make_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![normalization](https://www.analog.com/-/media/analog/en/app-note-images/data-loader-design-for-max78000-model-training/7600fig02.jpg?imgver=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wavelib path: /home/sam/Repositories/tinyml/bearings-max78000/firmware/synthed_net/bearingnet/cwt_verification/../../../../../wavelib/python/ \n",
      "Dataset already downloaded\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import scipy\n",
    "from firmware.synthed_net.bearingnet.cwt_verification.utils import *\n",
    "\n",
    "DATASET_NAME = \"ims_bearings\"\n",
    "DATASET_URL = \"https://data.nasa.gov/download/brfb-gzcv/application/zip\"\n",
    "TEMP_PATH = os.path.expanduser('~/Downloads') + \"/dataset/\"\n",
    "\n",
    "\n",
    "# Download the dataset to temp\n",
    "os.system(\"mkdir -p \" + TEMP_PATH)\n",
    "\n",
    "#check if exists first\n",
    "ZIPPED = TEMP_PATH + DATASET_NAME + \".zip\"\n",
    "if os.path.exists(ZIPPED):\n",
    "    print(\"Dataset already downloaded\")\n",
    "else:\n",
    "    #unzip it inside its own folder\n",
    "    os.system(\"wget -q -O \" + ZIPPED + \" \" + DATASET_URL)\n",
    "    os.system(\"unzip \" + ZIPPED + \" -d \" + TEMP_PATH + DATASET_NAME)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert get_image(np.ones(SAMPLE_LEN)).shape == (INTERP_SIZE, INTERP_SIZE), \"Image shape is wrong\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1st_test.rar\n",
      "2\n",
      "2nd_test.rar\n",
      "3\n",
      "3rd_test.rar\n",
      "exp1_b3_ch5_all_data.txt\n",
      "exp1_b3_ch5_first100k.txt\n",
      "exp1_b3_ch5_last100k.txt\n",
      "input_windows_from_averaging_30_windows.npy\n",
      "Readme Document for IMS Bearing Data.pdf\n"
     ]
    }
   ],
   "source": [
    "IMS_PATH = TEMP_PATH + DATASET_NAME + \"/IMS/\"\n",
    "os.chdir(IMS_PATH)\n",
    "os.system(\"ls\")\n",
    "\n",
    "#check if unrar exists\n",
    "if not os.path.exists(\"/usr/bin/unrar\"):\n",
    "    print(\"unrar not installed\")\n",
    "    sys.exit(1)\n",
    "\n",
    "if not os.path.exists(IMS_PATH + \"1/\"):\n",
    "    os.system(\"mkdir -p \" + IMS_PATH + \"1/\")\n",
    "    os.system(\"mkdir -p \" + IMS_PATH + \"2/\")\n",
    "    os.system(\"mkdir -p \" + IMS_PATH + \"3/\")\n",
    "\n",
    "    os.system(\"unrar e \" + TEMP_PATH + DATASET_NAME + \"/IMS/1st_test.rar 1/\")\n",
    "    os.system(\"unrar e \" + TEMP_PATH + DATASET_NAME + \"/IMS/2nd_test.rar 2/\")\n",
    "    os.system(\"unrar e \" + TEMP_PATH + DATASET_NAME + \"/IMS/3rd_test.rar 3/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def downsample_nparray(data, factor=2):\n",
    "    \"\"\"\n",
    "    Downsamples a numpy array by a given factor.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : array_like\n",
    "        Input data array.\n",
    "    factor : int\n",
    "        Factor by which to downsample the data.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    downsampled_data : array_like\n",
    "        Downsampled data array.\n",
    "    \"\"\"\n",
    "    downsampled_data = data[::factor]\n",
    "    \n",
    "    #normalise \n",
    "    downsampled_data = downsampled_data - np.mean(downsampled_data)\n",
    "    downsampled_data = downsampled_data / np.std(downsampled_data)\n",
    "    \n",
    "    return downsampled_data\n",
    "    \n",
    "\n",
    "def create_windows(data, window_length=128, overlap_length=32, window_type='rectangular'):\n",
    "    \"\"\"\n",
    "    Creates a list of windows from a numpy array.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : array_like\n",
    "        Input data array.\n",
    "    window_length : int\n",
    "        Length of the window.\n",
    "    overlap_length : int\n",
    "        Length of the overlap between windows.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    windows : list\n",
    "        List of windows.\n",
    "    \"\"\"\n",
    "    windows = []\n",
    "    for i in range(0, len(data) - window_length, window_length-overlap_length):\n",
    "        if window_type == 'hamming':\n",
