{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import healpy as hp\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "import lsst.sims.featureScheduler.utils as utils\n",
    "from survey_footprints import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def disp_footprints(footprints):\n",
    "    for i,key in enumerate(footprints):\n",
    "        hp.mollview(footprints[key], title=key, min=0,max=1, sub=(2,3,i+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classic Baseline Footprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc960bfba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "standard = utils.standard_goals()\n",
    "disp_footprints(standard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WFD area =  18346.9590843\n",
      "3.35717458084 32\n"
     ]
    }
   ],
   "source": [
    "standard = utils.standard_goals(nside=32)\n",
    "wfd_pix = np.where(standard['r'] == 1)[0]\n",
    "nside = hp.npix2nside(np.size(standard['r']))\n",
    "pix_area = hp.nside2pixarea(nside, degrees=True)\n",
    "print('WFD area = ', np.size(wfd_pix)*pix_area)\n",
    "print(pix_area, nside)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Galactic Plane as part of WFD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc96152ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprints = gp_smooth()\n",
    "disp_footprints(footprints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big Sky Footprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc991773438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = big_sky()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big Sky with no uiy in non-WFD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc981962668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = big_sky_nouiy()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big Sky with dust cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WFD area =  18155.6001332\n",
      "3.35717458084 32\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc98243c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = big_sky_dust()\n",
    "disp_footprints(footprint)\n",
    "\n",
    "wfd_pix = np.where(footprint['r'] == 1)[0]\n",
    "nside = hp.npix2nside(np.size(footprint['r']))\n",
    "pix_area = hp.nside2pixarea(nside, degrees=True)\n",
    "print('WFD area = ', np.size(wfd_pix)*pix_area)\n",
