notebooks/NXCals-example_bis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\"><h1>Integration of SWAN with NXCals</h1></div>\n",
    "<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates the use of __Spark in SWAN to access CERN Accelerator logging service data__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the cluster (NXCals)\n",
    "In the SWAN configuration menu:\n",
    "- Choose the NXCals project software stack\n",
    "- Choose the NXCals Hadoop cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To connect to a cluster, click on the star button on the top and follow the instructions\n",
    "* The star button only appears if you have selected a SPARK cluster in the configuration\n",
    "* The star button is active after the notebook kernel is ready\n",
    "* SELECT NXCALS configuration bundle\n",
    "* Access to the cluster and NXCALS data is controlled by acc-logging-team, please contact acc-logging-team@cern.ch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NXCals DataExtraction API - Examples\n",
    "See NXCals API documentation at: http://nxcals-docs.web.cern.ch/current/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nxcals.api.extraction.data.builders import DataQuery\n",
    "from pyspark.sql.functions import col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract scalar values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/12/13 19:42:34 WARN URLConfigurationSource: No URLs will be polled as dynamic configuration sources.\n"
     ]
    }
   ],
   "source": [
    "# This example shows how to extract scalar values\n",
    "\n",
    "df1 = ( DataQuery.builder(spark).byEntities()\n",
    "          .system('CMW') \n",
    "          .startTime('2023-06-29 00:00:00.000')\n",
    "          .endTime('2023-06-30 00:00:00.000')\n",
    "          .entity()\n",
    "          .keyValues({'device': 'LHC.LUMISERVER', 'property': 'CrossingAngleIP1'})\n",
    "          .build()\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- DeltaCrossingAngle: double (nullable = true)\n",
      " |-- Moving: boolean (nullable = true)\n",
      " |-- __record_timestamp__: long (nullable = true)\n",
      " |-- __record_version__: long (nullable = true)\n",
      " |-- acqStamp: long (nullable = true)\n",
      " |-- class: string (nullable = true)\n",
      " |-- cyclestamp: long (nullable = true)\n",
      " |-- device: string (nullable = true)\n",
      " |-- property: string (nullable = true)\n",
      " |-- selector: string (nullable = true)\n",
      " |-- nxcals_entity_id: long (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/12/13 19:42:42 WARN CheckAllocator: More than one DefaultAllocationManager on classpath. Choosing first found\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DeltaCrossingAngle</th>\n",
       "      <th>Moving</th>\n",
       "      <th>__record_timestamp__</th>\n",
       "      <th>__record_version__</th>\n",
       "      <th>acqStamp</th>\n",
       "      <th>class</th>\n",
       "      <th>cyclestamp</th>\n",
       "      <th>device</th>\n",
       "      <th>property</th>\n",
       "      <th>selector</th>\n",
       "      <th>nxcals_entity_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-75.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1688037433065102000</td>\n",
       "      <td>0</td>\n",
       "      <td>1688037433065102000</td>\n",
       "      <td>LhcLumiscan</td>\n",
       "      <td>0</td>\n",
       "      <td>LHC.LUMISERVER</td>\n",
       "      <td>CrossingAngleIP1</td>\n",
       "      <td>None</td>\n",
       "      <td>57336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-75.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1688037433066202000</td>\n",
       "      <td>0</td>\n",
       "      <td>1688037433066202000</td>\n",
       "      <td>LhcLumiscan</td>\n",
       "      <td>0</td>\n",
       "      <td>LHC.LUMISERVER</td>\n",
       "      <td>CrossingAngleIP1</td>\n",
       "      <td>None</td>\n",
       "      <td>57336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-75.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1688037433067349000</td>\n",
       "      <td>0</td>\n",
       "      <td>1688037433067349000</td>\n",
       "      <td>LhcLumiscan</td>\n",
       "      <td>0</td>\n",
       "      <td>LHC.LUMISERVER</td>\n",
       "      <td>CrossingAngleIP1</td>\n",
       "      <td>None</td>\n",
       "      <td>57336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DeltaCrossingAngle  Moving  __record_timestamp__  __record_version__  \\\n",
       "0               -75.0    True   1688037433065102000                   0   \n",
       "1               -75.0    True   1688037433066202000                   0   \n",
       "2               -75.0    True   1688037433067349000                   0   \n",
