{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Run the new window deformation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from openpiv import windef\n", "from openpiv import tools, scaling, validation, filters, preprocess\n", "import openpiv.pyprocess as process\n", "from openpiv import pyprocess\n", "from openpiv import widim\n", "import numpy as np\n", "import os\n", "from time import time\n", "import warnings\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "settings = windef.Settings()\n", "\n", "\n", "'Data related settings'\n", "# Folder with the images to process\n", "settings.filepath_images = '../test1/'\n", "# Folder for the outputs\n", "settings.save_path = '../test1/'\n", "# Root name of the output Folder for Result Files\n", "settings.save_folder_suffix = 'Test_1'\n", "# Format and Image Sequence\n", "settings.frame_pattern_a = 'exp1_001_a.bmp'\n", "settings.frame_pattern_b = 'exp1_001_b.bmp'\n", "\n", "'Region of interest'\n", "# (50,300,50,300) #Region of interest: (xmin,xmax,ymin,ymax) or 'full' for full image\n", "settings.ROI = 'full'\n", "\n", "'Image preprocessing'\n", "# 'None' for no masking, 'edges' for edges masking, 'intensity' for intensity masking\n", "# WARNING: This part is under development so better not to use MASKS\n", "settings.dynamic_masking_method = 'None'\n", "settings.dynamic_masking_threshold = 0.005\n", "settings.dynamic_masking_filter_size = 7\n", "\n", "settings.deformation_method = 'symmetric'\n", "\n", "'Processing Parameters'\n", "settings.correlation_method='circular' # 'circular' or 'linear'\n", "settings.normalized_correlation=False\n", "\n", "settings.num_iterations = 2 # select the number of PIV passes\n", "# add the interroagtion window size for each pass. \n", "# For the moment, it should be a power of 2 \n", "settings.windowsizes = (64, 32, 16) # if longer than n iteration the rest is ignored\n", "# The overlap of the interroagtion window for each pass.\n", "settings.overlap = (32, 16, 8) # This is 50% overlap\n", "# Has to be a value with base two. In general window size/2 is a good choice.\n", "# methode used for subpixel interpolation: 'gaussian','centroid','parabolic'\n", "settings.subpixel_method = 'gaussian'\n", "# order of the image interpolation for the window deformation\n", "settings.interpolation_order = 3\n", "settings.scaling_factor = 1 # scaling factor pixel/meter\n", "settings.dt = 1 # time between to frames (in seconds)\n", "'Signal to noise ratio options (only for the last pass)'\n", "# It is possible to decide if the S/N should be computed (for the last pass) or not\n", "settings.extract_sig2noise = True # 'True' or 'False' (only for the last pass)\n", "# method used to calculate the signal to noise ratio 'peak2peak' or 'peak2mean'\n", "settings.sig2noise_method = 'peak2peak'\n", "# select the width of the masked to masked out pixels next to the main peak\n", "settings.sig2noise_mask = 2\n", "# If extract_sig2noise==False the values in the signal to noise ratio\n", "# output column are set to NaN\n", "'vector validation options'\n", "# choose if you want to do validation of the first pass: True or False\n", "settings.validation_first_pass = True\n", "# only effecting the first pass of the interrogation the following passes\n", "# in the multipass will be validated\n", "'Validation Parameters'\n", "# The validation is done at each iteration based on three filters.\n", "# The first filter is based on the min/max ranges. Observe that these values are defined in\n", "# terms of minimum and maximum displacement in pixel/frames.\n", "settings.MinMax_U_disp = (-30, 30)\n", "settings.MinMax_V_disp = (-30, 30)\n", "# The second filter is based on the global STD threshold\n", "settings.std_threshold = 7 # threshold of the std validation\n", "# The third filter is the median test (not normalized at the moment)\n", "settings.median_threshold = 3 # threshold of the median validation\n", "# On the last iteration, an additional validation can be done based on the S/N.\n", "settings.median_size=1 #defines the size of the local median\n", "'Validation based on the signal to noise ratio'\n", "# Note: only available when extract_sig2noise==True and only for the last\n", "# pass of the interrogation\n", "# Enable the signal to noise ratio validation. Options: True or False\n", "settings.do_sig2noise_validation = False # This is time consuming\n", "# minmum signal to noise ratio that is need for a valid vector\n", "settings.sig2noise_threshold = 1.2\n", "'Outlier replacement or Smoothing options'\n", "# Replacment options for vectors which are masked as invalid by the validation\n", "settings.replace_vectors = True # Enable the replacment. Chosse: True or False\n", "settings.smoothn=True #Enables smoothing of the displacemenet field\n", "settings.smoothn_p=0.5 # This is a smoothing parameter\n", "# select a method to replace the outliers: 'localmean', 'disk', 'distance'\n", "settings.filter_method = 'localmean'\n", "# maximum iterations performed to replace the outliers\n", "settings.max_filter_iteration = 4\n", "settings.filter_kernel_size = 2 # kernel size for the localmean method\n", "'Output options'\n", "# Select if you want to save the plotted vectorfield: True or False\n", "settings.save_plot = False\n", "# Choose wether you want to see the vectorfield or not :True or False\n", "settings.show_plot = True\n", "settings.scale_plot = 200 # select a value to scale the quiver plot of the vectorfield\n", "# run the script with the given settings" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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cdhl3GaWwQ0Wh0Gd/6QfAagDzAMxGORUXQEMAnwNYVv5nA6+d3U2BsHGj2e23a7J9+pjl5/tyN99sNnKkL5eZaZaQQGq0h4ceMqtRg+kAPPTpY3bmmb7ckiWkUH//vS97zz1mBx3ky5mZ9e5NSr6HtDSz1FSmePBwzTVmZ53lyy1cyG9KSwuXKysz239/s5tu8tucOJG0eY/iXlpqdsopfntmZh9/rMm9+y7TAXjIyTEbOlRr8557/FQeMfTt66fIMGPqjS5dfLmCArPatbU5d955HHcPc+ea7bmnnybCzOy007T1lp9vdu65vpyZ2XPPmX37rS9XVmbWurXZggW+7MiRZj16CA+/6CLm8QjLUeEAYgqEylD0jf/nd6MBXFv+92sB3Ou1s7uK3sxs2zZNTpmcZsydMnGiJjt8uLZ5LFlidswxWpt33WX2yiua7MEHm5WU+HIrV2q5WczMHnnEz08Sw223aXLLlrFdBaedpsndf782TiUlZpdeqrW5bp0mp8LbXHbFrFma3IwZmrIxMxs7VtMhOTn6WF50kfZdL75oNmeOL1daqj/71Ve52SjwjIUYtm7Vx2nMGE325Ze5jl08/7z+8b8AVdFXhetmBIBx5X8fB+D4KngGAN5leLknYpgwQStT160bOQwK9tnH92sCeu50gP58hX1XWkoXhnLZ9sYbfAcPZWV0hyjM3Pvu05i5JSVkfSrU9TvuIB3fRXY2znzvL+jb1xe9/nq6+Dykp2sELIBMzh9/9OVWrPDzK8Xw5JNada2mTYGbb9banDtXI99lZ+tVwMrK6Arz0KsXs3d6SEigP1359p49NVcYwLsJ754NoHvl7LO1NufN06qV7b231kfo1w8j514pVyGrCCqq6A3AZ0EQ/BgEQazi5p5mFuOkbQIgZAXZPWzZoocxrV+vha8VFOgRMqmpWrRCFHKTyp1ITNTvcGrW1EIHExL0qlF162p51pOSuJiUvpeTOwUBmuWvkmKvs7K08DmFVBXD8uVUuB5WrNDHc8UKrc3MTL2ftm3TwnqLi7VQVYBzTokKS0jQUjoAjLRSotdUwwbg5a0S9ZOZqbe5ZYv2nikpYsRRp07YmFX71wijr3BSs4FmlhYEQVMAnwdB8P9kdTYzC4LgZ6dF+cYwCgBat269Ww+PQirbvFmTjVLcQGXA/dbkpoYN9YIesXhyb/I3a6YVHAeomJTQNLlqlVriCXrsc1aWRtoBmFpAyWWyYoWej2jTJi1RmlrsBojG81CVncqiLSjQOS5qLHsUZmxmptZPUdqMwsVRQ7SjkMAqggpZ9GaWVv7nZgDvAugNID0IguYAUP7nz6YMMrOxZtbLzHo12U0K8JYtmhUURTZKxxcXRyBGiIiq6BXrqmFDP5IlhkaNNMZr8+ZaLhGA/a4kjpLr0Eagsaqxzzt2aONuprVppiv6KDHvavlKoGoUfWmpRq4qLNQroFWFolfZtlEMO/U9q6K6VkWx24o+CILaQRDUjf0dwGEA5gOYAGBkudhIAO9X9CV/DllZDN9q2NAn+JhVTQHiKEWVVaSm6izWmjW1xGING+p5k5o00VwyURR9s2Ya30C26BMTZYteUcqFhTvdS16zd91FeS8i7r33eC/09df+Zvzee+TNeAmzsrLogqxf33e1FBZS0StWdRTmtropRFH0qgWsKvoNG9jnnsG0fTv7vFYtLUVHlDKKUfIRqazkiqAij9gTwNdBEMwBMAPAR2b2KYB7ABwaBMEyAMPL/13pSE4G/v1v4IEH/MvL22/XSovl5JCwU6sWd28PkZIXiahVS7fo69bVUsbWrk0r3bOq167lxHvxRb9PFy5kX73vbOOZmVSgkyeHf5cZv2f