{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output scan(s) to a SPEC data file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the common concerns is how to access data from bluesky's database.  The standard way is to replay the document stream from each of the scans through a bluesky callback that writes the data to the desired file format.  Here, we write data to the SPEC file format.\n",
    "\n",
    "## Setup\n",
    "\n",
    "First, we must setup a bluesky session.  We create a [temporary catalog](https://blueskyproject.io/databroker/how-to/file-backed-catalog.html?highlight=temporary#temporary-catalog) to store the data from this notebook's data collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.ion()\n",
    "\n",
    "from bluesky import RunEngine, plans as bp\n",
    "from bluesky.callbacks.best_effort import BestEffortCallback\n",
    "import databroker\n",
    "\n",
    "cat = databroker.temp()\n",
    "RE = RunEngine({})\n",
    "RE.subscribe(cat.v1.insert)\n",
    "RE.subscribe(BestEffortCallback())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SPEC data files usually contain a list of all the motor positions at the start of each scan.  Starting with apstools release 1.6.11, it became possible to have the list of all ophyd-labeled `motor` devices logged to a separate stream at the start of every scan.  The next configuration will create a `label_start_motor` stream with the positions.  The `SpecWriterCallback` will find this stream and record the motor names and values in the `#O` and `#P` control lines, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from apstools.plans import label_stream_wrapper\n",
    "\n",
    "def motor_start_preprocessor(plan):\n",
    "    return label_stream_wrapper(plan, \"motor\", when=\"start\")\n",
    "\n",
    "RE.preprocessors.append(motor_start_preprocessor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to create some scan data.  We'll use Gaussian peak profile calculated in a _userCalc_ (`swait` record) and a motor from our EPICS IOC simulator `gp:`.  Initialize the peak computation with some randomized parameters.  The peak will be at some `motor` position between -1 .. +1.  Print the actual values for our reference later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calc = D*(0.95+E*RNDM)/exp(((A-b)/c)^2)\n",
      "A: x = gp:m1.RBV\n",
      "B: center = 0.21141204623628096\n",
      "C: width = 0.04939034587707944\n",
      "D: scale = 102214.12035478499\n",
      "E: noise= 0.05216222581266805\n"
     ]
    }
   ],
   "source": [
    "from apstools.devices import UserCalcN, setup_gaussian_swait\n",
    "import numpy as np\n",
    "from ophyd import EpicsMotor, EpicsSignal\n",
    "\n",
    "IOC = \"gp:\"\n",
    "motor = EpicsMotor(f\"{IOC}m1\", name=\"motor\", labels=[\"motor\"])\n",
    "# define some extra motors to demonstrate #O & #P lines\n",
    "omega = EpicsMotor(f\"{IOC}m2\", name=\"omega\", labels=[\"motor\"])\n",
    "chi = EpicsMotor(f\"{IOC}m3\", name=\"chi\", labels=[\"motor\"])\n",
    "phi = EpicsMotor(f\"{IOC}m4\", name=\"phi\", labels=[\"motor\"])\n",
    "ttheta = EpicsMotor(f\"{IOC}m5\", name=\"ttheta\", labels=[\"motor\"])\n",
    "atth = EpicsMotor(f\"{IOC}m6\", name=\"atth\", labels=[\"motor\"])\n",
    "mx = EpicsMotor(f\"{IOC}m7\", name=\"mx\", labels=[\"motor\"])\n",
    "my = EpicsMotor(f\"{IOC}m8\", name=\"my\", labels=[\"motor\"])\n",
    "mz = EpicsMotor(f\"{IOC}m9\", name=\"mz\", labels=[\"motor\"])\n",
    "swait = UserCalcN(f\"{IOC}userCalc1\", name=\"swait\")\n",
    "det = EpicsSignal(swait.calculated_value.pvname, name=\"det\")\n",
    "\n",
    "motor.wait_for_connection()\n",
    "swait.wait_for_connection()\n",
    "det.wait_for_connection()\n",
    "\n",
    "setup_gaussian_swait(\n",
    "    swait, motor.user_readback, \n",
    "    center=-0.5 + np.random.uniform(), \n",
    "    width=0.01 + 0.2*np.random.uniform(), \n",
    "    noise=0.05*(.95 + .1*np.random.uniform()),\n",
    "    scale=100_000*(.95 + .1*np.random.uniform()), \n",
    "    )\n",
    "\n",
    "print(f\"calc = {swait.calculation.get()}\")\n",
    "print(f\"A: x = {swait.channels.A.input_pv.get()}\")\n",
    "print(f\"B: center = {swait.channels.B.input_value.get()}\")\n",
    "print(f\"C: width = {swait.channels.C.input_value.get()}\")\n",
    "print(f\"D: scale = {swait.channels.D.input_value.get()}\")\n",
    "print(f\"E: noise= {swait.channels.E.input_value.get()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, run the scan with the RunEngine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Transient Scan ID: 1     Time: 2023-02-14 13:16:56\n",
      "Persistent Unique Scan ID: '3df743d0-0768-467a-b5e8-1a4f84ad9c3d'\n",
      "New stream: 'label_start_motor'\n",
      "New stream: 'primary'\n",
      "+-----------+------------+------------+------------+\n",
      "|   seq_num |       time |      motor |        det |\n",
      "+-----------+------------+------------+------------+\n",
      "|         1 | 13:17:00.7 |   -1.20000 |    0.00000 |\n",
      "|         2 | 13:17:01.1 |   -1.14000 |    0.00000 |\n",
      "|         3 | 13:17:01.4 |   -1.08000 |    0.00000 |\n",
      "|         4 | 13:17:01.7 |   -1.02000 |    0.00000 |\n",
      "|         5 | 13:17:02.0 |   -0.96000 |    0.00000 |\n",
      "|         6 | 13:17:02.3 |   -0.90000 |    0.00000 |\n",
      "|         7 | 13:17:02.6 |   -0.84000 |    0.00000 |\n",
      "|         8 | 13:17:02.9 |   -0.78000 |    0.00000 |\n",
