jzuhone/jul2720_hack.ipynb

## jul2720_hack.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import acispy\n",
    "from cheta import fetch_sci as fetch\n",
    "from kadi import commands\n",
    "from kadi.commands import states\n",
    "import numpy as np\n",
    "from Chandra.Time import DateTime, date2secs\n",
    "from kadi.update_cmds import fix_nonload_cmds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get commands from JUL2720 from backstop\n",
    "backstop_file = \"/data/acis/LoadReviews/2020/JUL2720/oflsb/CR208_1806.backstop\"\n",
    "bs_cmds = commands.get_cmds_from_backstop(backstop_file)\n",
    "load_start = bs_cmds[0]['date']\n",
    "bs_dates = bs_cmds[\"date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get last telem from Ska archive.\n",
    "last_tlm_date = fetch.get_time_range('1dpamzt', format='date')[1]\n",
    "T_dpa0 = fetch.MSID(\"1dpamzt\", \"2020:190\").vals[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the runnning load termination time so we can handle continuity correctly\n",
    "ok = bs_cmds['event_type'] == 'RUNNING_LOAD_TERMINATION_TIME'\n",
    "rltt = DateTime(bs_dates[ok][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get approved commands from available telemetry through start of new loads\n",
    "cmds = commands.get_cmds(last_tlm_date, rltt, inclusive_stop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add backstop commands.\n",
    "cmds = cmds.add_cmds(bs_cmds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is where things get a bit hacky. We have to construct a table from scratch \n",
    "\n",
    "# The list of commands we are going to put in. Some of these are dummy values which don't matter\n",
    "cmd_dicts = [\n",
    "    {'cmd': u'ACISPKT',               \n",
    "     'date': u'2020:213:06:15:32.00',\n",
    "     'id': 0,  \n",
    "     'msid': None,  \n",
    "     'params': {},\n",
    "     'scs': 135, \n",
    "     'step': None, \n",
    "     'time': date2secs(u'2020:213:06:15:32.00'),\n",
    "     'timeline_id': None,  \n",
    "     'tlmsid': u'AA0000000',   \n",
    "     'vcdu': None},\n",
    "    {'cmd': u'ACISPKT',               \n",
    "     'date': u'2020:213:06:15:32.00',\n",
    "     'id': 0,  \n",
    "     'msid': None,  \n",
    "     'params': {},\n",
    "     'scs': 135, \n",
    "     'step': None, \n",
    "     'time': date2secs(u'2020:213:06:15:32.00'),\n",
    "     'timeline_id': None,  \n",
    "     'tlmsid': u'AA0000000',   \n",
    "     'vcdu': None},\n",
    "    {'cmd': u'ACISPKT',               \n",
    "     'date': u'2020:213:06:15:32.00',\n",
    "     'id': 0,  \n",
    "     'msid': None,  \n",
    "     'params': {},\n",
    "     'scs': 135, \n",
    "     'step': None, \n",
    "     'time': date2secs(u'2020:213:06:15:32.00'),\n",
    "     'timeline_id': None,  \n",
    "     'tlmsid': u'WSPOW00000',   \n",
    "     'vcdu': None},\n",
    "    {'cmd': u'ACISPKT',               \n",
    "     'date': u'2020:213:06:16:35.00',\n",
    "     'id': 0,  \n",
    "     'msid': None,  \n",
    "     'params': {},\n",
    "     'scs': 135, \n",
    "     'step': None, \n",
    "     'time': date2secs(u'2020:213:06:16:35.00'),\n",
    "     'timeline_id': None,  \n",
    "     'tlmsid': u'WSPOW0CF3F',   \n",
    "     'vcdu': None},\n",
    "    {'cmd': u'ACISPKT',               \n",
    "     'date': u'2020:213:06:16:47.00',\n",
    "     'id': 0,  \n",
    "     'msid': None,  \n",
    "     'params': {},\n",
    "     'scs': 135, \n",
    "     'step': None, \n",
    "     'time': date2secs(u'2020:213:06:16:47.00'),\n",
    "     'timeline_id': None,  \n",
    "     'tlmsid': u'XTZ0000005',   \n",
    "     'vcdu': None},\n",
    "    {'cmd': u'ACISPKT',               \n",
    "     'date': u'2020:213:10:04:59.00',\n",
    "     'id': 0,  \n",
    "     'msid': None,  \n",
    "     'params': {},\n",
    "     'scs': 135, \n",
    "     'step': None, \n",
    "     'time': date2secs(u'2020:213:10:04:59.00'),\n",
    "     'timeline_id': None,  \n",
    "     'tlmsid': u'AA0000000',   \n",
    "     'vcdu': None}\n",
    "]\n",
    "\n",
    "# Now we use the kadi magic to get everything into the format we need\n",
    "cmd_dicts = fix_nonload_cmds(cmd_dicts)\n",
    "ecs_cmds = commands.CommandTable(cmd_dicts)\n",
    "\n",
    "# Now add the ECS commands in\n",
    "new_cmds = cmds.add_cmds(ecs_cmds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate thermal model from the cmds without ECS\n",
    "tm1 = acispy.ThermalModelRunner.from_commands(\"1dpamzt\", cmds[\"date\"][0], cmds[\"date\"][-1], \n",
    "                                              cmds, T_dpa0, no_eclipse=True, get_msids=False)\n",
    "# Generate thermal model from the cmds with ECS\n",
    "tm2 = acispy.ThermalModelRunner.from_commands(\"1dpamzt\", new_cmds[\"date\"][0], new_cmds[\"date\"][-1], \n",
    "                                              new_cmds, T_dpa0, no_eclipse=True, get_msids=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<acispy.plots.DatePlot at 0x122c6a580>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare\n",
    "dp = acispy.DatePlot(tm1, \"1dpamzt\")\n",
    "dp2 = acispy.DatePlot(tm2, \"1dpamzt\", field2=\"fep_count\", plot=dp)\n",
    "dp2.set_ylim2(0, 6.5)\n",
    "dp.set_xlim(\"2020:213:00:00:00\", \"2020:214:03:00:00\")\n",
    "dp.add_hline(37.5)\n",
    "dp.add_hline(39.5, color='gold')\n",
    "dp.set_line_label(0, \"w/out ECS\")\n",
    "dp.set_line_label(1, \"w/ ECS\")\n",
    "dp.set_ylim(7, 40.5)\n",
    "dp.set_legend()\n",
    "dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write temperatures\n",
    "tm1.write_model(\"temperatures1.dat\", overwrite=True)\n",
    "tm2.write_model(\"temperatures2.dat\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write states\n",
    "tm1.write_states(\"states1.dat\", overwrite=True)\n",
    "tm2.write_states(\"states2.dat\", overwrite=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit ('ska-shiny': conda)",
   "language": "python",
   "name": "python38364bitskashinycondaf0ceebd4107e429bb7db45e11f67b89f"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}