7dr9UHEpKqHGcXW7NGi7O9evDla0ZL2KfecZ3ca1dy3a9uO9mzejLV+5xXnyRXAMve6UZ0z3fe6/v/37+eV7EegxegCSkVatIsArD2rUcw9g8+SUUF3NuBIHPdP7mG64j75Jz1SqSqqZO9U+n99/P5z/8cLhcUhJj3u+5x9+MP/yQCnzy5HA5IJrr5leDEppT1T+7G17ZvbsWLztmDANJvZji4mKWQKtZk2FXYZg4kWXARo70Q65efpnlxV58MVyuoMDsiCPMunb1w+3efdesWTOtROCJJ7Jcmxe2uWWLWUqKFlMd69NnngmX27yZbTZu7PfT8OGMzZ8wwXn4pZeSmHDffaFiX37Jdxw+3GnPzAYMMBs0yJd7/HGWEvSwbRu/RQlVvfVWs/3200L3unQxO+ccX27BAn7788/7siNHUnbVqnC5r7+m3CGH+G22a0fZlSvD5e6+m3JjxoTLlZSY1aun8UHeekv7HjO2d/bZvtyjj7LNJ54Il9u0yeyCC8z++U+zjIxw2a1bzQ44QAuR/iXgz1BKsE8frcJU796kovfrFy6XlMQSaEce6VtsXbvSYqtTx7fYioro5vCssBo1+LNiBd83DIMG0VpS7rH/8hcawN73N27McNXevf02R47k0bxnz3C5Jk3IUOzVy++no46ixehWeRo8mL6B4cNDxfr14/goIbiDBmlhdt26sWKWhwYNgGuv1UJVu3UD/vEP7dK+f39WwvKw775kuh5/vC975JGsbuYxY/v0oetEafPQQzk3vLDewYPpujjttHC5xESO58iR4XIAi7OoFa7UqlVDhlA/eHUVGjTgCe2NN3zf/8UXU4eMH+8/v8JQdoOq/tldi/7bbzUrKDfX7LHHtDZvuYXWsoKuXf0izWbcuZOSzDZs8GVfeYXFmhUceywLanvIz6clqPTVF1/4VksMF1ygMT+//FIrur1pEy0cF9nZNBmFDzrhBK3fJ00i29lDbq7G4jTTyHRmZHgrrGkz9qVKpHzjDU0uI0NnmJ96qtnq1b7cm2+SxeuhsNAvih7Dvff6J+0YlMLgZiRSKhZ1aanGmDfj+r3sMl/unnt42vUs/zDgz2DR9+rFnBIeatVizhUvARhA62btWu35Zx20BIOaLHbl6tenxdi8ud9m7dq+lQzw0rBRI7br4ZtvmAzLsxjLyugrVeqMTp7M3EHuHUVJCQ6e86hrsQEkzVxwgS+Xl1AHTx3+jmQCDxqkXVj36gW8/bYvV6sW7yWUcNX8fOCFF3y5Zs2ATz7RSl1266ZbgAkJ2lxOTNQjeZTTHsA5rJxQUlKAYcO0bz/1VD+RXQwbN2plPtu3Z0ZSDwkJPFFs2uTL9uql9efAgTxJ/Qo5zaq3ok9PZ8SCgvfe0yJP9txTn0yX13gSSel+DbTSUi2SBeClnFLEPCGBcexKrPBPP2k3+wkJZGh6icoAJilTmIdITETwxONSndOvv9ZDYN9YIlRwBxmsyrhv2OBnZYzhhRe0zWPBAuasUvDcc9qCX7OGWUYVvPmmFla7ZQs3GgXz5mnRVmZ6rq6vv9ZCerOyeCmq4OWXtUia1auBiRO1NidM0AIl+vThZbCHWEF0ta5wRVCtFf2GDcBee2myaWl+Jr2obdbI3SazbNQQKpXFGgVqHDvAOwJFibVrJ6Z1iBVWEMIh1Vz4DF/TWGhqlaco5Lt164BWrXy5pUv9zKpR21TJWkC08oRqWnQ10ixKRJoapRIl9FnlG2zdqt2jxGSVuZSaqhlgNWowevB3T5j6rRFFKW/cqLlONmzQNgQAOisjAtTY+BgUwpQaGw/w25Uj7957M4e5BLGiSDRFrz26shW9GcdHiRFXFb2ZzuBVUuXGoCr6KP1ZFXVTo3BcVKWYmal9exRFrxKm1GcD0b6pIqi2ij4vj1a6qujVCRqlzaoYpSiKvmZNLUdJkya6Rd+qleZ/btvWj/v+PzRvLjk3q0LRq8WfVeb0ihW01rwNdu5c/ihK+bvv6DL0xjI/P5qiVxVTFIu+KuqmqkxSlbVupqVqMIum6AHt3kFNvwDoG3xFUW0VfVoaMxM++6xPbnrqKQ78rFnhciUl0U4Jv7VFr6YMiFn0nhLNyGAI2Tvv+H7tmO/72Wedh+fmUjPNnOl+mMpQVBV9jEzmKeVYAZcZM/y59Le/sUjHxx+Hy82dS7+3Upjmqqv4bM+fv3gxyVJPPOHPkSefpBLzLhoLCvjc3Fz/JFdWVvkWvZm20WRk8ASZnOwT6m65hYaNd2k9bx6DOV57zZ9369dzbShrM4qi/7VQbRV9+/acJH36+FbLd9/xcsRje06axIu2ceMEP/mnn3IlCflIf/iBE3/u3HC53FxGyHz7LeNrwzBlCq27m25yH4977+WF13PPhcvVqcMLvKef9i2nHTs4+d1Lp8xMUk6vuirUxMvP5/u9+iqrTP0SCgqYpnfqVLJzw3DttTx1PPJIuFybNuyf9ev9ubTPPpTx0invsw//vPrqcDmAnIkOHRh9Eob99tvJM/BcR0lJVHaeXM2aVIgvveTLvvYa15LHti0tZarv6dP99McPPMATpFfmMzmZpQRHj/Y3mj32oBHSuXO4XJcuNBgbNfINhyuuoCvOq+hWWLhT0XsGRpQcQxVFtVX0CQkMTbr4Yl+2e3fgoIP80LADDmAkT9euwgCsWcPYtZ9+cp//5pu0xjyad+3aNHwnTvRPFZ06cTNQIjWGD6fV5IVt1qgBjBrF8oze9x9xBBeKR+xCy5bUigceGHruTU3lRrN+fXiZvJo1qcS2bvUXcrduXHgeYapmTcoq5LuuXUmY8nLD7LMPcPTR7EsP++1HReK5BVJS+J5KCOqAAVQ2Xm1bADjkEA6Rd3l58MFUoN26hcslJlIZr1rlf/+++9Jo8MayXj2u41NO8V1C/fqxTw86KFwuKYky550XLgdQLinJL084YwYNpXvv9Y3FBx6g4eLVSK4MVDRN8W+Kf/yDg++he3ftBr55c15Gjhrly+Lgg7nbCJUljjkGeOwxKkcPp5wCfPaZf5Tday9agAcf7Ld57LHUt0pfXXihFo4YBFROrqIHgIsukqq+nHYaT12eEj3kEG7aXiRTv35kaCrs4cMP12qM9umjhb82bAjcfbcvB5B1KfUjgCuv1J7fuTPXh+InHzpUe3bLllSgCtP4sMP4bC975qBB7CulBu/AgT6DFgB69OA8VvzpJ5/s1wkGyJg/9VT/xNerF11L/fr583jtWt75/BqXsb85K9YqwIwtLSWb0kNWFuUUpuJTT5mlpwsPLyuzzFNGSW0WFOhs159+Yt1a4fH22GMs0+chO9ts/HitzU2btBwh2dnM45Kb67eZvrHUbPp0t83161lf18MXX+hlBF97TWPvbtuml1DMyNDaLC7W29y61ayoyJcrKtLbXLdOe89167QxN+OcU9pctMjs6ae1Nu+/X2tz+nT92zdt0trMz9fYtjk5nHPKGPXubfbVV77cq6+ade6svecvAX8GZmxpqVYmrl49HpOUUoInnwyMGCE8PAhw6x4PSMcuM73wx7vv6uGdjz6q5Tt/7DGtPCHAE4KSI+Txx8kQ9U4eZWXAIcMSJErlQw9pz+7YUbOWExOBL77gHYmHnBzgjDN8OQC47DKt9N3KlXrpu3/8QytTt3q13ua112qktvx8LX8PwPsjpc3ERM5lBT/+qH17gwb6t195pd6f55/vy9WuzaAOhQh14olccx4GDqSLS2mzoqjWir6wUA/hUhP8Z2ToxJnMojrS7XpZmVYAAuCEUi5oYjwk5XjaurW20QQBn69EvnTsqE3QCDVCkJSk8QLy8vTjrkqciVLQQ+VkqCQoQCf0padrlagAPXQwI0MPMVRj87Oy9MiT7GwteC1KWK1adCVK4Q91jvTurX17y5bs90oO3PtZ/GkUvRpTvGWLPunVMKoot+uqogV4OafE0bdurefv2WcfP+InJqdaIup7JiVpJ48oUa1VoehVZbtunZZOAtDnXRRmrKroVcYnoM/5KCzWKJWboih6hRugPhvghqjMkczMaFXA1OdXBNVe0avsO3VAt26NYNGLDDi1/BrAiaxWmBIJp5EUfceODCPz0L69tiEAJCIpzNxYbVkPUXhqao3VKIq+pESbd+vXaxZ97BSjzJEoFn1urqbsVPYwoPd9lJKc6sYdRdHn5WkWfRRFr5YOjaLoVUJfRVGtFf3jj9PC8fzkS5aw89UET5Vt0asFlYFoFn2TJlz4XhqZRo3oGlixIlwuL4/hmq++6mf6XL2ax9433/Tfc889OU7ee6oWfWUXcC8q0q21RYvYnjdGX3/NO4wffvBdV//8Jxe8l1hs0SIm9VqyxE+uFTMAlP6Mwg5V53IUiz4/34/htwjlQKdP53h6dwlZWQyHzMvzDRHFpRhDFKMhShnHiqBaK/otWzj5vZjz2bNZJs1LabxiBTB2LMk4Xu3SL7/kBFmwwH/P997jpPr223C5oiISMj76yA/P/+EHTuhzz/X99Pfdxzj+Z54Jl0tNpYJ6911fka5YwcvGSZPC5X76ie/qXaJlZNCyW7MmnO2bmUlCVWmpv3E/+STbmjAhXG7zZrKsn37aDy29/noStbxMlzEycFaWrxjLyuj390Is99yTPL25c31r9dprOT7//W+43Ny5JAm+955/kvzgA8Z9e5fb6encjMrKfBbrV1/RUvbIhDk5wG23cW14BsvKlVyXHnu5bl3Okeef908+t9xCneDNpfz8nYre2xyibB4VRbVW9IceSkKGlzhq8GD+eeaZ4XJt23KCZGf71sj06ZxQSirWmTPZrueWSEmhzLx5JESFoUcPLqQWLXxFf/75lPHIK0FABVGnju8eOOookoe8vu/Wjd/UuHG4a6JGDUbd3H9/uNVWuzZlnnjCtwJ37KAC9ZiULVtyoXfu7FtiPXrwFHfCCeFy7drxXZVolu7dyXVo0yZcrmFDtqsQpmLz49RTw+U6daKrLiXFV3bffsuTnJe1tEYNGhcPPuiP0bRpVNxe+oe6dWn55uezD8IQ4wV4MfcJCYwyO/54/6RQVMRTqRcV9tNPnJt33+2f5G68kW0+/XS4XGWgWhOmhg3T/MnNmpGY4VSeQ2IiCQ8KU65/f048JSTvqKOA118nW9HD6afTUvUWXWKiHmrWsiXDRg8QUrgffTSVmLd5JCSwOLvnukpIIOPUy8lTpw5JS8nJ4UfZlBSSV/be27dq+/XjaU8hxPTvr41lz57sdy8IICGBpB2liEz37n7JvRiOOEIndh13nJ+orUYNbgrKnB8wgAbQcceFy9WvT6Ni+HDfLXHwwZzL3oYEkCinFFvfc0+GSMfyvYdh8GDfqAI4l3r29NdQjx489Qwe7H/7XnvxlKAQGSsML9AewHMANgOYv8vvGgL4HMCy8j8blP8+APAIgOUA5gLooQTz7y5hysxsxw5NbvZsTe6uu3wSkJlZXp7ZKadobebmsuyegpwcrQSbmdmaNRpxyMxsxgyN7GGmkdDM2J5CLtuwwey993y5F17QSDY33WQ2ZYovl5tr9p//+HJmJK8o/bNpk9mKFVqb6twsLNTKPJrphKGiIq2PzMxGj9bKI2ZkmF14odbmJZdoay4/3+ykk7Q2P/6YRCwFXlHyGFas0MY9PZ2F4RX068fi7B7mzjXr2FEf+58DKpEw9QKA/yXvXwtgkpntA2BS+b8B4EgA+5T/jAIg5O6rGK67TpNbsIAJwzxceindFx5SU2lZfvedL1uzpl4u7MMPtR3ejPk0vFMKwAvT7GzfhVFWBlxyiRbV8eqr/HbPYiwu5nsqOVd27NDi0485xve/AjwVNWrk+38BniZuvtmXi1UgW7TIl40lYPOQkgI8/LB2Oi0ro7/YQ3Iy3QhKCOxf/qIR0Bo2ZACA5yMHaFErFbtq1qRFrbS5//48GSuYMEHrz1q1gHvu8eWaNuWpVKnBcPbZvIj3sN9+vJdZs8aXrShcRW9mXwH43yuqEQDGlf99HIDjd/l9jGz/PYD6QRAIS3f3MXmyJrdkiVZQIyVFL4G2ZYtWQzIhQdsQAF5kKZM+CHgRqhCmcnL8LIIA33PSJO2SSIlqAHh8nThRi9RYv14qRIU2bVhGUcGCBTvTFYchK0tjuwKcHwoJbN06nWswaZIWXrlunR7W+sUXWpsbN+pFZKZP19IU16olViADN2JlzuXmSjkEAfBezEs5DfDiVB33b7/Vxr1XLz+zKsCxKSv7fdeM3dPMYpUjNwGI2YAtAOzKwVxf/rtqgyjkprIyTdECulyUsn8NG2oJyNq31zYPQK9GtddeDNn0oH43wE1BiaNXc6IDejWoKAxJtRrVpk3aCQXQC4ps2qTH0avPj1qpTXnPKG2q4YhRCJJKcjyA80NlrathvSrTF4hW9KUiqHDUTbmfKHKgUBAEo4IgmBkEwcwtap27XwFRyE1muqyKKIq+TRvt2BdF0e+9t2aJ7bWXdkKKQbHY1Dj631rRqwSjKCxWtcpSlDZVJaKmXwB00lCUkpxqWoWqUPRKDH8MqqKPwtz+vSv69JhLpvzPmGpKA7ArF7Bl+e/+fzCzsWbWy8x6NVGpqP+DWKy7YtWqiGLRm0WzWBXELGpF4bVpw3A3L71As2ZceBs3hsvl5ZHJOWWKH6Meqy2rsHhr1fIJPkA0Ra8yoitb0RcXaySX1at51C8q8vvojTf4nj/+GC63YQNdAnXq+GMexWBRre+CAl3RRrHoy8q0d1UVfawGr7KO1fkB6OURoyj6KGzfimB3Ff0EACPL/z4SwPu7/P6vAdEXQNYuLp5Kx+2388LlqafC5RYtYnm8hx7yGY2vvEIl5/n+Fy+mj/rjj/1F99JLVN5eFR2ABI7Jk/n/hGHzZl4Kjhrlk1IeeYSK57bbwuVKSkiwueEG/zj7yCO83Pb6PjubVpBCLFMVfVGRZtFv2sQNxttk8vM5PzZu9E9I//43lZhHRKpVi2UWX37Z78tvvuH4pKeHy5WUAGPGMBOr9/3/+hfnupexdf58ro/SUv/UtXy5viGoij7mc1+/PlyupISx+bNm+SVB336bffqf/4TLFRXxIva77/z7phkz2EfeBW92Nk98dev6vvdVq6g7PHJmZcBV9EEQvArgOwCdgiBYHwTBeQDuAXBoEATLAAwv/zcAfAxgJRhe+TQAof7T7uPoo9n5Rx4ZLtepExVtjRr+7rl8OS/QPIuteXNeds2Z4+/yrVqRZq5YAwccwBOKF6/btCldMikpvhshRhTzyE316jFGvH1738o55BBaql27hstNnUoyzPXXh8utW8fF+e674Sc0My7OBQv81BdffslLzltvDZdLTeVYfvyxnwqgbVtabDES3i+haVPyF5QCGD178nTmlSds1Yrvd955vgWcn8/8RrGShr+E9HRGqIwf77/nOedQyXr9/uabbPPRR8PlABo2ixf7hlVSEhXnkiV+4ZWBA7nePaZxSgo3kNWr/QpX779PveBtnPn5jNq74w5f0b/1Fr