    "            coeffs = np.hamming(window_length)\n",
    "        else:\n",
    "            coeffs = np.ones(window_length)\n",
    "            \n",
    "        windows.append(coeffs * data[i:i + window_length].flatten())\n",
    "    return np.array(windows)\n",
    "\n",
    "\n",
    "os.environ['SSQ_GPU'] = '1'\n",
    "from ssqueezepy import ssq_cwt, ssq_stft, cwt, wavelets\n",
    "from ssqueezepy.experimental import scale_to_freq\n",
    "\n",
    "def cwt_features(window):\n",
    "    #FEATURIZE START\n",
    "    #downsample window\n",
    "    window = downsample_nparray(window, factor=2)\n",
    "    \n",
    "    cwt_output = wl.cwt(window, MORLET_PARAM, N, J)\n",
    "    \n",
    "    #w =('morlet', {'mu': 5})\n",
    "    #_, Wx, *_ = ssq_cwt(window, wavelet=w, scales='log-piecewise', nv=16, maprange='energy', gamma=0)\n",
    "        \n",
    "    #detach tensors from GPU and convert to numpy\n",
    "    #Wx = np.abs(Wx.cpu().numpy())\n",
    "    \n",
    "    #downsample Tx to 64x64\n",
    "    #Wx = scipy.signal.resample(Wx, 64, axis=0)\n",
    "    #Wx = scipy.signal.resample(Wx, 64, axis=1)\n",
    "    \n",
    "    #min max normalisation\n",
    "    #features = (Wx - np.min(Wx)) / (np.max(Wx) - np.min(Wx))\n",
    "    ##FEATURIZE END\n",
    "    \n",
    "    return cwt_output\n",
    "    \n",
    "\n",
    "def convert_nasa_experiments_to_numpy(ims_path=IMS_PATH, limit=1e10):\n",
    "    experiment_files = {\"1\": os.listdir(ims_path + \"1/\"), \"2\": os.listdir(ims_path + \"2/\"), \"3\": os.listdir(ims_path + \"3/\")}\n",
    "    experiments_data = {\"1\": {\"bearing3_values\":[],\"bearing3_keys\":[]} , \"2\": {\"bearing3_values\":[],\"bearing3_keys\":[]}, \"3\": {\"bearing3_values\":[],\"bearing3_keys\":[]}}\n",
    "    \n",
    "    #sort experiment files by name\n",
    "    for experiment in experiment_files:\n",
    "        experiment_files[experiment] = sorted(experiment_files[experiment])\n",
    "        \n",
    "    \n",
    "    for experiment in experiment_files:\n",
    "        i=0\n",
    "        for file in experiment_files[experiment]:\n",
    "            #print(\"Converting exp %s file %s\" % (experiment, file))\n",
    "            #load the data in the file\n",
    "            bearing3_channel = 4 if experiment == \"1\" else 2\n",
    "            \n",
    "            data = np.loadtxt(ims_path + experiment + \"/\" + file)\n",
    "            vals = data[:,bearing3_channel]\n",
    "            \n",
    "            experiments_data[experiment]['bearing3_values'].append(vals)\n",
    "            experiments_data[experiment]['bearing3_keys'].append(file)\n",
    "            i+=1\n",
    "            \n",
    "            if i > limit:\n",
    "                break\n",
    "        \n",
    "    print(\"Converting NASA IMS dataset to spectrograms\")\n",
    "    return experiments_data,experiment_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting NASA IMS dataset to spectrograms\n"
     ]
    }
   ],
   "source": [
    "experiments_data,experiment_files = convert_nasa_experiments_to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sanity check\n",
    "all_data = np.concatenate(experiments_data['1']['bearing3_values'])\n",
    "xaxis_labels = experiment_files['1']\n",
    "ds_factor = 100\n",
    "xaxis_labels = [xaxis_labels[i] for i in range(0, len(xaxis_labels), ds_factor)] #downsample xaxis labels\n",
    "\n",
    "#plot windows with xaxis labels\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(all_data)\n",
    "\n",
    "#1 element of xaxis_labels for every 20480 samples\n",
    "#add xaxis labels\n",
    "plt.xticks(np.arange(0, len(all_data), ds_factor*20480), xaxis_labels, rotation=45)\n",
    "\n",
    "#add grid\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"exp1_b3_ch5_first100k.txt\", all_data[:100000])\n",
    "np.savetxt(\"exp1_b3_ch5_last100k.txt\", all_data[-100000:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bearing 3 failure labels\n",
    "\n",
    "from: https://github.com/Miltos-90/Failure_Classification_of_Bearings?tab=readme-ov-file\n",
    "\n",