       "\n",
       "              acqStamp        class  cyclestamp          device  \\\n",
       "0  1688037433065102000  LhcLumiscan           0  LHC.LUMISERVER   \n",
       "1  1688037433066202000  LhcLumiscan           0  LHC.LUMISERVER   \n",
       "2  1688037433067349000  LhcLumiscan           0  LHC.LUMISERVER   \n",
       "\n",
       "           property selector  nxcals_entity_id  \n",
       "0  CrossingAngleIP1     None             57336  \n",
       "1  CrossingAngleIP1     None             57336  \n",
       "2  CrossingAngleIP1     None             57336  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.limit(3).toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract vector values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example shows how to extract vector values\n",
    "\n",
    "df2 = ( DataQuery.builder(spark).byVariables()\n",
    "         .system('CMW') \n",
    "         .startTime('2018-05-21 00:00:00.000')\n",
    "         .endTime('2018-05-21 00:05:00.000')\n",
    "         .variable('SPS.BCTDC.51895:TOTAL_INTENSITY')\n",
    "         .build()\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- nxcals_value: struct (nullable = true)\n",
      " |    |-- elements: array (nullable = true)\n",
      " |    |    |-- element: double (containsNull = true)\n",
      " |    |-- dimensions: array (nullable = true)\n",
      " |    |    |-- element: integer (containsNull = true)\n",
      " |-- nxcals_entity_id: long (nullable = true)\n",
      " |-- nxcals_timestamp: long (nullable = true)\n",
      " |-- nxcals_variable_name: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StructField('nxcals_value', StructType([StructField('elements', ArrayType(DoubleType(), True), True), StructField('dimensions', ArrayType(IntegerType(), True), True)]), True),\n",
       " StructField('nxcals_entity_id', LongType(), True),\n",
       " StructField('nxcals_timestamp', LongType(), True),\n",
       " StructField('nxcals_variable_name', StringType(), True)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.schema.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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345.18246, 343.33658, 341.49066, 337.1836, 337.1836, 333.4918, 332.2612, 329.80002, 326.7235, 324.26233, 322.4164, 319.95523, 315.64813, 313.18695, 310.11044, 308.87985, 308.26453, 304.57275, 303.34216, 300.88098, 297.80447, 295.3433, 292.26678, 289.8056, 286.11383, 283.6526, 278.73022, 276.88434, 275.03845, 273.19254, 270.73135, 270.11603, 267.03955, 264.57834, 261.5019, 258.42538, 256.57947, 254.73358, 251.65709, 249.19589, 244.27351, 242.42763, 239.96642, 236.88994, 234.42873, 233.19814, 230.73695, 227.04515, 225.81456, 222.73807, 219.66158, 216.58508, 213.50858, 210.43208, 210.43208, 206.7403, 203.6638, 201.2026, 199.97202, 196.28023, 196.28023, 192.58842, 189.51195, 187.66605, 183.97426, 184.58955, 180.89777, 178.43657, 175.36008, 172.89888, 171.05298, 171.05298, 167.97649, 164.2847, 161.82352, 159.36232, 155.67052, 153.82463, 151.97873, 148.90224, 148.28694, 145.21045, 143.36455, 139.67276, 139.05745, 135.36568, 132.28917, 130.44328, 127.36679, 124.9056, 123.675, 120.59851, 119.36791, 116.90672, 113.83022, 111.36903, 110.138435, 108.29254, 105.83134, 103.98544, 100.90896, 99.67835, 97.83246, 96.60187, 95.37127, 94.755974, 92.91007, 92.91007, 91.06418, 89.218285, 90.44888, 89.218285, 87.987686, 87.987686, 85.52649, 84.911194, 83.680595, 83.0653, 82.450005, 81.2194, 83.0653, 80.6041, 80.6041, 80.6041, 80.6041, 78.75821, 79.373505, 77.52761, 77.52761, 76.912315, 76.29702, 75.066414, 74.45112, 74.45112, 72.605225, 71.98993, 57.838062, 11.690671, 0.6152985, 0.0, 0.6152985, 0.6152985, 0.0, 0.0, 1.230597, 1.230597, -0.6152985, -0.6152985, 1.8458955, 0.6152985, 1.8458955, 0.0, -0.6152985, 1.230597, 0.0, 1.230597, 0.6152985, 0.6152985, 0.6152985, 0.0, 1.230597, -0.6152985, 1.8458955, 2.461194])]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elements = df2.withColumn(\"nx_elements\", col(\"nxcals_value.elements\")).withColumn(\"nx_dimensions\", col(\"nxcals_value.dimensions\")).select(\"nx_elements\")\n",
    "elements.take(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract matrix values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This example shows how to extract matrix values\n",
    "\n",
    "df3 = ( DataQuery.builder(spark).byVariables()\n",
    "          .system('CMW')\n",
    "          .startTime('2018-08-15 00:00:00.000')\n",
    "          .endTime('2018-08-30 00:00:00.000')\n",
    "          .variable('HIE-BCAM-T2M03:RAWMEAS#NPIXELS')\n",
    "          .build()\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- nxcals_value: struct (nullable = true)\n",
      " |    |-- elements: array (nullable = true)\n",
