{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b775396",
   "metadata": {},
   "source": [
    "# Watch a temperature : scan temperature *v* time\n",
    "\n",
    "From *APS Python Training for Bluesky Data Acquisition*.\n",
    "\n",
    "**Objective**\n",
    "\n",
    "Measure the temperature for a short period and compute basics measures of the collected readings.\n",
    "\n",
    "## Start the `instrument` package\n",
    "\n",
    "Our instrument package is in the `bluesky` subdirectory here so we add that to the search path before importing it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a256149c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/prjemian/bluesky/instrument/_iconfig.py\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/docs/source/howto/.logs/ipython_console.log\n",
      "Mode           : rotate\n",
      "Output logging : True\n",
      "Raw input log  : False\n",
      "Timestamping   : True\n",
      "State          : active\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Thu-17:54:44 - ############################################################ startup\n",
      "I Thu-17:54:44 - logging started\n",
      "I Thu-17:54:44 - logging level = 10\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/session_logs.py\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/collection.py\n",
      "I Thu-17:54:44 - CONDA_PREFIX = /home/prjemian/.conda/envs/bluesky_2023_2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Minimal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I Thu-17:54:44 - xmode exception level: 'Minimal'\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/mpl/notebook.py\n",
      "I Thu-17:54:44 - #### Bluesky Framework ####\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/framework/check_python.py\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/framework/check_bluesky.py\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/framework/initialize.py\n",
      "I Thu-17:54:44 - RunEngine metadata saved in directory: /home/prjemian/Bluesky_RunEngine_md\n",
      "I Thu-17:54:44 - using databroker catalog 'training'\n",
      "I Thu-17:54:44 - using ophyd control layer: pyepics\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/framework/metadata.py\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/epics_signal_config.py\n",
      "I Thu-17:54:44 - Using RunEngine metadata for scan_id\n",
      "I Thu-17:54:44 - #### Devices ####\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/devices/area_detector.py\n",
      "I Thu-17:54:44 - /home/prjemian/bluesky/instrument/devices/calculation_records.py\n",
      "I Thu-17:54:47 - /home/prjemian/bluesky/instrument/devices/fourc_diffractometer.py\n",
      "I Thu-17:54:47 - /home/prjemian/bluesky/instrument/devices/ioc_stats.py\n",
      "I Thu-17:54:47 - /home/prjemian/bluesky/instrument/devices/kohzu_monochromator.py\n",
      "I Thu-17:54:47 - /home/prjemian/bluesky/instrument/devices/motors.py\n",
      "I Thu-17:54:47 - /home/prjemian/bluesky/instrument/devices/noisy_detector.py\n",
      "I Thu-17:54:47 - /home/prjemian/bluesky/instrument/devices/scaler.py\n",
      "I Thu-17:54:48 - /home/prjemian/bluesky/instrument/devices/shutter_simulator.py\n",
      "I Thu-17:54:48 - /home/prjemian/bluesky/instrument/devices/simulated_fourc.py\n",
      "I Thu-17:54:48 - /home/prjemian/bluesky/instrument/devices/simulated_kappa.py\n",
      "I Thu-17:54:48 - /home/prjemian/bluesky/instrument/devices/slits.py\n",
      "I Thu-17:54:48 - /home/prjemian/bluesky/instrument/devices/sixc_diffractometer.py\n",
      "I Thu-17:54:49 - /home/prjemian/bluesky/instrument/devices/temperature_signal.py\n",
      "I Thu-17:54:49 - #### Callbacks ####\n",
      "I Thu-17:54:49 - /home/prjemian/bluesky/instrument/callbacks/spec_data_file_writer.py\n",
      "I Thu-17:54:49 - #### Plans ####\n",
      "I Thu-17:54:49 - /home/prjemian/bluesky/instrument/plans/lup_plan.py\n",
      "I Thu-17:54:49 - /home/prjemian/bluesky/instrument/plans/peak_finder_example.py\n",
      "I Thu-17:54:49 - /home/prjemian/bluesky/instrument/utils/image_analysis.py\n",
      "I Thu-17:54:49 - #### Utilities ####\n",
      "I Thu-17:54:49 - writing to SPEC file: /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/docs/source/howto/20230413-175449.dat\n",
      "I Thu-17:54:49 -    >>>>   Using default SPEC file name   <<<<\n",
      "I Thu-17:54:49 -    file will be created when bluesky ends its next scan\n",
      "I Thu-17:54:49 -    to change SPEC file, use command:   newSpecFile('title')\n",
      "I Thu-17:54:49 - #### Startup is complete. ####\n"
     ]
    }
   ],
   "source": [
    "import pathlib, sys\n",