      "|         9 | 13:17:03.2 |   -0.72000 |    0.00000 |\n",
      "|        10 | 13:17:03.5 |   -0.66000 |    0.00000 |\n",
      "|        11 | 13:17:03.8 |   -0.60000 |    0.00000 |\n",
      "|        12 | 13:17:04.1 |   -0.54000 |    0.00000 |\n",
      "|        13 | 13:17:04.4 |   -0.48000 |    0.00000 |\n",
      "|        14 | 13:17:04.7 |   -0.42000 |    0.00000 |\n",
      "|        15 | 13:17:05.0 |   -0.36000 |    0.00000 |\n",
      "|        16 | 13:17:05.3 |   -0.30000 |    0.00000 |\n",
      "|        17 | 13:17:05.6 |   -0.24000 |    0.00000 |\n",
      "|        18 | 13:17:05.9 |   -0.18000 |    0.00000 |\n",
      "|        19 | 13:17:06.2 |   -0.12000 |    0.00000 |\n",
      "|        20 | 13:17:06.5 |   -0.06000 |    0.00000 |\n",
      "|        21 | 13:17:06.8 |    0.00000 |    0.00110 |\n",
      "|        22 | 13:17:07.1 |    0.06000 |    8.35809 |\n",
      "|        23 | 13:17:07.4 |    0.12000 | 3215.35530 |\n",
      "|        24 | 13:17:07.7 |    0.18000 | 66230.81346 |\n",
      "|        25 | 13:17:08.0 |    0.24000 | 73111.10621 |\n",
      "|        26 | 13:17:08.3 |    0.30000 | 4062.48995 |\n",
      "|        27 | 13:17:08.6 |    0.36000 |   11.45303 |\n",
      "|        28 | 13:17:08.9 |    0.42000 |    0.00181 |\n",
      "|        29 | 13:17:09.2 |    0.48000 |    0.00000 |\n",
      "|        30 | 13:17:09.5 |    0.54000 |    0.00000 |\n",
      "|        31 | 13:17:09.8 |    0.60000 |    0.00000 |\n",
      "|        32 | 13:17:10.1 |    0.66000 |    0.00000 |\n",
      "|        33 | 13:17:10.4 |    0.72000 |    0.00000 |\n",
      "|        34 | 13:17:10.7 |    0.78000 |    0.00000 |\n",
      "|        35 | 13:17:11.0 |    0.84000 |    0.00000 |\n",
      "|        36 | 13:17:11.3 |    0.90000 |    0.00000 |\n",
      "|        37 | 13:17:11.6 |    0.96000 |    0.00000 |\n",
      "|        38 | 13:17:11.9 |    1.02000 |    0.00000 |\n",
      "|        39 | 13:17:12.2 |    1.08000 |    0.00000 |\n",
      "|        40 | 13:17:12.5 |    1.14000 |    0.00000 |\n",
      "|        41 | 13:17:12.8 |    1.20000 |    0.00000 |\n",
      "+-----------+------------+------------+------------+\n",
      "generator scan ['3df743d0'] (scan num: 1)\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('3df743d0-0768-467a-b5e8-1a4f84ad9c3d',)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RE(bp.scan([det], motor, -1.2, 1.2, 41))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get the most recent scan, by steps\n",
    "\n",
    "The databroker instance, `cat`, provides access to its scans by several means.  One way is to consider `cat` as a list and retrieve the last item from the list.  This will return a *run*, the common reference to be used.\n",
    "\n",
    "For this first example, we'll work through the steps one by one.  The `cat.v2` interface is the easiest to use (at this writing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BlueskyRun\n",
       "  uid='3df743d0-0768-467a-b5e8-1a4f84ad9c3d'\n",
       "  exit_status='success'\n",
       "  2023-02-14 13:16:56.441 -- 2023-02-14 13:17:12.871\n",
       "  Streams:\n",
       "    * label_start_motor\n",
       "    * primary\n"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run = cat.v2[-1]\n",
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown, the run has one data stream, name `primary`.  The databroker provides a simple table view of this run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:              (time: 41)\n",
       "Coordinates:\n",
       "  * time                 (time) float64 1.676e+09 1.676e+09 ... 1.676e+09\n",
       "Data variables:\n",
       "    motor                (time) float64 -1.2 -1.14 -1.08 -1.02 ... 1.08 1.14 1.2\n",
       "    motor_user_setpoint  (time) float64 -1.2 -1.14 -1.08 -1.02 ... 1.08 1.14 1.2\n",
       "    det                  (time) float64 0.0 0.0 ... 3.032e-149 1.012e-169</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-50ee9458-f36c-494f-930f-1d1eee667d3f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-50ee9458-f36c-494f-930f-1d1eee667d3f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 41</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1990e21c-7102-4171-914a-c3955eabdabd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1990e21c-7102-4171-914a-c3955eabdabd' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.676e+09 1.676e+09 ... 1.676e+09</div><input id='attrs-f3398f82-dac5-4c2b-9061-54cbb2e536a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3398f82-dac5-4c2b-9061-54cbb2e536a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b0fbc32-55dc-4a56-996c-154eaeb2b2c0' class='xr-var-data-in' type='checkbox'><label for='data-5b0fbc32-55dc-4a56-996c-154eaeb2b2c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09, 1.676402e+09,\n",
       "       1.676402e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1df67234-eefc-40e5-a1fe-2f79920111cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1df67234-eefc-40e5-a1fe-2f79920111cc' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>motor</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.2 -1.14 -1.08 ... 1.08 1.14 1.2</div><input id='attrs-d249730e-16e0-4567-9609-a2b9cd25fcb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d249730e-16e0-4567-9609-a2b9cd25fcb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2d33a5b-4891-4625-89a9-22362889fb2f' class='xr-var-data-in' type='checkbox'><label for='data-e2d33a5b-4891-4625-89a9-22362889fb2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>motor</dd></dl></div><div class='xr-var-data'><pre>array([-1.2 , -1.14, -1.08, -1.02, -0.96, -0.9 , -0.84, -0.78, -0.72,\n",
