From 27d1871637f09c3c559828c1c1cfe46c4568b70f Mon Sep 17 00:00:00 2001 From: Erik Tjong Kim Sang Date: Mon, 29 Mar 2021 18:44:37 +0200 Subject: [PATCH] added comments and split code --- test_five_models.ipynb | 554 ++++++++++++++++++++++++++++++++--------- 1 file changed, 437 insertions(+), 117 deletions(-) diff --git a/test_five_models.ipynb b/test_five_models.ipynb index 28dd58a..5c9ef70 100644 --- a/test_five_models.ipynb +++ b/test_five_models.ipynb @@ -2,58 +2,232 @@ "cells": [ { "cell_type": "markdown", + "id": "dried-serve", "metadata": {}, "source": [ - "# `sequgen` demo" + "# `sequgen` demo with five signal models\n", + "\n", + "This sequgen demo shows how you can use five different signal models and how they can be combined. We start with importing the packages required for running the demo:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "final-integrity", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy\n", + "from sequgen.deterministic.sine import sine\n", + "from sequgen.deterministic.triangular_peak import triangular_peak\n", + "from sequgen.deterministic.constant import constant\n", + "from sequgen.deterministic.normal_peak import normal_peak\n", + "from sequgen.stochastic.gaussian import gaussian\n", + "from sequgen.parameter_space import ParameterSpace\n", + "from sequgen.dimension import Dimension" + ] + }, + { + "cell_type": "markdown", + "id": "tight-zealand", + "metadata": {}, + "source": [ + "If this command fails to load the `sequgen` models, please check if you followed the installation steps present in the [README](https://github.com/sequgen/notebooks#notebooks) before running this notebook. " ] }, { "cell_type": "markdown", + "id": "cheap-notification", "metadata": {}, "source": [ - "Print the version of the library as used in this demonstration:" + "We need a function to plot the signals:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "id": "impossible-consultation", + "metadata": {}, + "outputs": [], + "source": [ + "def plot(time_points, signal_points, title, parameters=None, values=None):\n", + " plt.figure()\n", + " plt.plot(time_points, signal_points, \".b-\")\n", + " if parameters != None:\n", + " title += \" (\" + parameters.format_str().format(**values) + \")\"\n", + " plt.title(title)\n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "interstate-positive", + "metadata": {}, + "source": [ + "## Signal 1\n", + "\n", + "The first signal is a constant. The signal has one parameter which can take a value between 1 and 2. We define the parameter of the signal like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "differential-color", + "metadata": {}, + "outputs": [], + "source": [ + "signal_1_parameter_space = ParameterSpace([\n", + " Dimension(\"value\", 1, 2)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "therapeutic-glasgow", + "metadata": {}, + "source": [ + "Next, we select an arbitrary values for the parameter with the instruction `sample`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "integrated-sunday", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'value': 1.0891081613281477}\n" + ] + } + ], + "source": [ + "signal_1_parameters = signal_1_parameter_space.sample()\n", + "print(signal_1_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "thermal-senate", + "metadata": {}, + "source": [ + "Then we define the time points (x=axis) as a space between 0 and 20 divided in 100 sections. We will re-use these time points for the other signals." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "renewable-limit", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 0.2 0.4 0.6 0.8 1. 1.2 1.4 1.6 1.8 2. 2.2 2.4 2.6\n", + " 2.8 3. 3.2 3.4 3.6 3.8 4. 4.2 4.4 4.6 4.8 5. 5.2 5.4\n", + " 5.6 5.8 6. 6.2 6.4 6.6 6.8 7. 7.2 7.4 7.6 7.8 8. 8.2\n", + " 8.4 8.6 8.8 9. 9.2 9.4 9.6 9.8 10. 10.2 10.4 10.6 10.8 11.\n", + " 11.2 11.4 11.6 11.8 12. 12.2 12.4 12.6 12.8 13. 13.2 13.4 13.6 13.8\n", + " 14. 14.2 14.4 14.6 14.8 15. 15.2 15.4 15.6 15.8 16. 16.2 16.4 16.6\n", + " 16.8 17. 17.2 17.4 17.6 17.8 18. 18.2 18.4 18.6 18.8 19. 19.2 19.4\n", + " 19.6 19.8 20. ]\n" + ] + } + ], + "source": [ + "time_points = numpy.linspace(0, 20, 101)\n", + "print(time_points)" + ] + }, + { + "cell_type": "markdown", + "id": "compatible-assist", + "metadata": {}, + "source": [ + "With the time points and the signal parameters, we can use the `constant` function to create the signal and plot the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "instrumental-sending", "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "'0.1.0'" + "
" ] }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "from sequgen.__version__ import __version__\n", - "\n", - "\n", - "__version__" + "signal_1_points = constant(time_points, **signal_1_parameters)\n", + "title_1 = \"constant\"\n", + "plot(time_points, signal_1_points, title_1, signal_1_parameter_space, signal_1_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "inner-floor", + "metadata": {}, + "source": [ + "## Signal 2" ] }, { "cell_type": "markdown", + "id": "bearing-conclusion", "metadata": {}, "source": [ - "Import core classes and run `sequgen`'s minimum viable product:" + "The second signal contains a triangular peak, similar to the signal in the notebook [test_mvp.ipynb](test_mvp.ipynb). We need four parameters which can take variable values: the height of the peak, the time position of the peak and the left and right widths of the trianglular peak. For example, the peak height can have a value between 1 and 2. We add a fifth constant parameter `sign` in order to get a negative peak." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, + "id": "black-choir", + "metadata": {}, + "outputs": [], + "source": [ + "signal_2_parameter_space = ParameterSpace([\n", + " Dimension(\"height\", 1, 2),\n", + " Dimension(\"placement\", 5, 9),\n", + " Dimension(\"width_base_left\", 0.1, 0.5),\n", + " Dimension(\"width_base_right\", 2.0, 3.0),\n", + " Dimension(\"sign\", -1)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "thirty-nursery", + "metadata": {}, + "source": [ + "Next, we select arbitrary values for the parameters and generate the signal using the function `triangular_peak` and the time points defined for the first signal. Then we plot the signal." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "beneficial-spoke", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
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" ] }, "metadata": { @@ -63,119 +237,265 @@ } ], "source": [ - "from matplotlib import pyplot as plt\n", - "import numpy\n", - "from sequgen.deterministic.sine import sine as signal1\n", - "from sequgen.deterministic.triangular_peak import triangular_peak as signal2\n", - "from sequgen.deterministic.constant import constant as signal3\n", - "from sequgen.deterministic.normal_peak import normal_peak as signal4\n", - "from sequgen.stochastic.gaussian import gaussian as noise1\n", - "from sequgen.parameter_space import ParameterSpace\n", - "from sequgen.dimension import Dimension\n", - "\n", - "\n", - "def test_five_models():\n", - "\n", - " def visualize():\n", - " plt.figure()\n", - "\n", - " # plot signal 1\n", - " plt.subplot(3, 2, 1)\n", - " plt.plot(t_predict, y_predict_signal1, \".b-\")\n", - " plt.title(signal1_parameter_space.format_str().format(**signal1_parameters))\n", - "\n", - " # plot signal 2\n", - " plt.subplot(3, 2, 2)\n", - " plt.plot(t_predict, y_predict_signal2, \".b-\")\n", - " plt.title(signal2_parameter_space.format_str().format(**signal2_parameters))\n", - "\n", - " # plot signal 3\n", - " plt.subplot(3, 2, 3)\n", - " plt.plot(t_predict, y_predict_signal3, \".b-\")\n", - " plt.title(signal3_parameter_space.format_str().format(**signal3_parameters))\n", - "\n", - " # plot signal 4\n", - " plt.subplot(3, 2, 4)\n", - " plt.plot(t_predict, y_predict_signal4, \".b-\")\n", - " plt.title(signal4_parameter_space.format_str().format(**signal4_parameters))\n", - "\n", - " # plot noise 1\n", - " plt.subplot(3, 2, 5)\n", - " plt.stem(t_predict, y_predict_noise1, \".b-\")\n", - " plt.title(noise1_parameter_space.format_str().format(**noise1_parameters))\n", - "\n", - " # plot everything stacked\n", - " plt.subplot(3, 2, 6)\n", - " plt.plot(t_predict, y_predict, \".b-\")\n", - " plt.title(\"combined\")\n", - "\n", - " plt.show()\n", - "\n", - " # where I want the model to predict values\n", - " t_predict = numpy.linspace(-2, 20, 100)\n", - "\n", - " # signal1: sine\n", - " signal1_parameter_space = ParameterSpace([\n", - " Dimension(\"amplitude\", 0, 1),\n", - " Dimension(\"average\", 0.1, 0.9),\n", - " Dimension(\"wavelength\", 3, 5),\n", - " ])\n", - "\n", - " # signal2: triangular_peak\n", - " signal2_parameter_space = ParameterSpace([\n", - " Dimension(\"height\", 1, 2),\n", - " Dimension(\"placement\", 0, 20),\n", - " Dimension(\"width_base_left\", 0.1, 0.5),\n", - " Dimension(\"width_base_right\", 2.0, 3.0),\n", - " Dimension(\"sign\", -1)\n", - " ])\n", - "\n", - " # signal3: constant\n", - " signal3_parameter_space = ParameterSpace([\n", - " Dimension(\"value\", 100, 200)\n", - " ])\n", - "\n", - " # signal4: normal_peak\n", - " signal4_parameter_space = ParameterSpace([\n", - " Dimension(\"location\", 3, 10),\n", - " Dimension(\"stddev\", 4, 6),\n", - " Dimension(\"height\", 0.5, 2)\n", - " ])\n", - "\n", - " # noise1: gaussian (colored)\n", - " noise1_parameter_space = ParameterSpace([\n", - " Dimension(\"stddev\", 1),\n", - " Dimension(\"correlation_length\", 2)\n", - " ])\n", + "signal_2_parameters = signal_2_parameter_space.sample()\n", + "signal_2_points = triangular_peak(time_points, **signal_2_parameters)\n", + "title_2 = \"triangular peak\"\n", + "plot(time_points, signal_2_points, title_2, signal_2_parameter_space, signal_2_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "false-participant", + "metadata": {}, + "source": [ + "## Signal 3\n", "\n", - " # draw a sample of the parameter space for each space\n", - " signal1_parameters = signal1_parameter_space.sample()\n", - " signal2_parameters = signal2_parameter_space.sample()\n", - " signal3_parameters = signal3_parameter_space.sample()\n", - " signal4_parameters = signal4_parameter_space.sample()\n", - " noise1_parameters = noise1_parameter_space.sample()\n", + "The third signal is a peak as well, but this time in the shape of a normal distribution. The signal has three parameters: the position on the time axis of the peak (`location`), the width of the peak (`stddev`, one-sided) and the height of the peak:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "italian-account", + "metadata": {}, + "outputs": [], + "source": [ + "signal_3_parameter_space = ParameterSpace([\n", + " Dimension(\"location\", 11, 14),\n", + " Dimension(\"stddev\", 1, 2),\n", + " Dimension(\"height\", 0.5, 2)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "aggregate-amateur", + "metadata": {}, + "source": [ + "Variable values for the parameters can be chosen with `sample` after which the signal can be created with `normal_peak` and can be plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "scheduled-filter", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "signal_3_parameters = signal_3_parameter_space.sample()\n", + "signal_3_points = normal_peak(time_points, **signal_3_parameters)\n", + "title_3 = \"normal peak\"\n", + "plot(time_points, signal_3_points, title_3, signal_3_parameter_space, signal_3_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "amino-access", + "metadata": {}, + "source": [ + "## Signal 4\n", "\n", - " # generate predictions of y at t_predict using the model and the parameterization\n", - " y_predict_signal1 = signal1(t_predict, **signal1_parameters)\n", - " y_predict_signal2 = signal2(t_predict, **signal2_parameters)\n", - " y_predict_signal3 = signal3(t_predict, **signal3_parameters)\n", - " y_predict_signal4 = signal4(t_predict, **signal4_parameters)\n", - " y_predict_noise1 = noise1(t_predict, **noise1_parameters)\n", + "The fourth signal has a trigonometric shape (sine). We need three parameters: the amplitude of the signal, its average value and the length of a wave (period). The three parameters are defined to have variable values." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "compact-volunteer", + "metadata": {}, + "outputs": [], + "source": [ + "signal_4_parameter_space = ParameterSpace([\n", + " Dimension(\"amplitude\", 0, 1),\n", + " Dimension(\"average\", 0.1, 0.9),\n", + " Dimension(\"wavelength\", 3, 5),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "daily-still", + "metadata": {}, + "source": [ + "Again variable values for the parameters can be chosen with `sample` and then the signal can be created with the function `sine` and be plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "endless-dublin", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5le3ee7UBBW1sksnyKFsyqeUyRrflUi7Q92/vXt0vtTqLyaEZuHtpP/tZ+ng5NDjTpumLCrBnD1x7LXziE3DuuaVdLoD77tNGBrRs995bPqS+Zk16v7JSy1QOOP74dJ01NsKECS2nLyXs2JHeFymtOot9Ds0gmVRiyIRl/1JGP2d9MxGoqIA5c+DWW+GMM0rPLpqJLVt0W+G82fffD9//fumXbds21YouuAAGD4aqKli7tjRt2Zl4803dfsZZr6zUvy+LXbvggQfguOP0V1UFRx0VtlS5IyaHZnDCCboVUftu27b6v6KitNg/G9au1e33vgdTpmgZofSJb/9+mDEDzjkHbrtNzYHGwMGDpV+2v/5VTS4336wkUVkJF19c+sS3b5/wm9+o1vrYY3DJJfA//wOrV4ctWfH42c9g0yb4wx+UJPbuhd+V0OoQMTk0g/p63U6Zoo1KMgndu6tpotTNE089BRMnagP6pS9BmzZ6vKqqtInv5Ze1h3311XDjjdpwWg2ibdvSLtv992vv8/jjVXM4//zyIL6XXqqmrg6uu07///Snalo677zSJTyAJ56An/wEzjoLTjoJjjlGTbe//rWaO0sBMTk0g6lToVMn+M1v0oRwwQXwzjv68pYq6upg9mz45Cf1fyIB//qX7n/lK6VNfI8+Cp07q+YAWpYvfUk1o6efLt2yzZunppcrrkgfu+oq3VrNthSJb8YM+N//Hc6IEar9AKxbp9/XggUa5VOKBJFKwWc/q2bp6dPTZfiv/4LNm1XjK4VyxeTQDF56SaNebK8a9APctk0JolTx7LO6teQAqtKPGgXr14cjkxfYvx8ef1x7nO3bp49feKH2sK1ZsBRxxx1qRho5Mn3slFNUkxg4sDQDJGyI5/btbVmzBmbO1OPJZLrzVaoakdtfuX9/ugxVVarJPvFEaZgCY3LIgrVrYdmydG/G4rTTdFuKL6zF00+rQ/r44w89fvLJ2pMr1VVjk0nYuhU+97lDj590km5nzQpcJE/wyivwyCNqPrrwwkMblNNPVwe8LWMpIZlMR8wdPJj+pmpqNGwcSte/N2mSbjO1uldeSX9fpUB8MTlkwdSpus0khyFDYNiw6Fdqc9i/H154QW2f1gltcfLJ2rguXRqObMXirrv0Q+zW7dDjffvCoEGlSw7/93/p/cwG5YQT1H69ZEngYhWNmhr7DppDGtBEQr+/Pn3UL1ZqGhFAb2edtc9+9lCtrqamtPx7MTlkgX05x45teq6mRnsApeh3uOce2LkTRoxoeu6UU3T7+uvByuQFXn9dVfV9+5T4MtX1CRNKlxy6dtVtZWVT34KNqHvrrcDFKhqJhH5jw4fXNzGLJRLaMbNRdaWG+fN1e9NNTcv1t7/p/jXXRJ/4YnLIgDHqb/j4x9ORLm6Uqt8hlYJvfUv3b721aQP6kY9Ajx5qWio1PPFEej+buj5hAqxYoZpRqaGhAY44QusssxE96igdlFmK5FBfD7W1UFOzJWsjOW6cDvp7//3gZSsW8+erZnD00U3PnX8+dOlSGhFLnpCDiNwnIptEJGuTKSKXich8EVkgIjNEZJzr3Crn+FwRmeOFPMXgr3/Vl3bo0OznS9Xv4HaSZWtAKyrSfodSw4ABuq2oyB65Y0fczp4dqFieYPFiGDNGx6RkNqKVldqIliI5LF6s22HDdmc9P85pIWwvvJQwb54GeFjfiRsiapEohc6lV5rDn4BzWzi/EjjNGHMM8CPgDxnnTzfGHGeMOdEjeQpCKgVf/rLu/+pX2aMJhgxRh+4990Q/2sANS2othT6efLJ+tNu2BSpa0ais1O13v5s9cmf8eC13KZqWFi/O3gO1OOEEePvt0jNz2sZx6NDs5HDssbotRXKYPz8tfzaMGaPlj3rwhyfkYIx5FWi2STHGzDDGWAVxJjDQi/t6jWRSnbZwaAiaG6mUjnpctKg0wtEshg/X7ac+1Xzo48kn67ZUymSxZImOSbnjjuzl6txZG9hSI4cdO2DDhtbJYdcuNZuVEhYu1JDjfv2y21f69YNevbQXXkrYvl3NYS2Rw9ix2gGrrQ1MrIIQxsR7VwLPuv4b4AURMcA9xphMrQIAEZkCTAGorq4mWaBdp76+vtlru3TpgsjxGANVVY106TKPZHLnIWkefHAwjY3DAKGhwXDffStpaFiTNT+v5PIC8+d3BY5n0qR5NDS8n5X49u6toKJiEjfdtINVq1YyZsxO3+UqFG65Uqlj6d+/Da+88maz6QcPPorXX+/JtGkzmkRq+SVXsVi0qDMwngMHFpBMZneYNDZ2Ak7kL39ZyOmnbw5ELi8wffoxDBrUlj17mpdr0KBxvP56Jclk8HazQp+X/c5gPslk9v7ygQPdgON48MF5nHhifk6VQOvRGOPJDxgKvNNKmtOBxUBP17EBzrYPMA84tbV7jR8/3hSKadOmtXi+e3djTjzRmBkzsp+fMcOYdu2MAd02l85ruYrFH/+oMr/3XvNpZswwRkTTdeig//2Wq1C45RoyxJhLL205/e9+p+VatcpXsTx9XvffrzIvWdJ8moYGY9q0Mea//is4ubzAwIHGfOELLcv1rW/pe3jgQHByWRT6vO66S+ts3brm09TVaZpf/jI4uSyAOSbHNj2waCURORa4FzjfGPNhN8gYs97ZbgIeB0KbsHfXLo2OuPDC5sPMEgl48EHd//a3ox+OZrFsmcZYDx7cfBp3h6QUBumARn2sWaPRVi3BOqVvvLF0zGaLF6t/yJoEs6FtW523p5Sc0jt26DQZY8a0nG7cOK3fZcuCkcsLzJ+vUX/9+zefpk8fHQsRdad0IOQgIoOBx4AvGmOWuo53FJHOdh84GwjtkVm7bbZxAG6cf76GqpUSli3TRqYluWtq0udLZb6e5cvVsdfaVMi7Hb/nww+Xjq9o8WKdMqO1d+2EE5Qcou7gtFi0SLfZxhG5UYpOaeuMbs10aZ3SUYZXoax/BVLAUSKyTkSuFJGrRMSZHoybgZ7A7zJCVquB10RkHjALeNoY85wXMhWC997T7ZFHtpyuqkqjlkrJCbhs2aFz82RDIqEzmYJOM1wKWpEdHdwaOdjBfcaUjlbUWqSSxQkn6BiOG24oDdKzjWJrmsPo0RqJVipOaTthYEvOaIuxY9UpH2VC9ypa6RJjTD9jTBtjzEBjzB+NMXcbY+52zn/VGNPdaLjqhyGrxpgVxphxzm+MMeZ2L+QpFMuX67Y1crBpLJlEHcZo2VojB0jPaNqli78yeQVLDq2ZlWpqSmv67oYG7XzkQg52Sob/+Z/S0IoWLtSBfUOGtJyuXTsdL1AqmsPKlaqh5koO9fWHru4XNcQjpF147z0Nn7NTFrSE4cNLR3PYsAE++KB1cxno3FGgL3opYMkSnZm0Y8eW0yUScNFFSgwvvRR9rWjZMu2JjhrVetp163Tb2FgaWtHChao1ZJuBIBP9+x867XWUYUksV3KAaJuWYnJw4b33ctMaQNNt26ZxzVGHdejlojn06qUNbakQ35IlrWsNFomENp65NLhhw44gzkVzsNpeqazt8M47rZuUQAkhmVQHdiloRE8/rVu7UFhLsOWPyaFEsHx5fuQApWFayoccRFQrKgXNwRidRTbXdXmtGWPVKt9E8gyLF2td5FK2REIJcuTI6K/tYAd/5UIOyaRO5w1qZouyRpRKwZ/+pPuf/GTrRNatm077EpNDCWDfPp0FMhfTC6TDC0uhh71smfYoBw3KLf2wYaVBDps3q+aWKznY+bJKhRyGDFHbfC4YPVp9D1EmBkjPSmqnPGkJNTXpRZqiPsW1m8hyNe3ZhZqiqhHF5OBg1Sq12eaqOVhyKBXN4cgjc/sgIU0OUY6kgNwjlSxKjRxyMSlZDBkCq1dHu85SKfjmN3X/e99rvVFMJODJJ3X/3/892sRniStX014qpUu/btwYXZNZTA4Ocg1jtejcWQeylIrmkItJyWLYMI262LLFP5m8QL7k0K2bRmFFnRxee02dtrkERlgMGaK27ihPcd3c8pkt4cwz9Tn4OeWJFzjuON2eeWZupr1CNI2gEZODAxvGmqtZCUojnLWxUWXMlxwg+qalJUs03LGlUd9uiKj2EGVySKXgrLO0Ef3HP3LvUVp/yurV/slWLAoNJx48ONohn5COGPvCF3LTcEphVbiYHBy8955G6fTpk/s1pUAO69bB3r35kUOp+FOWLFEyz9VcBtEnh+bWVm4NpUAOiYT6RoYPz89xXgrkYOXLtaOSSMDdd+v+LbdE02QWk4MDG8aaj/o6fLg6se3HHEXkE6lkYW3zUdcc7MjZfOy1lhyiapuvqUmTXb69a4g2OYCGpU6alF9jWI7kABrVBLkHHQSNmBwc5BPGanHkkWq2ifIHWQg5dOqk/pQok8OCBV1Zsyb/dTWGDk1PsBhFJBLwxS/q/gsv5N6I9uqlS4ZGuRE9eBDWr9conXwweLCGwOYyfiAsrF2rHUu7KmEu6N1bzaJRXSs7Jge0gV+5Mj9/A6TJJMrml2XLdFGVfF5aiH4464wZPYD850oqhYglEV3s5mMfy+8aG7EUVdTWKkHkGlJtYXvjUW1EQUm5ujr70qDNQUSJMqrliskB7c00NOSvOZRCOOsbb2i0xxtv5Hdd1AfC9e+/F2h+3ejmUArksG5d/r1riD452EawXMkhH5OSxaBB0S1XTA7kN+GeG/36aa88qppDKgUzZkBdXf6x1MOGaUNjw+2ihs6dNSbymmvyc27G5BAeiiWHKJvMYnIoU7zwgm537mw5XSbsVBNR1RySybTjNd9Y6mHDNJxy8+Y89OQAsWWLynXzzfk5N0thrEMx5LB5s06yGEUUSg79+6uGGFVyMEbLlm+5QK/ZsCGanbDDnhxSKZ3qGDRGOd+RilEOZ7WrnxUyIZsd61Bb28FzubzA5s3taN9eV93KB1Ef67Brl0b0FEoOEN1GdN06DRfv1i2/66qqlCCiWq5t25SQC9UcDh7UkdJRw2FPDu5Rm4WMVOzQAd59V803UYPtyVx0Uf4Tslly2LixvfeCeYAtW9oyYEBhI2ejTA7r1+u2EHKIuvll7VotVyF1FuVw1kLCWC3sNxpF09JhTw7FLAKTSsHjj+tUAFGcH8U2NFdfnf8gm8GD9blElxza5R2BZTF0aHTnIbIjbYvRHKLqdyjU9ALRJgfbsMfkUGZIJNSxfOyx+feu3fOj5DpXTJCw5NDSYufNoU0bHS2eSvWMHOmBmpUKaUBByWHnzmiuxWEbiULK1r+/DqArV3JYu1bDzqMGS1qF+hwgJodIwhgdEHXmmfn3rt3zo1RWRm9+FEsOhfSwUynYtAmWL+8UOa3ImOI1B4imaclqDoUQelWVkkoUyWH/frWrF0MO+/bpOxk1rFmj4xt6987/2m7d1A9TtuQgIveJyCYRybp0hSh+LSLLRWS+iJzgOne5iCxzfpd7IU8+2LlTnUmFfIyJBDz8sO5/+9vRmx9lwwaNzOnUKf9rk0nbS5PIzRq5dSvs319RtuTQu7eGSBeCqIazbtigpF4MOUA0TUtr1mi5cln2NBMi0Q1n9Upz+BNwbgvnJwMjnd8U4PcAItID+AEwEZgA/EBEunskU07YsEG3hTY0n/iEbjtEMKhn/frCSA9UC6qqAjCRW3qyGLs8pMnh3nujpRFB4WGsFlElh2LMZRBtcijGXAZlTg7GmFeBbS0kOR94wChmAt1EpB9wDvCiMWabMeZ94EVaJhnPUYxdHtSJ3bt3mmSihPXrCye9RAKuvRZA+Pvfo6UVFWMug/T6zM8+G71AAi/IYf36dAReVGAJvVw1h0Kc0RZRJYeqgO4zAHAXf51zrLnjTSAiU1Ctg+rqapIF2jnq6+sPuXbq1GrgaNaufYNkck9BeXbpciILFuwlmSx8QdhMubzAypUfZdy47SST7xZ0fdeuvYCxbNgwh2QyOrOevfxyP+Ao1qxJsXdvQ97XP/jgYGAYxggNDY3cd98qGhq8aXWKrcdVq05h6NBNJJPLCrp+795+HDx4FP/4R4rq6vSz8eP9ygfJ5CDgSFatms7mzekRX7nKZQx06DCJVGojyaT/A4tylevgQWHDhlNpbFxNMrmqoHsdPDiUurohvPjiq7Rp03IIXaD1aIzx5AcMBd5p5txTwCTX/6nAicD1wH+7jn8fuL61e40fP94UimnTph3y/8c/NgaMqa8vOEszebIxRYiUVa5icfCgMVVVxtxwQ+F5pFL6bJ5+2ju5vMD3v29MRUWj2bevsOtnzDCmokLL1qGD/vcKxdTj7t0q0+23F37/55/XPKZMObRcXr9f+eI//sOYLl2aHs9HrqOPNubCC72TqSXkKtfq1fq8//CHwu91772ax4oV3snVHIA5Jsc2PahopfWAW6Ec6Bxr7nhg2LBBJ6br2LHwPPr3j55ZafNmNS0UanoBDfGF6I3eXL8eunff92GkWL5IJOCcc7Te8w1f9hPFDICz2OYYd++9N1oms2Lt8hDNsQ7FDICziGo4a1Dk8ATwJSdq6aPADmPMRuB54GwR6e44os92jgWGDRsK9zdY9Ounk9tFaX6UYh3tAH37HppXVLB+PfTunb85yY1x4zRK7aMf9UgoD1CsXR5g6VLdNjZGa23imByaR1mTg4j8FUgBR4nIOhG5UkSuEpGrnCTPACuA5cD/Al8HMMZsA34EzHZ+tzrHAoMX5NC/v36MUYrBLtZpCxq73aXL/shpDuvWQa9exZFD374ae791q0dCeYBio7BA15+GwubT8hN26oxisWlTdAgPihsAZxFVcvDEIW2MuaSV8wb4RjPn7gPu80KOQrBhA5x6anF5WHLZsCFtigkbxUZhWfTs2cDGjQXab3zC+vUwcmRxa7Paeqqt1VXUogBLDsUQeiKhEUudO8Mf/hANk1lDgzbqxTSgqRT8+c+6P3kyvPxyNMo2e7aOSVmwoHB5OnXSwXBRI4fDeoS0MdqgF/MxwqHkEBWsX6+DcqxpqFD07LkvUuXavVunvSjWrBRFf8q6dTrLbLFrCg8bpo1NFBpPSHdUiiGHYifI9AOpFPzzn7B3b/H+nSiGsx7W5LBli5oWvDArQbTIYcMGXbawqkjdsGfPfZFqQG1DU6xZKark4IXppV8/1YiigkLXcXCjpkbNZKDvdBTMZelZBIonrE6dYM6c6AQQwGFODrYxL5YcqqvVxhslcihmdLQbPXs2UFsbnQnPYnJoHX37RqtcL7+s2y1bCs8jkYB//Uv3v/a1aGhFlqCK9e+kUjBrltZZlCLMYnKg+Ea0qkpnMI0aORRrLgPo0WNfpBy3lhyKNSt17Kh2+Sg1ol6Sw+7dUB+BcYupFPz4x7p/xRXFNXxnnaVBEoXOO+U1Jk5U0+1ppxUXEu2lBuIlYnLAmx52//7Rami88KUA9Oqljt+olM06bYvVHEC1h6iUyzptvTIrQTTK5qWvQESJLyoms61btVG/8MLiNJn0PGY6y3MUTGYQkwNQvNMWojUQbu9efXG9IL0ePbQRjkJDA6o5dO0KHToUb+eKkvnlqad0u3dv8XnZ9zkKjWgxi2llQ5TIwcpRbPuRSKS1q9/8JhomM4jJgV69VFUtFlEiBy8GwFn07LnvkDzDxvr13vSuITqaQyoFl12m+3feWbzNOUqaQyIBY8dqBJUXo9Gj5Gz3ihwgTZqFrAnhFw57cvCidw2az6ZNGv0UNvwghyg0NKBmJS/KBdEhh2Qy/d4cOFC8zTlKmgPoSPQJE7zpEUdJ2/OSHKJWZxCTg2fk0K+fjpuoq/Mmv2Lgxehoi3btGunWLTqaw8qVajJbuLBL0Xn166eO2127PBCsCNTU6EqC4I3NuUcPzSdKjagXDShoPjYEPWx4SQ59+hyaZxRw2JODV73QKI118Gp0tEVUetjTp2vD8NZbcN1148rG/JJIwMUXq23+pZeK72FXVGh4dRQamg8+UPKtrvYmP9sQR2GqmtpaHbBYyEqLmWjTRk3cUagzi8OWHA4c0Irw0qwE0SCHDRt0Zbpu3bzJLyrk8MwzujUG9u+Xos0vUSEH0GiVfv3glFO8yS8qdWY1aS81B4hGI7pxoz5nEW/yi5KzHQ5jcti0ScPQvCaHKHyQdoyDVy9tVJztRx2l24oKaNPGFG1+cc+vFDa8NL1AdBoaL00v7nyiUjav6ywKZmmLw5YcvBzjAGozrKiIRiO6eLFqRl6NtLS9UNPyIlW+w9plp0yBO++c50nkC0SD0OvqvDO9QHQ0h5gcckdUTIEWMTl4RA6VlVq5YZNDKgXz58OqVd4Nxe/XTwcwvf9+8XkVA9ur+u53YcyYnUXn1727xt5HpRH1uhdqF3wKEzE55A6r7YXdCbOIycEjcrB5hU0OyWT65fJqKH5U/CmWHLzqYdsRt2GTg10LxGvNwZjwHbd1dfqcvYrfb9dOST1scmho0M6S1+SwZ0/40XMWhy05zJqlL+17Hq5VHgVymDhRt14u9hIV80tdnUaGFDultRtRML9s26Y9fK8bGgi/EbXrZRQ7O7AbUSB0rx3t7rzCrjOLw5IcUil44AHtWZ1zjne2+SiQw9Chur3wQu/WR44KOXitxkM0yMFrjQjKu86i4Gz32lzmzisqTunDkhySyfR6z17OgnjwoMbhv/qqN/kVAvvSXnmld3O02IYmbOLz2mkL0SAHPxuaKDSiMTnkhqjUmYVXa0ifKyJLRGS5iNyQ5fwvRWSu81sqIttd5w66zj3hhTytoaYmHebplenFvYyhl9pIvvCjF9qpUzSmt/aLHLZtUxtyWPCjzmxDE3ad1dZ6X2flSg72OYVdNouiyUFEKoHfApOB0cAlIjLancYY8y1jzHHGmOOA3wCPuU7vseeMMZ8uVp5ckEioHfSEE7wzvbi1kf37w5uT3Q9bKESnh+1HuWzeYcGPhiYKjls7nYwfmkPY61XYb8GGV3uBnj016rFsyAGYACw3xqwwxuwDHgbObyH9JcBfPbhvwTAGduzQxUO8Mr3U1OgQeNAKDmtOdksOXs/u2LGjOvHD0oj279cevh+9UAiX+OrqVIPt2tXbfMN23O7cqVOQlyuh9+yZXrrUC0Rp2hPwhhwGAO6lsdc5x5pARIYAw4CXXYfbi8gcEZkpIhd4IE+r2L5dfQ1eNjSJBDz0kO5/+9vhzcleV6cvrSUqL2DHTqxeHd4yhjYk0w+zEoTbiFqNyKsR7RZhT2/th0bkzi/sstl3x0tEwWRm4WGAWU64GHjUGHPQdWyIMWa9iAwHXhaRBcaYJgGmIjIFmAJQXV1NskC7TX19PU8+OQuYwNati0gmvQsE79ixAjiVzZtXkEyuyVuuQsvkxjvvjKFTpyNIJmcXnReoXA8+uIKDB4cBQkNDI/fdt4qGhvzKVyyWLu0EnMjmze+QTG7x7Hlt2dIWOJlXX11K9+7Fe9wLkWvx4mM54ogqksm3ir6/GyJHs3JlF5LJNzx7Xvlg7tyuwPHU1s4lmdyeNU0hcq1d2xE4ialTF3LgwOZixSxIrqVLj6dDh4Mkk/M9vW+bNsewfHlbksk3C5LLUxhjivoBCeB51/8bgRubSfs2cHILef0JuKi1e44fP94UimnTpplk0hgw5qWXCs6mWXTubMy11xYmlxc45RRjTj/dk6yMMSrXjBnGtGmjz6xDB2NmzPAu/1zx9NN6/1QqLZcX2L/fGBFjvv99T7IrSK5x44w57zxv7u/GddcZ0769MY2N3j2vfPDww1pnCxY0n6YQuTZt0nx//evCZWsNrck1dKgxX/iC9/e94gpjBgxo/nyx9QjMMTm27V6YlWYDI0VkmIi0RbWDJlFHIjIK6A6kXMe6i0g7Z78XcAqwyAOZWoS1y3vpTLKorg43TtmPiJ5EQqesAI3ICsNk5kdED+jgrG7d4Nlnw40w87pcoCaKvXvV9h8G/AqOCNtxa4w/wRGQbj8ai18Ft2gUTQ7GmAPANcDzwGLgEWPMQhG5VUTc0UcXAw877GVxNDBHROYB04CfGGMCIwc/PshyJAdITyXt1foX+cKvOkul1Ac1Z044/pSDB9Wf4kdDE7Y/pbZWybdHD2/zDdtx65ejHTTPAwc0+CJseOJzMMY8AzyTcezmjP+3ZLluBnCMFzLkg02b9AXr2dP7vKurdVbUMGDnZfGL9CC8uXrq6nSshZdTZ0D2uaiC1Iy2btVeol+aA4TXiNoxDhU+DLUN03Hrl6PdnaeddiRMHJYjpOvqNNTTLs3oJcLUHPzUiKwJLqyy+TGYCg5dotOrAZH5wM+GxmoOd9/tzbKq+cIv0wuEG4kVBDlEYQqNw5Yc/GhoQPPdti2cNW7LmRz8qrNEAj7/eSUIL5bozBd+1tlaJ8D8kUe8WVY1X/hF6HB4aA5hIyYHjxGm+cXPhqZtWx1xW27kAHD88Wr7HzPGn/xbgm0E/Cjbm040pFfLquYLP0ZHW9hV08Jw3FofTkwOZQg/Gxrbww6DHPxsaEDLFpbPwU8TRZiE7mcv9PTTdWCdiDfLquaDxkb/ycFOdBk0amt1kGn37t7n3bkztG8fk0MosPO9+K05hNHD9jNEF8Lzp/g1dYZFmCazujptDDp39j7vRAJGjtSfF8uq5oOtW7Xx9pMcAG6/PfgIs/nzoUMHeOMN7/O2C1DF5BAC9uypZM+e8iWH7t110jU/EBY5+DV1hkWYdebX1BkWRx6ps+p6saxqPvBbi7Uaw113BRuCnErB889rOKtf943JISS8/77OlFWODY2fGhGERw5+DaayCNtP5HedlZu5DGDZMt02Nnq7JktrSCbTfg6/7mv9KWHjsCOHbdt0Rjq/PshOnVTlLEdy6NNHZ7MNeu0Dv3uhNp48TM3BL1g/UdCL1vtN6JMn69bL5XBzgb2P3/ddsSK8EfsWhx05+K05iITbw/a7FwrB90T9jMICdS727Fm+dbZvH+zeHewcmzNn6naNT/MznnGG1tukSd6tyZILJk7UQX2nnurPfVMpeOopXa8irBmQLQ5bcvDLaWvzLlcThb1PkPCbHGzeQdfZgQNqO/dbc4C0xhwEUim45x7d//Sn/WngRHQg3PDhwY5Nef99NSt95jP+3NevJYwLwWFHDtu360fi9WI4boShOezdqyafciUHP6bOcKNPn+DLtXmzmnuCqLPt2z1claYVJJNKfOBvAxdGnfkdEeheNKyqKrxFw+AwJIdt29p6vhhOJsIgB78jeiC8MRx+jrS1CKPOnn9etzt2+HcP+9ysxhwEamrS8yn5aZcPQ9vzW4tNJODXv9b9H/84vEXD4DAkh/ffbxtIQ7N5c1o9DAJBmV7c9woKfpvLIPiGJpWCq67S/R/+0D/bchhmpURCzT2jRvnrDwhDcwiiE3bGGbqNJ94LGEGRQ2OjDgQKCkGQQ8eO+itXcrBTMQeBZFJNLqAmGL9ML716qX0+SLMSQH29TvPuZ8/XEnqQkVh+m5XceYcdznoYkkObQBoaCLZygyAHm38YZiU/nbYQ/AdZU6M2ZfDX9FJVpQQRpFmpsVE15yDexf37dT2OoODndP8WdgqNsKaqsTgMySEYzQHKkxyCVuXt1BlLlvgb1hd0mG4iAZ/9rDbefs8G26dPOhAjCGzbpiZVP3vXEE4P207378caFRYi4ZjMMnFYkcOePfDBB1WBNKAQLPPX1kLXrtrj8BNBO26ffVa3L7/sb9x3GIReVQUDB8LJJ/t7H51GPjjNIQi7vDv/IL+zTZv8LxeEN7LdjcOKHII0vbjvFwSCsMtD8OTwwgu6Ncb/sEgI9oOsq/O/dw16jyDNSkFqse77BYGgvrNYcwgYQb203btrqGyQlbt0qTaefo+o7NNHB24FFYk1fLhuKyr8D4uEYOssyF5okGalIJy2EJ7mEAShh70WPXhEDiJyrogsEZHlInJDlvNfFpHNIjLX+X3Vde5yEVnm/C73Qp7mEBQ5BG0zTKVg3jxYtcr/IffV1dqLD2oefev4+/a3/Q2L7NBBHYHl2AutrlZz6p49/t8LgjMr9eypnYZyrbMw5sRyo2hyEJFK4LfAZGA0cImIjM6S9G/GmOOc373OtT2AHwATgQnAD0TEhyU0FEGRg71HUC9tMpl+ifwech90D9ve5wc/8H9AUJDTntiInqDMShBc2erqdNnVHj38vU9lpUZiBfUu1tfDBx8EV2cHDuh0HWHBC81hArDcGLPCGLMPeBg4P8drzwFeNMZsM8a8D7wInOuBTFlhPw4/p86wCJIcTjlFt0HMUBkGORxxhM526zeCrDMb0RNURwWCK9umTf5H9FgE6bgNSiNy3yNMp7QXUzUOANa6/q9DNYFMfFZETgWWAt8yxqxt5toB2W4iIlOAKQDV1dUkC+gev/DCaNq06ckf/zjP98VPjDmKtWu7k0zOzCl9fX19QWUC2Ly5LXAyp5yymYsvXktDw07PtIdMudasOQKYQDK5iDZt/H9zFywYRdeuXUkmD112q5jn1RwqK8ewYsURJJOzC84jV7lWrtTnuGXLIpJJf5/j2rWdgfG89NICPvjA/5GZixaNpWPH9iSTc1pNW2w9tmkzjmXLKkgm3y44j2zIJtfChV2AE6irm08yuc3T+2Viw4ZuwHE899zb1Nam51fx471vFsaYon7ARcC9rv9fBO7KSNMTaOfsfw142dm/HvhvV7rvA9e3ds/x48ebfDFjhjGVlcZAo+nQQf/7ie9+15i2bY1pbMwt/bRp0wq+15tvGgPGPP54wVk0i0y5tm3Te915p/f3yoazzjJm4sTW5fICX/uaMb17F5dHrnJNnarP8eWXi7tfLli1Su91773+38sYYyZM0HrLBcXW46WXGnPkkUVlkRXZ5PrnP/U5zpnj/f0ysWCB3uuRR1qXKx8Ac0yObbsXit96YJDr/0DnmJuAthpj7BIx9wLjc73WK6RXcJJApsLds0ft/zYU008E6Uvp1k1NV0GalYIoF+h9tmxJzyjqJ4I0UQQd8hlUFBYEG/gR5HcWhSk0vCCH2cBIERkmIm2Bi4En3AlEpJ/r76eBxc7+88DZItLdcUSf7RzzHDU1OkCsoqLRd7u8ez77Cy7wP7w0yJfWRmIFaecNkhyCisQKKtwTNBLriCMOBFJnxgQ3fgO0zqyj2G8E6bMMIxIrE0WTgzHmAHAN2qgvBh4xxiwUkVtF5NNOsmtFZKGIzAOuBb7sXLsN+BFKMLOBW51jniOR0FDIK65Y5fvKUUHNZ28RZC8UguutBRnRA8FG9WzaFExEj0X37vsCqbPdu1VrDvJdhGDqrK5ONed27fy/V2WlklCpO6QxxjwDPJNx7GbX/o3Ajc1cex9wnxdytIZEAhoa1pBIDPf1PjU1anrZuzeYBTtsRE/Hjv7ex6JtWx1XkUr5S7JbtwYX0QPBRvXY3nUQET0A3bvvD6xcEE6dDR3q772CNHFC+KOkD6sR0kEhkYBHH9X9b3zD//j8IF/aVApmz4YNG/wfcBdmQ+M3gjS9gGoOQWlEUL7aXpB1Fvb8SjE5+IRzz9VeoZ9LW1oESQ5px77/JrNyb2iC7IUGZVYqd0KPNYcYRcPaDMvtpQ1qHQIIvqHp2jW4SKygG5pu3fazdav/kVhBOtrd94k1B+8Rk4OPCGrEbZC90EQCbrpJ9//4R39NZkGTg4g6HF96yV9zmTHBNzTdu+8LJBIraG2vfXvo0sX/78yuKxK05hBUJFY2xOTgI4Igh4MHg43oAZg0Sbf9+rWcrlhs2qRaSnffZts6FKmUPsu33vLXn1JfH2xEDyg5gP890bo6ra+2Aa5KGkQPe/Nm3QatOUB4pqWYHHxEEOSwdav6AIJsaIJ6aa3TVsTf+1gENYFh0KYX0Ggl9739QtAaEQRjmw9ai3XfKyzTUkwOPsKSg5/T7ob50gbxQQZZrpoa9RWBv/6UoMelQFpzKLc6g2A0hzC+s7BHScfk4COqq3Wsw65d/t0jjJe2Z09tRGtr/b1P0BE9iQRceqlGmb34on/+lDDqzJLDX//qrz+lXDWHoH0pEGsOZY0gethhvLQVFcGp8kE3NOPHq5lu1Cj/7hGGWWnVKh0h+eyz/vpTwtIc/I7ECoPQ7TQdseZQhgiCHMJ4ae39/CyXnaMn6HL17atbP7WiMAh93rxugL9rce/bp4vThPEu+h2JtWmTRkYFsa6IRfv2Gl4daw5liKDIoW1bDcEMEn6Tw65d0NAQTkMD/tdZjx66znhQOO647R869v3yp4QR0eO+3x13+K8RBRUcYRHmQLiYHHxEUA1NkBE9Fn6TQximF0hrDn6bAoMu15gxOzn5ZC2fXxNPhqXFWlL67W/9M5ktXaph437PsJyJIFcnzERMDj6id29ttP1uRIP+GEEbGT8jscI0l4G/ZqWw6my0s7K7X472MMxlAMuW6bax0R+TWSoFs2bBunX+zyeWiTBHScfk4COqqjSyp9x6oaAv7b59sH27P/mHRQ5BLGYUFjnYhubgQX/yD6vOJk/WrV9rqAc1/iUbYrNSGSMI80tYDY29vx8IqxcqomXz2yEdBqH37au9a78ct7OdpbdXrfIn/+Zwxhm6xsIpp/hjMvvYx3TrF/m0hH37NBJr+vTg7mkRk4PP8JMc7Bw95UgOdXX6MQax6lYm/KyzhgbVtsIyBYI/ZXOvfnjeecGaXkRgwAAYMsQfk9lHPqLbT33KP39NNqRS8MADun/22cH7O2Jy8Bl+NjTbt2vPolzJoWfP9AywQcL6U/yAdZ6GWWd+aEVBr36Yib59/dP2bL6XXx4cMYA+Q2sC3L8/+Gcak4PP8JMcwjK9gP9RPWGZXsBfs1JYUVjgb53V1KRXtQva9AL+1pnN1z6/oFBTkw53rqwM/pnG5OAzqqt1Xd3du73POywHIKSn0PBTcwijXJB23NpFjbyE7f2FEYHip+aQSKhZZ/ToYE0vFkFoDkGTQyIBDz2k+9dfH/wz9YQcRORcEVkiIstF5IYs578tIotEZL6ITBWRIa5zB0VkrvN7wgt5ogQ/zS9hkkNFhfoD/Oxhh0UOffuqOr91q7f5plLwve/p/rXXBm9D7tRJVyb0q8527oTTTgu+EQOts61b1aTlNezzCuN9tJFYQa0P70bR5CAilcBvgcnAaOASERmdkext4ERjzLHAo8DPXOf2GGOOc36fLlaeqKFcycHet1zNSuB92ZJJtR1DODZkEf/8KTaqxu81PpqD7dX7oZHV1iqxBjl1hkWHDjqFht+TXGaDF5rDBGC5MWaFMWYf8DBwvjuBMWaaMcauZzQTGOjBfUsCfpNDRYWaeMKAX+Swd6/2Qt99N/jeNfhXZ0FNCd4S/LLN22cVtOnFws85sWprwysX+GsyawlexIIMANa6/q8DJraQ/krgWdf/9iIyBzgA/MQY889sF4nIFGAKQHV1NckCu1319fUFX1sINm9uByR49dUldOu20VO55s79CF279mL69BnFCdkCWpZrFGvWdCOZnOnpPV99tSdwDC++aHjllUbuvHMeY8bszEOu4rBmTQdgIi+/vIjKyvy6oq3JNXbssaxc2Ynbb3+HhoadgWkPVq6qqjG8994RJJOzPc3/3Xc7A+PZvHkByWTu9jiv6nH9er3/iy8uoL6+eHugW6533x1Hhw5CMjm36HwLQYcO41iyRO8faPtljCnqB1wE3Ov6/0XgrmbSfgHVHNq5jg1wtsOBVcCRrd1z/PjxplBMmzat4GsLQUODMWDMD3/YcrpC5Dr/fGOOOaYgsXJGS3J95zvGtGtnTGOjt/e8+mp9ZmBMZaUxd9yRn1zF4v339d533pn/ta3JNWmSMaedVohUxcHKdfXVxvTq5X3+Tzyhz2zWrMLkKharV+v9773Xk+wOkevoo4256CJv8i0EF19szIgRul/s8wLmmBzbdi/MSuuBQa7/A51jh0BEzgRuAj5tjGlwkdN6Z7sCSALHeyBTZNC2ra6p65dZKSx/A+i9Gxpgxw5v8x3ihCtUVoZjfunaVUfc+mWiCMsuD1pnW7akfR9eYaOjFIdVNj8jsQ5Xs5IX5DAbGCkiw0SkLXAxcEjUkYgcD9yDEsMm1/HuItLO2e8FnAIs8kCmSMEvJ+Dq1TqoKgy7PPhnm+/SRbff+U44YZF2Cg0/6mzjxnDJwS/HrW28wgoiaNdOO2FeN6INDbpGRdjkUF+vvyBRNDkYYw4A1wDPA4uBR4wxC0XkVhGx0Uc/BzoBf88IWT0amCMi84BpqM+h7MjBj4ZmxgxtaObPD36mSAu/yGHjRm2gf/SjcMIiwZ8627VLx7uErTmA92WrrdXAiLZtvc03H/jRww7b0Q7p9yXoCfg8mZzAGPMM8EzGsZtd+2c2c90M4BgvZIgyqqvhrbe8zfP553XrXtkrjIFH4A859OkTztQZFn37wpo13uYZ1mAqN/yK6gnbXAb+kEOU6mxj8/EsviAeIR0A/OiFHn20bisqwg2LBH/IIeyGxo86C9suD/6Rw8aN4TagUP7kELTfISaHAFBdrXH7e/d6myfAFVeEY5cHNSNUVJQnOfTt6/3aB1EgBz/NSjE5+IOYHMoY1pH0zDMtp8sHtqG57rrw7PKVlf5MobFxI/Tv722e+aK6WudW8nIKDVtnYTY0HTqow9/LOjMmGmal6mrvHbdhO9oBevXSby0mhzJDKgV33qn7l17qneN4wwbdRqER9bIXevCg5heFhga8LVttrZoAe/TwLs9C4HUPe8cO1YqjoDmA93XWq1d6dtQwUFGh72PscygzuOe593I+nQ0bdBK1zp29ya9QeB2mu3mz9tjDJgc/VHlrlxfxLs9C4DWhR8H04r6/l3VWWxvuWCKLMMY6xOTgM2pq0uF9Xs7Jbk0vYTc0FRWwdKl3GlEU7PLgj+YQBacteN/QlDs5hF0uiMmhLJFIwIsvaiN+6aXe+Qc2bAjfpJRKwUsv6Yp0Xo21sOayqJCD1w1N2OUC77W9qBB6OZNDv34xOZQlJk3yvpcfhYge9zKGXi0NGZWGpksXaN/e+0Y07HKBEt/27d5Fz0VFc+jVSzVZrxpR62gPu1yQJnQvo+daQ0wOAWHAgHSvuFgYEw3Nwb2MYZs23pjMohDRA0rk3bqpZuSFRrRvn85pFHa5wHvHbW2tTl/RrZs3+RWKykqNKvKKHHbtgj17olNnBw/Czp3BecZjcggI/ft7Rw5RmIYB1ET2q1/p/h13eGMy27hRo3natSs+r2KQSuk4h7lzvTGZ2bmMwq4z8N6fEhVHO3hrm4+KRuSWYdu24OYnickhIPTvD+ubzFVbGGzvOmzNAeCcc3Tbtas3+UXF9JJMpteQ9sJkFhVzGXhvm4+K6QW89adEiRzsexOTQxliwACd3XHPnuLziorTFtIE5SXxRaFcNTXpuZ28mJ4kKuYytwzlSg7lrDls3RqTQ9nBNqJemJaipDm0b6/TaJQbOSQS8NWv6v7TTxdvMrMNTRTKZkf7/v3v3vhTolJnkCYHXT+sOESJHKwp8P33Y3IoOwwYoFsvyCFKmgNo2bwgBxsdEgXSAzj5ZN0O9GDFc0voURhQ9eabup06tXh/yv790XG0g8qxf79q6cWitla1x7BHtAN06qS/2KxUhvBSc7Cjo+2iOGHDK3LYtk3t+1EiPYB164rPa+PG8KdhsLD+E/d074XCOtqjQg5ejk+xo6MrItJK9usXk0NZwkvbfFRGR1t4RQ5R04isxuBVnUWlXDU16QavWH9KlHwpkJbjF78o3mS2eLESaFgrLWaib1/YujW4ML6YHAJCt246I6ZXmkNUGhpQcti0SXuhxSBKET3greYQldHRoP6T886Djh2Ln+49Sr4USL9D999fnMls4cIuvPGGfmthrbSYib59Y82hLCHiXThrFKa0dsM2osXOGhk1cujYUUndK7NSVHrXACeeqGNljj++uHyi5LQFWLJEt42NxZnM5s7t9qFT26vR/8UiJocyhlcD4aKoOUDxxBc1cgA1LRVbrqisd+CGVyazWbN0u3Jlcfl4BTvuRqQ4k9mYMTs8ycdL7N8Pu3dXBUZUnpCDiJwrIktEZLmI3JDlfDsR+Ztz/g0RGeo6d6NzfImInOOFPFGFF1No2NHRUdQcvCCHzp21xx4VDBxYvOawbZt+2FEih0GDdLt2beF5pFJqvgGYPDkappdEAoYPh1GjijOZ9e7dAMCFF4a30qIbqRT88Y+6H9SzLpocRKQS+C0wGRgNXCIiozOSXQm8b4wZAfwS+Klz7WjgYmAMcC7wOye/soQ1KxUTgx01py14Sw5RKhdo2Yolh6g5bSGtORRTNvdaJVExvYASQ7t2xTXomze3B+Dqq8MnBjh0kksv14VpCV5oDhOA5caYFcaYfcDDwPkZac4H/uzsPwqcISLiHH/YGNNgjFkJLHfyK0v0768jpHfsKDyPqKwA50bPnvoxliM5DByo0zHs3194HlOn6tbLJUeLhRfkUFOTjpiLiukFYPDg4jQigLo6jQqyGlbYqKnRb6yiojGwZ13lQR4DAHdVrAMmNpfGGHNARHYAPZ3jMzOuHZDtJiIyBZgCUF1dTbJA6qyvry/42mKxY0cfYDSPPz6LYcM+KEiuqVM1j3XrZpFMftBq+mKRq1w9ekzkrbd2kkwuLvheK1ZMZNSo3PIIqh537+6HMUfx2GMpqqsb8pZr4cIuXH/9cUAF3/rWQYyZx5gxO32TN1e5ADp3PoWZMzeRTC4rON8jjjiFQYP2cM01y2lo2Jl3j9aPety/fzBbtw7nuedepX37xoLyWLdO1byVK19lw4bC8vAaP/95F2bN6sCECXsKetZ5wxhT1A+4CLjX9f+LwF0Zad4BBrr+vwf0Au4CvuA6/kfgotbuOX78eFMopk2bVvC1xeKVV4wBY154oem5XOX6+c81j+3bvZWtOeQq16RJxpx6auH3aWw0pkMHY771LW/lKhbPPKPP+/XXc0ufKdcddxgjonlUVur/MJDteR17rDGf/nThedbXa7mKKZMf9fjAAyrXkiWF53HeeetNr17eyeQVin1ewByTY9vuhVlpPeBWvgY6x7KmEZEqoCuwNcdrywZeTKGxcWO0RkdbFDsQbudONbm9+240HJsWxUb1eDngzGsMHFic+cVeO3iwN/J4BWsKWrOm8Dw2bWoXuXIFDS/IYTYwUkSGiUhb1MH8REaaJ4DLnf2LgJcdFnsCuNiJZhoGjARmeSBTJGHt6cU0ojaMNSqjoy0sORTqbH/mGd0+/3x0Bh1B8bb5RELHFPTvH42oFzcGDSrO52Ab36g1olaeYohv06Z2kfE3hIWiycEYcwC4BngeWAw8YoxZKCK3isinnWR/BHqKyHLg28ANzrULgUeARcBzwDeMMQEuhBcsjjhCB1UVozlEYQW4bBgwQJedLHTCs5de0m2xg5e8RrduWm/FNKI7d8LEidEiBlDi27y58OVCV6/W7ZAh3snkBayGXhw5tI8c6QUNLxzSGGOeAZ7JOHaza38v8Llmrr0duN0LOUoBxZpfVqyA7t21Zx2lxsYdzlrILJZ2GunKymiZX0SKqzNjtBE991xv5fICbpPZkUfmf/2aNWoyi1pnpV07nTCvUHLYuVMHm8WaQ4xAUcwo6VRKe7DvvBMt0wsUP9ahslIbmh/+MHrml2IGwm3ZAh98EL3eNaRt84WWbfVqrfcqT7qY3mLw4MJ9DpZUYnKIESiKIYdnn9WtF1Mte41iyWHlSv2gb7opWsQAxZGDNb0MHeqZOJ7Bag6F9rDXrIkm6YE27MWUC6LnSwkaMTkEjAEDNOLoYAGelWHDdFtRES3TCxQ/JfmqVdFsQCE97UljAeHuq1bpNoqNaLHO9jVrotuAWnIoJEAi1hwUMTkEjP79lRi+//38zUKdOun2G9+InumlXTtdzKYYzcGSX9QwcKCOkN68Of9ro6w5dOyo/qtCetgHD+p1USaH+vrCZiNQX4qJ3Gj9oBGTQ8DYtUu3P/1p/n6DFSt0e9tt0SIGi0Idt3v2qDYVZXKAwnrYq1frmJRu3TwVyTMUGs5aW6vzKkVRI4I0aRXid1i7Fnr1aoikLyVIxOQQMGwvrZCQzZUrtXcetQFwFoWSg/2Ao9i7huL8KatWRbcBhcL9KVG3yxcz6+zatelZWQ9nxOQQMD7tjPwoZJ74FSui27sGjVpZtix/c5ldCyCqZStWc4gq6UHho6StuawcyWHNGqiuLnDwRxkhJoeAcc452vM/6aT8/QYrVuhc9VFEKqWjnHfvzt9cZp22UW1E+/RR4itHzWHQoMIGwkVdc+jbV+ssX3JobNROQKw5xOQQCo4+Whe1yYcYDhzQ3lpUycE933wh5rI2baI3mMqiokKnJX/22fxIb/t2HVAVVdKDwueOWrNG/ShRNXFWVqo5MF+fw+bN0NBATjPwljticggBI0bA8uX5XbNunRJEVMmhpkbNZKAfZj7mMtu7rojo25hKaaPx9tv5aUVRnV7CjUIHwq1eHV2twaKQsQ42fe/esVkpop9jeWPECO3RNOTRObGRSlElh0QC/vlP3f/a1/LTiqIcxgqqBdkxDvloRVEe42BRqD8lygPgLIohhz59Ys0hJocQMGKEDs7JZ1F2mzaq5AA6f1CPHtqA5oOok0NNTXqKiHyCCKI8xsHCksNf/pKfySzKA+AsLDnkM3jRmqFis1JMDqFgxAjdLstjAa4VK7SBsh9zVPGRj8DSpbmnr6/X+Yei3IAmEjqtB+gi77lqRatWQYcOGn4cVcyfr9sXXsjdZLZzp/pTok4Ogwfr4MVNm3K/Zu1aaN8eunQpYl3YMkFMDiHAkkM+focVK1SNj/rAnHzJwZpeoqw5AJx3nm7bt8/9GhvGGrW1N9ywJrJ85uuyvetSMCsB/OhHuWtFViOKcp0FhZgcQkDPnhrpkS85RL0BBSWH9etVI8gFUQ9jtTjqKN2++27u16xeHf0GtKZGAwggd5NZ1MNYLbZt0+3dd+euFS1apFF3CxdGNAwrQMTkEAJE8o9YivIYBzc+8hHd5lq2qA+As+jUSU16+ZBDlCcTtEgkYMoU3X/iidxMZqUQhQXpII5cZyNIpWDhQnjvPbjuunGRmhI/DMTkEBLyIYddu9QuXwrkMHKkbnP1p1i7vF3sJ8oYNSp3cqivh61bo9+AAkyerFs7sWNrSKU07DifgIowYMuV62wEdkp8gP37JVJT4