/9gQfC5SoFSgxmVf/sbhx9UZFZ165aHOrf/mY2Zowv98UXZo0ba3HFhxxidvfdvlxJiVmzZmbz5vmyWVlmjRppsb1TppgdeqgvZ2Y2ahTjkD2sWaPFNZeWmrVr58d1FxWZNWniF1MpLTXbc0+zDh38Z48caZaQ4D87Pd0MMHvpJb/Nv//d7IwzfLnvvjMbMMCXMzM77TQWNPEwfz75GwqOPVbjOYwda3bUUb5cRgb76NNPfdlTTjFr3ZrzOQxz5rDNZ57x23zuObO6dckf8fDAA2annurLmZkdeKAWx3/HHWbnn+/LffKJWVKSxmPo3NnszDN9uR9+YD/9+KMv+0uAGEdfrZmxycm0GhQ/+bHH+tYnQGvgjDO0i5yjj9ZKoCUmMj4/rBZqDPXq0WJVXBODB+sFTa68UvNFtm7tF38GaFHeeadvAScnk+07cKDf3vHHa999+OG0Vr1nN21Khuixx/ptDhmiXbSpdYoBuhaVpHedOmnFLwCeTpSom549tUvbhg3JMFcK+PTowZQWnu973335bKHKJvr1IytW8VMfeqifJiKGSy7RmODDh2vht/37cx4pyd8OPlgrt3jggcxdf+CBvmxFUa1z3QB65rdOnbTLtpQU/wgdw8CBmnIwI7lK2ZBWrNCeb0ZXx1lnaW3WresvkrIytqkQtpYupXL0UFJCBdG3ry/bqxcXvochQzR6O8AFr8QpDxmiRX7UrEmDQQkAaNVK4y8kJfE+Qcmuuu++WsWs/fen68hLPQHs9H176NuXP55iTE6m62LxYr/Njh2Z1kFRtnvvrYer9uihxdF37qyt4Xr1gL/+VWvz2GO1cNHERFYr8+7YKgPVXtFLhbxBRp1KhPrXvzS5iRP1Ni+5RJP74ANesnoIAvpqlc3jgw80pl4Q6JblSy/5iagAKvqHH9Y22fnztVNHjRrMeqhg+nTeo3gIAv/yLoYHHtBIS+vW+RfgMdx9N33FHlatAu66y5dLTuaJy7vkBHiaGj3alzv4YAY0KCTB/ffnxbHy7PHjtTZXruTzFdx+u/btq1Zp7wkwy6cSUtyypd7mm29qfJSKotorejXVZ2KixrqMEi5ZsyYjDCqzzTZt9CIhDRpolqUaGx97T6VP69bVrJvkZJ35l5SkXViXlupx9Go8dZR4ZjV3fZQiNmodhPR0PY5ejXxZv16rhBUEnJtKMZW1a3U3S1qa9p5RSh6qXIcobap5+1VeABAtFLMiqPaKXkWMblyZUBU9oLM+q6LsX7t2OsVdVeB16mhy6gYLUJEom0yUQi5RiDMqQ3LbNk2JRClioyqRKMxYtQJaFMKUyiRds0ZX9CpZLErJQ5VtG6W6lqrA1X4H4oq+0pGQoBepVk8JURR9w4ZavGzr1nqSI7Vuq2rRA7QWlWN03bqV71tMSND6PgoZqKREs/7V0nOAViYvJ4cWdePGPjdg0SJuNB6xKjOTRkDTpn6bsX5UTpNpaZpFn5Ojk3vWrPFztwM6sQmIVglLZU9XRXWt7dv1wui/d8LU7wJvv02r8sMPw+Xy8jjhCwt98kzM+vQWUmEhLcWcHP8iaflyyiqZ9+bNo6/25ZfD5WKVbF580ScjzZ5NgtXzz4fLLV7MiXfffeFWeGwRZWZqNWtVBIFm/Ve2RV9Wprtu3n+fsl61sOJiXrQ9/LA/P8aPZ3z42LHhcjk55COMHu2fDm+6iS4z7w5pzRoaAV5FJDPKtm4d3h5A40e16F96iRatZ4gsX06ew8KFvoExbRqNAc8ISktjUEFiom+wxbK0eP2+YwfXxB57+DpkwQL2q3KXUFFUa0W/ahUHyxv4nBxGAfz7377F+J//0GryUpcGAcMQb7/dj2woKiIh5L33wuUAfs/69X66gtRUXl5OnOgfPbdtY5tevptmzcgSnDs33GJOSmL63TFjfDbnqlXsc68GbSydBCOLw2W3beP7eQr0hx/4/E8/DW+zrAy44gr2pRfRsnAhlZjnMmvQgBbtkCG+C6FPH37PyJHhci1bcqyPP94/fWzYQHed589fvJjGwujRvvV/1lkcRy/b49ixnO/PPRcuB9D4+e47/66peXMq+p9+8t08b77J8Zk+PVyurIwb8ZgxfkqNK65gf3rGUlYW9cKdd/p3U6+9xv5/9dVwucpAtVb0I0ZwN/bCEZs2Zchgv36+1TZgAC22/v3D5WJhmO3a+ZdTXbowXtZrEwBOOYU+WK/sHwBccw1dKJ7P9phjGMbmMQrr12dMsxe6GASsctSokc8+fPVVTubXXguXmzePi/7WW33r6pprmI/niy/C5WrXpuU9Z064EktKoqVaWMiwvDD068d2Tz45XA6gAleimPr0ISfDu5AMAo6NWkpw+HC/elK3bvzTi14LAirYggJ/bnbuTONKqajWvTu/36vEVbs24+KVSlj9+vGU4MWyt2zJPj/3XN8V2LMnjQVv3Fu14no87DBf1xx8MPtVqWxWUVRrwtQ++3AhKf6w44/XQveGDqX14BF8ACpl1b92zjlaubgaNYCLLtIU/fDhrIuplP274gpfKQOk7Ct5ac44gwrce/ZJJzF3jrcZd+1KRduihW+tHnooFb1XInDffbnwvPquAElDbdv6LqGDDmJKCeXy8IwzNK5B8+ZagRKAxDdvwwZIwlEiWZo146Zw/PG+7P77sy+9PuralWk0jj5ae0/lewCuTa8OLEBFf/rpvqINAm5G3kkKIH/ghBO0C94BA7jelfc89FDtfqTCUOizVf1TkVKCW7dqckuX6iXgXnlFkyssNJs6VZPNzOSPgowMvbyY+v25uSx/6KGsTG9T/fZBg7SUEhdcYPbEE77cokVmxxyjPfvSS5lWwsP8+WbffKO1uXGjJvdbIi9P63MzpnVQ8OKL2hwuK6OsgrQ0lhNUoJZRLCvjeleglhgtKDD78ktN9pNP9PWrPv+XgEosJfi7RUkJy3Yp+PBDjalXVkZihIInntBCDM3oElFCrh57jJdJnqVcVkaLVgkNe/RRujk8S7moiG4epc277hKjmHbswKPJV8qx7EoERPPmGuMT4ClOIUw1bw48+KDW5hNPaBWutmzRj+XXXceqSB7WrdOKwqemAldfzXscD82bay6R00+na81rMwhoASsupr324ilFKfu3bZuWfiII6Hv37oUAnsgvusiXq1FjZxIyD+3b6wTJsWO1b68oqrWiz8tjHKyCbdu03OlBoOePycnRaOsxookC9RY+IUF/z4QE7fnJyXq8/datfnpmAEBhIbqVOInWy7HHHtpmWFqqh8rm52shsCUlesGXDRu05+fmavMD4KWxEg64caOWyhmgQlbo/enpGscjMZGKTnFXrlunEwVnz9Y2+NWr9WirBQu0+PQtW8R5DF7wKiG4GRla/QWAkUFqWG9FUK0VfRSSi1okJAqLtUYNfZKoaNxYD1lMTdU2L7VqVRDo368Wi0BRkVwlxExbyPKzoVcBi1JwRv2kKGXqVOJMenq0UoIKi1YtjQjobN8oFaa2bdPu2dat01i5AOe78p4qsQrQiVBRmLFRYu4rgmqt6KOQXKqiGlSNGjq9X2V9Nmqkn1KaN/fDMAEqei8MMgaVLFYVil4lQkVR9KpsFEWvVjpSy9SVlWmKvqgomqJXyWJRFL1KmlLZtsXFjHpS1ub69bqiV6qAAXo6C4BjVLeuLxdF0UchoVUE1VbRm9E/npqqKaeqUvSqRV+rll/dCqBFn5GhfVPz5pz83lE+ZtGr7g4FMjs1oqKvCoteGfcoJCzlk8x4N7JihV8i8Ikn6GbxEott2kRf/vjxvkvovvuoRCZNCpfbto1KuVEj332jsm3LynRFP2cOo068+V5YqFv0+fnalMvO5ibXsKG/Nlav5jt63I3t2+kmrVXLP23H3KSqe68iqLaKPgiAf/yDaXU/+yxcduVKXnh89ZXvO4tZvl4agBgjdtOmnay5X8J33/G5Tz4ZLgeQkPHllz6LduFChhiOHOkvvGee4QWvd8k8YQIvOR9+OFxuzRpO6KVLhc2jChS96o6JIhvLHOmdkN57j4rbu7gNAm6us2b5YbXdu7PfvZDaVq3YbufO/nF//Xr+eHI7dpBFe9ddPiv5mmtohLz1VrjcBx9Q5pVXfAV+3nlM5ewxeN9+m+Uen3lGa3PzZp+gmJnJ0N977/UNtscf53z3SkgmJJC9fPfd4XKxNhcv5jdVNaqtogdYyCM/3y/r1rgxqeuTJ/uunnvuYed7NVtr1qTVdN99/vG4dm0q5kWLwuUALuYNG/zY4n335VF/r7185di5MxeoF0dfvz4VnZeTp7CQi/jpp51nmwG33MLbNmcl//gjoyTefddXOM88Q4vRI0x99RXZrrff7m9Id99NWS9CZ//9aVkql4fDhjEyynMNHHTQTsZrGIKAskpq7h49SETyCGBt2vDZJ57ou6Nq1KBR4xXw6dKFc7h9e98I6dyZ9wjeGu7alYpZqevQpAllPcJWq1b89uHDNSKl2c6ynL+EevU4R44/XmszCPw2KwPVWtEPG6YRoerV40Q67ji/zWOP5YB6ZI+kJC6O3r19f9wBB3CiHnyw//y//pWREl41qiAguclbyABJJvXr+wVFBg7kcfuQQ8LlOnYEOnQgM9l9ydq1GVLiPHzPPXk6W7bMt8C3bOGm4PmVe/XiiatFC38zPPJI+l+9b2/fnmxohRk7fDhJdR5q1KCBoZCwTjuNxCEPPXpoYYNBwA1BCa/s04dzxKuv2r49N7e//91vc//9WX1N2RCaNNEK7fTqxbXpzY8gIFtdCVft35/6RnEdDRyokbAGDCBhSr20rhCUYPuq/tldwtSWLVodVDOzBx80mzHDlysqMuvRQyM8fP652X/+oz3/nnvMVq7UZB98UJPLzjZ76y1NViEimZndeCPJIR4uu0wke7z4ola81MwOOMDs+ed9uU8/NWvTRhujo44ye+cdX27ZMr0e6X33ac8uKzPbsUNrs7RUk1OJOMXFGkHOzGzSJE0uPd3shRc02Ucf1eSmTtXqxZqZjRunyS1cyHrKCj7+WO9TlVj23Xf6eE6frsn9ElBZhKkgCJ4LgmBzEATzd/ndLUEQpAVBMLv856hd/tt1QRAsD4JgSRAEYlG+3UP9+no0Sdu2WvhaYiKtO+UCLymJR2kPZWU0bJW0rV9/rdXvLC2l31KpTTltmkbFLy5mzhclouSAA7T+LBg4HK+0/LcvCJ56lDqfffowR4syRvvtx1QZHlq10mr6Any2Ug2qoMDPrBrD559r/IkdO3wfOcC5OWmStj569OBc8tC0KS11JdLr9NPpU/dw8MG8a1Le85hjtDY7dWLgg5Juu18/rU2A7k+lzY4dtYpuAL9b5W9UBIrr5gUAR/zM7x80s+7lPx8DQBAEXQCcBmC/8v/niSAIxFiG6Ni8mREICqZN08hAJSW8TFIwZYpGjAgCRlYoimnKFGaP9JCQwOO+0uYXXzBpmIeCAvrdFXz3nVYCbXNCM4xbOUhqMzlZYxqXlPgX4DHEojWUNtXF+dlnWj6g/HxeuCl48UVN2S1Zor/nQw9p4b/Ll+vK7oUXtCiR1av1Np96Shv3tWuBN97w5RISGNSg8FHS09n3CsaP15Tyhg16RsrnnvudRN2Y2VcAhIJ1AIARAF4zs0IzWwVgOYDeFXi/UKil0gBGySg+0Cjx1ElJGksxSlinEpYVa1NtNzVV25CihC1mZWmsy4wMoGFjrdHSUi2muLhYi08HuHkoOe6jhMqq/anm1we0YiaAXtAD0EMco1SYUgt1bNyolzxUZaNUg8rJ0WLeCwv1uVRQoMlG4feoeqmiqMhl7D+CIJhb7tqJBXG1ALCr/bS+/HdVgo0bq4eiB3Qikqroo7RZFYpeJYVEYR6q1XZiJBsFagnJKGOp9qdaMQvgxqn05+rVWhbUggJGtSrjqVaYAnSC0aZN5HkoUEsJRlH02dmaoi8o0FyVMVlV0av1h7Ozf9+K/kkA7QF0B7ARwANRGwiCYFQQBDODIJi5RT2H/w82bNAnUxRFryoRtQ5sFKhpDaK2WRWKXrFA1fqqQDRFrxYHr4pawWp/zpzJb/IKdXz8Mf3zXqGMH35gCKiSa+eRR2glb9gQLpeZSddWixZ6vV5lfahWelmZfjKtbEVfVrZTeXvfvmOHduIrLuamXauWbziUlPzOmbFmlm5mpWZWBuBp7HTPpAHYNQCpZfnvfq6NsWbWy8x6NVH517tg4UL66xYs8BVjerrmAwSi1bBUrcCMDE5mz6edn0+LbdEiv27stGmU93yB2dkMWZwzx/dVFxfrxbzVjbMqLHqV2g/oij620L3x3LqVl5xvveX35zvv8G7Eq0a1bRvH3csKWbMmnz17tj9HX32Vsp5yWrSIFZbuv9/vp/vu47t6RKTp04FPPuHG5PXnrbfyHT3C1LJlfO6sWX5FuZdf5h2Od8FsxjoNn3xC0lYY3niD91I33BAuFyNyvvCCn5Xyvvs47rfeGi5XGdgtRR8Ewa529AkAYlN0AoDTgiCoEQTB3gD2AeB04e6hZUsSoFat8o9Jn3zCiXTLLX67Y8bQupowIVxu40aSqp580r+Jf+klXqLddVe4XM2anFDjx/tKNDeXm8FPP4XL1alDEtIrr/gK96qreBHskcVee40WzqefhsutW8f3y8nx0z98/z03RI9UZsYIlexsbvZhWL6cSjFW5zUMd93F9/XmSOPGvNSfO9f3a48YQWPAi6IaOFAjznTsSAV/xRXhcjHZU07x3Tz7788N/vjj/c2joIBGk2eXxUpSbtrkW/9paVrJw9h6X7rUXxulpXy2Z9wlJjLmv7DQr6o2bBgvto/4ubCUXZCUxGIqLVr4BMWePbku+vULl6sMKOGVrwL4DkCnIAjWB0FwHoDRQRDMC4JgLoBDAFwBAGa2AMAbABYC+BTAJWZWiRlWdqJePZJ2FFLIkCG0Vk46yZeNFUH2LryaN6e1UlzsT9KTTuLzhw0LlwsC5q3v3t0/og4bRhmP4BMEXPB9+/rWcvfudMl477l5MxeoF9VQqxYjKsaN8zfjpUvJSP7oo3C5IODGPXmy775o0YIb/Jw5Wlm33Fy/pB3AMRo61HdzDR7MkEDPxdWmDQk2np+8Rg0ShpSSlJ06sUayh7p1GdaqEqY6dfKf37o1jYoLL/Tb7NWLc9greZiaSgWqkJsGD+b9hEpqO/FEf5Pbe28q7mOP9dtUK2H170/doYRTVxhKsH1V/+wuYeryy0lwUnDAAVolm6++Mtt7b41EcfPNZldeqT3/4IPNtm3z5RYtMrvqKq3NSy/VCDmzZ5vdeqsvt2CBWa9evtyPP5rVrk3CloeOHc3uuMOXW7vWDDCbM8eXfeQRsw4dtDE64QSz66/35UpLzVq21CooLVpk9vTTvpwZiVgK0tMrV271ak3OzOyjjzS5zEyz++/XZC+7TBufGTPMXntNa/O667RqYWZmV1yhya1cqRPGVHLmnDlmy5drsuPHa3K/BIiEqd9cyVsFFP22bSyRp+DTT7WJV1Sks11/+IFlwzyUlelMvaIisyVLtDbVxVxQoJVRLC01e+45X6642OwfF5WYlZS4sueeq5d1O/poqUlbvFjfYJ95Rit5WFamsXLNOEYZGVqbapm8/HxtfpaUaMxlM66Nym5z61atzYwMrc2CArPt2yu3TTOzzZs1dmppqa5DCgq0+VlSEm3cVRbtz0FV9NU6101Ojpa/BiAJykvZCtAPp7IZ335bu+QtK+MFjYLbb9cIU6WldAsouOkmjTCVne1n5wPoh7y15Ab6TxyMGqUdtwEe971LOYB+8q++0tqcNctPLQvQh//YY1qb11/PvDwetm3Tj+WjRmmlBBcu1JNgnXaaVk5v3jzmWFIwapRGFtuwQSujWKMG21TmZ0YG8Je/+HIA75u89NAASViKiwdg7p5Zs3y5Vav09zzvPD8qqzJQrRX9rFl+oq4YVq3S4o937NCLBphpGXjVSBaAE18pfZeYqMd9q0VPior0mOKGJZulsJtmzfQ44YQETbaoSIuRBngvofS9Wi8A4PxQa9aqUAuKLF7sJxWLYdky3mNVZpurV2uErbVrefegID2d6RU8RIk5V0NwVZ5DFERp0yKk3K4IqrWi/+knXtAoWLtWyzynMj6BaHHnKmrW1BR9FLatStwpLJRTx8ssF5WhGJNVFrJa4QngYle4DlH6c489/FTOMaiLXlV2ixb5KawBGiy1a2sx71EUvTpGa9fyUlaByraNMpeiKHp17FXZKG1G4RFUBNVW0Zvpit6MA68osawsLVkXUHWKvrLr0KoWfRQFqjI9olhhKnkkyskjOVk/+ahQLXp1ARcXa1T87GxNKRcWMjmelzceoMtq0SJN0askOSCaoldJiirbFagaRQ9UvqKPW/QOxoxh7LWSqOzjj3cWI/CwY0c0i14hV8USS3lsypwcLtL0dL9ubKy0meeDLSripCst9S3bKApUNe2i5PKIYtGrJ48o7GXFHVZczPdcudIntd16K90nHi/h6qvp037qqXC58ePJ73jllXC5xET6nadO9d/xmWe4Pt58M1wOAM4/nxuDRy768ksm0ps/3zcwnnuO6+K778LlsrPJB1m3zieq/fAD19DXX4fLxZ6/ejXfOQyTJ1Pf3Huv/0333cdvnzjRb3PuXN4NVYULaVdUW0VfUMB4bmXXHj2ag+4xD/PymGVyyRLW+gzDTz8B337LGHFPkdx5J+8Ibr89XK5GDRJ37rzT3+Vff53v6SmRVauYQfFf/wqfTGYkQm3ZIlwOrVhBbedQFHNzOZETEvyNa8YMyngs0thJzsxP67tlC9tcssR3tUycyM+5555wueRkXlg/95yfGXKvvThHveIwbdvSwPBSSe+9N/vU4zkkJdEV0rev7ydv04bfPUhIMJqTQ0vds/7r1KGy3bDBt2xnz+Zm5K2hunW5eUyc6BO2CgrIzlUu7Lds4eWyl0qle3eupXr1/G9KTaUh4J2oOnbkCS3qqWK3oITmVPXP7oRXPvec2aBBWljWRRcxPlyR7dLFrHFjPz4/O9ssOdnsiCP8Nr/5hoGsSijmeeeZ9e7tyy1fzjaV0MGOHc1OOsmXO+YYtukWSHnySQpefHGoWFkZ+zI5mWF5YXj2WTZ57bX+e/btq8Xc5+aapaaaNWzoh8XNns02lbDNBx4wa9rUn08LF2qcjK+/Nhs82H/u4sVmBx6ozeNBg/h8D/Pm6W1eeaXWP7m5ZgkJZvPn+7Ljxpl16qQ9/69/5frwUFRkVq+e2fvv+7Lvv8/1oeDgg83ee8+X+/RTM1Wldetm9uGHmuzPAWJ4pZi+6/eHvfai5avshF267KSZexg0iFabd1KoU4cMPK/OJ0DLql07rZTgX/6iRV+0b8/2+vb1ZY87TiuqcdhhTO3gRifFTM/TTw8VCwL2UWGhz/SNNamE5B12GC11r5h2rVosIpOc7LvYDjiAfaSM0Rln8BivlL+7/HJfrnt34OKL/ee2bQv85z/aPL7mGu3Stl075m9R2jzgAK2ATa1aZKwrcy5WA1d5/pAhWsRPcjLX5WGH+bLDhmnlHgGuzeHDfbnBg7V5DOxkWVc5lN2gqn92x6JXy6SZ0bJRGbQvvaSVHDQze+ghs7Q0TVaxLsz4nj/9pMmqctOmmW3a5MstWmR2991Cg2VlPHYITI9HHjF79VWtSbHioE2bRpakgpde0olQ99yjEaHMaAkrKC7W5Cq7lGAUqG2qJCAzjWFsxpOW2uebN2uEJTOzdes0OTOdbVtYqLepErui6LGfA/7ohKnUVEY+3HGHL7vvvkDyK+P8LFigFaASpho00EqLlZbSAlTwwQfa5WFJiVZSDqAv20tXC9Bf6/nSAQBBgNdGvII58/zpc8ghGgEsCGgtKbIHHKCRoAD6a5Vc6wUFnE9Kps1XX/Xz7ABs7777fDkAGDuWPmAPGzeycpSCRx/VyhOmpTGtsYeaNXlxqFTsysvT2kxMZH+uXevLlpXxmxR88okWqLF1q15Vbdw4/+4O4L2MWlls/Hht3CuKaqvogZ0Z8iS88YZ0c5uX5+cFj2H2bK38WxDo5d8WLPAvJAEuELVU26xZ2oIvKmL6YwUztraXaoe2bctskwrWr9feMzFR3zhXrNDYyykpfvrdGObP1xb8tm06g/e117SIp5kztT4CeGGskP9mzdIULcDLfyUEdsECP01vDO+8o0WvpaX5ET8xTJ6sGQPZ2RpjHmDwhVI6Iy+PF8cKvv6a86SqUa0V/bRpWrQAAG7v7dq5Yps26SXQ1IozUSoN1aypl6lT21Rv9aMU9Cgt1eJ/o8YoK9/0W1eDqlPHz4kO6Pn1AVrqShGd2bM1NnhhIcdTCVedN8+/7wDYP5mZ2qln+XKtKDugrzn12YBeIjBKxIta2yBqsZuqDq0E/iyKPkYTFMyGTZu0y1CACkStDakiNVVjxkaBOpFKSvTqWiqHIMrzq0LRRynNCGjPr1tXOyWoij43l++pKJw5c3xFX1rKU4dCmCopobtMUfSbNmnsXUBPv2Cml+/cto3uUgVqicCoil5x2alywK/DigWqsaK/5x7G37o+5ZISsky6dJF6vyos+rIyTihPOcUIS7m5ft6V2AStzAkVpXKTygqOcvJQZaMspFq1NIseYIy0osBjFn3YO5SU0M0SBL578b//5T2C5wZ84QW26bkkcnOZ0Cw93b+beeQRuhkUctHJJ1PZeom93nmH91w//OCP55NPUsl7dzNbtjCRXG6u7+pYvZpjrtw3LVrEdef585cuZX9OmeLPvc8+o+/fcy/Om0d31KefxglTv4iffuKlkHtxmZgIXHIJbzmdW49Fi+gr/eknX9FOnMgJ//zz/iBdfz19oKNHh8sVFzPT5M03+xT7e+/l9990U7jcokWcUB984BN8srNpLXtygK7oY4vSK1KyciV/pk71TzRr17LPPT95QQGHf/t2nzC1aBFlPdZpbi7w8MP8Wbz4l+WSkqhEx43zU1o8/zwvD70Nac0afrvHdq1bF/93f+K5gxo25BgJXk0AnCPeSaFhQ1r0iYm+gfHVV4yR8Iyg+vV5Yf3SS34ahBkzyHR9QKhk/dRTvENavTpcLjWVa2jiRP/08f33dLF5456ayruEb7+tesu+2ir6PfZgik+PdYggYDjJsce6Z8nWrTnx0tL8I3e7djsvm7xB6t+f7oMBA8LlatdmHPfee5MnEIYBA2hVesf4Fi04mebN89MGXHEF3WFe1NG0aZzwd9zhWzc33cRJ79XvbNiQF9affeafkh57jGxXr9xjQgKff+utvhItLOTl/iefhMvVrk1GY3GxH6d+yCEce4+dGiv758WIt2xJJXvqqeFyQaDzTFq3Ziz70UeHywFcPldf7c+jTp14klK4Ad26sZSet46Tk/meZ53lnzqHDeN3K+mHYxXAPBdwq1asPqb004gRbNOrxNWhA+eQmm68Iqi2hKk2bZgfWkL79sCNN7pitWuT5KKUQOvQgYtEIVsceigXnlIC7pRTNNfRgAFU4h7Zol49EjiUvOgDBtAS8ogmPXvSYhw82LdujjqKFqu3QOrXZ3u9e4fLASz9dv/9JEOFISWFz/3+e43i3r27NkZnnkkL1DvRHHKI5ifv2FErPdeiBXDbbdpJatQo7Vtat2adXMWi7N8fOPtsX65ZM+aD90hyABW9QoKKPd/h6AHgcwcN8ucHwPkxdarmshwxgvPZw7BhnKNKm8cd9+so+t+cLGW7SZhSSuj9H777Tha9+mqdGHHTTTrRRSENmZE+PnmyJjtunCb34IMaHX7GDLNjj9XaPPJIs3ff9eWysrSUDmZmDz/MdBEeSkvN+vXTiD4TJ7LkooKHHmIpSQ+FhSRieUhP14hqatm5zEyd3KQStUpKKr9NM50IlJGhrze1n8xY/U2FSn5bu1bvK5VIqcr9EvBHJ0zVrUs/sVKZBn37YuVKLa72+uu1qjwArTC1wpRXET6GrVu18LmyMj0X/5Ah2imhe3fg8MP1Nnv18uVq19YKuAM8wnfs6MuZ0c2kWKEdOmiWXUkJ3QfKN23YoFl2DRpoMdLt2zMc0QvZrF+fLqawu4EYkpLoJ/cijhITeZehzPmkJLor1SgmJY6+YUPeOShttmihr80GDbRCMoWFegBCUZHWZkGBphcAfrda8KYiqLaKHuCEv+EGTfbmm3nh5iE7m0pEwUMPaWXAduwALrhAa/ORR/yUrQDf8/zztTZfeIEREB7y83l5qCA9Xfv2wkK9RN+bb2rfXlQE3H231uZ//6uFYhYXA//8pxYu+9BDWn9u2KDPpauv1khL8+ZpbHCA5SsVQt+sWX7Wzl3bVEhDP/2kj9Hll2vM7aVLmetHwY03cqPzkJZGN5OC226TyPXYsAG48kqtzVtu0TevisBV9EEQtAqCYEoQBAuDIFgQBMFl5b9vGATB50EQLCv/s0H574MgCB4JgmB5EARzgyDwrkt3GzNmAH36aLILF2pW9YoVtLAUrFunVa3asUMvZqLG/+bn88JLQSxO20OU8Eq1WEKUkodqNSi1ChfAE4ViLUZpU43jj1KtbNUqzVetzmOAkSRKOT81jh7QK7WplbAAWvRKkZItW/wUxTHk5elzXuWORAn//TVIUFGgLNcSAFeZWRcAfQFcEgRBFwDXAphkZvsAmFT+bwA4EsA+5T+jADxZ6W9djunTtcu70lIOvFKdJoqiT0vjcdJDlGImKqMvCllLnfRRmLFRyE1qzHuUsn/qQlIJU1HC29TNS1X0ZvrGrSr6rVvpFlE25HnzmD/Iw/btNFiUNlVFbxGqv0VR9CpZLYqiB7R5FwTRmLG/BtwhM7ONZjar/O/ZABYBaAFgBIDYQX8cgOPL/z4CwPjyu4LvAdQPgkAgd0fD66+TvKAsjqlTGbKoDFIURa+W3qsqi76yFX1VWTeqok9J0WL4Y89XEIUZq7apWvRq6b3Vq7WaqXPn0l3mWf7FxXQBduzo8we+/Rb48UffSi8tZe6cfff17xIWLSIzV2HGzp7N+yNvLmVl0SXSpInf94WFVPTe+jCjbGKir5gLCzk/iop82R072LbX99u2US4j43dGmAqCoC2AAwFMB7CnmcXSWm0CEEsc0ALArvnt1pf/rlKxahV9W4qf+G9/o5vnp5/C5T79FPjoI1o4XsePGUOl/M034XLFxYxnnj/fr3D1zTe8d/jgA78a1Pz5tIA9H6wZFZ1i3USx6GNtV5ZsVhafv3mzf2keW+gK47VWLS567/kxctXmzeFy69Yxqdj334crvB07OO7TpvmsyzPO4GWslwjr++/JM3jiiXC55GTei7z9tq8Uv/uOa+P558PlEhPpc3/jDV+BTZ5MnoWSvfLvf+ca9hKL5eQwx/7o0T4R6amnuCl6/vwg4P3ApEl+2b/vvmN/KkEAN9zAPvCS+c2fT4NVrTFQEciKPgiCOgDeBnC5mf0/S7E8zCfSnhQEwaggCGYGQTBzi3K78z9o0oRJk0aN8mVbtWKEikfK2GMPTpCcHL/jlyyhb9GzlJOTqcSWLvWTPDVsSIr76tXhzw8CTvjXX/cvsV54gRbbvfeGywFMAbtmDU9KYVi0iG6zV17x/do330zF6LEUExJ4IXjzzf6J5v77udFff324XHY208U++aQfqfLiixwj7/K0fn32+1tvhW+e9epxg8nI8Au5tG5N61MpJZiQwLKQHlq3Bi691D8ptG7N71DabNsWOPdcP+1zu3aco5dd5rfZqRM5Jh4fpEUL5qA66ijfaBkwgMaNUkSmRw8aDF7fDxjAze7gg33dcMQR/NPjo/Tvz3miRHBVFNJBPQiCZFDJv2xmseS46UEQNDezjeWumZgtlAZg14Ngy/Lf/T8ws7EAxgJAr169Ih9cmjShhaFYoG3balEvnTrxWK5M0AMO4OArIY6DBpE44ymwzp056f/yF7/NYcNoNXqhmD17Utl06uS3uWMHlbh3gbf33ow66d7dv0/o2pUK19tk69ZlPnozv5+OPhq47jqGY3pt7rWXVuf0xBPJ5FTaHD6cfeQt+IEDSdTy5Lp0YaUwz23Wrh1ZsUoI6n77adEk6oYAcA5de60v164dWalenwP89sGDNb9/794aSbJ7d25GChHpyCM5Pzw3T3IyFbLS5rHH0hDw3HZJSXz+scf6bVYYXqA9gADAeAAP/c/v7wNwbfnfrwUwuvzvRwP4pPz/6wtghveM3SFMbd2qkxcmTdLbveoqTW7aNLPXXtNkP/6YdUEVXHaZVp1m4kSzG2/05UpLWd9040Zf9q23zLp39+XMzI4/3uyuu3y5HTtYN1YhxTz/PCtSeSgrY23fzZt92ZdfNjv1VF/OzOy448zmzvXlnnnGbMIEX+6VV8xWrPDlJk7UqhwVFWl1WM10ctGWLfxRsGCBJldQwBq8CqZN08lVEydqcmZmb7yhyeXnm73zjib78cc6uUslPX71VcWqhkEkTCmKfiDolpkLYHb5z1EAGoHRNssAfAGgoe3cGB4HsALAPAC9vGfsjqKvKmRna3I5OXp5wrw8fTDVsmo5ORrb1Yy1vBVkZmrK24zF2RWlaGb29NOaXEaG2bJlmqxSpNmM4/nii5rsF19oTOfNm7V5opaTq4rygH92ROlTVfb3OE6qoleibr42s8DMDjCz7uU/H5tZhpkNM7N9zGy4mW0rlzczu8TM2pvZ/mY2syInjjAUFOhFeB98UEvFagaMHOnL1a5NX7FS8aa4mL5NBU8/rVW4KinRy9Rt3669Z2Kif2Ecw8aNfoZNgL7SL7/U2hw/HlLVqvx8vYzi2LFajHZ2NjMjKi6E55/XiDM5OdodUhCQjDNnji+7YQNdLQquv15j0a5apZOGrr5aq4C2bBnw739rbf7rX1rFrlWrtLsEgBeiyrevW6eT2v7zH23c163jJa8C9T0rimrNjN261Y+SiGHePG0Rr12rVQ8CGJqmhM9FaXPWLC28cuNGVtxRsGCB9u3Z2ZqiBRjto4QtmukTedMmLWVAUpKmFAG+pxclAvA9lUUM8MJarTClluibP18bI7WPAEZxKaUElyzRSW1ffqkVH1m0SI/K+uor7Y5g7Vo9tcC8eVoxk6ws8mEULF+uhf8WFOh1YFeurPxCQz+Haq/oVQLF5s3aBFVZegCjYxQ2o8qgBahovRTFQLQCKVu3agspSum70lIt5j4KuUklTCUlaXKAHpsfhcFrYlUitTANwM1IUcqbN+tzPj1dm/MrVujVoFROyJIl2qUxoBPL1HKLsTaV/ozCR1HDj9VaDYBeXauiqLaKvrSUeSLWraPFGoatW8mqU+pNrl2r0cbLyjjwivWt0sYBXSlv3Kgr+owMLWVsFEWvkquisASjMGPVuOMoil4ldplpC1lV9MXFmqIvKKDybtLE3zxjNX2V91y+XCMJRmGmLl2qRXqppDIguqJX2lXJhIA+56Mo+iiyFUG1VfSJibRqFy70J+mzzzKW/LbbwuUWLCBhavNmn5Tx4YecdJ6fetUqumPq1PEVTkaGrhQ3bdInfXa2ViQ6qqJXLBE170dZ2U5F7yncWHvKppCSop8SFEVfXMx3jf2EvePYsdzkvdJ7Dz/MeewlFsvLo3/+2Wf9il0XXkhr1SMixSocee2ZMZy5QQPyIsLw+efkgygb7OjRVMhehac5c1hoZt48v93XXuP3fPxxuFxxMfkTMfJlGL76iqd9r9gNQN7GsmX8f8LwySecG0olrIqi2ip6gAnNzjrLt5p69+Yi9uLT69WjAv/+e99Sv+ceplbw/LqFhVzwTzzhH/uuvJKTaezYcLl587ghzZ/vb0gxn7vnBywqoi89JcX3AS9YwMX+9df+xvTcc7QE33knXG7dOpKbbrjB93/ffTcV6M03h8utX89+evZZP+Pi00/TWn711XC5774jO/Rf//JJbatX82Ldy4d04IHcZL0EfQ0b8hTXvbtvWRcV0ffs5cXJzdVY40HAvpw82Xd1rF1Ltqun6ACy0adO9ddbs2a8H1i1ys+Lk5vLS2tv80pO5lr66ivfXVq7Njcb5dJ4xw626xHlDjiA36OmR6kIqm2FKYALQ6ng07Mn5byKOy1b0rpQCFP77svJ7xFsOnTgRnTppb67Yb/9aDl5G9Kee9JSS0z0F8itt/Jo/tRT4ZEAyck88axbR6p5mJsrOXknZfyaa8KfH8tR4p0o2rThd9Wu7R+5hwzhhtCzZ7hcixbc6BISfMVYXMzNoEGDcLnYePfv74/n8OG0qvfcM1yuTx8qshijMgy9e2uRZvvvz03YO/V17sx5pESJdOxIgo/nBtxnH24G//yn3+a++3Lj8t5zzz05nkr1t0GDuC6UUoLDhnHdewkPDzyQc+jEE/02jz6ait5z17ZowQpbI0b4bVYU1VrRjxihuRrq1ePxyFuYQcA2FfZb586aXFISadPK4uzRg8VMvIXUtCknpzLpe/emRes9PwioQJct8y+Y99mH1soJJ/jPP+445k8fPNiXPekkXwYgi7R1a5bqC0MQsE0lOmjECLI+vfdMTmZxluOO89scPlzz0depQxeGksHxpJO04jBdu/r9A3Aczz5bu5fq0gW45BJfrkMHMliVu6YuXXRFd8IJWrqAffbh85XL2KFDtUI/CQlkJXupEgCW7VRCUAF6JLx6tZUCJdi+qn9+T4QphUFqRoZiSYkmq5Yq27rV7PvvNdlLLtFKu82fb3bCCVqbKjPVzOwf/9D6qqyM5RkVLFpkNnOmJvvyy5rcDz+YffCBJnvDDZrc++9rDMmyMpLQFFQ2aUcla5lp5Q7N9LJ3ZWVm69drshs2aHJmZtu26bJqv+flkXyoYOtW/fkq8VJl2v4S8EcvJRjDm29qcrNnaxVnmjb1/ckA3SwzZ2px0p07A++958s1asTjtnfZBbCCzkcf+XL77svkSuvW+bKDBun+wkMP1S45YyX6FGRlaVEdRUX6pXFxsZZzpbBQs+wAWqoKf6OoiNkrFXz/vRbPXVCgjXuNGvQ9KxWmatemr9zDXnvRT+75voOAp5TPPvPbbN6cfv+MDF82IUFrE+Alp9JmUREvoxXMnau1mZWlVUoDyHVQ2qwoqr2iv+kmLVJl8mSNoVlYqJdqGz9eO6Jt3szMkAqeeYYXsh5KSrQ2ExLoL1Tqd9avz0tJBVOmaASjwkI9quDNN7UawEVF+hi98Ya2weflMfW02qYX0gtwk/EivWJ47TWNlZyVpTOix47VNvjVqznvFIwZoxH1Fi0CXn5Za3P0aI0ItXix3qb6nitXMmBAwWOPaRv8unV+QEUMTzzBCLqqRrVX9C1aaJZQw4Yao1AtJgKwPSU+PQq5SZXdvNm/5IshO1uz1KMQfNSY4ihl1dRY9iiEKaWoBFA17xmlPKEaBqrG8AOMXlKqqkWJj09L08J616zR/P4AT7BKpbb16/0UyTFs2qStD5VjAuhkMbX+A8DLenXNVQTVWtFv28aBf+45P7Y2pui9xRxV0SskrPR0XSlHUfQK6xGgolcWfBSWYFUQplSlnJyss1gTErQ2o76nuiEpcmVlO4ld3neprFxAH/eoJfoUToaq6M30WsVpabqiz8nR3lM11gCephRFr9ZoBqIZVxVBtVb0ZWU8Rj/+eHjEQmkpiRHvvMOY3TBs20ZF78WnA7QGFEUfxaJXSUtVoegLCqpG0auWslp2MEp5QlUpR3lPtc1XXqH74MUXw+WWLWNk1JVX+or+zjtJ7nn77XC5mTOpwL3qVnl5O9MqeN+fn6+NeWEh766UVCIbNuisbdWiz87WNsOiIt2iLy3l5uGtzeJiWv61avlz5McfqW/UHEsVQbVW9I0bA+ed5+fUSEyk7PLlfrX7yy/nBcmHH4bLvfQSLYxPPgmXW76cfsV58/wSeY8+ShmvrFlaGicJwAUQhpwcTnzPwjAD3n2XlPRFi8JlZ82isvnwQ98KnjOHC9/zva9cSdn33/e/aexYLlBPga5axf6fPNkPsVyzht/iMTSXLaMSfeUVv82iIm7yXh916kSDYb/9fOuuQQOOv3dxvHgx+9zbELZuZVjpY4/5fvJzzuGm4AUrTJhA4tn48f7mceaZ7HOPwfvtt2ScPvqor0AvvpibglceccMG3vGNHesnvrv7bvre7747XA5gCOq4cf692OrVJKq9/77fZkVRrRU9wPSqHvsPYJrgjh393XvAAC5Mj7wSW8Te4mjVitEPK1b4x75Nm0jw8ZRyjRqsx/nUU74F/tRTVKA33RQuFwS8YP38c//5jRoxmmThQt9f/Mor3Di82r4tWvCyfPZs32oyo7Xq9WfjxlQ6kyb53/Tgg9w8PTZn69Zku65c6bd5/PE8aQ4bFi4HkIijxMcfcwznu5f2I8YHOOuscLlWrejiiJW1C0OzZpzvHtegSxdaqr17+5Z18+ZU3AMHhst160ajZdgwP/VG585cnx7XoU0bnlC6dPFj7vffn88/6KBwueRkvuteezHiLQyHHML+OeWUcLnKQLUmTAEk91x9tS930EFaGbJDD2UYlads+vWjjMdirVGDk0QpZdizJyedN+kbN2a5tnPP9RXtwIG0qBUlMmwYrVTPt9qmDSexwhIcMYJW2JFHhsvVqEGZdu20Ni+/3K8zGiv7pxgCxx1HC1B5z6OP1rI91q9PdqjibjjpJM2ve9BBwPnn+3KtWpFc5IW2BgGZuUqbBx6oEfo6duQcPeccv82uXamYvXuxpk1J7lLes1cv9qdH2Iox2//2N7/NgQOpvJVNe+hQLc9Pw4bcuAcM8GUrDCXYvqp/fi3ClFIRqrjY7JNPfLnSUrMrr9See801Wrm01at1wtKZZ2qklKIis7ZttW//7juzMWO05//731o1rKIis6OO0tp86SWzqVM1WZXc9NRTZlOm+HI5OWaHHKK1+dprZt98o8nu2KHJlZXpZCi1splarevtt7Vnz55NApqC55/X5D79VCdsvf66Jrdli9mXX2qyL7+sEx/Vb5o1SyshaaaXfPwlQCRMBabeQFUhevXqZTNn7l4hqij5nKNELChQw61iqVi9Z5vpoZCLFvlHwxgmTNAo+yUl9NkqF8dqOmWA/nwlZW1WFq1aJQJDTS+7ZQv7XkkvMGuWRu7KyaHL7NfII/5rQF0XZWV6imh1XUZZk1Fk1fS/v3WbFUUQBD+aWS9Prtr76A88UAuLe/hhRjZ4MKOPTcE99/iXtgAHXclnEQTMyPj5575sq1Z+krYYZszQL3yUXCIA3THvvuvLFRbywk3B6NEaQzM3VztCAyQXeRd9ADdjtfzbTTfxPkNp8+CDtTavuEJ7zy1b9G+/8EKN9bl2LXDssb5cQgLdMV7aZYB3GEruoiBg6U4veybAi3A1H9LZZ9MF62HpUi35GcD39O6aAL6n59KN4cwzNZJgRVHtFX2bNhrrs3lzLV1BEGj+NYCWnRJzv22bnlpg61YtGVNBgW5RJiRo35SUxBA6BTVr+lFEAC1pL4pmV1klAVmNGnppxoQELVQ2IUEnN8Vivz1E+XZAn3eqXH6+djrKztZjubds0eanKgdwfSgx75mZmhzAdaQUHomFQipQ+ykvTwtDjbWphjRXBK6iD4KgVRAEU4IgWBgEwYIgCC4r//0tQRCkBUEwu/znqF3+n+uCIFgeBMGSIAiEa8DdR+/eWuHr1q31+p3qsUslGKnEKkCP640S865WboqCevU02nqUI2xqqqboVSISUDUVplRiV0qKtskAO8fI86RGifcvKtJcVirPAtDnZxQS1o4dmlLOzPTTSO8qq6w51f0KcL0rm0IU4mGUClcVgWLRlwC4ysy6AOgL4JIgCGJxDA+aWffyn48BoPy/nQZgPwBHAHgiCIIq8Waa8YZ90iQ/90jr1oyVVhezgqpQ9CpTLwp1OqqiVxRJ3bp6oWYVtWppij4KatSofEWvErY++IDumw8+CJebP59usJtv9k8Vzz7LOHovsVlhIX+UU18URa8qxih3OGrZvyiKXq2qFkXRq0o5il6o7Pn+S3AVvZltNLNZ5X/PBrAIQFhmihEAXjOzQjNbBWA5gN6V8bL/iyAg2WH8eBJEwvD661rZrnnzeCz3fHGlpRwkb0CLi3UG7dKlnHgKDX76dP7pnVI2byYx48cf/Sx5L7zAWP6rrgqXy8/n3cT77/v+xaIiKkaPkFJautOi99xHixdT3hujrVv5zQsX+s/PzGSbXrbH5cvZ3gcf+Ep55UoqZW9D7NKFbTVt6s+n7GyOp2cMfPAB73q8wjB5eeRaLF7sV0968026ojyuwYoVJFUtWOC79157jXPU27g2bSILfvFiP7HYhAmcQ979xLZt5INkZfkVyKZPp7wy3599lgEInk567TXghx+YibbKoYTmxH4AtAWwFkA9ALcAWA1gLoDnADQol3kMwFm7/D/PAjg5rN2KhFfOmmUWBH5Y3kcfmQFmr7wSLnfnnZQbPTpc7tVXzRo2NPvLX8Lltm0z23NPs3328fN533Ybn33jjeFyZmYHH0zZWbPC5QoLzerXN2vcmCGhYViwQPt2M7PDDjNLTvbDRp94wqxGDbORI8PlcnLM2rQxq1uXYaZhGD2a73n77eFyeXlmtWvz+70Qugsv5Dzywltzcsxq1jTr0SNczsxs1Sq2mZ7uy156qRbaOn06x9L7nq1b+WxlLPfbj3PZy41+xx3sd68WQH4+50avXv6zn3uObT71VLhcaSnHsX17PxQ0toZfeCFcrqTEbI892K6Xv//ll9nmVVeFy5mZHXQQ511ubrjcokVs8733/DZ/CajsfPRBENQB8DaAy81sB4AnAbQH0B3ARgCRStwGQTAqCIKZQRDM3OJtpyE48ECy/zx/4BFHkMjhpUCIkYA8ZuygQdzhvXJ2DRrQmm/Vyq9L2bcv/xw5MlwOIBGoQwefCp+SwoiKQw/1Q8O6dGF/KtWgzjqL9yOeBXrCCTzVxL7tl1C7NmWaNPEJW7EoieHDw+VSU0luGj7cd2EceyxPSB5hqnZt9qUSndS2LUl6Sk4ilRnbqxfTfnjf06gR56YSpTJ4MKsnef78fv3oYvEqi9Wsycg1hdw0aBCf60WpJCQwymzkSP/ep39/jpP37YmJJEKdcoofVBEj5512WrgcwKio447z3TydOjFEWikhWVFId8NBECSDSv5lM3sHAMwsfZf//jSAWKBhGoBdqyW2LP/d/wMzGwtgLMA4+t15+Rhuv9338SUksKCzlxenc2cuZK8WbYsWlFXCqPr108q69elDOYV1OWwYj/vKZeeJJ/J4quDCC6nsPRx/vHa53awZ31UpwXbaaZqveO+9qbx7udHDXOzKtw8dyhh6pe9HjNA5DHfdpckNGqTFaCck6Ef9667TmMaDB2tpgnv3Ztii4n8eOhQ4/XRfrn17ssYV3/vAgVqbvXtTTvHRDxqkMVObNaPy9gw7gPNdibYKAqYyUbPlVgieyQ8gADAewEP/8/vmu/z9CtAvD/ASdg6AGgD2BrASQGLYM34tZqzKgFu0SJMbN06Te+MN/xgXw4wZmlxRkdnixZpsbq7OPlT7yEwvwfbJJxrzMj/f7OOPtTYXLNDksrPN1q3TZKdN0+QyMqL1028FlWmblaXLKm4oM401HYNa9i9KKT+1ROHKlfq3r1ypyeXlcS7/GkAlum4GADgbwND/CaUcHQTBvCAI5gI4pFzZw8wWAHgDwEIAnwK4xMwqMdbl/4VaaejLL1muzYOZVkoQ4PFYIVCccAKzAyrYulVrMzFRIywBwNdfa9a3GUlLCj791L9sAnixPHeudvL44gvNsiws1IhVAEvPKTH3OTkaEQjge65a5ctlZTGFtoJXX/UzZwK8NP7vfyu3zeJiXsgqmDhRq1pVVqZXWfrkE60/g0D/9smTtTlfr57+nt98o7WZk+NnVo1h/HitcFJFoUTdfG1mgZkdYLuEUprZ2Wa2f/nvjzOzjbv8P3eaWXsz62RmTiLfiuHTT7VSXJs2+el/AU4mdZB++EFTdmZ6CbQff9TK1CUkMBWqgtWr/Tz8AL993DgtRnz5co11mZiol2qbNUv79iDgAlHw5Zfa4ty+ndEaCj78UCtTt3GjxnIGGNGihNqtXauVxASYolhxIaxfrzF9AW4eSqjumjV+dE4Mn3/OvvKQmanVywU4RkpFuW3b9DY/+cSPzgForHnpy2OYMMGPCKsMVHtm7IAB3Gk9dO6sKeUoiBKfrpJcatfW2ZRqPHf79n7oXAytW2sWW5s2Wm3bKISpPfbQJn1ysk5EUvOORCG5qHHnUfgTamlItURe7PkKJ2PrVp3ctHGjVkowSmEcdYyKijSmL6BXbiou1ttU8/dEaTNKRbuKoNor+oED6Zrw0LEjY1sVqIxGVdFHYTNGUfSNGmlWSxRF36mTtiGqih7QiVD162sXp1E2j6pQ9ArJJj+f/digge86+uEHKlsv3j43l8f8Zs20+aRuNFu26OQmlZFdFYo+qgKtbEVfVqYreoWRDOjvWVFUe0Xfvz9dCF7JtFq1aBEo+UxUhRNF0Ssw26kUPdJUdjYtu2+/9Rd9QQH757PPwuW2bWPVnVtv9ZV4aSk3TmWTbdLEJyIBukUfy6LoMV7z8tg3xcXhJ5+yMn57aqo/nl9/TavaK/9WowajvB5/3CcNffEF+/KVV8LlCgqAf/xjZ4WtMHzyCeeIlyxsxw5a6U2a+KfDLVuo6Lz5tnUr50/Dhv4YpaVRZt268HbN6IJMTPTdJ1u2sK+8Z5uxH5OTfeNq+/adJMkwlJXtzL3luZS/+op9r7rNKgTlxraqfyoSdfPEE2YpKSS8hOHzz81atjQ74YRwuZkzzXr29POdb9li9o9/mF10kR+J8N57JANNnhwul5tr1rWrWZMmZvPn+++ZlER5D488QmLGrbf6soMGsd3i4nC5G29km489Fi735ZfMhz9sWLhccbHZWWeZdetm9u234bK33mpWq5bZddeFy02fTrkuXfzIiq5dzerVM/v663C5GHHmmmvC5czMjjjC7MgjfbnPPjNLTNQiRfbe2+y003y5e+/lez75ZLjc+vUkN7VuzQilMJx4olmdOmbjx4fLrV7NZyt1EH74gbK9e4fLmXFdBgHHNQzjxpklJJidcorf5oEHktD31Vfhch9+yH4aOtRvs1s3ftOcOeFyn35Kucsu89v8JaCyCVO/V8Rir71Meb17a9kmGzXihahnsdWvTwrzyy/7McA//kgLx3Oz1KpFQlcQ+FWRevYkKUWJJf/b3/iOnTv7stdfT6KPl33vootoCXl8g969aQ15PuCkJFpDc+b4XIfhw2lZ9ekTLterF2Ophw71T1V9+3Ie9esXLhdLN3322eFyANs691xfrlcvxuYrvu9evbRqZX370iXgEXxatKCL5fDD/bjz2Jw8/vhwudatOd6nnea7RXr02Bmj7uGww/i+Xjm/4cM5l0aM8Ns85BCeJL2U34ccwjsxj1AHsC87dPDJmSpZrDJQ7UsJNmgA3Hab79+sVw+49FL/eNq2LQkkXjm/pCROpG3b/Ml83HHMXX/ooeFyABdHzDXhYdQoLV1u7dosmKwo+sMP12pY7rUX2ZT77Rcul5pKFq1X4xQA/vpXhpZ6F4h9+3KcPBJWQgKZsUqu9eHDqeg80lKrViSged8N8NneRghwDqthwmedpTGXe/Xi+Cipgvv0IdvWQ48e3OC8i+gg4AavMGNjY6Qo+sMPp4vFWxt77UVDSBn3I46gAej53mvV4nxTNo/DD9cKtNSqxfXuGReVAsXsr+qfihKmios1ssvWrSwD5+G558y+/96X+/hjP0eHGfN0eLleYigo0Eum7djhu3hi2LRJK2doppO71DJos2drpfeKi81uuUVr8403NLnPP/fzmJiZbd7MUooK1qzR5KoCKrnHTCeKffyx1u7q1WZz52pteq6QXfHjj9rzCwvpslSglI80I7FJfdcvvtDkCgrM5s3TZDdu1OR+CfizuG5WraJ17Vng27dzh/WqyVh56mMlDUD37loFoSAALrnElwPoklAs/9hFkmJZZmbSZeVFS8QuvJRUrBkZWkheWRmtSs/NEnvPiy/25UpLaTEq2H9/LQS1QQONrAXwBKcUaCkp0UJVAV7KKYECJSWMe1eQmKi1OWyYRtpp04anLSUirU8fnQjUvLnWZhBoZS4BRo8p356YyJQaCmJZRj0EgZ5O2UwveFMRVHtFf8wxWke9/jqJO94RLQjoM1PCo156SSMNAZpPFyBh6bXXNFklCRZAcpFK2FITLD31FPDee75caSnHSAlLu+8+poP1kJUFnHGGLwcwx7tSjjgtTXM1AMC//80c8h5Wr9Y2LgC48krWAfawfDldkAouu0yrvrZ0KWUV/OMfWqjukiV6aUb126P059VX+/dsAAloF16ot6kQ+lav1g27K67Qw74rgmqv6A86SFvIBxyg12ZMTtaKVTRrprFyg0AnN3XsqC3OIKB1tXWrL7vffppiCgI9xLFFC81iS06u/GpQUWKfc3K05FZRCmVs2qRZlqocwLBWL7spwBNCq1a+HMDxUU4paWlAy5Zam2vXas9Xnw1w41buEtSxBPTCPFHmUkmJViIwCrGrqChOmJKgEqa6dtWUHaArsT331BR9TNYrmADoih7g5apiCXXtqlkisTYVwpTaR4BOFlM3WHXBAbpyyMjQFX16us5iVRW9Wnpv/XpdKasFb9av15VyTo7GClY3LkCvMBVF0avM2JKSylf0UQhTarnHiuJPo+jr1uUur0SptGql+VabNdOIQICuGNu25dHPY1OWlTFV7vff+wSShg2pdLxkaUVF9G1OmuS32bIlFYRaTlBR9qpFH2VxVrZFn5+vLeS0NOZhysvzT0jPPMN+9HLYLFvGvDC5uf4dQXo6T5Fev5vpFn1BgWZ9xtpUNo+yMip6j2lcVsaxrF1ba1Ox6GP+8VhobxiKiznvvEiasjKOpUKmzMrifFd0UoWh3NhW9U9Fom4yMli96a23wuWKi81GjGDUTdgNf0kJCTn//S/Tt4Zh5UoSjFatCpdbt46VqM47z49oGTuWpJRLLgmXy8kx69CBhIu1a8Nln36aJKizzgqXy80lqQwgISwMo0aZpaayn8LwySdmrVqRwBOGtDSzk08mGch79sUX89snTgyXe+UVsxYtWHEoDOvXm/Xta3booYy+CcNFF3F8nngiXC4vjySotm39yl4x8pmXonnDBsr16uVHqZx8MsliXpWlKVPYl6ed5pPkzjyThC2P+Pfll2bt2pmddJLf5t13k6jmVcLKzzfbd1+u9YULw2WffppVo0aNCpczM+vfnxW7PvssXO6NN/ieRx8dLldWRsLUHnuwH8IwfjzJnied5L/nLwF/lqibadNo6bzxRrjckiW0VK+6KnxX3r6dqVAvucS3hu69l8/3st81asRnT5zoR7QcdhitDO/YW7s2yTgpKb7sX//KyAavclOtWrzsqlfPj2U//HBaTd26hcs1bszTkXeXsOeepIR//bX/7CDgpWSnTuFye+1Fy9KzQvfai+1lZvrukw4daFF7xJnUVN6NXHCBH5t/4IEsEOJdrjdrxve79FLfsuzShZaqV2WpUyd+e6NGvlvCjNFBHhFov/2YcmPPPf02O3ZkGgaPIFiz5s7cOR4fpF8/rmNvbgLs+8JCP3pu6FBa6l6RkiDg81NS/EjA4cNp0SskrIqi2hOmjjuOHeb5+Pbbj+XfPJdMo0YkcHzxhd/muecy+sRjp6amkp2qJgs74wyfHQpwM3r/fT+iJSWF1YYUd8fFF/PbPUVy3HF8V2/R9+xJZRJjlP4SEhN3lh30nn3EEXRZeRtXnz7s+1NPDZcLAsoec0y4HMCxPvJIutg8qMzYAw8k09jbEIKAobfe9wAM/R050ndbNWtGBapEiRxwAPvTc3E1bkz3pxIhM3Agn3/YYb5sjCCnbHItWuwsCxqGI47gXYbCmO/d2y+jCHDDVjJdNm9OEppCwqowFLO/qn8qSpiaN8/s2mt9uQULzE4/3ZebM8fsuON8ubIyHqMVItKKFWY33eTLmZEE5eXJiMHLzRJDQYFOrvIKjsfguctiuOsuFkn38PnnGlksO9vsvvu0Zyv5fczM7r9fq3S0YwfznihQiVVlZXqVJc9NF8OqVXqlNM+tFsPHH5P8puCBBzQ5M98NFsOUKXpVteee0+Sys83ef1+TVUl6WVlmkyZpsp770QNE101gakhEFaJXr142U4mRDIEadjZzppYfZtIkEkk8fPutnycjhihRGCambv0tob6jmr+9uJhuESXUTk0rrEZfRElTXB3GJgrUyI8oESJVkeddlQN4KarU4AX08Ywy7r/WHAmC4EczczVatffRn302O9VT8u+/zxKBnpI3A845R1Pyr7+upTM24xFeUfJvvEGfvzdJYm0qePllrdJRWRldTApeeEGrIFRUxHS9Cp5/XgsXzckhaUnB009rJQILCkjcUTBmjMbJ2LKFJBsF992nEXw2bQKuvVZr8557NDLO1q1MZuchJQW4807eiXlITwf+8x9fDuBdl9Lmpk16YfR779XClDds0N/znnu0NtPSgJtu0tq86y7t2yuKaq/ot2/XFkitWvQ9ewgCWv1KnvnCQj/fd6zNGTP0MKrvvtPa/P57rexffj6zQnpISNCZvtu2aUokOVkvKbd6tcZLKC5mRlAF8+ZpNWMzM3WG4owZWrjo5s1+nYQYlDEHqJjUtArTpmlx3+vX62kVpk7VQizXrOG7KvjmG81S37JFL6Dz44/aGs7M1CvP/fSTxsLPztZ5K/Pna/Ozoqj2in7YMLpZPPTooRd/btNGqzPapo1WfDkmq1RkilLyUI3Nb91arwZVt64WG9+kiVY/M8rxtUYNLedJlGOxyjxU3UuAXrlJLeUH6ISt9HS9cpNK7FLlosiqJQcB/dtVYhVAF6ASc68yaAF9LkUh9JnpLqaKoNor+qFDWfHdQ6NGDONSdvn27Rly5qFtW12BduigtRljxiqWevv2PPZ53xTbZBQyUsuWfrUfQFf0UaAq+ij+V7UmZ3a2zrpUFb3KTI0lqFNkoyjl3Fztm6K0qSq7KMzYHTu0TVYp4RiDqujV0ohm2lyy8opmqqKPMpcrAvcRQRDUDIJgRhAEc4IgWBAEwa3lv987CILpQRAsD4Lg9SAIUsp/X6P838vL/3vbqvyArl3puvHKtQEMu1q40M+9Equx6inbFi145PVYirm5XMQffuhb4J9+SmvI85muWcOY89NP97/npZd45P7vf8Pl5s7lCeXvf/fLun39NX+mTg1vc8ECWjgeezkjgwp08WK/QMszz1CRfPJJuNykSXTdvPxy+PcUFXFssrP9jfvjj+nT9k6HGRnAY4+RjeyN+X33ccyffz5cbsUKliacOtVn2z79NF0Cnrty5UoaSlu3+uX0PvmE/ej5lBcv5nhnZPgb9+uv87nTpoXLZWUx4d/Spb7r6s03mdXW49aUlAA33MBne7EgL79MmdtvD5cDgGuuoTvK8zRMnMg277tPTxOyu1D2kkIAQ82sG4DuAI4IgqAvgHsBPGhmHQBkAoiVLjgPQGb57x8sl6sy/PjjzgyJYdiwgRPv5JPDFXNBAanoDz7oD/5//0sXj3dBVKMGM0g+9phvPXTooCUqa9OGl7t16vhHzwMP5Ea4zz7hcvvuSyvdO04GAf3PP/7ouyZmz+ZJ5u23w+Xq1GF/Pv64b91t20YF5R33W7bkRuMVq0hJ4WX9u+/6z16+nGPupZNo1IhKaeVK37Jt0oTf5KUhaNuWl9UFBX5k0tq1NEI8I6BBA+DVV6nEPTLfG2/wHsO7y0hNpbL99FPfAl66lO+6cWO43B57cD1On+67hOrW1QywpCS2u3mzT67q04fr0iNrBQHX5ubNLJAThm7dOD/q1av6CB1X0ZeHa8auC5LLfwzAUABvlf9+HIDjy/8+ovzfKP/vw4Kg6j6jWzetuG/z5juZfWHH2Zo1uUiXL6fSDcPw4bSaPPJMUhIZuY0b+8fzrl1Zqk2pyPSvf1E5exgxgpPPq3aUnMyUrQqj8PzzeTT2nj9iBBWIVxWpRg2SsA45xA/LO/pojmfPnuFyHTsyGsurQQDw/Y480h+fGOtRSZPcrx8juJQyhi1a+KShxER+89//7j/7oIM4L73aBg0acJ5fcIH/nj17ch55TM7WrbmGLrrIf8++fdnnXnlCgOzV00/33SIDB3IOKZXShg0jscqbcx06sD89pjFAEtbhh/uuo2bNGAWoVNeqMJRgewCJAGYDyAEt9MYAlu/y31sBmF/+9/kAWu7y31YAaBzWfkUJU198YTZwoC/37bdm7dv7cgsXmjVtqj37yCM1ckRurtnxx2tt/vCDVs2mrIyFkBU884xWxSc9XSOblJWZ/fOf2rPPPJM5iTy8957Zo4/6ckVFWnFuMxLpvHwrZsyLoxBiior0amGPPKIRnEpLdQLY3Xcz74uHtDQ/x08M551ntn27L/fNN3qbp5+uvef27WaXX661OXasTuZT58f8+TppSakmZ0by28sva7JjxrBy1u4CImEqEoMVQH0AUwAMrKiiBzAKwEwAM1u3br37X1oOlQmnLihV7rPPqBwVLFumyZlRoShQS8t5ibV2hTrxFOVgpn93Xp6fIC6GHTs0uexsTS49XS+1uGFD5T7bTO9zRXnGoLJt1ZKDOTksSakgSrlFL5FcDNu26fNd7fuyMs0QMGPCQxWqbEWUvJmu6CPd95rZ9nJF3w9A/SAIYoeolgBiV05p5Yof5f99DwAZP9PWWDPrZWa9miiJuH8Bn37KP885J1xu+XL+eJV0zHhJolTcWbKERzov3M0M+Owz3xUUa3P1av8oWVZGEpTiFFuwwPeBAvTnTpqksR/V+N+SEj0yaflyLaKjuBj44QetzRUrtNj8+vW16lYAo1SU2gLJyf5ldQzz52t3M2b+xWUMy5f7F9sA3TdKqu/atelPz8z0ZRs25IWkglWrtDaTk3Wex6JFWpu5ufq4z5ql9WduLjkuCubO5d1hVUOJumkSBEH98r+nAjgUwCJQ4ce8nyMBvF/+9wnl/0b5f59cvvNUCW68kQvEU3gzZ5LN6SnQIAD++U8tPGryZF7ieQgCZhxUemHyZK1EX6xNBW+9pSsHtaTcG2/oC1lt87XXNOJQSYnOtn3hBf/iFGDEjcqQHDtWI8Rs3w7ccovW5uOPawzJ9euB++/X2hw9WiNCrVoFPPSQ1uadd2o1GJYt4zcpuOsurc1Vq4CHH9bavO8+7duj9OfDD2sEuIwMLToH0OdSRaFY9M0BTAmCYC6AHwB8bmYfArgGwJVBECwH0AjAs+XyzwJoVP77KwGIhO3dw9ChwJQpvlyfPvrOvddemgXcrp3OfFTJTVFKCQaBtnmoJKjERL3sn9pmUpLeZp062ikhJUWLtwfYRwonIUo8s5pzJUo8dV6eVpRdjeEHGEGlkrDUOHqVCBUljl59fpRvV2Pzo+Q4UsddrZQG6OuoonCnoZnNBXDgz/x+JYDeP/P7AgB/qZS3EzBsGMPi/uI8MVa5SVnQ++xDi8SbqFEU/b778jjphdDFnq2gYUMeT73JXxWTqW7dyqdu167tR08BXGyK8ga0KktA1Sj6KAzJKIq+QQOtTbVqVhS2rUpa2rBBZ8aqNWNVUhnA+a68Z1Uo+pQUjZgJ/HqKvtozYwcO1NwSQUBrWVGiqlWtpjUAGH+rELsaNiQhxMuhk5HB+OuJE8MVWUEBJ11amu+rXraMCs/LI5OTw2/55ht/o/vqKyoxL44+K4sb8YwZ/pH77bfZj16by5bxzuPjj/0TwKZNnCMe23f7dirwvDx/A8nMpKL34rm3bmV73uaVlUWfbmqqP4/mzeO4e37qzEzOt6ZNfeUU++9KOT3Voo9Zvl6bubm8F2nY0D8hrlzJ/vHy0pSUkEOwYYPPDZgyhS6WZ57xx/3dd9lm7P7wl5CRwTGdMUO7n6kQlBvbqv6paHjl4MFmH30ULlNaytzkd9wRHjlQVMT83McfbzZ3bnibn3xi1qyZH6FTUMDwz1q1zH78MVx2zBizIDC79NJwudmzGTN14IHhcqWlZm3aUHb9+nDZp5+m3BVXhMuZme2/P0vledENb73FNv/zn3C5sjKWqUtO9sstPvEE23z++XC5rCy2p0yvE05gv3shll99xbKMBx3ktzl0KMstvvdeuNy4cezLU08NlysqYnu1a/tRR1dfzT66//5wuTVrzBISOEe8iJ6RI80aNfJrBkyZwhKKJ5zgR7T8/e8s0edFzW3Zwm9v2dKvbfDgg/x2JWyzTx/OEa/NlSvZ5t//7rcZKw3phW0WFpo1bMjxVKPs/hf4s5QSXLKE1veZZ4bL5eUxFcCNN4b7z5KTSRt/7z3/KF+3Li1Bz2qqUYPkqrw8nwh1/vlk63kVprp1I9nES7uckACcdhojfrxizSedxO8/5JBwOYD93auXn0vl6KP5PR5hKghIhunb13dhnHgiv8sjA9Wrxzuco48OlwNIcklO5p9h6NePLqajjvLbHDyY3+URoYYOpZXqkYaSk9nnJ53k+5/79KH8X/8aLteqFd07J57oM6ybNqUF6pXd69qVp7OWLX3XVZs2PCF6YxmrWjVggO+6ir2fQmobNozj47W59950vyrErpNOYl96VdVSUkjqGjBAz92/u6j2pQQ7dQKOPRaYMCFcrk4dRmpccIFfF/Taa6nsPaXcvz8XlMJOvfRSRoB42ffq1gX+8Q8/XQHAb1EuJc88UzsaNmjAVBLeBAWolL18KwAn/OmnU0F6OOMMzQe6555k8HobF0Bmbo8evtyRR1Lhego0KYlKSVnwRxyx09UShpYtWfpPKWXYv7+/GQEse3f88f5cDwKyaBW2bc+eLKXn1VWIKWWFGXvQQdyIvbsrgHPotNN8uW7d2K5SYGjYML/2cAynnso5ojz/b3/TfP9nnqmn8a4QFLO/qn8q6rrJyDDr3duXKy7WjtxlZWYnn6w9+803zWbO1GTffluT27xZI7Hk5potWaK16bmMYliwQJMz04lDKsnGTCfaKKUJzdiXKllMLcs4ebJG3Ckp0VmXkydrcj/9pH1PWRllFahl75Yu1d/zoYc0uYwMsw8+0GQ/+kgnIs2Yocnl5+vEv5wcTc5Mn5+lpSwzurvAn6WUoNnOIhxeEiGAl5wH/v9iiP7/SEvTLMbSUl5SKTmtY+8aRxzVEWVlO8N6PUTNyR5fF7uHP00pwQEDOAE9Jf/ccyQneErejMdjRck/+STTl3pKPtamMpmfeIJteigr832lMdxxh5/SF+AdgucrjeH22xnx46GwUCvLCJA489FHldvmLbdoZRSzsjRfPkBilcLdyMykW1HBv/6lkcXWr6crTMHFF2uVxVauBM46y5dLSKDLbP58X3bVKp+tHsOFF2qkoeXLgZEjfTmAbiOl3OPq1dq3A3S/KsWLNm70w71juOYajZVcUVR7Rb/nntokad1aY0gGAeNaFd9348Y6uam4WKsv26iRVtIuIUEv/JGX54f4xaBQvAH2j0puUshnAL9JuUtISdHL1BUXa7UK1GcD7MvK7k+F3Q1Eo8uvWKFVZNq4UUs9ATBkVYllT0vTKzctX65xA7Zs0bkOGzZo3xRlbeTkaESoWrW0CnUxWbWMY0VQ7RX9gAEaFf+AA3gxpkCNj2/fXidMdemiFb6OFT2pTERhvKpITtZIIVGO5PXra5thlDZTU7WFnJio1/RNStK/XSV2qZWwohCmVMbppk1a4XqAsewKuSpKKcEtW/xLYyBaacacHK26ltrvADcZZTyjkAn32ks3WiqCaq/oBw7Ujj5Nm+4squGhXTsePRU5VSnHqlt5iKLoa9bUlJiahkBVoDELOSPDZ7LOnMnF9OWX4XK5uSTu/PCD3/dz5tCy8lwy27fz2StX+iGwBQXsI8/6Lyvbucl5G4PKygU0hVNaqrNDi4v1MnlRFH1pqeZ737hRT4FQUqKFF2Zk6IpeqQFsxj5S68AmJPBPT9nHNgRlU4grehE9epDJqSyo1q15pAqTzcujFTRpks8k/eYbWv6PPBIuV1xMpfPSS37WxZUr+dzXXw+XmzGDk+7RR/32pk0DXnnFV2K33EKf5bPPhsuVlAD33ANcd52/QGfO5Dt4m3GtWkwQ99JLPh1+8WK26TEPa9Rgsq577vFj86+9lhvxCy+Eyy1bxjJ9//qXr+jHjOH8ePXVcLkPPqBb8cknw+Wys1nA5u23/XJ611xDZTt2bLjcvHn8ni++8Dft55/nN3vM6cWLmVcqO9s/+cSye65eHS63YQMDLnJy/Hn89ttaqUkz4OqraYR4XoFvvwXeeQe44grfIHr0UT7fS5Jnxu//7DPtHqkiqPaKftEiWhkXXxwut3w5rd9TTw1X9ElJLGl3331+ut6EBB6PvQWSnEwFMWWKf5xcvpzKwXMzpabyUsy7cGrdmhbwmjV+7o+WLXk87t49XK5hQ8baDx7s99HJJ9Mt4pGwgoDknq5dfdfEccfxWwYODJdLTeVl6KBBvtV2xBFUSl6MeqdO/P4uXXwfdGoqN+127cLlOnfmKcbbjOrX53c3aMA49TC0aUOF6PXRPvvsvOPyKiJ98w3nZ15euFxCArObvvuubwi88ALXsJfyo1494KmnaAh4a6ioiHcEnqWckMA5v3074+7D0L8/9Ubv3lolrh07/LTkQcCT3Lx5+olqd1HtFX2TJlquirZt6V9MTw+/0ElJIWGpWTP/iHzEEVzwCmHqxhs5sF7ZwZNOooxXm3L//TmhBgwIl0tKorJTmJzHHsujpEIwOvFElkvz0LgxSTbeQgJIHhkyxJdLTSWj0FNiADf24cN9ucMPB/bbTyOqnXiiRloaMYLz0/v2Dh2omJWSh/37A+ee68v16sX+6dIlXK5mTW6uF1zgt9mzJ/vI6/cOHaiMFcJUr15cax5ZrE4d3rOdc45/ITt48E5GuIdDD+U4eQZLUhKjspTIrH796IJViIcXXcQx8NZ7haEE21f1T0UJU3feybwaHl580ezgg325bdvMjjtOe/Zzz5ktXqzJXnCBJvfwwywn6OHRR83mzPHl3n+fOVoUKKX8zMw2btQrR6lVo8x0wpTaZn6+Pj4quWnmTJ2oppbeu/NOjYT1xhsawSc31+zVV7Vn33GHRkT6/nuzxx7T2jzpJOZ4Utq84QatzUsuYc4ZVVbBmjVmH36oyX7wgU6aeuopvRqWWvbw54A/C2EK4IXK6NHATTeFy5WW0l97ww1+mwsW0HrxUFhI14RyQZWZqUVM5OTQ+veO0pmZPNJ6aQPy8/l+yoVXFKLLnxWxJaNcXqtRHapclHTKqmxBgRYKmZ9Pt4gSsrlqFfPDKM9OT+eJxsOKFX5akhi2b9dSHwN6+uEoxK4o45Sb66/1X8KfhjD16KOcpFdfHS737be88PIqE5mxMo6i5L/6inH0nmI0Ax57TFPy06ZxQnsDX1bGikzKBFXSCQNcxM8958sBvDxSOATFxf6FYAyTJmkhqCUlvEdR8MUXvBz0UFDAFLQKPv1Ui8rKzeUluILPPtNCerOzgRdf1NqcMEGL0c7Pp+/bQ2oqx0iJEqlfX/v2mjVJQlLabNBA788pU7RCP5mZXEcK3ntPa3P7dv09J0z4nZQS/L1j7FgtjGzpUi4mz2oKAiplBTNnarUhY20qh6e5c7UkR0HgR/vEoCq7wkJd2X32mZbbPwj8yKAYpk9nn3pITNRLyn3/vR8lAnBsnnhCa/PTT7UQ2KwsTYECjLJSCHDr12vlKwFg3DitqMWaNcCHH2ptvvCCFiO+ejWjiRS8+KKWIG/DBl7wKnj9dY0At2UL8OabWpvvvKOR//LyGKGkYPJkLey6oqj2ij5WuclDhw56fLpKjGjUSGc+qrItW2pWmJpzBNC/J8pxU0WUUoKNG2uLM/bdysbZuLGmQGvW9AtVxKD2Z1GRVmgdYJSG4hJRq0YBOrkpSilBVVYlQQG6S1OJjY9BrRwVZYzU9aGS9AD2kcpwrwiqvaLff38t90b79gwNU9CggaaUGzbUj11t22rH/Vat/BjpXaEouyiKXnEFRUHMovQmc2kpN8MtWzTrrmFDbYE0aaJvHiwX4ctWhaLPytJK323dqitQlUW7ebOu6NVSglWh6NVnA3ppxsLCylf0tWr5IagxqPOzoqj2ir5rV03RN2vmE6AA+pSbNuVxKmzRl5UxpHP2bD8G2IwXU4sX+zH3MYveI+OUldEK9RifWVmUzc72LdYZM6icvNxBc+bQV/v4435OoNGj6eJRXC233QY88IB/8vr8cxLfvARXJSXAW2+RQOO5w8aM4fwYPTpcbvZsfvuDD4Z/uxldLNu3+1yHL7+kS8Cbx5s20R2yebNvYHz1Fb/fu5tJT+e8rFvXnx8xheRtcoWF0RR9YaF2GRxF0SsWvdnOS3Bvgy8upkxZmS+blMR1pJwQYxa9Wkx8t+GF5QCoCWAGgDkAFgC4tfz3LwBYBWB2+U/38t8HAB4BsBzAXAA9vGdUJLxy/nyz4cP9MMMVK8w6d2b++DBMnWpWpw5L5XkYMYI2oFdyMD2dZfJq1DDLzAyXvf9+s7p1ze65J1zuhRcod8op4XLr1rH0XdOmfgjdOefwe7z84AUF7KPDDguXM2NOdMDsiy982RtvNKtZ0y+rlpPD8nNnn+23ed55fP7mzeFyH3xAOa+UYGEhn3344f6zjz2WbS5cGC43bpxJ5RaLi1mOsk0bPyf9lVeyTa9E34YNlGvZ0i/7d/bZHJ///jdcbtEilhzs3t0vNfnQQ5ybL78cLrd1q9mhh7LfvVoNzz3HNm+5JVyutJSlBFu2NJs2LVz2gw/M9tjD7JBDwuXMzP79b/bTbbeFyxUXs73Gjc2+/NJv9+eASiwlWAhgqJl1A9AdwBFBEMSSAl9tZt3Lf2aX/+5IAPuU/4wC4BC7K4bHH+dlo1cgfPVqWi5vvRUuN2iQTho691xaQh4ppWlTttu4sR/y1bUrrW8v3OyYY2gxdOsWLteyJWWGD/fdMkcdxWgfLwVwjRokGCkpeLt140///r7sueeSXOSFgdauTRZtz55+m+efT2aqZ10eeijdBx6DNyWF1agU4sxhh2mEuli66bPPDpdLSuI3n3uu70I46CBav6ecEi7XvDlTcp9zjh891q0brVkvBW/HjjxNdOvms1gTEnhC8Vw3jRrxZLh0qZ9CvH17tunlxUlI4N1dbi4rxYXhkEO43pR5PGwYZRUyY69edLEpc7kicCOmy3eN2D17cvlP2OFlBIDx5f/f90EQ1A+CoLmZiclqo+Ff/2LkTevW4XKHHELl7THQgoDKQYlrPeoo0vEVv/YVV2g39oceSnamp8AbNeLzFfbdccf5dOzYs485RrvEOvZYvw4swP58+GGtzXbtmD9HwYUXavcoffoA553ny9WoQdeRctE5YoSvGAAqeuUeoU0bKk+Flduvn8aMPegg5llX5nHfvpzzHnr14sbhMcYTEvTyhAMG0K2q1EE4+GD2lReE0LcvDTCFaXzYYTQsFONiyBCOvYehQ3nXpmwK553HKC4l02aFoJj9ABJB90wOgHttp+tmCeieeRBAjfLffwhg4C7/7yQAvcLarygz9m9/0xiir76qsQXT081mz9aevX69Jmeml0x7+mn/GG1GRl9+vi83bx6PvgpUxqfy3KqGWlZOYWiaaX0epb2yMn1+KKUjzczS0vRnq8xltRRmVpbZN99osi++qDFDi4vNbr1Va/OZZ3SW8+jRmtyGDSxRqOCtt/SylG+9pcmZ0XW3u0BVMGODIKgP4F0AlwLIALAJQAqAsQBWmNltQRB8COAeM/u6/P+ZBOAaM5v5P22NAl07aN26dc81ClvkZ5CRwQvHRo3Cw9Py83mhkp0dfvQz0/Ne5+bSmlcqTKlt5uXxwsfb4aO0mZNDi8XjEJjxcldJg1sVbUbpz8pus6yM80gN86tRw4/WiKU9VtrMymK0hmdZlpby+QrrMyuLlqjnkqmKNouL2fdKm5s28STltRmrVaCGoe6xh9+fJSX8dmWMtm/nuvTeM0qbO3ZwbqqRP/+LKmHGmtl2AFMAHGFmG8s3lUIAzwPoXS6WBmDX3Hoty3/3v22NNbNeZtariXo9/zPo359Hfm/wH3+cGfWUEoFKsiyA6RRUoona5oMPaqQQM81tAwB33qmXEjzsMK3NW2/VUqsWFOjlCe+8E/j4Y1+utFRzGwH6GOXna8nPAOD66/07IYCuOs9HHsM//+lHbwG8a/J8+TFccoleSlBtUy3Rt2qVXvbvqqu0okCbN+vvefnlWpurV1d+ecIo337ppdoYVRSuog+CoEm5JY8gCFIBHApgcRAEzct/FwA4HkAsOGwCgL8GRF8AWVZF/nmAloASy77XXpqPPEr1IjWeOkqbKoEiSlGLKKQlFbG4cw8pKXro2B57aGX/kpL0alBqWG2tWjrJRe33KAQflQilkqAAziOlTbWYCaC/p3riAvSwSbVqFKATptTCI1HaVHPnANEqm1UEikXfHMCUIAjmAvgBwOdm9iGAl4MgmAdgHoDGAO4ol/8YwEowvPJpAE6m+IqhY0etxmrz5nolF1UxJybqpeJUhVcVTDl1MkVJ2hSlj9SJXK+eVkoQ0JW9quijbMYqfmtFr7YZpUTf1q2abJSSh7+loldj+KO0GUXRV4UR9nNwFb2ZzTWzA83sADPrama3lf9+qJntX/67s8wsp/z3ZmaXmFn78v+++2kpBXTsqCXXUkt2xaw1T4Fv3cqFnJam5f5QTx5RFb1iXUapGasWqC4q4p8eYerFF2ndeUmecnKY9+Odd/zxXLWKCtQLlTWjYli3zmcbx8oTxioehT17yxa6WcI2muJiKuW6df0NKT+fys7bFIqKSHBSFX1enhZ1E6VEX36+xjjdtq36WPSqoldLM6rlFoFop9OKoNozY2MWvXfkr12bStmz/h98kMzUG28Ml0tMZJm6u+/2B3/aND778svD5QCmaVi4kNk2w/Dll1SyXmbIVavoS3/+eT+1wE030V87bly4XE4OQyb/8x//Eik/n8rJs3Dq1KFr7Ycf/OIs6enAd9/5CcOCgEzX8eP9pG4zZ5KR6907xKonvfZa+GJOTGTVs5df9p99661k+t57b7hcWhrn5bhx/ljedReNEa+P1q1jKcHvv/ep+LGEZpMmhctt2ECZ9HSfub1qFRWoxxjPy+O6rFnTT9S2ahXlvdNhScnOPFmbN4fLzp3L9rw0JmZcm1lZvq5ZtIjr7Z13ql7ZV2tFX1bGRfzoo35N0jFjmMXwo4/C5fr25UTy0hQ3aMALwSOO8JXY/vszQ6BifX/zDZWY5y9OTOSk8yZTmzbcPLZs8SMgWrakdeeRxdq0YbWfo47yTwDHH0+F6JGwAF609e3rbx59+rBPlapVsZhzj5By0kl8rnfJ26YNn+2RxRIS+MzWrUmCC8P++2sX4W3bcs536uSPZWoqFbfHL2nRgheMGzf