       "       -0.66, -0.6 , -0.54, -0.48, -0.42, -0.36, -0.3 , -0.24, -0.18,\n",
       "       -0.12, -0.06,  0.  ,  0.06,  0.12,  0.18,  0.24,  0.3 ,  0.36,\n",
       "        0.42,  0.48,  0.54,  0.6 ,  0.66,  0.72,  0.78,  0.84,  0.9 ,\n",
       "        0.96,  1.02,  1.08,  1.14,  1.2 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>motor_user_setpoint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.2 -1.14 -1.08 ... 1.08 1.14 1.2</div><input id='attrs-c66798db-b815-4425-b32e-c317016376e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c66798db-b815-4425-b32e-c317016376e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e135c62f-05a1-44a5-826b-04809cfc8d62' class='xr-var-data-in' type='checkbox'><label for='data-e135c62f-05a1-44a5-826b-04809cfc8d62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>motor</dd></dl></div><div class='xr-var-data'><pre>array([-1.2 , -1.14, -1.08, -1.02, -0.96, -0.9 , -0.84, -0.78, -0.72,\n",
       "       -0.66, -0.6 , -0.54, -0.48, -0.42, -0.36, -0.3 , -0.24, -0.18,\n",
       "       -0.12, -0.06,  0.  ,  0.06,  0.12,  0.18,  0.24,  0.3 ,  0.36,\n",
       "        0.42,  0.48,  0.54,  0.6 ,  0.66,  0.72,  0.78,  0.84,  0.9 ,\n",
       "        0.96,  1.02,  1.08,  1.14,  1.2 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>det</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... 3.032e-149 1.012e-169</div><input id='attrs-73471ee3-e989-47d0-b861-d97f46808b3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73471ee3-e989-47d0-b861-d97f46808b3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1987f586-544a-4e75-bf18-26198e438183' class='xr-var-data-in' type='checkbox'><label for='data-1987f586-544a-4e75-bf18-26198e438183' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>object :</span></dt><dd>det</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+000, 0.00000000e+000, 1.23288568e-292, 1.05908857e-265,\n",
       "       4.96804214e-240, 1.19582026e-215, 1.53222312e-192, 1.03407627e-170,\n",
       "       3.51623650e-150, 6.28275006e-131, 6.22191331e-113, 2.97778404e-096,\n",
       "       7.75910979e-081, 1.04981624e-066, 7.40910097e-054, 2.72598866e-042,\n",
       "       5.18839171e-032, 5.24623571e-023, 2.80339207e-015, 7.76777810e-009,\n",
       "       1.10175716e-003, 8.35809105e+000, 3.21535530e+003, 6.62308135e+004,\n",
       "       7.31111062e+004, 4.06248995e+003, 1.14530290e+001, 1.80963861e-003,\n",
       "       1.41688570e-008, 5.96728217e-015, 1.31565235e-022, 1.51883624e-031,\n",
       "       8.88541437e-042, 2.78721991e-053, 4.42531689e-066, 3.83313051e-080,\n",
       "       1.70769067e-095, 3.97423375e-112, 4.73296836e-130, 3.03157371e-149,\n",
       "       1.01227145e-169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbc685f2-1272-47ea-a962-8cb6dfb12252' class='xr-section-summary-in' type='checkbox'  ><label for='section-dbc685f2-1272-47ea-a962-8cb6dfb12252' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6b6c4c75-e753-4787-8262-e2d4b6f2f56c' class='xr-index-data-in' type='checkbox'/><label for='index-6b6c4c75-e753-4787-8262-e2d4b6f2f56c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1676402220.7818148, 1676402221.1092854,  1676402221.406919,\n",
       "              1676402221.7130668, 1676402222.0081055, 1676402222.3142998,\n",
       "              1676402222.6107583, 1676402222.9100828, 1676402223.2108445,\n",
       "               1676402223.517031,  1676402223.814489, 1676402224.1124578,\n",
       "              1676402224.4192667, 1676402224.7144701, 1676402225.0167778,\n",
       "              1676402225.3160782, 1676402225.6162107, 1676402225.9159987,\n",
       "              1676402226.2232482, 1676402226.5225663, 1676402226.8189554,\n",
       "               1676402227.118971,  1676402227.425652, 1676402227.7223291,\n",
       "              1676402228.0206509, 1676402228.3282225, 1676402228.6224885,\n",
       "              1676402228.9293768, 1676402229.2234843,  1676402229.529662,\n",
       "              1676402229.8257186, 1676402230.1299846, 1676402230.4262755,\n",
       "              1676402230.7270455, 1676402231.0265455, 1676402231.3306174,\n",
       "              1676402231.6540537, 1676402231.9291928, 1676402232.2293706,\n",
       "              1676402232.5302505, 1676402232.8377001],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2f8b2da-c4d7-4ee0-978e-aa762c0416b3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c2f8b2da-c4d7-4ee0-978e-aa762c0416b3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:              (time: 41)\n",
       "Coordinates:\n",
       "  * time                 (time) float64 1.676e+09 1.676e+09 ... 1.676e+09\n",
       "Data variables:\n",
       "    motor                (time) float64 -1.2 -1.14 -1.08 -1.02 ... 1.08 1.14 1.2\n",
       "    motor_user_setpoint  (time) float64 -1.2 -1.14 -1.08 -1.02 ... 1.08 1.14 1.2\n",
       "    det                  (time) float64 0.0 0.0 ... 3.032e-149 1.012e-169"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.primary.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support function: `specfile_example()`\n",
    "\n",
    "We need to call the `apstools.callbacks.SpecWriterCallback()` callback with the run's documents.\n",
    "\n",
