result_AN_RB_FPA_SNAP.ipynb 241 KB
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{
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     "status": "completed"
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   "source": [
    "\"\"\"Manual spark configuration based on the default Spark configuration from the NXCALS bundle\n",
    "and https://gitlab.cern.ch/msobiesz/spark-pipelines/-/blob/master/NXCALS-example-for-DAaaS.ipynb.\n",
    "Used unless the spark context is already created. (outside of SWAN service or pyspark)\n",
    "\"\"\"\n",
    "if 'spark' not in locals() and 'spark' not in globals():\n",
    "    import os\n",
    "    from pyspark import SparkContext, SparkConf\n",
    "    from pyspark.sql import SparkSession\n",
    "\n",
    "    nxcals_jars = os.getenv('NXCALS_JARS')\n",
    "    conf = SparkConf()\n",
    "    conf.set('spark.master', 'yarn')\n",
    "    conf.set(\"spark.driver.host\", \"spark-runner.cern.ch\")\n",
    "    conf.set(\"spark.driver.port\", '5001')\n",
    "    conf.set(\"spark.blockManager.port\", '5101')\n",
    "    conf.set(\"spark.ui.port\", '5201')\n",
    "    conf.set('spark.executorEnv.PYTHONPATH', os.getenv('PYTHONPATH'))\n",
    "    conf.set('spark.executorEnv.LD_LIBRARY_PATH', os.getenv('LD_LIBRARY_PATH'))\n",
    "    conf.set('spark.executorEnv.JAVA_HOME', os.getenv('JAVA_HOME'))\n",
    "    conf.set('spark.executorEnv.SPARK_HOME', os.getenv('SPARK_HOME'))\n",
    "    conf.set('spark.executorEnv.SPARK_EXTRA_CLASSPATH', os.getenv('SPARK_DIST_CLASSPATH'))\n",
    "    conf.set('spark.driver.extraClassPath', nxcals_jars)\n",
    "    conf.set('spark.executor.extraClassPath', nxcals_jars)\n",
    "    conf.set('spark.driver.extraJavaOptions',\n",
    "             \"-Dservice.url=https://cs-ccr-nxcals5.cern.ch:19093,https://cs-ccr-nxcals5.cern.ch:19094,\"\n",
    "             \"https://cs-ccr-nxcals6.cern.ch:19093,https://cs-ccr-nxcals6.cern.ch:19094,\"\n",
    "             \"https://cs-ccr-nxcals7.cern.ch:19093,https://cs-ccr-nxcals7.cern.ch:19094,\"\n",
    "             \"https://cs-ccr-nxcals8.cern.ch:19093,https://cs-ccr-nxcals8.cern.ch:19094,\"\n",
    "             \"https://cs-ccr-nxcalsstr4.cern.ch:19093,https://cs-ccr-nxcalsstr5.cern.ch:19093\")\n",
    "\n",
    "    sc = SparkContext(conf=conf)\n",
    "    spark = SparkSession(sc)\n"
   ]
  },
  {
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   "cell_type": "code",
   "execution_count": 2,
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   "id": "761f88d6",
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    "execution": {
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     "status": "completed"
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    "tags": [
     "injected-parameters"
    ]
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   },
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   "outputs": [],
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   "source": [
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    "# Parameters\n",
    "circuit_name = \"RB.A12\"\n",
    "timestamp_fgc = 1620970127420000000\n",
    "author = \"test\"\n",
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    "is_automatic = True\n",
    "parametrized_marker = None\n"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "166510fd",
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   "metadata": {
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     "duration": 0.063112,
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    "tags": []
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   "source": [
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    "<h1><center>Analysis of an FPA iQPS SNAP in an RB Circuit</center></h1>\n",
    "<img src=\"https://gitlab.cern.ch/LHCData/lhc-sm-hwc/raw/master/figures/rb/RB.png\" width=75%>\n",
    "source: Powering Procedure and Acceptance Criteria for the 13 kA Dipole Circuits, MP3 Procedure, <a href=\"https://edms.cern.ch/document/874713\">https://edms.cern.ch/document/874713</a>"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "def04de2",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.062815,
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     "exception": false,
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     "start_time": "2021-10-21T17:51:43.293131",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Analysis Assumptions\n",
    "- We consider standard analysis scenarios, i.e., all signals can be queried. If a signal is missing, an analysis can raise a warning and continue or an error and abort the analysis.\n",
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    "- In case a signal is not needed for the analysis, a particular analysis is skipped. In other words, all signals have to be available in order to perform an analysis.\n",
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    "- It is recommended to execute each cell one after another. However, since the signals are queried prior to analysis, any order of execution is allowed. In case an analysis cell is aborted, the following ones may not be executed (e.g. I\\_MEAS not present). \n",
    "\n",
    "# Plot Convention\n",
    "- Scales are labeled with signal name followed by a comma and a unit in square brackets, e.g., I_MEAS, [A].\n",
    "- If a reference signal is present, it is represented with a dashed line.\n",
    "- If the main current is present, its axis is on the left. Remaining signals are attached to the axis on the right. The legend of these signals is located on the lower left and upper right, respectively.\n",
    "- The grid comes from the left axis.\n",
    "- The title contains timestamp, circuit name, and signal name allowing to re-access the signal.\n",
    "- The plots assigned to the left scale have colors: blue (C0) and orange (C1). Plots presented on the right have colors red (C2) and green (C3).\n",
    "- Each plot has an individual time-synchronization mentioned explicitly in the description.\n",
    "- If an axis has a single signal, then the color of the label matches the signal's color. Otherwise, the label color is black."
