SeizureDetection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SeizureDetection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyMKf28E+iwfNGMOFXDz3mSH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/josephmhernandez/SeizureDetection/blob/master/SeizureDetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMZXK42oRwUB",
        "colab_type": "text"
      },
      "source": [
        "# Loading Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcZlUrmWS1a8",
        "colab_type": "text"
      },
      "source": [
        "## openPose"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PxEJKYWS36G",
        "colab_type": "code",
        "outputId": "930d53b8-b913-42da-ca46-a61c47245906",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Load Open Pose\n",
        "import os\n",
        "from os.path import exists, join, basename, splitext\n",
        "\n",
        "git_repo_url = 'https://github.com/CMU-Perceptual-Computing-Lab/openpose.git'\n",
        "project_name = splitext(basename(git_repo_url))[0]\n",
        "if not exists(project_name):\n",
        "  # see: https://github.com/CMU-Perceptual-Computing-Lab/openpose/issues/949\n",
        "  # install new CMake becaue of CUDA10\n",
        "  !wget -q https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.tar.gz\n",
        "  !tar xfz cmake-3.13.0-Linux-x86_64.tar.gz --strip-components=1 -C /usr/local\n",
        "  # clone openpose\n",
        "  !git clone -q --depth 1 $git_repo_url\n",
        "  !sed -i 's/execute_process(COMMAND git checkout master WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\\/3rdparty\\/caffe)/execute_process(COMMAND git checkout f019d0dfe86f49d1140961f8c7dec22130c83154 WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\\/3rdparty\\/caffe)/g' openpose/CMakeLists.txt\n",
        "  # install system dependencies\n",
        "  !apt-get -qq install -y libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev opencl-headers ocl-icd-opencl-dev libviennacl-dev\n",
        "  # install python dependencies\n",
        "  !pip install -q youtube-dl\n",
        "  # build openpose\n",
        "  !cd openpose && rm -rf build || true && mkdir build && cd build && cmake .. && make -j`nproc`\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Setting up libgoogle-glog-dev (0.3.5-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 3.4MB/s \n",
            "\u001b[?25h-- The C compiler identification is GNU 7.5.0\n",
            "-- The CXX compiler identification is GNU 7.5.0\n",
            "-- Check for working C compiler: /usr/bin/cc\n",
            "-- Check for working C compiler: /usr/bin/cc -- works\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++\n",
            "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- GCC detected, adding compile flags\n",
            "-- GCC detected, adding compile flags\n",
            "-- Looking for pthread.h\n",
            "-- Looking for pthread.h - found\n",
            "-- Looking for pthread_create\n",
            "-- Looking for pthread_create - not found\n",
            "-- Looking for pthread_create in pthreads\n",
            "-- Looking for pthread_create in pthreads - not found\n",
            "-- Looking for pthread_create in pthread\n",
            "-- Looking for pthread_create in pthread - found\n",
            "-- Found Threads: TRUE  \n",
            "-- Found CUDA: /usr/local/cuda (found version \"10.1\") \n",
            "-- Building with CUDA.\n",
            "-- CUDA detected: 10.1\n",
            "-- Found cuDNN: ver. 7.6.5 found (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so)\n",
            "-- Added CUDA NVCC flags for: sm_60\n",
            "-- Found cuDNN: ver. 7.6.5 found (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so)\n",
            "-- Found GFlags: /usr/include  \n",
            "-- Found gflags  (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so)\n",
            "-- Found Glog: /usr/include  \n",
            "-- Found glog    (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so)\n",
            "-- Found Protobuf: /usr/lib/x86_64-linux-gnu/libprotobuf.so;-lpthread (found version \"3.0.0\") \n",
            "-- Found OpenCV: /usr (found version \"3.2.0\") \n",
            "-- Caffe will be downloaded from source now. NOTE: This process might take several minutes depending\n",
            "        on your internet connection.\n",
            "Submodule '3rdparty/caffe' (https://github.com/CMU-Perceptual-Computing-Lab/caffe.git) registered for path '../3rdparty/caffe'\n",
            "Cloning into '/content/openpose/3rdparty/caffe'...\n",
            "Submodule path '../3rdparty/caffe': checked out 'b5ede488952e40861e84e51a9f9fd8fe2395cc8a'\n",
            "Previous HEAD position was b5ede488 Added TX2 JetPack3.3 support\n",
            "HEAD is now at c95002fb Added support for newer GPUs\n",
            "-- Caffe will be built from source now.\n",
            "-- Download the models.\n",
            "-- Downloading BODY_25 model...\n",
            "-- NOTE: This process might take several minutes depending on your internet connection.\n",
            "-- Not downloading body (COCO) model\n",
            "-- Not downloading body (MPI) model\n",
            "-- Downloading face model...\n",
            "-- NOTE: This process might take several minutes depending on your internet connection.\n",
            "-- Downloading hand model...\n",
            "-- NOTE: This process might take several minutes depending on your internet connection.\n",
            "-- Models Downloaded.\n",
            "-- Configuring done\n",
            "-- Generating done\n",
            "-- Build files have been written to: /content/openpose/build\n",
            "\u001b[35m\u001b[1mScanning dependencies of target openpose_lib\u001b[0m\n",
            "[ 12%] \u001b[34m\u001b[1mCreating directories for 'openpose_lib'\u001b[0m\n",
            "[ 25%] \u001b[34m\u001b[1mNo download step for 'openpose_lib'\u001b[0m\n",
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            "[ 62%] \u001b[34m\u001b[1mPerforming configure step for 'openpose_lib'\u001b[0m\n",
            "-- The C compiler identification is GNU 7.5.0\n",
            "-- The CXX compiler identification is GNU 7.5.0\n",
            "-- Check for working C compiler: /usr/bin/cc\n",
            "-- Check for working C compiler: /usr/bin/cc -- works\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++\n",
            "-- Check for working CXX compiler: /usr/bin/c++ -- works\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Looking for pthread.h\n",
            "-- Looking for pthread.h - found\n",
            "-- Looking for pthread_create\n",
            "-- Looking for pthread_create - not found\n",
            "-- Looking for pthread_create in pthreads\n",
            "-- Looking for pthread_create in pthreads - not found\n",
            "-- Looking for pthread_create in pthread\n",
            "-- Looking for pthread_create in pthread - found\n",
            "-- Found Threads: TRUE  \n",
            "-- Boost version: 1.65.1\n",
            "-- Found the following Boost libraries:\n",
            "--   system\n",
            "--   thread\n",
            "--   filesystem\n",
            "--   chrono\n",
            "--   date_time\n",
            "--   atomic\n",
            "-- Found GFlags: /usr/include  \n",
            "-- Found gflags  (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so)\n",
            "-- Found Glog: /usr/include  \n",
            "-- Found glog    (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so)\n",
            "-- Found Protobuf: /usr/lib/x86_64-linux-gnu/libprotobuf.so;-lpthread (found version \"3.0.0\") \n",
            "-- Found PROTOBUF Compiler: /usr/bin/protoc\n",
            "-- HDF5: Using hdf5 compiler wrapper to determine C configuration\n",
            "-- HDF5: Using hdf5 compiler wrapper to determine CXX configuration\n",
            "-- Found HDF5: /usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5_cpp.so;/usr/lib/x86_64-linux-gnu/hdf5/serial/libhdf5.so;/usr/lib/x86_64-linux-gnu/libpthread.so;/usr/lib/x86_64-linux-gnu/libsz.so;/usr/lib/x86_64-linux-gnu/libz.so;/usr/lib/x86_64-linux-gnu/libdl.so;/usr/lib/x86_64-linux-gnu/libm.so (found version \"1.10.0.1\") found components:  HL \n",
            "-- CUDA detected: 10.1\n",
            "-- Found cuDNN: ver. 7.6.5 found (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so)\n",
            "-- Added CUDA NVCC flags for: sm_60\n",
            "-- Found Atlas: /usr/include/x86_64-linux-gnu  \n",
            "-- Found Atlas (include: /usr/include/x86_64-linux-gnu library: /usr/lib/x86_64-linux-gnu/libatlas.so lapack: /usr/lib/x86_64-linux-gnu/liblapack.so\n",
            "-- Python interface is disabled or not all required dependencies found. Building without it...\n",
            "-- Found Git: /usr/bin/git (found version \"2.17.1\") \n",
            "-- \n",
            "-- ******************* Caffe Configuration Summary *******************\n",
            "-- General:\n",
            "--   Version           :   1.0.0\n",
            "--   Git               :   1.0-147-gc95002fb\n",
            "--   System            :   Linux\n",
            "--   C++ compiler      :   /usr/bin/c++\n",
            "--   Release CXX flags :   -O3 -DNDEBUG -fPIC -Wall -std=c++11 -Wno-sign-compare -Wno-uninitialized\n",
            "--   Debug CXX flags   :   -g -fPIC -Wall -std=c++11 -Wno-sign-compare -Wno-uninitialized\n",
            "--   Build type        :   Release\n",
            "-- \n",
            "--   BUILD_SHARED_LIBS :   ON\n",
            "--   BUILD_python      :   OFF\n",
            "--   BUILD_matlab      :   OFF\n",
            "--   BUILD_docs        :   OFF\n",
            "--   CPU_ONLY          :   OFF\n",
            "--   USE_OPENCV        :   OFF\n",
            "--   USE_LEVELDB       :   OFF\n",
            "--   USE_LMDB          :   OFF\n",
            "--   USE_NCCL          :   OFF\n",
            "--   ALLOW_LMDB_NOLOCK :   OFF\n",
            "--   USE_HDF5          :   ON\n",
            "-- \n",
            "-- Dependencies:\n",
            "--   BLAS              :   Yes (Atlas)\n",
            "--   Boost             :   Yes (ver. 1.65)\n",
            "--   glog              :   Yes\n",
            "--   gflags            :   Yes\n",
            "--   protobuf          :   Yes (ver. 3.0.0)\n",
            "--   CUDA              :   Yes (ver. 10.1)\n",
            "-- \n",
            "-- NVIDIA CUDA:\n",
            "--   Target GPU(s)     :   Auto\n",
            "--   GPU arch(s)       :   sm_60\n",
            "--   cuDNN             :   Yes (ver. 7.6.5)\n",
            "-- \n",
            "-- Install:\n",
            "--   Install path      :   /content/openpose/build/caffe\n",
            "-- \n",
            "-- Configuring done\n",
            "-- Generating done\n",
            "CMake Warning:\n",
            "  Manually-specified variables were not used by the project:\n",
            "\n",
            "    CUDA_ARCH_BIN\n",
            "\n",
            "\n",
            "-- Build files have been written to: /content/openpose/build/caffe/src/openpose_lib-build\n",
            "[ 75%] \u001b[34m\u001b[1mPerforming build step for 'openpose_lib'\u001b[0m\n",
            "[  1%] \u001b[34m\u001b[1mRunning C++/Python protocol buffer compiler on /content/openpose/3rdparty/caffe/src/caffe/proto/caffe.proto\u001b[0m\n",
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            "[  2%] \u001b[34m\u001b[1mBuilding NVCC (Device) object src/caffe/CMakeFiles/cuda_compile_1.dir/layers/cuda_compile_1_generated_base_data_layer.cu.o\u001b[0m\n",
            "In file included from /content/openpose/3rdparty/caffe/src/caffe/util/math_functions.cu:1:0:\n",
            "/usr/local/cuda/include/math_functions.h:54:2: warning: #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\" [-Wcpp]\n",
            " #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\"\n",
            "  ^~~~~~~\n",
            "In file included from /content/openpose/3rdparty/caffe/src/caffe/util/math_functions.cu:1:0:\n",
            "/usr/local/cuda/include/math_functions.h:54:2: warning: #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\" [-Wcpp]\n",
            " #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\"\n",
            "  ^~~~~~~\n",
            "In file included from /content/openpose/3rdparty/caffe/src/caffe/util/math_functions.cu:1:0:\n",
            "/usr/local/cuda/include/math_functions.h:54:2: warning: #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\" [-Wcpp]\n",
            " #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\"\n",
            "  ^~~~~~~\n",
            "In file included from /content/openpose/3rdparty/caffe/src/caffe/util/math_functions.cu:1:0:\n",
            "/usr/local/cuda/include/math_functions.h:54:2: warning: #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\" [-Wcpp]\n",
            " #warning \"math_functions.h is an internal header file and must not be used directly.  This file will be removed in a future CUDA release.  Please use cuda_runtime_api.h or cuda_runtime.h instead.\"\n",
            "  ^~~~~~~\n",
            "[  2%] \u001b[34m\u001b[1mBuilding NVCC (Device) object src/caffe/CMakeFiles/cuda_compile_1.dir/layers/cuda_compile_1_generated_batch_norm_layer.cu.o\u001b[0m\n",
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            "[100%] Built target convert_cifar_data\n",
            "[ 87%] \u001b[34m\u001b[1mPerforming install step for 'openpose_lib'\u001b[0m\n",
            "-- Boost version: 1.65.1\n",
            "-- Found the following Boost libraries:\n",
            "--   system\n",
            "--   thread\n",
            "--   filesystem\n",
            "--   chrono\n",
            "--   date_time\n",
            "--   atomic\n",
            "-- Found gflags  (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so)\n",
            "-- Found glog    (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so)\n",
            "-- Found PROTOBUF Compiler: /usr/bin/protoc\n",
            "-- HDF5: Using hdf5 compiler wrapper to determine C configuration\n",
            "-- HDF5: Using hdf5 compiler wrapper to determine CXX configuration\n",
            "-- CUDA detected: 10.1\n",
            "-- Found cuDNN: ver. 7.6.5 found (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so)\n",
            "-- Added CUDA NVCC flags for: sm_60\n",
            "-- Found Atlas (include: /usr/include/x86_64-linux-gnu library: /usr/lib/x86_64-linux-gnu/libatlas.so lapack: /usr/lib/x86_64-linux-gnu/liblapack.so\n",
            "-- Python interface is disabled or not all required dependencies found. Building without it...\n",
            "-- \n",
            "-- ******************* Caffe Configuration Summary *******************\n",
            "-- General:\n",
            "--   Version           :   1.0.0\n",
            "--   Git               :   1.0-147-gc95002fb\n",
            "--   System            :   Linux\n",
            "--   C++ compiler      :   /usr/bin/c++\n",
            "--   Release CXX flags :   -O3 -DNDEBUG -fPIC -Wall -std=c++11 -Wno-sign-compare -Wno-uninitialized\n",
            "--   Debug CXX flags   :   -g -fPIC -Wall -std=c++11 -Wno-sign-compare -Wno-uninitialized\n",
            "--   Build type        :   Release\n",
            "-- \n",
            "--   BUILD_SHARED_LIBS :   ON\n",
            "--   BUILD_python      :   OFF\n",
            "--   BUILD_matlab      :   OFF\n",
            "--   BUILD_docs        :   OFF\n",
            "--   CPU_ONLY          :   OFF\n",
            "--   USE_OPENCV        :   OFF\n",
            "--   USE_LEVELDB       :   OFF\n",
            "--   USE_LMDB          :   OFF\n",
            "--   USE_NCCL          :   OFF\n",
            "--   ALLOW_LMDB_NOLOCK :   OFF\n",
            "--   USE_HDF5          :   ON\n",
            "-- \n",
            "-- Dependencies:\n",
            "--   BLAS              :   Yes (Atlas)\n",
            "--   Boost             :   Yes (ver. 1.65)\n",
            "--   glog              :   Yes\n",
            "--   gflags            :   Yes\n",
            "--   protobuf          :   Yes (ver. 3.0.0)\n",
            "--   CUDA              :   Yes (ver. 10.1)\n",
            "-- \n",
            "-- NVIDIA CUDA:\n",
            "--   Target GPU(s)     :   Auto\n",
            "--   GPU arch(s)       :   sm_60\n",
            "--   cuDNN             :   Yes (ver. 7.6.5)\n",
            "-- \n",
            "-- Install:\n",
            "--   Install path      :   /content/openpose/build/caffe\n",
            "-- \n",
            "-- Configuring done\n",
            "-- Generating done\n",
            "-- Build files have been written to: /content/openpose/build/caffe/src/openpose_lib-build\n",
            "[  2%] Built target caffeproto\n",
            "\u001b[35m\u001b[1mScanning dependencies of target caffe\u001b[0m\n",
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            "[ 97%] Built target classification\n",
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            "[ 98%] Built target upgrade_net_proto_text\n",
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            "[100%] Built target convert_cifar_data\n",
            "\u001b[36mInstall the project...\u001b[0m\n",
            "-- Install configuration: \"Release\"\n",
            "-- Installing: /content/openpose/build/caffe/share/Caffe/CaffeConfig.cmake\n",
            "-- Installing: /content/openpose/build/caffe/share/Caffe/CaffeTargets.cmake\n",
            "-- Installing: /content/openpose/build/caffe/share/Caffe/CaffeTargets-release.cmake\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/solver_factory.hpp\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/util\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/util/format.hpp\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/filler.hpp\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/log_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_deconv_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_pooling_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/im2col_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/power_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_lrn_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/softmax_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/threshold_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/hdf5_data_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/slice_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/elu_layer.hpp\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/tanh_layer.hpp\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/filter_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/euclidean_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/argmax_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/embed_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/bnll_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/concat_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/softmax_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/bias_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/prelu_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/reshape_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/tile_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_softmax_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/batch_reindex_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/relu_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_lcn_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/lrn_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/batch_norm_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/dummy_data_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/conv_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/pooling_layer.hpp\n",
