Appendix 2. load dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Objective load a dataset and run a pre-defined set of analyses\n",
    "\n",
    "Two options\n",
    "1. Load the entire dataset, calculate absolute mean and write/use for modeling - see section Method 1\n",
    "2. Load the aggregates - absolute mean values for each sample (20480 events) - see section Method 2\n",
    "\n",
    "This set of notebooks is based upon \n",
    "1. NASA bearing dataset: http://data-acoustics.com/measurements/bearing-faults/bearing-4/\n",
    "   Set number 2: 4 accelerometers one on each bearing\n",
    "2. Tutorial: https://towardsdatascience.com/machine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770\n",
    "\n",
    "\n",
    "Acknowledgement is made for the measurements used in this work provided through data-acoustics.com Database\n",
    "\n",
    "Note: the dataset is 1GB (and only 85MB is loaded), check if someone else has already downloaded/loaded!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import configparser\n",
    "import logging\n",
    "from ocs_sample_library_preview import *\n",
    "import json\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import datetime\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.CRITICAL)\n",
    "#logger.setLevel(logging.DEBUG)\n",
    "\n",
    "config = configparser.ConfigParser()\n",
    "config.read('config.ini')\n",
    "\n",
    "ocsClient = OCSClient(config.get('Access', 'ApiVersion'), config.get('Access', 'Tenant'), config.get('Access', 'Resource'), \n",
    "                        config.get('Credentials', 'ClientId'), config.get('Credentials', 'ClientSecret'))\n",
    "        \n",
    "\n",
    "namespace_id = config.get('Configurations', 'Namespace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NASA.Bearing1: None\n",
      "NASA.Bearing4: None\n",
      "NASA.Bearing2: None\n",
      "NASA.Bearing3: None\n"
     ]
    }
   ],
   "source": [
    "# check status of the 4 streams, if they return values, check to see if the dataset has already been loaded.\n",
    "ocsClient.acceptverbosity = True\n",
    "for stream in ocsClient.Streams.getStreams(namespace_id,query='nasa.bearing*'):\n",
    "    value = ocsClient.Streams.getLastValue(namespace_id,stream.Id)\n",
    "    print(f'{stream.Id}: {value}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method 1\n",
    "\n",
    "Download the dataset from http://data-acoustics.com/measurements/bearing-faults/bearing-4/\n",
    "\n",
    "Extract the 2nd_test.rar file contents into a folder, for example: C:\\data\\IMS\\2nd_test and specify that folder in the variable data_dir in the next cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path(\"C:/data/IMS/2nd_test\")\n",
    "if not data_dir.is_dir():\n",
    "    print(\"Cannot find data directory\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "filename: 2004.02.19.06.22.39\n"
     ]
    }
   ],
   "source": [
    "# load the datafiles\n",
    "\n",
    "# function to bulk load data by bearing\n",
    "def write_to_sds(dataset):\n",
    "    # for each bearing 1 through 4\n",
    "    for bearing in range(1,5):\n",
    "            try:\n",
    "                # build a list of timestamp,index,bearing # channel data\n",
    "                values = [{ \"timestamp\": key.isoformat(), \"index\": int(row[f'index']),\"channel\": row[f'Bearing {bearing}'] } for key,row in dataset.iterrows()]\n",
    "                # write it!\n",
    "                ocsClient.Streams.insertValues(namespace_id,f'Nasa.bearing{bearing}',json.dumps(values))\n",
    "            except Exception as e:\n",
    "                print(f'Error: {str(e)}')\n",
    "\n",
    "# dataframe for loaded file\n",
    "dataset = pd.DataFrame(columns=['Bearing 1','Bearing 2','Bearing 3','Bearing 4'])\n",
    "\n",
    "# load datafiles\n",
    "for filename in os.listdir(data_dir):\n",
    "    #logging.debug(f'filename: {filename}')\n",
    "    print(f'filename: {filename}')\n",
    "    start_period = (datetime.datetime.strptime(filename,\"%Y.%m.%d.%H.%M.%S\")) \n",
    "    logging.debug(f'filename: {filename}, start_period: {start_period}')\n",