oeBmBxCwogR2jDmEtlTCpFKFtafkqvfYeXK6NvlLSw55DINdKn0rgFGj9btokWtp02l4KGHtDd+1lnRWnAqE4kETJiga67nMhuBnYW1ogLatDGRmhI/DBRFDiLSQ0ReFJFlzrZ7ljTHiUhKRBaKyHwR+bzr3J9EZKWIzHV+xxUjTylhxAj9wGwj0hKiPsbBjU6ddGRqPuQQdZOSxVFHaaROXV3raZ97Trd2Ft4oY+hQdbTnQg7FjIQPA5Mmqe9hwoTW01rSv/56uPPOeZGc+ThIFKs53ABMNcaMBKY6/zPxAfAlY8wY4FzgVyLSzXX+O8aY45zf3CLlKRnkE7FUSuQA+UUslYJd3mLUKN22ZlpKpeDGG3X/m9+Mdu8a1CE9alRu5HDKKbotZOLIMHDMMTpv1HvvtZ52wQINFPnJT2DMmJ2+yxZ1FEsO5wN/dvb/DFyQmcAYs9QYs8zZ3wBsAnoXed+SR77k0K2bLsxSCsiVHF54QePlS8GkBLmTQzKpU52AxtlHvXcNalrKhRy6dtXtv/1b9BacyoZjjtHtggWtp12wQNOXyvvoN4qNmq82xmx09muB6pYSi8gEoC3g5vHbReRmHM3DGJN1aKKITAGmAFRXV5Ms8Iurr68v+FovYQx06DCJZLKWY45Z3qJcc+YcQ+/ebUkm3wxWSAp7XhUVA9m2bQT/+tdrdO16IGuahQu78K1vjQMqueeeRo4+em5evbUw6tEYaN/+Y7z00kZGjcrO6vX19XTp8hZwPABVVY106TKPZDLcnmhrz6tDh8GsXj2cZ5+dTocOza9h+9xzfYFRfOITs2ho+KBo4vO7HvfurUDkYzz55Gp69lzVbDpjYO7cSZx5Zh3J5LLItBOZCFQuY0yLP+Al4J0sv/OB7Rlp328hn37AEuCjGccEaIdqHje3Jo8xhvHjx5tCMW3atIKv9RrHH2/MJz6h+y3JNXiwMWPHGjNjRjByuVHI83rySWOgZXnvuMOYigpNV1mp//2WywuMH2/MOec0f37atGlm/35j2rY15pRTwqmzbGjteT32mNbF7Nkt5/PNbxpzxBHGHDgQjFxeYORIYy68sOU0q1Zp+X//++DkKgTFygXMMTm0scaY1s1KxpgzjTFjs/z+BdSJSD8AZ5t1oLqIdAGeBm4yxsx05b3RkbkBuB/IwW1UPsglnPX113XQ0cKF+a+TEBbsWIeWwllratKzsJaC7doil3DWRYvUWfv1r0ff7GKRa8TS3Llw7LHpgXOlgGOOgXfeaTmNNTtZM1SM4n0OTwCXO/uXA//KTCAibYHHgQeMMY9mnLPEIqi/opUqLC+MGKHROgeyW14AeNR5YvlMbxA2hg3TxqMlv0MioeUfPrw0bNcWo0ZphNkHHzSf5k3H+nfiicHI5AWOPFJJuiVyUNMLHHdcUFJ5g7FjtRO2Z0/zaSw5jB0bjEylgGLJ4SfAWSKyDDjT+Y+InCgi9zpp/g04FfhylpDVB0VkAbAA6AXcVqQ8JYURI9RheeONzQ/Xd6+RUCo97DZtlCBaIoc9e/SD/fznS4cYID2NRkta0Zw5upiTDTooBVRVadlaIofVq2HHjtIjh2OO0bDxlsq2YIFOB2Id7jGKdEgbY7YCZ2Q5Pgf4qrP/f8D/NXP9x4u5f6nDLs34i19AmzbjOOGEpg3l+vU6DfZ118Hpp5dOQ9paxNJbb6nG9NGPBieTF3BHLI0blz3NnDkwfnx0Fy9qDqNHw+zZzZ9/+23dHn98MPJ4BWsqeucdrZdssJFKMdIosde3vGAHUzU2Nj9cf/p09TV873ulQwygg+EWLVKfSTbMdDxPEycGJ5MXGDlSQx2b8zscOCDMm1daJiWL0aPVzNmcyWzuXCW8UjO9HHkktGvXfDjrvn1an8ceG6xcUUdMDiHi3HPTvctsw/XXrNHfxz4WuGhFIZWCxx9Xk9mZZ2Z3os+cqaan6haDn6OH9u11moXHHsterlWrOtLQULrkYAwsWZL9/Ny5ano64ohAxSoaVVVatubIYckS1WJjzeFQxOQQIhIJ+OpXdf+WWxY20QymT9dtqZGDe4qFhobsTvSZM0vPpARKCLW1ukhOtuixJUs6A82bL6IMG7H0k59kJ75SdEZbtBSxFEcqZUdMDiHj3/9dt7t2NXX/TJ8OXbqU3ktbU6NqPKgJJlMjWr9e1ywuRXJIJtNz8GSLHluypDNdu6opo9SwZYtu//73psS3bZtqsaXmb7AYOxY2bEiv8eDG/PkaRGGDDWIoYnIIGSecoMtIzp7do8m56dN1LptSiikH1YimToWPf1wbUjuNt8Ubb+i2FMnBTXwVFU2Jb8mSzpx4YmlOwWD9Q9nCpufO1W0paw4AN9zQVCt69VUN+pgzJ3i5ooyYHEJGRYVOfTxnTo9DFkLfskUduqVmUrJIJODnP9eG5qmnDj03c6Y2sKXY0CQS8PLL0L+/+kzcpsCGBlixomNJmpRAia5NG91v0+ZQ4vvnP3WbyxTzUYSV+957D9WKZszQ97GurnQGmQaFmBwigHPOgfffb/vhYu8Ar72m21IlB1ATxKBB6YbFYuZM1ZjsGI5SQyKh0zovXXpo1NJDD8GBAxV0KdEVJhMJDSSorNQ1sy3xpVLwu9/p/uc+V5oNqPU3ZGpFv/99y2bCwxkxOUQAZ5+tW7sGAKhJqV07OOmkcGTyAiJw/vk6+6oNj5w+XRuXqK8/3Bouvli1voce0v+pFHzta7p/222l2YACfPKTWrbnn4fdu/XYo4+W1hoO2XD66U3NgY2NqjmIlNYg06AQk0ME0K8fDB9ez/PPp489+6wef+ut8OTyAhdcoKOhX3xRG8yzztKwwccfL90GFLRuPv5xePBB7Xn+4x8augulM013c7j6al3UyK74NnWqHi/lBtSaAwcOhJ49NdT473/X6fB/8AP40Y9KaxqXIFDslN0xPMJJJ23j0Uc7ceWVsHYtLF6sPZozzijtl/bUU3VA3K236lrRDc6E7AcPagNaquUCuPRSuOIK9ak8/LAeq6gwtG0rJdmAWpx8sg4I+93vlOjmzdNBmJ06KTGUap2dfDLcc49qR/ffrzMTjB0L3/9+6Y1mDwIxOUQEvXs3cPAg3Hdf+pjbPlqqH+ScOao5WA2ooqJ0VhFrDRdeqKakCy7QgVb33AOzZ6/kiiuGl2x9gdbP178OV10F116rvezbbivNCKxMTJ6s5bnmGiW+O+6IiaE5xI8lIvjgg8oPPz5d4Ly01XgL97iAigod11EuKvyiRWp2sVFmxxwDl122puTLBelJAw8eVGfuzJktpy8ViOhkj9YE+KMflbZ500/E5BARnHDCdtq3V0Jo1w7uuqs8GlE7LsCW6/LLdRbaUi6ThduvYM1k5YJZs9I96lL3oWRi3760FlSqDvYgEJuVIoIxY3Yydaq+qKVs182EHRBXbuUCLU/bttrAWA3P+lRKHZbU3WUrF5x+us6RVY5l8xIxOUQIiUR5NZ4W5VyuTOIrl15oOZN6OZfNS8TkECNGEShX4oO4bIc7Yp9DjBgxYsRogpgcYsSIESNGE8TkECNGjBgxmiAmhxgxYsSI0QQxOcSIESNGjCaIySFGjBgxYjSBGDu3QQlBRDYDqwu8vBewxUNxvEIsV36I5coPsVz5oVzlGmKM6Z1LwpIkh2IgInOMMSeGLUcmYrnyQyxXfojlyg+xXLFZKUaMGDFiZEFMDjFixIgRowkOR3L4Q9gCNINYrvwQy5UfYrnyw2Ev12Hnc4gRI0aMGK3jcNQcYsSIESNGK4jJIUaMGDFiNEHZkoOInCsiS0RkuYjckOV8OxH5m3P+DREZGoBMg0RkmogsEpGFIvLNLGlqRGSHiMx1fjf7LZdz31UissC555ws50VEfu08r/kickIAMh3leg5zRWSniPxnRppAnpeI3Ccim0TkHdexHiLyoogsc7bdm7n2cifNMhG5PAC5fi4i7zr19LiIdGvm2hbr3Ae5bhGR9a66+kQz17b47fog199cMq0SkbnNXOvn88raNoT6jhljyu4HVALvAcOBtsA8YHRGmq8Ddzv7FwN/C0CufsAJzn5nYGkWuWqAp0J4ZquAXi2c/wTwLCDAR4E3QqjTWnQQT+DPCzgVOAF4x3XsZ8ANzv4NwE+zXNcDWOFsuzv73X2W62ygytn/aTa5cqlzH+S6Bbg+h3pu8dv1Wq6M83cCN4fwvLK2DWG+Y+WqOUwAlhtjVhhj9gEPA+dnpDkf+LOz/yhwhohdWdYfGGM2GmPecvZ3AYuBAX7e00OcDzxgFDOBbiLSL8D7nwG8Z4wpdGR8UTDGvApsyzjsfof+DFyQ5dJzgBeNMduMMe8DLwLn+imXMeYFY8wB5+9MYKBX9ytGrhyRy7fri1zO9/9vwF+9ul+uaKFtCO0dK1dyGACsdf1fR9NG+MM0zoe0A+gZiHSAY8Y6Hngjy+mEiMwTkWdFZExAIhngBRF5U0SmZDmfyzP1ExfT/EcbxvMCqDbGbHT2a4HqLGnCfm5XoBpfNrRW537gGsfcdV8zJpIwn9fHgDpjzLJmzgfyvDLahtDesXIlh0hDRDoB/wD+0xizM+P0W6jpZBzwG+CfAYk1yRhzAjAZ+IaInBrQfVuFiLQFPg38PcvpsJ7XITCq30cqLlxEbgIOAA82kyToOv89cCRwHLARNeFECZfQstbg+/NqqW0I+h0rV3JYDwxy/R/oHMuaRkSqgK7AVr8FE5E2aOU/aIx5LPO8MWanMabe2X8GaCMivfyWyxiz3tluAh5H1Xs3cnmmfmEy8JYxpi7zRFjPy0GdNa05201Z0oTy3ETky8CngMucRqUJcqhzT2GMqTPGHDTGNAL/28z9wnpeVcCFwN+aS+P382qmbQjtHStXcpgNjBSRYU6v82LgiYw0TwDWq38R8HJzH5FXcGyafwQWG2N+0Uyavtb3ISIT0DrylbREpKOIdLb7qEPznYxkTwBfEsVHgR0udddvNNujC+N5ueB+hy4H/pUlzfPA2SLS3TGjnO0c8w0ici7wXeDTxpgPmkmTS517LZfbR/WZZu6Xy7frB84E3jXGrMt20u/n1ULbEN475ofnPQo/NLpmKRr5cJNz7Fb0gwFoj5oplgOzgOEByDQJVQvnA3Od3yeAq4CrnDTXAAvRKI2ZwMkByDXcud885972ebnlEuC3zvNcAJwYUD12RBv7rq5jgT8vlJw2AvtRm+6VqI9qKrAMeAno4aQ9EbjXde0Vznu2HPhKAHItR23Q9h2zUXn9gWdaqnOf5fqL8+7MRxu9fplyOf+bfLt+yuUc/5N9p1xpg3xezbUNob1j8fQZMWLEiBGjCcrVrBQjRowYMYpATA4xYsSIEaMJYnKIESNGjBhNEJNDjBgxYsRogpgcYsSIESNGE8TkECNGjBgxmiAmhxgxYsSI0QT/H4lvj4qwY4r/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "signal_4_parameters = signal_4_parameter_space.sample()\n", + "signal_4_points = sine(time_points, **signal_4_parameters)\n", + "title_4 = \"sine\"\n", + "plot(time_points, signal_4_points, title_4, signal_4_parameter_space, signal_4_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "professional-calibration", + "metadata": {}, + "source": [ + "## Signal 5\n", "\n", - " # combine signal and noise time series\n", - " y_predict = y_predict_signal1 + y_predict_signal2 + y_predict_signal3 + y_predict_signal4 + y_predict_noise1\n", + "The fifth signal is a noise signal. It has two parameters: its variance is defined by the parameter `stddev` while the relation between value and the previous value in the signal is registered in `correlation_length`: " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "instructional-river", + "metadata": {}, + "outputs": [], + "source": [ + "signal_5_parameter_space = ParameterSpace([\n", + " Dimension(\"stddev\", 1),\n", + " Dimension(\"correlation_length\", 2)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "unlikely-investigator", + "metadata": {}, + "source": [ + "We select arbitrary values for the parameters with the function `sample` and generate the Gaussian noise signal with `gaussian`:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "electronic-policy", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "signal_5_parameters = signal_5_parameter_space.sample()\n", + "signal_5_points = gaussian(time_points, **signal_5_parameters)\n", + "title_5 = \"gaussian\"\n", + "plot(time_points, signal_5_points, title_5, signal_5_parameter_space, signal_5_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "guilty-amendment", + "metadata": {}, + "source": [ + "## Signal 6\n", "\n", - " # plot to verify\n", - " visualize()\n", + "The sixth signal is the sum of the first five signals. Since the five signals were defined with respect to the same time axis, they can be added to each other and the result can be plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "serial-hundred", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "signal_6_points = signal_1_points + signal_2_points + signal_3_points + signal_4_points + signal_5_points\n", + "title_6 = \"combined\"\n", + "plot(time_points, signal_6_points, title_6)" + ] + }, + { + "cell_type": "markdown", + "id": "growing-boating", + "metadata": {}, + "source": [ + "## Create a combined graph\n", "\n", + "Finally, we plot the six signals in a single graph to make comparison