCYcyH44WBzu+999/X8477zx58803Zc2aNXL22WfLhRdeKJ9//rmt3ciRI23P7sMPPzwa3T8gBzu+fRQXF9v6n5mZ2fF3x/Pze/zxx23j2rlzp/Tq1Uu9ez3h+b333nty8803yyeffCJvv/22BINBOf/886W5ubnTY3rc7z6LHDYTJ060br755o6fw+GwlZuba82bNw+2v/TSS60LLrjAZissLLR+/OMfW5ZlWZFIxMrOzrZ++9vfdvx9XV2d5ff7reeff/4ojKBrDnZ8TkKhkJWUlGQ999xzHbZZs2ZZF1100ZHu6iFxsON79tlnrZSUlE7Pd6I9v0cffdRKSkqympqaOmw96fntQ0SsV199tcs2d9xxhzVy5Eib7bLLLrOmTp3a8fPh3q+jhcn4ECNGjLDuvffejp/vvvtua+zYsUeuY0cIk/EtX77cEhGrtra20zYn0vN79dVXLZfLZZWWlnbYeurzq6qqskTEeu+99zpt09N+93EH6DBpb2+XNWvWSFFRUYfN7XZLUVGRrFy5Eh6zcuVKW3sRkalTp3a0LykpkcrKSlublJQUKSws7PScR4tDGZ+TlpYWCQaD0qtXL5t9xYoVkpmZKQUFBXLTTTdJTU3NEe27CYc6vqamJhkwYID069dPLrroIvnqq686/u5Ee35PP/20zJw5UxISEmz2nvD8DpYDvXtH4n71JCKRiDQ2Nqp3b/PmzZKbmysDBw6UK664Qnbs2NFNPTw0xo0bJzk5OXLeeefJRx991GE/0Z7f008/LUVFRTJgwACbvSc+v/r6ehERNde+S0/73ccF0GFSXV0t4XBYsrKybPasrCzll95HZWVll+33/fdgznm0OJTxOfnZz34mubm5tkk9bdo0WbRokSxbtkx+85vfyHvvvSfTp0+XcDh8RPt/IA5lfAUFBfLMM8/I66+/Ln/+858lEonIaaedJmVlZSJyYj2/1atXy4YNG+T666+32XvK8ztYOnv3GhoapLW19YjM957EQw89JE1NTXLppZd22AoLC2XhwoWydOlSeeKJJ6SkpETOPPNMaWxs7MaempGTkyMLFiyQl19+WV5++WXp16+fTJkyRdauXSsiR+Z71VMoLy+Xf/zjH+rd64nPLxKJyK233iqnn366jBo1qtN2Pe13H6vBk6PKAw88IEuWLJEVK1bYhMIzZ87s+P/Ro0fLmDFjZNCgQbJixQo599xzu6OrxkyaNEkmTZrU8fNpp50mw4cPlz/84Q9y//33d2PPjjxPP/20jB49WiZOnGizH8/PL1pYvHix3HvvvfL666/bNDLTp0/v+P8xY8ZIYWGhDBgwQF588UW57rrruqOrxhQUFEhBQUHHz6eddpps3bpVHn30UfnTn/7UjT078jz33HOSmpoqM2bMsNl74vO7+eabZcOGDd2mJTtUuAN0mGRkZIjH45Hdu3fb7Lt375bs7Gx4THZ2dpft9/33YM55tDiU8e3joYcekgceeED++c9/ypgxY7psO3DgQMnIyJAtW7Ycdp8PhsMZ3z58Pp+cdNJJHX0/UZ5fc3OzLFmyxOij2l3P72Dp7N1LTk6WuLi4IzIfegJLliyR66+/Xl588UXlcnCSmpoqQ4cO7fHPrjMmTpzY0fcT5flZliXPPPOMXHXVVRITE9Nl2+5+frNnz5a///3vsnz5cunbt2+XbXva7z4ugA6TmJgYmTBhgixbtqzDFolEZNmyZbZdgu8yadIkW3sRkbfffrujfX5+vmRnZ9vaNDQ0yKpVqzo959HiUMYn8q2S//7775elS5fKySeffMDrlJWVSU1NjeTk5ByRfptyqOP7LuFwWL788suOvp8Iz0/k23DVQCAgV1555QGv013P72A50Lt3JOZDd/P888/LtddeK88//7wtdUFnNDU1ydatW3v8s+uMdevWdfT9RHh+It9GWG3ZssXoHx/d9fwsy5LZs2fLq6++Ku+++67k5+cf8Jge97vviMuqo5AlS5ZYfr/fWrhwofX1119bN954o5WammpVVlZalmVZV111lfXzn/+8o/1HH31keb1e66GHHrK++eYb6+6777Z8Pp/15ZdfdrR54IEHrNTUVOv111+31q9fb1100UVWfn6+1dra2uPH98ADD1gxMTHWX//6V6uioqLjT2Njo2VZltXY2Gj99Kc/tVauXGmVlJRY77zzjjV+/HhryJAhVltbW48f37333mu99dZb1tatW601a9ZYM2fOtGJjY62vvvqqo83x/Pz2ccYZZ1iXXXaZsvek59fY2Gh9/vnn1ueff26JiPXII49Yn3/+ubV9+3bLsizr5z//uXXVVVd1tN+2bZsVHx9v3X777dY333xjzZ8/3/J4PNbSpUs72hzofvXk8f3lL3+xvF6vNX/+fNu7V1dX19Hmtttus1asWGGVlJRYH330kVVUVGRlZGRYVVVVPX58jz76qPXaa69Zmzdvtr788kvrlltusdxut/XOO+90tDmen98+rrzySquwsBCes6c8v5tuuslKSUmxVqxYYZtrLS0tHW16+u8+LoCOEP/93/9t9e/f34qJibEmTpxoffLJJx1/N3nyZGvWrFm29i+++KI1dOhQKyYmxho5cqT1xhtv2P4+EolYd955p5WVlWX5/X7r3HPPtYqLi4/FUCAHM74BAwZYIqL+3H333ZZlWVZLS4t1/vnnW71797Z8Pp81YMAA64YbbuiWD9Q+DmZ8t956a0fbrKws63vf+561du1a2/mO5+dnWZa1ceNGS0Ssf/7zn+pcPen57QuLdv7ZN55Zs2ZZkydPVseMGzfOiomJsQYOHGg9++yz6rxd3a9jycGOb/LkyV22t6xvw/5zcnKsmJgYq0+fPtZll11mbdmy5dgO7P842PH95je/sQYNGmTFxsZavXr1sqZMmWK9++676rzH6/OzrG/DvuPi4qwnn3wSnrOnPD80LhGxvU89/Xef6/8GQgghhBASNVADRAghhJCogwsgQgghhEQdXAARQgghJOrgAogQQgghUQcXQIQQQgiJOrgAIoQQQkjUwQUQIYQQQqIOLoAIIcc9LpdLXC6XpKamHtRx11xzjSo2ebiUlpZ29GfcuHFH9NyEkCMHF0CEkB5BZ4uRFStWiMvlkrq6ui6Pf/bZZ2XTpk1Hp3MHQb9+/aSiokJuu+227u4KIaQLuAAihJwQpKamSmZmZqd/397efkz64fF4JDs7WxITE4/J9QghhwYXQISQE5J77rlHxo0bJ3/84x8lPz9fYmNjD/kc3+Wxxx6TvLy8I9NJQki34e3uDhBCyNFiy5Yt8vLLL8srr7wiHo+nu7tDCOlBcAFECOkx/P3vf1euo3A4fMjna29vl0WLFknv3r0Pt2uEkBMMLoAIIT2Gs88+W5544gmbbdWqVXLllVce0vkGDBjAxQ8hBMIFECGkx5CQkCCDBw+22crKyg7rfIeD2+0Wy7JstmAweFjnJIT0DCiCJoSQTujdu7dUVlbaFkHr1q3rvg4RQo4YXAARQqKGq6++WubOndvp3//+97+Xc889t+PnKVOmyJ49e+TBBx+UrVu3yvz58+Uf//iH7ZhXX31Vhg0bdtT6TAg5OnABRAiJGnbs2CEVFRWd/n11dbVs3bq14+fhw4fL//zP/8j8+fNl7Nixsnr1avnpT39qO6a+vl6Ki4uPWp8JIUcHl+V0cBNCyHGGy+WSV1999YiXtTgc7rnnHnnttdfoMiOkh8IFECHkuMflcklsbKykp6cflmj6SLBjxw4ZMWKEtLe3y4gRI7gAIqSHwigwQshxz+bNm0VEekSyw9zc3I5Fj9/v797OEEI6hTtAhBBCCIk6KIImhBBCSNTBBRAhhBBCog4ugAghhBASdXABRAghhJCogwsgQgghhEQdXAARQgghJOrgAogQQgghUQcXQIQQQgiJOrgAIoQQQkjU8f8D9BVMMbmebsMAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots()\n", "\n", "# Find the closest energy slice\n", "energy = 4.5\n", "EIdx = np.argmin(np.abs(E-energy))\n", "ax.pcolormesh(H[:, :, EIdx], L[:, :, EIdx], I[:, :, EIdx], vmin=0, vmax = 50)\n", "\n", "ax.set_title('E = {:.2f} meV'.format(e[EIdx]))\n", "ax.set_xlabel('H [r.l.u.]')\n", "ax.set_ylabel('L [r.l.u.]')" ] }, { "cell_type": "markdown", "id": "19859951-3686-46a3-b11e-0cc03b5e97fc", "metadata": {}, "source": [ "## Plot a constant H map" ] }, { "cell_type": "code", "execution_count": 28, "id": "5484ba62-d027-408b-9e34-e0f0e0ab4528", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'E [meV]')" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JZWTjYmF4DGalZGvQEOg+ES9ixVo0EIo3d3leD05Cg6awF0M0GSz4j9kLkc59WCN2SKoRTRVqFpHj+kPtIsN4zpkxQ/dP2rH99c1H66P4uUao9V6EgYYsqDLRn8KaL/gwqGnFjBkW3kdDJBtwXJkptXhcYkJFSFAf64vf+nKHyLM6eyrun5hbTimeN3pOmH1EajmyvwgxfCI7dmNfmM3le83sM/ZMD9Z6+5HNDJnh7UUJzRqV4eFh+9GPfmQ33XSTRSJMABRCCCHEaCM036Py2GOPWVdXl9144437bZNOpy2d/tNMtacH4+GFEEIIcfwQmk9U7r//frv88sutvr5+v22WLl1q5eXlIz8TJkw4ij0UQgghxNEmFBOVnTt32jPPPGM333zzu7ZbsmSJdXd3j/w0NTUdpR4KIYQQIh+E4k8/DzzwgNXW1tqVV175ru1SqZSlUrgwSAghhBDHJ3mfqDiOYw888IDdcMMNFo/nvTujirGnt0JtdxsaEkXbCqAGzEGzIF1YCLVOco0TZGF9EYmdYXaQMw5X118wO9hS8jX3zTtgG4csrO+tYSYMmkCDxTipTnWhqRDNoOEUFGbl+GGWTroSV+qn9mHfMsXYt1grnvOBSSTHhOAk0TRjeSIlVgE1N4n3Tq7Me44Ld+LNxOwYZvMwe4GZJW41sYhIdgrLWImk0Y5hBMnJYRlBNK+JZLEws8Qh91L/efjQRTN4r9P+JXF/nWd7g70qX8PrRU0gMq5s+x6oMcOHEX99G9QixJhx6jATKNqKx434TCC2LyPbMYMo2kjuJda3PRiwFmW5ToQos5mIuWbEDvPjbsN8ocRwHR4z7R1rNjsEbfZH3v/088wzz9iuXbvspptuyndXhBBCCBEy8v4RxiWXXGKuGyy1VgghhBCji7x/oiKEEEIIsT80URFCCCFEaNFERQghhBChJe9rVET+6B5AKyeawBX9/uwcJ4FritxmtChiJIcnTmSGoZm4+tuJo2lUOQPtkHPG4er9X26YC7XIXlw1X3xVF3bGR28zWiqRMmbuYH/LduK5jPdi5lD3ZDzGEMb/WM1reN5jxMDIFHntFX/GhplZphivTWEbXpzoML5FDNXhtWbbOincluWpMNsmmkbbgJkKA5O8lkdiOxo+jOF6POfRYXK9gtohJNslSowhZhHlqkmOTRA76E3smzsV85qYLcX2P1DLfm/F2sA40oyQLcT7ddzL3tq+eSSbqR2vfXbCNKjF+ydDLUasL4ZLjCmXnE+HmEXRaVNwW1/WDzO+mBkWwduQG0PE3Io2oFljaXx/cVeuw3bEjqLWG8m18veF9YPty/9+4MSCTz/0iYoQQgghQosmKkIIIYQILZqoCCGEECK0aKIihBBCiNCiiYoQQgghQousn1HMuLIeqDWvQmsgCc3QGOmeicYEmwX/9XXPQK08hsbI+LNwtfnKvqlQe8uvJJnZI++7D2r3tZ8PtWlF7Qfsxz8PXgQ1a0JbysFF7vuxKNCYKdmNJogZ7pAZPvtmsjwhbztm+KS60PpIV6G5VNCKFg2zb/rmo0VQvLkLaix3JzOlFmoUkgHjh+XfMFKNmB3DtqUGBiFCbBsjhk96Mo6B2ka9XouI2RduAV77bA3eX30N2K6/Du/NbBGUbOYlW6G2bjVaL4kGvK7Jtai07L7Se04icTL2JrwPmS1Y0Mkyt3D8icZm3JjYMRGSkxMdxnZ0W39mD8uXwj1xSP4Pwykh1mZvH9TitZhXxHKCXPJ8Rbb14jF854n1g+HPfnKiwT8n0ScqQgghhAgtmqgIIYQQIrRooiKEEEKI0KKJihBCCCFCiyYqQgghhAgtsn6EhygRULpP8a5yjxViFkeM7OsDM9dD7d/WnQ21hxf+AGot2QqoXVOxGmplEVyB3+OiqfH9hj+SHnr5XjeaKxdM2wy1Z20G1JweVCaYCdQzCX83cBJYq9qI5zhH2pXsxnZFO70r/1k2DzOIGLkyNEbM0I4pWb0btyUWQZZk7CRe2IDbnnEC1GKdaDSUtHgzVgZOqoc2zFxyS/GcMJw6NCYYbgqfgCgKE9Q2MmYW+e0NYoJ0XYh5MjEiqbSdj9e6ugFzsyyD/xx8ruE3UPtC7//EvpD7/wc3fQ+33ejdtnN7JbTJTsDsr/gmNIG6FmK7bCEaKFXFk6HG7n96bRgs68lnvjAzjuX/ONt2Qc1v1ZiZucTmifYGywRi/WWfUDgk/ylajs9repY37Cm5agu0idTgsx/3PyNZcrPuB32iIoQQQojQoomKEEIIIUKLJipCCCGECC2aqAghhBAitGiiIoQQQojQIutnFDOYRS1lqBoTKfx5HHXVuKL9IxNegdr4BOaTvFo9AWqnkZXqpyUHoPa9bswYuX/bWVA7Z9w2qFn1i1g7RJgJ9MwgWiqnzMaclJe24BjKVqHRkCnG3yGK+lHJSu098Mr5dCUaKRVr8RruPR1tg8J2tIoSLV1QY4ZP1JdXY2aWakHbxJmNGU6ZYnxrckhOjt+EYjYHNTCICcHydJj1Q/fHMmECGhiZ+gpsZ96aPyfFjOf1DOEltItOXQe1Z9bj/drQgPkvn1m9CGrDA/i+kSzCe/OmJz8BtUiZt100izlUxWVo8yQXovVCoomsrxyfpb0nYn8bHm+FGjXGnsVzFyUGVmSH13pzieHDcq4iDXVQy1Xj/mOd5F4imUPMZmP3NYMZflE8TZba4X2GM3Px+WXvETHfs+86+P6wP/SJihBCCCFCiyYqQgghhAgtmqgIIYQQIrRooiKEEEKI0KKJihBCCCFCi6yfUUzr2nFQqzsF8y52t3nzOJjhc2FRI9Q+3ng91J4/6TGo/bgXbY5eBzM7VvdOghrLGDEcln2r5RI87uQVntfjYziGexvfB7Uz63dC7atnPwq1+3bitiXEaDBDU2HfbPY7BNoLg3Nwpf6YNYPerfrRhHFKcbuS3WgRZIrRGGJWQs80tA0q1uKq/syUWqilK3FczHoqfwOtieSw10piZgXLSYkMo0XhVmPuDDN8nGrMOor2DUJtYEYN1FjuEDN6/DYTM7d6Z6GR9amzn4XaiQUtUNs5Gce6+Q3Mulp0DmZkPXnfuVDbd3qw33ndrLedG0fLcGAQr83UKjSStu1FxemyD6yE2q8fPQNqLVeibVO2g+RrkcypaGMz1IYXTPe8jv32Vdxu3olQiwyT3LQd+B7M7k3rQIMuwnKjCC4xhuKvoy3pTMZ7wv9MRNM4hsONPlERQgghRGjRREUIIYQQoUUTFSGEEEKEFk1UhBBCCBFaNFERQgghRGiR9TOKKWnGnI3TLmyCWveA18D5dPluaGOGJshp1bgvRlkMTRhmzOzpwWM4GZxrP/MIrvIveF8H1Ga88GnP6zPOeRPa/I/JmPXx0OunQ21NNa6Of+XUn0Ltrr3TofbBU9dC7YqffB5qzN5J7ctBLQj9E9DSKdnSA7VcAtuxDJ/yN7Af2RrclhkuhW1ozCRK0V6gGTs+aNYJMSFongrJIaKZQDvw/mfZLkWb8Z5zStBm8xs+Zmj5tJ2NdszCkzFzqjyG5/K2ZTdAbXgOZmlZEV7Dx5aj4dM/B9vVrkBzK5fA95eCLm9t7wnkd+U2NPnWN2GeTK4In4dZs9CYeWEhXofO7WjRFO7BviT6sdZz7jSolb3sfa9ziOETbd0DNZYHxQwfZrMZyRxyk3gdIuQYg/MnQy3Rf2i5XlnyrMZaDpxD5OaCTz/0iYoQQgghQosmKkIIIYQILZqoCCGEECK0aKIihBBCiNCS94nK7t277frrr7fq6morLCy0k08+2VatWpXvbgkhhBAiBOTV+tm3b5+dffbZdsEFF9ivfvUrGzNmjG3evNkqK0mugTjs9C3Elf+/a5oJtdfOWO553ZbD7JRxMVyB/u06zLtgfPHHaCUk53VBLd2JxgQjmiHFX5DcFV/pxXUzoM3qNsz6KCF96xvE7JzNGTxPf1+1BWp37T0Faoyu6fi4RknMRqrL247ZFw6JBCnBrpmTxG0jw3iC3ST2LdaDdlCcZJt0nYJZTxVr0cBhGTv+rBTWD4aTwnYOySFi1kOkBvubIYYTG/9QHbbrG4+mRt9E7+sZJ+6CNgNZvIjf2XAB1JJE8Em/hflSU/8Tr2t0GC2iTDGeu0Q/Wh7M8PJTtRHHHu9Hq4ido8IPtUPtn1687IDHNOMZRg+Voc3X8DMca2E7XtcgON29UIuOwbwidg9nyb3J7i+WOZVtRkutaBivV/rkiVBzSZ5QzJcTFCHPJXtWc2Xe98hsFvOr9kdeJyp33XWXTZgwwR544IGR2pQpU/LYIyGEEEKEibz+6eeXv/ylLViwwK699lqrra21U0891X7wgx/st306nbaenh7PjxBCCCGOX/I6Udm2bZvde++9NmPGDHv66aftM5/5jP3d3/2dPfjgg7T90qVLrby8fORnwoQJR7nHQgghhDia5HWi4jiOzZ8/377xjW/Yqaeeap/85CftE5/4hH3ve9+j7ZcsWWLd3d0jP01Nwb75VAghhBDHJnmdqNTV1dmJJ3q/aviEE06wXbtw4ZiZWSqVsrKyMs+PEEIIIY5f8rqY9uyzz7bGxkZPbdOmTTZp0qQ89Wh0ccaknVD78eQVB9yOGT6XvnlloGPuHcQcj2wh5pg4GyqgVtqJ+8vi7qxsJ2aAZIrRXqna6LULkj3BHoehDPatgCyXumzgb6H2yfm/h9riivVQa7sQV80/vmp+oP6VNnrHUfcC2gYs66dvOk78oxk8lywnh8EyQBglzWgvsJwgbpt4rR9mmrAsndRezJdiJlBQEo3NUGOZLR3z0F4p3YXnOD3OW9vSjNbH+HH7cLsetM+KUASxOrwNLbWDZB2lic0zaxzUomm0ueIdB7ZjEh14zt0U2iDsenVlsB+xk/AYuXK0iNb31B+wb2ZmTdfgtSlfi/ZhddLbl3gvnrdYeSkegGRJsfEntqPhZMTccerGQC3egDlkLBMr1Yg5SSzDyvFZSX6bx4z3NzLsO2YuuD2V109U/tf/+l/20ksv2Te+8Q3bsmWLPfTQQ/b973/fFi9enM9uCSGEECIk5HWicvrpp9ujjz5qy5cvt5NOOsm++tWv2t13322LFi3KZ7eEEEIIERLy+qcfM7MPfOAD9oEPfCDf3RBCCCFECMn7V+gLIYQQQuwPTVSEEEIIEVry/qcfkT+CGD5mZn2Od8V9SRRzQnbtRcMh3UaUHEI0idaPZdDSGfsK6gv7ZuIK/KEKnH9XbsJtW8/ybhsndsQQRnFYCbHny3fgCvaeBdiuL4vn7lO7MJ+E5bh89fyfQ+2fNl4KtajPmOo8GW2DxADaDGVb+6EW68S8IjeJ5grN63lyI9Syc6dCjdk8zPJIEbOkZ5rXJKhc+Ra0YZZStBevF7N+2LbpKryG2WK0I5gxNUy+UWHxV34KtSc753per3xhNrTZt6EOarWt+CyV7MaxJjrwZmfXNUKsn2QLWmSRXrx30sQO8psl+JQbNWEYFW9irWw7XsOmi/A94jVDszRSiPdXrAPPCbuGfeO9fc4UowkzbgdaNdaLz1eU5Os4k9HccV57A2rsH/Rs+x5sN41E1ZDzznK9/DWWL8TwbxdxWCgbR5+oCCGEECK0aKIihBBCiNCiiYoQQgghQosmKkIIIYQILZqoCCGEECK0yPoZxWzO4IrzGQm0HPyWz19uuQTafG7Os1D7p84roJaqxhXiLJ8k1YEr0Hefh6v3kyRjxyF3dc8UNDUG67xWxvjfoaXR14C5G5Wb0Ejpa8AxjHkGnYafbT8XO0eYeC4P5vTzkemvQO3fV13seV2ym4xrPP6OUtKMJ67nDLRZmFmT6CeGy4LpUGN5IjYFc2wYzLapWOs1JDL1Fdg3kjvSNx8tiqKd+DywHJOizR1Qa7sEDZy+BjzH5138GtQYK3d6rZSSZryX2HWN92OuDcvwYYaPu43ccwU4fmaqWA1aX8wOcvZ4A7uiDXjeXGK90HuJjCtSgu8Rta/iWJtINNncybuh9tog2kF3fvDHULtt2Q2e16W70L4amD8Zasxui6TxGkY7u6FmKXweGO65p2IxYDaVteCz7sya6O0bMeiYzeV/NrPZIbNtpCFBn6gIIYQQIrRooiKEEEKI0KKJihBCCCFCiyYqQgghhAgtmqgIIYQQIrTI+hnFMMOnLYcr+u/p/AvP6+becmhz38/+B9RSJCcnbbgqv3It3oYOLtSnsNyNLB7CynbiKvya1d616QliTCR7gmWHVL6JBka6Ate+xwewb8Ukn2XbzBqovVlVjxsTTrnCm7GzfessaBMlMRtOEn9vYTZP+/loarCsI5YnY8OYHRPvwJwYRhHJHXJ8lgfLDWJWUVDDh/WNmUUsEypXjOdufulOqP3sLQyFim/y5mSNWYPnMtaD5zzairkuVorPeaRzH9Tc2ZjD5L6JWkZ0DHuw8boasXcifouIbOe3Ssy4LeaWFkONjb8whebe9B/hfdI8cxoe93I0l2595VqoTX2/15javBmf1eqV+KaW6Cf5QsT6cYlpBefSzFxyPhPE8LEkyfUheU3M5oo2escaIRlBzCCCZymH9+/+0CcqQgghhAgtmqgIIYQQIrRooiKEEEKI0KKJihBCCCFCiyYqQgghhAgtsn5GMXftxfwMxsMbT/MWmtB6yc3EleqFbbjaPtaNtb4GPGYRRkzQDB+W9VO1EW2L4iZc0b796lLP62gGV+Wz/ae60NKJEemBtSvbgedp7wk4sPrluJL+4Q+fBrULpm2G2j9PeMLz+s2vPwdtbv7lJ6FmhhZBlIyrdkUr1JgJwyyaTA0aHdFhvF7M3mEZO67P6IhlcF/RNJoVkWGssfFna9As2foJtLncHryusWq0Gv557UVQmzoWx1Xkk1xYllTl7zHDiNkWbhLPJfsN1X8uzcxi5aXYkFgeFBRmqCECx2xBW4jZYhGyf2azxIgtxjKBalbh9Ypl0HBsPxuvdc0k7/vL5gTeh12z8P1gqAb7UdBB+gaV/dzDJJuHEvAasuc6M8PbG/ZcUquMZQkFRJ+oCCGEECK0aKIihBBCiNCiiYoQQgghQosmKkIIIYQILZqoCCGEECK0yPoZxTQkcHX9c92zD7hdSTNaD6kunPMOkqXqQzNxZX3BhoIDHtPMrGwnrqQfqMXjdk3H2lAF2gu1q737yxTjuKo3YH93vw8NjJrXcUV/LoH7K2zDzJax/bgCP5fAMZS8WAS1N6sxx+au2Hme139Tg9ZP8WTUmaLrKqDGMnx6ThkLtTjJSSrc2Q01ZqAM1aFZw7ZluTCD47yGBMvwifbhOWc2A8vOaTsP20UTGNh00twdUNu2FzNxhl/D/fX+ErW3lHnvzcqVxOYgJoy1kPGTrB+Wk8PsGJYTRHN9mEVSj/eJ67NS3Lox0CbaSe6bgDYTM2FYhg37DT1XjWNN9ONznWrD476Y8OYExQqxHwWdeI5YplligJhr5B5OT8YcntQwhnj587DMuOEVIRlDLCco4TOraA4RuR+6TvH2N5sZMmuEZhR9oiKEEEKI0KKJihBCCCFCiyYqQgghhAgtmqgIIYQQIrRooiKEEEKI0CLrZxTzZOdcqK3+zQnYcKbXcuiegyvaEx0kT2QWhnGU/BHtmyGUI6x/Aq5AHxiLK9UzNdiX2j9gO2bg9I33ztPjKAdQ+yZJhJSeSdiu6g2yAj+F5ylTjP2NDqNtwPo3+JM6qD11uddeeHzVfGjzqbOfhdp9Z1wAtZ6paDj5bSkzs66ZqC/EMnit2ViLN3dBbbget03twPOZKfae9wzJTtlzYQXU2LXZfR5um67BsX779J9C7eH2M6A2tAGPmyvH/ZU0o23kh5kb7LfMCInmYZYOy2KhkOwcbnmggRIhBorVeNtFiM3i7OnEfbFj4t6pbZKbPA5qsR1tUIsm8R4uW4vHLdmC16LFd49NvaYJ2ryeGQ+16Q2Y19Teg3lYztk4hoo3sW/MZktsJ5lQ5Hy6k7F/zPrCY6B56CTx7kzt876nxzL4Hr8/9ImKEEIIIUKLJipCCCGECC2aqAghhBAitGiiIoQQQojQkteJyle+8hWLRCKen9mzD/wV7kIIIYQYHeTd+pkzZ44988wzI6/j8bx3SQghhBAhIe+zgng8buPGoXoljjwvrp4JtQS5I3KD3mJhEzYanICasLVhiF56KipplRtQWe0mAYFJzNGzcS9jrW88ExcRvxYdR+vQssWoLDI9mZHaOwQ1pic7RIFO7UNltWgPjqtnMu4v3YNKsZ/ftc+CWnVDF9ROPXU31F5vRK19uAyP0TceVdHCdrxP+mdUQM2vHb8NCWHzKY9Uf24NplM3vH8X1CaVoMb7w5ZzoLbp19OgVtYKJSvZHSz4LTPLF1T45jbc2Rji9TNYYCALNAwYQOgOEZ2aqL1MgXZ9+4uQY0amop7rbsNrw2D7i7Vg+CqDHSNCznGkA/dX94L3vnujGu+HSDHeh9vaJkDtW3/3INQ+99T1UOueRoJWX8XnK9ZTjn0hoZRM7c8FCAx1SvH9Zudl2LciX65mLp0zewaaUfK+RmXz5s1WX19vU6dOtUWLFtmuXcFuSCGEEEIc/+T1E5UzzzzTli1bZrNmzbLW1la744477Nxzz7X169dbaSnOyNLptKXTf5rN9/SQX7GFEEIIcdyQ14nK5ZdfPvL/586da2eeeaZNmjTJHnnkEfv4xz8O7ZcuXWp33HHH0eyiEEIIIfJI3v/08+dUVFTYzJkzbcuWLfS/L1myxLq7u0d+mprwa4qFEEIIcfwQqolKX1+fbd261erqML/EzCyVSllZWZnnRwghhBDHL3n908/nP/95u+qqq2zSpEnW0tJit99+u8ViMfvwhz+cz26NGlKdaEgwoyWa9VoDBZgZZtEM3krMBKlsxLC9PRehHVP/OJoKuy9AYyLZE+wWTnWRMLjd3hozaBiJAdxXpgjn/D3TcMU8C6ArbMdauhItitQ+DHljU/VoxnvuBohUt62sBmrfImF7t75yLdQqPoQ3wOD2StIPvL8S/SRYkhhe6QqsxYbxnAxVeM97Fk85Db0cnoL33AeqdkLtlztOxo0JRcTwKSD3XCxD7h2/4WNoArkFaFa4xNyI9JLkSmLuMFjwYY6F3BFjyGFWDrF3aP8CtImU45pFv0FkxgMNo8TcYdsy2yhTg+c40YjbRnu9z/C4l/Fc7j0B319Y+CqzyhbO3wS1l1eiudcxF49RlQj2C31xE5539o7Ye6LXvuuYS4JbyfLR7pnesTqDwUMJ8zpRaW5utg9/+MPW2dlpY8aMsXPOOcdeeuklGzNmTD67JYQQQoiQkNeJysMPP5zPwwshhBAi5IRqjYoQQgghxJ+jiYoQQgghQosmKkIIIYQILXnP+hH5Iz0OcyEcEgrpTB3wvB4ewAyf+CDuf2AOmhXWiPZCtA1rPZPQ+iglcSdBSfSjbeG3aLJkVX4URRvIl9nf/qPDaDg5SfzdoHUhjr98K9sf9q9vPNbG/dqroGz6FGo/BWvxGn4x8UGoMRPo4fYzoNaUwBPVvQ2/ZmD3lXjPpZrQ8HLIO9PAWDx3mTqvgXH1vLXQZlwKVTaWdTS7sAVqP35rIXaEMHNdL9RYrlO8A80KljHjt3ycWWikxHa0YUcC5vU43dhfdzLeJ5Hfr8F25ZgdEyVWjjHDx2cM0X7Mm4H770UzLrcRTZjovBNxfzswrypSg7lRbstbUEt0kpwklk807L3/maFXtRF31T8Ln/NdyzEnaN8p+NzMmIsZUVtfR4Ns7wn43DBLrX0+XkMnge9hJc3e92ZmiiYv7IBaf3OFt5DFfe+PQBOVf/mXfwm8w3f42Mc+Rr8GXwghhBAiKIEmKrfccos1NDRYLIbfgcBoamqyD3zgA5qoCCGEEOI9EfhPP6tWrbLa2tpAbTVBEUIIIcThINBi2ttvv91KSsjfPvfDP/zDP1hVFf4NUAghhBDiYAj0icrtt99+UDtdsmTJIXVGCCGEEOLPCfynnwULFtjNN99sH/nIRxQGeJwQHSQWRTkaLQsneTNQamfiSv0VD6IJwmweluFSgovXLU6EAZaxw3ASeAwngWP1GzjMXMpiZIcN1jI7CFewxzCaxvoqsMgMn3QF9pfZRtXrsdP+zJZJT6P1MVCL/egswz/Z3rb2BqjFF3RB7Wen/gBq/+PFL0Dtp++/F2r3tZ8PtZdbJkGtdy+aSrFCrw3x+Kr50ObqBauh9pd1a6H2tXVXQK1yLV5rds4ZLK/JjGTHDJMQVp+pE+vsgyZOHUaN5MrwmUtsb4datAGP6RKzJjJtCvatF/vCTBinGdUSlrsDbRrRgoqQ/cdrcfzM8KGwrB82hmpiOHWi5uL6zkk8idc+sR3f1CbYeKi1nwYls0Sw9z63Csf179fcB7WbyHOdIc8Xs+gef+0Uz+tF81+GNiv34vPb6c8cygYbk9lBfI/KvHnz7Itf/KLV1dXZRz/6UVuxYkXggwghhBBCHAqBJyr333+/tbW12T333GNNTU124YUX2vTp0+0b3/iG7d4dcBYrhBBCCHEQHNQ30xYVFdmNN95oK1assE2bNtl1111n9913n02ePNmuvPJK+8UvfnGk+imEEEKIUcghf4X+tGnT7Gtf+5rt2LHDli9fbi+99JJde+21h7NvQgghhBjlvKev0F+xYoU98MAD9vOf/9zi8bh94hOfOFz9EkIIIYQ4+IlKc3OzLVu2zJYtW2bbtm2zc8891/71X//Vrr32WissJIqECC1OGeZHROK4Evvlld5clPG/wzZDZKV6URvaN6kutGN6JxJLh9yZ2WL8AJBZOWNXob2QKcZvVfbXWN/i/di3wnY8b8wEyhThtkV7cNt0BfatZDdpV4ntnCQeI1rs7UuWjL2kGc/R4Bg8mZP/51aoNfeiCfGL3lOgNv9iDDf52q4PQG0gi4bESbVojLz4FmbAPPx+r230TB9mvfxXy0lQ++WGuVBjlhqD5fX4s17MzIrSaGlFhvG6uh17sV2KZMz4YB+HR/vQSHJL0TSKdO7DjasrD3hMMzNLkmymErx3qOHjy/qhGUFk/7l6/F4uZgcFhdo85NzR88msJx/0/JJxFW/uglrB5BrctBu33TsWLZ2qauzbt1ougdp35j4Mtc+sXgS1VzsnQO2LC5/yvN6dxvuGZQ7Fhr3vVZEhfD72R+CJyiOPPGI//OEP7be//a3V1tbaDTfcYDfddJNNnz498MGEEEIIIQ6GwBOV66+/3q688kp79NFH7YorrrBo9JCXtwghhBBCBCLwRKW5uTlw1o8QQgghxOEg8Mcifz5J+Y//+A87++yzrb6+3nbufPtbS++++257/PHHD38PhRBCCDFqOei/39x7771266232hVXXGFdXV2Wy729IKaiosLuvvvuw90/IYQQQoxiDtr6+e53v2s/+MEP7JprrrE777xzpL5gwQL7/Oc/f1g7J44sqSZcSV7Uhu26Fg55Xld9Hr+JuOdn06A2TCKhmOETH8B2DolJSWLEhg2Mxdq+mWhvFLWjqZTzDZ/l9bD+JgaYpYMZG8w0Su1DOyQ6jMct3ImD7Z9Rge3a0EpIVxVAzQ8zV6o34O8tWwrxurJrs3UM/ll40z7MYmH0rULL4S//6pfY8GQsXfu7zxxw/7EO7HBBD17Xqo14j6S68Lpma9Ciib++DQ9MzKoIydgxYvj4c3yYfRKYlrewxnJtSvG5iaTxn4gIybGhNhOxYyI1PnuHtGHbxVqgRMfgt4r21y7SugfbESuH7Y9lAtHjBmqD99KYNSS/K4nPZl8HWlXtZ6NJUzsO782Pv3Aj1O4/ZxnUHt+H2VlP75njec2svcJW7O9gnff5ch1839sfB/2Jyvbt2+3UU0+FeiqVsv5+kiQnhBBCCHGIHPREZcqUKbZ27VqoP/XUU3bCCSccjj4JIYQQQpjZIfzp59Zbb7XFixfb0NCQua5rK1eutOXLl9vSpUvt3/7t345EH4UQQggxSjnoicrNN99shYWF9qUvfckGBgbsIx/5iNXX19t3vvMdu+66645EH4UQQggxSjmkrJ9FixbZokWLbGBgwPr6+vT9KkIIIYQ4IkRc1w2+9DZk9PT0WHl5uXV3d1tZGVFMxLsy7Sdfh1quB1e+Jzq889mCOV3QZvi1Cqilq3EFeuUGNGG6Z6FtEevH5VO5YmJldGC7ZA+UqB006Snv6vp0Ja5eZzk8LOtn74m4bdlOZpGg9ZOuwG37xuO4mA3Q14CmRuVKr+Wx7wwcfMVazJfZezrJZgkIs6OGZg5BrWATGknDc1D7iifw3slm8FokN3jzTgo6sW89U/Atbsp/Yt+iabyusR1Eg2PWBzFV/OaOmVm0E20ulsUzOMmbRVO0egfuv7sXaplz5kAt2YLtGJFelCGcPeSEzp4KJTauQAZOGtsEziYKaOm47BhDaGTRbCKWuRSgz+xcsn3lqvFeYvdc+uSJUIsOk/fNHhxX23kVUGPvh3EilsUXdEEt+Z/e/Q1jbBKt1b7qfb6ymSF76ckvB/r3+6A/Uens7LQvf/nL9uyzz1p7e7s5jvdk7d2Lb4BCCCGEEIfCQU9UPvrRj9qWLVvs4x//uI0dO9YiEfxNSgghhBDicHDQE5Xf//739sILL9i8efOORH+EEEIIIUY46O9RmT17tg0OvodvSRRCCCGECMhBT1T+9V//1f7P//k/9txzz1lnZ6f19PR4foQQQgghDhcH/aefiooK6+npsfe///2euuu6FolERkIKRfgpKsTV6/Hni6Dmz+zp3YttEuROapjZDrXubZh1UrwL58u9U0lmxUqSH4ExMZbqQssj3o9rqfyWT6aYmEYJ3I4ZPhVb0BjpG48nZagCLZ3yHbhSP0fyVN46vTDQcdOTq6Dmhxk+LK8oKNEM9rd8K1oOgyT+p+pnaAIx64lRstu7mD9TjNdrxgNorgQdf4yYJQMz8KaLZfCcJzrIJ8/EShmcVA81v+XDrI/Uul1YaySWUsD8G0aUZBOha7IfU4fJRj5jhllFUdZfZvgQiyYzBb8qg+VaRZlFRPbndhA5pB6Vmciwz+YLkv1jZu7KdVgrR2UmtQP7we7DTDG+5xTuwStWgnFtlK5MxQHbFHbg+23VG/i+VLTeG9iUdfB9b38c9ERl0aJFlkgk7KGHHtJiWiGEEEIcUQ56orJ+/Xpbs2aNzZo160j0RwghhBBihINeo7JgwQJramo6En0RQgghhPBw0J+o/O3f/q197nOfsy984Qt28sknWyLh/dv03LlzD1vnhBBCCDG6OeiJyoc+9CEzM7vppptGapFI5D0vpr3zzjttyZIl9rnPfc7uvvvuQ9qHEEIIIY4vDnqisn379sPeiVdeecXuu+8+fRpzlBnaUAG17ELMQBk/zrtCvr+ZGBPNuP/hxnFYm4btal7HVen9JDuGGR0D43DFeTSL7Xqn4jGGd3lv/ySJK0kM4HZZYgf1TMZHidlHqS6cyLPMDgYzfBL9B95f2Va0HvonoKXBMoeKm3DbbCnaEewY6Sq0eTJFeO7iZAxj1qARwIyGbLE3/6dsO96//TMqoMYMH2bRuNWVUPPbC29vHCwTJjcZn4mizR24bYC+Bc26YZlARmrM8GGwXB9m70QCZOdEy0sDHZPaQWTb+Otox7BcH5fkFdmb27AWMNfIbz25JOcpsgNVm3jDeDwmu5cIqb14r/N2WHNS+Cyx56tiC247UOt9hqvXYc5VtJcYPf5708Hcs/1x0BOVSZMmHewm70pfX58tWrTIfvCDH9jXvva1w7pvIYQQQhzbBFpM+8tf/tIymeCznyeffDLwt9cuXrzYrrzySrvooosO2DadTusL5oQQQohRRKCJyl/+5V9aV1dX4J1ed9111traesB2Dz/8sK1evdqWLl0aaL9Lly618vLykZ8JEyYE7pMQQgghjj0C/enHdV278cYbLZXCb9VkDA0d+G9nTU1N9rnPfc5+85vfWEEB/i2bsWTJErv11ltHXvf09GiyIoQQQhzHBJqo3HDDDQe100WLFllZWdm7tnn11Vetvb3d5s+fP1LL5XL2/PPP2//7f//P0um0xWLehXKpVCrwZEkIIYQQxz6BJioPPPDAYT/whRdeaOvWeXMOPvaxj9ns2bPt7//+72GSIg4/BXO6oBb9XQXUWqZ6sy1SDWh49ExBYyTZEyxeoWgxKkPODrQjnATJ4kgS6yeDx0104185/ZbPEMkNKtqIRs6+2bivApQSqOFTtBNXyLefVQG1cf+5E497Ln56GMQ2YrZU2XZcc+YksV0kjWOIG5olzARiVG7CtWvRNNpM7LgMvzHjlGAeEvvVJtZDrASSf+OU4taxXrz/XZLNxO5+ZkOwPJlIgCyeXD3mC8U68f5y2vdALT5tCrYj546ZMFFi80SmYhaRsw1NJb+pw6yqCMnhCWokWTVeh6g/h8fMsq+9AbV4LQmi6iNrLZmV0/KW95isTdAaIVeN9wO71taLNWaaJVq6cH/k+keG8dksxVMHuEl8XxqcP9nzOpsZMnvmwPsyOwTr53BRWlpqJ510kqdWXFxs1dXVUBdCCCHE6OSgv0JfCCGEEOJokbdPVBgrVqzIdxeEEEIIESL0iYoQQgghQosmKkIIIYQILYH/9HPFFVfY8uXLrby83MzeDhH89Kc/bRUVFWZm1tnZaeeee6698UaAJcEiFDDDJ0sW/qc6fPPZDszYYGvXh8vQyClqQxdi1+/RGEiQO7NsB65AHy7DhgO4yJ3m+BTt8e6vZhWumO+bjpp95ZtoAqX2oaWS6Mf+sv1Vr0ezIDOlFmqDY/D3CnZO/Pk/nXOIuZJh+UK4/1xZsK8DSHTgGJLEGIgQAyM9Ge2VRBr3l9jeDjV/xgqzFGIr0VzJnDMHaqlWtGOCuodsXMwEYriTMe8l4suTYYYPM4icZvyiTWqzEDskwsylgLYNG78RE8j/juASMyjC8n9YbhKznna04bbEoIr9979jnv0RO4aZNey6+vOEglhbZmaZ+gqoUSOnhQT2EGOIWVQ0d4fAnvVEy4G/YX5gBuqSBa1oxhXu9N7T2VywfpkdxCcqTz/9tKXTf9rxN77xDdu7908nL5vNWmNjY+ADCyGEEEIciMATFdd13/W1EEIIIcThRmtUhBBCCBFaAk9UIpGIRSIRqAkhhBBCHCkCL6b1BxMODQ3Zpz/9aSsufnsx25+vXxFCCCGEOBwEnqj4gwmvv/56aPPXf/3X771H4qgRJQv1HZIlCfbOBFwJPuaXmIDdOxE/sBs4fQD7sa0IayhvWMs56GCUbcd2URQELJbBNVV7T/Df/rhSv+1M/NRw+o9RIRqqK4Za00WoUI1/Hif0LHcmU4PbVm/AdukKNBDeWuAdV+kuNHzYdsVNuFKfZe4ws6Z/RgXUWK4Ry85hsPGnOvG8+22TvjlouOCVMUvtIBYFMzWIHWNJ4riRdg6zSIiV4nb34v58tg3NPvLly5iZufNmYLuA5kq2Bs9UvAPvCXZcZrm4JDsm6svOYYaPU4fX0L+dWfA8KEakBo2hyGubsSHpH7tekEVE7geXmEvx18n9xfJ/2L1J9hchNQo5Rvx1tOPc+rFQG673nhNm+DD8+T9uLvj1CzxRORLBhEIIIYQQ74YW0wohhBAitGiiIoQQQojQoomKEEIIIUKLJipCCCGECC2BF9OK449hYvg4p+CKdrfZawM4PbhivP1M3FeC5OsUrEXDZ/qVW6H2xu+nQa2kGQ2ckt1ooAyMwdt63yzctsgnL3RNx+0KOqFEjZS+8WhR1LzO8nSQ9rMqoFb7xy6oDUzClf+lb6C90jfem71R0kxsoUqS60IsCjeFphUznJwE/s7D2hVt7oBajJzjXDWO1anGfBZ//1g/mJFU/ByJ+yCGAzM8mFnDsljY+aRGCzNmAuQEMdOGZdMwO8Qh55dlKbFzTr89a5gc4zXMfYucMNPzmp1faq4QSydKspnY+Y3s2I01cu7A3DF+Hdhv947PcIoy64dkOkWJyUatH3bMPeTBIbD7lRlObPzWi0ZPssW3f5LXZLOn4v595lbUOQJZP0IIIYQQRxtNVIQQQggRWjRREUIIIURo0URFCCGEEKFFExUhhBBChBZZP6OYygtboda8nayar/Kuwk8VYUhQ0W8xEyMo2x5DwweTg8yyKNtQw6fzbLQGqv+AK+kdX8khT0PFFjR3uidjXk3lJswi6TwJO5wjFkGiH3OIgpo1vSeiDeHPBMoU48AyxbivXFmwHJ5sMZpAJVt6oMYygZjNQ/OEDjHHhVlQEWIuZOYSKyGN/Y0Qe4GRmVIbaH/pKryzC3eSYCv/vpgdwvpRXwE1ZvNEe9G4yNXjvcRsJpo7Q4iVE2PIfy2mToQ2/kwYM7MIMXyCEglo0TCY0RIZUw01v9HCcn3YNaTnnOVBVVfi/kgOESNdiuNPsW1Jn91SfB/yX58IuYZGnn2/GeXkgn9Ook9UhBBCCBFaNFERQgghRGjRREUIIYQQoUUTFSGEEEKEFk1UhBBCCBFaZP2MYgrjaO9ECg9sIKQ70WbJTkFzJVeExkxxExojg3XYLjKMiSLV67EvmWJsV7oeV7kP1UDJilu9fU514RhSXXiOCtuxv8zwSXVhO5a7E9QOYRS24/72nOrtS8UmHEP5G2jp9E3H8Kd4P9o3zKwZnISGB8v1yZaibZDsxbyXbA3aBuw8DY7zjrWEWCosr2awFg2nQpRjLBbQSEqPw+tfsppk54zDvBdmudib27zHJKYJM0YSLV24L2K9MBMqTa5hYS9eB7atS7JtIiSfx6Ad2T/J5glqGrH8HwrJJmJjcNJDuC3J2In6rg8de0Cy7TiGOBl/ZlYD1Nj1T+0gzwTLRCKmDnsO4RjEFmIWXKKx2fM66pBMp/2gT1SEEEIIEVo0URFCCCFEaNFERQghhBChRRMVIYQQQoQWTVSEEEIIEVpk/Yxidv2e5GyMw5XfpWO9q+EH2tAO4EYOGj7ds9CEKWzF+XLVRuxHyzm4v+r1aOpkC9EEYpk9QxXe46a60OZIdGCGT6YGDQ+W11O2Fe2IpkvQeilAiYAaQ2x/fuvFzGzsS16Lpms2MyZwNX9xE+6fGS7MUmH5P+8FZviwfJqSTp+pQQwXdr2YLUWNGWI0MGOkCFvRnJTizXiMSOc+3Nafn0LasEwYZsfQ/B8y1oJWYvOQrBuX5B9FiDHD8Bst8SReL3ySjJ5zagIFzfUh1xXfNfaTV8SMHv/+2Pkg/WX3tDvvRNyW2EzxDswNc0mWmLW8hce1AGMwswS7N/25Qx1oFcU78N73W0rZ7JBZQElLn6gIIYQQIrRooiKEEEKI0KKJihBCCCFCiyYqQgghhAgteZ2o3HvvvTZ37lwrKyuzsrIyW7hwof3qV7/KZ5eEEEIIESLyav00NDTYnXfeaTNmzDDXde3BBx+0q6++2tasWWNz5szJZ9dGBVGMgLFUGzE6miq826FEYftmYS1Tg+ZGogP3z/rRfhq2Y4ZP70Rcq88sGpYJlBjwmjV94/GY6Qpc9d9fR46JsTbWeTIaPuOfQ4uoZwrm+iT60fphxlDVRmznt3wqX+vGzhH2zcOxRjN4zova8YKVrUWzwCkhN0pAmG0UGcbj+i2H4Xo8R7kE/j4W7w2WM8LyX7Jz0XphlpKbQhOKWh5+i8LIWIkdwzKMWF5LYjtmDg2cVI/t+sm25Xg++yahvVLSSvrX3Aq1eK0vY4bYPBFm85B27P6K9uHzFdTAcUiGD4WYNRGfMeMyqwhPJTW+jNwPQbOOWA6TBcwdYpaaU4rbRhu9JhizoNLkOUyt8213EFk/eZ2oXHXVVZ7XX//61+3ee++1l156SRMVIYQQQoTne1RyuZz99Kc/tf7+flu4cGG+uyOEEEKIEJD3icq6dets4cKFNjQ0ZCUlJfboo4/aiSeSL7wxs3Q6ben0nz467enBqHohhBBCHD/k3fqZNWuWrV271l5++WX7zGc+YzfccIO98cYbtO3SpUutvLx85GfChAlHubdCCCGEOJrkfaKSTCZt+vTpdtppp9nSpUtt3rx59p3vfIe2XbJkiXV3d4/8NDU1HeXeCiGEEOJokvc//fhxHMfz550/J5VKWSqVOso9EkIIIUS+yOtEZcmSJXb55ZfbxIkTrbe31x566CFbsWKFPf300/ns1qhhaOYQ1KqqUeXr2lDteZ3sQT23aCH6uXs7UamrXY0f4vWND/bB3mANHne4DPXZgk5sV9iO6mXXTK9mmCU2baoL91/3RzxvTE9lejJrV7IbNb2BWlQg617kE3jc1juZH6pD7ZAF0DFS+4IFNfacMhZqLPiPqcKDk1CzLVrfgtvWowYZ84USJlt6oQ2DBStS6nFcLNCPhsEFPG5gpTbI/kmIYIQoxgXPrsN+jKmGGtOiWbAi02fdyeOw5tOzmU7LtOtoJyr29LwxyBjY+Y0UBPslmGny0CagTuzUjYEaGxe7v6jaTMhMqYUaU9ad6mB97j/P+10U7H5I7cCgQrgODo2fpOR1otLe3m5//dd/ba2trVZeXm5z5861p59+2i6++OJ8dksIIYQQISGvE5X7778/n4cXQgghRMjJ+2JaIYQQQoj9oYmKEEIIIUKLJipCCCGECC2h05PF0WPu5N1Qe23jJGxY413lXrAdV9H3rK2BWhH54uDdl6D1EiWL92tWo7nDiA9iu1QXBvXt+gjaK2WrvCvpWbBgugLn8pki1INiJLyvfAdaL/7AQDNuJDkJHBczZmIZHGvZdu/1YmFzjLLtaDMN1qIJwayEeD+aRelKNBUK23DbWAsaAsz8yJbifRft9R6DWSQsbI2NIUesBxYiaCRwziUBeSwgMNq6B/dH8NsgbDu6L2LusPHnZjXg/ojNRO2YTjJ+EqTHrmuQvrHzxsIhIym0wKgdQ7bloYHEXDpE+4rBQhqjxMhilhIdAwsvJDBLjYU8BrleZmbFvd5rlq3Ba0jNON+1dnPBpx/6REUIIYQQoUUTFSGEEEKEFk1UhBBCCBFaNFERQgghRGjRREUIIYQQoUXWzyhm/e46qC08eTPU1j55guf1vtNxxTgj2UMyNjLE0ukg+S8oEVm2CGvxAdKuEPdX8WIB1Co3eY2OTHEM2qQrsNZfh2Oo2ILWT994HH/Fm8EsgkQ/PprMwMka9i/qM4HiO0meSinuK95B8n/IMdOT0bYo3IlZLCzDx5/NY2bWN3881Eo2oNGSInkv6VnePJkkRgTRsUYb38K+4aaWqa/A/dWjqZFqbCNbI8zUYNkxkR0+I48ZKcyYIfkvbhKvA8Mh5pIxm8nft/0cN1COTQteh0gKnxuaCsNMIJbrQ2DHYLiT8d5kffG/4zDDh2UJOd0km2r2VNyWmFBBzx07RmTqxED7c2ZhO78dFCP5VYENuoDoExUhhBBChBZNVIQQQggRWjRREUIIIURo0URFCCGEEKFFExUhhBBChBZZP6OYHLFyXn/8BKil63x5Mplg89viVlwfPzAHDQdrQ9+CGT4FnVjrmYLHKGpDK2cII1AsXeHNBWmfj+MqacbtSndhvs4QyQRivPUXaIz0NeAYJj0dzKxKdeH5jA57+8eyOKJptAi6TkGbJ9GPY2Uw6yNG7JiBGahzMcOHWQORNOY1JVdt8bweXjAd2iQ6MNeHtWNZSkXrUSOi+S/MVGGGD8kiYoBtQqwPui9ivbBcI5oTRMwi2rf6sVBziPkRJZYWy5iB/ZMcHgoZK8uIopYSs35Y7c1tWCNWjt/yoRk+DHIuA1/roCZYwPswMxfHRU1A33li1zkzpRZq6SqveZnNDJk14u4Z+kRFCCGEEKFFExUhhBBChBZNVIQQQggRWjRREUIIIURo0URFCCGEEKFF1s8oJtWGl79/Kq44L2zytnOIHZGegCv1cx/qglrZ02h9ZEnEyHAZ1nreh8E+1b/CjVlOUJTIRn7Lp7IR7ZuS3Tguv1VjZrZvJvbDIRKBQ564MWtokgnADJ94L/bPv7reSaIFZZaASvkbPVBrvqwCanV/xH70nDsN+9aPlk4sg+eubw5mwhS2EVOF2AWOz45hhg/bDkdvZjV4DXP1aEJF0sQsYYYLwZ9NZGaW2rEXav5sF2ZpMMOFQXNimAlCzC26P1Ykpg67q8FmCmjV0DGQc0Jtphq8hizXiN0n0TGoCzIPzp+d45LcHApp5w5hJk6E9IMZPsPEtGPPRITYV7EeclxmG/msH2b4xFZuxH40eLPlsk7w7B99oiKEEEKI0KKJihBCCCFCiyYqQgghhAgtmqgIIYQQIrRooiKEEEKI0CLrZxTDsnPGLMRV8291eFdrOwlcz1/ciIrL8HrUb/qn4pr5wlacL8dxobqVEsOHUbEFDYGOuXir+3N8Cttxu30zU1BLdeEYynfgCvYgRo6ZWcc8dFAqV6IJwkyFvumoR5Wt9ZoEPadgnkiU2DfMrGC2FKP0DewvyxhyknitE8wOasH9DZxUD7XCFzd5CyQ7hVkJLOvInxtkRiwV4+eJGThuCjOsmDEWBGYfxXa0YUOSfxMYsi0zS5xSfCbY9aL4cnciPhPEzMxlWTfDAW9ENoYkPl8s/yhIDpFZwAwjkhvk1KHdRp85ZtqQvuU2YoZRsoUYaSQTKGhf2PX3m0oJln1Frqv52zkBM51Mn6gIIYQQIsRooiKEEEKI0KKJihBCCCFCiyYqQgghhAgtmqgIIYQQIrTI+hnFDL2vF2oDr+BqbbB8JuCK+eFMEdSYVZTqwLlxxRY0IXomBZtD5xKYPMKyiBj75nhtk2wh2gFJssA/0R/M3Bgch5bOwBh85Ep34f7Sk9HyyBQTi4TYO5n6Cu8xa/F8sHNUvBlKVtyK+2eZIG4SxxXvQHthcBJaCSz/h1kORZs7oJZeMN3zmp2jop1oJTBzIzMXM2aYpcSyedj4qZUzGbN+mJXit1xinWi9uCSvh2XHMGiejC+v5u2+BRsXs0jYOY6Q3B1owwwfYpa4JF+I7T+3cRPU4tOm4DGYMcVMIFbzb0usH2YasWsfNA8qHtDwYuYONXzItWZE/Vk/sxqwb+TZd3z3fi47ZEaimegxgzUTQgghhDj6aKIihBBCiNCiiYoQQgghQosmKkIIIYQILXmdqCxdutROP/10Ky0ttdraWrvmmmussbExn10SQgghRIjIq/Xz3HPP2eLFi+3000+3bDZr//AP/2CXXHKJvfHGG1ZcTDIGxGFlYtU+qHU0l0Kt533e1erRbWj4OFMHoJYdxHblW4mlUozmDrNtGFlym+ytw/k3M5AKOr2GSFE79q24CVev7zkNzxF7lBL9mIkUy2CtpBkNjMFazFPJkUX+TgDriY0raDZPUTsaGJkatJlYrtFQHe4vtXcIak4Kzx0zCTpPwuOOXeE1gRLEXMiV4bmM9uHJTLR0QY3ZJswOibJ2hGjjLqjRLBZfdhCzNKjhQ7KOWDZPtJdYP8S2CZqxE21FfYNdw8T29gPui+YaEcPH7UZrEZ8Gs3gD5jUZ2x/NNcK+RNKYTQXXh1wbZiRFOvE9OEVqzHDKEkst/vo2PAZUjFpJrC/suK7vHks0NkMbRqzXe0zXwXtwf+R1ovLUU095Xi9btsxqa2vt1Vdftfe973156pUQQgghwkKovkelu/vtX6Orqrhrn06nLZ3+0yysp6fnqPRLCCGEEPkhNItpHcexW265xc4++2w76aSTaJulS5daeXn5yM+ECROOci+FEEIIcTQJzURl8eLFtn79env44Yf322bJkiXW3d098tPU1HQUeyiEEEKIo00o/vTz2c9+1p544gl7/vnnraEBF2C9QyqVslQKF4YJIYQQ4vgkrxMV13Xtb//2b+3RRx+1FStW2JQpJH9BHDG6l+Gfzvadj6u8Y01e22LyQjQXdu3F3BFm+PROJEbO+zDDZW8nmhBVzxVArWITWgkd8zA/Y7gMSlArIkJC/4Rg9lmUxH9EieGz51Rcgx/N4OQ7RvaXKcJzV7Ibxz9Y632sy7ajacNIV+J5K97cBbW9p1dDreoVNDAKgtgRZhbrJMYMsQ3GNZKT4t8/M3KI4UNtHmLfDMyfDLWCVjTB3BRmDMVaiFlFTBiWnRTZsdv7mhgjDsnrcViuDbNeiPXBzjlrR02QyXiM2MqNuL9yrzFHM4KIGcWuTbQBc8mC2jzUcCLWS4zYgjQTyH9MYl8Z2T/dFzNyiGhIDR+WpcTOCckYYvcmPYbv3LFryPA/+24u+PQjrxOVxYsX20MPPWSPP/64lZaWWlvb22FX5eXlVliIKqIQQgghRhd5XaNy7733Wnd3t51//vlWV1c38vOTn/wkn90SQgghREjI+59+hBBCCCH2R2isHyGEEEIIP5qoCCGEECK0hEJPFvmhdyJJgcjg3NUZ57UL2h+bCG2yDfhnPAcXllP7puAXNVCLXoTZQSwnZ+cHsL/j/oC2Ufc0bBf3RhjZ3hPI2MkYoiT+pHQXGz+e30lP4wr8RMcg1DoWVECtZlUX1FieDssY8tN7ItoBhe0kc2hSOdQq3kRjhlkUw/WoKqQa26CWnjUOaiw7iFk0fkMiU18BTZwkufa9aJEw+8hJEkurD6+XdWJ/WWYNsyicWfg8RXzWiEMyjKLleH6jhjV/bpAZz+Zxq9Hcc7eRbKICkh3UScK5SP+YvQP7J23YuaR5RcTmcrehuhNhxhCBGU40O8dn77DnITcZ73N6T5MxsNykaDW5h9n+hskYUNIzI1lf7N70Pyf0eSDkfP11ssGXfugTFSGEEEKEFk1UhBBCCBFaNFERQgghRGjRREUIIYQQoUUTFSGEEEKEFlk/o5jBqbgavHAb5kzEF3hXeWcLMXMnV4SmzWAN5p+UofRgA2QBftErRVDrPAlXiZdvwmOkK3B/zNTx16IYQ2OFe3BcLHOnvw5dgIotZNti7G8ugavtKzfhSnqWJ5OuJOd4qzeLxknhY84Mn2gaT0DRdgxAYgYGI9lC1AKSY5LagaZC3xzMDyk0PK7fQGC5OXGSL+QSi4ZRsoHYMSQnxUiNWSnDC6ZDjY3ff4xMDUaKJIeJpcPsIGL4MJjhEx2DuU4uy0lisBwbv0XT3IptiC0U24G2GINZOsyOohDbyK3D8x4JeD6DwJ4lZp8lWrqC7ZAYPtRcItcm0diM2zITrMN7v9IcqpLDG4GjT1SEEEIIEVo0URFCCCFEaNFERQghhBChRRMVIYQQQoQWTVSEEEIIEVpk/YxiYh1oKnzwf/4ear/893M9r4fGoX0TG8A5b+/UHNSqp+yDWhHJ+umZCiUr245mTdA8oaqNaH70jffe/qkutHTK3+jB7abjAWpWBWvnJILlCfWNx2L5DrRIWP/8OUG1T++ENgMn1UONdMPSpF1q7xC2m4wr/1mGkVOKOTHMckjtI5oWgRo4QbYjBpURmcWfT2K2n0wgYjmw/JvoMBpeYMKYmeM7LstIYjgBjSxm5LjzZkAtQ4yx+OvkRAW0Tfyw3CDaN2Ya+fKQzMys5a0DHtPM6DlnRhrNMGI2k3/85HzYe8jmYcd09mCGEbuG7BiZKbXYjhhzEWLMAeTaRJgF58sEch083v7QJypCCCGECC2aqAghhBAitGiiIoQQQojQoomKEEIIIUKLJipCCCGECC2yfkYxzKJ5eONpUMvN8q38zuB2sWpcwZ3ahHk9fZ1o+BSRRfQlGDthA+Ow5pA7mG3rN3zM0PJJV+C8nZk7OdJflsXCKG7qh1q6CrOTSvrRmAqaE1TgH9csPHGFO9FmYEaOUxnMqhmoxXYJ0t/CNpJhRAwBZiAwUyfS6z2fbnX5AduYmcVIzS3Fc8kyZnrOnQa1speboOaQvtDMFmJ5sJygIPuPNmJejzN5/AH3ZcZtplhAOyY3Ge8xms/js1eYucLykJKrtuC+gsIyfMg9FxkOZppRK8cPsXQiaXym2T1nLcyiIUYSyWFKl2K7GOlv/HUMXWOZPczoAeeT9G24HvOVwFxzApzH/0afqAghhBAitGiiIoQQQojQoomKEEIIIUKLJipCCCGECC2aqAghhBAitMj6GcX0vG8AavEEWZk+eOD5bHQbGj7pcZgTESnEWjSD1stwGeYJJXvQNsrOxDFkO7EvjMpN3lX+sWE0AYaICZQYwEygeG+wFewOyU5h9EzBvrAsoswY7F/1ul7Pa2aQDE5CYyRdiVZNoh+P2T8BTYWSZjwGM3eG6ohZQ0yF1Dpir9SNgVr7pZM8r8f8sQPaMLOCGk7k2iSICVKyBfOVGP5sEzPjuT7dvVDLzWrw9oNYSnT/xMhx3ySGB8vYYXYMsT6cWROxL8w2GsLrH22o874m5grLiIqQcRkzt0j+T45ca9bfbDeacLFyYpEFOE9OUAuKwcZKYMZUktzrbnUlbsvOCXmfcMkzR+87H+wa5nw5VLnskNnuA+7q7WMGayaEEEIIcfTRREUIIYQQoUUTFSGEEEKEFk1UhBBCCBFaNFERQgghRGiR9TOKcd9C2yZdiJZHYUcA64fEZIw9fQ/Uep+og9oALtS3XBH2w3rQSkluQMOnfyJuW7MajaHd7/OufC/ARfRWtoNYSsNoJLG8nraFaO6Ub8VtS3ajCVKyG4+brsDx+w0fM7PBcb7cIf9r45k7feMxn6P8DTRcWP5RNI39ZflHLGMo0rkPaiw/hFGzqsvzmtkMNMOmE2/YSEmwvKZoJ8lJIrk7FHYMltnjO5/MvrFSfHC4bYKbMoPKhomlh63o+WS2TZRdV/84WIZNK75vZHwWlBnPTWJ5PbEWYgdhzyw670TcH7vWxLax2VN9x9yLbQLe04GyhIwbU0bOObvWtH/suMQYym7d7nkdOePkQPvvm+/NnMqSzLj9oU9UhBBCCBFaNFERQgghRGjRREUIIYQQoUUTFSGEEEKElrxOVJ5//nm76qqrrL6+3iKRiD322GP57I4QQgghQkZerZ/+/n6bN2+e3XTTTfbBD34wn10ZlRTvwnnqcDnW4r44HQdlFhusI/k3xPDpa8D19m4Sa+Wb0HCJ4+J9G8BDWOk2HENhO9oAuYR3ID1ToYmV7cBathj7Fs3g+JlFVPUKFvtnVEAtU4xjYHYQGD5mVrLBa03sOwONjBTJtfEbNGZmEWKCsKwbN4XnJLWDmAUMZkMENR98uSMuyWti5Koxr4Vl/bBxRdKYh0VNIGKHUFOD4M8nCpqvEyX2DYP1l5pFNVVYI+2Yv5EjBpI/T4YZXyybhhk+LDcp6H2TI7k+9ho5J8xoYVk85HwCbDuW/UQsHXYfsmwepxSvF7W0SCbQcD1er1Qj5hPFTpjp3Rd5HtKzcF/Fm7s8r7M57P/+yOtE5fLLL7fLL788n10QQgghRIg5pr5HJZ1OWzr9p1lYT0+wFFMhhBBCHJscU4tply5dauXl5SM/EyZMyHeXhBBCCHEEOaYmKkuWLLHu7u6Rn6ampnx3SQghhBBHkGPqTz+pVMpSKfyKbCGEEEIcnxxTExVxeEle2AG1gR7Mzsn4MoEckgcU68ZV6Q65u5jhkyJZQiz/p+gtrA3PGYBa/BUcQ994tEGGaryvq9fjuPrG4yCiRCwo6MJaxSY0jYbrMU+HUUQsJQbLHcrUVxxwX5liMi6S18NyfXJEXmCZQMysYTDbhlkeQ3VoKqT2eu+7bCl2LpfA+6uglShkpB/MrGDXMEXyadx5M/AYxMBg+DNrWF4NM3KcbWgCRcqxv8zwiZD9+XNdzMzcc0+FWrwD+0ctqpXrvNtNmwJtrIU86MyYYbYYqTH7KlYeLJspsgOtF2YlRXznk7Vh44qU4jPiN9nMzHIpbEdzjUiGFbW0yHFT6/DeCWIgMQsuQ8zIRNJ7P7g53G5/5HWi0tfXZ1u2bBl5vX37dlu7dq1VVVXZxImo4wkhhBBidJHXicqqVavsggsuGHl96623mpnZDTfcYMuWLctTr4QQQggRFvI6UTn//PPNdVngthBCCCHEMWb9CCGEEGJ0oYmKEEIIIUKLJipCCCGECC3Sk0cxPWtrsJjANUOxDIsc81K2Hduw8MLal7HWdjH6vpWvoGbY14DbFhEVOY52nznEZCzd5dWRE/2oJ3eehHP52tXYjim7LEQx0UE6ZxgsyAL9+uagKugQ9TbmC0gcqMULwcYaa8FjpojuG+/F6xU8NA2/Byn++jaoDSycCbXU3iGoRV7b7HmdmI3JkiymkIUtRtM4BgbTnamOyrYlyjY776APk7BBdi4jJTh+5008v9EGTPN0SvA+jNfiPWeNzVBySbheYpgEGvp0ZLcDxz68YDrUqDpbTRRjNlYSBBlUz3aaW3FbErYYGz6wih8hijULB2RBjTEWGEkUY3Y+2b3jJtnXE+A1ZOGVflWc3Tf+YNS3j+l9EiM5fAb3hz5REUIIIURo0URFCCGEEKFFExUhhBBChBZNVIQQQggRWjRREUIIIURokfUziik7BUMJ+1ahCVTgy/Tqa0DDZ98puIK7fAPeXu1nYj9K1+Nq+EQ/2kfxQTxukixKH0ChgeI3ZlL7MCSLBSEyYiSosGcK801wlX/HPGxXUYEr9eP92L+Sl7bicc+d5t3XkxuhDTMr9p07AWplW1FdYsF/iRc2QM0K0EqJEbOCheux0MBMDdoFSd8xIiTQrecUknBJKGxHS4lRtL4lULsYCY3zB0aamTlTaqEW78D7xA8Lg6OBdiQckcXBMfuIhfwFNWaMWCRsWz/MjGNWFTNS2Fgddp7YgUl/I+QednzBimYGNhMLDGRjYOZWDDMUqeHDwgYD22HknDDSs9Bw8l8fds+x+zyxvd3bByfY82amT1SEEEIIEWI0URFCCCFEaNFERQghhBChRRMVIYQQQoQWTVSEEEIIEVpk/YximOGTKccMmMo3/RWc33aT2I2+hQNQczO47cAwrkrvPQlXzZevRQMhS+SI8q04BieBxpDfLPJn5JiZlezGWlBYNlHZdqxlMa6Ikq7ELBrnL6ZALeobR9cVJ0AbZjhVrCXWByFXVQC1ODNLUvj2EuvBlf40d6cVs0KSw2hN9F7kHVvpGziGki09UGMM1eHNVED6y/JZWHYKG5fffDDjmTX+vBeWpWOTx2ONmCAOMzBaunBbxjAxYUhmDct7iZDcGbCDmGlDrn1mFj5MCZKJE23ETKAIM2YCwmymOKn5c2zcbdgPIzlULA+LZvgk0Qxk42L3IbW5GOS6Unx5Ssy0Yve5/7lxc8GnH/pERQghhBChRRMVIYQQQoQWTVSEEEIIEVo0URFCCCFEaDmmF9O67tuLIXt6gi2WE15yQ0NQc1K4eDTrW9eaS+P81hnEhYOROC6Uc7JkbjyEX5fvDOJi2lw62MLWLFkU67q4mDaS8S2m9Q/UzLKZYF81zXCGcKzZDBnXEB6DjSGHQ7AIaYf7wn7EyLiyuWBfaZ3N4H0Ty5J7KYZvL26OLJ7MkXvHIV+/Tvrn7wsbg5sLdg2zGVyszPbH+suOwceF+3PIMaK+8