    "sys.path.append(str(pathlib.Path.home() / \"bluesky\"))\n",
    "from instrument.collection import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a907e3e",
   "metadata": {},
   "source": [
    "## Describe\n",
    "\n",
    "Note the initial settings of the `temperature` (controller).  It's actually an EPICS *swait* record that simulates a temperature sensor and controller.  It has a setpoint, a readback, and a few other support attributes as shown.  A Random noise level is part of the simulation.  We'll compute that noise level from the standard deviation of the measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8699ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "=============================== =================================== ==========================\n",
       "data name                       value                               timestamp                 \n",
       "=============================== =================================== ==========================\n",
       "temperature_setpoint            25.0                                2023-04-13 17:54:49.157724\n",
       "temperature                     0.0                                 2023-04-13 17:54:49.159466\n",
       "temperature_done                False                               2023-04-13 17:54:49.178146\n",
       "temperature_calculation         A+max(-D,min(D,(B-A)))+C*(RNDM-0.5) 2023-04-13 17:54:49.160429\n",
       "temperature_description         temperature                         2023-04-13 17:54:49.154181\n",
       "temperature_max_change          2.0                                 2023-04-13 17:54:49.160349\n",
       "temperature_noise               1.0                                 2023-04-13 17:54:49.159466\n",
       "temperature_previous_value_pv   gp:userCalc8.VAL                    2023-04-13 17:54:49.157188\n",
       "temperature_scanning_rate       5                                   2023-04-13 17:54:49.160429\n",
       "temperature_tolerance           1.0                                 2023-04-13 17:54:49.160429\n",
       "temperature_report_dmov_changes False                               2023-04-13 17:54:49.156380\n",
       "=============================== =================================== =========================="
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listdevice(temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838eca41",
   "metadata": {},
   "source": [
    "## Count\n",
    "Use the bluesky RunEngine, `RE`, and a standard plan, `bp.count()`, to _count_ the temperature.  The `bp.count()` plan triggers (typically a scaler) and then reads from the detector.  In this case, the `temperature` device's `.trigger()` method does not do anything, yet it is still _countable_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87a37242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 958     Time: 2023-04-13 17:54:49\n",
      "Persistent Unique Scan ID: 'cd24d583-6fd9-43ce-9ea3-583b104c2a6d'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+-------------+\n",
      "|   seq_num |       time | temperature |\n",
      "+-----------+------------+-------------+\n",
      "|         1 | 17:54:49.4 |     0.00000 |\n",
      "+-----------+------------+-------------+\n",
      "generator count ['cd24d583'] (scan num: 958)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('cd24d583-6fd9-43ce-9ea3-583b104c2a6d',)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RE(bp.count([temperature]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3ff966",
   "metadata": {},
   "source": [
    "Take ten readings by adding the `num=10` keyword argument (a.k.a. *kwarg*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a9458d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 959     Time: 2023-04-13 17:54:49\n",
      "Persistent Unique Scan ID: '8b99ba9a-1361-4b5d-8910-3a7b1e0566e2'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+-------------+\n",
      "|   seq_num |       time | temperature |\n",
      "+-----------+------------+-------------+\n",
      "|         1 | 17:54:49.8 |     0.00000 |\n",
      "|         2 | 17:54:49.8 |     0.00000 |\n",
      "|         3 | 17:54:49.8 |     0.00000 |\n",
      "|         4 | 17:54:49.9 |     2.24478 |\n",
      "|         5 | 17:54:49.9 |     2.24478 |\n",
      "|         6 | 17:54:49.9 |     2.24478 |\n",
      "|         7 | 17:54:50.0 |     2.24478 |\n",
      "|         8 | 17:54:50.0 |     2.24478 |\n",
      "|         9 | 17:54:50.0 |     2.24478 |\n",
      "|        10 | 17:54:50.0 |     2.24478 |\n",
      "+-----------+------------+-------------+\n",