easier:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "arranged-tours", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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djWvXrqFmzZqIi4tD8+bNcf36dfMnOzsbc+fOBQD07NkT7dq1Q0ZGBm7cuIFBgwYVq4Nt27Zh6tSpWLNmTalyuMu993L3Sl/Y6c+cAXr3lpW8Vstt88IWLygPBISir1atGk45MUVR6r1KSnvz5s3Ytm2bQ//VarXo1KkTJk6ciJycHBw9ehTLly83H4+JiUGtWrWwYsUKFBYWYvHixTh58qTNtLKysqDT6RATEwOj0YjJkyfj5s2bDpdDYtOmTdi3bx/y8/Mxbtw4NG3aFHFxcXjuuedw7NgxfPHFFygoKEBBQQEOHjyIP//805x/lSpVEBoaigMHDuCrr74qlvZ9992HLVu2YPDgwVi3bp3TsjlDSAhw333AoUOqZlOMadOA+vWBS5d4712rlXvxo0cLJS8o+wSEoh89ejSmTp2KqKgozJw5s9TzIyIi8PHHH6Nr166oXLkyvvrqK7Rr187h/ObMmYMbN26gevXq6NOnD3r06IGQkBDz8QULFmDGjBmoWrUqjhw5gv/85z8202ndujXatGmDu+++GwkJCQgNDXXJDNSzZ09MmjQJVapUQXp6OlasWGEu57Zt27Bq1SrUrFkT1atXx8iRI5GXlwcA+OyzzzB+/HhERERg8uTJ6Nq1q830GzZsiA0bNmDAgAHYvHmz0/I5Q6NGvEfvwMuV2+TkAO3bA2+/DeTlcb/4Tz4RvXhB+cPvVphKSkoiV0IgqMnIkSNx8eJFLFu2zOt59+3bF7GxsZg6darX81aDjz8Ghg0DLlwAatZUL58VK/jkrMuXuYIn4j35KVP8O/yCQOAqjLF0srM6VUD06L3N0aNH8euvv4KIcODAASxatAgdO3b0tVhlAuWArBoQAW+8AfTpw5V8UJClucbTU8sFgkAgoL1u1CIrKws9evRAZmYmqlWrhhEjRqB9+/a+FqtM0LAh//7f/4Bnn/Vs2lu38l78H4q4qCYT8PLLchgDYa4RlEeE6UbgdWJjgagoYMECzyneuXOBwYNlE41OJweIEvZ4QXmgJNONqj16xtjzACYCuAfAw0QkNHg5x2AALl7kNvonnnBPCRsMPBjZ6dPA4sXFwxiIXrxAwFHbdPM7gE4A5pV2oqB8kJrKzSkADxGbmuqaIjYY+OSn3Fz++5FHgF9/5WmKCVACgSWqKnoi+hNwbFKQoHyQksIVcV4eN7E4OzhqMPCHw+7dspLXaLgb5YcfqhjmVSAIYMRgrMCrJCcD27cDrVoBzzzjnEI2GLi5JzeXm2kY40pe8qZJThYKXiCwhduKnjH2I4DqNg6NJaIfHEzDYilBQdnmsce4wj561Ln/ff21vM4nY0D//kDt2qIHLxCUhtuKnojsL/vkeBpuLyUoCCyaNwc2b+ZhCapVK/lcvZ4v/bZ+Pf+t0fBwCi+9JBS8QOAIwnQj8AmSbX7PHsAqoKYFW7dyf/vCQq7gZ8wACgpEL14gcAZVZ8Yyxjoyxs4DSAawkTG2Vc38BIFD48ZAxYp8UNUWBgMwaBDQpQtX8gA31xQUiEBkAoGzqO11sxbAWjXzEAQmQUFAs2bcS8aaffu462RBgXyuySRCGAgEriJMNwKfkZICjBkDXLkCREfzXvy6dcCyZbKS12pFCAOBwF2Eohf4jObN+feePUCNGlyR5+fzfTodd6EUk58EAvcRil7gM5KSuPfMu+9yt0lJyWu13HVS9OIFAs8gFL3AZ6SncxPNwYP8t1bLv0UvXiDwLELRC3xGaqociEyjAQYMEL14gUANhKIX+IyUFCA0VAQiEwjUxu/i0TPGLgM460YS0QCueEgcTyLksklERSAyAriZBWTdUhwQ9eUcQi7nKItyJRBRjK0Dfqfo3YUxlmYv+L4vEXI5h5DLOYRczlHe5BJrxgoEAkEZRyh6gUAgKOOURUU/39cC2EHI5RxCLucQcjlHuZKrzNnoBQKBQGCJ37lXRkdHU2Jioq/FEAgCllu3gKwsIDwcMBqBU6csF06XYExeqSsujkcJjYjgUUUFgUd6evoVe143fqfoExMTkZaW5msxBIKAQgoId+UKsHQpV/ClISl/IuDcOb6t0/Hw0FWrAq1b831iHd7AgDFm1y3d70w3SUlJFEiKnjGG48eP48477/RanqmpqejduzfOnz/vtTwF/ofBAHzzDe+xr1tXvNfOGA8FffAgV/xSiInCQr5NJMf6N5mKp8+YfF5wMLBjh1D2/gxjLN2ea6bf9egFAoF9DAa+6tbJk8CXXxZX7hoNV85S/P733+f7pV65re2qVYHhw/kMZca48ifiH+nN4PZtYMoU/uBo2VIo/EBDdUXPGFsM4DkA/xDR/WrnVxJGoxE6nXi2CQILg0GOCzRhQnGzjLVynz0buHrV0tyiVMy2ths0KK70pTeAggKe9+bN/BMUxNfwvXVLmHQCBiJS9QPgcQCNAfzuyPkPPfQQWZOQkEAzZsygBg0aUGRkJHXt2pVu375tPj5//nyqW7cuVa5cmdq2bUsXLlwwHwNAc+bMoTvvvJMSExNp165dVKtWLXrvvfcoJiaGqlevTmvXrqWNGzfSXXfdRZUrV6Zp06aZ///zzz9T06ZNqVKlSlS9enUaPHgw5eXlWaR//PjxYjITETVv3pxGjRpFTZo0oYiICGrXrh1dvXrVfNxgMFBycjJVqlSJHnjgAdq1a5f52OLFi6l+/foUHh5OtWvXps8//9x8TCqDxEcffUT33HMPZWRk2JRDELjo9UQhIVL/Wv4wRhQURKTVEoWFEc2bRzR9Oj/fE3lKaUnbAwYQaTTFZQgN9WzeAtcBkEb29LC9A578AEh0V9E3adKELly4QFevXqX69evT3LlziYhox44dVLVqVUpPT6fc3FwaMmQIPfbYY8rCU6tWrejq1auUk5NDu3btIq1WS5MmTaL8/HyaP38+RUdHU48ePejmzZv0+++/U2hoKJ06dYqIiNLS0shgMFBBQQGdPn2a6tevTx9++KFF+iUp+po1a9Jvv/1G2dnZ1KlTJ+rVqxcREZ0/f56qVKlCGzdupMLCQtq2bRtVqVKF/vnnHyIi2rBhA504cYJMJhOlpqZSWFgYpaenE5Glop80aRI1atTI/D9B2UCvJxo6lKh2bUvlqtOpo9wdkScsjOet1VrKpNHIMgll7zv8XtEDGAggDUBafHx8sQIkJCTQF198Yf795ptv0iuvvEJERP369aM333zTfCwrK4t0Oh2dPn1aKjzt2LHDfHzXrl0UGhpKRqORiIhu3rxJAOinn34yn9O4cWNau3atzcr88MMPqUOHDsrKLVHRjxw50vz7yJEjFBQUREajkd59913q3bu3xflPPfUULV261GZa7du3p9mzZ5vLULNmTXrttdeoWbNmdP36dZv/EQQWUu956lRLZSopV28rd3vyzZsnK33GLHv4/fqJ3r2vKEnR+4XBmojmo2hGWFJSkk03oOrVq5u3K1SogMzMTABAZmYmGjdubD4WHh6OqlWr4sKFC5D88ePi4izSqlq1KrRFBsiwsDAAQLVq1czHw8LCkJ2dDQA4duwYXn/9daSlpSEnJwdGoxEPPfSQw2VT5p2QkICCggJcuXIFZ8+exTfffIP169ebjxcUFKBFixYAgM2bN2PSpEk4duwYTCYTcnJy0KBBA/O5169fx/z587F69WpUqlTJYXkE/onBwAc58/IsB1i1Wv+J05+cbNumP2wYkJvL5V68mA/ohoYKLx1/wi8UvTvUrFkTZ8/K7qO3bt3C1atXUatWLfM+xpjL6b/66qto1KgRVq5ciYiICMyePRvffvutw//PyMgwb587dw5BQUGIjo5GXFwc+vTpgwULFhT7T15eHjp37ozly5ejffv2CAoKQocOHaS3HwBA5cqVsWLFCnTt2hVr165Fs2bNXC6jwHcYDMCPPwIbN3JlKeHva+baUvoHDwJr13K5JS+dRx8FWrTwP/nLGwEf66ZHjx5YsmQJDh06hLy8PIwZMwaPPPIIPDW7NisrC5GRkQgPD8fRo0cxd+5cp/6/YsUK/PHHH8jJycH48ePRpUsXaLVa9O7dG+vXr8fWrVtRWFiI3NxcpKam4vz588jPz0deXh5iYmKg0+mwefNmbNu2rVjaKSkp+PLLL9GpUyccOHDAI+UVeA+9nvfSx48Hfv5Z9p4JCwM+/ZQrykDoFScnA6NHA2++yWXXFGmVzZuBsWP5m4rB4FsZyzuqK3rG2EoABgD1GGPnGWMvezL9Vq1aYcqUKejcuTNq1KiBkydPYtWqVR5Lf+bMmfjqq68QERGBAQMGoFu3bk79v0+fPujbty+qV6+O3NxcfPzxxwC4SeeHH37A9OnTERMTg7i4OMyYMQMmkwkRERH4+OOP0bVrV1SuXBlfffUV2rVrZzP9J598EosXL0bbtm3xyy+/uF1egfoYDMCQIUDnzvKC6BoNMHCgrNwHDuTK09+VvJLkZC771KlcfulFOjcXePll4O23hcL3FWJmrIqkpKSgd+/e6N+/v69FEfgJ69cDHTvKM1KVJppA6L07isEAPPGE/CCTyhsUxP38b9zw/ZhDWUPMjBUIfExqKjB9OrBrl6z0tFqgf3//GGj1NFLvPjWVx9GZP59P6CooAAYP5m8wISFl6+HmzwhFLxCohMHAFfuJEzzQGBFXcMHBXNn760Crp5AGbA0GYNkyy969ycQHbGfOBJKSyt6Dzt8QphuBQAUMBu5tkpdnud+f3CW9iRTGQQqxkJdnGUgtNBTYubP81IcaCNONQOAlDAYebOyHH2Qlzxi3xUuxaMpyL94ettwxT5wAlizhbzq5uXwAt3174Nlny1/9qI3o0QsEbiL1VnNzgWnTbA+02go0Vt5RDtgSyT18nY5H3czNFfXlDKJHLxCohDSjVTnZCSjbA62ewt6ArdEIvP46fxMKCREmHU8gevQCgQsYDHyAdetWQDExu8y6S6qNsnfPmGUo5gYNgKefBjp0EPVZEiX16IWiFwgcxGDgyvvMGW5blkwNUtx2YaJxD+sBW2uTjlYLjBgBVKokwirYQih6gcBFJOUD8FAF1ot+lFcvGrWR6l1p0lEiFj8pjlD0AoETSEqmYkUev0Xy/5aw9qIRJhr1sDbpSMscSjAmv0n9+2/5VvpiMFYgKAVJuVeoALz1VnHlDggvGl+gHLBVmnQAWenn5QGvvior/Q8/BK5fF9dGiejRC8ot0szV3Fzg3Xf59HxrhP3dv7Blxwdkl1YlwcE8SFxhYfm4ZsJ0IyjXSMohJYX3/lavBq5c4bHTbSkIodwDA2eUvlYL9OrFz+3cGWjWzLJdlIVr63NFzxhrA+AjAFoAC4noXXvnCkUvcBTrG1X5u7AQ+P57rtjnzbPdW5dgjCsCYZYJXOwpfZPJ0qYvUacOH+iVYg5Nncr/U7TAm7kdKbf9vS34VNEzxrQAjgF4EsB5AAcB9CCiP2ydLxR9YKNUtoA629u385tz4kSuwLVaoE0bYNMm2705W0iLfEgDqkK5lx1sKX3G+LU2mfh2pUrcjm8LjUYOQCf9TzID5ebywWFA3XbuShv0taJPBjCRiFoX/R4NAET0jq3zXVX0RiOPMZKWxqPhAWV/u2FD4PBh28eIgPR0QFre1t62rfOtt3/5BWjcWN7fqBH/7y+/AA8+yPf/739ARARfGUlSvgBXvNINI22XtF+6Ea23XW2myt66VitPxhHKvXxgS+lL196Rnn9pMCY/DJQPBmV7VrZz6b6wt7+w0PXwzb72uqkFIEPx+zyARzydybZtQN++nk5V4CpKv2d728ret3JbecPZu/kYk3tewcHArFnAG2/wG7ckhQ4Ezqu4wH1sBVOTrr1ygXNJ6SvbjqR8jUZZgVu3RyJ5nyNtWzkPw959kZ/P5fJk+/QL90rG2EAAAwEgPj7epTTS0/mNL/UCpcovq9saDfDAA8Cvv/q+zAD/rVS+jvRcrG8qZ7Zt9cgbNXLsNVgo+PKJUulb/1Y+BIDi2yU9DBzZBoq3f+tev7QdHCzn7Sn8znTDGLsM4KytYyUTURG4627wdXD9y5VIHQi4kAHUigPA3EiHwXP1VSSTTgfczOK7IiNc26YooFJhyedk3fKQ3M4QDeCKD/ItDSGXc7ggV0RF19uzo9vh1YHsiy627QQiirF1wBuKXgc+GPsEgAvgg7E9ieiISvml2bNT+RIhl3MIuZxDyOUc5U0u1U03RGRkjA0BsBXcvXKxWkpeIBAIBMXxio2eiDYB2OSNvAQCgUBgicbXAqjAfF8LYAchl3MIuZxDyOUc5UouvwuBIBAIBALP4hfulUqio6MpMTHRK3ndugVkZfGJPoC8XbGiV7JXlexsXp7ISP67rJTt1i3gxg1eLsbKTrkAfs1u3uSzNoGyVbabN3n5ykrZlLrDaARycnib9GV50tPTr/jM68ZZ1A6BIEUsPHMGWLiw+ASI0NDAXaPSYAB++AE4cICXUULydQ8NDczY6dLKTufOAYsWyZNLJH/+QL9mq1YBf/7JQztISPMRAvWaAcDmzTzO0LFjvHwS0kxlV2eA+hqDgfu5FxRY6g+tlk/ajIsDnnrK++UqaWYsiMivPg899BA5yrRp0+jll192+Hy9nigkRJrLZv/z4INE48fz8wOFnTuJtNrSy9a5M9H06YFTNkevWZs2RHv3BlbZUlMdu2Y9ewZWuYiI5s4tvVyMEU2b5mtJnSM3lygpqfSyhYZ6/3oBSCM7etXnit3644yid5ZevSwbWVAQv9GCg7ky0WqJNBr5nLCwwLi5srOJateW5dZoipdNWS7GAqdsjlwzSVlqNHzb38um1/OORI0ajl8zjcb/y0XE5Xv++eKyW5dNOpaURDRhgv+Xi4jo+++JYmO53Dpd8Xao0/E2KpWtfXvvPqBLUvSq2+gZY4sBPAfgHyK6X+387LFxI39Flqbj24uBolyj8vZtYOtW/3613LED6N+fm6KCg+Wwq7bKduwYsHQpb4Z5eZ6Pp+FJDAbggw+ANWtKv2bNmwMffQR8/TXfp0asEE9hMAAtW/IoiID9VasAXoYjR3iwPpMpMK7Z44/LsWFCQuzHG9q1Czh4kIeSTksD3n/fv804333H49gDvDyffFL8WinDJBQWcjPq+vV+YqKy9wTw1AfA4wAaA/jdkfPt9ejfffddqlmzJoWHh9Pdd99NP/74I02YMIF69epFRESnT58mALR06VKKi4ujqlWr0tSpU4mI6KOPiDSaQoqOfodq1KhDYWFVqGXL5+nq1avF8tHrec9J6pHcdRfRpEn+2ePYuVOWMziYaN68knsQUtmkHsdrr/mnSUCv5+WReoMff1y6nNb/2bvXe/I6w8SJlj3dQYOcu2YLF3pXXmfo2lWWU6stvWzTp8vt15/NONnZRNWrW5Zt+nTb5+r1/Fjfvo6d70nga9MNgER3FP3Ro0cpNjaWLly4QERcqZ84ccKmou/fvz/l5OTQoUOHKDg4mGbN+qOowmcTY4/Q999nUG5uLg0cOJC6d+9us8Kki/Xyy/5txmnSxPnGpNcTjRtHVLOm/5oEXnvNtZtEryd67jn+vy5duOLwp3KZTETPPON8vev1RGPGcGVTpQrRW2/5V7mIiA4f5g9aZ8xn0kNMMnd06OB/HQ+TibclxmQTjaNlCw2VH2KbN6sva8Ar+uPHj1NMTAxt376d8vPzzfttKfqMjAzz8aSkJlS58soipVGfNJofzUojMzOTdDodFRQU2K246dPlRqjReOep7Chff00WtkJnlfWoUd7vcThCTg5RnTqyXK48hLp3l28wf3mI6fVEnTpxuV56yTWFtmKFf3Y8tmwhqlyZKDqaaP1658qm1/MHstRp8beOx8CBXK7Bg+UOoDNle/VVfo8+/DDR1KnqlsvvFT14iOI0AGnx8fE2C/Hll19Ss2bNKCoqirp160YXLlywqeiVijsurjkBC4pe6cMIiKDw8EpUqRL/hISE0Pnz5+1WnPVr8+rVDte5auj1RG++SVSxItEjjxDt2eOa0lB6s+h0/nFj6fXyDf/BB6737qZN86+HmLKu3TErWZs6fF0uIqL9+2WZQkJcb0dKk5Y/XDMiok8/9cyDVepUqd3xKEnR+0UIBCKaT0RJRJQUE2PT3x89e/bEvn37cPbsWTDGMHLkyBLTnDMHyMjgqzClpgLR0XH4/PPNyMq6juvX+Sc3Nxe1atWym0ZyMh9EGTmST4yYOJGvLWkwuFFYNzAY+DJmM2bwCRtvvgk89hgwerTzAz3JyXxA7D//4QNm2dnqyOwokm/ywYN8gLJpU9fKBfB1P0ND+TYRHyD0JT/+yAdSAT5IuXeva+mkpPCBPWn+QNWqHhPRZT74QJ7XYDTye80VnnrK8po1b+4R8VwmJwcYN07+LQ3wu4I0aZHIvXTcwS8UfWn89ddf2LlzJ/Ly8hAaGoqwsDBoNPZF374dGDqUb/9RtDLtmDGDsHLlWJw9y0PdX758GT/88EOpeScnA+++y5X9n38C48dzZesLZZ+aKntraDTci8YdkpO5ErrnHqBnT96wffUQ27RJXtaNyL2bITmZT6Dq0IEroe++A955x3dlO32af2s07i0qIXU8xo8Hqlfnq2rdvu0xMZ3m4kXulSatv+tu2XbuBJ5+ml8zqc58xbhxwLVr/MHqbtlSUoCwML5tMgGPPuopKZ3AXlffUx8AKwH8DaAAfBnBl0s635aN/vDhw9SkSRMKDw+nypUr07PPPlui6aZpU+mVqzkxtoCmTycqLCykWbNm0d13303h4eFUp04dGj16tMOvRdOn+/7VUrLLe9pGu2SJ722/0iClJ/3gTSZu3vKl7fe337