WFD footprint visible at other observatories.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wfd_dec_min = -63\n",
    "wfd_dec_max = 4.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kpno_lat = 32\n",
    "mk_lat = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def min_airmass(field_dec, obs_lat):\n",
    "    min_z = np.radians(np.abs(field_dec - obs_lat))\n",
    "    min_airmass = 1 / np.cos(min_z)\n",
    "    return min_airmass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dec_range(obs_lat, airmass_limit):\n",
    "    z_max = np.degrees(np.arccos(1.0/airmass_limit))\n",
    "    dec_min = obs_lat - z_max\n",
    "    dec_max = obs_lat + z_max\n",
    "    dec_min = np.max([dec_min, -90])\n",
    "    dec_max = np.min([dec_max, 90])\n",
    "    return dec_min, dec_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def overlap_wfd(obs_lat, airmass_limit, wfd_dec_min, wfd_dec_max):\n",
    "    dec_min, dec_max = dec_range(obs_lat, airmass_limit)\n",
    "    if dec_min > wfd_dec_max:\n",
    "        return 0\n",
    "    if dec_max < wfd_dec_min: \n",
    "        return 0\n",
    "    overlap_dec_max = np.min([dec_max, wfd_dec_max])\n",
    "    overlap_dec_min = np.max([dec_min, wfd_dec_min])\n",
    "    overlap_area = 2*np.pi*np.abs(np.sin(np.radians(overlap_dec_min)) - np.sin(np.radians(overlap_dec_max)))\n",
    "    wfd_area = 2*np.pi*np.abs(np.sin(np.radians(wfd_dec_min)) - np.sin(np.radians(wfd_dec_max)))\n",
    "    return overlap_area/wfd_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56518833392058943"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap_wfd(kpno_lat, 2, wfd_dec_min, wfd_dec_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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tWmbOtgRuizF+1piSuqzDmTOpF0aPzrbKOOAAOOggCD2ahJAk1SLvmbNNgJdC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110265e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "airmass_limits = np.arange(1, 2.5, 0.01)\n",
    "overlap = np.zeros(len(airmass_limits))\n",
    "for i, airmass in enumerate(airmass_limits):\n",
    "    overlap[i] = overlap_wfd(kpno_lat, airmass, wfd_dec_min, wfd_dec_max)\n",
    "plt.figure(figsize=[10, 8])\n",
    "plt.plot(airmass_limits, overlap)\n",
    "plt.xlabel('Airmass limit')\n",
    "plt.ylabel('Fraction of WFD footprint observable @ KPNO')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wuhOB+4CPE76vFKS91v/jj3DwwXDGGZXVmGkGIk6ZhCmXMOUSplzi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\nZlYgHpyZmZmZFcj/AXu7uYeVFvhfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e9ccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "airmass_limits = np.arange(1, 2.5, 0.05)\n",
    "overlap = np.zeros(len(airmass_limits))\n",
    "for i, airmass in enumerate(airmass_limits):\n",
    "    overlap[i] = overlap_wfd(mk_lat, airmass, wfd_dec_min, wfd_dec_max)\n",
    "plt.figure(figsize=[10, 8])\n",
    "plt.plot(airmass_limits, overlap)\n",
    "plt.xlabel('Airmass limit')\n",
    "plt.ylabel('Fraction of WFD footprint observable @ Mauna Kea')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}