preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66e38027",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b2510ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import zipfile\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7fec80a",
   "metadata": {},
   "source": [
    "Define absolute path to data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5c7736a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = ABSOLUTE_PATH_TO_DATA\n",
    "unzipped_path = data_path+\"/unzipped/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c3796f",
   "metadata": {},
   "source": [
    "Unzip data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b87dcf15",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    data_path = \"C:/Users/Tim/Desktop/makeathon/data/Messungen_PANE\"\n",
    "    for f in os.listdir(data_path):\n",
    "        with zipfile.ZipFile(data_path+\"/\"+f, 'r') as zip_ref:\n",
    "            zip_ref.extractall(data_path+\"/unzipped/\")\n",
    "except PermissionError:\n",
    "    unzipped_path = data_path+\"/unzipped/\"\n",
    "    os.remove(unzipped_path+\"OpcUA_PANE_BZ___2022_05_23__09_05_31.json.zip\")\n",
    "    print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279c5af7",
   "metadata": {},
   "source": [
    "Fix JSON formatting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b7766f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for file_ in os.listdir(unzipped_path):\n",
    "    with open(unzipped_path+file_, \"r\") as fin:\n",
    "        lines = fin.readlines()\n",
    "    modified = []\n",
    "    for line in lines:\n",
    "        modified.append(line[:-1] + \",\" + line[-1:])\n",
    "    modified[0] = \"[\" + modified[0]\n",
    "    modified[-1] = modified[-2] + \"]\"\n",
    "    modified[-1] = modified[-1][:-3] + modified[-1][-2:]\n",
    "    with open(unzipped_path+file_, \"w\") as fout:\n",
    "        fout.writelines(modified)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440b6639",
   "metadata": {},
   "source": [
    "Read all JSON files and gather them in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0094f032",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "files_json = []\n",
    "for file_ in os.listdir(unzipped_path):\n",
    "    if \".json\" in file_:\n",
    "        with open(unzipped_path+file_) as fin:\n",
    "            files_json.append(json.load(fin))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401b04c3",
   "metadata": {},
   "source": [
    "Select which columns to include in the reformatted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3c78179",
   "metadata": {},
   "outputs": [],
   "source": [
    "sensor_cols = [\"distanceSensors[0].calcValueInMm\", \"distanceSensors[1].calcValueInMm\", \"distanceSensors[2].calcValueInMm\",\n",
    "               \"quadroSensorsAutoCommands[0].setpointInMm\", \"quadroSensorsAutoCommands[1].setpointInMm\",\n",
    "               \"quadroSensorsAutoCommands[2].setpointInMm\", \"fConverters[3].rpdo.setpointPercentOfRpm\",\n",
    "               \"fConverters[3].speed.actualMillimeterPerSecond\", \"fConverters[4].rpdo.setpointPercentOfRpm\",\n",
    "               \".fConverters[4].speed.actualMillimeterPerSecond\", \"safetyIOs.image.i11\", \"safetyIOs.image.i12\",\n",
    "               \"safetyIOs.image.i15\", \"safetyIOs.image.iq2\", \"safetyIOs.image.iq3\",\n",
    "               \"fConverters[2].rpdo.setpointPercentOfRpm\", \"fConverters[2].speed.actualMillimeterPerSecond\",\n",
    "               \"fConverters[2].currentCurrent\", \"fConverters[2].nodeStatus\", \"fConverters[3].currentCurrent\",\n",
    "               \"fConverters[3].nodeStatus\", \"fConverters[4].currentCurrent\", \"fConverters[4].nodeStatus\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754bb6b4",
   "metadata": {},
   "source": [
    "Reformat the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3dc7983e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clean_file_names = [x[:-5] for x in os.listdir(unzipped_path) if \".json\" in x]\n",
    "for i, file in enumerate(files_json):\n",
    "    col_data = {col:[] for col in sensor_cols}  # make dict to hold values for each col\n",
    "    for row in file:\n",
    "        node_id = row[\"addressSpaceItems\"][0][\"nodeId\"]\n",
    "        payload = row[\"payload\"]\n",
    "        \n",
    "        if payload[\"statusCode\"][\"value\"] != 0:\n",
    "            # sensor error in this row\n",
    "            continue\n",