   ]
  },
  {
   "cell_type": "markdown",
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   "id": "fffdd4f7",
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   "metadata": {
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     "duration": 0.063322,
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     "exception": false,
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     "start_time": "2021-10-21T17:51:43.418702",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
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    "# Initialise Working Environment"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "id": "9a23eddf",
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   "metadata": {
    "deletable": false,
    "execution": {
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     "shell.execute_reply": "2021-10-21T17:51:47.544808Z"
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    },
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     "duration": 4.000181,
     "end_time": "2021-10-21T17:51:47.545029",
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     "exception": false,
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     "start_time": "2021-10-21T17:51:43.544848",
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     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis executed with lhc-sm-api version: 1.5.17\n",
      "Analysis executed with lhc-sm-hwc notebooks version: 1.5.65\n"
     ]
    }
   ],
   "source": [
    "# External libraries\n",
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    "print('Loading (1/12)'); import sys\n",
    "print('Loading (2/12)'); from IPython.display import display, Javascript, HTML, clear_output\n",
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    "\n",
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    "print('Loading (3/12)'); import pandas as pd\n",
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    "\n",
    "# Internal libraries\n",
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    "print('Loading (4/12)'); import lhcsmapi\n",
    "print('Loading (5/12)'); from lhcsmapi.Time import Time\n",
    "print('Loading (6/12)'); from lhcsmapi.Timer import Timer\n",
    "print('Loading (7/12)'); from lhcsmapi.analysis.RbCircuitQuery import RbCircuitQuery\n",
    "print('Loading (8/12)'); from lhcsmapi.analysis.RbCircuitAnalysis import RbCircuitAnalysis\n",
    "print('Loading (9/12)'); from lhcsmapi.analysis.report_template import apply_report_template\n",
    "print('Loading (10/12)'); from lhcsmapi.gui.DateTimeBaseModule import DateTimeBaseModule\n",
    "print('Loading (11/12)'); from lhcsmapi.gui.pc.FgcPmSearchModuleMediator import FgcPmSearchModuleMediator\n",
    "print('Loading (12/12)'); from lhcsmnb.parameters import are_all_parameters_injected, NbType\n",
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    "\n",
    "clear_output()\n",
    "lhcsmapi.get_lhcsmapi_version()\n",
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    "lhcsmapi.get_lhcsmhwc_version('../__init__.py')"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "0c07dcaf",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.065824,
     "end_time": "2021-10-21T17:51:47.680743",
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     "exception": false,
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     "start_time": "2021-10-21T17:51:47.614919",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
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    "# Select FGC Post Mortem Entry"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "6dfe0b59",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.065124,
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     "exception": false,
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     "start_time": "2021-10-21T17:51:47.747704",
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     "status": "completed"
    },
    "tags": [
     "skip_cell"
    ]
   },
   "source": [
    "In order to perform the analysis of a FPA in an RB circuit please:\n",
    "1. Select circuit name (e.g., RB.A12)\n",
    "2. Choose start and end time\n",
    "3. Choose analysis mode (Automatic by default)\n",
    "\n",
    "Once these inputs are provided, click 'Find FGC PM entries' button. This will trigger a search of the PM database in order to provide a list of timestamps of FGC events associated with the selected circuit name for the provided period of time. Select one timestamp from the 'FGC PM Entries' list to be processed by the following cells.\n",
    "\n",
    "**Note that 24 hours is the maximum duration of a single PM query for an event. To avoid delays in querying events, please restrict your query duration as much as possible.**"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "id": "90cf3ca1",
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   "metadata": {
    "deletable": false,
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     "shell.execute_reply": "2021-10-21T17:51:48.195272Z"
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    },
    "papermill": {
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     "duration": 0.351318,
