SlewExample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lsst.ts.observatory.model as om"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "observatoryModel = om.ObservatoryModel()\n",
    "observatoryModel.configure_from_module()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up grid to calculate slew times *to*\n",
    "altitude = np.arange(0, 90, 0.25)\n",
    "azimuth = np.arange(-180, 180, 0.5)\n",
    "alt, az = np.meshgrid(altitude, azimuth)\n",
    "#alt = np.ravel(alt)\n",
    "#az = np.ravel(az)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=7.7 ra=29.510 dec=-20.244 ang=180.000 filter=r track=False alt=80.000 az=0.000 pa=180.000 rot=0.000 telaz=0.000 telrot=0.000 mounted=['g', 'r', 'i', 'z', 'y'] unmounted=['u']\n"
     ]
    }
   ],
   "source": [
    "# Set location of telescope\n",
    "telAlt = 80.0\n",
    "telAz = 0\n",
    "observatoryModel.slew_altaz(time=0, alt_rad=np.radians(telAlt), az_rad=np.radians(telAz), \n",
    "                            rot_rad=0, band_filter='r')\n",
    "print(observatoryModel.current_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate slew times\n",
    "slewtimes = observatoryModel.get_approximate_slew_delay(alt_rad=np.radians(alt), az_rad=np.radians(az), \n",
    "                                                        goal_filter='r', lax_dome=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1111ec198>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x612dbebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot slew times ... (figure out how to do polar plot, with contourf .. or at least, a better imshow plot)\n",
    "cs = plt.imshow(slewtimes)\n",
    "plt.colorbar(cs)"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}