    "        \n",
    "        timestamp = payload[\"sourceTimestamp\"]\n",
    "        value = payload[\"value\"][\"value\"]\n",
    "        \n",
    "        for col in sensor_cols:\n",
    "            if col in node_id:\n",
    "                col_data[col].append([value, timestamp])\n",
    "    \n",
    "    # ignore file if empty\n",
    "    empty = False\n",
    "    for col in col_data.keys():\n",
    "        if len(col_data[col]) == 0:\n",
    "            empty = True\n",
    "            \n",
    "    if not empty:\n",
    "        for col in sensor_cols:\n",
    "            df_col = pd.DataFrame(col_data[col])\n",
    "            dt = pd.to_datetime(df_col.iloc[:, 1], infer_datetime_format=True)\n",
    "            sensor_dt = pd.Series(data=list(df_col.iloc[:, 0]), index=list(dt))\n",
    "            sensor_resampled = sensor_dt.resample(\"1000ms\").mean().interpolate()\n",
    "            col_data[col] = sensor_resampled\n",
    "\n",
    "        file_df = pd.DataFrame(data=col_data)\n",
    "        \n",
    "        for col in sensor_cols:\n",
    "            if col_data[col].shape[0] == 1:\n",
    "                # only one observation given, assume constant\n",
    "                file_df[col] = col_data[col].iloc[0]\n",
    "                \n",
    "        # as stated by WP, incomplete timeseries should be assumed to be constant till the end\n",
    "        file_df = file_df.interpolate(method=\"pad\", limit_direction=\"forward\")\n",
    "        file_df.to_csv(data_path+\"/csv/\"+clean_file_names[i]+\".csv\")  # save as csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe1a7d0",
   "metadata": {},
   "source": [
    "Load example csv file and plot data as a test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb4376ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distanceSensors[0].calcValueInMm</th>\n",
       "      <th>distanceSensors[1].calcValueInMm</th>\n",
       "      <th>distanceSensors[2].calcValueInMm</th>\n",
       "      <th>quadroSensorsAutoCommands[0].setpointInMm</th>\n",
       "      <th>quadroSensorsAutoCommands[1].setpointInMm</th>\n",
       "      <th>quadroSensorsAutoCommands[2].setpointInMm</th>\n",
       "      <th>fConverters[3].rpdo.setpointPercentOfRpm</th>\n",
       "      <th>fConverters[3].speed.actualMillimeterPerSecond</th>\n",
       "      <th>fConverters[4].rpdo.setpointPercentOfRpm</th>\n",
       "      <th>.fConverters[4].speed.actualMillimeterPerSecond</th>\n",
       "      <th>...</th>\n",
       "      <th>safetyIOs.image.iq2</th>\n",
       "      <th>safetyIOs.image.iq3</th>\n",
       "      <th>fConverters[2].rpdo.setpointPercentOfRpm</th>\n",
       "      <th>fConverters[2].speed.actualMillimeterPerSecond</th>\n",
       "      <th>fConverters[2].currentCurrent</th>\n",
       "      <th>fConverters[2].nodeStatus</th>\n",
       "      <th>fConverters[3].currentCurrent</th>\n",
       "      <th>fConverters[3].nodeStatus</th>\n",
       "      <th>fConverters[4].currentCurrent</th>\n",
       "      <th>fConverters[4].nodeStatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:42:13+00:00</th>\n",
       "      <td>101.585693</td>\n",
       "      <td>195.351257</td>\n",
       "      <td>33.785400</td>\n",
       "      <td>190.0</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>819.000000</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>819.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>22912.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>101.333333</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:42:14+00:00</th>\n",
       "      <td>102.183838</td>\n",
       "      <td>194.208374</td>\n",
       "      <td>35.694444</td>\n",
       "      <td>190.0</td>\n",
       "      <td>249.951923</td>\n",
       "      <td>180.0</td>\n",
       "      <td>842.314286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>825.5</td>\n",
       "      <td>9.833333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6513.777778</td>\n",
       "      <td>47.203704</td>\n",
       "      <td>22848.0</td>\n",
       "      <td>4.998883</td>\n",
       "      <td>97.916667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>125.666667</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:42:15+00:00</th>\n",
       "      <td>110.429688</td>\n",
       "      <td>184.488525</td>\n",
       "      <td>37.603488</td>\n",
       "      <td>190.0</td>\n",
       "      <td>249.903846</td>\n",