     "end_time": "2021-10-21T17:51:48.229989",
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     "exception": false,
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     "start_time": "2021-10-21T17:51:47.878671",
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     "status": "completed"
    },
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    "scrolled": true,
    "tags": []
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   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "model_id": "c4d2e37a5a5e4fb38f03db0ea7d96492",
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Dropdown(description='Circuit name:', options=('RB.A12', 'RB.A23', 'RB.A34', 'RB…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit_type = 'RB'\n",
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    "fgc_pm_search = FgcPmSearchModuleMediator(DateTimeBaseModule(start_date_time='2021-05-14 00:00:00+01:00',\n",
    "                                                             end_date_time='2021-05-15 00:00:00+01:00'), circuit_type=circuit_type)"
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   ]
  },
  {
   "cell_type": "markdown",
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   "id": "a4afb3ad",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.066309,
     "end_time": "2021-10-21T17:51:48.366335",
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     "exception": false,
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     "start_time": "2021-10-21T17:51:48.300026",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
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    "# Query All Signals Prior to Analysis"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "id": "29c53d47",
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   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-10-21T17:52:23.167849Z"
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    },
    "papermill": {
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     "duration": 34.734,
     "end_time": "2021-10-21T17:52:23.168879",
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     "exception": false,
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     "start_time": "2021-10-21T17:51:48.434879",
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     "status": "completed"
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    "scrolled": false,
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    "tags": []
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<text style=color:blue>Executing RB.A12 query function find_timestamp_pic: 1/4.</text>"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\tQuerying NXCALS signal(s) RB.A12.EVEN:ST_ABORT_PIC, RB.A12.ODD:ST_ABORT_PIC from 2021-05-14 07:28:46.420 to 2021-05-14 07:29:47.420\n"
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     ]
    },
    {
     "data": {
      "text/html": [
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       "<text style=color:blue>Executing RB.A12 query function query_pc_pm: 2/4.</text>"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\tQuerying PM event signal(s) IAB.I_A, IEARTH.I_EARTH, STATUS.I_EARTH_PCNT, STATUS.I_MEAS, STATUS.I_REF for system: RPTE.UA23.RB.A12, className: lhc_self_pmd, source: FGC at 2021-05-14 07:28:47.420\n"
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     ]
    },
    {
     "data": {
      "text/html": [
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       "<text style=color:blue>Executing RB.A12 query function get_timestamp_ref: 3/4.</text>"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<text style=color:blue>Executing RB.A12 query function query_pc_pm: 4/4.</text>"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\tQuerying PM event signal(s) IEARTH.IEARTH, STATUS.I_EARTH_PCNT, STATUS.I_MEAS for system: RPTE.UA23.RB.A12, className: 51_self_pmd, source: FGC at 2018-03-17 16:00:28.680\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\tQuerying PM event timestamps for system: QPS, className: DQAMCNMB_PMSTD, source: * from 2021-05-14 07:28:37.420 to 2021-05-14 07:28:57.420\n"
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     ]
    },
    {
     "data": {
      "text/html": [
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       "<text style=color:blue>Executing RB.A12 query function query_pm_iqps_board_type: 5/4.</text>"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
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      "application/vnd.jupyter.widget-view+json": {
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       "model_id": "dec838d7c469497895bd692fef56b11c",
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
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       "Querying PM:   0%|          | 0/2 [00:00<?, ?it/s]"
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      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
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       "<text style=color:blue>Executing RB.A12 query function query_voltage_logic_iqps: 6/4.</text>"
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      ],