      " |    |    |-- element: long (containsNull = true)\n",
      " |    |-- dimensions: array (nullable = true)\n",
      " |    |    |-- element: integer (containsNull = true)\n",
      " |-- nxcals_entity_id: long (nullable = true)\n",
      " |-- nxcals_timestamp: long (nullable = true)\n",
      " |-- nxcals_variable_name: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df3.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StructField('nxcals_value', StructType([StructField('elements', ArrayType(LongType(), True), True), StructField('dimensions', ArrayType(IntegerType(), True), True)]), True),\n",
       " StructField('nxcals_entity_id', LongType(), True),\n",
       " StructField('nxcals_timestamp', LongType(), True),\n",
       " StructField('nxcals_variable_name', StringType(), True)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.schema.fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(matrix=[14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 10, 17, 15, 0, 0, 0, 0, 0, 0, 13, 13, 19, 17, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 13, 12, 0, 0, 0, 0, 0, 0, 0, 0, 11, 11, 8, 9, 8, 9, 0, 0, 0, 0, 12, 13, 10, 10, 10, 9, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 12, 10, 0, 0, 0, 0, 0, 0, 0, 0, 12, 12, 0, 0, 0, 0, 0, 0, 0, 0, 13, 11, 0, 0, 0, 0, 0, 0, 0, 0, 12, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dim1=100, dim2=10),\n",
       " Row(matrix=[14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 10, 16, 13, 0, 0, 0, 0, 0, 0, 13, 13, 18, 17, 0, 0, 0, 0, 0, 0, 13, 12, 0, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 11, 11, 8, 9, 8, 8, 0, 0, 0, 0, 12, 12, 10, 9, 8, 9, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 0, 11, 10, 0, 0, 0, 0, 0, 0, 0, 0, 12, 13, 0, 0, 0, 0, 0, 0, 0, 0, 12, 13, 0, 0, 0, 0, 0, 0, 0, 0, 12, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dim1=100, dim2=10)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrices = (df3\n",
    "              .withColumn(\"matrix\", col(\"nxcals_value.elements\"))\n",
    "              .withColumn(\"dim1\", col(\"nxcals_value.dimensions\")[0])\n",
    "              .withColumn(\"dim2\", col(\"nxcals_value.dimensions\")[1])\n",
    "              .select(\"matrix\", \"dim1\", \"dim2\")\n",
    "           )\n",
    "\n",
    "matrices.take(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with timestamps\n",
    "See NXCals API documentation at: http://nxcals-docs.web.cern.ch/current/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nxcals.api.extraction.data.builders import DataQuery\n",
    "\n",
    "# Getting some sample data to use for demonstrating how to handle timestamps\n",
    "df = ( DataQuery.builder(spark).byEntities()\n",
    "         .system('CMW') \n",
    "         .startTime('2023-06-29 00:00:00.000')\n",
    "         .endTime('2023-06-30 01:00:00.000') \n",
    "         .entity()\n",
    "         .keyValue('device', 'LHC.LUMISERVER')\n",
    "         .keyValue('property', 'CrossingAngleIP1')\n",
    "         .build()\n",
    "     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stamp</th>\n",
       "      <th>acqStamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-06-29 13:17:13.065102</td>\n",
       "      <td>1688037433065102000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-06-29 13:17:13.066202</td>\n",
       "      <td>1688037433066202000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-06-29 13:17:13.067349</td>\n",
       "      <td>1688037433067349000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-06-29 13:20:09.764569</td>\n",
       "      <td>1688037609764569000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-06-29 13:20:28.496843</td>\n",
       "      <td>1688037628496843000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       stamp             acqStamp\n",
       "0 2023-06-29 13:17:13.065102  1688037433065102000\n",
       "1 2023-06-29 13:17:13.066202  1688037433066202000\n",
       "2 2023-06-29 13:17:13.067349  1688037433067349000\n",
       "3 2023-06-29 13:20:09.764569  1688037609764569000\n",
       "4 2023-06-29 13:20:28.496843  1688037628496843000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format the timestamp column and output result\n",
    "# timestamps are stored with nanosecond precision in the example data\n",
    "df.selectExpr(\"timestamp_micros(cast(acqStamp/1000 as long)) as stamp\", 'acqStamp').limit(5).toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:SparkMonitorKernel:Scala socket closed - empty data\n",
      "INFO:SparkMonitorKernel:Socket Exiting Client Loop\n",
      "INFO:SparkMonitorKernel:Starting socket thread, going to accept\n"
     ]
    }
   ],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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