    "\n",
    "#### Bearing 3\n",
    "* early: 2003.10.22.12.06.24 - 2003.11.01.21.41.44\n",
    "* normal: 2003.11.01.21.51.44 - 2003.11.22.09.16.56\n",
    "* suspect: 2003.11.22.09.26.56 - 2003.11.25.10.47.32\n",
    "* Inner race failure: 2003.11.25.10.57.32 - 2003.11.25.23.39.56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early start: 0\n",
      "Early end: 498\n",
      "Normal start: 499, normal end: 1789\n",
      "My normal end: 999\n",
      "Start of failure relative to suspect: 330\n"
     ]
    }
   ],
   "source": [
    "early_start = experiment_files['1'].index('2003.10.22.12.06.24')\n",
    "early_end = experiment_files['1'].index('2003.11.01.21.41.44')\n",
    "normal_start = experiment_files['1'].index('2003.11.01.21.51.44')\n",
    "real_normal_end = experiment_files['1'].index('2003.11.22.09.16.56')\n",
    "\n",
    "my_normal_end = normal_start + 500\n",
    "\n",
    "start_of_failure_relative_to_suspect = experiment_files['1'].index('2003.11.25.10.57.32') -experiment_files['1'].index('2003.11.22.09.26.56') \n",
    "\n",
    "print(\"Early start: %s\" % early_start)\n",
    "print(\"Early end: %s\" % early_end)\n",
    "print(\"Normal start: %s, normal end: %s\" % (normal_start, real_normal_end))\n",
    "print(\"My normal end: %s\" % my_normal_end)\n",
    "print(\"Start of failure relative to suspect: %s\" % start_of_failure_relative_to_suspect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "files: 1290\n",
      "all_training_sample_spectrograms.shape: (5160, 50, 50)\n",
      "all_training_labels.shape: (5160,)\n",
      "startfile: 2003.11.25.10.57.32\n",
      "endfile: 2003.11.25.23.39.56\n",
      "files: 35\n",
      "all_training_sample_spectrograms.shape: (140, 50, 50)\n",
      "all_training_labels.shape: (140,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gAQMGqHfv3srLy5PX6+1onQAAIAqFHFS2b9+uP/zhDxoxYkRQ/4wZM7RhwwatXbtWW7Zs0cGDBzVx4sQOFwoAAKJPSEHlyy+/1KRJk7Ry5Ur169cv0N/U1KSKigo98cQTGjNmjLKyslRZWam3335bNTU1YSsaAABEh5CCSkFBgcaNG6ecnJyg/traWrW2tgb1Z2ZmKj09XdXV1e3O5ff75fP5ghoAAIAkxdk9Yc2aNdqxY4e2b99+3DGPx6P4+Hj17ds3qN/pdMrj8bQ7X2lpqUpKSuyWAQAAooCtHZWGhgb9+te/1nPPPaeEhISwFFBcXKympqZAa2hoCMu8AADg1GcrqNTW1urw4cO65JJLFBcXp7i4OG3ZskVLlixRXFycnE6nWlpa1NjYGHSe1+uVy+Vqd06Hw6GkpKSgBgAAINn81c+PfvQj7d69O6jvjjvuUGZmph544AGlpaWpZ8+eqqqqUl5eniSprq5O9fX1crvd4asaAABEBVtBpU+fPrrggguC+nr16qUBAwYE+qdMmaLCwkL1799fSUlJmjZtmtxut0aOHBm+qgEAQFSwfTHt91m0aJFiY2OVl5cnv9+v3NxcLVu2LNx3AwAAokCHg8rmzZuDbickJKi8vFzl5eUdnRoAAEQ5PusHAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYKy47i4g2g0u2tjdJeAUZ+c5ZNLzzaRawuFEj+dA2bgurgSILOyoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGPZCirLly/XiBEjlJSUpKSkJLndbr3yyiuB483NzSooKNCAAQPUu3dv5eXlyev1hr1oAAAQHWwFlTPPPFNlZWWqra3Vu+++qzFjxuiGG27Q+++/L0maMWOGNmzYoLVr12rLli06ePCgJk6c2CmFAwCAyBdnZ/D48eODbs+fP1/Lly9XTU2NzjzzTFVUVGj16tUaM2aMJKmyslJDhw5VTU2NRo4cGb6qAQBAVAj5GpVjx45pzZo1Onr0qNxut2pra9Xa2qqcnJzAmMzMTKWnp6u6uvqE8/j9fvl8vqAGAAAg2dxRkaTdu3fL7XarublZvXv31rp163T++edr165dio+PV9++fYPGO51OeTyeE85XWlqqkpIS24V3pcFFG4/rO1A2rhsqAbpWe8/9U3WO75qnve9nO2MBdB7bOyrnnXeedu3apW3btumXv/yl8vPztWfPnpALKC4uVlNTU6A1NDSEPBcAAIgstndU4uPjdfbZZ0uSsrKytH37dv3ud7/TzTffrJaWFjU2Ngbtqni9XrlcrhPO53A45HA47FcOAAAiXof/jkpbW5v8fr+ysrLUs2dPVVVVBY7V1dWpvr5ebre7o3cDAACikK0dleLiYo0dO1bp6ek6cuSIVq9erc2bN+vVV19VcnKypkyZosLCQvXv319JSUmaNm2a3G437/gBAAAhsRVUDh8+rJ/97Gc6dOiQkpOTNWLECL366qv68Y9/LElatGiRYmNjlZeXJ7/fr9zcXC1btqxTCgcAAJHPVlCpqKj4zuMJCQkqLy9XeXl5h4oCAACQ+KwfAABgMIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLHiuruA7jC4aGO7/QfKxnVxJQBMcKLXhK7GaxNwPHZUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLFsBZXS0lJddtll6tOnjwYOHKgJEyaorq4uaExzc7MKCgo0YMAA9e7dW3l5efJ6vWEtGgAARAdbQWXLli0qKChQTU2NXnvtNbW2turaa6/V0aNHA2NmzJihDRs2aO3atdqyZYsOHjyoiRMnhr1wAAAQ+eLsDN60aVPQ7VWrVmngwIGqra3VD3/4QzU1NamiokKrV6/WmDFjJEmVlZUaOnSoampqNHLkyPBVDgAAIl6HrlFpamqSJPXv31+SVFtbq9bWVuXk5ATGZGZmKj09XdXV1e3O4ff75fP5ghoAAIBkc0flf7W1tWn69OkaNWqULrjgAkmSx+NRfHy8+vbtGzTW6XTK4/G0O09paalKSkpCLaPbDC7a2N0lAOgGfO8DXSvkHZWCggK99957WrNmTYcKKC4uVlNTU6A1NDR0aD4AABA5QtpRuffee/XSSy/pzTff1Jlnnhnod7lcamlpUWNjY9Cuitfrlcvlancuh8Mhh8MRShkAACDC2dpRsSxL9957r9atW6c33nhDGRkZQcezsrLUs2dPVVVVBfrq6upUX18vt9sdnooBAEDUsLWjUlBQoNWrV+uvf/2r+vTpE7juJDk5WYmJiUpOTtaUKVNUWFio/v37KykpSdOmTZPb7eYdPwAAwDZbQWX58uWSpKuvvjqov7KyUrfffrskadGiRYqNjVVeXp78fr9yc3O1bNmysBQLAACii62gYlnW945JSEhQeXm5ysvLQy4KAABA4rN+AACAwQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMaK6+4CACDaDC7aaMx9HigbF5Z5wjE30B52VAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxbAeVN998U+PHj1dqaqpiYmK0fv36oOOWZWn27NlKSUlRYmKicnJy9OGHH4arXgAAEEVsB5WjR4/qwgsvVHl5ebvHFy5cqCVLlmjFihXatm2bevXqpdzcXDU3N3e4WAAAEF3i7J4wduxYjR07tt1jlmVp8eLFevjhh3XDDTdIkp555hk5nU6tX79et9xyS8eqBQAAUSWs16js379fHo9HOTk5gb7k5GRlZ2erurq63XP8fr98Pl9QAwAAkELYUfkuHo9HkuR0OoP6nU5n4Ni3lZaWqqSk5KTvY3DRxpMee6Bs3EmPBYBTjZ3Xw86cw+7cJr02nwo1Rrtuf9dPcXGxmpqaAq2hoaG7SwIAAIYIa1BxuVySJK/XG9Tv9XoDx77N4XAoKSkpqAEAAEhhDioZGRlyuVyqqqoK9Pl8Pm3btk1utzucdwUAAKKA7WtUvvzyS3300UeB2/v379euXbvUv39/paena/r06Zo3b57OOeccZWRkaNasWUpNTdWECRPCWTcAAIgCtoPKu+++q2uuuSZwu7CwUJKUn5+vVatWaebMmTp69KimTp2qxsZGjR49Wps2bVJCQkL4qgYAAFHBdlC5+uqrZVnWCY/HxMRo7ty5mjt3bocKAwAA6PZ3/QAAAJwIQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjEVQAQAAxiKoAAAAYxFUAACAsQgqAADAWHHdXUBnGly0sVPHA8D3CcfrSrS/NrX3+A+UjeuGSuw50bqFo/ZwPCfs1tGZj+e7sKMCAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBYBBUAAGAsggoAADAWQQUAABiLoAIAAIxFUAEAAMYiqAAAAGMRVAAAgLEIKgAAwFgEFQAAYCyCCgAAMBZBBQAAGIugAgAAjNVpQaW8vFyDBw9WQkKCsrOz9c4773TWXQEAgAjVKUHlz3/+swoLCzVnzhzt2LFDF154oXJzc3X48OHOuDsAABChOiWoPPHEE7rrrrt0xx136Pzzz9eKFSt02mmn6amnnuqMuwMAABEqLtwTtrS0qLa2VsXFxYG+2NhY5eTkqLq6+rjxfr9ffr8/cLupqUmS5PP52p2/zf9VmCsGAHSlE72+n0h7r/t257Azd7jm74657QjHOvzvPN/817KsjhX2bVaYffbZZ5Yk6+233w7qv//++63LL7/8uPFz5syxJNFoNBqNRouA9vHHH4c1V4R9R8Wu4uJiFRYWBm43NjZq0KBBqq+vV3JycjdWBum/CTktLU0NDQ1KSkrq7nKiGmthDtbCHKyFOZqampSenq7+/fuHdd6wB5XTTz9dPXr0kNfrDer3er1yuVzHjXc4HHI4HMf1Jycn86QzSFJSEuthCNbCHKyFOVgLc8TGhvfy17BfTBsfH6+srCxVVVUF+tra2lRVVSW32x3uuwMAABGsU371U1hYqPz8fF166aW6/PLLtXjxYh09elR33HFHZ9wdAACIUJ0SVG6++Wb961//0uzZs+XxeHTRRRdp06ZNcjqd33uuw+HQnDlz2v11ELoe62EO1sIcrIU5WAtzdNZaxFhWuN9HBAAAEB581g8AADAWQQUAABiLoAIAAIxFUAEAAMbqlqBSXl6uwYMHKyEhQdnZ2XrnnXe+c/zatWuVmZmphIQEDR8+XC+//HIXVRr57KzFypUrdeWVV6pfv37q16+fcnJyvnftYI/d741vrFmzRjExMZowYULnFhhF7K5FY2OjCgoKlJKSIofDoXPPPZfXqjCxuxaLFy/Weeedp8TERKWlpWnGjBlqbm7uomoj15tvvqnx48crNTVVMTExWr9+/fees3nzZl1yySVyOBw6++yztWrVKvt3HNY/yH8S1qxZY8XHx1tPPfWU9f7771t33XWX1bdvX8vr9bY7/q233rJ69OhhLVy40NqzZ4/18MMPWz179rR2797dxZVHHrtrcdttt1nl5eXWzp07rb1791q33367lZycbH366addXHlksrse39i/f7/1gx/8wLryyiutG264oWuKjXB218Lv91uXXnqpdd1111lbt2619u/fb23evNnatWtXF1ceeeyuxXPPPWc5HA7rueees/bv32+9+uqrVkpKijVjxowurjzyvPzyy9ZDDz1kvfDCC5Yka926dd85ft++fdZpp51mFRYWWnv27LGWLl1q9ejRw9q0aZOt++3yoHL55ZdbBQUFgdvHjh2zUlNTrdLS0nbH33TTTda4ceOC+rKzs6277767U+uMBnbX4tu+/vprq0+fPtbTTz/dWSVGlVDW4+uvv7auuOIK649//KOVn59PUAkTu2uxfPly66yzzrJaWlq6qsSoYXctCgoKrDFjxgT1FRYWWqNGjerUOqPNyQSVmTNnWsOGDQvqu/nmm63c3Fxb99Wlv/ppaWlRbW2tcnJyAn2xsbHKyclRdXV1u+dUV1cHjZek3NzcE47HyQllLb7tq6++Umtra9g/gCoahboec+fO1cCBAzVlypSuKDMqhLIWL774otxutwoKCuR0OnXBBRdowYIFOnbsWFeVHZFCWYsrrrhCtbW1gV8P7du3Ty+//LKuu+66LqkZ/y9cP7+79NOTP//8cx07duy4v1DrdDr1z3/+s91zPB5Pu+M9Hk+n1RkNQlmLb3vggQeUmpp63BMR9oWyHlu3blVFRYV27drVBRVGj1DWYt++fXrjjTc0adIkvfzyy/roo490zz33qLW1VXPmzOmKsiNSKGtx22236fPPP9fo0aNlWZa+/vpr/eIXv9CDDz7YFSXjf5zo57fP59N//vMfJSYmntQ8vOsHISkrK9OaNWu0bt06JSQkdHc5UefIkSOaPHmyVq5cqdNPP727y4l6bW1tGjhwoJ588kllZWXp5ptv1kMPPaQVK1Z0d2lRZ/PmzVqwYIGWLVumHTt26IUXXtDGjRv16KOPdndpCFGX7qicfvrp6tGjh7xeb1C/1+uVy+Vq9xyXy2VrPE5OKGvxjccff1xlZWV6/fXXNWLEiM4sM2rYXY+PP/5YBw4c0Pjx4wN9bW1tkqS4uDjV1dVpyJAhnVt0hArleyMlJUU9e/ZUjx49An1Dhw6Vx+NRS0uL4uPjO7XmSBXKWsyaNUuTJ0/Wz3/+c0nS8OHDdfToUU2dOlUPPfSQYmP5//OucqKf30lJSSe9myJ18Y5KfHy8srKyVFVVFehra2tTVVWV3G53u+e43e6g8ZL02muvnXA8Tk4oayFJCxcu1KOPPqpNmzbp0ksv7YpSo4Ld9cjMzNTu3bu1a9euQPvJT36ia665Rrt27VJaWlpXlh9RQvneGDVqlD766KNAWJSkDz74QCkpKYSUDghlLb766qvjwsg3AdLio+26VNh+ftu7zrfj1qxZYzkcDmvVqlXWnj17rKlTp1p9+/a1PB6PZVmWNXnyZKuoqCgw/q233rLi4uKsxx9/3Nq7d681Z84c3p4cJnbXoqyszIqPj7f+8pe/WIcOHQq0I0eOdNdDiCh21+PbeNdP+Nhdi/r6eqtPnz7Wvffea9XV1VkvvfSSNXDgQGvevHnd9RAiht21mDNnjtWnTx/rT3/6k7Vv3z7rb3/7mzVkyBDrpptu6q6HEDGOHDli7dy509q5c6clyXriiSesnTt3Wp988ollWZZVVFRkTZ48OTD+m7cn33///dbevXut8vLyU+PtyZZlWUuXLrXS09Ot+Ph46/LLL7dqamoCx6666iorPz8/aPzzzz9vnXvuuVZ8fLw1bNgwa+PGjV1cceSysxaDBg2yJB3X5syZ0/WFRyi73xv/i6ASXnbX4u2337ays7Mth8NhnXXWWdb8+fOtr7/+uourjkx21qK1tdV65JFHrCFDhlgJCQlWWlqadc8991hffPFF1xceYf7+97+3+zPgm69/fn6+ddVVVx13zkUXXWTFx8dbZ511llVZWWn7fmMsi70wAABgJq4qAgAAxiKoAAAAYxFUAACAsQgqAADAWAQVAABgLIIKAAAwFkEFAAAYi6ACAACMRVABAADGIqgAAABjEVQAAICxCCoAAMBY/wd+MzhiUUQiXAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_torch_nasa_spectrograms(experiments_data, experiment_files, N=SAMPLE_LEN, wins_to_avg=30, name=\"train\", startfile = \"2003.11.01.21.51.44\", endfile= \"2003.11.22.09.16.56\", bearing=3, experiment=\"1\", save=False, plot=False):\n",