      "text/plain": [
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       "model_id": "15fdfa4647304c478866c9143037c617",
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       "version_major": 2,
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      },
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      "text/plain": [
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       "Querying iQPS logic PM:   0%|          | 0/2 [00:00<?, ?it/s]"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Elapsed: 34.644 s.\n"
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     ]
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    }
   ],
   "source": [
    "with Timer():\n",
    "    if not are_all_parameters_injected(NbType.FGC, locals()):\n",
    "        timestamp_fgc = fgc_pm_search.get_fgc_timestamp()\n",
    "        circuit_name = fgc_pm_search.get_fgc_circuit()\n",
    "        author = fgc_pm_search.get_author()\n",
    "        is_automatic = fgc_pm_search.is_automatic_mode()\n",
    "    \n",
    "    rb_query = RbCircuitQuery(circuit_type, circuit_name, max_executions=4)\n",
    "# PIC    \n",
    "    timestamp_pic = rb_query.find_timestamp_pic(timestamp_fgc, spark=spark)\n",
    "\n",
    "# PC Current\n",
    "    i_meas_df, i_a_df, i_earth_df, i_earth_pcnt_df, i_ref_df = rb_query.query_pc_pm(timestamp_fgc, timestamp_fgc, signal_names=['I_MEAS', 'I_A', 'I_EARTH', 'I_EARTH_PCNT', 'I_REF'])\n",
    "\n",
    "    timestamp_fgc_ref = rb_query.get_timestamp_ref(col='fgcPm')\n",
    "    i_meas_ref_df, i_earth_ref_df, i_earth_pcnt_ref_df = rb_query.query_pc_pm(timestamp_fgc_ref, timestamp_fgc_ref, signal_names=['I_MEAS', 'I_EARTH', 'I_EARTH_PCNT'])\n",
    "\n",
    "# QDS\n",
    "    source_timestamp_qds_df = rb_query.find_source_timestamp_qds_board_ab(timestamp_fgc, duration=[(10, 's'), (10, 's')])\n",
    "    \n",
    "    source_timestamp_qds_df.drop_duplicates(subset=['source', 'timestamp'], inplace=True)\n",
    "    source_timestamp_qds_df.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    iqps_board_type_df = rb_query.query_pm_iqps_board_type(source_timestamp_qds_df=source_timestamp_qds_df)\n",
    "\n",
    "    source_timestamp_qds_df['iqps_board_type'] = iqps_board_type_df['iqps_board_type']\n",
    "\n",
    "    u_qds_dfs = rb_query.query_voltage_logic_iqps(source_timestamp_qds_df=source_timestamp_qds_df, signal_names=['U_QS0', 'U_1', 'U_2'], filter_window=3)\n",
    "\n",
    "rb_analysis = RbCircuitAnalysis(circuit_type, None, is_automatic=is_automatic)"
   ]
  },
  {
   "cell_type": "markdown",
514
   "id": "591ae98f",
515
516
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   "metadata": {
    "deletable": false,
    "papermill": {
518
519
     "duration": 0.101574,
     "end_time": "2021-10-21T17:52:23.370507",
520
     "exception": false,
521
     "start_time": "2021-10-21T17:52:23.268933",
522
     "status": "completed"
523
    },
524
525
526
527
528
529
530
531
532
    "tags": []
   },
   "source": [
    "# PIC & Power Converter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
533
   "id": "eb19a29d",
534
535
   "metadata": {
    "execution": {
536
537
538
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     "iopub.execute_input": "2021-10-21T17:52:23.660164Z",
     "iopub.status.busy": "2021-10-21T17:52:23.653553Z",
     "iopub.status.idle": "2021-10-21T17:52:24.590112Z",
     "shell.execute_reply": "2021-10-21T17:52:24.590713Z"
540
    },
541
    "papermill": {
542
543
     "duration": 1.120734,
     "end_time": "2021-10-21T17:52:24.591013",
544
     "exception": false,
545
     "start_time": "2021-10-21T17:52:23.470279",
546
     "status": "completed"
547
    },
548
549
550
    "tags": []
   },
   "outputs": [
551
    {
552
     "name": "stdout",
553
554
     "output_type": "stream",
     "text": [
555
      "EVEN and ODD PIC timestamps (2021-05-14 07:28:47.399) and (2021-05-14 07:28:47.403) are within 1-5 ms away.\n"
556
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559
     ]
    },
    {
     "data": {
560
      "image/png": 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      "text/plain": [
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       "<Figure size 936x468 with 2 Axes>"
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      ]
     },
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      "needs_background": "light"
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     "output_type": "display_data"
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     "output_type": "stream",
     "text": [
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      "Analysis results\n",
      "----------------\n",
      "Circuit name:           RB.A12 \n",
      "\n",
      "PIC Abort:              2021-05-14 07:28:47.399 \n",
      "\n",
      "PC Off:                 2021-05-14 07:28:47.420 \n",
      "\n",
      "Reference:              2018-03-17 16:00:28.680 \n",
      "\n",
      "max(I_MEAS):            11741 A\n",
      "\n",
      "max(I_MEAS, Reference): 11080 A\n"
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     ]
    },
    {
     "data": {
      "text/html": [