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            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/split_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/python_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_conv_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/clip_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/spp_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/hinge_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_sigmoid_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/silence_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/data_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/mvn_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/exp_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/base_data_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/eltwise_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/base_conv_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/window_data_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/accuracy_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/reduction_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/deconv_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_tanh_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/cudnn_relu_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/scale_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/multinomial_logistic_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/hdf5_output_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/swish_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/sigmoid_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/inner_product_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/rnn_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/lstm_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/infogain_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/contrastive_loss_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layers/flatten_layer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/common.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/layer_factory.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/net.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/solver.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/syncedmem.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/parallel.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/data_transformer.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/test\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/test/test_caffe_main.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/test/test_gradient_check_util.hpp\n",
            "-- Installing: /content/openpose/build/caffe/include/caffe/proto/caffe.pb.h\n",
            "-- Installing: /content/openpose/build/caffe/lib/libcaffe.so.1.0.0\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/lib/libcaffe.so.1.0.0\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/lib/libcaffe.so\n",
            "-- Installing: /content/openpose/build/caffe/lib/libcaffeproto.a\n",
            "-- Installing: /content/openpose/build/caffe/python/caffe/proto/caffe_pb2.py\n",
            "-- Installing: /content/openpose/build/caffe/python/caffe/proto/__init__.py\n",
            "-- Installing: /content/openpose/build/caffe/bin/caffe\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/caffe\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/compute_image_mean\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/compute_image_mean\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/convert_imageset\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/convert_imageset\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/extract_features\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/extract_features\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/upgrade_net_proto_binary\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/upgrade_net_proto_binary\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/upgrade_net_proto_text\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/upgrade_net_proto_text\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/upgrade_solver_proto_text\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/upgrade_solver_proto_text\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/convert_cifar_data\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/convert_cifar_data\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/classification\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/classification\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/convert_mnist_data\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/convert_mnist_data\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "-- Installing: /content/openpose/build/caffe/bin/convert_mnist_siamese_data\n",
            "-- Set runtime path of \"/content/openpose/build/caffe/bin/convert_mnist_siamese_data\" to \"/content/openpose/build/caffe/lib:/usr/lib/x86_64-linux-gnu/hdf5/serial:/usr/local/cuda/lib64\"\n",
            "[100%] \u001b[34m\u001b[1mCompleted 'openpose_lib'\u001b[0m\n",
            "\u001b[34m\u001b[1mRerunning cmake after building Caffe submodule\u001b[0m\n",
            "-- GCC detected, adding compile flags\n",
            "-- GCC detected, adding compile flags\n",
            "-- Building with CUDA.\n",
            "-- CUDA detected: 10.1\n",
            "-- Found cuDNN: ver. 7.6.5 found (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so)\n",
            "-- Added CUDA NVCC flags for: sm_60\n",
            "-- Found cuDNN: ver. 7.6.5 found (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so)\n",
            "-- Found gflags  (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libgflags.so)\n",
            "-- Found glog    (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so)\n",
            "-- Caffe will be downloaded from source now. NOTE: This process might take several minutes depending\n",
            "        on your internet connection.\n",
            "-- Caffe has already been downloaded.\n",
            "HEAD is now at c95002fb Added support for newer GPUs\n",
            "-- Caffe will be built from source now.\n",
            "-- Adding Example calibration.bin\n",
            "-- Adding Example openpose.bin\n",
            "-- Adding Example 1_custom_post_processing.bin\n",
            "-- Adding Example 01_body_from_image_default.bin\n",
            "-- Adding Example 02_whole_body_from_image_default.bin\n",
            "-- Adding Example 03_keypoints_from_image.bin\n",
            "-- Adding Example 04_keypoints_from_images.bin\n",
            "-- Adding Example 05_keypoints_from_images_multi_gpu.bin\n",
            "-- Adding Example 06_face_from_image.bin\n",
            "-- Adding Example 07_hand_from_image.bin\n",
            "-- Adding Example 08_heatmaps_from_image.bin\n",
            "-- Adding Example 09_keypoints_from_heatmaps.bin\n",
            "-- Adding Example 10_asynchronous_custom_input.bin\n",
            "-- Adding Example 11_asynchronous_custom_output.bin\n",
            "-- Adding Example 12_asynchronous_custom_input_output_and_datum.bin\n",
            "-- Adding Example 13_synchronous_custom_input.bin\n",
            "-- Adding Example 14_synchronous_custom_preprocessing.bin\n",
            "-- Adding Example 15_synchronous_custom_postprocessing.bin\n",
            "-- Adding Example 16_synchronous_custom_output.bin\n",
            "-- Adding Example 17_synchronous_custom_all_and_datum.bin\n",
            "-- Adding Example 1_thread_user_processing_function.bin\n",
            "-- Adding Example 2_thread_user_input_processing_output_and_datum.bin\n",
            "-- Adding Example handFromJsonTest.bin\n",
            "-- Adding Example resizeTest.bin\n",
            "-- Download the models.\n",
            "-- Downloading BODY_25 model...\n",
            "-- Model already exists.\n",
            "-- Not downloading body (COCO) model\n",
            "-- Not downloading body (MPI) model\n",
            "-- Downloading face model...\n",
            "-- Model already exists.\n",
            "-- Downloading hand model...\n",
            "-- Model already exists.\n",
            "-- Models Downloaded.\n",
            "-- Configuring done\n",
            "-- Generating done\n",
            "-- Build files have been written to: /content/openpose/build\n",
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            "\u001b[01m\u001b[K/content/openpose/src/openpose/utilities/fileSystem.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbool op::compareNat(const string&, const string&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K/content/openpose/src/openpose/utilities/fileSystem.cpp:51:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "             for (auto i = 0 ; \u001b[01;35m\u001b[Ki < aNumberAsString.size()\u001b[m\u001b[K ; ++i)\n",
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            "\u001b[01m\u001b[K/content/openpose/src/openpose/utilities/fileSystem.cpp:51:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
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            "Built target openpose_lib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ydg6-2DmS9-o",
        "colab_type": "text"
      },
      "source": [
        "### Creating action landmarks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vU_hyToeTBlw",
        "colab_type": "code",
        "outputId": "da329a1e-50f0-4d4c-a293-a7d1a4136bfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#Connect to Google Drive.\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWycA1ZXzZtN",
        "colab_type": "code",
        "outputId": "55fb90c3-6a45-42c3-b0d9-1bbef01e00bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Function: save video in colab memory \n",
        "!pip install youtube_dl\n",
        "!pip install pafy\n",
        "\n",
        "import youtube_dl\n",
        "import os\n",
        "import pafy\n",
        "import numpy as np\n",
        "\n",
        "def create_vid(link):\n",
        "    ydl = youtube_dl.YoutubeDL({'outtmpl': '%(id)s%(ext)s',\n",
        "            'noplaylist' : True})\n",
        "    link_video = link\n",
        "    with ydl:\n",
        "        result = ydl.extract_info(\n",
        "            link_video,\n",
        "            download=True\n",
        "        )\n",
        "\n",
        "    if 'entries' in result:\n",
        "        # Can be a playlist or a list of videos\n",
        "        video = result['entries'][0]\n",
        "    else:\n",
        "        # Just a video\n",
        "        video = result\n",
        "\n",
        "    title = video['id']\n",
        "\n",
        "    file_list = np.array(os.listdir(path=None))\n",
        "    video_name = None\n",
        "    for a in file_list:\n",
        "        if(title in a):\n",
        "            print('here')\n",
        "            video_name = a\n",
        "            break\n",
        "\n",
        "    nvideo = pafy.new(link_video)\n",
        "    video_duration=nvideo.length \n",
        "\n",
        "    os.rename(video_name, nvideo.title + '.' + video['ext'])\n",
        "    video_name = nvideo.title + '.' + video['ext']\n",
        "    title = nvideo.title\n",
        "    return video_name, title"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: youtube_dl in /usr/local/lib/python3.6/dist-packages (2020.3.24)\n",
            "Collecting pafy\n",
            "  Downloading https://files.pythonhosted.org/packages/74/69/829919eeadff695338f98fa12bb99e45490761a2010c8d688d88b6df194a/pafy-0.5.5-py2.py3-none-any.whl\n",
            "Installing collected packages: pafy\n",
            "Successfully installed pafy-0.5.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdY6EinL7OBB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function\n",
        "# Iterate through JSON files of a video and save every 120 as an npy file. \n",
        "import json\n",
        "import numpy as np\n",
        "import os\n",
        "def create_120_npy_from_vid_json(read_json_folder, write_folder, save_name):\n",
        "    newJsonOutput = read_json_folder\n",
        "    print('Change JSON files to npy files....')\n",
        "    jsonFiles = np.array(os.listdir(newJsonOutput))\n",
        "    jsonFiles.sort()\n",
        "\n",
        "    rtnList = np.zeros((len(jsonFiles),50)) \n",
        "\n",
        "    print(len(jsonFiles))\n",
        "    print('total npy outputs: ' + str(len(jsonFiles) // 120))\n",
        "\n",
        "    for i,jsonFile in enumerate(jsonFiles): \n",
        "        if (i % 1000 == 0): print('\\tupdate:' + str(i))\n",
        "        with open(newJsonOutput + '/' + jsonFiles[i]) as jsonOutput:\n",
        "            data = json.load(jsonOutput)\n",
        "            try:\n",
        "                bodyLandmarks = data['people'][0]['pose_keypoints_2d']    \n",
        "                #Get rid of the confidence of points\n",
        "                bodyLandmarks = [xx for p, xx in enumerate(bodyLandmarks) if p%3 !=2]\n",
        "                #Y - coordinates are opposite. Makes printing out coordinates easier to visualize. \n",
        "                for inc in range(len(bodyLandmarks)):\n",
        "                    if inc % 2 == 1:\n",
        "                        bodyLandmarks[inc] = 1 - bodyLandmarks[inc]\n",
        "                \n",
        "                rtnList[i,] = bodyLandmarks\n",
        "            except:\n",
        "                print('oof.' + str(i))\n",
        "                continue\n",
        "\n",
        "    # !mkdir {write_folder}\n",
        "    print('write_npys')\n",
        "    _count_id = 1\n",
        "    for j in range(120, len(rtnList), 120):\n",
        "        _temp_data = rtnList[j-120:j]\n",
        "        np.save(write_folder + save_name + '_' + str(_count_id) + '.npy', _temp_data)\n",
        "        _count_id += 1\n",
        "    \n",
        "    print('finished')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFZxBKnl0RlU",
        "colab_type": "code",
        "outputId": "53afebf0-7c6b-4c60-d1a4-2a756a65578d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Mount google drive. \n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZA3nlEF1mU6",
        "colab_type": "text"
      },
      "source": [
        "Links: \n",
        "- jumping jacks : https://www.youtube.com/watch?v=8ebN7nWfcpc\n",
        "- stretching-vid-1-hd : https://www.youtube.com/watch?v=sY60bJFWU0A\n",
        "- laying-down-1-hd : https://www.youtube.com/watch?v=9j8ymDySdiM\n",
        "- Fold-vid-1-hd : https://www.youtube.com/watch?v=1UDruu4x_ag\n",
        "- Fold-vid-2-hd : https://www.youtube.com/watch?v=55Kq3f7k234\n",
        "- Laying-down-vid-2-hd : https://www.youtube.com/watch?v=dleh532-_9E\n",
        "- Laying-down-vid-3-hd : https://www.youtube.com/watch?v=zGduaygRisY\n",
        "- Working-our-vid-1-hd : https://www.youtube.com/watch?v=aKpabJokYjM\n",
        "- Reading-vid-1-hd : https://www.youtube.com/watch?v=ixKlzflnpYk\n",
        "- Reading-vid-2-hd : https://www.youtube.com/watch?v=BEePqUBFJbA\n",
        "- Sitting-watching-tv-hd : https://www.youtube.com/watch?v=fsV11nli8EU\n",
        "- Sitting-typing-hd : https://www.youtube.com/watch?v=Ku0XdzqW-3U\n",
        "- Working-out-vid-2-hd : https://www.youtube.com/watch?v=-riqhebsU9k\n",
        "- Stretching-vid-2-hd : https://www.youtube.com/watch?v=Q3fYKwUdz7g\n",
        "- Hanging-picture-hd : https://www.youtube.com/watch?v=sDPgUtSgz2o\n",
        "- Changing-clothes-hd : https://www.youtube.com/watch?v=cia96wLOBAI\n",
        "- Folding-clothes-3-hd : https://www.youtube.com/watch?v=RfvF_Kko5aQ\n",
        "- Eating-3-hd : https://www.youtube.com/watch?v=MWCLyicp1B8\n",
        "- Laying-down-typing-hd : https://www.youtube.com/watch?v=-yfRIPAhBNc\n",
        "- Seizure-vid-1-hd : https://www.youtube.com/watch?v=kxGVHBtKSYg\n",
        "- Seizure-vid-2-hd : https://www.youtube.com/watch?v=ITFZIJ8fJl4\n",
        "- Seizure-vid-3-hd : https://www.youtube.com/watch?v=QrE_FKI8FSY\n",
        "- Seizure-vid-4-hd : https://www.youtube.com/watch?v=EUVm5_p-4d4\n",
        "- Seizure-vid-5-hd : https://www.youtube.com/watch?v=S54-Uj2VWBI\n",
        "- Seizure-vid-6-hd : https://www.youtube.com/watch?v=borLntm8Fdc\n",
        "\n",
        "- Seizure-vid-7-hd : https://www.youtube.com/watch?v=LscZeT_PLic\n",
        "- Seizure-vid-8-hd : https://youtu.be/OfOSHEg7_Ng\n",
        "- Seizure-vid-9-hd : https://www.youtube.com/watch?v=tdamWt43VJM\n",
        "- Seizure-vid-11-hd : https://www.youtube.com/watch?v=8WxIwRZGPxE\n",
        "- Seizure-vid-12-hd : https://www.youtube.com/watch?v=jI16DtqY4vA\n",
        "- Seizure-vid-10-hd : https://www.youtube.com/watch?v=-dNp2mfvqRg\n",
        "- Sitting-Standing-vid-1-hd : https://www.youtube.com/watch?v=WFlSGyZVcsg\n",
        "- Sitting-Standing-vid-2-hd : https://www.youtube.com/watch?v=4qROxXaH61U\n",
        "- Sitting-Standing-vid-3-hd : https://www.youtube.com/watch?v=x2psE6ORYKM\n",
        "- Seizure-vid-13-hd : https://youtu.be/N2k3OBI2qOc\n",
        "- Seizure-vid-14-hd : https://youtu.be/6Ojn4Aqx2zE\n",
        "- Seizure-vid-15-hd : https://youtu.be/C3PbGmoYh-g\n",
        "- Seizure-vid-16-hd : https://youtu.be/1ZMPzTXZSuw\n",
        "- Seizure-vid-17-hd : https://youtu.be/eOY25TqsC_E\n",
        "- Seizure-vid-18-hd : https://youtu.be/A-2jhSJgc14\n",
        "- Seizure-vid-19-hd : https://youtu.be/WjuBm7aHJOU\n",
        "- Seizure-vid-20-hd : https://youtu.be/jAfkt9WZ7F4\n",
        "- Seizure-vid-21-hd : https://youtu.be/T87cq-_IJi4\n",
        "- Seizure-vid-22-hd : https://youtu.be/C2lRIjw5tRo\n",
        "- Seizure-vid-23-hd : https://youtu.be/ehanpXp81iA\n",
        "- Seizure-vid-24-hd : https://youtu.be/XqKFcTmcCgQ\n",
        "- Seizure-vid-25-hd : https://youtu.be/2Q-r2bD2te4\n",
        "- Seizure-vid-26-hd : https://youtu.be/bHi7EkoUZQk\n",
        "- Seizure-vid-27-hd : https://youtu.be/Dfpkb-OwpnM\n",
        "- Seizure-vid-28-hd : https://youtu.be/z5XH01Unb6M\n",
        "- Seizure-vid-29-hd : https://youtu.be/94Kh3u5QNP0\n",
        "- Seizure-vid-30-hd : https://youtu.be/gxbHI3jLH64\n",
        "- Seizure-vid-31-hd : https://youtu.be/l63cUMjvF6Q\n",
        "- Seizure-vid-32-hd : https://youtu.be/JlVx-tp-eIA\n",
        "- Seizure-vid-33-hd : https://youtu.be/evaUMGINrhk\n",
        "- Seizure-vid-34-hd : https://youtu.be/qMlJLhw1Mhw\n",
        "- Seizure-vid-35-hd : https://youtu.be/aIQo6X9ibeY\n",
        "- Seizure-vid-36-hd : https://youtu.be/vZ-Vinh21HI\n",
        "- Seizure-vid-37-hd : https://youtu.be/OXGFaSJ-wd8\n",
        "- Seizure-vid-38-hd : https://youtu.be/w0rOGHeDW74\n",
        "- Seizure-vid-39-hd : https://youtu.be/b0zwm1rdr5I\n",
        "- Seizure-vid-40-hd : https://youtu.be/_Q5NQfZ0V70\n",
        "\n",
        "Not processed yet. \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lCP-IklDv1l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# str_ = '/content/t_dataa/'\n",
        "# !mkdir {str_}\n",
        "# # !ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoHBSu3p0PGT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Entire data pipeline from start to all npy files\n",
        "#Links:\n",
        "# CopyofSeizure-vid-10-hd\n",
        "\n",
        "lst_links = ['https://youtu.be/b0zwm1rdr5I']\n",
        "json_output = '/content/temp'\n",
        "\n",
        "# train_folder = '/content/t_dataa/'\n",
        "train_folder = 'gdrive/My Drive/Seizure2/all_data_hd_npy/'\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/gdrive')\n",
        "#Mount google drive. \n",
        "\n",
        "print(len(os.listdir('gdrive/My Drive/Seizure2/all_data_hd_npy/')))\n",
        "for link in lst_links:\n",
        "    print(len(os.listdir('gdrive/My Drive/Seizure2/all_data_hd_npy/')))\n",
        "\n",
        "    print('New Video:::::::::::')\n",
        "\n",
        "    _name, title = create_vid(link)\n",
        "\n",
        "    newvidInput = _name\n",
        "    print(_name)\n",
        "    newJsonOutput = json_output\n",
        "    py_json_folder = json_output\n",
        "    !./openpose/build/examples/openpose/openpose.bin --tracking 10 --render_pose 0 --video {newvidInput} --write_json {newJsonOutput} --model_folder openpose/models --disable_blending --display 0 --number_people_max 1 --scale_number 4 --scale_gap 0.25 --keypoint_scale 3\n",
        "\n",
        "    write_npy_files = train_folder\n",
        "\n",