       "      <td>180.0</td>\n",
       "      <td>865.628571</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>832.0</td>\n",
       "      <td>9.599206</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6133.555556</td>\n",
       "      <td>44.407408</td>\n",
       "      <td>22784.0</td>\n",
       "      <td>4.997765</td>\n",
       "      <td>99.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:42:16+00:00</th>\n",
       "      <td>105.494995</td>\n",
       "      <td>184.232178</td>\n",
       "      <td>39.512533</td>\n",
       "      <td>190.0</td>\n",
       "      <td>249.855769</td>\n",
       "      <td>180.0</td>\n",
       "      <td>888.942857</td>\n",
       "      <td>8.253969</td>\n",
       "      <td>825.5</td>\n",
       "      <td>9.365079</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5753.333333</td>\n",
       "      <td>41.611111</td>\n",
       "      <td>22720.0</td>\n",
       "      <td>4.996648</td>\n",
       "      <td>100.750000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:42:17+00:00</th>\n",
       "      <td>109.062500</td>\n",
       "      <td>193.097534</td>\n",
       "      <td>41.421577</td>\n",
       "      <td>190.0</td>\n",
       "      <td>249.807692</td>\n",
       "      <td>180.0</td>\n",
       "      <td>912.257143</td>\n",
       "      <td>7.841270</td>\n",
       "      <td>819.0</td>\n",
       "      <td>9.130952</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5373.111111</td>\n",
       "      <td>38.814815</td>\n",
       "      <td>22656.0</td>\n",
       "      <td>4.995531</td>\n",
       "      <td>102.166667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:58:42+00:00</th>\n",
       "      <td>113.847656</td>\n",
       "      <td>193.588867</td>\n",
       "      <td>33.785400</td>\n",
       "      <td>190.0</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:58:43+00:00</th>\n",
       "      <td>113.206787</td>\n",
       "      <td>199.719849</td>\n",
       "      <td>33.785400</td>\n",
       "      <td>190.0</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>151.666667</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:58:44+00:00</th>\n",
       "      <td>112.181396</td>\n",
       "      <td>196.066895</td>\n",
       "      <td>33.785400</td>\n",
       "      <td>190.0</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>151.333333</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:58:45+00:00</th>\n",
       "      <td>112.459106</td>\n",
       "      <td>200.275269</td>\n",
       "      <td>33.785400</td>\n",
       "      <td>190.0</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25 07:58:46+00:00</th>\n",
       "      <td>112.800903</td>\n",
       "      <td>197.775879</td>\n",
       "      <td>33.785400</td>\n",
       "      <td>190.0</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>10.166667</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>994 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           distanceSensors[0].calcValueInMm  \\\n",
       "2022-05-25 07:42:13+00:00                        101.585693   \n",
       "2022-05-25 07:42:14+00:00                        102.183838   \n",
       "2022-05-25 07:42:15+00:00                        110.429688   \n",
       "2022-05-25 07:42:16+00:00                        105.494995   \n",
       "2022-05-25 07:42:17+00:00                        109.062500   \n",
       "...                                                     ...   \n",
       "2022-05-25 07:58:42+00:00                        113.847656   \n",
       "2022-05-25 07:58:43+00:00                        113.206787   \n",
       "2022-05-25 07:58:44+00:00                        112.181396   \n",
       "2022-05-25 07:58:45+00:00                        112.459106   \n",
       "2022-05-25 07:58:46+00:00                        112.800903   \n",
       "\n",
       "                           distanceSensors[1].calcValueInMm  \\\n",
       "2022-05-25 07:42:13+00:00                        195.351257   \n",
       "2022-05-25 07:42:14+00:00                        194.208374   \n",
       "2022-05-25 07:42:15+00:00                        184.488525   \n",
       "2022-05-25 07:42:16+00:00                        184.232178   \n",
       "2022-05-25 07:42:17+00:00                        193.097534   \n",
       "...                                                     ...   \n",
       "2022-05-25 07:58:42+00:00                        193.588867   \n",