    "\n",
    "    if startfile.__class__ == str:\n",
    "        print(\"startfile: %s\" % startfile)\n",
    "        print(\"endfile: %s\" % endfile)\n",
    "        index_start = experiments_data[experiment]['bearing%s_keys'%bearing].index(startfile)\n",
    "        index_end = experiments_data[experiment]['bearing%s_keys'%bearing].index(endfile)\n",
    "    else:\n",
    "        index_start = startfile\n",
    "        index_end = endfile\n",
    "\n",
    "    training_files = experiment_files[experiment][index_start:index_end]\n",
    "    training_data = experiments_data[experiment]['bearing%s_values'%bearing][index_start:index_end]\n",
    "    training_data = np.stack(training_data) #each row is 1 second of data, taken every 10 minutes\n",
    "    \n",
    "    print(\"files: %s\" % len(training_files))\n",
    "\n",
    "    #generate spectrograms\n",
    "    all_training_sample_spectrograms = []\n",
    "    all_training_labels = []\n",
    "    all_training_avg_windows = []\n",
    "    for i in range(0, len(training_data)):\n",
    "        #decimate the data\n",
    "        training_sample_windows = create_windows(training_data[i], window_type='rectangular', window_length=N, overlap_length=0)\n",
    "        \n",
    "        \n",
    "        for offset in range(0, len(training_sample_windows), wins_to_avg):\n",
    "            avg_window = np.mean(training_sample_windows[offset:offset+wins_to_avg], axis=0)\n",
    "            all_training_avg_windows.append(avg_window)\n",
    "            \n",
    "            avg_window_image = get_image(avg_window)\n",
    "            \n",
    "            #convert to float32\n",
    "            avg_window_image = avg_window_image.astype(np.float32)\n",
    "            \n",
    "            #if ther reference value is well chosen, the data needs to be normalised anymore for training\n",
    "            # the data will be between 0 and 1\n",
    "            #therefore no need to normalise\n",
    "            \n",
    "            all_training_sample_spectrograms.append(avg_window_image)\n",
    "            all_training_labels.append(training_files[i])\n",
    "            \n",
    "    all_training_sample_spectrograms = np.array(all_training_sample_spectrograms)\n",
    "    all_training_avg_windows = np.array(all_training_avg_windows)\n",
    "    \n",
    "    all_training_labels = np.array(all_training_labels)\n",
    "    \n",
    "    print(\"all_training_sample_spectrograms.shape: %s\" % str(all_training_sample_spectrograms.shape))\n",
    "    print(\"all_training_labels.shape: %s\" % str(all_training_labels.shape))\n",
    "    \n",
    "    if save:\n",
    "        #save the inpute WINDOWS for the on device demo\n",
    "        np.save('%s_%s_exp%s_b%s_input_windows_from_averaging_30_windows'% (DATASET_NAME,name,experiment,bearing), all_training_avg_windows)\n",
    "        \n",
    "        print(\"we have %s normal training samples of %s x %s spectrograms\" % (all_training_sample_spectrograms.shape[0], all_training_sample_spectrograms.shape[1], all_training_sample_spectrograms.shape[2]))\n",
    "        #save the dataset\n",
    "        torch.save(all_training_sample_spectrograms, \"%s_%s_exp%s_b%s_spectrograms.pt\" % (DATASET_NAME,name,experiment,bearing))\n",
    "        torch.save(all_training_labels, \"%s_%s_exp%s_b%s_labels.pt\" % (DATASET_NAME,name,experiment,bearing))\n",
    "        os.system(\"ls\")\n",
    "        \n",
    "            \n",
    "    return all_training_sample_spectrograms\n",
    "\n",