        "    # !mkdir gdrive/My\\ Drive/Seizure2/all_data_hd_npy/\n",
        "\n",
        "    create_120_npy_from_vid_json(read_json_folder=py_json_folder, write_folder=write_npy_files, save_name=title)\n",
        "\n",
        "\n",
        "    #Delete json folder\n",
        "    !rm -rf {newJsonOutput}\n",
        "    #Delete video \n",
        "    !rm -f {newvidInput}\n",
        "\n",
        "print(len(os.listdir('gdrive/My Drive/Seizure2/all_data_hd_npy/')))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVowJJT1G49k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Sometimes theres a timeout issue....\n",
        "create_120_npy_from_vid_json(read_json_folder=py_json_folder, write_folder=write_npy_files, save_name=title)\n",
        "\n",
        "#Delete json folder\n",
        "!rm -rf {newJsonOutput}\n",
        "#Delete video \n",
        "!rm -f {newvidInput}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYrq1x9n79td",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Visualize what's happening in the video output \n",
        "#import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "_t10 = np.load('gdrive/My Drive/Seizure2/all_data_hd_npy/Seizure-vid-39-hd_4.npy')\n",
        "\n",
        "print(_t10.shape)\n",
        "\n",
        "plt.figure()\n",
        "for i in range(0, len(_t10), 1):\n",
        "    for j in range(0,50,2):\n",
        "        plt.xlim(0, 1)\n",
        "        plt.ylim((0, 1))\n",
        "        plt.scatter(_t10[i][j], _t10[i][j+1])\n",
        "       \n",
        "\n",
        "    plt.pause(.0001)\n",
        "    \n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9QqO20PT_Kt",
        "colab_type": "text"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jsgsrcCXUY5Y",
        "colab_type": "text"
      },
      "source": [
        "## Function Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrfndpcZUdnj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Allows multiprocessing during training. \n",
        "import numpy as np\n",
        "import random\n",
        "import json\n",
        "import os\n",
        "import keras\n",
        "\n",
        "class DataGenerator(keras.utils.Sequence):\n",
        "\n",
        "    def __init__(self, list_IDs, labels, location=\"location__\", batch_size=32, dim=(32,32,32), n_channels=1,\n",
        "                 n_classes=10, shuffle=True):\n",
        "        #'Initialization'\n",
        "        #(number of channels, number of classes, batch size)\n",
        "        self.dim = dim\n",
        "        self.batch_size = batch_size\n",
        "        #Dictionary whwere labels[ID] = label\n",
        "        self.labels = labels\n",
        "\n",
        "        self.list_IDs = list_IDs\n",
        "        self.n_channels = n_channels\n",
        "        self.n_classes = n_classes\n",
        "        self.shuffle = shuffle\n",
        "        self.on_epoch_end()\n",
        "        self.location_data = location\n",
        "\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        #Updates indexes after each epoch\n",
        "        self.indexes = np.arange(len(self.list_IDs))\n",
        "        if self.shuffle == True:\n",
        "            np.random.shuffle(self.indexes)\n",
        "\n",
        "    def tester(self, list_IDs_temp):\n",
        "        [x1, x2, x3, x4, x5], y = self.__data_generation(list_IDs_temp)\n",
        "\n",
        "        return [x1, x2, x3, x4, x5], y\n",
        "\n",
        "        \n",
        "    def __data_generation(self, list_IDs_temp):\n",
        "        y = np.empty((self.batch_size), dtype=int)\n",
        "\n",
        "        r_arm_data = np.empty((self.batch_size, 120, 6))\n",
        "        l_arm_data = np.empty((self.batch_size, 120, 6))\n",
        "        r_leg_data = np.empty((self.batch_size, 120, 12))\n",
        "        l_leg_data = np.empty((self.batch_size, 120, 12))\n",
        "        torso_data = np.empty((self.batch_size, 120, 12))\n",
        "\n",
        "        # Generate data\n",
        "        for i, ID in enumerate(list_IDs_temp):\n",
        "            opened_data = np.load(self.location_data + ID) \n",
        "            #(120, 50)\n",
        "                \n",
        "            _temp_r_arm_data = np.empty((120,6))\n",
        "            _temp_l_arm_data = np.empty((120,6))\n",
        "            _temp_r_leg_data = np.empty((120, 12))\n",
        "            _temp_l_leg_data = np.empty((120, 12))\n",
        "            _temp_torso_data = np.empty((120, 12))\n",
        "\n",
        "\n",
        "            for j,mat in enumerate(opened_data):\n",
        "                #iterate 120 timestamps\n",
        "                mult = 2\n",
        "                #(120, 6) pad with 6 zeros \n",
        "                _temp_r_arm_data[j,] = np.array([mat[2*mult], mat[mult*2+1], \n",
        "                                          mat[3*mult], mat[mult*3+1],\n",
        "                                          mat[4*mult], mat[mult*4+1]])\n",
        "                \n",
        "                #(120, 6) pad with 6 zeros\n",
        "                _temp_l_arm_data[j,] = np.array([mat[5*mult], mat[mult*5+1], \n",
        "                                          mat[6*mult], mat[mult*6+1],\n",
        "                                          mat[7*mult], mat[mult*7+1]])\n",
        "                \n",
        "                #(120, 12)\n",
        "                _temp_r_leg_data[j,] = np.array([mat[9*mult], mat[mult*9+1], \n",
        "                                          mat[10*mult], mat[mult*10+1],\n",
        "                                          mat[11*mult], mat[mult*11+1],\n",
        "                                          mat[22*mult], mat[mult*22+1], \n",
        "                                          mat[23*mult], mat[mult*23+1],\n",
        "                                          mat[24*mult], mat[mult*24+1]])\n",
        "                #(120, 12)\n",
        "                _temp_l_leg_data[j,] = np.array([mat[12*mult], mat[mult*12+1], \n",
        "                                          mat[13*mult], mat[mult*13+1],\n",
        "                                          mat[14*mult], mat[mult*14+1],\n",
        "                                          mat[19*mult], mat[mult*19+1], \n",
        "                                          mat[20*mult], mat[mult*20+1],\n",
        "                                          mat[21*mult], mat[mult*21+1]]) \n",
        "                \n",
        "                #(120, 12)\n",
        "                _temp_torso_data[j,] = np.array([mat[1*mult], mat[mult*1+1], \n",
        "                                          mat[2*mult], mat[mult*2+1],\n",
        "                                          mat[5*mult], mat[mult*5+1],\n",
        "                                          mat[8*mult], mat[mult*8+1], \n",
        "                                          mat[9*mult], mat[mult*9+1],\n",
        "                                          mat[12*mult], mat[mult*12+1]])\n",
        "\n",
        "            r_arm_data[i,] = _temp_r_arm_data\n",
        "            l_arm_data[i,] = _temp_l_arm_data\n",
        "            r_leg_data[i,] = _temp_r_leg_data\n",
        "            l_leg_data[i,] = _temp_l_leg_data\n",
        "            torso_data[i,] = _temp_torso_data\n",
        "\n",
        "            # Store class\n",
        "            y[i] = self.labels[ID]\n",
        "\n",
        "        return [torso_data, l_arm_data, r_arm_data, l_leg_data, r_leg_data], y\n",
        "\n",
        "    def __len__(self):\n",
        "        #'Denotes the number of batches per epoch'\n",
        "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        #'Generate one batch of data'\n",
        "        # Generate indexes of the batch\n",
        "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
        "\n",
        "        # Find list of IDs\n",
        "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
        "\n",
        "        # Generate data\n",
        "        [x1, x2, x3, x4, x5], y = self.__data_generation(list_IDs_temp)\n",
        "\n",
        "        # print(\"HEREERERE: \")\n",
        "        # print(X.shape)\n",
        "\n",
        "###################TO DO make generators and have them here \n",
        "        return [x1, x2, x3, x4, x5], y\n",
        "        # return X, y\n",
        "\n",
        "    def getLandmarks(self, pyPath):\n",
        "        #Input -> Location of json files (openPose output).\n",
        "        #Return -> list of the first 80 outputs in the JSON folders with 75 body landmarks. \n",
        "        rtnList = np.zeros((80,50))\n",
        "        jsonFiles = np.array(os.listdir(pyPath))\n",
        "        # print('jsonFiles List:', jsonFiles)\n",
        "        jsonFiles.sort()\n",
        "\n",
        "        for i,fileName in enumerate(jsonFiles): \n",
        "            with open(pyPath + jsonFiles[i]) as jsonOutput:\n",
        "                data = json.load(jsonOutput)\n",
        "                try:\n",
        "                    bodyLandmarks = data['people'][0]['pose_keypoints_2d']\n",
        "                    #Get rid of the confidence of points\n",
        "                    bodyLandmarks = [xx for i, xx in enumerate(bodyLandmarks) if i%3 !=2]\n",
        "                    rtnList[i] = bodyLandmarks\n",
        "                except:\n",
        "                    continue\n",
        "            #Only extracts the first 80 frames/time_steps\n",
        "            if (i > 78):\n",
        "                break\n",
        "\n",
        "        #Should return (frames = 80, landmarks/features = 75)\n",
        "        return rtnList"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAYbp0HyUm9Z",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IROW7gkVuvA",
        "colab_type": "text"
      },
      "source": [
        "## Model Build\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0Y96i1mex5c",
        "colab_type": "code",
        "outputId": "31d84dbf-1701-46f4-d7f2-2ce4108223e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#If model shows @placeholder error then run this block. \n",
        "!pip install --upgrade pip setuptools wheel\n",
        "!pip install -I tensorflow\n",
        "!pip install -I keras"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: kaggle 1.5.6 has requirement urllib3<1.25,>=1.21.1, but you'll have urllib3 1.25.9 which is incompatible.\u001b[0m\n",
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            "Installing collected packages: six, numpy, keras-preprocessing, opt-einsum, grpcio, wrapt, scipy, gast, h5py, keras-applications, idna, chardet, certifi, urllib3, requests, setuptools, markdown, protobuf, wheel, werkzeug, absl-py, oauthlib, requests-oauthlib, cachetools, pyasn1, pyasn1-modules, rsa, google-auth, google-auth-oauthlib, tensorboard, termcolor, tensorflow-estimator, google-pasta, astor, tensorflow\n",
            "Successfully installed absl-py-0.9.0 astor-0.8.1 cachetools-4.1.0 certifi-2020.4.5.1 chardet-3.0.4 gast-0.3.3 google-auth-1.14.0 google-auth-oauthlib-0.4.1 google-pasta-0.2.0 grpcio-1.28.1 h5py-2.10.0 idna-2.9 keras-applications-1.0.8 keras-preprocessing-1.1.0 markdown-3.2.1 numpy-1.18.2 oauthlib-3.1.0 opt-einsum-3.2.1 protobuf-3.11.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-2.23.0 requests-oauthlib-1.3.0 rsa-4.0 scipy-1.4.1 setuptools-46.1.3 six-1.14.0 tensorboard-2.2.0 tensorflow-2.2.0rc2 tensorflow-estimator-2.2.0rc0 termcolor-1.1.0 urllib3-1.25.9 werkzeug-1.0.1 wheel-0.34.2 wrapt-1.12.1\n"
          ],
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                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
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          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy",
                  "six"
                ]
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8szFjk7QHaSe",
        "colab_type": "code",
        "outputId": "57e913d5-04fa-4c3b-9244-a6667cbffe6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from keras.utils import plot_model\n",
        "\n",
        "mod = build_model_rnn()\n",
        "mod.summary()\n",
        "# plot_model(mod, )\n",
        "plot_model(mod, show_shapes=False)\n",
        "# plot_model(mod, show_shapes=True, show_layer_names=True,to_file='model_shapes_names_r.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_46\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "Torso_Input (InputLayer)        (None, 120, 12)      0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Right_Arm_Input (InputLayer)    (None, 120, 6)       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Left_Arm_Input (InputLayer)     (None, 120, 6)       0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Right_Leg_Input (InputLayer)    (None, 120, 12)      0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Left_Leg_Input (InputLayer)     (None, 120, 12)      0                                            \n",
            "__________________________________________________________________________________________________\n",
            "torso_rnn (SimpleRNN)           (None, 120, 12)      300         Torso_Input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "r_arm_rnn (SimpleRNN)           (None, 120, 12)      228         Right_Arm_Input[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "l_arm_rnn (SimpleRNN)           (None, 120, 12)      228         Left_Arm_Input[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "r_leg_rnn (SimpleRNN)           (None, 120, 12)      300         Right_Leg_Input[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "l_leg_rnn (SimpleRNN)           (None, 120, 12)      300         Left_Leg_Input[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concat_torso_r_arm (Concatenate (None, 120, 24)      0           torso_rnn[0][0]                  \n",
            "                                                                 r_arm_rnn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concat_torso_l_arm (Concatenate (None, 120, 24)      0           torso_rnn[0][0]                  \n",
            "                                                                 l_arm_rnn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concat_torso_r_leg (Concatenate (None, 120, 24)      0           torso_rnn[0][0]                  \n",
            "                                                                 r_leg_rnn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concat_torso_l_leg (Concatenate (None, 120, 24)      0           torso_rnn[0][0]                  \n",
            "                                                                 l_leg_rnn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "Torso-Right-Arm_rnn (SimpleRNN) (None, 120, 30)      1650        concat_torso_r_arm[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "Torso-Left-Arm_rnn (SimpleRNN)  (None, 120, 30)      1650        concat_torso_l_arm[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "Torso-Right-Leg_rnn (SimpleRNN) (None, 120, 30)      1650        concat_torso_r_leg[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "Torso-Left-Leg_rnn (SimpleRNN)  (None, 120, 30)      1650        concat_torso_l_leg[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "concat_all_4_body_parts (Concat (None, 120, 120)     0           Torso-Right-Arm_rnn[0][0]        \n",
            "                                                                 Torso-Left-Arm_rnn[0][0]         \n",
            "                                                                 Torso-Right-Leg_rnn[0][0]        \n",
            "                                                                 Torso-Left-Leg_rnn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "gru_35 (GRU)                    (None, 128)          95616       concat_all_4_body_parts[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 128)          0           gru_35[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "Dense_1_Sigmoid (Dense)         (None, 1)            129         dropout_5[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 103,701\n",
            "Trainable params: 103,701\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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AVKuioiI3N1d2k+1gp6en+/j4uLm5/f333/Sigcqg66XBjIyMoqKijIyM3tSFRtcadIqlpeWMGTOEwlquc2nXrp2pqan6IwGoyueffy671lpeVVWVr6+v+vMAqFzPnj319PQU7uTz+Y6OjhMnTqQSCUCFHj58WPPsNPvBnpSUNGTIkLi4OBq5QJXQ9dJszs7O4eHhbxpGggHhoGtmzZpV88S1SCTq06cPlTwAqtKsWTMPDw+FH0z5fL6Hh4e9vT2tVAAq1K1bN3YEuDyGYdavX1/rb6YAmuX+/fu13s/n84VCYWRkpLu7u5ojgcqha63xhg4dunjx4lpPUOOsNeiaJk2afPvttwoHYdXV1ZjDDLTAhAkTFH4w5fF4OJsHWqNbt27sEFkZgUDQuXPnUaNG0YoEoEL379+v+es/n88XiUTHjx8fPnw4lVSgWuh6aYPg4OD+/fvX/E0XXWvQQXPmzFFoC1KpFHOYgRbw9vauORZp9OjRVMIAqJyLi4vCQtZSqXTDhg0Yggfa4f79+wrFLBAI9PX1T5482b9/f1qpQLXQ9dIGfD4/PDy8WbNmCj0KdK1BBzVt2jQoKEj+h2FDQ0PMnwxawNLSUv5XVIFA0L9//yZNmtBNBaAqAoFA/rNaKBR6eHigywFa4969exKJRHZTKBQaGBhcuHDhgw8+oJgKVAtdLy1hYWERFRUlEAjkfw/DD72gm2bPni37XYnH4/Xq1Qs/M4F2GD9+vGzELMMwEyZMoJsHQLXc3NxkP4xWVVVt2LCBbh4AFUpLS5PNjiQUCk1MTC5fvowL1rQMDje1R5cuXX7++Wf5Kc3QnQDdZGNjExgYyB6fiUQiLy8v2okAVGP48OGyKZRFIhFW+gUt061bN/Ywhi1vzOoE2uTx48fsP0Qikbm5+aVLl7p27Uo3Eqgcul5a5fPPP//mm29ka5+iaw06a+7cuWz9V1ZW4idh0BpGRkaffvqpSCQSCoUjRowwNjamnQhAld577zbZR10AACAASURBVD12knCpVLpq1SracQBUJjc3t6ysjBAiFAqbNm0aFxfXqVMn2qFA9dD10jabNm1ydXVlh4JjQDjoLFtbW39/f7YJuLm50Y4DoDLjxo2rqqqSSqWff/457SwAKtalSxcej8fj8caNG9exY0facQBU5sGDB4QQPp9vb29/9epVZ2dn2omgUWjwOoEHDx6kHYGjJk+e/O+//xYVFUVFRZmYmNCOoz04sn4sKr+eXFxcBAKBmZnZhQsXaGfRYD4+Pmp4lqdPn165ckUNT6QFpFKpWCxmGKa4uBifBvWk/k/vuLi4J0+eqPMZtYaVldXLly979eqF8q5bo1Y1PpNVLjY2lhBiY2Mzf/78uLi4uLg42olo4sgRdaNgNBbtdw50TkREBO2qZxhUPqiXeqo6IiKC9gsFbab+T+8xY8bQftGg5Rq1qvGZDI2KI0fUjUGDz1oTQiIiItRzRkUTbd68eeLEiebm5rSDaAlOja5H5dfT8+fPjx8//vXXX9MOopEOHjzo6+urzmdk8MtR/Vy4cIHH4/Xr1492EM1A69N7zJgxkZGRVJ5ao23dutXb29vGxoZ2EE5TT1XjM1mFdu/e7e3tjcNywrEjapXT7K411GHatGn4TAQdZ2dnN2nSJNopAFTs/fffpx0BoLH4+/vLFm8H0BpTpkyhHQHUAR9e2ky7fxYCqA8cooH2weoPoMXwoQ0AmgtfzwAAAAAAAABKQdcaAAAAAAAAQCnoWgMAAAAAAAAoBV1rAAAAAAAAAKWgaw0AAAAAAACgFHStAQAAAAAAAJSCrjUAAAAAAACAUtC1BgAAAAAAAFAKutYAAAAAAAAASkHXGgAAAAAAAEAp6FoDAAAAAAAAKAVdawAAAAAAAACloGutc27cuDF27NhWrVrp6+s3bdq0a9euK1eulD164sQJMzOzqKgo1T7phg0brK2teTzejh076tjs8OHDTk5OPDlisbhVq1ZffvnlgwcPat1swoQJ8nsYMGCAiYmJQCDo2LHj9evX67+lal8vXfV8t3UKyr7WLVX7eqGxoYxr3VK1r5fLtPKzHVVd65aqfb2NpEEFiepVFVQv1zEaixASERFBO4WGuXnzpqGh4fTp0x88eFBWVpaWljZ37tyPPvpItkF0dLSpqemxY8dU/tQZGRmEkO3bt791y9atW5uZmTEMI5VKc3JyfvvtN0NDQ2tr6xcvXihs1qRJE0JIdHS0/P0nT54cPnz4u21ZB+7UW32S1P/d1gUo+7q3fJOIiAi1fUeo87k0FMq47i3rQOXTe8yYMWPGjFH5brXssx1VXfeWdWjsqq7nZ3KDChLVqyqoXi7DWWvdsmHDBnNz89DQ0JYtW4rF4rZt265YscLAwEC2wZAhQwoLC4cNG0YxpAyfz7e2tp4wYUJQUFBubu7Zs2cVNti8eTOfz/fz8yssLKx7V/XfErQPyl5ryr6srMzDw4N2CjpQxlpTxiCDqkZVay5UL6q3Vuha65aXL18WFha+evVKdo+enp7KB6uonLOzMyEkOztb4X4PD48ZM2Y8e/Zs9uzZde+h/luC9kHZa03Z//TTT7m5ubRT0IEy1poyVj+GYSIjI3ft2kU7iCJUNar6rVC9qoXqbWza3LVet26doaGhiYlJbm7urFmzmjdvnpaWVvd/iYmJ6dChg5mZmVgs7ty58+nTp2vdT0BAgJGREZ/P79Gjh42NjUgkMjIy6t69e58+fVq0aCEWi83NzefOnftuIadPn25kZGRoaHj06NFPPvnE1NTU3t4+PDyc3X7btm11PEoIOXXqlKmpaUhISK1P17Nnz5KSkg8//DA2Nrbmo5cvX3ZwcODxeFu3biWEhIaG1v9lbt68WSwWW1tb+/v729nZicViDw+Pq1evvumFS6XSpUuXOjg4GBgYdOnShR169Cbs0JeuXbvWfGjlypVt27bds2dPzV/g3nlLXYOyR9lrhBkzZsyaNSszM5PH47EHBwzDbNy40cXFRV9f38LCYsSIEXfv3mU3rvXz/59//nFzczM0NDQ1Ne3cuXNRUVHdO6kbyhhlrKR3OEpR8Ka/hVQqXbVqVbt27QwMDJo2bdqqVatVq1b5+Pi8QyRUdU2oapVA9aJ6tRPd8ejKIPUYqb9o0SJCyPTp07ds2TJq1Kh///237u0jIyODg4NfvXr18uXL3r17N2nS5E37WbZsGSHk6tWrJSUlL168GDRoECHk+PHjeXl5JSUl06ZNI4SkpKTU54XU3Dl7z7lz5woLC3Nzc/v06WNkZFRZWSm//ZsejY6ONjEx+f7772t9rtLSUldXV/ZP36FDh7Vr1758+VJ+gydPnhBCtmzZwt5s0Mv08/MzMjK6c+dOeXl5ampqz549TUxMHj9+zD6qcGXI7Nmz9fX1Dx06lJ+fv3DhQj6fn5CQwD4kuzKEYZj8/Pxff/3V0NBwyJAhCq+ldevWDx48YBjmypUrfD6/ZcuWxcXFzBuuDKnnlnWoT72pR32SNOiKJpQ9yr5WHLzWevTo0a1bt5bdXLp0qZ6e3r59+woKCm7evNm9e/emTZtmZ2ezjyrUWGJioqmp6dq1a8vKyrKzs0eNGpWXl/fWndQNZcz9Mma4fa11Q49S6vm3CAkJEQgER48eLS0tTUpKsrGx6devXz2To6pR1YxarrVG9aJ6tZJOdK3LysreYf+rVq0ihOTm5ta6H7aFvH79mr25d+9eQsitW7fYm9euXSOEHDhwoD5PVHPnCveEhYURQu7du1efR9+qsrLyxx9/bN++PftxYG1tffHiRdmjtX4Q1PNl+vn5yRowwzAJCQmEkOXLl7M