btusPPh387MLEuuDf36crKtOXi/MtgYIqTPcNyAx+RjIOckyDEPBgffmxhuzvscsn5YwHspQsbv3//+2jk5fNajQcdPxkrPu29sLmnjkmP6F8++82y98+/4uxFxg7QKKc3NzTZhAuaTCCGEECL8NDU1WUMDUST/jGN6ouI4jrW0tFhpaalFIuTXzcNAT0+PTZgwwZqamqysrOyIHCOfaHzHLsfz2Mw0vmMdje/Y5WiMzXVd6+3ttfr6eotG330VyjH9p59oNHrAmdjhoqys7Li7Gf8cje/Y5Xgem5nGd6yj8R27HOmxlZeTL+AiaDGtEEIIIUKLJipCCCGECC2aqByAVCplt99+u6VS+DXvxwMa37HL8Tw2M43vWEfjO3YJ29iO6cW0QgghhDi+0ScqQgghhAgtmqgIIYQQIrRooiKEEEKI0KKJihBCCCFCy6ibqNxzzz02efJkKygosDPPPNNWrlz5ru1/+tOf2uzZs62goMBOPvlke/LJJz3/3XVd+/KXv2x1dXVWWFhoF110kW3evPlIDuFdOZjx/eAHP7Bzzz3XKisrrbKy0i666CJof+ONN1okEvH8XHbZZUd6GPvlYMa3bNky6HtBQYGnzbF8/c4//3wYXyQSsSuvvHKkTViu3/PPP29XXXWV1dfXWyQSsccee+yA26xYscLmz59vqVTKpk+fbsuWLYM2B/s8HykOdny/+MUv7OKLL7YxY8ZYWVmZLVy40J5++mlPm6985Stw7WbPnn0ER7F/DnZ8K1asoPdmW1ubp92xev3YcxWJRGzOnDkjbcJy/ZYuXWqnn366lZaWWm1trV1zzTXW2Nh4wO3C9G/fqJqo/OQnP7Fbb73Vbr/9dlu9erXNmzfPLr30Umtvb6ft//jHP9qHP/xh+/jHP25r1qyxa665xq655hpbv379SJt/+qd/sn/5l3+x733ve/byyy9bcXGxXXrppTZEAv+ONAc7vhUrVtiHP/xhe/bZZ+3FF1+0CRMm2CWXXGK7d+/2tLvsssustbV15Gf58uVHYzjAwY7P7O1vVvzzvu/cudPz34/l6/eLX/zCM7b169dbLBaza6+91tMuDNevv7/f5s2bZ/fcc0+g9tu3b7crr7zSLrjgAlu7dq3dcsstdvPNN3v+MT+U++FIcbDje/755+3iiy+2J5980l599VW74IIL7KqrrrI1a9Z42s2ZM8dz7V544YUj0f0DcrDje4fGxkZP/2tra0f+27F8/b7zne94xtXU1GRVVVXw7IXh+j333HO2ePFie+mll+w3v/mNZTIZu+SSS6y/v3+/24Tu3z53FHHGGWe4ixcvHnmdy+Xc+vp6d+nSpbT9X/3VX7lXXnmlp3bmmWe6n/rUp1zXdV3Hcdxx48a5//f//t+R/97V1eWmUil3+fLlR2AE787Bjs9PNpt1S0tL3QcffHCkdsMNN7hXX3314e7qIXGw43vggQfc8vLy/e7veLt+3/72t93S0lK3r69vpBam6/cOZuY++uij79rmi1/8ojtnzhxP7UMf+pB76aWXjrx+r+frSBFkfIwTTzzRveOOO0Ze33777e68efMOX8cOE0HG9+yzz7pm5u7bt2+/bY6n6/foo4+6kUjE3bFjx0gtrNevvb3dNTP3ueee22+bsP3bN2o+URkeHrZXX33VLrroopFaNBq1iy66yF588UW6zYsvvuhpb2Z26aWXjrTfvn27tbW1edqUl5fbmWeeud99HikOZXx+BgYGLJPJWFVVlae+YsUKq62ttVmzZtlnPvMZ6+zsPKx9D8Khjq+vr88mTZpkEyZMsKuvvto2bNgw8t+Ot+t3//3323XXXWfFxcWeehiu38FyoGfvcJyvMOE4jvX29sKzt3nzZquvr7epU6faokWLbNeuXXnq4aFxyimnWF1dnV188cX2hz/8YaR+vF2/+++/3y666CKbNGmSpx7G69fd3W1mBvfanxO2f/tGzUSlo6PDcrmcjR071lMfO3Ys/N30Hdra2t61/Tv/ezD7PFIcyvj8/P3f/73V19d7br7LLrvM/v3f/91++9vf2l133WXPPfecXX755ZbL5Q5r/w/EoYxv1qxZ9sMf/tAef/xx+9GPfmSO49hZZ51lzc3NZnZ8Xb+VK1fa+vXr7eabb/bUw3L9Dpb9PXs9PT02ODh4WO73MPHNb37T+vr67K/+6q9GameeeaYtW7bMnnrqKbv33ntt+/btdu6551pvb28eexqMuro6+973vmc///nP7ec//7lNmDDBzj//fFu9erWZHZ73q7DQ0tJiv/rVr+DZC+P1cxzHbrnlFjv77LPtpJNO2m+7sP3bd0ynJ4vDx5133mkPP/ywrVixwrPg9Lrrrhv5/yeffLLNnTvXpk2bZitWrLALL7wwH10NzMKFC23hwoUjr8866yw74YQT7L777rOvfvWreezZ4ef++++3k08+2c444wxP/Vi+fqOFhx56yO644w57/PHHPWs4Lr/88pH/P3fuXDvzzDNt0qRJ9sgjj9jHP/7xfHQ1MLNmzbJZs2aNvD7rrLNs69at9u1vf9v+4z/+I489O/w8+OCDVlFRYddcc42nHsbrt3jxYlu/fn3e1jodKqPmE5WamhqLxWL21ltveepvvfWWjRs3jm4zbty4d23/zv8ezD6PFIcyvnf45je/aXfeeaf9+te/trlz575r26lTp1pNTY1t2bLlPff5YHgv43uHRCJhp5566kjfj5fr19/fbw8//HCgN798Xb+DZX/PXllZmRUWFh6W+yEMPPzww3bzzTfbI488Ah+1+6moqLCZM2eG/trtjzPOOGOk78fL9XNd1374wx/aRz/6UUsmk+/aNt/X77Of/aw98cQT9uyzz1pDQ8O7tg3bv32jZqKSTCbttNNOs9/+9rcjNcdx7Le//a3nt+4/Z+HChZ72Zma/+c1vRtpPmTLFxo0b52nT09NjL7/88n73eaQ4lPGZvb1y+6tf/ao99dRTtmDBggMep7m52To7O62uru6w9Dsohzq+PyeXy9m6detG+n48XD+ztzXCdDpt119//QGPk6/rd7Ac6Nk7HPdDvlm+fLl97GMfs+XLl3uU8v3R19dnW7duDf212x9r164d6fvxcP3M3jZqtmzZEuiXhHxdP9d17bOf/aw9+uij9rvf/c6mTJlywG1C92/fYV+eG2IefvhhN5VKucuWLXPfeOMN95Of/KRbUVHhtrW1ua7ruh/96Efd2267baT9H/7wBzcej7vf/OY33Y0bN7q33367m0gk3HXr1o20ufPOO92Kigr38ccfd19//XX36quvdqdMmeIODg6Gfnx33nmnm0wm3Z/97Gdua2vryE9vb6/ruq7b29vrfv7zn3dffPFFd/v27e4zzzzjzp8/350xY4Y7NDQU+vHdcccd7tNPP+1u3brVffXVV93rrrvOLSgocDds2DDS5li+fu9wzjnnuB/60IegHqbr19vb665Zs8Zds2aNa2but771LXfNmjXuzp07Xdd13dtuu8396Ec/OtJ+27ZtblFRkfuFL3zB3bhxo3vPPfe4sVjMfeqpp0baHOh8hXl8P/7xj914PO7ec889nmevq6trpM3//t//212xYoW7fft29w9/+IN70UUXuTU1NW57e3vox/ftb3/bfeyxx9zNmze769atcz/3uc+50WjUfeaZZ0baHMvX7x2uv/5698wzz6T7DMv1+8xnPuOWl5e7K1as8NxrAwMDI23C/m/fqJqouK7rfve733UnTpzoJpNJ94wzznBfeumlkf923nnnuTfccIOn/SOPPOLOnDnTTSaT7pw5c9z/+q//8vx3x3Hcf/zHf3THjh3rplIp98ILL3QbGxuPxlAoBzO+SZMmuWYGP7fffrvruq47MDDgXnLJJe6YMWPcRCLhTpo0yf3EJz6RlzeSdziY8d1yyy0jbceOHeteccUV7urVqz37O5avn+u67ptvvumamfvrX/8a9hWm6/eOrur/eWc8N9xwg3veeefBNqeccoqbTCbdqVOnug888ADs993O19HkYMd33nnnvWt7131bx66rq3OTyaQ7fvx490Mf+pC7ZcuWozuw/+Zgx3fXXXe506ZNcwsKCtyqqir3/PPPd3/3u9/Bfo/V6+e6b+u4hYWF7ve//326z7BcPzYuM/M8T2H/ty/y3wMRQgghhAgdo2aNihBCCCGOPTRREUIIIURo0URFCCGEEKFFExUhhBBChBZNVIQQQggRWjRREUIIIURo0URFCCGEEKFFExUhxBHnxhtvtEgkYpFIxB577LHA261YscIikYh1dXUd1v6cf/75I/1Zu3btYd23EOLwoomKEOKgufHGGyEt9kBcdtll1tra6kmVzRe/+MUvbOXKlfnuhhAiAPF8d0AIMTpIpVIHTFbNZDKWSCSOeF+qqqqsp6fniB9HCPHe0ScqQoi8sGPHDotEIvaTn/zEzjvvPCsoKLAf//jHh7SPP//zTVdXl0UiEVuxYsXh7bAQIi/oExUhRF657bbb7J//+Z/t1FNPtYKCgnx3RwgRMjRREULklVtuucU++MEP5rsbQoiQoj/9CCHyyoIFC/LdBSFEiNFERQiRV4qLiw9522j07bcw13VHaplM5j33SQgRHjRREUIcs4wZM8bMzFpbW0dq+l4UIY4vNFERQhwS3d3dtnbtWs9PU1PTe9rno48+arNnz97vf9+9e7fNnj175DtQCgsL7S/+4i/szjvvtI0bN9pzzz1nX/rSl2C72bNn26OPPvqe+iaEyA+aqAghDokVK1bYqaee6vm544473tM+u7u7rbGxcb//PZPJWGNjow0MDIzUfvjDH1o2m7XTTjvNbrnlFvva174G2zU2Nlp3d/d76psQIj9E3D//464QQhwBbrzxRuvq6jqor88/0uzYscOmTJlia9assVNOOSXf3RFC7Ad9oiKEOCo88cQTVlJSYk888US+u2KXX365zZkzJ9/dEEIEQJ+oCCGOOO3t7SNfWV9XV/eeTJ/Dwe7du21wcNDMzCZOnGjJZDKv/RFC7B9NVIQQQggRWvSnHyGEEEKEFk1UhBBCCBFaNFERQgghRGjRREUIIYQQoUUTFSGEEEKEFk1UhBBCCBFaNFERQgghRGjRREUIIYQQoUUTFSGEEEKElv8PIUtUnrgU/fYAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots()\n", "\n", "h_value = 0.25\n", "HIdx = np.argmin(np.abs(h-h_value))\n", "ax.pcolormesh(L[:, HIdx, :],E[:, HIdx, :],I[:, HIdx, :], vmin=0, vmax=50)\n", "\n", "ax.set_xlabel('L [r.l.u.]')\n", "ax.set_ylabel('E [meV]')\n" ] }, { "cell_type": "markdown", "id": "2f7f37f2", "metadata": {}, "source": [ "## Run denoising algorithm" ] }, { "cell_type": "code", "execution_count": 42, "id": "3a58ab49", "metadata": {}, "outputs": [], "source": [ "# Initialize the AMBER background object\n", "\n", "AMBER = background(dtype=np.float32)\n", "\n", "# Set the grid sizes\n", "AMBER.set_gridcell_size(dqx = 0.022, dqy = 0.022, dE = 0.125)\n", "\n", "# Alternatively these can be set from the h, l, and e arrays like\n", "# AMBER.set_volume_from_limits([h[0],l[0],e[0]],[h[-1],l[-1],e[-1]],)\n", "\n", "# Input the data\n", "AMBER.set_binned_data(h, l, e, I)\n", "\n", "bins = int((q.max()-q.min())/0.022)\n", "\n", "# define maximum radius and number of bins\n", "AMBER.set_radial_bins(q.max(),n_bins=bins)" ] }, { "cell_type": "markdown", "id": "cfb11870", "metadata": {}, "source": [ "### Set algorithm parameters ($\\lambda, \\beta, \\mu$)\n", "\n", "lambda and mu will be determined as described in the paper and we select beta using cross validation\n", "\n", "beta_ is selected using cross validation, i.e. mask out the top q quantile of intensity. The beta value for the lowest Root-Mean-Square-Error is then chosen" ] }, { "cell_type": "code", "execution_count": 15, "id": "60e2fbf2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test - ( 0.1 )\n", "RMSE - ( 4.4478 0.1 55.50635141061015 ) : 3.4844415\n", "Test - ( 1.0 )\n", "RMSE - ( 4.4478 1.0 55.50635141061015 ) : 3.4835355\n", "Test - ( 10.0 )\n", "RMSE - ( 4.4478 10.0 55.50635141061015 ) : 3.4762373\n", "Test - ( 100.0 )\n", "RMSE - ( 4.4478 100.0 55.50635141061015 ) : 3.4504814\n", "Test - ( 200.0 )\n", "RMSE - ( 4.4478 200.0 55.50635141061015 ) : 3.443771\n", "Test - ( 300.0 )\n", "RMSE - ( 4.4478 300.0 55.50635141061015 ) : 3.4432797\n", "Test - ( 400.0 )\n", "RMSE - ( 4.4478 400.0 55.50635141061015 ) : 3.4455476\n", "Test - ( 500.0 )\n", "RMSE - ( 4.4478 500.0 55.50635141061015 ) : 3.44926\n" ] } ], "source": [ "\n", "lambda_tmp = AMBER.MAD_lambda()\n", "mu_tmp = AMBER.mu_estimator()\n", "\n", "\n", "beta_range_tmp = np.array([0.1,1.0,10.0,100.0,200.0,300.0,400.0,500.0])\n", "rmse = AMBER.cross_validation(q=0.3,beta_range= beta_range_tmp, lambda_=lambda_tmp, mu_=mu_tmp,n_epochs=15,verbose=False)\n", "\n", "\n", "beta_tmp = beta_range_tmp[np.argmin(rmse)]" ] }, { "cell_type": "markdown", "id": "e81bce2a", "metadata": {}, "source": [ "### Run the denoising algorithm using the parameters obtained using the heuristic" ] }, { "cell_type": "code", "execution_count": 18, "id": "1f9a8679", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Iteration 1\n", " Loss function: 24608190.0\n", " Iteration 2\n", " Loss function: 18320822.0\n", " Iteration 3\n", " Loss function: 15589522.0\n", " Iteration 4\n", " Loss function: 14332520.0\n", " Iteration 5\n", " Loss function: 13731277.0\n", " Iteration 6\n", " Loss function: 13459373.0\n", " Iteration 7\n", " Loss function: 13346343.0\n", " Iteration 8\n", " Loss function: 13302241.0\n", " Iteration 9\n", " Loss function: 13285422.0\n", " Iteration 10\n", " Loss function: 13278990.0\n", " Iteration 11\n", " Loss function: 13276512.0\n", " Iteration 12\n", " Loss function: 13275553.0\n", " Iteration 13\n", " Loss function: 13275184.0\n", " Iteration 14\n", " Loss function: 13275036.0\n", " Iteration 15\n", " Loss function: 13274984.0\n", " Iteration 16\n", " Loss function: 13274959.0\n", " Iteration 17\n", " Loss function: 13274950.0\n", " Iteration 18\n", " Loss function: 13274948.0\n" ] } ], "source": [ "# set number of epochs\n", "n_epochs = 20\n", "\n", "AMBER.denoising(AMBER.Ygrid,lambda_tmp,beta_tmp,mu_tmp,n_epochs,verbose=True) " ] }, { "cell_type": "markdown", "id": "e909ad23", "metadata": {}, "source": [ "## Compute substracted signal" ] }, { "cell_type": "code", "execution_count": 33, "id": "15d23f11", "metadata": {}, "outputs": [], "source": [ "# The subtracted signal is given by\n", "\n", "Y_sub = AMBER.Ygrid - AMBER.b_grid\n", "Y_back = AMBER.b_grid\n", "\n", "# reshape the data to fit \n", "Y_sub = Y_sub.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n", "Y_back = Y_back.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n", "\n", "# reshape the observation\n", "Y_obs = AMBER.Ygrid.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T" ] }, { "cell_type": "markdown", "id": "bf1032b5", "metadata": {}, "source": [ "## Display substracted signal" ] }, { "cell_type": "code", "execution_count": 46, "id": "e9e74383", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# reshape the observation\n", "Y_obs = AMBER.Ygrid.reshape(AMBER.E_size,AMBER.Qx_size,AMBER.Qy_size).T\n", "\n", "\n", "fig0 = plt.figure(figsize=(20, 9))\n", "\n", "# Plot 1: Observations Y\n", "ax0 = fig0.add_subplot(1, 2, 1)\n", "\n", "energy = 4.5\n", "EIdx = np.argmin(np.abs(E-energy))\n", "ax0.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_obs[:,:,EIdx],vmin=-2,vmax=50)\n", "\n", "ax0.set_xlabel('H [r.l.u.]')\n", "ax0.set_ylabel('L [r.l.u.]')\n", "ax0.set_title('Observations')\n", "\n", "# Plot 2: Subtracted Y\n", "ax1 = fig0.add_subplot(1, 2, 2)\n", "\n", "energy = 4.5\n", "EIdx = np.argmin(np.abs(E-energy))\n", "p = ax1.pcolormesh(H[:,:,EIdx],L[:,:,EIdx],Y_sub[:,:,EIdx],vmin=-2,vmax=50)\n", "\n", "ax1.set_xlabel('H [r.l.u.]')