    "Here, `specfile_example()` is a support function that can be used with one or more runs to create a SPEC data file (one Bluesky run will become one SPEC scan in the same file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from apstools.callbacks import SpecWriterCallback\n",
    "from databroker._drivers.mongo_normalized import BlueskyMongoCatalog\n",
    "from databroker._drivers.msgpack import BlueskyMsgpackCatalog\n",
    "import warnings\n",
    "\n",
    "DEMO_SPEC_FILE = \"test_specdata.txt\"\n",
    "\n",
    "def specfile_example(runs, filename=DEMO_SPEC_FILE):\n",
    "    \"\"\"write one or more headers (scans) to a SPEC data file\"\"\"\n",
    "    if isinstance(runs, databroker.core.BlueskyRun):\n",
    "        runs = [runs]\n",
    "    if not isinstance(runs, (list, BlueskyMsgpackCatalog, BlueskyMongoCatalog)):\n",
    "        raise TypeError(\"Must give run object or list of run objects.\")\n",
    "    if len(runs) == 0:\n",
    "        raise ValueError(\"Must provide one or more runs.\")\n",
    "\n",
    "    specwriter = SpecWriterCallback(filename=filename, auto_write=True)\n",
    "    for uid in runs:\n",
    "        if isinstance(uid, str):\n",
    "            run = runs[uid]\n",
    "        else:\n",
    "            run = uid\n",
    "        if not isinstance(run, databroker.core.BlueskyRun):\n",
    "            warnings.warn(f\"Skipping {run=}, it is not a BlueskyRun object.\")\n",
    "            continue\n",
    "        # to get the raw document stream, need the v1 interface\n",
    "        h = run.catalog_object.v1[run.name]  # header\n",
    "        for key, doc in h.db.get_documents(h):\n",
    "            specwriter.receiver(key, doc)\n",
    "        # lines = specwriter.prepare_scan_contents()\n",
    "    print(f\"Look at SPEC data file: {specwriter.spec_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the run as SPEC data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's write it as a SPEC data file (namely: `spec1.dat`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Look at SPEC data file: spec1.dat\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "spec1_dat = pathlib.Path(\"spec1.dat\")\n",
    "if spec1_dat.exists():  # re-write the file\n",
    "    spec1_dat.unlink()  # delete the existing file\n",
    "\n",
    "specfile_example(run, filename=spec1_dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view file `spec1.dat` from disk storage (using the [pycat](https://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=pycat#magic-pycat) IPython magic function):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m#F spec1.dat\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#E 1676402235\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#D Tue Feb 14 13:17:15 2023\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Bluesky  user = prjemian  host = zap\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#O0 atth  chi  motor  mx  my  mz  omega  phi\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#O1 ttheta\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#o0 atth  chi  motor  mx  my  mz  omega  phi\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#o1 ttheta\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#S 1  scan(detectors=['det'], num=41, args='['motor', -1.2, 1.2]', per_step='None')\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#D Tue Feb 14 13:16:56 2023\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:16:56 2023.  plan_type = generator\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:16:56 2023.  uid = 3df743d0-0768-467a-b5e8-1a4f84ad9c3d\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD uid = 3df743d0-0768-467a-b5e8-1a4f84ad9c3d\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD detectors = ['det']\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD motors = ['motor']\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD num_intervals = 40\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD num_points = 41\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD plan_pattern = inner_product\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD plan_pattern_args = {'num': 41, 'args': [\"EpicsMotor(prefix='gp:m1', name='motor', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", -1.2, 1.2]}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD plan_pattern_module = bluesky.plan_patterns\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD versions = {'ophyd': '1.7.0', 'bluesky': '1.10.0'}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#P0 0.35000000000000003 0.0 1.2 0.0 0.0 0.0 0.0 0.0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#P1 0.65\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#N 5\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#L motor  Epoch_float  Epoch  motor_user_setpoint  det\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.2\u001b[0m \u001b[0;36m4.340043544769287\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.2\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.1400000000000001\u001b[0m \u001b[0;36m4.667514085769653\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.14\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.08\u001b[0m \u001b[0;36m4.9651477336883545\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.08\u001b[0m \u001b[0;36m1.2328856838278386e-292\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.02\u001b[0m \u001b[0;36m5.271295547485352\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.02\u001b[0m \u001b[0;36m1.059088566676546e-265\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.96\u001b[0m \u001b[0;36m5.566334247589111\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.96\u001b[0m \u001b[0;36m4.968042135382428e-240\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;36m5.872528553009033\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.8999999999999999\u001b[0m \u001b[0;36m1.1958202643250899e-215\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.84\u001b[0m \u001b[0;36m6.168987035751343\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.84\u001b[0m \u001b[0;36m1.5322231228196394e-192\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.78\u001b[0m \u001b[0;36m6.468311548233032\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.78\u001b[0m \u001b[0;36m1.0340762669990406e-170\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.72\u001b[0m \u001b[0;36m6.769073247909546\u001b[0m \u001b[0;36m7\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.72\u001b[0m \u001b[0;36m3.51623650387244e-150\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.66\u001b[0m \u001b[0;36m7.075259685516357\u001b[0m \u001b[0;36m7\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.6599999999999999\u001b[0m \u001b[0;36m6.282750063287586e-131\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.6\u001b[0m \u001b[0;36m7.372717618942261\u001b[0m \u001b[0;36m7\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.6\u001b[0m \u001b[0;36m6.221913314981819e-113\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.54\u001b[0m \u001b[0;36m7.670686483383179\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.54\u001b[0m \u001b[0;36m2.9777840434537594e-96\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.48\u001b[0m \u001b[0;36m7.977495431900024\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.48\u001b[0m \u001b[0;36m7.759109794788586e-81\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.42\u001b[0m \u001b[0;36m8.272698879241943\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.41999999999999993\u001b[0m \u001b[0;36m1.0498162435302822e-66\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.36\u001b[0m \u001b[0;36m8.575006484985352\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.36\u001b[0m \u001b[0;36m7.409100974482037e-54\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.3\u001b[0m \u001b[0;36m8.874306917190552\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.30000000000000004\u001b[0m \u001b[0;36m2.725988662311212e-42\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.24\u001b[0m \u001b[0;36m9.174439430236816\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.24\u001b[0m \u001b[0;36m5.188391707598246e-32\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.18\u001b[0m \u001b[0;36m9.47422742843628\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.17999999999999994\u001b[0m \u001b[0;36m5.246235710806971e-23\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.12\u001b[0m \u001b[0;36m9.781476974487305\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.11999999999999988\u001b[0m \u001b[0;36m2.8033920736575236e-15\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.06\u001b[0m \u001b[0;36m10.080795049667358\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.06000000000000005\u001b[0m \u001b[0;36m7.767778104176514e-09\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.0\u001b[0m \u001b[0;36m10.37718415260315\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;36m0.0011017571616362435\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.06\u001b[0m \u001b[0;36m10.677199840545654\u001b[0m \u001b[0;36m11\u001b[0m \u001b[0;36m0.06000000000000005\u001b[0m \u001b[0;36m8.358091047426951\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.12\u001b[0m \u001b[0;36m10.983880758285522\u001b[0m \u001b[0;36m11\u001b[0m \u001b[0;36m0.11999999999999988\u001b[0m \u001b[0;36m3215.35530380413\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.18\u001b[0m \u001b[0;36m11.280557870864868\u001b[0m \u001b[0;36m11\u001b[0m \u001b[0;36m0.17999999999999994\u001b[0m \u001b[0;36m66230.8134586501\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.24\u001b[0m \u001b[0;36m11.578879594802856\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.24\u001b[0m \u001b[0;36m73111.10621036953\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.3\u001b[0m \u001b[0;36m11.886451244354248\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.30000000000000004\u001b[0m \u001b[0;36m4062.4899529906493\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.36\u001b[0m \u001b[0;36m12.18071722984314\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.3600000000000001\u001b[0m \u001b[0;36m11.453029013170354\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.42\u001b[0m \u001b[0;36m12.487605571746826\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.41999999999999993\u001b[0m \u001b[0;36m0.001809638606338164\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.48\u001b[0m \u001b[0;36m12.781713008880615\u001b[0m \u001b[0;36m13\u001b[0m \u001b[0;36m0.48\u001b[0m \u001b[0;36m1.4168856993691742e-08\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.54\u001b[0m \u001b[0;36m13.087890625\u001b[0m \u001b[0;36m13\u001b[0m \u001b[0;36m0.54\u001b[0m \u001b[0;36m5.967282173562889e-15\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.6\u001b[0m \u001b[0;36m13.383947372436523\u001b[0m \u001b[0;36m13\u001b[0m \u001b[0;36m0.5999999999999999\u001b[0m \u001b[0;36m1.3156523495521614e-22\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.66\u001b[0m \u001b[0;36m13.688213348388672\u001b[0m \u001b[0;36m14\u001b[0m \u001b[0;36m0.6599999999999999\u001b[0m \u001b[0;36m1.5188362400501979e-31\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.72\u001b[0m \u001b[0;36m13.984504222869873\u001b[0m \u001b[0;36m14\u001b[0m \u001b[0;36m0.72\u001b[0m \u001b[0;36m8.885414370735442e-42\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.78\u001b[0m \u001b[0;36m14.285274267196655\u001b[0m \u001b[0;36m14\u001b[0m \u001b[0;36m0.78\u001b[0m \u001b[0;36m2.7872199058127706e-53\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.84\u001b[0m \u001b[0;36m14.584774255752563\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m0.8400000000000001\u001b[0m \u001b[0;36m4.42531688732135e-66\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;36m14.888846158981323\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m0.9000000000000001\u001b[0m \u001b[0;36m3.8331305149188205e-80\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.96\u001b[0m \u001b[0;36m15.212282419204712\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m0.9600000000000002\u001b[0m \u001b[0;36m1.7076906675379237e-95\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.02\u001b[0m \u001b[0;36m15.48742151260376\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m1.0199999999999998\u001b[0m \u001b[0;36m3.9742337527757163e-112\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.08\u001b[0m \u001b[0;36m15.787599325180054\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;36m1.0799999999999998\u001b[0m \u001b[0;36m4.7329683607136035e-130\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.1400000000000001\u001b[0m \u001b[0;36m16.0884792804718\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;36m1.14\u001b[0m \u001b[0;36m3.031573705521703e-149\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.2\u001b[0m \u001b[0;36m16.395928859710693\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;36m1.2\u001b[0m \u001b[0;36m1.0122714506735151e-169\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:17:12 2023.  num_events_label_start_motor = 1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:17:12 2023.  num_events_primary = 41\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:17:12 2023.  exit_status = success\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "%pycat spec1.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the output of the `specfile_example()` command includes the content of the SPEC file.  For the remaining examples, we'll skip this additional step to view the SPEC file contents from disk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a specific scan\n",
    "\n",
    "The `cat` object allows us to access scans by UUID (or any shorter version that remains unique in the database).  We show an example but have commented it out since those runs do not exist in our temporary databroker catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specfile_example(cat[\"37c188c0\"], filename=\"spec3.dat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## specify a list of scans by UID\n",
    "\n",
    "Suppose we have a list of scans where we know the UID of each one, we can build a list of headers and write a SPEC data file with that list.  Here, we have such a list of tuning scans.  We show an example but have commented it out since those runs do not exist in our temporary databroker catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# runs = [cat[uid] for uid in \"957d83c c354fe37-e39f 42c\".split()]\n",
    "# specfile_example(runs, filename=\"spec_tunes.dat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find specific plans within a range of dates\n",
    "\n",
    "The `cat` object allows for filtering arguments based on any keywords in the *start* document and also by time.  Here, we filter between certain dates and also by `plan name`.  The dates are specified in [ISO8601 format](https://www.iso.org/iso-8601-date-and-time-format.html) and can include precision beyond a millisecond.  Here, we use the `v2` interface to do the searches.  We show examples how to pick between a set of dates.\n",
    "\n",
    "Also, we write to the default data file: `test_specdata.txt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/prjemian/micromamba/envs/bluesky_2023_1/lib/python3.10/site-packages/databroker/queries.py:89: PytzUsageWarning: The zone attribute is specific to pytz's interface; please migrate to a new time zone provider. For more details on how to do so, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  timezone = lz.zone\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3df743d0 2023-02-14 13:16:56.441771\n",
      "Look at SPEC data file: test_specdata.txt\n"
     ]
    }
   ],
   "source": [
    "from databroker.queries import TimeRange\n",
    "import datetime\n",
    "\n",
    "test_specdata_txt = pathlib.Path(\"test_specdata.txt\")\n",
    "if test_specdata_txt.exists():  # will re-write the file\n",
    "    test_specdata_txt.unlink()\n",
    "\n",
    "query = {}\n",
    "query.update(TimeRange(since=\"2019-02-19 17:00\"))\n",
    "query.update(TimeRange(until=\"2032-02-19 17:11:30\"))\n",
    "query.update(dict(plan_name=\"scan\"))\n",
    "\n",
    "runs = cat.v2.search(query)\n",
    "for uid in runs:\n",
    "    run = runs[uid]\n",
    "    start_time = run.metadata[\"start\"][\"time\"]\n",
    "    isodate = datetime.datetime.fromtimestamp(start_time).isoformat(sep=\" \")\n",
    "    print(uid[:8], isodate)\n",