    "print(pix_area, nside)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## newA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc982278588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = newA()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## newB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4ojwBAPBEeQIA4InyBADAE+UJAIAnyhMAAE+UJwAAnihPAAA8UZ4AAHiiPAEA8ER5AgDgifIEAMAT5QkAgCfKEwAAT5QnAACeKE8AADxRngAAeKI8AQDwRHkCAOCJ8gQAwBPlCQCAJ8oTAABPlCcAAJ4oTwAAPFGeAAB4ojwjM7NvmNnrYo8DAFCfOedijwEAgKyw8gQAwBPlGZmZfdfMfj32OJAXM7vazH6+8Oe4mX029riQPjN7p5n949JjHzKzv4g1phxRnkCGnHN3O+ee5Zx7lqSzJD0u6c7Iw0Ie7pB0hZk9R5LMbCLpGkl/G3VUmaE8gYyZ2UjS30v6rHPub2KPB+lzzj0p6QFJV80eukLS/zjn/iPeqPJDeQJ5e5+kZ0v6g9gDQVY+Jum62dvXSbo94liyRHkCmTKzayRdK+mtzrnt2ONBVj4l6WVm9lJJb5L0d5HHk51J7AEA8GdmF0r6S0mXO+f+O/Z4kBfn3NNm9g+qtvy/7Jz7r9hjyg0rTyBPV0p6rqQvLNxx+y+xB4WsfEzSr4kt20b4IQkAMEBm9quSvinpDOfcT2OPJzesPAFgYGZ3ab9D0l0UZzNc8wSAATGzI5J+KOk/VX2bChpg2xYAAE9s2wIA4CnWtu0glruXj646+KCa7pve09rflTircQz58UR+9iA/nsjPigMjbdtmH942g9mWQgI+iMmP/HSG/EQyoPxUB1Ke66UYUl+ZhbqoyY/89I78JKbA/FQHUp67SghqXQkHOtvJj/wkgfxkIPP8VAcOuTyHFNaDJBTmbCY/8rOL/PgjP7syy0914NDKk8AeLHKQk578yM/ByM/+yM/BMshPdeAQypPANhchyMlNfuSnOfJDfkIkmp/qwFLLk8C2r6cgJzH5kZ/2kR+ESCg/1YGllSeh7V7HIY46+ZGf7pEfhEggP9WBpZQnoe1fRyGOMvmRn/6RH4SImJ/qwNzLk9DG13KIe538yE985AchIuSnOjDX8iS06WkpxL1MfuQnPeQHIXrMT3VgbuVJaNMXGOJOJz/ykz7ygxA95EdSZr9VheDmIdXzlOq4sFeq5ynVcWGvvs5TFitPQpuvBl8Ftr5yID/5Ij8I0VF+JGWw8iS4eYt9/mK/PsLEPn+xXx9hujx/SZcnwS1DrPNIfspAfhCiq/OYbHkS3LL0fT7JT1nID0J0cT6TLE+CW6a+ziv5KRP5QYi2z2tyNwx1Edxv3XZJo+edd8ODe5573g0PtjWkQTvgIn7QDR9MfOUjPwjRQn6qA1Mqz8suff++Txh9/mFNX3PhqbcP0rQw62haovPx11Hn39jma/b9evd/7t37/a/Gk1+b+akj1Y9tjNckP/5S/djGeM3M8lMdmEp5rgvuQY5dPwkaTKhzbt+J+vq52ifAjSa/kPwgT+QHIQLyUx2YQnk2CW7swlx2zu07Onb9hCL1tCLA3pMfE99wkR+EaJif6sDY5ekb3NRKcxUK1M9SgL0mPyY+kB+EaJAfSVLyTZRDWS5bHDNFCgDlibryXPdVX46luQ4lut7CV3+1Vw6sGjBHfhDCMz+SElx5llaac/N/FyUKAPmLtvKcf9VXalmuw81Fq82++qu1cmDVgGXkByE88iMpYnlOnzq38xd542V+3/D8mftb/Y3kUdz85EXez/nAmQ91MBJ/ozO+LdWc/O46dnHHo5GOvvH1Xse/7TP/1tFI+vPh7/j9myXpxrPT+Hdfc85XJPIT1UDyIynRH8/XhiEWZ1NNCrd0Q5z4mmoyYZaO/NSXa36KLE/f4ixJKqvInPlOfCVJZRWQM/IzDFHKs48tWx+lrTqbFGgKq8+6uehjy81HaauGJhNgCquHurkgP90qPT9zRa48AQDoUnHlybXOSq6rz9i4VlXJdfUQG/mpDCE/xZUnAABdK6o8h3yj0CqsPv0M+UaPVYawemgT+dmr9PwUU54UZzuGercuE187hnS35SLy046c8hOlPGffDB9dqdc7Q8RcedbNxeybmaMr9XpViJgrh7q5ID/pyiE/c8WsPLHaUFeSaEdOKwGkp+T8DLY8WXXub8jXPeti1bC/nK5bxUJ+9pdLfooozybXO7lGut6QCrTJ9Squca2XywTYBvLTvhzyE60827zu2WQVycpzvb63e33z0OZ1qyarAFYO6/W9XeebB/KTttTzIw145Yn1WHkiRA4rh7aQn/blkJ+o5ZnKXbelS/2moaY5SOWuydKlftNH0xyQn36Ump/sV56sOhGCVQNCkJ/hil6erD6HLfT8s3oYttDzT36GLeT8Ry9PKWwC5caf7nR93bOtL5xCPgG4caM7XV+3aqv4yE+aUs9PEuUpNZ9I2bbNU9s7Dk0/Edh2y1PbK0byMyxt5CeZ8pT8J1SKM09dbdX7fkIw8eWpq61W8jMMbeUnqfKUuAaamra3brs+v1zDSkvbW29dn1/yk5aU85NceUr1J1iud+alry+M6n6CcL0qL30VG/kpU9v5SbI8pXoTbci2LVu+9bWx+ux7R6HOJ0rIthtbdvW1sXroe0VIftKRan6SLU9p/YRL+fUrpEBjbcWv+4Rh8upXyAQYayuV/KQjxfwkXZ5StxPvUAo4h9/R2ZUuJ96hTKA5/Y7FnF6f/HSvy/M36exvbtF8Ap4+da5ufvIiPXLdiyKPaJgWS/igH/kXuzQXzT+B7jp2sT78ndfrWb8feUADtTiJHvQj22KX5iLyk4bU8mPOuc5fZIXGL3rTw1dLUqsFWvqNR32vPG+58O6Qp1uNYxrn59X3/ZEktToBln7jSN8rhy9c/mchTyc/iSkwP5IyWXkumk/MN93RfokiTGBp9mL+ifXqW9ufBBEmcNLrBflJV9/5ya4855ZLVKJIY8mhNJctT4ISE2EsOZTmMvKTjlj5yW7bdj/z7VypWYmWvHXb1bZtR6XZ6bbbfubbcVKzSbDkrbeutt06mvTIT2IKzE91YCnlORdSoiUWaBfF2fFKM8rkNxcyCZY4AXYx8XW8UiA/CSk0P9WBpZXn3GKJSvWLtLQCbbM8e9qejTr5zS1OglL9ibC0CbDNya+n7TXyk5BC81MdWGp5Llpejb7kjsfWlmkpBdpGcUa4npnE5LdoeTXx81vXT4alTIBtTHwRrkeRn0QUnJ/qwCGU56LlFam0elWae4GGFGfkG4CSm/wWLa8opNUTYe4TYMjEF/kGIPKTgMLzUx04tPJcdFCR5lqgTYozoTtmk578Fh00EeY6ATaZ+BK6Y5b8RDaA/FQHDrk8l1322vfFHkJv7n/gPbGHsJ9sJr9l5CcJ5CcDmeenOpDyXK+EQCcc1FWynfxWIT+9Iz+JKTA/1YGUZzMphjqzkO6nqMlvP+SnM+QnkgHlpzqQ8uxOmwEvJJh1DGLyq4P8NEJ+ZshPI8mXJwAA2Ur+93kCAJAayhMAAE+UJwAAnihPAAA8UZ4AAHiiPAEA8DSJPYCYTh+d5bbthGS24o9O/dctvO/m3wW053FVj9vsG8hWvP2M97X7nFPfWWRu4X0nm71tcjJzs+E4jWZvj2aPj2aPjWZvV8dMdx+X00jTU2+PTx1jGkkaaaSHvnb8XufcFR1/yItCfshPCPKTd34GXZ7bOq5XHXmzdGhDNplIGxuzPxO5Q2NpPJbbGGu6MZbbGMmNTdONkaYT03TDNJ3Y7DHTdKLZH5Mbz94eS26iU++7PY+52WNObuykseQmU9nEycZTjSZOo/FU4/FUG5OT2pic1GQ01aHJjk4b72hzvKPNyY4OjXa0Od7W4fG2tsbbOjw6oc3RtrZGJ7Q1Oq6t0XFtjrZ1ZHRcR+yEtkYndMS2tWlTbZlpyybaGh3SxpmPnR77fOSG/JCfEOQn7/ywbQsAgKdB/4QhM/u6pKdjjyMRm865l8YeRE7Izx7kxxP52SO7/Ax621bS0865i2MPIgVm9pXYY8gQ+ZkhP42Qn5kc88O2LQAAnihPAAA8Db08b4s9gITwsfDHx2wXHwt/fMx2ZfexGPQNQwAANDH0lScAAN4oTwAAPA2yPM3sCjN71MyOmdm7Yo8nJjP7iJn9aPY9Z6iB/OwiP/7Iz66c8zO48jSzsaRbJb1B0gWSrjWzC+KOKqqjkrL6mZIxkZ9nOCryUxv5eYajyjQ/gytPSa+QdMw597hz7oSkuyRdGXlM0TjnHpD049jjyAj5WUB+vJGfBTnnZ4jl+XxJ31t4/4nZY0Ad5AchyE8hhlieAAAEGWJ5fl/SCxfef8HsMaAO8oMQ5KcQQyzPByWda2Znm9khSddI+nTkMSEf5AchyE8hBleezrkdSTdKulfSI5I+7pz7RtxRxWNmd0r6oqTzzewJM3t77DGljPzsRX78kJ+9cs4PP54PAABPg1t5AgAQivIEAMAT5QkAgCfKEwAAT5QnAACeKE8AADxRngAAePp/8HYHvoyg6+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc971761c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = newB()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bluer_footprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc981b8f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = bluer_footprint()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## stuck_rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc971cdfb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = stuck_rolling()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## no_gp_north"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc97174f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = no_gp_north()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## add_mag_clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9614d81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = add_mag_clouds()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## big_wfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc982699898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "footprint = big_wfd()\n",
    "disp_footprints(footprint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}