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       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_ac81c_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >min</th>        <th class=\"col_heading level0 col1\" >max</th>        <th class=\"col_heading level0 col2\" >act</th>        <th class=\"col_heading level0 col3\" >result</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ac81c_level0_row0\" class=\"row_heading level0 row0\" >tau_i_meas</th>\n",
       "                        <td id=\"T_ac81c_row0_col0\" class=\"data row0 col0\" >90</td>\n",
       "                        <td id=\"T_ac81c_row0_col1\" class=\"data row0 col1\" >110</td>\n",
       "                        <td id=\"T_ac81c_row0_col2\" class=\"data row0 col2\" >96</td>\n",
       "                        <td id=\"T_ac81c_row0_col3\" class=\"data row0 col3\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ac81c_level0_row1\" class=\"row_heading level0 row1\" >tau_i_meas_ref</th>\n",
       "                        <td id=\"T_ac81c_row1_col0\" class=\"data row1 col0\" >90</td>\n",
       "                        <td id=\"T_ac81c_row1_col1\" class=\"data row1 col1\" >110</td>\n",
       "                        <td id=\"T_ac81c_row1_col2\" class=\"data row1 col2\" >99</td>\n",
       "                        <td id=\"T_ac81c_row1_col3\" class=\"data row1 col3\" >True</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
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      ],
      "text/plain": [
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       "<pandas.io.formats.style.Styler at 0x7f9d10359c70>"
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      ]
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     "metadata": {},
     "output_type": "display_data"
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    }
   ],
   "source": [
    "rb_analysis.analyze_pic(timestamp_pic)\n",
    "rb_analysis.analyze_i_meas_pc(circuit_name, timestamp_fgc, timestamp_fgc_ref, min(timestamp_pic), i_meas_df, i_meas_ref_df)"
   ]
  },
  {
   "cell_type": "markdown",
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   "id": "a1f7bfc5",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.104295,
     "end_time": "2021-10-21T17:52:24.802394",
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     "exception": false,
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     "start_time": "2021-10-21T17:52:24.698099",
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     "status": "completed"
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    },
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    "tags": []
   },
   "source": [
    "# Analysis of Quench Detection Voltage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "id": "2f2d1b94",
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   "metadata": {
    "deletable": false,
    "execution": {
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     "iopub.execute_input": "2021-10-21T17:52:25.030307Z",
     "iopub.status.busy": "2021-10-21T17:52:25.029543Z",
     "iopub.status.idle": "2021-10-21T17:52:25.076338Z",
     "shell.execute_reply": "2021-10-21T17:52:25.075381Z"
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    },
    "papermill": {
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     "duration": 0.168414,
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     "exception": false,
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     "start_time": "2021-10-21T17:52:24.908136",
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     "status": "completed"
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    },
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    "scrolled": false,
    "tags": []
   },
   "outputs": [
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    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>magnet</th>\n",
       "      <th>timestamp_iqps</th>\n",
       "      <th>iqps_board_type</th>\n",
       "      <th>elec_position</th>\n",
       "      <th>U_QS0_max</th>\n",
       "      <th>t_U_QS0_max</th>\n",
       "      <th>U_QS0_min</th>\n",
       "      <th>t_U_QS0_min</th>\n",
       "      <th>datetime_iqps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31L2</td>\n",
       "      <td>1620970127388000000</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0.48977</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>-0.000235</td>\n",
       "      <td>-0.924</td>\n",
       "      <td>2021-05-14 07:28:47.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31L2</td>\n",
       "      <td>1620970127386000000</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0.48977</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.714</td>\n",
       "      <td>2021-05-14 07:28:47.386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      ],
      "text/plain": [
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       "  magnet       timestamp_iqps iqps_board_type  elec_position  U_QS0_max  \\\n",
       "1  A31L2  1620970127388000000               0            122    0.48977   \n",
       "0  A31L2  1620970127386000000               1            122    0.48977   \n",