       "2022-05-25 07:58:43+00:00                        199.719849   \n",
       "2022-05-25 07:58:44+00:00                        196.066895   \n",
       "2022-05-25 07:58:45+00:00                        200.275269   \n",
       "2022-05-25 07:58:46+00:00                        197.775879   \n",
       "\n",
       "                           distanceSensors[2].calcValueInMm  \\\n",
       "2022-05-25 07:42:13+00:00                         33.785400   \n",
       "2022-05-25 07:42:14+00:00                         35.694444   \n",
       "2022-05-25 07:42:15+00:00                         37.603488   \n",
       "2022-05-25 07:42:16+00:00                         39.512533   \n",
       "2022-05-25 07:42:17+00:00                         41.421577   \n",
       "...                                                     ...   \n",
       "2022-05-25 07:58:42+00:00                         33.785400   \n",
       "2022-05-25 07:58:43+00:00                         33.785400   \n",
       "2022-05-25 07:58:44+00:00                         33.785400   \n",
       "2022-05-25 07:58:45+00:00                         33.785400   \n",
       "2022-05-25 07:58:46+00:00                         33.785400   \n",
       "\n",
       "                           quadroSensorsAutoCommands[0].setpointInMm  \\\n",
       "2022-05-25 07:42:13+00:00                                      190.0   \n",
       "2022-05-25 07:42:14+00:00                                      190.0   \n",
       "2022-05-25 07:42:15+00:00                                      190.0   \n",
       "2022-05-25 07:42:16+00:00                                      190.0   \n",
       "2022-05-25 07:42:17+00:00                                      190.0   \n",
       "...                                                              ...   \n",
       "2022-05-25 07:58:42+00:00                                      190.0   \n",
       "2022-05-25 07:58:43+00:00                                      190.0   \n",
       "2022-05-25 07:58:44+00:00                                      190.0   \n",
       "2022-05-25 07:58:45+00:00                                      190.0   \n",
       "2022-05-25 07:58:46+00:00                                      190.0   \n",
       "\n",
       "                           quadroSensorsAutoCommands[1].setpointInMm  \\\n",
       "2022-05-25 07:42:13+00:00                                 250.000000   \n",
       "2022-05-25 07:42:14+00:00                                 249.951923   \n",
       "2022-05-25 07:42:15+00:00                                 249.903846   \n",
       "2022-05-25 07:42:16+00:00                                 249.855769   \n",
       "2022-05-25 07:42:17+00:00                                 249.807692   \n",
       "...                                                              ...   \n",
       "2022-05-25 07:58:42+00:00                                 230.000000   \n",
       "2022-05-25 07:58:43+00:00                                 230.000000   \n",
       "2022-05-25 07:58:44+00:00                                 230.000000   \n",
       "2022-05-25 07:58:45+00:00                                 230.000000   \n",
       "2022-05-25 07:58:46+00:00                                 230.000000   \n",
       "\n",
       "                           quadroSensorsAutoCommands[2].setpointInMm  \\\n",
       "2022-05-25 07:42:13+00:00                                      180.0   \n",
       "2022-05-25 07:42:14+00:00                                      180.0   \n",
       "2022-05-25 07:42:15+00:00                                      180.0   \n",
       "2022-05-25 07:42:16+00:00                                      180.0   \n",
       "2022-05-25 07:42:17+00:00                                      180.0   \n",
       "...                                                              ...   \n",
       "2022-05-25 07:58:42+00:00                                      180.0   \n",
       "2022-05-25 07:58:43+00:00                                      180.0   \n",
       "2022-05-25 07:58:44+00:00                                      180.0   \n",
       "2022-05-25 07:58:45+00:00                                      180.0   \n",
       "2022-05-25 07:58:46+00:00                                      180.0   \n",
       "\n",
       "                           fConverters[3].rpdo.setpointPercentOfRpm  \\\n",
       "2022-05-25 07:42:13+00:00                                819.000000   \n",
       "2022-05-25 07:42:14+00:00                                842.314286   \n",