    "specfile_example(runs, test_specdata_txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m#F test_specdata.txt\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#E 1676402235\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#D Tue Feb 14 13:17:15 2023\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Bluesky  user = prjemian  host = zap\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#O0 atth  chi  motor  mx  my  mz  omega  phi\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#O1 ttheta\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#o0 atth  chi  motor  mx  my  mz  omega  phi\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#o1 ttheta\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#S 1  scan(detectors=['det'], num=41, args='['motor', -1.2, 1.2]', per_step='None')\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#D Tue Feb 14 13:16:56 2023\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:16:56 2023.  plan_type = generator\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:16:56 2023.  uid = 3df743d0-0768-467a-b5e8-1a4f84ad9c3d\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD uid = 3df743d0-0768-467a-b5e8-1a4f84ad9c3d\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD detectors = ['det']\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD motors = ['motor']\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD num_intervals = 40\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD num_points = 41\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD plan_pattern = inner_product\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD plan_pattern_args = {'num': 41, 'args': [\"EpicsMotor(prefix='gp:m1', name='motor', settle_time=0.0, timeout=None, read_attrs=['user_readback', 'user_setpoint'], configuration_attrs=['user_offset', 'user_offset_dir', 'velocity', 'acceleration', 'motor_egu'])\", -1.2, 1.2]}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD plan_pattern_module = bluesky.plan_patterns\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#MD versions = {'ophyd': '1.7.0', 'bluesky': '1.10.0'}\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#P0 0.35000000000000003 0.0 1.2 0.0 0.0 0.0 0.0 0.0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#P1 0.65\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#N 5\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#L motor  Epoch_float  Epoch  motor_user_setpoint  det\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.2\u001b[0m \u001b[0;36m4.340043544769287\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.2\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.1400000000000001\u001b[0m \u001b[0;36m4.667514085769653\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.14\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.08\u001b[0m \u001b[0;36m4.9651477336883545\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.08\u001b[0m \u001b[0;36m1.2328856838278386e-292\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.02\u001b[0m \u001b[0;36m5.271295547485352\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.02\u001b[0m \u001b[0;36m1.059088566676546e-265\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.96\u001b[0m \u001b[0;36m5.566334247589111\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.96\u001b[0m \u001b[0;36m4.968042135382428e-240\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;36m5.872528553009033\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.8999999999999999\u001b[0m \u001b[0;36m1.1958202643250899e-215\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.84\u001b[0m \u001b[0;36m6.168987035751343\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.84\u001b[0m \u001b[0;36m1.5322231228196394e-192\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.78\u001b[0m \u001b[0;36m6.468311548233032\u001b[0m \u001b[0;36m6\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.78\u001b[0m \u001b[0;36m1.0340762669990406e-170\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.72\u001b[0m \u001b[0;36m6.769073247909546\u001b[0m \u001b[0;36m7\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.72\u001b[0m \u001b[0;36m3.51623650387244e-150\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.66\u001b[0m \u001b[0;36m7.075259685516357\u001b[0m \u001b[0;36m7\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.6599999999999999\u001b[0m \u001b[0;36m6.282750063287586e-131\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.6\u001b[0m \u001b[0;36m7.372717618942261\u001b[0m \u001b[0;36m7\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.6\u001b[0m \u001b[0;36m6.221913314981819e-113\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.54\u001b[0m \u001b[0;36m7.670686483383179\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.54\u001b[0m \u001b[0;36m2.9777840434537594e-96\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.48\u001b[0m \u001b[0;36m7.977495431900024\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.48\u001b[0m \u001b[0;36m7.759109794788586e-81\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.42\u001b[0m \u001b[0;36m8.272698879241943\u001b[0m \u001b[0;36m8\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.41999999999999993\u001b[0m \u001b[0;36m1.0498162435302822e-66\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.36\u001b[0m \u001b[0;36m8.575006484985352\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.36\u001b[0m \u001b[0;36m7.409100974482037e-54\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.3\u001b[0m \u001b[0;36m8.874306917190552\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.30000000000000004\u001b[0m \u001b[0;36m2.725988662311212e-42\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.24\u001b[0m \u001b[0;36m9.174439430236816\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.24\u001b[0m \u001b[0;36m5.188391707598246e-32\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.18\u001b[0m \u001b[0;36m9.47422742843628\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.17999999999999994\u001b[0m \u001b[0;36m5.246235710806971e-23\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.12\u001b[0m \u001b[0;36m9.781476974487305\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.11999999999999988\u001b[0m \u001b[0;36m2.8033920736575236e-15\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.06\u001b[0m \u001b[0;36m10.080795049667358\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.06000000000000005\u001b[0m \u001b[0;36m7.767778104176514e-09\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.0\u001b[0m \u001b[0;36m10.37718415260315\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;36m0.0011017571616362435\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.06\u001b[0m \u001b[0;36m10.677199840545654\u001b[0m \u001b[0;36m11\u001b[0m \u001b[0;36m0.06000000000000005\u001b[0m \u001b[0;36m8.358091047426951\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.12\u001b[0m \u001b[0;36m10.983880758285522\u001b[0m \u001b[0;36m11\u001b[0m \u001b[0;36m0.11999999999999988\u001b[0m \u001b[0;36m3215.35530380413\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.18\u001b[0m \u001b[0;36m11.280557870864868\u001b[0m \u001b[0;36m11\u001b[0m \u001b[0;36m0.17999999999999994\u001b[0m \u001b[0;36m66230.8134586501\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.24\u001b[0m \u001b[0;36m11.578879594802856\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.24\u001b[0m \u001b[0;36m73111.10621036953\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.3\u001b[0m \u001b[0;36m11.886451244354248\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.30000000000000004\u001b[0m \u001b[0;36m4062.4899529906493\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.36\u001b[0m \u001b[0;36m12.18071722984314\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.3600000000000001\u001b[0m \u001b[0;36m11.453029013170354\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.42\u001b[0m \u001b[0;36m12.487605571746826\u001b[0m \u001b[0;36m12\u001b[0m \u001b[0;36m0.41999999999999993\u001b[0m \u001b[0;36m0.001809638606338164\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.48\u001b[0m \u001b[0;36m12.781713008880615\u001b[0m \u001b[0;36m13\u001b[0m \u001b[0;36m0.48\u001b[0m \u001b[0;36m1.4168856993691742e-08\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.54\u001b[0m \u001b[0;36m13.087890625\u001b[0m \u001b[0;36m13\u001b[0m \u001b[0;36m0.54\u001b[0m \u001b[0;36m5.967282173562889e-15\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.6\u001b[0m \u001b[0;36m13.383947372436523\u001b[0m \u001b[0;36m13\u001b[0m \u001b[0;36m0.5999999999999999\u001b[0m \u001b[0;36m1.3156523495521614e-22\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.66\u001b[0m \u001b[0;36m13.688213348388672\u001b[0m \u001b[0;36m14\u001b[0m \u001b[0;36m0.6599999999999999\u001b[0m \u001b[0;36m1.5188362400501979e-31\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.72\u001b[0m \u001b[0;36m13.984504222869873\u001b[0m \u001b[0;36m14\u001b[0m \u001b[0;36m0.72\u001b[0m \u001b[0;36m8.885414370735442e-42\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.78\u001b[0m \u001b[0;36m14.285274267196655\u001b[0m \u001b[0;36m14\u001b[0m \u001b[0;36m0.78\u001b[0m \u001b[0;36m2.7872199058127706e-53\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.84\u001b[0m \u001b[0;36m14.584774255752563\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m0.8400000000000001\u001b[0m \u001b[0;36m4.42531688732135e-66\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;36m14.888846158981323\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m0.9000000000000001\u001b[0m \u001b[0;36m3.8331305149188205e-80\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m0.96\u001b[0m \u001b[0;36m15.212282419204712\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m0.9600000000000002\u001b[0m \u001b[0;36m1.7076906675379237e-95\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.02\u001b[0m \u001b[0;36m15.48742151260376\u001b[0m \u001b[0;36m15\u001b[0m \u001b[0;36m1.0199999999999998\u001b[0m \u001b[0;36m3.9742337527757163e-112\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.08\u001b[0m \u001b[0;36m15.787599325180054\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;36m1.0799999999999998\u001b[0m \u001b[0;36m4.7329683607136035e-130\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.1400000000000001\u001b[0m \u001b[0;36m16.0884792804718\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;36m1.14\u001b[0m \u001b[0;36m3.031573705521703e-149\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m1.2\u001b[0m \u001b[0;36m16.395928859710693\u001b[0m \u001b[0;36m16\u001b[0m \u001b[0;36m1.2\u001b[0m \u001b[0;36m1.0122714506735151e-169\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:17:12 2023.  num_events_label_start_motor = 1\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:17:12 2023.  num_events_primary = 41\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;31m#C Tue Feb 14 13:17:12 2023.  exit_status = success\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "%pycat test_specdata.txt"
   ]
  }
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