{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 4: Basic scan (& plot) of scaler *vs.* motor\n",
    "\n",
    "\n",
    "**Preparation**\n",
    "\n",
    "*`instrument` package*\n",
    "\n",
    "Make sure the `instrument` package is in the same directory \n",
    "as this jupyter notebook. The `instrument` package included with \n",
    "this lesson is a brief version of the standard package used \n",
    "with any APS instrument.  Since the notebook is for teaching,\n",
    "it does not connect with any mongodb database.  The scans are \n",
    "not kept by the databroker.  However, every scan is saved to a \n",
    "SPEC data file as described when the instrument package is loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "I Wed-13:14:37 - ############################################################ startup\n",
      "I Wed-13:14:37 - logging started\n",
      "I Wed-13:14:37 - logging level = 10\n",
      "I Wed-13:14:37 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/collection.py\n",
      "I Wed-13:14:37 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/mpl/notebook.py\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/.logs/ipython_console.log\n",
      "Mode           : rotate\n",
      "Output logging : True\n",
      "Raw input log  : False\n",
      "Timestamping   : True\n",
      "State          : active\n",
      "I Wed-13:14:37 - bluesky framework\n",
      "I Wed-13:14:37 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/framework/check_python.py\n",
      "I Wed-13:14:37 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/framework/check_bluesky.py\n",
      "I Wed-13:14:38 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/framework/initialize.py\n",
      "I Wed-13:14:39 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/framework/metadata.py\n",
      "I Wed-13:14:39 - /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/instrument/framework/callbacks.py\n",
      "I Wed-13:14:40 - writing to SPEC file: /home/prjemian/Documents/projects/BCDA-APS/bluesky_training/lessons/20201216-131440.dat\n",
      "I Wed-13:14:40 -    >>>>   Using default SPEC file name   <<<<\n",
      "I Wed-13:14:40 -    file will be created when bluesky ends its next scan\n",
      "I Wed-13:14:40 -    to change SPEC file, use command:   newSpecFile('title')\n"
     ]
    }
   ],
   "source": [
    "from instrument.collection import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for jupyterlab ...\n",
    "# https://stackoverflow.com/a/51932652\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get a motor and scaler from the ophyd simulators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ophyd.sim import motor as m1\n",
    "from ophyd.sim import det as scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scan the scaler *v.* the motor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 1     Time: 2020-12-16 13:14:40\n",
      "Persistent Unique Scan ID: '61993812-94b5-4701-be35-6c2f06f7e238'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |      motor |        det |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 13:14:40.4 |     -5.000 |      0.000 |\n",
      "|         2 | 13:14:40.5 |     -4.706 |      0.000 |\n",
      "|         3 | 13:14:40.5 |     -4.412 |      0.000 |\n",
      "|         4 | 13:14:40.6 |     -4.118 |      0.000 |\n",
      "|         5 | 13:14:40.6 |     -3.824 |      0.001 |\n",
      "|         6 | 13:14:40.7 |     -3.529 |      0.002 |\n",
      "|         7 | 13:14:40.7 |     -3.235 |      0.005 |\n",
      "|         8 | 13:14:40.8 |     -2.941 |      0.013 |\n",
      "|         9 | 13:14:40.8 |     -2.647 |      0.030 |\n",
      "|        10 | 13:14:40.8 |     -2.353 |      0.063 |\n",
      "|        11 | 13:14:40.9 |     -2.059 |      0.120 |\n",
      "|        12 | 13:14:40.9 |     -1.765 |      0.211 |\n",
      "|        13 | 13:14:41.0 |     -1.471 |      0.339 |\n",
      "|        14 | 13:14:41.0 |     -1.176 |      0.501 |\n",
      "|        15 | 13:14:41.1 |     -0.882 |      0.678 |\n",
      "|        16 | 13:14:41.1 |     -0.588 |      0.841 |\n",
      "|        17 | 13:14:41.1 |     -0.294 |      0.958 |\n",
      "|        18 | 13:14:41.2 |      0.000 |      1.000 |\n",
      "|        19 | 13:14:41.2 |      0.294 |      0.958 |\n",
      "|        20 | 13:14:41.2 |      0.588 |      0.841 |\n",
      "|        21 | 13:14:41.3 |      0.882 |      0.678 |\n",
      "|        22 | 13:14:41.3 |      1.176 |      0.501 |\n",
      "|        23 | 13:14:41.3 |      1.471 |      0.339 |\n",
      "|        24 | 13:14:41.4 |      1.765 |      0.211 |\n",
      "|        25 | 13:14:41.4 |      2.059 |      0.120 |\n",
      "|        26 | 13:14:41.4 |      2.353 |      0.063 |\n",
      "|        27 | 13:14:41.5 |      2.647 |      0.030 |\n",
      "|        28 | 13:14:41.5 |      2.941 |      0.013 |\n",
      "|        29 | 13:14:41.5 |      3.235 |      0.005 |\n",
      "|        30 | 13:14:41.6 |      3.529 |      0.002 |\n",
      "|        31 | 13:14:41.6 |      3.824 |      0.001 |\n",
      "|        32 | 13:14:41.6 |      4.118 |      0.000 |\n",
      "|        33 | 13:14:41.7 |      4.412 |      0.000 |\n",
      "|        34 | 13:14:41.7 |      4.706 |      0.000 |\n",
      "|        35 | 13:14:41.7 |      5.000 |      0.000 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator scan ['61993812'] (scan num: 1)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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     "metadata": {},
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "RE(bp.scan([scaler], m1, -5, 5, 35))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.2 64-bit (conda)",
   "language": "python",
   "name": "python38264bitcondaf8e76b08f7284c68a6b3de15f965a87a"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}