       "2022-05-25 07:42:15+00:00                                865.628571   \n",
       "2022-05-25 07:42:16+00:00                                888.942857   \n",
       "2022-05-25 07:42:17+00:00                                912.257143   \n",
       "...                                                             ...   \n",
       "2022-05-25 07:58:42+00:00                                944.000000   \n",
       "2022-05-25 07:58:43+00:00                                944.000000   \n",
       "2022-05-25 07:58:44+00:00                                944.000000   \n",
       "2022-05-25 07:58:45+00:00                                944.000000   \n",
       "2022-05-25 07:58:46+00:00                                944.000000   \n",
       "\n",
       "                           fConverters[3].speed.actualMillimeterPerSecond  \\\n",
       "2022-05-25 07:42:13+00:00                                        8.666667   \n",
       "2022-05-25 07:42:14+00:00                                        0.000000   \n",
       "2022-05-25 07:42:15+00:00                                        8.666667   \n",
       "2022-05-25 07:42:16+00:00                                        8.253969   \n",
       "2022-05-25 07:42:17+00:00                                        7.841270   \n",
       "...                                                                   ...   \n",
       "2022-05-25 07:58:42+00:00                                       10.166667   \n",
       "2022-05-25 07:58:43+00:00                                       10.166667   \n",
       "2022-05-25 07:58:44+00:00                                       10.166667   \n",
       "2022-05-25 07:58:45+00:00                                       10.166667   \n",
       "2022-05-25 07:58:46+00:00                                       10.166667   \n",
       "\n",
       "                           fConverters[4].rpdo.setpointPercentOfRpm  \\\n",
       "2022-05-25 07:42:13+00:00                                     819.0   \n",
       "2022-05-25 07:42:14+00:00                                     825.5   \n",
       "2022-05-25 07:42:15+00:00                                     832.0   \n",
       "2022-05-25 07:42:16+00:00                                     825.5   \n",
       "2022-05-25 07:42:17+00:00                                     819.0   \n",
       "...                                                             ...   \n",
       "2022-05-25 07:58:42+00:00                                    1163.0   \n",
       "2022-05-25 07:58:43+00:00                                    1163.0   \n",
       "2022-05-25 07:58:44+00:00                                    1163.0   \n",
       "2022-05-25 07:58:45+00:00                                    1163.0   \n",
       "2022-05-25 07:58:46+00:00                                    1163.0   \n",
       "\n",
       "                           .fConverters[4].speed.actualMillimeterPerSecond  \\\n",
       "2022-05-25 07:42:13+00:00                                         0.000000   \n",
       "2022-05-25 07:42:14+00:00                                         9.833333   \n",
       "2022-05-25 07:42:15+00:00                                         9.599206   \n",
       "2022-05-25 07:42:16+00:00                                         9.365079   \n",
       "2022-05-25 07:42:17+00:00                                         9.130952   \n",
       "...                                                                    ...   \n",
       "2022-05-25 07:58:42+00:00                                        14.166667   \n",
       "2022-05-25 07:58:43+00:00                                        14.166667   \n",
       "2022-05-25 07:58:44+00:00                                        14.166667   \n",
       "2022-05-25 07:58:45+00:00                                        14.166667   \n",
       "2022-05-25 07:58:46+00:00                                        14.166667   \n",
       "\n",
       "                           ...  safetyIOs.image.iq2  safetyIOs.image.iq3  \\\n",
       "2022-05-25 07:42:13+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:42:14+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:42:15+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:42:16+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:42:17+00:00  ...                  1.0                  1.0   \n",
       "...                        ...                  ...                  ...   \n",