       "\n",
       "   t_U_QS0_max  U_QS0_min  t_U_QS0_min            datetime_iqps  \n",
       "1       -0.032  -0.000235       -0.924  2021-05-14 07:28:47.388  \n",
       "0       -0.034  -0.004063       -0.714  2021-05-14 07:28:47.386  "
733
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      ]
     },
735
     "execution_count": 7,
736
     "metadata": {},
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "source_timestamp_qds_df.rename(columns={'source': 'magnet', 'timestamp': 'timestamp_iqps'}, inplace=True)\n",
    "\n",
    "rb_analysis.calc_min_max_iqps_u_qs0(u_qds_dfs, source_timestamp_qds_df, timestamp_fgc)\n",
    "source_timestamp_qds_df['datetime_iqps'] = source_timestamp_qds_df['timestamp_iqps'].apply(lambda row:Time.to_string_short(row))\n",
    "\n",
    "source_timestamp_qds_df.sort_values(['elec_position', 'iqps_board_type'])\n"
   ]
  },
  {
   "cell_type": "markdown",
754
   "id": "1fa9832e",
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   "metadata": {
    "papermill": {
757
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     "duration": 0.107677,
     "end_time": "2021-10-21T17:52:25.291695",
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     "exception": false,
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     "start_time": "2021-10-21T17:52:25.184018",
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     "status": "completed"
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    },
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    "tags": []
   },
   "source": [
    "# Interactive plots of U_QS0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
772
   "id": "47aab1a7",
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   "metadata": {
    "deletable": false,
    "execution": {
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     "iopub.execute_input": "2021-10-21T17:52:25.515200Z",
     "iopub.status.busy": "2021-10-21T17:52:25.514385Z",
     "iopub.status.idle": "2021-10-21T17:52:25.974831Z",
     "shell.execute_reply": "2021-10-21T17:52:25.975460Z"
780
    },
781
    "papermill": {
782
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     "duration": 0.576715,
     "end_time": "2021-10-21T17:52:25.975705",
784
     "exception": false,
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     "start_time": "2021-10-21T17:52:25.398990",
786
     "status": "completed"
787
    },
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    "tags": [
     "ignore"
    ]
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   },
   "outputs": [
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    {
     "data": {
795
      "application/vnd.jupyter.widget-view+json": {
796
       "model_id": "23f51919fefc41969708670b264b1477",
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       "version_major": 2,
       "version_minor": 0
      },
800
      "text/plain": [
801
       "VBox(children=(Label(value='Select a magnet for plotting'), Select(layout=Layout(width='100%'), options=('A31L…"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
809
      "image/png": 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\n",
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      "text/plain": [
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       "<Figure size 936x468 with 1 Axes>"
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      ]
     },
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     "metadata": {
      "needs_background": "light"
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>magnet</th>\n",
       "      <th>iqps_board_type</th>\n",
       "      <th>elec_position</th>\n",
       "      <th>U_QS0_max</th>\n",
       "      <th>t_U_QS0_max</th>\n",
       "      <th>U_QS0_min</th>\n",
       "      <th>t_U_QS0_min</th>\n",
       "      <th>datetime_iqps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A31L2</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0.48977</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.004063</td>\n",
       "      <td>-0.714</td>\n",
       "      <td>2021-05-14 07:28:47.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A31L2</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0.48977</td>\n",
       "      <td>-0.032</td>\n",
       "      <td>-0.000235</td>\n",
       "      <td>-0.924</td>\n",
       "      <td>2021-05-14 07:28:47.388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "rb_analysis.display_qps_signal_browser(u_qds_dfs, source_timestamp_qds_df, timestamp_fgc)"
   ]
  },
  {
   "cell_type": "markdown",
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   "id": "dc1e366d",
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   "metadata": {
    "papermill": {
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     "duration": 0.120163,
     "end_time": "2021-10-21T17:52:26.215839",
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     "exception": false,
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     "start_time": "2021-10-21T17:52:26.095676",
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     "status": "completed"
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    },