      "generator count ['8b99ba9a'] (scan num: 959)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('8b99ba9a-1361-4b5d-8910-3a7b1e0566e2',)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.count([temperature], num=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a979091",
   "metadata": {},
   "source": [
    "Do all the readings look the same?  Perhaps there are only two different values (first one, then the other)?  That's because our measurements were only *reading* the temperature without waiting for it to update.  (If there are two different values, we caught the moment when EPICS updated the calculation.)  The table above showed `temperature_scanning_rate 5` which means EPICS is only updating every 2 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd818f9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2 second'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature.scanning_rate.get(as_string=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c4fd5d",
   "metadata": {},
   "source": [
    "## Adjust the measurement interval\n",
    "We need to slow down the measurement to this interval by adding the `delay=2` kwarg, sampling every 2 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdadb6ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 960     Time: 2023-04-13 17:54:50\n",
      "Persistent Unique Scan ID: 'b55776f1-0a02-464c-9762-fe807c90c014'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+-------------+\n",
      "|   seq_num |       time | temperature |\n",
      "+-----------+------------+-------------+\n",
      "|         1 | 17:54:50.6 |     2.24478 |\n",
      "|         2 | 17:54:52.6 |     4.66694 |\n",
      "|         3 | 17:54:54.6 |     6.66461 |\n",
      "|         4 | 17:54:56.6 |     8.64790 |\n",
      "|         5 | 17:54:58.6 |    10.99491 |\n",
      "|         6 | 17:55:00.6 |    13.23049 |\n",
      "|         7 | 17:55:02.6 |    15.09944 |\n",
      "|         8 | 17:55:04.6 |    17.26870 |\n",
      "|         9 | 17:55:06.6 |    19.20524 |\n",
      "|        10 | 17:55:08.6 |    21.33919 |\n",
      "+-----------+------------+-------------+\n",
      "generator count ['b55776f1'] (scan num: 960)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('b55776f1-0a02-464c-9762-fe807c90c014',)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.count([temperature], num=10, delay=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5c8d75",
   "metadata": {},
   "source": [
    "Let's reduce the update interval that EPICS uses, to the shortest interval EPICS provides, so our measurements do not take so long."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "947b6aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Passive', 'Event', 'I/O Intr', '10 second', '5 second', '2 second', '1 second', '.5 second', '.2 second', '.1 second')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2 second'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the list of possible values\n",
    "print(temperature.scanning_rate.enum_strs)\n",
    "\n",
    "# set the shortest interval, by name\n",
    "temperature.scanning_rate.put(\".1 second\")\n",
    "temperature.scanning_rate.get(as_string=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36a9f40",
   "metadata": {},
   "source": [
    "## Collect our measurements\n",
    "Repeat the measurement, this time with more observations.\n",
    "\n",
    "Also, we'll add a metadata key:value pair so that later we can find this data more easily in the database.  Most plans take a `md={}` kwarg where the value is a Python dictionary with the key:value pair(s) to add to the run's metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "005c4c0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 961     Time: 2023-04-13 17:55:11\n",
      "Persistent Unique Scan ID: '51779bd1-f506-4622-8a89-f6002f281de4'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+-------------+\n",
      "|   seq_num |       time | temperature |\n",
      "+-----------+------------+-------------+\n",
      "|         1 | 17:55:11.1 |    24.67653 |\n",
      "|         2 | 17:55:11.2 |    25.31126 |\n",
      "|         3 | 17:55:11.3 |    25.33679 |\n",
      "|         4 | 17:55:11.4 |    25.47115 |\n",
      "|         5 | 17:55:11.5 |    25.43527 |\n",
      "|         6 | 17:55:11.6 |    25.44133 |\n",
      "|         7 | 17:55:11.7 |    24.72783 |\n",
      "|         8 | 17:55:11.8 |    24.95423 |\n",
      "|         9 | 17:55:11.9 |    25.01724 |\n",
      "|        10 | 17:55:12.0 |    24.70978 |\n",
      "|        11 | 17:55:12.1 |    25.28183 |\n",
      "|        12 | 17:55:12.2 |    25.21415 |\n",
      "|        13 | 17:55:12.3 |    25.47186 |\n",
      "|        14 | 17:55:12.4 |    24.53467 |\n",