       "2022-05-25 07:58:42+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:58:43+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:58:44+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:58:45+00:00  ...                  1.0                  1.0   \n",
       "2022-05-25 07:58:46+00:00  ...                  1.0                  1.0   \n",
       "\n",
       "                           fConverters[2].rpdo.setpointPercentOfRpm  \\\n",
       "2022-05-25 07:42:13+00:00                               6894.000000   \n",
       "2022-05-25 07:42:14+00:00                               6513.777778   \n",
       "2022-05-25 07:42:15+00:00                               6133.555556   \n",
       "2022-05-25 07:42:16+00:00                               5753.333333   \n",
       "2022-05-25 07:42:17+00:00                               5373.111111   \n",
       "...                                                             ...   \n",
       "2022-05-25 07:58:42+00:00                                  0.000000   \n",
       "2022-05-25 07:58:43+00:00                                  0.000000   \n",
       "2022-05-25 07:58:44+00:00                                  0.000000   \n",
       "2022-05-25 07:58:45+00:00                                  0.000000   \n",
       "2022-05-25 07:58:46+00:00                                  0.000000   \n",
       "\n",
       "                           fConverters[2].speed.actualMillimeterPerSecond  \\\n",
       "2022-05-25 07:42:13+00:00                                       50.000000   \n",
       "2022-05-25 07:42:14+00:00                                       47.203704   \n",
       "2022-05-25 07:42:15+00:00                                       44.407408   \n",
       "2022-05-25 07:42:16+00:00                                       41.611111   \n",
       "2022-05-25 07:42:17+00:00                                       38.814815   \n",
       "...                                                                   ...   \n",
       "2022-05-25 07:58:42+00:00                                        0.000000   \n",
       "2022-05-25 07:58:43+00:00                                        0.000000   \n",
       "2022-05-25 07:58:44+00:00                                        0.000000   \n",
       "2022-05-25 07:58:45+00:00                                        0.000000   \n",
       "2022-05-25 07:58:46+00:00                                        0.000000   \n",
       "\n",
       "                           fConverters[2].currentCurrent  \\\n",
       "2022-05-25 07:42:13+00:00                        22912.0   \n",
       "2022-05-25 07:42:14+00:00                        22848.0   \n",
       "2022-05-25 07:42:15+00:00                        22784.0   \n",
       "2022-05-25 07:42:16+00:00                        22720.0   \n",
       "2022-05-25 07:42:17+00:00                        22656.0   \n",
       "...                                                  ...   \n",
       "2022-05-25 07:58:42+00:00                            0.0   \n",
       "2022-05-25 07:58:43+00:00                            0.0   \n",
       "2022-05-25 07:58:44+00:00                            0.0   \n",
       "2022-05-25 07:58:45+00:00                            0.0   \n",
       "2022-05-25 07:58:46+00:00                            0.0   \n",
       "\n",
       "                           fConverters[2].nodeStatus  \\\n",
       "2022-05-25 07:42:13+00:00                   5.000000   \n",
       "2022-05-25 07:42:14+00:00                   4.998883   \n",
       "2022-05-25 07:42:15+00:00                   4.997765   \n",
       "2022-05-25 07:42:16+00:00                   4.996648   \n",
       "2022-05-25 07:42:17+00:00                   4.995531   \n",
       "...                                              ...   \n",
       "2022-05-25 07:58:42+00:00                   4.000000   \n",
       "2022-05-25 07:58:43+00:00                   4.000000   \n",
       "2022-05-25 07:58:44+00:00                   4.000000   \n",
       "2022-05-25 07:58:45+00:00                   4.000000   \n",
       "2022-05-25 07:58:46+00:00                   4.000000   \n",
       "\n",
       "                           fConverters[3].currentCurrent  \\\n",
       "2022-05-25 07:42:13+00:00                      96.500000   \n",
       "2022-05-25 07:42:14+00:00                      97.916667   \n",
       "2022-05-25 07:42:15+00:00                      99.333333   \n",
       "2022-05-25 07:42:16+00:00                     100.750000   \n",