gP+VNPeXag8ccfebkefdR3ZrdOnbi5ZuVKz5XNaCRq2pQoIoJo9GjflG3+fDIPDjtrl7eHXk/UuzdPVy3vIvjaRu/Mx90JUz/9JNudPak0lKPoGg3Rvn3up+kMJhNRq1ZEFSpwm58nbwBfDzpLSqtPH897Xfhy0HnvXj7BplIlosuXPZu2Xs/buLu2cVeZOpXnPWiQ59P+6ivfdTxSU+V7QY28n3+eX7fhwz2fdrlR9Pn5RA88QFSrFtH27Z5XGno9d7UCuM+6t5BmGwJ8gEiN9KVBZ8Z4Y/cWu3Zxl8FatbjHjafR63mPWppr4C2loXa+06dbhk8omjLiFbZuVVcR+7Lj8cQT6nYMNmxQr+5KUvQBYaN3lNde44tlDxkCtGrl+mCePZKT+ezLlBRgxAhgzBj17b7STMpvvuG20IYNPZ+HZPvt25c3wRUrvGPTNhiAJ5/kttArV4BDhzyfh2T7rVWLRxa85x7P52GLH37gQa8APkvS0wNwKSncbiwNVV2+7Nn0S2L8eHlbjcHFlBTLgdlHHvFs+vY4fhzYs4fb5N21y9vj11/lIIPSTGevYO8J4KuPqz36NWu897q3cqX38vL22IDUo/GGTXvYMO+VLT2dl6ljR+9MymneXC6XWvUo+aAnJxOFhxMpppCohlqmUWv0eqIBA3heI0d6Pn1rTCaiJ58kiowkWrdOvTZi7bK9caPn0kZ5MN1Mniy/7qmtNLz5ain58npr8s/o0d5RvoWFRA0aqK8MlUhT9NV+iK1fT2b7tTceKqdOcfPQffepm1dBAY/sWrOmOqZRW7z0Em8fw4apm5fkx//66+rlIaHX8/JotTxUgqcoF4peelJ6Q2lY27R37lQnn9xcovr1eZTDiRO9Y1/2lk17wQKex5gx3pv2Pn68+g+xnTuJoqKIEhOJ8vI8n74tvHXNpDcwb44HbNqk/tuz2mMO9pAcBQYM8Eye5ULRE3nOFcrRvKRXy7feUif9Vq14+ps2eT79kti3jyghgbu4Xbrk+fQ3bOAeTA8+yF+ZvYUy8FlQkOfbia88YawHZseP93we3jSNKvHG2/NDD3nnLdaanTs96+FTkqIvU4OxycmeH4AtKa/584F+/YCZM/kELU8NXkoDsD/+yAeFoqI8k66jNGvGJzDl5gJ9+nh2YNZgANq352kfPQr89JNn0nWE5GQ++NWgAb+tK1f2bPpffMFnhwLuzRJ1FmlgVqvlvw8f9mz6RMBbb8m/vTm7UzkwazIB9ep5Nv3t24H0dD4bW60BWHv89JM8MJubq3Kd2nsCePIDoA2AvwCcADCqpHPVXHhEDbZs8XxPR/JR9nXcj3795J6Up8omBRzzZdkuXuRBuBo29FyEy5wc/hbEmG8WP5HeZnv25HXbv7/n8p83T34L8lXZ3nyT5/vUU557C7x5k1+zevW4m6+3I2daR+9csMC99OBL0w0ALYCTAOoACAZwGMC99s4PNEWvRqApKTyyryP5TZ7sWaV84QI3B/nDSlDSoLOnBrlHjODpzZ7t21C7njYHrFnDzV0PPcQDmPmybHPm8HJ17uwZGTp3Jq/PibFGr+emtjvu4A+dyZNdL5uvFX0ygK2K36MBjLZ3fqApeuun8scfu5fWq69yRdi6te9jc1tHXXRnNvD+/UR3382VhienzLuKJyNcfv45mafM+xpPLuaxb5+cli/WQFVTng8+8OyD3l08IU9Jit4bNvpaADIUv88X7TPDGBvIGEtjjKVd9ubMDw8gTTaaMAGIjwcmT3YtOJgUmXLuXK5+Rozw3niDPaQIl+3acfvoypWu2eulyJTHjvGyJST4vmwtWlgGmmrc2LV0Nm4EXn2Vb2/d6rvAaRLWES7//df1tN5+W45MWVDgm6iLSvbskbdzc3l9u8KJE3yyI8DryFcRJZXk5srXTBV57D0BPPUB0AXAQsXvPgDm2Ds/0Hr0StwJDjZlivxff1vkxGQiSklx3Zz0wgueNQF5CukNSqvlwcGctdffuiUvFu1PZVNOpAoK4rZfZ9+gpEVOpMXX/aHXK709S736Ro2cv2ZZWXy+QUQEfyvwt7K5Iw+E6cY7KF3BGHPc37iwkJtq/MEub49x45xXaJIiDQry3SClI0j+4c68Nu/bxxWG5Lvuj2W7epUvP+hs3S9YwM9/8EGi3bt9b2ZTIg06S/Z1Z67Z/v38mjHGJ3x50x3bEdyVx9eKXgfgFIDakAdj77N3fiAremt7fZMmpS8ftn8/73kBfNkyf2p4SpTROwE+mFnaotbKNTNnzPDfsk2b5twDes8e2V8+KKj0Rdl9yZAhzj2gJQ8bf7HL20M5xuLIWMSOHfJcAzXmUPgDPlX0PH88A+AYuPfN2JLODWRFTyQ/lTt0KP2G+f57ufHpdFzp+zOSm1t0tP23D8ls8Pjjzr8B+ArrB/SDDxJNmlT8mun1RG+8QVStWmCVTRpQB4iGDrX9UNq/n68JINWBv5fN1jWbMMH2NXvzTR4hNRDK5Q4lKXrGj/sPSUlJlJaW5msx3Oadd4CxY3nTAnjUyaee4kvk5eTwCJE7dsgTbLRaYMoUPkjp74wezVfdknjqKR5hUKvl5VYumyetqrRjh28HX0vDYOADYIcO8QilAJ9EM2ECHyjLywM++kiOSKnT8WsbKGVbt45P6Lpwge8LDuYT/c6c4QO2y5bxgVeNBggK4u3S38tmMHBngcOHLa/ZyJF8QPrff4FPPpHvsaAgXkZ/L5erMMbSiSjJ5jGh6NVB8qLJz+cKQfJeUMIYb3yFhYHV+KSy5eXZLhfAFcaAAdzDJiUlMMoF8AeV0tvEFlotL1t8fGCVbeJE7hVW0i0fiGUry9fMGUpS9DpvC1NekNwuU1OBc+d4uASTSXahArgy7Ncv8BqfsmxnzgALF8plk+KjBwcDL74YOGWSkNwT8/N5eQoL+fVijCsLqRf/wguBV7bWrYH335fX5i0s5N/Smq9SbzfQylaWr5mn8LsePWPsMoCzbiQRDeCKh8TxEBEVgTvrAcz6AAHHjwFZt3whVRFu1ldEReCuu8ELR8CFDP4CfTPLzXL58DpGVAQiI/hLf604WJRNUwnIvujja2YLB+urpLJ55Lq5KJe7OF0uP9QTANyTK4GIYmwd8DtF7y6MsTR7ry++RMjlHEIu5xByOUd5k6tMRa8UCAQCQXGEohcIBIIyTllU9PN9LYAdhFzOIeRyDiGXc5Qrudyy0TPG2gD4CDwU8UIietfqeF8AMwAUee9iDhEtdDlDgUAgEDiNy+6VjDEtgE8BPAkekfIgY2wdEf1hdepqIhriaLrR0dGUmJjoqlhmbt0CsrKAiAigYkW3kxMIBAK/Jj09/Yo9rxt3/OgfBnCCiE4BAGNsFYD2AKwVvVMkJibC3QlTqal8Qg8AXLsWOBORBAKBwFUYY3bd0t2x0ZcaZ76IzoyxXxlj3zLG4tzIz2E+/JBP/jCZisd2NhhcXwP1yy+/xFNPPeUxOQUCgcAbqD0zdj2AlUSUxxh7BcAyAC2tT2KMDQQwEADi4+PdyrCgADhwQJ6BajIBTZrwY3o9X3Tb1TgevXr1Qq9evdySTyAQCLyNO4r+AgBlDz0W8qArAICIrip+LgTwvq2EiGg+ikabk5KS3JrBtWoVcPEin+p97BiwaBEPwLV0KbBtmxxwKy8PWL6c9/YDKfyAwHdIgc9SUngnIjWVr1QFiHYk8HPshbUs7QMH4swDqKHY7gjgp9LStRWmOD09nR588EEKDw+nLl26UNeuXWns2LF07do1evbZZyk6OpqioqLomWeepbvuyqD77+eLeSQkJNBzz203hycFJhBjvYq2bxPQi4AqBFSi+vWT6OLFi0REtGTJEqpduzaFh4dTYmIirVixwry/WbNmZrmGDh1KsbGxFBERQY0bN6Y9e/aYj02YMIGef/556tOnD4WHh9O9995LBw8edCDYqMAXrF3LV/kqKb5+cDAp2pJlPHQpzvm4cc6veiQQeAKosWYsERkBDAGwFcCfAL4moiOMscmMsXZFpw1ljB1hjB0GMBRAX2fzyc/PR8eOHdG3b19cu3YNPXr0wNq1awEAJpMJL730Es6ePYtz584hIyMMx48PQefOcnCtmBhuxgH49513AlOnAomJywDcAB9muIrY2M/x2Wdh2LnzFoYOHYrNmzcjKysLer0eDz74oE3ZmjRpgkOHDuHatWvo2bMnnn/+eeTm5pqPr1u3Dt27d8f169fRrl07DBnisPORwIssWgR07AiMH88H8W2N33z+uRwMzBrJQ7mggIeaHjvWfjoCgU+w9wTw1ce6R797926qWbMmmUwm875mzZrR2LFjLc7bv5+Isf8REGVeDCMhIYE++mi7eS1GnW4CPfVULyIiGjNmETGWTMBhi55ZaGg2hYdXom+//ZZycnIslvey7tFbExUVRYcOHSIi3qN/4oknzMeOHDlCoaGhdv8r8CyOLsuWlVX6ohSZmUSVKslL8gUH88U8rLd1OsuFO6ZMUa14AkExUEKP3u/DFGdmZqJWrVpgTI78GBfHhwZycnLw2muvYcuWLbh48d+inlUW8vIKkZqqBQDce68cUvfECdlGP3FiH1y6lIF167ojO/s6bt/uDaJpyM+viO7dV+Pzz2fixRdfxq1bzQDMQmhofYwYYSnbzJkzsWjRImRmZoIxhps3b+LKFTnwXPXq1c3bFSpUQG5uLoxGI3Q6v6/2gMZg4IPuBQWlD7oPG8ZdcLVaHt5Wp+O2don9+4G+fYHbt4Evv+RhmaXjkl1e2q5aFRg+XI7Tv24df7Ns0ULY7pXjG+W9LnyB32ucGjVq4MKFC3w5rCJln5GRgbp162LWrFn466+/8PPPP+P06epISTmE/PxGCA4mpKQAK1ZURE5ODlq14o1r0KCLZkUfFBSEhQsnAJiA7747gy5dngFRPZhMLyMqqjViYlrj4MHbAN4GMAC5uXuxZQtw5QpvtEbjXrz//vvYsWMH7rvvPmg0GlSuXFkajyjz+PON+/77fFUoQHavtSXjtGnA4sU8TvkLLwBPPw20aSOfazBwJV1QwBeISUwEevSQ/69MU9pu0IDnd/AgsHYtkJYGhIaW7bkcpbUFgwFo3px7u4WEADt3lt268Ff8PtZNcnIytFot5syZA6PRiB9++AEHDhwAAGRlZSEsLAxRUVGoV+8amjadBIB71yQnAw8++CBWrVqFgoICpKWl4dtvvzWnu2vXLvz2228oLCxESkok6tYNQrt2GlSrdgmzZ/+AGTNu4caNEGg04ZCqKT0dOHuW218PHMiCTqdDTEwMjEYjJk+ejJs3b3q9ftzB1TkF0o3rj7boP/8ENm2SfwcHW/bQJX74ga9KBADffANUqAB07Qrs3i0/JFatkpcONJks52PYIzmZL7WYlCS7+FrP5ShLbN0KPP44MGYMbxOTJgHTp1u2iYULeT0S8bp9803+kPWndlPW8XtFHxwcjO+++w6LFi1CVFQUVqxYgeeeew4hISEYPnw4bt++jejoaDRt2hQ9e7YBIPcWpkyZgpMnT6Jy5cqYMGECevbsaU734sWL6NKlCyIjI3HPPffg6aebY82aPujUyQTgAwA1AVRBtWq7MWzYXLz4oixTbi5w+3ZrtGnTBnfffTcSEhIQGhpqNikFAno9vzHffru4si7tAbBmjXzj5uV5bkKau+Tl8R53ZCTw3nt8X/fuxXuPJhMszHCSIu7TB7h+Hdi4ke/bvJkf12rtPzDs0aIF771KOPPfQMBgAAYNArp0kddkLSjgyxUqOwA5OcCWLfJqT4xxc5itdidQEXvGe199bLlXWvPwww/T4sWLXRyyKBm9nig0lA+uSYO60n7lqvOPP040dWrgutE9+6ztAci9e/mgokZjWX4lTz9t6V74+ed8/6pV/H+M2f+vmnTvzuWZMYP/7tqVqEIFPpiq5IMPZHdI5XUuKCCqXp2ofXvuJgkQvfeeY4O6ttDriZo35+n89pu7pfM927cTDRpE9NJLvI1I11+qR+vB6IkTiUaM4Nuffsrr8ZVX5HM0muID3wLXQQmDsT5X7NYfW4o+NTWV/v77byooKKClS5dSaGgoZVrfvR7EnseGXs99pFu3lr10fKHQ3OXgQX5jajTyDbd3L5HRSPTggyV7oBw7xs/v3p3o7beJYmOJoqKIHn1UTs/ef9Vk/Pji1+TECa6E2reXr+cXX3DZmjXjnlrW1/n11/lxxvgDzV2uXiWKjCTq0MH9tErCUS8jV/n4Y8uHu/I6DxrE8543j9e91A7i4ng9tm9vKWdYmPz/b79VR97ySMAr+nnz5tEdd9xBFStWpAYNGtCGDRs8VjmuMH263CvxtkJzl+3buTvhHXcQbdlC1KkTL8cLL8hKXrpRg4OLK47evXkvuWhuGa1cKd+0Gg1XrADv3TmidPR699+Mli4trnyka/L885bySduhobbzVKblqYf45Mk8vf/+Vx1FLCnPkt7C3Em7a1fLnrpGw6+v9VuvdP706UQDBtivR72e6K23eDtq3FhMMPMUAa/o/Q3JvCMpFX9ppGvWlHzT6PVcXoD7fkvnKc04QUHcFFOtGlFCAp9hLPHVV/yG79lT3jd9uqxApd5dnTpEtWoRKaY+2GT7dtvy2JJb6q1ab3fuzP+flETm+RJKxTJ2rP2eqK0H9LRpnn+Ib9/u+YeHkokTLZWwtcyO9PZtnaPXyw9uxuT5AmFhvPdeUprW7cJWPb71lpx2IL4Z+xtC0auAXs+VS3Aw0T//+FaWf/4hevLJ0m+aF1+0reiUikLaL/XU16zh5+zaJd+4yt6w1JtUKthly/h5e/fallevJxo+nE9CUirfoUMtz7E2B+h0XPEwJo8FSMpt1y77ykqSTzm5yV4d2SqPuyiVHmP8YeIJ9Hp+7e6+W65DxnhdSOzcWXwswppPP+X/s247zzxj2S4kE42jb2ql1eO0aaU/eG2lq6aJKpARil4l/viDzINOvsBkIpowgb8CK1+tGSMaONDyhsjP5z10aXanrYFm5X6jkejOO7kSeestopgY+zel9c2XlUVUsSJR//7FZbaOGSMpIcb4fwYP5qackBBZ+djqkTvSO7cln6u9W3ewHshv2dJ9c4XyrRIg6teP1zfA64+IDy7Xry+fo9FYKuv9+/k4hHXbmT5dflhrta4/9EqrR2VbsGUmtHW+9NAPDS39raK8IRS9ijz3HFeCOTneyU8aEJ41y/Imlnqryl6u8gb95BMq0YvE1k05erSlQi2tZ6jkxReJIiKIbt2y3N+3r+1e4pQp9hW5RiPb/4ODHe+d+xPSdWvZUlaoISGyN4qj8u/fz+3fdepY1o/0oOvYkT8wL1zgdSuNlyjrXGobysF4Zdtp1IhvJyURpaaqq0z37OGD1cnJpZ87dart9hEI198bqKboAbQB8BeAEwBG2TgeAmB10fGfASSWlmagKfpdu3gtdujgeGNztcdo3YsLD7e0J0tKs2lTyxv77bf5AGzLlqXbzZUola+zr+5SvXTtKvekX35ZdsGzVtDW5g1lT1LZc3O2d+5vKM0Vziorvb640raux5Mn+TmVK/NzRo7kx8aPJ6pb13be0rWdNk12B/WmAn37bX7tz50r+bxXX5Xbh3Jg3doc5mi7CMT2UxKqKHrwBcFPAqgDOUzxvVbn/BfA50Xb3cHXjy1Tip4HU5N7TlOnlv66Kr1+hoRws8ioUaWbEqZNI3rsMcte3IABtu2g1uaRunW5jMuXO1c2d+zV+/YVf7uQbspZs0q2pTsy2BeoKMsp1YnSZFIS3bqV/uBVPgy0Wn4dJPbvl+eI2Hsjmjat9EFUT3PqFM9v8mT75/z8My9XixZcRmnsRmpjr73Gz3PUA2nZMvmB4S9vBGvW8PkbrsqilqJPBrBV8Xs0gNFW52wFkFy0rQNwBQArKd1AU/TKnqjyprXXeJSmC+XHnteJdS/e2iRjr1ei13PTy333lS5TSbja67FVL6Upj7LWw7KH9UCzpKxmz7Z//uDB8kS2kh6806fLDxBbdV3aG5Eag9GO8MQTRImJll5eEpJLcLVqRNeuWco6bRqfE6HVEvXpQ9SkSeltzWQiql3b8p7ypou0rXrfu1e+X1ytd7UUfRcACxW/+wCYY3XO7wBiFb9PAoi2kdZAAGkA0uLj450voQ9R3hjK12rrxrNjB9Ejj8hKV7phlQNhUq9ESnfyZKKHH7Z8gLzyinPK0Bc9NCLnvV3KK5LnTO3afOKZ9dud8kHPGJ/168gbozt17YsHruTl1a+fPFD83//KYxoldYa2bi3u5y+1d1vnr19P5jEn6T+DB3unzOvWyeZL5fV57jnHOkMl4feKXvkJtB49UfEemnRT7t7Njyuf1jod0Zw5ludLPbDoaO73PWOGZSMsrRdXmmy+6KFJeQeyPd2bfPWVfL2Dg3kbmTTJ8kFfll0QpTGdkj72ym9rLkfz5vwePH7c8tyCAqJ77iG66y4+EDxuHPdGk+4zR+8RZ9v2xo189