35T8IysrKDA0Nx44dyz5UWlqqr68fGBjI3mzdurX8rz88Hm/lypWyTzoZWfNmGGbWrFmEkKCgIKbOD4K3blkH7nwQqLxrLQ9l39CXqcVlz/GudWlpqbGxsez9ZP7zp5EdBinUzO3bt0mNmVfeupO6oYy5X8aMJnSt63+UUs+/Rc+ePd3c3GT/a+rUqXw+v6Kioj5PgapGVTON37VG9aJ667mxxtHmAeFKEolEhBCpVFqfjfX09AghVVVV8v9XIpGoJAm78zftre5HaxKJRNOmTfv333/j4+NHjBiRm5vr7e2dn59f/yT1fJmurq6Ghoa1Dq1MS0srLS3t1KkTe9PAwMDW1lZ+S9kHypw5cxiGMTMzY5/rTVauXNmuXbuwsLDLly/X/RLqv6VuQtm/6blQ9lyTmppaXFwsO2lACOnZs6eent6bRs05OTlZW1uPHz8+ODj44cOH77aThkIZo4wbTx1/i/LycoZhZFtKpVKRSCQQCFTyvKhqVLXyUL2oXm2FrvX/OH78eL9+/aysrPT19et51ajm6tWr159//hkQEJCXl3fhwoXGeAp9ff28vLya95eUlBBCFi9eLFtt79GjR6WlpTW3XLJkia2t7cKFC9kf/95ELBb/8ssvPB7vyy+/LCsrU8mWugNlr1ooe/UoKCgghBgbG8vfaW5u/vr161q3NzAwOH/+vJeXV0hIiJOT09ixY8vKyhq6E45AGWtNGSujjr/F4MGDk5KSjh49WlZWlpiY+Ndffw0dOlRVnZNGgqrWqapG9TYUqldToGv9X48fPx45cqStre3Vq1cLCwvXrl1LO5HqjR49WvYLGYtd9r3WRqgkiURSUFBgb29f8yErKytCyKZNm+RHUMTFxdXc0sTEZM2aNa9fvw4MDKz76dzd3WfOnJmRkbFixQpVbakLUPaqhbJXG3Nzc0KIQh/4TW8+q2PHjlFRUVlZWfPmzYuIiGCXTmnoTmhBGTdoS11Qx98iODj4ww8/nDRpkqmp6ahRo3x8fHbv3k07by1Q1Q3aUpugehsE1atB0LX+r1u3bkkkksDAQCcnJ7FYzOPxaCdSvYqKijt37sjfw85H2qVLF5U/F3vBSe/evWs+xE6HmJKSUp/9TJw4sVevXtHR0QcPHqx7yxUrVrRv3z45Ofmt+6z/lloPZa9aKHu16dSpk7GxcWJiouyeq1evVlZW9ujRo9bts7Ky2DKwsrJavXp19+7d79y509CdUIQybuiWWq+Ov0VqampmZmZeXp5EInn8+PG2bdssLCzUn/CtUNUN3VJroHobBNWrQdC1/i8HBwdCyNmzZ8vLyzMyMlR1rZ2anTx5so6lAgghI0eOPHjwYEFBQWFh4dGjR+fPnz98+HBVfRBUV1fn5+dXVVXdvHlzxowZDg4OkyZNqrmZWCyePHlyeHj4tm3bioqKpFLp06dPnz9/Xus+eTze5s2beTzetGnT6r6ChR2aUp9RQ/XfUuuh7JWHslcbS0vLrKyshw8fvn79WiAQzJo168iRI/v37y8qKrp161ZAQICdnZ2fn1+t/zcrK8vf3//u3buVlZXJycmPHj3q3bu3WCxu0E4aFcpYR8pYVer4WwQFBTk4OBQXF9POiKpGVdcO1ftWqF5NVdccZ9xG3ja/3Nq1aw0MDAghLVq02LdvX332OW/ePEtLS3Nzc29vb3YlutatWwcFBSnsJzQ01NDQkBDSsmXLmJiYNWvWmJmZEUJsbGx+//33AwcO2NjYEEIsLCzCw8PrfsaaIcPCwtidt2nTJjMzc9euXaampoQQR0fH9PT0uh9lGObEiRMmJiYrV66s9enOnDnj6+vbunVrfX19PT29du3aBQcHszNGMAyzZcsWW1tbQoihoeGnn37a0Jfp5+cnEomaN28uFApNTU1HjBiRmZnJ7vmHH35gNzYyMho1ahTDMBUVFfPmzXNwcBAKhVZWVqNHj05NTY2NjW3bti1bmc2aNfP395clZz9QzM3NV69efeTIEXbOw6ZNm7IzE8qbM2eObJbC+m/5Vm+tN7V5a5Ka73bdUPYo+1pxcIbw69evOzo6GhgYeHl5ZWdnV1dXr1+/vk2bNiKRyMLCYuTIkWlpaeyWNWvs4cOHHh4eFhYWAoGgWbNmixYtqqqqYhimjp3UDWWsEWXMcHiG8IYepdTzb8EwzPnz55s0aSI70hOJRC4uLocPH37rU6CqUdWs+nwmN+hgA9WL6pXhzhF1Y9DmrjWomZ+fn6WlJe0UjYU79cadJMBoddlzsGsNjUSLy5jhcNe68YSFhc2YMUN2s6Ki4rvvvtPX1y8tLaUVSf1Q1cqg+JmM6mVQvZpMSABUp56LNgFoE5Q9aAGUsdbIzs6eNm2a/LWXenp6Dg4OEolEIpGwp/V0BKpa46B6ZVC9GkqHrrW+e/cu783Gjh2rNU8KIIOyB3g3KGNQM1WVnIGBgUgk+umnn3JyciQSSVZW1p49e5YuXTp27NisrCxUNTQGVC8AS4fOWrdv356RW4Nei5+UioULF/7yyy+VlZWtWrVav379mDFjaCcCQlD2jQxlr8VQxqBmqio5MzOzM2fOfP/9923bti0pKTE2Nu7YseOaNWumTp0qFApR1dAYUL0qhOrVaDrUtYZGtWrVqlWrVtFOAaBWKHvQAihj7dOnT5+///6bdgqaUNWaC9WL6tVoOjQgHAAAAAAAAKAxoGsNAAAAAAAAoBR0rQEAAAAAAACUgq41AAAAAAAAgFLQtQYAAAAAAABQCrrWAAAAAAAAAEpB1xoAAAAAAABAKehaAwAAAAAAACgFXWsAAAAAAAAApaBrDQAAAAAAAKAUIe0AAACgu/Lz8xMTE6Ojo2kHAW32+PFjqVQqEAhoBwEAAG2GrjUAAKhPcXFxcnJyQkJCYmJiQkLCvXv3CCFNmjShnQu02Zw5c4KDg7t169bjPzp06MDj8WjnAgAArYKuNQAANCKJRJKenp70HwkJCZWVlebm5h07dhw9erSnp2fPnj0vXbrk6+tLOylorQ0bNlhZWbEVuHPnzoqKClNT086dO/fo0cPLy6tPnz62tra0MwIAgMbT7K51XFwc7QgAFKDyQQ3eucykUundu3eT5JSXl5uYmHTp0qVHjx5Tp0590znDgwcPKp1a3SQSiUgkop1CHaRSKY/H4/M1coqWFi1a+Pj4TJw4kfznt57Y2NjLly+fPXt269at1dXVdnZ2shPaHh4eKhlG8fTpU00saQAZFDBAgzEai/Y7BzonIiKCdtUzDCof1KueZfns2bNjx47NmzfP09PT0NCQEGJkZOTp6Tlt2rS9e/fevn1bKpXW8d8jIiJov1DQZnV8ehcVFcXExISGhk6YMKFDhw7s9nZ2dt7e3qGhoTExMaWlpQ37jGYYhmHGjBlD9yWD1mvUYxJ8JkOj4sgRdWPgMThS120uLi5jxoxZsWIF7SAA3BUcHLxq1aoTJ058/PHHtLNwRVZWluykdFxc3MuXL4VCYdu2bWWn/tzc3PT09GjHbCy3bt3y9/e/du1aYGDgypUrTUxMaCdqdBkZGQEBARcuXPj66683bNigrS/5+fPniYmJbGHHx8e/ePFCpwr73QQGBu7du/f8+fO9evWinQVAfXJzc21sbC5cuNCvXz/aWYAr0LXWdRMmTMjLyzt16hTtIADcxTDMxIkTjx07Fhsb26lTJ9px6JDvcly7di03N1cgELRr107W5XB1dRWLxbRjNrrS0tJ169atXr26U6dOO3bs6NmzJ+1E6sMwzL59+2bNmqWnpxcaGurt7U07UaNjf0JiR48nJyeXlpYaGRlhOjR533///ffff3/48OHhw4fTzgKgVjdu3OjWrdu///7bvn172lmAK9C11nWbN29evnz5ixcvdPzgAKBulZWVAwYMePz4cXx8vLW1Ne046lBUVHTz5k3Zqek7d+4QQmSXpHp5ebm7uxsZGdGOqVbR0dHffvttQUFBcHBwUFCQbi7m9OrVqwULFuzevXvIkCFbt251dHSknUhNFCYRSExMrKioMDMz69SpE9si+vbta2NjQzumWoWHh48bN27Lli3ffPMN7SwA6nb69OlBgwYVFBSYmZnRzgJcga61rrty5Yqnp2dmZqaTkxPtLACc9vLly969e9vY2Jw7d05fX592HNUrLi5OSUmR9Rz+/fdfhmEaY3onTZSVlTV//vx9+/YNHTp0+/bt9vb2tBNRdunSJX9//0ePHi1dunT27Nk6+CuDRCK5efPm5cuX2fZy9+5dhenQPD09LS0tacdsRBcvXhw0aNCsWbNCQkJoZwGg4Ndffw0ICCgrK6MdBDgEXWtdV1ZWZmZmtn//fh8fH9pZALju33//9fDwGDhwYHh4uBYM9JAti8WOd2X7Bubm5q6urp6enuxlpbp2Fq6mqqqqsLCwJUuWWFtbh4WFDRw4kHYirpBIJBs3bly2bFn79u137typ49fZyo/yuHz58oMHDwghTk5ObFNiGRgY0I6pMqmpqV5eXoMGDfrjjz+04MMQ4B2sXr169+7d9+/fpx0EOARdayDdunXr37//+vXraQcB0AAXL14cOHDg4sWLlyxZQjtLg1VVVaWlpSmMaJUt8Mvq2LEj7Zgccv36dX9//5SUlJkzZwYHB+vCxeQNde/evYCAgPPnz3/99dfr1683NTWlnYgT5Of5qzkdmpeXV7du3TT3VH9WVpa7u3urVq1Onz6tlUN4AOpj+vTpiYmJsbGxtIMAh6BrDWTKlCn37t27cOEC7SAAmuGnn36aMmXKvn37xo0bRzvL292/f182ZvX69etlZWXGxsZdu3bFPEx1KygoWLZsWVhYmJeX1/bt211cXGgn4i52erPZs2cLhcI1a9awy0eDvJrToWluM3z9+nXfvn0rKysvX75sYWFBOw4ANb6+vlKp9NChQ7SDAIegaw1k586dc+bMKSgo4PP5tLMAaIaZM2du3779/Pnz7u7utLMokj9dduXKlVevXolEojZt2mjH6TL1iIqKCgwMLC0tXb169ZQpUzSlz0NXfn7+/PnzdXB6s4ZSGDySkJBQWVnJTofm5eXl6enJ5QsxJBLJ0KFDb926FRcXhz8x6Li+fft26dJl69attIMAh6BrDSQpKcnV1fXOnTs4LQNQT9XV1aNGjYqNjY2Li3N2dqYbRr4vffXq1by8PIWVeHVkWSyVyMzM/Oabb86cOTN+/PiNGzc2bdqUdiINc+nSpYCAgIcPH86ZM2fhwoVYAvqtSkpKkpOT65g+0MvLiyMnhxmG+fLLLw8fPvzPP/+89957tOMAUNa2bduJEycuXryYdhDgEHStgUgkElNT0127dk2YMIF2FgCNUVpa2q9fv+Li4itXrpibm6vzqQsLC2/duiUbX/r8+XNCiJ2dHXvKq0ePHt27dzc0NFRnJC3ATsoVHBzctm3bHTt2cHA8gqaQfyd37tzZu3dv2ok0iWw6tNjY2JiYmOzsbPK/06FR/KVs2bJlISEhf/7557Bhw6gEAOAUExOT0NDQr776inYQ4BB0rYEQQnr16tWrV6/NmzfTDgKgSbKysnr16tWhQ4fjx48LhcLGe6LXr1/fuHGjjvNaWr/MT2P7559/AgICHj16hHOtqnLv3r3AwMBz585hejNlyI9JiYuLe/nypWxMCvtTmouLi3ou5vr555+/+uqrbdu2BQQEqOHpADiupKTE2Ng4Ojp6yJAhtLMAh6BrDYQQEhQUdP369StXrtAOAqBhkpOT+/Tp89lnn+3evVuFu2WXxWJPSte6ZG6vXr2sra1V+Iw669WrVwsWLMAVwo0kMjIyKChIIBBgejOVyMrKkn0sqHNWwgsXLgwaNGj+/PnLly9X+c4BNNG9e/fatGmTlJTUvXt32lmAQ9C1BkL+s+p9UVGRSCSinQVAwxw/fnz48OE//PDD9OnT33kntS6Lxc5sJLve0snJSYWxgZ3XetasWXp6eqGhod7e3rQTaaf8/Pzg4OCtW7cOGjQoLCysZcuWtBNpiVqnQ5Nfl15VP8Ddvn3by8tr8ODBv//+O6b0A2DFxMT07dv32bNnzZo1o50FOARdayCEkNTU1E6dOiUnJ3fr1o12FgDNs27dugULFhw5cmT48OH1/C9SqfTu3buyw2Isi6VmN2/e9Pf3T0hICAwMXLlypYmJCe1EWi4mJsbf3//Bgwdz587FkPvG0EjToT179szd3b1169anTp3CEtYAMpGRkWPHjq2oqGjUy8FA46BrDYQQUl1dbW5u/sMPP0yZMoV2FgCNFBAQsH///suXL3ft2vVN28hfNhkbG5ufn88uiyWbfkxtl03qstLS0nXr1q1evbpz5847duxwdXWlnUhXYKI4dZKf7PDSpUs5OTkCgaBdu3YNWjigqKioT58+Uqn08uXLap6sEYDjNm/evGrVKnaiQQAZdK3h//Xt29fFxWXnzp20gwBoJIlEMnjw4Dt37ly9etXe3p69U74vHR8f/+LFC4VlsXr27IkTQeoUFRX17bffFhYWBgcHs9cA006kczIzMwMDA//+++/x48dv2rSpSZMmtBPphDqWu3/TdGgSiWTIkCGpqalxcXEODg60kgNw08KFC0+cOJGSkkI7CHALutbw/2bNmnXx4sWkpCTaQQA0VVFRUa9evaqrq8ePH3/t2rXExMSaC+f06NHDwMCAdlJdlJWVNWPGjMjISG9v7y1bttjY2NBOpNPY6c34fP7atWsxvZn63b9/n50LTXY1iomJSZcuXeSvRpk0adKff/556dIlXCkGUNPkyZOzs7NPnjxJOwhwC7rW8P/Cw8O/+OKLwsJCHPcD1J9sEVr56xvFYvFHH33k6ur6ztc3ggpVVVWFhYUtWbLExsYmLCxswIABtBMBIYQUFBQsW7Zs69at77///o4dO9q2bUs7kY6Snw4tNjY2JSVFKpUaGxuXlJSMGzfOx8end+/eVlZWtGMCcMsnn3xiZ2f3888/0w4C3IKuNfw/dhWB+Pj4Xr160c4CwF0KcwXVXBaLx+ONGTNmxowZa9asoR0WSFJSkr+//40bN2bOnBkcHPzWi0tBzS5fvuzv73///n1Mb8YRxcXFK1euXLdunbu7e0FBgcJ0aF5eXu7u7kZGRrRjAlDWrVu3wYMHr1q1inYQ4BZMagf/r3Xr1paWlgkJCehaA8irdYUbdlmsjz/+ODg4uE+fPra2tvL/Ze/evZ999lmrVq38/PxoxQbZSdG+ffvevHmzffv2tBNBLby8vJKTk7dt27Zo0aIjR47s2LHDw8ODdiidFhMT88MPPyxbtmzZsmXkP9OhsYtp79ixY/ny5QrToWHCCNBN2dnZCl/9AARnrUFe//79mzdv/uuvv9IOAkCTwrJYSUlJ5eXlNS9ErHtZrCVLlqxdu/bkyZMfffSR2pKDTGRk5LfffksIWbdu3YQJE7CGGffdv38/MDDwzJkzmN6MouvXr7///vvDhw/ft29fra0GyxwAEEKqqqrEYnF4eLi3tzftLMAt6FrDfy1cuPDo0aOpqam0gwCoG3u8yJ6ZSU5OLi0tFYlEXbp0kU0/1tDjRYZhxo8ff+rUqStXrrRr167xkoOCe/fuffPNN5iAWkNFRUUFBARUVVWtW7cO05up2dOnT93d3du0aXPq1Kl6jsyXnw6t1l8hO3bs2NixAdQvKyurefPmly5d6tOnD+0swC3oWsN/HTlyxNvbu6CgwMTEhHYWgMYlf+4lLi7u5cuXCstiubm5KXnZZ3l5+YcffpiTkxMfH49JgNRAtmxyu3bttm/fjmWTNZT8SP4dO3bglyn1KCws7NOnD8MwMTEx77aEtezaGfY3yprzULi7uzdt2lTlyQHU7/r16z169MjIyHB2dqadBbgFXWv4rydPnjg4OPzzzz99+/alnQVAxfLz85OSktgTLAkJCTk5OQpXDDbGslgvXrzo3bu3nZ3d2bNncTlio7p48WJAQMDjx4/nzJmzaNEikUhEOxEoJTY21t/fPzMzc+7cuQsWLEDzaVSVlZWDBw++e/duXFxcixYtVLLP4uLilJQUhdUT2J42O3q8e/fuhoaGKnkuADU7fvz40KFDX79+bWxsTDsLcAu61vA/7OzsZs+ePWvWLNpBAJSlsCzWnTt3CCHqn+f2zp07Dogd3AAAIABJREFUnp6ew4cPxywGjSQnJ2fOnDn79+8fMmRIWFiYg4MD7USgGuyqaYsXL27WrNn27ds//PBD2om0E8MwX3zxxdGjRy9dutS1a9dGepaCgoLExET2x81r167l5uZiOjTQXD/99NOMGTNev35NOwhwDrrW8D+GDh1qYmISHh5OOwhAg9VxkoTucMTTp08PHTp0+fLlCxcuVP+zazGGYfbt2zdz5kyxWPzjjz+OHj2adiJQvQcPHgQGBp4+fXr8+PEbN27EiGKVmz9//saNG48fP96/f3+1PWmt06EpM70FgDqtXLnyt99+S09Ppx0EOAdda/gfy5cv37dv371792gHAXg7iUSSnp4uOzhLSUmRSqXm5uaurq7s8Zmbm5uNjQ3tmIQQsmfPnqlTp+7fv//zzz+nnUVL3LhxIyAgICEhITAwMCQkBKPytFtUVFRgYGBpaenq1aunTJmCKd9VZffu3X5+fr/88ssXX3xBK0OtizKYmpp27tyZ7Wb36dOnVatWtOIB1BQUFHTz5s1Lly7RDgKcg641/I/jx48PGzYsLy8P0+oCByksMZ2YmFhRUSF/BMblCWmnT5++a9euCxcu9O7dm3YWzVZaWrpu3bpVq1a5ublt3769c+fOtBOBOhQWFi5dujQsLMzLy2vHjh1YqFx5J06cGD58eHBw8KJFi2hn+S/Zb6aYDg04a/To0UKhMCIignYQ4Bx0reF/5OXlWVtbnzlzRp0DwwDqkJWVxR5gJSUlsctiGRsbd+3aVXaYpSnjBqurq0eOHHnt2rX4+HhHR0facTRVVFRUUFBQUVFRcHDwt99+qxF/elCh69evT506NTU1dd68eZjeTBlJSUn9+vXz9fXds2cP7Sx1ef369Y0bN2qdNQPToQEtnp6erq6uP/74I+0gwDnoWoMiR0dHPz8/XBQKtMhfg3flypVXr16JRKI2bdrIph/r1q2bQCCgHfNdFBcXe3l5SSSSK1eumJmZ0Y6jYZ49ezZ9+vTDhw97e3tv3brV2tqadiKgQza9mZ2d3fbt2z/66CPaiTTPgwcP3N3d33vvvaioKKFQSDtOAzx//jwxMZH9grh69WpeXp7K100EeCsnJ6epU6fOnz+fdhDgHHStQdGYMWOqq6uPHDlCOwjoCvm+dK0zx7q6uorFYtoxVSMrK8vNza1Tp07R0dGadURLkXxXKiwsDGNqgBDy4MGDoKCgkydPYnqzhnr16pWnp6dIJIqJidH03/hkXx+xsbFxcXElJSUK06F16NABV+aDyhkZGYWFhU2aNIl2EOAcdK1B0Zo1a8LCwp48eUI7CGitwsLCW7duscdDly9ffvDgAdGlAX5JSUnvv//+559/vmvXLtpZNEBsbGxAQEBGRgYGAENNUVFR33zzTUlJCaY3q6fKyspBgwalp6fHx8fb29vTjqNKCtOhKUzG4eXl1adPH1tbW9oxQeMVFhaam5ufOnVq4MCBtLMA56BrDYrOnj3bv3//rKwsOzs72llASyhcLFdzWSwPDw+dmjnvyJEj3t7eP/74Y1BQEO0s3FVQULBs2bKtW7e+//7727Ztw7RVUCvZ9Gaenp47duxwcXGhnYi7GIaZMGFCVFRUTExMly5daMdpXOx0aLKpOmpOh6Zr3zugKnfv3nVxcUlJSWm8deBBc6FrDYoKCwstLCyOHTs2dOhQ9p6nT5+KRCKOLGIEGuGtxzS9evXS8WtlV69evWTJkj///HPYsGG0s3BRZGRkUFAQn89fu3btxIkTaccBrrt+/bqfn9+NGzdmzpy5fPlyjG6o1ezZszdv3nzy5EkdvED9TdOhsUOl2C8mAwMD2jFBA1y8ePGDDz7IycnR8cMYqBW61lCL1q1b9+rVq127dlevXr127drLly9Pnz49YMAA2rmAu2pdFsvMzKxTp07cXxaLFj8/v/Dw8MuXL8ufPsrKyrp8+bKPjw/FYHRlZGR88803586dGzduXGhoqKWlJe1EoBnYa/KXLFliY2Ozffv2jz/+mHYiaqqqqv78809vb2/5O3fu3BkQELB3794JEybQCsYdmA4N6i8jI2PIkCGWlpYtW7a0tbXNycmJjIyMioqyt7e3tra2trbGpSggg641EEJIYWFhYmJiQkJCQkJCXFzc8+fPCSFisbiysrK6upoQkpGR4ezsTDsmcIjCVW3Xr18vKytTWBYL88fUTSKRfPLJJ3fv3r169Wrz5s0JISkpKYMGDdLT03v06JEOvnXl5eVr1qxZs2aNi4vL9u3bsQA4vAN2JvkjR46MHz/+hx9+sLKyop2IgqNHj44cOXLZsmXLli1j74mOjh4xYsTKlSsxp3Gt5KdDu3LlSmlpqZGRUbdu3fB1BoQQhmGaNGmSn5/P4/FEIhEhpKqqij08JoQIBILPPvts3759VDMCV6BrDYQQcubMmYEDBwqFwurqatmHhYxAICgrK2M/TUCXyU/lHRsbm5+fL1sWix1TpylLTHPHq1ev3N3dTUxMLl26dPbs2bFjx1ZWVkql0vPnz3/wwQe006nepUuX+vbtW+tDFy5cCAwMfPLkyZIlS2bPnq2h66sBR7DTmxUXF69Zs+ZN05udPn1aW2chGjhw4NmzZwkhEyZM2L17940bN/r16/fZZ5/t3r2bdjQNUOt0aLJBWJgOTTeNGjXq2LFjUqm01kcxtBNk0LWG/zdy5MgTJ05UVlbWfKh58+ZPnz5VfySgTr4vXeuQuZ49e+KaRiVlZGS4u7s7OzsnJCQQQqqrq0Uikbe39++//047moqdPn16yJAhUVFRn3zyifz92dnZc+fO3bdv39ChQ8PCwhwcHGglBG1SUlKyYsWKDRs2eHp6bt++vUOHDvKP/v333wMHDjxw4ID2XXzx+PHjVq1asb+SC4VCNze39PT0Xr16/fXXX1jw7x1IJJKbN2+y84bUOnWIp6cnrlvRelu2bJk5c2ZVVZXC/Xw+v3379rdv38agBmChaw3/78mTJ23bti0vL6/5UL9+/S5cuKD+SKB+BQUFt2/fZk9Kx8TEZGdnE0KcnJxks7xgoheVk0qlPj4+CivJ6+npZWdnW1hY0Eqlcvfu3evevXtxcbG9vX1aWhpbRdXV1fv37//uu+/MzMy2bt06ePBg2jFB2yQnJ/v5+aWkpMycOTM4OFgsFhNCysrKXFxcHj9+rK+vf/XqVS2bK3vRokXr16+XSCTsTZFI1LRp0wsXLrRr145uMO1QVFR08+ZNhcUj8S2p9W7evFnrfOB8Pv/3338fO3as+iMBN6FrDf8VEhKybNkyheEuIpFo8uTJO3fupJUKGtVbl8XC7/GN6vXr197e3mfPnlVodwKBYMuWLQEBAbSCqVZxcbGrq2tmZmZVVZVQKJw3b97KlStTUlICAgKSkpICAgJCQkKMjY1pxwTtJD+92bZt2/r377948eK1a9ey1WhtbX3jxo2mTZvSjqkaVVVVzZo1y8vLk79TJBKZm5ufOXOmW7dutIJpK/mxXfHx8S9evJAf2+Xl5dWtWzdc3qIFGIYxNzcvKipSuN/e3v7BgwcYDwIy6FrDf1VWVrq4uDx69Ej+KF9PT+/777+fN28exWAgExsbW1lZqcxVuG8d29a7d2/dnPhH/R48eDBo0KAHDx7Izi/J8Hi8Ll26pKSkUAmmWgzDjB49Ojo6WvYyhULh1KlTd+7c6eHhsX37dkwdD2rw+PHjoKCg6OhoX1/fQ4cOyQZ2ikQiDw+Ps2fPasfB8aFDh3x8fGoe2gkEAj09vSNHjgwaNIhKMB3B9rTZhSeTk5NLS0tVOLvnrVu3MjMzR4wYodrMUE+ffvrpiRMn5I+QBQJBaGhoUFAQxVTAOQyAnNOnTytUCI/Hi4yMpJ0LmOzs7AkTJvB4vG+//bZB/1Eikdy+fXvv3r3Tpk3z9PRkVxMxMzPz9PScNm3awYMHs7KyGikz1EEikbi7u9f9+Xzjxg3aMVVg6dKlCpPbsbPf7dmzp7q6mnY60C2HDx92dnZWmJVTIBDMmjWLdjTV+OCDD950jpSd3PjixYu0M+qKOr58582bd+zYsezs7AbtcNOmTYSQgQMHZmRkNFJmqMPGjRsVfoAzNTUtLi6mnQu4RRt+owUVGjBgwLBhw06dOiU7v8QwjJOTE91UOq6qqmrbtm2LFi2qqKhgGObKlSt1b68wu2lSUlJ5ebmJiUmXLl169OgxdepUrCPCBUKh8J9//tm2bdvChQslEknNE9cikejnn38ODQ2lEk9Vjh49umLFCuZ/z6FJJJJ79+4JBAIUIahZfn5+ZmamQkFKpdIffvihc+fOX3zxBa1gKpGZmcn2nGs+JBAIbG1tN23a9KYp+kHlhEJhx44dO3bsOHHiRPK/Q8aioqLWrVvHNPDyq2vXrvH5/HPnznXo0GHBggXz58/HRd3q9P7778tPYyYSib777jsjIyOKkYCDMCAcFD1+/Lhdu3by85nl5+ebm5tTjKTLEhISpk6devPmTdmiaHp6esXFxQpnXeTX5IyLiyspKWFPDLJrYvXo0QPLYnFWVlbW3Llz//jjD4FAoDD7qJmZWU5OjubOwX737t0ePXqUlZXV/KLh8XimpqYZGRm4+gDU5uXLl87OzoWFhbUWpEgkunLlSo8ePahkU4l58+Zt2rRJ4Xc6kUgkEAjmzZs3f/58dhY34ALZdGhvmjTU1dVV4e/l4ODw5MkT9t9CobBJkyZbtmzx9vamkF4nSaVSc3Pz4uJi9qa+vv6TJ0/wFQYK0LWGWqxYsWL58uXs9SSmpqaFhYW0E+mi7OzsJUuW/PTTTzV7XMnJydbW1nVMndKjRw83Nzd2+BlohISEBH9//5SUFHZAEXsnj8eLiIjQ0COn/Pz89957Lysrq+YJeZZIJBo3btwvv/yi5mCgsyZMmBAREfGmgmTP66akpGjolGaVlZW2trb5+fmye4RCoVQqHTdu3IYNG/6PvTuPi6rsH/9/zTADw7Cr4A6KuUsqi6LC3Z6Z1p2KSmV961MJmqm5dmsZuaRWppWoLd7dZWWKdWfaclu2KaKBQqgkuJsS4sImIAzD/P44d/Obm2UYmGHODLyef/hw5lxc5z1nznuueZ855zrt27eXMTY0yHQ6tJSUlKtXrxrHdOkQefv27QMCAky/tCuVyurq6nvuuWf9+vXdu3eXMfjWY9SoUbt375bukTllypR169bJHREcDqU16lBRUSHdmESv1w8cOLBlzKXkRKT5bBctWlRZWVn7W6CLi4tWqy0pKXFxcenbt29ERER4eHh4ePjAgQOd9+dNCJM7URUXF0sHU1xcXG677bbvvvtO7tAaTa/X33333Xv37q2vjFGpVAaDobq6eu/evSNGjLBzeGiF9u3bJ50LrVarKysr62zj1FOabd269cEHH5S+1ElF1y233PLmm2+2sFuLtQbV1dU5OTmpf8nIyLhx44ZWqy0rK6vdWK1WKxQK6fxwzkpobq+++uqiRYt0Op1SqczJyenRo4fcEcHhUFqjbl9//fXo0aMVCsWECRO2bt0qdzityN69e+Pi4rKzs41ngNegUqnCw8NfffXVwYMHc5FPy1NQULB48eINGzYolUqdTqdQKM6dO9e1a1e542qcuXPnrl271nQmVYVC4erqWlFRIYTo0qXLrbfeGh4eHhUVNXjwYC5VgH0YZ2/+4YcfMjIydDqdq6trVVWV6Yeti4vLjBkzXn/9dRnjbJpbbrll37591dXVSqUyODj4jTfe4EbxLYN0kfaSJUu+/fbb+o4Kubi4dOrUacOGDaNHj7ZzeK3Kr7/+OnToUBcXl3Hjxm3btk3ucOCIKK1RrzFjxnz11VcLFy5cvny53LG0Cnl5eXPmzNmyZUvtM8Br6N+//9GjR+0WGOzv+PHj06dP37NnjxBiyZIlL7zwgtwRNcLHH388efJkIYRKpaqurq6urvbw8IiMjIyOjo6MjIyMjPTx8ZE7RrR2ZWVlaWlp+/fvT05OTk5OLigocHFxUalU0tGfjz766OGHH5Y7xkbIycnp06ePwWDw8fFZvnx5XFycM/7wDjPuv//+r776qr5j7uKvUxXGjBmzbt26oKAge8bWelRVVXl7e5eXlx86dCg0NFTucOCQ7DofeUNiYmLk3h6Ac7N/2sr9igEntnXrVnIWcGT2z1BOFQQsFxMTY/8kNcPhDmpGRkY+++yzckeB/9q+fXvfvn379+8vdyAtX0FBwaVLl/Lz86V/c3NzL126VFJSIi1VqVQKhcL0stVly5b17NnTtIeUlBS5btQ0a9asBm/RjKaprKz85ptv+vfvf9NNN8kdi0V+/vnntm3b9ujRg7vCNGjSpElyrZqcNaO8vPzkyZPXrl275ZZb5I7FIjqd7r333hs7dmyHDh3kjqVFkXFUFUKYFthXr16dNm2a8aF050Lj2W0ajcbf379Tp07SVGfSv+3atePMhebwxRdfBAcHM4WBg5Bu9u5QHC7runTpMnHiRLmjwH/df//95eXlfn5+cgfSSpWVlZ06der0X3JycnJyci5evKjT6Xx8fGpnilxfAoYNG0baNp/JkycbDAZnuQU0e4LlZCytydmWxGAwONfp605ExtLaNEP//e9/CyFUKlWnTp169uzZs2fP4ODg7t27BwcHBwcHc3tUexoxYkTnzp3ljgL/lZSUJHcINTlcaQ2HotFomHBSRlqtNiQkJCQkxPTJ6urqCxcuGDirszVxlroagP3x+dDiDRs27Ny5c126dGHSR9lRV8M8SmvAySiVysDAQLmjAAAA9sCp/oCz4OgXAAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWgMAAAAAYBVKawAAAAAArEJpDQAAAACAVSitAQAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWsOhZWdnP/PMM/379/fy8lKpVD4+Pr169Ro9enRKSorcoQF28tprrwUEBCgUio0bN8odSwNIWKAJ7JnjJCkgGpl0ZCgsR2kNx7Vp06aQkJDMzMzXX3/9jz/+KC0tTU9PX7p0aWFh4ZEjR+SODrCTuXPn7t+/X+4oGkbCAk1jtxwnSQFJo5KODIXlKK2bqLy8fPjw4c3RGJIDBw7ExcVFR0fv2bNn5MiRvr6+bm5uwcHBkyZNWrx4cWVlpf1Datb3kZ2kuZGzzYqEhc2Rs7ZFksLmSFIbIkNbBpXcATirTZs25efnN0djSJYtW6bX61esWKFS1dxLR44cOXLkSPuH1KzvIztJcyNnmxUJC5sjZxtkMBi2b99eUFAwZcqUBhuTpLA5ktQ8MlSuzuVkcCQxMTExMTGWtPzwww/DwsLc3Ny0Wm1QUNCSJUsMBkN1dfXq1av79Onj6urq6+v797///ffff5faJyYmarVad3f3L7744p577vHy8urcufMnn3zSYJ+//PJL3759vb293dzcBgwY8O233xoMhpkzZ7q6ukobsEePHuZDrd3YTJyrVq1yd3f39PS8dOnS7NmzO3XqdPz48Z9++ikiIsLd3d3Ly2vAgAFFRUXmOzGvzlWY/5M6N0LtfuLj47VarUKhCA0NDQgIUKlUWq128ODBUVFRXbp0cXNz8/HxmTdvnrHbb775xsvLa9myZbXXWFFRodFo2rZt2+DLkfFNr6qqeuGFF7p27arRaEJCQj799FNLVmpN5+Zt3bpVlowWQmzdurXBZuRsk3PWYDCcOHFCCLFhwwZLGpOwBmdIWIPFuWNz5KwDjrM1cry+Paqqqmr58uW9evWSMi4oKGjQoEEFBQXSUpK0ZYyqlq+XJLXbwEqG1veq5R1GLa8c7cYpS+s1a9YIIVasWHH16tVr1669/fbbDz/8sMFgWLx4saur6+bNmwsLCzMzM0NDQ9u1a5eXlyf91aJFi4QQe/bsKSoqys/Pj46O9vDwqKysNN9nUlJSQkLCtWvXrl69GhkZadzpx48f3+DniFGNxpbEOXPmzLfeemvcuHFpaWne3t6rVq0qLy/Py8sbN27c5cuXG+zEvBqraPBjqL6NULufF198UQhx8ODB0tLSK1eu3HPPPUKIr7766vLly6WlpTNmzBBCZGRkSH++a9cuLy8vKatryMnJEUJERkY2+FpkfNPnzp3r5uYmHY9cuHChUqlMTU1tcKVWdm6GI5fW5GyDnZjXqG8AJKzU3sET1uDYpTU522An5jV2nK2R4/XtUcuXL3dxcdmxY0dZWdmhQ4fat29/6623GjshSVvGqGrheknSBjsxz5rSmgx1hAw1UFo3yJINVFlZ6evre9tttxmfqaqqWrt2bVlZmaenZ2xsrPH5X3/9VQhh3IOlN768vFx6mJiYKIQ4efKkmT5rrPrll18WQuTn5xus+DRpbJxHjx4VQuzatcu0wwY7Ma/GKhrFdCPU7kf6pl5SUiI9/OCDD4QQR44cMQ3SkgNRaWlpQog777zTfDMZ3/Ty8nKtVmtcdVlZmZub27Rp08yv1PrOzXDY0pqctaQT8xr1DcAUCSs974AJa3Dg0pqctaQT8xo7zprmuJk9KiIiYsiQIca/mjJlilKprKioaLB/ktSJRlVL1kuSWtKJeU0urclQg2NkqMEhS2vnm8YsMzOzsLDQ9JIDFxeXmTNnHjt27Pr16+Hh4cbnIyIiXF1dDx48WGc/0nkIOp3OTJ81/kStVgsh9Hq9NfE3Ns7g4OCAgIDJkycnJCScPXu2aZ3YUKM2grSRq6qqTP9W2ubmeXp6CiHKysrMN5PxTc/Ozi4rKxswYID00N3dvUOHDsePHze/Upt37hTI2SZ0YiskrISEbRRytgmd2JCZPerGjRsGg8HYUq/Xq9VqFxeXBvskSUnSOvshSZuADBVkaP2cr7QuLi4WQvj6+tZ4vrCwUPy1axr5+vqWlJQ0uU8hxFdffXXrrbf6+/u7ubnNnz+/yWE3OU53d/cffvghKipq+fLlwcHBsbGx5eXl1rzYJrD5RmhQt27dNBqNdHqMGTK+6aWlpUKI559/XvGXc+fONfiZ2NydOyZy1s45S8LWRsI2Cjlr/3HWlJk96t577z106NCOHTvKy8vT0tK++OKLMWPGWPLFnSRtWucOiySVMUnJUDLUDOcrrTt16iSEuHLlSo3npX2lxr5VWFjYpUuXJvd5/vz5sWPHdujQ4eDBg0VFRatWrbIm8ibH2b9//507d+bm5i5YsGDr1q2vvfaaNS+2sZpjIzTIzc1t5MiRV65cSU5Orr302rVrTz75pJD1Tff39xdCrFmzxvQkkJSUFPMrbdbOHRY5a8+cJWHr/HMStlHIWTuPszWY2aMSEhJuv/32xx57zNvbe9y4cRMnTnz33Xct6ZMkbULnjowklTFJyVAy1AznK627devWpk2b3bt313h+wIABnp6e0rUKkoMHD1ZWVoaFhTW5zyNHjuh0umnTpgUHB2s0GoVCYX38jY0zNzc3KytLCOHv779ixYrQ0NCsrCxrXmxjNcdGsERCQoKbm9vs2bPLy8trLDp69Kh0ZwIZ33RpDsOMjIxGvahm7dxhkbP2zFkSts4/J2EbhZy18zhbg5k96tixY6dOnbp8+bJOpzt//vz69ev9/Pws7JYkJUmb1idJWgMZarlWmKHOV1q7ubktXLjwl19+mTFjxsWLF6urq0tKSrKysjQazZw5cz7//POPPvqouLj4yJEjU6dO7dixY1xcXJP7DAwMFEJ8//33N27cOHHihOmVDG3atMnNzT179mxJSUmD1yKaNnZxcWlUnLm5ufHx8cePH6+srExPTz937lxkZKQ1L7axzGwEK33zzTfe3t7Lly+vc+mgQYM+/vjjo0ePRkdHf/3110VFRTqd7syZM+++++4TTzwhXa0h45vu4uLy+OOPb9myZf369cXFxXq9/sKFC3/++af5lTZr5w6LnLVnzpKwxj8hYZuMnLXzOFuDRqOpb4+aPn16YGDg9evX6/xDkpQkJUntkKRkKBlqjrk5zuzO8nne1q1bFxISotFoNBrN4MGDExMTDQZDdXX1q6++2rNnT7Va7efnN3bs2OzsbKm9dNc1IUTPnj1PnTr1zjvveHt7CyGCgoJycnLM9LlgwYI2bdr4+vpOmDBh3bp1QogePXqcP3/+8OHDQUFB7u7uUVFRDc7yX6OxmTilW/kJIbp27bp582aDwXD27Nnhw4f7+fm5uLh06tRp0aJFVVVV5l+sebVX0aA6N8L06dNr9LN27VppI3fr1m3v3r0rV6708fERQrRv3/7jjz/+9NNP27dvL4Tw8/PbsmWLwWD4+uuv67u/n9H58+fnzp0bEhLi6enp4uLi6+s7ePDgJ554Ijk5WWog45teUVGxYMGCwMBAlUrl7+8/fvz4Y8eONbhSazo3/zY57AzhEnK2yTm7evVqKXc8PDzGjRvXYHsS1ikS1uDAM4RLyFm7jbO1c7y+PeqHH35o27at8SucWq3u27fvZ599JvVDkraMUdXy9ZKk9hlYyVDHzFCDQ84QrjCYTGQnuwkTJgghkpKS5A4EcD7btm2bNGmS/TNaoVBs3bp14sSJdl4v4Ozkyh1y1nmtX7/+xIkT0s1phRCVlZXPPffc+vXrCwoKpFIENiTXqCrXemE9MtTOHLByVMkdAAAAABqQl5c3Y8YM08sRXV1dAwMDdTqdTqfjizsgLzIUwhmvtXY0x48fV9QvNjbWweNxtPiB5uZo+3yj4nG04AE7cLTdXq5x1t3dXa1Wb9q06dKlSzqdLjc397333lu8eHFsbKx0BiYgF6dOUjIUNsSv1tbq06ePQ52009h4HC1+oLk52j7fqHgcLXjADhxtt5drnPXx8dm9e/eSJUt69epVWlrq6enZv3//lStXTpkyxfrOAWs4dZKSobAhSmsAAAAnEB0d/d1338kdBYC6kaHghHAAAAAAAKxCaQ0AAAAAgFUorQEAAAAAsAqlNQAAAAAAVqG0BgDyRCU3AAAgAElEQVQAAADAKpTWAAAAAABYhdIaAAAAAACrUFoDAAAAAGAVSmsAAAAAAKxCaQ0AAAAAgFUorQEAAAAAsAqlNQAAAAAAVqG0BgAAAADAKiq5A6hp+/btCoVC7igANMKkSZMmTZokdxQALEXOAg6OL8OAJWJiYuQO4X84Vmk9e/bsCRMmyB0F6jVp0qRZs2YNGzZM7kDgQLZu3Sp3CKgXOevghg8fbv+VkrMOaM2aNUKIZ599Vu5AIL/hw4eTpA6I8dQxde3aVe4Q/ofCYDDIHQOchkKh2Lp168SJE+UOBIBFyFnAKUhJum3bNrkDAVA3xlNYgmutAQAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWgMAAAAAYBVKawAAAAAArEJpDQAAAACAVSitAQAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWgMAAAAAYBVKawAAAAAArEJpDQAAAACAVSitAQAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWgMAAAAAYBVKawAAAAAArEJpDQAAAACAVSitAQAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWgMAAAAAYBVKawAAAAAArEJpDQAAAACAVVRyBwCHdu7cOb1eb/rMpUuXTp8+bXzYsWNHd3d3u8cFoG7kLOAUrly5UlxcbHxYWloqhDBNVW9v73bt2skQGQAhBOMpmkRhMBjkjgGOa9SoUd9++219S1UqVV5eXtu2be0ZEgAzyFnAKWzatOnJJ5800+C999574okn7BYPgBoYT9EEnBAOc2JjYxUKRZ2LlErlXXfdxWcK4FDIWcApjBs3Tq1W17dUrVaPGzfOnvEAqIHxFE1AaQ1zzI/9jzzyiD2DAdAgchZwCn5+fvfcc49KVcd1eSqVatSoUX5+fvaPCoAR4ymagNIa5nh5eY0ZM6bOTxa1Wn3ffffZPyQAZpCzgLOYPHlyjSs5JXq9fvLkyfaPB4ApxlM0AaU1GvDwww9XVVXVeFKlUo0dO9bT01OWkACYQc4CTuH++++vcw4kjUYzevRo+8cDoAbGUzQWpTUaMHr0aA8PjxpP6vX6hx9+WJZ4AJhHzgJOQaPRjB07tsZvYmq1evz48VqtVq6oABgxnqKxKK3RADc3t5iYGFdXV9MnPT097777brlCAmAGOQs4i4ceekin05k+o9PpHnroIbniAWCK8RSNRWmNhj300EOVlZXGh2