6bp7Vq6lsvbnZuDFPcB984AdJxDbCMWPC5VJSmIp8zBg/j8zkyUzncd994XJJSTQEnnnGP5XPnctTn2JcTJjA2HjvFL3nniR7zppV9TUgqrWiT0jg4sjN9ckmMfKGp8T69qXCUxTTiBEaQ7J+fZI3+vZ1RXH++ZwsvZyAqQEDuIg9xZSQQGV75JH+s485horMU0wASVhe+TeAboYrrtB8xaecokW+BAEwapSm6E8+mXKeG6F+fZY8VEguxx7L7/cweDDLI3ro2xc48EB/bgYBv2XUKL/NPn24JrzIrIQEyv7tb9p7Nm/ul1FMSeH8HTnSZ/DGWMFePyUlcc4PGeInYIutXSXiafhw9rtX13foUL3No45i33uM6IYNOUeUtVlhKMH2Vf1TEcJUXp7ZXnv5+VHMmFfCyzVjZvbss9qz16zRiUhTpph9/bUvV1ZmduqpWpvXX2+WkeHLTZzIHD4K1FKCc+boZdV27NDkoshmZppt2aLJfvutJrdihSa3bJlGBvrpJ7O1a325sjKz11/Xnj1unEbaycszGzNGa/Oee1g+00NGBuecgn//W+vP/Hyzvn21NkeP1slFffpoBLh33vHzSsVw6KFa3y9cyO9X8OijZj/+qMn+HPBnKiX45pt+/g2AfljPUgaqJi+7GS/oFGJEWpoW769eeBUV0RqMlxL8/UKNeIoSGaWWJ4xSyk6dc+vX82SsYMYMoHdvX27uXLqvlIvbH37QTnzbt/ObPIseoItJKeJtRjeXUkpx61b25+7qG5Uw9YdQ9FEmfxxxxBHHHwXVStEHQbAFQC6AXyEF/+8ajRHvAyDeD0C8D2KI9wPxS/3Qxszc1AK/C0UPAEEQzFR2pj8y4n1AxPsh3gcxxPuBqGg/VOuomzjiiCOOOHzEFX0cccQRxx8cvydF73A8/xSI9wER74d4H8QQ7weiQv3wu/HRxxFHHHHEUTX4PVn0ccQRRxxxVAF+c0UfBMERQRAsCYJgeRAE1/7W71OVCILguSAINgdBMH+X3zUMguDzIAiWlf/ZoPz3QRAEj5T3y9wgCIRy5b9/BEHQKgiCKUEQLAyCYEEQBJeV//7P1g81gyCYEQTBnPJ+uLX893sHQTC9/HtfD4Igpfz3Ncr/vbz8v7f9TT+gEhEEQWIQBD+VV6f7s/bB6iAI5gVBMDsIgpnlv6u0NfGbKvogCBIBPA7gSABdAJweBEGX3/KdqhgvAPjfTCHXAphkZvuA9XVjm92RAPYp/xkF4Mlf6R2rGiUArjKzLgD6ArikfMz/bP1QCGComXUD0B3AEeWFeu4F8KCZdQCQCSBW2vw8AJnlv3+wXO6PgssALNrl33/GPgCAQ8ys+y5hlJW3JpQ8CVX1A6AfgIm7/Ps6ANf9lu/0K3xzWwDzd/n3EgDNy//eHMCS8r8/BeD0n5P7I/0AeB+sWvan7QcAtQDMAtAHJMUklf/+/9YHgIkA+pX/PalcLvit370Svr1luRIbCuBDAMGfrQ/Kv2c1gMb/87tKWxO/teumBYBdS0KsL//dnwl72s5Si5sAxHLz/eH7pvzofSCA6fgT9kO5y2I2gM0APgewAsB2M4tlJ9/1W/+vH8r/exYAsVzI7xoPAfg3gFipn0b48/UBABiAz4Ig+DEIglh+0kpbE/H0Vb8jmJkFQfCnCIMKgqAOgLcBXG5mO4JdkhX9WfrBzEoBdC+vyfwuACFl1h8HQRAcA2Czmf0YBMGQ3/h1fmsMNLO0IAiaAvg8CIL/p1xNRdfEb23RpwFotcu/W5b/7s+E9F0KrTcHrTvgD9w3QRAkg0r+ZTN7p/zXf7p+iMHMtgOYArop6gdBEDPAdv3W/+uH8v++BwChOOXvGgMAHBcEwWoAr4Hum4fx5+oDAICZpZX/uRnc9HujEtfEb63ofwCwT/ktewqA0wBM+I3f6dfGBAAjy/8+EvRZx37/1/Ib9r4AsnY5xlVbBDTdnwWwyMx2rSv0Z+uHJuWWPIIgSAXvKRaBCv/kcrH/7YdY/5wMYLKVO2irK8zsOjNraWZtwbU/2czOxJ+oDwAgCILaQRDUjf0dwGEA5qMy18Tv4BLiKABLQf/kDb/1+1Txt74KYCOAYtCvdh7oY5wEYBmALwA0LJcNwIikFQDmAej1W79/JfXBQNAfORfA7PKfo/6E/XAAgJ/K+2E+gJvKf98OwAwAywG8CaBG+e9rlv97efl/b/dbf0Ml98cQAB/+Gfug/HvnlP8siOnBylwTcWZsHHHEEccfHL+16yaOOOKII44qRlzRxxFHHHH8wRFX9HHEEUccf3DEFX0cccQRxx8ccUUfRxxxxPEHR1zRxxFHHHH8wRFX9HHEEUccf3DEFX0cccQRxx8c/x9Th8AxXND0QQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Image Pair 1\n" ] } ], "source": [ "windef.piv(settings)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Run the extended search area PIV" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "# we can run it from any folder\n", "path = settings.filepath_images\n", "\n", "\n", "frame_a = tools.imread( os.path.join(path,settings.frame_pattern_a))\n", "frame_b = tools.imread( os.path.join(path,settings.frame_pattern_b))\n", "\n", "frame_a = (frame_a).astype(np.int32)\n", "frame_b = (frame_b).astype(np.int32)\n", "\n", "u, v, sig2noise = process.extended_search_area_piv( frame_a, frame_b, \\\n", " window_size=32, overlap=16, dt=1, search_area_size=64, sig2noise_method='peak2peak' )\n", "x, y = process.get_coordinates( image_size=frame_a.shape, \n", " search_area_size=64, overlap=16 )\n", "u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )\n", "u, v, mask = validation.global_val( u, v, (-1000, 2000), (-1000, 1000) )\n", "u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)\n", "x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 1)\n", "tools.save(x, y, u, v, mask, 'test1.vec' )\n", "tools.display_vector_field('test1.vec', scale=75, width=0.0035)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#Run the widim" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "lines_to_next_cell": 2, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------\n", "|-----> || The Open Source P article |\n", "| Open || I mage |\n", "| PIV || V elocimetry Toolbox |\n", "| <-----|| www.openpiv.net version 1.0 |\n", "----------------------------------------------------------\n", " \n", "('Algorithm : ', 'WiDIM')\n", " \n", "Parameters \n", "-----------------------------------\n", "(' ', 'Size of image', ' | ', [369, 511])\n", "(' ', 'total number of iterations', ' | ', 3)\n", "(' ', 'overlap ratio', ' | ', 0.0)\n", "(' ', 'coarse factor', ' | ', 2)\n", "(' ', 'time step', ' | ', 1.0)\n", "(' ', 'validation method', ' | ', 'mean_velocity')\n", "(' ', 'number of validation iterations', ' | ', 1)\n", "(' ', 'subpixel_method', ' | ', 'gaussian')\n", "(' ', 'Nrow', ' | ', array([ 5, 11, 23], dtype=int32))\n", "(' ', 'Ncol', ' | ', array([ 7, 15, 31], dtype=int32))\n", "(' ', 'Window sizes', ' | ', array([64, 32, 16], dtype=int32))\n", "-----------------------------------\n", "| STARTING |\n", "-----------------------------------\n", "('ITERATION # ', 0)\n", "..[DONE]\n", "(' --residual : ', 0.9999999615970089)\n", "no validation : trusting 1st iteration\n", "going to next iteration.. \n", "performing interpolation of the displacement field\n", " \n", "('..[DONE] -----> going to iteration ', 1)\n", " \n", "('ITERATION # ', 1)\n", "..[DONE]\n", "(' --residual : ', 0.0953833601767332)\n", "Starting validation..\n", "('Validation, iteration number ', 0)\n", " \n", "..[DONE]\n", " \n", "going to next iteration.. \n", "performing interpolation of the displacement field\n", " \n", "('..[DONE] -----> going to iteration ', 2)\n", " \n", "('ITERATION # ', 2)\n", "..[DONE]\n", "(' --residual : ', 0.2629027450643792)\n", "Starting validation..\n", "('Validation, iteration number ', 0)\n", " \n", "..[DONE]\n", " \n", "//////////////////////////////////////////////////////////////////\n", "end of iterative process.. Re-arranging vector fields..\n", "...[DONE]\n", "-------------------------------------------------------------\n", "('[DONE] ..after ', -27.409109354019165, 'seconds ')\n", "-------------------------------------------------------------\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaling_factor = 1\n", "\n", "# we can run it from any folder\n", "path = settings.filepath_images\n", "\n", "\n", "frame_a = tools.imread( os.path.join(path,settings.frame_pattern_a))\n", "frame_b = tools.imread( os.path.join(path,settings.frame_pattern_b))\n", "\n", "#no background removal will be performed so 'mark' is initialized to 1 everywhere\n", "mark = np.zeros(frame_a.shape, dtype=np.int32)\n", "for I in range(mark.shape[0]):\n", " for J in range(mark.shape[1]):\n", " mark[I,J]=1\n", "\n", "#main algorithm\n", "with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " x,y,u,v, mask=widim.WiDIM( frame_a.astype(np.int32), frame_b.astype(np.int32), mark, min_window_size=16, overlap_ratio=0.0, coarse_factor=2, dt=1, validation_method='mean_velocity', trust_1st_iter=1, validation_iter=1, tolerance=0.7, nb_iter_max=3, sig2noise_method='peak2peak')\n", "\n", "#display results\n", "x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = scaling_factor )\n", "\n", "tools.save(x, y, u, v, u*0, mask, '2image_00.txt' )\n", "\n", "tools.display_vector_field('2image_00.txt',widim=True,on_img=False, image_name=os.path.join(path,'../test2/2image_00.tif'), window_size=16, scaling_factor=scaling_factor, scale=200, width=0.001)\n", "\n", "#further validation can be performed to eliminate the few remaining wrong vectors" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python [conda env:openpiv] *", "language": "python", "name": "conda-env-openpiv-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }