318 lines
36 KiB
Plaintext
318 lines
36 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7a7d7566",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Needed Imports\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"import os\n",
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"import pickle\n",
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"import matplotlib.pyplot as plt\n",
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"from math import isqrt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "72dca74e",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # this is required\n",
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"os.environ['CUDA_VISIBLE_DEVICES'] = '2' # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "148e8cc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"delim = ';'\n",
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"user_count = 100\n",
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"base_path = '/opt/iui-datarelease1-sose2021/'\n",
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"Xpickle_file = './X.pickle'\n",
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"ypickle_file = './y.pickle'\n",
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"\n",
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"# Function that opens and reads pickle Data from FS and returns the read data as NumpyArray\n",
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"def load_pickles():\n",
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" _p = open(Xpickle_file, 'rb')\n",
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" X = pickle.load(_p)\n",
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" _p.close()\n",
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" \n",
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" _p = open(ypickle_file, 'rb')\n",
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" y = pickle.load(_p)\n",
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" _p.close()\n",
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" \n",
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" return (np.asarray(X, dtype = pd.DataFrame), np.asarray(y, dtype = str))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "863651d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Function used to save data as a pickle file\n",
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"def save_pickle():\n",
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"# _p = open(np.asarray(data, dtype=pd.DataFrame), 'wb')\n",
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" _p = open(Xpickle_file, 'wb')\n",
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" pickle.dump(X, _p)\n",
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" _p.close()\n",
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"\n",
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"# _p = open(np.asarray(label, dtype=str), 'wb')\n",
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" _p = open(ypickle_file, 'wb')\n",
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" pickle.dump(y, _p)\n",
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" _p.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "703abfd3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Function that loads data from the picklefiles and prints them into NumpyArrays (one for Data and one for Lables)\n",
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"def load_data():\n",
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" if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
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" return load_pickles()\n",
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" data = []\n",
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" label = []\n",
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" for user in range(0, user_count):\n",
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" user_path = base_path + str(user) + '/split_letters_csv/'\n",
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" for file in os.listdir(user_path):\n",
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" file_name = user_path + file\n",
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" letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
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" data.append(pd.read_csv(file_name, delim))\n",
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" label.append(letter)\n",
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" return (np.asarray(data, dtype = pd.DataFrame), np.asarray(label, dtype = str))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "c08e44d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Data\n",
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"X, y = load_data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "fc1766db",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"count 13102.000000\n",
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"mean 208.304457\n",
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"std 206.732342\n",
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"min 42.000000\n",
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"50% 185.000000\n",
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"90% 270.000000\n",
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"91% 276.000000\n",
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"92% 286.000000\n",
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"93% 299.000000\n",
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"94% 312.000000\n",
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"95% 333.000000\n",
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"96% 355.000000\n",
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"97% 388.000000\n",
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"98% 456.980000\n",
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"99% 701.940000\n",
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"max 11073.000000\n",
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"dtype: float64"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAc20lEQVR4nO3deXRc9Znm8e+r0i7Lkm3Jq7yCV8AY0BgIwUCAtPEEmExIsNMZyIazMd3p5CQDoUNn6NM9k2QO6WSGDnEgoUPSECAkuIMJoWnCEjYLMF6xkVdJXiRLsmRblrXUO3/UtSnLMirbJd1ans85dXTr3p+rHsulx1e3bt2fuTsiIpL+csIOICIiyaFCFxHJECp0EZEMoUIXEckQKnQRkQyRG9YTV1RU+JQpU8J6ehGRtPTGG2/sdffK/raFVuhTpkyhpqYmrKcXEUlLZrb9RNt0yEVEJEOo0EVEMoQKXUQkQwxY6Gb2MzNrNLO1J9huZvYjM6s1s9Vmdn7yY4qIyEAS2UN/AFj4PtuvAaYHt6XAj08/loiInKwBC93dXwBa3mfI9cAvPOZVoNzMxiUroIiIJCYZx9AnAHVx9+uDdccxs6VmVmNmNU1NTUl4ahEROWJIz0N392XAMoDq6mpdt1dEQtcbdXqiUXp6PXaLRumJeuzWGyz3xo0J1vdGne6o0xuN0t3rsfvB+p5epzsajOk9dkxPb5QrZ4/h3InlSf+7JKPQG4CJcfergnUikubcvf9C61Nux4zpZ7m/0uuOL8a+Y6JRegd8rti2I4/ZX7Zjni94jmPGRZ0wpoQYPbwwZQt9OXCrmT0MXAi0ufuuJDyuSNaJRp39h3toP9RNe2c37Yd6gq/dtHfG1nf1RgcuqqPL8SV4orKNf6z4Pc/YbahFcoxIjpGXY+RGcsjNMXIjRm5ODrmRI9tyYl+D+7mRHPJzcyg+Mj7+zwTLkZyco+PzIjnHPMd7jxU3JniO+OfOjc8Ut+2YxzzuOeJyR3LIMTCzQfneDVjoZvYQcDlQYWb1wN8BeQDufi+wAlgE1AIdwGcGJalIGohGnQNdQSH3U8ZHSrrt6PKx2w4c7hlwjzE/El80/ZReznvlcXRMTg4FBbnBtn6K7UgxHi3A+Mc8tsQikRzyTvAckcjxZZvXpzD7K9ujZW1GTs7glF02GLDQ3X3JANsd+ErSEomkEHen5WAXO1o6qGs9RF1LB80HuuLK+Nji3p9AIZcW5DK8KI/SwtjXCeVFzB5XyvDCPIYX5VFWlMfwYFtsXe7RbcMKcomo8OQEQrs4l0iqONTVS31rB3WtHexo7mBHyyHqWjuoa4ndDnb1HjO+JD8SK92gcMeXFzKrsDS4338RlwXrhhWqkGXwqNAlK7g7u9s7WVPfxvpd7Wxv7mBHS+zWtP/wMWOL8iJMHFnEpJHFXDRtFJNGFjNxZDGTRhZTNaKIkgL92Ehq0itTMo67U996iLUNbazd2caahnbWNbTRfLALgByDcWVFTBxZxBUzK5k4ophJo2KlPXFEMRXD8gftTSuRwaRCl7R2pLxX17exumFfrMQb2mk71A1Abo4xfUwpV84ezdkTyjhrfBlzxg2nKD8ScnKR5FOhS1ppbO+MlXf9Pt6ub2NNQxstwZ53fiSHmWNLWXTOOM6eMJyzx5cxc2wphXkqb8kOKnRJaXUtHTy1dhc121pZXd/G7vZOIHbYZMaYUq6aPZq5VeWcW1XOzLGl5OfqitCSvVToknLqWjp4cs0unly9izUNbQBMrSjhwmkjmVtVztyqMs4aP5zifL18ReLpJ0JSwo7mWImvWPNeiZ9bVca3Fs3imrPHMXFkccgJRVKfCl1CoxIXSS4VugypfR1dPFpTz/K3d75X4hPLVeIiSaBClyHx7p79/PzlbTz+Zj2d3VHtiYsMAhW6DKpVdfv4P09v5KXaveTn5vDReRP49CVTmD1ueNjRRDKOCl0GRW/Uuff5zdz9zCZGluTzjb+YyZL5kxhZkh92NJGMpUKXpKtr6eCbj63mlS3NfGTuOP7ho+dQVpQXdiyRjKdCl6Tp7o1y34tb+eGzm4iY8f0b5nLDBVW6LorIEFGhS1KsqW/j64+uYtOeAyw8ayx3XjuH8eVFYccSySoqdDltf1y3m796+C1GFOdz/83VXDl7TNiRRLKSCl1Ombtz34tb+cenNjC3qpz7bqqmsrQg7FgiWUuFLqek+cBhvvnYap59p5GFZ43lBzfO0yVpRUKmQpeT9sb2Fr70yzfZd6ib71w7h5s/MEVvfIqkABW6nJSWg1188ZdvUpwf4YHPzGfOeH1ASCRV6OLRkjB3547frmFfRxf//Jfnq8xFUowKXRL227caeGrtbv7m6hmcNb4s7Dgi0ocKXRKybe9B/u6JdVRPHsEXFpwRdhwR6YcKXQa0qm4fN9z7Mjk5xt2fmEckR2+AiqQiFbq8r+c2NrJ42SsU5Ud4/MsfYNIoXepWJFXpLBc5oe7eKHc8voYpo0r45ecvpGKYPjQkksq0hy4n9MSqnexs6+SbC2eqzEXSgApd+rWqbh93PrGWuVVlXD5jdNhxRCQBKnQ5zra9B/n0z1+nYlgB991UTY7eBBVJCzqGLsd56PUdHDzcwxNfuYTRwwvDjiMiCUpoD93MFprZRjOrNbPb+tk+ycyeM7O3zGy1mS1KflQZKq9va2FuVTmTR5WEHUVETsKAhW5mEeAe4BpgDrDEzOb0Gfa3wCPufh6wGPjnZAeVofHshj28tWMfH5ql4+Yi6SaRPfT5QK27b3H3LuBh4Po+Yxw4cmGPMmBn8iLKUGk52MX/+M0aZo0t5fOXTg07joicpEQKfQJQF3e/PlgX7zvAp8ysHlgB/Pf+HsjMlppZjZnVNDU1nUJcGSzuzrceX0P7oW5+cOM8CnJ1bXORdJOss1yWAA+4exWwCHjQzI57bHdf5u7V7l5dWVmZpKeWZPjdqgb+sG43X//wDGaP01UURdJRIoXeAEyMu18VrIv3OeARAHd/BSgEKpIRUIbGg69sDw61TAs7ioicokQKfSUw3cymmlk+sTc9l/cZswO4EsDMZhMrdB1TSROd3b2sbWjnshmVuvCWSBobsNDdvQe4FXga2EDsbJZ1ZnaXmV0XDPs6cIuZvQ08BHza3X2wQkty/ejZd+nqjXKFzmwRSWsJfbDI3VcQe7Mzft2dccvrgUuSG02GwhvbW7j3+c3cWD2Ri6aNCjuOiJwGffQ/y33vDxsZXVrIt6/t+9ECEUk3KvQstqpuH69tbeHzl05lWIGuAiGS7lToWeypNbvIj+SweP6ksKOISBKo0LPY69taOKeqTHvnIhlChZ6lnlmva7aIZBoVehZqOdjF7Y+vZva44dyiDxKJZAwVeha6/6UtNB/s4gc3nkt+rl4CIplCP81Z5uDhHh58ZTsLzxrLrLG6ZotIJlGhZ5naxgO0d/Zw/bzxYUcRkSRToWeZI9dj0KEWkcyjn+osU7OtBYCxw4tCTiIiyaZCzyJdPVHue3ErF00byZzxOn4ukmlU6Flk+ds72d3eyRcvOyPsKCIyCFToWSIadZa9sJlZY0u5bIZmixLJRCr0LPGnTY1s2nOAL1w2DTNNYiGSiVToWeLe57cwobyIj8zV6YoimUqFniXe2tHKonPGkhfRP7lIptJPdxZRmYtkNv2EZ4G9Bw7T3euUF+eFHUVEBpEKPQsc+TDR+ZNGhJxERAaTCj3DdXb3cvczmxhdWsA5VWVhxxGRQaSpajLcD57ZxKY9B/j5Z/4TBbmRsOOIyCDSHnoGa2zv5Od/3sbHL6jiipmamUgk06nQM9gDL2+jJxrl1g+dGXYUERkCKvQM9taOfcytKmfyqJKwo4jIEFChZ7h8nXsukjX0057Bdrd3UqZzz0Wyhgo9Q+09cJitew9ywWSdey6SLVToGeo3b9QDsGC6LpUrki1U6Bmos7uXn/15K5ecOUozE4lkERV6Bvr+0xvZ036YW6+YHnYUERlCCRW6mS00s41mVmtmt51gzCfMbL2ZrTOzf01uTEnUym0t3P/SVm66eDIXnzEq7DgiMoQG/Oi/mUWAe4CrgXpgpZk
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Show how many datasets are make how many percent \n",
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"X_len = np.asarray(list(map(len, X)))\n",
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"l = []\n",
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"sq_xlen = pd.Series(X_len)\n",
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"ptiles = [x*0.01 for x in range(100)]\n",
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"for i in ptiles:\n",
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" l.append(sq_xlen.quantile(i))\n",
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"plt.plot(l, ptiles)\n",
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"sq_xlen.describe(percentiles=[x*0.01 for x in range(90,100)])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "bbca15d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Remove outliner data from the dataset\n",
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"threshold_p = 0.99\n",
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"threshold = int(sq_xlen.quantile(threshold_p))\n",
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"len_mask = np.where(X_len <= threshold)\n",
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"\n",
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"X_filter = X[len_mask]\n",
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"y_filter = y[len_mask]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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|
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"id": "0577b868",
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"# Sliding Window Function\n",
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"def sliding_window(data):\n",
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" input_data = data\n",
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" _window_sz = 10\n",
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" sum_windows_passed = 0\n",
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" \n",
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" \n",
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" data_above_thresh = []\n",
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" thresh = 30\n",
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" \n",
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" values_sum = 0\n",
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" \n",
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" for i in range(0, len(input_data), _window_sz):\n",
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" for j in range(i, min(i + _window_sz, len(input_data))):\n",
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" values_sum += input_data[j]\n",
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" data_above_thresh.append(values_sum / _window_sz)\n",
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" \n",
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" return data_above_thresh"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "ae4b01be",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_data = X[5]['Force']\n",
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"window_sz = 10\n",
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"sum_windows_passed = 0\n",
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" \n",
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" \n",
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"win_above_thresh = []\n",
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" \n",
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" \n",
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"for i in range(0, len(input_data), window_sz):\n",
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" values_sum = 0\n",
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" for j in range(i, min(i + window_sz, len(input_data))): \n",
|
||
|
|
" values_sum += input_data[j]\n",
|
||
|
|
"\n",
|
||
|
|
" win_above_thresh.append(values_sum / window_sz)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"id": "7945cb5f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"thresh = 35\n",
|
||
|
|
"\n",
|
||
|
|
"_blep = np.where(np.asarray(win_above_thresh) > thresh)\n",
|
||
|
|
"ranges = []\n",
|
||
|
|
"for i in range(len(_blep[0])):\n",
|
||
|
|
" correlation = _blep[0][i] * window_sz\n",
|
||
|
|
" ranges.append(list(range(correlation, correlation + window_sz)))\n",
|
||
|
|
"ranges = np.array(ranges).flatten()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"id": "08ce93bc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f6a3ccd33a0>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"blepped = input_data[ranges]\n",
|
||
|
|
"plt.plot(range(len(blepped)), blepped)\n",
|
||
|
|
"plt.plot(range(len(input_data)), input_data)\n",
|
||
|
|
"plt.plot([140 for _ in range(2000)], [140 for _ in range(2000)])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "3455b49f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|