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    "tags": []
   },
   "source": [
    "# Dataframes to be saved to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "id": "c5678598",
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   "metadata": {
    "execution": {
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     "iopub.execute_input": "2021-10-21T17:52:26.482333Z",
     "iopub.status.busy": "2021-10-21T17:52:26.481477Z",
     "iopub.status.idle": "2021-10-21T17:52:26.484441Z",
     "shell.execute_reply": "2021-10-21T17:52:26.485075Z"
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    },
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    "papermill": {
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     "duration": 0.147421,
     "end_time": "2021-10-21T17:52:26.485364",
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     "exception": false,
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     "start_time": "2021-10-21T17:52:26.337943",
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     "status": "completed"
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    },
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "results_0_df = deepcopy(source_timestamp_qds_df[source_timestamp_qds_df['iqps_board_type']=='0'].sort_values(['elec_position']).reset_index(drop=True))\n",
    "results_0_df['U_QS0_max'] = results_0_df['U_QS0_max'].map('{:.3f}'.format)\n",
    "results_0_df['t_U_QS0_max'] = results_0_df['t_U_QS0_max'].map('{:.3f}'.format)\n",
    "results_0_df['U_QS0_min'] = results_0_df['U_QS0_min'].map('{:.3f}'.format)\n",
    "results_0_df['t_U_QS0_min'] = results_0_df['t_U_QS0_min'].map('{:.3f}'.format)\n",
    "\n",
    "results_1_df = deepcopy(source_timestamp_qds_df[source_timestamp_qds_df['iqps_board_type']=='1'].sort_values(['elec_position']).reset_index(drop=True))\n",
    "results_1_df['U_QS0_max'] = results_1_df['U_QS0_max'].map('{:.3f}'.format)\n",
    "results_1_df['t_U_QS0_max'] = results_1_df['t_U_QS0_max'].map('{:.3f}'.format)\n",
    "results_1_df['U_QS0_min'] = results_1_df['U_QS0_min'].map('{:.3f}'.format)\n",
    "results_1_df['t_U_QS0_min'] = results_1_df['t_U_QS0_min'].map('{:.3f}'.format)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "id": "1040278a",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.118255,
     "end_time": "2021-10-21T17:52:26.723585",
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     "exception": false,
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     "start_time": "2021-10-21T17:52:26.605330",
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     "status": "completed"
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    },
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    "tags": []
   },
   "source": [
    "# Final Report"
   ]
  },
  {
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   "cell_type": "raw",
   "execution_count": null,
   "id": "f2911cc0",
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   "metadata": {
    "deletable": false,
    "papermill": {
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     "duration": 0.119214,
     "end_time": "2021-10-21T17:52:26.961665",
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     "exception": false,
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     "start_time": "2021-10-21T17:52:26.842451",
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     "status": "completed"
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    },
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    "tags": [
     "ignore"
    ]
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   },
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   "outputs": [],
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   "source": [
    "analysis_start_time = Time.get_analysis_start_time()\n",
    "date_time_fgc = Time.to_datetime(timestamp_fgc).strftime(\"%Y-%m-%d-%Hh%M\")\n",
    "!mkdir -p /eos/project/m/mp3/RB/$circuit_name/FPA\n",
    "\n",
    "file_name = \"{}_FPA_SNAP-{}-{}\".format(circuit_name, date_time_fgc, analysis_start_time)\n",
    "\n",
    "iqps_board_type = '0'\n",
    "full_path = '/eos/project/m/mp3/RB/{}/FPA/{}_{}.csv'.format(circuit_name, file_name, iqps_board_type)\n",
    "results_0_df.to_csv(full_path, index=False)\n",
    "print('Board 0 results table saved to (Windows): ' + '\\\\\\\\cernbox-smb' + full_path.replace('/', '\\\\'))\n",
    "\n",
    "iqps_board_type = '1'\n",
    "full_path = '/eos/project/m/mp3/RB/{}/FPA/{}_{}.csv'.format(circuit_name, file_name, iqps_board_type)\n",
    "results_1_df.to_csv(full_path, index=False)\n",
    "print('Board 1 results table saved to (Windows): ' + '\\\\\\\\cernbox-smb' + full_path.replace('/', '\\\\'))\n",
    "\n",
    "apply_report_template()\n",
    "file_name_html = file_name + '.html'\n",
    "full_path = '/eos/project/m/mp3/RB/{}/FPA/{}.html'.format(circuit_name, file_name)\n",
    "print('Compact notebook report saved to (Windows): ' + '\\\\\\\\cernbox-smb' + full_path.replace('/', '\\\\'))\n",
    "display(Javascript('IPython.notebook.save_notebook();'))\n",
    "Time.sleep(5)\n",
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