    "    dataset=pd.read_csv(os.path.join(data_dir, filename), sep='\\t', header=None, names=['Bearing 1','Bearing 2','Bearing 3','Bearing 4'])\n",
    "    dataset['index'] = dataset.index\n",
    "    dataset['index'] += 1\n",
    "    dataset['timestamp'] = pd.date_range(start_period, periods=20480, freq='50us')\n",
    "    dataset.set_index('timestamp',inplace=True)\n",
    "    write_to_sds(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NASA.Bearing1: {'timestamp': '2004-02-19T06:22:40.02395Z', 'index': 20480.0, 'channel': -0.002}\n",
      "NASA.Bearing4: {'timestamp': '2004-02-19T06:22:40.02395Z', 'index': 20480.0, 'channel': -0.002}\n",
      "NASA.Bearing2: {'timestamp': '2004-02-19T06:22:40.02395Z', 'index': 20480.0, 'channel': 0.0}\n",
      "NASA.Bearing3: {'timestamp': '2004-02-19T06:22:40.02395Z', 'index': 20480.0, 'channel': 0.0}\n"
     ]
    }
   ],
   "source": [
    "ocsClient.acceptverbosity = True\n",
    "for stream in ocsClient.Streams.getStreams(namespace_id,query='nasa.bearing*'):\n",
    "    value = ocsClient.Streams.getLastValue(namespace_id,stream.Id)\n",
    "    print(f'{stream.Id}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(984, 4)\n",
      "                     bearing 1  bearing 2  bearing 3  bearing 4\n",
      "2004-02-12T10:32:39   0.058333   0.071832   0.083244   0.043066\n",
      "2004-02-12T10:42:39   0.058997   0.074008   0.084439   0.044541\n",
      "2004-02-12T10:52:39   0.060240   0.074224   0.083922   0.044443\n",
      "2004-02-12T11:02:39   0.061454   0.073843   0.084462   0.045082\n",
      "2004-02-12T11:12:39   0.061361   0.075607   0.082837   0.045119\n"
     ]
    }
   ],
   "source": [
    "# Retrieve mean values for each channel and sample\n",
    "# 20kHz (20480) events were collected 948 times\n",
    "# this step retrieves the mean for each set of events\n",
    "\n",
    "ocsClient.acceptverbosity=True\n",
    "\n",
    "merged_data = pd.DataFrame()\n",
    "df_values = pd.DataFrame()\n",
    "pd.options.display.float_format = '{:.3}'.format\n",
    "pd.options.display.float_format = None\n",
    "\n",
    "# start of data collection\n",
    "signal_datetime = (datetime.datetime.strptime(\"2004-02-12T10:32:39\",\"%Y-%m-%dT%H:%M:%S\"))\n",
    "# for each data collection cycle\n",
    "for _ in range(1,985):\n",
    "    #print(signal_datetime)\n",
    "    signal_means = pd.Series(name=signal_datetime.isoformat())\n",
    "    # Each bearing for a cycle\n",
    "    for bearing in range(1,5):\n",
    "        values = ocsClient.Streams.getRangeValues(namespace_id,f'nasa.bearing{bearing}',start=signal_datetime,skip=0,count=20480,value_class=None,reverse=False,boundary_type=SdsBoundaryType.Exact)\n",
    "        df_result = pd.DataFrame.from_dict(values)\n",
    "        signal_means[f'bearing {bearing}'] = round(df_result['channel'].astype(np.float32).abs().mean(),6)\n",
    "    merged_data = merged_data.append(signal_means)\n",
    "    signal_datetime += datetime.timedelta(minutes=10)\n",
    "print(merged_data.shape)\n",
    "print(merged_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write the mean values to OCS\n",
    "\n",
    "for i in range(1,5):\n",
    "    values = (merged_data[f'bearing {i}']\n",
    "                .reset_index()\n",
    "                .rename(columns={'index':'timestamp',f'bearing {i}':'channel'})\n",
    "                .to_json(orient='records'))\n",
    "    ocsClient.Streams.updateValues(namespace_id,f'Nasa.bearing{i}.agg',values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write values to a csv file for folks who want to use method two below\n",
    "# Note: only run if the file does not exist\n",
    "#merged_data.to_csv(path_or_buf='nasa.bearing.aggregates.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method2\n",
    "\n",
    "load the values from a csv file to OCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "merged_data = pd.read_csv('nasa.bearing.aggregates.csv',index_col='timestamp')\n",
    "# read values for each bearing and load into OCS\n",
    "for i in range(1,5):\n",
    "    values = (merged_data[f'bearing {i}']\n",