       "2022-05-25 07:42:17+00:00                     102.166667   \n",
       "...                                                  ...   \n",
       "2022-05-25 07:58:42+00:00                      98.000000   \n",
       "2022-05-25 07:58:43+00:00                      98.000000   \n",
       "2022-05-25 07:58:44+00:00                      98.000000   \n",
       "2022-05-25 07:58:45+00:00                      98.000000   \n",
       "2022-05-25 07:58:46+00:00                      98.000000   \n",
       "\n",
       "                           fConverters[3].nodeStatus  \\\n",
       "2022-05-25 07:42:13+00:00                        5.0   \n",
       "2022-05-25 07:42:14+00:00                        5.0   \n",
       "2022-05-25 07:42:15+00:00                        5.0   \n",
       "2022-05-25 07:42:16+00:00                        5.0   \n",
       "2022-05-25 07:42:17+00:00                        5.0   \n",
       "...                                              ...   \n",
       "2022-05-25 07:58:42+00:00                        5.0   \n",
       "2022-05-25 07:58:43+00:00                        5.0   \n",
       "2022-05-25 07:58:44+00:00                        5.0   \n",
       "2022-05-25 07:58:45+00:00                        5.0   \n",
       "2022-05-25 07:58:46+00:00                        5.0   \n",
       "\n",
       "                           fConverters[4].currentCurrent  \\\n",
       "2022-05-25 07:42:13+00:00                     101.333333   \n",
       "2022-05-25 07:42:14+00:00                     125.666667   \n",
       "2022-05-25 07:42:15+00:00                     150.000000   \n",
       "2022-05-25 07:42:16+00:00                     151.000000   \n",
       "2022-05-25 07:42:17+00:00                     152.000000   \n",
       "...                                                  ...   \n",
       "2022-05-25 07:58:42+00:00                     152.000000   \n",
       "2022-05-25 07:58:43+00:00                     151.666667   \n",
       "2022-05-25 07:58:44+00:00                     151.333333   \n",
       "2022-05-25 07:58:45+00:00                     151.000000   \n",
       "2022-05-25 07:58:46+00:00                     152.000000   \n",
       "\n",
       "                           fConverters[4].nodeStatus  \n",
       "2022-05-25 07:42:13+00:00                        4.5  \n",
       "2022-05-25 07:42:14+00:00                        4.5  \n",
       "2022-05-25 07:42:15+00:00                        4.5  \n",
       "2022-05-25 07:42:16+00:00                        4.5  \n",
       "2022-05-25 07:42:17+00:00                        4.5  \n",
       "...                                              ...  \n",
       "2022-05-25 07:58:42+00:00                        4.5  \n",
       "2022-05-25 07:58:43+00:00                        4.5  \n",
       "2022-05-25 07:58:44+00:00                        4.5  \n",
       "2022-05-25 07:58:45+00:00                        4.5  \n",
       "2022-05-25 07:58:46+00:00                        4.5  \n",
       "\n",
       "[994 rows x 23 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(data_path+\"/csv/OpcUA_PANE_BZ___2022_05_25__09_44_06.csv\", index_col=0)\n",
    "df.index = pd.to_datetime(df.index, infer_datetime_format=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63cbdbc",
   "metadata": {},
   "source": [
    "Durchlauf SOLL and IST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c542b275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"quadroSensorsAutoCommands[1].setpointInMm\"].plot()\n",
    "df[\"distanceSensors[1].calcValueInMm\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39d4dac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"quadroSensorsAutoCommands[1].setpointInMm\"].plot()\n",
    "df[\"distanceSensors[1].calcValueInMm\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6757b86e",
   "metadata": {},
   "source": [
    "Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "138b6e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"quadroSensorsAutoCommands[0].setpointInMm\"].plot()\n",
    "df[\"distanceSensors[0].calcValueInMm\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee8f7f3",
   "metadata": {},
   "source": [
    "Out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7972347b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"quadroSensorsAutoCommands[2].setpointInMm\"].plot()\n",
    "df[\"distanceSensors[2].calcValueInMm\"].plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}