      "|        15 | 17:55:12.5 |    24.83840 |\n",
      "|        16 | 17:55:12.6 |    25.45067 |\n",
      "|        17 | 17:55:12.7 |    25.04357 |\n",
      "|        18 | 17:55:12.8 |    24.97578 |\n",
      "|        19 | 17:55:12.9 |    25.04635 |\n",
      "|        20 | 17:55:13.0 |    25.32997 |\n",
      "|        21 | 17:55:13.1 |    25.01504 |\n",
      "|        22 | 17:55:13.2 |    25.23326 |\n",
      "|        23 | 17:55:13.3 |    24.75829 |\n",
      "|        24 | 17:55:13.4 |    24.64411 |\n",
      "|        25 | 17:55:13.5 |    24.60384 |\n",
      "|        26 | 17:55:13.6 |    24.87308 |\n",
      "|        27 | 17:55:13.7 |    24.62004 |\n",
      "|        28 | 17:55:13.8 |    24.71509 |\n",
      "|        29 | 17:55:13.9 |    25.42215 |\n",
      "|        30 | 17:55:14.0 |    25.08979 |\n",
      "|        31 | 17:55:14.1 |    24.82930 |\n",
      "|        32 | 17:55:14.2 |    24.50919 |\n",
      "|        33 | 17:55:14.3 |    25.06507 |\n",
      "|        34 | 17:55:14.4 |    24.93471 |\n",
      "|        35 | 17:55:14.5 |    25.09922 |\n",
      "|        36 | 17:55:14.6 |    25.28540 |\n",
      "|        37 | 17:55:14.7 |    24.68781 |\n",
      "|        38 | 17:55:14.8 |    24.59773 |\n",
      "|        39 | 17:55:14.9 |    24.75489 |\n",
      "|        40 | 17:55:15.0 |    25.42776 |\n",
      "|        41 | 17:55:15.1 |    24.93259 |\n",
      "|        42 | 17:55:15.2 |    24.84777 |\n",
      "|        43 | 17:55:15.3 |    24.81318 |\n",
      "|        44 | 17:55:15.4 |    24.78414 |\n",
      "|        45 | 17:55:15.5 |    25.26970 |\n",
      "|        46 | 17:55:15.6 |    24.61772 |\n",
      "|        47 | 17:55:15.7 |    25.15956 |\n",
      "|        48 | 17:55:15.8 |    24.77626 |\n",
      "|        49 | 17:55:15.9 |    24.84089 |\n",
      "+-----------+------------+-------------+\n",
      "|   seq_num |       time | temperature |\n",
      "+-----------+------------+-------------+\n",
      "|        50 | 17:55:16.0 |    25.26352 |\n",
      "+-----------+------------+-------------+\n",
      "generator count ['51779bd1'] (scan num: 961)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('51779bd1-f506-4622-8a89-f6002f281de4',)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.count([temperature], num=50, delay=0.1, md={\"motive\": \"tscan\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e23ea1",
   "metadata": {},
   "source": [
    "## Get the data (from the database)\n",
    "Let's get the measured data from the database now.  It's the most recent bluesky run, so an index of `-1` will identify it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0d72070",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = cat[-1].primary.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656b622b",
   "metadata": {},
   "source": [
    "Let's get the values as a 1-D numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64f93d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = dataset[\"temperature\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f163a0f2",
   "metadata": {},
   "source": [
    "## Compute the statistics\n",
    "Use the methods of numpy arrays to compute maximum, mean, standard deviation, and minimum.  Looks nicer in a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd1c200f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======= ==================\n",
      "measure value             \n",
      "======= ==================\n",
      "max     25.471862363622492\n",
      "mean    24.994794842450602\n",
      "dev     0.2892339831470962\n",
      "min     24.50918593118181 \n",
      "#       50                \n",
      "======= ==================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyRestTable\n",
    "\n",
    "table = pyRestTable.Table()\n",
    "table.addLabel(\"measure\")\n",
    "table.addLabel(\"value\")\n",
    "table.addRow((\"max\", T.max()))\n",
    "table.addRow((\"mean\", T.mean()))\n",
    "table.addRow((\"dev\", T.std()))\n",
    "table.addRow((\"min\", T.min()))\n",
    "table.addRow((\"#\", len(T)))\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e672d66a",
   "metadata": {},
   "source": [
    "Let's replot that data for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28c52cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b49565bd0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[\"temperature\"].plot(marker=\"o\", color=\"red\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "60aa360bcd8d3c8cfbc4e726e53a455fcd5c15cdf29caaf63c7ca2494eba79e9"
  },
  "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": "3.10.10"
  }
 },
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