q1OjY2tsYHDQDHQc4CTuHuu++uMV2Zr6/vnXfeKVc8AGpgPEWjUFqjYXfccUe7du2MDzmmDjg4chZwCiqVyvRrulqtfuihh8xMSgzAzhhP0SiU1miYUql86KGHjGO/v79/dHS0vCEBMIOcBZzFgw8+aPxNTKfTPfjgg/LGA8AU4ykahdIaFjGO/a6uro8++qiLi4vcEQEwh5wFnEJUVFSnTp2k/3fo0GHEiBHyxgOgBsZTWI7SGhYZOnRo165dhRCVlZWxsbFyhwOgAeQs4BQUCsXkyZNdXV3VavWjjz6qUCjkjgjA/2A8heUorWERhULx6KOPCiGCgoLCw8PlDgdAA8hZwFlIv4lxDSfgmBhPYTmVndf3+uuvp6Sk2HmlsIni4mIhhIeHx4QJE+SOBU2UlJRkfSdksbMgZ50OGdpqeXp6CiGWLVsmdyBonGHDhs2ePdv6fviUdnCMp85i9uzZw4YNkzEAe/9qnZKScuDAATuvFDbh7e3t4+PTpUsXuQNBU1y4cGH79u026YosdhbkrBMhQ1u5oKCgoKAguaNA4xw4cMBWh7G2b99+4cIFm3SF5sB46hS2b9/+xx9/yBuDvX+1FkJERkba5MA87O8///nPyJEj5Y4CTbFt27ZJkybZqjey2FmQs86CDG3lTp06JYTo0aOH3IGgEWz7A+azzz47ceJEG3YI22I8dXyOMFeFDKU1nBefKYBzIWcBp0BRDTg4xlNYgmnMAAAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKtQWgMAAAAAYBVKawAAAAAArEJpDQAAAACAVSitAQAAAACwCqU1AAAAAABWobQGAAAAAMAqlNYAAAAAAFiF0hoAAAAAAKs4YmkdGxurMGvXrl32j+qzzz4LDg42DUOj0XTv3v3//u//zpw5Y9ry66+/9vHx2blzZ4N9Pv744xqNRqFQ3Lhxw/oIP/nkE4VCMXz4cOu7cnC//fZbbGxs9+7d3dzc2rVrN3DgwGXLlhmXWr79G+W1114LCAhQKBQbN24008zC/cS02SOPPGLaw9133+3l5eXi4tK/f//Dhw9b3tK2r9dKjp/FHTp0mDx5cmN7qKiomDlzZocOHbRa7bffftuovyVDJWSoI3D8DHWQcdb6Dw1nQdrW2dK2r9cajp+zdhhVyUcJ+ejQDPYVExMTExNjvs2kSZN2795dWFio0+n+/PNPIcT9999fWVlZWlqan5//1FNP7dy50z7R1tajRw8fHx+DwaDX6y9duvThhx9qtdqAgIArV64Y2+zatcvb2/vLL7+0pMNFixYJIcrLy62PbfTo0T169BBCnDhxwvreHFZmZqZWq505c+aZM2fKy8uzs7Pnz59/xx13GBs0avs3yokTJ4QQGzZsaLClJfuJ1Kxt27ZCiF27dpk+/8033/z9739vWsv6bN261Vb53mKyuAmWL1/eq1evgoKCt99+OykpqVF/S4ZKyNA6kaFGDjjOWvOh4RRIW/Mt62NJrllICLF161YzDZwiZ5ugCaMq+Ug+1qfBPLIDR/zVWqFQjBgxwsfHR6VSGZ9Rq9Vardbf3z8sLEze8CRKpTIgIOCRRx6ZPn16fn7+999/b1w0evTooqKi++67z/q1lJeXW/gb19WrV7Oysl566SUhxIcffmj9qh3Wa6+95uvru3bt2m7dumk0ml69ei1dutTd3d3YwIbb33pm9hPJm2++qVQq4+LiioqKzHdleUtH4BRZ3ARffPFFeHi4r6/vlClTYmJiyNDayFAy1FYcbZxtwUhbx09bp8jZJmjyqNqCkY+On49mOGJpvWXLFq1WW9/SuLi4MWPG2DMe82666SYhRF5enoXtDQZDUlLSO++8Y/qkQqGos/GmTZvy8/Mt6Xbbtm2jR4++//77NRrN5s2bpSM3LdLVq1eLioquXbtmfMbV1dXmZ8XYXH37yfDhw2fNmnXx4sW5c+ea78Hylo7AubLYchcuXFCr1caHZGhtZCgZanMOMs5ar87AHAFp6/hp61w5a7kmj6rWIx9tq1XloxmOWFpbwmAwvP7663379nVzc/Pz83vggQeOHz8uLXrllVe0Wq2Xl1d+fv6cOXM6d+6cnZ39888/DxkyRKvVent7h4SEFBcXm+/EctK5EwMHDpQe7tu3LzAwUKFQrFu3TnpGr9e//PLLvXv3dnd3b9euXffu3V9++eWJEycae1AqlV999dWoUaN8fHw6duz4z3/+U3p+1qxZc+bMOXXqlEKhkPZXMz755JNx48Z5eXndfffdZ8+e3bt3r+nS2ttk6tSpHh4eSqUyLCysffv2arXaw8MjNDQ0Ojq6a9euGo3G19d3/vz5lmyB2p3PnDnTw8NDq9Xu2LFj1KhR3t7eXbp02bJli9R+/fr1ZpYKIb799ltvb+/ly5fXubqIiIjS0tLbb789OTm59tIa23/t2rWWv8w333xTo9EEBATEx8d37NhRo9EMHz784MGD9b1wvV6/ePHiwMBAd3f3m2++WTqfsz419hNTy5Yt69Wr13vvvVf7UF+TWzoFx8niGup8Z7/77rubbrrpzz///OCDDxQKhaenJxla5+rIUDK0pY6zZtS3szUYWH1IW9K2CRwnZ2tojlG1sasT5CP5aB92PgG9sdelSNeT1D7JfvHixa6urps3by4sLMzMzAwNDW3Xrl1eXp60VLqwaubMmW+99da4cePS0tK8vb1XrVpVXl6el5c3bty4y5cvN9hJnUwv8CgoKPjXv/6l1WpHjx5t2uaPP/4QQrz11lvSw+XLl7u4uOzYsaOsrOzQoUPt27e/9dZbjY2lUPfs2VNYWHjt2rV7773Xzc2ttLRUWjp+/PgePXo0uJXOnTvn7+9fVVVlMBg2b94shHjiiSdqtKmxTX7//fcXX3xRCHHw4MHS0tIrV67cc889Qoivvvrq8uXLpaWlM2bMEEJkZGQ0uPY6Oze+rqKiovz8/OjoaA8Pj8rKyhqvus6lu3bt8vLyWrJkSZ3rKisrCw8Pl/befv36rVq16urVq6YNamz/Rr3MuLg4Dw+PrKysGzduHDt2LCIiwsvL6/z589LSGpegzJ07183Nbfv27QUFBQsXLlQqlampqdIiS/YTqdmZM2cMBsP+/fuVSmW3bt2uX79uqOcSFAtb1sfOV3KacuQsrpOZd7Z9+/b/7//9P2NLMrQ2MpQMbXnjbJM/NMwHZh5p6/hpa89rrU05cs7WybajKvlIPtanUXnUTJyytC4rK/P09IyNjTU+8+uvvwohjPtojTlLjh49KmpdFt9gJ3WSZiEyUigUy5YtM6aKpMYeHxERMWTIEOPSKVOmKJXKioqKOkOVLsI8evSo9NDCL+4rVqx4/PHHpf8XFRW5ubl5e3uXlZWZtqk9j4uUiiUlJdLDDz74QAhx5MgR063x6aefNrj2Ojuv8UxiYqIQ4uTJk5YsbVBlZeUbb7zRp08f6V0ICAj46aefjEvr/MSx8GXGxcWZfl6npqYKIV566SXpoeknTnl5uVarNe4/ZWVlbm5u06ZNkx5asp8YTD5HDAbDnDlzhBDTp083mP3EabBlfRzti7u8WWxmVDb/zjattCZDydAGkaFGDjjONvlDw3xg5pG2jp+2DlVat55RlXwkH+vTqDxqJk55QvixY8euX79uPKIjhIiIiHB1da3vlIbg4OCAgIDJkycnJCScPXu2aZ0YGffIefPmGQwGHx8f06tEartx44bB5LpKvV6vVqtdXFzqbCx1pdPpzMdQg3SuqfR/b2/vu+++u7i4eMeOHY3qxNXVVQhRVVVlTSTmO6+vN/NLa1Or1TNmzPj9998PHDjwwAMP5OfnT5gwoaCgwPJILHyZ4eHhWq22zlOhsrOzy8rKBgwYID10d3fv0KGDacvG7ifLli3r3bt3YmLivn37zL8Ey1s6Mnmz2IwG39kmIEPJUKfDOGs5MztbowJrLNKWtDXVqkbVpq2OfKyNfLQ5pyytCwsLhRCenp6mT/r6+paUlNTZ3t3d/YcffoiKilq+fHlwcHBsbGx5ebn5Tnbt2mV6x7Y6b533wgsvdOjQYeHChdLRo/rce++9hw4d2rFjR3l5eVpa2hdffDFmzJgmZHJ9IR09evTIkSP33XefcZE01UHLnoVYMnTo0H//+99Tp069fPnyjz/+2ByrcHNzu3z5cu3nS0tLhRDPP/+8cbOfO3eurKysdksL9xONRvP+++8rFIr/+7//Ky8vt0lLR+YgWVyb5e9sDWRobWQoGWqmEycaZ80zs7PZKjB7Im2dNG0dJGdrs/mo2uTVkY91Ih9tyylLa19fXyFEjQ+LwsLCLl261Pcn/fv337lzZ25u7oIFC7Zu3SrNa2+mkzFjxpj+uP/RRx/V7tPLy2vlypUlJSXTpk0zE21CQsLtt9/+2GOPeXt7jxs3buLEie+++25jXu5/1RfSxx9//OCDD5ouunbtmru7++7duy2fT9WJjB8/3ngoTiLdX96Sj+nG0ul09e1X/v7+Qog1a9aYbvmUlJTaLS3cT4QQw4YNmz179okTJ5YuXWqrlg7LQbLY6JdfflmzZo1ozDtbAxkqIUMb1dJhOUiGOsg4WydLPjRsFVhzI20b1dIxOUjOGjXfqNrk1ZGPtZGPNueUpfWAAQM8PT3T0tKMzxw8eLCysrK+2/rl5uZmZWUJIfz9/VesWBEaGpqVldXYTur06KOPDh06dNeuXdu2bauvzbFjx06dOnX58mWdTnf+/Pn169f7+flZvgrzDAbDp59++vTTT5s+6efnN2HCBL1e/8knn9hqRY6joqJCejeNsrOzhRA333yzzdclXdkSGRlZe5E072JGRoYl/Viyn0iWLl3ap0+f9PT0Bvu0vKVjcpwslhw6dMjDw0M08p1tEBkqyFDn5DgZKvs4Wx9LPjRkCawJSNvGtnRAjpOzkmYaVa1ZHflYG/loc05ZWms0mjlz5nz++ecfffRRcXHxkSNHpk6d2rFjx7i4uDrb5+bmxsfHHz9+vLKyMj09/dy5c5GRkY3tpE4KheLNN99UKBQzZsyo7xKI6dOnBwYGXr9+vQmvtE2bNrm5uWfPni0pKanzMon9+/d7e3uPGDGixvNTp04VTnvG6TfffGPmngRCiLFjx27btq2wsLCoqGjHjh3PPffc3//+d1t94lRXVxcUFFRVVWVmZs6aNSswMPCxxx6r3Uyj0Tz++ONbtmxZv359cXGxXq+/cOGCNLNIbZbsJ8Zu33//fUtOT7K8pWNynCzW6XSXLl366aefpFG5Ue8sGVonMrRRLR2T42So7ONsbZZ/aFgTmG2Rti0+bR0nZ5t1VLVmdeSjhHxsXgb7snw2xeLi4r/97W9t2rQRQiiVyptuumn58uXGpdXV1a+++mrPnj3VarWfn9/YsWOzs7OlRatWrXJ3dxdCdO3adfPmzQaD4ezZs8OHD/fz83NxcenUqdOiRYuku+CY6aS25OTkXr16SRutU6dO8fHxxkXSHunr67tixYq33nqrQ4cOQgitVnv//fcbDIYffvihbdu2xg2uVqv79u372WefmYbas2fPU6dOffTRR9Lxsy5dukiTlx4+fDgoKMjd3T0qKqr2nQ+eeOIJDw8PlUo1cODAw4cPG59funRpx44dpdV17tw5MTGx9jZZu3atVqsVQnTr1m3v3r0rV6708fERQrRv3/7jjz/+9NNP27dvL4Tw8/PbsmWL+XeqdueJiYlS59Lreuedd7y9vYUQQUFBOTk55pcaDIavv/7ay8tr2bJlda5u9+7dkyZN6tGjh5ubm6ura+/evRMSEqSpKQwGQ43t39iXGRcXp1arO3furFKpvL29H3jggVOnTkk9r169Wmrs4eExbtw4g8FQUVGxYMGCwMBAlUrl7+8/fvz4Y8eOWbiffP7559Lkiu3atZOmQDQ1b94843SIlrc0T5b5hx0ti40bs06ff/651KzOd/bs2bODBw8WQqhUqtDQ0O3btxvI0LqQofW1NI8MNTjkOGvNh4b5wMwjbZ0ibe0/Q7ij5aydR1Xy0UA+mmVhHjUrxy2tW4zExMRZs2YZH1ZUVDz77LNubm41br0DRxAXF9emTRu5o2gWMt7aB7AVMtQSrTBDHXacddjA7KkFp61cN99C05CPhhadjwbHyCNVfcd+YBN5eXkzZswwvVDB1dU1MDBQp9PpdDrpGBgcil6vlzsEAPUiQ1GDw46zDhuY/ZG2kB35aEQ+NiunvNbaibi7u6vV6k2bNl26dEmn0+Xm5r733nuLFy+OjY2VTg5xfMePH1fULzY2Vu4AgVaNDEUr57DjrJnAcnNzSVvAnshH2AeldfPy8fHZvXv30aNHe/Xq5e7u3q9fv/fff3/lypUffPCB3KFZqk+fPmZOe/j000/lDtBmFi5c+P777xcVFXXv3n379u1yhwNYhAxFK+ew46yZwEhbwM7IR0E+2oXCYDDYc30TJkwQQiQlJdlzpQC2bds2adIkm+Q7WQzYHBkKOB0b5ppCodi6devEiROt7wpotRwhj/jVGgAAAAAAq1BaAwAAAABgFUprAAAAAACsQmkNAAAAAIBVKK0BAAAAALAKpTUAAAAAAFahtAYAAAAAwCqU1gAAAAAAWIXSGgAAAAAAq1BaAwAAAABgFUprAAAAAACsQmkNAAAAAIBVKK0BAAAAALCKyv6rPHDgwIQJE+y/XsDBXbx4UafT+fn5eXt7KxQK23Z+4cIFG/ZGFsOMK1eu+Pr6qlQyjC/Oiwx1HHl5eR06dJA7CjiBAwcOREZG2qq3NWvWJCUlNfnPy8vLCwoKSkpKevfubauQADSWvb/6DBs2zM5rBJzF5cuXz5w5o9frVSqVr6+vn5+fn59fmzZtPD09re+8S5cuMTEx1vcjyGKYZTAYUlNTDQbDoEGDOnXqJHc4ToMMdQTXr19PT0/Pz8+/9dZb27ZtK3c4cHSRkZG2SrcmpH9lZWVBQcG1a9cKCgoKCgrKy8sVCoWXl9dNN93k4uJik6gA5xITE9O1a1d5Y1AYDAZ5IwBgpNfrjx8/fsjEjRs3vL29Q0JCwv7Sv39/ucMEzLl27do//vGPd999d/To0YmJiYGBgXJHBDRAp9O9/vrrCQkJvXr12rBhw/Dhw+WOCKhJp9NlZmbu27dP+npw/Pjx6urqjh07Gr8eDB8+nENCgLworQHHpdPpcnJypEE0OTk5IyNDr9f7+vqGh4ePGDEiLCxsyJAh7du3lztMoA4///xzfHz8+fPnFy9ePHfuXH5FgcP65Zdfpk6devbs2Xnz5i1cuNDV1VXuiAAhhKiqqsrOzjYeak9NTa2srPTx8RkwYEBUVNSIESP4DgA4GkprwGlcv349IyPDOMr+/vvvBoOBI9ZwWNIvgS+++GLfvn03btw4dOhQuSMC/ofpGRbr1q0LCgqSOyK0drm5udLB9H379qWnp5eVlXl6eg4cONA40Pfr18/ms7EAsBVKa8BZFRcXZ2ZmGivtrKwsIYRUaUvHs0NDQ7VardxhorU7efLk1KlTf/jhhyeffPLVV1/19vaWOyJAGAyGzZs3z5kzx9XVdcWKFY8++qjcEaGVkmppSUpKytWrV1UqVa9evaRCOioqatCgQZz1AzgLSmughfjzzz/T0tKk4fnXX3/Nz893cXHp3bu38VB3eHi4RqORO0y0RlIZM3fuXJVKtXLlSsoYyCszM3Pq1Km//vrrtGnTli1b5uXlJXdEaEWKioqOHDki/TS9d+/evLw8IURwcLB0nReDNeDUKK2Blsn0QPj+/fuvXbumVqt79uxpPBA+ePBgpZI728N+CgoKnnvuOU6+hYzKyspeeeWVFStWDBgw4O233w4PD5c7IrR8paWl6enpZi7mioqK8vPzkztMADZAaQ20CqdPnzZOK3r48OHy8nIu34Isfvnll/j4+HPnzs2bN2/RokVqtVruiNBa7Nq1a/r06UVFRQkJCdOnT+ckWzSTOqcfM52CdOjQoQEBAXKHCcD2KK2BVqfGqJ+WllZRUSFNOmo8gh4cHCx3mGixjDc66t2798aNGyMjI+WOCC1cbm7uc889t3nz5gkTJrz11ltMqgybq3382svL6+abb+b4NdCqUFoDrZ10iy9pPtI6b5XJ8XU0h5MnT06bNm3Pnj1Mb4bmU1VVlZiY+MILLwQEBCQmJo4cOVLuiNBCmLnqSppJtG/fvlx1BbQ2lNYA/kdJSclvv/1m5qqwESNGtGnTRu4w0UIkJSU9/fTTTG+G5nD48OG4uLjffvtt9uzZCQkJTA0Fa0jTj0mHoVNTUy9dusRcoQBqoLQGYE6Dc5mGhYW5u7vLHSacWEFBQUJCwrp160aNGrVu3bpu3brJHRGcXmFh4YsvvpiYmBgVFbVhw4a+ffvKHRGcz/Xr1zMyMswcaI6Ojvb19ZU7TAAOhNIaQCOYngJ34MCBK1eumN6BMywsLCIiws3NTe4w4Xz27t0bHx9/9uzZefPmLVy40NXVVe6I4Kx27tw5derUqqqqV1555ZFHHuECV1jIdCKS5OTk9PT0GpdHRUZG+vv7yx0mAMdFaQ2g6Uwr7eTk5IKCAuliM+lKs7CwMC42g+WM05v16tXr7bffZnozNNapU6eefvrp3bt3T548ec2aNW3btpU7Ijg0vV5//PjxQyZu3LhRY/qx/v37yx0mAKdBaQ3ANiz5jsIUqWjQqVOnpk2b9t13302ePHnt2rVc2A9LmB6X2bhx47Bhw+SOCA6KI8IAmg+lNYBmUeeNPY23+IqKioqOju7QoYPcYcJBJSUlTZ8+XalUrlq1iunNYN7PP/88depU6WbpXE2AGgoLC9PS0qRbYPz666/5+fk1ph/jOiYAtkJpDcAeSktL09PTjZV27Vt8DRs2rF27dnKHCQcizUS1bt26e+65JzExkenNUNulS5fmzZv30UcfjR49et26dUFBQXJHBPnVuM9FVlaWEMI43ERFRQ0fPlyr1codJoAWiNIagAyKi4szMzPNfPUZNmyYh4eH3GFCfvv27YuPjz99+vT8+fP5QRJGBoNh8+bNs2fP1mg0a9eujYmJkTsiyEan0+Xk5EgneO/bt49DtwDkQmkNQH6mJ+xxv1DUoNPp1q9fv2jRouDg4LfffpvLaJGZmRkfH5+amjpt2rRly5Z5eXnJHRHsqs6pPby9vUNCQozHZ4ODg+UOE0CrQ2kNwOGYTjOTkpJy9erVGrf4GjJkCL9etjanT5+eNm0akz+3cmVlZa+88sqKFStCQkI2btwYHh4ud0SwE+O4kJycnJKSUlpaqlarb775ZmnuMaYfA+AIKK0BODrpG5V0pl96enpZWZmHh8egQYOMlTbfqFqPpKSkZ555RqFQML1ZK7Rz585nnnmmqKgoISHhmWeeIetbtj///DMtLU0qpw8ePHj58mWOsQJwcJTWAJxJg+cBchvSFs84vdktt9yyYcOG3r17yx0Rmt3FixdnzZq1ffv2CRMmrFu3LiAgQO6IYHv1TT9mvC1WaGgo048BcGSU1gCcWJ2z1/j6+oaHh0tfxYYMGdK+fXu5w4TtJScnx8fHnzp1iunNWraqqqrExMQXXnihffv269evv+uuu+SOCDYjfYBLn9513jli+PDhXPoBwIlQWgNoOa5fv56RkZeWkTEAACAASURBVGH80eP33383GAx8UWuppOnNnn/++U6dOm3cuPG2226TOyLY2KFDh+Lj4zMzM5999tmXXnqJmw87uxqnHaWlpVVUVPj4+AwYMECaeyw6OrpDhw5yhwkATURpDaDFKioqOnLkiHHmm9OnTwtOL2xxTp8+/fTTT//nP/+ZPHny66+/zi12Wgbjaf9/+9vfNmzY0KdPH7kjQhM1OFlGv379FAqF3GECgA1QWgNoLUwnHq9zUpyIiAh+FnNSO3funDZtmk6ne+WVVx555BG+qTu1pKSk6dOnK5VKJqtzRqaftAcOHLhy5QrTjwFoJSitAbRSpt//9u/ff+3aNbVa3bNnT+m8xBEjRjDxuHPhd84W4OTJk08//fR3333HLdacSHFxcWZmpvRZum/fvjNnzgghgoODjbfFCgsLc3d3lztMAGh2lNYAIIQQp0+flqbSOXTo0OHDh8vLyz09PQcOHMhZi87l0KFDcXFxWVlZ8+fP/8c//sFpCM6isrJyzZo1L774Yp8+fTZu3BgZGSl3RKiXTqfLzMw0fmDWntVixIgRbdq0kTtMALA3SmsAqKmqqio7O7u+uXbCwsKio6O7d+8ud5iomzSn9PPPP9+xY8cNGzbccccdckeEBvz0009Tp049f/784sWL58yZo1Kp5I4I/6PGR2JqamplZaX0kSid48O9GABAUFoDQIMavENMZGSkv7+/3GHif5w5c+bpp5/+9ttvmd7MkeXl5c2fP3/z5s1jxoxJTEwMDAyUOyL8V25urvFDjxN5AMASlNYA0DglJSW//fabmVt8RUVF+fn5yR0mhPhrerOysrIVK1Y89dRTVAKOw2AwbN68efbs2RqN5o033hg/frzcEbV2ptNPpKSkXL161XT6saioqMGDBzP9BACYQWkNAFYpLCw8evSo9PNOWlpaXl6eYAofR1JUVLR48eLExMSoqKiNGzcyvZkj+O233+Lj49PS0qZNm7Z8+XJPT0+5I2qNjLcnTE5O3rt3b15enouLS+/evY0fXOHh4RqNRu4wAcBpUFoDgC1x4xnHdPjw4bi4uN9++2327NkvvfQS05vJpaysbMmSJatXrx46dOjGjRsHDBggd0StyPXr1zMyMjjjBgCaCaU1ADQjY6WdnJyckpJSWlqqVqtvvvlm42/a3OLLbkynN1u/fv2dd94pd0Stzs6dO6dPn15SUvLiiy8+88wz7PnNzXT6seTk5IyMDL1e7+vrGx4eLn0EDR06NCAgQO4wAaCFoLQGADvR6/XHjx8/ZOLGjRteXl4333wzMwPZzcWLF2fMmPHvf/978uTJq1evZv45+7h48eLMmTM/++yzCRMmrFu3jnKu+dS+j2CND5n+/fvLHSMAtEyU1gAgjzrvZ+Pr69u/f3/pfjYREREdOnSQO8yWaefOnU8//fT169dXrlzJ9GbNipMFmpvpRSj79++/du2aWq3u2bOndIL3iBEjODUGAOyD0hoAHEJpaWl6errxK3LtW3wNGzaMO0jZUGlp6dKlS1977bURI0Zs3Lixb9++ckfUAiUnJ8fHx586dWr+/Pn/+Mc/uMTdJkynTkxNTb106VKN6cciIiLY1ABgf5TWAOCIiouLMzMzjZV2VlaWEMJYaUdFRQ0bNszDw0PuMJ1eenp6XFxcRkZGfdObHT58+Mcff5wzZ44s4Tk4vV4/Z86cVatW1d5uBQUFCQkJ69atu+WWW9avX8/E7NYwP/0YnwYA4CAorQHACRQWFqalpUmXUP7666/5+fncJsdWpDOWX3jhhfbt269fv/6uu+4yLtLr9WFhYUeOHPn6669HjhwpY5CO6bnnnlu1atWSJUteeOEF0+eTkpKmT5+uVCpXrVr16KOPyhWe89LpdDk5OdLcY/v27at9DktkZCTTBACAo6G0BgDnY3p1ZUpKytWrV01v8RUVFTVo0CAXFxe5w3Qmubm5M2bMkObZSkxMlOqWN99889lnnzUYDJ6enunp6T169JA7TAfy6aefPvTQQwaDQa1WZ2Vl3XTTTUKIEydOTJs27Ycffnj44YfXrl3bpk0bucN0DpbMccj0YwDg4CitAcDp5ebmSr9uHTp0KD09vayszNPTc+DAgUw83ljGu0OtXLlyzJgxvXr1Ki0tFUKo1eqgoKBDhw55e3vLHaNDyMjIiIyMrKyslErrqKior776atWqVStXruzbt+/GjRuHDh0qd4yOzvQAWXJyckFBgTT9mDT3GHfmAwCnQ2kNAC1KjYnH09LSKioqvL29Q0JCrP/5a/78+VqtduHCha6urrYN23EUFxc///zz69ev79evX3Z2dmVlpfS8SqUaNWrUjh07OEhx9erVQYMG5eXlVVVVSc8oFIqhQ4dmZWUtWbJk+vTpLfWMiZ9//jkhIeHHH39s2p83eFkH048BgFOjtAaAlqzBizaHDh1q+U2GQ0ND09PT+/Tp8/bbb//tb39r1sjl9eabb86cObPGk0qlcunSpQsXLpQlJAeh0+luv/32gwcP6nQ645MKhcLHx2f//v0tdaL1ixcvPvvss0lJSUKIs2fPBgUFWfJXJSUlv/32W52TEUo/TYeGhmq12uYNHQBgL5TWANCKmJ9qOCwsbMSIEfVdH1tZWenp6anT6VxcXPR6/YMPPrh69eqOHTva+SXYQVlZWe/evf/880+9Xl9jkUKh+PLLL8eMGSNLYI5g2rRp7777rvH3aiOVSjVlypTExERZomo+Op3ujTfeePHFF6uqqiorKxUKxbZt22JiYuprnJOTY7w6g1voAUCrQmkNAK1XUVHRkSNHpDJ73759Z86cEUJ07NjReLWn6a9qhw4dCg8PN/6tWq12cXFZsGBByzs/fM6cOW+++Wbt6lEIoVQqNRpNWlpaS/151rwPPvjgscceq2+pQqHYv39/ZGSkHSNqXr/88suUKVNOnjxpPMji6uo6a9asVatWSQ9rTD9W+/qL6Ojo7t27y/cKAAD2Q2kNAPivixcvpqampqWlpaampqamSvMqDRgwIDw8PCIi4sKFCy+//HKNglOpVHbr1m3jxo2m96xyahkZGWFhYdXV1fU1UKlU3bt3P3TokJeXlz0Dk11KSsott9xieh54DUqlsl+/fhkZGS3gWus///xz3rx5n3zyiVKprHHywpAhQ2bOnCnliDRroIeHx+DBgyP+Ik2WDgBobSitAQB1qzGDsY+Pz8WLF2tXVtL54ffee+/GjRu7du0qS6g2tHr16rfeeuvcuXNCCFdXV+M0ZqbUavWoUaO++OKL1jOl2cWLFwcNGlRQUFDnSfJqtVo6Wfqmm27617/+NXz4cFmCtAnpPucLFy7U6XR1HkdQq9UGg8F4r7uwsLAhQ4a0sBM3AABNQGkNAGiYXq/v06fPyZMn62vQws4PLyoqSk1N3bdv388//5ySklJRUaFSqYQQxh/tlUplQkLCCy+8IGuYdnLjxo0RI0YcOXLEWGqqVCqDwaDX693d3W+++eZbb711xIgRLeBC4r17906ZMiUnJ8fMaQtCiPT09EGDBtktKgCAU6C0BoAW5cKFC/v377d5txUVFY899pj5ekPSqVOnp556ql+/fjaPQS56vf7s2bM5OTk5OTlZWVmFhYXGH6vnzZsXFhYmb3h2kJiY+MsvvygUCoPBoFAo2rdv369fv169evXs2bNz584t46f7wsLCzZs379u3T6lUmt/PlUplfHz8Lbfc0hxhdO3addiwYc3RMwCguVFaA0CLsm3btkmTJskdBYCmiImJkW7xBQBwOiq5AwAA2J7ND5u+8cYbs2bNUiqVKpVKr9dLF9yqVKpOnTr17NmzV69ePXr0CP5L65nf68aNG5cvX24BV5jXx2AwnD59ukePHnIHYg86ne78+fNnzpw5/ZcTJ06cPn26uLhYCKFQKNzc3BQKRXl5+aBBg9LT020ewIQJE2zeJwDAbiitAQANO3ny5MCBA3v37m1aQnft2rUFzAVtDY1G04LraiGEQqFoJXW1EEKtVvfo0aP26y0sLDxt4sSJE3l5eVVVVdLl9wAASBgVAAANe+utt+QOAZCHr69vaGhoaGio3IEAAByaUu4AAAAAAABwbpTWAAAAAABYhdIaAAAAAACrUFoDAAAAAGAVSmsAAAAAAKxCaQ0AAAAAgFUorQEAAAAAsAqlNQAAAAAAVqG0BgAAAADAKpTWAAAAAABYhdIaAAAAAACrUFoDAAAAAGAVSmsAgLN67bXXAgICFArFxo0baz9slBs3bvTp0+f555+3pHFERISLi8ugQYMaHXEtTz75pJeXl0KhyMjIsL43R5Odnf3MM8/079/fy8tLpVL5+Pj06tVr9OjRKSkpcocGAICNUVoDAJzV3Llz9+/fX9/DRlm0aFF2draFjVNTU2+77bamraiG9957791337VJV45m06ZNISEhmZmZr7/++h9//FFaWpqenr506dLCwsIjR47IHR0AADZGaQ0AkEd5efnw4cPljkIIIfbv33/06NHG/pVCoWiOYByB9W/NgQMH4uLioqOj9+zZM3LkSF9fXzc3t+Dg4EmTJi1evLiystJWoVquWfc3x9mZAQByUckdAACgldq0aVN+fr7cUYjy8vJ58+a99957/fr1a9QfqtVqmwTggCW69W/NsmXL9Hr9ihUrVKqa3zRGjhw5cuRIazpvmmbd3xxkZwYAyIhfrQGgldq8eXN4eLhGo/Hw8OjWrdvSpUuFEAaD4fXXX+/bt6+bm5ufn98DDzxw/Phxqf369es9PDy0Wu2OHTtGjRrl7e3dpUuXLVu2NNjn3r17+/Xr5+Pjo9FoQkJC/vOf/wghZs2aNWfOnFOnTikUiptuuqnBaOvsxCYWLVr09NNP+/v7N/YPT5482adPHw8PD3d39+jo6H379hkXmdmM0tJXX321d+/ebm5uPj4+8+bNMy568sknFQqFQqHo0aNHenq6EOLxxx/XarU+Pj5ffvml+XjefPNNjUYTEBAQHx/fsWNHjUYzfPjwgwcPGhvUuQ1feeUVrVbr5eWVn58/Z86czp07jxo1qsZb8/PPPw8ZMkSr1Xp7e4eEhBQXFwshvv32W29v7+XLl9eOpLKycs+ePW3bth0yZIj5mGXc3/R6/eLFiwMDA93d3W+++eatW7daslJrOgcAtHAGAEALIn2Jb7DZmjVrhBArVqy4evXqtWvX3n777YcffthgMCxevNjV1XXz5s2FhYWZmZmhoaHt2rXLy8uT/mrRokVCiD179hQVFeXn50dHR3t4eFRWVprvMykpKSEh4dq1a1evXo2MjGzbtq3Ufvz48T169LDwddXXyYkTJ4QQGzZsqPNhg/bt23f//fcbDIbLly8LIRYtWmThH95xxx3BwcFnzpzR6XRHjx4dOnSoRqPJycmRlja4GRUKxerVqwsKCsrKyhITE4UQ6enp0tLx48e7uLhcvHjRuK6HHnroyy+/tCSquLg4Dw+PrKysGzduHDt2LCIiwsvL6/z589LS+rah9LbOnDnzrbfeGjdu3O+//2761ly/ft3b23vVqlXl5eV5eXnjxo27fPmywWDYtWuXl5fXkiVLaoeRk5MjhIiMjGwwYBn3t7lz57q5uW3fvr2goGDhwoVKpTI1NbXBlVrZuXkxMTExMTENNgMAOCZKawBoUSwprSsrK319fW+77TbjM1VVVWvXri0rK/P09IyNjTU+/+uvvwohjOWTVHWUl5dLD6Wa8OTJk2b6rLHql19+WQiRn59vaGRpXV8n1pTWZWVl4eHhFy5cMDSptB44cKDxYWZmphBi7ty5UrdmNmNZWZlWq73rrruMS6UfRY2l9ffffy+EWLZsmfSwqKioZ8+eVVVVlkQVFxfn4+NjfJiamiqEeOmll2q3NN2GNd5Ww/++NdJV6Lt27bIkAElaWpoQ4s477zTfTMb9rby8XKvVGlddVlbm5uY2bdo08yu1vnPzKK0BwKlxQjgAtDqZmZmFhYWm17u6uLjMnDnz2LFj169fDw8PNz4fERHh6upqelKxKVdXVyGETqcz02eNP5GuT9br9dbEb5NOhBALFy6cMmVK586drexHCBESEuLj4yMV2OY348mTJ8vKyu644476urr99tt79er1z3/+02AwCCE+/fTT2NhYFxeXJkQVHh6u1WpNz0U3snwbBgcHBwQETJ48OSEh4ezZs5as19PTUwhRVlZmvpmM+1t2dnZZWdmAAQOkh+7u7h06dKhzQ5mu1OadAwBaEkprAGh1pGtlfX19azxfWFgo/qqLjHx9fUtKSprcpxDiq6++uvXWW/39/d3c3ObPn9+0mG3Sial9+/YdOXLkySeftL4riVqtlgow85vxwoULQggzl3YrFIr4+PjTp0/v2bNHCPHhhx8+8cQTTY7Kzc1N+kFeNHUburu7//DDD1FRUcuXLw8ODo6NjS0vLzf/J926dZNOjzffTMb9rbS0VAjx/PPPK/5y7ty5Bo8FNHfnAACnRmkNAK1Op06dhBBXrlyp8bxUqNQobAoLC7t06dLkPs+fPz927NgOHTocPHiwqKho1apVTQjYJp3UsGnTpj179iiVSqn4kWrd5cuXKxQK6XzmRqmqqrp27VpgYKBoaDNqNBohREVFhZneHnvsMY1G895772VnZ3t7ewcFBTU2HolOpzOu15pt2L9//507d+bm5i5YsGDr1q2vvfaa+fZubm4jR468cuVKcnJy7aXXrl2TjmjIuL9Jb/eaNWtMT+RLSUkxv9Jm7RwA4OworQGg1enWrVubNm12795d4/kBAwZ4enqaFpYHDx6srKwMCwtrcp9HjhzR6XTTpk0LDg7WaDRNu9GUTTqp4f333zetfEyvtTY9RdlCP/74Y3V1dWhoqGhoMw4YMECpVP78889mevPz85s0adIXX3zx2muvPfXUU41+bX/56aefDAZDZGSksGIb5ubmZmVlCSH8/f1XrFgRGhoqPTQvISHBzc1t9uzZtX/iPnr0qHRHLhn3t65du2o0moyMjAZXZLfOAQDOjtIaAFodNze3hQsX/vLLLzNmzLh48WJ1dXVJSUlWVpZGo5kzZ87nn3/+0UcfFRcXHzlyZOrUqR07doyLi2tyn9IPud9///2NGzdOnDhhehltmzZtcnNzz549W1JSUue1rEZmOpFRZWVlUVFRVVXV4cOHZ8yYERQU9NhjjwkhzG9Gf3//8ePHb9++fdOmTcXFxZmZme+8807tzqdOnVpRUbFr16777ruvUVFVV1cXFBRUVVVlZmbOmjUrMDBQiqpR29D0rTl37lx8fPzx48crKyvT09PPnTsn1erffPNNfTffEkIMGjTo448/Pnr0aHR09Ndff11UVKTT6c6cOfPuu+8+8cQT0lXKMu5vLi4ujz/++JYtW9avX19cXKzX6y9cuPDnn3+aX2mzdg4AcHrNPU8aAMCeLLz5lsFgWLduXUhIiEaj0Wg0gwcPTkxMNBgM1dXVr776as+ePdVqtZ+f39ixY7Ozs6X2iYmJWq1WCNGzZ89Tp06988473t7eQoigoCDjTafq7HPBggVt2rTx9fWdMGHCunXrhBA9evQ4f/784cOHg4KC3N3do6KijPdbqk+dncyaNat9+/ZCCA8Pj3Hjxq1evdr0YaO2W2NnCH///fdvu+22gIAAlUrVtm3bBx988Ny5c8alZjajwWAoKSl58skn27Zt6+npGRUVtXjxYiFEly5dfvvtN9NVDB48+B//+EejXkVcXJxare7cubNKpfL29n7ggQdOnTplXFrnNpw+fbq7u7sQomvXrps3b5Zamr41Bw8eHD58uJ+fn4uLS6dOnRYtWiRNV/711197eXkZZzKv0/nz5+fOnRsSEuLp6eni4uLr6zt48OAnnngiOTm5wQ3V3PtbRUXFggULAgMDVSqVdLzj2LFjDa7Ums4bfPuYIRwAnJrCYDDYvZwHADSXbdu2TZo0ic/2FmD06NHr1q3r3r275X8SHx+flJR09erV5osKzWfChAlCiKSkJLkDAQA0BSeEAwDgKIwnxmdmZmo0mkbV1RLr70kGAACagNIaACCz48ePK+oXGxtr5z6bIx4LLViw4MSJEzk5OY8//vjSpUsdJCoAANAgldwBAABauz59+tj8DHZr+myOeCyk1Wr79OnTuXPnxMTEfv36NSqqhQsXvv/++5WVld27d3/11VdjYmKaOVgAAPD/41prAGhRuNYacFJcaw0ATo0TwgEAAAAAsAqlNQAAAAAAVqG0BgAAAADAKpTWAAAAAABYhdIaAAAAAACrUFoDAAAAAGAVSmsAAAAAAKxCaQ0AAAAAgFUorQEAAAAAsAqlNQAAAAAAVqG0BgAAAADAKpTWAAAAAABYhdIaAAAAAACrqOQOAABge9u2bZM7BACNc+HChS5dusgdBQCgiSitAaAFmjRpktwhAGi0mJgYuUMAADSRwmAwyB0DAACwiEKh2Lp168SJE+UOBAAA/A+utQYAAAAAwCqU1gAAAAAAWIXSGgAAAAAAq1BaAwAAAABgFUprAAAAAACsQmkNAAAAAIBVKK0BAAAAALAKpTUAAAAAAFahtAYAAAAAwCqU1gAAAAAAWIXSGgAAAAD+v/buNMqK6t4bcJ0eTs8DIpM0IDMK6lIEGSRBY6IGryLQTAoSFxFRQ7wSh+vAaxAIiIJDYLlQl3HhvTIqoFeJGoeVRCBgxAFFFBVQhG6RGaSb7vN+OLmdZh4KOd34PJ+o2rv2/ledD+3PXQOEIloDAABAKKI1AAAAhCJaAwAAQCiiNQAAAIQiWgMAAEAoojUAAACEIloDAABAKKI1AAAAhCJaAwAAQCiiNQAAAIQiWgMAAEAoojUAAACEIloDAABAKKI1AAAAhCJaAwAAQCiiNQAAAIQiWgMAAEAoojUAAACEIloDAABAKKI1AAAAhCJaAwAAQCiiNQAAAISSkugCAIADmjJlysaNGyvvmTt37hdffFGxOWjQoDp16hz3ugCAPURisViiawAA9m/IkCFTpkxJS0uLb8ZisUgkEv/37t278/Ly1q1bl5qamrgCAYAgcEM4AFRl/fr1C4Jg1/8pKSmp+HdSUlK/fv3kagCoCqxaA0DVVV5eXq9evaKiov22/u1vf+vcufNxLgkA2JdVawCoupKSkq6++upoNLpvU7169Tp16nT8SwIA9iVaA0CV1q9fv5KSkr12pqamDhw4sOK5awAgsdwQDgBVXZMmTSq/FTxu6dKlZ511VkLqAQD2YtUaAKq6gQMH7vW6siZNmsjVAFB1iNYAUNVdffXVpaWlFZupqam/+tWvElgPALAXN4QDQDVw5plnfvjhhxV/tVesWNG8efPElgQAVLBqDQDVwMCBA5OTk4MgiEQiZ599tlwNAFWKaA0A1UD//v3LysqCIEhOTr7mmmsSXQ4AsAfRGgCqgVNOOaVTp06RSKS8vLywsDDR5QAAexCtAaB6GDBgQCwW+8lPfnLKKackuhYAYA9eYwbAj0VhYeGsWbMSXQWB//YA4MSTkugCAOD46dChw3/+538muoqj9+CDDw4ZMiQ7OzvRhRylBQsWPPTQQ4muAgCOPdEagB+RgoKC3r17J7qKo9epU6eCgoJEVxGKaA3ACcmz1gBQbVT3XA0AJyrRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEgYcaNG9eqVauMjIysrKxWrVrdc889W7ZsqWgdNWpUZE9t2rQ5ovFXrFjxm9/8pnXr1rm5udFotFatWq1aterRo8fzzz8f7zB79uwmTZpUniI9Pb1x48bXXnvtF198UTHOww8/fMopp0QikaSkpBYtWrz22msVTZdddllubm5SUlKrVq3+/ve/h7seAFBdidYAkDB//etff/3rX69evXr9+vX33XffuHHjevXqdawG/9Of/nTGGWcsXbp0woQJq1ev3rp166JFix544IGSkpI///nP8T49e/b8/PPPmzZtmpeXF4vFysrKVq9ePXLkyOnTp3fo0GHDhg3xbr/97W/Xrl0bBEH79u1XrFhx0UUXVczy4osvzps378ILL1y+fHnnzp2PVfEAUL2I1gCQMNFo9MYbb6xVq1Z2dnZhYWH37t1fffXVb775pqLD1KlTY5V8+OGHhznywoULBw8e3KlTpzfeeOPiiy/Oz89PS0tr0qTJZZdd9sgjjxzoqKSkpNq1aw8YMOCmm24qKiqqvDoNAByEaA0ABxOLxWbOnDllypQfYvDnnnsuPT29YrN+/fpBEGzbti38yKNHjy4rK/vDH/6QkpKyV1OTJk0ee+yxgx/erFmzIAjWrVsXvhIA+DEQrQFgD2VlZWPGjGnZsmVGRsbJJ5/cuHHjMWPG9O7dOwiC+++/PzMzMycnp6ioaPjw4fXr17/44ouj0WjdunXjx954441ZWVmRSOTbb789iqk//fTT/Pz8Ro0aHU7n+fPn5+bmjh49et+mkpKS11577aSTTurQocNRlBGvJAiCs8466+gOB4AfG9EaAPYwbty4ESNGjB8//rvvvnvllVe+//77/Pz8/Pz8IAhuu+22W265Zdu2bWPGjGncuHGHDh0efvjheOqOmzRp0u9///sjnbG0tPTrr7/+4x//+Nprrz366KPRaLSi6b/+679q1KgRjUYbN27cvXv3xYsXVzSVlZUFQVBeXr7vgKtWrfr+++9btGhxpJUEQbBp06ann3560qRJ3bp169q161GMAAA/QnvfJAYAP3Jz5sxp27bt5ZdfHgTBOeecc8UVVzzxxBMlJSWVE+/YsWPT09NvuummYzJjgwYN1q9fX7Nmzfvvv79Pnz4V+6+55ppu3bo1b948Go3+85//vOGGG376058uXry4devWQRB069at8uvEK4vvz87OPvwaNm/eHIlEbukyvwAAHdlJREFU4v+ORCL33XffbbfddvSnBAA/MlatAWAP33//fSwWq9gsKytLTU1NTk7+4WZcs2ZNUVHR//zP/zz99NNnn312UVFRfH+DBg3OPvvs7OzsaDTaoUOHp556aufOnZMmTTrkgPFQvX379r32z5gxo3HjxvGPbJ122mkVEwVBEH9DeCwWu/XWW2OxWF5eXmpq6rE7RQA4wYnWALCHX/7yl++8887cuXN37ty5ZMmSOXPmXHbZZT9otE5NTa1Vq9YvfvGLadOmLVu2bMyYMfvtdsYZZyQnJ69YseKQAzZq1CgtLe2zzz7ba3/v3r2/+OKLRo0a1alT5+OPP65du/a+x95zzz1169a9884716xZs2/rfu8/j//fh0NWBQAnMNEaAPZw7733XnjhhYMGDcrNze3Ro0fv3r0ff/zx4zN1s2bNkpOTly1btt/W8vLy8vLytLS0Q46Tnp5+0UUXFRcXL1y48EhryMnJGTt27NatW2+44Ya9mk466aT416338sUXXzRo0OBIJwKAE4loDQB7WLZs2cqVK4uLi0tLS1evXj158uQaNWocpH9KSkppaelRTLRhw4b+/ftX3vPpp5+WlZVVxNSLL764cuvixYtjsVjHjh0PZ/Df//73qampt95661HUNnDgwPPOO+/FF1+cMWNG5f0XXnjh119//fbbb1feGYvF/vSnP5133nlHOgsAnEhEawDYw0033dSwYcPD/7h0s2bNvvvuuzlz5pSWlhYXF69ateowD8zKynrllVdef/31LVu2lJaWvvvuu9dcc01WVtYtt9wS7/D1119PmzZt06ZNpaWlCxYsGDx4cMOGDYcOHRpvffnllw/08a0gCNq2bTt16tR33nmna9eu8+fP/+abb3bv3r1q1aqpU6d+9913By8sEok88sgjkUhk2LBhGzdurNg/atSo/Pz8wsLC559/fvv27bt27Xrvvff69++/e/fuAQMGHOZZA8AJSbQGgD2MGTPmww8/rFGjRvx1X9Fo9PTTT3/uueeCILj//vsnTJgQBEGLFi2eeeaZeP8bbrjhggsu6NevX8uWLe+7776MjIwgCDp27LjfZ5UrS09P79y58+DBg+vXr5+Tk1NYWHjqqacuXLiwTZs28Q6XXHLJ3XffXVBQkJmZ2bt3786dOy9cuLBmzZqHeSJ9+vT56KOP2rdv/7vf/a558+Y5OTkXXHDB448/fuONN86cOTPe5+23327ZsuXKlSs3b95cv379itzevn37a665Zv369U2aNBk7dmx8Z8uWLd99991u3boNHz78pJNOqlGjRv/+/Vu0aPGXv/yl8uvTAeBHKFL5JagAcAIrLCwMgqAiVR7I5MmTP/3004kTJ8Y3S0pK7rjjjsmTJ2/cuDEemzlqM2bM6NOnj//2AODE47vWAPBv69atGzZs2NKlSyv2RKPRhg0blpaWlpaWitYAwH65IRwA/i0jIyM1NfXJJ59cv359aWnp2rVrn3jiiREjRvTt2zc3N/eIhlq+fHnkwPr27fsDnQIAcPxZtQaAf8vLy3vllVdGjhzZokWL7du3Z2dnt27deuzYsdddd92RDtWqVSt3PgPAj4RoDQB76NKly6uvvproKgCA6sQN4QAAABCKaA0AAAChiNYAAAAQimgNAAAAoYjWAAAAEIpoDQAAAKGI1gAAABCKaA0AAAChiNYAAAAQimgNAAAAoYjWAAAAEIpoDQAAAKGI1gAAABBKSqILAIDjZ9asWZFIJNFVAAAnmkgsFkt0DQBwPCxYsGDNmjWJriKUPn363HzzzR07dkx0IaH07t070SUAwDEmWgNAtRGJRKZPny6aAkBV41lrAAAACEW0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BgAAgFBSEl0AAHBAq1atKisrq7xn/fr1n3/+ecVmvXr1MjIyjntdAMAeIrFYLNE1AAD7d+mll86fP/9ArSkpKevWratZs+bxLAkA2JcbwgGg6urbt28kEtlvU1JS0s9//nO5GgCqAtEaAKquHj16pKamHqh1wIABx7MYAOBARGsAqLpycnIuu+yy/abr1NTU//iP/zj+JQEA+xKtAaBKu+qqq3bv3r3XzpSUlCuvvDI7OzshJQEAexGtAaBK69atW1ZW1l47y8rKrrrqqoTUAwDsS7QGgCotLS2tV69e0Wi08s7s7Oxf/OIXiSoJANiLaA0AVV3//v1LSkoqNlNTU/v27btX2AYAEsh3rQGgqisvL69Tp863335bseeNN97o2rVr4ioCAPZg1RoAqrqkpKT+/ftXLFPXqlWrS5cuiS0JAKhMtAaAaqBfv37xe8Kj0ejAgQOTk5MTXREA8G9uCAeAaiAWizVq1GjNmjVBECxevPjcc89NdEUAwL9ZtQaAaiASiQwcODAIgkaNGsnVAFDVpCS6AAA4mAULFkyYMCHRVVQJW7ZsCYIgKyursLAw0bVUCR07drzlllsSXQUABIFVawCquDVr1syaNSvRVVQJubm5eXl5BQUFiS6kSli4cOGCBQsSXQUA/ItVawCqgZkzZya6hCrhz3/+88UXX5zoKqoES/cAVClWrQGg2pCrAaBqEq0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEagBPN4MGDc3JyIpHI0qVLE11LKKNGjYrsqU2bNodz4OzZs5s0aVL5wGg0Wrt27a5du44fP37jxo0/dOUA8GMjWgNwonniiScef/zxRFeRSD179vz888+bNm2al5cXi8XKy8uLiopmzJjRuHHj22+/vXXr1kuWLEl0jQBwQhGtAeD42blzZ6dOnQ6//9SpU2OVfPjhh0cxaSQSyc/P79q161NPPTVjxoz169d369Zt8+bNRzHUD+pILw4AVB2iNQAnoEgkkugS9u/JJ58sKipKYAG9evUaNGhQUVHRY489lsAy9ivhFwcAjppoDcCJIBaLjR8/vmXLlmlpaXl5ebfeemtF0/3335+ZmZmTk1NUVDR8+PD69et/8sknsVhswoQJp512WlpaWo0aNbp37758+fJ4/0ceeSQ9Pb127drXX399vXr10tPTO3XqtGjRospzHejYYcOGRaPRunXrxjdvvPHGrKysSCTy7bffBkFw8803Dx8+fOXKlZFIpFmzZiFPef78+bm5uaNHjz7SAwcNGhQEwcsvvxycuBcHAI63GABUYdOnTz+cv1Z33XVXJBJ58MEHN27cuGPHjkmTJgVB8O6771a0BkHw29/+9tFHH+3Ro8fHH388YsSIaDQ6derUTZs2vf/+++ecc87JJ5+8bt26eP8hQ4ZkZWV99NFH33///bJly9q1a5eTk7N69ep468GPveqqq+rUqVNR2Pjx44MgKC4ujm/27NmzadOmh3nu9913X0FBQX5+fmpq6qmnnnrFFVf84x//qGh98cUXc3JyRo4ceaDDK5613suWLVuCIGjQoEG1vji9evXq1avXYXYGgB+aVWsAqr2dO3dOnDjxoosuuuWWW/Lz8zMyMk466aR9u40dO/amm26aPXt2o0aNJkyY0KNHj6uvvjovL++MM8547LHHvv322ylTplR0TklJiS+9nn766ZMnT966detTTz0Vn+uQxx4r11xzzbx589asWbNt27Znn3129erVP/3pT5ctWxZv7dat25YtW+65554jHTb++vStW7dW3lntLg4AVCmiNQDV3meffbZjx46f/exnh9l/2bJl27ZtO/fccyv2tGvXLhqNVr6xubJzzz03MzMzfmPzkR4bRoMGDc4+++zs7OxoNNqhQ4ennnpq586d8QX5MLZv3x6LxXJzc/fbWl0uDgBUKaI1ANXeV199FQRBrVq1DrP/pk2bgiDIzs6uvDM/P3+vhdzK0tLSiouLj+7YY+WMM85ITk5esWJFyHHiI7Rq1Wq/rdX04gBAYonWAFR76enpQRDs2rXrMPvn5+cHQbBX3tu0aVNBQcF++5eWlla0Humxx1B5eXl5eXlaWlrIcebPnx8EwaWXXrrf1mp6cQAgsURrAKq9Nm3aJCUlvfXWW4ffPzs7e8mSJRV7Fi1aVFJS0rZt2/32f/PNN2OxWIcOHQ7n2JSUlNLS0qM8kz1dfPHFlTcXL14ci8U6duwYZsx169ZNnDixoKDg2muv3W+H6nJxAKBKEa0BqPZq1arVs2fPWbNmPfnkk1u2bHn//fcP/t6s9PT04cOHP/fcc88888yWLVs++OCDoUOH1qtXb8iQIRV9ysvLN27cuHv37vfff//mm29u2LBh/JtVhzy2WbNm33333Zw5c0pLS4uLi1etWlV56pNOOmnt2rVffvnl1q1bDxkyv/7662nTpm3atKm0tHTBggWDBw9u2LDh0KFD460vv/zyIT++FYvFtm3bVl5eHovFiouLp0+f3rlz5+Tk5Dlz5hzoWevqcnEAoGpJ5OvJAeBQDvPjW1u3bh08eHDNmjWzs7PPP//8ESNGBEFQUFDw3nvvjRs3LiMjIwiCBg0aTJ06Nd6/vLx8/PjxzZs3T01NrVGjxpVXXhn/nnPckCFDUlNT69evn5KSkpub271795UrV1a0HvzYDRs2XHDBBenp6Y0bN/7Nb34T/8J2s2bN4p+n+uc//9moUaOMjIzzzz+/4pNUBzJ8+PCmTZtmZWWlpKQUFBT8+te/Xrt2bUXrSy+9lJOTM2rUqH0PnDdv3plnnpmZmRmNRpOSkoIgiEQi+fn57du3Hzly5IYNGyp6Vt+L4+NbAFQpkVgslsBgDwAHN2PGjD59+hznv1bXX3/9zJkzN2zYcDwnrS6qyMUpLCwMgmDmzJmJLQMA4twQDgD7UVZWlugSqi4XBwD2IloDQAIsX748cmB9+/ZNdIEAwBEQrQFgD3feeedTTz21efPmxo0bz5o16weapVWrVgd5XmvatGk/0LwhHZ+LAwDVjmetAajSEvKsNVWfZ60BqFKsWgMAAEAoojUAAACEIloDAABAKKI1AAAAhCJaAwAAQCiiNQAAAIQiWgMAAEAoojUAAACEIloDAABAKKI1AAAAhCJaAwAAQCiiNQAAAIQiWgMAAEAoKYkuAAAOrbCwMNElULUsXLiwQ4cOia4CAP7FqjUAVVqDBg169eqV6Cqqinnz5q1duzbRVVQJHTp06NixY6KrAIB/icRisUTXAAAclkgkMn369N69eye6EABgD1atAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACCUSCwWS3QNAMD+DRgwYOnSpRWbX375Za1atbKysuKbqampL7zwQv369RNUHQDwLymJLgAAOKCWLVs+88wzlfds27at4t+tWrWSqwGgKnBDOABUXf369YtEIvttSk1NHTRo0PEtBwDYPzeEA0CV1rZt26VLl5aXl++1PxKJfP7556eeemoiigIA9mDVGgCqtIEDByYl7f33OhKJtG/fXq4GgCpCtAaAKq1Pnz77LlknJSUNHDgwIfUAAPsSrQGgSqtbt26XLl2Sk5P32t+zZ8+E1AMA7Eu0BoCqbsCAAZU3k5KSLrjggjp16iSqHgBgL6I1AFR1hYWFez1uvVfYBgASS7QGgKouNzf3kksuSUlJiW8mJydfccUViS0JAKhMtAaAauDqq68uKysLgiAlJeXyyy/Py8tLdEUAwL+J1gBQDVx++eUZGRlBEJSVlV111VWJLgcA2INoDQDVQHp6eo8ePYIgyMzMvPTSSxNdDgCwh5REFwDAieyrr756++23E13FCaJBgwZBELRr127evHmJruUE0aBBg44dOya6CgBOBJFYLJboGgA4Yc2YMaNPnz6JrgL2r1evXjNnzkx0FQCcCKxaA/CD879xj5V777337rvvrnhVOGEUFhYmugQAThyetQaAakOuBoCqSbQGgGpDrgaAqkm0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BgAAgFBEawAAAAhFtAYAAIBQRGsAAAAIRbQGAACAUERrAAAACEW0BiDBZs+e3aRJk0gl0Wi0du3aXbt2HT9+/MaNGxNd4H6Ul5dPnDixU6dOR3rge++917dv38aNG6elpZ188slnnXXWqFGjKlpfeumlvLy8F1544ZgWe2gHmXfw4ME5OTmRSGTp0qWHHKc6/pQAcEyI1gAkWM+ePT///POmTZvm5eXFYrHy8vKioqIZM2Y0btz49ttvb9269ZIlSxJd4x4+/fTTn/zkJ7fccsuOHTuO6MAPPvigU6dOdevWfeONNzZv3vz2229fcsklb775ZkWHWCx2jGs9PAeZ94knnnj88ccPc5xq91MCwLGSkugCAGAPkUgkPz+/a9euXbt27datW58+fbp167ZixYq8vLxElxYEQfDee++NHDly6NCh27dvP9Ik/MADD+Tn5z/00EPxzRYtWtx33309e/as6NCtW7fNmzcfy3IPzw80bxX/KQHgGLJqDUDV1atXr0GDBhUVFT322GOJruVfzjrrrNmzZ1911VVpaWlHeuyGDRs2b9783XffVeyJRqOHf/t3LBabOXPmlClTjnTekCKRSPhBquBPCQDHkGgNQJU2aNCgIAhefvnl+GZZWdmIESMaNmyYkZFx5plnTp8+PQiCyZMnZ2VlZWZmzp0799JLL83NzS0oKHj22WcrBnnrrbfat2+fmZmZm5t7xhlnbNmy5UBD/aDatWu3ffv2Cy+88O9///u+rX/7298aNmwYiUT++Mc/VpzsmDFjWrZsmZGRcfLJJzdu3HjMmDG9e/cOguChhx7KyspKSkpq27ZtnTp1UlNTs7KyzjnnnC5dujRo0CA9PT0/P/+2226rGDwWi02YMOG0005LS0urUaNG9+7dly9ffqB5Y7HY+PHjW7ZsmZaWlpeXd+utt1auc/78+bm5uaNHjz7S0z+RfkoA2FsMAH4w8ZBzOD0rHtDdSzw7NWjQIL75u9/9Li0tbdasWRs3brzzzjuTkpIWL14ci8XuuuuuIAj+8pe/bN68uaioqEuXLllZWSUlJbFYbNu2bbm5uePGjdu5c+e6det69OhRXFx8kKEO03nnnXfWWWcdfv9YLLZjx45zzz03/vf39NNPHzdu3IYNGyp3WLNmTRAEjz76aHxz9OjRycnJc+fO3bFjxzvvvFOnTp2uXbtWdP5//+//BUGwaNGi7du3f/vtt5dcckkQBP/7v/9bXFy8ffv2YcOGBUGwdOnSeOcRI0ZEo9GpU6du2rTp/fffP+ecc04++eR169btd9677rorEok8+OCDGzdu3LFjx6RJk4IgePfdd+OtL774Yk5OzsiRIw90mtXlp+zVq1evXr0O2Q0ADodoDcAPKHy0jsVi8Ud2Y7HYzp07MzMz+/btG9+/Y8eOtLS0G264IfZ/eWznzp3xpnga/Oyzz2Kx2IcffhgEwYsvvlh5zIMMdZiOIlrHYrGSkpKHH364VatW8YBdu3btN998s6J1r4jbrl279u3bV7Red911SUlJu3btim/Go/XWrVvjm08//XQQBB988EF88x//+EcQBNOmTYufXXZ2dsXJVrRWxOPK8+7YsSMzM/PnP/95Ref4onFFtD6k6vJTitYAHENuCAegSou/LSw3NzcIgk8++WTHjh1t2rSJN2VkZNStW7fixubKotFoEASlpaVBEDRp0qR27dpXX331vffe++WXX8Y7HP5Qx1ZqauqwYcM+/vjjhQsXdu/evaioqLCw8EBfpfr+++9jld6UVlZWlpqampycvN/O8VPevXt3xUTB/12BZcuWbdu2rWLBPAiCdu3aRaPRRYsW7TvOZ599tmPHjp/97GdHeYYHdoL9lABQmWgNQJW2YsWKIAjiy7zbt28PguDuu++u+GzyqlWrDvkFrIyMjNdff/38888fPXp0kyZN+vbtu3PnzqMb6hg677zznn/++aFDhxYXF7/xxhv77fPLX/7ynXfemTt37s6dO5csWTJnzpzLLrvsQNH6IDZt2hQEQXZ2duWd+fn5W7du3bfzV199FQRBrVq1jnSWQzpRf0oACERrAKq4+fPnB0Fw6aWXBv+X9yZOnFj5/qsFCxYccpDWrVu/8MILa9euvf3226dPn/7AAw8c9VBh9OzZs2JVOW7AgAFBEBwoB957770XXnjhoEGDcnNze/To0bt378P/xHRl+fn5QRDsFaQ3bdpUUFCwb+f09PQgCHbt2nUUEx3cifRTAsBeRGsAqq5169ZNnDixoKDg2muvDYIg/u7rpUuXHtEga9eu/eijj4IgqFWr1h/+8Idzzjnno48+OrqhQtq1a1e8kgqffPJJEARnnnnmfvsvW7Zs5cqVxcXFpaWlq1evnjx5co0aNY5i3jZt2mRnZy9ZsqRiz6JFi0pKStq2bbvfzklJSW+99dZRTHQQJ9hPCQB7Ea0BqCpisdi2bdvKy8tjsVhxcfH06dM7d+6cnJw8Z86c+AO66enpv/rVr5599tnJkydv2bKlrKzsq6+++uabbw4+7Nq1a6+//vrly5eXlJS8++67q1at6tChw9ENFd6VV145Y8aMTZs2bd68ee7cuXfccccVV1xxoGh90003NWzYcNu2bSEnTU9PHz58+HPPPffMM89s2bLlgw8+GDp0aL169YYMGbJv51q1avXs2XPWrFlPPvnkli1b3n///b2+pP3yyy8f8uNbP4afEgD28AO8Gg0A/uVw3hA+b968M888MzMzMxqNJiUlBUEQf490+/btR44cudfnqXbt2nX77bc3bNgwJSUlHgKXLVs2adKkzMzMIAiaN2++cuXKKVOmxPNbo0aNVqxY8eWXX3bq1KlGjRrJycmnnHLKXXfdtXv37gMNdcgzWrBgQefOnevVqxf/M1q3bt1OnTq99dZbh3M1XnnllT59+jRt2jQtLS0ajbZs2fLee++Nv6ssFos9+uijdevWDYIgMzPz8ssvj8Vir7/+es2aNSv+ZKempp522mmzZ8+OxWIPPfRQ/JRPPfXUv/71r2PHjs3LywuCoE6dOv/93/89bdq0OnXqBEFQo0aNZ599NhaLlZeXjx8/vnnz5qmpqTVq1Ljyyis/+eSTA827devWwYMH16xZMzs7+/zzzx8xYkQQBAUFBe+9914sFnvppZdycnJGjRpV3X9KbwgH4BiKxCq9ehQAjq0ZM2b06dPH35qjM3ny5E8//XTixInxzZKSkjvuuGPy5MkbN27MyMhIbG0ngMLCwiAIZs6cmehCADgRpCS6AABgP9atWzds2LDKjxBHo9GGDRuWlpaWlpaK1gBQpXjWGgD+Zfny5ZED69u37w907H5lZGSkpqY++eST69evLy0tXbt27RNPPDFixIi+ffvG75EGAKoOq9YA8C+tWrU66nvXwxy7X3l5ea+88srIkSNbtGixffv27Ozs1q1bjx079rrrrjuGswAAx4RoDQBVVJcuXV599dVEVwEAHJobwgEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACAU0RoAAABCEa0BAAAgFNEaAAAAQhGtAQAAIBTRGgAAAEIRrQEAACCUlEQXAMCJb8aMGYkuAfb21VdfFRQUJLoKAE4QojUAP7g+ffokugTYj169eiW6BABOEJFYLJboGgAAAKAa86w1AAAAhCJaAwAAQCiiNQAAAIQiWgMAAEAo/x8Ey6sdHX2ObwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwh2RVpvVwav",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras.layers import Bidirectional\n",
        "import keras \n",
        "#Build the model function. \n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras.models import Model\n",
        "\n",
        "def build_model_hbl():\n",
        "    i = keras.initializers.RandomNormal(mean=0.0, stddev=.025, seed=None)\n",
        "    glo = keras.initializers.glorot_normal(seed=1)\n",
        "    he = keras.initializers.he_normal(seed=1)\n",
        "    input_torso = layers.Input(shape=(120,12), name='Torso_Input')\n",
        "    input_left_arm = layers.Input(shape=(120,6), name = 'Left_Arm_Input')\n",
        "    input_right_arm = layers.Input(shape=(120,6), name = 'Right_Arm_Input')\n",
        "    input_right_leg = layers.Input(shape=(120,12), name = 'Right_Leg_Input')\n",
        "    input_left_leg = layers.Input(shape=(120,12), name = 'Left_Leg_Input')\n",
        "\n",
        "    torso_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, name = 'lstm1') (input_torso)\n",
        "    r_arm_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, name='lstm2') (input_right_arm)\n",
        "    l_arm_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, name='lstm3') (input_left_arm)\n",
        "    l_leg_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, name='lstm4') (input_left_leg)\n",
        "    r_leg_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, name='lstm5') (input_right_leg)\n",
        "\n",
        "    torso_r_arm_con = layers.concatenate([torso_branch, r_arm_branch], axis=-1, name='concat1')\n",
        "    torso_l_arm_con = layers.concatenate([torso_branch, l_arm_branch], axis=-1, name='concat2')\n",
        "    torso_r_leg_con = layers.concatenate([torso_branch, r_leg_branch], axis=-1, name='concat3')\n",
        "    torso_l_leg_con = layers.concatenate([torso_branch, l_leg_branch], axis=-1, name='concat4')\n",
        "\n",
        "    torso_r_arm_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, name='Torso-Right-Arm') (torso_r_arm_con)\n",
        "    torso_l_arm_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, name='Torso-Left-Arm') (torso_l_arm_con)\n",
        "    torso_r_leg_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, name='Torso-Right-Leg') (torso_r_leg_con)\n",
        "    torso_l_leg_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, name='Torso-Left-Leg') (torso_l_leg_con)\n",
        "\n",
        "    all_con = layers.concatenate([torso_r_arm_out, torso_l_arm_out, torso_r_leg_out, torso_l_leg_out], name='concat5')\n",
        "\n",
        "    all_out_1 = layers.LSTM(100, activation='relu', kernel_initializer=he, return_sequences = False) (all_con)\n",
        "    # all_out_2 = layers.Dense(100, activation='relu')(all_out_1)\n",
        "    all_out_3 = layers.Dense(1, activation='sigmoid')(all_out_1)\n",
        "    \n",
        "    model = Model(inputs=[input_torso, input_left_arm, input_right_arm, input_left_leg, input_right_leg], outputs=all_out_3)\n",
        "    sgd = optimizers.SGD(lr=0.000001, decay=1e-6, momentum=0.5, nesterov=True)\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    \n",
        "    return model\n",
        "\n",
        "def build_model_rnn():\n",
        "    i = keras.initializers.RandomNormal(mean=0.0, stddev=.025, seed=None)\n",
        "    glo = keras.initializers.glorot_normal(seed=1)\n",
        "    he = keras.initializers.he_normal(seed=1)\n",
        "\n",
        "    l1 = keras.regularizers.l1(0.01)\n",
        "    l2 = keras.regularizers.l2(0.01)\n",
        "    l1_l2 = keras.regularizers.l1_l2(l1=0.01, l2=0.01)\n",
        "\n",
        "    zero = keras.initializers.Zeros()\n",
        "\n",
        "    input_torso = layers.Input(shape=(120,12), name='Torso_Input')\n",
        "    input_left_arm = layers.Input(shape=(120,6), name = 'Left_Arm_Input')\n",
        "    input_right_arm = layers.Input(shape=(120,6), name = 'Right_Arm_Input')\n",
        "    input_right_leg = layers.Input(shape=(120,12), name = 'Right_Leg_Input')\n",
        "    input_left_leg = layers.Input(shape=(120,12), name = 'Left_Leg_Input')\n",
        "\n",
        "    torso_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name = 'torso_rnn') (input_torso)\n",
        "    r_arm_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='r_arm_rnn') (input_right_arm)\n",
        "    l_arm_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='l_arm_rnn') (input_left_arm)\n",
        "    l_leg_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='l_leg_rnn') (input_left_leg)\n",
        "    r_leg_branch = layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='r_leg_rnn') (input_right_leg)\n",
        "\n",
        "    torso_r_arm_con = layers.concatenate([torso_branch, r_arm_branch], axis=-1, name='concat_torso_r_arm')\n",
        "    torso_l_arm_con = layers.concatenate([torso_branch, l_arm_branch], axis=-1, name='concat_torso_l_arm')\n",
        "    torso_r_leg_con = layers.concatenate([torso_branch, r_leg_branch], axis=-1, name='concat_torso_r_leg')\n",
        "    torso_l_leg_con = layers.concatenate([torso_branch, l_leg_branch], axis=-1, name='concat_torso_l_leg')\n",
        "\n",
        "    torso_r_arm_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Right-Arm_rnn') (torso_r_arm_con)\n",
        "    torso_l_arm_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Left-Arm_rnn') (torso_l_arm_con)\n",
        "    torso_r_leg_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Right-Leg_rnn') (torso_r_leg_con)\n",
        "    torso_l_leg_out = layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Left-Leg_rnn') (torso_l_leg_con)\n",
        "\n",
        "    all_con = layers.concatenate([torso_r_arm_out, torso_l_arm_out, torso_r_leg_out, torso_l_leg_out], name='concat_all_4_body_parts')\n",
        "    # bias_initializer=zero,\n",
        "    # bias_regularizer = l1,\n",
        "    # activity_regularizer=l1\n",
        "    # kernel_initializer=he, kernel_regularizer=l2, \n",
        "    # kernel_regularizer=l1,    \n",
        "    all_out_1 = layers.GRU(128, activation='relu', return_sequences=False, kernel_initializer=glo)(all_con)\n",
        "    all_out_2 = layers.Dropout(.3) (all_out_1)\n",
        "    # all_out_1 = layers.LSTM(100, activation='relu', return_sequences = False, name='Final_LSTM') (all_con)\n",
        "    # all_out_2 = layers.BatchNormalization(name='batch_normalization')(all_out_1)\n",
        "    # all_out_2 = layers.Dense(100, activation='relu')(all_out_1)\n",
        "    all_out_3 = layers.Dense(1, activation='sigmoid', name='Dense_1_Sigmoid')(all_out_2)\n",
        "    \n",
        "    model = Model(inputs=[input_torso, input_left_arm, input_right_arm, input_left_leg, input_right_leg], outputs=all_out_3)\n",
        "    # sgd = optimizers.SGD(lr=0.000001, decay=1e-6, momentum=0.5, nesterov=True)\n",
        "    # adam_1 = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    \n",
        "    return model\n",
        "\n",
        "\n",
        "#Trying to get more consistant training...\n",
        "def build_model_bi_gru():\n",
        "    i = keras.initializers.RandomNormal(mean=0.0, stddev=.025, seed=None)\n",
        "    glo = keras.initializers.glorot_normal(seed=1)\n",
        "    he = keras.initializers.he_normal(seed=1)\n",
        "\n",
        "    l1 = keras.regularizers.l1(0.01)\n",
        "    l2 = keras.regularizers.l2(0.01)\n",
        "    l1_l2 = keras.regularizers.l1_l2(l1=0.01, l2=0.01)\n",
        "\n",
        "    zero = keras.initializers.Zeros()\n",
        "\n",
        "    input_torso = layers.Input(shape=(120,12), name='Torso_Input')\n",
        "    input_left_arm = layers.Input(shape=(120,6), name = 'Left_Arm_Input')\n",
        "    input_right_arm = layers.Input(shape=(120,6), name = 'Right_Arm_Input')\n",
        "    input_right_leg = layers.Input(shape=(120,12), name = 'Right_Leg_Input')\n",
        "    input_left_leg = layers.Input(shape=(120,12), name = 'Left_Leg_Input')\n",
        "\n",
        "    torso_branch = Bidirectional(layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name = 'torso_gru')) (input_torso)\n",
        "    r_arm_branch = Bidirectional(layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='r_arm_gru')) (input_right_arm)\n",
        "    l_arm_branch = Bidirectional(layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='l_arm_gru')) (input_left_arm)\n",
        "    l_leg_branch = Bidirectional(layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='l_leg_gru')) (input_left_leg)\n",
        "    r_leg_branch = Bidirectional(layers.SimpleRNN(12, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='r_leg_gru')) (input_right_leg)\n",
        "\n",
        "    torso_r_arm_con = layers.concatenate([torso_branch, r_arm_branch], axis=-1, name='concat_torso_r_arm')\n",
        "    torso_l_arm_con = layers.concatenate([torso_branch, l_arm_branch], axis=-1, name='concat_torso_l_arm')\n",
        "    torso_r_leg_con = layers.concatenate([torso_branch, r_leg_branch], axis=-1, name='concat_torso_r_leg')\n",
        "    torso_l_leg_con = layers.concatenate([torso_branch, l_leg_branch], axis=-1, name='concat_torso_l_leg')\n",
        "\n",
        "    torso_r_arm_out = Bidirectional(layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Right-Arm_gru')) (torso_r_arm_con)\n",
        "    torso_l_arm_out = Bidirectional(layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Left-Arm_gru')) (torso_l_arm_con)\n",
        "    torso_r_leg_out = Bidirectional(layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Right-Leg_gru')) (torso_r_leg_con)\n",
        "    torso_l_leg_out = Bidirectional(layers.SimpleRNN(30, activation='tanh', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Left-Leg_gru')) (torso_l_leg_con)\n",
        "\n",
        "    all_con = layers.concatenate([torso_r_arm_out, torso_l_arm_out, torso_r_leg_out, torso_l_leg_out], name='concat_all_4_body_parts')\n",
        "    # bias_initializer=zero,\n",
        "    # bias_regularizer = l1,\n",
        "    # activity_regularizer=l1\n",
        "    # kernel_initializer=he, kernel_regularizer=l2, \n",
        "    # kernel_regularizer=l1, \n",
        "\n",
        "    all_out_1 = Bidirectional(layers.GRU(128, activation='relu', return_sequences=False, kernel_initializer=glo)(all_con))\n",
        "    # all_out_1 = layers.LSTM(100, activation='relu', return_sequences = False, name='Final_LSTM') (all_con)\n",
        "    # all_out_2 = layers.BatchNormalization(name='batch_normalization')(all_out_1)\n",
        "    # all_out_2 = layers.Dense(100, activation='relu')(all_out_1)\n",
        "    all_out_3 = layers.Dense(1, activation='sigmoid', name='Dense_1_Sigmoid')(all_out_1)\n",
        "    \n",
        "    model = Model(inputs=[input_torso, input_left_arm, input_right_arm, input_left_leg, input_right_leg], outputs=all_out_3)\n",
        "    # sgd = optimizers.SGD(lr=0.000001, decay=1e-6, momentum=0.5, nesterov=True)\n",
        "\n",
        "    adam_1 = optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "    model.compile(optimizer=adam_1, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    \n",
        "    return model\n",
        "\n",
        "def build_model_gru():\n",
        "    i = keras.initializers.RandomNormal(mean=0.0, stddev=.025, seed=None)\n",
        "    glo = keras.initializers.glorot_normal(seed=1)\n",
        "    he = keras.initializers.he_normal(seed=1)\n",
        "\n",
        "    l1 = keras.regularizers.l1(0.01)\n",
        "    l2 = keras.regularizers.l2(0.01)\n",
        "    l1_l2 = keras.regularizers.l1_l2(l1=0.01, l2=0.01)\n",
        "\n",
        "    zero = keras.initializers.Zeros()\n",
        "\n",
        "    input_torso = layers.Input(shape=(120,12), name='Torso_Input')\n",
        "    input_left_arm = layers.Input(shape=(120,6), name = 'Left_Arm_Input')\n",
        "    input_right_arm = layers.Input(shape=(120,6), name = 'Right_Arm_Input')\n",
        "    input_right_leg = layers.Input(shape=(120,12), name = 'Right_Leg_Input')\n",
        "    input_left_leg = layers.Input(shape=(120,12), name = 'Left_Leg_Input')\n",
        "\n",
        "    torso_branch = layers.GRU(16, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name = 'torso_gru') (input_torso)\n",
        "    r_arm_branch = layers.GRU(16, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='r_arm_gru') (input_right_arm)\n",
        "    l_arm_branch = layers.GRU(16, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='l_arm_gru') (input_left_arm)\n",
        "    l_leg_branch = layers.GRU(16, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='l_leg_gru') (input_left_leg)\n",
        "    r_leg_branch = layers.GRU(16, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='r_leg_gru') (input_right_leg)\n",
        "\n",
        "    torso_r_arm_con = layers.concatenate([torso_branch, r_arm_branch], axis=-1, name='concat_torso_r_arm')\n",
        "    torso_l_arm_con = layers.concatenate([torso_branch, l_arm_branch], axis=-1, name='concat_torso_l_arm')\n",
        "    torso_r_leg_con = layers.concatenate([torso_branch, r_leg_branch], axis=-1, name='concat_torso_r_leg')\n",
        "    torso_l_leg_con = layers.concatenate([torso_branch, l_leg_branch], axis=-1, name='concat_torso_l_leg')\n",
        "\n",
        "    torso_r_arm_out = layers.GRU(32, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Right-Arm_gru') (torso_r_arm_con)\n",
        "    torso_l_arm_out = layers.GRU(32, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Left-Arm_gru') (torso_l_arm_con)\n",
        "    torso_r_leg_out = layers.GRU(32, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Right-Leg_gru') (torso_r_leg_con)\n",
        "    torso_l_leg_out = layers.GRU(32, activation='relu', return_sequences = True, kernel_initializer=glo, bias_initializer = zero, name='Torso-Left-Leg_gru') (torso_l_leg_con)\n",
        "\n",
        "    all_con = layers.concatenate([torso_r_arm_out, torso_l_arm_out, torso_r_leg_out, torso_l_leg_out], name='concat_all_4_body_parts')\n",
        "    # bias_initializer=zero,\n",
        "    # bias_regularizer = l1,\n",
        "    # activity_regularizer=l1\n",
        "    # kernel_initializer=he, kernel_regularizer=l2, \n",
        "    # kernel_regularizer=l1, \n",
        "\n",
        "    all_out_1 = layers.GRU(128, activation='relu', return_sequences=False, kernel_initializer=glo) (all_con)\n",
        "    # all_out_1 = layers.LSTM(100, activation='relu', return_sequences = False, name='Final_LSTM') (all_con)\n",
        "    # all_out_2 = layers.BatchNormalization(name='batch_normalization')(all_out_1)\n",
        "    # all_out_2 = layers.Dense(100, activation='relu')(all_out_1)\n",
        "    all_out_3 = layers.Dense(1, activation='sigmoid', name='Dense_1_Sigmoid')(all_out_1)\n",
        "    \n",
        "    model = Model(inputs=[input_torso, input_left_arm, input_right_arm, input_left_leg, input_right_leg], outputs=all_out_3)\n",
        "    # sgd = optimizers.SGD(lr=0.000001, decay=1e-6, momentum=0.5, nesterov=True)\n",
        "\n",
        "    adam_1 = optimizers.Adam(learning_rate=0.0005, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "    model.compile(optimizer=adam_1, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jj3BY5WzWVNq",
        "colab_type": "text"
      },
      "source": [
        "## Train\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_G4ImZHKnCTk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip show tensorflow\n",
        "!pip show keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrH5c4OMWa6F",
        "colab_type": "code",
        "outputId": "f4180fb0-81cb-41b3-b283-ca0d1661655e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import random \n",
        "import os \n",
        "\n",
        "#For no more tensor flow warnings. \n",
        "from tensorflow.python.util import deprecation\n",
        "deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
        "\n",
        "#Mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5kdLNGb3HHw",
        "colab_type": "code",
        "outputId": "37d1b4bd-f740-4704-aee8-1f80b6dced43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Get good ratio of training/validation/testing data. \n",
        "import os \n",
        "import random\n",
        "import numpy as np\n",
        "print(len(os.listdir('gdrive/My Drive/Seizure2/all_data_hd_npy/')))\n",
        "\n",
        "data_files = os.listdir('gdrive/My Drive/Seizure2/all_data_hd_npy/')\n",
        "\n",
        "data = {}\n",
        "labels = {}\n",
        "\n",
        "for _name in data_files:\n",
        "    if 'Seizure' in _name:\n",
        "        labels[_name] = 1\n",
        "    else:\n",
        "        labels[_name] = 0\n",
        "\n",
        "random.seed(4)\n",
        "random.shuffle(data_files)\n",
        "\n",
        "num_seizure_files = len([ _name for _name in data_files if 'Seizure' in _name])\n",
        "\n",
        "train_index = int(len(data_files) * .8)\n",
        "validation_index = int(train_index * .15)\n",
        "\n",
        "data['test'] = np.array(data_files[train_index:])\n",
        "data['validation'] = np.array(data_files[:validation_index])\n",
        "data['train'] = np.array(data_files[validation_index:train_index])\n",
        "\n",
        "print('Number of Seizure data in dataset: ', num_seizure_files)\n",
        "print('Total number of files: ', len(data_files))\n",
        "print('Percentage of Seizure data in dataset: {} %'.format(num_seizure_files/len(data_files)*100))\n",
        "\n",
        "num_seizure_files_train = len([ _name for _name in data['train'] if 'Seizure' in _name])\n",
        "num_seizure_files_test = len([ _name for _name in data['test'] if 'Seizure' in _name])\n",
        "num_seizure_files_validation = len([ _name for _name in data['validation'] if 'Seizure' in _name])\n",
        "\n",
        "print('Number of training samples: ', len(data['train']))\n",
        "print('Number of testing samples: ', len(data['test']))\n",
        "print('Number of validation samples: ', len(data['validation']))\n",
        "print('Total: ', len(data['train']) + len(data['test']) + len(data['validation']))\n",
        "\n",
        "print('Percentage of Seizure data in training dataset: {} %'.format(num_seizure_files_train/len(data['train'])*100))\n",
        "print('Percentage of Seizure data in testing dataset: {} %'.format(num_seizure_files_test/len(data['test'])*100))\n",
        "print('Percentage of Seizure data in validation dataset: {} %'.format(num_seizure_files_validation/len(data['validation'])*100))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3124\n",
            "Number of Seizure data in dataset:  922\n",
            "Total number of files:  3124\n",
            "Percentage of Seizure data in dataset: 29.513444302176694 %\n",
            "Number of training samples:  2125\n",
            "Number of testing samples:  625\n",
            "Number of validation samples:  374\n",
            "Total:  3124\n",
            "Percentage of Seizure data in training dataset: 30.44705882352941 %\n",
            "Percentage of Seizure data in testing dataset: 26.88 %\n",
            "Percentage of Seizure data in validation dataset: 28.609625668449194 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsrrOZFHrVu2",
        "colab_type": "code",
        "outputId": "44dc5a71-0f00-4c94-ded0-1f7d3927019f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "import os\n",
        "import numpy as np\n",
        "import random\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Hyper-parameters \n",
        "\n",
        "batch_size = 64\n",
        "num_epochs = 50\n",
        "import numpy as np\n",
        "\n",
        "#Create dictionary entries of all data. \n",
        "py_train_path = 'gdrive/My Drive/Seizure2/all_data_hd_npy/'\n",
        "\n",
        "\n",
        "params ={'location': py_train_path,\n",
        "        'dim': (120, 12, 5),\n",
        "          'batch_size': batch_size,\n",
        "          'n_classes': 1,\n",
        "          'n_channels': 1,\n",
        "          'shuffle': True}\n",
        "\n",
        "validation_params ={'location': py_train_path,\n",
        "        'dim': (120, 12, 5),\n",
        "          'batch_size': len(data['validation']),\n",
        "          'n_classes': 1,\n",
        "          'n_channels': 1,\n",
        "          'shuffle': True}\n",
        "        \n",
        "test_params = {'location': py_train_path,\n",
        "        'dim': (120, 12, 5),\n",
        "          'batch_size': len(data['test']),\n",
        "          'n_classes': 1,\n",
        "          'n_channels': 1,\n",
        "          'shuffle': True}\n",
        "testing_generator = DataGenerator(data['test'], labels, **test_params)\n",
        "training_generator = DataGenerator(data['train'], labels, **params)\n",
        "validating_generator = DataGenerator(data['validation'], labels, **validation_params)\n",
        "\n",
        "histories = []\n",
        "# for i in range(3):\n",
        "# model = build_model_gru()\n",
        "model = build_model_rnn()\n",
        "filepath=\"gru-do-{epoch:02d}-{val_loss:.2f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "callbacks_list = [checkpoint]\n",
        "history = model.fit_generator(generator=training_generator, validation_data=validating_generator, callbacks = callbacks_list, validation_freq = 1, use_multiprocessing=True, workers=8, epochs=num_epochs,verbose=1)\n",
        "histories.append(history)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "33/33 [==============================] - 24s 713ms/step - loss: 0.5150 - accuracy: 0.7420 - val_loss: 0.3577 - val_accuracy: 0.8048\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.35772, saving model to gru-do-01-0.36.hdf5\n",
            "Epoch 2/50\n",
            "33/33 [==============================] - 18s 557ms/step - loss: 0.3508 - accuracy: 0.8116 - val_loss: 0.2896 - val_accuracy: 0.8396\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.35772 to 0.28957, saving model to gru-do-02-0.29.hdf5\n",
            "Epoch 3/50\n",
            "33/33 [==============================] - 19s 563ms/step - loss: 0.3102 - accuracy: 0.8395 - val_loss: 0.3102 - val_accuracy: 0.8396\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.28957\n",
            "Epoch 4/50\n",
            "33/33 [==============================] - 19s 571ms/step - loss: 0.2995 - accuracy: 0.8608 - val_loss: 0.2451 - val_accuracy: 0.8930\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.28957 to 0.24514, saving model to gru-do-04-0.25.hdf5\n",
            "Epoch 5/50\n",
            "33/33 [==============================] - 19s 563ms/step - loss: 0.2869 - accuracy: 0.8641 - val_loss: 0.2887 - val_accuracy: 0.8529\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.24514\n",
            "Epoch 6/50\n",
            "33/33 [==============================] - 18s 556ms/step - loss: 0.2674 - accuracy: 0.8712 - val_loss: 0.2289 - val_accuracy: 0.8904\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.24514 to 0.22888, saving model to gru-do-06-0.23.hdf5\n",
            "Epoch 7/50\n",
            "33/33 [==============================] - 18s 557ms/step - loss: 0.2500 - accuracy: 0.8859 - val_loss: 0.2162 - val_accuracy: 0.9064\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.22888 to 0.21615, saving model to gru-do-07-0.22.hdf5\n",
            "Epoch 8/50\n",
            "33/33 [==============================] - 19s 564ms/step - loss: 0.2200 - accuracy: 0.9025 - val_loss: 0.1961 - val_accuracy: 0.9171\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.21615 to 0.19606, saving model to gru-do-08-0.20.hdf5\n",
            "Epoch 9/50\n",
            "33/33 [==============================] - 18s 560ms/step - loss: 0.2005 - accuracy: 0.9100 - val_loss: 0.2008 - val_accuracy: 0.9118\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.19606\n",
            "Epoch 10/50\n",
            "33/33 [==============================] - 19s 562ms/step - loss: 0.1964 - accuracy: 0.9157 - val_loss: 0.1443 - val_accuracy: 0.9492\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.19606 to 0.14434, saving model to gru-do-10-0.14.hdf5\n",
            "Epoch 11/50\n",
            "33/33 [==============================] - 18s 551ms/step - loss: 0.1654 - accuracy: 0.9304 - val_loss: 0.1660 - val_accuracy: 0.9225\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.14434\n",
            "Epoch 12/50\n",
            "33/33 [==============================] - 19s 561ms/step - loss: 0.1578 - accuracy: 0.9394 - val_loss: 0.1157 - val_accuracy: 0.9492\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.14434 to 0.11566, saving model to gru-do-12-0.12.hdf5\n",
            "Epoch 13/50\n",
            "33/33 [==============================] - 19s 566ms/step - loss: 0.2184 - accuracy: 0.9001 - val_loss: 0.1669 - val_accuracy: 0.9305\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.11566\n",
            "Epoch 14/50\n",
            "33/33 [==============================] - 19s 561ms/step - loss: 0.1396 - accuracy: 0.9498 - val_loss: 0.1700 - val_accuracy: 0.9332\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.11566\n",
            "Epoch 15/50\n",
            "33/33 [==============================] - 18s 551ms/step - loss: 0.1411 - accuracy: 0.9427 - val_loss: 0.1473 - val_accuracy: 0.9599\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.11566\n",
            "Epoch 16/50\n",
            "33/33 [==============================] - 18s 553ms/step - loss: 0.1238 - accuracy: 0.9489 - val_loss: 0.1242 - val_accuracy: 0.9519\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.11566\n",
            "Epoch 17/50\n",
            "33/33 [==============================] - 19s 562ms/step - loss: 0.1045 - accuracy: 0.9598 - val_loss: 0.0974 - val_accuracy: 0.9599\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.11566 to 0.09744, saving model to gru-do-17-0.10.hdf5\n",
            "Epoch 18/50\n",
            "33/33 [==============================] - 18s 545ms/step - loss: 0.0797 - accuracy: 0.9706 - val_loss: 0.2204 - val_accuracy: 0.9198\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.09744\n",
            "Epoch 19/50\n",
            "33/33 [==============================] - 18s 554ms/step - loss: 0.1408 - accuracy: 0.9418 - val_loss: 0.1635 - val_accuracy: 0.9198\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.09744\n",
            "Epoch 20/50\n",
            "33/33 [==============================] - 18s 548ms/step - loss: 0.1200 - accuracy: 0.9517 - val_loss: 0.0657 - val_accuracy: 0.9706\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.09744 to 0.06571, saving model to gru-do-20-0.07.hdf5\n",
            "Epoch 21/50\n",
            "33/33 [==============================] - 18s 550ms/step - loss: 0.0635 - accuracy: 0.9759 - val_loss: 0.0443 - val_accuracy: 0.9759\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.06571 to 0.04434, saving model to gru-do-21-0.04.hdf5\n",
            "Epoch 22/50\n",
            "33/33 [==============================] - 18s 555ms/step - loss: 0.0612 - accuracy: 0.9749 - val_loss: 0.1051 - val_accuracy: 0.9599\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.04434\n",
            "Epoch 23/50\n",
            "33/33 [==============================] - 18s 555ms/step - loss: 0.0941 - accuracy: 0.9688 - val_loss: 0.1196 - val_accuracy: 0.9626\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.04434\n",
            "Epoch 24/50\n",
            "33/33 [==============================] - 18s 554ms/step - loss: 0.0544 - accuracy: 0.9782 - val_loss: 0.1118 - val_accuracy: 0.9652\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.04434\n",
            "Epoch 25/50\n",
            "33/33 [==============================] - 18s 552ms/step - loss: 0.0555 - accuracy: 0.9787 - val_loss: 0.0484 - val_accuracy: 0.9759\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.04434\n",
            "Epoch 26/50\n",
            "33/33 [==============================] - 18s 553ms/step - loss: 0.0553 - accuracy: 0.9796 - val_loss: 0.0460 - val_accuracy: 0.9813\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.04434\n",
            "Epoch 27/50\n",
            "33/33 [==============================] - 18s 560ms/step - loss: 0.0517 - accuracy: 0.9811 - val_loss: 0.0734 - val_accuracy: 0.9652\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.04434\n",
            "Epoch 28/50\n",
            "33/33 [==============================] - 18s 558ms/step - loss: 0.0275 - accuracy: 0.9886 - val_loss: 0.0725 - val_accuracy: 0.9840\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.04434\n",
            "Epoch 29/50\n",
            "33/33 [==============================] - 19s 568ms/step - loss: 0.0807 - accuracy: 0.9725 - val_loss: 0.0612 - val_accuracy: 0.9786\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.04434\n",
            "Epoch 30/50\n",
            "33/33 [==============================] - 19s 576ms/step - loss: 0.0396 - accuracy: 0.9858 - val_loss: 0.0335 - val_accuracy: 0.9893\n",
            "\n",
            "Epoch 00030: val_loss improved from 0.04434 to 0.03351, saving model to gru-do-30-0.03.hdf5\n",
            "Epoch 31/50\n",
            "33/33 [==============================] - 18s 554ms/step - loss: 0.0503 - accuracy: 0.9815 - val_loss: 0.0754 - val_accuracy: 0.9679\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.03351\n",
            "Epoch 32/50\n",
            "33/33 [==============================] - 19s 561ms/step - loss: 0.0746 - accuracy: 0.9711 - val_loss: 0.1220 - val_accuracy: 0.9545\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.03351\n",
            "Epoch 33/50\n",
            "33/33 [==============================] - 18s 560ms/step - loss: 0.0525 - accuracy: 0.9801 - val_loss: 0.0544 - val_accuracy: 0.9786\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.03351\n",
            "Epoch 34/50\n",
            "33/33 [==============================] - 18s 555ms/step - loss: 0.0273 - accuracy: 0.9901 - val_loss: 0.0413 - val_accuracy: 0.9866\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.03351\n",
            "Epoch 35/50\n",
            "33/33 [==============================] - 18s 560ms/step - loss: 0.0271 - accuracy: 0.9886 - val_loss: 0.1677 - val_accuracy: 0.9572\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.03351\n",
            "Epoch 36/50\n",
            "33/33 [==============================] - 18s 556ms/step - loss: 0.0806 - accuracy: 0.9744 - val_loss: 0.0632 - val_accuracy: 0.9813\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.03351\n",