    "train = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"train\", startfile = normal_start, endfile= real_normal_end, bearing=3, experiment=\"1\", save=False, plot=True)\n",
    "test = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"test\", startfile = \"2003.11.25.10.57.32\", endfile= \"2003.11.25.23.39.56\", bearing=3, experiment=\"1\", save=False, plot=True)\n",
    "\n",
    "\n",
    "#random index of training set\n",
    "tridx = np.random.randint(0, len(train))\n",
    "teidx = np.random.randint(0, len(test))\n",
    "\n",
    "plt.imshow(train[tridx], vmax=1, vmin=0)\n",
    "plt.figure()\n",
    "plt.imshow(test[teidx], vmax=1, vmin=0)\n",
    "#histograms\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(train[tridx].flatten(), bins=100)\n",
    "plt.xlim(0, 1)\n",
    "plt.figure()\n",
    "plt.hist(test[teidx].flatten(), bins=100)\n",
    "plt.xlim(0, 1)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set stats\n",
      "max: 1.4672838, min: 2.2917402e-05, mean: 0.23337433, std: 0.14315899\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([4.92000e+04, 1.44405e+05, 2.34170e+05, 3.18020e+05, 3.99060e+05,\n",
       "        4.63660e+05, 5.22015e+05, 5.67860e+05, 5.99870e+05, 6.18535e+05,\n",
       "        6.28300e+05, 6.30380e+05, 6.16970e+05, 6.02770e+05, 5.78535e+05,\n",
       "        5.48865e+05, 5.16895e+05, 4.77655e+05, 4.44405e+05, 4.12015e+05,\n",
       "        3.74470e+05, 3.41365e+05, 3.05755e+05, 2.74870e+05, 2.47655e+05,\n",
       "        2.21050e+05, 1.97655e+05, 1.74895e+05, 1.55480e+05, 1.39160e+05,\n",
       "        1.22300e+05, 1.09425e+05, 9.62200e+04, 8.60250e+04, 7.56050e+04,\n",
       "        6.69500e+04, 6.07800e+04, 5.31650e+04, 4.72700e+04, 4.26100e+04,\n",
       "        3.65050e+04, 3.28450e+04, 3.04550e+04, 2.60200e+04, 2.39300e+04,\n",
       "        2.20600e+04, 1.94000e+04, 1.68150e+04, 1.52800e+04, 1.30300e+04,\n",
       "        1.20450e+04, 1.07000e+04, 9.09500e+03, 7.87000e+03, 7.52000e+03,\n",
       "        6.41500e+03, 5.88000e+03, 5.24000e+03, 4.41500e+03, 3.95500e+03,\n",
       "        3.33000e+03, 2.92000e+03, 2.74500e+03, 2.07500e+03, 1.66000e+03,\n",
       "        1.75500e+03, 1.46500e+03, 1.10500e+03, 1.10500e+03, 9.10000e+02,\n",
       "        8.65000e+02, 5.90000e+02, 6.30000e+02, 4.80000e+02, 3.55000e+02,\n",
       "        4.20000e+02, 3.00000e+02, 2.50000e+02, 2.15000e+02, 7.50000e+01,\n",
       "        1.55000e+02, 1.15000e+02, 1.35000e+02, 1.10000e+02, 9.00000e+01,\n",
       "        8.00000e+01, 8.00000e+01, 5.00000e+01, 2.00000e+01, 1.00000e+01,\n",
       "        2.00000e+01, 2.00000e+01, 1.50000e+01, 1.00000e+01, 2.50000e+01,\n",
       "        1.50000e+01, 2.00000e+01, 0.00000e+00, 0.00000e+00, 1.50000e+01]),\n",
       " array([2.29174020e-05, 1.46955270e-02, 2.93681361e-02, 4.40407470e-02,\n",
       "        5.87133542e-02, 7.33859614e-02, 8.80585760e-02, 1.02731183e-01,\n",
       "        1.17403790e-01, 1.32076398e-01, 1.46749005e-01, 1.61421627e-01,\n",
       "        1.76094234e-01, 1.90766841e-01, 2.05439448e-01, 2.20112056e-01,\n",
       "        2.34784663e-01, 2.49457270e-01, 2.64129877e-01, 2.78802484e-01,\n",
       "        2.93475091e-01, 3.08147699e-01, 3.22820336e-01, 3.37492943e-01,\n",
       "        3.52165550e-01, 3.66838157e-01, 3.81510764e-01, 3.96183372e-01,\n",
       "        4.10855979e-01, 4.25528586e-01, 4.40201193e-01, 4.54873800e-01,\n",
       "        4.69546407e-01, 4.84219015e-01, 4.98891622e-01, 5.13564229e-01,\n",
       "        5.28236866e-01, 5.42909443e-01, 5.57582080e-01, 5.72254658e-01,\n",
       "        5.86927295e-01, 6.01599872e-01, 6.16272509e-01, 6.30945086e-01,\n",
       "        6.45617723e-01, 6.60290360e-01, 6.74962938e-01, 6.89635575e-01,\n",
       "        7.04308152e-01, 7.18980789e-01, 7.33653367e-01, 7.48326004e-01,\n",
       "        7.62998581e-01, 7.77671218e-01, 7.92343795e-01, 8.07016432e-01,\n",
       "        8.21689010e-01, 8.36361647e-01, 8.51034284e-01, 8.65706861e-01,\n",
       "        8.80379498e-01, 8.95052075e-01, 9.09724712e-01, 9.24397290e-01,\n",
