{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import healpy as hp\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from gen_new_map import generate_goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2DidDfi3FTK3290E175PE4On8VnJD9uPQwDavGgc3YBrGz/nUNnZ2GoV+K7kHnT65V2PHATnZttfz1M/+3OVPfW+/eT6Hkhuz1p/UA8hAFM5r/5dUjh1mHvv9liCcj0PJDbv+8H2D3MxqOyQCjGe8zO/ty9ei8IZDyT26evEuvvzJT6+9Gun429KQkxd6uX35k58WhRMwY5jIj/7l76y9Cmnsx6EdCuTgxV4+JkgOMmOIjWNKj31mFwB1sN+bnhnDxMwgXk4UQi5mDNsnBk9ixpCHbDwAd0Rh++zX5icMk7MRAZCB/dkyHErujMPL4zikDDmYNWyLGBxt9KFkH3Ddme1GJhABqJUgXI9DyZ2y0QFQI/undTmUjNnDMzikDG1yCLl+gnBSow8lC0NuCcSnCUNojyismyCchY+r4XI2zqfZwQBMx36nPmYMOcoM4nFmDqF++y/k3r587cVdBcTgIhxKZj4C8TBxCPU69GctReG6BOGihCHLEIn3iUOonyBcjxhcjc8xZBk+BxGApwjCdvnlE0ax0d8wEwF1s40uz/6hbQ4lM5neZxEdVoZ6CMLlCMEq+bga1mdwAGogCpdj3M/HjCGz6HX20KwhrEsULkMQVs9vJVOv3iJRHMI6tlHo42nmIQabIgxpQw+RKAxheR+8eXVv2xOF0xCDzfIeQ9rQwyBjhwTL84Jsej2M1zxkxpDVZZ5FtLOCZXlhNp4QTMWhZNqXMRCFISxHFI4jCFMShuSTKRQFIsxHEJ5HCHZBGJJXlkAUhzAPYXgaQdgVYUh+rQeiMIRpCcLTCMIuCUP602ooCkS43H4Uvn35WihuCEFCGNK7liJRGMJ4u/Hng6zviEH2CEOIEIiQ2f6HWO+e3itByBHCEA6pPRTFIYzTawwKQU4kDOEptUaiOITz9RSGYpARhCGMUVMsCkR4XA8xKAKZiDCES9QQiMIQjssehYKQiQlDmMrakSgQ4b6sH00jBpmRMIQ5LR2L4hAOazUIRSALE4awlKUiURzCfa1FoRhkRcIQ1jB3JIpDuNFKFIpBKiEMoSZLBuMpfwliqdN3XbJe22WP3c6h6z/1do9d/7HLnHK7ta7Xqe/PO+cxOuVDpqd6btVKAFI5YQi1WvuXWYBpiEEaIgyhFUIR2iAEaZgwhNYJRliHACQhYQjZCEWYhxCkA8IQeiMc4TDhB8IQuicU6ZUQhAeEIXCfUCQrIQhPEobAOAKSWgg+mIwwBKYjFpmbCIRZCUNgfoKRcwlAWIUwBOojJPMRetAEYQi0RzjWR/hBCsIQyEtAXk7wQVeEIQAAEXFBGD6fci0WNvpOAwDw0NXaKwAAQB2EIQAAESEMAQDYEIYAAESEMAQAYEMYAgAQEcIQAIANYQgAQEQIQwAANoQhAAARIQwBANgQhgAARIQwBABgQxgCABARwhAAgA1hCABARAhDAAA2hCEAABER8XztFYA1vVc+Mnw7vnXzTSlRtgvK7f/ulJ3vDy7fO63sLXz0+6eu98Zw7HKHbu/BZR9e9eHLxu19HR47z6HrP+M2zj3/7WXOOP/u6cOR00++7XNv9+iy4cmf5eHThwenH7zI7UP/4B7fewpvnk17l7l/W2XvBrbXWe5dZthZvvNvGe6ddu/yO/fpseUPr/vu/OXB9e8s213PA9e5f77Hrv/gsgfrfeB8JWJnRNk7z6Fld6f8zVe+9flhGD4RsAJhSNe+Hd+Kn7z6eJSrElGubv+Nq3K7JylXVzdf3/5bomzPE3Fz2u7y2+93l+9c54HlQyk38/c7y4ftnmzn9Hvni7j5vmyu42p7XXfLo8TtZbbn3V1+83VEXJXbrw8t3552e/6Ize09XHYbUGX/8jvru3vbB853dHncLT94uROv+7Hbeni/I6IMxy+/uzz2lw8759kuGx4sL9uvY/e822U7/26Wb89fyrB5atzFZinD5ilxt/xqG1F7p5UyxFUMt0+X29N2/r1ZPtwuv/df3H19b/nt6dd3y3ZOe3Z7nrvlz3a+v1m++Tqub6/7WbmOq9j8W65vL7N7+Wc7l9uePyI2X9/c9s15rm+ve3t7z7aX39zGs9iu+/Xt97fXHcO9db/5frMeEfGsRDzbPCg335e4ihLPYvNv2S672nx/81VExLOPvHsvYCUOJQMAEBHCEACADWEIAEBECEMAADaEIQAAESEMAQDYEIYAAESEMAQAYEMYAgAQEcIQAICNMgwP/54m9KKU8rcR8c211wNgx3cNw/DDa68EffK3kundN4dh+PG1VwJgq5TyxbXXgX45lAwAQEQIQwAANoQhvfuDtVcAYI9xidX45RMAACLCjCEAABvCEACAiBCGdKqU8olSyj+WUr5aSvnttdcHyO+pcaeU8qlSyldKKV8qpXyxlPJTO8s+U0r5+uazV2E23mNId0opzyLinyLi4xHxtYh4ExG/PAzD36+6YkBap4w7pZTvjYj/GoZhKKX8SET86TAMH9ss++mI+EZE/LEPv2ZOZgzp0U9ExFeHYfjnYRi+HRGvI+JTK68TkNuT484wDN8Y7mZrvicihp1lfx0R/7HUytIvYUiPvi8i/mXn+69tTgOYy0njTinl50sp/xARfxURv77QusEtYQgAlRiG4c82h49/LiI+vfb60B9hSI/+NSJ+YOf779+cBjCXs8adzaHjHyqlvDf3isEuYUiP3kTE+6WUHyylfEdEvIqIv1h5nYDcnhx3SikfLaWUzdc/FhHfGRH/vvia0rXna68ALG0Yhv8rpfxmRHw+Ip5FxGeGYfi7lVcLSOzYuFNK+Y3N8t+PiF+IiF8rpfxvRPxPRPzS9pdRSil/EhE/GxHvlVK+FhG/OwzDH65wV0jOx9UAABARDiUDALAhDAEAiAhhCADAhjAEACAihCEAABvCEACAiBCGAABs/D+f4GxfhC2nfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114777da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9a34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145d9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa01128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b489668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1e2b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nside=32\n",
    "ack=generate_goal(nside)\n",
    "for key in ack:\n",
    "    hp.mollview(ack[key], title=key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18259.672545214144"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# area of wfd\n",
    "hp.nside2pixarea(nside, degrees=True)*np.where(ack['r'] == 1)[0].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.2,  1. ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "np.unique(ack['r'])"
   ]
  },
  {
   "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
}