\n", "ax1.set_ylabel('L [r.l.u.]')\n", "ax1.set_title('Subtracted signal')\n", "\n", "fig0.colorbar(p)\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 45, "id": "da0f7346", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig0 = plt.figure(figsize=(20, 9))\n", "\n", "# Plot 1: Observations Y\n", "ax0 = fig0.add_subplot(1, 2, 1)\n", "\n", "h_value = 0.25\n", "HIdx = np.argmin(np.abs(h-h_value))\n", "ax0.pcolormesh(L[:,HIdx,:],E[:,HIdx,:],Y_obs[:,HIdx,:],vmin=-2,vmax=50)\n", "\n", "ax0.set_xlabel('H [r.l.u.]')\n", "ax0.set_ylabel('L [r.l.u.]')\n", "ax0.set_title('Observations')\n", "\n", "# Plot 2: Subtracted Y\n", "ax1 = fig0.add_subplot(1, 2, 2)\n", "\n", "\n", "HIdx = np.argmin(np.abs(h-h_value))\n", "p = ax1.pcolormesh(L[:,HIdx,:],E[:,HIdx,:],Y_sub[:,HIdx,:],vmin=-2,vmax=50)\n", "\n", "ax1.set_xlabel('L [r.l.u.]')\n", "ax1.set_ylabel('E [meV]')\n", "ax1.set_title('Subtracted signal')\n", "\n", "fig0.colorbar(p)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "71d4ed7e-dec7-4c84-842a-d03030e42db3", "metadata": {}, "source": [ "## Compare the signal with the subtracted signal in a 1D cut" ] }, { "cell_type": "code", "execution_count": 41, "id": "12829c7d-fbfd-4e4e-b3a1-c759d83f834e", "metadata": {}, "outputs": [ { "data": { "image/png": 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AfPLJJ7juuuvw3HPP2Z4PAI4dO4bs7GyMGTMG33//PT788EN88803mDp1qu24kyZNwqlTp7B161Z89NFHeO2111BWVuby5zYajdiwYQOqqqoknSeTyYTRo0cjJiYG3377Lf79739jzpw5TvedM2cOZs6ciYKCAnTo0AHjxo1DXV0dAKCmpga9evXC+vXrcfDgQTz88MO4//77kZ+fL2kcihHITkVFhQBAqKioCPRQiIjIA5cuXRIKCwuFS5cueX6QHz4ThHkGQZgX1+CfwfLvh88UGq29jz76SGjatKkQHR0t9O/fX5g9e7bw3Xff2b5fVFQkABD2799v2/bbb78JAIStW7cKgiAIW7duFQAIK1eutO1TXl4uNGrUSPjwww8FQRCEZcuWCQaDweH5U1NThdGjR7sd5/PPPy/06tXL9nVGRoYwfvx40f1TU1OFpUuX2m2bPHmy8PDDD9tt+/rrrwW9Xi9cunRJOHLkiABAyM/Pt33/0KFDAgCHY9W3fft24brrrhMiIiKEm266SZg+fbrwzTffiI7nP//5jxAeHi4UFxfbvr9p0yYBgLBmzRpBEK6d9zfffNO2zw8//CAAEA4dOiQ6lhEjRghPPPGE7etbbrlFmDZtmtN9Xb1u5cQmnLEjIiKqz2wC8nIACE6+eXVb3iyf3JYdM2YMzpw5g7Vr1yI7Oxvbtm1Dz549sXz5ctnHysjIsP3vZs2aoWPHjjh06JDbx910000O2z788EMMGDAARqMRTZo0wdNPP42TJ0/avl9QUIDbbrtN1vi+++47LF++3DZT2aRJE2RlZcFsNqOoqAiHDh1CeHg4evXqZXtMp06d3CZ93HzzzTh+/Di2bNmCu+++Gz/88AMGDRqEBQsWON3/yJEjaNWqFYxGo22bdbaxoW7dutn+d3JyMgDYZhBNJhMWLFiArl27olmzZmjSpAk2btxod578gYEdERFRfSd2ApVnXOwgAJWnLfv5QHR0NIYOHYq5c+di586dmDRpEubNmwcA0Ostb9uCcC3obLjWzVuNGze2+3rXrl0YP348hg8fjs8//xz79+/HnDlzcPnyZds+jRo1kv08Fy5cwJ/+9CcUFBTY/n333Xc4evQo2rVr59XPEBERgUGDBiEnJwdffPEFnnvuOSxYsMBuzJ4e18q6VtBsNgMAnn/+ebz88svIycnB1q1bUVBQgKysLK+fUy4GdkRERPVdKFV2Py+lp6fbEgqs2ZzWdWoA7BIp6tu9e7ftf//222/48ccf0blzZwBAZGQkTCZpM447d+5Eamoq5syZg5tuugnt27fHiRMn7Pbp1q0btmzZInoMZ8/Xs2dPFBYW4vrrr3f4FxkZiU6dOqGurg579+61PebIkSMe1edLT09HXV0dampqHL7XsWNHnDp1CqWl136fe/bskf0cO3bswKhRo3Dfffehe/fuaNu2LX788UfZx/EWy534mcksIL/oHMqqapAYG40+ac0Qpte5fyAREflHkyRl95OovLwcv//97/Hggw+iW7duiI2NxX//+18sXrwYo0aNAmCZGevXrx9yc3ORlpaGsrIyPP30006P99xzz6F58+ZISkrCnDlz0KJFC4wePRqAJSv0woUL2LJlC7p3746YmBjExMQ4PU779u1x8uRJrFy5Er1798b69euxZs0au33mzZuH2267De3atcPYsWNRV1eHDRs2ICcnx/Z8X331FcaOHYuoqCi0aNECOTk56NevH6ZOnYo//vGPaNy4MQoLC7Fp0ya88sor6NixI7Kzs/GnP/0Jr7/+OsLDwzF9+nS3s4ODBw/GuHHjcNNNN6F58+YoLCzEX/7yFwwZMgRxcXEO+w8dOhTt2rXDxIkTsXjxYlRVVdnOaf0MXnfat2+Pjz76CDt37kTTpk2xZMkSlJaWIj09XfIxlMAZOz/KO1iMgYu+xLg3dmPaygKMe2M3Bi76EnkHi90/mIiI/CO1PxCXAkDsTV0HxLW07KegJk2aoG/fvli6dCluvvlmdOnSBXPnzsVDDz2EV155xbbf22+/jbq6OvTq1QvTp0/HX//6V6fHy83NxbRp09CrVy+UlJRg3bp1tpp0/fv3x//8z//g3nvvRUJCAhYvXiw6rjvuuAOPP/44pk6dih49emDnzp22bFmrwYMHY/Xq1Vi7di169OiBW2+91S4b9LnnnsPPP/+Mdu3a2WYdu3Xrhu3bt+PHH3/EoEGDcOONN+KZZ55BSkqK7XHLli1DSkoKbrnlFtx11114+OGHkZiY6PI8ZmVlYcWKFfjd736Hzp0747HHHkNWVhZWrVrldP+wsDB8+umnuHDhAnr37o0//vGPtqzY6Ohol89V39NPP42ePXsiKysLgwcPhtFotAXS/qQT6t+oJ1RWVsJgMKCiosJpZO+pvIPFeOTdfQ5Lca2Xjdfv64nsLsmKPR8RUaiqqalBUVER0tLSZL0x2ylcC6yacPWL+lfuq1fte94B0u/wZpikYjt27MDAgQPx008/eb3eTypXr1s5sQln7PzAZBYwf12hq/wqzF9XCJOZMTYRkSqk32EJ3uIafOCOS2FQF4TWrFmDTZs24eeff8bmzZvx8MMPY8CAAX4L6pTENXZ+kF90DsUVjgs2rQQAxRU1yC86h4x2zf03MCIiEpd+B9BphCX79UKpZU1dan9AHxbokZHCqqqqkJOTg5MnT6JFixbIzMzEiy++GOhheYSBnR+UVYkHdZ7sR0REfqIPA9IGBXoU5GMTJkzAhAkT3O+oAbwV6weJsdLWeEjdj4iIiMgZzQR2zz77rK15sPVfp06dbN+vqanBlClT0Lx5czRp0gRjxoyxq0kTSH3SmiHZEO0qvwrJBkvpEyIiIiJPaSawA4AbbrjB1kC4uLgY33zzje17jz/+ONatW4fVq1dj+/btOHPmDO66664AjvaaML0O80Za6tg0DO6sX88bmc56dkREROQVTa2xCw8Pt+vlZlVRUYG33noL77//Pm699VYAlto3nTt3xu7du9GvXz9/D9VBdpdkvH5fT8xfV2iXSGE0RGPeyHSWOiEiIiKvaSqwO3r0KFJSUhAdHY2MjAwsXLgQrVu3xt69e3HlyhVkZmba9u3UqRNat26NXbt2uQzsamtrUVtba/u6srLSZ+PP7pKMoelGdp4gIiIin9BMYNe3b18sX74cHTt2RHFxMebPn49Bgwbh4MGDKCkpQWRkJOLj4+0ek5SUhJKSEpfHXbhwIebPn+/DkdsL0+tY0oSIiIh8QjNr7IYNG4bf//736NatG7KysrBhwwacP39etEWIVLNnz0ZFRYXt36lTpxQaMRERkfq0adMGL730UqCHoajly5c7TO6EKs0Edg3Fx8ejQ4cO+Omnn2A0GnH58mWcP3/ebp/S0lKna/Lqi4qKQlxcnN0/IiKiQJg0aZJd9YfmzZsjOzsb33//faCHRhqh2cDuwoULOHbsGJKTk9GrVy9ERERgy5Yttu8fOXIEJ0+eREZGRgBHSUREJE92drat+sOWLVsQHh6O22+/PdDDcuny5cuBHgJdpZnAbubMmdi+fTt+/vln7Ny5E3feeSfCwsIwbtw4GAwGTJ48GTNmzMDWrVuxd+9ePPDAA8jIyFBFRiwREWmTyWzCnpI92HB8A/aU7IHJbPL5c0ZFRcFoNMJoNKJHjx6YNWsWTp06hbNnzwIAcnJy0KFDB8TExKBt27aYO3curly5YneMdevWoXfv3oiOjkaLFi1w5513ij7fm2++ifj4eNvkSFVVFcaPH4/GjRsjOTkZS5cuxeDBgzF9+nTbY9q0aYMFCxZgwoQJiIuLw8MPPwwA+Pjjj3HDDTcgKioKbdq0cWjLpdPp8Omnn9pti4+Px/LlywEAP//8M3Q6HT755BMMGTIEMTEx6N69O3bt2mX3mOXLl6N169aIiYnBnXfeifLycsnnN9hpJnnil19+wbhx41BeXo6EhAQMHDgQu3fvRkJCAgBg6dKl0Ov1GDNmDGpra5GVlYXXXnstwKMmIiKt2nxiM3Lzc1Fafa3YfVJMEmb1mYXM1EwXj1TOhQsX8O677+L6669H8+aWxLvY2FgsX74cKSkpOHDgAB566CHExsbiqaeeAgCsX78ed955J+bMmYN33nkHly9fxoYNG5wef/HixVi8eDG++OIL9OnTBwAwY8YM7NixA2vXrkVSUhKeeeYZ7Nu3Dz169LB77AsvvIBnnnkG8+bNAwDs3bsX99xzD5599lnce++92LlzJx599FE0b94ckyZNkvVzz5kzBy+88ALat2+POXPmYNy4cfjpp58QHh6Ob7/9FpMnT8bChQsxevRo5OXl2cZAAASyU1FRIQAQKioqAj0UIiLywKVLl4TCwkLh0qVLHh9j08+bhK7Luwpdlnex+9d1eVeh6/KuwqafNyk44msmTpwohIWFCY0bNxYaN24sABCSk5OFvXv3ij7m+eefF3r16mX7OiMjQxg/frzo/qmpqcLSpUuFp556SkhOThYOHjxo+15lZaUQEREhrF692rbt/PnzQkxMjDBt2jS7Y4wePdruuH/4wx+EoUOH2m178sknhfT0dNvXAIQ1a9bY7WMwGIRly5YJgiAIRUVFAgDhzTfftH3/hx9+EAAIhw4dEgRBEMaNGycMHz7c7hj33nuvYDAYRH9mLXD1upUTm2jmViwRUagxmQXsOlaOzwpOY9excpjMQqCHFBJMZhNy83MhwPF8W7ctyl/ks9uyQ4YMQUFBAQoKCpCfn4+srCwMGzYMJ06cAAB8+OGHGDBgAIxGI5o0aYKnn34aJ0+etD2+oKAAt912m8vnePHFF/HGG2/gm2++wQ033GDbfvz4cVy5csU2ewcABoMBHTt2dDjGTTfdZPf1oUOHMGDAALttAwYMwNGjR2EyyTtX3bp1s/3v5GRLAf+ysjLb8/Tt29duf66nv4aBHRGRCuUdLMbARV9i3Bu7MW1lAca9sRsDF32JvIPFgR5a0NtXts/u9mtDAgSUVJdgX9k+nzx/48aNcf311+P6669H79698eabb+LixYt44403sGvXLowfPx7Dhw/H559/jv3792POnDl2yQuNGjVy+xyDBg2CyWTyqmRY48aNZT9Gp9NBEOwD5obrAwEgIiLC7jEAYDabZT9fKGJgR0SkMnkHi/HIu/vs2g8CQElFDR55dx+DOx87W31W0f28pdPpoNfrcenSJezcuROpqamYM2cObrrpJrRv3942k2fVrVs3uyoRzvTp0wf/+c9/8Pe//x0vvPCCbXvbtm0RERGBPXv22LZVVFTgxx9/dDvOzp07Y8eOHXbbduzYgQ4dOiAsLAwAkJCQgOLia6/fo0ePorq62u2xGz7Pt99+a7dt9+7dso4RzDSTPEFEFApMZgHz1xU6uQkICAB0AOavK8TQdCPbEfpIQkyCovvJVVtba+ua9Ntvv+GVV17BhQsXMHLkSFRWVuLkyZNYuXIlevfujfXr12PNmjV2j583bx5uu+02tGvXDmPHjkVdXR02bNiAnJwcu/369++PDRs2YNiwYQgPD8f06dMRGxuLiRMn4sknn0SzZs2QmJiIefPmQa/X22bOxDzxxBPo3bs3FixYgHvvvRe7du3CK6+8YpfIeOutt+KVV15BRkYGTCYTcnJy7GbnpPjzn/+MAQMG4IUXXsCoUaOwceNG5OXlyTpGMOOMHRGRiuQXnXOYqatPAFBcUYP8onP+G1SI6ZnYE0kxSdDBeSCjgw7GGCN6Jvb0yfPn5eUhOTkZycnJ6Nu3L/bs2YPVq1dj8ODBuOOOO/D4449j6tSp6NGjB3bu3Im5c+faPX7w4MFYvXo11q5dix49euDWW29Ffn6+0+caOHAg1q9fj6effhr/+7//CwBYsmQJMjIycPvttyMzMxMDBgxA586dER0d7XLcPXv2xKpVq7By5Up06dIFzzzzDJ577jm7jNgXX3wRrVq1wqBBg/CHP/wBM2fORExMjKzz069fP7zxxht4+eWX0b17d3zxxRd4+umnZR0jmOmEhje7Q1xlZSUMBgMqKipU0YXCZBaQX3QOZVU1SIyNRp+0ZvyUThTEPis4jWkrC9zu9/LYHhjVo6XvB6RBNTU1KCoqQlpamttgRMzmE5sxY9sMALBLorAGe0sGL/FbyZNAu3jxIlq2bIkXX3wRkydPDvRwgpar162c2IS3YlUs72Ax5q8rtPv0nmyIxryR6cjukhzAkRGRryTGSgtEpO5HnslMzcSSwUuc1rHL6ZMT1EHd/v37cfjwYfTp0wcVFRV47rnnAACjRo0K8MhICgZ2KmVdPN1wOtW6ePr1+3oyuCMKQn3SmiHZEI2Sihqn6+x0AIwGy+w9+VZmaiaGtBqCfWX7cLb6LBJiEtAzsSfC9GGBHprPvfDCCzhy5AgiIyPRq1cvfP3112jRokWgh0USMLBTIS6eJgpdYXod5o1MxyPv7oMOsLsOWP/a541M59++n4Tpw9Db2DvQw/CrG2+8EXv37g30MMhDTJ5QIS6eJgpt2V2S8fp9PWE02N9uNRqiOVtPRC5xxk6FyqrEgzpP9iMi7cnukoyh6UYmTxGRLAzsVIiLp0MbM6HJKkyvQ0a75oEehmax6ANpiVKvVwZ2KsTF06GLmdBE3rMWvK2urpbUXotIDawdOOQWbG6IgZ0KcfF0aGImNJEywsLCEB8fb2saHxMT47ZrAlGgCIKA6upqlJWVIT4+3tZ+zVMM7FTKuni64eyNkbM3QYmZ0ETKMhqNAGAL7ojULj4+3va69QYDOxXj4unQIScTmmuuiNzT6XRITk5GYmIirly5EujhELkUERHh9UydFQM7lePi6dDATGgi3wgLC1PsDZNIC1jHjkgFmAlNRERKYGBHpALWTGixm+w6WLJjmQlNRESuMLAjUgFrJjQAh+COmdBERCQVAzsilWAbKSIi8haTJ4hUhJnQRETkDQZ2RCrDTGgiIvIUAzsiogBjf2AiUgoDOyKiAGJ/YCJSEpMniIgCxNofuGHXEWt/4LyDxQEaGRFpFQM7IqIAcNcfGLD0BzaZne1BROQcAzsiogCQ0x+YiEgqBnZERAHA/sBE5AtMniAKEGZChjb2ByYiX2BgRxQAzIQka3/gkooap+vsdLB0HWF/YCKSg7diifyMmZAEsD8wEfkGAzsiP2ImJNXH/sBEpDTeiiXyIzmZkGwrFhrYH5iIlMTAjsiPmAlJzrA/MBEphbdiifyImZBERORLDOyI/MiaCSl2k00HS3YsMyGJiMgTDOyI/IiZkERE5EsM7Ij8jJmQRETkK0yeIAoAZkISEZEvMLAjChC5mZBsQUZERO4wsCPSALYgIyIiKbjGjkjl2IKMiIikYmBHpGJsQUZERHIwsCNSMTktyIiIiBjYEakYW5AREZEcmg3scnNzodPpMH36dNu2mpoaTJkyBc2bN0eTJk0wZswYlJaWBm6QRF5iCzIiIpJDk4Hdnj178K9//QvdunWz2/74449j3bp1WL16NbZv344zZ87grrvuCtAoibzHFmRERCSH5gK7CxcuYPz48XjjjTfQtGlT2/aKigq89dZbWLJkCW699Vb06tULy5Ytw86dO7F79+4AjpjIc2xBRkREcmgusJsyZQpGjBiBzMxMu+179+7FlStX7LZ36tQJrVu3xq5du/w9TCLFsAUZERFJpakCxStXrsS+ffuwZ88eh++VlJQgMjIS8fHxdtuTkpJQUlIiesza2lrU1tbavq6srFRsvERKYQsyIiKSQjOB3alTpzBt2jRs2rQJ0dHKLRRfuHAh5s+fr9jxiHxFbgsyIiIKPZq5Fbt3716UlZWhZ8+eCA8PR3h4OLZv345//OMfCA8PR1JSEi5fvozz58/bPa60tBRGo1H0uLNnz0ZFRYXt36lTp3z8kxARERH5hmZm7G677TYcOHDAbtsDDzyATp06IScnB61atUJERAS2bNmCMWPGAACOHDmCkydPIiMjQ/S4UVFRiIqK8unYiYiIiPxBM4FdbGwsunTpYretcePGaN68uW375MmTMWPGDDRr1gxxcXF47LHHkJGRgX79+gViyERERER+pZnAToqlS5dCr9djzJgxqK2tRVZWFl577bVAD4uIiIjIL3SCILB7eD2VlZUwGAyoqKhAXFxcoIdDRKHMbAJO7AQulAJNkoDU/oA+LNCjIiI/kxObBNWMHRFR0ChcC+TlAJVnrm2LSwGyFwHpdwRuXESkaprJiiUiChmFa4FVE+yDOgCoLLZsL1wbmHERkeoxsCMiUhOzyTJTB2erZK5uy5tl2Y+IqAEGdkREanJip+NMnR0BqDxt2Y+IqAEGdkREanKhVNn9iCikMLAjIlKTJknK7kdEIYWBHRGRmqT2t2S/Qieygw6Ia2nZj4ioAQZ2RERqog+zlDQB4BjcXf06O5f17IjIKQZ2RERqk34HcM87QFyy/fa4FMt21rEjIhEsUExEpEbpdwCdRrDzBBHJwsCOiEit9GFA2qBAj4KINIS3YomIiIiCBGfsgojJLCC/6BzKqmqQGBuNPmnNEKYXy6wjIiKiYMPALkjkHSzG/HWFKK6osW1LNkRj3sh0ZHdJdvFIIiIiCha8FRsE8g4W45F399kFdQBQUlGDR97dh7yDxQEaGREREfkTAzuNM5kFzF9X6KpdOOavK4TJ7GwPIiIiCiYM7DQuv+icw0xdfQKA4ooa5Bed89+giIiIKCAY2GlcWZV4UOfJfkRERKRdTJ7QoPrZr79W1Up6TGJstI9HRURERIHGwE5jnGW/6nWA2BI6HQCjwVL6hIiIiIIbAzsNsWa/NozhXAV1ADBvZDrr2RGpmdnE1mFEpAgGdhrhKvvVquHMnZF17IjUr3AtkJcDVJ65ti0uBcheZOkXS0QkAwM7jXCX/QpYgrq5IzqjRWwUO08QaUHhWmDVBKDhR7bKYsv2e95hcEdEsjCw0wipWa0tYqMwqkdLH4+GiLxmNllm6kSrUOqAvFlApxG8LUtEkrHciUZIzWp1tp/JLGDXsXJ8VnAau46Vs1gxkRqc2Gl/+9WBAFSetuxHRCQRZ+w0ok9aMyQbolFSUeP0871Y9it7yBKp1IVSZfcjIgJn7DQjTK/DvJHpAK5lu1qJZb+yhyyRijVJUnY/IiIwsNOU7C7JeP2+njAa7G+3Gg3ReP2+nnYzcOwhS6Ryqf0t2a8OH9WsdEBcS8t+REQS8VasxmR3ScbQdKOt84RY9qucHrIZ7Zr7eNRE5EAfZilpsmoCLMFd/Q9ZV/+es3OZOEFEsjCw06Awvc5tMMYeskQakH6HpaSJ0zp2uSx1QkSyMbALUt5k0RKRH6XfYSlpws4TRKQABnZBytMsWiIKAH0YkDYo0KMgoiDA5Ikg5UkWLamE2QQUfQ0c+MjyX7Mp0CMiIiKN4IxdELNm0TasY8cesirGvqFEROQFnSAIrHdRT2VlJQwGAyoqKhAXFxfo4SjCZBbcZtGSCoj1DbXOsbJvKBFRSJITm3DGLgRIyaKlAGPfUCJSED/Qhy4GdkRqIKdvKBfZE5ELbCUZ2pg8QaQG7BtKRApgK0liYOdnJrMJe0r2YMPxDdhTsgcmZjwSwL6hROQ1tpIkgLdi/Wrzic3Izc9FafW1WZekmCTM6jMLmamZARwZBZy1b2hlMZyvs9NZvs++oUQkgq0kCeCMnd9sPrEZM7bNsAvqAKCsugwzts3A5hObAzQyUgVr31AAopUH2TeUiFxgK0kCGNj5hclsQm5+LgQnMzHWbYvyF/G2rMaZzAJ2HSvHZwWnsetYufzbHda+oXENFjfHpbDUCRE5Vf+682tVraTH/FpV6/l1ilSPt2L9YF/ZPoeZuvoECCipLsH7h99H8+jmSIhJQM/Engjj7IxmKJaFxr6hRCSRs+uOXge4itX0OmDB+kO2r5ktG3xYoLgBXxQo3nB8A3K+zpH1GK690w5rFppIWWG8fl9PXjSJSFFi1x25eJ3SBjmxCW/F+kFCTILsx3DtnTYwC42I/M3VdceqYS1isdrEvE4FHwZ2ftAzsSeSYpKgc1gUL45r77RBThYaEZES3F13AMvt2LkjOuPlsT0wd0Rnl7dneZ0KLgzs/CBMH4ZZfWYBgOzgrqS6BPvK9vlqaOQlZqERkb9JvZ60iI3CqB4t0SI2StHjkrppJrB7/fXX0a1bN8TFxSEuLg4ZGRn4z3/+Y/t+TU0NpkyZgubNm6NJkyYYM2YMSkvVU6U/MzUTSwYvQWJMouzHnq0+64MRkRISY6MV3Y9ICq8zsEnT5F53eJ0KLZrJir3uuuuQm5uL9u3bQxAErFixAqNGjcL+/ftxww034PHHH8f69euxevVqGAwGTJ06FXfddRd27NgR6KHbZKZmYkirIdhXtg9nq8+ivKYci/csdvs4T9bokX/0SWuGZEM0SipqxMoKw2iwNOAmUgL7gJLc6w6vU6FFUlZss2byftk6nQ779u1DamqqxwOTolmzZnj++edx9913IyEhAe+//z7uvvtuAMDhw4fRuXNn7Nq1C/369ZN8TF9kxYoxmU3I+jgLZdVlTmvc6aBDUkwS8sbksfSJilmz0wD7nhHMNiOlMQObrORed3id0jY5sYmkGbvz58/jpZdegsFgcLuvIAh49NFHYTL5bsG/yWTC6tWrcfHiRWRkZGDv3r24cuUKMjOvlQbp1KkTWrduLTuw8yfr2rsZ22ZAB51dcGddi5fTJ4dBncpld0nG6/f1dJhFMXIWhRTkLgNbB0tm49B0I8LEUiApaMi97vA6FTok34odO3YsEhOlrQ977LHHPB6QKwcOHEBGRgZqamrQpEkTrFmzBunp6SgoKEBkZCTi4+Pt9k9KSkJJSYnLY9bW1qK29lq17srKSl8MXZR17Z2zHrI5fXJYx04jsrskY2i6EflF51BWVYPEWMttDb7BklLYB5Qaknvd4XUqNEgK7Mxms6yDVlVVeTQYdzp27IiCggJUVFTgo48+wsSJE7F9+3avjrlw4ULMnz9foRF6puHaO3ae0KYwvS4gb6gms8ALdQhgBjY5I/e6E6jrFPmP7OSJr776Cv3790d4uP1D6+rqsHPnTtx8882KDa6hyMhIXH/99QCAXr16Yc+ePXj55Zdx77334vLlyzh//rzdrF1paSmMRqPLY86ePRszZsywfV1ZWYlWrVr5ZPyuhOnD0NvY2+/PS9rGhfShg5mNRCSF7HInQ4YMwblzjkUMKyoqMGTIEEUGJZXZbEZtbS169eqFiIgIbNmyxfa9I0eO4OTJk8jIyHB5jKioKFsJFes/NTGZTdhTsgcbjm/AnpI9MJlNTrdR6LEuhm54e66kogaPvLsPeQeLAzQy8gVrZqPYXKwOlqCemY1EoU32jJ0gCNDpHC8t5eXlaNy4sSKDcmb27NkYNmwYWrdujaqqKrz//vvYtm0bNm7cCIPBgMmTJ2PGjBlo1qwZ4uLi8NhjjyEjI0O1iRNSbD6x2WHtnSHSAOiAitoK2zb2ldUoswk4sRO4UAo0SQJS+wMSb79zIX3oCdPrMG9kOh55dx90cJ7ZOG9kOn/fRCFOcmB31113AbCUMpk0aRKioq5VsjaZTPj+++/Rv39/5Ud4VVlZGSZMmIDi4mIYDAZ069YNGzduxNChQwEAS5cuhV6vx5gxY1BbW4usrCy89tprPhuPr20+sRkzts1wKINScbnCYV9rX9klg5cwuNOKwrVAXg5QeebatrgUIHsRkH6H24dzIX1oYmYjEbkjObCzljoRBAGxsbFo1KiR7XuRkZHo168fHnroIeVHeNVbb73l8vvR0dF49dVX8eqrr/psDP5iMpuQm5/rtLadMwIE6KDDovxFGNJqCJMu1K5wLbBqAtDw91tZbNl+zztugzsupA9dzGwkIlckB3bLli0DALRp0wYzZ8706W3XULevbJ/d7Vcp6veVVUsSBrM1LezOQ+MI9MvLgc7VTdS8WUCnES5vy3IhvTYp9TfBzEYiEiN7jd28efN8MQ6qx5vesGrpK8tsTYuG56GfvhAZkWdcPEIAKk9b1t6lDRLdiy2CtId/E0TkD5ICu549e2LLli1o2rQpbrzxRqfJE1b79u1TbHChypvesGroKyvW9siarRkqrWucnYdEnJf24AuuZ2y5kF5b+DdBRP4iKbAbNWqULVli9OjRvhwPAeiZ2BNJMUmiPWSdsfaV7ZnY08ejc43ZmhZi56EM8dIO0CTJcVuDLNrs9P5cSK8B/JsgIn+SFNjVv/3KW7G+56qHrDNq6ivLbE0LsfOQb+6EM0IzGHEOzt/DdZbs2NQGGeYiWbTZ2YswNGck1zKqGP8miMifZBcotrp8+TJ++eUXnDx50u4fKcPaQzYxxr4/b3xkPAxRBrttSTFJqil1wmxNC7Gfzww95l+ZYPnfDvH61WAsO9c+ccKaRVvZYG3e1SzasMPrkNGuOUb1aImMds0Z1KkM/yaIyJ9kJ0/8+OOPmDx5Mnbu3Gm33Vq42GRiFwSliPWQBaDavrLM1rRw9fNtNPfBI1emY17EO0hBvS4ucSmWoK5+qROzyTJT52UWLQUO/yaIyJ9kB3YPPPAAwsPD8fnnnyM5OdllIgV5T6yHrLNtJrMp4AEfszUt3J2HL8x9cDBqAL4aG42wi2XinSdO7HScqbMjLYuWAod/E0TkT7IDu4KCAuzduxedOnXyxXjIQ87ajwWi1RizNS2knIe5d3RFWFs3CQ5usmNl70d+x78JIvIn2Wvs0tPT8euvv/piLOQha/uxhkWNra3GNp/Y7NfxWNseGQ32t5aMhuiQKuugyHlwlh3rzX4UEPybICJ/0QmCIK2exlVffvklnn76afz9739H165dERERYff9uLg4RQfob5WVlTAYDKioqNDEz2Iym5D1cZZopwprGZS8MXl+vy3LzhMWXp0Hswl4qYslUULsRl5cCjD9ANfYaQD/JojIE3JiE9mBnV5vmeRruLYuWJIntBbY7SnZgwc3Puh2v7ez3lZNqzGSydZbFnB6I09Cb1kiItIuObGJ7DV2W7du9XhgpDypLcTU0mqMPJB+hyV4c1LHziGLlkIaZwSJSHZgd8stt/hiHOQhqS3E1NBqjLyQfoelpEm9zhNOs2gpZLEXLREBHgR2X331lcvv33zzzR4PhuRz135MLa3GSAH6MJY0IafYi5aIrGQHdoMHD3bYVn+9ndbX2GmNq/Zjamo1RkS+wV60RFSf7HInv/32m92/srIy5OXloXfv3vjiiy98MUZyQ6z9mJpajRGRb8jpRUtEwU/2jJ3BYHDYNnToUERGRmLGjBnYu3evIgMLOWaTV+unxNqPcaaOKLixFy055eV7CmmX7MBOTFJSEo4cOaLU4UJL4VqRjMdFsjIexdqPqaHVGBH5BnvRBh+vs5sVek8hbZId2H3//fd2XwuCgOLiYuTm5qJHjx5KjSt02GqUNVghU1ls2e5ljTK1tBojIt9gL9rg4nV2s4/fU0j9PCpQrNPp0PBh/fr1w9tvv635HrJ+LVBs6yog1uTdu64C1lZjDbNlrUkVXH9HFBysWbGA8160zIrVBrHsZsm/Rx+/p1DgyIlNZCdPFBUV4fjx4ygqKkJRURFOnDiB6upq7Ny5U/NBnd+d2OniDxAABKDytGU/mUxmE3Lzc52WQLFuW5S/CCYzs5iJtI69aLXPXXYzYMluNpldzMX48D2FtEP2rdjU1FRfjCM0XXDe39Xj/erZV7ZPtH8sYAnuSqpLsK9sH1uNEQWB7C7JGJpuZOcJjZKT3ZzRrrnznXz4nkLaoVjyBHmgSZKy+9XDVmNEoSdMrxN/0ydVk5q1/J+DxQDgPGiX+l5xoRQ48JFdtizb0QUPBnaBlNrfst6hshgOC10B2NZDpPaXfWi2GiMi0g6pWcvv7DqBd3adcJ5Q4fY9BYBOD2z8y7Wv41Kw/4ZZeHTfdWxHFyRkr7EjBenDLOnnAK4tj4X919m5Hi1ytbYa0zkc13p0HYwxRrYaIyJSAWt2s9Q5Mmu7uLyrM3gA3LynXCWY7b+sLEb3nX9Gtyr7dqFOj0+awMAu0NLvsKSfxzX4VBSX4lVaurXVGACH4I6txoiI1CVMr8O8kekAREMyO9b5uAVrD8B0/CvLrdWir4FOI5y/p+icv93rrh5pXsT/QY9rQZ/khA1SHdnlToKdX8ud1OejKuHO6tgZY4zI6ZMj2qmCBY2JiALDWR07MVn6fMyLeAcpunrt4qyFiDuNuPaecqHU/variLGXn8Zuc7rD9g8e6se1mwEmJzZRbI1dZmYmjh8/juPHjyt1yNCiDwPSBil+WLFWY1tPbUXWx1kOhYuHpw3HhqINLGhMRBQA9bOb/3OwGO/sOuF0vyx9Pl6PeMnxG84KER/4SNJzJ+K80+1sR6ctigV2d955J3799VelDkcKathqTKxwcWl1KZb9sMzh8WXVZZixbQYLGhMR+UH97GZngZ0eZsyLeMfyvx3u2woAdEDeLMusnT5McrZsGeKdbmc7Om1RLLCbMmWKUociH3JVuFiMAAE66LAofxGGtBrC27JERH4g1i6uj/6w/e1XB/UKEacNcpstaxaAEjRHvtm+yQDb0WmTV8kTp06dwqlTp5QaC/mBu8LFYuoXNCYiIt8TS6gQu2XqwFqI2EW2rHD16+eu3A9zvZDAute8kemsZ6cxsgO7uro6zJ07FwaDAW3atEGbNm1gMBjw9NNP48qVK74YIynI24LELGhMROQ/ztrFid0ydVD/FqxIBQZdXAq+6/8PfBd7s912tqPTLtm3Yh977DF88sknWLx4MTIyMgAAu3btwrPPPovy8nK8/vrrig+SlONtQWIWNCYi8i+HdnGNe0NY9zZ0covbp99hny17tQLDjfowfDOUnSeChexyJwaDAStXrsSwYcPstm/YsAHjxo1DRUWFogP0t4CVO/ETk9mErI+zUFZdJmudnQ46JMUkIW9MHtfYEREFWuFaS/YrAPvg7mow5kUdVFIfObGJ7FuxUVFRaNOmjcP2tLQ0REZGyj0c+ZmrwsViWNCYiEhlfFTcnrRP9ozdc889h8OHD2PZsmWIiooCANTW1mLy5Mlo37495s2b55OB+kuwz9hZiRUu7hg7CF+d2QQh7Lxtu64uHvd3+DNmDLiLhYuJiNTER8XtSV3kxCaSAru77rrL7uvNmzcjKioK3bt3BwB89913uHz5Mm677TZ88sknXgw98EIlsAPg0GHi7NlkTHnvOwgwIyymCLrwKgh1sTBXpyEsthDGtDxUXLlWq5CFi4mIiHxP8c4TBoPB7usxY8bYfd2qVSuZQyQ1qF+42GQWMHDZl1dXauhhqm5n2y889iCiW76Lisuwy5Rn4WIiIiJ1kRTYLVtm6UYgCAJOnTqFhIQENGrUyKcDI//KLzon0pvQjKikdZb/2WBJHgsXExERqYus5AlBEHD99dfjl19+8dV4KEDEegGGxRRBH1EBnUieBQsXExERqYesOnZ6vR7t27dHeXk52rdv76sxUQCI9QLUhVdJenwgChebzIGpuxSo55VLK+MkIiLlyC5QnJubiyeffBKvv/46unTp4osxUQCI9SQU6mIlPb68phwbjm/wW7Zs3sFizF9XaHf7ONkQjXkj031aKT1QzyuXVsZJRETKkl3upGnTpqiurkZdXR0iIyMd1tqdO+eqMbH6hVJWbEN5B4vxyLuWW6rXXhRmNL5+EXThzm/HCgKg1+khwGzb5utsWes4G75wrcPzVRucQD2vXFoZJxERSaN4uZP6VqxY4fL7EydOlHM41QnlwA5wPtMTEXsQUS3fBQC74M76ytHZ/p/1a8sXvsiWNZkFDFz0pUiih2UYRkM0vsm5VdHbjoF6Xrm0Mk4iIpJO8XIn9bkK3LQ+W0eOPQl/rarFgvWAcPo+RCWtgy6ifss4HQBBNFs299tcNIlognM15+xu0Tasnyfn1q149q71uYHiihrkF51DRrvmsn9+tT2vXFoZJxER+YbswM6ZL774Am+++SbWrVuHS5cuKXFICqAwvc72pv9ZwWkAQF1VF9RVpdsKF+vCqhBtXC96DAECSi+V4qFND9m2JcUkYXjacGwo2mDX8ULOrVux7F0p+4kFlFICTW+e15+0Mk4iIvINjwO7EydO4O2338aKFSvw22+/YdiwYXjnnXeUHJudhQsX4pNPPsHhw4fRqFEj9O/fH4sWLULHjh1t+9TU1OCJJ57AypUrUVtbi6ysLLz22mtISkry2biCnX227LXCxeFxBbKPVVpdimU/LHPYLqfQsVj2rj0zfjMdwobj39kCta2ntjq0UJMTaEp7Xun7+YpWxklERL4hK7C7fPkyPvnkE7z55pvYsWMHMjMz8csvv2D//v3o2rWrr8YIANi+fTumTJmC3r17o66uDn/5y1/wu9/9DoWFhWjcuDEA4PHHH8f69euxevVqGAwGTJ06FXfddRd27Njh07EFM2+zZaVwd+tWyniswmMPIib5c7x48LxtmyHSgIrLFQ77ygk03T2vde1an7RmMn5y5WllnERE5BuSkycee+wxfPDBB2jfvj3uu+8+jB07Fs2bN0dERAS+++47pKen+3qsds6ePYvExERs374dN998MyoqKpCQkID3338fd999NwDg8OHD6Ny5M3bt2oV+/fpJOm6oJ08440m2rBKsM2dDWg1x2tMWMENfr6etLuwiolu+r8h4dNAhKSYJeWPybMGl8/OgvmxTrYyTvMDG70QhxSdZseHh4cjJycGsWbMQG3tttiZQgd1PP/2E9u3b48CBA+jSpQu+/PJL3Hbbbfjtt98QHx9v2y81NRXTp0/H448/Lum4Pg/sNHpBlpMtqxQddBAgwBBlQEXttRm3pJgkdIq9GV+d2QQh7Py1Bwg6QCcrydutp3o/hebRzW0ziJsKyzRRH4517IJY4VogLweoPHNtW1wKkL0ISL8jcOMiIp/xSVbs//3f/+Htt99GcnIyRowYgfvvvx/Dhg3zerCeMJvNmD59OgYMGGArklxSUoLIyEi7oA4AkpKSUFJSInqs2tpa1NbW2r6urKz0yZgBaPqCLC9bVhnC1fmm+kEdYLmFWlq9GmgYDysc1AHA4j2Lbf/bOoP4Tc5tqu/o0PD3pdZxkkyFa4FVE4CGN9oriy3b73lH9dcSIvItyYHduHHjMG7cOBQVFWH58uWYMmUKqqurYTabUVhY6NcZuylTpuDgwYP45ptvvD7WwoULMX/+fAVG5YYKL8hyW05JyZYV6hojOmU1dOGVPrtFGyh2a+/a+ab4spLq/74oCJhNlg+GTldPCgB0QN4soNMITdwFICLf0Mt9QFpaGubPn4+ff/4Z7777LsaMGYP77rsP1113Hf785z/7Yox2pk6dis8//xxbt27FddddZ9tuNBpx+fJlnD9/3m7/0tJSGI1G0ePNnj0bFRUVtn+nTp1SftBuL8iwXJDNJuWfW0TewWIMXPQlxr2xG9NWFmDcG7sxcNGXyDtYLOnxzrJl6yp7wFTdHrWllgBVXulr9ROu/t+i/EUw+fF3RUHEbAKKvgYOfGT5r5zX0Ymd9rP9DgSg8rRlPyIKWbIDOyudToesrCysWrUKZ86cwcyZM7F9+3Ylx2ZHEARMnToVa9aswZdffom0tDS77/fq1QsRERHYsmWLbduRI0dw8uRJZGRkiB43KioKcXFxdv8Up7ILsnVxfcNCtiUVNXjk3X2Sgjtr9qWzSbm6qi6oOX0f9OZ4u+3GGCMeuOEBJMVou/xMSXUJ9pXtC/QwSGsK1wIvdQFW3A58PNny35e6WLZLcaHU/T5y9iOioKRIgeJmzZph+vTpmD59uhKHc2rKlCl4//338dlnnyE2Nta2bs5gMKBRo0YwGAyYPHkyZsyYgWbNmiEuLg6PPfYYMjIyJGfE+oyKLsgms4D56wpd3czB/HWFGJpudHtbdt7IdDzy7j5r/wkbHQBTVRfk3jEeCQnFDoV/p/WcZstybd6oOeZ8Mwdl1WW2NXVKE0vCMMYYMSxtmEMdOylKL5YpPUwKZkosxWgi8QOR1P2IKChJCuxmzJiBBQsW2OrFuTN79mw8+eSTaNZMuVpZr7/+OgBg8ODBdtuXLVuGSZMmAQCWLl0KvV6PMWPG2BUoDjgVXZCVbDmV3SUZr9/X0yH70miXfXmdw+PC9GHobext+3pWn1mYsW2GLQDzll6nh1kw275OiklCTp8ch7IpzgLNvadOYtXPr7p9jl/PR3k9TgoRSq2NS+1vSbaqLBY5ls7y/dT+igybiLRJUrmTsLAwlJSUICEhQdJB4+LiUFBQgLZt23o9QH/zSbkTs8lyy8XdBXn6AccLu8LlUT4rOI1pKwvc7vfy2B4Y1aOlpGPKTcJwZvOJzQ6dIeIj43H+8nnJAZ/u6o3hF255AU2jm3rUi3bN/pN4eu8fROvzCQIg1MVhcsfZ6NRSL/v4FIKKvrbcdnVn4udA2iDX+9hm/gCnVQqZFUsUlBQvdyIIAjp06ACdxDTHixcvStovZOjDLCVNVk0AnN64BJCd6xiw+aA8ii9aTimRfZmZmul0Rs1ZKzCxW6jWmTkpPWfFGOMao7Z0JKJbvgtBsK/PZ/sIpKvDsuOzgePXnldqr1sKQUouxUi/wxK8Ob0u5DKo0xglPhQTNSRpxm7FihWyD3z33XdLvnWrJj4tUOw0UGvp/IIstibHy0/mJrOAgYu+dNty6pucW1VzgTGZTU5voYpt9+65LOfnrPm/iEpaB329+nzmuhjowqotv4F6p8Y6Uyil1y2FICVn7Kw0WuicrmERcZLDJ50nQoUqOk/Ybt2KZdK6uHUrAVtOuXbt/NRvWea6Pp+zFmREALxbikFByXqNEfnYHvLXYHIkJzbxuNwJeUgfZvlU3vVuy3+dXch9XB7FmvRgNNjfbjUaonlBQf3zE2OrzwfooY8QL7osQGAZFHLOuhQDABwKBLlYikFByV1lAsBSmcBk5pwLeUaRciekMD+UR2HLKdcanp+fay7izSPuH3e2+qzvB0faw7VxdJWSlQkUxdv7QYOBnRr5qTwKW065Vv/87ClJkxTYJcRIyxynEJR+h6WkCd88Q1pZlXhQ58l+itBwH3NyxMBOjVivSnV6JvZEUkySaCFl6xq7nok9AzA6UhvRbEfrUgwKWb6oTOAVFfYxJ+8wsFMjT8ujkM+E6cNECylbs2Jz+uQwcYKY7UguWdsxuqtM0CdNuQL/opQqnk2qIjkr9q677pJ0wE8++cSrAQWaz7Ni5ZBTHoX8wlkhZWOM0WVnCwodzHYkKdxWJhjfHdlNinx/y94XpXjIJxQvUAxYerKSn3FNjuq4KqSc9XGWQ8FkvxQu5qJnVVCqDzMFP1ftGF/r+Qtu3DTUP+vdVNTHnJTDOnYNqGrGjjRh84nNmLFthsPaO78ULuaiZ9XYdawc497Y7Xa/Dx7qx6QlAuBkLWbNNwhbPRFKF6YXxRk7zWAdOyI/MZlNyM3PdZpQYd22KH8RTGaT8k9uXfTcsOahddFz4Vrln5NEqTLbkVTNmnk/qkdLZKTFI2zjLIivd4NlvZuS1xJrop5DfUUrnWX5DxP1NIWBHameySxg17FyfFZwGruOlXtcuFPsON4cf1/ZPrvbrw35rHCx20XPUP5NgFxSXbYjaYuPC9M7xeLZQYlZsaRqSmUYih3nju7JWPtdscfHl1qQWPHCxXLeBHgLxS9Ule1I2iN1HduhqzPxSq2lZfHsoMMZO1Ita+ZYwyrtJRU1eOTdfcg7WOzVcYoravCvr4q8Or7UgsSKFy7momfVCdPrMG9kOgDRuQ/MG5nOxAlyTmrB+fx/W9bFvdRFueUW6XcA0w9a1tKNecvy3+kHGNRpFAM7UiWl+im6Oo4YOce3Fi7WiaxR0UGHpEZJMJlN2HB8A/aU7FFmvZ2fupOQPOzDTB5zu96tAaXX0krpY06awFuxpEpK9VN0dxxvj++ucLEAATXmGjy06SHbdkXKoLA7iWoFtA8zS99ol8vC9M6wgDA5xxk78jspSQw7fvpV0rHcZRh6m4Eo5fGZqZlYMngJEmMS7bYbIi21HytqK+yPWV2GGdtmYPOJzZ4PjIueVc0u27Fdc/8EdYVrLbfnVtwOfDzZ7nadUglI5GPW9W5xUmd2fZBQQZrHGTvyKzlJDFK4yzD0NgNR6uMbFi5u3qg55nwzB7jsuK8AATrosCh/EYa0GuJ5dwoueiYrF/0+hVUTMCf8Say80MO2mS3OVKx+YfpDay1r6tzhWlqqh4Ed+Y1YuyVrEoMcUjMM3WUqenv8+sL0Yeht7A0A2FOyR3IZFOtjPMLuJOSm9I0gAH++8hZW4WWYr96ksSYIcd2fSlnXuwHSAjuupaV6eCuW/MKTJAYxcjIMXWUqKnF8MX4tg8JFz6HNTekbvQ5I0ZWjj/6wbZucBCEKIBYQJg8wsCO/8DSJwRm5GYZimYrJhmj86eY0JCuVwWg2WVr0HPgICRXSSrGU15Qrmy1LoUfibbhEnLf7un6CEKkU19KSB3grlvzC2ySGqUPaoX1SrMcZhq4yFZ/K7ux9BmODnq09ASS1boWyMJ3oLKVep8fiPYttXyuSLUuhR+JtuDLEO9/OFmfqxrW0JBMDO/ILb5MYBlyf4HXjdGumotTtkjlZuB4GYNav5ZiR2Bw6nfPgziyY7b62ZssuGbyEwR1J56b0jVkAStAc+eZOTh/OFmcawLW0JANvxZJfWJMY5K5Y08Fyy1S1bZhcLFzPrK7GkrJyJJrsv6fXOf+zs9bAW5S/iLdlSToXt+usHx3mX7nfljiBenuq+m+L7HEtLUnEwI78IlBJDD7nZuF6ZnU1Np48hbe7TceiQYvwVO+nHGbq6qufLUskmUj9s9pGRjx6ZTq+MPex266Jvy0i8ghvxZLfWJMYpNaxM2qh1paEhethAHqHG4C2w7Hh+AZJh1UkW5ZCi5PbdY1S+2N0YRm+a/A3p4m/LSLyCAM78iufJzH4m8yerQkxCZJ2t2bLJsQkoGdiT4Tpw2Aym2wFkOtvJ7KpX//sqoC2OCMiv9MJgsAiRvVUVlbCYDCgoqICcXFxgR4OqZ3ZZGnb5K5n6/QDwNXgLOvjLJRVl9n1la1Pr9Pb3a5NiknC8LTh2FC0wa7oMbNoiYhCg5zYhGvsiLwhs85UmD4Ms/rMuvpd5zMmDdfglVaXYtkPyxw6WSjSc7Ye9hMNPVL6NvO1QKQtnLFrgDN25JEGdewAWCrCi9SZ2nxiM3Lzc+2CtYYzdVLooENSTBLyxuR5dVtWrIcv12EFLzl9m/laIAosObEJA7sGGNiRx8wmWXWm6q+ZK68ptytWLNfbWW973HNWrIevdT6R/USDj9jvXAxfC0SBJSc2YfIEkVKcLFwHIBrwhenDbMGY1GxZMbvP7PYoqcJVD18Bljf0+esKMTTdyMX2QcKTvs18LRBpBwM7Il9yeos2xbIur94tWqnZsmL+feDftv8tJ6nCXQ/f+v1Eve38Qergad9mvhaItIHJE0S+Ym011rCAcWWxZXvhWtumnok9kRSTJJpQIYecpAqpfULZTzR4ePu75GuBSN0Y2FHQUUVGn4tWY7ZtebMs+0FatqxU1jIqud/mYveZ3dhwfAP2lOxx2qZMap9Q9hN1ThWvNZm8/V3ytUCkbrwVS0FFNdmdblqNAQJQedqy39V1eZmpmVgyeIlDtqwxxohhacMc6ti5IkBA6aVSPLTpIds2Z7dorT18SypqxKrwwch+ok6p5rUmk7vfuRi+Foi0gVmxDTArVrtUld154CPg48nu9xvzlqWpdz1iHSbqbz9WcQz//v7fIgd1zjoTuGTwErvgznreAPv5RWZCilPVa80DYr9zMVr5uYiCFQsUU8hxl90JWDL6/HarTGarsfqs2bLD2w5Hb2NvW4Zr/e39kvvJHpL1Fu2i/EV2t2WtPXyNBvtbbEZDNN/InVDda80DYr/zZEM0/nRzGpL5WiDSLN6KpaCguuzO1P6W7Fd3rcZS+3t0eGuyhavWZM4IEFBSXYL3D7+P5tHNbTOC7Ccqnepeax4Kur7NRASAgR0FCdVld1pbja2aAMuNLCc3Oeu1GpPLmmwxY9sM6KCTFdwBsCuGXH/tnZoDEbVQ3WvNC2F6ndPfudh2IlI/3oqloKDK7M70O4B73gHiGty+ikuxbHfSakwOa7JFYkyiV8dRuueslknJclXla42I6CrO2FFQUG12Z/odQKcRslqNyZGZmokhrYbYkiqaN2qOOd/MkXWLVoAAHXTI/TYXTSKa4FzNOdkdLAJNSsKJu59Japaral9rED8PRBQ6mBXbALNitYvZnRabT2zGjG0zAED2Ldr65HSw8BdngcvWU1sdSsQkxSRheNpwhxIxYj+T3CxXNb7WNp/Y7PQ8qO13SETyyYlNGNg1wMBO27RaW0xpzt7k5bKWR3nhlhfQNLppwGeBnP1MhkgDKi5XSD6Gs5IvJrOAgYu+FE2IsM7AfZNzq10CQSBfaw0D3N9qfsPM7TMdAnm1/Q6JyDMM7LzAwE77TGaBGX2wf/Mvrym3S5iQQ6/TwyyYbV8HYhbIOgvpzQyklQ46JDZKxF8H/hXnas6h7LdIPLv6EtwtOf7goX4OCQWBeK05C3Ab/o4aUsPvkIg8x8DOCwzsKBiZzCZkfZwluzyKM2KFjpVmDUxLL5Zi8Z7F+K32N589l/mKAbWlI1FX1UV0n5fH9sCoHi19NgYplApw/fU7VAo/rFGoC9oCxV999RVGjhyJlJQU6HQ6fPrpp3bfFwQBzzzzDJKTk9GoUSNkZmbi6NGjgRkskYoo3YtWgCCpF62nNp/YjKyPs/Dgxgcx+5vZPg3qAEAXXoHolu8iPPag6D6BznI1mU3Izc9VZNZSrFi1GuUdLMbARV9i3Bu7MW1lAca9sRsDF32JvIPFgR4akSppKiv24sWL6N69Ox588EHcddddDt9fvHgx/vGPf2DFihVIS0vD3LlzkZWVhcLCQkRHs/QAhTaxXrSeEutFWz9LV0qGqtT1Yr6k0wGCAEQlrYVgioYu/AKEuliYqtOgg14VWa7lNeWK/N6srMWq95XtQ29jb8WOqySxpJaSiho88u6+kEmIIpJDs7didTod1qxZg9GjRwOwzNalpKTgiSeewMyZMwEAFRUVSEpKwvLlyzF27FhJx+WtWAp29YMFT8qjiLEWSjZEGVBRey2hwVWGqrPt7taL+ZP1Fu0royaoIsvVFxYNWoThbYf79Dk84WlSC1EwkhObaGrGzpWioiKUlJQgM/PaehGDwYC+ffti165dooFdbW0tamtrbV9XVlb6fKxEgWTtOWvlTQeL+qyPrR/UAUBpdSmW/bDMYX+x7WoJ6gBAH16BRte9C32TG7Gn5Bev6uS5EshZy4SYBJ8/hyeCpXUbkb8FTWBXUlICAEhKsm+qnpSUZPueMwsXLsT8+fN9Oja/MZt8VgiXgpfYLVpBsLRC06l4MkRsltAYY8SwtGEOs4EePAEA4MmvnnTIKpVTJ89Kah0+vU7vUVDXcLbT1eynNTvYZDZhw/ENPglYvRFMrdtE8ZpNPhA0gZ2nZs+ejRkzZti+rqysRKtWrQI4Ig8VrgXycoDKM9e2xaVY+pX6sPMBqZjIm4azDMOGHSzKfovEX/P2ILrl+xAEqDa4S4pJQk6fHNF1fVN7/Bnvf7cNJytLcF1sIt4rWoyzHtx2bhgcic02WtuzOasb5yyAE6vDJ3fW0poQ8/zNz9s9r3XmD7AvVm0NiGvMNQ7rJD0JWH0hmFq3Oc3qPbxO/JrtZbvBQBLLYFbDh4VQETSBndFoBACUlpYiOfnaWpjS0lL06NFD9HFRUVGIiory9fB8q3Dt1WbzDd6sKouBVfcDjZoBl85d2x4EFw9yQyTQ33/DLDy67zrRorrWW7Qms4B/faHD2dN6RCWtgy5CehFgX2sa1RRP9X4KSY2T7N4cGiYAXCsgXAugKYArSEgaDqHZMsufig+CVWvw1HCGTyyAk1Nc2RVrgOss8Fqic5yNNUQacP7yecm3za0Bqz/Lo6i5dZsceQeL8ey6gzh75RB04VUQ6mJxt74CuXUvwgwB+6KjcDYsDAkmE3pWFiNs1QRFekkHgljR7rsGnsPGkn85/bAg9qGMPBd0yRMzZ87EE088AcAy+5aYmBjcyRNmE/BSF/s3cLeuvqNp9OJBbogE+gJ0EAQBj1yZjo3mPrbt7ttmmaGPKbr6ptQY0SmroQuv9Pssnpzaa2LZlAAQHnsQUUnroFdRsOqJp3o/hebRzSW9GTpLmJF7i1oHHZJikpA3Js9vb7xqbN0mR97BYkz97B2H11uLOjNGVFUhL7YxSsOvza8k1dVhVvl5ZIY3BaYf0NSdlWt/c2aE2a4XsdCFXUR0y/cdrheukq20VDzbXzORQVug+MKFC/jpp58AADfeeCOWLFmCIUOGoFmzZmjdujUWLVqE3Nxcu3In33//vaxyJ5oL7Iq+Blbc7sEDdZaZO41dPMgNN4G+WQBK0BwDa1+GuV4ZSzltsxKSjqCm2TKvky3cabg+zBhjFJ2Vqs9dNqVF/TefwAWrnvA2wNpTsgcPbnzQ4+d/O+ttv5ZH0WqbQJNZQL+Xl+JSU8sMqN1rq/7bbr1v6K5uf6HsVzQd+jecNSRrYhbL+jd31vxfhyBW7lpdLbXB82d/5qDNiv3vf/+LIUOG2L62ro2bOHEili9fjqeeegoXL17Eww8/jPPnz2PgwIHIy8sL7hp2FzxdGC4Alacta7DSBik6JAqgEztdzt7qdUAKytFHfxi7zem27WIZhtldkjE03dhgzcxwbD3VzeGCFh8Zj/OXz3sd8ImtF5N6YXeXTWmhh6m6ne2r2tI7EN3yXVWvJwSunZucPjkev8mdrT7r1Ri8fbxczl+D6uw8UX/2puRcOKpjP4YOTl5T1sKJDb4hXN3+ZGILmL9/ybY9KSYJT/V+SrWBTn7ROZw1/xfRLd91+J5OJ+9aILacIdAzeVIz1wOxbKEhTQV2gwcPhqsJRp1Oh+eeew7PPfecH0cVYE2S3O/jiseBIamSxN9nIs473e4swzBMr3MoJ9Ew2cJVgoBYhqrYdlfrxaTwJEuyrqoLak7f57Ce0NVsgz+CQGc9Xr05N4D35U0CUR7F2WtQbZz28I1w8QCxF49Oh4apM6XVpXhi+xN22wId6NRXUnkRUUnrACj3N9EwgchVYpKSAa63mesCBOigw6L8RRjSakhAgm9NBXbkRGp/yy3VymI4JE9I4W1gSOoi8fdZhnin2+VkGDashweIB3wNM1Rbxxnxh+6DERkejmk9p4l0pPCsP6inWZJ1VV1QV5WOZ3/fCIlNL7vMDLZ+vvRVcOftrKUrPRN7IikmSXZRah10SIxJwuULqfis4HSDjMfQ7uWqVA9fOfw1MyTld1th/tHna1Y9ncmTk6WrVOZ6oLu6MLDTOn2YJcN11QRYVkpJvbBcXWOX2t93Y2ONJv9zE+hb19jlmzvZbVcyw9BZwOeYoVqLf32x3SEb13F/+euq3GVTirGcgxjcf+OttmBFLDNYqDPgSkV3RDf9HkLYedt2uV0zXC0g93ZmToy1b7CcotTW/c7/koUJh1baFsYnRHTGqO4tsfa7Ys2tgVOKkj185fDHzJCrv8Oh6Ym2wKgCxxR/bjFyZvI2FZY5ZCQnRHTGmIHnHbJ0fZG57u9lC1aaSp7wB80lT1g5K29hK3PSMODzQ1asq7p6zMT1LVtWLFD/927Nin30ynTkSciKVYpYhqq7bFyp+7t6TkDaRx15mcGxMFenAdDj1fHdkZBQfG3dzU9fYOZPH1iet8GieAGAIbwxKkzVtu3WhJBAlHxwdutQ7PZ4fEQCSos7I8Lwnd3MjLXlWl1VF7tjayVrVQkeJ6MoWHLHFwktrv4Ow2IPwpiWh4orvyr6nN5o+KHKENECZcXpjq/Zuhjowqr9spZWyd9L0GbF+oNmAzvA+QzZ4fVOAqyWQHaub4M6Z3X1WGbFf5wG1i2x/4Ycl3XslCa336eS/UHFZhvu6J4sa4ZJ8uzh1YzkzXW/Ibd5U7syFsa6OuSUn8eQ8KbYN/ZtnK05p4oF8GKlGupvbxbdAlM//Bo1TZcDcH5Luub0fU6Du2Dt5Vr//ByrOIZ/f/9veQdQuI7iw10fRrv4doq9plz9HYbHHkR0y3ctw5fxMzQMvJRKthIlXHsHcvaa9WVg54vSQAzsvKDpwE6MP2+Juq2rxzIrfiOj84Sv3nh3HSvHuDd2u93vg4f6IaNdc9n7uyO+vkbeOZC0f73SQybAvvBsTS1sr/aJn2sqE33HT2V4eNtd0IVXiCaRCHUGXPwpB6hXQsdK6u9KK5zNdMqVFGPEcCczo3Jv5Ts/tvdJFeJ/h2Y0vn6R6GvBGVflS8SSEpTqFx2ILHc59TblCNpyJ+QhfZj/3kjclNtgmRU/Evm9+zPDUG6/T6X7g4r9rHLPgaT962UkhwHoXVPrdj8tyC/5r8uF8TodoIuoQFhMkV0JGStN93JtwJMkCWtP3r8O/CvONZipbZg4JNYCTg4lkirEfmdhMUWykyRcrRd1lmylxDmw8nVQZ5kW09mVdPHl+lipGNiRsqS+aWnszY08I7ffp9T9j5ZewK5j5erKypSaYa6xTHR9+AVJ++nCq5xu10IvVyk8SZKwzt7M6jsL/VL6OXzfWaKRsxZwcliTKnK/zUWTiCYOwaSUTgn2v7Nrxbz1kdLGJOfWsNRzoORMnlzObt9eW4YwDk9n90Zi08uqWF4BMLAjpQXpmxt5Rm6/T6kZra9s/QmvbP3JozVzPuO29JAfMtF9oG/rNnjziPv9dGFVCI8rgFAXC1N1GnTQa6KXq1T7yvbJDrY8mb0Rm8VavGex5OcXIKD0Uike2vSQ3ViGpw13WjeyYc/WZo1awGiIxK/mfR613+uX0s+rpAFfz+S51GD9o620kSkGuvBriU9CnSVxKEF/ky2TXi24xq6BoFxj50+2NXZu3ty4xi5kyO33KTej1ZmAZWWKZCRrOXHIZDbhlpWZOH/5V9E1dg1vR1mzZV8ZNUHTWbGeJEkoncjgzVikECu5ExMWi4t1lllYqckHvu4n7LQItIuZPLE1dq62C6YY6OsFcNcyv9PteuBaM+P9dY1h8oQXGNgpIAjf3Mg7cuvSOdtfroBlZYpkJPs0E93HNp/YjMe3Pe6YzWn9WmT70sFLVdEZwROeJkm8MfQt9Evp435HL3jb79cXfJU00JBYay9AZCZP5LXZMJDV1cWjuuR2hwDOdDWA0+ssdUCt/H1XgIGdFzQR2Gmh8G8QvrmRdzzNRN3x01m8stXzAqgBycrUwt+oTHJnS3w9e+NLYkkSrmarrNnB/x78CQZcn+jT8ZnMJmR9nCW7e4gvWWsyBiKQl1OTMSnGiFkNakeW/RaJZ1dfgrOs7vrmjuiMFrFRAVnHy8DOC6oP7LRU+DcI39xIee4Cvs8KTmPaygKPj//y2B4Y1aOlAiOl+rMl5TXlWLxnsdvHPNX7KTSPbi7p1qQakmCsQZPYTJ3rhfT34X9uGoX2SbE+H781+AR8vObMBV/ddvaElJqMYuOUeo1xdi3x12uW5U6ClVjh38piy3a13eL0Z5kV0iQpt2i9zaoMlqxMNaifwbjh+AZJj6kf/LmqseZNGzmppLz5l9eUu7z96nS2ru5aB476s8u+vF2XmZqJJYO9y6D1lrdJEkpyll3rant9crP3rfzxmvUEZ+waUO2MHQv/UpCR2jrMWgXfs96vwdn5QA08WeclVqz27NlkTHnvO6/ayLnj7HadWKaoFLVnh8B8OcluHVZD/kjiqR+UNm/UHHO+mePzW7Ravs3ujLtrjLNriRKtD+XgrVgvqDawq1fV3iWNVbWn0CS3dZhSvV9JOd6s82q4Nk9nikd1sfOF69ayKXICdLEF9koGO9UnHnJakLkhf3/A8PUtWn8lSfibnOx9JVsfSsVbscGIhX8piOQXnXOZ8SoAKK6oQX7ROWS0a47sLsl4/b6eknu/GlVwOyTYhenDMKvPLMzYNkN2v8+GCRdm/XlEt3xXtNREcUU6/m//FocisM5urYq1qVIyyNHVxV+dpXOv4WvZ18Ru0YolE4j1bBUrg6KGzgq+IHaNcXYtkXv98jcGdlrBwr8URDxpHZbdJRlD041OFyo/ld054IvuQ5FS67x0uqu1xcKq7beHV9gCvhcPXvue2C1UQ6QBFZcdi+kq1bHAdiv5tmcQO7gnyqpqcLT0Al7Z+pPbx/qztZqzAr9ibczEgmFrACd2nGDk6hpTn9KtD5XGwE4rgrSqPQUfKVlini5WVqr3KymnYRAhNVu2IacFb0UCvtLqUiz7YZnD/s6COiU5m63adaxcUmDn7yQeOckErgJBAAFJkBC7jvg6C1XKtcTT65e/MLDTCn2YpaTJqgm4VhHU6uqLOjuXiRMUUFKzxOS2GiN1qx8smMwmrPhhhWKZmr5u5O6Ou3ItwfJalpI96i9i1xG1tA9U++/cdTU+Upf0OywlTeIavIDjUtRX6oRCjnXxccO1JyUVNXjk3X3IO1hs2xam12HeyHQA9kXh6389b2Q6b6cqxWyyJGAd+MjyX7PJZ08Vpg9DlvFPloK9GkjNExujDjoYY4z4Q6c/YHjb4eht7O30FiRfy8oSu44UV9TgX18VSbq++Jraf+fMim1AtVmx9bHwL6mMp1liaq0DFVT8XNTc+lo4a/6vQwN5QbDcbQj0LJyVrdiw7f9Zv5af+cnXsvfcXUfEBKq0kT9/5yx34gVNBHZEKrPrWDnGvbHb7X7O2nupodtA0BIrau7Dvs32rwWzXfkSXdhFRLd83zICP/+KLe90Ouh0186F+YoBVyq6I8LwnV0A6ml7LL6WvSP1OiImEO0D2XmCiIKSN1liTHzwEbPJMlPndBXQ1U7oebOATiMUnfG3/x3rHWq91ZzWIyppHXT1Aimxkhtum7NKjA6vtfwaB8HU2KHB++Wz2Zh2exg6psCrzE++lr3jbRZpILJQ1fg7Z2BHRF5Te5ZYSDqx00WnGgAQgMrTlv0ULGru7ndcV9UFdVXpmHSrGfqIKrSOM+IP3Qfjq9PbHEpuxJvNOK/XQycIEJw1Z20Q3InVXqvf8ss5PeKETrhSGYU6IRrW5eecgfO9+uf416par471a1UtPis4HfK/KwZ2ROQ1tWeJhSSpxcqLtjuu1/ViHa+71wJgKRi8/Es9gKYAavGvL7Zj3sgbsHHMxmslN0oK0fOLBdga0wi5zZuiNPza25XRZMKwCxexoUlju+3Oaq81i26B6SsqUF11WXTMeh2wYP0h29dqysAMZs7WqOl1gNmDBWLOfoeh+rviGrsGuMaOyDNyWvKQH0htQ1hfXArQ5W7g4EdeJVso0gKu3vhNAPZFR+FsWBgSTCb0rKlFmHX7qCU4a0h2eQtV7nhkjZM8ItZrVSnB9rti8oQXGNgReY6ZgSpiNgEvdXFR1FwO+ckWcmdjHDIb3Y7/alH26QckzSYqNTsUqAzMYCIl+7Xh70ZsFlXWa0rDGNh5gYEdkXe4LklFbFmxgCLBnYxACnBcP1X/VpkYu8xG0fF7ltXryXgkjZNkkZr9OndEZ7SIjRLtPOHRa0qjmBVLRAGjxiyxkGUtat6wjp1H5Cdb1H8tfFZwWtJj7DIbxcYfl2LptCOzVIsn45E0TpJF6rlrERuFUT1a2m3z+jUVAhjYEcnFAtEAODOnGel3WEqaWF+zZYeBr5/3/HhSkzIa8DhzuuH4JfzNKdmvWPI4STKlsui9OU4wX78Y2BHJ4ecq/mrFtXQaow+7NstW9LV3gV2TJI8e5lXmdP3xu6FUv2IxzPD2nlJZ9J4eJ9ivX+wVSySVdb1Pw1talcWW7YVrAzMuP5PTEzbo+bEHq2JS+1s+jDh0uXRHB8S1tDzeA/7or6lUv2IxaugDGgyUei14cpxQuH4xsCOSwm0Vf1iq+Gvhjd0LJrOA+esKXZ0FzF9XCJMnhai0pnCtJWtzxe3Ax5Mt/32pi/oDfH2YZYYZgOyQJjvXq2UH2V2S8fp9PWE02N8aMxqivS5L4clrU2w8yYZo/OnmNCT7YJxkodRrQc5xQuX6xazYBpgVS05JrQk28XNFq/j7lYS1g970hA0qAejBqjinywpaAl3GOKlj19KjZAUxvljf5It+xcG8DkstlDrHUo6j5esXs2KJlCZ1wbiHC8sDTuLaQW96wgaNAPVgVZyrpITMZ32aIOSLzGlf9CtmhrfvKXWOpRwnVK5fDOyIpJC6YNzDheUBJTb7ZF07WG/2iT1hEbAerD4hlpQgI1khkDzpMxrUr00/kjvTpobZz1C5fjGwI5LCuuDcXRV8DxeWB4zM2Sf2hEXwz95qhKedLYL6tekncrNK1ZKFGirXLyZPEEnhcsG5MgvLA0LO7BP8k9moesE8e6sRYpmNroI6IARem34gN6tUTVmooXL9YmBH2uXvUhPWKvhxDT5hxqVoY7G8Mx7MPvkys1ET3JYL8a4sCLnmKrPRquH7csi8Nn1MblapGrNQQ+H6xVuxpE2BKhTsQRV8VfNw9im7SzKGphsDvmZGcVK6ilhnb1dNgCW4c9LDVIuzt97wYzeW/KJzLpvHA5aZO2d9Rsk77s69AKC4ogb5ReeQ0a657P39JWivX1cxsCPtkbHY3yc0srBcEi/WDgZdxqCcDwsK9zDVND9/yPKmzyh5oF7QbioJgx7hMLu52Wf9Hak5CzXorl/1MLAjbQmWUhNqwdknC08+LATb7K0nAvAhK1QyG1WhQdA+EMA3Uc0w/8oEbDT3EX2Y9dzzdxUYXGNH2iJzsT9JEIxrB+XwpquIdfa2692W/4ZSUBegbizWzEYXKxyRHASZjQEn0kLRqDuH1yNeQpY+3+EhDc89f1eBwcCOtIWlJnwj/Q5g+kFL54wxb1n+O/1A8Ad1AD8seMoX501CQlSoZDYGlIug3Ro0zIv4P+hhtm13du75uwoM3oolbWGpCd8JprWDcvDDgmeUPm8y1upZMxsb1kYzBqA2WlByE7TrdUAKytFHfxi7zZbAzXbu0xMtQfnV5QnZ6f35u/IzBnakLcFaKJgChx8WpKuf/So1YJNy3jxYq6epzEY/Zg0rQuLv9rlbW+BQix7Xzv3hdcBLjsF5dvYiDM0ZGZjfldbOvQIY2JG2cLE/KY0fFqRxNqOm0wOCWeQBEs+bFwlRmshsDFRpJm9I/BDTod316JB2NfPYTXAeds87yPD3z6vFc6+AoFxj9+qrr6JNmzaIjo5G3759kZ/vuMiTNCzUF/uTsoK1q4iSRBbSuwzqAGnnLZjXOIqdN+tMZOHawIzLHblFuAOUSOOSVs+9AoIusPvwww8xY8YMzJs3D/v27UP37t2RlZWFsrKyQA+NlBTKi/39zd8dPgLB3YeFTiOC/xyIcfmmfZWuwVuJnA9ZwbrGUY3BjlRyP+yoLTj3xbnX0HUw6G7FLlmyBA899BAeeOABAMA///lPrF+/Hm+//TZmzZoV4NGRony92D8E12Y4CKVbGWJ16Q6vB17qEhrnwBm3b9qwzNxl/d1yzuT+rWhxjaOUa4OcYEeNSUtyinCrLThX+txr7DoYVIHd5cuXsXfvXsyePdu2Ta/XIzMzE7t27XL6mNraWtTW1tq+rqys9Pk4SQM09ofsE4Hu8BEIDT8shOI5aEhOkkTXu+UfX2trHKVeG9QW7HhCahFutQXnSp57DV4DgupW7K+//gqTyYSkJPsXT1JSEkpKSpw+ZuHChTAYDLZ/rVq18sdQtUlsKlpDU9SShPDaDBst30ZSCs+Bha/ftP25xtHba5Wca4Pagh1PSSnCLXdNnq8pde41eg0Iqhk7T8yePRszZsywfV1ZWcngzhmxT6ld7gYOfhQ8M1tsWWah9dtISuA5sPBmRk3qcgZ/9N71dhZe7rVBazOR3lBbtQKlXrMXSjV5DQiqwK5FixYICwtDaan99GppaSmMRqPTx0RFRSEqKsofw9Mu0anoM8DOfzjur+Iparf4Zm4RDLeRvMVzYOHpm7bcQMqXvXeVuJ0m99qgtmDH1/wRnEul5GtWCpVdA4LqVmxkZCR69eqFLVu22LaZzWZs2bIFGRkZARyZxtS/XXFsm/uMOAfqnaJ2i2/mFsFyG0mMlFtywX4O5JBbYsjT5Qy+6L2r1O00T64NoVaaSU3VCpR6zUqhsmtAUM3YAcCMGTMwceJE3HTTTejTpw9eeuklXLx40ZYlS254+onFgUZntvhmbhHMt5GkziQF8znwhNQZNbUtZ1BqFt7Ta4MvZyLVSE2tCRV5zbqizmtA0AV29957L86ePYtnnnkGJSUl6NGjB/Ly8hwSKsgJsdsV3tDazBbfzC18cRspEOVjGj5ndTmwehIk3ZILtVtpUkh505YaSH37T8/Ko8il1Cy8N9cGNQU7oUaR16wz6r0GBF1gBwBTp07F1KlTAz0MbfH4E4sbWpvZ4pv5NUqumQlE+RixFlhyZpLUtG5IK6QGUhv/cu1/+/K1IPUadKHUcmteLNDktSF4eTIBoeJrgE4QBIXfybWtsrISBoMBFRUViIuLC/Rw/Kfoa2DF7Qoe8Oqn1+kHtHmhcxqItFTtH7JPeTvTJjoTfPXNUGytkTfP683s88TPHT/hs1i1dB5dS9y8FrxhNl0tMC020wbHnreuAk1eG4KP1Nesp0W4FSAnNmFg10DIBnYHPgI+nqzQwXx4kfanYH0z9+fPZXtTFbvNIfIBwJsZPrfP6caYtzwrtEsWUgIpp3z4YdAW6EPimDz80BGs14xg5/Y1G/iJCjmxSVDeiiUPeHLLNK4l0GWMSB27IPj0GozrYvx9S9SThevelqbwaL1MPVpbPqA2Lm9ZuuLDhCuxW+oNZ+rqj8VVkoezawO71WhXkN1mZ2BHFlIWBscmA3f+E7h41v7TaOaz/JSqBYFojSN34boSGZUeJ+yESGKMP4gFUlIokXDlbOasYYbkhVL7dX4OZCR5aLDtFDXg6XpaFc7SMrAjCymfWIYtAtre4vyxwTazFWwCVYJCbokIJUpTeDTjpr1P5aonO5C6ytsZU3czZ9bXzYGPpB3PXZKH2sq7kOfklqZR6SxtUBUoJi+FWjHNUCInYFKS3B6SSpSmcPucuJodWw9f475Rv+Bw3//xfT9RX/RydXecQP1tkW9ILZKt4p7inLEje6FWTDNUBKqjhty1K0oUiJbynGOWAY2b8zXuT75ex6R4L1dnnByH3Wr8Sw23PlU+S8vAjhzx1mrwCWRHDTlrV5QqEM36c+qk9O/Fm4btSiV5sFuN/6jl1qfKe4ozsCP1U8MnNK0LdEcNqTPBSs7qcPZZnZT6vSjRsF2JJI9A/20FK2+6xviaymdpGdiRuqnlE5rWqSGdX+pMsKtZnd/9HWjU1HWHAE+ek/zL29+LNwWo3fVylZvkoYa/rWCjRNcYX1L5LC0LFDcQsgWK1cjTjgUkTo1V86UWe60uBzbOdh7kc2YudHhcgFpikVlPi9Wq8W9Li5TuGuMLAShozM4TXmBgpxKedixwd0xWi1fXzyt1RtZlkC8AjZoBl865PgYFB3+0LBPtVOHDNnjkfdeYm58EEjr559x7+hrxEAM7LzCwUwmpF2+pn9DEAogud4t0zmBQ4HNSZ2Q9uthzVjdoedL+0JOZM87A+Z+SPcv9cR3342uELcVIGjV/ulRycapoVfgzwM5/OO7PavG+J6dcgEctwgJfcoB8ROq6JW8btjP5xv+UTDbwx3Vcpa8RBnahSu1JCUotTnUZQIhhUOBzcsoFeHyxD2zJAfIRqVmoff/H+79dJt/4l6LJBn66jqvwNcLOE6FIrRWzzSbLVPyBjyz/W4kq9R43hGe1eJ+SMyPr7cWehWGDizULFYDj9YFZqJrmSdcYl0LzOs4Zu1Cj1orZzmYQGzW9NiZPSwh4+6bOoEA+Kbf45czIetQhwIPnIu1gAerg0fB6kbXwar06CV1jyg4DXz/v/jlC7DrOwC7UqLFittgauEvnLf9t1NRJxqPEi7e3b+oMCuSReotfTlFXjzsEsDBsUFPp+iaSQex60f8xkaS2Btf9oq+lBXbOruNqXmPuJQZ2oUYNFbPr/0HFtHA/gxgeDUxYC1w8K/8P0OPZHgYFsokmqThZxCy3qKvYDI2tzAkLw4YkFa5vIolcXS92/i9w93L3/Zw97fqh9jXmXmJgF2oCXTFbdhsgAag6Y1lX0fVu+c/n0WwPgwLZPLnFL/d2mtgMzeH1vCVHpCVSrhdf/MV9nVJPun7I+QCqUQzsQk0g+xp6U1HcmxlE0QCiJdBljLQpf3LN01v8cm+nOZuh4S05Im1RckmQnA+Ial1jrjAGdqEmUH0NPSo7Uo+3M4iu3vwzn2VQ4C1vbvErcTuNt+SItEPpJUFSP9ypcY25DzCwC0WByCjzuOyIgjOIYm/+DAq8F+hb/ETkX94kH/jieiHlOq6GNeZ+wMAuVPn79pVHfyhc66YZgbzFT0T+5W3yQaCuFyHyAZQFikOZ9RNO17st//Vl8OTJH0pcSlAsZA0JLBpLFBqUKHAfqOuF2wLIEgvfqxwDO/IPKX9QsSmWsiZj3gImfm7JiGJQpx3WW/xxyfbbGaATBQe3yQewJB+YTe6PFYjrRYh8ANUJguDhavbgVFlZCYPBgIqKCsTFxQV6OMHFlhULOE3a4Jt/cAjiwp9EIa3oa2DF7e73m/i59HXLgbheOL2V3FLV1RDkxCZcY0f+wzZAoYHJKETByRfJB4G4XgR5iSQGduRfQf4HFVI4M0cUWrxJPlDb9SKIP4AysCP/C+I/qJAR5C15iMgJtvDSBCZPEJE8SmTFEZH2eJJ8wOuF3zGwIyLplMyKIyLtkZPNyutFQPBWLBFJFyIteYjIBbbwUjUGdkQkXYi05CEiN9jCS7UY2FHwUVv2VTAJkZY8RKQAXi8CgoEdBRdmX/kWe8ISkVS8XgQEkycoeDD7yvdCpCUPESmA14uAYGBHwYHZV/7DnrBEJBWvF37HW7EUHJh95V/sIEJEUvF64VcM7Cg4MPvK/9hBhIik4vXCb3grloIDs6+IiIgY2FGQsGZfOSzQtdIBcS2ZfUVEREGNgR0FB2ZfERERMbCjIMLsKyIiCnFMnqDgwuwrIiIKYQzsKPgw+4qIiEKUZm7F/u1vf0P//v0RExOD+Ph4p/ucPHkSI0aMQExMDBITE/Hkk0+irq7OvwMlIiIiChDNzNhdvnwZv//975GRkYG33nrL4fsmkwkjRoyA0WjEzp07UVxcjAkTJiAiIgJ///vfAzBiIiIiIv/SCYLgrAeTai1fvhzTp0/H+fPn7bb/5z//we23344zZ84gKclSq+yf//wncnJycPbsWURGRko6fmVlJQwGAyoqKhAXF6f08ImIiIhkkRObaOZWrDu7du1C165dbUEdAGRlZaGyshI//PBDAEdGRERE5B+auRXrTklJiV1QB8D2dUlJiejjamtrUVtba/u6srLSNwMkIiIi8rGAztjNmjULOp3O5b/Dhw/7dAwLFy6EwWCw/WvVqpVPn4+IiIjIVwI6Y/fEE09g0qRJLvdp27atpGMZjUbk5+fbbSstLbV9T8zs2bMxY8YM29eVlZUM7oiIiEiTAhrYJSQkICEhQZFjZWRk4G9/+xvKysqQmJgIANi0aRPi4uKQnp4u+rioqChERUUpMgYiIiKiQNLMGruTJ0/i3LlzOHnyJEwmEwoKCgAA119/PZo0aYLf/e53SE9Px/3334/FixejpKQETz/9NKZMmcLAjYiIiEKCZsqdTJo0CStWrHDYvnXrVgwePBgAcOLECTzyyCPYtm0bGjdujIkTJyI3Nxfh4dLjV5Y7ISIiIjWRE5toJrDzFwZ2REREpCZyYhPN3Ir1F2ucy7InREREpAbWmETKXBwDuwaqqqoAgJmxREREpCpVVVUwGAwu9+Gt2AbMZjPOnDmD2NhY6HQ6xY9vLady6tQp3uoFz0dDPB/2eD6u4bmwx/Nhj+fDXrCdD0EQUFVVhZSUFOj1rksQc8auAb1ej+uuu87nzxMXFxcULzal8HzY4/mwx/NxDc+FPZ4Pezwf9oLpfLibqbMKml6xRERERKGOgR0RERFRkGBg52dRUVGYN28eiyZfxfNhj+fDHs/HNTwX9ng+7PF82Avl88HkCSIiIqIgwRk7IiIioiDBwI6IiIgoSDCwIyIiIgoSDOwU8Oqrr6JNmzaIjo5G3759kZ+fL7rvG2+8gUGDBqFp06Zo2rQpMjMzHfafNGkSdDqd3b/s7Gxf/xiKkHMuli9f7vBzRkdH2+0jCAKeeeYZJCcno1GjRsjMzMTRo0d9/WMoRs75GDx4sMP50Ol0GDFihG0fLb82vvrqK4wcORIpKSnQ6XT49NNP3T5m27Zt6NmzJ6KionD99ddj+fLlDvvIOcdqIvd8fPLJJxg6dCgSEhIQFxeHjIwMbNy40W6fZ5991uH10alTJx/+FMqQey62bdvm9G+lpKTEbr9QeW04uy7odDrccMMNtn20+tpYuHAhevfujdjYWCQmJmL06NE4cuSI28etXr0anTp1QnR0NLp27YoNGzbYfV/r7y2uMLDz0ocffogZM2Zg3rx52LdvH7p3746srCyUlZU53X/btm0YN24ctm7dil27dqFVq1b43e9+h9OnT9vtl52djeLiYtu/Dz74wB8/jlfkngvAUjyy/s954sQJu+8vXrwY//jHP/DPf/4T3377LRo3boysrCzU1NT4+sfxmtzz8cknn9idi4MHDyIsLAy///3v7fbT4msDAC5evIju3bvj1VdflbR/UVERRowYgSFDhqCgoADTp0/HH//4R7tgxpPXnFrIPR9fffUVhg4dig0bNmDv3r0YMmQIRo4cif3799vtd8MNN9i9Pr755htfDF9Rcs+F1ZEjR+x+1sTERNv3Qum18fLLL9udh1OnTqFZs2YO1w4tvja2b9+OKVOmYPfu3di0aROuXLmC3/3ud7h48aLoY3bu3Ilx48Zh8uTJ2L9/P0aPHo3Ro0fj4MGDtn20/N7ilkBe6dOnjzBlyhTb1yaTSUhJSREWLlwo6fF1dXVCbGyssGLFCtu2iRMnCqNGjVJ6qD4n91wsW7ZMMBgMosczm82C0WgUnn/+edu28+fPC1FRUcIHH3yg2Lh9xdvXxtKlS4XY2FjhwoULtm1afW00BEBYs2aNy32eeuop4YYbbrDbdu+99wpZWVm2r709x2oh5Xw4k56eLsyfP9/29bx584Tu3bsrN7AAkHIutm7dKgAQfvvtN9F9Qvm1sWbNGkGn0wk///yzbVswvDYEQRDKysoEAML27dtF97nnnnuEESNG2G3r27ev8Kc//UkQBO2/t7jDGTsvXL58GXv37kVmZqZtm16vR2ZmJnbt2iXpGNXV1bhy5QqaNWtmt33btm1ITExEx44d8cgjj6C8vFzRsSvN03Nx4cIFpKamolWrVhg1ahR++OEH2/eKiopQUlJid0yDwYC+fftKPr+BosRr46233sLYsWPRuHFju+1ae214ateuXXbnDwCysrJs50+Jc6xlZrMZVVVVDteOo0ePIiUlBW3btsX48eNx8uTJAI3Q93r06IHk5GQMHToUO3bssG0P9dfGW2+9hczMTKSmptptD4bXRkVFBQA4vO7rc3ft0PJ7ixQM7Lzw66+/wmQyISkpyW57UlKSw1oPMTk5OUhJSbF7gWVnZ+Odd97Bli1bsGjRImzfvh3Dhg2DyWRSdPxK8uRcdOzYEW+//TY+++wzvPvuuzCbzejfvz9++eUXALA9zpvzGyjevjby8/Nx8OBB/PGPf7TbrsXXhqdKSkqcnr/KykpcunRJkb8/LXvhhRdw4cIF3HPPPbZtffv2xfLly5GXl4fXX38dRUVFGDRoEKqqqgI4UuUlJyfjn//8Jz7++GN8/PHHaNWqFQYPHox9+/YBUObarFVnzpzBf/7zH4drRzC8NsxmM6ZPn44BAwagS5cuovuJXTusv3stv7dIER7oAYSy3NxcrFy5Etu2bbNLGhg7dqztf3ft2hXdunVDu3btsG3bNtx2222BGKpPZGRkICMjw/Z1//790blzZ/zrX//CggULAjiywHvrrbfQtWtX9OnTx257qLw2yLX3338f8+fPx2effWa3rmzYsGG2/92tWzf07dsXqampWLVqFSZPnhyIofpEx44d0bFjR9vX/fv3x7Fjx7B06VL83//9XwBHFngrVqxAfHw8Ro8ebbc9GF4bU6ZMwcGDBzWxNjCQOGPnhRYtWiAsLAylpaV220tLS2E0Gl0+9oUXXkBubi6++OILdOvWzeW+bdu2RYsWLfDTTz95PWZf8eZcWEVERODGG2+0/ZzWx3lzzEDx5nxcvHgRK1eulHSx1cJrw1NGo9Hp+YuLi0OjRo0Uec1p0cqVK/HHP/4Rq1atcrjd1FB8fDw6dOgQlK+Phvr06WP7OUP1tSEIAt5++23cf//9iIyMdLmv1l4bU6dOxeeff46tW7fiuuuuc7mv2LXD+rvX8nuLFAzsvBAZGYlevXphy5Yttm1msxlbtmyxm4lqaPHixViwYAHy8vJw0003uX2eX375BeXl5UhOTlZk3L7g6bmoz2Qy4cCBA7afMy0tDUaj0e6YlZWV+PbbbyUfM1C8OR+rV69GbW0t7rvvPrfPo4XXhqcyMjLszh8AbNq0yXb+lHjNac0HH3yABx54AB988IFdGRwxFy5cwLFjx4Ly9dFQQUGB7ecMxdcGYMkg/emnnyR9KNTKa0MQBEydOhVr1qzBl19+ibS0NLePcXft0PJ7iySBzt7QupUrVwpRUVHC8uXLhcLCQuHhhx8W4uPjhZKSEkEQBOH+++8XZs2aZds/NzdXiIyMFD766COhuLjY9q+qqkoQBEGoqqoSZs6cKezatUsoKioSNm/eLPTs2VNo3769UFNTE5CfUSq552L+/PnCxo0bhWPHjgl79+4Vxo4dK0RHRws//PCDbZ/c3FwhPj5e+Oyzz4Tvv/9eGDVqlJCWliZcunTJ7z+fXHLPh9XAgQOFe++912G7ll8bgmAZ//79+4X9+/cLAIQlS5YI+/fvF06cOCEIgiDMmjVLuP/++237Hz9+XIiJiRGefPJJ4dChQ8Krr74qhIWFCXl5ebZ93J1jNZN7Pt577z0hPDxcePXVV+2uHefPn7ft88QTTwjbtm0TioqKhB07dgiZmZlCixYthLKyMr//fHLIPRdLly4VPv30U+Ho0aPCgQMHhGnTpgl6vV7YvHmzbZ9Qem1Y3XfffULfvn2dHlOrr41HHnlEMBgMwrZt2+xe99XV1bZ9Gl5Ld+zYIYSHhwsvvPCCcOjQIWHevHlCRESEcODAAds+Wn5vcYeBnQL+93//V2jdurUQGRkp9OnTR9i9e7fte7fccoswceJE29epqakCAId/8+bNEwRBEKqrq4Xf/e53QkJCghARESGkpqYKDz30kCYuRoIg71xMnz7dtm9SUpIwfPhwYd++fXbHM5vNwty5c4WkpCQhKipKuO2224QjR47468fxmpzzIQiCcPjwYQGA8MUXXzgcS+uvDWuJiob/rOdg4sSJwi233OLwmB49egiRkZFC27ZthWXLljkc19U5VjO55+OWW25xub8gWMrBJCcnC5GRkULLli2Fe++9V/jpp5/8+4N5QO65WLRokdCuXTshOjpaaNasmTB48GDhyy+/dDhuqLw2BMFSrqNRo0bCv//9b6fH1Oprw9l5AGB3LXB2LV21apXQoUMHITIyUrjhhhuE9evX231f6+8trugEQRB8Nh1IRERERH7DNXZEREREQYKBHREREVGQYGBHREREFCQY2BEREREFCQZ2REREREGCgR0RERFRkGBgR0RERBQkGNgRERERBQkGdkREEkyaNAk6nQ46nQ6ffvqp5Mdt27YNOp0O58+fV3Q8gwcPto2noKBA0WMTkXYxsCOikDRp0iSMHj1a1mOys7NRXFyMYcOG+WZQMnzyySfIz88P9DCISGXCAz0AIiKtiIqKgtFodLnPlStXEBER4fOxNGvWDJWVlT5/HiLSFs7YERF56Oeff4ZOp8OHH36IW265BdHR0Xjvvfc8Okb926nnz5+HTqfDtm3blB0wEQU9ztgREXlp1qxZePHFF3HjjTciOjo60MMhohDGwI6IyEvTp0/HXXfdFehhEBHxViwRkbduuummQA+BiAgAAzsiIq81btzY48fq9ZbLsCAItm1XrlzxekxEFJoY2BERBVBCQgIAoLi42LaNdemIyFMM7IgoZFVUVKCgoMDu36lTp7w65po1a9CpUyfR758+fRqdOnWy1aBr1KgR+vXrh9zcXBw6dAjbt2/H008/7fC4Tp06Yc2aNV6NjYiCHwM7IgpZ27Ztw4033mj3b/78+V4ds6KiAkeOHBH9/pUrV3DkyBFUV1fbtr399tuoq6tDr169MH36dPz1r391eNyRI0dQUVHh1diIKPjphPoLO4iIyKlJkybh/PnzstqJ+drPP/+MtLQ07N+/Hz169Aj0cIhIBThjR0Qk0eeff44mTZrg888/D/RQMGzYMNxwww2BHgYRqQxn7IiIJCgrK7O18EpOTvYqE1YJp0+fxqVLlwAArVu3RmRkZEDHQ0TqwMCOiIiIKEjwViwRERFRkGBgR0RERBQkGNgRERERBQkGdkRERERBgoEdERERUZBgYEdEREQUJBjYEREREQUJBnZEREREQYKBHREREVGQ+H/tQ8GQ7/fmWQAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig0 = plt.figure()\n", "\n", "# Plot 1: Observations Y\n", "ax0 = fig0.add_subplot(1, 1, 1)\n", "\n", "h_value = 0.25\n", "e_value = 5.5 # meV\n", "EIdx = np.argmin(np.abs(e-e_value))\n", "HIdx = np.argmin(np.abs(h-h_value))\n", "ax0.scatter(L[:,HIdx,EIdx],Y_obs[:,HIdx,EIdx],label='Observations')\n", "ax0.scatter(L[:,HIdx,EIdx],Y_sub[:,HIdx,EIdx],label='Subtracted Signal')\n", "ax0.scatter(L[:,HIdx,EIdx],Y_back[:,HIdx,EIdx],label='Background')\n", "\n", "\n", "\n", "ax0.set_xlabel('L [r.l.u.]')\n", "ax0.set_ylabel('I [Arb. unit]')\n", "ax0.legend()\n", "\n", "\n", "plt.tight_layout()\n", "plt.show()" ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }