diff --git a/README.md b/README.md index 7e3dfd7..79eb68e 100644 --- a/README.md +++ b/README.md @@ -1,58 +1,68 @@ # COUGHVID: A cough-based COVID-19 fast screening project # Overview: In the wake of the COVID-19 pandemic, mass coronavirus testing has proven essential to governments in monitoring the spread of the disease, isolating infected individuals, and effectively “flattening the curve” of infections over time. However, this oropharyngeal swab test is physically invasive and must be performed by a trained clinician. This requires patients to travel to a laboratory facility to get tested, thereby potentially infecting others along the way. Ideally, testing would be performed noninvasively at no cost, and administered at the homes of potential patients to minimize contamination risk. The World Health Organization (WHO) has reported that 67.7% of COVID-19 patients exhibit a “dry cough,” which may be audibly different from coughs caused by other pathologies. Such cough sounds analysis has proven successful in diagnosing respiratory conditions like pertussis, asthma, and pneumonia. At the Embedded Systems Laboratory (ESL) at EPFL, we have developed the COUGHVID database, which is an extensive dataset of COVID-19 cough sounds from around the world, partially validated by expert pulmonologists. We contribute our data, signal preprocessing source code, cough classification algorithm, and feature extraction methods to assist the global research community in developing algorithms to automatically screen for COVID-19 based on cough sounds. # Data access The COUGHVID dataset can be downloaded from the following Zenodo link: https://zenodo.org/record/4048312#.X22TIGgzY2w # How to use it: First install the Python library dependencies in a virtual environment. Pip: ``` pip install -r requirements.txt ``` Conda: ``` conda env create -f environment.yml ``` ## Notebooks The `coughvid_classification_example.ipynb` notebook illustrates the usage of the cough classifier model for removing unwanted recordings from a cough database. +The `segmentation_and_SNR_example.ipynb` notebook is an example of how to use the automatic cough segmentation and SNR estimation algorithm. + ## Source code +### Convert files + +A quick function to automatically convert all of the compressed .webm and .ogg files in the COUGHVID dataset to the more usable .wav format. Note: you must have FFMPEG installed for this to work. + ### DSP This file contains all digital signal processing functions, including filtering the recordings and classifying between cough and non-cough sounds. ### Features This file contains all of the functions used for the computation of audio signal features commonly used in cough classification. +### Segmentation + +This file contains a function for segmenting a recording into individual cough signals, and additional code to compute the SNR of the recording. + ## Models The `cough_classifier` is an XGB model that can be loaded and used in the `classify_cough` function to classify whether or not a given recording contains cough sounds. The `cough_classification_scaler` is a feature scaler also used in this function. # Citation Please cite the following ArXiv pre-print: https://arxiv.org/abs/2009.11644 # Contact For questions or suggestions, please contact coughvid@epfl.ch To donate a COVID-19 cough sound to our database, please visit https://coughvid.epfl.ch/ \ No newline at end of file diff --git a/environment.yml b/environment.yml index ee0eb40..baff379 100644 --- a/environment.yml +++ b/environment.yml @@ -1,114 +1,118 @@ name: coughvid_public channels: - defaults dependencies: - argon2-cffi=20.1.0=py38he774522_1 - attrs=20.1.0=py_0 - backcall=0.2.0=py_0 - blas=1.0=mkl - bleach=3.1.5=py_0 - ca-certificates=2020.7.22=0 - certifi=2020.6.20=py38_0 - cffi=1.14.2=py38h7a1dbc1_0 - colorama=0.4.3=py_0 - decorator=4.4.2=py_0 - defusedxml=0.6.0=py_0 - entrypoints=0.3=py38_0 - icc_rt=2019.0.0=h0cc432a_1 - icu=58.2=ha925a31_3 - importlib-metadata=1.7.0=py38_0 - importlib_metadata=1.7.0=0 - intel-openmp=2020.2=254 - ipykernel=5.3.4=py38h5ca1d4c_0 - ipython=7.18.1=py38h5ca1d4c_0 - ipython_genutils=0.2.0=py38_0 - ipywidgets=7.5.1=py_0 - jedi=0.17.2=py38_0 - jinja2=2.11.2=py_0 - jpeg=9b=hb83a4c4_2 - jsonschema=3.2.0=py38_0 - jupyter=1.0.0=py38_7 - jupyter_client=6.1.6=py_0 - jupyter_console=6.2.0=py_0 - jupyter_core=4.6.3=py38_0 - libpng=1.6.37=h2a8f88b_0 - libsodium=1.0.18=h62dcd97_0 - m2w64-gcc-libgfortran=5.3.0=6 - m2w64-gcc-libs=5.3.0=7 - m2w64-gcc-libs-core=5.3.0=7 - m2w64-gmp=6.1.0=2 - m2w64-libwinpthread-git=5.0.0.4634.697f757=2 - markupsafe=1.1.1=py38he774522_0 - mistune=0.8.4=py38he774522_1000 - mkl=2020.2=256 - mkl-service=2.3.0=py38hb782905_0 - mkl_fft=1.1.0=py38h45dec08_0 - mkl_random=1.1.1=py38h47e9c7a_0 - msys2-conda-epoch=20160418=1 - nbconvert=5.6.1=py38_0 - nbformat=5.0.7=py_0 - notebook=6.1.1=py38_0 - numpy=1.19.1=py38h5510c5b_0 - numpy-base=1.19.1=py38ha3acd2a_0 - openssl=1.1.1g=he774522_1 - packaging=20.4=py_0 - pandoc=2.10.1=0 - pandocfilters=1.4.2=py38_1 - parso=0.7.0=py_0 - pickleshare=0.7.5=py38_1000 - pip=20.2.2=py38_0 - prometheus_client=0.8.0=py_0 - prompt-toolkit=3.0.7=py_0 - prompt_toolkit=3.0.7=0 - pycparser=2.20=py_2 - pygments=2.6.1=py_0 - pyparsing=2.4.7=py_0 - pyqt=5.9.2=py38ha925a31_4 - pyrsistent=0.16.0=py38he774522_0 - python=3.8.5=h5fd99cc_1 - python-dateutil=2.8.1=py_0 - pywin32=227=py38he774522_1 - pywinpty=0.5.7=py38_0 - pyzmq=19.0.1=py38ha925a31_1 - qt=5.9.7=vc14h73c81de_0 - qtconsole=4.7.6=py_0 - qtpy=1.9.0=py_0 - scipy=1.5.0=py38h9439919_0 - send2trash=1.5.0=py38_0 - setuptools=49.6.0=py38_0 - sip=4.19.13=py38ha925a31_0 - six=1.15.0=py_0 - sqlite=3.33.0=h2a8f88b_0 - terminado=0.8.3=py38_0 - testpath=0.4.4=py_0 - tornado=6.0.4=py38he774522_1 - traitlets=4.3.3=py38_0 - vc=14.1=h0510ff6_4 - vs2015_runtime=14.16.27012=hf0eaf9b_3 - wcwidth=0.2.5=py_0 - webencodings=0.5.1=py38_1 - wheel=0.35.1=py_0 - widgetsnbextension=3.5.1=py38_0 - wincertstore=0.2=py38_0 - winpty=0.4.3=4 - zeromq=4.3.2=ha925a31_2 - zipp=3.1.0=py_0 - zlib=1.2.11=h62dcd97_4 - pip: - appdirs==1.4.4 - audioread==2.1.8 - chardet==3.0.4 + - cycler==0.10.0 - idna==2.10 - joblib==0.16.0 + - kiwisolver==1.3.1 - librosa==0.8.0 - llvmlite==0.34.0 + - matplotlib==3.3.4 - numba==0.51.2 + - pillow==8.1.0 - pooch==1.1.1 - requests==2.24.0 - resampy==0.2.2 - scikit-learn==0.22.1 - soundfile==0.10.3.post1 - threadpoolctl==2.1.0 - urllib3==1.25.10 - xgboost==0.90 prefix: C:\Users\Lara\Anaconda3\envs\coughvid_public diff --git a/notebooks/segmentation_and_SNR_example.ipynb b/notebooks/segmentation_and_SNR_example.ipynb new file mode 100644 index 0000000..cbd6039 --- /dev/null +++ b/notebooks/segmentation_and_SNR_example.ipynb @@ -0,0 +1,255 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import librosa\n", + "import os\n", + "import sys\n", + "sys.path.append(os.path.abspath('../src'))\n", + "from segmentation import segment_cough, compute_SNR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cough segmentation and SNR computation example\n", + "Learn how to use the automatic cough segmentation function. The target signal is comprised of a breath followed by two coughs and a throat-clearing noise. Our goal is to segment each individual cough sound using the segment_cough function, and then compute the SNR of the recording." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Input cough signal')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Visualize cough signal\n", + "file = \"../sample_recordings/cough.wav\"\n", + "x,fs = librosa.load(file, sr=None)\n", + "plt.plot(x)\n", + "plt.title(\"Input cough signal\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Segmentation with default parameters\n", + "By default, the minimal cough length (below which segments are discarded) is set to 200ms and the \"padding\" (extra signal samples before and after each detected cough) is 200ms. These thresholds are based on the physiology of cough sounds but can be adjusted to fit your applications." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Segmentation Output')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cough_segments, cough_mask = segment_cough(x,fs)\n", + "plt.plot(x)\n", + "plt.plot(cough_mask)\n", + "plt.title(\"Segmentation Output\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(len(cough_segments),1, figsize=(7,10))\n", + "for i in range(0,len(cough_segments)):\n", + " axs[i].plot(cough_segments[i])\n", + " axs[i].set_title(\"Cough segment \" + str(i))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove extra signal before/after cough\n", + "To reduce extra signal samples around each cough, modify \"cough_padding\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Segmentation Output')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cough_segments, cough_mask = segment_cough(x,fs, cough_padding=0)\n", + "plt.plot(x)\n", + "plt.plot(cough_mask)\n", + "plt.title(\"Segmentation Output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that both the breath and the throat-clearning noise at the end are successfully removed." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KAEMBHAPgAgAPE1FHuRBj7CHG2CjG2Khu3brltEN+t9Wbc9ZlHMsmfEIpPAPu69dmrA7VxvrtdULdwQ/N+Jlr8NtXvgrVhohOI+SaaXN6brNVoEspm84jk5fgwQ+W4JHJXxe6K80Sk1vJ5G7Z3dCEGvGZtuSUOAThKgD9hPd93WMiKwGMY4w1MMa+BrAAjmAsGGGdVZpTQL0JVz39BZ76dHno87jXbINmN/YZK/Of8LtYKIU1wnr3d812t5Zyx0+DM0nXePGjn2H0HW/H3S2Lhjgyy0wFMJSIBsERgOcD+I5U5hU4muC/iagrHFPpkhjazhplqITiaDbOMuMU2l1ONaacrhG6A6XsadkMhUBz0lotzQOTibKfA/p3H5mCLbUN+Eryurb3am7JWhAyxhqJ6GoAEwFUAHiUMTabiG4FMI0xNs797BtENAdAE4BrGWMbs207O/R3VrsWmZcldSNGGPDlUALm2zpvL13iz2/Ox9ptu3HP2QeEbzxHyGuEzZlmKMMtzRjl7jZJBiJkxBynhh17k+aUWHKNMsYmAJggHbtJeM0AXOP+Kwr8ZlhHDO2GP765AK2rK1LHgjTCyQs3YP323fjWgX0z21KUD7Ot09/eXQQARSUIZdPZ6i3qnTAsFouXJoV02+P6CThscBftOTaFYW4pm8wyMv5eXc6nravT84R0NgjnhpQz+F/0yBRc8/wMo8YYYx5B/Oxn4dfqOMp0bCGE7MDrxuPN2Wu1n8uzV/5Anje6n+f4r17QfPcCEuRpF7ROa+qpZ81WFk7Ke9rnntBNqnlssyX/lK0gnOru8K7cmFd4vbuhCYwxzwL3C9NW4IDfvYn5a82S5gatQ173UnTvzeE3TYx8Luf+9xZrP5OvD+/3sXt1z7rdXJOtgAp7fil5+UW9do1NybJOJm20L2eIa2snWfmhbAXh9t36xNb85ttR14BhN76Bv7y9MKUdJIjwnrsDw4J1hoIwSNqqzjGqWUOMCwo6E25zeD7D9nH5xlp8+58fhz5/227HOvDK9HAhL6XIXa/Pwwl//gDLDfPlcpZvrMUNL3/V7DOoVLjP3keL9NpdJI9xaxnNKWUrCP3ggmt3g7MO9tIXK5VxhKa381JpUDBxlikWsu1nIQOBA02jUmq0e99ZgM+XbTY+n1NCfkNZM9W9fht3htOOf/bcl/jPlOWYvmJzcOEY2FHXiAlfrYm93paCX4GOpHu/WAeY4sEKQgXy8MdYtG2Y/AhylsmmmdmSl2oQfm3J3eRrhM3BZGPaRZ3Lu/n5hgWLnN0NTVhgaO5v7lz7wgz8+D9fxG7G5RrhwC6ttWXCOMo1g8esJLCCUIE6oJ6/So962Yx/Qc9CNg/As1NXBBcyJGMfx5A9K6TANL3Gut/RtO/5lIObdtbj1tfmxBLwfs4DH+Oxj9IZZH7935l4w8dxqjkyf+12rNuW6dH8+izne3KrT9z4OuO5fwmETTvrjfIKl8hcq2ixglCByiSmyjWqun1N1ka21DYEihPTPQlzjV4YNP+5asq5RTPKFDr7j4rb/zcHj370Nd6Ylb3Amrp0M255bU7q/bSl8Zkli+XKnXTvBzj49+9oPw+jnZlgUhtvcv323TjwtrfwN7tXZ8GxglCBaoJmmmt0/XazeDqbRDf3BAkynk9z8fqd/ATv+YY/UT5/SZ7IIO4BHAASMY4GzUWDaaNInpEN/Ln2T+HofMjzA789NzO3sVzWklusIFSQYQ6Uwif8MF0vai73t66fpv3X7WSfD0z7qDMzGgvCAvyWYXLezlixJSO7kQrTLcZ8KcDFYIzh9v/Nwdcbdhqf06djKwDpNb248ZuoyHGEzWUsKGWsIFSh1Aj5zUtoclNDqLZr2lFXWtvXZKt5HPWHSTH1JHfovqGpabQYTaic+sYkzrz/I5z298mBZeMQhOmkKfnTCRfX7MS/Jn+NK56YFvrcXJlGTfY7NblE+byO5YwVhALcDKEyjYqu9tyh4LevzML7C7z7Jl786GeY5LMPIScXpq0g5q3dhnd8zDAq9EKi+DHX6DSxkszZ9PiPE+f77s9XiBn9jBVbjMxmD3+ozm2v+j5xjrn5Hb75c2v+Q/DvmquwRb/fJsz9kh6TGB58fzF21ZfWRLtYsIJQgN+gmZ6S6YdMnjVf/OhnmLtmm+fYpY9NNW4rn4y990Nc/ni4WbP8QO/Y3dhsNm3NVlNjAJ78ZBnum7QI//TNvhOtnfXbdmPZRnNzHu8T4KxvvjJd3u0sEx7sL/PSF5nnxmEmbC5mvlQGmLg1Qrc6PwEbpcVx01fjztfn4S9vL4jUL4s/VhAK6MwajCEVaL1TkdvzmQi5QpvDeLFw3faM7/bK9NU4477JzWLAC7vGlxk/ylDnbjflt9tGVK1izO/fwdF/eC/ayRCcfCKgmiRUJOLT47ig+ckzX+LSf38WW71xweNhc6YR+jzhqZhkA72Z35u73MmnX0YsS3TidZlq5jizQ1Lewv9wNYIttZkz7CjDR3PwBjvxLx8ojy9YtyPWdbGfPvMltu1uwGOXjomtTiB4sjF6YCdMXboZowd2jnR+qlyBfsqg3+CNWWvx4Pvm236K61FxfaXXFHtxxgFjDMf/+X38+JghGNGvg3MsQj3xL1Hol1fkNtNaqV9tmR9+tGgD9uvbAe1bVkXupcWL1QgF+C0nPxwMDD86ZjAAYFDXNhnnRVnQNnn8Xp2+ysjbT8XqLbn11gwaP44c2tW4rnEzVuO9+TXBBUMSNNnYp7czgLZtqZ4Pmo6RhVjvBYCNO+p9P7/7jXme9wOvG596HaXLW3c14IVp8SVryIa6xiSW1OyMvOtJeo0wN6ZR//AJtw8h6uPa49Zd9bjwX1Pwo6c+j95JSwZWEAqkbl7pJiYQBnR2Uia1aRGcSzBUWz787NnpRt5+HHEWethd70bolTlB3deZ2Wau3JK3Bf+gPgbNX8y3YTIrt3VXA3794kzsrMvCvCU0FZRBKBtD59/eyQzy/tULM3DtizMxZ/U2xRkOUS0FcRhlw9QRd+jC7oYmvPzlSmF5xSB8IsRu9px611S/YN2OSP20qLGCUIA/xKq0YrEbUCI8gUF5EflDEpYoPhJR+r+lth5n3PcRfv7cl+EbFFizdZfRumy+tmFqUWU2OfrHpEV4btoKPPXpsix6FQ9RLg0PAK9rDJ7IFGIj2ThNo2u27sKqEFaVu16fh188NwMful7ku/y8jEP0z2/kqWtsQqPN+B4LVhAK+Jk1TDJFhGor9BnApp1qD0COTqDtbmjCX97Se5vly7K3xA14njg7XAiHzCWPTsVvXvoKG3cE7HAQ8L3iyve6Z492RuWKaVXY9J5tbEqmt0bKswm4tr4RA68bj/snLcr4LNuucG1Mt5Z36J3v4vAQVhWez3Sb68xS62f1CNH39Ibg7qnCuXv99g2c/9Cn5pVZtFhBqEB+OBjzn5k1RRGEER7kqN7t/3xvMf6qMHVlU2+U/s9csSX8SQo27nTWxoKue9bhE0L9//7oa+1GzPl0fJK/0/PTVuA3L83Mqk4/S8PIW9/Cwb9/23PMz6QX56XgjmlPfpKpQWf72/JvENca4VpXEC7fFJwjONMHwQfNh7z/04RtwyzRiUUQEtFYIppPRIuI6Dqfct8mIkZEo+JoN27SGqH37lu/vc73AY9inYjyIEc1NvmZaaJSzNlUOEFjXOAaofB6d0MSp/7tQ7w6fRUGXjce64UdDQp5Jf7vxZl45rPsHFj8tJftdY3YEOCUoyKO4Hy/3d6zll8xxxF+uXwLAODdEMk0TDL5qGKaLfGTtSAkogoA9wM4GcBwABcQ0XBFuXYAfgZgSrZt5hrVzeZ3A0YyjebgjtbnBQ3QnCL0JdI54U/JqsJs25O/Y2OSpdYmF9UIzgqKhpZu2In9bp6IFQYaQqHJ1a7wz2fpYeq3zqjS5ILECmMs41nIVRyhbz/ClM1w3LPkgjg0wjEAFjHGljDG6gE8C+BMRbnbANwNwGx7hgKQcpZR3Klf1zjrW6qHM4p5Jco5wRqMNHv0SRmXLYHrazkcYMwTm6s7sXxjLQZeNx7z1vg7H6m341KUUwxtL36+EtvrGvHKl8HZXwpNnIJQvD7/mRI+0URQnaljqnIB9dwybjYG/WYCAME06n73mSu34LwHPzFyBMqWzIQdwR6mMjYFabzEIQj7ABCnfivdYymI6EAA/Rhj4+EDEV1BRNOIaFpNTfxxZUHoTKMA8KiwgalMlHEkn34HuYhzK1Ts3Lvz1qX2EZR7sGJTrSf9m66Hb85xcsV+smSj57j8u/t9Q3FCpLoUPHok7klILi57UyrTSXHhN9hHuQ6PC2uNsrPMGfd9hClfb9KuA8eJ3HXfNdfcdsXiknNnGSJKAPgzgF8GlWWMPcQYG8UYG9WtW7dcdy2zfemvKcN6mnkNZsv7Czb4F5A6fuv/5qC+MZkbM2yEc1oahhn4cdlj6lypySTDkfdMwtVPp0MzdN/bNAGCcYo2nzYKNWEIQ9ge+iaUzq4rxtpftg2l4wjz//uoNvnWlw133BKNOAThKgD9hPd93WOcdgD2BfAeES0FcAiAccXoMDNz5RYMvG48FoYMVh3crW3otqLcyKogZ0+d0vt/f7QU47/KTYqrKAMRDzPYQ5GdJ1IXhD5wgfPuvHRohs6hxzSlpvE2TEqNkNzPFOZVn7r+/s7CwN85DrIZSP29Rs0HeVWdvFufLN6IH2i2VdpV34Q5nkT34RoTf5NCrBHK+xH6oeueNY3GSxy5RqcCGEpEg+AIwPMBfId/yBjbCiCVb4uI3gPwK8ZY+M3Dcsx3Hnb8eMJmeI+yxpILr0vVoJtMAksNdziYs3obenVoiU5tqoPbiuL16j687VqpcyQyxkKlq+N9uO/dhfjjm+a/mWkLqryy6rWpzKNc2KpCPPza/5Mb7/nT44eadDGvhPnFwwbUy5leLntsasrbWbyEU5ZsxD0T56eS4IfvmVOfLsUaY8GJK7Il1CTEqn55IWuNkDHWCOBqABMBzAXwPGNsNhHdSkRnZFt/cyBfcYSBdWqOy3k8deagU/72Ic643yylWzaeprohMuo10QpBnVnJsN7vPz5NO/MOktelPGM3/WphLoGsQXtDftK/2HkPfSoJwfAwiLtPZN4NcwOcqLIljLOM7hMrH+Mllt0nGGMTAEyQjt2kKXtMHG0WE1sVO9UHkYu1I1WVqgHZr+kVm8zSSkXrvf9ZScaQ8Bk+M5xZgjxXjfulZtWWXb5tjL33A7RrWYkzR/TRFypScr02tnzTTkw3TKDQlGT4bOkmAP6bYmeDmHA8yVjqudhR14h/C45wDEBljNtRqZB3n/CzgmTsVAHvX0s82MwyAhcfOgAA0N9NsG3K/70YPrNHLm5k0zrjEMJRhFAqU5fmnCALc8ZMWlHGs5WQzlnGv5mATqRfzlu7HVOXbg7vbJJN8zHdOLkeSK986gvP+8amJF78fGUqXEFEzBurDJ6PuW/iNbxj/Fz87rU5wmcs1n0ZzfoT/A25Btsctm9rjlhBKMDdq/Ph6ZebgHrFepTimY7DQSDKGiEfBOs0mW6Crrv8qTrOzzm2o64Rt/1vTsbnOcHPtCV8FHV4XbphJ2773xwkk9707wcN6JR6/cXycOZCucvPT1sRuJaczT37r8lf41cvzMB/v1jpOf75sk1ei4rKqhFYe7pEXWMT7p+0yDcBvXifbdqZmTUn14IwtUQQYfeJAqV9LXnsxrwK/G6y+NZ+CncnywKHCKEy7QMR1wjdv/M0sVqBgtDANEpEeGfuOtz2vzlYujE/WV2CNNNsufKpzzFv7XY8Mvlrz/G2LdKP77f+8TGW3nWqcZ1yn8NYNaJ8tQ1u7KfogDRp3npc+thU9OnYStsvk/a4MKtrSOKRyV/jDxPno7oigR8ctYf2nJSXKlMfzyX8Pucxr3W+Qtt9IZlG4xg/1m/bjW/+42M89f2DlfuslhNWIywQ+XKWUXnvqdr+14fmO5k7dUTQCAPOCTSNZvRBXe7yx6flTAhyjUy8qrmeneciBVquTGxB1X69cSe21DqCa8Vm5zcSJ2G6SZJTt7ryP06cn6qH73VZW9+kLS8elh3dnDRs/t8hW/hz8IRrgfp6g14Tz5z8Mfdv9v0Y/9UarNqyC4/5JAspF6wgVJAP02i+4pdUE1yVl+u/P1oaqt4o3Q+6rLrrvqOuEV+t3KowE2WWz8aqFfUnyWWAeZg6Xpi2ArX1Zpv+mtziuRDAT09ZjpPu/UD7+W3/m6Nt16Q7qTAMMG15BpYOb5EKMeTeVpPPe8IPbgKO4vVealhBqCAf90VOZuSGHnf5cJZREbQzPdNYiPa9eSJOv28y6qVtPh796Gu8MWuN51iUDWHD/BZTl2auxfmdnU+3i2tfnIlbXzNbFxXXGnVf/w+uphUH4oRs3TbHTKq7Ni9r8rNqfyexIqEh3X2eZHpzqyoxtx9fLt8c/lkOUVwV5+j8TR9fv303NgTtzamAh6zYvX2tIAzNzJVbY6knN16jZrW+MG1lcKGgtiJIwp0B2krQzLSpyfv5E58sy/BONJE8unWgMEJLrOPPfpseh6hTZvWWXb4Ds8pBhudhDUKsUnffvDNXyNJj+HvHkShi+251OJJOw1P9bozpBSFjTDthalFZYfwNPlxYg2/+42M89vFSwzPc9sOU5YW5AOQbAwhlxtzxDkbd7t0z0gSuEao8ecsNKwgV5GOvvdx4jWYeq29K4pA9OnuOxeFNmYsrFLyGGNxqFA0sWweJ7e6u5KpqpgiJvcNcs8+XbcZhd73rO2nh7UaBad+o2eu3b0RuKywq0+i0pZsw/CZ1H8R9/UzWbv3GfSKzfQKBdMztgpApGcNYZBau99ad1ghDNamkgqxplGMFoYK8mEZzkmIt89j1L32FqopwP/NTny7DlU9+Hrot7+eZBaKuEZp+ni1xeAyu2rILc4U8mF+4G7aGhaf5mrZsU9Z9UiFeShOFQDZL55v7Jy1Co6ajCc3trV3j9DWNAp1aq1MAmrBfnw4AgK5t9WkK5fvYJGm/vEVcHEsrCasRprDhEwrWG5qXsiInS4SZlTYmw+XvBIDfvjLLoK3wqPr3/oJ0+rekNNburGv0mFPXbA3eyjKKLEt74mX/oxx+17sAgGtP2ktbxijZssd0aY4Yk+d3nmeNMMabUXcJx80wT/7OGNC9XQvj51DpGQ1gmcZzOMmYb5o/06vBr9vOukZ0blONTTvr0atDS3y1yn/5RL5GnVob5PaVzonHWcb5azVCqxGGpotBQmoTcjEJ006Ac5LOzb/O3Q1NaJC0CPHtHyfOx4Yddbj40c/SdUqP9xn3TcaYO95JvT/t78F5UHc35EdzUQncXDjGhHX+mebm4Zw0b32AW776tR86gblycy2O+cMkrNm6SztAcwcZD5pZi6qdSfP1+5OK1Yivu2i0MhbQdtjHZdyM1WhR6Qylb85ZF1A6msWJ15u6NjE80mlnGSsIrSAMiV/GijDkYx2ykExduhln//NjzzHRBHOfIvuH/DwurjHbNSMuVJpz1EmEWNWo29/COQ98rC+cAy59bKpx2Ttfnxeqblk4P/PZcizdWIv/fp69ExYQz9LEgrXbPdYGET8Tu5+TTUY9PgLEr4oo9xQXViw+OZh2lrEaoTWNhqUuprUSVWqnbNHdz/lyzJGZIXnY6tZ40nXm9oHcWdeINi0q8dEi7wbHuR4HNuyox4Yd9RjZv1NwYYnnpq3IQY+813quZ2+/YBbX7MDqrbtw0j49feuN3LcszuVC+o3Za/HG7LXq+pnPDigwvx9uGz9X+5m/WTo6fmuE23Y3YMP2OuwRYX/UcsdqhCGJSyP82bPTY6mnUESZRQatRXyyeCNena6OIcuWz5dtwj43T8S789ZlmK/yrZ0Xw/w7ijWM/3w/f246fig4U8U9kUiy6L/IR4s3BJZx9r3Uf2baut9Y4DchyIgNDPFteVnV77f/LW/iuD+9jw076vC5gZNVlJjbUsVqhCWF+oHqmIUXXLiW/AnyTrvWzXeZi22N+B52b8zK1BL4uKSLRzOFiHxPeGbKcu1nGe2aNxsJk+9l6njE4+hWbdkdypxtPgybl/zs62ABEKStxSHYfduQPly9JdgJTD7XT3iedf9HWLl5V6jcs+WO1QhLCJ259X8z1yiPZ0OUwaKQi/I76pysNs/HkEwACB6aVbPt7XX6uL8Vm+LLjWpSVxSda8E6dR7QWjdj0MrN0b6DLHDD3lthvaL9vUa9zjIH3vZWuM6k6vFr3/t+eYjfnp/qV//KzZkJ9D9atMHjvLa7oQmNrpu2XSK0grCkiBqzFoVI2zBJT1w+F+n/9s5C7Wf5Hgdq65vw5zfT6cvenbcOR94zyZMubtH6cEHaIj/+zxeBZUzmJHKgeIOU2ec/U5aF6lexwJheeMrOMlHX8v3XSqPfcalQnxDnfL5sEy781xT86c10BqRhN77R7Jdn4sSaRi2RiEMjLJaZ6Jotu0LvvqHC1FFEFsqzVznOKmL8mbzlUhjksJVcccPLs3DkkG5Z10PwDuxhd4AIu9LlqxEi9xO0bKpnGS/0bK1tQIfWVam0e0tq1JOrYnkOC0ksGiERjSWi+US0iIiuU3x+DRHNIaKZRPQOEQ2Io11L80J2likWt+3HP1mG28fPxeqtmSalqD0MY62b4K5bxuW8YHJZ/TTkMDQIWRBUJjkTZO0s7G0R9j7yD23I/PzjRRtCa79+PcrmvuenmtRRs8O79rhu2248PzU3XsjNnawFIRFVALgfwMkAhgO4gIiGS8W+BDCKMbY/gBcB3JNtu5bCEmk/QkON8JPFG9Uf5JhslzBJk/MyCB6+8PRn5s40fszXrOXlAvE39Avg90O+VvLPEDSp8NvYVgVj+nyijLEMIfOdf03BDS9nZltqJ2yMLGc9CrNGGAYm/Q3DjJVb8X//nenJPmRxiEMjHANgEWNsCWOsHsCzAM4UCzDGJjHG+IrwpwD6xtBuVuRhI+pmQ5QJ6tcbwjtGyBY73ax2w466WJ1HskG1w0OuyEVsaXPEWcPzHhOFjgz3FD2gbwez+qEPkQjjNXrYkC76NnyD9mNYIzSo4+PFGzHwuvFYJXulMvltcVhmCkkcgrAPAFHfXuke03E5gNdVHxDRFUQ0jYim1dToUyrFQeuqipzWX+r894vw3pdNSTmTjPoBJAJqIuyvlgve06T2shMpTrRB9AUhUUCG16iizh4dWgbW2brazOXBTyMLk1kmahvbstk1JGUaDS77tBuukxFTaO/dDPLqNUpEFwEYBeAPqs8ZYw8xxkYxxkZ165b9Irwfdg6Uf+Q1Qp1PB4FynmVGib0p8sa1L85MCUB5bXT+2u0Z+yqaeNF+ssTMpB6kremEzKT56zHwuvFY7Dqd7NXD2TXisMFdMnasyJWWFaVe2Ukt23CVWau2evaqLAXiEISrAPQT3vd1j3kgohMA3ADgDMZYwaf7ReKnUVbIgu8HT0xTliPKTVLyOFHdP3aiHQ+vK5IexImvRgi9oPyTG/LypRumxEv1VGiruRpf5B1a/Ji31lkrnjhbyqSUZd9O+/tkXP64+tltrsQhCKcCGEpEg4ioGsD5AMaJBYhoJIAH4QjB9TG0mTXF4rFYTsjXXBdITCj+jPh+G+YCVrksbvw0Qv2ns1Ztyygr/jVrITtiqVeqJIyZXxeC0dzJWhAyxhoBXA1gIoC5AJ5njM0moluJ6Ay32B8AtAXwAhFNJ6JxmuryhpWDagZ1bZOzusMIt0JMVD5bar4JblAy7GIX5MVEvjf9fX7aSm1aM4ZMr1EZ/nk672emwTKunMQy8SQ199YxcfY6/GHivMC6n/x0GY770/up96V0j8cSUM8YmwBggnTsJuH1CXG0EydWI1STy7U50wcnIGVn0SIHhVvioa6hKdb6HvpAnzzBcZbxP58nLODlkgzYUtt8QhJU3+/+SYvx42OGoI2Pd+6N0obdg6+fgIe/NwonDu8RdxfzTtmmWLPDVP4JM/lYmMdYuFzwRyGdVSmTjbxfrtlBXiYbL8uw+K0Rcri2x4u9Nz9/qz2xJATXVNLYpK98d0OTUuD94IlpOO5P72XfqQJTtinWrEaYf0w1wmUba/FCTJu8cvbr08GTwiwXlKMWOCkLIfCgj2ZWKFQB9TJ8+YCbGLfnUVDHkSxB9+1kk+mOuka0qEygqiKBYTe+oa1vSZ430M4F5asRlt+YpWX6ii2p10sNZ+lRMJ183Pn6PIwaEH4T20JTjrdUNsnBixGTgHqelaa5jiG651Cep+5780SjBO4AsGbrrmY9ESxbQWjJP36mF5lnY86JmA8LQDMeBywufnGEHB4P22wHfk23Vc/IW3PM4gUPvfNd/OO9xdn0qqBYQWjJG4V0MmuuY5Ylv5hkluE5c5vrPaV7Dn/x3HTcOWFu5Hrf12Rhag5YQWjJG4Vcl7VrwrmhhDzoAfDdJ/y/1OWPT8ONr8xqtt9dl53mw4Ubslq3bc45S60gtOSNxgKOHFYO5oa2Pu72zREGM+H+5KfLmu3AH/T9rnluusck+qsXZhjVO3XpZtw/aVE2XSsYVhBa8oa8DVNe27aSMCccskfnQnchVsJsCtxcb6nD73rX9/OXvlzlSX/4YggP7j9MnB+5X4XECkJL3jBNipwLmumYVfTkOSlMznE0QrO7pdk6y+SZJTU7il5TLC27hqWoKeR+e1YjzA2ldl1N1ghTZXPcl1Lhwn9NwZqtu3HRwQPQQdqlo1iwgtBSFuRrvO7ergXWby/45ip5o/QEYXD4RLpsbvvSXFlSswM765rw6ZKNmDBrDdZsdfK6ytuwFRNWEFrKgnwN2G1bVpaVICylxMtAONPowvXNOw1grhATc4s0FLEd3a4RWsqCfA3YpZBuKgxhkiQ0BxzTqFnZT5eY71Ziyd2OHHFgBaGlLChiq0yzpuRMowbbMFmiUVfEgtCaRi1lgR3cckMxr/tE4a9vL0TXti0K3Y2SpJhNo1YQRqRdy8q8Zp23ZIcVhLnhhpdnBRdqRixcvwMLSyyReLFgTaMlSEWCCt0FSwhKzKfDYml2FLNGaAVhRPhWLJbmgQ1+tlgKi9UISxCrEDYvSs3N32JpbtSVukZIRGOJaD4RLSKi6xSftyCi59zPpxDRwDjaLSRWI2xeWEFosRSWhlLWCImoAsD9AE4GMBzABUQ0XCp2OYDNjLEhAP4C4O5s2y00do2webHNOjZZLAWlvsQ1wjEAFjHGljDG6gE8C+BMqcyZAB53X78I4Hii5q1SWY3QYrFYzCl1Z5k+AFYI71e6x5RlGGONALYC6CJXRERXENE0IppWU1Pcux1bOWixWCzmWGcZQxhjDzHGRjHGRnXr1q3Q3fHFCkKLxWIxp9QF4SoA/YT3fd1jyjJEVAmgA4DCbU6XJQkCCFYSWiwWiyn1RZyXNg5BOBXAUCIaRETVAM4HME4qMw7Axe7rswG8y5pxYFeCyGqEFovFEoKttfVFG8+btSB01/yuBjARwFwAzzPGZhPRrUR0hlvsEQBdiGgRgGsAZIRYFAsmAo4IVh+0WCyWEPzt3UXYWd9U6G4oiSXXKGNsAoAJ0rGbhNe7AZwTR1u5JkEUmEiYiNDMnV4tFksMVCYIjTZG1ZiWlUXllpKiOHtVQEzCA20IocViAYDKCjsYhKGyojhFTnH2qoCYxAeSdZWxWCwAKhN2CC0F7K8oYSQICXaR0GKx2AxTJYIVhBJmptHS1wgP2aNzobvQbBncrU2hu2DJE1XWNFoSWEEokTCQhASUvLOMTSEXHXvtygdRI7Q726e57PBB6NymGkcO7YoZN3+j0N0JpKR3qE9G8OaSB7HKBGFI97aYt3Z7+mAZhE+s315X6C40W6wgLG3at6xMJXHna4StqirQuU0VNuxo3s/NAX07YMbKrVnXc9Ppw3HT6fLeC8VLSWuE01duCX2OrBAmiNCzQ8t4OtSM2FWk8T7NgQXrtwcXsjRbTj+gd+o19xotlWxTFx48AABwwyl746g9izvNZZxYjVAiYzav0P4c02jkbjULdtTZbYuiUqTJMywxUSnMlrlptFSyTZ07uh9O3b8X2rSoRBNj+GBBuM0Pnr3iEIwZmOlfMHpgJ7RrWRVXN2OnpDXCKKjWCOX1QKLSD6DYaQWhxaJkZP9OqdcpoVhCw0GbFo5+dPK+PUOf27KqQjmGvnDlYXj0ktFZ9y1XWEEoofKVUd3jpTD788Ouc1ksakYPSms8jW4i6UQJZptqVV0R+pxkMzWHWEEooRIAJXZ/m1GO39liMUCcLC/ZsBNAaeYf7t6uJZ68fEyocxat25Gj3uSWkhaEUeYmsiAk4f+e42UpHS2W4qNNBM0lG5STZZTmhPnIoeEcZg4dnLHferOgpAVhFFQZk2RzqXjDt6wq0UvYPC0cljLkkD3yN/j26dhKKfA21zaUpCAEgFP36+X7+TkH9cX0m07ElzeeiH6dW+epV/FSoqN4dExNo5T6W5p3f7HZ+i85bKBx2VZV4TWEv5x3QOhzLMVBPgVQpzZV2vXzUh0L7j1/BF696nDt5/v26YCOravRqU11HnsVLyUtCKOM5RUZHqLqG5wXUz0T+/ftEL7hIqPYBGH/EDPNKANjrp2DRg/sFFyoTPjZ8UNjrjF/Aqh9yypta6WqEVZVJNCupT7SLopTTbFR4oIw/GCeGUaojg9KCUJVHaFbLT6a8xZrUa5/rtd8m/P1jJuOreONJ5N/ulzm/7z08EHKSVPbFiUdkl3yPhElLQijYGIadTafcN1oSuQG6dq2BX5xwp6F7oaWXF9mu4lA80X+6Tq1zp2JrnV1hXKM6Nharynmi9YKzaxr23iuBa+nvY9m2JwpaUEYZRKu2lZFNo2SkEVCOUA3Q+F4/LDuOHBAx0J3IxaimDkXr9+Zg56E47Bm6nEHFDbhtPxzx7E10gH9Ourb042aWTz3Y/cJH7ye0bzi2PifHpl1vQDQrmUVFt5xMmbc/A18fecpsdRZTGQlCImoMxG9RUQL3b8ZCyFENIKIPiGi2UQ0k4jOy6bNMER5IDKzyPjf36ViGtWthZYL23c35LR+EzP9veeNyGkfckkhnY3k+zaO9d7qisxxIN1emutPGaY8bgrPTJOruXOP9vHlSa6qSLhKgLezHVoVb+o0U7LVCK8D8A5jbCiAd9z3MrUAvscY2wfAWAD3ElHHLNs1on2E3HbKzDKauCFAl5ItdLMFJ0jgF5qeYR7oCN8j19/dZI2we/uW+PD/js1tR3LEkUO74ZoTzUzrufDDOn5Y99TrODaNNxWuoqCJcg/t3at9+JOKhMHd2uDe80bgG8N7FLorWZPtLXMmgMfd148DOEsuwBhbwBhb6L5eDWA9gLykNWcRjKO6YNnMg+lku0blixwq8s2GR/TvaFy20M4yKhOn6Z0oxmEV6+7nPz9B7fX5zGfL89wTByLgoe+NSr2XPb+jVep9e/kRgwAAe/VsJ7WdLrh+W/gtmMJ0tVcMu+A8esko5fF/XHgg3r/2GON6Pvy/Y/HKVYfjrJF9SsJPIltB2IMxtsZ9vRaA79SAiMYAqAawWPP5FUQ0jYim1dSEy3quIsrMU9bwdBkjSPrr+ay53hgxd/uP5+jNZRce3D/w/AFdijs4V7cuFsVU9NrVR2Qc27dPcYbh6J6r3Q2F2bqLyDtpiMM0Ktdw4vAeWHrXqejatoXn+RaHi1VbdoVvJ0Rfg0qKdf321L0zNLUp1x+P44aph+g+HVthQJc2xn3p17l1Ue8mEZZAQUhEbxPRLMW/M8VyzFkE0YoeIuoF4EkAlzLGkqoyjLGHGGOjGGOjunXLXmmMJAgzsshkakqicGyuMk9G9ISNC791Md11O/ugvqnXyzbWpsuH6JvKXB1EtG+u/n6q7zY8wAS2XzOOPb3xtHAbsJ4/ph/OPqivb5B2GDLMmDFo0vJvKFZZXZEeNrN9ZnI1fHz/yD08WjIQ73phqREoCBljJzDG9lX8exXAOlfAcUG3XlUHEbUHMB7ADYyxT+P8Ar59j8s0qjj25fIt/NPUMT7YNUfZWCxrhGIX9urRTlvOtI5copPzqsHx2wf2idTGMXsV/+ao3ds5mrHp09a6uhJ/POcAX8/MUMheozHcyJnjQPp9dWUCL155KN755dFZh92EmVDzcWhEXNdN0Y9yJVvT6DgAF7uvLwbwqlyAiKoBvAzgCcbYi1m2F4o4MssA/jfJHl3T5oTqykRg+WJl4ux1sQuQKCbiPp1apV6LeVzDVBWl3TjN2aqqogTUF+ttJH+VQucKyMV18tMIAWDUwM4Y3K1t6r6J6gDEBa6JZsn7lMuEAeVKtoLwLgAnEtFCACe470FEo4joX26ZcwEcBeASIpru/huRZbs5Q70eqL/xxHWs9B6dze9GrdleF/vaZpTMPlcdOwQ/PGoPANGFU940Qs1xlVUhapajIst0pySUR28OUIU8xY02v2i2GmGEPujGF11dfTu18uTq/eeFB+LSwwd6ypT7/qNZCULG2EbG2PGMsaGuCXWTe3waY+z77uunGGNVjLERwr/pMfTdoH/hz5E99VTOMuJ78QZKPZDN9J4qhmehqiKBb+zjLOjrYreCiPI9KgKehKHd26Ze87yLOuGmaj+qPGsGchDDeztLAlGeN50XIwAcmsddJWQytmPT3FPrtu0GEM1RRtWOH4FFNZ9P/vVxuOWMfVLvT96vF24+fR9PmaqgB6DEKelvHyVxtDqzjP6IVyiGbq6oiLv7kQd/98Tos9Tw5wW11b192kP05tP3QevqCrSuVqebUtUV5V4s2ttJ+i7Z9FPnxQgA//n+wZHWif0sCScO75HanSTM89rQpP79ltRkl5FI7uqwnvrvSxkvNJ+H5OcnDMWePdoGFyxhSloQRhmIlcHzPneYV2vhpovmSTFohEB6MuLJeB9qjTB8m2HMsGcf1Bdzbh2LSs1ajfJoxFlBFJNq3By1p7/DDhf8cfc1kaBIsZTbdumzBF117JDUoO83+ZG/yry125TlsjaNKrzU9WWdz3SXJKq37M9P2LP5hnzFRGkLwggPZsa9RP5rfqSw3xVrIHQwxdHvEf064tdjh+FPPnGIcRNLELaLalDJtzjr0T6+3J+VAfcz/7qFF9kOfmZK8av4/eSTF23A3FvHpt7naneJ1NhisKry9QZH+6zZrg7cJwAfXXccplx/fHwdLBNKWxBGOCdjP0L4p2wSS++qdwKKm+vkKvZ+RxwZiQg/OmYwuggB62EckIJKfvvAvhlB70Fzl1BxjEqvUfOL8e9LRzttEkV2lhk1sHO0Ew0oFoHHqQsRyO9Z0/f5Tf94zgGeffZaRtjs2ag/fGxxL6rJM7hYY44lIvTp2MrGC0agtAVhFJd19XYSPuXTr79atRUA8NGijeEbzjMDA7K2dI5ht+nNtfXazwrpWXv5EYMy3eMNtR4R3f3Vq2OrjGNh7kWufTSX+ZTf4H30nt08IUZh6dYuWLOdvmKL7+df3HhiqDaX3nWqJ7EDAJywt3ot099aFNzWllrHjLt8U63xOTqO3at7cCGLkpIWhFHmruqk2/r3zTFUAgB+fMyQjGOiKTmOb/XEJ8u0n4VNdhBmgAhyfFF9PDpAgwoyD4qIruocMT4yDGGv02c3HI9Pf3N8XtW21DOgaLN/59b4yfGZ95opN5/uZK3p5LOZb9BShDipE++NMNdW14ZfHSbm9tmrnbVHPok2GU9ED2bO5F8fizu/tV/guSL3nL0/xv80M7VfOVLSgjBaijXZbdr/1myuZlDVl4p7F/WmgAr9cpFmQ5Ql2r17+Xsn/vIbewEwW3tTDYCDu+m98tpIG6pm43PSvV1L9OzQMlJWpagErRFm833aGmwEG8a72G+Z4+kfHIyrjh1sXBcA7O+TGi/usaG3m3R7iEIQ9u3UOpXQw5RzR/XDPr2bb2q/OCltQRjhnDdmr8045us1GqGNQrBvn/a+gueHR++BpCu4VA9aFPwGMQLh7IP6Gu94HS6OMPyvEnQO39JLjLf6yXFqTUe7cauCB797ECb+4ihNn6ILkYsOHhDtRAWipeDYvbrhssMHeRI6k6JcXJhoVWG2XfITmocN7oprTxqm/VzFMXvqzZHR7kPzMvzZufjQ+H7rcqW0BWFMz6Wx12gRkyDyrHtkOsemj8TlcfjT49Xb9eSaOPajk0lpPcI9dc6ofnjisjFZ1XvMXt3Qt5N3vVYUKFHv4cOGdM2mW1p+evxQdGpT7UnonEozpjknm+cwqG4gZFC653X2z67f2nIUT2R/6xMPzyJP23EkGS93SlwQ+j+BXRQOIaokxxk7UgA4c0Rv53UzuQczBJ/U8QQ5noaXHT4IfzzngFi+V+sYPe3CTDhMBkZFlEwk1Cn5nGDwP58bbPpVDcZM+CyfJk4dvAePXjIKI/t3yvicfwOdZ2w238DkdwkjcOKeuKrWDg8a0En7WSAm964UalEEoabNntIWhAGfqx6K/p1bS2UyyxGlb8Jm4yyjCAsRSZATvHzT6cPRq0M0x46AJj3kcoAP+kVUA0fQ+KP7XPX7ExEOH9IV3zqwr+KMEO3m4dZqYbCuxK+XToiotOW4MKmba0R/Oe8AXHeyv2kzQfH6EamE8AB3DMnVJJlP9L59YF8cN6w7fnxMuHVNSyalLQgD7njVjZrhLKM9l7R1NAf8PGGB3G8KazqB+N9PjsCtZ+4T+xphx9ZeawCBsPSuUwPPk60MYX7/t35xFJ683GtKjSK0dQSFxKgw6T/X9OSi3JOTX2+tRmj4JZ66/ODUazkptB9cGH1zZF9ceXRaKAzv1T7DwhN3cmmVGT6dASY3gwNXNNu2rMSjl4xGdxs3mDWlLQgjzP1SZgfKPOYpJ/0tdjJNo/J774H7vnMg/vujQ7NrM4aLs2+fDvjeoQOVn336m+PRUeFWH9RudSVlxLbJ5+wtbaSrW6tSNaVrfmiPdjhyqHdgVmahERoR25OtFTJxxH7KfE9wxJD7+spVh+Pub6dd9rP1Oj5iaFecvG9PAMCoAZ09bbau1pvZ5TUyvuTx+GVj8Nil0sSD4n1mVRoh96fKtUZoTaLxUdKCMEgOqu7TCsVsLtug2WIgyAQmL2e0bVGJgwbkLjsJv263nLEP2hmkr1Jd554d1DNh3Uz8ptOG465v7Ych3YMTOf/9gpHe9t2/JoNPmHsi0DIqtBe05BRlTSpIM2/TojJtGpU+G9ClDc4b3T/1XqX5dTcIiPejQ6sqXHfyMDz9g0O0ZeSvzb152yk8kj33RgzPrupeS+RYI/QJ27REpKQFYfAaYeYxPrtMaXxEqXJ8pklITyuLzWt0vz4dsOD2k1PvuRYRtHaiGxD7SoHgott8EL4TCPfvtw7si69+d5Jxnbp6HvzuQfjLeQdgRL+O+JngrSo6RHVoVYXzx/SHCvlnbCVpIOlYOemuivDzf3zdcdp2lW2kjvtzoMKRJVucNTXXNBrwXeX+3XbmPvjRMYMjJr9Pv77y6MEYJGnw4i4NssC55PBBWHrXqcq0aGLR+sZkhJ55UXlsZmMa9TuDTzTKfe/AXFDagjBQIzSbzfF7Xbzpi3WniYoEpQJrB3ZpndIS2ku5NU3NKpN/fZxni5buMSZzDoN2M1L3dxo1oBO+ObIvXrnqcM+g+cKVafNuk8+XluuvTBCW/P6U1Pse7Vri/NH98MjFowP7FaRl9RZSsPnvNmBu3p/w0yNx7Ul7GZUNQwUR+rnhHTyWUod8ec84oA8qKxIpCTmke1v84ez9Y+mXeA3DaMK6633BmH7K47efta9v0LyK1HgR8+DABTufBMQV72sBcpNSvUgIGkTWuptqiqjs+/zhEdcDUi+LTBLyfn183XFo17ISp/99MoBoacdySdyatPhLi1WLewb6aUxyd5KMeSY+iQThrm9nDuLK9eNsv5q4Rii81lXbu0PL1Oa4YQn2liXccsY+OHZYdxzQr2PIyp0//Dkc2a8jzhmlFjhhER1z/LZdklEJp8MGd8Gd31IL6IsOGYCLDgkXsM4Fs4nm1rVtC2zYod5NQubfl47GK1+uxuVHDMKhg7tgeK9ov7klk7LWCFWoNEJKfYaMY8UWPsH73btjK7RrWZVyYJB7KV+bxrjzq8WN9AX42pNq7W6dYoLTvV0L3xl00PUxPS9OnDhCf/59yWi8fNXhOetDgggtqypw0j49A8seOdQbxB9lQhDlmV2ywXxzXJVwinvbNN6Gyfcf1NXRtnUaqciALm3wsxOGgoiwT+8ORbcs05wpbUEY4Rw5LIJS/9OtB0TqWij+JjluhMF0fefpKcuN6gsl+HN4bc44wElowAffllXpW1ncr033vU87oJdRO4fs4e8wtDWENpItqnXeY4d1z+m2O2Hub513b5S6goqOHtgZxyqSX0SpN26BUlvfCMA0sYMVZsVAVqZRIuoM4DkAAwEsBXAuY2yzpmx7AHMAvMIYuzqbdk0Jswcch8u6dBBx+mYVZ3pydodcMnpgdCeItMeft6fyldlRl78BPQq6MeXus/fHz0/YE+0C1q9kvjmyL37x3AyhfvX1eeryg33XFlX3mFjV+J8eYRS0rmqbKO0g8eKVh3r2GLzzW/tpvWbjJIyydOLwHlh616nY56Y3sLO+KWeZTz649lj07dQKvw6hCXJIfHhdslUI7z1vBIb3bo9v/OUDAEBDk/OFTTRNOWGAVfIKQ7Ya4XUA3mGMDQXwjvtex20APsiyvVB8ujj8voCqWRw/VCF4lKacZfJw44aZNcou7LoHTC7nN3st5llri8oKDJRjAiP0V3dGZUUCLSr1MWwVAYlN9+ndwShcQ0T8zUShKHLBmP552X9u/74dI58rx16a/C4mzkH9u7RGIkEpj+gfHr1HiD4Bd35zPxy9Z1qbzNYL86yRfbBnj/RvnPbuDD7XxgQWB9kKwjMBPO6+fhzAWapCRHQQgB4A3syyvVA8+MES5XG/uLXU2p9wE6uESVojLLyQ6CmYxkyX+kwCw9NlhX0KC2QZzVjD8ynbxiAuMaN+qQFTLU5paiuCe8IUXU9bVCbw1i+OwlF7RjA/xjA7NMt44/xtUWE+jBGA4b3b43EhWXrc3p181xUj02jzuVVKmmwFYQ/G2Br39Vo4ws4DESUA/AnAr4IqI6IriGgaEU2rqanJsmvRyDR5pl+pskjkRSMMaOPT649PvQ7aA1BHvmOTctncqAimZHnw7trWLExEdbXj/G5pTSF8pZN/fWzkdufffjKG9ginyerIldnv1P2ddd7T3fViE1RCOu57f4+urlNWGI1QcSf9+dwD8NfzR8TYM4uOwKkzEb0NQOUydoP4hjHGiEg1LvwYwATG2MqgmSJj7CEADwHAqFGjcmYs8Ks47SyT7iu/SUWbfz7XCMO0Yez9KRXLxVYufr932DW9MFoGL9kty6wmJijXCLOsUxwUdaZRE+TtnUzJlVt+tvLm6e8f7PEQHdK9rVF+WJHGZGYQ/fsL4p108+8ZRiNUmUZ50vafPTs9pp5ZdAQKQsbYCbrPiGgdEfVijK0hol4A1iuKHQrgSCL6MYC2AKqJaAdjzG89Maf4JQH2u3cTHiFJwSe4HDesO96dp7o08dOkeNCB4G6KmTr88Kvme4cOwBOfLEu9r1QI1716tMPZB/XFd7PcTNRvTUX8qDmvvRAo9QVyNeEaPagz3psfsyCItbY0hw3pmvU+i6rHY3C3eAPT+TgRZT9CS2HI1jQ6DsDF7uuLAbwqF2CMXcgY688YGwjHPPpEIYUgAOysb9J+JjvBECE1sqr8IvKyJ2aINmSNkAv9TK9I53gfN0PHiP4do/fPRdaODt2jS0aZRILwg6P2UKa/8iMXu5/HgXpLp+xuClXS7VzFjN3/nQNTr//3kyPctnLSVFGg+m6hEwUYtmFyHdOmUfecWHtiMSXbzDJ3AXieiC4HsAzAuQBARKMAXMkY+36W9ecdlcmT36TeGR6fqZvfuvd/50C8On0V3pyzLqs++hF2jfDwIV1w3LAeOG5YsAeiSrCJyEIhTnOrPJP3G2TEj3I9qCszyxic9+TlY7Bi0y7lZ6I59IC+HTFz5dbUlkdxI+7qENdcQ66mGDYX5qh+m+qKeG8SXpt1lmk+ZKURMsY2MsaOZ4wNZYydwBjb5B6fphKCjLHH8hVDGJXUTSyESui0KueYed0VCVLu8K1CTHZtImy/dWAfAE7SbRPEQW/svj1T+Un9uHbsXr6aiclwF1Wzk2P5BvjsvcfDHUzNvYXgyKHd8J2D1QnARW48bTj+95MjMKBLm8CyYTl3VN+8ZCcJ4/BT6ZpdctUv1eSsRUjrRCA8O5XB6GrDJ4qDks4sE4WwTjAm5cQyR+1ptsbxn++nNyk1GRP4/m3y1jPy8/X+tcd46zb4BsUQDtBRShru59DRoXUVnrviEPzjwgONBpg3fn5k5H6p9gCMcwyvrkx4Nkn++wUjcdtZ+xqff+Npw7Wf3XP2AVn1TcdVxzrbIMkhKCbX5Xdn7oNLDhuI4w0sFFFQeQNfcZR5HKIJYTTCVAIPn2nkC1ceit+euncMPbPoKOmk21HwOsRoYgcVx0wIU7Zjq2q0qa7wXc8U0T1Ij14yGk99ugy93FjDAV3aYECXNnjmM7OUaiJhxvcHv3tQ6Pr9kGfyQRbgg10z7s465/qprv0DFx2IPh1bY1jP6F6So4VsL3885wA89MHiGNYI9V8uTKgAAFx+xCCMn7kaXyzfElh2oJv38mpXkEXlR8cMxo+OGRxcUEHXti1wyxn7ZNV+WNpGiDuND69GqLp3Rg/s7LnPLPFjBaEGcdzlN6k660x4t/6whcMNq97Se/dqjzu+uZ+mbFhBbl7YJElzNkRJnyczdl+zfKOmnH1QX5x9UN/Y6suFefA7B/fX5pVt17IqdDiCCcVu9QubAi8I/rPxVGsmZQtvcylvrGlUQh1H6KASjiKtDNYaTM2MxbiIHtSlfK5z2DUVc/ilOmavbvi9z6Qo1xThLQ0g/gnHp0s2AQCWGuRC5S23buGMHfnIH2vJxGqEEn55ApWbsIbUqEw96Lyej8UxhAR3I/i76QTYHd/cN1SGj2INpyhmfnJc2uR50AAzpy1LeF6bsRoAsKsheFmD3/J792qPv54/Asfv3QPjZ67xP8kSO1YQStw/aREAYN02ZysfAvmmiMqlI0mxCEAOgVLXYL8+HdCiMoFpy9KbjfgFJj/03YNwxZOfaz+/8OBwAfa5EoOVCSr43oy5bv2j645D59aZTj45o0gnLc9dcQimr9hS0D6I48eZI/qkXst7O1pyixWEEptr9dsRBTnL6OSWyrQaBBGlwzbMTzMmytgkfr+zRvbBxNlrPZ9fMKY/bh8/V3luf59whyiY9p+Hhfhtyivy0XXHYeOO+qjdipX4f3enRp5EIV9kkyYulxy8R5eUU1WhUF2TWb87KfZ1S4s/VhAG4BF0KtMogF+PHYa735inrSOZikMM0a6mDzryPelmjGVcDRPTZlzB1abOMp3bVOOJy8YYZw/p0b5l6I1u7zl7f/SKcW2H744SV53FopAVQxhOsdHR1czbVKeH4sJ6sZYnZXnF+3Vupc3q4Ydua6YfHe24i+9z0xvK88TNcU0HJWf/0PADRy5m3UGTAaM6Yh4EwzgVRNlKKAznjuoXa32jBnbG3y4YiRP3ztjMJSsKpZHt5e5iMVKTxu+pyw/GvLXb8tij3EJkPvn4xQlDMbR7W5wRMizGEi9lqX9fdYx5nFSQZma22aiishDkdh0yvrL50lzfvuYoz0aopcgZB/RGq+qYM574cP7oeIW5yMF7dMHkXx+b2k1B5oihXfH9I+MNai8kYZ7WFlUVuOyIQTnZ/cViTllqhFH3H0sl5BZ3KxSq0t3M4lqfadNhhV9Y+RLFRCn3KUwNcWojYXd8t/iz8I6Tc75TQtQtoUqdYls3LVfKUiP0Y4xBBgfdzXvicMeUde1Je2nOC2caTb8Brj9lmNmJOSQOjdCSf4JuuaqKhNVICoS96sWBFYQS8mAurtMpTaPCwSo3y64q/2S2fbriqGgpq3KJfDlCmYkteYN7IMa9E7tFTThLif1NioGyNI1yVDFjFYqZsepeZYgW2hBm1/QwdXdxhW+vkB6P+ZqT2se9cPz1/BF46tNlOKCv2c4kluwIsw5un4vioKw1wkp3H7JT90/nnFQJwjVbHQ/TbbsbU8fS+UeD20l7jTru+Z/85rjUZ7rcjmEniifv2xP3fWekcbLjMA+rWNZ3H0D7VBclvTq0wrUnDbPaRxFif5LioKwFIU8zdZ7g/t5ToVFN+MoJHJ/hZqFYtrEWdY3OTrGhkm67RXt1CA5oFs2MfkLr7xeMTPXjtP17o7KicD+pyZWwqdEspYpqj8xD9uiMP56j3+7KxlYWByUtCA/UxC1xenVohaV3neqJM7tS0qh0cm6cm09w2cbarPpogp+sDbstT1ztq+SZ1Tgs5cy4q4/ApF8d4zn27BWH+u5IYh+Z4qCkBWEUBnZpg5P26aH1/JTZtjudkk2fYo2vJ4bXHnNN2Hbsc2uxqOnQqgqDurYpdDcsEShPQegzmlckCA9+dxRG+qTkEjP3NzYlU69/evxQHDm0q2fNEYBv0m6/LubSiJiLuv2+np35WgrJGQf0Vu5OX2jsc1EcZCUIiagzEb1FRAvdv8q9XYioPxG9SURziWgOEQ3Mpt2ovH/tMXjnl0ebFfa5QUUHmVemr0697t2xFZ68/GC0b1kVsYdC82LYhvv3p8dlt3O4sp2I56kC8u1DbSlW/nbBSEz77Ql5aWv0QPMtruwaYXGQrUZ4HYB3GGNDAbzjvlfxBIA/MMb2BjAGwPos2zXr3Ml7o0/HVujRvgXat6zEgC5tfLcKEkllkQmIHTQhLv+Qa76xV052EA8D/+qMZWqVdo3QYlF7nluKm2zjCM8EcIz7+nEA7wH4tViAiIYDqGSMvQUAjLEdWbZpzJhBnfHRdcdlfmAgmPiYvnrL7ozPwqajihJzSCHLh+Wwwc72M1GT/YaVeVxLHm2Qucdiac4M69k+tUt9EHbuWBxkKwh7MMb4dsprAajS5e8JYAsRvQRgEIC3AVzHGMvYvpmIrgBwBQD0798/y65lR2OTI7yaFJu0bt2l37PQl5AJrp/74aF4dfqqnGzLMrhb26y1y8++NnvYAaB7+5Z48xdHKV3MLZZSYuy+PfHYx0vN0jXmoT+WYAJHWCJ6G0BPxUc3iG8YY4yIVLpWJYAjAYwEsBzAcwAuAfCIXJAx9hCAhwBg1KhRufMVMbj75q7Rbwszx+ezuCAiDO/dHsN7t895W6YkGcvKzBvHjhFVFXbosBQ3qWfE4Fa1ywnFQaAgZIxpV5iJaB0R9WKMrSGiXlCv/a0EMJ0xtsQ95xUAh0AhCPPFyfv2xKvTV+HnJwzVltlUG98u5eJ+hJwHLjoQCmWzKOEPqyfDTAHmstNvOtGuv1iKnjBLIfZuLg6ydZYZB+Bi9/XFAF5VlJkKoCMR8aj14wDMybLdrGjXsgr/+f4hedsaJjVBFO76sfv2win79VKWNyVfXnDF8rB2bF2NdjF45FosOcUnXEreJNoqhMVBtoLwLgAnEtFCACe470FEo4joXwDgrgX+CsA7RPQVnHH14SzbzTmi9qNKu1ZI7v/OgXjxykPzFhele1j30AQPX3PinjnsjcXSPFBZTZ64bIxnbd6aRouDrLwwGGMbARyvOD4NwPeF928B2D+btgrJXj3bYe223bjq2MG4f9JiHNi/I75YvsW8gphNoHLAfr5gDNinj7NmOaRHWyTd2cKJw3ukvFALHd5hsRSaZrLiYREo622Y/FAGjKdiC3MfPlFMpGIHwXDWiD7Yr08HDOneDjexWQCA3566NwZ0samlLBYgWiYpS2EpzxRrJghy8LuHDACAUB6cpyrW/5qrGYRPABhzvsOQ7o73Z9LNLmc3fLVY0vTq6CyljBmkD5/oHmJfUkvusRqhBlEfPGF4Dyy961RMW2oeN9ehdek4daQ1Qi/cNJqwnpwWS4rB3dri3V8ejYE+VpJXrjocM1duzWOvLH5YQajBb9888bOT9lHlEJDLx9KlgrFnj3aYuXJrRmA/TzZg5aDF4mWPgFSOvTu2Qu+OwfuSWvKDFYQaTDbPBYADNLtUtBOEhip8ojlx+1n74lsj+2BId+/DzeMgw6acs1gslmLCrhFq4ALu7m/vlzpmukEtAHRqUw0AuOSwgenzDds20TLzScuqChw2pGvG8R+5mxi3b1U6ZmCLxVJ+WI1Qw0EDOuGDa49F/4DcmH4mVABoUZkILCPSpU01HvzuKOPyheTyIwbh8iMGFbobFovFkhVWEPoQJAQBaNOkqWRfkAXxNycPwwnDi0sbtFgsllLHCsIIiDLOZMHbVB/84dGDI/XHYrFYLNGxa4ShSKt0z//wUPRs3xLfGtlHXVKp/VmnEovFYik2rEYYirRuN2ZQZ3x6fUZ2OfVZzTx8wmKxWEoZKwhdHv7eKAzSJJGWCavXNffwCYvFYillrCB0OTGEk0pUBc/KQYvFYik+rCAMwT69O+DgQZ1x42nDC90Vi8ViscSEFYQhaFlVged+eGjo87q5+wa2qKyIu0tFT2WC0KiLMbFYLJYiwArCPPCncw7AxNlrQ+1eUSp8fuOJaGxKFrobFovFosUKwhzD4OxEce7ofoXuSkHoYNOvWSz4v7F7oV+n4AQdlsJgBWGOsI4xFouF8+NjhhS6CxYfsgqoJ6LORPQWES10/3bSlLuHiGYT0Vwi+hs11x1qQxBmVeyes/fHCM0uFhaLxWLJLdlmlrkOwDuMsaEA3nHfeyCiwwAcDmB/APsCGA3g6CzbbTaYSPxzR/XDK1cdnvO+WCwWiyWTbAXhmQAed18/DuAsRRkGoCWAagAtAFQBWJdlu80G6y9psVgsxU22grAHY2yN+3otgIyodMbYJwAmAVjj/pvIGJurqoyIriCiaUQ0raamJsuuFZaSt/1aLBZLiRDoLENEbwPoqfjoBvENY4wRUYYCRERDAOwNoK976C0iOpIx9qFcljH2EICHAGDUqFFWmbJYLBZLzgkUhIyxE3SfEdE6IurFGFtDRL0ArFcU+yaATxljO9xzXgdwKIAMQWixWCwWS77J1jQ6DsDF7uuLAbyqKLMcwNFEVElEVXAcZZSm0VLi3FH9cOgeXfB9u4O7xWKxFDXZCsK7AJxIRAsBnOC+BxGNIqJ/uWVeBLAYwFcAZgCYwRh7Lct2i55ObarxzBWHoHv7loXuisVisVh8yCqgnjG2EUDGpnyMsWkAvu++bgLww2zasVgsFoslV9gd6i0Wi8VS1lhBaLFYLJayxgpCi8VisZQ1VhBaLBaLpayxgtBisVgsZY0VhBaLxWIpa6wgtFgsFktZYwWhxWKxWMoaYqw4c1sTUQ2AZTFU1RXAhhjqKXXsdTLHXisz7HUyw14nM/h1GsAY6xZnxUUrCOOCiKYxxkYVuh/Fjr1O5thrZYa9TmbY62RGLq+TNY1aLBaLpayxgtBisVgsZU05CMKHCt2BZoK9TubYa2WGvU5m2OtkRs6uU8mvEVosFovF4kc5aIQWi8VisWixgtBisVgsZU1JC0IiGktE84loERFdV+j+FAIiWkpEXxHRdCKa5h7rTERvEdFC928n9zgR0d/c6zWTiA4U6rnYLb+QiC4u1PeJCyJ6lIjWE9Es4Vhs14WIDnKv+yL3XMrvN4wHzXW6hYhWuffUdCI6RfjsN+53nk9EJwnHlc8iEQ0ioinu8eeIqDp/3y4+iKgfEU0iojlENJuIfuYet/eUgM91Kuw9xRgryX8AKgAsBrAHgGoAMwAML3S/CnAdlgLoKh27B8B17uvrANztvj4FwOsACMAhAKa4xzsDWOL+7eS+7lTo75bldTkKwIEAZuXiugD4zC1L7rknF/o7x3idbgHwK0XZ4e5z1gLAIPf5q/B7FgE8D+B89/UDAH5U6O8c8Tr1AnCg+7odgAXu9bD3lNl1Kug9Vcoa4RgAixhjSxhj9QCeBXBmgftULJwJ4HH39eMAzhKOP8EcPgXQkYh6ATgJwFuMsU2Msc0A3gIwNs99jhXG2AcANkmHY7ku7mftGWOfMudpfEKoq1mhuU46zgTwLGOsjjH2NYBFcJ5D5bPoajTHAXjRPV+85s0KxtgaxtgX7uvtAOYC6AN7T3nwuU468nJPlbIg7ANghfB+JfwveKnCALxJRJ8T0RXusR6MsTXu67UAerivddesXK5lXNelj/taPl5KXO2a9B7l5j6Ev05dAGxhjDVKx5s1RDQQwEgAU2DvKS3SdQIKeE+VsiC0OBzBGDsQwMkAriKio8QP3dmljaGRsNfFl38CGAxgBIA1AP5U0N4UEUTUFsB/AfycMbZN/MzeU2kU16mg91QpC8JVAPoJ7/u6x8oKxtgq9+96AC/DMSmsc00tcP+ud4vrrlm5XMu4rssq97V8vCRgjK1jjDUxxpIAHoZzTwHhr9NGOCbBSul4s4SIquAM7v9hjL3kHrb3lITqOhX6niplQTgVwFDXg6gawPkAxhW4T3mFiNoQUTv+GsA3AMyCcx24N9rFAF51X48D8D3Xo+0QAFtds85EAN8gok6uyeIb7rFSI5br4n62jYgOcdcsvifU1ezhA7vLN+HcU4Bznc4nohZENAjAUDgOHspn0dWQJgE42z1fvObNCvd3fgTAXMbYn4WP7D0loLtOBb+nCu1FlMt/cDyzFsDxLrqh0P0pwPffA4431QwAs/k1gGNHfwfAQgBvA+jsHicA97vX6ysAo4S6LoOzUL0IwKWF/m4xXJtn4JhgGuCsI1we53UBMMp9mBcDuA9uFqfm9k9znZ50r8NMd6DqJZS/wf3O8yF4NeqeRfce/cy9fi8AaFHo7xzxOh0Bx+w5E8B0998p9p4yvk4FvadsijWLxWKxlDWlbBq1WCwWiyUQKwgtFovFUtZYQWixWCyWssYKQovFYrGUNVYQWiwWi6WssYLQYrFYLGWNFYQWi8ViKWusILRYLBZLWWMFocVisVjKGisILRaLxVLWWEFosVgslrLGCkKLxWKxlDVWEFosFoulrLGC0GKxWCxljRWEFovFYilrrCC0WCwWS1ljBaHFYrFYyhorCC0Wi8VS1lhBaLHECBExIhpS6H5YLBZzrCC0lCRE9B0imkZEO4hoDRG9TkRHFLpfpQQRXUJEkwPKnEtEHxNRLRG9l6euWSyhsILQUnIQ0TUA7gXwewA9APQH8A8AZxawW+XKJji/xV0F7ofFosUKQktJQUQdANwK4CrG2EuMsZ2MsQbG2GuMsWvdMi2I6F4iWu3+u5eIWrifZWg5ormTiLoQ0WtEtI2IphLR7Qqt6AQiWkhEW4jofiIiTV/HuFrrNiJaR0R/Fj47xNWkthDRDCI6RvhsEBF9QETbiehtt42n3M8Guv29lIhWENFmIrqSiEYT0Uy3vvukflxGRHPdshOJaID03a+Uvw8R7Q3gAQCHulr3FtV3ZIy9zRh7HsBqn5/NYikoVhBaSo1DAbQE8LJPmRsAHAJgBIADAIwB8FvD+u8HsBNATwAXu/9kTgMwGsD+AM4FcJKmrr8C+CtjrD2AwQCeBwAi6gNgPIDbAXQG8CsA/yWibu55TwP4DEAXALcA+K6i7oMBDAVwHhyN7AYAJwDYB8C5RHS029aZAK4H8C0A3QB8COCZoO/DGJsL4EoAnzDG2jLGOmq+o8VS9FhBaCk1ugDYwBhr9ClzIYBbGWPrGWM1AH4HtTDxQEQVAL4N4GbGWC1jbA6AxxVF72KMbWGMLQcwCY7AVdEAYAgRdWWM7WCMfeoevwjABMbYBMZYkjH2FoBpAE4hov5whNJNjLF6xthkAOMUdd/GGNvNGHsTjuB+xv2+q+AIu5FuuSsB3MkYm+tes98DGCFqhSG+j8XSLLGC0FJqbATQlYgqfcr0BrBMeL/MPRZENwCVAFYIx1Yoyq0VXtcCaKup73IAewKY55pZT3OPDwBwjmuK3OKaHY8A0Mvt5ybGWG1AH9YJr3cp3vM+DQDwV6GdTQAIQJ8I38diaZZYQWgpNT4BUAfgLJ8yq+EIAE5/pNewdgJozT8gop5CuRoAjQD6Csf6Re0oY2whY+wCAN0B3A3gRSJqA0ewPckY6yj8a8MYuwvAGgCdiai1UFXkPrht/VBqqxVj7GOTr5BFuxZL0WAFoaWkYIxtBXATgPuJ6Cwiak1EVUR0MhHd4xZ7BsBviagbEXV1yz/lfjYDwD5ENIKIWsJZg+N1NwF4CcAtbr3DAHwval+J6CIi6sYYSwLY4h5Oun05nYhOIqIKImpJRMcQUV/G2DI4ZtJbiKiaiA4FcHrUPsBxePkNEe3j9qkDEZ1jeO46AH2JqFpXgPcfjiadcL9LVRb9tVhixwpCS8nBGPsTgGvgOMDUwNF6rgbwilvkdjjCZCaArwB84R4DY2wBHK/TtwEsBCB7hF4NoAMcc+GTcIRqXcSujgUwm4h2wHGcOZ8xtosxtgJOqMf1Qv+vRfp5vRCOU9BGt9/PRe0DY+xlONros0S0DcAsACcbnv4ugNkA1hLRBk2Z78Ixxf4TwJHu64ej9NViyRXEmLVuWCxRIaK7AfRkjKm8R/PVh+cAzGOM3VyoPlgszRmrEVosISCiYUS0vxtLNwaOw4tfqEYu+jCaiAYTUYKIxsLRHl/JZx8sllLCz7POYrFk0g6OObQ3nDWyPwF4Nc996AlnrbILgJUAfsQY+zLPfbBYSgZrGrVYLBZLWROLaZSIxhLRfCJaRETXKT7vT0STiOhLN83TKXG0a7FYLBZLtmStEbrZNhYAOBGOmWYqgAvcrBu8zEMAvmSM/ZOIhsPJmjHQr96uXbuygQN9i1gsFoulzPj88883MMa6BZc0J441wjEAFjHGlgAAET0LZ/F+jlCGAWjvvu4AgwS8AwcOxLRp02LonsVisVhKBSJaFlwqHHGYRvvAm+JpJbzpmQAnKPkiIloJYAKAn6gqIqIr3Gz802pqamLomsVisVgs/uQrfOICAI8xxvoCOAXAk0SU0TZj7CHG2CjG2Khu3WLVfC0Wi8ViURKHIFwFb67Dvu4xkcvhbjHDGPsEzjY5XWNo22KxWCyWrIhDEE4FMNTdLLQawPnI3BZmOYDjAcDd0LMlnNRRFovFYrEUlKwFobuH2dUAJgKYC+B5xthsIrqViM5wi/0SwA+IaAacYORLmA1gtFgsFksREEtmGcbYBDhOMOKxm4TXcwAcHkdbFovFYrHEic01arFYLJayxgpCi8VisZQ1ZS8IGWN48pOl2Lgj6pZy2fHajNW4ZdzsgrRtsVgsFisI8cQny3Djq7Nx0O1vp469M3cd1m7dnZf2f/LMl3js46V5actisVgsmZS9IJy1amvGscsfn4Zv/uOjAvTGYrFYLPmm7AWhjjV50ggtFovFUljKXhASed/b8EaLxWIpL8peEMokDeQgYwz1jcncd8ZisVgsOccKQomJs9cGlnns46XY87evY/02az61WCyW5k7ZC0LZErpsY23gOa9Od7ZTXLF5Vy66ZLFYLJY8YgWh9D5psEaYcNcVo64nvvTFSny8aEOkcy0Wi8USL2UvCGXBF6ezzJH3vIvT/z454/g1z8/Ad/41JbZ2LBaLxRKdWJJuN2skuWfiLMORPU5lVmzahRWw5lOLxWIpZspeI5TlnolCaAMsLBaLpXQoe0GYYRo1EHPpUwJUQovFYrEUPWUvCGUNMIxpdFd9U7ydsVgsFkvesYJQfm9gG52+YgsA4MZXZ8XfIYvFYrHklbIXhLJp1CR8gpOvHSosFovFkjvKXhDKKqFNNWqxWCzlRdkLwkxnmcJgk31bLBZLYSh7QZjpLBO/QGpsSmLmyi2YvFCfTSaMk47FYrFY4iMWQUhEY4loPhEtIqLrNGXOJaI5RDSbiJ6Oo904kMMlhvdqDwBo1yI410BQQD2nMclwxn0f4aJH9NlkrEZosVgshSHrzDJEVAHgfgAnAlgJYCoRjWOMzRHKDAXwGwCHM8Y2E1H3bNuNC1n+kCvdBndvG3hunFGEViO0WCyWwhCHRjgGwCLG2BLGWD2AZwGcKZX5AYD7GWObAYAxtj6GdmNBFkBcMzPV9kwwUfZyYZK1WCwWSzBxCMI+AFYI71e6x0T2BLAnEX1ERJ8S0VhVRUR0BRFNI6JpNTU1MXTNBK8AasqBaiYKubpGG4RvsVgsxUS+nGUqAQwFcAyACwA8TEQd5UKMsYcYY6MYY6O6deuWl47pMsskDFRCMlQbxSauf0kdhD93zTajuiwWi8USL3EIwlUA+gnv+7rHRFYCGMcYa2CMfQ1gARzBWHB0mWXidF4R65ry9UZlGbvJr8VisRSGOAThVABDiWgQEVUDOB/AOKnMK3C0QRBRVzim0iUxtJ01ccQR1jcmffOOitZWuxRosVgsxUXWgpAx1gjgagATAcwF8DxjbDYR3UpEZ7jFJgLYSERzAEwCcC1jTK0a5ZmMJUHm+eML1/TOeeBj7H3TG/o2DNYdE3YjC4vFYikIsWzMyxibAGCCdOwm4TUDcI37r6iQhZTJNkypc92iM1Zu9S0n1mjjBS0Wi6W4KPvMMvv26eB5z4WbiYJmKjRF4adTDnu0b2lUl8VisVjipewFYbuWXqWYhTKNmrXh0Qilmitdm2irqgqzyiwWi8USKyUtCDfuqMPUpZt8y8imyjCmUdOSYhOyRsgjMGxAvcVisRSGkhaE5z74Cc554BPfMrL8CSOPTNf7TMpZOWixWCyFoaQF4eKanYFldCnWTCBpJVGXlcavRl6H1QgtFoulMJS0IDRBNoWGEUfyudNXbFGX84sjpPDtWiyWwtKUZGhsSha6G5aYKHtBmKkRev/60VJycNFlXGOSu4znnFS7VhRaLM2F4//0Hva6UR87bGlelIUg9BUysrOMgUA656C+AIBLDx/kV1UKk8wyVg5aLM2HpRtrc5Kg31IYykIQ+t2v8kcm93bNjjoAQFWCPAH5uuwwonDNsIxa06jFYrEUlLIQhH5aHndS2aNbG6esQX3vza9JlRWdXHS7UfhpeylnGTu7tFgsloJQHoLQ7zMm/w0RR8i8dRtloxG1Q8aEOELjZi0Wi8USIyUtCH/1jT0B+IcmJDUC0EQuJRnz1K07R9d+kgG17q4VYQL5LRaLxRIfJS0It9c1AgBWb9mtLcMFEBdDoQLqpfKVmkXC+Wu3e87heASklYMWi8VSEEpaEL7ypbM/8D/fW6QtI5tGQwW2M+YRhOKu9qKG+c7c9RntyW1ZOWixWCyFoaQFYWXC+XqNPgtwqR3pJc3Qj+OGdU+VFYWZ93W6fEK4ylt3NQhtQyhvRaHFYrEUgpIWhBWJYI9M/lHSTRJhIo+48EwyKVReK9iCvUmtHLRYLJbCUBaCsMk8nt7IaUVcT9RrhOnXg7q2VtZjTaMWi8VSeEpaEHLfFX+N0DWJpv4G1yvuWciEdIMewSbUs2ePdup6FP2wWCwWS34paUGYXiMMTo6b1vKCk42KGqGoQepSqelTr6U/mDBzTWAfLRaLxRI/JS0IU+nLfE2j6fW+oLLyOYwxKRA+/WbJhh2p1zptT9QmX/h8ZXDDOWB3QxNq6xtDn7OjLtw5FjO27moI/XtYLJbsKGlBaIIcPxhuGya9p6gYMqGzzBaDOfSYP7yH4TdNDHXOUfdMwr43hzunlDjuj+/h3XnrclL3Ab97E0fcPSkndVssFjWxCEIiGktE84loERFd51Pu20TEiGhUHO0a9AuAYYo16b0fYjo2j6doUhSK6teeeoKbyjlrt+mTDehYv70uBz1pHnywoAZLNuzEZY9Ny1kbm3bW56xui8WSSdaCkIgqANwP4GQAwwFcQETDFeXaAfgZgCnZtmncN/dvGOFmoqWlYg6ZN3A+mzVCS/Ng9ZZdhe6CxWKJmTg0wjEAFjHGljDG6gE8C+BMRbnbANwNILwKkkPSzi5SqjWDc5NMFn7qcAhdIm8rCJsfCd3uyxaLpdkShyDsA2CF8H6leywFER0IoB9jbLxfRUR0BRFNI6JpNTU1MXSN4wic61/+Clc9/YWyRFKyjZrs5cvc/zLqgF5TVNVjaT7Y5OgWkTC71ViKl5w7yxBRAsCfAfwyqCxj7CHG2CjG2Khu3brF0Dav1/n79JTlGC+FKcjbL/Hb2k9b4x+t2rzLI+RMgutV9RSKFZtqC9uBZkgxbZe1u6EJv3lpJjbuKN8120Jjd6kvDeIQhKsA9BPe93WPcdoB2BfAe0S0FMAhAMblw2GGm7HM1v3gKeurEbql35yzTtL81AH1+m2YsnuIlm7YiV3uNk5RmLtmW1btFwt/nDgfR9z9bl7aKqaBb9z01XjmsxW45435he5K2dJU6NmsJRbiEIRTAQwlokFEVA3gfADj+IeMsa2Msa6MsYGMsYEAPgVwBmMsd253LibLORnhEwZOM/yj/p1ba51imjRCUSRbQXjMH9/D95+YmlUdpcB9kxZh5eb8OLHk0xTW2JT0bY9PyPh99OLnK62Wn2cMcnVYmgFZC0LGWCOAqwFMBDAXwPOMsdlEdCsRnZFt/XFgMnTJplGRuWu2YdrSTan3U752Xu/Vs52ULzT9uneHVsrj3jbTry85bKBBLzP5aNHGSOdZotG9fcu8tTXkhtdx7Yszjco2JRl+9cIMfPufH+e4VxYR6/BWGlTGUQljbAKACdKxmzRlj4mjTRNMwidkTVCVYe3kv34IAHjlqsMxol/H1PFkUo4jTL/u17mV8riqbQBo1zLcTxGHZkLWAzI0/Hfas0fbvLT34ucr8cdzDggsx++HDXa9MK9Y02hpUNKZZY7fuwcAoHfHVgElxbAJr7lJ5L356z3vmxjTOsWEXSMUiyyp2YG/vr3QV9jFvVRVTGtfRY17mRas2+FfLs+ECfuxZIcncYZ9bkqCkhaEVxy1BwCgbyc/QcidY7xOMqrbW5ZLTUkmeY2qy5qsEYrm0+89+hn+8vYC1PjM7uMwyYiCdnOtzWZiQr7GvbAav1VM8oe40bedQJYGJS0IxeDnoIGFSeXMBY2o1akD6nV1NTSJ56aP84ervlG/Eh/3wFdhzaRG5CuOMMz4SuTNdmTJLaLws6bR0qDEBaHzN5lk2pmb7CWauq+1QfBe4aXTCHXJuEV2N6RDH8Qi7VtWAXB2ItAhC9ettQ14Y1a4rZyY5rVFT74UAKOQH7cIgawAzCMNwqK/ve6lQUkLQkrFEXrNGUs37Mwom5F8W1Efg2TyhNkaoU6L0IVYtKquAADUhdAIf/7cl7jyqS9Cuc/fP2lR6rX1fnOo2V6HPX4zHuu3qzMBmpgspy3dhOemLs+qH2FiX4HCDsjbdzfg589+iS15Mq8/9ekyrNlauJyvjYIlx5pGS4OSFoRcI2TwaoSipiULwKSPaZSQOfjonWK8GuFe7i71hw3ukj4u9EkUlql+G8SQcVa5yaB3htjLbubKrZq+ly+j73gbSQZ88351GILJZTr7gU/w6/9+lVU/vGvM/o2KplFTNu6o8zW9h+HJT5fhlemr8c/3F8dSnx812+vw21dm4dJ/B8fP7qhrxMMfLIndoaWxKX3drCAsDUpaEOo0Qq+48kpCVfiESKZpVKMFSgNZZYXTl9autgdID5HwMiH0W4f8GT8nsjyzz7OHXQ3qjD35WyMMNq2nTKMU/nc/6Pa3cfnj8SRj4G3nIyE5vy4mW1X9ceJ83DFhLt6YvTbWPjRotluzNF9KWhACbnYZxtDQ5D/7lQc41Q3OIHmGgmlNoGK5pqTmuMa5JiUIfSSh3D8KkU5OXV+k00oWnRaWr3FPl8Dd05fUXUORxPOHCzdEOEvRD7d/iTz6W5l83227HctPbRZpCFVYjbD0KH1BCGdQeeC9tNmmrqEpFXicdpaB+97H+45l7jahC6gXy4kmVL0GmX7DJ9Z+z5jcP/5wbtgRbZ1mYsyz5kKwcN322OrSJRsohLOMrkmTWNV8wK8JIfeSMEoLcafFE729rRwsDUpeECaIwMDw5YotqWPfe/QzjLr9bU85vxRr3nLiG/0uE15NMV2vx/U6qS5vkizca6JlWLjeCfC+6/V5GWWXbtiJT5f4p2KbsXKL7+fZsmbrLlz/8leBmnk27KgzXx8NQjfgytc9VzDhMunug53u931v/vrImupujQk4DGnTqPP3kclfexyxcgFvc/nGWvz385XKMlwwx/0rNQozXmsaLQ3KQhAmJYElemMy6a+fswyQ6Q0qO8/I9TrH1QJPaxp1fxU/s4tOiKrMqcf88T2c/9Cn2rp058XJDS/PwtNTlmNyTOY4FRWGtjlmYConcq6xvBalS5qweWc9Bl43Hu/MXWfcXz90kyqRP721AACwZuvuyKP9/2aGC7lRwfvKtejb/jcHf5iYox0xUj+x0+Yvnp+OX74ww2OuTBUlT9HYsF6jpUfJC0KQ86AG3a+yk4zWWUYyeeo1Qtk0milgdQKSa4R+wbqNmgX7tiFzlqbriHSalvrGpFLbkAeO/36+Euc9+EksbZqa5p75bAWG3vA6Vm/xd8G/6/W5OPC2t7C1VoznVF/3eWsds+xDHywx77APYt1n3DcZt4ybnVHm4kMHCL2Kujac/Q/PUoIw66pC8/myzQDU9y/XmD9aHO/kq8GuEZYcJS8IEzzmwUTDYyxDM/SUlcvDK8x02oJOWDZp1hTJx1lm664GTFmy0fMwJhkwdp+eAIBzR/XNOMeEuE08J937AYbd+EbGcXGwZIzhly/MSO3mkS2tqs1u59dmrAagjiflEBEmfOWsm3KnC0B2lhLLu8dEjT+LQVI8dcG6HXjs46UZZTq2rlaWD0OQeXfcjNVYu1UdU8nhE7Z8ZCdKmTulbqvuXz45eXX66lj7IIadWNNoaVDygpBASDKz+bLo1KIrL2t0OoHn0Rw95+vqSh/nFr5Gxej2/cen4ryHPsX23en1MAaGFlXOTxl1R4m4d6L42hUyE75yTG/8u4iDiOr7ZUNVhdntnBJaEdrQ/d5pK1z62G3/mxOhBYf/e3FGyH6ZfxummYzJ7G5owk+f+RLfedjfrM7rSOTTbVRC9fXF32fR+vgcqcS1aKsQlgYlLwgTlGnCFJEFlp/XqCxQGfRu7vKAmfIa1WSuXy5khPELn5iz2tlV3pOeTdOHIDq3SWsUuRrDuDfqBwtqAAC/ey0tHMS1ljgI+upPT1nuGRD9yusuh27yoppI+Hnifr5sExbX6HewmDS/Rt85BWGupLff+nL8u64OyOLCy5mu0WYDfwI3ZqzdZl6BVlXpmN0XpqkdaqKgc3izNF9KXhCS6yxjIh9EDU8lUBql/Qcz1wi9n4mvVds7iWuA785Lb/HExxPVGiEfcL0u3IKglU7xrm95ufjQganXxw3rri2nQyd0xT3xZq3a6vls7ba0mU3M2bjZIEA6sD8Bn1//8lc46d4PU4N/o8/24rq6PBMhRSG/+4wxZ/Pc6Su24Nv//ATH/+n9gB77o5t4BZ6XVauZcGGQl8Ttms6rBOGZI/qkXsdp8dBZdSzNlzIQhI4QOm3/3uoC8pofz+KvKsoU5TXamM4ppsnzEKm79PZcRyj6zTblNUKO7KDywUK9ZiE61kTJCqIbA34/fm66jM/5qzanNY0bXvnKrZPh3XnrIoUmmJzTlGT4zF2T/Ns7C7XlRG1C10aQwX2NtLa2ubYBL36+Epf8+zPP8cc/XorHPvrat64gdoYIHTG9trr1OBl+r+fDNKpfssg8VlWR7k+cMtq7zm8FYSlQ8oKwriGJDTvq0aVtdWBZr2blvPhQECRNSYbZa9IajqNBajRCT71QappBIQuqh4w/z/VN6gV70fQYhDhQRJnZ6s3NZojOKnzN879frMJlj03D05+FT1pt2i7XphfX+DnLaNoQGvnvF6sMW+TnOifLVd88bjZuCfG7qbj6mS+My5qO3aaeqPw+zscSodabO+D+lbv20hcr8Y/3osU6NmmWNyzNl5IXhPVNSbw2Y7VPmirhNRPiCt0X330kPXtvSjJ85+EpnvJaM0mGaTSzTNBs0u9z0emEJc2EgPzQyuZbHY9O/hr73jwx47juFHHQ2aNrW229g7unP+Prhetc06moLXKCzKdBZkmTY0F1ib/fja/MwnvzHe1d5YAzqGsbb53uX9FMt2h9uJ3udzc04clPl2X0fdaqbcZ1yCFAMtzaYDo34rdVPnKN6oRzkDySu3bN8zNwzxvBsY6zVm3Fja/M8q7taya/hWL99t046La3MG+t+T1g8VLygpCjXs/JPJjelzB44GRSSL1XDmoeHGFZKmhTTz9BWGfowi0OAHJ7OlOuzK3/m4MddY0ZQcvac4Q2jxjSxfNRt3YtlOfz9TrSrI++8uUqjLztLcz0zYDDPOuT3r6qSvujio+Tv7Iqvo/TTorp5OeKY3KYHSDWbN2FP0ycjxtfmYWJs9dGzibjCfHIEKhbMfSG1/HuvHXpZyGgPl5HXkyjIdYI4+Cyx6biyU+XeZxzdOv8heK9eTXYuLMe//owO/N6OVM+glB1jCkcDpjwWkIeTP00Qlk5DAqoVyE/ZPWNydQoWq/IjhOELFhl823g+RmCNPgcPjiesLfjjHPZ4YOU5zc0Mazesis1S5eD4z9a5ARFz12jn/We++CnGHX728qZsXKg9NMgxZTWZG5CNtk6SxSsYZSoDdvrU1rxjrroqdE8110SxDwV4dtz1xvfV8k8Osvo+qSM+xUORdVW12+vc89PHxPng8VgGuXeuna9MjrlIwgNxkFx8FPdUipBoFsjlMMqZJOrXEaF+JC9/OVK7Pnb11NraXJQr24AFgWKPFh4+xL8EMlOliaCkLde6eaNEwcUWSP8xXPT9W1zbcpnQOPp0JYo1v5UZtWg7nNnF7HFIHOw2D+5p/zrioNymPE5yViq0mzynPqZ58W9MJnfwyCQ8hpNZNcvE8LsCqKK84yKbsJbDMKnEBl9So1YBCERjSWi+US0iIiuU3x+DRHNIaKZRPQOEQ2Io90wmDh2JAXBpiqfscYGvTDRmUybQjxE4ud/f8e7sF/XmNYIkoxpBYTYDz+N0GQAk6+J7pqKwpf3i+8JpwsxaUr6X4+UNiUfD9AEOJcoNnI1TdJ92F3vYoG7s4XcHv9+JsNhOiencH6IITrJmKd81CFYPK9DqypNKUoVDJokpUyjbqhSLhG7IoYGqe4dz8RVuOieSaRhh73LG8VlGuXkehJSymQtCImoAsD9AE4GMBzABUQ0XCr2JYBRjLH9AbwI4J5s2w2L2jQqa0jpnKRBM8x0+fSxqUs3YeB14/GP9xZlCkjFoBI0wHgyr0jjpcc0yvQee56ZbIZGp9ZmdcgPvYmjh7NprPo7e2fWSf+ZrUKbcurILKq6rnOzdCT4xl8+cLqhuU4mmgEv450omPdB9kqOvvdk+rz2rXS5aeWU8j71uR3bXFuPo+6ZpC23tbYB67f5p2sLwzkPfpzuQ8CESLzMFz2SdngzvYa6dcFiED7ZZEqyOMShEY4BsIgxtoQxVg/gWQBnigUYY5MYYzx1yqcAoiXEzAbVgyIdZhC8Rg1vK/H899xsIPe8MT8jA43KNBo0eIoPnywAZGcZk22D/GavJgMCkwTp/LXBaasI5E0QrtmkeO3W3b7akUqbEo+LqK5rXOOV7idTWhCkQ7yImIElrH+JuKNCVO1rnRDfmGTArvomfPMfH2H26q2e2MHUpDCgPl7u7bnrsconkfnoO97GmN+/E63TLuJlXrBuh/J4ql+aC/SZkNvWVKPTbq6du13FLHkkDkHYB8AK4f1K95iOywG8rvqAiK4gomlENK2mJlyKqSDUGmHm+7RpFBkeiEO7t/O8T0oaobcuEy0oyDSafi0PmN41QjMnggzTqEdrVZ8vDmzyoKHTZtZv3+0poxtExGu0bXejr3bES5oIwqg5TMX+jBnYRV1G576vGBB1pmSvsEh/Ib8sQIDz3cWcpibayLgZq/Hq9FVuHxl+P2EuLhByhyYZwxfLN+PL5Vtwh5AIAQAOvO0t5feQ4YIlqD/1MUgNffiE/0QkKC705S9XYvLCDajZrvE61jjIFJNp1BKdvDrLENFFAEYB+IPqc8bYQ4yxUYyxUd26dYu1bdXsMMP4IwoUluna3rLKe7nEWXNG3cz7mg8SXqEQJAjT7csa4X3CxqfJJEvVu3ev9p5yngfYZ41PZ7I6/K53PeV31afXJuV1yTVbd+HV6as835Eg5Wb0Mcf65710/vLr8M7cddi6q0EpgB6d/LW+Ih9EATqiXwdlGa1p1GCtUnWviN/5yU+X+vaPhPLOvRc8CP/0mS/xs2enAwAWrN+Ohz5Ygg07xFAAb39Vv0FQMzxtXj5kgq4N1bUVfxOdtYHfm794bgYuemQKRt/xtjJER7e2X2ivUcYYpi/fUtA+lAJxCMJVAPoJ7/u6xzwQ0QkAbgBwBmNMPe3KISYaobiWx8AC16OYtmbv7FjnNRo0QQ4zgeb1tqj0/qR+GqH4cH8obZi7s64RZ93/kbeuJMPf3k2nJZOHlu8+8hl+9uz0jI2PRQHjt8O7n2lULLtu225c/vg0XP30F0phMMcnxMIPMW2dbnhTrSsDOo1EXVYkjGWUiDy7rocdg1VChAlm9aBAe5nGpiQ+XpS+b/KhHe2sVzs4qdcIBUGoudCqPqtM/jqrThzxi+c88DEuCNg4W8cL01bi8U+WAcjPRKRUibaLq5epAIYS0SA4AvB8AN8RCxDRSAAPAhjLGFufWUXu0d0k3tyRSMWgJZl/ADp/r9UIhdc61+swGqFf2ECSpTVCvxAJWRCKmTWO2curgX+2dBOmuzFlqfMljVDMDAMAKzfXum16B1TTrBziV8z4Hu7fBFFKU19SszPWrZwaAnbDqNlepzdBu/3gG8UCmd9BtaOEWKI+oH1RIwTSv+2ePdp61su05ytuITEkw6+sE8DPMHbfXgCAG17+CrNXb/PcI3H8FA1NSdzzxjz8+Jgh6NQmMy3iqX+brDzPLzkGoJ9wyOveOnT3cBzhE1OXbg4upEHctcYSnaw1QsZYI4CrAUwEMBfA84yx2UR0KxGd4Rb7A4C2AF4goulENC7bdkP306QMY6n8k4yxDLOHfM8vrtnps0bofc3fh3mIkgzYUluPWau2+moOotDODJEwE7wvfu7dpkbVXmDcI8ssx8A8wqrJx1QrDr5yLKCY5YWXW7VlF/753mL/TgWwq74JZ9w3GV+t3JqZXEHirTnrlCE0gPrayHWo9icUy/Ro3yLjcx1rtu72rGebsHFHZiylbgMOue8/fPJzXPlUOp/pf6Ysz5goZWsmXL1lF4be8Doe/vBrjLztLRz3p/eMz1V7D6dfh9EIg8r53cP5plV1Ojm8VQijE8saIWNsAmNsT8bYYMbYHe6xmxhj49zXJzDGejDGRrj/zvCvMX508WbiUVmLk4VKkIu2rqyzjyEXVOkyQdrMfZMW4ZwHPsFpf58cuG+cag0SAKoFU6mf4F2pyO2Z0U6SeRJ1y9eUa2rrhPXGV6ev9rTrl9ZNNEXLdfNEAgTylNOZQUfc+mZqc2A/pq/Ygpkrt+K28XO0/RT7q7uCSk9VAJPmr8dF/5ri40iSPh6U/UScBPztnYWCuT2z7i219Rl9uvBfUzLKZUxGUr0KP6xmKxREbRpIT4Ze+mIlbn51Vui2PRqh5toqn2lFOd3afqG9RsXn0RKdMs8s45WEpgHjYcqkM3Xwv8IDJTxcfTq2Up6/0I3V85OZTDCNyoNfv06thb76djWQJGPo3EbMFaouJ24/9NnXmyQHGY+66MEvlRlfwyTyF5icLbUNeG7qCuVnIjyUwc/MLbalC4nQDcRXPvk5Ji/agN0N6hGT+dx/aoRr5HZGPm35xlqMuPUt/H6C1ws0qH3ALK5RFz/qN9GStcWNO+oytgzTcc3zM1LrYPr6g9s06RcApST0/E5F5DXKMzbFxavTV6WSR5QT5SMIFXd3xj0svV+20Wt/D6MR8uMVCTJaaO/URpfhw0HOsTmgi1fA8eUl/3yi5g+tblbcu2PLgFKK8zybCEP5GgAqSP8Z59Xpq/DYx0tT7/12uTfRaircJ6Ap6Q1HUJ1546uzsXWXOsThy+WbM44xBlS6gla3CbDYjsnuGp41Qk1f+X6Hjxh4z5qY9kV2NzRhopslyLQuAHhvgdc14KDb38Z3Hg52EDEVllHDJ1T3GV/rrhUcc3Sez/kKqP/PlGX4QnGP+VlowrCjrhGX/ttxdvvWPz4OPqHEKBtBqJv4eTzlpM9l05ppFhMI9SaIPGuE+gcqfa6JuUPWJFKmUR9nGdUaURiSzPt9dxomfhaFgF/4iKjpyd6vnImz1+GB99Prgr7rrAbjAtdCm1hmKIGKCV+tkZpwCj6syPxvMjCJRXbW+19POXFCancIqZ1Ntea/c4bDktuC7rJe//JX2rr8TP3/mJS5lvuF5PavElamzlCqS20y8VNpdH971wlN4mEncjlvQH3uBGFTkqUmwDe8PEspoCor4hnCv/vIlJQzl2nqwVKibASh0jTqIzSAzFm8+mFTt8ePyxqhx4uzSX3cZGLnXc9Kmw4zg+bT7y8wmIH78dacdR7PSr6rfBC6HTcyBKGQNWBE/44YeN14vPSF14nHr26ZUIZGxrwTE015eSsqP5pYOgesrr4wIQvyRCZtlvWWC7MLhH7HFHVn5HU8EVWyc840zXlbBKH9+qxMTdNU0ARZa7Rr+T71ezyANbGDYv8+WrQhZY7e3dCEl79cmZWW9pe3FuDkv36IRev1pkqThPAmfFnmsYjlIwi18X76MvIz4k155h7TPEgp06iUiFiXs1DXjg5RSPtmjwmsyZyPF2/Ab19JOy2s2BTsYAN4Z/V+WpeYPWetu854/yT/XcT9NIaHPlgS2DcuqGas3Gq0piS3p/O6BMwGYnE9NciU+/0npknhE0x5XigTOGPK+E1dFWu3xpcrFABG3Opkr6lrbML4mWsyPjcVhKr4QhMPbfNco0KfNJO5C/81BQ99sARfLN+MYTe+gV88NwOfLtmEqHCv3NVb9Nc8zvAhTvd2Xu/l9dt245UvM0LDS4ryEYQa06iIzhFClQ9yv74dAeiFmXfvubTp0iMURa3Op18qGjXapF8aNZlT9+uFId01O8hrzguzkSxH7KtX6/I2Imek8elGimw9FTcLGolXcKnrldck/XJrMiZ8D019twshFX7rnRzV7hOyMA5jrksytZauq6OuMZmTdTHdhML0u/zy+Rm+5/Lv9Z8pXqcbv+rFxz6M16howqzVJABQsVByUqms8F9flvuVDRcfOiD1urfkuHfpY1Px8+emB6YAbM6UkSBUm078Bj9+wz9w0UHOe+Gm48t4qR0FJGHpcZZJpgctTzySxmRoMs7odsxev73OY0rx0zKakixU0ufDBnfNOCYO5Ifs0Vl5ntY0Kj3fb88Nn2sh24Hgty+nNdw4vIRFTH5T8bC4tZYOXdKBb4500vsO7tYmlHdwkjE86GrOX67YkronfU3O8ctBbXumv+8ahaaqcsy64WVvGIbsJCXCQ3bk/iWTLOVtHOw1rn7AGpqSGHnrmxg3Y3Xq2InuDiecne5a3exV+kxJHo0wxO/yv5mrU/UDQIuqdDxipTQocCtAHLlii5UyEoSZx+okjzS5DBd8fGYm3nP8BucPgm5dpiJBboJk3ob/bDLKbFseK074s/BAZWi53pltmJ27e3bIDPj+l+CZ2L6l2vNVDCT3y30q8tWqrUZ9ylYQ7hLuARNX+DDNrd66O6VWmAjQ56f5r4cCkJxlnL+MMc+OFmGEdZKlYzHrG5PakAz5HFOem7pcmbtTRk5wz/HThoLQhSqJfLRog/b7iAO/x8ktmXZoCzSnp8KnmGd9eeuuBmyubcAt42Z7iov1dXYz66zXJAJ3+pKus0EjqN6bv97T9lcrt+Lqp7/ETa+m227XIp1krEIShHyI0NVfCpSNIFQ9aHe9Mc9XY2rj3hwVbqwOA8MBfTsAAA52tR/+gCSkm4ff0HyzUt6O1jTqvowyricZQ+8OLT3HJrvOM3J1j360VDhPP2NVXZeGJoazD9LvoKXru5hCyi/ptgj3KAwS09maRrfvTpt7TDS4qO3pzhIdTEzMzh4TunDPpD1Iw00Ovli+2TOJS6bq1NcR5hr8+r9f4Yz7Pgosd6li42TAfw02iCRj6NWhJaoqSNvnm8fNxltz1hnVJb6ucseEoMkTv7IPf7gEQ254PRV+w6+5LFy2Cfdjz/bOMy2v2Yl4szZl9uXDhTW45N9TPUn6/zXZsQC8vyCd8k+sp7JCFoTO+7oIyyLNhbIRhFsU8V/rtu3GxNnphyDTnd/5y00FL32xCsN6tkeP9i3QtoWj/fCbTxxMvjWyjyc3JmPqAYY/RERpwRNFIxSTenP45qNbJLv+R0KSZMYYdPG4qm40JRm6tM3M/8gxmb3rzKQ6gkqYBMH70aCJcdRNkKJqoCY/q3a9VuAJIbDc4yzDtUPDtjjjZ67BwK7pmFRVztqubb0D8RsK784wvK0QPF9vVHucivdU2GeDT/SIyFdgrd1qkFVJEoQVhhohn2hybZ9nXeL9kdeFufMQkA6N2OGzzih6nje4fbl/0iKc84CzTrl+m6MALHVDwaYu3YRXpzvm2FbV6Yffe529bfCR7fg/vZe3uMl8UzaCsEoRb5O5Y7v3PZ8lcRm3fFNtaleKhGTyEs0J1ZUJzxohY+l1CLENUWtkwkAWFsb0s/Srnv7C815+oHUa4bOKrCyNSebbQRMh4fXSNSCgUNBANHv1Njw3dbnyM1nwmPQ/qkY4e3WwqberzyRDhUojTDIWOtvJuaOczWNO279XyhzoV8X8LDOPfP+JaRnHdO2Jv8m2XeHi25JJBiJnkhrG1KuakIiKW1OSoYIIFQlCQ5LhwfcXa51ilm+qxcDrxqey8XCt3+Re42PWDmGt8ruPTEntCvPF8s3401sLAAAHDeiUMpP+YeL8lBWm1jX9c4vVOQ98kqrriCHpRPuiQJaFM6XGOmff0FIkjt0nmgXyAjCQOeuXb02Vtpdk3l0AGlPCLH2eqOElEtBumdMo1M8/jjLQOp5/ZmXlNSZdDkaVuaipKenbPxOvxyRj+GTxRlzw8Kf4yXFDAsub1OfH4poduPP1ecrPMhMmGJhGQ2qE3OHiEo3pTySstskD50WLg8406jeT57fA1l0NuMu9VjmMEw+FuE7n56Gr4iXX5b9NdYV/+jfp2mzaWY9zBYEhl0kyR7BUEGHCV2uwbGOt0lkH8FpgAGdiOvHnRxmFPfDfZamgLYvbpd3zRvq+rkyQcveU+9xt0/jWUn06tkpdR+/6oiAIfSw7m3bWo0Mr/yxYzZGy0QhN0qPxwYKbglJCTnJE8OwLJ2h+Ive+7dyAThyheid7j2lUoTGa4pcMWkbctsXRCIV6Ah7OxqR/Pk6TgbwpyfDGLCdebLI0SKhgcPJSausLuGByNha5L562DLRV3Vfco1sb336YEFYQbkuZ+5lHI1ThV/XkhRsBeJOlF3pXBY64btpa2GlBR832uow0eAnSrxECakH42VJv/F8yyVIZV5KutzURUOfmkP1S2olDV/eyjbUYceubaackxZ127oOOEOb3w0eLNirrFsecqoqEMtnD0O7tAAAj+nUE4LU6eLXc9Bs/Ie33LDZnykYQqj00pfdwbi6+HU7KEUZK8JhIpGdrqjLvCfvOEXeWUdxbXPglBI0wsiA0PG+x4JzBmLffQYNfU5L5DtYNBmuETNBCTbWrg25/W/tZUJNhnEeyCZ8Y4caVZkNYQZi2IiDw/vHTCP+ryN5TLIJQ1FT8PJzPH+2Yd0ff8TaOvPtdz2eJhGMa3aVJYWdy2f/7xUrse/NE1GyvQxNLm0Z5yMsMjSBUjTu7G5K+wuazrx0hHOSlKV6PigRl3D/jZqxOTTZTWpzmeW8Qzt22q0EbyrO9RE2jZSMIlXGEsmnU1dz4TCst5NJlHA2D0jkqFVqjaCbh2p5SIxTr56atCKuETtXm54m7qqvc8XU0Jpkn4bWMnKbp9AN6Z5SRTUzZYpaFx6whOW1dmPbiEBuhs4SkzKHiGrS6Dl2y8GJgcY1+U2FRGPiZ7FpXp1d55HWsBDm/7d43vaE810Tm8xjXddt2Oxqhaxrt0b6l73m636PJYNIYNDESBWFVRaZp9NMlaU2SfyI+755r25REz/YtcfK+PbF0Yy2OuHtS6jNxAsHDjVZsqk0lJ7/goU/x74++Dvw+xUzZrBGqBjB5Z2jGvFoSf/C8MVrOg5XhLKOZrZJQr4woRPnHq0Oug/A+hJnAP/3Zclx48ADHNKqJP2tVVeGJsQPM1gBF5CtSVUGeAGaTEMYgL7WgwWLZxlpjAWPiNaqrKg4NKrxGyCc0ac1YV8Mtr2VuCpxRX8xKoKmH4aM+u2SIptEoIR1d2lSDKCBBQIhpDDezJoiQUGhhMjrTvUlIXtB9K07QVRqhHA704ucrPZspi+EQjUmGygpKearWCLGL4jptrSsUj7zHEZRnHNAbnyzZiE+WbMSlhw8K/lJFStlohCZjOL9p01vnZJo9mfueH0k5vGhStDhmzyCNkFI37W3/C95DjpPObhFuIJ63ZnvqPLHbYhXXnLinor/h4ohkQdfQxPDmnHWhNMGl0lZYMkF1/fWdhUaJsvfv28EojlA38MUhREILQkEjDFoj3KbRCMWNm8Uzo0zIZD5ZrF7bCoM4WItLDjJNSYZJ872Zifbo1gaHDO7iLk/4CMIQl93xynWevYqEf1gG75cKk1CjoPtWdHSrrEhkLE2ImhxjDL96wZuGTtziqrGJoTKhvk779+mYrlOaHIuZcZozZSMITdajZDNnUiEIk4wBJKxz8XU+zZUkcmbramcZuPVHM62JWqnqfJ3JiT+ETAqfEGtpqXBMCJqhyvFmWicVrhH61mbGafv3CixjIl8GdvGmJgsfsxaDRhiyDl6ewes1qkIMnhY5Ykhm2jwAWLBOb640pTZgWymO37cWzXeqkB5OkjG8Nt07KCd5mAOR71pyGE/gpiRLOcskKFiI6u6LC/81xfe8FZtqjTINcaoUGuGMlemQHZVA9gjCZBKVFQll4vPGZDLlbDP1603a66VKCDFr1VYMvG58ymu1WCl5QfjDo/YAYOgI4f6Oskbo0fZc06m8+4TONMo1QlXz2cYRpvqlMb0e/6f3ledxEycPOL72pL0AOFrA+u27nRvaR4PVIc9y5dCMNtUV7uARnw3ONBVbEI4wSfcr4nJdVoTVCL3p0JzXfum4VIiCJu5gaVPB7jchEgdXPw0pyVgqXEJsvyLhPKvZbteV6kOSpU2jRIGane5jnuhC1607Xw+2DInXrSKRyFi6OHrPdJygKk/o7gZv+IQqxAxwvnO7ls4q2rgZq/GP99Q7wtTsqMO/PlyCRet3pO6r0/4+GQDw8+emY/qKLUWbpq3kBeFePR334ekar66BXVqnNoGV1wR1zjIJIZBQFWIho/Ma9a4R8kHN/LGsELRSxhiG92pvdB4XHk4oCFKbf/7kmekYc8c7+MVz05WDw4MB2xo1BdifR/TviD17tEsPDiHynOrw2x8vDExaZw2r4cUhREx3Y+c0JtP3TFTHI68gVJfR5QENwnRN2e82mCHkKdXF6gHqSUQyaZZZJrRpNMlSptEgC6fqPhIFlA4TbVq8blUVmUJZvCdVAki835rcNUKR5e6yREMTQ4vKtIVIZy1YUrMDt4+fixP+/D4uf9ybNGHumm046/6PMkyrxULJC0J+s+geIr7mBwhmTh8hl/Ia5e9NNEKd1yhLC9oo42hCWCNkANq2NPN9mrdWXCNMO+rw7Bjjv1oTOnAcyAyfkK9IRSLhyXzCPw+zA0auYPCahsN+/ziUqXXbwgmcdDq06KZZ0dNwyQb9xrpR8EvHJ+LXdTGlHF8vbKMw26t+Lkdg8exOPu2H0Akbm8JphKrlBJPfqnNr/2sn358VCcqYeDwuXLv6xsw2RaHU0JREZSKB//7osNSxo/4wCYDjGyBqi+0044y4V+UHrin+zBFpz/FOrau0ifkLTSyCkIjGEtF8IlpERNcpPm9BRM+5n08hooFxtGvUt4CVKMdL1HndJJlGuYbjWUdjjnDlx/igrnWWSbheo4rP+M3s2WMuxHhWmRAEOAsvUJir3aYcf8Q9DsNVhXlrt3lMLQAyJGGlu47x4ufetQ/dtcsrDJ4vzceZPtLebCrqGpsKEnfHJzS7Gpq0zjBBmDgSRcV0ZxNukTBFdb+oJi5cYDGwjCxC3nLmbTe5SSUSCedfkDlbFY8nhlzobhtZO5NxBKwUUO/TF5VpdOXmXVjvJlFobGKoqiAcNKBTRuKCxiavttiuZZVyrFm43qspNiWZJ7Xl5iLezzBrQUhEFQDuB3AygOEALiCi4VKxywFsZowNAfAXAHdn2655//w/F0MIZGeZRoW2x7UokoSn7qEnUMp0KaPSCMPMTvmAwDVOv4FHzChxYP+O7ndhHo3QJMUYZ78+HVKvt9TWY+y9H2aUkSchjlea/nvkmoMGdNJ+xqQrP9XNLGKS1mv9trrYQw/CMstnzzrOAa7Dg0h9yJCYMJgK2S+k+NMgKhV5g1WmT/5sr9i0S7s0AoR0luGmUdcJJ8iBTPW7iBqVzmQbNIngyxocJ8Wa/nrv0uRC/c1LXzn9SKbjp3nuWU5DMukRaO1aVqZ25hGRJxuDr58Q2txfKOLQCMcAWMQYW8IYqwfwLIAzpTJnAnjcff0igONJl+QyzzCWuSbIBR8PF/AklnFvwAxnGW34RIaykUIUvFtq67Fqy65QsXppky4Cdx343Rn7ZhxLJr0OLeJDHdQL0UlFzJgveiHKv3BlhTruqlLnchszXdrozU1MMi9O+XqTtqzMn99aUPDcnCabptZs8y4PjOzfMacaYegEAYaonjWVxyIXWEGE6WdTMpkSsAmFOdIE0XKi0yiDul1b3+SZZrauduJ+dfXpNr3mnt6isJPXhJuavPtdMsZQ35jECXt3l/qUKWxFc+nD3xul/0IFJo4RqA8A0a95pXtMWYYx1ghgK4AuckVEdAURTSOiaTU1+pihMJjI2wwzZ4Xea5SvKXJtR9TqNB0IdpYhws76Jhx+17vYv2+HzIIaRNOoPJDL9OoomGPcv365RnXOH3v3ao8T9u6hbUeMS5MvSWUi4XlQk0HXLmZUO5BwmOY3MuHlL1chHr/R3LJaWievIH8tIluy3TRZh8q7UdY8Pvt6EzbXNhhZG8JMBvgaYVojDH/95A3BlWUC9v478La3PMKyVXUlGMuMF+3athr9OuvN+zxHbqPgNfo/IYQimWRoSLLURsSAY0VoaEpib8k5b71ijXua68zWsXUVThyuHzcKTVE5yzDGHmKMjWKMjerWLdizyoSgx4AJwkB2fGlSCMK012j6HCK9wPULF+DjhJgI++EPvw7osVB3yjSKjKD9toLpokOrKhzYP9MsyFwzb4P70IlOQbePV7tvNyWTvoJLfGDkmaUcsMtn0ypTVy7wGxRl06gfqmoKrRFGIUHqHQviQhayt52VaZWIgup3nCQF2/PE1fPWBpuMw2iE3Gs04eYajaIR6hJ0i4TdBJev6212dyThVFcksGKT17w/sn9HTL3hBM8xJ7OM8xzefHp6ZWt7XSMaXUeaV646HIAz6Ugyp+7nf3goHrjoQADA2m1qh8TDh3TBlzeeGOr75Js4RqBVAESjcl/3mLIMEVUC6AAg+7QTBgQphAxpIcYfCDmOkAg4di9HMDtr1OTRIoVoisz24ZO30ucB1MX0iFRkaITpz3imfAA4ZI/OnvN4fxwTT/qhM9GIGpr81yJFrUsenOQ0UPz1pYcNDG44BvyuqaMRmg1qqoG4OW5YShSc2HnPHsGbBeuQNcJWVcG7R5hg8mxwPl0SbOI2MStzGpPMCctIOONGFPOvn+NOqk8GglBcg2+VEoRejVA3Qe/U2vHe5M++I+ycsqMHpseL7bsbUunXRvTriNbVFdjpji1VlQmMGdQZ+7sJ53UJuTu2qjayzBWSOAThVABDiWgQEVUDOB/AOKnMOAAXu6/PBvAuy9PIwW+WHu1boG+nTBMBN3MAmRqgaLoc0KUN2reszPC05LND3c+cIIIqHhHwT7PkF5fIqfBohD7rDVLvuAbKt5TiD51JvFhDU1KbRSeIyoqElPHeaffCQwZEqzB0+34aoblxU/VQi5c+Ls0n1yQMTKOtqqOnI26Q7se4TOBxO1eJptEBXVr7lm1KphPz+xkyDh7UWf+hAar9QP1o4/5OV0sbcSs3G2DOs5igtMAVNcJ9+3TAz08YCsDZDFk0m1ZVJPCfKc5G13wcaRkwwenQujhDJkSyFoTumt/VACYCmAvgecbYbCK6lYjOcIs9AqALES0CcA2AjBCLXCF6d6o0GaWzjMZ5hjukOAl3nfO595ZOSxIFofwA83HiyKGZaa5MZr1y+IRubiELrk0761PtJ4gCXbVFGptY5NldpUYjrArRfq7QaYQtKjMfEZUDhnjmoC7Z702YDxKJ4KB3VcyeKYskd/q4zMdxO1eJ12CZlNtWfmabkk4cbMJdI9Tx3A8PNWrbz4EriHXb06ZIbhqV46WTLDPuj3/b6spEShtuaEqiSviuY1ytcNvuhlT6NcBr8XneTXnXssr/9+jYDDbyjeWOYoxNYIztyRgbzBi7wz12E2NsnPt6N2PsHMbYEMbYGMaYf4qSGOE/LWNMbdKCICy584YoYCCES7D01kUpZ5mku1Gv1lkmXY8sQJokU6yISWYJ0Ws0yRg6aoJwdfqqow0Dv//mfoFt8QGxMZk0jg+TkU2jfNDJl9eoX0zp23PX4bLHpmUc79CqChN/flRg3c3RNJogCjQLtlW4yZvyt3cWet7rnFLkyYYqzEPExFrCUVmBZOTUbCKysGtM5RqlUP24+9uZz1h1RQIbd6bX9DqHFIritmetNBOWJGOZJkv3Xq2uSKQ0QjF8AgDau8Jr2y7HNFqV0gjTZbjWKGadEcOqOB3LQSMsdkQhpxq/uYYHpNfsUmuE7kyREs4gKmqEXk1TP8gmSPBG1QjCioiCgGtyPOn2fn06KG9EndzicYQ9O/jvqQakr1F9o7+zjJ88SJA6jjBfywdR2qlIEAYb7D4vfu8oe0oWgoTBGlcYa0EQOjOsPLG65DB/U7mJ/Onl3tMqjT4M9194oOc9D6ivSPhrhDLnje6fcUzWpHq2b4l/Xzo6Uj/baEzYfp7kLaoq8NqM1Xh/QQ12NzR5nNZ4BpgttQ2u1cz5TBSWR7mp4sRj13xjTzwvacMdW0XXevNFGQjCtOamvnHTXqPybvNNkkbIA9dFL9H0Rr2a9pEe/OUHmN+kYRb/xXpSJtuUJxvw2k+OyOyDpnM8jtBEwxvE3ayTzNcT108IVCS8TjycqBpmWKojDIqO6TiBr+88JXVM1V05GcFVxw6O1Md8kqDgEIc4nRzk4P0z3I2beaqvnu1bYuldpwZmgzK5X7iJMNv1RNnlvymZftayvW9lLe7asXsFjgXD3NzJQXVxVN6nKdOoq5Fe/Ohn2Fzb4NmDsH0rR7Bucr1Q+YRInMxkZJIC0LVNC4we2Al/PveA1LGyWCMsdvhtxc0ZMoylTS1yurT07hCu9yfSKdZEZxlHMGa2fcMpeyOREOrJWCNUrx0GwU2JYq5R8f2PjvEOwqrq12/bneH4o2Lab0/As1ccgvNGO47BjZrryNmwo177mc6UlK84wrH79gx9Dv+qQQLBqxECvzxxr9Bt5RuTgTyM1hOEbBrt39nrmCJrEjp0bvoq4p5k/X7CXGFj3uzqEr1o7/n2/jh2r+5Yudk/k5EqU8v4nx6RkRaNo/Lk5PeqrC2/PTftoMNN4txph5tERW9WVcxzh1ZVICJ868C+qWNls0ZYzKQ1N/VArPIaVW3M62iEjsaTELSo9xfUuFkeMuvu0LoqlWKN1yPCx4WwgpA/gLoNhAd19ZryVLVf++LMjHRxKjq2qsIhe3RJtVXfmPQVCjoXarF/psfjpE/HVpHaUZ3TqXU1LpN24xY1QkK4dSwdPdq3CC6UBWbJJuJrTzaN6iwkQablmhBbTemerV4GywEqauubBK9R/4vz9jXeteVPfnOc573okcvvF9M9HDl79miLfXp30ApCAJh761jlcT8LCTeT8t1duGlUFIQqb1GuSYp0ysIhKF+UviB0/zZJWVQ4DIIpVNLc0k4ukkBVCA/V7JDcc/Veo3rTqN+WSimN0O0ED4jn3qCyuUs1mDtBsSwVD6WDfybW4b9G6GMa1bQjHt6ja3F5XKp+GyLgptO96XRzsSo4uFv0GD4TTIRcHAKd09DEUplMAOAH7l6hnN5ugvNRA7ILPRDRTX50pkSTjZ55yFTQxKqfpPH26uB13GklrBHy5bmT9lFnXzl1P6dfcm5S/ra1tEZ4xzfTITytqivw0o8PS8VC93azTAUtFQzr2S41qa5KmUadBm89cx9P2W+7GqDKucpqhEUAv1e1XqMsXUYWTI3S7hM8e4toGh3eqz36dmql3ObJ8SZNbwEjN68TkIBjYtDBy3O7Pc9U8djHSwEonBIUz2tDUxJJFqwVpNYjE6Ig1J/j6yyjGVTFPtxyxj7KMqboXLmjmrFUv43q638m5Cbt3TG8tqFa+4mqKN94mpzzPpMfHzPYSEOOU1uvb0pif8GZq520JQ8fbPt1bo3Fv/dfkxWJtA6ouU9FLecXJ+ypLMOtS0HtVgXcdKIw5s+NzoOa71ghO8WIoRDipG1kv05495dHY+EdJwMADuzfCQ9/bxQuPLg/fj12mHOOFAgpr4cO6NIaG93YYh42wb2M9+ntnajfdPpwvHLV4cosUWG9YQtB2QhCHuYgw4RdG2RnGU/4BNL5KLmA42WIgCU1mdkiCF6vUXlQSQleyTNPt+bI4Tc8N1e0beE8UDweUX5AVYPZF8u3eNLL6VBqhD53jZ+XWpzrTTr+eeFByuMJ8t+TTkS8fvJ6K69Lx3u/OgZDuqsdGvzgWyqZtuNH93bBJtVfnLin0eQgjt+M70kXtMuD+HyaTrwA4Jsj5dTGaXT3t27vTrGPP3PDAzLKuGFHQf0K0qbFNcJnPnOC1OVnl3ss/9/YvXDP2fvjr+eP9HwuWmBEId6yKoE9urX1xP1VViRwxzf3wx4aS4McatKpdTW2uUsdcp7eti28k5gOraowQgp72b9vB3Rr1yJvKRSzofh7mCV87S7JAJUnOIM+oL5RcJYBcWcZ18FE0CL9vNwSRNodKpJJtYCsCDC78Hrk73O+66ItD166moK2bvLUQeJr/Tl+5haT58FvJ3ETdLPPMMO5+DMdJewmzrev8qtrYJGZdmV+evxQzLj5G6iqSJitEQq/mZyqzxQ+QJrE9Ilcc6JaI5PxS6aum1GeuHcPLL3r1IzjfjGFHJ1pVGfW1CE+K6e4pk95jPjfT47E9JtORMuqCpw7qh/26tkO156UdsRSPS6jB3bSCjuRGimT1M+P915v0dwqJ71o0yI40cK/Lh5lFINbDJS8IBRHLeWgz9IDnyqzDF8fJFcSJpm79of0ObqJHzeh6jTCJs0aYVCwbkoQun+/WLYlo125PhV8vdMEeY3w8CEZm4cAAO7+9v76OgxMWGF3hpfRfZ0w2pU4sanwfG9y28hOSzK15GXTziWa/K1tqitSZnfxux03rLuyvDgwD+gcTcifcUBvPHHZGFx48IBQ38nU5Ck+Pzwkg6Oq4oqj9sBVxw4x7geHZ1tJJplrGpX6oRHIut9CFOA8iYD8nVtVV2QkypB3w0m9dseTk/Yx844Ws+j88sQ9M8IcRAcc2YzarkXwul/3di2bhVkUKANBKN5WqsHQszEvyxSE6cGP71DgnQ36rbNxIap1lkl5jSYyzvMbA3g9vA/z1zlmtaUbd7rfOdPUqkLe3NMPsZzOzDi8V3vsqwjoT/XboDG/uLae7c0D/zOgEIHu4ndVmOiytRb+Q2O+9elGaMR9IUVEjVu8x7SC0NAk7kdFgnDUnt2QSARFCHoxuc59OrbylPv2QX09n6uq6N2hZSQnoM/czZod61LmGmGVos6ld52qXffm4Qt9OrbCSFcQhkmtKMMfnShJBFTOQ+IxWcibaITNidIXhAEPsug1qtqGid9zBHfz1iRfI0SqjH7sTYddAD4B9ZLZIcgjjT8IqvNSnRX7oalr++5Gc9MovAKBe/h52/Gvw2SG77fGKOdMDEM2aeE4mssbige/e1CkyYfIX88f4Xve5tp67bUWNe7569K5QHUmbc/z4+tdrO9PVM/T1MQjxDkrNnlzhf567LAMs15YTfuU/Xp6HJAak07iefl6+JpoFfDyFx2S1pRNnhF5k1wOf3aiJI5QCU9xDZNfw6cuPxiXHDawWaz7haG0vo2CII2wtr5JMI06f72mUa8WICfZbgrItCJm75AHBJ3JNBGgEfKbUKVJqvAdpAzHBLmc7D4d1I5TR3Bjfo4mopD84zkHKMvoTaOBTafr0JzH+5+NJ2X7luau5LpWurfz14wnzl6rFT6iQ/HcNem9+nRaRMJQEPpp+57PQly6KPJTdtg4eI8uWHjHKZ5jYX++f1x4EC4/YhAuPXwgACdbi2qyyp/Ly48YZFQvF1iipcJEEIoaocc0KtUbBnnTZkBtGj1iaNesPbuLkdIXhMJ9pbvJ0oIv6X0veVXyG407zwDOjMzXNOoTUJ92mfbXCMdIW7qkvEb1VkCpPuev6gExd5bxDohy3JJJXesMMoIM6a5f5O/byYnLGtS1TYb7dqqfmpGWQL6xmZ6yCuEHpC0K2ZhGE+QfYuLth7oh0RKgCrtoSjKt+UznjKQThN5roetpgJAUteoQktDk3mTMOxE1SRIedSLDQxgampLKgPqKBLDk96fgt6fubVQfFy7iT2KyfCBqYx4LCuP1mpktH7l4VOr1FmkfQ8CbDrEqy5ytxU5pfzt4Hzyd2U0UfIAQUC+uEYJSuUbF/Qd1gfqcREJIpaa5yTPCHRLegH05C0aVRiPkmUjkAZT39oNrj8XfLxjp/cznwROzxicMBsSgR3iKEGvH4Ru/TvrVMXjxSv8UW/+86ED8euwwPHHZGO2kxk8r1u3O4YdqEM9GIwwT86YrKbavqq/RZ/NknTOSOEma8NMjcdEhjgeyKFD9TJx+64dR832aXGf5/jVJEu5XrS6XJwBPdiVVUo0K18nN1PSqKhdmH1LAK0T5OGO6Rnj83j3wLTf05PtHZmqxu4QsNzHmVShKSl8QCj/gx4s3Ksusdc0CG908mWLqMq+zDHeOEU2jPlqI623Kxx7d8yHP3rfuavAMArKTSCqg3v3LB7GT91VnxeDV9+zQMpWhQv5MxWtXpxN4e9daI9hgoR4Qed8HdW2DUQM7Z3wu0rq6Ej86ZjD6dW7t66mrQh5UuTfbf390WGYdGq/RbJ1knDpCCEKD+0W8pnyrrEsOH6gVPjqNUNYiVOn/xGvRR1oj9tNkVNrp7wzMa3zjanmHDNEaIGdGErcE0uEnYC87YhDm3TbWE9Avn8e9reXvHGYt9JPfHJda3ws7sRKvp7iXYpQ1wtvO2hePXzZGmcWovZDUY/0287R2zZHSF4TCa90Nt9gNhn/W3WiS3+BJwRGGkN68VXSWYQGel0SZgfoyqgdIPLa51pvImi9cy+fpnAuMhJgCcbATz9J+X2mQlZPyqjxCVZMIkzRrOoGiu8byYT4IyXlZ5bIqM2n2GqFpiEiwBUG8Dt3cQPreHVt5XPvF3dJ1Xrny4Mmvj07oyl6mflofKSYTrQJ2NQfSz6PIPd/eH3sLJu5/XzLG83k2DlVH7dkNZxzQGy2rKpTfR9Q2KxTOMmHui4oEpe4C+bTrTxmG0/bvpXWK2iTsYSgmII+yRtimRSWOFmJlRS48OL111PF7h4uRbG6UviAUzUiGN2pCoRHyuzW1Qz3XCP3WCOENqNeZj1QzZvGhen7aSs9n3DSacuDxSdUmllPxv5lrtJ95QgeEvuseeHmI/ZewBgF4E/am68qsR5f1w3ue/prfdNrwVMyXrvz1p+yN6spE4MDpXRvlxwK7pyXMGqFOtni1NLE8KV//5/sHp17rBKFoThvUtU16XVtjGr3ymMGYcv3xys9EbpLSvfFS3EGEO6CoUO1fKG/yKq8pt6hMeARlGH7/zX2ViaQ5YvozVaxvGBNwBZGgEXo/u+KowbjvOwfizBHqjDlPfbpMeZzfV1GcZVSIa5Fx1VmslPa3g3527wcXTOL6Hz81vUO9g5/XKJGrEQaYQJT5LBX9Sb2XXJc9GXCg0n40HUSmtqnDGz5hdErGoKLqRtSgcT/T6GVHDMKpUvJkuZlzRvXDgttPznB5369Ph8C1OV7Xf39ktm2Qqg4TTEyjurRk4mvxfhEFoejUIQ50raorlI5c4jWvqqCU84jzmbqzXdp612XlYjefrjeRqu7bygr/DXGJCE9cNkb7ua5eIFiQiRqhKqA+zG9bmUikl0xCBuSIXqlf3Hhixudy8LslmJK/YuItZjpjUzrLuKemhKOgjek0vZ11TpxekGlUt8OB6jXgDdwVFVY+KB6/d4+UmQxA4B5nKt795dFSh9Iv+ff4pZQCa+bKrbpTAKg9QqNqV/pBh2vu3tHOVOAmSF9Wzrt6UIRdEsJmuPmOYJ5K1aERTuLEQ9eOKAhbKuLEOFcdOwT79mmv3cNRTg6tn5horAcGWrFYht/PCSL831jvXo9yE0FB6brECkG/jXiNVGkQ5f0W/aioSHuTh30G9u/b0f3bQZm5xU+rDUv7lpW+WzyVCqUvCDXmIhGeQ5LDZ5yNikTdyaTCNKqZ0c1fuyO1tui0r+6jatd2v2eDC3RupnXqTp/RoVUVpt5wAi4Yk+n5J6O7Jn65CvkZuq1s3vnl0XjrF0dl1K3yZou63qbfycL5e6S07jHD3aEjEJ/+yNYBIJ0aKxcQqbfj8mppatOlNqBekC5eDdL72wzs2gb/+8mRHk9bfj+p6tev2Xrf8/uqp8F+gGKV3JuTiNClrX9S8YoAz1GdEA66F72m0czyM1Zu8T0fSH+nygSlTL9hzY5Bc4g4BddnN5yg1DpLjTIQhOnXOnkgD+gVihk3n0gz5iQbTZtK/YO4iShlGtUNFrJXXOZ38J6XNuelTbcqc9GoAZ3c8/3q9m06XU54zQfcs6V0VpzB3dpiaI92GXWLprjjXWeLiAphYAjH4G5tsfSuU43SsunqyGyTWwfSJb45oremtB5jVxlyTLW6fsivPW1oGhFvNfEa6iZLYv08jlNV/q/njfA45XDkSeIVR+6Bp79/MI7ZS53SzXOuokuqXsptmKQpUxF0mqgR7qhriuQs8w13q6OqigTqGhxBGDYlGt/fT7VDPBCvIGxZVRGrhlmslL4gFF7rtAh5NqzaAuZv7ywE4GRgcGKI1CY4uR4icTsndTlVaqb+XdLejL85eZjnM75WwdO9AepBI1XOp4+mQ4bK6ahj62plBv903d7aRe2ifxdnUI2sERp6jdaHMFcBQP/OrVOCKiPRgSKgPsoap59Z8LDB6WTmBMIB/Triq1u+4SnjmdxpnmD9b54+7g07cCqSzde6byfH6x02pCue+2Hmmql8eRIJwmGaPKgZ5wqt86/jd7mvOtbZMivontJdmaClE3Gc+GTJxow1QlWcrMxfzx+JD//vWFQkCGe4k6gxg9QJ7HUM7NoGL/34MNx0mnp9VWepsejJShASUWcieouIFrp/OynKjCCiT4hoNhHNJKLzsmkzfB+F17oy0vug4HHHLOK85rtR6CoWvQR1g6bqAfzpcens+HJ8oKj9cUGsqkOMe4oT0xAMv/Eo2wTW4mD36CVp71S5PtHVHHD2bNTtnHHDKXvjrm/tl7qmPz3eux+dcm9Gg/7Lvw0P2RiqyaKT2k/QPU3ewDbI3E/QWxl0WUy6tWuBW04fjsclRxOdUOGbzvJdz2W4w0ZUjV9Gl51JpJM70QrUCDWThKBJjcdZhoC35673b0dBy6qK1M71Rw7thqV3naoM4QniwP6dMkyqfO9M6ywTnmyv2HUA3mGMDQXwjvtephbA9xhj+wAYC+BeIuqYZbshCDYjZcxahQOqh4Mn0wbcB9RHOzHxtmypMI2Inn7yrF8URA1N+gGCDwh+WmsUjcbU6chfEPK/2Zux9u2dNhEFeeA9efnB+M/3D1F+NnbfnmjTohI73Ywa8tck6S/gvX63KfKvAsCd39wv9ZqBYXjv9vj0N8fje4rteYjSzhCqazNmYGfP7ym2zwPNO7auTqULzKxfuB8T3uOXHD4oI1CeNCMEvwcf+O5B+EwIo+Bw55ZstpISYw39HEtk5xd+f3Ztq84kNHv1NuXxMDvONyUZvt6QuRl3Ifn12GH4+s5Tsrrm5Uq2gvBMAI+7rx8HcJZcgDG2gDG20H29GsB6AOppZA7w875MHfc5R6lpCdksmI9GmCCz+LvqgGwYqqTcqrYy++kc9NvaSD7tOwf31+bx5MzRDCSZfdI/kPyzBesyd2YPXbfBb2yCfK48oKi0WLEfI/tnGEQAAMcMy7zde3Zo6Rt2A3h/G3F9VZzXiGVuPn04XvrxYRjUtU1gYm7AbBLCS4jrWAO6pNcKW1RWoLtiHTaOvKxicueUVcXt0d3f3g/Pu6ZY2eJCRPjzuQfg5R8frqz33XlqTS5ofidqhGLWFQ7fTb6QWCEYjehpGBx6MMZ4RPZaAL7pB4hoDIBqAIs1n18B4AoA6N8/0208CvLs/ag9u+GDBTWeMvKAoAqilj83ud0czTFYI5Vd1zPr8cLrEWfCKnOlSQIB3r/7vjMSCaLUTtkyoqbQQTEIKOs2aHfjTrM4Rnm3co/WHpMBLvM+kD/nx9X3h1aweARX5v3QrkUltguew6p4UNFpRuXMBThC6UBXGHPzm4y4y7yJIJSF/9vXHI1uAV6bQHqvTdPkASrE4Hk5C8t5ozPHB/HbfOtAtSMXoF8/DRM+AeZMDuqEJBFWCDVfAjVCInqbiGYp/p0plmOOvUZ72xNRLwBPAriUMaa02zDGHmKMjWKMjerWLR6l0ZPeCVAG28r3r2pXck95uV7N/Z8gWRCrywUlCpYvqqiNclRCj8/K/caifp0dAXfa/r21QhBwnCj42tXwAI0x1b7Qp2evOET6zKiKFBnrdR6znvp1WDI0QknAqlLYmTiuiJlyxMH0/QWOZiIKQULanK76KgzMsxdk2MFXzFYSNqYRcO4DeSdzFau2OLGrz0/LTJNmikdepTTCTLq4sXRy1hkdsvmXEyZ8oomxjMxJVgw2XwIFIWPsBMbYvop/rwJY5wo4LuiUNgciag9gPIAbGGOfxvkFghBvzqAg7NS7gIHViSPUnS2e6w26NfV05Azu1gY92rfImFWLxU9wcwDq1jJVdBeC7U22reH0cgcQ2Vx8y+lOGi3Z+UPsEg/l4JgMwgdJ54hUSBOcMPXqCDJBq5xlxN9G13br6kr88ZwDcPZBfT1emVxYeNsQTKMBa9UAMGqg/hrJyE4kuxqctVC/eyDI4zmIHbszY2SjwK0fquvww6MH455v74+zNCnJZH587BDl8WCv0fTnBw/qjK6uZszDFX5w5B5G7VuKj2xNo+MAXAzgLvfvq3IBIqoG8DKAJxhjL2bZXmi86znBZeT3fJw7cmhXfLhwQ6oeUcj4JYA2a1/9wTu/PAZAZo5OVdorP0cxWZB+8H/HYtiNb+hP0Naj3k5qkBsk3aJK3ihYf40mL9oQ2J4osGV0Dk1ZaYQ+bTjvnb+fLEnvYvLazNXa80XOPqhvRtyleB25efRHRw/Gf6Ys19Yn/5YXHjwAI/t1wsrNtYrSad76xVEZmhzf3stv/OcOK786aS99IQXtWlZi++5G/OyEocGFNYimf7/wierKBM4d3c+4Xl0Ae3AcYfq8X35jL3y9YQcA5xrNuXWscfuW4iNbQXgXgOeJ6HIAywCcCwBENArAlYyx77vHjgLQhYgucc+7hDE2Pcu2jTARWH4D4BJ3Z4qfHj80JQhJEnA65OwTeo3Q+/6MA7xB2tWVCSy961Tc+fpcPPj+ktRMVKxTWbemj2KAbBjTGne6kU25JjXI33G6QaaXgwZ0wuuz1io3OtX4ymS1Xpixj6NUleoa86Bo1flBiOu6k399HDbsrMPgbm3xn8+WuxWmyzKN12RFgnDo4C4A/GPRhvbI3GcvlaHI57zKioRvrKgOLjRMLA4fX3ecMqhcsedsLOZH3c4XYcInKhKU+o66ra0szYesBCFjbCOADN9pxtg0AN93Xz8F4Kls2skGk7Epc20oE+/WN8HlgUwHFr0g9h7XPVb/d9IwfPeQAfh40cZUOb5s4WcS9HtMwwwsTZpdLtJejvradN/db/eH0QM746tbvpERRwfIGmFmX6IQ5DWqNJNrHFdMEDXCDq2rUhobP+oxwfI+yGb8cE16yMaMbIpJqE1vzZqdiF8IUBhuP2tfZQYcE6qkReCUIIw7UNeSd0o+8tLEmUEeXJR7kUnZZkwHX5Pga/m4rlxFgpw0V562XY1Q8d2iaGp+8LFINo2G0cJm/+4kzP7dScK5Pu0hM5ic49mBQdT6jXuSSaZlIPgcI69R3bkBac3ET5lGJcpuTZRXHrkKn7rd7xCzrM22vosOGeCZ4PCEAJN/fWzgubIlhL+3grD5k61ptOjxDpIajUw6LG9zBEgaoU9dMiYDpZ/Xqh+MpcsqvVtT3qXxBNRzE5A8Uahvcpwu5q4Jji9sI5nKwmwULJ933qh+6NuplWwbjUxG+IT0uepSqdLxmaL7ndPOMuljKWcRgz6Zkro/olehhT9CYfbok1GZRuPm4e+NQkMTM0pLJjsbVVuNsGQoL41Q80xmCEKlRpi+VKJnHxBkElS/3ktYs5GF0fWKNTEdfkm3/Yage88bAUCd1FmHbgPg6Su2AghOHq5iS22D9rOgIfTus/fHT44fqg1wB9RbP2nbk7Utg0FcZ6I1QbuRcuqvanKjbz8sWcioQPj9aDqpUyE6y/zETTmoWus0QXcfVFYkjHNzyhNkbhr1y+VraR6UvkYoPIdLN6o96+QBRwxG5nkAxUHr4Q+/xtkHCV5qhqZRsS+9OrbEfE1Wla4GActy/X7jjeo5PWtkHwzp3jbUbt5JjUYYhROH98Bbc9ZlXQ/grxCGGYgz1wSDz/WmLIvZNOqxjbrtZcQ2hmpS2U5c62+euhP8O8SjER43rEckpx3OhJ8embXAsqbR0qXkNUIjhPu7T8dWaCPMEPk+aLKWqPNa9KtbdZgofPaN9NJOer9E5S73AWPQvn06hBJq3DQqb8oaZajbu2e0mX0QGWnRQny/YCco54i4/U02VlltcmiFaZRrNOdJYQJxxE3mxjTKTfbZ1xEH1ZWJrLcTynCWSSTQojKB352hzjFraT6UvEYYFiK1Fic+lL8eO8wsxZpclyK9lvM3PRQdo8nm763XrYcB7y0IzoCv25E7LDxtluyYE2ksjtGLQvx2GRphiKle0P5y/K24G0hWzjKBXsTpz7u3b6nUiPya/PelozMGb9W5ubDsxeGRGpTzNt/IGmEiQZh/+8kF6o0lTkpeEJo85J5Zvbz+574RH4KWVQkjr1GSDFmqc8Rtmk7apwce/K43bVNQx3muzlp3x4RcktRohFEGvTjXpzxJqLNYQ5NLmqwp++Wl/eDaY7Weyn71J8j/c29f9IWODdj8Nh1HmAPTqNutbKyGxZa7s6oigVEDOmVo5ZbmT8kLQhPkxNhyflJA5R2o1vRk9BlQMtdQ4koenav60nGE3uNRhFrcfdPVy6+vLr+kSFDS7Uw9TZ5EeU/o30Wd+JrTuY16myCV12guSK8R5q7uOBxJ/DIM5ZsXf3RYobtgyQFWEMI7mC1zHWr42h1/oH29Rn0GLN1aojio5nqpPa6Bjs/uK+Q1wgLP3MWNSOX983jP7v72/oH1ZAg+jWA0zRAUxA2n7o1np2YmpU4n3c7tdeX9jfP+e+nHh2Hl5l24/91FALIXhO/96hjjZNoWS1SsIIRakHEBpVoj9OqD/vXq8mCKa4R8rPAzowWRDw/utGk0+wF6r/9v7/yj7KqqO/7ZM5PJhJBhJiE/JglJJhCUaIAkU0iA4I8kgFR+WatQWkMRseha9Ue1C8qqyy4rotYui9pqCq1pl7W0YCW1Wk2ia2lXWzRUQEqABGghGkISFAUKkuT0j3vevPveu/fd9969775f389ab+bcc88995x37jv7nnP22Xte7dsakgirv8eVrJbvttZp1XD0nOFKc3e1EmcsIAtffrXQjBeYVYtGWbVolAUj07jp67sa8r4eZknK64WoBWmNApetrLRaX+5poKTzL58+rbLWE+elIrxGOOl9u80duRQ9EVRXKqmFM8er28ZslEpnuoX/yWUs//7LvbxHtc91ryl6MshOrljob/NopmWZ1YtHufO6s5ia4HRaiHZAghA4/YSRirhCJ/GLF4MN39VGhHEC7DfXLI41+l3cK2ac6Z2lXn3OkvoKHurA8tjUW9j8Xt7hX3z6/IjU1WnWaKc828K3UpsT2tLjrff9JCZdMWFYiSqrEVZxjbDZU6PNU5YRopPoKUFY8N0X5ldXjEUKspePBJ3D9l3B9oRqtkbD/MvvnjMZPmZwIHJEOGv6YMlesTkzAtX41YvrNAYcyrvanqus+9PDR0o7zoLH8nqmTJumLBOTbS3fQbngKa9nVF6l093J94hi3bLjS46bafGl9D75rEUK0e701BrhMRGmlMwqFSyiKPc+UTrSK6Y7ucwEVEnf6sNrls6q7j4pAReaSv29jSfzqW2PRArCZr3pl+fbkNJF00aEpRlPrr/WcL9KR7zRx3GzAY205b0f2lhh4quQZ7O9Q0xmLzkoepyeEoSRSjFW2/tw+ZaK+L2D1a6zinSNvP2/9hVzmDl9kLefM85j3jlolCAs+F2bNb256ueFe1955qKar2na1GhZvpNOG2pZI2ygUFEKUPUwckzlFoq8tk9kablFiE6mtwRhXHydPU7cpvuovJKm0RrpfGfPmMp//eFGAJ54JnAcHGVTc/XiUW66bAUXnTZWcS4N5Zp8ZsYjf/yGyKnRC1fM4/GDlTZeq9X63GWzuX/vs8xuYP9YrS8otRDnjSLOkk1WgivKDVMzyMMfoRCdQE8JwniTVvVhWKwAq/QtaJHnilqpdd684n5BBtG2Ro3fqGOUVivDEWr/gxEexgH+/MrVkfHVOuH3bTyZK9csYuy45E3w5VSM71MoESXtKyyPG6zHnlsD98+ayZex5t5GiLan65VlotbowgRbHOrsCqxyc/zVZ4/7+0WPIoJw5bpiWs3AgtZiHm/3V589PjndmpZqxe3vs4aEYLV8s9TAjDPp1qhvxXLy0hotEDYiLkQv0vMjwmrrfdXyKZ/y/NBFy/nQRcur3rPUykw201/VRoRZE1fHRmieibVS0qgLxb1cVDPynQV5TY3OGR7i0289nde9srpNUiG6nZ4ShOUdy/DQANesW1oSV4s5J6P2jjyyL7X6NnpXo2D6rdMUH5qnLNO41mhy3pVxzdDLtYpA87g0wpiEEL1GTwnCcmFx/4fPB+Clw0XPDa+v4e24QlmmatqYEeGkIEy8XVUKdeo0QdgsKkeEjVvtWbEgesow7Mi2GXYMLKOXJCFEbaRaIzSzmWa2zcx2+/+jVdIOm9leM/tsmnumoSCUPnPFSra//9xifI2eJIr5lGsLxl9TKp/CijOWeG09dJogzG37RAOC6sTZgVZsuem9qGejOS6M8pkaFUIEpFWWuR7Y4ZxbBuzwx3F8BPhuyvulYtWiEQAuOm0+J80pbnzvixipFbhtU6V/QCvTlqk6IgyHI+6TViAUTKtFbZ9oZ5pnWSZGM7iB29V0TRPnRqvd/+Y3reCKM+QXT4gsSCsILwG2+PAW4NKoRGa2GpgLfCvl/VLxa6sWRsZH+R8sMDxtSihd8X+tHXlYkzBqFJl2+qvoI7DDBGFOxW22CdZC9o3seYyjFjdMl5+xiI+9Kdm1lBAimbSCcK5zbp8PP0Ug7Eowsz7gU8AHkjIzs2vNbKeZ7Txw4EDKogUcf2yxg6pls/VzLx2OPReezozbKF8t70mh59JZlgnTsYIwp/s0IgfruaYgaLOsT18NI0IhRHYkKsuY2XZgXsSpG8MHzjlnZlF9yLuArzvn9iathznnNgObASYmJjJ5ly8VhMnTZt944Kmyc8WT/WYcwVV4nzhapaRJ/gjTrhEWBGEWPgLzJG9nvs263czpg8wdnsqHL3pVZnlG2TQVQjSPREHonNsQd87M9pvZmHNun5mNAU9HJFsLrDOzdwHHAoNm9pxzrtp6Yq7UquzS1wccKWiNFk98+6GoahfyLoZ/eTjwb/d/Lx/JbI3wnGXHc/Fp8/ng+a9Il1HO5DXqcTXMjd62aYJ/23Mw4kxy4QYH+rj7D2J/Ig1htSwSCiEyI+32ia3AJuBm//+u8gTOuSsLYTO7CphoJyGYRFjgvfhyIMgMq3lKM7wGWJh2ffKZF1g085iK840wNKWfW65YmSqPVmBmvPt1J3LBq7K1gxp7vypCbf0pc1kf4aKrVWS1tUYIURtp1whvBjaa2W5ggz/GzCbM7Na0hWsHojojh0tUlpkyafqsGLd4ViD8FowWzYf1cmf3wfNfyQqZ94pFfgKFyIdUI0Ln3CFgfUT8TuCaiPgvAl9Mc8+8iRqxfe+Rg6xbNjv2mn997zpmevc64c6sYAXmyFEnp6g5MWlZpgOt6mpmVIh86CnLMllx+Kir2km9ct7wZDicrjBKPBK6Xp1dcznqGrcs02qavfVDCBHQge/J+RI1Iuwrsyyz0m/UjyLOV2Fx+0TnddCdRNExbwMXtQg9EULkiwRhAlFTaoH3iWJ3deb4rPjrQ73aGeMzuei0+dx02QqNCHOiMCLspLXYO+7ZC8Azz7/U4pII0RtoajSBqCm1vr5402kV14fODfb38Rmv4ZmVZRmRwOToro7vucVN8pNnXwTgsYPPt7YgQvQIGhEmEKk16so3x1e7PnpDfVaWZUR1jpkaOBLuNIMDQoj80IgwgajN9v19VrfHiop0k1Oj6qCbyRd+a4Kt9/5kcuuKEEKUoxFhAmE59daJwNr/8LQpNdsaTfRQLznYVBaMTOO6157YkS8cmjYXIh8kCBMId0bnnhzsHRw/fnpJmmpq7rGC0CrPizZB2xaE6CkkCBMILy1duGIed153Fr++emGJUNv8vcdir48aBQbhyvxFe9Hqd5Tpfn1TCNFcJAgTCHuWMDNWLx7FzBjsL351BWPaUTwe0vyL9D7RahVF0XZ85JLAk8WxU7WEL0QeSBAmcCTGx1Kta07ff/yZ0DVUhFs96hDtx+CAfpZC5Il+cQnECcJaSZ4alSRsN1YtHgVghkZkQvQE+qUn4FJqTriSqVUqDjrRGHQ7cud1a3lw3y8yyeujl72aq88eZ87wUCb51YtsjAqRLxKECSwYmZacqArhPs0iwlojzIbVi2eyevHMTPKaOtDP8vnDFfFpX4pqZdI+qp4NIXJB45EEZgxNSXV9X8S6YHlYdAaFkVqz9yRO2kfVr1OIXNBPrcm8Z/2y0FGlNRoJRFFOYVla68dC5IMEoWfhaLop0DhGvINeiB8dChHGTXrM0EMiRB5IEHqa1emUCr9KrVHReTS77Y4e7TzXUUJ0MhKEnieeeQFg0k1SVkRtmYDwPkL1dp1Gs5vsSGFqVJJQiFyQICzjyZ++kGl+cQoyNmlZRnQKLqd9DZoaFSJfJAjLyLqvi91Qrz6u4zjrpOMBOHXhSFPvM6k1qmdEiFzQPsIMGB6K/xpL1gVDHdvzLx0G4OUj8XZKRXtx/qvmcf+Hz2M45ZaaJKQ1KkS+pBoRmtlMM9tmZrv9/9GYdIvM7FtmtsvMHjSzJWnu227cUmVdMe6t/nPfeRSAf3/0UDOKJJpEs4UgFEeEWj8WIh/STo1eD+xwzi0DdvjjKP4G+KRz7hTgDODplPdtGqcuPK7ua46bFt85ajpU1Etx435ryyFEr5BWEF4CbPHhLcCl5QnMbDkw4JzbBuCce845l61GSoacdsJI3ddUe3OPUpARohb0tAiRD2kF4Vzn3D4ffgqYG5HmZOBnZvYVM/uhmX3SzCI9jprZtWa208x2HjhwIGXRGqPfC6tXL6i0NZl0TRThU3lpHYrOprCPUO9NQuRDorKMmW0H5kWcujF84JxzZhbV0w8A64CVwBPA7cBVwG3lCZ1zm4HNABMTE5lJjS+/Yw3D02rTC+r3i3p3/M5ZvFTF4W6Y/T9/kRVET6nKcLKol2mDwXvidLmBEiIXEn9pzrkNcefMbL+ZjTnn9pnZGNFrf3uBe51zj/lrvgqsIUIQNou1J86qOW1BU29oSj9DUyIHrhX89IVfxp7TW72ol7etXcJLh4/y9nPGW10UIXqCtFOjW4FNPrwJuCsizQ+AETOb7Y9fDzyY8r5NY6CBzVvV1v6kAi/qZXCgj3e/7iSmDtT2IiaESEdaQXgzsNHMdgMb/DFmNmFmtwI4544AHwB2mNmPCHQA/jLlfZtG1matwrlpiVAIIdqPVIsQzrlDwPqI+J3ANaHjbcCpae7VzpxeRdNUI0IhhGhvtBrvuf3aNWzftb+ha+cMT40/GSMHZwwN8IsXDzd0PyGEENkhQeg5c+kszlxau1JNmGqjvrhTKxeN8t1HWrNFRAghRBEZ3c6AasuKYSEZXn+85fLTm1giIYQQtSJBmAFVR4Sh8PzjhibDYc/1QgghWocEYZMJK4rKxJoQQrQfEoQZIM1QIYToXCQIM0AOVIUQonOR1mgGVBsRDg3Ev2u8ZWIh60+JslMuhBAiLyQIM6DazOhAf7wg/MSbT2tCaYQQQtSDpkYzQEowQgjRuWhEmANvmVio7RJCCNGmSBDmgKZAhRCifdHUqBBCiJ5GglAIIURPI0GYguVjw60ughBCiJRojTAFt79zDYee+2WriyGEECIFEoQpmDE0hRlDU1pdDCGEECnQ1KgQQoieRoJQCCFETyNBKIQQoqeRIBRCCNHTpBKEZjbTzLaZ2W7/fzQm3SfM7L/NbJeZ3WIyzimEEKJNSDsivB7Y4ZxbBuzwxyWY2VnA2cCpwKuBXwFek/K+QgghRCakFYSXAFt8eAtwaUQaBwwBg8BUYAqwP+V9hRBCiExIKwjnOuf2+fBTQIWXWefcfwDfAfb5zzedc7uiMjOza81sp5ntPHDgQMqiCSGEEMkkbqg3s+3AvIhTN4YPnHPOzFzE9ScBpwALfdQ2M1vnnPteeVrn3GZgM8DExERFXkIIIUTWJApC59yGuHNmtt/Mxpxz+8xsDHg6ItllwH86557z13wDWAtUCMIw99xzz0Ez+9+k8tXA8cDBDPLpBHqlrr1ST+iduqqe3Uez6ro46wzTmljbCmwCbvb/74pI8wTwDjP7GGAEijKfTsrYOTc7ZdkAMLOdzrmJLPJqd3qlrr1ST+iduqqe3Ucn1TXtGuHNwEYz2w1s8MeY2YSZ3erT3AE8CvwIuA+4zzn3zynvK4QQQmRCqhGhc+4QsD4ifidwjQ8fAd6Z5j5CCCFEs+gFyzKbW12AHOmVuvZKPaF36qp6dh8dU1dzTsqZQgghepdeGBEKIYQQsUgQCiGE6Gm6WhCa2QVm9rCZ7TGzCjuo7Y6ZnWBm3zGzB73R8vf4+Ehj5xZwi6/v/Wa2KpTXJp9+t5ltalWdqmFm/Wb2QzP7mj8eN7O7fX1uN7NBHz/VH+/x55eE8rjBxz9sZue3qCpVMbMRM7vDzB7yhujXdmObmtn7/HP7gJl92cyGuqVNzeyvzOxpM3sgFJdZG5rZajP7kb+mZY4KYur5Sf/s3m9m/2RmI6FzkW0V1xfHPQ+545zryg/QT7BtYymBndP7gOWtLleddRgDVvnwDOARYDnwCeB6H3898HEfvhD4BsF+zTXA3T5+JvCY/z/qw6Otrl9Efd8P/B3wNX/8D8DlPvx54DoffhfweR++HLjdh5f7dp4KjPv27291vSLquQW4xocHgZFua1NgAfA4MC3Ulld1S5sC5wKrgAdCcZm1IfB9n9b8tW9oo3qeBwz48MdD9YxsK6r0xXHPQ+71bPUD1cQGXEtg17RwfANwQ6vLlbJOdwEbgYeBMR83Bjzsw18Argilf9ifvwL4Qii+JF07fAhM8O0AXg98zXcAB0M/uMn2BL4JrPXhAZ/Oyts4nK5dPsBxBALCyuK7qk0JBOGTvpMf8G16fje1KbCkTEBk0ob+3EOh+JJ0ra5n2bnLgC/5cGRbEdMXV/uN5/3p5qnRwg+xwF4f15H4qaKVwN3EGzuPq3MnfBefBn4fOOqPZwE/c84d9sfhMk/Wx59/1qfvhHqOAweAv/bTwLea2XS6rE2dcz8G/oTAstQ+gja6h+5s0wJZteECHy6Pb0euJhixQv31rPYbz5VuFoRdg5kdC9wJvNc59/PwORe8SnX0HhgzeyPwtHPunlaXJQcGCKaa/sI5txJ4njI/nl3SpqMEbtrGgfnAdOCClhYqR7qhDZMwsxuBw8CXWl2WtHSzIPwxcELoeKGP6yjMbAqBEPySc+4rPnq/BUbOsVJj53F1bvfv4mzgYjP7H+DvCaZH/wwYMbOC9aNwmSfr488fBxyi/esJwVvvXufc3f74DgLB2G1tugF43Dl3wDn3MvAVgnbuxjYtkFUb/piit55wfNtgZlcBbwSu9EIf6q/nIeKfh1zpZkH4A2CZ10oaJFiA39riMtWF1xS7DdjlnPvT0KmCsXMoNXa+FXib11JbAzzrp2q+CZxnZqP+Tf08H9cWOOducM4tdM4tIWinbzvnriTwY/lmn6y8noX6v9mndz7+cq+BOA4sI1A6aBucc08BT5rZK3zUeuBBuqxNCaZE15jZMf45LtSz69o0RCZt6M/93MzW+O/ubUQ7NGgJZnYBwTLGxc65F0Kn4toqsi/27Rv3PORLqxZg8/gQaGs9QqCxdGOry9NA+c8hmF65H7jXfy4kmFvfAewGtgMzfXoDPkfRyPlEKK+rgT3+89utrluVOr+WotboUoIf0h7gH4GpPn7IH+/x55eGrr/R1/9hWqRpV0MdTwd2+nb9KoHGYNe1KfBHwEPAA8DfEmgTdkWbAl8mWPt8mWCU//Ys2xCY8N/bo8BnKVOuanE99xCs+RX6pM8ntRUxfXHc85D3RybWhBBC9DTdPDUqhBBCJCJBKIQQoqeRIBRCCNHTSBAKIYToaSQIhRBC9DQShEIIIXoaCUIhhBA9zf8DPUbOxHhRX88AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(len(cough_segments),1, figsize=(7,10))\n", + "for i in range(0,len(cough_segments)):\n", + " axs[i].plot(cough_segments[i])\n", + " axs[i].set_title(\"Cough segment \" + str(i))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute SNR of cough signal\n", + "Compute an estimate of the SNR by taking the ratio of the power of the cough part of the signal to the ratio of the rest of the signal (i.e. background noise)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The SNR of the cough signal is 24.53300952911377\n" + ] + } + ], + "source": [ + "snr = compute_SNR(x,fs)\n", + "print(\"The SNR of the cough signal is {0}\".format(snr))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index ba3d8a2..a5213c5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,111 +1,115 @@ # This file may be used to create an environment using: # $ conda create --name --file # platform: win-64 appdirs=1.4.4=pypi_0 argon2-cffi=20.1.0=py38he774522_1 attrs=20.1.0=py_0 audioread=2.1.8=pypi_0 backcall=0.2.0=py_0 blas=1.0=mkl bleach=3.1.5=py_0 ca-certificates=2020.7.22=0 certifi=2020.6.20=py38_0 cffi=1.14.2=py38h7a1dbc1_0 chardet=3.0.4=pypi_0 colorama=0.4.3=py_0 +cycler=0.10.0=pypi_0 decorator=4.4.2=py_0 defusedxml=0.6.0=py_0 entrypoints=0.3=py38_0 icc_rt=2019.0.0=h0cc432a_1 icu=58.2=ha925a31_3 idna=2.10=pypi_0 importlib-metadata=1.7.0=py38_0 importlib_metadata=1.7.0=0 intel-openmp=2020.2=254 ipykernel=5.3.4=py38h5ca1d4c_0 ipython=7.18.1=py38h5ca1d4c_0 ipython_genutils=0.2.0=py38_0 ipywidgets=7.5.1=py_0 jedi=0.17.2=py38_0 jinja2=2.11.2=py_0 joblib=0.16.0=pypi_0 jpeg=9b=hb83a4c4_2 jsonschema=3.2.0=py38_0 jupyter=1.0.0=py38_7 jupyter_client=6.1.6=py_0 jupyter_console=6.2.0=py_0 jupyter_core=4.6.3=py38_0 +kiwisolver=1.3.1=pypi_0 libpng=1.6.37=h2a8f88b_0 librosa=0.8.0=pypi_0 libsodium=1.0.18=h62dcd97_0 llvmlite=0.34.0=pypi_0 m2w64-gcc-libgfortran=5.3.0=6 m2w64-gcc-libs=5.3.0=7 m2w64-gcc-libs-core=5.3.0=7 m2w64-gmp=6.1.0=2 m2w64-libwinpthread-git=5.0.0.4634.697f757=2 markupsafe=1.1.1=py38he774522_0 +matplotlib=3.3.4=pypi_0 mistune=0.8.4=py38he774522_1000 mkl=2020.2=256 mkl-service=2.3.0=py38hb782905_0 mkl_fft=1.1.0=py38h45dec08_0 mkl_random=1.1.1=py38h47e9c7a_0 msys2-conda-epoch=20160418=1 nbconvert=5.6.1=py38_0 nbformat=5.0.7=py_0 notebook=6.1.1=py38_0 numba=0.51.2=pypi_0 numpy=1.19.1=py38h5510c5b_0 numpy-base=1.19.1=py38ha3acd2a_0 openssl=1.1.1g=he774522_1 packaging=20.4=py_0 pandoc=2.10.1=0 pandocfilters=1.4.2=py38_1 parso=0.7.0=py_0 pickleshare=0.7.5=py38_1000 +pillow=8.1.0=pypi_0 pip=20.2.2=py38_0 pooch=1.1.1=pypi_0 prometheus_client=0.8.0=py_0 prompt-toolkit=3.0.7=py_0 prompt_toolkit=3.0.7=0 pycparser=2.20=py_2 pygments=2.6.1=py_0 pyparsing=2.4.7=py_0 pyqt=5.9.2=py38ha925a31_4 pyrsistent=0.16.0=py38he774522_0 python=3.8.5=h5fd99cc_1 python-dateutil=2.8.1=py_0 pywin32=227=py38he774522_1 pywinpty=0.5.7=py38_0 pyzmq=19.0.1=py38ha925a31_1 qt=5.9.7=vc14h73c81de_0 qtconsole=4.7.6=py_0 qtpy=1.9.0=py_0 requests=2.24.0=pypi_0 resampy=0.2.2=pypi_0 scikit-learn=0.22.1=pypi_0 scipy=1.5.0=py38h9439919_0 send2trash=1.5.0=py38_0 setuptools=49.6.0=py38_0 sip=4.19.13=py38ha925a31_0 six=1.15.0=py_0 soundfile=0.10.3.post1=pypi_0 sqlite=3.33.0=h2a8f88b_0 terminado=0.8.3=py38_0 testpath=0.4.4=py_0 threadpoolctl=2.1.0=pypi_0 tornado=6.0.4=py38he774522_1 traitlets=4.3.3=py38_0 urllib3=1.25.10=pypi_0 vc=14.1=h0510ff6_4 vs2015_runtime=14.16.27012=hf0eaf9b_3 wcwidth=0.2.5=py_0 webencodings=0.5.1=py38_1 wheel=0.35.1=py_0 widgetsnbextension=3.5.1=py38_0 wincertstore=0.2=py38_0 winpty=0.4.3=4 xgboost=0.90=pypi_0 zeromq=4.3.2=ha925a31_2 zipp=3.1.0=py_0 zlib=1.2.11=h62dcd97_4 diff --git a/src/segmentation.py b/src/segmentation.py new file mode 100644 index 0000000..fdd4b85 --- /dev/null +++ b/src/segmentation.py @@ -0,0 +1,67 @@ +import numpy as np + +#Use old segmentation +def segment_cough(x,fs, cough_padding=0.2,min_cough_len=0.2, th_l_multiplier = 0.1, th_h_multiplier = 2): + """Preprocess the data by segmenting each file into individual coughs using a hysteresis comparator on the signal power + + Inputs: + *x (np.array): cough signal + *fs (float): sampling frequency in Hz + *cough_padding (float): number of seconds added to the beginning and end of each detected cough to make sure coughs are not cut short + *min_cough_length (float): length of the minimum possible segment that can be considered a cough + *th_l_multiplier (float): multiplier of the RMS energy used as a lower threshold of the hysteresis comparator + *th_h_multiplier (float): multiplier of the RMS energy used as a high threshold of the hysteresis comparator + + Outputs: + *coughSegments (np.array of np.arrays): a list of cough signal arrays corresponding to each cough + cough_mask (np.array): an array of booleans that are True at the indices where a cough is in progress""" + + cough_mask = np.array([False]*len(x)) + + + #Define hysteresis thresholds + rms = np.sqrt(np.mean(np.square(x))) + seg_th_l = th_l_multiplier * rms + seg_th_h = th_h_multiplier*rms + + #Segment coughs + coughSegments = [] + padding = round(fs*cough_padding) + min_cough_samples = round(fs*min_cough_len) + cough_start = 0 + cough_end = 0 + cough_in_progress = False + tolerance = round(0.01*fs) + below_th_counter = 0 + + for i, sample in enumerate(x**2): + if cough_in_progress: + if sample tolerance: + cough_end = i+padding if (i+padding < len(x)) else len(x)-1 + cough_in_progress = False + if (cough_end+1-cough_start-2*padding>min_cough_samples): + coughSegments.append(x[cough_start:cough_end+1]) + cough_mask[cough_start:cough_end+1] = True + elif i == (len(x)-1): + cough_end=i + cough_in_progress = False + if (cough_end+1-cough_start-2*padding>min_cough_samples): + coughSegments.append(x[cough_start:cough_end+1]) + else: + below_th_counter = 0 + else: + if sample>seg_th_h: + cough_start = i-padding if (i-padding >=0) else 0 + cough_in_progress = True + + return coughSegments, cough_mask + +def compute_SNR(x, fs): + """Compute the Signal-to-Noise ratio of the audio signal x (np.array) with sampling frequency fs (float)""" + segments, cough_mask = segment_cough(x,fs) + RMS_signal = 0 if len(x[cough_mask])==0 else np.sqrt(np.mean(np.square(x[cough_mask]))) + RMS_noise = np.sqrt(np.mean(np.square(x[~cough_mask]))) + SNR = 0 if (RMS_signal==0 or np.isnan(RMS_noise)) else 20*np.log10(RMS_signal/RMS_noise) + return SNR \ No newline at end of file