            "Epoch 37/50\n",
            "33/33 [==============================] - 18s 558ms/step - loss: 0.0243 - accuracy: 0.9924 - val_loss: 0.0200 - val_accuracy: 0.9947\n",
            "\n",
            "Epoch 00037: val_loss improved from 0.03351 to 0.02000, saving model to gru-do-37-0.02.hdf5\n",
            "Epoch 38/50\n",
            "33/33 [==============================] - 18s 556ms/step - loss: 0.0241 - accuracy: 0.9920 - val_loss: 0.0279 - val_accuracy: 0.9893\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.02000\n",
            "Epoch 39/50\n",
            "33/33 [==============================] - 19s 563ms/step - loss: 0.0187 - accuracy: 0.9938 - val_loss: 0.0627 - val_accuracy: 0.9813\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.02000\n",
            "Epoch 40/50\n",
            "33/33 [==============================] - 18s 557ms/step - loss: 0.0799 - accuracy: 0.9744 - val_loss: 0.1020 - val_accuracy: 0.9599\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.02000\n",
            "Epoch 41/50\n",
            "33/33 [==============================] - 19s 561ms/step - loss: 0.0523 - accuracy: 0.9815 - val_loss: 0.0304 - val_accuracy: 0.9893\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.02000\n",
            "Epoch 42/50\n",
            "33/33 [==============================] - 18s 558ms/step - loss: 0.0252 - accuracy: 0.9896 - val_loss: 0.0437 - val_accuracy: 0.9866\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.02000\n",
            "Epoch 43/50\n",
            "33/33 [==============================] - 19s 569ms/step - loss: 0.0224 - accuracy: 0.9934 - val_loss: 0.0243 - val_accuracy: 0.9866\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.02000\n",
            "Epoch 44/50\n",
            "33/33 [==============================] - 18s 554ms/step - loss: 0.0190 - accuracy: 0.9934 - val_loss: 0.0256 - val_accuracy: 0.9947\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.02000\n",
            "Epoch 45/50\n",
            "33/33 [==============================] - 19s 562ms/step - loss: 0.0159 - accuracy: 0.9943 - val_loss: 0.0240 - val_accuracy: 0.9920\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.02000\n",
            "Epoch 46/50\n",
            "33/33 [==============================] - 19s 563ms/step - loss: 0.0166 - accuracy: 0.9953 - val_loss: 0.0336 - val_accuracy: 0.9893\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.02000\n",
            "Epoch 47/50\n",
            "33/33 [==============================] - 19s 570ms/step - loss: 0.0126 - accuracy: 0.9967 - val_loss: 0.0146 - val_accuracy: 0.9920\n",
            "\n",
            "Epoch 00047: val_loss improved from 0.02000 to 0.01464, saving model to gru-do-47-0.01.hdf5\n",
            "Epoch 48/50\n",
            "33/33 [==============================] - 19s 563ms/step - loss: 0.0162 - accuracy: 0.9953 - val_loss: 0.0225 - val_accuracy: 0.9920\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.01464\n",
            "Epoch 49/50\n",
            "33/33 [==============================] - 18s 558ms/step - loss: 0.0198 - accuracy: 0.9948 - val_loss: 0.0143 - val_accuracy: 0.9947\n",
            "\n",
            "Epoch 00049: val_loss improved from 0.01464 to 0.01432, saving model to gru-do-49-0.01.hdf5\n",
            "Epoch 50/50\n",
            "33/33 [==============================] - 19s 566ms/step - loss: 0.0072 - accuracy: 0.9972 - val_loss: 0.0178 - val_accuracy: 0.9947\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.01432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BD-7rW-1Q6gO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm *.hdf5\n",
        "# !pip show tensorflow"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBYp5Zyz-Nkj",
        "colab_type": "code",
        "outputId": "d9124741-ae20-4877-a13e-951e4d786af6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "for h in history.history:\n",
        "    print(h)\n",
        "\n",
        "history = histories[0]\n",
        "plt.figure()\n",
        "val_loss = history.history['val_loss']\n",
        "loss = history.history['loss']\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "num_epochs = range(20)\n",
        "\n",
        "plt.plot(num_epochs, val_loss[:len(num_epochs)], 'r', label = 'Val Loss')\n",
        "plt.plot( num_epochs, loss[:len(num_epochs)], 'b', label = 'Train Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "val_loss = history.history['val_accuracy']\n",
        "loss = history.history['accuracy']\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('acc')\n",
        "num_epochs = range(20)\n",
        "\n",
        "plt.plot(num_epochs, val_loss[:len(num_epochs)], 'r', label = 'Val Acc')\n",
        "plt.plot( num_epochs, loss[:len(num_epochs)], 'b', label = 'Train Acc')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "val_loss\n",
            "val_accuracy\n",
            "loss\n",
            "accuracy\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MAkmQdLPXRL",
        "colab_type": "code",
        "outputId": "1daf52f8-fe09-4a5a-d485-a556cb38ac9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        }
      },
      "source": [
        "j_model = build_model_rnn()\n",
        "j_model.load_weights('gru-do-49-.01.hdf5')\n",
        "\n",
        "py_train_path = 'gdrive/My Drive/Seizure2/all_data_hd_npy/'\n",
        "test_params = {'location': py_train_path,\n",
        "        'dim': (120, 12, 5),\n",
        "          'batch_size': len(data['test']),\n",
        "          'n_classes': 1,\n",
        "          'n_channels': 1,\n",
        "          'shuffle': True}\n",
        "testing_generator = DataGenerator(data['test'], labels, **test_params)\n",
        "j_model.evaluate_generator(generator=testing_generator, steps=None, callbacks=None, max_queue_size=10, workers=0, use_multiprocessing=False, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-82-c7c58aa85f2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mj_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model_rnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mj_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gru-do-49-.01.hdf5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpy_train_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'gdrive/My Drive/Seizure2/all_data_hd_npy/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m test_params = {'location': py_train_path,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp_filepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mload_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mload_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/network.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name, skip_mismatch, reshape)\u001b[0m\n\u001b[1;32m   1219\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mh5py\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`load_weights` requires h5py.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m'layer_names'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m'model_weights'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m                 \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'model_weights'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)\u001b[0m\n\u001b[1;32m    406\u001b[0m                 fid = make_fid(name, mode, userblock_size,\n\u001b[1;32m    407\u001b[0m                                \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m                                swmr=swmr)\n\u001b[0m\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    171\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_RDWR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Unable to open file (unable to open file: name = 'gru-do-49-.01.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnmF4TUfj-0y",
        "colab_type": "code",
        "outputId": "cdf8b70e-9bd8-4203-ece2-0597b4be34da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.13054610788822174, 0.9391999840736389]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOa_NjHTXMoz",
        "colab_type": "text"
      },
      "source": [
        "## Fit the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpI-inUCXQZb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import random \n",
        "import os \n",
        "\n",
        "#For no more tensor flow warnings. \n",
        "from tensorflow.python.util import deprecation\n",
        "deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
        "\n",
        "#Mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1qKBSfzXwXB",
        "colab_type": "text"
      },
      "source": [
        "# Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WK2fLL9HXyJ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import random \n",
        "import os \n",
        "\n",
        "#For no more tensor flow warnings. \n",
        "from tensorflow.python.util import deprecation\n",
        "deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
        "\n",
        "#Mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "from keras import models\n",
        "\n",
        "#Load past model and evaluate the model\n",
        "model_path = 'gdrive/My Drive/Seizure DL/OverSample_2/weights.18.hdf5'\n",
        "train_data_path = 'gdrive/My Drive/Seizure DL/OverSample_2/Train/'\n",
        "test_data_path = 'gdrive/My Drive/Seizure DL/OverSample_2/Test/'\n",
        "\n",
        "#Load model \n",
        "dependencies = {\n",
        "    'f1_score': f1_score,\n",
        "    'precision_m': precision_m,\n",
        "    'recall_m': recall_m\n",
        "}\n",
        "# model = keras.models.load_model(self.output_directory + 'best_model.hdf5', custom_objects=dependencies)\n",
        "root = 'gdrive/My Drive/Seizure DL/OverSample_2/'\n",
        "# model_paths = ['weights.161.hdf5','weights.162.hdf5','weights.211.hdf5']\n",
        "model_paths = ['weights.340.hdf5']\n",
        "\n",
        "for mp in model_paths:\n",
        "    print('\\n')\n",
        "    print(mp)\n",
        "    # my_model = build_model_3()\n",
        "    my_model = load_model(root+mp, custom_objects=dependencies)\n",
        "    #Load testing data\n",
        "    (train_x, train_y, train_names), (test_x, test_y, test_names) = load_data(train_location=train_data_path, test_location=test_data_path, get_name=True)\n",
        "\n",
        "\n",
        "    h = my_model.evaluate(test_x, test_y, verbose=1)\n",
        "\n",
        "    for i in range(len(test_y)):\n",
        "        pred = my_model.predict(x=test_x[i].reshape(1,80,50))\n",
        "        print(test_names[i] + ' : ' + str(pred))\n",
        "\n",
        "    print('test output:')\n",
        "    print(h)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnMtD0gXu_Es",
        "colab_type": "text"
      },
      "source": [
        "# Visualizing Landmarks \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXj-YZzhvDDW",
        "colab_type": "code",
        "outputId": "e578e829-f241-40e6-d46b-c6797b21fb2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "import os\n",
        "import numpy as np\n",
        "import random\n",
        "#Create dictionary entries of all data. \n",
        "py_train_path = '/content/jj_npy/'\n",
        "\n",
        "partition = {}\n",
        "partition['train'] = np.array(os.listdir(py_train_path))\n",
        "\n",
        "\n",
        "labels = {}\n",
        "for p in partition:\n",
        "    for item in partition[p]:\n",
        "        if 'seizure' in item:\n",
        "            labels[item] = 1\n",
        "        else:\n",
        "            labels[item] = 0\n",
        "\n",
        "\n",
        "params ={'location': py_train_path,\n",
        "        'dim': (120, 12, 5),\n",
        "          'batch_size': batch_size,\n",
        "          'n_classes': 1,\n",
        "          'n_channels': 1,\n",
        "          'shuffle': True}\n",
        "\n",
        "training_generator = DataGenerator(partial_train_data, labels, **params)\n",
        "\n",
        "#[torso, right_arm, left_arm, right_leg, left_leg]\n",
        "[t, r_a, l_a, r_l, l_l], y = training_generator.tester(partial_train_data[:16])\n",
        "print(t.shape)\n",
        "print(r_a.shape)\n",
        "print(l_a.shape)\n",
        "print(r_l.shape)\n",
        "print(l_l.shape)\n",
        "\n",
        "#Full body 'video'\n",
        "print(len(partial_train_data))\n",
        "print(partial_train_data)\n",
        "f = np.load(py_train_path + partial_train_data[0])\n",
        "print('full shape:')\n",
        "print(f.shape)\n",
        "\n",
        "# _t10 = t[0] \n",
        "_t10 = f\n",
        "\n",
        "# print(_t10.shape)\n",
        "plt.figure()\n",
        "for i in range(0, len(_t10), 1):\n",
        "    for j in range(0,len(_t10[0]),2):\n",
        "        plt.xlim(0, 1)\n",
        "        plt.ylim((0, 1))\n",
        "        plt.scatter(_t10[i][j], _t10[i][j+1])\n",
        "    \n",
        "\n",
        "    plt.pause(.0001)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2, 120, 12)\n",
            "(2, 120, 6)\n",
            "(2, 120, 6)\n",
            "(2, 120, 12)\n",
            "(2, 120, 12)\n",
            "2\n",
            "['jj-vid_2.npy' 'jj-vid_1.npy']\n",
            "full shape:\n",
            "(120, 50)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kEAp8EZFAKPBFRAKhwBcRCYQCX0QkEAp8EZFAKPBFRAKhwBcRCYQCX0QkEAp8EZFAKPBF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\nFPgiIoFQ4IuIBOL/AwUvbXWdKl+zAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-33e95d918d6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.0001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpause\u001b[0;34m(interval)\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m         \u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mstart_event_loop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   2287\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_looping\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcounter\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtimestep\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2288\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2289\u001b[0;31m             \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2290\u001b[0m             \u001b[0mcounter\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cs9SjnLPvYLi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJCqLt8i1K4b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_data(list_IDs_temp=None, labels=None, location_data = \"\"):\n",
        "        dim1 = len(list_IDs_temp)\n",
        "\n",
        "        y = np.empty(dim1, dtype=int)\n",
        "        r_arm_data = np.empty((dim1, 120, 6))\n",
        "        l_arm_data = np.empty((dim1, 120, 6))\n",
        "        r_leg_data = np.empty((dim1, 120, 12))\n",
        "        l_leg_data = np.empty((dim1, 120, 12))\n",
        "        torso_data = np.empty((dim1, 120, 12))\n",
        "        # X = np.empty((self.batch_size), dtype=np.ndarray)\n",
        "        # Generate data\n",
        "        for i, ID in enumerate(list_IDs_temp):\n",
        "            # Store sample\n",
        "            opened_data = np.load(location_data + ID) \n",
        "            #(120, 50)\n",
        "                \n",
        "            _temp_r_arm_data = np.empty((120,6))\n",
        "            _temp_l_arm_data = np.empty((120,6))\n",
        "            _temp_r_leg_data = np.empty((120, 12))\n",
        "            _temp_l_leg_data = np.empty((120, 12))\n",
        "            _temp_torso_data = np.empty((120, 12))\n",
        "\n",
        "\n",
        "            for j,mat in enumerate(opened_data):\n",
        "                #iterate 120 timestamps\n",
        "                mult = 2\n",
        "                #(120, 6) pad with 6 zeros \n",
        "                _temp_r_arm_data[j,] = np.array([mat[2*mult], mat[mult*2+1], \n",
        "                                          mat[3*mult], mat[mult*3+1],\n",
        "                                          mat[4*mult], mat[mult*4+1]])\n",
        "                                        #   0, 0, \n",
        "                                        #   0, 0, \n",
        "                                        #   0, 0])\n",
        "                \n",
        "                #(120, 6) pad with 6 zeros\n",
        "                _temp_l_arm_data[j,] = np.array([mat[5*mult], mat[mult*5+1], \n",
        "                                          mat[6*mult], mat[mult*6+1],\n",
        "                                          mat[7*mult], mat[mult*7+1]])\n",
        "                                        #   ,\n",
        "                                        #   0, 0, \n",
        "                                        #   0, 0,\n",
        "                                        #   0, 0])\n",
        "                \n",
        "                #(120, 12)\n",
        "                _temp_r_leg_data[j,] = np.array([mat[9*mult], mat[mult*9+1], \n",
        "                                          mat[10*mult], mat[mult*10+1],\n",
        "                                          mat[11*mult], mat[mult*11+1],\n",
        "                                          mat[22*mult], mat[mult*22+1], \n",
        "                                          mat[23*mult], mat[mult*23+1],\n",
        "                                          mat[24*mult], mat[mult*24+1]])\n",
        "                #(120, 12)\n",
        "                _temp_l_leg_data[j,] = np.array([mat[12*mult], mat[mult*12+1], \n",
        "                                          mat[13*mult], mat[mult*13+1],\n",
        "                                          mat[14*mult], mat[mult*14+1],\n",
        "                                          mat[19*mult], mat[mult*19+1], \n",
        "                                          mat[20*mult], mat[mult*20+1],\n",
        "                                          mat[21*mult], mat[mult*21+1]]) \n",
        "                \n",
        "                #(120, 12)\n",
        "                _temp_torso_data[j,] = np.array([mat[1*mult], mat[mult*1+1], \n",
        "                                          mat[2*mult], mat[mult*2+1],\n",
        "                                          mat[5*mult], mat[mult*5+1],\n",
        "                                          mat[8*mult], mat[mult*8+1], \n",
        "                                          mat[9*mult], mat[mult*9+1],\n",
        "                                          mat[12*mult], mat[mult*12+1]])\n",
        "\n",
        "            r_arm_data[i,] = _temp_r_arm_data\n",
        "            l_arm_data[i,] = _temp_l_arm_data\n",
        "            r_leg_data[i,] = _temp_r_leg_data\n",
        "            l_leg_data[i,] = _temp_l_leg_data\n",
        "            torso_data[i,] = _temp_torso_data\n",
        "            # Store class\n",
        "            y[i] = labels[ID]\n",
        "\n",
        "        return [torso_data, l_arm_data, r_arm_data, l_leg_data, r_leg_data], y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lhf3gFOo19ra",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#More testing models.\n",
        "root = '/content/t_dataa/'\n",
        "names = os.listdir(root)\n",
        "\n",
        "labels = {}\n",
        "for _name in names:\n",
        "    if 'Seizure' in _name: \n",
        "        labels[_name] = 1\n",
        "    else:\n",
        "        labels[_name] = 0\n",
        "\n",
        "X, y = load_data(location_data=root, list_IDs_temp=names, labels = labels)\n",
        "\n",
        "from keras.models import load_model\n",
        "final_model = load_model('/content/rnn-imp.hdf5')\n",
        "\n",
        "for n in names:\n",
        "    x, y = load_data(location_data=root, list_IDs_temp=[n], labels = labels)\n",
        "    p = final_model.predict(x)\n",
        "    print(n, ':', p)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkLpWh0UOIlP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm *.hdf5"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}