    "                .reset_index()\n",
    "                .rename(columns={'index':'timestamp',f'bearing {i}':'channel'})\n",
    "                .to_json(orient='records'))\n",
    "    ocsClient.Streams.updateValues(namespace_id,f'Nasa.bearing{i}.agg',values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'channel': 0.058333, 'timestamp': '2004-02-12T10:32:39Z'},\n",
      " {'channel': 0.058997, 'timestamp': '2004-02-12T10:42:39Z'},\n",
      " {'channel': 0.06024, 'timestamp': '2004-02-12T10:52:39Z'},\n",
      " {'channel': 0.061454, 'timestamp': '2004-02-12T11:02:39Z'},\n",
      " {'channel': 0.061361, 'timestamp': '2004-02-12T11:12:39Z'},\n",
      " {'channel': 0.061669, 'timestamp': '2004-02-12T11:22:39Z'},\n",
      " {'channel': 0.061944, 'timestamp': '2004-02-12T11:32:39Z'},\n",
      " {'channel': 0.061232, 'timestamp': '2004-02-12T11:42:39Z'},\n",
      " {'channel': 0.062282, 'timestamp': '2004-02-12T11:52:39Z'},\n",
      " {'channel': 0.059893, 'timestamp': '2004-02-12T12:02:39Z'}]\n",
      "[{'channel': 0.071832, 'timestamp': '2004-02-12T10:32:39Z'},\n",
      " {'channel': 0.074008, 'timestamp': '2004-02-12T10:42:39Z'},\n",
      " {'channel': 0.074224, 'timestamp': '2004-02-12T10:52:39Z'},\n",
      " {'channel': 0.073843, 'timestamp': '2004-02-12T11:02:39Z'},\n",
      " {'channel': 0.075607, 'timestamp': '2004-02-12T11:12:39Z'},\n",
      " {'channel': 0.073281, 'timestamp': '2004-02-12T11:22:39Z'},\n",
      " {'channel': 0.074593, 'timestamp': '2004-02-12T11:32:39Z'},\n",
      " {'channel': 0.074171, 'timestamp': '2004-02-12T11:42:39Z'},\n",
      " {'channel': 0.075808, 'timestamp': '2004-02-12T11:52:39Z'},\n",
      " {'channel': 0.075117, 'timestamp': '2004-02-12T12:02:39Z'}]\n",
      "[{'channel': 0.083244, 'timestamp': '2004-02-12T10:32:39Z'},\n",
      " {'channel': 0.084439, 'timestamp': '2004-02-12T10:42:39Z'},\n",
      " {'channel': 0.083922, 'timestamp': '2004-02-12T10:52:39Z'},\n",
      " {'channel': 0.084462, 'timestamp': '2004-02-12T11:02:39Z'},\n",
      " {'channel': 0.082837, 'timestamp': '2004-02-12T11:12:39Z'},\n",
      " {'channel': 0.084886, 'timestamp': '2004-02-12T11:22:39Z'},\n",
      " {'channel': 0.082623, 'timestamp': '2004-02-12T11:32:39Z'},\n",
      " {'channel': 0.082021, 'timestamp': '2004-02-12T11:42:39Z'},\n",
      " {'channel': 0.084373, 'timestamp': '2004-02-12T11:52:39Z'},\n",
      " {'channel': 0.084504, 'timestamp': '2004-02-12T12:02:39Z'}]\n",
      "[{'channel': 0.043066, 'timestamp': '2004-02-12T10:32:39Z'},\n",
      " {'channel': 0.044541, 'timestamp': '2004-02-12T10:42:39Z'},\n",
      " {'channel': 0.044443, 'timestamp': '2004-02-12T10:52:39Z'},\n",
      " {'channel': 0.045082, 'timestamp': '2004-02-12T11:02:39Z'},\n",
      " {'channel': 0.045119, 'timestamp': '2004-02-12T11:12:39Z'},\n",
      " {'channel': 0.044172, 'timestamp': '2004-02-12T11:22:39Z'},\n",
      " {'channel': 0.044659, 'timestamp': '2004-02-12T11:32:39Z'},\n",
      " {'channel': 0.043841, 'timestamp': '2004-02-12T11:42:39Z'},\n",
      " {'channel': 0.044275, 'timestamp': '2004-02-12T11:52:39Z'},\n",
      " {'channel': 0.043973, 'timestamp': '2004-02-12T12:02:39Z'}]\n"
     ]
    }
   ],
   "source": [
    "# retrieve/check loaded data\n",
    "import pprint as pprint\n",
    "for i in range(1,5):\n",
    "    signal_starttime = ocsClient.Streams.getFirstValue(namespace_id,f'Nasa.bearing{i}.agg',None)['timestamp']\n",
    "    pprint.pprint(ocsClient.Streams.getRangeValues(namespace_id,f'Nasa.bearing{i}.agg',start=signal_starttime,count=10,value_class=None,skip=0,reverse=False,boundary_type=SdsBoundaryType.Inside))\n",
    "    # retrieve summary information\n",
    "    #signal_starttime = ocsClient.Streams.getFirstValue(namespace_id,f'Nasa.bearing{i}.agg',None)['timestamp']\n",
    "    #signal_endtime = ocsClient.Streams.getLastValue(namespace_id,f'Nasa.bearing{i}.agg',None)['timestamp']\n",
    "    #pprint.pprint(ocsClient.Streams.getSummaries(namespace_id, f'Nasa.bearing{i}.agg', value_class=None, start=signal_starttime, end=signal_endtime, count=1))"
   ]
  }
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