       "        9.39069927e-01, 9.53742504e-01, 9.68415141e-01, 9.83087718e-01,\n",
       "        9.97760355e-01, 1.01243293e+00, 1.02710557e+00, 1.04177821e+00,\n",
       "        1.05645084e+00, 1.07112336e+00, 1.08579600e+00, 1.10046864e+00,\n",
       "        1.11514127e+00, 1.12981379e+00, 1.14448643e+00, 1.15915906e+00,\n",
       "        1.17383170e+00, 1.18850422e+00, 1.20317686e+00, 1.21784949e+00,\n",
       "        1.23252213e+00, 1.24719465e+00, 1.26186728e+00, 1.27653992e+00,\n",
       "        1.29121256e+00, 1.30588520e+00, 1.32055771e+00, 1.33523035e+00,\n",
       "        1.34990299e+00, 1.36457562e+00, 1.37924814e+00, 1.39392078e+00,\n",
       "        1.40859342e+00, 1.42326605e+00, 1.43793857e+00, 1.45261121e+00,\n",
       "        1.46728384e+00]),\n",
       " <BarContainer object of 100 artists>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#training set stats\n",
    "print(\"training set stats\")\n",
    "print(\"max: %s, min: %s, mean: %s, std: %s\" % (train.max(), train.min(), train.mean(), train.std()))\n",
    "plt.figure()\n",
    "plt.hist(train.flatten(), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "startfile: 2003.11.01.21.51.44\n",
      "endfile: 2003.11.22.09.16.56\n",
      "all_training_sample_spectrograms.shape: (5160, 50, 50)\n",
      "all_training_labels.shape: (5160,)\n",
      "we have 5160 normal training samples of 50 x 50 spectrograms\n",
      "1\n",
      "1st_test.rar\n",
      "2\n",
      "2nd_test.rar\n",
      "3\n",
      "3rd_test.rar\n",
      "exp1_b3_ch5_all_data.txt\n",
      "exp1_b3_ch5_first100k.txt\n",
      "exp1_b3_ch5_last100k.txt\n",
      "ims_bearings_train_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_train_exp1_b3_labels.pt\n",
      "ims_bearings_train_exp1_b3_spectrograms.pt\n",
      "input_windows_from_averaging_30_windows.npy\n",
      "Readme Document for IMS Bearing Data.pdf\n",
      "startfile: 2003.11.22.09.26.56\n",
      "endfile: 2003.11.25.23.39.56\n",
      "all_training_sample_spectrograms.shape: (1460, 50, 50)\n",
      "all_training_labels.shape: (1460,)\n",
      "we have 1460 normal training samples of 50 x 50 spectrograms\n",
      "1\n",
      "1st_test.rar\n",
      "2\n",
      "2nd_test.rar\n",
      "3\n",
      "3rd_test.rar\n",
      "exp1_b3_ch5_all_data.txt\n",
      "exp1_b3_ch5_first100k.txt\n",
      "exp1_b3_ch5_last100k.txt\n",
      "ims_bearings_test_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_test_exp1_b3_labels.pt\n",
      "ims_bearings_test_exp1_b3_spectrograms.pt\n",
      "ims_bearings_train_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_train_exp1_b3_labels.pt\n",
      "ims_bearings_train_exp1_b3_spectrograms.pt\n",
      "input_windows_from_averaging_30_windows.npy\n",
      "Readme Document for IMS Bearing Data.pdf\n"
     ]
    }
   ],
   "source": [
    "train = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"train\", startfile = \"2003.11.01.21.51.44\", endfile= \"2003.11.22.09.16.56\", bearing=3, experiment=\"1\", save=True, plot=False) #discard early data\n",
    "test = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"test\", startfile = \"2003.11.22.09.26.56\", endfile= \"2003.11.25.23.39.56\", bearing=3, experiment=\"1\", save=True, plot=False) #starts at suspect!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4672838 2.2917402e-05 0.23337433 0.020494496\n",
      "6.3869233 0.00013786284 0.29650885 0.042972848\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train.max(), train.min(), train.mean(), train.var())\n",
    "print(test.max(), test.min(), test.mean(), test.var())\n",
    "\n",
    "#histogram of train\n",
    "plt.hist(train.flatten(), bins=100)\n",
    "plt.title(\"Histogram of train\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#histogram of test\n",
    "plt.hist(test.flatten(), bins=100)\n",
    "plt.title(\"Histogram of test\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the entire timeseries, so we can use it for microcontroller testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "startfile: 2003.11.01.21.51.44\n",
      "endfile: 2003.11.25.23.39.56\n",
      "files: 1656\n",
      "all_training_sample_spectrograms.shape: (6624, 50, 50)\n",
      "all_training_labels.shape: (6624,)\n",
      "we have 6624 normal training samples of 50 x 50 spectrograms\n",
      "1\n",
      "1st_test.rar\n",
      "2\n",
      "2nd_test.rar\n",
      "3\n",
      "3rd_test.rar\n",
      "exp1_b3_ch5_all_data.txt\n",
      "exp1_b3_ch5_first100k.txt\n",
      "exp1_b3_ch5_last100k.txt\n",
      "ims_bearings_all_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_all_exp1_b3_labels.pt\n",
      "ims_bearings_all_exp1_b3_spectrograms.pt\n",
      "ims_bearings_test_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_test_exp1_b3_labels.pt\n",
      "ims_bearings_test_exp1_b3_spectrograms.pt\n",
      "ims_bearings_train_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_train_exp1_b3_labels.pt\n",
      "ims_bearings_train_exp1_b3_spectrograms.pt\n",
      "input_windows_from_averaging_30_windows.npy\n",
      "Readme Document for IMS Bearing Data.pdf\n",
      "files: 984\n",
      "all_training_sample_spectrograms.shape: (3936, 50, 50)\n",
      "all_training_labels.shape: (3936,)\n",
      "we have 3936 normal training samples of 50 x 50 spectrograms\n",
      "1\n",
      "1st_test.rar\n",
      "2\n",
      "2nd_test.rar\n",
      "3\n",
      "3rd_test.rar\n",
      "exp1_b3_ch5_all_data.txt\n",
      "exp1_b3_ch5_first100k.txt\n",
      "exp1_b3_ch5_last100k.txt\n",
      "ims_bearings_all_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_all_exp1_b3_labels.pt\n",
      "ims_bearings_all_exp1_b3_spectrograms.pt\n",
      "ims_bearings_all_exp2_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_all_exp2_b3_labels.pt\n",
      "ims_bearings_all_exp2_b3_spectrograms.pt\n",
      "ims_bearings_test_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_test_exp1_b3_labels.pt\n",
      "ims_bearings_test_exp1_b3_spectrograms.pt\n",
      "ims_bearings_train_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_train_exp1_b3_labels.pt\n",
      "ims_bearings_train_exp1_b3_spectrograms.pt\n",
      "input_windows_from_averaging_30_windows.npy\n",
      "Readme Document for IMS Bearing Data.pdf\n",
      "files: 6324\n",
      "all_training_sample_spectrograms.shape: (25296, 50, 50)\n",
      "all_training_labels.shape: (25296,)\n",
      "we have 25296 normal training samples of 50 x 50 spectrograms\n",
      "1\n",
      "1st_test.rar\n",
      "2\n",
      "2nd_test.rar\n",
      "3\n",
      "3rd_test.rar\n",
      "exp1_b3_ch5_all_data.txt\n",
      "exp1_b3_ch5_first100k.txt\n",
      "exp1_b3_ch5_last100k.txt\n",
      "ims_bearings_all_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_all_exp1_b3_labels.pt\n",
      "ims_bearings_all_exp1_b3_spectrograms.pt\n",
      "ims_bearings_all_exp2_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_all_exp2_b3_labels.pt\n",
      "ims_bearings_all_exp2_b3_spectrograms.pt\n",
      "ims_bearings_all_exp3_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_all_exp3_b3_labels.pt\n",
      "ims_bearings_all_exp3_b3_spectrograms.pt\n",
      "ims_bearings_test_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_test_exp1_b3_labels.pt\n",
      "ims_bearings_test_exp1_b3_spectrograms.pt\n",
      "ims_bearings_train_exp1_b3_input_windows_from_averaging_30_windows.npy\n",
      "ims_bearings_train_exp1_b3_labels.pt\n",
      "ims_bearings_train_exp1_b3_spectrograms.pt\n",
      "input_windows_from_averaging_30_windows.npy\n",
      "Readme Document for IMS Bearing Data.pdf\n"
     ]
    }
   ],
   "source": [
    "all = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"all\", startfile = \"2003.11.01.21.51.44\", endfile= \"2003.11.25.23.39.56\", bearing=3, experiment=\"1\", save=True, plot=False)\n",
    "all2 = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"all\", startfile = None, endfile= None, bearing=3, experiment=\"2\", save=True, plot=False)\n",
    "all3 = create_torch_nasa_spectrograms(experiments_data, experiment_files, name=\"all\", startfile = None , endfile= None, bearing=3, experiment=\"3\", save=True, plot=False)"
   ]
  }
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