rjSXCd1Bk+f5g8xd+5zIvUUvTDd2ECvl/3VP/iA72vRwn5jlRrJyJG2G7e1S5yrMgXajV/esGfqcvdBHygoZ5tbl700by9bnZnMTP7AHDTI8rwOHXi6a9fK+6W4Ro4+THftslTY0seey+eWLZbjVcrxs4EDuRtzWBjRd9+5d5+qpegdWTN2sNVg7NelpRvoil6idWvuFSOFGCjNH9n6Rrc3xVxQNrE3SOuJB30gUJKpz5GBeVudmQEDeDoXL1qawDQabhpS/ld6gy5phjaR/EZg763D2qPMZJLHyZRvHBMnWrrIBgW5f31LUvQuLzxCREbGmLRmrBbAYipaM7Yow3UAFgH4gjF2AsC1ImVfLujTh8fq/u47Hpr1o4+AmzftL86QksLD2ubn85C4s2cDV6/658IeAs+TnCyvhCatVCW1hRdeKPttQFl+6xW8HCl7cnLx80aMABYsALp146GUpXUGGOPrDvznP/J/N24E2rYFmja1TEe5qAoR8NprfM2DoCAe7loKvWw08uMmE1+pTArf/dtvwJEj8vnK61lYyNcYBuT1DlS7zvaeAL76lJUevSuBz4SJRSAh2oL76PXFXVhLekuWTDi//85/z5hheQ8rx9lsze2YN8/SQ65NG35ukybczVVtDyeU0KNn/Lj/kJSURGlpab4Ww20MBr6wgtQrK8tLyQkE/sg77/AFTkwmvnbvgAFAQoL9t4SrV/nxRx7hve3du22nq9Xynvjo0cWPGQx8oZXvvwd+/ZXvCw21v3yiJ5fkZIylE1GSrWN+v2ZsoGL9KiqUvEDgXazNoS++WPJ9WLUq0L498NVX/LdGw00uRiNX7gB/AJS02phkQgoJ4WYbIr7ylj2zjC2TkxoIRa8i3rqIAoGgOK50thIT5W3GgJdeAuLjnR83aNGC9+Slh4yvl5IUphuBQCAowpMmV0+aZRyhJNON3yl6xthlAGfdSCIa3F/f3xByOYeQyzmEXM5RglwRFYHICOBmFpB1y6tSuVdfCUQUY+uA3yl6d2GMpdl7qvkSIZdzCLmcQ8jlHOVNLo2nExQIBAKBfyEUvUAgEJRxyqKin+9rAewg5HIOIZdzCLmco1zJVeZs9AKBQCCwxO/86KOjoylR6cwqEAgChlu3gKwsICKC/755k89MvXSJTx5iDKhXD6hY0bdylkXS09Ov2PO68TtFn5iYCOFHLygvKH2tgdK3/XkC3pYtQLt2fCYpY3yfyWR5DhFQqRLQpg3QurV/lyfQYIzZdUv3O9ONLyZMLV26FAsXLsS+fftsHn/66afRvXt3vPjiix7N98yZM6hduzYKCgqg0/ndM1egMtLknLw8WTEWFvJtxuQYLdJ2cDAwYwaQne1fSv/rr4FZs4D0dC6/NYzxEAImkxzQFyg5BozAeXwa64YxFgpgD4CQovy+JaIJaufrSTZv3uxrEQRlCIMB2LWLf27fLn5cqQyVPeK8PGDoUL4dEsL/72slOXkyMKHobtZo+MOosNAyfK8y7Pa5c8D8+bxcubnA2rW+L0N5wBvdyDwALYkomzEWBGAfY2wzEf3khbwFAr/CYABatrSMjc4YoNPJilGpJJXbgNwrzssDevQAunQBOnf2vrLcv59HhkxNlfcxBvTrV3JsGIMBWLaMhxgoLAQWLeLl6dRJKHxVsRe/WI0PgAoAfgHwiL1zbMWjP3fuHHXs2JGio6OpSpUqNHjwYCosLKQpU6ZQfHw8xcTEUJ8+fej69etERHT69GkCQIsXL6bY2FiKioqiuXPn0oEDB6hu3QYUGlqJOncebI4jPXbsEmrQ4D/UtOlgqlgxkurVq0c//vijOf/mzZvTggULiIhoyZIl1KxZMxoxYgRFRUVRYmIibdq0yXzu9evXqV+/flS9enWqWbMmjR07loxGIxERGY1GGjFiBFWtWpVq165Nc+bMIQBUUFBQYpxpQdmhVy/L+OgDB9pee9TWtnKNYWWcdZ2OL1vprfj1W7bIsdk1GucXfpfKM2aMXAaxkpr7QI2lBJ35gK9AdQhANoD3bBwfCCANQFp8fLyF8EajkR544AEaPnw4ZWdn0+3bt2nv3r20aNEiqlu3Lp08eZKysrKoY8eO1Lt3byKSFf0rr7xCt2/fpq1bt1JISAg99lh7Cg6+RMB5AmKIsdSiG24JAVpi7AMKDc2nyZNXUWRkJF29epWIiit6nU5H8+fPJ6PRSJ999hnVqFGDTCYTERF16NCBBg4cSNnZ2XTp0iVq0qQJff7550RENHfuXKpXrx6dO3eOrl69SikpKULRlxO2bydq3NixBTBKQlKSgwYVX7eUMXUVpl5P9N//8iUyrZfGc+Uho1w+kzHHFucR2Mfnit6cGRAFYBeA++2dY92j1+v1FB0dXUwZtmzZkj799FPz76NHj5JOp6OCggKzoj9//rz5eJUqVeiuu1YpboxOBHxYtL2EgBoEmIgxvrh3bGwTGjduOREVV/R169Y1p3vr1i0CQH///TddvHiRgoODKScnx3z8q6++opSUFCIiatGiBc2dO9d8bOvWrULRlwOUKxXpdESffup+79veGrNqKcz9++V1VaU1Tt1dGUkqg1Q3H3zgWZnLGyUpeq+6ehDRdcbYLgBtAPzuyH8yMjKQkJBQzCslMzMTCQkJ5t8JCQkwGo24dOmSeV+1atUAAPPmAdevh+HatWrQ6cwvi2As23yuyVQLAAMRtyECCXjnnUzExvIBpJMn5byrV69u3q5QoQIAIDs7G9euXUNBQQFq1KihSNeEuLg4s8zStiSzJylLrnplgU2b+CpE0kpDAG97N27YXp3IGWytMZuby9O/ft29tK0pLATeeIMvoAHwcYOXX5Zt8a62I6kMP/4ILF4MTJ8OXLsGPPOMaJuexhteNzEACoqUfBiAJwG85+j/4+LicO7cORiNRgtlX7NmTZw9K7uNnjt3DjqdDtWqVcP58+cBAP/9L1d+vxc9UoKCgDlz+Oh/aipfNqx2bT7JY968C3jtNcKvvzKsXg0A52A0tsOgQfzmmTWL+wiXJmtISAiuXLli012yRo0ayMjIsJDZU6xYwVfQsfZblgbzTCZLt72QELEAuZosXw707cvbjlbLB1slDxRPLUKhXNimQQPuqrhhAzBzJp+g9Mor7l/X1FRgyBC+wLXUSfLkguVSGWJj+UDu1Kn8XvP3pTftdar8VmZ7XX1PfQA8AOB/AH4F78WPL+l8a9ONZKMfMWKE2Ua/b98+WrBgAd1555106tQpysrKos6dO1OvXr2IiGjlSm66AQrMr7NALdJodplfa3v16kVTpkwhIm6O0Wq1NHv2bNqzJ5+Cg78mIIIYu1L0qtqcGFtA06fLg7FWr0x0/PhxIiJq164dDR06lG7cuEGFhYV04sQJSk1NJSKizz77jO655x7KyMiga9euUcuWLd023ezdyxchlmydznwY4/8TA2GeQ6/npj+dzjN2bGfZvt3STPTNN64vNL5+vdyugoIsF8T2NNOny3IzRjR1qufz8BQ7d1qasZSD4m+/TTRtmm/uJ/jYdPMvgOsAqnHlixvO/Fmr1WL9+vUYOnQo4uPjwRhDz549MXv2bGRmZuLxxx9Hbm4uWrdujU8++QQAXxRYQqPhPaqCAt4jsdebeuSRR3D8+HGMHx+NatWqoVWrb9G0aVUMH859nYm4+SY9nU/xtsfy5csxatQo3HvvvcjKykKdOnUwcuRIAMCAAQNw7NgxNGzYEJGRkXjjjTewc+dOZ6rDgvXrgQ4d5Ik1tnyYlWtdSttGo2UTzc+3v6alwHEMBqB5c9nEIV0PT/aAS+PgQd4WCgv5dX7+ef4b4G9xjvSUDQb+RrJ8ufyGaDLxtz93TU72SEnhE6gk89PGjXx/y5b+0y4NBmDJEmD1avkaKzEa+RsJ4H+TwVSfGcsYqwGgBhH9whiLAJAOoAMR/WHrfHdnxq5eDXTvbvma6Y6JwmAAtm7lCwYfP84VaGio718tjxwBmjXj9l6AK/EBA2z7MNvarlqVT76RZmVu2MBtowLXadOGtxWg+PXwVltRLoUXFATcfz8g3U7SGqh33llcJr0e2LyZK/QZM2w/rNRu85I5RK/n7REAwsJ8f68BwPbt/P6Q5jMEBfG6UnaqGON1JanURx4B2rb13sOqpJmxXvW6KXqo/ADgSXvHbfnRO8rXXxMFBxPdfz/Rnj2efc2cOFHuA2s0vnMF0+uJ+vcnqliRqEoV532YrdMaOJDX2d13E40fL0w4rrB/P1HLlrKZxl1vFHex9sFXerZIppGgIKKhQ7nZr0ED26Y9b5qclPiTGWf/fqLnnycKDbVfL7bmOSjrOyiI6P331a9H+JF7ZSKAcwAirfbb9aN3lN275coNDfV8hUo3jHTxhg3z/g2g13OlLN0Aa9a4bn9VMnOmXK6QkMBV9vYmHKnJ3r2yPV6rJZo71/vtojSkuujY0bFxG53Otw8r64dT06Zc2Xtblo0bLf38HXEpler6lVeKj5upPc/BLxQ9gHBws02nks5ztUf/xhuWT1w1etx6PdG4cUQ1a8o9e2/eDH37qlNG5cQVgOiBB4gmTfIvZWUL6abav19+m5MmI3mjZ331KtGdd6rf7jyF0vdep5MVqUZjqcTUHHR1Rtbp04natpXr15v3WmYmUUyM62831nWtVPhdu6pTvz5X9ACCAGwF8Hpp57qq6JUVq3aDGD3auze3ZK6Rbk5Pl1FZd0qF74/eOJIC+Ogj+e2mpI9a1+eLL4gqVy6uJP2tvqyxZWbwF+VuC6UZByDq3Fl9Odeu5WbR4GD3TaPKulbeW2p0EktS9N4YjGUAlgG4RkTDSzvfncFYpW+r2oNGUmAqjQbYs4cPjKqVV4sW8qDpjBl8oM3TZZTq7tw5PsGMiOc3bZp6nhbOYh0QTIIx4LHHgJ9/tvQ0ys/n3y+9BNx1l/t1Jg3M//STPOgaEgJ8/HFgzkfw1v3iDspQzpIHkBoOEQYDH4w+eRJYuZK3f09eW6mu//wT+OILvk+j4V46nrq/fDoYC+BRcLfKX8Hj3RwC8Iy9890ZjPUmej1Ru3b86Txjhnr5DBrk/bcHpX1UEbHBZ+j1RBMmEDVsWLy3ruxtWdvo336bqF49+fyQEPs919Js+vv22X6D8HdzTVlAujZK0yVjREXTYNxm9ericYPUurbWY33LlnkubfjadOPMJ1AUPRGRycQHuLRaHuzJk69hej3Rq69y5aSGuaa0vMePJ6pViyg6mmjsWN96kISEWL7yOmNumDKluMeJ0uukXz+i//s/rsRt1bFez8+JjrZMI5DMNWUF605I/frueYpt3kyUnGzZPrxhitPriUaN4mMA0dFEb73lmXxKUvTeMN0sBvAcgH+I6P7SzvfFClPusHUr8PTT8queJxaDsDbXvPsu98/19iv20qXc7AFwP+pPPvG+ieLll3kcFIC/6g4c6JxvutKvnKh4iAhrlHlcv87DCUj/8dTcDIHrSCaQv/6SYlK5Njnps8+AwYP5tkbDr600X8Bb13bFCqBPH74dFMTb2q1brufr0xWmACwFMAfAci/k5XV++YUrYyKumKdOBR591L1GMmcOTwuQZzn6wk7+9988f5OJK8pBg3hZg4KA8eN5mR2dDLJjB/+0beu4gp47l09UY0ye+evsDFNbwb/y8y0nt0jpFxbyskorICnRaoH+/b0/AUpgiRQb55135LaZmwusWuX4Ndm7l7cDCesFU7x1bTMy5DIUFADDhqk3IVN1RU9EexhjiWrn4ytSUnhPXloxZ9MmPqjjysXS6/ng56ZN8lqhngyC5SzKskm9YemBNnYsPycoCPjoI977TUnhDXb3bqBVK3581y4+wCX1yj/8sPTel8HA05IU8qxZ/GZ29Sa0Dv5lrfSlXtylS8D33/OHNyCvdSr14r0VxkBQOsq2aTJJEWr5bGSt1vYgs7S61fLlQI0awD//8Pbqq2urLAMgdzzUCEnilcXBixT9BnumG8bYQPBJU4iPj39IGZUyEJBeJ0+ckBWaox4rBgMP23DyJI/vQcQb6kcfATdv+r73KJVNqRgBWelLSG81yt9SL9maadOAMWPs5zloEL9xAV4XU6ao80Zjy+tEaeoRJhr/Rrp+Gg1vH1L7kxZaDw6WvWYiI4HXXuOKnTH+QI+J8b3Xka37y9VwEyWZbvxC0SsJNBu9EklJSIGZnn0W+M9/uL0dkBtVYSF34Tp9GtiyxVJBAuoqN3ewp/RtKXMl0pqokmlk8GBunrKV/jffcCV/+7ZsrvF2rJNAcDsUyLzzDjBunO12KCl95T3m7/dXoNroyw2SPXjnTm6+2biRfyS/butesBIpyqbJ5FtzTUmUZgKxt6i11DO+cgXYtg34/HNe3h49LHvSSh/5qVP5Ob5QtspyCvyflBTexqzboPXgu3QfBsL95WmEovcw0sVijNvciez3eK2VeyCZCWwpfUdWs2rUiNvwP/kEWLBAttevXy8rea1Wfh0XCEpDOeBuHaHVehwmUO4vT+ONFaZWAkgBEM0YOw9gAhEtUjtfX9OiBR+QdaSnG+iNz7onYm8bAA4dsvSWeO894N57eZxvQDbX+GOPS+C/2GuDyk5IoN5fnsArNnpnCGQbvTWOrOFa3hqfcrDT+k3njTeAKlXKZ70IBO4ibPQ+wpmebnlB+Zp97BiflAXwnnyVKsJcIxCogVD0Aq8jPQAl11LJhirMNQKBOvid6YYxdhmAO4700QCueEgcTyLksklERSAyAriZBWTdUhwQ9eUcQi7nKItyJRBRjK0Dfqfo3YUxlmbPTuVLhFzOIeRyDiGXc5Q3uTSeTlAgEAgE/oVQ9AKBQFDGKYuKfr6vBbCDkMs5hFzOIeRyjnIlV5mz0QsEAoHAkrLYoxcIBAKBAqHoBQKBoIwTkIqeMdaGMfYXY+wEY2yUjeMhjLHVRcd/9sbCJ4yxOMbYLsbYH4yxI4yxYTbOSWGM3WCMHSr6jFdbLkXeZxhjvxXlWyzGBON8XFRnvzLGGntBpnqKujjEGLvJGBtudY5X6owxtpgx9g9j7HfFviqMse2MseNF35Xt/PfFonOOM8Ze9IJcMxhjR4uu01rGWJSd/5Z4zVWQayJj7ILiWj1j578l3r8qyLVaIdMZxtghO/9Vs75s6gevtTF7i8n66weAFsBJAHUABAM4DOBeq3P+C+Dzou3uAFZ7Qa4aABoXbUcAOGZDrhTwuPy+qLczAKJLOP4MgM0AGICmAH72wXW9CD7pw+t1BuBxAI0B/K7Y9z6AUUXbowC8Z+N/VQCcKvquXLRdWWW5ngKgK9p+z5ZcjlxzFeSaCOANB65zifevp+WyOj4LwHgf1JdN/eCtNhaIPfqHAZwgolNElA9gFYD2Vue0B1C0dDC+BfAEY9ISBOpARH8T0S9F21kA/gRQS808PUx7AMuJ8xOAKMZYDS/m/wSAk0Tkk+XFiGgPgGtWu5XtaBmADjb+2hrAdiK6RkT/AtgOoI2achHRNiIyFv38CUCsp/JzRy4HceT+VUWuIh3QFcBKT+XnKCXoB6+0sUBU9LUAZCh+n0dxhWo+p+iGuAGgqlekg3lFrUYAfrZxOJkxdpgxtpkxdp+3ZAJAALYxxtIZX7rRGkfqVU26w/4N6Ks6q0ZEfxdtXwRQzcY5vq63fuBvYrYo7ZqrwZAik9JiO2YIX9bXYwAuEdFxO8e9Ul9W+sErbSwQFb1fwxgLB7AGwHAiuml1+Bdw00RDAJ8A+N6Loj1KRI0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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "signal_points = [ signal_1_points, signal_2_points, signal_3_points, signal_4_points, signal_5_points, signal_6_points ]\n", + "titles = [ title_1, title_2, title_3, title_4, title_5, title_6 ]\n", "\n", - "if __name__ == \"__main__\":\n", - " test_five_models()\n" + "plt.figure()\n", + "for i in range(0, len(signal_points)):\n", + " ax = plt.subplot(len(signal_points), 1, i+1)\n", + " plt.plot(time_points, signal_points[i], \".b-\")\n", + " plt.title(titles[i], x=0.01, y=0.45, loc=\"left\")\n", + "plt.show()" ] }, { "cell_type": "code", "execution_count": null, + "id": "anticipated-marathon", "metadata": {}, "outputs": [], "source": []