Downtime + Cloudy downtime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from astropy.time import Time, TimeDelta\n",
    "import lsst.sims.downtimeModel as dt\n",
    "import lsst.sims.cloudModel as cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tstart = Time('2022-10-01', format='isot', scale='tai')\n",
    "day_offset = TimeDelta(-0.34, format='jd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "First downtime start 2022-06-07T15:50:24.000 end 2022-06-14T15:50:24.000\n",
      "Last downtime start 2041-10-29T15:50:24.000 end 2041-11-05T15:50:24.000\n"
     ]
    }
   ],
   "source": [
    "# Read scheduled downtime data.\n",
    "scheduledDowntimeData = dt.ScheduledDowntimeData(tstart, start_of_night_offset=day_offset)\n",
    "scheduledDowntimeData.read_data()\n",
    "\n",
    "print(len(scheduledDowntimeData.downtime))\n",
    "print(\"First downtime\", \"start\", scheduledDowntimeData.downtime[0]['start'].isot, \n",
    "      \"end\", scheduledDowntimeData.downtime[0]['end'].isot)\n",
    "print(\"Last downtime\", \"start\", scheduledDowntimeData.downtime[-1]['start'].isot, \n",
    "      \"end\", scheduledDowntimeData.downtime[-1]['end'].isot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145\n",
      "First downtime start 2022-03-01T15:50:24.000 end 2022-03-02T15:50:24.000\n",
      "Last downtime start 2041-10-16T15:50:24.000 end 2041-10-17T15:50:24.000\n"
     ]
    }
   ],
   "source": [
    "# Read (generate) unscheduled downtime data\n",
    "unscheduledDowntimeData = dt.UnscheduledDowntimeData(tstart, start_of_night_offset=day_offset,\n",
    "                                                    survey_length=3650*2, seed=3)\n",
    "unscheduledDowntimeData.make_data()\n",
    "\n",
    "print(len(unscheduledDowntimeData.downtime))\n",
    "print(\"First downtime\", \"start\", unscheduledDowntimeData.downtime[0]['start'].isot, \n",
    "      \"end\", unscheduledDowntimeData.downtime[0]['end'].isot)\n",
    "print(\"Last downtime\", \"start\", unscheduledDowntimeData.downtime[-1]['start'].isot, \n",
    "      \"end\", unscheduledDowntimeData.downtime[-1]['end'].isot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read cloud data\n",
    "cloudData = cm.CloudData(tstart)\n",
    "cloudData.read_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('DowntimeModel_version', '0bec6a0'),\n",
       "             ('DowntimeModel_sha', '0bec6a04497600aa71a01ebe8d9343335ed55342'),\n",
       "             ('efd_columns', ['scheduled_downtimes', 'unscheduled_downtimes']),\n",
       "             ('efd_delta_time', 0.0),\n",
       "             ('target_columns', ['time'])])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up downtime model\n",
    "dtModel = dt.DowntimeModel()\n",
    "dtModel.config_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('CloudModel_version', 'unknown'),\n",
       "             ('CloudModel_sha', '73e205387426ea256f6a3cb809fe8ddccddb22b1 *'),\n",
       "             ('efd_columns', ['cloud']),\n",
       "             ('efd_delta_time', 0.0),\n",
       "             ('target_columns', ['altitude', 'azimuth'])])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up cloud model\n",
    "cloudModel = cm.CloudModel()\n",
    "cloudModel.config_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The EFD data for the downtime models is a little different than some of the other *Data values.\n",
    "# Basically, you don't repeatedly query it (although that may make sense in production),\n",
    "# and then you just pass the entire downtime arrays into the model.\n",
    "sched_downtime = scheduledDowntimeData()\n",
    "unsched_downtime = unscheduledDowntimeData()\n",
    "efdData = {'scheduled_downtimes': sched_downtime,\n",
    "           'unscheduled_downtimes': unsched_downtime}\n",
    "# But for the cloud model, the efdData is the cloud (single value, because we don't have sky maps yet)\n",
    "# value at that time.\n",
    "# i.e  efdData.update(cloudData(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So let's look at what we would see as a result of asking about 'downtime' and 'cloud' once per day for 10 years\n",
    "efdData = {'scheduled_downtimes': sched_downtime,\n",
    "           'unscheduled_downtimes': unsched_downtime}\n",
    "\n",
    "tStart = tstart - day_offset\n",
    "tEnd = Time('2032-10-01', format='isot', scale='tai')\n",
    "tStep = TimeDelta(1, format='jd')\n",
    "times = np.arange(tStart, tEnd, tStep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2022-10-01T08:09:36.000', '2032-09-30T08:09:36.000', 3652.0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times[0].isot, times[-1].isot, (times[-1] - times[0]).jd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloudstatus = np.zeros(len(times), bool)  # True = 1 = closed / downtime\n",
    "telestatus = np.zeros(len(times), bool)   # True = 1 = closed\n",
    "for i, t in enumerate(times):\n",
    "    efdData['cloud'] = cloudData(t)\n",
    "    targetDict = {'time': t, 'altitude': np.array([90]), 'azimuth': np.array([0])}\n",
    "    down = dtModel(efdData, targetDict)\n",
    "    telestatus[i] = down['status']\n",
    "    cloud = cloudModel(efdData, targetDict)\n",
    "    if cloud['cloud'][0] > 0.3:\n",
    "        cloudstatus[i] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total time  3653 cloud downtime  1033 telescope downtime  296 telescope and cloud downtime overlap  121 uptime  2445\n"
     ]
    }
   ],
   "source": [
    "print('total time ', len(times), 'cloud downtime ', len(np.where(cloudstatus)[0]), \n",
    "      'telescope downtime ', len(np.where(telestatus)[0]), \n",
    "      'telescope and cloud downtime overlap ', len(np.where(telestatus & cloudstatus)[0]), \n",
    "      'uptime ', len(np.where(~telestatus & ~cloudstatus)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "plt.plot(cloudstatus, color='r')\n",
    "plt.plot(telestatus)\n",
    "plt.ylim(0, 1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(262.0, 103.3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(times) - len(np.where(cloudstatus)[0]))/10, len(np.where(cloudstatus)[0])/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}